diff --git a/.github/workflows/cherry-pick.yml b/.github/workflows/cherry-pick.yml deleted file mode 100644 index e7b6b0471fd..00000000000 --- a/.github/workflows/cherry-pick.yml +++ /dev/null @@ -1,31 +0,0 @@ -name: Cherry Pick - -on: - pull_request_target: - branches: - - main - types: - - closed - -jobs: - cherry_pick: - runs-on: ubuntu-latest - - name: Cherry pick into 1.10.X-fixes - - if: github.event.pull_request.merged == true && contains(github.event.pull_request.labels.*.name, 'cherry-pick-v1') - - steps: - - name: Checkout - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - name: Cherry pick into 1.10.X-fixes - uses: carloscastrojumo/github-cherry-pick-action@v1.0.10 - with: - branch: 1.10.X-fixes - labels: from-cherry-pick - token: ${{ secrets.GITHUB_TOKEN }} - title: '[cherry-pick] {old_title}' - body: 'Cherry picking #{old_pull_request_id} onto this branch' diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 3aad3f57ed7..3d8c3889ed7 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -10,11 +10,12 @@ on: env: COLUMNS: 150 + UV_FROZEN: true jobs: lint: runs-on: ubuntu-latest - name: lint ${{ matrix.python-version }} + name: Lint ${{ matrix.python-version }} strategy: fail-fast: false matrix: @@ -22,16 +23,12 @@ jobs: steps: - uses: actions/checkout@v4 - - uses: pdm-project/setup-pdm@v4 + - uses: astral-sh/setup-uv@v3 with: - python-version: ${{ matrix.python-version }} - cache: true - allow-python-prereleases: true + enable-cache: true - - name: install - run: | - pdm venv create --with-pip --force $PYTHON - pdm install -G linting -G email + - name: Install dependencies + run: uv sync --python ${{ matrix.python-version }} --group linting --all-extras - uses: pre-commit/action@v3.0.1 with: @@ -44,20 +41,17 @@ jobs: steps: - uses: actions/checkout@v4 - - uses: pdm-project/setup-pdm@v4 + - uses: astral-sh/setup-uv@v3 with: - # cloudflare pages build containers run 3.8, hence using it here - python-version: '3.8' + enable-cache: true - - name: install dependencies + - name: Install dependencies # Unlike the docs build, we don't use mkdocs_material-insiders # Because the secret for accessing the library is not accessible from forks, but we still want to run # this job on public CI runs. - run: | - pdm venv create --with-pip --force $PYTHON - pdm install -G docs + run: uv sync --python 3.12 --group docs - - run: pdm run python -c 'import docs.plugins.main' + - run: uv run python -c 'import docs.plugins.main' # Adding local symlinks gets nice source locations like # pydantic_core/core_schema.py @@ -69,30 +63,26 @@ jobs: ln -s .venv/lib/python*/site-packages/pydantic_settings pydantic_settings ln -s .venv/lib/python*/site-packages/pydantic_extra_types pydantic_extra_types - - run: pdm run mkdocs build + - run: uv run mkdocs build test-memray: - name: test memray + name: Test memray runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - uses: pdm-project/setup-pdm@v4 + - uses: astral-sh/setup-uv@v3 with: - python-version: '3.12' - cache: true + enable-cache: true - name: install deps - run: | - pdm venv create --with-pip --force $PYTHON - pdm install -G testing -G testing-extra -G email -G memray - pdm add pytest-memray + run: uv sync --python 3.12 --group testing-extra - - name: test - run: pdm run pytest --ignore=tests/mypy/ --ignore=tests/test_docs.py --memray + - name: Run tests + run: uv run pytest --ignore=tests/mypy/ --ignore=tests/test_docs.py --memray test: - name: test ${{ matrix.os }} / ${{ matrix.python-version }} + name: Test ${{ matrix.os }} / ${{ matrix.python-version }} strategy: fail-fast: false matrix: @@ -124,30 +114,26 @@ jobs: env: OS: ${{ matrix.os }} DEPS: yes + UV_PYTHON: ${{ matrix.python-version }} + UV_PYTHON_PREFERENCE: only-managed runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v4 - - uses: pdm-project/setup-pdm@v4 + - uses: astral-sh/setup-uv@v3 with: - python-version: ${{ matrix.python-version }} - cache: true - allow-python-prereleases: true + enable-cache: true - - name: install deps - run: | - pdm venv create --with-pip --force $PYTHON - pdm install -G testing -G email + - name: Install dependencies + run: uv sync --extra timezone - - run: pdm info && pdm list - - - run: 'pdm run python -c "import pydantic.version; print(pydantic.version.version_info())"' + - run: 'uv run python -c "import pydantic.version; print(pydantic.version.version_info())"' - run: mkdir coverage - - name: test without deps + - name: Test without email-validator # speed up by skipping this step on pypy if: "!startsWith(matrix.python-version, 'pypy')" run: make test @@ -155,16 +141,16 @@ jobs: COVERAGE_FILE: coverage/.coverage.${{ runner.os }}-py${{ matrix.python-version }}-without-deps CONTEXT: ${{ runner.os }}-py${{ matrix.python-version }}-without-deps - - name: install extra deps - run: pdm install -G testing-extra -G email + - name: Install extra dependencies + run: uv sync --group testing-extra --all-extras - - name: test with deps + - name: Test with all extra dependencies run: make test env: COVERAGE_FILE: coverage/.coverage.${{ runner.os }}-py${{ matrix.python-version }}-with-deps CONTEXT: ${{ runner.os }}-py${{ matrix.python-version }}-with-deps - - name: store coverage files + - name: Store coverage files uses: actions/upload-artifact@v4 with: name: coverage-${{ matrix.os }}-${{ matrix.python-version }} @@ -174,7 +160,7 @@ jobs: test-fastapi: # If some tests start failing due to out-of-date schemas/validation errors/etc., # update the `tests/test_fastapi.sh` script to exclude tests that have known-acceptable failures. - name: test fastapi + name: Test FastAPI runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -184,110 +170,98 @@ jobs: with: python-version: '3.12' - - name: test + - name: Run tests run: make test-fastapi test-plugin: - name: test pydantic plugin + name: Test Pydantic plugin runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - uses: pdm-project/setup-pdm@v4 - with: - python-version: '3.12' - cache: true - - name: install deps - run: | - pdm venv create --with-pip --force $PYTHON - pdm install -G testing + - uses: astral-sh/setup-uv@v3 - - name: install example plugin - run: pdm add ./tests/plugin + - name: Install dependencies + run: uv sync --python 3.12 + + - name: Install example plugin + run: uv pip install ./tests/plugin - - run: pdm run pytest ./tests/plugin + - run: uv run pytest tests/plugin env: TEST_PLUGIN: 1 - # uninstalling is necessary to avoid creating a pdm cache shared with other jobs - # which includes the plugin - - name: uninstall example plugin - run: pdm remove ./tests/plugin - test-mypy: name: mypy ${{ matrix.mypy-version }} / ${{ matrix.python-version }} runs-on: ubuntu-latest strategy: fail-fast: false matrix: - # test recent mypy versions on 3.10 and mypy 1.0.1 on all supported python versions - # if you change the value in the lockfile add the old value to this list - mypy-version: ['1.0.1', 'lockfile', '1.2.0', '1.4.1', '1.5.1'] - python-version: ['3.10'] + # test the latest version on all supported Python versions and the rest on 3.12 + mypy-version: ['1.10.1', '1.11.2'] + python-version: ['3.12'] include: - - mypy-version: 'lockfile' + - mypy-version: '1.12.0' python-version: '3.8' - - mypy-version: 'lockfile' + - mypy-version: '1.12.0' python-version: '3.9' - - mypy-version: 'lockfile' + - mypy-version: '1.12.0' + python-version: '3.10' + - mypy-version: '1.12.0' python-version: '3.11' - # TODO: Once Python 3.7 is removed, and mypy is bumped on the lockfile, we can add 'lockfile' again. - - mypy-version: '1.5.1' + - mypy-version: '1.12.0' python-version: '3.12' - - mypy-version: '1.5.1' + - mypy-version: '1.12.0' python-version: '3.13' steps: - uses: actions/checkout@v4 - - uses: pdm-project/setup-pdm@v4 + - uses: astral-sh/setup-uv@v3 with: - python-version: ${{ matrix.python-version }} - cache: true - allow-python-prereleases: true + enable-cache: true - - name: install deps - run: | - pdm venv create --with-pip --force $PYTHON - pdm install -G testing -G mypy + - name: Install dependencies + run: uv sync --python ${{ matrix.python-version }} --group typechecking --all-extras - - name: install mypy + - name: Install mypy if: steps.cache.outputs.cache-hit != 'true' - run: | - if [ ${{ matrix.mypy-version }} != 'lockfile' ]; then - pdm remove -G linting mypy && pdm remove -G mypy mypy && pdm add mypy==${{ matrix.mypy-version }} - fi - pdm list + run: uv pip install 'mypy==${{ matrix.mypy-version }}' - run: mkdir coverage - - name: run mypy tests - run: pdm run coverage run -m pytest tests/mypy --test-mypy + - name: Run mypy tests + run: uv run coverage run -m pytest tests/mypy --test-mypy env: COVERAGE_FILE: coverage/.coverage.linux-py${{ matrix.python-version }}-mypy${{ matrix.mypy-version }} CONTEXT: linux-py${{ matrix.python-version }}-mypy${{ matrix.mypy-version }} - - name: install node for pyright - uses: actions/setup-node@v4 - with: - node-version: '18' - - - name: install pyright - run: npm install -g pyright@1.1.369 # try to keep this in sync with .pre-commit-config.yaml - - - name: run pyright tests - run: make test-pyright - env: - COVERAGE_FILE: coverage/.coverage.linux-py${{ matrix.python-version }}-pyright - CONTEXT: linux-py${{ matrix.python-version }}-pyright - - - name: store coverage files + - name: Store coverage files uses: actions/upload-artifact@v4 with: name: coverage-${{ matrix.python-version }}-mypy${{ matrix.mypy-version }} path: coverage include-hidden-files: true + test-typechecking-integration: + name: Typechecking integration tests + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - uses: astral-sh/setup-uv@v3 + with: + enable-cache: true + + - name: Install dependencies + run: uv sync --python 3.12 --group typechecking + + - name: Run typechecking integration tests (Pyright) + run: make test-typechecking-pyright + + - name: Run typechecking integration tests (Mypy) + run: make test-typechecking-mypy + coverage-combine: needs: [test, test-mypy] runs-on: ubuntu-latest @@ -299,7 +273,7 @@ jobs: with: python-version: '3.12' - - name: get coverage files + - name: Get coverage files uses: actions/download-artifact@v4 with: merge-multiple: true @@ -320,7 +294,7 @@ jobs: path: .coverage include-hidden-files: true - - name: Store coverage html + - name: Store coverage HTML uses: actions/upload-artifact@v4 with: name: coverage-html @@ -356,49 +330,30 @@ jobs: path: python-coverage-comment-action.txt test-typing-extensions: - name: test typing-extensions ${{ matrix.typing-extensions-version }} on Python ${{ matrix.python-version }} + name: Test typing-extensions (`main` branch) on Python ${{ matrix.python-version }} runs-on: ubuntu-latest strategy: fail-fast: false matrix: - # test the oldest supported version and main - typing-extensions-version: ['4.6.1', 'main'] python-version: ['3.8', '3.9', '3.10', '3.11', '3.12', '3.13'] - exclude: - - typing-extensions-version: '4.6.1' - python-version: '3.13' - include: - - typing-extensions-version: '4.12.2' - python-version: '3.13' - steps: - uses: actions/checkout@v4 - - uses: pdm-project/setup-pdm@v4 + - uses: astral-sh/setup-uv@v3 with: - python-version: ${{ matrix.python-version }} - cache: true - allow-python-prereleases: true + enable-cache: true - - name: install deps - run: | - pdm venv create --with-pip --force $PYTHON - pdm install -G testing -G email + - name: Install dependencies + run: uv sync --python ${{ matrix.python-version }} - - name: install typing-extensions - run: | - if [ ${{ matrix.typing-extensions-version }} == 'main' ]; then - pdm remove typing-extensions && pdm add 'typing-extensions @ git+https://github.com/python/typing_extensions.git' - else - pdm remove typing-extensions && pdm add 'typing-extensions==${{ matrix.typing-extensions-version }}' - fi - pdm list - - name: test + - name: Install typing-extensions + run: uv pip install 'typing-extensions @ git+https://github.com/python/typing_extensions.git' + + - name: Run tests run: make test # https://github.com/marketplace/actions/alls-green check: # This job does nothing and is only used for the branch protection - if: always() outputs: @@ -437,21 +392,20 @@ jobs: steps: - uses: actions/checkout@v4 - - name: set up python - uses: actions/setup-python@v5 + - uses: actions/setup-python@v5 with: python-version: '3.12' - - name: install + - name: Install 'build' library run: pip install -U build - - name: check version + - name: Check version id: check-tag uses: samuelcolvin/check-python-version@v4.1 with: version_file_path: pydantic/version.py - - name: build + - name: Build library run: python -m build - name: Upload package to PyPI diff --git a/.github/workflows/codspeed.yml b/.github/workflows/codspeed.yml index 2f9fba33e1f..a2204659daf 100644 --- a/.github/workflows/codspeed.yml +++ b/.github/workflows/codspeed.yml @@ -9,24 +9,29 @@ on: # performance analysis in order to generate initial data. workflow_dispatch: +env: + UV_FROZEN: true + jobs: codspeed-profiling: - name: codspeed profiling - runs-on: ubuntu-latest + name: CodSpeed profiling + runs-on: ubuntu-22.04 steps: - uses: actions/checkout@v4 - - uses: pdm-project/setup-pdm@v4 + - uses: astral-sh/setup-uv@v3 + with: + enable-cache: true + + # Using this action is still necessary for CodSpeed to work: + - uses: actions/setup-python@v5 with: python-version: '3.12' - cache: true - name: install deps - run: | - pdm venv create --with-pip --force $PYTHON - pdm install -G testing -G testing-extra -G email + run: uv sync --python 3.12 --group testing-extra --extra email --frozen - name: Run CodSpeed benchmarks uses: CodSpeedHQ/action@v3 with: - run: pdm run pytest ./tests/benchmarks --codspeed + run: uv run pytest ./tests/benchmarks --codspeed diff --git a/.github/workflows/dependencies-check.yml b/.github/workflows/dependencies-check.yml index d8db57827f2..4bc462b2fc2 100644 --- a/.github/workflows/dependencies-check.yml +++ b/.github/workflows/dependencies-check.yml @@ -5,6 +5,9 @@ on: - cron: '43 3 * * 6,3' workflow_dispatch: {} +env: + UV_FROZEN: true + jobs: find_dependency_cases: runs-on: ubuntu-latest @@ -20,7 +23,7 @@ jobs: mode: first-last test: - name: test py${{ matrix.python-version }} on ${{ matrix.PYTHON_DEPENDENCY_CASE }} + name: Test py${{ matrix.python-version }} on ${{ matrix.PYTHON_DEPENDENCY_CASE }} needs: - find_dependency_cases @@ -36,15 +39,15 @@ jobs: steps: - uses: actions/checkout@v4 - - name: set up python - uses: actions/setup-python@v5 + - uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - - run: pip install pdm && pdm install --no-isolation + - run: pip install uv + - run: uv sync --all-extras - - run: pdm run pip install ${{ matrix.PYTHON_DEPENDENCY_CASE }} + - run: uv pip install ${{ matrix.PYTHON_DEPENDENCY_CASE }} - - run: pdm run pip freeze + - run: uv pip freeze - run: make test diff --git a/.github/workflows/docs-update.yml b/.github/workflows/docs-update.yml index f71232afbbc..ba204477ffa 100644 --- a/.github/workflows/docs-update.yml +++ b/.github/workflows/docs-update.yml @@ -10,6 +10,7 @@ on: env: COLUMNS: 150 + UV_FROZEN: true jobs: lint: @@ -17,13 +18,12 @@ jobs: steps: - uses: actions/checkout@v4 - - uses: pdm-project/setup-pdm@v4 + - uses: astral-sh/setup-uv@v3 with: - python-version: '3.11' - cache: true + enable-cache: true - - name: install - run: pdm install -G linting -G email + - name: Install dependencies + run: uv sync --python 3.12 --group linting --all-extras - uses: pre-commit/action@v3.0.1 with: @@ -36,17 +36,14 @@ jobs: steps: - uses: actions/checkout@v4 - - uses: pdm-project/setup-pdm@v4 + - uses: astral-sh/setup-uv@v3 with: - python-version: '3.11' - cache: true + enable-cache: true - - name: install deps - run: pdm install -G testing -G email + - name: Install dependencies + run: uv sync --python 3.12 --group testing-extra --all-extras - - run: pdm info && pdm list - - - run: 'pdm run python -c "import pydantic.version; print(pydantic.version.version_info())"' + - run: 'uv run python -c "import pydantic.version; print(pydantic.version.version_info())"' - run: make test @@ -57,33 +54,31 @@ jobs: timeout-minutes: 30 steps: - - name: checkout docs-site + - name: Checkout docs-site uses: actions/checkout@v4 with: ref: docs-site - - name: checkout current branch + - name: Checkout current branch uses: actions/checkout@v4 - - uses: pdm-project/setup-pdm@v4 + - uses: astral-sh/setup-uv@v3 with: - python-version: '3.10' + enable-cache: true - - name: install - run: | - pdm venv create --with-pip --force $PYTHON - pdm install -G docs - pdm run python -m pip install --extra-index-url https://pydantic:${PPPR_TOKEN}@pppr.pydantic.dev/simple/ mkdocs-material + - run: uv sync --python 3.12 --group docs + + - run: uv pip install --default-index https://pydantic:${PPPR_TOKEN}@pppr.pydantic.dev/simple/ mkdocs-material env: PPPR_TOKEN: ${{ secrets.PPPR_TOKEN }} - - run: pdm run python -c 'import docs.plugins.main' + - run: uv run python -c 'import docs.plugins.main' # Adding local symlinks gets nice source locations like # pydantic_core/core_schema.py # instead of # .venv/lib/python3.10/site-packages/pydantic_core/core_schema.py - - name: prepare shortcuts for extra modules + - name: Prepare shortcuts for extra modules run: | ln -s .venv/lib/python*/site-packages/pydantic_core pydantic_core ln -s .venv/lib/python*/site-packages/pydantic_settings pydantic_settings @@ -94,7 +89,7 @@ jobs: git config --global user.name "${{ github.actor }}" git config --global user.email "${{ github.actor }}@users.noreply.github.com" - - run: pdm run mike deploy -b docs-site dev --push + - run: uv run mike deploy -b docs-site dev --push if: "github.ref == 'refs/heads/main'" - if: "github.ref == 'refs/heads/docs-update' || startsWith(github.ref, 'refs/tags/')" @@ -104,7 +99,7 @@ jobs: version_file_path: 'pydantic/version.py' skip_env_check: true - - run: pdm run mike deploy -b docs-site ${{ steps.check-version.outputs.VERSION_MAJOR_MINOR }} latest --update-aliases --push + - run: uv run mike deploy -b docs-site ${{ steps.check-version.outputs.VERSION_MAJOR_MINOR }} latest --update-aliases --push if: "(github.ref == 'refs/heads/docs-update' || startsWith(github.ref, 'refs/tags/')) && !fromJSON(steps.check-version.outputs.IS_PRERELEASE)" env: PYDANTIC_VERSION: v${{ steps.check-version.outputs.VERSION }} diff --git a/.github/workflows/integration.yml b/.github/workflows/integration.yml index 664f60c733a..db8ddf8b151 100644 --- a/.github/workflows/integration.yml +++ b/.github/workflows/integration.yml @@ -7,19 +7,19 @@ on: jobs: test-pydantic-settings: - name: test pydantic settings + name: Test pydantic settings runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - name: test + - name: Run tests run: make test-pydantic-settings test-pydantic-extra-types: - name: test pydantic extra types + name: Test pydantic extra types runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - name: test + - name: Run tests run: make test-pydantic-extra-types diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml index 90113a02fd7..32b669dd412 100644 --- a/.github/workflows/labeler.yml +++ b/.github/workflows/labeler.yml @@ -1,4 +1,4 @@ -name: release notes +name: Release notes on: pull_request_target: diff --git a/.github/workflows/update-pydantic-people.yml b/.github/workflows/update-pydantic-people.yml index f4e14ccd90c..4dfe180c48e 100644 --- a/.github/workflows/update-pydantic-people.yml +++ b/.github/workflows/update-pydantic-people.yml @@ -1,4 +1,4 @@ -name: Pydantic People Update +name: Pydantic people update on: schedule: diff --git a/.github/workflows/upload-previews.yml b/.github/workflows/upload-previews.yml index 945c39236c3..1fdbd6b5941 100644 --- a/.github/workflows/upload-previews.yml +++ b/.github/workflows/upload-previews.yml @@ -1,4 +1,4 @@ -name: Upload Previews +name: Upload previews on: workflow_run: diff --git a/.gitignore b/.gitignore index 9406afef4cf..79ddfafd933 100644 --- a/.gitignore +++ b/.gitignore @@ -52,4 +52,3 @@ pydantic/*.so .auto-format /sandbox/ /worktrees/ -.pdm-python diff --git a/.hyperlint/styles/config/vocabularies/hyperlint/accept.txt b/.hyperlint/styles/config/vocabularies/hyperlint/accept.txt index 449bab60657..3e376ae2f3e 100644 --- a/.hyperlint/styles/config/vocabularies/hyperlint/accept.txt +++ b/.hyperlint/styles/config/vocabularies/hyperlint/accept.txt @@ -6,6 +6,7 @@ Hyperlint preprocess tokenization tokenizer +tzdata API APIs SDKs diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index c52b16d6666..35f6640ac56 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.3.0 + rev: v5.0.0 hooks: - id: no-commit-to-branch # prevent direct commits to main branch - id: check-yaml @@ -10,12 +10,12 @@ repos: - id: trailing-whitespace - repo: https://github.com/codespell-project/codespell - rev: v2.2.4 + rev: v2.3.0 hooks: - id: codespell additional_dependencies: - tomli - exclude: '^pdm\.lock$' + exclude: '^uv\.lock$' - repo: local hooks: - id: lint @@ -26,14 +26,13 @@ repos: pass_filenames: false - id: usage_docs name: Usage docs links - entry: pdm run ./tests/check_usage_docs.py + entry: uv run ./tests/check_usage_docs.py files: '^pydantic/' types: [python] language: system - id: typecheck name: Typecheck - entry: pdm run pyright pydantic + entry: uv run pyright pydantic types: [python] - language: node + language: system pass_filenames: false - additional_dependencies: ["pyright@1.1.369"] # try to keep this in sync with .github/workflows/ci.yml diff --git a/CITATION.cff b/CITATION.cff index f249d9c7c3a..47f01263895 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -42,5 +42,5 @@ keywords: - hints - typing license: MIT -version: v2.9.0 -date-released: 2024-09-05 +version: v2.10.0 +date-released: 2024-11-20 diff --git a/HISTORY.md b/HISTORY.md index 7450645e6a5..309a86c4960 100644 --- a/HISTORY.md +++ b/HISTORY.md @@ -1,3 +1,178 @@ +## v2.10.0 (2024-11-20) + +The code released in v2.10.0 is practically identical to that of v2.10.0b2. + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.10.0) + +See the [v2.10 release blog post](https://pydantic.dev/articles/pydantic-v2-10-release) for the highlights! + +### What's Changed + +#### Packaging + +* Bump `pydantic-core` to `v2.27.0` by @sydney-runkle in [#10825](https://github.com/pydantic/pydantic/pull/10825) +* Replaced pdm with uv by @frfahim in [#10727](https://github.com/pydantic/pydantic/pull/10727) + +#### New Features + +* Support `fractions.Fraction` by @sydney-runkle in [#10318](https://github.com/pydantic/pydantic/pull/10318) +* Support `Hashable` for json validation by @sydney-runkle in [#10324](https://github.com/pydantic/pydantic/pull/10324) +* Add a `SocketPath` type for `linux` systems by @theunkn0wn1 in [#10378](https://github.com/pydantic/pydantic/pull/10378) +* Allow arbitrary refs in JSON schema `examples` by @sydney-runkle in [#10417](https://github.com/pydantic/pydantic/pull/10417) +* Support `defer_build` for Pydantic dataclasses by @Viicos in [#10313](https://github.com/pydantic/pydantic/pull/10313) +* Adding v1 / v2 incompatibility warning for nested v1 model by @sydney-runkle in [#10431](https://github.com/pydantic/pydantic/pull/10431) +* Add support for unpacked `TypedDict` to type hint variadic keyword arguments with `@validate_call` by @Viicos in [#10416](https://github.com/pydantic/pydantic/pull/10416) +* Support compiled patterns in `protected_namespaces` by @sydney-runkle in [#10522](https://github.com/pydantic/pydantic/pull/10522) +* Add support for `propertyNames` in JSON schema by @FlorianSW in [#10478](https://github.com/pydantic/pydantic/pull/10478) +* Adding `__replace__` protocol for Python 3.13+ support by @sydney-runkle in [#10596](https://github.com/pydantic/pydantic/pull/10596) +* Expose public `sort` method for JSON schema generation by @sydney-runkle in [#10595](https://github.com/pydantic/pydantic/pull/10595) +* Add runtime validation of `@validate_call` callable argument by @kc0506 in [#10627](https://github.com/pydantic/pydantic/pull/10627) +* Add `experimental_allow_partial` support by @samuelcolvin in [#10748](https://github.com/pydantic/pydantic/pull/10748) +* Support default factories taking validated data as an argument by @Viicos in [#10678](https://github.com/pydantic/pydantic/pull/10678) +* Allow subclassing `ValidationError` and `PydanticCustomError` by @Youssefares in [pydantic/pydantic-core#1413](https://github.com/pydantic/pydantic-core/pull/1413) +* Add `trailing-strings` support to `experimental_allow_partial` by @sydney-runkle in [#10825](https://github.com/pydantic/pydantic/pull/10825) +* Add `rebuild()` method for `TypeAdapter` and simplify `defer_build` patterns by @sydney-runkle in [#10537](https://github.com/pydantic/pydantic/pull/10537) +* Improve `TypeAdapter` instance repr by @sydney-runkle in [#10872](https://github.com/pydantic/pydantic/pull/10872) + +#### Changes + +* Don't allow customization of `SchemaGenerator` until interface is more stable by @sydney-runkle in [#10303](https://github.com/pydantic/pydantic/pull/10303) +* Cleanly `defer_build` on `TypeAdapters`, removing experimental flag by @sydney-runkle in [#10329](https://github.com/pydantic/pydantic/pull/10329) +* Fix `mro` of generic subclass by @kc0506 in [#10100](https://github.com/pydantic/pydantic/pull/10100) +* Strip whitespaces on JSON Schema title generation by @sydney-runkle in [#10404](https://github.com/pydantic/pydantic/pull/10404) +* Use `b64decode` and `b64encode` for `Base64Bytes` type by @sydney-runkle in [#10486](https://github.com/pydantic/pydantic/pull/10486) +* Relax protected namespace config default by @sydney-runkle in [#10441](https://github.com/pydantic/pydantic/pull/10441) +* Revalidate parametrized generics if instance's origin is subclass of OG class by @sydney-runkle in [#10666](https://github.com/pydantic/pydantic/pull/10666) +* Warn if configuration is specified on the `@dataclass` decorator and with the `__pydantic_config__` attribute by @sydney-runkle in [#10406](https://github.com/pydantic/pydantic/pull/10406) +* Recommend against using `Ellipsis` (...) with `Field` by @Viicos in [#10661](https://github.com/pydantic/pydantic/pull/10661) +* Migrate to subclassing instead of annotated approach for pydantic url types by @sydney-runkle in [#10662](https://github.com/pydantic/pydantic/pull/10662) +* Change JSON schema generation of `Literal`s and `Enums` by @Viicos in [#10692](https://github.com/pydantic/pydantic/pull/10692) +* Simplify unions involving `Any` or `Never` when replacing type variables by @Viicos in [#10338](https://github.com/pydantic/pydantic/pull/10338) +* Do not require padding when decoding `base64` bytes by @bschoenmaeckers in [pydantic/pydantic-core#1448](https://github.com/pydantic/pydantic-core/pull/1448) +* Support dates all the way to 1BC by @changhc in [pydantic/speedate#77](https://github.com/pydantic/speedate/pull/77) + +#### Performance + +* Schema cleaning: skip unnecessary copies during schema walking by @Viicos in [#10286](https://github.com/pydantic/pydantic/pull/10286) +* Refactor namespace logic for annotations evaluation by @Viicos in [#10530](https://github.com/pydantic/pydantic/pull/10530) +* Improve email regexp on edge cases by @AlekseyLobanov in [#10601](https://github.com/pydantic/pydantic/pull/10601) +* `CoreMetadata` refactor with an emphasis on documentation, schema build time performance, and reducing complexity by @sydney-runkle in [#10675](https://github.com/pydantic/pydantic/pull/10675) + +#### Fixes + +* Remove guarding check on `computed_field` with `field_serializer` by @nix010 in [#10390](https://github.com/pydantic/pydantic/pull/10390) +* Fix `Predicate` issue in `v2.9.0` by @sydney-runkle in [#10321](https://github.com/pydantic/pydantic/pull/10321) +* Fixing `annotated-types` bound by @sydney-runkle in [#10327](https://github.com/pydantic/pydantic/pull/10327) +* Turn `tzdata` install requirement into optional `timezone` dependency by @jakob-keller in [#10331](https://github.com/pydantic/pydantic/pull/10331) +* Use correct types namespace when building `namedtuple` core schemas by @Viicos in [#10337](https://github.com/pydantic/pydantic/pull/10337) +* Fix evaluation of stringified annotations during namespace inspection by @Viicos in [#10347](https://github.com/pydantic/pydantic/pull/10347) +* Fix `IncEx` type alias definition by @Viicos in [#10339](https://github.com/pydantic/pydantic/pull/10339) +* Do not error when trying to evaluate annotations of private attributes by @Viicos in [#10358](https://github.com/pydantic/pydantic/pull/10358) +* Fix nested type statement by @kc0506 in [#10369](https://github.com/pydantic/pydantic/pull/10369) +* Improve typing of `ModelMetaclass.mro` by @Viicos in [#10372](https://github.com/pydantic/pydantic/pull/10372) +* Fix class access of deprecated `computed_field`s by @Viicos in [#10391](https://github.com/pydantic/pydantic/pull/10391) +* Make sure `inspect.iscoroutinefunction` works on coroutines decorated with `@validate_call` by @MovisLi in [#10374](https://github.com/pydantic/pydantic/pull/10374) +* Fix `NameError` when using `validate_call` with PEP 695 on a class by @kc0506 in [#10380](https://github.com/pydantic/pydantic/pull/10380) +* Fix `ZoneInfo` with various invalid types by @sydney-runkle in [#10408](https://github.com/pydantic/pydantic/pull/10408) +* Fix `PydanticUserError` on empty `model_config` with annotations by @cdwilson in [#10412](https://github.com/pydantic/pydantic/pull/10412) +* Fix variance issue in `_IncEx` type alias, only allow `True` by @Viicos in [#10414](https://github.com/pydantic/pydantic/pull/10414) +* Fix serialization schema generation when using `PlainValidator` by @Viicos in [#10427](https://github.com/pydantic/pydantic/pull/10427) +* Fix schema generation error when serialization schema holds references by @Viicos in [#10444](https://github.com/pydantic/pydantic/pull/10444) +* Inline references if possible when generating schema for `json_schema_input_type` by @Viicos in [#10439](https://github.com/pydantic/pydantic/pull/10439) +* Fix recursive arguments in `Representation` by @Viicos in [#10480](https://github.com/pydantic/pydantic/pull/10480) +* Fix representation for builtin function types by @kschwab in [#10479](https://github.com/pydantic/pydantic/pull/10479) +* Add python validators for decimal constraints (`max_digits` and `decimal_places`) by @sydney-runkle in [#10506](https://github.com/pydantic/pydantic/pull/10506) +* Only fetch `__pydantic_core_schema__` from the current class during schema generation by @Viicos in [#10518](https://github.com/pydantic/pydantic/pull/10518) +* Fix `stacklevel` on deprecation warnings for `BaseModel` by @sydney-runkle in [#10520](https://github.com/pydantic/pydantic/pull/10520) +* Fix warning `stacklevel` in `BaseModel.__init__` by @Viicos in [#10526](https://github.com/pydantic/pydantic/pull/10526) +* Improve error handling for in-evaluable refs for discriminator application by @sydney-runkle in [#10440](https://github.com/pydantic/pydantic/pull/10440) +* Change the signature of `ConfigWrapper.core_config` to take the title directly by @Viicos in [#10562](https://github.com/pydantic/pydantic/pull/10562) +* Do not use the previous config from the stack for dataclasses without config by @Viicos in [#10576](https://github.com/pydantic/pydantic/pull/10576) +* Fix serialization for IP types with `mode='python'` by @sydney-runkle in [#10594](https://github.com/pydantic/pydantic/pull/10594) +* Support constraint application for `Base64Etc` types by @sydney-runkle in [#10584](https://github.com/pydantic/pydantic/pull/10584) +* Fix `validate_call` ignoring `Field` in `Annotated` by @kc0506 in [#10610](https://github.com/pydantic/pydantic/pull/10610) +* Raise an error when `Self` is invalid by @kc0506 in [#10609](https://github.com/pydantic/pydantic/pull/10609) +* Using `core_schema.InvalidSchema` instead of metadata injection + checks by @sydney-runkle in [#10523](https://github.com/pydantic/pydantic/pull/10523) +* Tweak type alias logic by @kc0506 in [#10643](https://github.com/pydantic/pydantic/pull/10643) +* Support usage of `type` with `typing.Self` and type aliases by @kc0506 in [#10621](https://github.com/pydantic/pydantic/pull/10621) +* Use overloads for `Field` and `PrivateAttr` functions by @Viicos in [#10651](https://github.com/pydantic/pydantic/pull/10651) +* Clean up the `mypy` plugin implementation by @Viicos in [#10669](https://github.com/pydantic/pydantic/pull/10669) +* Properly check for `typing_extensions` variant of `TypeAliasType` by @Daraan in [#10713](https://github.com/pydantic/pydantic/pull/10713) +* Allow any mapping in `BaseModel.model_copy()` by @Viicos in [#10751](https://github.com/pydantic/pydantic/pull/10751) +* Fix `isinstance` behavior for urls by @sydney-runkle in [#10766](https://github.com/pydantic/pydantic/pull/10766) +* Ensure `cached_property` can be set on Pydantic models by @Viicos in [#10774](https://github.com/pydantic/pydantic/pull/10774) +* Fix equality checks for primitives in literals by @sydney-runkle in [pydantic/pydantic-core#1459](https://github.com/pydantic/pydantic-core/pull/1459) +* Properly enforce `host_required` for URLs by @Viicos in [pydantic/pydantic-core#1488](https://github.com/pydantic/pydantic-core/pull/1488) +* Fix when `coerce_numbers_to_str` enabled and string has invalid Unicode character by @andrey-berenda in [pydantic/pydantic-core#1515](https://github.com/pydantic/pydantic-core/pull/1515) +* Fix serializing `complex` values in `Enum`s by @changhc in [pydantic/pydantic-core#1524](https://github.com/pydantic/pydantic-core/pull/1524) +* Refactor `_typing_extra` module by @Viicos in [#10725](https://github.com/pydantic/pydantic/pull/10725) +* Support intuitive equality for urls by @sydney-runkle in [#10798](https://github.com/pydantic/pydantic/pull/10798) +* Add `bytearray` to `TypeAdapter.validate_json` signature by @samuelcolvin in [#10802](https://github.com/pydantic/pydantic/pull/10802) +* Ensure class access of method descriptors is performed when used as a default with `Field` by @Viicos in [#10816](https://github.com/pydantic/pydantic/pull/10816) +* Fix circular import with `validate_call` by @sydney-runkle in [#10807](https://github.com/pydantic/pydantic/pull/10807) +* Fix error when using type aliases referencing other type aliases by @Viicos in [#10809](https://github.com/pydantic/pydantic/pull/10809) +* Fix `IncEx` type alias to be compatible with mypy by @Viicos in [#10813](https://github.com/pydantic/pydantic/pull/10813) +* Make `__signature__` a lazy property, do not deepcopy defaults by @Viicos in [#10818](https://github.com/pydantic/pydantic/pull/10818) +* Make `__signature__` lazy for dataclasses, too by @sydney-runkle in [#10832](https://github.com/pydantic/pydantic/pull/10832) +* Subclass all single host url classes from `AnyUrl` to preserve behavior from v2.9 by @sydney-runkle in [#10856](https://github.com/pydantic/pydantic/pull/10856) + +### New Contributors + +* @jakob-keller made their first contribution in [#10331](https://github.com/pydantic/pydantic/pull/10331) +* @MovisLi made their first contribution in [#10374](https://github.com/pydantic/pydantic/pull/10374) +* @joaopalmeiro made their first contribution in [#10405](https://github.com/pydantic/pydantic/pull/10405) +* @theunkn0wn1 made their first contribution in [#10378](https://github.com/pydantic/pydantic/pull/10378) +* @cdwilson made their first contribution in [#10412](https://github.com/pydantic/pydantic/pull/10412) +* @dlax made their first contribution in [#10421](https://github.com/pydantic/pydantic/pull/10421) +* @kschwab made their first contribution in [#10479](https://github.com/pydantic/pydantic/pull/10479) +* @santibreo made their first contribution in [#10453](https://github.com/pydantic/pydantic/pull/10453) +* @FlorianSW made their first contribution in [#10478](https://github.com/pydantic/pydantic/pull/10478) +* @tkasuz made their first contribution in [#10555](https://github.com/pydantic/pydantic/pull/10555) +* @AlekseyLobanov made their first contribution in [#10601](https://github.com/pydantic/pydantic/pull/10601) +* @NiclasvanEyk made their first contribution in [#10667](https://github.com/pydantic/pydantic/pull/10667) +* @mschoettle made their first contribution in [#10677](https://github.com/pydantic/pydantic/pull/10677) +* @Daraan made their first contribution in [#10713](https://github.com/pydantic/pydantic/pull/10713) +* @k4nar made their first contribution in [#10736](https://github.com/pydantic/pydantic/pull/10736) +* @UriyaHarpeness made their first contribution in [#10740](https://github.com/pydantic/pydantic/pull/10740) +* @frfahim made their first contribution in [#10727](https://github.com/pydantic/pydantic/pull/10727) + +## v2.10.0b2 (2024-11-13) + +Pre-release, see [the GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.10.0b2) for details. + +## v2.10.0b1 (2024-11-06) + +Pre-release, see [the GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.9.0b1) for details. + +## v2.9.2 (2024-09-17) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.9.2) + +### What's Changed + +#### Fixes +* Do not error when trying to evaluate annotations of private attributes by @Viicos in [#10358](https://github.com/pydantic/pydantic/pull/10358) +* Adding notes on designing sound `Callable` discriminators by @sydney-runkle in [#10400](https://github.com/pydantic/pydantic/pull/10400) +* Fix serialization schema generation when using `PlainValidator` by @Viicos in [#10427](https://github.com/pydantic/pydantic/pull/10427) +* Fix `Union` serialization warnings by @sydney-runkle in [pydantic/pydantic-core#1449](https://github.com/pydantic/pydantic-core/pull/1449) +* Fix variance issue in `_IncEx` type alias, only allow `True` by @Viicos in [#10414](https://github.com/pydantic/pydantic/pull/10414) +* Fix `ZoneInfo` validation with various invalid types by @sydney-runkle in [#10408](https://github.com/pydantic/pydantic/pull/10408) + +## v2.9.1 (2024-09-09) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.9.1) + +### What's Changed + +#### Fixes +* Fix Predicate issue in v2.9.0 by @sydney-runkle in [#10321](https://github.com/pydantic/pydantic/pull/10321) +* Fixing `annotated-types` bound to `>=0.6.0` by @sydney-runkle in [#10327](https://github.com/pydantic/pydantic/pull/10327) +* Turn `tzdata` install requirement into optional `timezone` dependency by @jakob-keller in [#10331](https://github.com/pydantic/pydantic/pull/10331) +* Fix `IncExc` type alias definition by @Viicos in [#10339](https://github.com/pydantic/pydantic/pull/10339) +* Use correct types namespace when building namedtuple core schemas by @Viicos in [#10337](https://github.com/pydantic/pydantic/pull/10337) +* Fix evaluation of stringified annotations during namespace inspection by @Viicos in [#10347](https://github.com/pydantic/pydantic/pull/10347) +* Fix tagged union serialization with alias generators by @sydney-runkle in [pydantic/pydantic-core#1442](https://github.com/pydantic/pydantic-core/pull/1442) + ## v2.9.0 (2024-09-05) [GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.9.0) @@ -1320,6 +1495,11 @@ See [this post](https://docs.pydantic.dev/blog/pydantic-v2-alpha/) for more deta +## v1.10.19 (2024-11-06) + +* Add warning when v2 model is nested in v1 model by @sydney-runkle in https://github.com/pydantic/pydantic/pull/10432 +* Fix deprecation warning in V1 `isinstance` check by @alicederyn in https://github.com/pydantic/pydantic/pull/10645 + ## v1.10.18 (2024-08-22) * Eval type fix in V1 by @sydney-runkle in https://github.com/pydantic/pydantic/pull/9751 @@ -2004,7 +2184,7 @@ Thank you to pydantic's sponsors: @matin, @tiangolo, @chdsbd, @jorgecarleitao, a * In examples, type nullable fields as `Optional`, so that these are valid mypy annotations, [#1248](https://github.com/pydantic/pydantic/pull/1248) by @kokes * Make `pattern_validator()` accept pre-compiled `Pattern` objects. Fix `str_validator()` return type to `str`, [#1237](https://github.com/pydantic/pydantic/pull/1237) by @adamgreg * Document how to manage Generics and inheritance, [#1229](https://github.com/pydantic/pydantic/pull/1229) by @esadruhn -* `update_forward_refs()` method of BaseModel now copies `__dict__` of class module instead of modyfying it, [#1228](https://github.com/pydantic/pydantic/pull/1228) by @paul-ilyin +* `update_forward_refs()` method of BaseModel now copies `__dict__` of class module instead of modifying it, [#1228](https://github.com/pydantic/pydantic/pull/1228) by @paul-ilyin * Support instance methods and class methods with `@validate_arguments`, [#1222](https://github.com/pydantic/pydantic/pull/1222) by @samuelcolvin * Add `default_factory` argument to `Field` to create a dynamic default value by passing a zero-argument callable, [#1210](https://github.com/pydantic/pydantic/pull/1210) by @prettywood * add support for `NewType` of `List`, `Optional`, etc, [#1207](https://github.com/pydantic/pydantic/pull/1207) by @Kazy @@ -2466,7 +2646,7 @@ Thank you to pydantic's sponsors: @matin, @tiangolo, @chdsbd, @jorgecarleitao, a * add `UUID1`, `UUID3`, `UUID4` and `UUID5` types [#167](https://github.com/pydantic/pydantic/pull/167), thanks @Gr1N * modify some inconsistent docstrings and annotations [#173](https://github.com/pydantic/pydantic/pull/173), thanks @YannLuo * fix type annotations for exotic types [#171](https://github.com/pydantic/pydantic/pull/171), thanks @Gr1N -* re-use type validators in exotic types [#171](https://github.com/pydantic/pydantic/pull/171) +* Reuse type validators in exotic types [#171](https://github.com/pydantic/pydantic/pull/171) * scheduled monthly requirements updates [#168](https://github.com/pydantic/pydantic/pull/168) * add `Decimal`, `ConstrainedDecimal` and `condecimal` types [#170](https://github.com/pydantic/pydantic/pull/170), thanks @Gr1N diff --git a/Makefile b/Makefile index 22e3f873b2d..4ec0a83c2f5 100644 --- a/Makefile +++ b/Makefile @@ -1,86 +1,84 @@ -.DEFAULT_GOAL := all +# .DEFAULT_GOAL := all sources = pydantic tests docs/plugins -.PHONY: .pdm ## Check that PDM is installed -.pdm: - @pdm -V || echo 'Please install PDM: https://pdm.fming.dev/latest/#installation' +.PHONY: .uv ## Check that uv is installed +.uv: + @uv -V || echo 'Please install uv: https://docs.astral.sh/uv/getting-started/installation/' .PHONY: .pre-commit ## Check that pre-commit is installed -.pre-commit: - @pre-commit -V || echo 'Please install pre-commit: https://pre-commit.com/' +.pre-commit: .uv + @uv run pre-commit -V || uv pip install pre-commit .PHONY: install ## Install the package, dependencies, and pre-commit for local development -install: .pdm .pre-commit - pdm info - pdm install --group :all +install: .uv + uv sync --frozen --group all --all-extras + uv pip install pre-commit pre-commit install --install-hooks -.PHONY: refresh-lockfiles ## Sync lockfiles with requirements files. -refresh-lockfiles: .pdm - pdm update --update-reuse --group :all - .PHONY: rebuild-lockfiles ## Rebuild lockfiles from scratch, updating all dependencies -rebuild-lockfiles: .pdm - pdm update --update-eager --group :all +rebuild-lockfiles: .uv + uv lock --upgrade .PHONY: format ## Auto-format python source files -format: .pdm - pdm run ruff check --fix $(sources) - pdm run ruff format $(sources) +format: .uv + uv run ruff check --fix $(sources) + uv run ruff format $(sources) .PHONY: lint ## Lint python source files -lint: .pdm - pdm run ruff check $(sources) - pdm run ruff format --check $(sources) +lint: .uv + uv run ruff check $(sources) + uv run ruff format --check $(sources) .PHONY: codespell ## Use Codespell to do spellchecking codespell: .pre-commit pre-commit run codespell --all-files .PHONY: typecheck ## Perform type-checking -typecheck: .pre-commit .pdm +typecheck: .pre-commit pre-commit run typecheck --all-files .PHONY: test-mypy ## Run the mypy integration tests -test-mypy: .pdm - pdm run coverage run -m pytest tests/mypy --test-mypy +test-mypy: .uv + uv run coverage run -m pytest tests/mypy --test-mypy .PHONY: test-mypy-update ## Update the mypy integration tests for the current mypy version -test-mypy-update: .pdm - pdm run coverage run -m pytest tests/mypy --test-mypy --update-mypy +test-mypy-update: .uv + uv run coverage run -m pytest tests/mypy --test-mypy --update-mypy .PHONY: test-mypy-update-all ## Update the mypy integration tests for all mypy versions -test-mypy-update-all: .pdm +test-mypy-update-all: .uv rm -rf tests/mypy/outputs - pip install --force mypy==1.0.1 && make test-mypy-update - pip install --force mypy==1.1.1 && make test-mypy-update - pip install --force mypy==1.2.0 && make test-mypy-update - pip install --force mypy==1.4.1 && make test-mypy-update - pip install --force mypy==1.5.0 && make test-mypy-update + uv pip install mypy==1.10.1 && make test-mypy-update + uv pip install mypy==1.11.2 && make test-mypy-update + uv pip install mypy==1.12.0 && make test-mypy-update + +.PHONY: test-typechecking-pyright ## Typechecking integration tests (Pyright) +test-typechecking-pyright: .uv + uv run bash -c 'cd tests/typechecking && pyright --version && pyright -p pyproject.toml' -.PHONY: test-pyright ## Run the pyright integration tests -test-pyright: .pdm - pdm run bash -c 'cd tests/pyright && pyright' +.PHONY: test-typechecking-mypy ## Typechecking integration tests (Mypy). Not to be confused with `test-mypy`. +test-typechecking-mypy: .uv + uv run bash -c 'cd tests/typechecking && mypy --version && mypy --cache-dir=/dev/null --config-file pyproject.toml .' .PHONY: test ## Run all tests, skipping the type-checker integration tests -test: .pdm - pdm run coverage run -m pytest --durations=10 +test: .uv + uv run coverage run -m pytest --durations=10 .PHONY: benchmark ## Run all benchmarks -benchmark: .pdm - pdm run coverage run -m pytest --durations=10 --benchmark-enable tests/benchmarks +benchmark: .uv + uv run coverage run -m pytest --durations=10 --benchmark-enable tests/benchmarks .PHONY: testcov ## Run tests and generate a coverage report, skipping the type-checker integration tests testcov: test @echo "building coverage html" - @pdm run coverage html + @uv run coverage html @echo "building coverage lcov" - @pdm run coverage lcov + @uv run coverage lcov .PHONY: test-examples ## Run only the tests from the documentation -test-examples: .pdm +test-examples: .uv @echo "running examples" - @find docs/examples -type f -name '*.py' | xargs -I'{}' sh -c 'pdm run python {} >/dev/null 2>&1 || (echo "{} failed")' + @find docs/examples -type f -name '*.py' | xargs -I'{}' sh -c 'uv run python {} >/dev/null 2>&1 || (echo "{} failed")' .PHONY: test-fastapi ## Run the FastAPI tests with this version of pydantic test-fastapi: @@ -88,18 +86,18 @@ test-fastapi: ./tests/test_fastapi.sh .PHONY: test-pydantic-settings ## Run the pydantic-settings tests with this version of pydantic -test-pydantic-settings: .pdm +test-pydantic-settings: .uv git clone https://github.com/pydantic/pydantic-settings.git --single-branch bash ./tests/test_pydantic_settings.sh .PHONY: test-pydantic-extra-types ## Run the pydantic-extra-types tests with this version of pydantic -test-pydantic-extra-types: .pdm +test-pydantic-extra-types: .uv git clone https://github.com/pydantic/pydantic-extra-types.git --single-branch bash ./tests/test_pydantic_extra_types.sh .PHONY: test-no-docs # Run all tests except the docs tests -test-no-docs: .pdm - pdm run pytest tests --ignore=tests/test_docs.py +test-no-docs: .uv + uv run pytest tests --ignore=tests/test_docs.py .PHONY: all ## Run the standard set of checks performed in CI all: lint typecheck codespell testcov @@ -127,7 +125,7 @@ clean: .PHONY: docs ## Generate the docs docs: - pdm run mkdocs build --strict + uv run mkdocs build --strict .PHONY: help ## Display this message help: @@ -138,4 +136,4 @@ help: .PHONY: update-v1 ## Update V1 namespace update-v1: - pdm run ./update_v1.sh + uv run ./update_v1.sh diff --git a/README.md b/README.md index 685f363ac91..8664b54d428 100644 --- a/README.md +++ b/README.md @@ -13,10 +13,10 @@ Data validation using Python type hints. Fast and extensible, Pydantic plays nicely with your linters/IDE/brain. Define how data should be in pure, canonical Python 3.8+; validate it with Pydantic. -## Pydantic Company :rocket: +## Pydantic Logfire :fire: -We've started a company based on the principles that I believe have led to Pydantic's success. -Learn more from the [Company Announcement](https://blog.pydantic.dev/blog/2023/02/16/company-announcement--pydantic/). +We've recently launched Pydantic Logfire to help you monitor your applications. +[Learn more](https://pydantic.dev/articles/logfire-announcement) ## Pydantic V1.10 vs. V2 @@ -38,7 +38,7 @@ see the [Install](https://docs.pydantic.dev/install/) section in the documentati ## A Simple Example -```py +```python from datetime import datetime from typing import List, Optional from pydantic import BaseModel diff --git a/build-docs.sh b/build-docs.sh index 68a1c27edf3..85cbba93d38 100755 --- a/build-docs.sh +++ b/build-docs.sh @@ -8,11 +8,9 @@ set -x python3 -V -python3 -m pip install --user pdm - -python3 -m pdm install -G docs - -python3 -m pdm run python -c 'import docs.plugins.main' +python3 -m pip install --user uv +python3 -m uv sync --python 3.12 --group docs --frozen +python3 -m uv run python -c 'import docs.plugins.main' # Adding local symlinks gets nice source locations like # pydantic_core/core_schema.py @@ -22,4 +20,4 @@ ln -s .venv/lib/python*/site-packages/pydantic_core pydantic_core ln -s .venv/lib/python*/site-packages/pydantic_settings pydantic_settings ln -s .venv/lib/python*/site-packages/pydantic_extra_types pydantic_extra_types -python3 -m pdm run mkdocs build +python3 -m uv run --no-sync mkdocs build diff --git a/docs/api/standard_library_types.md b/docs/api/standard_library_types.md index 40832d5492f..aedc8c8a713 100644 --- a/docs/api/standard_library_types.md +++ b/docs/api/standard_library_types.md @@ -21,7 +21,7 @@ A standard `bool` field will raise a `ValidationError` if the value is not one o Here is a script demonstrating some of these behaviors: -```py +```python from pydantic import BaseModel, ValidationError @@ -63,7 +63,7 @@ types: * `int` or `float` as a string (assumed as Unix time) * [`datetime.date`][] instances are accepted in lax mode, but not in strict mode -```py +```python from datetime import datetime from pydantic import BaseModel @@ -90,7 +90,7 @@ print(event.model_dump()) * `YYYY-MM-DD` * `int` or `float` as a string (assumed as Unix time) -```py +```python from datetime import date from pydantic import BaseModel @@ -113,7 +113,7 @@ print(my_birthday.model_dump()) * `str`; the following formats are accepted: * `HH:MM[:SS[.ffffff]][Z or [±]HH[:]MM]` -```py +```python from datetime import time from pydantic import BaseModel @@ -135,11 +135,11 @@ print(m.model_dump()) * `timedelta`; an existing `timedelta` object * `int` or `float`; assumed to be seconds * `str`; the following formats are accepted: - * `[-][DD]D[,][HH:MM:]SS[.ffffff]` + * `[-][[DD]D,]HH:MM:SS[.ffffff]` * Ex: `'1d,01:02:03.000004'` or `'1D01:02:03.000004'` or `'01:02:03'` * `[±]P[DD]DT[HH]H[MM]M[SS]S` ([ISO 8601](https://en.wikipedia.org/wiki/ISO_8601) format for timedelta) -```py +```python from datetime import timedelta from pydantic import BaseModel @@ -180,7 +180,7 @@ Pydantic supports the following numeric types from the Python standard library: * Serialization: Pydantic serializes [`Decimal`][decimal.Decimal] types as strings. You can use a custom serializer to override this behavior if desired. For example: -```py +```python from decimal import Decimal from typing_extensions import Annotated @@ -212,6 +212,16 @@ print(my_model.model_dump_json()) # (3)! 2. Using [`model_dump`][pydantic.main.BaseModel.model_dump] with `mode='json'`, `x` is serialized as a `string`, and `y` is serialized as a `float` because of the custom serializer applied. 3. Using [`model_dump_json`][pydantic.main.BaseModel.model_dump_json], `x` is serialized as a `string`, and `y` is serialized as a `float` because of the custom serializer applied. +### [`complex`][] + +* Validation: Pydantic supports `complex` types or `str` values that can be converted to a `complex` type. +* Serialization: Pydantic serializes [`complex`][] types as strings. + +### [`fractions.Fraction`][fractions.Fraction] + +* Validation: Pydantic attempts to convert the value to a `Fraction` using `Fraction(v)`. +* Serialization: Pydantic serializes [`Fraction`][fractions.Fraction] types as strings. + ## [`Enum`][enum.Enum] Pydantic uses Python's standard [`enum`][] classes to define choices. @@ -219,7 +229,7 @@ Pydantic uses Python's standard [`enum`][] classes to define choices. `enum.Enum` checks that the value is a valid `Enum` instance. Subclass of `enum.Enum` checks that the value is a valid member of the enum. -```py +```python from enum import Enum, IntEnum from pydantic import BaseModel, ValidationError @@ -266,7 +276,7 @@ When a generic parameter is provided, the appropriate validation is applied to a Handled the same as `list` above. -```py +```python from typing import List, Optional from pydantic import BaseModel @@ -292,7 +302,7 @@ When generic parameters are provided, the appropriate validation is applied to t Handled the same as `tuple` above. -```py +```python from typing import Optional, Tuple from pydantic import BaseModel @@ -316,7 +326,7 @@ Subclasses of [`typing.NamedTuple`][] are similar to `tuple`, but create instanc Subclasses of [`collections.namedtuple`][] are similar to subclass of [`typing.NamedTuple`][], but since field types are not specified, all fields are treated as having type [`Any`][typing.Any]. -```py +```python from typing import NamedTuple from pydantic import BaseModel, ValidationError @@ -353,7 +363,7 @@ When generic parameters are provided, the appropriate validation is applied to t Handled the same as `deque` above. -```py +```python from typing import Deque, Optional from pydantic import BaseModel @@ -378,7 +388,7 @@ When a generic parameter is provided, the appropriate validation is applied to a Handled the same as `set` above. -```py +```python from typing import Optional, Set from pydantic import BaseModel @@ -406,7 +416,7 @@ When a generic parameter is provided, the appropriate validation is applied to a Handled the same as `frozenset` above. -```py +```python from typing import FrozenSet, Optional from pydantic import BaseModel @@ -451,7 +461,7 @@ is provided, the post-validation value of a field of type [`typing.Iterable`][] Here is a simple example using [`typing.Sequence`][]: -```py +```python from typing import Sequence from pydantic import BaseModel @@ -476,7 +486,7 @@ validated against the type parameter of the `Sequence` (e.g. `int` in `Sequence[ However, if you have a generator that you _don't_ want to be eagerly consumed (e.g. an infinite generator or a remote data loader), you can use a field of type [`Iterable`][typing.Iterable]: -```py +```python from typing import Iterable from pydantic import BaseModel @@ -562,7 +572,7 @@ except ValidationError as e: `dict(v)` is used to attempt to convert a dictionary. see [`typing.Dict`][] below for sub-type constraints. -```py +```python from pydantic import BaseModel, ValidationError @@ -587,7 +597,7 @@ except ValidationError as e: ### [`typing.Dict`][] -```py +```python from typing import Dict from pydantic import BaseModel, ValidationError @@ -625,7 +635,7 @@ its instances to have a certain set of keys, where each key is associated with a It is same as [`dict`][] but Pydantic will validate the dictionary since keys are annotated. -```py +```python from typing_extensions import TypedDict from pydantic import TypeAdapter, ValidationError @@ -655,7 +665,7 @@ except ValidationError as e: You can define `__pydantic_config__` to change the model inherited from [`TypedDict`][typing.TypedDict]. See the [`ConfigDict` API reference][pydantic.config.ConfigDict] for more details. -```py +```python from typing import Optional from typing_extensions import TypedDict @@ -731,7 +741,7 @@ See below for more detail on parsing and validation Fields can also be of type [`Callable`][typing.Callable]: -```py +```python from typing import Callable from pydantic import BaseModel @@ -790,7 +800,7 @@ that are subclasses of `T`. Handled the same as `type` above. -```py +```python from typing import Type from pydantic import BaseModel, ValidationError @@ -827,7 +837,7 @@ except ValidationError as e: You may also use `Type` to specify that any class is allowed. -```py upgrade="skip" +```python {upgrade="skip"} from typing import Type from pydantic import BaseModel, ValidationError @@ -858,7 +868,7 @@ except ValidationError as e: [`TypeVar`][typing.TypeVar] is supported either unconstrained, constrained or with a bound. -```py +```python from typing import TypeVar from pydantic import BaseModel @@ -889,8 +899,11 @@ Allows only `None` value. ## Strings -`str`: Strings are accepted as-is. `bytes` and `bytearray` are converted using `v.decode()`. -Enum`s inheriting from `str` are converted using `v.value`. All other types cause an error. +- [`str`][]: Strings are accepted as-is. +- [`bytes`][] and [`bytearray`][] are converted using the [`decode()`][bytes.decode] method. +- Enums inheriting from [`str`][] are converted using the [`value`][enum.Enum.value] attribute. + +All other types cause an error. !!! warning "Strings aren't Sequences" @@ -901,7 +914,7 @@ Enum`s inheriting from `str` are converted using `v.value`. All other types caus As a result, Pydantic raises a `ValidationError` if you attempt to pass a `str` or `bytes` instance into a field of type `Sequence[str]` or `Sequence[bytes]`: -```py +```python from typing import Optional, Sequence from pydantic import BaseModel, ValidationError @@ -951,7 +964,7 @@ except ValidationError as e: Pydantic supports the use of [`typing.Literal`][] as a lightweight way to specify that a field may accept only specific literal values: -```py +```python from typing import Literal from pydantic import BaseModel, ValidationError @@ -977,7 +990,7 @@ except ValidationError as e: One benefit of this field type is that it can be used to check for equality with one or more specific values without needing to declare custom validators: -```py +```python from typing import ClassVar, List, Literal, Union from pydantic import BaseModel, ValidationError @@ -1016,7 +1029,7 @@ except ValidationError as e: With proper ordering in an annotated `Union`, you can use this to parse types of decreasing specificity: -```py +```python from typing import Literal, Optional, Union from pydantic import BaseModel @@ -1057,6 +1070,10 @@ print(type(Meal(dessert={'kind': 'cake'}).dessert).__name__) Allows any value, including `None`. +## [`typing.Hashable`][] + +* From Python, supports any data that passes an `isinstance(v, Hashable)` check. +* From JSON, first loads the data via an `Any` validator, then checks if the data is hashable with `isinstance(v, Hashable)`. ## [`typing.Annotated`][] diff --git a/docs/concepts/alias.md b/docs/concepts/alias.md index 3b2b460ab9f..3139e4b99ac 100644 --- a/docs/concepts/alias.md +++ b/docs/concepts/alias.md @@ -24,7 +24,7 @@ Pydantic provides two special types for convenience when using `validation_alias The `AliasPath` is used to specify a path to a field using aliases. For example: -```py lint="skip" +```python {lint="skip"} from pydantic import BaseModel, Field, AliasPath @@ -46,7 +46,7 @@ In the `'last_name'` field, we are using the alias `'names'` and the index `1` t `AliasChoices` is used to specify a choice of aliases. For example: -```py lint="skip" +```python {lint="skip"} from pydantic import BaseModel, Field, AliasChoices @@ -68,7 +68,7 @@ print(user) You can also use `AliasChoices` with `AliasPath`: -```py lint="skip" +```python {lint="skip"} from pydantic import BaseModel, Field, AliasPath, AliasChoices @@ -107,7 +107,7 @@ want to specify the alias for each field individually. Here's a basic example using a callable: -```py +```python from pydantic import BaseModel, ConfigDict @@ -141,7 +141,7 @@ but you don't want to specify the validation and serialization aliases for each For example: -```py +```python from pydantic import AliasGenerator, BaseModel, ConfigDict @@ -167,7 +167,7 @@ print(t.model_dump(by_alias=True)) If you specify an `alias` on the [`Field`][pydantic.fields.Field], it will take precedence over the generated alias by default: -```py +```python from pydantic import BaseModel, ConfigDict, Field diff --git a/docs/concepts/config.md b/docs/concepts/config.md index acb900a000c..7157eb9ea64 100644 --- a/docs/concepts/config.md +++ b/docs/concepts/config.md @@ -4,7 +4,7 @@ and as an argument to [`TypeAdapter`][pydantic.TypeAdapter]. !!! note Before **v2.0**, the `Config` class was used. This is still supported, but **deprecated**. -```py +```python from pydantic import BaseModel, ConfigDict, ValidationError @@ -26,7 +26,7 @@ except ValidationError as e: ``` Also, you can specify config options as model class kwargs: -```py +```python from pydantic import BaseModel, ValidationError @@ -48,7 +48,7 @@ except ValidationError as e: 1. See the [Extra Attributes](models.md#extra-fields) section for more details. Similarly, if using the [`@dataclass`][pydantic.dataclasses] decorator from Pydantic: -```py +```python from datetime import datetime from pydantic import ConfigDict, ValidationError @@ -80,7 +80,7 @@ except ValidationError as e: If using the `dataclass` from the standard library or `TypedDict`, you should use `__pydantic_config__` instead. -```py +```python from dataclasses import dataclass from datetime import datetime @@ -98,7 +98,7 @@ class User: Alternatively, the [`with_config`][pydantic.config.with_config] decorator can be used to comply with type checkers. -```py +```python from typing_extensions import TypedDict from pydantic import ConfigDict, with_config @@ -114,7 +114,7 @@ class Model(TypedDict): If you wish to change the behaviour of Pydantic globally, you can create your own custom `BaseModel` with custom `model_config` since the config is inherited: -```py +```python from pydantic import BaseModel, ConfigDict @@ -133,7 +133,7 @@ print(m.model_dump()) If you add a `model_config` to the `Model` class, it will _merge_ with the `model_config` from `Parent`: -```py +```python from pydantic import BaseModel, ConfigDict diff --git a/docs/concepts/dataclasses.md b/docs/concepts/dataclasses.md index f3685becc12..539366c6057 100644 --- a/docs/concepts/dataclasses.md +++ b/docs/concepts/dataclasses.md @@ -1,11 +1,12 @@ ??? api "API Documentation" [`pydantic.dataclasses.dataclass`][pydantic.dataclasses.dataclass]
-If you don't want to use Pydantic's `BaseModel` you can instead get the same data validation on standard -[dataclasses](https://docs.python.org/3/library/dataclasses.html) (introduced in Python 3.7). +If you don't want to use Pydantic's [`BaseModel`][pydantic.BaseModel] you can instead get the same data validation +on standard [dataclasses][dataclasses]. -```py +```python from datetime import datetime +from typing import Optional from pydantic.dataclasses import dataclass @@ -14,7 +15,7 @@ from pydantic.dataclasses import dataclass class User: id: int name: str = 'John Doe' - signup_ts: datetime = None + signup_ts: Optional[datetime] = None user = User(id='42', signup_ts='2032-06-21T12:00') @@ -25,28 +26,33 @@ User(id=42, name='John Doe', signup_ts=datetime.datetime(2032, 6, 21, 12, 0)) ``` !!! note - Keep in mind that `pydantic.dataclasses.dataclass` is **not** a replacement for `pydantic.BaseModel`. - `pydantic.dataclasses.dataclass` provides a similar functionality to `dataclasses.dataclass` with the addition of - Pydantic validation. - There are cases where subclassing `pydantic.BaseModel` is the better choice. + Keep in mind that Pydantic dataclasses are **not** a replacement for [Pydantic models](../concepts/models.md). + They provide a similar functionality to stdlib dataclasses with the addition of Pydantic validation. + + There are cases where subclassing using Pydantic models is the better choice. For more information and discussion see [pydantic/pydantic#710](https://github.com/pydantic/pydantic/issues/710). -Some differences between Pydantic dataclasses and `BaseModel` include: +Similarities between Pydantic dataclasses and models include support for: + +* [Configuration](#dataclass-config) support +* [Nested](./models.md#nested-models) classes +* [Generics](./models.md#generic-models) -* How [initialization hooks](#initialization-hooks) work -* [JSON dumping](#json-dumping) +Some differences between Pydantic dataclasses and models include: -You can use all the standard Pydantic field types. Note, however, that arguments passed to constructor will be copied in -order to perform validation and, where necessary coercion. +* [validators](#validators-and-initialization-hooks) +* The behavior with the [`extra`][pydantic.ConfigDict.extra] configuration value -To perform validation or generate a JSON schema on a Pydantic dataclass, you should now wrap the dataclass -with a [`TypeAdapter`][pydantic.type_adapter.TypeAdapter] and make use of its methods. +Similarly to Pydantic models, arguments used to instantiate the dataclass are [copied](./models.md#attribute-copies). -Fields that require a `default_factory` can be specified by either a `pydantic.Field` or a `dataclasses.field`. +To make use of the [various methods](./models.md#model-methods-and-properties) to validate, dump and generate a JSON Schema, +you can wrap the dataclass with a [`TypeAdapter`][pydantic.type_adapter.TypeAdapter] and make use of its methods. -```py +You can use both the Pydantic's [`Field()`][pydantic.Field] and the stdlib's [`field()`][dataclasses.field] functions: + +```python import dataclasses from typing import List, Optional @@ -61,7 +67,7 @@ class User: friends: List[int] = dataclasses.field(default_factory=lambda: [0]) age: Optional[int] = dataclasses.field( default=None, - metadata=dict(title='The age of the user', description='do not lie!'), + metadata={'title': 'The age of the user', 'description': 'do not lie!'}, ) height: Optional[int] = Field(None, title='The height in cm', ge=50, le=300) @@ -100,101 +106,46 @@ print(TypeAdapter(User).json_schema()) """ ``` -`pydantic.dataclasses.dataclass`'s arguments are the same as the standard decorator, except one extra -keyword argument `config` which has the same meaning as [model_config][pydantic.config.ConfigDict]. - -!!! warning - After v1.2, [The Mypy plugin](../integrations/mypy.md) must be installed to type check _pydantic_ dataclasses. - -For more information about combining validators with dataclasses, see -[dataclass validators](validators.md#dataclass-validators). +The Pydantic `@dataclass` decorator accepts the same arguments as the standard decorator, with the addition +of a `config` parameter. ## Dataclass config -If you want to modify the `config` like you would with a `BaseModel`, you have two options: +If you want to modify the configuration like you would with a [`BaseModel`][pydantic.BaseModel], you have two options: -* Apply config to the dataclass decorator as a dict -* Use `ConfigDict` as the config +* Use the `config` argument of the decorator. +* Define the configuration with the `__pydantic_config__` attribute. -```py +```python from pydantic import ConfigDict from pydantic.dataclasses import dataclass -# Option 1 - use directly a dict -# Note: `mypy` will still raise typo error -@dataclass(config=dict(validate_assignment=True)) # (1)! +# Option 1 -- using the decorator argument: +@dataclass(config=ConfigDict(validate_assignment=True)) # (1)! class MyDataclass1: a: int -# Option 2 - use `ConfigDict` -# (same as before at runtime since it's a `TypedDict` but with intellisense) -@dataclass(config=ConfigDict(validate_assignment=True)) +# Option 2 -- using an attribute: +@dataclass class MyDataclass2: a: int -``` - -1. You can read more about `validate_assignment` in [API reference][pydantic.config.ConfigDict.validate_assignment]. - -!!! note - Pydantic dataclasses support [`extra`][pydantic.config.ConfigDict.extra] configuration to `ignore`, `forbid`, or - `allow` extra fields passed to the initializer. However, some default behavior of stdlib dataclasses may prevail. - For example, any extra fields present on a Pydantic dataclass using `extra='allow'` are omitted when the dataclass - is `print`ed. - -## Nested dataclasses - -Nested dataclasses are supported both in dataclasses and normal models. - -```py -from pydantic import AnyUrl -from pydantic.dataclasses import dataclass - - -@dataclass -class NavbarButton: - href: AnyUrl - - -@dataclass -class Navbar: - button: NavbarButton - -navbar = Navbar(button={'href': 'https://example.com'}) -print(navbar) -#> Navbar(button=NavbarButton(href=Url('https://example.com/'))) + __pydantic_config__ = ConfigDict(validate_assignment=True) ``` -When used as fields, dataclasses (Pydantic or vanilla) should use dicts as validation inputs. - -## Generic dataclasses - -Pydantic supports generic dataclasses, including those with type variables. - -```py -from typing import Generic, TypeVar - -from pydantic import TypeAdapter -from pydantic.dataclasses import dataclass - -T = TypeVar('T') - - -@dataclass -class GenericDataclass(Generic[T]): - x: T +1. You can read more about `validate_assignment` in the [API reference][pydantic.config.ConfigDict.validate_assignment]. +!!! note + While Pydantic dataclasses support the [`extra`][pydantic.config.ConfigDict.extra] configuration value, some default + behavior of stdlib dataclasses may prevail. For example, any extra fields present on a Pydantic dataclass with + [`extra`][pydantic.config.ConfigDict.extra] set to `'allow'` are omitted in the dataclass' string representation. -validator = TypeAdapter(GenericDataclass) +## Rebuilding dataclass schema -assert validator.validate_python({'x': None}).x is None -assert validator.validate_python({'x': 1}).x == 1 -assert validator.validate_python({'x': 'a'}).x == 'a' -``` - -Note that, if you use the dataclass as a field of a `BaseModel` or via FastAPI you don't need a `TypeAdapter`. +The [`rebuild_dataclass()`][pydantic.dataclasses.rebuild_dataclass] can be used to rebuild the core schema of the dataclass. +See the [rebuilding model schema](./models.md#rebuilding-model-schema) section for more details. ## Stdlib dataclasses and Pydantic dataclasses @@ -203,7 +154,7 @@ Note that, if you use the dataclass as a field of a `BaseModel` or via FastAPI y Stdlib dataclasses (nested or not) can also be inherited and Pydantic will automatically validate all the inherited fields. -```py +```python import dataclasses import pydantic @@ -239,15 +190,14 @@ except pydantic.ValidationError as e: """ ``` -### Use of stdlib dataclasses with `BaseModel` +### Usage of stdlib dataclasses with `BaseModel` -Bear in mind that stdlib dataclasses (nested or not) are **automatically converted** into Pydantic -dataclasses when mixed with `BaseModel`! Furthermore the generated Pydantic dataclass will have -the **exact same configuration** (`order`, `frozen`, ...) as the original one. +When a standard library dataclass is used within a Pydantic model, a Pydantic dataclass or a [`TypeAdapter`][pydantic.TypeAdapter], +validation will be applied (and the [configuration](#dataclass-config) stays the same). This means that using a stdlib or a Pydantic +dataclass as a field annotation is functionally equivalent. -```py +```python import dataclasses -from datetime import datetime from typing import Optional from pydantic import BaseModel, ConfigDict, ValidationError @@ -258,40 +208,30 @@ class User: name: str -@dataclasses.dataclass -class File: - filename: str - last_modification_time: Optional[datetime] = None - - class Foo(BaseModel): - # Required so that pydantic revalidates the model attributes + # Required so that pydantic revalidates the model attributes: model_config = ConfigDict(revalidate_instances='always') - file: File user: Optional[User] = None -file = File( - filename=['not', 'a', 'string'], - last_modification_time='2020-01-01T00:00', -) # nothing is validated as expected -print(file) -""" -File(filename=['not', 'a', 'string'], last_modification_time='2020-01-01T00:00') -""" +# nothing is validated as expected: +user = User(name=['not', 'a', 'string']) +print(user) +#> User(name=['not', 'a', 'string']) + try: - Foo(file=file) + Foo(user=user) except ValidationError as e: print(e) """ 1 validation error for Foo - file.filename + user.name Input should be a valid string [type=string_type, input_value=['not', 'a', 'string'], input_type=list] """ -foo = Foo(file=File(filename='myfile'), user=User(name='pika')) +foo = Foo(user=User(name='pika')) try: foo.user.name = 'bulbi' except dataclasses.FrozenInstanceError as e: @@ -299,16 +239,17 @@ except dataclasses.FrozenInstanceError as e: #> cannot assign to field 'name' ``` -### Use custom types +### Using custom types -Since stdlib dataclasses are automatically converted to add validation, using -custom types may cause some unexpected behavior. -In this case you can simply add `arbitrary_types_allowed` in the config! +As said above, validation is applied on standard library dataclasses. If you make use +of custom types, you will get an error when trying to refer to the dataclass. To circumvent +the issue, you can set the [`arbitrary_types_allowed`][pydantic.ConfigDict.arbitrary_types_allowed] +configuration value on the dataclass: -```py +```python import dataclasses -from pydantic import BaseModel, ConfigDict +from pydantic import BaseModel from pydantic.errors import PydanticSchemaGenerationError @@ -326,7 +267,7 @@ class DC: b: str -# valid as it is a builtin dataclass without validation +# valid as it is a stdlib dataclass without validation: my_dc = DC(a=ArbitraryType(value=3), b='qwe') try: @@ -335,8 +276,6 @@ try: dc: DC other: str - # invalid as it is now a pydantic dataclass - Model(dc=my_dc, other='other') except PydanticSchemaGenerationError as e: print(e.message) """ @@ -346,23 +285,31 @@ except PydanticSchemaGenerationError as e: """ -class Model(BaseModel): - model_config = ConfigDict(arbitrary_types_allowed=True) +@dataclasses.dataclass +class DC2: + a: ArbitraryType + b: str + + __pydantic_config__ = {'arbitrary_types_allowed': True} + - dc: DC +class Model(BaseModel): + dc: DC2 other: str -m = Model(dc=my_dc, other='other') +m = Model(dc=DC2(a=ArbitraryType(value=3), b='qwe'), other='other') print(repr(m)) -#> Model(dc=DC(a=ArbitraryType(value=3), b='qwe'), other='other') +#> Model(dc=DC2(a=ArbitraryType(value=3), b='qwe'), other='other') ``` -### Checking if a dataclass is a pydantic dataclass +### Checking if a dataclass is a Pydantic dataclass -Pydantic dataclasses are still considered dataclasses, so using `dataclasses.is_dataclass` will return `True`. To check if a type is specifically a pydantic dataclass you can use `pydantic.dataclasses.is_pydantic_dataclass`. +Pydantic dataclasses are still considered dataclasses, so using [`dataclasses.is_dataclass`][] will return `True`. To check +if a type is specifically a pydantic dataclass you can use the [`is_pydantic_dataclass`][pydantic.dataclasses.is_pydantic_dataclass] +function. -```py +```python import dataclasses import pydantic @@ -386,123 +333,78 @@ print(pydantic.dataclasses.is_pydantic_dataclass(PydanticDataclass)) #> True ``` -## Initialization hooks - -When you initialize a dataclass, it is possible to execute code *before* or *after* validation -with the help of the [`@model_validator`](validators.md#model-validators) decorator `mode` parameter. +## Validators and initialization hooks -```py -from typing import Any, Dict +Validators also work with Pydantic dataclasses: -from typing_extensions import Self - -from pydantic import model_validator +```python +from pydantic import field_validator from pydantic.dataclasses import dataclass @dataclass -class Birth: - year: int - month: int - day: int - - -@dataclass -class User: - birth: Birth +class DemoDataclass: + product_id: str # should be a five-digit string, may have leading zeros - @model_validator(mode='before') + @field_validator('product_id', mode='before') @classmethod - def pre_root(cls, values: Dict[str, Any]) -> Dict[str, Any]: - print(f'First: {values}') - """ - First: ArgsKwargs((), {'birth': {'year': 1995, 'month': 3, 'day': 2}}) - """ - return values + def convert_int_serial(cls, v): + if isinstance(v, int): + v = str(v).zfill(5) + return v - @model_validator(mode='after') - def post_root(self) -> Self: - print(f'Third: {self}') - #> Third: User(birth=Birth(year=1995, month=3, day=2)) - return self - def __post_init__(self): - print(f'Second: {self.birth}') - #> Second: Birth(year=1995, month=3, day=2) - - -user = User(**{'birth': {'year': 1995, 'month': 3, 'day': 2}}) +print(DemoDataclass(product_id='01234')) +#> DemoDataclass(product_id='01234') +print(DemoDataclass(product_id=2468)) +#> DemoDataclass(product_id='02468') ``` -The `__post_init__` in Pydantic dataclasses is called in the _middle_ of validators. -Here is the order: - -* `model_validator(mode='before')` -* `field_validator(mode='before')` -* `field_validator(mode='after')` -* Inner validators. e.g. validation for types like `int`, `str`, ... -* `__post_init__`. -* `model_validator(mode='after')` - - -```py -from dataclasses import InitVar -from pathlib import Path -from typing import Optional - -from pydantic.dataclasses import dataclass - - -@dataclass -class PathData: - path: Path - base_path: InitVar[Optional[Path]] +The dataclass [`__post_init__()`][dataclasses.__post_init__] method is also supported, and will +be called between the calls to *before* and *after* model validators. - def __post_init__(self, base_path): - print(f'Received path={self.path!r}, base_path={base_path!r}') - #> Received path=PosixPath('world'), base_path=PosixPath('/hello') - if base_path is not None: - self.path = base_path / self.path +??? example + ```python + from pydantic_core import ArgsKwargs + from typing_extensions import Self -path_data = PathData('world', base_path='/hello') -# Received path='world', base_path='/hello' -assert path_data.path == Path('/hello/world') -``` + from pydantic import model_validator + from pydantic.dataclasses import dataclass -### Difference with stdlib dataclasses -Note that the `dataclasses.dataclass` from Python stdlib implements only the `__post_init__` method since it doesn't run a validation step. + @dataclass + class Birth: + year: int + month: int + day: int -## JSON dumping -Pydantic dataclasses do not feature a `.model_dump_json()` function. To dump them as JSON, you will need to -make use of the [RootModel](models.md#rootmodel-and-custom-root-types) as follows: + @dataclass + class User: + birth: Birth -```py output="json" -import dataclasses -from typing import List + @model_validator(mode='before') + @classmethod + def before(cls, values: ArgsKwargs) -> ArgsKwargs: + print(f'First: {values}') # (1)! + """ + First: ArgsKwargs((), {'birth': {'year': 1995, 'month': 3, 'day': 2}}) + """ + return values -from pydantic import RootModel -from pydantic.dataclasses import dataclass + @model_validator(mode='after') + def after(self) -> Self: + print(f'Third: {self}') + #> Third: User(birth=Birth(year=1995, month=3, day=2)) + return self + def __post_init__(self): + print(f'Second: {self.birth}') + #> Second: Birth(year=1995, month=3, day=2) -@dataclass -class User: - id: int - name: str = 'John Doe' - friends: List[int] = dataclasses.field(default_factory=lambda: [0]) + user = User(**{'birth': {'year': 1995, 'month': 3, 'day': 2}}) + ``` -user = User(id='42') -print(RootModel[User](User(id='42')).model_dump_json(indent=4)) -""" -{ - "id": 42, - "name": "John Doe", - "friends": [ - 0 - ] -} -""" -``` + 1. Unlike Pydantic models, the `values` parameter is of type [`ArgsKwargs`][pydantic_core.ArgsKwargs] diff --git a/docs/concepts/experimental.md b/docs/concepts/experimental.md index 43a51acd6c5..b585c3ee78e 100644 --- a/docs/concepts/experimental.md +++ b/docs/concepts/experimental.md @@ -51,7 +51,7 @@ from datetime import datetime from typing_extensions import Annotated from pydantic import BaseModel -from pydantic.experimental.pipeline import validate_as, validate_as_deferred +from pydantic.experimental.pipeline import validate_as class User(BaseModel): @@ -71,10 +71,6 @@ class User(BaseModel): ), ] friends: Annotated[list[User], validate_as(...).len(0, 100)] # (6)! - family: Annotated[ # (7)! - list[User], - validate_as_deferred(lambda: list[User]).transform(lambda x: x[1:]), - ] bio: Annotated[ datetime, validate_as(int) @@ -89,8 +85,7 @@ class User(BaseModel): 4. You can also use the lower level transform, constrain and predicate methods. 5. Use the `|` or `&` operators to combine steps (like a logical OR or AND). 6. Calling `validate_as(...)` with `Ellipsis`, `...` as the first positional argument implies `validate_as()`. Use `validate_as(Any)` to accept any type. -7. For recursive types you can use `validate_as_deferred` to reference the type itself before it's defined. -8. You can call `validate_as()` before or after other steps to do pre or post processing. +7. You can call `validate_as()` before or after other steps to do pre or post processing. ### Mapping from `BeforeValidator`, `AfterValidator` and `WrapValidator` @@ -154,3 +149,284 @@ This example uses plain idiomatic Python code that may be easier to understand, The approach you choose should really depend on your use case. You will have to compare verbosity, performance, ease of returning meaningful errors to your users, etc. to choose the right pattern. Just be mindful of abusing advanced patterns like the pipeline API just because you can. + +## Partial Validation + +Pydantic v2.10.0 introduces experimental support for "partial validation". + +This allows you to validate an incomplete JSON string, or a Python object representing incomplete input data. + +Partial validation is particularly helpful when processing the output of an LLM, where the model streams structured responses, and you may wish to begin validating the stream while you're still receiving data (e.g. to show partial data to users). + +!!! warning + Partial validation is an experimental feature and may change in future versions of Pydantic. The current implementation should be considered a proof of concept at this time and has a number of [limitations](#limitations-of-partial-validation). + +Partial validation can be enabled when using the three validation methods on `TypeAdapter`: [`TypeAdapter.validate_json()`][pydantic.TypeAdapter.validate_json], [`TypeAdapter.validate_python()`][pydantic.TypeAdapter.validate_python], and [`TypeAdapter.validate_strings()`][pydantic.TypeAdapter.validate_strings]. This allows you to parse and validation incomplete JSON, but also to validate Python objects created by parsing incomplete data of any format. + +The `experimental_allow_partial` flag can be passed to these methods to enable partial validation. +It can take the following values (and is `False`, by default): + +* `False` or `'off'` - disable partial validation +* `True` or `'on'` - enable partial validation, but don't support trailing strings +* `'trailing-strings'` - enable partial validation and support trailing strings + +!!! info "`'trailing-strings'` mode" + `'trailing-strings'` mode allows for trailing incomplete strings at the end of partial JSON to be included in the output. + For example, if you're validating against the following model: + + ```python + from typing import TypedDict + + + class Model(TypedDict): + a: str + b: str + ``` + + Then the following JSON input would be considered valid, despite the incomplete string at the end: + + ```json + '{"a": "hello", "b": "wor' + ``` + + And would be validated as: + + ```python {test="skip" lint="skip"} + {'a': 'hello', 'b': 'wor'} + ``` + +`experiment_allow_partial` in action: + +```python +from typing import List + +from annotated_types import MinLen +from typing_extensions import Annotated, NotRequired, TypedDict + +from pydantic import TypeAdapter + + +class Foobar(TypedDict): # (1)! + a: int + b: NotRequired[float] + c: NotRequired[Annotated[str, MinLen(5)]] + + +ta = TypeAdapter(List[Foobar]) + +v = ta.validate_json('[{"a": 1, "b"', experimental_allow_partial=True) # (2)! +print(v) +#> [{'a': 1}] + +v = ta.validate_json( + '[{"a": 1, "b": 1.0, "c": "abcd', experimental_allow_partial=True # (3)! +) +print(v) +#> [{'a': 1, 'b': 1.0}] + +v = ta.validate_json( + '[{"b": 1.0, "c": "abcde"', experimental_allow_partial=True # (4)! +) +print(v) +#> [] + +v = ta.validate_json( + '[{"a": 1, "b": 1.0, "c": "abcde"},{"a": ', experimental_allow_partial=True +) +print(v) +#> [{'a': 1, 'b': 1.0, 'c': 'abcde'}] + +v = ta.validate_python([{'a': 1}], experimental_allow_partial=True) # (5)! +print(v) +#> [{'a': 1}] + +v = ta.validate_python( + [{'a': 1, 'b': 1.0, 'c': 'abcd'}], experimental_allow_partial=True # (6)! +) +print(v) +#> [{'a': 1, 'b': 1.0}] + +v = ta.validate_json( + '[{"a": 1, "b": 1.0, "c": "abcdefg', + experimental_allow_partial='trailing-strings', # (7)! +) +print(v) +#> [{'a': 1, 'b': 1.0, 'c': 'abcdefg'}] +``` + +1. The TypedDict `Foobar` has three field, but only `a` is required, that means that a valid instance of `Foobar` can be created even if the `b` and `c` fields are missing. +2. Parsing JSON, the input is valid JSON up to the point where the string is truncated. +3. In this case truncation of the input means the value of `c` (`abcd`) is invalid as input to `c` field, hence it's omitted. +4. The `a` field is required, so validation on the only item in the list fails and is dropped. +5. Partial validation also works with Python objects, it should have the same semantics as with JSON except of course you can't have a genuinely "incomplete" Python object. +6. The same as above but with a Python object, `c` is dropped as it's not required and failed validation. +7. The `trailing-strings` mode allows for incomplete strings at the end of partial JSON to be included in the output, in this case the input is valid JSON up to the point where the string is truncated, so the last string is included. + +### How Partial Validation Works + +Partial validation follows the zen of Pydantic — it makes no guarantees about what the input data might have been, but it does guarantee to return a valid instance of the type you required, or raise a validation error. + +To do this, the `experimental_allow_partial` flag enables two pieces of behavior: + +#### 1. Partial JSON parsing + +The [jiter](https://github.com/pydantic/jiter) JSON parser used by Pydantic already supports parsing partial JSON, +`experimental_allow_partial` is simply passed to jiter via the `allow_partial` argument. + +!!! note + If you just want pure JSON parsing with support for partial JSON, you can use the [`jiter`](https://pypi.org/project/jiter/) Python library directly, or pass the `allow_partial` argument when calling [`pydantic_core.from_json`][pydantic_core.from_json]. + +#### 2. Ignore errors in the last element of the input {#2-ignore-errors-in-last} + +Only having access to part of the input data means errors can commonly occur in the last element of the input data. + +For example: + +* if a string has a constraint `MinLen(5)`, when you only see part of the input, validation might fail because part of the string is missing (e.g. `{"name": "Sam` instead of `{"name": "Samuel"}`) +* if an `int` field has a constraint `Ge(10)`, when you only see part of the input, validation might fail because the number is too small (e.g. `1` instead of `10`) +* if a `TypedDict` field has 3 required fields, but the partial input only has two of the fields, validation would fail because some field are missing +* etc. etc. — there are lost more cases like this + +The point is that if you only see part of some valid input data, validation errors can often occur in the last element of a sequence or last value of mapping. + +To avoid these errors breaking partial validation, Pydantic will ignore ALL errors in the last element of the input data. + +```python {title="Errors in last element ignored"} +from typing import List + +from annotated_types import MinLen +from typing_extensions import Annotated + +from pydantic import BaseModel, TypeAdapter + + +class MyModel(BaseModel): + a: int + b: Annotated[str, MinLen(5)] + + +ta = TypeAdapter(List[MyModel]) +v = ta.validate_json( + '[{"a": 1, "b": "12345"}, {"a": 1,', + experimental_allow_partial=True, +) +print(v) +#> [MyModel(a=1, b='12345')] +``` + +### Limitations of Partial Validation + +#### TypeAdapter only + +You can only pass `experiment_allow_partial` to [`TypeAdapter`][pydantic.TypeAdapter] methods, it's not yet supported via other Pydantic entry points like [`BaseModel`][pydantic.BaseModel]. + +#### Types supported + +Right now only a subset of collection validators know how to handle partial validation: + +- `list` +- `set` +- `frozenset` +- `dict` (as in `dict[X, Y]`) +- `TypedDict` — only non-required fields may be missing, e.g. via [`NotRequired`][typing.NotRequired] or [`total=False`][typing.TypedDict.__total__]) + +While you can use `experimental_allow_partial` while validating against types that include other collection validators, those types will be validated "all or nothing", and partial validation will not work on more nested types. + +E.g. in the [above](#2-ignore-errors-in-last) example partial validation works although the second item in the list is dropped completely since `BaseModel` doesn't (yet) support partial validation. + +But partial validation won't work at all in the follow example because `BaseModel` doesn't support partial validation so it doesn't forward the `allow_partial` instruction down to the list validator in `b`: + +```python +from typing import List + +from annotated_types import MinLen +from typing_extensions import Annotated + +from pydantic import BaseModel, TypeAdapter, ValidationError + + +class MyModel(BaseModel): + a: int = 1 + b: List[Annotated[str, MinLen(5)]] = [] # (1)! + + +ta = TypeAdapter(MyModel) +try: + v = ta.validate_json( + '{"a": 1, "b": ["12345", "12', experimental_allow_partial=True + ) +except ValidationError as e: + print(e) + """ + 1 validation error for MyModel + b.1 + String should have at least 5 characters [type=string_too_short, input_value='12', input_type=str] + """ +``` + +1. The list validator for `b` doesn't get the `allow_partial` instruction passed down to it by the model validator so it doesn't know to ignore errors in the last element of the input. + +#### Some invalid but complete JSON will be accepted + +The way [jiter](https://github.com/pydantic/jiter) (the JSON parser used by Pydantic) works means it's currently not possible to differentiate between complete JSON like `{"a": 1, "b": "12"}` and incomplete JSON like `{"a": 1, "b": "12`. + +This means that some invalid JSON will be accepted by Pydantic when using `experimental_allow_partial`, e.g.: + +```python +from annotated_types import MinLen +from typing_extensions import Annotated, TypedDict + +from pydantic import TypeAdapter + + +class Foobar(TypedDict, total=False): + a: int + b: Annotated[str, MinLen(5)] + + +ta = TypeAdapter(Foobar) + +v = ta.validate_json( + '{"a": 1, "b": "12', experimental_allow_partial=True # (1)! +) +print(v) +#> {'a': 1} + +v = ta.validate_json( + '{"a": 1, "b": "12"}', experimental_allow_partial=True # (2)! +) +print(v) +#> {'a': 1} +``` + +1. This will pass validation as expected although the last field will be omitted as it failed validation. +2. This will also pass validation since the binary representation of the JSON data passed to pydantic-core is indistinguishable from the previous case. + +#### Any error in the last field of the input will be ignored + +As described [above](#2-ignore-errors-in-last), many errors can result from truncating the input. Rather than trying to specifically ignore errors that could result from truncation, Pydantic ignores all errors in the last element of the input in partial validation mode. + +This means clearly invalid data will pass validation if the error is in the last field of the input: + +```python +from typing import List + +from annotated_types import Ge +from typing_extensions import Annotated + +from pydantic import TypeAdapter + +ta = TypeAdapter(List[Annotated[int, Ge(10)]]) +v = ta.validate_python([20, 30, 4], experimental_allow_partial=True) # (1)! +print(v) +#> [20, 30] + +ta = TypeAdapter(List[int]) + +v = ta.validate_python([1, 2, 'wrong'], experimental_allow_partial=True) # (2)! +print(v) +#> [1, 2] +``` + +1. As you would expect, this will pass validation since Pydantic correctly ignores the error in the (truncated) last item. +2. This will also pass validation since the error in the last item is ignored. diff --git a/docs/concepts/fields.md b/docs/concepts/fields.md index c08fdc09863..8aedcadb118 100644 --- a/docs/concepts/fields.md +++ b/docs/concepts/fields.md @@ -7,7 +7,7 @@ The [`Field`][pydantic.fields.Field] function is used to customize and add metad The `default` parameter is used to define a default value for a field. -```py +```python from pydantic import BaseModel, Field @@ -20,9 +20,12 @@ print(user) #> name='John Doe' ``` -You can also use `default_factory` to define a callable that will be called to generate a default value. +!!! note + If you use the [`Optional`][typing.Optional] annotation, it doesn't mean that the field has a default value of `None`! + +You can also use `default_factory` (but not both at the same time) to define a callable that will be called to generate a default value. -```py +```python from uuid import uuid4 from pydantic import BaseModel, Field @@ -32,17 +35,31 @@ class User(BaseModel): id: str = Field(default_factory=lambda: uuid4().hex) ``` -!!! info - The `default` and `default_factory` parameters are mutually exclusive. +The default factory can also take a single required argument, in which the case the already validated data will be passed as a dictionary. + +```python +from pydantic import BaseModel, EmailStr, Field + + +class User(BaseModel): + email: EmailStr + username: str = Field(default_factory=lambda data: data['email']) + + +user = User(email='user@example.com') +print(user.username) +#> user@example.com +``` + +The `data` argument will *only* contain the already validated data, based on the [order of model fields](./models.md#field-ordering) +(the above example would fail if `username` were to be defined before `email`). -!!! note - If you use `typing.Optional`, it doesn't mean that the field has a default value of `None`! ## Using `Annotated` The [`Field`][pydantic.fields.Field] function can also be used together with [`Annotated`][annotated]. -```py +```python from uuid import uuid4 from typing_extensions import Annotated @@ -65,9 +82,9 @@ For validation and serialization, you can define an alias for a field. There are three ways to define an alias: -* `Field(..., alias='foo')` -* `Field(..., validation_alias='foo')` -* `Field(..., serialization_alias='foo')` +* `Field(alias='foo')` +* `Field(validation_alias='foo')` +* `Field(serialization_alias='foo')` The `alias` parameter is used for both validation _and_ serialization. If you want to use _different_ aliases for validation and serialization respectively, you can use the`validation_alias` @@ -75,12 +92,12 @@ and `serialization_alias` parameters, which will apply only in their respective Here is an example of using the `alias` parameter: -```py +```python from pydantic import BaseModel, Field class User(BaseModel): - name: str = Field(..., alias='username') + name: str = Field(alias='username') user = User(username='johndoe') # (1)! @@ -102,12 +119,12 @@ print(user.model_dump(by_alias=True)) # (2)! If you want to use an alias _only_ for validation, you can use the `validation_alias` parameter: -```py +```python from pydantic import BaseModel, Field class User(BaseModel): - name: str = Field(..., validation_alias='username') + name: str = Field(validation_alias='username') user = User(username='johndoe') # (1)! @@ -122,12 +139,12 @@ print(user.model_dump(by_alias=True)) # (2)! If you only want to define an alias for _serialization_, you can use the `serialization_alias` parameter: -```py +```python from pydantic import BaseModel, Field class User(BaseModel): - name: str = Field(..., serialization_alias='username') + name: str = Field(serialization_alias='username') user = User(name='johndoe') # (1)! @@ -153,12 +170,12 @@ print(user.model_dump(by_alias=True)) # (2)! In VSCode, if you use the [Pylance](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance) extension, you won't see a warning when instantiating a model using a field's alias: - ```py + ```python from pydantic import BaseModel, Field class User(BaseModel): - name: str = Field(..., alias='username') + name: str = Field(alias='username') user = User(username='johndoe') # (1)! @@ -170,14 +187,14 @@ print(user.model_dump(by_alias=True)) # (2)! [Model Config][pydantic.config.ConfigDict.populate_by_name], VSCode will show a warning when instantiating a model using the field name (though it will work at runtime) — in this case, `'name'`: - ```py + ```python from pydantic import BaseModel, ConfigDict, Field class User(BaseModel): model_config = ConfigDict(populate_by_name=True) - name: str = Field(..., alias='username') + name: str = Field(alias='username') user = User(name='johndoe') # (1)! @@ -188,14 +205,14 @@ print(user.model_dump(by_alias=True)) # (2)! To "trick" VSCode into preferring the field name, you can use the `str` function to wrap the alias value. With this approach, though, a warning is shown when instantiating a model using the alias for the field: - ```py + ```python from pydantic import BaseModel, ConfigDict, Field class User(BaseModel): model_config = ConfigDict(populate_by_name=True) - name: str = Field(..., alias=str('username')) # noqa: UP018 + name: str = Field(alias=str('username')) # noqa: UP018 user = User(name='johndoe') # (1)! @@ -214,15 +231,15 @@ print(user.model_dump(by_alias=True)) # (2)! in the class initializer, you can instead specify both an `alias` and `serialization_alias`, as the `serialization_alias` will override the `alias` during serialization: - ```py + ```python from pydantic import BaseModel, Field class MyModel(BaseModel): - my_field: int = Field(..., validation_alias='myValidationAlias') + my_field: int = Field(validation_alias='myValidationAlias') ``` with: - ```py + ```python from pydantic import BaseModel, Field @@ -258,7 +275,7 @@ There are some keyword arguments that can be used to constrain numeric values: Here's an example: -```py +```python from pydantic import BaseModel, Field @@ -346,7 +363,7 @@ positive=1 non_negative=0 negative=-1 non_positive=0 even=2 love_for_pydantic=in In case you use field constraints with compound types, an error can happen in some cases. To avoid potential issues, you can use `Annotated`: - ```py + ```python from typing import Optional from typing_extensions import Annotated @@ -373,7 +390,7 @@ There are fields that can be used to constrain strings: Here's an example: -```py +```python from pydantic import BaseModel, Field @@ -438,7 +455,7 @@ There are fields that can be used to constrain decimals: Here's an example: -```py +```python from decimal import Decimal from pydantic import BaseModel, Field @@ -463,7 +480,7 @@ There are fields that can be used to constrain dataclasses: Here's an example: -```py +```python from pydantic import BaseModel, Field from pydantic.dataclasses import dataclass @@ -492,7 +509,7 @@ The parameter `validate_default` can be used to control whether the default valu By default, the default value of the field is not validated. -```py +```python from pydantic import BaseModel, Field, ValidationError @@ -516,7 +533,7 @@ except ValidationError as e: The parameter `repr` can be used to control whether the field should be included in the string representation of the model. -```py +```python from pydantic import BaseModel, Field @@ -540,7 +557,7 @@ approach can be useful when the discriminator fields aren't the same for all the The following example shows how to use `discriminator` with a field name: -```py +```python from typing import Literal, Union from pydantic import BaseModel, Field @@ -568,7 +585,7 @@ print(Model.model_validate({'pet': {'pet_type': 'cat', 'age': 12}})) # (1)! The following example shows how to use the `discriminator` keyword argument with a `Discriminator` instance: -```py +```python from typing import Literal, Union from typing_extensions import Annotated @@ -613,7 +630,7 @@ See the [Discriminated Unions] docs for more details. The `strict` parameter on a [`Field`][pydantic.fields.Field] specifies whether the field should be validated in "strict mode". In strict mode, Pydantic throws an error during validation instead of coercing data on the field where `strict=True`. -```py +```python from pydantic import BaseModel, Field @@ -641,7 +658,7 @@ assigned a new value after the model is created (immutability). See the [frozen dataclass documentation] for more details. -```py +```python from pydantic import BaseModel, Field, ValidationError @@ -672,7 +689,7 @@ model when exporting the model. See the following example: -```py +```python from pydantic import BaseModel, Field @@ -705,7 +722,7 @@ You can set the `deprecated` parameter as one of: ### `deprecated` as a string -```py +```python from typing_extensions import Annotated from pydantic import BaseModel, Field @@ -725,7 +742,7 @@ print(Model.model_json_schema()['properties']['deprecated_field']) You can only use the `deprecated` decorator in this way if you have `typing_extensions` >= 4.9.0 installed. -```py test="skip" +```python {test="skip"} import importlib.metadata from packaging.version import Version @@ -746,7 +763,7 @@ if Version(importlib.metadata.version('typing_extensions')) >= Version('4.9'): ### `deprecated` as a boolean -```py +```python from typing_extensions import Annotated from pydantic import BaseModel, Field @@ -769,7 +786,7 @@ print(Model.model_json_schema()['properties']['deprecated_field']) When accessing a deprecated field inside a validator, the deprecation warning will be emitted. You can use [`catch_warnings`][warnings.catch_warnings] to explicitly ignore it: - ```py + ```python import warnings from typing_extensions import Self @@ -805,7 +822,7 @@ Read more about JSON schema customization / modification with fields in the [Cus The [`computed_field`][pydantic.fields.computed_field] decorator can be used to include [`property`][] or [`cached_property`][functools.cached_property] attributes when serializing a model or dataclass. -The property will also be taken into account in the JSON Schema. +The property will also be taken into account in the JSON Schema (in serialization mode). !!! note Properties can be useful for fields that are computed from other fields, or for fields that @@ -814,9 +831,42 @@ The property will also be taken into account in the JSON Schema. However, note that Pydantic will *not* perform any additional logic on the wrapped property (validation, cache invalidation, etc.). -Here's an example: +Here's an example of the JSON schema (in serialization mode) generated for a model with a computed field: + +```python +from pydantic import BaseModel, computed_field + + +class Box(BaseModel): + width: float + height: float + depth: float + + @computed_field + @property # (1)! + def volume(self) -> float: + return self.width * self.height * self.depth + + +print(Box.model_json_schema(mode='serialization')) +""" +{ + 'properties': { + 'width': {'title': 'Width', 'type': 'number'}, + 'height': {'title': 'Height', 'type': 'number'}, + 'depth': {'title': 'Depth', 'type': 'number'}, + 'volume': {'readOnly': True, 'title': 'Volume', 'type': 'number'}, + }, + 'required': ['width', 'height', 'depth', 'volume'], + 'title': 'Box', + 'type': 'object', +} +""" +``` + +Here's an example using the `model_dump` method with a computed field: -```py +```python from pydantic import BaseModel, computed_field @@ -842,7 +892,7 @@ print(b.model_dump()) As with regular fields, computed fields can be marked as being deprecated: -```py +```python from typing_extensions import deprecated from pydantic import BaseModel, computed_field diff --git a/docs/concepts/postponed_annotations.md b/docs/concepts/forward_annotations.md similarity index 73% rename from docs/concepts/postponed_annotations.md rename to docs/concepts/forward_annotations.md index c5dd779c68f..3c8099fb362 100644 --- a/docs/concepts/postponed_annotations.md +++ b/docs/concepts/forward_annotations.md @@ -1,81 +1,45 @@ -Postponed annotations (as described in [PEP563](https://www.python.org/dev/peps/pep-0563/)) "just work". +Forward annotations (wrapped in quotes) or using the `from __future__ import annotations` [future statement] +(as introduced in [PEP563](https://www.python.org/dev/peps/pep-0563/)) are supported: -```py requires="3.9" +```python from __future__ import annotations -from typing import Any - from pydantic import BaseModel +MyInt = int + class Model(BaseModel): - a: list[int] - b: Any + a: MyInt + # Without the future import, equivalent to: + # a: 'MyInt' -print(Model(a=('1', 2, 3), b='ok')) -#> a=[1, 2, 3] b='ok' +print(Model(a='1')) +#> a=1 ``` -Internally, Pydantic will call a method similar to `typing.get_type_hints` to resolve annotations. - -Even without using `from __future__ import annotations`, in cases where the referenced type is not yet defined, a -`ForwardRef` or string can be used: - -```py -from typing import ForwardRef - -from pydantic import BaseModel - -Foo = ForwardRef('Foo') - - -class Foo(BaseModel): - a: int = 123 - b: Foo = None +As shown in the following sections, forward annotations are useful when you want to reference +a type that is not yet defined in your code. - -print(Foo()) -#> a=123 b=None -print(Foo(b={'a': '321'})) -#> a=123 b=Foo(a=321, b=None) -``` +The internal logic to resolve forward annotations is described in detail in [this section](../internals/resolving_annotations.md). ## Self-referencing (or "Recursive") Models -Models with self-referencing fields are also supported. Self-referencing fields will be automatically -resolved after model creation. - -Within the model, you can refer to the not-yet-constructed model using a string: - -```py -from pydantic import BaseModel - - -class Foo(BaseModel): - a: int = 123 - #: The sibling of `Foo` is referenced by string - sibling: 'Foo' = None - - -print(Foo()) -#> a=123 sibling=None -print(Foo(sibling={'a': '321'})) -#> a=123 sibling=Foo(a=321, sibling=None) -``` +Models with self-referencing fields are also supported. These annotations will be resolved during model creation. -If you use `from __future__ import annotations`, you can also just refer to the model by its type name: +Within the model, you can either add the `from __future__ import annotations` import or wrap the annotation +in a string: -```py -from __future__ import annotations +```python +from typing import Optional from pydantic import BaseModel class Foo(BaseModel): a: int = 123 - #: The sibling of `Foo` is referenced directly by type - sibling: Foo = None + sibling: 'Optional[Foo]' = None print(Foo()) @@ -90,10 +54,10 @@ When working with self-referencing recursive models, it is possible that you mig in validation inputs. For example, this can happen when validating ORM instances with back-references from attributes. -Rather than raising a Python `RecursionError` while attempting to validate data with cyclic references, Pydantic is able -to detect the cyclic reference and raise an appropriate `ValidationError`: +Rather than raising a [`RecursionError`][] while attempting to validate data with cyclic references, Pydantic is able +to detect the cyclic reference and raise an appropriate [`ValidationError`][pydantic_core.ValidationError]: -```py +```python from typing import Optional from pydantic import BaseModel, ValidationError @@ -124,7 +88,8 @@ except ValidationError as exc: ``` Because this error is raised without actually exceeding the maximum recursion depth, you can catch and -handle the raised `ValidationError` without needing to worry about the limited remaining recursion depth: +handle the raised [`ValidationError`][pydantic_core.ValidationError] without needing to worry about the limited +remaining recursion depth: ```python from contextlib import contextmanager @@ -179,10 +144,10 @@ print(Node.model_validate(node_data)) #> id=1 children=[Node(id=2, children=[Node(id=3, children=[])])] ``` -Similarly, if Pydantic encounters a recursive reference during _serialization_, rather than waiting for the maximum -recursion depth to be exceeded, a `ValueError` is raised immediately: +Similarly, if Pydantic encounters a recursive reference during _serialization_, rather than waiting +for the maximum recursion depth to be exceeded, a [`ValueError`][] is raised immediately: -```py +```python from pydantic import TypeAdapter # Create data with cyclic references representing the graph 1 -> 2 -> 3 -> 1 @@ -201,7 +166,7 @@ except ValueError as exc: This can also be handled if desired: -```py +```python from dataclasses import field from typing import Any, List @@ -266,3 +231,5 @@ print(TypeAdapter(Node).dump_python(nodes[0])) } """ ``` + +[future statement]: https://docs.python.org/3/reference/simple_stmts.html#future diff --git a/docs/concepts/json.md b/docs/concepts/json.md index fd027d67d21..17482dc1e63 100644 --- a/docs/concepts/json.md +++ b/docs/concepts/json.md @@ -1,6 +1,3 @@ -!!! warning "🚧 Work in Progress" - This page is a work in progress. - # JSON ## Json Parsing @@ -18,7 +15,7 @@ Pydantic provides builtin JSON parsing, which helps achieve: Here's an example of Pydantic's builtin JSON parsing via the [`model_validate_json`][pydantic.main.BaseModel.model_validate_json] method, showcasing the support for `strict` specifications while parsing JSON data that doesn't match the model's type annotations: -```py +```python from datetime import date from typing import Tuple @@ -64,7 +61,7 @@ in the original JSON input which contained the invalid value. **Starting in v2.7.0**, Pydantic's [JSON parser](https://docs.rs/jiter/latest/jiter/) offers support for partial JSON parsing, which is exposed via [`pydantic_core.from_json`][pydantic_core.from_json]. Here's an example of this feature in action: -```py +```python from pydantic_core import from_json partial_json_data = '["aa", "bb", "c' # (1)! @@ -86,7 +83,7 @@ print(result) # (3)! This also works for deserializing partial dictionaries. For example: -```py +```python from pydantic_core import from_json partial_dog_json = '{"breed": "lab", "name": "fluffy", "friends": ["buddy", "spot", "rufus"], "age' @@ -97,7 +94,7 @@ print(dog_dict) !!! tip "Validating LLM Output" This feature is particularly beneficial for validating LLM outputs. - We've written some blog posts about this topic, which you can find [here](https://blog.pydantic.dev/blog/category/llms/). + We've written some blog posts about this topic, which you can find [here](https://pydantic.dev/articles). In future versions of Pydantic, we expect to expand support for this feature through either Pydantic's other JSON validation functions ([`pydantic.main.BaseModel.model_validate_json`][pydantic.main.BaseModel.model_validate_json] and @@ -105,7 +102,7 @@ In future versions of Pydantic, we expect to expand support for this feature thr For now, you can use [`pydantic_core.from_json`][pydantic_core.from_json] in combination with [`pydantic.main.BaseModel.model_validate`][pydantic.main.BaseModel.model_validate] to achieve the same result. Here's an example: -```py +```python from pydantic_core import from_json from pydantic import BaseModel @@ -130,7 +127,7 @@ Check out the following example for a more in-depth look at how to use default v !!! example "Using default values with partial JSON parsing" - ```py + ```python from typing import Any, Optional, Tuple import pydantic_core diff --git a/docs/concepts/json_schema.md b/docs/concepts/json_schema.md index 0ee44189984..9703e29f8a2 100644 --- a/docs/concepts/json_schema.md +++ b/docs/concepts/json_schema.md @@ -42,7 +42,7 @@ Use the following functions to generate JSON schema: Here's an example of generating JSON schema from a `BaseModel`: -```py output="json" +```python {output="json"} import json from enum import Enum from typing import Union @@ -75,7 +75,7 @@ class MainModel(BaseModel): foo_bar: FooBar gender: Annotated[Union[Gender, None], Field(alias='Gender')] = None snap: int = Field( - 42, + default=42, title='The Snap', description='this is the value of snap', gt=30, @@ -167,7 +167,7 @@ Pydantic V1 (which is now deprecated). Here's an example of generating JSON schema from a [`TypeAdapter`][pydantic.type_adapter.TypeAdapter]: -```py +```python from typing import List from pydantic import TypeAdapter @@ -180,7 +180,7 @@ print(adapter.json_schema()) You can also generate JSON schemas for combinations of [`BaseModel`s][pydantic.main.BaseModel] and [`TypeAdapter`s][pydantic.type_adapter.TypeAdapter], as shown in this example: -```py output="json" +```python {output="json"} import json from typing import Union @@ -266,7 +266,7 @@ The [`JsonSchemaMode`][pydantic.json_schema.JsonSchemaMode] is a type alias that Here's an example of how to specify the `mode` parameter, and how it affects the generated JSON schema: -```py +```python from decimal import Decimal from pydantic import BaseModel @@ -334,7 +334,7 @@ Some field parameters are used exclusively to customize the generated JSON Schem Here's an example: -```py output="json" +```python {output="json"} import json from pydantic import BaseModel, EmailStr, Field, SecretStr @@ -403,14 +403,14 @@ If Pydantic finds constraints which are not being enforced, an error will be rai constraint to appear in the schema, even though it's not being checked upon parsing, you can use variadic arguments to [`Field`][pydantic.fields.Field] with the raw schema attribute name: -```py +```python from pydantic import BaseModel, Field, PositiveInt try: # this won't work since `PositiveInt` takes precedence over the # constraints defined in `Field`, meaning they're ignored class Model(BaseModel): - foo: PositiveInt = Field(..., lt=10) + foo: PositiveInt = Field(lt=10) except ValueError as e: print(e) @@ -422,7 +422,7 @@ except ValueError as e: class ModelB(BaseModel): # Here both constraints will be applied and the schema # will be generated correctly - foo: int = Field(..., gt=0, lt=10) + foo: int = Field(gt=0, lt=10) print(ModelB.model_json_schema()) @@ -445,7 +445,7 @@ print(ModelB.model_json_schema()) You can specify JSON schema modifications via the [`Field`][pydantic.fields.Field] constructor via [`typing.Annotated`][] as well: -```py output="json" +```python {output="json"} import json from uuid import uuid4 @@ -488,7 +488,7 @@ The `field_title_generator` parameter can be used to programmatically generate t See the following example: -```py +```python import json from pydantic import BaseModel, Field @@ -534,7 +534,6 @@ Specifically, the following config options are relevant: * [`title`][pydantic.config.ConfigDict.title] * [`json_schema_extra`][pydantic.config.ConfigDict.json_schema_extra] -* [`schema_generator`][pydantic.config.ConfigDict.schema_generator] * [`json_schema_mode_override`][pydantic.config.ConfigDict.json_schema_mode_override] * [`field_title_generator`][pydantic.config.ConfigDict.field_title_generator] * [`model_title_generator`][pydantic.config.ConfigDict.model_title_generator] @@ -549,7 +548,7 @@ You can pass a `dict` or a `Callable` to `json_schema_extra`. You can pass a `dict` to `json_schema_extra` to add extra information to the JSON schema: -```py output="json" +```python {output="json"} import json from pydantic import BaseModel, ConfigDict @@ -588,7 +587,7 @@ print(json.dumps(Model.model_json_schema(), indent=2)) You can pass a `Callable` to `json_schema_extra` to modify the JSON schema with a function: -```py output="json" +```python {output="json"} import json from pydantic import BaseModel, Field @@ -627,7 +626,7 @@ We viewed this change largely as a bug fix, as it resolves unintentional differe between `BaseModel` and `TypeAdapter` instances - see [this issue](https://github.com/pydantic/pydantic/issues/9210) for more details. -```py +```python import json from typing_extensions import Annotated, TypeAlias @@ -635,11 +634,11 @@ from typing_extensions import Annotated, TypeAlias from pydantic import Field, TypeAdapter ExternalType: TypeAlias = Annotated[ - int, Field(..., json_schema_extra={'key1': 'value1'}) + int, Field(json_schema_extra={'key1': 'value1'}) ] ta = TypeAdapter( - Annotated[ExternalType, Field(..., json_schema_extra={'key2': 'value2'})] + Annotated[ExternalType, Field(json_schema_extra={'key2': 'value2'})] ) print(json.dumps(ta.json_schema(), indent=2)) """ @@ -651,43 +650,9 @@ print(json.dumps(ta.json_schema(), indent=2)) """ ``` -If you would prefer for the last of your `json_schema_extra` specifications to override the previous ones, -you can use a `callable` to make more significant changes, including adding or removing keys, or modifying values. -You can use this pattern if you'd like to mimic the behavior of the `json_schema_extra` overrides present -in Pydantic v2.8 and earlier: - -```py -import json - -from typing_extensions import Annotated, TypeAlias - -from pydantic import Field, TypeAdapter -from pydantic.json_schema import JsonDict - -ExternalType: TypeAlias = Annotated[ - int, Field(..., json_schema_extra={'key1': 'value1', 'key2': 'value2'}) -] - - -def finalize_schema(s: JsonDict) -> None: - s.pop('key1') - s['key2'] = s['key2'] + '-final' - s['key3'] = 'value3-final' - - -ta = TypeAdapter( - Annotated[ExternalType, Field(..., json_schema_extra=finalize_schema)] -) -print(json.dumps(ta.json_schema(), indent=2)) -""" -{ - "key2": "value2-final", - "key3": "value3-final", - "type": "integer" -} -""" -``` - +!!! note + We no longer (and never fully did) support composing a mix of `dict` and `callable` type `json_schema_extra` specifications. + If this is a requirement for your use case, please [open a pydantic issue](https://github.com/pydantic/pydantic/issues/new/choose) and explain your situation - we'd be happy to reconsider this decision when presented with a compelling case. ### `WithJsonSchema` annotation @@ -711,7 +676,7 @@ would otherwise raise an error when producing a JSON schema because the [`PlainV is a `Callable`. However, by using the [`WithJsonSchema`][pydantic.json_schema.WithJsonSchema] annotation, we can override the generated JSON schema for the custom `MyInt` type: -```py output="json" +```python {output="json"} import json from typing_extensions import Annotated @@ -783,7 +748,7 @@ In this case the input is the type and the output is a `core_schema`. Here is an example of a custom type that *overrides* the generated `core_schema`: -```py +```python from dataclasses import dataclass from typing import Any, Dict, List, Type @@ -861,7 +826,7 @@ This will be the case for most custom types, so you almost never want to call in The process for `Annotated` metadata is much the same except that you can generally call into `handler` to have Pydantic handle generating the schema. -```py +```python from dataclasses import dataclass from typing import Any, Sequence, Type @@ -929,7 +894,7 @@ So far we have been wrapping the schema, but if you just want to *modify* it or To modify the schema, first call the handler, then mutate the result: -```py +```python from typing import Any, Type from pydantic_core import ValidationError, core_schema @@ -971,7 +936,7 @@ except ValidationError as e: To override the schema completely, do not call the handler and return your own `CoreSchema`: -```py +```python from typing import Any, Type from pydantic_core import ValidationError, core_schema @@ -1026,7 +991,7 @@ However, if you want to take advantage of storing metadata via `Annotated`, but schema, you can use the following approach with a no-op version of `__get_pydantic_core_schema__` implemented on the metadata class: -```py +```python from typing import Type from pydantic_core import CoreSchema @@ -1074,7 +1039,7 @@ Modifying this method only affects the JSON schema - it doesn't affect the core Here's an example of modifying the generated JSON schema: -```py output="json" +```python {output="json"} import json from typing import Any @@ -1156,7 +1121,7 @@ This is similar to the field level `field_title_generator`, but the `ConfigDict` See the following example: -```py +```python import json from pydantic import BaseModel, ConfigDict @@ -1200,7 +1165,7 @@ and accepts the model class as input. See the following example: -```py +```python import json from typing import Type @@ -1245,8 +1210,8 @@ print(json.dumps(Person.model_json_schema(), indent=2)) Types, custom field types, and constraints (like `max_length`) are mapped to the corresponding spec formats in the following priority order (when there is an equivalent available): -1. [JSON Schema Core](http://json-schema.org/latest/json-schema-core.html#rfc.section.4.3.1) -2. [JSON Schema Validation](http://json-schema.org/latest/json-schema-validation.html) +1. [JSON Schema Core](https://json-schema.org/draft/2020-12/json-schema-core) +2. [JSON Schema Validation](https://json-schema.org/draft/2020-12/json-schema-validation) 3. [OpenAPI Data Types](https://github.com/OAI/OpenAPI-Specification/blob/master/versions/3.0.2.md#data-types) 4. The standard `format` JSON field is used to define Pydantic extensions for more complex `string` sub-types. @@ -1260,7 +1225,7 @@ The field schema mapping from Python or Pydantic to JSON schema is done as follo You can also generate a top-level JSON schema that only includes a list of models and related sub-models in its `$defs`: -```py output="json" +```python {output="json"} import json from pydantic import BaseModel @@ -1342,7 +1307,7 @@ The various methods that can be used to produce JSON schema accept a keyword arg By design, this class breaks the JSON schema generation process into smaller methods that can be easily overridden in subclasses to modify the "global" approach to generating JSON schema. -```py +```python from pydantic import BaseModel from pydantic.json_schema import GenerateJsonSchema @@ -1373,7 +1338,7 @@ print(MyModel.model_json_schema(schema_generator=MyGenerateJsonSchema)) Below is an approach you can use to exclude any fields from the schema that don't have valid json schemas: -```py +```python from typing import Callable from pydantic_core import PydanticOmit, core_schema @@ -1415,6 +1380,68 @@ print(validation_schema) """ ``` +### JSON schema sorting + +By default, Pydantic recursively sorts JSON schemas by alphabetically sorting keys. Notably, Pydantic skips sorting the values of the `properties` key, +to preserve the order of the fields as they were defined in the model. + +If you would like to customize this behavior, you can override the `sort` method in your custom `GenerateJsonSchema` subclass. The below example +uses a no-op `sort` method to disable sorting entirely, which is reflected in the preserved order of the model fields and `json_schema_extra` keys: + +```python +import json +from typing import Optional + +from pydantic import BaseModel, Field +from pydantic.json_schema import GenerateJsonSchema, JsonSchemaValue + + +class MyGenerateJsonSchema(GenerateJsonSchema): + def sort( + self, value: JsonSchemaValue, parent_key: Optional[str] = None + ) -> JsonSchemaValue: + """No-op, we don't want to sort schema values at all.""" + return value + + +class Bar(BaseModel): + c: str + b: str + a: str = Field(json_schema_extra={'c': 'hi', 'b': 'hello', 'a': 'world'}) + + +json_schema = Bar.model_json_schema(schema_generator=MyGenerateJsonSchema) +print(json.dumps(json_schema, indent=2)) +""" +{ + "type": "object", + "properties": { + "c": { + "type": "string", + "title": "C" + }, + "b": { + "type": "string", + "title": "B" + }, + "a": { + "type": "string", + "c": "hi", + "b": "hello", + "a": "world", + "title": "A" + } + }, + "required": [ + "c", + "b", + "a" + ], + "title": "Bar" +} +""" +``` + ## Customizing the `$ref`s in JSON Schema The format of `$ref`s can be altered by calling [`model_json_schema()`][pydantic.main.BaseModel.model_json_schema] @@ -1423,7 +1450,7 @@ The definitions are always stored under the key `$defs`, but a specified prefix This is useful if you need to extend or modify the JSON schema default definitions location. For example, with OpenAPI: -```py output="json" +```python {output="json"} import json from pydantic import BaseModel diff --git a/docs/concepts/models.md b/docs/concepts/models.md index 3b3d7110837..c6695fddfa8 100644 --- a/docs/concepts/models.md +++ b/docs/concepts/models.md @@ -60,7 +60,7 @@ of the resultant model instance will conform to the field types defined on the m - The [Type System Guides](https://typing.readthedocs.io/en/latest/guides/index.html) - The [mypy documentation](https://mypy.readthedocs.io/en/latest/) -```py group="basic-model" +```python {group="basic-model"} from pydantic import BaseModel @@ -76,7 +76,7 @@ In this example, `User` is a model with two fields: The model can then be instantiated: -```py group="basic-model" +```python {group="basic-model"} user = User(id='123') ``` @@ -86,7 +86,7 @@ you know the resulting model instance is valid. Fields of a model can be accessed as normal attributes of the `user` object: -```py group="basic-model" +```python {group="basic-model"} assert user.name == 'Jane Doe' # (1)! assert user.id == 123 # (2)! assert isinstance(user.id, int) @@ -100,7 +100,7 @@ assert isinstance(user.id, int) The model instance can be serialized using the [`model_dump`][pydantic.BaseModel.model_dump] method: -```py group="basic-model" +```python {group="basic-model"} assert user.model_dump() == {'id': 123, 'name': 'Jane Doe'} ``` @@ -110,7 +110,7 @@ provides numerous arguments to customize the serialization result. By default, models are mutable and field values can be changed through attribute assignment: -```py group="basic-model" +```python {group="basic-model"} user.id = 321 assert user.id == 321 ``` @@ -120,7 +120,7 @@ assert user.id == 321 For example, the following will not behave as expected and would yield a validation error: - ```py test="skip" + ```python {test="skip"} from typing import Optional from pydantic import BaseModel @@ -157,7 +157,7 @@ Models possess the following methods and attributes: * [`model_extra`][pydantic.main.BaseModel.model_extra]: The extra fields set during validation. * [`model_fields_set`][pydantic.main.BaseModel.model_fields_set]: The set of fields which were explicitly provided when the model was initialized. * [`model_parametrized_name()`][pydantic.main.BaseModel.model_parametrized_name]: Computes the class name for parametrizations of generic classes. -* [`model_post_init()`][pydantic.main.BaseModel.model_post_init]: Performs additional actions after the model is initialized. +* [`model_post_init()`][pydantic.main.BaseModel.model_post_init]: Performs additional actions after the model is instantiated and all field validators are applied. * [`model_rebuild()`][pydantic.main.BaseModel.model_rebuild]: Rebuilds the model schema, which also supports building recursive generic models. See [Rebuilding model schema](#rebuilding-model-schema). @@ -172,7 +172,7 @@ Models possess the following methods and attributes: More complex hierarchical data structures can be defined using models themselves as types in annotations. -```py +```python from typing import List, Optional from pydantic import BaseModel @@ -207,17 +207,18 @@ print(m.model_dump()) """ ``` -Self-referencing models are supported. For more details, see [postponed annotations](postponed_annotations.md#self-referencing-or-recursive-models). +Self-referencing models are supported. For more details, see the documentation related to +[forward annotations](forward_annotations.md#self-referencing-or-recursive-models). ## Rebuilding model schema When you define a model class in your code, Pydantic will analyze the body of the class to collect a variety of information required to perform validation and serialization, gathered in a core schema. Notably, the model's type annotations are evaluated to -understand the valid types for each field (more information can be found in the [Architecture](../architecture.md) documentation). +understand the valid types for each field (more information can be found in the [Architecture](../internals/architecture.md) documentation). However, it might be the case that annotations refer to symbols not defined when the model class is being created. To circumvent this issue, the [`model_rebuild()`][pydantic.main.BaseModel.model_rebuild] method can be used: -```py +```python from pydantic import BaseModel, PydanticUserError @@ -254,9 +255,7 @@ print(Foo.model_json_schema()) ``` 1. `Bar` is not yet defined when the `Foo` class is being created. For this reason, - a [string annotation](https://typing.readthedocs.io/en/latest/spec/annotations.html#string-annotations) - is being used. Alternatively, postponed annotations can be used with the `from __future__ import annotations` import - (see [PEP 563](https://peps.python.org/pep-0563/)). + a [forward annotation](forward_annotations.md) is being used. Pydantic tries to determine when this is necessary automatically and error if it wasn't done, but you may want to call [`model_rebuild()`][pydantic.main.BaseModel.model_rebuild] proactively when dealing with recursive models or generics. @@ -278,7 +277,7 @@ To do this, set the [`from_attributes`][pydantic.config.ConfigDict.from_attribut The example here uses [SQLAlchemy](https://www.sqlalchemy.org/), but the same approach should work for any ORM. -```py +```python from typing import List from sqlalchemy import ARRAY, String @@ -329,7 +328,7 @@ deeper-nested attributes as appropriate. Here is an example demonstrating the principle: -```py +```python from typing import List from pydantic import BaseModel, ConfigDict @@ -384,7 +383,7 @@ See [Error Handling](../errors/errors.md) for details on standard and custom err As a demonstration: -```py +```python from typing import List from pydantic import BaseModel, ValidationError @@ -425,7 +424,7 @@ Pydantic provides three methods on models classes for parsing data: Learn more about JSON parsing in the [JSON](../concepts/json.md) section of the docs. * [`model_validate_strings()`][pydantic.main.BaseModel.model_validate_strings]: this takes a dictionary (can be nested) with string keys and values and validates the data in JSON mode so that said strings can be coerced into the correct types. -```py +```python from datetime import datetime from typing import Optional @@ -513,7 +512,7 @@ then pass it to [`model_validate`][pydantic.main.BaseModel.model_validate]. If you don't set this value, then validation will be skipped on model instances. See the below example: === ":x: `revalidate_instances='never'`" - ```py + ```python from pydantic import BaseModel @@ -530,7 +529,7 @@ then pass it to [`model_validate`][pydantic.main.BaseModel.model_validate]. ``` === ":white_check_mark: `revalidate_instances='always'`" - ```py + ```python from pydantic import BaseModel, ConfigDict, ValidationError @@ -611,7 +610,7 @@ In order to declare a generic model, you should follow the following steps: Here is an example using a generic Pydantic model to create an easily-reused HTTP response payload wrapper: -```py +```python from typing import Generic, List, Optional, TypeVar from pydantic import BaseModel, ValidationError @@ -667,7 +666,7 @@ you would expect if you were to declare a distinct type for each parametrization To inherit from a generic model and preserve the fact that it is generic, the subclass must also inherit from [`Generic`][typing.Generic]: -```py +```python from typing import Generic, TypeVar from pydantic import BaseModel @@ -691,7 +690,7 @@ print(ChildClass[int](X=1)) You can also create a generic subclass of a model that partially or fully replaces the type variables in the superclass: -```py +```python from typing import Generic, TypeVar from pydantic import BaseModel @@ -718,7 +717,7 @@ print(ChildClass[str, int](x='1', y='y', z='3')) If the name of the concrete subclasses is important, you can also override the default name generation by overriding the [`model_parametrized_name()`][pydantic.main.BaseModel.model_parametrized_name] method: -```py +```python from typing import Any, Generic, Tuple, Type, TypeVar from pydantic import BaseModel @@ -742,7 +741,7 @@ print(repr(Response[str](data='a'))) You can use parametrized generic models as types in other models: -```py +```python from typing import Generic, TypeVar from pydantic import BaseModel @@ -773,15 +772,9 @@ Order(id=1, product=ResponseModel[Product](content=Product(name='Apple', price=0 """ ``` -!!! tip - When using a parametrized generic model as a type in another model (like `product: ResponseModel[Product]`), - make sure to parametrize said generic model when you initialize the model instance - (like `response = ResponseModel[Product](content=product)`). If you don't, a [`ValidationError`][pydantic_core.ValidationError] - will be raised, as Pydantic doesn't infer the type of the generic model based on the data passed to it. - Using the same type variable in nested models allows you to enforce typing relationships at different points in your model: -```py +```python from typing import Generic, TypeVar from pydantic import BaseModel, ValidationError @@ -802,19 +795,20 @@ nested = InnerT[int](inner=1) print(OuterT[int](outer=1, nested=nested)) #> outer=1 nested=InnerT[int](inner=1) try: - nested = InnerT[str](inner='a') - print(OuterT[int](outer='a', nested=nested)) + print(OuterT[int](outer='a', nested=InnerT(inner='a'))) # (1)! except ValidationError as e: print(e) """ 2 validation errors for OuterT[int] outer Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str] - nested - Input should be a valid dictionary or instance of InnerT[int] [type=model_type, input_value=InnerT[str](inner='a'), input_type=InnerT[str]] + nested.inner + Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str] """ ``` +1. The `OuterT` model is parametrized with `int`, but the data associated with the the `T` annotations during validation is of type `str`, leading to validation errors. + !!! warning While it may not raise an error, we strongly advise against using parametrized generics in [`isinstance()`](https://docs.python.org/3/library/functions.html#isinstance) checks. @@ -822,12 +816,74 @@ except ValidationError as e: If you need to perform [`isinstance()`](https://docs.python.org/3/library/functions.html#isinstance) checks against parametrized generics, you can do this by subclassing the parametrized generic class: - ```py test="skip" lint="skip" + ```python {test="skip" lint="skip"} class MyIntModel(MyGenericModel[int]): ... isinstance(my_model, MyIntModel) ``` +!!! note "Implementation Details" + When using nested generic models, Pydantic sometimes performs revalidation in an attempt to produce the most intuitive validation result. + Specifically, if you have a field of type `GenericModel[SomeType]` and you validate data like `GenericModel[SomeCompatibleType]` against this field, + we will inspect the data, recognize that the input data is sort of a "loose" subclass of `GenericModel`, and revalidate the contained `SomeCompatibleType` data. + + This adds some validation overhead, but makes things more intuitive for cases like that shown below. + + ```python + from typing import Any, Generic, TypeVar + + from pydantic import BaseModel + + T = TypeVar('T') + + + class GenericModel(BaseModel, Generic[T]): + a: T + + + class Model(BaseModel): + inner: GenericModel[Any] + + + print(repr(Model.model_validate(Model(inner=GenericModel[int](a=1))))) + #> Model(inner=GenericModel[Any](a=1)) + ``` + + Note, validation will still fail if you, for example are validating against `GenericModel[int]` and pass in an instance `GenericModel[str](a='not an int')`. + + It's also worth noting that this pattern will re-trigger any custom validation as well, like additional model validators and the like. + Validators will be called once on the first pass, validating directly against `GenericModel[Any]`. That validation fails, as `GenericModel[int]` is not a subclass of `GenericModel[Any]`. This relates to the warning above about the complications of using parametrized generics in `isinstance()` and `issubclass()` checks. + Then, the validators will be called again on the second pass, during more lax force-revalidation phase, which succeeds. + To better understand this consequence, see below: + + ```python {test="skip"} + from typing import Any, Generic, Self, TypeVar + + from pydantic import BaseModel, model_validator + + T = TypeVar('T') + + + class GenericModel(BaseModel, Generic[T]): + a: T + + @model_validator(mode='after') + def validate_after(self: Self) -> Self: + print('after validator running custom validation...') + return self + + + class Model(BaseModel): + inner: GenericModel[Any] + + + m = Model.model_validate(Model(inner=GenericModel[int](a=1))) + #> after validator running custom validation... + #> after validator running custom validation... + print(repr(m)) + #> Model(inner=GenericModel[Any](a=1)) + ``` + ### Validation of unparametrized type variables When leaving type variables unparametrized, Pydantic treats generic models similarly to how it treats built-in generic @@ -839,7 +895,7 @@ types like [`list`][] and [`dict`][]: * If the type variable has a default type (as specified by [PEP 696](https://peps.python.org/pep-0696/)), it will be used. * For unbound or unconstrained type variables, Pydantic will fallback to [`Any`][typing.Any]. -```py +```python from typing import Generic from typing_extensions import TypeVar @@ -877,7 +933,7 @@ except ValidationError as exc: In some cases, validation against an unparametrized generic model can lead to data loss. Specifically, if a subtype of the type variable upper bound, constraints, or default is being used and the model isn't explicitly parametrized, the resulting type **will not be** the one being provided: - ```py + ```python from typing import Generic, TypeVar from pydantic import BaseModel @@ -916,7 +972,7 @@ The behavior of serialization differs when using type variables with [upper boun If a Pydantic model is used in a type variable upper bound and the type variable is never parametrized, then Pydantic will use the upper bound for validation but treat the value as [`Any`][typing.Any] in terms of serialization: -```py +```python from typing import Generic, TypeVar from pydantic import BaseModel @@ -967,7 +1023,7 @@ assert error.model_dump() == { Here's another example of the above behavior, enumerating all permutations regarding bound specification and generic type parametrization: -```py +```python from typing import Generic, TypeVar from pydantic import BaseModel @@ -1003,7 +1059,7 @@ will be used for both validation and serialization if the type variable is not p using [`SerializeAsAny`](./serialization.md#serializeasany-annotation): -```py +```python from typing import Generic from typing_extensions import TypeVar @@ -1068,7 +1124,7 @@ assert error.model_dump() == { There are some occasions where it is desirable to create a model using runtime information to specify the fields. For this Pydantic provides the `create_model` function to allow models to be created on the fly: -```py +```python from pydantic import BaseModel, create_model DynamicFoobarModel = create_model( @@ -1092,23 +1148,23 @@ Fields are defined by one of the following tuple forms: Using a `Field(...)` call as the second argument in the tuple (the default value) allows for more advanced field configuration. Thus, the following are analogous: -```py +```python from pydantic import BaseModel, Field, create_model DynamicModel = create_model( 'DynamicModel', - foo=(str, Field(..., description='foo description', alias='FOO')), + foo=(str, Field(description='foo description', alias='FOO')), ) class StaticModel(BaseModel): - foo: str = Field(..., description='foo description', alias='FOO') + foo: str = Field(description='foo description', alias='FOO') ``` The special keyword arguments `__config__` and `__base__` can be used to customize the new model. This includes extending a base model with extra fields. -```py +```python from pydantic import BaseModel, create_model @@ -1129,19 +1185,19 @@ print(BarModel.model_fields.keys()) #> dict_keys(['foo', 'bar', 'apple', 'banana']) ``` -You can also add validators by passing a dict to the `__validators__` argument. +You can also add validators by passing a dictionary to the `__validators__` argument. -```py rewrite_assert="false" +```python {rewrite_assert="false"} from pydantic import ValidationError, create_model, field_validator -def username_alphanumeric(cls, v): +def alphanum(cls, v): assert v.isalnum(), 'must be alphanumeric' return v validators = { - 'username_validator': field_validator('username')(username_alphanumeric) + 'username_validator': field_validator('username')(alphanum) # (1)! } UserModel = create_model( @@ -1163,11 +1219,16 @@ except ValidationError as e: """ ``` +1. Make sure that the validators names do not clash with any of the field names as + internally, Pydantic gathers all members into a namespace and mimics the normal + creation of a class using the [`types` module utilities](https://docs.python.org/3/library/types.html#dynamic-type-creation). + + !!! note To pickle a dynamically created model: - the model must be defined globally - - it must provide `__module__` + - the `__module__` argument must be provided ## `RootModel` and custom root types @@ -1182,7 +1243,7 @@ via the first and only argument. Here's an example of how this works: -```py +```python from typing import Dict, List from pydantic import RootModel @@ -1213,7 +1274,7 @@ print(PetsByName.model_validate({'Otis': 'dog', 'Milo': 'cat'})) If you want to access items in the `root` field directly or to iterate over the items, you can implement custom `__iter__` and `__getitem__` functions, as shown in the following example. -```py +```python from typing import List from pydantic import RootModel @@ -1238,7 +1299,7 @@ print([pet for pet in pets]) You can also create subclasses of the parametrized root model directly: -```py +```python from typing import List from pydantic import RootModel @@ -1269,7 +1330,7 @@ values of instance attributes will raise errors. See the [API reference][pydanti In Python, immutability is not enforced. Developers have the ability to modify objects that are conventionally considered "immutable" if they choose to do so. -```py +```python from pydantic import BaseModel, ConfigDict, ValidationError @@ -1309,7 +1370,7 @@ immutability of `foobar` doesn't stop `b` from being changed. Pydantic models can be used alongside Python's [Abstract Base Classes](https://docs.python.org/3/library/abc.html) (ABCs). -```py +```python import abc from pydantic import BaseModel @@ -1332,7 +1393,7 @@ Field order affects models in the following ways: * field order is preserved in [validation errors](#error-handling) * field order is preserved by [`.model_dump()` and `.model_dump_json()` etc.](serialization.md#model_dump) -```py +```python from pydantic import BaseModel, ValidationError @@ -1360,25 +1421,22 @@ print(error_locations) ## Required fields -To declare a field as required, you may declare it using an annotation, or an annotation in combination with a `Field` specification. -You may also use `Ellipsis`/`...` to emphasize that a field is required, especially when using the `Field` constructor. - -The [`Field`](fields.md) function is primarily used to configure settings like `alias` or `description` for an attribute. -The constructor supports `Ellipsis`/`...` as the sole positional argument. -This is used as a way to indicate that said field is mandatory, though it's the type hint that enforces this requirement. +To declare a field as required, you may declare it using an annotation, or an annotation in combination with a +[`Field`][pydantic.Field] function (without specifying any `default` or `default_factory` argument). -```py +```python from pydantic import BaseModel, Field class Model(BaseModel): a: int - b: int = ... + b: int = Field(alias='B') c: int = Field(..., alias='C') ``` -Here `a`, `b` and `c` are all required. However, this use of `b: int = ...` does not work properly with -[mypy](../integrations/mypy.md), and as of **v1.0** should be avoided in most cases. +Here `a`, `b` and `c` are all required. The field `c` uses the [ellipsis][Ellipsis] as a default argument, +emphasizing on the fact that it is required. However, the usage of the [ellipsis][Ellipsis] is discouraged +as it doesn't play well with type checkers. !!! note In Pydantic V1, fields annotated with `Optional` or `Any` would be given an implicit default of `None` even if no @@ -1400,7 +1458,7 @@ Pydantic also supports the use of a [`default_factory`](#fields-with-dynamic-def values, but it is not required. In the event that the default value is not hashable, Pydantic will deepcopy the default value when creating each instance of the model: -```py +```python from typing import Dict, List from pydantic import BaseModel @@ -1427,7 +1485,7 @@ To do this, you may want to use a `default_factory`. Here is an example: -```py +```python from datetime import datetime, timezone from uuid import UUID, uuid4 @@ -1456,7 +1514,7 @@ You can find more information in the documentation of the [`Field` function](fie Attributes annotated with `typing.ClassVar` are properly treated by Pydantic as class variables, and will not become fields on model instances: -```py +```python from typing import ClassVar from pydantic import BaseModel @@ -1489,7 +1547,7 @@ calls to `__init__`, `model_validate`, etc. Here is an example of usage: -```py +```python from datetime import datetime from random import randint @@ -1522,7 +1580,7 @@ Pydantic may cast input data to force it to conform to model field types, and in some cases this may result in a loss of information. For example: -```py +```python from pydantic import BaseModel @@ -1545,7 +1603,7 @@ Nevertheless, [strict type checking](strict_mode.md) is also supported. All Pydantic models will have their signature generated based on their fields: -```py +```python import inspect from pydantic import BaseModel, Field @@ -1566,7 +1624,7 @@ An accurate signature is useful for introspection purposes and libraries like `F The generated signature will also respect custom `__init__` functions: -```py +```python import inspect from pydantic import BaseModel @@ -1597,7 +1655,7 @@ a `**data` argument will be added. In addition, the `**data` argument will alway Pydantic supports structural pattern matching for models, as introduced by [PEP 636](https://peps.python.org/pep-0636/) in Python 3.10. -```py requires="3.10" lint="skip" +```python {requires="3.10" lint="skip"} from pydantic import BaseModel @@ -1630,7 +1688,7 @@ coercion. In this example, note that the ID of the list changes after the class is constructed because it has been copied during validation: -```py +```python from typing import List from pydantic import BaseModel @@ -1665,7 +1723,7 @@ print('id(c1.arr) == id(c2.arr):', id(c1.arr) == id(c2.arr)) By default, Pydantic models won't error when you provide data for unrecognized fields, they will just be ignored: -```py +```python from pydantic import BaseModel @@ -1677,9 +1735,10 @@ m = Model(x=1, y='a') assert m.model_dump() == {'x': 1} ``` -If you want this to raise an error, you can achieve this via `model_config`: +If you want this to raise an error, you can set the [`extra`][pydantic.ConfigDict.extra] configuration +value to `'forbid'`: -```py +```python from pydantic import BaseModel, ConfigDict, ValidationError @@ -1700,10 +1759,10 @@ except ValidationError as exc: """ ``` -To instead preserve any extra data provided, you can set `extra='allow'`. +To instead preserve any extra data provided, you can set [`extra`][pydantic.ConfigDict.extra] to `'allow'`. The extra fields will then be stored in `BaseModel.__pydantic_extra__`: -```py +```python from pydantic import BaseModel, ConfigDict @@ -1720,7 +1779,7 @@ assert m.__pydantic_extra__ == {'y': 'a'} By default, no validation will be applied to these extra items, but you can set a type for the values by overriding the type annotation for `__pydantic_extra__`: -```py +```python from typing import Dict from pydantic import BaseModel, ConfigDict, Field, ValidationError @@ -1752,7 +1811,7 @@ assert m.__pydantic_extra__ == {'y': 2} ``` 1. The `= Field(init=False)` does not have any effect at runtime, but prevents the `__pydantic_extra__` field from -being treated as an argument to the model's `__init__` method by type-checkers. + being included as a parameter to the model's `__init__` method by type checkers. The same configurations apply to `TypedDict` and `dataclass`' except the config is controlled by setting the `__pydantic_config__` attribute of the class to a valid `ConfigDict`. diff --git a/docs/concepts/performance.md b/docs/concepts/performance.md index cf08a79efdf..f402094ac13 100644 --- a/docs/concepts/performance.md +++ b/docs/concepts/performance.md @@ -23,7 +23,7 @@ the function is called. Instead, instantiate it once, and reuse it. === ":x: Bad" - ```py lint="skip" + ```python {lint="skip"} from typing import List from pydantic import TypeAdapter @@ -36,7 +36,7 @@ the function is called. Instead, instantiate it once, and reuse it. === ":white_check_mark: Good" - ```py lint="skip" + ```python {lint="skip"} from typing import List from pydantic import TypeAdapter @@ -61,7 +61,7 @@ If you know the value is a `dict`, use `dict` instead of `Mapping`. If you don't need to validate a value, use `Any` to keep the value unchanged. -```py +```python from typing import Any from pydantic import BaseModel @@ -78,7 +78,7 @@ model = Model(a=1) === "Don't do this" - ```py + ```python class CompletedStr(str): def __init__(self, s: str): self.s = s @@ -87,7 +87,7 @@ model = Model(a=1) === "Do this" - ```py + ```python from pydantic import BaseModel @@ -100,7 +100,7 @@ model = Model(a=1) Tagged union (or discriminated union) is a union with a field that indicates which type it is. -```py test="skip" +```python {test="skip"} from typing import Any, Literal from pydantic import BaseModel, Field @@ -145,7 +145,7 @@ Instead of using nested models, use `TypedDict` to define the structure of the d ??? info "Performance comparison" With a simple benchmark, `TypedDict` is about ~2.5x faster than nested models: - ```py test="skip" + ```python {test="skip"} from timeit import timeit from typing_extensions import TypedDict @@ -193,7 +193,7 @@ Starting in v2.8+, you can apply the `FailFast` annotation to sequence types to If you use this annotation, you won't get validation errors for the rest of the items in the sequence if one fails, so you're effectively trading off visibility for performance. -```py +```python from typing import List from typing_extensions import Annotated diff --git a/docs/concepts/serialization.md b/docs/concepts/serialization.md index 9e61dad8096..a0df7a6e9a0 100644 --- a/docs/concepts/serialization.md +++ b/docs/concepts/serialization.md @@ -33,7 +33,7 @@ This is the primary way of converting a model to a dictionary. Sub-models will b Example: -```py +```python from typing import Any, List, Optional from pydantic import BaseModel, Field, Json @@ -110,7 +110,7 @@ See [arguments][pydantic.main.BaseModel.model_dump_json] for more information. Pydantic can serialize many commonly used types to JSON that would otherwise be incompatible with a simple `json.dumps(foobar)` (e.g. `datetime`, `date` or `UUID`) . -```py +```python from datetime import datetime from pydantic import BaseModel @@ -147,7 +147,7 @@ sub-models will not be converted to dictionaries. Example: -```py +```python from pydantic import BaseModel @@ -187,7 +187,7 @@ Serialization can be customised on a field using the [`@field_serializer`][pydantic.functional_serializers.field_serializer] decorator, and on a model using the [`@model_serializer`][pydantic.functional_serializers.model_serializer] decorator. -```py +```python from datetime import datetime, timedelta, timezone from typing import Any, Dict @@ -238,7 +238,7 @@ Both serializers accept optional arguments including: `PlainSerializer` uses a simple function to modify the output of serialization. -```py +```python from typing_extensions import Annotated from pydantic import BaseModel @@ -263,7 +263,7 @@ print(MyModel(x=1234).model_dump(mode='json')) `WrapSerializer` receives the raw inputs along with a handler function that applies the standard serialization logic, and can modify the resulting value before returning it as the final output of serialization. -```py +```python from typing import Any from typing_extensions import Annotated @@ -294,7 +294,7 @@ print(MyModel(x=1234).model_dump(mode='json')) While the return value of `.model_dump()` can usually be described as `dict[str, Any]`, through the use of `@model_serializer` you can actually cause it to return a value that doesn't match this signature: -```py +```python from pydantic import BaseModel, model_serializer @@ -313,7 +313,7 @@ print(Model(x='not a dict').model_dump()) If you want to do this and still get proper type-checking for this method, you can override `.model_dump()` in an `if TYPE_CHECKING:` block: -```py +```python from typing import TYPE_CHECKING, Any, Literal from pydantic import BaseModel, model_serializer @@ -351,7 +351,7 @@ This trick is actually used in [`RootModel`](models.md#rootmodel-and-custom-root Subclasses of standard types are automatically dumped like their super-classes: -```py +```python from datetime import date, timedelta from typing import Any, Type @@ -397,7 +397,7 @@ the default behavior is to serialize the attribute value as though it was an ins even if it is a subclass. More specifically, only the fields from the _annotated_ type will be included in the dumped object: -```py +```python from pydantic import BaseModel @@ -451,7 +451,7 @@ We discuss these options below in more detail: If you want duck-typing serialization behavior, this can be done using the `SerializeAsAny` annotation on a type: -```py +```python from pydantic import BaseModel, SerializeAsAny @@ -505,7 +505,7 @@ which means that fields present in a subclass but not in the original schema wil For example: -```py +```python from pydantic import BaseModel @@ -543,7 +543,7 @@ but not the base class, are not included in serialization. This setting even takes effect with nested and recursive patterns as well. For example: -```py +```python from typing import List from pydantic import BaseModel @@ -601,7 +601,7 @@ You can override the default setting for `serialize_as_any` by configuring a sub For example, you could do the following if you want to use duck-typing serialization by default: -```py +```python from typing import Any, Dict from pydantic import BaseModel, SecretStr @@ -639,7 +639,7 @@ print(u.model_dump_json()) # (1)! Pydantic models support efficient pickling and unpickling. -```py test="skip" +```python {test="skip"} import pickle from pydantic import BaseModel @@ -666,7 +666,7 @@ print(m2) The `model_dump` and `model_dump_json` methods support `include` and `exclude` arguments which can either be sets or dictionaries. This allows nested selection of which fields to export: -```py +```python from pydantic import BaseModel, SecretStr @@ -700,14 +700,14 @@ print(t.model_dump(include={'id': True, 'user': {'id'}})) #> {'id': '1234567890', 'user': {'id': 42}} ``` -The `True` indicates that we want to exclude or include an entire key, just as if we included it in a set. +Using `True` indicates that we want to exclude or include an entire key, just as if we included it in a set (note that using `False` isn't supported). This can be done at any depth level. Special care must be taken when including or excluding fields from a list or tuple of submodels or dictionaries. In this scenario, `model_dump` and related methods expect integer keys for element-wise inclusion or exclusion. To exclude a field from **every** member of a list or tuple, the dictionary key `'__all__'` can be used, as shown here: -```py +```python import datetime from typing import List @@ -810,17 +810,17 @@ The same holds for the `model_dump_json` method. In addition to the explicit arguments `exclude` and `include` passed to `model_dump` and `model_dump_json` methods, we can also pass the `exclude: bool` arguments directly to the `Field` constructor: -Setting `exclude` on the field constructor (`Field(..., exclude=True)`) takes priority over the +Setting `exclude` on the field constructor (`Field(exclude=True)`) takes priority over the `exclude`/`include` on `model_dump` and `model_dump_json`: -```py +```python from pydantic import BaseModel, Field, SecretStr class User(BaseModel): id: int username: str - password: SecretStr = Field(..., exclude=True) + password: SecretStr = Field(exclude=True) class Transaction(BaseModel): @@ -841,10 +841,10 @@ print(t.model_dump(include={'id': True, 'value': True})) # (1)! 1. `value` excluded from the output because it excluded in `Field`. -That being said, setting `exclude` on the field constructor (`Field(..., exclude=True)`) does not take priority +That being said, setting `exclude` on the field constructor (`Field(exclude=True)`) does not take priority over the `exclude_unset`, `exclude_none`, and `exclude_default` parameters on `model_dump` and `model_dump_json`: -```py +```python from typing import Optional from pydantic import BaseModel, Field @@ -915,7 +915,7 @@ Similarly, you can [use a context for validation](../concepts/validators.md#vali Example: -```py +```python from pydantic import BaseModel diff --git a/docs/concepts/strict_mode.md b/docs/concepts/strict_mode.md index 04a79977ce7..7fc28f63a67 100644 --- a/docs/concepts/strict_mode.md +++ b/docs/concepts/strict_mode.md @@ -14,7 +14,7 @@ and will instead error if the data is not of the correct type. Here is a brief example showing the difference between validation behavior in strict and the default/"lax" mode: -```py +```python from pydantic import BaseModel, ValidationError @@ -54,7 +54,7 @@ will result in raising a `ValidationError`. Note that we are looser when validating data from JSON in strict mode. For example, when validating a `UUID` field, instances of `str` will be accepted when validating from JSON, but not from python: -```py +```python import json from uuid import UUID @@ -325,7 +325,7 @@ such as [`StrictInt`](../api/types.md#pydantic.types.StrictInt). If you want to enable strict mode for all fields on a complex input type, you can use [`ConfigDict(strict=True)`](../api/config.md#pydantic.config.ConfigDict) in the `model_config`: -```py +```python from pydantic import BaseModel, ConfigDict, ValidationError @@ -354,7 +354,7 @@ except ValidationError as exc: When using `strict=True` through a model's `model_config`, you can still override the strictness of individual fields by setting `strict=False` on individual fields: - ```py + ```python from pydantic import BaseModel, ConfigDict, Field diff --git a/docs/concepts/type_adapter.md b/docs/concepts/type_adapter.md index a6d54b6a1a9..b70b9321752 100644 --- a/docs/concepts/type_adapter.md +++ b/docs/concepts/type_adapter.md @@ -12,7 +12,7 @@ A [`TypeAdapter`][pydantic.type_adapter.TypeAdapter] instance exposes some of th [`BaseModel`][pydantic.main.BaseModel] instance methods for types that do not have such methods (such as dataclasses, primitive types, and more): -```py +```python from typing import List from typing_extensions import TypedDict @@ -67,7 +67,7 @@ but works with arbitrary Pydantic-compatible types. This is especially useful when you want to parse results into a type that is not a direct subclass of [`BaseModel`][pydantic.main.BaseModel]. For example: -```py +```python from typing import List from pydantic import BaseModel, TypeAdapter @@ -91,6 +91,36 @@ print(items) handle as fields of a [`BaseModel`][pydantic.main.BaseModel]. !!! info "Performance considerations" - When creating an instance of `TypeAdapter`, the provided type must be analyzed and converted into a pydantic-core + When creating an instance of [`TypeAdapter`][pydantic.type_adapter.TypeAdapter], the provided type must be analyzed and converted into a pydantic-core schema. This comes with some non-trivial overhead, so it is recommended to create a `TypeAdapter` for a given type just once and reuse it in loops or other performance-critical code. + + +## Rebuilding a `TypeAdapter`'s schema + +In v2.10+, [`TypeAdapter`][pydantic.type_adapter.TypeAdapter]'s support deferred schema building and manual rebuilds. This is helpful for the case of: + +* Types with forward references +* Types for which core schema builds are expensive + +When you initialize a [`TypeAdapter`][pydantic.type_adapter.TypeAdapter] with a type, Pydantic analyzes the type and creates a core schema for it. +This core schema contains the information needed to validate and serialize data for that type. +See the [architecture documentation](../internals/architecture.md) for more information on core schemas. + +If you set [`defer_build`][pydantic.config.ConfigDict.defer_build] to `True` when initializing a `TypeAdapter`, +Pydantic will defer building the core schema until the first time it is needed (for validation or serialization). + +In order to manually trigger the building of the core schema, you can call the +[`rebuild`][pydantic.type_adapter.TypeAdapter.rebuild] method on the [`TypeAdapter`][pydantic.type_adapter.TypeAdapter] instance: + +```python +from pydantic import ConfigDict, TypeAdapter + +ta = TypeAdapter('MyInt', config=ConfigDict(defer_build=True)) + +# some time later, the forward reference is defined +MyInt = int + +ta.rebuild() +assert ta.validate_python(1) == 1 +``` diff --git a/docs/concepts/types.md b/docs/concepts/types.md index c0d48e137fb..ed2c903f094 100644 --- a/docs/concepts/types.md +++ b/docs/concepts/types.md @@ -73,7 +73,7 @@ Pydantic takes advantage of this to allow you to create types that are identical For example, to create a type representing a positive int: -```py +```python # or `from typing import Annotated` for Python 3.9+ from typing_extensions import Annotated @@ -99,7 +99,7 @@ except ValidationError as exc: Note that you can also use constraints from [annotated-types](https://github.com/annotated-types/annotated-types) to make this Pydantic-agnostic: -```py +```python from annotated_types import Gt from typing_extensions import Annotated @@ -127,7 +127,7 @@ except ValidationError as exc: You can add or override validation, serialization, and JSON schemas to an arbitrary type using the markers that Pydantic exports: -```py +```python from typing_extensions import Annotated from pydantic import ( @@ -160,7 +160,7 @@ assert ta.json_schema(mode='serialization') == {'type': 'string'} #### Generics -You can use type variables within `Annotated` to make re-usable modifications to types: +You can use type variables within `Annotated` to make reusable modifications to types: ```python from typing import Any, List, Sequence, TypeVar @@ -220,7 +220,7 @@ This means that they will not be able to have a `title` in JSON schemas and thei You can use [PEP 695]'s `TypeAliasType` via its [typing-extensions] backport to make named aliases, allowing you to define a new type without creating subclasses. This new type can be as simple as a name or have complex validation logic attached to it: -```py +```python from typing import List from annotated_types import Gt @@ -287,7 +287,7 @@ print(Model2.model_json_schema()) These named type aliases can also be generic: -```py +```python from typing import Generic, List, TypeVar from annotated_types import Gt @@ -323,7 +323,7 @@ except ValidationError as exc: You can also use `TypeAliasType` to create recursive types: -```py +```python from typing import Any, Dict, List, Union from pydantic_core import PydanticCustomError @@ -401,7 +401,7 @@ handler at all. The following is an example of a type that uses `__get_pydantic_core_schema__` to customize how it gets validated. This is equivalent to implementing `__get_validators__` in Pydantic V1. -```py +```python from typing import Any from pydantic_core import CoreSchema, core_schema @@ -431,7 +431,7 @@ Often you'll want to parametrize your custom type by more than just generic type For example, if you were to implement `pydantic.AfterValidator` (see [Adding validation and serialization](#adding-validation-and-serialization)) yourself, you'd do something similar to the following: -```py +```python from dataclasses import dataclass from typing import Any, Callable @@ -470,7 +470,7 @@ assert Model(name='ABC').name == 'abc' # (2)! Another use case for the pattern in the previous section is to handle third party types. -```py +```python from typing import Any from pydantic_core import core_schema @@ -609,7 +609,7 @@ You can use this approach to e.g. define behavior for Pandas or Numpy types. You may notice that the above examples where we create a marker class require a good amount of boilerplate. For many simple cases you can greatly minimize this by using `pydantic.GetPydanticSchema`: -```py +```python from pydantic_core import core_schema from typing_extensions import Annotated @@ -659,7 +659,7 @@ Note that we do not do something like `handler(get_args(source_type)[0])` becaus schema for that generic parameter, not one that is influenced by the current context of `Annotated` metadata and such. This is less important for custom types, but crucial for annotated metadata that modifies schema building. -```py +```python from dataclasses import dataclass from typing import Any, Generic, TypeVar @@ -731,7 +731,7 @@ class Owner(Generic[ItemType]): lambda data: Owner( name=data['name'], item=data['item'] ), - # note that we re-use the same schema here as below + # note that we reuse the same schema here as below python_schema, ), ] diff --git a/docs/concepts/unions.md b/docs/concepts/unions.md index aa8f8bf291e..c4047807a03 100644 --- a/docs/concepts/unions.md +++ b/docs/concepts/unions.md @@ -34,7 +34,7 @@ If validation fails on all members, the validation error includes the errors fro `union_mode='left_to_right'` must be set as a [`Field`](../concepts/fields.md) parameter on union fields where you want to use it. -```py title="Union with left to right mode" +```python {title="Union with left to right mode"} from typing import Union from pydantic import BaseModel, Field, ValidationError @@ -64,7 +64,7 @@ except ValidationError as e: The order of members is very important in this case, as demonstrated by tweak the above example: -```py title="Union with left to right - unexpected results" +```python {title="Union with left to right - unexpected results"} from typing import Union from pydantic import BaseModel, Field @@ -142,7 +142,7 @@ In this mode, pydantic attempts to select the best match for the input from the 3. If validation succeeded on at least one member in "lax" mode, the leftmost match is returned. 4. Validation failed on all the members, return all the errors. -```py +```python from typing import Union from uuid import UUID @@ -200,7 +200,7 @@ To validate models based on that information you can set the same field - let's in each of the models with a discriminated value, which is one (or many) `Literal` value(s). For your `Union`, you can set the discriminator in its value: `Field(discriminator='my_discriminator')`. -```py +```python from typing import Literal, Union from pydantic import BaseModel, Field, ValidationError @@ -222,7 +222,7 @@ class Lizard(BaseModel): class Model(BaseModel): - pet: Union[Cat, Dog, Lizard] = Field(..., discriminator='pet_type') + pet: Union[Cat, Dog, Lizard] = Field(discriminator='pet_type') n: int @@ -248,7 +248,20 @@ In the case of a `Union` with multiple models, sometimes there isn't a single un across all models that you can use as a discriminator. This is the perfect use case for a callable `Discriminator`. -```py +!!! tip + When you're designing callable discriminators, remember that you might have to account + for both `dict` and model type inputs. This pattern is similar to that of `mode='before'` validators, + where you have to anticipate various forms of input. + + But wait! You ask, I only anticipate passing in `dict` types, why do I need to account for models? + Pydantic uses callable discriminators for serialization as well, at which point the input to your callable is + very likely to be a model instance. + + In the following examples, you'll see that the callable discriminators are designed to handle both `dict` and model inputs. + If you don't follow this practice, it's likely that you'll, in the best case, get warnings during serialization, + and in the worst case, get runtime errors during validation. + +```python from typing import Any, Literal, Union from typing_extensions import Annotated @@ -312,7 +325,7 @@ ThanksgivingDinner(dessert=PumpkinPie(time_to_cook=40, num_ingredients=6, fillin For example: -```py +```python from typing import Any, Union from typing_extensions import Annotated @@ -398,7 +411,7 @@ except ValidationError as e: Only one discriminator can be set for a field but sometimes you want to combine multiple discriminators. You can do it by creating nested `Annotated` types, e.g.: -```py +```python from typing import Literal, Union from typing_extensions import Annotated @@ -462,7 +475,7 @@ except ValidationError as e: In the context of the previous example, we have the following: - ```python lint="skip" test="skip" + ```python {lint="skip" test="skip"} type_adapter = TypeAdapter(Pet) pet = type_adapter.validate_python( @@ -484,7 +497,7 @@ the case with a matching discriminator value. You can also customize the error type, message, and context for a `Discriminator` by passing these specifications as parameters to the `Discriminator` constructor, as seen in the example below. -```py +```python from typing import Union from typing_extensions import Annotated @@ -587,7 +600,7 @@ print(m.model_dump()) You can also simplify error messages by labeling each case with a [`Tag`][pydantic.types.Tag]. This is especially useful when you have complex types like those in this example: -```py +```python from typing import Dict, List, Union from typing_extensions import Annotated diff --git a/docs/concepts/validation_decorator.md b/docs/concepts/validation_decorator.md index 370bdc5a606..076cf15ca98 100644 --- a/docs/concepts/validation_decorator.md +++ b/docs/concepts/validation_decorator.md @@ -1,7 +1,7 @@ ??? api "API Documentation" [`pydantic.validate_call_decorator.validate_call`][pydantic.validate_call_decorator.validate_call]
-The [`@validate_call`][pydantic.validate_call] decorator allows the arguments passed to a function to be parsed +The [`validate_call()`][pydantic.validate_call] decorator allows the arguments passed to a function to be parsed and validated using the function's annotations before the function is called. While under the hood this uses the same approach of model creation and initialisation @@ -10,7 +10,7 @@ to your code with minimal boilerplate. Example of usage: -```py +```python from pydantic import ValidationError, validate_call @@ -39,153 +39,162 @@ except ValidationError as exc: """ ``` -## Argument types +## Parameter types -Argument types are inferred from type annotations on the function, arguments without a type decorator are considered -as `Any`. All types listed in [types](../concepts/types.md) can be validated, including Pydantic models and -[custom types](../concepts/types.md#custom-types). -As with the rest of Pydantic, types can be coerced by the decorator before they're passed to the actual function: +Parameter types are inferred from type annotations on the function, or as [`Any`][typing.Any] if not annotated. All types listed in [types](types.md) can be validated, including Pydantic models and [custom types](types.md#custom-types). +As with the rest of Pydantic, types are by default coerced by the decorator before they're passed to the actual function: -```py test="no-print-intercept" -# TODO replace find_file with something that isn't affected the filesystem -import os -from pathlib import Path -from typing import Optional, Pattern +```python +from datetime import date -from pydantic import DirectoryPath, validate_call +from pydantic import validate_call @validate_call -def find_file(path: DirectoryPath, regex: Pattern, max=None) -> Optional[Path]: - for i, f in enumerate(path.glob('**/*')): - if max and i > max: - return - if f.is_file() and regex.fullmatch(str(f.relative_to(path))): - return f - +def greater_than(d1: date, d2: date, *, include_equal=False) -> date: # (1)! + if include_equal: + return d1 >= d2 + else: + return d1 > d2 -# note: this_dir is a string here -this_dir = os.path.dirname(__file__) -print(find_file(this_dir, '^validation.*')) -print(find_file(this_dir, '^foobar.*', max=3)) +d1 = '2000-01-01' # (2)! +d2 = date(2001, 1, 1) +greater_than(d1, d2, include_equal=True) ``` -A few notes: +1. Because `include_equal` has no type annotation, it will be inferred as [`Any`][typing.Any]. +2. Although `d1` is a string, it will be converted to a [`date`][datetime.date] object. -- Though they're passed as strings, `path` and `regex` are converted to a `Path` object and regex respectively - by the decorator. -- `max` has no type annotation, so will be considered as `Any` by the decorator. +Type coercion like this can be extremely helpful, but also confusing or not desired (see [model data conversion](models.md#data-conversion)). [Strict mode](strict_mode.md) +can be enabled by using a [custom configuration](#custom-configuration). -Type coercion like this can be extremely helpful, but also confusing or not desired. -See [Coercion and strictness](#coercion-and-strictness) for a discussion of `@validate_call`'s limitations -in this regard. +!!! note "Validating the return value" + By default, the return value of the function is **not** validated. To do so, the `validate_return` argument + of the decorator can be set to `True`. ## Function signatures -The `@validate_call` decorator is designed to work with functions using all possible parameter configurations and -all possible combinations of these: +The [`validate_call()`][pydantic.validate_call] decorator is designed to work with functions +using all possible [parameter configurations][parameter] and all possible combinations of these: -* Positional or keyword arguments with or without defaults. -* Variable positional arguments defined via `*` (often `*args`). -* Variable keyword arguments defined via `**` (often `**kwargs`). -* Keyword-only arguments: arguments after `*,`. -* Positional-only arguments: arguments before `, /` (new in Python 3.8). +* Positional or keyword parameters with or without defaults. +* Keyword-only parameters: parameters after `*,`. +* Positional-only parameters: parameters before `, /`. +* Variable positional parameters defined via `*` (often `*args`). +* Variable keyword parameters defined via `**` (often `**kwargs`). -To demonstrate all the above parameter types: +??? example -```py -from pydantic import validate_call + ```python + from pydantic import validate_call -@validate_call -def pos_or_kw(a: int, b: int = 2) -> str: - return f'a={a} b={b}' + @validate_call + def pos_or_kw(a: int, b: int = 2) -> str: + return f'a={a} b={b}' -print(pos_or_kw(1)) -#> a=1 b=2 -print(pos_or_kw(a=1)) -#> a=1 b=2 -print(pos_or_kw(1, 3)) -#> a=1 b=3 -print(pos_or_kw(a=1, b=3)) -#> a=1 b=3 + print(pos_or_kw(1, b=3)) + #> a=1 b=3 -@validate_call -def kw_only(*, a: int, b: int = 2) -> str: - return f'a={a} b={b}' + @validate_call + def kw_only(*, a: int, b: int = 2) -> str: + return f'a={a} b={b}' -print(kw_only(a=1)) -#> a=1 b=2 -print(kw_only(a=1, b=3)) -#> a=1 b=3 + print(kw_only(a=1)) + #> a=1 b=2 + print(kw_only(a=1, b=3)) + #> a=1 b=3 -@validate_call -def pos_only(a: int, b: int = 2, /) -> str: # python 3.8 only - return f'a={a} b={b}' + @validate_call + def pos_only(a: int, b: int = 2, /) -> str: + return f'a={a} b={b}' -print(pos_only(1)) -#> a=1 b=2 -print(pos_only(1, 2)) -#> a=1 b=2 + print(pos_only(1)) + #> a=1 b=2 -@validate_call -def var_args(*args: int) -> str: - return str(args) + @validate_call + def var_args(*args: int) -> str: + return str(args) -print(var_args(1)) -#> (1,) -print(var_args(1, 2)) -#> (1, 2) -print(var_args(1, 2, 3)) -#> (1, 2, 3) + print(var_args(1)) + #> (1,) + print(var_args(1, 2, 3)) + #> (1, 2, 3) -@validate_call -def var_kwargs(**kwargs: int) -> str: - return str(kwargs) + @validate_call + def var_kwargs(**kwargs: int) -> str: + return str(kwargs) -print(var_kwargs(a=1)) -#> {'a': 1} -print(var_kwargs(a=1, b=2)) -#> {'a': 1, 'b': 2} + print(var_kwargs(a=1)) + #> {'a': 1} + print(var_kwargs(a=1, b=2)) + #> {'a': 1, 'b': 2} -@validate_call -def armageddon( - a: int, - /, # python 3.8 only - b: int, - *c: int, - d: int, - e: int = None, - **f: int, -) -> str: - return f'a={a} b={b} c={c} d={d} e={e} f={f}' - - -print(armageddon(1, 2, d=3)) -#> a=1 b=2 c=() d=3 e=None f={} -print(armageddon(1, 2, 3, 4, 5, 6, d=8, e=9, f=10, spam=11)) -#> a=1 b=2 c=(3, 4, 5, 6) d=8 e=9 f={'f': 10, 'spam': 11} -``` + @validate_call + def armageddon( + a: int, + /, + b: int, + *c: int, + d: int, + e: int = None, + **f: int, + ) -> str: + return f'a={a} b={b} c={c} d={d} e={e} f={f}' + + + print(armageddon(1, 2, d=3)) + #> a=1 b=2 c=() d=3 e=None f={} + print(armageddon(1, 2, 3, 4, 5, 6, d=8, e=9, f=10, spam=11)) + #> a=1 b=2 c=(3, 4, 5, 6) d=8 e=9 f={'f': 10, 'spam': 11} + ``` + +!!! note "[`Unpack`][typing.Unpack] for keyword parameters" + [`Unpack`][typing.Unpack] and typed dictionaries can be used to annotate the variable + keyword parameters of a function: + + ```python + from typing_extensions import TypedDict, Unpack + + from pydantic import validate_call + -## Using Field to describe function arguments + class Point(TypedDict): + x: int + y: int -[Field](json_schema.md#field-level-customization) can also be used with `@validate_call` to provide extra information about -the field and validations. In general it should be used in a type hint with -[Annotated](types.md#composing-types-via-annotated), unless `default_factory` is specified, in which case it should be -used as the default value of the field: -```py + @validate_call + def add_coords(**kwargs: Unpack[Point]) -> int: + return kwargs['x'] + kwargs['y'] + + + add_coords(x=1, y=2) + ``` + + For reference, see the [related specification section] and [PEP 692]. + + [related specification section]: https://typing.readthedocs.io/en/latest/spec/callables.html#unpack-for-keyword-arguments + [PEP 692]: https://peps.python.org/pep-0692/ + +## Using the [`Field()`][pydantic.Field] function to describe function parameters + +The [`Field()` function](fields.md) can also be used with the decorator to provide extra information about +the field and validations. In general it should be used in a type hint with [Annotated](types.md#composing-types-via-annotated), +unless `default_factory` is specified, in which case it should be used as the default value of the field: + +```python from datetime import datetime from typing_extensions import Annotated @@ -218,9 +227,9 @@ print(type(when())) #> ``` -The [`alias`](fields.md#field-aliases) can be used with the decorator as normal. +[Aliases](fields.md#field-aliases) can be used with the decorator as normal: -```py +```python from typing_extensions import Annotated from pydantic import Field, validate_call @@ -234,20 +243,12 @@ def how_many(num: Annotated[int, Field(gt=10, alias='number')]): how_many(number=42) ``` +## Accessing the original function -## Usage with mypy - -The `validate_call` decorator should work "out of the box" with [mypy](http://mypy-lang.org/) since it's -defined to return a function with the same signature as the function it decorates. The only limitation is that -since we trick mypy into thinking the function returned by the decorator is the same as the function being -decorated; access to the [raw function](#raw-function) or other attributes will require `type: ignore`. - -## Raw function +The original function which was decorated can still be accessed by using the `raw_function` attribute. +This is useful if in some scenarios you trust your input arguments and want to call the function in the most efficient way (see [notes on performance](#performance) below): -The raw function which was decorated is accessible, this is useful if in some scenarios you trust your input -arguments and want to call the function in the most performant way (see [notes on performance](#performance) below): - -```py +```python from pydantic import validate_call @@ -268,9 +269,9 @@ print(b) ## Async functions -`@validate_call` can also be used on async functions: +[`validate_call()`][pydantic.validate_call] can also be used on async functions: -```py +```python class Connection: async def execute(self, sql, *args): return 'testing@example.com' @@ -319,16 +320,19 @@ asyncio.run(main()) # requires: `conn.execute()` that will return `'testing@example.com'` ``` -## Custom config +## Compatibility with type checkers -The model behind `@validate_call` can be customised using a `config` setting, which is equivalent to -setting the [`ConfigDict`][pydantic.config.ConfigDict] sub-class in normal models. +As the [`validate_call()`][pydantic.validate_call] decorator preserves the decorated function's signature, +it should be compatible with type checkers (such as mypy and pyright). However, due to current limitations in the Python type system, +the [`raw_function`](#accessing-the-original-function) or other attributes won't be recognized and you will +need to suppress the error using (usually with a `# type: ignore` comment). -Configuration is set using the `config` keyword argument to the decorator, it may be either a config class -or a dict of properties which are converted to a class later. +## Custom configuration -```py -from pydantic import ValidationError, validate_call +Similarly to Pydantic models, the `config` parameter of the decorator can be used to specify a custom configuration: + +```python +from pydantic import ConfigDict, ValidationError, validate_call class Foobar: @@ -342,7 +346,7 @@ class Foobar: return f'Foobar({self.v})' -@validate_call(config=dict(arbitrary_types_allowed=True)) +@validate_call(config=ConfigDict(arbitrary_types_allowed=True)) def add_foobars(a: Foobar, b: Foobar): return a + b @@ -367,11 +371,11 @@ except ValidationError as e: ## Extension — validating arguments before calling a function In some cases, it may be helpful to separate validation of a function's arguments from the function call itself. -This might be useful when a particular function is costly / time consuming. +This might be useful when a particular function is costly/time consuming. Here's an example of a workaround you can use for that pattern: -```py +```python from pydantic import validate_call @@ -392,31 +396,16 @@ print(foo()) ### Validation exception -Currently upon validation failure, a standard Pydantic [`ValidationError`][pydantic_core.ValidationError] is raised. -See [model error handling](models.md#error-handling) for details. - -This is helpful since its `str()` method provides useful details of the error which occurred and methods like -`.errors()` and `.json()` can be useful when exposing the errors to end users. However, `ValidationError` inherits -from `ValueError` **not** `TypeError`, which may be unexpected since Python would raise a `TypeError` upon invalid -or missing arguments. This may be addressed in future by either allowing a custom error or raising a different -exception by default, or both. - -### Coercion and strictness - -Pydantic currently leans on the side of trying to coerce types rather than raise an error if a type is wrong, -see [model data conversion](models.md#data-conversion) and `@validate_call` is no different. +Currently upon validation failure, a standard Pydantic [`ValidationError`][pydantic_core.ValidationError] is raised +(see [model error handling](models.md#error-handling) for details). This is also true for missing required arguments, +where Python normally raises a [`TypeError`][]. ### Performance -We've made a big effort to make Pydantic as performant as possible -and argument inspect and model creation is only performed once when the function is defined, however -there will still be a performance impact to using the `@validate_call` decorator compared to -calling the raw function. - -In many situations this will have little or no noticeable effect, however be aware that -`@validate_call` is not an equivalent or alternative to function definitions in strongly typed languages; -it never will be. - -### Return value +We've made a big effort to make Pydantic as performant as possible. While the inspection of the decorated +function is only performed once, there will still be a performance impact when making calls to the function +compared to using the original function. -The return value of the function may optionally be validated against its return type annotation. +In many situations, this will have little or no noticeable effect. However, be aware that +[`validate_call()`][pydantic.validate_call] is not an equivalent or alternative to function +definitions in strongly typed languages, and it never will be. diff --git a/docs/concepts/validators.md b/docs/concepts/validators.md index 592bbeb9801..31bce4c3555 100644 --- a/docs/concepts/validators.md +++ b/docs/concepts/validators.md @@ -9,7 +9,7 @@ Pydantic provides a way to apply validators via use of `Annotated`. You should use this whenever you want to bind validation to a type instead of model or field. -```py +```python from typing import Any, List from typing_extensions import Annotated @@ -134,7 +134,7 @@ Order of validation metadata within `Annotated` matters. Validation goes from right to left and back. That is, it goes from right to left running all "before" validators (or calling into "wrap" validators), then left to right back out calling all "after" validators. -```py +```python from typing import Any, Callable, List, cast from typing_extensions import Annotated, TypedDict @@ -269,7 +269,7 @@ This applies both to `@field_validator` validators and `Annotated` validators. You can force them to run with `Field(validate_default=True)`. Setting `validate_default` to `True` has the closest behavior to using `always=True` in `validator` in Pydantic v1. However, you are generally better off using a `@model_validator(mode='before')` where the function is called before the inner validator is called. -```py +```python from typing_extensions import Annotated from pydantic import BaseModel, Field, field_validator @@ -302,7 +302,7 @@ print(Model(x='foo', y='bar')) If you want to attach a validator to a specific field of a model you can use the `@field_validator` decorator. -```py +```python from pydantic import ( BaseModel, ValidationError, @@ -397,7 +397,7 @@ However, in most cases where you want to perform validation using multiple field Validation can also be performed on the entire model's data using `@model_validator`. -```py +```python from typing import Any from typing_extensions import Self @@ -475,7 +475,7 @@ except ValidationError as e: Here's an example of the unexpected behavior, and the warning you'll receive: - ```python test="skip" + ```python {test="skip"} from pydantic import BaseModel from pydantic.functional_validators import model_validator @@ -559,7 +559,7 @@ Pydantic provides a few special types that can be used to customize validation. - [`InstanceOf`][pydantic.functional_validators.InstanceOf] is a type that can be used to validate that a value is an instance of a given class. -```py +```python from typing import List from pydantic import BaseModel, InstanceOf, ValidationError @@ -595,7 +595,7 @@ except ValidationError as e: - [`SkipValidation`][pydantic.functional_validators.SkipValidation] is a type that can be used to skip validation on a field. -```py +```python from typing import List from pydantic import BaseModel, SkipValidation @@ -626,33 +626,6 @@ on subclasses of the model where the validator is defined. If you want to disable these checks during class creation, you can pass `check_fields=False` as a keyword argument to the validator. -## Dataclass validators - -Validators also work with Pydantic dataclasses. - -```py -from pydantic import field_validator -from pydantic.dataclasses import dataclass - - -@dataclass -class DemoDataclass: - product_id: str # should be a five-digit string, may have leading zeros - - @field_validator('product_id', mode='before') - @classmethod - def convert_int_serial(cls, v): - if isinstance(v, int): - v = str(v).zfill(5) - return v - - -print(DemoDataclass(product_id='01234')) -#> DemoDataclass(product_id='01234') -print(DemoDataclass(product_id=2468)) -#> DemoDataclass(product_id='02468') -``` - ## Validation Context You can pass a context object to the validation methods which can be accessed from the `info` @@ -810,7 +783,7 @@ The "naive" approach would be to write a separate function, then call it from mu Obviously, this entails a lot of repetition and boiler plate code. The following approach demonstrates how you can reuse a validator so that redundancy is minimized and the models become again almost declarative. -```py +```python from pydantic import BaseModel, field_validator diff --git a/docs/contributing.md b/docs/contributing.md index 19b7d6b1744..0bde161b654 100644 --- a/docs/contributing.md +++ b/docs/contributing.md @@ -49,10 +49,9 @@ Because of this, setting up and running the tests should be very simple. You'll need the following prerequisites: - Any Python version between **Python 3.9 and 3.12** -- **virtualenv** or other virtual environment tool +- [**uv**](https://docs.astral.sh/uv/getting-started/installation/) or other virtual environment tool - **git** - **make** -- [**PDM**](https://pdm.fming.dev/latest/#installation) ### Installation and setup @@ -63,13 +62,13 @@ Fork the repository on GitHub and clone your fork locally. git clone git@github.com:/pydantic.git cd pydantic -# Install PDM and pre-commit +# Install UV and pre-commit # We use pipx here, for other options see: -# https://pdm.fming.dev/latest/#installation +# https://docs.astral.sh/uv/getting-started/installation/ # https://pre-commit.com/#install # To get pipx itself: # https://pypa.github.io/pipx/ -pipx install pdm +pipx install uv pipx install pre-commit # Install pydantic, dependencies, test dependencies and doc dependencies @@ -107,13 +106,35 @@ make If you've made any changes to the documentation (including changes to function signatures, class definitions, or docstrings that will appear in the API documentation), make sure it builds successfully. +We use `mkdocs-material[imaging]` to support social previews. +You can find directions on how to install the required dependencies [here](https://squidfunk.github.io/mkdocs-material/plugins/requirements/image-processing/). + ```bash # Build documentation make docs # If you have changed the documentation, make sure it builds successfully. -# You can also use `pdm run mkdocs serve` to serve the documentation at localhost:8000 +# You can also use `uv run mkdocs serve` to serve the documentation at localhost:8000 ``` +If this isn't working due to issues with the imaging plugin, try commenting out the `social` plugin line in `mkdocs.yml` and running `make docs` again. + +#### Updating the documentation + +We push a new version of the documentation with each minor release, and we push to a `dev` path with each commit to `main`. + +If you're updating the documentation out of cycle with a minor release and want your changes to be reflected on `latest`, +do the following: + +1. Open a PR against `main` with your docs changes +2. Once the PR is merged, checkout the `docs-update` branch. This branch should be up to date with the latest patch release. +For example, if the latest release is `v2.9.2`, you should make sure `docs-update` is up to date with the `v2.9.2` tag. +3. Checkout a new branch from `docs-update` and cherry-pick your changes onto this branch. +4. Push your changes and open a PR against `docs-update`. +5. Once the PR is merged, the new docs will be built and deployed. + +!!! note + Maintainer shortcut - as a maintainer, you can skip the second PR and just cherry pick directly onto the `docs-update` branch. + ### Commit and push your changes Commit your changes, push your branch to GitHub, and create a pull request. diff --git a/docs/errors/errors.md b/docs/errors/errors.md index 6540b627dd7..6d542a5c659 100644 --- a/docs/errors/errors.md +++ b/docs/errors/errors.md @@ -34,7 +34,7 @@ The first item in the `loc` list will be the field where the error occurred, and As a demonstration: -```py +```python from typing import List from pydantic import BaseModel, ValidationError, conint @@ -135,7 +135,7 @@ In your custom data types or validators you should use `ValueError` or `Assertio See [validators](../concepts/validators.md) for more details on use of the `@validator` decorator. -```py +```python from pydantic import BaseModel, ValidationError, field_validator @@ -176,7 +176,7 @@ except ValidationError as e: You can also use [`PydanticCustomError`][pydantic_core.PydanticCustomError], to fully control the error structure: -```py +```python from pydantic_core import PydanticCustomError from pydantic import BaseModel, ValidationError, field_validator @@ -220,7 +220,7 @@ We've provided documentation for default error codes in the following sections: You can customize error messages by creating a custom error handler. -```py +```python from typing import Dict, List from pydantic_core import ErrorDetails @@ -285,7 +285,7 @@ dictionary of translations. Another example is customizing the way that the `'loc'` value of an error is represented. -```py +```python from typing import Any, Dict, List, Tuple, Union from pydantic import BaseModel, ValidationError diff --git a/docs/errors/usage_errors.md b/docs/errors/usage_errors.md index 0e06f190c0e..e2b26bb8f39 100644 --- a/docs/errors/usage_errors.md +++ b/docs/errors/usage_errors.md @@ -6,7 +6,7 @@ encounter when working with Pydantic, along with suggestions for addressing the This error is raised when a type referenced in an annotation of a pydantic-validated type (such as a subclass of `BaseModel`, or a pydantic `dataclass`) is not defined: -```py +```python from typing import ForwardRef from pydantic import BaseModel, PydanticUserError @@ -26,7 +26,7 @@ except PydanticUserError as exc_info: Or when the type has been defined after usage: -```py +```python from typing import Optional from pydantic import BaseModel, PydanticUserError @@ -53,7 +53,7 @@ foo = Foo(a={'b': {'a': None}}) For BaseModel subclasses, it can be fixed by defining the type and then calling `.model_rebuild()`: -```py +```python from typing import Optional from pydantic import BaseModel @@ -80,7 +80,7 @@ The `__modify_schema__` method is no longer supported in V2. You should use the The `__modify_schema__` used to receive a single argument representing the JSON schema. See the example below: -```py title="Old way" +```python {title="Old way"} from pydantic import BaseModel, PydanticUserError try: @@ -98,7 +98,7 @@ The new method `__get_pydantic_json_schema__` receives two arguments: the first and the second a callable `handler` that receives a `CoreSchema` as parameter, and returns a JSON schema. See the example below: -```py title="New way" +```python {title="New way"} from typing import Any, Dict from pydantic_core import CoreSchema @@ -127,7 +127,7 @@ print(Model.model_json_schema()) This error is raised when you define a decorator with a field that is not valid. -```py +```python from typing import Any from pydantic import BaseModel, PydanticUserError, field_validator @@ -147,7 +147,7 @@ except PydanticUserError as exc_info: You can use `check_fields=False` if you're inheriting from the model and intended this. -```py +```python from typing import Any from pydantic import BaseModel, create_model, field_validator @@ -166,7 +166,7 @@ model = create_model('FooModel', a=(str, 'cake'), __base__=Model) This error is raised when a model in discriminated unions doesn't define a discriminator field. -```py +```python from typing import Literal, Union from pydantic import BaseModel, Field, PydanticUserError @@ -184,7 +184,7 @@ class Dog(BaseModel): try: class Model(BaseModel): - pet: Union[Cat, Dog] = Field(..., discriminator='pet_type') + pet: Union[Cat, Dog] = Field(discriminator='pet_type') number: int except PydanticUserError as exc_info: @@ -195,7 +195,7 @@ except PydanticUserError as exc_info: This error is raised when you define a non-string alias on a discriminator field. -```py +```python from typing import Literal, Union from pydantic import AliasChoices, BaseModel, Field, PydanticUserError @@ -216,7 +216,7 @@ class Dog(BaseModel): try: class Model(BaseModel): - pet: Union[Cat, Dog] = Field(..., discriminator='pet_type') + pet: Union[Cat, Dog] = Field(discriminator='pet_type') number: int except PydanticUserError as exc_info: @@ -227,7 +227,7 @@ except PydanticUserError as exc_info: This error is raised when you define a non-`Literal` type on a discriminator field. -```py +```python from typing import Literal, Union from pydantic import BaseModel, Field, PydanticUserError @@ -246,7 +246,7 @@ class Dog(BaseModel): try: class Model(BaseModel): - pet: Union[Cat, Dog] = Field(..., discriminator='pet_type') + pet: Union[Cat, Dog] = Field(discriminator='pet_type') number: int except PydanticUserError as exc_info: @@ -257,7 +257,7 @@ except PydanticUserError as exc_info: This error is raised when you define different aliases on discriminator fields. -```py +```python from typing import Literal, Union from pydantic import BaseModel, Field, PydanticUserError @@ -276,7 +276,7 @@ class Dog(BaseModel): try: class Model(BaseModel): - pet: Union[Cat, Dog] = Field(..., discriminator='pet_type') + pet: Union[Cat, Dog] = Field(discriminator='pet_type') number: int except PydanticUserError as exc_info: @@ -290,7 +290,7 @@ This error is raised when you use a before, wrap, or plain validator on a discri This is disallowed because the discriminator field is used to determine the type of the model to use for validation, so you can't use a validator that might change its value. -```py +```python from typing import Literal, Union from pydantic import BaseModel, Field, PydanticUserError, field_validator @@ -314,7 +314,7 @@ class Dog(BaseModel): try: class Model(BaseModel): - pet: Union[Cat, Dog] = Field(..., discriminator='pet_type') + pet: Union[Cat, Dog] = Field(discriminator='pet_type') number: int except PydanticUserError as exc_info: @@ -323,7 +323,7 @@ except PydanticUserError as exc_info: This can be worked around by using a standard `Union`, dropping the discriminator: -```py +```python from typing import Literal, Union from pydantic import BaseModel, field_validator @@ -355,7 +355,7 @@ assert Model(pet={'pet_type': 'kitten'}).pet.pet_type == 'cat' This error is raised when a `Union` that uses a callable `Discriminator` doesn't have `Tag` annotations for all cases. -```py +```python from typing import Union from typing_extensions import Annotated @@ -407,7 +407,6 @@ except PydanticUserError as exc_info: assert exc_info.code == 'callable-discriminator-no-tag' ``` - ## `TypedDict` version {#typed-dict-version} This error is raised when you use [typing.TypedDict][] @@ -417,7 +416,7 @@ instead of `typing_extensions.TypedDict` on Python < 3.12. This error is raised when a field defined on a base class was overridden by a non-annotated attribute. -```py +```python from pydantic import BaseModel, PydanticUserError @@ -439,7 +438,7 @@ except PydanticUserError as exc_info: This error is raised when a field doesn't have an annotation. -```py +```python from pydantic import BaseModel, Field, PydanticUserError try: @@ -455,7 +454,7 @@ except PydanticUserError as exc_info: If the field is not meant to be a field, you may be able to resolve the error by annotating it as a `ClassVar`: -```py +```python from typing import ClassVar from pydantic import BaseModel @@ -467,7 +466,7 @@ class Model(BaseModel): Or updating `model_config['ignored_types']`: -```py +```python from pydantic import BaseModel, ConfigDict @@ -488,7 +487,7 @@ class MyModel(BaseModel): This error is raised when `class Config` and `model_config` are used together. -```py +```python from pydantic import BaseModel, ConfigDict, PydanticUserError try: @@ -511,7 +510,7 @@ This error is raised when the keyword arguments are not available in Pydantic V2 For example, `regex` is removed from Pydantic V2: -```py +```python from pydantic import BaseModel, Field, PydanticUserError try: @@ -523,11 +522,30 @@ except PydanticUserError as exc_info: assert exc_info.code == 'removed-kwargs' ``` +## Circular reference schema {#circular-reference-schema} + +This error is raised when a circular reference is found that would otherwise result in an infinite recursion. + +For example, this is a valid type alias: + +```python {test="skip" lint="skip" upgrade="skip"} +type A = list[A] | None +``` + +while these are not: + +```python {test="skip" lint="skip" upgrade="skip"} +type A = A + +type B = C +type C = B +``` + ## JSON schema invalid type {#invalid-for-json-schema} This error is raised when Pydantic fails to generate a JSON schema for some `CoreSchema`. -```py +```python from pydantic import BaseModel, ImportString, PydanticUserError @@ -541,7 +559,6 @@ except PydanticUserError as exc_info: assert exc_info.code == 'invalid-for-json-schema' ``` - ## JSON schema already used {#json-schema-already-used} This error is raised when the JSON schema generator has already been used to generate a JSON schema. @@ -551,7 +568,7 @@ You must create a new instance to generate a new JSON schema. This error is raised when you instantiate `BaseModel` directly. Pydantic models should inherit from `BaseModel`. -```py +```python from pydantic import BaseModel, PydanticUserError try: @@ -564,7 +581,7 @@ except PydanticUserError as exc_info: This error is raised when handling undefined annotations during `CoreSchema` generation. -```py +```python from pydantic import BaseModel, PydanticUndefinedAnnotation @@ -582,7 +599,7 @@ except PydanticUndefinedAnnotation as exc_info: This error is raised when Pydantic fails to generate a `CoreSchema` for some type. -```py +```python from pydantic import BaseModel, PydanticUserError try: @@ -605,7 +622,7 @@ See the [Migration Guide](../migration.md) for more information. This error is raised when you provide field definitions input in `create_model` that is not valid. -```py +```python from pydantic import PydanticUserError, create_model try: @@ -616,7 +633,7 @@ except PydanticUserError as exc_info: Or when you use [`typing.Annotated`][] with invalid input -```py +```python from typing_extensions import Annotated from pydantic import PydanticUserError, create_model @@ -631,7 +648,7 @@ except PydanticUserError as exc_info: This error is raised when you use both `__config__` and `__base__` together in `create_model`. -```py +```python from pydantic import BaseModel, ConfigDict, PydanticUserError, create_model try: @@ -647,7 +664,7 @@ except PydanticUserError as exc_info: This error is raised when you use validator bare (with no fields). -```py +```python from pydantic import BaseModel, PydanticUserError, field_validator try: @@ -665,7 +682,7 @@ except PydanticUserError as exc_info: Validators should be used with fields and keyword arguments. -```py +```python from pydantic import BaseModel, field_validator @@ -681,7 +698,7 @@ class Model(BaseModel): This error is raised when you use a validator with non-string fields. -```py +```python from pydantic import BaseModel, PydanticUserError, field_validator try: @@ -700,7 +717,7 @@ except PydanticUserError as exc_info: Fields should be passed as separate string arguments: -```py +```python from pydantic import BaseModel, field_validator @@ -717,7 +734,7 @@ class Model(BaseModel): This error is raised when you apply a validator on an instance method. -```py +```python from pydantic import BaseModel, PydanticUserError, field_validator try: @@ -738,7 +755,7 @@ except PydanticUserError as exc_info: This error is raised when you explicitly specify a value for the `json_schema_input_type` argument and `mode` isn't set to either `'before'`, `'plain'` or `'wrap'`. -```py +```python from pydantic import BaseModel, PydanticUserError, field_validator try: @@ -775,7 +792,7 @@ Please see the [Migration Guide](../migration.md) for more details. This error is raised when you apply `model_serializer` on an instance method without `self`: -```py +```python from pydantic import BaseModel, PydanticUserError, model_serializer try: @@ -793,7 +810,7 @@ except PydanticUserError as exc_info: Or on a class method: -```py +```python from pydantic import BaseModel, PydanticUserError, model_serializer try: @@ -824,7 +841,7 @@ The `field` argument is no longer available. This error is raised when you use an unsupported signature for Pydantic V1-style validator. -```py +```python import warnings from pydantic import BaseModel, PydanticUserError, validator @@ -848,7 +865,7 @@ except PydanticUserError as exc_info: This error is raised when a `field_validator` or `model_validator` function has the wrong signature. -```py +```python from pydantic import BaseModel, PydanticUserError, field_validator try: @@ -869,7 +886,7 @@ except PydanticUserError as exc_info: This error is raised when the `field_serializer` function has the wrong signature. -```py +```python from pydantic import BaseModel, PydanticUserError, field_serializer try: @@ -887,7 +904,7 @@ except PydanticUserError as exc_info: Valid field serializer signatures are: -```py test="skip" lint="skip" upgrade="skip" +```python {test="skip" lint="skip" upgrade="skip"} from pydantic import FieldSerializationInfo, SerializerFunctionWrapHandler, field_serializer # an instance method with the default mode or `mode='plain'` @@ -928,7 +945,7 @@ def ser_x(self, value: Any, handler: SerializerFunctionWrapHandler): ... This error is raised when the `model_serializer` function has the wrong signature. -```py +```python from pydantic import BaseModel, PydanticUserError, model_serializer try: @@ -946,7 +963,7 @@ except PydanticUserError as exc_info: Valid model serializer signatures are: -```py test="skip" lint="skip" upgrade="skip" +```python {test="skip" lint="skip" upgrade="skip"} from pydantic import SerializerFunctionWrapHandler, SerializationInfo, model_serializer # an instance method with the default mode or `mode='plain'` @@ -969,7 +986,7 @@ def mod_ser(self, handler: SerializerFunctionWrapHandler): ... This error is raised when multiple `model_serializer` functions are defined for a field. -```py +```python from pydantic import BaseModel, PydanticUserError, field_serializer try: @@ -994,7 +1011,7 @@ except PydanticUserError as exc_info: This error is raised when an annotation cannot annotate a type. -```py +```python from typing_extensions import Annotated from pydantic import BaseModel, FutureDate, PydanticUserError @@ -1013,7 +1030,7 @@ except PydanticUserError as exc_info: You will get this error if you try to pass `config` to `TypeAdapter` when the type is a type that has its own config that cannot be overridden (currently this is only `BaseModel`, `TypedDict` and `dataclass`): -```py +```python from typing_extensions import TypedDict from pydantic import ConfigDict, PydanticUserError, TypeAdapter @@ -1031,7 +1048,7 @@ except PydanticUserError as exc_info: Instead you'll need to subclass the type and override or set the config on it: -```py +```python from typing_extensions import TypedDict from pydantic import ConfigDict, TypeAdapter @@ -1052,7 +1069,7 @@ TypeAdapter(MyTypedDict) # ok Because `RootModel` is not capable of storing or even accepting extra fields during initialization, we raise an error if you try to specify a value for the config setting `'extra'` when creating a subclass of `RootModel`: -```py +```python from pydantic import PydanticUserError, RootModel try: @@ -1070,7 +1087,7 @@ except PydanticUserError as exc_info: Because type annotations are evaluated *after* assignments, you might get unexpected results when using a type annotation name that clashes with one of your fields. We raise an error in the following case: -```py test="skip" +```python {test="skip"} from datetime import date from pydantic import BaseModel, Field @@ -1082,7 +1099,7 @@ class Model(BaseModel): As a workaround, you can either use an alias or change your import: -```py lint="skip" +```python {lint="skip"} import datetime # Or `from datetime import date as _date` @@ -1100,7 +1117,7 @@ while any of the fields have `init=False` set. Thus, you may not do something like the following: -```py test="skip" +```python {test="skip"} from pydantic import ConfigDict, Field from pydantic.dataclasses import dataclass @@ -1121,14 +1138,14 @@ This combination is not allowed. The `init=False` and `init_var=True` settings are mutually exclusive. Doing so results in the `PydanticUserError` shown in the example below. -```py test="skip" +```python {test="skip"} from pydantic import Field from pydantic.dataclasses import dataclass @dataclass class Foo: - bar: str = Field(..., init=False, init_var=True) + bar: str = Field(init=False, init_var=True) """ @@ -1139,7 +1156,8 @@ pydantic.errors.PydanticUserError: Dataclass field bar has init=False and init_v ## `model_config` is used as a model field {#model-config-invalid-field-name} This error is raised when `model_config` is used as the name of a field. -```py + +```python from pydantic import BaseModel, PydanticUserError try: @@ -1151,12 +1169,11 @@ except PydanticUserError as exc_info: assert exc_info.code == 'model-config-invalid-field-name' ``` - ## [`with_config`][pydantic.config.with_config] is used on a `BaseModel` subclass {#with-config-on-model} -This error is raised when the [`with_config`][pydantic.config.with_config] decorator is used on a class which is already a Pydantic model (use the `model_config` attribute instead). +This error is raised when the [`with_config`][pydantic.config.with_config] decorator is used on a class which is already a Pydantic model (use the `model_config` attribute instead). -```py +```python from pydantic import BaseModel, PydanticUserError, with_config try: @@ -1169,13 +1186,12 @@ except PydanticUserError as exc_info: assert exc_info.code == 'with-config-on-model' ``` - ## `dataclass` is used on a `BaseModel` subclass {#dataclass-on-model} This error is raised when the Pydantic `dataclass` decorator is used on a class which is already a Pydantic model. -```py +```python from pydantic import BaseModel, PydanticUserError from pydantic.dataclasses import dataclass @@ -1188,3 +1204,189 @@ try: except PydanticUserError as exc_info: assert exc_info.code == 'dataclass-on-model' ``` + +## Unsupported type for `validate_call` {#validate-call-type} + +`validate_call` has some limitations on the callables it can validate. This error is raised when you try to use it with an unsupported callable. Currently the supported callables are functions (including lambdas) and methods and instances of [`partial`][functools.partial]. In the case of [`partial`][functools.partial], the function being partially applied must be one of the supported callables. + +### `@classmethod`, `@staticmethod`, and `@property` + +These decorators must be put before `validate_call`. + +```python +from pydantic import PydanticUserError, validate_call + +# error +try: + + class A: + @validate_call + @classmethod + def f1(cls): ... + +except PydanticUserError as exc_info: + assert exc_info.code == 'validate-call-type' + + +# correct +@classmethod +@validate_call +def f2(cls): ... +``` + +### Classes + +While classes are callables themselves, `validate_call` can't be applied on them, as it needs to know about which method to use (`__init__` or `__new__`) to fetch type annotations. If you want to validate the constructor of a class, you should put `validate_call` on top of the appropriate method instead. + +```python +from pydantic import PydanticUserError, validate_call + +# error +try: + + @validate_call + class A1: ... + +except PydanticUserError as exc_info: + assert exc_info.code == 'validate-call-type' + + +# correct +class A2: + @validate_call + def __init__(self): ... + + @validate_call + def __new__(cls): ... +``` + +### Custom callable + +Although you can create custom callable types in Python by implementing a `__call__` method, currently the instances of these types cannot be validated with `validate_call`. This may change in the future, but for now, you should use `validate_call` explicitly on `__call__` instead. + +```python +from pydantic import PydanticUserError, validate_call + +# error +try: + + class A1: + def __call__(self): ... + + validate_call(A1()) + +except PydanticUserError as exc_info: + assert exc_info.code == 'validate-call-type' + + +# correct +class A2: + @validate_call + def __call__(self): ... +``` + +### Invalid signature + +This is generally less common, but a possible reason is that you are trying to validate a method that doesn't have at least one argument (usually `self`). + +```python +from pydantic import PydanticUserError, validate_call + +try: + + class A: + def f(): ... + + validate_call(A().f) +except PydanticUserError as exc_info: + assert exc_info.code == 'validate-call-type' +``` + +## [`Unpack`][typing.Unpack] used without a [`TypedDict`][typing.TypedDict] {#unpack-typed-dict} + +This error is raised when [`Unpack`][typing.Unpack] is used with something other than +a [`TypedDict`][typing.TypedDict] class object to type hint variadic keyword parameters. + +For reference, see the [related specification section] and [PEP 692]. + +```python +from typing_extensions import Unpack + +from pydantic import PydanticUserError, validate_call + +try: + + @validate_call + def func(**kwargs: Unpack[int]): + pass + +except PydanticUserError as exc_info: + assert exc_info.code == 'unpack-typed-dict' +``` + +## Overlapping unpacked [`TypedDict`][typing.TypedDict] fields and arguments {#overlapping-unpack-typed-dict} + +This error is raised when the typed dictionary used to type hint variadic keywords parameters has field names +overlapping with other parameters (unless [positional only][positional-only_parameter]). + +For reference, see the [related specification section] and [PEP 692]. + +```python +from typing_extensions import TypedDict, Unpack + +from pydantic import PydanticUserError, validate_call + + +class TD(TypedDict): + a: int + + +try: + + @validate_call + def func(a: int, **kwargs: Unpack[TD]): + pass + +except PydanticUserError as exc_info: + assert exc_info.code == 'overlapping-unpack-typed-dict' +``` + +[related specification section]: https://typing.readthedocs.io/en/latest/spec/callables.html#unpack-for-keyword-arguments +[PEP 692]: https://peps.python.org/pep-0692/ + +## Invalid `Self` type {#invalid-self-type} + +Currently, [`Self`][typing.Self] can only be used to annotate a field of a class (specifically, subclasses of [`BaseModel`][pydantic.BaseModel], [`NamedTuple`][typing.NamedTuple], [`TypedDict`][typing.TypedDict], or dataclasses). Attempting to use [`Self`][typing.Self] in any other ways will raise this error. + +```python +from typing_extensions import Self + +from pydantic import PydanticUserError, validate_call + +try: + + @validate_call + def func(self: Self): + pass + +except PydanticUserError as exc_info: + assert exc_info.code == 'invalid-self-type' +``` + +The following example of [`validate_call()`][pydantic.validate_call] will also raise this error, even though it is correct from a type-checking perspective. This may be supported in the future. + +```python +from typing_extensions import Self + +from pydantic import BaseModel, PydanticUserError, validate_call + +try: + + class A(BaseModel): + @validate_call + def func(self, arg: Self): + pass + +except PydanticUserError as exc_info: + assert exc_info.code == 'invalid-self-type' +``` diff --git a/docs/errors/validation_errors.md b/docs/errors/validation_errors.md index e3ef9e01a0d..ab9dbd316ef 100644 --- a/docs/errors/validation_errors.md +++ b/docs/errors/validation_errors.md @@ -7,7 +7,7 @@ This error is raised when an object that would be passed as arguments to a funct a `tuple`, `list`, or `dict`. Because `NamedTuple` uses function calls in its implementation, that is one way to produce this error: -```py +```python from typing import NamedTuple from pydantic import BaseModel, ValidationError @@ -32,7 +32,7 @@ except ValidationError as exc: This error is raised when a failing `assert` statement is encountered during validation: -```py +```python from pydantic import BaseModel, ValidationError, field_validator @@ -57,7 +57,7 @@ except ValidationError as exc: This error is raised when the input value is a string that is not valid for coercion to a boolean: -```py +```python from pydantic import BaseModel, ValidationError @@ -78,7 +78,7 @@ except ValidationError as exc: This error is raised when the input value's type is not valid for a `bool` field: -```py +```python from pydantic import BaseModel, ValidationError @@ -98,7 +98,7 @@ except ValidationError as exc: This error is raised when a `bytes` value is invalid under the configured encoding. In the following example, `b'a'` is invalid hex (odd number of digits). -```py +```python from pydantic import BaseModel, ValidationError @@ -120,7 +120,7 @@ This error is also raised for strict fields when the input value is not an insta This error is raised when the length of a `bytes` value is greater than the field's `max_length` constraint: -```py +```python from pydantic import BaseModel, Field, ValidationError @@ -139,7 +139,7 @@ except ValidationError as exc: This error is raised when the length of a `bytes` value is less than the field's `min_length` constraint: -```py +```python from pydantic import BaseModel, Field, ValidationError @@ -158,7 +158,7 @@ except ValidationError as exc: This error is raised when the input value's type is not valid for a `bytes` field: -```py +```python from pydantic import BaseModel, ValidationError @@ -179,7 +179,7 @@ This error is also raised for strict fields when the input value is not an insta This error is raised when the input value is not valid as a `Callable`: -```py +```python from typing import Any, Callable from pydantic import BaseModel, ImportString, ValidationError @@ -203,7 +203,7 @@ except ValidationError as exc: This error is raised when the input value is a string but cannot be parsed as a complex number because it does not follow the [rule](https://docs.python.org/3/library/functions.html#complex) in Python: -```py +```python from pydantic import BaseModel, ValidationError @@ -224,7 +224,7 @@ except ValidationError as exc: This error is raised when the input value cannot be interpreted as a complex number: -```py +```python from pydantic import BaseModel, ValidationError @@ -243,7 +243,7 @@ except ValidationError as exc: This error is raised when validating a dataclass with `strict=True` and the input is not an instance of the dataclass: -```py +```python import pydantic.dataclasses from pydantic import TypeAdapter, ValidationError @@ -271,7 +271,7 @@ except ValidationError as exc: This error is raised when the input value is not valid for a `dataclass` field: -```py +```python from pydantic import ValidationError, dataclasses @@ -299,7 +299,7 @@ except ValidationError as exc: This error is raised when the input `datetime` value provided for a `date` field has a nonzero time component. For a timestamp to parse into a field of type `date`, the time components must all be zero: -```py +```python from datetime import date, datetime from pydantic import BaseModel, ValidationError @@ -323,7 +323,7 @@ except ValidationError as exc: This error is raised when the input value is a string that cannot be parsed for a `date` field: -```py +```python from datetime import date from pydantic import BaseModel, ValidationError @@ -344,7 +344,7 @@ except ValidationError as exc: This error is raised when the input value provided for a `FutureDate` field is not in the future: -```py +```python from datetime import date from pydantic import BaseModel, FutureDate, ValidationError @@ -365,7 +365,7 @@ except ValidationError as exc: This error is raised when validating JSON where the input value is string that cannot be parsed for a `date` field: -```py +```python import json from datetime import date @@ -387,7 +387,7 @@ except ValidationError as exc: This error is raised when the value provided for a `PastDate` field is not in the past: -```py +```python from datetime import date, timedelta from pydantic import BaseModel, PastDate, ValidationError @@ -408,7 +408,7 @@ except ValidationError as exc: This error is raised when the input value's type is not valid for a `date` field: -```py +```python from datetime import date from pydantic import BaseModel, ValidationError @@ -434,7 +434,7 @@ This error is also raised for strict fields when the input value is not an insta This error is raised when the input value is a string that cannot be parsed for a `datetime` field: -```py +```python from datetime import datetime from pydantic import BaseModel, ValidationError @@ -456,7 +456,7 @@ except ValidationError as exc: This error is raised when the value provided for a `FutureDatetime` field is not in the future: -```py +```python from datetime import datetime from pydantic import BaseModel, FutureDatetime, ValidationError @@ -477,7 +477,7 @@ except ValidationError as exc: This error is raised when something about the `datetime` object is not valid: -```py +```python from datetime import datetime, tzinfo from pydantic import AwareDatetime, BaseModel, ValidationError @@ -505,7 +505,7 @@ except ValidationError as exc: This error is raised when the value is a string that cannot be parsed for a `datetime` field: -```py +```python import json from datetime import datetime @@ -527,7 +527,7 @@ except ValidationError as exc: This error is raised when the value provided for a `PastDatetime` field is not in the past: -```py +```python from datetime import datetime, timedelta from pydantic import BaseModel, PastDatetime, ValidationError @@ -548,7 +548,7 @@ except ValidationError as exc: This error is raised when the input value's type is not valid for a `datetime` field: -```py +```python from datetime import datetime from pydantic import BaseModel, ValidationError @@ -571,7 +571,7 @@ This error is also raised for strict fields when the input value is not an insta This error is raised when the value provided for a `Decimal` has too many digits: -```py +```python from decimal import Decimal from pydantic import BaseModel, Field, ValidationError @@ -592,7 +592,7 @@ except ValidationError as exc: This error is raised when the value provided for a `Decimal` has too many digits after the decimal point: -```py +```python from decimal import Decimal from pydantic import BaseModel, Field, ValidationError @@ -613,7 +613,7 @@ except ValidationError as exc: This error is raised when the value provided for a `Decimal` could not be parsed as a decimal number: -```py +```python from decimal import Decimal from pydantic import BaseModel, Field, ValidationError @@ -634,7 +634,7 @@ except ValidationError as exc: This error is raised when the value provided for a `Decimal` is of the wrong type: -```py +```python from decimal import Decimal from pydantic import BaseModel, Field, ValidationError @@ -657,7 +657,7 @@ This error is also raised for strict fields when the input value is not an insta This error is raised when the value provided for a `Decimal` has more digits before the decimal point than `max_digits` - `decimal_places` (as long as both are specified): -```py +```python from decimal import Decimal from pydantic import BaseModel, Field, ValidationError @@ -680,7 +680,7 @@ This error is also raised for strict fields when the input value is not an insta This error is raised when the input value's type is not `dict` for a `dict` field: -```py +```python from pydantic import BaseModel, ValidationError @@ -699,7 +699,7 @@ except ValidationError as exc: This error is raised when the input value does not exist in an `enum` field members: -```py +```python from enum import Enum from pydantic import BaseModel, ValidationError @@ -724,7 +724,7 @@ except ValidationError as exc: This error is raised when the input value contains extra fields, but `model_config['extra'] == 'forbid'`: -```py +```python from pydantic import BaseModel, ConfigDict, ValidationError @@ -748,7 +748,7 @@ You can read more about the `extra` configuration in the [Extra Attributes][pyda This error is raised when the value is infinite, or too large to be represented as a 64-bit floating point number during validation: -```py +```python from pydantic import BaseModel, ValidationError @@ -767,7 +767,7 @@ except ValidationError as exc: This error is raised when the value is a string that can't be parsed as a `float`: -```py +```python from pydantic import BaseModel, ValidationError @@ -786,7 +786,7 @@ except ValidationError as exc: This error is raised when the input value's type is not valid for a `float` field: -```py +```python from pydantic import BaseModel, ValidationError @@ -805,7 +805,7 @@ except ValidationError as exc: This error is raised when you attempt to assign a value to a field with `frozen=True`, or to delete such a field: -```py +```python from pydantic import BaseModel, Field, ValidationError @@ -833,7 +833,7 @@ except ValidationError as exc: This error is raised when `model_config['frozen] == True` and you attempt to delete or assign a new value to any of the fields: -```py +```python from pydantic import BaseModel, ConfigDict, ValidationError @@ -862,7 +862,7 @@ except ValidationError as exc: This error is raised when the input value's type is not valid for a `frozenset` field: -```py +```python from pydantic import BaseModel, ValidationError @@ -881,7 +881,7 @@ except ValidationError as exc: This error is raised when `model_config['from_attributes'] == True` and an error is raised while reading the attributes: -```py +```python from pydantic import BaseModel, ConfigDict, ValidationError @@ -912,7 +912,7 @@ except ValidationError as exc: This error is raised when the value is not greater than the field's `gt` constraint: -```py +```python from pydantic import BaseModel, Field, ValidationError @@ -931,7 +931,7 @@ except ValidationError as exc: This error is raised when the value is not greater than or equal to the field's `ge` constraint: -```py +```python from pydantic import BaseModel, Field, ValidationError @@ -950,7 +950,7 @@ except ValidationError as exc: This error is raised when you provide a `float` value for an `int` field: -```py +```python from pydantic import BaseModel, ValidationError @@ -969,7 +969,7 @@ except ValidationError as exc: This error is raised when the value can't be parsed as `int`: -```py +```python from pydantic import BaseModel, ValidationError @@ -989,7 +989,7 @@ except ValidationError as exc: This error is raised when attempting to parse a python or JSON value from a string outside the maximum range that Python `str` to `int` parsing permits: -```py +```python import json from pydantic import BaseModel, ValidationError @@ -1021,7 +1021,7 @@ except ValidationError as exc: This error is raised when the input value's type is not valid for an `int` field: -```py +```python from pydantic import BaseModel, ValidationError @@ -1040,7 +1040,7 @@ except ValidationError as exc: This error is raised when attempting to validate a `dict` that has a key that is not an instance of `str`: -```py +```python from pydantic import BaseModel, ConfigDict, ValidationError @@ -1061,7 +1061,7 @@ except ValidationError as exc: This error is raised when the input value is not an instance of the expected type: -```py +```python from pydantic import BaseModel, ConfigDict, ValidationError @@ -1086,7 +1086,7 @@ except ValidationError as exc: This error is raised when the input value is not a subclass of the expected type: -```py +```python from typing import Type from pydantic import BaseModel, ValidationError @@ -1111,7 +1111,7 @@ except ValidationError as exc: This error is raised when the input value is not valid as an `Iterable`: -```py +```python from typing import Iterable from pydantic import BaseModel, ValidationError @@ -1132,7 +1132,7 @@ except ValidationError as exc: This error is raised when an error occurs during iteration: -```py +```python from typing import List from pydantic import BaseModel, ValidationError @@ -1158,7 +1158,7 @@ except ValidationError as exc: This error is raised when the input value is not a valid JSON string: -```py +```python from pydantic import BaseModel, Json, ValidationError @@ -1177,7 +1177,7 @@ except ValidationError as exc: This error is raised when the input value is of a type that cannot be parsed as JSON: -```py +```python from pydantic import BaseModel, Json, ValidationError @@ -1196,7 +1196,7 @@ except ValidationError as exc: This error is raised when the input value is not less than the field's `lt` constraint: -```py +```python from pydantic import BaseModel, Field, ValidationError @@ -1215,7 +1215,7 @@ except ValidationError as exc: This error is raised when the input value is not less than or equal to the field's `le` constraint: -```py +```python from pydantic import BaseModel, Field, ValidationError @@ -1234,7 +1234,7 @@ except ValidationError as exc: This error is raised when the input value's type is not valid for a `list` field: -```py +```python from typing import List from pydantic import BaseModel, ValidationError @@ -1255,7 +1255,7 @@ except ValidationError as exc: This error is raised when the input value is not one of the expected literal values: -```py +```python from typing import Literal from pydantic import BaseModel, ValidationError @@ -1279,7 +1279,7 @@ except ValidationError as exc: This error is raised when a problem occurs during validation due to a failure in a call to the methods from the `Mapping` protocol, such as `.items()`: -```py +```python from collections.abc import Mapping from typing import Dict @@ -1315,7 +1315,7 @@ except ValidationError as exc: This error is raised when there are required fields missing from the input value: -```py +```python from pydantic import BaseModel, ValidationError @@ -1335,7 +1335,7 @@ except ValidationError as exc: This error is raised when a required positional-or-keyword argument is not passed to a function decorated with `validate_call`: -```py +```python from pydantic import ValidationError, validate_call @@ -1355,7 +1355,7 @@ except ValidationError as exc: This error is raised when a required keyword-only argument is not passed to a function decorated with `validate_call`: -```py +```python from pydantic import ValidationError, validate_call @@ -1376,7 +1376,7 @@ except ValidationError as exc: This error is raised when a required positional-only argument is not passed to a function decorated with `validate_call`: -```py +```python from pydantic import ValidationError, validate_call @@ -1396,7 +1396,7 @@ except ValidationError as exc: This error is raised when the input value is not a valid dictionary, model instance, or instance that fields can be extracted from: -```py +```python from pydantic import BaseModel, ValidationError @@ -1431,7 +1431,7 @@ except ValidationError as exc: This error is raised when the input to a model is not an instance of the model or dict: -```py +```python from pydantic import BaseModel, ValidationError @@ -1461,7 +1461,7 @@ except ValidationError as exc: This error is raised when you provide multiple values for a single argument while calling a function decorated with `validate_call`: -```py +```python from pydantic import ValidationError, validate_call @@ -1481,7 +1481,7 @@ except ValidationError as exc: This error is raised when the input is not a multiple of a field's `multiple_of` constraint: -```py +```python from pydantic import BaseModel, Field, ValidationError @@ -1496,12 +1496,35 @@ except ValidationError as exc: #> 'multiple_of' ``` +## `needs_python_object` + +This type of error is raised when validation is attempted from a format that cannot be converted to a Python object. +For example, we cannot check `isinstance` or `issubclass` from JSON: + +```python +import json +from typing import Type + +from pydantic import BaseModel, ValidationError + + +class Model(BaseModel): + bm: Type[BaseModel] + + +try: + Model.model_validate_json(json.dumps({'bm': 'not a basemodel class'})) +except ValidationError as exc: + print(repr(exc.errors()[0]['type'])) + #> 'needs_python_object' +``` + ## `no_such_attribute` This error is raised when `validate_assignment=True` in the config, and you attempt to assign a value to an attribute that is not an existing field: -```py +```python from pydantic import ConfigDict, ValidationError, dataclasses @@ -1522,7 +1545,7 @@ except ValidationError as exc: This error is raised when the input value is not `None` for a field that requires `None`: -```py +```python from pydantic import BaseModel, ValidationError @@ -1541,7 +1564,8 @@ except ValidationError as exc: You may encounter this error when there is a naming collision in your model between a field name and its type. More specifically, this error is likely to be thrown when the default value of that field is `None`. For example, the following would yield the `none_required` validation error since the field `int` is set to a default value of `None` and has the exact same name as its type, which causes problems with validation. - ```py test="skip" + + ```python {test="skip"} from typing import Optional from pydantic import BaseModel @@ -1558,7 +1582,7 @@ except ValidationError as exc: This error is raised when a cyclic reference is detected: -```py +```python from typing import List from pydantic import BaseModel, ValidationError @@ -1581,7 +1605,7 @@ except ValidationError as exc: This error is raised when the value type is not valid for a `set` field: -```py +```python from typing import Set from pydantic import BaseModel, ValidationError @@ -1602,7 +1626,7 @@ except ValidationError as exc: This error is raised when the input value doesn't match the field's `pattern` constraint: -```py +```python from pydantic import BaseModel, Field, ValidationError @@ -1621,7 +1645,7 @@ except ValidationError as exc: This error is raised when the value is an instance of a strict subtype of `str` when the field is strict: -```py +```python from enum import Enum from pydantic import BaseModel, Field, ValidationError @@ -1646,7 +1670,7 @@ except ValidationError as exc: This error is raised when the input value is a string whose length is greater than the field's `max_length` constraint: -```py +```python from pydantic import BaseModel, Field, ValidationError @@ -1665,7 +1689,7 @@ except ValidationError as exc: This error is raised when the input value is a string whose length is less than the field's `min_length` constraint: -```py +```python from pydantic import BaseModel, Field, ValidationError @@ -1684,7 +1708,7 @@ except ValidationError as exc: This error is raised when the input value's type is not valid for a `str` field: -```py +```python from pydantic import BaseModel, ValidationError @@ -1705,7 +1729,7 @@ This error is also raised for strict fields when the input value is not an insta This error is raised when the value cannot be parsed as a Unicode string: -```py +```python from pydantic import BaseModel, ValidationError @@ -1724,7 +1748,7 @@ except ValidationError as exc: This error is raised when the input value is a string that cannot be parsed for a `timedelta` field: -```py +```python from datetime import timedelta from pydantic import BaseModel, ValidationError @@ -1745,7 +1769,7 @@ except ValidationError as exc: This error is raised when the input value's type is not valid for a `timedelta` field: -```py +```python from datetime import timedelta from pydantic import BaseModel, ValidationError @@ -1768,7 +1792,7 @@ This error is also raised for strict fields when the input value is not an insta This error is raised when the input value is a string that cannot be parsed for a `time` field: -```py +```python from datetime import time from pydantic import BaseModel, ValidationError @@ -1789,7 +1813,7 @@ except ValidationError as exc: This error is raised when the value type is not valid for a `time` field: -```py +```python from datetime import time from pydantic import BaseModel, ValidationError @@ -1813,7 +1837,7 @@ This error is also raised for strict fields when the input value is not an insta This error is raised when the `datetime` value provided for a timezone-aware `datetime` field doesn't have timezone information: -```py +```python from datetime import datetime from pydantic import AwareDatetime, BaseModel, ValidationError @@ -1835,7 +1859,7 @@ except ValidationError as exc: This error is raised when the `datetime` value provided for a timezone-naive `datetime` field has timezone info: -```py +```python from datetime import datetime, timezone from pydantic import BaseModel, NaiveDatetime, ValidationError @@ -1856,7 +1880,7 @@ except ValidationError as exc: This error is raised when the input value's length is greater than the field's `max_length` constraint: -```py +```python from typing import List from pydantic import BaseModel, Field, ValidationError @@ -1877,7 +1901,7 @@ except ValidationError as exc: This error is raised when the value length is less than the field's `min_length` constraint: -```py +```python from typing import List from pydantic import BaseModel, Field, ValidationError @@ -1898,7 +1922,7 @@ except ValidationError as exc: This error is raised when the input value's type is not valid for a `tuple` field: -```py +```python from typing import Tuple from pydantic import BaseModel, ValidationError @@ -1922,7 +1946,7 @@ This error is also raised for strict fields when the input value is not an insta This error is raised when you provide a value by keyword for a positional-only argument while calling a function decorated with `validate_call`: -```py +```python from pydantic import ValidationError, validate_call @@ -1940,7 +1964,7 @@ except ValidationError as exc: It is also raised when using pydantic.dataclasses and `extra=forbid`: -```py +```python from pydantic import TypeAdapter, ValidationError from pydantic.dataclasses import dataclass @@ -1962,7 +1986,7 @@ except ValidationError as exc: This error is raised when you provide a positional value for a keyword-only argument while calling a function decorated with `validate_call`: -```py +```python from pydantic import ValidationError, validate_call @@ -1982,7 +2006,7 @@ except ValidationError as exc: This error is raised when the input's discriminator is not one of the expected values: -```py +```python from typing import Literal, Union from pydantic import BaseModel, Field, ValidationError @@ -1997,7 +2021,7 @@ class WhiteCat(BaseModel): class Model(BaseModel): - cat: Union[BlackCat, WhiteCat] = Field(..., discriminator='pet_type') + cat: Union[BlackCat, WhiteCat] = Field(discriminator='pet_type') try: @@ -2011,7 +2035,7 @@ except ValidationError as exc: This error is raised when it is not possible to extract a discriminator value from the input: -```py +```python from typing import Literal, Union from pydantic import BaseModel, Field, ValidationError @@ -2026,7 +2050,7 @@ class WhiteCat(BaseModel): class Model(BaseModel): - cat: Union[BlackCat, WhiteCat] = Field(..., discriminator='pet_type') + cat: Union[BlackCat, WhiteCat] = Field(discriminator='pet_type') try: @@ -2040,7 +2064,7 @@ except ValidationError as exc: This error is raised when the input value cannot be parsed as a URL: -```py +```python from pydantic import AnyUrl, BaseModel, ValidationError @@ -2059,7 +2083,7 @@ except ValidationError as exc: This error is raised when the URL scheme is not valid for the URL type of the field: -```py +```python from pydantic import BaseModel, HttpUrl, ValidationError @@ -2078,7 +2102,7 @@ except ValidationError as exc: This error is raised when the URL syntax is not valid: -```py +```python from pydantic import BaseModel, Field, HttpUrl, ValidationError @@ -2097,7 +2121,7 @@ except ValidationError as exc: This error is raised when the URL length is greater than 2083: -```py +```python from pydantic import BaseModel, HttpUrl, ValidationError @@ -2116,7 +2140,7 @@ except ValidationError as exc: This error is raised when the input value's type is not valid for a URL field: -```py +```python from pydantic import BaseModel, HttpUrl, ValidationError @@ -2135,7 +2159,7 @@ except ValidationError as exc: This error is raised when the input value's type is not valid for a UUID field: -```py +```python from uuid import UUID from pydantic import BaseModel, ValidationError @@ -2156,7 +2180,7 @@ except ValidationError as exc: This error is raised when the input value's type is not valid instance for a UUID field (str, bytes or UUID): -```py +```python from uuid import UUID from pydantic import BaseModel, ValidationError @@ -2177,7 +2201,7 @@ except ValidationError as exc: This error is raised when the input value's type is not match UUID version: -```py +```python from pydantic import UUID5, BaseModel, ValidationError @@ -2196,7 +2220,7 @@ except ValidationError as exc: This error is raised when a `ValueError` is raised during validation: -```py +```python from pydantic import BaseModel, ValidationError, field_validator diff --git a/docs/examples/validators.md b/docs/examples/custom_validators.md similarity index 97% rename from docs/examples/validators.md rename to docs/examples/custom_validators.md index 9cafd33e241..a12f8ce8ca7 100644 --- a/docs/examples/validators.md +++ b/docs/examples/custom_validators.md @@ -1,9 +1,8 @@ -!!! warning "🚧 Work in Progress" - This page is a work in progress. - This page provides example snippets for creating more complex, custom validators in Pydantic. +Many of these examples are adapted from Pydantic issues and discussions, and are intended to showcase +the flexibility and power of Pydantic's validation system. -## Using Custom Validators with [`Annotated`][typing.Annotated] Metadata +## Custom `datetime` Validator via [`Annotated`][typing.Annotated] Metadata In this example, we'll construct a custom validator, attached to an [`Annotated`][typing.Annotated] type, that ensures a [`datetime`][datetime.datetime] object adheres to a given timezone constraint. @@ -12,7 +11,7 @@ The custom validator supports string specification of the timezone, and will rai We use `__get_pydantic_core_schema__` in the validator to customize the schema of the annotated type (in this case, [`datetime`][datetime.datetime]), which allows us to add custom validation logic. Notably, we use a `wrap` validator function so that we can perform operations both before and after the default `pydantic` validation of a [`datetime`][datetime.datetime]. -```py +```python import datetime as dt from dataclasses import dataclass from pprint import pprint @@ -101,7 +100,7 @@ except ValidationError as ve: We can also enforce UTC offset constraints in a similar way. Assuming we have a `lower_bound` and an `upper_bound`, we can create a custom validator to ensure our `datetime` has a UTC offset that is inclusive within the boundary we define: -```py +```python import datetime as dt from dataclasses import dataclass from pprint import pprint @@ -178,7 +177,7 @@ In this example, we construct a validator that checks that each user's password One way to do this is to place a custom validator on the outer model: -```py +```python from typing import List from typing_extensions import Self @@ -229,7 +228,7 @@ Alternatively, a custom validator can be used in the nested model class (`User`) !!! warning The ability to mutate the context within a validator adds a lot of power to nested validation, but can also lead to confusing or hard-to-debug code. Use this approach at your own risk! -```py +```python from typing import List from pydantic import BaseModel, ValidationError, ValidationInfo, field_validator diff --git a/docs/examples/files.md b/docs/examples/files.md new file mode 100644 index 00000000000..9272b8907d0 --- /dev/null +++ b/docs/examples/files.md @@ -0,0 +1,236 @@ +`pydantic` is a great tool for validating data coming from various sources. +In this section, we will look at how to validate data from different types of files. + +!!! Note: + If you're using any of the below file formats to parse configuration / settings, you might want to + consider using the [`pydantic-settings`][pydantic_settings] library, which offers builtin + support for parsing this type of data. + +## JSON data + +`.json` files are a common way to store key / value data in a human-readable format. +Here is an example of a `.json` file: + +```json +{ + "name": "John Doe", + "age": 30, + "email": "john@example.com" +} +``` + +To validate this data, we can use a `pydantic` model: + +```python {test="skip"} +import pathlib + +from pydantic import BaseModel, EmailStr, PositiveInt + + +class Person(BaseModel): + name: str + age: PositiveInt + email: EmailStr + + +json_string = pathlib.Path('person.json').read_text() +person = Person.model_validate_json(json_string) +print(repr(person)) +#> Person(name='John Doe', age=30, email='john@example.com') +``` + +If the data in the file is not valid, `pydantic` will raise a [`ValidationError`][pydantic_core.ValidationError]. +Let's say we have the following `.json` file: + +```json +{ + "age": -30, + "email": "not-an-email-address" +} +``` + +This data is flawed for three reasons: +1. It's missing the `name` field. +2. The `age` field is negative. +3. The `email` field is not a valid email address. + +When we try to validate this data, `pydantic` raises a [`ValidationError`][pydantic_core.ValidationError] with all of the +above issues: + +```python {test="skip"} +import pathlib + +from pydantic import BaseModel, EmailStr, PositiveInt, ValidationError + + +class Person(BaseModel): + name: str + age: PositiveInt + email: EmailStr + + +json_string = pathlib.Path('person.json').read_text() +try: + person = Person.model_validate_json(json_string) +except ValidationError as err: + print(err) + """ + 3 validation errors for Person + name + Field required [type=missing, input_value={'age': -30, 'email': 'not-an-email-address'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.10/v/missing + age + Input should be greater than 0 [type=greater_than, input_value=-30, input_type=int] + For further information visit https://errors.pydantic.dev/2.10/v/greater_than + email + value is not a valid email address: An email address must have an @-sign. [type=value_error, input_value='not-an-email-address', input_type=str] + """ +``` + +Often, it's the case that you have an abundance of a certain type of data within a `.json` file. +For example, you might have a list of people: + +```json +[ + { + "name": "John Doe", + "age": 30, + "email": "john@example.com" + }, + { + "name": "Jane Doe", + "age": 25, + "email": "jane@example.com" + } +] +``` + +In this case, you can validate the data against a `List[Person]` model: + +```python {test="skip"} +import pathlib +from typing import List + +from pydantic import BaseModel, EmailStr, PositiveInt, TypeAdapter + + +class Person(BaseModel): + name: str + age: PositiveInt + email: EmailStr + + +person_list_adapter = TypeAdapter(List[Person]) # (1)! + +json_string = pathlib.Path('people.json').read_text() +people = person_list_adapter.validate_json(json_string) +print(people) +#> [Person(name='John Doe', age=30, email='john@example.com'), Person(name='Jane Doe', age=25, email='jane@example.com')] +``` + +1. We use [`TypeAdapter`][pydantic.type_adapter.TypeAdapter] to validate a list of `Person` objects. +[`TypeAdapter`][pydantic.type_adapter.TypeAdapter] is a Pydantic construct used to validate data against a single type. + +## JSON lines files + +Similar to validating a list of objects from a `.json` file, you can validate a list of objects from a `.jsonl` file. +`.jsonl` files are a sequence of JSON objects separated by newlines. + +Consider the following `.jsonl` file: + +```json +{"name": "John Doe", "age": 30, "email": "john@example.com"} +{"name": "Jane Doe", "age": 25, "email": "jane@example.com"} +``` + +We can validate this data with a similar approach to the one we used for `.json` files: + +```python {test="skip"} +import pathlib + +from pydantic import BaseModel, EmailStr, PositiveInt + + +class Person(BaseModel): + name: str + age: PositiveInt + email: EmailStr + + +json_lines = pathlib.Path('people.jsonl').read_text().splitlines() +people = [Person.model_validate_json(line) for line in json_lines] +print(people) +#> [Person(name='John Doe', age=30, email='john@example.com'), Person(name='Jane Doe', age=25, email='jane@example.com')] +``` + +## CSV files + +CSV is one of the most common file formats for storing tabular data. +To validate data from a CSV file, you can use the `csv` module from the Python standard library to load +the data and validate it against a Pydantic model. + +Consider the following CSV file: + +```csv +name,age,email +John Doe,30,john@example.com +Jane Doe,25,jane@example.com +``` + +Here's how we validate that data: + +```python {test="skip"} +import csv + +from pydantic import BaseModel, EmailStr, PositiveInt + + +class Person(BaseModel): + name: str + age: PositiveInt + email: EmailStr + + +with open('people.csv') as f: + reader = csv.DictReader(f) + people = [Person.model_validate(row) for row in reader] + +print(people) +#> [Person(name='John Doe', age=30, email='john@example.com'), Person(name='Jane Doe', age=25, email='jane@example.com')] +``` + +## TOML files + +TOML files are often used for configuration due to their simplicity and readability. + +Consider the following TOML file: + +```toml +name = "John Doe" +age = 30 +email = "john@example.com" +``` + +Here's how we validate that data: + +```python {test="skip"} +import tomllib + +from pydantic import BaseModel, EmailStr, PositiveInt + + +class Person(BaseModel): + name: str + age: PositiveInt + email: EmailStr + + +with open('person.toml', 'rb') as f: + data = tomllib.load(f) + +person = Person.model_validate(data) +print(repr(person)) +#> Person(name='John Doe', age=30, email='john@example.com') +``` + + diff --git a/docs/examples/orms.md b/docs/examples/orms.md new file mode 100644 index 00000000000..ff0df8c6374 --- /dev/null +++ b/docs/examples/orms.md @@ -0,0 +1,53 @@ +Pydantic serves as a great tool for defining models for ORM (object relational mapping) libraries. +ORMs are used to map objects to database tables, and vice versa. + +## SQLAlchemy + +Pydantic can pair with SQLAlchemy, as it can be used to define the schema of the database models. + +!!! warning "Code Duplication" + If you use Pydantic with SQLAlchemy, you might experience some frustration with code duplication. + If you find yourself experiencing this difficulty, you might also consider [`SQLModel`](https://sqlmodel.tiangolo.com/) which integrates Pydantic with SQLAlchemy such that much of the code duplication is eliminated. + +If you'd prefer to use pure Pydantic with SQLAlchemy, we recommend using Pydantic models alongside of SQLAlchemy models +as shown in the example below. In this case, we take advantage of Pydantic's aliases feature to name a `Column` after a reserved SQLAlchemy field, thus avoiding conflicts. + +```python +import typing + +import sqlalchemy as sa +from sqlalchemy.orm import declarative_base + +from pydantic import BaseModel, ConfigDict, Field + + +class MyModel(BaseModel): + model_config = ConfigDict(from_attributes=True) + + metadata: typing.Dict[str, str] = Field(alias='metadata_') + + +Base = declarative_base() + + +class MyTableModel(Base): + __tablename__ = 'my_table' + id = sa.Column('id', sa.Integer, primary_key=True) + # 'metadata' is reserved by SQLAlchemy, hence the '_' + metadata_ = sa.Column('metadata', sa.JSON) + + +sql_model = MyTableModel(metadata_={'key': 'val'}, id=1) +pydantic_model = MyModel.model_validate(sql_model) + +print(pydantic_model.model_dump()) +#> {'metadata': {'key': 'val'}} +print(pydantic_model.model_dump(by_alias=True)) +#> {'metadata_': {'key': 'val'}} +``` + +!!! note + The example above works because aliases have priority over field names for + field population. Accessing `SQLModel`'s `metadata` attribute would lead to a `ValidationError`. + + diff --git a/docs/examples/queues.md b/docs/examples/queues.md new file mode 100644 index 00000000000..22cd31eed18 --- /dev/null +++ b/docs/examples/queues.md @@ -0,0 +1,67 @@ +Pydantic is quite helpful for validating data that goes into and comes out of queues. Below, +we'll explore how to validate / serialize data with various queue systems. + +## Redis queue + +Redis is a popular in-memory data structure store. + +In order to run this example locally, you'll first need to [install Redis](https://redis.io/docs/latest/operate/oss_and_stack/install/install-redis/) +and start your server up locally. + +Here's a simple example of how you can use Pydantic to: +1. Serialize data to push to the queue +2. Deserialize and validate data when it's popped from the queue + +```python {test="skip"} +import redis + +from pydantic import BaseModel, EmailStr + + +class User(BaseModel): + id: int + name: str + email: EmailStr + + +r = redis.Redis(host='localhost', port=6379, db=0) +QUEUE_NAME = 'user_queue' + + +def push_to_queue(user_data: User) -> None: + serialized_data = user_data.model_dump_json() + r.rpush(QUEUE_NAME, user_data.model_dump_json()) + print(f'Added to queue: {serialized_data}') + + +user1 = User(id=1, name='John Doe', email='john@example.com') +user2 = User(id=2, name='Jane Doe', email='jane@example.com') + +push_to_queue(user1) +#> Added to queue: {"id":1,"name":"John Doe","email":"john@example.com"} + +push_to_queue(user2) +#> Added to queue: {"id":2,"name":"Jane Doe","email":"jane@example.com"} + + +def pop_from_queue() -> None: + data = r.lpop(QUEUE_NAME) + + if data: + user = User.model_validate_json(data) + print(f'Validated user: {repr(user)}') + else: + print('Queue is empty') + + +pop_from_queue() +#> Validated user: User(id=1, name='John Doe', email='john@example.com') + +pop_from_queue() +#> Validated user: User(id=2, name='Jane Doe', email='jane@example.com') + +pop_from_queue() +#> Queue is empty +``` + + diff --git a/docs/examples/requests.md b/docs/examples/requests.md new file mode 100644 index 00000000000..80b51541555 --- /dev/null +++ b/docs/examples/requests.md @@ -0,0 +1,74 @@ +Pydantic models are a great way to validating and serializing data for requests and responses. +Pydantic is instrumental in many web frameworks and libraries, such as FastAPI, Django, Flask, and HTTPX. + +## `httpx` requests + +[`httpx`](https://www.python-httpx.org/) is a HTTP client for Python 3 with synchronous and asynchronous APIs. +In the below example, we query the [JSONPlaceholder API](https://jsonplaceholder.typicode.com/) to get a user's data and validate it with a Pydantic model. + +```python {test="skip"} +import httpx + +from pydantic import BaseModel, EmailStr + + +class User(BaseModel): + id: int + name: str + email: EmailStr + + +url = 'https://jsonplaceholder.typicode.com/users/1' + +response = httpx.get(url) +response.raise_for_status() + +user = User.model_validate(response.json()) +print(repr(user)) +#> User(id=1, name='Leanne Graham', email='Sincere@april.biz') +``` + +The [`TypeAdapter`][pydantic.type_adapter.TypeAdapter] tool from Pydantic often comes in quite +handy when working with HTTP requests. Consider a similar example where we are validating a list of users: + +```python {test="skip"} +from pprint import pprint +from typing import List + +import httpx + +from pydantic import BaseModel, EmailStr, TypeAdapter + + +class User(BaseModel): + id: int + name: str + email: EmailStr + + +url = 'https://jsonplaceholder.typicode.com/users/' # (1)! + +response = httpx.get(url) +response.raise_for_status() + +users_list_adapter = TypeAdapter(List[User]) + +users = users_list_adapter.validate_python(response.json()) +pprint([u.name for u in users]) +""" +['Leanne Graham', + 'Ervin Howell', + 'Clementine Bauch', + 'Patricia Lebsack', + 'Chelsey Dietrich', + 'Mrs. Dennis Schulist', + 'Kurtis Weissnat', + 'Nicholas Runolfsdottir V', + 'Glenna Reichert', + 'Clementina DuBuque'] +""" +``` + +1. Note, we're querying the `/users/` endpoint here to get a list of users. + + diff --git a/docs/examples/secrets.md b/docs/examples/secrets.md deleted file mode 100644 index 00c18b5fd01..00000000000 --- a/docs/examples/secrets.md +++ /dev/null @@ -1,139 +0,0 @@ -!!! warning "🚧 Work in Progress" - This page is a work in progress. - -## Serialize `SecretStr` and `SecretBytes` as plain-text - -By default, [`SecretStr`][pydantic.types.SecretStr] and [`SecretBytes`][pydantic.types.SecretBytes] -will be serialized as `**********` when serializing to json. - -You can use the [`field_serializer`][pydantic.functional_serializers.field_serializer] to dump the -secret as plain-text when serializing to json. - -```py -from pydantic import BaseModel, SecretBytes, SecretStr, field_serializer - - -class Model(BaseModel): - password: SecretStr - password_bytes: SecretBytes - - @field_serializer('password', 'password_bytes', when_used='json') - def dump_secret(self, v): - return v.get_secret_value() - - -model = Model(password='IAmSensitive', password_bytes=b'IAmSensitiveBytes') -print(model) -#> password=SecretStr('**********') password_bytes=SecretBytes(b'**********') -print(model.password) -#> ********** -print(model.model_dump()) -""" -{ - 'password': SecretStr('**********'), - 'password_bytes': SecretBytes(b'**********'), -} -""" -print(model.model_dump_json()) -#> {"password":"IAmSensitive","password_bytes":"IAmSensitiveBytes"} -``` - -## Create your own Secret field - -Pydantic provides the generic `Secret` class as a mechanism for creating custom secret types. - -??? api "API Documentation" - [`pydantic.types.Secret`][pydantic.types.Secret]
- -Pydantic provides the generic `Secret` class as a mechanism for creating custom secret types. -You can either directly parametrize `Secret`, or subclass from a parametrized `Secret` to customize the `str()` and `repr()` of a secret type. - -```py -from datetime import date - -from pydantic import BaseModel, Secret - -# Using the default representation -SecretDate = Secret[date] - - -# Overwriting the representation -class SecretSalary(Secret[float]): - def _display(self) -> str: - return '$****.**' - - -class Employee(BaseModel): - date_of_birth: SecretDate - salary: SecretSalary - - -employee = Employee(date_of_birth='1990-01-01', salary=42) - -print(employee) -#> date_of_birth=Secret('**********') salary=SecretSalary('$****.**') - -print(employee.salary) -#> $****.** - -print(employee.salary.get_secret_value()) -#> 42.0 - -print(employee.date_of_birth) -#> ********** - -print(employee.date_of_birth.get_secret_value()) -#> 1990-01-01 -``` - -You can enforce constraints on the underlying type through annotations: -For example: - -```py -from typing_extensions import Annotated - -from pydantic import BaseModel, Field, Secret, ValidationError - -SecretPosInt = Secret[Annotated[int, Field(gt=0, strict=True)]] - - -class Model(BaseModel): - sensitive_int: SecretPosInt - - -m = Model(sensitive_int=42) -print(m.model_dump()) -#> {'sensitive_int': Secret('**********')} - -try: - m = Model(sensitive_int=-42) # (1)! -except ValidationError as exc_info: - print(exc_info.errors(include_url=False, include_input=False)) - """ - [ - { - 'type': 'greater_than', - 'loc': ('sensitive_int',), - 'msg': 'Input should be greater than 0', - 'ctx': {'gt': 0}, - } - ] - """ - -try: - m = Model(sensitive_int='42') # (2)! -except ValidationError as exc_info: - print(exc_info.errors(include_url=False, include_input=False)) - """ - [ - { - 'type': 'int_type', - 'loc': ('sensitive_int',), - 'msg': 'Input should be a valid integer', - } - ] - """ -``` - -1. The input value is not greater than 0, so it raises a validation error. -2. The input value is not an integer, so it raises a validation error because the `SecretPosInt` type has strict mode enabled. diff --git a/docs/extra/fluff.js b/docs/extra/fluff.js index 493856309ac..e0fb8c0646e 100644 --- a/docs/extra/fluff.js +++ b/docs/extra/fluff.js @@ -1,11 +1,66 @@ -async function set_download_count(el) { - const r = await fetch('https://errors.pydantic.dev/download-count.txt'); - if (r.status === 200) { - el.innerText = await r.text(); +// set the download count in the "why pydantic" page +(async function() { + const downloadCount = document.getElementById('download-count'); + if (downloadCount) { + const r = await fetch('https://errors.pydantic.dev/download-count.txt'); + if (r.status === 200) { + downloadCount.innerText = await r.text(); + } } -} +})(); -const download_count = document.getElementById('download-count'); -if (download_count) { - set_download_count(download_count) -} +// update the announcement banner to change the app type +(function() { + const el = document.getElementById('logfire-app-type'); + const appTypes = [ + ['/integrations/pydantic/', 'Pydantic validations.'], + ['/integrations/fastapi/', 'FastAPI app.'], + ['/integrations/openai/', 'OpenAI integration.'], + ['/integrations/asyncpg/', 'Postgres queries.'], + ['/integrations/redis/', 'task queue.'], + ['/integrations/system-metrics/', 'system metrics.'], + ['/integrations/httpx/', 'API calls.'], + ['/integrations/logging/', 'std lib logging.'], + ['/integrations/django/', 'Django app.'], + ['/integrations/anthropic/', 'Anthropic API calls.'], + ['/integrations/fastapi/', 'Flask app.'], + ['/integrations/mysql/', 'MySQL queries.'], + ['/integrations/sqlalchemy/', 'SQLAlchemy queries.'], + ['/integrations/structlog/', 'Structlog logs.'], + ['/integrations/stripe/', 'Stripe API calls.'], + ]; + const docsUrl = 'https://logfire.pydantic.dev/docs'; + let counter = 0; + + const sleep = (ms) => new Promise((resolve) => setTimeout(resolve, ms)); + + // avoid multiple replaceText running at the same time (e.g. when the user has left the page) + let running = false; + + const replaceText = async () => { + if (running) { + return; + } + running = true; + try { + const text = el.textContent; + for (let i = text.length; i >= 0; i--) { + el.textContent = text.slice(0, i); + await sleep(30); + } + await sleep(30); + counter++; + // change the link halfway through the animation + const [link, newText] = appTypes[counter % appTypes.length]; + el.href = docsUrl + link; + await sleep(30); + for (let i = 0; i <= newText.length; i++) { + el.textContent = newText.slice(0, i); + await sleep(30); + } + } finally { + running = false; + } + }; + setInterval(replaceText, 4000); +})(); diff --git a/docs/extra/tweaks.css b/docs/extra/tweaks.css index 6257fc77d93..34e0bc7b7c7 100644 --- a/docs/extra/tweaks.css +++ b/docs/extra/tweaks.css @@ -111,6 +111,40 @@ aside.blog img { mask-image: var(--md-admonition-icon--api); } +/* Logfire link admonition */ +:root { + --md-admonition-icon--logfire: url('data:image/svg+xml;charset=utf-8, ') +} +.md-typeset .admonition.logfire, .md-typeset details.logfire { + border-color: #e620e9; +} +.md-typeset .logfire > .admonition-title, .md-typeset .logfire > summary { + background-color: #e620e91a; +} +.md-typeset .logfire > .admonition-title::before, .md-typeset .logfire > summary::before { + background-color: #e620e9; + -webkit-mask-image: var(--md-admonition-icon--logfire); + mask-image: var(--md-admonition-icon--logfire); +} + +/* Hide the run button in logfire admonitions */ +.admonition.logfire .run-code-btn { + display: none; +} + +/* add border to screenshots in the logfire admonitions `img[src*="logfire"]` to differentiate from emojis */ +.admonition.logfire img[src*="logfire"] { + border: 1px solid #448aff; + border-radius: 0.2rem; + padding: 0.2rem; +} + +/* banner slightly larger */ +.md-banner__inner { + font-size: 0.8rem; + margin: 0.3rem auto; +} + /* Revert hue value to that of pre mkdocs-material v9.4.0 */ [data-md-color-scheme="slate"] { --md-hue: 230; diff --git a/docs/img/basic_logfire.png b/docs/img/basic_logfire.png new file mode 100644 index 00000000000..d49cfa2998f Binary files /dev/null and b/docs/img/basic_logfire.png differ diff --git a/docs/img/logfire-pydantic-integration.png b/docs/img/logfire-pydantic-integration.png new file mode 100644 index 00000000000..750e784a295 Binary files /dev/null and b/docs/img/logfire-pydantic-integration.png differ diff --git a/docs/img/logfire_instrument.png b/docs/img/logfire_instrument.png new file mode 100644 index 00000000000..115749ebd88 Binary files /dev/null and b/docs/img/logfire_instrument.png differ diff --git a/docs/img/logfire_span.png b/docs/img/logfire_span.png new file mode 100644 index 00000000000..01bc28fe829 Binary files /dev/null and b/docs/img/logfire_span.png differ diff --git a/docs/img/rich_pydantic.png b/docs/img/rich_pydantic.png index 365f7b8e94c..d0903772236 100644 Binary files a/docs/img/rich_pydantic.png and b/docs/img/rich_pydantic.png differ diff --git a/docs/img/samuelcolvin.jpg b/docs/img/samuelcolvin.jpg deleted file mode 100644 index f4887096e01..00000000000 Binary files a/docs/img/samuelcolvin.jpg and /dev/null differ diff --git a/docs/img/terrencedorsey.jpg b/docs/img/terrencedorsey.jpg deleted file mode 100644 index 2c55d160629..00000000000 Binary files a/docs/img/terrencedorsey.jpg and /dev/null differ diff --git a/docs/index.md b/docs/index.md index 6cdda04b8f2..df0f32134d3 100644 --- a/docs/index.md +++ b/docs/index.md @@ -13,36 +13,48 @@ Pydantic is the most widely used data validation library for Python. Fast and extensible, Pydantic plays nicely with your linters/IDE/brain. Define how data should be in pure, canonical Python 3.8+; validate it with Pydantic. -!!! success "Migrating to Pydantic V2" - Using Pydantic V1? See the [Migration Guide](migration.md) for notes on upgrading to Pydantic V2 in your applications! +!!! logfire "Monitor Pydantic with Logfire :fire:" + Built by the same team as Pydantic, **[Logfire](https://pydantic.dev/logfire)** is an application monitoring tool that is as simple to use and powerful as Pydantic itself. -```py title="Pydantic Example" requires="3.10" -from datetime import datetime -from typing import Tuple + Logfire integrates with many popular Python libraries including FastAPI, OpenAI and Pydantic itself, so you can use Logfire to monitor Pydantic validations and understand why some inputs fail validation: -from pydantic import BaseModel + ```python {title="Monitoring Pydantic with Logfire" test="skip"} + from datetime import datetime + import logfire -class Delivery(BaseModel): - timestamp: datetime - dimensions: Tuple[int, int] + from pydantic import BaseModel + logfire.configure() + logfire.instrument_pydantic() # (1)! -m = Delivery(timestamp='2020-01-02T03:04:05Z', dimensions=['10', '20']) -print(repr(m.timestamp)) -#> datetime.datetime(2020, 1, 2, 3, 4, 5, tzinfo=TzInfo(UTC)) -print(m.dimensions) -#> (10, 20) -``` -!!! question "Why is Pydantic named the way it is?" + class Delivery(BaseModel): + timestamp: datetime + dimensions: tuple[int, int] + + + # this will record details of a successful validation to logfire + m = Delivery(timestamp='2020-01-02T03:04:05Z', dimensions=['10', '20']) + print(repr(m.timestamp)) + #> datetime.datetime(2020, 1, 2, 3, 4, 5, tzinfo=TzInfo(UTC)) + print(m.dimensions) + #> (10, 20) + + Delivery(timestamp='2020-01-02T03:04:05Z', dimensions=['10']) # (2)! + ``` + + 1. Set logfire record all both successful and failed validations, use `record='failure'` to only record failed validations, [learn more](https://logfire.pydantic.dev/docs/integrations/pydantic/). + 2. This will raise a `ValidationError` since there are too few `dimensions`, details of the input data and validation errors will be recorded in Logfire. + + Would give you a view like this in the Logfire platform: + + [![Logfire Pydantic Integration](img/logfire-pydantic-integration.png)](https://logfire.pydantic.dev/docs/guides/web-ui/live/) - The name "Pydantic" is a portmanteau of "Py" and "pedantic." The "Py" part indicates that the library is associated with Python, and - "pedantic" refers to the library's meticulous approach to data validation and type enforcement. + This is just a toy example, but hopefully makes clear the potential value of instrumenting a more complex application. - Combining these elements, "Pydantic" describes our Python library that provides detail-oriented, rigorous data validation. + **[Learn more about Pydantic Logfire](https://logfire.pydantic.dev/docs/)** - We’re aware of the irony that Pydantic V1 was not strict in its validation, so if we're being pedantic, "Pydantic" was a misnomer until V2 😉. ## Why use Pydantic? @@ -62,7 +74,7 @@ print(m.dimensions) To see Pydantic at work, let's start with a simple example, creating a custom class that inherits from `BaseModel`: -```py upgrade="skip" title="Validation Successful" requires="3.10" +```python {upgrade="skip" title="Validation Successful" requires="3.10"} from datetime import datetime from pydantic import BaseModel, PositiveInt @@ -118,7 +130,7 @@ print(user.model_dump()) # (10)! If validation fails, Pydantic will raise an error with a breakdown of what was wrong: -```py upgrade="skip" title="Validation Error" test="skip" lint="skip" +```python {upgrade="skip" title="Validation Error" test="skip" lint="skip"} # continuing the above example... from datetime import datetime diff --git a/docs/install.md b/docs/install.md index 6ab2d8161fb..c8c3064f9e9 100644 --- a/docs/install.md +++ b/docs/install.md @@ -1,12 +1,20 @@ Installation is as simple as: -```bash -pip install pydantic -``` +=== "pip" + + ```bash + pip install pydantic + ``` + +=== "uv" + + ```bash + uv add pydantic + ``` Pydantic has a few dependencies: -* [`pydantic-core`](https://pypi.org/project/pydantic-core/): Core validation logic for _pydantic_ written in rust. +* [`pydantic-core`](https://pypi.org/project/pydantic-core/): Core validation logic for Pydantic written in Rust. * [`typing-extensions`](https://pypi.org/project/typing-extensions/): Backport of the standard library [typing][] module. * [`annotated-types`](https://pypi.org/project/annotated-types/): Reusable constraint types to use with [`typing.Annotated`][]. @@ -23,22 +31,49 @@ conda install pydantic -c conda-forge Pydantic has the following optional dependencies: -* Email validation provided by the [email-validator](https://pypi.org/project/email-validator/) package. +* `email`: Email validation provided by the [email-validator](https://pypi.org/project/email-validator/) package. +* `timezone`: Fallback IANA time zone database provided by the [tzdata](https://pypi.org/project/tzdata/) package. To install optional dependencies along with Pydantic: -```bash -pip install pydantic[email] -``` -Of course, you can also install requirements manually with `pip install email-validator`. +=== "pip" + + ```bash + # with the `email` extra: + pip install 'pydantic[email]' + # or with `email` and `timezone` extras: + pip install 'pydantic[email,timezone]' + ``` + +=== "uv" + + ```bash + # with the `email` extra: + uv add 'pydantic[email]' + # or with `email` and `timezone` extras: + uv add 'pydantic[email,timezone]' + ``` + +Of course, you can also install requirements manually with `pip install email-validator tzdata`. ## Install from repository And if you prefer to install Pydantic directly from the repository: -```bash -pip install git+https://github.com/pydantic/pydantic@main#egg=pydantic -# or with the `email` extra: -pip install git+https://github.com/pydantic/pydantic@main#egg=pydantic[email] -``` + +=== "pip" + + ```bash + pip install 'git+https://github.com/pydantic/pydantic@main' + # or with `email` and `timezone` extras: + pip install 'git+https://github.com/pydantic/pydantic@main#egg=pydantic[email,timezone]' + ``` + +=== "uv" + + ```bash + uv add 'git+https://github.com/pydantic/pydantic@main' + # or with `email` and `timezone` extras: + uv add 'git+https://github.com/pydantic/pydantic@main#egg=pydantic[email,timezone]' + ``` diff --git a/docs/integrations/aws_lambda.md b/docs/integrations/aws_lambda.md index c2235532023..5b40601089e 100644 --- a/docs/integrations/aws_lambda.md +++ b/docs/integrations/aws_lambda.md @@ -53,7 +53,7 @@ error is a common issue that indicates you have installed `pydantic` incorrectly 1. Check the contents of the installed `pydantic-core` package. Are the compiled library and its type stubs both present? -```py test="skip" lint="skip" +```python {test="skip" lint="skip"} from importlib.metadata import files print([file for file in files('pydantic-core') if file.name.startswith('_pydantic_core')]) """ @@ -65,7 +65,7 @@ You should expect to see two files like those printed above. The compile library 2. Check that your lambda's Python version is compatible with the compiled library version found above. -```py test="skip" lint="skip" +```python {test="skip" lint="skip"} import sysconfig print(sysconfig.get_config_var("EXT_SUFFIX")) #> '.cpython-312-x86_64-linux-gnu.so' diff --git a/docs/integrations/datamodel_code_generator.md b/docs/integrations/datamodel_code_generator.md index 40465b0a26f..f2614dc8c33 100644 --- a/docs/integrations/datamodel_code_generator.md +++ b/docs/integrations/datamodel_code_generator.md @@ -74,7 +74,7 @@ person.json: ``` model.py: -```py upgrade="skip" requires="3.10" +```python {upgrade="skip" requires="3.10"} # generated by datamodel-codegen: # filename: person.json # timestamp: 2020-05-19T15:07:31+00:00 @@ -91,8 +91,8 @@ class Pet(BaseModel): class Person(BaseModel): - first_name: str = Field(..., description="The person's first name.") - last_name: str = Field(..., description="The person's last name.") + first_name: str = Field(description="The person's first name.") + last_name: str = Field(description="The person's last name.") age: conint(ge=0) | None = Field(None, description='Age in years.') pets: list[Pet] | None = None comment: Any | None = None diff --git a/docs/integrations/devtools.md b/docs/integrations/devtools.md index 58b4eb1fd6e..1a32e47673f 100644 --- a/docs/integrations/devtools.md +++ b/docs/integrations/devtools.md @@ -11,7 +11,7 @@ Pydantic integrates with *devtools* by implementing the `__pretty__` method on m In particular `debug()` is useful when inspecting models: -```py test="no-print-intercept" +```python {test="no-print-intercept"} from datetime import datetime from typing import List diff --git a/docs/integrations/logfire.md b/docs/integrations/logfire.md new file mode 100644 index 00000000000..3925ae5230c --- /dev/null +++ b/docs/integrations/logfire.md @@ -0,0 +1,78 @@ +Pydantic integrates seamlessly with **Pydantic Logfire**, an observability platform built by us on the same belief as our open source library — that the most powerful tools can be easy to use. + +## Getting Started + +Logfire has an out-of-the-box Pydantic integration that lets you understand the data passing through your Pydantic models and get analytics on validations. For existing Pydantic users, it delivers unparalleled insights into your usage of Pydantic models. + +[Getting started](https://logfire.pydantic.dev/docs/) with Logfire can be done in three simple steps: + +1. Set up your Logfire account. +2. Install the Logfire SDK. +3. Instrument your project. + +### Basic Usage + +Once you've got Logfire set up, you can start using it to monitor your Pydantic models and get insights into your data validation: + +```python {test="skip"} +from datetime import date + +import logfire + +from pydantic import BaseModel + +logfire.configure() # (1)! + + +class User(BaseModel): + name: str + country_code: str + dob: date + + +user = User(name='Anne', country_code='USA', dob='2000-01-01') +logfire.info('user processed: {user!r}', user=user) # (2)! +``` + +1. The `logfire.configure()` call is all you need to instrument your project with Logfire. +2. The `logfire.info()` call logs the `user` object to Logfire, with builtin support for Pydantic models. + +![basic pydantic logfire usage](../img/basic_logfire.png) + +### Pydantic Instrumentation + +You can even record information about the validation process automatically by +using the builtin [Pydantic integration](https://logfire.pydantic.dev/docs/why-logfire/pydantic/): + +```python {test="skip"} +from datetime import date + +import logfire + +from pydantic import BaseModel + +logfire.configure() +logfire.instrument_pydantic() # (1)! + + +class User(BaseModel): + name: str + country_code: str + dob: date + + +User(name='Anne', country_code='USA', dob='2000-01-01') +User(name='David', country_code='GBR', dob='invalid-dob') +``` + +1. The `logfire.instrument_pydantic()` call automatically logs validation information for all Pydantic models in your project. + +You'll see each successful and failed validation logged in Logfire: + +![logfire instrumentation](../img/logfire_instrument.png) + +And you can investigate each of the corresponding spans to get validation details: + +![logfire span details](../img/logfire_span.png) + + diff --git a/docs/integrations/mypy.md b/docs/integrations/mypy.md index 1d7ef0e6f13..28dc3c53194 100644 --- a/docs/integrations/mypy.md +++ b/docs/integrations/mypy.md @@ -1,11 +1,11 @@ Pydantic works well with [mypy](http://mypy-lang.org) right out of the box. -However, Pydantic also ships with a mypy plugin that adds a number of important pydantic-specific -features to mypy that improve its ability to type-check your code. +However, Pydantic also ships with a mypy plugin that adds a number of important Pydantic-specific +features that improve its ability to type-check your code. For example, consider the following script: -```py test="skip" +```python {test="skip" linenums="1"} from datetime import datetime from typing import List, Optional @@ -25,17 +25,18 @@ print(m.middle_name) # not a model field! Model() # will raise a validation error for age and list_of_ints ``` -Without any special configuration, mypy does not catch the [missing model field annotation](https://docs.pydantic.dev/2.5/errors/usage_errors/#model-field-missing-annotation) and warns about the `list_of_ints` argument which Pydantic parses correctly: +Without any special configuration, mypy does not catch the [missing model field annotation](../errors/usage_errors.md#model-field-missing-annotation) +and errors about the `list_of_ints` argument which Pydantic parses correctly: ``` -test.py:15: error: List item 1 has incompatible type "str"; expected "int" [list-item] -test.py:15: error: List item 2 has incompatible type "bytes"; expected "int" [list-item] -test.py:16: error: "Model" has no attribute "middle_name" [attr-defined] -test.py:17: error: Missing named argument "age" for "Model" [call-arg] -test.py:17: error: Missing named argument "list_of_ints" for "Model" [call-arg] +15: error: List item 1 has incompatible type "str"; expected "int" [list-item] +15: error: List item 2 has incompatible type "bytes"; expected "int" [list-item] +16: error: "Model" has no attribute "middle_name" [attr-defined] +17: error: Missing named argument "age" for "Model" [call-arg] +17: error: Missing named argument "list_of_ints" for "Model" [call-arg] ``` -But [with the plugin enabled](#enabling-the-plugin), it gives the correct error: +But [with the plugin enabled](#enabling-the-plugin), it gives the correct errors: ``` 9: error: Untyped fields disallowed [pydantic-field] 16: error: "Model" has no attribute "middle_name" [attr-defined] @@ -46,162 +47,160 @@ But [with the plugin enabled](#enabling-the-plugin), it gives the correct error: With the pydantic mypy plugin, you can fearlessly refactor your models knowing mypy will catch any mistakes if your field names or types change. -There are other benefits too! See below for more details. +Note that mypy already supports some features without using the Pydantic plugin, such as synthesizing a `__init__` +method for Pydantic models and dataclasses. See the [mypy plugin capabilities](#mypy-plugin-capabilities) for a list +of additional features. -## Using mypy without the plugin +## Enabling the Plugin -You can run your code through mypy with: +To enable the plugin, just add `pydantic.mypy` to the list of plugins in your +[mypy config file](https://mypy.readthedocs.io/en/latest/config_file.html): -```bash -mypy \ - --ignore-missing-imports \ - --follow-imports=skip \ - --strict-optional \ - pydantic_mypy_test.py -``` +=== "`mypy.ini`" + + ```ini + [mypy] + plugins = pydantic.mypy + ``` + +=== "`pyproject.toml`" + + ```toml + [tool.mypy] + plugins = ['pydantic.mypy'] + ``` -### Strict Optional +!!! note + + If you're using `pydantic.v1` models, you'll need to add `pydantic.v1.mypy` to your list of plugins. + +See the [plugin configuration](#configuring-the-plugin) for more details. -For your code to pass with `--strict-optional`, you need to use `Optional[]` or an alias of `Optional[]` -for all fields with `None` as the default. (This is standard with mypy.) +## Supported mypy versions -### Other Pydantic interfaces +Pydantic supports the mypy versions released less than 6 months ago. Older versions may still work with the plugin +but won't be tested. The list of released mypy versions can be found [here](https://mypy-lang.org/news.html). Note +that the version support policy is subject to change at discretion of contributors. -Pydantic [dataclasses](../concepts/dataclasses.md) and the [`validate_call` decorator](../concepts/validation_decorator.md) -should also work well with mypy. +## Mypy plugin capabilities -## Mypy Plugin Capabilities +### Generate a `__init__` signature for Pydantic models -### Generate a signature for `Model.__init__` * Any required fields that don't have dynamically-determined aliases will be included as required keyword arguments. -* If `Config.populate_by_name=True`, the generated signature will use the field names, - rather than aliases. -* If `Config.extra='forbid'` and you don't make use of dynamically-determined aliases, the generated signature - will not allow unexpected inputs. -* **Optional:** If the [`init_forbid_extra` **plugin setting**](#configuring-the-plugin) is set to `True`, unexpected inputs to - `__init__` will raise errors even if `Config.extra` is not `'forbid'`. -* **Optional:** If the [`init_typed` **plugin setting**](#configuring-the-plugin) is set to `True`, the generated signature - will use the types of the model fields (otherwise they will be annotated as `Any` to allow parsing). - -### Generate a typed signature for `Model.model_construct` -* The [`model_construct`](../concepts/models.md#creating-models-without-validation) method is an alternative to `__init__` - when input data is known to be valid and should not be parsed. Because this method performs no runtime validation, - static checking is important to detect errors. - -### Respect `Config.frozen` -* If `Config.frozen` is `True`, you'll get a mypy error if you try to change - the value of a model field; cf. [faux immutability](../concepts/models.md#faux-immutability). - -### Generate a signature for `dataclasses` -* classes decorated with [`@pydantic.dataclasses.dataclass`](../concepts/dataclasses.md) are type checked the same as standard Python dataclasses -* The `@pydantic.dataclasses.dataclass` decorator accepts a `config` keyword argument which has the same meaning as [the `Config` sub-class](../concepts/config.md). +* If the [`populate_by_name`][pydantic.ConfigDict.populate_by_name] model configuration value is set to + `True`, the generated signature will use the field names rather than aliases. +* The [`init_forbid_extra`](#init_forbid_extra) and [`init_typed`](#init_typed) plugin configuration + values can further fine-tune the synthesized `__init__` method. + +### Generate a typed signature for `model_construct` + +* The [`model_construct`][pydantic.BaseModel.model_construct] method is an alternative to model validation when input data is + known to be valid and should not be parsed (see the [documentation](../concepts/models.md#creating-models-without-validation)). + Because this method performs no runtime validation, static checking is important to detect errors. + +### Support for frozen models + +* If the [`frozen`][pydantic.ConfigDict.frozen] configuration is set to `True`, you will get + an error if you try mutating a model field (see [faux immutability](../concepts/models.md#faux-immutability)) ### Respect the type of the `Field`'s `default` and `default_factory` + * Field with both a `default` and a `default_factory` will result in an error during static checking. * The type of the `default` and `default_factory` value must be compatible with the one of the field. ### Warn about the use of untyped fields -* You'll get a mypy error any time you assign a public attribute on a model without annotating its type -* If your goal is to set a ClassVar, you should explicitly annotate the field using typing.ClassVar -## Optional Capabilities: +* While defining a field without an annotation will result in a [runtime error](../errors/usage_errors.md#model-field-missing-annotation), + the plugin will also emit a type checking error. + ### Prevent the use of required dynamic aliases -* If the [`warn_required_dynamic_aliases` **plugin setting**](#configuring-the-plugin) is set to `True`, you'll get a mypy - error any time you use a dynamically-determined alias or alias generator on a model with - `Config.populate_by_name=False`. -* This is important because if such aliases are present, mypy cannot properly type check calls to `__init__`. - In this case, it will default to treating all arguments as optional. +See the documentation of the [`warn_required_dynamic_aliases`](#warn_required_dynamic_aliases) plugin configuration value. -## Enabling the Plugin +## Configuring the Plugin -To enable the plugin, just add `pydantic.mypy` to the list of plugins in your -[mypy config file](https://mypy.readthedocs.io/en/latest/config_file.html) -(this could be `mypy.ini`, `pyproject.toml`, or `setup.cfg`). +To change the values of the plugin settings, create a section in your mypy config file called `[pydantic-mypy]`, +and add any key-value pairs for settings you want to override. -To get started, all you need to do is create a `mypy.ini` file with following contents: -```ini -[mypy] -plugins = pydantic.mypy -``` +A configuration file with all plugin strictness flags enabled (and some other mypy strictness flags, too) might look like: -!!! note - If you're using `pydantic.v1` models, you'll need to add `pydantic.v1.mypy` to your list of plugins. +=== "`mypy.ini`" -The plugin is compatible with mypy versions `>=0.930`. + ```ini + [mypy] + plugins = pydantic.mypy -See the [plugin configuration](#configuring-the-plugin) docs for more details. + follow_imports = silent + warn_redundant_casts = True + warn_unused_ignores = True + disallow_any_generics = True + no_implicit_reexport = True + disallow_untyped_defs = True -### Configuring the Plugin -To change the values of the plugin settings, create a section in your mypy config file called `[pydantic-mypy]`, -and add any key-value pairs for settings you want to override. + [pydantic-mypy] + init_forbid_extra = True + init_typed = True + warn_required_dynamic_aliases = True + ``` -A `mypy.ini` file with all plugin strictness flags enabled (and some other mypy strictness flags, too) might look like: +=== "`pyproject.toml`" -```ini -[mypy] -plugins = pydantic.mypy + ```toml + [tool.mypy] + plugins = ["pydantic.mypy"] -follow_imports = silent -warn_redundant_casts = True -warn_unused_ignores = True -disallow_any_generics = True -check_untyped_defs = True -no_implicit_reexport = True + follow_imports = "silent" + warn_redundant_casts = true + warn_unused_ignores = true + disallow_any_generics = true + no_implicit_reexport = true + disallow_untyped_defs = true -# for strict mypy: (this is the tricky one :-)) -disallow_untyped_defs = True + [tool.pydantic-mypy] + init_forbid_extra = true + init_typed = true + warn_required_dynamic_aliases = true + ``` -[pydantic-mypy] -init_forbid_extra = True -init_typed = True -warn_required_dynamic_aliases = True -``` +### `init_typed` -As of `mypy>=0.900`, mypy config may also be included in the `pyproject.toml` file rather than `mypy.ini`. -The same configuration as above would be: - -```toml -[tool.mypy] -plugins = [ - "pydantic.mypy" -] - -follow_imports = "silent" -warn_redundant_casts = true -warn_unused_ignores = true -disallow_any_generics = true -check_untyped_defs = true -no_implicit_reexport = true - -# for strict mypy: (this is the tricky one :-)) -disallow_untyped_defs = true - -[tool.pydantic-mypy] -init_forbid_extra = true -init_typed = true -warn_required_dynamic_aliases = true +Because Pydantic performs [data conversion](../concepts/models.md#data-conversion) by default, the following is still valid at runtime: + +```python {test="skip" lint="skip"} +class Model(BaseModel): + a: int + + +Model(a='1') ``` -#### Note on `--disallow-any-explicit` +For this reason, the plugin will use [`Any`][typing.Any] for field annotations when synthesizing the `__init__` method, +unless `init_typed` is set or [strict mode](../concepts/strict_mode.md) is enabled on the model. -If you're using the `--disallow-any-explicit` mypy config setting (or other settings that ban `Any`), you may encounter `no-any-explicit` errors when extending `BaseModel`. This is because by default, Pydantic's `mypy` plugin adds an `__init__` method with a signature like `def __init__(self, field_1: Any, field_2: Any, **kwargs: Any):` +### `init_forbid_extra` -!!! note "Why the extra signature?" - The Pydantic `mypy` plugin adds an `__init__` method with a signature like `def __init__(self, field_1: Any, field_2: Any, **kwargs: Any):` in - order to avoid type errors when initializing models with types that don't match the field annotations. - For example `Model(date='2024-01-01')` would raise a type error without this `Any` signature, but Pydantic has the ability to parse the string - `'2024-01-01'` into a `datetime.date` type. +By default, Pydantic allows (and ignores) any extra provided argument: + +```python {test="skip" lint="skip"} +class Model(BaseModel): + a: int = 1 -To resolve this issue, you need to enable strict mode settings for the Pydantic mypy plugin. Specifically, add these options to your `[pydantic-mypy]` section: -```toml -[tool.pydantic-mypy] -init_forbid_extra = true -init_typed = true +Model(unrelated=2) ``` -With `init_forbid_extra = True`, the `**kwargs` are removed from the generated `__init__` signature. With `init_typed = True`, the `Any` types for fields are replaced with their actual type hints. +For this reason, the plugin will add an extra `**kwargs: Any` parameter when synthesizing the `__init__` method, unless +`init_forbid_extra` is set or the [`extra`][pydantic.ConfigDict.extra] is set to `'forbid'`. + +### `warn_required_dynamic_aliases` + +Whether to error when using a dynamically-determined alias or alias generator on a model with +[`populate_by_name`][pydantic.ConfigDict.populate_by_name] set to `False`. If such aliases are +present, mypy cannot properly type check calls to `__init__`. In this case, it will default to +treating all arguments as not required. -This configuration allows you to use `--disallow-any-explicit` without getting errors on your Pydantic models. However, be aware that this stricter checking might flag some valid Pydantic use cases (like passing a string for a datetime field) as type errors. +!!! note "Compatibility with `Any` being disallowed" + Some mypy configuration options (such as [`disallow_any_explicit`](https://mypy.readthedocs.io/en/stable/config_file.html#confval-disallow_any_explicit)) + will error because the synthesized `__init__` method contains [`Any`][typing.Any] annotations. To circumvent the issue, you will have + to enable both `init_forbid_extra` and `init_typed`. diff --git a/docs/integrations/visual_studio_code.md b/docs/integrations/visual_studio_code.md index 3be46ac9dd4..2bf7e69494c 100644 --- a/docs/integrations/visual_studio_code.md +++ b/docs/integrations/visual_studio_code.md @@ -95,7 +95,7 @@ But more common cases where these strict errors would be inconvenient would be w For example, this is valid for Pydantic: -```Python hl_lines="12 17" +```python {hl_lines="12 17"} from pydantic import BaseModel @@ -129,13 +129,13 @@ Below are several techniques to achieve it. You can disable the errors for a specific line using a comment of: -```py +```python # type: ignore ``` or (to be specific to pylance/pyright): -```py +```python # pyright: ignore ``` @@ -143,7 +143,7 @@ or (to be specific to pylance/pyright): coming back to the example with `age='23'`, it would be: -```Python hl_lines="10" +```python {hl_lines="10"} from pydantic import BaseModel @@ -166,7 +166,7 @@ that way Pylance and mypy will ignore errors in that line. You can also create a variable with the value you want to use and declare its type explicitly with `Any`. -```Python hl_lines="1 11-12" +```python {hl_lines="1 11-12"} from typing import Any from pydantic import BaseModel @@ -194,7 +194,7 @@ The same idea from the previous example can be put on the same line with the hel This way, the type declaration of the value is overridden inline, without requiring another variable. -```Python hl_lines="1 11" +```python {hl_lines="1 11"} from typing import Any, cast from pydantic import BaseModel @@ -223,7 +223,7 @@ Pydantic has a rich set of [Model Configurations][pydantic.config.ConfigDict] av These configurations can be set in an internal `class Config` on each model: -```Python hl_lines="9-10" +```python {hl_lines="9-10"} from pydantic import BaseModel @@ -236,7 +236,7 @@ class Knight(BaseModel): or passed as keyword arguments when defining the model class: -```Python hl_lines="4" +```python {hl_lines="4"} from pydantic import BaseModel @@ -260,7 +260,7 @@ in a model that is "frozen". Pylance/pyright requires `default` to be a keyword argument to `Field` in order to infer that the field is optional. -```py +```python from pydantic import BaseModel, Field diff --git a/docs/architecture.md b/docs/internals/architecture.md similarity index 93% rename from docs/architecture.md rename to docs/internals/architecture.md index b58a58dd362..82ed9292f49 100644 --- a/docs/architecture.md +++ b/docs/internals/architecture.md @@ -1,5 +1,5 @@ -!!! warning "🚧 Work in Progress" - This page is a work in progress. +!!! note + This section is part of the *internals* documentation, and is partly targeted to contributors. Starting with Pydantic V2, part of the codebase is written in Rust in a separate package called `pydantic-core`. This was done partly in order to improve validation and serialization performance (with the cost of limited @@ -50,7 +50,7 @@ In the case of a Pydantic model, a core schema will be constructed and set as th To illustrate what a core schema looks like, we will take the example of the [`bool`][pydantic_core.core_schema.bool_schema] core schema: -```python lint="skip" test="skip" +```python {lint="skip" test="skip"} class BoolSchema(TypedDict, total=False): type: Required[Literal['bool']] strict: bool @@ -85,7 +85,7 @@ If we were to define a custom serialization function for `foo` (1), the `seriali 1. For example using the [`field_serializer`][pydantic.functional_serializers.field_serializer] decorator: - ```python test="skip" lint="skip" + ```python {test="skip" lint="skip"} class Model(BaseModel): foo: bool = Field(strict=True) @@ -94,7 +94,7 @@ If we were to define a custom serialization function for `foo` (1), the `seriali ... ``` -```python lint="skip" test="skip" +```python {lint="skip" test="skip"} { 'type': 'function-plain', 'function': , @@ -133,11 +133,11 @@ and will return the following JSON Schema: ### Customizing the core schema and JSON schema !!! abstract "Usage Documentation" - [Custom types](concepts/types.md#custom-types) + [Custom types](../concepts/types.md#custom-types) - [Implementing `__get_pydantic_core_schema__`](concepts/json_schema.md#implementing-__get_pydantic_core_schema__) + [Implementing `__get_pydantic_core_schema__`](../concepts/json_schema.md#implementing-__get_pydantic_core_schema__) - [Implementing `__get_pydantic_json_schema__`](concepts/json_schema.md#implementing-__get_pydantic_json_schema__) + [Implementing `__get_pydantic_json_schema__`](../concepts/json_schema.md#implementing-__get_pydantic_json_schema__) While the `GenerateSchema` and [`GenerateJsonSchema`][pydantic.json_schema.GenerateJsonSchema] classes handle the creation of the corresponding schemas, Pydantic offers a way to customize them in some cases, following a wrapper pattern. @@ -190,7 +190,7 @@ create an instance of a `GetCoreSchemaHandler` to be passed to the `MyGt.__get_p where a simple `{'type': 'int'}` schema is returned. The `source` argument depends on the core schema generation pattern. In the case of [`Annotated`][typing.Annotated], -the `source` will be the type being annotated. When [defining a custom type](concepts/types.md#as-a-method-on-a-custom-type), +the `source` will be the type being annotated. When [defining a custom type](../concepts/types.md#as-a-method-on-a-custom-type), the `source` will be the actual class where `__get_pydantic_core_schema__` is defined. ## Model validation and serialization diff --git a/docs/internals/resolving_annotations.md b/docs/internals/resolving_annotations.md new file mode 100644 index 00000000000..6e8a29fb565 --- /dev/null +++ b/docs/internals/resolving_annotations.md @@ -0,0 +1,268 @@ +!!! note + This section is part of the *internals* documentation, and is partly targeted to contributors. + +Pydantic heavily relies on [type hints][type hint] at runtime to build schemas for validation, serialization, etc. + +While type hints were primarily introduced for static type checkers (such as [Mypy] or [Pyright]), they are +accessible (and sometimes evaluated) at runtime. This means that the following would fail at runtime, +because `Node` has yet to be defined in the current module: + +```python {test="skip" lint="skip"} +class Node: + """Binary tree node.""" + + # NameError: name 'Node' is not defined: + def __init__(self, l: Node, r: Node) -> None: + self.left = l + self.right = r +``` + +To circumvent this issue, forward references can be used (by wrapping the annotation in quotes). + +In Python 3.7, [PEP 563] introduced the concept of _postponed evaluation of annotations_, meaning +with the `from __future__ import annotations` [future statement], type hints are stringified by default: + +```python {requires="3.12" lint="skip"} +from __future__ import annotations + +from pydantic import BaseModel + + +class Foo(BaseModel): + f: MyType + # Given the future import above, this is equivalent to: + # f: 'MyType' + + +type MyType = int + +print(Foo.__annotations__) +#> {'f': 'MyType'} +``` + +## The challenges of runtime evaluation + +Static type checkers make use of the AST to analyze the defined annotations. +Regarding the previous example, this has the benefit of being able to understand what `MyType` refers to when analyzing +the class definition of `Foo`, even if `MyType` isn't yet defined at runtime. + +However, for runtime tools such as Pydantic, it is more challenging to correctly resolve these forward annotations. +The Python standard library provides some tools to do so ([`typing.get_type_hints()`][typing.get_type_hints], +[`inspect.get_annotations()`][inspect.get_annotations]), but they come with some limitations. Thus, they are +being re-implemented in Pydantic with improved support for edge cases. + +As Pydantic as grown, it's adapted to support many edge cases requiring irregular patterns for annotation evaluation. +Some of these use cases aren't necessarily sound from a static type checking perspective. In v2.10, the internal +logic was refactored in an attempt to simplify and standardize annotation evaluation. Admittedly, backwards compatibility +posed some challenges, and there is still some noticeable scar tissue in the codebase because of this.There's a hope that +[PEP 649] (introduced in Python 3.14) will greatly simplify the process, especially when it comes to dealing with locals +of a function. + +To evaluate forward references, Pydantic roughly follows the same logic as described in the documentation of the +[`typing.get_type_hints()`][typing.get_type_hints] function. That is, the built-in [`eval()`][eval] function is used +by passing the forward reference, a global, and a local namespace. The namespace fetching logic is defined in the +sections below. + +## Resolving annotations at class definition + +The following example will be used as a reference throughout this section: + +```python {test="skip" lint="skip"} +# module1.py: +type MyType = int + +class Base: + f1: 'MyType' + +# module2.py: +from pydantic import BaseModel + +from module1 import Base + +type MyType = str + + +def inner() -> None: + type InnerType = bool + + class Model(BaseModel, Base): + type LocalType = bytes + + f2: 'MyType' + f3: 'InnerType' + f4: 'LocalType' + f5: 'UnknownType' + + type InnerType2 = complex +``` + +When the `Model` class is being built, different [namespaces][namespace] are at play. For each base class +of the `Model`'s [MRO][method resolution order] (in reverse order — that is, starting with `Base`), the +following logic is applied: + +1. Fetch the `__annotations__` key from the current base class' `__dict__`, if present. For `Base`, this will be + `{'f1': 'MyType'}`. +2. Iterate over the `__annotations__` items and try to evaluate the annotation [^1] using a custom wrapper around + the built-in [`eval()`][eval] function. This function takes two `globals` and `locals` arguments: + - The current module's `__dict__` is naturally used as `globals`. For `Base`, this will be + `sys.modules['module1'].__dict__`. + - For the `locals` argument, Pydantic will try to resolve symbols in the following namespaces, sorted by highest priority: + - A namespace created on the fly, containing the current class name (`{cls.__name__: cls}`). This is done + in order to support recursive references. + - The locals of the current class (i.e. `cls.__dict__`). For `Model`, this will include `LocalType`. + - The parent namespace of the class, if different from the globals described above. This is the + [locals][frame.f_locals] of the frame where the class is being defined. For `Base`, because the class is being + defined in the module directly, this namespace won't be used as it will result in the globals being used again. + For `Model`, the parent namespace is the locals of the frame of `inner()`. +3. If the annotation failed to evaluate, it is kept as is, so that the model can be rebuilt at a later stage. This will + be the case for `f5`. + +The following table lists the resolved type annotations for every field, once the `Model` class has been created: + +| Field name | Resolved annotation | +|------------|---------------------| +| `f1` | [`int`][] | +| `f2` | [`str`][] | +| `f3` | [`bool`][] | +| `f4` | [`bytes`][] | +| `f5` | `'UnkownType'` | + +### Limitations and backwards compatibility concerns + +While the namespace fetching logic is trying to be as accurate as possible, we still face some limitations: + +
+ +- The locals of the current class (`cls.__dict__`) may include irrelevant entries, most of them being dunder attributes. + This means that the following annotation: `f: '__doc__'` will successfully (and unexpectedly) be resolved. +- When the `Model` class is being created inside a function, we keep a copy of the [locals][frame.f_locals] of the frame. + This copy only includes the symbols defined in the locals when `Model` is being defined, meaning `InnerType2` won't be included + (and will **not be** if doing a model rebuild at a later point!). + - To avoid memory leaks, we use [weak references][weakref] to the locals of the function, meaning some forward references might + not resolve outside the function (1). + - Locals of the function are only taken into account for Pydantic models, but this pattern does not apply to dataclasses, typed + dictionaries or named tuples. + +
+ +1. Here is an example: + + ```python {test="skip" lint="skip"} + def func(): + A = int + + class Model(BaseModel): + f: 'A | Forward' + + return Model + + + Model = func() + + Model.model_rebuild(_types_namespace={'Forward': str}) + # pydantic.errors.PydanticUndefinedAnnotation: name 'A' is not defined + ``` + +[](){#backwards-compatibility-logic} + +For backwards compatibility reasons, and to be able to support valid use cases without having to rebuild models, +the namespace logic described above is a bit different when it comes to core schema generation. Taking the +following example: + +```python +from dataclasses import dataclass + +from pydantic import BaseModel + + +@dataclass +class Foo: + a: 'Bar | None' = None + + +class Bar(BaseModel): + b: Foo +``` + +Once the fields for `Bar` have been collected (meaning annotations resolved), the `GenerateSchema` class converts +every field into a core schema. When it encounters another class-like field type (such as a dataclass), it will +try to evaluate annotations, following roughly the same logic as [described above](#resolving-annotations-at-class-definition). +However, to evaluate the `'Bar | None'` annotation, `Bar` needs to be present in the globals or locals, which is normally +*not* the case: `Bar` is being created, so it is not "assigned" to the current module's `__dict__` at that point. + +To avoid having to call [`model_rebuild()`][pydantic.BaseModel.model_rebuild] on `Bar`, both the parent namespace +(if `Bar` was to be defined inside a function, and [the namespace provided during a model rebuild](#model-rebuild-semantics)) +and the `{Bar.__name__: Bar}` namespace are included in the locals during annotations evaluation of `Foo` +(with the lowest priority) (1). +{ .annotate } + +1. This backwards compatibility logic can introduce some inconsistencies, such as the following: + + ```python {lint="skip"} + from dataclasses import dataclass + + from pydantic import BaseModel + + + @dataclass + class Foo: + # `a` and `b` shouldn't resolve: + a: 'Model' + b: 'Inner' + + + def func(): + Inner = int + + class Model(BaseModel): + foo: Foo + + Model.__pydantic_complete__ + #> True, should be False. + ``` + +## Resolving annotations when rebuilding a model + +When a forward reference fails to evaluate, Pydantic will silently fail and stop the core schema +generation process. This can be seen by inspecting the `__pydantic_core_schema__` of a model class: + +```python {lint="skip"} +from pydantic import BaseModel + + +class Foo(BaseModel): + f: 'MyType' + + +Foo.__pydantic_core_schema__ +#> +``` + +If you then properly define `MyType`, you can rebuild the model: + +```python {test="skip" lint="skip"} +type MyType = int + +Foo.model_rebuild() +Foo.__pydantic_core_schema__ +#> {'type': 'model', 'schema': {...}, ...} +``` + +[](){#model-rebuild-semantics} + +The [`model_rebuild()`][pydantic.BaseModel.model_rebuild] method uses a *rebuild namespace*, with the following semantics: + +- If an explicit `_types_namespace` argument is provided, it is used as the rebuild namespace. +- If no namespace is provided, the namespace where the method is called will be used as the rebuild namespace. + +This *rebuild namespace* will be merged with the model's parent namespace (if it was defined in a function) and used as is +(see the [backwards compatibility logic](#backwards-compatibility-logic) described above). + +[Mypy]: https://www.mypy-lang.org/ +[Pyright]: https://github.com/microsoft/pyright/ +[PEP 563]: https://peps.python.org/pep-0563/ +[PEP 649]: https://peps.python.org/pep-0649/ +[future statement]: https://docs.python.org/3/reference/simple_stmts.html#future + +[^1]: This is done unconditionally, as forward annotations can be only present _as part_ of a type hint (e.g. `Optional['int']`), as dictated by + the [typing specification](https://typing.readthedocs.io/en/latest/spec/annotations.html#string-annotations). diff --git a/docs/migration.md b/docs/migration.md index 2e6743897ff..1f4404e8227 100644 --- a/docs/migration.md +++ b/docs/migration.md @@ -63,13 +63,13 @@ by importing through `pydantic.v1`. For example, you can use the `BaseModel` class from Pydantic V1 instead of the Pydantic V2 `pydantic.BaseModel` class: -```python test="skip" lint="skip" upgrade="skip" +```python {test="skip" lint="skip" upgrade="skip"} from pydantic.v1 import BaseModel ``` You can also import functions that have been removed from Pydantic V2, such as `lenient_isinstance`: -```python test="skip" lint="skip" upgrade="skip" +```python {test="skip" lint="skip" upgrade="skip"} from pydantic.v1.utils import lenient_isinstance ``` @@ -85,13 +85,13 @@ features, take the following steps: 1. Replace `pydantic<2` with `pydantic>=1.10.17` 2. Find and replace all occurrences of: -```python test="skip" lint="skip" upgrade="skip" +```python {test="skip" lint="skip" upgrade="skip"} from pydantic. import ``` with: -```python test="skip" lint="skip" upgrade="skip" +```python {test="skip" lint="skip" upgrade="skip"} from pydantic.v1. import ``` @@ -100,7 +100,7 @@ Here's how you can import `pydantic`'s v1 features based on your version of `pyd === "`pydantic>=1.10.17,<3`" As of `v1.10.17` the `.v1` namespace is available in V1, allowing imports as below: - ```python test="skip" lint="skip" upgrade="skip" + ```python {test="skip" lint="skip" upgrade="skip"} from pydantic.v1.fields import ModelField ``` @@ -108,7 +108,7 @@ Here's how you can import `pydantic`'s v1 features based on your version of `pyd All versions of Pydantic V1 and V2 support the following import pattern, in case you don't know which version of Pydantic you are using: - ```python test="skip" lint="skip" upgrade="skip" + ```python {test="skip" lint="skip" upgrade="skip"} try: from pydantic.v1.fields import ModelField except ImportError: @@ -188,7 +188,7 @@ to help ease migration, but calling them will emit `DeprecationWarning`s. If you'd still like to use said arguments, you can use [this workaround](https://github.com/pydantic/pydantic/issues/8825#issuecomment-1946206415). * JSON serialization of non-string key values is generally done with `str(key)`, leading to some changes in behavior such as the following: -```py +```python from typing import Dict, Optional from pydantic import BaseModel as V2BaseModel @@ -218,7 +218,7 @@ print(v2_model.model_dump_json()) * `model_dump_json()` results are compacted in order to save space, and don't always exactly match that of `json.dumps()` output. That being said, you can easily modify the separators used in `json.dumps()` results in order to align the two outputs: -```py +```python import json from typing import List @@ -382,7 +382,7 @@ See the [`ConfigDict` API reference][pydantic.config.ConfigDict] for more detail To avoid this, you can use the `validate_default` argument in the `Field` function. When set to `True`, it mimics the behavior of `always=True` in Pydantic v1. However, the new way of using `validate_default` is encouraged as it provides more flexibility and control. -```python test="skip" +```python {test="skip"} from pydantic import BaseModel, validator @@ -677,7 +677,7 @@ The following table describes the behavior of field annotations in V2: Any default value if provided makes a field not required. Here is a code example demonstrating the above: -```py +```python from typing import Optional from pydantic import BaseModel, ValidationError @@ -740,7 +740,7 @@ print(adapter.json_schema()) Due to limitations of inferring generic types with common type checkers, to get proper typing in some scenarios, you may need to explicitly specify the generic parameter: -```python test="skip" +```python {test="skip"} from pydantic import TypeAdapter adapter = TypeAdapter[str | int](str | int) @@ -838,7 +838,7 @@ Some of the URL validation differs slightly from the previous behavior in V1. One notable difference is that the new `Url` types append slashes to the validated version if no path is included, even if a slash is not specified in the argument to a `Url` type constructor. See the example below for this behavior: -```py +```python from pydantic import AnyUrl assert str(AnyUrl(url='https://google.com')) == 'https://google.com/' @@ -854,7 +854,7 @@ If you still want to use the old behavior without the appended slash, take a loo The `Constrained*` classes were _removed_, and you should replace them by `Annotated[, Field(...)]`, for example: -```py test="skip" +```python {test="skip"} from pydantic import BaseModel, ConstrainedInt @@ -868,7 +868,7 @@ class Model(BaseModel): ...becomes: -```py +```python from typing_extensions import Annotated from pydantic import BaseModel, Field diff --git a/docs/plugins/main.py b/docs/plugins/main.py index e1d4da064c8..9ef4c9074d3 100644 --- a/docs/plugins/main.py +++ b/docs/plugins/main.py @@ -40,9 +40,30 @@ def on_pre_build(config: Config) -> None: """ Before the build starts. """ + import mkdocs_redirects.plugin + add_changelog() add_mkdocs_run_deps() + # work around for very unfortunate bug in mkdocs-redirects: + # https://github.com/mkdocs/mkdocs-redirects/issues/65 + mkdocs_redirects.plugin.HTML_TEMPLATE = """ + + + + + Redirecting... + + + + + + +Redirecting... + + +""" + def on_files(files: Files, config: Config) -> Files: """ @@ -57,7 +78,6 @@ def on_page_markdown(markdown: str, page: Page, config: Config, files: Files) -> """ markdown = upgrade_python(markdown) markdown = insert_json_output(markdown) - markdown = remove_code_fence_attributes(markdown) if md := render_index(markdown, page): return md if md := render_why(markdown, page): @@ -94,13 +114,20 @@ def add_changelog() -> None: def add_mkdocs_run_deps() -> None: # set the pydantic, pydantic-core, pydantic-extra-types versions to configure for running examples in the browser pyproject_toml = (PROJECT_ROOT / 'pyproject.toml').read_text() - pydantic_core_version = re.search(r'pydantic-core==(.+?)["\']', pyproject_toml).group(1) + m = re.search(r'pydantic-core==(.+?)["\']', pyproject_toml) + if not m: + logger.info( + "Could not find pydantic-core version in pyproject.toml, this is expected if you're using a git ref" + ) + return + + pydantic_core_version = m.group(1) version_py = (PROJECT_ROOT / 'pydantic' / 'version.py').read_text() pydantic_version = re.search(r'^VERSION ?= (["\'])(.+)\1', version_py, flags=re.M).group(2) - pdm_lock = (PROJECT_ROOT / 'pdm.lock').read_text() - pydantic_extra_types_version = re.search(r'name = "pydantic-extra-types"\nversion = "(.+?)"', pdm_lock).group(1) + uv_lock = (PROJECT_ROOT / 'uv.lock').read_text() + pydantic_extra_types_version = re.search(r'name = "pydantic-extra-types"\nversion = "(.+?)"', uv_lock).group(1) mkdocs_run_deps = json.dumps( [ @@ -162,7 +189,7 @@ def add_tabs(match: re.Match[str]) -> str: else: return '\n\n'.join(output) - return re.sub(r'^(``` *py.*?)\n(.+?)^```(\s+(?:^\d+\. .+?\n)+)', add_tabs, markdown, flags=re.M | re.S) + return re.sub(r'^(``` *py.*?)\n(.+?)^```(\s+(?:^\d+\. .+?\n)*)', add_tabs, markdown, flags=re.M | re.S) def _upgrade_code(code: str, min_version: int) -> str: @@ -198,23 +225,6 @@ def replace_last_print(m2: re.Match[str]) -> str: return re.sub(r'(^ *```)([^\n]*?output="json"[^\n]*?\n)(.+?)\1', replace_json, markdown, flags=re.M | re.S) -def remove_code_fence_attributes(markdown: str) -> str: - """ - There's no way to add attributes to code fences that works with both pycharm and mkdocs, hence we use - `py key="value"` to provide attributes to pytest-examples, then remove those attributes here. - - https://youtrack.jetbrains.com/issue/IDEA-297873 & https://python-markdown.github.io/extensions/fenced_code_blocks/ - """ - - def remove_attrs(match: re.Match[str]) -> str: - suffix = re.sub( - r' (?:test|lint|upgrade|group|requires|output|rewrite_assert)=".+?"', '', match.group(2), flags=re.M - ) - return f'{match.group(1)}{suffix}' - - return re.sub(r'^( *``` *py)(.*)', remove_attrs, markdown, flags=re.M) - - def get_orgs_data() -> list[dict[str, str]]: with (THIS_DIR / 'orgs.toml').open('rb') as f: orgs_data = tomli.load(f) diff --git a/docs/plugins/people.yml b/docs/plugins/people.yml index 19836ce9b35..9771ea6d3aa 100644 --- a/docs/plugins/people.yml +++ b/docs/plugins/people.yml @@ -1,57 +1,57 @@ maintainers: -- login: samuelcolvin - answers: 295 - prs: 399 - avatarUrl: https://avatars.githubusercontent.com/u/4039449?u=42eb3b833047c8c4b4f647a031eaef148c16d93f&v=4 - url: https://github.com/samuelcolvin -- login: adriangb - answers: 41 - prs: 199 - avatarUrl: https://avatars.githubusercontent.com/u/1755071?u=612704256e38d6ac9cbed24f10e4b6ac2da74ecb&v=4 - url: https://github.com/adriangb +- login: Kludex + answers: 22 + prs: 112 + avatarUrl: https://avatars.githubusercontent.com/u/7353520?u=0934928aa44d3b75af26ad47cb0227c8af30c8ec&v=4 + url: https://github.com/Kludex - login: sydney-runkle answers: 36 - prs: 262 + prs: 292 avatarUrl: https://avatars.githubusercontent.com/u/54324534?u=3a4ffd00a8270b607922250d3a2d9c9af38b9cf9&v=4 url: https://github.com/sydney-runkle -- login: dmontagu - answers: 55 - prs: 315 - avatarUrl: https://avatars.githubusercontent.com/u/35119617?u=540f30c937a6450812628b9592a1dfe91bbe148e&v=4 - url: https://github.com/dmontagu -- login: davidhewitt - answers: 2 - prs: 40 - avatarUrl: https://avatars.githubusercontent.com/u/1939362?u=b4b48981c3a097daaad16c4c5417aa7a3e5e32d9&v=4 - url: https://github.com/davidhewitt -- login: hramezani - answers: 22 +- login: adriangb + answers: 41 prs: 199 - avatarUrl: https://avatars.githubusercontent.com/u/3122442?u=f387fc2dbc0c681f23e80e2ad705790fafcec9a2&v=4 - url: https://github.com/hramezani + avatarUrl: https://avatars.githubusercontent.com/u/1755071?u=612704256e38d6ac9cbed24f10e4b6ac2da74ecb&v=4 + url: https://github.com/adriangb +- login: samuelcolvin + answers: 295 + prs: 399 + avatarUrl: https://avatars.githubusercontent.com/u/4039449?u=42eb3b833047c8c4b4f647a031eaef148c16d93f&v=4 + url: https://github.com/samuelcolvin - login: alexmojaki answers: 0 prs: 18 avatarUrl: https://avatars.githubusercontent.com/u/3627481?u=9bb2e0cf1c5ef3d0609d2e639a135b7b4ca8b463&v=4 url: https://github.com/alexmojaki -- login: Kludex +- login: hramezani answers: 22 - prs: 111 - avatarUrl: https://avatars.githubusercontent.com/u/7353520?u=62adc405ef418f4b6c8caa93d3eb8ab107bc4927&v=4 - url: https://github.com/Kludex + prs: 199 + avatarUrl: https://avatars.githubusercontent.com/u/3122442?u=f387fc2dbc0c681f23e80e2ad705790fafcec9a2&v=4 + url: https://github.com/hramezani +- login: davidhewitt + answers: 2 + prs: 40 + avatarUrl: https://avatars.githubusercontent.com/u/1939362?u=b4b48981c3a097daaad16c4c5417aa7a3e5e32d9&v=4 + url: https://github.com/davidhewitt +- login: dmontagu + answers: 55 + prs: 315 + avatarUrl: https://avatars.githubusercontent.com/u/35119617?u=540f30c937a6450812628b9592a1dfe91bbe148e&v=4 + url: https://github.com/dmontagu experts: - login: PrettyWood count: 143 avatarUrl: https://avatars.githubusercontent.com/u/18406791?u=20a4953f7d7e9d49d054b81e1582b08e87b2125f&v=4 url: https://github.com/PrettyWood +- login: Viicos + count: 94 + avatarUrl: https://avatars.githubusercontent.com/u/65306057?u=fcd677dc1b9bef12aa103613e5ccb3f8ce305af9&v=4 + url: https://github.com/Viicos - login: uriyyo count: 93 avatarUrl: https://avatars.githubusercontent.com/u/32038156?u=bbcf79839cafe7b6249326c3b3b5383f2981595a&v=4 url: https://github.com/uriyyo -- login: Viicos - count: 87 - avatarUrl: https://avatars.githubusercontent.com/u/65306057?u=fcd677dc1b9bef12aa103613e5ccb3f8ce305af9&v=4 - url: https://github.com/Viicos - login: lesnik512 count: 21 avatarUrl: https://avatars.githubusercontent.com/u/2184855?u=9c720fa595336aa83eef20729d4230ba7424d9fa&v=4 @@ -70,7 +70,7 @@ experts: url: https://github.com/ybressler last_month_active: - login: Viicos - count: 15 + count: 7 avatarUrl: https://avatars.githubusercontent.com/u/65306057?u=fcd677dc1b9bef12aa103613e5ccb3f8ce305af9&v=4 url: https://github.com/Viicos top_contributors: @@ -79,7 +79,7 @@ top_contributors: avatarUrl: https://avatars.githubusercontent.com/u/18406791?u=20a4953f7d7e9d49d054b81e1582b08e87b2125f&v=4 url: https://github.com/PrettyWood - login: Viicos - count: 77 + count: 105 avatarUrl: https://avatars.githubusercontent.com/u/65306057?u=fcd677dc1b9bef12aa103613e5ccb3f8ce305af9&v=4 url: https://github.com/Viicos - login: dependabot-preview @@ -146,6 +146,10 @@ top_contributors: count: 8 avatarUrl: https://avatars.githubusercontent.com/u/70970900?u=573a3175906348e0d1529104d56b391e93ca0250&v=4 url: https://github.com/NeevCohen +- login: kc0506 + count: 8 + avatarUrl: https://avatars.githubusercontent.com/u/89458301?u=75f53e971fcba3ff61836c389505a420bddd865c&v=4 + url: https://github.com/kc0506 - login: layday count: 7 avatarUrl: https://avatars.githubusercontent.com/u/31134424?u=e8afd95a97b5556c467d1be27788950e67378ef1&v=4 @@ -174,10 +178,6 @@ top_contributors: count: 5 avatarUrl: https://avatars.githubusercontent.com/u/9677399?u=386c330f212ce467ce7119d9615c75d0e9b9f1ce&v=4 url: https://github.com/ofek -- login: kc0506 - count: 5 - avatarUrl: https://avatars.githubusercontent.com/u/89458301?u=75f53e971fcba3ff61836c389505a420bddd865c&v=4 - url: https://github.com/kc0506 - login: hmvp count: 4 avatarUrl: https://avatars.githubusercontent.com/u/1734544?v=4 @@ -218,11 +218,19 @@ top_contributors: count: 4 avatarUrl: https://avatars.githubusercontent.com/u/45747761?u=c1515d2ccf4877c0b64b5ea5a8c51631affe35de&v=4 url: https://github.com/dAIsySHEng1 +- login: AdolfoVillalobos + count: 4 + avatarUrl: https://avatars.githubusercontent.com/u/16639270?u=faa71bcfb3273a32cd81711a56998e115bca7fcc&v=4 + url: https://github.com/AdolfoVillalobos top_reviewers: - login: PrettyWood count: 211 avatarUrl: https://avatars.githubusercontent.com/u/18406791?u=20a4953f7d7e9d49d054b81e1582b08e87b2125f&v=4 url: https://github.com/PrettyWood +- login: Viicos + count: 115 + avatarUrl: https://avatars.githubusercontent.com/u/65306057?u=fcd677dc1b9bef12aa103613e5ccb3f8ce305af9&v=4 + url: https://github.com/Viicos - login: lig count: 103 avatarUrl: https://avatars.githubusercontent.com/u/38705?v=4 @@ -231,10 +239,6 @@ top_reviewers: count: 77 avatarUrl: https://avatars.githubusercontent.com/u/370316?u=eb206070cfe47f242d5fcea2e6c7514f4d0f27f5&v=4 url: https://github.com/tpdorsey -- login: Viicos - 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login: pilosus count: 6 avatarUrl: https://avatars.githubusercontent.com/u/6400248?u=2b30c6675f888c2e47640aed2f1c1a956baae224&v=4 @@ -291,10 +303,6 @@ top_reviewers: count: 6 avatarUrl: https://avatars.githubusercontent.com/u/10811879?u=c0cfe7f7be82474d0deb2ba27601ec96f4f43515&v=4 url: https://github.com/JeanArhancet -- login: MarkusSintonen - count: 6 - avatarUrl: https://avatars.githubusercontent.com/u/12939780?v=4 - url: https://github.com/MarkusSintonen - login: tlambert03 count: 5 avatarUrl: https://avatars.githubusercontent.com/u/1609449?u=922abf0524b47739b37095e553c99488814b05db&v=4 @@ -303,10 +311,10 @@ top_reviewers: count: 5 avatarUrl: https://avatars.githubusercontent.com/u/537700?u=7b64bd12eda862fbf72228495aada9c470df7a90&v=4 url: https://github.com/christianbundy -- login: hyperlint-ai +- login: nix010 count: 5 - avatarUrl: https://avatars.githubusercontent.com/in/718456?v=4 - url: https://github.com/apps/hyperlint-ai + avatarUrl: https://avatars.githubusercontent.com/u/16438204?u=f700f440b89e715795c3bc091800b8d3f39c58d9&v=4 + url: https://github.com/nix010 - login: graingert count: 4 avatarUrl: https://avatars.githubusercontent.com/u/413772?u=64b77b6aa405c68a9c6bcf45f84257c66eea5f32&v=4 @@ -335,7 +343,7 @@ top_reviewers: count: 4 avatarUrl: https://avatars.githubusercontent.com/u/59469646?u=86d6a20768cc4cc65622eafd86672147321bd8f8&v=4 url: https://github.com/JensHeinrich -- login: nix010 +- login: kc0506 count: 4 - avatarUrl: https://avatars.githubusercontent.com/u/16438204?u=f700f440b89e715795c3bc091800b8d3f39c58d9&v=4 - url: https://github.com/nix010 + avatarUrl: https://avatars.githubusercontent.com/u/89458301?u=75f53e971fcba3ff61836c389505a420bddd865c&v=4 + url: https://github.com/kc0506 diff --git a/docs/theme/announce.html b/docs/theme/announce.html index 41f585a8746..71941bdd598 100644 --- a/docs/theme/announce.html +++ b/docs/theme/announce.html @@ -1,5 +1,7 @@ -We're live! Pydantic Logfire is out in open beta! 🎉
-Logfire is a new observability tool for Python, from the creators of Pydantic, with great Pydantic support. -Please try it, and tell us what you think! +What's new — we've launched +Pydantic Logfire +🔥 +to help you monitor and understand your +Pydantic validations. diff --git a/docs/version-policy.md b/docs/version-policy.md index cb9237fe567..fecba91069f 100644 --- a/docs/version-policy.md +++ b/docs/version-policy.md @@ -45,7 +45,7 @@ We use one of the following naming conventions to indicate that a feature is exp 1. The feature is located in the [`experimental`](api/experimental.md) module. In this case, you can access the feature like this: - ```python test="skip" lint="skip" + ```python {test="skip" lint="skip"} from pydantic.experimental import feature_name ``` diff --git a/docs/why.md b/docs/why.md index 632977cb658..538eff411ad 100644 --- a/docs/why.md +++ b/docs/why.md @@ -15,7 +15,7 @@ and IDEs (like [PyCharm](https://www.jetbrains.com/pycharm/) and [VSCode](https: ???+ example "Example - just type hints" _(This example requires Python 3.9+)_ - ```py requires="3.9" + ```python {requires="3.9"} from typing import Annotated, Literal from annotated_types import Gt @@ -61,7 +61,7 @@ As a result, Pydantic is among the fastest data validation libraries for Python. In general, dedicated code should be much faster than a general-purpose validator, but in this example Pydantic is >300% faster than dedicated code when parsing JSON and validating URLs. - ```py title="Performance Example" test="skip" + ```python {title="Performance Example" test="skip"} import json import timeit from urllib.parse import urlparse @@ -137,7 +137,7 @@ In all three modes, the output can be customized by excluding specific fields, e ??? example "Example - Serialization 3 ways" - ```py + ```python from datetime import datetime from pydantic import BaseModel @@ -167,7 +167,7 @@ A [JSON Schema](https://json-schema.org/) can be generated for any Pydantic sche ??? example "Example - JSON Schema" - ```py + ```python from datetime import datetime from pydantic import BaseModel @@ -236,7 +236,7 @@ implemented in Rust, it's also very performant. ??? example "Example - Strict mode that's actually useful" - ```py + ```python from datetime import datetime from pydantic import BaseModel, ValidationError @@ -288,7 +288,7 @@ Pydantic provides four ways to create schemas and perform validation and seriali ??? example "Example - schema based on a [`TypedDict`][typing.TypedDict]" - ```py + ```python from datetime import datetime from typing_extensions import NotRequired, TypedDict @@ -338,7 +338,7 @@ Functional validators and serializers, as well as a powerful protocol for custom ??? example "Customisation Example - wrap validators" "wrap validators" are new in Pydantic V2 and are one of the most powerful ways to customize validation. - ```py + ```python from datetime import datetime, timezone from typing import Any diff --git a/mkdocs.yml b/mkdocs.yml index e4e16f01379..121ab2af8c2 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -7,6 +7,13 @@ theme: name: 'material' custom_dir: 'docs/theme' palette: + - media: "(prefers-color-scheme)" + scheme: default + primary: pink + accent: pink + toggle: + icon: material/lightbulb + name: "Switch to light mode" - media: "(prefers-color-scheme: light)" scheme: default primary: pink @@ -19,8 +26,8 @@ theme: primary: pink accent: pink toggle: - icon: material/lightbulb - name: "Switch to light mode" + icon: material/lightbulb-auto-outline + name: "Switch to system preference" features: - content.tabs.link - content.code.annotate @@ -97,7 +104,7 @@ nav: - Serialization: concepts/serialization.md - Validators: concepts/validators.md - Dataclasses: concepts/dataclasses.md - - Postponed Annotations: concepts/postponed_annotations.md + - Forward Annotations: concepts/forward_annotations.md - Strict Mode: concepts/strict_mode.md - Type Adapter: concepts/type_adapter.md - Validation Decorator: concepts/validation_decorator.md @@ -145,15 +152,21 @@ nav: - Semantic Version: api/pydantic_extra_types_semantic_version.md - Timezone Name: api/pydantic_extra_types_timezone_name.md - ULID: api/pydantic_extra_types_ulid.md -- Architecture: architecture.md +- Internals: + - Architecture: internals/architecture.md + - Resolving Annotations: internals/resolving_annotations.md - Examples: - - Secrets: examples/secrets.md - - Validators: examples/validators.md + - Validating File Data: examples/files.md + - Web and API Requests: examples/requests.md + - Queues: examples/queues.md + - Databases: examples/orms.md + - Custom Validators: examples/custom_validators.md - Error Messages: - Error Handling: errors/errors.md - Validation Errors: errors/validation_errors.md - Usage Errors: errors/usage_errors.md - Integrations: + - Pydantic Logfire: integrations/logfire.md - Dev Tools: - Mypy: integrations/mypy.md/. - PyCharm: integrations/pycharm.md @@ -192,6 +205,7 @@ hooks: - 'docs/plugins/main.py' plugins: +- social - mike: alias_type: symlink canonical_version: latest @@ -232,7 +246,7 @@ plugins: 'usage/rich.md': 'integrations/rich.md' 'usage/linting.md': 'integrations/linting.md' 'usage/types.md': 'concepts/types.md' - 'usage/types/secrets.md': 'examples/secrets.md' + 'usage/types/secrets.md': 'api/types.md#pydantic.types.Secret' 'usage/types/string_types.md': 'api/types.md#pydantic.types.StringConstraints' 'usage/types/file_types.md': 'api/types.md#pydantic.types.FilePath' 'api/main.md': 'api/base_model.md' @@ -270,7 +284,8 @@ plugins: 'usage/fields.md': 'concepts/fields.md' 'usage/json_schema.md': 'concepts/json_schema.md' 'usage/models.md': 'concepts/models.md' - 'usage/postponed_annotations.md': 'concepts/postponed_annotations.md' + 'usage/postponed_annotations.md': 'concepts/forward_annotations.md' + 'concepts/postponed_annotations.md': 'concepts/forward_annotations.md' 'usage/pydantic_settings.md': 'concepts/pydantic_settings.md' 'usage/serialization.md': 'concepts/serialization.md' 'usage/strict_mode.md': 'concepts/strict_mode.md' @@ -285,6 +300,9 @@ plugins: 'usage/types/sequence_iterable.md': 'api/standard_library_types.md#other-iterables' 'usage/types/set_types.md': 'api/standard_library_types.md#sets' 'usage/types/uuids.md': 'api/standard_library_types.md#uuid' - 'blog/pydantic-v2-alpha.md': 'https://blog.pydantic.dev/blog/2023/04/03/pydantic-v2-pre-release/' - 'blog/pydantic-v2-final.md': 'https://blog.pydantic.dev/blog/2023/06/30/pydantic-v2-is-here/' - 'blog/pydantic-v2.md': 'https://blog.pydantic.dev/blog/2022/07/10/pydantic-v2-plan/' + 'blog/pydantic-v2-alpha.md': 'https://pydantic.dev/articles/pydantic-v2-alpha' + 'blog/pydantic-v2-final.md': 'https://pydantic.dev/articles/pydantic-v2-final' + 'blog/pydantic-v2.md': 'https://pydantic.dev/articles/pydantic-v2' + 'examples/secrets.md': 'api/types.md#pydantic.types.Secret' + 'examples/validators.md': 'examples/custom_validators.md' + 'architecture.md': 'internals/architecture.md' diff --git a/pdm.lock b/pdm.lock deleted file mode 100644 index 2b73e296be6..00000000000 --- a/pdm.lock +++ /dev/null @@ -1,2136 +0,0 @@ -# This file is @generated by PDM. -# It is not intended for manual editing. - 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-[[package]] -name = "zipp" -version = "3.19.2" -requires_python = ">=3.8" -summary = "Backport of pathlib-compatible object wrapper for zip files" -files = [ - {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"}, - {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"}, -] diff --git a/pydantic/__init__.py b/pydantic/__init__.py index 72ee9688473..3c3c130f0fb 100644 --- a/pydantic/__init__.py +++ b/pydantic/__init__.py @@ -1,5 +1,6 @@ import typing from importlib import import_module +from warnings import warn from ._migration import getattr_migration from .version import VERSION @@ -17,7 +18,6 @@ ) from . import dataclasses - from ._internal._generate_schema import GenerateSchema as GenerateSchema from .aliases import AliasChoices, AliasGenerator, AliasPath from .annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler from .config import ConfigDict, with_config @@ -177,6 +177,7 @@ 'Secret', 'SecretStr', 'SecretBytes', + 'SocketPath', 'StrictBool', 'StrictBytes', 'StrictInt', @@ -217,8 +218,6 @@ # annotated handlers 'GetCoreSchemaHandler', 'GetJsonSchemaHandler', - # generate schema from ._internal - 'GenerateSchema', # pydantic_core 'ValidationError', 'ValidationInfo', @@ -341,6 +340,7 @@ 'PaymentCardNumber': (__spec__.parent, '.types'), 'ByteSize': (__spec__.parent, '.types'), 'PastDate': (__spec__.parent, '.types'), + 'SocketPath': (__spec__.parent, '.types'), 'FutureDate': (__spec__.parent, '.types'), 'PastDatetime': (__spec__.parent, '.types'), 'FutureDatetime': (__spec__.parent, '.types'), @@ -372,8 +372,6 @@ # annotated handlers 'GetCoreSchemaHandler': (__spec__.parent, '.annotated_handlers'), 'GetJsonSchemaHandler': (__spec__.parent, '.annotated_handlers'), - # generate schema from ._internal - 'GenerateSchema': (__spec__.parent, '._internal._generate_schema'), # pydantic_core stuff 'ValidationError': ('pydantic_core', '.'), 'ValidationInfo': ('pydantic_core', '.core_schema'), @@ -389,14 +387,23 @@ 'parse_obj_as': (__spec__.parent, '.deprecated.tools'), 'schema_of': (__spec__.parent, '.deprecated.tools'), 'schema_json_of': (__spec__.parent, '.deprecated.tools'), + # deprecated dynamic imports 'FieldValidationInfo': ('pydantic_core', '.core_schema'), + 'GenerateSchema': (__spec__.parent, '._internal._generate_schema'), } -_deprecated_dynamic_imports = {'FieldValidationInfo'} +_deprecated_dynamic_imports = {'FieldValidationInfo', 'GenerateSchema'} _getattr_migration = getattr_migration(__name__) def __getattr__(attr_name: str) -> object: + if attr_name in _deprecated_dynamic_imports: + warn( + f'Importing {attr_name} from `pydantic` is deprecated. This feature is either no longer supported, or is not public.', + DeprecationWarning, + stacklevel=2, + ) + dynamic_attr = _dynamic_imports.get(attr_name) if dynamic_attr is None: return _getattr_migration(attr_name) diff --git a/pydantic/_internal/_config.py b/pydantic/_internal/_config.py index 2634fcd8809..6d491c29027 100644 --- a/pydantic/_internal/_config.py +++ b/pydantic/_internal/_config.py @@ -2,6 +2,7 @@ import warnings from contextlib import contextmanager +from re import Pattern from typing import ( TYPE_CHECKING, Any, @@ -18,7 +19,7 @@ from ..aliases import AliasGenerator from ..config import ConfigDict, ExtraValues, JsonDict, JsonEncoder, JsonSchemaExtraCallable from ..errors import PydanticUserError -from ..warnings import PydanticDeprecatedSince20 +from ..warnings import PydanticDeprecatedSince20, PydanticDeprecatedSince210 if not TYPE_CHECKING: # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915 @@ -76,10 +77,9 @@ class ConfigWrapper: # whether to validate default values during validation, default False validate_default: bool validate_return: bool - protected_namespaces: tuple[str, ...] + protected_namespaces: tuple[str | Pattern[str], ...] hide_input_in_errors: bool defer_build: bool - experimental_defer_build_mode: tuple[Literal['model', 'type_adapter'], ...] plugin_settings: dict[str, object] | None schema_generator: type[GenerateSchema] | None json_schema_serialization_defaults_required: bool @@ -123,7 +123,7 @@ def for_model(cls, bases: tuple[type[Any], ...], namespace: dict[str, Any], kwar config_dict_from_namespace = namespace.get('model_config') raw_annotations = namespace.get('__annotations__', {}) - if raw_annotations.get('model_config') and not config_dict_from_namespace: + if raw_annotations.get('model_config') and config_dict_from_namespace is None: raise PydanticUserError( '`model_config` cannot be used as a model field name. Use `model_config` for model configuration.', code='model-config-invalid-field-name', @@ -154,23 +154,28 @@ def __getattr__(self, name: str) -> Any: except KeyError: raise AttributeError(f'Config has no attribute {name!r}') from None - def core_config(self, obj: Any) -> core_schema.CoreConfig: - """Create a pydantic-core config, `obj` is just used to populate `title` if not set in config. - - Pass `obj=None` if you do not want to attempt to infer the `title`. + def core_config(self, title: str | None) -> core_schema.CoreConfig: + """Create a pydantic-core config. We don't use getattr here since we don't want to populate with defaults. Args: - obj: An object used to populate `title` if not set in config. + title: The title to use if not set in config. Returns: A `CoreConfig` object created from config. """ config = self.config_dict + if config.get('schema_generator') is not None: + warnings.warn( + 'The `schema_generator` setting has been deprecated since v2.10. This setting no longer has any effect.', + PydanticDeprecatedSince210, + stacklevel=2, + ) + core_config_values = { - 'title': config.get('title') or (obj and obj.__name__), + 'title': config.get('title') or title or None, 'extra_fields_behavior': config.get('extra'), 'allow_inf_nan': config.get('allow_inf_nan'), 'populate_by_name': config.get('populate_by_name'), @@ -258,13 +263,12 @@ def push(self, config_wrapper: ConfigWrapper | ConfigDict | None): ser_json_inf_nan='null', validate_default=False, validate_return=False, - protected_namespaces=('model_',), + protected_namespaces=('model_validate', 'model_dump'), hide_input_in_errors=False, json_encoders=None, defer_build=False, - experimental_defer_build_mode=('model',), - plugin_settings=None, schema_generator=None, + plugin_settings=None, json_schema_serialization_defaults_required=False, json_schema_mode_override=None, coerce_numbers_to_str=False, diff --git a/pydantic/_internal/_core_metadata.py b/pydantic/_internal/_core_metadata.py index b3caf7d807f..c8d452443cb 100644 --- a/pydantic/_internal/_core_metadata.py +++ b/pydantic/_internal/_core_metadata.py @@ -1,84 +1,95 @@ from __future__ import annotations as _annotations -import typing -from typing import Any, cast +from typing import TYPE_CHECKING, Any, TypedDict, cast +from warnings import warn -import typing_extensions - -if typing.TYPE_CHECKING: +if TYPE_CHECKING: from pydantic_core import CoreSchema + from ..config import JsonDict, JsonSchemaExtraCallable from ._schema_generation_shared import ( - CoreSchemaOrField, GetJsonSchemaFunction, ) -class CoreMetadata(typing_extensions.TypedDict, total=False): +class CoreMetadata(TypedDict, total=False): """A `TypedDict` for holding the metadata dict of the schema. Attributes: - pydantic_js_functions: List of JSON schema functions. + pydantic_js_functions: List of JSON schema functions that resolve refs during application. + pydantic_js_annotation_functions: List of JSON schema functions that don't resolve refs during application. pydantic_js_prefer_positional_arguments: Whether JSON schema generator will prefer positional over keyword arguments for an 'arguments' schema. - """ - - pydantic_js_functions: list[GetJsonSchemaFunction] - pydantic_js_annotation_functions: list[GetJsonSchemaFunction] - - # If `pydantic_js_prefer_positional_arguments` is True, the JSON schema generator will - # prefer positional over keyword arguments for an 'arguments' schema. - pydantic_js_prefer_positional_arguments: bool | None - pydantic_js_input_core_schema: CoreSchema | None + pydantic_js_input_core_schema: Schema associated with the input value for the associated + custom validation function. Only applies to before, plain, and wrap validators. + pydantic_js_udpates: key / value pair updates to apply to the JSON schema for a type. + pydantic_js_extra: WIP, either key/value pair updates to apply to the JSON schema, or a custom callable. + TODO: Perhaps we should move this structure to pydantic-core. At the moment, though, + it's easier to iterate on if we leave it in pydantic until we feel there is a semi-stable API. -class CoreMetadataHandler: - """Because the metadata field in pydantic_core is of type `Dict[str, Any]`, we can't assume much about its contents. - - This class is used to interact with the metadata field on a CoreSchema object in a consistent way throughout pydantic. - - TODO: We'd like to refactor the storage of json related metadata to be more explicit, and less functionally oriented. - This should make its way into our v2.10 release. It's inevitable that we need to store some json schema related information + TODO: It's unfortunate how functionally oriented JSON schema generation is, especially that which occurs during + the core schema generation process. It's inevitable that we need to store some json schema related information on core schemas, given that we generate JSON schemas directly from core schemas. That being said, debugging related issues is quite difficult when JSON schema information is disguised via dynamically defined functions. """ - __slots__ = ('_schema',) - - def __init__(self, schema: CoreSchemaOrField): - self._schema = schema - - metadata = schema.get('metadata') - if metadata is None: - schema['metadata'] = CoreMetadata() # type: ignore - elif not isinstance(metadata, dict): - raise TypeError(f'CoreSchema metadata should be a dict; got {metadata!r}.') - - @property - def metadata(self) -> CoreMetadata: - """Retrieves the metadata dict from the schema, initializing it to a dict if it is None - and raises an error if it is not a dict. - """ - metadata = self._schema.get('metadata') - if metadata is None: - self._schema['metadata'] = metadata = CoreMetadata() # type: ignore - if not isinstance(metadata, dict): - raise TypeError(f'CoreSchema metadata should be a dict; got {metadata!r}.') - return cast(CoreMetadata, metadata) - - -def build_metadata_dict( - *, # force keyword arguments to make it easier to modify this signature in a backwards-compatible way - js_functions: list[GetJsonSchemaFunction] | None = None, - js_annotation_functions: list[GetJsonSchemaFunction] | None = None, - js_prefer_positional_arguments: bool | None = None, - js_input_core_schema: CoreSchema | None = None, -) -> dict[str, Any]: - """Builds a dict to use as the metadata field of a CoreSchema object in a manner that is consistent with the `CoreMetadataHandler` class.""" - metadata = CoreMetadata( - pydantic_js_functions=js_functions or [], - pydantic_js_annotation_functions=js_annotation_functions or [], - pydantic_js_prefer_positional_arguments=js_prefer_positional_arguments, - pydantic_js_input_core_schema=js_input_core_schema, - ) - return {k: v for k, v in metadata.items() if v is not None} + pydantic_js_functions: list[GetJsonSchemaFunction] + pydantic_js_annotation_functions: list[GetJsonSchemaFunction] + pydantic_js_prefer_positional_arguments: bool + pydantic_js_input_core_schema: CoreSchema + pydantic_js_updates: JsonDict + pydantic_js_extra: JsonDict | JsonSchemaExtraCallable + + +def update_core_metadata( + core_metadata: Any, + /, + *, + pydantic_js_functions: list[GetJsonSchemaFunction] | None = None, + pydantic_js_annotation_functions: list[GetJsonSchemaFunction] | None = None, + pydantic_js_updates: JsonDict | None = None, + pydantic_js_extra: JsonDict | JsonSchemaExtraCallable | None = None, +) -> None: + from ..json_schema import PydanticJsonSchemaWarning + + """Update CoreMetadata instance in place. When we make modifications in this function, they + take effect on the `core_metadata` reference passed in as the first (and only) positional argument. + + First, cast to `CoreMetadata`, then finish with a cast to `dict[str, Any]` for core schema compatibility. + We do this here, instead of before / after each call to this function so that this typing hack + can be easily removed if/when we move `CoreMetadata` to `pydantic-core`. + + For parameter descriptions, see `CoreMetadata` above. + """ + core_metadata = cast(CoreMetadata, core_metadata) + + if pydantic_js_functions: + core_metadata.setdefault('pydantic_js_functions', []).extend(pydantic_js_functions) + + if pydantic_js_annotation_functions: + core_metadata.setdefault('pydantic_js_annotation_functions', []).extend(pydantic_js_annotation_functions) + + if pydantic_js_updates: + if (existing_updates := core_metadata.get('pydantic_js_updates')) is not None: + core_metadata['pydantic_js_updates'] = {**existing_updates, **pydantic_js_updates} + else: + core_metadata['pydantic_js_updates'] = pydantic_js_updates + + if pydantic_js_extra is not None: + existing_pydantic_js_extra = core_metadata.get('pydantic_js_extra') + if existing_pydantic_js_extra is None: + core_metadata['pydantic_js_extra'] = pydantic_js_extra + if isinstance(existing_pydantic_js_extra, dict): + if isinstance(pydantic_js_extra, dict): + core_metadata['pydantic_js_extra'] = {**existing_pydantic_js_extra, **pydantic_js_extra} + if callable(pydantic_js_extra): + warn( + 'Composing `dict` and `callable` type `json_schema_extra` is not supported.' + 'The `callable` type is being ignored.' + "If you'd like support for this behavior, please open an issue on pydantic.", + PydanticJsonSchemaWarning, + ) + if callable(existing_pydantic_js_extra): + # if ever there's a case of a callable, we'll just keep the last json schema extra spec + core_metadata['pydantic_js_extra'] = pydantic_js_extra diff --git a/pydantic/_internal/_core_utils.py b/pydantic/_internal/_core_utils.py index 493954c789c..e4c1aed9c1f 100644 --- a/pydantic/_internal/_core_utils.py +++ b/pydantic/_internal/_core_utils.py @@ -2,20 +2,16 @@ import os from collections import defaultdict -from typing import ( - Any, - Callable, - Hashable, - TypeVar, - Union, -) +from typing import Any, Callable, Hashable, TypeVar, Union from pydantic_core import CoreSchema, core_schema from pydantic_core import validate_core_schema as _validate_core_schema -from typing_extensions import TypeAliasType, TypeGuard, get_args, get_origin +from typing_extensions import TypeGuard, get_args, get_origin +from ..errors import PydanticUserError from . import _repr -from ._typing_extra import is_generic_alias +from ._core_metadata import CoreMetadata +from ._typing_extra import is_generic_alias, is_type_alias_type AnyFunctionSchema = Union[ core_schema.AfterValidatorFunctionSchema, @@ -44,10 +40,6 @@ """ Used in a `Tag` schema to specify the tag used for a discriminated union. """ -HAS_INVALID_SCHEMAS_METADATA_KEY = 'pydantic.internal.invalid' -"""Used to mark a schema that is invalid because it refers to a definition that was not yet defined when the -schema was first encountered. -""" def is_core_schema( @@ -89,7 +81,7 @@ def get_type_ref(type_: type[Any], args_override: tuple[type[Any], ...] | None = args = generic_metadata['args'] or args module_name = getattr(origin, '__module__', '') - if isinstance(origin, TypeAliasType): + if is_type_alias_type(origin): type_ref = f'{module_name}.{origin.__name__}:{id(origin)}' else: try: @@ -128,7 +120,7 @@ def _record_valid_refs(s: core_schema.CoreSchema, recurse: Recurse) -> core_sche defs[ref] = s return recurse(s, _record_valid_refs) - walk_core_schema(schema, _record_valid_refs) + walk_core_schema(schema, _record_valid_refs, copy=False) return defs @@ -146,10 +138,7 @@ def define_expected_missing_refs( expected_missing_refs = allowed_missing_refs.difference(refs) if expected_missing_refs: definitions: list[core_schema.CoreSchema] = [ - # TODO: Replace this with a (new) CoreSchema that, if present at any level, makes validation fail - # Issue: https://github.com/pydantic/pydantic-core/issues/619 - core_schema.none_schema(ref=ref, metadata={HAS_INVALID_SCHEMAS_METADATA_KEY: True}) - for ref in expected_missing_refs + core_schema.invalid_schema(ref=ref) for ref in expected_missing_refs ] return core_schema.definitions_schema(schema, definitions) return None @@ -160,14 +149,14 @@ def collect_invalid_schemas(schema: core_schema.CoreSchema) -> bool: def _is_schema_valid(s: core_schema.CoreSchema, recurse: Recurse) -> core_schema.CoreSchema: nonlocal invalid - if 'metadata' in s: - metadata = s['metadata'] - if HAS_INVALID_SCHEMAS_METADATA_KEY in metadata: - invalid = metadata[HAS_INVALID_SCHEMAS_METADATA_KEY] - return s + + if s['type'] == 'invalid': + invalid = True + return s + return recurse(s, _is_schema_valid) - walk_core_schema(schema, _is_schema_valid) + walk_core_schema(schema, _is_schema_valid, copy=False) return invalid @@ -180,10 +169,16 @@ def _is_schema_valid(s: core_schema.CoreSchema, recurse: Recurse) -> core_schema # TODO: Should we move _WalkCoreSchema into pydantic_core proper? # Issue: https://github.com/pydantic/pydantic-core/issues/615 +CoreSchemaT = TypeVar('CoreSchemaT') + class _WalkCoreSchema: - def __init__(self): + def __init__(self, *, copy: bool = True): self._schema_type_to_method = self._build_schema_type_to_method() + self._copy = copy + + def _copy_schema(self, schema: CoreSchemaT) -> CoreSchemaT: + return schema.copy() if self._copy else schema # pyright: ignore[reportAttributeAccessIssue] def _build_schema_type_to_method(self) -> dict[core_schema.CoreSchemaType, Recurse]: mapping: dict[core_schema.CoreSchemaType, Recurse] = {} @@ -197,7 +192,7 @@ def walk(self, schema: core_schema.CoreSchema, f: Walk) -> core_schema.CoreSchem return f(schema, self._walk) def _walk(self, schema: core_schema.CoreSchema, f: Walk) -> core_schema.CoreSchema: - schema = self._schema_type_to_method[schema['type']](schema.copy(), f) + schema = self._schema_type_to_method[schema['type']](self._copy_schema(schema), f) ser_schema: core_schema.SerSchema | None = schema.get('serialization') # type: ignore if ser_schema: schema['serialization'] = self._handle_ser_schemas(ser_schema, f) @@ -213,7 +208,7 @@ def _handle_ser_schemas(self, ser_schema: core_schema.SerSchema, f: Walk) -> cor schema: core_schema.CoreSchema | None = ser_schema.get('schema', None) return_schema: core_schema.CoreSchema | None = ser_schema.get('return_schema', None) if schema is not None or return_schema is not None: - ser_schema = ser_schema.copy() + ser_schema = self._copy_schema(ser_schema) if schema is not None: ser_schema['schema'] = self.walk(schema, f) # type: ignore if return_schema is not None: @@ -243,7 +238,7 @@ def handle_definitions_schema(self, schema: core_schema.DefinitionsSchema, f: Wa # This means we'd be returning a "trivial" definitions schema that just wrapped the inner schema return new_inner_schema - new_schema = schema.copy() + new_schema = self._copy_schema(schema) new_schema['schema'] = new_inner_schema new_schema['definitions'] = new_definitions return new_schema @@ -329,13 +324,13 @@ def handle_model_fields_schema(self, schema: core_schema.ModelFieldsSchema, f: W replaced_fields: dict[str, core_schema.ModelField] = {} replaced_computed_fields: list[core_schema.ComputedField] = [] for computed_field in schema.get('computed_fields', ()): - replaced_field = computed_field.copy() + replaced_field = self._copy_schema(computed_field) replaced_field['return_schema'] = self.walk(computed_field['return_schema'], f) replaced_computed_fields.append(replaced_field) if replaced_computed_fields: schema['computed_fields'] = replaced_computed_fields for k, v in schema['fields'].items(): - replaced_field = v.copy() + replaced_field = self._copy_schema(v) replaced_field['schema'] = self.walk(v['schema'], f) replaced_fields[k] = replaced_field schema['fields'] = replaced_fields @@ -347,14 +342,14 @@ def handle_typed_dict_schema(self, schema: core_schema.TypedDictSchema, f: Walk) schema['extras_schema'] = self.walk(extras_schema, f) replaced_computed_fields: list[core_schema.ComputedField] = [] for computed_field in schema.get('computed_fields', ()): - replaced_field = computed_field.copy() + replaced_field = self._copy_schema(computed_field) replaced_field['return_schema'] = self.walk(computed_field['return_schema'], f) replaced_computed_fields.append(replaced_field) if replaced_computed_fields: schema['computed_fields'] = replaced_computed_fields replaced_fields: dict[str, core_schema.TypedDictField] = {} for k, v in schema['fields'].items(): - replaced_field = v.copy() + replaced_field = self._copy_schema(v) replaced_field['schema'] = self.walk(v['schema'], f) replaced_fields[k] = replaced_field schema['fields'] = replaced_fields @@ -364,13 +359,13 @@ def handle_dataclass_args_schema(self, schema: core_schema.DataclassArgsSchema, replaced_fields: list[core_schema.DataclassField] = [] replaced_computed_fields: list[core_schema.ComputedField] = [] for computed_field in schema.get('computed_fields', ()): - replaced_field = computed_field.copy() + replaced_field = self._copy_schema(computed_field) replaced_field['return_schema'] = self.walk(computed_field['return_schema'], f) replaced_computed_fields.append(replaced_field) if replaced_computed_fields: schema['computed_fields'] = replaced_computed_fields for field in schema['fields']: - replaced_field = field.copy() + replaced_field = self._copy_schema(field) replaced_field['schema'] = self.walk(field['schema'], f) replaced_fields.append(replaced_field) schema['fields'] = replaced_fields @@ -379,7 +374,7 @@ def handle_dataclass_args_schema(self, schema: core_schema.DataclassArgsSchema, def handle_arguments_schema(self, schema: core_schema.ArgumentsSchema, f: Walk) -> core_schema.CoreSchema: replaced_arguments_schema: list[core_schema.ArgumentsParameter] = [] for param in schema['arguments_schema']: - replaced_param = param.copy() + replaced_param = self._copy_schema(param) replaced_param['schema'] = self.walk(param['schema'], f) replaced_arguments_schema.append(replaced_param) schema['arguments_schema'] = replaced_arguments_schema @@ -397,9 +392,10 @@ def handle_call_schema(self, schema: core_schema.CallSchema, f: Walk) -> core_sc _dispatch = _WalkCoreSchema().walk +_dispatch_no_copy = _WalkCoreSchema(copy=False).walk -def walk_core_schema(schema: core_schema.CoreSchema, f: Walk) -> core_schema.CoreSchema: +def walk_core_schema(schema: core_schema.CoreSchema, f: Walk, *, copy: bool = True) -> core_schema.CoreSchema: """Recursively traverse a CoreSchema. Args: @@ -409,11 +405,12 @@ def walk_core_schema(schema: core_schema.CoreSchema, f: Walk) -> core_schema.Cor (not the same one you passed into this function, one level down). 2. The "next" `f` to call. This lets you for example use `f=functools.partial(some_method, some_context)` to pass data down the recursive calls without using globals or other mutable state. + copy: Whether schema should be recursively copied. Returns: core_schema.CoreSchema: A processed CoreSchema. """ - return f(schema.copy(), _dispatch) + return f(schema.copy() if copy else schema, _dispatch if copy else _dispatch_no_copy) def simplify_schema_references(schema: core_schema.CoreSchema) -> core_schema.CoreSchema: # noqa: C901 @@ -458,11 +455,28 @@ def count_refs(s: core_schema.CoreSchema, recurse: Recurse) -> core_schema.CoreS return s current_recursion_ref_count[ref] += 1 - recurse(definitions[ref], count_refs) + if 'serialization' in s: + # Even though this is a `'definition-ref'` schema, there might + # be more references inside the serialization schema: + recurse(s, count_refs) + + next_s = definitions[ref] + visited: set[str] = set() + while next_s['type'] == 'definition-ref': + if next_s['schema_ref'] in visited: + raise PydanticUserError( + f'{ref} contains a circular reference to itself.', code='circular-reference-schema' + ) + + visited.add(next_s['schema_ref']) + ref_counts[next_s['schema_ref']] += 1 + next_s = definitions[next_s['schema_ref']] + + recurse(next_s, count_refs) current_recursion_ref_count[ref] -= 1 return s - schema = walk_core_schema(schema, count_refs) + schema = walk_core_schema(schema, count_refs, copy=False) assert all(c == 0 for c in current_recursion_ref_count.values()), 'this is a bug! please report it' @@ -475,19 +489,21 @@ def can_be_inlined(s: core_schema.DefinitionReferenceSchema, ref: str) -> bool: return False if 'metadata' in s: metadata = s['metadata'] - for k in ( - 'pydantic_js_functions', - 'pydantic_js_annotation_functions', + for k in [ + *CoreMetadata.__annotations__.keys(), 'pydantic.internal.union_discriminator', - ): + 'pydantic.internal.tagged_union_tag', + ]: if k in metadata: # we need to keep this as a ref return False return True def inline_refs(s: core_schema.CoreSchema, recurse: Recurse) -> core_schema.CoreSchema: - if s['type'] == 'definition-ref': + # Assume there are no infinite loops, because we already checked for that in `count_refs` + while s['type'] == 'definition-ref': ref = s['schema_ref'] + # Check if the reference is only used once, not involved in recursion and does not have # any extra keys (like 'serialization') if can_be_inlined(s, ref): @@ -498,14 +514,12 @@ def inline_refs(s: core_schema.CoreSchema, recurse: Recurse) -> core_schema.Core # in particular this is needed for `serialization` if 'serialization' in s: new['serialization'] = s['serialization'] - s = recurse(new, inline_refs) - return s + s = new else: - return recurse(s, inline_refs) - else: - return recurse(s, inline_refs) + break + return recurse(s, inline_refs) - schema = walk_core_schema(schema, inline_refs) + schema = walk_core_schema(schema, inline_refs, copy=False) def_values = [v for v in definitions.values() if ref_counts[v['ref']] > 0] # type: ignore diff --git a/pydantic/_internal/_dataclasses.py b/pydantic/_internal/_dataclasses.py index a537cd18b32..208220ccbad 100644 --- a/pydantic/_internal/_dataclasses.py +++ b/pydantic/_internal/_dataclasses.py @@ -6,7 +6,7 @@ import typing import warnings from functools import partial, wraps -from typing import Any, Callable, ClassVar +from typing import Any, ClassVar from pydantic_core import ( ArgsKwargs, @@ -19,26 +19,22 @@ from ..errors import PydanticUndefinedAnnotation from ..plugin._schema_validator import PluggableSchemaValidator, create_schema_validator from ..warnings import PydanticDeprecatedSince20 -from . import _config, _decorators, _typing_extra +from . import _config, _decorators from ._fields import collect_dataclass_fields from ._generate_schema import GenerateSchema from ._generics import get_standard_typevars_map from ._mock_val_ser import set_dataclass_mocks +from ._namespace_utils import NsResolver from ._schema_generation_shared import CallbackGetCoreSchemaHandler from ._signature import generate_pydantic_signature +from ._utils import LazyClassAttribute if typing.TYPE_CHECKING: + from _typeshed import DataclassInstance as StandardDataclass + from ..config import ConfigDict from ..fields import FieldInfo - class StandardDataclass(typing.Protocol): - __dataclass_fields__: ClassVar[dict[str, Any]] - __dataclass_params__: ClassVar[Any] # in reality `dataclasses._DataclassParams` - __post_init__: ClassVar[Callable[..., None]] - - def __init__(self, *args: object, **kwargs: object) -> None: - pass - class PydanticDataclass(StandardDataclass, typing.Protocol): """A protocol containing attributes only available once a class has been decorated as a Pydantic dataclass. @@ -68,18 +64,20 @@ class PydanticDataclass(StandardDataclass, typing.Protocol): def set_dataclass_fields( cls: type[StandardDataclass], - types_namespace: dict[str, Any] | None = None, + ns_resolver: NsResolver | None = None, config_wrapper: _config.ConfigWrapper | None = None, ) -> None: """Collect and set `cls.__pydantic_fields__`. Args: cls: The class. - types_namespace: The types namespace, defaults to `None`. + ns_resolver: Namespace resolver to use when getting dataclass annotations. config_wrapper: The config wrapper instance, defaults to `None`. """ typevars_map = get_standard_typevars_map(cls) - fields = collect_dataclass_fields(cls, types_namespace, typevars_map=typevars_map, config_wrapper=config_wrapper) + fields = collect_dataclass_fields( + cls, ns_resolver=ns_resolver, typevars_map=typevars_map, config_wrapper=config_wrapper + ) cls.__pydantic_fields__ = fields # type: ignore @@ -89,7 +87,8 @@ def complete_dataclass( config_wrapper: _config.ConfigWrapper, *, raise_errors: bool = True, - types_namespace: dict[str, Any] | None, + ns_resolver: NsResolver | None = None, + _force_build: bool = False, ) -> bool: """Finish building a pydantic dataclass. @@ -101,7 +100,10 @@ def complete_dataclass( cls: The class. config_wrapper: The config wrapper instance. raise_errors: Whether to raise errors, defaults to `True`. - types_namespace: The types namespace. + ns_resolver: The namespace resolver instance to use when collecting dataclass fields + and during schema building. + _force_build: Whether to force building the dataclass, no matter if + [`defer_build`][pydantic.config.ConfigDict.defer_build] is set. Returns: `True` if building a pydantic dataclass is successfully completed, `False` otherwise. @@ -109,42 +111,53 @@ def complete_dataclass( Raises: PydanticUndefinedAnnotation: If `raise_error` is `True` and there is an undefined annotations. """ + original_init = cls.__init__ + + # dataclass.__init__ must be defined here so its `__qualname__` can be changed since functions can't be copied, + # and so that the mock validator is used if building was deferred: + def __init__(__dataclass_self__: PydanticDataclass, *args: Any, **kwargs: Any) -> None: + __tracebackhide__ = True + s = __dataclass_self__ + s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s) + + __init__.__qualname__ = f'{cls.__qualname__}.__init__' + + cls.__init__ = __init__ # type: ignore + cls.__pydantic_config__ = config_wrapper.config_dict # type: ignore + + if not _force_build and config_wrapper.defer_build: + set_dataclass_mocks(cls, cls.__name__) + return False + if hasattr(cls, '__post_init_post_parse__'): warnings.warn( 'Support for `__post_init_post_parse__` has been dropped, the method will not be called', DeprecationWarning ) - if types_namespace is None: - types_namespace = _typing_extra.merge_cls_and_parent_ns(cls) - - set_dataclass_fields(cls, types_namespace, config_wrapper=config_wrapper) + set_dataclass_fields(cls, ns_resolver, config_wrapper=config_wrapper) typevars_map = get_standard_typevars_map(cls) gen_schema = GenerateSchema( config_wrapper, - types_namespace, - typevars_map, + ns_resolver=ns_resolver, + typevars_map=typevars_map, ) - # This needs to be called before we change the __init__ - sig = generate_pydantic_signature( - init=cls.__init__, - fields=cls.__pydantic_fields__, # type: ignore - config_wrapper=config_wrapper, - is_dataclass=True, + # set __signature__ attr only for model class, but not for its instances + # (because instances can define `__call__`, and `inspect.signature` shouldn't + # use the `__signature__` attribute and instead generate from `__call__`). + cls.__signature__ = LazyClassAttribute( + '__signature__', + partial( + generate_pydantic_signature, + # It's' important that we reference the original_init here + init=original_init, + fields=cls.__pydantic_fields__, # type: ignore + populate_by_name=config_wrapper.populate_by_name, + extra=config_wrapper.extra, + is_dataclass=True, + ), ) - - # dataclass.__init__ must be defined here so its `__qualname__` can be changed since functions can't be copied. - def __init__(__dataclass_self__: PydanticDataclass, *args: Any, **kwargs: Any) -> None: - __tracebackhide__ = True - s = __dataclass_self__ - s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s) - - __init__.__qualname__ = f'{cls.__qualname__}.__init__' - - cls.__init__ = __init__ # type: ignore - cls.__pydantic_config__ = config_wrapper.config_dict # type: ignore - cls.__signature__ = sig # type: ignore get_core_schema = getattr(cls, '__get_pydantic_core_schema__', None) try: if get_core_schema: @@ -164,7 +177,7 @@ def __init__(__dataclass_self__: PydanticDataclass, *args: Any, **kwargs: Any) - set_dataclass_mocks(cls, cls.__name__, f'`{e.name}`') return False - core_config = config_wrapper.core_config(cls) + core_config = config_wrapper.core_config(title=cls.__name__) try: schema = gen_schema.clean_schema(schema) @@ -203,7 +216,7 @@ def is_builtin_dataclass(_cls: type[Any]) -> TypeGuard[type[StandardDataclass]]: - `_cls` does not inherit from a processed pydantic dataclass (and thus have a `__pydantic_validator__`) - `_cls` does not have any annotations that are not dataclass fields e.g. - ```py + ```python import dataclasses import pydantic.dataclasses diff --git a/pydantic/_internal/_decorators.py b/pydantic/_internal/_decorators.py index 52a8ec17a6c..bb0f7207eae 100644 --- a/pydantic/_internal/_decorators.py +++ b/pydantic/_internal/_decorators.py @@ -15,7 +15,9 @@ from ..errors import PydanticUserError from ._core_utils import get_type_ref from ._internal_dataclass import slots_true +from ._namespace_utils import GlobalsNamespace, MappingNamespace from ._typing_extra import get_function_type_hints +from ._utils import can_be_positional if TYPE_CHECKING: from ..fields import ComputedFieldInfo @@ -548,9 +550,7 @@ def inspect_validator(validator: Callable[..., Any], mode: FieldValidatorModes) ) -def inspect_field_serializer( - serializer: Callable[..., Any], mode: Literal['plain', 'wrap'], computed_field: bool = False -) -> tuple[bool, bool]: +def inspect_field_serializer(serializer: Callable[..., Any], mode: Literal['plain', 'wrap']) -> tuple[bool, bool]: """Look at a field serializer function and determine if it is a field serializer, and whether it takes an info argument. @@ -559,8 +559,6 @@ def inspect_field_serializer( Args: serializer: The serializer function to inspect. mode: The serializer mode, either 'plain' or 'wrap'. - computed_field: When serializer is applied on computed_field. It doesn't require - info signature. Returns: Tuple of (is_field_serializer, info_arg). @@ -587,13 +585,8 @@ def inspect_field_serializer( f'Unrecognized field_serializer function signature for {serializer} with `mode={mode}`:{sig}', code='field-serializer-signature', ) - if info_arg and computed_field: - raise PydanticUserError( - 'field_serializer on computed_field does not use info signature', code='field-serializer-signature' - ) - else: - return is_field_serializer, info_arg + return is_field_serializer, info_arg def inspect_annotated_serializer(serializer: Callable[..., Any], mode: Literal['plain', 'wrap']) -> bool: @@ -761,7 +754,10 @@ def unwrap_wrapped_function( def get_function_return_type( - func: Any, explicit_return_type: Any, types_namespace: dict[str, Any] | None = None + func: Any, + explicit_return_type: Any, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, ) -> Any: """Get the function return type. @@ -771,7 +767,8 @@ def get_function_return_type( Args: func: The function to get its return type. explicit_return_type: The explicit return type. - types_namespace: The types namespace, defaults to `None`. + globalns: The globals namespace to use during type annotation evaluation. + localns: The locals namespace to use during type annotation evaluation. Returns: The function return type. @@ -779,7 +776,10 @@ def get_function_return_type( if explicit_return_type is PydanticUndefined: # try to get it from the type annotation hints = get_function_type_hints( - unwrap_wrapped_function(func), include_keys={'return'}, types_namespace=types_namespace + unwrap_wrapped_function(func), + include_keys={'return'}, + globalns=globalns, + localns=localns, ) return hints.get('return', PydanticUndefined) else: @@ -804,14 +804,10 @@ def count_positional_required_params(sig: Signature) -> int: # First argument is the value being validated/serialized, and can have a default value # (e.g. `float`, which has signature `(x=0, /)`). We assume other parameters (the info arg # for instance) should be required, and thus without any default value. - and (param.default is Parameter.empty or param == parameters[0]) + and (param.default is Parameter.empty or param is parameters[0]) ) -def can_be_positional(param: Parameter) -> bool: - return param.kind in (Parameter.POSITIONAL_ONLY, Parameter.POSITIONAL_OR_KEYWORD) - - def ensure_property(f: Any) -> Any: """Ensure that a function is a `property` or `cached_property`, or is a valid descriptor. diff --git a/pydantic/_internal/_decorators_v1.py b/pydantic/_internal/_decorators_v1.py index 0957e018bab..2dfa3f2a985 100644 --- a/pydantic/_internal/_decorators_v1.py +++ b/pydantic/_internal/_decorators_v1.py @@ -9,7 +9,7 @@ from typing_extensions import Protocol from ..errors import PydanticUserError -from ._decorators import can_be_positional +from ._utils import can_be_positional class V1OnlyValueValidator(Protocol): diff --git a/pydantic/_internal/_discriminated_union.py b/pydantic/_internal/_discriminated_union.py index a090c36f0de..29a50a5ad79 100644 --- a/pydantic/_internal/_discriminated_union.py +++ b/pydantic/_internal/_discriminated_union.py @@ -55,7 +55,7 @@ def inner(s: core_schema.CoreSchema, recurse: _core_utils.Recurse) -> core_schem s = apply_discriminator(s, discriminator, global_definitions) return s - return _core_utils.walk_core_schema(schema, inner) + return _core_utils.walk_core_schema(schema, inner, copy=False) def apply_discriminator( @@ -161,7 +161,7 @@ def __init__(self, discriminator: str, definitions: dict[str, core_schema.CoreSc # in the output TaggedUnionSchema that will replace the union from the input schema self._tagged_union_choices: dict[Hashable, core_schema.CoreSchema] = {} - # `_used` is changed to True after applying the discriminator to prevent accidental re-use + # `_used` is changed to True after applying the discriminator to prevent accidental reuse self._used = False def apply(self, schema: core_schema.CoreSchema) -> core_schema.CoreSchema: diff --git a/pydantic/_internal/_fields.py b/pydantic/_internal/_fields.py index 170464016f7..688b69d65b1 100644 --- a/pydantic/_internal/_fields.py +++ b/pydantic/_internal/_fields.py @@ -3,13 +3,14 @@ from __future__ import annotations as _annotations import dataclasses -import sys import warnings from copy import copy from functools import lru_cache -from typing import TYPE_CHECKING, Any +from inspect import Parameter, ismethoddescriptor, signature +from typing import TYPE_CHECKING, Any, Callable, Pattern from pydantic_core import PydanticUndefined +from typing_extensions import TypeIs from pydantic.errors import PydanticUserError @@ -17,8 +18,9 @@ from ._config import ConfigWrapper from ._docs_extraction import extract_docstrings_from_cls from ._import_utils import import_cached_base_model, import_cached_field_info +from ._namespace_utils import NsResolver from ._repr import Representation -from ._typing_extra import get_cls_type_hints_lenient, is_classvar, is_finalvar +from ._utils import can_be_positional if TYPE_CHECKING: from annotated_types import BaseMetadata @@ -73,7 +75,7 @@ def collect_model_fields( # noqa: C901 cls: type[BaseModel], bases: tuple[type[Any], ...], config_wrapper: ConfigWrapper, - types_namespace: dict[str, Any] | None, + ns_resolver: NsResolver | None, *, typevars_map: dict[Any, Any] | None = None, ) -> tuple[dict[str, FieldInfo], set[str]]: @@ -87,7 +89,7 @@ def collect_model_fields( # noqa: C901 cls: BaseModel or dataclass. bases: Parents of the class, generally `cls.__bases__`. config_wrapper: The config wrapper instance. - types_namespace: Optional extra namespace to look for types in. + ns_resolver: Namespace resolver to use when getting model annotations. typevars_map: A dictionary mapping type variables to their concrete types. Returns: @@ -102,7 +104,12 @@ def collect_model_fields( # noqa: C901 BaseModel = import_cached_base_model() FieldInfo_ = import_cached_field_info() - type_hints = get_cls_type_hints_lenient(cls, types_namespace) + parent_fields_lookup: dict[str, FieldInfo] = {} + for base in reversed(bases): + if model_fields := getattr(base, '__pydantic_fields__', None): + parent_fields_lookup.update(model_fields) + + type_hints = _typing_extra.get_cls_type_hints(cls, ns_resolver=ns_resolver, lenient=True) # https://docs.python.org/3/howto/annotations.html#accessing-the-annotations-dict-of-an-object-in-python-3-9-and-older # annotations is only used for finding fields in parent classes @@ -116,26 +123,39 @@ def collect_model_fields( # noqa: C901 # Note: we may need to change this logic if/when we introduce a `BareModel` class with no # protected namespaces (where `model_config` might be allowed as a field name) continue + for protected_namespace in config_wrapper.protected_namespaces: - if ann_name.startswith(protected_namespace): + ns_violation: bool = False + if isinstance(protected_namespace, Pattern): + ns_violation = protected_namespace.match(ann_name) is not None + elif isinstance(protected_namespace, str): + ns_violation = ann_name.startswith(protected_namespace) + + if ns_violation: for b in bases: if hasattr(b, ann_name): - if not (issubclass(b, BaseModel) and ann_name in b.model_fields): + if not (issubclass(b, BaseModel) and ann_name in getattr(b, '__pydantic_fields__', {})): raise NameError( f'Field "{ann_name}" conflicts with member {getattr(b, ann_name)}' f' of protected namespace "{protected_namespace}".' ) else: - valid_namespaces = tuple( - x for x in config_wrapper.protected_namespaces if not ann_name.startswith(x) - ) + valid_namespaces = () + for pn in config_wrapper.protected_namespaces: + if isinstance(pn, Pattern): + if not pn.match(ann_name): + valid_namespaces += (f're.compile({pn.pattern})',) + else: + if not ann_name.startswith(pn): + valid_namespaces += (pn,) + warnings.warn( f'Field "{ann_name}" in {cls.__name__} has conflict with protected namespace "{protected_namespace}".' '\n\nYou may be able to resolve this warning by setting' f" `model_config['protected_namespaces'] = {valid_namespaces}`.", UserWarning, ) - if is_classvar(ann_type): + if _typing_extra.is_classvar_annotation(ann_type): class_vars.add(ann_name) continue if _is_finalvar_with_default_val(ann_type, getattr(cls, ann_name, PydanticUndefined)): @@ -185,13 +205,10 @@ def collect_model_fields( # noqa: C901 else: # if field has no default value and is not in __annotations__ this means that it is # defined in a base class and we can take it from there - model_fields_lookup: dict[str, FieldInfo] = {} - for x in cls.__bases__[::-1]: - model_fields_lookup.update(getattr(x, 'model_fields', {})) - if ann_name in model_fields_lookup: + if ann_name in parent_fields_lookup: # The field was present on one of the (possibly multiple) base classes # copy the field to make sure typevar substitutions don't cause issues with the base classes - field_info = copy(model_fields_lookup[ann_name]) + field_info = copy(parent_fields_lookup[ann_name]) else: # The field was not found on any base classes; this seems to be caused by fields not getting # generated thanks to models not being fully defined while initializing recursive models. @@ -199,6 +216,13 @@ def collect_model_fields( # noqa: C901 field_info = FieldInfo_.from_annotation(ann_type) else: _warn_on_nested_alias_in_annotation(ann_type, ann_name) + if isinstance(default, FieldInfo_) and ismethoddescriptor(default.default): + # the `getattr` call above triggers a call to `__get__` for descriptors, so we do + # the same if the `= field(default=...)` form is used. Note that we only do this + # for method descriptors for now, we might want to extend this to any descriptor + # in the future (by simply checking for `hasattr(default.default, '__get__')`). + default.default = default.default.__get__(None, cls) + field_info = FieldInfo_.from_annotated_attribute(ann_type, default) # attributes which are fields are removed from the class namespace: # 1. To match the behaviour of annotation-only fields @@ -217,10 +241,9 @@ def collect_model_fields( # noqa: C901 if typevars_map: for field in fields.values(): - field.apply_typevars_map(typevars_map, types_namespace) + field.apply_typevars_map(typevars_map) _update_fields_from_docstrings(cls, fields, config_wrapper) - return fields, class_vars @@ -243,7 +266,7 @@ def _warn_on_nested_alias_in_annotation(ann_type: type[Any], ann_name: str) -> N def _is_finalvar_with_default_val(type_: type[Any], val: Any) -> bool: FieldInfo = import_cached_field_info() - if not is_finalvar(type_): + if not _typing_extra.is_finalvar(type_): return False elif val is PydanticUndefined: return False @@ -255,8 +278,8 @@ def _is_finalvar_with_default_val(type_: type[Any], val: Any) -> bool: def collect_dataclass_fields( cls: type[StandardDataclass], - types_namespace: dict[str, Any] | None, *, + ns_resolver: NsResolver | None = None, typevars_map: dict[Any, Any] | None = None, config_wrapper: ConfigWrapper | None = None, ) -> dict[str, FieldInfo]: @@ -264,7 +287,8 @@ def collect_dataclass_fields( Args: cls: dataclass. - types_namespace: Optional extra namespace to look for types in. + ns_resolver: Namespace resolver to use when getting dataclass annotations. + Defaults to an empty instance. typevars_map: A dictionary mapping type variables to their concrete types. config_wrapper: The config wrapper instance. @@ -274,50 +298,66 @@ def collect_dataclass_fields( FieldInfo_ = import_cached_field_info() fields: dict[str, FieldInfo] = {} - dataclass_fields: dict[str, dataclasses.Field] = cls.__dataclass_fields__ - cls_localns = dict(vars(cls)) # this matches get_cls_type_hints_lenient, but all tests pass with `= None` instead + ns_resolver = ns_resolver or NsResolver() + dataclass_fields = cls.__dataclass_fields__ + + # The logic here is similar to `_typing_extra.get_cls_type_hints`, + # although we do it manually as stdlib dataclasses already have annotations + # collected in each class: + for base in reversed(cls.__mro__): + if not dataclasses.is_dataclass(base): + continue - source_module = sys.modules.get(cls.__module__) - if source_module is not None: - types_namespace = {**source_module.__dict__, **(types_namespace or {})} + with ns_resolver.push(base): + for ann_name, dataclass_field in dataclass_fields.items(): + if ann_name not in base.__dict__.get('__annotations__', {}): + # `__dataclass_fields__`contains every field, even the ones from base classes. + # Only collect the ones defined on `base`. + continue - for ann_name, dataclass_field in dataclass_fields.items(): - ann_type = _typing_extra.eval_type_lenient(dataclass_field.type, types_namespace, cls_localns) - if is_classvar(ann_type): - continue + globalns, localns = ns_resolver.types_namespace + ann_type = _typing_extra.eval_type(dataclass_field.type, globalns, localns, lenient=True) - if ( - not dataclass_field.init - and dataclass_field.default == dataclasses.MISSING - and dataclass_field.default_factory == dataclasses.MISSING - ): - # TODO: We should probably do something with this so that validate_assignment behaves properly - # Issue: https://github.com/pydantic/pydantic/issues/5470 - continue + if _typing_extra.is_classvar_annotation(ann_type): + continue - if isinstance(dataclass_field.default, FieldInfo_): - if dataclass_field.default.init_var: - if dataclass_field.default.init is False: - raise PydanticUserError( - f'Dataclass field {ann_name} has init=False and init_var=True, but these are mutually exclusive.', - code='clashing-init-and-init-var', - ) + if ( + not dataclass_field.init + and dataclass_field.default is dataclasses.MISSING + and dataclass_field.default_factory is dataclasses.MISSING + ): + # TODO: We should probably do something with this so that validate_assignment behaves properly + # Issue: https://github.com/pydantic/pydantic/issues/5470 + continue - # TODO: same note as above re validate_assignment - continue - field_info = FieldInfo_.from_annotated_attribute(ann_type, dataclass_field.default) - else: - field_info = FieldInfo_.from_annotated_attribute(ann_type, dataclass_field) + if isinstance(dataclass_field.default, FieldInfo_): + if dataclass_field.default.init_var: + if dataclass_field.default.init is False: + raise PydanticUserError( + f'Dataclass field {ann_name} has init=False and init_var=True, but these are mutually exclusive.', + code='clashing-init-and-init-var', + ) - fields[ann_name] = field_info + # TODO: same note as above re validate_assignment + continue + field_info = FieldInfo_.from_annotated_attribute(ann_type, dataclass_field.default) + else: + field_info = FieldInfo_.from_annotated_attribute(ann_type, dataclass_field) - if field_info.default is not PydanticUndefined and isinstance(getattr(cls, ann_name, field_info), FieldInfo_): - # We need this to fix the default when the "default" from __dataclass_fields__ is a pydantic.FieldInfo - setattr(cls, ann_name, field_info.default) + fields[ann_name] = field_info + + if field_info.default is not PydanticUndefined and isinstance( + getattr(cls, ann_name, field_info), FieldInfo_ + ): + # We need this to fix the default when the "default" from __dataclass_fields__ is a pydantic.FieldInfo + setattr(cls, ann_name, field_info.default) if typevars_map: for field in fields.values(): - field.apply_typevars_map(typevars_map, types_namespace) + # We don't pass any ns, as `field.annotation` + # was already evaluated. TODO: is this method relevant? + # Can't we juste use `_generics.replace_types`? + field.apply_typevars_map(typevars_map) if config_wrapper is not None: _update_fields_from_docstrings(cls, fields, config_wrapper) @@ -331,3 +371,19 @@ def is_valid_field_name(name: str) -> bool: def is_valid_privateattr_name(name: str) -> bool: return name.startswith('_') and not name.startswith('__') + + +def takes_validated_data_argument( + default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any], +) -> TypeIs[Callable[[dict[str, Any]], Any]]: + """Whether the provided default factory callable has a validated data parameter.""" + try: + sig = signature(default_factory) + except (ValueError, TypeError): + # `inspect.signature` might not be able to infer a signature, e.g. with C objects. + # In this case, we assume no data argument is present: + return False + + parameters = list(sig.parameters.values()) + + return len(parameters) == 1 and can_be_positional(parameters[0]) and parameters[0].default is Parameter.empty diff --git a/pydantic/_internal/_generate_schema.py b/pydantic/_internal/_generate_schema.py index 40ac2ddbdd9..bf5d4d9ffc7 100644 --- a/pydantic/_internal/_generate_schema.py +++ b/pydantic/_internal/_generate_schema.py @@ -12,16 +12,17 @@ import sys import typing import warnings -from contextlib import ExitStack, contextmanager +from contextlib import contextmanager from copy import copy, deepcopy from decimal import Decimal from enum import Enum +from fractions import Fraction from functools import partial from inspect import Parameter, _ParameterKind, signature from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network from itertools import chain from operator import attrgetter -from types import FunctionType, LambdaType, MethodType +from types import BuiltinFunctionType, BuiltinMethodType, FunctionType, LambdaType, MethodType from typing import ( TYPE_CHECKING, Any, @@ -56,7 +57,7 @@ from ..aliases import AliasChoices, AliasGenerator, AliasPath from ..annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler -from ..config import ConfigDict, JsonDict, JsonEncoder +from ..config import ConfigDict, JsonDict, JsonEncoder, JsonSchemaExtraCallable from ..errors import PydanticSchemaGenerationError, PydanticUndefinedAnnotation, PydanticUserError from ..functional_validators import AfterValidator, BeforeValidator, FieldValidatorModes, PlainValidator, WrapValidator from ..json_schema import JsonSchemaValue @@ -64,9 +65,8 @@ from ..warnings import PydanticDeprecatedSince20 from . import _core_utils, _decorators, _discriminated_union, _known_annotated_metadata, _typing_extra from ._config import ConfigWrapper, ConfigWrapperStack -from ._core_metadata import CoreMetadataHandler, build_metadata_dict +from ._core_metadata import update_core_metadata from ._core_utils import ( - CoreSchemaOrField, collect_invalid_schemas, define_expected_missing_refs, get_ref, @@ -91,13 +91,13 @@ inspect_validator, ) from ._docs_extraction import extract_docstrings_from_cls -from ._fields import collect_dataclass_fields +from ._fields import collect_dataclass_fields, takes_validated_data_argument from ._forward_ref import PydanticRecursiveRef from ._generics import get_standard_typevars_map, has_instance_in_type, recursively_defined_type_refs, replace_types from ._import_utils import import_cached_base_model, import_cached_field_info from ._mock_val_ser import MockCoreSchema +from ._namespace_utils import NamespacesTuple, NsResolver from ._schema_generation_shared import CallbackGetCoreSchemaHandler -from ._typing_extra import get_cls_type_hints_lenient, is_annotated, is_finalvar, is_self_type, is_zoneinfo_type from ._utils import lenient_issubclass, smart_deepcopy if TYPE_CHECKING: @@ -149,6 +149,19 @@ ] DEQUE_TYPES: list[type] = [collections.deque, typing.Deque] +# Note: This does not play very well with type checkers. For example, +# `a: LambdaType = lambda x: x` will raise a type error by Pyright. +ValidateCallSupportedTypes = Union[ + LambdaType, + FunctionType, + MethodType, + BuiltinFunctionType, + BuiltinMethodType, + partial, +] + +VALIDATE_CALL_SUPPORTED_TYPES = get_args(ValidateCallSupportedTypes) + _mode_to_validator: dict[ FieldValidatorModes, type[BeforeValidator | AfterValidator | PlainValidator | WrapValidator] ] = {'before': BeforeValidator, 'after': AfterValidator, 'plain': PlainValidator, 'wrap': WrapValidator} @@ -245,43 +258,17 @@ def apply_each_item_validators( return schema -def modify_model_json_schema( - schema_or_field: CoreSchemaOrField, - handler: GetJsonSchemaHandler, - *, - cls: Any, - title: str | None = None, -) -> JsonSchemaValue: - """Add title and description for model-like classes' JSON schema. - - Args: - schema_or_field: The schema data to generate a JSON schema from. - handler: The `GetCoreSchemaHandler` instance. - cls: The model-like class. - title: The title to set for the model's schema, defaults to the model's name - - Returns: - JsonSchemaValue: The updated JSON schema. - """ - from ..dataclasses import is_pydantic_dataclass - from ..root_model import RootModel - from ._dataclasses import is_builtin_dataclass - - BaseModel = import_cached_base_model() - - json_schema = handler(schema_or_field) - original_schema = handler.resolve_ref_schema(json_schema) - if title is not None: - original_schema['title'] = title - elif 'title' not in original_schema: - original_schema['title'] = cls.__name__ - # BaseModel + Dataclass; don't use cls.__doc__ as it will contain the verbose class signature by default - docstring = None if cls is BaseModel or is_builtin_dataclass(cls) or is_pydantic_dataclass(cls) else cls.__doc__ - if docstring and 'description' not in original_schema: - original_schema['description'] = inspect.cleandoc(docstring) - elif issubclass(cls, RootModel) and cls.model_fields['root'].description: - original_schema['description'] = cls.model_fields['root'].description - return json_schema +def _extract_json_schema_info_from_field_info( + info: FieldInfo | ComputedFieldInfo, +) -> tuple[JsonDict | None, JsonDict | JsonSchemaExtraCallable | None]: + json_schema_updates = { + 'title': info.title, + 'description': info.description, + 'deprecated': bool(info.deprecated) or info.deprecated == '' or None, + 'examples': to_jsonable_python(info.examples), + } + json_schema_updates = {k: v for k, v in json_schema_updates.items() if v is not None} + return (json_schema_updates or None, info.json_schema_extra) JsonEncoders = Dict[Type[Any], JsonEncoder] @@ -321,30 +308,6 @@ def _add_custom_serialization_from_json_encoders( return schema -TypesNamespace = Union[Dict[str, Any], None] - - -class TypesNamespaceStack: - """A stack of types namespaces.""" - - def __init__(self, types_namespace: TypesNamespace): - self._types_namespace_stack: list[TypesNamespace] = [types_namespace] - - @property - def tail(self) -> TypesNamespace: - return self._types_namespace_stack[-1] - - @contextmanager - def push(self, for_type: type[Any]): - types_namespace = _typing_extra.get_module_ns_of(for_type).copy() - types_namespace.update(self.tail or {}) - self._types_namespace_stack.append(types_namespace) - try: - yield - finally: - self._types_namespace_stack.pop() - - def _get_first_non_null(a: Any, b: Any) -> Any: """Return the first argument if it is not None, otherwise return the second argument. @@ -359,7 +322,7 @@ class GenerateSchema: __slots__ = ( '_config_wrapper_stack', - '_types_namespace_stack', + '_ns_resolver', '_typevars_map', 'field_name_stack', 'model_type_stack', @@ -369,62 +332,37 @@ class GenerateSchema: def __init__( self, config_wrapper: ConfigWrapper, - types_namespace: dict[str, Any] | None, + ns_resolver: NsResolver | None = None, typevars_map: dict[Any, Any] | None = None, ) -> None: - # we need a stack for recursing into child models + # we need a stack for recursing into nested models self._config_wrapper_stack = ConfigWrapperStack(config_wrapper) - self._types_namespace_stack = TypesNamespaceStack(types_namespace) + self._ns_resolver = ns_resolver or NsResolver() self._typevars_map = typevars_map self.field_name_stack = _FieldNameStack() self.model_type_stack = _ModelTypeStack() self.defs = _Definitions() - @classmethod - def __from_parent( - cls, - config_wrapper_stack: ConfigWrapperStack, - types_namespace_stack: TypesNamespaceStack, - model_type_stack: _ModelTypeStack, - typevars_map: dict[Any, Any] | None, - defs: _Definitions, - ) -> GenerateSchema: - obj = cls.__new__(cls) - obj._config_wrapper_stack = config_wrapper_stack - obj._types_namespace_stack = types_namespace_stack - obj.model_type_stack = model_type_stack - obj._typevars_map = typevars_map - obj.field_name_stack = _FieldNameStack() - obj.defs = defs - return obj + def __init_subclass__(cls) -> None: + super().__init_subclass__() + warnings.warn( + 'Subclassing `GenerateSchema` is not supported. The API is highly subject to change in minor versions.', + UserWarning, + stacklevel=2, + ) @property def _config_wrapper(self) -> ConfigWrapper: return self._config_wrapper_stack.tail @property - def _types_namespace(self) -> dict[str, Any] | None: - return self._types_namespace_stack.tail - - @property - def _current_generate_schema(self) -> GenerateSchema: - cls = self._config_wrapper.schema_generator or GenerateSchema - return cls.__from_parent( - self._config_wrapper_stack, - self._types_namespace_stack, - self.model_type_stack, - self._typevars_map, - self.defs, - ) + def _types_namespace(self) -> NamespacesTuple: + return self._ns_resolver.types_namespace @property def _arbitrary_types(self) -> bool: return self._config_wrapper.arbitrary_types_allowed - def str_schema(self) -> CoreSchema: - """Generate a CoreSchema for `str`""" - return core_schema.str_schema() - # the following methods can be overridden but should be considered # unstable / private APIs def _list_schema(self, items_type: Any) -> CoreSchema: @@ -475,7 +413,7 @@ def get_json_schema(schema: CoreSchema, handler: GetJsonSchemaHandler) -> JsonSc return json_schema # we don't want to add the missing to the schema if it's the default one - default_missing = getattr(enum_type._missing_, '__func__', None) == Enum._missing_.__func__ # type: ignore + default_missing = getattr(enum_type._missing_, '__func__', None) is Enum._missing_.__func__ # pyright: ignore[reportFunctionMemberAccess] enum_schema = core_schema.enum_schema( enum_type, cases, @@ -512,9 +450,9 @@ def get_json_schema_no_cases(_, handler: GetJsonSchemaHandler) -> JsonSchemaValu ) def _ip_schema(self, tp: Any) -> CoreSchema: - from ._validators import IP_VALIDATOR_LOOKUP + from ._validators import IP_VALIDATOR_LOOKUP, IpType - ip_type_json_schema_format = { + ip_type_json_schema_format: dict[type[IpType], str] = { IPv4Address: 'ipv4', IPv4Network: 'ipv4network', IPv4Interface: 'ipv4interface', @@ -523,11 +461,13 @@ def _ip_schema(self, tp: Any) -> CoreSchema: IPv6Interface: 'ipv6interface', } - def ser_ip(ip: Any) -> str: + def ser_ip(ip: Any, info: core_schema.SerializationInfo) -> str | IpType: if not isinstance(ip, (tp, str)): raise PydanticSerializationUnexpectedValue( f"Expected `{tp}` but got `{type(ip)}` with value `'{ip}'` - serialized value may not be as expected." ) + if info.mode == 'python': + return ip return str(ip) return core_schema.lax_or_strict_schema( @@ -536,10 +476,27 @@ def ser_ip(ip: Any) -> str: json_schema=core_schema.no_info_after_validator_function(tp, core_schema.str_schema()), python_schema=core_schema.is_instance_schema(tp), ), - serialization=core_schema.plain_serializer_function_ser_schema(ser_ip), - metadata=build_metadata_dict( - js_functions=[lambda _1, _2: {'type': 'string', 'format': ip_type_json_schema_format[tp]}] + serialization=core_schema.plain_serializer_function_ser_schema(ser_ip, info_arg=True, when_used='always'), + metadata={ + 'pydantic_js_functions': [lambda _1, _2: {'type': 'string', 'format': ip_type_json_schema_format[tp]}] + }, + ) + + def _fraction_schema(self) -> CoreSchema: + """Support for [`fractions.Fraction`][fractions.Fraction].""" + from ._validators import fraction_validator + + # TODO: note, this is a fairly common pattern, re lax / strict for attempted type coercion, + # can we use a helper function to reduce boilerplate? + return core_schema.lax_or_strict_schema( + lax_schema=core_schema.no_info_plain_validator_function(fraction_validator), + strict_schema=core_schema.json_or_python_schema( + json_schema=core_schema.no_info_plain_validator_function(fraction_validator), + python_schema=core_schema.is_instance_schema(Fraction), ), + # use str serialization to guarantee round trip behavior + serialization=core_schema.to_string_ser_schema(when_used='always'), + metadata={'pydantic_js_functions': [lambda _1, _2: {'type': 'string', 'format': 'fraction'}]}, ) def _arbitrary_type_schema(self, tp: Any) -> CoreSchema: @@ -607,15 +564,15 @@ def collect_definitions(self, schema: CoreSchema) -> CoreSchema: ) def _add_js_function(self, metadata_schema: CoreSchema, js_function: Callable[..., Any]) -> None: - metadata = CoreMetadataHandler(metadata_schema).metadata + metadata = metadata_schema.get('metadata', {}) pydantic_js_functions = metadata.setdefault('pydantic_js_functions', []) # because of how we generate core schemas for nested generic models # we can end up adding `BaseModel.__get_pydantic_json_schema__` multiple times # this check may fail to catch duplicates if the function is a `functools.partial` - # or something like that - # but if it does it'll fail by inserting the duplicate + # or something like that, but if it does it'll fail by inserting the duplicate if js_function not in pydantic_js_functions: pydantic_js_functions.append(js_function) + metadata_schema['metadata'] = metadata def generate_schema( self, @@ -670,7 +627,7 @@ def _model_schema(self, cls: type[BaseModel]) -> core_schema.CoreSchema: if maybe_schema is not None: return maybe_schema - fields = cls.model_fields + fields = getattr(cls, '__pydantic_fields__', {}) decorators = cls.__pydantic_decorators__ computed_fields = decorators.computed_fields check_decorator_fields_exist( @@ -682,15 +639,10 @@ def _model_schema(self, cls: type[BaseModel]) -> core_schema.CoreSchema: {*fields.keys(), *computed_fields.keys()}, ) config_wrapper = ConfigWrapper(cls.model_config, check=False) - core_config = config_wrapper.core_config(cls) - title = self._get_model_title_from_config(cls, config_wrapper) - metadata = build_metadata_dict(js_functions=[partial(modify_model_json_schema, cls=cls, title=title)]) - + core_config = config_wrapper.core_config(title=cls.__name__) model_validators = decorators.model_validators.values() - with self._config_wrapper_stack.push(config_wrapper), self._types_namespace_stack.push(cls): - self = self._current_generate_schema - + with self._config_wrapper_stack.push(config_wrapper), self._ns_resolver.push(cls): extras_schema = None if core_config.get('extra_fields_behavior') == 'allow': assert cls.__mro__[0] is cls @@ -705,7 +657,7 @@ def _model_schema(self, cls: type[BaseModel]) -> core_schema.CoreSchema: _typing_extra._make_forward_ref( extras_annotation, is_argument=False, is_class=True ), - self._types_namespace, + *self._types_namespace, ) tp = get_origin(extras_annotation) if tp not in (Dict, dict): @@ -716,10 +668,12 @@ def _model_schema(self, cls: type[BaseModel]) -> core_schema.CoreSchema: extras_annotation, required=True, )[1] - if extra_items_type is not Any: + if not _typing_extra.is_any(extra_items_type): extras_schema = self.generate_schema(extra_items_type) break + generic_origin: type[BaseModel] | None = getattr(cls, '__pydantic_generic_metadata__', {}).get('origin') + if cls.__pydantic_root_model__: root_field = self._common_field_schema('root', fields['root'], decorators) inner_schema = root_field['schema'] @@ -727,12 +681,12 @@ def _model_schema(self, cls: type[BaseModel]) -> core_schema.CoreSchema: model_schema = core_schema.model_schema( cls, inner_schema, + generic_origin=generic_origin, custom_init=getattr(cls, '__pydantic_custom_init__', None), root_model=True, post_init=getattr(cls, '__pydantic_post_init__', None), config=core_config, ref=model_ref, - metadata=metadata, ) else: fields_schema: core_schema.CoreSchema = core_schema.model_fields_schema( @@ -753,12 +707,12 @@ def _model_schema(self, cls: type[BaseModel]) -> core_schema.CoreSchema: model_schema = core_schema.model_schema( cls, inner_schema, + generic_origin=generic_origin, custom_init=getattr(cls, '__pydantic_custom_init__', None), root_model=False, post_init=getattr(cls, '__pydantic_post_init__', None), config=core_config, ref=model_ref, - metadata=metadata, ) schema = self._apply_model_serializers(model_schema, decorators.model_serializers.values()) @@ -766,26 +720,6 @@ def _model_schema(self, cls: type[BaseModel]) -> core_schema.CoreSchema: self.defs.definitions[model_ref] = schema return core_schema.definition_reference_schema(model_ref) - @staticmethod - def _get_model_title_from_config( - model: type[BaseModel | StandardDataclass], config_wrapper: ConfigWrapper | None = None - ) -> str | None: - """Get the title of a model if `model_title_generator` or `title` are set in the config, else return None""" - if config_wrapper is None: - return None - - if config_wrapper.title: - return config_wrapper.title - - model_title_generator = config_wrapper.model_title_generator - if model_title_generator: - title = model_title_generator(model) - if not isinstance(title, str): - raise TypeError(f'model_title_generator {model_title_generator} must return str, not {title.__class__}') - return title - - return None - def _unpack_refs_defs(self, schema: CoreSchema) -> CoreSchema: """Unpack all 'definitions' schemas into `GenerateSchema.defs.definitions` and return the inner schema. @@ -797,6 +731,12 @@ def _unpack_refs_defs(self, schema: CoreSchema) -> CoreSchema: return schema['schema'] return schema + def _resolve_self_type(self, obj: Any) -> Any: + obj = self.model_type_stack.get() + if obj is None: + raise PydanticUserError('`typing.Self` is invalid in this context', code='invalid-self-type') + return obj + def _generate_schema_from_property(self, obj: Any, source: Any) -> core_schema.CoreSchema | None: """Try to generate schema from either the `__get_pydantic_core_schema__` function or `__pydantic_core_schema__` property. @@ -805,8 +745,8 @@ def _generate_schema_from_property(self, obj: Any, source: Any) -> core_schema.C decide whether to use a `__pydantic_core_schema__` attribute, or generate a fresh schema. """ # avoid calling `__get_pydantic_core_schema__` if we've already visited this object - if is_self_type(obj): - obj = self.model_type_stack.get() + if _typing_extra.is_self(obj): + obj = self._resolve_self_type(obj) with self.defs.get_schema_or_ref(obj) as (_, maybe_schema): if maybe_schema is not None: return maybe_schema @@ -822,16 +762,27 @@ def _generate_schema_from_property(self, obj: Any, source: Any) -> core_schema.C source, CallbackGetCoreSchemaHandler(self._generate_schema_inner, self, ref_mode=ref_mode) ) elif ( - (existing_schema := getattr(obj, '__pydantic_core_schema__', None)) is not None + hasattr(obj, '__dict__') + # In some cases (e.g. a stdlib dataclass subclassing a Pydantic dataclass), + # doing an attribute access to get the schema will result in the parent schema + # being fetched. Thus, only look for the current obj's dict: + and (existing_schema := obj.__dict__.get('__pydantic_core_schema__')) is not None and not isinstance(existing_schema, MockCoreSchema) - and existing_schema.get('cls', None) == obj ): schema = existing_schema elif (validators := getattr(obj, '__get_validators__', None)) is not None: - warn( - '`__get_validators__` is deprecated and will be removed, use `__get_pydantic_core_schema__` instead.', - PydanticDeprecatedSince20, - ) + from pydantic.v1 import BaseModel as BaseModelV1 + + if issubclass(obj, BaseModelV1): + warn( + f'Mixing V1 models and V2 models (or constructs, like `TypeAdapter`) is not supported. Please upgrade `{obj.__name__}` to V2.', + UserWarning, + ) + else: + warn( + '`__get_validators__` is deprecated and will be removed, use `__get_pydantic_core_schema__` instead.', + PydanticDeprecatedSince20, + ) schema = core_schema.chain_schema([core_schema.with_info_plain_validator_function(v) for v in validators()]) else: # we have no existing schema information on the property, exit early so that we can go generate a schema @@ -861,7 +812,7 @@ def _resolve_forward_ref(self, obj: Any) -> Any: # class Model(BaseModel): # x: SomeImportedTypeAliasWithAForwardReference try: - obj = _typing_extra.eval_type_backport(obj, globalns=self._types_namespace) + obj = _typing_extra.eval_type_backport(obj, *self._types_namespace) except NameError as e: raise PydanticUndefinedAnnotation.from_name_error(e) from e @@ -883,7 +834,13 @@ def _get_args_resolving_forward_refs(self, obj: Any) -> tuple[Any, ...] | None: def _get_args_resolving_forward_refs(self, obj: Any, required: bool = False) -> tuple[Any, ...] | None: args = get_args(obj) if args: - args = tuple([self._resolve_forward_ref(a) if isinstance(a, ForwardRef) else a for a in args]) + if sys.version_info >= (3, 9): + from types import GenericAlias + + if isinstance(obj, GenericAlias): + # PEP 585 generic aliases don't convert args to ForwardRefs, unlike `typing.List/Dict` etc. + args = (_typing_extra._make_forward_ref(a) if isinstance(a, str) else a for a in args) + args = tuple(self._resolve_forward_ref(a) if isinstance(a, ForwardRef) else a for a in args) elif required: # pragma: no cover raise TypeError(f'Expected {obj} to have generic parameters but it had none') return args @@ -904,7 +861,7 @@ def _get_first_two_args_or_any(self, obj: Any) -> tuple[Any, Any]: return args[0], args[1] def _generate_schema_inner(self, obj: Any) -> core_schema.CoreSchema: - if is_annotated(obj): + if _typing_extra.is_annotated(obj): return self._annotated_schema(obj) if isinstance(obj, dict): @@ -942,7 +899,7 @@ def match_type(self, obj: Any) -> core_schema.CoreSchema: # noqa: C901 as they get requested and we figure out what the right API for them is. """ if obj is str: - return self.str_schema() + return core_schema.str_schema() elif obj is bytes: return core_schema.bytes_schema() elif obj is int: @@ -953,7 +910,7 @@ def match_type(self, obj: Any) -> core_schema.CoreSchema: # noqa: C901 return core_schema.bool_schema() elif obj is complex: return core_schema.complex_schema() - elif obj is Any or obj is object: + elif _typing_extra.is_any(obj) or obj is object: return core_schema.any_schema() elif obj is datetime.date: return core_schema.date_schema() @@ -969,6 +926,8 @@ def match_type(self, obj: Any) -> core_schema.CoreSchema: # noqa: C901 return core_schema.uuid_schema() elif obj is Url: return core_schema.url_schema() + elif obj is Fraction: + return self._fraction_schema() elif obj is MultiHostUrl: return core_schema.multi_host_url_schema() elif obj is None or obj is _typing_extra.NoneType: @@ -987,13 +946,13 @@ def match_type(self, obj: Any) -> core_schema.CoreSchema: # noqa: C901 return self._sequence_schema(Any) elif obj in DICT_TYPES: return self._dict_schema(Any, Any) - elif isinstance(obj, TypeAliasType): + elif _typing_extra.is_type_alias_type(obj): return self._type_alias_type_schema(obj) elif obj is type: return self._type_schema() - elif _typing_extra.is_callable_type(obj): + elif _typing_extra.is_callable(obj): return core_schema.callable_schema() - elif _typing_extra.is_literal_type(obj): + elif _typing_extra.is_literal(obj): return self._literal_schema(obj) elif is_typeddict(obj): return self._typed_dict_schema(obj, None) @@ -1002,26 +961,26 @@ def match_type(self, obj: Any) -> core_schema.CoreSchema: # noqa: C901 elif _typing_extra.is_new_type(obj): # NewType, can't use isinstance because it fails <3.10 return self.generate_schema(obj.__supertype__) - elif obj == re.Pattern: + elif obj is re.Pattern: return self._pattern_schema(obj) - elif obj is collections.abc.Hashable or obj is typing.Hashable: + elif _typing_extra.is_hashable(obj): return self._hashable_schema() elif isinstance(obj, typing.TypeVar): return self._unsubstituted_typevar_schema(obj) - elif is_finalvar(obj): + elif _typing_extra.is_finalvar(obj): if obj is Final: return core_schema.any_schema() return self.generate_schema( self._get_first_arg_or_any(obj), ) - elif isinstance(obj, (FunctionType, LambdaType, MethodType, partial)): - return self._callable_schema(obj) + elif isinstance(obj, VALIDATE_CALL_SUPPORTED_TYPES): + return self._call_schema(obj) elif inspect.isclass(obj) and issubclass(obj, Enum): return self._enum_schema(obj) - elif is_zoneinfo_type(obj): + elif _typing_extra.is_zoneinfo_type(obj): return self._zoneinfo_schema() - if _typing_extra.is_dataclass(obj): + if dataclasses.is_dataclass(obj): return self._dataclass_schema(obj, None) origin = get_origin(obj) @@ -1038,15 +997,12 @@ def match_type(self, obj: Any) -> core_schema.CoreSchema: # noqa: C901 return self._unknown_type_schema(obj) def _match_generic_type(self, obj: Any, origin: Any) -> CoreSchema: # noqa: C901 - if isinstance(origin, TypeAliasType): - return self._type_alias_type_schema(obj) - # Need to handle generic dataclasses before looking for the schema properties because attribute accesses # on _GenericAlias delegate to the origin type, so lose the information about the concrete parametrization # As a result, currently, there is no way to cache the schema for generic dataclasses. This may be possible # to resolve by modifying the value returned by `Generic.__class_getitem__`, but that is a dangerous game. - if _typing_extra.is_dataclass(origin): - return self._dataclass_schema(obj, origin) + if dataclasses.is_dataclass(origin): + return self._dataclass_schema(obj, origin) # pyright: ignore[reportArgumentType] if _typing_extra.is_namedtuple(origin): return self._namedtuple_schema(obj, origin) @@ -1054,7 +1010,9 @@ def _match_generic_type(self, obj: Any, origin: Any) -> CoreSchema: # noqa: C90 if from_property is not None: return from_property - if _typing_extra.origin_is_union(origin): + if _typing_extra.is_type_alias_type(origin): + return self._type_alias_type_schema(obj) + elif _typing_extra.origin_is_union(origin): return self._union_schema(obj) elif origin in TUPLE_TYPES: return self._tuple_schema(obj) @@ -1263,15 +1221,10 @@ def _common_field_schema( # C901 ) -> _CommonField: # Update FieldInfo annotation if appropriate: FieldInfo = import_cached_field_info() - if has_instance_in_type(field_info.annotation, (ForwardRef, str)): - types_namespace = self._types_namespace - if self._typevars_map: - types_namespace = (types_namespace or {}).copy() - # Ensure that typevars get mapped to their concrete types: - types_namespace.update({k.__name__: v for k, v in self._typevars_map.items()}) - - evaluated = _typing_extra.eval_type_lenient(field_info.annotation, types_namespace) + # TODO Can we use field_info.apply_typevars_map here? Shouldn't we use lenient=False? + evaluated = _typing_extra.eval_type(field_info.annotation, *self._types_namespace, lenient=True) + evaluated = replace_types(evaluated, self._typevars_map) if evaluated is not field_info.annotation and not has_instance_in_type(evaluated, PydanticRecursiveRef): new_field_info = FieldInfo.from_annotation(evaluated) field_info.annotation = new_field_info.annotation @@ -1334,18 +1287,10 @@ def set_discriminator(schema: CoreSchema) -> CoreSchema: ) self._apply_field_title_generator_to_field_info(self._config_wrapper, field_info, name) - json_schema_updates = { - 'title': field_info.title, - 'description': field_info.description, - 'deprecated': bool(field_info.deprecated) or field_info.deprecated == '' or None, - 'examples': to_jsonable_python(field_info.examples), - } - json_schema_updates = {k: v for k, v in json_schema_updates.items() if v is not None} - - json_schema_extra = field_info.json_schema_extra - - metadata = build_metadata_dict( - js_annotation_functions=[get_json_schema_update_func(json_schema_updates, json_schema_extra)] + pydantic_js_updates, pydantic_js_extra = _extract_json_schema_info_from_field_info(field_info) + core_metadata: dict[str, Any] = {} + update_core_metadata( + core_metadata, pydantic_js_updates=pydantic_js_updates, pydantic_js_extra=pydantic_js_extra ) alias_generator = self._config_wrapper.alias_generator @@ -1363,7 +1308,7 @@ def set_discriminator(schema: CoreSchema) -> CoreSchema: validation_alias=validation_alias, serialization_alias=field_info.serialization_alias, frozen=field_info.frozen, - metadata=metadata, + metadata=core_metadata, ) def _union_schema(self, union_type: Any) -> core_schema.CoreSchema: @@ -1393,21 +1338,18 @@ def _union_schema(self, union_type: Any) -> core_schema.CoreSchema: s = core_schema.nullable_schema(s) return s - def _type_alias_type_schema( - self, - obj: Any, # TypeAliasType - ) -> CoreSchema: + def _type_alias_type_schema(self, obj: TypeAliasType) -> CoreSchema: with self.defs.get_schema_or_ref(obj) as (ref, maybe_schema): if maybe_schema is not None: return maybe_schema - origin = get_origin(obj) or obj + origin: TypeAliasType = get_origin(obj) or obj annotation = origin.__value__ typevars_map = get_standard_typevars_map(obj) - with self._types_namespace_stack.push(origin): - annotation = _typing_extra.eval_type_lenient(annotation, self._types_namespace) + with self._ns_resolver.push(origin): + annotation = _typing_extra.eval_type(annotation, *self._types_namespace, lenient=True) annotation = replace_types(annotation, typevars_map) schema = self.generate_schema(annotation) assert schema['type'] != 'definitions' @@ -1417,7 +1359,7 @@ def _type_alias_type_schema( def _literal_schema(self, literal_type: Any) -> CoreSchema: """Generate schema for a Literal.""" - expected = _typing_extra.all_literal_values(literal_type) + expected = _typing_extra.literal_values(literal_type) assert expected, f'literal "expected" cannot be empty, obj={literal_type}' schema = core_schema.literal_schema(expected) @@ -1468,10 +1410,8 @@ def _typed_dict_schema(self, typed_dict_cls: Any, origin: Any) -> core_schema.Co except AttributeError: config = None - with self._config_wrapper_stack.push(config), self._types_namespace_stack.push(typed_dict_cls): - core_config = self._config_wrapper.core_config(typed_dict_cls) - - self = self._current_generate_schema + with self._config_wrapper_stack.push(config): + core_config = self._config_wrapper.core_config(title=typed_dict_cls.__name__) required_keys: frozenset[str] = typed_dict_cls.__required_keys__ @@ -1484,17 +1424,24 @@ def _typed_dict_schema(self, typed_dict_cls: Any, origin: Any) -> core_schema.Co else: field_docstrings = None - for field_name, annotation in get_cls_type_hints_lenient(typed_dict_cls, self._types_namespace).items(): + try: + annotations = _typing_extra.get_cls_type_hints( + typed_dict_cls, ns_resolver=self._ns_resolver, lenient=False + ) + except NameError as e: + raise PydanticUndefinedAnnotation.from_name_error(e) from e + + for field_name, annotation in annotations.items(): annotation = replace_types(annotation, typevars_map) required = field_name in required_keys - if get_origin(annotation) == _typing_extra.Required: + if _typing_extra.is_required(annotation): required = True annotation = self._get_args_resolving_forward_refs( annotation, required=True, )[0] - elif get_origin(annotation) == _typing_extra.NotRequired: + elif _typing_extra.is_not_required(annotation): required = False annotation = self._get_args_resolving_forward_refs( annotation, @@ -1513,10 +1460,6 @@ def _typed_dict_schema(self, typed_dict_cls: Any, origin: Any) -> core_schema.Co field_name, field_info, decorators, required=required ) - title = self._get_model_title_from_config(typed_dict_cls, ConfigWrapper(config)) - metadata = build_metadata_dict( - js_functions=[partial(modify_model_json_schema, cls=typed_dict_cls, title=title)], - ) td_schema = core_schema.typed_dict_schema( fields, cls=typed_dict_cls, @@ -1525,7 +1468,6 @@ def _typed_dict_schema(self, typed_dict_cls: Any, origin: Any) -> core_schema.Co for d in decorators.computed_fields.values() ], ref=typed_dict_ref, - metadata=metadata, config=core_config, ) @@ -1546,10 +1488,15 @@ def _namedtuple_schema(self, namedtuple_cls: Any, origin: Any) -> core_schema.Co if origin is not None: namedtuple_cls = origin - annotations: dict[str, Any] = get_cls_type_hints_lenient(namedtuple_cls, self._types_namespace) + try: + annotations = _typing_extra.get_cls_type_hints( + namedtuple_cls, ns_resolver=self._ns_resolver, lenient=False + ) + except NameError as e: + raise PydanticUndefinedAnnotation.from_name_error(e) from e if not annotations: # annotations is empty, happens if namedtuple_cls defined via collections.namedtuple(...) - annotations = {k: Any for k in namedtuple_cls._fields} + annotations: dict[str, Any] = {k: Any for k in namedtuple_cls._fields} if typevars_map: annotations = { @@ -1560,11 +1507,13 @@ def _namedtuple_schema(self, namedtuple_cls: Any, origin: Any) -> core_schema.Co arguments_schema = core_schema.arguments_schema( [ self._generate_parameter_schema( - field_name, annotation, default=namedtuple_cls._field_defaults.get(field_name, Parameter.empty) + field_name, + annotation, + default=namedtuple_cls._field_defaults.get(field_name, Parameter.empty), ) for field_name, annotation in annotations.items() ], - metadata=build_metadata_dict(js_prefer_positional_arguments=True), + metadata={'pydantic_js_prefer_positional_arguments': True}, ) return core_schema.call_schema(arguments_schema, namedtuple_cls, ref=namedtuple_ref) @@ -1583,9 +1532,8 @@ def _generate_parameter_schema( else: field = FieldInfo.from_annotated_attribute(annotation, default) assert field.annotation is not None, 'field.annotation should not be None when generating a schema' - source_type, annotations = field.annotation, field.metadata with self.field_name_stack.push(name): - schema = self._apply_annotations(source_type, annotations) + schema = self._apply_annotations(field.annotation, [field]) if not field.is_required(): schema = wrap_default(field, schema) @@ -1654,10 +1602,10 @@ def validate_str_is_valid_iana_tz(value: Any, /) -> ZoneInfo: return value try: return ZoneInfo(value) - except (ZoneInfoNotFoundError, ValueError): + except (ZoneInfoNotFoundError, ValueError, TypeError): raise PydanticCustomError('zoneinfo_str', 'invalid timezone: {value}', {'value': value}) - metadata = build_metadata_dict(js_functions=[lambda _1, _2: {'type': 'string', 'format': 'zoneinfo'}]) + metadata = {'pydantic_js_functions': [lambda _1, _2: {'type': 'string', 'format': 'zoneinfo'}]} return core_schema.no_info_plain_validator_function( validate_str_is_valid_iana_tz, serialization=core_schema.to_string_ser_schema(), @@ -1672,10 +1620,14 @@ def _union_is_subclass_schema(self, union_type: Any) -> core_schema.CoreSchema: def _subclass_schema(self, type_: Any) -> core_schema.CoreSchema: """Generate schema for a Type, e.g. `Type[int]`.""" type_param = self._get_first_arg_or_any(type_) + # Assume `type[Annotated[, ...]]` is equivalent to `type[]`: type_param = _typing_extra.annotated_type(type_param) or type_param - if type_param == Any: + + if _typing_extra.is_any(type_param): return self._type_schema() + elif _typing_extra.is_type_alias_type(type_param): + return self.generate_schema(typing.Type[type_param.__value__]) elif isinstance(type_param, typing.TypeVar): if type_param.__bound__: if _typing_extra.origin_is_union(get_origin(type_param.__bound__)): @@ -1690,6 +1642,11 @@ def _subclass_schema(self, type_: Any) -> core_schema.CoreSchema: elif _typing_extra.origin_is_union(get_origin(type_param)): return self._union_is_subclass_schema(type_param) else: + if _typing_extra.is_self(type_param): + type_param = self._resolve_self_type(type_param) + + if not inspect.isclass(type_param): + raise TypeError(f'Expected a class, got {type_param!r}') return core_schema.is_subclass_schema(type_param) def _sequence_schema(self, items_type: Any) -> core_schema.CoreSchema: @@ -1701,7 +1658,7 @@ def _sequence_schema(self, items_type: Any) -> core_schema.CoreSchema: json_schema = smart_deepcopy(list_schema) python_schema = core_schema.is_instance_schema(typing.Sequence, cls_repr='Sequence') - if items_type != Any: + if not _typing_extra.is_any(items_type): from ._validators import sequence_validator python_schema = core_schema.chain_schema( @@ -1724,11 +1681,11 @@ def _iterable_schema(self, type_: Any) -> core_schema.GeneratorSchema: def _pattern_schema(self, pattern_type: Any) -> core_schema.CoreSchema: from . import _validators - metadata = build_metadata_dict(js_functions=[lambda _1, _2: {'type': 'string', 'format': 'regex'}]) + metadata = {'pydantic_js_functions': [lambda _1, _2: {'type': 'string', 'format': 'regex'}]} ser = core_schema.plain_serializer_function_ser_schema( attrgetter('pattern'), when_used='json', return_schema=core_schema.str_schema() ) - if pattern_type == typing.Pattern or pattern_type == re.Pattern: + if pattern_type is typing.Pattern or pattern_type is re.Pattern: # bare type return core_schema.no_info_plain_validator_function( _validators.pattern_either_validator, serialization=ser, metadata=metadata @@ -1751,7 +1708,12 @@ def _pattern_schema(self, pattern_type: Any) -> core_schema.CoreSchema: def _hashable_schema(self) -> core_schema.CoreSchema: return core_schema.custom_error_schema( - core_schema.is_instance_schema(collections.abc.Hashable), + schema=core_schema.json_or_python_schema( + json_schema=core_schema.chain_schema( + [core_schema.any_schema(), core_schema.is_instance_schema(collections.abc.Hashable)] + ), + python_schema=core_schema.is_instance_schema(collections.abc.Hashable), + ), custom_error_type='is_hashable', custom_error_message='Input should be hashable', ) @@ -1771,39 +1733,23 @@ def _dataclass_schema( if origin is not None: dataclass = origin - with ExitStack() as dataclass_bases_stack: - # Pushing a namespace prioritises items already in the stack, so iterate though the MRO forwards - for dataclass_base in dataclass.__mro__: - if dataclasses.is_dataclass(dataclass_base): - dataclass_bases_stack.enter_context(self._types_namespace_stack.push(dataclass_base)) + config = getattr(dataclass, '__pydantic_config__', ConfigDict()) - # Pushing a config overwrites the previous config, so iterate though the MRO backwards - config = None - for dataclass_base in reversed(dataclass.__mro__): - if dataclasses.is_dataclass(dataclass_base): - config = getattr(dataclass_base, '__pydantic_config__', None) - dataclass_bases_stack.enter_context(self._config_wrapper_stack.push(config)) - - core_config = self._config_wrapper.core_config(dataclass) - - self = self._current_generate_schema - - from ..dataclasses import is_pydantic_dataclass + from ..dataclasses import is_pydantic_dataclass + with self._ns_resolver.push(dataclass), self._config_wrapper_stack.push(config): if is_pydantic_dataclass(dataclass): fields = deepcopy(dataclass.__pydantic_fields__) if typevars_map: for field in fields.values(): - field.apply_typevars_map(typevars_map, self._types_namespace) + field.apply_typevars_map(typevars_map, *self._types_namespace) else: fields = collect_dataclass_fields( dataclass, - self._types_namespace, typevars_map=typevars_map, ) - # disallow combination of init=False on a dataclass field and extra='allow' on a dataclass - if self._config_wrapper_stack.tail.extra == 'allow': + if self._config_wrapper.extra == 'allow': # disallow combination of init=False on a dataclass field and extra='allow' on a dataclass for field_name, field in fields.items(): if field.init is False: @@ -1838,20 +1784,17 @@ def _dataclass_schema( model_validators = decorators.model_validators.values() inner_schema = apply_model_validators(inner_schema, model_validators, 'inner') - title = self._get_model_title_from_config(dataclass, ConfigWrapper(config)) - metadata = build_metadata_dict( - js_functions=[partial(modify_model_json_schema, cls=dataclass, title=title)] - ) + core_config = self._config_wrapper.core_config(title=dataclass.__name__) dc_schema = core_schema.dataclass_schema( dataclass, inner_schema, + generic_origin=origin, post_init=has_post_init, ref=dataclass_ref, fields=[field.name for field in dataclasses.fields(dataclass)], slots=has_slots, config=core_config, - metadata=metadata, # we don't use a custom __setattr__ for dataclasses, so we must # pass along the frozen config setting to the pydantic-core schema frozen=self._config_wrapper_stack.tail.frozen, @@ -1861,19 +1804,13 @@ def _dataclass_schema( self.defs.definitions[dataclass_ref] = schema return core_schema.definition_reference_schema(dataclass_ref) - # Type checkers seem to assume ExitStack may suppress exceptions and therefore - # control flow can exit the `with` block without returning. - assert False, 'Unreachable' - - def _callable_schema(self, function: Callable[..., Any]) -> core_schema.CallSchema: + def _call_schema(self, function: ValidateCallSupportedTypes) -> core_schema.CallSchema: """Generate schema for a Callable. TODO support functional validators once we support them in Config """ sig = signature(function) - type_hints = _typing_extra.get_function_type_hints(function, types_namespace=self._types_namespace) - mode_lookup: dict[_ParameterKind, Literal['positional_only', 'positional_or_keyword', 'keyword_only']] = { Parameter.POSITIONAL_ONLY: 'positional_only', Parameter.POSITIONAL_OR_KEYWORD: 'positional_or_keyword', @@ -1883,12 +1820,19 @@ def _callable_schema(self, function: Callable[..., Any]) -> core_schema.CallSche arguments_list: list[core_schema.ArgumentsParameter] = [] var_args_schema: core_schema.CoreSchema | None = None var_kwargs_schema: core_schema.CoreSchema | None = None + var_kwargs_mode: core_schema.VarKwargsMode | None = None for name, p in sig.parameters.items(): if p.annotation is sig.empty: annotation = typing.cast(Any, Any) else: - annotation = type_hints[name] + # Note: This was originally get by `_typing_extra.get_function_type_hints`, + # but we switch to simply `p.annotation` to support bultins (e.g. `sorted`). + # May need to revisit if anything breaks. + annotation = ( + _typing_extra._make_forward_ref(p.annotation) if isinstance(p.annotation, str) else p.annotation + ) + annotation = self._resolve_forward_ref(annotation) parameter_mode = mode_lookup.get(p.kind) if parameter_mode is not None: @@ -1898,19 +1842,43 @@ def _callable_schema(self, function: Callable[..., Any]) -> core_schema.CallSche var_args_schema = self.generate_schema(annotation) else: assert p.kind == Parameter.VAR_KEYWORD, p.kind - var_kwargs_schema = self.generate_schema(annotation) + + unpack_type = _typing_extra.unpack_type(annotation) + if unpack_type is not None: + if not is_typeddict(unpack_type): + raise PydanticUserError( + f'Expected a `TypedDict` class, got {unpack_type.__name__!r}', code='unpack-typed-dict' + ) + non_pos_only_param_names = { + name for name, p in sig.parameters.items() if p.kind != Parameter.POSITIONAL_ONLY + } + overlapping_params = non_pos_only_param_names.intersection(unpack_type.__annotations__) + if overlapping_params: + raise PydanticUserError( + f'Typed dictionary {unpack_type.__name__!r} overlaps with parameter' + f"{'s' if len(overlapping_params) >= 2 else ''} " + f"{', '.join(repr(p) for p in sorted(overlapping_params))}", + code='overlapping-unpack-typed-dict', + ) + + var_kwargs_mode = 'unpacked-typed-dict' + var_kwargs_schema = self._typed_dict_schema(unpack_type, None) + else: + var_kwargs_mode = 'uniform' + var_kwargs_schema = self.generate_schema(annotation) return_schema: core_schema.CoreSchema | None = None config_wrapper = self._config_wrapper if config_wrapper.validate_return: - return_hint = type_hints.get('return') - if return_hint is not None: + return_hint = sig.return_annotation + if return_hint is not sig.empty: return_schema = self.generate_schema(return_hint) return core_schema.call_schema( core_schema.arguments_schema( arguments_list, var_args_schema=var_args_schema, + var_kwargs_mode=var_kwargs_mode, var_kwargs_schema=var_kwargs_schema, populate_by_name=config_wrapper.populate_by_name, ), @@ -1954,7 +1922,7 @@ def _computed_field_schema( field_serializers: dict[str, Decorator[FieldSerializerDecoratorInfo]], ) -> core_schema.ComputedField: try: - return_type = _decorators.get_function_return_type(d.func, d.info.return_type, self._types_namespace) + return_type = _decorators.get_function_return_type(d.func, d.info.return_type, *self._types_namespace) except NameError as e: raise PydanticUndefinedAnnotation.from_name_error(e) from e if return_type is PydanticUndefined: @@ -1973,7 +1941,6 @@ def _computed_field_schema( return_type_schema = self._apply_field_serializers( return_type_schema, filter_field_decorator_info_by_field(field_serializers.values(), d.cls_var_name), - computed_field=True, ) alias_generator = self._config_wrapper.alias_generator @@ -1983,35 +1950,15 @@ def _computed_field_schema( ) self._apply_field_title_generator_to_field_info(self._config_wrapper, d.info, d.cls_var_name) - def set_computed_field_metadata(schema: CoreSchemaOrField, handler: GetJsonSchemaHandler) -> JsonSchemaValue: - json_schema = handler(schema) - - json_schema['readOnly'] = True - - title = d.info.title - if title is not None: - json_schema['title'] = title - - description = d.info.description - if description is not None: - json_schema['description'] = description - - if d.info.deprecated or d.info.deprecated == '': - json_schema['deprecated'] = True - - examples = d.info.examples - if examples is not None: - json_schema['examples'] = to_jsonable_python(examples) - - json_schema_extra = d.info.json_schema_extra - if json_schema_extra is not None: - add_json_schema_extra(json_schema, json_schema_extra) - - return json_schema - - metadata = build_metadata_dict(js_annotation_functions=[set_computed_field_metadata]) + pydantic_js_updates, pydantic_js_extra = _extract_json_schema_info_from_field_info(d.info) + core_metadata: dict[str, Any] = {} + update_core_metadata( + core_metadata, + pydantic_js_updates={'readOnly': True, **(pydantic_js_updates if pydantic_js_updates else {})}, + pydantic_js_extra=pydantic_js_extra, + ) return core_schema.computed_field( - d.cls_var_name, return_schema=return_type_schema, alias=d.info.alias, metadata=metadata + d.cls_var_name, return_schema=return_type_schema, alias=d.info.alias, metadata=core_metadata ) def _annotated_schema(self, annotated_type: Any) -> core_schema.CoreSchema: @@ -2103,8 +2050,8 @@ def inner_handler(obj: Any) -> CoreSchema: schema = get_inner_schema(source_type) if pydantic_js_annotation_functions: - metadata = CoreMetadataHandler(schema).metadata - metadata.setdefault('pydantic_js_annotation_functions', []).extend(pydantic_js_annotation_functions) + core_metadata = schema.setdefault('metadata', {}) + update_core_metadata(core_metadata, pydantic_js_annotation_functions=pydantic_js_annotation_functions) return _add_custom_serialization_from_json_encoders(self._config_wrapper.json_encoders, source_type, schema) def _apply_single_annotation(self, schema: core_schema.CoreSchema, metadata: Any) -> core_schema.CoreSchema: @@ -2157,19 +2104,12 @@ def _apply_single_annotation_json_schema( if isinstance(metadata, FieldInfo): for field_metadata in metadata.metadata: schema = self._apply_single_annotation_json_schema(schema, field_metadata) - json_schema_update: JsonSchemaValue = {} - if metadata.title: - json_schema_update['title'] = metadata.title - if metadata.description: - json_schema_update['description'] = metadata.description - if metadata.examples: - json_schema_update['examples'] = to_jsonable_python(metadata.examples) - - json_schema_extra = metadata.json_schema_extra - if json_schema_update or json_schema_extra: - CoreMetadataHandler(schema).metadata.setdefault('pydantic_js_annotation_functions', []).append( - get_json_schema_update_func(json_schema_update, json_schema_extra) - ) + + pydantic_js_updates, pydantic_js_extra = _extract_json_schema_info_from_field_info(metadata) + core_metadata = schema.setdefault('metadata', {}) + update_core_metadata( + core_metadata, pydantic_js_updates=pydantic_js_updates, pydantic_js_extra=pydantic_js_extra + ) return schema def _get_wrapped_inner_schema( @@ -2198,7 +2138,6 @@ def _apply_field_serializers( self, schema: core_schema.CoreSchema, serializers: list[Decorator[FieldSerializerDecoratorInfo]], - computed_field: bool = False, ) -> core_schema.CoreSchema: """Apply field serializers to a schema.""" if serializers: @@ -2215,13 +2154,11 @@ def _apply_field_serializers( # use the last serializer to make it easy to override a serializer set on a parent model serializer = serializers[-1] - is_field_serializer, info_arg = inspect_field_serializer( - serializer.func, serializer.info.mode, computed_field=computed_field - ) + is_field_serializer, info_arg = inspect_field_serializer(serializer.func, serializer.info.mode) try: return_type = _decorators.get_function_return_type( - serializer.func, serializer.info.return_type, self._types_namespace + serializer.func, serializer.info.return_type, *self._types_namespace ) except NameError as e: raise PydanticUndefinedAnnotation.from_name_error(e) from e @@ -2261,7 +2198,7 @@ def _apply_model_serializers( try: return_type = _decorators.get_function_return_type( - serializer.func, serializer.info.return_type, self._types_namespace + serializer.func, serializer.info.return_type, *self._types_namespace ) except NameError as e: raise PydanticUndefinedAnnotation.from_name_error(e) from e @@ -2416,7 +2353,10 @@ def wrap_default(field_info: FieldInfo, schema: core_schema.CoreSchema) -> core_ """ if field_info.default_factory: return core_schema.with_default_schema( - schema, default_factory=field_info.default_factory, validate_default=field_info.validate_default + schema, + default_factory=field_info.default_factory, + default_factory_takes_data=takes_validated_data_argument(field_info.default_factory), + validate_default=field_info.validate_default, ) elif field_info.default is not PydanticUndefined: return core_schema.with_default_schema( @@ -2448,7 +2388,7 @@ def _extract_get_pydantic_json_schema(tp: Any, schema: CoreSchema) -> GetJsonSch ) # handle GenericAlias' but ignore Annotated which "lies" about its origin (in this case it would be `int`) - if hasattr(tp, '__origin__') and not is_annotated(tp): + if hasattr(tp, '__origin__') and not _typing_extra.is_annotated(tp): return _extract_get_pydantic_json_schema(tp.__origin__, schema) if js_modify_function is None: @@ -2457,28 +2397,6 @@ def _extract_get_pydantic_json_schema(tp: Any, schema: CoreSchema) -> GetJsonSch return js_modify_function -def get_json_schema_update_func( - json_schema_update: JsonSchemaValue, json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None -) -> GetJsonSchemaFunction: - def json_schema_update_func( - core_schema_or_field: CoreSchemaOrField, handler: GetJsonSchemaHandler - ) -> JsonSchemaValue: - json_schema = {**handler(core_schema_or_field), **json_schema_update} - add_json_schema_extra(json_schema, json_schema_extra) - return json_schema - - return json_schema_update_func - - -def add_json_schema_extra( - json_schema: JsonSchemaValue, json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None -): - if isinstance(json_schema_extra, dict): - json_schema.update(to_jsonable_python(json_schema_extra)) - elif callable(json_schema_extra): - json_schema_extra(json_schema) - - class _CommonField(TypedDict): schema: core_schema.CoreSchema validation_alias: str | list[str | int] | list[list[str | int]] | None diff --git a/pydantic/_internal/_generics.py b/pydantic/_internal/_generics.py index fad5f9479b2..d6fea96cb76 100644 --- a/pydantic/_internal/_generics.py +++ b/pydantic/_internal/_generics.py @@ -7,14 +7,14 @@ from contextlib import contextmanager from contextvars import ContextVar from types import prepare_class -from typing import TYPE_CHECKING, Any, Iterator, List, Mapping, MutableMapping, Tuple, TypeVar +from typing import TYPE_CHECKING, Any, Iterator, Mapping, MutableMapping, Tuple, TypeVar from weakref import WeakValueDictionary import typing_extensions +from . import _typing_extra from ._core_utils import get_type_ref from ._forward_ref import PydanticRecursiveRef -from ._typing_extra import TypeVarType, typing_base from ._utils import all_identical, is_model_class if sys.version_info >= (3, 10): @@ -109,7 +109,7 @@ def __delitem__(self, key: KT) -> None: class PydanticGenericMetadata(typing_extensions.TypedDict): origin: type[BaseModel] | None # analogous to typing._GenericAlias.__origin__ args: tuple[Any, ...] # analogous to typing._GenericAlias.__args__ - parameters: tuple[type[Any], ...] # analogous to typing.Generic.__parameters__ + parameters: tuple[TypeVar, ...] # analogous to typing.Generic.__parameters__ def create_generic_submodel( @@ -184,7 +184,7 @@ def _get_caller_frame_info(depth: int = 2) -> tuple[str | None, bool]: DictValues: type[Any] = {}.values().__class__ -def iter_contained_typevars(v: Any) -> Iterator[TypeVarType]: +def iter_contained_typevars(v: Any) -> Iterator[TypeVar]: """Recursively iterate through all subtypes and type args of `v` and yield any typevars that are found. This is inspired as an alternative to directly accessing the `__parameters__` attribute of a GenericAlias, @@ -217,7 +217,7 @@ def get_origin(v: Any) -> Any: return typing_extensions.get_origin(v) -def get_standard_typevars_map(cls: type[Any]) -> dict[TypeVarType, Any] | None: +def get_standard_typevars_map(cls: Any) -> dict[TypeVar, Any] | None: """Package a generic type's typevars and parametrization (if present) into a dictionary compatible with the `replace_types` function. Specifically, this works with standard typing generics and typing._GenericAlias. """ @@ -230,11 +230,11 @@ def get_standard_typevars_map(cls: type[Any]) -> dict[TypeVarType, Any] | None: # In this case, we know that cls is a _GenericAlias, and origin is the generic type # So it is safe to access cls.__args__ and origin.__parameters__ args: tuple[Any, ...] = cls.__args__ # type: ignore - parameters: tuple[TypeVarType, ...] = origin.__parameters__ + parameters: tuple[TypeVar, ...] = origin.__parameters__ return dict(zip(parameters, args)) -def get_model_typevars_map(cls: type[BaseModel]) -> dict[TypeVarType, Any] | None: +def get_model_typevars_map(cls: type[BaseModel]) -> dict[TypeVar, Any] | None: """Package a generic BaseModel's typevars and concrete parametrization (if present) into a dictionary compatible with the `replace_types` function. @@ -261,7 +261,7 @@ def replace_types(type_: Any, type_map: Mapping[Any, Any] | None) -> Any: `typevar_map` keys recursively replaced. Example: - ```py + ```python from typing import List, Tuple, Union from pydantic._internal._generics import replace_types @@ -274,27 +274,29 @@ def replace_types(type_: Any, type_map: Mapping[Any, Any] | None) -> Any: return type_ type_args = get_args(type_) - origin_type = get_origin(type_) - if origin_type is typing_extensions.Annotated: + if _typing_extra.is_annotated(type_): annotated_type, *annotations = type_args annotated = replace_types(annotated_type, type_map) for annotation in annotations: annotated = typing_extensions.Annotated[annotated, annotation] return annotated - # Having type args is a good indicator that this is a typing module - # class instantiation or a generic alias of some sort. + origin_type = get_origin(type_) + + # Having type args is a good indicator that this is a typing special form + # instance or a generic alias of some sort. if type_args: resolved_type_args = tuple(replace_types(arg, type_map) for arg in type_args) if all_identical(type_args, resolved_type_args): # If all arguments are the same, there is no need to modify the # type or create a new object at all return type_ + if ( origin_type is not None - and isinstance(type_, typing_base) - and not isinstance(origin_type, typing_base) + and isinstance(type_, _typing_extra.typing_base) + and not isinstance(origin_type, _typing_extra.typing_base) and getattr(type_, '_name', None) is not None ): # In python < 3.9 generic aliases don't exist so any of these like `list`, @@ -302,6 +304,18 @@ def replace_types(type_: Any, type_map: Mapping[Any, Any] | None) -> Any: # See: https://www.python.org/dev/peps/pep-0585 origin_type = getattr(typing, type_._name) assert origin_type is not None + + if _typing_extra.origin_is_union(origin_type): + if any(_typing_extra.is_any(arg) for arg in resolved_type_args): + # `Any | T` ~ `Any`: + resolved_type_args = (Any,) + # `Never | T` ~ `T`: + resolved_type_args = tuple( + arg + for arg in resolved_type_args + if not (_typing_extra.is_no_return(arg) or _typing_extra.is_never(arg)) + ) + # PEP-604 syntax (Ex.: list | str) is represented with a types.UnionType object that does not have __getitem__. # We also cannot use isinstance() since we have to compare types. if sys.version_info >= (3, 10) and origin_type is types.UnionType: @@ -323,7 +337,7 @@ def replace_types(type_: Any, type_map: Mapping[Any, Any] | None) -> Any: # Handle special case for typehints that can have lists as arguments. # `typing.Callable[[int, str], int]` is an example for this. - if isinstance(type_, (List, list)): + if isinstance(type_, list): resolved_list = [replace_types(element, type_map) for element in type_] if all_identical(type_, resolved_list): return type_ @@ -340,13 +354,12 @@ def has_instance_in_type(type_: Any, isinstance_target: Any) -> bool: """ if isinstance(type_, isinstance_target): return True + if _typing_extra.is_annotated(type_): + return has_instance_in_type(type_.__origin__, isinstance_target) + if _typing_extra.is_literal(type_): + return False type_args = get_args(type_) - origin_type = get_origin(type_) - - if origin_type is typing_extensions.Annotated: - annotated_type, *annotations = type_args - return has_instance_in_type(annotated_type, isinstance_target) # Having type args is a good indicator that this is a typing module # class instantiation or a generic alias of some sort. @@ -356,7 +369,11 @@ def has_instance_in_type(type_: Any, isinstance_target: Any) -> bool: # Handle special case for typehints that can have lists as arguments. # `typing.Callable[[int, str], int]` is an example for this. - if isinstance(type_, (List, list)) and not isinstance(type_, typing_extensions.ParamSpec): + if ( + isinstance(type_, list) + # On Python < 3.10, typing_extensions implements `ParamSpec` as a subclass of `list`: + and not isinstance(type_, typing_extensions.ParamSpec) + ): for element in type_: if has_instance_in_type(element, isinstance_target): return True @@ -488,7 +505,7 @@ def _union_orderings_key(typevar_values: Any) -> Any: for value in typevar_values: args_data.append(_union_orderings_key(value)) return tuple(args_data) - elif typing_extensions.get_origin(typevar_values) is typing.Union: + elif _typing_extra.is_union(typevar_values): return get_args(typevar_values) else: return () diff --git a/pydantic/_internal/_known_annotated_metadata.py b/pydantic/_internal/_known_annotated_metadata.py index efdb8924c7a..78de89ecd77 100644 --- a/pydantic/_internal/_known_annotated_metadata.py +++ b/pydantic/_internal/_known_annotated_metadata.py @@ -3,7 +3,7 @@ from collections import defaultdict from copy import copy from functools import lru_cache, partial -from typing import TYPE_CHECKING, Any, Callable, Iterable +from typing import TYPE_CHECKING, Any, Iterable from pydantic_core import CoreSchema, PydanticCustomError, ValidationError, to_jsonable_python from pydantic_core import core_schema as cs @@ -12,7 +12,7 @@ from ._import_utils import import_cached_field_info if TYPE_CHECKING: - from ..annotated_handlers import GetJsonSchemaHandler + pass STRICT = {'strict'} FAIL_FAST = {'fail_fast'} @@ -96,22 +96,6 @@ CONSTRAINTS_TO_ALLOWED_SCHEMAS[c].update(schemas) -def add_js_update_schema(s: cs.CoreSchema, f: Callable[[], dict[str, Any]]) -> None: - def update_js_schema(s: cs.CoreSchema, handler: GetJsonSchemaHandler) -> dict[str, Any]: - js_schema = handler(s) - js_schema.update(f()) - return js_schema - - if 'metadata' in s: - metadata = s['metadata'] - if 'pydantic_js_functions' in s: - metadata['pydantic_js_functions'].append(update_js_schema) - else: - metadata['pydantic_js_functions'] = [update_js_schema] - else: - s['metadata'] = {'pydantic_js_functions': [update_js_schema]} - - def as_jsonable_value(v: Any) -> Any: if type(v) not in (int, str, float, bytes, bool, type(None)): return to_jsonable_python(v) @@ -128,7 +112,7 @@ def expand_grouped_metadata(annotations: Iterable[Any]) -> Iterable[Any]: An iterable of expanded annotations. Example: - ```py + ```python from annotated_types import Ge, Len from pydantic._internal._known_annotated_metadata import expand_grouped_metadata @@ -201,7 +185,7 @@ def apply_known_metadata(annotation: Any, schema: CoreSchema) -> CoreSchema | No """ import annotated_types as at - from ._validators import forbid_inf_nan_check, get_constraint_validator + from ._validators import NUMERIC_VALIDATOR_LOOKUP, forbid_inf_nan_check schema = schema.copy() schema_update, other_metadata = collect_known_metadata([annotation]) @@ -263,10 +247,8 @@ def _apply_constraint_with_incompatibility_info( _apply_constraint_with_incompatibility_info, cs.str_schema(**{constraint: value}) ) ) - elif constraint in {*NUMERIC_CONSTRAINTS, *LENGTH_CONSTRAINTS}: - if constraint in NUMERIC_CONSTRAINTS: - json_schema_constraint = constraint - elif constraint in LENGTH_CONSTRAINTS: + elif constraint in NUMERIC_VALIDATOR_LOOKUP: + if constraint in LENGTH_CONSTRAINTS: inner_schema = schema while inner_schema['type'] in {'function-before', 'function-wrap', 'function-after'}: inner_schema = inner_schema['schema'] # type: ignore @@ -274,14 +256,24 @@ def _apply_constraint_with_incompatibility_info( if inner_schema_type == 'list' or ( inner_schema_type == 'json-or-python' and inner_schema['json_schema']['type'] == 'list' # type: ignore ): - json_schema_constraint = 'minItems' if constraint == 'min_length' else 'maxItems' + js_constraint_key = 'minItems' if constraint == 'min_length' else 'maxItems' else: - json_schema_constraint = 'minLength' if constraint == 'min_length' else 'maxLength' + js_constraint_key = 'minLength' if constraint == 'min_length' else 'maxLength' + else: + js_constraint_key = constraint schema = cs.no_info_after_validator_function( - partial(get_constraint_validator(constraint), **{'constraint_value': value}), schema + partial(NUMERIC_VALIDATOR_LOOKUP[constraint], **{constraint: value}), schema ) - add_js_update_schema(schema, lambda: {json_schema_constraint: as_jsonable_value(value)}) # noqa: B023 + metadata = schema.get('metadata', {}) + if (existing_json_schema_updates := metadata.get('pydantic_js_updates')) is not None: + metadata['pydantic_js_updates'] = { + **existing_json_schema_updates, + **{js_constraint_key: as_jsonable_value(value)}, + } + else: + metadata['pydantic_js_updates'] = {js_constraint_key: as_jsonable_value(value)} + schema['metadata'] = metadata elif constraint == 'allow_inf_nan' and value is False: schema = cs.no_info_after_validator_function( forbid_inf_nan_check, @@ -295,8 +287,11 @@ def _apply_constraint_with_incompatibility_info( for annotation in other_metadata: if (annotation_type := type(annotation)) in (at_to_constraint_map := _get_at_to_constraint_map()): constraint = at_to_constraint_map[annotation_type] + validator = NUMERIC_VALIDATOR_LOOKUP.get(constraint) + if validator is None: + raise ValueError(f'Unknown constraint {constraint}') schema = cs.no_info_after_validator_function( - partial(get_constraint_validator(constraint), {constraint: getattr(annotation, constraint)}), schema + partial(validator, {constraint: getattr(annotation, constraint)}), schema ) continue elif isinstance(annotation, (at.Predicate, at.Not)): @@ -319,6 +314,8 @@ def val_func(v: Any) -> Any: f'Not of {predicate_name} failed', # type: ignore # noqa: B023 ) + return v + schema = cs.no_info_after_validator_function(val_func, schema) else: # ignore any other unknown metadata @@ -341,7 +338,7 @@ def collect_known_metadata(annotations: Iterable[Any]) -> tuple[dict[str, Any], A tuple contains a dict of known metadata and a list of unknown annotations. Example: - ```py + ```python from annotated_types import Gt, Len from pydantic._internal._known_annotated_metadata import collect_known_metadata diff --git a/pydantic/_internal/_mock_val_ser.py b/pydantic/_internal/_mock_val_ser.py index 3e55af70dc7..624c68e4461 100644 --- a/pydantic/_internal/_mock_val_ser.py +++ b/pydantic/_internal/_mock_val_ser.py @@ -11,6 +11,7 @@ if TYPE_CHECKING: from ..dataclasses import PydanticDataclass from ..main import BaseModel + from ..type_adapter import TypeAdapter ValSer = TypeVar('ValSer', bound=Union[SchemaValidator, PluggableSchemaValidator, SchemaSerializer]) @@ -59,9 +60,9 @@ def _get_built(self) -> CoreSchema: def rebuild(self) -> CoreSchema | None: self._built_memo = None if self._attempt_rebuild: - val_ser = self._attempt_rebuild() - if val_ser is not None: - return val_ser + schema = self._attempt_rebuild() + if schema is not None: + return schema else: raise PydanticUserError(self._error_message, code=self._code) return None @@ -108,8 +109,48 @@ def rebuild(self) -> ValSer | None: return None +def set_type_adapter_mocks(adapter: TypeAdapter, type_repr: str) -> None: + """Set `core_schema`, `validator` and `serializer` to mock core types on a type adapter instance. + + Args: + adapter: The type adapter instance to set the mocks on + type_repr: Name of the type used in the adapter, used in error messages + """ + undefined_type_error_message = ( + f'`TypeAdapter[{type_repr}]` is not fully defined; you should define `{type_repr}` and all referenced types,' + f' then call `.rebuild()` on the instance.' + ) + + def attempt_rebuild_fn(attr_fn: Callable[[TypeAdapter], T]) -> Callable[[], T | None]: + def handler() -> T | None: + if adapter.rebuild(raise_errors=False, _parent_namespace_depth=5) is not False: + return attr_fn(adapter) + else: + return None + + return handler + + adapter.core_schema = MockCoreSchema( # pyright: ignore[reportAttributeAccessIssue] + undefined_type_error_message, + code='class-not-fully-defined', + attempt_rebuild=attempt_rebuild_fn(lambda ta: ta.core_schema), + ) + adapter.validator = MockValSer( # pyright: ignore[reportAttributeAccessIssue] + undefined_type_error_message, + code='class-not-fully-defined', + val_or_ser='validator', + attempt_rebuild=attempt_rebuild_fn(lambda ta: ta.validator), + ) + adapter.serializer = MockValSer( # pyright: ignore[reportAttributeAccessIssue] + undefined_type_error_message, + code='class-not-fully-defined', + val_or_ser='serializer', + attempt_rebuild=attempt_rebuild_fn(lambda ta: ta.serializer), + ) + + def set_model_mocks(cls: type[BaseModel], cls_name: str, undefined_name: str = 'all referenced types') -> None: - """Set `__pydantic_validator__` and `__pydantic_serializer__` to `MockValSer`s on a model. + """Set `__pydantic_core_schema__`, `__pydantic_validator__` and `__pydantic_serializer__` to mock core types on a model. Args: cls: The model class to set the mocks on @@ -130,18 +171,18 @@ def handler() -> T | None: return handler - cls.__pydantic_core_schema__ = MockCoreSchema( # type: ignore[assignment] + cls.__pydantic_core_schema__ = MockCoreSchema( # pyright: ignore[reportAttributeAccessIssue] undefined_type_error_message, code='class-not-fully-defined', attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_core_schema__), ) - cls.__pydantic_validator__ = MockValSer( # type: ignore[assignment] + cls.__pydantic_validator__ = MockValSer( # pyright: ignore[reportAttributeAccessIssue] undefined_type_error_message, code='class-not-fully-defined', val_or_ser='validator', attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_validator__), ) - cls.__pydantic_serializer__ = MockValSer( # type: ignore[assignment] + cls.__pydantic_serializer__ = MockValSer( # pyright: ignore[reportAttributeAccessIssue] undefined_type_error_message, code='class-not-fully-defined', val_or_ser='serializer', @@ -175,20 +216,20 @@ def handler() -> T | None: return handler - cls.__pydantic_core_schema__ = MockCoreSchema( # type: ignore[assignment] + cls.__pydantic_core_schema__ = MockCoreSchema( # pyright: ignore[reportAttributeAccessIssue] undefined_type_error_message, code='class-not-fully-defined', attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_core_schema__), ) - cls.__pydantic_validator__ = MockValSer( # type: ignore[assignment] + cls.__pydantic_validator__ = MockValSer( # pyright: ignore[reportAttributeAccessIssue] undefined_type_error_message, code='class-not-fully-defined', val_or_ser='validator', attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_validator__), ) - cls.__pydantic_serializer__ = MockValSer( # type: ignore[assignment] + cls.__pydantic_serializer__ = MockValSer( # pyright: ignore[reportAttributeAccessIssue] undefined_type_error_message, code='class-not-fully-defined', - val_or_ser='validator', + val_or_ser='serializer', attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_serializer__), ) diff --git a/pydantic/_internal/_model_construction.py b/pydantic/_internal/_model_construction.py index 86c9bf05fe4..db87d66acb9 100644 --- a/pydantic/_internal/_model_construction.py +++ b/pydantic/_internal/_model_construction.py @@ -11,11 +11,10 @@ from abc import ABCMeta from functools import lru_cache, partial from types import FunctionType -from typing import Any, Callable, ForwardRef, Generic, Literal, NoReturn +from typing import Any, Callable, Generic, Literal, NoReturn, TypeVar, cast -import typing_extensions from pydantic_core import PydanticUndefined, SchemaSerializer -from typing_extensions import dataclass_transform, deprecated +from typing_extensions import TypeAliasType, dataclass_transform, deprecated, get_args from ..errors import PydanticUndefinedAnnotation, PydanticUserError from ..plugin._schema_validator import create_schema_validator @@ -27,21 +26,21 @@ from ._generics import PydanticGenericMetadata, get_model_typevars_map from ._import_utils import import_cached_base_model, import_cached_field_info from ._mock_val_ser import set_model_mocks +from ._namespace_utils import NsResolver from ._schema_generation_shared import CallbackGetCoreSchemaHandler from ._signature import generate_pydantic_signature from ._typing_extra import ( + _make_forward_ref, eval_type_backport, is_annotated, - is_classvar, - merge_cls_and_parent_ns, + is_classvar_annotation, parent_frame_namespace, ) -from ._utils import ClassAttribute, SafeGetItemProxy -from ._validate_call import ValidateCallWrapper +from ._utils import LazyClassAttribute, SafeGetItemProxy if typing.TYPE_CHECKING: + from ..fields import ComputedFieldInfo, FieldInfo, ModelPrivateAttr from ..fields import Field as PydanticModelField - from ..fields import FieldInfo, ModelPrivateAttr from ..fields import PrivateAttr as PydanticModelPrivateAttr from ..main import BaseModel else: @@ -132,13 +131,14 @@ def wrapped_model_post_init(self: BaseModel, context: Any, /) -> None: namespace['__class_vars__'] = class_vars namespace['__private_attributes__'] = {**base_private_attributes, **private_attributes} + if __pydantic_generic_metadata__: + namespace['__pydantic_generic_metadata__'] = __pydantic_generic_metadata__ - cls: type[BaseModel] = super().__new__(mcs, cls_name, bases, namespace, **kwargs) # type: ignore - - BaseModel = import_cached_base_model() + cls = cast('type[BaseModel]', super().__new__(mcs, cls_name, bases, namespace, **kwargs)) + BaseModel_ = import_cached_base_model() mro = cls.__mro__ - if Generic in mro and mro.index(Generic) < mro.index(BaseModel): + if Generic in mro and mro.index(Generic) < mro.index(BaseModel_): warnings.warn( GenericBeforeBaseModelWarning( 'Classes should inherit from `BaseModel` before generic classes (e.g. `typing.Generic[T]`) ' @@ -148,7 +148,9 @@ def wrapped_model_post_init(self: BaseModel, context: Any, /) -> None: ) cls.__pydantic_custom_init__ = not getattr(cls.__init__, '__pydantic_base_init__', False) - cls.__pydantic_post_init__ = None if cls.model_post_init is BaseModel.model_post_init else 'model_post_init' + cls.__pydantic_post_init__ = ( + None if cls.model_post_init is BaseModel_.model_post_init else 'model_post_init' + ) cls.__pydantic_decorators__ = DecoratorInfos.build(cls) @@ -210,12 +212,13 @@ def wrapped_model_post_init(self: BaseModel, context: Any, /) -> None: if __pydantic_reset_parent_namespace__: cls.__pydantic_parent_namespace__ = build_lenient_weakvaluedict(parent_frame_namespace()) - parent_namespace = getattr(cls, '__pydantic_parent_namespace__', None) + parent_namespace: dict[str, Any] | None = getattr(cls, '__pydantic_parent_namespace__', None) if isinstance(parent_namespace, dict): parent_namespace = unpack_lenient_weakvaluedict(parent_namespace) - types_namespace = merge_cls_and_parent_ns(cls, parent_namespace) - set_model_fields(cls, bases, config_wrapper, types_namespace) + ns_resolver = NsResolver(parent_namespace=parent_namespace) + + set_model_fields(cls, bases, config_wrapper, ns_resolver) if config_wrapper.frozen and '__hash__' not in namespace: set_default_hash_func(cls, bases) @@ -225,13 +228,15 @@ def wrapped_model_post_init(self: BaseModel, context: Any, /) -> None: cls_name, config_wrapper, raise_errors=False, - types_namespace=types_namespace, + ns_resolver=ns_resolver, create_model_module=_create_model_module, ) # If this is placed before the complete_model_class call above, # the generic computed fields return type is set to PydanticUndefined - cls.model_computed_fields = {k: v.info for k, v in cls.__pydantic_decorators__.computed_fields.items()} + cls.__pydantic_computed_fields__ = { + k: v.info for k, v in cls.__pydantic_decorators__.computed_fields.items() + } set_deprecated_descriptors(cls) @@ -250,6 +255,60 @@ def wrapped_model_post_init(self: BaseModel, context: Any, /) -> None: namespace.get('__annotations__', {}).clear() return super().__new__(mcs, cls_name, bases, namespace, **kwargs) + def mro(cls) -> list[type[Any]]: + original_mro = super().mro() + + if cls.__bases__ == (object,): + return original_mro + + generic_metadata: PydanticGenericMetadata | None = cls.__dict__.get('__pydantic_generic_metadata__') + if not generic_metadata: + return original_mro + + assert_err_msg = 'Unexpected error occurred when generating MRO of generic subclass. Please report this issue on GitHub: https://github.com/pydantic/pydantic/issues.' + + origin: type[BaseModel] | None + origin, args = ( + generic_metadata['origin'], + generic_metadata['args'], + ) + if not origin: + return original_mro + + target_params = origin.__pydantic_generic_metadata__['parameters'] + param_dict = dict(zip(target_params, args)) + + indexed_origins = {origin} + + new_mro: list[type[Any]] = [cls] + for base in original_mro[1:]: + base_origin: type[BaseModel] | None = getattr(base, '__pydantic_generic_metadata__', {}).get('origin') + base_params: tuple[TypeVar, ...] = getattr(base, '__pydantic_generic_metadata__', {}).get('parameters', ()) + + if base_origin in indexed_origins: + continue + elif base not in indexed_origins and base_params: + assert set(base_params) <= param_dict.keys(), assert_err_msg + new_base_args = tuple(param_dict[param] for param in base_params) + new_base = base[new_base_args] # type: ignore + new_mro.append(new_base) + + indexed_origins.add(base_origin or base) + + if base_origin is not None: + # dropped previous indexed origins + continue + else: + indexed_origins.add(base_origin or base) + + # Avoid redundunt case such as + # class A(BaseModel, Generic[T]): ... + # A[T] is A # True + if base is not new_mro[-1]: + new_mro.append(base) + + return new_mro + if not typing.TYPE_CHECKING: # pragma: no branch # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access @@ -281,7 +340,7 @@ def _collect_bases_data(bases: tuple[type[Any], ...]) -> tuple[set[str], set[str for base in bases: if issubclass(base, BaseModel) and base is not BaseModel: # model_fields might not be defined yet in the case of generics, so we use getattr here: - field_names.update(getattr(base, 'model_fields', {}).keys()) + field_names.update(getattr(base, '__pydantic_fields__', {}).keys()) class_vars.update(base.__class_vars__) private_attributes.update(base.__private_attributes__) return field_names, class_vars, private_attributes @@ -290,9 +349,29 @@ def _collect_bases_data(bases: tuple[type[Any], ...]) -> tuple[set[str], set[str @deprecated('The `__fields__` attribute is deprecated, use `model_fields` instead.', category=None) def __fields__(self) -> dict[str, FieldInfo]: warnings.warn( - 'The `__fields__` attribute is deprecated, use `model_fields` instead.', PydanticDeprecatedSince20 + 'The `__fields__` attribute is deprecated, use `model_fields` instead.', + PydanticDeprecatedSince20, + stacklevel=2, ) - return self.model_fields # type: ignore + return self.model_fields + + @property + def model_fields(self) -> dict[str, FieldInfo]: + """Get metadata about the fields defined on the model. + + Returns: + A mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects. + """ + return getattr(self, '__pydantic_fields__', {}) + + @property + def model_computed_fields(self) -> dict[str, ComputedFieldInfo]: + """Get metadata about the computed fields defined on the model. + + Returns: + A mapping of computed field names to [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects. + """ + return getattr(self, '__pydantic_computed_fields__', {}) def __dir__(self) -> list[str]: attributes = list(super().__dir__()) @@ -407,8 +486,8 @@ def inspect_namespace( # noqa C901 elif var_name.startswith('__'): continue elif is_valid_privateattr_name(var_name): - if var_name not in raw_annotations or not is_classvar(raw_annotations[var_name]): - private_attributes[var_name] = PrivateAttr(default=value) + if var_name not in raw_annotations or not is_classvar_annotation(raw_annotations[var_name]): + private_attributes[var_name] = cast(ModelPrivateAttr, PrivateAttr(default=value)) del namespace[var_name] elif var_name in base_class_vars: continue @@ -436,7 +515,9 @@ def inspect_namespace( # noqa C901 is_valid_privateattr_name(ann_name) and ann_name not in private_attributes and ann_name not in ignored_names - and not is_classvar(ann_type) + # This condition can be a false negative when `ann_type` is stringified, + # but it is handled in most cases in `set_model_fields`: + and not is_classvar_annotation(ann_type) and ann_type not in all_ignored_types and getattr(ann_type, '__module__', None) != 'functools' ): @@ -445,11 +526,17 @@ def inspect_namespace( # noqa C901 # (as the model class wasn't created yet, we unfortunately can't use `cls.__module__`): frame = sys._getframe(2) if frame is not None: - ann_type = eval_type_backport( - ForwardRef(ann_type), globalns=frame.f_globals, localns=frame.f_locals - ) + try: + ann_type = eval_type_backport( + _make_forward_ref(ann_type, is_argument=False, is_class=True), + globalns=frame.f_globals, + localns=frame.f_locals, + ) + except (NameError, TypeError): + pass + if is_annotated(ann_type): - _, *metadata = typing_extensions.get_args(ann_type) + _, *metadata = get_args(ann_type) private_attr = next((v for v in metadata if isinstance(v, ModelPrivateAttr)), None) if private_attr is not None: private_attributes[ann_name] = private_attr @@ -473,7 +560,7 @@ def set_default_hash_func(cls: type[BaseModel], bases: tuple[type[Any], ...]) -> def make_hash_func(cls: type[BaseModel]) -> Any: - getter = operator.itemgetter(*cls.model_fields.keys()) if cls.model_fields else lambda _: 0 + getter = operator.itemgetter(*cls.__pydantic_fields__.keys()) if cls.__pydantic_fields__ else lambda _: 0 def hash_func(self: Any) -> int: try: @@ -489,20 +576,23 @@ def hash_func(self: Any) -> int: def set_model_fields( - cls: type[BaseModel], bases: tuple[type[Any], ...], config_wrapper: ConfigWrapper, types_namespace: dict[str, Any] + cls: type[BaseModel], + bases: tuple[type[Any], ...], + config_wrapper: ConfigWrapper, + ns_resolver: NsResolver | None, ) -> None: - """Collect and set `cls.model_fields` and `cls.__class_vars__`. + """Collect and set `cls.__pydantic_fields__` and `cls.__class_vars__`. Args: cls: BaseModel or dataclass. bases: Parents of the class, generally `cls.__bases__`. config_wrapper: The config wrapper instance. - types_namespace: Optional extra namespace to look for types in. + ns_resolver: Namespace resolver to use when getting model annotations. """ typevars_map = get_model_typevars_map(cls) - fields, class_vars = collect_model_fields(cls, bases, config_wrapper, types_namespace, typevars_map=typevars_map) + fields, class_vars = collect_model_fields(cls, bases, config_wrapper, ns_resolver, typevars_map=typevars_map) - cls.model_fields = fields + cls.__pydantic_fields__ = fields cls.__class_vars__.update(class_vars) for k in class_vars: @@ -524,7 +614,7 @@ def complete_model_class( config_wrapper: ConfigWrapper, *, raise_errors: bool = True, - types_namespace: dict[str, Any] | None, + ns_resolver: NsResolver | None = None, create_model_module: str | None = None, ) -> bool: """Finish building a model class. @@ -537,7 +627,7 @@ def complete_model_class( cls_name: The model or dataclass name. config_wrapper: The config wrapper instance. raise_errors: Whether to raise errors. - types_namespace: Optional extra namespace to look for types in. + ns_resolver: The namespace resolver instance to use during schema building. create_model_module: The module of the class to be created, if created by `create_model`. Returns: @@ -547,10 +637,14 @@ def complete_model_class( PydanticUndefinedAnnotation: If `PydanticUndefinedAnnotation` occurs in`__get_pydantic_core_schema__` and `raise_errors=True`. """ + if config_wrapper.defer_build: + set_model_mocks(cls, cls_name) + return False + typevars_map = get_model_typevars_map(cls) gen_schema = GenerateSchema( config_wrapper, - types_namespace, + ns_resolver, typevars_map, ) @@ -560,10 +654,6 @@ def complete_model_class( ref_mode='unpack', ) - if config_wrapper.defer_build and 'model' in config_wrapper.experimental_defer_build_mode: - set_model_mocks(cls, cls_name) - return False - try: schema = cls.__get_pydantic_core_schema__(cls, handler) except PydanticUndefinedAnnotation as e: @@ -572,7 +662,7 @@ def complete_model_class( set_model_mocks(cls, cls_name, f'`{e.name}`') return False - core_config = config_wrapper.core_config(cls) + core_config = config_wrapper.core_config(title=cls.__name__) try: schema = gen_schema.clean_schema(schema) @@ -596,22 +686,30 @@ def complete_model_class( cls.__pydantic_complete__ = True # set __signature__ attr only for model class, but not for its instances - cls.__signature__ = ClassAttribute( + # (because instances can define `__call__`, and `inspect.signature` shouldn't + # use the `__signature__` attribute and instead generate from `__call__`). + cls.__signature__ = LazyClassAttribute( '__signature__', - generate_pydantic_signature(init=cls.__init__, fields=cls.model_fields, config_wrapper=config_wrapper), + partial( + generate_pydantic_signature, + init=cls.__init__, + fields=cls.__pydantic_fields__, + populate_by_name=config_wrapper.populate_by_name, + extra=config_wrapper.extra, + ), ) return True def set_deprecated_descriptors(cls: type[BaseModel]) -> None: """Set data descriptors on the class for deprecated fields.""" - for field, field_info in cls.model_fields.items(): + for field, field_info in cls.__pydantic_fields__.items(): if (msg := field_info.deprecation_message) is not None: desc = _DeprecatedFieldDescriptor(msg) desc.__set_name__(cls, field) setattr(cls, field, desc) - for field, computed_field_info in cls.model_computed_fields.items(): + for field, computed_field_info in cls.__pydantic_computed_fields__.items(): if ( (msg := computed_field_info.deprecation_message) is not None # Avoid having two warnings emitted: @@ -623,7 +721,7 @@ def set_deprecated_descriptors(cls: type[BaseModel]) -> None: class _DeprecatedFieldDescriptor: - """Data descriptor used to emit a runtime deprecation warning before accessing a deprecated field. + """Read-only data descriptor used to emit a runtime deprecation warning before accessing a deprecated field. Attributes: msg: The deprecation message to be emitted. @@ -642,6 +740,8 @@ def __set_name__(self, cls: type[BaseModel], name: str) -> None: def __get__(self, obj: BaseModel | None, obj_type: type[BaseModel] | None = None) -> Any: if obj is None: + if self.wrapped_property is not None: + return self.wrapped_property.__get__(None, obj_type) raise AttributeError(self.field_name) warnings.warn(self.msg, builtins.DeprecationWarning, stacklevel=2) @@ -650,7 +750,7 @@ def __get__(self, obj: BaseModel | None, obj_type: type[BaseModel] | None = None return self.wrapped_property.__get__(obj, obj_type) return obj.__dict__[self.field_name] - # Defined to take precedence over the instance's dictionary + # Defined to make it a data descriptor and take precedence over the instance's dictionary. # Note that it will not be called when setting a value on a model instance # as `BaseModel.__setattr__` is defined and takes priority. def __set__(self, obj: Any, value: Any) -> NoReturn: @@ -732,12 +832,17 @@ def unpack_lenient_weakvaluedict(d: dict[str, Any] | None) -> dict[str, Any] | N def default_ignored_types() -> tuple[type[Any], ...]: from ..fields import ComputedFieldInfo - return ( + ignored_types = [ FunctionType, property, classmethod, staticmethod, PydanticDescriptorProxy, ComputedFieldInfo, - ValidateCallWrapper, - ) + TypeAliasType, # from `typing_extensions` + ] + + if sys.version_info >= (3, 12): + ignored_types.append(typing.TypeAliasType) + + return tuple(ignored_types) diff --git a/pydantic/_internal/_namespace_utils.py b/pydantic/_internal/_namespace_utils.py new file mode 100644 index 00000000000..9b956d1db29 --- /dev/null +++ b/pydantic/_internal/_namespace_utils.py @@ -0,0 +1,278 @@ +from __future__ import annotations + +import sys +from collections.abc import Generator +from contextlib import contextmanager +from functools import cached_property +from typing import Any, Callable, Iterator, Mapping, NamedTuple, TypeVar + +from typing_extensions import TypeAlias, TypeAliasType + +GlobalsNamespace: TypeAlias = 'dict[str, Any]' +"""A global namespace. + +In most cases, this is a reference to the `__dict__` attribute of a module. +This namespace type is expected as the `globals` argument during annotations evaluation. +""" + +MappingNamespace: TypeAlias = Mapping[str, Any] +"""Any kind of namespace. + +In most cases, this is a local namespace (e.g. the `__dict__` attribute of a class, +the [`f_locals`][frame.f_locals] attribute of a frame object, when dealing with types +defined inside functions). +This namespace type is expected as the `locals` argument during annotations evaluation. +""" + + +class NamespacesTuple(NamedTuple): + """A tuple of globals and locals to be used during annotations evaluation. + + This datastructure is defined as a named tuple so that it can easily be unpacked: + + ```python {lint="skip" test="skip"} + def eval_type(typ: type[Any], ns: NamespacesTuple) -> None: + return eval(typ, *ns) + ``` + """ + + globals: GlobalsNamespace + """The namespace to be used as the `globals` argument during annotations evaluation.""" + + locals: MappingNamespace + """The namespace to be used as the `locals` argument during annotations evaluation.""" + + +def get_module_ns_of(obj: Any) -> dict[str, Any]: + """Get the namespace of the module where the object is defined. + + Caution: this function does not return a copy of the module namespace, so the result + should not be mutated. The burden of enforcing this is on the caller. + """ + module_name = getattr(obj, '__module__', None) + if module_name: + try: + return sys.modules[module_name].__dict__ + except KeyError: + # happens occasionally, see https://github.com/pydantic/pydantic/issues/2363 + return {} + return {} + + +# Note that this class is almost identical to `collections.ChainMap`, but need to enforce +# immutable mappings here: +class LazyLocalNamespace(Mapping[str, Any]): + """A lazily evaluated mapping, to be used as the `locals` argument during annotations evaluation. + + While the [`eval`][eval] function expects a mapping as the `locals` argument, it only + performs `__getitem__` calls. The [`Mapping`][collections.abc.Mapping] abstract base class + is fully implemented only for type checking purposes. + + Args: + *namespaces: The namespaces to consider, in ascending order of priority. + + Example: + ```python {lint="skip" test="skip"} + ns = LazyLocalNamespace({'a': 1, 'b': 2}, {'a': 3}) + ns['a'] + #> 3 + ns['b'] + #> 2 + ``` + """ + + def __init__(self, *namespaces: MappingNamespace) -> None: + self._namespaces = namespaces + + @cached_property + def data(self) -> dict[str, Any]: + return {k: v for ns in self._namespaces for k, v in ns.items()} + + def __len__(self) -> int: + return len(self.data) + + def __getitem__(self, key: str) -> Any: + return self.data[key] + + def __contains__(self, key: object) -> bool: + return key in self.data + + def __iter__(self) -> Iterator[str]: + return iter(self.data) + + +def ns_for_function(obj: Callable[..., Any], parent_namespace: MappingNamespace | None = None) -> NamespacesTuple: + """Return the global and local namespaces to be used when evaluating annotations for the provided function. + + The global namespace will be the `__dict__` attribute of the module the function was defined in. + The local namespace will contain the `__type_params__` introduced by PEP 695. + + Args: + obj: The object to use when building namespaces. + parent_namespace: Optional namespace to be added with the lowest priority in the local namespace. + If the passed function is a method, the `parent_namespace` will be the namespace of the class + the method is defined in. Thus, we also fetch type `__type_params__` from there (i.e. the + class-scoped type variables). + """ + locals_list: list[MappingNamespace] = [] + if parent_namespace is not None: + locals_list.append(parent_namespace) + + # Get the `__type_params__` attribute introduced by PEP 695. + # Note that the `typing._eval_type` function expects type params to be + # passed as a separate argument. However, internally, `_eval_type` calls + # `ForwardRef._evaluate` which will merge type params with the localns, + # essentially mimicking what we do here. + type_params: tuple[TypeVar, ...] = () + if parent_namespace is not None: + # We also fetch type params from the parent namespace. If present, it probably + # means the function was defined in a class. This is to support the following: + # https://github.com/python/cpython/issues/124089. + type_params += parent_namespace.get('__type_params__', ()) + + locals_list.append({t.__name__: t for t in type_params}) + + # What about short-cirtuiting to `obj.__globals__`? + globalns = get_module_ns_of(obj) + + return NamespacesTuple(globalns, LazyLocalNamespace(*locals_list)) + + +class NsResolver: + """A class responsible for the namespaces resolving logic for annotations evaluation. + + This class handles the namespace logic when evaluating annotations mainly for class objects. + + It holds a stack of classes that are being inspected during the core schema building, + and the `types_namespace` property exposes the globals and locals to be used for + type annotation evaluation. Additionally -- if no class is present in the stack -- a + fallback globals and locals can be provided using the `namespaces_tuple` argument + (this is useful when generating a schema for a simple annotation, e.g. when using + `TypeAdapter`). + + The namespace creation logic is unfortunately flawed in some cases, for backwards + compatibility reasons and to better support valid edge cases. See the description + for the `parent_namespace` argument and the example for more details. + + Args: + namespaces_tuple: The default globals and locals to use if no class is present + on the stack. This can be useful when using the `GenerateSchema` class + with `TypeAdapter`, where the "type" being analyzed is a simple annotation. + parent_namespace: An optional parent namespace that will be added to the locals + with the lowest priority. For a given class defined in a function, the locals + of this function are usually used as the parent namespace: + + ```python {lint="skip" test="skip"} + from pydantic import BaseModel + + def func() -> None: + SomeType = int + + class Model(BaseModel): + f: 'SomeType' + + # when collecting fields, an namespace resolver instance will be created + # this way: + # ns_resolver = NsResolver(parent_namespace={'SomeType': SomeType}) + ``` + + For backwards compatibility reasons and to support valid edge cases, this parent + namespace will be used for *every* type being pushed to the stack. In the future, + we might want to be smarter by only doing so when the type being pushed is defined + in the same module as the parent namespace. + + Example: + ```python {lint="skip" test="skip"} + ns_resolver = NsResolver( + parent_namespace={'fallback': 1}, + ) + + class Sub: + m: 'Model' + + class Model: + some_local = 1 + sub: Sub + + ns_resolver = NsResolver() + + # This is roughly what happens when we build a core schema for `Model`: + with ns_resolver.push(Model): + ns_resolver.types_namespace + #> NamespacesTuple({'Sub': Sub}, {'Model': Model, 'some_local': 1}) + # First thing to notice here, the model being pushed is added to the locals. + # Because `NsResolver` is being used during the model definition, it is not + # yet added to the globals. This is useful when resolving self-referencing annotations. + + with ns_resolver.push(Sub): + ns_resolver.types_namespace + #> NamespacesTuple({'Sub': Sub}, {'Sub': Sub, 'Model': Model}) + # Second thing to notice: `Sub` is present in both the globals and locals. + # This is not an issue, just that as described above, the model being pushed + # is added to the locals, but it happens to be present in the globals as well + # because it is already defined. + # Third thing to notice: `Model` is also added in locals. This is a backwards + # compatibility workaround that allows for `Sub` to be able to resolve `'Model'` + # correctly (as otherwise models would have to be rebuilt even though this + # doesn't look necessary). + ``` + """ + + def __init__( + self, + namespaces_tuple: NamespacesTuple | None = None, + parent_namespace: MappingNamespace | None = None, + ) -> None: + self._base_ns_tuple = namespaces_tuple or NamespacesTuple({}, {}) + self._parent_ns = parent_namespace + self._types_stack: list[type[Any] | TypeAliasType] = [] + + @cached_property + def types_namespace(self) -> NamespacesTuple: + """The current global and local namespaces to be used for annotations evaluation.""" + if not self._types_stack: + # TODO: should we merge the parent namespace here? + # This is relevant for TypeAdapter, where there are no types on the stack, and we might + # need access to the parent_ns. Right now, we sidestep this in `type_adapter.py` by passing + # locals to both parent_ns and the base_ns_tuple, but this is a bit hacky. + # we might consider something like: + # if self._parent_ns is not None: + # # Hacky workarounds, see class docstring: + # # An optional parent namespace that will be added to the locals with the lowest priority + # locals_list: list[MappingNamespace] = [self._parent_ns, self._base_ns_tuple.locals] + # return NamespacesTuple(self._base_ns_tuple.globals, LazyLocalNamespace(*locals_list)) + return self._base_ns_tuple + + typ = self._types_stack[-1] + + globalns = get_module_ns_of(typ) + + locals_list: list[MappingNamespace] = [] + # Hacky workarounds, see class docstring: + # An optional parent namespace that will be added to the locals with the lowest priority + if self._parent_ns is not None: + locals_list.append(self._parent_ns) + if len(self._types_stack) > 1: + first_type = self._types_stack[0] + locals_list.append({first_type.__name__: first_type}) + + if hasattr(typ, '__dict__'): + # TypeAliasType is the exception. + locals_list.append(vars(typ)) + + # The len check above presents this from being added twice: + locals_list.append({typ.__name__: typ}) + + return NamespacesTuple(globalns, LazyLocalNamespace(*locals_list)) + + @contextmanager + def push(self, typ: type[Any] | TypeAliasType, /) -> Generator[None]: + """Push a type to the stack.""" + self._types_stack.append(typ) + # Reset the cached property: + self.__dict__.pop('types_namespace', None) + try: + yield + finally: + self._types_stack.pop() + self.__dict__.pop('types_namespace', None) diff --git a/pydantic/_internal/_repr.py b/pydantic/_internal/_repr.py index 54fb1d7bea3..de81c8bd4f6 100644 --- a/pydantic/_internal/_repr.py +++ b/pydantic/_internal/_repr.py @@ -46,12 +46,17 @@ def __repr_args__(self) -> ReprArgs: if not attrs_names and hasattr(self, '__dict__'): attrs_names = self.__dict__.keys() attrs = ((s, getattr(self, s)) for s in attrs_names) - return [(a, v) for a, v in attrs if v is not None] + return [(a, v if v is not self else self.__repr_recursion__(v)) for a, v in attrs if v is not None] def __repr_name__(self) -> str: """Name of the instance's class, used in __repr__.""" return self.__class__.__name__ + def __repr_recursion__(self, object: Any) -> str: + """Returns the string representation of a recursive object.""" + # This is copied over from the stdlib `pprint` module: + return f'' + def __repr_str__(self, join_str: str) -> str: return join_str.join(repr(v) if a is None else f'{a}={v!r}' for a, v in self.__repr_args__()) @@ -88,13 +93,13 @@ def display_as_type(obj: Any) -> str: Takes some logic from `typing._type_repr`. """ - if isinstance(obj, types.FunctionType): + if isinstance(obj, (types.FunctionType, types.BuiltinFunctionType)): return obj.__name__ elif obj is ...: return '...' elif isinstance(obj, Representation): return repr(obj) - elif isinstance(obj, typing_extensions.TypeAliasType): + elif isinstance(obj, typing.ForwardRef) or _typing_extra.is_type_alias_type(obj): return str(obj) if not isinstance(obj, (_typing_extra.typing_base, _typing_extra.WithArgsTypes, type)): @@ -104,14 +109,14 @@ def display_as_type(obj: Any) -> str: args = ', '.join(map(display_as_type, typing_extensions.get_args(obj))) return f'Union[{args}]' elif isinstance(obj, _typing_extra.WithArgsTypes): - if typing_extensions.get_origin(obj) == typing_extensions.Literal: + if _typing_extra.is_literal(obj): args = ', '.join(map(repr, typing_extensions.get_args(obj))) else: args = ', '.join(map(display_as_type, typing_extensions.get_args(obj))) try: return f'{obj.__qualname__}[{args}]' except AttributeError: - return str(obj) # handles TypeAliasType in 3.12 + return str(obj).replace('typing.', '').replace('typing_extensions.', '') # handles TypeAliasType in 3.12 elif isinstance(obj, type): return obj.__qualname__ else: diff --git a/pydantic/_internal/_schema_generation_shared.py b/pydantic/_internal/_schema_generation_shared.py index f35c665d26d..a6e3391d913 100644 --- a/pydantic/_internal/_schema_generation_shared.py +++ b/pydantic/_internal/_schema_generation_shared.py @@ -13,6 +13,7 @@ from ..json_schema import GenerateJsonSchema, JsonSchemaValue from ._core_utils import CoreSchemaOrField from ._generate_schema import GenerateSchema + from ._namespace_utils import NamespacesTuple GetJsonSchemaFunction = Callable[[CoreSchemaOrField, GetJsonSchemaHandler], JsonSchemaValue] HandlerOverride = Callable[[CoreSchemaOrField], JsonSchemaValue] @@ -90,7 +91,7 @@ def __call__(self, source_type: Any, /) -> core_schema.CoreSchema: else: # ref_mode = 'unpack return self.resolve_ref_schema(schema) - def _get_types_namespace(self) -> dict[str, Any] | None: + def _get_types_namespace(self) -> NamespacesTuple: return self._generate_schema._types_namespace def generate_schema(self, source_type: Any, /) -> core_schema.CoreSchema: diff --git a/pydantic/_internal/_signature.py b/pydantic/_internal/_signature.py index 0e3c8af814d..2273577ccb2 100644 --- a/pydantic/_internal/_signature.py +++ b/pydantic/_internal/_signature.py @@ -6,13 +6,22 @@ from pydantic_core import PydanticUndefined -from ._config import ConfigWrapper from ._utils import is_valid_identifier if TYPE_CHECKING: + from ..config import ExtraValues from ..fields import FieldInfo +# Copied over from stdlib dataclasses +class _HAS_DEFAULT_FACTORY_CLASS: + def __repr__(self): + return '' + + +_HAS_DEFAULT_FACTORY = _HAS_DEFAULT_FACTORY_CLASS() + + def _field_name_for_signature(field_name: str, field_info: FieldInfo) -> str: """Extract the correct name to use for the field when generating a signature. @@ -71,7 +80,8 @@ def _process_param_defaults(param: Parameter) -> Parameter: def _generate_signature_parameters( # noqa: C901 (ignore complexity, could use a refactor) init: Callable[..., None], fields: dict[str, FieldInfo], - config_wrapper: ConfigWrapper, + populate_by_name: bool, + extra: ExtraValues | None, ) -> dict[str, Parameter]: """Generate a mapping of parameter names to Parameter objects for a pydantic BaseModel or dataclass.""" from itertools import islice @@ -97,7 +107,7 @@ def _generate_signature_parameters( # noqa: C901 (ignore complexity, could use merged_params[param.name] = param if var_kw: # if custom init has no var_kw, fields which are not declared in it cannot be passed through - allow_names = config_wrapper.populate_by_name + allow_names = populate_by_name for field_name, field in fields.items(): # when alias is a str it should be used for signature generation param_name = _field_name_for_signature(field_name, field) @@ -112,12 +122,21 @@ def _generate_signature_parameters( # noqa: C901 (ignore complexity, could use use_var_kw = True continue - kwargs = {} if field.is_required() else {'default': field.get_default(call_default_factory=False)} + if field.is_required(): + default = Parameter.empty + elif field.default_factory is not None: + # Mimics stdlib dataclasses: + default = _HAS_DEFAULT_FACTORY + else: + default = field.default merged_params[param_name] = Parameter( - param_name, Parameter.KEYWORD_ONLY, annotation=field.rebuild_annotation(), **kwargs + param_name, + Parameter.KEYWORD_ONLY, + annotation=field.rebuild_annotation(), + default=default, ) - if config_wrapper.extra == 'allow': + if extra == 'allow': use_var_kw = True if var_kw and use_var_kw: @@ -143,20 +162,25 @@ def _generate_signature_parameters( # noqa: C901 (ignore complexity, could use def generate_pydantic_signature( - init: Callable[..., None], fields: dict[str, FieldInfo], config_wrapper: ConfigWrapper, is_dataclass: bool = False + init: Callable[..., None], + fields: dict[str, FieldInfo], + populate_by_name: bool, + extra: ExtraValues | None, + is_dataclass: bool = False, ) -> Signature: """Generate signature for a pydantic BaseModel or dataclass. Args: init: The class init. fields: The model fields. - config_wrapper: The config wrapper instance. + populate_by_name: The `populate_by_name` value of the config. + extra: The `extra` value of the config. is_dataclass: Whether the model is a dataclass. Returns: The dataclass/BaseModel subclass signature. """ - merged_params = _generate_signature_parameters(init, fields, config_wrapper) + merged_params = _generate_signature_parameters(init, fields, populate_by_name, extra) if is_dataclass: merged_params = {k: _process_param_defaults(v) for k, v in merged_params.items()} diff --git a/pydantic/_internal/_std_types_schema.py b/pydantic/_internal/_std_types_schema.py index 57bd7275e1d..84a7a4a4225 100644 --- a/pydantic/_internal/_std_types_schema.py +++ b/pydantic/_internal/_std_types_schema.py @@ -14,7 +14,7 @@ import os import typing from functools import partial -from typing import Any, Callable, Iterable, Tuple, TypeVar +from typing import Any, Callable, Iterable, Tuple, TypeVar, cast import typing_extensions from pydantic_core import ( @@ -164,13 +164,13 @@ class DequeValidator: metadata: dict[str, Any] def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema: - if self.item_source_type is Any: + if _typing_extra.is_any(self.item_source_type): items_schema = None else: items_schema = handler.generate_schema(self.item_source_type) # if we have a MaxLen annotation might as well set that as the default maxlen on the deque - # this lets us re-use existing metadata annotations to let users set the maxlen on a dequeue + # this lets us reuse existing metadata annotations to let users set the maxlen on a dequeue # that e.g. comes from JSON coerce_instance_wrap = partial( core_schema.no_info_wrap_validator_function, @@ -300,7 +300,8 @@ def type_var_default_factory() -> None: else: field_info = None if field_info and field_info.default_factory: - default_default_factory = field_info.default_factory + # Assume the default factory does not take any argument: + default_default_factory = cast(Callable[[], Any], field_info.default_factory) else: default_default_factory = infer_default() return default_default_factory @@ -319,11 +320,11 @@ def serialize_mapping_via_dict(self, v: Any, handler: core_schema.SerializerFunc return handler(v) def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema: - if self.keys_source_type is Any: + if _typing_extra.is_any(self.keys_source_type): keys_schema = None else: keys_schema = handler.generate_schema(self.keys_source_type) - if self.values_source_type is Any: + if _typing_extra.is_any(self.values_source_type): values_schema = None else: values_schema = handler.generate_schema(self.values_source_type) diff --git a/pydantic/_internal/_typing_extra.py b/pydantic/_internal/_typing_extra.py index 50566e5a24a..65dec2d9351 100644 --- a/pydantic/_internal/_typing_extra.py +++ b/pydantic/_internal/_typing_extra.py @@ -1,170 +1,416 @@ -"""Logic for interacting with type annotations, mostly extensions, shims and hacks to wrap python's typing module.""" +"""Logic for interacting with type annotations, mostly extensions, shims and hacks to wrap Python's typing module.""" -from __future__ import annotations as _annotations +from __future__ import annotations -import dataclasses +import collections.abc import re import sys import types import typing import warnings -from collections.abc import Callable -from functools import partial -from types import GetSetDescriptorType -from typing import TYPE_CHECKING, Any, Final, Iterable +from functools import lru_cache, partial +from typing import Any, Callable -from typing_extensions import Annotated, Literal, TypeAliasType, TypeGuard, deprecated, get_args, get_origin +import typing_extensions +from typing_extensions import TypeIs, deprecated, get_args, get_origin -if TYPE_CHECKING: - from ._dataclasses import StandardDataclass +from ._namespace_utils import GlobalsNamespace, MappingNamespace, NsResolver, get_module_ns_of -try: - from typing import _TypingBase # type: ignore[attr-defined] -except ImportError: - from typing import _Final as _TypingBase # type: ignore[attr-defined] +if sys.version_info < (3, 10): + NoneType = type(None) + EllipsisType = type(Ellipsis) +else: + from types import EllipsisType as EllipsisType + from types import NoneType as NoneType -typing_base = _TypingBase +# See https://typing-extensions.readthedocs.io/en/latest/#runtime-use-of-types: -if sys.version_info < (3, 9): - # python < 3.9 does not have GenericAlias (list[int], tuple[str, ...] and so on) - TypingGenericAlias = () -else: - from typing import GenericAlias as TypingGenericAlias # type: ignore +@lru_cache(maxsize=None) +def _get_typing_objects_by_name_of(name: str) -> tuple[Any, ...]: + """Get the member named `name` from both `typing` and `typing-extensions` (if it exists).""" + result = tuple(getattr(module, name) for module in (typing, typing_extensions) if hasattr(module, name)) + if not result: + raise ValueError(f'Neither `typing` nor `typing_extensions` has an object called {name!r}') + return result -if sys.version_info < (3, 11): - from typing_extensions import NotRequired, Required -else: - from typing import NotRequired, Required # noqa: F401 +# As suggested by the `typing-extensions` documentation, we could apply caching to this method, +# but it doesn't seem to improve performance. This also requires `obj` to be hashable, which +# might not be always the case: +def _is_typing_name(obj: object, name: str) -> bool: + """Return whether `obj` is the member of the typing modules (includes the `typing-extensions` one) named `name`.""" + # Using `any()` is slower: + for thing in _get_typing_objects_by_name_of(name): + if obj is thing: + return True + return False -if sys.version_info < (3, 10): - def origin_is_union(tp: type[Any] | None) -> bool: - return tp is typing.Union +def is_any(tp: Any, /) -> bool: + """Return whether the provided argument is the `Any` special form. - WithArgsTypes = (TypingGenericAlias,) + ```python {test="skip" lint="skip"} + is_any(Any) + #> True + ``` + """ + return _is_typing_name(tp, name='Any') -else: - def origin_is_union(tp: type[Any] | None) -> bool: - return tp is typing.Union or tp is types.UnionType +def is_union(tp: Any, /) -> bool: + """Return whether the provided argument is a `Union` special form. - WithArgsTypes = typing._GenericAlias, types.GenericAlias, types.UnionType # type: ignore[attr-defined] + ```python {test="skip" lint="skip"} + is_union(Union[int, str]) + #> True + is_union(int | str) + #> False + ``` + """ + return _is_typing_name(get_origin(tp), name='Union') -if sys.version_info < (3, 10): - NoneType = type(None) - EllipsisType = type(Ellipsis) -else: - from types import NoneType as NoneType +def is_literal(tp: Any, /) -> bool: + """Return whether the provided argument is a `Literal` special form. + ```python {test="skip" lint="skip"} + is_literal(Literal[42]) + #> True + ``` + """ + return _is_typing_name(get_origin(tp), name='Literal') -LITERAL_TYPES: set[Any] = {Literal} -if hasattr(typing, 'Literal'): - LITERAL_TYPES.add(typing.Literal) # type: ignore -# Check if `deprecated` is a type to prevent errors when using typing_extensions < 4.9.0 -DEPRECATED_TYPES: tuple[Any, ...] = (deprecated,) if isinstance(deprecated, type) else () -if hasattr(warnings, 'deprecated'): - DEPRECATED_TYPES = (*DEPRECATED_TYPES, warnings.deprecated) # type: ignore +# TODO remove and replace with `get_args` when we drop support for Python 3.8 +# (see https://docs.python.org/3/whatsnew/3.9.html#id4). +def literal_values(tp: Any, /) -> list[Any]: + """Return the values contained in the provided `Literal` special form.""" + if not is_literal(tp): + return [tp] -NONE_TYPES: tuple[Any, ...] = (None, NoneType, *(tp[None] for tp in LITERAL_TYPES)) + values = get_args(tp) + return [x for value in values for x in literal_values(value)] -TypeVarType = Any # since mypy doesn't allow the use of TypeVar as a type +def is_annotated(tp: Any, /) -> bool: + """Return whether the provided argument is a `Annotated` special form. + ```python {test="skip" lint="skip"} + is_annotated(Annotated[int, ...]) + #> True + ``` + """ + return _is_typing_name(get_origin(tp), name='Annotated') -def is_none_type(type_: Any) -> bool: - return type_ in NONE_TYPES +def annotated_type(tp: Any, /) -> Any | None: + """Return the type of the `Annotated` special form, or `None`.""" + return get_args(tp)[0] if is_annotated(tp) else None -def is_callable_type(type_: type[Any]) -> bool: - return type_ is Callable or get_origin(type_) is Callable +def is_unpack(tp: Any, /) -> bool: + """Return whether the provided argument is a `Unpack` special form. -def is_literal_type(type_: type[Any]) -> bool: - return Literal is not None and get_origin(type_) in LITERAL_TYPES + ```python {test="skip" lint="skip"} + is_unpack(Unpack[Ts]) + #> True + ``` + """ + return _is_typing_name(get_origin(tp), name='Unpack') -def is_deprecated_instance(instance: Any) -> TypeGuard[deprecated]: - return isinstance(instance, DEPRECATED_TYPES) +def unpack_type(tp: Any, /) -> Any | None: + """Return the type wrapped by the `Unpack` special form, or `None`.""" + return get_args(tp)[0] if is_unpack(tp) else None -def literal_values(type_: type[Any]) -> tuple[Any, ...]: - return get_args(type_) +def is_self(tp: Any, /) -> bool: + """Return whether the provided argument is the `Self` special form. + ```python {test="skip" lint="skip"} + is_self(Self) + #> True + ``` + """ + return _is_typing_name(tp, name='Self') -def all_literal_values(type_: type[Any]) -> list[Any]: - """This method is used to retrieve all Literal values as - Literal can be used recursively (see https://www.python.org/dev/peps/pep-0586) - e.g. `Literal[Literal[Literal[1, 2, 3], "foo"], 5, None]`. + +def is_new_type(tp: Any, /) -> bool: + """Return whether the provided argument is a `NewType`. + + ```python {test="skip" lint="skip"} + is_new_type(NewType('MyInt', int)) + #> True + ``` """ - if not is_literal_type(type_): - return [type_] + if sys.version_info < (3, 10): + # On Python < 3.10, `typing.NewType` is a function + return hasattr(tp, '__supertype__') + else: + return _is_typing_name(type(tp), name='NewType') - values = literal_values(type_) - return [x for value in values for x in all_literal_values(value)] +def is_hashable(tp: Any, /) -> bool: + """Return whether the provided argument is the `Hashable` class. + + ```python {test="skip" lint="skip"} + is_hashable(Hashable) + #> True + ``` + """ + # `get_origin` is documented as normalizing any typing-module aliases to `collections` classes, + # hence the second check: + return tp is collections.abc.Hashable or get_origin(tp) is collections.abc.Hashable + + +def is_callable(tp: Any, /) -> bool: + """Return whether the provided argument is a `Callable`, parametrized or not. + + ```python {test="skip" lint="skip"} + is_callable(Callable[[int], str]) + #> True + is_callable(typing.Callable) + #> True + is_callable(collections.abc.Callable) + #> True + ``` + """ + # `get_origin` is documented as normalizing any typing-module aliases to `collections` classes, + # hence the second check: + return tp is collections.abc.Callable or get_origin(tp) is collections.abc.Callable -def is_annotated(ann_type: Any) -> bool: - return get_origin(ann_type) is Annotated +_PARAMSPEC_TYPES: tuple[type[typing_extensions.ParamSpec], ...] = (typing_extensions.ParamSpec,) +if sys.version_info >= (3, 10): + _PARAMSPEC_TYPES = (*_PARAMSPEC_TYPES, typing.ParamSpec) # pyright: ignore[reportAssignmentType] -def annotated_type(type_: Any) -> Any | None: - return get_args(type_)[0] if is_annotated(type_) else None +def is_paramspec(tp: Any, /) -> bool: + """Return whether the provided argument is a `ParamSpec`. -def is_namedtuple(type_: type[Any]) -> bool: - """Check if a given class is a named tuple. - It can be either a `typing.NamedTuple` or `collections.namedtuple`. + ```python {test="skip" lint="skip"} + P = ParamSpec('P') + is_paramspec(P) + #> True + ``` """ - from ._utils import lenient_issubclass + return isinstance(tp, _PARAMSPEC_TYPES) + - return lenient_issubclass(type_, tuple) and hasattr(type_, '_fields') +_TYPE_ALIAS_TYPES: tuple[type[typing_extensions.TypeAliasType], ...] = (typing_extensions.TypeAliasType,) +if sys.version_info >= (3, 12): + _TYPE_ALIAS_TYPES = (*_TYPE_ALIAS_TYPES, typing.TypeAliasType) -test_new_type = typing.NewType('test_new_type', str) +def is_type_alias_type(tp: Any, /) -> TypeIs[typing_extensions.TypeAliasType]: + """Return whether the provided argument is an instance of `TypeAliasType`. + + ```python {test="skip" lint="skip"} + type Int = int + is_type_alias_type(Int) + #> True + Str = TypeAliasType('Str', str) + is_type_alias_type(Str) + #> True + ``` + """ + return isinstance(tp, _TYPE_ALIAS_TYPES) -def is_new_type(type_: type[Any]) -> bool: - """Check whether type_ was created using typing.NewType. +def is_classvar(tp: Any, /) -> bool: + """Return whether the provided argument is a `ClassVar` special form, parametrized or not. - Can't use isinstance because it fails <3.10. + Note that in most cases, you will want to use the `is_classvar_annotation` function, + which is used to check if an annotation (in the context of a Pydantic model or dataclass) + should be treated as being a class variable. + + ```python {test="skip" lint="skip"} + is_classvar(ClassVar[int]) + #> True + is_classvar(ClassVar) + #> True """ - return isinstance(type_, test_new_type.__class__) and hasattr(type_, '__supertype__') # type: ignore[arg-type] + # ClassVar is not necessarily parametrized: + return _is_typing_name(tp, name='ClassVar') or _is_typing_name(get_origin(tp), name='ClassVar') -classvar_re = re.compile(r'(\w+\.)?ClassVar\[') +_classvar_re = re.compile(r'((\w+\.)?Annotated\[)?(\w+\.)?ClassVar\[') -def _check_classvar(v: type[Any] | None) -> bool: - return v is not None and v.__class__ is typing.ClassVar.__class__ and getattr(v, '_name', None) == 'ClassVar' +def is_classvar_annotation(tp: Any, /) -> bool: + """Return whether the provided argument represents a class variable annotation. + Although not explicitly stated by the typing specification, `ClassVar` can be used + inside `Annotated` and as such, this function checks for this specific scenario. -def is_classvar(ann_type: type[Any]) -> bool: - if _check_classvar(ann_type) or _check_classvar(get_origin(ann_type)): + Because this function is used to detect class variables before evaluating forward references + (or because evaluation failed), we also implement a naive regex match implementation. This is + required because class variables are inspected before fields are collected, so we try to be + as accurate as possible. + """ + if is_classvar(tp) or (anntp := annotated_type(tp)) is not None and is_classvar(anntp): return True - # this is an ugly workaround for class vars that contain forward references and are therefore themselves - # forward references, see #3679 - if ann_type.__class__ == typing.ForwardRef and classvar_re.match(ann_type.__forward_arg__): + str_ann: str | None = None + if isinstance(tp, typing.ForwardRef): + str_ann = tp.__forward_arg__ + if isinstance(tp, str): + str_ann = tp + + if str_ann is not None and _classvar_re.match(str_ann): + # stdlib dataclasses do something similar, although a bit more advanced + # (see `dataclass._is_type`). return True return False -def _check_finalvar(v: type[Any] | None) -> bool: - """Check if a given type is a `typing.Final` type.""" - if v is None: +# TODO implement `is_finalvar_annotation` as Final can be wrapped with other special forms: +def is_finalvar(tp: Any, /) -> bool: + """Return whether the provided argument is a `Final` special form, parametrized or not. + + ```python {test="skip" lint="skip"} + is_finalvar(Final[int]) + #> True + is_finalvar(Final) + #> True + """ + # Final is not necessarily parametrized: + return _is_typing_name(tp, name='Final') or _is_typing_name(get_origin(tp), name='Final') + + +def is_required(tp: Any, /) -> bool: + """Return whether the provided argument is a `Required` special form. + + ```python {test="skip" lint="skip"} + is_required(Required[int]) + #> True + """ + return _is_typing_name(get_origin(tp), name='Required') + + +def is_not_required(tp: Any, /) -> bool: + """Return whether the provided argument is a `NotRequired` special form. + + ```python {test="skip" lint="skip"} + is_required(Required[int]) + #> True + """ + return _is_typing_name(get_origin(tp), name='NotRequired') + + +def is_no_return(tp: Any, /) -> bool: + """Return whether the provided argument is the `NoReturn` special form. + + ```python {test="skip" lint="skip"} + is_no_return(NoReturn) + #> True + ``` + """ + return _is_typing_name(tp, name='NoReturn') + + +def is_never(tp: Any, /) -> bool: + """Return whether the provided argument is the `Never` special form. + + ```python {test="skip" lint="skip"} + is_never(Never) + #> True + ``` + """ + return _is_typing_name(tp, name='Never') + + +_DEPRECATED_TYPES: tuple[type[typing_extensions.deprecated], ...] = (typing_extensions.deprecated,) +if hasattr(warnings, 'deprecated'): + _DEPRECATED_TYPES = (*_DEPRECATED_TYPES, warnings.deprecated) # pyright: ignore[reportAttributeAccessIssue] + + +def is_deprecated_instance(obj: Any, /) -> TypeIs[deprecated]: + """Return whether the argument is an instance of the `warnings.deprecated` class or the `typing_extensions` backport.""" + return isinstance(obj, _DEPRECATED_TYPES) + + +_NONE_TYPES: tuple[Any, ...] = (None, NoneType, typing.Literal[None], typing_extensions.Literal[None]) + + +def is_none_type(tp: Any, /) -> bool: + """Return whether the argument represents the `None` type as part of an annotation. + + ```python {test="skip" lint="skip"} + is_none_type(None) + #> True + is_none_type(NoneType) + #> True + is_none_type(Literal[None]) + #> True + is_none_type(type[None]) + #> False + """ + return tp in _NONE_TYPES + + +def is_namedtuple(tp: Any, /) -> bool: + """Return whether the provided argument is a named tuple class. + + The class can be created using `typing.NamedTuple` or `collections.namedtuple`. + Parametrized generic classes are *not* assumed to be named tuples. + """ + from ._utils import lenient_issubclass # circ. import + + return lenient_issubclass(tp, tuple) and hasattr(tp, '_fields') + + +if sys.version_info < (3, 9): + + def is_zoneinfo_type(tp: Any, /) -> bool: + """Return whether the provided argument is the `zoneinfo.ZoneInfo` type.""" return False - return v.__class__ == Final.__class__ and (sys.version_info < (3, 8) or getattr(v, '_name', None) == 'Final') +else: + from zoneinfo import ZoneInfo + + def is_zoneinfo_type(tp: Any, /) -> TypeIs[type[ZoneInfo]]: + """Return whether the provided argument is the `zoneinfo.ZoneInfo` type.""" + return tp is ZoneInfo + +if sys.version_info < (3, 10): + + def origin_is_union(tp: Any, /) -> bool: + """Return whether the provided argument is the `Union` special form.""" + return _is_typing_name(tp, name='Union') -def is_finalvar(ann_type: Any) -> bool: - return _check_finalvar(ann_type) or _check_finalvar(get_origin(ann_type)) + def is_generic_alias(type_: type[Any]) -> bool: + return isinstance(type_, typing._GenericAlias) # pyright: ignore[reportAttributeAccessIssue] + +else: + + def origin_is_union(tp: Any, /) -> bool: + """Return whether the provided argument is the `Union` special form or the `UnionType`.""" + return _is_typing_name(tp, name='Union') or tp is types.UnionType + + def is_generic_alias(tp: Any, /) -> bool: + return isinstance(tp, (types.GenericAlias, typing._GenericAlias)) # pyright: ignore[reportAttributeAccessIssue] + + +# TODO: Ideally, we should avoid relying on the private `typing` constructs: + +if sys.version_info < (3, 9): + WithArgsTypes: tuple[Any, ...] = (typing._GenericAlias,) # pyright: ignore[reportAttributeAccessIssue] +elif sys.version_info < (3, 10): + WithArgsTypes: tuple[Any, ...] = (typing._GenericAlias, types.GenericAlias) # pyright: ignore[reportAttributeAccessIssue] +else: + WithArgsTypes: tuple[Any, ...] = (typing._GenericAlias, types.GenericAlias, types.UnionType) # pyright: ignore[reportAttributeAccessIssue] + + +# Similarly, we shouldn't rely on this `_Final` class, which is even more private than `_GenericAlias`: +typing_base: Any = typing._Final # pyright: ignore[reportAttributeAccessIssue] + + +### Annotation evaluations functions: def parent_frame_namespace(*, parent_depth: int = 2, force: bool = False) -> dict[str, Any] | None: @@ -185,7 +431,7 @@ def parent_frame_namespace(*, parent_depth: int = 2, force: bool = False) -> dic In other cases, like during initial schema build, if a class is defined at the top module level, we don't need to fetch that module's namespace, because the class' __module__ attribute can be used to access the parent namespace. - This is done in `_typing_extra.get_module_ns_of`. Thus, there's no need to cache the parent frame namespace in this case. + This is done in `_namespace_utils.get_module_ns_of`. Thus, there's no need to cache the parent frame namespace in this case. """ frame = sys._getframe(parent_depth) @@ -203,51 +449,46 @@ def parent_frame_namespace(*, parent_depth: int = 2, force: bool = False) -> dic return frame.f_locals -def get_module_ns_of(obj: Any) -> dict[str, Any]: - """Get the namespace of the module where the object is defined. - - Caution: this function does not return a copy of the module namespace, so it should not be mutated. - The burden of enforcing this is on the caller. - """ - module_name = getattr(obj, '__module__', None) - if module_name: - try: - return sys.modules[module_name].__dict__ - except KeyError: - # happens occasionally, see https://github.com/pydantic/pydantic/issues/2363 - return {} - return {} - - -def merge_cls_and_parent_ns(cls: type[Any], parent_namespace: dict[str, Any] | None = None) -> dict[str, Any]: - ns = get_module_ns_of(cls).copy() - if parent_namespace is not None: - ns.update(parent_namespace) - ns[cls.__name__] = cls - return ns - - -def get_cls_type_hints_lenient( - obj: Any, globalns: dict[str, Any] | None = None, mro: Iterable[type[Any]] | None = None +def get_cls_type_hints( + obj: type[Any], *, ns_resolver: NsResolver | None = None, lenient: bool = False ) -> dict[str, Any]: """Collect annotations from a class, including those from parent classes. - Unlike `typing.get_type_hints`, this function will not error if a forward reference is not resolvable. + Args: + obj: The class to inspect. + ns_resolver: A namespace resolver instance to use. Defaults to an empty instance. + lenient: Whether to keep unresolvable annotations as is or re-raise the `NameError` exception. Default: re-raise. """ hints = {} - if mro is None: - mro = reversed(obj.__mro__) - for base in mro: - ann = base.__dict__.get('__annotations__') - localns = dict(vars(base)) - if ann is not None and ann is not GetSetDescriptorType: + ns_resolver = ns_resolver or NsResolver() + + for base in reversed(obj.__mro__): + ann: dict[str, Any] | None = base.__dict__.get('__annotations__') + if not ann or isinstance(ann, types.GetSetDescriptorType): + continue + with ns_resolver.push(base): + globalns, localns = ns_resolver.types_namespace for name, value in ann.items(): - hints[name] = eval_type_lenient(value, globalns, localns) + hints[name] = eval_type(value, globalns, localns, lenient=lenient) return hints -def eval_type_lenient(value: Any, globalns: dict[str, Any] | None = None, localns: dict[str, Any] | None = None) -> Any: - """Behaves like typing._eval_type, except it won't raise an error if a forward reference can't be resolved.""" +def eval_type( + value: Any, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, + *, + lenient: bool = False, +) -> Any: + """Evaluate the annotation using the provided namespaces. + + Args: + value: The value to evaluate. If `None`, it will be replaced by `type[None]`. If an instance + of `str`, it will be converted to a `ForwardRef`. + localns: The global namespace to use during annotation evaluation. + globalns: The local namespace to use during annotation evaluation. + lenient: Whether to keep unresolvable annotations as is or re-raise the `NameError` exception. Default: re-raise. + """ if value is None: value = NoneType elif isinstance(value, str): @@ -256,15 +497,29 @@ def eval_type_lenient(value: Any, globalns: dict[str, Any] | None = None, localn try: return eval_type_backport(value, globalns, localns) except NameError: + if not lenient: + raise # the point of this function is to be tolerant to this case return value +@deprecated( + '`eval_type_lenient` is deprecated, use `eval_type` with `lenient=True` instead.', + category=None, +) +def eval_type_lenient( + value: Any, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, +) -> Any: + return eval_type(value, globalns, localns, lenient=True) + + def eval_type_backport( value: Any, - globalns: dict[str, Any] | None = None, - localns: dict[str, Any] | None = None, - type_params: tuple[Any] | None = None, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, + type_params: tuple[Any, ...] | None = None, ) -> Any: """An enhanced version of `typing._eval_type` which will fall back to using the `eval_type_backport` package if it's installed to let older Python versions use newer typing constructs. @@ -295,9 +550,9 @@ def eval_type_backport( def _eval_type_backport( value: Any, - globalns: dict[str, Any] | None = None, - localns: dict[str, Any] | None = None, - type_params: tuple[Any] | None = None, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, + type_params: tuple[Any, ...] | None = None, ) -> Any: try: return _eval_type(value, globalns, localns, type_params) @@ -315,14 +570,19 @@ def _eval_type_backport( '`typing` constructs or install the `eval_type_backport` package.' ) from e - return eval_type_backport(value, globalns, localns, try_default=False) + return eval_type_backport( + value, + globalns, + localns, # pyright: ignore[reportArgumentType], waiting on a new `eval_type_backport` release. + try_default=False, + ) def _eval_type( value: Any, - globalns: dict[str, Any] | None = None, - localns: dict[str, Any] | None = None, - type_params: tuple[Any] | None = None, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, + type_params: tuple[Any, ...] | None = None, ) -> Any: if sys.version_info >= (3, 13): return typing._eval_type( # type: ignore @@ -346,10 +606,20 @@ def is_backport_fixable_error(e: TypeError) -> bool: def get_function_type_hints( - function: Callable[..., Any], *, include_keys: set[str] | None = None, types_namespace: dict[str, Any] | None = None + function: Callable[..., Any], + *, + include_keys: set[str] | None = None, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, ) -> dict[str, Any]: - """Like `typing.get_type_hints`, but doesn't convert `X` to `Optional[X]` if the default value is `None`, also - copes with `partial`. + """Return type hints for a function. + + This is similar to the `typing.get_type_hints` function, with a few differences: + - Support `functools.partial` by using the underlying `func` attribute. + - If `function` happens to be a built-in type (e.g. `int`), assume it doesn't have annotations + but specify the `return` key as being the actual type. + - Do not wrap type annotation of a parameter with `Optional` if it has a default value of `None` + (related bug: https://github.com/python/cpython/issues/90353, only fixed in 3.11+). """ try: if isinstance(function, partial): @@ -365,9 +635,15 @@ def get_function_type_hints( type_hints.setdefault('return', function) return type_hints - globalns = get_module_ns_of(function) + if globalns is None: + globalns = get_module_ns_of(function) + type_params: tuple[Any, ...] | None = None + if localns is None: + # If localns was specified, it is assumed to already contain type params. This is because + # Pydantic has more advanced logic to do so (see `_namespace_utils.ns_for_function`). + type_params = getattr(function, '__type_params__', ()) + type_hints = {} - type_params: tuple[Any] = getattr(function, '__type_params__', ()) # type: ignore for name, value in annotations.items(): if include_keys is not None and name not in include_keys: continue @@ -376,7 +652,7 @@ def get_function_type_hints( elif isinstance(value, str): value = _make_forward_ref(value) - type_hints[name] = eval_type_backport(value, globalns, types_namespace, type_params) + type_hints[name] = eval_type_backport(value, globalns, localns, type_params) return type_hints @@ -540,42 +816,3 @@ def get_type_hints( # noqa: C901 value = typing.Optional[value] hints[name] = value return hints if include_extras else {k: typing._strip_annotations(t) for k, t in hints.items()} # type: ignore - - -def is_dataclass(_cls: type[Any]) -> TypeGuard[type[StandardDataclass]]: - # The dataclasses.is_dataclass function doesn't seem to provide TypeGuard functionality, - # so I created this convenience function - return dataclasses.is_dataclass(_cls) - - -def origin_is_type_alias_type(origin: Any) -> TypeGuard[TypeAliasType]: - return isinstance(origin, TypeAliasType) - - -if sys.version_info >= (3, 10): - - def is_generic_alias(type_: type[Any]) -> bool: - return isinstance(type_, (types.GenericAlias, typing._GenericAlias)) # type: ignore[attr-defined] - -else: - - def is_generic_alias(type_: type[Any]) -> bool: - return isinstance(type_, typing._GenericAlias) # type: ignore - - -def is_self_type(tp: Any) -> bool: - """Check if a given class is a Self type (from `typing` or `typing_extensions`)""" - return isinstance(tp, typing_base) and getattr(tp, '_name', None) == 'Self' - - -if sys.version_info >= (3, 9): - from zoneinfo import ZoneInfo - - def is_zoneinfo_type(tp: Any) -> bool: - """Check if a give class is a zone_info.ZoneInfo type""" - return tp is ZoneInfo - -else: - - def is_zoneinfo_type(tp: Any) -> bool: - return False diff --git a/pydantic/_internal/_utils.py b/pydantic/_internal/_utils.py index 494b8f6f281..861271b7c32 100644 --- a/pydantic/_internal/_utils.py +++ b/pydantic/_internal/_utils.py @@ -11,9 +11,11 @@ import weakref from collections import OrderedDict, defaultdict, deque from copy import deepcopy +from functools import cached_property +from inspect import Parameter from itertools import zip_longest from types import BuiltinFunctionType, CodeType, FunctionType, GeneratorType, LambdaType, ModuleType -from typing import Any, Mapping, TypeVar +from typing import Any, Callable, Mapping, TypeVar from typing_extensions import TypeAlias, TypeGuard @@ -62,6 +64,25 @@ } +def can_be_positional(param: Parameter) -> bool: + """Return whether the parameter accepts a positional argument. + + ```python {test="skip" lint="skip"} + def func(a, /, b, *, c): + pass + + params = inspect.signature(func).parameters + can_be_positional(params['a']) + #> True + can_be_positional(params['b']) + #> True + can_be_positional(params['c']) + #> False + ``` + """ + return param.kind in (Parameter.POSITIONAL_ONLY, Parameter.POSITIONAL_OR_KEYWORD) + + def sequence_like(v: Any) -> bool: return isinstance(v, (list, tuple, set, frozenset, GeneratorType, deque)) @@ -278,18 +299,23 @@ def __repr_args__(self) -> _repr.ReprArgs: if typing.TYPE_CHECKING: - def ClassAttribute(name: str, value: T) -> T: ... + def LazyClassAttribute(name: str, get_value: Callable[[], T]) -> T: ... else: - class ClassAttribute: - """Hide class attribute from its instances.""" + class LazyClassAttribute: + """A descriptor exposing an attribute only accessible on a class (hidden from instances). - __slots__ = 'name', 'value' + The attribute is lazily computed and cached during the first access. + """ - def __init__(self, name: str, value: Any) -> None: + def __init__(self, name: str, get_value: Callable[[], Any]) -> None: self.name = name - self.value = value + self.get_value = get_value + + @cached_property + def value(self) -> Any: + return self.get_value() def __get__(self, instance: Any, owner: type[Any]) -> None: if instance is None: diff --git a/pydantic/_internal/_validate_call.py b/pydantic/_internal/_validate_call.py index 52f6593e70b..f04da826d93 100644 --- a/pydantic/_internal/_validate_call.py +++ b/pydantic/_internal/_validate_call.py @@ -1,5 +1,6 @@ from __future__ import annotations as _annotations +import functools import inspect from functools import partial from typing import Any, Awaitable, Callable @@ -8,60 +9,74 @@ from ..config import ConfigDict from ..plugin._schema_validator import create_schema_validator -from . import _generate_schema, _typing_extra from ._config import ConfigWrapper +from ._generate_schema import GenerateSchema, ValidateCallSupportedTypes +from ._namespace_utils import MappingNamespace, NsResolver, ns_for_function + + +def extract_function_name(func: ValidateCallSupportedTypes) -> str: + """Extract the name of a `ValidateCallSupportedTypes` object.""" + return f'partial({func.func.__name__})' if isinstance(func, functools.partial) else func.__name__ + + +def extract_function_qualname(func: ValidateCallSupportedTypes) -> str: + """Extract the qualname of a `ValidateCallSupportedTypes` object.""" + return f'partial({func.func.__qualname__})' if isinstance(func, functools.partial) else func.__qualname__ + + +def update_wrapper_attributes(wrapped: ValidateCallSupportedTypes, wrapper: Callable[..., Any]): + """Update the `wrapper` function with the attributes of the `wrapped` function. Return the updated function.""" + if inspect.iscoroutinefunction(wrapped): + + @functools.wraps(wrapped) + async def wrapper_function(*args, **kwargs): # type: ignore + return await wrapper(*args, **kwargs) + else: + + @functools.wraps(wrapped) + def wrapper_function(*args, **kwargs): + return wrapper(*args, **kwargs) + + # We need to manually update this because `partial` object has no `__name__` and `__qualname__`. + wrapper_function.__name__ = extract_function_name(wrapped) + wrapper_function.__qualname__ = extract_function_qualname(wrapped) + wrapper_function.raw_function = wrapped # type: ignore + + return wrapper_function class ValidateCallWrapper: """This is a wrapper around a function that validates the arguments passed to it, and optionally the return value.""" - __slots__ = ( - '__pydantic_validator__', - '__name__', - '__qualname__', - '__annotations__', - '__dict__', # required for __module__ - ) + __slots__ = ('__pydantic_validator__', '__return_pydantic_validator__') def __init__( self, - function: Callable[..., Any], + function: ValidateCallSupportedTypes, config: ConfigDict | None, validate_return: bool, - namespace: dict[str, Any] | None, - ): + parent_namespace: MappingNamespace | None, + ) -> None: if isinstance(function, partial): - func = function.func - schema_type = func - self.__name__ = f'partial({func.__name__})' - self.__qualname__ = f'partial({func.__qualname__})' - self.__module__ = func.__module__ + schema_type = function.func + module = function.func.__module__ else: schema_type = function - self.__name__ = function.__name__ - self.__qualname__ = function.__qualname__ - self.__module__ = function.__module__ - - global_ns = _typing_extra.get_module_ns_of(function) - # TODO: this is a bit of a hack, we should probably have a better way to handle this - # specifically, we shouldn't be pumping the namespace full of type_params - # when we take namespace and type_params arguments in eval_type_backport - type_params = getattr(schema_type, '__type_params__', ()) - namespace = { - **{param.__name__: param for param in type_params}, - **(global_ns or {}), - **(namespace or {}), - } + module = function.__module__ + qualname = extract_function_qualname(function) + + ns_resolver = NsResolver(namespaces_tuple=ns_for_function(schema_type, parent_namespace=parent_namespace)) + config_wrapper = ConfigWrapper(config) - gen_schema = _generate_schema.GenerateSchema(config_wrapper, namespace) + gen_schema = GenerateSchema(config_wrapper, ns_resolver) schema = gen_schema.clean_schema(gen_schema.generate_schema(function)) - core_config = config_wrapper.core_config(self) + core_config = config_wrapper.core_config(title=qualname) self.__pydantic_validator__ = create_schema_validator( schema, schema_type, - self.__module__, - self.__qualname__, + module, + qualname, 'validate_call', core_config, config_wrapper.plugin_settings, @@ -70,13 +85,13 @@ def __init__( if validate_return: signature = inspect.signature(function) return_type = signature.return_annotation if signature.return_annotation is not signature.empty else Any - gen_schema = _generate_schema.GenerateSchema(config_wrapper, namespace) + gen_schema = GenerateSchema(config_wrapper, ns_resolver) schema = gen_schema.clean_schema(gen_schema.generate_schema(return_type)) validator = create_schema_validator( schema, schema_type, - self.__module__, - self.__qualname__, + module, + qualname, 'validate_call', core_config, config_wrapper.plugin_settings, @@ -96,4 +111,5 @@ def __call__(self, *args: Any, **kwargs: Any) -> Any: res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs)) if self.__return_pydantic_validator__: return self.__return_pydantic_validator__(res) - return res + else: + return res diff --git a/pydantic/_internal/_validators.py b/pydantic/_internal/_validators.py index e97073740c9..5d165c0420a 100644 --- a/pydantic/_internal/_validators.py +++ b/pydantic/_internal/_validators.py @@ -8,12 +8,13 @@ import math import re import typing +from decimal import Decimal +from fractions import Fraction from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network -from typing import Any, Callable +from typing import Any, Callable, Union from pydantic_core import PydanticCustomError, core_schema from pydantic_core._pydantic_core import PydanticKnownError -from pydantic_core.core_schema import ErrorType def sequence_validator( @@ -236,75 +237,184 @@ def ip_v6_interface_validator(input_value: Any, /) -> IPv6Interface: raise PydanticCustomError('ip_v6_interface', 'Input is not a valid IPv6 interface') +def fraction_validator(input_value: Any, /) -> Fraction: + if isinstance(input_value, Fraction): + return input_value + + try: + return Fraction(input_value) + except ValueError: + raise PydanticCustomError('fraction_parsing', 'Input is not a valid fraction') + + def forbid_inf_nan_check(x: Any) -> Any: if not math.isfinite(x): raise PydanticKnownError('finite_number') return x -_InputType = typing.TypeVar('_InputType') +def _safe_repr(v: Any) -> int | float | str: + """The context argument for `PydanticKnownError` requires a number or str type, so we do a simple repr() coercion for types like timedelta. + + See tests/test_types.py::test_annotated_metadata_any_order for some context. + """ + if isinstance(v, (int, float, str)): + return v + return repr(v) + + +def greater_than_validator(x: Any, gt: Any) -> Any: + try: + if not (x > gt): + raise PydanticKnownError('greater_than', {'gt': _safe_repr(gt)}) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'gt' to supplied value {x}") + + +def greater_than_or_equal_validator(x: Any, ge: Any) -> Any: + try: + if not (x >= ge): + raise PydanticKnownError('greater_than_equal', {'ge': _safe_repr(ge)}) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'ge' to supplied value {x}") + + +def less_than_validator(x: Any, lt: Any) -> Any: + try: + if not (x < lt): + raise PydanticKnownError('less_than', {'lt': _safe_repr(lt)}) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'lt' to supplied value {x}") + + +def less_than_or_equal_validator(x: Any, le: Any) -> Any: + try: + if not (x <= le): + raise PydanticKnownError('less_than_equal', {'le': _safe_repr(le)}) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'le' to supplied value {x}") + + +def multiple_of_validator(x: Any, multiple_of: Any) -> Any: + try: + if x % multiple_of: + raise PydanticKnownError('multiple_of', {'multiple_of': _safe_repr(multiple_of)}) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'multiple_of' to supplied value {x}") + + +def min_length_validator(x: Any, min_length: Any) -> Any: + try: + if not (len(x) >= min_length): + raise PydanticKnownError( + 'too_short', {'field_type': 'Value', 'min_length': min_length, 'actual_length': len(x)} + ) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'min_length' to supplied value {x}") + + +def max_length_validator(x: Any, max_length: Any) -> Any: + try: + if len(x) > max_length: + raise PydanticKnownError( + 'too_long', + {'field_type': 'Value', 'max_length': max_length, 'actual_length': len(x)}, + ) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'max_length' to supplied value {x}") + +def _extract_decimal_digits_info(decimal: Decimal) -> tuple[int, int]: + """Compute the total number of digits and decimal places for a given [`Decimal`][decimal.Decimal] instance. -def create_constraint_validator( - constraint_id: str, - predicate: Callable[[_InputType, Any], bool], - error_type: ErrorType, - context_gen: Callable[[Any, Any], dict[str, Any]] | None = None, -) -> Callable[[_InputType, Any], _InputType]: - """Create a validator function for a given constraint. + This function handles both normalized and non-normalized Decimal instances. + Example: Decimal('1.230') -> 4 digits, 3 decimal places Args: - constraint_id: The constraint identifier, used to identify the constraint in error messages, ex 'gt'. - predicate: The predicate function to apply to the input value, ex `lambda x, gt: x > gt`. - error_type: The error type to raise if the predicate fails. - context_gen: A function to generate the error context from the constraint value and the input value. + decimal (Decimal): The decimal number to analyze. + + Returns: + tuple[int, int]: A tuple containing the number of decimal places and total digits. + + Though this could be divided into two separate functions, the logic is easier to follow if we couple the computation + of the number of decimals and digits together. """ + decimal_tuple = decimal.as_tuple() + if not isinstance(decimal_tuple.exponent, int): + raise TypeError(f'Unable to extract decimal digits info from supplied value {decimal}') + exponent = decimal_tuple.exponent + num_digits = len(decimal_tuple.digits) + + if exponent >= 0: + # A positive exponent adds that many trailing zeros + # Ex: digit_tuple=(1, 2, 3), exponent=2 -> 12300 -> 0 decimal places, 5 digits + num_digits += exponent + decimal_places = 0 + else: + # If the absolute value of the negative exponent is larger than the + # number of digits, then it's the same as the number of digits, + # because it'll consume all the digits in digit_tuple and then + # add abs(exponent) - len(digit_tuple) leading zeros after the decimal point. + # Ex: digit_tuple=(1, 2, 3), exponent=-2 -> 1.23 -> 2 decimal places, 3 digits + # Ex: digit_tuple=(1, 2, 3), exponent=-4 -> 0.0123 -> 4 decimal places, 4 digits + decimal_places = abs(exponent) + num_digits = max(num_digits, decimal_places) - def validator(x: _InputType, constraint_value: Any) -> _InputType: - try: - if not predicate(x, constraint_value): - raise PydanticKnownError( - error_type, context_gen(constraint_value, x) if context_gen else {constraint_id: constraint_value} - ) - except TypeError: - raise TypeError(f"Unable to apply constraint '{constraint_id}' to supplied value {x}") + return decimal_places, num_digits + + +def max_digits_validator(x: Any, max_digits: Any) -> Any: + _, num_digits = _extract_decimal_digits_info(x) + _, normalized_num_digits = _extract_decimal_digits_info(x.normalize()) + + try: + if (num_digits > max_digits) and (normalized_num_digits > max_digits): + raise PydanticKnownError( + 'decimal_max_digits', + {'max_digits': max_digits}, + ) return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'max_digits' to supplied value {x}") - return validator - - -_CONSTRAINT_TO_VALIDATOR_LOOKUP: dict[str, Callable] = { - 'gt': create_constraint_validator('gt', lambda x, gt: x > gt, 'greater_than'), - 'ge': create_constraint_validator('ge', lambda x, ge: x >= ge, 'greater_than_equal'), - 'lt': create_constraint_validator('lt', lambda x, lt: x < lt, 'less_than'), - 'le': create_constraint_validator('le', lambda x, le: x <= le, 'less_than_equal'), - 'multiple_of': create_constraint_validator( - 'multiple_of', lambda x, multiple_of: x % multiple_of == 0, 'multiple_of' - ), - 'min_length': create_constraint_validator( - 'min_length', - lambda x, min_length: len(x) >= min_length, - 'too_short', - lambda c_val, x: {'field_type': 'Value', 'min_length': c_val, 'actual_length': len(x)}, - ), - 'max_length': create_constraint_validator( - 'max_length', - lambda x, max_length: len(x) <= max_length, - 'too_long', - lambda c_val, x: {'field_type': 'Value', 'max_length': c_val, 'actual_length': len(x)}, - ), -} +def decimal_places_validator(x: Any, decimal_places: Any) -> Any: + decimal_places_, _ = _extract_decimal_digits_info(x) + normalized_decimal_places, _ = _extract_decimal_digits_info(x.normalize()) -def get_constraint_validator(constraint: str) -> Callable: - """Fetch the validator function for the given constraint.""" try: - return _CONSTRAINT_TO_VALIDATOR_LOOKUP[constraint] - except KeyError: - raise TypeError(f'Unknown constraint {constraint}') + if (decimal_places_ > decimal_places) and (normalized_decimal_places > decimal_places): + raise PydanticKnownError( + 'decimal_max_places', + {'decimal_places': decimal_places}, + ) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'decimal_places' to supplied value {x}") + + +NUMERIC_VALIDATOR_LOOKUP: dict[str, Callable] = { + 'gt': greater_than_validator, + 'ge': greater_than_or_equal_validator, + 'lt': less_than_validator, + 'le': less_than_or_equal_validator, + 'multiple_of': multiple_of_validator, + 'min_length': min_length_validator, + 'max_length': max_length_validator, + 'max_digits': max_digits_validator, + 'decimal_places': decimal_places_validator, +} +IpType = Union[IPv4Address, IPv6Address, IPv4Network, IPv6Network, IPv4Interface, IPv6Interface] -IP_VALIDATOR_LOOKUP: dict[type, Callable] = { +IP_VALIDATOR_LOOKUP: dict[type[IpType], Callable] = { IPv4Address: ip_v4_address_validator, IPv6Address: ip_v6_address_validator, IPv4Network: ip_v4_network_validator, diff --git a/pydantic/aliases.py b/pydantic/aliases.py index 0325bedacbe..a9b806da05e 100644 --- a/pydantic/aliases.py +++ b/pydantic/aliases.py @@ -14,7 +14,7 @@ @dataclasses.dataclass(**_internal_dataclass.slots_true) class AliasPath: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/alias#aliaspath-and-aliaschoices + """Usage docs: https://docs.pydantic.dev/2.10/concepts/alias#aliaspath-and-aliaschoices A data class used by `validation_alias` as a convenience to create aliases. @@ -55,7 +55,7 @@ def search_dict_for_path(self, d: dict) -> Any: @dataclasses.dataclass(**_internal_dataclass.slots_true) class AliasChoices: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/alias#aliaspath-and-aliaschoices + """Usage docs: https://docs.pydantic.dev/2.10/concepts/alias#aliaspath-and-aliaschoices A data class used by `validation_alias` as a convenience to create aliases. @@ -85,7 +85,7 @@ def convert_to_aliases(self) -> list[list[str | int]]: @dataclasses.dataclass(**_internal_dataclass.slots_true) class AliasGenerator: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/alias#using-an-aliasgenerator + """Usage docs: https://docs.pydantic.dev/2.10/concepts/alias#using-an-aliasgenerator A data class used by `alias_generator` as a convenience to create various aliases. diff --git a/pydantic/annotated_handlers.py b/pydantic/annotated_handlers.py index ac3d213910f..d0cb5d3d461 100644 --- a/pydantic/annotated_handlers.py +++ b/pydantic/annotated_handlers.py @@ -7,6 +7,7 @@ from pydantic_core import core_schema if TYPE_CHECKING: + from ._internal._namespace_utils import NamespacesTuple from .json_schema import JsonSchemaMode, JsonSchemaValue CoreSchemaOrField = Union[ @@ -116,6 +117,6 @@ def field_name(self) -> str | None: """Get the name of the closest field to this validator.""" raise NotImplementedError - def _get_types_namespace(self) -> dict[str, Any] | None: + def _get_types_namespace(self) -> NamespacesTuple: """Internal method used during type resolution for serializer annotations.""" raise NotImplementedError diff --git a/pydantic/config.py b/pydantic/config.py index 86d2ecd7406..e5e5e7c554b 100644 --- a/pydantic/config.py +++ b/pydantic/config.py @@ -2,6 +2,7 @@ from __future__ import annotations as _annotations +from re import Pattern from typing import TYPE_CHECKING, Any, Callable, Dict, List, Type, TypeVar, Union from typing_extensions import Literal, TypeAlias, TypedDict @@ -67,16 +68,14 @@ class ConfigDict(TypedDict, total=False): * `forbid` - Forbid any extra attributes. * `ignore` - Ignore any extra attributes. - ```py + ```python from pydantic import BaseModel, ConfigDict - class User(BaseModel): model_config = ConfigDict(extra='ignore') # (1)! name: str - user = User(name='John Doe', age=20) # (2)! print(user) #> name='John Doe' @@ -87,16 +86,14 @@ class User(BaseModel): Instead, with `extra='allow'`, the `age` argument is included: - ```py + ```python from pydantic import BaseModel, ConfigDict - class User(BaseModel): model_config = ConfigDict(extra='allow') name: str - user = User(name='John Doe', age=20) # (1)! print(user) #> name='John Doe' age=20 @@ -106,16 +103,14 @@ class User(BaseModel): With `extra='forbid'`, an error is raised: - ```py + ```python from pydantic import BaseModel, ConfigDict, ValidationError - class User(BaseModel): model_config = ConfigDict(extra='forbid') name: str - try: User(name='John Doe', age=20) except ValidationError as e: @@ -123,7 +118,7 @@ class User(BaseModel): ''' 1 validation error for User age - Extra inputs are not permitted [type=extra_forbidden, input_value=20, input_type=int] + Extra inputs are not permitted [type=extra_forbidden, input_value=20, input_type=int] ''' ``` """ @@ -147,17 +142,15 @@ class User(BaseModel): The name of this configuration setting was changed in **v2.0** from `allow_population_by_field_name` to `populate_by_name`. - ```py + ```python from pydantic import BaseModel, ConfigDict, Field - class User(BaseModel): model_config = ConfigDict(populate_by_name=True) name: str = Field(alias='full_name') # (1)! age: int - user = User(full_name='John Doe', age=20) # (2)! print(user) #> name='John Doe' age=20 @@ -181,29 +174,28 @@ class User(BaseModel): for said Field to ensure that the `use_enum_values` flag takes effect on the default, as extracting an enum's value occurs during validation, not serialization. - ```py + ```python from enum import Enum from typing import Optional from pydantic import BaseModel, ConfigDict, Field - class SomeEnum(Enum): FOO = 'foo' BAR = 'bar' BAZ = 'baz' - class SomeModel(BaseModel): model_config = ConfigDict(use_enum_values=True) some_enum: SomeEnum - another_enum: Optional[SomeEnum] = Field(default=SomeEnum.FOO, validate_default=True) - + another_enum: Optional[SomeEnum] = Field( + default=SomeEnum.FOO, validate_default=True + ) model1 = SomeModel(some_enum=SomeEnum.BAR) print(model1.model_dump()) - # {'some_enum': 'bar', 'another_enum': 'foo'} + #> {'some_enum': 'bar', 'another_enum': 'foo'} model2 = SomeModel(some_enum=SomeEnum.BAR, another_enum=SomeEnum.BAZ) print(model2.model_dump()) @@ -219,7 +211,7 @@ class SomeModel(BaseModel): In case the user changes the data after the model is created, the model is _not_ revalidated. - ```py + ```python from pydantic import BaseModel class User(BaseModel): @@ -238,7 +230,7 @@ class User(BaseModel): In case you want to revalidate the model when the data is changed, you can use `validate_assignment=True`: - ```py + ```python from pydantic import BaseModel, ValidationError class User(BaseModel, validate_assignment=True): # (1)! @@ -267,7 +259,7 @@ class User(BaseModel, validate_assignment=True): # (1)! """ Whether arbitrary types are allowed for field types. Defaults to `False`. - ```py + ```python from pydantic import BaseModel, ConfigDict, ValidationError # This is not a pydantic model, it's an arbitrary class @@ -339,7 +331,7 @@ class Model(BaseModel): you can automatically generate aliases using `alias_generator`. Here's an example with a basic callable: - ```py + ```python from pydantic import BaseModel, ConfigDict from pydantic.alias_generators import to_pascal @@ -359,7 +351,7 @@ class Voice(BaseModel): If you want to use different alias generators for validation and serialization, you can use [`AliasGenerator`][pydantic.aliases.AliasGenerator]. - ```py + ```python from pydantic import AliasGenerator, BaseModel, ConfigDict from pydantic.alias_generators import to_camel, to_pascal @@ -420,7 +412,7 @@ class without an annotation and has a type that is not in this tuple (or otherwi To configure strict mode for all fields on a model, you can set `strict=True` on the model. - ```py + ```python from pydantic import BaseModel, ConfigDict class Model(BaseModel): @@ -448,7 +440,7 @@ class Model(BaseModel): By default, model and dataclass instances are not revalidated during validation. - ```py + ```python from typing import List from pydantic import BaseModel @@ -485,7 +477,7 @@ class Transaction(BaseModel): If you want to revalidate instances during validation, you can set `revalidate_instances` to `'always'` in the model's config. - ```py + ```python from typing import List from pydantic import BaseModel, ValidationError @@ -528,7 +520,7 @@ class Transaction(BaseModel): It's also possible to set `revalidate_instances` to `'subclass-instances'` to only revalidate instances of subclasses of the model. - ```py + ```python from typing import List from pydantic import BaseModel @@ -606,17 +598,26 @@ class Transaction(BaseModel): """Whether to validate default values during validation. Defaults to `False`.""" validate_return: bool - """whether to validate the return value from call validators. Defaults to `False`.""" + """Whether to validate the return value from call validators. Defaults to `False`.""" - protected_namespaces: tuple[str, ...] + protected_namespaces: tuple[str | Pattern[str], ...] """ - A `tuple` of strings that prevent model to have field which conflict with them. - Defaults to `('model_', )`). + A `tuple` of strings and/or patterns that prevent models from having fields with names that conflict with them. + For strings, we match on a prefix basis. Ex, if 'dog' is in the protected namespace, 'dog_name' will be protected. + For patterns, we match on the entire field name. Ex, if `re.compile(r'^dog$')` is in the protected namespace, 'dog' will be protected, but 'dog_name' will not be. + Defaults to `('model_validate', 'model_dump',)`. - Pydantic prevents collisions between model attributes and `BaseModel`'s own methods by - namespacing them with the prefix `model_`. + The reason we've selected these is to prevent collisions with other validation / dumping formats + in the future - ex, `model_validate_{some_newly_supported_format}`. - ```py + Before v2.10, Pydantic used `('model_',)` as the default value for this setting to + prevent collisions between model attributes and `BaseModel`'s own methods. This was changed + in v2.10 given feedback that this restriction was limiting in AI and data science contexts, + where it is common to have fields with names like `model_id`, `model_input`, `model_output`, etc. + + For more details, see https://github.com/pydantic/pydantic/issues/10315. + + ```python import warnings from pydantic import BaseModel @@ -626,56 +627,65 @@ class Transaction(BaseModel): try: class Model(BaseModel): - model_prefixed_field: str + model_dump_something: str except UserWarning as e: print(e) ''' - Field "model_prefixed_field" in Model has conflict with protected namespace "model_". + Field "model_dump_something" in Model has conflict with protected namespace "model_dump". - You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`. + You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ('model_validate',)`. ''' ``` You can customize this behavior using the `protected_namespaces` setting: - ```py + ```python {test="skip"} + import re import warnings from pydantic import BaseModel, ConfigDict - warnings.filterwarnings('error') # Raise warnings as errors - - try: + with warnings.catch_warnings(record=True) as caught_warnings: + warnings.simplefilter('always') # Catch all warnings class Model(BaseModel): - model_prefixed_field: str + safe_field: str also_protect_field: str + protect_this: str model_config = ConfigDict( - protected_namespaces=('protect_me_', 'also_protect_') + protected_namespaces=( + 'protect_me_', + 'also_protect_', + re.compile('^protect_this$'), + ) ) - except UserWarning as e: - print(e) + for warning in caught_warnings: + print(f'{warning.message}') ''' Field "also_protect_field" in Model has conflict with protected namespace "also_protect_". + You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ('protect_me_', re.compile('^protect_this$'))`. - You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ('protect_me_',)`. + Field "protect_this" in Model has conflict with protected namespace "re.compile('^protect_this$')". + You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ('protect_me_', 'also_protect_')`. ''' ``` While Pydantic will only emit a warning when an item is in a protected namespace but does not actually have a collision, an error _is_ raised if there is an actual collision with an existing attribute: - ```py - from pydantic import BaseModel + ```python + from pydantic import BaseModel, ConfigDict try: class Model(BaseModel): model_validate: str + model_config = ConfigDict(protected_namespaces=('model_',)) + except NameError as e: print(e) ''' @@ -690,7 +700,7 @@ class Model(BaseModel): Pydantic shows the input value and type when it raises `ValidationError` during the validation. - ```py + ```python from pydantic import BaseModel, ValidationError class Model(BaseModel): @@ -709,7 +719,7 @@ class Model(BaseModel): You can hide the input value and type by setting the `hide_input_in_errors` config to `True`. - ```py + ```python from pydantic import BaseModel, ConfigDict, ValidationError class Model(BaseModel): @@ -736,43 +746,20 @@ class Model(BaseModel): used nested within other models, or when you want to manually define type namespace via [`Model.model_rebuild(_types_namespace=...)`][pydantic.BaseModel.model_rebuild]. - See also [`experimental_defer_build_mode`][pydantic.config.ConfigDict.experimental_defer_build_mode]. - - !!! note - `defer_build` does not work by default with FastAPI Pydantic models. By default, the validator and serializer - for said models is constructed immediately for FastAPI routes. You also need to define - [`experimental_defer_build_mode=('model', 'type_adapter')`][pydantic.config.ConfigDict.experimental_defer_build_mode] with FastAPI - models in order for `defer_build=True` to take effect. This additional (experimental) parameter is required for - the deferred building due to FastAPI relying on `TypeAdapter`s. - """ - - experimental_defer_build_mode: tuple[Literal['model', 'type_adapter'], ...] - """ - Controls when [`defer_build`][pydantic.config.ConfigDict.defer_build] is applicable. Defaults to `('model',)`. - - Due to backwards compatibility reasons [`TypeAdapter`][pydantic.type_adapter.TypeAdapter] does not by default - respect `defer_build`. Meaning when `defer_build` is `True` and `experimental_defer_build_mode` is the default `('model',)` - then `TypeAdapter` immediately constructs its validator and serializer instead of postponing said construction until - the first model validation. Set this to `('model', 'type_adapter')` to make `TypeAdapter` respect the `defer_build` - so it postpones validator and serializer construction until the first validation or serialization. - - !!! note - The `experimental_defer_build_mode` parameter is named with an underscore to suggest this is an experimental feature. It may - be removed or changed in the future in a minor release. + Since v2.10, this setting also applies to pydantic dataclasses and TypeAdapter instances. """ plugin_settings: dict[str, object] | None - """A `dict` of settings for plugins. Defaults to `None`. - """ + """A `dict` of settings for plugins. Defaults to `None`.""" schema_generator: type[_GenerateSchema] | None """ - A custom core schema generator class to use when generating JSON schemas. - Useful if you want to change the way types are validated across an entire model/schema. Defaults to `None`. - - The `GenerateSchema` interface is subject to change, currently only the `string_schema` method is public. + !!! warning + `schema_generator` is deprecated in v2.10. - See [#6737](https://github.com/pydantic/pydantic/pull/6737) for details. + Prior to v2.10, this setting was advertised as highly subject to change. + It's possible that this interface may once again become public once the internal core schema generation + API is more stable, but that will likely come after significant performance improvements have been made. """ json_schema_serialization_defaults_required: bool @@ -786,7 +773,7 @@ class Model(BaseModel): between validation and serialization, and don't mind fields with defaults being marked as not required during serialization. See [#7209](https://github.com/pydantic/pydantic/issues/7209) for more details. - ```py + ```python from pydantic import BaseModel, ConfigDict class Model(BaseModel): @@ -829,7 +816,7 @@ class Model(BaseModel): the validation and serialization schemas (since both will use the specified schema), and so prevents the suffixes from being added to the definition references. - ```py + ```python from pydantic import BaseModel, ConfigDict, Json class Model(BaseModel): @@ -875,7 +862,7 @@ class ForceInputModel(Model): Pydantic doesn't allow number types (`int`, `float`, `Decimal`) to be coerced as type `str` by default. - ```py + ```python from decimal import Decimal from pydantic import BaseModel, ConfigDict, ValidationError @@ -921,7 +908,7 @@ class Model(BaseModel): If you use a compiled regex pattern, the python-re engine will be used regardless of this setting. This is so that flags such as `re.IGNORECASE` are respected. - ```py + ```python from pydantic import BaseModel, ConfigDict, Field, ValidationError class Model(BaseModel): @@ -962,7 +949,7 @@ class Model(BaseModel): Available in Pydantic v2.7+. - ```py + ```python from pydantic import BaseModel, ConfigDict, Field @@ -1017,7 +1004,7 @@ class Model(BaseModel): def with_config(config: ConfigDict) -> Callable[[_TypeT], _TypeT]: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/config/#configuration-with-dataclass-from-the-standard-library-or-typeddict + """Usage docs: https://docs.pydantic.dev/2.10/concepts/config/#configuration-with-dataclass-from-the-standard-library-or-typeddict A convenience decorator to set a [Pydantic configuration](config.md) on a `TypedDict` or a `dataclass` from the standard library. @@ -1026,7 +1013,7 @@ def with_config(config: ConfigDict) -> Callable[[_TypeT], _TypeT]: !!! example "Usage" - ```py + ```python from typing_extensions import TypedDict from pydantic import ConfigDict, TypeAdapter, with_config diff --git a/pydantic/dataclasses.py b/pydantic/dataclasses.py index 1d30991f155..fe7709f81fb 100644 --- a/pydantic/dataclasses.py +++ b/pydantic/dataclasses.py @@ -10,7 +10,7 @@ from typing_extensions import Literal, TypeGuard, dataclass_transform -from ._internal import _config, _decorators, _typing_extra +from ._internal import _config, _decorators, _namespace_utils, _typing_extra from ._internal import _dataclasses as _pydantic_dataclasses from ._migration import getattr_migration from .config import ConfigDict @@ -19,6 +19,7 @@ if TYPE_CHECKING: from ._internal._dataclasses import PydanticDataclass + from ._internal._namespace_utils import MappingNamespace __all__ = 'dataclass', 'rebuild_dataclass' @@ -108,7 +109,7 @@ def dataclass( kw_only: bool = False, slots: bool = False, ) -> Callable[[type[_T]], type[PydanticDataclass]] | type[PydanticDataclass]: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/dataclasses/ + """Usage docs: https://docs.pydantic.dev/2.10/concepts/dataclasses/ A decorator used to create a Pydantic-enhanced dataclass, similar to the standard Python `dataclass`, but with added validation. @@ -201,6 +202,17 @@ def create_dataclass(cls: type[Any]) -> type[PydanticDataclass]: original_cls = cls + # we warn on conflicting config specifications, but only if the class doesn't have a dataclass base + # because a dataclass base might provide a __pydantic_config__ attribute that we don't want to warn about + has_dataclass_base = any(dataclasses.is_dataclass(base) for base in cls.__bases__) + if not has_dataclass_base and config is not None and hasattr(cls, '__pydantic_config__'): + warn( + f'`config` is set via both the `dataclass` decorator and `__pydantic_config__` for dataclass {cls.__name__}. ' + f'The `config` specification from `dataclass` decorator will take priority.', + category=UserWarning, + stacklevel=2, + ) + # if config is not explicitly provided, try to read it from the type config_dict = config if config is not None else getattr(cls, '__pydantic_config__', None) config_wrapper = _config.ConfigWrapper(config_dict) @@ -258,7 +270,10 @@ def create_dataclass(cls: type[Any]) -> type[PydanticDataclass]: cls.__module__ = original_cls.__module__ cls.__qualname__ = original_cls.__qualname__ cls.__pydantic_complete__ = False # `complete_dataclass` will set it to `True` if successful. - _pydantic_dataclasses.complete_dataclass(cls, config_wrapper, raise_errors=False, types_namespace=None) + # TODO `parent_namespace` is currently None, but we could do the same thing as Pydantic models: + # fetch the parent ns using `parent_frame_namespace` (if the dataclass was defined in a function), + # and possibly cache it (see the `__pydantic_parent_namespace__` logic for models). + _pydantic_dataclasses.complete_dataclass(cls, config_wrapper, raise_errors=False) return cls return create_dataclass if _cls is None else create_dataclass(_cls) @@ -286,7 +301,7 @@ def rebuild_dataclass( force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, - _types_namespace: dict[str, Any] | None = None, + _types_namespace: MappingNamespace | None = None, ) -> bool | None: """Try to rebuild the pydantic-core schema for the dataclass. @@ -308,25 +323,32 @@ def rebuild_dataclass( """ if not force and cls.__pydantic_complete__: return None + + if '__pydantic_core_schema__' in cls.__dict__: + delattr(cls, '__pydantic_core_schema__') # delete cached value to ensure full rebuild happens + + if _types_namespace is not None: + rebuild_ns = _types_namespace + elif _parent_namespace_depth > 0: + rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {} else: - if _types_namespace is not None: - types_namespace: dict[str, Any] | None = _types_namespace.copy() - else: - if _parent_namespace_depth > 0: - frame_parent_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth) or {} - # Note: we may need to add something similar to cls.__pydantic_parent_namespace__ from BaseModel - # here when implementing handling of recursive generics. See BaseModel.model_rebuild for reference. - types_namespace = frame_parent_ns - else: - types_namespace = {} - - types_namespace = _typing_extra.merge_cls_and_parent_ns(cls, types_namespace) - return _pydantic_dataclasses.complete_dataclass( - cls, - _config.ConfigWrapper(cls.__pydantic_config__, check=False), - raise_errors=raise_errors, - types_namespace=types_namespace, - ) + rebuild_ns = {} + + ns_resolver = _namespace_utils.NsResolver( + parent_namespace=rebuild_ns, + ) + + return _pydantic_dataclasses.complete_dataclass( + cls, + _config.ConfigWrapper(cls.__pydantic_config__, check=False), + raise_errors=raise_errors, + ns_resolver=ns_resolver, + # We could provide a different config instead (with `'defer_build'` set to `True`) + # of this explicit `_force_build` argument, but because config can come from the + # decorator parameter or the `__pydantic_config__` attribute, `complete_dataclass` + # will overwrite `__pydantic_config__` with the provided config above: + _force_build=True, + ) def is_pydantic_dataclass(class_: type[Any], /) -> TypeGuard[type[PydanticDataclass]]: diff --git a/pydantic/deprecated/copy_internals.py b/pydantic/deprecated/copy_internals.py index efe5de28996..00e0a8a9512 100644 --- a/pydantic/deprecated/copy_internals.py +++ b/pydantic/deprecated/copy_internals.py @@ -40,11 +40,11 @@ def _iter( # The extra "is not None" guards are not logically necessary but optimizes performance for the simple case. if exclude is not None: exclude = _utils.ValueItems.merge( - {k: v.exclude for k, v in self.model_fields.items() if v.exclude is not None}, exclude + {k: v.exclude for k, v in self.__pydantic_fields__.items() if v.exclude is not None}, exclude ) if include is not None: - include = _utils.ValueItems.merge({k: True for k in self.model_fields}, include, intersect=True) + include = _utils.ValueItems.merge({k: True for k in self.__pydantic_fields__}, include, intersect=True) allowed_keys = _calculate_keys(self, include=include, exclude=exclude, exclude_unset=exclude_unset) # type: ignore if allowed_keys is None and not (to_dict or by_alias or exclude_unset or exclude_defaults or exclude_none): @@ -68,15 +68,15 @@ def _iter( if exclude_defaults: try: - field = self.model_fields[field_key] + field = self.__pydantic_fields__[field_key] except KeyError: pass else: if not field.is_required() and field.default == v: continue - if by_alias and field_key in self.model_fields: - dict_key = self.model_fields[field_key].alias or field_key + if by_alias and field_key in self.__pydantic_fields__: + dict_key = self.__pydantic_fields__[field_key].alias or field_key else: dict_key = field_key diff --git a/pydantic/deprecated/decorator.py b/pydantic/deprecated/decorator.py index 0c0ea7445c6..27ee4603845 100644 --- a/pydantic/deprecated/decorator.py +++ b/pydantic/deprecated/decorator.py @@ -170,10 +170,10 @@ def build_values(self, args: Tuple[Any, ...], kwargs: Dict[str, Any]) -> Dict[st duplicate_kwargs = [] fields_alias = [ field.alias - for name, field in self.model.model_fields.items() + for name, field in self.model.__pydantic_fields__.items() if name not in (self.v_args_name, self.v_kwargs_name) ] - non_var_fields = set(self.model.model_fields) - {self.v_args_name, self.v_kwargs_name} + non_var_fields = set(self.model.__pydantic_fields__) - {self.v_args_name, self.v_kwargs_name} for k, v in kwargs.items(): if k in non_var_fields or k in fields_alias: if k in self.positional_only_args: @@ -193,7 +193,11 @@ def build_values(self, args: Tuple[Any, ...], kwargs: Dict[str, Any]) -> Dict[st return values def execute(self, m: BaseModel) -> Any: - d = {k: v for k, v in m.__dict__.items() if k in m.__pydantic_fields_set__ or m.model_fields[k].default_factory} + d = { + k: v + for k, v in m.__dict__.items() + if k in m.__pydantic_fields_set__ or m.__pydantic_fields__[k].default_factory + } var_kwargs = d.pop(self.v_kwargs_name, {}) if self.v_args_name in d: diff --git a/pydantic/errors.py b/pydantic/errors.py index b29dde88b59..c7d46842a36 100644 --- a/pydantic/errors.py +++ b/pydantic/errors.py @@ -37,6 +37,7 @@ 'model-field-missing-annotation', 'config-both', 'removed-kwargs', + 'circular-reference-schema', 'invalid-for-json-schema', 'json-schema-already-used', 'base-model-instantiated', @@ -66,6 +67,10 @@ 'model-config-invalid-field-name', 'with-config-on-model', 'dataclass-on-model', + 'validate-call-type', + 'unpack-typed-dict', + 'overlapping-unpack-typed-dict', + 'invalid-self-type', ] diff --git a/pydantic/fields.py b/pydantic/fields.py index fb26f70b19d..e96b44fc8be 100644 --- a/pydantic/fields.py +++ b/pydantic/fields.py @@ -9,7 +9,7 @@ from copy import copy from dataclasses import Field as DataclassField from functools import cached_property -from typing import Any, ClassVar +from typing import Any, Callable, ClassVar, TypeVar, cast, overload from warnings import warn import annotated_types @@ -19,9 +19,11 @@ from . import types from ._internal import _decorators, _fields, _generics, _internal_dataclass, _repr, _typing_extra, _utils +from ._internal._namespace_utils import GlobalsNamespace, MappingNamespace from .aliases import AliasChoices, AliasPath from .config import JsonDict from .errors import PydanticUserError +from .json_schema import PydanticJsonSchemaWarning from .warnings import PydanticDeprecatedSince20 if typing.TYPE_CHECKING: @@ -48,13 +50,13 @@ class _FromFieldInfoInputs(typing_extensions.TypedDict, total=False): """This class exists solely to add type checking for the `**kwargs` in `FieldInfo.from_field`.""" annotation: type[Any] | None - default_factory: typing.Callable[[], Any] | None + default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None alias: str | None alias_priority: int | None validation_alias: str | AliasPath | AliasChoices | None serialization_alias: str | None title: str | None - field_title_generator: typing_extensions.Callable[[str, FieldInfo], str] | None + field_title_generator: Callable[[str, FieldInfo], str] | None description: str | None examples: list[Any] | None exclude: bool | None @@ -73,7 +75,7 @@ class _FromFieldInfoInputs(typing_extensions.TypedDict, total=False): union_mode: Literal['smart', 'left_to_right'] | None discriminator: str | types.Discriminator | None deprecated: Deprecated | str | bool | None - json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None frozen: bool | None validate_default: bool | None repr: bool @@ -103,7 +105,8 @@ class FieldInfo(_repr.Representation): Attributes: annotation: The type annotation of the field. default: The default value of the field. - default_factory: The factory function used to construct the default for the field. + default_factory: A callable to generate the default value. The callable can either take 0 arguments + (in which case it is called as is) or a single argument containing the already validated data. alias: The alias name of the field. alias_priority: The priority of the field's alias. validation_alias: The validation alias of the field. @@ -128,19 +131,19 @@ class FieldInfo(_repr.Representation): annotation: type[Any] | None default: Any - default_factory: typing.Callable[[], Any] | None + default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None alias: str | None alias_priority: int | None validation_alias: str | AliasPath | AliasChoices | None serialization_alias: str | None title: str | None - field_title_generator: typing.Callable[[str, FieldInfo], str] | None + field_title_generator: Callable[[str, FieldInfo], str] | None description: str | None examples: list[Any] | None exclude: bool | None discriminator: str | types.Discriminator | None deprecated: Deprecated | str | bool | None - json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None frozen: bool | None validate_default: bool | None repr: bool @@ -208,6 +211,10 @@ def __init__(self, **kwargs: Unpack[_FieldInfoInputs]) -> None: default = kwargs.pop('default', PydanticUndefined) if default is Ellipsis: self.default = PydanticUndefined + # Also remove it from the attributes set, otherwise + # `GenerateSchema._common_field_schema` mistakenly + # uses it: + self._attributes_set.pop('default', None) else: self.default = default @@ -446,11 +453,24 @@ def merge_field_infos(*field_infos: FieldInfo, **overrides: Any) -> FieldInfo: try: json_schema_extra = attributes_set.pop('json_schema_extra') - existing_json_schema_extra = merged_field_info_kwargs.get('json_schema_extra', {}) + existing_json_schema_extra = merged_field_info_kwargs.get('json_schema_extra') - if isinstance(existing_json_schema_extra, dict) and isinstance(json_schema_extra, dict): - merged_field_info_kwargs['json_schema_extra'] = {**existing_json_schema_extra, **json_schema_extra} - else: + if existing_json_schema_extra is None: + merged_field_info_kwargs['json_schema_extra'] = json_schema_extra + if isinstance(existing_json_schema_extra, dict): + if isinstance(json_schema_extra, dict): + merged_field_info_kwargs['json_schema_extra'] = { + **existing_json_schema_extra, + **json_schema_extra, + } + if callable(json_schema_extra): + warn( + 'Composing `dict` and `callable` type `json_schema_extra` is not supported.' + 'The `callable` type is being ignored.' + "If you'd like support for this behavior, please open an issue on pydantic.", + PydanticJsonSchemaWarning, + ) + elif callable(json_schema_extra): # if ever there's a case of a callable, we'll just keep the last json schema extra spec merged_field_info_kwargs['json_schema_extra'] = json_schema_extra except KeyError: @@ -486,13 +506,13 @@ def _from_dataclass_field(dc_field: DataclassField[Any]) -> FieldInfo: default = _Unset if dc_field.default_factory is dataclasses.MISSING: - default_factory: typing.Callable[[], Any] | None = _Unset + default_factory = _Unset else: default_factory = dc_field.default_factory # use the `Field` function so in correct kwargs raise the correct `TypeError` dc_field_metadata = {k: v for k, v in dc_field.metadata.items() if k in _FIELD_ARG_NAMES} - return Field(default=default, default_factory=default_factory, repr=dc_field.repr, **dc_field_metadata) + return Field(default=default, default_factory=default_factory, repr=dc_field.repr, **dc_field_metadata) # pyright: ignore[reportCallIssue] @staticmethod def _extract_metadata(annotation: type[Any] | None) -> tuple[type[Any] | None, list[Any]]: @@ -549,7 +569,13 @@ def deprecation_message(self) -> str | None: return 'deprecated' if self.deprecated else None return self.deprecated if isinstance(self.deprecated, str) else self.deprecated.message - def get_default(self, *, call_default_factory: bool = False) -> Any: + @overload + def get_default(self, *, call_default_factory: Literal[True], validated_data: dict[str, Any]) -> Any: ... + + @overload + def get_default(self, *, call_default_factory: Literal[False] = ...) -> Any: ... + + def get_default(self, *, call_default_factory: bool = False, validated_data: dict[str, Any] | None = None) -> Any: """Get the default value. We expose an option for whether to call the default_factory (if present), as calling it may @@ -557,7 +583,8 @@ def get_default(self, *, call_default_factory: bool = False) -> Any: be called (namely, when instantiating a model via `model_construct`). Args: - call_default_factory: Whether to call the default_factory or not. Defaults to `False`. + call_default_factory: Whether to call the default factory or not. + validated_data: The already validated data to be passed to the default factory. Returns: The default value, calling the default factory if requested or `None` if not set. @@ -565,7 +592,13 @@ def get_default(self, *, call_default_factory: bool = False) -> Any: if self.default_factory is None: return _utils.smart_deepcopy(self.default) elif call_default_factory: - return self.default_factory() + if validated_data is None: + raise ValueError("'validated_data' must be provided if 'call_default_factory' is True.") + if _fields.takes_validated_data_argument(self.default_factory): + return self.default_factory(validated_data) + else: + fac = cast(Callable[[], Any], self.default_factory) # Pyright doesn't narrow correctly + return fac() else: return None @@ -596,7 +629,12 @@ def rebuild_annotation(self) -> Any: # Annotated arguments must be a tuple return typing_extensions.Annotated[(self.annotation, *self.metadata)] # type: ignore - def apply_typevars_map(self, typevars_map: dict[Any, Any] | None, types_namespace: dict[str, Any] | None) -> None: + def apply_typevars_map( + self, + typevars_map: dict[Any, Any] | None, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, + ) -> None: """Apply a `typevars_map` to the annotation. This method is used when analyzing parametrized generic types to replace typevars with their concrete types. @@ -605,13 +643,14 @@ def apply_typevars_map(self, typevars_map: dict[Any, Any] | None, types_namespac Args: typevars_map: A dictionary mapping type variables to their concrete types. - types_namespace (dict | None): A dictionary containing related types to the annotated type. + globalns: The globals namespace to use during type annotation evaluation. + localns: The locals namespace to use during type annotation evaluation. See Also: pydantic._internal._generics.replace_types is used for replacing the typevars with their concrete types. """ - annotation = _typing_extra.eval_type_lenient(self.annotation, types_namespace) + annotation = _typing_extra.eval_type(self.annotation, globalns, localns, lenient=True) self.annotation = _generics.replace_types(annotation, typevars_map) def __repr_args__(self) -> ReprArgs: @@ -682,22 +721,182 @@ class _EmptyKwargs(typing_extensions.TypedDict): } +_T = TypeVar('_T') + + +# NOTE: Actual return type is 'FieldInfo', but we want to help type checkers +# to understand the magic that happens at runtime with the following overloads: +@overload # type hint the return value as `Any` to avoid type checking regressions when using `...`. +def Field( + default: _typing_extra.EllipsisType, + *, + alias: str | None = _Unset, + alias_priority: int | None = _Unset, + validation_alias: str | AliasPath | AliasChoices | None = _Unset, + serialization_alias: str | None = _Unset, + title: str | None = _Unset, + field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset, + description: str | None = _Unset, + examples: list[Any] | None = _Unset, + exclude: bool | None = _Unset, + discriminator: str | types.Discriminator | None = _Unset, + deprecated: Deprecated | str | bool | None = _Unset, + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset, + frozen: bool | None = _Unset, + validate_default: bool | None = _Unset, + repr: bool = _Unset, + init: bool | None = _Unset, + init_var: bool | None = _Unset, + kw_only: bool | None = _Unset, + pattern: str | typing.Pattern[str] | None = _Unset, + strict: bool | None = _Unset, + coerce_numbers_to_str: bool | None = _Unset, + gt: annotated_types.SupportsGt | None = _Unset, + ge: annotated_types.SupportsGe | None = _Unset, + lt: annotated_types.SupportsLt | None = _Unset, + le: annotated_types.SupportsLe | None = _Unset, + multiple_of: float | None = _Unset, + allow_inf_nan: bool | None = _Unset, + max_digits: int | None = _Unset, + decimal_places: int | None = _Unset, + min_length: int | None = _Unset, + max_length: int | None = _Unset, + union_mode: Literal['smart', 'left_to_right'] = _Unset, + fail_fast: bool | None = _Unset, + **extra: Unpack[_EmptyKwargs], +) -> Any: ... +@overload # `default` argument set +def Field( + default: _T, + *, + alias: str | None = _Unset, + alias_priority: int | None = _Unset, + validation_alias: str | AliasPath | AliasChoices | None = _Unset, + serialization_alias: str | None = _Unset, + title: str | None = _Unset, + field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset, + description: str | None = _Unset, + examples: list[Any] | None = _Unset, + exclude: bool | None = _Unset, + discriminator: str | types.Discriminator | None = _Unset, + deprecated: Deprecated | str | bool | None = _Unset, + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset, + frozen: bool | None = _Unset, + validate_default: bool | None = _Unset, + repr: bool = _Unset, + init: bool | None = _Unset, + init_var: bool | None = _Unset, + kw_only: bool | None = _Unset, + pattern: str | typing.Pattern[str] | None = _Unset, + strict: bool | None = _Unset, + coerce_numbers_to_str: bool | None = _Unset, + gt: annotated_types.SupportsGt | None = _Unset, + ge: annotated_types.SupportsGe | None = _Unset, + lt: annotated_types.SupportsLt | None = _Unset, + le: annotated_types.SupportsLe | None = _Unset, + multiple_of: float | None = _Unset, + allow_inf_nan: bool | None = _Unset, + max_digits: int | None = _Unset, + decimal_places: int | None = _Unset, + min_length: int | None = _Unset, + max_length: int | None = _Unset, + union_mode: Literal['smart', 'left_to_right'] = _Unset, + fail_fast: bool | None = _Unset, + **extra: Unpack[_EmptyKwargs], +) -> _T: ... +@overload # `default_factory` argument set +def Field( + *, + default_factory: Callable[[], _T] | Callable[[dict[str, Any]], _T], + alias: str | None = _Unset, + alias_priority: int | None = _Unset, + validation_alias: str | AliasPath | AliasChoices | None = _Unset, + serialization_alias: str | None = _Unset, + title: str | None = _Unset, + field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset, + description: str | None = _Unset, + examples: list[Any] | None = _Unset, + exclude: bool | None = _Unset, + discriminator: str | types.Discriminator | None = _Unset, + deprecated: Deprecated | str | bool | None = _Unset, + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset, + frozen: bool | None = _Unset, + validate_default: bool | None = _Unset, + repr: bool = _Unset, + init: bool | None = _Unset, + init_var: bool | None = _Unset, + kw_only: bool | None = _Unset, + pattern: str | typing.Pattern[str] | None = _Unset, + strict: bool | None = _Unset, + coerce_numbers_to_str: bool | None = _Unset, + gt: annotated_types.SupportsGt | None = _Unset, + ge: annotated_types.SupportsGe | None = _Unset, + lt: annotated_types.SupportsLt | None = _Unset, + le: annotated_types.SupportsLe | None = _Unset, + multiple_of: float | None = _Unset, + allow_inf_nan: bool | None = _Unset, + max_digits: int | None = _Unset, + decimal_places: int | None = _Unset, + min_length: int | None = _Unset, + max_length: int | None = _Unset, + union_mode: Literal['smart', 'left_to_right'] = _Unset, + fail_fast: bool | None = _Unset, + **extra: Unpack[_EmptyKwargs], +) -> _T: ... +@overload +def Field( # No default set + *, + alias: str | None = _Unset, + alias_priority: int | None = _Unset, + validation_alias: str | AliasPath | AliasChoices | None = _Unset, + serialization_alias: str | None = _Unset, + title: str | None = _Unset, + field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset, + description: str | None = _Unset, + examples: list[Any] | None = _Unset, + exclude: bool | None = _Unset, + discriminator: str | types.Discriminator | None = _Unset, + deprecated: Deprecated | str | bool | None = _Unset, + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset, + frozen: bool | None = _Unset, + validate_default: bool | None = _Unset, + repr: bool = _Unset, + init: bool | None = _Unset, + init_var: bool | None = _Unset, + kw_only: bool | None = _Unset, + pattern: str | typing.Pattern[str] | None = _Unset, + strict: bool | None = _Unset, + coerce_numbers_to_str: bool | None = _Unset, + gt: annotated_types.SupportsGt | None = _Unset, + ge: annotated_types.SupportsGe | None = _Unset, + lt: annotated_types.SupportsLt | None = _Unset, + le: annotated_types.SupportsLe | None = _Unset, + multiple_of: float | None = _Unset, + allow_inf_nan: bool | None = _Unset, + max_digits: int | None = _Unset, + decimal_places: int | None = _Unset, + min_length: int | None = _Unset, + max_length: int | None = _Unset, + union_mode: Literal['smart', 'left_to_right'] = _Unset, + fail_fast: bool | None = _Unset, + **extra: Unpack[_EmptyKwargs], +) -> Any: ... def Field( # noqa: C901 default: Any = PydanticUndefined, *, - default_factory: typing.Callable[[], Any] | None = _Unset, + default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None = _Unset, alias: str | None = _Unset, alias_priority: int | None = _Unset, validation_alias: str | AliasPath | AliasChoices | None = _Unset, serialization_alias: str | None = _Unset, title: str | None = _Unset, - field_title_generator: typing_extensions.Callable[[str, FieldInfo], str] | None = _Unset, + field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset, description: str | None = _Unset, examples: list[Any] | None = _Unset, exclude: bool | None = _Unset, discriminator: str | types.Discriminator | None = _Unset, deprecated: Deprecated | str | bool | None = _Unset, - json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None = _Unset, + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset, frozen: bool | None = _Unset, validate_default: bool | None = _Unset, repr: bool = _Unset, @@ -721,7 +920,7 @@ def Field( # noqa: C901 fail_fast: bool | None = _Unset, **extra: Unpack[_EmptyKwargs], ) -> Any: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/fields + """Usage docs: https://docs.pydantic.dev/2.10/concepts/fields Create a field for objects that can be configured. @@ -733,7 +932,8 @@ def Field( # noqa: C901 Args: default: Default value if the field is not set. - default_factory: A callable to generate the default value, such as :func:`~datetime.utcnow`. + default_factory: A callable to generate the default value. The callable can either take 0 arguments + (in which case it is called as is) or a single argument containing the already validated data. alias: The name to use for the attribute when validating or serializing by alias. This is often used for things like converting between snake and camel case. alias_priority: Priority of the alias. This affects whether an alias generator is used. @@ -905,12 +1105,15 @@ class ModelPrivateAttr(_repr.Representation): attribute if not provided. """ - __slots__ = 'default', 'default_factory' + __slots__ = ('default', 'default_factory') def __init__( self, default: Any = PydanticUndefined, *, default_factory: typing.Callable[[], Any] | None = None ) -> None: - self.default = default + if default is Ellipsis: + self.default = PydanticUndefined + else: + self.default = default self.default_factory = default_factory if not typing.TYPE_CHECKING: @@ -953,13 +1156,32 @@ def __eq__(self, other: Any) -> bool: ) +# NOTE: Actual return type is 'ModelPrivateAttr', but we want to help type checkers +# to understand the magic that happens at runtime. +@overload # `default` argument set +def PrivateAttr( + default: _T, + *, + init: Literal[False] = False, +) -> _T: ... +@overload # `default_factory` argument set +def PrivateAttr( + *, + default_factory: Callable[[], _T], + init: Literal[False] = False, +) -> _T: ... +@overload # No default set +def PrivateAttr( + *, + init: Literal[False] = False, +) -> Any: ... def PrivateAttr( default: Any = PydanticUndefined, *, - default_factory: typing.Callable[[], Any] | None = None, + default_factory: Callable[[], Any] | None = None, init: Literal[False] = False, ) -> Any: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/models/#private-model-attributes + """Usage docs: https://docs.pydantic.dev/2.10/concepts/models/#private-model-attributes Indicates that an attribute is intended for private use and not handled during normal validation/serialization. @@ -1084,13 +1306,13 @@ def computed_field( repr: bool | None = None, return_type: Any = PydanticUndefined, ) -> PropertyT | typing.Callable[[PropertyT], PropertyT]: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/fields#the-computed_field-decorator + """Usage docs: https://docs.pydantic.dev/2.10/concepts/fields#the-computed_field-decorator Decorator to include `property` and `cached_property` when serializing models or dataclasses. This is useful for fields that are computed from other fields, or for fields that are expensive to compute and should be cached. - ```py + ```python from pydantic import BaseModel, computed_field class Rectangle(BaseModel): @@ -1118,7 +1340,7 @@ def area(self) -> int: [pyright](https://github.com/microsoft/pyright) supports `@computed_field` without error. - ```py + ```python import random from pydantic import BaseModel, computed_field @@ -1158,7 +1380,7 @@ def random_number(self) -> int: `mypy` complains about this behavior if allowed, and `dataclasses` doesn't allow this pattern either. See the example below: - ```py + ```python from pydantic import BaseModel, computed_field class Parent(BaseModel): @@ -1179,7 +1401,7 @@ def a(self) -> str: Private properties decorated with `@computed_field` have `repr=False` by default. - ```py + ```python from functools import cached_property from pydantic import BaseModel, computed_field @@ -1199,7 +1421,7 @@ def _private_property(self) -> int: m = Model(foo=1) print(repr(m)) - #> M(foo=1) + #> Model(foo=1) ``` Args: diff --git a/pydantic/functional_serializers.py b/pydantic/functional_serializers.py index d1579bbd8ae..b653c8a6313 100644 --- a/pydantic/functional_serializers.py +++ b/pydantic/functional_serializers.py @@ -63,9 +63,13 @@ def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaH The Pydantic core schema. """ schema = handler(source_type) + globalns, localns = handler._get_types_namespace() try: return_type = _decorators.get_function_return_type( - self.func, self.return_type, handler._get_types_namespace() + self.func, + self.return_type, + globalns=globalns, + localns=localns, ) except NameError as e: raise PydanticUndefinedAnnotation.from_name_error(e) from e @@ -160,9 +164,13 @@ def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaH The generated core schema of the class. """ schema = handler(source_type) + globalns, localns = handler._get_types_namespace() try: return_type = _decorators.get_function_return_type( - self.func, self.return_type, handler._get_types_namespace() + self.func, + self.return_type, + globalns=globalns, + localns=localns, ) except NameError as e: raise PydanticUndefinedAnnotation.from_name_error(e) from e diff --git a/pydantic/functional_validators.py b/pydantic/functional_validators.py index 8779f72b74b..16f645d39ad 100644 --- a/pydantic/functional_validators.py +++ b/pydantic/functional_validators.py @@ -12,7 +12,7 @@ from pydantic_core import core_schema as _core_schema from typing_extensions import Annotated, Literal, Self, TypeAlias -from ._internal import _core_metadata, _decorators, _generics, _internal_dataclass +from ._internal import _decorators, _generics, _internal_dataclass from .annotated_handlers import GetCoreSchemaHandler from .errors import PydanticUserError @@ -26,7 +26,7 @@ @dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true) class AfterValidator: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/validators/#annotated-validators + """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#annotated-validators A metadata class that indicates that a validation should be applied **after** the inner validation logic. @@ -34,7 +34,7 @@ class AfterValidator: func: The validator function. Example: - ```py + ```python from typing_extensions import Annotated from pydantic import AfterValidator, BaseModel, ValidationError @@ -86,7 +86,7 @@ def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValid @dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true) class BeforeValidator: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/validators/#annotated-validators + """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#annotated-validators A metadata class that indicates that a validation should be applied **before** the inner validation logic. @@ -96,7 +96,7 @@ class BeforeValidator: JSON Schema (in validation mode). Example: - ```py + ```python from typing_extensions import Annotated from pydantic import BaseModel, BeforeValidator @@ -127,7 +127,14 @@ def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaH if self.json_schema_input_type is PydanticUndefined else handler.generate_schema(self.json_schema_input_type) ) - metadata = _core_metadata.build_metadata_dict(js_input_core_schema=input_schema) + # Try to resolve the original schema if required, because schema cleaning + # won't inline references in metadata: + if input_schema is not None: + try: + input_schema = handler.resolve_ref_schema(input_schema) + except LookupError: + pass + metadata = {'pydantic_js_input_core_schema': input_schema} if input_schema is not None else {} info_arg = _inspect_validator(self.func, 'before') if info_arg: @@ -152,29 +159,47 @@ def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValid @dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true) class PlainValidator: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/validators/#annotated-validators + """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#annotated-validators A metadata class that indicates that a validation should be applied **instead** of the inner validation logic. + !!! note + Before v2.9, `PlainValidator` wasn't always compatible with JSON Schema generation for `mode='validation'`. + You can now use the `json_schema_input_type` argument to specify the input type of the function + to be used in the JSON schema when `mode='validation'` (the default). See the example below for more details. + Attributes: func: The validator function. json_schema_input_type: The input type of the function. This is only used to generate the appropriate JSON Schema (in validation mode). If not provided, will default to `Any`. Example: - ```py + ```python + from typing import Union + from typing_extensions import Annotated from pydantic import BaseModel, PlainValidator - MyInt = Annotated[int, PlainValidator(lambda v: int(v) + 1)] + MyInt = Annotated[ + int, + PlainValidator( + lambda v: int(v) + 1, json_schema_input_type=Union[str, int] # (1)! + ), + ] class Model(BaseModel): a: MyInt print(Model(a='1').a) #> 2 + + print(Model(a=1).a) + #> 2 ``` + + 1. In this example, we've specified the `json_schema_input_type` as `Union[str, int]` which indicates to the JSON schema + generator that in validation mode, the input type for the `a` field can be either a `str` or an `int`. """ func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction @@ -190,16 +215,27 @@ def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaH try: schema = handler(source_type) - serialization = core_schema.wrap_serializer_function_ser_schema( - function=lambda v, h: h(v), - schema=schema, - return_schema=schema, + # TODO if `schema['serialization']` is one of `'include-exclude-dict/sequence', + # schema validation will fail. That's why we use 'type ignore' comments below. + serialization = schema.get( + 'serialization', + core_schema.wrap_serializer_function_ser_schema( + function=lambda v, h: h(v), + schema=schema, + return_schema=handler.generate_schema(source_type), + ), ) except PydanticSchemaGenerationError: serialization = None input_schema = handler.generate_schema(self.json_schema_input_type) - metadata = _core_metadata.build_metadata_dict(js_input_core_schema=input_schema) + # Try to resolve the original schema if required, because schema cleaning + # won't inline references in metadata: + try: + input_schema = handler.resolve_ref_schema(input_schema) + except LookupError: + pass + metadata = {'pydantic_js_input_core_schema': input_schema} if input_schema is not None else {} info_arg = _inspect_validator(self.func, 'plain') if info_arg: @@ -207,12 +243,16 @@ def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaH return core_schema.with_info_plain_validator_function( func, field_name=handler.field_name, - serialization=serialization, + serialization=serialization, # pyright: ignore[reportArgumentType] metadata=metadata, ) else: func = cast(core_schema.NoInfoValidatorFunction, self.func) - return core_schema.no_info_plain_validator_function(func, serialization=serialization, metadata=metadata) + return core_schema.no_info_plain_validator_function( + func, + serialization=serialization, # pyright: ignore[reportArgumentType] + metadata=metadata, + ) @classmethod def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self: @@ -224,7 +264,7 @@ def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValid @dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true) class WrapValidator: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/validators/#annotated-validators + """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#annotated-validators A metadata class that indicates that a validation should be applied **around** the inner validation logic. @@ -233,7 +273,7 @@ class WrapValidator: json_schema_input_type: The input type of the function. This is only used to generate the appropriate JSON Schema (in validation mode). - ```py + ```python from datetime import datetime from typing_extensions import Annotated @@ -272,7 +312,14 @@ def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaH if self.json_schema_input_type is PydanticUndefined else handler.generate_schema(self.json_schema_input_type) ) - metadata = _core_metadata.build_metadata_dict(js_input_core_schema=input_schema) + # Try to resolve the original schema if required, because schema cleaning + # won't inline references in metadata: + if input_schema is not None: + try: + input_schema = handler.resolve_ref_schema(input_schema) + except LookupError: + pass + metadata = {'pydantic_js_input_core_schema': input_schema} if input_schema is not None else {} info_arg = _inspect_validator(self.func, 'wrap') if info_arg: @@ -385,12 +432,12 @@ def field_validator( check_fields: bool | None = None, json_schema_input_type: Any = PydanticUndefined, ) -> Callable[[Any], Any]: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/validators/#field-validators + """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#field-validators Decorate methods on the class indicating that they should be used to validate fields. Example usage: - ```py + ```python from typing import Any from pydantic import ( @@ -642,12 +689,12 @@ def model_validator( *, mode: Literal['wrap', 'before', 'after'], ) -> Any: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/validators/#model-validators + """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#model-validators Decorate model methods for validation purposes. Example usage: - ```py + ```python from typing_extensions import Self from pydantic import BaseModel, ValidationError, model_validator @@ -709,7 +756,7 @@ class InstanceOf: '''Generic type for annotating a type that is an instance of a given class. Example: - ```py + ```python from pydantic import BaseModel, InstanceOf class Foo: @@ -788,7 +835,7 @@ def __class_getitem__(cls, item: Any) -> Any: @classmethod def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: original_schema = handler(source) - metadata = _core_metadata.build_metadata_dict(js_annotation_functions=[lambda _c, h: h(original_schema)]) + metadata = {'pydantic_js_annotation_functions': [lambda _c, h: h(original_schema)]} return core_schema.any_schema( metadata=metadata, serialization=core_schema.wrap_serializer_function_ser_schema( diff --git a/pydantic/json_schema.py b/pydantic/json_schema.py index 64c659e8d22..f31280014a6 100644 --- a/pydantic/json_schema.py +++ b/pydantic/json_schema.py @@ -14,11 +14,11 @@ import dataclasses import inspect import math +import os import re import warnings from collections import defaultdict from copy import deepcopy -from dataclasses import is_dataclass from enum import Enum from typing import ( TYPE_CHECKING, @@ -56,7 +56,7 @@ _typing_extra, ) from .annotated_handlers import GetJsonSchemaHandler -from .config import JsonDict, JsonSchemaExtraCallable, JsonValue +from .config import JsonDict, JsonValue from .errors import PydanticInvalidForJsonSchema, PydanticSchemaGenerationError, PydanticUserError if TYPE_CHECKING: @@ -92,27 +92,7 @@ _MODE_TITLE_MAPPING: dict[JsonSchemaMode, str] = {'validation': 'Input', 'serialization': 'Output'} -@deprecated( - '`update_json_schema` is deprecated, use a simple `my_dict.update(update_dict)` call instead.', - category=None, -) -def update_json_schema(schema: JsonSchemaValue, updates: dict[str, Any]) -> JsonSchemaValue: - """Update a JSON schema in-place by providing a dictionary of updates. - - This function sets the provided key-value pairs in the schema and returns the updated schema. - - Args: - schema: The JSON schema to update. - updates: A dictionary of key-value pairs to set in the schema. - - Returns: - The updated JSON schema. - """ - schema.update(updates) - return schema - - -JsonSchemaWarningKind = Literal['skipped-choice', 'non-serializable-default'] +JsonSchemaWarningKind = Literal['skipped-choice', 'non-serializable-default', 'skipped-discriminator'] """ A type alias representing the kinds of warnings that can be emitted during JSON schema generation. @@ -231,7 +211,7 @@ def remap_json_schema(self, schema: Any) -> Any: class GenerateJsonSchema: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/json_schema/#customizing-the-json-schema-generation-process + """Usage docs: https://docs.pydantic.dev/2.10/concepts/json_schema/#customizing-the-json-schema-generation-process A class for generating JSON schemas. @@ -260,7 +240,7 @@ class GenerateJsonSchema: ref_template: The format string to use when generating reference names. Raises: - JsonSchemaError: If the instance of the class is inadvertently re-used after generating a schema. + JsonSchemaError: If the instance of the class is inadvertently reused after generating a schema. """ schema_dialect = 'https://json-schema.org/draft/2020-12/schema' @@ -301,7 +281,7 @@ def __init__(self, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLA # store the error raised and re-throw it if we end up needing that def self._core_defs_invalid_for_json_schema: dict[DefsRef, PydanticInvalidForJsonSchema] = {} - # This changes to True after generating a schema, to prevent issues caused by accidental re-use + # This changes to True after generating a schema, to prevent issues caused by accidental reuse # of a single instance of a schema generator self._used = False @@ -328,7 +308,7 @@ def build_schema_type_to_method( TypeError: If no method has been defined for generating a JSON schema for a given pydantic core schema type. """ mapping: dict[CoreSchemaOrFieldType, Callable[[CoreSchemaOrField], JsonSchemaValue]] = {} - core_schema_types: list[CoreSchemaOrFieldType] = _typing_extra.all_literal_values( + core_schema_types: list[CoreSchemaOrFieldType] = _typing_extra.literal_values( CoreSchemaOrFieldType # type: ignore ) for key in core_schema_types: @@ -336,6 +316,8 @@ def build_schema_type_to_method( try: mapping[key] = getattr(self, method_name) except AttributeError as e: # pragma: no cover + if os.environ['PYDANTIC_PRIVATE_ALLOW_UNHANDLED_SCHEMA_TYPES'] == '1': + continue raise TypeError( f'No method for generating JsonSchema for core_schema.type={key!r} ' f'(expected: {type(self).__name__}.{method_name})' @@ -389,7 +371,7 @@ def generate_definitions( json_schema = {'$defs': self.definitions} json_schema = definitions_remapping.remap_json_schema(json_schema) self._used = True - return json_schemas_map, _sort_json_schema(json_schema['$defs']) # type: ignore + return json_schemas_map, self.sort(json_schema['$defs']) # type: ignore def generate(self, schema: CoreSchema, mode: JsonSchemaMode = 'validation') -> JsonSchemaValue: """Generates a JSON schema for a specified schema in a specified mode. @@ -438,7 +420,7 @@ def generate(self, schema: CoreSchema, mode: JsonSchemaMode = 'validation') -> J # json_schema['$schema'] = self.schema_dialect self._used = True - return _sort_json_schema(json_schema) + return self.sort(json_schema) def generate_inner(self, schema: CoreSchemaOrField) -> JsonSchemaValue: # noqa: C901 """Generates a JSON schema for a given core schema. @@ -448,6 +430,10 @@ def generate_inner(self, schema: CoreSchemaOrField) -> JsonSchemaValue: # noqa: Returns: The generated JSON schema. + + TODO: the nested function definitions here seem like bad practice, I'd like to unpack these + in a future PR. It'd be great if we could shorten the call stack a bit for JSON schema generation, + and I think there's potential for that here. """ # If a schema with the same CoreRef has been handled, just return a reference to it # Note that this assumes that it will _never_ be the case that the same CoreRef is used @@ -458,15 +444,11 @@ def generate_inner(self, schema: CoreSchemaOrField) -> JsonSchemaValue: # noqa: if core_mode_ref in self.core_to_defs_refs and self.core_to_defs_refs[core_mode_ref] in self.definitions: return {'$ref': self.core_to_json_refs[core_mode_ref]} - # Generate the JSON schema, accounting for the json_schema_override and core_schema_override - metadata_handler = _core_metadata.CoreMetadataHandler(schema) - def populate_defs(core_schema: CoreSchema, json_schema: JsonSchemaValue) -> JsonSchemaValue: if 'ref' in core_schema: core_ref = CoreRef(core_schema['ref']) # type: ignore[typeddict-item] defs_ref, ref_json_schema = self.get_cache_defs_ref_schema(core_ref) json_ref = JsonRef(ref_json_schema['$ref']) - self.json_to_defs_refs[json_ref] = defs_ref # Replace the schema if it's not a reference to itself # What we want to avoid is having the def be just a ref to itself # which is what would happen if we blindly assigned any @@ -517,7 +499,38 @@ def handler_func(schema_or_field: CoreSchemaOrField) -> JsonSchemaValue: current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, handler_func) - for js_modify_function in metadata_handler.metadata.get('pydantic_js_functions', ()): + metadata = cast(_core_metadata.CoreMetadata, schema.get('metadata', {})) + + # TODO: I dislike that we have to wrap these basic dict updates in callables, is there any way around this? + + if js_updates := metadata.get('pydantic_js_updates'): + + def js_updates_handler_func( + schema_or_field: CoreSchemaOrField, + current_handler: GetJsonSchemaHandler = current_handler, + ) -> JsonSchemaValue: + json_schema = {**current_handler(schema_or_field), **js_updates} + return json_schema + + current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, js_updates_handler_func) + + if js_extra := metadata.get('pydantic_js_extra'): + + def js_extra_handler_func( + schema_or_field: CoreSchemaOrField, + current_handler: GetJsonSchemaHandler = current_handler, + ) -> JsonSchemaValue: + json_schema = current_handler(schema_or_field) + if isinstance(js_extra, dict): + json_schema.update(to_jsonable_python(js_extra)) + elif callable(js_extra): + # similar to typing issue in _update_class_schema when we're working with callable js extra + js_extra(json_schema) # type: ignore + return json_schema + + current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, js_extra_handler_func) + + for js_modify_function in metadata.get('pydantic_js_functions', ()): def new_handler_func( schema_or_field: CoreSchemaOrField, @@ -535,7 +548,7 @@ def new_handler_func( current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, new_handler_func) - for js_modify_function in metadata_handler.metadata.get('pydantic_js_annotation_functions', ()): + for js_modify_function in metadata.get('pydantic_js_annotation_functions', ()): def new_handler_func( schema_or_field: CoreSchemaOrField, @@ -554,7 +567,45 @@ def new_handler_func( json_schema = populate_defs(schema, json_schema) return json_schema + def sort(self, value: JsonSchemaValue, parent_key: str | None = None) -> JsonSchemaValue: + """Override this method to customize the sorting of the JSON schema (e.g., don't sort at all, sort all keys unconditionally, etc.) + + By default, alphabetically sort the keys in the JSON schema, skipping the 'properties' and 'default' keys to preserve field definition order. + This sort is recursive, so it will sort all nested dictionaries as well. + """ + sorted_dict: dict[str, JsonSchemaValue] = {} + keys = value.keys() + if parent_key not in ('properties', 'default'): + keys = sorted(keys) + for key in keys: + sorted_dict[key] = self._sort_recursive(value[key], parent_key=key) + return sorted_dict + + def _sort_recursive(self, value: Any, parent_key: str | None = None) -> Any: + """Recursively sort a JSON schema value.""" + if isinstance(value, dict): + sorted_dict: dict[str, JsonSchemaValue] = {} + keys = value.keys() + if parent_key not in ('properties', 'default'): + keys = sorted(keys) + for key in keys: + sorted_dict[key] = self._sort_recursive(value[key], parent_key=key) + return sorted_dict + elif isinstance(value, list): + sorted_list: list[JsonSchemaValue] = [] + for item in value: + sorted_list.append(self._sort_recursive(item, parent_key)) + return sorted_list + else: + return value + # ### Schema generation methods + + def invalid_schema(self, schema: core_schema.InvalidSchema) -> JsonSchemaValue: + """Placeholder - should never be called.""" + + raise RuntimeError('Cannot generate schema for invalid_schema. This is a bug! Please report it.') + def any_schema(self, schema: core_schema.AnySchema) -> JsonSchemaValue: """Generates a JSON schema that matches any value. @@ -737,9 +788,11 @@ def literal_schema(self, schema: core_schema.LiteralSchema) -> JsonSchemaValue: # jsonify the expected values expected = [to_jsonable_python(v) for v in expected] - result: dict[str, Any] = {'enum': expected} + result: dict[str, Any] = {} if len(expected) == 1: result['const'] = expected[0] + else: + result['enum'] = expected types = {type(e) for e in expected} if types == {str}: @@ -777,8 +830,6 @@ def enum_schema(self, schema: core_schema.EnumSchema) -> JsonSchemaValue: expected = [to_jsonable_python(v.value) for v in schema['members']] result['enum'] = expected - if len(expected) == 1: - result['const'] = expected[0] types = {type(e) for e in expected} if isinstance(enum_type, str) or types == {str}: @@ -958,17 +1009,26 @@ def dict_schema(self, schema: core_schema.DictSchema) -> JsonSchemaValue: """ json_schema: JsonSchemaValue = {'type': 'object'} - keys_schema = self.generate_inner(schema['keys_schema']).copy() if 'keys_schema' in schema else {} + keys_schema = self.resolve_ref_schema( + self.generate_inner(schema['keys_schema']).copy() if 'keys_schema' in schema else {} + ) keys_pattern = keys_schema.pop('pattern', None) values_schema = self.generate_inner(schema['values_schema']).copy() if 'values_schema' in schema else {} - values_schema.pop('title', None) # don't give a title to the additionalProperties + # don't give a title to additionalProperties, patternProperties and propertyNames + values_schema.pop('title', None) + keys_schema.pop('title', None) if values_schema or keys_pattern is not None: # don't add additionalProperties if it's empty if keys_pattern is None: json_schema['additionalProperties'] = values_schema else: json_schema['patternProperties'] = {keys_pattern: values_schema} + # The len check indicates that constraints are probably present: + if keys_schema.get('type') == 'string' and len(keys_schema) > 1: + keys_schema.pop('type') + json_schema['propertyNames'] = keys_schema + self.update_with_validations(json_schema, schema, self.ValidationsMapping.object) return json_schema @@ -981,8 +1041,9 @@ def function_before_schema(self, schema: core_schema.BeforeValidatorFunctionSche Returns: The generated JSON schema. """ - metadata = _core_metadata.CoreMetadataHandler(schema).metadata - if self._mode == 'validation' and (input_schema := metadata.get('pydantic_js_input_core_schema')): + if self._mode == 'validation' and ( + input_schema := schema.get('metadata', {}).get('pydantic_js_input_core_schema') + ): return self.generate_inner(input_schema) return self.generate_inner(schema['schema']) @@ -1007,8 +1068,9 @@ def function_plain_schema(self, schema: core_schema.PlainValidatorFunctionSchema Returns: The generated JSON schema. """ - metadata = _core_metadata.CoreMetadataHandler(schema).metadata - if self._mode == 'validation' and (input_schema := metadata.get('pydantic_js_input_core_schema')): + if self._mode == 'validation' and ( + input_schema := schema.get('metadata', {}).get('pydantic_js_input_core_schema') + ): return self.generate_inner(input_schema) return self.handle_invalid_for_json_schema( @@ -1024,8 +1086,9 @@ def function_wrap_schema(self, schema: core_schema.WrapValidatorFunctionSchema) Returns: The generated JSON schema. """ - metadata = _core_metadata.CoreMetadataHandler(schema).metadata - if self._mode == 'validation' and (input_schema := metadata.get('pydantic_js_input_core_schema')): + if self._mode == 'validation' and ( + input_schema := schema.get('metadata', {}).get('pydantic_js_input_core_schema') + ): return self.generate_inner(input_schema) return self.generate_inner(schema['schema']) @@ -1201,9 +1264,14 @@ def _extract_discriminator( continue # this means that the "alias" does not represent a field alias_is_present_on_all_choices = True for choice in one_of_choices: - while '$ref' in choice: - assert isinstance(choice['$ref'], str) - choice = self.get_schema_from_definitions(JsonRef(choice['$ref'])) or {} + try: + choice = self.resolve_ref_schema(choice) + except RuntimeError as exc: + # TODO: fixme - this is a workaround for the fact that we can't always resolve refs + # for tagged union choices at this point in the schema gen process, we might need to do + # another pass at the end like we do for core schemas + self.emit_warning('skipped-discriminator', str(exc)) + choice = {} properties = choice.get('properties', {}) if not isinstance(properties, dict) or alias not in properties: alias_is_present_on_all_choices = False @@ -1284,15 +1352,10 @@ def typed_dict_schema(self, schema: core_schema.TypedDictSchema) -> JsonSchemaVa with self._config_wrapper_stack.push(config): json_schema = self._named_required_fields_schema(named_required_fields) - json_schema_extra = config.get('json_schema_extra') - extra = schema.get('extra_behavior') - if extra is None: - extra = config.get('extra', 'ignore') - if cls is not None: - title = config.get('title') or cls.__name__ - json_schema = self._update_class_schema(json_schema, title, extra, cls, json_schema_extra) + self._update_class_schema(json_schema, cls, config) else: + extra = config.get('extra') if extra == 'forbid': json_schema['additionalProperties'] = False elif extra == 'allow': @@ -1409,13 +1472,56 @@ def model_schema(self, schema: core_schema.ModelSchema) -> JsonSchemaValue: # because it could lead to inconsistent refs handling, etc. cls = cast('type[BaseModel]', schema['cls']) config = cls.model_config - title = config.get('title') with self._config_wrapper_stack.push(config): json_schema = self.generate_inner(schema['schema']) + self._update_class_schema(json_schema, cls, config) + + return json_schema + + def _update_class_schema(self, json_schema: JsonSchemaValue, cls: type[Any], config: ConfigDict) -> None: + """Update json_schema with the following, extracted from `config` and `cls`: + + * title + * description + * additional properties + * json_schema_extra + * deprecated + + Done in place, hence there's no return value as the original json_schema is mutated. + No ref resolving is involved here, as that's not appropriate for simple updates. + """ + from .main import BaseModel + from .root_model import RootModel + + if config_title := config.get('title'): + json_schema.setdefault('title', config_title) + elif model_title_generator := config.get('model_title_generator'): + title = model_title_generator(cls) + if not isinstance(title, str): + raise TypeError(f'model_title_generator {model_title_generator} must return str, not {title.__class__}') + json_schema.setdefault('title', title) + if 'title' not in json_schema: + json_schema['title'] = cls.__name__ + + # BaseModel and dataclasses; don't use cls.__doc__ as it will contain the verbose class signature by default + docstring = None if cls is BaseModel or dataclasses.is_dataclass(cls) else cls.__doc__ + + if docstring: + json_schema.setdefault('description', inspect.cleandoc(docstring)) + elif issubclass(cls, RootModel) and (root_description := cls.__pydantic_fields__['root'].description): + json_schema.setdefault('description', root_description) + + extra = config.get('extra') + if 'additionalProperties' not in json_schema: + if extra == 'allow': + json_schema['additionalProperties'] = True + elif extra == 'forbid': + json_schema['additionalProperties'] = False + json_schema_extra = config.get('json_schema_extra') - if cls.__pydantic_root_model__: + if issubclass(cls, BaseModel) and cls.__pydantic_root_model__: root_json_schema_extra = cls.model_fields['root'].json_schema_extra if json_schema_extra and root_json_schema_extra: raise ValueError( @@ -1425,44 +1531,18 @@ def model_schema(self, schema: core_schema.ModelSchema) -> JsonSchemaValue: if root_json_schema_extra: json_schema_extra = root_json_schema_extra - json_schema = self._update_class_schema(json_schema, title, config.get('extra', None), cls, json_schema_extra) - - return json_schema - - def _update_class_schema( - self, - json_schema: JsonSchemaValue, - title: str | None, - extra: Literal['allow', 'ignore', 'forbid'] | None, - cls: type[Any], - json_schema_extra: JsonDict | JsonSchemaExtraCallable | None, - ) -> JsonSchemaValue: - if '$ref' in json_schema: - schema_to_update = self.get_schema_from_definitions(JsonRef(json_schema['$ref'])) or json_schema - else: - schema_to_update = json_schema - - if title is not None: - # referenced_schema['title'] = title - schema_to_update.setdefault('title', title) - - if 'additionalProperties' not in schema_to_update: - if extra == 'allow': - schema_to_update['additionalProperties'] = True - elif extra == 'forbid': - schema_to_update['additionalProperties'] = False - if isinstance(json_schema_extra, (staticmethod, classmethod)): # In older versions of python, this is necessary to ensure staticmethod/classmethods are callable json_schema_extra = json_schema_extra.__get__(cls) if isinstance(json_schema_extra, dict): - schema_to_update.update(json_schema_extra) + json_schema.update(json_schema_extra) elif callable(json_schema_extra): + # FIXME: why are there type ignores here? We support two signatures for json_schema_extra callables... if len(inspect.signature(json_schema_extra).parameters) > 1: - json_schema_extra(schema_to_update, cls) # type: ignore + json_schema_extra(json_schema, cls) # type: ignore else: - json_schema_extra(schema_to_update) # type: ignore + json_schema_extra(json_schema) # type: ignore elif json_schema_extra is not None: raise ValueError( f"model_config['json_schema_extra']={json_schema_extra} should be a dict, callable, or None" @@ -1471,9 +1551,7 @@ def _update_class_schema( if hasattr(cls, '__deprecated__'): json_schema['deprecated'] = True - return json_schema - - def resolve_schema_to_update(self, json_schema: JsonSchemaValue) -> JsonSchemaValue: + def resolve_ref_schema(self, json_schema: JsonSchemaValue) -> JsonSchemaValue: """Resolve a JsonSchemaValue to the non-ref schema if it is a $ref schema. Args: @@ -1482,14 +1560,14 @@ def resolve_schema_to_update(self, json_schema: JsonSchemaValue) -> JsonSchemaVa Returns: The resolved schema. """ - if '$ref' in json_schema: - schema_to_update = self.get_schema_from_definitions(JsonRef(json_schema['$ref'])) - if schema_to_update is None: - raise RuntimeError(f'Cannot update undefined schema for $ref={json_schema["$ref"]}') - return self.resolve_schema_to_update(schema_to_update) - else: - schema_to_update = json_schema - return schema_to_update + if '$ref' not in json_schema: + return json_schema + + ref = json_schema['$ref'] + schema_to_update = self.get_schema_from_definitions(JsonRef(ref)) + if schema_to_update is None: + raise RuntimeError(f'Cannot update undefined schema for $ref={ref}') + return self.resolve_ref_schema(schema_to_update) def model_fields_schema(self, schema: core_schema.ModelFieldsSchema) -> JsonSchemaValue: """Generates a JSON schema that matches a schema that defines a model's fields. @@ -1510,7 +1588,7 @@ def model_fields_schema(self, schema: core_schema.ModelFieldsSchema) -> JsonSche json_schema = self._named_required_fields_schema(named_required_fields) extras_schema = schema.get('extras_schema', None) if extras_schema is not None: - schema_to_update = self.resolve_schema_to_update(json_schema) + schema_to_update = self.resolve_ref_schema(json_schema) schema_to_update['additionalProperties'] = self.generate_inner(extras_schema) return json_schema @@ -1584,18 +1662,18 @@ def dataclass_schema(self, schema: core_schema.DataclassSchema) -> JsonSchemaVal Returns: The generated JSON schema. """ + from ._internal._dataclasses import is_builtin_dataclass + cls = schema['cls'] config: ConfigDict = getattr(cls, '__pydantic_config__', cast('ConfigDict', {})) - title = config.get('title') or cls.__name__ with self._config_wrapper_stack.push(config): json_schema = self.generate_inner(schema['schema']).copy() - json_schema_extra = config.get('json_schema_extra') - json_schema = self._update_class_schema(json_schema, title, config.get('extra', None), cls, json_schema_extra) + self._update_class_schema(json_schema, cls, config) # Dataclass-specific handling of description - if is_dataclass(cls) and not hasattr(cls, '__pydantic_validator__'): + if is_builtin_dataclass(cls): # vanilla dataclass; don't use cls.__doc__ as it will contain the class signature by default description = None else: @@ -1614,8 +1692,7 @@ def arguments_schema(self, schema: core_schema.ArgumentsSchema) -> JsonSchemaVal Returns: The generated JSON schema. """ - metadata = _core_metadata.CoreMetadataHandler(schema).metadata - prefer_positional = metadata.get('pydantic_js_prefer_positional_arguments') + prefer_positional = schema.get('metadata', {}).get('pydantic_js_prefer_positional_arguments') arguments = schema['arguments_schema'] kw_only_arguments = [a for a in arguments if a.get('mode') == 'keyword_only'] @@ -1899,7 +1976,7 @@ def get_title_from_name(self, name: str) -> str: Returns: The title. """ - return name.title().replace('_', ' ') + return name.title().replace('_', ' ').strip() def field_title_should_be_set(self, schema: CoreSchemaOrField) -> bool: """Returns true if a field with the given schema should have a title set based on the field name. @@ -2172,7 +2249,9 @@ def _add_json_refs(schema: Any) -> None: if not json_ref.startswith(('http://', 'https://')): raise - for v in schema.values(): + for k, v in schema.items(): + if k == 'examples': + continue # skip refs processing for examples, allow arbitrary values / refs _add_json_refs(v) elif isinstance(schema, list): for v in schema: @@ -2349,27 +2428,9 @@ def _make_json_hashable(value: JsonValue) -> _HashableJsonValue: return value -def _sort_json_schema(value: JsonSchemaValue, parent_key: str | None = None) -> JsonSchemaValue: - if isinstance(value, dict): - sorted_dict: dict[str, JsonSchemaValue] = {} - keys = value.keys() - if (parent_key != 'properties') and (parent_key != 'default'): - keys = sorted(keys) - for key in keys: - sorted_dict[key] = _sort_json_schema(value[key], parent_key=key) - return sorted_dict - elif isinstance(value, list): - sorted_list: list[JsonSchemaValue] = [] - for item in value: # type: ignore - sorted_list.append(_sort_json_schema(item, parent_key)) - return sorted_list # type: ignore - else: - return value - - @dataclasses.dataclass(**_internal_dataclass.slots_true) class WithJsonSchema: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/json_schema/#withjsonschema-annotation + """Usage docs: https://docs.pydantic.dev/2.10/concepts/json_schema/#withjsonschema-annotation Add this as an annotation on a field to override the (base) JSON schema that would be generated for that field. This provides a way to set a JSON schema for types that would otherwise raise errors when producing a JSON schema, @@ -2484,6 +2545,8 @@ def _get_all_json_refs(item: Any) -> set[JsonRef]: current = stack.pop() if isinstance(current, dict): for key, value in current.items(): + if key == 'examples': + continue # skip examples, allow arbitrary values / refs if key == '$ref' and isinstance(value, str): refs.add(JsonRef(value)) elif isinstance(value, dict): @@ -2504,12 +2567,12 @@ def _get_all_json_refs(item: Any) -> set[JsonRef]: @dataclasses.dataclass(**_internal_dataclass.slots_true) class SkipJsonSchema: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/json_schema/#skipjsonschema-annotation + """Usage docs: https://docs.pydantic.dev/2.10/concepts/json_schema/#skipjsonschema-annotation Add this as an annotation on a field to skip generating a JSON schema for that field. Example: - ```py + ```python from typing import Union from pydantic import BaseModel @@ -2517,13 +2580,11 @@ class SkipJsonSchema: from pprint import pprint - class Model(BaseModel): a: Union[int, None] = None # (1)! b: Union[int, SkipJsonSchema[None]] = None # (2)! c: SkipJsonSchema[Union[int, None]] = None # (3)! - pprint(Model.model_json_schema()) ''' { diff --git a/pydantic/main.py b/pydantic/main.py index 4b3d8eff730..3885e3022c3 100644 --- a/pydantic/main.py +++ b/pydantic/main.py @@ -8,6 +8,7 @@ import typing import warnings from copy import copy, deepcopy +from functools import cached_property from typing import ( TYPE_CHECKING, Any, @@ -16,6 +17,7 @@ Dict, Generator, Literal, + Mapping, Set, Tuple, TypeVar, @@ -38,6 +40,7 @@ _import_utils, _mock_val_ser, _model_construction, + _namespace_utils, _repr, _typing_extra, _utils, @@ -51,19 +54,13 @@ from .plugin._schema_validator import PluggableSchemaValidator from .warnings import PydanticDeprecatedSince20 -# Always define certain types that are needed to resolve method type hints/annotations -# (even when not type checking) via typing.get_type_hints. -ModelT = TypeVar('ModelT', bound='BaseModel') -TupleGenerator = Generator[Tuple[str, Any], None, None] -IncEx: TypeAlias = Union[Set[int], Set[str], Dict[int, 'IncEx'], Dict[str, 'IncEx'], None] - - if TYPE_CHECKING: from inspect import Signature from pathlib import Path from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator + from ._internal._namespace_utils import MappingNamespace from ._internal._utils import AbstractSetIntStr, MappingIntStrAny from .deprecated.parse import Protocol as DeprecatedParseProtocol from .fields import ComputedFieldInfo, FieldInfo, ModelPrivateAttr @@ -74,11 +71,18 @@ __all__ = 'BaseModel', 'create_model' +# Keep these type aliases available at runtime: +TupleGenerator: TypeAlias = Generator[Tuple[str, Any], None, None] +# NOTE: In reality, `bool` should be replaced by `Literal[True]` but mypy fails to correctly apply bidirectional +# type inference (e.g. when using `{'a': {'b': True}}`): +# NOTE: Keep this type alias in sync with the stub definition in `pydantic-core`: +IncEx: TypeAlias = Union[Set[int], Set[str], Mapping[int, Union['IncEx', bool]], Mapping[str, Union['IncEx', bool]]] + _object_setattr = _model_construction.object_setattr class BaseModel(metaclass=_model_construction.ModelMetaclass): - """Usage docs: https://docs.pydantic.dev/2.9/concepts/models/ + """Usage docs: https://docs.pydantic.dev/2.10/concepts/models/ A base class for creating Pydantic models. @@ -100,15 +104,16 @@ class BaseModel(metaclass=_model_construction.ModelMetaclass): __pydantic_serializer__: The `pydantic-core` `SchemaSerializer` used to dump instances of the model. __pydantic_validator__: The `pydantic-core` `SchemaValidator` used to validate instances of the model. + __pydantic_fields__: A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects. + __pydantic_computed_fields__: A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects. + __pydantic_extra__: A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`. __pydantic_fields_set__: The names of fields explicitly set during instantiation. __pydantic_private__: Values of private attributes set on the model instance. """ - # Class attributes: - # `model_fields` and `__pydantic_decorators__` must be set for - # `GenerateSchema.model_schema` to work for a plain `BaseModel` annotation. + # Note: Many of the below class vars are defined in the metaclass, but we define them here for type checking purposes. model_config: ClassVar[ConfigDict] = ConfigDict() """ @@ -117,17 +122,6 @@ class BaseModel(metaclass=_model_construction.ModelMetaclass): # Because `dict` is in the local namespace of the `BaseModel` class, we use `Dict` for annotations. # TODO v3 fallback to `dict` when the deprecated `dict` method gets removed. - model_fields: ClassVar[Dict[str, FieldInfo]] = {} # noqa: UP006 - """ - Metadata about the fields defined on the model, - mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects. - - This replaces `Model.__fields__` from Pydantic V1. - """ - - model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {} # noqa: UP006 - """A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects.""" - __class_vars__: ClassVar[set[str]] """The names of the class variables defined on the model.""" @@ -146,6 +140,7 @@ class BaseModel(metaclass=_model_construction.ModelMetaclass): __pydantic_custom_init__: ClassVar[bool] """Whether the model has a custom `__init__` method.""" + # Must be set for `GenerateSchema.model_schema` to work for a plain `BaseModel` annotation. __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = _decorators.DecoratorInfos() """Metadata containing the decorators defined on the model. This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.""" @@ -169,6 +164,14 @@ class BaseModel(metaclass=_model_construction.ModelMetaclass): __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] """The `pydantic-core` `SchemaValidator` used to validate instances of the model.""" + __pydantic_fields__: ClassVar[Dict[str, FieldInfo]] # noqa: UP006 + """A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects. + This replaces `Model.__fields__` from Pydantic V1. + """ + + __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] # noqa: UP006 + """A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.""" + __pydantic_extra__: dict[str, Any] | None = _model_construction.NoInitField(init=False) """A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`.""" @@ -214,12 +217,41 @@ def __init__(self, /, **data: Any) -> None: 'A custom validator is returning a value other than `self`.\n' "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n" 'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.', - category=None, + stacklevel=2, ) # The following line sets a flag that we use to determine when `__init__` gets overridden by the user __init__.__pydantic_base_init__ = True # pyright: ignore[reportFunctionMemberAccess] + # TODO: V3 - remove `model_fields` and `model_computed_fields` properties from the `BaseModel` class - they should only + # be accessible on the model class, not on instances. We have these purely for backwards compatibility with Pydantic dict[str, FieldInfo]: + """Get metadata about the fields defined on the model. + + Deprecation warning: you should be getting this information from the model class, not from an instance. + In V3, this property will be removed from the `BaseModel` class. + + Returns: + A mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects. + """ + # Must be set for `GenerateSchema.model_schema` to work for a plain `BaseModel` annotation, hence the default here. + return getattr(self, '__pydantic_fields__', {}) + + @property + def model_computed_fields(self) -> dict[str, ComputedFieldInfo]: + """Get metadata about the computed fields defined on the model. + + Deprecation warning: you should be getting this information from the model class, not from an instance. + In V3, this property will be removed from the `BaseModel` class. + + Returns: + A mapping of computed field names to [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects. + """ + # Must be set for `GenerateSchema.model_schema` to work for a plain `BaseModel` annotation, hence the default here. + return getattr(self, '__pydantic_computed_fields__', {}) + @property def model_extra(self) -> dict[str, Any] | None: """Get extra fields set during validation. @@ -266,7 +298,7 @@ def model_construct(cls, _fields_set: set[str] | None = None, **values: Any) -> fields_values: dict[str, Any] = {} fields_set = set() - for name, field in cls.model_fields.items(): + for name, field in cls.__pydantic_fields__.items(): if field.alias is not None and field.alias in values: fields_values[name] = values.pop(field.alias) fields_set.add(name) @@ -295,7 +327,7 @@ def model_construct(cls, _fields_set: set[str] | None = None, **values: Any) -> fields_values[name] = values.pop(name) fields_set.add(name) elif not field.is_required(): - fields_values[name] = field.get_default(call_default_factory=True) + fields_values[name] = field.get_default(call_default_factory=True, validated_data=fields_values) if _fields_set is None: _fields_set = fields_set @@ -320,8 +352,8 @@ def model_construct(cls, _fields_set: set[str] | None = None, **values: Any) -> return m - def model_copy(self, *, update: dict[str, Any] | None = None, deep: bool = False) -> Self: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy + def model_copy(self, *, update: Mapping[str, Any] | None = None, deep: bool = False) -> Self: + """Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy Returns a copy of the model. @@ -337,7 +369,7 @@ def model_copy(self, *, update: dict[str, Any] | None = None, deep: bool = False if update: if self.model_config.get('extra') == 'allow': for k, v in update.items(): - if k in self.model_fields: + if k in self.__pydantic_fields__: copied.__dict__[k] = v else: if copied.__pydantic_extra__ is None: @@ -352,8 +384,8 @@ def model_dump( self, *, mode: Literal['json', 'python'] | str = 'python', - include: IncEx = None, - exclude: IncEx = None, + include: IncEx | None = None, + exclude: IncEx | None = None, context: Any | None = None, by_alias: bool = False, exclude_unset: bool = False, @@ -363,7 +395,7 @@ def model_dump( warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False, ) -> dict[str, Any]: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump + """Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. @@ -405,8 +437,8 @@ def model_dump_json( self, *, indent: int | None = None, - include: IncEx = None, - exclude: IncEx = None, + include: IncEx | None = None, + exclude: IncEx | None = None, context: Any | None = None, by_alias: bool = False, exclude_unset: bool = False, @@ -416,7 +448,7 @@ def model_dump_json( warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False, ) -> str: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json + """Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json Generates a JSON representation of the model using Pydantic's `to_json` method. @@ -516,7 +548,7 @@ def model_rebuild( force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, - _types_namespace: dict[str, Any] | None = None, + _types_namespace: MappingNamespace | None = None, ) -> bool | None: """Try to rebuild the pydantic-core schema for the model. @@ -535,37 +567,32 @@ def model_rebuild( """ if not force and cls.__pydantic_complete__: return None + + if '__pydantic_core_schema__' in cls.__dict__: + delattr(cls, '__pydantic_core_schema__') # delete cached value to ensure full rebuild happens + + if _types_namespace is not None: + rebuild_ns = _types_namespace + elif _parent_namespace_depth > 0: + rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {} else: - if '__pydantic_core_schema__' in cls.__dict__: - delattr(cls, '__pydantic_core_schema__') # delete cached value to ensure full rebuild happens - if _types_namespace is not None: - types_namespace: dict[str, Any] | None = _types_namespace.copy() - else: - if _parent_namespace_depth > 0: - frame_parent_ns = ( - _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {} - ) - cls_parent_ns = ( - _model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {} - ) - types_namespace = {**cls_parent_ns, **frame_parent_ns} - cls.__pydantic_parent_namespace__ = _model_construction.build_lenient_weakvaluedict(types_namespace) - else: - types_namespace = _model_construction.unpack_lenient_weakvaluedict( - cls.__pydantic_parent_namespace__ - ) - - types_namespace = _typing_extra.merge_cls_and_parent_ns(cls, types_namespace) - - # manually override defer_build so complete_model_class doesn't skip building the model again - config = {**cls.model_config, 'defer_build': False} - return _model_construction.complete_model_class( - cls, - cls.__name__, - _config.ConfigWrapper(config, check=False), - raise_errors=raise_errors, - types_namespace=types_namespace, - ) + rebuild_ns = {} + + parent_ns = _model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {} + + ns_resolver = _namespace_utils.NsResolver( + parent_namespace={**rebuild_ns, **parent_ns}, + ) + + # manually override defer_build so complete_model_class doesn't skip building the model again + config = {**cls.model_config, 'defer_build': False} + return _model_construction.complete_model_class( + cls, + cls.__name__, + _config.ConfigWrapper(config, check=False), + raise_errors=raise_errors, + ns_resolver=ns_resolver, + ) @classmethod def model_validate( @@ -604,7 +631,7 @@ def model_validate_json( strict: bool | None = None, context: Any | None = None, ) -> Self: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing + """Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing Validate the given JSON data against the Pydantic model. @@ -733,7 +760,7 @@ def __class_getitem__( _generics.check_parameters_count(cls, typevar_values) # Build map from generic typevars to passed params - typevars_map: dict[_typing_extra.TypeVarType, type[Any]] = dict( + typevars_map: dict[TypeVar, type[Any]] = dict( zip(cls.__pydantic_generic_metadata__['parameters'], typevar_values) ) @@ -754,21 +781,26 @@ def __class_getitem__( ) # use dict as ordered set with _generics.generic_recursion_self_type(origin, args) as maybe_self_type: - if maybe_self_type is not None: - return maybe_self_type - + # Check cached first otherwise `mro` may return `PydanticRecursiveRef` cached = _generics.get_cached_generic_type_late(cls, typevar_values, origin, args) if cached is not None: return cached + if maybe_self_type is not None: + return maybe_self_type + # Attempt to rebuild the origin in case new types have been defined try: - # depth 3 gets you above this __class_getitem__ call - origin.model_rebuild(_parent_namespace_depth=3) + # depth 2 gets you above this __class_getitem__ call. + # Note that we explicitly provide the parent ns, otherwise + # `model_rebuild` will use the parent ns no matter if it is the ns of a module. + # We don't want this here, as this has unexpected effects when a model + # is being parametrized during a forward annotation evaluation. + parent_ns = _typing_extra.parent_frame_namespace(parent_depth=2) or {} + origin.model_rebuild(_types_namespace=parent_ns) except PydanticUndefinedAnnotation: # It's okay if it fails, it just means there are still undefined types # that could be evaluated later. - # TODO: Make sure validation fails if there are still undefined types, perhaps using MockValidator pass submodel = _generics.create_generic_submodel(model_name, origin, args, params) @@ -818,6 +850,10 @@ def __deepcopy__(self, memo: dict[int, Any] | None = None) -> Self: return m + def __replace__(self, **changes: Any) -> Self: + """Creates a new instance of the model, replacing fields with values from changes. Relevant for v3.13+.""" + return self.model_copy(update=changes) + if not TYPE_CHECKING: # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access # The same goes for __setattr__ and __delattr__, see: https://github.com/pydantic/pydantic/issues/8643 @@ -874,14 +910,20 @@ def __setattr__(self, name: str, value: Any) -> None: self._check_frozen(name, value) attr = getattr(self.__class__, name, None) + # NOTE: We currently special case properties and `cached_property`, but we might need + # to generalize this to all data/non-data descriptors at some point. For non-data descriptors + # (such as `cached_property`), it isn't obvious though. `cached_property` caches the value + # to the instance's `__dict__`, but other non-data descriptors might do things differently. if isinstance(attr, property): attr.__set__(self, value) + elif isinstance(attr, cached_property): + self.__dict__[name] = value elif self.model_config.get('validate_assignment', None): self.__pydantic_validator__.validate_assignment(self, name, value) - elif self.model_config.get('extra') != 'allow' and name not in self.model_fields: + elif self.model_config.get('extra') != 'allow' and name not in self.__pydantic_fields__: # TODO - matching error raise ValueError(f'"{self.__class__.__name__}" object has no field "{name}"') - elif self.model_config.get('extra') == 'allow' and name not in self.model_fields: + elif self.model_config.get('extra') == 'allow' and name not in self.__pydantic_fields__: if self.model_extra and name in self.model_extra: self.__pydantic_extra__[name] = value # type: ignore else: @@ -913,7 +955,7 @@ def __delattr__(self, item: str) -> Any: self._check_frozen(item, None) - if item in self.model_fields: + if item in self.__pydantic_fields__: object.__delattr__(self, item) elif self.__pydantic_extra__ is not None and item in self.__pydantic_extra__: del self.__pydantic_extra__[item] @@ -926,7 +968,7 @@ def __delattr__(self, item: str) -> Any: def _check_frozen(self, name: str, value: Any) -> None: if self.model_config.get('frozen', None): typ = 'frozen_instance' - elif getattr(self.model_fields.get(name), 'frozen', False): + elif getattr(self.__pydantic_fields__.get(name), 'frozen', False): typ = 'frozen_field' else: return @@ -983,7 +1025,7 @@ def __eq__(self, other: Any) -> bool: # We don't want to trigger unnecessary costly filtering of __dict__ on all unequal objects, so we return # early if there are no keys to ignore (we would just return False later on anyway) - model_fields = type(self).model_fields.keys() + model_fields = type(self).__pydantic_fields__.keys() if self.__dict__.keys() <= model_fields and other.__dict__.keys() <= model_fields: return False @@ -1020,7 +1062,7 @@ def __init_subclass__(cls, **kwargs: Unpack[ConfigDict]): """This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs. - ```py + ```python from pydantic import BaseModel class MyModel(BaseModel, extra='allow'): ... @@ -1050,10 +1092,12 @@ def __repr__(self) -> str: def __repr_args__(self) -> _repr.ReprArgs: for k, v in self.__dict__.items(): - field = self.model_fields.get(k) + field = self.__pydantic_fields__.get(k) if field and field.repr: - yield k, v - + if v is not self: + yield k, v + else: + yield k, self.__repr_recursion__(v) # `__pydantic_extra__` can fail to be set if the model is not yet fully initialized. # This can happen if a `ValidationError` is raised during initialization and the instance's # repr is generated as part of the exception handling. Therefore, we use `getattr` here @@ -1065,10 +1109,11 @@ def __repr_args__(self) -> _repr.ReprArgs: if pydantic_extra is not None: yield from ((k, v) for k, v in pydantic_extra.items()) - yield from ((k, getattr(self, k)) for k, v in self.model_computed_fields.items() if v.repr) + yield from ((k, getattr(self, k)) for k, v in self.__pydantic_computed_fields__.items() if v.repr) # take logic from `_repr.Representation` without the side effects of inheritance, see #5740 __repr_name__ = _repr.Representation.__repr_name__ + __repr_recursion__ = _repr.Representation.__repr_recursion__ __repr_str__ = _repr.Representation.__repr_str__ __pretty__ = _repr.Representation.__pretty__ __rich_repr__ = _repr.Representation.__rich_repr__ @@ -1083,7 +1128,9 @@ def __str__(self) -> str: ) def __fields__(self) -> dict[str, FieldInfo]: warnings.warn( - 'The `__fields__` attribute is deprecated, use `model_fields` instead.', category=PydanticDeprecatedSince20 + 'The `__fields__` attribute is deprecated, use `model_fields` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, ) return self.model_fields @@ -1096,6 +1143,7 @@ def __fields_set__(self) -> set[str]: warnings.warn( 'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.', category=PydanticDeprecatedSince20, + stacklevel=2, ) return self.__pydantic_fields_set__ @@ -1103,14 +1151,18 @@ def __fields_set__(self) -> set[str]: def dict( # noqa: D102 self, *, - include: IncEx = None, - exclude: IncEx = None, + include: IncEx | None = None, + exclude: IncEx | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, ) -> Dict[str, Any]: # noqa UP006 - warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20) + warnings.warn( + 'The `dict` method is deprecated; use `model_dump` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) return self.model_dump( include=include, exclude=exclude, @@ -1124,8 +1176,8 @@ def dict( # noqa: D102 def json( # noqa: D102 self, *, - include: IncEx = None, - exclude: IncEx = None, + include: IncEx | None = None, + exclude: IncEx | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, @@ -1135,7 +1187,9 @@ def json( # noqa: D102 **dumps_kwargs: Any, ) -> str: warnings.warn( - 'The `json` method is deprecated; use `model_dump_json` instead.', category=PydanticDeprecatedSince20 + 'The `json` method is deprecated; use `model_dump_json` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, ) if encoder is not PydanticUndefined: raise TypeError('The `encoder` argument is no longer supported; use field serializers instead.') @@ -1156,7 +1210,9 @@ def json( # noqa: D102 @typing_extensions.deprecated('The `parse_obj` method is deprecated; use `model_validate` instead.', category=None) def parse_obj(cls, obj: Any) -> Self: # noqa: D102 warnings.warn( - 'The `parse_obj` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20 + 'The `parse_obj` method is deprecated; use `model_validate` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, ) return cls.model_validate(obj) @@ -1179,6 +1235,7 @@ def parse_raw( # noqa: D102 'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, ' 'otherwise load the data then use `model_validate` instead.', category=PydanticDeprecatedSince20, + stacklevel=2, ) from .deprecated import parse @@ -1232,6 +1289,7 @@ def parse_file( # noqa: D102 'The `parse_file` method is deprecated; load the data from file, then if your data is JSON ' 'use `model_validate_json`, otherwise `model_validate` instead.', category=PydanticDeprecatedSince20, + stacklevel=2, ) from .deprecated import parse @@ -1255,6 +1313,7 @@ def from_orm(cls, obj: Any) -> Self: # noqa: D102 'The `from_orm` method is deprecated; set ' "`model_config['from_attributes']=True` and use `model_validate` instead.", category=PydanticDeprecatedSince20, + stacklevel=2, ) if not cls.model_config.get('from_attributes', None): raise PydanticUserError( @@ -1266,7 +1325,9 @@ def from_orm(cls, obj: Any) -> Self: # noqa: D102 @typing_extensions.deprecated('The `construct` method is deprecated; use `model_construct` instead.', category=None) def construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self: # noqa: D102 warnings.warn( - 'The `construct` method is deprecated; use `model_construct` instead.', category=PydanticDeprecatedSince20 + 'The `construct` method is deprecated; use `model_construct` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, ) return cls.model_construct(_fields_set=_fields_set, **values) @@ -1290,7 +1351,7 @@ def copy( If you need `include` or `exclude`, use: - ```py + ```python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) @@ -1309,6 +1370,7 @@ def copy( 'The `copy` method is deprecated; use `model_copy` instead. ' 'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.', category=PydanticDeprecatedSince20, + stacklevel=2, ) from .deprecated import copy_internals @@ -1352,7 +1414,9 @@ def schema( # noqa: D102 cls, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE ) -> Dict[str, Any]: # noqa UP006 warnings.warn( - 'The `schema` method is deprecated; use `model_json_schema` instead.', category=PydanticDeprecatedSince20 + 'The `schema` method is deprecated; use `model_json_schema` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, ) return cls.model_json_schema(by_alias=by_alias, ref_template=ref_template) @@ -1367,6 +1431,7 @@ def schema_json( # noqa: D102 warnings.warn( 'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.', category=PydanticDeprecatedSince20, + stacklevel=2, ) import json @@ -1382,7 +1447,9 @@ def schema_json( # noqa: D102 @typing_extensions.deprecated('The `validate` method is deprecated; use `model_validate` instead.', category=None) def validate(cls, value: Any) -> Self: # noqa: D102 warnings.warn( - 'The `validate` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20 + 'The `validate` method is deprecated; use `model_validate` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, ) return cls.model_validate(value) @@ -1395,6 +1462,7 @@ def update_forward_refs(cls, **localns: Any) -> None: # noqa: D102 warnings.warn( 'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.', category=PydanticDeprecatedSince20, + stacklevel=2, ) if localns: # pragma: no cover raise TypeError('`localns` arguments are not longer accepted.') @@ -1407,6 +1475,7 @@ def _iter(self, *args: Any, **kwargs: Any) -> Any: warnings.warn( 'The private method `_iter` will be removed and should no longer be used.', category=PydanticDeprecatedSince20, + stacklevel=2, ) from .deprecated import copy_internals @@ -1420,6 +1489,7 @@ def _copy_and_set_values(self, *args: Any, **kwargs: Any) -> Any: warnings.warn( 'The private method `_copy_and_set_values` will be removed and should no longer be used.', category=PydanticDeprecatedSince20, + stacklevel=2, ) from .deprecated import copy_internals @@ -1434,6 +1504,7 @@ def _get_value(cls, *args: Any, **kwargs: Any) -> Any: warnings.warn( 'The private method `_get_value` will be removed and should no longer be used.', category=PydanticDeprecatedSince20, + stacklevel=2, ) from .deprecated import copy_internals @@ -1447,12 +1518,16 @@ def _calculate_keys(self, *args: Any, **kwargs: Any) -> Any: warnings.warn( 'The private method `_calculate_keys` will be removed and should no longer be used.', category=PydanticDeprecatedSince20, + stacklevel=2, ) from .deprecated import copy_internals return copy_internals._calculate_keys(self, *args, **kwargs) +ModelT = TypeVar('ModelT', bound=BaseModel) + + @overload def create_model( model_name: str, @@ -1496,7 +1571,7 @@ def create_model( # noqa: C901 __slots__: tuple[str, ...] | None = None, **field_definitions: Any, ) -> type[ModelT]: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/models/#dynamic-model-creation + """Usage docs: https://docs.pydantic.dev/2.10/concepts/models/#dynamic-model-creation Dynamically creates and returns a new Pydantic model, in other words, `create_model` dynamically creates a subclass of [`BaseModel`][pydantic.BaseModel]. @@ -1516,6 +1591,8 @@ def create_model( # noqa: C901 **field_definitions: Attributes of the new model. They should be passed in the format: `=(, )`, `=(, )`, or `typing.Annotated[, ]`. Any additional metadata in `typing.Annotated[, , ...]` will be ignored. + Note, `FieldInfo` instances should be created via `pydantic.Field(...)`. + Initializing `FieldInfo` instances directly is not supported. Returns: The new [model][pydantic.BaseModel]. diff --git a/pydantic/mypy.py b/pydantic/mypy.py index 0ce0a258d96..9ad3a3ff179 100644 --- a/pydantic/mypy.py +++ b/pydantic/mypy.py @@ -46,13 +46,11 @@ from mypy.plugin import ( CheckerPluginInterface, ClassDefContext, - FunctionContext, MethodContext, Plugin, ReportConfigContext, SemanticAnalyzerPluginInterface, ) -from mypy.plugins import dataclasses from mypy.plugins.common import ( deserialize_and_fixup_type, ) @@ -65,7 +63,6 @@ CallableType, Instance, NoneType, - Overloaded, Type, TypeOfAny, TypeType, @@ -80,12 +77,6 @@ from pydantic._internal import _fields from pydantic.version import parse_mypy_version -try: - from mypy.types import TypeVarDef # type: ignore[attr-defined] -except ImportError: # pragma: no cover - # Backward-compatible with TypeVarDef from Mypy 0.930. - from mypy.types import TypeVarType as TypeVarDef - CONFIGFILE_KEY = 'pydantic-mypy' METADATA_KEY = 'pydantic-mypy-metadata' BASEMODEL_FULLNAME = 'pydantic.main.BaseModel' @@ -106,7 +97,7 @@ MYPY_VERSION_TUPLE = parse_mypy_version(mypy_version) -BUILTINS_NAME = 'builtins' if MYPY_VERSION_TUPLE >= (0, 930) else '__builtins__' +BUILTINS_NAME = 'builtins' # Increment version if plugin changes and mypy caches should be invalidated __version__ = 2 @@ -135,7 +126,7 @@ def __init__(self, options: Options) -> None: self._plugin_data = self.plugin_config.to_data() super().__init__(options) - def get_base_class_hook(self, fullname: str) -> Callable[[ClassDefContext], bool] | None: + def get_base_class_hook(self, fullname: str) -> Callable[[ClassDefContext], None] | None: """Update Pydantic model class.""" sym = self.lookup_fully_qualified(fullname) if sym and isinstance(sym.node, TypeInfo): # pragma: no branch @@ -150,28 +141,12 @@ def get_metaclass_hook(self, fullname: str) -> Callable[[ClassDefContext], None] return self._pydantic_model_metaclass_marker_callback return None - def get_function_hook(self, fullname: str) -> Callable[[FunctionContext], Type] | None: - """Adjust the return type of the `Field` function.""" - sym = self.lookup_fully_qualified(fullname) - if sym and sym.fullname == FIELD_FULLNAME: - return self._pydantic_field_callback - return None - def get_method_hook(self, fullname: str) -> Callable[[MethodContext], Type] | None: """Adjust return type of `from_orm` method call.""" if fullname.endswith('.from_orm'): return from_attributes_callback return None - def get_class_decorator_hook(self, fullname: str) -> Callable[[ClassDefContext], None] | None: - """Mark pydantic.dataclasses as dataclass. - - Mypy version 1.1.1 added support for `@dataclass_transform` decorator. - """ - if fullname == DATACLASS_FULLNAME and MYPY_VERSION_TUPLE < (1, 1): - return dataclasses.dataclass_class_maker_callback # type: ignore[return-value] - return None - def report_config_data(self, ctx: ReportConfigContext) -> dict[str, Any]: """Return all plugin config data. @@ -179,9 +154,9 @@ def report_config_data(self, ctx: ReportConfigContext) -> dict[str, Any]: """ return self._plugin_data - def _pydantic_model_class_maker_callback(self, ctx: ClassDefContext) -> bool: + def _pydantic_model_class_maker_callback(self, ctx: ClassDefContext) -> None: transformer = PydanticModelTransformer(ctx.cls, ctx.reason, ctx.api, self.plugin_config) - return transformer.transform() + transformer.transform() def _pydantic_model_metaclass_marker_callback(self, ctx: ClassDefContext) -> None: """Reset dataclass_transform_spec attribute of ModelMetaclass. @@ -196,54 +171,6 @@ def _pydantic_model_metaclass_marker_callback(self, ctx: ClassDefContext) -> Non if getattr(info_metaclass.type, 'dataclass_transform_spec', None): info_metaclass.type.dataclass_transform_spec = None - def _pydantic_field_callback(self, ctx: FunctionContext) -> Type: - """Extract the type of the `default` argument from the Field function, and use it as the return type. - - In particular: - * Check whether the default and default_factory argument is specified. - * Output an error if both are specified. - * Retrieve the type of the argument which is specified, and use it as return type for the function. - """ - default_any_type = ctx.default_return_type - - assert ctx.callee_arg_names[0] == 'default', '"default" is no longer first argument in Field()' - assert ctx.callee_arg_names[1] == 'default_factory', '"default_factory" is no longer second argument in Field()' - default_args = ctx.args[0] - default_factory_args = ctx.args[1] - - if default_args and default_factory_args: - error_default_and_default_factory_specified(ctx.api, ctx.context) - return default_any_type - - if default_args: - default_type = ctx.arg_types[0][0] - default_arg = default_args[0] - - # Fallback to default Any type if the field is required - if not isinstance(default_arg, EllipsisExpr): - return default_type - - elif default_factory_args: - default_factory_type = ctx.arg_types[1][0] - - # Functions which use `ParamSpec` can be overloaded, exposing the callable's types as a parameter - # Pydantic calls the default factory without any argument, so we retrieve the first item - if isinstance(default_factory_type, Overloaded): - default_factory_type = default_factory_type.items[0] - - if isinstance(default_factory_type, CallableType): - ret_type = default_factory_type.ret_type - # mypy doesn't think `ret_type` has `args`, you'd think mypy should know, - # add this check in case it varies by version - args = getattr(ret_type, 'args', None) - if args: - if all(isinstance(arg, TypeVarType) for arg in args): - # Looks like the default factory is a type like `list` or `dict`, replace all args with `Any` - ret_type.args = tuple(default_any_type for _ in args) # type: ignore[attr-defined] - return ret_type - - return default_any_type - class PydanticPluginConfig: """A Pydantic mypy plugin config holder. @@ -377,7 +304,6 @@ def expand_type( self, current_info: TypeInfo, api: SemanticAnalyzerPluginInterface, force_typevars_invariant: bool = False ) -> Type | None: """Based on mypy.plugins.dataclasses.DataclassAttribute.expand_type.""" - # The getattr in the next line is used to prevent errors in legacy versions of mypy without this attribute if force_typevars_invariant: # In some cases, mypy will emit an error "Cannot use a covariant type variable as a parameter" # To prevent that, we add an option to replace typevars with invariant ones while building certain @@ -388,13 +314,15 @@ def expand_type( modified_type.variance = INVARIANT self.type = modified_type - if self.type is not None and getattr(self.info, 'self_type', None) is not None: - # In general, it is not safe to call `expand_type()` during semantic analyzis, + if self.type is not None and self.info.self_type is not None: + # In general, it is not safe to call `expand_type()` during semantic analysis, # however this plugin is called very late, so all types should be fully ready. # Also, it is tricky to avoid eager expansion of Self types here (e.g. because # we serialize attributes). with state.strict_optional_set(api.options.strict_optional): filled_with_typevars = fill_typevars(current_info) + # Cannot be TupleType as current_info represents a Pydantic model: + assert isinstance(filled_with_typevars, Instance) if force_typevars_invariant: for arg in filled_with_typevars.args: if isinstance(arg, TypeVarType): @@ -692,15 +620,18 @@ def collect_fields_and_class_vars( current_class_vars_names: set[str] = set() for stmt in self._get_assignment_statements_from_block(cls.defs): maybe_field = self.collect_field_or_class_var_from_stmt(stmt, model_config, found_class_vars) + if maybe_field is None: + continue + + lhs = stmt.lvalues[0] + assert isinstance(lhs, NameExpr) # collect_field_or_class_var_from_stmt guarantees this if isinstance(maybe_field, PydanticModelField): - lhs = stmt.lvalues[0] if is_root_model and lhs.name != 'root': error_extra_fields_on_root_model(self._api, stmt) else: current_field_names.add(lhs.name) found_fields[lhs.name] = maybe_field elif isinstance(maybe_field, PydanticModelClassVar): - lhs = stmt.lvalues[0] current_class_vars_names.add(lhs.name) found_class_vars[lhs.name] = maybe_field @@ -912,19 +843,17 @@ def add_initializer( force_typevars_invariant=True, ) - if is_root_model and MYPY_VERSION_TUPLE <= (1, 0, 1): - # convert root argument to positional argument - # This is needed because mypy support for `dataclass_transform` isn't complete on 1.0.1 - args[0].kind = ARG_POS if args[0].kind == ARG_NAMED else ARG_OPT - if is_settings: base_settings_node = self._api.lookup_fully_qualified(BASESETTINGS_FULLNAME).node + assert isinstance(base_settings_node, TypeInfo) if '__init__' in base_settings_node.names: base_settings_init_node = base_settings_node.names['__init__'].node + assert isinstance(base_settings_init_node, FuncDef) if base_settings_init_node is not None and base_settings_init_node.type is not None: func_type = base_settings_init_node.type + assert isinstance(func_type, CallableType) for arg_idx, arg_name in enumerate(func_type.arg_names): - if arg_name.startswith('__') or not arg_name.startswith('_'): + if arg_name is None or arg_name.startswith('__') or not arg_name.startswith('_'): continue analyzed_variable_type = self._api.anal_type(func_type.arg_types[arg_idx]) variable = Var(arg_name, analyzed_variable_type) @@ -1280,11 +1209,6 @@ def error_extra_fields_on_root_model(api: CheckerPluginInterface, context: Conte api.fail('Only `root` is allowed as a field of a `RootModel`', context, code=ERROR_EXTRA_FIELD_ROOT_MODEL) -def error_default_and_default_factory_specified(api: CheckerPluginInterface, context: Context) -> None: - """Emits an error when `Field` has both `default` and `default_factory` together.""" - api.fail('Field default and default_factory cannot be specified together', context, code=ERROR_FIELD_DEFAULTS) - - def add_method( api: SemanticAnalyzerPluginInterface | CheckerPluginInterface, cls: ClassDef, @@ -1292,7 +1216,7 @@ def add_method( args: list[Argument], return_type: Type, self_type: Type | None = None, - tvar_def: TypeVarDef | None = None, + tvar_def: TypeVarType | None = None, is_classmethod: bool = False, ) -> None: """Very closely related to `mypy.plugins.common.add_method_to_class`, with a few pydantic-specific changes.""" diff --git a/pydantic/networks.py b/pydantic/networks.py index 256f2bf1e4d..33799578c06 100644 --- a/pydantic/networks.py +++ b/pydantic/networks.py @@ -4,17 +4,24 @@ import dataclasses as _dataclasses import re +from dataclasses import fields +from functools import lru_cache from importlib.metadata import version from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, ClassVar -from pydantic_core import MultiHostUrl, PydanticCustomError, Url, core_schema +from pydantic_core import MultiHostHost, PydanticCustomError, core_schema +from pydantic_core import MultiHostUrl as _CoreMultiHostUrl +from pydantic_core import Url as _CoreUrl from typing_extensions import Annotated, Self, TypeAlias +from pydantic.errors import PydanticUserError + from ._internal import _fields, _repr, _schema_generation_shared from ._migration import getattr_migration from .annotated_handlers import GetCoreSchemaHandler from .json_schema import JsonSchemaValue +from .type_adapter import TypeAdapter if TYPE_CHECKING: import email_validator @@ -86,136 +93,602 @@ def __hash__(self) -> int: ) ) + @property + def defined_constraints(self) -> dict[str, Any]: + """Fetch a key / value mapping of constraints to values that are not None. Used for core schema updates.""" + return {field.name: getattr(self, field.name) for field in fields(self)} -AnyUrl = Url -"""Base type for all URLs. -* Any scheme allowed -* Top-level domain (TLD) not required -* Host required +class _BaseUrl: + _constraints: ClassVar[UrlConstraints] = UrlConstraints() + _url: _CoreUrl -Assuming an input URL of `http://samuel:pass@example.com:8000/the/path/?query=here#fragment=is;this=bit`, -the types export the following properties: + def __init__(self, url: str | _CoreUrl | _BaseUrl) -> None: + self._url = _build_type_adapter(self.__class__).validate_python(url) -- `scheme`: the URL scheme (`http`), always set. -- `host`: the URL host (`example.com`), always set. -- `username`: optional username if included (`samuel`). -- `password`: optional password if included (`pass`). -- `port`: optional port (`8000`). -- `path`: optional path (`/the/path/`). -- `query`: optional URL query (for example, `GET` arguments or "search string", such as `query=here`). -- `fragment`: optional fragment (`fragment=is;this=bit`). -""" -AnyHttpUrl = Annotated[Url, UrlConstraints(allowed_schemes=['http', 'https'])] -"""A type that will accept any http or https URL. + @property + def scheme(self) -> str: + """The scheme part of the URL. -* TLD not required -* Host required -""" -HttpUrl = Annotated[Url, UrlConstraints(max_length=2083, allowed_schemes=['http', 'https'])] -"""A type that will accept any http or https URL. - -* TLD not required -* Host required -* Max length 2083 - -```py -from pydantic import BaseModel, HttpUrl, ValidationError - -class MyModel(BaseModel): - url: HttpUrl - -m = MyModel(url='http://www.example.com') # (1)! -print(m.url) -#> http://www.example.com/ - -try: - MyModel(url='ftp://invalid.url') -except ValidationError as e: - print(e) - ''' - 1 validation error for MyModel - url - URL scheme should be 'http' or 'https' [type=url_scheme, input_value='ftp://invalid.url', input_type=str] - ''' - -try: - MyModel(url='not a url') -except ValidationError as e: - print(e) - ''' - 1 validation error for MyModel - url - Input should be a valid URL, relative URL without a base [type=url_parsing, input_value='not a url', input_type=str] - ''' -``` - -1. Note: mypy would prefer `m = MyModel(url=HttpUrl('http://www.example.com'))`, but Pydantic will convert the string to an HttpUrl instance anyway. - -"International domains" (e.g. a URL where the host or TLD includes non-ascii characters) will be encoded via -[punycode](https://en.wikipedia.org/wiki/Punycode) (see -[this article](https://www.xudongz.com/blog/2017/idn-phishing/) for a good description of why this is important): - -```py -from pydantic import BaseModel, HttpUrl - -class MyModel(BaseModel): - url: HttpUrl - -m1 = MyModel(url='http://puny£code.com') -print(m1.url) -#> http://xn--punycode-eja.com/ -m2 = MyModel(url='https://www.аррӏе.com/') -print(m2.url) -#> https://www.xn--80ak6aa92e.com/ -m3 = MyModel(url='https://www.example.珠宝/') -print(m3.url) -#> https://www.example.xn--pbt977c/ -``` - - -!!! warning "Underscores in Hostnames" - In Pydantic, underscores are allowed in all parts of a domain except the TLD. - Technically this might be wrong - in theory the hostname cannot have underscores, but subdomains can. - - To explain this; consider the following two cases: - - - `exam_ple.co.uk`: the hostname is `exam_ple`, which should not be allowed since it contains an underscore. - - `foo_bar.example.com` the hostname is `example`, which should be allowed since the underscore is in the subdomain. - - Without having an exhaustive list of TLDs, it would be impossible to differentiate between these two. Therefore - underscores are allowed, but you can always do further validation in a validator if desired. - - Also, Chrome, Firefox, and Safari all currently accept `http://exam_ple.com` as a URL, so we're in good - (or at least big) company. -""" -AnyWebsocketUrl = Annotated[Url, UrlConstraints(allowed_schemes=['ws', 'wss'])] -"""A type that will accept any ws or wss URL. + e.g. `https` in `https://user:pass@host:port/path?query#fragment` + """ + return self._url.scheme -* TLD not required -* Host required -""" -WebsocketUrl = Annotated[Url, UrlConstraints(max_length=2083, allowed_schemes=['ws', 'wss'])] -"""A type that will accept any ws or wss URL. + @property + def username(self) -> str | None: + """The username part of the URL, or `None`. -* TLD not required -* Host required -* Max length 2083 -""" -FileUrl = Annotated[Url, UrlConstraints(allowed_schemes=['file'])] -"""A type that will accept any file URL. + e.g. `user` in `https://user:pass@host:port/path?query#fragment` + """ + return self._url.username -* Host not required -""" -FtpUrl = Annotated[Url, UrlConstraints(allowed_schemes=['ftp'])] -"""A type that will accept ftp URL. + @property + def password(self) -> str | None: + """The password part of the URL, or `None`. -* TLD not required -* Host required -""" -PostgresDsn = Annotated[ - MultiHostUrl, - UrlConstraints( + e.g. `pass` in `https://user:pass@host:port/path?query#fragment` + """ + return self._url.password + + @property + def host(self) -> str | None: + """The host part of the URL, or `None`. + + If the URL must be punycode encoded, this is the encoded host, e.g if the input URL is `https://£££.com`, + `host` will be `xn--9aaa.com` + """ + return self._url.host + + def unicode_host(self) -> str | None: + """The host part of the URL as a unicode string, or `None`. + + e.g. `host` in `https://user:pass@host:port/path?query#fragment` + + If the URL must be punycode encoded, this is the decoded host, e.g if the input URL is `https://£££.com`, + `unicode_host()` will be `£££.com` + """ + return self._url.unicode_host() + + @property + def port(self) -> int | None: + """The port part of the URL, or `None`. + + e.g. `port` in `https://user:pass@host:port/path?query#fragment` + """ + return self._url.port + + @property + def path(self) -> str | None: + """The path part of the URL, or `None`. + + e.g. `/path` in `https://user:pass@host:port/path?query#fragment` + """ + return self._url.path + + @property + def query(self) -> str | None: + """The query part of the URL, or `None`. + + e.g. `query` in `https://user:pass@host:port/path?query#fragment` + """ + return self._url.query + + def query_params(self) -> list[tuple[str, str]]: + """The query part of the URL as a list of key-value pairs. + + e.g. `[('foo', 'bar')]` in `https://user:pass@host:port/path?foo=bar#fragment` + """ + return self._url.query_params() + + @property + def fragment(self) -> str | None: + """The fragment part of the URL, or `None`. + + e.g. `fragment` in `https://user:pass@host:port/path?query#fragment` + """ + return self._url.fragment + + def unicode_string(self) -> str: + """The URL as a unicode string, unlike `__str__()` this will not punycode encode the host. + + If the URL must be punycode encoded, this is the decoded string, e.g if the input URL is `https://£££.com`, + `unicode_string()` will be `https://£££.com` + """ + return self._url.unicode_string() + + def __str__(self) -> str: + """The URL as a string, this will punycode encode the host if required.""" + return str(self._url) + + def __repr__(self) -> str: + return f'{self.__class__.__name__}({str(self._url)!r})' + + def __deepcopy__(self, memo: dict) -> Self: + return self.__class__(self._url) + + def __eq__(self, other: Any) -> bool: + return self.__class__ is other.__class__ and self._url == other._url + + @classmethod + def build( + cls, + *, + scheme: str, + username: str | None = None, + password: str | None = None, + host: str, + port: int | None = None, + path: str | None = None, + query: str | None = None, + fragment: str | None = None, + ) -> Self: + """Build a new `Url` instance from its component parts. + + Args: + scheme: The scheme part of the URL. + username: The username part of the URL, or omit for no username. + password: The password part of the URL, or omit for no password. + host: The host part of the URL. + port: The port part of the URL, or omit for no port. + path: The path part of the URL, or omit for no path. + query: The query part of the URL, or omit for no query. + fragment: The fragment part of the URL, or omit for no fragment. + + Returns: + An instance of URL + """ + return cls( + _CoreUrl.build( + scheme=scheme, + username=username, + password=password, + host=host, + port=port, + path=path, + query=query, + fragment=fragment, + ) + ) + + @classmethod + def __get_pydantic_core_schema__( + cls, source: type[_BaseUrl], handler: GetCoreSchemaHandler + ) -> core_schema.CoreSchema: + if issubclass(cls, source): + + def wrap_val(value, handler): + if isinstance(value, source): + return value + if isinstance(value, _BaseUrl): + value = str(value) + core_url = handler(value) + instance = source.__new__(source) + instance._url = core_url + return instance + + return core_schema.no_info_wrap_validator_function( + wrap_val, + schema=core_schema.url_schema(**cls._constraints.defined_constraints), + serialization=core_schema.to_string_ser_schema(), + ) + else: + schema = handler(source) + # TODO: this logic is used in types.py as well in the _check_annotated_type function, should we move that to somewhere more central? + if annotated_type := schema['type'] not in ('url', 'multi-host-url'): + raise PydanticUserError( + f"'{cls.__name__}' cannot annotate '{annotated_type}'.", code='invalid-annotated-type' + ) + for constraint_key, constraint_value in cls._constraints.defined_constraints.items(): + schema[constraint_key] = constraint_value + return schema + + +class _BaseMultiHostUrl: + _constraints: ClassVar[UrlConstraints] = UrlConstraints() + _url: _CoreMultiHostUrl + + def __init__(self, url: str | _CoreMultiHostUrl | _BaseMultiHostUrl) -> None: + self._url = _build_type_adapter(self.__class__).validate_python(url) + + @property + def scheme(self) -> str: + """The scheme part of the URL. + + e.g. `https` in `https://foo.com,bar.com/path?query#fragment` + """ + return self._url.scheme + + @property + def path(self) -> str | None: + """The path part of the URL, or `None`. + + e.g. `/path` in `https://foo.com,bar.com/path?query#fragment` + """ + return self._url.path + + @property + def query(self) -> str | None: + """The query part of the URL, or `None`. + + e.g. `query` in `https://foo.com,bar.com/path?query#fragment` + """ + return self._url.query + + def query_params(self) -> list[tuple[str, str]]: + """The query part of the URL as a list of key-value pairs. + + e.g. `[('foo', 'bar')]` in `https://foo.com,bar.com/path?query#fragment` + """ + return self._url.query_params() + + @property + def fragment(self) -> str | None: + """The fragment part of the URL, or `None`. + + e.g. `fragment` in `https://foo.com,bar.com/path?query#fragment` + """ + return self._url.fragment + + def hosts(self) -> list[MultiHostHost]: + '''The hosts of the `MultiHostUrl` as [`MultiHostHost`][pydantic_core.MultiHostHost] typed dicts. + + ```python + from pydantic_core import MultiHostUrl + + mhu = MultiHostUrl('https://foo.com:123,foo:bar@bar.com/path') + print(mhu.hosts()) + """ + [ + {'username': None, 'password': None, 'host': 'foo.com', 'port': 123}, + {'username': 'foo', 'password': 'bar', 'host': 'bar.com', 'port': 443} + ] + ``` + Returns: + A list of dicts, each representing a host. + ''' + return self._url.hosts() + + def unicode_string(self) -> str: + """The URL as a unicode string, unlike `__str__()` this will not punycode encode the hosts.""" + return self._url.unicode_string() + + def __str__(self) -> str: + """The URL as a string, this will punycode encode the host if required.""" + return str(self._url) + + def __repr__(self) -> str: + return f'{self.__class__.__name__}({str(self._url)!r})' + + def __deepcopy__(self, memo: dict) -> Self: + return self.__class__(self._url) + + def __eq__(self, other: Any) -> bool: + return self.__class__ is other.__class__ and self._url == other._url + + @classmethod + def build( + cls, + *, + scheme: str, + hosts: list[MultiHostHost] | None = None, + username: str | None = None, + password: str | None = None, + host: str | None = None, + port: int | None = None, + path: str | None = None, + query: str | None = None, + fragment: str | None = None, + ) -> Self: + """Build a new `MultiHostUrl` instance from its component parts. + + This method takes either `hosts` - a list of `MultiHostHost` typed dicts, or the individual components + `username`, `password`, `host` and `port`. + + Args: + scheme: The scheme part of the URL. + hosts: Multiple hosts to build the URL from. + username: The username part of the URL. + password: The password part of the URL. + host: The host part of the URL. + port: The port part of the URL. + path: The path part of the URL. + query: The query part of the URL, or omit for no query. + fragment: The fragment part of the URL, or omit for no fragment. + + Returns: + An instance of `MultiHostUrl` + """ + return cls( + _CoreMultiHostUrl.build( + scheme=scheme, + hosts=hosts, + username=username, + password=password, + host=host, + port=port, + path=path, + query=query, + fragment=fragment, + ) + ) + + @classmethod + def __get_pydantic_core_schema__( + cls, source: type[_BaseMultiHostUrl], handler: GetCoreSchemaHandler + ) -> core_schema.CoreSchema: + if issubclass(cls, source): + + def wrap_val(value, handler): + if isinstance(value, source): + return value + if isinstance(value, _BaseMultiHostUrl): + value = str(value) + core_url = handler(value) + instance = source.__new__(source) + instance._url = core_url + return instance + + return core_schema.no_info_wrap_validator_function( + wrap_val, + schema=core_schema.multi_host_url_schema(**cls._constraints.defined_constraints), + serialization=core_schema.to_string_ser_schema(), + ) + else: + schema = handler(source) + # TODO: this logic is used in types.py as well in the _check_annotated_type function, should we move that to somewhere more central? + if annotated_type := schema['type'] not in ('url', 'multi-host-url'): + raise PydanticUserError( + f"'{cls.__name__}' cannot annotate '{annotated_type}'.", code='invalid-annotated-type' + ) + for constraint_key, constraint_value in cls._constraints.defined_constraints.items(): + schema[constraint_key] = constraint_value + return schema + + +@lru_cache +def _build_type_adapter(cls: type[_BaseUrl | _BaseMultiHostUrl]) -> TypeAdapter: + return TypeAdapter(cls) + + +class AnyUrl(_BaseUrl): + """Base type for all URLs. + + * Any scheme allowed + * Top-level domain (TLD) not required + * Host required + + Assuming an input URL of `http://samuel:pass@example.com:8000/the/path/?query=here#fragment=is;this=bit`, + the types export the following properties: + + - `scheme`: the URL scheme (`http`), always set. + - `host`: the URL host (`example.com`), always set. + - `username`: optional username if included (`samuel`). + - `password`: optional password if included (`pass`). + - `port`: optional port (`8000`). + - `path`: optional path (`/the/path/`). + - `query`: optional URL query (for example, `GET` arguments or "search string", such as `query=here`). + - `fragment`: optional fragment (`fragment=is;this=bit`). + """ + + _constraints = UrlConstraints(host_required=True) + + @property + def host(self) -> str: + """The required URL host.""" + return self._url.host # pyright: ignore[reportReturnType] + + +# Note: all single host urls inherit from `AnyUrl` to preserve compatibility with pre-v2.10 code +# Where urls were annotated variants of `AnyUrl`, which was an alias to `pydantic_core.Url` + + +class AnyHttpUrl(AnyUrl): + """A type that will accept any http or https URL. + + * TLD not required + * Host required + """ + + _constraints = UrlConstraints(host_required=True, allowed_schemes=['http', 'https']) + + +class HttpUrl(AnyUrl): + """A type that will accept any http or https URL. + + * TLD not required + * Host required + * Max length 2083 + + ```python + from pydantic import BaseModel, HttpUrl, ValidationError + + class MyModel(BaseModel): + url: HttpUrl + + m = MyModel(url='http://www.example.com') # (1)! + print(m.url) + #> http://www.example.com/ + + try: + MyModel(url='ftp://invalid.url') + except ValidationError as e: + print(e) + ''' + 1 validation error for MyModel + url + URL scheme should be 'http' or 'https' [type=url_scheme, input_value='ftp://invalid.url', input_type=str] + ''' + + try: + MyModel(url='not a url') + except ValidationError as e: + print(e) + ''' + 1 validation error for MyModel + url + Input should be a valid URL, relative URL without a base [type=url_parsing, input_value='not a url', input_type=str] + ''' + ``` + + 1. Note: mypy would prefer `m = MyModel(url=HttpUrl('http://www.example.com'))`, but Pydantic will convert the string to an HttpUrl instance anyway. + + "International domains" (e.g. a URL where the host or TLD includes non-ascii characters) will be encoded via + [punycode](https://en.wikipedia.org/wiki/Punycode) (see + [this article](https://www.xudongz.com/blog/2017/idn-phishing/) for a good description of why this is important): + + ```python + from pydantic import BaseModel, HttpUrl + + class MyModel(BaseModel): + url: HttpUrl + + m1 = MyModel(url='http://puny£code.com') + print(m1.url) + #> http://xn--punycode-eja.com/ + m2 = MyModel(url='https://www.аррӏе.com/') + print(m2.url) + #> https://www.xn--80ak6aa92e.com/ + m3 = MyModel(url='https://www.example.珠宝/') + print(m3.url) + #> https://www.example.xn--pbt977c/ + ``` + + + !!! warning "Underscores in Hostnames" + In Pydantic, underscores are allowed in all parts of a domain except the TLD. + Technically this might be wrong - in theory the hostname cannot have underscores, but subdomains can. + + To explain this; consider the following two cases: + + - `exam_ple.co.uk`: the hostname is `exam_ple`, which should not be allowed since it contains an underscore. + - `foo_bar.example.com` the hostname is `example`, which should be allowed since the underscore is in the subdomain. + + Without having an exhaustive list of TLDs, it would be impossible to differentiate between these two. Therefore + underscores are allowed, but you can always do further validation in a validator if desired. + + Also, Chrome, Firefox, and Safari all currently accept `http://exam_ple.com` as a URL, so we're in good + (or at least big) company. + """ + + _constraints = UrlConstraints(max_length=2083, allowed_schemes=['http', 'https'], host_required=True) + + +class AnyWebsocketUrl(AnyUrl): + """A type that will accept any ws or wss URL. + + * TLD not required + * Host required + """ + + _constraints = UrlConstraints(allowed_schemes=['ws', 'wss'], host_required=True) + + +class WebsocketUrl(AnyUrl): + """A type that will accept any ws or wss URL. + + * TLD not required + * Host required + * Max length 2083 + """ + + _constraints = UrlConstraints(max_length=2083, allowed_schemes=['ws', 'wss'], host_required=True) + + @property + def host(self) -> str: + """The required URL host.""" + return self._url.host # type: ignore + + +class FileUrl(AnyUrl): + """A type that will accept any file URL. + + * Host not required + """ + + _constraints = UrlConstraints(allowed_schemes=['file']) + + @property + def host(self) -> str | None: # pyright: ignore[reportIncompatibleMethodOverride] + """The host part of the URL, or `None`.""" + return self._url.host + + +class FtpUrl(AnyUrl): + """A type that will accept ftp URL. + + * TLD not required + * Host required + """ + + _constraints = UrlConstraints(allowed_schemes=['ftp'], host_required=True) + + @property + def host(self) -> str | None: # pyright: ignore[reportIncompatibleMethodOverride] + """The host part of the URL, or `None`.""" + return self._url.host + + +class PostgresDsn(_BaseMultiHostUrl): + """A type that will accept any Postgres DSN. + + * User info required + * TLD not required + * Host required + * Supports multiple hosts + + If further validation is required, these properties can be used by validators to enforce specific behaviour: + + ```python + from pydantic import ( + BaseModel, + HttpUrl, + PostgresDsn, + ValidationError, + field_validator, + ) + + class MyModel(BaseModel): + url: HttpUrl + + m = MyModel(url='http://www.example.com') + + # the repr() method for a url will display all properties of the url + print(repr(m.url)) + #> HttpUrl('http://www.example.com/') + print(m.url.scheme) + #> http + print(m.url.host) + #> www.example.com + print(m.url.port) + #> 80 + + class MyDatabaseModel(BaseModel): + db: PostgresDsn + + @field_validator('db') + def check_db_name(cls, v): + assert v.path and len(v.path) > 1, 'database must be provided' + return v + + m = MyDatabaseModel(db='postgres://user:pass@localhost:5432/foobar') + print(m.db) + #> postgres://user:pass@localhost:5432/foobar + + try: + MyDatabaseModel(db='postgres://user:pass@localhost:5432') + except ValidationError as e: + print(e) + ''' + 1 validation error for MyDatabaseModel + db + Assertion failed, database must be provided + assert (None) + + where None = PostgresDsn('postgres://user:pass@localhost:5432').path [type=assertion_error, input_value='postgres://user:pass@localhost:5432', input_type=str] + ''' + ``` + """ + + _constraints = UrlConstraints( host_required=True, allowed_schemes=[ 'postgres', @@ -228,130 +701,113 @@ class MyModel(BaseModel): 'postgresql+py-postgresql', 'postgresql+pygresql', ], - ), -] -"""A type that will accept any Postgres DSN. - -* User info required -* TLD not required -* Host required -* Supports multiple hosts - -If further validation is required, these properties can be used by validators to enforce specific behaviour: - -```py -from pydantic import ( - BaseModel, - HttpUrl, - PostgresDsn, - ValidationError, - field_validator, -) - -class MyModel(BaseModel): - url: HttpUrl - -m = MyModel(url='http://www.example.com') - -# the repr() method for a url will display all properties of the url -print(repr(m.url)) -#> Url('http://www.example.com/') -print(m.url.scheme) -#> http -print(m.url.host) -#> www.example.com -print(m.url.port) -#> 80 - -class MyDatabaseModel(BaseModel): - db: PostgresDsn - - @field_validator('db') - def check_db_name(cls, v): - assert v.path and len(v.path) > 1, 'database must be provided' - return v - -m = MyDatabaseModel(db='postgres://user:pass@localhost:5432/foobar') -print(m.db) -#> postgres://user:pass@localhost:5432/foobar - -try: - MyDatabaseModel(db='postgres://user:pass@localhost:5432') -except ValidationError as e: - print(e) - ''' - 1 validation error for MyDatabaseModel - db - Assertion failed, database must be provided - assert (None) - + where None = MultiHostUrl('postgres://user:pass@localhost:5432').path [type=assertion_error, input_value='postgres://user:pass@localhost:5432', input_type=str] - ''' -``` -""" + ) -CockroachDsn = Annotated[ - Url, - UrlConstraints( + @property + def host(self) -> str: + """The required URL host.""" + return self._url.host # pyright: ignore[reportAttributeAccessIssue] + + +class CockroachDsn(AnyUrl): + """A type that will accept any Cockroach DSN. + + * User info required + * TLD not required + * Host required + """ + + _constraints = UrlConstraints( host_required=True, allowed_schemes=[ 'cockroachdb', 'cockroachdb+psycopg2', 'cockroachdb+asyncpg', ], - ), -] -"""A type that will accept any Cockroach DSN. + ) -* User info required -* TLD not required -* Host required -""" -AmqpDsn = Annotated[Url, UrlConstraints(allowed_schemes=['amqp', 'amqps'])] -"""A type that will accept any AMQP DSN. -* User info required -* TLD not required -* Host required -""" -RedisDsn = Annotated[ - Url, - UrlConstraints(allowed_schemes=['redis', 'rediss'], default_host='localhost', default_port=6379, default_path='/0'), -] -"""A type that will accept any Redis DSN. +class AmqpDsn(AnyUrl): + """A type that will accept any AMQP DSN. -* User info required -* TLD not required -* Host required (e.g., `rediss://:pass@localhost`) -""" -MongoDsn = Annotated[MultiHostUrl, UrlConstraints(allowed_schemes=['mongodb', 'mongodb+srv'], default_port=27017)] -"""A type that will accept any MongoDB DSN. + * User info required + * TLD not required + * Host not required + """ -* User info not required -* Database name not required -* Port not required -* User info may be passed without user part (e.g., `mongodb://mongodb0.example.com:27017`). -""" -KafkaDsn = Annotated[Url, UrlConstraints(allowed_schemes=['kafka'], default_host='localhost', default_port=9092)] -"""A type that will accept any Kafka DSN. + _constraints = UrlConstraints(allowed_schemes=['amqp', 'amqps']) -* User info required -* TLD not required -* Host required -""" -NatsDsn = Annotated[ - MultiHostUrl, - UrlConstraints(allowed_schemes=['nats', 'tls', 'ws', 'wss'], default_host='localhost', default_port=4222), -] -"""A type that will accept any NATS DSN. + @property + def host(self) -> str | None: # pyright: ignore[reportIncompatibleMethodOverride] + """The host part of the URL, or `None`.""" + return self._url.host -NATS is a connective technology built for the ever increasingly hyper-connected world. -It is a single technology that enables applications to securely communicate across -any combination of cloud vendors, on-premise, edge, web and mobile, and devices. -More: https://nats.io -""" -MySQLDsn = Annotated[ - Url, - UrlConstraints( + +class RedisDsn(AnyUrl): + """A type that will accept any Redis DSN. + + * User info required + * TLD not required + * Host required (e.g., `rediss://:pass@localhost`) + """ + + _constraints = UrlConstraints( + allowed_schemes=['redis', 'rediss'], + default_host='localhost', + default_port=6379, + default_path='/0', + host_required=True, + ) + + +class MongoDsn(_BaseMultiHostUrl): + """A type that will accept any MongoDB DSN. + + * User info not required + * Database name not required + * Port not required + * User info may be passed without user part (e.g., `mongodb://mongodb0.example.com:27017`). + """ + + _constraints = UrlConstraints(allowed_schemes=['mongodb', 'mongodb+srv'], default_port=27017) + + +class KafkaDsn(AnyUrl): + """A type that will accept any Kafka DSN. + + * User info required + * TLD not required + * Host required + """ + + _constraints = UrlConstraints( + allowed_schemes=['kafka'], default_host='localhost', default_port=9092, host_required=True + ) + + +class NatsDsn(_BaseMultiHostUrl): + """A type that will accept any NATS DSN. + + NATS is a connective technology built for the ever increasingly hyper-connected world. + It is a single technology that enables applications to securely communicate across + any combination of cloud vendors, on-premise, edge, web and mobile, and devices. + More: https://nats.io + """ + + _constraints = UrlConstraints( + allowed_schemes=['nats', 'tls', 'ws', 'wss'], default_host='localhost', default_port=4222 + ) + + +class MySQLDsn(AnyUrl): + """A type that will accept any MySQL DSN. + + * User info required + * TLD not required + * Host required + """ + + _constraints = UrlConstraints( allowed_schemes=[ 'mysql', 'mysql+mysqlconnector', @@ -363,54 +819,53 @@ def check_db_name(cls, v): 'mysql+pyodbc', ], default_port=3306, - ), -] -"""A type that will accept any MySQL DSN. + host_required=True, + ) -* User info required -* TLD not required -* Host required -""" -MariaDBDsn = Annotated[ - Url, - UrlConstraints( + +class MariaDBDsn(AnyUrl): + """A type that will accept any MariaDB DSN. + + * User info required + * TLD not required + * Host required + """ + + _constraints = UrlConstraints( allowed_schemes=['mariadb', 'mariadb+mariadbconnector', 'mariadb+pymysql'], default_port=3306, - ), -] -"""A type that will accept any MariaDB DSN. + host_required=True, + ) -* User info required -* TLD not required -* Host required -""" -ClickHouseDsn = Annotated[ - Url, - UrlConstraints( + +class ClickHouseDsn(AnyUrl): + """A type that will accept any ClickHouse DSN. + + * User info required + * TLD not required + * Host required + """ + + _constraints = UrlConstraints( allowed_schemes=['clickhouse+native', 'clickhouse+asynch'], default_host='localhost', default_port=9000, - ), -] -"""A type that will accept any ClickHouse DSN. + host_required=True, + ) -* User info required -* TLD not required -* Host required -""" -SnowflakeDsn = Annotated[ - Url, - UrlConstraints( + +class SnowflakeDsn(AnyUrl): + """A type that will accept any Snowflake DSN. + + * User info required + * TLD not required + * Host required + """ + + _constraints = UrlConstraints( allowed_schemes=['snowflake'], host_required=True, - ), -] -"""A type that will accept any Snowflake DSN. - -* User info required -* TLD not required -* Host required -""" + ) def import_email_validator() -> None: @@ -439,7 +894,7 @@ class EmailStr: Validate email addresses. - ```py + ```python from pydantic import BaseModel, EmailStr class Model(BaseModel): @@ -488,7 +943,7 @@ class NameEmail(_repr.Representation): The `NameEmail` has two properties: `name` and `email`. In case the `name` is not provided, it's inferred from the email address. - ```py + ```python from pydantic import BaseModel, NameEmail class User(BaseModel): @@ -574,7 +1029,7 @@ def __str__(self) -> str: class IPvAnyAddress: """Validate an IPv4 or IPv6 address. - ```py + ```python from pydantic import BaseModel from pydantic.networks import IPvAnyAddress @@ -722,7 +1177,7 @@ def _build_pretty_email_regex() -> re.Pattern[str]: name_chars = r'[\w!#$%&\'*+\-/=?^_`{|}~]' unquoted_name_group = rf'((?:{name_chars}+\s+)*{name_chars}+)' quoted_name_group = r'"((?:[^"]|\")+)"' - email_group = r'<\s*(.+)\s*>' + email_group = r'<(.+)>' return re.compile(rf'\s*(?:{unquoted_name_group}|{quoted_name_group})?\s*{email_group}\s*') @@ -737,6 +1192,13 @@ def _build_pretty_email_regex() -> re.Pattern[str]: def validate_email(value: str) -> tuple[str, str]: """Email address validation using [email-validator](https://pypi.org/project/email-validator/). + Returns: + A tuple containing the local part of the email (or the name for "pretty" email addresses) + and the normalized email. + + Raises: + PydanticCustomError: If the email is invalid. + Note: Note that: diff --git a/pydantic/plugin/__init__.py b/pydantic/plugin/__init__.py index 952f6031923..d821566012e 100644 --- a/pydantic/plugin/__init__.py +++ b/pydantic/plugin/__init__.py @@ -1,4 +1,4 @@ -"""Usage docs: https://docs.pydantic.dev/2.9/concepts/plugins#build-a-plugin +"""Usage docs: https://docs.pydantic.dev/2.10/concepts/plugins#build-a-plugin Plugin interface for Pydantic plugins, and related types. """ diff --git a/pydantic/root_model.py b/pydantic/root_model.py index b408c5c8fa8..8351651dfa2 100644 --- a/pydantic/root_model.py +++ b/pydantic/root_model.py @@ -33,7 +33,7 @@ class _RootModelMetaclass(_model_construction.ModelMetaclass): ... class RootModel(BaseModel, typing.Generic[RootModelRootType], metaclass=_RootModelMetaclass): - """Usage docs: https://docs.pydantic.dev/2.9/concepts/models/#rootmodel-and-custom-root-types + """Usage docs: https://docs.pydantic.dev/2.10/concepts/models/#rootmodel-and-custom-root-types A Pydantic `BaseModel` for the root object of the model. @@ -148,7 +148,9 @@ def model_dump( # type: ignore def __eq__(self, other: Any) -> bool: if not isinstance(other, RootModel): return NotImplemented - return self.model_fields['root'].annotation == other.model_fields['root'].annotation and super().__eq__(other) + return self.__pydantic_fields__['root'].annotation == other.__pydantic_fields__[ + 'root' + ].annotation and super().__eq__(other) def __repr_args__(self) -> _repr.ReprArgs: yield 'root', self.root diff --git a/pydantic/type_adapter.py b/pydantic/type_adapter.py index 3619fdff3c7..3de858642f8 100644 --- a/pydantic/type_adapter.py +++ b/pydantic/type_adapter.py @@ -3,34 +3,27 @@ from __future__ import annotations as _annotations import sys -from contextlib import contextmanager from dataclasses import is_dataclass -from functools import cached_property, wraps from typing import ( - TYPE_CHECKING, Any, - Callable, - Dict, Generic, Iterable, - Iterator, Literal, - Set, TypeVar, - Union, cast, final, overload, ) from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator, Some -from typing_extensions import Concatenate, ParamSpec, is_typeddict +from typing_extensions import ParamSpec, is_typeddict from pydantic.errors import PydanticUserError -from pydantic.main import BaseModel +from pydantic.main import BaseModel, IncEx -from ._internal import _config, _generate_schema, _mock_val_ser, _typing_extra, _utils +from ._internal import _config, _generate_schema, _mock_val_ser, _namespace_utils, _repr, _typing_extra, _utils from .config import ConfigDict +from .errors import PydanticUndefinedAnnotation from .json_schema import ( DEFAULT_REF_TEMPLATE, GenerateJsonSchema, @@ -46,66 +39,6 @@ TypeAdapterT = TypeVar('TypeAdapterT', bound='TypeAdapter') -if TYPE_CHECKING: - # should be `set[int] | set[str] | dict[int, IncEx] | dict[str, IncEx] | None`, but mypy can't cope - IncEx = Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any]] - - -def _get_schema(type_: Any, config_wrapper: _config.ConfigWrapper, parent_depth: int) -> CoreSchema: - """`BaseModel` uses its own `__module__` to find out where it was defined - and then looks for symbols to resolve forward references in those globals. - On the other hand this function can be called with arbitrary objects, - including type aliases, where `__module__` (always `typing.py`) is not useful. - So instead we look at the globals in our parent stack frame. - - This works for the case where this function is called in a module that - has the target of forward references in its scope, but - does not always work for more complex cases. - - For example, take the following: - - a.py - ```python - from typing import Dict, List - - IntList = List[int] - OuterDict = Dict[str, 'IntList'] - ``` - - b.py - ```python test="skip" - from a import OuterDict - - from pydantic import TypeAdapter - - IntList = int # replaces the symbol the forward reference is looking for - v = TypeAdapter(OuterDict) - v({'x': 1}) # should fail but doesn't - ``` - - If `OuterDict` were a `BaseModel`, this would work because it would resolve - the forward reference within the `a.py` namespace. - But `TypeAdapter(OuterDict)` can't determine what module `OuterDict` came from. - - In other words, the assumption that _all_ forward references exist in the - module we are being called from is not technically always true. - Although most of the time it is and it works fine for recursive models and such, - `BaseModel`'s behavior isn't perfect either and _can_ break in similar ways, - so there is no right or wrong between the two. - - But at the very least this behavior is _subtly_ different from `BaseModel`'s. - """ - local_ns = _typing_extra.parent_frame_namespace(parent_depth=parent_depth) - global_ns = sys._getframe(max(parent_depth - 1, 1)).f_globals.copy() - global_ns.update(local_ns or {}) - gen = (config_wrapper.schema_generator or _generate_schema.GenerateSchema)( - config_wrapper, types_namespace=global_ns, typevars_map={} - ) - schema = gen.generate_schema(type_) - schema = gen.clean_schema(schema) - return schema - - def _getattr_no_parents(obj: Any, attribute: str) -> Any: """Returns the attribute value without attempting to look up attributes from parent types.""" if hasattr(obj, '__dict__'): @@ -131,26 +64,9 @@ def _type_has_config(type_: Any) -> bool: return False -# This is keeping track of the frame depth for the TypeAdapter functions. This is required for _parent_depth used for -# ForwardRef resolution. We may enter the TypeAdapter schema building via different TypeAdapter functions. Hence, we -# need to keep track of the frame depth relative to the originally provided _parent_depth. -def _frame_depth( - depth: int, -) -> Callable[[Callable[Concatenate[TypeAdapterT, P], R]], Callable[Concatenate[TypeAdapterT, P], R]]: - def wrapper(func: Callable[Concatenate[TypeAdapterT, P], R]) -> Callable[Concatenate[TypeAdapterT, P], R]: - @wraps(func) - def wrapped(self: TypeAdapterT, *args: P.args, **kwargs: P.kwargs) -> R: - with self._with_frame_depth(depth + 1): # depth + 1 for the wrapper function - return func(self, *args, **kwargs) - - return wrapped - - return wrapper - - @final class TypeAdapter(Generic[T]): - """Usage docs: https://docs.pydantic.dev/2.9/concepts/type_adapter/ + """Usage docs: https://docs.pydantic.dev/2.10/concepts/type_adapter/ Type adapters provide a flexible way to perform validation and serialization based on a Python type. @@ -159,17 +75,61 @@ class TypeAdapter(Generic[T]): **Note:** `TypeAdapter` instances are not types, and cannot be used as type annotations for fields. - **Note:** By default, `TypeAdapter` does not respect the - [`defer_build=True`][pydantic.config.ConfigDict.defer_build] setting in the - [`model_config`][pydantic.BaseModel.model_config] or in the `TypeAdapter` constructor `config`. You need to also - explicitly set [`experimental_defer_build_mode=('model', 'type_adapter')`][pydantic.config.ConfigDict.experimental_defer_build_mode] of the - config to defer the model validator and serializer construction. Thus, this feature is opt-in to ensure backwards - compatibility. - Attributes: core_schema: The core schema for the type. validator (SchemaValidator): The schema validator for the type. serializer: The schema serializer for the type. + + ??? info "Namespace management nuances and implementation details" + + Here, we collect some notes on namespace management, and subtle differences from `BaseModel`: + + `BaseModel` uses its own `__module__` to find out where it was defined + and then looks for symbols to resolve forward references in those globals. + On the other hand, `TypeAdapter` can be initialized with arbitrary objects, + which may not be types and thus do not have a `__module__` available. + So instead we look at the globals in our parent stack frame. + + It is expected that the `ns_resolver` passed to this function will have the correct + namespace for the type we're adapting. See `TypeAdapter.__init__` and `TypeAdapter.rebuild` + for various ways to construct this namespace. + + This works for the case where this function is called in a module that + has the target of forward references in its scope, but + does not always work for more complex cases. + + For example, take the following: + + a.py + ```python + from typing import Dict, List + + IntList = List[int] + OuterDict = Dict[str, 'IntList'] + ``` + + b.py + ```python {test="skip"} + from a import OuterDict + + from pydantic import TypeAdapter + + IntList = int # replaces the symbol the forward reference is looking for + v = TypeAdapter(OuterDict) + v({'x': 1}) # should fail but doesn't + ``` + + If `OuterDict` were a `BaseModel`, this would work because it would resolve + the forward reference within the `a.py` namespace. + But `TypeAdapter(OuterDict)` can't determine what module `OuterDict` came from. + + In other words, the assumption that _all_ forward references exist in the + module we are being called from is not technically always true. + Although most of the time it is and it works fine for recursive models and such, + `BaseModel`'s behavior isn't perfect either and _can_ break in similar ways, + so there is no right or wrong between the two. + + But at the very least this behavior is _subtly_ different from `BaseModel`'s. """ @overload @@ -208,7 +168,9 @@ def __init__( Args: type: The type associated with the `TypeAdapter`. config: Configuration for the `TypeAdapter`, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict]. - _parent_depth: depth at which to search the parent namespace to construct the local namespace. + _parent_depth: Depth at which to search the for the parent namespace used for schema building. + We also use this as a reference level to find the global namespace (_parent_depth - 1). + Defaults to 2 because we expect to reference the frame that called the `TypeAdapter` constructor. module: The module that passes to plugin if provided. !!! note @@ -225,7 +187,7 @@ def __init__( Depending on the type used, `mypy` might raise an error when instantiating a `TypeAdapter`. As a workaround, you can explicitly annotate your variable: - ```py + ```python from typing import Union from pydantic import TypeAdapter @@ -248,44 +210,83 @@ def __init__( self._type = type self._config = config self._parent_depth = _parent_depth - if module is None: - f = sys._getframe(1) - self._module_name = cast(str, f.f_globals.get('__name__', '')) - else: - self._module_name = module - - self._core_schema: CoreSchema | None = None - self._validator: SchemaValidator | PluggableSchemaValidator | None = None - self._serializer: SchemaSerializer | None = None - - if not self._defer_build(): - # Immediately initialize the core schema, validator and serializer - with self._with_frame_depth(1): # +1 frame depth for this __init__ - # Model itself may be using deferred building. For backward compatibility we don't rebuild model mocks - # here as part of __init__ even though TypeAdapter itself is not using deferred building. - self._init_core_attrs(rebuild_mocks=False) - - @contextmanager - def _with_frame_depth(self, depth: int) -> Iterator[None]: - self._parent_depth += depth - try: - yield - finally: - self._parent_depth -= depth - @_frame_depth(1) - def _init_core_attrs(self, rebuild_mocks: bool) -> None: + self.core_schema: CoreSchema + self.validator: SchemaValidator | PluggableSchemaValidator + self.serializer: SchemaSerializer + self.pydantic_complete: bool = False + + localns = _typing_extra.parent_frame_namespace(parent_depth=self._parent_depth) or {} + globalns = sys._getframe(max(self._parent_depth - 1, 1)).f_globals + self._module_name = module or cast(str, globalns.get('__name__', '')) + self._init_core_attrs( + ns_resolver=_namespace_utils.NsResolver( + namespaces_tuple=_namespace_utils.NamespacesTuple(locals=localns, globals=globalns), + parent_namespace=localns, + ), + force=False, + ) + + def _init_core_attrs( + self, ns_resolver: _namespace_utils.NsResolver, force: bool, raise_errors: bool = False + ) -> bool: + """Initialize the core schema, validator, and serializer for the type. + + Args: + ns_resolver: The namespace resolver to use when building the core schema for the adapted type. + force: Whether to force the construction of the core schema, validator, and serializer. + If `force` is set to `False` and `_defer_build` is `True`, the core schema, validator, and serializer will be set to mocks. + raise_errors: Whether to raise errors if initializing any of the core attrs fails. + + Returns: + `True` if the core schema, validator, and serializer were successfully initialized, otherwise `False`. + + Raises: + PydanticUndefinedAnnotation: If `PydanticUndefinedAnnotation` occurs in`__get_pydantic_core_schema__` + and `raise_errors=True`. + """ + if not force and self._defer_build: + _mock_val_ser.set_type_adapter_mocks(self, str(self._type)) + self.pydantic_complete = False + return False + try: - self._core_schema = _getattr_no_parents(self._type, '__pydantic_core_schema__') - self._validator = _getattr_no_parents(self._type, '__pydantic_validator__') - self._serializer = _getattr_no_parents(self._type, '__pydantic_serializer__') + self.core_schema = _getattr_no_parents(self._type, '__pydantic_core_schema__') + self.validator = _getattr_no_parents(self._type, '__pydantic_validator__') + self.serializer = _getattr_no_parents(self._type, '__pydantic_serializer__') + + # TODO: we don't go through the rebuild logic here directly because we don't want + # to repeat all of the namespace fetching logic that we've already done + # so we simply skip to the block below that does the actual schema generation + if ( + isinstance(self.core_schema, _mock_val_ser.MockCoreSchema) + or isinstance(self.validator, _mock_val_ser.MockValSer) + or isinstance(self.serializer, _mock_val_ser.MockValSer) + ): + raise AttributeError() except AttributeError: config_wrapper = _config.ConfigWrapper(self._config) + + schema_generator = _generate_schema.GenerateSchema(config_wrapper, ns_resolver=ns_resolver) + + try: + core_schema = schema_generator.generate_schema(self._type) + except PydanticUndefinedAnnotation: + if raise_errors: + raise + _mock_val_ser.set_type_adapter_mocks(self, str(self._type)) + return False + + try: + self.core_schema = schema_generator.clean_schema(core_schema) + except schema_generator.CollectedInvalid: + _mock_val_ser.set_type_adapter_mocks(self, str(self._type)) + return False + core_config = config_wrapper.core_config(None) - self._core_schema = _get_schema(self._type, config_wrapper, parent_depth=self._parent_depth) - self._validator = create_schema_validator( - schema=self._core_schema, + self.validator = create_schema_validator( + schema=self.core_schema, schema_type=self._type, schema_type_module=self._module_name, schema_type_name=str(self._type), @@ -293,61 +294,70 @@ def _init_core_attrs(self, rebuild_mocks: bool) -> None: config=core_config, plugin_settings=config_wrapper.plugin_settings, ) - self._serializer = SchemaSerializer(self._core_schema, core_config) - - if rebuild_mocks and isinstance(self._core_schema, _mock_val_ser.MockCoreSchema): - self._core_schema.rebuild() - self._init_core_attrs(rebuild_mocks=False) - assert not isinstance(self._core_schema, _mock_val_ser.MockCoreSchema) - assert not isinstance(self._validator, _mock_val_ser.MockValSer) - assert not isinstance(self._serializer, _mock_val_ser.MockValSer) - - @cached_property - @_frame_depth(2) # +2 for @cached_property and core_schema(self) - def core_schema(self) -> CoreSchema: - """The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.""" - if self._core_schema is None or isinstance(self._core_schema, _mock_val_ser.MockCoreSchema): - self._init_core_attrs(rebuild_mocks=True) # Do not expose MockCoreSchema from public function - assert self._core_schema is not None and not isinstance(self._core_schema, _mock_val_ser.MockCoreSchema) - return self._core_schema - - @cached_property - @_frame_depth(2) # +2 for @cached_property + validator(self) - def validator(self) -> SchemaValidator | PluggableSchemaValidator: - """The pydantic-core SchemaValidator used to validate instances of the model.""" - if not isinstance(self._validator, (SchemaValidator, PluggableSchemaValidator)): - self._init_core_attrs(rebuild_mocks=True) # Do not expose MockValSer from public function - assert isinstance(self._validator, (SchemaValidator, PluggableSchemaValidator)) - return self._validator - - @cached_property - @_frame_depth(2) # +2 for @cached_property + serializer(self) - def serializer(self) -> SchemaSerializer: - """The pydantic-core SchemaSerializer used to dump instances of the model.""" - if not isinstance(self._serializer, SchemaSerializer): - self._init_core_attrs(rebuild_mocks=True) # Do not expose MockValSer from public function - assert isinstance(self._serializer, SchemaSerializer) - return self._serializer + self.serializer = SchemaSerializer(self.core_schema, core_config) + self.pydantic_complete = True + return True + + @property def _defer_build(self) -> bool: - config = self._config if self._config is not None else self._model_config() - return self._is_defer_build_config(config) if config is not None else False + config = self._config if self._config is not None else self._model_config + if config: + return config.get('defer_build') is True + return False + @property def _model_config(self) -> ConfigDict | None: type_: Any = _typing_extra.annotated_type(self._type) or self._type # Eg FastAPI heavily uses Annotated if _utils.lenient_issubclass(type_, BaseModel): return type_.model_config return getattr(type_, '__pydantic_config__', None) - @staticmethod - def _is_defer_build_config(config: ConfigDict) -> bool: - # TODO reevaluate this logic when we have a better understanding of how defer_build should work with TypeAdapter - # Should we drop the special experimental_defer_build_mode check? - return config.get('defer_build', False) is True and 'type_adapter' in config.get( - 'experimental_defer_build_mode', () + def __repr__(self) -> str: + return f'TypeAdapter({_repr.display_as_type(self._type)})' + + def rebuild( + self, + *, + force: bool = False, + raise_errors: bool = True, + _parent_namespace_depth: int = 2, + _types_namespace: _namespace_utils.MappingNamespace | None = None, + ) -> bool | None: + """Try to rebuild the pydantic-core schema for the adapter's type. + + This may be necessary when one of the annotations is a ForwardRef which could not be resolved during + the initial attempt to build the schema, and automatic rebuilding fails. + + Args: + force: Whether to force the rebuilding of the type adapter's schema, defaults to `False`. + raise_errors: Whether to raise errors, defaults to `True`. + _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. + _types_namespace: The types namespace, defaults to `None`. + + Returns: + Returns `None` if the schema is already "complete" and rebuilding was not required. + If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`. + """ + if not force and self.pydantic_complete: + return None + + if _types_namespace is not None: + rebuild_ns = _types_namespace + elif _parent_namespace_depth > 0: + rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {} + else: + rebuild_ns = {} + + # we have to manually fetch globals here because there's no type on the stack for the NsResolver + # and so we skip the globalns = get_module_ns_of(typ) call that would normally happen + globalns = sys._getframe(max(_parent_namespace_depth - 1, 1)).f_globals + ns_resolver = _namespace_utils.NsResolver( + namespaces_tuple=_namespace_utils.NamespacesTuple(locals=rebuild_ns, globals=globalns), + parent_namespace=rebuild_ns, ) + return self._init_core_attrs(ns_resolver=ns_resolver, force=True, raise_errors=raise_errors) - @_frame_depth(1) def validate_python( self, object: Any, @@ -356,6 +366,7 @@ def validate_python( strict: bool | None = None, from_attributes: bool | None = None, context: dict[str, Any] | None = None, + experimental_allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False, ) -> T: """Validate a Python object against the model. @@ -364,6 +375,11 @@ def validate_python( strict: Whether to strictly check types. from_attributes: Whether to extract data from object attributes. context: Additional context to pass to the validator. + experimental_allow_partial: **Experimental** whether to enable + [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams. + * False / 'off': Default behavior, no partial validation. + * True / 'on': Enable partial validation. + * 'trailing-strings': Enable partial validation and allow trailing strings in the input. !!! note When using `TypeAdapter` with a Pydantic `dataclass`, the use of the `from_attributes` @@ -372,13 +388,24 @@ def validate_python( Returns: The validated object. """ - return self.validator.validate_python(object, strict=strict, from_attributes=from_attributes, context=context) + return self.validator.validate_python( + object, + strict=strict, + from_attributes=from_attributes, + context=context, + allow_partial=experimental_allow_partial, + ) - @_frame_depth(1) def validate_json( - self, data: str | bytes, /, *, strict: bool | None = None, context: dict[str, Any] | None = None + self, + data: str | bytes | bytearray, + /, + *, + strict: bool | None = None, + context: dict[str, Any] | None = None, + experimental_allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False, ) -> T: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing + """Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing Validate a JSON string or bytes against the model. @@ -386,27 +413,47 @@ def validate_json( data: The JSON data to validate against the model. strict: Whether to strictly check types. context: Additional context to use during validation. + experimental_allow_partial: **Experimental** whether to enable + [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams. + * False / 'off': Default behavior, no partial validation. + * True / 'on': Enable partial validation. + * 'trailing-strings': Enable partial validation and allow trailing strings in the input. Returns: The validated object. """ - return self.validator.validate_json(data, strict=strict, context=context) + return self.validator.validate_json( + data, strict=strict, context=context, allow_partial=experimental_allow_partial + ) - @_frame_depth(1) - def validate_strings(self, obj: Any, /, *, strict: bool | None = None, context: dict[str, Any] | None = None) -> T: + def validate_strings( + self, + obj: Any, + /, + *, + strict: bool | None = None, + context: dict[str, Any] | None = None, + experimental_allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False, + ) -> T: """Validate object contains string data against the model. Args: obj: The object contains string data to validate. strict: Whether to strictly check types. context: Additional context to use during validation. + experimental_allow_partial: **Experimental** whether to enable + [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams. + * False / 'off': Default behavior, no partial validation. + * True / 'on': Enable partial validation. + * 'trailing-strings': Enable partial validation and allow trailing strings in the input. Returns: The validated object. """ - return self.validator.validate_strings(obj, strict=strict, context=context) + return self.validator.validate_strings( + obj, strict=strict, context=context, allow_partial=experimental_allow_partial + ) - @_frame_depth(1) def get_default_value(self, *, strict: bool | None = None, context: dict[str, Any] | None = None) -> Some[T] | None: """Get the default value for the wrapped type. @@ -419,7 +466,6 @@ def get_default_value(self, *, strict: bool | None = None, context: dict[str, An """ return self.validator.get_default_value(strict=strict, context=context) - @_frame_depth(1) def dump_python( self, instance: T, @@ -472,7 +518,6 @@ def dump_python( context=context, ) - @_frame_depth(1) def dump_json( self, instance: T, @@ -490,7 +535,7 @@ def dump_json( serialize_as_any: bool = False, context: dict[str, Any] | None = None, ) -> bytes: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-serialization + """Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-serialization Serialize an instance of the adapted type to JSON. @@ -527,7 +572,6 @@ def dump_json( context=context, ) - @_frame_depth(1) def json_schema( self, *, @@ -548,6 +592,9 @@ def json_schema( The JSON schema for the model as a dictionary. """ schema_generator_instance = schema_generator(by_alias=by_alias, ref_template=ref_template) + if isinstance(self.core_schema, _mock_val_ser.MockCoreSchema): + self.core_schema.rebuild() + assert not isinstance(self.core_schema, _mock_val_ser.MockCoreSchema), 'this is a bug! please report it' return schema_generator_instance.generate(self.core_schema, mode=mode) @staticmethod @@ -587,8 +634,13 @@ def json_schemas( inputs_ = [] for key, mode, adapter in inputs: - with adapter._with_frame_depth(1): # +1 for json_schemas staticmethod - inputs_.append((key, mode, adapter.core_schema)) + # This is the same pattern we follow for model json schemas - we attempt a core schema rebuild if we detect a mock + if isinstance(adapter.core_schema, _mock_val_ser.MockCoreSchema): + adapter.core_schema.rebuild() + assert not isinstance( + adapter.core_schema, _mock_val_ser.MockCoreSchema + ), 'this is a bug! please report it' + inputs_.append((key, mode, adapter.core_schema)) json_schemas_map, definitions = schema_generator_instance.generate_definitions(inputs_) diff --git a/pydantic/types.py b/pydantic/types.py index 58b7899645f..2cbbb6567b4 100644 --- a/pydantic/types.py +++ b/pydantic/types.py @@ -46,6 +46,7 @@ __all__ = ( 'Strict', 'StrictStr', + 'SocketPath', 'conbytes', 'conlist', 'conset', @@ -112,7 +113,7 @@ @_dataclasses.dataclass class Strict(_fields.PydanticMetadata, BaseMetadata): - """Usage docs: https://docs.pydantic.dev/2.9/concepts/strict_mode/#strict-mode-with-annotated-strict + """Usage docs: https://docs.pydantic.dev/2.10/concepts/strict_mode/#strict-mode-with-annotated-strict A field metadata class to indicate that a field should be validated in strict mode. Use this class as an annotation via [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated), as seen below. @@ -164,7 +165,7 @@ def conint( The reason is that `conint` returns a type, which doesn't play well with static analysis tools. === ":x: Don't do this" - ```py + ```python from pydantic import BaseModel, conint class Foo(BaseModel): @@ -172,7 +173,7 @@ class Foo(BaseModel): ``` === ":white_check_mark: Do this" - ```py + ```python from typing_extensions import Annotated from pydantic import BaseModel, Field @@ -194,7 +195,7 @@ class Foo(BaseModel): Returns: The wrapped integer type. - ```py + ```python from pydantic import BaseModel, ValidationError, conint class ConstrainedExample(BaseModel): @@ -234,7 +235,7 @@ class ConstrainedExample(BaseModel): PositiveInt = Annotated[int, annotated_types.Gt(0)] """An integer that must be greater than zero. -```py +```python from pydantic import BaseModel, PositiveInt, ValidationError class Model(BaseModel): @@ -265,7 +266,7 @@ class Model(BaseModel): NegativeInt = Annotated[int, annotated_types.Lt(0)] """An integer that must be less than zero. -```py +```python from pydantic import BaseModel, NegativeInt, ValidationError class Model(BaseModel): @@ -296,7 +297,7 @@ class Model(BaseModel): NonPositiveInt = Annotated[int, annotated_types.Le(0)] """An integer that must be less than or equal to zero. -```py +```python from pydantic import BaseModel, NonPositiveInt, ValidationError class Model(BaseModel): @@ -327,7 +328,7 @@ class Model(BaseModel): NonNegativeInt = Annotated[int, annotated_types.Ge(0)] """An integer that must be greater than or equal to zero. -```py +```python from pydantic import BaseModel, NonNegativeInt, ValidationError class Model(BaseModel): @@ -358,7 +359,7 @@ class Model(BaseModel): StrictInt = Annotated[int, Strict()] """An integer that must be validated in strict mode. -```py +```python from pydantic import BaseModel, StrictInt, ValidationError class StrictIntModel(BaseModel): @@ -424,7 +425,7 @@ def confloat( The reason is that `confloat` returns a type, which doesn't play well with static analysis tools. === ":x: Don't do this" - ```py + ```python from pydantic import BaseModel, confloat class Foo(BaseModel): @@ -432,7 +433,7 @@ class Foo(BaseModel): ``` === ":white_check_mark: Do this" - ```py + ```python from typing_extensions import Annotated from pydantic import BaseModel, Field @@ -455,7 +456,7 @@ class Foo(BaseModel): Returns: The wrapped float type. - ```py + ```python from pydantic import BaseModel, ValidationError, confloat class ConstrainedExample(BaseModel): @@ -495,7 +496,7 @@ class ConstrainedExample(BaseModel): PositiveFloat = Annotated[float, annotated_types.Gt(0)] """A float that must be greater than zero. -```py +```python from pydantic import BaseModel, PositiveFloat, ValidationError class Model(BaseModel): @@ -526,7 +527,7 @@ class Model(BaseModel): NegativeFloat = Annotated[float, annotated_types.Lt(0)] """A float that must be less than zero. -```py +```python from pydantic import BaseModel, NegativeFloat, ValidationError class Model(BaseModel): @@ -557,7 +558,7 @@ class Model(BaseModel): NonPositiveFloat = Annotated[float, annotated_types.Le(0)] """A float that must be less than or equal to zero. -```py +```python from pydantic import BaseModel, NonPositiveFloat, ValidationError class Model(BaseModel): @@ -588,7 +589,7 @@ class Model(BaseModel): NonNegativeFloat = Annotated[float, annotated_types.Ge(0)] """A float that must be greater than or equal to zero. -```py +```python from pydantic import BaseModel, NonNegativeFloat, ValidationError class Model(BaseModel): @@ -619,7 +620,7 @@ class Model(BaseModel): StrictFloat = Annotated[float, Strict(True)] """A float that must be validated in strict mode. -```py +```python from pydantic import BaseModel, StrictFloat, ValidationError class StrictFloatModel(BaseModel): @@ -639,7 +640,7 @@ class StrictFloatModel(BaseModel): FiniteFloat = Annotated[float, AllowInfNan(False)] """A float that must be finite (not ``-inf``, ``inf``, or ``nan``). -```py +```python from pydantic import BaseModel, FiniteFloat class Model(BaseModel): @@ -687,7 +688,7 @@ def conbytes( @_dataclasses.dataclass(frozen=True) class StringConstraints(annotated_types.GroupedMetadata): - """Usage docs: https://docs.pydantic.dev/2.9/concepts/fields/#string-constraints + """Usage docs: https://docs.pydantic.dev/2.10/concepts/fields/#string-constraints A field metadata class to apply constraints to `str` types. Use this class as an annotation via [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated), as seen below. @@ -708,6 +709,7 @@ class StringConstraints(annotated_types.GroupedMetadata): from pydantic.types import StringConstraints ConstrainedStr = Annotated[str, StringConstraints(min_length=1, max_length=10)] + ``` """ strip_whitespace: bool | None = None @@ -760,7 +762,7 @@ def constr( The reason is that `constr` returns a type, which doesn't play well with static analysis tools. === ":x: Don't do this" - ```py + ```python from pydantic import BaseModel, constr class Foo(BaseModel): @@ -768,7 +770,7 @@ class Foo(BaseModel): ``` === ":white_check_mark: Do this" - ```py + ```python from typing_extensions import Annotated from pydantic import BaseModel, StringConstraints @@ -784,7 +786,7 @@ class Foo(BaseModel): A wrapper around `str` that allows for additional constraints. - ```py + ```python from pydantic import BaseModel, constr class Foo(BaseModel): @@ -904,18 +906,17 @@ def conlist( else: class ImportString: - """A type that can be used to import a type from a string. + """A type that can be used to import a Python object from a string. `ImportString` expects a string and loads the Python object importable at that dotted path. - Attributes of modules may be separated from the module by `:` or `.`, e.g. if `'math:cos'` was provided, - the resulting field value would be the function`cos`. If a `.` is used and both an attribute and submodule + Attributes of modules may be separated from the module by `:` or `.`, e.g. if `'math:cos'` is provided, + the resulting field value would be the function `cos`. If a `.` is used and both an attribute and submodule are present at the same path, the module will be preferred. On model instantiation, pointers will be evaluated and imported. There is some nuance to this behavior, demonstrated in the examples below. - **Good behavior:** - ```py + ```python import math from pydantic import BaseModel, Field, ImportString, ValidationError @@ -969,7 +970,7 @@ class ImportThingsMissingValidateDefault(BaseModel): Serializing an `ImportString` type to json is also possible. - ```py lint="skip" + ```python from pydantic import BaseModel, ImportString class ImportThings(BaseModel): @@ -1055,7 +1056,7 @@ def condecimal( The reason is that `condecimal` returns a type, which doesn't play well with static analysis tools. === ":x: Don't do this" - ```py + ```python from pydantic import BaseModel, condecimal class Foo(BaseModel): @@ -1063,7 +1064,7 @@ class Foo(BaseModel): ``` === ":white_check_mark: Do this" - ```py + ```python from decimal import Decimal from typing_extensions import Annotated @@ -1087,7 +1088,7 @@ class Foo(BaseModel): decimal_places: The number of decimal places. Defaults to `None`. allow_inf_nan: Whether to allow infinity and NaN. Defaults to `None`. - ```py + ```python from decimal import Decimal from pydantic import BaseModel, ValidationError, condecimal @@ -1141,9 +1142,10 @@ class UuidVersion: Example: ```python - from typing_extensions import Annotated from uuid import UUID + from typing_extensions import Annotated + from pydantic.types import UuidVersion UUID1 = Annotated[UUID, UuidVersion(1)] @@ -1178,7 +1180,7 @@ def __hash__(self) -> int: UUID1 = Annotated[UUID, UuidVersion(1)] """A [UUID](https://docs.python.org/3/library/uuid.html) that must be version 1. -```py +```python import uuid from pydantic import UUID1, BaseModel @@ -1192,7 +1194,7 @@ class Model(BaseModel): UUID3 = Annotated[UUID, UuidVersion(3)] """A [UUID](https://docs.python.org/3/library/uuid.html) that must be version 3. -```py +```python import uuid from pydantic import UUID3, BaseModel @@ -1206,7 +1208,7 @@ class Model(BaseModel): UUID4 = Annotated[UUID, UuidVersion(4)] """A [UUID](https://docs.python.org/3/library/uuid.html) that must be version 4. -```py +```python import uuid from pydantic import UUID4, BaseModel @@ -1220,7 +1222,7 @@ class Model(BaseModel): UUID5 = Annotated[UUID, UuidVersion(5)] """A [UUID](https://docs.python.org/3/library/uuid.html) that must be version 5. -```py +```python import uuid from pydantic import UUID5, BaseModel @@ -1238,7 +1240,7 @@ class Model(BaseModel): @_dataclasses.dataclass class PathType: - path_type: Literal['file', 'dir', 'new'] + path_type: Literal['file', 'dir', 'new', 'socket'] def __get_pydantic_json_schema__( self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler @@ -1253,6 +1255,7 @@ def __get_pydantic_core_schema__(self, source: Any, handler: GetCoreSchemaHandle 'file': cast(core_schema.WithInfoValidatorFunction, self.validate_file), 'dir': cast(core_schema.WithInfoValidatorFunction, self.validate_directory), 'new': cast(core_schema.WithInfoValidatorFunction, self.validate_new), + 'socket': cast(core_schema.WithInfoValidatorFunction, self.validate_socket), } return core_schema.with_info_after_validator_function( @@ -1267,6 +1270,13 @@ def validate_file(path: Path, _: core_schema.ValidationInfo) -> Path: else: raise PydanticCustomError('path_not_file', 'Path does not point to a file') + @staticmethod + def validate_socket(path: Path, _: core_schema.ValidationInfo) -> Path: + if path.is_socket(): + return path + else: + raise PydanticCustomError('path_not_socket', 'Path does not point to a socket') + @staticmethod def validate_directory(path: Path, _: core_schema.ValidationInfo) -> Path: if path.is_dir(): @@ -1290,7 +1300,7 @@ def __hash__(self) -> int: FilePath = Annotated[Path, PathType('file')] """A path that must point to a file. -```py +```python from pathlib import Path from pydantic import BaseModel, FilePath, ValidationError @@ -1332,7 +1342,7 @@ class Model(BaseModel): DirectoryPath = Annotated[Path, PathType('dir')] """A path that must point to a directory. -```py +```python from pathlib import Path from pydantic import BaseModel, DirectoryPath, ValidationError @@ -1374,6 +1384,8 @@ class Model(BaseModel): NewPath = Annotated[Path, PathType('new')] """A path for a new file or directory that must not already exist. The parent directory must already exist.""" +SocketPath = Annotated[Path, PathType('socket')] +"""A path to an existing socket file""" # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ JSON TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -1389,7 +1401,7 @@ class Json: You can use the `Json` data type to make Pydantic first load a raw JSON string before validating the loaded data into the parametrized type: - ```py + ```python from typing import Any, List from pydantic import BaseModel, Json, ValidationError @@ -1441,7 +1453,7 @@ class ConstrainedJsonModel(BaseModel): When you dump the model using `model_dump` or `model_dump_json`, the dumped value will be the result of validation, not the original JSON string. However, you can use the argument `round_trip=True` to get the original JSON string back: - ```py + ```python from typing import List from pydantic import BaseModel, Json @@ -1521,7 +1533,7 @@ class Secret(_SecretBase[SecretType]): 1. Directly parametrizing `Secret` with a type: - ```py + ```python from pydantic import BaseModel, Secret SecretBool = Secret[bool] @@ -1542,7 +1554,7 @@ class Model(BaseModel): 2. Subclassing from parametrized `Secret`: - ```py + ```python from datetime import date from pydantic import BaseModel, Secret @@ -1566,6 +1578,56 @@ class Model(BaseModel): ``` The value returned by the `_display` method will be used for `repr()` and `str()`. + + You can enforce constraints on the underlying type through annotations: + For example: + + ```python + from typing_extensions import Annotated + + from pydantic import BaseModel, Field, Secret, ValidationError + + SecretPosInt = Secret[Annotated[int, Field(gt=0, strict=True)]] + + class Model(BaseModel): + sensitive_int: SecretPosInt + + m = Model(sensitive_int=42) + print(m.model_dump()) + #> {'sensitive_int': Secret('**********')} + + try: + m = Model(sensitive_int=-42) # (1)! + except ValidationError as exc_info: + print(exc_info.errors(include_url=False, include_input=False)) + ''' + [ + { + 'type': 'greater_than', + 'loc': ('sensitive_int',), + 'msg': 'Input should be greater than 0', + 'ctx': {'gt': 0}, + } + ] + ''' + + try: + m = Model(sensitive_int='42') # (2)! + except ValidationError as exc_info: + print(exc_info.errors(include_url=False, include_input=False)) + ''' + [ + { + 'type': 'int_type', + 'loc': ('sensitive_int',), + 'msg': 'Input should be a valid integer', + } + ] + ''' + ``` + + 1. The input value is not greater than 0, so it raises a validation error. + 2. The input value is not an integer, so it raises a validation error because the `SecretPosInt` type has strict mode enabled. """ def _display(self) -> str | bytes: @@ -1685,7 +1747,7 @@ class SecretStr(_SecretField[str]): When the secret value is nonempty, it is displayed as `'**********'` instead of the underlying value in calls to `repr()` and `str()`. If the value _is_ empty, it is displayed as `''`. - ```py + ```python from pydantic import BaseModel, SecretStr class User(BaseModel): @@ -1701,6 +1763,39 @@ class User(BaseModel): print((SecretStr('password'), SecretStr(''))) #> (SecretStr('**********'), SecretStr('')) ``` + + As seen above, by default, [`SecretStr`][pydantic.types.SecretStr] (and [`SecretBytes`][pydantic.types.SecretBytes]) + will be serialized as `**********` when serializing to json. + + You can use the [`field_serializer`][pydantic.functional_serializers.field_serializer] to dump the + secret as plain-text when serializing to json. + + ```python + from pydantic import BaseModel, SecretBytes, SecretStr, field_serializer + + class Model(BaseModel): + password: SecretStr + password_bytes: SecretBytes + + @field_serializer('password', 'password_bytes', when_used='json') + def dump_secret(self, v): + return v.get_secret_value() + + model = Model(password='IAmSensitive', password_bytes=b'IAmSensitiveBytes') + print(model) + #> password=SecretStr('**********') password_bytes=SecretBytes(b'**********') + print(model.password) + #> ********** + print(model.model_dump()) + ''' + { + 'password': SecretStr('**********'), + 'password_bytes': SecretBytes(b'**********'), + } + ''' + print(model.model_dump_json()) + #> {"password":"IAmSensitive","password_bytes":"IAmSensitiveBytes"} + ``` """ _inner_schema: ClassVar[CoreSchema] = core_schema.str_schema() @@ -1720,7 +1815,7 @@ class SecretBytes(_SecretField[bytes]): When the secret value is nonempty, it is displayed as `b'**********'` instead of the underlying value in calls to `repr()` and `str()`. If the value _is_ empty, it is displayed as `b''`. - ```py + ```python from pydantic import BaseModel, SecretBytes class User(BaseModel): @@ -1883,7 +1978,7 @@ class ByteSize(int): !!! info Note that `1b` will be parsed as "1 byte" and not "1 bit". - ```py + ```python from pydantic import BaseModel, ByteSize class MyModel(BaseModel): @@ -2250,7 +2345,7 @@ def decode(cls, data: bytes) -> bytes: The decoded data. """ try: - return base64.decodebytes(data) + return base64.b64decode(data) except ValueError as e: raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)}) @@ -2264,7 +2359,7 @@ def encode(cls, value: bytes) -> bytes: Returns: The encoded data. """ - return base64.encodebytes(value) + return base64.b64encode(value) @classmethod def get_json_format(cls) -> Literal['base64']: @@ -2322,7 +2417,7 @@ class EncodedBytes: `EncodedBytes` needs an encoder that implements `EncoderProtocol` to operate. - ```py + ```python from typing_extensions import Annotated from pydantic import BaseModel, EncodedBytes, EncoderProtocol, ValidationError @@ -2381,9 +2476,11 @@ def __get_pydantic_json_schema__( return field_schema def __get_pydantic_core_schema__(self, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + schema = handler(source) + _check_annotated_type(schema['type'], 'bytes', self.__class__.__name__) return core_schema.with_info_after_validator_function( function=self.decode, - schema=core_schema.bytes_schema(), + schema=schema, serialization=core_schema.plain_serializer_function_ser_schema(function=self.encode), ) @@ -2414,12 +2511,12 @@ def __hash__(self) -> int: @_dataclasses.dataclass(**_internal_dataclass.slots_true) -class EncodedStr(EncodedBytes): +class EncodedStr: """A str type that is encoded and decoded using the specified encoder. `EncodedStr` needs an encoder that implements `EncoderProtocol` to operate. - ```py + ```python from typing_extensions import Annotated from pydantic import BaseModel, EncodedStr, EncoderProtocol, ValidationError @@ -2468,14 +2565,25 @@ class Model(BaseModel): ``` """ + encoder: type[EncoderProtocol] + + def __get_pydantic_json_schema__( + self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler + ) -> JsonSchemaValue: + field_schema = handler(core_schema) + field_schema.update(type='string', format=self.encoder.get_json_format()) + return field_schema + def __get_pydantic_core_schema__(self, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + schema = handler(source) + _check_annotated_type(schema['type'], 'str', self.__class__.__name__) return core_schema.with_info_after_validator_function( function=self.decode_str, - schema=super(EncodedStr, self).__get_pydantic_core_schema__(source=source, handler=handler), # noqa: UP008 + schema=schema, serialization=core_schema.plain_serializer_function_ser_schema(function=self.encode_str), ) - def decode_str(self, data: bytes, _: core_schema.ValidationInfo) -> str: + def decode_str(self, data: str, _: core_schema.ValidationInfo) -> str: """Decode the data using the specified encoder. Args: @@ -2484,7 +2592,7 @@ def decode_str(self, data: bytes, _: core_schema.ValidationInfo) -> str: Returns: The decoded data. """ - return data.decode() + return self.encoder.decode(data.encode()).decode() def encode_str(self, value: str) -> str: """Encode the data using the specified encoder. @@ -2495,7 +2603,7 @@ def encode_str(self, value: str) -> str: Returns: The encoded data. """ - return super(EncodedStr, self).encode(value=value.encode()).decode() # noqa: UP008 + return self.encoder.encode(value.encode()).decode() # noqa: UP008 def __hash__(self) -> int: return hash(self.encoder) @@ -2505,12 +2613,53 @@ def __hash__(self) -> int: """A bytes type that is encoded and decoded using the standard (non-URL-safe) base64 encoder. Note: - Under the hood, `Base64Bytes` use standard library `base64.encodebytes` and `base64.decodebytes` functions. + Under the hood, `Base64Bytes` uses the standard library `base64.b64encode` and `base64.b64decode` functions. As a result, attempting to decode url-safe base64 data using the `Base64Bytes` type may fail or produce an incorrect decoding. -```py +Warning: + In versions of Pydantic prior to v2.10, `Base64Bytes` used [`base64.encodebytes`][base64.encodebytes] + and [`base64.decodebytes`][base64.decodebytes] functions. According to the [base64 documentation](https://docs.python.org/3/library/base64.html), + these methods are considered legacy implementation, and thus, Pydantic v2.10+ now uses the modern + [`base64.b64encode`][base64.b64encode] and [`base64.b64decode`][base64.b64decode] functions. + + If you'd still like to use these legacy encoders / decoders, you can achieve this by creating a custom annotated type, + like follows: + + ```python + import base64 + from typing import Literal + + from pydantic_core import PydanticCustomError + from typing_extensions import Annotated + + from pydantic import EncodedBytes, EncoderProtocol + + class LegacyBase64Encoder(EncoderProtocol): + @classmethod + def decode(cls, data: bytes) -> bytes: + try: + return base64.decodebytes(data) + except ValueError as e: + raise PydanticCustomError( + 'base64_decode', + "Base64 decoding error: '{error}'", + {'error': str(e)}, + ) + + @classmethod + def encode(cls, value: bytes) -> bytes: + return base64.encodebytes(value) + + @classmethod + def get_json_format(cls) -> Literal['base64']: + return 'base64' + + LegacyBase64Bytes = Annotated[bytes, EncodedBytes(encoder=LegacyBase64Encoder)] + ``` + +```python from pydantic import Base64Bytes, BaseModel, ValidationError class Model(BaseModel): @@ -2525,7 +2674,7 @@ class Model(BaseModel): # Serialize into the base64 form print(m.model_dump()) -#> {'base64_bytes': b'VGhpcyBpcyB0aGUgd2F5\n'} +#> {'base64_bytes': b'VGhpcyBpcyB0aGUgd2F5'} # Validate base64 data try: @@ -2543,12 +2692,21 @@ class Model(BaseModel): """A str type that is encoded and decoded using the standard (non-URL-safe) base64 encoder. Note: - Under the hood, `Base64Bytes` use standard library `base64.encodebytes` and `base64.decodebytes` functions. + Under the hood, `Base64Str` uses the standard library `base64.b64encode` and `base64.b64decode` functions. As a result, attempting to decode url-safe base64 data using the `Base64Str` type may fail or produce an incorrect decoding. -```py +Warning: + In versions of Pydantic prior to v2.10, `Base64Str` used [`base64.encodebytes`][base64.encodebytes] + and [`base64.decodebytes`][base64.decodebytes] functions. According to the [base64 documentation](https://docs.python.org/3/library/base64.html), + these methods are considered legacy implementation, and thus, Pydantic v2.10+ now uses the modern + [`base64.b64encode`][base64.b64encode] and [`base64.b64decode`][base64.b64decode] functions. + + See the [`Base64Bytes`][pydantic.types.Base64Bytes] type for more information on how to + replicate the old behavior with the legacy encoders / decoders. + +```python from pydantic import Base64Str, BaseModel, ValidationError class Model(BaseModel): @@ -2563,7 +2721,7 @@ class Model(BaseModel): # Serialize into the base64 form print(m.model_dump()) -#> {'base64_str': 'VGhlc2UgYXJlbid0IHRoZSBkcm9pZHMgeW91J3JlIGxvb2tpbmcgZm9y\n'} +#> {'base64_str': 'VGhlc2UgYXJlbid0IHRoZSBkcm9pZHMgeW91J3JlIGxvb2tpbmcgZm9y'} # Validate base64 data try: @@ -2587,7 +2745,7 @@ class Model(BaseModel): As a result, the `Base64UrlBytes` type can be used to faithfully decode "vanilla" base64 data (using `'+'` and `'/'`). -```py +```python from pydantic import Base64UrlBytes, BaseModel class Model(BaseModel): @@ -2608,7 +2766,7 @@ class Model(BaseModel): As a result, the `Base64UrlStr` type can be used to faithfully decode "vanilla" base64 data (using `'+'` and `'/'`). -```py +```python from pydantic import Base64UrlStr, BaseModel class Model(BaseModel): @@ -2627,7 +2785,7 @@ class Model(BaseModel): @_dataclasses.dataclass(**_internal_dataclass.slots_true) class GetPydanticSchema: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/types/#using-getpydanticschema-to-reduce-boilerplate + """Usage docs: https://docs.pydantic.dev/2.10/concepts/types/#using-getpydanticschema-to-reduce-boilerplate A convenience class for creating an annotation that provides pydantic custom type hooks. @@ -2687,7 +2845,7 @@ class Tag: The primary role of the `Tag` here is to map the return value from the callable `Discriminator` function to the appropriate member of the `Union` in question. - ```py + ```python from typing import Any, Union from typing_extensions import Annotated, Literal @@ -2763,7 +2921,7 @@ def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaH @_dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True) class Discriminator: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/unions/#discriminated-unions-with-callable-discriminator + """Usage docs: https://docs.pydantic.dev/2.10/concepts/unions/#discriminated-unions-with-callable-discriminator Provides a way to use a custom callable as the way to extract the value of a union discriminator. @@ -2776,7 +2934,7 @@ class Discriminator: Consider this example, which is much more performant with the use of `Discriminator` and thus a `TaggedUnion` than it would be as a normal `Union`. - ```py + ```python from typing import Any, Union from typing_extensions import Annotated, Literal @@ -2972,7 +3130,7 @@ def __get_pydantic_core_schema__(cls, source_type: Any, handler: GetCoreSchemaHa The following example demonstrates how to use `JsonValue` to validate JSON data, and what kind of errors to expect when input data is not json serializable. - ```py + ```python import json from pydantic import BaseModel, JsonValue, ValidationError @@ -3050,9 +3208,11 @@ class FailFast(_fields.PydanticMetadata, BaseMetadata): You might want to enable this setting if you want to validate your data faster (basically, if you use this, validation will be more performant with the caveat that you get less information). - ```py + ```python from typing import List + from typing_extensions import Annotated + from pydantic import BaseModel, FailFast, ValidationError class Model(BaseModel): diff --git a/pydantic/v1/main.py b/pydantic/v1/main.py index cd376037c42..68af6f55444 100644 --- a/pydantic/v1/main.py +++ b/pydantic/v1/main.py @@ -301,7 +301,7 @@ def __instancecheck__(self, instance: Any) -> bool: See #3829 and python/cpython#92810 """ - return hasattr(instance, '__fields__') and super().__instancecheck__(instance) + return hasattr(instance, '__post_root_validators__') and super().__instancecheck__(instance) object_setattr = object.__setattr__ diff --git a/pydantic/v1/validators.py b/pydantic/v1/validators.py index 7c39aa9a7aa..c0940e8114a 100644 --- a/pydantic/v1/validators.py +++ b/pydantic/v1/validators.py @@ -27,6 +27,7 @@ Union, ) from uuid import UUID +from warnings import warn from pydantic.v1 import errors from pydantic.v1.datetime_parse import parse_date, parse_datetime, parse_duration, parse_time @@ -762,4 +763,6 @@ def find_validators( # noqa: C901 (ignore complexity) if config.arbitrary_types_allowed: yield make_arbitrary_type_validator(type_) else: + if hasattr(type_, '__pydantic_core_schema__'): + warn(f'Mixing V1 and V2 models is not supported. `{type_.__name__}` is a V2 model.', UserWarning) raise RuntimeError(f'no validator found for {type_}, see `arbitrary_types_allowed` in Config') diff --git a/pydantic/v1/version.py b/pydantic/v1/version.py index 46e3a32791f..47be0b7573a 100644 --- a/pydantic/v1/version.py +++ b/pydantic/v1/version.py @@ -1,6 +1,6 @@ __all__ = 'compiled', 'VERSION', 'version_info' -VERSION = '1.10.18' +VERSION = '1.10.19' try: import cython # type: ignore diff --git a/pydantic/validate_call_decorator.py b/pydantic/validate_call_decorator.py index 1151694515d..20ee9e084b9 100644 --- a/pydantic/validate_call_decorator.py +++ b/pydantic/validate_call_decorator.py @@ -2,10 +2,12 @@ from __future__ import annotations as _annotations -import functools -from typing import TYPE_CHECKING, Any, Callable, TypeVar, overload +import inspect +from functools import partial +from typing import TYPE_CHECKING, Any, Callable, TypeVar, cast, overload -from ._internal import _typing_extra, _validate_call +from ._internal import _generate_schema, _typing_extra, _validate_call +from .errors import PydanticUserError __all__ = ('validate_call',) @@ -15,6 +17,55 @@ AnyCallableT = TypeVar('AnyCallableT', bound=Callable[..., Any]) +_INVALID_TYPE_ERROR_CODE = 'validate-call-type' + + +def _check_function_type(function: object) -> None: + """Check if the input function is a supported type for `validate_call`.""" + if isinstance(function, _generate_schema.VALIDATE_CALL_SUPPORTED_TYPES): + try: + inspect.signature(cast(_generate_schema.ValidateCallSupportedTypes, function)) + except ValueError: + raise PydanticUserError( + f"Input function `{function}` doesn't have a valid signature", code=_INVALID_TYPE_ERROR_CODE + ) + + if isinstance(function, partial): + try: + assert not isinstance(partial.func, partial), 'Partial of partial' + _check_function_type(function.func) + except PydanticUserError as e: + raise PydanticUserError( + f'Partial of `{function.func}` is invalid because the type of `{function.func}` is not supported by `validate_call`', + code=_INVALID_TYPE_ERROR_CODE, + ) from e + + return + + if isinstance(function, (classmethod, staticmethod, property)): + name = type(function).__name__ + raise PydanticUserError( + f'The `@{name}` decorator should be applied after `@validate_call` (put `@{name}` on top)', + code=_INVALID_TYPE_ERROR_CODE, + ) + + if inspect.isclass(function): + raise PydanticUserError( + f'Unable to validate {function}: `validate_call` should be applied to functions, not classes (put `@validate_call` on top of `__init__` or `__new__` instead)', + code=_INVALID_TYPE_ERROR_CODE, + ) + if callable(function): + raise PydanticUserError( + f'Unable to validate {function}: `validate_call` should be applied to functions, not instances or other callables. Use `validate_call` explicitly on `__call__` instead.', + code=_INVALID_TYPE_ERROR_CODE, + ) + + raise PydanticUserError( + f'Unable to validate {function}: `validate_call` should be applied to one of the following: function, method, partial, or lambda', + code=_INVALID_TYPE_ERROR_CODE, + ) + + @overload def validate_call( *, config: ConfigDict | None = None, validate_return: bool = False @@ -32,7 +83,7 @@ def validate_call( config: ConfigDict | None = None, validate_return: bool = False, ) -> AnyCallableT | Callable[[AnyCallableT], AnyCallableT]: - """Usage docs: https://docs.pydantic.dev/2.9/concepts/validation_decorator/ + """Usage docs: https://docs.pydantic.dev/2.10/concepts/validation_decorator/ Returns a decorated wrapper around the function that validates the arguments and, optionally, the return value. @@ -46,24 +97,16 @@ def validate_call( Returns: The decorated function. """ - local_ns = _typing_extra.parent_frame_namespace() + parent_namespace = _typing_extra.parent_frame_namespace() def validate(function: AnyCallableT) -> AnyCallableT: - if isinstance(function, (classmethod, staticmethod)): - name = type(function).__name__ - raise TypeError(f'The `@{name}` decorator should be applied after `@validate_call` (put `@{name}` on top)') - - validate_call_wrapper = _validate_call.ValidateCallWrapper(function, config, validate_return, local_ns) - - @functools.wraps(function) - def wrapper_function(*args, **kwargs): - return validate_call_wrapper(*args, **kwargs) - - wrapper_function.raw_function = function # type: ignore - - return wrapper_function # type: ignore + _check_function_type(function) + validate_call_wrapper = _validate_call.ValidateCallWrapper( + cast(_generate_schema.ValidateCallSupportedTypes, function), config, validate_return, parent_namespace + ) + return _validate_call.update_wrapper_attributes(function, validate_call_wrapper.__call__) # type: ignore - if func: + if func is not None: return validate(func) else: return validate diff --git a/pydantic/version.py b/pydantic/version.py index 42e19011e41..73523dfd3fc 100644 --- a/pydantic/version.py +++ b/pydantic/version.py @@ -4,7 +4,7 @@ __all__ = 'VERSION', 'version_info' -VERSION = '2.9.0' +VERSION = '2.10.0' """The version of Pydantic.""" @@ -63,16 +63,16 @@ def version_info() -> str: return '\n'.join('{:>30} {}'.format(k + ':', str(v).replace('\n', ' ')) for k, v in info.items()) -def parse_mypy_version(version: str) -> tuple[int, ...]: - """Parse mypy string version to tuple of ints. +def parse_mypy_version(version: str) -> tuple[int, int, int]: + """Parse `mypy` string version to a 3-tuple of ints. - It parses normal version like `0.930` and extra info followed by a `+` sign - like `0.940+dev.04cac4b5d911c4f9529e6ce86a27b44f28846f5d.dirty`. + It parses normal version like `1.11.0` and extra info followed by a `+` sign + like `1.11.0+dev.d6d9d8cd4f27c52edac1f537e236ec48a01e54cb.dirty`. Args: version: The mypy version string. Returns: - A tuple of ints. e.g. (0, 930). + A triple of ints, e.g. `(1, 11, 0)`. """ - return tuple(map(int, version.partition('+')[0].split('.'))) + return tuple(map(int, version.partition('+')[0].split('.'))) # pyright: ignore[reportReturnType] diff --git a/pydantic/warnings.py b/pydantic/warnings.py index 9a0736a525b..4cbd52da519 100644 --- a/pydantic/warnings.py +++ b/pydantic/warnings.py @@ -67,6 +67,13 @@ def __init__(self, message: str, *args: object) -> None: super().__init__(message, *args, since=(2, 9), expected_removal=(3, 0)) +class PydanticDeprecatedSince210(PydanticDeprecationWarning): + """A specific `PydanticDeprecationWarning` subclass defining functionality deprecated since Pydantic 2.10.""" + + def __init__(self, message: str, *args: object) -> None: + super().__init__(message, *args, since=(2, 10), expected_removal=(3, 0)) + + class GenericBeforeBaseModelWarning(Warning): pass diff --git a/pyproject.toml b/pyproject.toml index 377a52bcc14..ee5c90a514e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,6 +17,7 @@ authors = [ {name = 'Sydney Runkle', email = 'sydneymarierunkle@gmail.com'}, {name = 'David Hewitt', email = 'mail@davidhewitt.io'}, {name = 'Alex Hall', email='alex.mojaki@gmail.com'}, + {name = 'Victorien Plot', email='contact@vctrn.dev'}, ] license = 'MIT' classifiers = [ @@ -34,12 +35,8 @@ classifiers = [ 'Programming Language :: Python :: 3.13', 'Intended Audience :: Developers', 'Intended Audience :: Information Technology', - 'Intended Audience :: System Administrators', 'License :: OSI Approved :: MIT License', - 'Operating System :: Unix', - 'Operating System :: POSIX :: Linux', - 'Environment :: Console', - 'Environment :: MacOS X', + 'Operating System :: OS Independent', 'Framework :: Hypothesis', 'Framework :: Pydantic', 'Topic :: Software Development :: Libraries :: Python Modules', @@ -47,17 +44,18 @@ classifiers = [ ] requires-python = '>=3.8' dependencies = [ - 'typing-extensions>=4.6.1; python_version < "3.13"', - 'typing-extensions>=4.12.2; python_version >= "3.13"', - 'annotated-types>=0.4.0', - "pydantic-core==2.23.2", - # See: https://docs.python.org/3/library/zoneinfo.html#data-sources - 'tzdata; python_version >= "3.9"', + 'typing-extensions>=4.12.2', + 'annotated-types>=0.6.0', + 'pydantic-core==2.27.0', ] dynamic = ['version', 'readme'] [project.optional-dependencies] email = ['email-validator>=2.0.0'] +timezone = [ + # See: https://docs.python.org/3/library/zoneinfo.html#data-sources + 'tzdata; python_version >= "3.9" and platform_system == "Windows"', +] [project.urls] Homepage = 'https://github.com/pydantic/pydantic' @@ -66,6 +64,65 @@ Funding = 'https://github.com/sponsors/samuelcolvin' Source = 'https://github.com/pydantic/pydantic' Changelog = 'https://docs.pydantic.dev/latest/changelog/' +[dependency-groups] +dev = [ + 'coverage[toml]', + 'pytz', + 'dirty-equals', + 'eval-type-backport', + 'pytest', + 'pytest-mock', + 'pytest-pretty', + 'pytest-examples', + 'faker', + 'pytest-benchmark', + 'pytest-codspeed', + 'pytest-memray; platform_python_implementation == "CPython" and platform_system != "Windows"', + 'packaging', + 'jsonschema', +] +docs = [ + 'autoflake', + 'mkdocs', + 'mkdocs-exclude', + 'mkdocs-material[imaging]', + 'mkdocs-redirects', + 'mkdocstrings-python', + 'tomli', + 'pyupgrade', + 'mike', + 'pydantic-settings', + 'pydantic-extra-types @ git+https://github.com/pydantic/pydantic-extra-types.git@main', + 'requests', +] +linting = [ + 'eval-type-backport', + 'ruff', + 'pyright', +] +testing-extra = [ + 'cloudpickle', + # used when generate devtools docs example + 'ansi2html', + 'devtools', + # used in docs tests + 'sqlalchemy', + 'greenlet; python_version < "3.13"', +] +typechecking = [ + 'mypy', + 'pyright', + 'pydantic-settings', +] + +all = [ + { include-group = 'dev' }, + { include-group = 'docs' }, + { include-group = 'linting' }, + { include-group = 'testing-extra' }, + { include-group = 'typechecking' }, +] + [tool.hatch.version] path = 'pydantic/version.py' @@ -88,82 +145,24 @@ include = [ content-type = 'text/markdown' # construct the PyPI readme from README.md and HISTORY.md fragments = [ - {path = "README.md"}, + {path = 'README.md'}, {text = "\n## Changelog\n\n"}, - {path = "HISTORY.md", pattern = "(.+?)"}, + {path = 'HISTORY.md', pattern = '(.+?)'}, {text = "\n... see [here](https://docs.pydantic.dev/changelog/#v0322-2019-08-17) for earlier changes.\n"}, ] -# convert GitHuB issue/PR numbers and handles to links +# convert GitHub issue/PR numbers and handles to links substitutions = [ {pattern = '(\s+)#(\d+)', replacement = '\1[#\2](https://github.com/pydantic/pydantic/issues/\2)'}, {pattern = '(\s+)@([\w\-]+)', replacement = '\1[@\2](https://github.com/\2)'}, {pattern = '@@', replacement = '@'}, ] -[tool.pdm.dev-dependencies] -docs = [ - "autoflake", - "mkdocs", - "mkdocs-exclude", - "mkdocs-material", - "mkdocs-redirects", - "mkdocstrings-python", - "tomli", - "pyupgrade", - "mike", - "pytest-examples>=0.0.13", - "pydantic-settings>=2.0b1", - "pydantic-extra-types @ git+https://github.com/pydantic/pydantic-extra-types.git@main", - "requests", -] -linting = [ - "eval-type-backport>=0.1.3", - "ruff==0.5.6", - "mypy~=1.1.1", -] -testing = [ - "cloudpickle", - "coverage[toml]", - "pytz", - "dirty-equals", - "eval-type-backport", - "pytest>=8.2.2", - "pytest-mock", - "pytest-pretty", - "pytest-examples", - "faker>=18.13.0", - "pytest-benchmark>=4.0.0", - # TODO: remove bound once pytest-codspeed supports 3.13, blocked by `python-cffi` - "pytest-codspeed~=2.2.0; python_version < '3.13'", - "packaging>=23.2", - "jsonschema>=4.23.0", -] -testing-extra = [ - # used when generate devtools docs example - "ansi2html", - "devtools", - # used in docs tests - # TODO: remove bound once sqlalchemy supports 3.13 - "sqlalchemy>=2.0,<3.0; python_version < '3.13'", - "greenlet>=3.0.0rc3; python_version < '3.13'", -] -mypy = [ - "mypy", - "pydantic-settings>=2.0.0", -] -# TODO: remove once memray supports 3.13 -memray = ["pytest-memray; python_version < '3.13' and platform_system != 'Windows'"] - [tool.pytest.ini_options] testpaths = 'tests' xfail_strict = true filterwarnings = [ 'error', 'ignore:path is deprecated.*:DeprecationWarning:', - # Work around https://github.com/pytest-dev/pytest/issues/10977 for Python 3.12 - 'ignore:(ast\.Str|ast\.NameConstant|ast\.Num|Attribute s) is deprecated and will be removed.*:DeprecationWarning:', - # pytest-examples needs to remove `co_lnotab` in favor of `co_lines`. - 'ignore:co_lnotab is deprecated, use co_lines instead.:DeprecationWarning:', ] addopts = [ '--benchmark-columns', 'min,mean,stddev,outliers,rounds,iterations', @@ -175,6 +174,9 @@ markers = [ 'skip_json_schema_validation: Disable JSON Schema validation.', ] +[tool.uv] +default-groups = ['dev'] + # configuring https://github.com/pydantic/hooky [tool.hooky] reviewers = ['sydney-runkle'] @@ -217,8 +219,7 @@ pydocstyle = { convention = 'google' } [tool.ruff.lint.extend-per-file-ignores] "docs/**/*.py" = ['T'] "tests/**/*.py" = ['T', 'E721', 'F811'] -"tests/benchmarks/test_fastapi_startup_generics.py" = ['UP006', 'UP007'] -"tests/benchmarks/test_fastapi_startup_simple.py" = ['UP006', 'UP007'] +"tests/benchmarks/**/*.py" = ['UP006', 'UP007'] [tool.ruff.format] quote-style = 'single' @@ -260,7 +261,7 @@ strict = ['tests/test_pipeline.py'] enableExperimentalFeatures = true [tool.codespell] -skip = '.git,env*,pydantic/v1/*' +skip = '.git,env*,pydantic/v1/*,uv.lock' # `ser` - abbreviation for "ser"ialisation # `crate` - a rust crate ignore-words-list = 'gir,ser,crate' diff --git a/release/README.md b/release/README.md index 5e41ec15748..04e17b88c81 100644 --- a/release/README.md +++ b/release/README.md @@ -7,9 +7,9 @@ Prerequisites: * Run `gh auth login` to authenticate with GitHub, which is needed for the API calls made in the release process. To create a new release: -1. Edit `pydantic/version.py` to set the new version number. +1. Edit `pydantic/version.py` to set the new version number and run `uv lock -P pydantic` 2. **(If the new version is a new minor or major release)** run `pre-commit run -a usage_docs` to update the usage links in docstrings. -3. Run `pdm run release/make_history.py` to update `HISTORY.md` and `CITATION.cff`. +3. Run `uv run release/make_history.py` to update `HISTORY.md` and `CITATION.cff`. 4. **Important:** curate the changes in `HISTORY.md`: - make sure the markdown is valid; in particular, check text that should be in `code-blocks` is. - mark any breaking changes with `**Breaking Change:**` diff --git a/tests/benchmarks/basemodel_eq_performance.py b/tests/benchmarks/basemodel_eq_performance.py index 2f7e05f585f..2e47b71f7f1 100644 --- a/tests/benchmarks/basemodel_eq_performance.py +++ b/tests/benchmarks/basemodel_eq_performance.py @@ -550,7 +550,7 @@ def run_benchmark( if __name__ == '__main__': - # run with `pdm run tests/benchmarks/test_basemodel_eq_performance.py` + # run with `uv run tests/benchmarks/test_basemodel_eq_performance.py` import argparse import pathlib diff --git a/tests/benchmarks/shared.py b/tests/benchmarks/shared.py index 7a1e5d5c980..d84192bafe9 100644 --- a/tests/benchmarks/shared.py +++ b/tests/benchmarks/shared.py @@ -1,6 +1,72 @@ -from typing import Dict, List, Optional, Union +from collections import deque +from datetime import date, datetime, time, timedelta +from decimal import Decimal +from enum import Enum, IntEnum +from ipaddress import ( + IPv4Address, + IPv4Interface, + IPv4Network, + IPv6Address, + IPv6Interface, + IPv6Network, +) +from pathlib import Path +from re import Pattern +from typing import ( + Any, + Callable, + Deque, + Dict, + FrozenSet, + Iterable, + List, + NamedTuple, + Optional, + Sequence, + Set, + Tuple, + Type, + Union, +) +from uuid import UUID, uuid4, uuid5 -from pydantic import BaseModel +from typing_extensions import Literal, TypedDict + +from pydantic import ( + UUID1, + UUID3, + UUID4, + UUID5, + Base64Bytes, + Base64Str, + Base64UrlBytes, + Base64UrlStr, + BaseModel, + ByteSize, + DirectoryPath, + FilePath, + FiniteFloat, + FutureDate, + ImportString, + Json, + JsonValue, + NegativeFloat, + NegativeInt, + NewPath, + NonNegativeFloat, + NonNegativeInt, + NonPositiveFloat, + NonPositiveInt, + OnErrorOmit, + PastDate, + PastDatetime, + PositiveFloat, + PositiveInt, + Secret, + SecretBytes, + SecretStr, + StrictBool, +) class SimpleModel(BaseModel): @@ -24,3 +90,152 @@ class ComplexModel(BaseModel): field1: Union[str, int, float] field2: List[Dict[str, Union[int, float]]] field3: Optional[List[Union[str, int]]] + + +class Color(Enum): + RED = 'red' + GREEN = 'green' + BLUE = 'blue' + + +class ToolEnum(IntEnum): + spanner = 1 + wrench = 2 + screwdriver = 3 + + +class Point(NamedTuple): + x: int + y: int + + +class User(TypedDict): + name: str + id: int + + +class Foo: + pass + + +StdLibTypes = [ + deque, # collections.deque + Deque[str], # typing.Deque + Deque[int], # typing.Deque + Deque[float], # typing.Deque + Deque[bytes], # typing.Deque + str, # str + int, # int + float, # float + complex, # complex + bool, # bool + bytes, # bytes + date, # datetime.date + datetime, # datetime.datetime + time, # datetime.time + timedelta, # datetime.timedelta + Decimal, # decimal.Decimal + Color, # enum + ToolEnum, # int enum + IPv4Address, # ipaddress.IPv4Address + IPv6Address, # ipaddress.IPv6Address + IPv4Interface, # ipaddress.IPv4Interface + IPv6Interface, # ipaddress.IPv6Interface + IPv4Network, # ipaddress.IPv4Network + IPv6Network, # ipaddress.IPv6Network + Path, # pathlib.Path + Pattern, # typing.Pattern + UUID, # uuid.UUID + uuid4, # uuid.uuid4 + uuid5, # uuid.uuid5 + Point, # named tuple + list, # built-in list + List[int], # typing.List + List[str], # typing.List + List[bytes], # typing.List + List[float], # typing.List + dict, # built-in dict + Dict[str, float], # typing.Dict + Dict[str, bytes], # typing.Dict + Dict[str, int], # typing.Dict + Dict[str, str], # typing.Dict + User, # TypedDict + tuple, # tuple + Tuple[int, str, float], # typing.Tuple + set, # built-in set + Set[int], # typing.Set + Set[str], # typing.Set + frozenset, # built-in frozenset + FrozenSet[int], # typing.FrozenSet + FrozenSet[str], # typing.FrozenSet + Optional[int], # typing.Optional + Optional[str], # typing.Optional + Optional[float], # typing.Optional + Optional[bytes], # typing.Optional + Optional[bool], # typing.Optional + Sequence[int], # typing.Sequence + Sequence[str], # typing.Sequence + Sequence[bytes], # typing.Sequence + Sequence[float], # typing.Sequence + Iterable[int], # typing.Iterable + Iterable[str], # typing.Iterable + Iterable[bytes], # typing.Iterable + Iterable[float], # typing.Iterable + Callable[[int], int], # typing.Callable + Callable[[str], str], # typing.Callable + Literal['apple', 'pumpkin'], # + Type[Foo], # typing.Type + Any, # typing.Any +] + +PydanticTypes = [ + StrictBool, + PositiveInt, + PositiveFloat, + NegativeInt, + NegativeFloat, + NonNegativeInt, + NonPositiveInt, + NonNegativeFloat, + NonPositiveFloat, + FiniteFloat, + UUID1, + UUID3, + UUID4, + UUID5, + FilePath, + DirectoryPath, + NewPath, + Base64Bytes, + Base64Str, + Base64UrlBytes, + Base64UrlStr, + JsonValue, + OnErrorOmit, + ImportString, + Json[Any], + Json[List[int]], + Json[List[str]], + Json[List[bytes]], + Json[List[float]], + Json[List[Any]], + Secret[bool], + Secret[int], + Secret[float], + Secret[str], + Secret[bytes], + SecretStr, + SecretBytes, + ByteSize, + PastDate, + FutureDate, + PastDatetime, +] + + +class DeferredModel(BaseModel): + model_config = {'defer_build': True} + + +def rebuild_model(model: Type[BaseModel]) -> None: + model.model_rebuild(force=True, _types_namespace={}) diff --git a/tests/benchmarks/test_discriminated_unions.py b/tests/benchmarks/test_discriminated_unions.py index 741fd65c2fd..041d684ef44 100644 --- a/tests/benchmarks/test_discriminated_unions.py +++ b/tests/benchmarks/test_discriminated_unions.py @@ -22,13 +22,15 @@ class LeafState(BaseModel): state_type: Literal['leaf'] -AnyState = Annotated[Union[NestedState, LoopState, LeafState], Field(..., discriminator='state_type')] +AnyState = Annotated[Union[NestedState, LoopState, LeafState], Field(discriminator='state_type')] @pytest.mark.benchmark -def test_schema_build() -> None: - adapter = TypeAdapter(AnyState) - assert adapter.core_schema['schema']['type'] == 'tagged-union' +def test_schema_build(benchmark) -> None: + @benchmark + def run(): + adapter = TypeAdapter(AnyState) + assert adapter.core_schema['schema']['type'] == 'tagged-union' any_state_adapter = TypeAdapter(AnyState) @@ -42,8 +44,10 @@ def build_nested_state(n): @pytest.mark.benchmark -def test_efficiency_with_highly_nested_examples() -> None: +def test_efficiency_with_highly_nested_examples(benchmark) -> None: # can go much higher, but we keep it reasonably low here for a proof of concept - for i in range(1, 12): - very_nested_input = build_nested_state(i) - any_state_adapter.validate_python(very_nested_input) + @benchmark + def run(): + for i in range(1, 12): + very_nested_input = build_nested_state(i) + any_state_adapter.validate_python(very_nested_input) diff --git a/tests/benchmarks/test_fastapi_startup_generics.py b/tests/benchmarks/test_fastapi_startup_generics.py index 8de4a20f864..17417ddfa23 100644 --- a/tests/benchmarks/test_fastapi_startup_generics.py +++ b/tests/benchmarks/test_fastapi_startup_generics.py @@ -124,7 +124,7 @@ def bench(): if __name__ == '__main__': - # run with `pdm run tests/benchmarks/test_fastapi_startup.py` + # run with `uv run tests/benchmarks/test_fastapi_startup.py` import cProfile import sys import time diff --git a/tests/benchmarks/test_fastapi_startup_simple.py b/tests/benchmarks/test_fastapi_startup_simple.py index a6bbe0095ef..bbe1b571b74 100644 --- a/tests/benchmarks/test_fastapi_startup_simple.py +++ b/tests/benchmarks/test_fastapi_startup_simple.py @@ -7,6 +7,7 @@ from typing import Any, Callable, Dict, List, Tuple from uuid import UUID +import pytest from annotated_types import Gt from typing_extensions import Annotated @@ -14,7 +15,13 @@ from pydantic.functional_validators import AfterValidator from pydantic.types import StringConstraints +try: + import email_validator +except ImportError: + email_validator = None + +@pytest.mark.skipif(not email_validator, reason='email_validator not installed') def test_fastapi_startup_perf(benchmark: Callable[[Callable[[], Any]], None]): def run() -> None: class User(BaseModel): @@ -91,7 +98,7 @@ class Category(BaseModel): if __name__ == '__main__': - # run with `pdm run tests/benchmarks/test_fastapi_startup_simple.py` + # run with `uv run tests/benchmarks/test_fastapi_startup_simple.py` import cProfile import sys import time diff --git a/tests/benchmarks/test_imports.py b/tests/benchmarks/test_imports.py index cd971f2bd65..8b8a8074f62 100644 --- a/tests/benchmarks/test_imports.py +++ b/tests/benchmarks/test_imports.py @@ -2,14 +2,18 @@ @pytest.mark.benchmark -def test_import_basemodel() -> None: - from pydantic import BaseModel +def test_import_basemodel(benchmark) -> None: + @benchmark + def run(): + from pydantic import BaseModel - assert BaseModel + assert BaseModel @pytest.mark.benchmark -def test_import_field() -> None: - from pydantic import Field +def test_import_field(benchmark) -> None: + @benchmark + def run(): + from pydantic import Field - assert Field + assert Field diff --git a/tests/benchmarks/test_isinstance.py b/tests/benchmarks/test_isinstance.py index 4d4e1be50a9..535d8562f52 100644 --- a/tests/benchmarks/test_isinstance.py +++ b/tests/benchmarks/test_isinstance.py @@ -1,5 +1,3 @@ -import pytest - from pydantic import BaseModel @@ -10,7 +8,8 @@ class ModelV2(BaseModel): mv2 = ModelV2(my_str='hello') -@pytest.mark.benchmark -def test_isinstance_basemodel() -> None: - for _ in range(10000): - assert isinstance(mv2, BaseModel) +def test_isinstance_basemodel(benchmark) -> None: + @benchmark + def run(): + for _ in range(10000): + assert isinstance(mv2, BaseModel) diff --git a/tests/benchmarks/test_model_schema_generation.py b/tests/benchmarks/test_model_schema_generation.py index 28c05fe7b6c..3782b607f2b 100644 --- a/tests/benchmarks/test_model_schema_generation.py +++ b/tests/benchmarks/test_model_schema_generation.py @@ -1,4 +1,15 @@ -from typing import Any, Callable, Dict, Generic, List, Literal, Optional, TypeVar, Union, get_origin +from typing import ( + Any, + Dict, + Generic, + List, + Literal, + Optional, + Type, + TypeVar, + Union, + get_origin, +) import pytest from typing_extensions import Annotated, Self @@ -11,96 +22,101 @@ Field, PlainSerializer, PlainValidator, - SerializerFunctionWrapHandler, - ValidatorFunctionWrapHandler, + Tag, WrapSerializer, WrapValidator, create_model, - field_serializer, - field_validator, model_serializer, model_validator, ) -from pydantic.dataclasses import dataclass -from pydantic.functional_validators import ModelWrapValidatorHandler +from pydantic.dataclasses import dataclass, rebuild_dataclass + +from .shared import DeferredModel, PydanticTypes, StdLibTypes, rebuild_model @pytest.mark.benchmark(group='model_schema_generation') def test_simple_model_schema_generation(benchmark) -> None: - def generate_schema(): - class SimpleModel(BaseModel): - field1: str - field2: int - field3: float + class SimpleModel(DeferredModel): + field1: str + field2: int + field3: float - benchmark(generate_schema) + benchmark(rebuild_model, SimpleModel) @pytest.mark.benchmark(group='model_schema_generation') -def test_nested_model_schema_generation(benchmark) -> None: - def generate_schema(): - class NestedModel(BaseModel): - field1: str - field2: List[int] - field3: Dict[str, float] +def test_simple_model_schema_lots_of_fields_generation(benchmark) -> None: + IntStr = Union[int, str] - class OuterModel(BaseModel): - nested: NestedModel - optional_nested: Optional[NestedModel] + Model = create_model( + 'Model', + __config__={'defer_build': True}, + **{f'f{i}': (IntStr, ...) for i in range(100)}, + ) - benchmark(generate_schema) + benchmark(rebuild_model, Model) @pytest.mark.benchmark(group='model_schema_generation') -def test_complex_model_schema_generation(benchmark) -> None: - def generate_schema(): - class ComplexModel(BaseModel): - field1: Union[str, int, float] - field2: List[Dict[str, Union[int, float]]] - field3: Optional[List[Union[str, int]]] +def test_nested_model_schema_generation(benchmark) -> None: + class NestedModel(BaseModel): + field1: str + field2: List[int] + field3: Dict[str, float] - benchmark(generate_schema) + class OuterModel(DeferredModel): + nested: NestedModel + optional_nested: Optional[NestedModel] + benchmark(rebuild_model, OuterModel) -@pytest.mark.benchmark(group='model_schema_generation') -def test_recursive_model_schema_generation(benchmark) -> None: - def generate_schema(): - class RecursiveModel(BaseModel): - name: str - children: Optional[List['RecursiveModel']] = None - benchmark(generate_schema) +@pytest.mark.benchmark(group='model_schema_generation') +def test_complex_model_schema_generation(benchmark) -> None: + class ComplexModel(DeferredModel): + field1: Union[str, int, float] + field2: List[Dict[str, Union[int, float]]] + field3: Optional[List[Union[str, int]]] + benchmark(rebuild_model, ComplexModel) -@dataclass(frozen=True, kw_only=True) -class Cat: - type: Literal['cat'] = 'cat' +@pytest.mark.benchmark(group='model_schema_generation') +def test_recursive_model_schema_generation(benchmark) -> None: + class RecursiveModel(DeferredModel): + name: str + children: Optional[List['RecursiveModel']] = None -@dataclass(frozen=True, kw_only=True) -class Dog: - type: Literal['dog'] = 'dog' + benchmark(rebuild_model, RecursiveModel) -@dataclass(frozen=True, kw_only=True) -class NestedDataClass: - animal: Annotated[Union[Cat, Dog], Discriminator('type')] +@pytest.mark.benchmark(group='model_schema_generation') +def test_construct_dataclass_schema(benchmark): + @dataclass(frozen=True, kw_only=True) + class Cat: + type: Literal['cat'] = 'cat' + @dataclass(frozen=True, kw_only=True) + class Dog: + type: Literal['dog'] = 'dog' -class NestedModel(BaseModel): - animal: Annotated[Union[Cat, Dog], Discriminator('type')] + @dataclass(frozen=True, kw_only=True) + class NestedDataClass: + animal: Annotated[Union[Cat, Dog], Discriminator('type')] + class NestedModel(BaseModel): + animal: Annotated[Union[Cat, Dog], Discriminator('type')] -@pytest.mark.benchmark(group='model_schema_generation') -def test_construct_schema(): - @dataclass(frozen=True, kw_only=True) + @dataclass(frozen=True, kw_only=True, config={'defer_build': True}) class Root: data_class: NestedDataClass model: NestedModel + benchmark(lambda: rebuild_dataclass(Root, force=True, _types_namespace={})) + @pytest.mark.benchmark(group='model_schema_generation') -def test_lots_of_models_with_lots_of_fields(): +def test_lots_of_models_with_lots_of_fields(benchmark): T = TypeVar('T') class GenericModel(BaseModel, Generic[T]): @@ -131,6 +147,8 @@ class Product(BaseModel): metadata: Dict[str, str] # Repeat the pattern for other models up to Model_99 + models: list[type[BaseModel]] = [] + for i in range(100): model_fields = {} @@ -170,163 +188,114 @@ class Product(BaseModel): model_fields[f'field_{j}'] = (field_type, ...) model_name = f'Model_{i}' - model = create_model(model_name, **model_fields) - globals()[model_name] = model - - -@pytest.mark.parametrize('validator_mode', ['before', 'after', 'plain']) -@pytest.mark.benchmark(group='model_schema_generation') -def test_custom_field_validator_via_decorator(benchmark, validator_mode) -> None: - def schema_gen() -> None: - class ModelWithFieldValidator(BaseModel): - field: Any - - @field_validator('field', mode=validator_mode) - @classmethod - def validate_field(cls, v: Any): - return v + models.append(create_model(model_name, __config__={'defer_build': True}, **model_fields)) - benchmark(schema_gen) + def rebuild_models(models: List[Type[BaseModel]]) -> None: + for model in models: + rebuild_model(model) - -@pytest.mark.benchmark(group='model_schema_generation') -def test_custom_wrap_field_validator_via_decorator(benchmark) -> None: - def schema_gen() -> None: - class ModelWithWrapFieldValidator(BaseModel): - field: Any - - @field_validator('field', mode='wrap') - @classmethod - def validate_field(cls, v: Any, handler: ValidatorFunctionWrapHandler) -> Any: - return handler(v) - - benchmark(schema_gen) + benchmark(rebuild_models, models) -@pytest.mark.parametrize('validator_constructor', [BeforeValidator, AfterValidator, PlainValidator]) @pytest.mark.benchmark(group='model_schema_generation') -def test_custom_field_validator_via_annotation(benchmark, validator_constructor) -> None: - def validate_field(v: Any) -> Any: - return v +def test_field_validators_serializers(benchmark) -> None: + class ModelWithFieldValidatorsSerializers(DeferredModel): + field1: Annotated[Any, BeforeValidator(lambda v: v)] + field2: Annotated[Any, AfterValidator(lambda v: v)] + field3: Annotated[Any, PlainValidator(lambda v: v)] + field4: Annotated[Any, WrapValidator(lambda v, h: h(v))] + field5: Annotated[Any, PlainSerializer(lambda x: x, return_type=Any)] + field6: Annotated[Any, WrapSerializer(lambda x, nxt: nxt(x), when_used='json')] - def schema_gen(validation_func) -> None: - class ModelWithFieldValidator(BaseModel): - field: Annotated[Any, validator_constructor(validation_func)] - - benchmark(schema_gen, validate_field) + benchmark(rebuild_model, ModelWithFieldValidatorsSerializers) @pytest.mark.benchmark(group='model_schema_generation') -def test_custom_wrap_field_validator_via_annotation(benchmark) -> None: - def validate_field(v: Any, handler: ValidatorFunctionWrapHandler) -> Any: - return handler(v) - - def schema_gen(validator_func: Callable) -> None: - class ModelWithWrapFieldValidator(BaseModel): - field: Annotated[Any, WrapValidator(validator_func)] +def test_model_validators_serializers(benchmark): + class ModelWithValidator(DeferredModel): + field: Any - benchmark(schema_gen, validate_field) + @model_validator(mode='before') + @classmethod + def validate_model_before(cls, data: Any) -> Any: + return data + @model_validator(mode='after') + def validate_model_after(self) -> Self: + return self -@pytest.mark.benchmark(group='model_schema_generation') -def test_custom_model_validator_before(benchmark): - def schema_gen() -> None: - class ModelWithBeforeValidator(BaseModel): - field: Any + @model_serializer + def serialize_model(self) -> Any: + return self.field - @model_validator(mode='before') - @classmethod - def validate_model_before(cls, data: Any) -> Any: - return data - - benchmark(schema_gen) + benchmark(rebuild_model, ModelWithValidator) @pytest.mark.benchmark(group='model_schema_generation') -def test_custom_model_validator_after(benchmark) -> None: - def schema_gen() -> None: - class ModelWithAfterValidator(BaseModel): - field: Any - - @model_validator(mode='after') - def validate_model_after(self: 'ModelWithAfterValidator') -> 'ModelWithAfterValidator': - return self - - benchmark(schema_gen) +def test_tagged_union_with_str_discriminator_schema_generation(benchmark): + class Cat(BaseModel): + pet_type: Literal['cat'] + meows: int + class Dog(BaseModel): + pet_type: Literal['dog'] + barks: float -@pytest.mark.benchmark(group='model_schema_generation') -def test_custom_model_validator_wrap(benchmark) -> None: - def schema_gen() -> None: - class ModelWithWrapValidator(BaseModel): - field: Any + class Lizard(BaseModel): + pet_type: Literal['reptile', 'lizard'] + scales: bool - @model_validator(mode='wrap') - @classmethod - def validate_model_wrap(cls, values: Any, handler: ModelWrapValidatorHandler[Self]) -> Any: - return handler(values) + class Model(DeferredModel): + pet: Union[Cat, Dog, Lizard] = Field(discriminator='pet_type') + n: int - benchmark(schema_gen) + benchmark(rebuild_model, Model) @pytest.mark.benchmark(group='model_schema_generation') -def test_custom_field_serializer_plain(benchmark) -> None: - def schema_gen() -> None: - class ModelWithFieldSerializer(BaseModel): - field1: int - - @field_serializer('field1', mode='plain') - def serialize_field(cls, v: int) -> str: - return str(v) +def test_tagged_union_with_callable_discriminator_schema_generation(benchmark): + class Pie(BaseModel): + time_to_cook: int + num_ingredients: int - benchmark(schema_gen) + class ApplePie(Pie): + fruit: Literal['apple'] = 'apple' + class PumpkinPie(Pie): + filling: Literal['pumpkin'] = 'pumpkin' -@pytest.mark.benchmark(group='model_schema_generation') -def test_custom_field_serializer_wrap(benchmark) -> None: - def schema_gen() -> None: - class ModelWithFieldSerializer(BaseModel): - field1: int - - @field_serializer('field1', mode='wrap') - def serialize_field(cls, v: int, nxt: SerializerFunctionWrapHandler) -> str: - return nxt(v) - - benchmark(schema_gen) - - -@pytest.mark.benchmark(group='model_schema_generation') -def test_custom_model_serializer_decorator(benchmark) -> None: - def schema_gen() -> None: - class ModelWithModelSerializer(BaseModel): - field1: Any - - @model_serializer - def serialize_model(self) -> Any: - return self.field1 - - benchmark(schema_gen) + def get_discriminator_value(v: Any) -> str: + if isinstance(v, dict): + return v.get('fruit', v.get('filling')) + return getattr(v, 'fruit', getattr(v, 'filling', None)) + class ThanksgivingDinner(DeferredModel): + dessert: Annotated[ + Union[ + Annotated[ApplePie, Tag('apple')], + Annotated[PumpkinPie, Tag('pumpkin')], + ], + Discriminator(get_discriminator_value), + ] -@pytest.mark.benchmark(group='model_schema_generation') -def test_custom_serializer_plain_annotated(benchmark) -> None: - def schema_gen() -> None: - def serialize_idempotent(x: Any) -> Any: - return x + benchmark(rebuild_model, ThanksgivingDinner) - class ModelWithAnnotatedSerializer(BaseModel): - field: Annotated[List, PlainSerializer(serialize_idempotent, return_type=Any)] - benchmark(schema_gen) +@pytest.mark.parametrize('field_type', StdLibTypes) +@pytest.mark.benchmark(group='stdlib_schema_generation') +@pytest.mark.skip('Clutters codspeed CI, but should be enabled on branches where we modify schema building.') +def test_stdlib_type_schema_generation(benchmark, field_type): + class StdlibTypeModel(DeferredModel): + field: field_type + benchmark(rebuild_model, StdlibTypeModel) -@pytest.mark.benchmark(group='model_schema_generation') -def test_custom_serializer_wrap_annotated(benchmark) -> None: - def schema_gen() -> None: - def serialize_idempotent(x: Any, nxt: SerializerFunctionWrapHandler) -> Any: - return nxt(x) - class ModelWithAnnotatedSerializer(BaseModel): - field: Annotated[List, WrapSerializer(serialize_idempotent, when_used='json')] +@pytest.mark.parametrize('field_type', PydanticTypes) +@pytest.mark.benchmark(group='pydantic_custom_types_schema_generation') +@pytest.mark.skip('Clutters codspeed CI, but should be enabled on branches where we modify schema building.') +def test_pydantic_custom_types_schema_generation(benchmark, field_type): + class PydanticTypeModel(DeferredModel): + field: field_type - benchmark(schema_gen) + benchmark(rebuild_model, PydanticTypeModel) diff --git a/tests/benchmarks/test_model_schema_generation_recursive.py b/tests/benchmarks/test_model_schema_generation_recursive.py new file mode 100644 index 00000000000..731d7f1cd87 --- /dev/null +++ b/tests/benchmarks/test_model_schema_generation_recursive.py @@ -0,0 +1,86 @@ +from typing import Dict, Generic, Literal, Optional, TypeVar, Union + +import pytest + +from pydantic import Field + +from .shared import DeferredModel, rebuild_model + + +@pytest.mark.benchmark(group='model_schema_generation_recursive') +def test_simple_recursive_model_schema_generation(benchmark): + class Foo(DeferredModel): + a: int = 123 + sibling: 'Foo' = None + + benchmark(rebuild_model, Foo) + + +@pytest.mark.benchmark(group='model_schema_generation_recursive') +def test_generic_recursive_model_schema_generation(benchmark): + T = TypeVar('T') + + class GenericFoo(DeferredModel, Generic[T]): + value: T + sibling: Optional['GenericFoo[T]'] = None + + benchmark(rebuild_model, GenericFoo[int]) + + +@pytest.mark.benchmark(group='model_schema_generation_recursive') +def test_nested_recursive_model_schema_generation(benchmark): + class Node(DeferredModel): + value: int + left: Optional['Node'] = None + right: Optional['Node'] = None + + class Tree(DeferredModel): + root: Node + metadata: Dict[str, 'Tree'] = Field(default_factory=dict) + + benchmark(rebuild_model, Tree) + + +@pytest.mark.benchmark(group='model_schema_generation_recursive') +def test_nested_recursive_generic_model_schema_generation(benchmark): + T = TypeVar('T') + + class GenericNode(DeferredModel, Generic[T]): + value: T + left: Optional['GenericNode[T]'] = None + right: Optional['GenericNode[T]'] = None + + class GenericTree(DeferredModel, Generic[T]): + root: GenericNode[T] + metadata: Dict[str, 'GenericTree[T]'] = Field(default_factory=dict) + + benchmark(rebuild_model, GenericTree[int]) + + +@pytest.mark.benchmark(group='model_schema_generation_recursive') +def test_recursive_discriminated_union_with_base_model(benchmark) -> None: + class Foo(DeferredModel): + type: Literal['foo'] + x: 'Foobar' + + class Bar(DeferredModel): + type: Literal['bar'] + + class Foobar(DeferredModel): + value: Union[Foo, Bar] = Field(discriminator='type') + + benchmark(rebuild_model, Foobar) + + +@pytest.mark.benchmark(group='model_schema_generation_recursive') +def test_deeply_nested_recursive_model_schema_generation(benchmark): + class A(DeferredModel): + b: 'B' + + class B(DeferredModel): + c: 'C' + + class C(DeferredModel): + a: Optional['A'] + + benchmark(rebuild_model, C) diff --git a/tests/conftest.py b/tests/conftest.py index 24aef2cfa7b..cc83add5fdc 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -17,7 +17,7 @@ from _pytest.assertion.rewrite import AssertionRewritingHook from jsonschema import Draft202012Validator, SchemaError -from pydantic import GenerateSchema +from pydantic._internal._generate_schema import GenerateSchema from pydantic.json_schema import GenerateJsonSchema @@ -43,7 +43,11 @@ def _extract_source_code_from_function(function: FunctionType): def _create_module_file(code: str, tmp_path: Path, name: str) -> tuple[str, str]: - name = f'{name}_{secrets.token_hex(5)}' + # Max path length in Windows is 260. Leaving some buffer here + max_name_len = 240 - len(str(tmp_path)) + # Windows does not allow these characters in paths. Linux bans slashes only. + sanitized_name = re.sub('[' + re.escape('<>:"/\\|?*') + ']', '-', name)[:max_name_len] + name = f'{sanitized_name}_{secrets.token_hex(5)}' path = tmp_path / f'{name}.py' path.write_text(code) return name, str(path) diff --git a/tests/mypy/README.md b/tests/mypy/README.md new file mode 100644 index 00000000000..af6e22973ec --- /dev/null +++ b/tests/mypy/README.md @@ -0,0 +1,76 @@ +# Mypy plugin type checking suite + +> [!WARNING] +> The test suite is subject to changes. It is currently not user friendly as the output and configuration +> files are separated from the source modules, making it hard to navigate. In the future, we may switch +> to using the [`pytest-mypy-plugins`][https://github.com/TypedDjango/pytest-mypy-plugins] library, which +> provides more flexibility when it comes to merging different mypy configurations. + + +The `test_mypy_results` test defined in [`test_mypy.py`](./test_mypy.py) runs Mypy on the files +defined in [`modules/`](./modules/), using the configuration files from [`configs/`](./configs/). + +The Mypy output is merged with the source file and saved in the [`outputs/`](./outputs/) folder. + +For instance, with the following file: + +```python +from pydantic import BaseModel + + +class Model(BaseModel): + a: int + + +model = Model(a=1, b=2) +``` + +The output will look like: + +```python +from pydantic import BaseModel + + +class Model(BaseModel): + a: int + + +model = Model(a=1, b=2) +# MYPY: error: Unexpected keyword argument "b" for "Model" [call-arg] +``` + +## Adding a new test + +1. Define a new file in the [`modules/`](./modules/) folder: + + ```python + # modules/new_test.py + + class Model(BaseModel): + a: int + + + model = Model(a=1, b=2) + ``` + +2. Add the new file in the defined `cases` in [`test_mypy.py`](./test_mypy.py), together + with a configuration file: + + ```python + cases: list[ParameterSet | tuple[str, str]] = [ + ..., + # One-off cases + *[ + ('mypy-plugin.ini', 'custom_constructor.py'), + ('mypy-plugin.ini', 'config_conditional_extra.py'), + ..., + ('mypy-plugin.ini', 'new_test.py'), # <-- new test added. + ] + ``` + +3. Run `make test-mypy-update-all`. It should create a new output with your new file. + +4. Make sure the output contains the expected Mypy error message/code. + +> [!NOTE] +> You can also edit existing module files. In that case, only step 3 and 4 are relevant. diff --git a/tests/mypy/configs/mypy-default.ini b/tests/mypy/configs/mypy-default.ini index 4000deb8e0c..65af6605a92 100644 --- a/tests/mypy/configs/mypy-default.ini +++ b/tests/mypy/configs/mypy-default.ini @@ -7,8 +7,15 @@ disallow_any_generics = True check_untyped_defs = True no_implicit_reexport = True +python_version = 3.10 + # for strict mypy: (this is the tricky one :-)) disallow_untyped_defs = True +# TODO 3.9 drop the following line: +force_uppercase_builtins = True +# TODO 3.10 drop the following line: +force_union_syntax = True + [mypy-pydantic_core.*] follow_imports = skip diff --git a/tests/mypy/configs/mypy-plugin-strict-no-any.ini b/tests/mypy/configs/mypy-plugin-strict-no-any.ini index 87eba0fa7b6..9d513baf10d 100644 --- a/tests/mypy/configs/mypy-plugin-strict-no-any.ini +++ b/tests/mypy/configs/mypy-plugin-strict-no-any.ini @@ -13,10 +13,19 @@ disallow_untyped_defs = True disallow_any_decorated = True disallow_any_expr = True disallow_any_explicit = True -disallow_any_unimported = True +# The following should be set to True, but results in a Mypy crash +# (https://github.com/python/mypy/issues/17954) +disallow_any_unimported = False disallow_subclassing_any = True warn_return_any = True +python_version = 3.10 + +# TODO 3.9 drop the following line: +force_uppercase_builtins = True +# TODO 3.10 drop the following line: +force_union_syntax = True + [pydantic-mypy] init_forbid_extra = True init_typed = True diff --git a/tests/mypy/configs/mypy-plugin-strict.ini b/tests/mypy/configs/mypy-plugin-strict.ini index a8d047ad1ab..f98247c89c2 100644 --- a/tests/mypy/configs/mypy-plugin-strict.ini +++ b/tests/mypy/configs/mypy-plugin-strict.ini @@ -10,6 +10,13 @@ check_untyped_defs = True no_implicit_reexport = True disallow_untyped_defs = True +python_version = 3.10 + +# TODO 3.9 drop the following line: +force_uppercase_builtins = True +# TODO 3.10 drop the following line: +force_union_syntax = True + [pydantic-mypy] init_forbid_extra = True init_typed = True diff --git a/tests/mypy/configs/mypy-plugin-very-strict.ini b/tests/mypy/configs/mypy-plugin-very-strict.ini index b10cceee55e..843dee9f05d 100644 --- a/tests/mypy/configs/mypy-plugin-very-strict.ini +++ b/tests/mypy/configs/mypy-plugin-very-strict.ini @@ -1,6 +1,14 @@ [mypy] plugins = pydantic.mypy strict = True +follow_imports = silent + +python_version = 3.10 + +# TODO 3.9 drop the following line: +force_uppercase_builtins = True +# TODO 3.10 drop the following line: +force_union_syntax = True [pydantic-mypy] init_forbid_extra = True diff --git a/tests/mypy/configs/mypy-plugin.ini b/tests/mypy/configs/mypy-plugin.ini index f969fd37699..c1e4e6acb2b 100644 --- a/tests/mypy/configs/mypy-plugin.ini +++ b/tests/mypy/configs/mypy-plugin.ini @@ -9,8 +9,15 @@ disallow_any_generics = True check_untyped_defs = True no_implicit_reexport = True +python_version = 3.10 + # for strict mypy: (this is the tricky one :-)) disallow_untyped_defs = True +# TODO 3.9 drop the following line: +force_uppercase_builtins = True +# TODO 3.10 drop the following line: +force_union_syntax = True + [mypy-pydantic_core.*] follow_imports = skip diff --git a/tests/mypy/configs/pyproject-default.toml b/tests/mypy/configs/pyproject-default.toml index 0e7846b4e0f..20928f6f128 100644 --- a/tests/mypy/configs/pyproject-default.toml +++ b/tests/mypy/configs/pyproject-default.toml @@ -26,6 +26,13 @@ check_untyped_defs = true no_implicit_reexport = true disallow_untyped_defs = true +python_version = '3.10' + +# TODO 3.9 drop the following line: +force_uppercase_builtins = true +# TODO 3.10 drop the following line: +force_union_syntax = true + [[tool.mypy.overrides]] module = [ 'pydantic_core.*', diff --git a/tests/mypy/configs/pyproject-plugin-bad-param.toml b/tests/mypy/configs/pyproject-plugin-bad-param.toml index 090f5693c0a..daea77551e5 100644 --- a/tests/mypy/configs/pyproject-plugin-bad-param.toml +++ b/tests/mypy/configs/pyproject-plugin-bad-param.toml @@ -29,6 +29,13 @@ check_untyped_defs = true no_implicit_reexport = true disallow_untyped_defs = true +python_version = '3.10' + +# TODO 3.9 drop the following line: +force_uppercase_builtins = true +# TODO 3.10 drop the following line: +force_union_syntax = true + [tool.pydantic-mypy] init_forbid_extra = "foo" # this will raise a ValueError for the config diff --git a/tests/mypy/configs/pyproject-plugin-no-strict-optional.toml b/tests/mypy/configs/pyproject-plugin-no-strict-optional.toml index f646735e84f..02021559ae1 100644 --- a/tests/mypy/configs/pyproject-plugin-no-strict-optional.toml +++ b/tests/mypy/configs/pyproject-plugin-no-strict-optional.toml @@ -29,6 +29,13 @@ check_untyped_defs = true no_implicit_reexport = true disallow_untyped_defs = true +python_version = '3.10' + +# TODO 3.9 drop the following line: +force_uppercase_builtins = true +# TODO 3.10 drop the following line: +force_union_syntax = true + [[tool.mypy.overrides]] module = [ 'pydantic_core.*', diff --git a/tests/mypy/configs/pyproject-plugin-strict-equality.toml b/tests/mypy/configs/pyproject-plugin-strict-equality.toml index 2c84122275e..bdbaff29096 100644 --- a/tests/mypy/configs/pyproject-plugin-strict-equality.toml +++ b/tests/mypy/configs/pyproject-plugin-strict-equality.toml @@ -35,6 +35,13 @@ disallow_untyped_defs = true disallow_untyped_decorators = false strict_equality = true +python_version = '3.10' + +# TODO 3.9 drop the following line: +force_uppercase_builtins = true +# TODO 3.10 drop the following line: +force_union_syntax = true + [tool.pydantic-mypy] init_forbid_extra = true init_typed = true diff --git a/tests/mypy/configs/pyproject-plugin-strict.toml b/tests/mypy/configs/pyproject-plugin-strict.toml index 5fd821b651b..9c4be7ad28b 100644 --- a/tests/mypy/configs/pyproject-plugin-strict.toml +++ b/tests/mypy/configs/pyproject-plugin-strict.toml @@ -29,6 +29,13 @@ check_untyped_defs = true no_implicit_reexport = true disallow_untyped_defs = true +python_version = '3.10' + +# TODO 3.9 drop the following line: +force_uppercase_builtins = true +# TODO 3.10 drop the following line: +force_union_syntax = true + [tool.pydantic-mypy] init_forbid_extra = true init_typed = true diff --git a/tests/mypy/configs/pyproject-plugin.toml b/tests/mypy/configs/pyproject-plugin.toml index 2a4d3f27f61..6aaf45e1ae8 100644 --- a/tests/mypy/configs/pyproject-plugin.toml +++ b/tests/mypy/configs/pyproject-plugin.toml @@ -29,6 +29,13 @@ check_untyped_defs = true no_implicit_reexport = true disallow_untyped_defs = true +python_version = '3.10' + +# TODO 3.9 drop the following line: +force_uppercase_builtins = true +# TODO 3.10 drop the following line: +force_union_syntax = true + [[tool.mypy.overrides]] module = [ 'pydantic_core.*', diff --git a/tests/mypy/modules/computed_fields.py b/tests/mypy/modules/computed_fields.py deleted file mode 100644 index 695f72c9b35..00000000000 --- a/tests/mypy/modules/computed_fields.py +++ /dev/null @@ -1,37 +0,0 @@ -from pydantic import BaseModel, computed_field - - -class Square(BaseModel): - side: float - - @computed_field - @property - def area(self) -> float: - return self.side**2 - - @area.setter - def area(self, area: float) -> None: - self.side = area**0.5 - - -sq = Square(side=10) -y = 12.4 + sq.area -z = 'x' + sq.area - -try: - from functools import cached_property -except ImportError: - pass -else: - - class Square2(BaseModel): - side: float - - @computed_field - @cached_property - def area(self) -> float: - return self.side**2 - - sq = Square(side=10) - y = 12.4 + sq.area - z = 'x' + sq.area diff --git a/tests/mypy/modules/config_conditional_extra.py b/tests/mypy/modules/config_conditional_extra.py index d44f95dcd48..9027d476b5e 100644 --- a/tests/mypy/modules/config_conditional_extra.py +++ b/tests/mypy/modules/config_conditional_extra.py @@ -1,3 +1,8 @@ +"""Test that the mypy plugin does not change the config type checking. + +This test can most likely be removed when we drop support for the old V1 `Config` class. +""" + from pydantic import BaseModel, ConfigDict diff --git a/tests/mypy/modules/fail1.py b/tests/mypy/modules/fail1.py deleted file mode 100644 index 9bb0a8a7c01..00000000000 --- a/tests/mypy/modules/fail1.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -Test mypy failure with missing attribute -""" -from datetime import datetime -from typing import List, Optional - -from pydantic import BaseModel -from pydantic.types import Json - - -class Model(BaseModel): - age: int - first_name = 'John' - last_name: Optional[str] = None - signup_ts: Optional[datetime] = None - list_of_ints: List[int] - json_list_of_ints: Json[List[int]] - - -m = Model(age=42, list_of_ints=[1, '2', b'3']) - -print(m.age + 'not integer') -m.json_list_of_ints[0] + 'not integer' diff --git a/tests/mypy/modules/fail2.py b/tests/mypy/modules/fail2.py deleted file mode 100644 index 760db0ba5eb..00000000000 --- a/tests/mypy/modules/fail2.py +++ /dev/null @@ -1,20 +0,0 @@ -""" -Test mypy failure with invalid types. -""" -from datetime import datetime -from typing import List, Optional - -from pydantic import BaseModel - - -class Model(BaseModel): - age: int - first_name = 'John' - last_name: Optional[str] = None - signup_ts: Optional[datetime] = None - list_of_ints: List[int] - - -m = Model(age=42, list_of_ints=[1, '2', b'3']) - -print(m.foobar) diff --git a/tests/mypy/modules/fail3.py b/tests/mypy/modules/fail3.py deleted file mode 100644 index 37c054caf2f..00000000000 --- a/tests/mypy/modules/fail3.py +++ /dev/null @@ -1,21 +0,0 @@ -""" -Test mypy failure with invalid types. -""" -from typing import Generic, List, TypeVar - -from pydantic import BaseModel - -T = TypeVar('T') - - -class Model(BaseModel): - list_of_ints: List[int] - - -class WrapperModel(BaseModel, Generic[T]): - payload: T - - -model_instance = Model(list_of_ints=[1]) -wrapper_instance = WrapperModel[Model](payload=model_instance) -wrapper_instance.payload.list_of_ints.append('1') diff --git a/tests/mypy/modules/fail4.py b/tests/mypy/modules/fail4.py deleted file mode 100644 index 0d4d6fa277e..00000000000 --- a/tests/mypy/modules/fail4.py +++ /dev/null @@ -1,49 +0,0 @@ -from typing import Any - -from pydantic import BaseModel, root_validator, validate_call - - -@validate_call -def foo(a: int, *, c: str = 'x') -> str: - return c * a - - -# ok -x: str = foo(1, c='hello') -# fails -foo('x') -foo(1, c=1) -foo(1, 2) -foo(1, d=2) -# mypy assumes foo is just a function -callable(foo.raw_function) - - -@validate_call -def bar() -> str: - return 'x' - - -# return type should be a string -y: int = bar() - - -# Demonstrate type errors for root_validator signatures -class Model(BaseModel): - @root_validator() - @classmethod - def validate_1(cls, values: Any) -> Any: - return values - - @root_validator(pre=True, skip_on_failure=True) - @classmethod - def validate_2(cls, values: Any) -> Any: - return values - - @root_validator(pre=False) - @classmethod - def validate_3(cls, values: Any) -> Any: - return values - - -Model.non_existent_attribute diff --git a/tests/mypy/modules/generics.py b/tests/mypy/modules/generics.py index d8ec9247c2f..ed33bd577e7 100644 --- a/tests/mypy/modules/generics.py +++ b/tests/mypy/modules/generics.py @@ -1,5 +1,7 @@ from typing import Any, Dict, Generic, Optional, TypeVar +from typing_extensions import assert_type + from pydantic import BaseModel Tbody = TypeVar('Tbody') @@ -35,7 +37,7 @@ def custom_method(self) -> None: resp = HtmlResponse.model_validate(example) resp.custom_method() -doctype = resp.body.doctype +assert_type(resp.body, HtmlBody) T = TypeVar('T', int, str) @@ -50,4 +52,4 @@ class DomainType(HistoryField[int]): thing = DomainType(value=None) -var: Optional[int] = thing.value +assert_type(thing.value, Optional[int]) diff --git a/tests/mypy/modules/plugin_default_factory.py b/tests/mypy/modules/plugin_default_factory.py deleted file mode 100644 index 4f81566f1a1..00000000000 --- a/tests/mypy/modules/plugin_default_factory.py +++ /dev/null @@ -1,21 +0,0 @@ -""" -See https://github.com/pydantic/pydantic/issues/4457 -""" - -from typing import Dict, List - -from pydantic import BaseModel, Field - - -def new_list() -> List[int]: - return [] - - -class Model(BaseModel): - l1: List[str] = Field(default_factory=list) - l2: List[int] = Field(default_factory=new_list) - l3: List[str] = Field(default_factory=lambda: list()) - l4: Dict[str, str] = Field(default_factory=dict) - l5: int = Field(default_factory=lambda: 123) - l6_error: List[str] = Field(default_factory=new_list) - l7_error: int = Field(default_factory=list) diff --git a/tests/mypy/modules/plugin_fail.py b/tests/mypy/modules/plugin_fail.py index ec129fa9281..f5789512724 100644 --- a/tests/mypy/modules/plugin_fail.py +++ b/tests/mypy/modules/plugin_fail.py @@ -251,28 +251,6 @@ class InheritingModel2(FrozenModel): inheriting2.y = 'd' -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) - f: int = None - - # Default factory - g: str = Field(default_factory=set) - h: int = Field(default_factory=_default_factory) - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) - - # Default and default factory - m: int = Field(default=1, default_factory=list) - - class ModelWithAnnotatedValidator(BaseModel): name: str diff --git a/tests/mypy/modules/plugin_fail_baseConfig.py b/tests/mypy/modules/plugin_fail_baseConfig.py index f5facd0dfad..c11b9438eb8 100644 --- a/tests/mypy/modules/plugin_fail_baseConfig.py +++ b/tests/mypy/modules/plugin_fail_baseConfig.py @@ -271,28 +271,6 @@ class Config: inheriting2.y = 'd' -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) - f: int = None - - # Default factory - g: str = Field(default_factory=set) - h: int = Field(default_factory=_default_factory) - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) - - # Default and default factory - m: int = Field(default=1, default_factory=list) - - class ModelWithAnnotatedValidator(BaseModel): name: str diff --git a/tests/mypy/modules/success.py b/tests/mypy/modules/success.py deleted file mode 100644 index 5669dc7ad6b..00000000000 --- a/tests/mypy/modules/success.py +++ /dev/null @@ -1,339 +0,0 @@ -""" -Test pydantic's compliance with mypy. - -Do a little skipping about with types to demonstrate its usage. -""" -import os -from datetime import date, datetime, timedelta, timezone -from pathlib import Path, PurePath -from typing import Any, ClassVar, Dict, ForwardRef, Generic, List, Optional, Type, TypeVar -from uuid import UUID - -from typing_extensions import Annotated, TypedDict - -from pydantic import ( - UUID1, - AwareDatetime, - BaseModel, - ConfigDict, - DirectoryPath, - FilePath, - FutureDate, - FutureDatetime, - ImportString, - Json, - NaiveDatetime, - NegativeFloat, - NegativeInt, - NonNegativeFloat, - NonNegativeInt, - NonPositiveFloat, - NonPositiveInt, - PastDate, - PastDatetime, - PositiveFloat, - PositiveInt, - StrictBool, - StrictBytes, - StrictFloat, - StrictInt, - StrictStr, - UrlConstraints, - WrapValidator, - create_model, - field_validator, - model_validator, - root_validator, - validate_call, -) -from pydantic.fields import Field, PrivateAttr -from pydantic.json_schema import Examples -from pydantic.networks import AnyUrl - - -class Flags(BaseModel): - strict_bool: StrictBool = False - - def __str__(self) -> str: - return f'flag={self.strict_bool}' - - -class Model(BaseModel): - age: int - first_name: str = 'John' - last_name: Optional[str] = None - signup_ts: Optional[datetime] = None - list_of_ints: List[int] - - @field_validator('age') - def check_age(cls, value: int) -> int: - assert value < 100, 'too old' - return value - - @root_validator(skip_on_failure=True) - def root_check(cls, values: Dict[str, Any]) -> Dict[str, Any]: - return values - - @root_validator(pre=True, allow_reuse=False) - def pre_root_check(cls, values: Dict[str, Any]) -> Dict[str, Any]: - return values - - -def dog_years(age: int) -> int: - return age * 7 - - -def day_of_week(dt: datetime) -> int: - return dt.date().isoweekday() - - -m = Model(age=21, list_of_ints=[1, 2, 3]) - -assert m.age == 21, m.age -m.age = 42 -assert m.age == 42, m.age -assert m.first_name == 'John', m.first_name -assert m.last_name is None, m.last_name -assert m.list_of_ints == [1, 2, 3], m.list_of_ints - -dog_age = dog_years(m.age) -assert dog_age == 294, dog_age - -Model(age=2, first_name='Woof', last_name='Woof', signup_ts=datetime(2017, 6, 7), list_of_ints=[1, 2, 3]) -m = Model.model_validate( - { - 'age': 2, - 'first_name': b'Woof', - 'last_name': b'Woof', - 'signup_ts': '2017-06-07 00:00', - 'list_of_ints': [1, '2', b'3'], - } -) - -assert m.first_name == 'Woof', m.first_name -assert m.last_name == 'Woof', m.last_name -assert m.signup_ts == datetime(2017, 6, 7), m.signup_ts -assert day_of_week(m.signup_ts) == 3 - -data = {'age': 10, 'first_name': 'Alena', 'last_name': 'Sousova', 'list_of_ints': [410]} -m_from_obj = Model.model_validate(data) - -assert isinstance(m_from_obj, Model) -assert m_from_obj.age == 10 -assert m_from_obj.first_name == data['first_name'] -assert m_from_obj.last_name == data['last_name'] -assert m_from_obj.list_of_ints == data['list_of_ints'] - -m_copy = m_from_obj.model_copy() - -assert isinstance(m_copy, Model) -assert m_copy.age == m_from_obj.age -assert m_copy.first_name == m_from_obj.first_name -assert m_copy.last_name == m_from_obj.last_name -assert m_copy.list_of_ints == m_from_obj.list_of_ints - -T = TypeVar('T') - - -class WrapperModel(BaseModel, Generic[T]): - payload: T - - -int_instance = WrapperModel[int](payload=1) -int_instance.payload += 1 -assert int_instance.payload == 2 - -str_instance = WrapperModel[str](payload='a') -str_instance.payload += 'a' -assert str_instance.payload == 'aa' - -model_instance = WrapperModel[Model](payload=m) -model_instance.payload.list_of_ints.append(4) -assert model_instance.payload.list_of_ints == [1, 2, 3, 4] - - -class WithField(BaseModel): - age: int - first_name: str = Field('John', max_length=42) - - -# simple decorator -@validate_call -def foo(a: int, *, c: str = 'x') -> str: - return c * a - - -foo(1, c='thing') -foo(1) - - -# nested decorator should not produce an error -@validate_call(config={'arbitrary_types_allowed': True}) -def bar(a: int, *, c: str = 'x') -> str: - return c * a - - -bar(1, c='thing') -bar(1) - - -class Foo(BaseModel): - a: int - - -FooRef = ForwardRef('Foo') - - -class MyConf(BaseModel): - str_pyobject: ImportString[Type[date]] = Field(...) - callable_pyobject: ImportString[Type[date]] = Field(default=date) - - -conf = MyConf(str_pyobject='datetime.date') -var1: date = conf.str_pyobject(2020, 12, 20) -var2: date = conf.callable_pyobject(2111, 1, 1) - - -class MyPrivateAttr(BaseModel): - _private_field: str = PrivateAttr() - - -class PydanticTypes(BaseModel): - model_config = ConfigDict(validate_default=True) - - # Boolean - my_strict_bool: StrictBool = True - # Integer - my_positive_int: PositiveInt = 1 - my_negative_int: NegativeInt = -1 - my_non_positive_int: NonPositiveInt = -1 - my_non_negative_int: NonNegativeInt = 1 - my_strict_int: StrictInt = 1 - # Float - my_positive_float: PositiveFloat = 1.1 - my_negative_float: NegativeFloat = -1.1 - my_non_positive_float: NonPositiveFloat = -1.1 - my_non_negative_float: NonNegativeFloat = 1.1 - my_strict_float: StrictFloat = 1.1 - # Bytes - my_strict_bytes: StrictBytes = b'pika' - # String - my_strict_str: StrictStr = 'pika' - # ImportString - import_string_str: ImportString[Any] = 'datetime.date' # type: ignore[misc] - import_string_callable: ImportString[Any] = date - # UUID - my_uuid1: UUID1 = UUID('a8098c1a-f86e-11da-bd1a-00112444be1e') - my_uuid1_str: UUID1 = 'a8098c1a-f86e-11da-bd1a-00112444be1e' - # Path - my_file_path: FilePath = Path(__file__) - my_file_path_str: FilePath = __file__ - my_dir_path: DirectoryPath = Path('.') - my_dir_path_str: DirectoryPath = '.' - # Json - my_json: Json[Dict[str, str]] = '{"hello": "world"}' - my_json_list: Json[List[str]] = '["hello", "world"]' - # Date - my_past_date: PastDate = date.today() - timedelta(1) - my_future_date: FutureDate = date.today() + timedelta(1) - # Datetime - my_past_datetime: PastDatetime = datetime.now() - timedelta(1) - my_future_datetime: FutureDatetime = datetime.now() + timedelta(1) - my_aware_datetime: AwareDatetime = datetime.now(tz=timezone.utc) - my_naive_datetime: NaiveDatetime = datetime.now() - # Complex - my_complex: complex = complex(1.0, 2.0) - - -validated = PydanticTypes() -validated.import_string_str(2021, 1, 1) -validated.import_string_callable(2021, 1, 1) -validated.my_uuid1.hex -validated.my_file_path.absolute() -validated.my_file_path_str.absolute() -validated.my_dir_path.absolute() -validated.my_dir_path_str.absolute() -validated.my_json['hello'].capitalize() -validated.my_json_list[0].capitalize() - - -class UrlModel(BaseModel): - x: Annotated[AnyUrl, UrlConstraints(allowed_schemes=['http'])] = Field(default=None) - y: Annotated[AnyUrl, UrlConstraints(allowed_schemes=['http'])] = Field(default=None) - z: Annotated[AnyUrl, UrlConstraints(allowed_schemes=['s3', 's3n', 's3a'])] = Field(default=None) - - -url_model = UrlModel(x='http://example.com') -assert url_model.x.host == 'example.com' - - -class SomeDict(TypedDict): - val: int - name: str - - -obj: SomeDict = { - 'val': 12, - 'name': 'John', -} - -config = ConfigDict(title='Record', extra='ignore', str_max_length=1234) - - -class CustomPath(PurePath): - def __init__(self, *args: str): - self.path = os.path.join(*args) - - def __fspath__(self) -> str: - return f'a/custom/{self.path}' - - -DynamicModel = create_model('DynamicModel') - -examples = Examples([]) - - -def double(value: Any, handler: Any) -> int: - return handler(value) * 2 - - -class WrapValidatorModel(BaseModel): - x: Annotated[int, WrapValidator(double)] - - -class Abstract(BaseModel): - class_id: ClassVar - - -class Concrete(Abstract): - class_id = 1 - - -def two_dim_shape_validator(v: Dict[str, Any]) -> Dict[str, Any]: - assert 'volume' not in v, 'shape is 2d, cannot have volume' - return v - - -class Square(BaseModel): - width: float - height: float - - free_validator = model_validator(mode='before')(two_dim_shape_validator) - - -class SomeModel(BaseModel): - field: int - - @field_validator("field") - @classmethod - def validate_field(cls, v: int) -> int: - return v - - -class OtherModel(BaseModel): - field: int - - @field_validator("field") - @classmethod - def validate_field(cls, v: int) -> int: - return SomeModel.validate_field(v) diff --git a/tests/mypy/modules/with_config_decorator.py b/tests/mypy/modules/with_config_decorator.py deleted file mode 100644 index e56a835654a..00000000000 --- a/tests/mypy/modules/with_config_decorator.py +++ /dev/null @@ -1,11 +0,0 @@ -from typing import TypedDict - -from pydantic import ConfigDict, with_config - - -@with_config(ConfigDict(str_to_lower=True)) -class Model(TypedDict): - a: str - - -model = Model(a='ABC') diff --git a/tests/mypy/outputs/1.0.1/mypy-default_ini/fail1.py b/tests/mypy/outputs/1.0.1/mypy-default_ini/fail1.py deleted file mode 100644 index 51ed34606a2..00000000000 --- a/tests/mypy/outputs/1.0.1/mypy-default_ini/fail1.py +++ /dev/null @@ -1,25 +0,0 @@ -""" -Test mypy failure with missing attribute -""" -from datetime import datetime -from typing import List, Optional - -from pydantic import BaseModel -from pydantic.types import Json - - -class Model(BaseModel): - age: int - first_name = 'John' - last_name: Optional[str] = None - signup_ts: Optional[datetime] = None - list_of_ints: List[int] - json_list_of_ints: Json[List[int]] - - -m = Model(age=42, list_of_ints=[1, '2', b'3']) - -print(m.age + 'not integer') -# MYPY: error: Unsupported operand types for + ("int" and "str") [operator] -m.json_list_of_ints[0] + 'not integer' -# MYPY: error: Unsupported operand types for + ("int" and "str") [operator] diff --git a/tests/mypy/outputs/1.0.1/mypy-default_ini/fail2.py b/tests/mypy/outputs/1.0.1/mypy-default_ini/fail2.py deleted file mode 100644 index a5aadf2df82..00000000000 --- a/tests/mypy/outputs/1.0.1/mypy-default_ini/fail2.py +++ /dev/null @@ -1,21 +0,0 @@ -""" -Test mypy failure with invalid types. -""" -from datetime import datetime -from typing import List, Optional - -from pydantic import BaseModel - - -class Model(BaseModel): - age: int - first_name = 'John' - last_name: Optional[str] = None - signup_ts: Optional[datetime] = None - list_of_ints: List[int] - - -m = Model(age=42, list_of_ints=[1, '2', b'3']) - -print(m.foobar) -# MYPY: error: "Model" has no attribute "foobar" [attr-defined] diff --git a/tests/mypy/outputs/1.0.1/mypy-default_ini/fail3.py b/tests/mypy/outputs/1.0.1/mypy-default_ini/fail3.py deleted file mode 100644 index 4e7abaeeac7..00000000000 --- a/tests/mypy/outputs/1.0.1/mypy-default_ini/fail3.py +++ /dev/null @@ -1,22 +0,0 @@ -""" -Test mypy failure with invalid types. -""" -from typing import Generic, List, TypeVar - -from pydantic import BaseModel - -T = TypeVar('T') - - -class Model(BaseModel): - list_of_ints: List[int] - - -class WrapperModel(BaseModel, Generic[T]): - payload: T - - -model_instance = Model(list_of_ints=[1]) -wrapper_instance = WrapperModel[Model](payload=model_instance) -wrapper_instance.payload.list_of_ints.append('1') -# MYPY: error: Argument 1 to "append" of "list" has incompatible type "str"; expected "int" [arg-type] diff --git a/tests/mypy/outputs/1.0.1/mypy-default_ini/fail4.py b/tests/mypy/outputs/1.0.1/mypy-default_ini/fail4.py deleted file mode 100644 index c52c8511e13..00000000000 --- a/tests/mypy/outputs/1.0.1/mypy-default_ini/fail4.py +++ /dev/null @@ -1,73 +0,0 @@ -from typing import Any - -from pydantic import BaseModel, root_validator, validate_call - - -@validate_call -def foo(a: int, *, c: str = 'x') -> str: -# MYPY: note: "foo" defined here - return c * a - - -# ok -x: str = foo(1, c='hello') -# fails -foo('x') -# MYPY: error: Argument 1 to "foo" has incompatible type "str"; expected "int" [arg-type] -foo(1, c=1) -# MYPY: error: Argument "c" to "foo" has incompatible type "int"; expected "str" [arg-type] -foo(1, 2) -# MYPY: error: Too many positional arguments for "foo" [misc] -# MYPY: error: Argument 2 to "foo" has incompatible type "int"; expected "str" [arg-type] -foo(1, d=2) -# MYPY: error: Unexpected keyword argument "d" for "foo" [call-arg] -# mypy assumes foo is just a function -callable(foo.raw_function) -# MYPY: error: "Callable[[int, DefaultNamedArg(str, 'c')], str]" has no attribute "raw_function" [attr-defined] - - -@validate_call -def bar() -> str: - return 'x' - - -# return type should be a string -y: int = bar() -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - - -# Demonstrate type errors for root_validator signatures -class Model(BaseModel): - @root_validator() -# MYPY: error: All overload variants of "root_validator" require at least one argument [call-overload] -# MYPY: note: Possible overload variants: -# MYPY: note: def root_validator(*, skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[False], skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] - @classmethod - def validate_1(cls, values: Any) -> Any: - return values - - @root_validator(pre=True, skip_on_failure=True) -# MYPY: error: No overload variant of "root_validator" matches argument types "bool", "bool" [call-overload] -# MYPY: note: Possible overload variants: -# MYPY: note: def root_validator(*, skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[False], skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] - @classmethod - def validate_2(cls, values: Any) -> Any: - return values - - @root_validator(pre=False) -# MYPY: error: No overload variant of "root_validator" matches argument type "bool" [call-overload] -# MYPY: note: Possible overload variants: -# MYPY: note: def root_validator(*, skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[False], skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] - @classmethod - def validate_3(cls, values: Any) -> Any: - return values - - -Model.non_existent_attribute -# MYPY: error: "Type[Model]" has no attribute "non_existent_attribute" [attr-defined] diff --git a/tests/mypy/outputs/1.0.1/mypy-default_ini/plugin_success.py b/tests/mypy/outputs/1.0.1/mypy-default_ini/plugin_success.py deleted file mode 100644 index 8f599e82c55..00000000000 --- a/tests/mypy/outputs/1.0.1/mypy-default_ini/plugin_success.py +++ /dev/null @@ -1,320 +0,0 @@ -from dataclasses import InitVar -from typing import Any, ClassVar, Generic, List, Optional, TypeVar, Union - -from typing_extensions import Self - -from pydantic import BaseModel, ConfigDict, Field, RootModel, create_model, field_validator, model_validator, validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - x: float - y: str - - model_config = ConfigDict(from_attributes=True) - - -class SelfReferencingModel(BaseModel): - submodel: Optional['SelfReferencingModel'] - - @property - def prop(self) -> None: - ... - - -SelfReferencingModel.model_rebuild() - -model = Model(x=1, y='y') -Model(x=1, y='y', z='z') -model.x = 2 -model.model_validate(model) - -self_referencing_model = SelfReferencingModel(submodel=SelfReferencingModel(submodel=None)) - - -class KwargsModel(BaseModel, from_attributes=True): - x: float - y: str - - -kwargs_model = KwargsModel(x=1, y='y') -KwargsModel(x=1, y='y', z='z') -kwargs_model.x = 2 -kwargs_model.model_validate(kwargs_model.__dict__) - - -class InheritingModel(Model): - z: int = 1 - - -InheritingModel.model_validate(model.__dict__) - - -class ForwardReferencingModel(Model): - future: 'FutureModel' - - -class FutureModel(Model): - pass - - -ForwardReferencingModel.model_rebuild() -future_model = FutureModel(x=1, y='a') -forward_model = ForwardReferencingModel(x=1, y='a', future=future_model) - - -class NoMutationModel(BaseModel): - x: int - - model_config = ConfigDict(frozen=True) - - -class MutationModel(NoMutationModel): - a: int = 1 - - model_config = ConfigDict(frozen=False, from_attributes=True) - - -MutationModel(x=1).x = 2 -MutationModel.model_validate(model.__dict__) - - -class KwargsNoMutationModel(BaseModel, frozen=True): - x: int - - -class KwargsMutationModel(KwargsNoMutationModel, frozen=False, from_attributes=True): - a: int = 1 - - -KwargsMutationModel(x=1).x = 2 -KwargsMutationModel.model_validate(model.__dict__) - - -class OverrideModel(Model): - x: int - - -OverrideModel(x=1, y='b') - - -class Mixin: - def f(self) -> None: - pass - - -class MultiInheritanceModel(BaseModel, Mixin): - pass - - -MultiInheritanceModel().f() - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - - -alias_model = AliasModel(y='hello') -assert alias_model.x == 'hello' - - -class ClassVarModel(BaseModel): - x: int - y: ClassVar[int] = 1 - - -ClassVarModel(x=1) - - -@dataclass(config={'validate_assignment': True}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') -# MYPY: error: Unexpected keyword argument "name" for "AddProject" [call-arg] -# MYPY: error: Unexpected keyword argument "slug" for "AddProject" [call-arg] -# MYPY: error: Unexpected keyword argument "description" for "AddProject" [call-arg] - - -class TypeAliasAsAttribute(BaseModel): - __type_alias_attribute__ = Union[str, bytes] - - -class NestedModel(BaseModel): - class Model(BaseModel): - id: str - - model: Model - - -_ = NestedModel.Model - - -DynamicModel = create_model('DynamicModel', __base__=Model) - -dynamic_model = DynamicModel(x=1, y='y') -dynamic_model.x = 2 - - -class FrozenModel(BaseModel): - x: int - - model_config = ConfigDict(frozen=True) - - -class NotFrozenModel(FrozenModel): - a: int = 1 - - model_config = ConfigDict(frozen=False, from_attributes=True) - - -NotFrozenModel(x=1).x = 2 -NotFrozenModel.model_validate(model.__dict__) - - -class KwargsFrozenModel(BaseModel, frozen=True): - x: int - - -class KwargsNotFrozenModel(FrozenModel, frozen=False, from_attributes=True): - a: int = 1 - - -KwargsNotFrozenModel(x=1).x = 2 -KwargsNotFrozenModel.model_validate(model.__dict__) - - -class ModelWithSelfField(BaseModel): - self: str - - -def f(name: str) -> str: - return name - - -class ModelWithAllowReuseValidator(BaseModel): - name: str - normalize_name = field_validator('name')(f) - - -model_with_allow_reuse_validator = ModelWithAllowReuseValidator(name='xyz') - - -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(cls, name: str) -> str: - return name - - -def _default_factory_str() -> str: - return 'x' - - -def _default_factory_list() -> List[int]: - return [1, 2, 3] - - -class FieldDefaultTestingModel(BaseModel): - # Required - a: int - b: int = Field() - c: int = Field(...) - - # Default - d: int = Field(1) - - # Default factory - g: List[int] = Field(default_factory=_default_factory_list) - h: str = Field(default_factory=_default_factory_str) - i: str = Field(default_factory=lambda: 'test') - - -_TModel = TypeVar('_TModel') -_TType = TypeVar('_TType') - - -class OrmMixin(Generic[_TModel, _TType]): - @classmethod - def from_orm(cls, model: _TModel) -> _TType: - raise NotImplementedError - - @classmethod - def from_orm_optional(cls, model: Optional[_TModel]) -> Optional[_TType]: - if model is None: - return None - return cls.from_orm(model) - - -@dataclass -class MyDataClass: - foo: InitVar[str] - bar: str - - -MyDataClass(foo='foo', bar='bar') -# MYPY: error: Unexpected keyword argument "foo" for "MyDataClass" [call-arg] -# MYPY: error: Unexpected keyword argument "bar" for "MyDataClass" [call-arg] - - -def get_my_custom_validator(field_name: str) -> Any: - @validator(field_name, allow_reuse=True) - def my_custom_validator(cls: Any, v: int) -> int: - return v - - return my_custom_validator - - -def foo() -> None: - class MyModel(BaseModel): - number: int - custom_validator = get_my_custom_validator('number') # type: ignore[pydantic-field] -# MYPY: error: Unused "type: ignore" comment - - @model_validator(mode='before') - @classmethod - def validate_before(cls, values: Any) -> Any: - return values - - @model_validator(mode='after') - def validate_after(self) -> Self: - return self - - MyModel(number=2) - - -class InnerModel(BaseModel): - my_var: Union[str, None] = Field(default=None) - - -class OuterModel(InnerModel): - pass - - -m = OuterModel() -if m.my_var is None: - # In https://github.com/pydantic/pydantic/issues/7399, this was unreachable - print('not unreachable') - - -class Foo(BaseModel): - pass - - -class Bar(Foo, RootModel[int]): - pass diff --git a/tests/mypy/outputs/1.0.1/mypy-default_ini/plugin_success_baseConfig.py b/tests/mypy/outputs/1.0.1/mypy-default_ini/plugin_success_baseConfig.py deleted file mode 100644 index 65748006348..00000000000 --- a/tests/mypy/outputs/1.0.1/mypy-default_ini/plugin_success_baseConfig.py +++ /dev/null @@ -1,248 +0,0 @@ -from typing import ClassVar, Generic, List, Optional, TypeVar, Union - -from pydantic import BaseModel, Field, create_model, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - x: float - y: str - - model_config = dict(from_attributes=True) - - class NotConfig: - frozen = True - - -class SelfReferencingModel(BaseModel): - submodel: Optional['SelfReferencingModel'] - - @property - def prop(self) -> None: - ... - - -SelfReferencingModel.model_rebuild() - -model = Model(x=1, y='y') -Model(x=1, y='y', z='z') -model.x = 2 -model.model_validate(model) - -self_referencing_model = SelfReferencingModel(submodel=SelfReferencingModel(submodel=None)) - - -class KwargsModel(BaseModel, from_attributes=True): - x: float - y: str - - class NotConfig: - frozen = True - - -kwargs_model = KwargsModel(x=1, y='y') -KwargsModel(x=1, y='y', z='z') -kwargs_model.x = 2 -kwargs_model.model_validate(kwargs_model.__dict__) - - -class InheritingModel(Model): - z: int = 1 - - -InheritingModel.model_validate(model.__dict__) - - -class ForwardReferencingModel(Model): - future: 'FutureModel' - - -class FutureModel(Model): - pass - - -ForwardReferencingModel.model_rebuild() -future_model = FutureModel(x=1, y='a') -forward_model = ForwardReferencingModel(x=1, y='a', future=future_model) - - -class NoMutationModel(BaseModel): - x: int - - model_config = dict(frozen=True) - - -class MutationModel(NoMutationModel): - a: int = 1 - - model_config = dict(frozen=False, from_attributes=True) - - -MutationModel(x=1).x = 2 -MutationModel.model_validate(model.__dict__) - - -class KwargsNoMutationModel(BaseModel, frozen=True): - x: int - - -class KwargsMutationModel(KwargsNoMutationModel, frozen=False, from_attributes=True): - a: int = 1 - - -KwargsMutationModel(x=1).x = 2 -KwargsMutationModel.model_validate(model.__dict__) - - -class OverrideModel(Model): - x: int - - -OverrideModel(x=1, y='b') - - -class Mixin: - def f(self) -> None: - pass - - -class MultiInheritanceModel(BaseModel, Mixin): - pass - - -MultiInheritanceModel().f() - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - - -alias_model = AliasModel(y='hello') -assert alias_model.x == 'hello' - - -class ClassVarModel(BaseModel): - x: int - y: ClassVar[int] = 1 - - -ClassVarModel(x=1) - - -@dataclass(config=dict(validate_assignment=True)) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') -# MYPY: error: Unexpected keyword argument "name" for "AddProject" [call-arg] -# MYPY: error: Unexpected keyword argument "slug" for "AddProject" [call-arg] -# MYPY: error: Unexpected keyword argument "description" for "AddProject" [call-arg] - - -class TypeAliasAsAttribute(BaseModel): - __type_alias_attribute__ = Union[str, bytes] - - -class NestedModel(BaseModel): - class Model(BaseModel): - id: str - - model: Model - - -_ = NestedModel.Model - - -DynamicModel = create_model('DynamicModel', __base__=Model) - -dynamic_model = DynamicModel(x=1, y='y') -dynamic_model.x = 2 - - -class FrozenModel(BaseModel): - x: int - - model_config = dict(frozen=True) - - -class NotFrozenModel(FrozenModel): - a: int = 1 - - model_config = dict(frozen=False, from_attributes=True) - - -NotFrozenModel(x=1).x = 2 -NotFrozenModel.model_validate(model.__dict__) - - -class KwargsFrozenModel(BaseModel, frozen=True): - x: int - - -class KwargsNotFrozenModel(FrozenModel, frozen=False, from_attributes=True): - a: int = 1 - - -KwargsNotFrozenModel(x=1).x = 2 -KwargsNotFrozenModel.model_validate(model.__dict__) - - -class ModelWithSelfField(BaseModel): - self: str - - -def f(name: str) -> str: - return name - - -class ModelWithAllowReuseValidator(BaseModel): - name: str - normalize_name = field_validator('name')(f) - - -model_with_allow_reuse_validator = ModelWithAllowReuseValidator(name='xyz') - - -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(cls, name: str) -> str: - return name - - -def _default_factory_str() -> str: - return 'x' - - -def _default_factory_list() -> List[int]: - return [1, 2, 3] - - -class FieldDefaultTestingModel(BaseModel): - # Required - a: int - b: int = Field() - c: int = Field(...) - - # Default - d: int = Field(1) - - # Default factory - g: List[int] = Field(default_factory=_default_factory_list) - h: str = Field(default_factory=_default_factory_str) - i: str = Field(default_factory=lambda: 'test') diff --git a/tests/mypy/outputs/1.0.1/mypy-default_ini/pydantic_settings.py b/tests/mypy/outputs/1.0.1/mypy-default_ini/pydantic_settings.py deleted file mode 100644 index c2a077dd806..00000000000 --- a/tests/mypy/outputs/1.0.1/mypy-default_ini/pydantic_settings.py +++ /dev/null @@ -1,24 +0,0 @@ -from pydantic_settings import BaseSettings, SettingsConfigDict - - -class Settings(BaseSettings): - foo: str - - -s = Settings() - -s = Settings(foo='test', _case_sensitive=True, _env_prefix='test__', _env_file='test') - -s = Settings(foo='test', _case_sensitive=1, _env_prefix=2, _env_file=3) -# MYPY: error: Argument "_case_sensitive" to "Settings" has incompatible type "int"; expected "Optional[bool]" [arg-type] -# MYPY: error: Argument "_env_prefix" to "Settings" has incompatible type "int"; expected "Optional[str]" [arg-type] -# MYPY: error: Argument "_env_file" to "Settings" has incompatible type "int"; expected "Optional[Union[Path, str, List[Union[Path, str]], Tuple[Union[Path, str], ...]]]" [arg-type] - - -class SettingsWithConfigDict(BaseSettings): - bar: str - - model_config = SettingsConfigDict(env_file='.env', env_file_encoding='utf-8') - - -scd = SettingsWithConfigDict() diff --git a/tests/mypy/outputs/1.0.1/mypy-default_ini/success.py b/tests/mypy/outputs/1.0.1/mypy-default_ini/success.py deleted file mode 100644 index e900c11c4a4..00000000000 --- a/tests/mypy/outputs/1.0.1/mypy-default_ini/success.py +++ /dev/null @@ -1,345 +0,0 @@ -""" -Test pydantic's compliance with mypy. - -Do a little skipping about with types to demonstrate its usage. -""" -import os -from datetime import date, datetime, timedelta, timezone -from pathlib import Path, PurePath -from typing import Any, ClassVar, Dict, ForwardRef, Generic, List, Optional, Type, TypeVar -from uuid import UUID - -from typing_extensions import Annotated, TypedDict - -from pydantic import ( - UUID1, - AwareDatetime, - BaseModel, - ConfigDict, - DirectoryPath, - FilePath, - FutureDate, - FutureDatetime, - ImportString, - Json, - NaiveDatetime, - NegativeFloat, - NegativeInt, - NonNegativeFloat, - NonNegativeInt, - NonPositiveFloat, - NonPositiveInt, - PastDate, - PastDatetime, - PositiveFloat, - PositiveInt, - StrictBool, - StrictBytes, - StrictFloat, - StrictInt, - StrictStr, - UrlConstraints, - WrapValidator, - create_model, - field_validator, - model_validator, - root_validator, - validate_call, -) -from pydantic.fields import Field, PrivateAttr -from pydantic.json_schema import Examples -from pydantic.networks import AnyUrl - - -class Flags(BaseModel): - strict_bool: StrictBool = False - - def __str__(self) -> str: - return f'flag={self.strict_bool}' - - -class Model(BaseModel): - age: int - first_name: str = 'John' - last_name: Optional[str] = None - signup_ts: Optional[datetime] = None - list_of_ints: List[int] - - @field_validator('age') - def check_age(cls, value: int) -> int: - assert value < 100, 'too old' - return value - - @root_validator(skip_on_failure=True) - def root_check(cls, values: Dict[str, Any]) -> Dict[str, Any]: - return values - - @root_validator(pre=True, allow_reuse=False) - def pre_root_check(cls, values: Dict[str, Any]) -> Dict[str, Any]: - return values - - -def dog_years(age: int) -> int: - return age * 7 - - -def day_of_week(dt: datetime) -> int: - return dt.date().isoweekday() - - -m = Model(age=21, list_of_ints=[1, 2, 3]) - -assert m.age == 21, m.age -m.age = 42 -assert m.age == 42, m.age -assert m.first_name == 'John', m.first_name -assert m.last_name is None, m.last_name -assert m.list_of_ints == [1, 2, 3], m.list_of_ints - -dog_age = dog_years(m.age) -assert dog_age == 294, dog_age - -Model(age=2, first_name='Woof', last_name='Woof', signup_ts=datetime(2017, 6, 7), list_of_ints=[1, 2, 3]) -m = Model.model_validate( - { - 'age': 2, - 'first_name': b'Woof', - 'last_name': b'Woof', - 'signup_ts': '2017-06-07 00:00', - 'list_of_ints': [1, '2', b'3'], - } -) - -assert m.first_name == 'Woof', m.first_name -assert m.last_name == 'Woof', m.last_name -assert m.signup_ts == datetime(2017, 6, 7), m.signup_ts -assert day_of_week(m.signup_ts) == 3 - -data = {'age': 10, 'first_name': 'Alena', 'last_name': 'Sousova', 'list_of_ints': [410]} -m_from_obj = Model.model_validate(data) - -assert isinstance(m_from_obj, Model) -assert m_from_obj.age == 10 -assert m_from_obj.first_name == data['first_name'] -assert m_from_obj.last_name == data['last_name'] -assert m_from_obj.list_of_ints == data['list_of_ints'] - -m_copy = m_from_obj.model_copy() - -assert isinstance(m_copy, Model) -assert m_copy.age == m_from_obj.age -assert m_copy.first_name == m_from_obj.first_name -assert m_copy.last_name == m_from_obj.last_name -assert m_copy.list_of_ints == m_from_obj.list_of_ints - -T = TypeVar('T') - - -class WrapperModel(BaseModel, Generic[T]): - payload: T - - -int_instance = WrapperModel[int](payload=1) -int_instance.payload += 1 -assert int_instance.payload == 2 - -str_instance = WrapperModel[str](payload='a') -str_instance.payload += 'a' -assert str_instance.payload == 'aa' - -model_instance = WrapperModel[Model](payload=m) -model_instance.payload.list_of_ints.append(4) -assert model_instance.payload.list_of_ints == [1, 2, 3, 4] - - -class WithField(BaseModel): - age: int - first_name: str = Field('John', max_length=42) - - -# simple decorator -@validate_call -def foo(a: int, *, c: str = 'x') -> str: - return c * a - - -foo(1, c='thing') -foo(1) - - -# nested decorator should not produce an error -@validate_call(config={'arbitrary_types_allowed': True}) -def bar(a: int, *, c: str = 'x') -> str: - return c * a - - -bar(1, c='thing') -bar(1) - - -class Foo(BaseModel): - a: int - - -FooRef = ForwardRef('Foo') - - -class MyConf(BaseModel): - str_pyobject: ImportString[Type[date]] = Field(...) - callable_pyobject: ImportString[Type[date]] = Field(default=date) - - -conf = MyConf(str_pyobject='datetime.date') -var1: date = conf.str_pyobject(2020, 12, 20) -var2: date = conf.callable_pyobject(2111, 1, 1) - - -class MyPrivateAttr(BaseModel): - _private_field: str = PrivateAttr() - - -class PydanticTypes(BaseModel): - model_config = ConfigDict(validate_default=True) - - # Boolean - my_strict_bool: StrictBool = True - # Integer - my_positive_int: PositiveInt = 1 - my_negative_int: NegativeInt = -1 - my_non_positive_int: NonPositiveInt = -1 - my_non_negative_int: NonNegativeInt = 1 - my_strict_int: StrictInt = 1 - # Float - my_positive_float: PositiveFloat = 1.1 - my_negative_float: NegativeFloat = -1.1 - my_non_positive_float: NonPositiveFloat = -1.1 - my_non_negative_float: NonNegativeFloat = 1.1 - my_strict_float: StrictFloat = 1.1 - # Bytes - my_strict_bytes: StrictBytes = b'pika' - # String - my_strict_str: StrictStr = 'pika' - # ImportString - import_string_str: ImportString[Any] = 'datetime.date' # type: ignore[misc] -# MYPY: error: Unused "type: ignore" comment - import_string_callable: ImportString[Any] = date - # UUID - my_uuid1: UUID1 = UUID('a8098c1a-f86e-11da-bd1a-00112444be1e') - my_uuid1_str: UUID1 = 'a8098c1a-f86e-11da-bd1a-00112444be1e' -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "UUID") [assignment] - # Path - my_file_path: FilePath = Path(__file__) - my_file_path_str: FilePath = __file__ -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "Path") [assignment] - my_dir_path: DirectoryPath = Path('.') - my_dir_path_str: DirectoryPath = '.' -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "Path") [assignment] - # Json - my_json: Json[Dict[str, str]] = '{"hello": "world"}' -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "Dict[str, str]") [assignment] - my_json_list: Json[List[str]] = '["hello", "world"]' -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "List[str]") [assignment] - # Date - my_past_date: PastDate = date.today() - timedelta(1) - my_future_date: FutureDate = date.today() + timedelta(1) - # Datetime - my_past_datetime: PastDatetime = datetime.now() - timedelta(1) - my_future_datetime: FutureDatetime = datetime.now() + timedelta(1) - my_aware_datetime: AwareDatetime = datetime.now(tz=timezone.utc) - my_naive_datetime: NaiveDatetime = datetime.now() - # Complex - my_complex: complex = complex(1.0, 2.0) - - -validated = PydanticTypes() -validated.import_string_str(2021, 1, 1) -validated.import_string_callable(2021, 1, 1) -validated.my_uuid1.hex -validated.my_file_path.absolute() -validated.my_file_path_str.absolute() -validated.my_dir_path.absolute() -validated.my_dir_path_str.absolute() -validated.my_json['hello'].capitalize() -validated.my_json_list[0].capitalize() - - -class UrlModel(BaseModel): - x: Annotated[AnyUrl, UrlConstraints(allowed_schemes=['http'])] = Field(default=None) - y: Annotated[AnyUrl, UrlConstraints(allowed_schemes=['http'])] = Field(default=None) - z: Annotated[AnyUrl, UrlConstraints(allowed_schemes=['s3', 's3n', 's3a'])] = Field(default=None) - - -url_model = UrlModel(x='http://example.com') -assert url_model.x.host == 'example.com' - - -class SomeDict(TypedDict): - val: int - name: str - - -obj: SomeDict = { - 'val': 12, - 'name': 'John', -} - -config = ConfigDict(title='Record', extra='ignore', str_max_length=1234) - - -class CustomPath(PurePath): - def __init__(self, *args: str): - self.path = os.path.join(*args) - - def __fspath__(self) -> str: - return f'a/custom/{self.path}' - - -DynamicModel = create_model('DynamicModel') - -examples = Examples([]) - - -def double(value: Any, handler: Any) -> int: - return handler(value) * 2 - - -class WrapValidatorModel(BaseModel): - x: Annotated[int, WrapValidator(double)] - - -class Abstract(BaseModel): - class_id: ClassVar - - -class Concrete(Abstract): - class_id = 1 - - -def two_dim_shape_validator(v: Dict[str, Any]) -> Dict[str, Any]: - assert 'volume' not in v, 'shape is 2d, cannot have volume' - return v - - -class Square(BaseModel): - width: float - height: float - - free_validator = model_validator(mode='before')(two_dim_shape_validator) - - -class SomeModel(BaseModel): - field: int - - @field_validator("field") - @classmethod - def validate_field(cls, v: int) -> int: - return v - - -class OtherModel(BaseModel): - field: int - - @field_validator("field") - @classmethod - def validate_field(cls, v: int) -> int: - return SomeModel.validate_field(v) diff --git a/tests/mypy/outputs/1.0.1/mypy-plugin-strict_ini/plugin_default_factory.py b/tests/mypy/outputs/1.0.1/mypy-plugin-strict_ini/plugin_default_factory.py deleted file mode 100644 index 454d82f15a0..00000000000 --- a/tests/mypy/outputs/1.0.1/mypy-plugin-strict_ini/plugin_default_factory.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -See https://github.com/pydantic/pydantic/issues/4457 -""" - -from typing import Dict, List - -from pydantic import BaseModel, Field - - -def new_list() -> List[int]: - return [] - - -class Model(BaseModel): - l1: List[str] = Field(default_factory=list) - l2: List[int] = Field(default_factory=new_list) - l3: List[str] = Field(default_factory=lambda: list()) - l4: Dict[str, str] = Field(default_factory=dict) - l5: int = Field(default_factory=lambda: 123) - l6_error: List[str] = Field(default_factory=new_list) -# MYPY: error: Incompatible types in assignment (expression has type "List[int]", variable has type "List[str]") [assignment] - l7_error: int = Field(default_factory=list) -# MYPY: error: Incompatible types in assignment (expression has type "List[Any]", variable has type "int") [assignment] diff --git a/tests/mypy/outputs/1.0.1/mypy-plugin-strict_ini/plugin_fail.py b/tests/mypy/outputs/1.0.1/mypy-plugin-strict_ini/plugin_fail.py deleted file mode 100644 index 21f670b9e49..00000000000 --- a/tests/mypy/outputs/1.0.1/mypy-plugin-strict_ini/plugin_fail.py +++ /dev/null @@ -1,344 +0,0 @@ -from typing import Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, ConfigDict, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - x: int - y: str - - def method(self) -> None: - pass - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) - - -class ForbidExtraModel(BaseModel): - model_config = ConfigDict(extra=Extra.forbid) - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - model_config = ConfigDict(extra=1) # type: ignore[typeddict-item] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - model_config = ConfigDict(from_attributes={}) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - model_config = ConfigDict(from_attributes=list) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - model_config = ConfigDict(frozen=False) - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -# MYPY: error: Argument "x" to "InheritingModel" has incompatible type "str"; expected "int" [arg-type] -Model(x='1', y='2') -# MYPY: error: Argument "x" to "Model" has incompatible type "str"; expected "int" [arg-type] - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) -# MYPY: error: Argument "data" to "Response" has incompatible type "int"; expected "Model" [arg-type] - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) -# MYPY: error: Argument "y" to "AliasModel" has incompatible type "int"; expected "str" [arg-type] - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - z: int - - -DynamicAliasModel(y='y', z='1') -# MYPY: error: Argument "z" to "DynamicAliasModel" has incompatible type "str"; expected "int" [arg-type] - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - model_config = ConfigDict(populate_by_name=True) - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int - - model_config = ConfigDict(alias_generator=lambda x: x + '_') -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') - - model_config = ConfigDict(alias_generator=lambda x: x + '_') # type: ignore[pydantic-alias] - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "AliasGeneratorModel2" [call-arg] -AliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "AliasGeneratorModel2" [call-arg] - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsAliasGeneratorModel2" [call-arg] -KwargsAliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "KwargsAliasGeneratorModel2" [call-arg] - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - model_config = ConfigDict(frozen=False) - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.0.1/mypy-plugin-strict_ini/plugin_fail_baseConfig.py b/tests/mypy/outputs/1.0.1/mypy-plugin-strict_ini/plugin_fail_baseConfig.py deleted file mode 100644 index 8ca24eb0174..00000000000 --- a/tests/mypy/outputs/1.0.1/mypy-plugin-strict_ini/plugin_fail_baseConfig.py +++ /dev/null @@ -1,363 +0,0 @@ -from typing import Any, Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - x: int - y: str - - def method(self) -> None: - pass - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - def config_method(self) -> None: - ... - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) - - -class ForbidExtraModel(BaseModel): - class Config: - extra = 'forbid' - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - class Config: - extra = 1 # type: ignore[pydantic-config] - extra = 1 -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - class Config: - from_attributes: Any = {} # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - class Config: - from_attributes = list # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - class Config: - frozen = False - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -# MYPY: error: Argument "x" to "InheritingModel" has incompatible type "str"; expected "int" [arg-type] -Model(x='1', y='2') -# MYPY: error: Argument "x" to "Model" has incompatible type "str"; expected "int" [arg-type] - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) -# MYPY: error: Argument "data" to "Response" has incompatible type "int"; expected "Model" [arg-type] - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) -# MYPY: error: Argument "y" to "AliasModel" has incompatible type "int"; expected "str" [arg-type] - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - z: int - - -DynamicAliasModel(y='y', z='1') -# MYPY: error: Argument "z" to "DynamicAliasModel" has incompatible type "str"; expected "int" [arg-type] - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - class Config: - populate_by_name = True - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int - - class Config: -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - alias_generator = lambda x: x + '_' # noqa E731 - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') - - class Config: # type: ignore[pydantic-alias] - alias_generator = lambda x: x + '_' # noqa E731 - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "AliasGeneratorModel2" [call-arg] -AliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "AliasGeneratorModel2" [call-arg] - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsAliasGeneratorModel2" [call-arg] -KwargsAliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "KwargsAliasGeneratorModel2" [call-arg] - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - class Config: - frozen = False - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.0.1/mypy-plugin_ini/plugin_fail.py b/tests/mypy/outputs/1.0.1/mypy-plugin_ini/plugin_fail.py deleted file mode 100644 index e3ff6795808..00000000000 --- a/tests/mypy/outputs/1.0.1/mypy-plugin_ini/plugin_fail.py +++ /dev/null @@ -1,328 +0,0 @@ -from typing import Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, ConfigDict, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - x: int - y: str - - def method(self) -> None: - pass - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) - - -class ForbidExtraModel(BaseModel): - model_config = ConfigDict(extra=Extra.forbid) - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - model_config = ConfigDict(extra=1) # type: ignore[typeddict-item] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - model_config = ConfigDict(from_attributes={}) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - model_config = ConfigDict(from_attributes=list) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - model_config = ConfigDict(frozen=False) - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -Model(x='1', y='2') - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - -DynamicAliasModel(y='y', z='1') - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - model_config = ConfigDict(populate_by_name=True) - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Missing named argument "x" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Missing named argument "x" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): - x: int - - model_config = ConfigDict(alias_generator=lambda x: x + '_') - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): - x: int = Field(..., alias='y') - - model_config = ConfigDict(alias_generator=lambda x: x + '_') # type: ignore[pydantic-alias] -# MYPY: error: Unused "type: ignore" comment - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -AliasGeneratorModel2(y=1, z=1) - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): - x: int - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): - x: int = Field(..., alias='y') - - -KwargsAliasGeneratorModel2(x=1) -KwargsAliasGeneratorModel2(y=1, z=1) - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - model_config = ConfigDict(frozen=False) - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.0.1/mypy-plugin_ini/plugin_fail_baseConfig.py b/tests/mypy/outputs/1.0.1/mypy-plugin_ini/plugin_fail_baseConfig.py deleted file mode 100644 index 0087d4b14f1..00000000000 --- a/tests/mypy/outputs/1.0.1/mypy-plugin_ini/plugin_fail_baseConfig.py +++ /dev/null @@ -1,347 +0,0 @@ -from typing import Any, Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - x: int - y: str - - def method(self) -> None: - pass - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - def config_method(self) -> None: - ... - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) - - -class ForbidExtraModel(BaseModel): - class Config: - extra = 'forbid' - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - class Config: - extra = 1 # type: ignore[pydantic-config] - extra = 1 -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - class Config: - from_attributes: Any = {} # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - class Config: - from_attributes = list # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - class Config: - frozen = False - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -Model(x='1', y='2') - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - -DynamicAliasModel(y='y', z='1') - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - class Config: - populate_by_name = True - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Missing named argument "x" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Missing named argument "x" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): - x: int - - class Config: - alias_generator = lambda x: x + '_' # noqa E731 - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): - x: int = Field(..., alias='y') - - class Config: # type: ignore[pydantic-alias] -# MYPY: error: Unused "type: ignore" comment - alias_generator = lambda x: x + '_' # noqa E731 - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -AliasGeneratorModel2(y=1, z=1) - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): - x: int - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): - x: int = Field(..., alias='y') - - -KwargsAliasGeneratorModel2(x=1) -KwargsAliasGeneratorModel2(y=1, z=1) - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - class Config: - frozen = False - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.0.1/pyproject-default_toml/computed_fields.py b/tests/mypy/outputs/1.0.1/pyproject-default_toml/computed_fields.py deleted file mode 100644 index ebd875c452e..00000000000 --- a/tests/mypy/outputs/1.0.1/pyproject-default_toml/computed_fields.py +++ /dev/null @@ -1,41 +0,0 @@ -from pydantic import BaseModel, computed_field - - -class Square(BaseModel): - side: float - - @computed_field -# MYPY: error: Decorators on top of @property are not supported [misc] - @property - def area(self) -> float: - return self.side**2 - - @area.setter - def area(self, area: float) -> None: - self.side = area**0.5 - - -sq = Square(side=10) -y = 12.4 + sq.area -z = 'x' + sq.area -# MYPY: error: Unsupported operand types for + ("str" and "float") [operator] - -try: - from functools import cached_property -except ImportError: - pass -else: - - class Square2(BaseModel): - side: float - - @computed_field -# MYPY: error: Decorators on top of @property are not supported [misc] - @cached_property - def area(self) -> float: - return self.side**2 - - sq = Square(side=10) - y = 12.4 + sq.area - z = 'x' + sq.area -# MYPY: error: Unsupported operand types for + ("str" and "float") [operator] diff --git a/tests/mypy/outputs/1.0.1/pyproject-default_toml/fail1.py b/tests/mypy/outputs/1.0.1/pyproject-default_toml/fail1.py deleted file mode 100644 index 51ed34606a2..00000000000 --- a/tests/mypy/outputs/1.0.1/pyproject-default_toml/fail1.py +++ /dev/null @@ -1,25 +0,0 @@ -""" -Test mypy failure with missing attribute -""" -from datetime import datetime -from typing import List, Optional - -from pydantic import BaseModel -from pydantic.types import Json - - -class Model(BaseModel): - age: int - first_name = 'John' - last_name: Optional[str] = None - signup_ts: Optional[datetime] = None - list_of_ints: List[int] - json_list_of_ints: Json[List[int]] - - -m = Model(age=42, list_of_ints=[1, '2', b'3']) - -print(m.age + 'not integer') -# MYPY: error: Unsupported operand types for + ("int" and "str") [operator] -m.json_list_of_ints[0] + 'not integer' -# MYPY: error: Unsupported operand types for + ("int" and "str") [operator] diff --git a/tests/mypy/outputs/1.0.1/pyproject-default_toml/fail2.py b/tests/mypy/outputs/1.0.1/pyproject-default_toml/fail2.py deleted file mode 100644 index a5aadf2df82..00000000000 --- a/tests/mypy/outputs/1.0.1/pyproject-default_toml/fail2.py +++ /dev/null @@ -1,21 +0,0 @@ -""" -Test mypy failure with invalid types. -""" -from datetime import datetime -from typing import List, Optional - -from pydantic import BaseModel - - -class Model(BaseModel): - age: int - first_name = 'John' - last_name: Optional[str] = None - signup_ts: Optional[datetime] = None - list_of_ints: List[int] - - -m = Model(age=42, list_of_ints=[1, '2', b'3']) - -print(m.foobar) -# MYPY: error: "Model" has no attribute "foobar" [attr-defined] diff --git a/tests/mypy/outputs/1.0.1/pyproject-default_toml/fail3.py b/tests/mypy/outputs/1.0.1/pyproject-default_toml/fail3.py deleted file mode 100644 index 4e7abaeeac7..00000000000 --- a/tests/mypy/outputs/1.0.1/pyproject-default_toml/fail3.py +++ /dev/null @@ -1,22 +0,0 @@ -""" -Test mypy failure with invalid types. -""" -from typing import Generic, List, TypeVar - -from pydantic import BaseModel - -T = TypeVar('T') - - -class Model(BaseModel): - list_of_ints: List[int] - - -class WrapperModel(BaseModel, Generic[T]): - payload: T - - -model_instance = Model(list_of_ints=[1]) -wrapper_instance = WrapperModel[Model](payload=model_instance) -wrapper_instance.payload.list_of_ints.append('1') -# MYPY: error: Argument 1 to "append" of "list" has incompatible type "str"; expected "int" [arg-type] diff --git a/tests/mypy/outputs/1.0.1/pyproject-default_toml/fail4.py b/tests/mypy/outputs/1.0.1/pyproject-default_toml/fail4.py deleted file mode 100644 index c52c8511e13..00000000000 --- a/tests/mypy/outputs/1.0.1/pyproject-default_toml/fail4.py +++ /dev/null @@ -1,73 +0,0 @@ -from typing import Any - -from pydantic import BaseModel, root_validator, validate_call - - -@validate_call -def foo(a: int, *, c: str = 'x') -> str: -# MYPY: note: "foo" defined here - return c * a - - -# ok -x: str = foo(1, c='hello') -# fails -foo('x') -# MYPY: error: Argument 1 to "foo" has incompatible type "str"; expected "int" [arg-type] -foo(1, c=1) -# MYPY: error: Argument "c" to "foo" has incompatible type "int"; expected "str" [arg-type] -foo(1, 2) -# MYPY: error: Too many positional arguments for "foo" [misc] -# MYPY: error: Argument 2 to "foo" has incompatible type "int"; expected "str" [arg-type] -foo(1, d=2) -# MYPY: error: Unexpected keyword argument "d" for "foo" [call-arg] -# mypy assumes foo is just a function -callable(foo.raw_function) -# MYPY: error: "Callable[[int, DefaultNamedArg(str, 'c')], str]" has no attribute "raw_function" [attr-defined] - - -@validate_call -def bar() -> str: - return 'x' - - -# return type should be a string -y: int = bar() -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - - -# Demonstrate type errors for root_validator signatures -class Model(BaseModel): - @root_validator() -# MYPY: error: All overload variants of "root_validator" require at least one argument [call-overload] -# MYPY: note: Possible overload variants: -# MYPY: note: def root_validator(*, skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[False], skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] - @classmethod - def validate_1(cls, values: Any) -> Any: - return values - - @root_validator(pre=True, skip_on_failure=True) -# MYPY: error: No overload variant of "root_validator" matches argument types "bool", "bool" [call-overload] -# MYPY: note: Possible overload variants: -# MYPY: note: def root_validator(*, skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[False], skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] - @classmethod - def validate_2(cls, values: Any) -> Any: - return values - - @root_validator(pre=False) -# MYPY: error: No overload variant of "root_validator" matches argument type "bool" [call-overload] -# MYPY: note: Possible overload variants: -# MYPY: note: def root_validator(*, skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[False], skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] - @classmethod - def validate_3(cls, values: Any) -> Any: - return values - - -Model.non_existent_attribute -# MYPY: error: "Type[Model]" has no attribute "non_existent_attribute" [attr-defined] diff --git a/tests/mypy/outputs/1.0.1/pyproject-default_toml/success.py b/tests/mypy/outputs/1.0.1/pyproject-default_toml/success.py deleted file mode 100644 index e900c11c4a4..00000000000 --- a/tests/mypy/outputs/1.0.1/pyproject-default_toml/success.py +++ /dev/null @@ -1,345 +0,0 @@ -""" -Test pydantic's compliance with mypy. - -Do a little skipping about with types to demonstrate its usage. -""" -import os -from datetime import date, datetime, timedelta, timezone -from pathlib import Path, PurePath -from typing import Any, ClassVar, Dict, ForwardRef, Generic, List, Optional, Type, TypeVar -from uuid import UUID - -from typing_extensions import Annotated, TypedDict - -from pydantic import ( - UUID1, - AwareDatetime, - BaseModel, - ConfigDict, - DirectoryPath, - FilePath, - FutureDate, - FutureDatetime, - ImportString, - Json, - NaiveDatetime, - NegativeFloat, - NegativeInt, - NonNegativeFloat, - NonNegativeInt, - NonPositiveFloat, - NonPositiveInt, - PastDate, - PastDatetime, - PositiveFloat, - PositiveInt, - StrictBool, - StrictBytes, - StrictFloat, - StrictInt, - StrictStr, - UrlConstraints, - WrapValidator, - create_model, - field_validator, - model_validator, - root_validator, - validate_call, -) -from pydantic.fields import Field, PrivateAttr -from pydantic.json_schema import Examples -from pydantic.networks import AnyUrl - - -class Flags(BaseModel): - strict_bool: StrictBool = False - - def __str__(self) -> str: - return f'flag={self.strict_bool}' - - -class Model(BaseModel): - age: int - first_name: str = 'John' - last_name: Optional[str] = None - signup_ts: Optional[datetime] = None - list_of_ints: List[int] - - @field_validator('age') - def check_age(cls, value: int) -> int: - assert value < 100, 'too old' - return value - - @root_validator(skip_on_failure=True) - def root_check(cls, values: Dict[str, Any]) -> Dict[str, Any]: - return values - - @root_validator(pre=True, allow_reuse=False) - def pre_root_check(cls, values: Dict[str, Any]) -> Dict[str, Any]: - return values - - -def dog_years(age: int) -> int: - return age * 7 - - -def day_of_week(dt: datetime) -> int: - return dt.date().isoweekday() - - -m = Model(age=21, list_of_ints=[1, 2, 3]) - -assert m.age == 21, m.age -m.age = 42 -assert m.age == 42, m.age -assert m.first_name == 'John', m.first_name -assert m.last_name is None, m.last_name -assert m.list_of_ints == [1, 2, 3], m.list_of_ints - -dog_age = dog_years(m.age) -assert dog_age == 294, dog_age - -Model(age=2, first_name='Woof', last_name='Woof', signup_ts=datetime(2017, 6, 7), list_of_ints=[1, 2, 3]) -m = Model.model_validate( - { - 'age': 2, - 'first_name': b'Woof', - 'last_name': b'Woof', - 'signup_ts': '2017-06-07 00:00', - 'list_of_ints': [1, '2', b'3'], - } -) - -assert m.first_name == 'Woof', m.first_name -assert m.last_name == 'Woof', m.last_name -assert m.signup_ts == datetime(2017, 6, 7), m.signup_ts -assert day_of_week(m.signup_ts) == 3 - -data = {'age': 10, 'first_name': 'Alena', 'last_name': 'Sousova', 'list_of_ints': [410]} -m_from_obj = Model.model_validate(data) - -assert isinstance(m_from_obj, Model) -assert m_from_obj.age == 10 -assert m_from_obj.first_name == data['first_name'] -assert m_from_obj.last_name == data['last_name'] -assert m_from_obj.list_of_ints == data['list_of_ints'] - -m_copy = m_from_obj.model_copy() - -assert isinstance(m_copy, Model) -assert m_copy.age == m_from_obj.age -assert m_copy.first_name == m_from_obj.first_name -assert m_copy.last_name == m_from_obj.last_name -assert m_copy.list_of_ints == m_from_obj.list_of_ints - -T = TypeVar('T') - - -class WrapperModel(BaseModel, Generic[T]): - payload: T - - -int_instance = WrapperModel[int](payload=1) -int_instance.payload += 1 -assert int_instance.payload == 2 - -str_instance = WrapperModel[str](payload='a') -str_instance.payload += 'a' -assert str_instance.payload == 'aa' - -model_instance = WrapperModel[Model](payload=m) -model_instance.payload.list_of_ints.append(4) -assert model_instance.payload.list_of_ints == [1, 2, 3, 4] - - -class WithField(BaseModel): - age: int - first_name: str = Field('John', max_length=42) - - -# simple decorator -@validate_call -def foo(a: int, *, c: str = 'x') -> str: - return c * a - - -foo(1, c='thing') -foo(1) - - -# nested decorator should not produce an error -@validate_call(config={'arbitrary_types_allowed': True}) -def bar(a: int, *, c: str = 'x') -> str: - return c * a - - -bar(1, c='thing') -bar(1) - - -class Foo(BaseModel): - a: int - - -FooRef = ForwardRef('Foo') - - -class MyConf(BaseModel): - str_pyobject: ImportString[Type[date]] = Field(...) - callable_pyobject: ImportString[Type[date]] = Field(default=date) - - -conf = MyConf(str_pyobject='datetime.date') -var1: date = conf.str_pyobject(2020, 12, 20) -var2: date = conf.callable_pyobject(2111, 1, 1) - - -class MyPrivateAttr(BaseModel): - _private_field: str = PrivateAttr() - - -class PydanticTypes(BaseModel): - model_config = ConfigDict(validate_default=True) - - # Boolean - my_strict_bool: StrictBool = True - # Integer - my_positive_int: PositiveInt = 1 - my_negative_int: NegativeInt = -1 - my_non_positive_int: NonPositiveInt = -1 - my_non_negative_int: NonNegativeInt = 1 - my_strict_int: StrictInt = 1 - # Float - my_positive_float: PositiveFloat = 1.1 - my_negative_float: NegativeFloat = -1.1 - my_non_positive_float: NonPositiveFloat = -1.1 - my_non_negative_float: NonNegativeFloat = 1.1 - my_strict_float: StrictFloat = 1.1 - # Bytes - my_strict_bytes: StrictBytes = b'pika' - # String - my_strict_str: StrictStr = 'pika' - # ImportString - import_string_str: ImportString[Any] = 'datetime.date' # type: ignore[misc] -# MYPY: error: Unused "type: ignore" comment - import_string_callable: ImportString[Any] = date - # UUID - my_uuid1: UUID1 = UUID('a8098c1a-f86e-11da-bd1a-00112444be1e') - my_uuid1_str: UUID1 = 'a8098c1a-f86e-11da-bd1a-00112444be1e' -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "UUID") [assignment] - # Path - my_file_path: FilePath = Path(__file__) - my_file_path_str: FilePath = __file__ -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "Path") [assignment] - my_dir_path: DirectoryPath = Path('.') - my_dir_path_str: DirectoryPath = '.' -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "Path") [assignment] - # Json - my_json: Json[Dict[str, str]] = '{"hello": "world"}' -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "Dict[str, str]") [assignment] - my_json_list: Json[List[str]] = '["hello", "world"]' -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "List[str]") [assignment] - # Date - my_past_date: PastDate = date.today() - timedelta(1) - my_future_date: FutureDate = date.today() + timedelta(1) - # Datetime - my_past_datetime: PastDatetime = datetime.now() - timedelta(1) - my_future_datetime: FutureDatetime = datetime.now() + timedelta(1) - my_aware_datetime: AwareDatetime = datetime.now(tz=timezone.utc) - my_naive_datetime: NaiveDatetime = datetime.now() - # Complex - my_complex: complex = complex(1.0, 2.0) - - -validated = PydanticTypes() -validated.import_string_str(2021, 1, 1) -validated.import_string_callable(2021, 1, 1) -validated.my_uuid1.hex -validated.my_file_path.absolute() -validated.my_file_path_str.absolute() -validated.my_dir_path.absolute() -validated.my_dir_path_str.absolute() -validated.my_json['hello'].capitalize() -validated.my_json_list[0].capitalize() - - -class UrlModel(BaseModel): - x: Annotated[AnyUrl, UrlConstraints(allowed_schemes=['http'])] = Field(default=None) - y: Annotated[AnyUrl, UrlConstraints(allowed_schemes=['http'])] = Field(default=None) - z: Annotated[AnyUrl, UrlConstraints(allowed_schemes=['s3', 's3n', 's3a'])] = Field(default=None) - - -url_model = UrlModel(x='http://example.com') -assert url_model.x.host == 'example.com' - - -class SomeDict(TypedDict): - val: int - name: str - - -obj: SomeDict = { - 'val': 12, - 'name': 'John', -} - -config = ConfigDict(title='Record', extra='ignore', str_max_length=1234) - - -class CustomPath(PurePath): - def __init__(self, *args: str): - self.path = os.path.join(*args) - - def __fspath__(self) -> str: - return f'a/custom/{self.path}' - - -DynamicModel = create_model('DynamicModel') - -examples = Examples([]) - - -def double(value: Any, handler: Any) -> int: - return handler(value) * 2 - - -class WrapValidatorModel(BaseModel): - x: Annotated[int, WrapValidator(double)] - - -class Abstract(BaseModel): - class_id: ClassVar - - -class Concrete(Abstract): - class_id = 1 - - -def two_dim_shape_validator(v: Dict[str, Any]) -> Dict[str, Any]: - assert 'volume' not in v, 'shape is 2d, cannot have volume' - return v - - -class Square(BaseModel): - width: float - height: float - - free_validator = model_validator(mode='before')(two_dim_shape_validator) - - -class SomeModel(BaseModel): - field: int - - @field_validator("field") - @classmethod - def validate_field(cls, v: int) -> int: - return v - - -class OtherModel(BaseModel): - field: int - - @field_validator("field") - @classmethod - def validate_field(cls, v: int) -> int: - return SomeModel.validate_field(v) diff --git a/tests/mypy/outputs/1.0.1/pyproject-plugin-strict_toml/plugin_fail.py b/tests/mypy/outputs/1.0.1/pyproject-plugin-strict_toml/plugin_fail.py deleted file mode 100644 index 21f670b9e49..00000000000 --- a/tests/mypy/outputs/1.0.1/pyproject-plugin-strict_toml/plugin_fail.py +++ /dev/null @@ -1,344 +0,0 @@ -from typing import Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, ConfigDict, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - x: int - y: str - - def method(self) -> None: - pass - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) - - -class ForbidExtraModel(BaseModel): - model_config = ConfigDict(extra=Extra.forbid) - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - model_config = ConfigDict(extra=1) # type: ignore[typeddict-item] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - model_config = ConfigDict(from_attributes={}) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - model_config = ConfigDict(from_attributes=list) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - model_config = ConfigDict(frozen=False) - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -# MYPY: error: Argument "x" to "InheritingModel" has incompatible type "str"; expected "int" [arg-type] -Model(x='1', y='2') -# MYPY: error: Argument "x" to "Model" has incompatible type "str"; expected "int" [arg-type] - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) -# MYPY: error: Argument "data" to "Response" has incompatible type "int"; expected "Model" [arg-type] - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) -# MYPY: error: Argument "y" to "AliasModel" has incompatible type "int"; expected "str" [arg-type] - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - z: int - - -DynamicAliasModel(y='y', z='1') -# MYPY: error: Argument "z" to "DynamicAliasModel" has incompatible type "str"; expected "int" [arg-type] - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - model_config = ConfigDict(populate_by_name=True) - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int - - model_config = ConfigDict(alias_generator=lambda x: x + '_') -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') - - model_config = ConfigDict(alias_generator=lambda x: x + '_') # type: ignore[pydantic-alias] - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "AliasGeneratorModel2" [call-arg] -AliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "AliasGeneratorModel2" [call-arg] - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsAliasGeneratorModel2" [call-arg] -KwargsAliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "KwargsAliasGeneratorModel2" [call-arg] - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - model_config = ConfigDict(frozen=False) - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.0.1/pyproject-plugin-strict_toml/plugin_fail_baseConfig.py b/tests/mypy/outputs/1.0.1/pyproject-plugin-strict_toml/plugin_fail_baseConfig.py deleted file mode 100644 index 8ca24eb0174..00000000000 --- a/tests/mypy/outputs/1.0.1/pyproject-plugin-strict_toml/plugin_fail_baseConfig.py +++ /dev/null @@ -1,363 +0,0 @@ -from typing import Any, Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - x: int - y: str - - def method(self) -> None: - pass - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - def config_method(self) -> None: - ... - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) - - -class ForbidExtraModel(BaseModel): - class Config: - extra = 'forbid' - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - class Config: - extra = 1 # type: ignore[pydantic-config] - extra = 1 -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - class Config: - from_attributes: Any = {} # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - class Config: - from_attributes = list # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - class Config: - frozen = False - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -# MYPY: error: Argument "x" to "InheritingModel" has incompatible type "str"; expected "int" [arg-type] -Model(x='1', y='2') -# MYPY: error: Argument "x" to "Model" has incompatible type "str"; expected "int" [arg-type] - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) -# MYPY: error: Argument "data" to "Response" has incompatible type "int"; expected "Model" [arg-type] - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) -# MYPY: error: Argument "y" to "AliasModel" has incompatible type "int"; expected "str" [arg-type] - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - z: int - - -DynamicAliasModel(y='y', z='1') -# MYPY: error: Argument "z" to "DynamicAliasModel" has incompatible type "str"; expected "int" [arg-type] - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - class Config: - populate_by_name = True - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int - - class Config: -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - alias_generator = lambda x: x + '_' # noqa E731 - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') - - class Config: # type: ignore[pydantic-alias] - alias_generator = lambda x: x + '_' # noqa E731 - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "AliasGeneratorModel2" [call-arg] -AliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "AliasGeneratorModel2" [call-arg] - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsAliasGeneratorModel2" [call-arg] -KwargsAliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "KwargsAliasGeneratorModel2" [call-arg] - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - class Config: - frozen = False - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.0.1/pyproject-plugin_toml/plugin_fail.py b/tests/mypy/outputs/1.0.1/pyproject-plugin_toml/plugin_fail.py deleted file mode 100644 index e3ff6795808..00000000000 --- a/tests/mypy/outputs/1.0.1/pyproject-plugin_toml/plugin_fail.py +++ /dev/null @@ -1,328 +0,0 @@ -from typing import Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, ConfigDict, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - x: int - y: str - - def method(self) -> None: - pass - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) - - -class ForbidExtraModel(BaseModel): - model_config = ConfigDict(extra=Extra.forbid) - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - model_config = ConfigDict(extra=1) # type: ignore[typeddict-item] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - model_config = ConfigDict(from_attributes={}) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - model_config = ConfigDict(from_attributes=list) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - model_config = ConfigDict(frozen=False) - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -Model(x='1', y='2') - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - -DynamicAliasModel(y='y', z='1') - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - model_config = ConfigDict(populate_by_name=True) - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Missing named argument "x" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Missing named argument "x" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): - x: int - - model_config = ConfigDict(alias_generator=lambda x: x + '_') - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): - x: int = Field(..., alias='y') - - model_config = ConfigDict(alias_generator=lambda x: x + '_') # type: ignore[pydantic-alias] -# MYPY: error: Unused "type: ignore" comment - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -AliasGeneratorModel2(y=1, z=1) - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): - x: int - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): - x: int = Field(..., alias='y') - - -KwargsAliasGeneratorModel2(x=1) -KwargsAliasGeneratorModel2(y=1, z=1) - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - model_config = ConfigDict(frozen=False) - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.0.1/pyproject-plugin_toml/plugin_fail_baseConfig.py b/tests/mypy/outputs/1.0.1/pyproject-plugin_toml/plugin_fail_baseConfig.py deleted file mode 100644 index 0087d4b14f1..00000000000 --- a/tests/mypy/outputs/1.0.1/pyproject-plugin_toml/plugin_fail_baseConfig.py +++ /dev/null @@ -1,347 +0,0 @@ -from typing import Any, Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - x: int - y: str - - def method(self) -> None: - pass - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - def config_method(self) -> None: - ... - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) - - -class ForbidExtraModel(BaseModel): - class Config: - extra = 'forbid' - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - class Config: - extra = 1 # type: ignore[pydantic-config] - extra = 1 -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - class Config: - from_attributes: Any = {} # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - class Config: - from_attributes = list # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - class Config: - frozen = False - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -Model(x='1', y='2') - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - -DynamicAliasModel(y='y', z='1') - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - class Config: - populate_by_name = True - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Missing named argument "x" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Missing named argument "x" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): - x: int - - class Config: - alias_generator = lambda x: x + '_' # noqa E731 - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): - x: int = Field(..., alias='y') - - class Config: # type: ignore[pydantic-alias] -# MYPY: error: Unused "type: ignore" comment - alias_generator = lambda x: x + '_' # noqa E731 - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -AliasGeneratorModel2(y=1, z=1) - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): - x: int - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): - x: int = Field(..., alias='y') - - -KwargsAliasGeneratorModel2(x=1) -KwargsAliasGeneratorModel2(y=1, z=1) - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - class Config: - frozen = False - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.0.1/pyproject-plugin_toml/pydantic_settings.py b/tests/mypy/outputs/1.0.1/pyproject-plugin_toml/pydantic_settings.py deleted file mode 100644 index c2a077dd806..00000000000 --- a/tests/mypy/outputs/1.0.1/pyproject-plugin_toml/pydantic_settings.py +++ /dev/null @@ -1,24 +0,0 @@ -from pydantic_settings import BaseSettings, SettingsConfigDict - - -class Settings(BaseSettings): - foo: str - - -s = Settings() - -s = Settings(foo='test', _case_sensitive=True, _env_prefix='test__', _env_file='test') - -s = Settings(foo='test', _case_sensitive=1, _env_prefix=2, _env_file=3) -# MYPY: error: Argument "_case_sensitive" to "Settings" has incompatible type "int"; expected "Optional[bool]" [arg-type] -# MYPY: error: Argument "_env_prefix" to "Settings" has incompatible type "int"; expected "Optional[str]" [arg-type] -# MYPY: error: Argument "_env_file" to "Settings" has incompatible type "int"; expected "Optional[Union[Path, str, List[Union[Path, str]], Tuple[Union[Path, str], ...]]]" [arg-type] - - -class SettingsWithConfigDict(BaseSettings): - bar: str - - model_config = SettingsConfigDict(env_file='.env', env_file_encoding='utf-8') - - -scd = SettingsWithConfigDict() diff --git a/tests/mypy/outputs/1.1.1/mypy-default_ini/fail1.py b/tests/mypy/outputs/1.1.1/mypy-default_ini/fail1.py deleted file mode 100644 index 3207217f144..00000000000 --- a/tests/mypy/outputs/1.1.1/mypy-default_ini/fail1.py +++ /dev/null @@ -1,28 +0,0 @@ -""" -Test mypy failure with missing attribute -""" -from datetime import datetime -from typing import List, Optional - -from pydantic import BaseModel -from pydantic.types import Json - - -class Model(BaseModel): - age: int - first_name = 'John' - last_name: Optional[str] = None - signup_ts: Optional[datetime] = None - list_of_ints: List[int] - json_list_of_ints: Json[List[int]] - - -m = Model(age=42, list_of_ints=[1, '2', b'3']) -# MYPY: error: Missing named argument "json_list_of_ints" for "Model" [call-arg] -# MYPY: error: List item 1 has incompatible type "str"; expected "int" [list-item] -# MYPY: error: List item 2 has incompatible type "bytes"; expected "int" [list-item] - -print(m.age + 'not integer') -# MYPY: error: Unsupported operand types for + ("int" and "str") [operator] -m.json_list_of_ints[0] + 'not integer' -# MYPY: error: Unsupported operand types for + ("int" and "str") [operator] diff --git a/tests/mypy/outputs/1.1.1/mypy-default_ini/fail2.py b/tests/mypy/outputs/1.1.1/mypy-default_ini/fail2.py deleted file mode 100644 index 3da0618094c..00000000000 --- a/tests/mypy/outputs/1.1.1/mypy-default_ini/fail2.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -Test mypy failure with invalid types. -""" -from datetime import datetime -from typing import List, Optional - -from pydantic import BaseModel - - -class Model(BaseModel): - age: int - first_name = 'John' - last_name: Optional[str] = None - signup_ts: Optional[datetime] = None - list_of_ints: List[int] - - -m = Model(age=42, list_of_ints=[1, '2', b'3']) -# MYPY: error: List item 1 has incompatible type "str"; expected "int" [list-item] -# MYPY: error: List item 2 has incompatible type "bytes"; expected "int" [list-item] - -print(m.foobar) -# MYPY: error: "Model" has no attribute "foobar" [attr-defined] diff --git a/tests/mypy/outputs/1.1.1/mypy-default_ini/plugin_success.py b/tests/mypy/outputs/1.1.1/mypy-default_ini/plugin_success.py deleted file mode 100644 index 5fbf5e4bb5b..00000000000 --- a/tests/mypy/outputs/1.1.1/mypy-default_ini/plugin_success.py +++ /dev/null @@ -1,321 +0,0 @@ -from dataclasses import InitVar -from typing import Any, ClassVar, Generic, List, Optional, TypeVar, Union - -from typing_extensions import Self - -from pydantic import BaseModel, ConfigDict, Field, RootModel, create_model, field_validator, model_validator, validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - x: float - y: str - - model_config = ConfigDict(from_attributes=True) - - -class SelfReferencingModel(BaseModel): - submodel: Optional['SelfReferencingModel'] - - @property - def prop(self) -> None: - ... - - -SelfReferencingModel.model_rebuild() - -model = Model(x=1, y='y') -Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model.x = 2 -model.model_validate(model) - -self_referencing_model = SelfReferencingModel(submodel=SelfReferencingModel(submodel=None)) - - -class KwargsModel(BaseModel, from_attributes=True): - x: float - y: str - - -kwargs_model = KwargsModel(x=1, y='y') -KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model.x = 2 -kwargs_model.model_validate(kwargs_model.__dict__) - - -class InheritingModel(Model): - z: int = 1 - - -InheritingModel.model_validate(model.__dict__) - - -class ForwardReferencingModel(Model): - future: 'FutureModel' - - -class FutureModel(Model): - pass - - -ForwardReferencingModel.model_rebuild() -future_model = FutureModel(x=1, y='a') -forward_model = ForwardReferencingModel(x=1, y='a', future=future_model) - - -class NoMutationModel(BaseModel): - x: int - - model_config = ConfigDict(frozen=True) - - -class MutationModel(NoMutationModel): - a: int = 1 - - model_config = ConfigDict(frozen=False, from_attributes=True) - - -MutationModel(x=1).x = 2 -MutationModel.model_validate(model.__dict__) - - -class KwargsNoMutationModel(BaseModel, frozen=True): -# MYPY: error: Cannot inherit frozen dataclass from a non-frozen one [misc] - x: int - - -class KwargsMutationModel(KwargsNoMutationModel, frozen=False, from_attributes=True): -# MYPY: error: Cannot inherit non-frozen dataclass from a frozen one [misc] - a: int = 1 - - -KwargsMutationModel(x=1).x = 2 -# MYPY: error: Property "x" defined in "KwargsNoMutationModel" is read-only [misc] -KwargsMutationModel.model_validate(model.__dict__) - - -class OverrideModel(Model): - x: int - - -OverrideModel(x=1, y='b') - - -class Mixin: - def f(self) -> None: - pass - - -class MultiInheritanceModel(BaseModel, Mixin): - pass - - -MultiInheritanceModel().f() - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - - -alias_model = AliasModel(y='hello') -assert alias_model.x == 'hello' - - -class ClassVarModel(BaseModel): - x: int - y: ClassVar[int] = 1 - - -ClassVarModel(x=1) - - -@dataclass(config={'validate_assignment': True}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -class TypeAliasAsAttribute(BaseModel): - __type_alias_attribute__ = Union[str, bytes] - - -class NestedModel(BaseModel): - class Model(BaseModel): - id: str - - model: Model - - -_ = NestedModel.Model - - -DynamicModel = create_model('DynamicModel', __base__=Model) - -dynamic_model = DynamicModel(x=1, y='y') -dynamic_model.x = 2 - - -class FrozenModel(BaseModel): - x: int - - model_config = ConfigDict(frozen=True) - - -class NotFrozenModel(FrozenModel): - a: int = 1 - - model_config = ConfigDict(frozen=False, from_attributes=True) - - -NotFrozenModel(x=1).x = 2 -NotFrozenModel.model_validate(model.__dict__) - - -class KwargsFrozenModel(BaseModel, frozen=True): -# MYPY: error: Cannot inherit frozen dataclass from a non-frozen one [misc] - x: int - - -class KwargsNotFrozenModel(FrozenModel, frozen=False, from_attributes=True): - a: int = 1 - - -KwargsNotFrozenModel(x=1).x = 2 -KwargsNotFrozenModel.model_validate(model.__dict__) - - -class ModelWithSelfField(BaseModel): - self: str - - -def f(name: str) -> str: - return name - - -class ModelWithAllowReuseValidator(BaseModel): - name: str - normalize_name = field_validator('name')(f) - - -model_with_allow_reuse_validator = ModelWithAllowReuseValidator(name='xyz') - - -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(cls, name: str) -> str: - return name - - -def _default_factory_str() -> str: - return 'x' - - -def _default_factory_list() -> List[int]: - return [1, 2, 3] - - -class FieldDefaultTestingModel(BaseModel): - # Required - a: int - b: int = Field() - c: int = Field(...) - - # Default - d: int = Field(1) - - # Default factory - g: List[int] = Field(default_factory=_default_factory_list) - h: str = Field(default_factory=_default_factory_str) - i: str = Field(default_factory=lambda: 'test') - - -_TModel = TypeVar('_TModel') -_TType = TypeVar('_TType') - - -class OrmMixin(Generic[_TModel, _TType]): - @classmethod - def from_orm(cls, model: _TModel) -> _TType: - raise NotImplementedError - - @classmethod - def from_orm_optional(cls, model: Optional[_TModel]) -> Optional[_TType]: - if model is None: - return None - return cls.from_orm(model) - - -@dataclass -class MyDataClass: - foo: InitVar[str] - bar: str - - -MyDataClass(foo='foo', bar='bar') - - -def get_my_custom_validator(field_name: str) -> Any: - @validator(field_name, allow_reuse=True) - def my_custom_validator(cls: Any, v: int) -> int: - return v - - return my_custom_validator - - -def foo() -> None: - class MyModel(BaseModel): - number: int - custom_validator = get_my_custom_validator('number') # type: ignore[pydantic-field] -# MYPY: error: Unused "type: ignore" comment - - @model_validator(mode='before') - @classmethod - def validate_before(cls, values: Any) -> Any: - return values - - @model_validator(mode='after') - def validate_after(self) -> Self: - return self - - MyModel(number=2) - - -class InnerModel(BaseModel): - my_var: Union[str, None] = Field(default=None) - - -class OuterModel(InnerModel): - pass - - -m = OuterModel() -if m.my_var is None: - # In https://github.com/pydantic/pydantic/issues/7399, this was unreachable - print('not unreachable') - - -class Foo(BaseModel): - pass - - -class Bar(Foo, RootModel[int]): - pass diff --git a/tests/mypy/outputs/1.1.1/mypy-default_ini/plugin_success_baseConfig.py b/tests/mypy/outputs/1.1.1/mypy-default_ini/plugin_success_baseConfig.py deleted file mode 100644 index 0b2e5a6eebd..00000000000 --- a/tests/mypy/outputs/1.1.1/mypy-default_ini/plugin_success_baseConfig.py +++ /dev/null @@ -1,251 +0,0 @@ -from typing import ClassVar, Generic, List, Optional, TypeVar, Union - -from pydantic import BaseModel, Field, create_model, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - x: float - y: str - - model_config = dict(from_attributes=True) - - class NotConfig: - frozen = True - - -class SelfReferencingModel(BaseModel): - submodel: Optional['SelfReferencingModel'] - - @property - def prop(self) -> None: - ... - - -SelfReferencingModel.model_rebuild() - -model = Model(x=1, y='y') -Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model.x = 2 -model.model_validate(model) - -self_referencing_model = SelfReferencingModel(submodel=SelfReferencingModel(submodel=None)) - - -class KwargsModel(BaseModel, from_attributes=True): - x: float - y: str - - class NotConfig: - frozen = True - - -kwargs_model = KwargsModel(x=1, y='y') -KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model.x = 2 -kwargs_model.model_validate(kwargs_model.__dict__) - - -class InheritingModel(Model): - z: int = 1 - - -InheritingModel.model_validate(model.__dict__) - - -class ForwardReferencingModel(Model): - future: 'FutureModel' - - -class FutureModel(Model): - pass - - -ForwardReferencingModel.model_rebuild() -future_model = FutureModel(x=1, y='a') -forward_model = ForwardReferencingModel(x=1, y='a', future=future_model) - - -class NoMutationModel(BaseModel): - x: int - - model_config = dict(frozen=True) - - -class MutationModel(NoMutationModel): - a: int = 1 - - model_config = dict(frozen=False, from_attributes=True) - - -MutationModel(x=1).x = 2 -MutationModel.model_validate(model.__dict__) - - -class KwargsNoMutationModel(BaseModel, frozen=True): -# MYPY: error: Cannot inherit frozen dataclass from a non-frozen one [misc] - x: int - - -class KwargsMutationModel(KwargsNoMutationModel, frozen=False, from_attributes=True): -# MYPY: error: Cannot inherit non-frozen dataclass from a frozen one [misc] - a: int = 1 - - -KwargsMutationModel(x=1).x = 2 -# MYPY: error: Property "x" defined in "KwargsNoMutationModel" is read-only [misc] -KwargsMutationModel.model_validate(model.__dict__) - - -class OverrideModel(Model): - x: int - - -OverrideModel(x=1, y='b') - - -class Mixin: - def f(self) -> None: - pass - - -class MultiInheritanceModel(BaseModel, Mixin): - pass - - -MultiInheritanceModel().f() - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - - -alias_model = AliasModel(y='hello') -assert alias_model.x == 'hello' - - -class ClassVarModel(BaseModel): - x: int - y: ClassVar[int] = 1 - - -ClassVarModel(x=1) - - -@dataclass(config=dict(validate_assignment=True)) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -class TypeAliasAsAttribute(BaseModel): - __type_alias_attribute__ = Union[str, bytes] - - -class NestedModel(BaseModel): - class Model(BaseModel): - id: str - - model: Model - - -_ = NestedModel.Model - - -DynamicModel = create_model('DynamicModel', __base__=Model) - -dynamic_model = DynamicModel(x=1, y='y') -dynamic_model.x = 2 - - -class FrozenModel(BaseModel): - x: int - - model_config = dict(frozen=True) - - -class NotFrozenModel(FrozenModel): - a: int = 1 - - model_config = dict(frozen=False, from_attributes=True) - - -NotFrozenModel(x=1).x = 2 -NotFrozenModel.model_validate(model.__dict__) - - -class KwargsFrozenModel(BaseModel, frozen=True): -# MYPY: error: Cannot inherit frozen dataclass from a non-frozen one [misc] - x: int - - -class KwargsNotFrozenModel(FrozenModel, frozen=False, from_attributes=True): - a: int = 1 - - -KwargsNotFrozenModel(x=1).x = 2 -KwargsNotFrozenModel.model_validate(model.__dict__) - - -class ModelWithSelfField(BaseModel): - self: str - - -def f(name: str) -> str: - return name - - -class ModelWithAllowReuseValidator(BaseModel): - name: str - normalize_name = field_validator('name')(f) - - -model_with_allow_reuse_validator = ModelWithAllowReuseValidator(name='xyz') - - -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(cls, name: str) -> str: - return name - - -def _default_factory_str() -> str: - return 'x' - - -def _default_factory_list() -> List[int]: - return [1, 2, 3] - - -class FieldDefaultTestingModel(BaseModel): - # Required - a: int - b: int = Field() - c: int = Field(...) - - # Default - d: int = Field(1) - - # Default factory - g: List[int] = Field(default_factory=_default_factory_list) - h: str = Field(default_factory=_default_factory_str) - i: str = Field(default_factory=lambda: 'test') diff --git a/tests/mypy/outputs/1.1.1/mypy-plugin_ini/plugin_strict_fields.py b/tests/mypy/outputs/1.1.1/mypy-plugin_ini/plugin_strict_fields.py deleted file mode 100644 index 7cea5ba47b5..00000000000 --- a/tests/mypy/outputs/1.1.1/mypy-plugin_ini/plugin_strict_fields.py +++ /dev/null @@ -1,56 +0,0 @@ -from pydantic import BaseModel, Field - - -class Model(BaseModel): - a: int - b: int = Field(strict=True) - c: int = Field(strict=False) - - -# expected error: b -Model(a='1', b='2', c='3') -# MYPY: error: Argument "b" to "Model" has incompatible type "str"; expected "int" [arg-type] - - -class ModelStrictMode(BaseModel): - model_config = {'strict': True} - - a: int - b: int = Field(strict=True) - c: int = Field(strict=False) - - -# expected error: a, b -ModelStrictMode(a='1', b='2', c='3') -# MYPY: error: Argument "a" to "ModelStrictMode" has incompatible type "str"; expected "int" [arg-type] -# MYPY: error: Argument "b" to "ModelStrictMode" has incompatible type "str"; expected "int" [arg-type] - - -class ModelOverride1(Model): - b: int = Field(strict=False) - c: int = Field(strict=True) - - -# expected error: c -ModelOverride1(a='1', b='2', c='3') -# MYPY: error: Argument "c" to "ModelOverride1" has incompatible type "str"; expected "int" [arg-type] - - -class ModelOverride2(ModelStrictMode): - b: int = Field(strict=False) - c: int = Field(strict=True) - - -# expected error: a, c -ModelOverride2(a='1', b='2', c='3') -# MYPY: error: Argument "a" to "ModelOverride2" has incompatible type "str"; expected "int" [arg-type] -# MYPY: error: Argument "c" to "ModelOverride2" has incompatible type "str"; expected "int" [arg-type] - - -class ModelOverrideStrictMode(ModelStrictMode): - model_config = {'strict': False} - - -# expected error: b -ModelOverrideStrictMode(a='1', b='2', c='3') -# MYPY: error: Argument "b" to "ModelOverrideStrictMode" has incompatible type "str"; expected "int" [arg-type] diff --git a/tests/mypy/outputs/1.1.1/pyproject-default_toml/fail1.py b/tests/mypy/outputs/1.1.1/pyproject-default_toml/fail1.py deleted file mode 100644 index 3207217f144..00000000000 --- a/tests/mypy/outputs/1.1.1/pyproject-default_toml/fail1.py +++ /dev/null @@ -1,28 +0,0 @@ -""" -Test mypy failure with missing attribute -""" -from datetime import datetime -from typing import List, Optional - -from pydantic import BaseModel -from pydantic.types import Json - - -class Model(BaseModel): - age: int - first_name = 'John' - last_name: Optional[str] = None - signup_ts: Optional[datetime] = None - list_of_ints: List[int] - json_list_of_ints: Json[List[int]] - - -m = Model(age=42, list_of_ints=[1, '2', b'3']) -# MYPY: error: Missing named argument "json_list_of_ints" for "Model" [call-arg] -# MYPY: error: List item 1 has incompatible type "str"; expected "int" [list-item] -# MYPY: error: List item 2 has incompatible type "bytes"; expected "int" [list-item] - -print(m.age + 'not integer') -# MYPY: error: Unsupported operand types for + ("int" and "str") [operator] -m.json_list_of_ints[0] + 'not integer' -# MYPY: error: Unsupported operand types for + ("int" and "str") [operator] diff --git a/tests/mypy/outputs/1.1.1/pyproject-default_toml/fail2.py b/tests/mypy/outputs/1.1.1/pyproject-default_toml/fail2.py deleted file mode 100644 index 3da0618094c..00000000000 --- a/tests/mypy/outputs/1.1.1/pyproject-default_toml/fail2.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -Test mypy failure with invalid types. -""" -from datetime import datetime -from typing import List, Optional - -from pydantic import BaseModel - - -class Model(BaseModel): - age: int - first_name = 'John' - last_name: Optional[str] = None - signup_ts: Optional[datetime] = None - list_of_ints: List[int] - - -m = Model(age=42, list_of_ints=[1, '2', b'3']) -# MYPY: error: List item 1 has incompatible type "str"; expected "int" [list-item] -# MYPY: error: List item 2 has incompatible type "bytes"; expected "int" [list-item] - -print(m.foobar) -# MYPY: error: "Model" has no attribute "foobar" [attr-defined] diff --git a/tests/mypy/outputs/1.1.1/mypy-default_ini/metaclass_args.py b/tests/mypy/outputs/1.10.1/mypy-default_ini/metaclass_args.py similarity index 100% rename from tests/mypy/outputs/1.1.1/mypy-default_ini/metaclass_args.py rename to tests/mypy/outputs/1.10.1/mypy-default_ini/metaclass_args.py diff --git a/tests/mypy/outputs/1.4.1/mypy-default_ini/plugin_success.py b/tests/mypy/outputs/1.10.1/mypy-default_ini/plugin_success.py similarity index 100% rename from tests/mypy/outputs/1.4.1/mypy-default_ini/plugin_success.py rename to tests/mypy/outputs/1.10.1/mypy-default_ini/plugin_success.py diff --git a/tests/mypy/outputs/1.2.0/mypy-default_ini/plugin_success_baseConfig.py b/tests/mypy/outputs/1.10.1/mypy-default_ini/plugin_success_baseConfig.py similarity index 100% rename from tests/mypy/outputs/1.2.0/mypy-default_ini/plugin_success_baseConfig.py rename to tests/mypy/outputs/1.10.1/mypy-default_ini/plugin_success_baseConfig.py diff --git a/tests/mypy/outputs/1.1.1/mypy-default_ini/pydantic_settings.py b/tests/mypy/outputs/1.10.1/mypy-default_ini/pydantic_settings.py similarity index 100% rename from tests/mypy/outputs/1.1.1/mypy-default_ini/pydantic_settings.py rename to tests/mypy/outputs/1.10.1/mypy-default_ini/pydantic_settings.py diff --git a/tests/mypy/outputs/1.1.1/mypy-default_ini/root_models.py b/tests/mypy/outputs/1.10.1/mypy-default_ini/root_models.py similarity index 100% rename from tests/mypy/outputs/1.1.1/mypy-default_ini/root_models.py rename to tests/mypy/outputs/1.10.1/mypy-default_ini/root_models.py diff --git a/tests/mypy/outputs/1.0.1/mypy-plugin-strict_ini/fail_defaults.py b/tests/mypy/outputs/1.10.1/mypy-plugin-strict_ini/fail_defaults.py similarity index 100% rename from tests/mypy/outputs/1.0.1/mypy-plugin-strict_ini/fail_defaults.py rename to tests/mypy/outputs/1.10.1/mypy-plugin-strict_ini/fail_defaults.py diff --git a/tests/mypy/outputs/1.4.1/mypy-plugin-strict_ini/plugin_fail.py b/tests/mypy/outputs/1.10.1/mypy-plugin-strict_ini/plugin_fail.py similarity index 88% rename from tests/mypy/outputs/1.4.1/mypy-plugin-strict_ini/plugin_fail.py rename to tests/mypy/outputs/1.10.1/mypy-plugin-strict_ini/plugin_fail.py index 0c594200132..a89a3e52f3d 100644 --- a/tests/mypy/outputs/1.4.1/mypy-plugin-strict_ini/plugin_fail.py +++ b/tests/mypy/outputs/1.10.1/mypy-plugin-strict_ini/plugin_fail.py @@ -100,7 +100,7 @@ class DefaultTestingModel(BaseModel): # Required a: int b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] +# MYPY: error: Incompatible types in assignment (expression has type "EllipsisType", variable has type "int") [assignment] c: int = Field(...) d: Union[int, str] e = ... @@ -303,34 +303,6 @@ class InheritingModel2(FrozenModel): inheriting2.y = 'd' -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Callable[[], Any] | None" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - class ModelWithAnnotatedValidator(BaseModel): name: str diff --git a/tests/mypy/outputs/1.4.1/mypy-plugin-strict_ini/plugin_fail_baseConfig.py b/tests/mypy/outputs/1.10.1/mypy-plugin-strict_ini/plugin_fail_baseConfig.py similarity index 88% rename from tests/mypy/outputs/1.4.1/mypy-plugin-strict_ini/plugin_fail_baseConfig.py rename to tests/mypy/outputs/1.10.1/mypy-plugin-strict_ini/plugin_fail_baseConfig.py index 43ee0a6a092..d758ec11a54 100644 --- a/tests/mypy/outputs/1.4.1/mypy-plugin-strict_ini/plugin_fail_baseConfig.py +++ b/tests/mypy/outputs/1.10.1/mypy-plugin-strict_ini/plugin_fail_baseConfig.py @@ -112,7 +112,7 @@ class DefaultTestingModel(BaseModel): # Required a: int b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] +# MYPY: error: Incompatible types in assignment (expression has type "EllipsisType", variable has type "int") [assignment] c: int = Field(...) d: Union[int, str] e = ... @@ -322,34 +322,6 @@ class Config: inheriting2.y = 'd' -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Callable[[], Any] | None" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - class ModelWithAnnotatedValidator(BaseModel): name: str diff --git a/tests/mypy/outputs/1.0.1/mypy-plugin-strict_ini/plugin_success.py b/tests/mypy/outputs/1.10.1/mypy-plugin-strict_ini/plugin_success.py similarity index 100% rename from tests/mypy/outputs/1.0.1/mypy-plugin-strict_ini/plugin_success.py rename to tests/mypy/outputs/1.10.1/mypy-plugin-strict_ini/plugin_success.py diff --git a/tests/mypy/outputs/1.0.1/mypy-plugin-strict_ini/plugin_success_baseConfig.py b/tests/mypy/outputs/1.10.1/mypy-plugin-strict_ini/plugin_success_baseConfig.py similarity index 100% rename from tests/mypy/outputs/1.0.1/mypy-plugin-strict_ini/plugin_success_baseConfig.py rename to tests/mypy/outputs/1.10.1/mypy-plugin-strict_ini/plugin_success_baseConfig.py diff --git a/tests/mypy/outputs/1.0.1/mypy-plugin-very-strict_ini/metaclass_args.py b/tests/mypy/outputs/1.10.1/mypy-plugin-very-strict_ini/metaclass_args.py similarity index 100% rename from tests/mypy/outputs/1.0.1/mypy-plugin-very-strict_ini/metaclass_args.py rename to tests/mypy/outputs/1.10.1/mypy-plugin-very-strict_ini/metaclass_args.py diff --git a/tests/mypy/outputs/1.0.1/mypy-plugin_ini/custom_constructor.py b/tests/mypy/outputs/1.10.1/mypy-plugin_ini/custom_constructor.py similarity index 100% rename from tests/mypy/outputs/1.0.1/mypy-plugin_ini/custom_constructor.py rename to tests/mypy/outputs/1.10.1/mypy-plugin_ini/custom_constructor.py diff --git a/tests/mypy/outputs/1.0.1/mypy-plugin_ini/frozen_field.py b/tests/mypy/outputs/1.10.1/mypy-plugin_ini/frozen_field.py similarity index 100% rename from tests/mypy/outputs/1.0.1/mypy-plugin_ini/frozen_field.py rename to tests/mypy/outputs/1.10.1/mypy-plugin_ini/frozen_field.py diff --git a/tests/mypy/outputs/1.4.1/mypy-plugin_ini/plugin_fail.py b/tests/mypy/outputs/1.10.1/mypy-plugin_ini/plugin_fail.py similarity index 86% rename from tests/mypy/outputs/1.4.1/mypy-plugin_ini/plugin_fail.py rename to tests/mypy/outputs/1.10.1/mypy-plugin_ini/plugin_fail.py index 15ad880fcf0..40b7495377f 100644 --- a/tests/mypy/outputs/1.4.1/mypy-plugin_ini/plugin_fail.py +++ b/tests/mypy/outputs/1.10.1/mypy-plugin_ini/plugin_fail.py @@ -100,7 +100,7 @@ class DefaultTestingModel(BaseModel): # Required a: int b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] +# MYPY: error: Incompatible types in assignment (expression has type "EllipsisType", variable has type "int") [assignment] c: int = Field(...) d: Union[int, str] e = ... @@ -287,34 +287,6 @@ class InheritingModel2(FrozenModel): inheriting2.y = 'd' -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Callable[[], Any] | None" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - class ModelWithAnnotatedValidator(BaseModel): name: str diff --git a/tests/mypy/outputs/1.4.1/mypy-plugin_ini/plugin_fail_baseConfig.py b/tests/mypy/outputs/1.10.1/mypy-plugin_ini/plugin_fail_baseConfig.py similarity index 86% rename from tests/mypy/outputs/1.4.1/mypy-plugin_ini/plugin_fail_baseConfig.py rename to tests/mypy/outputs/1.10.1/mypy-plugin_ini/plugin_fail_baseConfig.py index d616b902787..f92c0f9d868 100644 --- a/tests/mypy/outputs/1.4.1/mypy-plugin_ini/plugin_fail_baseConfig.py +++ b/tests/mypy/outputs/1.10.1/mypy-plugin_ini/plugin_fail_baseConfig.py @@ -112,7 +112,7 @@ class DefaultTestingModel(BaseModel): # Required a: int b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] +# MYPY: error: Incompatible types in assignment (expression has type "EllipsisType", variable has type "int") [assignment] c: int = Field(...) d: Union[int, str] e = ... @@ -306,34 +306,6 @@ class Config: inheriting2.y = 'd' -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Callable[[], Any] | None" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - class ModelWithAnnotatedValidator(BaseModel): name: str diff --git a/tests/mypy/outputs/1.0.1/mypy-plugin_ini/plugin_optional_inheritance.py b/tests/mypy/outputs/1.10.1/mypy-plugin_ini/plugin_optional_inheritance.py similarity index 100% rename from tests/mypy/outputs/1.0.1/mypy-plugin_ini/plugin_optional_inheritance.py rename to tests/mypy/outputs/1.10.1/mypy-plugin_ini/plugin_optional_inheritance.py diff --git a/tests/mypy/outputs/1.0.1/mypy-plugin_ini/plugin_strict_fields.py b/tests/mypy/outputs/1.10.1/mypy-plugin_ini/plugin_strict_fields.py similarity index 100% rename from tests/mypy/outputs/1.0.1/mypy-plugin_ini/plugin_strict_fields.py rename to tests/mypy/outputs/1.10.1/mypy-plugin_ini/plugin_strict_fields.py diff --git a/tests/mypy/outputs/1.0.1/mypy-plugin_ini/pydantic_settings.py b/tests/mypy/outputs/1.10.1/mypy-plugin_ini/pydantic_settings.py similarity index 86% rename from tests/mypy/outputs/1.0.1/mypy-plugin_ini/pydantic_settings.py rename to tests/mypy/outputs/1.10.1/mypy-plugin_ini/pydantic_settings.py index c2a077dd806..4d5ea25f157 100644 --- a/tests/mypy/outputs/1.0.1/mypy-plugin_ini/pydantic_settings.py +++ b/tests/mypy/outputs/1.10.1/mypy-plugin_ini/pydantic_settings.py @@ -12,7 +12,7 @@ class Settings(BaseSettings): s = Settings(foo='test', _case_sensitive=1, _env_prefix=2, _env_file=3) # MYPY: error: Argument "_case_sensitive" to "Settings" has incompatible type "int"; expected "Optional[bool]" [arg-type] # MYPY: error: Argument "_env_prefix" to "Settings" has incompatible type "int"; expected "Optional[str]" [arg-type] -# MYPY: error: Argument "_env_file" to "Settings" has incompatible type "int"; expected "Optional[Union[Path, str, List[Union[Path, str]], Tuple[Union[Path, str], ...]]]" [arg-type] +# MYPY: error: Argument "_env_file" to "Settings" has incompatible type "int"; expected "Optional[Union[Path, str, Sequence[Union[Path, str]]]]" [arg-type] class SettingsWithConfigDict(BaseSettings): diff --git a/tests/mypy/outputs/1.0.1/mypy-plugin_ini/root_models.py b/tests/mypy/outputs/1.10.1/mypy-plugin_ini/root_models.py similarity index 100% rename from tests/mypy/outputs/1.0.1/mypy-plugin_ini/root_models.py rename to tests/mypy/outputs/1.10.1/mypy-plugin_ini/root_models.py diff --git a/tests/mypy/outputs/1.1.1/pyproject-default_toml/pydantic_settings.py b/tests/mypy/outputs/1.10.1/pyproject-default_toml/pydantic_settings.py similarity index 100% rename from tests/mypy/outputs/1.1.1/pyproject-default_toml/pydantic_settings.py rename to tests/mypy/outputs/1.10.1/pyproject-default_toml/pydantic_settings.py diff --git a/tests/mypy/outputs/1.1.1/pyproject-default_toml/root_models.py b/tests/mypy/outputs/1.10.1/pyproject-default_toml/root_models.py similarity index 100% rename from tests/mypy/outputs/1.1.1/pyproject-default_toml/root_models.py rename to tests/mypy/outputs/1.10.1/pyproject-default_toml/root_models.py diff --git a/tests/mypy/outputs/1.0.1/pyproject-plugin-strict-equality_toml/strict_equality.py b/tests/mypy/outputs/1.10.1/pyproject-plugin-strict-equality_toml/strict_equality.py similarity index 100% rename from tests/mypy/outputs/1.0.1/pyproject-plugin-strict-equality_toml/strict_equality.py rename to tests/mypy/outputs/1.10.1/pyproject-plugin-strict-equality_toml/strict_equality.py diff --git a/tests/mypy/outputs/1.0.1/pyproject-plugin-strict_toml/fail_defaults.py b/tests/mypy/outputs/1.10.1/pyproject-plugin-strict_toml/fail_defaults.py similarity index 100% rename from tests/mypy/outputs/1.0.1/pyproject-plugin-strict_toml/fail_defaults.py rename to tests/mypy/outputs/1.10.1/pyproject-plugin-strict_toml/fail_defaults.py diff --git a/tests/mypy/outputs/1.4.1/pyproject-plugin-strict_toml/plugin_fail.py b/tests/mypy/outputs/1.10.1/pyproject-plugin-strict_toml/plugin_fail.py similarity index 88% rename from tests/mypy/outputs/1.4.1/pyproject-plugin-strict_toml/plugin_fail.py rename to tests/mypy/outputs/1.10.1/pyproject-plugin-strict_toml/plugin_fail.py index 0c594200132..a89a3e52f3d 100644 --- a/tests/mypy/outputs/1.4.1/pyproject-plugin-strict_toml/plugin_fail.py +++ b/tests/mypy/outputs/1.10.1/pyproject-plugin-strict_toml/plugin_fail.py @@ -100,7 +100,7 @@ class DefaultTestingModel(BaseModel): # Required a: int b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] +# MYPY: error: Incompatible types in assignment (expression has type "EllipsisType", variable has type "int") [assignment] c: int = Field(...) d: Union[int, str] e = ... @@ -303,34 +303,6 @@ class InheritingModel2(FrozenModel): inheriting2.y = 'd' -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Callable[[], Any] | None" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - class ModelWithAnnotatedValidator(BaseModel): name: str diff --git a/tests/mypy/outputs/1.4.1/pyproject-plugin-strict_toml/plugin_fail_baseConfig.py b/tests/mypy/outputs/1.10.1/pyproject-plugin-strict_toml/plugin_fail_baseConfig.py similarity index 88% rename from tests/mypy/outputs/1.4.1/pyproject-plugin-strict_toml/plugin_fail_baseConfig.py rename to tests/mypy/outputs/1.10.1/pyproject-plugin-strict_toml/plugin_fail_baseConfig.py index 43ee0a6a092..d758ec11a54 100644 --- a/tests/mypy/outputs/1.4.1/pyproject-plugin-strict_toml/plugin_fail_baseConfig.py +++ b/tests/mypy/outputs/1.10.1/pyproject-plugin-strict_toml/plugin_fail_baseConfig.py @@ -112,7 +112,7 @@ class DefaultTestingModel(BaseModel): # Required a: int b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] +# MYPY: error: Incompatible types in assignment (expression has type "EllipsisType", variable has type "int") [assignment] c: int = Field(...) d: Union[int, str] e = ... @@ -322,34 +322,6 @@ class Config: inheriting2.y = 'd' -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Callable[[], Any] | None" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - class ModelWithAnnotatedValidator(BaseModel): name: str diff --git a/tests/mypy/outputs/1.0.1/pyproject-plugin-strict_toml/plugin_success.py b/tests/mypy/outputs/1.10.1/pyproject-plugin-strict_toml/plugin_success.py similarity index 100% rename from tests/mypy/outputs/1.0.1/pyproject-plugin-strict_toml/plugin_success.py rename to tests/mypy/outputs/1.10.1/pyproject-plugin-strict_toml/plugin_success.py diff --git a/tests/mypy/outputs/1.0.1/pyproject-plugin-strict_toml/plugin_success_baseConfig.py b/tests/mypy/outputs/1.10.1/pyproject-plugin-strict_toml/plugin_success_baseConfig.py similarity index 100% rename from tests/mypy/outputs/1.0.1/pyproject-plugin-strict_toml/plugin_success_baseConfig.py rename to tests/mypy/outputs/1.10.1/pyproject-plugin-strict_toml/plugin_success_baseConfig.py diff --git a/tests/mypy/outputs/1.4.1/pyproject-plugin_toml/plugin_fail.py b/tests/mypy/outputs/1.10.1/pyproject-plugin_toml/plugin_fail.py similarity index 86% rename from tests/mypy/outputs/1.4.1/pyproject-plugin_toml/plugin_fail.py rename to tests/mypy/outputs/1.10.1/pyproject-plugin_toml/plugin_fail.py index 15ad880fcf0..40b7495377f 100644 --- a/tests/mypy/outputs/1.4.1/pyproject-plugin_toml/plugin_fail.py +++ b/tests/mypy/outputs/1.10.1/pyproject-plugin_toml/plugin_fail.py @@ -100,7 +100,7 @@ class DefaultTestingModel(BaseModel): # Required a: int b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] +# MYPY: error: Incompatible types in assignment (expression has type "EllipsisType", variable has type "int") [assignment] c: int = Field(...) d: Union[int, str] e = ... @@ -287,34 +287,6 @@ class InheritingModel2(FrozenModel): inheriting2.y = 'd' -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Callable[[], Any] | None" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - class ModelWithAnnotatedValidator(BaseModel): name: str diff --git a/tests/mypy/outputs/1.4.1/pyproject-plugin_toml/plugin_fail_baseConfig.py b/tests/mypy/outputs/1.10.1/pyproject-plugin_toml/plugin_fail_baseConfig.py similarity index 86% rename from tests/mypy/outputs/1.4.1/pyproject-plugin_toml/plugin_fail_baseConfig.py rename to tests/mypy/outputs/1.10.1/pyproject-plugin_toml/plugin_fail_baseConfig.py index d616b902787..f92c0f9d868 100644 --- a/tests/mypy/outputs/1.4.1/pyproject-plugin_toml/plugin_fail_baseConfig.py +++ b/tests/mypy/outputs/1.10.1/pyproject-plugin_toml/plugin_fail_baseConfig.py @@ -112,7 +112,7 @@ class DefaultTestingModel(BaseModel): # Required a: int b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] +# MYPY: error: Incompatible types in assignment (expression has type "EllipsisType", variable has type "int") [assignment] c: int = Field(...) d: Union[int, str] e = ... @@ -306,34 +306,6 @@ class Config: inheriting2.y = 'd' -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Callable[[], Any] | None" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - class ModelWithAnnotatedValidator(BaseModel): name: str diff --git a/tests/mypy/outputs/1.0.1/pyproject-default_toml/pydantic_settings.py b/tests/mypy/outputs/1.10.1/pyproject-plugin_toml/pydantic_settings.py similarity index 86% rename from tests/mypy/outputs/1.0.1/pyproject-default_toml/pydantic_settings.py rename to tests/mypy/outputs/1.10.1/pyproject-plugin_toml/pydantic_settings.py index c2a077dd806..4d5ea25f157 100644 --- a/tests/mypy/outputs/1.0.1/pyproject-default_toml/pydantic_settings.py +++ b/tests/mypy/outputs/1.10.1/pyproject-plugin_toml/pydantic_settings.py @@ -12,7 +12,7 @@ class Settings(BaseSettings): s = Settings(foo='test', _case_sensitive=1, _env_prefix=2, _env_file=3) # MYPY: error: Argument "_case_sensitive" to "Settings" has incompatible type "int"; expected "Optional[bool]" [arg-type] # MYPY: error: Argument "_env_prefix" to "Settings" has incompatible type "int"; expected "Optional[str]" [arg-type] -# MYPY: error: Argument "_env_file" to "Settings" has incompatible type "int"; expected "Optional[Union[Path, str, List[Union[Path, str]], Tuple[Union[Path, str], ...]]]" [arg-type] +# MYPY: error: Argument "_env_file" to "Settings" has incompatible type "int"; expected "Optional[Union[Path, str, Sequence[Union[Path, str]]]]" [arg-type] class SettingsWithConfigDict(BaseSettings): diff --git a/tests/mypy/outputs/1.2.0/mypy-default_ini/plugin_success.py b/tests/mypy/outputs/1.2.0/mypy-default_ini/plugin_success.py deleted file mode 100644 index c23d0a736ad..00000000000 --- a/tests/mypy/outputs/1.2.0/mypy-default_ini/plugin_success.py +++ /dev/null @@ -1,319 +0,0 @@ -from dataclasses import InitVar -from typing import Any, ClassVar, Generic, List, Optional, TypeVar, Union - -from typing_extensions import Self - -from pydantic import BaseModel, ConfigDict, Field, RootModel, create_model, field_validator, model_validator, validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - x: float - y: str - - model_config = ConfigDict(from_attributes=True) - - -class SelfReferencingModel(BaseModel): - submodel: Optional['SelfReferencingModel'] - - @property - def prop(self) -> None: - ... - - -SelfReferencingModel.model_rebuild() - -model = Model(x=1, y='y') -Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model.x = 2 -model.model_validate(model) - -self_referencing_model = SelfReferencingModel(submodel=SelfReferencingModel(submodel=None)) - - -class KwargsModel(BaseModel, from_attributes=True): - x: float - y: str - - -kwargs_model = KwargsModel(x=1, y='y') -KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model.x = 2 -kwargs_model.model_validate(kwargs_model.__dict__) - - -class InheritingModel(Model): - z: int = 1 - - -InheritingModel.model_validate(model.__dict__) - - -class ForwardReferencingModel(Model): - future: 'FutureModel' - - -class FutureModel(Model): - pass - - -ForwardReferencingModel.model_rebuild() -future_model = FutureModel(x=1, y='a') -forward_model = ForwardReferencingModel(x=1, y='a', future=future_model) - - -class NoMutationModel(BaseModel): - x: int - - model_config = ConfigDict(frozen=True) - - -class MutationModel(NoMutationModel): - a: int = 1 - - model_config = ConfigDict(frozen=False, from_attributes=True) - - -MutationModel(x=1).x = 2 -MutationModel.model_validate(model.__dict__) - - -class KwargsNoMutationModel(BaseModel, frozen=True): - x: int - - -class KwargsMutationModel(KwargsNoMutationModel, frozen=False, from_attributes=True): -# MYPY: error: Cannot inherit non-frozen dataclass from a frozen one [misc] - a: int = 1 - - -KwargsMutationModel(x=1).x = 2 -# MYPY: error: Property "x" defined in "KwargsNoMutationModel" is read-only [misc] -KwargsMutationModel.model_validate(model.__dict__) - - -class OverrideModel(Model): - x: int - - -OverrideModel(x=1, y='b') - - -class Mixin: - def f(self) -> None: - pass - - -class MultiInheritanceModel(BaseModel, Mixin): - pass - - -MultiInheritanceModel().f() - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - - -alias_model = AliasModel(y='hello') -assert alias_model.x == 'hello' - - -class ClassVarModel(BaseModel): - x: int - y: ClassVar[int] = 1 - - -ClassVarModel(x=1) - - -@dataclass(config={'validate_assignment': True}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -class TypeAliasAsAttribute(BaseModel): - __type_alias_attribute__ = Union[str, bytes] - - -class NestedModel(BaseModel): - class Model(BaseModel): - id: str - - model: Model - - -_ = NestedModel.Model - - -DynamicModel = create_model('DynamicModel', __base__=Model) - -dynamic_model = DynamicModel(x=1, y='y') -dynamic_model.x = 2 - - -class FrozenModel(BaseModel): - x: int - - model_config = ConfigDict(frozen=True) - - -class NotFrozenModel(FrozenModel): - a: int = 1 - - model_config = ConfigDict(frozen=False, from_attributes=True) - - -NotFrozenModel(x=1).x = 2 -NotFrozenModel.model_validate(model.__dict__) - - -class KwargsFrozenModel(BaseModel, frozen=True): - x: int - - -class KwargsNotFrozenModel(FrozenModel, frozen=False, from_attributes=True): - a: int = 1 - - -KwargsNotFrozenModel(x=1).x = 2 -KwargsNotFrozenModel.model_validate(model.__dict__) - - -class ModelWithSelfField(BaseModel): - self: str - - -def f(name: str) -> str: - return name - - -class ModelWithAllowReuseValidator(BaseModel): - name: str - normalize_name = field_validator('name')(f) - - -model_with_allow_reuse_validator = ModelWithAllowReuseValidator(name='xyz') - - -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(cls, name: str) -> str: - return name - - -def _default_factory_str() -> str: - return 'x' - - -def _default_factory_list() -> List[int]: - return [1, 2, 3] - - -class FieldDefaultTestingModel(BaseModel): - # Required - a: int - b: int = Field() - c: int = Field(...) - - # Default - d: int = Field(1) - - # Default factory - g: List[int] = Field(default_factory=_default_factory_list) - h: str = Field(default_factory=_default_factory_str) - i: str = Field(default_factory=lambda: 'test') - - -_TModel = TypeVar('_TModel') -_TType = TypeVar('_TType') - - -class OrmMixin(Generic[_TModel, _TType]): - @classmethod - def from_orm(cls, model: _TModel) -> _TType: - raise NotImplementedError - - @classmethod - def from_orm_optional(cls, model: Optional[_TModel]) -> Optional[_TType]: - if model is None: - return None - return cls.from_orm(model) - - -@dataclass -class MyDataClass: - foo: InitVar[str] - bar: str - - -MyDataClass(foo='foo', bar='bar') - - -def get_my_custom_validator(field_name: str) -> Any: - @validator(field_name, allow_reuse=True) - def my_custom_validator(cls: Any, v: int) -> int: - return v - - return my_custom_validator - - -def foo() -> None: - class MyModel(BaseModel): - number: int - custom_validator = get_my_custom_validator('number') # type: ignore[pydantic-field] -# MYPY: error: Unused "type: ignore" comment - - @model_validator(mode='before') - @classmethod - def validate_before(cls, values: Any) -> Any: - return values - - @model_validator(mode='after') - def validate_after(self) -> Self: - return self - - MyModel(number=2) - - -class InnerModel(BaseModel): - my_var: Union[str, None] = Field(default=None) - - -class OuterModel(InnerModel): - pass - - -m = OuterModel() -if m.my_var is None: - # In https://github.com/pydantic/pydantic/issues/7399, this was unreachable - print('not unreachable') - - -class Foo(BaseModel): - pass - - -class Bar(Foo, RootModel[int]): - pass diff --git a/tests/mypy/outputs/1.2.0/mypy-plugin-strict_ini/plugin_fail.py b/tests/mypy/outputs/1.2.0/mypy-plugin-strict_ini/plugin_fail.py deleted file mode 100644 index 3aa10919fe9..00000000000 --- a/tests/mypy/outputs/1.2.0/mypy-plugin-strict_ini/plugin_fail.py +++ /dev/null @@ -1,346 +0,0 @@ -from typing import Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, ConfigDict, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - x: int - y: str - - def method(self) -> None: - pass - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) -# MYPY: error: "Model" does not have from_attributes=True [pydantic-orm] - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) -# MYPY: error: "KwargsModel" does not have from_attributes=True [pydantic-orm] - - -class ForbidExtraModel(BaseModel): - model_config = ConfigDict(extra=Extra.forbid) - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - model_config = ConfigDict(extra=1) # type: ignore[typeddict-item] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - model_config = ConfigDict(from_attributes={}) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - model_config = ConfigDict(from_attributes=list) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - model_config = ConfigDict(frozen=False) - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -# MYPY: error: Argument "x" to "InheritingModel" has incompatible type "str"; expected "int" [arg-type] -Model(x='1', y='2') -# MYPY: error: Argument "x" to "Model" has incompatible type "str"; expected "int" [arg-type] - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) -# MYPY: error: Argument "data" to "Response" has incompatible type "int"; expected "Model" [arg-type] - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) -# MYPY: error: Argument "y" to "AliasModel" has incompatible type "int"; expected "str" [arg-type] - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - z: int - - -DynamicAliasModel(y='y', z='1') -# MYPY: error: Argument "z" to "DynamicAliasModel" has incompatible type "str"; expected "int" [arg-type] - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - model_config = ConfigDict(populate_by_name=True) - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int - - model_config = ConfigDict(alias_generator=lambda x: x + '_') -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') - - model_config = ConfigDict(alias_generator=lambda x: x + '_') # type: ignore[pydantic-alias] - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "AliasGeneratorModel2" [call-arg] -AliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "AliasGeneratorModel2" [call-arg] - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsAliasGeneratorModel2" [call-arg] -KwargsAliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "KwargsAliasGeneratorModel2" [call-arg] - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - model_config = ConfigDict(frozen=False) - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.2.0/mypy-plugin-strict_ini/plugin_fail_baseConfig.py b/tests/mypy/outputs/1.2.0/mypy-plugin-strict_ini/plugin_fail_baseConfig.py deleted file mode 100644 index 245e547adb7..00000000000 --- a/tests/mypy/outputs/1.2.0/mypy-plugin-strict_ini/plugin_fail_baseConfig.py +++ /dev/null @@ -1,365 +0,0 @@ -from typing import Any, Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - x: int - y: str - - def method(self) -> None: - pass - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - def config_method(self) -> None: - ... - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) -# MYPY: error: "Model" does not have from_attributes=True [pydantic-orm] - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) -# MYPY: error: "KwargsModel" does not have from_attributes=True [pydantic-orm] - - -class ForbidExtraModel(BaseModel): - class Config: - extra = 'forbid' - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - class Config: - extra = 1 # type: ignore[pydantic-config] - extra = 1 -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - class Config: - from_attributes: Any = {} # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - class Config: - from_attributes = list # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - class Config: - frozen = False - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -# MYPY: error: Argument "x" to "InheritingModel" has incompatible type "str"; expected "int" [arg-type] -Model(x='1', y='2') -# MYPY: error: Argument "x" to "Model" has incompatible type "str"; expected "int" [arg-type] - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) -# MYPY: error: Argument "data" to "Response" has incompatible type "int"; expected "Model" [arg-type] - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) -# MYPY: error: Argument "y" to "AliasModel" has incompatible type "int"; expected "str" [arg-type] - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - z: int - - -DynamicAliasModel(y='y', z='1') -# MYPY: error: Argument "z" to "DynamicAliasModel" has incompatible type "str"; expected "int" [arg-type] - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - class Config: - populate_by_name = True - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int - - class Config: -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - alias_generator = lambda x: x + '_' # noqa E731 - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') - - class Config: # type: ignore[pydantic-alias] - alias_generator = lambda x: x + '_' # noqa E731 - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "AliasGeneratorModel2" [call-arg] -AliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "AliasGeneratorModel2" [call-arg] - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsAliasGeneratorModel2" [call-arg] -KwargsAliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "KwargsAliasGeneratorModel2" [call-arg] - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - class Config: - frozen = False - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.2.0/mypy-plugin_ini/plugin_fail.py b/tests/mypy/outputs/1.2.0/mypy-plugin_ini/plugin_fail.py deleted file mode 100644 index 0e324d1b7cf..00000000000 --- a/tests/mypy/outputs/1.2.0/mypy-plugin_ini/plugin_fail.py +++ /dev/null @@ -1,330 +0,0 @@ -from typing import Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, ConfigDict, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - x: int - y: str - - def method(self) -> None: - pass - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) -# MYPY: error: "Model" does not have from_attributes=True [pydantic-orm] - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) -# MYPY: error: "KwargsModel" does not have from_attributes=True [pydantic-orm] - - -class ForbidExtraModel(BaseModel): - model_config = ConfigDict(extra=Extra.forbid) - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - model_config = ConfigDict(extra=1) # type: ignore[typeddict-item] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - model_config = ConfigDict(from_attributes={}) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - model_config = ConfigDict(from_attributes=list) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - model_config = ConfigDict(frozen=False) - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -Model(x='1', y='2') - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - -DynamicAliasModel(y='y', z='1') - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - model_config = ConfigDict(populate_by_name=True) - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Missing named argument "x" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Missing named argument "x" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): - x: int - - model_config = ConfigDict(alias_generator=lambda x: x + '_') - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): - x: int = Field(..., alias='y') - - model_config = ConfigDict(alias_generator=lambda x: x + '_') # type: ignore[pydantic-alias] -# MYPY: error: Unused "type: ignore" comment - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -AliasGeneratorModel2(y=1, z=1) - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): - x: int - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): - x: int = Field(..., alias='y') - - -KwargsAliasGeneratorModel2(x=1) -KwargsAliasGeneratorModel2(y=1, z=1) - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - model_config = ConfigDict(frozen=False) - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.2.0/mypy-plugin_ini/plugin_fail_baseConfig.py b/tests/mypy/outputs/1.2.0/mypy-plugin_ini/plugin_fail_baseConfig.py deleted file mode 100644 index 993f93f2e0e..00000000000 --- a/tests/mypy/outputs/1.2.0/mypy-plugin_ini/plugin_fail_baseConfig.py +++ /dev/null @@ -1,349 +0,0 @@ -from typing import Any, Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - x: int - y: str - - def method(self) -> None: - pass - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - def config_method(self) -> None: - ... - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) -# MYPY: error: "Model" does not have from_attributes=True [pydantic-orm] - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) -# MYPY: error: "KwargsModel" does not have from_attributes=True [pydantic-orm] - - -class ForbidExtraModel(BaseModel): - class Config: - extra = 'forbid' - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - class Config: - extra = 1 # type: ignore[pydantic-config] - extra = 1 -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - class Config: - from_attributes: Any = {} # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - class Config: - from_attributes = list # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - class Config: - frozen = False - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -Model(x='1', y='2') - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - -DynamicAliasModel(y='y', z='1') - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - class Config: - populate_by_name = True - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Missing named argument "x" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Missing named argument "x" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): - x: int - - class Config: - alias_generator = lambda x: x + '_' # noqa E731 - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): - x: int = Field(..., alias='y') - - class Config: # type: ignore[pydantic-alias] -# MYPY: error: Unused "type: ignore" comment - alias_generator = lambda x: x + '_' # noqa E731 - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -AliasGeneratorModel2(y=1, z=1) - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): - x: int - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): - x: int = Field(..., alias='y') - - -KwargsAliasGeneratorModel2(x=1) -KwargsAliasGeneratorModel2(y=1, z=1) - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - class Config: - frozen = False - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.2.0/pyproject-plugin-strict_toml/plugin_fail.py b/tests/mypy/outputs/1.2.0/pyproject-plugin-strict_toml/plugin_fail.py deleted file mode 100644 index 3aa10919fe9..00000000000 --- a/tests/mypy/outputs/1.2.0/pyproject-plugin-strict_toml/plugin_fail.py +++ /dev/null @@ -1,346 +0,0 @@ -from typing import Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, ConfigDict, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - x: int - y: str - - def method(self) -> None: - pass - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) -# MYPY: error: "Model" does not have from_attributes=True [pydantic-orm] - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) -# MYPY: error: "KwargsModel" does not have from_attributes=True [pydantic-orm] - - -class ForbidExtraModel(BaseModel): - model_config = ConfigDict(extra=Extra.forbid) - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - model_config = ConfigDict(extra=1) # type: ignore[typeddict-item] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - model_config = ConfigDict(from_attributes={}) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - model_config = ConfigDict(from_attributes=list) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - model_config = ConfigDict(frozen=False) - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -# MYPY: error: Argument "x" to "InheritingModel" has incompatible type "str"; expected "int" [arg-type] -Model(x='1', y='2') -# MYPY: error: Argument "x" to "Model" has incompatible type "str"; expected "int" [arg-type] - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) -# MYPY: error: Argument "data" to "Response" has incompatible type "int"; expected "Model" [arg-type] - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) -# MYPY: error: Argument "y" to "AliasModel" has incompatible type "int"; expected "str" [arg-type] - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - z: int - - -DynamicAliasModel(y='y', z='1') -# MYPY: error: Argument "z" to "DynamicAliasModel" has incompatible type "str"; expected "int" [arg-type] - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - model_config = ConfigDict(populate_by_name=True) - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int - - model_config = ConfigDict(alias_generator=lambda x: x + '_') -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') - - model_config = ConfigDict(alias_generator=lambda x: x + '_') # type: ignore[pydantic-alias] - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "AliasGeneratorModel2" [call-arg] -AliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "AliasGeneratorModel2" [call-arg] - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsAliasGeneratorModel2" [call-arg] -KwargsAliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "KwargsAliasGeneratorModel2" [call-arg] - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - model_config = ConfigDict(frozen=False) - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.2.0/pyproject-plugin-strict_toml/plugin_fail_baseConfig.py b/tests/mypy/outputs/1.2.0/pyproject-plugin-strict_toml/plugin_fail_baseConfig.py deleted file mode 100644 index 245e547adb7..00000000000 --- a/tests/mypy/outputs/1.2.0/pyproject-plugin-strict_toml/plugin_fail_baseConfig.py +++ /dev/null @@ -1,365 +0,0 @@ -from typing import Any, Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - x: int - y: str - - def method(self) -> None: - pass - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - def config_method(self) -> None: - ... - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) -# MYPY: error: "Model" does not have from_attributes=True [pydantic-orm] - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) -# MYPY: error: "KwargsModel" does not have from_attributes=True [pydantic-orm] - - -class ForbidExtraModel(BaseModel): - class Config: - extra = 'forbid' - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - class Config: - extra = 1 # type: ignore[pydantic-config] - extra = 1 -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - class Config: - from_attributes: Any = {} # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - class Config: - from_attributes = list # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - class Config: - frozen = False - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -# MYPY: error: Argument "x" to "InheritingModel" has incompatible type "str"; expected "int" [arg-type] -Model(x='1', y='2') -# MYPY: error: Argument "x" to "Model" has incompatible type "str"; expected "int" [arg-type] - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) -# MYPY: error: Argument "data" to "Response" has incompatible type "int"; expected "Model" [arg-type] - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) -# MYPY: error: Argument "y" to "AliasModel" has incompatible type "int"; expected "str" [arg-type] - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - z: int - - -DynamicAliasModel(y='y', z='1') -# MYPY: error: Argument "z" to "DynamicAliasModel" has incompatible type "str"; expected "int" [arg-type] - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - class Config: - populate_by_name = True - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Unexpected keyword argument "y" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int - - class Config: -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - alias_generator = lambda x: x + '_' # noqa E731 - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') - - class Config: # type: ignore[pydantic-alias] - alias_generator = lambda x: x + '_' # noqa E731 - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "AliasGeneratorModel2" [call-arg] -AliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "AliasGeneratorModel2" [call-arg] - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - x: int = Field(..., alias='y') -# MYPY: error: Required dynamic aliases disallowed [pydantic-alias] - - -KwargsAliasGeneratorModel2(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsAliasGeneratorModel2" [call-arg] -KwargsAliasGeneratorModel2(y=1, z=1) -# MYPY: error: Unexpected keyword argument "z" for "KwargsAliasGeneratorModel2" [call-arg] - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - class Config: - frozen = False - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.2.0/pyproject-plugin_toml/plugin_fail.py b/tests/mypy/outputs/1.2.0/pyproject-plugin_toml/plugin_fail.py deleted file mode 100644 index 0e324d1b7cf..00000000000 --- a/tests/mypy/outputs/1.2.0/pyproject-plugin_toml/plugin_fail.py +++ /dev/null @@ -1,330 +0,0 @@ -from typing import Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, ConfigDict, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - x: int - y: str - - def method(self) -> None: - pass - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) -# MYPY: error: "Model" does not have from_attributes=True [pydantic-orm] - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) -# MYPY: error: "KwargsModel" does not have from_attributes=True [pydantic-orm] - - -class ForbidExtraModel(BaseModel): - model_config = ConfigDict(extra=Extra.forbid) - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - model_config = ConfigDict(extra=1) # type: ignore[typeddict-item] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - model_config = ConfigDict(from_attributes={}) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - model_config = ConfigDict(from_attributes=list) # type: ignore[typeddict-item] -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] -# MYPY: note: Error code "pydantic-config" not covered by "type: ignore" comment - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - model_config = ConfigDict(frozen=False) - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -Model(x='1', y='2') - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - -DynamicAliasModel(y='y', z='1') - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - model_config = ConfigDict(populate_by_name=True) - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Missing named argument "x" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Missing named argument "x" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): - x: int - - model_config = ConfigDict(alias_generator=lambda x: x + '_') - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): - x: int = Field(..., alias='y') - - model_config = ConfigDict(alias_generator=lambda x: x + '_') # type: ignore[pydantic-alias] -# MYPY: error: Unused "type: ignore" comment - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -AliasGeneratorModel2(y=1, z=1) - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): - x: int - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): - x: int = Field(..., alias='y') - - -KwargsAliasGeneratorModel2(x=1) -KwargsAliasGeneratorModel2(y=1, z=1) - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - model_config = ConfigDict(alias_generator=None, frozen=True, extra=Extra.forbid) - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - model_config = ConfigDict(frozen=False) - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.2.0/pyproject-plugin_toml/plugin_fail_baseConfig.py b/tests/mypy/outputs/1.2.0/pyproject-plugin_toml/plugin_fail_baseConfig.py deleted file mode 100644 index 993f93f2e0e..00000000000 --- a/tests/mypy/outputs/1.2.0/pyproject-plugin_toml/plugin_fail_baseConfig.py +++ /dev/null @@ -1,349 +0,0 @@ -from typing import Any, Generic, List, Optional, Set, TypeVar, Union - -from pydantic import BaseModel, Extra, Field, field_validator -from pydantic.dataclasses import dataclass - - -class Model(BaseModel): - x: int - y: str - - def method(self) -> None: - pass - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - def config_method(self) -> None: - ... - - -model = Model(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "Model" [call-arg] -model = Model(x=1) -# MYPY: error: Missing named argument "y" for "Model" [call-arg] -model.y = 'a' -# MYPY: error: Property "y" defined in "Model" is read-only [misc] -Model.from_orm({}) -# MYPY: error: "Model" does not have from_attributes=True [pydantic-orm] - - -class KwargsModel(BaseModel, alias_generator=None, frozen=True, extra=Extra.forbid): - x: int - y: str - - def method(self) -> None: - pass - - -kwargs_model = KwargsModel(x=1, y='y', z='z') -# MYPY: error: Unexpected keyword argument "z" for "KwargsModel" [call-arg] -kwargs_model = KwargsModel(x=1) -# MYPY: error: Missing named argument "y" for "KwargsModel" [call-arg] -kwargs_model.y = 'a' -# MYPY: error: Property "y" defined in "KwargsModel" is read-only [misc] -KwargsModel.from_orm({}) -# MYPY: error: "KwargsModel" does not have from_attributes=True [pydantic-orm] - - -class ForbidExtraModel(BaseModel): - class Config: - extra = 'forbid' - - -ForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "ForbidExtraModel" [call-arg] - - -class KwargsForbidExtraModel(BaseModel, extra='forbid'): - pass - - -KwargsForbidExtraModel(x=1) -# MYPY: error: Unexpected keyword argument "x" for "KwargsForbidExtraModel" [call-arg] - - -class BadExtraModel(BaseModel): - class Config: - extra = 1 # type: ignore[pydantic-config] - extra = 1 -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - - -class KwargsBadExtraModel(BaseModel, extra=1): -# MYPY: error: Invalid value for "Config.extra" [pydantic-config] - pass - - -class BadConfig1(BaseModel): - class Config: - from_attributes: Any = {} # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig1(BaseModel, from_attributes={}): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class BadConfig2(BaseModel): - class Config: - from_attributes = list # not sensible, but should still be handled gracefully -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - - -class KwargsBadConfig2(BaseModel, from_attributes=list): -# MYPY: error: Invalid value for "Config.from_attributes" [pydantic-config] - pass - - -class InheritingModel(Model): - class Config: - frozen = False - - -class KwargsInheritingModel(KwargsModel, frozen=False): - pass - - -class DefaultTestingModel(BaseModel): - # Required - a: int - b: int = ... -# MYPY: error: Incompatible types in assignment (expression has type "ellipsis", variable has type "int") [assignment] - c: int = Field(...) - d: Union[int, str] - e = ... -# MYPY: error: Untyped fields disallowed [pydantic-field] - - # Not required - f: Optional[int] - g: int = 1 - h: int = Field(1) - i: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - j = 1 -# MYPY: error: Untyped fields disallowed [pydantic-field] - - -DefaultTestingModel() -# MYPY: error: Missing named argument "a" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "b" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "c" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "d" for "DefaultTestingModel" [call-arg] -# MYPY: error: Missing named argument "f" for "DefaultTestingModel" [call-arg] - - -class UndefinedAnnotationModel(BaseModel): - undefined: Undefined # noqa F821 -# MYPY: error: Name "Undefined" is not defined [name-defined] - - -UndefinedAnnotationModel() -# MYPY: error: Missing named argument "undefined" for "UndefinedAnnotationModel" [call-arg] - - -Model.model_construct(x=1) -# MYPY: error: Missing named argument "y" for "model_construct" of "Model" [call-arg] -Model.model_construct(_fields_set={'x'}, x=1, y='2') -Model.model_construct(x='1', y='2') -# MYPY: error: Argument "x" to "model_construct" of "Model" has incompatible type "str"; expected "int" [arg-type] - -# Strict mode fails -inheriting = InheritingModel(x='1', y='1') -Model(x='1', y='2') - - -class Blah(BaseModel): - fields_set: Optional[Set[str]] = None - - -# (comment to keep line numbers unchanged) -T = TypeVar('T') - - -class Response(BaseModel, Generic[T]): - data: T - error: Optional[str] - - -response = Response[Model](data=model, error=None) -response = Response[Model](data=1, error=None) - - -class AliasModel(BaseModel): - x: str = Field(..., alias='y') - z: int - - -AliasModel(y=1, z=2) - -x_alias = 'y' - - -class DynamicAliasModel(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - -DynamicAliasModel(y='y', z='1') - - -class DynamicAliasModel2(BaseModel): - x: str = Field(..., alias=x_alias) - z: int - - class Config: - populate_by_name = True - - -DynamicAliasModel2(y='y', z=1) -# MYPY: error: Missing named argument "x" for "DynamicAliasModel2" [call-arg] -DynamicAliasModel2(x='y', z=1) - - -class KwargsDynamicAliasModel(BaseModel, populate_by_name=True): - x: str = Field(..., alias=x_alias) - z: int - - -KwargsDynamicAliasModel(y='y', z=1) -# MYPY: error: Missing named argument "x" for "KwargsDynamicAliasModel" [call-arg] -KwargsDynamicAliasModel(x='y', z=1) - - -class AliasGeneratorModel(BaseModel): - x: int - - class Config: - alias_generator = lambda x: x + '_' # noqa E731 - - -AliasGeneratorModel(x=1) -AliasGeneratorModel(x_=1) -AliasGeneratorModel(z=1) - - -class AliasGeneratorModel2(BaseModel): - x: int = Field(..., alias='y') - - class Config: # type: ignore[pydantic-alias] -# MYPY: error: Unused "type: ignore" comment - alias_generator = lambda x: x + '_' # noqa E731 - - -class UntypedFieldModel(BaseModel): - x: int = 1 - y = 2 -# MYPY: error: Untyped fields disallowed [pydantic-field] - z = 2 # type: ignore[pydantic-field] - - -AliasGeneratorModel2(x=1) -AliasGeneratorModel2(y=1, z=1) - - -class KwargsAliasGeneratorModel(BaseModel, alias_generator=lambda x: x + '_'): - x: int - - -KwargsAliasGeneratorModel(x=1) -KwargsAliasGeneratorModel(x_=1) -KwargsAliasGeneratorModel(z=1) - - -class KwargsAliasGeneratorModel2(BaseModel, alias_generator=lambda x: x + '_'): - x: int = Field(..., alias='y') - - -KwargsAliasGeneratorModel2(x=1) -KwargsAliasGeneratorModel2(y=1, z=1) - - -class CoverageTester(Missing): # noqa F821 -# MYPY: error: Name "Missing" is not defined [name-defined] - def from_orm(self) -> None: - pass - - -CoverageTester().from_orm() - - -@dataclass(config={}) -class AddProject: - name: str - slug: Optional[str] - description: Optional[str] - - -p = AddProject(name='x', slug='y', description='z') - - -# Same as Model, but with frozen = True -class FrozenModel(BaseModel): - x: int - y: str - - class Config: - alias_generator = None - frozen = True - extra = Extra.forbid - - -frozenmodel = FrozenModel(x=1, y='b') -frozenmodel.y = 'a' -# MYPY: error: Property "y" defined in "FrozenModel" is read-only [misc] - - -class InheritingModel2(FrozenModel): - class Config: - frozen = False - - -inheriting2 = InheritingModel2(x=1, y='c') -inheriting2.y = 'd' - - -def _default_factory() -> str: - return 'x' - - -test: List[str] = [] - - -class FieldDefaultTestingModel(BaseModel): - # Default - e: int = Field(None) -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - f: int = None -# MYPY: error: Incompatible types in assignment (expression has type "None", variable has type "int") [assignment] - - # Default factory - g: str = Field(default_factory=set) -# MYPY: error: Incompatible types in assignment (expression has type "Set[Any]", variable has type "str") [assignment] - h: int = Field(default_factory=_default_factory) -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - i: List[int] = Field(default_factory=list) - l_: str = Field(default_factory=3) -# MYPY: error: Argument "default_factory" to "Field" has incompatible type "int"; expected "Optional[Callable[[], Any]]" [arg-type] - - # Default and default factory - m: int = Field(default=1, default_factory=list) -# MYPY: error: Field default and default_factory cannot be specified together [pydantic-field] - - -class ModelWithAnnotatedValidator(BaseModel): - name: str - - @field_validator('name') - def noop_validator_with_annotations(self, name: str) -> str: - # This is a mistake: the first argument to a validator is the class itself, - # like a classmethod. - self.instance_method() -# MYPY: error: Missing positional argument "self" in call to "instance_method" of "ModelWithAnnotatedValidator" [call-arg] - return name - - def instance_method(self) -> None: - ... diff --git a/tests/mypy/outputs/1.4.1/mypy-default_ini/fail4.py b/tests/mypy/outputs/1.4.1/mypy-default_ini/fail4.py deleted file mode 100644 index 68bbd313183..00000000000 --- a/tests/mypy/outputs/1.4.1/mypy-default_ini/fail4.py +++ /dev/null @@ -1,73 +0,0 @@ -from typing import Any - -from pydantic import BaseModel, root_validator, validate_call - - -@validate_call -def foo(a: int, *, c: str = 'x') -> str: -# MYPY: note: "foo" defined here - return c * a - - -# ok -x: str = foo(1, c='hello') -# fails -foo('x') -# MYPY: error: Argument 1 to "foo" has incompatible type "str"; expected "int" [arg-type] -foo(1, c=1) -# MYPY: error: Argument "c" to "foo" has incompatible type "int"; expected "str" [arg-type] -foo(1, 2) -# MYPY: error: Too many positional arguments for "foo" [misc] -# MYPY: error: Argument 2 to "foo" has incompatible type "int"; expected "str" [arg-type] -foo(1, d=2) -# MYPY: error: Unexpected keyword argument "d" for "foo" [call-arg] -# mypy assumes foo is just a function -callable(foo.raw_function) -# MYPY: error: "Callable[[int, DefaultNamedArg(str, 'c')], str]" has no attribute "raw_function" [attr-defined] - - -@validate_call -def bar() -> str: - return 'x' - - -# return type should be a string -y: int = bar() -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - - -# Demonstrate type errors for root_validator signatures -class Model(BaseModel): - @root_validator() -# MYPY: error: All overload variants of "root_validator" require at least one argument [call-overload] -# MYPY: note: Possible overload variants: -# MYPY: note: def root_validator(*, skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[False], skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] - @classmethod - def validate_1(cls, values: Any) -> Any: - return values - - @root_validator(pre=True, skip_on_failure=True) -# MYPY: error: No overload variant of "root_validator" matches argument types "bool", "bool" [call-overload] -# MYPY: note: Possible overload variants: -# MYPY: note: def root_validator(*, skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[False], skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] - @classmethod - def validate_2(cls, values: Any) -> Any: - return values - - @root_validator(pre=False) -# MYPY: error: No overload variant of "root_validator" matches argument type "bool" [call-overload] -# MYPY: note: Possible overload variants: -# MYPY: note: def root_validator(*, skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[False], skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] - @classmethod - def validate_3(cls, values: Any) -> Any: - return values - - -Model.non_existent_attribute -# MYPY: error: "type[Model]" has no attribute "non_existent_attribute" [attr-defined] diff --git a/tests/mypy/outputs/1.4.1/mypy-plugin-strict_ini/plugin_default_factory.py b/tests/mypy/outputs/1.4.1/mypy-plugin-strict_ini/plugin_default_factory.py deleted file mode 100644 index a61c1d45675..00000000000 --- a/tests/mypy/outputs/1.4.1/mypy-plugin-strict_ini/plugin_default_factory.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -See https://github.com/pydantic/pydantic/issues/4457 -""" - -from typing import Dict, List - -from pydantic import BaseModel, Field - - -def new_list() -> List[int]: - return [] - - -class Model(BaseModel): - l1: List[str] = Field(default_factory=list) - l2: List[int] = Field(default_factory=new_list) - l3: List[str] = Field(default_factory=lambda: list()) - l4: Dict[str, str] = Field(default_factory=dict) - l5: int = Field(default_factory=lambda: 123) - l6_error: List[str] = Field(default_factory=new_list) -# MYPY: error: Incompatible types in assignment (expression has type "list[int]", variable has type "list[str]") [assignment] - l7_error: int = Field(default_factory=list) -# MYPY: error: Incompatible types in assignment (expression has type "list[Any]", variable has type "int") [assignment] diff --git a/tests/mypy/outputs/1.4.1/mypy-plugin_ini/plugin_optional_inheritance.py b/tests/mypy/outputs/1.4.1/mypy-plugin_ini/plugin_optional_inheritance.py deleted file mode 100644 index 1145652dfc5..00000000000 --- a/tests/mypy/outputs/1.4.1/mypy-plugin_ini/plugin_optional_inheritance.py +++ /dev/null @@ -1,24 +0,0 @@ -from typing import Optional - -from pydantic import BaseModel - - -class Foo(BaseModel): - id: Optional[int] - - -class Bar(BaseModel): - foo: Optional[Foo] - - -class Baz(Bar): - name: str - - -b = Bar(foo={'id': 1}) -assert b.foo.id == 1 -# MYPY: error: Item "None" of "Foo | None" has no attribute "id" [union-attr] - -z = Baz(foo={'id': 1}, name='test') -assert z.foo.id == 1 -# MYPY: error: Item "None" of "Foo | None" has no attribute "id" [union-attr] diff --git a/tests/mypy/outputs/1.4.1/mypy-plugin_ini/pydantic_settings.py b/tests/mypy/outputs/1.4.1/mypy-plugin_ini/pydantic_settings.py deleted file mode 100644 index 2ab257d1fd2..00000000000 --- a/tests/mypy/outputs/1.4.1/mypy-plugin_ini/pydantic_settings.py +++ /dev/null @@ -1,24 +0,0 @@ -from pydantic_settings import BaseSettings, SettingsConfigDict - - -class Settings(BaseSettings): - foo: str - - -s = Settings() - -s = Settings(foo='test', _case_sensitive=True, _env_prefix='test__', _env_file='test') - -s = Settings(foo='test', _case_sensitive=1, _env_prefix=2, _env_file=3) -# MYPY: error: Argument "_case_sensitive" to "Settings" has incompatible type "int"; expected "bool | None" [arg-type] -# MYPY: error: Argument "_env_prefix" to "Settings" has incompatible type "int"; expected "str | None" [arg-type] -# MYPY: error: Argument "_env_file" to "Settings" has incompatible type "int"; expected "Path | str | list[Path | str] | tuple[Path | str, ...] | None" [arg-type] - - -class SettingsWithConfigDict(BaseSettings): - bar: str - - model_config = SettingsConfigDict(env_file='.env', env_file_encoding='utf-8') - - -scd = SettingsWithConfigDict() diff --git a/tests/mypy/outputs/1.4.1/pyproject-default_toml/fail4.py b/tests/mypy/outputs/1.4.1/pyproject-default_toml/fail4.py deleted file mode 100644 index 68bbd313183..00000000000 --- a/tests/mypy/outputs/1.4.1/pyproject-default_toml/fail4.py +++ /dev/null @@ -1,73 +0,0 @@ -from typing import Any - -from pydantic import BaseModel, root_validator, validate_call - - -@validate_call -def foo(a: int, *, c: str = 'x') -> str: -# MYPY: note: "foo" defined here - return c * a - - -# ok -x: str = foo(1, c='hello') -# fails -foo('x') -# MYPY: error: Argument 1 to "foo" has incompatible type "str"; expected "int" [arg-type] -foo(1, c=1) -# MYPY: error: Argument "c" to "foo" has incompatible type "int"; expected "str" [arg-type] -foo(1, 2) -# MYPY: error: Too many positional arguments for "foo" [misc] -# MYPY: error: Argument 2 to "foo" has incompatible type "int"; expected "str" [arg-type] -foo(1, d=2) -# MYPY: error: Unexpected keyword argument "d" for "foo" [call-arg] -# mypy assumes foo is just a function -callable(foo.raw_function) -# MYPY: error: "Callable[[int, DefaultNamedArg(str, 'c')], str]" has no attribute "raw_function" [attr-defined] - - -@validate_call -def bar() -> str: - return 'x' - - -# return type should be a string -y: int = bar() -# MYPY: error: Incompatible types in assignment (expression has type "str", variable has type "int") [assignment] - - -# Demonstrate type errors for root_validator signatures -class Model(BaseModel): - @root_validator() -# MYPY: error: All overload variants of "root_validator" require at least one argument [call-overload] -# MYPY: note: Possible overload variants: -# MYPY: note: def root_validator(*, skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[False], skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] - @classmethod - def validate_1(cls, values: Any) -> Any: - return values - - @root_validator(pre=True, skip_on_failure=True) -# MYPY: error: No overload variant of "root_validator" matches argument types "bool", "bool" [call-overload] -# MYPY: note: Possible overload variants: -# MYPY: note: def root_validator(*, skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[False], skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] - @classmethod - def validate_2(cls, values: Any) -> Any: - return values - - @root_validator(pre=False) -# MYPY: error: No overload variant of "root_validator" matches argument type "bool" [call-overload] -# MYPY: note: Possible overload variants: -# MYPY: note: def root_validator(*, skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] -# MYPY: note: def root_validator(*, pre: Literal[False], skip_on_failure: Literal[True], allow_reuse: bool = ...) -> Callable[[_V1RootValidatorFunctionType], _V1RootValidatorFunctionType] - @classmethod - def validate_3(cls, values: Any) -> Any: - return values - - -Model.non_existent_attribute -# MYPY: error: "type[Model]" has no attribute "non_existent_attribute" [attr-defined] diff --git a/tests/mypy/outputs/1.4.1/pyproject-plugin_toml/pydantic_settings.py b/tests/mypy/outputs/1.4.1/pyproject-plugin_toml/pydantic_settings.py deleted file mode 100644 index 2ab257d1fd2..00000000000 --- a/tests/mypy/outputs/1.4.1/pyproject-plugin_toml/pydantic_settings.py +++ /dev/null @@ -1,24 +0,0 @@ -from pydantic_settings import BaseSettings, SettingsConfigDict - - -class Settings(BaseSettings): - foo: str - - -s = Settings() - -s = Settings(foo='test', _case_sensitive=True, _env_prefix='test__', _env_file='test') - -s = Settings(foo='test', _case_sensitive=1, _env_prefix=2, _env_file=3) -# MYPY: error: Argument "_case_sensitive" to "Settings" has incompatible type "int"; expected "bool | None" [arg-type] -# MYPY: error: Argument "_env_prefix" to "Settings" has incompatible type "int"; expected "str | None" [arg-type] -# MYPY: error: Argument "_env_file" to "Settings" has incompatible type "int"; expected "Path | str | list[Path | str] | tuple[Path | str, ...] | None" [arg-type] - - -class SettingsWithConfigDict(BaseSettings): - bar: str - - model_config = SettingsConfigDict(env_file='.env', env_file_encoding='utf-8') - - -scd = SettingsWithConfigDict() diff --git a/tests/mypy/test_mypy.py b/tests/mypy/test_mypy.py index b052996166e..4ab5067c7cc 100644 --- a/tests/mypy/test_mypy.py +++ b/tests/mypy/test_mypy.py @@ -1,25 +1,38 @@ +from __future__ import annotations + import dataclasses import importlib import os import re import sys from bisect import insort +from collections.abc import Collection from pathlib import Path -from typing import Any, List, Optional, Tuple, Union +from typing import TYPE_CHECKING import pytest +from _pytest.mark import Mark, MarkDecorator +from _pytest.mark.structures import ParameterSet +from typing_extensions import TypeAlias -try: +# Pyright doesn't like try/expect blocks for imports: +if TYPE_CHECKING: from mypy import api as mypy_api from mypy.version import __version__ as mypy_version from pydantic.version import parse_mypy_version +else: + try: + from mypy import api as mypy_api + from mypy.version import __version__ as mypy_version + + from pydantic.version import parse_mypy_version -except ImportError: - mypy_api = None - mypy_version = None + except ImportError: + mypy_api = None + mypy_version = None - parse_mypy_version = lambda _: (0,) # noqa: E731 + parse_mypy_version = lambda _: (0,) # noqa: E731 MYPY_VERSION_TUPLE = parse_mypy_version(mypy_version) @@ -35,52 +48,39 @@ os.chdir(Path(__file__).parent.parent.parent) -@dataclasses.dataclass -class MypyCasesBuilder: - configs: Union[str, List[str]] - modules: Union[str, List[str]] - marks: Any = None - - def build(self) -> List[Union[Tuple[str, str], Any]]: - """ - Produces the cartesian product of the configs and modules, optionally with marks. - """ - if isinstance(self.configs, str): - self.configs = [self.configs] - if isinstance(self.modules, str): - self.modules = [self.modules] - built_cases = [] - for config in self.configs: - for module in self.modules: - built_cases.append((config, module)) - if self.marks is not None: - built_cases = [pytest.param(config, module, marks=self.marks) for config, module in built_cases] - return built_cases - - -cases = ( +# Type hint taken from the signature of `pytest.param`: +Marks: TypeAlias = 'MarkDecorator | Collection[MarkDecorator | Mark]' + + +def build_cases( + configs: list[str], + modules: list[str], + marks: Marks = (), +) -> list[ParameterSet]: + """Produces the cartesian product of the configs and modules, optionally with marks.""" + + return [pytest.param(config, module, marks=marks) for config in configs for module in modules] + + +cases: list[ParameterSet | tuple[str, str]] = [ # No plugin - MypyCasesBuilder( + *build_cases( ['mypy-default.ini', 'pyproject-default.toml'], - ['fail1.py', 'fail2.py', 'fail3.py', 'fail4.py', 'pydantic_settings.py'], - ).build() - + MypyCasesBuilder( + ['pydantic_settings.py'], + ), + *build_cases( ['mypy-default.ini', 'pyproject-default.toml'], - 'success.py', - pytest.mark.skipif(MYPY_VERSION_TUPLE > (1, 0, 1), reason='Need to handle some more things for mypy >=1.1.1'), - ).build() - + MypyCasesBuilder( - ['mypy-default.ini', 'pyproject-default.toml'], - 'root_models.py', + ['root_models.py'], pytest.mark.skipif( MYPY_VERSION_TUPLE < (1, 1, 1), reason='`dataclass_transform` only supported on mypy >= 1.1.1' ), - ).build() - + MypyCasesBuilder( - 'mypy-default.ini', ['plugin_success.py', 'plugin_success_baseConfig.py', 'metaclass_args.py'] - ).build() + ), + *build_cases( + ['mypy-default.ini'], + ['plugin_success.py', 'plugin_success_baseConfig.py', 'metaclass_args.py'], + ), # Default plugin config - + MypyCasesBuilder( + *build_cases( ['mypy-plugin.ini', 'pyproject-plugin.toml'], [ 'plugin_success.py', @@ -89,9 +89,9 @@ def build(self) -> List[Union[Tuple[str, str], Any]]: 'plugin_fail_baseConfig.py', 'pydantic_settings.py', ], - ).build() + ), # Strict plugin config - + MypyCasesBuilder( + *build_cases( ['mypy-plugin-strict.ini', 'pyproject-plugin-strict.toml'], [ 'plugin_success.py', @@ -100,9 +100,9 @@ def build(self) -> List[Union[Tuple[str, str], Any]]: 'plugin_success_baseConfig.py', 'plugin_fail_baseConfig.py', ], - ).build() + ), # One-off cases - + [ + *[ ('mypy-plugin.ini', 'custom_constructor.py'), ('mypy-plugin.ini', 'config_conditional_extra.py'), ('mypy-plugin.ini', 'covariant_typevar.py'), @@ -111,31 +111,27 @@ def build(self) -> List[Union[Tuple[str, str], Any]]: ('mypy-plugin.ini', 'generics.py'), ('mypy-plugin.ini', 'root_models.py'), ('mypy-plugin.ini', 'plugin_strict_fields.py'), - ('mypy-plugin-strict.ini', 'plugin_default_factory.py'), ('mypy-plugin-strict-no-any.ini', 'dataclass_no_any.py'), ('mypy-plugin-very-strict.ini', 'metaclass_args.py'), - ('pyproject-default.toml', 'computed_fields.py'), - ('pyproject-default.toml', 'with_config_decorator.py'), ('pyproject-plugin-no-strict-optional.toml', 'no_strict_optional.py'), ('pyproject-plugin-strict-equality.toml', 'strict_equality.py'), ('pyproject-plugin.toml', 'from_orm_v1_noconflict.py'), - ] -) - - -@dataclasses.dataclass -class MypyTestTarget: - parsed_mypy_version: Tuple[int, ...] - output_path: Path + ], +] @dataclasses.dataclass class MypyTestConfig: - existing: Optional[MypyTestTarget] # the oldest target with an output that is no older than the installed mypy - current: MypyTestTarget # the target for the current installed mypy + existing_output_path: Path | None + """The path pointing to the existing test result, or `None` if this is the first time the test is run.""" + + current_output_path: Path + """The path pointing to the current test result to be created or compared to the existing one.""" def get_test_config(module_path: Path, config_path: Path) -> MypyTestConfig: + """Given a file to test with a specific config, get a test config.""" + outputs_dir = PYDANTIC_ROOT / 'tests/mypy/outputs' outputs_dir.mkdir(exist_ok=True) existing_versions = [ @@ -145,10 +141,10 @@ def get_test_config(module_path: Path, config_path: Path) -> MypyTestConfig: def _convert_to_output_path(v: str) -> Path: return outputs_dir / v / config_path.name.replace('.', '_') / module_path.name - existing = None + existing: Path | None = None # Build sorted list of (parsed_version, version) pairs, including the current mypy version being used - parsed_version_pairs = sorted([(parse_mypy_version(v), v) for v in existing_versions]) + parsed_version_pairs = sorted((parse_mypy_version(v), v) for v in existing_versions) if MYPY_VERSION_TUPLE not in [x[0] for x in parsed_version_pairs]: insort(parsed_version_pairs, (MYPY_VERSION_TUPLE, mypy_version)) @@ -157,15 +153,23 @@ def _convert_to_output_path(v: str) -> Path: continue output_path = _convert_to_output_path(version) if output_path.exists(): - existing = MypyTestTarget(parsed_version, output_path) + existing = output_path break - current = MypyTestTarget(MYPY_VERSION_TUPLE, _convert_to_output_path(mypy_version)) - return MypyTestConfig(existing, current) + return MypyTestConfig(existing, _convert_to_output_path(mypy_version)) -@pytest.mark.filterwarnings('ignore:ast.:DeprecationWarning') # these are produced by mypy in python 3.12 -@pytest.mark.parametrize('config_filename,python_filename', cases) +def get_expected_return_code(source_code: str) -> int: + """Return 1 if at least one `# MYPY:` comment was found, else 0.""" + if re.findall(r'^\s*# MYPY:', source_code, flags=re.MULTILINE): + return 1 + return 0 + + +@pytest.mark.parametrize( + ['config_filename', 'python_filename'], + cases, +) def test_mypy_results(config_filename: str, python_filename: str, request: pytest.FixtureRequest) -> None: input_path = PYDANTIC_ROOT / 'tests/mypy/modules' / python_filename config_path = PYDANTIC_ROOT / 'tests/mypy/configs' / config_filename @@ -195,10 +199,10 @@ def test_mypy_results(config_filename: str, python_filename: str, request: pytes input_code = input_path.read_text() - existing_output_code: Optional[str] = None - if test_config.existing is not None: - existing_output_code = test_config.existing.output_path.read_text() - print(f'Comparing output with {test_config.existing.output_path}') + existing_output_code: str | None = None + if test_config.existing_output_path is not None: + existing_output_code = test_config.existing_output_path.read_text() + print(f'Comparing output with {test_config.existing_output_path}') else: print(f'Comparing output with {input_path} (expecting no mypy errors)') @@ -208,8 +212,8 @@ def test_mypy_results(config_filename: str, python_filename: str, request: pytes # Test passed, no changes needed pass elif request.config.getoption('update_mypy'): - test_config.current.output_path.parent.mkdir(parents=True, exist_ok=True) - test_config.current.output_path.write_text(merged_output) + test_config.current_output_path.parent.mkdir(parents=True, exist_ok=True) + test_config.current_output_path.write_text(merged_output) else: print('**** Merged Output ****') print(merged_output) @@ -222,14 +226,9 @@ def test_mypy_results(config_filename: str, python_filename: str, request: pytes def test_bad_toml_config() -> None: full_config_filename = 'tests/mypy/configs/pyproject-plugin-bad-param.toml' - full_filename = 'tests/mypy/modules/success.py' + full_filename = 'tests/mypy/modules/generics.py' # File doesn't matter - # Specifying a different cache dir for each configuration dramatically speeds up subsequent execution - # It also prevents cache-invalidation-related bugs in the tests - cache_dir = '.mypy_cache/test-pyproject-plugin-bad-param' - command = [full_filename, '--config-file', full_config_filename, '--cache-dir', cache_dir, '--show-error-codes'] - if MYPY_VERSION_TUPLE >= (0, 990): - command.append('--disable-recursive-aliases') + command = [full_filename, '--config-file', full_config_filename, '--show-error-codes'] print(f"\nExecuting: mypy {' '.join(command)}") # makes it easier to debug as necessary with pytest.raises(ValueError) as e: mypy_api.run(command) @@ -237,50 +236,26 @@ def test_bad_toml_config() -> None: assert str(e.value) == 'Configuration value must be a boolean for key: init_forbid_extra' -def get_expected_return_code(source_code: str) -> int: - if re.findall(r'^\s*# MYPY:', source_code, flags=re.MULTILINE): - return 1 - return 0 - - @pytest.mark.parametrize('module', ['dataclass_no_any', 'plugin_success', 'plugin_success_baseConfig']) -@pytest.mark.filterwarnings('ignore:.*is deprecated.*:DeprecationWarning') -@pytest.mark.filterwarnings('ignore:.*are deprecated.*:DeprecationWarning') def test_success_cases_run(module: str) -> None: """ Ensure the "success" files can actually be executed """ - importlib.import_module(f'tests.mypy.modules.{module}') - - -def test_explicit_reexports(): - from pydantic import __all__ as root_all - from pydantic.deprecated.tools import __all__ as tools - from pydantic.main import __all__ as main - from pydantic.networks import __all__ as networks - from pydantic.types import __all__ as types - - for name, export_all in [('main', main), ('network', networks), ('tools', tools), ('types', types)]: - for export in export_all: - assert export in root_all, f'{export} is in {name}.__all__ but missing from re-export in __init__.py' - - -def test_explicit_reexports_exist(): - import pydantic - - for name in pydantic.__all__: - assert hasattr(pydantic, name), f'{name} is in pydantic.__all__ but missing from pydantic' + module_name = f'tests.mypy.modules.{module}' + try: + importlib.import_module(module_name) + except Exception: + pytest.fail(reason=f'Unable to execute module {module_name}') @pytest.mark.parametrize( - 'v_str,v_tuple', + ['v_str', 'v_tuple'], [ - ('0', (0,)), - ('0.930', (0, 930)), - ('0.940+dev.04cac4b5d911c4f9529e6ce86a27b44f28846f5d.dirty', (0, 940)), + ('1.11.0', (1, 11, 0)), + ('1.11.0+dev.d6d9d8cd4f27c52edac1f537e236ec48a01e54cb.dirty', (1, 11, 0)), ], ) -def test_parse_mypy_version(v_str, v_tuple): +def test_parse_mypy_version(v_str: str, v_tuple: tuple[int, int, int]) -> None: assert parse_mypy_version(v_str) == v_tuple diff --git a/tests/pyright/base_model.py b/tests/pyright/base_model.py deleted file mode 100644 index 4fffa3b8503..00000000000 --- a/tests/pyright/base_model.py +++ /dev/null @@ -1,57 +0,0 @@ -""" -This file is used to test pyright's ability to check Pydantic's `BaseModel` related code. -""" - -from functools import cached_property - -from typing_extensions import assert_type - -from pydantic import BaseModel, Field, computed_field - - -class MyModel(BaseModel): - x: str - y: list[int] - - -m1 = MyModel(x='hello', y=[1, 2, 3]) - -m2 = MyModel(x='hello') # pyright: ignore[reportCallIssue] - - -class Knight(BaseModel): - title: str = Field(default='Sir Lancelot') # this is okay - age: int = Field(23) # this works fine at runtime but will case an error for pyright - - -k = Knight() # pyright: ignore[reportCallIssue] - - -class Square(BaseModel): - side: float - - @computed_field - @property - def area(self) -> float: - return self.side**2 - - @area.setter - def area(self, area: float) -> None: - self.side = area**0.5 - - -sq = Square(side=10) -assert_type(sq.area, float) - - -class Square2(BaseModel): - side: float - - @computed_field - @cached_property - def area(self) -> float: - return self.side**2 - - -sq = Square(side=10) -assert_type(sq.area, float) diff --git a/tests/pyright/pipeline_api.py b/tests/pyright/pipeline_api.py deleted file mode 100644 index b95ddbb36da..00000000000 --- a/tests/pyright/pipeline_api.py +++ /dev/null @@ -1,19 +0,0 @@ -import datetime -from typing import Annotated - -from pydantic.experimental.pipeline import validate_as - -# this test works by adding type ignores and having pyright fail with -# an unused type ignore error if the type checking isn't working -Annotated[str, validate_as(int)] # type: ignore -Annotated[str, validate_as(str).transform(lambda x: int(x))] # type: ignore -Annotated[float, validate_as(float).gt(0)] # should be able to compare float to int -Annotated[datetime.datetime, validate_as(datetime.datetime).datetime_tz_naive()] -Annotated[datetime.datetime, validate_as(str).datetime_tz_naive()] # type: ignore -Annotated[ - datetime.datetime, - ( - validate_as(str).transform(str.strip).validate_as(datetime.datetime).datetime_tz_naive() - | validate_as(int).transform(datetime.datetime.fromtimestamp).datetime_tz_aware() - ), -] diff --git a/tests/pyright/pyproject.toml b/tests/pyright/pyproject.toml deleted file mode 100644 index 761b6282f5d..00000000000 --- a/tests/pyright/pyproject.toml +++ /dev/null @@ -1,6 +0,0 @@ -[tool.pyright] -extraPaths = ['../..'] -reportUnnecessaryTypeIgnoreComment = true -pythonVersion = '3.10' -enableExperimentalFeatures = true -reportDeprecated = true diff --git a/tests/pyright/type_adapter.py b/tests/pyright/type_adapter.py deleted file mode 100644 index 8a40bf91198..00000000000 --- a/tests/pyright/type_adapter.py +++ /dev/null @@ -1,25 +0,0 @@ -from typing import Annotated - -from typing_extensions import assert_type - -from pydantic import TypeAdapter - -ta1 = TypeAdapter(int) -assert_type(ta1, TypeAdapter[int]) - -assert_type(ta1.validate_python('1'), int) -ta1.dump_python(1) -ta1.dump_python('1') # pyright: ignore[reportArgumentType] -ta1.dump_json(1) -ta1.dump_json('1') # pyright: ignore[reportArgumentType] - -# The following use cases require PEP 747: TypeExpr: - -ta2 = TypeAdapter(Annotated[int, ...]) -assert_type(ta2, TypeAdapter[int]) # type: ignore[reportAssertTypeFailure] - -ta3: TypeAdapter[int] = TypeAdapter(Annotated[int, ...]) -assert_type(ta3, TypeAdapter[int]) - -ta4 = TypeAdapter(int | str) -assert_type(ta4, TypeAdapter[int | str]) # type: ignore[reportAssertTypeFailure] diff --git a/tests/pyright/validate_call.py b/tests/pyright/validate_call.py deleted file mode 100644 index 81f5b7d85ef..00000000000 --- a/tests/pyright/validate_call.py +++ /dev/null @@ -1,17 +0,0 @@ -from typing_extensions import assert_type - -from pydantic import validate_call - - -@validate_call -def foo(a: int, *, c: str = 'x') -> str: - return c * a - - -a = foo(1, c='a') -assert_type(a, str) - -foo('', c=1) # pyright: ignore[reportArgumentType] - -# Not possible to type check currently (see https://github.com/pydantic/pydantic/issues/9883): -foo.raw_function(1, c='a') # pyright: ignore[reportFunctionMemberAccess] diff --git a/tests/test_aliases.py b/tests/test_aliases.py index 0a860565174..c88dd4505b1 100644 --- a/tests/test_aliases.py +++ b/tests/test_aliases.py @@ -141,8 +141,8 @@ class Parent(BaseModel): x: int = Field(alias='x1', gt=0) class Child(Parent): - x: int = Field(..., alias='x2') - y: int = Field(..., alias='y2') + x: int = Field(alias='x2') + y: int = Field(alias='y2') assert Parent.model_fields['x'].alias == 'x1' assert Child.model_fields['x'].alias == 'x2' @@ -209,7 +209,7 @@ class Child(Parent): def test_alias_generator_on_parent(alias_generator): class Parent(BaseModel): model_config = ConfigDict(alias_generator=alias_generator) - x: bool = Field(..., alias='a_b_c') + x: bool = Field(alias='a_b_c') y: str class Child(Parent): @@ -226,7 +226,7 @@ class Child(Parent): @pytest.mark.parametrize('alias_generator', upper_alias_generator) def test_alias_generator_on_child(alias_generator): class Parent(BaseModel): - x: bool = Field(..., alias='abc') + x: bool = Field(alias='abc') y: str class Child(Parent): @@ -245,12 +245,12 @@ class Model(BaseModel): model_config = ConfigDict(alias_generator=alias_generator) a: str - b: str = Field(..., alias='b_alias') - c: str = Field(..., validation_alias='c_val_alias') - d: str = Field(..., serialization_alias='d_ser_alias') - e: str = Field(..., alias='e_alias', validation_alias='e_val_alias') - f: str = Field(..., alias='f_alias', serialization_alias='f_ser_alias') - g: str = Field(..., alias='g_alias', validation_alias='g_val_alias', serialization_alias='g_ser_alias') + b: str = Field(alias='b_alias') + c: str = Field(validation_alias='c_val_alias') + d: str = Field(serialization_alias='d_ser_alias') + e: str = Field(alias='e_alias', validation_alias='e_val_alias') + f: str = Field(alias='f_alias', serialization_alias='f_ser_alias') + g: str = Field(alias='g_alias', validation_alias='g_val_alias', serialization_alias='g_ser_alias') assert { name: {k: getattr(f, k) for k in ('alias', 'validation_alias', 'serialization_alias')} @@ -301,9 +301,9 @@ class Model(BaseModel): @pytest.mark.parametrize('alias_generator', upper_alias_generator) def test_low_priority_alias(alias_generator): class Parent(BaseModel): - w: bool = Field(..., alias='w_', validation_alias='w_val_alias', serialization_alias='w_ser_alias') + w: bool = Field(alias='w_', validation_alias='w_val_alias', serialization_alias='w_ser_alias') x: bool = Field( - ..., alias='abc', alias_priority=1, validation_alias='x_val_alias', serialization_alias='x_ser_alias' + alias='abc', alias_priority=1, validation_alias='x_val_alias', serialization_alias='x_ser_alias' ) y: str @@ -403,7 +403,7 @@ def test_populate_by_name_config( class Foo(BaseModel): model_config = ConfigDict(populate_by_name=populate_by_name_config) - bar_: int = Field(..., alias='bar') + bar_: int = Field(alias='bar') with expectation: if use_construct: diff --git a/tests/test_allow_partial.py b/tests/test_allow_partial.py new file mode 100644 index 00000000000..93f20df3117 --- /dev/null +++ b/tests/test_allow_partial.py @@ -0,0 +1,87 @@ +from typing import Dict, List, Tuple + +import pytest +from annotated_types import Ge +from typing_extensions import Annotated, TypedDict + +from pydantic import TypeAdapter, ValidationError + +from .conftest import Err + + +@pytest.mark.parametrize( + 'mode,value,expected', + [ + ('python', {'a': 1, 'b': 'b', 'c': (3, '4')}, {'a': 1, 'b': 'b', 'c': (3, '4')}), + ('python', {'a': 1, 'b': 'b', 'c': (3,)}, {'a': 1, 'b': 'b'}), + ('python', {'a': 1, 'b': 'b'}, {'a': 1, 'b': 'b'}), + ('json', '{"a": 1, "b": "b", "c": [3, "4"]}', {'a': 1, 'b': 'b', 'c': (3, '4')}), + ('json', '{"a": 1, "b": "b", "c": [3, "4"]}', {'a': 1, 'b': 'b', 'c': (3, '4')}), + ('json', '{"a": 1, "b": "b", "c": [3]}', {'a': 1, 'b': 'b'}), + ('json', '{"a": 1, "b": "b", "c": [3', {'a': 1, 'b': 'b'}), + ('json', '{"a": 1, "b": "b', {'a': 1}), + ('json', '{"a": 1, "b": ', {'a': 1}), + ('python', {'a': 1, 'c': (3,), 'b': 'b'}, Err(r'c\.1\s+Field required')), + ('json', '{"a": 1, "c": [3], "b": "b"}', Err(r'c\.1\s+Field required')), + ], +) +def test_typed_dict(mode, value, expected): + class Foobar(TypedDict, total=False): + a: int + b: str + c: Tuple[int, str] + + ta = TypeAdapter(Foobar) + if mode == 'python': + if isinstance(expected, Err): + with pytest.raises(ValidationError, match=expected.message): + ta.validate_python(value, experimental_allow_partial=True) + else: + assert ta.validate_python(value, experimental_allow_partial=True) == expected + else: + if isinstance(expected, Err): + with pytest.raises(ValidationError, match=expected.message): + ta.validate_json(value, experimental_allow_partial=True) + else: + assert ta.validate_json(value, experimental_allow_partial=True) == expected + + +@pytest.mark.parametrize( + 'mode,value,expected', + [ + ('python', [10, 20, 30], [10, 20, 30]), + ('python', ['10', '20', '30'], [10, 20, 30]), + ('python', [10, 20, 30], [10, 20, 30]), + ('python', [10, 20, 3], [10, 20]), + ('json', '[10, 20, 30]', [10, 20, 30]), + ('json', '[10, 20, 30', [10, 20, 30]), + ('json', '[10, 20, 3', [10, 20]), + ], +) +def test_list(mode, value, expected): + ta = TypeAdapter(List[Annotated[int, Ge(10)]]) + if mode == 'python': + if isinstance(expected, Err): + with pytest.raises(ValidationError, match=expected.message): + ta.validate_python(value, experimental_allow_partial=True) + else: + assert ta.validate_python(value, experimental_allow_partial=True) == expected + else: + if isinstance(expected, Err): + with pytest.raises(ValidationError, match=expected.message): + ta.validate_json(value, experimental_allow_partial=True) + else: + assert ta.validate_json(value, experimental_allow_partial=True) == expected + + +def test_dict(): + ta = TypeAdapter(Dict[str, Annotated[int, Ge(10)]]) + eap = dict(experimental_allow_partial=True) + + assert ta.validate_python({'a': 10, 'b': 20, 'c': 30}, **eap) == {'a': 10, 'b': 20, 'c': 30} + assert ta.validate_python({'a': 10, 'b': 20, 'c': 3}, **eap) == {'a': 10, 'b': 20} + assert ta.validate_strings({'a': '10', 'b': '20', 'c': '30'}, strict=True, **eap) == {'a': 10, 'b': 20, 'c': 30} + assert ta.validate_strings({'a': '10', 'b': '20', 'c': '3'}, strict=True, **eap) == {'a': 10, 'b': 20} + assert ta.validate_json('{"a": 10, "b": 20, "c": 30}', **eap) == {'a': 10, 'b': 20, 'c': 30} + assert ta.validate_json('{"a": 10, "b": 20, "c": 3', **eap) == {'a': 10, 'b': 20} + assert ta.validate_json('{"a": 10, "b": 20, "c": 3}', **eap) == {'a': 10, 'b': 20} diff --git a/tests/test_annotated.py b/tests/test_annotated.py index e0d3da79968..15b1ad1eafe 100644 --- a/tests/test_annotated.py +++ b/tests/test_annotated.py @@ -1,6 +1,7 @@ import datetime as dt import sys from dataclasses import dataclass +from decimal import Decimal from typing import Any, Callable, Generic, Iterator, List, Optional, Set, TypeVar import pytest @@ -385,6 +386,12 @@ def test_validate_float_inf_nan_python() -> None: ] +def test_predicate_success_python() -> None: + ta = TypeAdapter(Annotated[int, Predicate(lambda x: x > 0)]) + + assert ta.validate_python(1) == 1 + + def test_predicate_error_python() -> None: ta = TypeAdapter(Annotated[int, Predicate(lambda x: x > 0)]) @@ -638,3 +645,20 @@ def test_compatible_metadata_raises_correct_validation_error() -> None: ta = TypeAdapter(Annotated[str, BeforeValidator(lambda x: x), Field(pattern='abc')]) with pytest.raises(ValidationError, match="String should match pattern 'abc'"): ta.validate_python('def') + + +def test_decimal_constraints_after_annotation() -> None: + DecimalAnnotation = Annotated[Decimal, BeforeValidator(lambda v: v), Field(max_digits=10, decimal_places=4)] + + ta = TypeAdapter(DecimalAnnotation) + assert ta.validate_python(Decimal('123.4567')) == Decimal('123.4567') + + with pytest.raises(ValidationError) as e: + ta.validate_python(Decimal('123.45678')) + + assert e.value.errors()[0]['type'] == 'decimal_max_places' + + with pytest.raises(ValidationError) as e: + ta.validate_python(Decimal('12345678.901')) + + assert e.value.errors()[0]['type'] == 'decimal_max_digits' diff --git a/tests/test_computed_fields.py b/tests/test_computed_fields.py index 99182f8de5f..ef5841d7d8b 100644 --- a/tests/test_computed_fields.py +++ b/tests/test_computed_fields.py @@ -1,6 +1,7 @@ import random import sys from abc import ABC, abstractmethod +from functools import cached_property, lru_cache, singledispatchmethod from typing import Any, Callable, ClassVar, Generic, List, Tuple, TypeVar import pytest @@ -10,6 +11,7 @@ from pydantic import ( BaseModel, Field, + FieldSerializationInfo, GetCoreSchemaHandler, PrivateAttr, TypeAdapter, @@ -21,13 +23,6 @@ from pydantic.alias_generators import to_camel from pydantic.errors import PydanticUserError -try: - from functools import cached_property, lru_cache, singledispatchmethod -except ImportError: - cached_property = None - lru_cache = None - singledispatchmethod = None - def test_computed_fields_get(): class Rectangle(BaseModel): @@ -180,7 +175,6 @@ def fullname(self): assert user.model_dump() == {'first': '', 'last': '', 'fullname': ' '} -@pytest.mark.skipif(cached_property is None, reason='cached_property not available') def test_cached_property(): class Model(BaseModel): minimum: int = Field(alias='min') @@ -211,6 +205,13 @@ def _cached_property_3(self) -> int: assert rect.model_dump(by_alias=True) == {'min': 10, 'max': 10_000, 'the magic number': first_n} assert rect.model_dump(by_alias=True, exclude={'random_number'}) == {'min': 10, 'max': 10000} + # `cached_property` is a non-data descriptor, assert that you can assign a value to it: + rect2 = Model(min=1, max=1) + rect2.cached_property_2 = 1 + rect2._cached_property_3 = 2 + assert rect2.cached_property_2 == 1 + assert rect2._cached_property_3 == 2 + def test_properties_and_computed_fields(): class Model(BaseModel): @@ -256,7 +257,6 @@ def triple(self) -> int: assert repr(Model(x=2)) == 'Model(x=2, triple=6)' -@pytest.mark.skipif(singledispatchmethod is None, reason='singledispatchmethod not available') def test_functools(): class Model(BaseModel, frozen=True): x: int @@ -798,3 +798,42 @@ def id(self) -> str: m = Model(bar=42) assert m.model_dump() == {'bar': 42, 'id': 'id: {"bar":42}'} + + +def test_computed_field_with_field_serializer(): + class MyModel(BaseModel): + other_field: int = 42 + + @computed_field + @property + def my_field(self) -> str: + return 'foo' + + @field_serializer('*') + def my_field_serializer(self, value: Any, info: FieldSerializationInfo) -> Any: + return f'{info.field_name} = {value}' + + assert MyModel().model_dump() == {'my_field': 'my_field = foo', 'other_field': 'other_field = 42'} + + +def test_fields_on_instance_and_cls() -> None: + """For now, we support `model_fields` and `model_computed_fields` access on both instances and classes. + + In V3, we should only support class access, though we need to preserve the current behavior for V2 compatibility.""" + + class Rectangle(BaseModel): + x: int + y: int + + @computed_field + @property + def area(self) -> int: + return self.x * self.y + + r = Rectangle(x=10, y=5) + + for attr in {'model_fields', 'model_computed_fields'}: + assert getattr(r, attr) == getattr(Rectangle, attr) + + assert set(r.model_fields) == {'x', 'y'} + assert set(r.model_computed_fields) == {'area'} diff --git a/tests/test_config.py b/tests/test_config.py index a6259c73d6e..17b599b53f2 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -4,7 +4,7 @@ from contextlib import nullcontext as does_not_raise from decimal import Decimal from inspect import signature -from typing import Any, ContextManager, Dict, Iterable, NamedTuple, Optional, Tuple, Type, Union +from typing import Any, ContextManager, Dict, Iterable, NamedTuple, Optional, Type, Union from dirty_equals import HasRepr, IsPartialDict from pydantic_core import SchemaError, SchemaSerializer, SchemaValidator @@ -13,7 +13,6 @@ BaseConfig, BaseModel, Field, - GenerateSchema, PrivateAttr, PydanticDeprecatedSince20, PydanticSchemaGenerationError, @@ -24,21 +23,23 @@ with_config, ) from pydantic._internal._config import ConfigWrapper, config_defaults +from pydantic._internal._generate_schema import GenerateSchema from pydantic._internal._mock_val_ser import MockValSer from pydantic._internal._typing_extra import get_type_hints from pydantic.config import ConfigDict, JsonValue from pydantic.dataclasses import dataclass as pydantic_dataclass +from pydantic.dataclasses import rebuild_dataclass from pydantic.errors import PydanticUserError from pydantic.fields import ComputedFieldInfo, FieldInfo from pydantic.type_adapter import TypeAdapter -from pydantic.warnings import PydanticDeprecationWarning +from pydantic.warnings import PydanticDeprecatedSince210, PydanticDeprecationWarning from .conftest import CallCounter if sys.version_info < (3, 9): - from typing_extensions import Annotated, Literal + from typing_extensions import Annotated else: - from typing import Annotated, Literal + from typing import Annotated import pytest @@ -112,7 +113,7 @@ def test_base_config_custom_init_signature(self): class MyModel(BaseModel): id: int name: str = 'John Doe' - f__: str = Field(..., alias='foo') + f__: str = Field(alias='foo') model_config = ConfigDict(extra='allow') @@ -143,7 +144,7 @@ def __init__(self, a: float, b: int): def test_base_config_use_field_name(self): class Foo(BaseModel): - foo: str = Field(..., alias='this is invalid') + foo: str = Field(alias='this is invalid') model_config = ConfigDict(populate_by_name=True) @@ -151,7 +152,7 @@ class Foo(BaseModel): def test_base_config_does_not_use_reserved_word(self): class Foo(BaseModel): - from_: str = Field(..., alias='from') + from_: str = Field(alias='from') model_config = ConfigDict(populate_by_name=True) @@ -325,7 +326,7 @@ def test_populate_by_name_config( expected_value: int = 7 class Foo(BaseModel): - bar_: int = Field(..., alias='bar') + bar_: int = Field(alias='bar') model_config = dict(populate_by_name=populate_by_name_config) @@ -558,7 +559,7 @@ def check_foo(cls, v): Foo(foo=4) # Confirm python error attached as a cause, and error location specified in a note: assert exc_info.value.__cause__ is not None - assert isinstance(exc_info.value.__cause__, ExceptionGroup) + assert isinstance(exc_info.value.__cause__, ExceptionGroup) # noqa: F821 assert len(exc_info.value.__cause__.exceptions) == 1 src_exc = exc_info.value.__cause__.exceptions[0] assert repr(src_exc) == "AssertionError('Must be greater than 5\\nassert 4 > 5')" @@ -705,26 +706,22 @@ def test_json_encoders_type_adapter() -> None: assert json.loads(ta.dump_json(1)) == '2' -@pytest.mark.parametrize('defer_build_mode', [None, tuple(), ('model',), ('type_adapter',), ('model', 'type_adapter')]) -def test_config_model_defer_build( - defer_build_mode: Optional[Tuple[Literal['model', 'type_adapter'], ...]], generate_schema_calls: CallCounter -): - config = ConfigDict(defer_build=True) - if defer_build_mode is not None: - config['experimental_defer_build_mode'] = defer_build_mode +@pytest.mark.parametrize('defer_build', [True, False]) +def test_config_model_defer_build(defer_build: bool, generate_schema_calls: CallCounter): + config = ConfigDict(defer_build=defer_build) class MyModel(BaseModel): model_config = config x: int - if defer_build_mode is None or 'model' in defer_build_mode: + if defer_build: assert isinstance(MyModel.__pydantic_validator__, MockValSer) assert isinstance(MyModel.__pydantic_serializer__, MockValSer) - assert generate_schema_calls.count == 0, 'Should respect experimental_defer_build_mode' + assert generate_schema_calls.count == 0, 'Should respect defer_build' else: assert isinstance(MyModel.__pydantic_validator__, SchemaValidator) assert isinstance(MyModel.__pydantic_serializer__, SchemaSerializer) - assert generate_schema_calls.count == 1, 'Should respect experimental_defer_build_mode' + assert generate_schema_calls.count == 1, 'Should respect defer_build' m = MyModel(x=1) assert m.x == 1 @@ -737,20 +734,57 @@ class MyModel(BaseModel): assert generate_schema_calls.count == 1, 'Should not build duplicated core schemas' -@pytest.mark.parametrize('defer_build_mode', [None, tuple(), ('model',), ('type_adapter',), ('model', 'type_adapter')]) -def test_config_model_type_adapter_defer_build( - defer_build_mode: Optional[Tuple[Literal['model', 'type_adapter'], ...]], generate_schema_calls: CallCounter -): +@pytest.mark.parametrize('defer_build', [True, False]) +def test_config_dataclass_defer_build(defer_build: bool, generate_schema_calls: CallCounter) -> None: + config = ConfigDict(defer_build=defer_build) + + @pydantic_dataclass(config=config) + class MyDataclass: + x: int + + if defer_build: + assert isinstance(MyDataclass.__pydantic_validator__, MockValSer) + assert isinstance(MyDataclass.__pydantic_serializer__, MockValSer) + assert generate_schema_calls.count == 0, 'Should respect defer_build' + else: + assert isinstance(MyDataclass.__pydantic_validator__, SchemaValidator) + assert isinstance(MyDataclass.__pydantic_serializer__, SchemaSerializer) + assert generate_schema_calls.count == 1, 'Should respect defer_build' + + m = MyDataclass(x=1) + assert m.x == 1 + + assert isinstance(MyDataclass.__pydantic_validator__, SchemaValidator) + assert isinstance(MyDataclass.__pydantic_serializer__, SchemaSerializer) + assert generate_schema_calls.count == 1, 'Should not build duplicated core schemas' + + +def test_dataclass_defer_build_override_on_rebuild_dataclass(generate_schema_calls: CallCounter) -> None: config = ConfigDict(defer_build=True) - if defer_build_mode is not None: - config['experimental_defer_build_mode'] = defer_build_mode + + @pydantic_dataclass(config=config) + class MyDataclass: + x: int + + assert isinstance(MyDataclass.__pydantic_validator__, MockValSer) + assert isinstance(MyDataclass.__pydantic_serializer__, MockValSer) + assert generate_schema_calls.count == 0, 'Should respect defer_build' + + rebuild_dataclass(MyDataclass, force=True) + assert isinstance(MyDataclass.__pydantic_validator__, SchemaValidator) + assert isinstance(MyDataclass.__pydantic_serializer__, SchemaSerializer) + assert generate_schema_calls.count == 1, 'Should have called generate_schema once' + + +@pytest.mark.parametrize('defer_build', [True, False]) +def test_config_model_type_adapter_defer_build(defer_build: bool, generate_schema_calls: CallCounter): + config = ConfigDict(defer_build=defer_build) class MyModel(BaseModel): model_config = config x: int - is_deferred = defer_build_mode is None or 'model' in defer_build_mode - assert generate_schema_calls.count == (0 if is_deferred else 1) + assert generate_schema_calls.count == (0 if defer_build is True else 1) generate_schema_calls.reset() ta = TypeAdapter(MyModel) @@ -762,21 +796,16 @@ class MyModel(BaseModel): assert ta.dump_python(MyModel.model_construct(x=1))['x'] == 1 assert ta.json_schema()['type'] == 'object' - assert generate_schema_calls.count == (1 if is_deferred else 0), 'Should not build duplicate core schemas' + assert generate_schema_calls.count == (1 if defer_build is True else 0), 'Should not build duplicate core schemas' -@pytest.mark.parametrize('defer_build_mode', [None, tuple(), ('model',), ('type_adapter',), ('model', 'type_adapter')]) -def test_config_plain_type_adapter_defer_build( - defer_build_mode: Optional[Tuple[Literal['model', 'type_adapter'], ...]], generate_schema_calls: CallCounter -): - config = ConfigDict(defer_build=True) - if defer_build_mode is not None: - config['experimental_defer_build_mode'] = defer_build_mode - is_deferred = defer_build_mode is not None and 'type_adapter' in defer_build_mode +@pytest.mark.parametrize('defer_build', [True, False]) +def test_config_plain_type_adapter_defer_build(defer_build: bool, generate_schema_calls: CallCounter): + config = ConfigDict(defer_build=defer_build) ta = TypeAdapter(Dict[str, int], config=config) - assert generate_schema_calls.count == (0 if is_deferred else 1) + assert generate_schema_calls.count == (0 if defer_build else 1) generate_schema_calls.reset() assert ta.validate_python({}) == {} @@ -784,16 +813,12 @@ def test_config_plain_type_adapter_defer_build( assert ta.dump_python({'x': 2}) == {'x': 2} assert ta.json_schema()['type'] == 'object' - assert generate_schema_calls.count == (1 if is_deferred else 0), 'Should not build duplicate core schemas' + assert generate_schema_calls.count == (1 if defer_build else 0), 'Should not build duplicate core schemas' -@pytest.mark.parametrize('defer_build_mode', [None, ('model',), ('type_adapter',), ('model', 'type_adapter')]) -def test_config_model_defer_build_nested( - defer_build_mode: Optional[Tuple[Literal['model', 'type_adapter'], ...]], generate_schema_calls: CallCounter -): - config = ConfigDict(defer_build=True) - if defer_build_mode: - config['experimental_defer_build_mode'] = defer_build_mode +@pytest.mark.parametrize('defer_build', [True, False]) +def test_config_model_defer_build_nested(defer_build: bool, generate_schema_calls: CallCounter): + config = ConfigDict(defer_build=defer_build) assert generate_schema_calls.count == 0 @@ -807,10 +832,10 @@ class MyModel(BaseModel): assert isinstance(MyModel.__pydantic_validator__, SchemaValidator) assert isinstance(MyModel.__pydantic_serializer__, SchemaSerializer) - expected_schema_count = 1 if defer_build_mode is None or 'model' in defer_build_mode else 2 - assert generate_schema_calls.count == expected_schema_count, 'Should respect experimental_defer_build_mode' + expected_schema_count = 1 if defer_build is True else 2 + assert generate_schema_calls.count == expected_schema_count, 'Should respect defer_build' - if defer_build_mode is None or 'model' in defer_build_mode: + if defer_build: assert isinstance(MyNestedModel.__pydantic_validator__, MockValSer) assert isinstance(MyNestedModel.__pydantic_serializer__, MockValSer) else: @@ -823,7 +848,7 @@ class MyModel(BaseModel): assert m.model_validate({'y': {'x': 1}}).y.x == 1 assert m.model_json_schema()['type'] == 'object' - if defer_build_mode is None or 'model' in defer_build_mode: + if defer_build: assert isinstance(MyNestedModel.__pydantic_validator__, MockValSer) assert isinstance(MyNestedModel.__pydantic_serializer__, MockValSer) else: @@ -889,7 +914,7 @@ class MyModel(BaseModel): assert exc_info.value.code == 'model-config-invalid-field-name' -def test_dataclass_allowes_model_config_as_model_field(): +def test_dataclass_allows_model_config_as_model_field(): config_title = 'from_config' field_title = 'from_field' @@ -911,3 +936,19 @@ def test_with_config_disallowed_with_model(): @with_config({'coerce_numbers_to_str': True}) class Model(BaseModel): pass + + +def test_empty_config_with_annotations(): + class Model(BaseModel): + model_config: ConfigDict = {} + + assert Model.model_config == {} + + +def test_generate_schema_deprecation_warning() -> None: + with pytest.warns( + PydanticDeprecatedSince210, match='The `schema_generator` setting has been deprecated since v2.10.' + ): + + class Model(BaseModel): + model_config = ConfigDict(schema_generator=GenerateSchema) diff --git a/tests/test_create_model.py b/tests/test_create_model.py index f896422046b..c39042820f3 100644 --- a/tests/test_create_model.py +++ b/tests/test_create_model.py @@ -558,14 +558,9 @@ def test_create_model_tuple_3(): def test_create_model_protected_namespace_default(): with pytest.warns( - UserWarning, match='Field "model_prefixed_field" in Model has conflict with protected namespace "model_"' + UserWarning, match='Field "model_dump_something" in Model has conflict with protected namespace "model_dump"' ): - create_model('Model', model_prefixed_field=(str, ...)) - - -def test_create_model_protected_namespace_real_conflict(): - with pytest.raises(NameError, match='Field "model_dump" conflicts with member .* of protected namespace "model_"'): - create_model('Model', model_dump=(str, ...)) + create_model('Model', model_dump_something=(str, ...)) def test_create_model_custom_protected_namespace(): diff --git a/tests/test_dataclasses.py b/tests/test_dataclasses.py index 050d94f0f37..f69600d6d31 100644 --- a/tests/test_dataclasses.py +++ b/tests/test_dataclasses.py @@ -12,7 +12,7 @@ import pytest from dirty_equals import HasRepr -from pydantic_core import ArgsKwargs, CoreSchema, SchemaValidator, core_schema +from pydantic_core import ArgsKwargs, SchemaValidator from typing_extensions import Annotated, Literal import pydantic @@ -20,8 +20,8 @@ BaseModel, BeforeValidator, ConfigDict, - GenerateSchema, PydanticDeprecatedSince20, + PydanticSchemaGenerationError, PydanticUndefinedAnnotation, PydanticUserError, RootModel, @@ -819,12 +819,14 @@ class Meta: modified_date: Optional[datetime] seen_count: int + __pydantic_config__ = {'revalidate_instances': 'always'} + @dataclasses.dataclass class File: filename: str meta: Meta - FileChecked = pydantic.dataclasses.dataclass(File, config=ConfigDict(revalidate_instances='always')) + FileChecked = pydantic.dataclasses.dataclass(File) f = FileChecked(filename=b'thefilename', meta=Meta(modified_date='2020-01-01T00:00', seen_count='7')) assert f.filename == 'thefilename' assert f.meta.modified_date == datetime(2020, 1, 1, 0, 0) @@ -1377,13 +1379,13 @@ class Top: 'required': ['sub'], '$defs': { 'A': { - 'properties': {'l': {'const': 'a', 'enum': ['a'], 'title': 'L', 'type': 'string'}}, + 'properties': {'l': {'const': 'a', 'title': 'L', 'type': 'string'}}, 'required': ['l'], 'title': 'A', 'type': 'object', }, 'B': { - 'properties': {'l': {'const': 'b', 'enum': ['b'], 'title': 'L', 'type': 'string'}}, + 'properties': {'l': {'const': 'b', 'title': 'L', 'type': 'string'}}, 'required': ['l'], 'title': 'B', 'type': 'object', @@ -2488,23 +2490,31 @@ class Model: def test_model_config_override_in_decorator() -> None: - @pydantic.dataclasses.dataclass(config=ConfigDict(str_to_lower=False, str_strip_whitespace=True)) - class Model: - x: str - __pydantic_config__ = ConfigDict(str_to_lower=True) + with pytest.warns( + UserWarning, match='`config` is set via both the `dataclass` decorator and `__pydantic_config__`' + ): - ta = TypeAdapter(Model) - assert ta.validate_python({'x': 'ABC '}).x == 'ABC' + @pydantic.dataclasses.dataclass(config=ConfigDict(str_to_lower=False, str_strip_whitespace=True)) + class Model: + x: str + __pydantic_config__ = ConfigDict(str_to_lower=True) + + ta = TypeAdapter(Model) + assert ta.validate_python({'x': 'ABC '}).x == 'ABC' def test_model_config_override_in_decorator_empty_config() -> None: - @pydantic.dataclasses.dataclass(config=ConfigDict()) - class Model: - x: str - __pydantic_config__ = ConfigDict(str_to_lower=True) + with pytest.warns( + UserWarning, match='`config` is set via both the `dataclass` decorator and `__pydantic_config__`' + ): - ta = TypeAdapter(Model) - assert ta.validate_python({'x': 'ABC '}).x == 'ABC ' + @pydantic.dataclasses.dataclass(config=ConfigDict()) + class Model: + x: str + __pydantic_config__ = ConfigDict(str_to_lower=True) + + ta = TypeAdapter(Model) + assert ta.validate_python({'x': 'ABC '}).x == 'ABC ' def test_dataclasses_with_config_decorator(): @@ -2654,19 +2664,6 @@ class B: assert B().b == 1 -def test_schema_generator() -> None: - class LaxStrGenerator(GenerateSchema): - def str_schema(self) -> CoreSchema: - return core_schema.no_info_plain_validator_function(str) - - @pydantic.dataclasses.dataclass - class Model: - x: str - __pydantic_config__ = ConfigDict(schema_generator=LaxStrGenerator) - - assert Model(x=1).x == '1' - - @pytest.mark.parametrize('decorator1', **dataclass_decorators()) def test_annotated_before_validator_called_once(decorator1): count = 0 @@ -2858,7 +2855,7 @@ def test_disallow_init_false_and_init_var_true() -> None: @pydantic.dataclasses.dataclass class Foo: - bar: str = Field(..., init=False, init_var=True) + bar: str = Field(init=False, init_var=True) def test_annotations_valid_for_field_inheritance() -> None: @@ -3045,3 +3042,33 @@ def test_warns_on_double_frozen() -> None: @pydantic.dataclasses.dataclass(frozen=True, config=ConfigDict(frozen=True)) class DC: x: int + + +def test_warns_on_double_config() -> None: + with pytest.warns( + UserWarning, match='`config` is set via both the `dataclass` decorator and `__pydantic_config__`' + ): + + @pydantic.dataclasses.dataclass(config=ConfigDict(title='from decorator')) + class Foo: + __pydantic_config__ = ConfigDict(title='from __pydantic_config__') + + +def test_do_not_leak_config_from_other_types_during_building() -> None: + """Regression test, where dataclasses without any config would use the precedent type's config.""" + + class ArbitraryType: + pass + + @dataclasses.dataclass + class DC: + a: ArbitraryType + b: str + + with pytest.raises(PydanticSchemaGenerationError): + + class Model(BaseModel): + model_config = ConfigDict(arbitrary_types_allowed=True) + + dc: DC + other: str diff --git a/tests/test_datetime.py b/tests/test_datetime.py index 0bcabf99c28..2b95f7acaa2 100644 --- a/tests/test_datetime.py +++ b/tests/test_datetime.py @@ -87,7 +87,7 @@ class DateModel(BaseModel): (float('inf'), Err('Input should be a valid date or datetime, dates after 9999')), (int('1' + '0' * 100), Err('Input should be a valid date or datetime, dates after 9999')), (1e1000, Err('Input should be a valid date or datetime, dates after 9999')), - (float('-infinity'), Err('Input should be a valid date or datetime, dates before 1600')), + (float('-infinity'), Err('Input should be a valid date or datetime, dates before 0000')), (float('nan'), Err('Input should be a valid date or datetime, NaN values not permitted')), ], ) @@ -184,7 +184,7 @@ class DatetimeModel(BaseModel): (1_549_316_052_104_324, Err('Input should be a valid datetime, dates after 9999')), # nowish in μs (1_549_316_052_104_324_096, Err('Input should be a valid datetime, dates after 9999')), # nowish in ns (float('inf'), Err('Input should be a valid datetime, dates after 9999')), - (float('-inf'), Err('Input should be a valid datetime, dates before 1600')), + (float('-inf'), Err('Input should be a valid datetime, dates before 0000')), (1e50, Err('Input should be a valid datetime, dates after 9999')), (float('nan'), Err('Input should be a valid datetime, NaN values not permitted')), ], diff --git a/tests/test_deprecated_fields.py b/tests/test_deprecated_fields.py index 43559a9e54a..7835127b913 100644 --- a/tests/test_deprecated_fields.py +++ b/tests/test_deprecated_fields.py @@ -229,3 +229,24 @@ class Model(BaseModel): instance = Model(a=1, b=1) pytest.warns(DeprecationWarning, lambda: instance.a, match='deprecated') + + +def test_computed_field_deprecated_class_access() -> None: + class Model(BaseModel): + @computed_field(deprecated=True) + def prop(self) -> int: + return 1 + + assert isinstance(Model.prop, property) + + +def test_computed_field_deprecated_subclass() -> None: + """https://github.com/pydantic/pydantic/issues/10384""" + + class Base(BaseModel): + @computed_field(deprecated=True) + def prop(self) -> int: + return 1 + + class Sub(Base): + pass diff --git a/tests/test_discriminated_union.py b/tests/test_discriminated_union.py index b7abb4d91b8..af43953adb9 100644 --- a/tests/test_discriminated_union.py +++ b/tests/test_discriminated_union.py @@ -35,7 +35,7 @@ def test_discriminated_union_type(): ): class Model(BaseModel): - x: str = Field(..., discriminator='qwe') + x: str = Field(discriminator='qwe') @pytest.mark.parametrize('union', [True, False]) @@ -46,9 +46,9 @@ class InnerModel(BaseModel): class Model(BaseModel): if union: - x: Union[InnerModel] = Field(..., discriminator='qwe') + x: Union[InnerModel] = Field(discriminator='qwe') else: - x: InnerModel = Field(..., discriminator='qwe') + x: InnerModel = Field(discriminator='qwe') assert Model(x={'qwe': 'qwe', 'y': 1}).x.qwe == 'qwe' with pytest.raises(ValidationError) as exc_info: @@ -69,7 +69,7 @@ class InnerModel(BaseModel): qwe: Literal['qwe'] class Model(BaseModel): - x: Union[InnerModel] = Field(..., discriminator='qwe') + x: Union[InnerModel] = Field(discriminator='qwe') assert Model(x={'qwe': 'qwe'}).x.qwe == 'qwe' @@ -80,7 +80,7 @@ def test_discriminated_union_invalid_type(): ): class Model(BaseModel): - x: Union[str, int] = Field(..., discriminator='qwe') + x: Union[str, int] = Field(discriminator='qwe') def test_discriminated_union_defined_discriminator(): @@ -94,7 +94,7 @@ class Dog(BaseModel): with pytest.raises(PydanticUserError, match="Model 'Cat' needs a discriminator field for key 'pet_type'"): class Model(BaseModel): - pet: Union[Cat, Dog] = Field(..., discriminator='pet_type') + pet: Union[Cat, Dog] = Field(discriminator='pet_type') number: int @@ -110,7 +110,7 @@ class Dog(BaseModel): with pytest.raises(PydanticUserError, match="Model 'Cat' needs field 'pet_type' to be of type `Literal`"): class Model(BaseModel): - pet: Union[Cat, Dog] = Field(..., discriminator='pet_type') + pet: Union[Cat, Dog] = Field(discriminator='pet_type') number: int @@ -126,7 +126,7 @@ class WhiteCat(BaseModel): class Dog(BaseModel): pet_type: Literal['dog'] - CatDog = TypeAdapter(Annotated[Union[Cat, Dog], Field(..., discriminator='pet_type')]).validate_python + CatDog = TypeAdapter(Annotated[Union[Cat, Dog], Field(discriminator='pet_type')]).validate_python CatDog({'pet_type': 'blackcat'}) CatDog({'pet_type': 'whitecat'}) CatDog({'pet_type': 'dog'}) @@ -345,7 +345,7 @@ class B(BaseModel): foo: Literal['b'] class Top(BaseModel): - sub: Union[A, B] = Field(..., discriminator='foo') + sub: Union[A, B] = Field(discriminator='foo') t = Top(sub=A(foo='a')) assert isinstance(t, Top) @@ -360,7 +360,7 @@ class B(BaseModel): literal: Literal['b'] = Field(alias='lit') class Top(BaseModel): - sub: Union[A, B] = Field(..., discriminator='literal') + sub: Union[A, B] = Field(discriminator='literal') with pytest.raises(ValidationError) as exc_info: Top(sub=A(literal='a')) @@ -381,7 +381,7 @@ class B(BaseModel): m: Literal[2] class Top(BaseModel): - sub: Union[A, B] = Field(..., discriminator='m') + sub: Union[A, B] = Field(discriminator='m') assert isinstance(Top.model_validate({'sub': {'m': 2}}).sub, B) with pytest.raises(ValidationError) as exc_info: @@ -430,7 +430,7 @@ class B(BaseModel): m: Literal[EnumValue.b] class Top(BaseModel): - sub: Union[A, B] = Field(..., discriminator='m') + sub: Union[A, B] = Field(discriminator='m') assert isinstance(Top.model_validate({'sub': {'m': EnumValue.b}}).sub, B) if isinstance(EnumValue.b, (int, str)): @@ -497,7 +497,7 @@ class Lizard(BaseModel): name: str class Model(BaseModel): - pet: Union[CommonPet, Lizard] = Field(..., discriminator='pet_type') + pet: Union[CommonPet, Lizard] = Field(discriminator='pet_type') n: int assert isinstance(Model(**{'pet': {'pet_type': 'dog', 'name': 'Milou'}, 'n': 5}).pet, Dog) @@ -1240,11 +1240,10 @@ class TestModel(BaseModel): }, 'UnionModel1': { 'properties': { - 'type': {'const': 1, 'default': 1, 'enum': [1], 'title': 'Type', 'type': 'integer'}, + 'type': {'const': 1, 'default': 1, 'title': 'Type', 'type': 'integer'}, 'other': { 'const': 'UnionModel1', 'default': 'UnionModel1', - 'enum': ['UnionModel1'], 'title': 'Other', 'type': 'string', }, @@ -1254,11 +1253,10 @@ class TestModel(BaseModel): }, 'UnionModel2': { 'properties': { - 'type': {'const': 2, 'default': 2, 'enum': [2], 'title': 'Type', 'type': 'integer'}, + 'type': {'const': 2, 'default': 2, 'title': 'Type', 'type': 'integer'}, 'other': { 'const': 'UnionModel2', 'default': 'UnionModel2', - 'enum': ['UnionModel2'], 'title': 'Other', 'type': 'string', }, @@ -1339,7 +1337,7 @@ class Model(BaseModel): '$defs': { 'Cat': { 'properties': { - 'pet_type': {'const': 'cat', 'enum': ['cat'], 'title': 'Pet Type', 'type': 'string'}, + 'pet_type': {'const': 'cat', 'title': 'Pet Type', 'type': 'string'}, 'meows': {'title': 'Meows', 'type': 'integer'}, }, 'required': ['pet_type', 'meows'], @@ -1348,7 +1346,7 @@ class Model(BaseModel): }, 'Dog': { 'properties': { - 'pet_type': {'const': 'dog', 'enum': ['dog'], 'title': 'Pet Type', 'type': 'string'}, + 'pet_type': {'const': 'dog', 'title': 'Pet Type', 'type': 'string'}, 'barks': {'title': 'Barks', 'type': 'number'}, }, 'required': ['pet_type', 'barks'], @@ -1803,7 +1801,9 @@ class DiscriminatedModel(BaseModel): assert exc_info.code == 'callable-discriminator-no-tag' -@pytest.mark.xfail(reason='Issue not yet fixed, see: https://github.com/pydantic/pydantic/issues/8271.') +@pytest.mark.xfail( + reason='Issue not yet fixed, see: https://github.com/pydantic/pydantic/issues/8271. At the moment, JSON schema gen warns with a PydanticJsonSchemaWarning.' +) def test_presence_of_discriminator_when_generating_type_adaptor_json_schema_definitions() -> None: class ItemType(str, Enum): ITEM1 = 'item1' @@ -1829,11 +1829,11 @@ class CreateObjectDto(BaseModel): ] ] - adaptor = TypeAdapter( + adapter = TypeAdapter( Annotated[CreateObjectDto, FieldInfo(examples=[{'id': 1, 'items': [{'id': 3, 'type': 'ITEM1'}]}])] ) - schema_map, definitions = GenerateJsonSchema().generate_definitions([(adaptor, 'validation', adaptor.core_schema)]) + schema_map, definitions = GenerateJsonSchema().generate_definitions([(adapter, 'validation', adapter.core_schema)]) assert definitions == { 'CreateItem1': { 'properties': {'id': {'title': 'Id', 'type': 'integer'}, 'type': {'const': 'item1', 'title': 'Type'}}, @@ -1904,7 +1904,7 @@ class SubModel(MyModel): 'Step_A': { 'properties': { 'count': {'title': 'Count', 'type': 'integer'}, - 'type': {'const': 'stepA', 'enum': ['stepA'], 'title': 'Type', 'type': 'string'}, + 'type': {'const': 'stepA', 'title': 'Type', 'type': 'string'}, }, 'required': ['type', 'count'], 'title': 'Step_A', @@ -1912,7 +1912,7 @@ class SubModel(MyModel): }, 'Step_B': { 'properties': { - 'type': {'const': 'stepB', 'enum': ['stepB'], 'title': 'Type', 'type': 'string'}, + 'type': {'const': 'stepB', 'title': 'Type', 'type': 'string'}, 'value': {'title': 'Value', 'type': 'number'}, }, 'required': ['type', 'value'], @@ -1932,7 +1932,7 @@ class SubModel(MyModel): 'title': 'Steps', }, 'sub_models': {'items': {'$ref': '#/$defs/SubModel'}, 'title': 'Sub Models', 'type': 'array'}, - 'type': {'const': 'mixed', 'enum': ['mixed'], 'title': 'Type', 'type': 'string'}, + 'type': {'const': 'mixed', 'title': 'Type', 'type': 'string'}, }, 'required': ['type', 'sub_models', 'blending'], 'title': 'SubModel', @@ -1950,7 +1950,7 @@ class SubModel(MyModel): 'title': 'Steps', }, 'sub_models': {'items': {'$ref': '#/$defs/SubModel'}, 'title': 'Sub Models', 'type': 'array'}, - 'type': {'const': 'mixed', 'enum': ['mixed'], 'title': 'Type', 'type': 'string'}, + 'type': {'const': 'mixed', 'title': 'Type', 'type': 'string'}, }, 'required': ['type', 'sub_models'], 'title': 'MyModel', @@ -1971,13 +1971,13 @@ class LoopState(BaseModel): class LeafState(BaseModel): state_type: Literal['leaf'] - AnyState = Annotated[Union[NestedState, LoopState, LeafState], Field(..., discriminator='state_type')] + AnyState = Annotated[Union[NestedState, LoopState, LeafState], Field(discriminator='state_type')] adapter = TypeAdapter(AnyState) assert adapter.core_schema['schema']['type'] == 'tagged-union' for definition in adapter.core_schema['definitions']: if definition['schema']['model_name'] in ['NestedState', 'LoopState']: - assert definition['schema']['fields']['substate']['schema']['schema']['type'] == 'tagged-union' + assert definition['schema']['fields']['substate']['schema']['type'] == 'tagged-union' def test_recursive_discriminiated_union_with_typed_dict() -> None: @@ -2115,7 +2115,7 @@ class Cat(BaseModel, Generic[T]): friends: List['GenericPet'] id: T - GenericPet = Annotated[Union[Dog[T], Cat[T]], Field(..., discriminator='type_')] + GenericPet = Annotated[Union[Dog[T], Cat[T]], Field(discriminator='type_')] ta = TypeAdapter(Dog[int]) int_dog = { diff --git a/tests/test_dunder_all.py b/tests/test_dunder_all.py new file mode 100644 index 00000000000..90f60e786cf --- /dev/null +++ b/tests/test_dunder_all.py @@ -0,0 +1,17 @@ +def test_explicit_reexports() -> None: + from pydantic import __all__ as root_all + from pydantic.deprecated.tools import __all__ as tools + from pydantic.main import __all__ as main + from pydantic.networks import __all__ as networks + from pydantic.types import __all__ as types + + for name, export_all in [('main', main), ('networks', networks), ('deprecated.tools', tools), ('types', types)]: + for export in export_all: + assert export in root_all, f'{export} is in `pydantic.{name}.__all__` but missing in `pydantic.__all__`' + + +def test_explicit_reexports_exist() -> None: + import pydantic + + for name in pydantic.__all__: + assert hasattr(pydantic, name), f'{name} is in `pydantic.__all__` but `from pydantic import {name}` fails' diff --git a/tests/test_edge_cases.py b/tests/test_edge_cases.py index 517f49e0932..5dba203f7bc 100644 --- a/tests/test_edge_cases.py +++ b/tests/test_edge_cases.py @@ -8,6 +8,7 @@ from enum import Enum, auto from typing import ( Any, + Callable, Dict, ForwardRef, FrozenSet, @@ -24,15 +25,15 @@ import pytest from dirty_equals import HasRepr, IsStr -from pydantic_core import ErrorDetails, InitErrorDetails, PydanticSerializationError, core_schema -from typing_extensions import Annotated, Literal, TypedDict, get_args +from pydantic_core import ErrorDetails, InitErrorDetails, PydanticSerializationError, PydanticUndefined, core_schema +from typing_extensions import Annotated, Literal, TypeAliasType, TypedDict, get_args from pydantic import ( BaseModel, ConfigDict, GetCoreSchemaHandler, + PrivateAttr, PydanticDeprecatedSince20, - PydanticInvalidForJsonSchema, PydanticSchemaGenerationError, RootModel, TypeAdapter, @@ -648,15 +649,15 @@ class Model(BaseModel): def test_advanced_exclude_by_alias(): class SubSubModel(BaseModel): a: str - aliased_b: str = Field(..., alias='b_alias') + aliased_b: str = Field(alias='b_alias') class SubModel(BaseModel): - aliased_c: str = Field(..., alias='c_alias') - aliased_d: List[SubSubModel] = Field(..., alias='d_alias') + aliased_c: str = Field(alias='c_alias') + aliased_d: List[SubSubModel] = Field(alias='d_alias') class Model(BaseModel): - aliased_e: str = Field(..., alias='e_alias') - aliased_f: SubModel = Field(..., alias='f_alias') + aliased_e: str = Field(alias='e_alias') + aliased_f: SubModel = Field(alias='f_alias') m = Model( e_alias='e', @@ -1395,7 +1396,61 @@ class Model(BaseModel): assert Model.model_fields.keys() == set('abcdefg') -def test_annotated_inside_type(): +def test_type_union(): + class Model(BaseModel): + a: Type[Union[str, bytes]] + b: Type[Union[Any, str]] + + m = Model(a=bytes, b=int) + assert m.model_dump() == {'a': bytes, 'b': int} + assert m.a == bytes + + +def test_type_on_none(): + class Model(BaseModel): + a: Type[None] + + Model(a=type(None)) + + with pytest.raises(ValidationError) as exc_info: + Model(a=None) + + assert exc_info.value.errors(include_url=False) == [ + { + 'type': 'is_subclass_of', + 'loc': ('a',), + 'msg': 'Input should be a subclass of NoneType', + 'input': None, + 'ctx': {'class': 'NoneType'}, + } + ] + + +def test_type_on_typealias(): + Float = TypeAliasType('Float', float) + + class MyFloat(float): ... + + adapter = TypeAdapter(Type[Float]) + + adapter.validate_python(float) + adapter.validate_python(MyFloat) + + with pytest.raises(ValidationError) as exc_info: + adapter.validate_python(str) + + assert exc_info.value.errors(include_url=False) == [ + { + 'type': 'is_subclass_of', + 'loc': (), + 'msg': 'Input should be a subclass of float', + 'input': str, + 'ctx': {'class': 'float'}, + } + ] + + +def test_type_on_annotated(): class Model(BaseModel): a: Type[Annotated[int, ...]] @@ -1415,7 +1470,7 @@ class Model(BaseModel): ] -def test_assign_type(): +def test_type_assign(): class Parent: def echo(self): return 'parent' @@ -1438,10 +1493,10 @@ class Model(BaseModel): Model(v=Different) assert exc_info.value.errors(include_url=False) == [ { - 'ctx': {'class': 'test_assign_type..Parent'}, - 'input': HasRepr(".Different'>"), + 'ctx': {'class': Parent.__qualname__}, + 'input': HasRepr(repr(Different)), 'loc': ('v',), - 'msg': 'Input should be a subclass of test_assign_type..Parent', + 'msg': f'Input should be a subclass of {Parent.__qualname__}', 'type': 'is_subclass_of', } ] @@ -1508,7 +1563,7 @@ def check_a(cls, v): def test_optional_field_constraints(): class MyModel(BaseModel): - my_int: Optional[int] = Field(..., ge=3) + my_int: Optional[int] = Field(ge=3) with pytest.raises(ValidationError) as exc_info: MyModel(my_int=2) @@ -1846,6 +1901,24 @@ class Model(BaseModel): ] +def test_ellipsis_forward_ref_annotated() -> None: + """This previously resulted in the ellipsis being used as a default value.""" + + class Model(BaseModel): + f: 'Forward' + + Forward = Annotated[int, Field(...)] + + assert Model.model_fields['f'].default is PydanticUndefined + + +def test_private_attr_ellipsis() -> None: + class Model(BaseModel): + _a: int = PrivateAttr(...) + + assert not hasattr(Model(), '_a') + + def test_required_any(): class Model(BaseModel): optional1: Any @@ -2107,17 +2180,55 @@ def __hash__(self): m.model_dump_json() +def test_hashable_validate_json(): + class Model(BaseModel): + v: Hashable + + ta = TypeAdapter(Model) + + # Test a large nested dict + for validate in (Model.model_validate_json, ta.validate_json): + for testcase in ( + '{"v": "a"}', + '{"v": 1}', + '{"v": 1.0}', + '{"v": true}', + '{"v": null}', + ): + assert hash(validate(testcase).v) == hash(validate(testcase).v) + + +@pytest.mark.parametrize( + 'non_hashable', + [ + '{"v": []}', + '{"v": {"a": 0}}', + ], +) +def test_hashable_invalid_json(non_hashable) -> None: + """This is primarily included in order to document the behavior / limitations of the `Hashable` type's validation logic. + + Specifically, we don't do any coercions to arrays / dicts when loading from JSON, and thus they are not considered hashable. + This would be different if we, for example, coerced arrays to tuples. + """ + + class Model(BaseModel): + v: Hashable + + with pytest.raises(ValidationError): + Model.model_validate_json(non_hashable) + + def test_hashable_json_schema(): class Model(BaseModel): v: Hashable - with pytest.raises( - PydanticInvalidForJsonSchema, - match=re.escape( - "Cannot generate a JsonSchema for core_schema.IsInstanceSchema ()" - ), - ): - Model.model_json_schema() + assert Model.model_json_schema() == { + 'properties': {'v': {'title': 'V'}}, + 'required': ['v'], + 'title': 'Model', + 'type': 'object', + } def test_default_factory_called_once(): @@ -2199,7 +2310,7 @@ class MyModel(BaseModel): def test_frozen_config_and_field(): class Foo(BaseModel): model_config = ConfigDict(frozen=False, validate_assignment=True) - a: str = Field(...) + a: str assert Foo.model_fields['a'].metadata == [] @@ -2209,7 +2320,7 @@ class Foo(BaseModel): class Bar(BaseModel): model_config = ConfigDict(validate_assignment=True) - a: str = Field(..., frozen=True) + a: str = Field(frozen=True) c: Annotated[str, Field(frozen=True)] assert Bar.model_fields['a'].frozen @@ -2581,16 +2692,6 @@ class Model(BaseModel): assert m.model_dump_json() == f'{{"value":{json_value},"value_types_reversed":{json_value_reversed}}}' -def test_type_union(): - class Model(BaseModel): - a: Type[Union[str, bytes]] - b: Type[Union[Any, str]] - - m = Model(a=bytes, b=int) - assert m.model_dump() == {'a': bytes, 'b': int} - assert m.a == bytes - - def test_model_repr_before_validation(): log = [] @@ -2698,7 +2799,7 @@ class Outer(BaseModel): '$defs': { 'Model1': { 'properties': { - 'kind': {'const': '1', 'default': '1', 'enum': ['1'], 'title': 'Kind', 'type': 'string'}, + 'kind': {'const': '1', 'default': '1', 'title': 'Kind', 'type': 'string'}, 'two': {'anyOf': [{'$ref': '#/$defs/Model2'}, {'type': 'null'}]}, }, 'required': ['two'], @@ -2707,7 +2808,7 @@ class Outer(BaseModel): }, 'Model2': { 'properties': { - 'kind': {'const': '2', 'default': '2', 'enum': ['2'], 'title': 'Kind', 'type': 'string'}, + 'kind': {'const': '2', 'default': '2', 'title': 'Kind', 'type': 'string'}, 'one': {'anyOf': [{'$ref': '#/$defs/Model1'}, {'type': 'null'}]}, }, 'required': ['one'], @@ -2771,3 +2872,31 @@ def test_model_metaclass_on_other_class() -> None: class OtherClass(metaclass=ModelMetaclass): pass + + +@pytest.mark.skipif(sys.version_info < (3, 12), reason='requires Python 3.12+') +def test_nested_type_statement(): + # https://docs.python.org/3/reference/simple_stmts.html#type + + globs = {} + exec( + """ +from pydantic import BaseModel +class A(BaseModel): + type Int = int + a: Int +""", + globs, + ) + A = globs['A'] + assert A(a=1).a == 1 + + +def test_method_descriptors_default() -> None: + class SomeModel(BaseModel): + @staticmethod + def default_int_factory() -> int: ... + + int_factory: Callable[[], int] = Field(default=default_int_factory) + + assert SomeModel.model_fields['int_factory'].default is SomeModel.default_int_factory diff --git a/tests/test_fastapi.sh b/tests/test_fastapi.sh index 6e8e7b050b3..984bbcbe2fc 100755 --- a/tests/test_fastapi.sh +++ b/tests/test_fastapi.sh @@ -21,5 +21,14 @@ cd .. && pip install . && cd fastapi # To skip a specific test, add '--deselect path/to/test.py::test_name' to the end of this command # # To update the list of deselected tests, remove all deselections, run the tests, and re-add any remaining failures -# TODO remove this once that test is fixed, see https://github.com/pydantic/pydantic/pull/10029 -./scripts/test.sh -vv --deselect tests/test_openapi_examples.py::test_openapi_schema + +# Remove the first one once that test is fixed, see https://github.com/pydantic/pydantic/pull/10029 +# the remaining tests all failing bc we now correctly add a `'deprecated': True` attribute to the JSON schema, +# So it's the FastAPI tests that need to be updated here +./scripts/test.sh -vv \ + --deselect tests/test_openapi_examples.py::test_openapi_schema \ + --deselect tests/test_tutorial/test_query_params_str_validations/test_tutorial010.py::test_openapi_schema \ + --deselect tests/test_tutorial/test_query_params_str_validations/test_tutorial010_an.py::test_openapi_schema \ + --deselect tests/test_tutorial/test_query_params_str_validations/test_tutorial010_an_py310.py::test_openapi_schema \ + --deselect tests/test_tutorial/test_query_params_str_validations/test_tutorial010_an_py39.py::test_openapi_schema \ + --deselect tests/test_tutorial/test_query_params_str_validations/test_tutorial010_py310.py::test_openapi_schema \ diff --git a/tests/test_fastapi_json_schema.py b/tests/test_fastapi_json_schema.py index c9cd4c2b607..d38d8bfd99a 100644 --- a/tests/test_fastapi_json_schema.py +++ b/tests/test_fastapi_json_schema.py @@ -16,7 +16,6 @@ from dirty_equals import HasRepr, IsInstance, IsStr from pydantic import BaseModel, ConfigDict -from pydantic._internal._core_metadata import CoreMetadataHandler from pydantic._internal._core_utils import CoreSchemaOrField from pydantic.errors import PydanticInvalidForJsonSchema from pydantic.json_schema import GenerateJsonSchema, JsonSchemaValue @@ -46,9 +45,7 @@ class FastAPIGenerateJsonSchema(GenerateJsonSchema): """ def handle_invalid_for_json_schema(self, schema: CoreSchemaOrField, error_info: str) -> JsonSchemaValue: - # NOTE: I think it may be a good idea to rework this method to either not use CoreMetadataHandler, - # and/or to make CoreMetadataHandler a public API. - if CoreMetadataHandler(schema).metadata.get('pydantic_js_modify_function') is not None: + if schema.get('metadata', {}).get('pydantic_js_modify_function') is not None: # Since there is a json schema modify function, assume that this type is meant to be handled, # and the modify function will set all properties as appropriate return {} diff --git a/tests/test_forward_ref.py b/tests/test_forward_ref.py index d9280ff6be7..c7041997386 100644 --- a/tests/test_forward_ref.py +++ b/tests/test_forward_ref.py @@ -431,7 +431,7 @@ def module(): Foo = ForwardRef('Foo') class Foo(BaseModel): - c: List[Foo] = Field(..., gt=0) + c: List[Foo] = Field(gt=0) with pytest.raises(TypeError, match=re.escape("Unable to apply constraint 'gt' to supplied value []")): Foo(c=[Foo(c=[])]) @@ -446,7 +446,7 @@ def test_forward_ref_optional(create_module): class Spec(BaseModel): - spec_fields: list[str] = Field(..., alias="fields") + spec_fields: list[str] = Field(alias="fields") filter: str | None = None sort: str | None @@ -558,13 +558,13 @@ class Dog(BaseModel): 'Cat': { 'title': 'Cat', 'type': 'object', - 'properties': {'type': {'const': 'cat', 'enum': ['cat'], 'title': 'Type', 'type': 'string'}}, + 'properties': {'type': {'const': 'cat', 'title': 'Type', 'type': 'string'}}, 'required': ['type'], }, 'Dog': { 'title': 'Dog', 'type': 'object', - 'properties': {'type': {'const': 'dog', 'enum': ['dog'], 'title': 'Type', 'type': 'string'}}, + 'properties': {'type': {'const': 'dog', 'title': 'Type', 'type': 'string'}}, 'required': ['type'], }, }, @@ -576,15 +576,26 @@ def test_class_var_as_string(create_module): # language=Python """ from __future__ import annotations -from typing import ClassVar +from typing import ClassVar, ClassVar as CV +from typing_extensions import Annotated from pydantic import BaseModel class Model(BaseModel): a: ClassVar[int] + _b: ClassVar[int] + _c: ClassVar[Forward] + _d: Annotated[ClassVar[int], ...] + _e: CV[int] + _f: Annotated[CV[int], ...] + # Doesn't work as of today: + # _g: CV[Forward] + +Forward = int """ ) - assert module.Model.__class_vars__ == {'a'} + assert module.Model.__class_vars__ == {'a', '_b', '_c', '_d', '_e', '_f'} + assert module.Model.__private_attributes__ == {} def test_json_encoder_str(create_module): @@ -1125,3 +1136,217 @@ class Foo(BaseModel): ] assert extras_schema == {'type': 'int'} + + +@pytest.mark.xfail( + reason='While `get_cls_type_hints` uses the correct module ns for each base, `collect_model_fields` ' + 'will still use the `FieldInfo` instances from each base (see the `parent_fields_lookup` logic). ' + 'This means that `f` is still a forward ref in `Foo.model_fields`, and it gets evaluated in ' + '`GenerateSchema._model_schema`, where only the module of `Foo` is considered.' +) +def test_uses_the_correct_globals_to_resolve_model_forward_refs(create_module): + @create_module + def module_1(): + from pydantic import BaseModel + + class Bar(BaseModel): + f: 'A' + + A = int + + module_2 = create_module( + f""" +from {module_1.__name__} import Bar + +A = str + +class Foo(Bar): + pass + """ + ) + + assert module_2.Foo.model_fields['f'].annotation is int + + +@pytest.mark.xfail( + reason='We should keep a reference to the parent frame, not `f_locals`. ' + "It's probably only reasonable to support this in Python 3.14 with PEP 649." +) +def test_can_resolve_forward_refs_in_parent_frame_after_class_definition(): + def func(): + class Model(BaseModel): + a: 'A' + + class A(BaseModel): + pass + + return Model + + Model = func() + + Model.model_rebuild() + + +def test_uses_correct_global_ns_for_type_defined_in_separate_module(create_module): + @create_module + def module_1(): + from dataclasses import dataclass + + @dataclass + class Bar: + f: 'A' + + A = int + + module_2 = create_module( + f""" +from pydantic import BaseModel +from {module_1.__name__} import Bar + +A = str + +class Foo(BaseModel): + bar: Bar + """ + ) + + module_2.Foo(bar={'f': 1}) + + +def test_uses_the_local_namespace_when_generating_schema(): + def func(): + A = int + + class Model(BaseModel): + __pydantic_extra__: 'dict[str, A]' + + model_config = {'defer_build': True, 'extra': 'allow'} + + return Model + + Model = func() + + A = str # noqa: F841 + + Model.model_rebuild() + Model(extra_value=1) + + +def test_uses_the_correct_globals_to_resolve_dataclass_forward_refs(create_module): + @create_module + def module_1(): + from dataclasses import dataclass + + A = int + + @dataclass + class DC1: + a: 'A' + + module_2 = create_module(f""" +from dataclasses import dataclass + +from pydantic import BaseModel + +from {module_1.__name__} import DC1 + +A = str + +@dataclass +class DC2(DC1): + b: 'A' + +class Model(BaseModel): + dc: DC2 + """) + + Model = module_2.Model + + Model(dc=dict(a=1, b='not_an_int')) + + +@pytest.mark.skipif(sys.version_info < (3, 12), reason='Requires PEP 695 syntax') +def test_class_locals_are_kept_during_schema_generation(create_module): + create_module( + """ +from pydantic import BaseModel + +class Model(BaseModel): + type Test = int + a: 'Test | Forward' + +Forward = str + +Model.model_rebuild() + """ + ) + + +def test_validate_call_does_not_override_the_global_ns_with_the_local_ns_where_it_is_used(create_module): + from pydantic import validate_call + + @create_module + def module_1(): + A = int + + def func(a: 'A'): + pass + + def inner(): + A = str # noqa: F841 + + from module_1 import func + + func_val = validate_call(func) + + func_val(a=1) + + +@pytest.mark.xfail( + reason='In `GenerateSchema`, only the current class module is taken into account. ' + 'This is similar to `test_uses_the_correct_globals_to_resolve_model_forward_refs`.' +) +def test_uses_the_correct_globals_to_resolve_forward_refs_on_serializers(create_module): + @create_module + def module_1(): + from pydantic import BaseModel, field_serializer # or model_serializer + + MyStr = str + + class Model(BaseModel): + a: int + + @field_serializer('a') + def ser(self) -> 'MyStr': + return str(self.a) + + class Sub(module_1.Model): + pass + + Sub.model_rebuild() + + +@pytest.mark.xfail(reason='parent namespace is used for every type in `NsResolver`, for backwards compatibility.') +def test_do_not_use_parent_ns_when_outside_the_function(create_module): + @create_module + def module_1(): + import dataclasses + + from pydantic import BaseModel + + @dataclasses.dataclass + class A: + a: 'Model' # shouldn't resolve + b: 'Test' # same + + def inner(): + Test = int # noqa: F841 + + class Model(BaseModel, A): + pass + + return Model + + ReturnedModel = inner() # noqa: F841 + + assert module_1.ReturnedModel.__pydantic_complete__ is False diff --git a/tests/test_generics.py b/tests/test_generics.py index 2980af5d6c3..2e11d871f85 100644 --- a/tests/test_generics.py +++ b/tests/test_generics.py @@ -36,6 +36,7 @@ from typing_extensions import ( Annotated, Literal, + Never, NotRequired, ParamSpec, TypedDict, @@ -358,7 +359,7 @@ class MyGenericModel(BaseModel, Generic[T]): assert len(_GENERIC_TYPES_CACHE) == cache_size + 15 class Model(BaseModel): - x: MyGenericModel[Callable[[C], Iterable[str]]] = Field(...) + x: MyGenericModel[Callable[[C], Iterable[str]]] models.append(Model) assert len(_GENERIC_TYPES_CACHE) == cache_size + 15 @@ -475,7 +476,10 @@ class M(BaseModel): Working = B[m] generics.append(Working) - target_size = cache_size + count_create_models * 3 + 2 + # A[int, C] -> 2 caches + # A[int, C][M] -> 3 caches // this is generated by `mro` + # B[M] -> 3 caches + target_size = cache_size + count_create_models * 3 * 2 + 2 assert len(_GENERIC_TYPES_CACHE) < target_size + _LIMITED_DICT_SIZE del models del generics @@ -685,10 +689,7 @@ class OuterT_SameType(BaseModel, Generic[AT]): OuterT_SameType[int](i={'a': 8}) OuterT_SameType[int](i=inner_int) OuterT_SameType[str](i=inner_str) - # TODO: The next line is failing, but passes in v1. - # Should re-parse-from-dict if the pydantic_generic_origin is the same - # OuterT_SameType[str](i=inner_int_any) - OuterT_SameType[int](i=inner_int_any.model_dump()) + OuterT_SameType[int](i=inner_int_any) with pytest.raises(ValidationError) as exc_info: OuterT_SameType[int](i=inner_str.model_dump()) @@ -706,11 +707,10 @@ class OuterT_SameType(BaseModel, Generic[AT]): # insert_assert(exc_info.value.errors(include_url=False)) assert exc_info.value.errors(include_url=False) == [ { - 'type': 'model_type', - 'loc': ('i',), - 'msg': 'Input should be a valid dictionary or instance of InnerT[int]', - 'input': InnerT[str](a='ate'), - 'ctx': {'class_name': 'InnerT[int]'}, + 'type': 'int_parsing', + 'loc': ('i', 'a'), + 'msg': 'Input should be a valid integer, unable to parse string as an integer', + 'input': 'ate', } ] @@ -724,13 +724,7 @@ class OuterT_SameType(BaseModel, Generic[AT]): OuterT_SameType[int](i=inner_dict_any) # insert_assert(exc_info.value.errors(include_url=False)) assert exc_info.value.errors(include_url=False) == [ - { - 'type': 'model_type', - 'loc': ('i',), - 'msg': 'Input should be a valid dictionary or instance of InnerT[int]', - 'input': InnerT[Any](a={}), - 'ctx': {'class_name': 'InnerT[int]'}, - } + {'type': 'int_type', 'loc': ('i', 'a'), 'msg': 'Input should be a valid integer', 'input': {}} ] @@ -2320,7 +2314,7 @@ def test_construct_generic_model_with_validation(): class Page(BaseModel, Generic[T]): page: int = Field(ge=42) items: Sequence[T] - unenforced: PositiveInt = Field(..., lt=10) + unenforced: PositiveInt = Field(lt=10) with pytest.raises(ValidationError) as exc_info: Page[int](page=41, items=[], unenforced=11) @@ -2416,7 +2410,7 @@ class Model(BaseModel, Generic[T]): # insert_assert(Model[MyEnum].model_json_schema()) assert Model[MyEnum].model_json_schema() == { - '$defs': {'MyEnum': {'const': 1, 'enum': [1], 'title': 'MyEnum', 'type': 'integer'}}, + '$defs': {'MyEnum': {'enum': [1], 'title': 'MyEnum', 'type': 'integer'}}, 'properties': {'x': {'$ref': '#/$defs/MyEnum'}}, 'required': ['x'], 'title': 'Model[test_generic_enum_bound..MyEnum]', @@ -2468,7 +2462,7 @@ class Model(BaseModel, Generic[T]): # insert_assert(Model[MyEnum].model_json_schema()) assert Model[MyEnum].model_json_schema() == { - '$defs': {'MyEnum': {'const': 1, 'enum': [1], 'title': 'MyEnum', 'type': 'integer'}}, + '$defs': {'MyEnum': {'enum': [1], 'title': 'MyEnum', 'type': 'integer'}}, 'properties': {'x': {'$ref': '#/$defs/MyEnum'}}, 'required': ['x'], 'title': 'Model[test_generic_intenum_bound..MyEnum]', @@ -2875,3 +2869,233 @@ def test_generic_with_allow_extra(): # This used to raise an error related to accessing the __annotations__ attribute of the Generic class class AllowExtraGeneric(BaseModel, Generic[T], extra='allow'): data: T + + +def test_generic_mro_single(): + T = TypeVar('T') + + # 1. + class A(BaseModel, Generic[T]): ... + + class B(A, Generic[T]): ... + + assert B[int].__mro__ == (B[int], B, A[int], A, BaseModel, Generic, object) + + # 2. + class A(BaseModel, Generic[T]): ... + + class B(A[T]): ... + + class C(B[int]): ... + + assert B.__mro__ == (B, A, BaseModel, Generic, object) + assert C.__mro__ == (C, B[int], B, A[int], A, BaseModel, Generic, object) + + # 3. + class A(BaseModel, Generic[T]): ... + + class B(A[T], Generic[T]): ... + + class C(B[T], Generic[T]): ... + + assert B.__mro__ == (B, A, BaseModel, Generic, object) + assert B[int].__mro__ == (B[int], B, A[int], A, BaseModel, Generic, object) + assert C[int].__mro__ == (C[int], C, B[int], B, A[int], A, BaseModel, Generic, object) + + # 4. + class A(BaseModel, Generic[T]): ... + + class B(A[int], Generic[T]): ... + + class C(B[str], Generic[T]): ... + + assert B.__mro__ == (B, A[int], A, BaseModel, Generic, object) + assert C.__mro__ == (C, B[str], B, A[int], A, BaseModel, Generic, object) + + +def test_generic_mro_multi(): + T1 = TypeVar('T1') + T2 = TypeVar('T2') + T3 = TypeVar('T3') + + # 1. + class A(BaseModel, Generic[T1, T2]): ... + + class B(A[int, T2], BaseModel, Generic[T2]): ... + + assert A[int, T2][str].__mro__ == (A[int, T2][str], A, BaseModel, Generic, object) + assert B[str].__mro__ == (B[str], B, A[int, T2][str], A, BaseModel, Generic, object) + + # 2. + class A(BaseModel, Generic[T1, T2]): ... + + B = A[int, T2] + C = B[str] + + assert B.__mro__ == (A[int, T2], A, BaseModel, Generic, object) + assert C.__mro__ == (B[str], A, BaseModel, Generic, object) + + # 3. + + class A(BaseModel, Generic[T1]): ... + + class B1(A[T1], Generic[T1, T2]): ... + + class B2(A[T2], Generic[T1, T2]): ... + + assert B1[T1, str].__mro__ == (B1[T1, str], B1, A, BaseModel, Generic, object) + assert B2[T1, str].__mro__ == (B2[T1, str], B2, A[str], A, BaseModel, Generic, object) + + assert B1[T1, str][int].__mro__ == (B1[T1, str][int], B1, A[int], A, BaseModel, Generic, object) + assert B2[T1, str][int].__mro__ == (B2[T1, str][int], B2, A[str], A, BaseModel, Generic, object) + + assert B1[int, str].__mro__ == (B1[int, str], B1, A[int], A, BaseModel, Generic, object) + assert B2[int, str].__mro__ == (B2[int, str], B2, A[str], A, BaseModel, Generic, object) + + # 4. + + class A(BaseModel, Generic[T1]): ... + + class B(A[T2], Generic[T2]): ... + + class C1(B[T3], Generic[T3, T1]): ... + + class C2(B[T3], Generic[T3, T2]): ... + + assert C1[int, str].__mro__ == (C1[int, str], C1, B[int], B, A[int], A, BaseModel, Generic, object) + assert C2[int, str].__mro__ == (C2[int, str], C2, B[int], B, A[int], A, BaseModel, Generic, object) + + +def test_generic_mro_circular_typevar(): + T1 = TypeVar('T1') + T2 = TypeVar('T2') + T3 = TypeVar('T3') + + class A(BaseModel, Generic[T1]): ... + + class B(A[T2], Generic[T2]): ... + + class C(B[T3], Generic[T3]): ... + + class D(C[T1], Generic[T1]): ... + + class E(D[T2], Generic[T2]): ... + + assert E[int].__mro__ == (E[int], E, D[int], D, C[int], C, B[int], B, A[int], A, BaseModel, Generic, object) + + +def test_generic_mro_swap(): + T1 = TypeVar('T1') + T2 = TypeVar('T2') + + class A(BaseModel, Generic[T1]): ... + + class B(A[T1], Generic[T1, T2]): ... + + class BSwap(B[T2, T1], Generic[T1, T2]): ... + + C = BSwap[T1, str] + + assert C.__mro__ == (C, BSwap, B[str, T1], B, A[str], A, BaseModel, Generic, object) + assert C[int].__mro__ == (C[int], BSwap, B[str, int], B, A[str], A, BaseModel, Generic, object) + + assert BSwap[int, str].__mro__[1:] == C[int].__mro__[1:] + + +def test_generic_field(): + # https://github.com/pydantic/pydantic/issues/10039 + + T = TypeVar('T') + + class A(BaseModel, Generic[T]): ... + + class B(A[T]): ... + + class C(B[bool]): ... + + class Model(BaseModel): + input_bool: A[bool] + + Model(input_bool=C()) + + assert C.__mro__ == (C, B[bool], B, A[bool], A, BaseModel, Generic, object) + assert issubclass(C, A[bool]) and issubclass(C, A[int]) is False + + +def test_generic_any_or_never() -> None: + T = TypeVar('T') + + class GenericModel(BaseModel, Generic[T]): + f: Union[T, int] + + any_json_schema = GenericModel[Any].model_json_schema() + assert any_json_schema['properties']['f'] == {'title': 'F'} # any type + + never_json_schema = GenericModel[Never].model_json_schema() + assert never_json_schema['properties']['f'] == {'type': 'integer', 'title': 'F'} + + +def test_revalidation_against_any() -> None: + T = TypeVar('T') + + class ResponseModel(BaseModel, Generic[T]): + content: T + + class Product(BaseModel): + name: str + price: float + + class Order(BaseModel): + id: int + product: ResponseModel[Any] + + product = Product(name='Apple', price=0.5) + response1: ResponseModel[Any] = ResponseModel[Any](content=product) + response2: ResponseModel[Any] = ResponseModel(content=product) + response3: ResponseModel[Any] = ResponseModel[Product](content=product) + + for response in response1, response2, response3: + order = Order(id=1, product=response) + assert isinstance(order.product.content, Product) + + +def test_revalidation_without_explicit_parametrization() -> None: + """Note, this is seen in the test above as well, but is added here for thoroughness.""" + + T1 = TypeVar('T1', bound=BaseModel) + + class InnerModel(BaseModel, Generic[T1]): + model: T1 + + T2 = TypeVar('T2', bound=InnerModel) + + class OuterModel(BaseModel, Generic[T2]): + inner: T2 + + class MyModel(BaseModel): + foo: int + + # Construct two instances, with and without generic annotation in the constructor: + inner1 = InnerModel[MyModel](model=MyModel(foo=42)) + inner2 = InnerModel(model=MyModel(foo=42)) + assert inner1 == inner2 + + outer1 = OuterModel[InnerModel[MyModel]](inner=inner1) + outer2 = OuterModel[InnerModel[MyModel]](inner=inner2) + # implies that validation succeeds for both + assert outer1 == outer2 + + +def test_revalidation_with_basic_inference() -> None: + T = TypeVar('T') + + class Inner(BaseModel, Generic[T]): + inner: T + + class Holder(BaseModel, Generic[T]): + inner: Inner[T] + + holder1 = Holder[int](inner=Inner[int](inner=1)) + holder2 = Holder(inner=Inner(inner=1)) + # implies that validation succeeds for both + assert holder1 == holder2 diff --git a/tests/test_internal.py b/tests/test_internal.py index 862fe30c5c2..85f1763779f 100644 --- a/tests/test_internal.py +++ b/tests/test_internal.py @@ -3,19 +3,20 @@ """ from dataclasses import dataclass +from decimal import Decimal import pytest from pydantic_core import CoreSchema, SchemaValidator from pydantic_core import core_schema as cs from pydantic._internal._core_utils import ( - HAS_INVALID_SCHEMAS_METADATA_KEY, Walk, collect_invalid_schemas, simplify_schema_references, walk_core_schema, ) from pydantic._internal._repr import Representation +from pydantic._internal._validators import _extract_decimal_digits_info def remove_metadata(schema: CoreSchema) -> CoreSchema: @@ -203,7 +204,21 @@ class Obj(Representation): def test_schema_is_valid(): assert collect_invalid_schemas(cs.none_schema()) is False - assert ( - collect_invalid_schemas(cs.nullable_schema(cs.int_schema(metadata={HAS_INVALID_SCHEMAS_METADATA_KEY: True}))) - is True - ) + assert collect_invalid_schemas(cs.invalid_schema()) is True + assert collect_invalid_schemas(cs.nullable_schema(cs.invalid_schema())) is True + + +@pytest.mark.parametrize( + 'decimal,decimal_places,digits', + [ + (Decimal('0.0'), 1, 1), + (Decimal('0.'), 0, 1), + (Decimal('0.000'), 3, 3), + (Decimal('0.0001'), 4, 4), + (Decimal('.0001'), 4, 4), + (Decimal('123.123'), 3, 6), + (Decimal('123.1230'), 4, 7), + ], +) +def test_decimal_digits_calculation(decimal: Decimal, decimal_places: int, digits: int) -> None: + assert _extract_decimal_digits_info(decimal) == (decimal_places, digits) diff --git a/tests/test_json_schema.py b/tests/test_json_schema.py index 089b9c51cd5..bad671d1177 100644 --- a/tests/test_json_schema.py +++ b/tests/test_json_schema.py @@ -62,7 +62,6 @@ field_serializer, field_validator, ) -from pydantic._internal._core_metadata import CoreMetadataHandler, build_metadata_dict from pydantic.color import Color from pydantic.config import ConfigDict from pydantic.dataclasses import dataclass @@ -77,7 +76,15 @@ model_json_schema, models_json_schema, ) -from pydantic.networks import AnyUrl, EmailStr, IPvAnyAddress, IPvAnyInterface, IPvAnyNetwork, MultiHostUrl, NameEmail +from pydantic.networks import ( + AnyUrl, + EmailStr, + IPvAnyAddress, + IPvAnyInterface, + IPvAnyNetwork, + NameEmail, + _CoreMultiHostUrl, +) from pydantic.type_adapter import TypeAdapter from pydantic.types import ( UUID1, @@ -101,6 +108,7 @@ SecretStr, StrictBool, StrictStr, + StringConstraints, conbytes, condate, condecimal, @@ -220,7 +228,7 @@ class Bar(BaseModel): def test_schema_class(): class Model(BaseModel): foo: int = Field(4, title='Foo is Great') - bar: str = Field(..., description='this description of bar') + bar: str = Field(description='this description of bar') with pytest.raises(ValidationError): Model() @@ -420,7 +428,7 @@ class Names(str, Enum): class Foo(BaseModel): enum: Names - extra_enum: Names = Field(..., json_schema_extra={'extra': 'Extra field'}) + extra_enum: Names = Field(json_schema_extra={'extra': 'Extra field'}) assert Foo.model_json_schema() == { '$defs': { @@ -933,7 +941,7 @@ class Model(BaseModel): Annotated[AnyUrl, Field(max_length=2**16)], {'title': 'A', 'type': 'string', 'format': 'uri', 'minLength': 1, 'maxLength': 2**16}, ), - (MultiHostUrl, {'title': 'A', 'type': 'string', 'format': 'multi-host-uri', 'minLength': 1}), + (_CoreMultiHostUrl, {'title': 'A', 'type': 'string', 'format': 'multi-host-uri', 'minLength': 1}), ], ) def test_special_str_types(field_type, expected_schema): @@ -1481,7 +1489,7 @@ class Bar(BaseModel): pass class Spam(BaseModel): - a: Union[Foo, Bar] = Field(..., description='xxx') + a: Union[Foo, Bar] = Field(description='xxx') assert Spam.model_json_schema()['properties'] == { 'a': { @@ -1726,6 +1734,48 @@ class UserModel(BaseModel): assert default_value == MyEnum.FOO.value +def test_enum_dict(): + class MyEnum(str, Enum): + FOO = 'foo' + BAR = 'bar' + + class MyModel(BaseModel): + enum_dict: Dict[MyEnum, str] + + assert MyModel.model_json_schema() == { + 'title': 'MyModel', + 'type': 'object', + 'properties': { + 'enum_dict': { + 'title': 'Enum Dict', + 'type': 'object', + 'additionalProperties': {'type': 'string'}, + 'propertyNames': {'enum': ['foo', 'bar']}, + } + }, + 'required': ['enum_dict'], + } + + +def test_property_names_constraint(): + class MyModel(BaseModel): + my_dict: Dict[Annotated[str, StringConstraints(max_length=1)], str] + + assert MyModel.model_json_schema() == { + 'properties': { + 'my_dict': { + 'additionalProperties': {'type': 'string'}, + 'propertyNames': {'maxLength': 1}, + 'title': 'My Dict', + 'type': 'object', + } + }, + 'required': ['my_dict'], + 'title': 'MyModel', + 'type': 'object', + } + + def test_dict_default(): class UserModel(BaseModel): friends: Dict[str, float] = {'a': 1.1, 'b': 2.2} @@ -1760,6 +1810,7 @@ class Outer(BaseModel): 'a': { 'additionalProperties': {'type': 'string'}, 'default': {'.': ''}, + 'propertyNames': {'format': 'path'}, 'title': 'A', 'type': 'object', } @@ -2311,12 +2362,12 @@ class Model(BaseModel): # insert_assert(Model.model_json_schema()) assert Model.model_json_schema() == { 'properties': { - 'a': {'const': 1, 'enum': [1], 'title': 'A', 'type': 'integer'}, - 'b': {'const': 'a', 'enum': ['a'], 'title': 'B', 'type': 'string'}, + 'a': {'const': 1, 'title': 'A', 'type': 'integer'}, + 'b': {'const': 'a', 'title': 'B', 'type': 'string'}, 'c': {'enum': ['a', 1], 'title': 'C'}, 'd': {'enum': ['a', 'b', 1, 2], 'title': 'D'}, - 'e': {'const': 1.0, 'enum': [1.0], 'title': 'E', 'type': 'number'}, - 'f': {'const': ['a', 1], 'enum': [['a', 1]], 'title': 'F', 'type': 'array'}, + 'e': {'const': 1.0, 'title': 'E', 'type': 'number'}, + 'f': {'const': ['a', 1], 'title': 'F', 'type': 'array'}, }, 'required': ['a', 'b', 'c', 'd', 'e', 'f'], 'title': 'Model', @@ -2336,7 +2387,7 @@ class Model(BaseModel): # insert_assert(Model.model_json_schema()) assert Model.model_json_schema() == { 'properties': { - 'kind': {'const': 'foo', 'enum': ['foo'], 'title': 'Kind', 'type': 'string'}, + 'kind': {'const': 'foo', 'title': 'Kind', 'type': 'string'}, 'other': {'enum': ['foo', 'bar'], 'title': 'Other', 'type': 'string'}, }, 'required': ['kind', 'other'], @@ -2378,7 +2429,7 @@ class Model(BaseModel): 'int_literal': {'enum': [123, 456], 'title': 'Int Literal', 'type': 'integer'}, 'float_literal': {'$ref': '#/$defs/FloatEnum'}, 'bool_literal': {'enum': [True, False], 'title': 'Bool Literal', 'type': 'boolean'}, - 'none_literal': {'const': None, 'enum': [None], 'title': 'None Literal', 'type': 'null'}, + 'none_literal': {'const': None, 'title': 'None Literal', 'type': 'null'}, 'list_literal': {'$ref': '#/$defs/ListEnum'}, 'mixed_literal': {'enum': [123, 'abc'], 'title': 'Mixed Literal'}, }, @@ -2507,23 +2558,6 @@ class Model(TypedDict): } -def test_typeddict_with_extra_behavior_allow(): - class Model: - @classmethod - def __get_pydantic_core_schema__(cls, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema: - return core_schema.typed_dict_schema( - {'a': core_schema.typed_dict_field(core_schema.str_schema())}, - extra_behavior='allow', - ) - - assert TypeAdapter(Model).json_schema() == { - 'type': 'object', - 'properties': {'a': {'title': 'A', 'type': 'string'}}, - 'required': ['a'], - 'additionalProperties': True, - } - - def test_typeddict_with_extra_ignore(): class Model(TypedDict): __pydantic_config__ = ConfigDict(extra='ignore') # type: ignore @@ -2537,22 +2571,6 @@ class Model(TypedDict): } -def test_typeddict_with_extra_behavior_ignore(): - class Model: - @classmethod - def __get_pydantic_core_schema__(cls, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema: - return core_schema.typed_dict_schema( - {'a': core_schema.typed_dict_field(core_schema.str_schema())}, - extra_behavior='ignore', - ) - - assert TypeAdapter(Model).json_schema() == { - 'type': 'object', - 'properties': {'a': {'title': 'A', 'type': 'string'}}, - 'required': ['a'], - } - - def test_typeddict_with_extra_forbid(): @pydantic.dataclasses.dataclass class Model: @@ -2568,23 +2586,6 @@ class Model: } -def test_typeddict_with_extra_behavior_forbid(): - class Model: - @classmethod - def __get_pydantic_core_schema__(cls, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema: - return core_schema.typed_dict_schema( - {'a': core_schema.typed_dict_field(core_schema.str_schema())}, - extra_behavior='forbid', - ) - - assert TypeAdapter(Model).json_schema() == { - 'type': 'object', - 'properties': {'a': {'title': 'A', 'type': 'string'}}, - 'required': ['a'], - 'additionalProperties': False, - } - - def test_typeddict_with_title(): class Model(TypedDict): __pydantic_config__ = ConfigDict(title='Test') # type: ignore @@ -2686,7 +2687,7 @@ class Model(TypedDict): ) def test_enforced_constraints(annotation, kwargs, field_schema): class Model(BaseModel): - a: annotation = Field(..., **kwargs) + a: annotation = Field(**kwargs) schema = Model.model_json_schema() # debug(schema['properties']['a']) @@ -2696,7 +2697,7 @@ class Model(BaseModel): def test_real_constraints(): class Model1(BaseModel): model_config = ConfigDict(title='Test Model') - foo: int = Field(..., gt=123) + foo: int = Field(gt=123) with pytest.raises(ValidationError, match='should be greater than 123'): Model1(foo=123) @@ -3266,7 +3267,7 @@ def __get_pydantic_core_schema__( ) -> core_schema.PlainValidatorFunctionSchema: source_args = getattr(source, '__args__', [Any]) param = source_args[0] - metadata = build_metadata_dict(js_functions=[lambda _c, h: h(handler.generate_schema(param))]) + metadata = {'pydantic_js_functions': [lambda _c, h: h(handler.generate_schema(param))]} return core_schema.with_info_plain_validator_function( GenModel, metadata=metadata, @@ -3436,7 +3437,7 @@ def __get_pydantic_core_schema__( ) -> core_schema.CoreSchema: if hasattr(source, '__args__'): # the js_function ignores the schema we were given and gets a new Tuple CoreSchema - metadata = build_metadata_dict(js_functions=[lambda _c, h: h(handler(Tuple[source.__args__]))]) + metadata = {'pydantic_js_functions': [lambda _c, h: h(handler(Tuple[source.__args__]))]} return core_schema.with_info_plain_validator_function( GenTwoParams, metadata=metadata, @@ -3689,19 +3690,19 @@ class Lizard(BaseModel): pet_type: Literal['reptile', 'lizard'] class Model(BaseModel): - pet: Union[Cat, Dog, Lizard] = Field(..., discriminator='pet_type') + pet: Union[Cat, Dog, Lizard] = Field(discriminator='pet_type') # insert_assert(Model.model_json_schema()) assert Model.model_json_schema() == { '$defs': { 'Cat': { - 'properties': {'pet_type': {'const': 'cat', 'enum': ['cat'], 'title': 'Pet Type', 'type': 'string'}}, + 'properties': {'pet_type': {'const': 'cat', 'title': 'Pet Type', 'type': 'string'}}, 'required': ['pet_type'], 'title': 'Cat', 'type': 'object', }, 'Dog': { - 'properties': {'pet_type': {'const': 'dog', 'enum': ['dog'], 'title': 'Pet Type', 'type': 'string'}}, + 'properties': {'pet_type': {'const': 'dog', 'title': 'Pet Type', 'type': 'string'}}, 'required': ['pet_type'], 'title': 'Dog', 'type': 'object', @@ -3745,19 +3746,19 @@ class Lizard(BaseModel): pet_type: Literal['reptile', 'lizard'] class Model(BaseModel): - pet: Annotated[Union[Cat, Dog, Lizard], Field(..., discriminator='pet_type')] + pet: Annotated[Union[Cat, Dog, Lizard], Field(discriminator='pet_type')] # insert_assert(Model.model_json_schema()) assert Model.model_json_schema() == { '$defs': { 'Cat': { - 'properties': {'pet_type': {'const': 'cat', 'enum': ['cat'], 'title': 'Pet Type', 'type': 'string'}}, + 'properties': {'pet_type': {'const': 'cat', 'title': 'Pet Type', 'type': 'string'}}, 'required': ['pet_type'], 'title': 'Cat', 'type': 'object', }, 'Dog': { - 'properties': {'pet_type': {'const': 'dog', 'enum': ['dog'], 'title': 'Pet Type', 'type': 'string'}}, + 'properties': {'pet_type': {'const': 'dog', 'title': 'Pet Type', 'type': 'string'}}, 'required': ['pet_type'], 'title': 'Dog', 'type': 'object', @@ -3815,9 +3816,9 @@ class Cat(BaseModel): '$defs': { 'BlackCatWithHeight': { 'properties': { - 'color': {'const': 'black', 'enum': ['black'], 'title': 'Color', 'type': 'string'}, + 'color': {'const': 'black', 'title': 'Color', 'type': 'string'}, 'height': {'title': 'Height', 'type': 'number'}, - 'info': {'const': 'height', 'enum': ['height'], 'title': 'Info', 'type': 'string'}, + 'info': {'const': 'height', 'title': 'Info', 'type': 'string'}, }, 'required': ['color', 'info', 'height'], 'title': 'BlackCatWithHeight', @@ -3825,8 +3826,8 @@ class Cat(BaseModel): }, 'BlackCatWithWeight': { 'properties': { - 'color': {'const': 'black', 'enum': ['black'], 'title': 'Color', 'type': 'string'}, - 'info': {'const': 'weight', 'enum': ['weight'], 'title': 'Info', 'type': 'string'}, + 'color': {'const': 'black', 'title': 'Color', 'type': 'string'}, + 'info': {'const': 'weight', 'title': 'Info', 'type': 'string'}, 'weight': {'title': 'Weight', 'type': 'number'}, }, 'required': ['color', 'info', 'weight'], @@ -3835,7 +3836,7 @@ class Cat(BaseModel): }, 'WhiteCat': { 'properties': { - 'color': {'const': 'white', 'enum': ['white'], 'title': 'Color', 'type': 'string'}, + 'color': {'const': 'white', 'title': 'Color', 'type': 'string'}, 'white_cat_info': {'title': 'White Cat Info', 'type': 'string'}, }, 'required': ['color', 'white_cat_info'], @@ -3918,9 +3919,9 @@ class Model(BaseModel): 'BlackCatWithHeight': { 'properties': { 'black_infos': {'title': 'Black Infos', 'type': 'string'}, - 'color': {'const': 'black', 'enum': ['black'], 'title': 'Color', 'type': 'string'}, - 'info': {'const': 'height', 'enum': ['height'], 'title': 'Info', 'type': 'string'}, - 'pet_type': {'const': 'cat', 'enum': ['cat'], 'title': 'Pet Type', 'type': 'string'}, + 'color': {'const': 'black', 'title': 'Color', 'type': 'string'}, + 'info': {'const': 'height', 'title': 'Info', 'type': 'string'}, + 'pet_type': {'const': 'cat', 'title': 'Pet Type', 'type': 'string'}, }, 'required': ['pet_type', 'color', 'info', 'black_infos'], 'title': 'BlackCatWithHeight', @@ -3929,9 +3930,9 @@ class Model(BaseModel): 'BlackCatWithWeight': { 'properties': { 'black_infos': {'title': 'Black Infos', 'type': 'string'}, - 'color': {'const': 'black', 'enum': ['black'], 'title': 'Color', 'type': 'string'}, - 'info': {'const': 'weight', 'enum': ['weight'], 'title': 'Info', 'type': 'string'}, - 'pet_type': {'const': 'cat', 'enum': ['cat'], 'title': 'Pet Type', 'type': 'string'}, + 'color': {'const': 'black', 'title': 'Color', 'type': 'string'}, + 'info': {'const': 'weight', 'title': 'Info', 'type': 'string'}, + 'pet_type': {'const': 'cat', 'title': 'Pet Type', 'type': 'string'}, }, 'required': ['pet_type', 'color', 'info', 'black_infos'], 'title': 'BlackCatWithWeight', @@ -3940,7 +3941,7 @@ class Model(BaseModel): 'Dog': { 'properties': { 'dog_name': {'title': 'Dog Name', 'type': 'string'}, - 'pet_type': {'const': 'dog', 'enum': ['dog'], 'title': 'Pet Type', 'type': 'string'}, + 'pet_type': {'const': 'dog', 'title': 'Pet Type', 'type': 'string'}, }, 'required': ['pet_type', 'dog_name'], 'title': 'Dog', @@ -3948,8 +3949,8 @@ class Model(BaseModel): }, 'WhiteCat': { 'properties': { - 'color': {'const': 'white', 'enum': ['white'], 'title': 'Color', 'type': 'string'}, - 'pet_type': {'const': 'cat', 'enum': ['cat'], 'title': 'Pet Type', 'type': 'string'}, + 'color': {'const': 'white', 'title': 'Color', 'type': 'string'}, + 'pet_type': {'const': 'cat', 'title': 'Pet Type', 'type': 'string'}, 'white_infos': {'title': 'White Infos', 'type': 'string'}, }, 'required': ['pet_type', 'color', 'white_infos'], @@ -4102,9 +4103,9 @@ class Model(BaseModel): 'BlackCatWithHeight': { 'properties': { 'black_infos': {'title': 'Black Infos', 'type': 'string'}, - 'color': {'const': 'black', 'enum': ['black'], 'title': 'Color', 'type': 'string'}, - 'info': {'const': 0, 'enum': [0], 'title': 'Info', 'type': 'integer'}, - 'pet_type': {'const': 'cat', 'enum': ['cat'], 'title': 'Pet Type', 'type': 'string'}, + 'color': {'const': 'black', 'title': 'Color', 'type': 'string'}, + 'info': {'const': 0, 'title': 'Info', 'type': 'integer'}, + 'pet_type': {'const': 'cat', 'title': 'Pet Type', 'type': 'string'}, }, 'required': ['pet_type', 'color', 'info', 'black_infos'], 'title': 'BlackCatWithHeight', @@ -4113,9 +4114,9 @@ class Model(BaseModel): 'BlackCatWithWeight': { 'properties': { 'black_infos': {'title': 'Black Infos', 'type': 'string'}, - 'color': {'const': 'black', 'enum': ['black'], 'title': 'Color', 'type': 'string'}, - 'info': {'const': 1, 'enum': [1], 'title': 'Info', 'type': 'integer'}, - 'pet_type': {'const': 'cat', 'enum': ['cat'], 'title': 'Pet Type', 'type': 'string'}, + 'color': {'const': 'black', 'title': 'Color', 'type': 'string'}, + 'info': {'const': 1, 'title': 'Info', 'type': 'integer'}, + 'pet_type': {'const': 'cat', 'title': 'Pet Type', 'type': 'string'}, }, 'required': ['pet_type', 'color', 'info', 'black_infos'], 'title': 'BlackCatWithWeight', @@ -4124,7 +4125,7 @@ class Model(BaseModel): 'Dog': { 'properties': { 'dog_name': {'title': 'Dog Name', 'type': 'string'}, - 'pet_type': {'const': 'dog', 'enum': ['dog'], 'title': 'Pet Type', 'type': 'string'}, + 'pet_type': {'const': 'dog', 'title': 'Pet Type', 'type': 'string'}, }, 'required': ['pet_type', 'dog_name'], 'title': 'Dog', @@ -4132,8 +4133,8 @@ class Model(BaseModel): }, 'WhiteCat': { 'properties': { - 'color': {'const': 'white', 'enum': ['white'], 'title': 'Color', 'type': 'string'}, - 'pet_type': {'const': 'cat', 'enum': ['cat'], 'title': 'Pet Type', 'type': 'string'}, + 'color': {'const': 'white', 'title': 'Color', 'type': 'string'}, + 'pet_type': {'const': 'cat', 'title': 'Pet Type', 'type': 'string'}, 'white_infos': {'title': 'White Infos', 'type': 'string'}, }, 'required': ['pet_type', 'color', 'white_infos'], @@ -4252,7 +4253,7 @@ class Model(BaseModel): 'Cat': { 'properties': { 'c': {'title': 'C', 'type': 'string'}, - 'typeOfPet': {'const': 'cat', 'enum': ['cat'], 'title': 'Typeofpet', 'type': 'string'}, + 'typeOfPet': {'const': 'cat', 'title': 'Typeofpet', 'type': 'string'}, }, 'required': ['typeOfPet', 'c'], 'title': 'Cat', @@ -4261,7 +4262,7 @@ class Model(BaseModel): 'Dog': { 'properties': { 'd': {'title': 'D', 'type': 'string'}, - 'typeOfPet': {'const': 'dog', 'enum': ['dog'], 'title': 'Typeofpet', 'type': 'string'}, + 'typeOfPet': {'const': 'dog', 'title': 'Typeofpet', 'type': 'string'}, }, 'required': ['typeOfPet', 'd'], 'title': 'Dog', @@ -4349,8 +4350,8 @@ class Model(BaseModel): 'BlackCat': { 'properties': { 'black_name': {'title': 'Black Name', 'type': 'string'}, - 'color': {'const': 'black', 'enum': ['black'], 'title': 'Color', 'type': 'string'}, - 'pet_type': {'const': 'cat', 'enum': ['cat'], 'title': 'Pet Type', 'type': 'string'}, + 'color': {'const': 'black', 'title': 'Color', 'type': 'string'}, + 'pet_type': {'const': 'cat', 'title': 'Pet Type', 'type': 'string'}, }, 'required': ['pet_type', 'color', 'black_name'], 'title': 'BlackCat', @@ -4359,7 +4360,7 @@ class Model(BaseModel): 'Dog': { 'properties': { 'name': {'title': 'Name', 'type': 'string'}, - 'pet_type': {'const': 'dog', 'enum': ['dog'], 'title': 'Pet Type', 'type': 'string'}, + 'pet_type': {'const': 'dog', 'title': 'Pet Type', 'type': 'string'}, }, 'required': ['pet_type', 'name'], 'title': 'Dog', @@ -4367,8 +4368,8 @@ class Model(BaseModel): }, 'WhiteCat': { 'properties': { - 'color': {'const': 'white', 'enum': ['white'], 'title': 'Color', 'type': 'string'}, - 'pet_type': {'const': 'cat', 'enum': ['cat'], 'title': 'Pet Type', 'type': 'string'}, + 'color': {'const': 'white', 'title': 'Color', 'type': 'string'}, + 'pet_type': {'const': 'cat', 'title': 'Pet Type', 'type': 'string'}, 'white_name': {'title': 'White Name', 'type': 'string'}, }, 'required': ['pet_type', 'color', 'white_name'], @@ -5139,18 +5140,6 @@ class C(RootModel[A]): } -def test_core_metadata_core_schema_metadata(): - with pytest.raises(TypeError, match=re.escape("CoreSchema metadata should be a dict; got 'test'.")): - CoreMetadataHandler({'metadata': 'test'}) - - core_metadata_handler = CoreMetadataHandler({}) - core_metadata_handler._schema = {} - assert core_metadata_handler.metadata == {} - core_metadata_handler._schema = {'metadata': 'test'} - with pytest.raises(TypeError, match=re.escape("CoreSchema metadata should be a dict; got 'test'.")): - core_metadata_handler.metadata - - def test_type_adapter_json_schemas_title_description(): class Model(BaseModel): a: str @@ -5578,7 +5567,6 @@ def __get_pydantic_json_schema__( self, schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler ) -> JsonSchemaValue: json_schema = handler(schema) - assert '$ref' in json_schema json_schema['title'] = 'Set from annotation' return json_schema @@ -5590,7 +5578,6 @@ def __get_pydantic_json_schema__( cls, schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler ) -> JsonSchemaValue: json_schema = handler(schema) - assert json_schema['type'] == 'object' and '$ref' not in json_schema return json_schema ta = TypeAdapter(Annotated[Model, Annotation()]) @@ -6063,7 +6050,7 @@ class AllowedValues(str, Enum): VAL2 = 'Val2' class ModelA(BaseModel): - modelA_1: AllowedValues = Field(..., max_length=60) + modelA_1: AllowedValues = Field(max_length=60) class ModelB(ModelA): modelB_1: typing.List[ModelA] @@ -6107,7 +6094,7 @@ def test_required_fields_in_annotated_with_create_model() -> None: 'test_model', foo=(int, ...), bar=(Annotated[int, Field(description='Bar description')], ...), - baz=(Annotated[int, Field(..., description='Baz description')], ...), + baz=(Annotated[int, Field(description='Baz description')], ...), ) assert Model.model_json_schema() == { @@ -6422,17 +6409,6 @@ class Model(BaseModel): } -def test_remove_key_from_like_parent_annotation() -> None: - class Model(BaseModel): - a: Annotated[ - int, - Field(json_schema_extra={'key_to_remove': 'value'}), - Field(json_schema_extra=lambda _: None), - ] - - assert Model.model_json_schema()['properties']['a'] == {'title': 'A', 'type': 'integer'} - - def test_ta_and_bm_same_json_schema() -> None: MyStr = Annotated[str, Field(json_schema_extra={'key1': 'value1'}), Field(json_schema_extra={'key2': 'value2'})] @@ -6547,3 +6523,89 @@ def validate_f(cls, value: Any) -> int: ... 'e': {'title': 'E', 'type': 'integer'}, 'f': {'title': 'F', 'type': 'integer'}, } + + +@pytest.mark.parametrize( + 'validator', + [ + PlainValidator(lambda v: v, json_schema_input_type='Sub'), + BeforeValidator(lambda v: v, json_schema_input_type='Sub'), + WrapValidator(lambda v, h: h(v), json_schema_input_type='Sub'), + ], +) +def test_json_schema_input_type_with_refs(validator) -> None: + """Test that `'definition-ref` schemas for `json_schema_input_type` are inlined. + + See: https://github.com/pydantic/pydantic/issues/10434. + """ + + class Sub(BaseModel): + pass + + class Model(BaseModel): + sub: Annotated[ + Sub, + PlainSerializer(lambda v: v, return_type=Sub), + validator, + ] + + json_schema = Model.model_json_schema() + + assert 'Sub' in json_schema['$defs'] + assert json_schema['properties']['sub']['$ref'] == '#/$defs/Sub' + + +@pytest.mark.parametrize( + 'validator', + [ + PlainValidator(lambda v: v, json_schema_input_type='Model'), + BeforeValidator(lambda v: v, json_schema_input_type='Model'), + WrapValidator(lambda v, h: h(v), json_schema_input_type='Model'), + ], +) +def test_json_schema_input_type_with_recursive_refs(validator) -> None: + """Test that recursive `'definition-ref` schemas for `json_schema_input_type` are not inlined.""" + + class Model(BaseModel): + model: Annotated[ + 'Model', + PlainSerializer(lambda v: v, return_type='Model'), + validator, + ] + + json_schema = Model.model_json_schema() + + assert 'Model' in json_schema['$defs'] + assert json_schema['$ref'] == '#/$defs/Model' + + +def test_title_strip() -> None: + class Model(BaseModel): + some_field: str = Field(alias='_some_field') + + assert Model.model_json_schema()['properties']['_some_field']['title'] == 'Some Field' + + +def test_arbitrary_ref_in_json_schema() -> None: + """See https://github.com/pydantic/pydantic/issues/9981.""" + + class Test(BaseModel): + x: dict = Field(examples=[{'$ref': '#/components/schemas/Pet'}]) + + assert Test.model_json_schema() == { + 'properties': {'x': {'examples': [{'$ref': '#/components/schemas/Pet'}], 'title': 'X', 'type': 'object'}}, + 'required': ['x'], + 'title': 'Test', + 'type': 'object', + } + + # raises KeyError: '#/components/schemas/Pet' + + +def test_warn_on_mixed_compose() -> None: + with pytest.warns( + PydanticJsonSchemaWarning, match='Composing `dict` and `callable` type `json_schema_extra` is not supported.' + ): + + class Model(BaseModel): + field: Annotated[int, Field(json_schema_extra={'a': 'dict'}), Field(json_schema_extra=lambda x: x.pop('a'))] # type: ignore diff --git a/tests/test_main.py b/tests/test_main.py index bd06dbe79bb..cfcece87bb1 100644 --- a/tests/test_main.py +++ b/tests/test_main.py @@ -1,6 +1,7 @@ import json import platform import re +import sys import warnings from collections import defaultdict from copy import deepcopy @@ -35,7 +36,6 @@ BaseModel, ConfigDict, Field, - GenerateSchema, GetCoreSchemaHandler, PrivateAttr, PydanticUndefinedAnnotation, @@ -48,8 +48,10 @@ constr, field_validator, ) +from pydantic._internal._generate_schema import GenerateSchema from pydantic._internal._mock_val_ser import MockCoreSchema from pydantic.dataclasses import dataclass as pydantic_dataclass +from pydantic.v1 import BaseModel as BaseModelV1 def test_success(): @@ -116,6 +118,20 @@ def test_ultra_simple_repr(UltraSimpleModel): assert str(m) == 'a=10.2 b=10' +def test_recursive_repr() -> None: + class A(BaseModel): + a: object = None + + class B(BaseModel): + a: Optional[A] = None + + a = A() + a.a = a + b = B(a=a) + + assert re.match(r"B\(a=A\(a=''\)\)", repr(b)) is not None + + def test_default_factory_field(): def myfunc(): return 1 @@ -999,13 +1015,6 @@ class TypeTypeModel(BaseModel): ] -def test_annotation_field_name_shadows_attribute(): - with pytest.raises(NameError): - # When defining a model that has an attribute with the name of a built-in attribute, an exception is raised - class BadModel(BaseModel): - model_json_schema: str # This conflicts with the BaseModel's model_json_schema() class method - - def test_value_field_name_shadows_attribute(): with pytest.raises(PydanticUserError, match="A non-annotated attribute was detected: `model_json_schema = 'abc'`"): @@ -1502,7 +1511,7 @@ class Sub(BaseModel): class Parent(BaseModel): model_config = ConfigDict(fields={'a': {'exclude': ...}, 's': {'exclude': {'s1'}}}) a: int - b: int = Field(..., exclude=...) + b: int = Field(exclude=...) c: int d: int s: Sub = Sub() @@ -1767,8 +1776,38 @@ class Outer(BaseModel): assert repr(parsed) == 'Outer(inner_1=Inner(val=0), inner_2=Inner(val=0))' +def test_default_factory_validated_data_arg() -> None: + class Model(BaseModel): + a: int = 1 + b: int = Field(default_factory=lambda data: data['a']) + + model = Model() + assert model.b == 1 + + class InvalidModel(BaseModel): + a: int = Field(default_factory=lambda data: data['b']) + b: int + + with pytest.raises(KeyError): + InvalidModel(b=2) + + +def test_default_factory_validated_data_arg_not_required() -> None: + def fac(data: Optional[Dict[str, Any]] = None): + if data is not None: + return data['a'] + return 3 + + class Model(BaseModel): + a: int = 1 + b: int = Field(default_factory=fac) + + model = Model() + assert model.b == 3 + + def test_reuse_same_field(): - required_field = Field(...) + required_field = Field() class Model1(BaseModel): required: str = required_field @@ -1943,7 +1982,7 @@ class Model(BaseModel): ) @pytest.mark.parametrize( 'value', - [None, Field(...)], + [None, Field()], ids=['none', 'field'], ) def test_frozen_field_decl_without_default_val(ann, value): @@ -2587,20 +2626,11 @@ class Model(BaseModel): def test_protected_namespace_default(): with pytest.warns( - UserWarning, match='Field "model_prefixed_field" in Model has conflict with protected namespace "model_"' + UserWarning, match='Field "model_dump_something" in Model has conflict with protected namespace "model_dump"' ): class Model(BaseModel): - model_prefixed_field: str - - -def test_protected_namespace_real_conflict(): - with pytest.raises( - NameError, match=r'Field "model_validate" conflicts with member .* of protected namespace "model_"\.' - ): - - class Model(BaseModel): - model_validate: str + model_dump_something: str def test_custom_protected_namespace(): @@ -2626,6 +2656,15 @@ class Model(BaseModel): model_config = ConfigDict(protected_namespaces=('protect_me_', 'also_protect_')) +def test_protected_namespace_pattern() -> None: + with pytest.warns(UserWarning, match=r'Field "perfect_match" in Model has conflict with protected namespace .*'): + + class Model(BaseModel): + perfect_match: str + + model_config = ConfigDict(protected_namespaces=(re.compile(r'^perfect_match$'),)) + + def test_model_get_core_schema() -> None: class Model(BaseModel): @classmethod @@ -3131,62 +3170,7 @@ class Model3(BaseModel): ___: int = Field(default=1) -def test_schema_generator_customize_type() -> None: - class LaxStrGenerator(GenerateSchema): - def str_schema(self) -> CoreSchema: - return core_schema.no_info_plain_validator_function(str) - - class Model(BaseModel): - x: str - model_config = ConfigDict(schema_generator=LaxStrGenerator) - - assert Model(x=1).x == '1' - - -def test_schema_generator_customize_type_constraints() -> None: - class LaxStrGenerator(GenerateSchema): - def str_schema(self) -> CoreSchema: - return core_schema.no_info_plain_validator_function(str) - - class Model(BaseModel): - x: Annotated[str, Field(pattern='^\\d+$')] - y: Annotated[float, Field(gt=0)] - z: Annotated[List[int], Field(min_length=1)] - model_config = ConfigDict(schema_generator=LaxStrGenerator) - - # insert_assert(Model(x='123', y=1, z=[-1]).model_dump()) - assert Model(x='123', y=1, z=[-1]).model_dump() == {'x': '123', 'y': 1.0, 'z': [-1]} - - with pytest.raises(ValidationError) as exc_info: - Model(x='abc', y=-1, z=[]) - - # insert_assert(exc_info.value.errors(include_url=False)) - assert exc_info.value.errors(include_url=False) == [ - { - 'type': 'string_pattern_mismatch', - 'loc': ('x',), - 'msg': "String should match pattern '^\\d+$'", - 'input': 'abc', - 'ctx': {'pattern': '^\\d+$'}, - }, - { - 'type': 'greater_than', - 'loc': ('y',), - 'msg': 'Input should be greater than 0', - 'input': -1, - 'ctx': {'gt': 0.0}, - }, - { - 'type': 'too_short', - 'loc': ('z',), - 'msg': 'List should have at least 1 item after validation, not 0', - 'input': [], - 'ctx': {'field_type': 'List', 'min_length': 1, 'actual_length': 0}, - }, - ] - - -def test_schema_generator_customize_type_constraints_order() -> None: +def test_customize_type_constraints_order() -> None: class Model(BaseModel): # whitespace will be stripped first, then max length will be checked, should pass on ' 1 ' x: Annotated[str, AfterValidator(lambda x: x.strip()), StringConstraints(max_length=1)] @@ -3368,3 +3352,34 @@ def model_post_init(self, context: Any) -> None: assert m == copy assert id(m) != id(copy) + + +def test_subclassing_gen_schema_warns() -> None: + with pytest.warns(UserWarning, match='Subclassing `GenerateSchema` is not supported.'): + + class MyGenSchema(GenerateSchema): ... + + +def test_nested_v1_model_warns() -> None: + with pytest.warns( + UserWarning, + match=r'Mixing V1 models and V2 models \(or constructs, like `TypeAdapter`\) is not supported. Please upgrade `V1Model` to V2.', + ): + + class V1Model(BaseModelV1): + a: int + + class V2Model(BaseModel): + inner: V1Model + + +@pytest.mark.skipif(sys.version_info < (3, 13), reason='requires python 3.13') +def test_replace() -> None: + from copy import replace + + class Model(BaseModel): + x: int + y: int + + m = Model(x=1, y=2) + assert replace(m, x=3) == Model(x=3, y=2) diff --git a/tests/test_model_signature.py b/tests/test_model_signature.py index e37ac4b0933..46f2fb15661 100644 --- a/tests/test_model_signature.py +++ b/tests/test_model_signature.py @@ -23,13 +23,14 @@ def _equals(a: Union[str, Iterable[str]], b: Union[str, Iterable[str]]) -> bool: def test_model_signature(): class Model(BaseModel): - a: float = Field(..., title='A') + a: float = Field(title='A') b: int = Field(10) + c: int = Field(default_factory=lambda: 1) sig = signature(Model) assert sig != signature(BaseModel) - assert _equals(map(str, sig.parameters.values()), ('a: float', 'b: int = 10')) - assert _equals(str(sig), '(*, a: float, b: int = 10) -> None') + assert _equals(map(str, sig.parameters.values()), ('a: float', 'b: int = 10', 'c: int = ')) + assert _equals(str(sig), '(*, a: float, b: int = 10, c: int = ) -> None') def test_generic_model_signature(): @@ -48,7 +49,7 @@ def test_custom_init_signature(): class MyModel(BaseModel): id: int name: str = 'John Doe' - f__: str = Field(..., alias='foo') + f__: str = Field(alias='foo') model_config = ConfigDict(extra='allow') @@ -92,7 +93,7 @@ def test_invalid_identifiers_signature(): def test_use_field_name(): class Foo(BaseModel): - foo: str = Field(..., alias='this is invalid') + foo: str = Field(alias='this is invalid') model_config = ConfigDict(populate_by_name=True) @@ -101,7 +102,7 @@ class Foo(BaseModel): def test_does_not_use_reserved_word(): class Foo(BaseModel): - from_: str = Field(..., alias='from') + from_: str = Field(alias='from') model_config = ConfigDict(populate_by_name=True) diff --git a/tests/test_networks.py b/tests/test_networks.py index 34bf46899cc..7935ae74253 100644 --- a/tests/test_networks.py +++ b/tests/test_networks.py @@ -7,6 +7,7 @@ from pydantic import ( AmqpDsn, + AnyHttpUrl, AnyUrl, BaseModel, ClickHouseDsn, @@ -24,11 +25,12 @@ RedisDsn, SnowflakeDsn, Strict, + TypeAdapter, UrlConstraints, ValidationError, WebsocketUrl, ) -from pydantic.networks import validate_email +from pydantic.networks import import_email_validator, validate_email try: import email_validator @@ -107,7 +109,6 @@ 'http://example.org/path#fragment', 'http://example.org/path?query#', 'http://example.org/path?query#fragment', - 'file://localhost/foo/bar', ], ) def test_any_url_success(value): @@ -168,7 +169,7 @@ class Model(BaseModel): def test_any_url_parts(): url = validate_url('http://example.org') assert str(url) == 'http://example.org/' - assert repr(url) == "Url('http://example.org/')" + assert repr(url) == "AnyUrl('http://example.org/')" assert url.scheme == 'http' assert url.host == 'example.org' assert url.port == 80 @@ -177,7 +178,7 @@ def test_any_url_parts(): def test_url_repr(): url = validate_url('http://user:password@example.org:1234/the/path/?query=here#fragment=is;this=bit') assert str(url) == 'http://user:password@example.org:1234/the/path/?query=here#fragment=is;this=bit' - assert repr(url) == "Url('http://user:password@example.org:1234/the/path/?query=here#fragment=is;this=bit')" + assert repr(url) == "AnyUrl('http://user:password@example.org:1234/the/path/?query=here#fragment=is;this=bit')" assert url.scheme == 'http' assert url.username == 'user' assert url.password == 'password' @@ -908,6 +909,8 @@ class Model(BaseModel): ('FOO bar ', 'FOO bar', 'foobar@example.com'), (' Whatever ', 'Whatever', 'foobar@example.com'), ('Whatever < foobar@example.com>', 'Whatever', 'foobar@example.com'), + ('Whatever ', 'Whatever', 'foobar@example.com'), + ('Whatever < foobar@example.com >', 'Whatever', 'foobar@example.com'), (' ', 'FOOBAR', 'FOOBAR@example.com'), ('ñoñó@example.com', 'ñoñó', 'ñoñó@example.com'), ('我買@example.com', '我買', '我買@example.com'), @@ -970,10 +973,27 @@ def test_address_invalid(value: str, reason: Union[str, None]): validate_email(value) -@pytest.mark.skipif(email_validator, reason='email_validator is installed') -def test_email_validator_not_installed(): +def test_email_validator_not_installed(mocker): + mocker.patch('pydantic.networks.email_validator', None) + m = mocker.patch('pydantic.networks.import_email_validator', side_effect=ImportError) with pytest.raises(ImportError): validate_email('s@muelcolvin.com') + m.assert_called_once() + + +def test_import_email_validator_not_installed(mocker): + mocker.patch.dict('sys.modules', {'email_validator': None}) + with pytest.raises(ImportError, match=r'email-validator is not installed, run `pip install pydantic\[email\]`'): + import_email_validator() + + +@pytest.mark.skipif(not email_validator, reason='email_validator not installed') +def test_import_email_validator_invalid_version(mocker): + mocker.patch('pydantic.networks.version', return_value='1.0.0') + with pytest.raises( + ImportError, match=r'email-validator version >= 2.0 required, run pip install -U email-validator' + ): + import_email_validator() @pytest.mark.skipif(not email_validator, reason='email_validator not installed') @@ -1013,3 +1033,42 @@ class Model(BaseModel): obj = json.loads(m.model_dump_json()) Model(email=obj['email']) + + +def test_specialized_urls() -> None: + ta = TypeAdapter(HttpUrl) + + http_url = ta.validate_python('http://example.com/something') + assert str(http_url) == 'http://example.com/something' + assert repr(http_url) == "HttpUrl('http://example.com/something')" + assert http_url.__class__ == HttpUrl + assert http_url.host == 'example.com' + assert http_url.path == '/something' + assert http_url.username is None + assert http_url.password is None + + http_url2 = ta.validate_python(http_url) + assert str(http_url2) == 'http://example.com/something' + assert repr(http_url2) == "HttpUrl('http://example.com/something')" + assert http_url2.__class__ == HttpUrl + assert http_url2.host == 'example.com' + assert http_url2.path == '/something' + assert http_url2.username is None + assert http_url2.password is None + + +def test_url_equality() -> None: + # works for descendants of _BaseUrl and _BaseMultiHostUrl + assert HttpUrl('http://example.com/something') == HttpUrl('http://example.com/something') + assert PostgresDsn('postgres://user:pass@localhost:5432/app') == PostgresDsn( + 'postgres://user:pass@localhost:5432/app' + ) + + +def test_url_subclasses_any_url() -> None: + http_url = AnyHttpUrl('https://localhost') + assert isinstance(http_url, AnyUrl) + assert isinstance(http_url, AnyHttpUrl) + + url = TypeAdapter(AnyUrl).validate_python(http_url) + assert url is http_url diff --git a/tests/test_pickle.py b/tests/test_pickle.py index 241ebd1c83f..c61178acb25 100644 --- a/tests/test_pickle.py +++ b/tests/test_pickle.py @@ -3,7 +3,6 @@ import pickle from typing import Optional, Type -import cloudpickle import pytest import pydantic @@ -11,6 +10,13 @@ from pydantic._internal._model_construction import _PydanticWeakRef from pydantic.config import ConfigDict +try: + import cloudpickle +except ImportError: + cloudpickle = None + +pytestmark = pytest.mark.skipif(cloudpickle is None, reason='cloudpickle is not installed') + class IntWrapper: def __init__(self, v: int): diff --git a/tests/test_root_model.py b/tests/test_root_model.py index c5b09fd59a5..a491cba61f9 100644 --- a/tests/test_root_model.py +++ b/tests/test_root_model.py @@ -182,7 +182,7 @@ class Base64Root(RootModel[Base64Str]): pass v = Base64Root.model_construct('test') - assert v.model_dump() == 'dGVzdA==\n' + assert v.model_dump() == 'dGVzdA==' def test_construct_nested(): @@ -190,7 +190,7 @@ class Base64RootProperty(BaseModel): data: RootModel[Base64Str] v = Base64RootProperty.model_construct(data=RootModel[Base64Str].model_construct('test')) - assert v.model_dump() == {'data': 'dGVzdA==\n'} + assert v.model_dump() == {'data': 'dGVzdA=='} # Note: model_construct requires the inputs to be valid; the root model value does not get "validated" into # an actual root model instance: diff --git a/tests/test_serialize.py b/tests/test_serialize.py index 505396e7a36..95271f8af0e 100644 --- a/tests/test_serialize.py +++ b/tests/test_serialize.py @@ -17,7 +17,6 @@ BaseModel, Field, FieldSerializationInfo, - PydanticUserError, SerializationInfo, SerializerFunctionWrapHandler, TypeAdapter, @@ -893,6 +892,21 @@ def ser_model(self, v) -> int: assert return_serializer == 'return_serializer: Any' +def test_serializer_return_type_model() -> None: + """https://github.com/pydantic/pydantic/issues/10443""" + + class Sub(BaseModel): + pass + + class Model(BaseModel): + sub: Annotated[ + Sub, + PlainSerializer(lambda v: v, return_type=Sub), + ] + + assert Model(sub=Sub()).model_dump() == {'sub': {}} + + def test_type_adapter_dump_json(): class Model(TypedDict): x: int @@ -1073,24 +1087,6 @@ def quadruple_x_plus_one(self) -> Annotated[int, PlainSerializer(lambda v: v + 1 } -def test_computed_field_custom_serializer_bad_signature(): - error_msg = 'field_serializer on computed_field does not use info signature' - - with pytest.raises(PydanticUserError, match=error_msg): - - class Model(BaseModel): - x: int - - @computed_field - @property - def two_x(self) -> int: - return self.x * 2 - - @field_serializer('two_x') - def ser_two_x_bad_signature(self, v, _info): - return f'The double of x is {v}' - - @pytest.mark.skipif( sys.version_info < (3, 9) or sys.version_info >= (3, 13), reason='@computed_field @classmethod @property only works in 3.9-3.12', diff --git a/tests/test_titles.py b/tests/test_titles.py index 6eb601fad59..bcbe6f804f8 100644 --- a/tests/test_titles.py +++ b/tests/test_titles.py @@ -411,6 +411,8 @@ def test_model_title_generator_returns_invalid_type(invalid_return_value, TypedD class Model(BaseModel): model_config = ConfigDict(model_title_generator=lambda m: invalid_return_value) + Model.model_json_schema() + with pytest.raises( TypeError, match=f'model_title_generator .* must return str, not {invalid_return_value.__class__}' ): @@ -419,6 +421,8 @@ class Model(BaseModel): class MyDataclass: pass + TypeAdapter(MyDataclass).json_schema() + with pytest.raises( TypeError, match=f'model_title_generator .* must return str, not {invalid_return_value.__class__}' ): @@ -427,7 +431,7 @@ class MyTypedDict(TypedDict): __pydantic_config__ = ConfigDict(model_title_generator=lambda m: invalid_return_value) pass - TypeAdapter(MyTypedDict) + TypeAdapter(MyTypedDict).json_schema() @pytest.mark.parametrize('invalid_return_value', (1, 2, 3, tuple(), list(), object())) diff --git a/tests/test_type_adapter.py b/tests/test_type_adapter.py index 74bc98a9351..8f30064d6a7 100644 --- a/tests/test_type_adapter.py +++ b/tests/test_type_adapter.py @@ -5,23 +5,21 @@ from typing import Any, Dict, ForwardRef, Generic, List, NamedTuple, Optional, Tuple, TypeVar, Union import pytest -from pydantic_core import ValidationError, core_schema +from pydantic_core import ValidationError from typing_extensions import Annotated, TypeAlias, TypedDict from pydantic import BaseModel, Field, TypeAdapter, ValidationInfo, create_model, field_validator -from pydantic._internal._generate_schema import GenerateSchema +from pydantic._internal import _mock_val_ser from pydantic._internal._typing_extra import annotated_type from pydantic.config import ConfigDict from pydantic.dataclasses import dataclass as pydantic_dataclass -from pydantic.errors import PydanticUserError +from pydantic.errors import PydanticUndefinedAnnotation, PydanticUserError from pydantic.type_adapter import _type_has_config ItemType = TypeVar('ItemType') NestedList = List[List[ItemType]] -DEFER_ENABLE_MODE = ('model', 'type_adapter') - class PydanticModel(BaseModel): x: int @@ -74,7 +72,7 @@ def test_types(tp: Any, val: Any, expected: Any): @pytest.mark.parametrize('defer_build', [False, True]) @pytest.mark.parametrize('method', ['validate', 'serialize', 'json_schema', 'json_schemas']) def test_global_namespace_variables(defer_build: bool, method: str, generate_schema_calls): - config = ConfigDict(defer_build=True, experimental_defer_build_mode=DEFER_ENABLE_MODE) if defer_build else None + config = ConfigDict(defer_build=True) if defer_build else None ta = TypeAdapter(OuterDict, config=config) assert generate_schema_calls.count == (0 if defer_build else 1), 'Should be built deferred' @@ -95,9 +93,7 @@ def test_global_namespace_variables(defer_build: bool, method: str, generate_sch @pytest.mark.parametrize('method', ['validate', 'serialize', 'json_schema', 'json_schemas']) def test_model_global_namespace_variables(defer_build: bool, method: str, generate_schema_calls): class MyModel(BaseModel): - model_config = ( - ConfigDict(defer_build=True, experimental_defer_build_mode=DEFER_ENABLE_MODE) if defer_build else None - ) + model_config = ConfigDict(defer_build=defer_build) x: OuterDict ta = TypeAdapter(MyModel) @@ -122,7 +118,7 @@ def test_local_namespace_variables(defer_build: bool, method: str, generate_sche IntList = List[int] # noqa: F841 OuterDict = Dict[str, 'IntList'] - config = ConfigDict(defer_build=True, experimental_defer_build_mode=DEFER_ENABLE_MODE) if defer_build else None + config = ConfigDict(defer_build=True) if defer_build else None ta = TypeAdapter(OuterDict, config=config) assert generate_schema_calls.count == (0 if defer_build else 1), 'Should be built deferred' @@ -145,9 +141,7 @@ def test_model_local_namespace_variables(defer_build: bool, method: str, generat IntList = List[int] # noqa: F841 class MyModel(BaseModel): - model_config = ( - ConfigDict(defer_build=True, experimental_defer_build_mode=DEFER_ENABLE_MODE) if defer_build else None - ) + model_config = ConfigDict(defer_build=defer_build) x: Dict[str, 'IntList'] ta = TypeAdapter(MyModel) @@ -170,7 +164,7 @@ class MyModel(BaseModel): @pytest.mark.parametrize('method', ['validate', 'serialize', 'json_schema', 'json_schemas']) @pytest.mark.skipif(sys.version_info < (3, 9), reason="ForwardRef doesn't accept module as a parameter in Python < 3.9") def test_top_level_fwd_ref(defer_build: bool, method: str, generate_schema_calls): - config = ConfigDict(defer_build=True, experimental_defer_build_mode=DEFER_ENABLE_MODE) if defer_build else None + config = ConfigDict(defer_build=True) if defer_build else None FwdRef = ForwardRef('OuterDict', module=__name__) ta = TypeAdapter(FwdRef, config=config) @@ -398,7 +392,7 @@ class UnrelatedClass: def test_validate_strings( field_type, input_value, expected, raises_match, strict, defer_build: bool, generate_schema_calls ): - config = ConfigDict(defer_build=True, experimental_defer_build_mode=DEFER_ENABLE_MODE) if defer_build else None + config = ConfigDict(defer_build=True) if defer_build else None ta = TypeAdapter(field_type, config=config) assert generate_schema_calls.count == (0 if defer_build else 1), 'Should be built deferred' @@ -519,10 +513,8 @@ class TypedDictModel(TypedDict): CONFIGS = [ - ConfigDict(defer_build=False, experimental_defer_build_mode=('model',)), - ConfigDict(defer_build=False, experimental_defer_build_mode=DEFER_ENABLE_MODE), - ConfigDict(defer_build=True, experimental_defer_build_mode=('model',)), - ConfigDict(defer_build=True, experimental_defer_build_mode=DEFER_ENABLE_MODE), + ConfigDict(defer_build=False), + ConfigDict(defer_build=True), ] MODELS_CONFIGS: List[Tuple[Any, ConfigDict]] = [ (model, config) for config in CONFIGS for model in defer_build_test_models(config) @@ -538,17 +530,19 @@ def test_core_schema_respects_defer_build(model: Any, config: ConfigDict, method type_adapter = TypeAdapter(model) if _type_has_config(model) else TypeAdapter(model, config=config) - if config['defer_build'] and 'type_adapter' in config['experimental_defer_build_mode']: + if config.get('defer_build'): assert generate_schema_calls.count == 0, 'Should be built deferred' - assert type_adapter._core_schema is None, 'Should be initialized deferred' - assert type_adapter._validator is None, 'Should be initialized deferred' - assert type_adapter._serializer is None, 'Should be initialized deferred' + assert isinstance(type_adapter.core_schema, _mock_val_ser.MockCoreSchema), 'Should be initialized deferred' + assert isinstance(type_adapter.validator, _mock_val_ser.MockValSer), 'Should be initialized deferred' + assert isinstance(type_adapter.serializer, _mock_val_ser.MockValSer), 'Should be initialized deferred' else: built_inside_type_adapter = 'Dict' in str(model) or 'Annotated' in str(model) assert generate_schema_calls.count == (1 if built_inside_type_adapter else 0), f'Should be built ({model})' - assert type_adapter._core_schema is not None, 'Should be initialized before usage' - assert type_adapter._validator is not None, 'Should be initialized before usage' - assert type_adapter._serializer is not None, 'Should be initialized before usage' + assert not isinstance( + type_adapter.core_schema, _mock_val_ser.MockCoreSchema + ), 'Should be initialized before usage' + assert not isinstance(type_adapter.validator, _mock_val_ser.MockValSer), 'Should be initialized before usage' + assert not isinstance(type_adapter.validator, _mock_val_ser.MockValSer), 'Should be initialized before usage' if method == 'schema': json_schema = type_adapter.json_schema() # Use it @@ -565,14 +559,36 @@ def test_core_schema_respects_defer_build(model: Any, config: ConfigDict, method assert json.loads(raw.decode())['x'] == 1 # Sanity check assert generate_schema_calls.count < 2, 'Should not build duplicates' - assert type_adapter._core_schema is not None, 'Should be initialized after the usage' - assert type_adapter._validator is not None, 'Should be initialized after the usage' - assert type_adapter._serializer is not None, 'Should be initialized after the usage' + assert not isinstance( + type_adapter.core_schema, _mock_val_ser.MockCoreSchema + ), 'Should be initialized after the usage' + assert not isinstance(type_adapter.validator, _mock_val_ser.MockValSer), 'Should be initialized after the usage' + assert not isinstance(type_adapter.validator, _mock_val_ser.MockValSer), 'Should be initialized after the usage' + + +def test_defer_build_raise_errors() -> None: + ta = TypeAdapter('MyInt', config=ConfigDict(defer_build=True)) # pyright: ignore[reportUndefinedVariable] + assert isinstance(ta.core_schema, _mock_val_ser.MockCoreSchema) + + with pytest.raises(PydanticUndefinedAnnotation): + # `True` is the `raise_errors` default for the `rebuild` method, but we include here for clarity + ta.rebuild(raise_errors=True) + + ta.rebuild(raise_errors=False) + assert isinstance(ta.core_schema, _mock_val_ser.MockCoreSchema) + MyInt = int # noqa: F841 + + ta.rebuild(raise_errors=True) + assert not isinstance(ta.core_schema, _mock_val_ser.MockCoreSchema) + + +@dataclass +class SimpleDataclass: + x: int -def test_custom_schema_gen_respected() -> None: - class LaxStrGenerator(GenerateSchema): - def str_schema(self) -> core_schema.CoreSchema: - return core_schema.no_info_plain_validator_function(str) - assert TypeAdapter(str, config=ConfigDict(schema_generator=LaxStrGenerator)).validate_python(1) == '1' +@pytest.mark.parametrize('type_,repr_', [(int, 'int'), (List[int], 'List[int]'), (SimpleDataclass, 'SimpleDataclass')]) +def test_ta_repr(type_: Any, repr_: str) -> None: + ta = TypeAdapter(type_) + assert repr(ta) == f'TypeAdapter({repr_})' diff --git a/tests/test_type_alias_type.py b/tests/test_type_alias_type.py index c4a1086b00d..dd3c311185c 100644 --- a/tests/test_type_alias_type.py +++ b/tests/test_type_alias_type.py @@ -1,13 +1,12 @@ import datetime from dataclasses import dataclass -from typing import Dict, Generic, List, Tuple, TypeVar, Union +from typing import Dict, Generic, List, Sequence, Tuple, TypeVar, Union import pytest from annotated_types import MaxLen from typing_extensions import Annotated, Literal, TypeAliasType -from pydantic import BaseModel, Field, ValidationError -from pydantic.type_adapter import TypeAdapter +from pydantic import BaseModel, Field, PydanticUserError, TypeAdapter, ValidationError T = TypeVar('T') @@ -388,3 +387,59 @@ class Div(BaseModel): 'type': 'literal_error', } ] + + +def test_intermediate_type_aliases() -> None: + # https://github.com/pydantic/pydantic/issues/8984 + MySeq = TypeAliasType('MySeq', Sequence[T], type_params=(T,)) + MyIntSeq = TypeAliasType('MyIntSeq', MySeq[int]) + + class MyModel(BaseModel): + my_int_seq: MyIntSeq + + assert MyModel(my_int_seq=range(1, 4)).my_int_seq == [1, 2, 3] + + assert MyModel.model_json_schema() == { + '$defs': {'MySeq_int_': {'items': {'type': 'integer'}, 'type': 'array'}}, + 'properties': {'my_int_seq': {'$ref': '#/$defs/MySeq_int_'}}, + 'required': ['my_int_seq'], + 'title': 'MyModel', + 'type': 'object', + } + + +def test_intermediate_type_aliases_json_type() -> None: + JSON = TypeAliasType('JSON', Union[str, int, bool, 'JSONSeq', 'JSONObj', None]) + JSONObj = TypeAliasType('JSONObj', Dict[str, JSON]) + JSONSeq = TypeAliasType('JSONSeq', List[JSON]) + MyJSONAlias1 = TypeAliasType('MyJSONAlias1', JSON) + MyJSONAlias2 = TypeAliasType('MyJSONAlias2', MyJSONAlias1) + JSONs = TypeAliasType('JSONs', List[MyJSONAlias2]) + + adapter = TypeAdapter(JSONs) + + assert adapter.validate_python([{'a': 1}, 2, '3', [4, 5], True, None]) == [{'a': 1}, 2, '3', [4, 5], True, None] + + +def test_intermediate_type_aliases_chain() -> None: + A = TypeAliasType('A', int) + B = TypeAliasType('B', A) + C = TypeAliasType('C', B) + D = TypeAliasType('D', C) + E = TypeAliasType('E', D) + + TypeAdapter(E) + + +def test_circular_type_aliases() -> None: + A = TypeAliasType('A', 'C') + B = TypeAliasType('B', A) + C = TypeAliasType('C', B) + + with pytest.raises(PydanticUserError) as exc_info: + + class MyModel(BaseModel): + a: C + + assert exc_info.value.code == 'circular-reference-schema' + assert exc_info.value.message.startswith('tests.test_type_alias_type.C') diff --git a/tests/test_types.py b/tests/test_types.py index 0180a3633ca..f9d6c521604 100644 --- a/tests/test_types.py +++ b/tests/test_types.py @@ -14,6 +14,7 @@ from datetime import date, datetime, time, timedelta, timezone from decimal import Decimal from enum import Enum, IntEnum +from fractions import Fraction from numbers import Number from pathlib import Path from typing import ( @@ -62,6 +63,7 @@ Base64UrlBytes, Base64UrlStr, BaseModel, + BeforeValidator, ByteSize, ConfigDict, DirectoryPath, @@ -100,6 +102,7 @@ SecretStr, SerializeAsAny, SkipValidation, + SocketPath, Strict, StrictBool, StrictBytes, @@ -389,7 +392,7 @@ class ConListModel(BaseModel): def test_conlist(): class Model(BaseModel): - foo: List[int] = Field(..., min_length=2, max_length=4) + foo: List[int] = Field(min_length=2, max_length=4) bar: conlist(str, min_length=1, max_length=4) = None assert Model(foo=[1, 2], bar=['spoon']).model_dump() == {'foo': [1, 2], 'bar': ['spoon']} @@ -604,7 +607,7 @@ class ConSetModel(BaseModel): def test_conset(): class Model(BaseModel): - foo: Set[int] = Field(..., min_length=2, max_length=4) + foo: Set[int] = Field(min_length=2, max_length=4) bar: conset(str, min_length=1, max_length=4) = None assert Model(foo=[1, 2], bar=['spoon']).model_dump() == {'foo': {1, 2}, 'bar': {'spoon'}} @@ -653,7 +656,7 @@ class Model(BaseModel): def test_confrozenset(): class Model(BaseModel): - foo: FrozenSet[int] = Field(..., min_length=2, max_length=4) + foo: FrozenSet[int] = Field(min_length=2, max_length=4) bar: confrozenset(str, min_length=1, max_length=4) = None m = Model(foo=[1, 2], bar=['spoon']) @@ -2806,7 +2809,7 @@ class Model(BaseModel): def test_strict_bytes_max_length(): class Model(BaseModel): - u: StrictBytes = Field(..., max_length=5) + u: StrictBytes = Field(max_length=5) assert Model(u=b'foo').u == b'foo' @@ -2839,7 +2842,7 @@ class Model(BaseModel): def test_strict_str_max_length(): class Model(BaseModel): - u: StrictStr = Field(..., max_length=5) + u: StrictStr = Field(max_length=5) assert Model(u='foo').u == 'foo' @@ -3082,11 +3085,11 @@ class StrictByConfig(BaseModel): model_config = ConfigDict(strict=True) class StrictByField(BaseModel): - a: UUID1 = Field(..., strict=True) - b: UUID3 = Field(..., strict=True) - c: UUID4 = Field(..., strict=True) - d: UUID5 = Field(..., strict=True) - e: uuid.UUID = Field(..., strict=True) + a: UUID1 = Field(strict=True) + b: UUID3 = Field(strict=True) + c: UUID4 = Field(strict=True) + d: UUID5 = Field(strict=True) + e: uuid.UUID = Field(strict=True) a = uuid.UUID('7fb48116-ca6b-11ed-a439-3274d3adddac') # uuid1 b = uuid.UUID('6fa459ea-ee8a-3ca4-894e-db77e160355e') # uuid3 @@ -3698,6 +3701,43 @@ class Model(BaseModel): ] +@pytest.mark.skipif( + not sys.platform.startswith('linux'), + reason='Test only works for linux systems. Windows excluded (unsupported). Mac excluded due to CI issues.', +) +def test_socket_exists(tmp_path): + import socket + + # Working around path length limits by reducing character count where possible. + target = tmp_path / 's' + with socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) as sock: + sock.bind(str(target)) + + class Model(BaseModel): + path: SocketPath + + assert Model(path=target).path == target + + +def test_socket_not_exists(tmp_path): + target = tmp_path / 's' + + class Model(BaseModel): + path: SocketPath + + with pytest.raises(ValidationError) as exc_info: + Model(path=target) + + assert exc_info.value.errors(include_url=False) == [ + { + 'type': 'path_not_socket', + 'loc': ('path',), + 'msg': 'Path does not point to a socket', + 'input': target, + } + ] + + @pytest.mark.parametrize('value', ('/nonexistentdir/foo.py', Path('/nonexistentdir/foo.py'))) def test_new_path_validation_parent_does_not_exist(value): class Model(BaseModel): @@ -4166,7 +4206,7 @@ def test_compiled_pattern_in_field(use_field): if use_field: class Foobar(BaseModel): - str_regex: str = Field(..., pattern=field_pattern) + str_regex: str = Field(pattern=field_pattern) else: class Foobar(BaseModel): @@ -5255,20 +5295,20 @@ class Model(BaseModel): 'title': 'Model', 'type': 'object', 'properties': { - 'my_none': {'const': None, 'enum': [None], 'title': 'My None', 'type': 'null'}, + 'my_none': {'const': None, 'title': 'My None', 'type': 'null'}, 'my_none_list': { - 'items': {'const': None, 'enum': [None], 'type': 'null'}, + 'items': {'const': None, 'type': 'null'}, 'title': 'My None List', 'type': 'array', }, 'my_none_dict': { - 'additionalProperties': {'const': None, 'enum': [None], 'type': 'null'}, + 'additionalProperties': {'const': None, 'type': 'null'}, 'title': 'My None Dict', 'type': 'object', }, 'my_json_none': { 'contentMediaType': 'application/json', - 'contentSchema': {'const': None, 'enum': [None], 'type': 'null'}, + 'contentSchema': {'const': None, 'type': 'null'}, 'title': 'My Json None', 'type': 'string', }, @@ -5510,23 +5550,19 @@ class Model(BaseModel): @pytest.mark.parametrize( ('field_type', 'input_data', 'expected_value', 'serialized_data'), [ - pytest.param(Base64Bytes, b'Zm9vIGJhcg==\n', b'foo bar', b'Zm9vIGJhcg==\n', id='Base64Bytes-reversible'), - pytest.param(Base64Str, 'Zm9vIGJhcg==\n', 'foo bar', 'Zm9vIGJhcg==\n', id='Base64Str-reversible'), - pytest.param(Base64Bytes, b'Zm9vIGJhcg==', b'foo bar', b'Zm9vIGJhcg==\n', id='Base64Bytes-bytes-input'), - pytest.param(Base64Bytes, 'Zm9vIGJhcg==', b'foo bar', b'Zm9vIGJhcg==\n', id='Base64Bytes-str-input'), + pytest.param(Base64Bytes, b'Zm9vIGJhcg==', b'foo bar', b'Zm9vIGJhcg==', id='Base64Bytes-bytes-input'), + pytest.param(Base64Bytes, 'Zm9vIGJhcg==', b'foo bar', b'Zm9vIGJhcg==', id='Base64Bytes-str-input'), pytest.param( - Base64Bytes, bytearray(b'Zm9vIGJhcg=='), b'foo bar', b'Zm9vIGJhcg==\n', id='Base64Bytes-bytearray-input' - ), - pytest.param(Base64Str, b'Zm9vIGJhcg==', 'foo bar', 'Zm9vIGJhcg==\n', id='Base64Str-bytes-input'), - pytest.param(Base64Str, 'Zm9vIGJhcg==', 'foo bar', 'Zm9vIGJhcg==\n', id='Base64Str-str-input'), - pytest.param( - Base64Str, bytearray(b'Zm9vIGJhcg=='), 'foo bar', 'Zm9vIGJhcg==\n', id='Base64Str-bytearray-input' + Base64Bytes, bytearray(b'Zm9vIGJhcg=='), b'foo bar', b'Zm9vIGJhcg==', id='Base64Bytes-bytearray-input' ), + pytest.param(Base64Str, b'Zm9vIGJhcg==', 'foo bar', 'Zm9vIGJhcg==', id='Base64Str-bytes-input'), + pytest.param(Base64Str, 'Zm9vIGJhcg==', 'foo bar', 'Zm9vIGJhcg==', id='Base64Str-str-input'), + pytest.param(Base64Str, bytearray(b'Zm9vIGJhcg=='), 'foo bar', 'Zm9vIGJhcg==', id='Base64Str-bytearray-input'), pytest.param( Base64Bytes, b'BCq+6+1/Paun/Q==', b'\x04*\xbe\xeb\xed\x7f=\xab\xa7\xfd', - b'BCq+6+1/Paun/Q==\n', + b'BCq+6+1/Paun/Q==', id='Base64Bytes-bytes-alphabet-vanilla', ), ], @@ -6858,6 +6894,7 @@ class Model(BaseModel): assert Model(a='abc').a == 'abc' +@pytest.mark.skipif(not email_validator, reason='email_validator not installed') def test_constraints_on_str_like() -> None: """See https://github.com/pydantic/pydantic/issues/8577 for motivation.""" @@ -6882,9 +6919,9 @@ class Foo(BaseModel): pytest.param(True, FailFast(), id='fail-fast-default'), pytest.param(True, FailFast(True), id='fail-fast-true'), pytest.param(False, FailFast(False), id='fail-fast-false'), - pytest.param(False, Field(...), id='field-default'), - pytest.param(True, Field(..., fail_fast=True), id='field-true'), - pytest.param(False, Field(..., fail_fast=False), id='field-false'), + pytest.param(False, Field(), id='field-default'), + pytest.param(True, Field(fail_fast=True), id='field-true'), + pytest.param(False, Field(fail_fast=False), id='field-false'), ], ) def test_fail_fast(tp, fail_fast, decl) -> None: @@ -6944,3 +6981,82 @@ def test_ser_ip_with_unexpected_value() -> None: with pytest.warns(UserWarning, match='serialized value may not be as expected.'): assert ta.dump_python(123) + + +def test_ser_ip_python_and_json() -> None: + ta = TypeAdapter(ipaddress.IPv4Address) + + ip = ta.validate_python('127.0.0.1') + assert ta.dump_python(ip) == ip + assert ta.dump_python(ip, mode='json') == '127.0.0.1' + assert ta.dump_json(ip) == b'"127.0.0.1"' + + +@pytest.mark.parametrize('input_data', ['1/3', 1.333, Fraction(1, 3), Decimal('1.333')]) +def test_fraction_validation_lax(input_data) -> None: + ta = TypeAdapter(Fraction) + fraction = ta.validate_python(input_data) + assert isinstance(fraction, Fraction) + + +def test_fraction_validation_strict() -> None: + ta = TypeAdapter(Fraction, config=ConfigDict(strict=True)) + + assert ta.validate_python(Fraction(1 / 3)) == Fraction(1 / 3) + + # only fractions accepted in strict mode + for lax_fraction in ['1/3', 1.333, Decimal('1.333')]: + with pytest.raises(ValidationError): + ta.validate_python(lax_fraction) + + +def test_fraction_serialization() -> None: + ta = TypeAdapter(Fraction) + assert ta.dump_python(Fraction(1, 3)) == '1/3' + assert ta.dump_json(Fraction(1, 3)) == b'"1/3"' + + +def test_fraction_json_schema() -> None: + ta = TypeAdapter(Fraction) + assert ta.json_schema() == {'type': 'string', 'format': 'fraction'} + + +def test_annotated_metadata_any_order() -> None: + def validator(v): + if isinstance(v, (int, float)): + return timedelta(days=v) + return v + + class BeforeValidatorAfterLe(BaseModel): + v: Annotated[timedelta, annotated_types.Le(timedelta(days=365)), BeforeValidator(validator)] + + class BeforeValidatorBeforeLe(BaseModel): + v: Annotated[timedelta, BeforeValidator(validator), annotated_types.Le(timedelta(days=365))] + + try: + BeforeValidatorAfterLe(v=366) + except ValueError as ex: + assert '365 days' in str(ex) + + # in this case, the Le constraint comes after the BeforeValidator, so we use functional validators + # from pydantic._internal._validators.py (in this case, less_than_or_equal_validator) + # which doesn't have access to fancy pydantic-core formatting for timedelta, so we get + # the raw timedelta repr in the error `datetime.timedelta(days=365`` vs the above `365 days` + try: + BeforeValidatorBeforeLe(v=366) + except ValueError as ex: + assert 'datetime.timedelta(days=365)' in str(ex) + + +@pytest.mark.parametrize('base64_type', [Base64Bytes, Base64Str, Base64UrlBytes, Base64UrlStr]) +def test_base64_with_invalid_min_length(base64_type) -> None: + """Check that an error is raised when the length of the base64 type's value is less or more than the min_length and max_length.""" + + class Model(BaseModel): + base64_value: base64_type = Field(min_length=3, max_length=5) # type: ignore + + with pytest.raises(ValidationError): + Model(**{'base64_value': b''}) + + with pytest.raises(ValidationError): + Model(**{'base64_value': b'123456'}) diff --git a/tests/test_types_namedtuple.py b/tests/test_types_namedtuple.py index 3774558275c..91829a626fd 100644 --- a/tests/test_types_namedtuple.py +++ b/tests/test_types_namedtuple.py @@ -136,6 +136,24 @@ class Model(BaseModel): Model.model_validate({'t': [-1]}) +def test_namedtuple_different_module(create_module) -> None: + """https://github.com/pydantic/pydantic/issues/10336""" + + @create_module + def other_module(): + from typing import NamedTuple + + TestIntOtherModule = int + + class Tup(NamedTuple): + f: 'TestIntOtherModule' + + class Model(BaseModel): + tup: other_module.Tup + + assert Model(tup={'f': 1}).tup.f == 1 + + def test_namedtuple_arbitrary_type(): class CustomClass: pass diff --git a/tests/test_types_self.py b/tests/test_types_self.py index 7a052f0b050..b21465c3d30 100644 --- a/tests/test_types_self.py +++ b/tests/test_types_self.py @@ -1,13 +1,14 @@ import dataclasses import re +import sys import typing -from typing import List, Optional, Union +from typing import List, Optional, Type, Union import pytest import typing_extensions from typing_extensions import NamedTuple, TypedDict -from pydantic import BaseModel, Field, TypeAdapter, ValidationError +from pydantic import BaseModel, Field, PydanticUserError, TypeAdapter, ValidationError, computed_field, validate_call @pytest.fixture( @@ -108,7 +109,7 @@ class SubSelfRef(SelfRef): def test_self_type_with_field(Self): class SelfRef(BaseModel): x: int - refs: typing.List[Self] = Field(..., gt=0) + refs: typing.List[Self] = Field(gt=0) with pytest.raises(TypeError, match=re.escape("Unable to apply constraint 'gt' to supplied value []")): SelfRef(x=1, refs=[SelfRef(x=2, refs=[])]) @@ -171,3 +172,73 @@ class Model(BaseModel): assert m.item.ref.x == 2 with pytest.raises(dataclasses.FrozenInstanceError): m.item.ref.x = 3 + + +def test_invalid_validate_call(Self): + with pytest.raises(PydanticUserError, match='`typing.Self` is invalid in this context'): + + @validate_call + def foo(self: Self): + pass + + +def test_invalid_validate_call_of_method(Self): + with pytest.raises(PydanticUserError, match='`typing.Self` is invalid in this context'): + + class A(BaseModel): + @validate_call + def foo(self: Self): + pass + + +def test_type_of_self(Self): + class A(BaseModel): + self_type: Type[Self] + + @computed_field + def self_types1(self) -> List[Type[Self]]: + return [type(self), self.self_type] + + # make sure forward refs are supported: + @computed_field + def self_types2(self) -> List[Type['Self']]: + return [type(self), self.self_type] + + @computed_field + def self_types3(self) -> 'List[Type[Self]]': + return [type(self), self.self_type] + + if sys.version_info >= (3, 9): + # standard container types are supported in 3.9+ + + @computed_field + def self_types4(self) -> 'list[type[Self]]': + return [type(self), self.self_type] + + @computed_field + def self_types5(self) -> list['type[Self]']: + return [type(self), self.self_type] + + class B(A): ... + + A(self_type=A) + A(self_type=B) + B(self_type=B) + + a = A(self_type=B) + for prop in (a.self_types1, a.self_types2, a.self_types3): + assert prop == [A, B] + + for invalid_type in (type, int, A, object): + with pytest.raises(ValidationError) as exc_info: + B(self_type=invalid_type) + + assert exc_info.value.errors(include_url=False) == [ + { + 'type': 'is_subclass_of', + 'loc': ('self_type',), + 'msg': f'Input should be a subclass of {B.__qualname__}', + 'input': invalid_type, + 'ctx': {'class': B.__qualname__}, + } + ] diff --git a/tests/test_types_typeddict.py b/tests/test_types_typeddict.py index 8255504d6f8..aeb137ef4bd 100644 --- a/tests/test_types_typeddict.py +++ b/tests/test_types_typeddict.py @@ -9,14 +9,13 @@ import pytest import typing_extensions from annotated_types import Lt -from pydantic_core import CoreSchema, core_schema +from pydantic_core import core_schema from typing_extensions import Annotated, TypedDict from pydantic import ( BaseModel, ConfigDict, Field, - GenerateSchema, GetCoreSchemaHandler, PositiveInt, PydanticUserError, @@ -881,20 +880,6 @@ class Model(Base): assert ta.validate_python({'x': 'ABC'}) == {'x': 'abc'} -def test_schema_generator() -> None: - class LaxStrGenerator(GenerateSchema): - def str_schema(self) -> CoreSchema: - return core_schema.no_info_plain_validator_function(str) - - class Model(TypedDict): - x: str - __pydantic_config__ = ConfigDict(schema_generator=LaxStrGenerator) # type: ignore - - ta = TypeAdapter(Model) - - assert ta.validate_python(dict(x=1))['x'] == '1' - - def test_grandparent_config(): class MyTypedDict(TypedDict): __pydantic_config__ = ConfigDict(str_to_lower=True) diff --git a/tests/test_types_zoneinfo.py b/tests/test_types_zoneinfo.py index 0c8becf2d70..f633c7eb3af 100644 --- a/tests/test_types_zoneinfo.py +++ b/tests/test_types_zoneinfo.py @@ -1,6 +1,9 @@ +from datetime import timezone +from typing import Union + import pytest -from pydantic import BaseModel, ValidationError +from pydantic import BaseModel, ConfigDict, TypeAdapter, ValidationError zoneinfo = pytest.importorskip('zoneinfo', reason='zoneinfo requires >=3.9') @@ -48,3 +51,8 @@ def test_zoneinfo_json_schema(): 'properties': {'tz': {'type': 'string', 'format': 'zoneinfo', 'title': 'Tz'}}, 'required': ['tz'], } + + +def test_zoneinfo_union() -> None: + ta = TypeAdapter(Union[zoneinfo.ZoneInfo, timezone], config=ConfigDict(arbitrary_types_allowed=True)) + assert ta.validate_python(timezone.utc) is timezone.utc diff --git a/tests/test_typing.py b/tests/test_typing.py index e42bc7fc51f..4316d0960ff 100644 --- a/tests/test_typing.py +++ b/tests/test_typing.py @@ -4,15 +4,15 @@ from typing import Callable, ClassVar, ForwardRef, NamedTuple import pytest -from typing_extensions import Literal, get_origin +from typing_extensions import Annotated, Literal, get_origin from pydantic import BaseModel, Field # noqa: F401 from pydantic._internal._typing_extra import ( NoneType, - eval_type_lenient, + eval_type, get_function_type_hints, - is_classvar, - is_literal_type, + is_classvar_annotation, + is_literal, is_namedtuple, is_none_type, origin_is_union, @@ -64,7 +64,7 @@ def test_is_none_type(): 'union', [ typing.Union[int, str], - eval_type_lenient('int | str'), + eval_type('int | str'), *([int | str] if sys.version_info >= (3, 10) else []), ], ) @@ -76,19 +76,19 @@ def test_is_union(union): def test_is_literal_with_typing_extension_literal(): from typing_extensions import Literal - assert is_literal_type(Literal) is False - assert is_literal_type(Literal['foo']) is True + assert is_literal(Literal) is False + assert is_literal(Literal['foo']) is True def test_is_literal_with_typing_literal(): from typing import Literal - assert is_literal_type(Literal) is False - assert is_literal_type(Literal['foo']) is True + assert is_literal(Literal) is False + assert is_literal(Literal['foo']) is True @pytest.mark.parametrize( - 'ann_type,extepcted', + ['ann_type', 'extepcted'], ( (None, False), (ForwardRef('Other[int]'), False), @@ -96,11 +96,15 @@ def test_is_literal_with_typing_literal(): (ForwardRef('ClassVar[int]'), True), (ForwardRef('t.ClassVar[int]'), True), (ForwardRef('typing.ClassVar[int]'), True), + (ForwardRef('Annotated[ClassVar[int], ...]'), True), + (ForwardRef('Annotated[t.ClassVar[int], ...]'), True), + (ForwardRef('t.Annotated[t.ClassVar[int], ...]'), True), (ClassVar[int], True), + (Annotated[ClassVar[int], ...], True), ), ) -def test_is_classvar(ann_type, extepcted): - assert is_classvar(ann_type) is extepcted +def test_is_classvar_annotation(ann_type, extepcted): + assert is_classvar_annotation(ann_type) is extepcted def test_parent_frame_namespace(mocker): diff --git a/tests/test_utils.py b/tests/test_utils.py index 37547e4367b..820fe9d322c 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -3,6 +3,7 @@ import os import pickle import sys +import time from copy import copy, deepcopy from typing import Callable, Dict, Generic, List, NewType, Tuple, TypeVar, Union @@ -14,10 +15,10 @@ from pydantic import BaseModel from pydantic._internal import _repr from pydantic._internal._core_utils import _WalkCoreSchema, pretty_print_core_schema -from pydantic._internal._typing_extra import all_literal_values, get_origin, is_new_type +from pydantic._internal._typing_extra import get_origin, is_new_type, literal_values from pydantic._internal._utils import ( BUILTIN_COLLECTIONS, - ClassAttribute, + LazyClassAttribute, ValueItems, all_identical, deep_update, @@ -76,6 +77,7 @@ def get(self) -> T: ... (Tuple[str, ...], 'Tuple[str, ...]'), (Union[int, List[str], Tuple[str, int]], 'Union[int, List[str], Tuple[str, int]]'), (foobar, 'foobar'), + (time.time_ns, 'time_ns'), (LoggedVar, 'LoggedVar'), (LoggedVar(), 'LoggedVar'), ], @@ -366,7 +368,7 @@ def test_undefined_copy(): def test_class_attribute(): class Foo: - attr = ClassAttribute('attr', 'foo') + attr = LazyClassAttribute('attr', lambda: 'foo') assert Foo.attr == 'foo' @@ -378,21 +380,21 @@ class Foo: assert f.attr == 'not foo' -def test_all_literal_values(): +def test_literal_values(): L1 = Literal['1'] - assert all_literal_values(L1) == ['1'] + assert literal_values(L1) == ['1'] L2 = Literal['2'] L12 = Literal[L1, L2] - assert all_literal_values(L12) == IsList('1', '2', check_order=False) + assert literal_values(L12) == IsList('1', '2', check_order=False) L312 = Literal['3', Literal[L1, L2]] - assert all_literal_values(L312) == IsList('3', '1', '2', check_order=False) + assert literal_values(L312) == IsList('3', '1', '2', check_order=False) @pytest.mark.parametrize( 'obj', - (1, 1.0, '1', b'1', int, None, test_all_literal_values, len, test_all_literal_values.__code__, lambda: ..., ...), + (1, 1.0, '1', b'1', int, None, test_literal_values, len, test_literal_values.__code__, lambda: ..., ...), ) def test_smart_deepcopy_immutable_non_sequence(obj, mocker): # make sure deepcopy is not used diff --git a/tests/test_v1.py b/tests/test_v1.py index 60ffbaf5758..b8c4ee28fa0 100644 --- a/tests/test_v1.py +++ b/tests/test_v1.py @@ -1,4 +1,7 @@ +import warnings + from pydantic import VERSION +from pydantic import BaseModel as V2BaseModel from pydantic.v1 import VERSION as V1_VERSION from pydantic.v1 import BaseModel as V1BaseModel from pydantic.v1 import root_validator as v1_root_validator @@ -21,3 +24,22 @@ def root_validator(cls, values): model = Model(v='value') assert model.v == 'value-v1' + + +def test_isinstance_does_not_raise_deprecation_warnings(): + class V1Model(V1BaseModel): + v: int + + class V2Model(V2BaseModel): + v: int + + v1_obj = V1Model(v=1) + v2_obj = V2Model(v=2) + + with warnings.catch_warnings(): + warnings.simplefilter('error') + + assert isinstance(v1_obj, V1BaseModel) + assert not isinstance(v1_obj, V2BaseModel) + assert not isinstance(v2_obj, V1BaseModel) + assert isinstance(v2_obj, V2BaseModel) diff --git a/tests/test_validate_call.py b/tests/test_validate_call.py index 12547f5a4f7..a265359c212 100644 --- a/tests/test_validate_call.py +++ b/tests/test_validate_call.py @@ -4,17 +4,152 @@ import sys from datetime import datetime, timezone from functools import partial -from typing import Any, List, Tuple +from typing import Any, List, Literal, Tuple, Union import pytest from pydantic_core import ArgsKwargs -from typing_extensions import Annotated, TypedDict +from typing_extensions import Annotated, Required, TypedDict, Unpack + +from pydantic import ( + AfterValidator, + BaseModel, + BeforeValidator, + Field, + PydanticInvalidForJsonSchema, + PydanticUserError, + Strict, + TypeAdapter, + ValidationError, + validate_call, + with_config, +) + + +def test_wrap() -> None: + @validate_call + def foo_bar(a: int, b: int): + """This is the foo_bar method.""" + return f'{a}, {b}' + + assert foo_bar.__doc__ == 'This is the foo_bar method.' + assert foo_bar.__name__ == 'foo_bar' + assert foo_bar.__module__ == 'tests.test_validate_call' + assert foo_bar.__qualname__ == 'test_wrap..foo_bar' + assert callable(foo_bar.raw_function) + assert repr(inspect.signature(foo_bar)) == '' + + +def test_func_type() -> None: + def f(x: int): ... + + class A: + def m(self, x: int): ... + + for func in (f, lambda x: None, A.m, A().m): + assert validate_call(func).__name__ == func.__name__ + assert validate_call(func).__qualname__ == func.__qualname__ + assert validate_call(partial(func)).__name__ == f'partial({func.__name__})' + assert validate_call(partial(func)).__qualname__ == f'partial({func.__qualname__})' + + with pytest.raises( + PydanticUserError, + match=(f'Partial of `{list}` is invalid because the type of `{list}` is not supported by `validate_call`'), + ): + validate_call(partial(list)) + + with pytest.raises( + PydanticUserError, + match=('`validate_call` should be applied to one of the following: function, method, partial, or lambda'), + ): + validate_call([]) -from pydantic import Field, PydanticInvalidForJsonSchema, TypeAdapter, ValidationError, validate_call -from pydantic.main import BaseModel +def test_validate_class() -> None: + class A: + @validate_call + def __new__(cls, x: int): + return super().__new__(cls) -def test_args(): + @validate_call + def __init__(self, x: int) -> None: + self.x = x + + class M(type): ... + + for cls in (A, int, type, Exception, M): + with pytest.raises( + PydanticUserError, + match=re.escape( + '`validate_call` should be applied to functions, not classes (put `@validate_call` on top of `__init__` or `__new__` instead)' + ), + ): + validate_call(cls) + + assert A('5').x == 5 + + +def test_validate_custom_callable() -> None: + class A: + def __call__(self, x: int) -> int: + return x + + with pytest.raises( + PydanticUserError, + match=re.escape( + '`validate_call` should be applied to functions, not instances or other callables. Use `validate_call` explicitly on `__call__` instead.' + ), + ): + validate_call(A()) + + a = A() + assert validate_call(a.__call__)('5') == 5 # Note: dunder methods cannot be overridden at instance level + + class B: + @validate_call + def __call__(self, x: int) -> int: + return x + + assert B()('5') == 5 + + +def test_invalid_signature() -> None: + # In some versions, these functions may not have a valid signature + for func in (max, min, breakpoint, sorted, compile, print, [].append, {}.popitem, int().bit_length): + try: + inspect.signature(func) + assert validate_call(func).__name__ == func.__name__ + assert validate_call(func).__qualname__ == func.__qualname__ + assert validate_call(partial(func)).__name__ == f'partial({func.__name__})' + assert validate_call(partial(func)).__qualname__ == f'partial({func.__qualname__})' + except ValueError: + with pytest.raises(PydanticUserError, match=(f"Input function `{func}` doesn't have a valid signature")): + validate_call(func) + + class A: + def f(): ... + + # A method require at least one positional arg (i.e. `self`), so the signature is invalid + func = A().f + with pytest.raises(PydanticUserError, match=(f"Input function `{func}` doesn't have a valid signature")): + validate_call(func) + + +@pytest.mark.parametrize('decorator', [staticmethod, classmethod]) +def test_classmethod_order_error(decorator) -> None: + name = decorator.__name__ + with pytest.raises( + PydanticUserError, + match=re.escape(f'The `@{name}` decorator should be applied after `@validate_call` (put `@{name}` on top)'), + ): + + class A: + @validate_call + @decorator + def method(self, x: int): + pass + + +def test_args() -> None: @validate_call def foo(a: int, b: int): return f'{a}, {b}' @@ -81,20 +216,6 @@ def foo_bar(a: int = None): ] -def test_wrap(): - @validate_call - def foo_bar(a: int, b: int): - """This is the foo_bar method.""" - return f'{a}, {b}' - - assert foo_bar.__doc__ == 'This is the foo_bar method.' - assert foo_bar.__name__ == 'foo_bar' - assert foo_bar.__module__ == 'tests.test_validate_call' - assert foo_bar.__qualname__ == 'test_wrap..foo_bar' - assert callable(foo_bar.raw_function) - assert repr(inspect.signature(foo_bar)) == '' - - def test_kwargs(): @validate_call def foo(*, a: int, b: int): @@ -162,6 +283,90 @@ def foo(a, b, *args, d=3, **kwargs): assert foo(1, 2, kwargs=4, e=5) == "a=1, b=2, args=(), d=3, kwargs={'kwargs': 4, 'e': 5}" +def test_unpacked_typed_dict_kwargs_invalid_type() -> None: + with pytest.raises(PydanticUserError) as exc: + + @validate_call + def foo(**kwargs: Unpack[int]): + pass + + assert exc.value.code == 'unpack-typed-dict' + + +def test_unpacked_typed_dict_kwargs_overlaps() -> None: + class TD(TypedDict, total=False): + a: int + b: int + c: int + + with pytest.raises(PydanticUserError) as exc: + + @validate_call + def foo(a: int, b: int, **kwargs: Unpack[TD]): + pass + + assert exc.value.code == 'overlapping-unpack-typed-dict' + assert exc.value.message == "Typed dictionary 'TD' overlaps with parameters 'a', 'b'" + + # Works for a pos-only argument + @validate_call + def foo(a: int, /, **kwargs: Unpack[TD]): + pass + + foo(1, a=1) + + +def test_unpacked_typed_dict_kwargs() -> None: + @with_config({'strict': True}) + class TD(TypedDict, total=False): + a: int + b: Required[str] + + @validate_call + def foo1(**kwargs: Unpack[TD]): + pass + + @validate_call + def foo2(**kwargs: 'Unpack[TD]'): + pass + + for foo in (foo1, foo2): + foo(a=1, b='test') + foo(b='test') + + with pytest.raises(ValidationError) as exc: + foo(a='1') + + assert exc.value.errors()[0]['type'] == 'int_type' + assert exc.value.errors()[0]['loc'] == ('a',) + assert exc.value.errors()[1]['type'] == 'missing' + assert exc.value.errors()[1]['loc'] == ('b',) + + # Make sure that when called without any arguments, + # empty kwargs are still validated against the typed dict: + with pytest.raises(ValidationError) as exc: + foo() + + assert exc.value.errors()[0]['type'] == 'missing' + assert exc.value.errors()[0]['loc'] == ('b',) + + +def test_unpacked_typed_dict_kwargs_functional_syntax() -> None: + TD = TypedDict('TD', {'in': int, 'x-y': int}) + + @validate_call + def foo(**kwargs: Unpack[TD]): + pass + + foo(**{'in': 1, 'x-y': 2}) + + with pytest.raises(ValidationError) as exc: + foo(**{'in': 'not_an_int', 'x-y': 1}) + + assert exc.value.errors()[0]['type'] == 'int_parsing' + assert exc.value.errors()[0]['loc'] == ('in',) + + def test_field_can_provide_factory() -> None: @validate_call def foo(a: int, b: int = Field(default_factory=lambda: 99), *args: int) -> int: @@ -174,7 +379,7 @@ def foo(a: int, b: int = Field(default_factory=lambda: 99), *args: int) -> int: def test_annotated_field_can_provide_factory() -> None: @validate_call - def foo2(a: int, b: Annotated[int, Field(default_factory=lambda: 99)], *args: int) -> int: + def foo2(a: int, b: 'Annotated[int, Field(default_factory=lambda: 99)]', *args: int) -> int: """mypy reports Incompatible default for argument "b" if we don't supply ANY as default""" return a + b + sum(args) @@ -257,32 +462,6 @@ def foo(args: int, kwargs: int): ] -def test_v_args(): - @validate_call - def foo1(v__args: int): - return v__args - - assert foo1(123) == 123 - - @validate_call - def foo2(v__kwargs: int): - return v__kwargs - - assert foo2(123) == 123 - - @validate_call - def foo3(v__positional_only: int): - return v__positional_only - - assert foo3(123) == 123 - - @validate_call - def foo4(v__duplicate_kwargs: int): - return v__duplicate_kwargs - - assert foo4(123) == 123 - - def test_async(): @validate_call async def foo(a, b): @@ -292,6 +471,9 @@ async def run(): v = await foo(1, 2) assert v == 'a=1 b=2' + # insert_assert(inspect.iscoroutinefunction(foo) is True) + assert inspect.iscoroutinefunction(foo) is True + asyncio.run(run()) with pytest.raises(ValidationError) as exc_info: asyncio.run(foo('x')) @@ -463,7 +645,7 @@ def foo(**scores: int) -> str: } @validate_call - def foo(a: Annotated[int, Field(..., alias='A')]): + def foo(a: Annotated[int, Field(alias='A')]): return a assert foo(1) == 1 @@ -523,6 +705,97 @@ def foo(a: int, b: List[str]): ] +def test_annotated_num(): + @validate_call + def f(a: Annotated[int, Field(gt=0), Field(lt=10)]): + return a + + assert f(5) == 5 + + with pytest.raises(ValidationError) as exc_info: + f(0) + assert exc_info.value.errors(include_url=False) == [ + {'type': 'greater_than', 'loc': (0,), 'msg': 'Input should be greater than 0', 'input': 0, 'ctx': {'gt': 0}} + ] + + with pytest.raises(ValidationError) as exc_info: + f(10) + assert exc_info.value.errors(include_url=False) == [ + {'type': 'less_than', 'loc': (0,), 'msg': 'Input should be less than 10', 'input': 10, 'ctx': {'lt': 10}} + ] + + +def test_annotated_discriminator(): + class Cat(BaseModel): + type: Literal['cat'] = 'cat' + food: str + meow: int + + class Dog(BaseModel): + type: Literal['dog'] = 'dog' + food: str + bark: int + + Pet = Annotated[Union[Cat, Dog], Field(discriminator='type')] + + @validate_call + def f(pet: Pet): + return pet + + with pytest.raises(ValidationError) as exc_info: + f({'food': 'fish'}) + + assert exc_info.value.errors(include_url=False) == [ + { + 'type': 'union_tag_not_found', + 'loc': (0,), + 'msg': "Unable to extract tag using discriminator 'type'", + 'input': {'food': 'fish'}, + 'ctx': {'discriminator': "'type'"}, + } + ] + + with pytest.raises(ValidationError) as exc_info: + f({'type': 'dog', 'food': 'fish'}) + + assert exc_info.value.errors(include_url=False) == [ + { + 'type': 'missing', + 'loc': (0, 'dog', 'bark'), + 'msg': 'Field required', + 'input': {'type': 'dog', 'food': 'fish'}, + } + ] + + +def test_annotated_validator(): + @validate_call + def f(x: Annotated[int, BeforeValidator(lambda x: x + '2'), AfterValidator(lambda x: x + 1)]): + return x + + assert f('1') == 13 + + +def test_annotated_strict(): + @validate_call + def f1(x: Annotated[int, Strict()]): + return x + + @validate_call + def f2(x: 'Annotated[int, Strict()]'): + return x + + for f in (f1, f2): + assert f(1) == 1 + + with pytest.raises(ValidationError) as exc_info: + f('1') + + assert exc_info.value.errors(include_url=False) == [ + {'type': 'int_type', 'loc': (0,), 'msg': 'Input should be a valid integer', 'input': '1'} + ] + + def test_annotated_use_of_alias(): @validate_call def foo(a: Annotated[int, Field(alias='b')], c: Annotated[int, Field()], d: Annotated[int, Field(alias='')]): @@ -716,21 +989,6 @@ def bar(self, value: str) -> str: assert foo.bar('test') == 'bar-test' -@pytest.mark.parametrize('decorator', [staticmethod, classmethod]) -def test_classmethod_order_error(decorator): - name = decorator.__name__ - with pytest.raises( - TypeError, - match=re.escape(f'The `@{name}` decorator should be applied after `@validate_call` (put `@{name}` on top)'), - ): - - class A: - @validate_call - @decorator - def method(self, x: int): - pass - - def test_async_func() -> None: @validate_call(validate_return=True) async def foo(a: Any) -> int: @@ -818,13 +1076,54 @@ def foo(bar: 'list[int | str]') -> 'list[int | str]': ] +def test_eval_namespace_basic(create_module): + module = create_module( + """ +from __future__ import annotations +from typing import TypeVar +from pydantic import validate_call + +T = TypeVar('T', bound=int) + +@validate_call +def f(x: T): ... + +def g(): + MyList = list + + @validate_call + def h(x: MyList[int]): ... + return h +""" + ) + f = module.f + f(1) + with pytest.raises(ValidationError) as exc_info: + f('x') + assert exc_info.value.errors(include_url=False) == [ + { + 'input': 'x', + 'loc': (0,), + 'msg': 'Input should be a valid integer, unable to parse string as an integer', + 'type': 'int_parsing', + } + ] + + h = module.g() + with pytest.raises(ValidationError) as exc_info: + h('not a list') + assert exc_info.value.errors(include_url=False) == [ + {'input': 'not a list', 'loc': (0,), 'msg': 'Input should be a valid list', 'type': 'list_type'} + ] + + @pytest.mark.skipif(sys.version_info < (3, 12), reason='requires Python 3.12+ for PEP 695 syntax with generics') -def test_validate_call_with_pep_695_syntax() -> None: +def test_validate_call_with_pep_695_syntax(create_module) -> None: """Note: validate_call still doesn't work properly with generics, see https://github.com/pydantic/pydantic/issues/7796. This test is just to ensure that the syntax is accepted and doesn't raise a NameError.""" - globs = {} - exec( + + module = create_module( """ from typing import Iterable from pydantic import validate_call @@ -836,10 +1135,10 @@ def find_max_no_validate_return[T](args: Iterable[T]) -> T: @validate_call(validate_return=True) def find_max_validate_return[T](args: Iterable[T]) -> T: return sorted(args, reverse=True)[0] - """, - globs, + """ ) - functions = [globs['find_max_no_validate_return'], globs['find_max_validate_return']] + + functions = [module.find_max_no_validate_return, module.find_max_validate_return] for find_max in functions: assert len(find_max.__type_params__) == 1 assert find_max([1, 2, 10, 5]) == 10 @@ -848,6 +1147,77 @@ def find_max_validate_return[T](args: Iterable[T]) -> T: find_max(1) +@pytest.mark.skipif(sys.version_info < (3, 12), reason='requires Python 3.12+ for PEP 695 syntax with generics') +def test_pep695_with_class(create_module): + """Primarily to ensure that the syntax is accepted and doesn't raise a `NameError` with `T`. + The validation is not expected to work properly when parameterized at this point.""" + + for import_annotations in ('from __future__ import annotations', ''): + module = create_module( + f""" +{import_annotations} +from pydantic import validate_call + +class A[T]: + @validate_call(validate_return=True) + def f(self, a: T) -> T: + return str(a) + """ + ) + A = module.A + a = A[int]() + # these two are undesired behavior, but it's what happens now + assert a.f(1) == '1' + assert a.f('1') == '1' + + +@pytest.mark.skipif(sys.version_info < (3, 12), reason='requires Python 3.12+ for PEP 695 syntax with generics') +def test_pep695_with_nested_scopes(create_module): + """Nested scopes generally cannot be caught by `parent_frame_namespace`, + so currently this test is expected to fail. + """ + + module = create_module( + """ +from __future__ import annotations +from pydantic import validate_call + +class A[T]: + def g(self): + @validate_call(validate_return=True) + def inner(a: T) -> T: ... + + def h[S](self): + @validate_call(validate_return=True) + def inner(a: T) -> S: ... + """ + ) + + A = module.A + a = A[int]() + with pytest.raises(NameError): + a.g() + with pytest.raises(NameError): + a.h() + + with pytest.raises(NameError): + create_module( + """ +from __future__ import annotations +from pydantic import validate_call + +class A[T]: + class B: + @validate_call(validate_return=True) + def f(a: T) -> T: ... + + class C[S]: + @validate_call(validate_return=True) + def f(a: T) -> S: ... + """ + ) + + class M0(BaseModel): z: int @@ -865,10 +1235,12 @@ def foo(): class M2(BaseModel): z: int - M = M2 + M = M2 # noqa: F841 - @validate_call - def bar(m: M) -> M: + @validate_call(validate_return=True) + def bar(m: 'M') -> 'M': return m assert bar({'z': 1}) == M2(z=1) + + foo() diff --git a/tests/test_validators.py b/tests/test_validators.py index 51dd636ea60..a8598c0d42c 100644 --- a/tests/test_validators.py +++ b/tests/test_validators.py @@ -1816,7 +1816,7 @@ def post_root(self) -> 'B': def test_validating_assignment_pre_root_validator_fail(): class Model(BaseModel): - current_value: float = Field(..., alias='current') + current_value: float = Field(alias='current') max_value: float model_config = ConfigDict(validate_assignment=True) @@ -1844,7 +1844,7 @@ def values_are_not_string(cls, values: Dict[str, Any]) -> Dict[str, Any]: def test_validating_assignment_model_validator_before_fail(): class Model(BaseModel): - current_value: float = Field(..., alias='current') + current_value: float = Field(alias='current') max_value: float model_config = ConfigDict(validate_assignment=True) @@ -2869,6 +2869,45 @@ class Blah(BaseModel): assert isinstance(data['bar'], ser_type) +def test_plain_validator_plain_serializer_single_ser_call() -> None: + """https://github.com/pydantic/pydantic/issues/10385""" + + ser_count = 0 + + def ser(v): + nonlocal ser_count + ser_count += 1 + return v + + class Model(BaseModel): + foo: Annotated[bool, PlainSerializer(ser), PlainValidator(lambda v: v)] + + model = Model(foo=True) + data = model.model_dump() + + assert data == {'foo': True} + assert ser_count == 1 + + +@pytest.mark.xfail(reason='https://github.com/pydantic/pydantic/issues/10428') +def test_plain_validator_with_filter_dict_schema() -> None: + class MyDict: + @classmethod + def __get_pydantic_core_schema__(cls, source, handler): + return core_schema.dict_schema( + keys_schema=handler.generate_schema(str), + values_schema=handler.generate_schema(int), + serialization=core_schema.filter_dict_schema( + include={'a'}, + ), + ) + + class Model(BaseModel): + f: Annotated[MyDict, PlainValidator(lambda v: v)] + + assert Model(f={'a': 1, 'b': 1}).model_dump() == {'f': {'a': 1}} + + def test_plain_validator_with_unsupported_type() -> None: class UnsupportedClass: pass diff --git a/tests/typechecking/README.md b/tests/typechecking/README.md new file mode 100644 index 00000000000..9866313edd5 --- /dev/null +++ b/tests/typechecking/README.md @@ -0,0 +1,33 @@ +# Type checking test suite + +This test suite is meant to assert the correct behavior of the type hints we use in the Pydantic code. +In CI, we run both Mypy and Pyright on these files, using the [`pyproject.toml`](./pyproject.toml) +configuration file. + +Note that these tests do not relate to the Mypy plugin, which is tested under the [`mypy/`](../mypy/) folder. + +## Assertions + +Use [`assert_type`](https://docs.python.org/3/library/typing.html#typing.assert_type) to make assertions: + +```python +from typing_extensions import assert_type + +from pydantic import TypeAdapter + +ta1 = TypeAdapter(int) +assert_type(ta1, TypeAdapter[int]) +``` + +To assert on invalid cases, add a `type: ignore` (for Mypy, must go first) and/or a `pyright: ignore` (for Pyright) comment: + +```python +from pydantic import BaseModel + + +class Model(BaseModel): + a: int + + +Model() # type: ignore[call-arg] # pyright: ignore[reportCallIssue] +``` diff --git a/tests/typechecking/base_model.py b/tests/typechecking/base_model.py new file mode 100644 index 00000000000..bcbb979e5e5 --- /dev/null +++ b/tests/typechecking/base_model.py @@ -0,0 +1,30 @@ +""" +This file is used to test pyright's ability to check Pydantic's `BaseModel` related code. +""" + +from pydantic import BaseModel, Field + + +class MyModel(BaseModel): + x: str + y: list[int] + z: int = 1 + + +m1 = MyModel(x='hello', y=[1, 2, 3]) + +m2 = MyModel(x='hello') # type: ignore[call-arg] # pyright: ignore[reportCallIssue] + +m3 = MyModel(x='hello', y=[1, '2', b'3']) # type: ignore[list-item] # pyright: ignore[reportArgumentType] + +m1.z + 'not an int' # type: ignore[operator] # pyright: ignore[reportOperatorIssue] + +m1.foobar # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] + + +class Knight(BaseModel): + title: str = Field(default='Sir Lancelot') # this is okay + age: int = Field(23) # this works fine at runtime but will case an error for pyright + + +k = Knight() # type: ignore[call-arg] # pyright: ignore[reportCallIssue] diff --git a/tests/typechecking/computed_field.py b/tests/typechecking/computed_field.py new file mode 100644 index 00000000000..c7e15f94393 --- /dev/null +++ b/tests/typechecking/computed_field.py @@ -0,0 +1,26 @@ +from functools import cached_property + +from pydantic import BaseModel, computed_field + + +class Square(BaseModel): + side: float + + # mypy limitation, see: + # https://mypy.readthedocs.io/en/stable/error_code_list.html#decorator-preceding-property-not-supported-prop-decorator + @computed_field # type: ignore[prop-decorator] + @property + def area(self) -> float: + return self.side**2 + + @computed_field # type: ignore[prop-decorator] + @cached_property + def area_cached(self) -> float: + return self.side**2 + + +sq = Square(side=10) +y = 12.4 + sq.area +z = 'x' + sq.area # type: ignore[operator] # pyright: ignore[reportOperatorIssue] +y_cached = 12.4 + sq.area_cached +z_cached = 'x' + sq.area_cached # type: ignore[operator] # pyright: ignore[reportOperatorIssue] diff --git a/tests/pyright/decorators.py b/tests/typechecking/decorators.py similarity index 85% rename from tests/pyright/decorators.py rename to tests/typechecking/decorators.py index e6e030d0e87..28f0e6031fa 100644 --- a/tests/pyright/decorators.py +++ b/tests/typechecking/decorators.py @@ -45,14 +45,17 @@ def valid_staticmethod(value: Any) -> Any: ... class WrapModelValidator(BaseModel): - @model_validator(mode='wrap') # pyright: ignore[reportArgumentType] + # mypy randomly does not catch the type error here (https://github.com/python/mypy/issues/18125) + # so we also ignore the `unused-ignore` code: + @model_validator(mode='wrap') # type: ignore[arg-type, unused-ignore] # pyright: ignore[reportArgumentType] def no_classmethod(cls, value: Any, handler: ModelWrapValidatorHandler[Self]) -> Self: ... - @model_validator(mode='wrap') # pyright: ignore[reportArgumentType] + @model_validator(mode='wrap') # type: ignore[arg-type] # pyright: ignore[reportArgumentType] @classmethod def no_handler(cls, value: Any) -> Self: ... - @model_validator(mode='wrap') # pyright: ignore[reportArgumentType] + # Mypy somehow reports "Cannot infer function type argument" here: + @model_validator(mode='wrap') # type:ignore[misc] # pyright: ignore[reportArgumentType] @classmethod def incompatible_type_var(cls, value: Any, handler: ModelWrapValidatorHandler[int]) -> int: """ @@ -80,7 +83,8 @@ def valid_info(cls, value: Any, handler: ModelWrapValidatorHandler[Self], info: class AfterModelValidator(BaseModel): - @model_validator(mode='after') # pyright: ignore[reportArgumentType] + # Mypy somehow reports "Cannot infer function type argument" here: + @model_validator(mode='after') # type:ignore[misc] # pyright: ignore[reportArgumentType] def missing_return_value(self) -> None: ... @model_validator(mode='after') @@ -104,7 +108,7 @@ def no_classmethod(self, value: Any) -> Any: @classmethod def valid_classmethod(cls, value: Any) -> Any: ... - @field_validator('foo', mode='before') # pyright: ignore[reportArgumentType] + @field_validator('foo', mode='before') # type: ignore[type-var] # pyright: ignore[reportArgumentType] @classmethod def invalid_with_info(cls, value: Any, info: int) -> Any: ... @@ -118,7 +122,7 @@ class AfterFieldValidator(BaseModel): @classmethod def valid_classmethod(cls, value: Any) -> Any: ... - @field_validator('foo', mode='after', json_schema_input_type=int) # pyright: ignore[reportCallIssue, reportArgumentType] + @field_validator('foo', mode='after', json_schema_input_type=int) # type: ignore[call-overload] # pyright: ignore[reportCallIssue, reportArgumentType] @classmethod def invalid_input_type_not_allowed(cls, value: Any) -> Any: ... @@ -134,7 +138,7 @@ def invalid_missing_handler(cls, value: Any) -> Any: `pydantic_core.core_schema.NoInfoWrapValidatorFunction`. """ - @field_validator('foo', mode='wrap') # pyright: ignore[reportArgumentType] + @field_validator('foo', mode='wrap') # type: ignore[type-var] # pyright: ignore[reportArgumentType] @classmethod def invalid_handler(cls, value: Any, handler: int) -> Any: ... @@ -148,7 +152,7 @@ def valid_with_info(cls, value: Any, handler: ValidatorFunctionWrapHandler, info class PlainModelSerializer(BaseModel): - @model_serializer # pyright: ignore[reportArgumentType] + @model_serializer # type: ignore[type-var] # pyright: ignore[reportArgumentType] def too_many_arguments(self, info: SerializationInfo, unrelated: Any) -> Any: ... @model_serializer @@ -162,7 +166,7 @@ def valid_plain_serializer_info(self, info: SerializationInfo) -> Any: ... class WrapModelSerializer(BaseModel): - @model_serializer(mode='wrap') # pyright: ignore[reportArgumentType] + @model_serializer(mode='wrap') # type: ignore[type-var] # pyright: ignore[reportArgumentType] def no_handler(self) -> Any: ... @model_serializer(mode='wrap') @@ -185,7 +189,7 @@ def valid_method_no_info_1(self, value: Any) -> Any: ... @field_serializer('a', mode='plain') def valid_method_no_info_2(self, value: Any) -> Any: ... - @field_serializer('a', mode='plain') # pyright: ignore[reportArgumentType] + @field_serializer('a', mode='plain') # type: ignore[type-var] # pyright: ignore[reportArgumentType] def invalid_method_info_1(self, value: Any, info: int) -> Any: ... @field_serializer('a', mode='plain') @@ -238,7 +242,7 @@ def no_handler(self, value: Any) -> Any: `pydantic_core.core_schema.GeneralWrapNoInfoSerializerFunction`. """ - @field_serializer('a', mode='wrap') # pyright: ignore[reportArgumentType] + @field_serializer('a', mode='wrap') # type: ignore[type-var] # pyright: ignore[reportArgumentType] @staticmethod def staticmethod_no_handler(value: Any) -> Any: ... diff --git a/tests/typechecking/fields.py b/tests/typechecking/fields.py new file mode 100644 index 00000000000..38721d730e5 --- /dev/null +++ b/tests/typechecking/fields.py @@ -0,0 +1,40 @@ +from pydantic import BaseModel, Field, PrivateAttr + + +# private attributes should be excluded from +# the synthesized `__init__` method: +class ModelWithPrivateAttr(BaseModel): + _private_field: str = PrivateAttr() + + +m = ModelWithPrivateAttr() + + +def new_list() -> list[int]: + return [] + + +class Model(BaseModel): + # `default` and `default_factory` are mutually exclusive: + f1: int = Field(default=1, default_factory=int) # type: ignore[call-overload] # pyright: ignore[reportCallIssue] + + # `default` and `default_factory` matches the annotation: + f2: int = Field(default='1') # type: ignore[assignment] # pyright: ignore[reportAssignmentType] + f3: int = Field(default_factory=str) # type: ignore[assignment] # pyright: ignore[reportAssignmentType] + + f4: int = PrivateAttr(default='1') # type: ignore[assignment] # pyright: ignore[reportAssignmentType] + f5: int = PrivateAttr(default_factory=str) # type: ignore[assignment] # pyright: ignore[reportAssignmentType] + + f6: list[str] = Field(default_factory=list) + f7: list[int] = Field(default_factory=new_list) + f8: list[str] = Field(default_factory=lambda: list()) + f9: dict[str, str] = Field(default_factory=dict) + f10: int = Field(default_factory=lambda: 123) + + # Note: mypy may require a different error code for `f12` (see https://github.com/python/mypy/issues/17986). + # Seems like this is not the case anymore. But could pop up at any time. + f11: list[str] = Field(default_factory=new_list) # type: ignore[arg-type] # pyright: ignore[reportAssignmentType] + f12: int = Field(default_factory=list) # type: ignore[arg-type] # pyright: ignore[reportAssignmentType] + + # Do not error on the ellipsis: + f13: int = Field(...) diff --git a/tests/pyright/json_schema_examples.py b/tests/typechecking/json_schema_examples.py similarity index 100% rename from tests/pyright/json_schema_examples.py rename to tests/typechecking/json_schema_examples.py diff --git a/tests/typechecking/misc.py b/tests/typechecking/misc.py new file mode 100644 index 00000000000..707f5dc9d10 --- /dev/null +++ b/tests/typechecking/misc.py @@ -0,0 +1,30 @@ +from pydantic import BaseModel + + +class Sub(BaseModel): + a: int + b: int + + +class Model(BaseModel): + subs: list[Sub] + + +def func(model: Model) -> None: + model.model_dump( + include={'a': {1: True}}, + ) + model.model_dump( + include={'a': {'__all__': True}}, + ) + model.model_dump( + include={'a': {1: {'a'}}}, + ) + model.model_dump( + include={'a': {1, 2}}, + ) + + # Invalid cases, should fail but the `IncEx` alias uses `bool` due to mypy limitations: + model.model_dump( + include={'a': {1: False}}, + ) diff --git a/tests/typechecking/pipeline_api.py b/tests/typechecking/pipeline_api.py new file mode 100644 index 00000000000..bb4ccf80968 --- /dev/null +++ b/tests/typechecking/pipeline_api.py @@ -0,0 +1,20 @@ +import datetime +from typing import Annotated + +from pydantic.experimental.pipeline import validate_as + +# TODO: since Pyright 1.1.384, support for PEP 746 was disabled. +# `a1` and `a2` should have a `pyright: ignore[reportInvalidTypeArguments]` comment. +a1 = Annotated[str, validate_as(int)] +a2 = Annotated[str, validate_as(str).transform(lambda x: int(x))] +a3 = Annotated[float, validate_as(float).gt(0)] # should be able to compare float to int + +a4 = Annotated[datetime.datetime, validate_as(datetime.datetime).datetime_tz_naive()] +a5 = Annotated[datetime.datetime, validate_as(str).datetime_tz_naive()] # pyright: ignore[reportAttributeAccessIssue] +a6 = Annotated[ + datetime.datetime, + ( + validate_as(str).transform(str.strip).validate_as(datetime.datetime).datetime_tz_naive() + | validate_as(int).transform(datetime.datetime.fromtimestamp).datetime_tz_aware() + ), +] diff --git a/tests/typechecking/pyproject.toml b/tests/typechecking/pyproject.toml new file mode 100644 index 00000000000..7ad8af273cd --- /dev/null +++ b/tests/typechecking/pyproject.toml @@ -0,0 +1,13 @@ +[tool.pyright] +extraPaths = ['../..'] +pythonVersion = '3.10' +enableExperimentalFeatures = true +enableTypeIgnoreComments = false +reportUnnecessaryTypeIgnoreComment = true +reportDeprecated = true +reportUnusedExpression = false + +[tool.mypy] +python_version = '3.10' +disable_error_code = ['empty-body'] +warn_unused_ignores = true diff --git a/tests/pyright/root_model.py b/tests/typechecking/root_model.py similarity index 75% rename from tests/pyright/root_model.py rename to tests/typechecking/root_model.py index 7d6b0f1121a..1149472b58c 100644 --- a/tests/pyright/root_model.py +++ b/tests/typechecking/root_model.py @@ -5,7 +5,7 @@ IntRootModel = RootModel[int] int_root_model = IntRootModel(1) -bad_root_model = IntRootModel('1') # pyright: ignore[reportArgumentType] +bad_root_model = IntRootModel('1') # type: ignore[arg-type] # pyright: ignore[reportArgumentType] assert_type(int_root_model.root, int) diff --git a/tests/typechecking/type_adapter.py b/tests/typechecking/type_adapter.py new file mode 100644 index 00000000000..042b1c52e5c --- /dev/null +++ b/tests/typechecking/type_adapter.py @@ -0,0 +1,27 @@ +# to be removed with PEP 747: +# mypy: disable_error_code=var-annotated +from typing import Annotated + +from typing_extensions import assert_type + +from pydantic import TypeAdapter + +ta1 = TypeAdapter(int) +assert_type(ta1, TypeAdapter[int]) + +assert_type(ta1.validate_python('1'), int) +ta1.dump_python(1) +ta1.dump_python('1') # type: ignore[arg-type] # pyright: ignore[reportArgumentType] +ta1.dump_json(1) +ta1.dump_json('1') # type: ignore[arg-type] # pyright: ignore[reportArgumentType] + +# The following use cases require PEP 747: TypeExpr: + +ta2 = TypeAdapter(Annotated[int, ...]) +assert_type(ta2, TypeAdapter[int]) # type: ignore[assert-type] # pyright: ignore[reportAssertTypeFailure] + +ta3: TypeAdapter[int] = TypeAdapter(Annotated[int, ...]) +assert_type(ta3, TypeAdapter[int]) + +ta4 = TypeAdapter(int | str) +assert_type(ta4, TypeAdapter[int | str]) # type: ignore[assert-type] # pyright: ignore[reportAssertTypeFailure] diff --git a/tests/typechecking/validate_call.py b/tests/typechecking/validate_call.py new file mode 100644 index 00000000000..f5f2666dce3 --- /dev/null +++ b/tests/typechecking/validate_call.py @@ -0,0 +1,27 @@ +from typing_extensions import assert_type + +from pydantic import validate_call + + +@validate_call +def foo(a: int, *, c: str = 'x') -> str: + return c * 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