diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml
index 7cd9c3ec..e0059f85 100644
--- a/.github/workflows/pypi.yml
+++ b/.github/workflows/pypi.yml
@@ -5,22 +5,46 @@ on:
types: [published]
jobs:
- deploy:
+ build:
+ name: build
runs-on: ubuntu-latest
steps:
- - uses: actions/checkout@v4
- - name: Set up Python
- uses: actions/setup-python@v5
- with:
- python-version: '3.9'
- - name: Install dependencies
- run: |
- pip install --upgrade pip uv
- uv pip install build twine
- - name: Build and publish
- env:
- TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
- TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
- run: |
- python -m build
- twine upload dist/*
+ - name: Clone repo
+ uses: actions/checkout@v4.2.2
+
+ - name: Set up python
+ uses: actions/setup-python@v5.6.0
+ with:
+ python-version: '3.13'
+
+ - name: Install pip dependencies
+ run: pip install build
+
+ - name: List pip dependencies
+ run: pip list
+
+ - name: Build project
+ run: python3 -m build
+
+ - name: Upload artifacts
+ uses: actions/upload-artifact@v4.6.2
+ with:
+ name: pypi-dist
+ path: dist/
+
+ pypi:
+ name: pypi
+ needs:
+ - build
+ permissions:
+ id-token: write
+ runs-on: ubuntu-latest
+ steps:
+ - name: Download artifacts
+ uses: actions/download-artifact@v4.3.0
+ with:
+ name: pypi-dist
+ path: dist/
+
+ - name: Publish to PyPI
+ uses: pypa/gh-action-pypi-publish@v1.12.4
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index bf04ab0a..fbce9a45 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -51,7 +51,7 @@ jobs:
run: uv pip list
- name: Test with PyTest
- run: uv run pytest -v -rsx -n 2 --cov=segmentation_models_pytorch --cov-report=xml --cov-config=pyproject.toml -k "not logits_match"
+ run: uv run pytest -v -rsx -n 2 --cov=segmentation_models_pytorch --cov-report=xml --cov-config=pyproject.toml --non-marked-only
- name: Upload coverage reports to Codecov
uses: codecov/codecov-action@v5
@@ -73,7 +73,52 @@ jobs:
- name: Show installed packages
run: uv pip list
- name: Test with PyTest
- run: RUN_SLOW=1 uv run pytest -v -rsx -n 2 -k "logits_match"
+ run: RUN_SLOW=1 uv run pytest -v -rsx -n 2 -m "logits_match"
+
+ test_torch_compile:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+ - name: Set up Python
+ uses: astral-sh/setup-uv@v5
+ with:
+ python-version: "3.10"
+ - name: Install dependencies
+ run: uv pip install -r requirements/required.txt -r requirements/test.txt
+ - name: Show installed packages
+ run: uv pip list
+ - name: Test with PyTest
+ run: uv run pytest -v -rsx -n 2 -m "compile"
+
+ test_torch_export:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+ - name: Set up Python
+ uses: astral-sh/setup-uv@v5
+ with:
+ python-version: "3.10"
+ - name: Install dependencies
+ run: uv pip install -r requirements/required.txt -r requirements/test.txt
+ - name: Show installed packages
+ run: uv pip list
+ - name: Test with PyTest
+ run: uv run pytest -v -rsx -n 2 -m "torch_export"
+
+ test_torch_script:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+ - name: Set up Python
+ uses: astral-sh/setup-uv@v5
+ with:
+ python-version: "3.10"
+ - name: Install dependencies
+ run: uv pip install -r requirements/required.txt -r requirements/test.txt
+ - name: Show installed packages
+ run: uv pip list
+ - name: Test with PyTest
+ run: uv run pytest -v -rsx -n 2 -m "torch_script"
minimum:
runs-on: ubuntu-latest
@@ -88,4 +133,4 @@ jobs:
- name: Show installed packages
run: uv pip list
- name: Test with pytest
- run: uv run pytest -v -rsx -n 2 -k "not logits_match"
+ run: uv run pytest -v -rsx -n 2 --non-marked-only
diff --git a/Makefile b/Makefile
index a58d230f..9cbf9bdc 100644
--- a/Makefile
+++ b/Makefile
@@ -1,26 +1,39 @@
-.PHONY: test
+.PHONY: test # Declare the 'test' target as phony to avoid conflicts with files named 'test'
+# Variables to store the paths of the python, pip, pytest, and ruff executables
+PYTHON := $(shell which python)
+PIP := $(shell which pip)
+PYTEST := $(shell which pytest)
+RUFF := $(shell which ruff)
+
+# Target to create a Python virtual environment
.venv:
- python3 -m venv .venv
+ $(PYTHON) -m venv $(shell dirname $(PYTHON))
+# Target to install development dependencies in the virtual environment
install_dev: .venv
- .venv/bin/pip install -e ".[test]"
+ $(PIP) install -e ".[test]"
+# Target to run tests with pytest, using 2 parallel processes and only non-marked tests
test: .venv
- .venv/bin/pytest -v -rsx -n 2 tests/ -k "not logits_match"
+ $(PYTEST) -v -rsx -n 2 tests/ --non-marked-only
+# Target to run all tests with pytest, including slow tests, using 2 parallel processes
test_all: .venv
- RUN_SLOW=1 .venv/bin/pytest -v -rsx -n 2 tests/
+ RUN_SLOW=1 $(PYTEST) -v -rsx -n 2 tests/
+# Target to generate a table by running a Python script
table:
- .venv/bin/python misc/generate_table.py
+ $(PYTHON) misc/generate_table.py
+# Target to generate a table for timm by running a Python script
table_timm:
- .venv/bin/python misc/generate_table_timm.py
+ $(PYTHON) misc/generate_table_timm.py
+# Target to fix and format code using ruff
fixup:
- .venv/bin/ruff check --fix
- .venv/bin/ruff format
+ $(RUFF) check --fix
+ $(RUFF) format
+# Target to run code formatting and tests
all: fixup test
-
diff --git a/README.md b/README.md
index b3a0b3ff..df00ff8c 100644
--- a/README.md
+++ b/README.md
@@ -1,28 +1,51 @@
-**Python library with Neural Networks for Image
+**Python library with Neural Networks for Image Semantic
Segmentation based on [PyTorch](https://pytorch.org/).**
-[](https://github.com/qubvel/segmentation_models.pytorch/blob/main/LICENSE)
+
[](https://github.com/qubvel/segmentation_models.pytorch/actions/workflows/tests.yml)
+
[](https://smp.readthedocs.io/en/latest/)
-[](https://pypi.org/project/segmentation-models-pytorch/)
-[](https://pepy.tech/project/segmentation-models-pytorch)
-
-[](https://pepy.tech/project/segmentation-models-pytorch)
+[](https://pypi.org/project/segmentation-models-pytorch/)
+[](https://pepy.tech/project/segmentation-models-pytorch)
[](https://pepy.tech/project/segmentation-models-pytorch)
+
+[](https://github.com/qubvel/segmentation_models.pytorch/blob/main/LICENSE)
+[](https://pepy.tech/project/segmentation-models-pytorch)
-The main features of this library are:
-
- - High-level API (just two lines to create a neural network)
- - 11 models architectures for binary and multi class segmentation (including legendary Unet)
- - 124 available encoders (and 500+ encoders from [timm](https://github.com/rwightman/pytorch-image-models))
- - All encoders have pre-trained weights for faster and better convergence
- - Popular metrics and losses for training routines
+The main features of the library are:
+
+ - Super simple high-level API (just two lines to create a neural network)
+ - 12 encoder-decoder model architectures (Unet, Unet++, Segformer, DPT, ...)
+ - 800+ **pretrained** convolution- and transform-based encoders, including [timm](https://github.com/huggingface/pytorch-image-models) support
+ - Popular metrics and losses for training routines (Dice, Jaccard, Tversky, ...)
+ - ONNX export and torch script/trace/compile friendly
+
+### Community-Driven Project, Supported By
+
### [π Project Documentation π](http://smp.readthedocs.io/)
@@ -31,21 +54,18 @@ Visit [Read The Docs Project Page](https://smp.readthedocs.io/) or read the foll
### π Table of content
1. [Quick start](#start)
2. [Examples](#examples)
- 3. [Models](#models)
- 1. [Architectures](#architectures)
- 2. [Encoders](#encoders)
- 3. [Timm Encoders](#timm)
+ 3. [Models and encoders](#models-and-encoders)
4. [Models API](#api)
1. [Input channels](#input-channels)
2. [Auxiliary classification output](#auxiliary-classification-output)
3. [Depth](#depth)
5. [Installation](#installation)
- 6. [Competitions won with the library](#competitions-won-with-the-library)
+ 6. [Competitions won with the library](#competitions)
7. [Contributing](#contributing)
8. [Citing](#citing)
9. [License](#license)
-### β³ Quick start
+## β³ Quick start
#### 1. Create your first Segmentation model with SMP
@@ -76,361 +96,71 @@ preprocess_input = get_preprocessing_fn('resnet18', pretrained='imagenet')
Congratulations! You are done! Now you can train your model with your favorite framework!
-### π‘ Examples
- - Training model for pets binary segmentation with Pytorch-Lightning [notebook](https://github.com/qubvel/segmentation_models.pytorch/blob/main/examples/binary_segmentation_intro.ipynb) and [](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/binary_segmentation_intro.ipynb)
- - Training model for cars segmentation on CamVid dataset [here](https://github.com/qubvel/segmentation_models.pytorch/blob/main/examples/cars%20segmentation%20(camvid).ipynb).
- - Training SMP model with [Catalyst](https://github.com/catalyst-team/catalyst) (high-level framework for PyTorch), [TTAch](https://github.com/qubvel/ttach) (TTA library for PyTorch) and [Albumentations](https://github.com/albu/albumentations) (fast image augmentation library) - [here](https://github.com/catalyst-team/catalyst/blob/v21.02rc0/examples/notebooks/segmentation-tutorial.ipynb) [](https://colab.research.google.com/github/catalyst-team/catalyst/blob/v21.02rc0/examples/notebooks/segmentation-tutorial.ipynb)
- - Training SMP model with [Pytorch-Lightning](https://pytorch-lightning.readthedocs.io) framework - [here](https://github.com/ternaus/cloths_segmentation) (clothes binary segmentation by [@ternaus](https://github.com/ternaus)).
- - Export trained model to ONNX - [notebook](https://github.com/qubvel/segmentation_models.pytorch/blob/main/examples/convert_to_onnx.ipynb) [](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/convert_to_onnx.ipynb)
-
-### π¦ Models
-
-#### Architectures
- - Unet [[paper](https://arxiv.org/abs/1505.04597)] [[docs](https://smp.readthedocs.io/en/latest/models.html#unet)]
- - Unet++ [[paper](https://arxiv.org/pdf/1807.10165.pdf)] [[docs](https://smp.readthedocs.io/en/latest/models.html#id2)]
- - MAnet [[paper](https://ieeexplore.ieee.org/abstract/document/9201310)] [[docs](https://smp.readthedocs.io/en/latest/models.html#manet)]
- - Linknet [[paper](https://arxiv.org/abs/1707.03718)] [[docs](https://smp.readthedocs.io/en/latest/models.html#linknet)]
- - FPN [[paper](http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf)] [[docs](https://smp.readthedocs.io/en/latest/models.html#fpn)]
- - PSPNet [[paper](https://arxiv.org/abs/1612.01105)] [[docs](https://smp.readthedocs.io/en/latest/models.html#pspnet)]
- - PAN [[paper](https://arxiv.org/abs/1805.10180)] [[docs](https://smp.readthedocs.io/en/latest/models.html#pan)]
- - DeepLabV3 [[paper](https://arxiv.org/abs/1706.05587)] [[docs](https://smp.readthedocs.io/en/latest/models.html#deeplabv3)]
- - DeepLabV3+ [[paper](https://arxiv.org/abs/1802.02611)] [[docs](https://smp.readthedocs.io/en/latest/models.html#id9)]
- - UPerNet [[paper](https://arxiv.org/abs/1807.10221)] [[docs](https://smp.readthedocs.io/en/latest/models.html#upernet)]
- - Segformer [[paper](https://arxiv.org/abs/2105.15203)] [[docs](https://smp.readthedocs.io/en/latest/models.html#segformer)]
-
-#### Encoders
-
-The following is a list of supported encoders in the SMP. Select the appropriate family of encoders and click to expand the table and select a specific encoder and its pre-trained weights (`encoder_name` and `encoder_weights` parameters).
-
-
-ResNet
-
-
+The library provides a wide range of **pretrained** encoders (also known as backbones) for segmentation models. Instead of using features from the final layer of a classification model, we extract **intermediate features** and feed them into the decoder for segmentation tasks.
-
-Inception
-
+All encoders come with **pretrained weights**, which help achieve **faster and more stable convergence** when training segmentation models.
-|Encoder |Weights |Params, M |
-|--------------------------------|:------------------------------:|:------------------------------:|
-|inceptionresnetv2 |imagenet / imagenet+background |54M |
-|inceptionv4 |imagenet / imagenet+background |41M |
-|xception |imagenet |22M |
+Given the extensive selection of supported encoders, you can choose the best one for your specific use case, for example:
+- **Lightweight encoders** for low-latency applications or real-time inference on edge devices (mobilenet/mobileone).
+- **High-capacity architectures** for complex tasks involving a large number of segmented classes, providing superior accuracy (convnext/swin/mit).
-
-
-|Encoder |Weights |Params, M |
-|--------------------------------|:------------------------------:|:------------------------------:|
-|mobilenet_v2 |imagenet |2M |
-|timm-mobilenetv3_large_075 |imagenet |1.78M |
-|timm-mobilenetv3_large_100 |imagenet |2.97M |
-|timm-mobilenetv3_large_minimal_100|imagenet |1.41M |
-|timm-mobilenetv3_small_075 |imagenet |0.57M |
-|timm-mobilenetv3_small_100 |imagenet |0.93M |
-|timm-mobilenetv3_small_minimal_100|imagenet |0.43M |
+All encoders and corresponding pretrained weight are listed in the documentation:
+ - [table](https://smp.readthedocs.io/en/latest/encoders.html) with natively ported encoders
+ - [table](https://smp.readthedocs.io/en/latest/encoders_timm.html) with [timm](https://github.com/huggingface/pytorch-image-models) encoders supported
-
-
+## π Models API
-
-DPN
-
+### Input channels
-|Encoder |Weights |Params, M |
-|--------------------------------|:------------------------------:|:------------------------------:|
-|dpn68 |imagenet |11M |
-|dpn68b |imagenet+5k |11M |
-|dpn92 |imagenet+5k |34M |
-|dpn98 |imagenet |58M |
-|dpn107 |imagenet+5k |84M |
-|dpn131 |imagenet |76M |
+The input channels parameter allows you to create a model that can process a tensor with an arbitrary number of channels.
+If you use pretrained weights from ImageNet, the weights of the first convolution will be reused:
+ - For the 1-channel case, it would be a sum of the weights of the first convolution layer.
+ - Otherwise, channels would be populated with weights like `new_weight[:, i] = pretrained_weight[:, i % 3]`, and then scaled with `new_weight * 3 / new_in_channels`.
-
-
-Backbone from SegFormer pretrained on Imagenet! Can be used with other decoders from package, you can combine Mix Vision Transformer with Unet, FPN and others!
-
-Limitations:
-
- - encoder is **not** supported by Linknet, Unet++
- - encoder is supported by FPN only for encoder **depth = 5**
-
-|Encoder |Weights |Params, M |
-|--------------------------------|:------------------------------:|:------------------------------:|
-|mit_b0 |imagenet |3M |
-|mit_b1 |imagenet |13M |
-|mit_b2 |imagenet |24M |
-|mit_b3 |imagenet |44M |
-|mit_b4 |imagenet |60M |
-|mit_b5 |imagenet |81M |
-
-
-
-
-
-MobileOne
-
-
-Apple's "sub-one-ms" Backbone pretrained on Imagenet! Can be used with all decoders.
-
-Note: In the official github repo the s0 variant has additional num_conv_branches, leading to more params than s1.
-
-|Encoder |Weights |Params, M |
-|--------------------------------|:------------------------------:|:------------------------------:|
-|mobileone_s0 |imagenet |4.6M |
-|mobileone_s1 |imagenet |4.0M |
-|mobileone_s2 |imagenet |6.5M |
-|mobileone_s3 |imagenet |8.8M |
-|mobileone_s4 |imagenet |13.6M |
-
-
-
-
-
-\* `ssl`, `swsl` - semi-supervised and weakly-supervised learning on ImageNet ([repo](https://github.com/facebookresearch/semi-supervised-ImageNet1K-models)).
-
-#### Timm Encoders
-
-[docs](https://smp.readthedocs.io/en/latest/encoders_timm.html)
-
-Pytorch Image Models (a.k.a. timm) has a lot of pretrained models and interface which allows using these models as encoders in smp, however, not all models are supported
-
- - not all transformer models have ``features_only`` functionality implemented that is required for encoder
- - some models have inappropriate strides
-
-Total number of supported encoders: 549
- - [table with available encoders](https://smp.readthedocs.io/en/latest/encoders_timm.html)
-
-### π Models API
-
- - `model.encoder` - pretrained backbone to extract features of different spatial resolution
- - `model.decoder` - depends on models architecture (`Unet`/`Linknet`/`PSPNet`/`FPN`)
- - `model.segmentation_head` - last block to produce required number of mask channels (include also optional upsampling and activation)
- - `model.classification_head` - optional block which create classification head on top of encoder
- - `model.forward(x)` - sequentially pass `x` through model\`s encoder, decoder and segmentation head (and classification head if specified)
-
-##### Input channels
-Input channels parameter allows you to create models, which process tensors with arbitrary number of channels.
-If you use pretrained weights from imagenet - weights of first convolution will be reused. For
-1-channel case it would be a sum of weights of first convolution layer, otherwise channels would be
-populated with weights like `new_weight[:, i] = pretrained_weight[:, i % 3]` and than scaled with `new_weight * 3 / new_in_channels`.
```python
model = smp.FPN('resnet34', in_channels=1)
mask = model(torch.ones([1, 1, 64, 64]))
```
-##### Auxiliary classification output
+### Auxiliary classification output
+
All models support `aux_params` parameters, which is default set to `None`.
If `aux_params = None` then classification auxiliary output is not created, else
model produce not only `mask`, but also `label` output with shape `NC`.
@@ -447,50 +177,54 @@ model = smp.Unet('resnet34', classes=4, aux_params=aux_params)
mask, label = model(x)
```
-##### Depth
+### Depth
+
Depth parameter specify a number of downsampling operations in encoder, so you can make
your model lighter if specify smaller `depth`.
```python
model = smp.Unet('resnet34', encoder_depth=4)
```
-
-### π Installation
+## π Installation
PyPI version:
+
```bash
$ pip install segmentation-models-pytorch
````
-Latest version from source:
+
+The latest version from GitHub:
+
```bash
$ pip install git+https://github.com/qubvel/segmentation_models.pytorch
````
-### π Competitions won with the library
+## π Competitions won with the library
-`Segmentation Models` package is widely used in the image segmentation competitions.
+`Segmentation Models` package is widely used in image segmentation competitions.
[Here](https://github.com/qubvel/segmentation_models.pytorch/blob/main/HALLOFFAME.md) you can find competitions, names of the winners and links to their solutions.
-### π€ Contributing
+## π€ Contributing
-#### Install SMP
+1. Install SMP in dev mode
```bash
-make install_dev # create .venv, install SMP in dev mode
+make install_dev # Create .venv, install SMP in dev mode
```
-#### Run tests and code checks
+2. Run tests and code checks
```bash
+make test # Run tests suite with pytest
make fixup # Ruff for formatting and lint checks
```
-#### Update table with encoders
+3. Update a table (in case you added an encoder)
```bash
-make table # generate a table with encoders and print to stdout
+make table # Generates a table with encoders and print to stdout
```
-### π Citing
+## π Citing
```
@misc{Iakubovskii:2019,
Author = {Pavel Iakubovskii},
@@ -502,5 +236,5 @@ make table # generate a table with encoders and print to stdout
}
```
-### π‘οΈ License
+## π‘οΈ License
The project is primarily distributed under [MIT License](https://github.com/qubvel/segmentation_models.pytorch/blob/main/LICENSE), while some files are subject to other licenses. Please refer to [LICENSES](licenses/LICENSES.md) and license statements in each file for careful check, especially for commercial use.
diff --git a/docs/conf.py b/docs/conf.py
index c7dde9e5..4cc70a6b 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -100,9 +100,7 @@ def get_version():
"timm",
"cv2",
"PIL",
- "pretrainedmodels",
"torchvision",
- "efficientnet-pytorch",
"segmentation_models_pytorch.encoders",
"segmentation_models_pytorch.utils",
# 'segmentation_models_pytorch.base',
diff --git a/docs/encoders.rst b/docs/encoders.rst
index 652745b7..2de35dec 100644
--- a/docs/encoders.rst
+++ b/docs/encoders.rst
@@ -1,363 +1,141 @@
π Available Encoders
=====================
-ResNet
-~~~~~~
-
-+-------------+-------------------------+-------------+
-| Encoder | Weights | Params, M |
-+=============+=========================+=============+
-| resnet18 | imagenet / ssl / swsl | 11M |
-+-------------+-------------------------+-------------+
-| resnet34 | imagenet | 21M |
-+-------------+-------------------------+-------------+
-| resnet50 | imagenet / ssl / swsl | 23M |
-+-------------+-------------------------+-------------+
-| resnet101 | imagenet | 42M |
-+-------------+-------------------------+-------------+
-| resnet152 | imagenet | 58M |
-+-------------+-------------------------+-------------+
-
-ResNeXt
-~~~~~~~
-
-+----------------------+-------------------------------------+-------------+
-| Encoder | Weights | Params, M |
-+======================+=====================================+=============+
-| resnext50\_32x4d | imagenet / ssl / swsl | 22M |
-+----------------------+-------------------------------------+-------------+
-| resnext101\_32x4d | ssl / swsl | 42M |
-+----------------------+-------------------------------------+-------------+
-| resnext101\_32x8d | imagenet / instagram / ssl / swsl | 86M |
-+----------------------+-------------------------------------+-------------+
-| resnext101\_32x16d | instagram / ssl / swsl | 191M |
-+----------------------+-------------------------------------+-------------+
-| resnext101\_32x32d | instagram | 466M |
-+----------------------+-------------------------------------+-------------+
-| resnext101\_32x48d | instagram | 826M |
-+----------------------+-------------------------------------+-------------+
-
-ResNeSt
-~~~~~~~
-
-+----------------------------+------------+-------------+
-| Encoder | Weights | Params, M |
-+============================+============+=============+
-| timm-resnest14d | imagenet | 8M |
-+----------------------------+------------+-------------+
-| timm-resnest26d | imagenet | 15M |
-+----------------------------+------------+-------------+
-| timm-resnest50d | imagenet | 25M |
-+----------------------------+------------+-------------+
-| timm-resnest101e | imagenet | 46M |
-+----------------------------+------------+-------------+
-| timm-resnest200e | imagenet | 68M |
-+----------------------------+------------+-------------+
-| timm-resnest269e | imagenet | 108M |
-+----------------------------+------------+-------------+
-| timm-resnest50d\_4s2x40d | imagenet | 28M |
-+----------------------------+------------+-------------+
-| timm-resnest50d\_1s4x24d | imagenet | 23M |
-+----------------------------+------------+-------------+
-
-Res2Ne(X)t
-~~~~~~~~~~
-
-+----------------------------+------------+-------------+
-| Encoder | Weights | Params, M |
-+============================+============+=============+
-| timm-res2net50\_26w\_4s | imagenet | 23M |
-+----------------------------+------------+-------------+
-| timm-res2net101\_26w\_4s | imagenet | 43M |
-+----------------------------+------------+-------------+
-| timm-res2net50\_26w\_6s | imagenet | 35M |
-+----------------------------+------------+-------------+
-| timm-res2net50\_26w\_8s | imagenet | 46M |
-+----------------------------+------------+-------------+
-| timm-res2net50\_48w\_2s | imagenet | 23M |
-+----------------------------+------------+-------------+
-| timm-res2net50\_14w\_8s | imagenet | 23M |
-+----------------------------+------------+-------------+
-| timm-res2next50 | imagenet | 22M |
-+----------------------------+------------+-------------+
-
-RegNet(x/y)
-~~~~~~~~~~
-
-+---------------------+------------+-------------+
-| Encoder | Weights | Params, M |
-+=====================+============+=============+
-| timm-regnetx\_002 | imagenet | 2M |
-+---------------------+------------+-------------+
-| timm-regnetx\_004 | imagenet | 4M |
-+---------------------+------------+-------------+
-| timm-regnetx\_006 | imagenet | 5M |
-+---------------------+------------+-------------+
-| timm-regnetx\_008 | imagenet | 6M |
-+---------------------+------------+-------------+
-| timm-regnetx\_016 | imagenet | 8M |
-+---------------------+------------+-------------+
-| timm-regnetx\_032 | imagenet | 14M |
-+---------------------+------------+-------------+
-| timm-regnetx\_040 | imagenet | 20M |
-+---------------------+------------+-------------+
-| timm-regnetx\_064 | imagenet | 24M |
-+---------------------+------------+-------------+
-| timm-regnetx\_080 | imagenet | 37M |
-+---------------------+------------+-------------+
-| timm-regnetx\_120 | imagenet | 43M |
-+---------------------+------------+-------------+
-| timm-regnetx\_160 | imagenet | 52M |
-+---------------------+------------+-------------+
-| timm-regnetx\_320 | imagenet | 105M |
-+---------------------+------------+-------------+
-| timm-regnety\_002 | imagenet | 2M |
-+---------------------+------------+-------------+
-| timm-regnety\_004 | imagenet | 3M |
-+---------------------+------------+-------------+
-| timm-regnety\_006 | imagenet | 5M |
-+---------------------+------------+-------------+
-| timm-regnety\_008 | imagenet | 5M |
-+---------------------+------------+-------------+
-| timm-regnety\_016 | imagenet | 10M |
-+---------------------+------------+-------------+
-| timm-regnety\_032 | imagenet | 17M |
-+---------------------+------------+-------------+
-| timm-regnety\_040 | imagenet | 19M |
-+---------------------+------------+-------------+
-| timm-regnety\_064 | imagenet | 29M |
-+---------------------+------------+-------------+
-| timm-regnety\_080 | imagenet | 37M |
-+---------------------+------------+-------------+
-| timm-regnety\_120 | imagenet | 49M |
-+---------------------+------------+-------------+
-| timm-regnety\_160 | imagenet | 80M |
-+---------------------+------------+-------------+
-| timm-regnety\_320 | imagenet | 141M |
-+---------------------+------------+-------------+
-
-GERNet
-~~~~~~
-
-+-------------------------+------------+-------------+
-| Encoder | Weights | Params, M |
-+=========================+============+=============+
-| timm-gernet\_s | imagenet | 6M |
-+-------------------------+------------+-------------+
-| timm-gernet\_m | imagenet | 18M |
-+-------------------------+------------+-------------+
-| timm-gernet\_l | imagenet | 28M |
-+-------------------------+------------+-------------+
-
-SE-Net
-~~~~~~
-
-+-------------------------+------------+-------------+
-| Encoder | Weights | Params, M |
-+=========================+============+=============+
-| senet154 | imagenet | 113M |
-+-------------------------+------------+-------------+
-| se\_resnet50 | imagenet | 26M |
-+-------------------------+------------+-------------+
-| se\_resnet101 | imagenet | 47M |
-+-------------------------+------------+-------------+
-| se\_resnet152 | imagenet | 64M |
-+-------------------------+------------+-------------+
-| se\_resnext50\_32x4d | imagenet | 25M |
-+-------------------------+------------+-------------+
-| se\_resnext101\_32x4d | imagenet | 46M |
-+-------------------------+------------+-------------+
-
-SK-ResNe(X)t
-~~~~~~~~~~~~
-
-+---------------------------+------------+-------------+
-| Encoder | Weights | Params, M |
-+===========================+============+=============+
-| timm-skresnet18 | imagenet | 11M |
-+---------------------------+------------+-------------+
-| timm-skresnet34 | imagenet | 21M |
-+---------------------------+------------+-------------+
-| timm-skresnext50\_32x4d | imagenet | 25M |
-+---------------------------+------------+-------------+
-
-DenseNet
-~~~~~~~~
-
-+---------------+------------+-------------+
-| Encoder | Weights | Params, M |
-+===============+============+=============+
-| densenet121 | imagenet | 6M |
-+---------------+------------+-------------+
-| densenet169 | imagenet | 12M |
-+---------------+------------+-------------+
-| densenet201 | imagenet | 18M |
-+---------------+------------+-------------+
-| densenet161 | imagenet | 26M |
-+---------------+------------+-------------+
-
-Inception
-~~~~~~~~~
-
-+---------------------+----------------------------------+-------------+
-| Encoder | Weights | Params, M |
-+=====================+==================================+=============+
-| inceptionresnetv2 | imagenet / imagenet+background | 54M |
-+---------------------+----------------------------------+-------------+
-| inceptionv4 | imagenet / imagenet+background | 41M |
-+---------------------+----------------------------------+-------------+
-| xception | imagenet | 22M |
-+---------------------+----------------------------------+-------------+
-
-EfficientNet
-~~~~~~~~~~~~
-
-+------------------------+--------------------------------------+-------------+
-| Encoder | Weights | Params, M |
-+========================+======================================+=============+
-| efficientnet-b0 | imagenet | 4M |
-+------------------------+--------------------------------------+-------------+
-| efficientnet-b1 | imagenet | 6M |
-+------------------------+--------------------------------------+-------------+
-| efficientnet-b2 | imagenet | 7M |
-+------------------------+--------------------------------------+-------------+
-| efficientnet-b3 | imagenet | 10M |
-+------------------------+--------------------------------------+-------------+
-| efficientnet-b4 | imagenet | 17M |
-+------------------------+--------------------------------------+-------------+
-| efficientnet-b5 | imagenet | 28M |
-+------------------------+--------------------------------------+-------------+
-| efficientnet-b6 | imagenet | 40M |
-+------------------------+--------------------------------------+-------------+
-| efficientnet-b7 | imagenet | 63M |
-+------------------------+--------------------------------------+-------------+
-| timm-efficientnet-b0 | imagenet / advprop / noisy-student | 4M |
-+------------------------+--------------------------------------+-------------+
-| timm-efficientnet-b1 | imagenet / advprop / noisy-student | 6M |
-+------------------------+--------------------------------------+-------------+
-| timm-efficientnet-b2 | imagenet / advprop / noisy-student | 7M |
-+------------------------+--------------------------------------+-------------+
-| timm-efficientnet-b3 | imagenet / advprop / noisy-student | 10M |
-+------------------------+--------------------------------------+-------------+
-| timm-efficientnet-b4 | imagenet / advprop / noisy-student | 17M |
-+------------------------+--------------------------------------+-------------+
-| timm-efficientnet-b5 | imagenet / advprop / noisy-student | 28M |
-+------------------------+--------------------------------------+-------------+
-| timm-efficientnet-b6 | imagenet / advprop / noisy-student | 40M |
-+------------------------+--------------------------------------+-------------+
-| timm-efficientnet-b7 | imagenet / advprop / noisy-student | 63M |
-+------------------------+--------------------------------------+-------------+
-| timm-efficientnet-b8 | imagenet / advprop | 84M |
-+------------------------+--------------------------------------+-------------+
-| timm-efficientnet-l2 | noisy-student / noisy-student-475 | 474M |
-+------------------------+--------------------------------------+-------------+
-| timm-efficientnet-lite0| imagenet | 4M |
-+------------------------+--------------------------------------+-------------+
-| timm-efficientnet-lite1| imagenet | 4M |
-+------------------------+--------------------------------------+-------------+
-| timm-efficientnet-lite2| imagenet | 6M |
-+------------------------+--------------------------------------+-------------+
-| timm-efficientnet-lite3| imagenet | 8M |
-+------------------------+--------------------------------------+-------------+
-| timm-efficientnet-lite4| imagenet | 13M |
-+------------------------+--------------------------------------+-------------+
-
-MobileNet
-~~~~~~~~~
-
-+---------------------------------------+------------+-------------+
-| Encoder | Weights | Params, M |
-+=======================================+============+=============+
-| mobilenet\_v2 | imagenet | 2M |
-+---------------------------------------+------------+-------------+
-| timm-mobilenetv3\_large\_075 | imagenet | 1.78M |
-+---------------------------------------+------------+-------------+
-| timm-mobilenetv3\_large\_100 | imagenet | 2.97M |
-+---------------------------------------+------------+-------------+
-| timm-mobilenetv3\_large\_minimal\_100 | imagenet | 1.41M |
-+---------------------------------------+------------+-------------+
-| timm-mobilenetv3\_small\_075 | imagenet | 0.57M |
-+---------------------------------------+------------+-------------+
-| timm-mobilenetv3\_small\_100 | imagenet | 0.93M |
-+---------------------------------------+------------+-------------+
-| timm-mobilenetv3\_small\_minimal\_100 | imagenet | 0.43M |
-+---------------------------------------+------------+-------------+
-
-DPN
-~~~
-
-+-----------+---------------+-------------+
-| Encoder | Weights | Params, M |
-+===========+===============+=============+
-| dpn68 | imagenet | 11M |
-+-----------+---------------+-------------+
-| dpn68b | imagenet+5k | 11M |
-+-----------+---------------+-------------+
-| dpn92 | imagenet+5k | 34M |
-+-----------+---------------+-------------+
-| dpn98 | imagenet | 58M |
-+-----------+---------------+-------------+
-| dpn107 | imagenet+5k | 84M |
-+-----------+---------------+-------------+
-| dpn131 | imagenet | 76M |
-+-----------+---------------+-------------+
-
-VGG
-~~~
-
-+-------------+------------+-------------+
-| Encoder | Weights | Params, M |
-+=============+============+=============+
-| vgg11 | imagenet | 9M |
-+-------------+------------+-------------+
-| vgg11\_bn | imagenet | 9M |
-+-------------+------------+-------------+
-| vgg13 | imagenet | 9M |
-+-------------+------------+-------------+
-| vgg13\_bn | imagenet | 9M |
-+-------------+------------+-------------+
-| vgg16 | imagenet | 14M |
-+-------------+------------+-------------+
-| vgg16\_bn | imagenet | 14M |
-+-------------+------------+-------------+
-| vgg19 | imagenet | 20M |
-+-------------+------------+-------------+
-| vgg19\_bn | imagenet | 20M |
-+-------------+------------+-------------+
-
-
-Mix Visual Transformer
-~~~~~~~~~~~~~~~~~~~~~
-
-+-----------+----------+------------+
-| Encoder | Weights | Params, M |
-+===========+==========+============+
-| mit\_b0 | imagenet | 3M |
-+-----------+----------+------------+
-| mit\_b1 | imagenet | 13M |
-+-----------+----------+------------+
-| mit\_b2 | imagenet | 24M |
-+-----------+----------+------------+
-| mit\_b3 | imagenet | 44M |
-+-----------+----------+------------+
-| mit\_b4 | imagenet | 60M |
-+-----------+----------+------------+
-| mit\_b5 | imagenet | 81M |
-+-----------+----------+------------+
-
-MobileOne
-~~~~~~~~~~~~~~~~~~~~~
-
-+-----------------+----------+------------+
-| Encoder | Weights | Params, M |
-+=================+==========+============+
-| mobileone\_s0 | imagenet | 4.6M |
-+-----------------+----------+------------+
-| mobileone\_s1 | imagenet | 4.0M |
-+-----------------+----------+------------+
-| mobileone\_s2 | imagenet | 6.5M |
-+-----------------+----------+------------+
-| mobileone\_s3 | imagenet | 8.8M |
-+-----------------+----------+------------+
-| mobileone\_s4 | imagenet | 13.6M |
-+-----------------+----------+------------+
+**Segmentation Models PyTorch** provides support for a wide range of encoders.
+This flexibility allows you to use these encoders with any model in the library by
+specifying the encoder name in the ``encoder_name`` parameter during model initialization.
+
+Hereβs a quick example of using a ResNet34 encoder with the ``Unet`` model:
+
+.. code-block:: python
+
+ from segmentation_models_pytorch import Unet
+
+ # Initialize Unet with ResNet34 encoder pre-trained on ImageNet
+ model = Unet(encoder_name="resnet34", encoder_weights="imagenet")
+
+
+The following encoder families are supported by the library, enabling you to choose the one that best fits your use case:
+
+- **Mix Vision Transformer (mit)**
+- **MobileOne**
+- **MobileNet**
+- **EfficientNet**
+- **ResNet**
+- **ResNeXt**
+- **SENet**
+- **DPN**
+- **VGG**
+- **DenseNet**
+- **Xception**
+- **Inception**
+
+Choosing the Right Encoder
+--------------------------
+
+1. **Small Models for Edge Devices**
+ Consider encoders like **MobileNet** or **MobileOne**, which have a smaller parameter count and are optimized for lightweight deployment.
+
+2. **High Performance**
+ If you require state-of-the-art accuracy **Mix Vision Transformer (mit)**, **EfficientNet** families offer excellent balance between performance and computational efficiency.
+
+For each encoder, the table below provides detailed information:
+
+1. **Pretrained Weights**
+ Specifies the available pretrained weights (e.g., ``imagenet``, ``imagenet21k``).
+
+2. **Params, M**:
+ The total number of parameters in the encoder, measured in millions. This metric helps you assess the model's size and computational requirements.
+
+3. **Script**:
+ Indicates whether the encoder can be scripted with ``torch.jit.script``.
+
+4. **Compile**:
+ Indicates whether the encoder is compatible with ``torch.compile(model, fullgraph=True, dynamic=True, backend="eager")``.
+ You may still get some issues with another backends, such as ``inductor``, depending on the torch/cuda/... dependencies version,
+ but most of the time it will work.
+
+5. **Export**:
+ Indicates whether the encoder can be exported using ``torch.export.export``, making it suitable for deployment in different environments (e.g., ONNX).
+
+
+============================ ==================================== =========== ======== ========= ========
+Encoder Pretrained weights Params, M Script Compile Export
+============================ ==================================== =========== ======== ========= ========
+resnet18 imagenet / ssl / swsl 11M β β β
+resnet34 imagenet 21M β β β
+resnet50 imagenet / ssl / swsl 23M β β β
+resnet101 imagenet 42M β β β
+resnet152 imagenet 58M β β β
+resnext50_32x4d imagenet / ssl / swsl 22M β β β
+resnext101_32x4d ssl / swsl 42M β β β
+resnext101_32x8d imagenet / instagram / ssl / swsl 86M β β β
+resnext101_32x16d instagram / ssl / swsl 191M β β β
+resnext101_32x32d instagram 466M β β β
+resnext101_32x48d instagram 826M β β β
+dpn68 imagenet 11M β β β
+dpn68b imagenet+5k 11M β β β
+dpn92 imagenet+5k 34M β β β
+dpn98 imagenet 58M β β β
+dpn107 imagenet+5k 84M β β β
+dpn131 imagenet 76M β β β
+vgg11 imagenet 9M β β β
+vgg11_bn imagenet 9M β β β
+vgg13 imagenet 9M β β β
+vgg13_bn imagenet 9M β β β
+vgg16 imagenet 14M β β β
+vgg16_bn imagenet 14M β β β
+vgg19 imagenet 20M β β β
+vgg19_bn imagenet 20M β β β
+senet154 imagenet 113M β β β
+se_resnet50 imagenet 26M β β β
+se_resnet101 imagenet 47M β β β
+se_resnet152 imagenet 64M β β β
+se_resnext50_32x4d imagenet 25M β β β
+se_resnext101_32x4d imagenet 46M β β β
+densenet121 imagenet 6M β β β
+densenet169 imagenet 12M β β β
+densenet201 imagenet 18M β β β
+densenet161 imagenet 26M β β β
+inceptionresnetv2 imagenet / imagenet+background 54M β β β
+inceptionv4 imagenet / imagenet+background 41M β β β
+efficientnet-b0 imagenet / advprop 4M β β β
+efficientnet-b1 imagenet / advprop 6M β β β
+efficientnet-b2 imagenet / advprop 7M β β β
+efficientnet-b3 imagenet / advprop 10M β β β
+efficientnet-b4 imagenet / advprop 17M β β β
+efficientnet-b5 imagenet / advprop 28M β β β
+efficientnet-b6 imagenet / advprop 40M β β β
+efficientnet-b7 imagenet / advprop 63M β β β
+mobilenet_v2 imagenet 2M β β β
+xception imagenet 20M β β β
+timm-efficientnet-b0 imagenet / advprop / noisy-student 4M β β β
+timm-efficientnet-b1 imagenet / advprop / noisy-student 6M β β β
+timm-efficientnet-b2 imagenet / advprop / noisy-student 7M β β β
+timm-efficientnet-b3 imagenet / advprop / noisy-student 10M β β β
+timm-efficientnet-b4 imagenet / advprop / noisy-student 17M β β β
+timm-efficientnet-b5 imagenet / advprop / noisy-student 28M β β β
+timm-efficientnet-b6 imagenet / advprop / noisy-student 40M β β β
+timm-efficientnet-b7 imagenet / advprop / noisy-student 63M β β β
+timm-efficientnet-b8 imagenet / advprop 84M β β β
+timm-efficientnet-l2 noisy-student / noisy-student-475 474M β β β
+timm-tf_efficientnet_lite0 imagenet 3M β β β
+timm-tf_efficientnet_lite1 imagenet 4M β β β
+timm-tf_efficientnet_lite2 imagenet 4M β β β
+timm-tf_efficientnet_lite3 imagenet 6M β β β
+timm-tf_efficientnet_lite4 imagenet 11M β β β
+timm-skresnet18 imagenet 11M β β β
+timm-skresnet34 imagenet 21M β β β
+timm-skresnext50_32x4d imagenet 23M β β β
+mit_b0 imagenet 3M β β β
+mit_b1 imagenet 13M β β β
+mit_b2 imagenet 24M β β β
+mit_b3 imagenet 44M β β β
+mit_b4 imagenet 60M β β β
+mit_b5 imagenet 81M β β β
+mobileone_s0 imagenet 4M β β β
+mobileone_s1 imagenet 3M β β β
+mobileone_s2 imagenet 5M β β β
+mobileone_s3 imagenet 8M β β β
+mobileone_s4 imagenet 12M β β β
+============================ ==================================== =========== ======== ========= ========
diff --git a/docs/encoders_dpt.rst b/docs/encoders_dpt.rst
new file mode 100644
index 00000000..9ce3af31
--- /dev/null
+++ b/docs/encoders_dpt.rst
@@ -0,0 +1,461 @@
+.. _dpt-encoders:
+
+DPT Encoders
+============
+
+This is a list of Vision Transformer encoders that are compatible with the DPT architecture.
+While other Vision Transformer encoders from timm may also be compatible, the ones listed below are tested to work properly.
+
+.. list-table:: Encoder Name
+ :widths: 100
+ :header-rows: 0
+
+ * - tu-fastvit_ma36.apple_dist_in1k
+ * - tu-fastvit_ma36.apple_in1k
+ * - tu-fastvit_mci0.apple_mclip
+ * - tu-fastvit_mci1.apple_mclip
+ * - tu-fastvit_mci2.apple_mclip
+ * - tu-fastvit_s12.apple_dist_in1k
+ * - tu-fastvit_s12.apple_in1k
+ * - tu-fastvit_sa12.apple_dist_in1k
+ * - tu-fastvit_sa12.apple_in1k
+ * - tu-fastvit_sa24.apple_dist_in1k
+ * - tu-fastvit_sa24.apple_in1k
+ * - tu-fastvit_sa36.apple_dist_in1k
+ * - tu-fastvit_sa36.apple_in1k
+ * - tu-fastvit_t8.apple_dist_in1k
+ * - tu-fastvit_t8.apple_in1k
+ * - tu-fastvit_t12.apple_dist_in1k
+ * - tu-fastvit_t12.apple_in1k
+ * - tu-flexivit_base.300ep_in1k
+ * - tu-flexivit_base.300ep_in21k
+ * - tu-flexivit_base.600ep_in1k
+ * - tu-flexivit_base.1000ep_in21k
+ * - tu-flexivit_base.1200ep_in1k
+ * - tu-flexivit_base.patch16_in21k
+ * - tu-flexivit_base.patch30_in21k
+ * - tu-flexivit_large.300ep_in1k
+ * - tu-flexivit_large.600ep_in1k
+ * - tu-flexivit_large.1200ep_in1k
+ * - tu-flexivit_small.300ep_in1k
+ * - tu-flexivit_small.600ep_in1k
+ * - tu-flexivit_small.1200ep_in1k
+ * - tu-maxvit_base_tf_224.in1k
+ * - tu-maxvit_base_tf_224.in21k
+ * - tu-maxvit_base_tf_384.in1k
+ * - tu-maxvit_base_tf_384.in21k_ft_in1k
+ * - tu-maxvit_base_tf_512.in1k
+ * - tu-maxvit_base_tf_512.in21k_ft_in1k
+ * - tu-maxvit_large_tf_224.in1k
+ * - tu-maxvit_large_tf_224.in21k
+ * - tu-maxvit_large_tf_384.in1k
+ * - tu-maxvit_large_tf_384.in21k_ft_in1k
+ * - tu-maxvit_large_tf_512.in1k
+ * - tu-maxvit_large_tf_512.in21k_ft_in1k
+ * - tu-maxvit_nano_rw_256.sw_in1k
+ * - tu-maxvit_rmlp_base_rw_224.sw_in12k
+ * - tu-maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k
+ * - tu-maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k
+ * - tu-maxvit_rmlp_nano_rw_256.sw_in1k
+ * - tu-maxvit_rmlp_pico_rw_256.sw_in1k
+ * - tu-maxvit_rmlp_small_rw_224.sw_in1k
+ * - tu-maxvit_rmlp_tiny_rw_256.sw_in1k
+ * - tu-maxvit_small_tf_224.in1k
+ * - tu-maxvit_small_tf_384.in1k
+ * - tu-maxvit_small_tf_512.in1k
+ * - tu-maxvit_tiny_rw_224.sw_in1k
+ * - tu-maxvit_tiny_tf_224.in1k
+ * - tu-maxvit_tiny_tf_384.in1k
+ * - tu-maxvit_tiny_tf_512.in1k
+ * - tu-maxvit_xlarge_tf_224.in21k
+ * - tu-maxvit_xlarge_tf_384.in21k_ft_in1k
+ * - tu-maxvit_xlarge_tf_512.in21k_ft_in1k
+ * - tu-maxxvit_rmlp_nano_rw_256.sw_in1k
+ * - tu-maxxvit_rmlp_small_rw_256.sw_in1k
+ * - tu-maxxvitv2_nano_rw_256.sw_in1k
+ * - tu-maxxvitv2_rmlp_base_rw_224.sw_in12k
+ * - tu-maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k
+ * - tu-maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k
+ * - tu-mobilevit_s.cvnets_in1k
+ * - tu-mobilevit_xs.cvnets_in1k
+ * - tu-mobilevit_xxs.cvnets_in1k
+ * - tu-mobilevitv2_050.cvnets_in1k
+ * - tu-mobilevitv2_075.cvnets_in1k
+ * - tu-mobilevitv2_100.cvnets_in1k
+ * - tu-mobilevitv2_125.cvnets_in1k
+ * - tu-mobilevitv2_150.cvnets_in1k
+ * - tu-mobilevitv2_150.cvnets_in22k_ft_in1k
+ * - tu-mobilevitv2_150.cvnets_in22k_ft_in1k_384
+ * - tu-mobilevitv2_175.cvnets_in1k
+ * - tu-mobilevitv2_175.cvnets_in22k_ft_in1k
+ * - tu-mobilevitv2_175.cvnets_in22k_ft_in1k_384
+ * - tu-mobilevitv2_200.cvnets_in1k
+ * - tu-mobilevitv2_200.cvnets_in22k_ft_in1k
+ * - tu-mobilevitv2_200.cvnets_in22k_ft_in1k_384
+ * - tu-mvitv2_base.fb_in1k
+ * - tu-mvitv2_base_cls.fb_inw21k
+ * - tu-mvitv2_huge_cls.fb_inw21k
+ * - tu-mvitv2_large.fb_in1k
+ * - tu-mvitv2_large_cls.fb_inw21k
+ * - tu-mvitv2_small.fb_in1k
+ * - tu-mvitv2_tiny.fb_in1k
+ * - tu-samvit_base_patch16.sa1b
+ * - tu-samvit_huge_patch16.sa1b
+ * - tu-samvit_large_patch16.sa1b
+ * - tu-test_vit2.r160_in1k
+ * - tu-test_vit3.r160_in1k
+ * - tu-test_vit.r160_in1k
+ * - tu-vit_base_mci_224.apple_mclip
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+ * - tu-xcit_large_24_p8_384.fb_dist_in1k
+ * - tu-xcit_large_24_p16_224.fb_dist_in1k
+ * - tu-xcit_large_24_p16_224.fb_in1k
+ * - tu-xcit_large_24_p16_384.fb_dist_in1k
+ * - tu-xcit_medium_24_p8_224.fb_dist_in1k
+ * - tu-xcit_medium_24_p8_224.fb_in1k
+ * - tu-xcit_medium_24_p8_384.fb_dist_in1k
+ * - tu-xcit_medium_24_p16_224.fb_dist_in1k
+ * - tu-xcit_medium_24_p16_224.fb_in1k
+ * - tu-xcit_medium_24_p16_384.fb_dist_in1k
+ * - tu-xcit_nano_12_p8_224.fb_dist_in1k
+ * - tu-xcit_nano_12_p8_224.fb_in1k
+ * - tu-xcit_nano_12_p8_384.fb_dist_in1k
+ * - tu-xcit_nano_12_p16_224.fb_dist_in1k
+ * - tu-xcit_nano_12_p16_224.fb_in1k
+ * - tu-xcit_nano_12_p16_384.fb_dist_in1k
+ * - tu-xcit_small_12_p8_224.fb_dist_in1k
+ * - tu-xcit_small_12_p8_224.fb_in1k
+ * - tu-xcit_small_12_p8_384.fb_dist_in1k
+ * - tu-xcit_small_12_p16_224.fb_dist_in1k
+ * - tu-xcit_small_12_p16_224.fb_in1k
+ * - tu-xcit_small_12_p16_384.fb_dist_in1k
+ * - tu-xcit_small_24_p8_224.fb_dist_in1k
+ * - tu-xcit_small_24_p8_224.fb_in1k
+ * - tu-xcit_small_24_p8_384.fb_dist_in1k
+ * - tu-xcit_small_24_p16_224.fb_dist_in1k
+ * - tu-xcit_small_24_p16_224.fb_in1k
+ * - tu-xcit_small_24_p16_384.fb_dist_in1k
+ * - tu-xcit_tiny_12_p8_224.fb_dist_in1k
+ * - tu-xcit_tiny_12_p8_224.fb_in1k
+ * - tu-xcit_tiny_12_p8_384.fb_dist_in1k
+ * - tu-xcit_tiny_12_p16_224.fb_dist_in1k
+ * - tu-xcit_tiny_12_p16_224.fb_in1k
+ * - tu-xcit_tiny_12_p16_384.fb_dist_in1k
+ * - tu-xcit_tiny_24_p8_224.fb_dist_in1k
+ * - tu-xcit_tiny_24_p8_224.fb_in1k
+ * - tu-xcit_tiny_24_p8_384.fb_dist_in1k
+ * - tu-xcit_tiny_24_p16_224.fb_dist_in1k
+ * - tu-xcit_tiny_24_p16_224.fb_in1k
+ * - tu-xcit_tiny_24_p16_384.fb_dist_in1k
\ No newline at end of file
diff --git a/docs/models.rst b/docs/models.rst
index c2037afb..ab04bb5e 100644
--- a/docs/models.rst
+++ b/docs/models.rst
@@ -81,3 +81,18 @@ Segformer
~~~~~~~~~
.. autoclass:: segmentation_models_pytorch.Segformer
+
+.. _dpt:
+
+DPT
+~~~
+
+.. note::
+
+ See full list of DPT-compatible timm encoders in :ref:`dpt-encoders`.
+
+.. note::
+
+ For some encoders, the model requires ``dynamic_img_size=True`` to be passed in order to work with resolutions different from what the encoder was trained for.
+
+.. autoclass:: segmentation_models_pytorch.DPT
diff --git a/docs/quickstart.rst b/docs/quickstart.rst
index 7fc04dd7..e6627b83 100644
--- a/docs/quickstart.rst
+++ b/docs/quickstart.rst
@@ -53,7 +53,7 @@ You are done! Now you can train your model with your favorite framework, or as s
for images, gt_masks in dataloader:
- predicted_mask = model(image)
+ predicted_mask = model(images)
loss = loss_fn(predicted_mask, gt_masks)
loss.backward()
diff --git a/docs/save_load.rst b/docs/save_load.rst
index e90e4eba..15434eb6 100644
--- a/docs/save_load.rst
+++ b/docs/save_load.rst
@@ -40,6 +40,14 @@ For example:
# Alternatively, load the model directly from the Hugging Face Hub
model = smp.from_pretrained('username/my-model')
+Loading pre-trained model with different number of classes for fine-tuning:
+
+.. code:: python
+
+ import segmentation_models_pytorch as smp
+
+ model = smp.from_pretrained('', classes=5, strict=False)
+
Saving model Metrics and Dataset Name
-------------------------------------
diff --git a/examples/binary_segmentation_buildings.py b/examples/binary_segmentation_buildings.py
new file mode 100644
index 00000000..1dd2cf0a
--- /dev/null
+++ b/examples/binary_segmentation_buildings.py
@@ -0,0 +1,498 @@
+"""
+This script demonstrates how to train a binary segmentation model using the
+CamVid dataset and segmentation_models_pytorch. The CamVid dataset is a
+collection of videos with pixel-level annotations for semantic segmentation.
+The dataset includes 367 training images, 101 validation images, and 233 test.
+Each training image has a corresponding mask that labels each pixel as belonging
+to these classes with the numerical labels as follows:
+- Sky: 0
+- Building: 1
+- Pole: 2
+- Road: 3
+- Pavement: 4
+- Tree: 5
+- SignSymbol: 6
+- Fence: 7
+- Car: 8
+- Pedestrian: 9
+- Bicyclist: 10
+- Unlabelled: 11
+
+In this script, we focus on binary segmentation, where the goal is to classify
+each pixel as whether belonging to a certain class (Foregorund) or
+not (Background).
+
+Class Labels:
+- 0: Background
+- 1: Foreground
+
+The script includes the following steps:
+1. Set the device to GPU if available, otherwise use CPU.
+2. Download the CamVid dataset if it is not already present.
+3. Define hyperparameters for training.
+4. Define a custom dataset class for loading and preprocessing the CamVid
+ dataset.
+5. Define a function to visualize images and masks.
+6. Create datasets and dataloaders for training, validation, and testing.
+7. Define a model class for the segmentation task.
+8. Train the model using the training and validation datasets.
+9. Evaluate the model using the test dataset and save the output masks and
+ metrics.
+"""
+
+import logging
+import os
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from torch.optim import lr_scheduler
+from torch.utils.data import DataLoader
+from torch.utils.data import Dataset as BaseDataset
+from tqdm import tqdm
+
+import segmentation_models_pytorch as smp
+
+logging.basicConfig(
+ level=logging.INFO,
+ format="%(asctime)s - %(message)s",
+ datefmt="%d:%m:%Y %H:%M:%S",
+)
+
+# ----------------------------
+# Set the device to GPU if available
+# ----------------------------
+device = "cuda" if torch.cuda.is_available() else "cpu"
+logging.info(f"Using device: {device}")
+if device == "cpu":
+ os.system("export OMP_NUM_THREADS=64")
+ torch.set_num_threads(os.cpu_count())
+
+# ----------------------------
+# Download the CamVid dataset, if needed
+# ----------------------------
+# Change this to your desired directory
+main_dir = "./examples/binary_segmentation_data/"
+
+data_dir = os.path.join(main_dir, "dataset")
+if not os.path.exists(data_dir):
+ logging.info("Loading data...")
+ os.system(f"git clone https://github.com/alexgkendall/SegNet-Tutorial {data_dir}")
+ logging.info("Done!")
+
+# Create a directory to store the output masks
+output_dir = os.path.join(main_dir, "output_images")
+os.makedirs(output_dir, exist_ok=True)
+
+# ----------------------------
+# Define the hyperparameters
+# ----------------------------
+epochs_max = 200 # Number of epochs to train the model
+adam_lr = 2e-4 # Learning rate for the Adam optimizer
+eta_min = 1e-5 # Minimum learning rate for the scheduler
+batch_size = 8 # Batch size for training
+input_image_reshape = (320, 320) # Desired shape for the input images and masks
+foreground_class = 1 # 1 for binary segmentation
+
+
+# ----------------------------
+# Define a custom dataset class for the CamVid dataset
+# ----------------------------
+class Dataset(BaseDataset):
+ """
+ A custom dataset class for binary segmentation tasks.
+
+ Parameters:
+ ----------
+
+ - images_dir (str): Directory containing the input images.
+ - masks_dir (str): Directory containing the corresponding masks.
+ - input_image_reshape (tuple, optional): Desired shape for the input
+ images and masks. Default is (320, 320).
+ - foreground_class (int, optional): The class value in the mask to be
+ considered as the foreground. Default is 1.
+ - augmentation (callable, optional): A function/transform to apply to the
+ images and masks for data augmentation.
+ """
+
+ def __init__(
+ self,
+ images_dir,
+ masks_dir,
+ input_image_reshape=(320, 320),
+ foreground_class=1,
+ augmentation=None,
+ ):
+ self.ids = os.listdir(images_dir)
+ self.images_filepaths = [
+ os.path.join(images_dir, image_id) for image_id in self.ids
+ ]
+ self.masks_filepaths = [
+ os.path.join(masks_dir, image_id) for image_id in self.ids
+ ]
+
+ self.input_image_reshape = input_image_reshape
+ self.foreground_class = foreground_class
+ self.augmentation = augmentation
+
+ def __getitem__(self, i):
+ """
+ Retrieves the image and corresponding mask at index `i`.
+
+ Parameters:
+ ----------
+
+ - i (int): Index of the image and mask to retrieve.
+ Returns:
+ - A tuple containing:
+ - image (torch.Tensor): The preprocessed image tensor of shape
+ (1, input_image_reshape) - e.g., (1, 320, 320) - normalized to [0, 1].
+ - mask_remap (torch.Tensor): The preprocessed mask tensor of
+ shape input_image_reshape with values 0 or 1.
+ """
+ # Read the image
+ image = cv2.imread(
+ self.images_filepaths[i], cv2.IMREAD_GRAYSCALE
+ ) # Read image as grayscale
+ image = np.expand_dims(image, axis=-1) # Add channel dimension
+
+ # resize image to input_image_reshape
+ image = cv2.resize(image, self.input_image_reshape)
+
+ # Read the mask in grayscale mode
+ mask = cv2.imread(self.masks_filepaths[i], 0)
+
+ # Update the mask: Set foreground_class to 1 and the rest to 0
+ mask_remap = np.where(mask == self.foreground_class, 1, 0).astype(np.uint8)
+
+ # resize mask to input_image_reshape
+ mask_remap = cv2.resize(mask_remap, self.input_image_reshape)
+
+ if self.augmentation:
+ sample = self.augmentation(image=image, mask=mask_remap)
+ image, mask_remap = sample["image"], sample["mask"]
+
+ # Convert to PyTorch tensors
+ # Add channel dimension if missing
+ if image.ndim == 2:
+ image = np.expand_dims(image, axis=-1)
+
+ # HWC -> CHW and normalize to [0, 1]
+ image = torch.tensor(image).float().permute(2, 0, 1) / 255.0
+
+ # Ensure mask is LongTensor
+ mask_remap = torch.tensor(mask_remap).long()
+
+ return image, mask_remap
+
+ def __len__(self):
+ return len(self.ids)
+
+
+# Define a class for the CamVid model
+class CamVidModel(torch.nn.Module):
+ """
+ A PyTorch model for binary segmentation using the Segmentation Models
+ PyTorch library.
+
+ Parameters:
+ ----------
+
+ - arch (str): The architecture name of the segmentation model
+ (e.g., 'Unet', 'FPN').
+ - encoder_name (str): The name of the encoder to use
+ (e.g., 'resnet34', 'vgg16').
+ - in_channels (int, optional): Number of input channels (e.g., 3 for RGB).
+ - out_classes (int, optional): Number of output classes (e.g., 1 for binary)
+ **kwargs: Additional keyword arguments to pass to the model
+ creation function.
+ """
+
+ def __init__(self, arch, encoder_name, in_channels=3, out_classes=1, **kwargs):
+ super().__init__()
+ self.mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
+ self.std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
+ self.model = smp.create_model(
+ arch,
+ encoder_name=encoder_name,
+ in_channels=in_channels,
+ classes=out_classes,
+ **kwargs,
+ )
+
+ def forward(self, image):
+ # Normalize image
+ image = (image - self.mean) / self.std
+ mask = self.model(image)
+ return mask
+
+
+def visualize(output_dir, image_filename, **images):
+ """PLot images in one row."""
+ n = len(images)
+ plt.figure(figsize=(16, 5))
+ for i, (name, image) in enumerate(images.items()):
+ plt.subplot(1, n, i + 1)
+ plt.xticks([])
+ plt.yticks([])
+ plt.title(" ".join(name.split("_")).title())
+ plt.imshow(image)
+ plt.show()
+ plt.savefig(os.path.join(output_dir, image_filename))
+ plt.close()
+
+
+# Use multiple CPUs in parallel
+def train_and_evaluate_one_epoch(
+ model, train_dataloader, valid_dataloader, optimizer, scheduler, loss_fn, device
+):
+ # Set the model to training mode
+ model.train()
+ train_loss = 0
+ for batch in tqdm(train_dataloader, desc="Training"):
+ images, masks = batch
+ images, masks = images.to(device), masks.to(device)
+
+ optimizer.zero_grad()
+ outputs = model(images)
+
+ loss = loss_fn(outputs, masks)
+ loss.backward()
+ optimizer.step()
+
+ train_loss += loss.item()
+
+ scheduler.step()
+ avg_train_loss = train_loss / len(train_dataloader)
+
+ # Set the model to evaluation mode
+ model.eval()
+ val_loss = 0
+ with torch.inference_mode():
+ for batch in tqdm(valid_dataloader, desc="Evaluating"):
+ images, masks = batch
+ images, masks = images.to(device), masks.to(device)
+
+ outputs = model(images)
+ loss = loss_fn(outputs, masks)
+
+ val_loss += loss.item()
+
+ avg_val_loss = val_loss / len(valid_dataloader)
+ return avg_train_loss, avg_val_loss
+
+
+def train_model(
+ model,
+ train_dataloader,
+ valid_dataloader,
+ optimizer,
+ scheduler,
+ loss_fn,
+ device,
+ epochs,
+):
+ train_losses = []
+ val_losses = []
+
+ for epoch in range(epochs):
+ avg_train_loss, avg_val_loss = train_and_evaluate_one_epoch(
+ model,
+ train_dataloader,
+ valid_dataloader,
+ optimizer,
+ scheduler,
+ loss_fn,
+ device,
+ )
+ train_losses.append(avg_train_loss)
+ val_losses.append(avg_val_loss)
+
+ logging.info(
+ f"Epoch {epoch + 1}/{epochs}, Training Loss: {avg_train_loss:.2f}, Validation Loss: {avg_val_loss:.2f}"
+ )
+
+ history = {
+ "train_losses": train_losses,
+ "val_losses": val_losses,
+ }
+ return history
+
+
+def test_model(model, output_dir, test_dataloader, loss_fn, device):
+ # Set the model to evaluation mode
+ model.eval()
+ test_loss = 0
+ tp, fp, fn, tn = 0, 0, 0, 0
+ with torch.inference_mode():
+ for batch in tqdm(test_dataloader, desc="Evaluating"):
+ images, masks = batch
+ images, masks = images.to(device), masks.to(device)
+
+ outputs = model(images)
+ loss = loss_fn(outputs, masks)
+
+ for i, output in enumerate(outputs):
+ input = images[i].cpu().numpy().transpose(1, 2, 0)
+ output = output.squeeze().cpu().numpy()
+
+ visualize(
+ output_dir,
+ f"output_{i}.png",
+ input_image=input,
+ output_mask=output,
+ binary_mask=output > 0.5,
+ )
+
+ test_loss += loss.item()
+
+ prob_mask = outputs.sigmoid().squeeze(1)
+ pred_mask = (prob_mask > 0.5).long()
+ batch_tp, batch_fp, batch_fn, batch_tn = smp.metrics.get_stats(
+ pred_mask, masks, mode="binary"
+ )
+ tp += batch_tp.sum().item()
+ fp += batch_fp.sum().item()
+ fn += batch_fn.sum().item()
+ tn += batch_tn.sum().item()
+
+ test_loss_mean = test_loss / len(test_dataloader)
+ logging.info(f"Test Loss: {test_loss_mean:.2f}")
+
+ iou_score = smp.metrics.iou_score(
+ torch.tensor([tp]),
+ torch.tensor([fp]),
+ torch.tensor([fn]),
+ torch.tensor([tn]),
+ reduction="micro",
+ )
+
+ return test_loss_mean, iou_score.item()
+
+
+# ----------------------------
+# Define the data directories and create the datasets
+# ----------------------------
+x_train_dir = os.path.join(data_dir, "CamVid", "train")
+y_train_dir = os.path.join(data_dir, "CamVid", "trainannot")
+
+x_val_dir = os.path.join(data_dir, "CamVid", "val")
+y_val_dir = os.path.join(data_dir, "CamVid", "valannot")
+
+x_test_dir = os.path.join(data_dir, "CamVid", "test")
+y_test_dir = os.path.join(data_dir, "CamVid", "testannot")
+
+train_dataset = Dataset(
+ x_train_dir,
+ y_train_dir,
+ input_image_reshape=input_image_reshape,
+ foreground_class=foreground_class,
+)
+valid_dataset = Dataset(
+ x_val_dir,
+ y_val_dir,
+ input_image_reshape=input_image_reshape,
+ foreground_class=foreground_class,
+)
+test_dataset = Dataset(
+ x_test_dir,
+ y_test_dir,
+ input_image_reshape=input_image_reshape,
+ foreground_class=foreground_class,
+)
+
+image, mask = train_dataset[0]
+logging.info(f"Unique values in mask: {np.unique(mask)}")
+logging.info(f"Image shape: {image.shape}")
+logging.info(f"Mask shape: {mask.shape}")
+
+# ----------------------------
+# Create the dataloaders using the datasets
+# ----------------------------
+logging.info(f"Train size: {len(train_dataset)}")
+logging.info(f"Valid size: {len(valid_dataset)}")
+logging.info(f"Test size: {len(test_dataset)}")
+
+train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)
+test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
+
+# ----------------------------
+# Lets look at some samples
+# ----------------------------
+# Visualize and save train sample
+sample = train_dataset[0]
+visualize(
+ output_dir,
+ "train_sample.png",
+ train_image=sample[0].numpy().transpose(1, 2, 0),
+ train_mask=sample[1].squeeze(),
+)
+
+# Visualize and save validation sample
+sample = valid_dataset[0]
+visualize(
+ output_dir,
+ "validation_sample.png",
+ validation_image=sample[0].numpy().transpose(1, 2, 0),
+ validation_mask=sample[1].squeeze(),
+)
+
+# Visualize and save test sample
+sample = test_dataset[0]
+visualize(
+ output_dir,
+ "test_sample.png",
+ test_image=sample[0].numpy().transpose(1, 2, 0),
+ test_mask=sample[1].squeeze(),
+)
+
+# ----------------------------
+# Create and train the model
+# ----------------------------
+max_iter = epochs_max * len(train_dataloader) # Total number of iterations
+
+model = CamVidModel("Unet", "resnet34", in_channels=3, out_classes=1)
+
+# Training loop
+model = model.to(device)
+
+# Define the Adam optimizer
+optimizer = torch.optim.Adam(model.parameters(), lr=adam_lr)
+
+# Define the learning rate scheduler
+scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=max_iter, eta_min=eta_min)
+
+# Define the loss function
+loss_fn = smp.losses.DiceLoss(smp.losses.BINARY_MODE, from_logits=True)
+
+# Train the model
+history = train_model(
+ model,
+ train_dataloader,
+ valid_dataloader,
+ optimizer,
+ scheduler,
+ loss_fn,
+ device,
+ epochs_max,
+)
+
+# Visualize the training and validation losses
+plt.figure(figsize=(10, 5))
+plt.plot(history["train_losses"], label="Train Loss")
+plt.plot(history["val_losses"], label="Validation Loss")
+plt.xlabel("Epochs")
+plt.ylabel("Loss")
+plt.title("Training and Validation Losses")
+plt.legend()
+plt.savefig(os.path.join(output_dir, "train_val_losses.png"))
+plt.close()
+
+
+# Evaluate the model
+test_loss = test_model(model, output_dir, test_dataloader, loss_fn, device)
+
+logging.info(f"Test Loss: {test_loss[0]}, IoU Score: {test_loss[1]}")
+logging.info(f"The output masks are saved in {output_dir}.")
diff --git a/examples/binary_segmentation_intro.ipynb b/examples/binary_segmentation_intro.ipynb
index 3c5b6175..bbdf329d 100644
--- a/examples/binary_segmentation_intro.ipynb
+++ b/examples/binary_segmentation_intro.ipynb
@@ -1,4211 +1,1094 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/binary_segmentation_intro.ipynb)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "U3WUb8t2P2e5"
- },
- "source": [
- "π πͺ π± π± π΄ π\n",
- "\n",
- "This example shows how to use `segmentation-models-pytorch` for **binary** semantic segmentation. We will use the [The Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) (this is an adopted example from Albumentations package [docs](https://albumentations.ai/docs/examples/pytorch_semantic_segmentation/), which is strongly recommended to read, especially if you never used this package for augmentations before). \n",
- "\n",
- "The task will be to classify each pixel of an input image either as pet πΆπ± or as a background.\n",
- "\n",
- "\n",
- "What we are going to overview in this example: \n",
- "\n",
- " - π `Datasets` and `DataLoaders` preparation (with predefined dataset class). \n",
- " - π¦ `LightningModule` preparation: defining training, validation and test routines. \n",
- " - π Writing `IoU` metric inside the `LightningModule` for measuring quality of segmentation. \n",
- " - πΆ Results visualization.\n",
- "\n",
- "\n",
- "> It is expected you are familiar with Python, PyTorch and have some experience with training neural networks before!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-08-18T04:37:36.751747Z",
- "iopub.status.busy": "2024-08-18T04:37:36.750812Z",
- "iopub.status.idle": "2024-08-18T04:38:26.758872Z",
- "shell.execute_reply": "2024-08-18T04:38:26.757586Z",
- "shell.execute_reply.started": "2024-08-18T04:37:36.751710Z"
- },
- "id": "DYNdz8s56qOu",
- "outputId": "7f343747-532d-417c-fc72-fda5c713d4e3",
- "trusted": true
- },
- "outputs": [],
- "source": [
- "%%capture\n",
- "!pip install -U git+https://github.com/qubvel-org/segmentation_models.pytorch\n",
- "!pip install lightning albumentations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
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- "source": [
- "import os\n",
- "\n",
- "import torch\n",
- "import matplotlib.pyplot as plt\n",
- "import pytorch_lightning as pl\n",
- "from torch.optim import lr_scheduler\n",
- "import segmentation_models_pytorch as smp\n",
- "from torch.utils.data import DataLoader"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "H4RKHF535Twz"
- },
- "source": [
- "## Dataset"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "lkghwALE5fIc"
- },
- "source": [
- "In this example we will use predefined `Dataset` class for simplicity. The dataset actually read pairs of images and masks from disk and return `sample` - dictionary with keys `image`, `mask` and others (not relevant for this example).\n",
- "\n",
- "β οΈ **Dataset preparation checklist** β οΈ\n",
- "\n",
- "In case you writing your own dataset, please, make sure that:\n",
- "\n",
- "1. **Images** πΌ \n",
- " β Images from dataset have **the same size**, required for packing images to a batch. \n",
- " β Images height and width are **divisible by 32**. This step is important for segmentation, because almost all models have skip-connections between encoder and decoder and all encoders have 5 downsampling stages (2 ^ 5 = 32). Very likely you will face with error when model will try to concatenate encoder and decoder features if height or width is not divisible by 32. \n",
- " β Images have **correct axes order**. PyTorch works with CHW order, we read images in HWC [height, width, channels], don`t forget to transpose image.\n",
- "2. **Masks** π³ \n",
- " β Masks have **the same sizes** as images. \n",
- " β Masks have only `0` - background and `1` - target class values (for binary segmentation). \n",
- " β Even if mask don`t have channels, you need it. Convert each mask from **HW to 1HW** format for binary segmentation (expand the first dimension).\n",
- "\n",
- "Some of these checks are included in LightningModule below during the training.\n",
- "\n",
- "βοΈ And the main rule: your train, validation and test sets are not intersects with each other!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-08-18T04:38:37.025511Z",
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- "shell.execute_reply.started": "2024-08-18T04:38:37.025486Z"
- },
- "id": "NP_DttTvvyQN",
- "trusted": true
- },
- "outputs": [],
- "source": [
- "from segmentation_models_pytorch.datasets import SimpleOxfordPetDataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-08-18T04:38:37.032330Z",
- "iopub.status.busy": "2024-08-18T04:38:37.032035Z",
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- "shell.execute_reply": "2024-08-18T04:39:42.743179Z",
- "shell.execute_reply.started": "2024-08-18T04:38:37.032282Z"
- },
- "id": "OVHVkntIS6Cr",
- "trusted": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "images.tar.gz: 100%|ββββββββββ| 755M/755M [00:51<00:00, 15.5MB/s] \n",
- "annotations.tar.gz: 100%|ββββββββββ| 18.3M/18.3M [00:05<00:00, 3.44MB/s] \n"
- ]
- }
- ],
- "source": [
- "# download data\n",
- "root = \".\"\n",
- "SimpleOxfordPetDataset.download(root)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-08-18T04:39:42.745259Z",
- "iopub.status.busy": "2024-08-18T04:39:42.744995Z",
- "iopub.status.idle": "2024-08-18T04:39:42.761041Z",
- "shell.execute_reply": "2024-08-18T04:39:42.760018Z",
- "shell.execute_reply.started": "2024-08-18T04:39:42.745236Z"
- },
- "id": "5Qyuw1YA5b7y",
- "outputId": "1d60699d-9dab-44d4-ba4c-fc0182b4a5d8",
- "trusted": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Train size: 3312\n",
- "Valid size: 368\n",
- "Test size: 3669\n"
- ]
- }
- ],
- "source": [
- "# init train, val, test sets\n",
- "train_dataset = SimpleOxfordPetDataset(root, \"train\")\n",
- "valid_dataset = SimpleOxfordPetDataset(root, \"valid\")\n",
- "test_dataset = SimpleOxfordPetDataset(root, \"test\")\n",
- "\n",
- "# It is a good practice to check datasets don`t intersects with each other\n",
- "assert set(test_dataset.filenames).isdisjoint(set(train_dataset.filenames))\n",
- "assert set(test_dataset.filenames).isdisjoint(set(valid_dataset.filenames))\n",
- "assert set(train_dataset.filenames).isdisjoint(set(valid_dataset.filenames))\n",
- "\n",
- "print(f\"Train size: {len(train_dataset)}\")\n",
- "print(f\"Valid size: {len(valid_dataset)}\")\n",
- "print(f\"Test size: {len(test_dataset)}\")\n",
- "\n",
- "n_cpu = os.cpu_count()\n",
- "train_dataloader = DataLoader(\n",
- " train_dataset, batch_size=64, shuffle=True, num_workers=n_cpu\n",
- ")\n",
- "valid_dataloader = DataLoader(\n",
- " valid_dataset, batch_size=64, shuffle=False, num_workers=n_cpu\n",
- ")\n",
- "test_dataloader = DataLoader(\n",
- " test_dataset, batch_size=64, shuffle=False, num_workers=n_cpu\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-08-18T04:39:42.762445Z",
- "iopub.status.busy": "2024-08-18T04:39:42.762171Z",
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- "shell.execute_reply.started": "2024-08-18T04:39:42.762422Z"
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Wy9U3BkOsZlCstdEvK4QkdkwXcx0meWxKswQPdkSdS5qWxIDxaNCeQlcMVgzBGGwqSxYZgRSTHWFtSuGklFFmSUxOGWRQMn4dNy79Do/2PT1ngDb+LIOx/ttpztdz63klVmILnJzL3kRwk3QLOqx4e9oevhsn7gy26NmCDCoyBBozOXkUvSHa+VFmHJHRSQJZcRI+ByD6cSa2Jn0nPkm/HU1MUH+8Aum3Orf9tKGxWLZnkbI2Zfx+1pkE+pQJoqhRCAmojPZyrDuJnyRAsQVKxixI1FFFRm87/ROBRPItgS1wEgmg9FokpUU76Ct0ttkYDT6uM4GVHYOyi13s4rWMRx6g9LqLcaktA0ARURCLSaI+MRZji0RbJ+YlAD0boAmEKKhFbL5zTVs0Brz0AMWoxhQORazMIVfgRNGsmAhSAmO79FF1SAIpcZtp/Wki39Jc5BRCGsdQxJvel9Hr/Dy6LY+i27ifw9Hbhid5e0Nhl6T0F5msGpYd3/L3ZMdLaQDdglnj55f7xrZXy3nNyGigad2jfcmplZ5JGcYw1r7krZz3AQmpkEUM4yPYMz1BGI7zCMWJYWBEckosfx76oY72cPRX0J5FGbJkem6pDILjeRgII4CyzaCc9z+BMZBJAKX/IRP/lcYcQiC4rt8+jNYfFFUHyQAuppd8fG8Xu9jFLl6jeMQBynhCG4SXMAAUUIxIcmMVnPOYwlBaSwg+SlXN4JWSQUq+qZX+Yp7e8GnOMnF5oxCrPgpyGggTK388khxRo1V6MCaOJVus9wDFjsBKZhpkG6CMpvrMmuSJeCsVNQIpW6zJORAxsAfb7Eg/fwUZiIGQAMkIpOjo9eiGfxjfaH1pj4atC71+Z3vDMowrHYqXMjdjaLX9fU3rzEzIdionPzOwZqPodUjQg4xRJi0yHulg9yXDPTjO/XnY7rGTPfoldUMyMlrf8LkqYOK4VYYDmwFlBBxE5oJtUBKCkv1OtgBK6jUV3WS3Acq4h5O1Bu893jskW+Jnmilovw7yOjW6zmZH2l3sYhe7eK3ikQYow6RMPw8NPVIEY4TsohqBiqEsTbTrxlCYkcNqyAAF+rJLlKyWjdvJfVcMJuQ7WzAYgsQ0j6ZHLE8eHGJNmtjUZBVMHHMvoE2C2JxSyBNf3rV+Yu93N41VMqiS/v3xxD1Mu9t38qIjO3UATROkDm9lTcoYtGwxE2l2HZevvhSY0E/mETwM+GwMamBwys1M0RiEZHASv7e9j3ESl2Hi1RG4yisaV8eMWZXRa0mTf/yZM+hILsOaQcMAHuM2BsCCyAB2E8iJQCekTJL0YGccSjy28ZCOWwTSA6NBZ3KOMUnpy7EmJahPDEdmUXz6DcKQ/hEwxuBSE8z4C+V+PTGFSQjgx8LYaJlPZlV2KZ5d7GIXr2E80gAlToCjyeocSOmX61mJNLFbS9B4X2klqRTzHbNK1LMEQ9YKIBoN1YCefheiloUIUDBR4GpkJIZNqZwgEZSogUAEI3EdaUITAU0VO+cED70SJd1Nj+/qhyZ6w51/H6r9kemnu54VSs9jcHL+4IZz4EQTMNDxXTiDFX6fBkvrDnlA6RdKTMGYFZGtAY6+3b8cjsVQoZW//5IR95+JRr5AtkBIehESWMrpI5W+S3NkL0yf/otpsSF9KOOdyI/xmBMI6MfJuMBb46Qfy6F6lmhI7+hWCi4XCw/HdQAk20zJkO7p2Zbz5cBJj5LXEU8xoW2bON6kZYkMycDQxIaa+cf3vUFcr0PZMSi72MUuXsN4pAHKudt5ehBybu7Yeh8iuwG966yxJoln05QiJs6wSbwaL/Q2bUl7oDPYlCcgYkw0dcvgJK3LiMVLmrAyw5Imv5zW0ZDvzGE8MfcsRP4rTURmxD5sw7Fhzt96Pj//j9iG4XU6BipJdzCwI1lbOb5777c9Ylr68SbWQkeDkNF+kStzdBuonN+XcYym6pfgk/Mpp7jPwxh7TJNYstgsT0bgJUO9EN17YUiznXvE31B7NqXvVAzgNbnFytCtuN+0juz2R+Awp3ryc/5vC19ld1iN1TSjLsa5105vpJY7dSdxbXSUDT2LBYLzHRocvSYlNxcMmaUZAZvsRJvSPFGDsvNB2cUudvHaxqMNUFLJ49Cufpv6HwtN+1JdGRwvxFh8iLR1dIsFEm3dd7IVTRNQnEmzQVc2yIqfmMicmAQ+jEExiAqm70qcsADbAKWfxMaTHGTeJO/o1l+9tOFlkzcvja2UTVq7JoM37RmQfhcjOAk53aO9DkU1llZzHqAkkfEWbsigpN9kZhVSxYwkoJD3QvOnDFVJuQS3RxxbWGKY48epKBiEvRABAOOZflhWfdyXQbsznCuRTBtcZcWAmuTwm38qkxgWzfsk/T4rgoSIgTJAzhUz/ehEeqGsJGYlpAM6nC/xG9lATxmlcRLbkfvtkFM7GbSQQIckNiUdBudcAhqhBy6xfDiBjy0GJn82lBf3y+wAyi52sYvXMB5tgJLpZhUEuwVGMjgZp3tkVAkj+W8bQYqXEHuPmBCdOsl3/CPqJbnMxoobIjWetAomJvUJ6Q7bJLv6mOohgRJiuienPhKropk1eZkQGWb9AWPlqTR5W4R+/h8dl/EqR8hB4/deQqmMmBLNE/nodQYr9A+Ggo+8vX5F0o9zmGJJBmXp+5mNyKCkX4g++9GLNftjNHwOGQjkVerWe30KSuln+swkSRh5haR9HGBrPtAjMWz6nQjxHAma0zjD+RGB5sjHRekBCz3QkMiwSAZiaRkDuaw35BLkvB+SmBPJyxAZlHQctWc2IrsRgYvvWRYkVa+lc8a5jhBGJmyawUcCOJk5SaXE+d9YPAd8aiao/bZ2sYtd7OK1ikcaoEhydAWDmIDIoBM5P+FvMSijCV8AsRYXAt47bN8sLqD4qFUQg0pImgQD4vPsEj1PspYha1By2qafwEyauoc0zoh/YJtqYHgvcwyjiX6YRkepii0AEumOXKmSJ/4+LZIBRY4eeOSbZO2Zlaix7Gd8sigYVSRID2KG7Y+BCj1bEgeuA/Loya4RiNj6vXL6LAGUnAMagxQGJmJsBd+vbvxePkTpWRJzku1FIiDNbQVIBm05fWPps34icYJOeiPRqCHSdE6piUBKlGTKtn0OjiQ/EbRkMe3oJw9ZD5LFsbnseHT8M3DoBa0JWGRxbFCfjo3Hp/ppTe6wuSInjNiQfM7E3z6ncOJ7/e+eU0cjXcvD6Ga8i13s4nMnHm2Aoi5a1mcdhQZIJb1bKREZAYF8K02cILLmw5pUbhli1YbJt87nvjoIP+NF3OaJK2/UGMQMLrDGQE4MBQQj0ZR+y+SL0SS1Fbr9rOO/R4AjsxADnUCalba+J6qpWGlIgWhK3fSgZARUSAAlvz+sj77qaXhfB0Yl7ZcmUBEPcQYaDMhwa/HREUhiVM3fGz2/VJfBaNs63nzah5SO0gRMA+BDv3wEKSlNk1M8ZA2KiWZqNp8LEtM6aYySvzdK1SVqLo4rsypmGPdw7jGkfHq/lKwIiWMOI5pKE1U2rt7JFTo9axJy6mf4TBCCakzreAcaK3ZI5cn5t4uaksH8TXoNSk4jDeCEVNYcdgBlF7vYxWsYjzRAIQTIHVWFbfOwc+md9NY54JK/mpqwGYv3ARcCpUl3wSFT9/E2WoymO0nFdy6+V6S7b2MwxsROyHmtSY+Sx9i/TpNgul9mK4dBYh9GbERu+CY9JZLAQZpkpAck488YpToyiBmBlK1nohByC6QAI7ahZyoY3Xj7BE7SsHIaJQITfcnELJJ/iEG/oRIn0e3lRsXGMgiSMwRJyok+1TEcFh1Wk9gfzYAqNa4Wn8rIsyxDib4vKhFTJAYlSECNjaLhNG5T2B6rIoLJ1VdKD2j7BtQZ1KTjMVB22i/eV++M30/MSegHn/c7A5eRWJUIUII6NOgIoOR/F0rXdem4RCAjIf+GL1+VM6R/FBLwGdJBQxpId1U8n1aYyQSs5X/8xR9mcu6a9MvFV/3Fb+Hy//CB0Y3JLnbxuRWPNEAxvkOCjyAhkNIIAdFBcLqlS2FU+JmYk35CTcsUVvDO47xSmH6miZUZPqZ4VD1+s6FZrij9jGIxT3b1EjUKkvr89AgoxFJkJJqOjjQVWTMhsn0d6tM4mZ1If9OXfY4OhDLQ7b2V+bZuZJzxyE3tstFXZkRENeK9nmnQgUEJaVSqRM+R1HgusAWI8s/Qszr98U+l1clsNwMtTXoUzfsq2fhsACLjSTyzC/2O58mUnJbQtI/0KZye8Ql5P2OqRr1iApgETlTBI+n3k2TwF2L/JWPAgs/prVyBlRs7hth7SE129h00LGHoOJmaWGtPcOXKnUjY+X6x4VwIfS1OX2acq3KS0NXj+r81VeDE5plC1zZxOTSmd/D9OdGzIyO2RLJ4eiSKzYAkjPrwkAS4u/jlo3j8Bl/zz36WP3X4LLD4jL7749/213j3B/8w9l/+xGsytl3s4o0ejzhA8ZjgkxlanqDjbBAFrANb0U9m9PPBVqpjPCuIESQozvvYzw+TePh4oe6altMHz7M5PWEy2WfBY9jpHFMLIkXM9Ej0PyHpUMaphz5blDc9Ig/6EWU0wblJdwucjAHLsCw6msT7fdTRx+fASxg/6FM8cf4bWJUwnvDThCl5YsszK8NwehIhJhoiCDPS6zn6b5xDZ5mdiM9hmLQJgzaj307YOmbaj4sBaPWpHE0MSt6/tF9+nIFK6TmJAEWMID4DFEFsFEqLiYxZUJN6Oxp8/r0T4szuwP1vLyQLlBGzZEasUBad9gAtaU7I7EdmMrLGxBHU9RU+PfgAvI+aKu1BSSwhVqJuK/924749GbCMOyL3201MmjKq6NkxKL9s2C94G9e/9/kETn5lcfvXT7iw+HIm/+jHXsWR7WIXj0Y80gBFvEe8B3HxQh8KRHw/MUV8IjmvsPVdHU2I/frShd6QJ594UQ9iiL0IHeoamtUnOVt+CNd6umaB+I5q7zKzi1fA1inlM0y+Q9cc+uc+TUCupn0ZZmQEONKgR8vkO948OQ13xZFFSuvs38urkFHqY1RGmgFKvCmP7qSZOfGZ8k/AI4Gn8+LUHrzo+GhnoMGQ4kgi4i2WCYY0z+hgBZPgleiYSxgdodEkSwado5RXHv9I8CtZY5PelwRa4mYjQBn8Twxi4/kg3iCFIMEkoJXASvqcxKJpLnvP4tneul9T+bD2OAYFTO/h2qe28vkQJJuwxbTNuNeOMlTs5GMnEn8757oRkIh+J2H0e2s6d9BYOj6A1eQqOwYoIfmg9OdTypXtUg+vGOZLfg2874jvferffFbr+Zlv+W4AvuT//F9z7a/+21djaLvYxSMTjzRA0dBB6CLl7kc4JOsAwmhCJF/AR0BFz78YJnwRxRjB+0RtGxBKmtXH+NiH388nn/0EmJqrFx6jaR8wWV4leEd95UnKwxJFkwU+verEp4nbjDedZ/MRMzHMXMQJBs1iFfqGhj0wiGXRPXPCiOrogURIf0qvJYmYJvSW5hqU4MMWQAlpEs8MyhgvDXfeDCAoxInVnBeeEJkUkyqk1DCAlcwLDT9BD0A0+4KIJtYhTd15kifvX04x5Ql3SGNl3UlmgjJAyfslSa4huaIn/yeCEjtOS0gdp40gajDBINYM+hQVxMbUT1BBbTLcM5Fd6Y+djNiTgcaLh25o/sQAwnJKJxAIiGY2ZPAv8b0JGxgpCN7RjcFJ0qqMGZJeOLwFYBN7ErLANi2LHx3jdL7l7e9SPC8b9gveBu874h+/7R+/auv8V//t/5V3Xn4vb/pzP/qqrXMXu3ijxyMNUIx3GOd6hmJ8l97rPIyNE3a+I85A5fzNX19yme500x2mlagCCD4gruGFZ/89P/FT/5YXby/p1HD5wou86fp1nrz2DIVVGt9yOP81mLLqhxT1FbmSZxhqnkNz6ibOsT1SGViKESjJE1dfhdLf0eowqYTQryukFE7e3xCy2VoCKiEQfEp3hID6QNJCpj4s2k/wUeORWYHheMU7b4a0CsNEn0OM4MWntEdMgY3gC0PzPu3/n1mFDFCyFiX0Hv8Dq5OPUQYrgwZFtwFKiOm7MTjBa58hjM2FkyFbdhP2EZCIjQBFVZBgERNIJ1kELqM+S1l30vc8ggiUVVEzxqCaDG2VIUOUAWY6ApJBiGdweY2fRSO26L3jfYf3HdlwrS8THlXo9CmxkAFIBqwZyI7Ppbye0WepnLkHN7t4SSyfOeBfve0HXtV1Hpgp/+Tr/xJf/84/yNEPP8Zjf3nHpuziV3882gDFOYzr4tSUwIkmYzTI7p3Rrp741pi6GGKk2RgYigQECNhUbXJ6/5f46Ed/hgcnQuuUtnO8cPM+m9WGzrUU1nN9UeGOLlNfeAwtbJqDhsm3L+rQoYvQFpvCSD+SmZHMjmS9R152rBXoqfsBOGQ2ZcvnJE9GQVNH2vTaK977CFR8fM/7xMb01aU6GuZw5y2j93vRb6QLeqFoxIfD674qZ5ipGfJiCXhI5hKi9gSTAEpiUbJ0RVV7IDh2lY1lxXnfE1BLjAohID6BlSwM1gGgxPPHRdO9rDsJNrIpKhjjkcKmc8siNhBSxZdq1NmkPpMokX1RT9SkZMCMptTVcB7GgWSNSRxQUA/q0v5lkJB+VwERG/UmPjrEDm6vif1LXihhdH5IAjBD2fI4rZO+1/ug+ARyM8Dx8bfaAZSXRPH0U3zjf/+/vCbrfqZc8P/7wr/PM8/9ER57Tbawi128seKRBih4B74F0WR3n2zmgxB5+ywjSFNmdoEFtmBBf+efdAAjrYeQ5rjmlNsf+zGe+9gnsWafxVTw5ZoQPJtmyYc/8nHWp2s63/J0NaEqSti7CGVJTuqMWZsxX3C+vw3pvTwB9GxLP7FAnlwiQxB6X5Kx8FEyuxJG6w9p0vGZUYgTV3CJPfGRUQneR2alL80dxqNx88P4QgYrcWQG4rFOgx962pDSbZFB6deVWZecAunzIUQNxog9UclKlAGg5N9qa8JURYP0eoueHctgy6WJO8S0V2S4YiWPyRoUjeBWQkzrmNyrSQ2hMFgGhxs09nSKGZGhLD0ClozaojqGlLaC0f7150TuoZPeV4/i47ujyprI9EVjQOcc3ntEsuZER8uNUnxjMDvyMxmDzbxdJSS6b1gmrzf96D0Ds4shtK74Q/t3X9Nt/Pv/9P/Gb3/vn+Gx9/3oDiTu4ld1PNoAxXXgHL3oRAxYg/jx5EicHHLflIQ6XvLPepRK2RJdIgieZvkin/zIz3N694wWx97hBUSmoCvEK2ebludeuIPFELzhbfWE0hrs7BLBKtGNNo1LhzVLTmOMtqsQ77I1WbKP6Pp8Z5z/k0Tf9x15dTwhR8BB8m1RNLH0IQlEA9FYNKawvEtAJYQRm0ISkoZeYwJslfBKf7wkMSpx/u3ZolQN06ffJE/g9EcAHTREQfqtJPIp9GkdTRO19p+ln/ocQBl0NqNjGzRW8HhQr8mJOK5I0iOuS/pWBWhM8ZjCYkIEIlJYDBbVkABKBCJGM1sn2w+hByb5MA3GcxpTSejA3hGixT2DGDaM2BNNB8aWBV3XEbyLx7pP4+jWI6b88u8f+nOD3j12ABsRmOQUk47Ajk+/f0AICQjvAMrDiMt2zk/+N99J+G+Ur/n9fwTzr3/yYQ9pF7t4TeLRBijBgeuGahAjiBsMvno9rNHojZIt6PPt95bsIU1uI5o7viWo6zi6/SHu3XyBsPEENkjXcnDpAGtn4DfM3IblWeD45JgP/9LPM5ksePrtc4yp0ekiNkNOpc/9nX+eUHvGY9CO9BPEiI4fxLAjl8/em2IEFLIOxacKjFxiqtqLX4OP6R3v42vnIzjJzIn3PjIvLoIUwhgs0QORnrkIWbcj5FSJQASFZmQdnx5GsuX/CGVk8Jj7MWZxLPmZZFKWf69tmDnuv5OP47jSqK/eCRBcnGRjyicBFKKWQzT6oZispFGTAIMlanBiiiWyLhGgCCZCTp+ee+MWSb+9RJO/3M047XYYGhoRWb8EUFLeKfSMSgISRLArtqBtWzT4eN5kbREjcMLArL2czqT3MRlrU5IINoIU6cFL7o7cs4u8jI5rF69bWDFY4J/9wPfx7j/wh7H/4ice9pB2sYtXPR5pgKKi4F3vN4HLTEq8TR0qdhSMRjrfDIbm591b+7vt9JXMZoT2lPu3PkqzavCtp5qUWF1hwpzZ3gVEGqbdhnnd0rYOCcKnPvYhFvsXuWQN1nwepp73moqMnCRvU8+NIU34QyUGff6/T+mMafl+ufy91NE2ARSC70WRmoBJBCkQvMe5CEi88wTnI3jxUZeiLuSb+tizRrfHKCNw0h9TZXD1NRGMGJMYLpNdWumBZW9alxiXnNLJqxvu/wc2abB7JwGSPjE0/JR5+SwSTqsNQdHe7p5tgJKASVQjjQSwohhyWwVJQlqTmkHG1/n/OvJSyfaAUQYVRgxK+js9R9fdnq4ii6OTFLZnriRpq3zXEHxiXvpjIUkUnQFsPE/6EuGehRmzJiMAM6rQIaWF+hwfo3MRhnN5F0MYy/Nfc/V13+wP/a2/zm/5338zkwee+of+/eu+/V0MYeZzHvzeL3zZzw7+1gde59E8+vFIAxSsjXx90uxFFsWk+d8Ml888ocoIlJgsnB1AypAmSJNdiCt2mxOO7t6kaYTgwQQfJzHXIK5jsreP1AvazTHTuqNpHJ1fc+eFDzGbTpkFi1x4BqmmPY2vmY3o5w7t73YjM5ImqzSOsX6EbJClYfQd39/pxrJh37MohMSgpFSO+oBP7Il3kT3xzvePDFCiJkVHItlhnD3FPxq3atZt5AqexJyYWOEiJrUEEJPM2thmspJB2eCuqhnvJNv3NGXn9FWflsgAZWDNVJPepGen8oKSDmHYes+o6XUzGaD0cMUKBh+Bio3jDy6BlwRgRA0Gg1HbMyg5RTQ8wlDBI4pIBh46ABRJlTqZB9SRiNYYrDGpI7Ef/GiECEzSsQmjFE0GI/mgjJ1hhx+WPpUTRmCELTCjPeoLEEvJd/hkK8yk5qf/zHe/7tutpeQD/9338A+WC/7sl/8hHvvRjuqf7oDK6xX3/ui7OHsiXju6/cBHv/Z7Xna5t73tT/bXmKs/6Zj+g5353n8sHm2AUlVRAOs96iN7Ihl4jPM31gI2zYhmSKNslbomu/X8WS9uVLrNCacnxyxXjs6FeNDUYFRR16Kdp5hNmBcHuGZNUXmadcPZ6QPuvfARKluD1NhLT0NhRxOm9iMdO3j2WoLg42cpjZNZlVw1I70A1o+WGdiTDFC894QQgYcfPZyPIKVzDu+i7sR1GaAM4GRse58ZH+mb8CWTsGyZn9kDcmrHYI0l9M6qpn+/1wj1ACXP5dp/Nih0MjgJiTQaMyiDiVufNkpsiYZhYgUgSE9aSdKpCLHyJjMpQYkgSgagYlQwWILmNFDSnYhNyxoUG8chMS2UGZTxuRhPrYSeCH1KJ/Ykin9rHjwkZiMxUAiuc2hwaV0jEJN2ahtMjNiU/hwbpTCTBqVnUUhsWGJapBfVjsS2aY8iwN8hlDdS/J75Gb/nj343b178CT7/nz7s0XxuxO1v+gr++z/91/kd0/+4J9AHv/Gv9a+f+Sd/hLf+g9dwYL9K4tEGKJMJioOmQYNDggE/lBnHyHfQAYyNgtK+adtohuxrVuO3JN/FBmiWJ5yerlivHSFAYSoKU4DaOOF1LX4NdjKlmlZYt8Ko0rYNt27fxNYzbtQLdLVPWFxKfVLShBsYnDyJd799W/sRVZ+9LCRoIheiWFRiniYtGwFKCCGBtoB6l3QlfgAmLuCDp/MhApTOxfecxzkftSguCShjxiF5h9BX7KjPE+Ag2h3gnumZExFLMD5NsNGsDWMwYnrDMun1KDpoUHrQMqR3Qg/A8k81gJRhDKT0kYzKorcBCpm9Sr+0KITECMS2BmByjXAaczCCSRUuRk0vlA0SEkCxGPHpHEzsS0jpH5E+zRREo3/KSGciyYRFBcRksJGAgxistYgIXdv2aZkhlZWARmKwzoOQAbDQf3d4zpVlWYMCvYdKPr4jJoUEUAbWcQdQdvG5G3f/2Ls+bXByPr7zt/wt/sIf+gYO/+ed8d4vF480QNHpHOhiyqPtwHexUkaJs1wu4TEBDRaM78W0uVeKysukeoh33tGNVmmbFatVS9MqqEGDwXeAWixFFEt2gUBHNZ1S15ZWhNaswXvu377FhQv7zMoZppqgVWwaJqOJM1PzGiKYyCmeKOr0g9YDRlU7Id5NZ/Yka0x8Fz0xXAQoPpWhxofifKBzHuc9nQu49Np1HpfBifdxtSMjs3jTnybPrEd52YgsgzFRzBny63TMjbWpTxHpt0jfSo30svwhHaQhhZNYpQGgwGBqNjAtwyh0C4jE1UlfKRsPfNynXFkUNPqcBHzPoASNDIoHxBqcA4vFSNGzJ4KPKhQZpbF8QlqqiIkPehPAzJ5E1+JI+oQoSk7HD8Cmsuxms4YRGBuELDnNNQKL57QlGahlBqoHP6O00KBZCXltDLqT/Fk+YLt4I8c/fM/7+MM/9V4O/x+7ye+1iuV73sn3/R/exxdVk1/R93/XbMN/+7hw+OoO61ddPNoApZyhpkPaLvpaaIjeKGTwoaAF2DTThnj3njrYJXGjJKHmkGfoL8FpQtbg8Kr4dKF3LrBuW8qmYzJVKrWIKTDqcZsz/KSmqvewtsBtNmi35sELn2I6P4DjPcLhM2hZjeh1EthIBl2JLYmAYzDJIgz9U2LZZ9ovH3qQEoGJJ/gWTekA5zJIiWmdCFAcXRfovKdL2hPnAq7zeBciuFGNAMWlDr7aa1dfNsbpjJzyiGXdFtXIAhhj4oSXS4/TXCupb81YtpEnypgNS6msNOHGquc0ieZjSH7S0WP8fkrZpDxwxi5Rk5tHr6nkOC8egYsPaf8UHIGKisIoYiwSIluiwSDBRgZFpAcrJI2JibkxVEJ6W3N+L20r9GNSsVhboKo0zYaeyuozVoOfj44AWmY6hrLyMbDY9oTJx67vv0NmSTKwGwOUcx2lFHo91y7eUPEF1ZT/z3f8ZX7P7W+m+uH/8LCH86sqpK7xX/YO/t9/5X1csLPPal1qGZj7XbxsPNoApajRokQmLjYNbDex9Lifr9IFN9jYO8XayKz0JmJRt0JKMQCDw+lW2sfjneJVsYXBW8UBTdexXm2wCHZW4CcFRV2DOlynWFuD8Xi34YVPPMt0scfhtQmUc9i/3jM1IYlXQy4X7VmUQUsiYbjzlaxPyQAlC2O9TwDFEXz0x1DXJfDhElMS6BKD0nbx0XmHd56ui4/gdAugRPZEkssqvYkZ+TX0r/M0H4EIiM2sQUythTAImSVkfxR6diVrLzW9l8tfI8OUU19jBsX3KYwIBca1Jbo1xnESKmtP0KwySfvQV+okBiYBXSORWWnbDo+CjaXKNmlQjDGRpfNJ2CwDYzT4hkS2JLvivgREyXAMi6IAAm3bRUFv8h/RHgTm7w0iWNX8t+9ZkbG4tUc3WTx7jlHJVTyZedF0oPu2R4Ax+d9OYn528YaMAzPlX3zf3+B3fcV/hvvEpx72cB75KJ58Ap3W/MN/8YOU8u+Azw6cAPzCf/3dfOUv/XHmf+/fffYD/FUajzRACWWJry34aHkfnWWbCFKy6DV4sHnyMLE3TwImOtagmFFZcv9sQAPdZkOzCXROMJXBi9IR6FTpnKdrWloUq0JRBgoDagKmnFJSxA4qRced559jPptTlguCrQmTg8ii+Og/guqgF3FuELr2ACWXlSbgoin1k1JC6n0EJ67rAUpI7EnXRZDSOk+bwEjrPE3naV1H17ohxePDVqdfQ6xwsTKeyE1/vPKkOkzIJgk3SR4i2ykejAx/BwaQgvT6kwTFAO1t3aPg05MrcbZ7yyTAIWN4klQpQk84yNZdf6q2SfsVNRmZKUpwJ/uvJO+TTddiCkNRmThBG0WMQY3p9ShI9OORvH4TMDanczJrMaaiMkCOnZCtLfAh4LsuubiGaJWvgISUsUl6kJ7t0O1jMgIm41YAMmJJQp8ajECvR35p2ZxiTIRXAoyp4/IOmzwSce833eBgB1A+q5Av/QK+9m//cHIItv/R5Xfx6sUjDVAoLFpUhHqKeIe4DkkpjuiPEhLtblCTJg/r6F1ne+Mwk5xmAUbARRSCo9msaDsXJ05r8UZwQOs9rXNoVRPUwqajJRCmFRSCyoa6mmLNAWVR0i1Pufvsx7jIBGvmOFOhpoi+HBq9SIIf9CLBdVE/MCoblqw1CS46oeaJ27kokHUdoYtN44Lr8D5EEWzX0TlH4zxN29F0MbWz6Rxt5+g6h0/pnTxXCZk1iRoMK+MCXNOLjc0ImORnYwzGBkQLlLAFUMQIapINmpF4Vy6JvQoafwtGHIHm3jOpaimkSp6st1DtQUuvZ8n/y2W9Wd85SkPlJfM+aqZu0sYlgaPOd6yaJatuxapbc7jYo5oYjE3HKUT3YMmdj0M6Qj51gcpVNjbrTTLwGRieeOoVWKliGqlb98wIRM+aPG7tCZQRZxTzYD3zscWc6HA0s8V9rysJ25oTTfs9fCcDoEEYKyn9tos3fnzgL30P/6sf/HK0ax/2UB7ZWP2fVq95+4JdvHyY//gin1n8+T//54eJKD3e/va3959vNhv+1J/6U1y6dInFYsF73vMebt269SvaVkiOmlpNCJMZTGdoWSVg0UWXWR/t8KVroWugbeJz10DXxodLz138Dj6yMeI9dC2u2SACtjQYGxkYReh8YNN2NE2DbxtCF+jWHrdxhI2nO1uyOTuJk4ud4I3l+OiY1b3nKJrnMZu7kfFQjw8e7x0uPSLr4ei6Dtd1uK7FdS1d19G1La7t4uv0cF1H17R0bUvbNrRNQ9N0bJqOddOyalqWm5blpuGsaTht1pysV5yt1pytNixXG87WG1brhvWmpWlb2vzoWjrX0bqWbvRwvktj7vDBpX3YfoTgIhsQfP+IFUUOF9K+eo/Ly/jwMusYyqQjwxTw3uG9I/T6mQTs3EgUnL1d8nrSuvv1jR7e5/F1+BD3rXUdy80ZNx+8yCdvfYTn7n6ITftJ4B7eL7eOgQ9dP6a4fYf3Ld418Tl/Nlpm2K+YkolpIqVtN3jXot4NFVrp+IRU0RUbPfqecdMsrM5l5v0yWVjsU2VX3l5k4GKJuk/i7Lxs6P+Oy26zMpmRGaf3Ptt4Pa8bn2vx4f/Lr3/YQ3jkwv6at/Lhv/pOPvxX38l/9dQuBfOw4jVhUL7gC76Af/7P//mwkWLYzJ/+03+aH/qhH+Lv/t2/y8HBAd/0Td/E7/t9v48f+ZEf+Yy3oyKotYhWhCogkwbpNohvEN9EJsWkZm5mzJiAih36w6S0jySxrIhBjYApoGvx3Sbd6RtIBmdGFG8CbWhZCxSqTOsaocRvOqhKitLimw7DmmAK7OSA5vQ+d+/eZLK/oNSadlbiJ/tp4kxaFOdxLk54kQ1Kk0kqHx5MtELPsJDSOcF1uC5OgC6VDzcuTrZN59h0HZu2pWkdTedoGxd9ULpYXkzI9mNEbU1WP0hyShWJ8gliczwj8b0QDNak1A+QO+0qsVBH1fRsS3wjsS/ZT0Nlq0MxjFUWSRcRcqWTjlI+edKMy2ZdhOZ0hEoq6JKRrmV8FuVUTNqWag+GVs2SB6c3OWnuMl0EruwLT16bs9ibsGyUbuNwvsBaMCGyP8bHFcW9yj4ruVsPfU8hSayHimBMgTUV6hXnl/SC1LQPuRIpyJAKymONKZdYqp5bEfQMSlxokBoHIFnnSxLQatgu06YXcOnWttHckFPJTQBe7Xi9rhufa/Gz/8Vf5Qsu/Ene+g0//rCH8khE8fgNLvyN23zs6R982EP5nI/XBKAURcH169df8v7x8THf+73fy9/5O3+H3/7bfzsA3/d938c73vEOPvCBD/Abf+Nv/Iy2EyyEVMpKUWGqGVInZqRN7EmX3TkHQWzUOQypHWz0sdCxNsWYWFXRtnTNJt6lex+b67k0rVnBoWy8o/AOEyyFg6qsKespdlpiCnDB0Z48QEzFhQvXoFtydPcWl8sZJTWdPEXQMrq7uhEL4DLocEkYm7UnuconNQv0EcT4BFIyMOm6kNI6EZw0rmPddKzbjqbpaLukPUkW9wRNhmSCBbyAFaHAxMlRJVUFp2Z6PgIUK4I1FlQQUYxEkBNUUuGJJk+U6H2iOkr15ElwVPo95vXCWLSZegiN+8qEbLevGRRlrUduDkkPhgZt0bZBXxhELwQNtO2adXPC/dNPUc82fP6TFZcuzdifTdi7MGP/4CJ37025+RwETW0EfDxuPhnQBZRsZRYhcl/T1KcPRQVsrNbpXBdZEDoUxUjs+7MVvdh3/I8gDMLudEzGBdex6mnwaeg7JfdOutqLkIfXMRUk4y0lwXKfJEupvFczXq/rxudazEzFT7z7/86X/s/fxFv+0E887OG8ocNMJvyOH/4l3nvxYw97KLvgNQIoH/7wh7lx4waTyYR3vetdfMd3fAdPPfUUP/7jP07Xdbz73e/ul33729/OU089xY/+6I++4oWmaRqapun/Pjk5AUDFEGx2+iwI5QQmc0zXxRROFs4C2TxMjSR9BSA2Tow+ihzB9E6niAWrkCpcXIiX9sJaiqLAFqYfgwNW3qGdsCgKKlvgGkdoHWKVzrU06zPONg1d67h4eMiDkzNM8QJXqinOCK68gQ8G50NK6XSRVeki6EAV71zqXuxTs7oonvUueqHEvjoxrdE5R+scnetYdZ6u69h0jk3TsWk72rajayNACc4n63ftmROPUEhMowVCz6qIyMCkGJuqXAyqgjWeXOCjxqCp4U5kD+KkaSSzBqYX1mrSkMCo9DhFyAZ0JKlJGABKSNVLIYwn0jCsM7MxCZyM0wfjST5A2qZn3aw4ObvFanOb2f6ap585ZP+whKJlpQFxJRessjcpuSUdzjvUR1CrGDBZyJxhQmRsJMk6RIjnIGBsSVUUdF2D77rENEX/FZ8oncykyHlwklisQSCbWaRBPxKfBh1Lb7yWXXcD2wAlH2Sy2NYzpHEEkfHl4tVnUF7t6wa88rXjcy0u2Blf8vSzLB/2QN7A8Rt/uuM/2/8xvrSuHvZQdpHiVQco73znO/mbf/Nv8ra3vY0XX3yRb//2b+c3/+bfzM/93M9x8+ZNqqri8PBw6zvXrl3j5s2br7jO7/iO7+Dbv/3bX/J+nhzFKqghlJYQJlg3w3YrZL0a0iCEIbVAyp+bNDEGk6o9zJAKMj5V2MRqFK+CKSxFVVJUFmtMnBwJtD5EbOOFsuvQ9RprGgQhhEA9qaj3rzK9GPCrM1Ynx5RVzdnZhsXpbWpbsQxTPHO8N7gEMHzXJWYjYK1Nd9nJt0JHbrMuvs66C+ccjXN91c6qbSOT0uYUT4fv4udZA5LN3wQICZwgBkuIrBJRdyMSJ91gDIUCiRcIeIg6UDQxIyHE7rwiJqUSQjq2sconlhknoBIig2Jid8CRQDb3lokRyGmegUXSEUBRQp/JI2eSEiuTK3bMVil5nJNDCDTdkvtHn8LJHfYueB5/fM58T3CibFrAFuyZinKyhy5rAi2q0dCOxBRF2iRgbByAEjNwxkbQItaARtbP2JKm7fBdi+B7B1wVg4gfBMmJDdrGBBnIjYzW0vHqnezSXsaU0RiIjAzf0iP0KaCxB0oU9W413cxbz8f1VYrX4roBr3zt+FyM/+XN/4w3/+1v4M1/+BfQEWj7XA0pK8zBHgCP/9CGb7/yI8AOnLyR4lUHKF/91V/dv/6iL/oi3vnOd/KmN72JH/zBH2Q6nf6K1vmt3/qtvPe97+3/Pjk54cknnyQkC/JowAZiC6Qo8dUEmcyxkw20bSo9JvbrMbkSIqcUsplWBiemn0RREOdiukIEnzwxhFxZE3Aa0NQhuTSWddfSqTJbLJjv76c0TYcUFdO9mpOm4e6DBxS2YD7fQ7nFjaJib79m4x/DuxqXy4I7j08izImpUQPtxg0AJUSw4hOr4n0yW3OO1nWxpLjrWLcx7dN0EaTEdbvEtkSxZjaDMwmIZP+PEKfOmMZJOhQVwQRwpMNp0v15UFSiHiOyJpJ8P+grfhQhJBNVMYLJ6Z+kbAhhmIhVc4nvIM4MQaPTbRJtDj2BsgilJxfSz6qp6iWBIcm+HslzRUF9oGmW3F++wKp7gUtXhatXF1y4fIA3kU2yYUpZTxBjmE0v8sAGgiZX2+hhH0uInZCQWywc0nQqKViTfExsSWUtrmtwXRMBjcmwQTBpzOPS7aHj8wAKBi3NOQYkC1pzN8QesEj/WdT6nEvthJjCySDHpGSRiIAd0nGaWLBXE6C8FtcNeOVrx+dqfOS3fR+f95f+OG//P/4i/nOUTcJY5Ne/g2f/031+/pte/+aO4zh90rKo6x1gfIV4zcuMDw8Peetb38pHPvIRfufv/J20bcvR0dHW3dCtW7deNveco65r6rp+yfu6NZkKYmwUvJYVoZ5hJhtoV4hrEdfFa7RPF/tkpJV1J71GxSSBbDD51prKFliRWAXSeZwFa23UWqSLtHOeFQ2b1mHMhnXT0gZl72CfojScnT7gwb01bduCBuaTijZ47t/bMJne43o9Ya+acOYOaTulSxU73jlC5/Gdp64tnTpC5/pUx1YlSki29W32O+louo6mjemeNlf9uG7kLpuqNjQ3rEvpj6RYNUSNCekePPIlZlCzihLUI6kJY0itpTWJbbPuREy2QzNZ9ZEcXSWu0SSh5zidoYobpyyyiDgM5cXqU0+i8yeHkHQeCXPG/Ak+pBQTERE4dZwu73O2uYUtTtk/8Cz2K/YvLjD1hHXbsNp0rM7O6G7f57ELM/SJJp5HIXmSBIVg0iEZ0lZpCPQNDlUxpqQsLV07gBMRRQcEEhmWkR5qCwiMAUrazwGckIDc4JcT3x83DByBGckAJS6Tmapt51gIEjAJJKmJG42uwK9+mifHq3HdgFe+dnwux8f+87/OWzZ/krf8xZ//nAQpd//Il/Pjf/6v/ccXfB3ip//Md/PV7//96M/80sMeyhsyXnOAcnZ2xkc/+lH+4B/8g3zpl34pZVny/ve/n/e85z0AfPCDH+RTn/oU73rXuz7jdatEZgNN2hJiGsaEEuopfjpHuiW2bdCuSxUw+Q7dD2xJZlBSRY+IoDYDFKUwlsKA+IBrPEVhsCWxiiUqK1EVnB8as7Vth0spoNn+HLUGdcLZ/WVMu0w7rl5WNptA84mWyaRg/8YBE1FO24K2VVrnCF1IgEQJFKhVmpTqyd4gGZyEoHgXEjhx0S3WOVoftSid63B+KKl16XtDjiIJJIEoHhl3hs66jsRWJaFpnJ/TJJpSCRCGyp38WwWJODCBEGNsn0pIioyeacgKkSiRiMBEUmonhAxQEnuSwFW2LcuR2RNSyij+L4qiYweD6A1ztHzA7ZNPcPWS5/qVimp2wP7lA6rZlC4I6w5O1x1HD85ojjfs+45udUbXVHjnk/7UoD7uhxpBvPQtAUQVE2kWMAWFKSM4cU0ELOl0FBOPpqRzWtJ5KGKS8bFsgbdxuqX3QRlpUoY+RGG0zKBT6augRg0Vx0BKRPE6APDMsvRng8T9fq3itbxu7AI+/F/9Nb767/yX8FO/8LCH8rrF7W/6CjaX4ef+6HfyWp67u3j14lUHKN/yLd/C7/7dv5s3velNvPDCC3zbt30b1lq+7uu+joODA77xG7+R9773vVy8eJH9/X2++Zu/mXe9612/IiV+EMEnYNLT9saihcX4Cqln2MkC3WygaZEkJh0mMp/u8JNgIelKMIKoTRNcfDIpjeO7eNcsRhAbdQ35ou6T66YGxatgnWd1tkZVqSZ1bMzXeowtcUE4Ojml9IEH9x5wsD9levCAS2XgxFvWXU3XaWre51AfaJcd9aTEA51zqQty8sPIAMX71F8n0Caw4nx69Jb3DufjZO9D7l6bSk/HACXR+baveMqcFXmJmAYT09+tx7lUIdiYDkvdeyW50ULqDhzSse/TNyF2FO4nW3oxbeddrzWJDrWDYRshVfqQUkF5bLkcWjXe9Sd5SNZtbLoVtx7c5Gh1i729hhvXL3Dp4pTp4R5UNV0QPLHBX3AtrgXphBJDCMJq3eCciyBMtCc2op2+YDT19LHxmJrCItZGzxzfAg5JKTQjggnJUj85GovGYzY4HGdGJdXRpIOvI6ASNIPE0AOVLbZkC4xE87hcZjykhjIQVYzG/FTun2TSLyxklmfY9mcbr+d1Yxcxbn574PrXzQir1cMeymsS5kt+DR/6loE9+/tf+ZdTc78dOHlU4lUHKM899xxf93Vfx71797hy5Qpf+ZVfyQc+8AGuXLkCwPve9z6MMbznPe+haRq+6qu+iu/+7l9ZHjDeHGcX06QpMYZgDFiLKWu0nqOTNWw20DRIcPFOtxcq6HDBD0Ks7DGoSchEDDZV9uTcPZb4eZo4InkQJwXv40RrjMHbAofgW6UNG8TA4ZULdMFjpjV1WWLXazand/nER5/n8pULXHp8yuVZwWbd0PqCrmVgDRIBZKoCt2lTyW1I7ElIJmcuApQRKPH9c4hVQr0Z2nYn21zFE5BxJiHzJ+k5pOZ6SWMhmUHJvVmyFbsM3wsmlR9L9AEJg/9JnkiDgoRBkKnENJoYE6t0MlPiA8GnFEQCKRmgnDs78EHJxbBiBJ+aMW7cihfvPcfNB89zcOC5dnmP/cMJs8N96r19WjUYJ5QGJr5gUSkrznA0LKY1xkxZb47wPpXvntdiaM5+Rc+QsiwQa+mahs51gI/H0WTBrkSWxCiq2XE3Oh9nzQ8pXZbPt1wqrSNQpunE7oWy+XPRTHVtpXhCGBg/EtuSwXbulihJpGyyJov82766GpTX87qxixg/+WXfz9fUvw1eJYBy+5u+grf8/g9+Wsu6YFj+J3dele2+XBRvepJf9zd/ln9y9WdH7/7KOg/v4uHFqw5Qvv/7v/+X/XwymfBd3/VdfNd3fddnv7F0oU6sNUDvYRIKiVb41QQ7nWM3K1ivoG1RP4gG+6aACeSoCbGqRyzY6GZhMBSFEBSK0mBtnkS1v2jLaHIQDJPZguneHicnJ2w2HVcfu86kgM3RMZuzFWHjWDx5laIWLk9vcOtjz/Nz/+FnedpZbjzzZlzb4l3HOgjOGRwBDZawapnMarCGrnXEKhJPSHqUni3Jbq3ZVTWE4TkEXPDRwyNT/QmgIFEl4lWTHX00XjMSj0UATE4HJG8MFUnsR8j310AqaNGsOY7alahkGXXt1fzbJZYghQK2sFEA7GO3Xe+zQ2pCAD6nKLRPYWSwEH/S7OkBRgXvPQ9WR9w8fZH7q7tMa8fTjx9y5eqccloj1YQgBWCTnjQQWsWvOyYUmKpkfzrBdYZm1UYPFgmxnDqfEYnFiIyDUNUlxlhWqw3Bt0DXT+xZGCskE0CjsZ+PyWXcpgcs8QhlgCJ9yfxWhZOQPGH6gxpBTAgvASUiDMex18kkcKnZkj/99jnVp/l8zwDlV/Bv9hXidb1u7OJVDf2KL+brv+8f8YV1Zig+vfjbH7zE//Tsb6J496vbK8hMJvzRf/4v+D3zs1d1vbt4/eOR7sUTNJadar67TNUkBosJMdVDVeEnC5huKFbLyKR0Ln5ZoTfuEEY9eAQRH4GKsfEG1IBaG5kTogGZSXexSHTZFJHUzVZpztZ4F5gu5hS25PT4GF8arAjTg0Nm+/ucnSy5f/smVWG4cPGQmzdvsvmx/4D1HdefegbfONatp+kM6oskglXESuzOrBB8TOs4VbRnRlwCJx2qofdG6d1qVXsdByFbmcdJXpSeug8a4qSXdBEmHd2QAEeQDEhyqiEnAYAMRGJiLLJKqliTC6kEM8zrfeTKHUQpKmF93KRqpR6BJj3KkPKJ4Ko/K3qBbEgABQmICRyt7/OJe59i6Y65emXKxcWE69f2uXhxH1NPKMopRTFltVzRblq65Zr12RK3XFM6x6w07E/n+G5D03SE4FDswEBksiIoWlqKcoJiWJ6dRXBiQjq30mnnMxsCRgPiE6NnwBibvOvyuRU9VvpqY+3Lfuh766RfQrIgtkftDA0Xs4g2ma5pSCAz6XxSBiw+G01WQSY3zM7CILK/yy4e7fgrP/WP+OY3/abP6DtSFGAtUhT89V/8YSbyI1y1cz5ThuIP7N3jv3jH3+fL/+Ef4Orv/fCWoeCvJKQo+H0/+wJfM/8QTxSLz2pdu3hjxCMNUCKoiBNhtg7P70sssYm3qUUJkyk6maHrJXQNEjwSNK8kfW+kRTEB1MbmlT6k9EOcrGNfF+0Nt9B4EbcCLvmRdO2a1dmSB/ceINZwcPGQ6fWr3L97k/mkgq5gWlnme3scHZ8QdM388JB6UvOJj3yE+eFFLl64SLM+xnfC0SYQpESD4DaOYlqiRawq8okFGbQoYaDvGdJP40ec1JIJWlpOxjPOlsbBjB4pssZB0vxM1DSYlO4KqVpHAdFYvp0BS7ah16AR8CVQ0h9OBVtZyrqma4+2KmFCCBEEZi1KAgRBc/4h9FxM8AGnG7yuWXZnPHt0m3urI556bM5bnjjk4HDKYm/GZO+QarZAVeg6ZV4vKPyajXVsghLaFu06bK0URUXnA52PrJXiU3PgkMBJdBiuTAEK6/US75o4rpwWTKdmsn6J52uImhmxxAqjfDqb6BFjTEyNiUlW8zIwJwN7lH64MDAo+dj2LrMZoKBDr8CcasuMCgysjUSWxgTDFuyRYZldPLrxhC0pPu9pPhO0eeFvH/O3nv6X6a/PDgiUYvnJL/t+nvneb+Tt/9sPEU5PP+N12MMD5MIhb/97z/LHDl74rMe0izdOPNIAxYviJd8NxhLYeI2WWG5sLVJYTBlTPTqZIfUEs1mhbYfxgyOYjHP9IYObqH2wGkttRQOuA9d5CgvBhtThN27Xpo69hKiPyCWus9mMg/kC1zo6F1gtV/H9vX3q6ZQF4H1D07Wcrjd0R2sOP/FR3vbrLnP18pxle0RwBSs1rFtwTjBqMWWBplRD8KFP3/hzICWEcZM8twVkNN215Elds1BWFJut6dOkpFkrQgYEEehE0WQ67pqFolFAnNkUkcS+kMpxUVSG6ptea5JGU1TRxIwwsD0hhJieG/XiIWkmotGYxyaBjA+BzresumOW3RHHzTFrf8aliwVPPbHP5Us1s8UMU1psUVHYGh977VHZgqKYUtaOrlyxVolupKWhKGcs2+gn40NiiBJjoyEgRUFZlCDCankWBbGSDevoq3UyMzXoXwVLdHfVYAgmFnRLSvVEb5Xs4RIFxoNYNutGyGgxnoMjkWzfGiCDk8S+jat6MjtCn5IjugSHLIDORncmtjrY4ZNHPmam4of+zT942MPg41/1vXz+X/gTvO0vfBD/4MGn/b3i+jV+8b97nI+9+396DUe3i4cVjzZAwdNpiILDdGcqEhMRamIaRAqDugJTVkg9x0wXhPUK3bRDP5t0UY93hilVIwmgFJbCFFRliZFA54FQRuIlXdOtjC/eEstlTVRvTPdmLA72cH6FFeHpd7yVrtlQV1X0OelOODzcYzK5RLtacv/mbYKWfOyjH+fakze4fO0pLiwLXNdSS2zet/ZRcFlNZrS2RV3XA47c8TY+dCTc9X3H4VyenMtzITMfw4SJSOxRlBglNUIw0aBNE3MViF4bIU2UgqDaJ4IiUNGYCkuJMfpqnFyHO6riyam2gKcoLafHp316J3bxHUpjcypIg0/MjCIaXxsDm/aMk+aY++sHNLoC8Tx5fY8nrs+5fu2QclJgypKiKFEXO1fXxQRbVlgVmqZF2yZWfnkldB5jLaaacrJqadokMs4AQaJLbFnGxpPr5QrnGpCASuhTNRlQ9NY7kjUoWXQsWB+rhyQBXmNMcsHNjEsS0cY1jf5FZDACBI0puS3WjJ51giy+ThAyMShKMnFLbJkx5++sh+3tGJRdvJrx0a/9Hj6fP8Fb/9zPf1pMir12lV/4i0/y8Xf/jddhdK9N/Jcf/22Y4+WQrd3FVjzaAEUDPqV3cp+Y7BmBROdXY0wCKgVuMsFM55jJDNZLQtul9M1w0e71LAIY6V0zbWEhaR+8kh6xpDO6lNr4FQuW1M/HGCaTAltAPZkx25tRLybMD/boWsfq6AispQ2B9fEppycPqOuKsjCcnh5z8xMf48LehGtXDmk3t5m0a2a2Zh0sd5YbhJJ6YmmbzG6M+tTkZ5SQNAohT+opNUM6VH2kSdRaSxAoyprCjrQQEtMmyTgVm7iV3l1U47ZEJcMTSGWrKrEsnJCMyTJIIVVHJSpFAVMaNCjdpqEvQz4PTvK+igc84BDjCOJZ+Q23zu5wtDmhDQ1VLezPS564vs/j1/aYzWd0EkWsRVEhWqAuUBbCtK5jr6FuTSuKphJvEaGeTgmm5vh0RecGkKcIxhYU9QQVYXl2hvcOCKiJYIM08SeRzwickEBt0p8khsKaEH12jIl9kkIWx2bmJYtUB1mynrOwDyNgct6oLabLBj3tAGI0gaeAMXZ0zow0LYlv2wGUXbza8dGv/R6e2fsjvO1P/BTq3CsuZ/b2+ND7bvDx3/roghOAT73vrcw/+e8e9jDesPFoAxQ8jtigzuZmc1kTkXPkIhhresGsTmeE2RxZHhNWm8iieN+XzcYLfrKv12T37qEwRazeaYSu7bCliV4XNuorDILR2N2XMnX7NbFME+9xDZy0D2jXKyaTOW3bsdlssLbg6OSY9WoFvmV/OonzrTG8+OID9vc+yjNvfQc3ru1z79YDQihpCKhzLHVFsZixPjM4nydxUsonVekQQUXQXNEy6BNEonYml1obI5RFQVWVeBWqekJZ5FLXmDIIbRcFnSF1diaXF0eAFDkX20942TMDUYxJrEtIjFcIBBP1F9pXUUE9mdKsN2iXxp3KjNkCJz7pMTzWeIK0rLszTtdL7q5OuHd2isNzcb/i4n7FpcMJe3sVRVViipJ5VWIl9sOxpqQyFRNTMrGWojDQlayrgqKwWBHKumRxcEinBSdnG0IXxcVBFFOUlPUEDcpqvcS7DjRE/5WQGQnt0zwZiPQpHEmVMumcNUYJxmBDPGZiZPBHySkhkmGeZiYjS1xHqZ2enRp+9+FtSQ742yxLJg6j9iTb9I9+y55dNIPmaxe7eBXj41/zN/iN/6//nM6/sl/JvOr4yBf+zddvULt4KPHIA5QOH0WFsc43ggoGgaFNYlljDdiCUFbIZIrUU0Kxij0QRkLLKOpMzrQ2bseoYV7NqCths4S2cVixyESRssBY05ecRp1BpOZ9CKzXG5qmxRYWUwht17BeN2xWm9jXZbGHIOwdHDKrS07u3+fs9IyJCC/cOaZpN9TllGfe/oWEpuHkeEUJ+MoxCQY3h/ag4ui4Qb1JfXnyHbImM1glV21kykSEqM/JE6MxlEXBbDZhUteoWurSUhTJWA2D94prNtjg8c0G13hEI0sQhZdmuNOOysxkwZHEyLksV6KhS+x27CP/pdFJFo3jak5z39WRdiKJe6PJmaMowciGTlccr064d3bM3bMlp21H4x3TScHBouLxqwdcvrxPXRk2mxaxHYcXrjKZTOmcR1tHKQWVraIOxLcYDRRFQVnXlKWlYMrewSXW3nO2agjex70qCqrJDKeBZr2iaRuMRGYphMSWACrJfl6yZww9SCGBEpMYLCMGYxNITqe2tab/bvz9UkUQkd0yIknjmgBjXz6cdSaD0Dg+zMBuJV1KCCStEEgoUhlyBCp5fSSGUUbn0i528WrHB77k7z3sIeziDRCPNEAJgEd7pgBS6arGVjpBNT4XAsEipcFUFWY6I8wX6NkJfq1ocKlLcLr/1KRhGVWaTMua0hoET3BK10V+3ipYC2IDkid7okjTu0DXBWwRvTVsWYJYVk2LSsF0Muf4ZMV6teLgcJ8uKC/evU+BYb5YcLA35/bdY37ipz/IhUuHXLr6FIJnebTkcFKycC1duaa6PmMqDfdsx4NTR7sBwlAGmnUFhU13xmoBQ2ELiqJIZcuW6WTCYj5lWpdU1lBYoSqT1kYsrRfW6wqrysnRMUE78A5LLO1GU4FNSExKnjATyFABrGCwZOfYvtQ46R5sXRAaR9u28fhn7xMfGQtjHLboQBzGtmy6E54/fsDNo5PYIDHApvPMZiVXDmsuHc6YzWrKsmJS1mjhMFrSbDqqaspstkBLh1WDUY90bay62cTyZgGK0jLbXzDdv8jdVcNq4/AoxlZU0yld17HerHGu7dNcUaMTesZhzDTBS9M0EaBEZslagwmxEaaYEAWrWcwt5x7EFFEga2NzH56c0hl5pei48aJA+h3icjpav/Tan2G7JqY+UzWXZKpyF7vYxS5eo3ikAYon4MSPtBaQQYrRWCmRCx2cKMYKWhi0nMBsD6YzwtkJTtvU22bIzxNiCkKtEqwwtRWT0mJwJHIeVcEHjWkmnwSFBtTKqKJCcK2yaVq6ozO0FC5euczFS5dZrZbcvncPK4reD1y4dpWL167jN2smiymTecmkrbl1dMZHPviLvLWsuXL5BvgX2JyuKCUw6wLTy/tMwx7TekPnO9YbJbRZHxGZCWNiqkXV9MLYsqooyxpbCCZVn9gCylLYn5WE0FJXlsIIdVXTdHCHDu8F6pgaCU2b2AQDLtatxjv5qFUxIaUeEnALIWDw0f7N+CjMTQyLC56qmtKtNqnDckgPB3Q4XVNIB6al8ytOVqfcPV7ywtGaTmEyKYHAbGK4vF/x5LV9LhzMgMDpySlN1TCfzTHBs1mtKYqKopwwnc4pAuAdrmkJ3QbXtjjX4p2jqisOLl2imOxxdO8OzgGmoJxMY7fo1Qrnu97RNilzeqCSIwNFyS6to1LemMKJv5MPGjUoKa1jjMGbyIaJDKxKTPTk45e3QK85kvhWLnJDMQmQhARQUjVWquQZ9C02poBeXqiUlbrsEMoudrGL1zIeaYDiEt2vQp+eQSwFYDVWMXhSjt4YxJhotlYYtKyhnqHVlCAtXrvY2ZdRb7l0p6lYymJGPS2xpkGbQCiUUEIQxWvAaAQ01hCBgIk5fu8CbdexajyOwGQ+ZbXsODr+JM63TCZTrly6wP2793jxxdvsHx4yXRiCVYKZcLq6z9nRhudfPOXqtZvM9y5w4dpj3Fl/mLbrKLsJJqy4dnFOXdecNg1Hp0vWJmo2jCVWliAp9QBdAmPWCmUp2LKkqid0rmHTdJQm0FVwdnZEWRou7O8zw1NKoJCOjfeogaKu8MaibYdzCtJhJFY1afJLU1FMyDb58c5eU+dcI0lEKwL45D+juHYDGt1jnbZ43eDDmk13HMFDaDhbr7i36jhee3yAqjS4rqO2wt6s5PGr+xzu1xijTKdT2s6z3jQUZc3efBEBWwB1ASrBFCZqWoLEVFbQ+JkqVVVRz+a0GE5WDYilnkxwrmW1WuFdF+HISJia/Xm0/y+xSSmtnhmU3IzPGJNSPxGQBCu9eNYYxVrF+22hrDERR/eZ+gTKe7FrSufEz5IhWxY1w5aINmnC+4aNIQzgRMYARXqJLK9mL55d7OJzLT6w8RTLXf3OLxePNEDxGkA9AcUnn1NUUWI1T2xIHBudBRugMNgAWhqoSnQ6w01nhHKJa1qcRhv1RJLHCcx4UDBaUJQFpY0TbdcFKKOAshATTRBTvxzjFWwEKM5FJ1dbGOqqJojw4O4DGtdhC8Pe3kVOly33j88wRYGt1lw4nOJ8w4PlGZPZnHu3j/jUrVOu371HMf04jz/5Dg4uXODO8nkav6Q4K5hevwhGeOzCHrfvnbBetShCWVqMqfGuheAIIeBUwYPzTSxptQZjDd06LuO8oXGWZdNCqxzsH3K6XtM6F0FG8BRGCBjstCSUJe06WrgH2iSo9UiI4uVozkaatDPDEBBjsUUsSNbgKeuS1m3wvsUHh/Md625J409ZtWesNkvWXcOm61g3Hudj9+q6Ag0eY5XDgzlXL065fnVBWRQxLWRLDhYHFGWNSAHB07aOxUQxGjsrg+J9h0kGbN5rTPt5RYylqCacrDecbjxlPaHpOjarJc65wW8mpfbGsCTvcdAAYmL5V2IrTM7RJIGySWkUY5Tgo34qCmSj/mcslO3LjTEjCJGqszSXIOfmf+kfjA7jUZWeSesjgaZ++V5nIkRFbmJ/JO7KkJ/bxS528ZnGN/6P38wT/+TfPuxhvKHjkQYoDtcDE+mfY9Gxpry/ihJMTNuIid2ObRFNzsJkgs7m+MkJbrWiS4ZnyZcWlZGoUCxVUVNNhLIVui7QNrH82JbxblIlAhTvAgFHp0rnY6+aooxOqm3jaV30nyjrKYGSO7fvYKspN27cQNVRlgVXrl7i9Pg+7aZl78I+J6sVz996wGwy4WDvNovFRQ4OTnn+Ux+n1MDksc+jKAwX5hMu7U84OlnhGsUUsemew+A6pXMdLgiFrVE8znUYcRixKJ7KGib1BKRg07ioozA1XQicth2u9TStI3ilnMyYTipciIyDrSzeGlznMcTJHiWKlaGfC9VAMbFMphVVaei6aGBXTISz9SnOL+lCw9n6jKP1KWfNKcv1ho3zbJyncQErwqSIfZFC8FhrOFjUXL20x7XLU+aziqqaUtYTympCUdbYosQai1UQ5ynEIt4T2gYtBN820G5wvqNtW5pNi2tdbFdQzzg5WeODZbPZ0KzWeN8N4EQjkxZCIHI/SYPCUPaNDCmgCFBIoCBWU5nUQNAmBsWLicDFamI3zpcaQ68HCRLXnxoDim6XcacND4LlnN5JH5jsvBwk9Voa/0uT3q8lAyHo9b+72MUudvGaxCMNUDrxCB6vZP4kll8CQ948mU8xSvUUFi0tWpXotMZPpnT2hI42TTS9fCTm84FgCmbVPtX0LlMH61Oh3fjoVQFIIRQGsEJwiusCjtjLJogk0BIBS+ciSDGzCbfv38d1LY8//gTLzYqje/epJyUuXOJwf5/9coELN7m3XPPii2tuXFpz9uAei9k+h9ef4P79e9y/f4vi7osUiysInqpwiK6RECdQK5bCGNQWtG5D6zyLwwXGzmjbFUVZsJjNWa6PwHvm8wWqhs2mAxGWG2UymdHiuf3gLqdH96kqy2N7F5iUsFyv8LTMJhO0nNC20MkKnCc4BxrFxEYjs1XWwuKwZLFXUlplvfScNQ5vA2er26xXp6zbDffPzri/2rDctGw63/+kRWEpy5iiWHcdk8pwsJhwcX/CfCLMpxVVVVGWscljXVeU9RyVCGMntqbAUgaPOA/rJTKtESN0qjRtx2bTsNm0oMpi/xDqOetmxXrZsD5bElxiWkiuvUkQ7EPAZy2IDuAlQ4OYYiFV7qSKXaJ+KRu2eWMwXnqwYoPtGZesVRkqepIKRUl+K5HhQ9nSv8AghM1JoSg9GRgVxSAmkDkgMpiR/O8pJXd65mfXtn4Xu9jFaxePNECJJm2BLl5agXQRVxDsUOYggksFCGoFtQZbWqgLQj2ByZRQ1XSbhuBCuhM2yVwsXqa9WOrJHuWkovINVSeszwJN67GpFBebUhkmVReFDHIUF4jpiGnFbL/EVCX1YsZ0OuVgf8FsNuPWrbt4jrl155hls+Ztb32Kz3vmTVw6XNA0v8jHnz/i6ccvcOXgjPXBA+YXb3DlTW/j+PhHOX7+WeyNgvXKsVmdEtojKhVC0+KLAovBJ9v4UpTDeYktSk7XJUUpEBoKKVi2KwiB2WxBVUxYtx2rpgFjscWCVXePOw9OOTxY4LzStS2uWaG+A1Mwn06wztKpR7ziNhtwHuuhEMEWymRh2Duw7M1BQ0vTrPGu4XTdcvfoJidnS07WLcerjtPG0TjFB6Uu4WBSMp/VOI1pntLC4d6ES4dTZrWlrGK6qihrsAWtU2znmUwLimpKYQxWFVwUOxeADYpvNwQCXVDa1tM1Ld57ZrMFexcvc+oCd+7cpTk7w3cJnKTGiz679gbwmho3pnTKoPNIpd9kBmVI1ZjshZJSJ8YEjImNJdUMeh15GQZFcqsBoPdZSe9k2Tikypy+1Fz75bNjX7Tsj5U7IWS/lGR3q9tdlLOZ3I5A2cUufmXxn/zs7+VN/89bfHbtEX/1xyMNUEISwiqB6DmY+t/096o2pnWIFvEYwSf6vLQGKQu0jiwK0ylhucKrw2mUCoQo1Yh3w16xZoqtC2jWlJWNug0HnY9VQ6S8vgp4MdFaXsGp4lTxnaM2FfP9PS5eu8x0b850VuPahtoK03nF/HDG/t6UWVUwq2Z0TUug4/Bwxi9+6EU+9KkjLkxKZmWByIT9/ctcv/E0n3ruDmcvPM/GWXyz5qDwqMBJu2a6d8jp6THaGkpxGPEQVhxMDzHBcro8wc5KriwmrE5vszm9z4X9Qy7s7+Pu3aU5uw/dGcXsMaqionMdTRtLf1ftisIoUwuFiSXXVmPLc+0C6j1elE4ChVWmFUwmSlHECpn1esWDkyOO2sCtozOev/uAk3XL2caxdlHQG6taYFqXTKclPnga5zDWsj+vuXgwY29ecOlwn8PDGXVlqes59fwQ9VFLFNqAMT7phzYYdRQSvXEwBYOhWYGiqcGfZX7xKraecO/Fuxw9OI3NGRNLkkFKBLTRdyYC0wRYkvZkMMkTMpPSm7UlYGIkRCbORO1UNGuL/j026BY4iLoUyKmXGNlfJVUG9f9L/y5knJ7JVUQjb5xoY5MIleyPMk71mL7UOJvv7ZxktyOs1/yWP/7H+Fd//X942EPZxRs8nn3uEm/90H942MN4w8cjDVCiRNBgJNnPozhcujinO0WNQlbEoCYgVgjBEIpkX19awqREpyVaFjgEF6BLYMMD6hWvSjAVRTUhcAoSfU+CE5wTimBwVvrJKCAEFZzzdJqpfWGzdjy4f0LjPFdvXMGYwP379+laz2M3HufNz8xZnhxzdnrCnQd3KeaWzfoYW9eU8xmfuH/M40cFVy7tU965je8C164/w+mDJWf3b7PWgqJtuHGwR7NeU+iGz3v8gLt3W0RrTjctd49X3L71PNfmFVcXNSf3jjmsL7K3mPPJj59x89kjDqcTrhxMeeGFU+7fus/1yxfwUjItFO8cJyfH4FvOzk7YmxoO6hpjDW2zwftoeua9R0tQY/F4WhNoxbNqW9xRy6Y5YbXacPdoyVIn3Lq34ub9DetWox6IWCNbFYa9WcVsEj1kOh8oCuXK/ow3PXaBSxemzKclk7pEFKxarBP80RICWIQORSYVs+mcugBQjHq8C5G1KGxkD5yj7RydQjmdUU7mbJoNx8cPWDcOl/oaucQ0RNHxCKBoTJCEVK0UGwoS3XBh7Hc2MmtLhoIiSGCo3BHFJHGsSWJZEWLvQTMqUzYDIBEGZmNgW6IHi4z+5eTPBz1JLEnvY7Bs6VmeHqBIBCe6Ayjbocr8Q/cf9ih2sYtfNfFIAxSjQpHsXkVSmafE6h5HvFuWVMoqRKMwMfHCGqzBlAZTW8LE4qclYVrRnjV0rkvVGOCRZPomqMY0gWIIGpKvB7gu3uljoG+unJQvgSji9CFgCoulwLVKt3Yc3X3A/bt3me0tuPr4E+xdvsLd23e4fe+U4wfHYAJ1vc/b3/x27usn+bVveYrTowfcOur42PN3uXjcUj845S1Pfx43DmeEzRpxjhdv32NjCiazKVf3a25UjqduHLK3d4FlgA/83Cf52Keeo/z8J5kbwZ3e58HNArnU4pZLjtdLurPblIt97t2/y7SyXFzU1OUxpdSUZU2zWXH/3m3c6pj1sefxq5dAYN06yrJmr7aU04oHpytWqzWOaO1/0i0hNHjXsFwuaRrHujXoxHL/aM3RxtH56AhcGFhMKw7mJYVVWudoQnRVvXRQ88wTh1w7rJnPS6bTGXVV07UdohZtA6UGnA+44CmMIajSIhSLGaWpsMFhvCOsz+hMOm+6Bu8UO7lIUdSY0NE2jtPTJa5ztEpqvKgJlGQGJTI9XnXrvaxBCRrLvoG+DYMweJ+EkP42Q2mx78uMo7LKiI/AxGSDu4GFIYtYcwomIaABoND/e4iS5ZExm8nOtzmtk/1WBoYmi2RzCc8Y3OxiF7vYxWsRjzRAiUoTG/vgEEWpmkoto3FbfBiJjQP7MsrU6TgUgpQWrQtCXaCTAq0NrhE8AyujQQmpRNaYmtynJJVF4HzAton6lqhFwMc71sDIb0KiDkRCwFhh0zRs2g3BGq4ZQ9duaEKH1hWbLqZKbj17k7lVnF9zenrMJ2894PhBw4svPuDtT1ziiauXWJQlFyu4cfUKulxy+xMvcHR6gtF9pC6YWOFgOmE+nzKzBYfzioO6QLolRgqMtmxWS+6bOyybFV4D2q25/eIJy/Wak2NHKcKbqppCPQYPoWN9eo/aCHfuP2BewNVrsLAVpW2xds3h/ID1MrDsTmk3jpOzUzarFevNhq5zNG2H60BsTS1wum5pXYi+JsYwmwh7c4u1wrppcaqUdc2sUh67tODKwYzZxHLp8ALz1DIgqpJBO09hKyqxhM4RXEdZVJS2wDct6h2lesR3hODo8LHaSwqq6T4SDLiOQjwnm46zdRe7QgfFex8BayCCkqC9UDZ2lE6gJAGVvvw9CWmjRnbQkwQlaToigxKMSbb32WQvPvd9BjN7IhGYS6phQ2xypAVNtEovqqWXYyEmpnb6ZJCmZoV9a2YY4Mjg1QLJhj+Dl10P1l3s4jOOD3VL6ueqhz2MRyIeaYBixFJQAIolIOLxRAojG64FSI6yMdlvxn12rEBpkLogTDNAKXDW4VzAp4qeXNMQRLBmhikLvHYxwSSC+oDvbMzhE70z8AETopurV0BMSg+0LGZzyrqkqC2Xr1/mscdvsJhMuHnnDsd37rHYO+TgC69y79nneHDvFj/3SysWs4qzs5bnH7SsTgO1NFydnbFXlnwCmD71GNevXOTSZMJ8b87JgyOakwccXr2OrWY0ApuTY5bOs1qfcu1gwl7h0LbB4FitVzxYdxyfNlxeFGyWx5ytA4vplKN2yd2TJU+0G/YWFRPrCKJI1zBfzHmx8ZydrXnr41BUBtRzvLpPh6Pwp2hzQrducU3DZt1wdLph1TpapwQH08UE3wVWrQcMdRE4mFkO5pbgA6eNp/OBurIsJpbHL+/x2OUD5pOa0ka2oCoqRAyhC9iywNZQmgLB4FuHb1NFDBJZk3ZD17WELjrABgNaWmRaRIv3dUNdBEQDx6crzpYNznu8115sHIJGhiZV62Sg4v3gieLVJx3HSwGKGelKQtagiCBBUWMQjeXoxgwur7ANbjJDEhmWuB9Z0yJGQG2fTjK5XFh10MAAJulPxmXLOSU0wJSsf4mGh3EcuyqeXeziM43/3cf+N7zp23b+J59OPNoABdMzKEECogaHx/fS1gwu4q1n0NgtV62JrwtBS4HaorOCMCtgVqJnLa6L3h4+2+gHITgIRYWpSjArxASssTgHXRfwIrEpXKRNEJ8b9w1N2abTKfWkxhQFhxcPOby4z/LsmLu3PsHx3ROwNZN9z9Xrh1y9eplPffQT3L31CYJCYaLu4sVuwycetFTFCmsLpLIUsxmhMthGuHLxkM39+5Rl4MbehLZpaChZnS25++A+p0f32K+VvQJONh2zScFydcrxpuT0dM0zly6gGnhwvES9p3OKNA4JnpKOQlu8a1mdwWEJoVPOVg3WtYg6RB1zLXju1nNUtuNiHehO16hVtIAjhab1tA6CGmqx0VslePZnJQdTy6QQ2s6z7DwuRNZgMa148so+N64cMCkEayyFtbTrllM5ZTKZYRE0dIgtYlve4CG46HfiOwIekYBvO8KmIbQugtpJTTWb4Ty0ZydYr1QHU1xwPDhZst5E/YkPiveR5RmYk+E5fpare8btE7TXn4yLfccpmNjKKQGBZJsimSWRETCBCBTS97M2xYjHBkNILIpRE03bEkAhfycWJpOLlHNhTwZS/TKSqncwAzCJK0LE9mXNu9jFLj69+PGm5cW/9zRXeeFhD+WRiEcaoBAkNnkTwYTtqoIIUiQ1bdM+v68hMihBDMEaXGEIlYVphcxrwqyC6Qa/CXQuimR9NtvyigO8sVAaVEKk1I3Eu+vWoCY5hW6pIaMuxViLiuF0ucYb2Luwz7379+i6NYpj79IB+3sHuNBxfO8mN556Bzd+82/h9PjtHD/7Sfz6iKevr/jYJ4/5kZ9/keePWxblKdMJ3Ltzh/V6ybrpaF3DYm/CVDwz2yE4No1y53jDsjXsT2usd5weHfPC3SU2wLWDOau2YVIJVoSTlef5O2d0KMdnLZcOpwie1dkZbes4Wa2pCkv23e1CYNN1NMuWqW3Zn+/x4Pg+V/dKHlvMWJ2cMZGAdMq9AmqTGhEUwnxa0m0aDiaGy/PYpHC5CSxbh7eKiKEqLJcuzHjsyh7zWcG8LNlf7GGTO2u7crjNKZO6YlbXBHV477EIfrOmW60jSCgtBiV0Du0cwQWoC4rJJIKO5Rq/WlFPK+qq4NQ5Hpw0tN0ASlzWnoTIsmlIJceJVelTPAmk5Mk/NqNMbq9oLI7P7IaQugknDBACRkZ/90JV6VmWrAuJaaDI8KkJ2GSOl4GR7bUk0WrfJEVtz47kDcRFYvTak5Elfy+UzfqUHYPyknjxNp//A3+Cj37t9zzskeziDRj/fv0MV79rx558uvFoAxTJuXsT7whVsWKSP0q86MZUjyYaPFLUqiZWltiCrigIZYFWNTqtCPMSnVpYOryTWE2SvC4UpfWB5SZa60sR9SSiJk5Czifwk8uf0wgSVvHO050uMZVltrfH8ckZpvDs700xtuLi5ctMKsvzzz3L8ckLHD+4z5vf8oU8/bYv5r6d87P/9l9zdHIf71sOpsphbTlqAh+7dUrzgZ9if3/OtChZ7B+gZcWma/DGU1vFnS25d+cOWk25cekKbnnCp164x3MPGvb3Ci4u5nzwuWOqQmiCcnracNLA3rzAuYZm07FpHWVdsumgcYbjVcPZqmBaG5x33Dw6wYoiE9gPLRdKYbPa0BjDpFSmRujWnqlRZnWBVaWoCi4tStZhjUpF8B1HK0+TmABRmBQF1y7OefrGBa5e2kcJVGXJfDGnMAXtpgOvWIQCgwZBTDQ30xBQHyIg8R7vJIGJEB1Xy4pyMsFrwK+XhNWGsFlTzEpsYTk+3XC07Gg7n8BJYlFCIGjodSmqUWSa1x1GICWk8ndNvji5dFdSt2NJ0pnITKRqm17YSt+/ZwxQssu8MYINsWu3MSaWMpuXVumY5INiRqDEGENMPQ0+Jya51+bP82MQ76Y0j1h2vXheGv7khCfeH+BrH/ZIdrGLRz8ebYAy1BXwkjvC7DkRNZODwJDYeyVYS1CLLyy+tISqxE8rZFEh8wo57dCNx2u8Y/aJ/t50gePTlrbpKNMdsLFQaHQhzZFfhdy2PkDnHCrCop7QtY7uqOGxJ69ycHjAfDplb15x//5dmuWG1fESt/J8qPt5PvqRZzn8vCd48cTysQ+fsuocy1aw6tFSubMyHJ9tmN9dcWUx5Zl6j+liysfv3OXy4xe4e/cuG+cotKFpwdQXMF3HcSvcXnrqWlivN6xapdk4js+WnG48Z6uO65cuMJ84VJTWGc66DaerNV0bcJMCVWFaKKcb5XjZcHlWcvdow7y0vPXqPh+8fcRx1zKfVxgHbePZmzjOGo+Ulums5HBaUm0Cq6Dc2RhWTSyxrauSyggXL1S87c2XeMuTV5lNKjZdS/AOKZSqLmP1zqpFnMY2BmJALFgb2YXSI7ImuAbX+XRCRHBSTCY4AW0bgmtp2hXWOybW4AM8OF5xuo5VXa5nSMIghk3Pmo3a+r8jEAnpkTsOh9Gcnp1cB5YkpnaS1ppYpQOiCcQwaFGipiRWl2Hi6+xhEqIYKnbXzmGS23IAzZU/o/M0/wvK5cRGxqBkJM7NgEUM7GymdrGLXbyG8UgDFO0vr9rbcycLqsSphP5v7S/0Jvo9GAe2hFARygpXl7hJjc4nyF6NnmzQVYtvAl2edFCcF9Yr4eik5aCqKI2hqKGowHQG53K5Jr2IUn1qzqbxlrhtO05Ozzi8vGBvfw+ModGW5z/+HC+88AJnJxtWyyhmbbvbuC7w1C9e44u+/Cs5WW742Z//MCvnCQpOgFWgxmEoaGrl+TsnfF51jZO14cHZmnbVctJteHDaUU+EB/fvUFRzjtqSsy5w5yR25N2bl1SVYd157h9vMFJy43DO3eMVxhRcXFR0XcekKNibbLgxL3Ea2KsKJqWyP61pcPzSC0sqLO94U8nFScFx46hnBa3rmEwMb7k+RXTFugFTF+xPC2QZ2GwCoh4RxQUBF1jsldy4OOfN169xuDcnqKcyFc3GYGzFdDGjKmu6jac5PqFdneJb0LLCbZRSLNo5CAbfBULoMNZi6inl3h6qDtc2iCpu4zCdZ1EX1JOS07bl5smKtunwnj6N470fGBNPrzUJIf/WyUlYhwqu3Pcmi2RhJHrtU4F5wh8qb/Jy26LY+JkXiekcDVgG47XBij6X2Q8sXvrnEqvQVBEbtVFCTH2JgBWDNUX0GEoVb2JKTLLVz863WSy7i13sYhevRTzSAMUTKyhsL/lLF+KsHuwv9NK3FSHl5yXYmAawBdiSUBb4SUWYVsisxs0r/GlLt+xos57AR5ofDKu1p/CO/YWlmhYYMRSNslo5CAk8hEDno/+FaBpjAB86EMtsVtO1K5wLEDzGK4t6jiuglYZ4L6zUZQGbjrOPf4Jf+2vfRrtRPvbxj3DatTRrh849ngItLW7tse0RN950nVBZPnHnlGuHU4KUnK1WGKPcvttyb33GipJqskfQNccrx52zDYd7k9i7p1CemFuu71t+2iu2UBbzinm94IkXH+D39igwBNfixTCfWmqrBK1onefZB0sOFxV1FUWrm42laz1FbbiyNwVnuXm0QSY1e1Zx1rKo4LiAjY/MVV0Erh/WvOWZa1y4sECModu0TKdTFrMKW1WoWGxdMd2bwuEhp/cfcHzrLqvTFfgO6QLSRbMa9R1GCqin1IcHBO1oVhvUuZie8475pObgcIbUFbcfrLh3vI6lzwlwep9SOyFqkoYyY+3t7jWXuOeqnVxmnjo7k0Tbox5+nEcQY4ACEaQoI+dZYxh68cST2yRdSAgBY6PXSS/O1QEQqaZ1j4S3JMBh+godYp8pE03k+nTPCCXtyox3sYtdvJbxSAOUDo8jlqZCEiDmkmBiN9d0md6aEIwIwRhEI0jBWrQoCFVkUZjVuEWNzhr0pME1UXsQmRGhsCWCxStIKdhawIdYNYIHDdFF1ieAokKJYoyhKC1lGe/Q69qyPDvBhxbxguscy7MNm1VD8J5pVVBKSHfYDZgl7ugmX/br3oGElk/d/BSVxoaGVVAET1FVXD+cosbyYAlnJyvurVre/OQlqnrJ7aMNzsPtE8epq5BiwYNWOF2eok65sq9snCM4xxPXZ3Rdg/Mt+1JRasuDkw2bztOuWx47nHPtYMqd4wZrhU3bMKlrplXJ6cbxiRePuXFlQgUsVw0X9moWE4trGhaTwLQILPbnGN9SGUNlArURaiuU1nJpv+Lzn7jCm25cRYynaddoaClszaVLe4itMFJixGIKQzFdcDhZYOyUkxdfoDk+JjiHNk0EJ8ZQzmbU+3uoaOxe7Dx+1WJCYF4XHO5NOTg44NgYbp2uOVk5nKfXmvRlxOl8GIOTeH5oX94+Bic6ZjGEQYjyMqGqfQomh/SAO4IDJfQ6FD84msSO2qmnTtS7DDb7+XlcEWQklzybganpNSiDg60xQyWPpDQQOw3KLnbxaccqtLzvZ38HT/MzD3soj0w80gBlQ0uDw4tN4r/YXdbjcZKcZHtDqiRQJKeAUtmk2igisSValriqJMxq/LwmLCrM1KKrDhdCX4ZZVVW0yS/AlBDwuM7TtR7vh/ROfmjSv9TGokgU3Cq0TYcNgbZtOTtb0zaOrvMEH8HMpCiwE8G1G+o6MJk0TPSYPRb8ul/zFuahpdnc5cFqgwBNUDYdYODkdEVZ1Hzy+fs8XlQ80XRIUfCRmxsKYzhZee6ebihnDltPCXbKY3uWaW24u+wwRqiKQKNR03Ewn6BqOF5tOF47TBAW85rHr+3h9QFFVXDvtKFp19QFTMsJp13LaRPYKy0r7ahLy7S2nDiYzSyTEkoDzTp2DS6sMi8FY0s8yuXDCTeuXGQx2yNohzrPdDZnWtcUBkLwlGVNKQbxAUqwkxmzS1fwziNq6OQ0egq7gqKuKRYzNDhk3aEh4FuPto5ZUXBhOmFvNsVOZtw/XnLzeMOmzbqSMDAmPvme9KmdDEoyc5JLinWwuE//jxmZl5/YtxRMKtsgpdekRCdkUVBjyE10YmmyYkYppVimPKYOt7e21QsoMyXWYK3FWhuBiR2DE9s/0zM4u9jFLj6deNG3PP21O3DymcQjDVBW2jLVhpoCm7iSICGCFI13mC/bc1WT20Pm0Y2BwqKlxU8K3LTEzSvCXgGLAnMCNNEpVFSZ1CWTqaWwAVvFfjubtaPbeFxn4p10z35L0iQITedwjcdYxRZCVVkCntVqzXLTMp1MoyuuTb4YBC4eLCiKim7TsD83TIwi3U1uHFxn+vbP42d//oyu7mhUWbXK8arh+fuKKU+YzPeQ0uC9cPtoybqBm8eO0hpO1oGztce4M8RuuH75IoeXDvFuyawq0IVhbzaJmhOJY3/+zglVXbKoC2Z1zaQUTjctRWGorGCC8mDZcWFqcQgPNp5l45gXJa5T7h2vqMopRVEjZsPlC/t01nJ63GLVU1mYlUJtSmypPHVtj0sHc1y7wljD3mLB3nzOdDahrMpYVeNaXBebNQZpwMb2Ana6oNzrYnmMxNJpaw0amiiwDYprPaENTIuKw/mE/VlFOS058YFP3Dvh7vGGtvME7/sKnQhOSI0CE1ORGgNGYDCwFmPdSXzeOgXH/wNICZNBV3UeU2R7fLLGSpWgEZgEVWz2MtGXOeehBzwDMBmYEpst9E0ytLOCtQwMiuTKHTv63k6D8nKx+IXb/Lp///v5yS/7/oc9lF3s4pGORxqgrLVlrS2KUuDJLeVDL5mNF/KQVAGITUqV0V1tcshSa9CigKokTEvCvCbMa1hU2GmB3Xi8j9soy4L5ogQcCGw2jvXa45oQzdw0zosKyc0TXIjGbwLUdY1QcHy0pvUtILQdTGphb3+OqKPrOlyzwbmWq1cOufugZd01bPyK2hhOXlhS7z3NjSc+n1u/9DOsXceqU0qUm0fKuj3isacuYozlxdtrms5T2JKmCyw3KfUksGkU6OjWpwRqfLWPLU8puzUXDmrKYsada46T1RJcy2xWUNmYOpsUjqKaEbTg5v0le3XFehOYlsKDtSMbwGyCstp0dM6xX8fj7Z1jNt+jlYqu89TW4lGqUvESuHgw5eJehXdnnB53qCh7+3vM5/Euv6wqpuWc9XpN8ApiooNvaCFYkBJTVJTTKUVhCb4lNCvEdYgInVdc47Ei7M8n7O3VlFODTCbcO93wyTsnnK265Bzr8V6ixX0uJU5C06HfTkorqg46E14ZnPT6k3MgRBnVIZ9DKGoiQI65nWTClkqEx0v3IlwYpXO2tzPujtyXFYvFGNtX6UgPSiKDYtKySN7mjkF5uXAf+wThX34FfNnDHskudvFoxyMNUDbqWNESUEosRaqS8Qmi5MSPaKo8IDZKC6JJp5g8HdQipkRshS0cVA1MK5jXsKgp5wVu2RCcR30su5xNCzrv6drAehVYb6KdenA+mmRJ7vYqvQ26UWE+mzCdlKxWGzZdh7HCpK6oqzr1X/EUKMEHOgPHqxUHqwmXDqacnK4Q7bAKm1OhudXw9nd8KdUnn+P2nVvYytK6wLpTzlZLFhfX2GLC7ZMV627NfC/QOaVxcd9bFw3WZhNL2zQc3T9hsbdHqKawbjhcVFy7dInn7xzTNpa92YzCGKwopydLDp65xsH+AZ98/pQ7JxsWlw0XZpZ1F9iflIDQek/rA/NJgQhsnOJ9Q9Mq0znYMnUD7jyti6mt0sYmgWVh6XxHWcS7fUwA4/Hq6FxLUU6wRUHXtWhwFFL3bq1lUeFNgRaGoBZtO1APRYHrlK7rCCGwmNfM92dUNdhJga9m3Dk74t5pS+sisAy9BoXe48Tn1I6GZFVHYk9gW5w6StzoAIxVB8HqSz5Pz7kAp2c+0h8haGzw1/uqJIF40mJlKW4899N3yeXD2Qo/u9Ka1F/HxOodMQmkDA8xduSBYntR7Q6e7GIXu3gt45EGKJ141nR4hYoQtQgqiT8JFDnPToglk5obq41LJGJO3ZoCa0uMLbBliZtWMJ9gFlPYW1OdNNA4fAi0KGVtEGdYHbc0a0/bahTTOjBFvMuXJMzNOhRjDPVkQtNsaJoudtnF0DYtVgWxlgMzA4kMjCeCiNsPTnnzmy7g1yua1uE2nqMTx92jJevwC3zxF30Jz//wP0OrgC1LTk431DZwfHLMYj6HB8esOs9EYt+i1iU3VFXmtTCxgBf8agOFpVwccvDE46w3gu9aNpsNx2eOs/WGtgk0jeLFIqFiZmP7APFC1zn2K8Px2lFT4XysKDmYwF5dsmo9R2cdi7qkcy3dSrlyEeoJtKuAd2AL4dL+lMuHC/bnCw735tSTirIqsaWhsDVVMYsAq12jUiOmInSBbtNSVgvCZoPbLCmqitDWbFZ3sV0TxdFYvG/xraO0lvliSjkvCUVAJhNWAZ4/Sd4nPvucxKodNKd5zmtNzgliYQucDMBkEKy+hGGBHq0MoCZVfvVN/DJwefkUTp+1HLEjQ9fk1Izw3GcReNioNbHnTNpsBiUjMJ/9WnoAvotd7GIXr0080gDFaWCjHY6Ak5JKY35cU12DEst7IzjxqaFapKijc0q8s7QSAUppKkLpo2197QmzDt1rsHs1dl5iNw2dCp2B2gAauxo7Fx+th84FbDAUpWKsDjIDhaIoOT4+BXVMpzW12GiA1nUQLN4XNJ1gJjXTxQRTRbt9MzEs24JWptxbbVgvhfsnHS8ebbh1/yMsDq7yzNvewQc/8vPMq1hd4b1wcrJhf++A5dIRjGfWBmxRgHiCBgpRLh3OaFcNk9IynVp8swaxzOcXoTpkuV7TAIvKoG3HAxVeuN/wpisVnXecrBsuzkrc3pRJaZjNKxbNGS/cO2X/YIYCjSqVCIUF9RrvyOsJ1bQiuAZRoSorqlJYNR2TwnBhMeXyhX0uXLgQGzsaIQRH2zq60jGZVKkfjWEyrekksDrb4NtTiqLEecVvjvHrFTYkTi1YfOdpGwcKs1nFbGox1qOVJVQVd49XPHv7hLYZ9d3xSewcot392NsksycZnIToyvay7Ml2ake33g+cByfDwpKAtknvjQFKrt9JSZ/EjGSgMjjBRu+SQXtiUwWPNTnLmZdNbIkFayWBlGjaNpQXD80Dd/HyYTfKi+6Mx4rFwx7KLnbxyMYjDVA8SovDjTgTq0Dq7ho0m7UpKgXZPjzJaQmajdwMVgoK6/FU1IVHKkc3ceiiQfYmsF9hVgXWORyGjRPaTdQwWBM7xkbhpKRJKtrdI9HISxXW6w0aHHVdRPfZMt651rMpRTWlqEqaxtE0K9brJf9/9v40xrb1vO/Efu+0pj1W1ZnvQF6SGmhblmzLVtNxDKlJWKQSQYbUMYhmAtlWS7Y77LQhBGrI8BDJRoQYCtqQ4FjJFw+AFHQHCBjASMutWBaEjilqZGRTEsXhkrzDmWra05reKR/etYe6HMQrkTz3kPVcblbtofZ+zzmr1vtfz/MfehuoG0tuDKenGrRm3Sia1uC9wJJC9/7dr/4Wf/Ev/kVeeXRK3zxilGv61rNYrPm6r38zmdb0gPcWISNKRka5ItMmgTMlidHTWkUmAjeynqo75db8JlKfcO/mEhXO8UFztu6ZV4pveeGYhxcrylHBMzfHmOBQWmK9x4gExqRPEQOXtcflkdvTnCMDq9qxWVuKica2a8pM4YRNwY9CJh5J7JHREYPHI7B9T9+3icwZgTgmC4ooLVIr0IoQLaHtMZNblJObLJe/C/0KIQ0hpLFO3/a4rqfMkout0clhVWlDLzSvXD7iYtHg3Rag7H1OYuBKlwQOgMfQJUuP7Y/Rq2OddLS9FpxsCbaHdeX+AATCln6yAy77p7chgjAAlf2P7XxStkBEHRBjpZQoKdFy3z1JvJPDDsw2TuKgrsHJF6yb/+yD/Plv+mFe/Mv/tye9lOu6rqe2XjcN/5d/+Zf57u/+bu7du4cQgg984ANXno8x8vf//t/n7t27lGXJu971Lj72sY9dec35+Tnve9/7mE6nzOdzfuAHfoD1ev26F+8IdDgaemp66tgn6XHsaXG02EGK3NNh6XH00WPx+AG4sJvNK5Q0GJGRq5xcl5i8QJU5elxgJhl5mVEaSaEERm/txiNq+FsMAlyMuK2aI+zVHN57vPMgkoW6CwFlNNV4jMkL1nXN+fkFZ2fnNHWD9x6twBiBtZbFuuPTL1/y6VfOuP94weUmUORjTFXSipZPf/KjvOm55zEyRwBFqZDCAT3zyYhRpsmV2l095xq8d6xWNYWWlFrSbCwiQiYc89JQSMcnPvWA3kpsNPQ+UOWCO/MChSIGQSXA1z2ZhFIbMi25eTzm7lHF6WVNDIJ5bug7i/VhSGWGcaZRQuOsI8+SQDyKwNE4Yz4xGOkIrqberNhsVlxcnlNvmsQUDQNhdfBr8c6hVE41OSYrSkK/hn5FVZQolfJmQoTeeqz1SCkoi4yi0AgVwWhEPmLpBPcv17SdGDgmh5b2B26xW1Js3EvKdx2U13qfcBXUbEdC4WA09Nqf+XxgJcY9+BEHnZSdtmfXJdn6mez5rEJugwIPSLFbn5PDkY9QO8O33Who935y58Oyu32R9UY6b1zXdT2J+p7f+BtPeglPXb1ugLLZbPjmb/5m/uk//aef8/l//I//MT/1Uz/Fz/zMz/ChD32I0WjEd37nd9K27e4173vf+/jIRz7CL/zCL/Cv//W/5pd/+Zf5oR/6ode9eIenjZ46WtaxY0PHJvZsYs96+FpjqUUCLjbanbmbH5rqkmTtraXCCI2Rhkxm5Logyyt0OUKNK8ykpJzklLmhNAajFEKSVENDpD2ExEqUr6ENDLfdda9IqhNlctresVitWSwXWNdDDCk/RqQhVZalzwkIfNRkeYExht5Z2q4BJRiPxiyWFxQq8NyzzxODQMuI1orVasV0NiI4S3SBMs/ItGRS6WQcJyWTImM2zhgVknmpMVJjjKRdrlB2yeXpeRqfOI9wgd46fu/T57z5RkWBQHmQUoF3VHmGkYJJHigLTWYk945zZpXGuUDrIspoJuMco9Iwrree3glsSJY0s5FmlIMSjug7Mg250eRZwaiaUFVjsiwHIiE4YgipEzUek41GeNsSNhepk6BzEGH4XpNVBePZhPG0xBiZQvryApdVnNY99y8arGXXOdlKiQ+5KH5Llo2JJLs3Z4s70PFZnBT2Y5+dLHnb39t2Xj7PjeE9t8RX2B9fQopErh68TOQhOEmCqau8kh0Q2Y999oZsWzLs3jF2B0wGdc8O4JCCOr/YeiOdN67rur7S5WPg2b/y0Se9jKeuXveI5z3veQ/vec97PudzMUb+yT/5J/zdv/t3+Z7v+R4A/tW/+lfcvn2bD3zgA7z3ve/ld3/3d/n5n/95fu3Xfo1v/dZvBeCnf/qn+a7v+i5+8id/knv37n3Ra/ExYKPH4pJRGS4F+AEKgUFghdp5ouQodMohhijRpASSxFuRBClQA8dEaIjGQ94SRhVqUmGmLawdoS7I3DDWgZS/E9Mmoc0+ZA0hEIEdKdf7pDAqypy8rGh7S9+3WOeHTSTubkrJlB6cFZzcOsJkI/JZRAgDw+bZdh1N0yGDJ2pB1605mh8xO5vj7JI6eh7cv+D5596EePAQ6wKZSWOUKk98lyw6VIwYrSl0ZDIyzGYl6z5iT5dMRhm+XxCCI6BYtwEfBZNSMS5KQgwcTXIWDxq64BFKEEKgUJK33pmwrmva3lFmGkGkzBSd83gp6XvLou6RIW2Cnff0QTKdlpwcTfEoyCDPDVmu2Wx6gt/m1Sik0ri+o+1rXFBU45tU0xMyBM35K4S+Q8SAkAJT5mSTKUZrcunJ6YAeHzzWR9ablhcfPOb0vME5P4CSbQeMPdfk8PZaAMLAe+I1o50tCIniSndkK0tOYx9BFMkJ5fNxOw5NB7eM2B0/5bPIsfvRjzxodojB7+SKW6w6ADJX3GMZlGWDcywHn/Ga1fxB9UY6b1zXdV3X01FfUqelF198kQcPHvCud71r99hsNuPbvu3b+OAHPwjABz/4Qebz+e4kA/Cud70LKSUf+tCHPuf7dl3Hcrm8coPkGeEJtDhWsWMRWhaxY0nHKqbbJnY00dLjh01GIGICJlqkm0KhhcYIQ7Yd8aiMTOVkWYkuK1Q1Rk8KssqQqxwVVVqDFMNoJ2W7CilQKs3ww46KOziAAiFKkDnWRfq+T5wQBVpLjJFoI/HeI5VifnLCrbvPcPPmXfJ8glEZmTaMqpybxxPu3b7Fc/fu8uyzd6mmY7ro6D3cOL6D9xIXAxeXayBSlkUKTJRJibJpPdY5tIgpzbjt6X3gctNR944HF5aXTje0vaMLEaFS52fTW5ztuTkpeXDRcLGu2TQNx6OMcZnT1T2us5RGM84k48LQdSmgbzzKmY0yJlUGJuN8tebxoqa2A2FZKYIQGGMwuSbLFWWhyIxgMiqYTgoQAec9vU0W9H3vaTcN69Nzlo8fYNsl2XhMMb+N1Dp1pISkGk24ceMmd+7d4ejOCdl0jFeK1vWcLxe8+MoDPvGZhyxWbeqUDGMcH66OYBiOvH2HhIPb53o87tDLIbAJB++XXrJrsV0Z5WxLDE/Ew/sckmIPXGG33ZRtR2UHOLZBf3IHQra29WLbUdkmFwsGc7ah63LVXWVAPK9Z5B+yvlznDfj8547ruq7reuPXl5Qk++DBAwBu37595fHbt2/vnnvw4AG3bt26ugitOT4+3r3mtfUTP/ET/NiP/dhnPa6R9CJio6fFIYRAE1BEtBDkURCjxgi5U0koBkCCQkWF2rpyiqSDUCK5aIoYCNonT5C8Qo5GqFGBrmrydYbBJI6JjHjiwD1JfhkqxkTmJLl8wrBpKElA0blAc7liOjVkKm01tndkpgDhMJlhfnxCXh3RdJFHjxdsNjU+SspqRJ5VZEYgYvqzZLmmqAw6eoKQTKfHvHq/pOvrJLsOPUomAq7UAuc9qyZCDJzMx1SF4tF5i9CaZe0QZ2s2NcynkvVK47yn7j3eSySRqlBsmo5Ce0yVs9447pyUuGWNEMkrRiiFc45RmdFby7LrGY8mjEYj3KbDBEFmJB7FsnFMitSRanrHqmmZdjnVuCIrFDoX6EyhdEnbg8kNJqsIQVI3lrZuUUHh2pa+XTEZHycQmRVIXaNFpMzTGGs8qQiypC4yVsHT1SvOlxtePnecLlucF3ulzraDEoY+SNx3VbbE1j35lQFApORqdq+BAX4cdEx2Lx++7rstHPQltt0YDmTxabQirnRG9iObLcjYf586cofjHHnQPZEHIx65BzFb8LIzbdu/XgwIZpsN9KWoL9d5Az7/ueO6rusrWe/5K38d4T78pJfx1NVT4VX9oz/6oywWi93tpZdeAkAPAMBFRxcdbfSDu6yjiY4Ojxtyb2QQyChRUaCjREWJRqK33ZMBtBihyDAYmZENXRSTlaiiRFYj1LggL3MKXaS2vBIEuc9lsT7Q9p7OBXofcAF8TB2WCJRVxXK1YbVu6PoeiCghMFoBAWMMJycnnBzfYLVs+eQnXubRowvmRzOef+YW89kUowxKSDIjqXLFqDDkRuMR+OgYjcfcOLmLEBqlFOvNGmMyui4mfopWOA95pjmZGWJ0+BhY1R2di5yvHF1vyWTg5ccLtIJNH3i87Ile4HpYNy3jwnBzPkEoyWqxJjeSk6MpAcEr52variNTCm0ycqVYLDecLtZ0vWc2LjkuBfPpCJVnSK0JQmCjpHaRVWtxUiCNJM8VSkKRZ4zHFXlhmMzGjKZTJsc3KEczvPOsL9esH55z+cqnWTz+NH3XD1bwoH1LGWpG0jIpS6ZHN8nnJzhVsGg8l+uezgZi3I4utrGTB+BkyxkR8TVdEz67c8L++Z0cOcI+tO+1nRfYup8d8pa2TYqdh8lrvEzkwWPyCpF1D16U2Otw0kjotZ0XsRvvbEmxe2M2uQcnB4TZLxU4+XLX5zt3fCXqG/53v8l3fOR7vmKfd11vzDr1G8ynHj3pZTyV9SXtoNy5cweAhw8fcvfu3d3jDx8+5Fu+5Vt2r3n06Oo/lnOO8/Pz3c+/tvI8J8/zz3pckUzZLIF+yN9RInFPiJI4dEQMGoPBkDomCtBCDeAkjWPiVnIsFFEIgjAE6QkqR5iCUJTo0QQz2cByQ5FVKKlBWrRRCBWINhCQiUhp0/xeC1BCoAUUVUlvLdY6dCZ3RmBaSIxKLp/z+YyT4xOW65r7r76CD5JnnrlLVRYoCVKplA8TIsJI8lwgCZRaE31ACo+LHc899wyPL16lbjY8enTBvbt3eHh6SgwSrSTWO3ItGJdw2kDvPXmmsV4QHRzPC5Z1JKBYti1NJ7Ax4JsIXvK2m2OUFjRdCgEUQZIZw6ZtaVrLatNTHJVYH3A+4nqHULBaNcznR2RKMskF0yoSfCTXkvXK4oNg1XjkRUMbJDcs3L0zTrLYTGFMidAGkxtizCiDxPXQLhsIPb7raW2P3WzQShK8R3tHDAaMQ+YSMxpBMabop5hRRRcvqG2ysZdCEEVKfIxiD1IOFTifSx68G+eQQEjilYQdcZYtADkkxR6wYw97KAwvFftv93BApP/bgwyR/Erk1fGOGkzV9v4nB7wUDgDPAfDYdWG2jx22bQSDF8r288VA5P6j15frvAGf/9zxlajoHD48FdeA1/VlrPf+b/4r1Cu/+aSX8VTWl/S354UXXuDOnTv823/7b3ePLZdLPvShD/GOd7wDgHe84x1cXl7yG7/xG7vX/OIv/iIhBL7t277tdX2eHP7b8j16Ana4yhUINBIjNNlw0yh0VIl7wpZ/IoabRAuVRkBCoWXipBhp0NqgshxZFqhRiRnlFFVGZrK0/ahkyha28tMQ8SHgQsq8cSEBF6E1TdugNGgl0UqhlUYbidaKIs84mp+w3rQ8fPCQssx47pm7jApDs9lgrUWpSG5AyQFUhXRTKimRskwS6JjMRjxz53mUEHSdpygMVZETY1KiuABZrplMC0yWkeWGPoD1ERtg0wZOG4GXJSufs6x7Vq1n1Ue0FkxLjVSCtndEb1FKpcDEPjCuCo4mJetVz+Pzhr7tKDLD8WREYQzWQ9c21G1PoQTzyhC8o8w1WmlOFy0PzmseLVpefnjB4/MFPia/Gu8dAM6Hwe3U0PaW5XrDuqnZNC1d22O7jm6zpl0t6ZdL3OUCd7EiLC+hW6K1pBqPGc3nmKoEIdASMrMljO59P3Zg5ABAfJaM+ADEQCBGPzwmroCZz/ZA+RzghC0gOQQscRjvsAcnhwF/h/Jh9qOgvRoHpNqTY/fusHuwsbe/34OXLWBJwYCDJ8ofQmb8heorfd64ruu6rqejXncHZb1e8/GPf3x3/8UXX+TDH/4wx8fHPP/88/ztv/23+Uf/6B/xdV/3dbzwwgv8vb/397h37x5/+S//ZQDe/va38+53v5sf/MEf5Gd+5mew1vL+97+f9773va+biW+EJEeTozGxpyOmHJ4ogIAUaZxjUAMYGfxO0AMHZUsATDgtDkyVSEwtcSlRUiFlRjAZFAViVJJPCkZVRpGpHbdAKNLNXT1vp01JILMspRlHnzguIuCDB6GRKlIozcnJCVJIHj54QNs6bt6+SwiSrl5TVSMQAts1FFmWeBFxSO5Valh5QAmFlIGmb3jr297C2dlLXCxWbOoNmTF0ziO1BJtcUWWeEWJNbx2rNpJJmTa4aszRrVsIIcn8io1bEwk4H/DR8PhyM2i0M0YjyXhWcn6ejNuObxsuNx2f/uQpmzYwLTRVrlE4pDGp49U1nC0axmXB2rVoozjKBZ0LtJ0jEqn6yCWW08WaYjQiSI02qRvlvaPvGprGARGlFbVzOGeRWhONAu8JbUfse3qr8WVJbFvoa5QQFKMp05t3qO4/QmWXGKMG/gnEnp28WHwWMHktB4UrXxmAiM4ysqICKejblrZpX8M3OeiisGefiG2AThL4cPCC14xn9tJidSAZFlKgBMNIZ+uRcvVTto/twckBb0WKXaeF4TnBVenx653wvJHOG9d1XV+p+s8+8S6y+0v8H/zS6/oc9boByq//+q/zHd/xHbv7P/zDPwzA93//9/Mv/sW/4Ed+5EfYbDb80A/9EJeXl/yFv/AX+Pmf/3mKotj9zM/+7M/y/ve/n3e+851IKfm+7/s+fuqnfup1L94IQyUVk9Bjh9FGH8M+SXiYuydi7NAZ2RJkReq+qCvJrHLHMZDsrb+V0gSdQZ4TqwJdFUwnY25ORnzm9DK9XqWuSIdDKrEnWA7vrIxhtVqlv3QtURK89zjnkFJwPJtwcnLE2dklm01NNZohpWa5XDCbFFSl4vx8RZHnCDzWWvKioO8ty0VLby3OObTRjEYTyiIyP5py95nnWK4+mj5bgevTZj4dRZSI+CiQMpJJhSTQ2kiIngLNqmnpnaD1aWwVBTgbuKgdD00kLzpUITjWYzrnCMCoyHnm2ZvIR5ds5iuK2jIqDMV4Sj4ZYTLDZnVJs7aMSoPJDONZwbjMycXQCWlbJqMKhSZE2HSOdd2RZRVFKREyYm3PZtVwcbqkbzwxgOstoW0JUqKFIHoLfU8ZI1ZI/CAfjs4ifYcu5+TTY8r5jLx4lTzfhu1txzMJAEYGd+AwdEMGXtMWlIQr6pstABFUownT+RFCCjbrNU19/yqp9jUV42ts7MXgN7Ib6RyqdQYn2C2AOCC7qoPOymsly3tgcgBOdtJjdu8hlTx4D7kb74jhDcRV1PMH1hvpvPGVrO5n7/Bv/4HineX1FvW1WJ/6V1/HyUc/+KSX8dTW6wYo3/7t3/55T7CQTno//uM/zo//+I9/3tccHx/zcz/3c6/3oz+rTNSUEWaiJBLxwJoOQRxAyeBzMkiLBcOVYroeHJ7bT7muXNuKbTt7q2bIiKogFAWyLBiN57wwf5bT6ZrLxRmNiCATF8D7NGQaYllAKlyA3lpyLSnyZIQmhB9SciXT2ZQYA6dn52QmZz6bsVovgUBVjVmvLsmzjCLL8H2LUYroPaePHnFxsSJ4BzISUSh1xnQyR6J581vfxqOHj2iac5yQNJ0DoTieFxyNBJ21KJNxfASn6w3WB5SUdC5C07JYN+TlhEDquAgkXYC1k5wuLKM8p+t7XOewncUXliLPqKqcyaSg7wNFOSKfHRGLCSI3qHZNaTKmZU9WFExHN1FaoV1N8B3T8RSTFyzajiglvfUoaciUREcPfQ9R06w2XDx6jOsiXdNRrzfQJ1ccGSH2HdgeYRSdlvTe4wN464jdklCMCbpiPLvFdPoSXd2jhB/s5OX+WACi44Aoe8BFGbglWwLt9kiKCJCSxWqdfFiU2nVcdkfZYffk82z2V9xixbb7sSe9Kg7GO3Lvh3LFUVaIg/fZgpR9Ns/O1WTgmVxR8nyO99mDky9+QvxGOm98JWv+rz7Ih/73b+Od5bVJ13Vd1+utpzqLRyLIUIzI8QQ64QjBE0XEDB2SXducw5b3VYuppG04/G9o4YtIlIIoBSiN1AaynFAVZKMRt6a3eX72mI89uuQchxcCoSQx+AMrc9BZhu16BKB0xBgoSzWoNQJGZxhTcXa+YLlaM5vfRAhwfctsOqfrehaXS55//k00dY3RkvF4xP2HD1itLnB9smSPMnFftIxc2gu6xvGWt9zjTW96no9/ouZisWHdWKRwZFnkmRuTFFQ4XJ77mKTQpZHkRhMQECNloZOEWyQFivUBU0jOV5ZJ3qFdx9F8hDaRvFJIDbfunHD+aImpPSbXWBc5f3zG0dGYiZa4TGLykqwqedx5FusV80IyqhJXpmkajAhok1FlOVoIXN/gjB9SqxWub/HWUa9rurqjrVvoLUYqxJDiaIhIo/FK0g5kamU9qt3QyAUPHrds6p6qzBiVEilS8rWIcms+MvwvmawFmWTIh3D2Ktl1Ly0OIWJ9nzb9PL8y1om7Y+9zldg1J8T2/3aM2YPxDhyMdg6kxFuQwoEUmQOQM7xum8WzTSzevY+Ug+W92t1PRFoFYvv9Nfnzuq7rC9ULP/9f8Pb/50evxzt/hHqqAYqQAi0UWdSUwlDFDCuSib3e9kaGTSOIZEMvxHbDCUSxdfY8ACci2ZeHLfFRQJSSqBLJNWiDzzNMZtBFxrw6YlqWCNXhQ3rPxECMRL9tiUuc65AySYOlDIBDkngOmckRUnN+sURITV6UrNc1MQrKquThw0doY1BK0LYb8vGEtl0TQ8PxUcVS9lxcNMQQCEEQlUNniuXigo/+3u9x++ZNXn04pX68obEBrRTnlz2rueX2yYy1WmE7x1GZRk83xopWRR7UYeAzpO6QJrnERueRIdCQnHw7X+JDRBtNPpkRTUVXb1A6ZQ557+n7nrZ1eB+JIvFmZrMSTMW5TcO16SinYExdN5QKZkdTaiSZlHRdCzEg9QhlSpzrhit+Rd939F1LdAHfO6RO//BRJOdcU+WIMscaiUsMWKQI4C22XhN9T5FLqsIgIihSFyXghvwdEvBVAh8iUgpiEIN0ePA4GQjSSaUzUFy3x9bB/QPl8BVwsqWbxBh3E8ctTXcHNuR+JPPamziQBF/J4tmCkwPVz478KlJQoFIKNYx19onGEqXkFZAih8yqlPHwdMiMr+u6nlTpxwZ/dv6kl/FU11MNUORwEjdCYqKmIiOKQEfAxDhY2CdwEmLAxZTBE8Sg9Ilhxz6JIvFF/C4ZeYgTFOyySIKSCKUJRhNLjSqThHGUaYyWINPPbK9+o9hyCiIueIwW5Lkmz0CJBIYQUOQl1kbW64bMpPc8X19glE58k37Ds/eeoW9btJCUueb+/UesNg2RyM1bY+YnMx68ekHTOMpcMakUBM+LH/99RlWOKacofUnnWlzwCKNYbizeWapqjJSe6NdorXn+huZRm3Ha1Ww6aG2kLHNEjLRdsrMX0XE0ydKGpwxFnhKWVZYTpWDV9nRdpG0CNguUswzVBrz11H3HLFMYLaljQBsYVTnT0RjtPLFvmIynFNWI07YlKol1/cC50CAU3rshC8lCBO/iDvwIJZBKgAdTSCgkscgQRUWvM6xSGBSESMShZKTKDW1pEICRYvi3ETCEIsYoCaFHqwGcbMEIYedvIon4uAcgsFXtbBO1BTHs7ey3W7yHnfJGiOTbgiD9WcTebI3DDsmOL7LtiIAiJVVvRzMcAJyr3JbtsUmKhUIghNp3U4bxTYzJmC0OLZsoxT5jSpB++Lr+wPpYfQsff3fgu13X10Jd+BrVXoP4P2o91QAlXWUqDJGSQBQ5EtDRAh41XIOGQd3jY8CJiCOiBgHoNoo+xAHIEBOAGXwwgNRWl5IoJChF1ApRGFRpqMYlVZmjtUQIN1whD0qKOIToDUApXc2mDoYArA1orSmKEXXdUdeW+dEY5yLWWo7mR3iXHHLHo4r1ap3SeWNEig6NpWs9pw96pvMTnnvuHu2mQROZH404v7yk7zteefUVMMdkZUm4uCA4T2EEbQgEM+Ho6IRAZD7v0AimhaJfBvTjl9BBMa6mvOlNJTFGus5S+A3HR4FxLsnLOdIYKA1laMmMwltP63JaUZLNT1CTMU6VKNOBlmTlMeW4xAaLySqydkOUoPMKlUmqbMKoEMTgKJmwdJGLXnNnekRWHSd1Fpa+bRHZBFUE/EWNMAX55ITpKAO3wduWrCoQ4zFxXGHzklWUxD7gO0EdI33f0XU9RldMZ5Jq5NksLpCiSdECuaSoPOvas6l7Fss1LkRklEgZkxGcUuRZjpCS4ANd12GtHYDKbrhCDGE4HpMLalGWdG1HdO6qrFjsb1sya8K5cXjsNbLgA4O1PU9ly2H9HNwRwa4rotS+W3Jo2rYPDbxq0LaVGO9GT9f1B9ar/8mK//Ci5VuekB/LdX3l63/+az/Im/7Bv3/Sy3jq66kGKIkSmkCKEhl6CAA0CHrp0gk8JgKjI2IJOHyS+h7O+AcOShj+izHs02K3pltCgdQEqfHK4I1CFwpTaPI8jV+28faBRDSFg6vXQemTFCASISLBB4JIm8tmvaHrHEJmOBsI3qOkpO86MqUxSmH7jvF4TNd3EFMysVGR3kXWy0v0Zs3NmyeMigpjNEIFTh+fs1qtUFVJUVUIqYguoKRgNJ2hyiOWmzXNekUeIp13bM4FZxuH7FZo73n84FOsehBKM6kKnrl7TJ61aNVSzm7Q2Y5NzKgyl9bTdmTZmHVccGkbzk5rxnWPjg29krjZmAfrkGjKXU/fW6K1LJsWrQ1SljgNGQ0xZmzaGmsD50tLXjhmszFBRHxYoLIR0TgYzbHW0TqHj3Dz5h1YXuCrkssoWa8dZ65m3HvGfWTca5zecH7xkEXTIKIhBI3JS6bTNOIpVUndS1TjySooO4vKMx4/OsMHjwCyomA8nWGKEik0wTvatmFxeQlREBm6dDEgRALCymQcnZwwn885Pzvl8uxsAB/i4Jg8UNwM93Zy4sPU4uHo3XVfDtBNAha7dzoYEe07JVuQIrZ+J0oilUCq14KWfRLyHqRcI5Qvtt73m3+dj7zjZ5/0Mq7rup6qeqoBStj6lQz5OkqoQa2THg8xnbhDDDjhcNHhQiKz+q1qgXRiP+QGwPaO2L17FAEvJUEpnE43rUFoQW4URh8Erck0fhLDpmKURKt01RqHcYQ0IoUDapWItTGlxkol8cGjlcQoRb1uiUMWjHcWrQTWB3rr03W5lojoGBlD1ziW5wsm9wqKIkcYuLxcsjlf4e0CZSZkWY71nkJnzKdT6nrFyASevXWEdCmoUIVIObaURnB6dsbjukXFEqUVtq1ZLgUnN+eUCi5XNefnj9C3TziaebQWqFyjgZdeeshnHp+BKHjLMyOmhcc7waoOrOs+5RAFhxnGE8uLgI+SLCs4Gt9C6hFt7Ti7XCOlIpOaV3zgct0yn4xB5bi+4XJTs+4jZ4/PwDs27YSsqIhRcfHwnKZrmZUZVSGZTyusn9GHiFWC1XrNetPgncBFwXQyYlZoxnLGRR05X15wudrQ9gGpcmbzGX1vcafnkCvG82OCEJydn9N1Fq0U08mE+dFRIhQ37e7YijGiTcb8+Jgsz9nUG0ajMZvlEmdt6o4Mh98OnIg9yN2OdyT7jsj2+b1V/V6ODFx5nRSgZCLWKikGjslg9DYAErXrnqTjGLknxV5VB31FfsW/aurZ/+x3sC97jFBPeinX9WWu/7E2FP/D9Ekv46uinmqA4kQAkQIATZRkaJRIJmgiQh+T66gn0GFp6TFolIBkeD803wcgs70qFEKgohhAzsAOEH4AKAKvJFYJlPIILTAm8Sm0kigF0Q/mGDJtCFqBlumM3tvAKgaqqMi0QJlhvh/DsIlICGEY4wiIyZXUe08IASnS+ymVyLxK5/je4bqeXGdApOk6qvEkbfTHMz5z/5Jm0zA+mlLkOa6tKcucIjPI2PKmZ9/EnUnO5vSS1aIjuMD4ZIIJDm1rqnHFMk7oY7LH7zaXXNQ9zz47Y9GDCFsX2ASyqknF2gUyHTHFhFs37zCbK4TfcPuZZzk+GlM3HVpJ6qZltVgSnOX45gl5liGkJMtzOhfYLJdMi4zZ8ZwbR3NChNOzM9pNw3Q8QagVq2bNcr0mq0rKskRrxappWW0aTs8uuHdzysnxnPHIcON4ws07N6jGE1adZVHXTKqMo8kEiEidOCCXvmG5fECmBc/cvsG67nj14Tm9dRwfHVHXNVFkSKV4/PgUKSXHR6mLs6lrZtMJRVmyaRNACYDUipMbNzBZxmKxYFRV6Gy7YcXh2ItXwMgWQMuB5LpV5iT1zjCm+Ty29sOnDgBm752idl4pB6nHKhGXlU5hmVJtx0AKqdSOhLtHJgdo6rqu67oA+Ejf8A/+/t/k5OeuvU++FPVUAxSLG7ooYLauJgJi8Hg8DNyTSKTFsqFLnRYkOrqUZCwEMg724AM5UB3IjqWIICJ+d7kqCULipSBqQZTJ8ExJUEqgtSA4TxgAjlICpRLXJfqIHWTMqk+PKe/pnafrXZJ2JuYnMWxTdEEpSVHmGKMgBvLcMJ1OcN7hQ6QMhs0q0PYtuda0TUdr+2F/8mRFxuK8hxBSKKFIfJuiMAixRkRBUWWE3LKozzlfttx9c0GpArcmI+7Ob/Fbn3jE5XLD5MYRpizwtmM6uovTLZvcUBUZWRZprWeWaUJoKXKJyUrA4x1kSjCfVxyNYCxTRyjLJGJUYfuWm5OcygSc7widZ1WDF57bt24yyjyl7DGjOYv1msvLBcdHx5STESZTlKMSozTN4oLLtkfmFU6kbb/IMuzqnIikuik5Hs0pKksXLVIGykIyKSwitgTv8XrMerMgV4LbJ0c41zEtx2Q64/defIXMFBwdHdG5yOnlGqMNJyfHbDY1eW6YzWeEELC9TZJjAcYYbt29i5aas/NzxtUIozVnjx9jrQX2ycV7eivDI69V48irHRT2EvrPRg0DK0pc5ZiIwSVZKTWMc9TQUTkwaBs6ejvS7TUgua7r+oL1ipsy/blfedLL+Kqpp5pW3kWHi2FniKYQZOh0iykeUCKJQI9nQ8eGji70ONzAB4m7E7yKEhMVOqSsHhMUJqqdE60crPKjUngliVqhs4xRXlJlGmm2JNjt+ChiNGRZShBWWpBpQSaTCVgIae0hCjwSH5J3aSQQgsd5n2TDMSCDZ1QV6EwRBHRdT9cm/kbw6T1CTAZlUnosHlNprHOJWCv2YEcJhZFQlAapFZebmk0o8WpG7TRn5z3BRco85+6NY95ya8Id05CHDuUa7hxPmBUBJ5L7rHWRru2RZYYwGmctRkkCCiIslys2TU/rIj5YctVjsETbYoQjK0qEUETfUMSGkhZEZLPZoGOgyEt0OcWpiqYLGKkwMmCUZjafkGUp4PH87JTVcklwHRJLrjUyprFRLwvaaBEKtPYQLN7alF4sBDIM7rKxJ6Do25b5dMQ4C2i3QnRLbswqpqOCvtswnU0wWuGtZT6bsF4tOXv0kIcP7rNYXHJxfs5yudqZsc3nc7Is5+HjR5RlQVkWnJ+f0tQbYgxXNv/UzRs6KrtR5BZEJ6m83PFOto8Bg+qMzzmCibtOiVTpONAqeZ0oqRM4GUBKincwKKn2QEhe5Z1s13RdX2TFyP/y+/7ak17FdV3XU1VPdQelw+N2suB0pZicNZOM1EZPEn86XPB0CHrR0wmDjY4gDLANHUxjHbE79QNiILwSh+6GQkgNSuG1wmuJyTQn1ZSjsiTXLa1I3hlx54uxNcASEAW5SR4cSQ6aQu8iSarb9g7vPUWeAQwjHYXtHL1N/IboHeNxxZlSOOcgKkIMaJ3hrcUNmum+qTm5NWc2n1Dev8SoBMOkFBQGCukojWd6fI/1ZsPLjx4g6oAt75CNJUZqZKWZTXNmE8Pb33zEsreMx4ob8wxvc4SK5HmBEJGiUFRlzniUowWgAq211A60CHTWUlUZWZ5jcoFtLUIKiqLiYhWIUmGyAq17vI0okeP7BW2/4eHpY7SUdE0LYhhPaIUXCpPnmEyTmQQq+qYFKQkRpjdHdD7w0v1HPPv8De7cvEs+GuODw3YO52PSy8a0AcsIUiWSMsCozMhER6agFx5Pw8l8zisPH1IUBbruUFqilabZbIhEbN/z+NFDYogcHZ+ktw+BzWbDyy+/wmw6YTqZcProEfVmcwWA7AmofFbH4ioP5UCZcwUk7KMJtz8jt2McJYeuyV69o/S+g5KASnrNrmuy9T85SDxm6Kxs3/+6vvhSLz540ku4ri9jLULDf/sn/hOgfdJL+aqppxugRIsl4AeAEknkVC0SHyVxUOLwfMCJ1FmwwmMHT5TtVaEaiLbpOvXA9D4KVAy752LUeBRRSpySGK0YZwWTMkexTzMeeK2DUdlgjh7T52mTcnsgJuVJ0xHROB/praUq89TxiJHpZETbLhECJtMxm82KzGSMpxPa056+dzRNjxhIwdYmYq0LgrOH50znBaNKUeZJFaSVJteSMvMYveJ4fouqLHG+5WxzTtMFTu4dMz0Zc/5oidSa8bjgxrQgCx4dU6BgNp2QZdlA1o2MZyXVKGM6nVCNcla93alATJ4htUZlBmUyrLMopSkrlUACYtC6SMoiwxhF13gQPUbnHE3nKRIgKpQxSBEo8pxMhiSZ1opJYVhohROCznq8r9HlhnI0ZbU649HZQ05OSlZU5D1Et6LtNZ6QiIsi/YMFHxNRWEKmJSY4MgmZinSxYVyNkDJgtMBoiVES8GksR+oseO8GqJCA6mpVs9lsEEJw48YNHj98RLPZ7I3Y9gKerf5m//0B4XU30/mco5yD9xi6dzs7/CHt+Ao4GbgliXMiEWoLPpLnj5AKpBru7xizn2eMdF3XdV0Aob0GJ1/KeqoBSk8CGVtzte2sXQ+kWYWAGIdnhw1o2DSSeVvcBbTJHTslAYcohvA3IZDRI2M63QcBFoEK6WumQWaKXCeTrxggTc6SrNR7TwS0Ttk54EmW4YK+D/TO0bQtRTFB64y+Sxt7ZhRCOEbVBEVksVhy9+5NrDcILSmrgoE5idaGvkt8FCkcRVmRVzmL5RqhAlVZcPM4o0saaIxSKGnwrufs0aeYzW6gdUlZjLk8fYiYzji5dxcfNL6zZOWUIGE6zjCFROUaoRxIKIuC8aSimo4oJznKZKgsJ8sMhRaURhFD6mgoo/BIVF5iu57V8pKIwdkcKRTlaIIyNUJGRiJHKM2sHPP8nWNcs8TODL3tEUJSlhot11ysIBMeVWVMxjnLxRLvPT4E1hcXTG8e88IL95iNFN73fOxTnya85TkKHXBhAAPDfhtiJHq3/UdEbhUvKpIZCAi0imQajBIYnThGiRydOhth6HAobZJSZ9PgvcOYgr5vWa/XFHnOWkrC4HFzmLdzCAF2Ix6uxjQML/7sDsZrfNN2CiD2Xby9Z8rASZESoZO0OIEVjZIGqa7a3DOsM4o9fPpC2TrXdV3XdV1/1HqqOSiBiI8ehyeIBFGSVfk+TC15oQyOnjEycF6HoQ5sY2nFsD3sLMCHePndRhEHp9kYcUAnBF0MdKGj8S3eJz9QH2JyC43J7M3HiHMOY8zOuCsiEm/DRqyHruvIjWZU5ikbB4ExGW1do6RkOp1Rb2qyLGM0HtF7RzkeUVQlWmtm8zFZVSCVZDzOKccZs5tTbj9zE+ctLjpms4LJKEfKQF5EhAZHyWrjuP/gJT79qU8SgmM8HZFXmqwccXz3FnJcoKoSkynmE83xPENlSbo9P54wm2YcH40IShCzgqg1oppioyTPJeNCYXuLB/IiQ2c5fZBcrtd4IlFKhDJk1QincnqVg5YYoyiykr7fsLl8RL94QHv6GZrHnyEsHuAW92nOX8W1S5RwFBlUpUYqgRt8bLIsIt0FNyrHW58/4eaNI5pmzf1HK6yo8DHZ1YcheToGR4x28KvxWOdAapQCpSDLS6xtyY0m0wKdXPPRmSbLDHoANFlecHxyAykVdVNTVSVvftPzTCYTHj56hNSa6WyW1DFi3/m4+su4b4fsxkCfg1fyWXeHRt3WyOdQprzzMzm0r9/yTnZjnW3nRO3GPDvfk9eMlK5HPK+vYt3wP/vt733Sy7iuL1P92f/pbzzpJXzV1VMNULbmanE34rkawqaHbooWqZsi475PopE7ECNg8E8Z7PO3J3oYuCT7fB5PUuJYKXBK4QT44IDA1uJg6ya7BSTWWUyW7Xr2zkc6G/BBEKNgvVojCMwmY5xNxNeqrOi7NL4pyglN27LabMjKCpUZqvGE+fwGCIXzjrIyjGc5eakJwKZpUUYxmlaUk4Lbd28wrjR5FhlPBOO5QGcjoj7CihFBZdS9xxGRBpy16KqgOBrT2Z6yLBlVJfPJCCU0PgSOZiWziWZ+MqMPjk4UiHLOxmVcrFaUowIlwWhFkRvG1Yiu99Q2EnWBqWYg05hk3bR0HrLxlGJyzHg04uRogrUNq3WHFxVRVch8gipn6HxCVkzIjMGHDutbYugJvsfHSDXKedPbnuWFt9zlZG7QqqUa5TiZUXceEbMBtIJ1iWxtY8RFT64iUio2m4YoNEIptClQuqCu11RljooOo9KoLgbPdDpBa4XJMk5OTsiynOVyhXMeYwwIuHF8gtaai8Ul1XjMqKqGTT9l24TtmOjgtpUbw1WA8lqVz9ZWP4GtuOtuHAYJXgkJ3PFOBGrI5Nmasx1m8oikRd6BFA7AynX/5PVVWK0Y/x/GT3oZ1/Vlqrf+4ItPeglfdfVUj3ggdVFCTADFiTB0RVKLXkdJgcYLg4gRTSLPaqHQcbiSFDLZlgsxoLVtmErcvbfH71xo7eBKK6Ukao3KSyajCXcnM46Lcx6ofuCfJPJljBD8lnuiidEREPhBfRQj1E1D1zYcHc3YrBc0Tc3sZMpmGbhYrrhx4wZCZ7z64BHPVzk60/QucnTrJlEpHrzyEN9bsiqnrBIPxEbFYrlkMi+58+xzZLKk69fMKkGmDdNZgRfw6sNzpIK8KOibhmBrbt+cs1wsMNUY1ztEmZFXOVkWGY0MKMOmAyUj05mmlTdZrS94dLnGyowQlzTWMZ0eUzw8xyjPKFMYo7m4vMRNRhTFMc2qxjer4aodlo1jZScUqsDomhuzEQ8fFJyendFNjkEW2BCpO0EWFVprmtYSAhS54XiaMZ8ZnHbcPhlx88YxMpPMTiqyTHLZdPQ2YjJDDBYRPVprVsuathqTZcdoAr63VFXBxeUlJs8Yl7cR1nP6+BJrLScnJzi7QhMYlTmX6zXz2QnPPpfRB+it4+L8kqoqh2A+Qb3Z0NYNN2/c4PGjx9Sbhul8jnOOvusGUJEUOWwzW4QgwfD0dUtWSUBl6O0NKCHGfX5fCiU8GBQdeKZsuyJqkBZrpRM3Seqd7b084KEIodgnGasdONlzYa7ruq7rm/7Jf8m9zYee9DK+6uqpBihyGJcEktrGx5D6FsOVqBaKImZEQrpyJAGWLCbXWRnlQdt6f7LdkhvTe3psdPQ4uri9eVQIBAQmK5nPTyjvCNwisFp+kk+2G5yTaV0x4gMEbymKnLZxBB8H+XAaB/kQWa1WPPfsPSbjEtvWSDnDZJr1esnNmyfcuXuby9UZnW0ZTW+yXNWMxmP+2J94OzdPTnj46BFNn0zBeilQWlCqnGoy4cbRDZrNhtFMMpqEJL+VSd1UlBmv3j/DsSb4wM3jKVJrLpcLjvIpTWMJc4GPkaIq8MIzPhrTR6it5/lnRvhC0rmOi+WSumsp8pxbN44xXvLiKy26rrk9V9y+M+XxoublVx+AkNysRoSuQ2iJ0SUvv/oq682SG7MJX/fsmKOZ4U3PPcPHXnyJ09MH9H1k06URTFloCqPxEXSA+SjnSE/xfsN02XDvzhHjecHjywUPznqkgstNjVSa49mMvtkQQp8Iua7nk68+pixzjiYlmWuZFIaN1Lz66gOyvKSzPYtVy2wyYVwaTteniGA4mlZcLDfcf/SAohzRtJbVas1kkjKOHjx8hHOOECLn5+fcygzHx8ecn59TFDnzo2POTh/T9ykMEfaH4uERua2rx2rq1cWBMJWmmFtwMjy//XYANsmQbWvCJnZjHqFkkhtLnX43xJYgK2Cr4Lmu67quz6o/+ZP/Jc/8098gBv+kl/JVV081QNGJBrsb8aQkna3VvUSjEskx7o3cSnKK3b1k0radq8eBZ7Id5bjocTi6aGlDRxt6mtDTeocKgT5GolSU+YjZiWH+Qka76VnUL/JgYSEIUvc+4n3LaDShaWq8T8m729a8FLDerIjBcXw05+IsmXfNj45Zv/wK55fnPP/CM9Rhw8sPz1huQMmcvq8RKMbjE4QwnF2eUTctRmvGR2NMkTGdjBHak0VD2WmqcUREgYuOdXPOyc1bSJ2xqh0gKAvN+cUZd77uWY7vnjCuDL7f0DQdbSdZnbY8d2L4+hfeRF4Yuq5DRsfdO3fofCBYy3w24tZI4i4Dz949ohIb3nwz585RjskLXvUR23VkOoIWSO3Jcs1oVGIyyXw6oipGxFBzclRiw3NcXFyyqh1y3aHzjPHIkCnwUSC04WhUIhsH8YjZ0YiTmydM796knJScXSxx3jOazJlOpshoads1CI/WBbPpiEePzmk6R5FlRFcjQuD4aIqPkcv1ghAUR7MpR0dTLi/PEN4SrCXPCp65d4uXXj3l/DyZwZ2cHGO0YbNZY4xGazUYCDouzs65fecO0+mUumm4fesmve25ODsbuicDS+kg+Xar5tl9/1m/CQczyd0jW+k9Ox8TubO2V7sOilIM+Tt65xorB3+UrXrnkCS77Zxcw5U/fPkYrpONv4pq9qIndt2TXsZXZT3dACVuYcZAgBUMrrBJlZOCAyU6SvLBtC0XmgyFismEKkZ26cVbwBDidpyzBSc9TeioQ0MdWtpgUdHTErDRE4VAS8OsKvkTx0f86ughj5eL5DI7WIfHkGz3pdJ0XT8obhhCBQV913JxccbNG8c0m4LFcs3NW7cZTWacnp4zmR/T2hEf/fhnaJsLvuEb3sqb3jxOOTXCMsorqvEtxlMI9ExPxslGPQqc67A+4qMjLyQxKhABL2rOzl+i63KclVgbuTyvaTLHn3r7baZixdKfEaMjyyAIh7Mg+iWFNDRNjXARUxhK1XIyHkOQKGlx9YZCbXhu1nFDFhyfjDA0FEpx+2SMbyXN+oLMSLT2VGOYndzkeH7CvPTofoX3DRUNt/LIaKJYKU8VA7qUTCYZVa4IMe6IrFGUKHmMaRLfZJJBefeEGycn9C7Q9y3L1ZLVcoGRfdrWe0uVj3j+3l2i90TXsF5eIrDIUc/JjTnTo5OUxxQ61utHuK5OICIGmtU5RXXEW55/hrr3WBdpm5b7rz5AmZwbN2+ilOby8hIRwXUdy4tLbt+9g9GKsiyRQL1e4myHEJEoxNVOyJYoywE4iXGPS+J+xJN4KGFQ3wBy6JaoPUFWbQ3atnwTJQZgMoAUmbxREBKhtkBlkCELCWl6+TlIu9f1B9av/Dbv+Dv/W371J/7Zk17JdX0J6mW3RtprNtaXq55qgJKIr3vfBxHj4GciBoJs6rCYIaRPINBC7bone3JhwON3F6A+BvroaLG0saOJHavYsIwN69hQxxYTesrgaKPDDUF/KEllNEeZoVCCLgyun4N7Z9PW5HlBvbHs2ZBiyN1Jzqm3bt6gmox5+PAUnW+YzU9Yrje8+KlXyca3kOaEzeKc//j7L/PgvKEsSqIKTEc51juKPONoWrG2NUUhyTKJUYLednTtBp1JhDT44FEagnXYrmO1bFmtSZ4to5zzizWvhk+QS0sxGlPkmuN5BVJQ6JaLBy8xeeY2XR9QRoKw0PcQPEJDlIIqi8zzHiMUqsxwRHANwvdIGZCZwnpFXhmq0lCZDiM2ySLeu2TkJiPWrum6NWJdE9Y1ba0x/YhiVqKHq3+GTTcYRe4UPvb0doFWGRMjqL1ltb5ks6gRUeFU6jHgA21dE6MnuA7bNoTOYgSI9gyh1wiVEyJY6+ldB35LYAUZejaXp7iY0UfFuu5Zrmq6tia2DW3bJNWWtengipH1eol72SUpuUwqM0K4Yr52JZAvDrTrPclk9zuwpYUPtKnBGflA9bPtnkh2XZPEvVIDWXborCiVwMj2tuOgDKRYKXfvO7R5rusPWcXC80uN5NvL8KSXcl1/xHr3P/0RnvnX//5JL+Ortp5qgDKwRYbRThiM1yIyKlRM6p0kJlbDCfWql0TimWxTe3ZURHz09Hi60NPGnk1oWYSahd+wdA2t78iDZRw8PREvA0Jp8rxgVo04rnLKXOH7kDYGKVASnOspioK8zOmaDkEi0EqZdpKmqTk9O+Po+AZZ2XF2vsRkhnvP3OHh2SmPL05BT5nfnnN6dsan7r/M8dGE0WyM79LfxbJ1nF7W5EZR5prJVHPjOEOrlk29RMiIzjXRCYQMSBWpKsNy3dE0PQFYSsfHHyxwm8DzNwxRtvQudXtMKREEsjwjuo5iNB6kup7eR8ajEXk2xAv4iJl5RoDMDHXfY0MNAmRWUpUjiAZtQBQFQWeILMOMpmB7ZJYT/BnoGiEl2hgKk7PsLJu2Iy+SsVuRGWLocT4SHQTX0m9qxlVOVuR4BK7rWC2WLFc1RmmU1knD5T0xWLx3BGdx1uKdR8aIjAJkA3IzKG3kTnm1jUjQUqCio+ssbe/pu0hwDimgt5beOq6MpkVylm3qDd3QmFDb7ogYiNXEPbrYH+rDMfva419s5WbbL/tOi2BQ77DL0xHbgEG9lRjvybF7k7bBRVYMBm1bIL1DPKk1c33d+Ier8gO/yt/803+D3/svrrso13VdX6ieeoDioscyGGohsUMIIGIY9EQ5KB22LYvhfC6GIMEYcQQkfndSdySn2S5YmtiziS3r0LL0LUvf0PkO6y11cLREnAwECUppJmXJjSqjygWNT2nGUqa2PcHjbMNkVAzfO0CiBp5Kay33H5zhYkZRTtmsTzm7OOcbv/F5qnnJ2X94mcePHjM5mnPv3j2C70BsUGZBkG0i5gaDxOAs1E3kfNXRNJ7paEWzWVMUSa2kdLKkM5kgDGZy3pMM5KznUy+dYieWUld0vUdIyappmeYjnIPpcYXvA6JKuTC+64nRoHVGMRrhnMf7RErGWtq+o3UeL0rINFpn6CxjuejQWUlWjcgLjTZp5CVNjoqSkNWILAc25EZT5Zp12+F6i+0NWonkr6I1MXqiSK69dVvTtQ3laAxC0PYtl5uadWPJdeQgRDh10LzH+4DrI9EGREjU67S5C6QcVDSkzKPhUEGKOHQntqnVEaMlIeik0nKRFFB9YBcLu6RiDo5NKeQOXMQYiSJyxb11eO3WzH5Hio0QQ/KUOQQ1gq01vnyNkmdIK1YCqfQepOxuet9FGWTGe4kxAz9GHJBxr+u6vvbqv3n4Ldz6rf5JL+Orup5qgCJJu4wbQIYTSQLsCJgYCGJ74k8+JzsibUwkRB8TSLkCYGIiyfbR0sWeNlha75LPR3C0wVHHHhc61rGniRYvRDK2UoIszzguc6aZ5qJ1O4JiYstGvHVo6SlyQx1cch/1niAEbRfxfU/dn3L79h3GsyOcu+RydcmN27e4dfs2pxevcnp2xrPj29x79oj5yZRs3LPaWJaXiq7WCG8AhbeBvos8fNzw4P4pk6rnRBuUdzsXUWUkto/kRmE0uCjoLDx4XOObwO255+K85+goARXnAy6CLkxy2BUh5fx4RZ7l5HmBrub4zqG8QjhP19Rs2p7OgheCKAP9ZkVwnvXGI7zgZCaYjRU3judMjmbkWpKpHNs1dL2ltY4YPVmmyDNFHwTBpw5CDBGUQGqF8BGtFK53rNYrqtEIoTVt07KpO9rWEXXE68gODsSADwHnPK6PBBtJ6CIBkKR6iUjpB/WT3CrZEUOmkpQRLQVaCYwSeAXOS4JMZn1i4IfAVYOzvcjmc5uxHd6Pu+8H9c7QQdlxULZgZfeDe4M2NRiv7U3ZFELpAbBo5FbBs+WgHHRShFBsu48JmFxPeK7ruv77X/+zfP3/+GtPehlf1fVUAxQdxODYGnBCEmQgxC0M8UTpCVEgoiSS/ExcdEQCymukkETBQIpNuT3EJKnto6UNlib0iQgbQITkX9IHhwuWdUhdFEfKNCnzjGx2xFtv3Obeg1Pu1z0hpg3ED7bqkkhwPZkusDrQ9z3OQd8FNnVMScBtysB54YW7PHvrGZxbcHZ+ilYFxycnrLuGmDs6vSSfGu49W7HaWJCRJRpXp+C3YqSJS8/54yWPH5xy54aiqALaRJQyRJmsy01mKHJNZiTegveR1dqRy5baTqmXa4o8kFeKoDxegsgMlanQEjIp6W3AZBJTFMisIoaOqDt639PajkXdcLnsOL1o+cxnLnnp/hprLePMUJkcLS1HE0NVaP7025/lxnHBc8/eoAoe17QoqSjKAmstF+ua1nq63mKs2nUUXEwp0II0juq7jr5rwRlWm4ZN42haR9DgdNyl9RIDwQesC1gbUligDxADUkSMEigZhqTf1O1KXRSBFBEtSbcBnDiV7isp8DISAgNt4+q2njp2Q7yfuPrEFsRs/2yHFUlqMzXcOeDLXnlteou9nHjnc7JT8Kg9OFEmkWSHW/Km2Y549uTYLTS5Zk9c13Vd15e7nmqAkk7UkQBIAi5E/NBFsSRugSNdKR96mkAkI6KFhMhgxOYHx9hkn2+Dp4ueLni8T+Ak3SB4sD6wiZ4aTz9IBpWS5KMpb7v1DH/8+FU+cX7JeZukzxGPiMlVNMaAc5Y8z2l6T2MjTQdN55AikolA2y5o24Lx+Fmm82d5fLrgY586pesUs/mUe2855s6zmlGxJEjLfD5ivepZLyJt51FKMqo0OpfU/UP64Hj1UUdZhmSrX5H8M1AonbwxjBK0fUDJ1Jvqe2j7jiJKjFRYCyIaQlDYPlBmmnGm6esGGSLTcUU2mhB1hlQtve3o+5a6bXnwcMVv/Yf7/P6n1zxa9CzbgIiBosgxxhOsQ8sVGsdvv7jmz711zp/9Y56vv1uhQxohtdEjVVK+tL6hs57cepzzaJUs9Zu2pesdAkEIAdenztflsmXTOLouErzDR4mWaQQSQyCEiHOC3oFzguiSR40UEadSZ0T7NMYRUu5GLLsuyy63R6BlTK9XAhcEQoKMV52OU+0hy2uoJUSRyK6DexuIoVty+Jqh2xej2HdQDt9byIEgK7lidS8SaBFCJJCiEzE2EWX1AUF263+yXaX6bM3zdV3X12C998X/lLf/3U9z7Xzy5a2nGqCkJOMk3w0hJsvumMBCQJGa8ZYtlHFDt0UJgQekP7S8Sr4RdgAnfejpQ8D6MFiiy2SHH5N7p4uBznv66HFovIIYJYKMG7Mb/Om7z/CRszPWp4vUPYmgtAYpiD7gfY/OFXmZc7G2dDYgZKAoNFUuKTNBs77k0UNFUT7HrZu3uHVL8PjiIcvLBUergiK7xc2bGegW5SXzI8GjBy3ORnrbU0TBC2+9ydnlb+Kl5/K+4MF9z3zUkxmFzgzKaLzt0SYlMxMDWiaZqouBx2cr3jKfcXm2QqicclYmMGNU2rzbhna1YDqekucjpB6BzIbk5PR3dnHR8P/9jVf4td85Z9U6hAGZSYr5nGf/9HdQzG8Tz1/k/MXfp1lZfu/xBW+9WyGzGdZ1aGnQSuGtJyKpRiVr6+mtxQdP27RoKQep+WDDHiPBe5qmZd1H1k1P1wf6PknREZ44BPwRU5fD+YD3EecCtk/dNAl4BZkmdUNiGBoLcnvgDB2bBPK0BzUE72kPyoMXIXWrQtw5v36uzX1LgB0MZIlbUHIAPOLB63Zfd0qe7fAl7sZFWyAipDjwPlG7DB6hUrdpq+5JzrEH451dkvHQ1RGvAVPX9Ycq4QVdtOTCPOmlXNfrLB8DL6/mjB5+8kkv5au+nmqA0kaHI/k+5KgkFI4Q0Tg8g+8V2x5GytMJZELhRaL4pRD5xEbYmbMFn0L+QuqqbOWdydhNIqMiBoWLIvFfosMGi4/pJJ7nBS+c3OLrT+Z8YlVTO4sIiTArRNyFDvZNizIjpuMJtl8mPxUVyLSgKATaRFbrFS+/8go3b9ziaDbizq0brNoWaQW+iUyzY6w453x5hhURjGfTbOis4+7b3swzbxrxmfsOKQP1hWax8FxceI6OBEokJUuMAq2TB4b3LqlqlEALaOqIm3msl8wnBqMEudGMq4JRnqE8RFswOR4jsjE+DEJvHwn9hvViwX/8+Cm/+YnHbBCYsUJrSZYpZveO+fP/i+/iW7/pz9EtP8ZHfvUDfPi3Fjz88O+QjyrGJzNiPMU1jr5L45tc5QhlyLMCNxBbg7MQA3legNAEWmTX4HxkWXdcNJ62jQSXuirWB4QXCDxq6AYEH/EenI+4AC4pfxExDtEFMeXuRFBqC1IEYVC1JLVWAipaC3SIaB3RQeCjILp9x4VBHp/8Tfbsjteilx3rY6suFnvjtW2lYzwBcMFWBZSeSNYlgyJHDnk7ctstSSnGYnCRPfRIEWo72tkatSXSedyuTQwU2esOyh+6nv+xf8+f+IYf5GPf/i+e9FKu63XW/+XyBUbvvgYnX4l6ygGKpXOpi6GBWjiaaBkLQzZQaFXcxsIHYgyIKDBCk4tIJjQZcncF6ga7/O3oyMdkQx8IiAAySGRIIAUSOOmCpw1go907d2rDeDTjzmhEpQVdkIQQCD6CTid3HwRd5/DNGpONmM+mtO2ashCYLKI0SC2QRtP3HY8e3WfVKNbrgIsZMijwAu9gejTik5cvcZEJHuqWlxfnVCanDhc8uHzEaKZpFh2Z8dQOVmuPdRLvA3oYDaQNOkAQA0dBEGxAKoV1gsnEYIzCeU0UGXmWIYXAdZbgIkJliHIKOkcohSKwOj/nt37nM/z7//AKUSvmY0PXS/JccOOOobrpuXtH8effeof7D1+ieW7Jpz++4DO+o3WO0dEc0/cszi44u9zQ9z2m6BG5QZsMokRpgylGqKxKG60HpCYESec8XddzurFsmkAKmU7RA96l3CYhE9Hah2FEGCIuCFxInjAihkSIDQncBCQ6BpSKKSpBJZWNEgxjHdARjGIYDaWE6+BF6qIctkI4pJ4ObZMrm/5VOupevXM4LEqdnh05Ng7vOXRNxCA13kuJxVUeij5U76QkYwaCrJSH/JMtQElrujr6ua7ruq7r+tLXUw1QlrHHRkmLT1e40bKMLSOhKdDkKDLSSCYGTySgUWTRUOCoRI4QOdlwsk2b11Z+zC7FOMYIXsBAlBUxJR77EGi8pYkeF21KVpYSkWmqyRFvOr7FjerTLKwjoIgxJGKujBDTZzjn6O2KrKg4OZojRYeRDiFBZwYpZSL8BoGzG5aLmsfnnrZf4PUFDzee7J7jcX/GWet5ZSWIusBqy1n/GW5IQ340Rd4/xZjUublcOFYry2ScYXxACpV4NdahVERJSYx+CIgTqTOlQJiK4xu3mB2NKasRVaZ5fL4gOkUxvoEsjxDaEGNPvVnwkd99mV/88H0a1XPzWU0fJYuXHEIZqhsV5YnHyvus7H2W6/8JVX+EqZa0m1Nqe0zUiiwb0wvFsuk5v6yxomU0LhhVOVFLxqZA52N0OQHpCNYRhCQIw7rzLJqe01XPqvF0vdsBBCcGmqdKG3gIKQk4DAIeH5LsOoZECI0yDsKeQFRp9GKICBmHboJAiaTkMTKpeLQWaC9xPqJUQhBe7JDJ1a9fzOBk6OTsHXvS9yExva9kDO74JnKfTrztlKit/4nUKGlQyiCUgkODtq3V/dAhirvMKnadnut8nuv6WqtFaPhv/z/v4ev4lSe9lK+JeqoByiUOHzUtjm3Anw6CEkElNBMMaYgBMqYrZoOiGOzZkjcFIMAIhRhUIC6mmw8eH33KzokeFwLeh2GsEOl9oA6OJgiscMmDQ0hQObqK3Jsfc6sq+fSyHdQckd56vD+44hUpFdj2G7KsZFTm5FmGEC4BBl1RFBoZYVRlzCaOR48uU4KxXJGft6gLi5pLKDJylbHebIiqw20Cp69qhHUD6VPgfWTTBNa1p7eBzFq0MUlayuD3wdaNNNI2FnVzwuzoBuVoSlGmDohWmhAjzsd0NV5OQBcIEZDe8+rLj/h3v/UyfW659UwgYnl8ZrAWogR1MqW403Buf4fffCyJq39H19xH+9vgI8JA19eIsaaYjlGZobbw6rIhrwM3jyXTScGk7wi2wfsSnZeowiNaj4uSRQunK8/FxtM2Duc9iaqy9xEmJp5GiGlcE6MkBE+MAh8lMSTidAxpNLeV9qYRSkSFiBjAjpIKrcQuwNAEgdVgghqCIdMxsOeavHaDj9uGyG59+67JIZa5GgyY+FNDGrfYv/dOvbO1td+ClW0woDKDikchlUFIPUiP1QBO5U6uvAUquxWl9tOX9Pf5a61u/r8KfvbPnPC+ydmTXsp1fZH1qot83X99DU6+UvVUA5RH0eID9HiCiLjUiycHpmiOCZgIyRUk8U0yNJaIFQEv4iApdhToRH4lpPTiIRjQBU8IYKOnDxYbQuIqhMR92QRPHUVylB2ubYWUoA2jvOIoyzEC2mF7G+w1hvC2iBICo9KmHWNP0zm0KhiPSpRJHReTGXzvyI3h1knB4zPD+UXP8qylKjU3KCmMAhx122FbSzEB1Qa6M5u4N53B9QIfAl3vWS57bJ/GGEqlUQaRgcjp0doQnaUsxkzGx5R5hYoWLSN5XqC0QYmA7SxFrkFKhNIE2yKd48P/8VUeLlccvTmgji2n54qmD6lTQ6TJFdVYs2w+ySdefpXb/X2UEQSfiLVaSaQEU+SMjueM5iPyy5b2oqFpPMeqwqHofRq3EFqCN+TFBGECfVyx7iKXtWPTOLwNhKHzFWMYFC5pmKfYSmjj7j8fSaOZlB5JEAMFdVDUCBkRYbjJ9LPbPVyIgSgb0tjH60iMCu/T+yWQsmN07Mc7Vyc6+4r7LwmID+qeGJKMnkT83TFYxFXXWDFk6MitB8rOjE3vviZwcrWDcghQhBCfpUG6Bih/tJr8d7/Cv/nbf5z3TX75SS/luq7rDVlPNUD5eGgI0RPxEFLbPpJ4FbPo6ERgFAWFSG6ykkghHE1UlMHQSUeHo0YjgkAFgRlGQk3o2IQW5x0EiQueNnTY4HAh0IZAFxwLZ9lESY9P/BXFcIUryE3BSTWmUIpFG5Krp0p8lhgDUoESyc1V6HTy986zWtUE23N8Y0JZFMxns0TCRDA/atH5hI99coX1AWU94bSlZ4N1PeGBZFKmHBzTRTIT2XSSbi2wrYAocT6wWvfUteVonidwQlqfkgJjVOJUlAXPPnuH4BTdekE5G+O6gMkrdD5GhppRnlEoiD6ANCAc61XLb37kU5wcK6p5zoXswAiCSAnPnpxFcEwzQ+0a1otLJnFF5iOv3n+ED46izBhPJqjCUM2PmJzMqc7WHB+NaV0ajWXlBCc6gpBIHPiOPipiNqUTl9TdOXXn6K3fGavFARwoKVA+7kiyV/gfW28S0kiMEFJXQyTllxRJ0SMHkKJlIlnHQUUkB5m2jhETBilwAKMjISTJ+SFh9nXVQDTZdXIiKVxwK0lmqwROIxkpxUCOHUzadBrxJGNBvZcWq73NPVuAMhBi90Bly32JO67LdV3X10r5GPiv//O/heD/96SX8jVTTzVAuYx9UjDEdMJP9uSRPkqsEDQSjlBMUBQiomKgjYIiKlrhqaNihKNEwyAdjjGgQiRGTxftcD9Jbpexw4YeG5LD7Mp7Rs5yGSMb2eFiwAuPFgEhJVU55+uPTrhZvspp7Qhb5ZAELwfOQiYoKkWWCwKK9arDtrBuPOF8jQ09zjYcH02YjDXHM4VRI/pNw0c++pj1eaBaRMa3kzusbgQ3CgUEwkZio0U6yfpSsFlbohNECW0TWS0c9sST5XrYgAJlLsmKpFJ65uYtpBT07SVVecR4WpLlhlIrjPAEk1PkGaFpEMoghMJ6eOXBKZ+5v+Et3xApDFx20K88QhqigjgF8+YZ8bijbVbEKgMxYrFR3H95SWU0z9w+ZjI+xhQe16wZTQvu3Jzjo+Ky9RijmVUjjJJEhg03BqRvaWqHkyW9KOldjQ+DJAcGDlDysglyGJ0MwAIhcUOnZNsNST2WvVW9CCCCREaQIaK2Nq5DZwXiDkxqCUYnPkvQEhcTwTZGwG8bI3H48S0YYC/TCen5oCWRAFEmMCKS9ixGQdiuf1CbbX9cwuDzslXq6J3XiVQKpXVS8hx2UKRGCL0b74hdS2i7pkNRfiTKa4ByXV9bJX7lPz7pJXxN1VMNUKAnxuT4ubNsi5GIpBMaFz21kBRRMouKUigqoI+eDBhFgcSnzgEpg6ePDhvS+/rokQiyqHZX3mEYAbXBsfY9C2dZhMBad7Siw+KSBbzU6KrkzvwGt8qS32eDCxH00M0RSYZqcslorMgLTdt7TCdxncO6ZDG/XrcpJyYEIhPmsxFV5fjGb6xAzPmPv3vJ+rylrSWFNmgRkMqhjGCxAcYl3gkWjy19BwgPQtL3gXrTYfsK30lEgCJTODRlUTAqZogA56cPOc4EgglFrpjPRuQmOeIKoahGIzAGo8C7lti3fPrlR6xax7PPHLNiibjU9AuHcIlkOp1k3DgylJnHhI71xRkPNjX3P6I4O43cmpbce+YIU5YE0aHyimo8ZXbUcb5qaIOgKHLKQlPmFXku0t+3qcBb6m7D+eUCIZMcue/sMAqJA1F58BaJ+07ENu5gu7kL9l2UsG+noORAng4JHIRh1Jfy8wZ5sIxIse2iJLKsj5EsaKKKBJXCFYFd1yN1X/b8lLjLvtlzUbZf92s//D6J5veW+YcjnjQuk0rslDvbToo6cI6VB+RYMciTt3+m7fRJHKxVym2g0XVd11d3fcKuWYVrz5qvdL3uIfIv//Iv893f/d3cu3cPIQQf+MAHrjz/V//qX92ZZW1v7373u6+85vz8nPe9731Mp1Pm8zk/8AM/wHq9fv2rDw6ihdhD6NNXLGARweKjpY2WRbQ8xPEgWu5Hy0PhuMCzjp4kDk4bSiYNudIIKekI9IPDrB9IkFqkE7qQAkTAR6iBZQgsfc8qNHSuI9ieaC0iCOam4lZRkukhYG1QB0FIfiAGEH3y7lCOqpIUpR7UGZqulzx8WPPJj59zcV5jbY+Sgtm45E++/QZ/7luOePZGRRYU9bJndRnYLBT1UuAaxeYisrkICCvJpcHotAFZD3XT4/oe17TgE2gyWlPlI6R3uHqFDJbcGIJzGCXJtCS6jtB3GARBpMA5QsB3Dc1ywcsvn3K2aOnWEeENooloB8oFqjLw/D3DXVpObMNMgYk9F48FH/9ooKktd27OuH3rBG0ESIGUBqUkvWsJwuN8R2YkBEfwLm2eOgNlaH1guWm4uLhkvVqTZQVFWSWZ7MA29SFl7yTpd/q6BQByIJeKARxEEnl2y0lJPJLEpUnfJ+XX1itlm62nhmwepQRGS4yWaK3QUqEHyS9whbey9bu/Ap6G70OMw5GaQEwIydMnDC64W8UZcCAxPlDySJVydq6EApodKVbuuihyz0HZmrRtAYvYutDKHZj5YusNdd54A9WvvvQm1qF90su4rj+g/ub/+r/iv3nh27gaTX5dX+563R2UzWbDN3/zN/PX//pf53u/93s/52ve/e5388//+T/f3c/z/Mrz73vf+7h//z6/8Au/gLWWv/bX/ho/9EM/xM/93M+9vsXEHoJGkPSgSWcx2K+J1PJOWbuKVkIfBZsISwQVng0ujXWAOZIxGaUwGGlQbOhDj4gBLSRGKTKh0RFaramUplIZSuZ0AZbRs5Ab1qzIESiRIVzEIJhkBp0uPQlhkKVKECKgjWY0zshHGmc99drTt+mPEEUaB6w3gr5xvPzSJb7vuXkyZTIrKQrJN7z1mOfuHfHw8Yr7Dy5ZrxwiKHIjOZ5JqjK5pz4473j1NHK+TiZ1MUDTRjbrhjLL8Ra0yKnKCbL34DaMM8lsVICLBCsQDnzbQ5HjmxqpM7IyQzQdMXqCt5w/OuflVy7oOrh43DF9NoIVZCFSisgztwXPnXiONyvoG7xf0V96PvFbjldeDIwywze+9RluzKfE0BC9o2s2NPUGiJRlzkwYxmWWHG8F9NajnUOrQB8Em9bTtY5m04M0FEUBMdLUdepcBECLZJgXIjJs5cJDB0SkjJ2tn8jwL7Z7vQ/J20QFmTopAcROehtRbCciYqBGy72Kx6jkuaKGsdFOnvPZip6UaLz18RFDNMG2qyOQ7IMCtySZbWqxEolzsnWQ1XovLT7M3JFSI+Sg4JFqGPOoIW55MGXbjnm2bZS4VXp98dc3b6jzxhuo3vRX/gP/79+/zV8ZL570Uq7rut5w9boBynve8x7e8573fMHX5HnOnTt3Pudzv/u7v8vP//zP82u/9mt867d+KwA//dM/zXd913fxkz/5k9y7d++LX4xvIOhhtLONLxMgFBGXrv5imtcTAkEogpBYBG0IbIRkQ8rTuUPgnlAcxYKJytBI2thgZY9EUESDlpIsCrzpcVlLzC2xs9jOsQiBs9Aw86vkvSIylIvYaNFSoYYTvlTJcRQhUBKyXJDlAq0lUgRCLmgLgcnTBrZ1NwXJo9OO9dKyvGi5e3fE7HhEWWbcHBluH2e85fkJ61VLtJZcRcaVQoTA+cKhlaDvO+rO0oXk7dI0kdUyUOXgrWY0HiFjQNmaSSGYVQajI65z4GOSbHtP6DqQgtCsGY3LNOLwHb675Pz0jItFx82TOW0D+VLiWkMuIWrPbC6YxA3iApwQdG3gpd9xfOYjEVdH/vjX3+Jb/9RbOT4aEWyL7TuaeklXN/StxXlHkeWMqoxRkWMyiTQQkNgg6IOkd2EQuTi6Ltnhl+UIJTX1ZgXRp65JVMO4JhCC2HU1BMkCX243Zhg6WluflBQWGAaL/C1wGI6+1HQYNnSNJGzNAGMkBImParDVH8z7hk/4LJDyOSgecWs8KAbTN7FX9+wOf8FOVnxob58CAQ1KDl91NtzXu9DAbQbPjhg7jIwYPiM9NKiQ5BcPUN5Q543ruq7XUd/xke+heuViCFW5rq9kfVl0gr/0S7/ErVu3+IZv+Ab+1t/6W5yd7XX+H/zgB5nP57uTDMC73vUupJR86EMf+pzv13Udy+Xyyg1IAMXXw9dufwsd+H7/feyHUZAHPMn83tFgOYsdr8SOz1DzctxwFltcDOTCkKsco7I02hGSQmSMdcUsqzjKR5wUEyb5GEzBRkjOfM/jfsFpc8HFZsHF5oLzZk0fAlqq3UXp1kNDG9BZRKkwcEM8SkeyLFKUApMN7p0y4oDGwrIOPHrccf64ZX3Zs1pbur4nShhPK0azktE8Ix9n9DFipUHkhixTlJkm1wKNQMRI13lOzyxnFwHnMoIP9M0ag6PUgnFuyIiUmSRTEt93iOBxbYtvarrFguh6pAzY1RnN45dZXi4Zz074E3/s6ynNBHeRIV2GjJoqM0xySeY94dJx/umej/1Gz+/9VmSzjNw5HvPnv/UFvv4tN5Eq0rY1bbuh7VqatqO3nryoGI9L5pMRo1FFWU4oqzlFNUfqinVj2dQt3m/TqQNdW9M0G7I8ZzI9Qmo9jEniwXgkARUIBxyOyIAlgYEsO/Bt4+BnEg7fh5B+RoCUEj3kERktMEZijCLLFMYotBls5YXYvWca21z1PtlmRG1dcK+MfGLcWe2/VuacZOyHCcZDB0UZlNZpVKk0UhqEMkg5eOEMI57tSEhts3nEQVaPVLvR0JeyvtTnDfgC5443UP2f/s//+fWY5w1c6//7PdwnP/Wkl/E1WV9ykuy73/1uvvd7v5cXXniBT3ziE/ydv/N3eM973sMHP/hBlFI8ePCAW7duXV2E1hwfH/PgwYPP+Z4/8RM/wY/92I999hO+SUluMLS4hyu/mPKN0+MqmV+IYfQTBQmkpA3GRslykJV21NQEnsFzJJLMMrW7E/cgikghDEGVeOOQuWBdWHTXU9s1j0KPth2x72l8ge4Di3rFyvbpqhx2VuPbSZRUafOKKimIjBaUpaK3ka6NCCVRCNo++bEgoOng8WlL1wZ0Lrlxc8StWyV5mZFnhp5A3/WEkJKK0VuX0KTsSBofsC7Q2pyuL4m2RfqaQnqkMUgCIngqnSNkQAOx68E6XHD03hLEBmUiSkmaxwsePlrzmU9dUOYz5uOSu6KnLDz3ujmffnzJ6eKcftNSZ5oH93s+80rPgweO2AnefHfOX/yzX8c7vvVtjCtDu7mkWZ3hmprV43MuF2sckqwaMS4KCiPSRppVyMwQgK63rNqO5bqlaV1S7wBE6NoNMURGoynT6Zz18hzvE0HaB4H0MZnlcCim2Y5MRHKAjamTEiP4LaiIKcNpq6TZ+pvIwWwtKpLFvUgjGh3BhIi2HqUFPqTjdftz+2N56IpEsQNCPsaUijx0UUJI0vmo0rp2Ypttt07K3ehmm8GzV/ToxCtRCgaDNqRO6h152EE5bOSk74LY3vvSAZQvx3kDvsC54w1UN/6vH6T+u57xk17IdV3XG6y+5ADlve997+77b/qmb+JP/sk/yVvf+lZ+6Zd+iXe+851/qPf80R/9UX74h394d3+5XPLcc8+B68AeuF3KdLIdzEiGrnnYSUyRavfabfBaINJHwSJGahlY4HhMz3PkHEdFHgNRBKRIulChcrKoGWcF0oMqLbHr6Pqex21H029YdS0n1lC00NQNl73Dx/S5avA+QSRFiNYJrBijMFphseRRkPcCZRJgkQrqLuKsQ2UKHz1tHxCbSLeMnC86Ls8Ljo8yprOCqsgRJsfJiLMWawO297jeDbwKCTJQFSPK6ojLizVG1JR5QGVpZjAqCoJ1SCkp8gwpQEtJtBYZBYGAzCT9ZoUUitOXz/jkJy94+dMXWLPg7r0TvuFexTPPzIiF5nSx5DMPz/nYq5d89NUNl48bRrHgrTfg2bsz/uyfegt/5pvexrP3bqCFo+3WrB6fcv74lMuzJbbrmM6PmExHjPIMX9e4CFZJZCxwUfBoteT+2SWruqO3LnE8GOTCEfq+xXnPbDphPj9mtbrEB4sMkSBjijKQcm/ItmWlHFAwoth3N8JWSRMEUe4VOAyaGobwQIEcxiZbHgtkJmBdSk8OPh2nh2nEW+lxIKK23icD3yTubIiTn04McgeuEIfeJ2rfRblyOxzpHGbuiB042ZJiD6ZXw59s9zH7ROcvQX05zhvwBc4d13Vd1/WGry+7zPgtb3kLN27c4OMf/zjvfOc7uXPnDo8ePbryGucc5+fnn3f+nOf5ZxHm0g924IaTpJSkP87QIN/ZcgModgP77f2D5Njt4KcTgXV0XMaOxzTcFTm3haaKEWRkHRyWQDVcZWbaUOQlfVlh+5ZFY1lR0/qayz6S1ZFm3fFKU9P6IVM5BFARpUBrgLRRRRso8gxRKIQIVE7SjtL4wLrIaGyo1w4fwQVB7yW0gc5G6joS+5bFZcO4WnI0KyhGOc5B9JEQPL6P+OE+CMaTCZPxjIuLSzJq5iNF6CS9DzTS0pYWEXuE9YgqwLiEAME6QOKDRZiC2Hf4KHj84Jzf++irnK4j05HDbCTH42cZzSbk04rJtGI+L/mWP/0m6hhpnUHFguBAK8l4NqEoSqToaOslDz/zGV76+KcJLoX3zY9PuHnnFrfu3KVfr6ibGpMZVJ7RR03dWV5+dM6n7j9iVfcEt+9IxIGcHHH46Lm89MyncybTOfVmSQwudchIHYkQt3yL5Gkihdh1VRJBdWsxn8iqu3HMziE2/axQg40++25ECBKnwRiNsQ7nJF6GRKYNYi87FulzlBgkx9sje9dBGf5wIZFvB3rvjtgrDrN3hpFOSiveEmQTD2U72kmS4gFIbTsobDs529+Ug6+CwY/ly1NfivMGfIFzxxuszrzg1rVq+w1Xb/vZv8Vb/8WvPullfM3Wlx2gvPzyy5ydnXH37l0A3vGOd3B5eclv/MZv8Gf+zJ8B4Bd/8RcJIfBt3/Ztr+/Ngwc/UJei5IpbxNCl2G0rUSRnLiVAHOrZBVuDLYaUYhvhHE8rPAupmUdFEQMlIkk7o8FICEYgvIa8oC9LurohGkWUkdr3KOeo+5bH3tHFCHKwCxcDwVAEhIxkWZ5s3gkoo4gRlHFkBVQBrJMILbA2EpyktZHcgDaSLE9XuT4EFqtA3UjarsWoBi0URgqs92yaSNcHkIr5fIwpCparFa5bMZ4kwOT9EJCH5nzZJNnx8YgiSmwIKTBOCAiRrumQIZJlYrjiBo+k6x3r0GA3GUoEsvEMMz2iX13AqkEHxXFZoPKCgESqkiBMusrHsTw751O//0lOH57jWo+1nvnxMUfHx1STMUoJMiPxZUlRzcjHcy43jvOzJS89POPioqbvPGk8F/ebqkiTv+gDPlhW6xWz6YTxeEpbr/CuH3bewUpeDMBEyCQ03ilZ4m7Ms7/ts5USgTWATOBGqIFs61NnxviI1QmU6V13Iwz8l93Ru7dE2xJSDh47hAwHg6HdY9u1yoGHsvNC0Qqp5dA5STcGOXHqmAxJxkM44C6U8EBldKWT9GUMC/yynjfegPXD3/if8j988jrj5Y1UL7s1+YW4lhY/wXrdAGW9XvPxj398d//FF1/kwx/+MMfHxxwfH/NjP/ZjfN/3fR937tzhE5/4BD/yIz/C2972Nr7zO78TgLe//e28+93v5gd/8Af5mZ/5Gay1vP/97+e9733v62fib0kAsLvyTOfRsCUR7K5cEyfF7e+LwzGQTD/DECs/SDA20dEFySWKCkEF1NFhZc4IiZKRqCI2U/S5wVUGO1L0VpG1oBS0IlIjCEahY1LuBOGSZ4aSKKN2jqXJYi4tUWtBWSVDL1+npWotcEHig6d1gcxDLpI9vvNgbcqQiSuHRGCkZ1QqQoBN4/FIxuMxpihZLtfYek2mI0bpQT6bQNqmj3Q2JHGtasmzCpQhSokymugdrg+0oUMGRZSgM4WUEe97RJ7ReUcQAT2qEOMpVZbjELhuQ7vpkE5QTqagU0ijbVbUF+e88smX+dTHX2XTOYoiR0oNSpGVBZPZnMxogs0RFcPfnaK1Na8+esiDR+f0jSe4xBfaphOnVsdwzEQIMWD7htUqMp1OKKoJXb3Ge7tXw8S465pswa4QCZHEAy7K4S2RZVPQoGAPELYjkwh4FTGagTSr0S5iXUD4YRQ5HJ5yS9TloKvC/s+wNQ0Uw3EcBjdcIcVubJM+X+26KHpQ8qjdiEcddE/2QGX/O7MbVu3/Ig4xyesAKG+o88Z1XdcXUX/pV/8mz/0f//2TXsbXdL1ugPLrv/7rfMd3fMfu/na++/3f//38s3/2z/jt3/5t/uW//JdcXl5y7949/tJf+kv8w3/4D6+0WX/2Z3+W97///bzzne9ESsn3fd/38VM/9VOvf/W7y8tth4TE4DsY37ClhMZA8hffnlQ1B9ep7ADKEBwXkRDBIVhFyRqBFpGlEDTRcZuMClDS4wx0uaItFfVIs+4VRacoW4/XEpcphI9In4h9aZYQkCZdXQfvCQR0ZnA+mYcJAZkBmwm8E/gA1ga89wjAC0FjA9ZHjNRIEcmyNGZwPv3ZnPdIHen6yLoL5OUYaUoWyzVdU5ObQKYVvRV0vScGSS8gOIuRicsQRcO4krTW0fuQJLNRYHtPs+7AZ5hCY7RgXCpG+SCXdh677MiaHln1uOAYjUY4GfHthtXZivXFBqENbd2yPD/n8vEZ56cLWg9129M7z717d7h56xaTyYQsy9Fa0g/8EB8jIgac71mu12zWDdFFRCRxjIa2RtyanA0kVjmMT/quY7mMTKZjimpE1zY4112dDrLHuluscOjeuoMOw2a9tTZJgYLsQENEDsGBHh1Au+RNYnTAKo8TDABo28jZk163qcc7b1vB7rOTymeLKZI3ytZsbtc5UWpIMVYDKVYdGLJJEGroFO1HSVd+zXY4JO5WkUY8X/yv6hvqvPEGrOgDf+63/lf86p/6fzzppVwX8CutR39w+qSX8TVfrxugfPu3f/tVxcFr6t/8m3/zB77H8fHxl8hcKdlgEUPqgkQOCLFi4KUMI5XdpW6SkhLtfs6+2xUkia8CRL9rsCQXUkH//2fv34NvybK7Tuyz9t6ZeR6/17311KMfQu8GYQTDmBZjRtiMhEcYA8JEmDASYTzgpkUYhGcICMIMM2E6BMNAGEtoPDZMEGMFMxiBsHh4GMDNAD3AyC2Q6AcS6lZL3VVdVffe3+s8MnPvvfzH2jtP/m61pG5RpVtXfVbFqfM75+TJ3Pm4Z3/zu77ruwQeOiFmZRTl1Amdy6hP5AY2i5ardYfGLavouDdAe6ukQcjqkV5JzoCOCzpV84gIHlf0ITbpNG1AyMRoIkr1wjgKw5CJsQgsR0VypmvMmD0m86k4W3sDMllxvXmDLE7OyeJ4dHnF9nZPF4Tl0jEm5WYXzWBMdDJx8yVF0O0dw6DEmIkxkRS8CkOMDPsRlxOL2OCd48Vn1rx0ORpw8I6bRzfcfOJjLPpbxt2G293Abhh4cHnJ7Xbg+mbL9e3A7WZHv48MY0SCo10tWa7WnF+see6FZ7i4d0a36mjaliYskZVCvkb9mqhCjEqMOuFUBcg64dSDdqNO6VJM/JRh6Lm5zpyenLBcn9DvHUNfSz6rnkQAS/XMQcFBfHJomydFtHHQohgr4r2lTEIQmmzOsm3jiNExRk+MvuhJMlIzlPUyFnCSMQihZTtFcSKHZUV0AjRu5v4qlU1x3nrzuApUDuCkimQPtBGTjksrYKsg7WfxL/Wt9bvx1gsdB579vQne/6RHcgyAP//av8UX/Mkje/Kk4+nuxaOUmkd3eENzofPDIcVDFf2V70w0fkkFaS6rKIBGxQBK/VWWQn+rIzvhhpGoytIJnVOci0iT2HXK9cqRomcxwLBXzk+EZlScQiIQs2kTQhDw9uOv1fW83qaLoAKuEZbrBhFluB5LLxXzNDWtiEeyY7MbQISsZvi26RMepXFCFsfp+Tn9KFxdXdPvdwSvrBeergsMNyMx2t2+ioK3vjQZaMUD5kbaNJYqyDHicib4wGZMbOPIGEdYLjhZdawbx+VuYEiJ62Gk3+3hpVe4urrkZj9yuev51Ks3bCP0MfHqw2tShq6zFETtC7M6PeG5F5/l/N4afKJdndCuVrjsCO2KoelxzrMfMg9vNnYMtICIXE3hpaRkdAZQKL1k7PQ6hHEci2W6slytEefZbTeTUFYK1tVcroiSk6tiWVsn07qz6tQjSkr2RNUaCPpsvihtC2NUmphpx0QM3gTN5fqsXIaxIopTN6X/jOezsnWn5dIVKZj8wJ4c7O0PbIpMXidu8jdhEvHONC1iQGwCfHeAyeOy2WO8EaGf+jRf/hfew498y5990kP5vI4f7Hs+/H/+GpYcxbFPOp5ugJLBfp6tSeAk5qjURy63nzV3frDEnASR04oKLX/4sLAzdZ1S0kDiSQ42quwUnEuIT6CJ3Cqx80gX2CwDeeXIa8+9QWlRUgbpM43zSONwwSp6ko5ozgTvkWB3silnssuoE3yjOG/b9w6a4BnUmJV696tEK5HNyn43suiE5arj9OycmITNzSV53LNeQOcDi5aD3gKKt0dNVZRZT9Xu/EUZY0TVvEYAVIQx13QK7MY9uMCz9xdcfnIg43m4H3ntasv9E+FqO/Jos2OfFde0bG83bPcjSYWoQhoSbePJqUc0sn7mnG65IOWBtl2zWJ/jw4q8uyKNGwRFxz1jr1zd3LDd9laJU0Unhd1QnelGHr+Bl8KsZCHFyO3tDVkzJ6enZFXi7Q3V9t6JmDapurjKTIPCfPK260pzLiycXUci2czbgrnXNklpG09sPGNwNF5I1fi44GaHlocUP1pXk5DT+17AixDEHIoP4MThxOMLOHG+gpIDUKl+KfV9Lf9Gpn8VMt+3u/CkYuljvHGRNxu+6O9H+JYnPZLP7/hkOmf5fUdw8laIpxugTGWOcnhMv57z5R7/3nxKmYkLyAf9it7t62NmWiA5lUnJkR2koEBCNFnr2s6jnYNlYLvyPFgLPjYsfaYNgm4SISltA9o6CAauMpExKc5lnDjUgRNP0kh2meUqsN1Fxgg+eNw+s9uMpCjmCqoNookmZJZdYLFYcn52Qc7K5aMrGhc5ObNOyt5ZhctmZ6kx5zKpYrRc+r9Q0woJ5z2DZvqUaJPHiy3jnKff9+gYebgbWd5b8/a33eN6E8k5cb0dePV6zyjC9T6zGRz9ODAOPcuFJ2nGNQtCF7i+2bE8WXN6cYp0gRe+8AXOzs9YNpHzszPatiU0K8Z+x75/mdjvaViQB2UYBvrBOkAnzVOlSYVgWgBK0vklUNMhlH0FjYnN5hYFTk9OQeHm5hbMr7V4f9TraUK5h+xHSZOYR0omTwAlI+LwZQWqSsqeNmVi9MTBE4Mn+mjEXVmfF7NC86j11hH721WtSfmsARo4LGvcWknTYSyKhEPKB29reOzfyRxkZWr2Sid51zyVVX1gjvHGxuJv/zPe9Z2/mw+997ue9FCOcYwnHk85QKnMiHInjWNt1FApwtcqplXlMHvNGRcOqZ6qU9FcgE44cNyCUfdlvSYSFJOttDCJcZeYnmQUtqPjZa+cLwMnK+G5505ZRWGZRhodcHmEcUAGUzikCOJs24L1q5HgCV1ifeYY48jYZ7os5OjZJyXmhAO6xtM2Qte1nJ6dEePA2N+wXox0rdAGIScT3KYMCWNq5vNUzop608QsWs96FQjekRG2w4CokoMQVImq7IeRlDIuNJysl5yfr/nyt4289uiaRdOSM+z7xM028eB6YLvfsVg5Ts7WNN0e8Z4xJs7OFkSE8/unXDz/HN2iwbnMc8/eo1204BqSQG5a2u6c3Fs36f0Y2e4Hdn0R8aqxDjqxJjqxAJOmek6klUsoZWs8SFJ221sEWK9PUBXi1TUpDYeDpAbQKKW4Qk0lMq1YS1GOai4OwqVCLHuCV5qgpOBJIZEKkxKjQ7IjaULUQIYTSw01RfzqxXQovljqewdBlCDmEhycw5eHczIrlfYHK/tZlc405Pk1ILNjBpP9vu3aQbMVjwDlDQ8dB5qnu0HzMY7xhsXTDVAmtCEHvcmU8iifK2W2SNXCtXy3zlTusByl0icXEHKnTKGUJUuh7uuttCvbkcr1e+gCRI/0gp46tj6TA/QBdDOg6hHvCE1nbMnOhJEuK6MmNJmPh3O2fWmFHEElI87cSMes4ATfOojW/G7MyiqsOD2/R+MzY9qw6GKp1glFBxEYorLdj8XALE8Td+UeUjaVbHAQGs8I7IZUAEpGkqPF0jw4h6ZM2zqWi5bVsuNtX3SPNI70Q6Lf94gmlqsVpyGwf5j5yQfX3EuecbtlfbLg5HQJ4rhYr1menOKIBMmcn1zQLVeE7gTfLlDfIK7DhQ7XdsQ+sd0PXO8G+jEdTNMeu0Kq6LSedbIxJsYKmODGeZkATc6Z3W6DqtAtVizXmZiukTxAkcqaWqmmjw7XyQEQle7FpRw4OBOxVjF2UI9vMj44QnC0wRG9R3wiJplkUV6gcRBkLpgt7IhhY4ITGicEMW4kiMM7sYe4qfHhoYRYDtf2BPB1Ah8TMKnkYiUYqcvWz48A5Rg/vyJp5v/56XcDj570UI7B0w5Q5oo+CjhxFThUPh7QjKiV0eJSARmVbSlaE9SAB9HAzNzkTepywqHh4Gw7dfrzIEHQ1kMM6LIIEBvP0AnpFvKYyPuemNXuqltol42JFfuIG815NOdMzNlEkgJZPK6BbtmQUmIYqrZBrRxYHV275PTshCYkhv01jUS6xrFYNKZhUTXn/wS1qFRULK1TNCjirHmdC4JzMKTMkJTNMLLpEw6HJMjBM4gH38CYINm62sazfu6c/abndrOnaRxdCEjr6FZrhiHyyVeu+eTLNywaGHKPtAsWSwN5q9NTVmcrVs3IatHhmzW+PQXX4jHdRHKKDw159Gz3kavbPf2YjAWpbNfsacYFHKBFFbiW02pztkymazln9vstGVgsFqScubm+JuV0+P5sfq5NA105fgdflCLa1YxzxnwYQHGE5Kyqp3GkxpMau27EGxCuYtimABIPB2AiIAWENPUhYmDGGdPiJ6BS2BRKekfcAZdMhyhPjFA9UpN1jBYwN/uCYj/mx3jj4/QnE3/h+lm+5ey1Jz2Uz7u4znse/MojOHmrxNMNUObVO+ImB8w7Bm3VJjwXjUkFMhNDUh8FoGgCjQcDOCm3sqV5YJ3opnVNVT4CXu17jTMWJbcQBFkKsvdoyIxEditlHyE3DSE4QlZ0O6A3A+wjcR9Jw0jSERE18aRa2iq0wmIpaErsisNh9p6TbsH9i1OWTSKPN0gazazMwTiY0LYNnpSVcYzkpHgndG0gppFalYJaWiljqaWUYYzCfoTbfUIkkIMjS8PgMj5EXGgIztM48/o4P1ux+LIvZNv3bG63BNcyRGXIyv2Tjre9eJ+XH1yimogZHl3tONM1i/UCHxasViecLBLLkyW+6RDf4sQX47KMOEfTrhj7yO0wstn1jGMiZ5teJxnSDMDOpJ9lcp3hF1EDajNg4QQ0J/a7DU3KLBdLVIWbG3OdFTHGqXYVvsO26aSNNfv7AlDAxLY4IXtH4x2pcRA82nhy8EhIuORQnVx5aASCyAF4OClNJ02nYqAEWid2DsQZm+I4pIVqqqeavxVmZOJM5C4wmYOTqZJnFgbwjgDlzYj1X/7H/Ie/+pv5lt/0nz3poXzexS/9W/8HvoJ/+qSHcYwSTzlAqZb1pvDUKWUDE8MB3BHQ5pKmEXfAJnVGsdmpiAcKiyLmqKouzNZdGRaYLPZL/ad6hVZAA0KLBgdRCr5JJJTtQnmYYOE8y2ZBi6c9he5CkMEeeZfZbzfsdleMw44+DWafHjJtZ/UcwXv2veLdinsXJzgdyP0GKYADcfRDZhyV0IJ2hr1SSWl0Cxiy0icYeqsyqk7t+5gYckPCc9snwj6D2zNkx7hqGXyDbxqapCxSYt16zpctq8UC33SszgInzSm73Y7Uj2QC+z6TvZAQNCuvPbpmteoQD6fnJ5zeO6fpAierhuWiwYVAxpfaFYHi1JrV+hFd7ve8erthN5j3Si3emXv1mScIaLZUR2U+ctaJYJOS2jkAVmNXDKRE9rsNwxBpF2vOzi64ubkkj2MRilJASramgdNsXliWiUVJCIo4xdeyZ49dH8GVZ48Lgi86oXo1N0bCGYviDsDFYcCkEaVzZbkKWKSyKMW4jSpwtf2bugvVLGjZ6wpO8qx2J0/46wDCMkqsit5jvOHxzr8a+SP/k1/IH33uXzzpoXzexJf/hffwlX/4n74OjB/jycXTDVB8W0BEBRM1xUMBIG4mPpBZWgem2+XDbWTJ9sx0K0VUq9PEVYWzeZqEJt0KpVOyL7Nk40EbuzOVTEwJmkTqIn2O3A6Zje7o454XNLDuheUGQi+0saGVBd35KeG0JeeRGCP7/Y797Zbh0ZZ97q3d/LMnODqGfovkgRCUOFrvHNMyCF1r7ENW64zcLh0uKy6Z5qVPDZtxJJVpyWUYMvQJRhx9gs0Au9jTp0AKp3B2Cllw256uaVgsPIvW0zUN3je40EDwLJxDQ88wJMQ5LnTFfohcnHSkvAYPoTUPlNOzc5555pyTVYNIIiNWeu3coYoGYz+uN1t+7OWX+djLD7ja9qSspUUBTCXn5TyLFrGpM5bEXHnT5IsiCK4ADTNwmzFrAmRlHHrGmFiuTjk9u2Bzc0UeB2sRoNZw0tdLquLdAk5yqQ4TybiiQwkqdwBKBSmu8fiYiL7oktT0J4G6mNTqYNOnCAQvtF5ofFlGZHquzrKVcRGnIAZFrPtTEXyb1y5Z7WrOMrP0lwJetObCSrsAjgDlzYrmv/0B/ptPfdURoPwcxpf/hQekGJ/0MI4xi6cboEiw2ZQqQHRTwc0BoMhBTjIj+u/E9GZlXWb5gUlBmkqap2x6+stSPSr1+yUlFATVcNCvFO2Binmd5D5zmyKv5cgiKwnY7yNdTPjbRL6JaDJvlLZd0CwaFusVZ88+Q3ph5Pql13BJ6doVjx7ecHuzwTs1Ka/AmDPDLuPxeK/4nPA4+zsImhxJzDAu51QmJVNDZlVigj4qfcx477jaDKDWeNAtYNWeowT0ZoOGzPK0pV0ucM4Rh57gGmuQFwUdHGm3I+fEerHg4t4pLz+4YblsSzpJWa5WnJ2vaRsYdtdWudSsymSfTIBhUIKc4ep2w4+/9AqfevWG26053LrqHFyAh8mRLK2RCvugTszkbjuio3ULDsGZr4gzXYeUMmrTXRSfEIVx6LlJmfXJGSdn52yvL9E0knM9t49dVmVin0ziCiMHpvUJ3oHPaHBoqCkehzQOnzySsolgxQSwTowhsTJxG1fjLfVjoMQRnPWAciWtU59FKnMy50sO17exJJYgM9akiGWlvq6sihQzunxkUN7kuP+/H/gzf+Md/J57P/6kh/LzPn7pf/wenv+RH3jSwzjGY/F0AxTnDxoTUQQ3GYdNtpviD42OJ6CSD8CD+p5nqsQB7NDUH+C6bKYyJYemclKWq7186kQkNlMo0GabYEXMICtEaCIpwmtJkdHzBSirlU0EXb2j3ybG2DP0G9gKPFSarmO1PmV9fkZ/m3j5pQek3LM8DUhOXL42sL0U0gjDoAQXcW1Lr7DSzMnK7q59UHzOZFGudok+qYk4UTrnWASHy5k4KvucSDHiXYPowNmpEvwJo3ds2xPiWjh5vmFx74R2uQTx5EJe5QS7XY+o0K2XuPUJLDLPv7bln//YS4RWOL+3pl21tK03sOA7nBN88CAD4pbgg2XWWBKT47XrWz75YMvNrpQWO2+dgRtwWXAR0AG3cIQ2QONInSMFyCnjbgPcCs4prhW8d0jOqCZ0TNY7CVDncblM2F4YYs94c8nF+pzT0/vsb67Q3BtT4pxlELWWGJd0SQF9WXUSuVrhV2H8QjJA2wguOdzoaLyxdAK0TmcVOsYE4SorZAxKF2ZiWRzBeXwxYHMqODVwIlp1OpVFSQZb1JO0eJ5o+VSyXdnKBNYonyUSfTrebb6ZEX/8J/hEfx84ApQ3O04+ldBx+JkXPMbPaTzdAEVKQv5xsd5nsgyt9D8FwUyOo3la5ABY5nfi+YBPKvioVDcVnNTIh/c9TJoVKa+dR61jDmry9wAAdmRJREFUINKMMESGMfGamCbg2RSmySRkb7u1B5GMSiSPoMkqcVyIqFzj2xu2lz1DLyxbj6jgnZm+xaQ0bcvNfsSRaEOLd56mdagI4pSwj2U3lUXraJ3QOcdpFzhfek5bTxsc0iYal3j2ouH5s8C9RaR3K261ZZ8jo2sJJ2eo8wTnkTTSx5F+2HN79QhInLhT/Pk5q+dfZPXKBvnxl0gusFgsWCwaskvW4Tks8aFFnTOWxjcYl9Az6J6H+4FPXQ9c90DoCEsBpzQrh18JTVBkq+g24jpFThysA7JuyI35iix6hZtkpdudo20DjCN5N8JNhGsT60hjehnpE+wdkoT9MHKZHnF6ds7y4oy4vybn0SrUHXhX9Btq1WKPW+07jy3oStOj4MhB0MZD9LjGo7GIajkIZENhT5wvrI7DWBPvDJgU4W3wbkrv3LE8mcaRpxRPZcxU7P2MpXcSmZQzyVGSP1UYa9xL0kSvxx/0Nzv++S9V/sQPfyn//v1/9aSH8vM2Pjxs8f1RefJWjKcboNCARCbKuupKikzkkLEplTvAofqmmKppOihDJ7OtmmuvpQ5z+kUPy0zPwtSksFYQwcSamErTlcYpJoY0GsNBk+hD4kFT9C4inIrSpYiPHpGMJLX0TPa0zZIgjn53A+y5uNdychLY3UbGraD7zOmzDbvtyNV1ZLkMxGi7GbwnpkTjhLZtaBeOAc9qFRnGbP4ZxTa9dZl14zltYbXwtOLpBM4bz7OtcBL3nK0bbsPA9eWWq2eX+PU5XfDoOKJjhnHkdjfSS0D7hNyMhHNHs1pw/wu+mC+8uqYJwhe8+Awn9y9AI5vNxgzFfGTZrcn7SBz3+DaQx4Gr655PXo58cjPSd4FwsSJrB0vBnTrkFGgU2SWahxFHJp3CcK+hP2vZByU5BxH8Ppn2onOMXUBjhKs9i6tMeHVPGBTXONQpaT8il5H8UHBbJaXI9uaG5vyc5eqc1G9BB0s1Og5sXLkcDRhYibIghwykBzG/esQLEoTkhezt2vOqpjMpNvZV9Ep97e0RagWPczNhrJZLMCNSy+lz0docWETVTFYhl3Rb0gJQyOVvLdU9h948SSNDPgKUn4v4b3/RKZ/8gX+DP/0F/8OTHsrPy/gt/9f/I1/4t46NAd+K8XQDFA0H3CC1/sBMzsot30wI62b44jB5GBdfgEr1TqntW6cGLmqMi6spoJnYtioiK5NSDdwqSHH1FrawPd4hXlHvDaAMEVxi75XXXCaGTApwRmCR1SamQXB0LBYrGnFs97fkvEVItD4gI7QtPHvewItCIw03jwbOrnpQJaXAGIW2UUt3OY9rPe2i4XzRcf/hyM02MiS1fjYO1q23ElWNNGrgZCEeryN52DA8fIWwv+GUHbmB82efozm9R0BJes0YIeF49WrLg5cvWXnHKjmaceS0CSzPLrg4vWCxXNK1C2RwxDSS/EBYLgliOgezjo9oHhnJPEqJR8B4doqXntD3ZK/ElSOdOYa1I0sk7COrewPNoIydsrvfsL1YsG0cg4cUlTAaqM2N4LqGnBLcbDm9GTm551nsFRcc6jOyj7gHka7tyQ+EtHOklLnZbvAnp6xPTsnDLTn3VtFTGvAZgyHIXfVsEa0W3xRvIlkDKKZFycFs8n3Wqc+OLxkh5wqYcQ7vzTU2FK+USa+C+aAIWHqnbFfrtTp7XbiUIvY1IBLJJFGSZiKZKLnoUYpGSRN77d+0f9rHuBsf/fol/7O/8uv5O+/6a096KD+v4s88egf3fvSYqnyrxtMNUAgcRK3VQO0gAjx0civLVEakpnUqppks7gvLUbUk1TulmlIkZ8u6YsCGMBm3TZ2RYeqgXKtAClgRJwaqHKapcB5cQnzGNxkaGBeO/UJYNIlFs0K2A90I57omjMLm6hE5NzhOER1xTslDxGnCe0e3MMGn9w3LhTXh2+4jKQWSZkIbaFYdbtHiVguWCPde6Hnlwcjlo4E+woASvHAxeFaNECMkD1kio3pevbrlteuBexcX3Hv+Ps+97XleeMcX4k6eQcceHQfi7Q37qCCeOCZyt2I7OF791CWb+DK5aelCYNF4fNMwOshty+m9Z1mfP4fz3vobeQdNS3YNt/2GV7aRh6eepnmR83TKOFyxz3vGRWZYO8aVZ9QE2z3jxchizPSSuTlruT1dsG08Owd9SjTZGK3ceFxoyDkRT1sW2x3nFw3nA3jvQTJhTLTPRZp7e8IrC9xVj7/tkdvEMGxYdCvWF+eM/YY07ijyWJMjqd41RSuXhSulOM6bXklC6UHQeFL0BaBAgxRTNmNHKjhxE1NiAtmmVPbUbjtStdkTWDeNDZrQ2j8KR1I5VO+gxAJQYmFRIokR63M0VfrkRK/jm/GP+hifIfLNDc0ffCd835Meyc+v+E//u2/kK/7qsTHgWzWeboAiTeHIHUj1/tACGkpqpoAUUUGlVNnoDDwA1ctbyKhWEzf7rqhJAqfSVS0gY2JKKntSmZp5OqgAk1rNYbx8McwCFQ+SUJ/J40gKjm0KZA8bRrbieK5dccES9sruZgMnS5quxWvC5URQxeuOPiqbTeLyskeBxgmd87SdN2v8UVEf8F3Adx7pWnKzQLzn5H7k3rOJvlc2NyPDmHl023O6cKyCJ+iIBMEtA/sdXD7Y07SO0+dP+YIveSdvf+dznD73Am55Dm6L9FuGITNs99y/OGPhIPiGREPTdtxue3IXcKt7pFXHNgSSDqZ1CR4lEhGyb6zCxXl2KfGJYc+PCbxyEognLdKsaN0Ji7hjZE8fRnadkkTZNrBtAqc5s3ORqy6w6Tzb4BmdYyy+qniHhoB33pr4LTzXi8DVasEiG0DxXlhmZbnZ0z23Y3E5Em5G/NXA4tWecBXJBLT1nJ5dEDcNw35jPZWcOZCI1OJluz5ESrNCZ12SfXBI43EpQxPIycCAT5lQNdelx45V8DA1/3POlWaCtQ3gdOXNsLoewMnEniRQb94yYv4xSZUkVqETyYwaDaBIxlx8LGJOjPkIUH4uw/3YJ/mSv/o7+dhv+L896aH8vIg/9OlfzJf9l8c05Vs5nmqAIrJAnSL05iALJSUjB1aEeuc4d46tAgBX2BBFUjYRYy6TippEUWv1Tq6VOmJ2+ZVFqaWjQkkPVbfa8j4GTibhLYq6GYApYCkLEB1NaItwsqFZNKyHjnGbGOWWRlrCssHvE36MkAY0JxxCm5Tt5Q0PHu7ZbTNdJ5wsHKeLlrEfEe8LlhO0bekBUiT4gF8tOH9hxWYbGcdEnzP9qDzaRe63mVYTTYZ2EbjcZEa35st/8Vfx1b/8F/JFb3+ei/trfHeCikdDgzRLFosVOUaaZQcvPoNvl4TVOdG3bHrF+RbXteiyo897+rgnayTj2AeP+mAAxXkGhEfa87KLvLpQLhvHNidGn4mtR90a1YZt3nDteqJzPFo6fIazDL0TrhtldKlUp2Burs4bY+Yh+yJJzQFkya5bMIiVDjfOsxXBnfTI6YKT5yNdH+luR+69uqO5zeSo7LcR1wun5/fpbjqGzXUBE4IrjIeBk4yIt/cB9Q7xkH2Axpg7E7IKMkaqRaDZ27vJjVYwXxuKrb1TV6rpCwiq5nXVj0UTTlNp+xBRnGlPsMs2A1GUSDJQoomBsbAn1SnFYtRk3j7H+DmL9OAhX/1/+lf8mr/0v2XzH1zxgf/RX37SQ3pq4y9cP8sHv/VduH/+g096KMf4aeIpByglxTM1YatVNlU4WwWxNaqGZM5wuPrJTG9SJivbCJPBhSpWqkFhUXxZTWkeONWPymz9dbs1/TQDRjWb5AVxnYk7Y7Y72SwMUfmx4QH7zSUn+z3P7Bzr60yzUfyYaMS0BdJHFupZn53ifcftVc/N9Zarq5HdxvZnsbKUiYiw6JRmZR4tQ07gHIuzJet7I5vbgTgqmpT9kNnuE0tVznyDazqcW/G2t72NL/vqL+Xtv+Cd3Hv2nG7RoT6UZoYOXfasnnuesF4i3uG6JX51iizvkcOKtTpr39OsiK4hD1uGuGfIPWPcEWNPrwODjqQsXKfEJ+KWn8gbLhnoXWbvlVuJJDHh5rWHn4iR7TiQvKUoxGe2Egs7UBIYpvAsbFd3OP82sxuz4cWurRDM78OZmjU3gdS1bFGWOXOyi/h7PW7I7IaBZrtnuIzIFTxztmDxcEG/vSzNHx1JlEbsynKikw29BtDk8AHIjRWDASLRqppKcx9Xqr1EZtd00bk4tNjYH/4p1GaGFaBrTuQckZTMf0UTqo6srghiTYMSSSQSI5FYGJQk9V9NscTXRDqmeH7OI732AP/3HvDK7/jaJz2UpzY+PGz5r77+l5Ff/siTHsoxfoZ4qgHKgsBOnFXvSAEPkwl4SfPUqI6xd8Svj72e3GMzSHgMaJR1pQJ+ql2+KwDJFQGKb5gca4tvRPmiPcQqekQUtWYq5eOEamIviYXzrEPHJXse6RU0W74gZZZbxamCd+b74bzpE0Km00jOeyQYIzP0sItWKpqTEDd7fApIFmIY6ELAuxYXhEGU1EJ70bG4WdDvEhqtWV0/RAYci7OO5567x/nqec6ff4bVaUuzcITFEm3XJAlAxKe96Tm6BifL0vLI2UQYE+pGCAsSHmkCfYrcyp7LfM3teEtKAzmN3MSRjSiDCJ9OAx9LW17zkWs3ciuZR5J5oCOxMF9OPfs8kuIAuz3EoXiTmDssUQtQKCDUKXQ9QoP6FoiAm0ppK/hVB9EVE7sCWlStV1HuMv1J4lYzLmbyvuf6dsv17Yb4MPL8+QXLVxzx8pasSjtZz9v14xyQisNrEDNp42DCJt7jmoxPCUn5AExKn6iJEPSW9jng4YMY1yp0LB2Yc9Gd6Aga0GxAODkliyeqME7MielOBmIBL3Y9m+DX0Eqq/aqO8XMeX/Yt/5zf+t/9ar7nS/7ekx7KUxc/Mj5LfPnTT3oYx/gs4qkGKM/R8YrAvoATlQCM2C91MVord8Z3KnJqafFnekz1yWkGUErkQpsz61qSpRjIVqallFuIfz0ImizxD63tpx4+oriY6ZqWU98R88hl0xPXAWlWPGoTXRdYnAjcJvJlz+Z6iw4j2g82iWlmEYTQBp557oR43hH7xGazR5MjpkwcEm6M0A+0jSPulX5IjDmjjdCuG3zniPvRypKzkrLS+cCz95ZcNwFR5eykIwRIMeEbB2FBTgOp3+Jvb0nXj9A0Ij6gXcd4+wj0Er8+RU9eJDUn5ARj9gws2LvEbVAeMbKVyF73XA17Xok9P5l7Pi0je+fZItxo4jaP9BrNz0NAvVraQnfI/hZNe8jJ+u9IcfqtFVW1PULuIXf2NwVwTrroAgiaBioDU8rVoyo9jtY3pNCiKjgV8iJxs1pye2/JcHJD6ga+uH2GVdOQrq/N46ZcEc5saxEH3iuSBWkMnHgBFwI+GfBxOZknS2X/svmZZCkdcwTEOxNh1w0Uv5NcgEnOCSnPpkMZyRWgZCVJIiGmO5FE1CKMJVkLBDmkIxUha7Imicd4MpETP/jSF3H7jj0nbvGkR/PUxP/j6kX+63d9AXC8dp+GeKoByguuIws8AHbANLuIYwIqVR8ypXWYAZK6JqlK1tnnxsbY2wfRbLVZsWqesnyW4quemHr+VDM3YQZUOGhUJjdbJpp+0S640BWxj1w2mbjukCYgw4C2kZs9uDyiu8gyKO2yMQHnuqUTZbzew2jTyihGzavLNMtAMi97QteyPF3gVwsS0Pcj210kJ2GM5mjqO+vOPCbogV1Wogq77cBVvuWF0zPWZ2uadolKaWyngg8dtAuGbNVDqpEUR1yMZAW/XKMuoBrp445BEhvNvLJ/yCfHG15jx4Y9MUVu08gn4oaPDRteyQOXXom+ISpFqzJCNk2J+ftnnGaIA6IDDHtUR+smLMUQzXkIobBWaqzVMOVTrLJqdo2YqNoVPbUr58ujogwqXKOMCDs8C/G44BncAm091wI3XLEPmXe2Z5x+OqC3W6vsKf2iJrErgsvGojhx+ODwWQgJfLZSY6kNLBU0J9OVkM2fRI2Nc6GAlArCJpCSSdlYFM3RTOWyN3CiELOScYwOY1BcotcCTkRrh6bp34oqxBxJRx+UJxpv+80/zL/xX/97/LNf+efopPmZv/B5HH/k1V/IDzx6O/k37kEfPenhHOOzjKcaoNx3LVmsLDIB/cRUuAIOxnJ3PGNClJlgVczRcy6crULZKmgVmEzdyvftrrUsV5sUapkoczIRrVnJHqxXhIOWBQ5sDiDiOGXJWheMmrgOI9HZl3LOuBgZ2PFIExsie688E+A0KCsJkM1OfthHZBxxwdH3SuwVklq1RzBHVNeaFkJTJCVhjJkUlRgzcTSzunbVse8iw25kJ47bpLyy7ckfvyQ8t+CrXrygOzlHfWuslQtkcVYV0nSkbs0uQxMC6hVpWpxr0PUF8eScISW2aUPvIo/Gnlf6R/zkeMmndMdWzEL9cuj5+Paan+yv2UkmNh6kBZdR7SFHJGpp9yvgjfUgRevd0w9I7NE82vlrHDQBaMCV9J06SAIx2gNnGhrAwGYs57GcRbUiXNQASyRzo7Cx3tIEAtllvChpGXhwf8FV2LIPmV8sp1xs1qTrWyTmSTTrsZSPOPBNSdtJQ1ADKT5D0JLaK6BWkzEhKVvprznBiqX+QoN4XwxTiql9YVJSSmgqAtkcyVmIWczvBGt4GCUzplQSO5koNeVVhLdYL56YRsZ49EF50vGO3/JDfNV//rv52Df95096KG/p+Jt/6ldx77/4wJMexjE+x3iqAco9WZjzpWXGeU2UcQ5QEFSKCY8Ck5tmEbemChjqYZiMUew559ezILVSpz5KKmia2ETsTjdbpc8EZipK0TrdaelIG1jLgnXu2OWRWwZGl8vkOAIjSXr2oQef2DcZbQW3MnaHbabfRthEGufxXWDcj2y2AzkqTupdteCbFlxgNyoaIxGI2ZGzEsdEHDMpgWs9zbql7yPbaHftP/Jox0PZ8su+5lnuv/PtsFgxlt5HtucJKWAgdS16viY3pg0afUNuOsYQiHlgFyPXceA6X/Hy7RWf2D/kx/ItH/cjDyVx2+/Z7Pfc7Lbs4xb1Cm0DdOAz5GhAZCxpl9ZDU3xHklU3kQYYdlD7a0R/AI8uQGjAtXZOjEYAZ/12pnOd43SNqDg7J3kspzEA3iphxIEkhmJ2IprRIAyLlt4pox/wXvma3Yp7JyvcgxtjxYo3jwNowCdH8IHGdQQJBlJUCBx66ZCNEck5knM0kBLNZDCLM8ataZEQps7eZmOvpkfKCWIk+5HshZghiiOJELMyipmypZJCMut7wVFTTNj200g89uJ5S8RX/K7/H1/xx97Dv/yWP/ukh/KWjF/1Q7+RZ//pw2Pv7acwnmqAcl8WOIGAo1UDA6+IdRmBmSaEPAMnFIQwc3ydUkA17VLBR2FkKkBROJi9lfekCBJrj55cWZlo6/eH8mKoFnB2J+olcM6KTgM3umPrRhK5iCAj+IQELUNxVkAUHdsIlzhoHGMDaxwuJcZ9wg+gyZxqY8qICi57m0SjMI4DNA3OB4ZoYuCsQr/N7DcjacgQHe1yQVpldtc76/7r4f4zF1y84wvQ7pybMdMsBMkJP+7QPFhVSNUwnK7Z60DUTBbPLif2w5Z93rHJynXe8mB3xaduHvHJYcfH8sC/8iOXjZK2O/LtBt1uLL3WipX/al+u2ITEaC68OEgtmizFpjoi4x7yiI4RhkN6DskQMlKuehWPUS/lvJbOx1P1S4olRZhNU4Qa8CGCOGOPpAEaJuSJs5RSbS7YtnzyPDB2Cb2Br1o0PNdd0D64hb4v2m4xcOICQRqCa2h8hxdPEE/A4RWcih3jZMAk50xKIzHGqZRdvcM1wcrKJxalXJpayuVzIuWRnCBJJhGIwCjK6DLRFXv74oirQFZH5ZZUMzklcjpW8bwlIie+9D/8IO/a/G4+9J7vetKjeUvFr/+RX8vJ72tIH/rokx7KMX4W8VQDlAuWNEAjno6AAr0oVzhDy8UrQqXS9XDowVPX4mz2FV9SO+mgIZlYkvIsB6q/tHelmrNpdZ2VetddQIkUUzcx3YmKouLxeC5Y0annOm3ZuMGAlWJN5KrmxQPBodkhLZCEiHAbHNoLY1BGURauo30tI7cDjW9xS/Au4ghoFjQpKcOYBcHh1RH7cvctjnGAcZ9Io+IyrNYrOHfsNwMxK7JasXruHvsRXnn5ETpmgjrOTlbgekgZzZFRI3vdMebISKTPiT7t2aJsVLkZlcsEl+PA1XDLJ3fXfGxzw6fTwKMwkjqHDBHdG0CRnNFlARJ5X8xAsjXTi7W8OyNiZc7kHt33yDDAmGEs58sJBAOQpjUVm6y92mfeF28bDucchWgs1pQWyoMBJRFwjaUQJdnfmgqQwcYoCZwSHbyy8PyLEIht5kvWHV/ctqweXJH7wfQne3BqDIr3DT40NK4luIYgHoelhDQbQHEpWlfmFPFxIJdGf+pAgjeQEhzinV3O1AyjGbalbA6yKUPOShar5kmai2g2o+XaFnFIbcipguYCdI4+KG+ZyPs97/iTP8j//K/9r/noty/5sX/nzz3pIT3R+Ds7z3/ym34L7uEN6Sf+5ZMezjF+lvGUA5QFS3E04lngCSpE4OMol2rumFq9UKY0zYz9qCBFCvNRAces07GkeBBjwoxtmb58WGd9PelY0qH8WItgF/AqnPoFi1zBya5U9YRDJYmUSbXYoeM9NIKUCXYQSF7ZexhwnO5hKdks78WxWLS4tmHcZ3IE3wZz8R8iWVzpWiSMOeNDwDUNfqGoRFz2uNZSDNIGJGfa0w63aLjd7Lm8vKRtbL+348CiVPQEp2zGHY/yDb3ekiQxCvRppEd5MPR8fLPlE7ueV4cd+7zjerzh4faWfhhRl3GtAY487mEY0X4sLmoeQmVCZsc4CEhCtTEWaxxgN6DbaBPoOB4AysIb41EdiOu5896qdbwrgug0AympVCDXEvHSedGpXSee6VqpJn/T9VDAi0omiuMnG0+8aLlae4bG8Quac1aXO/LNloVT1In5xniz/w++pfEdThq8M5BbGRRiwsWEpEiKDZIjBoAxYN4EJFQtiiuXtu1vRqwyRxOqJpTNKqZlyaU6SKyazYz0J1cgUMXljMuKz2/IP+NjvEGRt1v4Zx/mK35nxy//y7+F//5r/yJ+apL6+ROP0pY/+bW/lnz94cf73B/jKYunGqCcuwXJBVocK/F0CJIzDcrHVXioe0apVTPGcIh6Juv6qbqmVvrMIifIEU0RSWNxki1RnDynzsV1Uiq6hGqdb5qFyqLYokE9p9KxUMejfM2WwcSdzhULLCkTSVmPL3fyQUqjN5tAs+biEOrZt5nrc8eJLDhZBda32X6YUjaxrXpy0cNoaqxrbVZ0GZBRUBdIwwhBCKlDEgwKY8r4k9aO3mlg60du2DB299j7iJMe3IKbMUFWnEvcjBte6h9yzY69y4xFaLwfRj613fDR2yt+YnfJLm7RNE5l0uxMv5K7whrFbI+haEqqXnXMlkITDDS0Av0AXbCy4H5Eb3roI7gR0XGq9JHUoNmDtnauxB/ASRMM7CjWDyf2h/SdA0ItP68l5LEIsEcOgFUrTVHEpOV8Oft753p+UuGyUy7PYBMWfMW9M04+fUV86RFBPS4EQtMQQoP3HS4s8L4l+DCNJ6eIpkRKEUZLd6UUgViuJXOuleCNRXG1xYJMCisTz866FKvglGLJb72DyhWDU51wtmB9eyLWI+gYb73Qvuf+r/uX/Mb3/7v85S/76zSV1fs8iP9+n/hkfJF0ff2kh3KMNyCeaoBy4lrwLSFL+bEstDqKeKuseSCZWIWxk0yq6EJq35xSMUMuKALs/ZxMjFlYlIkmcaUCxJU+QPU9oHplUMt9Moj98hNwnNPR4bgdbtj7oXi6FX8JKXfvFJbAeeshlBXcaEyKYkxCSmjnSN7GsQ2BfeO4WTbcuxUuNiMhZ/JJg0vOdgUhmueoAZSkkD2qjrgX0iLhspBHSH0kqeDvLZFG0OcW3J5kHoUNl27PeiGEpXArAylbV+Ax9Twarvlkf8Un44Zryew0M6TINkde2W957faScXsNwx4XbYLVmGDM1veoL+fPlf2OCfoEvYE0osJQTkWTYQF0AzQBdQ4ZEtyMMGSkSyCjVaBEMdHsNh9Yhbb2cpKJTJmQajYWRUqZupFvVbdUrqXaBVsqOK16pUPbBKemdVHvUEkM2vMoj2wax+2pMNw74yvWz7PoI911MgYleHzb4kODbxp86HChsctUi0A2miNsbiNpTEhMuGypnsk40Nm1oWV/VRyZ4glD+bsIzJ0IUj7PCA7FIVakJnIwa8Z4laCOoJ9/d+dPU/T/9st88/u/ib/25X/rSQ/l5yS+8/Jt/I1/92uJH//Ekx7KMd6geKoBylIa60/jahmm4YZsP7nFO0J5OFX3FFElcgAWpZpGy8RiepSasql3xXqYjArLQbXZdzItNq1XfCnLZMI0DQ0XrPCqXKUNOxmMflR3AEpouSsvd+rTEF3xWqlCz3Kn7ovNaRl/ksCuEXKnjEsIOdk0M3oYLf01AporQMkI3uQ5q0COlOpaQaOHW+BUYOnR51u6ZxxnJ8KndUMeLnkmwTpn1An7OLCLOz69e8SP7R7yY/0Nj4jsycRsd/1DvyNtb+HmFtnvjQkYk+lEMkWkqqjLpZrGWCCGmj7BzsVe7Xi0Nl5iBD8aYEwU7YmlVowKyMgY0R3F5t4VsquB1JjOJMfCrMDUpykNKBlxxXSveJZMvZWk6pkKGJjuVOu1kosDbQW+paqGkcE5XvKOD7obbhYtJxenrPJIlwTxAQkB17T4ZkFolrjQ2PHRZOkcPyI52djdiLpU9LzmjmKXjYGpXDHX5ItigDULZK8Fq9u/C4fQVO+XAk4ODOQE7Yl4wtP98/F5Ef2//TJf81d+Ky+e3fC3v/r//aSH86bEn3n0Dv7cj76bF/7jgH78h5/0cI7xBsZT/QuzlIZWCtEsgmrtFevMmaJOaMBDhLHk1kW1aAPsh1erCZdTE6hKUya7mrrhAFIoAllXUQ1MXhni7G9XDb8SOGil4VxOCdlxmW/pZTSLrSwHQaxUcOQOYKfe7bqiQ9Eq6C3fSyVFUdgg5xzaQN8mHi1aJOZSyhqQJNYhN2PHKYGkfNDcjIpGrR3jkAi68JA8sgrEixY9E5an0C4ie73iakis/A5Jntu052F/w6dvL/n45hGv7jcMmsjFRI0U0WGPbLdmWLYfDViMGQaQZOdPWged2v4mOzaSFB0tOSEAAyaSHQtQSWpiV422xCgwVu2IB28MD1pSRjrCooWmiF9jY0yNL5qhanaW7Xxbh+vSA6cKnqXQY3ZxMFVyVbSoZb8r6+IK6MyAWGKld54fdztu/MBz54FFXtANDTkuyMFKobVpya7B+RbxvjAzI048KSUDmCrkPJA0kIjF4VUhqZnlwWTPry6QRi2pnlBcjCsI8Yi3xoOilVWRaRehdvdWAp4gT/XPx+dNfOFv/BDhC17ka77rt/JD/+PvedLDeUPjuy+/iP/qj/xanv9//eODDPAYP2/iqf6FaV3DUlr7ES13gIL93QBtmQ9ytrTGA5Q09c0Zy11uZUWyTVYu2511bkCGclfsZsChTOpThY8c0gSUaiBpCr7wLHzDBUtIykO3Y3CpAKlyL5oLMCpz2x3mpr4hhT3wdRtloqzW5oJR+V7QkGHhGZPiRjUQ5AOqDomKRJ0AisYZ4EpibEwhmjRlOG0Bh3YBPem4PW15de1pm8RORi7TFjeOjNlxPex4ZXvNg9tLtlfXjNstKglpDExpjtD36O0ebnoYEsSMjFj6plcDFgsPp94qd1OyHjroZFGjZBvrUH6OBkwx7GZsVwFYICgmLrb9w7oF+8LKxCKkrWJaX1ipoj8yVqVoXsopMvBSqC3Frp/qPKyVoavnDg7WwwW81utPleyEwQk3y5YP33f4nHnHJvM1LPCDI4qS02gVUynStivrc4T119GsxCTECMOYiONISlZybJhDyRliSuRcmcVMJJg7rOrE+hgZpIh6nLeKoeCqSLaGAcSkGU+iOaZ4npqIL73M27+9411/4n/Dh77uv3zSw/nXjk/EW37D+/59Tn8isf7+f/ykh3OMNymeboAijlZCQc4yFdiYe4N1BX5OzdMhSmbQxJWzCVpUoLiCHnrzlPWIQ10oZaTZxJg5A76wL7lUBxWvE+Eue1JKUle+4YIFQ0pc+Q2jxELoFMZFc5kZ6iSW74zjAIooIIRpWyKFFXCKiEedoE6R4A2UqByqqcWAkyaBmIv2BGMopsk/lMnYBJKaEqRQ0iEBXS3JXceu9XzSRZqsSB5Jo7CPmX2/p9/vGfd7dLeHzdb2zSvqS1nqmGATTSOyx5iPhIGFvQELHQoD0RV79xFUSxou50MKrFT/TgCl9LFBFR21ZljsgHkP0RkrdFLSZVZfa8JkAdVopzMrxD2MO0ilAih607tASeW4A3s2mfaVNgfMUn+5si2VeQnc8dkRNV0KysfXwoPhlnv9Fu0WfJVbstrcIuMN+2FAnXBvcZ91d4oopNgTYyKOA2nsGfc70jiQSg8h56WYt2F9dqqBIZRSYqvigQCacD5BE8wRV62Fgu2uWAURxZCvCGZHPOF4y/pURfzYj/Mlv/9t/Jvf9b/in3ztX3rSw/ms4g+/8jV84Pf/m69738XMc+8/OsP+fI+nG6AQaPGHuVtAXXHMJNOjnGjinhv4Aloeacc2ZwZqs79y5zvdKRtA0NJzBReM9s+xaE5yqQAqqsmZXf2hnNP8NBZuwZksGXLkyu8YBbubl/wYQVLASdaiWykCSy07VCfZKkgRV7zFSiNENXMuU+OWCT87RL1NkK6mJ5wxJ1mtOiZrMZTD1l33JZf9ysnSHuoMqLUNuQ3cOLjJEc2DzbO9sSMae/I4IKOlboxcUHQYD8c4Zdgl2Ans1PBhzpbmiYUZUYVtKlhNDHzkjLqiz3Flgk8UkOGnY6OBQ3olVe0QtszG2+en3nruiB7Kx0VNGhpH6/ETtzDubf8ByakAvQo05qk/q56RUg9j6zKweuhsUParnkfnyznKaNozeMcDaXjY7fj0RU+TX+Z6f8oXJmC7Yzts2MY9z/t7vLB8hqW0pt+JkTRav6PY95YKE+t0HUKD89aNOfuEVjZJlZgdKUVyDtO5Ed8gOuK1NUCY7fve8qSYsaArpcnVPubArRzj6Yj44z/Bs99yn286/fUA/Gd//3v44nDyRMf073z4f0H7O36Ka2nfE17+gZ/bAR3jLROfE0B53/vex/d+7/fykY98hOVyydd93dfxHd/xHXzlV37ltMx+v+f3//7fz1/8i3+Rvu/5xm/8Rr7ru76LF154YVrmE5/4BO95z3v4e3/v73FycsK3fuu38r73vY8QPje8FHAECWUeMqeSLEp0maiZUTNRIoO0PKvK80QeysCQrRy4CgN10hDYZDNpTepdrwvlM8pdstrkosaqWMqoTliepSy4kBOSJi7dSBSxfH9NJU3opFYVledKnlSwhDI5104To5sBm5Ly0QJkvEzMgeGeMi4XzKq9+oekCkTK3b1ymEDVzMBs8i9KytI6ILvMqFZWq9UjRM12njRCHlAdTbAaMoyl+qkfrex3FGNKxlk2K5n+haiHYzHUHytnDEnNnAQsXeOsd41WjZFy2BdXj222deayf1ENGO0N8Ji4doC4sQqfgLFmapVb5LGcg3BgY8TPzkFJDxbdyUQmaAGP9cX0ZknViS+grxwb2wmSc8gisHGOH9pviNd7fhEdzy48D4YbrvYPeSk/4vnNa5z5JS4rbkz4mqpLmY5AIwHvGtrGKn8IZsRGdiZ3SkpKmdio2d7niAYHYcRpwGe79lzQWluG8w2uXD+uXG9B0+dUxfNW++34fI704CE8eAjA/+5L/6cADL/6F/Nf/N//9OuWfftj4OVR2nKjmd/5ze+BH3xj3Fld+hRxbuNwjGOU+Jz+Vb///e/nve99L7/8l/9yYoz8oT/0h/iGb/gGPvShD7FerwH4fb/v9/HX//pf5y/9pb/E+fk53/Zt38Zv+k2/iX/4D/8hACklvumbvokXX3yRf/SP/hEvvfQS3/It30LTNPyxP/bHPqfBi3N4sTtDLU3NIkqXMyuNRAlEGgY6dpJ5npYr7RhIbAmlJNl65kx33ne3ADIDHwW31AUt7WCpCFW7C1/6wIU7ISblSvaMPiNTqqbQ7AKTR0oFLap3SJVD6gAm9sRxGKPMnhVoHVLnu8l0rizgq25iDkgK8FAs1VGrURQTB8dU2Bg9jCdVdqcOooAUVzxBajqsqSkkZWqq2GfYlFSO5pKqKutRCkabMTixVDHFoh9x2OQeTPejWutpZuMRNxVAGXOUkSgHomtQKzPeZlgVMW6PGbSJmPaoGrBlPRz7WkVVUmdTNRFlH+oxyRSAlJhSdBUkVlGt1hNmAFiclUfjpTi3Nmyl4yfPMoshsu2Fl33kMu9Y7m95Nd9w7joD52OmyQZOO3WcsGBFR+s62pzwqcEljzhL+VTr+xyE1HgjnxpHbjy5cfgYCN2Ipog25rMjjdouuQYnHidSWiO6wl1+dvFW++04hoWWXlXNf/M/8O+9/d963ee//kMPeDFc8XWLT/H/2f4C/vR3/WZe+L/8I+CHfo5HeozPxxDVabb5nOPVV1/l+eef5/3vfz+/6lf9Kq6urnjuuef4nu/5Hn7zb/7NAHzkIx/hq7/6q/nABz7Ar/gVv4K/+Tf/Jr/u1/06PvWpT013Rt/93d/NH/gDf4BXX32Vtm1/xu1eX19zfn7OP//oR1mdrsmaGdWs1fca2enITgeudc8NO67oeU12vJK3vJy3fERv+UndMdCD7EB3EHdmzpWHwx10ihB7pN/BsEeHvhh4RStdDQIhIO0a152wWJxzHu4zqHDpR8aQD5PwdMSlHPhsnXZ1NLCiRc+CUpvhma4BLC3gDpUgNWWgYHqW8l4FBakKMgOTO+3s7l8q3Y+UybgIRauFf9ZSBRMp6kqmqpSsBwaiTsylSoexsCX78tiaqys3Ebkc4SqigyJOUVfumJLAtgCFpKWSqqS9xCPZUaUSdAKtFoAgpVOzokHts4UrDEs5jmO2tEcWW4+IlU2/6OE54ELgJEDbQbeAtoVQSopjb/sqLTQLWK6Q1Qm66ApeTIVl2du5Ew/SUVsaWDoolHPgSmVXKAAlIqTCbDW2DeeNzUkJYc292HJ+OZA+9irr65Ht1SNurx5wlpRnpWWZHCGZK7HLyloDp7rgRJZ00tG5xhoP+kDjPK3zeNfgiuW/Bk/2igZHah25EWgdYdEQupamXbBs1yzbBV3o6HxD45qpcmeMIy+9+grf+hv+A66urjg7O/ssfzUsnvRvx9fzvyRI8zmN+fM1/Jd9CR/5fc/z5e89ilGP8a8fUUf+v3zfZ/W78a/Fi15dXQFw//59AH7gB36AcRz5Nb/m10zLfNVXfRVvf/vbpx+ZD3zgA3zN13zNHdr2G7/xG3nPe97Dv/gX/4Kv/dqvfd12+r6n7w+t3a+LS6AreXGb8q0EM5DpcCQJLGhIZJJCr4m9REbJfJEk9qo8VGUvCWv+NhoToKXSonpolBZpk1VJqRIyMy6jLBxwIh2nack277kOI7GmAnR2l1nZCzGDrCldoAVQwF2twoQdZ3TJgWIp71VjN3+oBrJb+bIfetifkvpQnVExdcdyyWNMFUVplsao2yysTk61PKqwQOVvSebR0pTUyoClivYZtor2WDmzz0wKy8qQRC3Eg9m9a8l3aQVI2RJWmirrYikurY71qaSJBKsA8hge8JiwORdg01QQWBiRzKGKh1yqt6QIiMvxmXow1XQS5XUu+qURJCHOuhvbjhRQCBx6MRXwkotAtlaAUdbnfGGsRm4kswkjusxcbJSuWdIsz3m0v+F67FlGWGbBK/iYWavnlMgZI4vc0BAI3rN0LSvXsXItrU80zqz0xWc0gHoleSE1Ap1Dx0geE3mpuOxKVRyIChJqmsd6O72ecfzs40n/dhzjs4/0ox/jy9/7sSc9jGN8HsbPGqDknPm9v/f38it/5a/kF/2iXwTAyy+/TNu2XFxc3Fn2hRde4OWXX56Wmf/A1M/rZ58p3ve+9/FH/+gffd37Th0eN5m/BlUaMW1IB0TJRdaqjGR6TYwu8aJ29GQyiQcyMmpjQtCcDqkKKULKWgEjhcHIswlFwWvgVNYsdclm2HDdJqJYmbJI8TGpgKK6w1J/24uwok5etTsycHCkLcBiZvR1iJpOmM0UVS1MdUHV0lelTI7VGE5tAha1aiSV0rl3yt4URoV0ACEpH9ieyqaksu76PZftqiryHLLCXtGtwo5DGklK1VUlj+oD6wJchcMHkFZA1agH6U4ojImIufXW6t9ieypBDpk7LamutqRbkpiPTDQNkUjxjCHadnIBKKFeD5i2JFcdCQZOsmlvjN2qDQdNu2ID/Qx36XeyifOqoM7WkyIjGXzEt7DtlGGApa7o2obbq0fcDltCVmSMyBhZq+fCj2x0YKEBl0yjdeKXnLk1p37B2kUWzpgQ79T0KU6tSWAA1zl0iFYdlDOSC4ZTLWSZ0rqMw5Vuyj87hPJW+O04xjGO8daPnzVAee9738sP//AP8w/+wT94I8fzGeMP/sE/yLd/+7dPr6+vr3nb2942yQ3AoaJ452iKGFUVEg2qSizg5FxbBonsSTzHyI1ruNWWJJEsTSknrhUZlZUIphPw3sCJ1nJTIUjDeTij0yXXacc+KNGsbI0AmTQJNUdSusLeYTAsPWNplypyqWmKVJiNA4NyADfM/lJLB01vuTKplkcVdU66ifodLYLOaPuuyuTxUcFF1YtUxsDlkg4qIs+sZsdf2R2YQJ4RDw4dsRTOMAccRZw8qJULVwZFy3YrsJvWqWgsL+bLzbTE8+xKbVI9ATZxSDFhK5jMSp2DrVOrHsgVkFgBSgVqMuW5DnKSfGBQJAsqHisVkrJ8nuSzOulODuf8wKpYqk0kID6gLmI10QNZR8ZxS+c8N+OGZfKcrs+4JrO/3aDjHt33DBIYWhgdLBlx2fQp+zgySGbwI0mWRNfSuUzjFV+OUXJqQGUAGT2aE66UIUcyMSVSSmirZJ8JxSQu5toh/HOLt8JvxzGOcYy3fvysAMq3fdu38f3f//38/b//9/niL/7i6f0XX3yRYRi4vLy8cyf06U9/mhdffHFa5p/8k39yZ32f/vSnp88+U3RdR9d1r3vfiU3s1uDMSmldVrwEGqBTJamjU88SzyCBNYFbAic0PKMN29wQpaUn2WRdbMMnYCCg3uOcL94oJhQNPnDhT+hkwRU921qp44rmQGdVM0CdkIwkqbPqgS3RO11HK0ApUZabXD3RQ9pAoTqePlZKYpqLabauYs2afnCz17VyqW5eijPrPH1TJthUQIoraTHFxJ0TQijj1XwwUg1l4q8aGTCdTM7GiFSr+7oD+fDnhEKn8mc5fJ45lCdnMf+XjilDhi+QTkulVajpnQxDqRDqy7Jk80tRE5Liva0n1NYHZd9yOpyrXMAopQw575maEBamRX1hzkQM1BXQZsuFQgyV9Joo6hYY67Kz9caBcdhwdbVF+5EhKmftBefLZ7jaRXbpEk0D0cNGBDz0Wa3/YcqM454xZnbjwMDIuVtwEhJLn2l9hwseLQyayaGs14/mSCaSciKmoZQlZ8amo5UGspLiyOcab5XfjmMc4xhv/fjs6wQxivfbvu3b+Ct/5a/wd//u3+VLvuRL7nz+y37ZL6NpGv7O3/k703sf/ehH+cQnPsG73/1uAN797nfzQz/0Q7zyyivTMn/7b/9tzs7OeNe73vWz2AEpD4dT06R4cYT6cJ5WPAs8Cw0scmCpjjWBcw3ck5aOFrO3r0JGdwAAJbWTfUstKQ6+5Txc0IQVl25k61Lpe1J0B660uReYZssKCLRUleRapVM0HXeAjDt8p7IX0504JVXEYXwTcMmzR5qlY4pOIg+WkphcT6MtW/U2gj2XMl7xJS1STL9s0i+plDv6lse1MYVZyNHW1XrovKVkwNiSvmhT+oxEa1Fnh0pm+8xh3yrrkfTwGLI9eksjsVVjRSrw6BXdgw6YeVu2yhRjXqSsjwJgyjlzHkIDTQtNB6EIWEVmAuF4EBVPJm31GNfjW1HUjCm5AzArDVO/M6J5NF2TZKu8CWY9r+LQ7Giajgjc7G6Iccu9k1OWywtctyIHT0TZk9mQ2QrsnGODcjP2PNzf8ur+hgf9hst+x2Yc6GNkHBMpajldSuwjw2ZkuN2zv92z2+zYbrbsdrds9rds9lu2/Y79sGcfD9qOnyneir8dxzjGMd7a8TkxKO9973v5nu/5Hr7v+76P09PTKe97fn7Ocrnk/Pyc3/E7fgff/u3fzv379zk7O+P3/J7fw7vf/W5+xa/4FQB8wzd8A+9617v4bb/tt/HH//gf5+WXX+YP/+E/zHvf+97P+U6n+nzUudVhqQYvnkwm4GkwgDKqM6CiBlRW4jmj4UYja3XciiMREA2oBCaRaNGg4AIEj6SWc79mIR1XecPOR/LUEbdORG4+yPo/JvHBVKpr5b21u+zkbVKBx8R62LdVZ2AADturpcXzElZgcsqFAoQESv+VA3BSAyiTf0gdezGsqyLUuh9JZsCEGbshh0nXFcGsS8YgLICVQwZLm+k+F3Aglgkp2zukkg6Ha8I91XVWywf1e7kezxmLc8j9gReb5KeKX5v8DVDOUixuBlJ8EcqKzl6X81NTPzoWp9lqPDMXGZfjfEfMXI/5HJzUcZdSdRdAB5TGdDSrFjlfwkZhbKDf4xLEzYab2w1ni/vce/5tPHr1FXbbR+TYsx96BvG0oaXVQEemT5FmUEaSiYbrvwwNkMUqe7wzC/1spdvZwegTMfe4lBhzZsyJ2EZis8AT6EuJ6mcTb7XfjmMc4xhv/ficAMqf/bN/FoCv//qvv/P+n//zf57f/tt/OwB/6k/9KZxzfPM3f/Mds6Ua3nu+//u/n/e85z28+93vZr1e863f+q38R//Rf/Q5D/6gPRV80Xho+c/jCSgNnoinlYZGI400LLWhJ9JL4lwTFzLyUK3ZGhKK02iZDdVAinMRH1oumlPa7Lkcr9i7HhWHqLm0TpHrRD27c57fPddHLmmlSXNRWZvSAHBiVWaT3JT6qJNrmY1d+aBqUSpQ0dGqWKz7CqXRDpb/qJVCVedSUxHOhLW1kSEcwEMzAzJSAE6imKIdRMYSsjUHXiis1VgOLePMnuozoo/t3p19LId/0gLXwhqpi8hh+cqwjApjASUqxtp0RX8S1ESyHbAIsHT2uhFoPNK0qGu4Ay6mUuFqiFfZqVoeXtN1hQnz2LJuzq6kaeCHrF89X7aMaYFqPXVhzpoGd37KaXufzfhphocDXhfo5pZ+v+FhcpydNDxz7xmu+oHNzSNSHMghkLtMdAtiUlwcCNHs7n121gFcg+lmghB8wKugUh7ZFDNJMikmUoyEOLKPI/vFwKpd0rmOcfjsGZS32m/HMY5xjLd+fE4A5bOxTFksFnznd34n3/md3/lTLvOOd7yDv/E3/sbnsumfejz1x76UgooUHkUF872splIOr/Z3S2BBYEngVDz3teHTGujFm9CxTt6lakdcoHPCabemGROP4jX7EM06Xn0xYnPT3fDUlyW7Q3qgDLMM/CBAnUSYlYVwJli12tkygdUZ2NZjtvVFI1E1JvXvqfKlAA5VlIjkWN73BXwVZqhuZ6JJDt+bWI3qO1L3QaWAqWDsy1hTGnHaFw0Ky7JcjmZ7TxljdMVuPx/WWT+rDFNlm6ph2sQOHUgUYzjK37UTdWVXkpa0VNmux4DIQgyYrFtYNZZ66jy0LRq6ojnBUjjM930GQAv7YV42eXbMS3ptAncFnORUxLeukGRFVDwHtSjoALQgHaLesOpqRdO0hOdPSOMtuo3IooONJ8U9VzevgDvjdHWBbHtudyNZEzkkoiRzvRHrvxN0ZJMG2nFPiA4XFK+W8szirRCpMFFKIksmRmWMyj5CHyP92DMuepZ+ybj/7N0/32q/Hcc4xjHe+vFU+0Pb3H243RapSR+HE8Wp4Iomxasn4GjU0RU9ylICawmcFBGtU0eqWhHnQRMinqVbcBoEiSOP0i07P9qPuBTqXyhVOEB1C83CZBgGHO72K7VfJq46idX0jYYZFsmz5XWakG3+LtU/IpYOqJP6VJpcD4sBIa1381M6ogKx+STppgnqsI6KAGoqqHw1SRGdygyYzVIgXqEtu5+kFLCIjWXvrBfPZHk/AyHVkbWe11TBFo+xULNUTtXGTPoYSslwBVl6EMk2Al2ARQvLFukC2gboWmiaAkrj4ZykUlqszJgwmfDTBDZzGVOtQq/jrIzL5E3jmTxwqsYoKxO4K9eFloqjFAK7nNB7C9xlR5OEsQlI16G7npT23I4OTUtW3RkuO26HDTGan0rKgSAOgmeMsNNEOw404mmyo6XH4WhcS6AyPEoeFM0JbSCNmWFM7Mee3egYhp6xHYn7+bVzjGMc4xhvbDzVAAUw0aOTGUCwO1QRsWKMokmxrLs1F1zgiQT2BFoaVgTWGvB4cqEMVALOJRa+4wxnE0Hu2YeMpkL3i5hFeakmusM+KFiap9Ybl8+q0DJXgFLZk7pHeZp/7+Q6ppRNSTuU9/TO55i+Ysoi6d1U1ZSOSCaYZc7w1PX6+eqYTMcQAyiTpX0Zi59tq/YPymrfqcNcVt8QRXtFOgdNRp0rZnfMwIlOtvdSGRRhqmqahuXsPXFq5cSTmLceaj0Aivp956w6p2mhtQaI2rUGVtoWCY01JRwxYFI6HpuzcHF9q8ertgZIlSUqYCPJ7JiUlJBEDv4vlkIS1Czu1RcdT7TqrpxQGUBH1CADAwPaOcL5KemVTxrYbDtkyGhMxHHkZjeQc8NqcYI4x3V/QxpH67ftvAEUYD9k2jTQqKP1js7tCM5SYE4aQK3toSoxZVLMjC7RDwPDEJFe6FcDQ5fR8XPS2B/jGMc4xucUTz1AmTMABgHsPyfGnjg8nkRwjkZ9Se8koiar6sGzUs8FgZZgJmuMOFUWbsWZKkPccCMDY1A0m8+FaOkDI7ZlnfddOahaZ6mCmnooVTW5pm5mDIE+9l3HbH0zFmUOWGaiVmSWRqkpJK3ahjLWiZGpZmu1esUxGYtVwDFtquR5prTPrPIHStlxBj+rD64ETBBYGK2gSWEL2iZ7XziUDVfWpHzPaQGfZXWSKxQrbIhUwDJjV0QOTQRryssX5sQDvgCUEJA5QGk7pGmhCSXLVAXKgx2TauevilTBNKDaU7tFg7N9SQ5J2dxyK5DLaTqFNa2jtdGjNHbMJSHVxKVW9piTGuOwQUaH90LuPHL/HNUtetvD5pY8KAyOWw2kGDnxLae64ma3Ibme6K1EPiGQE9uxx2elcdCJx+NwTkoBlUwYMZEZYySSGMaRXT+Q+0w/joxL5WBaeIxjHOMYb3w81QBlKmipDMDhk5LmAafZdCgqtOLpCCQSA5kuR1o8HZ4z8ZxpYE9DwrMSxzmBPt9yLZExYM6jtAUIzAHG7G590haU8SiFWSh303lkspVXuFvp/ThlLoe7dWbrr6LMyTpf767iTjVPBTYc0iZT+mgEhgIKAkjLZOxW92vOpqgrr6taVSuSoFbtTO67qYCkCT+JeY8sFNpcAEpJhSWdHUuQyjZMz/VkHwDhpE+BiV2ZDn1lTLyDxkHroPWW2mkb6Fq0rWXE9tBQu1YLNPP+RuMMBIuBlwJQkObAOokaG5ILEEvZUk41pZbruUyW2qklylLW4+wYqHN2LHMFkIL2O7iJDJcR0hZZniD3T9HtHrnZoNtbNDrUt2z7BLHnfHFCCI6Hu0vyAiIG1mPOjDGxT8rGO7qxoXHWr8cheDyqUuz7MkkTkUQcEykkxpQZsxJzYY2OcYxjHONNiqcaoNzV3UmZVrU0mC3+KOrKT3MRyEoFKIlWAq16WjxrDTxDwyMNdLLiHGUY91y7TMQZOHGtWaJXC/g6qc4FpdOMPJtclTIJz7QNdZn6PJsEp6fKFExzdz58RbFJcO5fUgFFXWD6DO5YrUKZWBNob6AkR6by4YOQ48DCZMXu6Ms6Jnan7JcbwceyerFlK2NTWYxWZoDBWYVNsdeXXADIlC6SybzVsIqWNET1kdEDi1KB00TguKI5qcDEWdVO1xhICcH0Jm13eITaVFGK/qgcw+wLE1LTYbXpYgDXlkc8pHgm9qkCt/k5mwmp3YxxE9t2FrtW1Q2W5kmlzJ0AcSTfXOMfXuHaCHTk4NCTU9x1RvsehhFNme1ugF3m/OKc++sLHu1u0CZDEnLKjFHpc6RxA7s0sEgNXQw4hEZawBHJjHlgyNFaRkgmRyUnJedI1t6E2sc4xjGO8SbFUw1QoM4jNnlNnYKxe11FEJ2BFLGET9BASzKxLNZU8ISGZwjcyIJRGuJ4w5WLDAKiwYCJ84U5SXa3PIlYZymUO6LTO4OcJi7DHHU2K+N27jESSJhElLVipyhktG53UtMWBc60Xg7AZjogs9cT62NsxaFEetYccNLT1EqjKvScT0plH6aUT7J3nJ9N0mXbTg6VNE0BK6X5XE3WTPhKbVwyG2+eJn7bpuEoG7tkndgdnbxjnDEooYChzpVqHW/bD85ASWjBt1Za7jyTaZ6Tw3vFuE5rOmeqnGrKowUZZsctH4BIPT4o1bjOyphn10oBPE4aFtKSpWef98ZwSYDVGtkLDK+SLzfQJHIupe2LBZxldBeRXUKHBIOyHbcAXNy/x/lKuNnfEnMiqzKqXSVBEzsdWcaRvRsI6ibgFImkHMnlO+oPbFrOStbRrsNjHOMYx3iT4qkHKDUmoDIREDLNy+bdJVZmrI6mpHU6KeXG2rCUhhdZIgQ+Ol7zUJRRrFBZXVPmRRO2Wu+ZGThJcrhTntu9T2kexdgKAzJ6R6tSgYh77HvlfaRkXAqzITZ9WFUOvI5VeZ0Wpm5DD8tPvWXq3TszMDHb/pTaqSXBdxDUoUppZoVvFUUJU5rOtu0oLAoGFlpnnireoaTDLsIEWBSZdk3q/k2YTLD+NZjFfVKrfKml2uIPAKUpwKTz0DVI26DBW/dh5wsQaQprhTFCUvQqWOm6ej3oTUQKaCwAxY2IK/2bKlM1N+tTyvGuYtlDhZSIKyLrQHANJ25BoiHqNUn2JtptG2u3gEdDQBtvWuXNaKTRwpgivUlI6TKcgc2wJz+64uLZc8KJ5/J2R5SB5GHMykCi18gujXRDIDRFjwTEolHKmslTywPQlAtogSSffZnxMY5xjGN8rvHzBqDUOXUqsp0mO1ehCh5Pg6JiDQRbIp02rGTknlvgE1zHAe8ACQidYZBppXaHqbUTYJ55ZdjGDgChMB5Qm+qNlHa9E6V/ZzKddmJ2V5qLzgShNJaxaTv70s8nlUlRD+XWTg/rE8fdWb2ChzRtW6seYrJ0ndMulQWpY2D2XQ7LTaCmgrYiLp3SIeXZY21mWi2MhhpQ6eUgx5jGYOkmVUcVPh8qygtTVjoYH7DgDOyFoj/pPCwaAyjBQxOKM2ypnvFFU+ILQCmVTrY9Y0N0ssEXc6aV0hDQJdR3RVdU022uiIaZneN6qAo4qWkovF1LOBoxwNyV9NitWxP9aOxQBMbBAMHaI41DcouMDne9NaJl0UEb0ZRsP8s1uE1b8nXm4uyCeycX3MQbhthj7jjKmBL7PLJLAZeE5BUvhX+Ukpksp6SSlDlnYs7EOV49xjGOcYw3OJ5qgDK71594icNduKU9agLEiydrJogjqSNIoFFL75xKR0qJK1W8E56Vhp6WS2DwWoCI3qX3q3C1CkGrYneyqa93nTVNMqcH9LA+5w7LV+HkVGpcGQg5rL+sZGJhKnqq5cq5fo/DNmdVKIYjZuutJm9VWCtS0mP62P7UFM80SzFDDEwM0dT7J0AurqxSTeXUAFTjoE3GpoQZEzPpXuo6y/Ps0L3O76toem3Ol1JuXLQni3B4dI3pTnx1Ci7iVtxhH11AyMWAr7IxuYCYUl7spKSwAJrCvARw0Rx7nTPLel/AysSozc/f7FyIx+M5kY6A0JfWB0qLyAk4xTUZbRurAPcG7nx7gqYRvR1RidB5ZOnNF8e3SFZ0GAGlTwOPNtecLy84OznlNkK/762CWjNREwORkEGjlrJjcFMrBYP45iskphfKJqA9xjGOcYw3K55qgDKVbNZ5mMN8ABULVEdZJePJKNXxpKNhJZk+jeSkOHGcSOA5GkbJJIRLVTKplIXa3a6xDfkw6WYB0p3y14Pg0xbXWkkzaU9sAjvAqKoIzbO/obI2BoYKFHtcZKuOO7brFVjM5viDhramKKQwLZXxqYAjF21BnWAfY1SmybUKhcvb9TuTzwuWCnOUdIgezNKCzh5mSy9zF9zChkxYbvZcsdQc59kHBZgEPzEnsmzQdYB1A6sOll1xjG3AdyAtBqAKSHSC4gu4qAAiW6rHFRZoAm31HDfgWzRb6bhUB+L6+TTq2YU59WKy8+vU0YlHRbjRkZQjUbGeUFUX0i5BIzIOOPG4AG69ZjjpcZcbcuztX3PnDKTFcmyKg28/DFwPl5x1Z5yuTu3U7QaiZgYiOx1AlCxKEFdKjh3O2THVohWyxpzgspDzEaAc4xjHePPi6QYoMM1WkxspTFkHLQkeFSGriWSTGlhp1NNKYJ96SIp4ayR4pg3RZXoyvSb26tnSlpmxpGPmAGVqsiezeUiYqnXuMAKVz5Fi01/M5aqr7LwsePI8YZqwDy/ybJl6IGSWHZqBFAyI6GR/X0GW2KSLn4GiCjjq7FY1MHUcUspfRw42/OW4KHfpDc3o1KywOrZJzWwU4zSxzQcpleIztmQGVO6sWwp0qeRPPebeHcqKFx5WDXrWwFkDpws4XcKqPTApbYeG1hiQClIqePOh+NY4JuDpChMEINZ9WfGFPWnAdaCDaXDuMF4zFKWzAWs5niXVFjUzKuyI5NrnB8V5h2SPNg10DS5aqXrcX9I2ZwjW2E9StnFnM+HTiuIy5T1ln7boTjlfnXFxcsZmvCbtRnpNiMYJoDTisESm4L3H5YD4CpTL8U/KUYJyjGMc482MpxqgzG+gp/kfDumKKcodei041kwrjiHZ3b53nq5Y4KtrWEvmTBM7Ri5x7M3qjYlpELVJuvbtcXWbc4CBMQIpWb+W2QB18jWpiKL6olSaQw7P0yQ3/3wmWJ0EN/rYLtvkZ31fsG05byyMA6hgyxUBbC4MDaXkuIypajWq2EPLvmucAYoZs+T0MKban4dY1lV0HIGSNgHCvAKr7vdjp292iqlHUQ4vnHdWcts6c61dB+SsQy86uGgNoJwsYdkWDUpnbrIhIL70X5LZ8XAYaHSFmZpSQAWgqM60zMW4zQcmkDcPNz83M4BYAZpaZ+WeRFQtzrqpACB75DiiPiMna7TryFGR3Z7+tS0as+lnug5kRL1DdoMxV9XFeFCIBj4Hei7jNRfrMy5O7rEdHzHsRqt2E7X0nrjJ6NCrWnPq7HDekSRbrx/yrBP1MY5xjGO88fFUA5S7E5l8xrdFxLwlUIrLBMF5YrS7+9Z1dGRW2hCLxmIF9JLZ6sgpI9eayEWvoFI69mphUqR05q25h8qcZDWFYa66kgI27rAMauuovV7u7MH8LnyW/phX7VQwhB5YDGZjwSpEbCIsy7jZhsQZAyC1oV3Zjqsl1HV7dR3efEEmcDUf9Hy2muVl6vhyMSvLpWooqFXxBLFJVfKh/PpxoUl5PWG1ij+rhid4NBQjtlWAsw7OOzhbGDhZd7BcWL+dtuhGihZFq7uslEd11dXSzkA5HPN5BU+qDFIFXgWgZC0FPHLIfE35tQpQOIA9l1HJxaWniryrADsaSMkZSSNWZp4syRIC6M5KoMcBjdEM4kIw2/5oDSDVCcSI9HYNagMxJa6ubvAdnK4vuB1vGfc9QQQvxmYFsWs9a0I0I+qQ7MiiJE0kDKgc4xjHOMabFU83QJnCZi2bt2fsiRyeSrIH75y5zWfTnAQcnQaW0hZ4ItaHhMiehvs0XJG4REkU+h4Ki1L61Ig/TGTVhVXvpim0vj9PyUylqDP2RQ4jnssW5imeO617ps/qxFeAQk3/TJbqh+N0ADLle65MjTqr+tGyrgp26rhcawxKbag3ByHTumfjrZN8TpAKC1NZlKY8i0wsiszTVrNuv3d2p871XtDg0GBpHTlp4LxFLxr0vC0AZWn6k65D2hZCYyJW5wpIkcLmVLGsHI6LdUU8XE8VFE0DrOdsli7DAMfhENRzPDved76fUVU6HGtxRI0MYoJc1eor4wx8jIMByChws4fbPW65QMST91t01KlTAVh5tOXQUsHIh3TZOIw8uH0E63PWp6f04si7npQzyRkArYVIWdUs/J1Y+yGyPfLdq/AYxzjGMd7IeLoBymwC/0z+aNOcoAY8PEJMCQW8b/AKQZWWxEobmCjuTNLISMtOIxtVBgcbCpMypZCKy+ckPil3lLU0V2tprHCoZKlgBA41sqUSZ86YzO3m53BEdT5vH5abRLF6YHA8BjrmPivusVRKPYhTz5jCJGgDEg/+J1q/W8cFMDA1IZwvV09IXX9tJJhT0XVUj5I8a/BX03C1AaLc2W3mR2Eq5/WW2ll4OG3gXgv3O3h2Ac8sCkBZwWJh4GRewSP+0GDQu7sbqmTXdB4eL8HWAytSv+BmgKba8k90zwyISu2/U8rfy6WSRLknnl5arlCiPzBroopu9siYYblC+oSMAziPX52SVWCZkNsb2O1h4e0STO7Azk242q45jUocMo/6R6SYOF+tEfGkbU/O2VKDpRVB1uLxUi6diVg6EijHOMYx3sR4ugEKlEzI69HJdBOuRllLzqSUEOcIDlLKhGp/XzQmUTNJEr06FniWEjij4RkyvSqeyE4zA7k0B/SvT0fYVpHJ46K0tq0gQeBwVz4HIVWkWV7WSW1KpczSQNM2a2qiMkbVRr8yG3U9VewqB7ZkYj44vHbZmJHszdtDBmBk6tyLt2Vq+ikF+xzrLyQSTSR69+TUQ3JIiWipDqpdiCt5U3ZfJmB3AFEyMUBlzUIRxZa0zqmH84DeC3DRWJpnvTRwslhBKOkcF5hKn4uPyh3QVkCiOaGUCqsJkM7Zoup7Usvb6/mtAFQOD5mtm8K0FFFuxTEOoQGe1YZGHJc4YzUYkXGLPLxCH9zYaoKBXVkE4s0WmtYaKyaBIaIVdCWdlbcXflHLZZLts5gS11fXuJh59uQctwoM2z1ZK1OXjefJ2XBO1UPPcfYxjnGMY7wJ8VQDlImQgIMYFKZkT70Xt/4huehRrILGF+v7BkcSx6iBQMJnAy0Nnk4DKxrOJZGw8ssrIo/IDMyAwZzpUEvzmPCyWKWjHHq5lElO0122oQon7+R1CnCRcgvs8qGyBJi0D3fu5ot2ZF5FotVYv2xnVo0yzTpQvlfGTdGaKGWs5e6/rhuY0iDqwcUizJz1KKoAY8psVOamCnALe+LdlBap7YDuwr4JtZV1uUN/n4WHtYezUrVz2li1zmIB3RLahVXrtI19RzxoQKS0LXCVuZpt4/FUWwVLc1A5wx9atTrzUU9ushVozg+EnZNaWRXUsZBAEjvqz9EyoiSEiKDsYBfhU9d2bTUrxLcGPnY9iEfw5KqNGYDGFSBSxuIdZC16FrHdLkONKXO72bDAc//0Au8Cu83mYHOvmZjt35PMsdcxw3OMYxzjTYynG6A8xp7Uv6tPiMORMbtuIxAcSrZJUIr1vXga9TSSadUz4mirLkUbehJnLpNRVISAMEriUpxNCHWirXfak46hVoXU6h+KrXn5VVfusi939ChwmPAKCJDMoRPubLKsDMo0/6W76y0VP4rR9YeJdaaNqJOoA2MXxmk+tkMay7C8AYn6/TQDKDqWVM5QBLez3ahEkZ9N+BVvOEG9iTllBtBqL6WqPdHZ8uKcgZouoKsGTjs4b+2xbmHRId0C7RbWsbhU7Ji5R/F+KQ61E0EyARJ97Hl+TuoOzeIOeJmtZ3IILtdIuU4s81VBqrkaB4WlOnLO4GCNY6mOrQjkQGqW6OkFOnjY9bi2RV4b0X3RAnVLGAWXPVkd9BlStlM/FParCKU1ZjQ5nKoxVSUblZNyu9kS8Nw7O2d1csL29gZSJGUlFUBVNUIiMvPjOcYxjnGMNz6eaoAylynYJKYTSHFiLpkpxlIFbJNdFqPugxpYyeIIBBZkMo1VKKiZtCUEM6jXAnAiDZGRyKiZjdiPftZ0qNbRWuUSCyPiS4bHlz57yTojT6Zqs9vR6otROXRHoRRMQaMSZnsLh266iil/a7nvHdqCKTWh9W5eDp9PLFD9riuajFq15CzdMyEFLAXkElN3Xl/WldWs7DVZWWtJhRhBZNU6kzC2GLZZjxtFxCY/RcukqWVPy7OY4V6VceRFIK+DlRHfb+FeVzxPlrBaoYsFdAto19YM0LkixLWqG63mcdUt1pWS7FnzR63grTrHTuXd+e7FV914SUzM0zy9BVStkU5dpbVUx0hhQJQFYVI0neK5yUKvCZWM3juBF85wH3tEfrSFByNERYhoNyDR2XBGZwAxRBvLUIZbL52saNKpaMtVskiVFCO3tzdITty//wxn52dcXl6Sx2QNAnOuSS9EZKpKP8YxjnGMNyOeaoCiNa9en8v7ImJdcGJCnJ9o6oP80iy7vTq8ZDyCV09DopFAh1X0ZFWiJFJJqWhhHXa0XJPZaSKpMAlRJ01KFVTUu+hyp05J3M8zMjbgw514yoe/q3alLl63Ub/jwiFtJEWcWe3blcMY5vb5lbkRmCbWaSyV3SlgxZfmOYoBrrpMTRE5SllyAVZ+ZiCnEZuwxcSadTzVjr72q5l0KOUxw1OGC+u5dYaHvCM3Hl0Gq9R5pkOeWcD9BXqxNN3Jao0s18YsNB3BNzTe2zkXJeXM4MSKeOfE1Z20jh7A251UXC0vrmmeCjgcaKgXJtMBknp9zJctTB8GzJwIC3EsxeHFOiw5xdIxw4BuryGOyNmaLNe4qxG9teofbTJcblDfoinDrpznRTmu9ZoSG9ckzFWldrGuu5kLqN/stuhDePbZZzm/uMdrrz0gxYGc1ZiX8qV0h1k6xjGOcYw3Np5qgFJ5/0o1z1M8KSWcVHCC9VcpM40Tuw90Ys4ovrjMmmA2kMV+/LNTUjHLKve8qCobl1lr5IqRKA6ppZy1umQukiziWK2GXZniB1IYkvojX+3j8wygTBNjLmCEA7tSSlvFuQN4qR4dlMm1motNjQ3LNmo5sdSUh3IoGa7MygyE1KqkKVVxSFFMImCnZhimZXLPERPYlv2t+zmRN66AFL1TfXtHClLP6UQICdp68ro1Eez9BTzXoc91cH8JZ0tYLaFbos0CCS1taFj7jqWYVqcRUMlci7IhMlIn6jwB3nJCJmBXey7q4w6/04DLs6od/0kYZeBUJM+ybm7aJ61eIjnhvIEYL55ODaQEVXLs0e0GfuIV9KUN9FiZdCrna9EiquQ4mj5pNIdXdR6Wrpy/WLBiZXr0QKploUpqKjeUFfb7PQ8ePODi/ILT83P6Bw+JcSRrASlOrHroGMc4xjHepHiqAYqRAZ8ZnEzalBmIqQDFCA37z6kUBkXwOFpxZAIqSspKFKtjyEAUYcyZJZGlOkJlGrT4nFQ7+YmpKBubBpzvainncof57FCdXQWQcDCCm3rjyAQ+pm7ErpbCzMFRLRmutEQu61LrylsMzQ8pi/nAZ+LOus3XGXMVgFIBi1R/EYFQzOxy1a+4w1fqSVADavW86MSWHJabDqGI6UhWLZw38EwDz7XwXGclxecrOFkj3cr61rQLfNOx9g0XrmEh3nxsUNR5vESz7Zts/ZUDipg/zx/5sdd1sHKXfZlSawYa70o1ZGIubDWJlBN4ocNN0p1UDj05wzDA5Qa5GsmjoguPrByyV1BnOGg/QhTTlSQx894kTP4uEyvnDvrpLFNFuhPzrU1ogZzCbt+T0iPW6xNOT895ND4ijtaA0PwFj2U8xzjGMd68eLoBShXuOfvBTymRcyaEYEAFPYgrpWbPCxsw3dBbZY/VQTgDKaW1YHaT2TgJGFF6aVgw0orHq0eMkOfu5JQOE0K941YK+KgT4XySKu8lLV4hBaAAU3WPr3stB+bj8fLVmuaZ1l9FmjU1UcBPYXMOJbSVmqllH0zMj5UW613PiwlB1HSIMzBS2QNf0h3qTDQ7pRhmNaq19VB9TBPoYc11Y+rF3GLXrdnXP9fAiwvkhQ59dgEXJ3ByCqsVtCsISyR0rF3DPRdYUYuIhK1mdiiDVtipd7c4ub4WQFKaKOoEUGpn69n+z1NBJZVyuBYKkzXbv8pgCKZ3ySR6MkgoWTNlo5GdptKVOCE+oPc62O+sC8EzC/R6hHEgLxYwCAypeJ+oiWN9suaJvjrf2r5JAaO1l47tmQl4a6vIXASx+11Pv4+s1mvOzs+5vb5h2Pflsj0aoRzjGMd48+KpBihZdZrfUUvLOO8Pd9137lwL01K/e2dNxqZUgJJQmtL5OJEYcXRYr56ulCa3OBbi2apndMUCXigsQ52463ZnzqzT7XSdxMqEmOf6jSK4FQ5/q/U+mdJITg7rmVIKM1FszTrcOQR1W3owcpvG4phM16QyKLOJdgIlZV+cs5RCrTtVLJWjcOhdU74qqUx7BTzN+iIW5fKdc3FgJxziHdIE8qJBz5fw7AJ5oYEXO3huARdLOFnDegWLJRq6KbVz7lpOnZ90HaowiHIriVGVce4SW/McdxgRLeBkTnvV8T3OmlSgaI7Fymyf6jGZLtZyXhHIVmU2ongFnLBxias4sieRpZgBDhHWGfdsBzfJGBKciWWTWEflfmO2NCpIUgMsxQBPC3CdslRZ7mSqSnE8tXKqXiopJ1IcGcaR1fqUk5NTblXY73fHKp5jHOMYb2o81QClFr1Y23fBO/96HcH0V2ENanlnKWkVHKKKU4cXJeCnbyVy0aZ4PAFPxuNp1bPGcy6ePY4bPNk1h4no7qbL37U6xCa7Kv60ObACE53dfdfMiR6YjyqyrOW44gqFX3UmcbbRAjCmdIIedCjiDpVEvqyHaswldximw35URWtlD9xhXydmqrIlBdDM7eRdOrAOWW0CnTIlxm5VczQojJH35CbAsiWfNOgzLfJ8A883xqJcLOBkBculVew0SwhLWt9x6lvOXKATjxPYF3DlsQk7FfAhj02yOoHIetyscmXCmxN+0hlOkXKo6rGr603ToTcvGpn9Xc5DWSCrshDHnsxLac+juEXHnWk9upVtYzvYMWw8qgPSOGTZof0Ao7fNJUVyYb+iMUDqS/ovlwZ/E4vHtK8yjRPrt4OVIedspobDmIjxmpOTE05OTlFVdrsNxzjGMY7xZsVTDVBExBwuVXGueIxovZPVKQWkd5IG5Yc5W1WPSgEtswnIIQRxJHV4MV4FkulVVGjUfCrO8GxdYJ8zvdQUgAED0Xo3bZqDaeJTnea2aThZmCptZJ73oLxO5TuV6eDAOoggorN7di1skqCT9X4RyeYCkFJNarmi8cwmDJ5buqsi1elLMzLTVVQfj4Pxm1I9X4QqOi0P51DvwQ+IjFS2Cp0d9yI01QJsJFiaSEIgrz35rLEy4udb9MUGnu+Q+yv0/ATWa1gYQJF2gfMNJwSekcDKBdoCAgKJPUoQYS2eXiO1mNgxn6zL8ZtYnAJsbWBMYuJy7K2dQT33ZY2aCyFTFSXlmOZYtB92vErBLirCXjIRCNkx5swQe3S4tVJi51G/QPYOvdwj7cKOd0zoosG7BsaB2HjT/hQWhWJ1b3pnmRC9ZAMfEziZ7b7WfzcFsWgBTzlnhn7gJl0j52dcXFwAyvXN/qf653mMYxzjGP9a8VQDlDENZBY4Z6kGpdLZejeHo6C5lhrbB1Uo6MoEUdr+WTdXavdjj9dMwNNQK2JNs9IBJzhW4glOGO8ACGe6jZxKnj4hWlw46+R9By850LZMdNEe4gqnUEqXUynLlWJqIU1hTjyKt8ZwkoAW1Lxfan8czYVdKZoB0YwWg7WsNTXlEA0caAF3+LswBjofs9ou2pxeU1oeNCI5T8yCFA97dS3qGpBkHaEdVsHjHc5n05h03oqQnEMaR14G9CzgLhrk3gJ5tkWfb8nPLOD8FDk9hdUJslxBsyS4BSfS8Lxrue8anJOSxVJjTpw1IFgh3DqIWgXOFTFW0Fc4BQ3lEOSDbmNWBXUAJnBgmA4CZ8sSlf5O2R1SKmVMIrEsF9iqcJUzz0vLM27BlTj6XU++vkH2BkpldYobAry6Jd+OtpbdiEoDTUDaDK0i2carydm5sJNuTJZiItpChMm0rzVbV4SzpXIt52w3AdlO+jCOXD66RO7d45lnnuPTrzz86f+RHuMYxzjGzzKeaoByfrLg7HQNzOQA08x/uEufP+rHuTArpjBQsmjp0aolJ5+JKJFET2KrI3sie41sGbllYEvkWjKXGhnyYIxFjpATUgCBTr4Z4+EOHah35VYkkybBppCxBMQsjSIzVUNhK8Q1U4pHvd0pm1nbaHfNZW2WcchoTja5gpUe+8ZKl+elp1MlEpPzqDX5u8smGIOTzaejMkM5G4gqYsxpvFpSOhFkUHTI6D7DTtG9wEZhE5EeiO6wzwHoBFkGZOnRtUdPS8+dZYsulki3hKax6h7f0rmGE/GciKNxzhgMBRVlT2Kj0eCEwtYlUs717M9STRmpepkMU0puaidggNOurZoOqqzKLFVnB7G8XQFQrKe+HM8AfgGypPEdz/qOZ6VjkyKfCpf0/hHaXqGDIssBBmfD+clHuN2INiYG11K6LvuMDAKDpXKMUMwTzpwMgxU033WCPSQ8D8/131BWLQDfxi4CznmcEz704Y/+tP9Gj3GMYxzjZxtPNUB55t49Tk9PD0Zt8vpl5qkUa/42S+fANJmY7fkkMDik6KcEka3cbEHqdx7bhsyXLEvUyX9K68j8G9RRwXzwBUjNfSbmZMadbdQ16jRegybuMA++/rCUZWTGihzG8NN+bxr+4/sxG+d82dmopjcVSx/NFjroT+arqmPKd/Z7dpbu7I2b1nX3/Ez7Ny89LxSaPM6eTGuQO6PLVKHpZzouj5+/8t6BkCnn/+46JyFtEXtX7YoUYzd9tkAmcYhm04WI4HIdvx0Pa+VwSKtNvSNffxC4e3YfG7PcGfZhyZoNeiyur6/543/iP3n9B8c4xjGO8QbEUw1QgM8wrT3++etfH2CEzkyz7n5j4mHKDG7VM0x3kGVq4/WQ4TDxHbgS04k4nS0zPc1ByGOTZAU8BTw8DiHq67tzx2Hi+emOC6UPywxdPbaWzwRA6nZl2vvXffrYpFwP2J3skFNqWkgOi/yUwEg4pJvuHsHDWusnmc98TcwPrUy9k+ZAVWYbPqy36kQEHkvR3B3hz3C0P8OYbbQyO2BSQFCWCi/LsVOmRpf178P1h6X3pjUr6mopsdQV/xTn+bP4t1OHJ3Cs2jnGMY7xcxlPNUARkckD5Wde+PCHPP6mfIbp5XVY4qcAFZ9pfY+9P01MP+VQ5c7T3anM7pT1MwACeP29+093NOoEU9NdzpWJbUY9TZOQPPa9eXXS/PVPtz0OVVPT+JxpMWoSYWKjZmhJy5juHtmfZnszcOEw3cQYI03TICLEGFFVQgjT3yJCCGHSWARv/xSm18Fep2RpGe89KaVpvN778nkihHCo9JbHLhw+80t7q5rhvZ4RkxkIrICkal0fX+Xdb88qxOTxhT+Lfys/zSLy2Dl//PUxjnGMY7yR8VQDlCcdb+7P80+99jvagc8GKJQJuXag3W63vPbaa7z44ot0XXdn2Zwzu92OxWLB7e0tp6enBmR+irvn6tzb9z1d1+G9vzMmEWEcRx48eMByueT8/Pwz754cJtpJL/TZ7t9dCozLy0s++tGP8kt+yS+h6zo+8YlPMI4jX/zFX8xHP/pRHj58yMXFBV/6pV/KSy+9xMOHD/nyL/9yzs/P+ZEf+RFubm740i/9UhaLBT/yIz+CqvLOd76Tj3/841xfX9M0DV/xFV9BzpnLy0ve+c53slwuP8cJ+zMv+ziP9bnHETQc4xjH+PkRR4DyFMR8wp6Dk/q3c+6npN/nLQC0lIteXl7yQz/0QzjneOGFF2jbdgIx2+2WH/3RH+Xtb387H/7wh3nXu97FxcUFKaUJfNRtxRhxzrHf7/mxH/sx3vGOd3BycgLcZRj6vufjH/84zz777AHwcAA3IoJzzvonOTeVj9fx55wRkWmd831/HBSklPjQhz7EBz7wAb7yK7+SGCMf/OAHaZqG09NTPvjBD7Jer/He89JLL/GDP/iD3NzcsN/v+cIv/EI++MEPcv/+fXa7Haenp3z4wx+m73tijGw2Gx4+fMiDBw84Pz/n1Vdf5dVXX+XFF19ktVr9a5zhYxzjGMc4xuPxOQGU973vfXzv934vH/nIR1gul3zd130d3/Ed38FXfuVXTst8/dd/Pe9///vvfO93/a7fxXd/93dPrz/xiU/wnve85//f3r3FRHH9cQD/7mV2md1luSywy3JzERQoeImioSamiQRqTVPbPlnTmqapqYUHrTHGptVektLYpA9tTPumfaht06TWlKgtFYs1IlYEAbkVKiz3i7AXYO9z/g90RxbBAn+BXfx9kknYmcPsmR/Lb86eOXMGV65cgUajwb59+1BSUiJ2q5OZDQ8Po7W1FeHh4eK0/t3d3di4cSNaW1uRlJSEgYEBcBwHhUIBpVIJxhhiY2MRHh6OqqoqjI6OIj4+HmazGe3t7cjMzERSUhIcDge0Wi2am5vR1NSENWvW4ObNm5iYmEB8fDzq6+thMpnA8zx6enoQHx+P1tZWyGQyGI1GVFZWYvPmzXC73RgYGEB+fj5sNhuuX7+OhIQEyOVyjIyMYHR0FGvXroVWq0V/fz8uX74Mo9GIhIQE1NTUQK/XIzU1FXfu3IHdbodGo4HX60VKSgrWrVsHhULxUFymNlIsFgu6urogk8kgCAKGhoZgsVgQGxsLl8sFjuOwevVqxMXFQSKRICMjAz6fD11dXbBarYiOjkZmZiYkEgkcDgc2bdoEs9mM8fFx5ObmYmRkBNXV1VAqlRgeHgYA8dIPXfKYHeUOQsh8zeu/uqKiAkVFRcjNzYXX68W7776LgoICNDY2Qq1Wi+XefPNNfPTRR+Lrqd8ufT4fdu3aBYPBgOvXr6Ovrw+vvfYaOI7DJ5988hgOaWXyn/x6enrgdrvh9Xqh1WrhcDjQ0NCAhoYGWCwW+Hw+cBwHi8UCmUwGhUKBgoICWK1W9PT0YNWqVZDL5eB5Hk899RSam5uhUChgs9mg1+vhcDiQmJiI9PR0mM1mxMXFoba2FlqtFt3d3XA6nbDb7eIJPCMjAzzPw2g0ir0MGo0GN2/exOjoKBITE+F2uzE2NoZ//vkHJpMJHMdBEATU1taC53kIgoA7d+4gMjISHR0dkEgkaG5uxpYtW1BTU4O0tDT09fUhLS0NCoUCPp8PY2NjUCqVUCgUYo+M1+tFY2MjdDodRkdHMTY2ht7eXoSFhUGhUMDr9WJkZAS3b9+GIAjYvXs3MjMzcevWLXAch/v376OtrQ3Nzc3IyMjAM888A5/Ph9bWVuh0OvA8j87OTkRERMBmswGAWH/yaJQ7CCHzNa8GyqVLlwJenzlzBnFxcaiursb27dvF9SqVCgaDYcZ9/Pbbb2hsbMTvv/8OvV6PDRs24OOPP8bRo0fxwQcfzPgNmUxe0lCpVOA4Dn19fZDJZOB5HiaTCVVVVdBqtRgYGIDJZILL5cL4+DgEQUB4eLh4AuA4DnV1dUhKSoJKpYJOpxP3LwgCPB4POI4Tx4qEh4dDqVRicHBQbBDJZDLk5OTAaDSira0N1dXVyMnJgVqthiAIiIyMREREBKqrq+F0OrF27VooFApUVFTg3r174ufC4/HAarUiOzsbOp0Ot2/fRkJCAqxWKyYmJhAXF4fU1FSYzWYkJiZiYGBAbAgIgiD26BiNRvFyz/j4OOrr6yGXy9HR0YGamhoMDQ1heHgYg4ODMBqNyM/PR1JSEkpLS9HT04Ph4WH09PQgOzsbLpcLCoUCTz/9NMrLyzE8PIyuri74fD6kpKRAKpWivb0dGzduRFNTE7q7uyGRSJCSkoKYmBj6Fv8IlDsIIfMl/e8is7NarQCA6OjogPXffvstYmJikJ2djWPHjmFiYkLcVllZiZycHOj1enFdYWEhbDYb7t69O+P7uFwu2Gy2gOVJI5FIwHFcwBgPf8/FyMgIDAYDXC6XeKL0X97RaDQYHByE3W5HYmIi1Go1hoaGxLEfwOQYltHRUQwNDU1Oae52iw2csbEx6HQ6JCQkwGg0QqPRQKFQgOM4ZGZmQi6Xo6urC16vFz6fDzabDb29vdDpdNBoNLh37x6am5shl8uRk5MDp9OJgYEB9PX1QaVSobOzE21tbXC73ejr64PL5UJYWJg4FsW/TCWVSpGamvpgwO2//O+h0+kgl8uhVquRnp6OqKgoyGQyOBwOdHd3o7u7Gx6PB0NDQ/jzzz8hCALCwsIQHx8PiUSCkZERSKVSNDU1obq6Woy5w+GA1WpFVFQUVq9eDb1eD5lMFjCmhswN5Q5CyH9Z8Fc+QRBw8OBBbNu2DdnZ2eL6V155BSkpKTAajairq8PRo0fR0tKCn376CQDQ398fkGAAiK/7+/tnfK+SkhJ8+OGHC63qiiGTycRLNDKZDDExMYiNjcWGDRuQkZEBjUaDxMREsXfE6XQiNjYWo6OjkEgkcDqd0Gg00Ov1kEgk0Gq1MJlMSEhIQG9vL6RSKYxGI+x2OyYmJpCcnAy73Y61a9eiq6sLGo0GRqNR7Emx2WyIjo5Genq6eEkJmJzAa/PmzbBYLGhtbYXBYEBqaio0Gg2Gh4cxNjYGj8eDNWvWoLa2FiqVCiaTCW1tbYiPj0dSUhKkUil4nkdycjIiIiLAGAPHcQAmGyjx8fEPxYfneeTl5cHhcECtViM3NxdqtRpJSUmw2+1iY66hoQF6vR6xsbEwm83ipaD09HSMjo6ira0NaWlpYo/SwMCA2DtlMpmg0+mQmJiIpKQk9PX1ISUlJWAAL3k0yh2EkLmQsAV+7Ttw4AAuXryIa9euITExcdZy5eXl2LFjB9ra2rB69Wrs378fnZ2d+PXXX8UyExMTUKvVuHDhAnbu3PnQPlwuF1wul/jaZrMhKSkJVqsVWq12IdUPKVPv4PF6vfB6veJdLXK5HOPj41AqlfD+O/eH+PwUxsTBosDkNXyv1yv2rsjlcrjdbiiVSoyPj0MqlYpjNfx36wiCAI7j4HQ6oVQqxTtuZDIZ7HY7gMluef/8Iv47czQaDXw+n1g3/x1A/p4b//tPTEyA4zjI5XI4HA7xZ389/QNbfT5fwHiTqXGZKV4OhwM8z0MqlYq9O/7jcDqd4raJiQkIggClUgme5+F0OuF2u6FSqeDz+eB0Tj4Mj+d5KBQK8bPqn1/FP2fK9NurnwQ2mw0RERHz/j8MhtzxDF6AXMLNuc6EkMfDyzz4A+fnlDcW1INSXFyM0tJSXL169ZEJBgC2bt0KAGKSMRgMuHnzZkCZgYEBAJj12rNSqXxovo4nkf8yD8dxASdn/wlzcsIwScAtuFPvLpl+a66/kSCRSAIul0yPNWNMbJz4X0//Hf+tylNJpdJ/n3r7gL8XxG/qB9Q/sRpjTBxP4J9fZPrvTT2+6eskEok4uNJ/jP7xITzPB2zzv6f/2FQqFdRqtbhfnucD3st/uQcAjTlZAModhJC5mtcYFMYYiouLce7cOZSXl8NkMv3n79TW1gKA2CWfl5eH+vp6DA4OimXKysqg1WqRlZU1n+o8UWY7EU/d/uCZRJKAn2czveHyqHLT9zPTZHHTJ2ib7f1m2vdc6/yoOsw00+lM66bOK+Mf6zJ9fzOZqa6z1YcEotxBCJmveX0FLCoqwtmzZ3H+/HmEh4eL130jIiLA8zza29tx9uxZPPfcc9DpdKirq8OhQ4ewfft2rFu3DgBQUFCArKwsvPrqqzh58iT6+/vx3nvvoaioaM7fdPwngidlwNv0E/F8ToRzPfmGkrnG4HGXm4sn6TKP//9vLrELttzhhWfGZ10SQhaXFx4AczwnsXnAg8ehBiynT59mjDFmNpvZ9u3bWXR0NFMqlSwtLY0dOXKEWa3WgP10dHSwnTt3Mp7nWUxMDDt8+DDzeDxzrkdXV9esdaGFFlqWdunq6gqZ3NHe3r7s8aKFFlrmljcWPEh2OQmCgJaWFmRlZaGrq+uJGCi71PyDCSm+i2MlxJcxJt4dNXXwcjCzWCyIioqC2Wx+6DZ18v9bCZ/rYLYS4jufvBGSo/ykUikSEhIATA6yDNU/VCig+C6uUI9vqJ3k/QkxIiIipOMe7EL9cx3sQj2+c80bofG1hxBCCCFPFGqgEEIIISTohGwDRalU4sSJEzTHwSKh+C4uiu/yoLgvLorv4nrS4huSg2QJIYQQsrKFbA8KIYQQQlYuaqAQQgghJOhQA4UQQgghQYcaKIQQQggJOiHZQDl16hRWrVqFsLAwbN269aEnnJKZXb16Fc8//zyMRiMkEgl+/vnngO2MMRw/fhzx8fHgeR75+fn4+++/A8qMjIxg79690Gq1iIyMxBtvvIGxsbElPIrgVVJSgtzcXISHhyMuLg67d+9GS0tLQBmn04mioiLodDpoNBq8/PLL4hN5/cxmM3bt2gWVSoW4uDgcOXIEXq93KQ9lxaLcsTCUOxYP5Y3ZhVwD5YcffsA777yDEydO4Pbt21i/fj0KCwsDnnBKZjY+Po7169fj1KlTM24/efIkvvjiC3z99deoqqqCWq1GYWEhnE6nWGbv3r24e/cuysrKUFpaiqtXr2L//v1LdQhBraKiAkVFRbhx4wbKysrg8XhQUFCA8fFxscyhQ4fwyy+/4Mcff0RFRQV6e3vx0ksvidt9Ph927doFt9uN69ev45tvvsGZM2dw/Pjx5TikFYVyx8JR7lg8lDceYc5P2QoSW7ZsYUVFReJrn8/HjEYjKykpWcZahR4A7Ny5c+JrQRCYwWBgn332mbjOYrEwpVLJvvvuO8YYY42NjQwA++uvv8QyFy9eZBKJhPX09CxZ3UPF4OAgA8AqKioYY5Px5DiO/fjjj2KZpqYmBoBVVlYyxhi7cOECk0qlrL+/Xyzz1VdfMa1Wy1wu19IewApDuePxoNyxuChvPBBSPShutxvV1dXIz88X10mlUuTn56OysnIZaxb67t27h/7+/oDYRkREYOvWrWJsKysrERkZic2bN4tl8vPzIZVKUVVVteR1DnZWqxUAEB0dDQCorq6Gx+MJiHFGRgaSk5MDYpyTkwO9Xi+WKSwshM1mw927d5ew9isL5Y7FQ7nj8aK88UBINVCGh4fh8/kC/ggAoNfr0d/fv0y1Whn88XtUbPv7+xEXFxewXS6XIzo6muI/jSAIOHjwILZt24bs7GwAk/FTKBSIjIwMKDs9xjP9DfzbyMJQ7lg8lDseH8obgULyacaEBLuioiI0NDTg2rVry10VQkiIoLwRKKR6UGJiYiCTyR4avTwwMACDwbBMtVoZ/PF7VGwNBsNDAwq9Xi9GRkYo/lMUFxejtLQUV65cQWJiorjeYDDA7XbDYrEElJ8e45n+Bv5tZGEodyweyh2PB+WNh4VUA0WhUGDTpk24fPmyuE4QBFy+fBl5eXnLWLPQZzKZYDAYAmJrs9lQVVUlxjYvLw8WiwXV1dVimfLycgiCgK1bty55nYMNYwzFxcU4d+4cysvLYTKZArZv2rQJHMcFxLilpQVmszkgxvX19QHJvKysDFqtFllZWUtzICsQ5Y7FQ7nj/0N54xGWe5TufH3//fdMqVSyM2fOsMbGRrZ//34WGRkZMHqZzMxut7OamhpWU1PDALDPP/+c1dTUsM7OTsYYY59++imLjIxk58+fZ3V1deyFF15gJpOJORwOcR/PPvss27hxI6uqqmLXrl1j6enpbM+ePct1SEHlwIEDLCIigv3xxx+sr69PXCYmJsQyb731FktOTmbl5eXs1q1bLC8vj+Xl5YnbvV4vy87OZgUFBay2tpZdunSJxcbGsmPHji3HIa0olDsWjnLH4qG8MbuQa6AwxtiXX37JkpOTmUKhYFu2bGE3btxY7iqFhCtXrjAADy379u1jjE3eLvj+++8zvV7PlEol27FjB2tpaQnYx/3799mePXuYRqNhWq2Wvf7668xuty/D0QSfmWILgJ0+fVos43A42Ntvv82ioqKYSqViL774Iuvr6wvYT0dHB9u5cyfjeZ7FxMSww4cPM4/Hs8RHszJR7lgYyh2Lh/LG7CSMMbZ0/TWEEEIIIf8tpMagEEIIIeTJQA0UQgghhAQdaqAQQgghJOhQA4UQQgghQYcaKIQQQggJOtRAIYQQQkjQoQYKIYQQQoIONVAIIYQQEnSogUIIIYSQoEMNFEIIIYQEHWqgEEIIISToUAOFEEIIIUHnf5YgPenn5g89AAAAAElFTkSuQmCC",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
+ "cells": [
{
- "data": {
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",
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/binary_segmentation_intro.ipynb)"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "U3WUb8t2P2e5"
+ },
+ "source": [
+ "\ud83c\udded \ud83c\uddea \ud83c\uddf1 \ud83c\uddf1 \ud83c\uddf4 \ud83d\udc4b\n",
+ "\n",
+ "This example shows how to use `segmentation-models-pytorch` for **binary** semantic segmentation. We will use the [The Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) (this is an adopted example from Albumentations package [docs](https://albumentations.ai/docs/examples/pytorch_semantic_segmentation/), which is strongly recommended to read, especially if you never used this package for augmentations before). \n",
+ "\n",
+ "The task will be to classify each pixel of an input image either as pet \ud83d\udc36\ud83d\udc31 or as a background.\n",
+ "\n",
+ "\n",
+ "What we are going to overview in this example: \n",
+ "\n",
+ " - \ud83d\udcdc `Datasets` and `DataLoaders` preparation (with predefined dataset class). \n",
+ " - \ud83d\udce6 `LightningModule` preparation: defining training, validation and test routines. \n",
+ " - \ud83d\udcc8 Writing `IoU` metric inside the `LightningModule` for measuring quality of segmentation. \n",
+ " - \ud83d\udc36 Results visualization.\n",
+ "\n",
+ "\n",
+ "> It is expected you are familiar with Python, PyTorch and have some experience with training neural networks before!"
]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# lets look at some samples\n",
- "\n",
- "sample = train_dataset[0]\n",
- "plt.subplot(1, 2, 1)\n",
- "# for visualization we have to transpose back to HWC\n",
- "plt.imshow(sample[\"image\"].transpose(1, 2, 0))\n",
- "plt.subplot(1, 2, 2)\n",
- "# for visualization we have to remove 3rd dimension of mask\n",
- "plt.imshow(sample[\"mask\"].squeeze())\n",
- "plt.show()\n",
- "\n",
- "sample = valid_dataset[0]\n",
- "plt.subplot(1, 2, 1)\n",
- "# for visualization we have to transpose back to HWC\n",
- "plt.imshow(sample[\"image\"].transpose(1, 2, 0))\n",
- "plt.subplot(1, 2, 2)\n",
- "# for visualization we have to remove 3rd dimension of mask\n",
- "plt.imshow(sample[\"mask\"].squeeze())\n",
- "plt.show()\n",
- "\n",
- "sample = test_dataset[0]\n",
- "plt.subplot(1, 2, 1)\n",
- "# for visualization we have to transpose back to HWC\n",
- "plt.imshow(sample[\"image\"].transpose(1, 2, 0))\n",
- "plt.subplot(1, 2, 2)\n",
- "# for visualization we have to remove 3rd dimension of mask\n",
- "plt.imshow(sample[\"mask\"].squeeze())\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "jg4_bxKV5BaQ"
- },
- "source": [
- "## Model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
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- "outputs": [],
- "source": [
- "# Some training hyperparameters\n",
- "EPOCHS = 10\n",
- "T_MAX = EPOCHS * len(train_dataloader)\n",
- "OUT_CLASSES = 1"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-08-18T04:39:44.509551Z",
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- "shell.execute_reply.started": "2024-08-18T04:39:44.509528Z"
- },
- "id": "PeGCIYNlVx5y",
- "trusted": true
- },
- "outputs": [],
- "source": [
- "class PetModel(pl.LightningModule):\n",
- " def __init__(self, arch, encoder_name, in_channels, out_classes, **kwargs):\n",
- " super().__init__()\n",
- " self.model = smp.create_model(\n",
- " arch,\n",
- " encoder_name=encoder_name,\n",
- " in_channels=in_channels,\n",
- " classes=out_classes,\n",
- " **kwargs,\n",
- " )\n",
- " # preprocessing parameteres for image\n",
- " params = smp.encoders.get_preprocessing_params(encoder_name)\n",
- " self.register_buffer(\"std\", torch.tensor(params[\"std\"]).view(1, 3, 1, 1))\n",
- " self.register_buffer(\"mean\", torch.tensor(params[\"mean\"]).view(1, 3, 1, 1))\n",
- "\n",
- " # for image segmentation dice loss could be the best first choice\n",
- " self.loss_fn = smp.losses.DiceLoss(smp.losses.BINARY_MODE, from_logits=True)\n",
- "\n",
- " # initialize step metics\n",
- " self.training_step_outputs = []\n",
- " self.validation_step_outputs = []\n",
- " self.test_step_outputs = []\n",
- "\n",
- " def forward(self, image):\n",
- " # normalize image here\n",
- " image = (image - self.mean) / self.std\n",
- " mask = self.model(image)\n",
- " return mask\n",
- "\n",
- " def shared_step(self, batch, stage):\n",
- " image = batch[\"image\"]\n",
- "\n",
- " # Shape of the image should be (batch_size, num_channels, height, width)\n",
- " # if you work with grayscale images, expand channels dim to have [batch_size, 1, height, width]\n",
- " assert image.ndim == 4\n",
- "\n",
- " # Check that image dimensions are divisible by 32,\n",
- " # encoder and decoder connected by `skip connections` and usually encoder have 5 stages of\n",
- " # downsampling by factor 2 (2 ^ 5 = 32); e.g. if we have image with shape 65x65 we will have\n",
- " # following shapes of features in encoder and decoder: 84, 42, 21, 10, 5 -> 5, 10, 20, 40, 80\n",
- " # and we will get an error trying to concat these features\n",
- " h, w = image.shape[2:]\n",
- " assert h % 32 == 0 and w % 32 == 0\n",
- "\n",
- " mask = batch[\"mask\"]\n",
- " assert mask.ndim == 4\n",
- "\n",
- " # Check that mask values in between 0 and 1, NOT 0 and 255 for binary segmentation\n",
- " assert mask.max() <= 1.0 and mask.min() >= 0\n",
- "\n",
- " logits_mask = self.forward(image)\n",
- "\n",
- " # Predicted mask contains logits, and loss_fn param `from_logits` is set to True\n",
- " loss = self.loss_fn(logits_mask, mask)\n",
- "\n",
- " # Lets compute metrics for some threshold\n",
- " # first convert mask values to probabilities, then\n",
- " # apply thresholding\n",
- " prob_mask = logits_mask.sigmoid()\n",
- " pred_mask = (prob_mask > 0.5).float()\n",
- "\n",
- " # We will compute IoU metric by two ways\n",
- " # 1. dataset-wise\n",
- " # 2. image-wise\n",
- " # but for now we just compute true positive, false positive, false negative and\n",
- " # true negative 'pixels' for each image and class\n",
- " # these values will be aggregated in the end of an epoch\n",
- " tp, fp, fn, tn = smp.metrics.get_stats(\n",
- " pred_mask.long(), mask.long(), mode=\"binary\"\n",
- " )\n",
- " return {\n",
- " \"loss\": loss,\n",
- " \"tp\": tp,\n",
- " \"fp\": fp,\n",
- " \"fn\": fn,\n",
- " \"tn\": tn,\n",
- " }\n",
- "\n",
- " def shared_epoch_end(self, outputs, stage):\n",
- " # aggregate step metics\n",
- " tp = torch.cat([x[\"tp\"] for x in outputs])\n",
- " fp = torch.cat([x[\"fp\"] for x in outputs])\n",
- " fn = torch.cat([x[\"fn\"] for x in outputs])\n",
- " tn = torch.cat([x[\"tn\"] for x in outputs])\n",
- "\n",
- " # per image IoU means that we first calculate IoU score for each image\n",
- " # and then compute mean over these scores\n",
- " per_image_iou = smp.metrics.iou_score(\n",
- " tp, fp, fn, tn, reduction=\"micro-imagewise\"\n",
- " )\n",
- "\n",
- " # dataset IoU means that we aggregate intersection and union over whole dataset\n",
- " # and then compute IoU score. The difference between dataset_iou and per_image_iou scores\n",
- " # in this particular case will not be much, however for dataset\n",
- " # with \"empty\" images (images without target class) a large gap could be observed.\n",
- " # Empty images influence a lot on per_image_iou and much less on dataset_iou.\n",
- " dataset_iou = smp.metrics.iou_score(tp, fp, fn, tn, reduction=\"micro\")\n",
- " metrics = {\n",
- " f\"{stage}_per_image_iou\": per_image_iou,\n",
- " f\"{stage}_dataset_iou\": dataset_iou,\n",
- " }\n",
- "\n",
- " self.log_dict(metrics, prog_bar=True)\n",
- "\n",
- " def training_step(self, batch, batch_idx):\n",
- " train_loss_info = self.shared_step(batch, \"train\")\n",
- " # append the metics of each step to the\n",
- " self.training_step_outputs.append(train_loss_info)\n",
- " return train_loss_info\n",
- "\n",
- " def on_train_epoch_end(self):\n",
- " self.shared_epoch_end(self.training_step_outputs, \"train\")\n",
- " # empty set output list\n",
- " self.training_step_outputs.clear()\n",
- " return\n",
- "\n",
- " def validation_step(self, batch, batch_idx):\n",
- " valid_loss_info = self.shared_step(batch, \"valid\")\n",
- " self.validation_step_outputs.append(valid_loss_info)\n",
- " return valid_loss_info\n",
- "\n",
- " def on_validation_epoch_end(self):\n",
- " self.shared_epoch_end(self.validation_step_outputs, \"valid\")\n",
- " self.validation_step_outputs.clear()\n",
- " return\n",
- "\n",
- " def test_step(self, batch, batch_idx):\n",
- " test_loss_info = self.shared_step(batch, \"test\")\n",
- " self.test_step_outputs.append(test_loss_info)\n",
- " return test_loss_info\n",
- "\n",
- " def on_test_epoch_end(self):\n",
- " self.shared_epoch_end(self.test_step_outputs, \"test\")\n",
- " # empty set output list\n",
- " self.test_step_outputs.clear()\n",
- " return\n",
- "\n",
- " def configure_optimizers(self):\n",
- " optimizer = torch.optim.Adam(self.parameters(), lr=2e-4)\n",
- " scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=T_MAX, eta_min=1e-5)\n",
- " return {\n",
- " \"optimizer\": optimizer,\n",
- " \"lr_scheduler\": {\n",
- " \"scheduler\": scheduler,\n",
- " \"interval\": \"step\",\n",
- " \"frequency\": 1,\n",
- " },\n",
- " }\n",
- " return"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
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- "id": "8d_wsmYArTt6",
- "trusted": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.cache/torch/hub/checkpoints/resnet34-333f7ec4.pth\n",
- "100%|ββββββββββ| 83.3M/83.3M [00:01<00:00, 74.5MB/s]\n"
- ]
- }
- ],
- "source": [
- "model = PetModel(\"FPN\", \"resnet34\", in_channels=3, out_classes=1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "v-YUI8oH-sfL"
- },
- "source": [
- "## Training"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-08-18T04:39:46.416557Z",
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- "trusted": true
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- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2024-08-18 04:39:48.833488: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
- "2024-08-18 04:39:48.833619: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
- "2024-08-18 04:39:48.968760: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
- ]
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- "Sanity Checking: | | 0/? [00:00"
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# lets look at some samples\n",
+ "\n",
+ "sample = train_dataset[0]\n",
+ "plt.subplot(1, 2, 1)\n",
+ "# for visualization we have to transpose back to HWC\n",
+ "plt.imshow(sample[\"image\"].transpose(1, 2, 0))\n",
+ "plt.subplot(1, 2, 2)\n",
+ "# for visualization we have to remove 3rd dimension of mask\n",
+ "plt.imshow(sample[\"mask\"].squeeze())\n",
+ "plt.show()\n",
+ "\n",
+ "sample = valid_dataset[0]\n",
+ "plt.subplot(1, 2, 1)\n",
+ "# for visualization we have to transpose back to HWC\n",
+ "plt.imshow(sample[\"image\"].transpose(1, 2, 0))\n",
+ "plt.subplot(1, 2, 2)\n",
+ "# for visualization we have to remove 3rd dimension of mask\n",
+ "plt.imshow(sample[\"mask\"].squeeze())\n",
+ "plt.show()\n",
+ "\n",
+ "sample = test_dataset[0]\n",
+ "plt.subplot(1, 2, 1)\n",
+ "# for visualization we have to transpose back to HWC\n",
+ "plt.imshow(sample[\"image\"].transpose(1, 2, 0))\n",
+ "plt.subplot(1, 2, 2)\n",
+ "# for visualization we have to remove 3rd dimension of mask\n",
+ "plt.imshow(sample[\"mask\"].squeeze())\n",
+ "plt.show()"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "",
- "version_major": 2,
- "version_minor": 0
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jg4_bxKV5BaQ"
},
- "text/plain": [
- "Validation: | | 0/? [00:00 5, 10, 20, 40, 80\n",
+ " # and we will get an error trying to concat these features\n",
+ " h, w = image.shape[2:]\n",
+ " assert h % 32 == 0 and w % 32 == 0\n",
+ "\n",
+ " mask = batch[\"mask\"]\n",
+ " assert mask.ndim == 4\n",
+ "\n",
+ " # Check that mask values in between 0 and 1, NOT 0 and 255 for binary segmentation\n",
+ " assert mask.max() <= 1.0 and mask.min() >= 0\n",
+ "\n",
+ " logits_mask = self.forward(image)\n",
+ "\n",
+ " # Predicted mask contains logits, and loss_fn param `from_logits` is set to True\n",
+ " loss = self.loss_fn(logits_mask, mask)\n",
+ "\n",
+ " # Lets compute metrics for some threshold\n",
+ " # first convert mask values to probabilities, then\n",
+ " # apply thresholding\n",
+ " prob_mask = logits_mask.sigmoid()\n",
+ " pred_mask = (prob_mask > 0.5).float()\n",
+ "\n",
+ " # We will compute IoU metric by two ways\n",
+ " # 1. dataset-wise\n",
+ " # 2. image-wise\n",
+ " # but for now we just compute true positive, false positive, false negative and\n",
+ " # true negative 'pixels' for each image and class\n",
+ " # these values will be aggregated in the end of an epoch\n",
+ " tp, fp, fn, tn = smp.metrics.get_stats(\n",
+ " pred_mask.long(), mask.long(), mode=\"binary\"\n",
+ " )\n",
+ " return {\n",
+ " \"loss\": loss,\n",
+ " \"tp\": tp,\n",
+ " \"fp\": fp,\n",
+ " \"fn\": fn,\n",
+ " \"tn\": tn,\n",
+ " }\n",
+ "\n",
+ " def shared_epoch_end(self, outputs, stage):\n",
+ " # aggregate step metics\n",
+ " tp = torch.cat([x[\"tp\"] for x in outputs])\n",
+ " fp = torch.cat([x[\"fp\"] for x in outputs])\n",
+ " fn = torch.cat([x[\"fn\"] for x in outputs])\n",
+ " tn = torch.cat([x[\"tn\"] for x in outputs])\n",
+ "\n",
+ " # per image IoU means that we first calculate IoU score for each image\n",
+ " # and then compute mean over these scores\n",
+ " per_image_iou = smp.metrics.iou_score(\n",
+ " tp, fp, fn, tn, reduction=\"micro-imagewise\"\n",
+ " )\n",
+ "\n",
+ " # dataset IoU means that we aggregate intersection and union over whole dataset\n",
+ " # and then compute IoU score. The difference between dataset_iou and per_image_iou scores\n",
+ " # in this particular case will not be much, however for dataset\n",
+ " # with \"empty\" images (images without target class) a large gap could be observed.\n",
+ " # Empty images influence a lot on per_image_iou and much less on dataset_iou.\n",
+ " dataset_iou = smp.metrics.iou_score(tp, fp, fn, tn, reduction=\"micro\")\n",
+ " metrics = {\n",
+ " f\"{stage}_per_image_iou\": per_image_iou,\n",
+ " f\"{stage}_dataset_iou\": dataset_iou,\n",
+ " }\n",
+ "\n",
+ " self.log_dict(metrics, prog_bar=True)\n",
+ "\n",
+ " def training_step(self, batch, batch_idx):\n",
+ " train_loss_info = self.shared_step(batch, \"train\")\n",
+ " # append the metics of each step to the\n",
+ " self.training_step_outputs.append(train_loss_info)\n",
+ " return train_loss_info\n",
+ "\n",
+ " def on_train_epoch_end(self):\n",
+ " self.shared_epoch_end(self.training_step_outputs, \"train\")\n",
+ " # empty set output list\n",
+ " self.training_step_outputs.clear()\n",
+ " return\n",
+ "\n",
+ " def validation_step(self, batch, batch_idx):\n",
+ " valid_loss_info = self.shared_step(batch, \"valid\")\n",
+ " self.validation_step_outputs.append(valid_loss_info)\n",
+ " return valid_loss_info\n",
+ "\n",
+ " def on_validation_epoch_end(self):\n",
+ " self.shared_epoch_end(self.validation_step_outputs, \"valid\")\n",
+ " self.validation_step_outputs.clear()\n",
+ " return\n",
+ "\n",
+ " def test_step(self, batch, batch_idx):\n",
+ " test_loss_info = self.shared_step(batch, \"test\")\n",
+ " self.test_step_outputs.append(test_loss_info)\n",
+ " return test_loss_info\n",
+ "\n",
+ " def on_test_epoch_end(self):\n",
+ " self.shared_epoch_end(self.test_step_outputs, \"test\")\n",
+ " # empty set output list\n",
+ " self.test_step_outputs.clear()\n",
+ " return\n",
+ "\n",
+ " def configure_optimizers(self):\n",
+ " optimizer = torch.optim.Adam(self.parameters(), lr=2e-4)\n",
+ " scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=T_MAX, eta_min=1e-5)\n",
+ " return {\n",
+ " \"optimizer\": optimizer,\n",
+ " \"lr_scheduler\": {\n",
+ " \"scheduler\": scheduler,\n",
+ " \"interval\": \"step\",\n",
+ " \"frequency\": 1,\n",
+ " },\n",
+ " }\n",
+ " return"
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