diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..5f836d9ef80708971179d66432e754ee5a1a3d6a 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +mmcv_full-1.5.0-cp310-cp310-linux_x86_64.whl filter=lfs diff=lfs merge=lfs -text diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b2ed3b081f4f69727ba546cda7b9ddece0ef75fd --- /dev/null +++ b/.github/workflows/lint.yaml @@ -0,0 +1,39 @@ +name: Lint + +on: + push: + branches: + - main + pull_request: + branches: + - master + - 'gh/**' + +jobs: + run-linters: + name: Run linters + runs-on: ubuntu-20.04 + + steps: + - name: Checkout repository + uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: 3.9 + cache: 'pip' + cache-dependency-path: '**/requirements*.txt' + - name: Install Python (development) dependencies + run: | + pip install -r requirements-dev.txt + - name: Run flake8 + run: | + flake8 + - name: Run black + if: always() + run: | + black --check dinov2 + - name: Run pylint + if: always() + run: | + pylint --exit-zero dinov2 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..f6893ca30f324f6ed3e18ae9c726af8377c57c69 --- /dev/null +++ b/.gitignore @@ -0,0 +1,11 @@ +build/ +dist/ +*.egg-info/ +**/__pycache__/ + +**/.ipynb_checkpoints +**/.ipynb_checkpoints/** + +*.swp + +.vscode/ diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000000000000000000000000000000000000..2b2502c69f9298ce3c619c93297fe4c3f459ba4d --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,16 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Current File", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": false + } + ] +} \ No newline at end of file diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000000000000000000000000000000000000..3232ed665566ec047ce55a929db1581dbda266a1 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,80 @@ +# Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to make participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or +advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic +address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a +professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies within all project spaces, and it also applies when +an individual is representing the project or its community in public spaces. +Examples of representing a project or community include using an official +project e-mail address, posting via an official social media account, or acting +as an appointed representative at an online or offline event. Representation of +a project may be further defined and clarified by project maintainers. + +This Code of Conduct also applies outside the project spaces when there is a +reasonable belief that an individual's behavior may have a negative impact on +the project or its community. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at . All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000000000000000000000000000000000000..afc89823fc90b920f0758f50e4d808df6a884a34 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,31 @@ +# Contributing to DINOv2 +We want to make contributing to this project as easy and transparent as +possible. + +## Pull Requests +We actively welcome your pull requests. + +1. Fork the repo and create your branch from `main`. +2. If you've added code that should be tested, add tests. +3. If you've changed APIs, update the documentation. +4. Ensure the test suite passes. +5. Make sure your code lints. +6. If you haven't already, complete the Contributor License Agreement ("CLA"). + +## Contributor License Agreement ("CLA") +In order to accept your pull request, we need you to submit a CLA. You only need +to do this once to work on any of Meta's open source projects. + +Complete your CLA here: + +## Issues +We use GitHub issues to track public bugs. Please ensure your description is +clear and has sufficient instructions to be able to reproduce the issue. + +Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe +disclosure of security bugs. In those cases, please go through the process +outlined on that page and do not file a public issue. + +## License +By contributing to DINOv2, you agree that your contributions will be licensed +under the LICENSE file in the root directory of this source tree. diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..eca1bd9e454e55beab6e78164f624460c8c10f11 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,11 @@ +FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel + +COPY . /dinov2 +WORKDIR /dinov2 + +RUN pip install -r requirements.txt +RUN pip install -r requirements-extras.txt + +RUN apt-get update && apt-get install ffmpeg libsm6 libxext6 -y + + \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..5471dc10377b76db85a2feca7a99a7eef4980ba8 --- /dev/null +++ b/LICENSE @@ -0,0 +1,203 @@ + + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/MODEL_CARD.md b/MODEL_CARD.md new file mode 100644 index 0000000000000000000000000000000000000000..d158d5828aec4225c881f3621aec1e06d06c8a6c --- /dev/null +++ b/MODEL_CARD.md @@ -0,0 +1,201 @@ +# Model Card for DINOv2-S/B/L/g + +These are Vision Transformer models trained following the method described in the paper: +"DINOv2: Learning Robust Visual Features without Supervision" + +We provide 4 models: 1 ViT-g trained from scratch, and 3 ViT-S/B/L models distilled from the ViT-g. + +## Model Details +The model takes an image as input and returns a class token and patch tokens. + +The embedding dimension is: +- 384 for ViT-S. +- 768 for ViT-B. +- 1024 for ViT-L. +- 1536 for ViT-g. + +The models follow a Transformer architecture, with a patch size of 14. + +For a 224x224 image, this results in 1 class token + 256 patch tokens. + +The models can accept larger images provided the image shapes are multiples of the patch size (14). +If this condition is not verified, the model will crop to the closest smaller multiple of the patch size. + +### Model Description + +- **Developed by:** Meta AI +- **Model type:** Vision Transformer +- **License:** Apache License 2.0 + +- **Repository:** https://github.com/facebookresearch/dinov2 +- **Paper:** https://arxiv.org/abs/2304.07193 +- **Demo:** https://dinov2.metademolab.com/ + +## Uses + +The models are vision backbones providing multi-purpose features for downstream tasks. + +### Direct Use + +The models can be used without fine-tuning, with downstream classifiers as simple as linear layers, to obtain competitive results: +- on depth estimation, semantic segmentation, using linear layers. +- on image classification, using k-NN classifiers on the class token. +- on image classification, with logistic regression classifiers applied on the class token. +- on image classification, with a linear layer applied on the class token and the average of the patch tokens. +- on image retrieval using nearest neighbors. + +### Downstream Use + +It is technically possible to perform fine-tuning on the models, for small gains (we measured +2% on ImageNet-1k classification). +We recommend keeping this as a very last step and only when necessary, as the features already provide good performance out-of-the-box. + +## Bias, Risks, and Limitations + +Despite improvements thanks to the training method not using annotations, we still observe significant biases in our models toward rich households from Western countries. + +### Recommendations + +We expect fine-tuning will increase the biases in the features produced by the model as they will be tuned to the fine-tuning labels. + +## How to Get Started with the Model + +Use the code below to get started with the model. + +```python +import torch +dinov2_vits14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14') +dinov2_vitb14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14') +dinov2_vitl14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14') +dinov2_vitg14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14') +``` + +## Training Details + +### Training Data + +- **Training data:** LVD-142M (see paper) +- **Training regime:** fp16 using PyTorch-FSDP mixed-precision. + +### Training Procedure + +- **Training objective:** + - DINO self-distillation loss with multi-crop + - iBOT masked-image modeling loss + - KoLeo regularization on [CLS] tokens +- **Architectures:** + - ViT-S (21M params): Patch size 14, embedding dimension 384, 6 heads, MLP FFN + - ViT-B (86M params): Patch size 14, embedding dimension 768, 12 heads, MLP FFN + - ViT-L (0.3B params): Patch size 14, embedding dimension 1024, 16 heads, MLP FFN + - ViT-g (1.1B params): Patch size 14, embedding dimension 1536, 24 heads, SwiGLU FFN +- **Distillation:** + - Distillation follows the standard DINOv2 pretraining procedure, except the teacher is a pretrained ViT-g, frozen. + +## Evaluation + +We refer users to the associated paper for the evaluation protocols. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
modelImageNet-1kNYU-Depth v2SUN-RGBDADE20kiNaturalist 2018Oxford-H
taskclassif. (acc)classif. (acc)classif. V2 (acc)depth (RMSE)depth (RMSE)segm. (mAP)classif. (acc)retrieval (mAP)
k-NNlinearlinearlinear
4 layers		NYU-D transfermultiscalelinearnearest neighbor
ViT-S/1479.0%81.1%70.8%0.4170.43147.269.5%43.2
ViT-B/1482.1%84.5%74.9%0.3620.40051.376.3%49.5
ViT-L/1483.5%86.3%77.6%0.3330.39653.179.8%54.0
ViT-g/1483.5%86.5%78.4%0.2980.36253.081.6%52.3
+ +## Environmental Impact + +- **Hardware Type:** Nvidia A100 +- **Hours used:** 22,000 for ViT-g, 4,500 for ViT-S distillation, 5,300 for ViT-B distillation, 8,000 for ViT-L distillation +- **Cloud Provider:** Private infra +- **Compute Region:** USA +- **Carbon Emitted:** 7t CO2eq + +#### Hardware + +Nvidia A100 GPUs + +#### Software + +PyTorch 2.0, +xFormers 0.0.18 + +**BibTeX** + +``` +@misc{oquab2023dinov2, + title={DINOv2: Learning Robust Visual Features without Supervision}, + author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr}, + journal={arXiv:2304.07193}, + year={2023} +} +``` diff --git a/README.md b/README.md index 9da6476b6788c9094f876fa73d3981cbfb724743..fd4b273623b83e19f1214c864892ff0fbb42cb40 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,501 @@ --- -title: DinoV2 Semantic Segmentation -emoji: 📊 -colorFrom: gray -colorTo: pink -sdk: gradio -sdk_version: 3.44.1 +title: DinoV2_Semantic_Segmentation app_file: app.py -pinned: false +sdk: gradio +sdk_version: 3.42.0 --- +# DINOv2: Learning Robust Visual Features without Supervision + +**[Meta AI Research, FAIR](https://ai.facebook.com/research/)** + +Maxime Oquab, +Timothée Darcet, +Théo Moutakanni, +Huy V. Vo, +Marc Szafraniec, +Vasil Khalidov, +Patrick Labatut, +Armand Joulin, +Piotr Bojanowski + +[[`Paper`](https://arxiv.org/abs/2304.07193)] [[`Blog`](https://ai.facebook.com/blog/dino-v2-computer-vision-self-supervised-learning/)] [[`Demo`](https://dinov2.metademolab.com)] [[`BibTeX`](#citing-dinov2)] + +PyTorch implementation and pretrained models for DINOv2. For details, see the paper: **[DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193)**. + +DINOv2 models produce high-performance visual features that can be directly employed with classifiers as simple as linear layers on a variety of computer vision tasks; these visual features are robust and perform well across domains without any requirement for fine-tuning. The models were pretrained on a dataset of 142 M images without using any labels or annotations. + +https://github.com/facebookresearch/dinov2/assets/60359573/f168823e-7922-415a-b429-578badf5c356 + +
+ Visualization of the three first principal components of the patch features of all frames, mapped to RGB values. +
+ +## Pretrained models + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
model# of
params		ImageNet
k-NN		ImageNet
linear		download
ViT-S/14 distilled21 M79.0%81.1%backbone only
ViT-B/14 distilled86 M82.1%84.5%backbone only
ViT-L/14 distilled300 M83.5%86.3%backbone only
ViT-g/141,100 M83.5%86.5%backbone only
+ +### Pretrained backbones (via PyTorch Hub) + +Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install PyTorch (the only required dependency for loading the model). Installing PyTorch with CUDA support is strongly recommended. + +A corresponding [model card](MODEL_CARD.md) is included in the repository. + +```python +import torch + +dinov2_vits14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14') +dinov2_vitb14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14') +dinov2_vitl14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14') +dinov2_vitg14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14') +``` + +### Pretrained heads - Image classification + + + + + + + + + + + + + + + + + + + + + + + + + + +
backbonedownload
ImageNet
ViT-S/14 distilled + linear head (1 layer, + 4 layers) +
ViT-B/14 distilled + linear head (1 layer, + 4 layers) +
ViT-L/14 distilled + linear head (1 layer, + 4 layers) +
ViT-g/14 + linear head (1 layer, + 4 layers) +
+ +The (full) classifier models can be loaded via PyTorch Hub: + +```python +import torch + +dinov2_vits14_lc = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14_lc') +dinov2_vitb14_lc = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14_lc') +dinov2_vitl14_lc = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14_lc') +dinov2_vitg14_lc = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14_lc') +``` + +### Pretrained heads - Depth estimation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
backbonedownload head
NYUdKITTI
ViT-S/14 distilled + linear (1 layer, + 4 layers), + DPT + + linear (1 layer, + 4 layers), + DPT +
ViT-B/14 distilled + linear (1 layer, + 4 layers), + DPT + + linear (1 layer, + 4 layers), + DPT +
ViT-L/14 distilled + linear (1 layer, + 4 layers), + DPT + + linear (1 layer, + 4 layers), + DPT +
ViT-g/14 + linear (1 layer, + 4 layers), + DPT + + linear (1 layer, + 4 layers), + DPT +
+ +### Pretrained heads - Semantic segmentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
backbonedownload modeldownload head
ADE20KADE20KVOC2012
ViT-S/14 distilled + linear, + multi-scale + + linear, + multi-scale +
ViT-B/14 distilled + linear, + multi-scale + + linear, + multi-scale +
ViT-L/14 distilled + linear, + multi-scale + + linear, + multi-scale +
ViT-g/14 + Mask2Former + + linear, + multi-scale + + linear, + multi-scale +
+ +## Installation + +The training and evaluation code requires PyTorch 2.0 and [xFormers](https://github.com/facebookresearch/xformers) 0.0.18 as well as a number of other 3rd party packages. Note that the code has only been tested with the specified versions and also expects a Linux environment. To setup all the required dependencies for training and evaluation, please follow the instructions below: + +*[conda](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html)* **(Recommended)** - Clone the repository and then create and activate a `dinov2` conda environment using the provided environment definition: + +```shell +conda env create -f conda.yaml +conda activate dinov2 +``` + +*[pip](https://pip.pypa.io/en/stable/getting-started/)* - Clone the repository and then use the provided `requirements.txt` to install the dependencies: + +```shell +pip install -r requirements.txt +``` + +For dense tasks (depth estimation and semantic segmentation), there are additional dependencies (specific versions of `mmcv` and `mmsegmentation`) which are captured in the `extras` dependency specifications: + +*[conda](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html)* **(Recommended)**: + +```shell +conda env create -f conda-extras.yaml +conda activate dinov2-extras +``` + +*[pip](https://pip.pypa.io/en/stable/getting-started/)*: + +```shell +pip install -r requirements.txt -r requirements-extras.txt +``` + +## Data preparation + +### ImageNet-1k + +The root directory of the dataset should hold the following contents: + +- `/test/ILSVRC2012_test_00000001.JPEG` +- `/test/[..]` +- `/test/ILSVRC2012_test_00100000.JPEG` +- `/train/n01440764/n01440764_10026.JPEG` +- `/train/[...]` +- `/train/n15075141/n15075141_9993.JPEG` +- `/val/n01440764/ILSVRC2012_val_00000293.JPEG` +- `/val/[...]` +- `/val/n15075141/ILSVRC2012_val_00049174.JPEG` +- `/labels.txt` + +The provided dataset implementation expects a few additional metadata files to be present under the extra directory: + +- `/class-ids-TRAIN.npy` +- `/class-ids-VAL.npy` +- `/class-names-TRAIN.npy` +- `/class-names-VAL.npy` +- `/entries-TEST.npy` +- `/entries-TRAIN.npy` +- `/entries-VAL.npy` + +These metadata files can be generated (once) with the following lines of Python code: + +```python +from dinov2.data.datasets import ImageNet + +for split in ImageNet.Split: + dataset = ImageNet(split=split, root="", extra="") + dataset.dump_extra() +``` + +Note that the root and extra directories do not have to be distinct directories. + +### ImageNet-22k + +Please adapt the [dataset class](dinov2/data/datasets/image_net_22k.py) to match your local setup. + +
+ +:warning: To execute the commands provided in the next sections for training and evaluation, the `dinov2` package should be included in the Python module search path, i.e. simply prefix the command to run with `PYTHONPATH=.`. + +## Training + +### Fast setup: training DINOv2 ViT-L/16 on ImageNet-1k + +Run DINOv2 training on 4 A100-80GB nodes (32 GPUs) in a SLURM cluster environment with submitit: + +```shell +python dinov2/run/train/train.py \ + --nodes 4 \ + --config-file dinov2/configs/train/vitl16_short.yaml \ + --output-dir \ + train.dataset_path=ImageNet:split=TRAIN:root=:extra= +``` + +Training time is approximately 1 day and the resulting checkpoint should reach 81.6% on k-NN eval and 82.9% on linear eval. + +The training code saves the weights of the teacher in the `eval` folder every 12500 iterations for evaluation. + +### Long setup: training DINOv2 ViT-L/14 on ImageNet-22k + +Run DINOv2 training on 12 A100-80GB nodes (96 GPUs) in a SLURM cluster environment with submitit: + +```shell +python dinov2/run/train/train.py \ + --nodes 12 \ + --config-file dinov2/configs/train/vitl14.yaml \ + --output-dir \ + train.dataset_path=ImageNet22k:root=:extra= +``` + +Training time is approximately 3.3 days and the resulting checkpoint should reach 82.0% on k-NN eval and 84.5% on linear eval. + +The training code saves the weights of the teacher in the `eval` folder every 12500 iterations for evaluation. + + +## Evaluation + +The training code regularly saves the teacher weights. In order to evaluate the model, run the following evaluation on a single node: + +### k-NN classification on ImageNet-1k + +```shell +python dinov2/run/eval/knn.py \ + --config-file /config.yaml \ + --pretrained-weights /eval/training_24999/teacher_checkpoint.pth \ + --output-dir /eval/training_24999/knn \ + --train-dataset ImageNet:split=TRAIN:root=:extra= \ + --val-dataset ImageNet:split=VAL:root=:extra= +``` + +### Logistic regression classification on ImageNet-1k + +```shell +python dinov2/run/eval/log_regression.py \ + --config-file /config.yaml \ + --pretrained-weights /eval/training_24999/teacher_checkpoint.pth \ + --output-dir /eval/training_24999/logreg \ + --train-dataset ImageNet:split=TRAIN:root=:extra= \ + --val-dataset ImageNet:split=VAL:root=:extra= +``` + +### Linear classification with data augmentation on ImageNet-1k + +```shell +python dinov2/run/eval/linear.py \ + --config-file /config.yaml \ + --pretrained-weights /eval/training_24999/teacher_checkpoint.pth \ + --output-dir /eval/training_24999/linear \ + --train-dataset ImageNet:split=TRAIN:root=:extra= \ + --val-dataset ImageNet:split=VAL:root=:extra= +``` + +We release the weights from evaluating the different models: + + + + + + + + + + + + + + + + + + + + + + + + + + + +
modelImageNet
top-1		linear evaluation
ViT-S/14 distilled81.1%linear head weights
ViT-B/14 distilled84.5%linear head weights
ViT-L/14 distilled86.3%linear head weights
ViT-g/1486.5%linear head weights
+ +The performance of the provided pretrained model weights can be evaluated as follows on ImageNet-1k: + +```shell +python dinov2/run/eval/linear.py \ + --config-file dinov2/configs/eval/vitg14_pretrain.yaml \ + --pretrained-weights https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_pretrain.pth \ + --train-dataset ImageNet:split=TRAIN:root=:extra= \ + --val-dataset ImageNet:split=VAL:root=:extra= +``` + +## Notebooks + +A few notebooks are provided to help the community leverage the models and code: + +
    +
  • Depth estimation - How to load and use the depth heads in combination with a matching backbone via mmcv
    • +
  • Semantic segmentation - How to load and use the segmentation heads in combination with a matching backbone via mmcv, and also how to load and use the Mask2Former-based segmentation model trained on ADE20K
    • +
+ +## License + +DINOv2 code and model weights are released under the Apache License 2.0. See [LICENSE](LICENSE) for additional details. + +## Contributing + +See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md). + +## Citing DINOv2 + +If you find this repository useful, please consider giving a star :star: and citation :t-rex:: -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +``` +@misc{oquab2023dinov2, + title={DINOv2: Learning Robust Visual Features without Supervision}, + author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy V. and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr}, + journal={arXiv:2304.07193}, + year={2023} +} +``` diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..3b5de3b50fd7bdb9c44024c075fcabdf1338128d --- /dev/null +++ b/app.py @@ -0,0 +1,137 @@ +import os + +os.system("pip uninstall -y mmcv-full") +os.system("pip uninstall -y mmsegmentation") +os.system("pip install ./mmcv_full-1.5.0-cp310-cp310-linux_x86_64.whl") +os.system("pip install -r requirements-extras.txt") +# os.system("cp /home/user/data/dinov2_vitg14_ade20k_m2f.pth /home/user/.cache/torch/hub/checkpoints/dinov2_vitg14_ade20k_m2f.pth") + +import gradio as gr + +import base64 +import cv2 +import math +import itertools +from functools import partial +from PIL import Image +import numpy as np +import pandas as pd + +import dinov2.eval.segmentation.utils.colormaps as colormaps + +import torch +import torch.nn.functional as F +from mmseg.apis import init_segmentor, inference_segmentor + +import dinov2.eval.segmentation.models +import dinov2.eval.segmentation_m2f.models.segmentors + +import urllib + +import mmcv +from mmcv.runner import load_checkpoint + +model = None +model_loaded = False + +DINOV2_BASE_URL = "https://dl.fbaipublicfiles.com/dinov2" +CONFIG_URL = f"{DINOV2_BASE_URL}/dinov2_vitg14/dinov2_vitg14_ade20k_m2f_config.py" +CHECKPOINT_URL = f"{DINOV2_BASE_URL}/dinov2_vitg14/dinov2_vitg14_ade20k_m2f.pth" + + +def load_config_from_url(url: str) -> str: + with urllib.request.urlopen(url) as f: + return f.read().decode() + + +cfg_str = load_config_from_url(CONFIG_URL) +cfg = mmcv.Config.fromstring(cfg_str, file_format=".py") + + +DATASET_COLORMAPS = { + "ade20k": colormaps.ADE20K_COLORMAP, + "voc2012": colormaps.VOC2012_COLORMAP, +} + +model = init_segmentor(cfg) +load_checkpoint(model, CHECKPOINT_URL, map_location="cpu") +model.cuda() +model.eval() + +class CenterPadding(torch.nn.Module): + def __init__(self, multiple): + super().__init__() + self.multiple = multiple + + def _get_pad(self, size): + new_size = math.ceil(size / self.multiple) * self.multiple + pad_size = new_size - size + pad_size_left = pad_size // 2 + pad_size_right = pad_size - pad_size_left + return pad_size_left, pad_size_right + + @torch.inference_mode() + def forward(self, x): + pads = list(itertools.chain.from_iterable(self._get_pad(m) for m in x.shape[:1:-1])) + output = F.pad(x, pads) + return output + + +def create_segmenter(cfg, backbone_model): + model = init_segmentor(cfg) + model.backbone.forward = partial( + backbone_model.get_intermediate_layers, + n=cfg.model.backbone.out_indices, + reshape=True, + ) + if hasattr(backbone_model, "patch_size"): + model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(backbone_model.patch_size)(x[0])) + model.init_weights() + return model + + +def render_segmentation(segmentation_logits, dataset): + colormap = DATASET_COLORMAPS[dataset] + colormap_array = np.array(colormap, dtype=np.uint8) + segmentation_logits += 1 + segmentation_values = colormap_array[segmentation_logits] + unique_labels = np.unique(segmentation_logits) + + colormap_array = colormap_array[unique_labels] + df = pd.read_csv("labelmap.txt", sep="\t") + + html_output = '
' + import matplotlib.pyplot as plt + + for idx, color in enumerate(colormap_array): + color_box = np.zeros((20, 20, 3), dtype=np.uint8) + color_box[:, :] = color + color_box = cv2.cvtColor(color_box, cv2.COLOR_RGB2BGR) + _, img_data = cv2.imencode(".jpg", color_box) + img_base64 = base64.b64encode(img_data).decode("utf-8") + img_data_uri = f"data:image/jpg;base64,{img_base64}" + html_output += f'

{df.iloc[unique_labels[idx]-1]["Name"]}

' + + html_output += "
" + + return Image.fromarray(segmentation_values), html_output + + +def predict(image_file): + array = np.array(image_file)[:, :, ::-1] # BGR + segmentation_logits = inference_segmentor(model, array)[0] + segmented_image, html_output = render_segmentation(segmentation_logits, "ade20k") + return np.array(segmented_image), html_output + +description = "Gradio demo for Semantic segmentation. To use it, simply upload your image" + +demo = gr.Interface( + title="Semantic Segmentation - DinoV2", + fn=predict, + inputs=gr.inputs.Image(), + outputs=[gr.outputs.Image(type="numpy"), gr.outputs.HTML()], + examples=["example_1.jpg", "example_2.jpg"], + description=description, +) + +demo.launch() diff --git a/conda-extras.yaml b/conda-extras.yaml new file mode 100644 index 0000000000000000000000000000000000000000..71574c4d32e31c1e134ffb2102daa86a14867bb8 --- /dev/null +++ b/conda-extras.yaml @@ -0,0 +1,24 @@ +name: dinov2-extras +channels: + - defaults + - pytorch + - nvidia + - xformers + - conda-forge +dependencies: + - python=3.9 + - pytorch::pytorch=2.0.0 + - pytorch::pytorch-cuda=11.7.0 + - pytorch::torchvision=0.15.0 + - omegaconf + - torchmetrics=0.10.3 + - fvcore + - iopath + - xformers::xformers=0.0.18 + - pip + - pip: + - git+https://github.com/facebookincubator/submitit + - --extra-index-url https://pypi.nvidia.com + - cuml-cu11 + - mmcv-full==1.5.0 + - mmsegmentation==0.27.0 diff --git a/conda.yaml b/conda.yaml new file mode 100644 index 0000000000000000000000000000000000000000..35dfc30adc275da51b58ff2340dd1d53d2cb9250 --- /dev/null +++ b/conda.yaml @@ -0,0 +1,22 @@ +name: dinov2 +channels: + - defaults + - pytorch + - nvidia + - xformers + - conda-forge +dependencies: + - python=3.9 + - pytorch::pytorch=2.0.0 + - pytorch::pytorch-cuda=11.7.0 + - pytorch::torchvision=0.15.0 + - omegaconf + - torchmetrics=0.10.3 + - fvcore + - iopath + - xformers::xformers=0.0.18 + - pip + - pip: + - git+https://github.com/facebookincubator/submitit + - --extra-index-url https://pypi.nvidia.com + - cuml-cu11 diff --git a/demo.py b/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..1606ed61e51f8585f9fc11fd69e05634810c5f4c --- /dev/null +++ b/demo.py @@ -0,0 +1,153 @@ +import sys +REPO_PATH="." +sys.path.append("/dino_v2") + +import math +import itertools +from functools import partial + +import torch +import torch.nn.functional as F +from mmseg.apis import init_segmentor, inference_segmentor + +import dinov2.eval.segmentation.models + + +class CenterPadding(torch.nn.Module): + def __init__(self, multiple): + super().__init__() + self.multiple = multiple + + def _get_pad(self, size): + new_size = math.ceil(size / self.multiple) * self.multiple + pad_size = new_size - size + pad_size_left = pad_size // 2 + pad_size_right = pad_size - pad_size_left + return pad_size_left, pad_size_right + + @torch.inference_mode() + def forward(self, x): + pads = list(itertools.chain.from_iterable(self._get_pad(m) for m in x.shape[:1:-1])) + output = F.pad(x, pads) + return output + + +def create_segmenter(cfg, backbone_model): + model = init_segmentor(cfg) + model.backbone.forward = partial( + backbone_model.get_intermediate_layers, + n=cfg.model.backbone.out_indices, + reshape=True, + ) + if hasattr(backbone_model, "patch_size"): + model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(backbone_model.patch_size)(x[0])) + model.init_weights() + return model + +# BACKBONE_SIZE = "small" # in ("small", "base", "large" or "giant") + + +# backbone_archs = { +# "small": "vits14", +# "base": "vitb14", +# "large": "vitl14", +# "giant": "vitg14", +# } +# backbone_arch = backbone_archs[BACKBONE_SIZE] +# backbone_name = f"dinov2_{backbone_arch}" + +# backbone_model = torch.hub.load(repo_or_dir="facebookresearch/dinov2", model=backbone_name) +# backbone_model.eval() +# backbone_model.cuda() + +import urllib + +import mmcv +from mmcv.runner import load_checkpoint + + +def load_config_from_url(url: str) -> str: + with urllib.request.urlopen(url) as f: + return f.read().decode() + + +# HEAD_SCALE_COUNT = 3 # more scales: slower but better results, in (1,2,3,4,5) +# HEAD_DATASET = "voc2012" # in ("ade20k", "voc2012") +# HEAD_TYPE = "ms" # in ("ms, "linear") + + +DINOV2_BASE_URL = "https://dl.fbaipublicfiles.com/dinov2" +# head_config_url = f"{DINOV2_BASE_URL}/{backbone_name}/{backbone_name}_{HEAD_DATASET}_{HEAD_TYPE}_config.py" +# head_checkpoint_url = f"{DINOV2_BASE_URL}/{backbone_name}/{backbone_name}_{HEAD_DATASET}_{HEAD_TYPE}_head.pth" + +# cfg_str = load_config_from_url(head_config_url) +# cfg = mmcv.Config.fromstring(cfg_str, file_format=".py") +# if HEAD_TYPE == "ms": +# cfg.data.test.pipeline[1]["img_ratios"] = cfg.data.test.pipeline[1]["img_ratios"][:HEAD_SCALE_COUNT] +# print("scales:", cfg.data.test.pipeline[1]["img_ratios"]) + +# model = create_segmenter(cfg, backbone_model=backbone_model) +# load_checkpoint(model, head_checkpoint_url, map_location="cpu") +# model.cuda() +# model.eval() + +import urllib + +from PIL import Image + + +def load_image_from_url(url: str) -> Image: + with urllib.request.urlopen(url) as f: + return Image.open(f).convert("RGB") + + +EXAMPLE_IMAGE_URL = "https://dl.fbaipublicfiles.com/dinov2/images/example.jpg" + + +# image = load_image_from_url(EXAMPLE_IMAGE_URL) +image = Image.open("bridge_2.JPG").convert("RGB") + +image.show() + +import numpy as np + +import dinov2.eval.segmentation.utils.colormaps as colormaps + + +DATASET_COLORMAPS = { + "ade20k": colormaps.ADE20K_COLORMAP, + "voc2012": colormaps.VOC2012_COLORMAP, +} + + +def render_segmentation(segmentation_logits, dataset): + colormap = DATASET_COLORMAPS[dataset] + colormap_array = np.array(colormap, dtype=np.uint8) + print(len(colormap)) + segmentation_values = colormap_array[segmentation_logits + 1] + return Image.fromarray(segmentation_values) + + +# array = np.array(image)[:, :, ::-1] # BGR +# segmentation_logits = inference_segmentor(model, array)[0] +# segmented_image = render_segmentation(segmentation_logits, HEAD_DATASET) +# segmented_image.save("output.jpg") + +import dinov2.eval.segmentation_m2f.models.segmentors + +CONFIG_URL = f"{DINOV2_BASE_URL}/dinov2_vitg14/dinov2_vitg14_ade20k_m2f_config.py" +CHECKPOINT_URL = f"{DINOV2_BASE_URL}/dinov2_vitg14/dinov2_vitg14_ade20k_m2f.pth" + +cfg_str = load_config_from_url(CONFIG_URL) +cfg = mmcv.Config.fromstring(cfg_str, file_format=".py") + +model = init_segmentor(cfg) +load_checkpoint(model, CHECKPOINT_URL, map_location="cpu") +model.cuda() +model.eval() + +array = np.array(image)[:, :, ::-1] # BGR +segmentation_logits = inference_segmentor(model, array)[0] +print(np.unique(segmentation_logits, return_counts=True)) +segmented_image = render_segmentation(segmentation_logits, "ade20k") +segmented_image.save("output.jpg") diff --git a/dinov2/__init__.py b/dinov2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ae847e46898077fe3d8701b8a181d7b4e3d41cd9 --- /dev/null +++ b/dinov2/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +__version__ = "0.0.1" diff --git a/dinov2/__pycache__/__init__.cpython-310.pyc b/dinov2/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bb0c8f5bd535a985010f66f1b77f61e75df172d0 Binary files /dev/null and b/dinov2/__pycache__/__init__.cpython-310.pyc differ diff --git a/dinov2/configs/__init__.py b/dinov2/configs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..68e0830c62ea19649b6cd2361995f6df309d7640 --- /dev/null +++ b/dinov2/configs/__init__.py @@ -0,0 +1,22 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import pathlib + +from omegaconf import OmegaConf + + +def load_config(config_name: str): + config_filename = config_name + ".yaml" + return OmegaConf.load(pathlib.Path(__file__).parent.resolve() / config_filename) + + +dinov2_default_config = load_config("ssl_default_config") + + +def load_and_merge_config(config_name: str): + default_config = OmegaConf.create(dinov2_default_config) + loaded_config = load_config(config_name) + return OmegaConf.merge(default_config, loaded_config) diff --git a/dinov2/configs/eval/vitb14_pretrain.yaml b/dinov2/configs/eval/vitb14_pretrain.yaml new file mode 100644 index 0000000000000000000000000000000000000000..117d0f027ca26cd8ce6c010bb78d5a8fac42c70e --- /dev/null +++ b/dinov2/configs/eval/vitb14_pretrain.yaml @@ -0,0 +1,6 @@ +student: + arch: vit_base + patch_size: 14 +crops: + global_crops_size: 518 # this is to set up the position embeddings properly + local_crops_size: 98 \ No newline at end of file diff --git a/dinov2/configs/eval/vitg14_pretrain.yaml b/dinov2/configs/eval/vitg14_pretrain.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a96dd5b117b4d59ee210b65037821f1b3e3f16e3 --- /dev/null +++ b/dinov2/configs/eval/vitg14_pretrain.yaml @@ -0,0 +1,7 @@ +student: + arch: vit_giant2 + patch_size: 14 + ffn_layer: swiglufused +crops: + global_crops_size: 518 # this is to set up the position embeddings properly + local_crops_size: 98 \ No newline at end of file diff --git a/dinov2/configs/eval/vitl14_pretrain.yaml b/dinov2/configs/eval/vitl14_pretrain.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7a984548bd034f762d455419d7193917fa462dd8 --- /dev/null +++ b/dinov2/configs/eval/vitl14_pretrain.yaml @@ -0,0 +1,6 @@ +student: + arch: vit_large + patch_size: 14 +crops: + global_crops_size: 518 # this is to set up the position embeddings properly + local_crops_size: 98 \ No newline at end of file diff --git a/dinov2/configs/eval/vits14_pretrain.yaml b/dinov2/configs/eval/vits14_pretrain.yaml new file mode 100644 index 0000000000000000000000000000000000000000..afbdb4ba14f1c97130a25b579360f4d817cda495 --- /dev/null +++ b/dinov2/configs/eval/vits14_pretrain.yaml @@ -0,0 +1,6 @@ +student: + arch: vit_small + patch_size: 14 +crops: + global_crops_size: 518 # this is to set up the position embeddings properly + local_crops_size: 98 \ No newline at end of file diff --git a/dinov2/configs/ssl_default_config.yaml b/dinov2/configs/ssl_default_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a4ef04545ce9d6cc52b5179236008adc8a9bbda2 --- /dev/null +++ b/dinov2/configs/ssl_default_config.yaml @@ -0,0 +1,115 @@ +MODEL: + WEIGHTS: '' +compute_precision: + grad_scaler: true + teacher: + backbone: + sharding_strategy: SHARD_GRAD_OP + mixed_precision: + param_dtype: fp16 + reduce_dtype: fp16 + buffer_dtype: fp32 + dino_head: + sharding_strategy: SHARD_GRAD_OP + mixed_precision: + param_dtype: fp16 + reduce_dtype: fp16 + buffer_dtype: fp32 + ibot_head: + sharding_strategy: SHARD_GRAD_OP + mixed_precision: + param_dtype: fp16 + reduce_dtype: fp16 + buffer_dtype: fp32 + student: + backbone: + sharding_strategy: SHARD_GRAD_OP + mixed_precision: + param_dtype: fp16 + reduce_dtype: fp16 + buffer_dtype: fp32 + dino_head: + sharding_strategy: SHARD_GRAD_OP + mixed_precision: + param_dtype: fp16 + reduce_dtype: fp32 + buffer_dtype: fp32 + ibot_head: + sharding_strategy: SHARD_GRAD_OP + mixed_precision: + param_dtype: fp16 + reduce_dtype: fp32 + buffer_dtype: fp32 +dino: + loss_weight: 1.0 + head_n_prototypes: 65536 + head_bottleneck_dim: 256 + head_nlayers: 3 + head_hidden_dim: 2048 + koleo_loss_weight: 0.1 +ibot: + loss_weight: 1.0 + mask_sample_probability: 0.5 + mask_ratio_min_max: + - 0.1 + - 0.5 + separate_head: false + head_n_prototypes: 65536 + head_bottleneck_dim: 256 + head_nlayers: 3 + head_hidden_dim: 2048 +train: + batch_size_per_gpu: 64 + dataset_path: ImageNet:split=TRAIN + output_dir: . + saveckp_freq: 20 + seed: 0 + num_workers: 10 + OFFICIAL_EPOCH_LENGTH: 1250 + cache_dataset: true + centering: "centering" # or "sinkhorn_knopp" +student: + arch: vit_large + patch_size: 16 + drop_path_rate: 0.3 + layerscale: 1.0e-05 + drop_path_uniform: true + pretrained_weights: '' + ffn_layer: "mlp" + block_chunks: 0 + qkv_bias: true + proj_bias: true + ffn_bias: true +teacher: + momentum_teacher: 0.992 + final_momentum_teacher: 1 + warmup_teacher_temp: 0.04 + teacher_temp: 0.07 + warmup_teacher_temp_epochs: 30 +optim: + epochs: 100 + weight_decay: 0.04 + weight_decay_end: 0.4 + base_lr: 0.004 # learning rate for a batch size of 1024 + lr: 0. # will be set after applying scaling rule + warmup_epochs: 10 + min_lr: 1.0e-06 + clip_grad: 3.0 + freeze_last_layer_epochs: 1 + scaling_rule: sqrt_wrt_1024 + patch_embed_lr_mult: 0.2 + layerwise_decay: 0.9 + adamw_beta1: 0.9 + adamw_beta2: 0.999 +crops: + global_crops_scale: + - 0.32 + - 1.0 + local_crops_number: 8 + local_crops_scale: + - 0.05 + - 0.32 + global_crops_size: 224 + local_crops_size: 96 +evaluation: + eval_period_iterations: 12500 diff --git a/dinov2/configs/train/vitg14.yaml b/dinov2/configs/train/vitg14.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d05cf0d59e07ac6e4a2b0f9bdcb6131d7c508962 --- /dev/null +++ b/dinov2/configs/train/vitg14.yaml @@ -0,0 +1,26 @@ +dino: + head_n_prototypes: 131072 + head_bottleneck_dim: 384 +ibot: + separate_head: true + head_n_prototypes: 131072 +train: + batch_size_per_gpu: 12 + dataset_path: ImageNet22k + centering: sinkhorn_knopp +student: + arch: vit_giant2 + patch_size: 14 + drop_path_rate: 0.4 + ffn_layer: swiglufused + block_chunks: 4 +teacher: + momentum_teacher: 0.994 +optim: + epochs: 500 + weight_decay_end: 0.2 + base_lr: 2.0e-04 # learning rate for a batch size of 1024 + warmup_epochs: 80 + layerwise_decay: 1.0 +crops: + local_crops_size: 98 \ No newline at end of file diff --git a/dinov2/configs/train/vitl14.yaml b/dinov2/configs/train/vitl14.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d9b491dcc6a522c71328fc2933dd0501123c8f6b --- /dev/null +++ b/dinov2/configs/train/vitl14.yaml @@ -0,0 +1,26 @@ +dino: + head_n_prototypes: 131072 + head_bottleneck_dim: 384 +ibot: + separate_head: true + head_n_prototypes: 131072 +train: + batch_size_per_gpu: 32 + dataset_path: ImageNet22k + centering: sinkhorn_knopp +student: + arch: vit_large + patch_size: 14 + drop_path_rate: 0.4 + ffn_layer: swiglufused + block_chunks: 4 +teacher: + momentum_teacher: 0.994 +optim: + epochs: 500 + weight_decay_end: 0.2 + base_lr: 2.0e-04 # learning rate for a batch size of 1024 + warmup_epochs: 80 + layerwise_decay: 1.0 +crops: + local_crops_size: 98 \ No newline at end of file diff --git a/dinov2/configs/train/vitl16_short.yaml b/dinov2/configs/train/vitl16_short.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3e7e72864c92175a1354142ac1d64da8070d1e5e --- /dev/null +++ b/dinov2/configs/train/vitl16_short.yaml @@ -0,0 +1,6 @@ +# this corresponds to the default config +train: + dataset_path: ImageNet:split=TRAIN + batch_size_per_gpu: 64 +student: + block_chunks: 4 diff --git a/dinov2/data/__init__.py b/dinov2/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2ded47ea63a7b184ff74a040e2c2c514cda273ef --- /dev/null +++ b/dinov2/data/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .adapters import DatasetWithEnumeratedTargets +from .loaders import make_data_loader, make_dataset, SamplerType +from .collate import collate_data_and_cast +from .masking import MaskingGenerator +from .augmentations import DataAugmentationDINO diff --git a/dinov2/data/adapters.py b/dinov2/data/adapters.py new file mode 100644 index 0000000000000000000000000000000000000000..2097bad046fb1052267d5f2bb99c798045f00c92 --- /dev/null +++ b/dinov2/data/adapters.py @@ -0,0 +1,28 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from typing import Any, Tuple + +from torch.utils.data import Dataset + + +class DatasetWithEnumeratedTargets(Dataset): + def __init__(self, dataset): + self._dataset = dataset + + def get_image_data(self, index: int) -> bytes: + return self._dataset.get_image_data(index) + + def get_target(self, index: int) -> Tuple[Any, int]: + target = self._dataset.get_target(index) + return (index, target) + + def __getitem__(self, index: int) -> Tuple[Any, Tuple[Any, int]]: + image, target = self._dataset[index] + target = index if target is None else target + return image, (index, target) + + def __len__(self) -> int: + return len(self._dataset) diff --git a/dinov2/data/augmentations.py b/dinov2/data/augmentations.py new file mode 100644 index 0000000000000000000000000000000000000000..05b1eaa942c14f75b88d9e14732e141e8909b0a1 --- /dev/null +++ b/dinov2/data/augmentations.py @@ -0,0 +1,118 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import logging + +from torchvision import transforms + +from .transforms import ( + GaussianBlur, + make_normalize_transform, +) + + +logger = logging.getLogger("dinov2") + + +class DataAugmentationDINO(object): + def __init__( + self, + global_crops_scale, + local_crops_scale, + local_crops_number, + global_crops_size=224, + local_crops_size=96, + ): + self.global_crops_scale = global_crops_scale + self.local_crops_scale = local_crops_scale + self.local_crops_number = local_crops_number + self.global_crops_size = global_crops_size + self.local_crops_size = local_crops_size + + logger.info("###################################") + logger.info("Using data augmentation parameters:") + logger.info(f"global_crops_scale: {global_crops_scale}") + logger.info(f"local_crops_scale: {local_crops_scale}") + logger.info(f"local_crops_number: {local_crops_number}") + logger.info(f"global_crops_size: {global_crops_size}") + logger.info(f"local_crops_size: {local_crops_size}") + logger.info("###################################") + + # random resized crop and flip + self.geometric_augmentation_global = transforms.Compose( + [ + transforms.RandomResizedCrop( + global_crops_size, scale=global_crops_scale, interpolation=transforms.InterpolationMode.BICUBIC + ), + transforms.RandomHorizontalFlip(p=0.5), + ] + ) + + self.geometric_augmentation_local = transforms.Compose( + [ + transforms.RandomResizedCrop( + local_crops_size, scale=local_crops_scale, interpolation=transforms.InterpolationMode.BICUBIC + ), + transforms.RandomHorizontalFlip(p=0.5), + ] + ) + + # color distorsions / blurring + color_jittering = transforms.Compose( + [ + transforms.RandomApply( + [transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1)], + p=0.8, + ), + transforms.RandomGrayscale(p=0.2), + ] + ) + + global_transfo1_extra = GaussianBlur(p=1.0) + + global_transfo2_extra = transforms.Compose( + [ + GaussianBlur(p=0.1), + transforms.RandomSolarize(threshold=128, p=0.2), + ] + ) + + local_transfo_extra = GaussianBlur(p=0.5) + + # normalization + self.normalize = transforms.Compose( + [ + transforms.ToTensor(), + make_normalize_transform(), + ] + ) + + self.global_transfo1 = transforms.Compose([color_jittering, global_transfo1_extra, self.normalize]) + self.global_transfo2 = transforms.Compose([color_jittering, global_transfo2_extra, self.normalize]) + self.local_transfo = transforms.Compose([color_jittering, local_transfo_extra, self.normalize]) + + def __call__(self, image): + output = {} + + # global crops: + im1_base = self.geometric_augmentation_global(image) + global_crop_1 = self.global_transfo1(im1_base) + + im2_base = self.geometric_augmentation_global(image) + global_crop_2 = self.global_transfo2(im2_base) + + output["global_crops"] = [global_crop_1, global_crop_2] + + # global crops for teacher: + output["global_crops_teacher"] = [global_crop_1, global_crop_2] + + # local crops: + local_crops = [ + self.local_transfo(self.geometric_augmentation_local(image)) for _ in range(self.local_crops_number) + ] + output["local_crops"] = local_crops + output["offsets"] = () + + return output diff --git a/dinov2/data/collate.py b/dinov2/data/collate.py new file mode 100644 index 0000000000000000000000000000000000000000..b3e32f357a76e6f32162cee14cb6ae1665a4827a --- /dev/null +++ b/dinov2/data/collate.py @@ -0,0 +1,49 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch +import random + + +def collate_data_and_cast(samples_list, mask_ratio_tuple, mask_probability, dtype, n_tokens=None, mask_generator=None): + # dtype = torch.half # TODO: Remove + + n_global_crops = len(samples_list[0][0]["global_crops"]) + n_local_crops = len(samples_list[0][0]["local_crops"]) + + collated_global_crops = torch.stack([s[0]["global_crops"][i] for i in range(n_global_crops) for s in samples_list]) + + collated_local_crops = torch.stack([s[0]["local_crops"][i] for i in range(n_local_crops) for s in samples_list]) + + B = len(collated_global_crops) + N = n_tokens + n_samples_masked = int(B * mask_probability) + probs = torch.linspace(*mask_ratio_tuple, n_samples_masked + 1) + upperbound = 0 + masks_list = [] + for i in range(0, n_samples_masked): + prob_min = probs[i] + prob_max = probs[i + 1] + masks_list.append(torch.BoolTensor(mask_generator(int(N * random.uniform(prob_min, prob_max))))) + upperbound += int(N * prob_max) + for i in range(n_samples_masked, B): + masks_list.append(torch.BoolTensor(mask_generator(0))) + + random.shuffle(masks_list) + + collated_masks = torch.stack(masks_list).flatten(1) + mask_indices_list = collated_masks.flatten().nonzero().flatten() + + masks_weight = (1 / collated_masks.sum(-1).clamp(min=1.0)).unsqueeze(-1).expand_as(collated_masks)[collated_masks] + + return { + "collated_global_crops": collated_global_crops.to(dtype), + "collated_local_crops": collated_local_crops.to(dtype), + "collated_masks": collated_masks, + "mask_indices_list": mask_indices_list, + "masks_weight": masks_weight, + "upperbound": upperbound, + "n_masked_patches": torch.full((1,), fill_value=mask_indices_list.shape[0], dtype=torch.long), + } diff --git a/dinov2/data/datasets/__init__.py b/dinov2/data/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5550fdc5ce16269bc0c28795a389f0182e8bc6c8 --- /dev/null +++ b/dinov2/data/datasets/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .image_net import ImageNet +from .image_net_22k import ImageNet22k diff --git a/dinov2/data/datasets/decoders.py b/dinov2/data/datasets/decoders.py new file mode 100644 index 0000000000000000000000000000000000000000..3769f7750d94f7e0f7bce281ef3ff186970fc9cd --- /dev/null +++ b/dinov2/data/datasets/decoders.py @@ -0,0 +1,31 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from io import BytesIO +from typing import Any + +from PIL import Image + + +class Decoder: + def decode(self) -> Any: + raise NotImplementedError + + +class ImageDataDecoder(Decoder): + def __init__(self, image_data: bytes) -> None: + self._image_data = image_data + + def decode(self) -> Image: + f = BytesIO(self._image_data) + return Image.open(f).convert(mode="RGB") + + +class TargetDecoder(Decoder): + def __init__(self, target: Any): + self._target = target + + def decode(self) -> Any: + return self._target diff --git a/dinov2/data/datasets/extended.py b/dinov2/data/datasets/extended.py new file mode 100644 index 0000000000000000000000000000000000000000..f60b619a3c797823cccfc89e262cdb230f9188f0 --- /dev/null +++ b/dinov2/data/datasets/extended.py @@ -0,0 +1,38 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from typing import Any, Tuple + +from torchvision.datasets import VisionDataset + +from .decoders import TargetDecoder, ImageDataDecoder + + +class ExtendedVisionDataset(VisionDataset): + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) # type: ignore + + def get_image_data(self, index: int) -> bytes: + raise NotImplementedError + + def get_target(self, index: int) -> Any: + raise NotImplementedError + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + try: + image_data = self.get_image_data(index) + image = ImageDataDecoder(image_data).decode() + except Exception as e: + raise RuntimeError(f"can not read image for sample {index}") from e + target = self.get_target(index) + target = TargetDecoder(target).decode() + + if self.transforms is not None: + image, target = self.transforms(image, target) + + return image, target + + def __len__(self) -> int: + raise NotImplementedError diff --git a/dinov2/data/datasets/image_net.py b/dinov2/data/datasets/image_net.py new file mode 100644 index 0000000000000000000000000000000000000000..8d08446147986c58360163e468896e994197c657 --- /dev/null +++ b/dinov2/data/datasets/image_net.py @@ -0,0 +1,290 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import csv +from enum import Enum +import logging +import os +from typing import Callable, List, Optional, Tuple, Union + +import numpy as np + +from .extended import ExtendedVisionDataset + + +logger = logging.getLogger("dinov2") +_Target = int + + +class _Split(Enum): + TRAIN = "train" + VAL = "val" + TEST = "test" # NOTE: torchvision does not support the test split + + @property + def length(self) -> int: + split_lengths = { + _Split.TRAIN: 1_281_167, + _Split.VAL: 50_000, + _Split.TEST: 100_000, + } + return split_lengths[self] + + def get_dirname(self, class_id: Optional[str] = None) -> str: + return self.value if class_id is None else os.path.join(self.value, class_id) + + def get_image_relpath(self, actual_index: int, class_id: Optional[str] = None) -> str: + dirname = self.get_dirname(class_id) + if self == _Split.TRAIN: + basename = f"{class_id}_{actual_index}" + else: # self in (_Split.VAL, _Split.TEST): + basename = f"ILSVRC2012_{self.value}_{actual_index:08d}" + return os.path.join(dirname, basename + ".JPEG") + + def parse_image_relpath(self, image_relpath: str) -> Tuple[str, int]: + assert self != _Split.TEST + dirname, filename = os.path.split(image_relpath) + class_id = os.path.split(dirname)[-1] + basename, _ = os.path.splitext(filename) + actual_index = int(basename.split("_")[-1]) + return class_id, actual_index + + +class ImageNet(ExtendedVisionDataset): + Target = Union[_Target] + Split = Union[_Split] + + def __init__( + self, + *, + split: "ImageNet.Split", + root: str, + extra: str, + transforms: Optional[Callable] = None, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + ) -> None: + super().__init__(root, transforms, transform, target_transform) + self._extra_root = extra + self._split = split + + self._entries = None + self._class_ids = None + self._class_names = None + + @property + def split(self) -> "ImageNet.Split": + return self._split + + def _get_extra_full_path(self, extra_path: str) -> str: + return os.path.join(self._extra_root, extra_path) + + def _load_extra(self, extra_path: str) -> np.ndarray: + extra_full_path = self._get_extra_full_path(extra_path) + return np.load(extra_full_path, mmap_mode="r") + + def _save_extra(self, extra_array: np.ndarray, extra_path: str) -> None: + extra_full_path = self._get_extra_full_path(extra_path) + os.makedirs(self._extra_root, exist_ok=True) + np.save(extra_full_path, extra_array) + + @property + def _entries_path(self) -> str: + return f"entries-{self._split.value.upper()}.npy" + + @property + def _class_ids_path(self) -> str: + return f"class-ids-{self._split.value.upper()}.npy" + + @property + def _class_names_path(self) -> str: + return f"class-names-{self._split.value.upper()}.npy" + + def _get_entries(self) -> np.ndarray: + if self._entries is None: + self._entries = self._load_extra(self._entries_path) + assert self._entries is not None + return self._entries + + def _get_class_ids(self) -> np.ndarray: + if self._split == _Split.TEST: + assert False, "Class IDs are not available in TEST split" + if self._class_ids is None: + self._class_ids = self._load_extra(self._class_ids_path) + assert self._class_ids is not None + return self._class_ids + + def _get_class_names(self) -> np.ndarray: + if self._split == _Split.TEST: + assert False, "Class names are not available in TEST split" + if self._class_names is None: + self._class_names = self._load_extra(self._class_names_path) + assert self._class_names is not None + return self._class_names + + def find_class_id(self, class_index: int) -> str: + class_ids = self._get_class_ids() + return str(class_ids[class_index]) + + def find_class_name(self, class_index: int) -> str: + class_names = self._get_class_names() + return str(class_names[class_index]) + + def get_image_data(self, index: int) -> bytes: + entries = self._get_entries() + actual_index = entries[index]["actual_index"] + + class_id = self.get_class_id(index) + + image_relpath = self.split.get_image_relpath(actual_index, class_id) + image_full_path = os.path.join(self.root, image_relpath) + with open(image_full_path, mode="rb") as f: + image_data = f.read() + return image_data + + def get_target(self, index: int) -> Optional[Target]: + entries = self._get_entries() + class_index = entries[index]["class_index"] + return None if self.split == _Split.TEST else int(class_index) + + def get_targets(self) -> Optional[np.ndarray]: + entries = self._get_entries() + return None if self.split == _Split.TEST else entries["class_index"] + + def get_class_id(self, index: int) -> Optional[str]: + entries = self._get_entries() + class_id = entries[index]["class_id"] + return None if self.split == _Split.TEST else str(class_id) + + def get_class_name(self, index: int) -> Optional[str]: + entries = self._get_entries() + class_name = entries[index]["class_name"] + return None if self.split == _Split.TEST else str(class_name) + + def __len__(self) -> int: + entries = self._get_entries() + assert len(entries) == self.split.length + return len(entries) + + def _load_labels(self, labels_path: str) -> List[Tuple[str, str]]: + labels_full_path = os.path.join(self.root, labels_path) + labels = [] + + try: + with open(labels_full_path, "r") as f: + reader = csv.reader(f) + for row in reader: + class_id, class_name = row + labels.append((class_id, class_name)) + except OSError as e: + raise RuntimeError(f'can not read labels file "{labels_full_path}"') from e + + return labels + + def _dump_entries(self) -> None: + split = self.split + if split == ImageNet.Split.TEST: + dataset = None + sample_count = split.length + max_class_id_length, max_class_name_length = 0, 0 + else: + labels_path = "labels.txt" + logger.info(f'loading labels from "{labels_path}"') + labels = self._load_labels(labels_path) + + # NOTE: Using torchvision ImageFolder for consistency + from torchvision.datasets import ImageFolder + + dataset_root = os.path.join(self.root, split.get_dirname()) + dataset = ImageFolder(dataset_root) + sample_count = len(dataset) + max_class_id_length, max_class_name_length = -1, -1 + for sample in dataset.samples: + _, class_index = sample + class_id, class_name = labels[class_index] + max_class_id_length = max(len(class_id), max_class_id_length) + max_class_name_length = max(len(class_name), max_class_name_length) + + dtype = np.dtype( + [ + ("actual_index", " old_percent: + logger.info(f"creating entries: {percent}%") + old_percent = percent + + actual_index = index + 1 + class_index = np.uint32(-1) + class_id, class_name = "", "" + entries_array[index] = (actual_index, class_index, class_id, class_name) + else: + class_names = {class_id: class_name for class_id, class_name in labels} + + assert dataset + old_percent = -1 + for index in range(sample_count): + percent = 100 * (index + 1) // sample_count + if percent > old_percent: + logger.info(f"creating entries: {percent}%") + old_percent = percent + + image_full_path, class_index = dataset.samples[index] + image_relpath = os.path.relpath(image_full_path, self.root) + class_id, actual_index = split.parse_image_relpath(image_relpath) + class_name = class_names[class_id] + entries_array[index] = (actual_index, class_index, class_id, class_name) + + logger.info(f'saving entries to "{self._entries_path}"') + self._save_extra(entries_array, self._entries_path) + + def _dump_class_ids_and_names(self) -> None: + split = self.split + if split == ImageNet.Split.TEST: + return + + entries_array = self._load_extra(self._entries_path) + + max_class_id_length, max_class_name_length, max_class_index = -1, -1, -1 + for entry in entries_array: + class_index, class_id, class_name = ( + entry["class_index"], + entry["class_id"], + entry["class_name"], + ) + max_class_index = max(int(class_index), max_class_index) + max_class_id_length = max(len(str(class_id)), max_class_id_length) + max_class_name_length = max(len(str(class_name)), max_class_name_length) + + class_count = max_class_index + 1 + class_ids_array = np.empty(class_count, dtype=f"U{max_class_id_length}") + class_names_array = np.empty(class_count, dtype=f"U{max_class_name_length}") + for entry in entries_array: + class_index, class_id, class_name = ( + entry["class_index"], + entry["class_id"], + entry["class_name"], + ) + class_ids_array[class_index] = class_id + class_names_array[class_index] = class_name + + logger.info(f'saving class IDs to "{self._class_ids_path}"') + self._save_extra(class_ids_array, self._class_ids_path) + + logger.info(f'saving class names to "{self._class_names_path}"') + self._save_extra(class_names_array, self._class_names_path) + + def dump_extra(self) -> None: + self._dump_entries() + self._dump_class_ids_and_names() diff --git a/dinov2/data/datasets/image_net_22k.py b/dinov2/data/datasets/image_net_22k.py new file mode 100644 index 0000000000000000000000000000000000000000..52b36a2c664a7b72e30173b03b4e2aef1cd2fcd9 --- /dev/null +++ b/dinov2/data/datasets/image_net_22k.py @@ -0,0 +1,302 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass +from enum import Enum +from functools import lru_cache +from gzip import GzipFile +from io import BytesIO +from mmap import ACCESS_READ, mmap +import os +from typing import Any, Callable, List, Optional, Set, Tuple +import warnings + +import numpy as np + +from .extended import ExtendedVisionDataset + + +_Labels = int + +_DEFAULT_MMAP_CACHE_SIZE = 16 # Warning: This can exhaust file descriptors + + +@dataclass +class _ClassEntry: + block_offset: int + maybe_filename: Optional[str] = None + + +@dataclass +class _Entry: + class_index: int # noqa: E701 + start_offset: int + end_offset: int + filename: str + + +class _Split(Enum): + TRAIN = "train" + VAL = "val" + + @property + def length(self) -> int: + return { + _Split.TRAIN: 11_797_647, + _Split.VAL: 561_050, + }[self] + + def entries_path(self): + return f"imagenet21kp_{self.value}.txt" + + +def _get_tarball_path(class_id: str) -> str: + return f"{class_id}.tar" + + +def _make_mmap_tarball(tarballs_root: str, mmap_cache_size: int): + @lru_cache(maxsize=mmap_cache_size) + def _mmap_tarball(class_id: str) -> mmap: + tarball_path = _get_tarball_path(class_id) + tarball_full_path = os.path.join(tarballs_root, tarball_path) + with open(tarball_full_path) as f: + return mmap(fileno=f.fileno(), length=0, access=ACCESS_READ) + + return _mmap_tarball + + +class ImageNet22k(ExtendedVisionDataset): + _GZIPPED_INDICES: Set[int] = { + 841_545, + 1_304_131, + 2_437_921, + 2_672_079, + 2_795_676, + 2_969_786, + 6_902_965, + 6_903_550, + 6_903_628, + 7_432_557, + 7_432_589, + 7_813_809, + 8_329_633, + 10_296_990, + 10_417_652, + 10_492_265, + 10_598_078, + 10_782_398, + 10_902_612, + 11_203_736, + 11_342_890, + 11_397_596, + 11_589_762, + 11_705_103, + 12_936_875, + 13_289_782, + } + Labels = _Labels + + def __init__( + self, + *, + root: str, + extra: str, + transforms: Optional[Callable] = None, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + mmap_cache_size: int = _DEFAULT_MMAP_CACHE_SIZE, + ) -> None: + super().__init__(root, transforms, transform, target_transform) + self._extra_root = extra + + entries_path = self._get_entries_path(root) + self._entries = self._load_extra(entries_path) + + class_ids_path = self._get_class_ids_path(root) + self._class_ids = self._load_extra(class_ids_path) + + self._gzipped_indices = ImageNet22k._GZIPPED_INDICES + self._mmap_tarball = _make_mmap_tarball(self._tarballs_root, mmap_cache_size) + + def _get_entries_path(self, root: Optional[str] = None) -> str: + return "entries.npy" + + def _get_class_ids_path(self, root: Optional[str] = None) -> str: + return "class-ids.npy" + + def _find_class_ids(self, path: str) -> List[str]: + class_ids = [] + + with os.scandir(path) as entries: + for entry in entries: + root, ext = os.path.splitext(entry.name) + if ext != ".tar": + continue + class_ids.append(root) + + return sorted(class_ids) + + def _load_entries_class_ids(self, root: Optional[str] = None) -> Tuple[List[_Entry], List[str]]: + root = self.get_root(root) + entries: List[_Entry] = [] + class_ids = self._find_class_ids(root) + + for class_index, class_id in enumerate(class_ids): + path = os.path.join(root, "blocks", f"{class_id}.log") + class_entries = [] + + try: + with open(path) as f: + for line in f: + line = line.rstrip() + block, filename = line.split(":") + block_offset = int(block[6:]) + filename = filename[1:] + + maybe_filename = None + if filename != "** Block of NULs **": + maybe_filename = filename + _, ext = os.path.splitext(filename) + # assert ext == ".JPEG" + + class_entry = _ClassEntry(block_offset, maybe_filename) + class_entries.append(class_entry) + except OSError as e: + raise RuntimeError(f'can not read blocks file "{path}"') from e + + assert class_entries[-1].maybe_filename is None + + for class_entry1, class_entry2 in zip(class_entries, class_entries[1:]): + assert class_entry1.block_offset <= class_entry2.block_offset + start_offset = 512 * class_entry1.block_offset + end_offset = 512 * class_entry2.block_offset + assert class_entry1.maybe_filename is not None + filename = class_entry1.maybe_filename + entry = _Entry(class_index, start_offset, end_offset, filename) + # Skip invalid image files (PIL throws UnidentifiedImageError) + if filename == "n06470073_47249.JPEG": + continue + entries.append(entry) + + return entries, class_ids + + def _load_extra(self, extra_path: str) -> np.ndarray: + extra_root = self._extra_root + extra_full_path = os.path.join(extra_root, extra_path) + return np.load(extra_full_path, mmap_mode="r") + + def _save_extra(self, extra_array: np.ndarray, extra_path: str) -> None: + extra_root = self._extra_root + extra_full_path = os.path.join(extra_root, extra_path) + os.makedirs(extra_root, exist_ok=True) + np.save(extra_full_path, extra_array) + + @property + def _tarballs_root(self) -> str: + return self.root + + def find_class_id(self, class_index: int) -> str: + return str(self._class_ids[class_index]) + + def get_image_data(self, index: int) -> bytes: + entry = self._entries[index] + class_id = entry["class_id"] + class_mmap = self._mmap_tarball(class_id) + + start_offset, end_offset = entry["start_offset"], entry["end_offset"] + try: + mapped_data = class_mmap[start_offset:end_offset] + data = mapped_data[512:] # Skip entry header block + + if len(data) >= 2 and tuple(data[:2]) == (0x1F, 0x8B): + assert index in self._gzipped_indices, f"unexpected gzip header for sample {index}" + with GzipFile(fileobj=BytesIO(data)) as g: + data = g.read() + except Exception as e: + raise RuntimeError(f"can not retrieve image data for sample {index} " f'from "{class_id}" tarball') from e + + return data + + def get_target(self, index: int) -> Any: + return int(self._entries[index]["class_index"]) + + def get_targets(self) -> np.ndarray: + return self._entries["class_index"] + + def get_class_id(self, index: int) -> str: + return str(self._entries[index]["class_id"]) + + def get_class_ids(self) -> np.ndarray: + return self._entries["class_id"] + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + return super().__getitem__(index) + + def __len__(self) -> int: + return len(self._entries) + + def _dump_entries(self, *args, **kwargs) -> None: + entries, class_ids = self._load_entries_class_ids(*args, **kwargs) + + max_class_id_length, max_filename_length, max_class_index = -1, -1, -1 + for entry in entries: + class_id = class_ids[entry.class_index] + max_class_index = max(entry.class_index, max_class_index) + max_class_id_length = max(len(class_id), max_class_id_length) + max_filename_length = max(len(entry.filename), max_filename_length) + + dtype = np.dtype( + [ + ("class_index", " None: + entries_path = self._get_entries_path(*args, **kwargs) + entries_array = self._load_extra(entries_path) + + max_class_id_length, max_class_index = -1, -1 + for entry in entries_array: + class_index, class_id = entry["class_index"], entry["class_id"] + max_class_index = max(int(class_index), max_class_index) + max_class_id_length = max(len(str(class_id)), max_class_id_length) + + class_ids_array = np.empty(max_class_index + 1, dtype=f"U{max_class_id_length}") + for entry in entries_array: + class_index, class_id = entry["class_index"], entry["class_id"] + class_ids_array[class_index] = class_id + class_ids_path = self._get_class_ids_path(*args, **kwargs) + self._save_extra(class_ids_array, class_ids_path) + + def _dump_extra(self, *args, **kwargs) -> None: + self._dump_entries(*args, *kwargs) + self._dump_class_ids(*args, *kwargs) + + def dump_extra(self, root: Optional[str] = None) -> None: + return self._dump_extra(root) diff --git a/dinov2/data/loaders.py b/dinov2/data/loaders.py new file mode 100644 index 0000000000000000000000000000000000000000..d6a2f0210efa0fa96be764665b5d6792191b1e72 --- /dev/null +++ b/dinov2/data/loaders.py @@ -0,0 +1,222 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import logging +from enum import Enum +from typing import Any, Callable, List, Optional, TypeVar + +import torch +from torch.utils.data import Sampler + +from .datasets import ImageNet, ImageNet22k +from .samplers import EpochSampler, InfiniteSampler, ShardedInfiniteSampler + + +logger = logging.getLogger("dinov2") + + +class SamplerType(Enum): + DISTRIBUTED = 0 + EPOCH = 1 + INFINITE = 2 + SHARDED_INFINITE = 3 + SHARDED_INFINITE_NEW = 4 + + +def _make_bool_str(b: bool) -> str: + return "yes" if b else "no" + + +def _make_sample_transform(image_transform: Optional[Callable] = None, target_transform: Optional[Callable] = None): + def transform(sample): + image, target = sample + if image_transform is not None: + image = image_transform(image) + if target_transform is not None: + target = target_transform(target) + return image, target + + return transform + + +def _parse_dataset_str(dataset_str: str): + tokens = dataset_str.split(":") + + name = tokens[0] + kwargs = {} + + for token in tokens[1:]: + key, value = token.split("=") + assert key in ("root", "extra", "split") + kwargs[key] = value + + if name == "ImageNet": + class_ = ImageNet + if "split" in kwargs: + kwargs["split"] = ImageNet.Split[kwargs["split"]] + elif name == "ImageNet22k": + class_ = ImageNet22k + else: + raise ValueError(f'Unsupported dataset "{name}"') + + return class_, kwargs + + +def make_dataset( + *, + dataset_str: str, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, +): + """ + Creates a dataset with the specified parameters. + + Args: + dataset_str: A dataset string description (e.g. ImageNet:split=TRAIN). + transform: A transform to apply to images. + target_transform: A transform to apply to targets. + + Returns: + The created dataset. + """ + logger.info(f'using dataset: "{dataset_str}"') + + class_, kwargs = _parse_dataset_str(dataset_str) + dataset = class_(transform=transform, target_transform=target_transform, **kwargs) + + logger.info(f"# of dataset samples: {len(dataset):,d}") + + # Aggregated datasets do not expose (yet) these attributes, so add them. + if not hasattr(dataset, "transform"): + setattr(dataset, "transform", transform) + if not hasattr(dataset, "target_transform"): + setattr(dataset, "target_transform", target_transform) + + return dataset + + +def _make_sampler( + *, + dataset, + type: Optional[SamplerType] = None, + shuffle: bool = False, + seed: int = 0, + size: int = -1, + advance: int = 0, +) -> Optional[Sampler]: + sample_count = len(dataset) + + if type == SamplerType.INFINITE: + logger.info("sampler: infinite") + if size > 0: + raise ValueError("sampler size > 0 is invalid") + return InfiniteSampler( + sample_count=sample_count, + shuffle=shuffle, + seed=seed, + advance=advance, + ) + elif type in (SamplerType.SHARDED_INFINITE, SamplerType.SHARDED_INFINITE_NEW): + logger.info("sampler: sharded infinite") + if size > 0: + raise ValueError("sampler size > 0 is invalid") + # TODO: Remove support for old shuffling + use_new_shuffle_tensor_slice = type == SamplerType.SHARDED_INFINITE_NEW + return ShardedInfiniteSampler( + sample_count=sample_count, + shuffle=shuffle, + seed=seed, + advance=advance, + use_new_shuffle_tensor_slice=use_new_shuffle_tensor_slice, + ) + elif type == SamplerType.EPOCH: + logger.info("sampler: epoch") + if advance > 0: + raise NotImplementedError("sampler advance > 0 is not supported") + size = size if size > 0 else sample_count + logger.info(f"# of samples / epoch: {size:,d}") + return EpochSampler( + size=size, + sample_count=sample_count, + shuffle=shuffle, + seed=seed, + ) + elif type == SamplerType.DISTRIBUTED: + logger.info("sampler: distributed") + if size > 0: + raise ValueError("sampler size > 0 is invalid") + if advance > 0: + raise ValueError("sampler advance > 0 is invalid") + return torch.utils.data.DistributedSampler( + dataset=dataset, + shuffle=shuffle, + seed=seed, + drop_last=False, + ) + + logger.info("sampler: none") + return None + + +T = TypeVar("T") + + +def make_data_loader( + *, + dataset, + batch_size: int, + num_workers: int, + shuffle: bool = True, + seed: int = 0, + sampler_type: Optional[SamplerType] = SamplerType.INFINITE, + sampler_size: int = -1, + sampler_advance: int = 0, + drop_last: bool = True, + persistent_workers: bool = False, + collate_fn: Optional[Callable[[List[T]], Any]] = None, +): + """ + Creates a data loader with the specified parameters. + + Args: + dataset: A dataset (third party, LaViDa or WebDataset). + batch_size: The size of batches to generate. + num_workers: The number of workers to use. + shuffle: Whether to shuffle samples. + seed: The random seed to use. + sampler_type: Which sampler to use: EPOCH, INFINITE, SHARDED_INFINITE, SHARDED_INFINITE_NEW, DISTRIBUTED or None. + sampler_size: The number of images per epoch (when applicable) or -1 for the entire dataset. + sampler_advance: How many samples to skip (when applicable). + drop_last: Whether the last non-full batch of data should be dropped. + persistent_workers: maintain the workers Dataset instances alive after a dataset has been consumed once. + collate_fn: Function that performs batch collation + """ + + sampler = _make_sampler( + dataset=dataset, + type=sampler_type, + shuffle=shuffle, + seed=seed, + size=sampler_size, + advance=sampler_advance, + ) + + logger.info("using PyTorch data loader") + data_loader = torch.utils.data.DataLoader( + dataset, + sampler=sampler, + batch_size=batch_size, + num_workers=num_workers, + pin_memory=True, + drop_last=drop_last, + persistent_workers=persistent_workers, + collate_fn=collate_fn, + ) + + try: + logger.info(f"# of batches: {len(data_loader):,d}") + except TypeError: # data loader has no length + logger.info("infinite data loader") + return data_loader diff --git a/dinov2/data/masking.py b/dinov2/data/masking.py new file mode 100644 index 0000000000000000000000000000000000000000..ab12aa7bf138b916b16a9a2ed1a628a2759dbec6 --- /dev/null +++ b/dinov2/data/masking.py @@ -0,0 +1,86 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import random +import math +import numpy as np + + +class MaskingGenerator: + def __init__( + self, + input_size, + num_masking_patches=None, + min_num_patches=4, + max_num_patches=None, + min_aspect=0.3, + max_aspect=None, + ): + if not isinstance(input_size, tuple): + input_size = (input_size,) * 2 + self.height, self.width = input_size + + self.num_patches = self.height * self.width + self.num_masking_patches = num_masking_patches + + self.min_num_patches = min_num_patches + self.max_num_patches = num_masking_patches if max_num_patches is None else max_num_patches + + max_aspect = max_aspect or 1 / min_aspect + self.log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect)) + + def __repr__(self): + repr_str = "Generator(%d, %d -> [%d ~ %d], max = %d, %.3f ~ %.3f)" % ( + self.height, + self.width, + self.min_num_patches, + self.max_num_patches, + self.num_masking_patches, + self.log_aspect_ratio[0], + self.log_aspect_ratio[1], + ) + return repr_str + + def get_shape(self): + return self.height, self.width + + def _mask(self, mask, max_mask_patches): + delta = 0 + for _ in range(10): + target_area = random.uniform(self.min_num_patches, max_mask_patches) + aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio)) + h = int(round(math.sqrt(target_area * aspect_ratio))) + w = int(round(math.sqrt(target_area / aspect_ratio))) + if w < self.width and h < self.height: + top = random.randint(0, self.height - h) + left = random.randint(0, self.width - w) + + num_masked = mask[top : top + h, left : left + w].sum() + # Overlap + if 0 < h * w - num_masked <= max_mask_patches: + for i in range(top, top + h): + for j in range(left, left + w): + if mask[i, j] == 0: + mask[i, j] = 1 + delta += 1 + + if delta > 0: + break + return delta + + def __call__(self, num_masking_patches=0): + mask = np.zeros(shape=self.get_shape(), dtype=bool) + mask_count = 0 + while mask_count < num_masking_patches: + max_mask_patches = num_masking_patches - mask_count + max_mask_patches = min(max_mask_patches, self.max_num_patches) + + delta = self._mask(mask, max_mask_patches) + if delta == 0: + break + else: + mask_count += delta + + return mask diff --git a/dinov2/data/samplers.py b/dinov2/data/samplers.py new file mode 100644 index 0000000000000000000000000000000000000000..6562197d94652bb9a75a5fc722fcb2c65ca161be --- /dev/null +++ b/dinov2/data/samplers.py @@ -0,0 +1,229 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import itertools +from typing import Any, Optional +import warnings + +import numpy as np +import torch +from torch.utils.data.sampler import Sampler + +import dinov2.distributed as distributed + + +class EpochSampler(Sampler): + def __init__( + self, + *, + size: int, + sample_count: int, + shuffle: bool = False, + seed: int = 0, + start: Optional[int] = None, + step: Optional[int] = None, + ): + self._size = size + self._sample_count = sample_count + self._shuffle = shuffle + self._seed = seed + self._start = distributed.get_global_rank() if start is None else start + self._step = distributed.get_global_size() if step is None else step + self._epoch = 0 + + def __iter__(self): + count = (self._size + self._sample_count - 1) // self._sample_count + tiled_indices = np.tile(np.arange(self._sample_count), count) + if self._shuffle: + seed = self._seed * self._epoch if self._seed != 0 else self._epoch + rng = np.random.default_rng(seed) + iterable = rng.choice(tiled_indices, self._size, replace=False) + else: + iterable = tiled_indices[: self._size] + + yield from itertools.islice(iterable, self._start, None, self._step) + + def __len__(self): + return (self._size - self._start + self._step - 1) // self._step + + def set_epoch(self, epoch): + self._epoch = epoch + + +def _get_numpy_dtype(size: int) -> Any: + return np.int32 if size <= 2**31 else np.int64 + + +def _get_torch_dtype(size: int) -> Any: + return torch.int32 if size <= 2**31 else torch.int64 + + +def _generate_randperm_indices(*, size: int, generator: torch.Generator): + """Generate the indices of a random permutation.""" + dtype = _get_torch_dtype(size) + # This is actually matching PyTorch's CPU implementation, see: https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorFactories.cpp#L900-L921 + perm = torch.arange(size, dtype=dtype) + for i in range(size): + j = torch.randint(i, size, size=(1,), generator=generator).item() + + # Always swap even if no-op + value = perm[j].item() + perm[j] = perm[i].item() + perm[i] = value + yield value + + +class InfiniteSampler(Sampler): + def __init__( + self, + *, + sample_count: int, + shuffle: bool = False, + seed: int = 0, + start: Optional[int] = None, + step: Optional[int] = None, + advance: int = 0, + ): + self._sample_count = sample_count + self._seed = seed + self._shuffle = shuffle + self._start = distributed.get_global_rank() if start is None else start + self._step = distributed.get_global_size() if step is None else step + self._advance = advance + + def __iter__(self): + if self._shuffle: + iterator = self._shuffled_iterator() + else: + iterator = self._iterator() + + yield from itertools.islice(iterator, self._advance, None) + + def _iterator(self): + assert not self._shuffle + + while True: + iterable = range(self._sample_count) + yield from itertools.islice(iterable, self._start, None, self._step) + + def _shuffled_iterator(self): + assert self._shuffle + + # Instantiate a generator here (rather than in the ctor) to keep the class + # picklable (requirement of mp.spawn) + generator = torch.Generator().manual_seed(self._seed) + + while True: + iterable = _generate_randperm_indices(size=self._sample_count, generator=generator) + yield from itertools.islice(iterable, self._start, None, self._step) + + +# The following function is somewhat equivalent to _new_shuffle_tensor_slice below, +# but avoids a full in-place random permutation generation. +def _shuffle_tensor_slice( + *, tensor: torch.Tensor, start: int = 0, step: int = 1, generator: torch.Generator +) -> np.ndarray: + stop = len(tensor) + count = stop // step + drop_count = stop - step * count + if drop_count: + warnings.warn(f"# of dropped samples: {drop_count}") + + dtype = _get_numpy_dtype(stop) + result = np.empty(count, dtype=dtype) + + for i in range(count): + j = torch.randint(0, i + 1, size=(1,), generator=generator).item() if i > 0 else 0 + + result[i] = result[j] + result[j] = tensor[start + i * step].item() + + return result + + +def _new_shuffle_tensor_slice( + *, tensor: torch.Tensor, start: int = 0, step: int = 1, generator: torch.Generator +) -> np.ndarray: + stop = len(tensor) + count = stop // step + dtype = torch.int64 # Needed for using randperm result as indices + count = stop // step + drop_count = stop - step * count + if drop_count: + warnings.warn(f"# of dropped samples: {drop_count}") + indices = torch.randperm(count, dtype=dtype, generator=generator) + return tensor[start::step][indices].numpy() + + +def _make_seed(seed: int, start: int, iter_count: int) -> int: + # NOTE: Tried a few variants (including iter_count << 32), this one worked best. + return seed + start + (iter_count << 24) + + +class ShardedInfiniteSampler(Sampler): + def __init__( + self, + *, + sample_count: int, + shuffle: bool = False, + seed: int = 0, + start: Optional[int] = None, + step: Optional[int] = None, + advance: int = 0, + use_new_shuffle_tensor_slice: bool = False, + ): + self._sample_count = sample_count + self._seed = seed + self._shuffle = shuffle + self._start = distributed.get_global_rank() if start is None else start + self._step = distributed.get_global_size() if step is None else step + self._advance = advance + self._iter_count = 0 + self._shuffle_tensor_slice_fn = ( + _new_shuffle_tensor_slice if use_new_shuffle_tensor_slice else _shuffle_tensor_slice + ) + + def __iter__(self): + iter_count = self._advance // self._sample_count + if iter_count > 0: + self._advance -= iter_count * self._sample_count + self._iter_count += iter_count + + if self._shuffle: + iterator = self._shuffled_iterator() + else: + iterator = self._iterator() + + yield from itertools.islice(iterator, self._advance, None) + + def _iterator(self): + assert not self._shuffle + + while True: + iterable = range(self._sample_count) + yield from itertools.islice(iterable, self._start, None, self._step) + + def _shuffled_iterator(self): + assert self._shuffle + + # Instantiate a generator here (rather than in the ctor) to be keep the class + # picklable (requirement of mp.spawn) + generator = torch.Generator() + + # Always shuffle everything first + generator.manual_seed(self._seed) + dtype = _get_torch_dtype(self._sample_count) + perm = torch.randperm(self._sample_count, dtype=dtype, generator=generator) + + while True: + # Re-seed on each iteration to allow skipping whole permutations + seed = _make_seed(self._seed, self._start, self._iter_count) + generator.manual_seed(seed) + + iterable = self._shuffle_tensor_slice_fn( + tensor=perm, start=self._start, step=self._step, generator=generator + ) + yield from iterable + self._iter_count += 1 diff --git a/dinov2/data/transforms.py b/dinov2/data/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..eb5f252b50c54d58f160528c9f2b00fad47103c7 --- /dev/null +++ b/dinov2/data/transforms.py @@ -0,0 +1,91 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from typing import Sequence + +import torch +from torchvision import transforms + + +class GaussianBlur(transforms.RandomApply): + """ + Apply Gaussian Blur to the PIL image. + """ + + def __init__(self, *, p: float = 0.5, radius_min: float = 0.1, radius_max: float = 2.0): + # NOTE: torchvision is applying 1 - probability to return the original image + keep_p = 1 - p + transform = transforms.GaussianBlur(kernel_size=9, sigma=(radius_min, radius_max)) + super().__init__(transforms=[transform], p=keep_p) + + +class MaybeToTensor(transforms.ToTensor): + """ + Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor, or keep as is if already a tensor. + """ + + def __call__(self, pic): + """ + Args: + pic (PIL Image, numpy.ndarray or torch.tensor): Image to be converted to tensor. + Returns: + Tensor: Converted image. + """ + if isinstance(pic, torch.Tensor): + return pic + return super().__call__(pic) + + +# Use timm's names +IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406) +IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225) + + +def make_normalize_transform( + mean: Sequence[float] = IMAGENET_DEFAULT_MEAN, + std: Sequence[float] = IMAGENET_DEFAULT_STD, +) -> transforms.Normalize: + return transforms.Normalize(mean=mean, std=std) + + +# This roughly matches torchvision's preset for classification training: +# https://github.com/pytorch/vision/blob/main/references/classification/presets.py#L6-L44 +def make_classification_train_transform( + *, + crop_size: int = 224, + interpolation=transforms.InterpolationMode.BICUBIC, + hflip_prob: float = 0.5, + mean: Sequence[float] = IMAGENET_DEFAULT_MEAN, + std: Sequence[float] = IMAGENET_DEFAULT_STD, +): + transforms_list = [transforms.RandomResizedCrop(crop_size, interpolation=interpolation)] + if hflip_prob > 0.0: + transforms_list.append(transforms.RandomHorizontalFlip(hflip_prob)) + transforms_list.extend( + [ + MaybeToTensor(), + make_normalize_transform(mean=mean, std=std), + ] + ) + return transforms.Compose(transforms_list) + + +# This matches (roughly) torchvision's preset for classification evaluation: +# https://github.com/pytorch/vision/blob/main/references/classification/presets.py#L47-L69 +def make_classification_eval_transform( + *, + resize_size: int = 256, + interpolation=transforms.InterpolationMode.BICUBIC, + crop_size: int = 224, + mean: Sequence[float] = IMAGENET_DEFAULT_MEAN, + std: Sequence[float] = IMAGENET_DEFAULT_STD, +) -> transforms.Compose: + transforms_list = [ + transforms.Resize(resize_size, interpolation=interpolation), + transforms.CenterCrop(crop_size), + MaybeToTensor(), + make_normalize_transform(mean=mean, std=std), + ] + return transforms.Compose(transforms_list) diff --git a/dinov2/distributed/__init__.py b/dinov2/distributed/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..23226f4536bf5acf4ffac242e9903d92863b246d --- /dev/null +++ b/dinov2/distributed/__init__.py @@ -0,0 +1,270 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import os +import random +import re +import socket +from typing import Dict, List + +import torch +import torch.distributed as dist + +_LOCAL_RANK = -1 +_LOCAL_WORLD_SIZE = -1 + + +def is_enabled() -> bool: + """ + Returns: + True if distributed training is enabled + """ + return dist.is_available() and dist.is_initialized() + + +def get_global_size() -> int: + """ + Returns: + The number of processes in the process group + """ + return dist.get_world_size() if is_enabled() else 1 + + +def get_global_rank() -> int: + """ + Returns: + The rank of the current process within the global process group. + """ + return dist.get_rank() if is_enabled() else 0 + + +def get_local_rank() -> int: + """ + Returns: + The rank of the current process within the local (per-machine) process group. + """ + if not is_enabled(): + return 0 + assert 0 <= _LOCAL_RANK < _LOCAL_WORLD_SIZE + return _LOCAL_RANK + + +def get_local_size() -> int: + """ + Returns: + The size of the per-machine process group, + i.e. the number of processes per machine. + """ + if not is_enabled(): + return 1 + assert 0 <= _LOCAL_RANK < _LOCAL_WORLD_SIZE + return _LOCAL_WORLD_SIZE + + +def is_main_process() -> bool: + """ + Returns: + True if the current process is the main one. + """ + return get_global_rank() == 0 + + +def _restrict_print_to_main_process() -> None: + """ + This function disables printing when not in the main process + """ + import builtins as __builtin__ + + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop("force", False) + if is_main_process() or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def _get_master_port(seed: int = 0) -> int: + MIN_MASTER_PORT, MAX_MASTER_PORT = (20_000, 60_000) + + master_port_str = os.environ.get("MASTER_PORT") + if master_port_str is None: + rng = random.Random(seed) + return rng.randint(MIN_MASTER_PORT, MAX_MASTER_PORT) + + return int(master_port_str) + + +def _get_available_port() -> int: + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + # A "" host address means INADDR_ANY i.e. binding to all interfaces. + # Note this is not compatible with IPv6. + s.bind(("", 0)) + port = s.getsockname()[1] + return port + + +_TORCH_DISTRIBUTED_ENV_VARS = ( + "MASTER_ADDR", + "MASTER_PORT", + "RANK", + "WORLD_SIZE", + "LOCAL_RANK", + "LOCAL_WORLD_SIZE", +) + + +def _collect_env_vars() -> Dict[str, str]: + return {env_var: os.environ[env_var] for env_var in _TORCH_DISTRIBUTED_ENV_VARS if env_var in os.environ} + + +def _is_slurm_job_process() -> bool: + return "SLURM_JOB_ID" in os.environ + + +def _parse_slurm_node_list(s: str) -> List[str]: + nodes = [] + # Extract "hostname", "hostname[1-2,3,4-5]," substrings + p = re.compile(r"(([^\[]+)(?:\[([^\]]+)\])?),?") + for m in p.finditer(s): + prefix, suffixes = s[m.start(2) : m.end(2)], s[m.start(3) : m.end(3)] + for suffix in suffixes.split(","): + span = suffix.split("-") + if len(span) == 1: + nodes.append(prefix + suffix) + else: + width = len(span[0]) + start, end = int(span[0]), int(span[1]) + 1 + nodes.extend([prefix + f"{i:0{width}}" for i in range(start, end)]) + return nodes + + +def _check_env_variable(key: str, new_value: str): + # Only check for difference with preset environment variables + if key in os.environ and os.environ[key] != new_value: + raise RuntimeError(f"Cannot export environment variables as {key} is already set") + + +class _TorchDistributedEnvironment: + def __init__(self): + self.master_addr = "127.0.0.1" + self.master_port = 0 + self.rank = -1 + self.world_size = -1 + self.local_rank = -1 + self.local_world_size = -1 + + if _is_slurm_job_process(): + return self._set_from_slurm_env() + + env_vars = _collect_env_vars() + if not env_vars: + # Environment is not set + pass + elif len(env_vars) == len(_TORCH_DISTRIBUTED_ENV_VARS): + # Environment is fully set + return self._set_from_preset_env() + else: + # Environment is partially set + collected_env_vars = ", ".join(env_vars.keys()) + raise RuntimeError(f"Partially set environment: {collected_env_vars}") + + if torch.cuda.device_count() > 0: + return self._set_from_local() + + raise RuntimeError("Can't initialize PyTorch distributed environment") + + # Slurm job created with sbatch, submitit, etc... + def _set_from_slurm_env(self): + # logger.info("Initialization from Slurm environment") + job_id = int(os.environ["SLURM_JOB_ID"]) + node_count = int(os.environ["SLURM_JOB_NUM_NODES"]) + nodes = _parse_slurm_node_list(os.environ["SLURM_JOB_NODELIST"]) + assert len(nodes) == node_count + + self.master_addr = nodes[0] + self.master_port = _get_master_port(seed=job_id) + self.rank = int(os.environ["SLURM_PROCID"]) + self.world_size = int(os.environ["SLURM_NTASKS"]) + assert self.rank < self.world_size + self.local_rank = int(os.environ["SLURM_LOCALID"]) + self.local_world_size = self.world_size // node_count + assert self.local_rank < self.local_world_size + + # Single node job with preset environment (i.e. torchrun) + def _set_from_preset_env(self): + # logger.info("Initialization from preset environment") + self.master_addr = os.environ["MASTER_ADDR"] + self.master_port = os.environ["MASTER_PORT"] + self.rank = int(os.environ["RANK"]) + self.world_size = int(os.environ["WORLD_SIZE"]) + assert self.rank < self.world_size + self.local_rank = int(os.environ["LOCAL_RANK"]) + self.local_world_size = int(os.environ["LOCAL_WORLD_SIZE"]) + assert self.local_rank < self.local_world_size + + # Single node and GPU job (i.e. local script run) + def _set_from_local(self): + # logger.info("Initialization from local") + self.master_addr = "127.0.0.1" + self.master_port = _get_available_port() + self.rank = 0 + self.world_size = 1 + self.local_rank = 0 + self.local_world_size = 1 + + def export(self, *, overwrite: bool) -> "_TorchDistributedEnvironment": + # See the "Environment variable initialization" section from + # https://pytorch.org/docs/stable/distributed.html for the complete list of + # environment variables required for the env:// initialization method. + env_vars = { + "MASTER_ADDR": self.master_addr, + "MASTER_PORT": str(self.master_port), + "RANK": str(self.rank), + "WORLD_SIZE": str(self.world_size), + "LOCAL_RANK": str(self.local_rank), + "LOCAL_WORLD_SIZE": str(self.local_world_size), + } + if not overwrite: + for k, v in env_vars.items(): + _check_env_variable(k, v) + + os.environ.update(env_vars) + return self + + +def enable(*, set_cuda_current_device: bool = True, overwrite: bool = False, allow_nccl_timeout: bool = False): + """Enable distributed mode + + Args: + set_cuda_current_device: If True, call torch.cuda.set_device() to set the + current PyTorch CUDA device to the one matching the local rank. + overwrite: If True, overwrites already set variables. Else fails. + """ + + global _LOCAL_RANK, _LOCAL_WORLD_SIZE + if _LOCAL_RANK >= 0 or _LOCAL_WORLD_SIZE >= 0: + raise RuntimeError("Distributed mode has already been enabled") + torch_env = _TorchDistributedEnvironment() + torch_env.export(overwrite=overwrite) + + if set_cuda_current_device: + torch.cuda.set_device(torch_env.local_rank) + + if allow_nccl_timeout: + # This allows to use torch distributed timeout in a NCCL backend + key, value = "NCCL_ASYNC_ERROR_HANDLING", "1" + if not overwrite: + _check_env_variable(key, value) + os.environ[key] = value + + dist.init_process_group(backend="nccl") + dist.barrier() + + # Finalize setup + _LOCAL_RANK = torch_env.local_rank + _LOCAL_WORLD_SIZE = torch_env.local_world_size + _restrict_print_to_main_process() diff --git a/dinov2/eval/__init__.py b/dinov2/eval/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b88da6bf80be92af00b72dfdb0a806fa64a7a2d9 --- /dev/null +++ b/dinov2/eval/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. diff --git a/dinov2/eval/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..516d17c69979722f102abbfbd74298f8ecb33009 Binary files /dev/null and b/dinov2/eval/__pycache__/__init__.cpython-310.pyc differ diff --git a/dinov2/eval/depth/__init__.py b/dinov2/eval/depth/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b88da6bf80be92af00b72dfdb0a806fa64a7a2d9 --- /dev/null +++ b/dinov2/eval/depth/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. diff --git a/dinov2/eval/depth/models/__init__.py b/dinov2/eval/depth/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9a5825181dc2189424b5c58d245b36919cbc5b2e --- /dev/null +++ b/dinov2/eval/depth/models/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .backbones import * # noqa: F403 +from .builder import BACKBONES, DEPTHER, HEADS, LOSSES, build_backbone, build_depther, build_head, build_loss +from .decode_heads import * # noqa: F403 +from .depther import * # noqa: F403 +from .losses import * # noqa: F403 diff --git a/dinov2/eval/depth/models/backbones/__init__.py b/dinov2/eval/depth/models/backbones/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..520d75bc6e064b9d64487293604ac1bda6e2b6f7 --- /dev/null +++ b/dinov2/eval/depth/models/backbones/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .vision_transformer import DinoVisionTransformer diff --git a/dinov2/eval/depth/models/backbones/vision_transformer.py b/dinov2/eval/depth/models/backbones/vision_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..69bda46fd69eb7dabb8f5b60e6fa459fdc21aeab --- /dev/null +++ b/dinov2/eval/depth/models/backbones/vision_transformer.py @@ -0,0 +1,16 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from mmcv.runner import BaseModule + +from ..builder import BACKBONES + + +@BACKBONES.register_module() +class DinoVisionTransformer(BaseModule): + """Vision Transformer.""" + + def __init__(self, *args, **kwargs): + super().__init__() diff --git a/dinov2/eval/depth/models/builder.py b/dinov2/eval/depth/models/builder.py new file mode 100644 index 0000000000000000000000000000000000000000..c152643435308afcff60b07cd68ea979fe1d90cb --- /dev/null +++ b/dinov2/eval/depth/models/builder.py @@ -0,0 +1,49 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import warnings + +from mmcv.cnn import MODELS as MMCV_MODELS +from mmcv.cnn.bricks.registry import ATTENTION as MMCV_ATTENTION +from mmcv.utils import Registry + +MODELS = Registry("models", parent=MMCV_MODELS) +ATTENTION = Registry("attention", parent=MMCV_ATTENTION) + + +BACKBONES = MODELS +NECKS = MODELS +HEADS = MODELS +LOSSES = MODELS +DEPTHER = MODELS + + +def build_backbone(cfg): + """Build backbone.""" + return BACKBONES.build(cfg) + + +def build_neck(cfg): + """Build neck.""" + return NECKS.build(cfg) + + +def build_head(cfg): + """Build head.""" + return HEADS.build(cfg) + + +def build_loss(cfg): + """Build loss.""" + return LOSSES.build(cfg) + + +def build_depther(cfg, train_cfg=None, test_cfg=None): + """Build depther.""" + if train_cfg is not None or test_cfg is not None: + warnings.warn("train_cfg and test_cfg is deprecated, " "please specify them in model", UserWarning) + assert cfg.get("train_cfg") is None or train_cfg is None, "train_cfg specified in both outer field and model field " + assert cfg.get("test_cfg") is None or test_cfg is None, "test_cfg specified in both outer field and model field " + return DEPTHER.build(cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg)) diff --git a/dinov2/eval/depth/models/decode_heads/__init__.py b/dinov2/eval/depth/models/decode_heads/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bd0f0754a5b01d7622c1f26bf3f60daea19da4e8 --- /dev/null +++ b/dinov2/eval/depth/models/decode_heads/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .dpt_head import DPTHead +from .linear_head import BNHead diff --git a/dinov2/eval/depth/models/decode_heads/decode_head.py b/dinov2/eval/depth/models/decode_heads/decode_head.py new file mode 100644 index 0000000000000000000000000000000000000000..f8c867a3ec687090b280d90bb86aee435320acda --- /dev/null +++ b/dinov2/eval/depth/models/decode_heads/decode_head.py @@ -0,0 +1,225 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import copy +from abc import ABCMeta, abstractmethod + +import mmcv +import numpy as np +import torch +import torch.nn as nn +from mmcv.runner import BaseModule, auto_fp16, force_fp32 + +from ...ops import resize +from ..builder import build_loss + + +class DepthBaseDecodeHead(BaseModule, metaclass=ABCMeta): + """Base class for BaseDecodeHead. + + Args: + in_channels (List): Input channels. + channels (int): Channels after modules, before conv_depth. + conv_cfg (dict|None): Config of conv layers. Default: None. + act_cfg (dict): Config of activation layers. + Default: dict(type='ReLU') + loss_decode (dict): Config of decode loss. + Default: dict(type='SigLoss'). + sampler (dict|None): The config of depth map sampler. + Default: None. + align_corners (bool): align_corners argument of F.interpolate. + Default: False. + min_depth (int): Min depth in dataset setting. + Default: 1e-3. + max_depth (int): Max depth in dataset setting. + Default: None. + norm_cfg (dict|None): Config of norm layers. + Default: None. + classify (bool): Whether predict depth in a cls.-reg. manner. + Default: False. + n_bins (int): The number of bins used in cls. step. + Default: 256. + bins_strategy (str): The discrete strategy used in cls. step. + Default: 'UD'. + norm_strategy (str): The norm strategy on cls. probability + distribution. Default: 'linear' + scale_up (str): Whether predict depth in a scale-up manner. + Default: False. + """ + + def __init__( + self, + in_channels, + channels=96, + conv_cfg=None, + act_cfg=dict(type="ReLU"), + loss_decode=dict(type="SigLoss", valid_mask=True, loss_weight=10), + sampler=None, + align_corners=False, + min_depth=1e-3, + max_depth=None, + norm_cfg=None, + classify=False, + n_bins=256, + bins_strategy="UD", + norm_strategy="linear", + scale_up=False, + ): + super(DepthBaseDecodeHead, self).__init__() + + self.in_channels = in_channels + self.channels = channels + self.conv_cfg = conv_cfg + self.act_cfg = act_cfg + if isinstance(loss_decode, dict): + self.loss_decode = build_loss(loss_decode) + elif isinstance(loss_decode, (list, tuple)): + self.loss_decode = nn.ModuleList() + for loss in loss_decode: + self.loss_decode.append(build_loss(loss)) + self.align_corners = align_corners + self.min_depth = min_depth + self.max_depth = max_depth + self.norm_cfg = norm_cfg + self.classify = classify + self.n_bins = n_bins + self.scale_up = scale_up + + if self.classify: + assert bins_strategy in ["UD", "SID"], "Support bins_strategy: UD, SID" + assert norm_strategy in ["linear", "softmax", "sigmoid"], "Support norm_strategy: linear, softmax, sigmoid" + + self.bins_strategy = bins_strategy + self.norm_strategy = norm_strategy + self.softmax = nn.Softmax(dim=1) + self.conv_depth = nn.Conv2d(channels, n_bins, kernel_size=3, padding=1, stride=1) + else: + self.conv_depth = nn.Conv2d(channels, 1, kernel_size=3, padding=1, stride=1) + + self.fp16_enabled = False + self.relu = nn.ReLU() + self.sigmoid = nn.Sigmoid() + + def extra_repr(self): + """Extra repr.""" + s = f"align_corners={self.align_corners}" + return s + + @auto_fp16() + @abstractmethod + def forward(self, inputs, img_metas): + """Placeholder of forward function.""" + pass + + def forward_train(self, img, inputs, img_metas, depth_gt, train_cfg): + """Forward function for training. + Args: + inputs (list[Tensor]): List of multi-level img features. + img_metas (list[dict]): List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `depth/datasets/pipelines/formatting.py:Collect`. + depth_gt (Tensor): GT depth + train_cfg (dict): The training config. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + depth_pred = self.forward(inputs, img_metas) + losses = self.losses(depth_pred, depth_gt) + + log_imgs = self.log_images(img[0], depth_pred[0], depth_gt[0], img_metas[0]) + losses.update(**log_imgs) + + return losses + + def forward_test(self, inputs, img_metas, test_cfg): + """Forward function for testing. + Args: + inputs (list[Tensor]): List of multi-level img features. + img_metas (list[dict]): List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `depth/datasets/pipelines/formatting.py:Collect`. + test_cfg (dict): The testing config. + + Returns: + Tensor: Output depth map. + """ + return self.forward(inputs, img_metas) + + def depth_pred(self, feat): + """Prediction each pixel.""" + if self.classify: + logit = self.conv_depth(feat) + + if self.bins_strategy == "UD": + bins = torch.linspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device) + elif self.bins_strategy == "SID": + bins = torch.logspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device) + + # following Adabins, default linear + if self.norm_strategy == "linear": + logit = torch.relu(logit) + eps = 0.1 + logit = logit + eps + logit = logit / logit.sum(dim=1, keepdim=True) + elif self.norm_strategy == "softmax": + logit = torch.softmax(logit, dim=1) + elif self.norm_strategy == "sigmoid": + logit = torch.sigmoid(logit) + logit = logit / logit.sum(dim=1, keepdim=True) + + output = torch.einsum("ikmn,k->imn", [logit, bins]).unsqueeze(dim=1) + + else: + if self.scale_up: + output = self.sigmoid(self.conv_depth(feat)) * self.max_depth + else: + output = self.relu(self.conv_depth(feat)) + self.min_depth + return output + + @force_fp32(apply_to=("depth_pred",)) + def losses(self, depth_pred, depth_gt): + """Compute depth loss.""" + loss = dict() + depth_pred = resize( + input=depth_pred, size=depth_gt.shape[2:], mode="bilinear", align_corners=self.align_corners, warning=False + ) + if not isinstance(self.loss_decode, nn.ModuleList): + losses_decode = [self.loss_decode] + else: + losses_decode = self.loss_decode + for loss_decode in losses_decode: + if loss_decode.loss_name not in loss: + loss[loss_decode.loss_name] = loss_decode(depth_pred, depth_gt) + else: + loss[loss_decode.loss_name] += loss_decode(depth_pred, depth_gt) + return loss + + def log_images(self, img_path, depth_pred, depth_gt, img_meta): + show_img = copy.deepcopy(img_path.detach().cpu().permute(1, 2, 0)) + show_img = show_img.numpy().astype(np.float32) + show_img = mmcv.imdenormalize( + show_img, + img_meta["img_norm_cfg"]["mean"], + img_meta["img_norm_cfg"]["std"], + img_meta["img_norm_cfg"]["to_rgb"], + ) + show_img = np.clip(show_img, 0, 255) + show_img = show_img.astype(np.uint8) + show_img = show_img[:, :, ::-1] + show_img = show_img.transpose(0, 2, 1) + show_img = show_img.transpose(1, 0, 2) + + depth_pred = depth_pred / torch.max(depth_pred) + depth_gt = depth_gt / torch.max(depth_gt) + + depth_pred_color = copy.deepcopy(depth_pred.detach().cpu()) + depth_gt_color = copy.deepcopy(depth_gt.detach().cpu()) + + return {"img_rgb": show_img, "img_depth_pred": depth_pred_color, "img_depth_gt": depth_gt_color} diff --git a/dinov2/eval/depth/models/decode_heads/dpt_head.py b/dinov2/eval/depth/models/decode_heads/dpt_head.py new file mode 100644 index 0000000000000000000000000000000000000000..c6c6d9470d78e1d944cc505f97865f026a9458d3 --- /dev/null +++ b/dinov2/eval/depth/models/decode_heads/dpt_head.py @@ -0,0 +1,270 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn as nn +from mmcv.cnn import ConvModule, Linear, build_activation_layer +from mmcv.runner import BaseModule + +from ...ops import resize +from ..builder import HEADS +from .decode_head import DepthBaseDecodeHead + + +class Interpolate(nn.Module): + def __init__(self, scale_factor, mode, align_corners=False): + super(Interpolate, self).__init__() + self.interp = nn.functional.interpolate + self.scale_factor = scale_factor + self.mode = mode + self.align_corners = align_corners + + def forward(self, x): + x = self.interp(x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners) + return x + + +class HeadDepth(nn.Module): + def __init__(self, features): + super(HeadDepth, self).__init__() + self.head = nn.Sequential( + nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1), + Interpolate(scale_factor=2, mode="bilinear", align_corners=True), + nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1), + nn.ReLU(), + nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), + ) + + def forward(self, x): + x = self.head(x) + return x + + +class ReassembleBlocks(BaseModule): + """ViTPostProcessBlock, process cls_token in ViT backbone output and + rearrange the feature vector to feature map. + Args: + in_channels (int): ViT feature channels. Default: 768. + out_channels (List): output channels of each stage. + Default: [96, 192, 384, 768]. + readout_type (str): Type of readout operation. Default: 'ignore'. + patch_size (int): The patch size. Default: 16. + init_cfg (dict, optional): Initialization config dict. Default: None. + """ + + def __init__( + self, in_channels=768, out_channels=[96, 192, 384, 768], readout_type="ignore", patch_size=16, init_cfg=None + ): + super(ReassembleBlocks, self).__init__(init_cfg) + + assert readout_type in ["ignore", "add", "project"] + self.readout_type = readout_type + self.patch_size = patch_size + + self.projects = nn.ModuleList( + [ + ConvModule( + in_channels=in_channels, + out_channels=out_channel, + kernel_size=1, + act_cfg=None, + ) + for out_channel in out_channels + ] + ) + + self.resize_layers = nn.ModuleList( + [ + nn.ConvTranspose2d( + in_channels=out_channels[0], out_channels=out_channels[0], kernel_size=4, stride=4, padding=0 + ), + nn.ConvTranspose2d( + in_channels=out_channels[1], out_channels=out_channels[1], kernel_size=2, stride=2, padding=0 + ), + nn.Identity(), + nn.Conv2d( + in_channels=out_channels[3], out_channels=out_channels[3], kernel_size=3, stride=2, padding=1 + ), + ] + ) + if self.readout_type == "project": + self.readout_projects = nn.ModuleList() + for _ in range(len(self.projects)): + self.readout_projects.append( + nn.Sequential(Linear(2 * in_channels, in_channels), build_activation_layer(dict(type="GELU"))) + ) + + def forward(self, inputs): + assert isinstance(inputs, list) + out = [] + for i, x in enumerate(inputs): + assert len(x) == 2 + x, cls_token = x[0], x[1] + feature_shape = x.shape + if self.readout_type == "project": + x = x.flatten(2).permute((0, 2, 1)) + readout = cls_token.unsqueeze(1).expand_as(x) + x = self.readout_projects[i](torch.cat((x, readout), -1)) + x = x.permute(0, 2, 1).reshape(feature_shape) + elif self.readout_type == "add": + x = x.flatten(2) + cls_token.unsqueeze(-1) + x = x.reshape(feature_shape) + else: + pass + x = self.projects[i](x) + x = self.resize_layers[i](x) + out.append(x) + return out + + +class PreActResidualConvUnit(BaseModule): + """ResidualConvUnit, pre-activate residual unit. + Args: + in_channels (int): number of channels in the input feature map. + act_cfg (dict): dictionary to construct and config activation layer. + norm_cfg (dict): dictionary to construct and config norm layer. + stride (int): stride of the first block. Default: 1 + dilation (int): dilation rate for convs layers. Default: 1. + init_cfg (dict, optional): Initialization config dict. Default: None. + """ + + def __init__(self, in_channels, act_cfg, norm_cfg, stride=1, dilation=1, init_cfg=None): + super(PreActResidualConvUnit, self).__init__(init_cfg) + + self.conv1 = ConvModule( + in_channels, + in_channels, + 3, + stride=stride, + padding=dilation, + dilation=dilation, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + bias=False, + order=("act", "conv", "norm"), + ) + + self.conv2 = ConvModule( + in_channels, + in_channels, + 3, + padding=1, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + bias=False, + order=("act", "conv", "norm"), + ) + + def forward(self, inputs): + inputs_ = inputs.clone() + x = self.conv1(inputs) + x = self.conv2(x) + return x + inputs_ + + +class FeatureFusionBlock(BaseModule): + """FeatureFusionBlock, merge feature map from different stages. + Args: + in_channels (int): Input channels. + act_cfg (dict): The activation config for ResidualConvUnit. + norm_cfg (dict): Config dict for normalization layer. + expand (bool): Whether expand the channels in post process block. + Default: False. + align_corners (bool): align_corner setting for bilinear upsample. + Default: True. + init_cfg (dict, optional): Initialization config dict. Default: None. + """ + + def __init__(self, in_channels, act_cfg, norm_cfg, expand=False, align_corners=True, init_cfg=None): + super(FeatureFusionBlock, self).__init__(init_cfg) + + self.in_channels = in_channels + self.expand = expand + self.align_corners = align_corners + + self.out_channels = in_channels + if self.expand: + self.out_channels = in_channels // 2 + + self.project = ConvModule(self.in_channels, self.out_channels, kernel_size=1, act_cfg=None, bias=True) + + self.res_conv_unit1 = PreActResidualConvUnit(in_channels=self.in_channels, act_cfg=act_cfg, norm_cfg=norm_cfg) + self.res_conv_unit2 = PreActResidualConvUnit(in_channels=self.in_channels, act_cfg=act_cfg, norm_cfg=norm_cfg) + + def forward(self, *inputs): + x = inputs[0] + if len(inputs) == 2: + if x.shape != inputs[1].shape: + res = resize(inputs[1], size=(x.shape[2], x.shape[3]), mode="bilinear", align_corners=False) + else: + res = inputs[1] + x = x + self.res_conv_unit1(res) + x = self.res_conv_unit2(x) + x = resize(x, scale_factor=2, mode="bilinear", align_corners=self.align_corners) + x = self.project(x) + return x + + +@HEADS.register_module() +class DPTHead(DepthBaseDecodeHead): + """Vision Transformers for Dense Prediction. + This head is implemented of `DPT `_. + Args: + embed_dims (int): The embed dimension of the ViT backbone. + Default: 768. + post_process_channels (List): Out channels of post process conv + layers. Default: [96, 192, 384, 768]. + readout_type (str): Type of readout operation. Default: 'ignore'. + patch_size (int): The patch size. Default: 16. + expand_channels (bool): Whether expand the channels in post process + block. Default: False. + """ + + def __init__( + self, + embed_dims=768, + post_process_channels=[96, 192, 384, 768], + readout_type="ignore", + patch_size=16, + expand_channels=False, + **kwargs + ): + super(DPTHead, self).__init__(**kwargs) + + self.in_channels = self.in_channels + self.expand_channels = expand_channels + self.reassemble_blocks = ReassembleBlocks(embed_dims, post_process_channels, readout_type, patch_size) + + self.post_process_channels = [ + channel * math.pow(2, i) if expand_channels else channel for i, channel in enumerate(post_process_channels) + ] + self.convs = nn.ModuleList() + for channel in self.post_process_channels: + self.convs.append(ConvModule(channel, self.channels, kernel_size=3, padding=1, act_cfg=None, bias=False)) + self.fusion_blocks = nn.ModuleList() + for _ in range(len(self.convs)): + self.fusion_blocks.append(FeatureFusionBlock(self.channels, self.act_cfg, self.norm_cfg)) + self.fusion_blocks[0].res_conv_unit1 = None + self.project = ConvModule(self.channels, self.channels, kernel_size=3, padding=1, norm_cfg=self.norm_cfg) + self.num_fusion_blocks = len(self.fusion_blocks) + self.num_reassemble_blocks = len(self.reassemble_blocks.resize_layers) + self.num_post_process_channels = len(self.post_process_channels) + assert self.num_fusion_blocks == self.num_reassemble_blocks + assert self.num_reassemble_blocks == self.num_post_process_channels + self.conv_depth = HeadDepth(self.channels) + + def forward(self, inputs, img_metas): + assert len(inputs) == self.num_reassemble_blocks + x = [inp for inp in inputs] + x = self.reassemble_blocks(x) + x = [self.convs[i](feature) for i, feature in enumerate(x)] + out = self.fusion_blocks[0](x[-1]) + for i in range(1, len(self.fusion_blocks)): + out = self.fusion_blocks[i](out, x[-(i + 1)]) + out = self.project(out) + out = self.depth_pred(out) + return out diff --git a/dinov2/eval/depth/models/decode_heads/linear_head.py b/dinov2/eval/depth/models/decode_heads/linear_head.py new file mode 100644 index 0000000000000000000000000000000000000000..3da1436f6a3f0bcc389d74ed86d44d455d2f7a87 --- /dev/null +++ b/dinov2/eval/depth/models/decode_heads/linear_head.py @@ -0,0 +1,89 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + +from ...ops import resize +from ..builder import HEADS +from .decode_head import DepthBaseDecodeHead + + +@HEADS.register_module() +class BNHead(DepthBaseDecodeHead): + """Just a batchnorm.""" + + def __init__(self, input_transform="resize_concat", in_index=(0, 1, 2, 3), upsample=1, **kwargs): + super().__init__(**kwargs) + self.input_transform = input_transform + self.in_index = in_index + self.upsample = upsample + # self.bn = nn.SyncBatchNorm(self.in_channels) + if self.classify: + self.conv_depth = nn.Conv2d(self.channels, self.n_bins, kernel_size=1, padding=0, stride=1) + else: + self.conv_depth = nn.Conv2d(self.channels, 1, kernel_size=1, padding=0, stride=1) + + def _transform_inputs(self, inputs): + """Transform inputs for decoder. + Args: + inputs (list[Tensor]): List of multi-level img features. + Returns: + Tensor: The transformed inputs + """ + + if "concat" in self.input_transform: + inputs = [inputs[i] for i in self.in_index] + if "resize" in self.input_transform: + inputs = [ + resize( + input=x, + size=[s * self.upsample for s in inputs[0].shape[2:]], + mode="bilinear", + align_corners=self.align_corners, + ) + for x in inputs + ] + inputs = torch.cat(inputs, dim=1) + elif self.input_transform == "multiple_select": + inputs = [inputs[i] for i in self.in_index] + else: + inputs = inputs[self.in_index] + + return inputs + + def _forward_feature(self, inputs, img_metas=None, **kwargs): + """Forward function for feature maps before classifying each pixel with + ``self.cls_seg`` fc. + Args: + inputs (list[Tensor]): List of multi-level img features. + Returns: + feats (Tensor): A tensor of shape (batch_size, self.channels, + H, W) which is feature map for last layer of decoder head. + """ + # accept lists (for cls token) + inputs = list(inputs) + for i, x in enumerate(inputs): + if len(x) == 2: + x, cls_token = x[0], x[1] + if len(x.shape) == 2: + x = x[:, :, None, None] + cls_token = cls_token[:, :, None, None].expand_as(x) + inputs[i] = torch.cat((x, cls_token), 1) + else: + x = x[0] + if len(x.shape) == 2: + x = x[:, :, None, None] + inputs[i] = x + x = self._transform_inputs(inputs) + # feats = self.bn(x) + return x + + def forward(self, inputs, img_metas=None, **kwargs): + """Forward function.""" + output = self._forward_feature(inputs, img_metas=img_metas, **kwargs) + output = self.depth_pred(output) + + return output diff --git a/dinov2/eval/depth/models/depther/__init__.py b/dinov2/eval/depth/models/depther/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..be99743bf6c773d05f2b74524116e368c0cfcba0 --- /dev/null +++ b/dinov2/eval/depth/models/depther/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .base import BaseDepther +from .encoder_decoder import DepthEncoderDecoder diff --git a/dinov2/eval/depth/models/depther/base.py b/dinov2/eval/depth/models/depther/base.py new file mode 100644 index 0000000000000000000000000000000000000000..e133a825a888167f90d95d67803609d6cac7ff55 --- /dev/null +++ b/dinov2/eval/depth/models/depther/base.py @@ -0,0 +1,194 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from abc import ABCMeta, abstractmethod +from collections import OrderedDict + +import torch +import torch.distributed as dist +from mmcv.runner import BaseModule, auto_fp16 + + +class BaseDepther(BaseModule, metaclass=ABCMeta): + """Base class for depther.""" + + def __init__(self, init_cfg=None): + super(BaseDepther, self).__init__(init_cfg) + self.fp16_enabled = False + + @property + def with_neck(self): + """bool: whether the depther has neck""" + return hasattr(self, "neck") and self.neck is not None + + @property + def with_auxiliary_head(self): + """bool: whether the depther has auxiliary head""" + return hasattr(self, "auxiliary_head") and self.auxiliary_head is not None + + @property + def with_decode_head(self): + """bool: whether the depther has decode head""" + return hasattr(self, "decode_head") and self.decode_head is not None + + @abstractmethod + def extract_feat(self, imgs): + """Placeholder for extract features from images.""" + pass + + @abstractmethod + def encode_decode(self, img, img_metas): + """Placeholder for encode images with backbone and decode into a + semantic depth map of the same size as input.""" + pass + + @abstractmethod + def forward_train(self, imgs, img_metas, **kwargs): + """Placeholder for Forward function for training.""" + pass + + @abstractmethod + def simple_test(self, img, img_meta, **kwargs): + """Placeholder for single image test.""" + pass + + @abstractmethod + def aug_test(self, imgs, img_metas, **kwargs): + """Placeholder for augmentation test.""" + pass + + def forward_test(self, imgs, img_metas, **kwargs): + """ + Args: + imgs (List[Tensor]): the outer list indicates test-time + augmentations and inner Tensor should have a shape NxCxHxW, + which contains all images in the batch. + img_metas (List[List[dict]]): the outer list indicates test-time + augs (multiscale, flip, etc.) and the inner list indicates + images in a batch. + """ + for var, name in [(imgs, "imgs"), (img_metas, "img_metas")]: + if not isinstance(var, list): + raise TypeError(f"{name} must be a list, but got " f"{type(var)}") + num_augs = len(imgs) + if num_augs != len(img_metas): + raise ValueError(f"num of augmentations ({len(imgs)}) != " f"num of image meta ({len(img_metas)})") + # all images in the same aug batch all of the same ori_shape and pad + # shape + for img_meta in img_metas: + ori_shapes = [_["ori_shape"] for _ in img_meta] + assert all(shape == ori_shapes[0] for shape in ori_shapes) + img_shapes = [_["img_shape"] for _ in img_meta] + assert all(shape == img_shapes[0] for shape in img_shapes) + pad_shapes = [_["pad_shape"] for _ in img_meta] + assert all(shape == pad_shapes[0] for shape in pad_shapes) + + if num_augs == 1: + return self.simple_test(imgs[0], img_metas[0], **kwargs) + else: + return self.aug_test(imgs, img_metas, **kwargs) + + @auto_fp16(apply_to=("img",)) + def forward(self, img, img_metas, return_loss=True, **kwargs): + """Calls either :func:`forward_train` or :func:`forward_test` depending + on whether ``return_loss`` is ``True``. + + Note this setting will change the expected inputs. When + ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor + and List[dict]), and when ``resturn_loss=False``, img and img_meta + should be double nested (i.e. List[Tensor], List[List[dict]]), with + the outer list indicating test time augmentations. + """ + if return_loss: + return self.forward_train(img, img_metas, **kwargs) + else: + return self.forward_test(img, img_metas, **kwargs) + + def train_step(self, data_batch, optimizer, **kwargs): + """The iteration step during training. + + This method defines an iteration step during training, except for the + back propagation and optimizer updating, which are done in an optimizer + hook. Note that in some complicated cases or models, the whole process + including back propagation and optimizer updating is also defined in + this method, such as GAN. + + Args: + data (dict): The output of dataloader. + optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of + runner is passed to ``train_step()``. This argument is unused + and reserved. + + Returns: + dict: It should contain at least 3 keys: ``loss``, ``log_vars``, + ``num_samples``. + ``loss`` is a tensor for back propagation, which can be a + weighted sum of multiple losses. + ``log_vars`` contains all the variables to be sent to the + logger. + ``num_samples`` indicates the batch size (when the model is + DDP, it means the batch size on each GPU), which is used for + averaging the logs. + """ + losses = self(**data_batch) + + # split losses and images + real_losses = {} + log_imgs = {} + for k, v in losses.items(): + if "img" in k: + log_imgs[k] = v + else: + real_losses[k] = v + + loss, log_vars = self._parse_losses(real_losses) + + outputs = dict(loss=loss, log_vars=log_vars, num_samples=len(data_batch["img_metas"]), log_imgs=log_imgs) + + return outputs + + def val_step(self, data_batch, **kwargs): + """The iteration step during validation. + + This method shares the same signature as :func:`train_step`, but used + during val epochs. Note that the evaluation after training epochs is + not implemented with this method, but an evaluation hook. + """ + output = self(**data_batch, **kwargs) + return output + + @staticmethod + def _parse_losses(losses): + """Parse the raw outputs (losses) of the network. + + Args: + losses (dict): Raw output of the network, which usually contain + losses and other necessary information. + + Returns: + tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor + which may be a weighted sum of all losses, log_vars contains + all the variables to be sent to the logger. + """ + log_vars = OrderedDict() + for loss_name, loss_value in losses.items(): + if isinstance(loss_value, torch.Tensor): + log_vars[loss_name] = loss_value.mean() + elif isinstance(loss_value, list): + log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value) + else: + raise TypeError(f"{loss_name} is not a tensor or list of tensors") + + loss = sum(_value for _key, _value in log_vars.items() if "loss" in _key) + + log_vars["loss"] = loss + for loss_name, loss_value in log_vars.items(): + # reduce loss when distributed training + if dist.is_available() and dist.is_initialized(): + loss_value = loss_value.data.clone() + dist.all_reduce(loss_value.div_(dist.get_world_size())) + log_vars[loss_name] = loss_value.item() + + return loss, log_vars diff --git a/dinov2/eval/depth/models/depther/encoder_decoder.py b/dinov2/eval/depth/models/depther/encoder_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..6b0ec2dd314fdf8ccf4414d81afb95326b7dc0c9 --- /dev/null +++ b/dinov2/eval/depth/models/depther/encoder_decoder.py @@ -0,0 +1,236 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F + +from ...models import builder +from ...models.builder import DEPTHER +from ...ops import resize +from .base import BaseDepther + + +def add_prefix(inputs, prefix): + """Add prefix for dict. + + Args: + inputs (dict): The input dict with str keys. + prefix (str): The prefix to add. + + Returns: + + dict: The dict with keys updated with ``prefix``. + """ + + outputs = dict() + for name, value in inputs.items(): + outputs[f"{prefix}.{name}"] = value + + return outputs + + +@DEPTHER.register_module() +class DepthEncoderDecoder(BaseDepther): + """Encoder Decoder depther. + + EncoderDecoder typically consists of backbone, (neck) and decode_head. + """ + + def __init__(self, backbone, decode_head, neck=None, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None): + super(DepthEncoderDecoder, self).__init__(init_cfg) + if pretrained is not None: + assert backbone.get("pretrained") is None, "both backbone and depther set pretrained weight" + backbone.pretrained = pretrained + self.backbone = builder.build_backbone(backbone) + self._init_decode_head(decode_head) + + if neck is not None: + self.neck = builder.build_neck(neck) + + self.train_cfg = train_cfg + self.test_cfg = test_cfg + + assert self.with_decode_head + + def _init_decode_head(self, decode_head): + """Initialize ``decode_head``""" + self.decode_head = builder.build_head(decode_head) + self.align_corners = self.decode_head.align_corners + + def extract_feat(self, img): + """Extract features from images.""" + x = self.backbone(img) + if self.with_neck: + x = self.neck(x) + return x + + def encode_decode(self, img, img_metas, rescale=True, size=None): + """Encode images with backbone and decode into a depth estimation + map of the same size as input.""" + x = self.extract_feat(img) + out = self._decode_head_forward_test(x, img_metas) + # crop the pred depth to the certain range. + out = torch.clamp(out, min=self.decode_head.min_depth, max=self.decode_head.max_depth) + if rescale: + if size is None: + if img_metas is not None: + size = img_metas[0]["ori_shape"][:2] + else: + size = img.shape[2:] + out = resize(input=out, size=size, mode="bilinear", align_corners=self.align_corners) + return out + + def _decode_head_forward_train(self, img, x, img_metas, depth_gt, **kwargs): + """Run forward function and calculate loss for decode head in + training.""" + losses = dict() + loss_decode = self.decode_head.forward_train(img, x, img_metas, depth_gt, self.train_cfg, **kwargs) + losses.update(add_prefix(loss_decode, "decode")) + return losses + + def _decode_head_forward_test(self, x, img_metas): + """Run forward function and calculate loss for decode head in + inference.""" + depth_pred = self.decode_head.forward_test(x, img_metas, self.test_cfg) + return depth_pred + + def forward_dummy(self, img): + """Dummy forward function.""" + depth = self.encode_decode(img, None) + + return depth + + def forward_train(self, img, img_metas, depth_gt, **kwargs): + """Forward function for training. + + Args: + img (Tensor): Input images. + img_metas (list[dict]): List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `depth/datasets/pipelines/formatting.py:Collect`. + depth_gt (Tensor): Depth gt + used if the architecture supports depth estimation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + + x = self.extract_feat(img) + + losses = dict() + + # the last of x saves the info from neck + loss_decode = self._decode_head_forward_train(img, x, img_metas, depth_gt, **kwargs) + + losses.update(loss_decode) + + return losses + + def whole_inference(self, img, img_meta, rescale, size=None): + """Inference with full image.""" + depth_pred = self.encode_decode(img, img_meta, rescale, size=size) + + return depth_pred + + def slide_inference(self, img, img_meta, rescale): + """Inference by sliding-window with overlap. + + If h_crop > h_img or w_crop > w_img, the small patch will be used to + decode without padding. + """ + + h_stride, w_stride = self.test_cfg.stride + h_crop, w_crop = self.test_cfg.crop_size + batch_size, _, h_img, w_img = img.size() + h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1 + w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1 + preds = img.new_zeros((batch_size, 1, h_img, w_img)) + count_mat = img.new_zeros((batch_size, 1, h_img, w_img)) + for h_idx in range(h_grids): + for w_idx in range(w_grids): + y1 = h_idx * h_stride + x1 = w_idx * w_stride + y2 = min(y1 + h_crop, h_img) + x2 = min(x1 + w_crop, w_img) + y1 = max(y2 - h_crop, 0) + x1 = max(x2 - w_crop, 0) + crop_img = img[:, :, y1:y2, x1:x2] + depth_pred = self.encode_decode(crop_img, img_meta, rescale) + preds += F.pad(depth_pred, (int(x1), int(preds.shape[3] - x2), int(y1), int(preds.shape[2] - y2))) + + count_mat[:, :, y1:y2, x1:x2] += 1 + assert (count_mat == 0).sum() == 0 + if torch.onnx.is_in_onnx_export(): + # cast count_mat to constant while exporting to ONNX + count_mat = torch.from_numpy(count_mat.cpu().detach().numpy()).to(device=img.device) + preds = preds / count_mat + return preds + + def inference(self, img, img_meta, rescale, size=None): + """Inference with slide/whole style. + + Args: + img (Tensor): The input image of shape (N, 3, H, W). + img_meta (dict): Image info dict where each dict has: 'img_shape', + 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `depth/datasets/pipelines/formatting.py:Collect`. + rescale (bool): Whether rescale back to original shape. + + Returns: + Tensor: The output depth map. + """ + + assert self.test_cfg.mode in ["slide", "whole"] + ori_shape = img_meta[0]["ori_shape"] + assert all(_["ori_shape"] == ori_shape for _ in img_meta) + if self.test_cfg.mode == "slide": + depth_pred = self.slide_inference(img, img_meta, rescale) + else: + depth_pred = self.whole_inference(img, img_meta, rescale, size=size) + output = depth_pred + flip = img_meta[0]["flip"] + if flip: + flip_direction = img_meta[0]["flip_direction"] + assert flip_direction in ["horizontal", "vertical"] + if flip_direction == "horizontal": + output = output.flip(dims=(3,)) + elif flip_direction == "vertical": + output = output.flip(dims=(2,)) + + return output + + def simple_test(self, img, img_meta, rescale=True): + """Simple test with single image.""" + depth_pred = self.inference(img, img_meta, rescale) + if torch.onnx.is_in_onnx_export(): + # our inference backend only support 4D output + depth_pred = depth_pred.unsqueeze(0) + return depth_pred + depth_pred = depth_pred.cpu().numpy() + # unravel batch dim + depth_pred = list(depth_pred) + return depth_pred + + def aug_test(self, imgs, img_metas, rescale=True): + """Test with augmentations. + + Only rescale=True is supported. + """ + # aug_test rescale all imgs back to ori_shape for now + assert rescale + # to save memory, we get augmented depth logit inplace + depth_pred = self.inference(imgs[0], img_metas[0], rescale) + for i in range(1, len(imgs)): + cur_depth_pred = self.inference(imgs[i], img_metas[i], rescale, size=depth_pred.shape[-2:]) + depth_pred += cur_depth_pred + depth_pred /= len(imgs) + depth_pred = depth_pred.cpu().numpy() + # unravel batch dim + depth_pred = list(depth_pred) + return depth_pred diff --git a/dinov2/eval/depth/models/losses/__init__.py b/dinov2/eval/depth/models/losses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2f86242e342776da2e0acc61150d15a8d58ff1e0 --- /dev/null +++ b/dinov2/eval/depth/models/losses/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .gradientloss import GradientLoss +from .sigloss import SigLoss diff --git a/dinov2/eval/depth/models/losses/gradientloss.py b/dinov2/eval/depth/models/losses/gradientloss.py new file mode 100644 index 0000000000000000000000000000000000000000..1599878a6b70cdff4f8467e1e875f0d13ea89eca --- /dev/null +++ b/dinov2/eval/depth/models/losses/gradientloss.py @@ -0,0 +1,69 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + +from ...models.builder import LOSSES + + +@LOSSES.register_module() +class GradientLoss(nn.Module): + """GradientLoss. + + Adapted from https://www.cs.cornell.edu/projects/megadepth/ + + Args: + valid_mask (bool): Whether filter invalid gt (gt > 0). Default: True. + loss_weight (float): Weight of the loss. Default: 1.0. + max_depth (int): When filtering invalid gt, set a max threshold. Default: None. + """ + + def __init__(self, valid_mask=True, loss_weight=1.0, max_depth=None, loss_name="loss_grad"): + super(GradientLoss, self).__init__() + self.valid_mask = valid_mask + self.loss_weight = loss_weight + self.max_depth = max_depth + self.loss_name = loss_name + + self.eps = 0.001 # avoid grad explode + + def gradientloss(self, input, target): + input_downscaled = [input] + [input[:: 2 * i, :: 2 * i] for i in range(1, 4)] + target_downscaled = [target] + [target[:: 2 * i, :: 2 * i] for i in range(1, 4)] + + gradient_loss = 0 + for input, target in zip(input_downscaled, target_downscaled): + if self.valid_mask: + mask = target > 0 + if self.max_depth is not None: + mask = torch.logical_and(target > 0, target <= self.max_depth) + N = torch.sum(mask) + else: + mask = torch.ones_like(target) + N = input.numel() + input_log = torch.log(input + self.eps) + target_log = torch.log(target + self.eps) + log_d_diff = input_log - target_log + + log_d_diff = torch.mul(log_d_diff, mask) + + v_gradient = torch.abs(log_d_diff[0:-2, :] - log_d_diff[2:, :]) + v_mask = torch.mul(mask[0:-2, :], mask[2:, :]) + v_gradient = torch.mul(v_gradient, v_mask) + + h_gradient = torch.abs(log_d_diff[:, 0:-2] - log_d_diff[:, 2:]) + h_mask = torch.mul(mask[:, 0:-2], mask[:, 2:]) + h_gradient = torch.mul(h_gradient, h_mask) + + gradient_loss += (torch.sum(h_gradient) + torch.sum(v_gradient)) / N + + return gradient_loss + + def forward(self, depth_pred, depth_gt): + """Forward function.""" + + gradient_loss = self.loss_weight * self.gradientloss(depth_pred, depth_gt) + return gradient_loss diff --git a/dinov2/eval/depth/models/losses/sigloss.py b/dinov2/eval/depth/models/losses/sigloss.py new file mode 100644 index 0000000000000000000000000000000000000000..e12fad3e6151e4b975dd055193fdaec0206d4a14 --- /dev/null +++ b/dinov2/eval/depth/models/losses/sigloss.py @@ -0,0 +1,65 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + +from ...models.builder import LOSSES + + +@LOSSES.register_module() +class SigLoss(nn.Module): + """SigLoss. + + This follows `AdaBins `_. + + Args: + valid_mask (bool): Whether filter invalid gt (gt > 0). Default: True. + loss_weight (float): Weight of the loss. Default: 1.0. + max_depth (int): When filtering invalid gt, set a max threshold. Default: None. + warm_up (bool): A simple warm up stage to help convergence. Default: False. + warm_iter (int): The number of warm up stage. Default: 100. + """ + + def __init__( + self, valid_mask=True, loss_weight=1.0, max_depth=None, warm_up=False, warm_iter=100, loss_name="sigloss" + ): + super(SigLoss, self).__init__() + self.valid_mask = valid_mask + self.loss_weight = loss_weight + self.max_depth = max_depth + self.loss_name = loss_name + + self.eps = 0.001 # avoid grad explode + + # HACK: a hack implementation for warmup sigloss + self.warm_up = warm_up + self.warm_iter = warm_iter + self.warm_up_counter = 0 + + def sigloss(self, input, target): + if self.valid_mask: + valid_mask = target > 0 + if self.max_depth is not None: + valid_mask = torch.logical_and(target > 0, target <= self.max_depth) + input = input[valid_mask] + target = target[valid_mask] + + if self.warm_up: + if self.warm_up_counter < self.warm_iter: + g = torch.log(input + self.eps) - torch.log(target + self.eps) + g = 0.15 * torch.pow(torch.mean(g), 2) + self.warm_up_counter += 1 + return torch.sqrt(g) + + g = torch.log(input + self.eps) - torch.log(target + self.eps) + Dg = torch.var(g) + 0.15 * torch.pow(torch.mean(g), 2) + return torch.sqrt(Dg) + + def forward(self, depth_pred, depth_gt): + """Forward function.""" + + loss_depth = self.loss_weight * self.sigloss(depth_pred, depth_gt) + return loss_depth diff --git a/dinov2/eval/depth/ops/__init__.py b/dinov2/eval/depth/ops/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..78181c29581a281b5f42cf12078636aaeb43b5a5 --- /dev/null +++ b/dinov2/eval/depth/ops/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .wrappers import resize diff --git a/dinov2/eval/depth/ops/wrappers.py b/dinov2/eval/depth/ops/wrappers.py new file mode 100644 index 0000000000000000000000000000000000000000..15880ee0cb7652d4b41c489b927bf6a156b40e5e --- /dev/null +++ b/dinov2/eval/depth/ops/wrappers.py @@ -0,0 +1,28 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import warnings + +import torch.nn.functional as F + + +def resize(input, size=None, scale_factor=None, mode="nearest", align_corners=None, warning=False): + if warning: + if size is not None and align_corners: + input_h, input_w = tuple(int(x) for x in input.shape[2:]) + output_h, output_w = tuple(int(x) for x in size) + if output_h > input_h or output_w > output_h: + if ( + (output_h > 1 and output_w > 1 and input_h > 1 and input_w > 1) + and (output_h - 1) % (input_h - 1) + and (output_w - 1) % (input_w - 1) + ): + warnings.warn( + f"When align_corners={align_corners}, " + "the output would more aligned if " + f"input size {(input_h, input_w)} is `x+1` and " + f"out size {(output_h, output_w)} is `nx+1`" + ) + return F.interpolate(input, size, scale_factor, mode, align_corners) diff --git a/dinov2/eval/knn.py b/dinov2/eval/knn.py new file mode 100644 index 0000000000000000000000000000000000000000..f3a4845da1313a6db6b8345bb9a98230fcd24acf --- /dev/null +++ b/dinov2/eval/knn.py @@ -0,0 +1,404 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import argparse +from functools import partial +import json +import logging +import os +import sys +from typing import List, Optional + +import torch +from torch.nn.functional import one_hot, softmax + +import dinov2.distributed as distributed +from dinov2.data import SamplerType, make_data_loader, make_dataset +from dinov2.data.transforms import make_classification_eval_transform +from dinov2.eval.metrics import AccuracyAveraging, build_topk_accuracy_metric +from dinov2.eval.setup import get_args_parser as get_setup_args_parser +from dinov2.eval.setup import setup_and_build_model +from dinov2.eval.utils import ModelWithNormalize, evaluate, extract_features + + +logger = logging.getLogger("dinov2") + + +def get_args_parser( + description: Optional[str] = None, + parents: Optional[List[argparse.ArgumentParser]] = None, + add_help: bool = True, +): + parents = parents or [] + setup_args_parser = get_setup_args_parser(parents=parents, add_help=False) + parents = [setup_args_parser] + parser = argparse.ArgumentParser( + description=description, + parents=parents, + add_help=add_help, + ) + parser.add_argument( + "--train-dataset", + dest="train_dataset_str", + type=str, + help="Training dataset", + ) + parser.add_argument( + "--val-dataset", + dest="val_dataset_str", + type=str, + help="Validation dataset", + ) + parser.add_argument( + "--nb_knn", + nargs="+", + type=int, + help="Number of NN to use. 20 is usually working the best.", + ) + parser.add_argument( + "--temperature", + type=float, + help="Temperature used in the voting coefficient", + ) + parser.add_argument( + "--gather-on-cpu", + action="store_true", + help="Whether to gather the train features on cpu, slower" + "but useful to avoid OOM for large datasets (e.g. ImageNet22k).", + ) + parser.add_argument( + "--batch-size", + type=int, + help="Batch size.", + ) + parser.add_argument( + "--n-per-class-list", + nargs="+", + type=int, + help="Number to take per class", + ) + parser.add_argument( + "--n-tries", + type=int, + help="Number of tries", + ) + parser.set_defaults( + train_dataset_str="ImageNet:split=TRAIN", + val_dataset_str="ImageNet:split=VAL", + nb_knn=[10, 20, 100, 200], + temperature=0.07, + batch_size=256, + n_per_class_list=[-1], + n_tries=1, + ) + return parser + + +class KnnModule(torch.nn.Module): + """ + Gets knn of test features from all processes on a chunk of the train features + + Each rank gets a chunk of the train features as well as a chunk of the test features. + In `compute_neighbors`, for each rank one after the other, its chunk of test features + is sent to all devices, partial knns are computed with each chunk of train features + then collated back on the original device. + """ + + def __init__(self, train_features, train_labels, nb_knn, T, device, num_classes=1000): + super().__init__() + + self.global_rank = distributed.get_global_rank() + self.global_size = distributed.get_global_size() + + self.device = device + self.train_features_rank_T = train_features.chunk(self.global_size)[self.global_rank].T.to(self.device) + self.candidates = train_labels.chunk(self.global_size)[self.global_rank].view(1, -1).to(self.device) + + self.nb_knn = nb_knn + self.max_k = max(self.nb_knn) + self.T = T + self.num_classes = num_classes + + def _get_knn_sims_and_labels(self, similarity, train_labels): + topk_sims, indices = similarity.topk(self.max_k, largest=True, sorted=True) + neighbors_labels = torch.gather(train_labels, 1, indices) + return topk_sims, neighbors_labels + + def _similarity_for_rank(self, features_rank, source_rank): + # Send the features from `source_rank` to all ranks + broadcast_shape = torch.tensor(features_rank.shape).to(self.device) + torch.distributed.broadcast(broadcast_shape, source_rank) + + broadcasted = features_rank + if self.global_rank != source_rank: + broadcasted = torch.zeros(*broadcast_shape, dtype=features_rank.dtype, device=self.device) + torch.distributed.broadcast(broadcasted, source_rank) + + # Compute the neighbors for `source_rank` among `train_features_rank_T` + similarity_rank = torch.mm(broadcasted, self.train_features_rank_T) + candidate_labels = self.candidates.expand(len(similarity_rank), -1) + return self._get_knn_sims_and_labels(similarity_rank, candidate_labels) + + def _gather_all_knn_for_rank(self, topk_sims, neighbors_labels, target_rank): + # Gather all neighbors for `target_rank` + topk_sims_rank = retrieved_rank = None + if self.global_rank == target_rank: + topk_sims_rank = [torch.zeros_like(topk_sims) for _ in range(self.global_size)] + retrieved_rank = [torch.zeros_like(neighbors_labels) for _ in range(self.global_size)] + + torch.distributed.gather(topk_sims, topk_sims_rank, dst=target_rank) + torch.distributed.gather(neighbors_labels, retrieved_rank, dst=target_rank) + + if self.global_rank == target_rank: + # Perform a second top-k on the k * global_size retrieved neighbors + topk_sims_rank = torch.cat(topk_sims_rank, dim=1) + retrieved_rank = torch.cat(retrieved_rank, dim=1) + results = self._get_knn_sims_and_labels(topk_sims_rank, retrieved_rank) + return results + return None + + def compute_neighbors(self, features_rank): + for rank in range(self.global_size): + topk_sims, neighbors_labels = self._similarity_for_rank(features_rank, rank) + results = self._gather_all_knn_for_rank(topk_sims, neighbors_labels, rank) + if results is not None: + topk_sims_rank, neighbors_labels_rank = results + return topk_sims_rank, neighbors_labels_rank + + def forward(self, features_rank): + """ + Compute the results on all values of `self.nb_knn` neighbors from the full `self.max_k` + """ + assert all(k <= self.max_k for k in self.nb_knn) + + topk_sims, neighbors_labels = self.compute_neighbors(features_rank) + batch_size = neighbors_labels.shape[0] + topk_sims_transform = softmax(topk_sims / self.T, 1) + matmul = torch.mul( + one_hot(neighbors_labels, num_classes=self.num_classes), + topk_sims_transform.view(batch_size, -1, 1), + ) + probas_for_k = {k: torch.sum(matmul[:, :k, :], 1) for k in self.nb_knn} + return probas_for_k + + +class DictKeysModule(torch.nn.Module): + def __init__(self, keys): + super().__init__() + self.keys = keys + + def forward(self, features_dict, targets): + for k in self.keys: + features_dict = features_dict[k] + return {"preds": features_dict, "target": targets} + + +def create_module_dict(*, module, n_per_class_list, n_tries, nb_knn, train_features, train_labels): + modules = {} + mapping = create_class_indices_mapping(train_labels) + for npc in n_per_class_list: + if npc < 0: # Only one try needed when using the full data + full_module = module( + train_features=train_features, + train_labels=train_labels, + nb_knn=nb_knn, + ) + modules["full"] = ModuleDictWithForward({"1": full_module}) + continue + all_tries = {} + for t in range(n_tries): + final_indices = filter_train(mapping, npc, seed=t) + k_list = list(set(nb_knn + [npc])) + k_list = sorted([el for el in k_list if el <= npc]) + all_tries[str(t)] = module( + train_features=train_features[final_indices], + train_labels=train_labels[final_indices], + nb_knn=k_list, + ) + modules[f"{npc} per class"] = ModuleDictWithForward(all_tries) + + return ModuleDictWithForward(modules) + + +def filter_train(mapping, n_per_class, seed): + torch.manual_seed(seed) + final_indices = [] + for k in mapping.keys(): + index = torch.randperm(len(mapping[k]))[:n_per_class] + final_indices.append(mapping[k][index]) + return torch.cat(final_indices).squeeze() + + +def create_class_indices_mapping(labels): + unique_labels, inverse = torch.unique(labels, return_inverse=True) + mapping = {unique_labels[i]: (inverse == i).nonzero() for i in range(len(unique_labels))} + return mapping + + +class ModuleDictWithForward(torch.nn.ModuleDict): + def forward(self, *args, **kwargs): + return {k: module(*args, **kwargs) for k, module in self._modules.items()} + + +def eval_knn( + model, + train_dataset, + val_dataset, + accuracy_averaging, + nb_knn, + temperature, + batch_size, + num_workers, + gather_on_cpu, + n_per_class_list=[-1], + n_tries=1, +): + model = ModelWithNormalize(model) + + logger.info("Extracting features for train set...") + train_features, train_labels = extract_features( + model, train_dataset, batch_size, num_workers, gather_on_cpu=gather_on_cpu + ) + logger.info(f"Train features created, shape {train_features.shape}.") + + val_dataloader = make_data_loader( + dataset=val_dataset, + batch_size=batch_size, + num_workers=num_workers, + sampler_type=SamplerType.DISTRIBUTED, + drop_last=False, + shuffle=False, + persistent_workers=True, + ) + num_classes = train_labels.max() + 1 + metric_collection = build_topk_accuracy_metric(accuracy_averaging, num_classes=num_classes) + + device = torch.cuda.current_device() + partial_module = partial(KnnModule, T=temperature, device=device, num_classes=num_classes) + knn_module_dict = create_module_dict( + module=partial_module, + n_per_class_list=n_per_class_list, + n_tries=n_tries, + nb_knn=nb_knn, + train_features=train_features, + train_labels=train_labels, + ) + postprocessors, metrics = {}, {} + for n_per_class, knn_module in knn_module_dict.items(): + for t, knn_try in knn_module.items(): + postprocessors = { + **postprocessors, + **{(n_per_class, t, k): DictKeysModule([n_per_class, t, k]) for k in knn_try.nb_knn}, + } + metrics = {**metrics, **{(n_per_class, t, k): metric_collection.clone() for k in knn_try.nb_knn}} + model_with_knn = torch.nn.Sequential(model, knn_module_dict) + + # ============ evaluation ... ============ + logger.info("Start the k-NN classification.") + _, results_dict = evaluate(model_with_knn, val_dataloader, postprocessors, metrics, device) + + # Averaging the results over the n tries for each value of n_per_class + for n_per_class, knn_module in knn_module_dict.items(): + first_try = list(knn_module.keys())[0] + k_list = knn_module[first_try].nb_knn + for k in k_list: + keys = results_dict[(n_per_class, first_try, k)].keys() # keys are e.g. `top-1` and `top-5` + results_dict[(n_per_class, k)] = { + key: torch.mean(torch.stack([results_dict[(n_per_class, t, k)][key] for t in knn_module.keys()])) + for key in keys + } + for t in knn_module.keys(): + del results_dict[(n_per_class, t, k)] + + return results_dict + + +def eval_knn_with_model( + model, + output_dir, + train_dataset_str="ImageNet:split=TRAIN", + val_dataset_str="ImageNet:split=VAL", + nb_knn=(10, 20, 100, 200), + temperature=0.07, + autocast_dtype=torch.float, + accuracy_averaging=AccuracyAveraging.MEAN_ACCURACY, + transform=None, + gather_on_cpu=False, + batch_size=256, + num_workers=5, + n_per_class_list=[-1], + n_tries=1, +): + transform = transform or make_classification_eval_transform() + + train_dataset = make_dataset( + dataset_str=train_dataset_str, + transform=transform, + ) + val_dataset = make_dataset( + dataset_str=val_dataset_str, + transform=transform, + ) + + with torch.cuda.amp.autocast(dtype=autocast_dtype): + results_dict_knn = eval_knn( + model=model, + train_dataset=train_dataset, + val_dataset=val_dataset, + accuracy_averaging=accuracy_averaging, + nb_knn=nb_knn, + temperature=temperature, + batch_size=batch_size, + num_workers=num_workers, + gather_on_cpu=gather_on_cpu, + n_per_class_list=n_per_class_list, + n_tries=n_tries, + ) + + results_dict = {} + if distributed.is_main_process(): + for knn_ in results_dict_knn.keys(): + top1 = results_dict_knn[knn_]["top-1"].item() * 100.0 + top5 = results_dict_knn[knn_]["top-5"].item() * 100.0 + results_dict[f"{knn_} Top 1"] = top1 + results_dict[f"{knn_} Top 5"] = top5 + logger.info(f"{knn_} classifier result: Top1: {top1:.2f} Top5: {top5:.2f}") + + metrics_file_path = os.path.join(output_dir, "results_eval_knn.json") + with open(metrics_file_path, "a") as f: + for k, v in results_dict.items(): + f.write(json.dumps({k: v}) + "\n") + + if distributed.is_enabled(): + torch.distributed.barrier() + return results_dict + + +def main(args): + model, autocast_dtype = setup_and_build_model(args) + eval_knn_with_model( + model=model, + output_dir=args.output_dir, + train_dataset_str=args.train_dataset_str, + val_dataset_str=args.val_dataset_str, + nb_knn=args.nb_knn, + temperature=args.temperature, + autocast_dtype=autocast_dtype, + accuracy_averaging=AccuracyAveraging.MEAN_ACCURACY, + transform=None, + gather_on_cpu=args.gather_on_cpu, + batch_size=args.batch_size, + num_workers=5, + n_per_class_list=args.n_per_class_list, + n_tries=args.n_tries, + ) + return 0 + + +if __name__ == "__main__": + description = "DINOv2 k-NN evaluation" + args_parser = get_args_parser(description=description) + args = args_parser.parse_args() + sys.exit(main(args)) diff --git a/dinov2/eval/linear.py b/dinov2/eval/linear.py new file mode 100644 index 0000000000000000000000000000000000000000..1bd4c5de5a041be8a188f007257d1e91b6d6921e --- /dev/null +++ b/dinov2/eval/linear.py @@ -0,0 +1,625 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import argparse +from functools import partial +import json +import logging +import os +import sys +from typing import List, Optional + +import numpy as np +import torch +import torch.nn as nn +from torch.nn.parallel import DistributedDataParallel +from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer + +from dinov2.data import SamplerType, make_data_loader, make_dataset +from dinov2.data.transforms import make_classification_eval_transform, make_classification_train_transform +import dinov2.distributed as distributed +from dinov2.eval.metrics import MetricType, build_metric +from dinov2.eval.setup import get_args_parser as get_setup_args_parser +from dinov2.eval.setup import setup_and_build_model +from dinov2.eval.utils import ModelWithIntermediateLayers, evaluate +from dinov2.logging import MetricLogger + + +logger = logging.getLogger("dinov2") + + +def get_args_parser( + description: Optional[str] = None, + parents: Optional[List[argparse.ArgumentParser]] = None, + add_help: bool = True, +): + parents = parents or [] + setup_args_parser = get_setup_args_parser(parents=parents, add_help=False) + parents = [setup_args_parser] + parser = argparse.ArgumentParser( + description=description, + parents=parents, + add_help=add_help, + ) + parser.add_argument( + "--train-dataset", + dest="train_dataset_str", + type=str, + help="Training dataset", + ) + parser.add_argument( + "--val-dataset", + dest="val_dataset_str", + type=str, + help="Validation dataset", + ) + parser.add_argument( + "--test-datasets", + dest="test_dataset_strs", + type=str, + nargs="+", + help="Test datasets, none to reuse the validation dataset", + ) + parser.add_argument( + "--epochs", + type=int, + help="Number of training epochs", + ) + parser.add_argument( + "--batch-size", + type=int, + help="Batch Size (per GPU)", + ) + parser.add_argument( + "--num-workers", + type=int, + help="Number de Workers", + ) + parser.add_argument( + "--epoch-length", + type=int, + help="Length of an epoch in number of iterations", + ) + parser.add_argument( + "--save-checkpoint-frequency", + type=int, + help="Number of epochs between two named checkpoint saves.", + ) + parser.add_argument( + "--eval-period-iterations", + type=int, + help="Number of iterations between two evaluations.", + ) + parser.add_argument( + "--learning-rates", + nargs="+", + type=float, + help="Learning rates to grid search.", + ) + parser.add_argument( + "--no-resume", + action="store_true", + help="Whether to not resume from existing checkpoints", + ) + parser.add_argument( + "--val-metric-type", + type=MetricType, + choices=list(MetricType), + help="Validation metric", + ) + parser.add_argument( + "--test-metric-types", + type=MetricType, + choices=list(MetricType), + nargs="+", + help="Evaluation metric", + ) + parser.add_argument( + "--classifier-fpath", + type=str, + help="Path to a file containing pretrained linear classifiers", + ) + parser.add_argument( + "--val-class-mapping-fpath", + type=str, + help="Path to a file containing a mapping to adjust classifier outputs", + ) + parser.add_argument( + "--test-class-mapping-fpaths", + nargs="+", + type=str, + help="Path to a file containing a mapping to adjust classifier outputs", + ) + parser.set_defaults( + train_dataset_str="ImageNet:split=TRAIN", + val_dataset_str="ImageNet:split=VAL", + test_dataset_strs=None, + epochs=10, + batch_size=128, + num_workers=8, + epoch_length=1250, + save_checkpoint_frequency=20, + eval_period_iterations=1250, + learning_rates=[1e-5, 2e-5, 5e-5, 1e-4, 2e-4, 5e-4, 1e-3, 2e-3, 5e-3, 1e-2, 2e-2, 5e-2, 0.1], + val_metric_type=MetricType.MEAN_ACCURACY, + test_metric_types=None, + classifier_fpath=None, + val_class_mapping_fpath=None, + test_class_mapping_fpaths=[None], + ) + return parser + + +def has_ddp_wrapper(m: nn.Module) -> bool: + return isinstance(m, DistributedDataParallel) + + +def remove_ddp_wrapper(m: nn.Module) -> nn.Module: + return m.module if has_ddp_wrapper(m) else m + + +def _pad_and_collate(batch): + maxlen = max(len(targets) for image, targets in batch) + padded_batch = [ + (image, np.pad(targets, (0, maxlen - len(targets)), constant_values=-1)) for image, targets in batch + ] + return torch.utils.data.default_collate(padded_batch) + + +def create_linear_input(x_tokens_list, use_n_blocks, use_avgpool): + intermediate_output = x_tokens_list[-use_n_blocks:] + output = torch.cat([class_token for _, class_token in intermediate_output], dim=-1) + if use_avgpool: + output = torch.cat( + ( + output, + torch.mean(intermediate_output[-1][0], dim=1), # patch tokens + ), + dim=-1, + ) + output = output.reshape(output.shape[0], -1) + return output.float() + + +class LinearClassifier(nn.Module): + """Linear layer to train on top of frozen features""" + + def __init__(self, out_dim, use_n_blocks, use_avgpool, num_classes=1000): + super().__init__() + self.out_dim = out_dim + self.use_n_blocks = use_n_blocks + self.use_avgpool = use_avgpool + self.num_classes = num_classes + self.linear = nn.Linear(out_dim, num_classes) + self.linear.weight.data.normal_(mean=0.0, std=0.01) + self.linear.bias.data.zero_() + + def forward(self, x_tokens_list): + output = create_linear_input(x_tokens_list, self.use_n_blocks, self.use_avgpool) + return self.linear(output) + + +class AllClassifiers(nn.Module): + def __init__(self, classifiers_dict): + super().__init__() + self.classifiers_dict = nn.ModuleDict() + self.classifiers_dict.update(classifiers_dict) + + def forward(self, inputs): + return {k: v.forward(inputs) for k, v in self.classifiers_dict.items()} + + def __len__(self): + return len(self.classifiers_dict) + + +class LinearPostprocessor(nn.Module): + def __init__(self, linear_classifier, class_mapping=None): + super().__init__() + self.linear_classifier = linear_classifier + self.register_buffer("class_mapping", None if class_mapping is None else torch.LongTensor(class_mapping)) + + def forward(self, samples, targets): + preds = self.linear_classifier(samples) + return { + "preds": preds[:, self.class_mapping] if self.class_mapping is not None else preds, + "target": targets, + } + + +def scale_lr(learning_rates, batch_size): + return learning_rates * (batch_size * distributed.get_global_size()) / 256.0 + + +def setup_linear_classifiers(sample_output, n_last_blocks_list, learning_rates, batch_size, num_classes=1000): + linear_classifiers_dict = nn.ModuleDict() + optim_param_groups = [] + for n in n_last_blocks_list: + for avgpool in [False, True]: + for _lr in learning_rates: + lr = scale_lr(_lr, batch_size) + out_dim = create_linear_input(sample_output, use_n_blocks=n, use_avgpool=avgpool).shape[1] + linear_classifier = LinearClassifier( + out_dim, use_n_blocks=n, use_avgpool=avgpool, num_classes=num_classes + ) + linear_classifier = linear_classifier.cuda() + linear_classifiers_dict[ + f"classifier_{n}_blocks_avgpool_{avgpool}_lr_{lr:.5f}".replace(".", "_") + ] = linear_classifier + optim_param_groups.append({"params": linear_classifier.parameters(), "lr": lr}) + + linear_classifiers = AllClassifiers(linear_classifiers_dict) + if distributed.is_enabled(): + linear_classifiers = nn.parallel.DistributedDataParallel(linear_classifiers) + + return linear_classifiers, optim_param_groups + + +@torch.no_grad() +def evaluate_linear_classifiers( + feature_model, + linear_classifiers, + data_loader, + metric_type, + metrics_file_path, + training_num_classes, + iteration, + prefixstring="", + class_mapping=None, + best_classifier_on_val=None, +): + logger.info("running validation !") + + num_classes = len(class_mapping) if class_mapping is not None else training_num_classes + metric = build_metric(metric_type, num_classes=num_classes) + postprocessors = {k: LinearPostprocessor(v, class_mapping) for k, v in linear_classifiers.classifiers_dict.items()} + metrics = {k: metric.clone() for k in linear_classifiers.classifiers_dict} + + _, results_dict_temp = evaluate( + feature_model, + data_loader, + postprocessors, + metrics, + torch.cuda.current_device(), + ) + + logger.info("") + results_dict = {} + max_accuracy = 0 + best_classifier = "" + for i, (classifier_string, metric) in enumerate(results_dict_temp.items()): + logger.info(f"{prefixstring} -- Classifier: {classifier_string} * {metric}") + if ( + best_classifier_on_val is None and metric["top-1"].item() > max_accuracy + ) or classifier_string == best_classifier_on_val: + max_accuracy = metric["top-1"].item() + best_classifier = classifier_string + + results_dict["best_classifier"] = {"name": best_classifier, "accuracy": max_accuracy} + + logger.info(f"best classifier: {results_dict['best_classifier']}") + + if distributed.is_main_process(): + with open(metrics_file_path, "a") as f: + f.write(f"iter: {iteration}\n") + for k, v in results_dict.items(): + f.write(json.dumps({k: v}) + "\n") + f.write("\n") + + return results_dict + + +def eval_linear( + *, + feature_model, + linear_classifiers, + train_data_loader, + val_data_loader, + metrics_file_path, + optimizer, + scheduler, + output_dir, + max_iter, + checkpoint_period, # In number of iter, creates a new file every period + running_checkpoint_period, # Period to update main checkpoint file + eval_period, + metric_type, + training_num_classes, + resume=True, + classifier_fpath=None, + val_class_mapping=None, +): + checkpointer = Checkpointer(linear_classifiers, output_dir, optimizer=optimizer, scheduler=scheduler) + start_iter = checkpointer.resume_or_load(classifier_fpath or "", resume=resume).get("iteration", -1) + 1 + + periodic_checkpointer = PeriodicCheckpointer(checkpointer, checkpoint_period, max_iter=max_iter) + iteration = start_iter + logger.info("Starting training from iteration {}".format(start_iter)) + metric_logger = MetricLogger(delimiter=" ") + header = "Training" + + for data, labels in metric_logger.log_every( + train_data_loader, + 10, + header, + max_iter, + start_iter, + ): + data = data.cuda(non_blocking=True) + labels = labels.cuda(non_blocking=True) + + features = feature_model(data) + outputs = linear_classifiers(features) + + losses = {f"loss_{k}": nn.CrossEntropyLoss()(v, labels) for k, v in outputs.items()} + loss = sum(losses.values()) + + # compute the gradients + optimizer.zero_grad() + loss.backward() + + # step + optimizer.step() + scheduler.step() + + # log + if iteration % 10 == 0: + torch.cuda.synchronize() + metric_logger.update(loss=loss.item()) + metric_logger.update(lr=optimizer.param_groups[0]["lr"]) + print("lr", optimizer.param_groups[0]["lr"]) + + if iteration - start_iter > 5: + if iteration % running_checkpoint_period == 0: + torch.cuda.synchronize() + if distributed.is_main_process(): + logger.info("Checkpointing running_checkpoint") + periodic_checkpointer.save("running_checkpoint_linear_eval", iteration=iteration) + torch.cuda.synchronize() + periodic_checkpointer.step(iteration) + + if eval_period > 0 and (iteration + 1) % eval_period == 0 and iteration != max_iter - 1: + _ = evaluate_linear_classifiers( + feature_model=feature_model, + linear_classifiers=remove_ddp_wrapper(linear_classifiers), + data_loader=val_data_loader, + metrics_file_path=metrics_file_path, + prefixstring=f"ITER: {iteration}", + metric_type=metric_type, + training_num_classes=training_num_classes, + iteration=iteration, + class_mapping=val_class_mapping, + ) + torch.cuda.synchronize() + + iteration = iteration + 1 + + val_results_dict = evaluate_linear_classifiers( + feature_model=feature_model, + linear_classifiers=remove_ddp_wrapper(linear_classifiers), + data_loader=val_data_loader, + metrics_file_path=metrics_file_path, + metric_type=metric_type, + training_num_classes=training_num_classes, + iteration=iteration, + class_mapping=val_class_mapping, + ) + return val_results_dict, feature_model, linear_classifiers, iteration + + +def make_eval_data_loader(test_dataset_str, batch_size, num_workers, metric_type): + test_dataset = make_dataset( + dataset_str=test_dataset_str, + transform=make_classification_eval_transform(), + ) + test_data_loader = make_data_loader( + dataset=test_dataset, + batch_size=batch_size, + num_workers=num_workers, + sampler_type=SamplerType.DISTRIBUTED, + drop_last=False, + shuffle=False, + persistent_workers=False, + collate_fn=_pad_and_collate if metric_type == MetricType.IMAGENET_REAL_ACCURACY else None, + ) + return test_data_loader + + +def test_on_datasets( + feature_model, + linear_classifiers, + test_dataset_strs, + batch_size, + num_workers, + test_metric_types, + metrics_file_path, + training_num_classes, + iteration, + best_classifier_on_val, + prefixstring="", + test_class_mappings=[None], +): + results_dict = {} + for test_dataset_str, class_mapping, metric_type in zip(test_dataset_strs, test_class_mappings, test_metric_types): + logger.info(f"Testing on {test_dataset_str}") + test_data_loader = make_eval_data_loader(test_dataset_str, batch_size, num_workers, metric_type) + dataset_results_dict = evaluate_linear_classifiers( + feature_model, + remove_ddp_wrapper(linear_classifiers), + test_data_loader, + metric_type, + metrics_file_path, + training_num_classes, + iteration, + prefixstring="", + class_mapping=class_mapping, + best_classifier_on_val=best_classifier_on_val, + ) + results_dict[f"{test_dataset_str}_accuracy"] = 100.0 * dataset_results_dict["best_classifier"]["accuracy"] + return results_dict + + +def run_eval_linear( + model, + output_dir, + train_dataset_str, + val_dataset_str, + batch_size, + epochs, + epoch_length, + num_workers, + save_checkpoint_frequency, + eval_period_iterations, + learning_rates, + autocast_dtype, + test_dataset_strs=None, + resume=True, + classifier_fpath=None, + val_class_mapping_fpath=None, + test_class_mapping_fpaths=[None], + val_metric_type=MetricType.MEAN_ACCURACY, + test_metric_types=None, +): + seed = 0 + + if test_dataset_strs is None: + test_dataset_strs = [val_dataset_str] + if test_metric_types is None: + test_metric_types = [val_metric_type] * len(test_dataset_strs) + else: + assert len(test_metric_types) == len(test_dataset_strs) + assert len(test_dataset_strs) == len(test_class_mapping_fpaths) + + train_transform = make_classification_train_transform() + train_dataset = make_dataset( + dataset_str=train_dataset_str, + transform=train_transform, + ) + training_num_classes = len(torch.unique(torch.Tensor(train_dataset.get_targets().astype(int)))) + sampler_type = SamplerType.SHARDED_INFINITE + # sampler_type = SamplerType.INFINITE + + n_last_blocks_list = [1, 4] + n_last_blocks = max(n_last_blocks_list) + autocast_ctx = partial(torch.cuda.amp.autocast, enabled=True, dtype=autocast_dtype) + feature_model = ModelWithIntermediateLayers(model, n_last_blocks, autocast_ctx) + sample_output = feature_model(train_dataset[0][0].unsqueeze(0).cuda()) + + linear_classifiers, optim_param_groups = setup_linear_classifiers( + sample_output, + n_last_blocks_list, + learning_rates, + batch_size, + training_num_classes, + ) + + optimizer = torch.optim.SGD(optim_param_groups, momentum=0.9, weight_decay=0) + max_iter = epochs * epoch_length + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, max_iter, eta_min=0) + checkpointer = Checkpointer(linear_classifiers, output_dir, optimizer=optimizer, scheduler=scheduler) + start_iter = checkpointer.resume_or_load(classifier_fpath or "", resume=resume).get("iteration", -1) + 1 + train_data_loader = make_data_loader( + dataset=train_dataset, + batch_size=batch_size, + num_workers=num_workers, + shuffle=True, + seed=seed, + sampler_type=sampler_type, + sampler_advance=start_iter, + drop_last=True, + persistent_workers=True, + ) + val_data_loader = make_eval_data_loader(val_dataset_str, batch_size, num_workers, val_metric_type) + + checkpoint_period = save_checkpoint_frequency * epoch_length + + if val_class_mapping_fpath is not None: + logger.info(f"Using class mapping from {val_class_mapping_fpath}") + val_class_mapping = np.load(val_class_mapping_fpath) + else: + val_class_mapping = None + + test_class_mappings = [] + for class_mapping_fpath in test_class_mapping_fpaths: + if class_mapping_fpath is not None and class_mapping_fpath != "None": + logger.info(f"Using class mapping from {class_mapping_fpath}") + class_mapping = np.load(class_mapping_fpath) + else: + class_mapping = None + test_class_mappings.append(class_mapping) + + metrics_file_path = os.path.join(output_dir, "results_eval_linear.json") + val_results_dict, feature_model, linear_classifiers, iteration = eval_linear( + feature_model=feature_model, + linear_classifiers=linear_classifiers, + train_data_loader=train_data_loader, + val_data_loader=val_data_loader, + metrics_file_path=metrics_file_path, + optimizer=optimizer, + scheduler=scheduler, + output_dir=output_dir, + max_iter=max_iter, + checkpoint_period=checkpoint_period, + running_checkpoint_period=epoch_length, + eval_period=eval_period_iterations, + metric_type=val_metric_type, + training_num_classes=training_num_classes, + resume=resume, + val_class_mapping=val_class_mapping, + classifier_fpath=classifier_fpath, + ) + results_dict = {} + if len(test_dataset_strs) > 1 or test_dataset_strs[0] != val_dataset_str: + results_dict = test_on_datasets( + feature_model, + linear_classifiers, + test_dataset_strs, + batch_size, + 0, # num_workers, + test_metric_types, + metrics_file_path, + training_num_classes, + iteration, + val_results_dict["best_classifier"]["name"], + prefixstring="", + test_class_mappings=test_class_mappings, + ) + results_dict["best_classifier"] = val_results_dict["best_classifier"]["name"] + results_dict[f"{val_dataset_str}_accuracy"] = 100.0 * val_results_dict["best_classifier"]["accuracy"] + logger.info("Test Results Dict " + str(results_dict)) + + return results_dict + + +def main(args): + model, autocast_dtype = setup_and_build_model(args) + run_eval_linear( + model=model, + output_dir=args.output_dir, + train_dataset_str=args.train_dataset_str, + val_dataset_str=args.val_dataset_str, + test_dataset_strs=args.test_dataset_strs, + batch_size=args.batch_size, + epochs=args.epochs, + epoch_length=args.epoch_length, + num_workers=args.num_workers, + save_checkpoint_frequency=args.save_checkpoint_frequency, + eval_period_iterations=args.eval_period_iterations, + learning_rates=args.learning_rates, + autocast_dtype=autocast_dtype, + resume=not args.no_resume, + classifier_fpath=args.classifier_fpath, + val_metric_type=args.val_metric_type, + test_metric_types=args.test_metric_types, + val_class_mapping_fpath=args.val_class_mapping_fpath, + test_class_mapping_fpaths=args.test_class_mapping_fpaths, + ) + return 0 + + +if __name__ == "__main__": + description = "DINOv2 linear evaluation" + args_parser = get_args_parser(description=description) + args = args_parser.parse_args() + sys.exit(main(args)) diff --git a/dinov2/eval/log_regression.py b/dinov2/eval/log_regression.py new file mode 100644 index 0000000000000000000000000000000000000000..5f36ec134e0ce25697428a0b3f21cdc2f0145645 --- /dev/null +++ b/dinov2/eval/log_regression.py @@ -0,0 +1,444 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import argparse +import gc +import logging +import sys +import time +from typing import List, Optional + +from cuml.linear_model import LogisticRegression +import torch +import torch.backends.cudnn as cudnn +import torch.distributed +from torch import nn +from torch.utils.data import TensorDataset +from torchmetrics import MetricTracker + +from dinov2.data import make_dataset +from dinov2.data.transforms import make_classification_eval_transform +from dinov2.distributed import get_global_rank, get_global_size +from dinov2.eval.metrics import MetricType, build_metric +from dinov2.eval.setup import get_args_parser as get_setup_args_parser +from dinov2.eval.setup import setup_and_build_model +from dinov2.eval.utils import evaluate, extract_features +from dinov2.utils.dtype import as_torch_dtype + + +logger = logging.getLogger("dinov2") + +DEFAULT_MAX_ITER = 1_000 +C_POWER_RANGE = torch.linspace(-6, 5, 45) +_CPU_DEVICE = torch.device("cpu") + + +def get_args_parser( + description: Optional[str] = None, + parents: Optional[List[argparse.ArgumentParser]] = None, + add_help: bool = True, +): + parents = parents or [] + setup_args_parser = get_setup_args_parser(parents=parents, add_help=False) + parents = [setup_args_parser] + parser = argparse.ArgumentParser( + description=description, + parents=parents, + add_help=add_help, + ) + parser.add_argument( + "--train-dataset", + dest="train_dataset_str", + type=str, + help="Training dataset", + ) + parser.add_argument( + "--val-dataset", + dest="val_dataset_str", + type=str, + help="Validation dataset", + ) + parser.add_argument( + "--finetune-dataset-str", + dest="finetune_dataset_str", + type=str, + help="Fine-tuning dataset", + ) + parser.add_argument( + "--finetune-on-val", + action="store_true", + help="If there is no finetune dataset, whether to choose the " + "hyperparameters on the val set instead of 10%% of the train dataset", + ) + parser.add_argument( + "--metric-type", + type=MetricType, + choices=list(MetricType), + help="Metric type", + ) + parser.add_argument( + "--train-features-device", + type=str, + help="Device to gather train features (cpu, cuda, cuda:0, etc.), default: %(default)s", + ) + parser.add_argument( + "--train-dtype", + type=str, + help="Data type to convert the train features to (default: %(default)s)", + ) + parser.add_argument( + "--max-train-iters", + type=int, + help="Maximum number of train iterations (default: %(default)s)", + ) + parser.set_defaults( + train_dataset_str="ImageNet:split=TRAIN", + val_dataset_str="ImageNet:split=VAL", + finetune_dataset_str=None, + metric_type=MetricType.MEAN_ACCURACY, + train_features_device="cpu", + train_dtype="float64", + max_train_iters=DEFAULT_MAX_ITER, + finetune_on_val=False, + ) + return parser + + +class LogRegModule(nn.Module): + def __init__( + self, + C, + max_iter=DEFAULT_MAX_ITER, + dtype=torch.float64, + device=_CPU_DEVICE, + ): + super().__init__() + self.dtype = dtype + self.device = device + self.estimator = LogisticRegression( + penalty="l2", + C=C, + max_iter=max_iter, + output_type="numpy", + tol=1e-12, + linesearch_max_iter=50, + ) + + def forward(self, samples, targets): + samples_device = samples.device + samples = samples.to(dtype=self.dtype, device=self.device) + if self.device == _CPU_DEVICE: + samples = samples.numpy() + probas = self.estimator.predict_proba(samples) + return {"preds": torch.from_numpy(probas).to(samples_device), "target": targets} + + def fit(self, train_features, train_labels): + train_features = train_features.to(dtype=self.dtype, device=self.device) + train_labels = train_labels.to(dtype=self.dtype, device=self.device) + if self.device == _CPU_DEVICE: + # both cuML and sklearn only work with numpy arrays on CPU + train_features = train_features.numpy() + train_labels = train_labels.numpy() + self.estimator.fit(train_features, train_labels) + + +def evaluate_model(*, logreg_model, logreg_metric, test_data_loader, device): + postprocessors = {"metrics": logreg_model} + metrics = {"metrics": logreg_metric} + return evaluate(nn.Identity(), test_data_loader, postprocessors, metrics, device) + + +def train_for_C(*, C, max_iter, train_features, train_labels, dtype=torch.float64, device=_CPU_DEVICE): + logreg_model = LogRegModule(C, max_iter=max_iter, dtype=dtype, device=device) + logreg_model.fit(train_features, train_labels) + return logreg_model + + +def train_and_evaluate( + *, + C, + max_iter, + train_features, + train_labels, + logreg_metric, + test_data_loader, + train_dtype=torch.float64, + train_features_device, + eval_device, +): + logreg_model = train_for_C( + C=C, + max_iter=max_iter, + train_features=train_features, + train_labels=train_labels, + dtype=train_dtype, + device=train_features_device, + ) + return evaluate_model( + logreg_model=logreg_model, + logreg_metric=logreg_metric, + test_data_loader=test_data_loader, + device=eval_device, + ) + + +def sweep_C_values( + *, + train_features, + train_labels, + test_data_loader, + metric_type, + num_classes, + train_dtype=torch.float64, + train_features_device=_CPU_DEVICE, + max_train_iters=DEFAULT_MAX_ITER, +): + if metric_type == MetricType.PER_CLASS_ACCURACY: + # If we want to output per-class accuracy, we select the hyperparameters with mean per class + metric_type = MetricType.MEAN_PER_CLASS_ACCURACY + logreg_metric = build_metric(metric_type, num_classes=num_classes) + metric_tracker = MetricTracker(logreg_metric, maximize=True) + ALL_C = 10**C_POWER_RANGE + logreg_models = {} + + train_features = train_features.to(dtype=train_dtype, device=train_features_device) + train_labels = train_labels.to(device=train_features_device) + + for i in range(get_global_rank(), len(ALL_C), get_global_size()): + C = ALL_C[i].item() + logger.info( + f"Training for C = {C:.5f}, dtype={train_dtype}, " + f"features: {train_features.shape}, {train_features.dtype}, " + f"labels: {train_labels.shape}, {train_labels.dtype}" + ) + logreg_models[C] = train_for_C( + C=C, + max_iter=max_train_iters, + train_features=train_features, + train_labels=train_labels, + dtype=train_dtype, + device=train_features_device, + ) + + gather_list = [None for _ in range(get_global_size())] + torch.distributed.all_gather_object(gather_list, logreg_models) + + logreg_models_gathered = {} + for logreg_dict in gather_list: + logreg_models_gathered.update(logreg_dict) + + for i in range(len(ALL_C)): + metric_tracker.increment() + C = ALL_C[i].item() + evals = evaluate_model( + logreg_model=logreg_models_gathered[C], + logreg_metric=metric_tracker, + test_data_loader=test_data_loader, + device=torch.cuda.current_device(), + ) + logger.info(f"Trained for C = {C:.5f}, accuracies = {evals}") + + best_stats, which_epoch = metric_tracker.best_metric(return_step=True) + best_stats_100 = {k: 100.0 * v for k, v in best_stats.items()} + if which_epoch["top-1"] == i: + best_C = C + logger.info(f"Sweep best {best_stats_100}, best C = {best_C:.6f}") + + return best_stats, best_C + + +def eval_log_regression( + *, + model, + train_dataset, + val_dataset, + finetune_dataset, + metric_type, + batch_size, + num_workers, + finetune_on_val=False, + train_dtype=torch.float64, + train_features_device=_CPU_DEVICE, + max_train_iters=DEFAULT_MAX_ITER, +): + """ + Implements the "standard" process for log regression evaluation: + The value of C is chosen by training on train_dataset and evaluating on + finetune_dataset. Then, the final model is trained on a concatenation of + train_dataset and finetune_dataset, and is evaluated on val_dataset. + If there is no finetune_dataset, the value of C is the one that yields + the best results on a random 10% subset of the train dataset + """ + + start = time.time() + + train_features, train_labels = extract_features( + model, train_dataset, batch_size, num_workers, gather_on_cpu=(train_features_device == _CPU_DEVICE) + ) + val_features, val_labels = extract_features( + model, val_dataset, batch_size, num_workers, gather_on_cpu=(train_features_device == _CPU_DEVICE) + ) + val_data_loader = torch.utils.data.DataLoader( + TensorDataset(val_features, val_labels), + batch_size=batch_size, + drop_last=False, + num_workers=0, + persistent_workers=False, + ) + + if finetune_dataset is None and finetune_on_val: + logger.info("Choosing hyperparameters on the val dataset") + finetune_features, finetune_labels = val_features, val_labels + elif finetune_dataset is None and not finetune_on_val: + logger.info("Choosing hyperparameters on 10% of the train dataset") + torch.manual_seed(0) + indices = torch.randperm(len(train_features), device=train_features.device) + finetune_index = indices[: len(train_features) // 10] + train_index = indices[len(train_features) // 10 :] + finetune_features, finetune_labels = train_features[finetune_index], train_labels[finetune_index] + train_features, train_labels = train_features[train_index], train_labels[train_index] + else: + logger.info("Choosing hyperparameters on the finetune dataset") + finetune_features, finetune_labels = extract_features( + model, finetune_dataset, batch_size, num_workers, gather_on_cpu=(train_features_device == _CPU_DEVICE) + ) + # release the model - free GPU memory + del model + gc.collect() + torch.cuda.empty_cache() + finetune_data_loader = torch.utils.data.DataLoader( + TensorDataset(finetune_features, finetune_labels), + batch_size=batch_size, + drop_last=False, + ) + + if len(train_labels.shape) > 1: + num_classes = train_labels.shape[1] + else: + num_classes = train_labels.max() + 1 + + logger.info("Using cuML for logistic regression") + + best_stats, best_C = sweep_C_values( + train_features=train_features, + train_labels=train_labels, + test_data_loader=finetune_data_loader, + metric_type=metric_type, + num_classes=num_classes, + train_dtype=train_dtype, + train_features_device=train_features_device, + max_train_iters=max_train_iters, + ) + + if not finetune_on_val: + logger.info("Best parameter found, concatenating features") + train_features = torch.cat((train_features, finetune_features)) + train_labels = torch.cat((train_labels, finetune_labels)) + + logger.info("Training final model") + logreg_metric = build_metric(metric_type, num_classes=num_classes) + evals = train_and_evaluate( + C=best_C, + max_iter=max_train_iters, + train_features=train_features, + train_labels=train_labels, + logreg_metric=logreg_metric.clone(), + test_data_loader=val_data_loader, + eval_device=torch.cuda.current_device(), + train_dtype=train_dtype, + train_features_device=train_features_device, + ) + + best_stats = evals[1]["metrics"] + + best_stats["best_C"] = best_C + + logger.info(f"Log regression evaluation done in {int(time.time() - start)}s") + return best_stats + + +def eval_log_regression_with_model( + model, + train_dataset_str="ImageNet:split=TRAIN", + val_dataset_str="ImageNet:split=VAL", + finetune_dataset_str=None, + autocast_dtype=torch.float, + finetune_on_val=False, + metric_type=MetricType.MEAN_ACCURACY, + train_dtype=torch.float64, + train_features_device=_CPU_DEVICE, + max_train_iters=DEFAULT_MAX_ITER, +): + cudnn.benchmark = True + + transform = make_classification_eval_transform(resize_size=224) + target_transform = None + + train_dataset = make_dataset(dataset_str=train_dataset_str, transform=transform, target_transform=target_transform) + val_dataset = make_dataset(dataset_str=val_dataset_str, transform=transform, target_transform=target_transform) + if finetune_dataset_str is not None: + finetune_dataset = make_dataset( + dataset_str=finetune_dataset_str, transform=transform, target_transform=target_transform + ) + else: + finetune_dataset = None + + with torch.cuda.amp.autocast(dtype=autocast_dtype): + results_dict_logreg = eval_log_regression( + model=model, + train_dataset=train_dataset, + val_dataset=val_dataset, + finetune_dataset=finetune_dataset, + metric_type=metric_type, + batch_size=256, + num_workers=0, # 5, + finetune_on_val=finetune_on_val, + train_dtype=train_dtype, + train_features_device=train_features_device, + max_train_iters=max_train_iters, + ) + + results_dict = { + "top-1": results_dict_logreg["top-1"].cpu().numpy() * 100.0, + "top-5": results_dict_logreg.get("top-5", torch.tensor(0.0)).cpu().numpy() * 100.0, + "best_C": results_dict_logreg["best_C"], + } + logger.info( + "\n".join( + [ + "Training of the supervised logistic regression on frozen features completed.\n" + "Top-1 test accuracy: {acc:.1f}".format(acc=results_dict["top-1"]), + "Top-5 test accuracy: {acc:.1f}".format(acc=results_dict["top-5"]), + "obtained for C = {c:.6f}".format(c=results_dict["best_C"]), + ] + ) + ) + + torch.distributed.barrier() + return results_dict + + +def main(args): + model, autocast_dtype = setup_and_build_model(args) + eval_log_regression_with_model( + model=model, + train_dataset_str=args.train_dataset_str, + val_dataset_str=args.val_dataset_str, + finetune_dataset_str=args.finetune_dataset_str, + autocast_dtype=autocast_dtype, + finetune_on_val=args.finetune_on_val, + metric_type=args.metric_type, + train_dtype=as_torch_dtype(args.train_dtype), + train_features_device=torch.device(args.train_features_device), + max_train_iters=args.max_train_iters, + ) + return 0 + + +if __name__ == "__main__": + description = "DINOv2 logistic regression evaluation" + args_parser = get_args_parser(description=description) + args = args_parser.parse_args() + sys.exit(main(args)) diff --git a/dinov2/eval/metrics.py b/dinov2/eval/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..52be81a859dddde82da93c3657c35352d2bb0a48 --- /dev/null +++ b/dinov2/eval/metrics.py @@ -0,0 +1,113 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from enum import Enum +import logging +from typing import Any, Dict, Optional + +import torch +from torch import Tensor +from torchmetrics import Metric, MetricCollection +from torchmetrics.classification import MulticlassAccuracy +from torchmetrics.utilities.data import dim_zero_cat, select_topk + + +logger = logging.getLogger("dinov2") + + +class MetricType(Enum): + MEAN_ACCURACY = "mean_accuracy" + MEAN_PER_CLASS_ACCURACY = "mean_per_class_accuracy" + PER_CLASS_ACCURACY = "per_class_accuracy" + IMAGENET_REAL_ACCURACY = "imagenet_real_accuracy" + + @property + def accuracy_averaging(self): + return getattr(AccuracyAveraging, self.name, None) + + def __str__(self): + return self.value + + +class AccuracyAveraging(Enum): + MEAN_ACCURACY = "micro" + MEAN_PER_CLASS_ACCURACY = "macro" + PER_CLASS_ACCURACY = "none" + + def __str__(self): + return self.value + + +def build_metric(metric_type: MetricType, *, num_classes: int, ks: Optional[tuple] = None): + if metric_type.accuracy_averaging is not None: + return build_topk_accuracy_metric( + average_type=metric_type.accuracy_averaging, + num_classes=num_classes, + ks=(1, 5) if ks is None else ks, + ) + elif metric_type == MetricType.IMAGENET_REAL_ACCURACY: + return build_topk_imagenet_real_accuracy_metric( + num_classes=num_classes, + ks=(1, 5) if ks is None else ks, + ) + + raise ValueError(f"Unknown metric type {metric_type}") + + +def build_topk_accuracy_metric(average_type: AccuracyAveraging, num_classes: int, ks: tuple = (1, 5)): + metrics: Dict[str, Metric] = { + f"top-{k}": MulticlassAccuracy(top_k=k, num_classes=int(num_classes), average=average_type.value) for k in ks + } + return MetricCollection(metrics) + + +def build_topk_imagenet_real_accuracy_metric(num_classes: int, ks: tuple = (1, 5)): + metrics: Dict[str, Metric] = {f"top-{k}": ImageNetReaLAccuracy(top_k=k, num_classes=int(num_classes)) for k in ks} + return MetricCollection(metrics) + + +class ImageNetReaLAccuracy(Metric): + is_differentiable: bool = False + higher_is_better: Optional[bool] = None + full_state_update: bool = False + + def __init__( + self, + num_classes: int, + top_k: int = 1, + **kwargs: Any, + ) -> None: + super().__init__(**kwargs) + self.num_classes = num_classes + self.top_k = top_k + self.add_state("tp", [], dist_reduce_fx="cat") + + def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore + # preds [B, D] + # target [B, A] + # preds_oh [B, D] with 0 and 1 + # select top K highest probabilities, use one hot representation + preds_oh = select_topk(preds, self.top_k) + # target_oh [B, D + 1] with 0 and 1 + target_oh = torch.zeros((preds_oh.shape[0], preds_oh.shape[1] + 1), device=target.device, dtype=torch.int32) + target = target.long() + # for undefined targets (-1) use a fake value `num_classes` + target[target == -1] = self.num_classes + # fill targets, use one hot representation + target_oh.scatter_(1, target, 1) + # target_oh [B, D] (remove the fake target at index `num_classes`) + target_oh = target_oh[:, :-1] + # tp [B] with 0 and 1 + tp = (preds_oh * target_oh == 1).sum(dim=1) + # at least one match between prediction and target + tp.clip_(max=1) + # ignore instances where no targets are defined + mask = target_oh.sum(dim=1) > 0 + tp = tp[mask] + self.tp.append(tp) # type: ignore + + def compute(self) -> Tensor: + tp = dim_zero_cat(self.tp) # type: ignore + return tp.float().mean() diff --git a/dinov2/eval/segmentation/__init__.py b/dinov2/eval/segmentation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b88da6bf80be92af00b72dfdb0a806fa64a7a2d9 --- /dev/null +++ b/dinov2/eval/segmentation/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. diff --git a/dinov2/eval/segmentation/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/segmentation/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..98fa3d0e5d0b7140c7411bde65fc4497132a480f Binary files /dev/null and b/dinov2/eval/segmentation/__pycache__/__init__.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation/hooks/__init__.py b/dinov2/eval/segmentation/hooks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..738cc2d2069521ea0353acd0cb0a03e3ddf1fa51 --- /dev/null +++ b/dinov2/eval/segmentation/hooks/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .optimizer import DistOptimizerHook diff --git a/dinov2/eval/segmentation/hooks/optimizer.py b/dinov2/eval/segmentation/hooks/optimizer.py new file mode 100644 index 0000000000000000000000000000000000000000..f593f26a84475bbf7ebda9607a4d10914b13a443 --- /dev/null +++ b/dinov2/eval/segmentation/hooks/optimizer.py @@ -0,0 +1,40 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +try: + import apex +except ImportError: + print("apex is not installed") + +from mmcv.runner import OptimizerHook, HOOKS + + +@HOOKS.register_module() +class DistOptimizerHook(OptimizerHook): + """Optimizer hook for distributed training.""" + + def __init__(self, update_interval=1, grad_clip=None, coalesce=True, bucket_size_mb=-1, use_fp16=False): + self.grad_clip = grad_clip + self.coalesce = coalesce + self.bucket_size_mb = bucket_size_mb + self.update_interval = update_interval + self.use_fp16 = use_fp16 + + def before_run(self, runner): + runner.optimizer.zero_grad() + + def after_train_iter(self, runner): + runner.outputs["loss"] /= self.update_interval + if self.use_fp16: + # runner.outputs['loss'].backward() + with apex.amp.scale_loss(runner.outputs["loss"], runner.optimizer) as scaled_loss: + scaled_loss.backward() + else: + runner.outputs["loss"].backward() + if self.every_n_iters(runner, self.update_interval): + if self.grad_clip is not None: + self.clip_grads(runner.model.parameters()) + runner.optimizer.step() + runner.optimizer.zero_grad() diff --git a/dinov2/eval/segmentation/models/__init__.py b/dinov2/eval/segmentation/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..88e4563d4c162d67e7900955a06bd9248d4c9a48 --- /dev/null +++ b/dinov2/eval/segmentation/models/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .backbones import * # noqa: F403 +from .decode_heads import * # noqa: F403 diff --git a/dinov2/eval/segmentation/models/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/segmentation/models/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..aaa3ea8d8e9cd49bc1a50d3f35c27a29c1334ecc Binary files /dev/null and b/dinov2/eval/segmentation/models/__pycache__/__init__.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation/models/backbones/__init__.py b/dinov2/eval/segmentation/models/backbones/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..520d75bc6e064b9d64487293604ac1bda6e2b6f7 --- /dev/null +++ b/dinov2/eval/segmentation/models/backbones/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .vision_transformer import DinoVisionTransformer diff --git a/dinov2/eval/segmentation/models/backbones/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/segmentation/models/backbones/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d699a5f6911dc55b2825d995c9046437060f3c8b Binary files /dev/null and b/dinov2/eval/segmentation/models/backbones/__pycache__/__init__.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation/models/backbones/__pycache__/vision_transformer.cpython-310.pyc b/dinov2/eval/segmentation/models/backbones/__pycache__/vision_transformer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9dc4e1670173fa0d64e93de386467f2ee86b1c55 Binary files /dev/null and b/dinov2/eval/segmentation/models/backbones/__pycache__/vision_transformer.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation/models/backbones/vision_transformer.py b/dinov2/eval/segmentation/models/backbones/vision_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..c3e9753ae92a36be52f100e3004cbeeff777d14a --- /dev/null +++ b/dinov2/eval/segmentation/models/backbones/vision_transformer.py @@ -0,0 +1,19 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from mmcv.runner import BaseModule +from mmseg.models.builder import BACKBONES + + +@BACKBONES.register_module() +class DinoVisionTransformer(BaseModule): + """Vision Transformer.""" + + def __init__( + self, + *args, + **kwargs, + ): + super().__init__() diff --git a/dinov2/eval/segmentation/models/decode_heads/__init__.py b/dinov2/eval/segmentation/models/decode_heads/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c55317875262dadf8970c2b3882f016b8d4731ac --- /dev/null +++ b/dinov2/eval/segmentation/models/decode_heads/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .linear_head import BNHead diff --git a/dinov2/eval/segmentation/models/decode_heads/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/segmentation/models/decode_heads/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..517290f1826fe94275034efaea1c7f1d132a10ee 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2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + +from mmseg.models.builder import HEADS +from mmseg.models.decode_heads.decode_head import BaseDecodeHead +from mmseg.ops import resize + + +@HEADS.register_module() +class BNHead(BaseDecodeHead): + """Just a batchnorm.""" + + def __init__(self, resize_factors=None, **kwargs): + super().__init__(**kwargs) + assert self.in_channels == self.channels + self.bn = nn.SyncBatchNorm(self.in_channels) + self.resize_factors = resize_factors + + def _forward_feature(self, inputs): + """Forward function for feature maps before classifying each pixel with + ``self.cls_seg`` fc. + + Args: + inputs (list[Tensor]): List of multi-level img features. + + Returns: + feats (Tensor): A tensor of shape (batch_size, self.channels, + H, W) which is feature map for last layer of decoder head. + """ + # print("inputs", [i.shape for i in inputs]) + x = self._transform_inputs(inputs) + # print("x", x.shape) + feats = self.bn(x) + # print("feats", feats.shape) + return feats + + def _transform_inputs(self, inputs): + """Transform inputs for decoder. + Args: + inputs (list[Tensor]): List of multi-level img features. + Returns: + Tensor: The transformed inputs + """ + + if self.input_transform == "resize_concat": + # accept lists (for cls token) + input_list = [] + for x in inputs: + if isinstance(x, list): + input_list.extend(x) + else: + input_list.append(x) + inputs = input_list + # an image descriptor can be a local descriptor with resolution 1x1 + for i, x in enumerate(inputs): + if len(x.shape) == 2: + inputs[i] = x[:, :, None, None] + # select indices + inputs = [inputs[i] for i in self.in_index] + # Resizing shenanigans + # print("before", *(x.shape for x in inputs)) + if self.resize_factors is not None: + assert len(self.resize_factors) == len(inputs), (len(self.resize_factors), len(inputs)) + inputs = [ + resize(input=x, scale_factor=f, mode="bilinear" if f >= 1 else "area") + for x, f in zip(inputs, self.resize_factors) + ] + # print("after", *(x.shape for x in inputs)) + upsampled_inputs = [ + resize(input=x, size=inputs[0].shape[2:], mode="bilinear", align_corners=self.align_corners) + for x in inputs + ] + inputs = torch.cat(upsampled_inputs, dim=1) + elif self.input_transform == "multiple_select": + inputs = [inputs[i] for i in self.in_index] + else: + inputs = inputs[self.in_index] + + return inputs + + def forward(self, inputs): + """Forward function.""" + output = self._forward_feature(inputs) + output = self.cls_seg(output) + return output diff --git a/dinov2/eval/segmentation/utils/__init__.py b/dinov2/eval/segmentation/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b88da6bf80be92af00b72dfdb0a806fa64a7a2d9 --- /dev/null +++ b/dinov2/eval/segmentation/utils/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. diff --git a/dinov2/eval/segmentation/utils/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/segmentation/utils/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a4b494e003d0459f922c237038c5b3eed0db1cd0 Binary files /dev/null and b/dinov2/eval/segmentation/utils/__pycache__/__init__.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation/utils/__pycache__/colormaps.cpython-310.pyc b/dinov2/eval/segmentation/utils/__pycache__/colormaps.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1533629d1ea58d2dd73da786d57ba2c21c5c3a2c Binary files /dev/null and b/dinov2/eval/segmentation/utils/__pycache__/colormaps.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation/utils/colormaps.py b/dinov2/eval/segmentation/utils/colormaps.py new file mode 100644 index 0000000000000000000000000000000000000000..e6ef604b2c75792e95e438abfd51ab03d40de340 --- /dev/null +++ b/dinov2/eval/segmentation/utils/colormaps.py @@ -0,0 +1,362 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +ADE20K_COLORMAP = [ + (0, 0, 0), + (120, 120, 120), + (180, 120, 120), + (6, 230, 230), + (80, 50, 50), + (4, 200, 3), + (120, 120, 80), + (140, 140, 140), + (204, 5, 255), + (230, 230, 230), + (4, 250, 7), + (224, 5, 255), + (235, 255, 7), + (150, 5, 61), + (120, 120, 70), + (8, 255, 51), + (255, 6, 82), + (143, 255, 140), + (204, 255, 4), + (255, 51, 7), + (204, 70, 3), + (0, 102, 200), + (61, 230, 250), + (255, 6, 51), + (11, 102, 255), + (255, 7, 71), + (255, 9, 224), + (9, 7, 230), + (220, 220, 220), + (255, 9, 92), + (112, 9, 255), + (8, 255, 214), + (7, 255, 224), + (255, 184, 6), + (10, 255, 71), + (255, 41, 10), + (7, 255, 255), + (224, 255, 8), + (102, 8, 255), + (255, 61, 6), + (255, 194, 7), + (255, 122, 8), + (0, 255, 20), + (255, 8, 41), + (255, 5, 153), + (6, 51, 255), + (235, 12, 255), + (160, 150, 20), + (0, 163, 255), + (140, 140, 140), + (250, 10, 15), + (20, 255, 0), + (31, 255, 0), + (255, 31, 0), + (255, 224, 0), + (153, 255, 0), + (0, 0, 255), + (255, 71, 0), + (0, 235, 255), + (0, 173, 255), + (31, 0, 255), + (11, 200, 200), + (255, 82, 0), + (0, 255, 245), + (0, 61, 255), + (0, 255, 112), + (0, 255, 133), + (255, 0, 0), + (255, 163, 0), + (255, 102, 0), + (194, 255, 0), + (0, 143, 255), + (51, 255, 0), + (0, 82, 255), + (0, 255, 41), + (0, 255, 173), + (10, 0, 255), + (173, 255, 0), + (0, 255, 153), + (255, 92, 0), + (255, 0, 255), + (255, 0, 245), + (255, 0, 102), + (255, 173, 0), + (255, 0, 20), + (255, 184, 184), + (0, 31, 255), + (0, 255, 61), + (0, 71, 255), + (255, 0, 204), + (0, 255, 194), + (0, 255, 82), + (0, 10, 255), + (0, 112, 255), + (51, 0, 255), + (0, 194, 255), + (0, 122, 255), + (0, 255, 163), + (255, 153, 0), + (0, 255, 10), + (255, 112, 0), + (143, 255, 0), + (82, 0, 255), + (163, 255, 0), + (255, 235, 0), + (8, 184, 170), + (133, 0, 255), + (0, 255, 92), + (184, 0, 255), + (255, 0, 31), + (0, 184, 255), + (0, 214, 255), + (255, 0, 112), + (92, 255, 0), + (0, 224, 255), + (112, 224, 255), + (70, 184, 160), + (163, 0, 255), + (153, 0, 255), + (71, 255, 0), + (255, 0, 163), + (255, 204, 0), + (255, 0, 143), + (0, 255, 235), + (133, 255, 0), + (255, 0, 235), + (245, 0, 255), + (255, 0, 122), + (255, 245, 0), + (10, 190, 212), + (214, 255, 0), + (0, 204, 255), + (20, 0, 255), + (255, 255, 0), + (0, 153, 255), + (0, 41, 255), + (0, 255, 204), + (41, 0, 255), + (41, 255, 0), + (173, 0, 255), + (0, 245, 255), + (71, 0, 255), + (122, 0, 255), + (0, 255, 184), + (0, 92, 255), + (184, 255, 0), + (0, 133, 255), + (255, 214, 0), + (25, 194, 194), + (102, 255, 0), + (92, 0, 255), +] + +ADE20K_CLASS_NAMES = [ + "", + "wall", + "building;edifice", + "sky", + "floor;flooring", + "tree", + "ceiling", + "road;route", + "bed", + "windowpane;window", + "grass", + "cabinet", + "sidewalk;pavement", + "person;individual;someone;somebody;mortal;soul", + "earth;ground", + "door;double;door", + "table", + "mountain;mount", + "plant;flora;plant;life", + "curtain;drape;drapery;mantle;pall", + "chair", + "car;auto;automobile;machine;motorcar", + "water", + "painting;picture", + "sofa;couch;lounge", + "shelf", + "house", + "sea", + "mirror", + "rug;carpet;carpeting", + "field", + "armchair", + "seat", + "fence;fencing", + "desk", + "rock;stone", + "wardrobe;closet;press", + "lamp", + "bathtub;bathing;tub;bath;tub", + "railing;rail", + "cushion", + "base;pedestal;stand", + "box", + "column;pillar", + "signboard;sign", + "chest;of;drawers;chest;bureau;dresser", + "counter", + "sand", + "sink", + "skyscraper", + "fireplace;hearth;open;fireplace", + "refrigerator;icebox", + "grandstand;covered;stand", + "path", + "stairs;steps", + "runway", + "case;display;case;showcase;vitrine", + "pool;table;billiard;table;snooker;table", + "pillow", + "screen;door;screen", + "stairway;staircase", + "river", + "bridge;span", + "bookcase", + "blind;screen", + "coffee;table;cocktail;table", + "toilet;can;commode;crapper;pot;potty;stool;throne", + "flower", + "book", + "hill", + "bench", + "countertop", + "stove;kitchen;stove;range;kitchen;range;cooking;stove", + "palm;palm;tree", + "kitchen;island", + "computer;computing;machine;computing;device;data;processor;electronic;computer;information;processing;system", + "swivel;chair", + "boat", + "bar", + "arcade;machine", + "hovel;hut;hutch;shack;shanty", + "bus;autobus;coach;charabanc;double-decker;jitney;motorbus;motorcoach;omnibus;passenger;vehicle", + "towel", + "light;light;source", + "truck;motortruck", + "tower", + "chandelier;pendant;pendent", + "awning;sunshade;sunblind", + "streetlight;street;lamp", + "booth;cubicle;stall;kiosk", + "television;television;receiver;television;set;tv;tv;set;idiot;box;boob;tube;telly;goggle;box", + "airplane;aeroplane;plane", + "dirt;track", + "apparel;wearing;apparel;dress;clothes", + "pole", + "land;ground;soil", + "bannister;banister;balustrade;balusters;handrail", + "escalator;moving;staircase;moving;stairway", + "ottoman;pouf;pouffe;puff;hassock", + "bottle", + "buffet;counter;sideboard", + "poster;posting;placard;notice;bill;card", + "stage", + "van", + "ship", + "fountain", + "conveyer;belt;conveyor;belt;conveyer;conveyor;transporter", + "canopy", + "washer;automatic;washer;washing;machine", + "plaything;toy", + "swimming;pool;swimming;bath;natatorium", + "stool", + "barrel;cask", + "basket;handbasket", + "waterfall;falls", + "tent;collapsible;shelter", + "bag", + "minibike;motorbike", + "cradle", + "oven", + "ball", + "food;solid;food", + "step;stair", + "tank;storage;tank", + "trade;name;brand;name;brand;marque", + "microwave;microwave;oven", + "pot;flowerpot", + "animal;animate;being;beast;brute;creature;fauna", + "bicycle;bike;wheel;cycle", + "lake", + "dishwasher;dish;washer;dishwashing;machine", + "screen;silver;screen;projection;screen", + "blanket;cover", + "sculpture", + "hood;exhaust;hood", + "sconce", + "vase", + "traffic;light;traffic;signal;stoplight", + "tray", + "ashcan;trash;can;garbage;can;wastebin;ash;bin;ash-bin;ashbin;dustbin;trash;barrel;trash;bin", + "fan", + "pier;wharf;wharfage;dock", + "crt;screen", + "plate", + "monitor;monitoring;device", + "bulletin;board;notice;board", + "shower", + "radiator", + "glass;drinking;glass", + "clock", + "flag", +] + + +VOC2012_COLORMAP = [ + (0, 0, 0), + (128, 0, 0), + (0, 128, 0), + (128, 128, 0), + (0, 0, 128), + (128, 0, 128), + (0, 128, 128), + (128, 128, 128), + (64, 0, 0), + (192, 0, 0), + (64, 128, 0), + (192, 128, 0), + (64, 0, 128), + (192, 0, 128), + (64, 128, 128), + (192, 128, 128), + (0, 64, 0), + (128, 64, 0), + (0, 192, 0), + (128, 192, 0), + (0, 64, 128), +] + + +VOC2012_CLASS_NAMES = [ + "", + "aeroplane", + "bicycle", + "bird", + "boat", + "bottle", + "bus", + "car", + "cat", + "chair", + "cow", + "diningtable", + "dog", + "horse", + "motorbike", + "person", + "pottedplant", + "sheep", + "sofa", + "train", + "tvmonitor", +] diff --git a/dinov2/eval/segmentation_m2f/__init__.py b/dinov2/eval/segmentation_m2f/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6c678fdf8f1dee14d7cf9be70af14e6f9a1441c3 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .core import * # noqa: F403 +from .models import * # noqa: F403 +from .ops import * # noqa: F403 diff --git a/dinov2/eval/segmentation_m2f/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/segmentation_m2f/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4c9ee44d7bf7cc8995b39e7a11f4b9d48a0ea8d5 Binary files /dev/null and b/dinov2/eval/segmentation_m2f/__pycache__/__init__.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/core/__init__.py b/dinov2/eval/segmentation_m2f/core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..92599806fbd221c1418d179892a0f46dc0b7d4db --- /dev/null +++ b/dinov2/eval/segmentation_m2f/core/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from mmseg.core.evaluation import * # noqa: F403 +from mmseg.core.seg import * # noqa: F403 + +from .anchor import * # noqa: F403 +from .box import * # noqa: F403 +from .utils import * # noqa: F403 diff --git a/dinov2/eval/segmentation_m2f/core/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/segmentation_m2f/core/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..818749ab55f046626f2f31110ba9a62e4aec82c1 Binary files /dev/null and b/dinov2/eval/segmentation_m2f/core/__pycache__/__init__.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/core/anchor/__init__.py b/dinov2/eval/segmentation_m2f/core/anchor/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e71ac4d6e01462221ae01aa16d0e1231cda7e2e7 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/core/anchor/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .point_generator import MlvlPointGenerator # noqa: F403 diff --git 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0000000000000000000000000000000000000000..38326a50aeebaa8126b9f5facc1d6b1bde981bac Binary files /dev/null and b/dinov2/eval/segmentation_m2f/core/anchor/__pycache__/point_generator.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/core/anchor/builder.py b/dinov2/eval/segmentation_m2f/core/anchor/builder.py new file mode 100644 index 0000000000000000000000000000000000000000..6dba90e22de76d2f23a86d3c057f196d55a99690 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/core/anchor/builder.py @@ -0,0 +1,21 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import warnings + +from mmcv.utils import Registry, build_from_cfg + +PRIOR_GENERATORS = Registry("Generator for anchors and points") + +ANCHOR_GENERATORS = PRIOR_GENERATORS + + +def build_prior_generator(cfg, default_args=None): + return build_from_cfg(cfg, PRIOR_GENERATORS, default_args) + + +def build_anchor_generator(cfg, default_args=None): + warnings.warn("``build_anchor_generator`` would be deprecated soon, please use " "``build_prior_generator`` ") + return build_prior_generator(cfg, default_args=default_args) diff --git a/dinov2/eval/segmentation_m2f/core/anchor/point_generator.py b/dinov2/eval/segmentation_m2f/core/anchor/point_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..574d71939080e22284fe99087fb2e7336657bd97 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/core/anchor/point_generator.py @@ -0,0 +1,205 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +from torch.nn.modules.utils import _pair + +from .builder import PRIOR_GENERATORS + + +@PRIOR_GENERATORS.register_module() +class MlvlPointGenerator: + """Standard points generator for multi-level (Mlvl) feature maps in 2D + points-based detectors. + + Args: + strides (list[int] | list[tuple[int, int]]): Strides of anchors + in multiple feature levels in order (w, h). + offset (float): The offset of points, the value is normalized with + corresponding stride. Defaults to 0.5. + """ + + def __init__(self, strides, offset=0.5): + self.strides = [_pair(stride) for stride in strides] + self.offset = offset + + @property + def num_levels(self): + """int: number of feature levels that the generator will be applied""" + return len(self.strides) + + @property + def num_base_priors(self): + """list[int]: The number of priors (points) at a point + on the feature grid""" + return [1 for _ in range(len(self.strides))] + + def _meshgrid(self, x, y, row_major=True): + yy, xx = torch.meshgrid(y, x) + if row_major: + # warning .flatten() would cause error in ONNX exporting + # have to use reshape here + return xx.reshape(-1), yy.reshape(-1) + + else: + return yy.reshape(-1), xx.reshape(-1) + + def grid_priors(self, featmap_sizes, dtype=torch.float32, device="cuda", with_stride=False): + """Generate grid points of multiple feature levels. + + Args: + featmap_sizes (list[tuple]): List of feature map sizes in + multiple feature levels, each size arrange as + as (h, w). + dtype (:obj:`dtype`): Dtype of priors. Default: torch.float32. + device (str): The device where the anchors will be put on. + with_stride (bool): Whether to concatenate the stride to + the last dimension of points. + + Return: + list[torch.Tensor]: Points of multiple feature levels. + The sizes of each tensor should be (N, 2) when with stride is + ``False``, where N = width * height, width and height + are the sizes of the corresponding feature level, + and the last dimension 2 represent (coord_x, coord_y), + otherwise the shape should be (N, 4), + and the last dimension 4 represent + (coord_x, coord_y, stride_w, stride_h). + """ + + assert self.num_levels == len(featmap_sizes) + multi_level_priors = [] + for i in range(self.num_levels): + priors = self.single_level_grid_priors( + featmap_sizes[i], level_idx=i, dtype=dtype, device=device, with_stride=with_stride + ) + multi_level_priors.append(priors) + return multi_level_priors + + def single_level_grid_priors(self, featmap_size, level_idx, dtype=torch.float32, device="cuda", with_stride=False): + """Generate grid Points of a single level. + + Note: + This function is usually called by method ``self.grid_priors``. + + Args: + featmap_size (tuple[int]): Size of the feature maps, arrange as + (h, w). + level_idx (int): The index of corresponding feature map level. + dtype (:obj:`dtype`): Dtype of priors. Default: torch.float32. + device (str, optional): The device the tensor will be put on. + Defaults to 'cuda'. + with_stride (bool): Concatenate the stride to the last dimension + of points. + + Return: + Tensor: Points of single feature levels. + The shape of tensor should be (N, 2) when with stride is + ``False``, where N = width * height, width and height + are the sizes of the corresponding feature level, + and the last dimension 2 represent (coord_x, coord_y), + otherwise the shape should be (N, 4), + and the last dimension 4 represent + (coord_x, coord_y, stride_w, stride_h). + """ + feat_h, feat_w = featmap_size + stride_w, stride_h = self.strides[level_idx] + shift_x = (torch.arange(0, feat_w, device=device) + self.offset) * stride_w + # keep featmap_size as Tensor instead of int, so that we + # can convert to ONNX correctly + shift_x = shift_x.to(dtype) + + shift_y = (torch.arange(0, feat_h, device=device) + self.offset) * stride_h + # keep featmap_size as Tensor instead of int, so that we + # can convert to ONNX correctly + shift_y = shift_y.to(dtype) + shift_xx, shift_yy = self._meshgrid(shift_x, shift_y) + if not with_stride: + shifts = torch.stack([shift_xx, shift_yy], dim=-1) + else: + # use `shape[0]` instead of `len(shift_xx)` for ONNX export + stride_w = shift_xx.new_full((shift_xx.shape[0],), stride_w).to(dtype) + stride_h = shift_xx.new_full((shift_yy.shape[0],), stride_h).to(dtype) + shifts = torch.stack([shift_xx, shift_yy, stride_w, stride_h], dim=-1) + all_points = shifts.to(device) + return all_points + + def valid_flags(self, featmap_sizes, pad_shape, device="cuda"): + """Generate valid flags of points of multiple feature levels. + + Args: + featmap_sizes (list(tuple)): List of feature map sizes in + multiple feature levels, each size arrange as + as (h, w). + pad_shape (tuple(int)): The padded shape of the image, + arrange as (h, w). + device (str): The device where the anchors will be put on. + + Return: + list(torch.Tensor): Valid flags of points of multiple levels. + """ + assert self.num_levels == len(featmap_sizes) + multi_level_flags = [] + for i in range(self.num_levels): + point_stride = self.strides[i] + feat_h, feat_w = featmap_sizes[i] + h, w = pad_shape[:2] + valid_feat_h = min(int(np.ceil(h / point_stride[1])), feat_h) + valid_feat_w = min(int(np.ceil(w / point_stride[0])), feat_w) + flags = self.single_level_valid_flags((feat_h, feat_w), (valid_feat_h, valid_feat_w), device=device) + multi_level_flags.append(flags) + return multi_level_flags + + def single_level_valid_flags(self, featmap_size, valid_size, device="cuda"): + """Generate the valid flags of points of a single feature map. + + Args: + featmap_size (tuple[int]): The size of feature maps, arrange as + as (h, w). + valid_size (tuple[int]): The valid size of the feature maps. + The size arrange as as (h, w). + device (str, optional): The device where the flags will be put on. + Defaults to 'cuda'. + + Returns: + torch.Tensor: The valid flags of each points in a single level \ + feature map. + """ + feat_h, feat_w = featmap_size + valid_h, valid_w = valid_size + assert valid_h <= feat_h and valid_w <= feat_w + valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device) + valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device) + valid_x[:valid_w] = 1 + valid_y[:valid_h] = 1 + valid_xx, valid_yy = self._meshgrid(valid_x, valid_y) + valid = valid_xx & valid_yy + return valid + + def sparse_priors(self, prior_idxs, featmap_size, level_idx, dtype=torch.float32, device="cuda"): + """Generate sparse points according to the ``prior_idxs``. + + Args: + prior_idxs (Tensor): The index of corresponding anchors + in the feature map. + featmap_size (tuple[int]): feature map size arrange as (w, h). + level_idx (int): The level index of corresponding feature + map. + dtype (obj:`torch.dtype`): Date type of points. Defaults to + ``torch.float32``. + device (obj:`torch.device`): The device where the points is + located. + Returns: + Tensor: Anchor with shape (N, 2), N should be equal to + the length of ``prior_idxs``. And last dimension + 2 represent (coord_x, coord_y). + """ + height, width = featmap_size + x = (prior_idxs % width + self.offset) * self.strides[level_idx][0] + y = ((prior_idxs // width) % height + self.offset) * self.strides[level_idx][1] + prioris = torch.stack([x, y], 1).to(dtype) + prioris = prioris.to(device) + return prioris diff --git a/dinov2/eval/segmentation_m2f/core/box/__init__.py b/dinov2/eval/segmentation_m2f/core/box/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bf35a613f81acd77ecab2dfb75a722fa8e5c0787 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/core/box/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .builder import * # noqa: F403 +from .samplers import MaskPseudoSampler # noqa: F403 diff --git 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b/dinov2/eval/segmentation_m2f/core/box/builder.py @@ -0,0 +1,19 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from mmcv.utils import Registry, build_from_cfg + +BBOX_SAMPLERS = Registry("bbox_sampler") +BBOX_CODERS = Registry("bbox_coder") + + +def build_sampler(cfg, **default_args): + """Builder of box sampler.""" + return build_from_cfg(cfg, BBOX_SAMPLERS, default_args) + + +def build_bbox_coder(cfg, **default_args): + """Builder of box coder.""" + return build_from_cfg(cfg, BBOX_CODERS, default_args) diff --git a/dinov2/eval/segmentation_m2f/core/box/samplers/__init__.py b/dinov2/eval/segmentation_m2f/core/box/samplers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..19c363e3fabc365d92aeaf1e78189d710db279e9 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/core/box/samplers/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .mask_pseudo_sampler import MaskPseudoSampler # noqa: F403 diff --git a/dinov2/eval/segmentation_m2f/core/box/samplers/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/segmentation_m2f/core/box/samplers/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b8b94ecb165ac0fca3dff5b183fe2190dccc5247 Binary files /dev/null and b/dinov2/eval/segmentation_m2f/core/box/samplers/__pycache__/__init__.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/core/box/samplers/__pycache__/base_sampler.cpython-310.pyc b/dinov2/eval/segmentation_m2f/core/box/samplers/__pycache__/base_sampler.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..da183b101f8fdc95214d8fd6d1ed3531b6a21f1d Binary files 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differ diff --git a/dinov2/eval/segmentation_m2f/core/box/samplers/__pycache__/sampling_result.cpython-310.pyc b/dinov2/eval/segmentation_m2f/core/box/samplers/__pycache__/sampling_result.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8ce0b47151f62442a7d18bcf5a5f7c1713c8ef25 Binary files /dev/null and b/dinov2/eval/segmentation_m2f/core/box/samplers/__pycache__/sampling_result.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/core/box/samplers/base_sampler.py b/dinov2/eval/segmentation_m2f/core/box/samplers/base_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..c45cec3ed7af5b49bb54b92d6e6bcf59b06b4c99 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/core/box/samplers/base_sampler.py @@ -0,0 +1,92 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from abc import ABCMeta, abstractmethod + +import torch + +from .sampling_result import SamplingResult + + +class BaseSampler(metaclass=ABCMeta): + """Base class of samplers.""" + + def __init__(self, num, pos_fraction, neg_pos_ub=-1, add_gt_as_proposals=True, **kwargs): + self.num = num + self.pos_fraction = pos_fraction + self.neg_pos_ub = neg_pos_ub + self.add_gt_as_proposals = add_gt_as_proposals + self.pos_sampler = self + self.neg_sampler = self + + @abstractmethod + def _sample_pos(self, assign_result, num_expected, **kwargs): + """Sample positive samples.""" + pass + + @abstractmethod + def _sample_neg(self, assign_result, num_expected, **kwargs): + """Sample negative samples.""" + pass + + def sample(self, assign_result, bboxes, gt_bboxes, gt_labels=None, **kwargs): + """Sample positive and negative bboxes. + + This is a simple implementation of bbox sampling given candidates, + assigning results and ground truth bboxes. + + Args: + assign_result (:obj:`AssignResult`): Bbox assigning results. + bboxes (Tensor): Boxes to be sampled from. + gt_bboxes (Tensor): Ground truth bboxes. + gt_labels (Tensor, optional): Class labels of ground truth bboxes. + + Returns: + :obj:`SamplingResult`: Sampling result. + + Example: + >>> from mmdet.core.bbox import RandomSampler + >>> from mmdet.core.bbox import AssignResult + >>> from mmdet.core.bbox.demodata import ensure_rng, random_boxes + >>> rng = ensure_rng(None) + >>> assign_result = AssignResult.random(rng=rng) + >>> bboxes = random_boxes(assign_result.num_preds, rng=rng) + >>> gt_bboxes = random_boxes(assign_result.num_gts, rng=rng) + >>> gt_labels = None + >>> self = RandomSampler(num=32, pos_fraction=0.5, neg_pos_ub=-1, + >>> add_gt_as_proposals=False) + >>> self = self.sample(assign_result, bboxes, gt_bboxes, gt_labels) + """ + if len(bboxes.shape) < 2: + bboxes = bboxes[None, :] + + bboxes = bboxes[:, :4] + + gt_flags = bboxes.new_zeros((bboxes.shape[0],), dtype=torch.uint8) + if self.add_gt_as_proposals and len(gt_bboxes) > 0: + if gt_labels is None: + raise ValueError("gt_labels must be given when add_gt_as_proposals is True") + bboxes = torch.cat([gt_bboxes, bboxes], dim=0) + assign_result.add_gt_(gt_labels) + gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8) + gt_flags = torch.cat([gt_ones, gt_flags]) + + num_expected_pos = int(self.num * self.pos_fraction) + pos_inds = self.pos_sampler._sample_pos(assign_result, num_expected_pos, bboxes=bboxes, **kwargs) + # We found that sampled indices have duplicated items occasionally. + # (may be a bug of PyTorch) + pos_inds = pos_inds.unique() + num_sampled_pos = pos_inds.numel() + num_expected_neg = self.num - num_sampled_pos + if self.neg_pos_ub >= 0: + _pos = max(1, num_sampled_pos) + neg_upper_bound = int(self.neg_pos_ub * _pos) + if num_expected_neg > neg_upper_bound: + num_expected_neg = neg_upper_bound + neg_inds = self.neg_sampler._sample_neg(assign_result, num_expected_neg, bboxes=bboxes, **kwargs) + neg_inds = neg_inds.unique() + + sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes, assign_result, gt_flags) + return sampling_result diff --git a/dinov2/eval/segmentation_m2f/core/box/samplers/mask_pseudo_sampler.py b/dinov2/eval/segmentation_m2f/core/box/samplers/mask_pseudo_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..3e67ea61ed0fd65cca0addde1893a3c1e176bf15 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/core/box/samplers/mask_pseudo_sampler.py @@ -0,0 +1,45 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py + +import torch + +from ..builder import BBOX_SAMPLERS +from .base_sampler import BaseSampler +from .mask_sampling_result import MaskSamplingResult + + +@BBOX_SAMPLERS.register_module() +class MaskPseudoSampler(BaseSampler): + """A pseudo sampler that does not do sampling actually.""" + + def __init__(self, **kwargs): + pass + + def _sample_pos(self, **kwargs): + """Sample positive samples.""" + raise NotImplementedError + + def _sample_neg(self, **kwargs): + """Sample negative samples.""" + raise NotImplementedError + + def sample(self, assign_result, masks, gt_masks, **kwargs): + """Directly returns the positive and negative indices of samples. + + Args: + assign_result (:obj:`AssignResult`): Assigned results + masks (torch.Tensor): Bounding boxes + gt_masks (torch.Tensor): Ground truth boxes + Returns: + :obj:`SamplingResult`: sampler results + """ + pos_inds = torch.nonzero(assign_result.gt_inds > 0, as_tuple=False).squeeze(-1).unique() + neg_inds = torch.nonzero(assign_result.gt_inds == 0, as_tuple=False).squeeze(-1).unique() + gt_flags = masks.new_zeros(masks.shape[0], dtype=torch.uint8) + sampling_result = MaskSamplingResult(pos_inds, neg_inds, masks, gt_masks, assign_result, gt_flags) + return sampling_result diff --git a/dinov2/eval/segmentation_m2f/core/box/samplers/mask_sampling_result.py b/dinov2/eval/segmentation_m2f/core/box/samplers/mask_sampling_result.py new file mode 100644 index 0000000000000000000000000000000000000000..270ffd35a5f120dd0560a7fea7fe83ef0bab66bb --- /dev/null +++ b/dinov2/eval/segmentation_m2f/core/box/samplers/mask_sampling_result.py @@ -0,0 +1,63 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py + +import torch + +from .sampling_result import SamplingResult + + +class MaskSamplingResult(SamplingResult): + """Mask sampling result.""" + + def __init__(self, pos_inds, neg_inds, masks, gt_masks, assign_result, gt_flags): + self.pos_inds = pos_inds + self.neg_inds = neg_inds + self.pos_masks = masks[pos_inds] + self.neg_masks = masks[neg_inds] + self.pos_is_gt = gt_flags[pos_inds] + + self.num_gts = gt_masks.shape[0] + self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1 + + if gt_masks.numel() == 0: + # hack for index error case + assert self.pos_assigned_gt_inds.numel() == 0 + self.pos_gt_masks = torch.empty_like(gt_masks) + else: + self.pos_gt_masks = gt_masks[self.pos_assigned_gt_inds, :] + + if assign_result.labels is not None: + self.pos_gt_labels = assign_result.labels[pos_inds] + else: + self.pos_gt_labels = None + + @property + def masks(self): + """torch.Tensor: concatenated positive and negative boxes""" + return torch.cat([self.pos_masks, self.neg_masks]) + + def __nice__(self): + data = self.info.copy() + data["pos_masks"] = data.pop("pos_masks").shape + data["neg_masks"] = data.pop("neg_masks").shape + parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())] + body = " " + ",\n ".join(parts) + return "{\n" + body + "\n}" + + @property + def info(self): + """Returns a dictionary of info about the object.""" + return { + "pos_inds": self.pos_inds, + "neg_inds": self.neg_inds, + "pos_masks": self.pos_masks, + "neg_masks": self.neg_masks, + "pos_is_gt": self.pos_is_gt, + "num_gts": self.num_gts, + "pos_assigned_gt_inds": self.pos_assigned_gt_inds, + } diff --git a/dinov2/eval/segmentation_m2f/core/box/samplers/sampling_result.py b/dinov2/eval/segmentation_m2f/core/box/samplers/sampling_result.py new file mode 100644 index 0000000000000000000000000000000000000000..aaee3fe55aeb8c6da7edefbbd382d94b67b6a6b4 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/core/box/samplers/sampling_result.py @@ -0,0 +1,152 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch + + +class SamplingResult: + """Bbox sampling result. + + Example: + >>> # xdoctest: +IGNORE_WANT + >>> from mmdet.core.bbox.samplers.sampling_result import * # NOQA + >>> self = SamplingResult.random(rng=10) + >>> print(f'self = {self}') + self = + """ + + def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result, gt_flags): + self.pos_inds = pos_inds + self.neg_inds = neg_inds + self.pos_bboxes = bboxes[pos_inds] + self.neg_bboxes = bboxes[neg_inds] + self.pos_is_gt = gt_flags[pos_inds] + + self.num_gts = gt_bboxes.shape[0] + self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1 + + if gt_bboxes.numel() == 0: + # hack for index error case + assert self.pos_assigned_gt_inds.numel() == 0 + self.pos_gt_bboxes = torch.empty_like(gt_bboxes).view(-1, 4) + else: + if len(gt_bboxes.shape) < 2: + gt_bboxes = gt_bboxes.view(-1, 4) + + self.pos_gt_bboxes = gt_bboxes[self.pos_assigned_gt_inds.long(), :] + + if assign_result.labels is not None: + self.pos_gt_labels = assign_result.labels[pos_inds] + else: + self.pos_gt_labels = None + + @property + def bboxes(self): + """torch.Tensor: concatenated positive and negative boxes""" + return torch.cat([self.pos_bboxes, self.neg_bboxes]) + + def to(self, device): + """Change the device of the data inplace. + + Example: + >>> self = SamplingResult.random() + >>> print(f'self = {self.to(None)}') + >>> # xdoctest: +REQUIRES(--gpu) + >>> print(f'self = {self.to(0)}') + """ + _dict = self.__dict__ + for key, value in _dict.items(): + if isinstance(value, torch.Tensor): + _dict[key] = value.to(device) + return self + + def __nice__(self): + data = self.info.copy() + data["pos_bboxes"] = data.pop("pos_bboxes").shape + data["neg_bboxes"] = data.pop("neg_bboxes").shape + parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())] + body = " " + ",\n ".join(parts) + return "{\n" + body + "\n}" + + @property + def info(self): + """Returns a dictionary of info about the object.""" + return { + "pos_inds": self.pos_inds, + "neg_inds": self.neg_inds, + "pos_bboxes": self.pos_bboxes, + "neg_bboxes": self.neg_bboxes, + "pos_is_gt": self.pos_is_gt, + "num_gts": self.num_gts, + "pos_assigned_gt_inds": self.pos_assigned_gt_inds, + } + + @classmethod + def random(cls, rng=None, **kwargs): + """ + Args: + rng (None | int | numpy.random.RandomState): seed or state. + kwargs (keyword arguments): + - num_preds: number of predicted boxes + - num_gts: number of true boxes + - p_ignore (float): probability of a predicted box assigned to \ + an ignored truth. + - p_assigned (float): probability of a predicted box not being \ + assigned. + - p_use_label (float | bool): with labels or not. + + Returns: + :obj:`SamplingResult`: Randomly generated sampling result. + + Example: + >>> from mmdet.core.bbox.samplers.sampling_result import * # NOQA + >>> self = SamplingResult.random() + >>> print(self.__dict__) + """ + from mmdet.core.bbox import demodata + from mmdet.core.bbox.assigners.assign_result import AssignResult + from mmdet.core.bbox.samplers.random_sampler import RandomSampler + + rng = demodata.ensure_rng(rng) + + # make probabalistic? + num = 32 + pos_fraction = 0.5 + neg_pos_ub = -1 + + assign_result = AssignResult.random(rng=rng, **kwargs) + + # Note we could just compute an assignment + bboxes = demodata.random_boxes(assign_result.num_preds, rng=rng) + gt_bboxes = demodata.random_boxes(assign_result.num_gts, rng=rng) + + if rng.rand() > 0.2: + # sometimes algorithms squeeze their data, be robust to that + gt_bboxes = gt_bboxes.squeeze() + bboxes = bboxes.squeeze() + + if assign_result.labels is None: + gt_labels = None + else: + gt_labels = None + + if gt_labels is None: + add_gt_as_proposals = False + else: + add_gt_as_proposals = True # make probabalistic? + + sampler = RandomSampler( + num, pos_fraction, neg_pos_ub=neg_pos_ub, add_gt_as_proposals=add_gt_as_proposals, rng=rng + ) + self = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels) + return self diff --git a/dinov2/eval/segmentation_m2f/core/utils/__init__.py 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--- /dev/null +++ b/dinov2/eval/segmentation_m2f/core/utils/dist_utils.py @@ -0,0 +1,15 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch.distributed as dist + + +def reduce_mean(tensor): + """ "Obtain the mean of tensor on different GPUs.""" + if not (dist.is_available() and dist.is_initialized()): + return tensor + tensor = tensor.clone() + dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM) + return tensor diff --git a/dinov2/eval/segmentation_m2f/core/utils/misc.py b/dinov2/eval/segmentation_m2f/core/utils/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..e07579e7b182b62153e81fe637ffd0f3081ef2a3 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/core/utils/misc.py @@ -0,0 +1,47 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from functools import partial + + +def multi_apply(func, *args, **kwargs): + """Apply function to a list of arguments. + + Note: + This function applies the ``func`` to multiple inputs and + map the multiple outputs of the ``func`` into different + list. Each list contains the same type of outputs corresponding + to different inputs. + + Args: + func (Function): A function that will be applied to a list of + arguments + + Returns: + tuple(list): A tuple containing multiple list, each list contains \ + a kind of returned results by the function + """ + pfunc = partial(func, **kwargs) if kwargs else func + map_results = map(pfunc, *args) + return tuple(map(list, zip(*map_results))) + + +def add_prefix(inputs, prefix): + """Add prefix for dict. + + Args: + inputs (dict): The input dict with str keys. + prefix (str): The prefix to add. + + Returns: + + dict: The dict with keys updated with ``prefix``. + """ + + outputs = dict() + for name, value in inputs.items(): + outputs[f"{prefix}.{name}"] = value + + return outputs diff --git a/dinov2/eval/segmentation_m2f/models/__init__.py b/dinov2/eval/segmentation_m2f/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ed89bb0064d82b4360af020798eab3d2f5a47937 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .backbones import * # noqa: F403 +from .builder import MASK_ASSIGNERS, MATCH_COST, TRANSFORMER, build_assigner, build_match_cost +from .decode_heads import * # noqa: F403 +from .losses import * # noqa: F403 +from .plugins import * # noqa: F403 +from .segmentors import * # noqa: F403 diff --git a/dinov2/eval/segmentation_m2f/models/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/segmentation_m2f/models/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0dc83022a6ecd660184c6177862e0dfb1aaf050b Binary files /dev/null and b/dinov2/eval/segmentation_m2f/models/__pycache__/__init__.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/models/__pycache__/builder.cpython-310.pyc b/dinov2/eval/segmentation_m2f/models/__pycache__/builder.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..317edb0ae1007d7f692904865db45bf227c02a18 Binary files /dev/null and b/dinov2/eval/segmentation_m2f/models/__pycache__/builder.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/models/backbones/__init__.py b/dinov2/eval/segmentation_m2f/models/backbones/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c4bf73bcbcee710676f81cb6517ae787f4d61cc6 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/backbones/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .vit_adapter import ViTAdapter diff --git 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0000000000000000000000000000000000000000..d7438d99965b05423feda6f098dfa23d5270e60e Binary files /dev/null and b/dinov2/eval/segmentation_m2f/models/backbones/__pycache__/vit_adapter.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/models/backbones/adapter_modules.py b/dinov2/eval/segmentation_m2f/models/backbones/adapter_modules.py new file mode 100644 index 0000000000000000000000000000000000000000..26bfdf8f6ae6c107d22d61985cce34d4b5ce275f --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/backbones/adapter_modules.py @@ -0,0 +1,442 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from functools import partial + +import torch +import torch.nn as nn +import torch.utils.checkpoint as cp + +from ...ops.modules import MSDeformAttn +from .drop_path import DropPath + + +def get_reference_points(spatial_shapes, device): + reference_points_list = [] + for lvl, (H_, W_) in enumerate(spatial_shapes): + ref_y, ref_x = torch.meshgrid( + torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device), + torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device), + ) + ref_y = ref_y.reshape(-1)[None] / H_ + ref_x = ref_x.reshape(-1)[None] / W_ + ref = torch.stack((ref_x, ref_y), -1) + reference_points_list.append(ref) + reference_points = torch.cat(reference_points_list, 1) + reference_points = reference_points[:, :, None] + return reference_points + + +def deform_inputs(x, patch_size): + bs, c, h, w = x.shape + spatial_shapes = torch.as_tensor( + [(h // 8, w // 8), (h // 16, w // 16), (h // 32, w // 32)], dtype=torch.long, device=x.device + ) + level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) + reference_points = get_reference_points([(h // patch_size, w // patch_size)], x.device) + deform_inputs1 = [reference_points, spatial_shapes, level_start_index] + + spatial_shapes = torch.as_tensor([(h // patch_size, w // patch_size)], dtype=torch.long, device=x.device) + level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) + reference_points = get_reference_points([(h // 8, w // 8), (h // 16, w // 16), (h // 32, w // 32)], x.device) + deform_inputs2 = [reference_points, spatial_shapes, level_start_index] + + return deform_inputs1, deform_inputs2 + + +class ConvFFN(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.dwconv = DWConv(hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x, H, W): + x = self.fc1(x) + x = self.dwconv(x, H, W) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class DWConv(nn.Module): + def __init__(self, dim=768): + super().__init__() + self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim) + + def forward(self, x, H, W): + B, N, C = x.shape + n = N // 21 + x1 = x[:, 0 : 16 * n, :].transpose(1, 2).view(B, C, H * 2, W * 2).contiguous() + x2 = x[:, 16 * n : 20 * n, :].transpose(1, 2).view(B, C, H, W).contiguous() + x3 = x[:, 20 * n :, :].transpose(1, 2).view(B, C, H // 2, W // 2).contiguous() + x1 = self.dwconv(x1).flatten(2).transpose(1, 2) + x2 = self.dwconv(x2).flatten(2).transpose(1, 2) + x3 = self.dwconv(x3).flatten(2).transpose(1, 2) + x = torch.cat([x1, x2, x3], dim=1) + return x + + +class Extractor(nn.Module): + def __init__( + self, + dim, + num_heads=6, + n_points=4, + n_levels=1, + deform_ratio=1.0, + with_cffn=True, + cffn_ratio=0.25, + drop=0.0, + drop_path=0.0, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + with_cp=False, + ): + super().__init__() + self.query_norm = norm_layer(dim) + self.feat_norm = norm_layer(dim) + self.attn = MSDeformAttn( + d_model=dim, n_levels=n_levels, n_heads=num_heads, n_points=n_points, ratio=deform_ratio + ) + self.with_cffn = with_cffn + self.with_cp = with_cp + if with_cffn: + self.ffn = ConvFFN(in_features=dim, hidden_features=int(dim * cffn_ratio), drop=drop) + self.ffn_norm = norm_layer(dim) + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + def forward(self, query, reference_points, feat, spatial_shapes, level_start_index, H, W): + def _inner_forward(query, feat): + + attn = self.attn( + self.query_norm(query), reference_points, self.feat_norm(feat), spatial_shapes, level_start_index, None + ) + query = query + attn + + if self.with_cffn: + query = query + self.drop_path(self.ffn(self.ffn_norm(query), H, W)) + return query + + if self.with_cp and query.requires_grad: + query = cp.checkpoint(_inner_forward, query, feat) + else: + query = _inner_forward(query, feat) + + return query + + +class Injector(nn.Module): + def __init__( + self, + dim, + num_heads=6, + n_points=4, + n_levels=1, + deform_ratio=1.0, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + init_values=0.0, + with_cp=False, + ): + super().__init__() + self.with_cp = with_cp + self.query_norm = norm_layer(dim) + self.feat_norm = norm_layer(dim) + self.attn = MSDeformAttn( + d_model=dim, n_levels=n_levels, n_heads=num_heads, n_points=n_points, ratio=deform_ratio + ) + self.gamma = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True) + + def forward(self, query, reference_points, feat, spatial_shapes, level_start_index): + def _inner_forward(query, feat): + + attn = self.attn( + self.query_norm(query), reference_points, self.feat_norm(feat), spatial_shapes, level_start_index, None + ) + return query + self.gamma * attn + + if self.with_cp and query.requires_grad: + query = cp.checkpoint(_inner_forward, query, feat) + else: + query = _inner_forward(query, feat) + + return query + + +class InteractionBlock(nn.Module): + def __init__( + self, + dim, + num_heads=6, + n_points=4, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + drop=0.0, + drop_path=0.0, + with_cffn=True, + cffn_ratio=0.25, + init_values=0.0, + deform_ratio=1.0, + extra_extractor=False, + with_cp=False, + ): + super().__init__() + + self.injector = Injector( + dim=dim, + n_levels=3, + num_heads=num_heads, + init_values=init_values, + n_points=n_points, + norm_layer=norm_layer, + deform_ratio=deform_ratio, + with_cp=with_cp, + ) + self.extractor = Extractor( + dim=dim, + n_levels=1, + num_heads=num_heads, + n_points=n_points, + norm_layer=norm_layer, + deform_ratio=deform_ratio, + with_cffn=with_cffn, + cffn_ratio=cffn_ratio, + drop=drop, + drop_path=drop_path, + with_cp=with_cp, + ) + if extra_extractor: + self.extra_extractors = nn.Sequential( + *[ + Extractor( + dim=dim, + num_heads=num_heads, + n_points=n_points, + norm_layer=norm_layer, + with_cffn=with_cffn, + cffn_ratio=cffn_ratio, + deform_ratio=deform_ratio, + drop=drop, + drop_path=drop_path, + with_cp=with_cp, + ) + for _ in range(2) + ] + ) + else: + self.extra_extractors = None + + def forward(self, x, c, blocks, deform_inputs1, deform_inputs2, H_c, W_c, H_toks, W_toks): + x = self.injector( + query=x, + reference_points=deform_inputs1[0], + feat=c, + spatial_shapes=deform_inputs1[1], + level_start_index=deform_inputs1[2], + ) + for idx, blk in enumerate(blocks): + x = blk(x, H_toks, W_toks) + c = self.extractor( + query=c, + reference_points=deform_inputs2[0], + feat=x, + spatial_shapes=deform_inputs2[1], + level_start_index=deform_inputs2[2], + H=H_c, + W=W_c, + ) + if self.extra_extractors is not None: + for extractor in self.extra_extractors: + c = extractor( + query=c, + reference_points=deform_inputs2[0], + feat=x, + spatial_shapes=deform_inputs2[1], + level_start_index=deform_inputs2[2], + H=H_c, + W=W_c, + ) + return x, c + + +class InteractionBlockWithCls(nn.Module): + def __init__( + self, + dim, + num_heads=6, + n_points=4, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + drop=0.0, + drop_path=0.0, + with_cffn=True, + cffn_ratio=0.25, + init_values=0.0, + deform_ratio=1.0, + extra_extractor=False, + with_cp=False, + ): + super().__init__() + + self.injector = Injector( + dim=dim, + n_levels=3, + num_heads=num_heads, + init_values=init_values, + n_points=n_points, + norm_layer=norm_layer, + deform_ratio=deform_ratio, + with_cp=with_cp, + ) + self.extractor = Extractor( + dim=dim, + n_levels=1, + num_heads=num_heads, + n_points=n_points, + norm_layer=norm_layer, + deform_ratio=deform_ratio, + with_cffn=with_cffn, + cffn_ratio=cffn_ratio, + drop=drop, + drop_path=drop_path, + with_cp=with_cp, + ) + if extra_extractor: + self.extra_extractors = nn.Sequential( + *[ + Extractor( + dim=dim, + num_heads=num_heads, + n_points=n_points, + norm_layer=norm_layer, + with_cffn=with_cffn, + cffn_ratio=cffn_ratio, + deform_ratio=deform_ratio, + drop=drop, + drop_path=drop_path, + with_cp=with_cp, + ) + for _ in range(2) + ] + ) + else: + self.extra_extractors = None + + def forward(self, x, c, cls, blocks, deform_inputs1, deform_inputs2, H_c, W_c, H_toks, W_toks): + x = self.injector( + query=x, + reference_points=deform_inputs1[0], + feat=c, + spatial_shapes=deform_inputs1[1], + level_start_index=deform_inputs1[2], + ) + x = torch.cat((cls, x), dim=1) + for idx, blk in enumerate(blocks): + x = blk(x, H_toks, W_toks) + cls, x = ( + x[ + :, + :1, + ], + x[ + :, + 1:, + ], + ) + c = self.extractor( + query=c, + reference_points=deform_inputs2[0], + feat=x, + spatial_shapes=deform_inputs2[1], + level_start_index=deform_inputs2[2], + H=H_c, + W=W_c, + ) + if self.extra_extractors is not None: + for extractor in self.extra_extractors: + c = extractor( + query=c, + reference_points=deform_inputs2[0], + feat=x, + spatial_shapes=deform_inputs2[1], + level_start_index=deform_inputs2[2], + H=H_c, + W=W_c, + ) + return x, c, cls + + +class SpatialPriorModule(nn.Module): + def __init__(self, inplanes=64, embed_dim=384, with_cp=False): + super().__init__() + self.with_cp = with_cp + + self.stem = nn.Sequential( + *[ + nn.Conv2d(3, inplanes, kernel_size=3, stride=2, padding=1, bias=False), + nn.SyncBatchNorm(inplanes), + nn.ReLU(inplace=True), + nn.Conv2d(inplanes, inplanes, kernel_size=3, stride=1, padding=1, bias=False), + nn.SyncBatchNorm(inplanes), + nn.ReLU(inplace=True), + nn.Conv2d(inplanes, inplanes, kernel_size=3, stride=1, padding=1, bias=False), + nn.SyncBatchNorm(inplanes), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), + ] + ) + self.conv2 = nn.Sequential( + *[ + nn.Conv2d(inplanes, 2 * inplanes, kernel_size=3, stride=2, padding=1, bias=False), + nn.SyncBatchNorm(2 * inplanes), + nn.ReLU(inplace=True), + ] + ) + self.conv3 = nn.Sequential( + *[ + nn.Conv2d(2 * inplanes, 4 * inplanes, kernel_size=3, stride=2, padding=1, bias=False), + nn.SyncBatchNorm(4 * inplanes), + nn.ReLU(inplace=True), + ] + ) + self.conv4 = nn.Sequential( + *[ + nn.Conv2d(4 * inplanes, 4 * inplanes, kernel_size=3, stride=2, padding=1, bias=False), + nn.SyncBatchNorm(4 * inplanes), + nn.ReLU(inplace=True), + ] + ) + self.fc1 = nn.Conv2d(inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True) + self.fc2 = nn.Conv2d(2 * inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True) + self.fc3 = nn.Conv2d(4 * inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True) + self.fc4 = nn.Conv2d(4 * inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True) + + def forward(self, x): + def _inner_forward(x): + c1 = self.stem(x) + c2 = self.conv2(c1) + c3 = self.conv3(c2) + c4 = self.conv4(c3) + c1 = self.fc1(c1) + c2 = self.fc2(c2) + c3 = self.fc3(c3) + c4 = self.fc4(c4) + + bs, dim, _, _ = c1.shape + # c1 = c1.view(bs, dim, -1).transpose(1, 2) # 4s + c2 = c2.view(bs, dim, -1).transpose(1, 2) # 8s + c3 = c3.view(bs, dim, -1).transpose(1, 2) # 16s + c4 = c4.view(bs, dim, -1).transpose(1, 2) # 32s + + return c1, c2, c3, c4 + + if self.with_cp and x.requires_grad: + outs = cp.checkpoint(_inner_forward, x) + else: + outs = _inner_forward(x) + return outs diff --git a/dinov2/eval/segmentation_m2f/models/backbones/drop_path.py b/dinov2/eval/segmentation_m2f/models/backbones/drop_path.py new file mode 100644 index 0000000000000000000000000000000000000000..864eb8738c44652d12b979fc811503f21cbb00dd --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/backbones/drop_path.py @@ -0,0 +1,32 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py + +from torch import nn + + +def drop_path(x, drop_prob: float = 0.0, training: bool = False): + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0: + random_tensor.div_(keep_prob) + return x * random_tensor + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: float = 0.0): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) diff --git a/dinov2/eval/segmentation_m2f/models/backbones/vit.py b/dinov2/eval/segmentation_m2f/models/backbones/vit.py new file mode 100644 index 0000000000000000000000000000000000000000..8a147570451bd2fbd016ddfafbbfa33035cbd4f8 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/backbones/vit.py @@ -0,0 +1,552 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +"""Vision Transformer (ViT) in PyTorch. + +A PyTorch implement of Vision Transformers as described in: + +'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' + - https://arxiv.org/abs/2010.11929 + +`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers` + - https://arxiv.org/abs/2106.10270 + +The official jax code is released and available at https://github.com/google-research/vision_transformer + +DeiT model defs and weights from https://github.com/facebookresearch/deit, +paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 + +Acknowledgments: +* The paper authors for releasing code and weights, thanks! +* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out +for some einops/einsum fun +* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT +* Bert reference code checks against Huggingface Transformers and Tensorflow Bert + +Hacked together by / Copyright 2021 Ross Wightman +""" +import logging +import math +from functools import partial +from itertools import repeat +from typing import Callable, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from mmcv.runner import BaseModule, load_checkpoint +from mmseg.ops import resize +from mmseg.utils import get_root_logger +from torch import Tensor + +from .drop_path import DropPath + + +def to_2tuple(x): + return tuple(repeat(x, 2)) + + +class Mlp(nn.Module): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Callable[..., nn.Module] = nn.GELU, + drop: float = 0.0, + bias: bool = True, + ) -> None: + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features, bias=bias) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features, bias=bias) + self.drop = nn.Dropout(drop) + + def forward(self, x: Tensor) -> Tensor: + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class SwiGLUFFN(nn.Module): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Callable[..., nn.Module] = None, + drop: float = 0.0, + ) -> None: + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + swiglu_hidden_features = int(2 * hidden_features / 3) + align_as = 8 + swiglu_hidden_features = (swiglu_hidden_features + align_as - 1) // align_as * align_as + self.w1 = nn.Linear(in_features, swiglu_hidden_features) + self.w2 = nn.Linear(in_features, swiglu_hidden_features) + self.w3 = nn.Linear(swiglu_hidden_features, out_features) + + def forward(self, x: Tensor) -> Tensor: + x1 = self.w1(x) + x2 = self.w2(x) + hidden = F.silu(x1) * x2 + return self.w3(hidden) + + +class PatchEmbed(nn.Module): + """2D Image to Patch Embedding.""" + + def __init__( + self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True, bias=True + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + self.img_size = img_size + self.patch_size = patch_size + self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) + self.num_patches = self.grid_size[0] * self.grid_size[1] + self.flatten = flatten + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def forward(self, x): + x = self.proj(x) + _, _, H, W = x.shape + if self.flatten: + x = x.flatten(2).transpose(1, 2) # BCHW -> BNC + x = self.norm(x) + return x, H, W + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, H, W): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple) + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class MemEffAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = False, + attn_drop: float = 0.0, + proj_drop: float = 0.0, + ) -> None: + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x: Tensor, H, W) -> Tensor: + from xformers.ops import memory_efficient_attention, unbind + + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) + + q, k, v = unbind(qkv, 2) + + x = memory_efficient_attention(q, k, v) + x = x.reshape([B, N, C]) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowedAttention(nn.Module): + def __init__( + self, dim, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0, window_size=14, pad_mode="constant" + ): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.window_size = window_size + self.pad_mode = pad_mode + + def forward(self, x, H, W): + B, N, C = x.shape + N_ = self.window_size * self.window_size + H_ = math.ceil(H / self.window_size) * self.window_size + W_ = math.ceil(W / self.window_size) * self.window_size + + qkv = self.qkv(x) # [B, N, C] + qkv = qkv.transpose(1, 2).reshape(B, C * 3, H, W) # [B, C, H, W] + qkv = F.pad(qkv, [0, W_ - W, 0, H_ - H], mode=self.pad_mode) + + qkv = F.unfold( + qkv, kernel_size=(self.window_size, self.window_size), stride=(self.window_size, self.window_size) + ) + B, C_kw_kw, L = qkv.shape # L - the num of windows + qkv = qkv.reshape(B, C * 3, N_, L).permute(0, 3, 2, 1) # [B, L, N_, C] + qkv = qkv.reshape(B, L, N_, 3, self.num_heads, C // self.num_heads).permute(3, 0, 1, 4, 2, 5) + q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple) + + # q,k,v [B, L, num_head, N_, C/num_head] + attn = (q @ k.transpose(-2, -1)) * self.scale # [B, L, num_head, N_, N_] + # if self.mask: + # attn = attn * mask + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) # [B, L, num_head, N_, N_] + # attn @ v = [B, L, num_head, N_, C/num_head] + x = (attn @ v).permute(0, 2, 4, 3, 1).reshape(B, C_kw_kw // 3, L) + + x = F.fold( + x, + output_size=(H_, W_), + kernel_size=(self.window_size, self.window_size), + stride=(self.window_size, self.window_size), + ) # [B, C, H_, W_] + x = x[:, :, :H, :W].reshape(B, C, N).transpose(-1, -2) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +# class WindowedAttention(nn.Module): +# def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., window_size=14, pad_mode="constant"): +# super().__init__() +# self.num_heads = num_heads +# head_dim = dim // num_heads +# self.scale = head_dim ** -0.5 +# +# self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) +# self.attn_drop = nn.Dropout(attn_drop) +# self.proj = nn.Linear(dim, dim) +# self.proj_drop = nn.Dropout(proj_drop) +# self.window_size = window_size +# self.pad_mode = pad_mode +# +# def forward(self, x, H, W): +# B, N, C = x.shape +# +# N_ = self.window_size * self.window_size +# H_ = math.ceil(H / self.window_size) * self.window_size +# W_ = math.ceil(W / self.window_size) * self.window_size +# x = x.view(B, H, W, C) +# x = F.pad(x, [0, 0, 0, W_ - W, 0, H_- H], mode=self.pad_mode) +# +# x = window_partition(x, window_size=self.window_size)# nW*B, window_size, window_size, C +# x = x.view(-1, N_, C) +# +# qkv = self.qkv(x).view(-1, N_, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) +# q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple) +# attn = (q @ k.transpose(-2, -1)) * self.scale # [B, L, num_head, N_, N_] +# attn = attn.softmax(dim=-1) +# attn = self.attn_drop(attn) # [B, L, num_head, N_, N_] +# x = (attn @ v).transpose(1, 2).reshape(-1, self.window_size, self.window_size, C) +# +# x = window_reverse(x, self.window_size, H_, W_) +# x = x[:, :H, :W, :].reshape(B, N, C).contiguous() +# x = self.proj(x) +# x = self.proj_drop(x) +# return x + + +class Block(nn.Module): + def __init__( + self, + dim, + num_heads, + mlp_ratio=4.0, + qkv_bias=False, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + windowed=False, + window_size=14, + pad_mode="constant", + layer_scale=False, + with_cp=False, + ffn_layer=Mlp, + memeff=False, + ): + super().__init__() + self.with_cp = with_cp + self.norm1 = norm_layer(dim) + if windowed: + self.attn = WindowedAttention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + attn_drop=attn_drop, + proj_drop=drop, + window_size=window_size, + pad_mode=pad_mode, + ) + elif memeff: + self.attn = MemEffAttention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop + ) + else: + self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = ffn_layer(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + self.layer_scale = layer_scale + if layer_scale: + self.gamma1 = nn.Parameter(torch.ones((dim)), requires_grad=True) + self.gamma2 = nn.Parameter(torch.ones((dim)), requires_grad=True) + + def forward(self, x, H, W): + def _inner_forward(x): + if self.layer_scale: + x = x + self.drop_path(self.gamma1 * self.attn(self.norm1(x), H, W)) + x = x + self.drop_path(self.gamma2 * self.mlp(self.norm2(x))) + else: + x = x + self.drop_path(self.attn(self.norm1(x), H, W)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + if self.with_cp and x.requires_grad: + x = cp.checkpoint(_inner_forward, x) + else: + x = _inner_forward(x) + + return x + + +class TIMMVisionTransformer(BaseModule): + """Vision Transformer. + + A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` + - https://arxiv.org/abs/2010.11929 + + Includes distillation token & head support for `DeiT: Data-efficient Image Transformers` + - https://arxiv.org/abs/2012.12877 + """ + + def __init__( + self, + img_size=224, + patch_size=16, + in_chans=3, + num_classes=1000, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4.0, + qkv_bias=True, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.0, + layer_scale=True, + embed_layer=PatchEmbed, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + act_layer=nn.GELU, + window_attn=False, + window_size=14, + pretrained=None, + with_cp=False, + pre_norm=False, + ffn_type="mlp", + memeff=False, + ): + """ + Args: + img_size (int, tuple): input image size + patch_size (int, tuple): patch size + in_chans (int): number of input channels + num_classes (int): number of classes for classification head + embed_dim (int): embedding dimension + depth (int): depth of transformer + num_heads (int): number of attention heads + mlp_ratio (int): ratio of mlp hidden dim to embedding dim + qkv_bias (bool): enable bias for qkv if True + drop_rate (float): dropout rate + attn_drop_rate (float): attention dropout rate + drop_path_rate (float): stochastic depth rate + embed_layer (nn.Module): patch embedding layer + norm_layer: (nn.Module): normalization layer + pretrained: (str): pretrained path + """ + super().__init__() + self.num_classes = num_classes + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + self.num_tokens = 1 + norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) + act_layer = act_layer or nn.GELU + self.norm_layer = norm_layer + self.act_layer = act_layer + self.pretrain_size = img_size + self.drop_path_rate = drop_path_rate + self.drop_rate = drop_rate + self.patch_size = patch_size + + window_attn = [window_attn] * depth if not isinstance(window_attn, list) else window_attn + window_size = [window_size] * depth if not isinstance(window_size, list) else window_size + logging.info("window attention:", window_attn) + logging.info("window size:", window_size) + logging.info("layer scale:", layer_scale) + + self.patch_embed = embed_layer( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, bias=not pre_norm + ) + num_patches = self.patch_embed.num_patches + + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + ffn_types = {"mlp": Mlp, "swiglu": SwiGLUFFN} + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.Sequential( + *[ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + act_layer=act_layer, + windowed=window_attn[i], + window_size=window_size[i], + layer_scale=layer_scale, + with_cp=with_cp, + ffn_layer=ffn_types[ffn_type], + memeff=memeff, + ) + for i in range(depth) + ] + ) + + # self.norm = norm_layer(embed_dim) + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + # For CLIP + if pre_norm: + norm_pre = norm_layer(embed_dim) + self.norm_pre = norm_pre + else: + self.norm_pre = nn.Identity() + self.init_weights(pretrained) + + def init_weights(self, pretrained=None): + if isinstance(pretrained, str): + logger = get_root_logger() + load_checkpoint(self, pretrained, map_location="cpu", strict=False, logger=logger) + + def forward_features(self, x): + x, H, W = self.patch_embed(x) + cls_token = self.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_token, x), dim=1) + x = self.pos_drop(x + self.pos_embed) + + # For CLIP + x = self.norm_pre(x) + + for blk in self.blocks: + x = blk(x, H, W) + x = self.norm(x) + return x + + def forward(self, x): + x = self.forward_features(x) + return x + + @staticmethod + def resize_pos_embed(pos_embed, input_shpae, pos_shape, mode): + """Resize pos_embed weights. + + Resize pos_embed using bicubic interpolate method. + Args: + pos_embed (torch.Tensor): Position embedding weights. + input_shpae (tuple): Tuple for (downsampled input image height, + downsampled input image width). + pos_shape (tuple): The resolution of downsampled origin training + image. + mode (str): Algorithm used for upsampling: + ``'nearest'`` | ``'linear'`` | ``'bilinear'`` | ``'bicubic'`` | + ``'trilinear'``. Default: ``'nearest'`` + Return: + torch.Tensor: The resized pos_embed of shape [B, L_new, C] + """ + assert pos_embed.ndim == 3, "shape of pos_embed must be [B, L, C]" + pos_h, pos_w = pos_shape + # keep dim for easy deployment + cls_token_weight = pos_embed[:, 0:1] + pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w) :] + pos_embed_weight = pos_embed_weight.reshape(1, pos_h, pos_w, pos_embed.shape[2]).permute(0, 3, 1, 2) + pos_embed_weight = resize(pos_embed_weight, size=input_shpae, align_corners=False, mode=mode) + pos_embed_weight = torch.flatten(pos_embed_weight, 2).transpose(1, 2) + pos_embed = torch.cat((cls_token_weight, pos_embed_weight), dim=1) + return pos_embed diff --git a/dinov2/eval/segmentation_m2f/models/backbones/vit_adapter.py b/dinov2/eval/segmentation_m2f/models/backbones/vit_adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..ebc4f0f65e04ed764464d141607b3b2073220f6b --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/backbones/vit_adapter.py @@ -0,0 +1,217 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmseg.models.builder import BACKBONES +from torch.nn.init import normal_ + +from ...ops.modules import MSDeformAttn +from .adapter_modules import InteractionBlock, InteractionBlockWithCls, SpatialPriorModule, deform_inputs +from .vit import TIMMVisionTransformer + + +@BACKBONES.register_module() +class ViTAdapter(TIMMVisionTransformer): + def __init__( + self, + pretrain_size=224, + num_heads=12, + conv_inplane=64, + n_points=4, + deform_num_heads=6, + init_values=0.0, + interaction_indexes=None, + with_cffn=True, + cffn_ratio=0.25, + deform_ratio=1.0, + add_vit_feature=True, + pretrained=None, + use_extra_extractor=True, + freeze_vit=False, + use_cls=True, + with_cp=False, + *args, + **kwargs + ): + + super().__init__(num_heads=num_heads, pretrained=pretrained, with_cp=with_cp, *args, **kwargs) + if freeze_vit: + for param in self.parameters(): + param.requires_grad = False + + # self.num_classes = 80 + self.use_cls = use_cls + if not self.use_cls: + self.cls_token = None + self.num_block = len(self.blocks) + self.pretrain_size = (pretrain_size, pretrain_size) + self.interaction_indexes = interaction_indexes + self.add_vit_feature = add_vit_feature + embed_dim = self.embed_dim + + block_fn = InteractionBlockWithCls if use_cls else InteractionBlock + + self.level_embed = nn.Parameter(torch.zeros(3, embed_dim)) + self.spm = SpatialPriorModule(inplanes=conv_inplane, embed_dim=embed_dim, with_cp=False) + self.interactions = nn.Sequential( + *[ + block_fn( + dim=embed_dim, + num_heads=deform_num_heads, + n_points=n_points, + init_values=init_values, + drop_path=self.drop_path_rate, + norm_layer=self.norm_layer, + with_cffn=with_cffn, + cffn_ratio=cffn_ratio, + deform_ratio=deform_ratio, + extra_extractor=((True if i == len(interaction_indexes) - 1 else False) and use_extra_extractor), + with_cp=with_cp, + ) + for i in range(len(interaction_indexes)) + ] + ) + self.up = nn.ConvTranspose2d(embed_dim, embed_dim, 2, 2) + self.norm1 = nn.SyncBatchNorm(embed_dim) + self.norm2 = nn.SyncBatchNorm(embed_dim) + self.norm3 = nn.SyncBatchNorm(embed_dim) + self.norm4 = nn.SyncBatchNorm(embed_dim) + + self.up.apply(self._init_weights) + self.spm.apply(self._init_weights) + self.interactions.apply(self._init_weights) + self.apply(self._init_deform_weights) + normal_(self.level_embed) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + torch.nn.init.trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm) or isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d): + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + fan_out //= m.groups + m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) + if m.bias is not None: + m.bias.data.zero_() + + def _get_pos_embed(self, pos_embed, H, W): + pos_embed = pos_embed.reshape( + 1, self.pretrain_size[0] // self.patch_size, self.pretrain_size[1] // self.patch_size, -1 + ).permute(0, 3, 1, 2) + pos_embed = ( + F.interpolate(pos_embed, size=(H, W), mode="bicubic", align_corners=False) + .reshape(1, -1, H * W) + .permute(0, 2, 1) + ) + return pos_embed + + def _init_deform_weights(self, m): + if isinstance(m, MSDeformAttn): + m._reset_parameters() + + def _add_level_embed(self, c2, c3, c4): + c2 = c2 + self.level_embed[0] + c3 = c3 + self.level_embed[1] + c4 = c4 + self.level_embed[2] + return c2, c3, c4 + + def forward(self, x): + deform_inputs1, deform_inputs2 = deform_inputs(x, self.patch_size) + + # SPM forward + c1, c2, c3, c4 = self.spm(x) + c2, c3, c4 = self._add_level_embed(c2, c3, c4) + c = torch.cat([c2, c3, c4], dim=1) + + # Patch Embedding forward + H_c, W_c = x.shape[2] // 16, x.shape[3] // 16 + x, H_toks, W_toks = self.patch_embed(x) + # print("H_toks, W_toks =", H_toks, W_toks) + bs, n, dim = x.shape + pos_embed = self._get_pos_embed(self.pos_embed[:, 1:], H_toks, W_toks) + if self.use_cls: + cls_token = self.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_token, x), dim=1) + pos_embed = torch.cat((self.pos_embed[:, :1], pos_embed), dim=1) + x = self.pos_drop(x + pos_embed) + # For CLIP + x = self.norm_pre(x) + + # Interaction + if self.use_cls: + cls, x = ( + x[ + :, + :1, + ], + x[ + :, + 1:, + ], + ) + outs = list() + for i, layer in enumerate(self.interactions): + indexes = self.interaction_indexes[i] + if self.use_cls: + x, c, cls = layer( + x, + c, + cls, + self.blocks[indexes[0] : indexes[-1] + 1], + deform_inputs1, + deform_inputs2, + H_c, + W_c, + H_toks, + W_toks, + ) + else: + x, c = layer( + x, + c, + self.blocks[indexes[0] : indexes[-1] + 1], + deform_inputs1, + deform_inputs2, + H_c, + W_c, + H_toks, + W_toks, + ) + outs.append(x.transpose(1, 2).view(bs, dim, H_toks, W_toks).contiguous()) + + # Split & Reshape + c2 = c[:, 0 : c2.size(1), :] + c3 = c[:, c2.size(1) : c2.size(1) + c3.size(1), :] + c4 = c[:, c2.size(1) + c3.size(1) :, :] + + c2 = c2.transpose(1, 2).view(bs, dim, H_c * 2, W_c * 2).contiguous() + c3 = c3.transpose(1, 2).view(bs, dim, H_c, W_c).contiguous() + c4 = c4.transpose(1, 2).view(bs, dim, H_c // 2, W_c // 2).contiguous() + c1 = self.up(c2) + c1 + + if self.add_vit_feature: + x1, x2, x3, x4 = outs + + x1 = F.interpolate(x1, size=(4 * H_c, 4 * W_c), mode="bilinear", align_corners=False) + x2 = F.interpolate(x2, size=(2 * H_c, 2 * W_c), mode="bilinear", align_corners=False) + x3 = F.interpolate(x3, size=(1 * H_c, 1 * W_c), mode="bilinear", align_corners=False) + x4 = F.interpolate(x4, size=(H_c // 2, W_c // 2), mode="bilinear", align_corners=False) + # print(c1.shape, c2.shape, c3.shape, c4.shape, x1.shape, x2.shape, x3.shape, x4.shape, H_c, H_toks) + c1, c2, c3, c4 = c1 + x1, c2 + x2, c3 + x3, c4 + x4 + + # Final Norm + f1 = self.norm1(c1) + f2 = self.norm2(c2) + f3 = self.norm3(c3) + f4 = self.norm4(c4) + return [f1, f2, f3, f4] diff --git a/dinov2/eval/segmentation_m2f/models/builder.py b/dinov2/eval/segmentation_m2f/models/builder.py new file mode 100644 index 0000000000000000000000000000000000000000..d7cf7b919f6b0e8e00bde45bc244d9c29a36fed6 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/builder.py @@ -0,0 +1,25 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from mmcv.utils import Registry + +TRANSFORMER = Registry("Transformer") +MASK_ASSIGNERS = Registry("mask_assigner") +MATCH_COST = Registry("match_cost") + + +def build_match_cost(cfg): + """Build Match Cost.""" + return MATCH_COST.build(cfg) + + +def build_assigner(cfg): + """Build Assigner.""" + return MASK_ASSIGNERS.build(cfg) + + +def build_transformer(cfg): + """Build Transformer.""" + return TRANSFORMER.build(cfg) diff --git a/dinov2/eval/segmentation_m2f/models/decode_heads/__init__.py b/dinov2/eval/segmentation_m2f/models/decode_heads/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..01f08b88950750337781fc671adfea2a935ea8fe --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/decode_heads/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .mask2former_head import Mask2FormerHead diff --git a/dinov2/eval/segmentation_m2f/models/decode_heads/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/segmentation_m2f/models/decode_heads/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8b34a0f0dd830e6c5b81a45e2af4177e42dd66b7 Binary files /dev/null and b/dinov2/eval/segmentation_m2f/models/decode_heads/__pycache__/__init__.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/models/decode_heads/__pycache__/mask2former_head.cpython-310.pyc b/dinov2/eval/segmentation_m2f/models/decode_heads/__pycache__/mask2former_head.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2c0fbc768b2cd1f1ef5c46cfa4966ad9d89bc30e Binary files /dev/null and b/dinov2/eval/segmentation_m2f/models/decode_heads/__pycache__/mask2former_head.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/models/decode_heads/mask2former_head.py b/dinov2/eval/segmentation_m2f/models/decode_heads/mask2former_head.py new file mode 100644 index 0000000000000000000000000000000000000000..d1705fc444fa8d1583d88fca36d7fe1e060db9e7 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/decode_heads/mask2former_head.py @@ -0,0 +1,544 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import copy + +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import Conv2d, build_plugin_layer, caffe2_xavier_init +from mmcv.cnn.bricks.transformer import build_positional_encoding, build_transformer_layer_sequence +from mmcv.ops import point_sample +from mmcv.runner import ModuleList, force_fp32 +from mmseg.models.builder import HEADS, build_loss +from mmseg.models.decode_heads.decode_head import BaseDecodeHead + +from ...core import build_sampler, multi_apply, reduce_mean +from ..builder import build_assigner +from ..utils import get_uncertain_point_coords_with_randomness + + +@HEADS.register_module() +class Mask2FormerHead(BaseDecodeHead): + """Implements the Mask2Former head. + + See `Masked-attention Mask Transformer for Universal Image + Segmentation `_ for details. + + Args: + in_channels (list[int]): Number of channels in the input feature map. + feat_channels (int): Number of channels for features. + out_channels (int): Number of channels for output. + num_things_classes (int): Number of things. + num_stuff_classes (int): Number of stuff. + num_queries (int): Number of query in Transformer decoder. + pixel_decoder (:obj:`mmcv.ConfigDict` | dict): Config for pixel + decoder. Defaults to None. + enforce_decoder_input_project (bool, optional): Whether to add + a layer to change the embed_dim of tranformer encoder in + pixel decoder to the embed_dim of transformer decoder. + Defaults to False. + transformer_decoder (:obj:`mmcv.ConfigDict` | dict): Config for + transformer decoder. Defaults to None. + positional_encoding (:obj:`mmcv.ConfigDict` | dict): Config for + transformer decoder position encoding. Defaults to None. + loss_cls (:obj:`mmcv.ConfigDict` | dict): Config of the classification + loss. Defaults to None. + loss_mask (:obj:`mmcv.ConfigDict` | dict): Config of the mask loss. + Defaults to None. + loss_dice (:obj:`mmcv.ConfigDict` | dict): Config of the dice loss. + Defaults to None. + train_cfg (:obj:`mmcv.ConfigDict` | dict): Training config of + Mask2Former head. + test_cfg (:obj:`mmcv.ConfigDict` | dict): Testing config of + Mask2Former head. + init_cfg (dict or list[dict], optional): Initialization config dict. + Defaults to None. + """ + + def __init__( + self, + in_channels, + feat_channels, + out_channels, + num_things_classes=80, + num_stuff_classes=53, + num_queries=100, + num_transformer_feat_level=3, + pixel_decoder=None, + enforce_decoder_input_project=False, + transformer_decoder=None, + positional_encoding=None, + loss_cls=None, + loss_mask=None, + loss_dice=None, + train_cfg=None, + test_cfg=None, + init_cfg=None, + **kwargs, + ): + super(Mask2FormerHead, self).__init__( + in_channels=in_channels, + channels=feat_channels, + num_classes=(num_things_classes + num_stuff_classes), + init_cfg=init_cfg, + input_transform="multiple_select", + **kwargs, + ) + self.num_things_classes = num_things_classes + self.num_stuff_classes = num_stuff_classes + self.num_classes = self.num_things_classes + self.num_stuff_classes + self.num_queries = num_queries + self.num_transformer_feat_level = num_transformer_feat_level + self.num_heads = transformer_decoder.transformerlayers.attn_cfgs.num_heads + self.num_transformer_decoder_layers = transformer_decoder.num_layers + assert pixel_decoder.encoder.transformerlayers.attn_cfgs.num_levels == num_transformer_feat_level + pixel_decoder_ = copy.deepcopy(pixel_decoder) + pixel_decoder_.update(in_channels=in_channels, feat_channels=feat_channels, out_channels=out_channels) + self.pixel_decoder = build_plugin_layer(pixel_decoder_)[1] + self.transformer_decoder = build_transformer_layer_sequence(transformer_decoder) + self.decoder_embed_dims = self.transformer_decoder.embed_dims + + self.decoder_input_projs = ModuleList() + # from low resolution to high resolution + for _ in range(num_transformer_feat_level): + if self.decoder_embed_dims != feat_channels or enforce_decoder_input_project: + self.decoder_input_projs.append(Conv2d(feat_channels, self.decoder_embed_dims, kernel_size=1)) + else: + self.decoder_input_projs.append(nn.Identity()) + self.decoder_positional_encoding = build_positional_encoding(positional_encoding) + self.query_embed = nn.Embedding(self.num_queries, feat_channels) + self.query_feat = nn.Embedding(self.num_queries, feat_channels) + # from low resolution to high resolution + self.level_embed = nn.Embedding(self.num_transformer_feat_level, feat_channels) + + self.cls_embed = nn.Linear(feat_channels, self.num_classes + 1) + self.mask_embed = nn.Sequential( + nn.Linear(feat_channels, feat_channels), + nn.ReLU(inplace=True), + nn.Linear(feat_channels, feat_channels), + nn.ReLU(inplace=True), + nn.Linear(feat_channels, out_channels), + ) + self.conv_seg = None # fix a bug here (conv_seg is not used) + + self.test_cfg = test_cfg + self.train_cfg = train_cfg + if train_cfg: + self.assigner = build_assigner(self.train_cfg.assigner) + self.sampler = build_sampler(self.train_cfg.sampler, context=self) + self.num_points = self.train_cfg.get("num_points", 12544) + self.oversample_ratio = self.train_cfg.get("oversample_ratio", 3.0) + self.importance_sample_ratio = self.train_cfg.get("importance_sample_ratio", 0.75) + + self.class_weight = loss_cls.class_weight + self.loss_cls = build_loss(loss_cls) + self.loss_mask = build_loss(loss_mask) + self.loss_dice = build_loss(loss_dice) + + def init_weights(self): + for m in self.decoder_input_projs: + if isinstance(m, Conv2d): + caffe2_xavier_init(m, bias=0) + + self.pixel_decoder.init_weights() + + for p in self.transformer_decoder.parameters(): + if p.dim() > 1: + nn.init.xavier_normal_(p) + + def get_targets(self, cls_scores_list, mask_preds_list, gt_labels_list, gt_masks_list, img_metas): + """Compute classification and mask targets for all images for a decoder + layer. + + Args: + cls_scores_list (list[Tensor]): Mask score logits from a single + decoder layer for all images. Each with shape [num_queries, + cls_out_channels]. + mask_preds_list (list[Tensor]): Mask logits from a single decoder + layer for all images. Each with shape [num_queries, h, w]. + gt_labels_list (list[Tensor]): Ground truth class indices for all + images. Each with shape (n, ), n is the sum of number of stuff + type and number of instance in a image. + gt_masks_list (list[Tensor]): Ground truth mask for each image, + each with shape (n, h, w). + img_metas (list[dict]): List of image meta information. + + Returns: + tuple[list[Tensor]]: a tuple containing the following targets. + + - labels_list (list[Tensor]): Labels of all images. + Each with shape [num_queries, ]. + - label_weights_list (list[Tensor]): Label weights of all + images.Each with shape [num_queries, ]. + - mask_targets_list (list[Tensor]): Mask targets of all images. + Each with shape [num_queries, h, w]. + - mask_weights_list (list[Tensor]): Mask weights of all images. + Each with shape [num_queries, ]. + - num_total_pos (int): Number of positive samples in all + images. + - num_total_neg (int): Number of negative samples in all + images. + """ + ( + labels_list, + label_weights_list, + mask_targets_list, + mask_weights_list, + pos_inds_list, + neg_inds_list, + ) = multi_apply( + self._get_target_single, cls_scores_list, mask_preds_list, gt_labels_list, gt_masks_list, img_metas + ) + + num_total_pos = sum((inds.numel() for inds in pos_inds_list)) + num_total_neg = sum((inds.numel() for inds in neg_inds_list)) + return (labels_list, label_weights_list, mask_targets_list, mask_weights_list, num_total_pos, num_total_neg) + + def _get_target_single(self, cls_score, mask_pred, gt_labels, gt_masks, img_metas): + """Compute classification and mask targets for one image. + + Args: + cls_score (Tensor): Mask score logits from a single decoder layer + for one image. Shape (num_queries, cls_out_channels). + mask_pred (Tensor): Mask logits for a single decoder layer for one + image. Shape (num_queries, h, w). + gt_labels (Tensor): Ground truth class indices for one image with + shape (num_gts, ). + gt_masks (Tensor): Ground truth mask for each image, each with + shape (num_gts, h, w). + img_metas (dict): Image informtation. + + Returns: + tuple[Tensor]: A tuple containing the following for one image. + + - labels (Tensor): Labels of each image. \ + shape (num_queries, ). + - label_weights (Tensor): Label weights of each image. \ + shape (num_queries, ). + - mask_targets (Tensor): Mask targets of each image. \ + shape (num_queries, h, w). + - mask_weights (Tensor): Mask weights of each image. \ + shape (num_queries, ). + - pos_inds (Tensor): Sampled positive indices for each \ + image. + - neg_inds (Tensor): Sampled negative indices for each \ + image. + """ + # sample points + num_queries = cls_score.shape[0] + num_gts = gt_labels.shape[0] + + point_coords = torch.rand((1, self.num_points, 2), device=cls_score.device) + # shape (num_queries, num_points) + mask_points_pred = point_sample(mask_pred.unsqueeze(1), point_coords.repeat(num_queries, 1, 1)).squeeze(1) + # shape (num_gts, num_points) + gt_points_masks = point_sample(gt_masks.unsqueeze(1).float(), point_coords.repeat(num_gts, 1, 1)).squeeze(1) + + # assign and sample + assign_result = self.assigner.assign(cls_score, mask_points_pred, gt_labels, gt_points_masks, img_metas) + sampling_result = self.sampler.sample(assign_result, mask_pred, gt_masks) + pos_inds = sampling_result.pos_inds + neg_inds = sampling_result.neg_inds + + # label target + labels = gt_labels.new_full((self.num_queries,), self.num_classes, dtype=torch.long) + labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds] + label_weights = gt_labels.new_ones((self.num_queries,)) + + # mask target + mask_targets = gt_masks[sampling_result.pos_assigned_gt_inds] + mask_weights = mask_pred.new_zeros((self.num_queries,)) + mask_weights[pos_inds] = 1.0 + + return (labels, label_weights, mask_targets, mask_weights, pos_inds, neg_inds) + + def loss_single(self, cls_scores, mask_preds, gt_labels_list, gt_masks_list, img_metas): + """Loss function for outputs from a single decoder layer. + + Args: + cls_scores (Tensor): Mask score logits from a single decoder layer + for all images. Shape (batch_size, num_queries, + cls_out_channels). Note `cls_out_channels` should includes + background. + mask_preds (Tensor): Mask logits for a pixel decoder for all + images. Shape (batch_size, num_queries, h, w). + gt_labels_list (list[Tensor]): Ground truth class indices for each + image, each with shape (num_gts, ). + gt_masks_list (list[Tensor]): Ground truth mask for each image, + each with shape (num_gts, h, w). + img_metas (list[dict]): List of image meta information. + + Returns: + tuple[Tensor]: Loss components for outputs from a single \ + decoder layer. + """ + num_imgs = cls_scores.size(0) + cls_scores_list = [cls_scores[i] for i in range(num_imgs)] + mask_preds_list = [mask_preds[i] for i in range(num_imgs)] + ( + labels_list, + label_weights_list, + mask_targets_list, + mask_weights_list, + num_total_pos, + num_total_neg, + ) = self.get_targets(cls_scores_list, mask_preds_list, gt_labels_list, gt_masks_list, img_metas) + # shape (batch_size, num_queries) + labels = torch.stack(labels_list, dim=0) + # shape (batch_size, num_queries) + label_weights = torch.stack(label_weights_list, dim=0) + # shape (num_total_gts, h, w) + mask_targets = torch.cat(mask_targets_list, dim=0) + # shape (batch_size, num_queries) + mask_weights = torch.stack(mask_weights_list, dim=0) + + # classfication loss + # shape (batch_size * num_queries, ) + cls_scores = cls_scores.flatten(0, 1) + labels = labels.flatten(0, 1) + label_weights = label_weights.flatten(0, 1) + + class_weight = cls_scores.new_tensor(self.class_weight) + loss_cls = self.loss_cls(cls_scores, labels, label_weights, avg_factor=class_weight[labels].sum()) + + num_total_masks = reduce_mean(cls_scores.new_tensor([num_total_pos])) + num_total_masks = max(num_total_masks, 1) + + # extract positive ones + # shape (batch_size, num_queries, h, w) -> (num_total_gts, h, w) + mask_preds = mask_preds[mask_weights > 0] + + if mask_targets.shape[0] == 0: + # zero match + loss_dice = mask_preds.sum() + loss_mask = mask_preds.sum() + return loss_cls, loss_mask, loss_dice + + with torch.no_grad(): + points_coords = get_uncertain_point_coords_with_randomness( + mask_preds.unsqueeze(1), None, self.num_points, self.oversample_ratio, self.importance_sample_ratio + ) + # shape (num_total_gts, h, w) -> (num_total_gts, num_points) + mask_point_targets = point_sample(mask_targets.unsqueeze(1).float(), points_coords).squeeze(1) + # shape (num_queries, h, w) -> (num_queries, num_points) + mask_point_preds = point_sample(mask_preds.unsqueeze(1), points_coords).squeeze(1) + + # dice loss + loss_dice = self.loss_dice(mask_point_preds, mask_point_targets, avg_factor=num_total_masks) + + # mask loss + # shape (num_queries, num_points) -> (num_queries * num_points, ) + mask_point_preds = mask_point_preds.reshape(-1, 1) + # shape (num_total_gts, num_points) -> (num_total_gts * num_points, ) + mask_point_targets = mask_point_targets.reshape(-1) + loss_mask = self.loss_mask(mask_point_preds, mask_point_targets, avg_factor=num_total_masks * self.num_points) + + return loss_cls, loss_mask, loss_dice + + @force_fp32(apply_to=("all_cls_scores", "all_mask_preds")) + def loss(self, all_cls_scores, all_mask_preds, gt_labels_list, gt_masks_list, img_metas): + """Loss function. + + Args: + all_cls_scores (Tensor): Classification scores for all decoder + layers with shape [num_decoder, batch_size, num_queries, + cls_out_channels]. + all_mask_preds (Tensor): Mask scores for all decoder layers with + shape [num_decoder, batch_size, num_queries, h, w]. + gt_labels_list (list[Tensor]): Ground truth class indices for each + image with shape (n, ). n is the sum of number of stuff type + and number of instance in a image. + gt_masks_list (list[Tensor]): Ground truth mask for each image with + shape (n, h, w). + img_metas (list[dict]): List of image meta information. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + num_dec_layers = len(all_cls_scores) + all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)] + all_gt_masks_list = [gt_masks_list for _ in range(num_dec_layers)] + img_metas_list = [img_metas for _ in range(num_dec_layers)] + losses_cls, losses_mask, losses_dice = multi_apply( + self.loss_single, all_cls_scores, all_mask_preds, all_gt_labels_list, all_gt_masks_list, img_metas_list + ) + + loss_dict = dict() + # loss from the last decoder layer + loss_dict["loss_cls"] = losses_cls[-1] + loss_dict["loss_mask"] = losses_mask[-1] + loss_dict["loss_dice"] = losses_dice[-1] + # loss from other decoder layers + num_dec_layer = 0 + for loss_cls_i, loss_mask_i, loss_dice_i in zip(losses_cls[:-1], losses_mask[:-1], losses_dice[:-1]): + loss_dict[f"d{num_dec_layer}.loss_cls"] = loss_cls_i + loss_dict[f"d{num_dec_layer}.loss_mask"] = loss_mask_i + loss_dict[f"d{num_dec_layer}.loss_dice"] = loss_dice_i + num_dec_layer += 1 + return loss_dict + + def forward_head(self, decoder_out, mask_feature, attn_mask_target_size): + """Forward for head part which is called after every decoder layer. + + Args: + decoder_out (Tensor): in shape (num_queries, batch_size, c). + mask_feature (Tensor): in shape (batch_size, c, h, w). + attn_mask_target_size (tuple[int, int]): target attention + mask size. + + Returns: + tuple: A tuple contain three elements. + + - cls_pred (Tensor): Classification scores in shape \ + (batch_size, num_queries, cls_out_channels). \ + Note `cls_out_channels` should includes background. + - mask_pred (Tensor): Mask scores in shape \ + (batch_size, num_queries,h, w). + - attn_mask (Tensor): Attention mask in shape \ + (batch_size * num_heads, num_queries, h, w). + """ + decoder_out = self.transformer_decoder.post_norm(decoder_out) + decoder_out = decoder_out.transpose(0, 1) + # shape (num_queries, batch_size, c) + cls_pred = self.cls_embed(decoder_out) + # shape (num_queries, batch_size, c) + mask_embed = self.mask_embed(decoder_out) + # shape (num_queries, batch_size, h, w) + mask_pred = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_feature) + attn_mask = F.interpolate(mask_pred, attn_mask_target_size, mode="bilinear", align_corners=False) + # shape (num_queries, batch_size, h, w) -> + # (batch_size * num_head, num_queries, h, w) + attn_mask = attn_mask.flatten(2).unsqueeze(1).repeat((1, self.num_heads, 1, 1)).flatten(0, 1) + attn_mask = attn_mask.sigmoid() < 0.5 + attn_mask = attn_mask.detach() + + return cls_pred, mask_pred, attn_mask + + def forward(self, feats, img_metas): + """Forward function. + + Args: + feats (list[Tensor]): Multi scale Features from the + upstream network, each is a 4D-tensor. + img_metas (list[dict]): List of image information. + + Returns: + tuple: A tuple contains two elements. + + - cls_pred_list (list[Tensor)]: Classification logits \ + for each decoder layer. Each is a 3D-tensor with shape \ + (batch_size, num_queries, cls_out_channels). \ + Note `cls_out_channels` should includes background. + - mask_pred_list (list[Tensor]): Mask logits for each \ + decoder layer. Each with shape (batch_size, num_queries, \ + h, w). + """ + batch_size = len(img_metas) + mask_features, multi_scale_memorys = self.pixel_decoder(feats) + # multi_scale_memorys (from low resolution to high resolution) + decoder_inputs = [] + decoder_positional_encodings = [] + for i in range(self.num_transformer_feat_level): + decoder_input = self.decoder_input_projs[i](multi_scale_memorys[i]) + # shape (batch_size, c, h, w) -> (h*w, batch_size, c) + decoder_input = decoder_input.flatten(2).permute(2, 0, 1) + level_embed = self.level_embed.weight[i].view(1, 1, -1) + decoder_input = decoder_input + level_embed + # shape (batch_size, c, h, w) -> (h*w, batch_size, c) + mask = decoder_input.new_zeros((batch_size,) + multi_scale_memorys[i].shape[-2:], dtype=torch.bool) + decoder_positional_encoding = self.decoder_positional_encoding(mask) + decoder_positional_encoding = decoder_positional_encoding.flatten(2).permute(2, 0, 1) + decoder_inputs.append(decoder_input) + decoder_positional_encodings.append(decoder_positional_encoding) + # shape (num_queries, c) -> (num_queries, batch_size, c) + query_feat = self.query_feat.weight.unsqueeze(1).repeat((1, batch_size, 1)) + query_embed = self.query_embed.weight.unsqueeze(1).repeat((1, batch_size, 1)) + + cls_pred_list = [] + mask_pred_list = [] + cls_pred, mask_pred, attn_mask = self.forward_head(query_feat, mask_features, multi_scale_memorys[0].shape[-2:]) + cls_pred_list.append(cls_pred) + mask_pred_list.append(mask_pred) + + for i in range(self.num_transformer_decoder_layers): + level_idx = i % self.num_transformer_feat_level + # if a mask is all True(all background), then set it all False. + attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False + + # cross_attn + self_attn + layer = self.transformer_decoder.layers[i] + attn_masks = [attn_mask, None] + query_feat = layer( + query=query_feat, + key=decoder_inputs[level_idx], + value=decoder_inputs[level_idx], + query_pos=query_embed, + key_pos=decoder_positional_encodings[level_idx], + attn_masks=attn_masks, + query_key_padding_mask=None, + # here we do not apply masking on padded region + key_padding_mask=None, + ) + cls_pred, mask_pred, attn_mask = self.forward_head( + query_feat, mask_features, multi_scale_memorys[(i + 1) % self.num_transformer_feat_level].shape[-2:] + ) + + cls_pred_list.append(cls_pred) + mask_pred_list.append(mask_pred) + + return cls_pred_list, mask_pred_list + + def forward_train(self, x, img_metas, gt_semantic_seg, gt_labels, gt_masks): + """Forward function for training mode. + + Args: + x (list[Tensor]): Multi-level features from the upstream network, + each is a 4D-tensor. + img_metas (list[Dict]): List of image information. + gt_semantic_seg (list[tensor]):Each element is the ground truth + of semantic segmentation with the shape (N, H, W). + train_cfg (dict): The training config, which not been used in + maskformer. + gt_labels (list[Tensor]): Each element is ground truth labels of + each box, shape (num_gts,). + gt_masks (list[BitmapMasks]): Each element is masks of instances + of a image, shape (num_gts, h, w). + + Returns: + losses (dict[str, Tensor]): a dictionary of loss components + """ + + # forward + all_cls_scores, all_mask_preds = self(x, img_metas) + + # loss + losses = self.loss(all_cls_scores, all_mask_preds, gt_labels, gt_masks, img_metas) + + return losses + + def forward_test(self, inputs, img_metas, test_cfg): + """Test segment without test-time aumengtation. + + Only the output of last decoder layers was used. + + Args: + inputs (list[Tensor]): Multi-level features from the + upstream network, each is a 4D-tensor. + img_metas (list[dict]): List of image information. + test_cfg (dict): Testing config. + + Returns: + seg_mask (Tensor): Predicted semantic segmentation logits. + """ + all_cls_scores, all_mask_preds = self(inputs, img_metas) + cls_score, mask_pred = all_cls_scores[-1], all_mask_preds[-1] + ori_h, ori_w, _ = img_metas[0]["ori_shape"] + + # semantic inference + cls_score = F.softmax(cls_score, dim=-1)[..., :-1] + mask_pred = mask_pred.sigmoid() + seg_mask = torch.einsum("bqc,bqhw->bchw", cls_score, mask_pred) + return seg_mask diff --git a/dinov2/eval/segmentation_m2f/models/losses/__init__.py b/dinov2/eval/segmentation_m2f/models/losses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..229a887817372f4991b32354180592cfb236d728 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/losses/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .cross_entropy_loss import CrossEntropyLoss, binary_cross_entropy, cross_entropy, mask_cross_entropy +from .dice_loss import DiceLoss +from .match_costs import ClassificationCost, CrossEntropyLossCost, DiceCost diff --git a/dinov2/eval/segmentation_m2f/models/losses/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/segmentation_m2f/models/losses/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1a976b9e5f2a39a468e80601b9e7b1b31d11604b Binary files /dev/null and b/dinov2/eval/segmentation_m2f/models/losses/__pycache__/__init__.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/models/losses/__pycache__/cross_entropy_loss.cpython-310.pyc b/dinov2/eval/segmentation_m2f/models/losses/__pycache__/cross_entropy_loss.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..717cd684fdba97da2389820fb558af0de344a966 Binary files /dev/null and b/dinov2/eval/segmentation_m2f/models/losses/__pycache__/cross_entropy_loss.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/models/losses/__pycache__/dice_loss.cpython-310.pyc b/dinov2/eval/segmentation_m2f/models/losses/__pycache__/dice_loss.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fed0ff001da4c7954ee2fa5d4c38042aab9ff363 Binary files /dev/null and b/dinov2/eval/segmentation_m2f/models/losses/__pycache__/dice_loss.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/models/losses/__pycache__/match_costs.cpython-310.pyc b/dinov2/eval/segmentation_m2f/models/losses/__pycache__/match_costs.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..28d93c590c982a454a2ff63031cc8449ce989e9e Binary files /dev/null and b/dinov2/eval/segmentation_m2f/models/losses/__pycache__/match_costs.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/models/losses/cross_entropy_loss.py b/dinov2/eval/segmentation_m2f/models/losses/cross_entropy_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..0a1f9dd4aa52ebe94cc527db36b1c7fa2f53813e --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/losses/cross_entropy_loss.py @@ -0,0 +1,279 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import warnings + +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmseg.models.builder import LOSSES +from mmseg.models.losses.utils import get_class_weight, weight_reduce_loss + + +def cross_entropy( + pred, + label, + weight=None, + class_weight=None, + reduction="mean", + avg_factor=None, + ignore_index=-100, + avg_non_ignore=False, +): + """cross_entropy. The wrapper function for :func:`F.cross_entropy` + + Args: + pred (torch.Tensor): The prediction with shape (N, 1). + label (torch.Tensor): The learning label of the prediction. + weight (torch.Tensor, optional): Sample-wise loss weight. + Default: None. + class_weight (list[float], optional): The weight for each class. + Default: None. + reduction (str, optional): The method used to reduce the loss. + Options are 'none', 'mean' and 'sum'. Default: 'mean'. + avg_factor (int, optional): Average factor that is used to average + the loss. Default: None. + ignore_index (int): Specifies a target value that is ignored and + does not contribute to the input gradients. When + ``avg_non_ignore `` is ``True``, and the ``reduction`` is + ``''mean''``, the loss is averaged over non-ignored targets. + Defaults: -100. + avg_non_ignore (bool): The flag decides to whether the loss is + only averaged over non-ignored targets. Default: False. + `New in version 0.23.0.` + """ + + # class_weight is a manual rescaling weight given to each class. + # If given, has to be a Tensor of size C element-wise losses + loss = F.cross_entropy(pred, label, weight=class_weight, reduction="none", ignore_index=ignore_index) + + # apply weights and do the reduction + # average loss over non-ignored elements + # pytorch's official cross_entropy average loss over non-ignored elements + # refer to https://github.com/pytorch/pytorch/blob/56b43f4fec1f76953f15a627694d4bba34588969/torch/nn/functional.py#L2660 # noqa + if (avg_factor is None) and avg_non_ignore and reduction == "mean": + avg_factor = label.numel() - (label == ignore_index).sum().item() + if weight is not None: + weight = weight.float() + loss = weight_reduce_loss(loss, weight=weight, reduction=reduction, avg_factor=avg_factor) + + return loss + + +def _expand_onehot_labels(labels, label_weights, target_shape, ignore_index): + """Expand onehot labels to match the size of prediction.""" + bin_labels = labels.new_zeros(target_shape) + valid_mask = (labels >= 0) & (labels != ignore_index) + inds = torch.nonzero(valid_mask, as_tuple=True) + + if inds[0].numel() > 0: + if labels.dim() == 3: + bin_labels[inds[0], labels[valid_mask], inds[1], inds[2]] = 1 + else: + bin_labels[inds[0], labels[valid_mask]] = 1 + + valid_mask = valid_mask.unsqueeze(1).expand(target_shape).float() + + if label_weights is None: + bin_label_weights = valid_mask + else: + bin_label_weights = label_weights.unsqueeze(1).expand(target_shape) + bin_label_weights = bin_label_weights * valid_mask + + return bin_labels, bin_label_weights, valid_mask + + +def binary_cross_entropy( + pred, + label, + weight=None, + reduction="mean", + avg_factor=None, + class_weight=None, + ignore_index=-100, + avg_non_ignore=False, + **kwargs, +): + """Calculate the binary CrossEntropy loss. + + Args: + pred (torch.Tensor): The prediction with shape (N, 1). + label (torch.Tensor): The learning label of the prediction. + Note: In bce loss, label < 0 is invalid. + weight (torch.Tensor, optional): Sample-wise loss weight. + reduction (str, optional): The method used to reduce the loss. + Options are "none", "mean" and "sum". + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + class_weight (list[float], optional): The weight for each class. + ignore_index (int): The label index to be ignored. Default: -100. + avg_non_ignore (bool): The flag decides to whether the loss is + only averaged over non-ignored targets. Default: False. + `New in version 0.23.0.` + + Returns: + torch.Tensor: The calculated loss + """ + if pred.size(1) == 1: + # For binary class segmentation, the shape of pred is + # [N, 1, H, W] and that of label is [N, H, W]. + assert label.max() <= 1, "For pred with shape [N, 1, H, W], its label must have at " "most 2 classes" + pred = pred.squeeze() + if pred.dim() != label.dim(): + assert (pred.dim() == 2 and label.dim() == 1) or (pred.dim() == 4 and label.dim() == 3), ( + "Only pred shape [N, C], label shape [N] or pred shape [N, C, " "H, W], label shape [N, H, W] are supported" + ) + # `weight` returned from `_expand_onehot_labels` + # has been treated for valid (non-ignore) pixels + label, weight, valid_mask = _expand_onehot_labels(label, weight, pred.shape, ignore_index) + else: + # should mask out the ignored elements + valid_mask = ((label >= 0) & (label != ignore_index)).float() + if weight is not None: + weight = weight * valid_mask + else: + weight = valid_mask + # average loss over non-ignored and valid elements + if reduction == "mean" and avg_factor is None and avg_non_ignore: + avg_factor = valid_mask.sum().item() + + loss = F.binary_cross_entropy_with_logits(pred, label.float(), pos_weight=class_weight, reduction="none") + # do the reduction for the weighted loss + loss = weight_reduce_loss(loss, weight, reduction=reduction, avg_factor=avg_factor) + + return loss + + +def mask_cross_entropy( + pred, target, label, reduction="mean", avg_factor=None, class_weight=None, ignore_index=None, **kwargs +): + """Calculate the CrossEntropy loss for masks. + + Args: + pred (torch.Tensor): The prediction with shape (N, C), C is the number + of classes. + target (torch.Tensor): The learning label of the prediction. + label (torch.Tensor): ``label`` indicates the class label of the mask' + corresponding object. This will be used to select the mask in the + of the class which the object belongs to when the mask prediction + if not class-agnostic. + reduction (str, optional): The method used to reduce the loss. + Options are "none", "mean" and "sum". + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + class_weight (list[float], optional): The weight for each class. + ignore_index (None): Placeholder, to be consistent with other loss. + Default: None. + + Returns: + torch.Tensor: The calculated loss + """ + assert ignore_index is None, "BCE loss does not support ignore_index" + assert reduction == "mean" and avg_factor is None + num_rois = pred.size()[0] + inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device) + pred_slice = pred[inds, label].squeeze(1) + return F.binary_cross_entropy_with_logits(pred_slice, target, weight=class_weight, reduction="mean")[None] + + +@LOSSES.register_module(force=True) +class CrossEntropyLoss(nn.Module): + """CrossEntropyLoss. + + Args: + use_sigmoid (bool, optional): Whether the prediction uses sigmoid + of softmax. Defaults to False. + use_mask (bool, optional): Whether to use mask cross entropy loss. + Defaults to False. + reduction (str, optional): . Defaults to 'mean'. + Options are "none", "mean" and "sum". + class_weight (list[float] | str, optional): Weight of each class. If in + str format, read them from a file. Defaults to None. + loss_weight (float, optional): Weight of the loss. Defaults to 1.0. + loss_name (str, optional): Name of the loss item. If you want this loss + item to be included into the backward graph, `loss_` must be the + prefix of the name. Defaults to 'loss_ce'. + avg_non_ignore (bool): The flag decides to whether the loss is + only averaged over non-ignored targets. Default: False. + `New in version 0.23.0.` + """ + + def __init__( + self, + use_sigmoid=False, + use_mask=False, + reduction="mean", + class_weight=None, + loss_weight=1.0, + loss_name="loss_ce", + avg_non_ignore=False, + ): + super(CrossEntropyLoss, self).__init__() + assert (use_sigmoid is False) or (use_mask is False) + self.use_sigmoid = use_sigmoid + self.use_mask = use_mask + self.reduction = reduction + self.loss_weight = loss_weight + self.class_weight = get_class_weight(class_weight) + self.avg_non_ignore = avg_non_ignore + if not self.avg_non_ignore and self.reduction == "mean": + warnings.warn( + "Default ``avg_non_ignore`` is False, if you would like to " + "ignore the certain label and average loss over non-ignore " + "labels, which is the same with PyTorch official " + "cross_entropy, set ``avg_non_ignore=True``." + ) + + if self.use_sigmoid: + self.cls_criterion = binary_cross_entropy + elif self.use_mask: + self.cls_criterion = mask_cross_entropy + else: + self.cls_criterion = cross_entropy + self._loss_name = loss_name + + def extra_repr(self): + """Extra repr.""" + s = f"avg_non_ignore={self.avg_non_ignore}" + return s + + def forward( + self, cls_score, label, weight=None, avg_factor=None, reduction_override=None, ignore_index=-100, **kwargs + ): + """Forward function.""" + assert reduction_override in (None, "none", "mean", "sum") + reduction = reduction_override if reduction_override else self.reduction + if self.class_weight is not None: + class_weight = cls_score.new_tensor(self.class_weight) + else: + class_weight = None + # Note: for BCE loss, label < 0 is invalid. + loss_cls = self.loss_weight * self.cls_criterion( + cls_score, + label, + weight, + class_weight=class_weight, + reduction=reduction, + avg_factor=avg_factor, + avg_non_ignore=self.avg_non_ignore, + ignore_index=ignore_index, + **kwargs, + ) + return loss_cls + + @property + def loss_name(self): + """Loss Name. + + This function must be implemented and will return the name of this + loss function. This name will be used to combine different loss items + by simple sum operation. In addition, if you want this loss item to be + included into the backward graph, `loss_` must be the prefix of the + name. + + Returns: + str: The name of this loss item. + """ + return self._loss_name diff --git a/dinov2/eval/segmentation_m2f/models/losses/dice_loss.py b/dinov2/eval/segmentation_m2f/models/losses/dice_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..1bc5ba893c502861032ed531283f225e183eb693 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/losses/dice_loss.py @@ -0,0 +1,153 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +from mmseg.models.builder import LOSSES +from mmseg.models.losses.utils import weight_reduce_loss + + +def dice_loss(pred, target, weight=None, eps=1e-3, reduction="mean", avg_factor=None): + """Calculate dice loss, which is proposed in + `V-Net: Fully Convolutional Neural Networks for Volumetric + Medical Image Segmentation `_. + + Args: + pred (torch.Tensor): The prediction, has a shape (n, *) + target (torch.Tensor): The learning label of the prediction, + shape (n, *), same shape of pred. + weight (torch.Tensor, optional): The weight of loss for each + prediction, has a shape (n,). Defaults to None. + eps (float): Avoid dividing by zero. Default: 1e-3. + reduction (str, optional): The method used to reduce the loss into + a scalar. Defaults to 'mean'. + Options are "none", "mean" and "sum". + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + """ + + input = pred.flatten(1) + target = target.flatten(1).float() + + a = torch.sum(input * target, 1) + b = torch.sum(input * input, 1) + eps + c = torch.sum(target * target, 1) + eps + d = (2 * a) / (b + c) + loss = 1 - d + if weight is not None: + assert weight.ndim == loss.ndim + assert len(weight) == len(pred) + loss = weight_reduce_loss(loss, weight, reduction, avg_factor) + return loss + + +def naive_dice_loss(pred, target, weight=None, eps=1e-3, reduction="mean", avg_factor=None): + """Calculate naive dice loss, the coefficient in the denominator is the + first power instead of the second power. + + Args: + pred (torch.Tensor): The prediction, has a shape (n, *) + target (torch.Tensor): The learning label of the prediction, + shape (n, *), same shape of pred. + weight (torch.Tensor, optional): The weight of loss for each + prediction, has a shape (n,). Defaults to None. + eps (float): Avoid dividing by zero. Default: 1e-3. + reduction (str, optional): The method used to reduce the loss into + a scalar. Defaults to 'mean'. + Options are "none", "mean" and "sum". + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + """ + input = pred.flatten(1) + target = target.flatten(1).float() + + a = torch.sum(input * target, 1) + b = torch.sum(input, 1) + c = torch.sum(target, 1) + d = (2 * a + eps) / (b + c + eps) + loss = 1 - d + if weight is not None: + assert weight.ndim == loss.ndim + assert len(weight) == len(pred) + loss = weight_reduce_loss(loss, weight, reduction, avg_factor) + return loss + + +@LOSSES.register_module(force=True) +class DiceLoss(nn.Module): + def __init__(self, use_sigmoid=True, activate=True, reduction="mean", naive_dice=False, loss_weight=1.0, eps=1e-3): + """Dice Loss, there are two forms of dice loss is supported: + + - the one proposed in `V-Net: Fully Convolutional Neural + Networks for Volumetric Medical Image Segmentation + `_. + - the dice loss in which the power of the number in the + denominator is the first power instead of the second + power. + + Args: + use_sigmoid (bool, optional): Whether to the prediction is + used for sigmoid or softmax. Defaults to True. + activate (bool): Whether to activate the predictions inside, + this will disable the inside sigmoid operation. + Defaults to True. + reduction (str, optional): The method used + to reduce the loss. Options are "none", + "mean" and "sum". Defaults to 'mean'. + naive_dice (bool, optional): If false, use the dice + loss defined in the V-Net paper, otherwise, use the + naive dice loss in which the power of the number in the + denominator is the first power instead of the second + power.Defaults to False. + loss_weight (float, optional): Weight of loss. Defaults to 1.0. + eps (float): Avoid dividing by zero. Defaults to 1e-3. + """ + + super(DiceLoss, self).__init__() + self.use_sigmoid = use_sigmoid + self.reduction = reduction + self.naive_dice = naive_dice + self.loss_weight = loss_weight + self.eps = eps + self.activate = activate + + def forward(self, pred, target, weight=None, reduction_override=None, avg_factor=None): + """Forward function. + + Args: + pred (torch.Tensor): The prediction, has a shape (n, *). + target (torch.Tensor): The label of the prediction, + shape (n, *), same shape of pred. + weight (torch.Tensor, optional): The weight of loss for each + prediction, has a shape (n,). Defaults to None. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + reduction_override (str, optional): The reduction method used to + override the original reduction method of the loss. + Options are "none", "mean" and "sum". + + Returns: + torch.Tensor: The calculated loss + """ + + assert reduction_override in (None, "none", "mean", "sum") + reduction = reduction_override if reduction_override else self.reduction + + if self.activate: + if self.use_sigmoid: + pred = pred.sigmoid() + else: + raise NotImplementedError + + if self.naive_dice: + loss = self.loss_weight * naive_dice_loss( + pred, target, weight, eps=self.eps, reduction=reduction, avg_factor=avg_factor + ) + else: + loss = self.loss_weight * dice_loss( + pred, target, weight, eps=self.eps, reduction=reduction, avg_factor=avg_factor + ) + + return loss diff --git a/dinov2/eval/segmentation_m2f/models/losses/match_costs.py b/dinov2/eval/segmentation_m2f/models/losses/match_costs.py new file mode 100644 index 0000000000000000000000000000000000000000..4917d2a939c01398dd49c0d90b06f4c37d283ce0 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/losses/match_costs.py @@ -0,0 +1,153 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F + +from ..builder import MATCH_COST + + +@MATCH_COST.register_module() +class ClassificationCost: + """ClsSoftmaxCost.Borrow from + mmdet.core.bbox.match_costs.match_cost.ClassificationCost. + + Args: + weight (int | float, optional): loss_weight + + Examples: + >>> import torch + >>> self = ClassificationCost() + >>> cls_pred = torch.rand(4, 3) + >>> gt_labels = torch.tensor([0, 1, 2]) + >>> factor = torch.tensor([10, 8, 10, 8]) + >>> self(cls_pred, gt_labels) + tensor([[-0.3430, -0.3525, -0.3045], + [-0.3077, -0.2931, -0.3992], + [-0.3664, -0.3455, -0.2881], + [-0.3343, -0.2701, -0.3956]]) + """ + + def __init__(self, weight=1.0): + self.weight = weight + + def __call__(self, cls_pred, gt_labels): + """ + Args: + cls_pred (Tensor): Predicted classification logits, shape + [num_query, num_class]. + gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,). + + Returns: + torch.Tensor: cls_cost value with weight + """ + # Following the official DETR repo, contrary to the loss that + # NLL is used, we approximate it in 1 - cls_score[gt_label]. + # The 1 is a constant that doesn't change the matching, + # so it can be omitted. + cls_score = cls_pred.softmax(-1) + cls_cost = -cls_score[:, gt_labels] + return cls_cost * self.weight + + +@MATCH_COST.register_module() +class DiceCost: + """Cost of mask assignments based on dice losses. + + Args: + weight (int | float, optional): loss_weight. Defaults to 1. + pred_act (bool, optional): Whether to apply sigmoid to mask_pred. + Defaults to False. + eps (float, optional): default 1e-12. + """ + + def __init__(self, weight=1.0, pred_act=False, eps=1e-3): + self.weight = weight + self.pred_act = pred_act + self.eps = eps + + def binary_mask_dice_loss(self, mask_preds, gt_masks): + """ + Args: + mask_preds (Tensor): Mask prediction in shape (N1, H, W). + gt_masks (Tensor): Ground truth in shape (N2, H, W) + store 0 or 1, 0 for negative class and 1 for + positive class. + + Returns: + Tensor: Dice cost matrix in shape (N1, N2). + """ + mask_preds = mask_preds.reshape((mask_preds.shape[0], -1)) + gt_masks = gt_masks.reshape((gt_masks.shape[0], -1)).float() + numerator = 2 * torch.einsum("nc,mc->nm", mask_preds, gt_masks) + denominator = mask_preds.sum(-1)[:, None] + gt_masks.sum(-1)[None, :] + loss = 1 - (numerator + self.eps) / (denominator + self.eps) + return loss + + def __call__(self, mask_preds, gt_masks): + """ + Args: + mask_preds (Tensor): Mask prediction logits in shape (N1, H, W). + gt_masks (Tensor): Ground truth in shape (N2, H, W). + + Returns: + Tensor: Dice cost matrix in shape (N1, N2). + """ + if self.pred_act: + mask_preds = mask_preds.sigmoid() + dice_cost = self.binary_mask_dice_loss(mask_preds, gt_masks) + return dice_cost * self.weight + + +@MATCH_COST.register_module() +class CrossEntropyLossCost: + """CrossEntropyLossCost. + + Args: + weight (int | float, optional): loss weight. Defaults to 1. + use_sigmoid (bool, optional): Whether the prediction uses sigmoid + of softmax. Defaults to True. + """ + + def __init__(self, weight=1.0, use_sigmoid=True): + assert use_sigmoid, "use_sigmoid = False is not supported yet." + self.weight = weight + self.use_sigmoid = use_sigmoid + + def _binary_cross_entropy(self, cls_pred, gt_labels): + """ + Args: + cls_pred (Tensor): The prediction with shape (num_query, 1, *) or + (num_query, *). + gt_labels (Tensor): The learning label of prediction with + shape (num_gt, *). + Returns: + Tensor: Cross entropy cost matrix in shape (num_query, num_gt). + """ + cls_pred = cls_pred.flatten(1).float() + gt_labels = gt_labels.flatten(1).float() + n = cls_pred.shape[1] + pos = F.binary_cross_entropy_with_logits(cls_pred, torch.ones_like(cls_pred), reduction="none") + neg = F.binary_cross_entropy_with_logits(cls_pred, torch.zeros_like(cls_pred), reduction="none") + cls_cost = torch.einsum("nc,mc->nm", pos, gt_labels) + torch.einsum("nc,mc->nm", neg, 1 - gt_labels) + cls_cost = cls_cost / n + + return cls_cost + + def __call__(self, cls_pred, gt_labels): + """ + Args: + cls_pred (Tensor): Predicted classification logits. + gt_labels (Tensor): Labels. + Returns: + Tensor: Cross entropy cost matrix with weight in + shape (num_query, num_gt). + """ + if self.use_sigmoid: + cls_cost = self._binary_cross_entropy(cls_pred, gt_labels) + else: + raise NotImplementedError + + return cls_cost * self.weight diff --git a/dinov2/eval/segmentation_m2f/models/plugins/__init__.py b/dinov2/eval/segmentation_m2f/models/plugins/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..81a60db4de31238cb38e078683e5ca265839fe60 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/plugins/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .msdeformattn_pixel_decoder import MSDeformAttnPixelDecoder diff --git a/dinov2/eval/segmentation_m2f/models/plugins/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/segmentation_m2f/models/plugins/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8791833fdab443506aaa7855a6924ccae9d4e850 Binary files /dev/null and b/dinov2/eval/segmentation_m2f/models/plugins/__pycache__/__init__.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/models/plugins/__pycache__/msdeformattn_pixel_decoder.cpython-310.pyc b/dinov2/eval/segmentation_m2f/models/plugins/__pycache__/msdeformattn_pixel_decoder.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b2a73a589e74aa21f4a8cca0c6fbc3a9e363ffdf Binary files /dev/null and b/dinov2/eval/segmentation_m2f/models/plugins/__pycache__/msdeformattn_pixel_decoder.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/models/plugins/msdeformattn_pixel_decoder.py b/dinov2/eval/segmentation_m2f/models/plugins/msdeformattn_pixel_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..db1947175917f73f3f24184cb09c78e092d46ef8 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/plugins/msdeformattn_pixel_decoder.py @@ -0,0 +1,242 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import PLUGIN_LAYERS, Conv2d, ConvModule, caffe2_xavier_init, normal_init, xavier_init +from mmcv.cnn.bricks.transformer import build_positional_encoding, build_transformer_layer_sequence +from mmcv.runner import BaseModule, ModuleList + +from ...core.anchor import MlvlPointGenerator +from ..utils.transformer import MultiScaleDeformableAttention + + +@PLUGIN_LAYERS.register_module() +class MSDeformAttnPixelDecoder(BaseModule): + """Pixel decoder with multi-scale deformable attention. + + Args: + in_channels (list[int] | tuple[int]): Number of channels in the + input feature maps. + strides (list[int] | tuple[int]): Output strides of feature from + backbone. + feat_channels (int): Number of channels for feature. + out_channels (int): Number of channels for output. + num_outs (int): Number of output scales. + norm_cfg (:obj:`mmcv.ConfigDict` | dict): Config for normalization. + Defaults to dict(type='GN', num_groups=32). + act_cfg (:obj:`mmcv.ConfigDict` | dict): Config for activation. + Defaults to dict(type='ReLU'). + encoder (:obj:`mmcv.ConfigDict` | dict): Config for transformer + encoder. Defaults to `DetrTransformerEncoder`. + positional_encoding (:obj:`mmcv.ConfigDict` | dict): Config for + transformer encoder position encoding. Defaults to + dict(type='SinePositionalEncoding', num_feats=128, + normalize=True). + init_cfg (:obj:`mmcv.ConfigDict` | dict): Initialization config dict. + """ + + def __init__( + self, + in_channels=[256, 512, 1024, 2048], + strides=[4, 8, 16, 32], + feat_channels=256, + out_channels=256, + num_outs=3, + norm_cfg=dict(type="GN", num_groups=32), + act_cfg=dict(type="ReLU"), + encoder=dict( + type="DetrTransformerEncoder", + num_layers=6, + transformerlayers=dict( + type="BaseTransformerLayer", + attn_cfgs=dict( + type="MultiScaleDeformableAttention", + embed_dims=256, + num_heads=8, + num_levels=3, + num_points=4, + im2col_step=64, + dropout=0.0, + batch_first=False, + norm_cfg=None, + init_cfg=None, + ), + feedforward_channels=1024, + ffn_dropout=0.0, + operation_order=("self_attn", "norm", "ffn", "norm"), + ), + init_cfg=None, + ), + positional_encoding=dict(type="SinePositionalEncoding", num_feats=128, normalize=True), + init_cfg=None, + ): + super().__init__(init_cfg=init_cfg) + self.strides = strides + self.num_input_levels = len(in_channels) + self.num_encoder_levels = encoder.transformerlayers.attn_cfgs.num_levels + assert self.num_encoder_levels >= 1, "num_levels in attn_cfgs must be at least one" + input_conv_list = [] + # from top to down (low to high resolution) + for i in range(self.num_input_levels - 1, self.num_input_levels - self.num_encoder_levels - 1, -1): + input_conv = ConvModule( + in_channels[i], feat_channels, kernel_size=1, norm_cfg=norm_cfg, act_cfg=None, bias=True + ) + input_conv_list.append(input_conv) + self.input_convs = ModuleList(input_conv_list) + + self.encoder = build_transformer_layer_sequence(encoder) + self.postional_encoding = build_positional_encoding(positional_encoding) + # high resolution to low resolution + self.level_encoding = nn.Embedding(self.num_encoder_levels, feat_channels) + + # fpn-like structure + self.lateral_convs = ModuleList() + self.output_convs = ModuleList() + self.use_bias = norm_cfg is None + # from top to down (low to high resolution) + # fpn for the rest features that didn't pass in encoder + for i in range(self.num_input_levels - self.num_encoder_levels - 1, -1, -1): + lateral_conv = ConvModule( + in_channels[i], feat_channels, kernel_size=1, bias=self.use_bias, norm_cfg=norm_cfg, act_cfg=None + ) + output_conv = ConvModule( + feat_channels, + feat_channels, + kernel_size=3, + stride=1, + padding=1, + bias=self.use_bias, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + ) + self.lateral_convs.append(lateral_conv) + self.output_convs.append(output_conv) + + self.mask_feature = Conv2d(feat_channels, out_channels, kernel_size=1, stride=1, padding=0) + + self.num_outs = num_outs + self.point_generator = MlvlPointGenerator(strides) + + def init_weights(self): + """Initialize weights.""" + for i in range(0, self.num_encoder_levels): + xavier_init(self.input_convs[i].conv, gain=1, bias=0, distribution="uniform") + + for i in range(0, self.num_input_levels - self.num_encoder_levels): + caffe2_xavier_init(self.lateral_convs[i].conv, bias=0) + caffe2_xavier_init(self.output_convs[i].conv, bias=0) + + caffe2_xavier_init(self.mask_feature, bias=0) + + normal_init(self.level_encoding, mean=0, std=1) + for p in self.encoder.parameters(): + if p.dim() > 1: + nn.init.xavier_normal_(p) + + # init_weights defined in MultiScaleDeformableAttention + for layer in self.encoder.layers: + for attn in layer.attentions: + if isinstance(attn, MultiScaleDeformableAttention): + attn.init_weights() + + def forward(self, feats): + """ + Args: + feats (list[Tensor]): Feature maps of each level. Each has + shape of (batch_size, c, h, w). + + Returns: + tuple: A tuple containing the following: + + - mask_feature (Tensor): shape (batch_size, c, h, w). + - multi_scale_features (list[Tensor]): Multi scale \ + features, each in shape (batch_size, c, h, w). + """ + # generate padding mask for each level, for each image + batch_size = feats[0].shape[0] + encoder_input_list = [] + padding_mask_list = [] + level_positional_encoding_list = [] + spatial_shapes = [] + reference_points_list = [] + for i in range(self.num_encoder_levels): + level_idx = self.num_input_levels - i - 1 + feat = feats[level_idx] + feat_projected = self.input_convs[i](feat) + h, w = feat.shape[-2:] + + # no padding + padding_mask_resized = feat.new_zeros((batch_size,) + feat.shape[-2:], dtype=torch.bool) + pos_embed = self.postional_encoding(padding_mask_resized) + level_embed = self.level_encoding.weight[i] + level_pos_embed = level_embed.view(1, -1, 1, 1) + pos_embed + # (h_i * w_i, 2) + reference_points = self.point_generator.single_level_grid_priors( + feat.shape[-2:], level_idx, device=feat.device + ) + # normalize + factor = feat.new_tensor([[w, h]]) * self.strides[level_idx] + reference_points = reference_points / factor + + # shape (batch_size, c, h_i, w_i) -> (h_i * w_i, batch_size, c) + feat_projected = feat_projected.flatten(2).permute(2, 0, 1) + level_pos_embed = level_pos_embed.flatten(2).permute(2, 0, 1) + padding_mask_resized = padding_mask_resized.flatten(1) + + encoder_input_list.append(feat_projected) + padding_mask_list.append(padding_mask_resized) + level_positional_encoding_list.append(level_pos_embed) + spatial_shapes.append(feat.shape[-2:]) + reference_points_list.append(reference_points) + # shape (batch_size, total_num_query), + # total_num_query=sum([., h_i * w_i,.]) + padding_masks = torch.cat(padding_mask_list, dim=1) + # shape (total_num_query, batch_size, c) + encoder_inputs = torch.cat(encoder_input_list, dim=0) + level_positional_encodings = torch.cat(level_positional_encoding_list, dim=0) + device = encoder_inputs.device + # shape (num_encoder_levels, 2), from low + # resolution to high resolution + spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=device) + # shape (0, h_0*w_0, h_0*w_0+h_1*w_1, ...) + level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) + reference_points = torch.cat(reference_points_list, dim=0) + reference_points = reference_points[None, :, None].repeat(batch_size, 1, self.num_encoder_levels, 1) + valid_radios = reference_points.new_ones((batch_size, self.num_encoder_levels, 2)) + # shape (num_total_query, batch_size, c) + memory = self.encoder( + query=encoder_inputs, + key=None, + value=None, + query_pos=level_positional_encodings, + key_pos=None, + attn_masks=None, + key_padding_mask=None, + query_key_padding_mask=padding_masks, + spatial_shapes=spatial_shapes, + reference_points=reference_points, + level_start_index=level_start_index, + valid_radios=valid_radios, + ) + # (num_total_query, batch_size, c) -> (batch_size, c, num_total_query) + memory = memory.permute(1, 2, 0) + + # from low resolution to high resolution + num_query_per_level = [e[0] * e[1] for e in spatial_shapes] + outs = torch.split(memory, num_query_per_level, dim=-1) + outs = [x.reshape(batch_size, -1, spatial_shapes[i][0], spatial_shapes[i][1]) for i, x in enumerate(outs)] + + for i in range(self.num_input_levels - self.num_encoder_levels - 1, -1, -1): + x = feats[i] + cur_feat = self.lateral_convs[i](x) + y = cur_feat + F.interpolate(outs[-1], size=cur_feat.shape[-2:], mode="bilinear", align_corners=False) + y = self.output_convs[i](y) + outs.append(y) + multi_scale_features = outs[: self.num_outs] + + mask_feature = self.mask_feature(outs[-1]) + return mask_feature, multi_scale_features diff --git a/dinov2/eval/segmentation_m2f/models/segmentors/__init__.py b/dinov2/eval/segmentation_m2f/models/segmentors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..adf0062691e4889612e118f28ced853cd0bc33db --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/segmentors/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .encoder_decoder_mask2former import EncoderDecoderMask2Former diff --git a/dinov2/eval/segmentation_m2f/models/segmentors/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/segmentation_m2f/models/segmentors/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fae3d70c831529e42ce615b8169a7e9863a1fc61 Binary files /dev/null and b/dinov2/eval/segmentation_m2f/models/segmentors/__pycache__/__init__.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/models/segmentors/__pycache__/encoder_decoder_mask2former.cpython-310.pyc b/dinov2/eval/segmentation_m2f/models/segmentors/__pycache__/encoder_decoder_mask2former.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3f687a0371f911dcfe981af7183a531ed54a4e74 Binary files /dev/null and b/dinov2/eval/segmentation_m2f/models/segmentors/__pycache__/encoder_decoder_mask2former.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/models/segmentors/encoder_decoder_mask2former.py b/dinov2/eval/segmentation_m2f/models/segmentors/encoder_decoder_mask2former.py new file mode 100644 index 0000000000000000000000000000000000000000..cfe572c9d317303bff8d51b85217d144906ebfe7 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/segmentors/encoder_decoder_mask2former.py @@ -0,0 +1,271 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmseg.core import add_prefix +from mmseg.models import builder +from mmseg.models.builder import SEGMENTORS +from mmseg.models.segmentors.base import BaseSegmentor +from mmseg.ops import resize + + +@SEGMENTORS.register_module() +class EncoderDecoderMask2Former(BaseSegmentor): + """Encoder Decoder segmentors. + + EncoderDecoder typically consists of backbone, decode_head, auxiliary_head. + Note that auxiliary_head is only used for deep supervision during training, + which could be dumped during inference. + """ + + def __init__( + self, + backbone, + decode_head, + neck=None, + auxiliary_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None, + ): + super(EncoderDecoderMask2Former, self).__init__(init_cfg) + if pretrained is not None: + assert backbone.get("pretrained") is None, "both backbone and segmentor set pretrained weight" + backbone.pretrained = pretrained + self.backbone = builder.build_backbone(backbone) + if neck is not None: + self.neck = builder.build_neck(neck) + decode_head.update(train_cfg=train_cfg) + decode_head.update(test_cfg=test_cfg) + self._init_decode_head(decode_head) + self._init_auxiliary_head(auxiliary_head) + + self.train_cfg = train_cfg + self.test_cfg = test_cfg + + assert self.with_decode_head + + def _init_decode_head(self, decode_head): + """Initialize ``decode_head``""" + self.decode_head = builder.build_head(decode_head) + self.align_corners = self.decode_head.align_corners + self.num_classes = self.decode_head.num_classes + + def _init_auxiliary_head(self, auxiliary_head): + """Initialize ``auxiliary_head``""" + if auxiliary_head is not None: + if isinstance(auxiliary_head, list): + self.auxiliary_head = nn.ModuleList() + for head_cfg in auxiliary_head: + self.auxiliary_head.append(builder.build_head(head_cfg)) + else: + self.auxiliary_head = builder.build_head(auxiliary_head) + + def extract_feat(self, img): + """Extract features from images.""" + x = self.backbone(img) + if self.with_neck: + x = self.neck(x) + return x + + def encode_decode(self, img, img_metas): + """Encode images with backbone and decode into a semantic segmentation + map of the same size as input.""" + x = self.extract_feat(img) + out = self._decode_head_forward_test(x, img_metas) + out = resize(input=out, size=img.shape[2:], mode="bilinear", align_corners=self.align_corners) + return out + + def _decode_head_forward_train(self, x, img_metas, gt_semantic_seg, **kwargs): + """Run forward function and calculate loss for decode head in + training.""" + losses = dict() + loss_decode = self.decode_head.forward_train(x, img_metas, gt_semantic_seg, **kwargs) + + losses.update(add_prefix(loss_decode, "decode")) + return losses + + def _decode_head_forward_test(self, x, img_metas): + """Run forward function and calculate loss for decode head in + inference.""" + seg_logits = self.decode_head.forward_test(x, img_metas, self.test_cfg) + return seg_logits + + def _auxiliary_head_forward_train(self, x, img_metas, gt_semantic_seg): + """Run forward function and calculate loss for auxiliary head in + training.""" + losses = dict() + if isinstance(self.auxiliary_head, nn.ModuleList): + for idx, aux_head in enumerate(self.auxiliary_head): + loss_aux = aux_head.forward_train(x, img_metas, gt_semantic_seg, self.train_cfg) + losses.update(add_prefix(loss_aux, f"aux_{idx}")) + else: + loss_aux = self.auxiliary_head.forward_train(x, img_metas, gt_semantic_seg, self.train_cfg) + losses.update(add_prefix(loss_aux, "aux")) + + return losses + + def forward_dummy(self, img): + """Dummy forward function.""" + seg_logit = self.encode_decode(img, None) + + return seg_logit + + def forward_train(self, img, img_metas, gt_semantic_seg, **kwargs): + """Forward function for training. + + Args: + img (Tensor): Input images. + img_metas (list[dict]): List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmseg/datasets/pipelines/formatting.py:Collect`. + gt_semantic_seg (Tensor): Semantic segmentation masks + used if the architecture supports semantic segmentation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + + x = self.extract_feat(img) + + losses = dict() + + loss_decode = self._decode_head_forward_train(x, img_metas, gt_semantic_seg, **kwargs) + losses.update(loss_decode) + + if self.with_auxiliary_head: + loss_aux = self._auxiliary_head_forward_train(x, img_metas, gt_semantic_seg) + losses.update(loss_aux) + + return losses + + def slide_inference(self, img, img_meta, rescale): + """Inference by sliding-window with overlap. + + If h_crop > h_img or w_crop > w_img, the small patch will be used to + decode without padding. + """ + + h_stride, w_stride = self.test_cfg.stride + h_crop, w_crop = self.test_cfg.crop_size + batch_size, _, h_img, w_img = img.size() + num_classes = self.num_classes + h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1 + w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1 + preds = img.new_zeros((batch_size, num_classes, h_img, w_img)) + count_mat = img.new_zeros((batch_size, 1, h_img, w_img)) + for h_idx in range(h_grids): + for w_idx in range(w_grids): + y1 = h_idx * h_stride + x1 = w_idx * w_stride + y2 = min(y1 + h_crop, h_img) + x2 = min(x1 + w_crop, w_img) + y1 = max(y2 - h_crop, 0) + x1 = max(x2 - w_crop, 0) + crop_img = img[:, :, y1:y2, x1:x2] + crop_seg_logit = self.encode_decode(crop_img, img_meta) + preds += F.pad(crop_seg_logit, (int(x1), int(preds.shape[3] - x2), int(y1), int(preds.shape[2] - y2))) + + count_mat[:, :, y1:y2, x1:x2] += 1 + assert (count_mat == 0).sum() == 0 + if torch.onnx.is_in_onnx_export(): + # cast count_mat to constant while exporting to ONNX + count_mat = torch.from_numpy(count_mat.cpu().detach().numpy()).to(device=img.device) + preds = preds / count_mat + if rescale: + preds = resize( + preds, + size=img_meta[0]["ori_shape"][:2], + mode="bilinear", + align_corners=self.align_corners, + warning=False, + ) + return preds + + def whole_inference(self, img, img_meta, rescale): + """Inference with full image.""" + + seg_logit = self.encode_decode(img, img_meta) + if rescale: + # support dynamic shape for onnx + if torch.onnx.is_in_onnx_export(): + size = img.shape[2:] + else: + size = img_meta[0]["ori_shape"][:2] + seg_logit = resize(seg_logit, size=size, mode="bilinear", align_corners=self.align_corners, warning=False) + + return seg_logit + + def inference(self, img, img_meta, rescale): + """Inference with slide/whole style. + + Args: + img (Tensor): The input image of shape (N, 3, H, W). + img_meta (dict): Image info dict where each dict has: 'img_shape', + 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmseg/datasets/pipelines/formatting.py:Collect`. + rescale (bool): Whether rescale back to original shape. + + Returns: + Tensor: The output segmentation map. + """ + + assert self.test_cfg.mode in ["slide", "whole"] + ori_shape = img_meta[0]["ori_shape"] + assert all(_["ori_shape"] == ori_shape for _ in img_meta) + if self.test_cfg.mode == "slide": + seg_logit = self.slide_inference(img, img_meta, rescale) + else: + seg_logit = self.whole_inference(img, img_meta, rescale) + output = F.softmax(seg_logit, dim=1) + flip = img_meta[0]["flip"] + if flip: + flip_direction = img_meta[0]["flip_direction"] + assert flip_direction in ["horizontal", "vertical"] + if flip_direction == "horizontal": + output = output.flip(dims=(3,)) + elif flip_direction == "vertical": + output = output.flip(dims=(2,)) + + return output + + def simple_test(self, img, img_meta, rescale=True): + """Simple test with single image.""" + seg_logit = self.inference(img, img_meta, rescale) + seg_pred = seg_logit.argmax(dim=1) + if torch.onnx.is_in_onnx_export(): + # our inference backend only support 4D output + seg_pred = seg_pred.unsqueeze(0) + return seg_pred + seg_pred = seg_pred.cpu().numpy() + # unravel batch dim + seg_pred = list(seg_pred) + return seg_pred + + def aug_test(self, imgs, img_metas, rescale=True): + """Test with augmentations. + + Only rescale=True is supported. + """ + # aug_test rescale all imgs back to ori_shape for now + assert rescale + # to save memory, we get augmented seg logit inplace + seg_logit = self.inference(imgs[0], img_metas[0], rescale) + for i in range(1, len(imgs)): + cur_seg_logit = self.inference(imgs[i], img_metas[i], rescale) + seg_logit += cur_seg_logit + seg_logit /= len(imgs) + seg_pred = seg_logit.argmax(dim=1) + seg_pred = seg_pred.cpu().numpy() + # unravel batch dim + seg_pred = list(seg_pred) + return seg_pred diff --git a/dinov2/eval/segmentation_m2f/models/utils/__init__.py b/dinov2/eval/segmentation_m2f/models/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e7fdc1668b1015c8feea8fa1a4691bc0ebdbd936 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/utils/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .assigner import MaskHungarianAssigner +from .point_sample import get_uncertain_point_coords_with_randomness +from .positional_encoding import LearnedPositionalEncoding, SinePositionalEncoding +from .transformer import DetrTransformerDecoder, DetrTransformerDecoderLayer, DynamicConv, Transformer diff --git a/dinov2/eval/segmentation_m2f/models/utils/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/segmentation_m2f/models/utils/__pycache__/__init__.cpython-310.pyc new file 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b/dinov2/eval/segmentation_m2f/models/utils/assigner.py new file mode 100644 index 0000000000000000000000000000000000000000..3cb08fc1bb2e36336989b45a1d3850f260c05963 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/utils/assigner.py @@ -0,0 +1,157 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from abc import ABCMeta, abstractmethod + +import torch + +from ..builder import MASK_ASSIGNERS, build_match_cost + +try: + from scipy.optimize import linear_sum_assignment +except ImportError: + linear_sum_assignment = None + + +class AssignResult(metaclass=ABCMeta): + """Collection of assign results.""" + + def __init__(self, num_gts, gt_inds, labels): + self.num_gts = num_gts + self.gt_inds = gt_inds + self.labels = labels + + @property + def info(self): + info = { + "num_gts": self.num_gts, + "gt_inds": self.gt_inds, + "labels": self.labels, + } + return info + + +class BaseAssigner(metaclass=ABCMeta): + """Base assigner that assigns boxes to ground truth boxes.""" + + @abstractmethod + def assign(self, masks, gt_masks, gt_masks_ignore=None, gt_labels=None): + """Assign boxes to either a ground truth boxes or a negative boxes.""" + pass + + +@MASK_ASSIGNERS.register_module() +class MaskHungarianAssigner(BaseAssigner): + """Computes one-to-one matching between predictions and ground truth for + mask. + + This class computes an assignment between the targets and the predictions + based on the costs. The costs are weighted sum of three components: + classification cost, regression L1 cost and regression iou cost. The + targets don't include the no_object, so generally there are more + predictions than targets. After the one-to-one matching, the un-matched + are treated as backgrounds. Thus each query prediction will be assigned + with `0` or a positive integer indicating the ground truth index: + + - 0: negative sample, no assigned gt + - positive integer: positive sample, index (1-based) of assigned gt + + Args: + cls_cost (obj:`mmcv.ConfigDict`|dict): Classification cost config. + mask_cost (obj:`mmcv.ConfigDict`|dict): Mask cost config. + dice_cost (obj:`mmcv.ConfigDict`|dict): Dice cost config. + """ + + def __init__( + self, + cls_cost=dict(type="ClassificationCost", weight=1.0), + dice_cost=dict(type="DiceCost", weight=1.0), + mask_cost=dict(type="MaskFocalCost", weight=1.0), + ): + self.cls_cost = build_match_cost(cls_cost) + self.dice_cost = build_match_cost(dice_cost) + self.mask_cost = build_match_cost(mask_cost) + + def assign(self, cls_pred, mask_pred, gt_labels, gt_masks, img_meta, gt_masks_ignore=None, eps=1e-7): + """Computes one-to-one matching based on the weighted costs. + + This method assign each query prediction to a ground truth or + background. The `assigned_gt_inds` with -1 means don't care, + 0 means negative sample, and positive number is the index (1-based) + of assigned gt. + The assignment is done in the following steps, the order matters. + + 1. assign every prediction to -1 + 2. compute the weighted costs + 3. do Hungarian matching on CPU based on the costs + 4. assign all to 0 (background) first, then for each matched pair + between predictions and gts, treat this prediction as foreground + and assign the corresponding gt index (plus 1) to it. + + Args: + mask_pred (Tensor): Predicted mask, shape [num_query, h, w] + cls_pred (Tensor): Predicted classification logits, shape + [num_query, num_class]. + gt_masks (Tensor): Ground truth mask, shape [num_gt, h, w]. + gt_labels (Tensor): Label of `gt_masks`, shape (num_gt,). + img_meta (dict): Meta information for current image. + gt_masks_ignore (Tensor, optional): Ground truth masks that are + labelled as `ignored`. Default None. + eps (int | float, optional): A value added to the denominator for + numerical stability. Default 1e-7. + + Returns: + :obj:`AssignResult`: The assigned result. + """ + assert gt_masks_ignore is None, "Only case when gt_masks_ignore is None is supported." + num_gts, num_queries = gt_labels.shape[0], cls_pred.shape[0] + + # 1. assign -1 by default + assigned_gt_inds = cls_pred.new_full((num_queries,), -1, dtype=torch.long) + assigned_labels = cls_pred.new_full((num_queries,), -1, dtype=torch.long) + if num_gts == 0 or num_queries == 0: + # No ground truth or boxes, return empty assignment + if num_gts == 0: + # No ground truth, assign all to background + assigned_gt_inds[:] = 0 + return AssignResult(num_gts, assigned_gt_inds, labels=assigned_labels) + + # 2. compute the weighted costs + # classification and maskcost. + if self.cls_cost.weight != 0 and cls_pred is not None: + cls_cost = self.cls_cost(cls_pred, gt_labels) + else: + cls_cost = 0 + + if self.mask_cost.weight != 0: + # mask_pred shape = [nq, h, w] + # gt_mask shape = [ng, h, w] + # mask_cost shape = [nq, ng] + mask_cost = self.mask_cost(mask_pred, gt_masks) + else: + mask_cost = 0 + + if self.dice_cost.weight != 0: + dice_cost = self.dice_cost(mask_pred, gt_masks) + else: + dice_cost = 0 + cost = cls_cost + mask_cost + dice_cost + + # 3. do Hungarian matching on CPU using linear_sum_assignment + cost = cost.detach().cpu() + if linear_sum_assignment is None: + raise ImportError('Please run "pip install scipy" ' "to install scipy first.") + + matched_row_inds, matched_col_inds = linear_sum_assignment(cost) + matched_row_inds = torch.from_numpy(matched_row_inds).to(cls_pred.device) + matched_col_inds = torch.from_numpy(matched_col_inds).to(cls_pred.device) + + # 4. assign backgrounds and foregrounds + # assign all indices to backgrounds first + assigned_gt_inds[:] = 0 + # assign foregrounds based on matching results + assigned_gt_inds[matched_row_inds] = matched_col_inds + 1 + assigned_labels[matched_row_inds] = gt_labels[matched_col_inds] + return AssignResult(num_gts, assigned_gt_inds, labels=assigned_labels) diff --git a/dinov2/eval/segmentation_m2f/models/utils/point_sample.py b/dinov2/eval/segmentation_m2f/models/utils/point_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..9f1134082bafb51432618a9632592db070f87284 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/utils/point_sample.py @@ -0,0 +1,86 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch +from mmcv.ops import point_sample + + +def get_uncertainty(mask_pred, labels): + """Estimate uncertainty based on pred logits. + + We estimate uncertainty as L1 distance between 0.0 and the logits + prediction in 'mask_pred' for the foreground class in `classes`. + + Args: + mask_pred (Tensor): mask predication logits, shape (num_rois, + num_classes, mask_height, mask_width). + + labels (list[Tensor]): Either predicted or ground truth label for + each predicted mask, of length num_rois. + + Returns: + scores (Tensor): Uncertainty scores with the most uncertain + locations having the highest uncertainty score, + shape (num_rois, 1, mask_height, mask_width) + """ + if mask_pred.shape[1] == 1: + gt_class_logits = mask_pred.clone() + else: + inds = torch.arange(mask_pred.shape[0], device=mask_pred.device) + gt_class_logits = mask_pred[inds, labels].unsqueeze(1) + return -torch.abs(gt_class_logits) + + +def get_uncertain_point_coords_with_randomness( + mask_pred, labels, num_points, oversample_ratio, importance_sample_ratio +): + """Get ``num_points`` most uncertain points with random points during + train. + + Sample points in [0, 1] x [0, 1] coordinate space based on their + uncertainty. The uncertainties are calculated for each point using + 'get_uncertainty()' function that takes point's logit prediction as + input. + + Args: + mask_pred (Tensor): A tensor of shape (num_rois, num_classes, + mask_height, mask_width) for class-specific or class-agnostic + prediction. + labels (list): The ground truth class for each instance. + num_points (int): The number of points to sample. + oversample_ratio (int): Oversampling parameter. + importance_sample_ratio (float): Ratio of points that are sampled + via importnace sampling. + + Returns: + point_coords (Tensor): A tensor of shape (num_rois, num_points, 2) + that contains the coordinates sampled points. + """ + assert oversample_ratio >= 1 + assert 0 <= importance_sample_ratio <= 1 + batch_size = mask_pred.shape[0] + num_sampled = int(num_points * oversample_ratio) + point_coords = torch.rand(batch_size, num_sampled, 2, device=mask_pred.device) + point_logits = point_sample(mask_pred, point_coords) + # It is crucial to calculate uncertainty based on the sampled + # prediction value for the points. Calculating uncertainties of the + # coarse predictions first and sampling them for points leads to + # incorrect results. To illustrate this: assume uncertainty func( + # logits)=-abs(logits), a sampled point between two coarse + # predictions with -1 and 1 logits has 0 logits, and therefore 0 + # uncertainty value. However, if we calculate uncertainties for the + # coarse predictions first, both will have -1 uncertainty, + # and sampled point will get -1 uncertainty. + point_uncertainties = get_uncertainty(point_logits, labels) + num_uncertain_points = int(importance_sample_ratio * num_points) + num_random_points = num_points - num_uncertain_points + idx = torch.topk(point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1] + shift = num_sampled * torch.arange(batch_size, dtype=torch.long, device=mask_pred.device) + idx += shift[:, None] + point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view(batch_size, num_uncertain_points, 2) + if num_random_points > 0: + rand_roi_coords = torch.rand(batch_size, num_random_points, 2, device=mask_pred.device) + point_coords = torch.cat((point_coords, rand_roi_coords), dim=1) + return point_coords diff --git a/dinov2/eval/segmentation_m2f/models/utils/positional_encoding.py b/dinov2/eval/segmentation_m2f/models/utils/positional_encoding.py new file mode 100644 index 0000000000000000000000000000000000000000..bf5d6fabe946d06fe97cc799da47bae93758b34e --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/utils/positional_encoding.py @@ -0,0 +1,152 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn as nn +from mmcv.cnn.bricks.transformer import POSITIONAL_ENCODING +from mmcv.runner import BaseModule + + +@POSITIONAL_ENCODING.register_module() +class SinePositionalEncoding(BaseModule): + """Position encoding with sine and cosine functions. + + See `End-to-End Object Detection with Transformers + `_ for details. + + Args: + num_feats (int): The feature dimension for each position + along x-axis or y-axis. Note the final returned dimension + for each position is 2 times of this value. + temperature (int, optional): The temperature used for scaling + the position embedding. Defaults to 10000. + normalize (bool, optional): Whether to normalize the position + embedding. Defaults to False. + scale (float, optional): A scale factor that scales the position + embedding. The scale will be used only when `normalize` is True. + Defaults to 2*pi. + eps (float, optional): A value added to the denominator for + numerical stability. Defaults to 1e-6. + offset (float): offset add to embed when do the normalization. + Defaults to 0. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__( + self, num_feats, temperature=10000, normalize=False, scale=2 * math.pi, eps=1e-6, offset=0.0, init_cfg=None + ): + super(SinePositionalEncoding, self).__init__(init_cfg) + if normalize: + assert isinstance(scale, (float, int)), ( + "when normalize is set," "scale should be provided and in float or int type, " f"found {type(scale)}" + ) + self.num_feats = num_feats + self.temperature = temperature + self.normalize = normalize + self.scale = scale + self.eps = eps + self.offset = offset + + def forward(self, mask): + """Forward function for `SinePositionalEncoding`. + + Args: + mask (Tensor): ByteTensor mask. Non-zero values representing + ignored positions, while zero values means valid positions + for this image. Shape [bs, h, w]. + + Returns: + pos (Tensor): Returned position embedding with shape + [bs, num_feats*2, h, w]. + """ + # For convenience of exporting to ONNX, it's required to convert + # `masks` from bool to int. + mask = mask.to(torch.int) + not_mask = 1 - mask # logical_not + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + y_embed = (y_embed + self.offset) / (y_embed[:, -1:, :] + self.eps) * self.scale + x_embed = (x_embed + self.offset) / (x_embed[:, :, -1:] + self.eps) * self.scale + dim_t = torch.arange(self.num_feats, dtype=torch.float32, device=mask.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_feats) + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + # use `view` instead of `flatten` for dynamically exporting to ONNX + B, H, W = mask.size() + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).view(B, H, W, -1) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).view(B, H, W, -1) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + def __repr__(self): + """str: a string that describes the module""" + repr_str = self.__class__.__name__ + repr_str += f"(num_feats={self.num_feats}, " + repr_str += f"temperature={self.temperature}, " + repr_str += f"normalize={self.normalize}, " + repr_str += f"scale={self.scale}, " + repr_str += f"eps={self.eps})" + return repr_str + + +@POSITIONAL_ENCODING.register_module() +class LearnedPositionalEncoding(BaseModule): + """Position embedding with learnable embedding weights. + + Args: + num_feats (int): The feature dimension for each position + along x-axis or y-axis. The final returned dimension for + each position is 2 times of this value. + row_num_embed (int, optional): The dictionary size of row embeddings. + Default 50. + col_num_embed (int, optional): The dictionary size of col embeddings. + Default 50. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, num_feats, row_num_embed=50, col_num_embed=50, init_cfg=dict(type="Uniform", layer="Embedding")): + super(LearnedPositionalEncoding, self).__init__(init_cfg) + self.row_embed = nn.Embedding(row_num_embed, num_feats) + self.col_embed = nn.Embedding(col_num_embed, num_feats) + self.num_feats = num_feats + self.row_num_embed = row_num_embed + self.col_num_embed = col_num_embed + + def forward(self, mask): + """Forward function for `LearnedPositionalEncoding`. + + Args: + mask (Tensor): ByteTensor mask. Non-zero values representing + ignored positions, while zero values means valid positions + for this image. Shape [bs, h, w]. + + Returns: + pos (Tensor): Returned position embedding with shape + [bs, num_feats*2, h, w]. + """ + h, w = mask.shape[-2:] + x = torch.arange(w, device=mask.device) + y = torch.arange(h, device=mask.device) + x_embed = self.col_embed(x) + y_embed = self.row_embed(y) + pos = ( + torch.cat((x_embed.unsqueeze(0).repeat(h, 1, 1), y_embed.unsqueeze(1).repeat(1, w, 1)), dim=-1) + .permute(2, 0, 1) + .unsqueeze(0) + .repeat(mask.shape[0], 1, 1, 1) + ) + return pos + + def __repr__(self): + """str: a string that describes the module""" + repr_str = self.__class__.__name__ + repr_str += f"(num_feats={self.num_feats}, " + repr_str += f"row_num_embed={self.row_num_embed}, " + repr_str += f"col_num_embed={self.col_num_embed})" + return repr_str diff --git a/dinov2/eval/segmentation_m2f/models/utils/transformer.py b/dinov2/eval/segmentation_m2f/models/utils/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..8befe6011a34d5ccecb82c8b17b61e19f732f96b --- /dev/null +++ b/dinov2/eval/segmentation_m2f/models/utils/transformer.py @@ -0,0 +1,989 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import math +import warnings +from typing import Sequence + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from mmcv.cnn import Linear, build_activation_layer, build_norm_layer, xavier_init +from mmcv.cnn.bricks.drop import build_dropout +from mmcv.cnn.bricks.registry import FEEDFORWARD_NETWORK, TRANSFORMER_LAYER, TRANSFORMER_LAYER_SEQUENCE +from mmcv.cnn.bricks.transformer import BaseTransformerLayer, TransformerLayerSequence, build_transformer_layer_sequence +from mmcv.runner.base_module import BaseModule, Sequential +from mmcv.utils import deprecated_api_warning, to_2tuple +from torch.nn.init import normal_ + +from ..builder import TRANSFORMER + +try: + from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention + +except ImportError: + warnings.warn( + "`MultiScaleDeformableAttention` in MMCV has been moved to " + "`mmcv.ops.multi_scale_deform_attn`, please update your MMCV" + ) + from mmcv.cnn.bricks.transformer import MultiScaleDeformableAttention + + +class AdaptivePadding(nn.Module): + """Applies padding to input (if needed) so that input can get fully covered + by filter you specified. It support two modes "same" and "corner". The + "same" mode is same with "SAME" padding mode in TensorFlow, pad zero around + input. The "corner" mode would pad zero to bottom right. + + Args: + kernel_size (int | tuple): Size of the kernel: + stride (int | tuple): Stride of the filter. Default: 1: + dilation (int | tuple): Spacing between kernel elements. + Default: 1 + padding (str): Support "same" and "corner", "corner" mode + would pad zero to bottom right, and "same" mode would + pad zero around input. Default: "corner". + Example: + >>> kernel_size = 16 + >>> stride = 16 + >>> dilation = 1 + >>> input = torch.rand(1, 1, 15, 17) + >>> adap_pad = AdaptivePadding( + >>> kernel_size=kernel_size, + >>> stride=stride, + >>> dilation=dilation, + >>> padding="corner") + >>> out = adap_pad(input) + >>> assert (out.shape[2], out.shape[3]) == (16, 32) + >>> input = torch.rand(1, 1, 16, 17) + >>> out = adap_pad(input) + >>> assert (out.shape[2], out.shape[3]) == (16, 32) + """ + + def __init__(self, kernel_size=1, stride=1, dilation=1, padding="corner"): + + super(AdaptivePadding, self).__init__() + + assert padding in ("same", "corner") + + kernel_size = to_2tuple(kernel_size) + stride = to_2tuple(stride) + padding = to_2tuple(padding) + dilation = to_2tuple(dilation) + + self.padding = padding + self.kernel_size = kernel_size + self.stride = stride + self.dilation = dilation + + def get_pad_shape(self, input_shape): + input_h, input_w = input_shape + kernel_h, kernel_w = self.kernel_size + stride_h, stride_w = self.stride + output_h = math.ceil(input_h / stride_h) + output_w = math.ceil(input_w / stride_w) + pad_h = max((output_h - 1) * stride_h + (kernel_h - 1) * self.dilation[0] + 1 - input_h, 0) + pad_w = max((output_w - 1) * stride_w + (kernel_w - 1) * self.dilation[1] + 1 - input_w, 0) + return pad_h, pad_w + + def forward(self, x): + pad_h, pad_w = self.get_pad_shape(x.size()[-2:]) + if pad_h > 0 or pad_w > 0: + if self.padding == "corner": + x = F.pad(x, [0, pad_w, 0, pad_h]) + elif self.padding == "same": + x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]) + return x + + +class PatchMerging(BaseModule): + """Merge patch feature map. + + This layer groups feature map by kernel_size, and applies norm and linear + layers to the grouped feature map. Our implementation uses `nn.Unfold` to + merge patch, which is about 25% faster than original implementation. + Instead, we need to modify pretrained models for compatibility. + + Args: + in_channels (int): The num of input channels. + to gets fully covered by filter and stride you specified.. + Default: True. + out_channels (int): The num of output channels. + kernel_size (int | tuple, optional): the kernel size in the unfold + layer. Defaults to 2. + stride (int | tuple, optional): the stride of the sliding blocks in the + unfold layer. Default: None. (Would be set as `kernel_size`) + padding (int | tuple | string ): The padding length of + embedding conv. When it is a string, it means the mode + of adaptive padding, support "same" and "corner" now. + Default: "corner". + dilation (int | tuple, optional): dilation parameter in the unfold + layer. Default: 1. + bias (bool, optional): Whether to add bias in linear layer or not. + Defaults: False. + norm_cfg (dict, optional): Config dict for normalization layer. + Default: dict(type='LN'). + init_cfg (dict, optional): The extra config for initialization. + Default: None. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size=2, + stride=None, + padding="corner", + dilation=1, + bias=False, + norm_cfg=dict(type="LN"), + init_cfg=None, + ): + super().__init__(init_cfg=init_cfg) + self.in_channels = in_channels + self.out_channels = out_channels + if stride: + stride = stride + else: + stride = kernel_size + + kernel_size = to_2tuple(kernel_size) + stride = to_2tuple(stride) + dilation = to_2tuple(dilation) + + if isinstance(padding, str): + self.adap_padding = AdaptivePadding( + kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding + ) + # disable the padding of unfold + padding = 0 + else: + self.adap_padding = None + + padding = to_2tuple(padding) + self.sampler = nn.Unfold(kernel_size=kernel_size, dilation=dilation, padding=padding, stride=stride) + + sample_dim = kernel_size[0] * kernel_size[1] * in_channels + + if norm_cfg is not None: + self.norm = build_norm_layer(norm_cfg, sample_dim)[1] + else: + self.norm = None + + self.reduction = nn.Linear(sample_dim, out_channels, bias=bias) + + def forward(self, x, input_size): + """ + Args: + x (Tensor): Has shape (B, H*W, C_in). + input_size (tuple[int]): The spatial shape of x, arrange as (H, W). + Default: None. + + Returns: + tuple: Contains merged results and its spatial shape. + + - x (Tensor): Has shape (B, Merged_H * Merged_W, C_out) + - out_size (tuple[int]): Spatial shape of x, arrange as + (Merged_H, Merged_W). + """ + B, L, C = x.shape + assert isinstance(input_size, Sequence), f"Expect " f"input_size is " f"`Sequence` " f"but get {input_size}" + + H, W = input_size + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C).permute([0, 3, 1, 2]) # B, C, H, W + # Use nn.Unfold to merge patch. About 25% faster than original method, + # but need to modify pretrained model for compatibility + + if self.adap_padding: + x = self.adap_padding(x) + H, W = x.shape[-2:] + + x = self.sampler(x) + # if kernel_size=2 and stride=2, x should has shape (B, 4*C, H/2*W/2) + + out_h = ( + H + 2 * self.sampler.padding[0] - self.sampler.dilation[0] * (self.sampler.kernel_size[0] - 1) - 1 + ) // self.sampler.stride[0] + 1 + out_w = ( + W + 2 * self.sampler.padding[1] - self.sampler.dilation[1] * (self.sampler.kernel_size[1] - 1) - 1 + ) // self.sampler.stride[1] + 1 + + output_size = (out_h, out_w) + x = x.transpose(1, 2) # B, H/2*W/2, 4*C + x = self.norm(x) if self.norm else x + x = self.reduction(x) + return x, output_size + + +def inverse_sigmoid(x, eps=1e-5): + """Inverse function of sigmoid. + + Args: + x (Tensor): The tensor to do the + inverse. + eps (float): EPS avoid numerical + overflow. Defaults 1e-5. + Returns: + Tensor: The x has passed the inverse + function of sigmoid, has same + shape with input. + """ + x = x.clamp(min=0, max=1) + x1 = x.clamp(min=eps) + x2 = (1 - x).clamp(min=eps) + return torch.log(x1 / x2) + + +@FEEDFORWARD_NETWORK.register_module(force=True) +class FFN(BaseModule): + """Implements feed-forward networks (FFNs) with identity connection. + Args: + embed_dims (int): The feature dimension. Same as + `MultiheadAttention`. Defaults: 256. + feedforward_channels (int): The hidden dimension of FFNs. + Defaults: 1024. + num_fcs (int, optional): The number of fully-connected layers in + FFNs. Default: 2. + act_cfg (dict, optional): The activation config for FFNs. + Default: dict(type='ReLU') + ffn_drop (float, optional): Probability of an element to be + zeroed in FFN. Default 0.0. + add_identity (bool, optional): Whether to add the + identity connection. Default: `True`. + dropout_layer (obj:`ConfigDict`): The dropout_layer used + when adding the shortcut. + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Default: None. + """ + + @deprecated_api_warning({"dropout": "ffn_drop", "add_residual": "add_identity"}, cls_name="FFN") + def __init__( + self, + embed_dims=256, + feedforward_channels=1024, + num_fcs=2, + act_cfg=dict(type="ReLU", inplace=True), + ffn_drop=0.0, + dropout_layer=None, + add_identity=True, + init_cfg=None, + with_cp=False, + **kwargs, + ): + super().__init__(init_cfg) + assert num_fcs >= 2, "num_fcs should be no less " f"than 2. got {num_fcs}." + self.embed_dims = embed_dims + self.feedforward_channels = feedforward_channels + self.num_fcs = num_fcs + self.act_cfg = act_cfg + self.activate = build_activation_layer(act_cfg) + self.with_cp = with_cp + layers = [] + in_channels = embed_dims + for _ in range(num_fcs - 1): + layers.append(Sequential(Linear(in_channels, feedforward_channels), self.activate, nn.Dropout(ffn_drop))) + in_channels = feedforward_channels + layers.append(Linear(feedforward_channels, embed_dims)) + layers.append(nn.Dropout(ffn_drop)) + self.layers = Sequential(*layers) + self.dropout_layer = build_dropout(dropout_layer) if dropout_layer else torch.nn.Identity() + self.add_identity = add_identity + + @deprecated_api_warning({"residual": "identity"}, cls_name="FFN") + def forward(self, x, identity=None): + """Forward function for `FFN`. + The function would add x to the output tensor if residue is None. + """ + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(self.layers, x) + else: + out = self.layers(x) + + if not self.add_identity: + return self.dropout_layer(out) + if identity is None: + identity = x + return identity + self.dropout_layer(out) + + +@TRANSFORMER_LAYER.register_module() +class DetrTransformerDecoderLayer(BaseTransformerLayer): + """Implements decoder layer in DETR transformer. + + Args: + attn_cfgs (list[`mmcv.ConfigDict`] | list[dict] | dict )): + Configs for self_attention or cross_attention, the order + should be consistent with it in `operation_order`. If it is + a dict, it would be expand to the number of attention in + `operation_order`. + feedforward_channels (int): The hidden dimension for FFNs. + ffn_dropout (float): Probability of an element to be zeroed + in ffn. Default 0.0. + operation_order (tuple[str]): The execution order of operation + in transformer. Such as ('self_attn', 'norm', 'ffn', 'norm'). + Default：None + act_cfg (dict): The activation config for FFNs. Default: `LN` + norm_cfg (dict): Config dict for normalization layer. + Default: `LN`. + ffn_num_fcs (int): The number of fully-connected layers in FFNs. + Default：2. + """ + + def __init__( + self, + attn_cfgs, + feedforward_channels, + ffn_dropout=0.0, + operation_order=None, + act_cfg=dict(type="ReLU", inplace=True), + norm_cfg=dict(type="LN"), + ffn_num_fcs=2, + **kwargs, + ): + super(DetrTransformerDecoderLayer, self).__init__( + attn_cfgs=attn_cfgs, + feedforward_channels=feedforward_channels, + ffn_dropout=ffn_dropout, + operation_order=operation_order, + act_cfg=act_cfg, + norm_cfg=norm_cfg, + ffn_num_fcs=ffn_num_fcs, + **kwargs, + ) + assert len(operation_order) == 6 + assert set(operation_order) == set(["self_attn", "norm", "cross_attn", "ffn"]) + + +@TRANSFORMER_LAYER_SEQUENCE.register_module() +class DetrTransformerEncoder(TransformerLayerSequence): + """TransformerEncoder of DETR. + + Args: + post_norm_cfg (dict): Config of last normalization layer. Default： + `LN`. Only used when `self.pre_norm` is `True` + """ + + def __init__(self, *args, post_norm_cfg=dict(type="LN"), **kwargs): + super(DetrTransformerEncoder, self).__init__(*args, **kwargs) + if post_norm_cfg is not None: + self.post_norm = build_norm_layer(post_norm_cfg, self.embed_dims)[1] if self.pre_norm else None + else: + assert not self.pre_norm, f"Use prenorm in " f"{self.__class__.__name__}," f"Please specify post_norm_cfg" + self.post_norm = None + + def forward(self, *args, **kwargs): + """Forward function for `TransformerCoder`. + + Returns: + Tensor: forwarded results with shape [num_query, bs, embed_dims]. + """ + x = super(DetrTransformerEncoder, self).forward(*args, **kwargs) + if self.post_norm is not None: + x = self.post_norm(x) + return x + + +@TRANSFORMER_LAYER_SEQUENCE.register_module() +class DetrTransformerDecoder(TransformerLayerSequence): + """Implements the decoder in DETR transformer. + + Args: + return_intermediate (bool): Whether to return intermediate outputs. + post_norm_cfg (dict): Config of last normalization layer. Default： + `LN`. + """ + + def __init__(self, *args, post_norm_cfg=dict(type="LN"), return_intermediate=False, **kwargs): + + super(DetrTransformerDecoder, self).__init__(*args, **kwargs) + self.return_intermediate = return_intermediate + if post_norm_cfg is not None: + self.post_norm = build_norm_layer(post_norm_cfg, self.embed_dims)[1] + else: + self.post_norm = None + + def forward(self, query, *args, **kwargs): + """Forward function for `TransformerDecoder`. + + Args: + query (Tensor): Input query with shape + `(num_query, bs, embed_dims)`. + + Returns: + Tensor: Results with shape [1, num_query, bs, embed_dims] when + return_intermediate is `False`, otherwise it has shape + [num_layers, num_query, bs, embed_dims]. + """ + if not self.return_intermediate: + x = super().forward(query, *args, **kwargs) + if self.post_norm: + x = self.post_norm(x)[None] + return x + + intermediate = [] + for layer in self.layers: + query = layer(query, *args, **kwargs) + if self.return_intermediate: + if self.post_norm is not None: + intermediate.append(self.post_norm(query)) + else: + intermediate.append(query) + return torch.stack(intermediate) + + +@TRANSFORMER.register_module() +class Transformer(BaseModule): + """Implements the DETR transformer. + + Following the official DETR implementation, this module copy-paste + from torch.nn.Transformer with modifications: + + * positional encodings are passed in MultiheadAttention + * extra LN at the end of encoder is removed + * decoder returns a stack of activations from all decoding layers + + See `paper: End-to-End Object Detection with Transformers + `_ for details. + + Args: + encoder (`mmcv.ConfigDict` | Dict): Config of + TransformerEncoder. Defaults to None. + decoder ((`mmcv.ConfigDict` | Dict)): Config of + TransformerDecoder. Defaults to None + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Defaults to None. + """ + + def __init__(self, encoder=None, decoder=None, init_cfg=None): + super(Transformer, self).__init__(init_cfg=init_cfg) + self.encoder = build_transformer_layer_sequence(encoder) + self.decoder = build_transformer_layer_sequence(decoder) + self.embed_dims = self.encoder.embed_dims + + def init_weights(self): + # follow the official DETR to init parameters + for m in self.modules(): + if hasattr(m, "weight") and m.weight.dim() > 1: + xavier_init(m, distribution="uniform") + self._is_init = True + + def forward(self, x, mask, query_embed, pos_embed): + """Forward function for `Transformer`. + + Args: + x (Tensor): Input query with shape [bs, c, h, w] where + c = embed_dims. + mask (Tensor): The key_padding_mask used for encoder and decoder, + with shape [bs, h, w]. + query_embed (Tensor): The query embedding for decoder, with shape + [num_query, c]. + pos_embed (Tensor): The positional encoding for encoder and + decoder, with the same shape as `x`. + + Returns: + tuple[Tensor]: results of decoder containing the following tensor. + + - out_dec: Output from decoder. If return_intermediate_dec \ + is True output has shape [num_dec_layers, bs, + num_query, embed_dims], else has shape [1, bs, \ + num_query, embed_dims]. + - memory: Output results from encoder, with shape \ + [bs, embed_dims, h, w]. + """ + bs, c, h, w = x.shape + # use `view` instead of `flatten` for dynamically exporting to ONNX + x = x.view(bs, c, -1).permute(2, 0, 1) # [bs, c, h, w] -> [h*w, bs, c] + pos_embed = pos_embed.view(bs, c, -1).permute(2, 0, 1) + query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) # [num_query, dim] -> [num_query, bs, dim] + mask = mask.view(bs, -1) # [bs, h, w] -> [bs, h*w] + memory = self.encoder(query=x, key=None, value=None, query_pos=pos_embed, query_key_padding_mask=mask) + target = torch.zeros_like(query_embed) + # out_dec: [num_layers, num_query, bs, dim] + out_dec = self.decoder( + query=target, key=memory, value=memory, key_pos=pos_embed, query_pos=query_embed, key_padding_mask=mask + ) + out_dec = out_dec.transpose(1, 2) + memory = memory.permute(1, 2, 0).reshape(bs, c, h, w) + return out_dec, memory + + +@TRANSFORMER_LAYER_SEQUENCE.register_module() +class DeformableDetrTransformerDecoder(TransformerLayerSequence): + """Implements the decoder in DETR transformer. + + Args: + return_intermediate (bool): Whether to return intermediate outputs. + coder_norm_cfg (dict): Config of last normalization layer. Default： + `LN`. + """ + + def __init__(self, *args, return_intermediate=False, **kwargs): + + super(DeformableDetrTransformerDecoder, self).__init__(*args, **kwargs) + self.return_intermediate = return_intermediate + + def forward(self, query, *args, reference_points=None, valid_ratios=None, reg_branches=None, **kwargs): + """Forward function for `TransformerDecoder`. + + Args: + query (Tensor): Input query with shape + `(num_query, bs, embed_dims)`. + reference_points (Tensor): The reference + points of offset. has shape + (bs, num_query, 4) when as_two_stage, + otherwise has shape ((bs, num_query, 2). + valid_ratios (Tensor): The radios of valid + points on the feature map, has shape + (bs, num_levels, 2) + reg_branch: (obj:`nn.ModuleList`): Used for + refining the regression results. Only would + be passed when with_box_refine is True, + otherwise would be passed a `None`. + + Returns: + Tensor: Results with shape [1, num_query, bs, embed_dims] when + return_intermediate is `False`, otherwise it has shape + [num_layers, num_query, bs, embed_dims]. + """ + output = query + intermediate = [] + intermediate_reference_points = [] + for lid, layer in enumerate(self.layers): + if reference_points.shape[-1] == 4: + reference_points_input = ( + reference_points[:, :, None] * torch.cat([valid_ratios, valid_ratios], -1)[:, None] + ) + else: + assert reference_points.shape[-1] == 2 + reference_points_input = reference_points[:, :, None] * valid_ratios[:, None] + output = layer(output, *args, reference_points=reference_points_input, **kwargs) + output = output.permute(1, 0, 2) + + if reg_branches is not None: + tmp = reg_branches[lid](output) + if reference_points.shape[-1] == 4: + new_reference_points = tmp + inverse_sigmoid(reference_points) + new_reference_points = new_reference_points.sigmoid() + else: + assert reference_points.shape[-1] == 2 + new_reference_points = tmp + new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points) + new_reference_points = new_reference_points.sigmoid() + reference_points = new_reference_points.detach() + + output = output.permute(1, 0, 2) + if self.return_intermediate: + intermediate.append(output) + intermediate_reference_points.append(reference_points) + + if self.return_intermediate: + return torch.stack(intermediate), torch.stack(intermediate_reference_points) + + return output, reference_points + + +@TRANSFORMER.register_module() +class DeformableDetrTransformer(Transformer): + """Implements the DeformableDETR transformer. + + Args: + as_two_stage (bool): Generate query from encoder features. + Default: False. + num_feature_levels (int): Number of feature maps from FPN: + Default: 4. + two_stage_num_proposals (int): Number of proposals when set + `as_two_stage` as True. Default: 300. + """ + + def __init__(self, as_two_stage=False, num_feature_levels=4, two_stage_num_proposals=300, **kwargs): + super(DeformableDetrTransformer, self).__init__(**kwargs) + self.as_two_stage = as_two_stage + self.num_feature_levels = num_feature_levels + self.two_stage_num_proposals = two_stage_num_proposals + self.embed_dims = self.encoder.embed_dims + self.init_layers() + + def init_layers(self): + """Initialize layers of the DeformableDetrTransformer.""" + self.level_embeds = nn.Parameter(torch.Tensor(self.num_feature_levels, self.embed_dims)) + + if self.as_two_stage: + self.enc_output = nn.Linear(self.embed_dims, self.embed_dims) + self.enc_output_norm = nn.LayerNorm(self.embed_dims) + self.pos_trans = nn.Linear(self.embed_dims * 2, self.embed_dims * 2) + self.pos_trans_norm = nn.LayerNorm(self.embed_dims * 2) + else: + self.reference_points = nn.Linear(self.embed_dims, 2) + + def init_weights(self): + """Initialize the transformer weights.""" + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + for m in self.modules(): + if isinstance(m, MultiScaleDeformableAttention): + m.init_weights() + if not self.as_two_stage: + xavier_init(self.reference_points, distribution="uniform", bias=0.0) + normal_(self.level_embeds) + + def gen_encoder_output_proposals(self, memory, memory_padding_mask, spatial_shapes): + """Generate proposals from encoded memory. + + Args: + memory (Tensor) : The output of encoder, + has shape (bs, num_key, embed_dim). num_key is + equal the number of points on feature map from + all level. + memory_padding_mask (Tensor): Padding mask for memory. + has shape (bs, num_key). + spatial_shapes (Tensor): The shape of all feature maps. + has shape (num_level, 2). + + Returns: + tuple: A tuple of feature map and bbox prediction. + + - output_memory (Tensor): The input of decoder, \ + has shape (bs, num_key, embed_dim). num_key is \ + equal the number of points on feature map from \ + all levels. + - output_proposals (Tensor): The normalized proposal \ + after a inverse sigmoid, has shape \ + (bs, num_keys, 4). + """ + + N, S, C = memory.shape + proposals = [] + _cur = 0 + for lvl, (H, W) in enumerate(spatial_shapes): + mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H * W)].view(N, H, W, 1) + valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1) + valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1) + + grid_y, grid_x = torch.meshgrid( + torch.linspace(0, H - 1, H, dtype=torch.float32, device=memory.device), + torch.linspace(0, W - 1, W, dtype=torch.float32, device=memory.device), + ) + grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) + + scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N, 1, 1, 2) + grid = (grid.unsqueeze(0).expand(N, -1, -1, -1) + 0.5) / scale + wh = torch.ones_like(grid) * 0.05 * (2.0**lvl) + proposal = torch.cat((grid, wh), -1).view(N, -1, 4) + proposals.append(proposal) + _cur += H * W + output_proposals = torch.cat(proposals, 1) + output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True) + output_proposals = torch.log(output_proposals / (1 - output_proposals)) + output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float("inf")) + output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf")) + + output_memory = memory + output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0)) + output_memory = output_memory.masked_fill(~output_proposals_valid, float(0)) + output_memory = self.enc_output_norm(self.enc_output(output_memory)) + return output_memory, output_proposals + + @staticmethod + def get_reference_points(spatial_shapes, valid_ratios, device): + """Get the reference points used in decoder. + + Args: + spatial_shapes (Tensor): The shape of all + feature maps, has shape (num_level, 2). + valid_ratios (Tensor): The radios of valid + points on the feature map, has shape + (bs, num_levels, 2) + device (obj:`device`): The device where + reference_points should be. + + Returns: + Tensor: reference points used in decoder, has \ + shape (bs, num_keys, num_levels, 2). + """ + reference_points_list = [] + for lvl, (H, W) in enumerate(spatial_shapes): + ref_y, ref_x = torch.meshgrid( + torch.linspace(0.5, H - 0.5, H, dtype=torch.float32, device=device), + torch.linspace(0.5, W - 0.5, W, dtype=torch.float32, device=device), + ) + ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H) + ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W) + ref = torch.stack((ref_x, ref_y), -1) + reference_points_list.append(ref) + reference_points = torch.cat(reference_points_list, 1) + reference_points = reference_points[:, :, None] * valid_ratios[:, None] + return reference_points + + def get_valid_ratio(self, mask): + """Get the valid radios of feature maps of all level.""" + _, H, W = mask.shape + valid_H = torch.sum(~mask[:, :, 0], 1) + valid_W = torch.sum(~mask[:, 0, :], 1) + valid_ratio_h = valid_H.float() / H + valid_ratio_w = valid_W.float() / W + valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) + return valid_ratio + + def get_proposal_pos_embed(self, proposals, num_pos_feats=128, temperature=10000): + """Get the position embedding of proposal.""" + scale = 2 * math.pi + dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device) + dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats) + # N, L, 4 + proposals = proposals.sigmoid() * scale + # N, L, 4, 128 + pos = proposals[:, :, :, None] / dim_t + # N, L, 4, 64, 2 + pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2) + return pos + + def forward( + self, mlvl_feats, mlvl_masks, query_embed, mlvl_pos_embeds, reg_branches=None, cls_branches=None, **kwargs + ): + """Forward function for `Transformer`. + + Args: + mlvl_feats (list(Tensor)): Input queries from + different level. Each element has shape + [bs, embed_dims, h, w]. + mlvl_masks (list(Tensor)): The key_padding_mask from + different level used for encoder and decoder, + each element has shape [bs, h, w]. + query_embed (Tensor): The query embedding for decoder, + with shape [num_query, c]. + mlvl_pos_embeds (list(Tensor)): The positional encoding + of feats from different level, has the shape + [bs, embed_dims, h, w]. + reg_branches (obj:`nn.ModuleList`): Regression heads for + feature maps from each decoder layer. Only would + be passed when + `with_box_refine` is True. Default to None. + cls_branches (obj:`nn.ModuleList`): Classification heads + for feature maps from each decoder layer. Only would + be passed when `as_two_stage` + is True. Default to None. + + + Returns: + tuple[Tensor]: results of decoder containing the following tensor. + + - inter_states: Outputs from decoder. If + return_intermediate_dec is True output has shape \ + (num_dec_layers, bs, num_query, embed_dims), else has \ + shape (1, bs, num_query, embed_dims). + - init_reference_out: The initial value of reference \ + points, has shape (bs, num_queries, 4). + - inter_references_out: The internal value of reference \ + points in decoder, has shape \ + (num_dec_layers, bs,num_query, embed_dims) + - enc_outputs_class: The classification score of \ + proposals generated from \ + encoder's feature maps, has shape \ + (batch, h*w, num_classes). \ + Only would be returned when `as_two_stage` is True, \ + otherwise None. + - enc_outputs_coord_unact: The regression results \ + generated from encoder's feature maps., has shape \ + (batch, h*w, 4). Only would \ + be returned when `as_two_stage` is True, \ + otherwise None. + """ + assert self.as_two_stage or query_embed is not None + + feat_flatten = [] + mask_flatten = [] + lvl_pos_embed_flatten = [] + spatial_shapes = [] + for lvl, (feat, mask, pos_embed) in enumerate(zip(mlvl_feats, mlvl_masks, mlvl_pos_embeds)): + bs, c, h, w = feat.shape + spatial_shape = (h, w) + spatial_shapes.append(spatial_shape) + feat = feat.flatten(2).transpose(1, 2) + mask = mask.flatten(1) + pos_embed = pos_embed.flatten(2).transpose(1, 2) + lvl_pos_embed = pos_embed + self.level_embeds[lvl].view(1, 1, -1) + lvl_pos_embed_flatten.append(lvl_pos_embed) + feat_flatten.append(feat) + mask_flatten.append(mask) + feat_flatten = torch.cat(feat_flatten, 1) + mask_flatten = torch.cat(mask_flatten, 1) + lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) + spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=feat_flatten.device) + level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) + valid_ratios = torch.stack([self.get_valid_ratio(m) for m in mlvl_masks], 1) + + reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=feat.device) + + feat_flatten = feat_flatten.permute(1, 0, 2) # (H*W, bs, embed_dims) + lvl_pos_embed_flatten = lvl_pos_embed_flatten.permute(1, 0, 2) # (H*W, bs, embed_dims) + memory = self.encoder( + query=feat_flatten, + key=None, + value=None, + query_pos=lvl_pos_embed_flatten, + query_key_padding_mask=mask_flatten, + spatial_shapes=spatial_shapes, + reference_points=reference_points, + level_start_index=level_start_index, + valid_ratios=valid_ratios, + **kwargs, + ) + + memory = memory.permute(1, 0, 2) + bs, _, c = memory.shape + if self.as_two_stage: + output_memory, output_proposals = self.gen_encoder_output_proposals(memory, mask_flatten, spatial_shapes) + enc_outputs_class = cls_branches[self.decoder.num_layers](output_memory) + enc_outputs_coord_unact = reg_branches[self.decoder.num_layers](output_memory) + output_proposals + + topk = self.two_stage_num_proposals + topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1] + topk_coords_unact = torch.gather(enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)) + topk_coords_unact = topk_coords_unact.detach() + reference_points = topk_coords_unact.sigmoid() + init_reference_out = reference_points + pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact))) + query_pos, query = torch.split(pos_trans_out, c, dim=2) + else: + query_pos, query = torch.split(query_embed, c, dim=1) + query_pos = query_pos.unsqueeze(0).expand(bs, -1, -1) + query = query.unsqueeze(0).expand(bs, -1, -1) + reference_points = self.reference_points(query_pos).sigmoid() + init_reference_out = reference_points + + # decoder + query = query.permute(1, 0, 2) + memory = memory.permute(1, 0, 2) + query_pos = query_pos.permute(1, 0, 2) + inter_states, inter_references = self.decoder( + query=query, + key=None, + value=memory, + query_pos=query_pos, + key_padding_mask=mask_flatten, + reference_points=reference_points, + spatial_shapes=spatial_shapes, + level_start_index=level_start_index, + valid_ratios=valid_ratios, + reg_branches=reg_branches, + **kwargs, + ) + + inter_references_out = inter_references + if self.as_two_stage: + return inter_states, init_reference_out, inter_references_out, enc_outputs_class, enc_outputs_coord_unact + return inter_states, init_reference_out, inter_references_out, None, None + + +@TRANSFORMER.register_module() +class DynamicConv(BaseModule): + """Implements Dynamic Convolution. + + This module generate parameters for each sample and + use bmm to implement 1*1 convolution. Code is modified + from the `official github repo `_ . + + Args: + in_channels (int): The input feature channel. + Defaults to 256. + feat_channels (int): The inner feature channel. + Defaults to 64. + out_channels (int, optional): The output feature channel. + When not specified, it will be set to `in_channels` + by default + input_feat_shape (int): The shape of input feature. + Defaults to 7. + with_proj (bool): Project two-dimentional feature to + one-dimentional feature. Default to True. + act_cfg (dict): The activation config for DynamicConv. + norm_cfg (dict): Config dict for normalization layer. Default + layer normalization. + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Default: None. + """ + + def __init__( + self, + in_channels=256, + feat_channels=64, + out_channels=None, + input_feat_shape=7, + with_proj=True, + act_cfg=dict(type="ReLU", inplace=True), + norm_cfg=dict(type="LN"), + init_cfg=None, + ): + super(DynamicConv, self).__init__(init_cfg) + self.in_channels = in_channels + self.feat_channels = feat_channels + self.out_channels_raw = out_channels + self.input_feat_shape = input_feat_shape + self.with_proj = with_proj + self.act_cfg = act_cfg + self.norm_cfg = norm_cfg + self.out_channels = out_channels if out_channels else in_channels + + self.num_params_in = self.in_channels * self.feat_channels + self.num_params_out = self.out_channels * self.feat_channels + self.dynamic_layer = nn.Linear(self.in_channels, self.num_params_in + self.num_params_out) + + self.norm_in = build_norm_layer(norm_cfg, self.feat_channels)[1] + self.norm_out = build_norm_layer(norm_cfg, self.out_channels)[1] + + self.activation = build_activation_layer(act_cfg) + + num_output = self.out_channels * input_feat_shape**2 + if self.with_proj: + self.fc_layer = nn.Linear(num_output, self.out_channels) + self.fc_norm = build_norm_layer(norm_cfg, self.out_channels)[1] + + def forward(self, param_feature, input_feature): + """Forward function for `DynamicConv`. + + Args: + param_feature (Tensor): The feature can be used + to generate the parameter, has shape + (num_all_proposals, in_channels). + input_feature (Tensor): Feature that + interact with parameters, has shape + (num_all_proposals, in_channels, H, W). + + Returns: + Tensor: The output feature has shape + (num_all_proposals, out_channels). + """ + input_feature = input_feature.flatten(2).permute(2, 0, 1) + + input_feature = input_feature.permute(1, 0, 2) + parameters = self.dynamic_layer(param_feature) + + param_in = parameters[:, : self.num_params_in].view(-1, self.in_channels, self.feat_channels) + param_out = parameters[:, -self.num_params_out :].view(-1, self.feat_channels, self.out_channels) + + # input_feature has shape (num_all_proposals, H*W, in_channels) + # param_in has shape (num_all_proposals, in_channels, feat_channels) + # feature has shape (num_all_proposals, H*W, feat_channels) + features = torch.bmm(input_feature, param_in) + features = self.norm_in(features) + features = self.activation(features) + + # param_out has shape (batch_size, feat_channels, out_channels) + features = torch.bmm(features, param_out) + features = self.norm_out(features) + features = self.activation(features) + + if self.with_proj: + features = features.flatten(1) + features = self.fc_layer(features) + features = self.fc_norm(features) + features = self.activation(features) + + return features diff --git a/dinov2/eval/segmentation_m2f/ops/modules/__init__.py b/dinov2/eval/segmentation_m2f/ops/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..49aa8fe612fd4c088e294707c5ee16bd1cb5b5e7 --- /dev/null +++ b/dinov2/eval/segmentation_m2f/ops/modules/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/fundamentalvision/Deformable-DETR/tree/main/models/ops/modules +# https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 + +from .ms_deform_attn import MSDeformAttn diff --git a/dinov2/eval/segmentation_m2f/ops/modules/__pycache__/__init__.cpython-310.pyc b/dinov2/eval/segmentation_m2f/ops/modules/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..29108b4783e363bf3625ff57e3bd467af2f05021 Binary files /dev/null and b/dinov2/eval/segmentation_m2f/ops/modules/__pycache__/__init__.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/ops/modules/__pycache__/ms_deform_attn.cpython-310.pyc b/dinov2/eval/segmentation_m2f/ops/modules/__pycache__/ms_deform_attn.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..09364b5ffd5a96fa4ba8635c6cfb38c7dbbbbc5c Binary files /dev/null and b/dinov2/eval/segmentation_m2f/ops/modules/__pycache__/ms_deform_attn.cpython-310.pyc differ diff --git a/dinov2/eval/segmentation_m2f/ops/modules/ms_deform_attn.py b/dinov2/eval/segmentation_m2f/ops/modules/ms_deform_attn.py new file mode 100644 index 0000000000000000000000000000000000000000..d8b4fa23712e87d1a2682b57e71ee37fe8524cff --- /dev/null +++ b/dinov2/eval/segmentation_m2f/ops/modules/ms_deform_attn.py @@ -0,0 +1,185 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import math +import warnings + +import torch +import torch.nn.functional as F +from torch import nn +from torch.autograd import Function +from torch.cuda.amp import custom_fwd +from torch.nn.init import constant_, xavier_uniform_ + + +class MSDeformAttnFunction(Function): + @staticmethod + @custom_fwd(cast_inputs=torch.float32) + def forward( + ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step + ): + output = ms_deform_attn_core_pytorch( + value, + value_spatial_shapes, + # value_level_start_index, + sampling_locations, + attention_weights, + ) + return output + + +def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights): + # for debug and test only, + # need to use cuda version instead + N_, S_, M_, D_ = value.shape + _, Lq_, M_, L_, P_, _ = sampling_locations.shape + value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1) + sampling_grids = 2 * sampling_locations - 1 + sampling_value_list = [] + for lid_, (H_, W_) in enumerate(value_spatial_shapes): + # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_ + value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_ * M_, D_, H_, W_) + # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2 + sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1) + # N_*M_, D_, Lq_, P_ + sampling_value_l_ = F.grid_sample( + value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False + ) + sampling_value_list.append(sampling_value_l_) + # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_) + attention_weights = attention_weights.transpose(1, 2).reshape(N_ * M_, 1, Lq_, L_ * P_) + output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_ * D_, Lq_) + return output.transpose(1, 2).contiguous() + + +def _is_power_of_2(n): + if (not isinstance(n, int)) or (n < 0): + raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))) + return (n & (n - 1) == 0) and n != 0 + + +class MSDeformAttn(nn.Module): + def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4, ratio=1.0): + """Multi-Scale Deformable Attention Module. + + :param d_model hidden dimension + :param n_levels number of feature levels + :param n_heads number of attention heads + :param n_points number of sampling points per attention head per feature level + """ + super().__init__() + if d_model % n_heads != 0: + raise ValueError("d_model must be divisible by n_heads, " "but got {} and {}".format(d_model, n_heads)) + _d_per_head = d_model // n_heads + # you'd better set _d_per_head to a power of 2 + # which is more efficient in our CUDA implementation + if not _is_power_of_2(_d_per_head): + warnings.warn( + "You'd better set d_model in MSDeformAttn to make " + "the dimension of each attention head a power of 2 " + "which is more efficient in our CUDA implementation." + ) + + self.im2col_step = 64 + + self.d_model = d_model + self.n_levels = n_levels + self.n_heads = n_heads + self.n_points = n_points + self.ratio = ratio + self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2) + self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points) + self.value_proj = nn.Linear(d_model, int(d_model * ratio)) + self.output_proj = nn.Linear(int(d_model * ratio), d_model) + + self._reset_parameters() + + def _reset_parameters(self): + constant_(self.sampling_offsets.weight.data, 0.0) + thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = ( + (grid_init / grid_init.abs().max(-1, keepdim=True)[0]) + .view(self.n_heads, 1, 1, 2) + .repeat(1, self.n_levels, self.n_points, 1) + ) + for i in range(self.n_points): + grid_init[:, :, i, :] *= i + 1 + + with torch.no_grad(): + self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) + constant_(self.attention_weights.weight.data, 0.0) + constant_(self.attention_weights.bias.data, 0.0) + xavier_uniform_(self.value_proj.weight.data) + constant_(self.value_proj.bias.data, 0.0) + xavier_uniform_(self.output_proj.weight.data) + constant_(self.output_proj.bias.data, 0.0) + + def forward( + self, + query, + reference_points, + input_flatten, + input_spatial_shapes, + input_level_start_index, + input_padding_mask=None, + ): + """ + :param query (N, Length_{query}, C) + :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area + or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes + :param input_flatten (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l, C) + :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})] + :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}] + :param input_padding_mask (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l), True for padding elements, False for non-padding elements + + :return output (N, Length_{query}, C) + """ + # print(query.shape) + # print(reference_points.shape) + # print(input_flatten.shape) + # print(input_spatial_shapes.shape) + # print(input_level_start_index.shape) + # print(input_spatial_shapes) + # print(input_level_start_index) + + N, Len_q, _ = query.shape + N, Len_in, _ = input_flatten.shape + assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in + + value = self.value_proj(input_flatten) + if input_padding_mask is not None: + value = value.masked_fill(input_padding_mask[..., None], float(0)) + + value = value.view(N, Len_in, self.n_heads, int(self.ratio * self.d_model) // self.n_heads) + sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2) + attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points) + attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points) + + if reference_points.shape[-1] == 2: + offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1) + sampling_locations = ( + reference_points[:, :, None, :, None, :] + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + ) + elif reference_points.shape[-1] == 4: + sampling_locations = ( + reference_points[:, :, None, :, None, :2] + + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 + ) + else: + raise ValueError( + "Last dim of reference_points must be 2 or 4, but get {} instead.".format(reference_points.shape[-1]) + ) + output = MSDeformAttnFunction.apply( + value, + input_spatial_shapes, + input_level_start_index, + sampling_locations, + attention_weights, + self.im2col_step, + ) + output = self.output_proj(output) + return output diff --git a/dinov2/eval/setup.py b/dinov2/eval/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..959128c0673cc51036dbf17dcc4ee68a037988fb --- /dev/null +++ b/dinov2/eval/setup.py @@ -0,0 +1,75 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import argparse +from typing import Any, List, Optional, Tuple + +import torch +import torch.backends.cudnn as cudnn + +from dinov2.models import build_model_from_cfg +from dinov2.utils.config import setup +import dinov2.utils.utils as dinov2_utils + + +def get_args_parser( + description: Optional[str] = None, + parents: Optional[List[argparse.ArgumentParser]] = None, + add_help: bool = True, +): + parser = argparse.ArgumentParser( + description=description, + parents=parents or [], + add_help=add_help, + ) + parser.add_argument( + "--config-file", + type=str, + help="Model configuration file", + ) + parser.add_argument( + "--pretrained-weights", + type=str, + help="Pretrained model weights", + ) + parser.add_argument( + "--output-dir", + default="", + type=str, + help="Output directory to write results and logs", + ) + parser.add_argument( + "--opts", + help="Extra configuration options", + default=[], + nargs="+", + ) + return parser + + +def get_autocast_dtype(config): + teacher_dtype_str = config.compute_precision.teacher.backbone.mixed_precision.param_dtype + if teacher_dtype_str == "fp16": + return torch.half + elif teacher_dtype_str == "bf16": + return torch.bfloat16 + else: + return torch.float + + +def build_model_for_eval(config, pretrained_weights): + model, _ = build_model_from_cfg(config, only_teacher=True) + dinov2_utils.load_pretrained_weights(model, pretrained_weights, "teacher") + model.eval() + model.cuda() + return model + + +def setup_and_build_model(args) -> Tuple[Any, torch.dtype]: + cudnn.benchmark = True + config = setup(args) + model = build_model_for_eval(config, args.pretrained_weights) + autocast_dtype = get_autocast_dtype(config) + return model, autocast_dtype diff --git a/dinov2/eval/utils.py b/dinov2/eval/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c50576b1940587ee64b7a422e2e96b475d60fd39 --- /dev/null +++ b/dinov2/eval/utils.py @@ -0,0 +1,146 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import logging +from typing import Dict, Optional + +import torch +from torch import nn +from torchmetrics import MetricCollection + +from dinov2.data import DatasetWithEnumeratedTargets, SamplerType, make_data_loader +import dinov2.distributed as distributed +from dinov2.logging import MetricLogger + + +logger = logging.getLogger("dinov2") + + +class ModelWithNormalize(torch.nn.Module): + def __init__(self, model): + super().__init__() + self.model = model + + def forward(self, samples): + return nn.functional.normalize(self.model(samples), dim=1, p=2) + + +class ModelWithIntermediateLayers(nn.Module): + def __init__(self, feature_model, n_last_blocks, autocast_ctx): + super().__init__() + self.feature_model = feature_model + self.feature_model.eval() + self.n_last_blocks = n_last_blocks + self.autocast_ctx = autocast_ctx + + def forward(self, images): + with torch.inference_mode(): + with self.autocast_ctx(): + features = self.feature_model.get_intermediate_layers( + images, self.n_last_blocks, return_class_token=True + ) + return features + + +@torch.inference_mode() +def evaluate( + model: nn.Module, + data_loader, + postprocessors: Dict[str, nn.Module], + metrics: Dict[str, MetricCollection], + device: torch.device, + criterion: Optional[nn.Module] = None, +): + model.eval() + if criterion is not None: + criterion.eval() + + for metric in metrics.values(): + metric = metric.to(device) + + metric_logger = MetricLogger(delimiter=" ") + header = "Test:" + + for samples, targets, *_ in metric_logger.log_every(data_loader, 10, header): + outputs = model(samples.to(device)) + targets = targets.to(device) + + if criterion is not None: + loss = criterion(outputs, targets) + metric_logger.update(loss=loss.item()) + + for k, metric in metrics.items(): + metric_inputs = postprocessors[k](outputs, targets) + metric.update(**metric_inputs) + + metric_logger.synchronize_between_processes() + logger.info(f"Averaged stats: {metric_logger}") + + stats = {k: metric.compute() for k, metric in metrics.items()} + metric_logger_stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()} + return metric_logger_stats, stats + + +def all_gather_and_flatten(tensor_rank): + tensor_all_ranks = torch.empty( + distributed.get_global_size(), + *tensor_rank.shape, + dtype=tensor_rank.dtype, + device=tensor_rank.device, + ) + tensor_list = list(tensor_all_ranks.unbind(0)) + torch.distributed.all_gather(tensor_list, tensor_rank.contiguous()) + return tensor_all_ranks.flatten(end_dim=1) + + +def extract_features(model, dataset, batch_size, num_workers, gather_on_cpu=False): + dataset_with_enumerated_targets = DatasetWithEnumeratedTargets(dataset) + sample_count = len(dataset_with_enumerated_targets) + data_loader = make_data_loader( + dataset=dataset_with_enumerated_targets, + batch_size=batch_size, + num_workers=num_workers, + sampler_type=SamplerType.DISTRIBUTED, + drop_last=False, + shuffle=False, + ) + return extract_features_with_dataloader(model, data_loader, sample_count, gather_on_cpu) + + +@torch.inference_mode() +def extract_features_with_dataloader(model, data_loader, sample_count, gather_on_cpu=False): + gather_device = torch.device("cpu") if gather_on_cpu else torch.device("cuda") + metric_logger = MetricLogger(delimiter=" ") + features, all_labels = None, None + for samples, (index, labels_rank) in metric_logger.log_every(data_loader, 10): + samples = samples.cuda(non_blocking=True) + labels_rank = labels_rank.cuda(non_blocking=True) + index = index.cuda(non_blocking=True) + features_rank = model(samples).float() + + # init storage feature matrix + if features is None: + features = torch.zeros(sample_count, features_rank.shape[-1], device=gather_device) + labels_shape = list(labels_rank.shape) + labels_shape[0] = sample_count + all_labels = torch.full(labels_shape, fill_value=-1, device=gather_device) + logger.info(f"Storing features into tensor of shape {features.shape}") + + # share indexes, features and labels between processes + index_all = all_gather_and_flatten(index).to(gather_device) + features_all_ranks = all_gather_and_flatten(features_rank).to(gather_device) + labels_all_ranks = all_gather_and_flatten(labels_rank).to(gather_device) + + # update storage feature matrix + if len(index_all) > 0: + features.index_copy_(0, index_all, features_all_ranks) + all_labels.index_copy_(0, index_all, labels_all_ranks) + + logger.info(f"Features shape: {tuple(features.shape)}") + logger.info(f"Labels shape: {tuple(all_labels.shape)}") + + assert torch.all(all_labels > -1) + + return features, all_labels diff --git a/dinov2/fsdp/__init__.py b/dinov2/fsdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ed454480e0b76e761d657cc40fd097bd339d15a2 --- /dev/null +++ b/dinov2/fsdp/__init__.py @@ -0,0 +1,157 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import os +from typing import Any + +import torch +import dinov2.distributed as distributed +from functools import partial +from fvcore.common.checkpoint import Checkpointer +from torch.distributed.fsdp import FullyShardedDataParallel as FSDP +from torch.distributed.fsdp import ShardingStrategy +from torch.distributed.fsdp import MixedPrecision +from torch.distributed.fsdp import StateDictType +from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler +from torch.distributed.fsdp.wrap import ModuleWrapPolicy +from torch.distributed.fsdp._runtime_utils import _reshard + + +def get_fsdp_wrapper(model_cfg, modules_to_wrap=set()): + sharding_strategy_dict = { + "NO_SHARD": ShardingStrategy.NO_SHARD, + "SHARD_GRAD_OP": ShardingStrategy.SHARD_GRAD_OP, + "FULL_SHARD": ShardingStrategy.FULL_SHARD, + } + + dtype_dict = { + "fp32": torch.float32, + "fp16": torch.float16, + "bf16": torch.bfloat16, + } + + mixed_precision_config = MixedPrecision( + param_dtype=dtype_dict[model_cfg.mixed_precision.param_dtype], + reduce_dtype=dtype_dict[model_cfg.mixed_precision.reduce_dtype], + buffer_dtype=dtype_dict[model_cfg.mixed_precision.buffer_dtype], + ) + + sharding_strategy_config = sharding_strategy_dict[model_cfg.sharding_strategy] + + local_rank = distributed.get_local_rank() + + fsdp_wrapper = partial( + FSDP, + sharding_strategy=sharding_strategy_config, + mixed_precision=mixed_precision_config, + device_id=local_rank, + sync_module_states=True, + use_orig_params=True, + auto_wrap_policy=ModuleWrapPolicy(modules_to_wrap), + ) + return fsdp_wrapper + + +def is_fsdp(x): + return isinstance(x, FSDP) + + +def is_sharded_fsdp(x): + return is_fsdp(x) and x.sharding_strategy is not ShardingStrategy.NO_SHARD + + +def free_if_fsdp(x): + if is_sharded_fsdp(x): + handles = x._handles + true_list = [True for h in handles] + _reshard(x, handles, true_list) + + +def get_fsdp_modules(x): + return FSDP.fsdp_modules(x) + + +def reshard_fsdp_model(x): + for m in get_fsdp_modules(x): + free_if_fsdp(m) + + +def rankstr(): + return f"rank_{distributed.get_global_rank()}" + + +class FSDPCheckpointer(Checkpointer): + def save(self, name: str, **kwargs: Any) -> None: + """ + Dump model and checkpointables to a file. + + Args: + name (str): name of the file. + kwargs (dict): extra arbitrary data to save. + """ + if not self.save_dir or not self.save_to_disk: + return + + data = {} + with FSDP.state_dict_type(self.model, StateDictType.LOCAL_STATE_DICT): + data["model"] = self.model.state_dict() + + # data["model"] = self.model.state_dict() + for key, obj in self.checkpointables.items(): + data[key] = obj.state_dict() + data.update(kwargs) + + basename = f"{name}.{rankstr()}.pth" + save_file = os.path.join(self.save_dir, basename) + assert os.path.basename(save_file) == basename, basename + self.logger.info("Saving checkpoint to {}".format(save_file)) + with self.path_manager.open(save_file, "wb") as f: + torch.save(data, f) + self.tag_last_checkpoint(basename) + + def load(self, *args, **kwargs): + with FSDP.state_dict_type(self.model, StateDictType.LOCAL_STATE_DICT): + return super().load(*args, **kwargs) + + def has_checkpoint(self) -> bool: + """ + Returns: + bool: whether a checkpoint exists in the target directory. + """ + save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}") + return self.path_manager.exists(save_file) + + def get_checkpoint_file(self) -> str: + """ + Returns: + str: The latest checkpoint file in target directory. + """ + save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}") + try: + with self.path_manager.open(save_file, "r") as f: + last_saved = f.read().strip() + except IOError: + # if file doesn't exist, maybe because it has just been + # deleted by a separate process + return "" + # pyre-fixme[6]: For 2nd param expected `Union[PathLike[str], str]` but got + # `Union[bytes, str]`. + return os.path.join(self.save_dir, last_saved) + + def tag_last_checkpoint(self, last_filename_basename: str) -> None: + """ + Tag the last checkpoint. + + Args: + last_filename_basename (str): the basename of the last filename. + """ + if distributed.is_enabled(): + torch.distributed.barrier() + save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}") + with self.path_manager.open(save_file, "w") as f: + f.write(last_filename_basename) # pyre-ignore + + +ShardedGradScaler = ShardedGradScaler diff --git a/dinov2/layers/__init__.py b/dinov2/layers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..05a0b61868e43abb821ca05a813bab2b8b43629e --- /dev/null +++ b/dinov2/layers/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .dino_head import DINOHead +from .mlp import Mlp +from .patch_embed import PatchEmbed +from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused +from .block import NestedTensorBlock +from .attention import MemEffAttention diff --git a/dinov2/layers/attention.py b/dinov2/layers/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..0fb76ef2816164729a58cceb18d0f000cfb18777 --- /dev/null +++ b/dinov2/layers/attention.py @@ -0,0 +1,89 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py + +import logging +import os +import warnings + +from torch import Tensor +from torch import nn + + +logger = logging.getLogger("dinov2") + + +XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None +try: + if XFORMERS_ENABLED: + from xformers.ops import memory_efficient_attention, unbind + + XFORMERS_AVAILABLE = True + warnings.warn("xFormers is available (Attention)") + else: + warnings.warn("xFormers is disabled (Attention)") + raise ImportError +except ImportError: + XFORMERS_AVAILABLE = False + warnings.warn("xFormers is not available (Attention)") + + +class Attention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = False, + proj_bias: bool = True, + attn_drop: float = 0.0, + proj_drop: float = 0.0, + ) -> None: + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim, bias=proj_bias) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x: Tensor) -> Tensor: + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + + q, k, v = qkv[0] * self.scale, qkv[1], qkv[2] + attn = q @ k.transpose(-2, -1) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class MemEffAttention(Attention): + def forward(self, x: Tensor, attn_bias=None) -> Tensor: + if not XFORMERS_AVAILABLE: + if attn_bias is not None: + raise AssertionError("xFormers is required for using nested tensors") + return super().forward(x) + + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) + + q, k, v = unbind(qkv, 2) + + x = memory_efficient_attention(q, k, v, attn_bias=attn_bias) + x = x.reshape([B, N, C]) + + x = self.proj(x) + x = self.proj_drop(x) + return x diff --git a/dinov2/layers/block.py b/dinov2/layers/block.py new file mode 100644 index 0000000000000000000000000000000000000000..930787b262faac4f2264797496faff75ac56b7cc --- /dev/null +++ b/dinov2/layers/block.py @@ -0,0 +1,260 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py + +import logging +import os +from typing import Callable, List, Any, Tuple, Dict +import warnings + +import torch +from torch import nn, Tensor + +from .attention import Attention, MemEffAttention +from .drop_path import DropPath +from .layer_scale import LayerScale +from .mlp import Mlp + + +logger = logging.getLogger("dinov2") + + +XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None +try: + if XFORMERS_ENABLED: + from xformers.ops import fmha, scaled_index_add, index_select_cat + + XFORMERS_AVAILABLE = True + warnings.warn("xFormers is available (Block)") + else: + warnings.warn("xFormers is disabled (Block)") + raise ImportError +except ImportError: + XFORMERS_AVAILABLE = False + + warnings.warn("xFormers is not available (Block)") + + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + mlp_ratio: float = 4.0, + qkv_bias: bool = False, + proj_bias: bool = True, + ffn_bias: bool = True, + drop: float = 0.0, + attn_drop: float = 0.0, + init_values=None, + drop_path: float = 0.0, + act_layer: Callable[..., nn.Module] = nn.GELU, + norm_layer: Callable[..., nn.Module] = nn.LayerNorm, + attn_class: Callable[..., nn.Module] = Attention, + ffn_layer: Callable[..., nn.Module] = Mlp, + ) -> None: + super().__init__() + # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}") + self.norm1 = norm_layer(dim) + self.attn = attn_class( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + proj_bias=proj_bias, + attn_drop=attn_drop, + proj_drop=drop, + ) + self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() + self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = ffn_layer( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + bias=ffn_bias, + ) + self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() + self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + self.sample_drop_ratio = drop_path + + def forward(self, x: Tensor) -> Tensor: + def attn_residual_func(x: Tensor) -> Tensor: + return self.ls1(self.attn(self.norm1(x))) + + def ffn_residual_func(x: Tensor) -> Tensor: + return self.ls2(self.mlp(self.norm2(x))) + + if self.training and self.sample_drop_ratio > 0.1: + # the overhead is compensated only for a drop path rate larger than 0.1 + x = drop_add_residual_stochastic_depth( + x, + residual_func=attn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + ) + x = drop_add_residual_stochastic_depth( + x, + residual_func=ffn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + ) + elif self.training and self.sample_drop_ratio > 0.0: + x = x + self.drop_path1(attn_residual_func(x)) + x = x + self.drop_path1(ffn_residual_func(x)) # FIXME: drop_path2 + else: + x = x + attn_residual_func(x) + x = x + ffn_residual_func(x) + return x + + +def drop_add_residual_stochastic_depth( + x: Tensor, + residual_func: Callable[[Tensor], Tensor], + sample_drop_ratio: float = 0.0, +) -> Tensor: + # 1) extract subset using permutation + b, n, d = x.shape + sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1) + brange = (torch.randperm(b, device=x.device))[:sample_subset_size] + x_subset = x[brange] + + # 2) apply residual_func to get residual + residual = residual_func(x_subset) + + x_flat = x.flatten(1) + residual = residual.flatten(1) + + residual_scale_factor = b / sample_subset_size + + # 3) add the residual + x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor) + return x_plus_residual.view_as(x) + + +def get_branges_scales(x, sample_drop_ratio=0.0): + b, n, d = x.shape + sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1) + brange = (torch.randperm(b, device=x.device))[:sample_subset_size] + residual_scale_factor = b / sample_subset_size + return brange, residual_scale_factor + + +def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None): + if scaling_vector is None: + x_flat = x.flatten(1) + residual = residual.flatten(1) + x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor) + else: + x_plus_residual = scaled_index_add( + x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor + ) + return x_plus_residual + + +attn_bias_cache: Dict[Tuple, Any] = {} + + +def get_attn_bias_and_cat(x_list, branges=None): + """ + this will perform the index select, cat the tensors, and provide the attn_bias from cache + """ + batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list] + all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list)) + if all_shapes not in attn_bias_cache.keys(): + seqlens = [] + for b, x in zip(batch_sizes, x_list): + for _ in range(b): + seqlens.append(x.shape[1]) + attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens) + attn_bias._batch_sizes = batch_sizes + attn_bias_cache[all_shapes] = attn_bias + + if branges is not None: + cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1]) + else: + tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list) + cat_tensors = torch.cat(tensors_bs1, dim=1) + + return attn_bias_cache[all_shapes], cat_tensors + + +def drop_add_residual_stochastic_depth_list( + x_list: List[Tensor], + residual_func: Callable[[Tensor, Any], Tensor], + sample_drop_ratio: float = 0.0, + scaling_vector=None, +) -> Tensor: + # 1) generate random set of indices for dropping samples in the batch + branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list] + branges = [s[0] for s in branges_scales] + residual_scale_factors = [s[1] for s in branges_scales] + + # 2) get attention bias and index+concat the tensors + attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges) + + # 3) apply residual_func to get residual, and split the result + residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias)) # type: ignore + + outputs = [] + for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors): + outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x)) + return outputs + + +class NestedTensorBlock(Block): + def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]: + """ + x_list contains a list of tensors to nest together and run + """ + assert isinstance(self.attn, MemEffAttention) + + if self.training and self.sample_drop_ratio > 0.0: + + def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.attn(self.norm1(x), attn_bias=attn_bias) + + def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.mlp(self.norm2(x)) + + x_list = drop_add_residual_stochastic_depth_list( + x_list, + residual_func=attn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None, + ) + x_list = drop_add_residual_stochastic_depth_list( + x_list, + residual_func=ffn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None, + ) + return x_list + else: + + def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias)) + + def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.ls2(self.mlp(self.norm2(x))) + + attn_bias, x = get_attn_bias_and_cat(x_list) + x = x + attn_residual_func(x, attn_bias=attn_bias) + x = x + ffn_residual_func(x) + return attn_bias.split(x) + + def forward(self, x_or_x_list): + if isinstance(x_or_x_list, Tensor): + return super().forward(x_or_x_list) + elif isinstance(x_or_x_list, list): + if not XFORMERS_AVAILABLE: + raise AssertionError("xFormers is required for using nested tensors") + return self.forward_nested(x_or_x_list) + else: + raise AssertionError diff --git a/dinov2/layers/dino_head.py b/dinov2/layers/dino_head.py new file mode 100644 index 0000000000000000000000000000000000000000..0ace8ffd6297a1dd480b19db407b662a6ea0f565 --- /dev/null +++ b/dinov2/layers/dino_head.py @@ -0,0 +1,58 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +from torch.nn.init import trunc_normal_ +from torch.nn.utils import weight_norm + + +class DINOHead(nn.Module): + def __init__( + self, + in_dim, + out_dim, + use_bn=False, + nlayers=3, + hidden_dim=2048, + bottleneck_dim=256, + mlp_bias=True, + ): + super().__init__() + nlayers = max(nlayers, 1) + self.mlp = _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=hidden_dim, use_bn=use_bn, bias=mlp_bias) + self.apply(self._init_weights) + self.last_layer = weight_norm(nn.Linear(bottleneck_dim, out_dim, bias=False)) + self.last_layer.weight_g.data.fill_(1) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x): + x = self.mlp(x) + eps = 1e-6 if x.dtype == torch.float16 else 1e-12 + x = nn.functional.normalize(x, dim=-1, p=2, eps=eps) + x = self.last_layer(x) + return x + + +def _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=None, use_bn=False, bias=True): + if nlayers == 1: + return nn.Linear(in_dim, bottleneck_dim, bias=bias) + else: + layers = [nn.Linear(in_dim, hidden_dim, bias=bias)] + if use_bn: + layers.append(nn.BatchNorm1d(hidden_dim)) + layers.append(nn.GELU()) + for _ in range(nlayers - 2): + layers.append(nn.Linear(hidden_dim, hidden_dim, bias=bias)) + if use_bn: + layers.append(nn.BatchNorm1d(hidden_dim)) + layers.append(nn.GELU()) + layers.append(nn.Linear(hidden_dim, bottleneck_dim, bias=bias)) + return nn.Sequential(*layers) diff --git a/dinov2/layers/drop_path.py b/dinov2/layers/drop_path.py new file mode 100644 index 0000000000000000000000000000000000000000..1d640e0b969b8dcba96260243473700b4e5b24b5 --- /dev/null +++ b/dinov2/layers/drop_path.py @@ -0,0 +1,34 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py + + +from torch import nn + + +def drop_path(x, drop_prob: float = 0.0, training: bool = False): + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0: + random_tensor.div_(keep_prob) + output = x * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) diff --git a/dinov2/layers/layer_scale.py b/dinov2/layers/layer_scale.py new file mode 100644 index 0000000000000000000000000000000000000000..51df0d7ce61f2b41fa9e6369f52391dd7fe7d386 --- /dev/null +++ b/dinov2/layers/layer_scale.py @@ -0,0 +1,27 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +# Modified from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L103-L110 + +from typing import Union + +import torch +from torch import Tensor +from torch import nn + + +class LayerScale(nn.Module): + def __init__( + self, + dim: int, + init_values: Union[float, Tensor] = 1e-5, + inplace: bool = False, + ) -> None: + super().__init__() + self.inplace = inplace + self.gamma = nn.Parameter(init_values * torch.ones(dim)) + + def forward(self, x: Tensor) -> Tensor: + return x.mul_(self.gamma) if self.inplace else x * self.gamma diff --git a/dinov2/layers/mlp.py b/dinov2/layers/mlp.py new file mode 100644 index 0000000000000000000000000000000000000000..bbf9432aae9258612caeae910a7bde17999e328e --- /dev/null +++ b/dinov2/layers/mlp.py @@ -0,0 +1,40 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/mlp.py + + +from typing import Callable, Optional + +from torch import Tensor, nn + + +class Mlp(nn.Module): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Callable[..., nn.Module] = nn.GELU, + drop: float = 0.0, + bias: bool = True, + ) -> None: + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features, bias=bias) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features, bias=bias) + self.drop = nn.Dropout(drop) + + def forward(self, x: Tensor) -> Tensor: + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x diff --git a/dinov2/layers/patch_embed.py b/dinov2/layers/patch_embed.py new file mode 100644 index 0000000000000000000000000000000000000000..8b7c0804784a42cf80c0297d110dcc68cc85b339 --- /dev/null +++ b/dinov2/layers/patch_embed.py @@ -0,0 +1,88 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py + +from typing import Callable, Optional, Tuple, Union + +from torch import Tensor +import torch.nn as nn + + +def make_2tuple(x): + if isinstance(x, tuple): + assert len(x) == 2 + return x + + assert isinstance(x, int) + return (x, x) + + +class PatchEmbed(nn.Module): + """ + 2D image to patch embedding: (B,C,H,W) -> (B,N,D) + + Args: + img_size: Image size. + patch_size: Patch token size. + in_chans: Number of input image channels. + embed_dim: Number of linear projection output channels. + norm_layer: Normalization layer. + """ + + def __init__( + self, + img_size: Union[int, Tuple[int, int]] = 224, + patch_size: Union[int, Tuple[int, int]] = 16, + in_chans: int = 3, + embed_dim: int = 768, + norm_layer: Optional[Callable] = None, + flatten_embedding: bool = True, + ) -> None: + super().__init__() + + image_HW = make_2tuple(img_size) + patch_HW = make_2tuple(patch_size) + patch_grid_size = ( + image_HW[0] // patch_HW[0], + image_HW[1] // patch_HW[1], + ) + + self.img_size = image_HW + self.patch_size = patch_HW + self.patches_resolution = patch_grid_size + self.num_patches = patch_grid_size[0] * patch_grid_size[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.flatten_embedding = flatten_embedding + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def forward(self, x: Tensor) -> Tensor: + _, _, H, W = x.shape + patch_H, patch_W = self.patch_size + + assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}" + assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}" + + x = self.proj(x) # B C H W + H, W = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) # B HW C + x = self.norm(x) + if not self.flatten_embedding: + x = x.reshape(-1, H, W, self.embed_dim) # B H W C + return x + + def flops(self) -> float: + Ho, Wo = self.patches_resolution + flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) + if self.norm is not None: + flops += Ho * Wo * self.embed_dim + return flops diff --git a/dinov2/layers/swiglu_ffn.py b/dinov2/layers/swiglu_ffn.py new file mode 100644 index 0000000000000000000000000000000000000000..5e9dafa4592a408f6874d54853e8f60db5c41f74 --- /dev/null +++ b/dinov2/layers/swiglu_ffn.py @@ -0,0 +1,72 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import os +from typing import Callable, Optional +import warnings + +from torch import Tensor, nn +import torch.nn.functional as F + + +class SwiGLUFFN(nn.Module): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Callable[..., nn.Module] = None, + drop: float = 0.0, + bias: bool = True, + ) -> None: + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias) + self.w3 = nn.Linear(hidden_features, out_features, bias=bias) + + def forward(self, x: Tensor) -> Tensor: + x12 = self.w12(x) + x1, x2 = x12.chunk(2, dim=-1) + hidden = F.silu(x1) * x2 + return self.w3(hidden) + + +XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None +try: + if XFORMERS_ENABLED: + from xformers.ops import SwiGLU + + XFORMERS_AVAILABLE = True + warnings.warn("xFormers is available (SwiGLU)") + else: + warnings.warn("xFormers is disabled (SwiGLU)") + raise ImportError +except ImportError: + SwiGLU = SwiGLUFFN + XFORMERS_AVAILABLE = False + + warnings.warn("xFormers is not available (SwiGLU)") + + +class SwiGLUFFNFused(SwiGLU): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Callable[..., nn.Module] = None, + drop: float = 0.0, + bias: bool = True, + ) -> None: + out_features = out_features or in_features + hidden_features = hidden_features or in_features + hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8 + super().__init__( + in_features=in_features, + hidden_features=hidden_features, + out_features=out_features, + bias=bias, + ) diff --git a/dinov2/logging/__init__.py b/dinov2/logging/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..04a7f02204316d4d1ef38bf6080dae3d66241c25 --- /dev/null +++ b/dinov2/logging/__init__.py @@ -0,0 +1,102 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import functools +import logging +import os +import sys +from typing import Optional + +import dinov2.distributed as distributed +from .helpers import MetricLogger, SmoothedValue + + +# So that calling _configure_logger multiple times won't add many handlers +@functools.lru_cache() +def _configure_logger( + name: Optional[str] = None, + *, + level: int = logging.DEBUG, + output: Optional[str] = None, +): + """ + Configure a logger. + + Adapted from Detectron2. + + Args: + name: The name of the logger to configure. + level: The logging level to use. + output: A file name or a directory to save log. If None, will not save log file. + If ends with ".txt" or ".log", assumed to be a file name. + Otherwise, logs will be saved to `output/log.txt`. + + Returns: + The configured logger. + """ + + logger = logging.getLogger(name) + logger.setLevel(level) + logger.propagate = False + + # Loosely match Google glog format: + # [IWEF]yyyymmdd hh:mm:ss.uuuuuu threadid file:line] msg + # but use a shorter timestamp and include the logger name: + # [IWEF]yyyymmdd hh:mm:ss logger threadid file:line] msg + fmt_prefix = "%(levelname).1s%(asctime)s %(process)s %(name)s %(filename)s:%(lineno)s] " + fmt_message = "%(message)s" + fmt = fmt_prefix + fmt_message + datefmt = "%Y%m%d %H:%M:%S" + formatter = logging.Formatter(fmt=fmt, datefmt=datefmt) + + # stdout logging for main worker only + if distributed.is_main_process(): + handler = logging.StreamHandler(stream=sys.stdout) + handler.setLevel(logging.DEBUG) + handler.setFormatter(formatter) + logger.addHandler(handler) + + # file logging for all workers + if output: + if os.path.splitext(output)[-1] in (".txt", ".log"): + filename = output + else: + filename = os.path.join(output, "logs", "log.txt") + + if not distributed.is_main_process(): + global_rank = distributed.get_global_rank() + filename = filename + ".rank{}".format(global_rank) + + os.makedirs(os.path.dirname(filename), exist_ok=True) + + handler = logging.StreamHandler(open(filename, "a")) + handler.setLevel(logging.DEBUG) + handler.setFormatter(formatter) + logger.addHandler(handler) + + return logger + + +def setup_logging( + output: Optional[str] = None, + *, + name: Optional[str] = None, + level: int = logging.DEBUG, + capture_warnings: bool = True, +) -> None: + """ + Setup logging. + + Args: + output: A file name or a directory to save log files. If None, log + files will not be saved. If output ends with ".txt" or ".log", it + is assumed to be a file name. + Otherwise, logs will be saved to `output/log.txt`. + name: The name of the logger to configure, by default the root logger. + level: The logging level to use. + capture_warnings: Whether warnings should be captured as logs. + """ + logging.captureWarnings(capture_warnings) + _configure_logger(name, level=level, output=output) diff --git a/dinov2/logging/helpers.py b/dinov2/logging/helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..c6e70bb15505cbbc4c4732b069ee919bf921a74f --- /dev/null +++ b/dinov2/logging/helpers.py @@ -0,0 +1,194 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from collections import defaultdict, deque +import datetime +import json +import logging +import time + +import torch + +import dinov2.distributed as distributed + + +logger = logging.getLogger("dinov2") + + +class MetricLogger(object): + def __init__(self, delimiter="\t", output_file=None): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + self.output_file = output_file + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append("{}: {}".format(name, str(meter))) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def dump_in_output_file(self, iteration, iter_time, data_time): + if self.output_file is None or not distributed.is_main_process(): + return + dict_to_dump = dict( + iteration=iteration, + iter_time=iter_time, + data_time=data_time, + ) + dict_to_dump.update({k: v.median for k, v in self.meters.items()}) + with open(self.output_file, "a") as f: + f.write(json.dumps(dict_to_dump) + "\n") + pass + + def log_every(self, iterable, print_freq, header=None, n_iterations=None, start_iteration=0): + i = start_iteration + if not header: + header = "" + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt="{avg:.6f}") + data_time = SmoothedValue(fmt="{avg:.6f}") + + if n_iterations is None: + n_iterations = len(iterable) + + space_fmt = ":" + str(len(str(n_iterations))) + "d" + + log_list = [ + header, + "[{0" + space_fmt + "}/{1}]", + "eta: {eta}", + "{meters}", + "time: {time}", + "data: {data}", + ] + if torch.cuda.is_available(): + log_list += ["max mem: {memory:.0f}"] + + log_msg = self.delimiter.join(log_list) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == n_iterations - 1: + self.dump_in_output_file(iteration=i, iter_time=iter_time.avg, data_time=data_time.avg) + eta_seconds = iter_time.global_avg * (n_iterations - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + logger.info( + log_msg.format( + i, + n_iterations, + eta=eta_string, + meters=str(self), + time=str(iter_time), + data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB, + ) + ) + else: + logger.info( + log_msg.format( + i, + n_iterations, + eta=eta_string, + meters=str(self), + time=str(iter_time), + data=str(data_time), + ) + ) + i += 1 + end = time.time() + if i >= n_iterations: + break + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + logger.info("{} Total time: {} ({:.6f} s / it)".format(header, total_time_str, total_time / n_iterations)) + + +class SmoothedValue: + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.4f} ({global_avg:.4f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, num=1): + self.deque.append(value) + self.count += num + self.total += value * num + + def synchronize_between_processes(self): + """ + Distributed synchronization of the metric + Warning: does not synchronize the deque! + """ + if not distributed.is_enabled(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda") + torch.distributed.barrier() + torch.distributed.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value, + ) diff --git a/dinov2/loss/__init__.py b/dinov2/loss/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d6b0115b74edbd74b324c9056a57fade363c58fd --- /dev/null +++ b/dinov2/loss/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .dino_clstoken_loss import DINOLoss +from .ibot_patch_loss import iBOTPatchLoss +from .koleo_loss import KoLeoLoss diff --git a/dinov2/loss/dino_clstoken_loss.py b/dinov2/loss/dino_clstoken_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..c31808e36e6c38ee6dae13ba0443bf1946242117 --- /dev/null +++ b/dinov2/loss/dino_clstoken_loss.py @@ -0,0 +1,99 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import nn + + +class DINOLoss(nn.Module): + def __init__( + self, + out_dim, + student_temp=0.1, + center_momentum=0.9, + ): + super().__init__() + self.student_temp = student_temp + self.center_momentum = center_momentum + self.register_buffer("center", torch.zeros(1, out_dim)) + self.updated = True + self.reduce_handle = None + self.len_teacher_output = None + self.async_batch_center = None + + @torch.no_grad() + def softmax_center_teacher(self, teacher_output, teacher_temp): + self.apply_center_update() + # teacher centering and sharpening + return F.softmax((teacher_output - self.center) / teacher_temp, dim=-1) + + @torch.no_grad() + def sinkhorn_knopp_teacher(self, teacher_output, teacher_temp, n_iterations=3): + teacher_output = teacher_output.float() + world_size = dist.get_world_size() if dist.is_initialized() else 1 + Q = torch.exp(teacher_output / teacher_temp).t() # Q is K-by-B for consistency with notations from our paper + B = Q.shape[1] * world_size # number of samples to assign + K = Q.shape[0] # how many prototypes + + # make the matrix sums to 1 + sum_Q = torch.sum(Q) + if dist.is_initialized(): + dist.all_reduce(sum_Q) + Q /= sum_Q + + for it in range(n_iterations): + # normalize each row: total weight per prototype must be 1/K + sum_of_rows = torch.sum(Q, dim=1, keepdim=True) + if dist.is_initialized(): + dist.all_reduce(sum_of_rows) + Q /= sum_of_rows + Q /= K + + # normalize each column: total weight per sample must be 1/B + Q /= torch.sum(Q, dim=0, keepdim=True) + Q /= B + + Q *= B # the columns must sum to 1 so that Q is an assignment + return Q.t() + + def forward(self, student_output_list, teacher_out_softmaxed_centered_list): + """ + Cross-entropy between softmax outputs of the teacher and student networks. + """ + # TODO: Use cross_entropy_distribution here + total_loss = 0 + for s in student_output_list: + lsm = F.log_softmax(s / self.student_temp, dim=-1) + for t in teacher_out_softmaxed_centered_list: + loss = torch.sum(t * lsm, dim=-1) + total_loss -= loss.mean() + return total_loss + + @torch.no_grad() + def update_center(self, teacher_output): + self.reduce_center_update(teacher_output) + + @torch.no_grad() + def reduce_center_update(self, teacher_output): + self.updated = False + self.len_teacher_output = len(teacher_output) + self.async_batch_center = torch.sum(teacher_output, dim=0, keepdim=True) + if dist.is_initialized(): + self.reduce_handle = dist.all_reduce(self.async_batch_center, async_op=True) + + @torch.no_grad() + def apply_center_update(self): + if self.updated is False: + world_size = dist.get_world_size() if dist.is_initialized() else 1 + + if self.reduce_handle is not None: + self.reduce_handle.wait() + _t = self.async_batch_center / (self.len_teacher_output * world_size) + + self.center = self.center * self.center_momentum + _t * (1 - self.center_momentum) + + self.updated = True diff --git a/dinov2/loss/ibot_patch_loss.py b/dinov2/loss/ibot_patch_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..6732cda0c311c69f193669ebc950fc8665871442 --- /dev/null +++ b/dinov2/loss/ibot_patch_loss.py @@ -0,0 +1,151 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import nn + +import logging + + +logger = logging.getLogger("dinov2") + + +try: + from xformers.ops import cross_entropy + + def lossfunc(t, s, temp): + s = s.float() + t = t.float() + if s.ndim == 2: + return -cross_entropy(s.unsqueeze(0), t.unsqueeze(0), temp, bw_inplace=True).squeeze(0) + elif s.ndim == 3: + return -cross_entropy(s, t, temp, bw_inplace=True) + +except ImportError: + + def lossfunc(t, s, temp): + return torch.sum(t * F.log_softmax(s / temp, dim=-1), dim=-1) + + +class iBOTPatchLoss(nn.Module): + def __init__(self, patch_out_dim, student_temp=0.1, center_momentum=0.9): + super().__init__() + self.student_temp = student_temp + self.center_momentum = center_momentum + self.register_buffer("center", torch.zeros(1, 1, patch_out_dim)) + self.updated = True + self.reduce_handle = None + self.len_teacher_patch_tokens = None + self.async_batch_center = None + + @torch.no_grad() + def softmax_center_teacher(self, teacher_patch_tokens, teacher_temp): + self.apply_center_update() + # teacher centering and sharpening + # + # WARNING: + # as self.center is a float32, everything gets casted to float32 afterwards + # + # teacher_patch_tokens = teacher_patch_tokens.float() + # return F.softmax((teacher_patch_tokens.sub_(self.center.to(teacher_patch_tokens.dtype))).mul_(1 / teacher_temp), dim=-1) + + return F.softmax((teacher_patch_tokens - self.center) / teacher_temp, dim=-1) + + # this is experimental, keep everything in float16 and let's see what happens: + # return F.softmax((teacher_patch_tokens.sub_(self.center)) / teacher_temp, dim=-1) + + @torch.no_grad() + def sinkhorn_knopp_teacher(self, teacher_output, teacher_temp, n_masked_patches_tensor, n_iterations=3): + teacher_output = teacher_output.float() + # world_size = dist.get_world_size() if dist.is_initialized() else 1 + Q = torch.exp(teacher_output / teacher_temp).t() # Q is K-by-B for consistency with notations from our paper + # B = Q.shape[1] * world_size # number of samples to assign + B = n_masked_patches_tensor + dist.all_reduce(B) + K = Q.shape[0] # how many prototypes + + # make the matrix sums to 1 + sum_Q = torch.sum(Q) + if dist.is_initialized(): + dist.all_reduce(sum_Q) + Q /= sum_Q + + for it in range(n_iterations): + # normalize each row: total weight per prototype must be 1/K + sum_of_rows = torch.sum(Q, dim=1, keepdim=True) + if dist.is_initialized(): + dist.all_reduce(sum_of_rows) + Q /= sum_of_rows + Q /= K + + # normalize each column: total weight per sample must be 1/B + Q /= torch.sum(Q, dim=0, keepdim=True) + Q /= B + + Q *= B # the columns must sum to 1 so that Q is an assignment + return Q.t() + + def forward(self, student_patch_tokens, teacher_patch_tokens, student_masks_flat): + """ + Cross-entropy between softmax outputs of the teacher and student networks. + student_patch_tokens: (B, N, D) tensor + teacher_patch_tokens: (B, N, D) tensor + student_masks_flat: (B, N) tensor + """ + t = teacher_patch_tokens + s = student_patch_tokens + loss = torch.sum(t * F.log_softmax(s / self.student_temp, dim=-1), dim=-1) + loss = torch.sum(loss * student_masks_flat.float(), dim=-1) / student_masks_flat.sum(dim=-1).clamp(min=1.0) + return -loss.mean() + + def forward_masked( + self, + student_patch_tokens_masked, + teacher_patch_tokens_masked, + student_masks_flat, + n_masked_patches=None, + masks_weight=None, + ): + t = teacher_patch_tokens_masked + s = student_patch_tokens_masked + # loss = torch.sum(t * F.log_softmax(s / self.student_temp, dim=-1), dim=-1) + loss = lossfunc(t, s, self.student_temp) + if masks_weight is None: + masks_weight = ( + (1 / student_masks_flat.sum(-1).clamp(min=1.0)) + .unsqueeze(-1) + .expand_as(student_masks_flat)[student_masks_flat] + ) + if n_masked_patches is not None: + loss = loss[:n_masked_patches] + loss = loss * masks_weight + return -loss.sum() / student_masks_flat.shape[0] + + @torch.no_grad() + def update_center(self, teacher_patch_tokens): + self.reduce_center_update(teacher_patch_tokens) + + @torch.no_grad() + def reduce_center_update(self, teacher_patch_tokens): + self.updated = False + self.len_teacher_patch_tokens = len(teacher_patch_tokens) + self.async_batch_center = torch.sum(teacher_patch_tokens.mean(1), dim=0, keepdim=True) + if dist.is_initialized(): + self.reduce_handle = dist.all_reduce(self.async_batch_center, async_op=True) + + @torch.no_grad() + def apply_center_update(self): + if self.updated is False: + world_size = dist.get_world_size() if dist.is_initialized() else 1 + + if self.reduce_handle is not None: + self.reduce_handle.wait() + _t = self.async_batch_center / (self.len_teacher_patch_tokens * world_size) + + self.center = self.center * self.center_momentum + _t * (1 - self.center_momentum) + + self.updated = True diff --git a/dinov2/loss/koleo_loss.py b/dinov2/loss/koleo_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..b5cbcd91e0fc0b857f477b0910f957f02a6c4335 --- /dev/null +++ b/dinov2/loss/koleo_loss.py @@ -0,0 +1,48 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import logging + +import torch +import torch.nn as nn +import torch.nn.functional as F + +# import torch.distributed as dist + + +logger = logging.getLogger("dinov2") + + +class KoLeoLoss(nn.Module): + """Kozachenko-Leonenko entropic loss regularizer from Sablayrolles et al. - 2018 - Spreading vectors for similarity search""" + + def __init__(self): + super().__init__() + self.pdist = nn.PairwiseDistance(2, eps=1e-8) + + def pairwise_NNs_inner(self, x): + """ + Pairwise nearest neighbors for L2-normalized vectors. + Uses Torch rather than Faiss to remain on GPU. + """ + # parwise dot products (= inverse distance) + dots = torch.mm(x, x.t()) + n = x.shape[0] + dots.view(-1)[:: (n + 1)].fill_(-1) # Trick to fill diagonal with -1 + # max inner prod -> min distance + _, I = torch.max(dots, dim=1) # noqa: E741 + return I + + def forward(self, student_output, eps=1e-8): + """ + Args: + student_output (BxD): backbone output of student + """ + with torch.cuda.amp.autocast(enabled=False): + student_output = F.normalize(student_output, eps=eps, p=2, dim=-1) + I = self.pairwise_NNs_inner(student_output) # noqa: E741 + distances = self.pdist(student_output, student_output[I]) # BxD, BxD -> B + loss = -torch.log(distances + eps).mean() + return loss diff --git a/dinov2/models/__init__.py b/dinov2/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e7c92d9edfd96f69b80a1f1bbc791c8a18508ecf --- /dev/null +++ b/dinov2/models/__init__.py @@ -0,0 +1,40 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import logging + +from . import vision_transformer as vits + + +logger = logging.getLogger("dinov2") + + +def build_model(args, only_teacher=False, img_size=224): + args.arch = args.arch.removesuffix("_memeff") + if "vit" in args.arch: + vit_kwargs = dict( + img_size=img_size, + patch_size=args.patch_size, + init_values=args.layerscale, + ffn_layer=args.ffn_layer, + block_chunks=args.block_chunks, + qkv_bias=args.qkv_bias, + proj_bias=args.proj_bias, + ffn_bias=args.ffn_bias, + ) + teacher = vits.__dict__[args.arch](**vit_kwargs) + if only_teacher: + return teacher, teacher.embed_dim + student = vits.__dict__[args.arch]( + **vit_kwargs, + drop_path_rate=args.drop_path_rate, + drop_path_uniform=args.drop_path_uniform, + ) + embed_dim = student.embed_dim + return student, teacher, embed_dim + + +def build_model_from_cfg(cfg, only_teacher=False): + return build_model(cfg.student, only_teacher=only_teacher, img_size=cfg.crops.global_crops_size) diff --git a/dinov2/models/vision_transformer.py b/dinov2/models/vision_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..d572572be5f0154ae391bff28f82d095178b4a34 --- /dev/null +++ b/dinov2/models/vision_transformer.py @@ -0,0 +1,357 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/main/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py + +from functools import partial +import math +import logging +from typing import Sequence, Tuple, Union, Callable + +import torch +import torch.nn as nn +import torch.utils.checkpoint +from torch.nn.init import trunc_normal_ + +from dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block + + +logger = logging.getLogger("dinov2") + + +def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module: + if not depth_first and include_root: + fn(module=module, name=name) + for child_name, child_module in module.named_children(): + child_name = ".".join((name, child_name)) if name else child_name + named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True) + if depth_first and include_root: + fn(module=module, name=name) + return module + + +class BlockChunk(nn.ModuleList): + def forward(self, x): + for b in self: + x = b(x) + return x + + +class DinoVisionTransformer(nn.Module): + def __init__( + self, + img_size=224, + patch_size=16, + in_chans=3, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4.0, + qkv_bias=True, + ffn_bias=True, + proj_bias=True, + drop_path_rate=0.0, + drop_path_uniform=False, + init_values=None, # for layerscale: None or 0 => no layerscale + embed_layer=PatchEmbed, + act_layer=nn.GELU, + block_fn=Block, + ffn_layer="mlp", + block_chunks=1, + ): + """ + Args: + img_size (int, tuple): input image size + patch_size (int, tuple): patch size + in_chans (int): number of input channels + embed_dim (int): embedding dimension + depth (int): depth of transformer + num_heads (int): number of attention heads + mlp_ratio (int): ratio of mlp hidden dim to embedding dim + qkv_bias (bool): enable bias for qkv if True + proj_bias (bool): enable bias for proj in attn if True + ffn_bias (bool): enable bias for ffn if True + drop_path_rate (float): stochastic depth rate + drop_path_uniform (bool): apply uniform drop rate across blocks + weight_init (str): weight init scheme + init_values (float): layer-scale init values + embed_layer (nn.Module): patch embedding layer + act_layer (nn.Module): MLP activation layer + block_fn (nn.Module): transformer block class + ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity" + block_chunks: (int) split block sequence into block_chunks units for FSDP wrap + """ + super().__init__() + norm_layer = partial(nn.LayerNorm, eps=1e-6) + + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + self.num_tokens = 1 + self.n_blocks = depth + self.num_heads = num_heads + self.patch_size = patch_size + + self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) + + if drop_path_uniform is True: + dpr = [drop_path_rate] * depth + else: + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + + if ffn_layer == "mlp": + logger.info("using MLP layer as FFN") + ffn_layer = Mlp + elif ffn_layer == "swiglufused" or ffn_layer == "swiglu": + logger.info("using SwiGLU layer as FFN") + ffn_layer = SwiGLUFFNFused + elif ffn_layer == "identity": + logger.info("using Identity layer as FFN") + + def f(*args, **kwargs): + return nn.Identity() + + ffn_layer = f + else: + raise NotImplementedError + + blocks_list = [ + block_fn( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + proj_bias=proj_bias, + ffn_bias=ffn_bias, + drop_path=dpr[i], + norm_layer=norm_layer, + act_layer=act_layer, + ffn_layer=ffn_layer, + init_values=init_values, + ) + for i in range(depth) + ] + if block_chunks > 0: + self.chunked_blocks = True + chunked_blocks = [] + chunksize = depth // block_chunks + for i in range(0, depth, chunksize): + # this is to keep the block index consistent if we chunk the block list + chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize]) + self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks]) + else: + self.chunked_blocks = False + self.blocks = nn.ModuleList(blocks_list) + + self.norm = norm_layer(embed_dim) + self.head = nn.Identity() + + self.mask_token = nn.Parameter(torch.zeros(1, embed_dim)) + + self.init_weights() + + def init_weights(self): + trunc_normal_(self.pos_embed, std=0.02) + nn.init.normal_(self.cls_token, std=1e-6) + named_apply(init_weights_vit_timm, self) + + def interpolate_pos_encoding(self, x, w, h): + previous_dtype = x.dtype + npatch = x.shape[1] - 1 + N = self.pos_embed.shape[1] - 1 + if npatch == N and w == h: + return self.pos_embed + pos_embed = self.pos_embed.float() + class_pos_embed = pos_embed[:, 0] + patch_pos_embed = pos_embed[:, 1:] + dim = x.shape[-1] + w0 = w // self.patch_size + h0 = h // self.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + w0, h0 = w0 + 0.1, h0 + 0.1 + + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2), + scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), + mode="bicubic", + ) + + assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype) + + def prepare_tokens_with_masks(self, x, masks=None): + B, nc, w, h = x.shape + x = self.patch_embed(x) + if masks is not None: + x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x) + + x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1) + x = x + self.interpolate_pos_encoding(x, w, h) + + return x + + def forward_features_list(self, x_list, masks_list): + x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)] + for blk in self.blocks: + x = blk(x) + + all_x = x + output = [] + for x, masks in zip(all_x, masks_list): + x_norm = self.norm(x) + output.append( + { + "x_norm_clstoken": x_norm[:, 0], + "x_norm_patchtokens": x_norm[:, 1:], + "x_prenorm": x, + "masks": masks, + } + ) + return output + + def forward_features(self, x, masks=None): + if isinstance(x, list): + return self.forward_features_list(x, masks) + + x = self.prepare_tokens_with_masks(x, masks) + + for blk in self.blocks: + x = blk(x) + + x_norm = self.norm(x) + return { + "x_norm_clstoken": x_norm[:, 0], + "x_norm_patchtokens": x_norm[:, 1:], + "x_prenorm": x, + "masks": masks, + } + + def _get_intermediate_layers_not_chunked(self, x, n=1): + x = self.prepare_tokens_with_masks(x) + # If n is an int, take the n last blocks. If it's a list, take them + output, total_block_len = [], len(self.blocks) + blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n + for i, blk in enumerate(self.blocks): + x = blk(x) + if i in blocks_to_take: + output.append(x) + assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" + return output + + def _get_intermediate_layers_chunked(self, x, n=1): + x = self.prepare_tokens_with_masks(x) + output, i, total_block_len = [], 0, len(self.blocks[-1]) + # If n is an int, take the n last blocks. If it's a list, take them + blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n + for block_chunk in self.blocks: + for blk in block_chunk[i:]: # Passing the nn.Identity() + x = blk(x) + if i in blocks_to_take: + output.append(x) + i += 1 + assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" + return output + + def get_intermediate_layers( + self, + x: torch.Tensor, + n: Union[int, Sequence] = 1, # Layers or n last layers to take + reshape: bool = False, + return_class_token: bool = False, + norm=True, + ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]: + if self.chunked_blocks: + outputs = self._get_intermediate_layers_chunked(x, n) + else: + outputs = self._get_intermediate_layers_not_chunked(x, n) + if norm: + outputs = [self.norm(out) for out in outputs] + class_tokens = [out[:, 0] for out in outputs] + outputs = [out[:, 1:] for out in outputs] + if reshape: + B, _, w, h = x.shape + outputs = [ + out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous() + for out in outputs + ] + if return_class_token: + return tuple(zip(outputs, class_tokens)) + return tuple(outputs) + + def forward(self, *args, is_training=False, **kwargs): + ret = self.forward_features(*args, **kwargs) + if is_training: + return ret + else: + return self.head(ret["x_norm_clstoken"]) + + +def init_weights_vit_timm(module: nn.Module, name: str = ""): + """ViT weight initialization, original timm impl (for reproducibility)""" + if isinstance(module, nn.Linear): + trunc_normal_(module.weight, std=0.02) + if module.bias is not None: + nn.init.zeros_(module.bias) + + +def vit_small(patch_size=16, **kwargs): + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=384, + depth=12, + num_heads=6, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + **kwargs, + ) + return model + + +def vit_base(patch_size=16, **kwargs): + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + **kwargs, + ) + return model + + +def vit_large(patch_size=16, **kwargs): + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=1024, + depth=24, + num_heads=16, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + **kwargs, + ) + return model + + +def vit_giant2(patch_size=16, **kwargs): + """ + Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64 + """ + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=1536, + depth=40, + num_heads=24, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + **kwargs, + ) + return model diff --git a/dinov2/run/__init__.py b/dinov2/run/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b88da6bf80be92af00b72dfdb0a806fa64a7a2d9 --- /dev/null +++ b/dinov2/run/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. diff --git a/dinov2/run/eval/knn.py b/dinov2/run/eval/knn.py new file mode 100644 index 0000000000000000000000000000000000000000..d11918445cdfe415fe58ac8b3ad0bf29702e3457 --- /dev/null +++ b/dinov2/run/eval/knn.py @@ -0,0 +1,59 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +from dinov2.eval.knn import get_args_parser as get_knn_args_parser +from dinov2.logging import setup_logging +from dinov2.run.submit import get_args_parser, submit_jobs + + +logger = logging.getLogger("dinov2") + + +class Evaluator: + def __init__(self, args): + self.args = args + + def __call__(self): + from dinov2.eval.knn import main as knn_main + + self._setup_args() + knn_main(self.args) + + def checkpoint(self): + import submitit + + logger.info(f"Requeuing {self.args}") + empty = type(self)(self.args) + return submitit.helpers.DelayedSubmission(empty) + + def _setup_args(self): + import submitit + + job_env = submitit.JobEnvironment() + self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id)) + logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}") + logger.info(f"Args: {self.args}") + + +def main(): + description = "Submitit launcher for DINOv2 k-NN evaluation" + knn_args_parser = get_knn_args_parser(add_help=False) + parents = [knn_args_parser] + args_parser = get_args_parser(description=description, parents=parents) + args = args_parser.parse_args() + + setup_logging() + + assert os.path.exists(args.config_file), "Configuration file does not exist!" + submit_jobs(Evaluator, args, name="dinov2:knn") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/dinov2/run/eval/linear.py b/dinov2/run/eval/linear.py new file mode 100644 index 0000000000000000000000000000000000000000..e1dc3293e88512a5cf885ab775dc08e01aed6724 --- /dev/null +++ b/dinov2/run/eval/linear.py @@ -0,0 +1,59 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +from dinov2.eval.linear import get_args_parser as get_linear_args_parser +from dinov2.logging import setup_logging +from dinov2.run.submit import get_args_parser, submit_jobs + + +logger = logging.getLogger("dinov2") + + +class Evaluator: + def __init__(self, args): + self.args = args + + def __call__(self): + from dinov2.eval.linear import main as linear_main + + self._setup_args() + linear_main(self.args) + + def checkpoint(self): + import submitit + + logger.info(f"Requeuing {self.args}") + empty = type(self)(self.args) + return submitit.helpers.DelayedSubmission(empty) + + def _setup_args(self): + import submitit + + job_env = submitit.JobEnvironment() + self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id)) + logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}") + logger.info(f"Args: {self.args}") + + +def main(): + description = "Submitit launcher for DINOv2 linear evaluation" + linear_args_parser = get_linear_args_parser(add_help=False) + parents = [linear_args_parser] + args_parser = get_args_parser(description=description, parents=parents) + args = args_parser.parse_args() + + setup_logging() + + assert os.path.exists(args.config_file), "Configuration file does not exist!" + submit_jobs(Evaluator, args, name="dinov2:linear") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/dinov2/run/eval/log_regression.py b/dinov2/run/eval/log_regression.py new file mode 100644 index 0000000000000000000000000000000000000000..cdf02181122de72cfa463ef38494967219df9cf3 --- /dev/null +++ b/dinov2/run/eval/log_regression.py @@ -0,0 +1,59 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +from dinov2.eval.log_regression import get_args_parser as get_log_regression_args_parser +from dinov2.logging import setup_logging +from dinov2.run.submit import get_args_parser, submit_jobs + + +logger = logging.getLogger("dinov2") + + +class Evaluator: + def __init__(self, args): + self.args = args + + def __call__(self): + from dinov2.eval.log_regression import main as log_regression_main + + self._setup_args() + log_regression_main(self.args) + + def checkpoint(self): + import submitit + + logger.info(f"Requeuing {self.args}") + empty = type(self)(self.args) + return submitit.helpers.DelayedSubmission(empty) + + def _setup_args(self): + import submitit + + job_env = submitit.JobEnvironment() + self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id)) + logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}") + logger.info(f"Args: {self.args}") + + +def main(): + description = "Submitit launcher for DINOv2 logistic evaluation" + log_regression_args_parser = get_log_regression_args_parser(add_help=False) + parents = [log_regression_args_parser] + args_parser = get_args_parser(description=description, parents=parents) + args = args_parser.parse_args() + + setup_logging() + + assert os.path.exists(args.config_file), "Configuration file does not exist!" + submit_jobs(Evaluator, args, name="dinov2:logreg") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/dinov2/run/submit.py b/dinov2/run/submit.py new file mode 100644 index 0000000000000000000000000000000000000000..4d1f718e704cf9a48913422404c25a7fcc50e738 --- /dev/null +++ b/dinov2/run/submit.py @@ -0,0 +1,122 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os +from pathlib import Path +from typing import List, Optional + +import submitit + +from dinov2.utils.cluster import ( + get_slurm_executor_parameters, + get_slurm_partition, + get_user_checkpoint_path, +) + + +logger = logging.getLogger("dinov2") + + +def get_args_parser( + description: Optional[str] = None, + parents: Optional[List[argparse.ArgumentParser]] = None, + add_help: bool = True, +) -> argparse.ArgumentParser: + parents = parents or [] + slurm_partition = get_slurm_partition() + parser = argparse.ArgumentParser( + description=description, + parents=parents, + add_help=add_help, + ) + parser.add_argument( + "--ngpus", + "--gpus", + "--gpus-per-node", + default=8, + type=int, + help="Number of GPUs to request on each node", + ) + parser.add_argument( + "--nodes", + "--nnodes", + default=1, + type=int, + help="Number of nodes to request", + ) + parser.add_argument( + "--timeout", + default=2800, + type=int, + help="Duration of the job", + ) + parser.add_argument( + "--partition", + default=slurm_partition, + type=str, + help="Partition where to submit", + ) + parser.add_argument( + "--use-volta32", + action="store_true", + help="Request V100-32GB GPUs", + ) + parser.add_argument( + "--comment", + default="", + type=str, + help="Comment to pass to scheduler, e.g. priority message", + ) + parser.add_argument( + "--exclude", + default="", + type=str, + help="Nodes to exclude", + ) + return parser + + +def get_shared_folder() -> Path: + user_checkpoint_path = get_user_checkpoint_path() + if user_checkpoint_path is None: + raise RuntimeError("Path to user checkpoint cannot be determined") + path = user_checkpoint_path / "experiments" + path.mkdir(exist_ok=True) + return path + + +def submit_jobs(task_class, args, name: str): + if not args.output_dir: + args.output_dir = str(get_shared_folder() / "%j") + + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + executor = submitit.AutoExecutor(folder=args.output_dir, slurm_max_num_timeout=30) + + kwargs = {} + if args.use_volta32: + kwargs["slurm_constraint"] = "volta32gb" + if args.comment: + kwargs["slurm_comment"] = args.comment + if args.exclude: + kwargs["slurm_exclude"] = args.exclude + + executor_params = get_slurm_executor_parameters( + nodes=args.nodes, + num_gpus_per_node=args.ngpus, + timeout_min=args.timeout, # max is 60 * 72 + slurm_signal_delay_s=120, + slurm_partition=args.partition, + **kwargs, + ) + executor.update_parameters(name=name, **executor_params) + + task = task_class(args) + job = executor.submit(task) + + logger.info(f"Submitted job_id: {job.job_id}") + str_output_dir = os.path.abspath(args.output_dir).replace("%j", str(job.job_id)) + logger.info(f"Logs and checkpoints will be saved at: {str_output_dir}") diff --git a/dinov2/run/train/train.py b/dinov2/run/train/train.py new file mode 100644 index 0000000000000000000000000000000000000000..c2366e9bf79765e6abcd70dda6b43f31cb7093eb --- /dev/null +++ b/dinov2/run/train/train.py @@ -0,0 +1,59 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +from dinov2.logging import setup_logging +from dinov2.train import get_args_parser as get_train_args_parser +from dinov2.run.submit import get_args_parser, submit_jobs + + +logger = logging.getLogger("dinov2") + + +class Trainer(object): + def __init__(self, args): + self.args = args + + def __call__(self): + from dinov2.train import main as train_main + + self._setup_args() + train_main(self.args) + + def checkpoint(self): + import submitit + + logger.info(f"Requeuing {self.args}") + empty = type(self)(self.args) + return submitit.helpers.DelayedSubmission(empty) + + def _setup_args(self): + import submitit + + job_env = submitit.JobEnvironment() + self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id)) + logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}") + logger.info(f"Args: {self.args}") + + +def main(): + description = "Submitit launcher for DINOv2 training" + train_args_parser = get_train_args_parser(add_help=False) + parents = [train_args_parser] + args_parser = get_args_parser(description=description, parents=parents) + args = args_parser.parse_args() + + setup_logging() + + assert os.path.exists(args.config_file), "Configuration file does not exist!" + submit_jobs(Trainer, args, name="dinov2:train") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/dinov2/train/__init__.py b/dinov2/train/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5f1752922d04fff0112eb7796be28ff6b68c6073 --- /dev/null +++ b/dinov2/train/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from .train import get_args_parser, main +from .ssl_meta_arch import SSLMetaArch diff --git a/dinov2/train/ssl_meta_arch.py b/dinov2/train/ssl_meta_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..3ccf15e904ebeb6134dfb4f5c99da4fc8d41b8e4 --- /dev/null +++ b/dinov2/train/ssl_meta_arch.py @@ -0,0 +1,400 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from functools import partial +import logging + +import torch +from torch import nn + +from dinov2.loss import DINOLoss, iBOTPatchLoss, KoLeoLoss +from dinov2.models import build_model_from_cfg +from dinov2.layers import DINOHead +from dinov2.utils.utils import has_batchnorms +from dinov2.utils.param_groups import get_params_groups_with_decay, fuse_params_groups +from dinov2.fsdp import get_fsdp_wrapper, ShardedGradScaler, get_fsdp_modules, reshard_fsdp_model + +from dinov2.models.vision_transformer import BlockChunk + + +try: + from xformers.ops import fmha +except ImportError: + raise AssertionError("xFormers is required for training") + + +logger = logging.getLogger("dinov2") + + +class SSLMetaArch(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.fp16_scaler = ShardedGradScaler() if cfg.compute_precision.grad_scaler else None + + student_model_dict = dict() + teacher_model_dict = dict() + + student_backbone, teacher_backbone, embed_dim = build_model_from_cfg(cfg) + student_model_dict["backbone"] = student_backbone + teacher_model_dict["backbone"] = teacher_backbone + logger.info(f"OPTIONS -- architecture : embed_dim: {embed_dim}") + + if cfg.student.pretrained_weights: + chkpt = torch.load(cfg.student.pretrained_weights) + logger.info(f"OPTIONS -- pretrained weights: loading from {cfg.student.pretrained_weights}") + student_backbone.load_state_dict(chkpt["model"], strict=False) + + self.embed_dim = embed_dim + self.dino_out_dim = cfg.dino.head_n_prototypes + + self.do_dino = cfg.dino.loss_weight > 0 + self.do_koleo = cfg.dino.koleo_loss_weight > 0 + self.do_ibot = cfg.ibot.loss_weight > 0 + self.ibot_separate_head = cfg.ibot.separate_head + + logger.info("OPTIONS -- DINO") + if self.do_dino: + logger.info(f"OPTIONS -- DINO -- loss_weight: {cfg.dino.loss_weight}") + logger.info(f"OPTIONS -- DINO -- head_n_prototypes: {cfg.dino.head_n_prototypes}") + logger.info(f"OPTIONS -- DINO -- head_bottleneck_dim: {cfg.dino.head_bottleneck_dim}") + logger.info(f"OPTIONS -- DINO -- head_hidden_dim: {cfg.dino.head_hidden_dim}") + self.dino_loss_weight = cfg.dino.loss_weight + dino_head = partial( + DINOHead, + in_dim=embed_dim, + out_dim=cfg.dino.head_n_prototypes, + hidden_dim=cfg.dino.head_hidden_dim, + bottleneck_dim=cfg.dino.head_bottleneck_dim, + nlayers=cfg.dino.head_nlayers, + ) + self.dino_loss = DINOLoss(self.dino_out_dim) + if self.do_koleo: + logger.info("OPTIONS -- DINO -- applying KOLEO regularization") + self.koleo_loss = KoLeoLoss() + + else: + logger.info("OPTIONS -- DINO -- not using DINO") + + if self.do_dino or self.do_ibot: + student_model_dict["dino_head"] = dino_head() + teacher_model_dict["dino_head"] = dino_head() + + logger.info("OPTIONS -- IBOT") + logger.info(f"OPTIONS -- IBOT -- loss_weight: {cfg.ibot.loss_weight}") + logger.info(f"OPTIONS -- IBOT masking -- ibot_mask_ratio_tuple: {cfg.ibot.mask_ratio_min_max}") + logger.info(f"OPTIONS -- IBOT masking -- ibot_mask_sample_probability: {cfg.ibot.mask_sample_probability}") + if self.do_ibot: + self.ibot_loss_weight = cfg.ibot.loss_weight + assert max(cfg.ibot.mask_ratio_min_max) > 0, "please provide a positive mask ratio tuple for ibot" + assert cfg.ibot.mask_sample_probability > 0, "please provide a positive mask probability for ibot" + self.ibot_out_dim = cfg.ibot.head_n_prototypes if self.ibot_separate_head else cfg.dino.head_n_prototypes + self.ibot_patch_loss = iBOTPatchLoss(self.ibot_out_dim) + if self.ibot_separate_head: + logger.info(f"OPTIONS -- IBOT -- loss_weight: {cfg.ibot.loss_weight}") + logger.info(f"OPTIONS -- IBOT -- head_n_prototypes: {cfg.ibot.head_n_prototypes}") + logger.info(f"OPTIONS -- IBOT -- head_bottleneck_dim: {cfg.ibot.head_bottleneck_dim}") + logger.info(f"OPTIONS -- IBOT -- head_hidden_dim: {cfg.ibot.head_hidden_dim}") + ibot_head = partial( + DINOHead, + in_dim=embed_dim, + out_dim=cfg.ibot.head_n_prototypes, + hidden_dim=cfg.ibot.head_hidden_dim, + bottleneck_dim=cfg.ibot.head_bottleneck_dim, + nlayers=cfg.ibot.head_nlayers, + ) + student_model_dict["ibot_head"] = ibot_head() + teacher_model_dict["ibot_head"] = ibot_head() + else: + logger.info("OPTIONS -- IBOT -- head shared with DINO") + + self.need_to_synchronize_fsdp_streams = True + + self.student = nn.ModuleDict(student_model_dict) + self.teacher = nn.ModuleDict(teacher_model_dict) + + # there is no backpropagation through the teacher, so no need for gradients + for p in self.teacher.parameters(): + p.requires_grad = False + logger.info(f"Student and Teacher are built: they are both {cfg.student.arch} network.") + + def forward(self, inputs): + raise NotImplementedError + + def backprop_loss(self, loss): + if self.fp16_scaler is not None: + self.fp16_scaler.scale(loss).backward() + else: + loss.backward() + + def forward_backward(self, images, teacher_temp): + n_global_crops = 2 + assert n_global_crops == 2 + n_local_crops = self.cfg.crops.local_crops_number + + global_crops = images["collated_global_crops"].cuda(non_blocking=True) + local_crops = images["collated_local_crops"].cuda(non_blocking=True) + + masks = images["collated_masks"].cuda(non_blocking=True) + mask_indices_list = images["mask_indices_list"].cuda(non_blocking=True) + n_masked_patches_tensor = images["n_masked_patches"].cuda(non_blocking=True) + n_masked_patches = mask_indices_list.shape[0] + upperbound = images["upperbound"] + masks_weight = images["masks_weight"].cuda(non_blocking=True) + + n_local_crops_loss_terms = max(n_local_crops * n_global_crops, 1) + n_global_crops_loss_terms = (n_global_crops - 1) * n_global_crops + + do_dino = self.do_dino + do_ibot = self.do_ibot + + # loss scales + ibot_loss_scale = 1.0 / n_global_crops + + # teacher output + @torch.no_grad() + def get_teacher_output(): + x, n_global_crops_teacher = global_crops, n_global_crops + teacher_backbone_output_dict = self.teacher.backbone(x, is_training=True) + teacher_cls_tokens = teacher_backbone_output_dict["x_norm_clstoken"] + teacher_cls_tokens = teacher_cls_tokens.chunk(n_global_crops_teacher) + # watch out: these are chunked and cat'd in reverse so A is matched to B in the global crops dino loss + teacher_cls_tokens = torch.cat((teacher_cls_tokens[1], teacher_cls_tokens[0])) + ibot_teacher_patch_tokens = teacher_backbone_output_dict["x_norm_patchtokens"] + _dim = ibot_teacher_patch_tokens.shape[-1] + n_cls_tokens = teacher_cls_tokens.shape[0] + + if do_ibot and not self.ibot_separate_head: + buffer_tensor_teacher = ibot_teacher_patch_tokens.new_zeros(upperbound + n_cls_tokens, _dim) + buffer_tensor_teacher[:n_cls_tokens].copy_(teacher_cls_tokens) + torch.index_select( + ibot_teacher_patch_tokens.flatten(0, 1), + dim=0, + index=mask_indices_list, + out=buffer_tensor_teacher[n_cls_tokens : n_cls_tokens + n_masked_patches], + ) + tokens_after_head = self.teacher.dino_head(buffer_tensor_teacher) + teacher_cls_tokens_after_head = tokens_after_head[:n_cls_tokens] + masked_teacher_patch_tokens_after_head = tokens_after_head[ + n_cls_tokens : n_cls_tokens + n_masked_patches + ] + elif do_ibot and self.ibot_separate_head: + buffer_tensor_teacher = ibot_teacher_patch_tokens.new_zeros(upperbound, _dim) + torch.index_select( + ibot_teacher_patch_tokens.flatten(0, 1), + dim=0, + index=mask_indices_list, + out=buffer_tensor_teacher[:n_masked_patches], + ) + teacher_cls_tokens_after_head = self.teacher.dino_head(teacher_cls_tokens) + masked_teacher_patch_tokens_after_head = self.teacher.ibot_head(buffer_tensor_teacher)[ + :n_masked_patches + ] + else: + teacher_cls_tokens_after_head = self.teacher.dino_head(teacher_cls_tokens) + masked_teacher_ibot_softmaxed_centered = None + + if self.cfg.train.centering == "centering": + teacher_dino_softmaxed_centered_list = self.dino_loss.softmax_center_teacher( + teacher_cls_tokens_after_head, teacher_temp=teacher_temp + ).view(n_global_crops_teacher, -1, *teacher_cls_tokens_after_head.shape[1:]) + self.dino_loss.update_center(teacher_cls_tokens_after_head) + if do_ibot: + masked_teacher_patch_tokens_after_head = masked_teacher_patch_tokens_after_head.unsqueeze(0) + masked_teacher_ibot_softmaxed_centered = self.ibot_patch_loss.softmax_center_teacher( + masked_teacher_patch_tokens_after_head[:, :n_masked_patches], teacher_temp=teacher_temp + ) + masked_teacher_ibot_softmaxed_centered = masked_teacher_ibot_softmaxed_centered.squeeze(0) + self.ibot_patch_loss.update_center(masked_teacher_patch_tokens_after_head[:n_masked_patches]) + + elif self.cfg.train.centering == "sinkhorn_knopp": + teacher_dino_softmaxed_centered_list = self.dino_loss.sinkhorn_knopp_teacher( + teacher_cls_tokens_after_head, teacher_temp=teacher_temp + ).view(n_global_crops_teacher, -1, *teacher_cls_tokens_after_head.shape[1:]) + + if do_ibot: + masked_teacher_ibot_softmaxed_centered = self.ibot_patch_loss.sinkhorn_knopp_teacher( + masked_teacher_patch_tokens_after_head, + teacher_temp=teacher_temp, + n_masked_patches_tensor=n_masked_patches_tensor, + ) + + else: + raise NotImplementedError + + return teacher_dino_softmaxed_centered_list, masked_teacher_ibot_softmaxed_centered + + teacher_dino_softmaxed_centered_list, masked_teacher_ibot_softmaxed_centered = get_teacher_output() + reshard_fsdp_model(self.teacher) + + loss_dict = {} + + loss_accumulator = 0 # for backprop + student_global_backbone_output_dict, student_local_backbone_output_dict = self.student.backbone( + [global_crops, local_crops], masks=[masks, None], is_training=True + ) + + inputs_for_student_head_list = [] + + # 1a: local crops cls tokens + student_local_cls_tokens = student_local_backbone_output_dict["x_norm_clstoken"] + inputs_for_student_head_list.append(student_local_cls_tokens.unsqueeze(0)) + + # 1b: global crops cls tokens + student_global_cls_tokens = student_global_backbone_output_dict["x_norm_clstoken"] + inputs_for_student_head_list.append(student_global_cls_tokens.unsqueeze(0)) + + # 1c: global crops patch tokens + if do_ibot: + _dim = student_global_backbone_output_dict["x_norm_clstoken"].shape[-1] + ibot_student_patch_tokens = student_global_backbone_output_dict["x_norm_patchtokens"] + buffer_tensor_patch_tokens = ibot_student_patch_tokens.new_zeros(upperbound, _dim) + buffer_tensor_patch_tokens[:n_masked_patches].copy_( + torch.index_select(ibot_student_patch_tokens.flatten(0, 1), dim=0, index=mask_indices_list) + ) + if not self.ibot_separate_head: + inputs_for_student_head_list.append(buffer_tensor_patch_tokens.unsqueeze(0)) + else: + student_global_masked_patch_tokens_after_head = self.student.ibot_head(buffer_tensor_patch_tokens)[ + :n_masked_patches + ] + + # 2: run + _attn_bias, cat_inputs = fmha.BlockDiagonalMask.from_tensor_list(inputs_for_student_head_list) + outputs_list = _attn_bias.split(self.student.dino_head(cat_inputs)) + + # 3a: local crops cls tokens + student_local_cls_tokens_after_head = outputs_list.pop(0).squeeze(0) + + # 3b: global crops cls tokens + student_global_cls_tokens_after_head = outputs_list.pop(0).squeeze(0) + + # 3c: global crops patch tokens + if do_ibot and not self.ibot_separate_head: + student_global_masked_patch_tokens_after_head = outputs_list.pop(0).squeeze(0)[:n_masked_patches] + + if n_local_crops > 0: + dino_local_crops_loss = self.dino_loss( + student_output_list=student_local_cls_tokens_after_head.chunk(n_local_crops), + teacher_out_softmaxed_centered_list=teacher_dino_softmaxed_centered_list, + ) / (n_global_crops_loss_terms + n_local_crops_loss_terms) + + # store for display + loss_dict["dino_local_crops_loss"] = dino_local_crops_loss + + # accumulate loss + loss_accumulator += self.dino_loss_weight * dino_local_crops_loss + + # process global crops + loss_scales = 2 # this is here since we process global crops together + + if do_dino: + # compute loss + dino_global_crops_loss = ( + self.dino_loss( + student_output_list=[student_global_cls_tokens_after_head], + teacher_out_softmaxed_centered_list=[ + teacher_dino_softmaxed_centered_list.flatten(0, 1) + ], # these were chunked and stacked in reverse so A is matched to B + ) + * loss_scales + / (n_global_crops_loss_terms + n_local_crops_loss_terms) + ) + + loss_dict["dino_global_crops_loss"] = dino_global_crops_loss + + # accumulate loss + loss_accumulator += self.dino_loss_weight * dino_global_crops_loss + + student_cls_tokens = student_global_cls_tokens + + if self.do_koleo: + koleo_loss = self.cfg.dino.koleo_loss_weight * sum( + self.koleo_loss(p) for p in student_cls_tokens.chunk(2) + ) # we don't apply koleo loss between cls tokens of a same image + loss_accumulator += koleo_loss + loss_dict["koleo_loss"] = ( + koleo_loss / loss_scales + ) # this is to display the same losses as before but we can remove eventually + + if do_ibot: + # compute loss + ibot_patch_loss = ( + self.ibot_patch_loss.forward_masked( + student_global_masked_patch_tokens_after_head, + masked_teacher_ibot_softmaxed_centered, + student_masks_flat=masks, + n_masked_patches=n_masked_patches, + masks_weight=masks_weight, + ) + * loss_scales + * ibot_loss_scale + ) + + # store for display + loss_dict["ibot_loss"] = ibot_patch_loss / 2 + + # accumulate loss + loss_accumulator += self.ibot_loss_weight * ibot_patch_loss + + self.backprop_loss(loss_accumulator) + + self.fsdp_synchronize_streams() + + return loss_dict + + def fsdp_synchronize_streams(self): + if self.need_to_synchronize_fsdp_streams: + torch.cuda.synchronize() + self.student.dino_head._streams = ( + self.teacher.dino_head._streams + ) = self.student.backbone._streams = self.teacher.backbone._streams + self.need_to_synchronize_fsdp_streams = False + + def update_teacher(self, m): + student_param_list = [] + teacher_param_list = [] + with torch.no_grad(): + for k in self.student.keys(): + for ms, mt in zip(get_fsdp_modules(self.student[k]), get_fsdp_modules(self.teacher[k])): + student_param_list += ms.params + teacher_param_list += mt.params + torch._foreach_mul_(teacher_param_list, m) + torch._foreach_add_(teacher_param_list, student_param_list, alpha=1 - m) + + def train(self): + super().train() + self.teacher.eval() + + def get_maybe_fused_params_for_submodel(self, m): + params_groups = get_params_groups_with_decay( + model=m, + lr_decay_rate=self.cfg.optim.layerwise_decay, + patch_embed_lr_mult=self.cfg.optim.patch_embed_lr_mult, + ) + fused_params_groups = fuse_params_groups(params_groups) + logger.info("fusing param groups") + + for g in fused_params_groups: + g["foreach"] = True + return fused_params_groups + + def get_params_groups(self): + all_params_groups = [] + for m in self.student.values(): + all_params_groups += self.get_maybe_fused_params_for_submodel(m) + return all_params_groups + + def prepare_for_distributed_training(self): + logger.info("DISTRIBUTED FSDP -- preparing model for distributed training") + if has_batchnorms(self.student): + raise NotImplementedError + # below will synchronize all student subnetworks across gpus: + for k, v in self.student.items(): + self.teacher[k].load_state_dict(self.student[k].state_dict()) + student_model_cfg = self.cfg.compute_precision.student[k] + self.student[k] = get_fsdp_wrapper(student_model_cfg, modules_to_wrap={BlockChunk})(self.student[k]) + teacher_model_cfg = self.cfg.compute_precision.teacher[k] + self.teacher[k] = get_fsdp_wrapper(teacher_model_cfg, modules_to_wrap={BlockChunk})(self.teacher[k]) diff --git a/dinov2/train/train.py b/dinov2/train/train.py new file mode 100644 index 0000000000000000000000000000000000000000..473b8d01473654182de9f91c94a2d8720fe096a5 --- /dev/null +++ b/dinov2/train/train.py @@ -0,0 +1,318 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import argparse +import logging +import math +import os +from functools import partial + +from fvcore.common.checkpoint import PeriodicCheckpointer +import torch + +from dinov2.data import SamplerType, make_data_loader, make_dataset +from dinov2.data import collate_data_and_cast, DataAugmentationDINO, MaskingGenerator +import dinov2.distributed as distributed +from dinov2.fsdp import FSDPCheckpointer +from dinov2.logging import MetricLogger +from dinov2.utils.config import setup +from dinov2.utils.utils import CosineScheduler + +from dinov2.train.ssl_meta_arch import SSLMetaArch + + +torch.backends.cuda.matmul.allow_tf32 = True # PyTorch 1.12 sets this to False by default +logger = logging.getLogger("dinov2") + + +def get_args_parser(add_help: bool = True): + parser = argparse.ArgumentParser("DINOv2 training", add_help=add_help) + parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file") + parser.add_argument( + "--no-resume", + action="store_true", + help="Whether to not attempt to resume from the checkpoint directory. ", + ) + parser.add_argument("--eval-only", action="store_true", help="perform evaluation only") + parser.add_argument("--eval", type=str, default="", help="Eval type to perform") + parser.add_argument( + "opts", + help=""" +Modify config options at the end of the command. For Yacs configs, use +space-separated "PATH.KEY VALUE" pairs. +For python-based LazyConfig, use "path.key=value". + """.strip(), + default=None, + nargs=argparse.REMAINDER, + ) + parser.add_argument( + "--output-dir", + "--output_dir", + default="", + type=str, + help="Output directory to save logs and checkpoints", + ) + + return parser + + +def build_optimizer(cfg, params_groups): + return torch.optim.AdamW(params_groups, betas=(cfg.optim.adamw_beta1, cfg.optim.adamw_beta2)) + + +def build_schedulers(cfg): + OFFICIAL_EPOCH_LENGTH = cfg.train.OFFICIAL_EPOCH_LENGTH + lr = dict( + base_value=cfg.optim["lr"], + final_value=cfg.optim["min_lr"], + total_iters=cfg.optim["epochs"] * OFFICIAL_EPOCH_LENGTH, + warmup_iters=cfg.optim["warmup_epochs"] * OFFICIAL_EPOCH_LENGTH, + start_warmup_value=0, + ) + wd = dict( + base_value=cfg.optim["weight_decay"], + final_value=cfg.optim["weight_decay_end"], + total_iters=cfg.optim["epochs"] * OFFICIAL_EPOCH_LENGTH, + ) + momentum = dict( + base_value=cfg.teacher["momentum_teacher"], + final_value=cfg.teacher["final_momentum_teacher"], + total_iters=cfg.optim["epochs"] * OFFICIAL_EPOCH_LENGTH, + ) + teacher_temp = dict( + base_value=cfg.teacher["teacher_temp"], + final_value=cfg.teacher["teacher_temp"], + total_iters=cfg.teacher["warmup_teacher_temp_epochs"] * OFFICIAL_EPOCH_LENGTH, + warmup_iters=cfg.teacher["warmup_teacher_temp_epochs"] * OFFICIAL_EPOCH_LENGTH, + start_warmup_value=cfg.teacher["warmup_teacher_temp"], + ) + + lr_schedule = CosineScheduler(**lr) + wd_schedule = CosineScheduler(**wd) + momentum_schedule = CosineScheduler(**momentum) + teacher_temp_schedule = CosineScheduler(**teacher_temp) + last_layer_lr_schedule = CosineScheduler(**lr) + + last_layer_lr_schedule.schedule[ + : cfg.optim["freeze_last_layer_epochs"] * OFFICIAL_EPOCH_LENGTH + ] = 0 # mimicking the original schedules + + logger.info("Schedulers ready.") + + return ( + lr_schedule, + wd_schedule, + momentum_schedule, + teacher_temp_schedule, + last_layer_lr_schedule, + ) + + +def apply_optim_scheduler(optimizer, lr, wd, last_layer_lr): + for param_group in optimizer.param_groups: + is_last_layer = param_group["is_last_layer"] + lr_multiplier = param_group["lr_multiplier"] + wd_multiplier = param_group["wd_multiplier"] + param_group["weight_decay"] = wd * wd_multiplier + param_group["lr"] = (last_layer_lr if is_last_layer else lr) * lr_multiplier + + +def do_test(cfg, model, iteration): + new_state_dict = model.teacher.state_dict() + + if distributed.is_main_process(): + iterstring = str(iteration) + eval_dir = os.path.join(cfg.train.output_dir, "eval", iterstring) + os.makedirs(eval_dir, exist_ok=True) + # save teacher checkpoint + teacher_ckp_path = os.path.join(eval_dir, "teacher_checkpoint.pth") + torch.save({"teacher": new_state_dict}, teacher_ckp_path) + + +def do_train(cfg, model, resume=False): + model.train() + inputs_dtype = torch.half + fp16_scaler = model.fp16_scaler # for mixed precision training + + # setup optimizer + + optimizer = build_optimizer(cfg, model.get_params_groups()) + ( + lr_schedule, + wd_schedule, + momentum_schedule, + teacher_temp_schedule, + last_layer_lr_schedule, + ) = build_schedulers(cfg) + + # checkpointer + checkpointer = FSDPCheckpointer(model, cfg.train.output_dir, optimizer=optimizer, save_to_disk=True) + + start_iter = checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, resume=resume).get("iteration", -1) + 1 + + OFFICIAL_EPOCH_LENGTH = cfg.train.OFFICIAL_EPOCH_LENGTH + max_iter = cfg.optim.epochs * OFFICIAL_EPOCH_LENGTH + + periodic_checkpointer = PeriodicCheckpointer( + checkpointer, + period=3 * OFFICIAL_EPOCH_LENGTH, + max_iter=max_iter, + max_to_keep=3, + ) + + # setup data preprocessing + + img_size = cfg.crops.global_crops_size + patch_size = cfg.student.patch_size + n_tokens = (img_size // patch_size) ** 2 + mask_generator = MaskingGenerator( + input_size=(img_size // patch_size, img_size // patch_size), + max_num_patches=0.5 * img_size // patch_size * img_size // patch_size, + ) + + data_transform = DataAugmentationDINO( + cfg.crops.global_crops_scale, + cfg.crops.local_crops_scale, + cfg.crops.local_crops_number, + global_crops_size=cfg.crops.global_crops_size, + local_crops_size=cfg.crops.local_crops_size, + ) + + collate_fn = partial( + collate_data_and_cast, + mask_ratio_tuple=cfg.ibot.mask_ratio_min_max, + mask_probability=cfg.ibot.mask_sample_probability, + n_tokens=n_tokens, + mask_generator=mask_generator, + dtype=inputs_dtype, + ) + + # setup data loader + + dataset = make_dataset( + dataset_str=cfg.train.dataset_path, + transform=data_transform, + target_transform=lambda _: (), + ) + # sampler_type = SamplerType.INFINITE + sampler_type = SamplerType.SHARDED_INFINITE + data_loader = make_data_loader( + dataset=dataset, + batch_size=cfg.train.batch_size_per_gpu, + num_workers=cfg.train.num_workers, + shuffle=True, + seed=start_iter, # TODO: Fix this -- cfg.train.seed + sampler_type=sampler_type, + sampler_advance=0, # TODO(qas): fix this -- start_iter * cfg.train.batch_size_per_gpu, + drop_last=True, + collate_fn=collate_fn, + ) + + # training loop + + iteration = start_iter + + logger.info("Starting training from iteration {}".format(start_iter)) + metrics_file = os.path.join(cfg.train.output_dir, "training_metrics.json") + metric_logger = MetricLogger(delimiter=" ", output_file=metrics_file) + header = "Training" + + for data in metric_logger.log_every( + data_loader, + 10, + header, + max_iter, + start_iter, + ): + current_batch_size = data["collated_global_crops"].shape[0] / 2 + if iteration > max_iter: + return + + # apply schedules + + lr = lr_schedule[iteration] + wd = wd_schedule[iteration] + mom = momentum_schedule[iteration] + teacher_temp = teacher_temp_schedule[iteration] + last_layer_lr = last_layer_lr_schedule[iteration] + apply_optim_scheduler(optimizer, lr, wd, last_layer_lr) + + # compute losses + + optimizer.zero_grad(set_to_none=True) + loss_dict = model.forward_backward(data, teacher_temp=teacher_temp) + + # clip gradients + + if fp16_scaler is not None: + if cfg.optim.clip_grad: + fp16_scaler.unscale_(optimizer) + for v in model.student.values(): + v.clip_grad_norm_(cfg.optim.clip_grad) + fp16_scaler.step(optimizer) + fp16_scaler.update() + else: + if cfg.optim.clip_grad: + for v in model.student.values(): + v.clip_grad_norm_(cfg.optim.clip_grad) + optimizer.step() + + # perform teacher EMA update + + model.update_teacher(mom) + + # logging + + if distributed.get_global_size() > 1: + for v in loss_dict.values(): + torch.distributed.all_reduce(v) + loss_dict_reduced = {k: v.item() / distributed.get_global_size() for k, v in loss_dict.items()} + + if math.isnan(sum(loss_dict_reduced.values())): + logger.info("NaN detected") + raise AssertionError + losses_reduced = sum(loss for loss in loss_dict_reduced.values()) + + metric_logger.update(lr=lr) + metric_logger.update(wd=wd) + metric_logger.update(mom=mom) + metric_logger.update(last_layer_lr=last_layer_lr) + metric_logger.update(current_batch_size=current_batch_size) + metric_logger.update(total_loss=losses_reduced, **loss_dict_reduced) + + # checkpointing and testing + + if cfg.evaluation.eval_period_iterations > 0 and (iteration + 1) % cfg.evaluation.eval_period_iterations == 0: + do_test(cfg, model, f"training_{iteration}") + torch.cuda.synchronize() + periodic_checkpointer.step(iteration) + + iteration = iteration + 1 + metric_logger.synchronize_between_processes() + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + + +def main(args): + cfg = setup(args) + + model = SSLMetaArch(cfg).to(torch.device("cuda")) + model.prepare_for_distributed_training() + + logger.info("Model:\n{}".format(model)) + if args.eval_only: + iteration = ( + FSDPCheckpointer(model, save_dir=cfg.train.output_dir) + .resume_or_load(cfg.MODEL.WEIGHTS, resume=not args.no_resume) + .get("iteration", -1) + + 1 + ) + return do_test(cfg, model, f"manual_{iteration}") + + do_train(cfg, model, resume=not args.no_resume) + + +if __name__ == "__main__": + args = get_args_parser(add_help=True).parse_args() + main(args) diff --git a/dinov2/utils/__init__.py b/dinov2/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b88da6bf80be92af00b72dfdb0a806fa64a7a2d9 --- /dev/null +++ b/dinov2/utils/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. diff --git a/dinov2/utils/cluster.py b/dinov2/utils/cluster.py new file mode 100644 index 0000000000000000000000000000000000000000..3df87dc3e1eb4f0f8a280dc3137cfef031886314 --- /dev/null +++ b/dinov2/utils/cluster.py @@ -0,0 +1,95 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from enum import Enum +import os +from pathlib import Path +from typing import Any, Dict, Optional + + +class ClusterType(Enum): + AWS = "aws" + FAIR = "fair" + RSC = "rsc" + + +def _guess_cluster_type() -> ClusterType: + uname = os.uname() + if uname.sysname == "Linux": + if uname.release.endswith("-aws"): + # Linux kernel versions on AWS instances are of the form "5.4.0-1051-aws" + return ClusterType.AWS + elif uname.nodename.startswith("rsc"): + # Linux kernel versions on RSC instances are standard ones but hostnames start with "rsc" + return ClusterType.RSC + + return ClusterType.FAIR + + +def get_cluster_type(cluster_type: Optional[ClusterType] = None) -> Optional[ClusterType]: + if cluster_type is None: + return _guess_cluster_type() + + return cluster_type + + +def get_checkpoint_path(cluster_type: Optional[ClusterType] = None) -> Optional[Path]: + cluster_type = get_cluster_type(cluster_type) + if cluster_type is None: + return None + + CHECKPOINT_DIRNAMES = { + ClusterType.AWS: "checkpoints", + ClusterType.FAIR: "checkpoint", + ClusterType.RSC: "checkpoint/dino", + } + return Path("/") / CHECKPOINT_DIRNAMES[cluster_type] + + +def get_user_checkpoint_path(cluster_type: Optional[ClusterType] = None) -> Optional[Path]: + checkpoint_path = get_checkpoint_path(cluster_type) + if checkpoint_path is None: + return None + + username = os.environ.get("USER") + assert username is not None + return checkpoint_path / username + + +def get_slurm_partition(cluster_type: Optional[ClusterType] = None) -> Optional[str]: + cluster_type = get_cluster_type(cluster_type) + if cluster_type is None: + return None + + SLURM_PARTITIONS = { + ClusterType.AWS: "learnlab", + ClusterType.FAIR: "learnlab", + ClusterType.RSC: "learn", + } + return SLURM_PARTITIONS[cluster_type] + + +def get_slurm_executor_parameters( + nodes: int, num_gpus_per_node: int, cluster_type: Optional[ClusterType] = None, **kwargs +) -> Dict[str, Any]: + # create default parameters + params = { + "mem_gb": 0, # Requests all memory on a node, see https://slurm.schedmd.com/sbatch.html + "gpus_per_node": num_gpus_per_node, + "tasks_per_node": num_gpus_per_node, # one task per GPU + "cpus_per_task": 10, + "nodes": nodes, + "slurm_partition": get_slurm_partition(cluster_type), + } + # apply cluster-specific adjustments + cluster_type = get_cluster_type(cluster_type) + if cluster_type == ClusterType.AWS: + params["cpus_per_task"] = 12 + del params["mem_gb"] + elif cluster_type == ClusterType.RSC: + params["cpus_per_task"] = 12 + # set additional parameters / apply overrides + params.update(kwargs) + return params diff --git a/dinov2/utils/config.py b/dinov2/utils/config.py new file mode 100644 index 0000000000000000000000000000000000000000..c9de578787bbcb376f8bd5a782206d0eb7ec1f52 --- /dev/null +++ b/dinov2/utils/config.py @@ -0,0 +1,72 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import math +import logging +import os + +from omegaconf import OmegaConf + +import dinov2.distributed as distributed +from dinov2.logging import setup_logging +from dinov2.utils import utils +from dinov2.configs import dinov2_default_config + + +logger = logging.getLogger("dinov2") + + +def apply_scaling_rules_to_cfg(cfg): # to fix + if cfg.optim.scaling_rule == "sqrt_wrt_1024": + base_lr = cfg.optim.base_lr + cfg.optim.lr = base_lr + cfg.optim.lr *= math.sqrt(cfg.train.batch_size_per_gpu * distributed.get_global_size() / 1024.0) + logger.info(f"sqrt scaling learning rate; base: {base_lr}, new: {cfg.optim.lr}") + else: + raise NotImplementedError + return cfg + + +def write_config(cfg, output_dir, name="config.yaml"): + logger.info(OmegaConf.to_yaml(cfg)) + saved_cfg_path = os.path.join(output_dir, name) + with open(saved_cfg_path, "w") as f: + OmegaConf.save(config=cfg, f=f) + return saved_cfg_path + + +def get_cfg_from_args(args): + args.output_dir = os.path.abspath(args.output_dir) + args.opts += [f"train.output_dir={args.output_dir}"] + default_cfg = OmegaConf.create(dinov2_default_config) + cfg = OmegaConf.load(args.config_file) + cfg = OmegaConf.merge(default_cfg, cfg, OmegaConf.from_cli(args.opts)) + return cfg + + +def default_setup(args): + distributed.enable(overwrite=True) + seed = getattr(args, "seed", 0) + rank = distributed.get_global_rank() + + global logger + setup_logging(output=args.output_dir, level=logging.INFO) + logger = logging.getLogger("dinov2") + + utils.fix_random_seeds(seed + rank) + logger.info("git:\n {}\n".format(utils.get_sha())) + logger.info("\n".join("%s: %s" % (k, str(v)) for k, v in sorted(dict(vars(args)).items()))) + + +def setup(args): + """ + Create configs and perform basic setups. + """ + cfg = get_cfg_from_args(args) + os.makedirs(args.output_dir, exist_ok=True) + default_setup(args) + apply_scaling_rules_to_cfg(cfg) + write_config(cfg, args.output_dir) + return cfg diff --git a/dinov2/utils/dtype.py b/dinov2/utils/dtype.py new file mode 100644 index 0000000000000000000000000000000000000000..80f4cd74d99faa2731dbe9f8d3a13d71b3f8e3a8 --- /dev/null +++ b/dinov2/utils/dtype.py @@ -0,0 +1,37 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + + +from typing import Dict, Union + +import numpy as np +import torch + + +TypeSpec = Union[str, np.dtype, torch.dtype] + + +_NUMPY_TO_TORCH_DTYPE: Dict[np.dtype, torch.dtype] = { + np.dtype("bool"): torch.bool, + np.dtype("uint8"): torch.uint8, + np.dtype("int8"): torch.int8, + np.dtype("int16"): torch.int16, + np.dtype("int32"): torch.int32, + np.dtype("int64"): torch.int64, + np.dtype("float16"): torch.float16, + np.dtype("float32"): torch.float32, + np.dtype("float64"): torch.float64, + np.dtype("complex64"): torch.complex64, + np.dtype("complex128"): torch.complex128, +} + + +def as_torch_dtype(dtype: TypeSpec) -> torch.dtype: + if isinstance(dtype, torch.dtype): + return dtype + if isinstance(dtype, str): + dtype = np.dtype(dtype) + assert isinstance(dtype, np.dtype), f"Expected an instance of nunpy dtype, got {type(dtype)}" + return _NUMPY_TO_TORCH_DTYPE[dtype] diff --git a/dinov2/utils/param_groups.py b/dinov2/utils/param_groups.py new file mode 100644 index 0000000000000000000000000000000000000000..c8d9a9e2a99bedffeb3ed63aa68c2480f4f0b013 --- /dev/null +++ b/dinov2/utils/param_groups.py @@ -0,0 +1,93 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from collections import defaultdict +import logging + + +logger = logging.getLogger("dinov2") + + +def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12, force_is_backbone=False, chunked_blocks=False): + """ + Calculate lr decay rate for different ViT blocks. + Args: + name (string): parameter name. + lr_decay_rate (float): base lr decay rate. + num_layers (int): number of ViT blocks. + Returns: + lr decay rate for the given parameter. + """ + layer_id = num_layers + 1 + if name.startswith("backbone") or force_is_backbone: + if ".pos_embed" in name or ".patch_embed" in name or ".mask_token" in name or ".cls_token" in name: + layer_id = 0 + elif force_is_backbone and ( + "pos_embed" in name or "patch_embed" in name or "mask_token" in name or "cls_token" in name + ): + layer_id = 0 + elif ".blocks." in name and ".residual." not in name: + layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1 + elif chunked_blocks and "blocks." in name and "residual." not in name: + layer_id = int(name[name.find("blocks.") :].split(".")[2]) + 1 + elif "blocks." in name and "residual." not in name: + layer_id = int(name[name.find("blocks.") :].split(".")[1]) + 1 + + return lr_decay_rate ** (num_layers + 1 - layer_id) + + +def get_params_groups_with_decay(model, lr_decay_rate=1.0, patch_embed_lr_mult=1.0): + chunked_blocks = False + if hasattr(model, "n_blocks"): + logger.info("chunked fsdp") + n_blocks = model.n_blocks + chunked_blocks = model.chunked_blocks + elif hasattr(model, "blocks"): + logger.info("first code branch") + n_blocks = len(model.blocks) + elif hasattr(model, "backbone"): + logger.info("second code branch") + n_blocks = len(model.backbone.blocks) + else: + logger.info("else code branch") + n_blocks = 0 + all_param_groups = [] + + for name, param in model.named_parameters(): + name = name.replace("_fsdp_wrapped_module.", "") + if not param.requires_grad: + continue + decay_rate = get_vit_lr_decay_rate( + name, lr_decay_rate, num_layers=n_blocks, force_is_backbone=n_blocks > 0, chunked_blocks=chunked_blocks + ) + d = {"params": param, "is_last_layer": False, "lr_multiplier": decay_rate, "wd_multiplier": 1.0, "name": name} + + if "last_layer" in name: + d.update({"is_last_layer": True}) + + if name.endswith(".bias") or "norm" in name or "gamma" in name: + d.update({"wd_multiplier": 0.0}) + + if "patch_embed" in name: + d.update({"lr_multiplier": d["lr_multiplier"] * patch_embed_lr_mult}) + + all_param_groups.append(d) + logger.info(f"""{name}: lr_multiplier: {d["lr_multiplier"]}, wd_multiplier: {d["wd_multiplier"]}""") + + return all_param_groups + + +def fuse_params_groups(all_params_groups, keys=("lr_multiplier", "wd_multiplier", "is_last_layer")): + fused_params_groups = defaultdict(lambda: {"params": []}) + for d in all_params_groups: + identifier = "" + for k in keys: + identifier += k + str(d[k]) + "_" + + for k in keys: + fused_params_groups[identifier][k] = d[k] + fused_params_groups[identifier]["params"].append(d["params"]) + + return fused_params_groups.values() diff --git a/dinov2/utils/utils.py b/dinov2/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..68f8e2c3be5f780bbb7e00359b5ac4fd0ba0785f --- /dev/null +++ b/dinov2/utils/utils.py @@ -0,0 +1,95 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import logging +import os +import random +import subprocess +from urllib.parse import urlparse + +import numpy as np +import torch +from torch import nn + + +logger = logging.getLogger("dinov2") + + +def load_pretrained_weights(model, pretrained_weights, checkpoint_key): + if urlparse(pretrained_weights).scheme: # If it looks like an URL + state_dict = torch.hub.load_state_dict_from_url(pretrained_weights, map_location="cpu") + else: + state_dict = torch.load(pretrained_weights, map_location="cpu") + if checkpoint_key is not None and checkpoint_key in state_dict: + logger.info(f"Take key {checkpoint_key} in provided checkpoint dict") + state_dict = state_dict[checkpoint_key] + # remove `module.` prefix + state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()} + # remove `backbone.` prefix induced by multicrop wrapper + state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()} + msg = model.load_state_dict(state_dict, strict=False) + logger.info("Pretrained weights found at {} and loaded with msg: {}".format(pretrained_weights, msg)) + + +def fix_random_seeds(seed=31): + """ + Fix random seeds. + """ + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + np.random.seed(seed) + random.seed(seed) + + +def get_sha(): + cwd = os.path.dirname(os.path.abspath(__file__)) + + def _run(command): + return subprocess.check_output(command, cwd=cwd).decode("ascii").strip() + + sha = "N/A" + diff = "clean" + branch = "N/A" + try: + sha = _run(["git", "rev-parse", "HEAD"]) + subprocess.check_output(["git", "diff"], cwd=cwd) + diff = _run(["git", "diff-index", "HEAD"]) + diff = "has uncommitted changes" if diff else "clean" + branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"]) + except Exception: + pass + message = f"sha: {sha}, status: {diff}, branch: {branch}" + return message + + +class CosineScheduler(object): + def __init__(self, base_value, final_value, total_iters, warmup_iters=0, start_warmup_value=0, freeze_iters=0): + super().__init__() + self.final_value = final_value + self.total_iters = total_iters + + freeze_schedule = np.zeros((freeze_iters)) + + warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters) + + iters = np.arange(total_iters - warmup_iters - freeze_iters) + schedule = final_value + 0.5 * (base_value - final_value) * (1 + np.cos(np.pi * iters / len(iters))) + self.schedule = np.concatenate((freeze_schedule, warmup_schedule, schedule)) + + assert len(self.schedule) == self.total_iters + + def __getitem__(self, it): + if it >= self.total_iters: + return self.final_value + else: + return self.schedule[it] + + +def has_batchnorms(model): + bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm) + for name, module in model.named_modules(): + if isinstance(module, bn_types): + return True + return False diff --git a/example_1.jpg b/example_1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..8759cb39d3e3f8462642eebefacb0fad9043fcff Binary files /dev/null and b/example_1.jpg differ diff --git a/example_2.jpg b/example_2.jpg new file mode 100644 index 0000000000000000000000000000000000000000..deb49df069042fa90cf81817bd8aaa381558746f Binary files /dev/null and b/example_2.jpg differ diff --git a/hubconf.py b/hubconf.py new file mode 100644 index 0000000000000000000000000000000000000000..fc6a4eee7963f0c6bf58accc2f6d9c9c1430acd0 --- /dev/null +++ b/hubconf.py @@ -0,0 +1,181 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + + +dependencies = ["torch"] + + +_DINOV2_BASE_URL = "https://dl.fbaipublicfiles.com/dinov2" + + +def _make_dinov2_model_name(arch_name: str, patch_size: int) -> str: + compact_arch_name = arch_name.replace("_", "")[:4] + return f"dinov2_{compact_arch_name}{patch_size}" + + +def _make_dinov2_model( + *, + arch_name: str = "vit_large", + img_size: int = 518, + patch_size: int = 14, + init_values: float = 1.0, + ffn_layer: str = "mlp", + block_chunks: int = 0, + pretrained: bool = True, + **kwargs, +): + from dinov2.models import vision_transformer as vits + + model_name = _make_dinov2_model_name(arch_name, patch_size) + vit_kwargs = dict( + img_size=img_size, + patch_size=patch_size, + init_values=init_values, + ffn_layer=ffn_layer, + block_chunks=block_chunks, + ) + vit_kwargs.update(**kwargs) + model = vits.__dict__[arch_name](**vit_kwargs) + + if pretrained: + url = _DINOV2_BASE_URL + f"/{model_name}/{model_name}_pretrain.pth" + state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu") + model.load_state_dict(state_dict, strict=False) + + return model + + +def dinov2_vits14(*, pretrained: bool = True, **kwargs): + """ + DINOv2 ViT-S/14 model (optionally) pretrained on the LVD-142M dataset. + """ + return _make_dinov2_model(arch_name="vit_small", pretrained=pretrained, **kwargs) + + +def dinov2_vitb14(*, pretrained: bool = True, **kwargs): + """ + DINOv2 ViT-B/14 model pretrained on the LVD-142M dataset. + """ + return _make_dinov2_model(arch_name="vit_base", pretrained=pretrained, **kwargs) + + +def dinov2_vitl14(*, pretrained: bool = True, **kwargs): + """ + DINOv2 ViT-L/14 model (optionally) pretrained on the LVD-142M dataset. + """ + return _make_dinov2_model(arch_name="vit_large", pretrained=pretrained, **kwargs) + + +def dinov2_vitg14(*, pretrained: bool = True, **kwargs): + """ + DINOv2 ViT-g/14 model (optionally) pretrained on the LVD-142M dataset. + """ + return _make_dinov2_model(arch_name="vit_giant2", ffn_layer="swiglufused", pretrained=pretrained, **kwargs) + + +def _make_dinov2_linear_head( + *, + model_name: str = "dinov2_vitl14", + embed_dim: int = 1024, + layers: int = 4, + pretrained: bool = True, + **kwargs, +): + assert layers in (1, 4), f"Unsupported number of layers: {layers}" + linear_head = nn.Linear((1 + layers) * embed_dim, 1_000) + + if pretrained: + layers_str = str(layers) if layers == 4 else "" + url = _DINOV2_BASE_URL + f"/{model_name}/{model_name}_linear{layers_str}_head.pth" + state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu") + linear_head.load_state_dict(state_dict, strict=False) + + return linear_head + + +class _LinearClassifierWrapper(nn.Module): + def __init__(self, *, backbone: nn.Module, linear_head: nn.Module, layers: int = 4): + super().__init__() + self.backbone = backbone + self.linear_head = linear_head + self.layers = layers + + def forward(self, x): + if self.layers == 1: + x = self.backbone.forward_features(x) + cls_token = x["x_norm_clstoken"] + patch_tokens = x["x_norm_patchtokens"] + # fmt: off + linear_input = torch.cat([ + cls_token, + patch_tokens.mean(dim=1), + ], dim=1) + # fmt: on + elif self.layers == 4: + x = self.backbone.get_intermediate_layers(x, n=4, return_class_token=True) + # fmt: off + linear_input = torch.cat([ + x[0][1], + x[1][1], + x[2][1], + x[3][1], + x[3][0].mean(dim=1), + ], dim=1) + # fmt: on + else: + assert False, f"Unsupported number of layers: {self.layers}" + return self.linear_head(linear_input) + + +def _make_dinov2_linear_classifier( + *, + arch_name: str = "vit_large", + layers: int = 4, + pretrained: bool = True, + **kwargs, +): + backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs) + + embed_dim = backbone.embed_dim + patch_size = backbone.patch_size + model_name = _make_dinov2_model_name(arch_name, patch_size) + linear_head = _make_dinov2_linear_head( + model_name=model_name, embed_dim=embed_dim, layers=layers, pretrained=pretrained + ) + + return _LinearClassifierWrapper(backbone=backbone, linear_head=linear_head, layers=layers) + + +def dinov2_vits14_lc(*, layers: int = 4, pretrained: bool = True, **kwargs): + """ + Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-S/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. + """ + return _make_dinov2_linear_classifier(arch_name="vit_small", layers=layers, pretrained=pretrained, **kwargs) + + +def dinov2_vitb14_lc(*, layers: int = 4, pretrained: bool = True, **kwargs): + """ + Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-B/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. + """ + return _make_dinov2_linear_classifier(arch_name="vit_base", layers=layers, pretrained=pretrained, **kwargs) + + +def dinov2_vitl14_lc(*, layers: int = 4, pretrained: bool = True, **kwargs): + """ + Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-L/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. + """ + return _make_dinov2_linear_classifier(arch_name="vit_large", layers=layers, pretrained=pretrained, **kwargs) + + +def dinov2_vitg14_lc(*, layers: int = 4, pretrained: bool = True, **kwargs): + """ + Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-g/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. + """ + return _make_dinov2_linear_classifier( + arch_name="vit_giant2", layers=layers, ffn_layer="swiglufused", pretrained=pretrained, **kwargs + ) diff --git a/labelmap.txt b/labelmap.txt new file mode 100644 index 0000000000000000000000000000000000000000..2771e137f4f51835ce1a50fcf293aa7365612727 --- /dev/null +++ b/labelmap.txt @@ -0,0 +1,151 @@ +Idx Ratio Train Val Name +1 0.1576 11664 1172 wall +2 0.1072 6046 612 building, edifice +3 0.0878 8265 796 sky +4 0.0621 9336 917 floor, flooring +5 0.0480 6678 641 tree +6 0.0450 6604 643 ceiling +7 0.0398 4023 408 road, route +8 0.0231 1906 199 bed +9 0.0198 4688 460 windowpane, window +10 0.0183 2423 225 grass +11 0.0181 2874 294 cabinet +12 0.0166 3068 310 sidewalk, pavement +13 0.0160 5075 526 person, individual, someone, somebody, mortal, soul +14 0.0151 1804 190 earth, ground +15 0.0118 6666 796 door, double door +16 0.0110 4269 411 table +17 0.0109 1691 160 mountain, mount +18 0.0104 3999 441 plant, flora, plant life +19 0.0104 2149 217 curtain, drape, drapery, mantle, pall +20 0.0103 3261 318 chair +21 0.0098 3164 306 car, auto, automobile, machine, motorcar +22 0.0074 709 75 water +23 0.0067 3296 315 painting, picture +24 0.0065 1191 106 sofa, couch, lounge +25 0.0061 1516 162 shelf +26 0.0060 667 69 house +27 0.0053 651 57 sea +28 0.0052 1847 224 mirror +29 0.0046 1158 128 rug, carpet, carpeting +30 0.0044 480 44 field +31 0.0044 1172 98 armchair +32 0.0044 1292 184 seat +33 0.0033 1386 138 fence, fencing +34 0.0031 698 61 desk +35 0.0030 781 73 rock, stone +36 0.0027 380 43 wardrobe, closet, press +37 0.0026 3089 302 lamp +38 0.0024 404 37 bathtub, bathing tub, bath, tub +39 0.0024 804 99 railing, rail +40 0.0023 1453 153 cushion +41 0.0023 411 37 base, pedestal, stand +42 0.0022 1440 162 box +43 0.0022 800 77 column, pillar +44 0.0020 2650 298 signboard, sign +45 0.0019 549 46 chest of drawers, chest, bureau, dresser +46 0.0019 367 36 counter +47 0.0018 311 30 sand +48 0.0018 1181 122 sink +49 0.0018 287 23 skyscraper +50 0.0018 468 38 fireplace, hearth, open fireplace +51 0.0018 402 43 refrigerator, icebox +52 0.0018 130 12 grandstand, covered stand +53 0.0018 561 64 path +54 0.0017 880 102 stairs, steps +55 0.0017 86 12 runway +56 0.0017 172 11 case, display case, showcase, vitrine +57 0.0017 198 18 pool table, billiard table, snooker table +58 0.0017 930 109 pillow +59 0.0015 139 18 screen door, screen +60 0.0015 564 52 stairway, staircase +61 0.0015 320 26 river +62 0.0015 261 29 bridge, span +63 0.0014 275 22 bookcase +64 0.0014 335 60 blind, screen +65 0.0014 792 75 coffee table, cocktail table +66 0.0014 395 49 toilet, can, commode, crapper, pot, potty, stool, throne +67 0.0014 1309 138 flower +68 0.0013 1112 113 book +69 0.0013 266 27 hill +70 0.0013 659 66 bench +71 0.0012 331 31 countertop +72 0.0012 531 56 stove, kitchen stove, range, kitchen range, cooking stove +73 0.0012 369 36 palm, palm tree +74 0.0012 144 9 kitchen island +75 0.0011 265 29 computer, computing machine, computing device, data processor, electronic computer, information processing system +76 0.0010 324 33 swivel chair +77 0.0009 304 27 boat +78 0.0009 170 20 bar +79 0.0009 68 6 arcade machine +80 0.0009 65 8 hovel, hut, hutch, shack, shanty +81 0.0009 248 25 bus, autobus, coach, charabanc, double-decker, jitney, motorbus, motorcoach, omnibus, passenger vehicle +82 0.0008 492 49 towel +83 0.0008 2510 269 light, light source +84 0.0008 440 39 truck, motortruck +85 0.0008 147 18 tower +86 0.0008 583 56 chandelier, pendant, pendent +87 0.0007 533 61 awning, sunshade, sunblind +88 0.0007 1989 239 streetlight, street lamp +89 0.0007 71 5 booth, cubicle, stall, kiosk +90 0.0007 618 53 television receiver, television, television set, tv, tv set, idiot box, boob tube, telly, goggle box +91 0.0007 135 12 airplane, aeroplane, plane +92 0.0007 83 5 dirt track +93 0.0007 178 17 apparel, wearing apparel, dress, clothes +94 0.0006 1003 104 pole +95 0.0006 182 12 land, ground, soil +96 0.0006 452 50 bannister, banister, balustrade, balusters, handrail +97 0.0006 42 6 escalator, moving staircase, moving stairway +98 0.0006 307 31 ottoman, pouf, pouffe, puff, hassock +99 0.0006 965 114 bottle +100 0.0006 117 13 buffet, counter, sideboard +101 0.0006 354 35 poster, posting, placard, notice, bill, card +102 0.0006 108 9 stage +103 0.0006 557 55 van +104 0.0006 52 4 ship +105 0.0005 99 5 fountain +106 0.0005 57 4 conveyer belt, conveyor belt, conveyer, conveyor, transporter +107 0.0005 292 31 canopy +108 0.0005 77 9 washer, automatic washer, washing machine +109 0.0005 340 38 plaything, toy +110 0.0005 66 3 swimming pool, swimming bath, natatorium +111 0.0005 465 49 stool +112 0.0005 50 4 barrel, cask +113 0.0005 622 75 basket, handbasket +114 0.0005 80 9 waterfall, falls +115 0.0005 59 3 tent, collapsible shelter +116 0.0005 531 72 bag +117 0.0005 282 30 minibike, motorbike +118 0.0005 73 7 cradle +119 0.0005 435 44 oven +120 0.0005 136 25 ball +121 0.0005 116 24 food, solid food +122 0.0004 266 31 step, stair +123 0.0004 58 12 tank, storage tank +124 0.0004 418 83 trade name, brand name, brand, marque +125 0.0004 319 43 microwave, microwave oven +126 0.0004 1193 139 pot, flowerpot +127 0.0004 97 23 animal, animate being, beast, brute, creature, fauna +128 0.0004 347 36 bicycle, bike, wheel, cycle +129 0.0004 52 5 lake +130 0.0004 246 22 dishwasher, dish washer, dishwashing machine +131 0.0004 108 13 screen, silver screen, projection screen +132 0.0004 201 30 blanket, cover +133 0.0004 285 21 sculpture +134 0.0004 268 27 hood, exhaust hood +135 0.0003 1020 108 sconce +136 0.0003 1282 122 vase +137 0.0003 528 65 traffic light, traffic signal, stoplight +138 0.0003 453 57 tray +139 0.0003 671 100 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +140 0.0003 397 44 fan +141 0.0003 92 8 pier, wharf, wharfage, dock +142 0.0003 228 18 crt screen +143 0.0003 570 59 plate +144 0.0003 217 22 monitor, monitoring device +145 0.0003 206 19 bulletin board, notice board +146 0.0003 130 14 shower +147 0.0003 178 28 radiator +148 0.0002 504 57 glass, drinking glass +149 0.0002 775 96 clock +150 0.0002 421 56 flag diff --git a/mmcv_full-1.5.0-cp310-cp310-linux_x86_64.whl b/mmcv_full-1.5.0-cp310-cp310-linux_x86_64.whl new file mode 100644 index 0000000000000000000000000000000000000000..06ae2c9ecd3e8a523c96a742a6f1f2d111660a60 --- /dev/null +++ b/mmcv_full-1.5.0-cp310-cp310-linux_x86_64.whl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c402d8d300ed5d1c125bdc85fd3b6a2dbd026f763db854a5e462e4aba2bab0c1 +size 26812199 diff --git a/notebooks/depth_estimation.ipynb b/notebooks/depth_estimation.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..717739a3c93d3077426c14e6af86c1aa45365053 --- /dev/null +++ b/notebooks/depth_estimation.ipynb @@ -0,0 +1,482 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) Meta Platforms, Inc. and affiliates." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Depth Estimation \"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "INSTALL = False # Switch this to install dependencies\n", + "if INSTALL: # Try installing package with extras\n", + " REPO_URL = \"https://github.com/facebookresearch/dinov2\"\n", + " !{sys.executable} -m pip install -e {REPO_URL}'[extras]' --extra-index-url https://download.pytorch.org/whl/cu117 --extra-index-url https://pypi.nvidia.com\n", + "else:\n", + " REPO_PATH = \"\" # Specify a local path to the repository (or use installed package instead)\n", + " sys.path.append(REPO_PATH)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Utilities" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "import itertools\n", + "from functools import partial\n", + "\n", + "import torch\n", + "import torch.nn.functional as F\n", + "\n", + "from dinov2.eval.depth.models import build_depther\n", + "\n", + "\n", + "class CenterPadding(torch.nn.Module):\n", + " def __init__(self, multiple):\n", + " super().__init__()\n", + " self.multiple = multiple\n", + "\n", + " def _get_pad(self, size):\n", + " new_size = math.ceil(size / self.multiple) * self.multiple\n", + " pad_size = new_size - size\n", + " pad_size_left = pad_size // 2\n", + " pad_size_right = pad_size - pad_size_left\n", + " return pad_size_left, pad_size_right\n", + "\n", + " @torch.inference_mode()\n", + " def forward(self, x):\n", + " pads = list(itertools.chain.from_iterable(self._get_pad(m) for m in x.shape[:1:-1]))\n", + " output = F.pad(x, pads)\n", + " return output\n", + "\n", + "\n", + "def create_depther(cfg, backbone_model, backbone_size, head_type):\n", + " train_cfg = cfg.get(\"train_cfg\")\n", + " test_cfg = cfg.get(\"test_cfg\")\n", + " depther = build_depther(cfg.model, train_cfg=train_cfg, test_cfg=test_cfg)\n", + "\n", + " depther.backbone.forward = partial(\n", + " backbone_model.get_intermediate_layers,\n", + " n=cfg.model.backbone.out_indices,\n", + " reshape=True,\n", + " return_class_token=cfg.model.backbone.output_cls_token,\n", + " norm=cfg.model.backbone.final_norm,\n", + " )\n", + "\n", + " if hasattr(backbone_model, \"patch_size\"):\n", + " depther.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(backbone_model.patch_size)(x[0]))\n", + "\n", + " return depther" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load pretrained backbone" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using cache found in /private/home/plabatut/.cache/torch/hub/facebookresearch_dinov2_main\n" + ] + }, + { + "data": { + "text/plain": [ + "DinoVisionTransformer(\n", + " (patch_embed): PatchEmbed(\n", + " (proj): Conv2d(3, 384, kernel_size=(14, 14), stride=(14, 14))\n", + " (norm): Identity()\n", + " )\n", + " (blocks): ModuleList(\n", + " (0-11): 12 x NestedTensorBlock(\n", + " (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", + " (attn): MemEffAttention(\n", + " (qkv): Linear(in_features=384, out_features=1152, bias=True)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=384, out_features=384, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (ls1): LayerScale()\n", + " (drop_path1): Identity()\n", + " (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=384, out_features=1536, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (fc2): Linear(in_features=1536, out_features=384, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (ls2): LayerScale()\n", + " (drop_path2): Identity()\n", + " )\n", + " )\n", + " (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", + " (head): Identity()\n", + ")" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "BACKBONE_SIZE = \"small\" # in (\"small\", \"base\", \"large\" or \"giant\")\n", + "\n", + "\n", + "backbone_archs = {\n", + " \"small\": \"vits14\",\n", + " \"base\": \"vitb14\",\n", + " \"large\": \"vitl14\",\n", + " \"giant\": \"vitg14\",\n", + "}\n", + "backbone_arch = backbone_archs[BACKBONE_SIZE]\n", + "backbone_name = f\"dinov2_{backbone_arch}\"\n", + "\n", + "backbone_model = torch.hub.load(repo_or_dir=\"facebookresearch/dinov2\", model=backbone_name)\n", + "backbone_model.eval()\n", + "backbone_model.cuda()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load pretrained depth head" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_nyu_dpt_head.pth\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_nyu_dpt_head.pth\" to /private/home/plabatut/.cache/torch/hub/checkpoints/dinov2_vits14_nyu_dpt_head.pth\n", + "100%|██████████| 160M/160M [00:06<00:00, 27.2MB/s] \n" + ] + }, + { + "data": { + "text/plain": [ + "DepthEncoderDecoder(\n", + " (backbone): DinoVisionTransformer()\n", + " (decode_head): DPTHead(\n", + " align_corners=False\n", + " (loss_decode): ModuleList(\n", + " (0): SigLoss()\n", + " (1): GradientLoss()\n", + " )\n", + " (conv_depth): HeadDepth(\n", + " (head): Sequential(\n", + " (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (1): Interpolate()\n", + " (2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (3): ReLU()\n", + " (4): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + " )\n", + " (relu): ReLU()\n", + " (sigmoid): Sigmoid()\n", + " (reassemble_blocks): ReassembleBlocks(\n", + " (projects): ModuleList(\n", + " (0): ConvModule(\n", + " (conv): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + " (1): ConvModule(\n", + " (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + " (2): ConvModule(\n", + " (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + " (3): ConvModule(\n", + " (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + " )\n", + " (resize_layers): ModuleList(\n", + " (0): ConvTranspose2d(48, 48, kernel_size=(4, 4), stride=(4, 4))\n", + " (1): ConvTranspose2d(96, 96, kernel_size=(2, 2), stride=(2, 2))\n", + " (2): Identity()\n", + " (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", + " )\n", + " (readout_projects): ModuleList(\n", + " (0-3): 4 x Sequential(\n", + " (0): Linear(in_features=768, out_features=384, bias=True)\n", + " (1): GELU(approximate='none')\n", + " )\n", + " )\n", + " )\n", + " (convs): ModuleList(\n", + " (0): ConvModule(\n", + " (conv): Conv2d(48, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (1): ConvModule(\n", + " (conv): Conv2d(96, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (2): ConvModule(\n", + " (conv): Conv2d(192, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (3): ConvModule(\n", + " (conv): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " )\n", + " (fusion_blocks): ModuleList(\n", + " (0): FeatureFusionBlock(\n", + " (project): ConvModule(\n", + " (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + " (res_conv_unit1): None\n", + " (res_conv_unit2): PreActResidualConvUnit(\n", + " (conv1): ConvModule(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (activate): ReLU(inplace=True)\n", + " )\n", + " (conv2): ConvModule(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (activate): ReLU(inplace=True)\n", + " )\n", + " )\n", + " )\n", + " (1-3): 3 x FeatureFusionBlock(\n", + " (project): ConvModule(\n", + " (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + " (res_conv_unit1): PreActResidualConvUnit(\n", + " (conv1): ConvModule(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (activate): ReLU(inplace=True)\n", + " )\n", + " (conv2): ConvModule(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (activate): ReLU(inplace=True)\n", + " )\n", + " )\n", + " (res_conv_unit2): PreActResidualConvUnit(\n", + " (conv1): ConvModule(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (activate): ReLU(inplace=True)\n", + " )\n", + " (conv2): ConvModule(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (activate): ReLU(inplace=True)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (project): ConvModule(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (activate): ReLU(inplace=True)\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import urllib\n", + "\n", + "import mmcv\n", + "from mmcv.runner import load_checkpoint\n", + "\n", + "\n", + "def load_config_from_url(url: str) -> str:\n", + " with urllib.request.urlopen(url) as f:\n", + " return f.read().decode()\n", + "\n", + "\n", + "HEAD_DATASET = \"nyu\" # in (\"nyu\", \"kitti\")\n", + "HEAD_TYPE = \"dpt\" # in (\"linear\", \"linear4\", \"dpt\")\n", + "\n", + "\n", + "DINOV2_BASE_URL = \"https://dl.fbaipublicfiles.com/dinov2\"\n", + "head_config_url = f\"{DINOV2_BASE_URL}/{backbone_name}/{backbone_name}_{HEAD_DATASET}_{HEAD_TYPE}_config.py\"\n", + "head_checkpoint_url = f\"{DINOV2_BASE_URL}/{backbone_name}/{backbone_name}_{HEAD_DATASET}_{HEAD_TYPE}_head.pth\"\n", + "\n", + "cfg_str = load_config_from_url(head_config_url)\n", + "cfg = mmcv.Config.fromstring(cfg_str, file_format=\".py\")\n", + "\n", + "model = create_depther(\n", + " cfg,\n", + " backbone_model=backbone_model,\n", + " backbone_size=BACKBONE_SIZE,\n", + " head_type=HEAD_TYPE,\n", + ")\n", + "\n", + "load_checkpoint(model, head_checkpoint_url, map_location=\"cpu\")\n", + "model.eval()\n", + "model.cuda()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load sample image" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import urllib\n", + "\n", + "from PIL import Image\n", + "\n", + "\n", + "def load_image_from_url(url: str) -> Image:\n", + " with urllib.request.urlopen(url) as f:\n", + " return Image.open(f).convert(\"RGB\")\n", + "\n", + "\n", + "EXAMPLE_IMAGE_URL = \"https://dl.fbaipublicfiles.com/dinov2/images/example.jpg\"\n", + "\n", + "\n", + "image = load_image_from_url(EXAMPLE_IMAGE_URL)\n", + "display(image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimate depth on sample image" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib\n", + "from torchvision import transforms\n", + "\n", + "\n", + "def make_depth_transform() -> transforms.Compose:\n", + " return transforms.Compose([\n", + " transforms.ToTensor(),\n", + " lambda x: 255.0 * x[:3], # Discard alpha component and scale by 255\n", + " transforms.Normalize(\n", + " mean=(123.675, 116.28, 103.53),\n", + " std=(58.395, 57.12, 57.375),\n", + " ),\n", + " ])\n", + "\n", + "\n", + "def render_depth(values, colormap_name=\"magma_r\") -> Image:\n", + " min_value, max_value = values.min(), values.max()\n", + " normalized_values = (values - min_value) / (max_value - min_value)\n", + "\n", + " colormap = matplotlib.colormaps[colormap_name]\n", + " colors = colormap(normalized_values, bytes=True) # ((1)xhxwx4)\n", + " colors = colors[:, :, :3] # Discard alpha component\n", + " return Image.fromarray(colors)\n", + "\n", + "\n", + "transform = make_depth_transform()\n", + "\n", + "scale_factor = 1\n", + "rescaled_image = image.resize((scale_factor * image.width, scale_factor * image.height))\n", + "transformed_image = transform(rescaled_image)\n", + "batch = transformed_image.unsqueeze(0).cuda() # Make a batch of one image\n", + "\n", + "with torch.inference_mode():\n", + " result = model.whole_inference(batch, img_meta=None, rescale=True)\n", + "\n", + "depth_image = render_depth(result.squeeze().cpu())\n", + "display(depth_image)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/semantic_segmentation.ipynb b/notebooks/semantic_segmentation.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..60dd3ef5505be2ad9bc3a9d98f8b3dbe1db99c5a --- /dev/null +++ b/notebooks/semantic_segmentation.ipynb @@ -0,0 +1,1476 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "b470389d-a897-416e-9601-aeacb39cd694", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) Meta Platforms, Inc. and affiliates." + ] + }, + { + "cell_type": "markdown", + "id": "eb5c8577-7dff-41b1-9b04-2dca12940e02", + "metadata": {}, + "source": [ + "# Semantic Segmentation \"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "febdf412-5ad0-4bbc-9530-754f92dcc491", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "INSTALL = False # Switch this to install dependencies\n", + "if INSTALL: # Try installing package with extras\n", + " REPO_URL = \"https://github.com/facebookresearch/dinov2\"\n", + " !{sys.executable} -m pip install -e {REPO_URL}'[extras]' --extra-index-url https://download.pytorch.org/whl/cu117 --extra-index-url https://pypi.nvidia.com\n", + "else:\n", + " REPO_PATH = \"\" # Specify a local path to the repository (or use installed package instead)\n", + " sys.path.append(REPO_PATH)" + ] + }, + { + "cell_type": "markdown", + "id": "efdf378b-0591-4879-9db6-6a4ab582d49f", + "metadata": {}, + "source": [ + "## Utilities" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "90223c04-e7da-4738-bb16-d4f7025aa3eb", + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "import itertools\n", + "from functools import partial\n", + "\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from mmseg.apis import init_segmentor, inference_segmentor\n", + "\n", + "import dinov2.eval.segmentation.models\n", + "\n", + "\n", + "class CenterPadding(torch.nn.Module):\n", + " def __init__(self, multiple):\n", + " super().__init__()\n", + " self.multiple = multiple\n", + "\n", + " def _get_pad(self, size):\n", + " new_size = math.ceil(size / self.multiple) * self.multiple\n", + " pad_size = new_size - size\n", + " pad_size_left = pad_size // 2\n", + " pad_size_right = pad_size - pad_size_left\n", + " return pad_size_left, pad_size_right\n", + "\n", + " @torch.inference_mode()\n", + " def forward(self, x):\n", + " pads = list(itertools.chain.from_iterable(self._get_pad(m) for m in x.shape[:1:-1]))\n", + " output = F.pad(x, pads)\n", + " return output\n", + "\n", + "\n", + "def create_segmenter(cfg, backbone_model):\n", + " model = init_segmentor(cfg)\n", + " model.backbone.forward = partial(\n", + " backbone_model.get_intermediate_layers,\n", + " n=cfg.model.backbone.out_indices,\n", + " reshape=True,\n", + " )\n", + " if hasattr(backbone_model, \"patch_size\"):\n", + " model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(backbone_model.patch_size)(x[0]))\n", + " model.init_weights()\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "id": "a5724efc-b2b8-46ed-94e1-7fee59a39ed9", + "metadata": {}, + "source": [ + "## Load pretrained backbone" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2d51b932-1157-45ce-997f-572ad417a12f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using cache found in /private/home/plabatut/.cache/torch/hub/facebookresearch_dinov2_main\n", + "/private/home/plabatut/github/patricklabatut/dinov2/dinov2/layers/swiglu_ffn.py:43: UserWarning: xFormers is available (SwiGLU)\n", + " warnings.warn(\"xFormers is available (SwiGLU)\")\n", + "/private/home/plabatut/github/patricklabatut/dinov2/dinov2/layers/attention.py:27: UserWarning: xFormers is available (Attention)\n", + " warnings.warn(\"xFormers is available (Attention)\")\n", + "/private/home/plabatut/github/patricklabatut/dinov2/dinov2/layers/block.py:33: UserWarning: xFormers is available (Block)\n", + " warnings.warn(\"xFormers is available (Block)\")\n" + ] + }, + { + "data": { + "text/plain": [ + "DinoVisionTransformer(\n", + " (patch_embed): PatchEmbed(\n", + " (proj): Conv2d(3, 384, kernel_size=(14, 14), stride=(14, 14))\n", + " (norm): Identity()\n", + " )\n", + " (blocks): ModuleList(\n", + " (0-11): 12 x NestedTensorBlock(\n", + " (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", + " (attn): MemEffAttention(\n", + " (qkv): Linear(in_features=384, out_features=1152, bias=True)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=384, out_features=384, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (ls1): LayerScale()\n", + " (drop_path1): Identity()\n", + " (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=384, out_features=1536, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (fc2): Linear(in_features=1536, out_features=384, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (ls2): LayerScale()\n", + " (drop_path2): Identity()\n", + " )\n", + " )\n", + " (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", + " (head): Identity()\n", + ")" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "BACKBONE_SIZE = \"small\" # in (\"small\", \"base\", \"large\" or \"giant\")\n", + "\n", + "\n", + "backbone_archs = {\n", + " \"small\": \"vits14\",\n", + " \"base\": \"vitb14\",\n", + " \"large\": \"vitl14\",\n", + " \"giant\": \"vitg14\",\n", + "}\n", + "backbone_arch = backbone_archs[BACKBONE_SIZE]\n", + "backbone_name = f\"dinov2_{backbone_arch}\"\n", + "\n", + "backbone_model = torch.hub.load(repo_or_dir=\"facebookresearch/dinov2\", model=backbone_name)\n", + "backbone_model.eval()\n", + "backbone_model.cuda()" + ] + }, + { + "cell_type": "markdown", + "id": "c1c90501-d6ef-436e-b1a1-72e63b0534e3", + "metadata": {}, + "source": [ + "## Load pretrained segmentation head" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d0bf0b7f-ad98-4cfb-8120-f076df8f8933", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/private/home/plabatut/.conda/envs/dinov2-extras-conda/lib/python3.9/site-packages/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``.\n", + " warnings.warn(\n", + "2023-08-31 06:29:03,743 - mmcv - INFO - initialize BNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}\n", + "2023-08-31 06:29:03,744 - mmcv - INFO - \n", + "decode_head.conv_seg.weight - torch.Size([21, 1536, 1, 1]): \n", + "NormalInit: mean=0, std=0.01, bias=0 \n", + " \n", + "2023-08-31 06:29:03,745 - mmcv - INFO - \n", + "decode_head.conv_seg.bias - torch.Size([21]): \n", + "NormalInit: mean=0, std=0.01, bias=0 \n", + " \n", + "2023-08-31 06:29:03,745 - mmcv - INFO - \n", + "decode_head.bn.weight - torch.Size([1536]): \n", + "The value is the same before and after calling `init_weights` of EncoderDecoder \n", + " \n", + "2023-08-31 06:29:03,746 - mmcv - INFO - \n", + "decode_head.bn.bias - torch.Size([1536]): \n", + "The value is the same before and after calling `init_weights` of EncoderDecoder \n", + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scales: [1.0, 1.32, 1.73]\n", + "load checkpoint from http path: https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_voc2012_ms_head.pth\n" + ] + }, + { + "data": { + "text/plain": [ + "EncoderDecoder(\n", + " (backbone): DinoVisionTransformer()\n", + " (decode_head): BNHead(\n", + " input_transform=resize_concat, ignore_index=255, align_corners=False\n", + " (loss_decode): CrossEntropyLoss(avg_non_ignore=False)\n", + " (conv_seg): Conv2d(1536, 21, kernel_size=(1, 1), stride=(1, 1))\n", + " (bn): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}\n", + ")" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import urllib\n", + "\n", + "import mmcv\n", + "from mmcv.runner import load_checkpoint\n", + "\n", + "\n", + "def load_config_from_url(url: str) -> str:\n", + " with urllib.request.urlopen(url) as f:\n", + " return f.read().decode()\n", + "\n", + "\n", + "HEAD_SCALE_COUNT = 3 # more scales: slower but better results, in (1,2,3,4,5)\n", + "HEAD_DATASET = \"voc2012\" # in (\"ade20k\", \"voc2012\")\n", + "HEAD_TYPE = \"ms\" # in (\"ms, \"linear\")\n", + "\n", + "\n", + "DINOV2_BASE_URL = \"https://dl.fbaipublicfiles.com/dinov2\"\n", + "head_config_url = f\"{DINOV2_BASE_URL}/{backbone_name}/{backbone_name}_{HEAD_DATASET}_{HEAD_TYPE}_config.py\"\n", + "head_checkpoint_url = f\"{DINOV2_BASE_URL}/{backbone_name}/{backbone_name}_{HEAD_DATASET}_{HEAD_TYPE}_head.pth\"\n", + "\n", + "cfg_str = load_config_from_url(head_config_url)\n", + "cfg = mmcv.Config.fromstring(cfg_str, file_format=\".py\")\n", + "if HEAD_TYPE == \"ms\":\n", + " cfg.data.test.pipeline[1][\"img_ratios\"] = cfg.data.test.pipeline[1][\"img_ratios\"][:HEAD_SCALE_COUNT]\n", + " print(\"scales:\", cfg.data.test.pipeline[1][\"img_ratios\"])\n", + "\n", + "model = create_segmenter(cfg, backbone_model=backbone_model)\n", + "load_checkpoint(model, head_checkpoint_url, map_location=\"cpu\")\n", + "model.cuda()\n", + "model.eval()" + ] + }, + { + "cell_type": "markdown", + "id": "2dc1b106-d28c-41cc-9ddd-f558d66a4715", + "metadata": {}, + "source": [ + "## Load sample image" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "44511634-8243-4662-a512-4531014adb32", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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s6zrXVzBiQEEAEImA2csH83lzfvZoXp+IyIcffmgs7737wSuvvC6WYoze+7quiTnL64cPH8YoALDZbEqLPMa4artNt4mSkkYzO52dxjZuLm8oQVy1cdtera8363WULqEhIwAQg1nv4mZGMzEThyBdzFYCmqCBqm7Xm1kV8kso9u0Wi4VDcs4hjStCUUtni/nZYl57B70Y6geZFU/XdZr6LrL/7NH9B1Xtkezy6rlojG3XdR0RzGaz6+vr7XabPdUiUkrwUXEWIn4PFxPMlub+WEYNOtXfYzuU9Vn/YTaYSt0/ipry5fhwGMAx/lquMvNz9h2GEJqm8d43TdM087zkFemthxBCXj0PW78dFBy6m4IRoQJov6wcZYUqDIsfYh73j8V2m4O7HUkEU8uwGOBs2UOU+9tB4Bg77ANHDlGD/ZbwbvE9oguKTssPJ1bXDr+Z9wDQALMrwyyKdiygwNFu2paCrx1XQI7Dq9W83azW0kUFAAQyF6qT2s9OF7UP1x9dXG1W5J0hbbrtHBo36f5wyhOayHvAAP1WzRiwcBRWiIjsVcHM1ICZb9ars3jmnAMyZiYVIlIxVUXILmdEBCIkIkAZF5fzefP06dNnz56dn59670VSpte8h5E9cmbEzKomIpJ2M8rIMENVWC23dV1fX61+65vf/fZ336zremA2MVOR6JgLs/qIippITCK8ex/xNgV2FOCHRHCgAAygkOC3UKiZYWHu3aF9J+3fRgZ3fzUUGpxSZQu7l6PRltmml6Aw7EcOu1CzGT17dtF1XVVVkTFU89W6VSWPDObVGBCIbR3NV/OuTSnidrMFcu3VMjjq2vUs4IMHc41LAI2pJXREDEYKRoYGJnkDmizT3IH9tGdTH+rgo2xyiMqSz80M7EhrR02xAV9QvkEiRFwul0+fX+RVjoJleeecU9hsYxd8MIQoycxijHzK6LiaVR98+M7f/Bs//6f+qT9T1f76+rrruu3g5vHeLxan19fXeblshKCQ15FXV1e/8/VfUwBXB/a+ruvH919eYMXG3XIdqrlsu9S1hrbtutVmqYyOiIjMBJHZqXOuD64BaLtNVfm85MpaYb1ez2azEBwzDjuF0nXdvG6c54orAADQ3u8X42KxYEcB/AiTfqvKzDkadSH3CkAXJ3Pv/Wq1urq6MkmqiQjMYLvdXlxcOOfyTmcWaxPD6BBBEwI4tKjKT/IqPJfSqwy4Y8aseNDocEcP7iyjMoNh5TN+O8QEiA12bR5hXuPmxtfrddOszYzI5aio0W0wOA/Mex6De1Q1pdTvLVjGJ4EKINoQk4hZrZqK5oi2pGBagGIcuWE/ZkPIITYlU0z5qAfadJmxk8myW/j1sosMe6bWrIOh9DyNu8VYeg+nEnKCUzNDyHvBaGaK0Gnyvlp13dPLixBcCCEtW3T8IFTPyD+PqGZKaGgJkiKE4B+cn8/Jz66X17Hbdi0mirFzh6jND6Pvvnxv5WbJvqtk9MCMNW2IqkiqbRtDcM65GNNms7l3756pMLMTVlLVZD1W89o662AiIuuNOAWA5dKefPTs4cP7i8Xi5uY6SqorD+LGeIFxdQ4AsYtEhMhmkJJ03abrutVqs1rdXF3dPH361JAQ8eTkZLNZzWazbEOoqvdV8LzeJABQscE1jvuTKmmFMtfAx+kt2JfdH8tmd7Rzx7f9qAztlkCtkVennxw8H/75AoVKD/mh9s2/7s9CcswdDCJ1Pp9fXV21bXtycpaYPni+/JVf+sY2dpvtTahO0GpVFVtG3RhSVZ2hr9Vwm1QSe1WTzUsPT/7IP/ITc1+rrAGRGEzNVAFYLa9vjnv2RuMgl1E0TEAEx5wZR8tRUJfi5rBy8dN07eWcq+s6L2EREQb9gQ4BIKVU1UREMcau69wZ5QhHInrw4MEPffUreYlzeXnpvM9k0DSNjXvJO/Oot4RSSufn5+y9EgiCCaCicyzb5MGhaOy23WZ7095c3Vy3Fn1TUR9FnJe/fbglDPuUi8Uia0dVRdWU0mxez2bBOWcmnhgARr+0p4oARQTREFS6tpnVjpAc4+DLJSI0DwCttnXwAzwJwJJI08yI4IP33l9eX42utfV6fXFxEWO/Vi6pNFu0IxwO8VWW8adSZY4Po9YxEAOjHFxaLMtgzzPPoxPb9stt5FQOsixd1/Xh2UMIdy40xGPn2L2UUsZO/io7pcsBZCsKhgX62CAacD93MlCF3mnTr2vBACE3i4gZqlm7i4hqUtVscJQe+2Fevb7EApKICKhm063xF5FL2K9dd/iCfY4uXdKT1nZ/Imr25xQ/IWIUkQDIvOy26223mIkCLkIlYg9cdSUhQtcxKkLSZIqs5J07efDw/N6Dd5599N5HT8gxKriRCEZLCvblwoQOSkRmfO9HPE3nkJIg4qZtQ3DE3mLsui5jK/MPI8kAdURkQBlgxEjmQA1VEQDB4MmTp5+4edUTMrsYOykOtORIvTxCMAIQREbElHS93q7X667rRKSNSVVnixPvq/V6vV5vnSNVzSrfhv0h0C1oVmEEhdIqOa0Ey2iRlWiGA99vieZJg3cr74m4779Cm+zH9L9m+/Kgr7tbLud1N33f8WvmTAAYvDWHumo3/uLXHAzVv/S+evLkZr3edl0Hc67m82dX8Qtf/MKrn3z03e+89Wu/+q0vffErX/ihTzVz++7bb/7CL/z6K6+9jBS4jTfddQMBQJ8v26TcSouq7FiSgAHYsIkDCqZIAmajD3AYocA+lx7C55BHflc2yoRCjrZwVPKqSIzROTfu1QFAjiIezGVlRudINSH3qBSR7XbdxvbevXshhPV21cXIrmd8731WwKvV6vT0VGA4fmOoYoT84ME9dHy92ZKpgBKxI5+27er6Zq3WpTZBEhAj886TQ5JejiMi0bgQtBy1e7pY1D70ER5m6/XaOVdVFRGpincVIopIVVWI6EMwM02JiDNv1qHK7uW8OBt6IUTM25xmliOZVVXVP3hwjwkuLp/F1GaplePIttttCGG73eZApBhThuqEVUtxf4zO0cxGhtnDGqoo5q2zvEc0WZ+UaEVEQyu3J62IHL6NHnrDa5DeY8miOP+6F0w3iO4MumzlZDVsgw98XLWP7WTGHIUbEYEaQrZ78lK4dzIjIhECAhD11iFAJr8Rhv1qpRiz4Wif9wp73ITpwTusfengmOVxKTRdK/dKtpBFL6SDdyMc+spLTzLIK3siutms51XN4C9urhofqpMzjWlW+QdVc2nblSQ1a5kQwRmSmUOIErmqzk5OP3zytJWubk4cmiGAiqAZEanpMFsFsEwMAECEZiAiaoqI7DAfdhjPumW1WiI7P7N3XRcBgM/P23YDADc3q7OzNSJmVjEzZmaGdts55wCIARIYMzun2y7m4AgRXSz8t7/99pe+9MXZ/RNZx8yrvq5IRVtSlCw4EC2l2Elq15t2G7uuizEN+Cbvq36JPPDbiM5sSeTVQCZiot6MHck9P0PhBzCzDJMSzSNxjIxXVhgJ+pCAjgrl8mFPpR2SX25zdOUfHB7oqXIwXMp/Rz7cp9fjCzU4YICJZjpqzyEiImlhNu2RCnPXxSw4zCAjIskKbX7v5MErL73yyVcfgegv/vyv/+jv/5rS5vvv/8aP/ejvv7lp33zz6vzBq1XlTlGdzoi6q+fvXm9Tc6+O6y0SgvWbqBlkaGAgJmIghQVzBCbl1KxYHU7Qp6pIe5GuRxFalhI+UBAGjZ9OB7C3CBhdhWbmmGPbhhAQMau07XadN2VExDk6OZtXXWD2beyyYx8RY4w5HLJt281m03Xdzc1Ns5gT8ma7nd2b55W0bluqPAAsl+t5s0ADjQkNmKCqKlZcrbZAaAjOuS7F4GoAIHKZm1Q1qagqO0wp5R7NjIiW642IuirM500GRTLNGrRpGkk6W3giYnbtNubWvPfsMpBGP7PliWeF6gbDIoTwO7/zOz/5kz+pqpeXlwDQdhs13Ww2FxcXGfje+3EfbXSAqSpl3BogYHZZEhJosR88SGUc4pAniDYFZR1nOrpw83qjR2/2HQ5LPsVeyORfR71VMtG4nD1kt7EmFQeTsoAd/8zPOc4OEdu2xRzzCJBlaekrLgXXOE1VBRiOE4jm4KsegEnEIFRO8oagI1BznjzvjiPbICdHrX9ozmYdb2YGMp4SxH038kj5o7qeiE3cX96UECj9W2WxwrXZBwFkh3OeL0A+WwEABqZmCQDQWkmg2AGst5s1+wDEqI9PT9ak737wzJ0GY4pq2iZWcFXVgoApml1fXswWJ9ebm+E40Dj6PXtkqiRwZ2sPdh8Z2J4YLfckEFElAwk3bUsAjsO6XWcvNIKNMQIiggRJIsCgAIaoLOpLVde4XF5+9NGTBw/vee9VRTVtNi0zV1WVophZjHGzaa+urm5uViJiigDj6SY2056DFMEIgQHEDG2IEB615kCXU7PoUCmW+DuKWhj4cALqEuVlF+OvJXUeUYR4RLKP2vdoX4e9HNZ8wXJ0vocvD5U0wB6rmPX+ZzNVAQDI8UEAkGPRMa1rODtr6p/5v/yFP/8//RfuLcLq8oknfP+DD/6f/9f/8xc+/7/81Gsv/ebXv3/v5NGs4eDdpq28c6KQxNabyIaqhsNeOWqmxkMz+cVnvbeUOYr0El82SpCPb7k/JnUrbAu4ZZ0BCI44n5UPngkgdREIY4wfffTR/XsvbTabk7NPfvKTr333u982hEePHsV4+tprr43yPaXknHMuqCo6NrPNeuu9z8kZ5vM5ejOmCnC2ODHJK0u5urqaz+dE0MW4bNc37TLzRh0aRkZCJjBEtlFWYM6P4fvFbn9mRkQYKYQ6T4cZVXvDq0spa9YYYwgBBte0Yz/K01Eb5TKbzfKh3qqqvv/97//kT/7kl7/85b/za1/P4sXM2ra9urqKMWY3/oAX2kmzAbwvSA9wIPrHB0mmlEYxmEUYIgbnsSz5QB4CoJbSoBQXpRDA/WQvk2HfMfKy5fKrMgVHXurkdg5X2IjAzGrSi1BCNCQkMwU1ZtY+INSC92TIDm0I0h6GR6Nn7i6ZgwpDtNRRLjsK8/Gl2d63t8Fh5LhxEVzw4FS4jkod8nYFgpgRMjkGQ0XXqW3atmI/r2rq0im71xb3vq/rTdsBcSCqnEspGaCrwtPvfT9u2lQ3QuBKbi9HMJn2qI9HYtXhjHaBzVtgBJxUlsvlvGnq4G1rowNtNPpU1RGnlHL0S84tRZy3LnqfErM/OQnPnj27uV55NiKK0VwI3nsw7Nrl8+eXOcCk61J2RCPCGOljQ3Yb0711ZKnqAMBMmSlUrmsTohuttqOqd3imSSqWo3AoX040621lHOdUBx/s8N7m49pv6mMUJNxC8XcPL5fbPrlTVWeOVQB2znWph2HeQ1qtVszcSXLED+6dB0cXF09effXVJBEkLU5mGNzy5no+q2eh8o5St1HcIM3W65vLy8sYI9NMokaTxjsTNRDEfMw2u5GyvaKTUX0sUg4nNTEyjuArV7s1DcBey0Wzez9laZi37pqmyf6qtm1zngBm3m7XSdl5r5ZibK+uLtv26ee/8BlmvlktY0yqimQppTEgNgvcHPAvYM+fXWxgmw0gRLx//77XGThed5GcJ0FntF6vzSSpIWFC3VoXQbliMWEAYk9oyOTIDASp32Xcxs7MmqZB1y83x/1pX1fkuGtbTyQisZMqNJq2VVU751PsyLsUdb1eT9ykWf+W8koVmqZ5//13v/KVr/zhP/yHr6+vnz9/7oi7bUtEXZueP39e1/XgrivDXBRA4UgI4ceUYW13zK1lBEN0iA6R1dGAhqjvXf3s98CdU2S3HQsyrrgBFam3wzzlrXQbz/hlcgOwvHF7OJ5DGV6Of2T8bCtkUVmug6lPkTT8iQQEaEpIeaQMRqaEGILDTskAVJDGDWnVPjx5T2Hks0cFwPf5sXjY++ro7jgMboO9H/dOH5WCd09nl/7F4vPhlASW40SAqKJgm9Y6M3LVmrqa3AkgErHCeWg+cfbw+fX7a4hC7Im6dYs+oPMpxXffffvRo4fX222Y1Q4OZC4iosFhCA8MhpIVpX9Pu5QapXvBzIB6az3GlCpFJu/9drtt29YxjU7IEfGSbBQ0AMAppdTnLNxutyHUH330dLlcPnp8r9vmrJMhxvTRR0/ffffd6+slIlZVZWZ5dZu1b6ZGzAeTQA1kl1DQ+i3t8jwoIoYQujaZCQ4T2UEpb8kPKQMzAA5hdXcZW5u4Io/WKf/MxHBb5YPB7ALEytlNPrxNl8BthL5nr/TAnNS5pQudrPDGh5T6rAVEUNcBAC4uLtq2bQ3dzG9iWpzfW3cSkX/4R37kvadPX3n94T/wB34cw0wiLbfta3VlapV3to31ojo/qeJmTTgzYASVZJjjn01y0tN+YIowXZF/zNbvhG/vAObABVOIHULp8Kuj9DDyBTPnc8AAkHeOQM2SxBiBCHOiCdY+dyxADrqpqgodry5XH330UV3Nzs7Ozs7O5vM5kQshKMLzJ09yJofVanV2diYiKanDPpxeRBi9im7WN11qQ90I6Hq7TaQcmByTUCA25HzIlQiVHDMTubzqErOqagYLGLddZGZH2YDOme8ogzY0ddpaUgGAnOkCABC5qup8PiKDohREiJh911dXV5/73A/+9E//tKq8/fbb19fX0B/ZoKx9MxRHaFux4WrDJsXdpZTXZqOfbA9pOKjWfUHRr+/LPaDepziQZc7RcdhjSRg4nicu3hRElXUYly8nn49wGxfEo4nQr1KGsy17egERjbKPngwQNKchATCJKW+fOOcq522zVlVFzdHU2dyx4UiSqmbNXqYoGWdaenrgTudcqcrNbMjNhVOhfXsLJcv3b2zP8sXhnETWwaZKBooQnNO8FFZIAKt2q9tORFz9cKYKwg1i3WrNsJGUAKqmXm+7um6ePn1uZjmLM6ruKeBSsuj+jveIvIkHY0IHJWjySzVERzgk7sgcfnOzOj8/n89OsqcrR0CUvmsARTAi4N7jDabg2DPT86fXm81WxACgqqqLi6vvfve77733Xs7toqpg5Difbx79/gAACLwLi0PNBIqIBiBiRLv5mhkRIJloBOw320a67EW2YfYaTeAzioOj5HL4ZvyqbORonR1FHCsFJU4XZEU7Rz+9ayH74gPb/Wm4o3m0g8r9sX0AQBxNUXHOg0JKKSVJKYUAV1dXqpp4HpW5ab76B37/vZdfWyv+gT/4k7/5m99zp/yP/GP/+Gpz8/Vvfmt2erI4P5GlkoeX56ypvZ6H64un9Oo9BPPEKpHADERg4p76GH8A7OPoBbXvPtDUeoxk1t4T0uW/1vNgyYlTlBX0SVniqCqqeeeSKAGq9LuYOeipaRoRSSqnp6di2G67s7OzbInO5/Ocf4qZU0r1fPbo4ePvfOc7mSlijHkflzyJal3Ptl0rInW9SN4DmqKtus1HN88jClXeTDzmgEpHDISATKyAyAoAxEQOgHKPAJDjgEIIla8dh8y2+XgoswN0NSCTFwVVUIUxczJ7R8hUHMIZOY6dv7y8fP2NT/7UT/2Uc+6dd97JWXq6rqvr2Ztvvrlerdi5lLSqXLnyKyOe9g+nHKeB29BtRgAIk8QA1q/MeslW5NvPLfT6ifaD8obYwGPDgXHiA41NVWyuAog4qGEsChT6CRF3ef6HlkfHZK6/W46bEXBWwAzGgEaIYIBEDAhECN4xGogIgnKRSX7sbrSddjxyi694wnF3F8QjknGQSAXcwI5ss97eRU6bZ9ivo/NoyUCSJDAFQ0TxIECdQQvapVj7ugJ36twrszORzUfaqndRzFehbdvVahVVTFJKXWWNywH4tm/pHLFKdr/mN/37cRtmPF0ABUUSkaplN5GYdl3nGavgus12s9mcnZ5k5s84yUTA7AF0yA8NiP3uqST13jNxXbubm5ubm3lT8eXl5S/+0q/XdX16ck5EKgBAOeR92HPBnDM2o8HMALU0u4gop8BlRrP+MICqIBIzxrg7Rd4/aJ+t5ZAhJ+VQPx19f/h5ydXHlNxxQhlsveNXIAzNHmnztnIb3Y9cUUBgX0JByTlH9fd4VGnIoZMDRpiIyDl0zs3ns2fPni2Xy+r+Irar07PqP/qZf+/t974Yk4o21zebX/67/+Ubn3p5s9ls1upD8yu/8jcRsZ7Nnd54hG69bG8uSUUNwAxEjWDwByJkIyEnizjYAL4bp7CfBLSUEYdCcALDjwX7HeaOiBDBaPPlGIW6aTar5fPnzwU1JnPOxYT5coLFYuGcOzk5yTUfPXqUFLzzT568LyJNPX/27NlsNnv55ZdtyOveu7VVQwjMHhH7nJGSTBWMGLHdbq6uLg0AHNwsV5eba62IHWkCj+SBzAcGY4dEAApGKNIfJNWcedh63RljXMxPK3a9ywoRgFJScgzAlWvm83keWx2alLTrkvfk2A8r4OxotVGJinQPHz788R/7CSK6urparVYfffTUkqWkz58/f/LkSVVValjXHgcHOOwvlV4QTbfhbrTqxn2rocVdHQeYw3lKgWBmKnvyIafYMjPivUGOBDaO/3DA+1S3E3Rl5VEpwr5uHoE5tjl+OESomRJkQxEwZ8tHylIa0UBCCGqJJBGgp/7w+qHVMrzpp1uOeaKbByV6XEnj/lf9v3rrEqgPxdxfpUwMqcNeco6QvLDI6zZHCJpaSaq67lokD0CrFC+vl4yOkSoXHlWnF6uOokJF6Mihw06++Y3fruaVmCLiV7/05f4mDUa0iXVPU5DtTcN2yc8G0Q8TIE5Ms+xKrkMgdFVV3dzcnJ+e5V7Mdhc8FJC1IfxKAMjXIcdN1PWsbSMYSbLvfe8tImZ2VJxaVtUhO1uOvRqVUz8wIgNA09EcQ1Xps4Wo5cw4ORn9mBd+QhylsTJM9lb8lR8eAgfuFLslEdzd/rHuxlXUXYvXSe9He3lxI+OFhdf0fLBznM/AqPaZ4q+uLlJKs3YTqurhif2zf+q/V83r5aar63MACBW27cb7ut3K4vQEIHWdsq8tXc9mM4updowaISX2PBhfuTs1RNCszWB6EP7j5O/E/oB9B9Jt5tQdbR42CAUkJx3hsF+Tt0Jns9mbb765XF0DgSDHtvX1XBFSyokFYwghk/Gmax0HcpyzT5jZZrMZjOZUzZpnz5699+775+fnq9Vqs9mcnJzl/MAUPANvUwIDIt6s1l3XzU5mrnKtdp1FMJ8kBSRU8C4IM8Nwls8AgVRVkuYbMHIAdt4FyMFQDrhqahlyM6aUENj7SqXzvjLD8eqCEMJsVpfaYiQeGy4b+PEf/1Hv/bOnz5PE9957b7lcAlDctu+8805d15LMBTfKmd+teXQUd7f8OdG//ZEexD5pz5j4D4a1rBZpdKFXe2KKWsjkUm1PtFo5hr1RoWSf9lh54liGwg1eNlU+lCHZ1isxBQMANUIk0rwnzSqS6qqCXv+hmtEx4QO3CLQ8+PKD3Rz3/QovIg/xwDLeNbgPJTuovzeYYUAKMG5dSBQDdc6JqYJ1JqoCLazAZlU985Vv+TRUJxAqoBVCMmWw5dV1U7vmZNGKXrx38alPvuHgFsordcwOGUOyMETMWeNHzMVODkevCjnqxVchdtvcTD7/E2MXY6yb4cIT5zAfAxg0hyOSPotKr5WZvXPUqeTQza5bi0hdz4gmygyzY23wMmXJxcNVBIM/EBVgyPYyhGVZsXQbT6nfCp//hrpnr5RhnP9tykBYuzcDOo4YQ5M6R998rOYoShHmMCQAGcaDhRdaRiv7sNMcTwPQX+IWQlitUtd1Z6Fer5+fhub0jYfPLy/nJ43zLJaQ4qn3KmDzoHKNpLV3kkwDWdw6wCYE7SKqaRpOIvbOHoDhkjEwArx1JXFbOaosp5ryFoD/Nyi5BSIiJBhkdLYvEfH89PTBgwfk6Onltarm/BJEtNmszCyfOanr+sOnH15evH9+73HtcbFYIOInzj4x5PSoZDhhnN2PdV2v1+t8cVlKiRwjqpm2bVsxP3r06EL1nffe/ujJk8126zw5QnasKaEaGBoTEzvHVuRPUATvfeX6/cukkp3kOJxFtv7ETkJE55xSXn+zcy77KnI++XEDGBHHBLEAIBJfe+21+/cfrtdL7/1b3//ezc3NbDa7eH79rW99y3u/3XSLxWLTbvNx4QnfFfja/XkUdwdqe0yBsBeGqYV9P9DDuD7OfewaLNWODasIRNxJ/IMBTCgQCpU86uD+TVb5Y0ahYlTjmzENy/hhGR47wqqM9FTLM0EwI8QcPatJfOMRkYnytYY25LaEQdMTUS+TR8gUi9cJUnYL3P33h4bCHkyKw77Tj/uZ4JigDfpV2XQ3elKy71rVcmJv6K+FRVOLKsGz8559yIfuVDW2XfBN46uG6hsTJVTVi2fPvQudpG1Mjx+fzuvGKY2iiYZgPCTi0uDaxcSraSFomBmBh7RnNi4is5LDfPDLkBBTF5k8mK02W/an3rm2Ta1Axd55U2g1JtU0xiLkiRkAMzd1SCmJdPm4cEq63W6///13r6+vL69iVVVmgLgLIiihqTpkIwO13ozivPwyRQPF4USHCiB4IhIBIq+KGSAqgLRLUkNsRJjdmjBOEwHRZezRflQtwAiKPeukqIOIexw1uvRHJtkL1BpSJ4781reTgyGpJEczM8r+9tyjIo0DyPtPRbqM/EYHAdS/298yMdqxzWCy5AmOB1t78pBxzLtNTQTAbddrCFIUk2wtIZIqgCIjq0FwFaNDgOvr6/a1R62tzQSjnp6eqoBqvLy5RkRy3HWd976LknM2+ao7PWky9rvt2lWVQBQAJBxDXE0NwAgN++iYPY/xyLSjn3OCTeohnP1YAxPuxOmeK2wPQ4WhXaJ7ovVz3h0ENsvGIgMaASKgGLAREZr2LNluutNQzVnBmQdCC0Shs613LioK6cX1h8mWqmeQ6Fu/9Z17955z7Srvuu32q1/9oYvnH7399tv379//0g995fR0kVICpKTivXeORLskVs0aSpXvfAVXVH20Wn+gjr/15O11wqvNsxC6GkS6YEbKcI1L1plzXoGNfHCMwJCiM4FETX2CiNfR4aWmp8+vr9Nift/a6Cgw+U5iAmxFqaqU2LFjF6KYAiu5KBTmZ0yEzGb9FWjeEaK13YZMX3nl1YeP7l9dP0+qz549u7y+Ruab6+v3PvioS+rZVU0dJbF3bcxpv5TJq6qqqKHjCvMuGCaEQQ1Dzuo1JDvs8UkDbec0F5NzpZm3+uO/NuzyjvLQmEu6GJmv6HQMOjEzFe0Zdhev2vdBo5jNRnYftoN78sfMsngR7fl5Qn6gRyIoR8kzUfZm5jXGXgiREgsCADgBVnHEV5tN9fhR5Xy32TpHkUg15YxaiEjDNZSIyOx2XRRXOkLRb/5v/zxo0pH98kDVdhkaUBH6o60AoKPgykeKc5YuHDrJP/V3GZY3CWG/2s7olt5gt2E1BjpsChAQCDB4BZWoqcbk3WobP7y+qkJ4cDJ30L3EuFyDbe26DteA335ygVx5RWhXb3zhB8KZuQHcezIIRsfIXmDU3ipztCtGnTfu3fYHl4eYOhxjp1UBKMaoSt77y+urugl+iD7IiMmKpFRdmYKzG8Q5F0Louu7p06fjAUHcWZcvVHYqp6C5vE3lnEPCkWeq0HTbiIVUncDk0Gg6apGVkn18Mwr6yTlUHAzP2yyyo532PCMAOOWZfX1vk68mL/PNB4efFBp3L4b5aPtl72VrI2CzfCkXAQhEhCJqhGNCgJzyafyTmbMPdrFYAAA5Pjk5SSktmLsuLZfL+XyePaiOeLFYtG3rBl/IHdM/BH6B2cOA7ReltMPGyzKB6u7hzvqjQB9hx94pZj8HbjabRTWr61osqapzoamrtm1F5OTk5Kd+6qdijM+vnnddV9+/r6p5k9i5QEQ/8aM/9ld/7q9fXl1XVZU5sWs7QCOqurZtqrrt0mp5dXHxjGkGRO+/976qOnQ5pDl2gog+oMTWPAP0m5QuEPZit8vc2q7bd6+u8/n+qqq7KDAYnYOJQ+xcN5zTpSGlYvaLiKJYRMQ+XTxo0zSn89njx4+TxK7rxOyjjz7K0Z3vf/jBk2dPfRUyNznnJKbF6UkOjQ4BKRuomkSimTGzGIQQ8kUFoxIdV35jXrydvJ74Ng9oHvbZv0zjM2lhbOcolU7I6Sjz2uA+LIdU9ntYmDzsCfYj4nHSr1l/+etYb1SToMpIo4df1VyZ2Kcwba0o5QqYDsCCt0djlVi4m90m5RDI4/vDmmWdyaisWAhlNxI417axbVtZNDF1wbmXz+/frG8uJL7z5ElM3byZicRO0qc+9amTZu5G0XQ4GihQeBu5lDWzmlRN4w0NOfp8lGIGYGYiggiV5+VymR7cc3UwM/aOUxaUNhnASP35spDsKMtR01VVldu05SA/Hh+o+QbLUZX2oVvqgCylBKMlqDQuhA55Y0ocxblh21spHocw9MT8MY7o0RaZTK180/fV21L9G7Xd8syKCNve6ZSrFnlZR0adoL5PhQM2upTLIcHgJjl6qqrkE3ZccvhYM3/OTDkdEjM2tVsul8OGRRQRlhQ7UVVFCCGkLpLjlFIg2qyXb37nu4jYxfVms3n88NFXvvIV7z0CxBgd8e/CQNsbs5THOW6ps6dHp3WGmrRPD4fScyijiwJKH2QJKCLK6xkRaRZzZIqivqpcFYwwJRFT7/3Nzc3pfCZiMYqqzucNeXeiJ6vValbVNzerT7z62quvfsLMTHE2W3zitdcvLr+RL6ECgHyPrJEQUdQ2pY4cKUhq265LT549nc0rRIyChM6AzUAF1TonnofgTgBDE5HI7GezWUrJETnnRKJjTClh3WUDazz64pxj5/oTaRkUogKQE1RVVYXgEVFVwMTMHHNo6sub6xC89/69995r2zYleXZx8fbb75hyXdfZr25mIbj18uZkPhMRANu22+Vync24DN5ZPV+tbnISR0Rk50UE8kICBn9uj469jFRjGYc9ckepgEtsTuRqyU2HCn6PRO7YQP04sTdp+WgQ4sc0sauW/2+q/YIhJfPEIoL97PbmOE6ZittuYF+I3VEOdVD5VVbktw3+tveHNxTuhoEAOl01TcZcfqKqbdu2M3aO265bpxR8CM6fJjuh4MjWcSNmEIXZzOGDx48Y0JUTyGUCmpEsxoj/yTTHvxHRTLIas2HrPrt8s7uPiJB3YijfV+p978dzznVdVwqmkkydc4iSPxyze9+BtjsIdzcHtCzgiKjrurx/1u85keVOVTeIaEpQeHfNdhedTruA3Zgnv45/jvMqEEmTOwAO96gm9Fc2cnQ8++1PwTJaqSWKJxpl/DefK9CewHfXj+TSnw9htt662uXnO0REl/oAOcoOtIH8cxSo91Uy7drkvX/w4MHy+iaHzmqR7qAPF8ioV6t8iCkBwKd/4I0stNEgC1AeRnW40T4xm47g8dgeGxwTuIfNHv+paIoOJM4OSlM3Sc6SvcuF2yNLFQBijK++/gkf9KOLZ7DumL33Pllyzm03cnN5vT49cc6Rd+T1ankzm81OT89PTs4CU4ptzgZM5LabzV/+y3+5jSmEOmtrM6yquuu6lDpy3G1adAoJVOHZ88vvvPl9RGQXzJIBEPkczaYqxCASY1TnAImYsWnq89NFG0VVHYV8gnmzWrZti0ghBGayvMo0MkPvKnKu9p6ZQVStlyd9ZmOzUAVmbtutab4FOa+wU0oRAJbL5fX1zdXN9d/5lV/13oPzffJ5s5S64Jv1arlaLu/fP7+5uZnP55/9PV/5zne+4z3HKPnqpLqe5b1wZhdjQkSRMd39zlbeccc+O5f0M0rO8WW51i+/LZuaUNRRMjv86ahKhluod3woA573Ce84GetgBxqWd4aBmWlMqFD7YJ0QoiI4R6MpMxnennQqrqKCCUseM0TKKZTuSdxvHw8+NNslzy8VzQRKuzcHqXMOJzJWzuV6u3YULjcbXN/cq84acSfKD1wTUvvmm2/ev//Qd3q1uvriV7+wOJ2n2O3dgGFFutExJ/AomsctDYBSjU0cdFO3ZF+pPwECqhZjZCZAJKLlalVVnpljbK0/VkXlhzCQqQ6ZWcrBHIXFBF4Ax0F2KH/zgSgtAvdHX7qZgaKYGefLy/Qo4QIAFsEXEwwd1cdGaZ+ddEIZk/qwTytj5TJbdVmNbPohFJx/qIAtX6d+4Iwq6QQActSqITLQqIBhWEAfXs6xG1iWtuP2FQ20p/0Ik0FKyswnJ/P+grwB3dJFMSV0OZeTmkWV7DD07F59+ZUQwmp1U+6ebDYbM8sJaSegG58PeXsPQZajUUb9N61WgvRu3Tx+qEPsyWF3ZX0s9LSZGQiCG+UmAKSUyLuzh4uO7Hp9sd5uwuwEkFJK3oemaXISjHyI1jknqkzkkNq2rUJQVQL03pvicrm6WW1eeumlq8ubfBwsxsjMqmAgQAYGHz199uFHz957++mzp8/Pzl9yzhkpYb7xDfKoFDoUZGcGAqBMwIzMPCO/WW7ynjoR1fUsm4A80B4Nxy6qqkIO5CgzGgM6T6paVYGZc+Ck995MVYwdIlqMMXukr6+vN217vbz51b/7a+PgQ6iyk7mu/JMnH33h85/92te+9jd+7q8Fx1/96lfPTs+/+c2/X1XNYt60XVosFuv1OoSgqobALmT9EWNEMgNA2+0ZYeE5LBn8UCVP9GspHkclfUg/I1fuqasDorrjzUTEvWA5ZIq9dmh3jSANmeSMUEQYoPaB8k2XBg4Jh6MsAJDPOpeTLXnwkP7v+HNXuZBUhRG7wwzuryUmO0iZDe/sd1wX9UddBsNABsTuQswQSQ03KaEKromIZjWeVYsFufb5NXaGkjO/us9/9nOni5P24sq1bVt2POJ79AjtqzrUHMx0i5MkLzgGjsrb+5iVygB6i5K8OOc9M67X63vnp6Gu2naThbgiqO5mfQiO0sNzWKd8eYtEy00PYfdDLFy+qS2pxE0anOfAzFQoJwBQzddj3b5y0h23lBVKBisNC6Pdm6HscmvfXUoitj79BRYvrfh3WsYZTQCV94BJpygeDbJy5IiA2reQUZ+rjSGXh3CYGgGD3mfvxsFUtZdk2SzbbDb5+tg8KUYih6q9uxvVUtvlXfvl1TUzr7art956K6X06NGj1197DQDcsV3tCZ2Uem4y4BIpk5x2vSlyi949pMDJ9K2E52G1/XdqCaAPDylft13nqvDyK6/4cP/k5CTftB2TSpL1cuU/4Z1zGpOIOF+ZmSVB7xFgu91eX17MZrP53EJddQkANMYoKS3Ozohovd6261Uzh+ChCgGNHdfLm+3TJ5f37993viI28gERLWdENEG0qBYGH6NIJJo7ZlVhxhhbAFqtblIXZ7NZ3pv3zjNz5koRAcAQakNSGGW0AgAaOOcco3cNogXnCbVtEzMyo5nmfCNdih988ME3fuM3va/yKtaH2szYuSw2QOF/8i/9jx8/vH9z8XSxWLCv6rr+gz/x43/9r//XdV2fnsy3263jQWugiykCQDLtUy8CACnuDqeo2XHiKfm3fJ7EG5d6Y3weWXgkzvFhJJ5Dehtp5zaSG8uLKONDhdcPuKyDAJa3GJWJNcmicjU50w0O03HDCjgT/KT9w39vW3GWkIEDD9ZEBRyVeLfOFBFv0SYAvalRNl5Wm8jzfn3oXQuwza6+JBG7pgn32L96MlfE5fpmcTJ/7ZWXtusVOHDjHirulwJS/f9uHeLBIMqNwAyNok0b4xqEKXWxS3FGDXmHWizjdgtR2JubGeIegQ6YOAKdPSjftghGzWeQENGwDwHg4abM7OgeW+jbNwKQwbyYei2Qj6xpEJFsF22Ixa9U1CmGemsEVtndAIfpWnY3OzM7ym+IiHh4RA8HA2Wc2lGCK+YLcIsX7u5/dw/9P0OCfrAhQ68y82q1atv2/Pwciqg0iclUVIzJ5WsDRqrLKY4/+clPEtGsrkfX36G74jb5crdsQtyTAtO5HJSjOji/V92LFYCpEaBjRt/xV+oztu6NX8BEBJjq+ayqKmZGUuJKGNerzfLmalY3iEYEXbth70xwvV5KSsHxdrtdrVZPnz51oZrNT4IPZhaCG5Jm8PPVattC5VNdhSp0AO69d58sV9398+CcQ69VTSpgygAQo4bAHmdVVTkmU+y6tNls1ut1XmEjMpGtVzeXl5fOufl8Pp8388pxzzIEgMw8n8/FIGlCBtEoEhHBh3CyWCAaYYVos3mz3dp6vXTe+yrE2BHCs2fP3nnn3d/+7d9erVbMPoQakVNKVe1BzTv46IMn//P/2b/yhc//wLtvv3P/3um98/sigux+4sd+dD6f/5X//L9IAghw/9GDfAZ6UTVt2/q6sbZldmoybkNl7TuifkJOJS0dStQR6RPJProYx/qlv/ooRR3TDVMyPuz6kBoPSfTW9m33vtR/igJgp7M5m5ooMiGapt3tDgpWbs+NczGzvXTQtw/mEIxj78NIdhV039s8UdhHG79jvnd/UjKydN2W2TEEUUZHRNF07sIbJ/c+M7/3reVzI3v11Ze92axulqtrN/EWjpNMKcGeq3kXB7/r3giGYyflYa9RAZc6UlWxSPc4LqxzlGZOf5E2icjZ4OSc2EP5vGBKKaVdR3ef3xp188F7G52KuUjvSchaqY/czsLdeiWBuDNsyUCOIhX37bKJjD7Kh8eoqt81L789+jwq4CNsZgQIBjvXdGlFjqx+KAgAcp7NPR4bU3znMmZUpuKUBRxbuE8gMOKrJwzAfLaciATME+WADiQLITx9+nS1Wo2kwsP9pvkWATMDsOAZh6tPzYSZz87OYLhZ0rMbDb7buHdv4rd4uj5W105+OorWiSSdsO6oa8sW+jrYe3p7OOMOZcRIzOvtarPxzrl5M2tllZI6HzTQW29+jwnAjBEVCVREzDunEtUSaLp49vTDp0/e+t7bP/wP/v4vfPHLzlF27zNj3KZPfepTTHR9fS2pNYGrZ9fvvvPk1VfeqKs5gkOvzmFUcYGZKgCrG5+kcey9d0SwXK/ef//DZ88uiIjJf+pTn26a+YMH905O5tfX1ym1MbIBGgiZkDlkCr72vlKgNm6jtG1nqhqcn81m2tQMJgLk3GzexNh2XccOnfB6vW7b9p133vnt3/6dm5sb771zIR9qYIdmFrttUy/A4GTeMGEV+OxkETz5WSNqi9Ozn/7D//APfenLzy6ev//eBz/7sz97fn5fVW9urmbzk67det9fyjJw2s5veYft1TNIQXhWWKsTqhiZcWy2FCOHlScqsGyv5O7x7dGDAAC7Xc47pGhZBMyUoGf/3WnaqBIMah8oqYoSEZCpabkBXM500uyhB2giWo9ykxX7viXvlI3s9YJ7iBvbVwB3zKzJ9Y9CuxQIJX49h9V20zSVqq6ub9b3qnkTJLYvh8Urrnlf4Tq2n/nMp5uqXq9uANFNsD5IZBiuMdhpUBjShI57sTpkmMsybrILiIiikdDlmsSQLdy6rmZ1YyDOOTJYLpf3z88dh5TWHLylnmrNdhd9WHGursTohExLiVaA8gi+i8QRNHrUmf3gKKbxZq5ds7TzGQLskUzZaTmGQ4k8CRUeXbWjfTMoEhsnOBn33W+OUS2WGncc2EQlH7ZGB77xfaj2upYAy5ke8thkwOUhrnEMipAFnJkR9ZlE224TKpdD7UbgjL2UXhYzG3MFm+V7HSylxEjjGmKiaMehjtfKjuMp8XIoOvcZb4zPl8kn48Bu0+hH3yOiakLniy72UCYaY8S6rtfbdkS3pM4754nvnZ1vW5nXjaoHCwyRqDk9Pe267vn1M2S3Xq83q3Xbtpv18urqan1zfX5+fv/+/foLn3v+/GmMLQCIxHGXh5k/+donVqubpx+9f3X1/m/8xt8/O324mJ9V4XTdbnNiEF85FYfgzs7OiRD4gUhkRwA6axZMftuuzayuZjF2MfrFYoG4aGZhu11fX1/HiAAz733btrPF/ORkYVYZ4bbzzy9bREQytcREJ01dVZWIpZQePLi3Xi9Xq9Xp2eLi4mKz2Xz44Qe/+Zu/uVyuYNB53jMREvkQHGpczBoC+Obf/8ZPfu1HGe2N119DRERG9s4HF6qH98/F0Azb7eav/rW/+YnXXt50sYsCJp4qEWGHXdd5cl3sqhA0e+xu2SraCfdCahUCdupKseJehJFNxugz2Jdvt8kHMyNk00EUJ4HdftBhSg8AOH5sAY5Jkvync0HNpN/5zfdXqwKGUK2fXZ+9vKjZr0Tz4skzxxi99zkhDOxLhrJxHHVNsXiDQgEdjrwHxbHxIyIC2v6945gXgXbQQgHw3ee36ePhIV/wXL7J44wx1k0wkXUnN0IX1VIcnbBbAH/5ldff315bCPcfnHexRQREdCVcEBH6wAKbSK4REKMKMd2L1DWzMbVN74VGRcQkXQ9Ngaqq5vO5cywigJrHLKKr1Wo+n/e5eQugmOmBbZTLXj7nstiRVemeIYmFthvRk+9dKUDcX9ozvimF74BCHhPzHv476XFs4ag+m4x/eJyeDz4sR1vY9YIKiIdXY96m2l+8i72+dNraoVC4u5EcV1nAZ3dQ0jm3XC7zUeAx8A0RD6dQGEZ7+MIxdhpp7GsyzsN2Jjw/qXCE2wtJesi9Y5tla7f1TkTYb1eO7gQdj0LVdY3AQ4ACiuRUcagiRHR6ehpaQRKRlqmZzajr2vfefqdZzC8uLp4/v3z6/Nnl84vLi2fvvfde6hIAfOlLn/uBH/iBR3UlSiEEk+ScS6mrQ1BL333z27JdLxazs7P617/+3baNr776CbRaDOtqASTkmNkxNbWvnXOqaZsqR957JlQD8T4458Ds7PQUzFJsTetmVp+ePFJL772H3hQkieRbEV3la8TGiKsmXN1cACgjee9DcEREDIgUQp1ThWy32/fff3+5vlkul9/85jevrq6bptlutwAAJsxMSKrdO29/yAgXTy9/5B/80h/+qX9YU0dEs6ZCRGIfQs2+QiZTEIOmaf7YH/npn/trf/PJRx+K2b3ze/PZ6Xa7VRATZIQY27qquq7znu3YquuQwm1/x+E2qru7jILxUM4c1ilvUyh7v63cNuyx2ZGGy9tnaWA2wJw/BjxCXG00pURKQIwunxrtL7Ya+FFEypgSAFDoM4/g0PUdorJkqANpvwcNLNxyfSN6vPLkPNLHCsnbhsTOI4BFQXbqaK3Rp67qukWoH81PHs/m1YP798/PkUklSkw5tdsuEWh51ggOEDNC0Mzy1mkJqVKXq6X+kABSpoaqqZum8cxgoGbOsZkRg5qtVuvFYpHPJ4hGLIzEEo7lMLJuxt3Cbrpcux2C/QlFM1Ppta+IEPVOCMSdhD2uPHbXYjLuZ70oP5x+REc8Ckepp5g4f6wOBoDD+KCPLXcqxV7xK5YHr/Y/p35lfdjgHdLhaBnvA9wHnSG6HOyajwKPzhUcor1Gis1f7bTyLnOr2TCBMRSc8m0aBb6I9nz4owYtt1om7uuRILXonfaSrxWwKkrZ/iSZ/gjPThIwmaEBUH+ZrjrmfNW8iKSo+U6hfCFYt25FMiVD08wNOqPk0RPWhN5uLv/e13/j7Ozk4uJqudqsVivTVFXVZz/9KedZkzx++Ojy6iI+1+ub1euvv+49b9uN956Zl8vlW2+9dfXko7px3ss777x1//794Jsk3rkayRkZe+e4cjzzrnbMItHNGjNFNJNWNNXO1XXtuHcPtG17eXkZY/Pw0f3FYvHo0QNrN8wwOHiZyFXVDB1vWk3SxRhFUuXDvXv3PDETqCp75wNrSh9++GHdhKfPnn3wwQfL1XVd16pahyoL/cCcUry6uPizf/qP31xdr5eX//Q/9ace3DtfLpfzWR27WVZLVeXrumbv8/0T683285/99L/9b/6rv/Qrf+fb3/72r/zqbxKCITx8cJ+IrldLxyQSmfs1EBxwwYSjD82v0lyDA0lVyLQpnx5V3mUvh88lBcKxchurjsOeSKpeQSBkgcPYY87M2EHjg3aRwBQBqU/mrweL2sO+yu5KljkUQWUjeSA7iNleCzBYHoe63PZ9CbfJQ9s/2jSB20RN9L14RhVNAuxak+u4rbvKgnRd11T+wXx++uhhFdw6dY7IkXMT7VtelXWIp17zag5c2qnq0c0y1h+D/QDMOa6qajab5Y3eESiWJF8csl6vRdS5PoB2zP1tfcz3bV674xbQbap3NAazPM0nB7NpVkjbKR0fZyoAyqMyKnO/3VYODYhigjh5CXvYpcMhHW1zOL9kwwDHWrcq5tt4ctDlH2M1D2OYAv+oWPnYMsJ5+ETJwOHOvTyiIz+Ml2gBgJXJT2QHsN5JbgAAbdv2piHujWqXaBB7M7mUOGUUTOmpzupWFMZPbL+UICpfjo3nZAX7kAQAqBwXCrjnTSZKKS0WixgjAr/2+idvVmvovYvoXaXWqdh8ccpBFBKgEnqmaj5vRLddl4Kv792bnZycSdqqauVYJeVj9++8846KrbZt3jRtQuWZVbVt22fPnsm2ChWmuHKe7p0/XK9kMTtnXxsCEpEPCAHMAwYkZsy3HQiCiDClrUGqgmuq0MxqIrq6umjbNqU+dO7hw8ftzbMubkQUEVNKW+2ck6aedWmTAZtSQrST2ZwQU+q8Z++9C66uw3J1/fx5ev/D90Skrmb5pJD3AURPT+br9frJk+f/4r/4Z/+xP/rHzKTdbpoqrDfLR/fvxa67d+9ef9sEWkqdmRgLM8+aoKo//A/83q9+5Ycuri5Xy/U3vvGNn/0v/9pv/Ma3BODRw/OYRERcFTablsjJXsDpNNinlNGlPDnUwUc5ZZ8jdm8+lo/gFsH1saWsf/TbnoyN+tyQZgSoqAKGABU56sQ5FyEB9S7SpP2uZXaR7k4l7c9UcW+mh0w0+WnCUOOfh+pgfGNmeMupVDiWlOOOMnJ9+QYAokoFgGqiukld1Hi/WSAREIQ6vPbKq/bKA1KIkgg55Cs/RxlRBlKNToxJl8PVBTsH9bgHvKtJRoNeB9WmaZqmweIsr1qCIkRNRUXEe++QE+okjvo2BOzPfIq5QzYo0WCKSUxMB33MZeWJaXMHGsyGW8RxlyligpURVWXLL9h+MbU9Pr/jkxcsR6lwn9U5X3gyqXAI/Mm8Dlv+2GlO6iBml1avaDebzRjqPLHzylYPsYaIPLzM9ExEsL+cLRKY6ISQcIjtKkWGFft5+ch3WWEiFHD/OMquwhTUOxh6yifTyIZACoD+VuzcmgosTs+ykaqqzJ7Za9ttY6pnJ9XMRV13XavCAK6ZVcg1otW1z9kZTavKc9u2jjGl2Lat9z4m4VBVVROjiKiIIHBdze7fe3ja+CRrH6yBJlTz4BugxofGmIAQyCEEU88UmD0AAMYYW4kbICTvGN18Xi1mc++5aZp53VxeXq62q6dPnhO6R48fiHPbVnOqLBHrtmukxoUagAiQcKeuLOe98tx2m5Oz06Zpfv3Xv/7GJz+VIQAA3vuTxWK1unnppUff/Pu/rQZ//l/5l374q182jefn513lLy4uKu+32+1iPo8xOseUL1NzOSMvMBESRBGT9mQ+a6oH+NLjz/3AG1/7iR+/uLzquvSv/mv/GwAgom3b5tNxMGGHAaElEQL0ocmjKii1wqEOvo3Ny2bv0o6Fbsj/Hiq8ox/CPhPdVmfUU2b56hVTBDH1BmhgKpTz5BCpKg5ZVnDwBiOiEUo+Slh0SkSGBrIzec0M9iOZbxs/lgs1AMgrQJjuAU+V8YFA/t2WESMj+3eiQgiEyRRFCFwraSsRPSrYw8cP9MG9SzMPBGqQ89eUevQA1jC+6esIlNq3NO3HdbCZZf+M9362aJwbjmKq9gYTIgMqEYAykQBst9smVMw8JtfOsBmobW8ksEcxVDwf0P3BRBBYkiRNRcoIGg7+TrecS3d02WyvIAq/NwLZgbt4opwmKM+goyLD0dEBD4VKHfwi5SgDj11/bP2S+v//WHDMlblfpgbvYNXVPnhiBkTmMRJ7oPgpCx0+c4G+UaFaIaGgQBMiEvWp8MeArxKJtr98McjngnqHynSm2aU8JHejAr9H28/VomhWwFBKWyLnqOtiCHW+/iTGGIJT1RRVEohiF9X5ysAjkrFDCGgEIN4xIlCfYtkxRFVtmobQ2nZbVRURx5Solaurqy4KEQVfA0CMURWa+XnbkSk555Jy05wb1uwrDKRmSJ6wZqpDqJm9mTG36/VyvVIC57gKDk/ms2ZWBefNpGlmROxvfCdpu+2ePrmseQzPYWaOra5WKyBCpzkTuPMUKica0aByHkBms5mk7vHjx3/8f/AnfuEXfmE+P+m6zrlexL/+6mu/9mu/8Xt+z+f/h3/mzz54cG+7WTa1r4N3bKAnMcamqZGormvvXWZA3hlLhI6a2puZSAemIbiU5JOvvvTaKy+xr/7cP//P/ev/xr/5iU+98eTZJRGJAlqEfQV2lCBHuWH7q+EJC5S6c8IXE516h4KcjKEUMoelFD6TRo42ng2jPulAnsjg+J3NGibqj0bnDByG46UyqpozGEYV6+966kXoXpkYH1b+8kIL+rLaIax0uF1mgpoXafm27so/CS0BgEM28wrMuG63l+tr8ucJBWeniUG6tkIyBCXcKeBxrKNimMxBBfJdrWW1En87OwuRiPJdpIumSSmZKDomopSSqXniJAkRzSCf57m6uql98N4XopCK2IEjTuZ9ENzlyRm/MrOUJN8YNZD73tp3v00t/bdW+EiPUj8CjWFZh6PF2ze2DxUwTqiwLx8flpXbK8GFByvyu6F0UO7q1I7tkUwoe6zZv3wBpT42i4g5XxIR0X5YnEgqYYiI+a7M8u4mAtB9Bdyjr1B4uP9sZtmkG13ck5olX9iQzgURx8srx8n2kdv7i48JZm9DRL5ApmxQVfPWJiHlvrouJxMFMyR0wbvZLERl81VlMxVm8AAJsSVQSdbMAqI5i13cNlUdY5vv+4sxsguG/OZ336pns4cPHnddUoXZbHF+fk7Y3L83V1iJxM0GiWryC/YOPBoA+UDcMAXvmn7XKcX5fO4cEapncwyzOhCaiHjiUAXvKgDYbDYiutm0rk751LuZVVVF4NsONpsthx5L+f4VMzM1IGZmVTGzx48f/8k/+Sf/w7/4H77y6idyhXtnpx98+N5bb7715/75P/3P/FN/crNZffD+eyCxqfy9s5PNZlP7IBIzagiRmfpVRy+RgQjVJDgPgOg5hNBt28XJSdd1qRNXVX/kH/mpX/21v/dXfvbnzh48kISikYpjdYeIPvrnRJVOvi1fTjh3FBGHIuuQx0cZPmlnUm7TaqUpUPa753MqvkspnZ09qJwHXQH3/MjUHxGcaIrd4G8ZWKGAP2bVCwW/YwEKVYUD8PZgx72Rl/L5xb3Qh1I0z9EhrS2xoxABFS3Jqt3UTTg1BRdwVrWgZubVhDESuHHTF4aAdSuChMfJ5Jjn0i9txdYvIuZwc0TMRzKy25mIYux6t7MomAV2NgTp9a5mAyLabDYpaVU5LW6NLYmgnGQJ04kTckJMmWJK26LrOqCdtDW7TbvsqdKxhXFgRDSxzsqPD78t9SscLI4PPzywBl7QRpu63w8bv+3XcbQlig+HMal5u/nyuyuH9gdkX992O5721iHLmKqOmQszq9udAzhQnHt1yvma9UuUTLRHsWajWw939zTcdpzjgFz3UFBSVP7JMVm+H2w4+AuDe6nPtq0y5KsBRHAuSDIRA/bNLLAycBIQUK/JHDk1REiEsFgsVqsVk54192K39d7PmyqlVFWV85UC1YIu5HSP/VGue/ceVO4kySq4mXpZzE9FquVagm/AKXnyYWboVdgoO3Bxu4lV7WusVTqE5L2r6xoNVGLlfAbs+dn9qlpd3dxkQweGS2dDCHVY0Eq2sdtsNiMMey+xAQKqRu8r5zjG9tVXX/03/rf/u//wP/qL2db/Wz//X/1DP/m1f/1f+1/96B/4/Wcnzc/83/9vb731Fkv85Cc/+ejRo0ePHl1ePq/rEGPsdUnGT5LsvcjIpgHLdfDOOU3RMbqmmc1ove3Oz8/+/J//849eeu3f+Xf/vS988YsffPDBQJl6iGXYF/0lvZUqzQ5s+qMavfx18Ngd8/AVsQvwAqLjNla93TTMnrE+OwJZf7Kgi312MxHNAlZEhBmGXZ7iiqRdnuO9ZgekTETBoQFxtwQbBr8z9UdhW6q2yaR20viFl8ITUTniJaGhZxOBKKrQASjC1dXVvLqPjAnFI0OrQNQROpPhaAfu1Fs+CNF2HRHxsBIlhiRS3siAiESYI7vZ7SKfm2Y2n88JMKWEhDq468wgmYLsLu4FNSInIt5Vl9fLerZAU1AhMAUFtbxrZ7QXXFoSaJSWmcflEWUjVoSdi0mZ2Po4DjSA9Wajlq+N3MUCDDIO9j2u/XWbfUa8Xd+KiGB9CGyPNhwucbIjtF6OdqKY9xC/I50jmB5HNXEGTD4cl+zjSjDfcA491Y5u0mIXZ5+GsDB7tThnxbDjBBp9OAMIYJ+ID/XNdKDjOAx67wUAqOXDQmKm0K/s2m7bdtvFyTylRAD9xhLuw7nXqLvHfvwA5dxg3/Ewni0egTD81F/5rHqYXltFabC2p/Y77q8YDrFTtHM8aBwRO0U0pLzm1/H05uCyYxSzpJoXwapgpMiU1iYiPjhLPio7RnJElSdgwEQQiQ2MT/yp4bXExKFCxBRjZx0BgHGSTkQMVbT2XKmCsfNNUzUu2JmZRTFfLSAmB9uTs1NiL5Kv/nYcGADUoqk1FZsKqqgqEde+IfRmkZmNLFTeIYgIkENq1us1QiRgM1Yx75k5VTO/7eDiaq0KCBUqBPTOjInq4H01BwDPDh3MG/rj/8RPf+1HfiiEIGIfffQ/evXll1577TVJHar+o3/0j/zar/3a3/75X/rlX/36zab7o//oH/nEG6/E2CEqxOQUZy5AEmMfKa1Th96ZI2L2VcPMaEDOzbkGteB93LZnVbXeLn/gpQf/0p/7M3/rb/zsb3/zm5//whuXF2ndboN3XYpkYEm8c5SMGWOMhqaMHLhLHQcvItXWDIG9ayVFNQwOBjdkdk9npjCzzG/EBakP5iHcntytPNde5oef1B+59VCu5nIYA9TzlDfr1CdA4OhwE1hAZsS4hZeAXdcZQYpdxUFUraKmqlMXe21NJGDMrIOuHR1oo1vYBl0Mo1trSHZ7VOmWpozkBILj6nHwNpdb4LTnWOrbINrBwcpL1VEVdremYoGHQwMoj8EZztSwVQW89kpEta/Wqy7N2b90/6rhpQeOMvfEiKEVd2iOAfRyNQck6xDukXee8rKViHIMQp9RnXMuuui9Pz09ndWNqiZJtJ+8wsyw1FsAyATWC8G8W5ZjWqzIvGGmarsgrwlZ5GV3eboLEZk5J/RPpmjgve/atGm3iIyDUiktrPLPscCoY/oyvT9rRNILulXhmMC9o/4tivkuA22fRves78lPhyOZ9GiD8wCL4JH/NuU25XR3UdVxyZLTM8I+/CfD3h//1A8xeX8bV98xztu6e5Fy+O0I3h1N4u7UbzESw7zgHd6MFVLqRAQJnHPENYmjJIb5Pk1wzrFj75iZwFiVAFSCAACIdsCOGMDUkhqpahXCbDZjRnNc1/XlxXXui4iYMptjU0MIDgCzkUqEkN2wotBPRAHROefyRiAjICMg9Ulb8qiaEEJd1+160253N4oyMzsP5MKmFsmHNwJQHzSeUmrmM+dcCN7XfbLYT3ziE03TAMDrr79uktq2JQJGeuWVVz796U//8O/5fb/0S7/4y3/3l//Sf/L/+Yf+0B/83Oc+651j5wNy3LZ105hZt10HrpQxocyRAhiqEDlPlKfGDtnXm83m/ORs1XYPX3v4F/79f/eP/4k/9dvffeul+y89enC22m4MwAAEjQKmNiqRenVVUEliYmYSk5mBY2TqRIBdHVhMxQBUmcjASodqThG072f9eJesFWViW8MB49xNq2WFwmy1jEYYbv/jYe3iiM0MCBlZVPXgEuKRbPGW3u9QtLeNdjw3u+vilg3vw5d7fPe7LBPg2OAWlXwsy0wBVNVl9zCoI0xoqprViGTRSsN1hGZmYIUVAKOjIMWoqtkllZ1yo75k5nxVXFbMs1zqGhFUxWwMVJoOFwavGhENTAv5VEDlhmkoE4GIHO7DT1YPGfwFKBkRzKKA5ajRbdt1KdroOTzmGJw0u5OMWSoecy1iv/b9eG16WF6kcmkTFAO7q/J+y7m+4oFyzbkdJoM5JP2SH363FHpU+cHvktbNTESyF1qPff6x2vc2NQyFs640uY6O/7axHbWQ7tDch+OfaN/hWwWEEkGTMo7XDABVrUM0ZnSejTwQR4lVVTEFR55YgRQgB1sgszNDh2RkYIbBEaGkzp8szIy9qyuuqjqJtW13/3zRJQBA772IqQEZUyBGMkMmU8k+WzMwQFNTRMsmBCM7T86RIwZCxwigRIhggJrzeIcQGGabzTMcxFYOggvB523dHOadZYUiKlA7hB9777PrOl+HRUSO0NAxs3ckklLqUkpf+vIPvvzKo9c/+cov/MLf+rmf/dlv/9ZvffGLX3ztlVebe/e5mWWBdlKHCNJJ8iAhaiAEJnYuBK+QKcSiSn3StF1nZCnFL3/5i3/pL/3HP/MzP/Mz/6d//+IjiAqvvPp4tVl77wyFKlIDDk3bdWJa+5D1FqhGBgQEdiKSr9F0zPnixd7KHG1+UkMwJcAjCRRvo7dR+95Be6Uyvk0DHb7BYSUNwDYoURNDl+UkmFm73aKqcwyM5Khp+sgA7V3XvQs63waGxYChGHNpN4xvSqf6HXr6timPLyepN+9q5M7zpbdhQQAYnJkoWDIjMBFZtVK/dF8dgRoJIKIwqijbkHg532uB45lFgLw3bP01y2lMzQhGRKSQRhBkX+5isZg3sxB8tltxl89v3we7PwEdcoPlP5fLZbjXkGOQVM6KAdMxFVBS20BJmHKQNiEBS5L1er3tWudcviDlUJWM6qeUwmOzvbm3X7mvf1uiihdYJ31shcPKL/jJ3fxZlL2Agxck6zuGN3meDON31ZrtuR1w03apuDF0orFKaQLFlHAgNhxcxjt+Hv+H2SMDk/Fn7O/rRSyavFXBl7O448+jb0oUqxmTQp+JFqHnUAPrvWk4jAlQzFRARBJxMuvDXrz3hB6AMK+eGRmZHAciE3HOEYHEmhgcYZJ2ubxeb5an/lRSt5FYN3PvsGvbyi/A0DsfyVJUYiQ0NEEgUckpbchyP4ZgqCagDOCcD5z30Y2ySZzv+yUiZudc1qCp5TIiRETU0IUAAFnU5LzfUYUYVPOpIUZEUGvmDQ6R7c65nPpfVdvtmgjzshhRHj++/7Wf+LFXXn70y7/wi3/3V3/l1//Orz5+/Pinfuqnf+j3fJW8FxH2rtskIiBgrLw5R86hD+KdmolGRgpca0xEGtTYaHtx/Xu/8OV/43/xv/4X/sQ/941vfOM/+U//v3/jb/78J157ZRO7dRfJcQIwIGIvUQmAiTEpM1kF+egX5cjhptGYum2bN7mTGYEqgiIo7kIcJ/R5hxq+g+/KyhOuOSxlcBkWxSzpEI0xaE5IMd6bN7OqlvWyD7hSAyDnwuASn94qcbSUCvLwp8N52cGe8ajLj04NC4/mx8i6j8vucGjW538FzAGimhIYghF2KsnspfOF1Z4InKo5TAiQFA1c3ugygEnyZzPbrNfZ7lABZk9EKaa8KmXmEDiltN1uq6q6d3Z+cnKSUsx3X+dwzTILyajPxtWkDbsUjjjXDCHc3NzcO6+Zmcip9tEZhLur5svW9vWuDdtlBgCm/VUEy+Xy6mZdVR6Ru5Scc6bpbmiWxWyaam5ED9EgAF/MV3lY7UVU1KFiuKPyYeOTRg76Gons+GJrj2P1SINld6UKnwz1Be2GQ5qGIg6LmaGwoyf97j782J5usRjKoe4PGDMV9L183FSmqvdOW3tnIhwYCqMRUFqxAJYvERnIH0SiWlKVJF1AREQmxryF3MsipczfiETsAI3JsWNCR+wYY2w3y9X77767Xi/jw80bb7zhqiCSCCD43r2MiIGZLCGyAuVAcTRM+dJfBEJyDIqQ1NQUGdkhM+brPBlzKgJiRnaIiPkaQUSXpwDgGIfU6wqIKKmfYTYmvPfMfRxo27aLk5mZaRLvmcAq70QsJwiLsfXeE2EIzsy62DpzIfhPfuLV6ms//tqrL//Wb/3OW9///n/2V/7zbYo/+KUvnp6eAgCaVgigRrU3x+CCOU4EZgDks9L3oWJ0rqbUJVDU5aZ24Qtf/sKXfvj3RI3/8X/2s89/+7sAAAytAHtIER4+fFjXMxM4nddx2zoXNryKMYpI1dSaZLPZgCgTMaAhjPKyJ4yBZEYqLbXvhGhzoWOZ5HH/gpwDCj9SbrMv8wCSict3RJMCcOq6ew9fbkK1sZtdTYOsCMbxEFF/p431uvuw3xcXcWX98Sschoujs3v484Vb1hc/fVk2i30ctalaPr6at1PblKwJdDYXT6jqECOAECioF3A9dAih2DIUkZwoChEJHYAgIhMpYj5KFGNUQ1ALzs+b2WKxAOhPEo8HvxD7M7o7YTosdjMW8x4zEIL2B7TX63VK2jQVUZrYdIwoB9LVDo5LISJYfyT3erm6Wa2z60IBEVFEx9jl0qyD2wVxEUuX/Xj5p1vXlxPhfludo7g8fDkxMqC3k17kMNK02TtZrk+qONoxL0KsExU70b7ln7d9dbTBnjULsm7bdgj7PKI1x696KO03eDjrEphjtVvG2TvJJhO5zVY4+nKUPrdNfGKyGAgAgHE+TqmqSIRoaKPw7f14+XNVHS+vDJWD5MWQEIgIwZnlc9eWcSJqgbBfQQJ6XzHBzc3V8+fP5/M5ETx9+rRt28zjr732+v1HDyU5M+M+INwhIpEDI0SMTjiBmTH3R3pEwPoIMvAMjokIHCMzsiMy9SEf5hY0QBu3p8wsEaP33hEDucp5GlxozDyb1U0dUC2l2LbbGLuT07lzBKDOVYNHmjNrMDPVtWNyjtq2VVQzCVUI7p5HntXzs5Pzxy+9/Pd/65t/5+/96lbil7/0xUenp/eqGmMkFEnCiN5bPnyeNIs+I0QwVe1A8aSpNzdr6NYQXIddQPkDX/vh//df/AvPn15cX19fXF2/9+4HT54/f/rh02/8xm9dPH3KQJfACqqA1Ul1fn6eUuq6aGbEXM1mses0X/lqeeFpgOZyhuRej+zswrt586jRX1LsC+rgw14ynogQCA1BEQiAAQFUElSOLYmJAOe03gzZDZOjJo/lnzpuSYyMM478YF5lfSnS+PdC+5YZHe3uYFQvKlrtwPTPA2AANCVCQFMzUWtTnJ/fh8WsAwVVBFAyGVDpYPBu5dSSvRdINWfSoPG6JLPMDL0mVkWDummapqmqyswy32ZmUFUDIUaAne++9NdOhSYCDHHq2+22rutCN+7uPmLAMX3f7tseBNQfKDJS1ZR0025vbm5UwDvqkiCnqqpEpL+Adl9Wjsg79qZM8XjrFHZtGtmxJI6HYvoFGeBQ+u/T2ADeY8cS7mjkcL6HemJk1JENjn5etjBh79K4+VilXo5hbCQr4PHukY9X53d2VMLBBsVb9ljU3DsCvntPmm8NOSxHZVYZZXrbJ5NRDb+YmmGOdCpsVlWgfsAAQxBKtpjrakYuRAEgIGLCAEBmEbkjNgRnyjWDc44BmdE77rotIodmBtqpViLN1dVV27avv/7G48ePRfJWKwzWT0oiAMQOvXecLPXxksiMqpgQmDiRMZJnch6CQ++ZGFElJwf2TACEZFk6j3lnnXOzukIKYpmS+/fM2DRNCM5UnOPtetPF7Xq9NpPZrAYAHxxYBqAxO2Zuu62KInIIQRG896lr2WDx8FFVNWqwODu79+jhr//mN7735ncDWPXGp0/Oz3C15ZSa1AIzzGpoZuA5IAAh5LRfIVRpC2bw/KqpZ9DeAHT1jCD5z33y8edefwxJbLlG53Xbpgg3N6v/9C//lQ8+fLoRebpcRoTltn3zu29/8NFHbbtlx2Imqst2Q0SOyCx7E4AHi4pskNTDnSojrZbUUuyoghXxqhNeLnnwRXQw7LPk8I4Q8+2YWeQaEZHBLFSklnMZJUNvrEmsj+LGcufVjl0OsRv/gaTNSelxn0/v5vHDCneYv8Wvv4uFzfj5pOWALJiP/CQTVdIO7bWXH3FTiQnlFaMqOgJEBXN588YMRgU8psTCIb+P2zvIJQAQQlgsFjnBpIjE2I15ngEABycZWaF393E5UkDv5QZrY0dM2+12jJ0bz2KOn1C/QNghLG9OA4zKz0Ssjd3V5TURkSdRBQMREzHngsm21A0jeib3Iheq6wjcj1pzxc9ke8Hsu0+OLgrHCodLqLL+LfbveGEOwD5B3MF+h50W9kT5bfnVXZqv1L5HLYyP1b6wTxjlm3zl5aEBUc50WBvimL79KDzhgGcOFXj2OUPhKTmoooB4R5DUpMdDWEGB3+knQ3CfWWHQKO7ufUQAADJgAAGIMcYoXdclZeccuoZEyTEAIHgzzAYbkiAykCeMZqJEgYMa3Nys1ttN8JUops1mPj9rmvnZ2b1PfepTTD7GGIfc6X3XpgjK7IiAuT84k/3JIqAmhLk2ODZHyI7YgSNzVQDQ4Jzz/dk/7312UeZ5M3Nd16bcdqISU9upRJO8YaRqAmYhcGw5VA5ARaJZ1XZb59k5BwjM/XZyHaq2bc3MN40omIFbnIAabDtf1y+/+oq7qKrTxexk8fZb37t58uS5c9WHTy6/99b66QU8edfM0PnQ1L4K7H2oK++5qioOHhGb+UwU0Dk1m80WN5V6YlCr2Klq8HXbdcDO++pB1fyJH//9T5Y3337ywRXK1tEG5Pqd3/cLv/iLz2+ufue7b7774fMHjx+YSYqCngAAGMkILV/ibACGKACAtuOOUgEj9hvFt1H75M1t8uEoKRZdFBJbzTwiEaCBKBo4JAUI5CCp5WWQqHOuuNUnB+RiWaZd3yK1DocxKfvSph/nqOBLru8ntZ9rtgDLNJPd3aU8wloihfKNvcygyNluqP3py4+4DtpusU9IoGxgiIbmRDQnl8kNjfFWlpO0cW/sF9u01ycni9PT0xz/LCKI/eEzAMBeFAJAYbgdYrHAQX5Q1WxrP31+Uc/mTdOYRiJGTNlMztPTnBNkmPagnkdRBV3XrTbbzXrrnJOeihiHqxe897C7FzOPYg9nh8QB+zQxGfYRlbn7mwAywU41SlluY5vDaG3bj7/Y/zwfz5nmbT46i5JoxvmOGm5fMUyJtfyk/HMyi0NbodSdR+dbVEA1s8HmzVGUm80mh8XCcBx5zFTVG1+a3ej5oiY4HNIEMpNBqtrOBC8+nwCthB6gmAECH1bOZXJA4hA+JUx2kCysn/78/ZhmtuvqyqkKM4qIATTB3yw37TZtt6mZ195XjEG7iJRvBKzMMEUwMPYOwYuAd8M1Pkxt23YpqiEQq+Bicdq27Xx+8vjlV12YiQi5CgUwxy6bAhl7YofeIZERYgh+HB4GpvmC87YTGgAwYx3yhXQphBA85wwq+ZPK+ZyKi4hAgcAIDT1FEecZUBkNUKvai4iphOBEpMoNJqHKqySJ1G035itEJEDnHBMCoQtsZqCC2UtqBExQM8RESWfzE64CMc4cdc+fd88u//63fu28U726CesrIgKjJM+3amjAjERUeSYiX1fsnBiAY/LBzMQRIrr++nPF4FzwSqyAzXz26mc+88l7p87Lr7775k1MEPjx/Uf/3T/204Lw9d/6rW+9+b3/9K/83PnD01A5SYqIsU2g6l2FO7MSEFFMzSz7F/OpvEwbw1l2D7tz7UfEVylGyiUNFOeDx2ojWZY5K0bJg4QxxpwVAA2cd13XOYZ5Vadtm/mHmJxzdRVms1nBQTZ6iHvtVdoNsrfwG9mkjwUr7ikYJ1Iy3fCmdwWMnpWjYgcKaVb+VOqmnfFQxKONSVcy5CfV+r6Seu87FQKtnH/v+bMv/6EfnZ2ftilmR2KURAQgZkiC6gBgu92GELLNCIPII6Ku65i8d06Hg//L5fL09GQ+n4cQMkTyrYImfQJu2xluuxlORNgEfJNqzNy27Xw+J+/IwDmXV95jI2yTADXKcWSEbivtcr3pr4CWBNb76kZ6UlXal5WD/aiHqDrUxLfJ9MOaMNLWx12XhLdYdoctj5ryjsYA4A41fHScR8uoAyZKYiTZkvgm2q7kH9hHNBT8PPlXRMYwuv1x0mazyWfecD+WxMxAhy2gMVq499XtBa2UD+Mg+z9HY3F/hV12dIj33RvMh5OPKNoJtMs6E1Yva8aYhiz//fH6PBJiqus6dtE5R0SiyTF47wlDTLpZt/MTns9PLq7bpmlUlZ0LHIicBAIMzAboVdjzNncaYzJTlw/jthtml5I1zfz09NS72hQQnJoQGaIRoRk4RwjknAuBRrz3QRio+V61RdUQkQ+MiCl1ZuYcMQcmzCYUIjqCIVgEYoyxjZmoz8/PYydExM537SqlrUNwZDmkwyQKQBOafCFSpoeUutUqet9VVTUCmSinsjBVTQgA4AwICciACYPj5Be1d55rU6srQP87X/+t0NkMXPPwtZxXC4EY0bMLIeQDwVA5qCtwDD6AY3AOmHqCyxWYwBEwggI4BzFCK3px/WA++8T5/fXFB1GBHRDQvbPTL3zuMw8e3PvMZz797/0//oPnT69f+8TLMYqRAvaBS8w+gXkyVUECRx4GN2RVVaWuYsY+W2sf0XaEDif6ZuTcCeVP6Lz8tSdaA0TUMW++GZgF5oqcRxF2CQXNpIuhqdk5tbTPDsWpyHIAQx06SD932/PIREflZ38Gp5jCHWJ2zHs40UaTr0rVcFtrKKoMhkBEmmRxdhJO5sqImrICFgBQc2AKIISu67qqqjJeEXE+ny+Xy3y01zlnpiLRORdji2rnJ4vF2UleqgIYY39bXjrwiw6rQTt0rOfRl2HupVBzzq3X67OzMyKyJDlX7WhxjIuEEii5qW3XbjYbEUEgRUCcBs2qJpFIfOSY9m2aadJXWaaNDPuC5e1/ow7O7ugJTZdKohznhP5uM+LGnw7otdyhzNuIY53+JRyw5SEopjCxPR1Wdj2pXJqQdyihiUY0szEtaBmERUQ5Kf9g1fYg6jNY9fo2p4EeFSpAv6jFHF6MiHmNS4RjzHHvnhncq7AP8zG4D4sCR/ECAkZHpdhoMRzirmwfCnTkrk1Ri+s+EbEPna0qM0sphRCSgHO0Wm43m6hqMYqZNU2jgN7n25TZsQdE6xN6O/QOQbK/Kp9lmM1mjoAZU+QY3WzWnJ+eO+dU861R7BgA8n4KkmRxD/kIQL6qKN9tAUDEQETeATMG74ihQ46xQ0DCfHu3EaEr9vLz1nXOY0VEdV0BtECoCsGhafQOZ3VVV5zaTpWapmJkonx/AuRs7Zm5ttttSimlKoTgnOtDxUGQggII5aQhDGzZ182MlXdBUl3Vb333ncsnz05do9GeWmoUa8gXB3BNXjlERmCq64rqAI5hVkOowHmoA+RU0maAAExA0F9UbwD1AtZrSqlSeqU+vcTLznDpsUvx5ubq/vni/fff/eEf+qGX/uV/+W/8V//1z/31v0UEL7/8yrZL6+3Gh3obW3K8XW+qKqctaxExeKfKkhLRbovLzBA1ryLKxMsjx9m+NXyUII+WifaFQab3NGygAGZaVxWbSkyIOZabsrvRe9/FvZUoAIw59idqrK8zSA8isnGxcexsRSktx3fl4HtV8QLadzJlK8dTdnewIQgHNUMIQgoWEamN3fnLj5uTedtumDnf7m69VuzD2RwCr1dbZm7bGEJYr7aPHr4UKvftb39bVYPzANB1nSc+OT+ZzWah8v2dvoNPGPZu/526745KYTiQxbvJkOu6brPZjFZe7zNRmWArF2ZKKW3buN1uU1JyzmzcGAYYwkTHZ8W9HgdouhffA9ipT+DBU3qr/Zi7MD1+aHgEwgQUWCz1Drs+MpJbl2h0WGdQyQQgh12XK2wscpZP4hhHmi41x/i+5PmJLBjxAvu8cVv0tZqJaoySUkop5esBc+IFzP7nYx6zQ8YeNzLHzZtjDHzXaqD3CA6GIJQ2BBrartmj3H5oXZUMsjM4OK+/cyMkYjmyV7U3Rr2rEM1MPMP52VlK+uSj51c3q5Oz16qqikkQgRicA+cwMBB7A1UVQyYM3pmZiRgaMNJmsxHmqqqYIIQwn8/qplIxRHPOMZtC5jtEBGNCRO/IMSIaABLkHeJe9BMRo4JqTMrGiBqCA1S15NghGQNkNZwnKyKqJiJtG4kBTSrnqoq6mDS1oB2gIQmZmgkB1qHyHHRIi911nQ/syBmIar6+SVXVV67HshojG5mCKapDov7+b1BVAVucnl0+f+ut9947uXf/wzffac4fbCSiVqCKIKjaIVZMDMjMrRBGQXAcEUgdm1MQAoeEwABgjoRJGJkZuhQAoPa6arvL67O5f61ePF1dX9VxNmvW7RZBX3np0Qfvfv8zb/zAyU//4a/84Bf/9t/+xV//+jcWp4uH52c3m3XlsY2x4j5VQ55yjBH65Z0AEJjpPrFNgmbysw7Z1KGQMIfEfwcLjD8RIBIKWE7RSKYmOp83KKpdzBsJSAySvPeGRzgasj1ng/OgfI8oYGXYUK5jhGi3iDjcezk+939+vJkxvQCwtFF2DH7QRTnmUsQlFUMFNSXbWHzp/vnsZLGG2B80YcrBHWC96e9ywPPDhw/Pzs6ePn368OFDRLy4uMh9Z2dIXdfnJ6dN06SUVJOqDGIaywXW5N+7jawx1+jEKDMzNVyvt1VV5agK55zmq5OgPyMIBQLUbL3ZbLcdIpJzImqmzCxSWgO91BMRz3TbkOBO4turpphZ7jZhfdjm5P1IQ6Xhkn8qHybAxP2MMHd0PTGGJvba0GnevYPy5on9h8I2tD1MlTQ3aXlUeEcHedQaG4Yx0MDOcDc0E5H1ZgMAzjnE3UnH3Fke29AIFibsXhclO+2hYz8oGocEbeMeUo4hGMxfsyGVaWlJAIDtn3ovUJBt0/GgV/9veQtICZLRq5439kREwDz1m2FDMMRZ6qIIPHhw/63vv/f88uqb3/r2H/xDf6zrOkQKVZjNAzMzee8bIgV0ZkLkmRqRm9gJgVBANJPYqvdMII68d3XlEfucVpo6ov5EHyJyTqlB5D2HfPefiVl/KaRDYgbnCBFU1USTtMzs64qIU0reczYtBlxA7gegT85x7/RMNTnvAGCTWiQ9v3cmIsGx8xz8PIdkAkC+oxBRJUbPtfOUd2qj5FteOiDrt5ZNASIQGogZKwMroAqqmeq23XIIGkJiqu7f3z65uEYy6NS6pIyWEJkEOCqYU7G4WfoquLqyzYaC16pOzLFxwCEgow9QeeYqESljWDQgAg4ocVo+r/38fvCyxg8Iu3ZTB59ieunB/cunz5aXFy8/fHjv5PTzP/DGr//6l37hF3/5N7/zPQN48PD83r2Ttm37BQ8iGBmSgA3EDJkBSiN4jGDFodj+dYQTc3xkipJoD6sd4eKcsBpQVRezuSOWQUQgIjB578tUlKUQu83HWzLs5M/JeCbj3I1qMpGipYO+pgvfnXw4ZhwjoulUJR/222kEZs8cRTRwOJkxoweOIoKgKAYGINrH1ZlDZBG7f/++c+7evXuqenFx8e6774WKicjUmqa5f/9+YJddwSL9gZBMFkMIxpElOQGq7RJZjCIVCh1wOJls3nZdBwDe+1xNkiEiSL/vkK/gyG60zbbLVmHOyJwbFN1B3kwGIJqqIFSI/X1U4xhGSV7iu0TMHtYHoQPHytFJmRnktOIHODskr/xmslKcfFg+j/9OKKxvLV+FsSP5nTOgaCrbKP3dVkWn00UbHDBqSf0ldd5mH5Q7+oeKKr+X3iGNZiaW2rZ9/vx5G7umqvPlWn37uZbqcC8wAgx50IZNJkJSG5J1W39XRzlUy8ftinVD/jNvMebbL8r6uAsJ3mNFYhhV6hiXlNE7YvkQfRNwIeZUkYaI+ZZ44N7cQCQRqes6MwgRVRUuFotZ1TDTn/kzf+aNN944OTtLwgpyctIAAJFz+XrjnFmLPSEzBHQxmpkIIgbnoRIRMOOsfVNKBCIqURIzo/MAQGiI5gio3+UkdghiqgpoaIagBH3uD0eokLMIGEOoQlDHfQCLKCAhIRowOwD4vb/3927Xm5TSw3v3Ea2qPDNfry436yWhKCRHOKsrVWAkx0TIxOAcg1rKOZYlAlDTNDnlsg2XrWVq9yBArKoKioCIxAaAmFQR6GqzOX386BNf/OLXf/ZvdIRgct4nvM804RKYAyEV02QpcV1XwN57p4omQFY7hu0GlMA2UNdwD9xiJmjJkgMA72A+q65Dt7qpUR/V9W/GNG+aqCKpa5rm9ddffffd92bzmgjrwH/wx37ksz/wxne+853f+tbv/MbXv/H06WVz7ySE4GufVLquQ0CHzsyi5uvMAXWaLOhQZB1y6yEFHpYJrdouwHa/msGsadCADJIlA1CwWVU1TRPpeOOIqCZ47Oo/xN0lCjAE7gDkGw73dK2ZKQIX4zmc+O1Z+o9E0ux08G0rXTguA8tOqfYkFkU6jaePH87vnYGopAgehPKaHgDAENQMzFze63327OLhw4cicnFx8ezZs6ap81Gi8/Pzs7MzMkgSiXFi0RRDF0S3G9BO6BzRvoeDLkEA0B8niFECu+xvHFsYxbyZxhjbtt22kdmTR0mmpgCITChjs+N9PkDQny8BAOz3L16o7OlU2wUnj0Rpw2oSoF8nlaqxmOBd1mVZ2e64Q3sIrh4HdgjAssEhSKlYE9Oui33FD6qWUgc77OxANN6AZEVE4mQKhwEUx6ewP9qhqV1figBG+dpX9lzXddd1KSU3dykNzmei0dk9JA4iAGDyh1BFxPGYWWmvAID3NC6txlkMKplxvwAAueMevEzc5cSz7EipD+8sf7JhrxdzYP/wa15fDnbtxsw4eO+9akqp88xXV1cAcHV58+FH73/2s581EzD68le/8iM/8iMPHjyIYuzwwf0HSKlpKsaAwGrJLAIAogfzyhbZtUigZqzqnIEnMHaAaKBCkIwsE7LPl/4SMTNxPglljtgzMpH1fkREyycgANTIIREyOCY0BGIMITDnsK+YqY6zC5KRge7fvw/npqrterVer2dNM5s3zXUwSMzYthFQmbndrDpD5xxYp5acYzShbGIw9+FdJpI9ZAkE0fodFiIGU4uS0NjYkSEgSkrIJIorkUeffuPs9dd/7be/Y/PTE7MYBUlcxYgoZqrqSYNzyYTBlMlVFToHroK6hjlDZ5AANp0st1zVsHAIYESqQm2ChL6epydrZqwUz2bNto3eO6v8ZrtanC3sfeikRQUfnMn2wel8/sXP/+CnPvkHf98Pv/3227/07e8/e/ZstVo1TTOrGwVLKeUFCQ7eGkPLdjYcOGZKeivJfqw80SKHmru0EUcZrmBmgAYMaAZ1XesmDR2RmVVVVVVVZ+k2CWA2zSIJd4qLcTzjQ57x5BMr4rmgUMD71XYJ/MuyG8zBknrizzuU3mNJYJQSqiXUlz/1+smDM0T0SC2aEgAAqRmAIOQ04/1tSO+8887l5WVVVdvterFYrNdrE3n8+PHZySkAmCTchUGBSMxeIxx81GVEFfbycMDW/jjHoY+OxEOjjIi6lLquq6r+guHdbNHyBn72nHdd55xXNVVjdqoaRQDA+9Cr7SFyC8Bwoq6KgQ17Kofj1L0/C+07vhxD7cZPj0rnF7Q0dyReHGIpPy8CE/bW0+O9zqVqRERCNwEyEUFxz2DZvg43cIwNj09cKNcSjHlrCgbEHeWokv9F03gJWTmqIt4CkTDnWjKzrHE3m8319XVwvm23OUjYrA97M+tzvauCmXVyJNUoHDDwWETioQJm5hh3rryyBbE0GbkNK3tJVmIhn+gzw7x7nT1G5Tn7IWxVcoBV7quqvKoC9EfvTs7P5vO5SPzwww/X6+XpYtF1nff+ZH567/yBiHzxB7/4+c//4Hy2wLxoZt+225PTivMSHhEVDfqjuaZ5m0acc03TOOeYIEay2ofgYrvqOgFgSck0IZGBMFf5Ol7HObMeVJ699yZiLGaEQ7AYoxFB1KSDcAAEMwFQ5oCIKSUzUQWRPkpDVVPqunYTQkhti4iXlxer1fKt77/1/vvvM/Nytb64eLbeLFMSRE5tJ0bE0HWdSfSeM9yqqkpJUkqa00SAgSAAG0gXE0MQ0qgRknMBBAjzaYvUGUKn2qp96ff98NPvvfPeb3/nldqJilF2DpIBJsCE1CEmgFW3TZt15x2pOtVFFZYKtWPn/n+U/XnQbEl2H4adczLz3ltV37681++97vd6ep2tMTM9AAhgBstgIUFKIglSAiASoMQwF1FeRNqhoGxG2PqHfzpM0iFbdthhB2WLIsV9B0hRAAhoCBAzwKzAzPQMeu9++7dV1b03M8/xHyczK6u+7w3pGx2v66u6S97Mk2c/v+PEy/l84QY/8bwAACIjOEELxkC75eDUCEjv7RRb6xigMTZCZObD44O79+/dunEzjB5ZCJk4TK3Zeur60dbs+KUP371//+7d+w8ePHj8+PFyuVTdxUYSwiQUEfiqBoU16daCFitnTynn25C1deV9vfHpKhnZWhOjt5qUZ42EFHIe/WgvbcMrOWHN60SkDnilp1fJjP82AhtzcArXzKQnHmvbvNgqG00gYHPXXz4Cx46obewCYHawB4biGJzBESu7HlITZWTFOwNoW+fHPoa+bVvmKByefeZ2N2kBQCWZHga13ZLJ/r/an5zEs85fWlPmVQk5rDyriBiEjTF+HDXb0HuvVVwkpJtzuZwbg9ZaY4nQAIYQOTI6ZyLjxXy56EdrNStS1C4jgFblgYTGADOTIaE11Y8lIqD+VwYfo6esLSGiyifhkEyzFbtcWSpFGF9yyqSF5IxJAgAsTEiXAxKICJElSR2VXibBpa6ymWJKsFRdLAt4wVVWBSIykjJ9JkwtGtNTogpHA6lgWidcb63PSfSEgs7peuUBpFbQpYG29uktSTTK7Fa/rqctlNQPZhZCIvIhGCCkTb1tbRigsNMRCRDA4oSRY4yf//WvQFa8VJjt7+/v7u4CwPn5+b17984vlvBkMJsNx1L5c8Ne4PVL0pdSfSNrv0pyDGhezxW+K1VnDCWaK+7uollqWRFkM30xPyVSxEciogfvv3c3RgBummbiGmdsM3Wz2fbWbMd7/5GPvPKh7/j43uHRdGvPWjuOy8ZBY5sOqQVjjSGDzEaCrs4QYxyFu46MgWEIxoBzLo4g0vTDomm66MPgewBo2yY5ul1wDo2JavsSESEbZHQoorkgCtUFIhAkkmUi8r43xjjrQCJH70c2xuhnjh4RQwwShEP0fiCivl8YY+aLReea0Yflol+czR8+fIiIGPgDT98+Pj4+vThXpI6maWIcW2cuLpYxemvM+dlZ2zbqIOPUdkWbyWA0RuaLtm2ZpZcRBQcigyAiDVGcD865JeOZxf3v/thXxvnjB4+3ukmIXnreNVMAsMbGRU/OTdEaMeHRPAzouha7tu/vb5020DTgLDLvyjiePCSCrb196AWUgsMIEu12F+6/a2P83nfOHmxPX7N4vrPP6Cy0h3vte/cfP17OdydTs/TiuUUcyJ/359jAh+fjK3sOu+vj8f69hw8fjOPd4L/63vsLsfPBd81kcX7BHJYwYiuRYhNsLh/PibFCUIVFVL+E7N2dTPeXywtCNhZAIggBWmayzoYwsvKVGJxpxjEQ2aENzRidCLdm0RqcL2+ayc4AYwgLii3SxANZ4xsKjjpxIY6g8a3sSWYAFKAU80qKuyAmlxGy1ggLpHbjoE2xrDGwinsyBwmKV1Gn0RjdrpINcRZZgVIIAKBGJOEqs2f1vfr5RFILoo0Ez/Vkz5rvIeJexNHw+/7i+MVnp/tbPURvg3POMmxFjIJBQNAQIAGiIxtC6LoOAHRjaFXSs88+i7zCplgJ2ooBQZYi5TNcpRcksQeC2SbWq0gS+EauiNA8FzREGmxWDmWttcZpIQoANE0TQjg9PfM+KrTk5brYWr/bcORittLq2UcUFCrWmy5Afbt1OYEFpbaUHpVfoXbDgto0VEayMch0UJqTYnqmUMvKhU5ZxAIAmFxvI2tjxATcTcS4qh/FHFMkDX+iKYPJsVguYyk++TI5V0xX1lE2FOqNl9I/i2BGRAIEFgMrK7kutAVYYbmICNUVONYiAArEEFX0qmlojBn9cHZ+quj8TesOW2eM4bip7BfK3Bheea5cOtL3VbS3XEJrCsqKKiCHHuqdqdNaf5lXrEZRSPFRnW1ntgsNpFZuCETphtpEyLl2Op0eH19/+eWXt7a2ptNp13XOpfRgPZDUKYxEBAaYAzMSkVWYaLSIEmOUgKNAiGPXdWEc8lCFiFQz0NAAIlprnLECrHgXABCj5p2t3C0iwhKJSLM3SumU1haqgqiTMI5jytUK43K51BkIIYzGak/xj33sY9/61rcAoGmaBw8eWGvB0KNHj0Bs27bOudmkBQBj0CNCbkyOiMZa56JG0Ms0EhEjM/MwDDpORyalbRsTmE9PT4HwA889d/q1f97s42w6Hfs+tA0YiILQmqWEaWuD97OmOZV+C6EfBufckgLw0nJnJ620nSfyDTrDgRAIgNlg7BzKxMynXVxetAajDe10etbPp7SFw6KdNB++fefzX/tS99wHiDiid5bkIszExXkfGIDBIDJIO50c7u0v5/OLb3zz0dLPdve3t7cf3L3nnCFLghw8G04iShs7aiS7JlfJUBJpEQN3bUsmjMNiOp1ChCgQPABBN5tECTF6RuDIYz80DbJlA+CMDcYgIVrbQGPJDBJVz2YOBE6BIrQpdc0uyj6qOfC/8dA7bBo6TzD3Nz5vPGvjqkp4r10CcMWw68HAOtsvD/LRR6LlMMy2Jk3TDMgGDTNj6h+l57K2EIMYrLE4jEstN/Lez2azg4ODpmk4+FoA6/95vRZo4/GXX1hEhDBZ71n0lhMcGcUAYQ79sp9MJoQUY5xOpyqVZd1Ism3jfbg4XwzDAECaRXJ5jq6UDeWbIgDKQSBIq+oUQMYVWhYI5M6XmS+rby85xmWt9wgiGlv479VpwBtjQxREWElZZlSAP+HCoDGpLExEsNISrtDCVM7XM2Jg1XGvFifqACwaKCTFcVWzVLgq1iqIAc2NKu9VMqAuU0UtY3ScxV8NlaZSPmN2fNUvxcIGgQyhEAoRiDMpBxA4cvAcI4F0TYIpmHRNTRJlbFr2etlJnlPBV6JXD9skDOSiPOmSmMrxUB6hYEmYzdZCvVgJ6TIYEQHk0iimrK+e0GoHA4CC4KbbUJv8AEAz6SaT2a2bzzz//ItPPXWjaWdb06nLoezij6lj3kiggRIy0JAmeZEIETAYax2EQN77GHokcc6KaEe+9OaISATaKYElEqCGpdVBIhIRTaEQFXKleykiavy+73tr7TiO6jTu+17LRt988/Uvf/nLzrlbt25puslsNgOAtk1NC5bL5ePHj5um6WZT771wCCFYa8d+IZKSthBga2tL599YG2OZVUTEpmmiCJIw8zimmQ/OaVoDR2Dm0/PzcQgHBwdwdPzw0cMWYH8622o6huhBFmOPrVvEHhHO43K77R6fn+7v7SByjKExjTOAEAWgIRfjOCzObDtBayR4BBAeYz8YCZG5QeYxmtZM7MQZhCBxuTzcnR7tbT948P7u7r5rnfjYNE046XdsO/e9lyhEfYyRDFo3BP/m+4/NxG07C4QX84vdne04BkBsmoYXXjddCnVEMIYpJxWqUlWc1d77rmsJ4+nZOYdBHA2DFzYhctdNQYCE0ZAACEForSEzDEtL1jkHCMGIM3ZiHCQVzRIhIRhnt7amidozFULZ1xWLKCRas1C8KpflSgFc7n+ZzZbtrE8tN6Eqkn2lzNp4BDwhYnylbBYRtIaB9w8Pjq9f9zEM0auotVhKTvS2KCzCbJ2xYBJ7Ojo62traIkL1J4cQUjY/JB9sMTfrx9evUQu//Fktv82ImkJcIco49k3TtG07juP+/j4AAbAawSLIzISJepajPz8/Xy60IzeGXA5Yz93GnNbctnDStbUXXrN9L1EGVnfeuKGIIDKAQBJCQGhQp6jkAlyhWzEA1D5wSGL4ishoEZklRK0TWzwKkHdaUWwBAKKaTRpGreuJWQpgCCKiFMEPKKQ/8Zr0XR9DynqFgtcIpGgMiKiA+ADAOdaAWZYnAc9c0HopY51i1gak8o3XpBLyAjG7EpcqR3FxK2cJIZCsBOoGyWFWv6rl2wx91TMPAOo5rxfdXioZV+5Wi+oYvZp6iEhka6Uz6zlSLP4i3fUc64wxhjDlYKvvR6df33FvZ//6jVt37nzg5s1bCsk+nU67pmEARVdW49WkPqzRAIn6Y1AZMTILEBm0HiVZgyiE4kfbda0x4L1HEkCOHJ1rjMFiLNIKSpDKshKBNhYUEUWQrr3rfd+r3HXOLRYLTTIe+9RgA4AQzaNHJ8Pg9/b2Jk17+/ZtZj47uzDGhcAh8DiG8/P5cvTGmHEIalL3qOtOTdM4a5WDuaZpmkYkOw+YrW0EEbwn0rIMaRouxIOMMQ7GWUTT+xERD5575lt33zt7680XDq+1LDvTyQTdlNw4Bh/D9vY2Bx8v+sOdbV4soWmO2EMTIQQwDhoL1gP1YBtoe7AWggdhGHs7v9jiAJGgj9dnzXyU5Rad+ovp1lY/7xugZ28//YWvfLXZPVQMNjLWbs0u5vNROyMxL2McBZfjeLHsBSAKt207DIOoZTIE72PxlIiAgu/6MZYNqFqR7jIt8hzH0WD/znvv/tiP/cDuzuTN17/VNZPzswWQGXrf+zEwEMEweCLLnufzBQMbQ5OmRWTrgLxMuglLCCEYZECMAlakaZrAXkQE1kTdk47Ccyout3lOEdIbd9v4Rps31Jdc/lD/W/O3+syrRXKWoJcj7mkXG3p8evbCCx/d2ds9GZagSKXWQlzz1KohxVoHrIA4W1uzyWQiIkrKmeEGrQ8pPOhK0Xv5uPTmVGQwrFymWuxv5vP5dDp98cUXvfd9P969e1ckTieTcRwRcTqdRu97P56eno7jiMaAOu6sDSEQriqI6uxcIkCUHNRLFjwRaH0wZdTGYrhUXv4rvOi1TCrWEkmWooAIyYqO6y5Q1Z8q/zMWEP964XXAtTAoBoS1lCcQiAiFuHI/l8EUnpIXGEgbrGq7kix4mKPaCVKl9ulp6XJcLdzGrqgXFFe+hHwOAggIpxsYJACI6+738rmI0jL++lf9yeiE0trMl9y0Issx54Jaa6Vx7BkuESrkkrZqXdI5dT26VIezLs1MBhPQACgBiph6rxKhITSGkCDdA4EwBS8AIWd1iOL/MAsIWGdEqKxCGbAlJSQEQQC0ZKjRiWmZeTrdun79+rPPPnt0dNw0zaSbta1TdytVMh6AraUYhRmRJEethAiQ2BqVT8aN2knFM0fmiCjWEpLVxAXnDCJaJGutPgIJtDk3V2gk+c+o0bioLlC0HGHpB/VDav6ztfbi4mIclsYYbbBBRDHG4+NjY8zFxcVisbg4PRORra2tYRhUbBtjlsslALTTCQCAUAihbVtMRUdJqEy6zjnXtG3XdW3rm6ZRwgDiafa+FDAcYwxz0JcKnjkKEI4+EpE/PmjvPPPul7968s1vvf76m4ftZKvrms4Z5wD5dDKZts04LOddN51OmtZOPRtjmq4DY6B1YCwAwHQKMcJkmur7QgA/ABEEDzw1/XB2eiEv79v9yfkYxhYX47Kx7njv4OH7927demY+Xw4QwcGFBCMIgnHwDy/mQHaJ+PDiomnBE+3v79977x4ieu+NMWJoGAaMIAK64erNhSmlMa2FTqn3oTk0xsBHPvzSzrSbOoLIy+VAtp1fLBf9cjksGGG5XHIkS4uxH4JA27lu0gByRGATZtMpRIYYqDFACDHFQTxHIOHAULHUQjUbm7EwYf39ail4ydITUQflplsY1mVTrUBfKWWvlLX1wZeSRWC9v19OQoeI4IFnuzueIxAZ50ShbNJzUy6UZioTohVha5u9vd3JZFIonkPI0iuDHlSW++Wjfp/yhuWqJ70Us1hrl8vlbDb74Ac/eHh4eHFx8eu//vlhGJqm8WMcfa8xv3EcF4sFABhy6ksTkRAV1GYtEAuV17SMZ004cUJw0e+KIFlJFFzJmPrydHOFKKpy6MuF+YnJQKzl08Ygy4WISIBcA06lyynrEJtajqIulkmuBVJ5aAmmiohi39dR4Y33Qo3+qi8aasCZtfXdIADM7tZLw1O0tZIypu51jXESAao1B0gYA+VMeCpBUN2cqtGkiV05GABRkGKMrMlo+dWSDyAGETG02mz1PGuSQcnkB0hy0dkGqh1Y5lMQStuAkolmjJHIUtUppBlG7YIqiBlbWhvRGxOv2tsiAsCYsgFTcEhymIYIjDE6hzrtZI1qydeuXTs6una0fzSdzgw59TwXzQElipBumTJ7CKAiWc1fQjGGjCERocYyE6Iwuxi9MSayFyBrLaJ0rlEtJ1mTMURMIXMGMDkNU13KnLsiarkqAGhsPn0Zgs7e4mLe9/0G2TBz0zRqkMXRP3jwgIgWi4WyI8n55BGEiPwYjTHOOYPSNI1G01UIOefacRyGoW3Htm0TQzetJrprLjcg65nOuXEctZYFEW3TCaHn+Mi66XPPHRt3/vrbD87OTvoBF3MOIwoQx9ZYI7w9myEwM9+9/37equSce+rGjRu3nzbOoXVgiKyNPvi+Bz92REZ4fn6x9WD4tYfvffef/lk72e+DB2sbsRhH7/3+8bV37/3W1nJprGWOw7znxgyLEZgvlv398wvXdgPi3QcPyLqunbimuVheWEvjOBpHZIjQhjjWqmqRPWUJoGLOk0nX9wuOcHSw/+Du28C+a9vowzAsgX3bmMg2CotrRgRlL8TYNE1jXYQo7Id+aI9szKxYibGZtDXTyDsaoRJUVx5YsVolEJE1hoSV6rwhNZ8kaxBRsg8Z0/684niynFp7dBmAqdzS5aUQsedwfOOpdmvaD4OZtYP3iAgYSUBNWU5wAbr7we7s7Ozt7SV3hDHWWh8GwlVSu+6Bcmy8dq1xXJaFAKD26aZDQSICdF336NGjg4ODT37yk7u7u3fv3n377bcfP368vb1NRMuhB5YRRt2H3vtJN2PmwNFao6UdugM3pmklStc9G3n8azHXIn3TQSl9biW2BRR0LU19NdflQ226aY0jZAM0jSGLzDKWPC6BnJymNZVaQY24AphLYk+ThGGVaJsXhAGk2PqS2H8SP8y8YXDX2kZKRUbQeDJU+kqtgkAl6VfzlK3qEt1XQiy/gkCJcZZZKhMrsspwghzdQBBeR8mIMZJhnUx1JOgoSUiEjSEAieyJyJosrtarkOq71YSxmtg8zeWE+tq8rNn9gCAG8ySvyF5lmz6t1lEQsXVNflYswYJae8sTmpimIbBkDBkiEkAdjrW2nXTHx8c3b96cTrfUB9s2LRG1raIfEzNo9nsUQazrudUtxAAogmTB2OS4KQVdiCgxGhTg0PtAAiCiyI7GGB/GEvwudVaK5uhj8N5HSTll2lFjGLyIeD9oAYUKYCKyZEpFVln0yB4RZ7NZqjQzdhzH+/fvq26h5hoAjOMoC3TOBc+IGMZeRLquY1Y/Nsxms6Ksj2PQZCtmtk0XQqfGrjZs0Lyw6dZMX9y5lpB2dnbu339wcXGxFOrJnk8mXz4/McMIPFpDwmwBJcTj6dQv+8evv9401jjzqR/8wZs/+CPPPvvsCy+80G5PYTqF6QSGAawDQxACCEAIMA7gYzw7PXn4aPLNd1+etv7WtW9cPJ4hXpyczWwzXw5RgMldu33nm2+8dePadfReIvjFeDF4CHEeoneWmi4In1zMwTrbuPl8vlgsuukEBaKEGAJUATUNAOuREyAsc0qCExEtRTt79PiTr36UQE7PTizhcn5OQAaFowcE4AjCkUPkpGyxsNP0LoYw+sV8rn6/GGOMQGgQ1P+8WmipuOXlQ0QQCzcT3auXxaGyxvo+NZuC9Uv4Epcu5wsSyqb3uGYFNX+AJ4xcOcrlQSLi2bB86cUPdFuzniIIa3TMkhvHkYiRCFcFuoiI9vj4OMQxxFFARu8dGQMYOdbaU1mzWsquz+Bmm5rCXyA7zSuNJhXevPfee7dv3/6u7/rk9vbu6enpe++99xu/8ds3bh6quEVEMql0xxCZ1oUQGET3rbIegMsO402hu8FwUzXjhtzVS5DrNuwGECF5IDdmQ8+vpRQiAotilcFGuBEFqWhem7HVeiIREDk1Flib6hysBQCOa29UBBjlpo2QpUIJTJZpKSFzJAABSjJy5aC+kqRgJcsrgVFJtXImlfkgUpu0TFT5ULwFyGvPMoQZALnyRcegmhIpfhIgQEJQKin0IQRrbHl9Mmv+4bXhVXpSeVO6VI++IYPLVG/cs9CD3rZpmnJVzA09MSfrIaImVRGtLizTWM+/IdU2WASJyDjrnGvbdnd3d3d3dzKZaf2uiDRN07YTSsUukDUwAWbdQcYYS9opICodCiAJWtT2gkBkY4xB40NRK3QLvB2pxkAIwXuF6ylTkR0qwXs/Bl+M4BCCMU7lrvex70cRidEzMwGyc8qni2gPIYy+F5HFYoGIXdephylt/5wokFKmCfu+j2N0zhWEgBiZiBFxPp9rWYRzTl9ET2gFRUQDELouercQQtO1bTsBAO89GaugtkuPCGYJ5rfffe9wZyd4v1wu2Ydp20GIv/mt39nu2t/z+378h37sRz766nc89dwH5gc3Ly7O3pnPHz16dPbeO/P5PMZ4eO24H4aLi4vpZHKwu3ewt3/t2vH09u1DY+F7llND0LbTk9PQ+5N337n3+utLfx7HcTTQtrPpZOedd+5ut62fn2OMS+YY2BtyO7tkXOj7k/NlcDR1TruEoXWISZ8tAhgROa5oTJmW5B5fLi9EjJEFbt95uu97ACADbevCKN4vGkuL0TOz9h8Ucc45BZtMNeHa4zWCMwYDIwkjCILJnBmznl1v8w05V2+rFZu9yhiVZJusabeQARC//YHF5aWfNaiaIfPK/de58b/50BdMsNiFmVua7e0004nnfgjBOKuYnZz7ahOqsZH2vo1+wFx8KlRVHK2/bUkYqXl0/ZmqFK3aFNZtQJS+JJS+HyeTyenp449+9MPPP/9813Xz+fyLX/ziV7/6zZs392MV3N64P1mTLWlkZszmdRlqLRgAVtU4kF1klLMB60NEBKKAGLIqGFik3IyvGo/qNYhIICW1llEQBVCK6xJyftbGMl++YSbHivvnZGgAILMmEspGgsqy1FSdIsDqhybBkw1tRCLFY1lZeGtrWi8955a0vIIdTUVTiRh4RbhlMMKCAiuZpNDH2eiFGEBWrUWS9hD8msjkiIjGWhExxtauMx22ISOMhpxpnIhwZIRUnl7evb6hsvWcVAiFmFE7ylVuZahZQ8EcLUsWN3Ht19ncanKUwWnWC2Twc2NyeQYAgFibtCKVYcYYa0lEDFlmaJqmaScAcHh4OJ1O9/b2VPoiYucaADAGXWMEYvSivHIcg2Y5AUDTWiCjGWGrJLsU0F1F0HXAmmJNZEXGxJqZrbV+TKbkMAycC7tLBpn3YRg04yzJ4Pl8yVkDGMdRJyqEkUMsFc/MrFIQAFjiMAwa8RURXOXnpzkp9XV6gkETwigSnXPLhBBOml9NOdWobuwR5nPNmi5jVt/49vauCC4WvbPNYrH4+te//vDhQ+fc6UXcnU2HIYDYCGaIEtG0O9uvv/3OzWvXfvd/8B/86O/53R/92HdwS198+82/+w/+zr/85V//7Gc/+/o3X1fyUQX+r/yV/+O3vvWtb3zjGzdu3PjkJ1791X/1r156/qXWNU3TvPDCC7eff/GZ55+f7B9agOPj4xdffPG1r3zxX/3aZ9956+2Bw9O3bv/6G+8t7SLGES30Q2xdE9EMHJcxPDybDxyRcWd3V839aduIgGuafhyCj9qZWFO7y2wrDygLlwxTEVWqnnnmmcg+hHE2m549PgGxAKAAbs658+UcyIzLUR046AgRQwhRwtnJKQEAR4msedExxqbtJrNpLDE4TIW8ab9kGVf2fuJKa5tIHUUpgCfMSpA2FyBQTr3U7coZ4qIW6jWPLdwA6/QFxb6Vq/F2VgK+hPPWA6waANYUUUhprxhjPLh5/fDG9T70Htk4FzgSYNO2uislSyJDAAIxRltrH1AdNXOp2dnGOeWEWn1Yu5ZF23+qm+7i/GJ/f//k5NErH/7Iyy+/TM7O5/Mvfekr3/zmN69d21VaWU2H/iMgqOBnZQAJ+L6U3OC6Q698LmlZUGwgAzn2BllAAgDanLYll16hFjDlA6KgbEaglcJqP2c9UfUdrjyQ1vCE0wvnkqhMfIar3iZFXtZvnVEfknEMKJLdw5T68D1R3SsWYflG/6TKq68V9MmRCKa+VU0JxX+QrxKVvlIZf2n0HDduUo+QL9WjX35c+VfpRFgAFXQ4XWiMQb1PmToEuCqjEtYXeuOD5tRh/jI1RzIUNductPcfsLAgAa01PFcXtN5HK9qZIUZNRUxTEWMktMaYrmtns62mnVhrd3f39/f3Z9tbGtrUbriNsdZa7VXHzHEQjhzCGGMAYEuALFzwvTE5VLyPIbA+Li+TQUAEgxg0CBVjjDGM4+C9NyQxV9sPw2ASRDbku/lhGNRRXF5QWfw4jn3f67UcPCKqmZXXN0YOepr6pRNHXieMmgKVG6gTtWjVWSn09Y7QoFUKjphIAGr8cggxyXXq+zGEAIDzxcJa+9prr/36r3/+M5/5DI/RbG1Nm8YgkYD3cWt79/W33/l3fuIPfupTn3761u3ej3/jH/7Dv/rX/9pvfe01IOgifODOtU+8eGdnZ2cymXRdd+upp+7sbXe3nvpdH3y+bdtXPvIdi/fe/sKvfdYY85UvfeU3Xnvz+PrRD//u3/sTP/VTr7766s1rh267e+G7X927dfw//Itf+Ht/7x/MpvuvvPKJf/rz/+Tg+v7Fcjm1rQGaj+NiuSS083HoJpOzxXLazS7mZ0SgoNAQgoiQMguhGsIP1gyqVXCKiM7OztoWXn3147/5+V8dx3FsnG2bcRHQOB4HTpCTGGNEQ7GP5Z7MPPpRYrQZoUj1PzBUdGUAMER8SWrUA7t8lB3KnIS1FIcirFgx5M9ZCpfygkQ2V3I3ufxcMli1RalJ7srhrc4kBAADRpGIwhgFMTBfu3bEhJFAAHzw+jq5qEcrjxLpqi86FQ+sxr3OgGBdBj/puJKb61WtM94rfDwE7/f29u7de/+jH/rw888/H0LgcfzlX/7ld9+9f/Pm9X4YADbhs0XWovAVf085LMBQPABFSIgIgpqjOXEcktmE2fivb1gDRGwI3dohWf2krDzdrLZHCwv5t5yr8tCN08pylLMlV/hsDL6cqa7I4txIzihtD57D2JgQlLVdo2QFpoqMMgivEQNkR2t+/RQmh4paNt5iRTCXpqEIpNXECnPlw6lvhZXbnHKSZG2OlzNpBSZFKgOwShPT8Rebr1ZZNDVZTWBELLRU5v2SSI4bFjCiIbI5prPK8c7LR1XEXwRSPld5tZQcpGCWMZIFa23TdNPpbHtnr+u6rpvu7u7u7Oy0k05tay3bazs36yattcoXyQD7VYy2FCIjChGZXF4sObpE2nU4Jnk5DEMJ0KpYHYYRUYLv1RLK4BsRAAY/Nk0DqZNgEsBFvSgCWO+Joq24V0UQmheqI1R39BrN5GUtArVeaOYVo9X0bBGJEbuu012IqUY8+/85AnCMTbGhY4wcARE9x/l84b3f2dkRwZOTk69+9as3nnpmOXdE0E1sHwdx+ObDuzc/9MJ3/ugP3Xruhde+8dqf+7N/Fhhe+MAHfvxHfmQYljHOmXmxWDy6uNgCjgYne9uDxKObTzln2qY5Gy4enJ0sOZw8enBw66l/74MvhDG+9sXf/Mn/5r/57u/9rp/9j372B77/Ux/+8EtHt5/+d3/6J5/9yCv/+f/6P/8Pf+Lff+4DL37+C79+9NRxJIgMaNxiPA+hf/Dw8Rh5MpkAwMXFRSEkANCmfcJrKnuh9HpudQ4nk8np6fmP/Z4fMIbeevuNndnMe+/HUcj0fa+o+iwxMHvvXbMVQupIBoaEpe/7EGTmwCLlUgIxxri2SeSn5F9pALEqnoSkAQNeJVjShtkQTLL2FnlLrqzbWpA9oQHsGvPPw0hR4RVPuGRwVtt/PS2XUEIQRCBikKZrd4/2hjgEQDEozM45Z6zkmFRMzXG0TWxEBAtXsJg1/r4xlMvvAxW7vDz05XKpSf9+jG3b3r373vd813c/99xzzLxcLj/72c+enJ7duHFtvliIYF0r8qTH4ZOtlg3XazlKXMQYI1U1lChEVMVxC8FuzPilZaByTwUqlBxbrQX2k6bridPIULZKGQZWRGoAY4WJWt+NcnS5zEMaIQBQvb6ss7V+uQCUdUQREcWLW19TZiYCIrMmgVJswsG64AHdYJdISOfHUEp45tyob4MIgQyQQWMJoebdRabW754XEOsxSJU4VkQOrG+hDQ1Gx1zr2OXm62tVO0Jq6OwrVBCsjDkiYgbMHRULMEhhHMaYtmsnk0nbTra2tre3t7tuOp1OZ7MZ2ZTLZq1tWts0rm1b1xiNxVDOBjdIgiknI7/1iiYRUbR0HRHQsMQiOFXuqt9YD+8HRByHQaVpSilAg4aYeRl6FWZ+GEP0Ne9Tq1fvxiHWJTHMrNK32MorOtdZrXyJV+7lMvnFk4/WqE6mgWoiCxCBAImAeRh8jNEYBddLDtgYhJm7bjIu++U4LBeDRD4+PHrz9Tds027NWj/2ghBiFEP3zpe/71PfNVh56/Te//fv/k03mzz3zJ2pbfyiR+DBufnFcrZ3aBDtZBKJ3nx87r75+gdfenFvunUe/cWDR3Zv188mInJyvnj4+J0H7zyQpf99P/KDxtn/7D/5X/3wj37vT/3UT33qM5+58/zLt19+6TM//uOf++IXv/Ojr3zxC7/Zn8yJrDHufL4YOS764Xy+dLYliwiwnC80HmGMCVEQRdOsCimKCOR2XnmeVyyu67qzs/Pj48N33nqDQ2waywjaVV2QWACI2TMzB46dwYSunwGox9Ezg+ucMcargm5EK4wZFGBXgNfwk+sNoswBEuup3cWc9wPIuv8S1py1kn8qrql/g82K64k1tahiAbPe6ejKyzcpEAQF1IuFhsahv3X92tbujgdGZ4yhGNNDNQXdpyQVSnSuAXW914adV4/427/P5c/1iyFi2zrvvTXGNM3Z2ckPfOrTWvZ3fn7+K7/yK48ePd47PGRmaxvKTVivnIjLqkD9dKzM3zQ72UhSll2dkKWSDjCL3yu1nifs/1UhPwAIgUKO1ukGV87bhlx/0pRipYtdeRROd/nmtJ5cRkSSa50Li1yNfO0pIpcqkKQ6dFJL0UUlXdSi5XwCAIBcyvYvk2nNFeJz40zI6hQRQZWyWL8Xracj1GOG9dSqsr68Xr2aDy6qV4HVybtaTxa45JWp5kf1oQQhUqa3yBsNjEnO1cJ8oWb5csocRgAwxjRNM5lMtra2Jt10a2tne3t3Mpm0besyMrNWKOlBhMl7sD4zBld/6vhDCMxEBokIOBUFITIAMqb8t6ZpNCEZkmwbQwjAwiGy8DAMAGCN08UtFJUyqkIEUHBbLMIVWCSqjEwGrvfehwHUigIocr1eeszYnMWrtPp13dtR4sRK8p4j50aNxqx8/jYBehFAgiAWQEGIzGM/WGvjxcVicS4is9lsGIYv/dZXb926hYTDOHKMAWTLwQdu3nnq4Pjk7OKrX/jSjRs33r93d29n11Mcx9G1bnu61bkpGGIP2Jjo5fT+6fSVaWfb8+X49jvvPLx3goEmbnpw63Dn1kvPHN/Enn/1s7/2G7/5G9/36U++/NwLf+ev//d/46/9jT/5v/zPnnv5I5/+vu//Zw/OTk5Ovu93fc/f+ft/54P7ryAasq7rphfzIQr3ff/UU09ppMA5y8xROEYWQdB8Bg3YIWg0RHQ/otae5qQHgMBRELrGPXhwb2trJiJawuRHL+q4iRgUkFgABYIfCESAWERY202C8m1dlAgRDWkFNqoKyAK0yQxrFpd+qnivyFozoppXFO1hfQ+WnbhqonCZLdQE9gSufrXVu8GspLIoBESj61HYOjfOw8HRERv0gUmEpY7BV/fUXRlZRKAI4PoBl0n/yumAdRF1pWiEhA7Yee8fP374qe/53jt37gjw+++/90u/+C9jjIeHB0IUY7TWaZi6dufWNy98Od9YIahQIU83GOsGyy6BK9Q6I7X/Kl8xZizly2+9sSSYRXi5UICRAAQFAXMR6priVpuzlx6xmlJZaf1YmWsAIAICa8blxlNEUlFsHam9PNoNCKeNYUjusJ0kFiSPH2QXdG38rYyqdb0HUQSv0DEvL035qZ6W+sX1S/Ux1g99knaip5kq7qv6nGKkSORUAl5SNtQBJmnPb9wxTaMk39fqKSxIleqZNJNIhgRQtRgEgBL+KG6IvDqV7iI6sQp5gYht67qum0wmO9s7k8msm06m021FkCg57SlrEHMkqUyFglaSGDD1REn23OpKRZCCcCcizIE5BAmpwj3V6QmwRB9i9MLJnAUAYQhZfjvXapq0+nsBgIAAQYE4gAutJlTOGGOIow5YUt1E0GSCPOFlZdUs1gVd+68i58TiWduMqk4GZa+tXp8lcgaHEQkAqvQwMy8XF3qVqgKTtrVEF4vhnffuHu/uo2lDfzHb3okPTmkZbu8/dWPLv/L8B7/0pS/dfPppsLBgFke7QdoxOvRNO/E+OsB24B3bHraz/dkuzpfLpqN+wGWPUfZ297e3Dz7w3IvPP/38H/7pn/2dt976u3/7b3/9tdeOZvunj89++id+6v/xV/8/n/j4Jz/1qU/9rb/+3/7QZ77/6a987t779z/yI6/cvf/gnXfvIZoQWASn02kYeomBbFsIIAqX5j9Z3Szw8sV2TBqJiFxcXBgDB3u7F/MTBDYWz06WCIaIvO/JNFo/Jjl8E2MAAOcckdXcdUQo7hMCFhRjjG2avJEtc2RZkyZpJAjFn4xVnW7mk5kSUNvelByueu+vSYdEutXOFRFG2Ci1gHW2CRVLYQGUKwTfkzi25pogERoSkCjSNM327s7JOEbNG2Ox1goIIam2pJcrjJLGCqhUY9avUX/O31DCVlp3uG1M65VH2zTz+TyE8Yd/+Idv3rxJBt95551//I//B2Pp4OBA1zhEWS6XTdMFXnMn1ny5epCiJa9sPsolN9USrkmjejY11aqcU3bylVdVm30Tr6NMWn0OQNY2r1rFzTP1G4HSbad+6yvXBSryqqWXwk6V8dcjrGdg456bgxQGluIYLMuKWSjWM1OsiuJHBQAghIRH8cTEvXW6WjPd6s9lNaGCktZ/Sw5nPcJy/8IRlO+XP+t3qefn8suiZrHJ1dEpQJZLuKp5qqVUGdUTrlFeWh1aRBtUArWtm81mk8lkMplMp9Odvd3tnd2dvf12Mm2aZjqdNk1jTaM30WxetYPJACCr4CweXSKyVCGZiyhQM1EG7DJGEKJwXF3ExWkcgvZI0L6BQa1zhU8aB59BJSUEHsdR87k4w6CWEiM/qAfb6211rVOSMyKkyuCRU8+AtH/1wMqGromnpg3IrvgCval3yLAkq5XVz6UCW8eDLMgCkSVGAOi6zhm7WCym0+nW1tak2377nfd9hBt3buNsNiAEgK987evz+XLabv2hf+cPjsueWFrXQeQxxGEYfBz39/d2dna2dncOjo/0Xx/D6fnZ6P3J6SlZbDrnOjfGEE7Gf/H3/8Vf+Uv/l3/wD3/+6Onbf+4v/IV//z/8o7/++S+EED/9vZ/+hV/8xS/+9leneztb+7uvv//W7/53f/zLX/7ywf7hJz7xyU9/+tPXr19/9OjRZDIxiBcXF6oV6StrhEJj23mXrbb/hqzSmRnH8fr168vFBcfQNBYiK+jeGEPxzWiE3lob/QCRDQo5G0E0vU4FcFkvzKBpNXvZ+Hx5P218eXn7bHCMb3NcCSJ09Znrd8NLUnbjKCeoo1v/jZlvEFEI4fD4qOs6IFxRcvZ+rXo+UqLM8naWqqzXmi1WXInqbzY+f/tB6+6dTrtPfepTu1vbAPCtb33zl3/plw/3Z1pIrgg4AmKMGbw3xqAk9orrUfHLU7bBUgFWwcLC0OEJMgAgIqpWAZl61kRjObOWZPnDE5enunIzu6Bm1nLJhkO5Agpj7Zyk1lcmQ82VVkAfKxGr/yrmUS2oREQgZx0XaGjRO2OukIGMz5FmsngmNh5kyIhIFBaoYzlreJBrL8IxeYqIBFfGbj3JK3JnXva91vsys/ZdFxFnUiNYTS2DvFEBQJHMdRRE2RwVRiJhRkKkUnQkACLrOnLunYubS7+KXSFiWdyVY6Y6X0RWy1GKoQFE43P6CkWQUAWUv7W1dbB/OJ1Ot7d3vNeC11Z5HBloGqv5zwrkVOCvAQDBlAEohJap+jRjlXh4WSut6HMVl5VsN3NklZ2afh84lQsXT6MuVO4Bh6zdEkLKcI7R6ytruJpTchwoYhfzCgs23Somhn5525bxcy6JJKIIqe5LRAp3Y0bJIDAhBGEG8CDEzFqLFQLHGPu+nxlDxuzt7T1+/HjsB+fc/s7+g/uPv+a+tb+///RLzePTkw9ev/Z3fu6f2tn2/+xn//gf+SM/8/TNZ/7y//kv/doXf/13ffpTs92t/a1uMpldf+oGI4Uo1tqu65qDnTnCCOGcQ3u0G95taXe7Abr2zDM3mr1HuP35L3zxz/9v/gv48//FX/5//t9fuv2Bwxs3ljGejcFubw0k5753s/bn//nP/Zk/86f+0B/4g/fv3fvIx24+c/vZb37zd5RUjo+P33rrd6DUWAIAFiC81fJtHJTru/Iq0K1bt9rWnZ49mrY2hMFae3F+MY5BxDBzyU43hvwYAFn5jPd+uRxCYLJgIMmbyGwzCHl5hI7kSnqDq7Tn+ifdoEVjAMgohOunFW/H+pd5A15N51cPA1d5MFcfK5mYWUQU9jE42/oYDg8PydnAI5G1iIpeRyDOudEHRCREQtFyCUYwgIRkJcPyiLBI2Hg8VbE0HWXQ7OMCWMG6W8A6G0ZPRIQSOcYYp7Pu4uJib7t59dVPTBsLGF977Vu/8sv/0+7BITOHSLaZahhKIxUtMghHZV7V9OmfRIRmpTuwiPIsRwCpHTWgcI4+orFG1MAHuyb8GEujLsRVg8lsZmGSUOl0AggiK5Q15tK+t0rewczHEaIE/RVWsPVX0AcWJd2sYTMBAGU/dnJoszrNs8cymf8QYhQAay0hAkjTuKKI6ODhkggUIUXk1g5lepaWGUSFuhHWSF7Nr436QEmBwdEYkzy6GkCXiAAIomA5mIv/kFhkDaUsRWvQ6rCipLWS7DoWiVzaSitxMYOFMQYUQsQYBiLbuYbYCqeUuiiCBILAEq217IubsnKHGIoxGjIIyBEgI7OJAHDZkwwiJWGtFldlsUQktZNOc8z6iixA0JTZFiT1g2hLUbUJMtezSfALlt6Cwctk0rVNu7tzsNVtdU3njDVIHOJift51nYAwY4yCBoDQAI0hSIqtaNOFAIQGTWRdwbRYyiiSeBOJIThHKCDZNAVmYJYYMbcDYQ6DHwc/avXw6L3GHY0xBOjHlHtF4q21SjTGWWbQMus4LJRlSowoASWqjIxZshKRMdqzNgWDEYkEOYYy81SFooyydXKJnnG1UxCxLf08kFQYJGWICABCjAQGyGSPCEbFJGEWgcmkA5DOuiEO25Pu/sV5A9DZ5bV9e3Lv9fmDt3Z3d3cij8v5d71w5+/91f/39Vn7R//YH/u9f/D3fO+Pfvqf/NzPf/W3vnb3wf2j/b3t7e2jo6P9/f1O8aibpus6fbXucL+xpy99wCzm88PDw6eeeoqI5hfL7/zMD//eP/yHv/Clr/zN/9dff3x6cnRw8K8/96u/67s++YPf9eqt/a233njzjd/+rcdv3/udL772nZ985X//X/4f/vSf/jPf/4M/Qjxw9DduXds+3H3vNx+2WzshemcaAzAOAwkBMAoRyhCiNUaECS0A6j6IkRGdCFtLtjX9MBzfOODhpBHfuMnFuWduBDvAASzxyIEpMkV2o48nZ4uegSEe2E5G5hCdM/0Y290JAEDk1tgYuWtai8ZLJLKDjwasyQ5BRDAKWwEgUYhWYI5J2uqejCCCBIYrD02iYxRhzl5lLUFZDxHK6oZKBowgGTay0kefEMDKPuz6no6BCSMhE5BoaZ+yaTHOoo8zoDj4ZtY0zx692fXOuMDMITpjJEYhHPwYCTh43YYWEuIQA48oBd0mSm7CWo/vCj2UpPS9QRFAReIpUS5gFmaezrq7d+/evn37u179iMq23/qtr/3qr/7r3f09Vf8LUEtiW0mkrrG/zQHkXlJYOUUBRJO6AVawK7iWKFRmFiT73CErj8VXmUWXlgVLqcxZG8l669ONWSoPrcdfBPBlz3bRpy4TxGpaKnoqjy4+1Zxykht/XuVyX/9wZWetK4pANsYpIFq5VI88DymZmIIIG4nohLSuVJaXvfwsEcmbamUPGQMBilJISeLntrLMrC0IYmQ0ZIyr71rP2KVXvvy+a3Ar5QWL7xqyybXScpABVh2NkFJ/aEQUrJPUVngX2hmJV/nJlKk0kYf3Xp17URI8coxxuVxq6xsAkJzJWYK46l/FZElbU7lk9UUKqSidhOBNBRbGIWjOs+IW5mlPJyOgmvyiPuScVBUz9FV+X5Xj6vbAGDXImlRkjSIr0EcRqxp7wmy8wiW2WDwEZp2qKxUTNta3zLOsI9VA7umpjIKZARkgl6UZbNt2a2trPp/P5/PpZNL3/bTtQggnjx6rKc8MTz9947/+r/9vX/ryl//4n/qTv/8P/MSf/lN/8v6D+2++/dbd9+4hUdu2BwcH169f397ezq2IJTtpGFgK1IkIvvvuu+/fvX98fPwDP/RDb7755l/77/7bn//5f/qf/Kk/8f2f+r7jo4PHDx/88i//kvf+5Zdf/pVf+ZXnX3z2zp07v/Irv3L3/uPf/MIXEWEymZyfninWpjFU6vQk5XAoVnmpNw1qVIQgzrkYPYKMQ3CNBYDrR8eBl4w0DsFH7kc/9N4YZ10zX56enZ09OjkVER/5/PyCGWazzhgTh4GZIWYfSXJXFBFAEkP6DHQpCFvv97XP6d/cTa4sHxHh5QqLouxWfBI22PUTjsuP/jaHinAQSG1V8lUSotWWNs6eLi5uPffcdDoVqwmPBcwBtRg9xljoNovOxIltXcC8McqNV4VslmlCUAVXrKzKI4AxbhiX0+n05OTRhz/8wVdeecWhMMjXv/71X/u1zx/s7zOCMTZ4rrcbYoHTW83Ihh6QZRKuym9Tli+IlrIkj+mGyMkWqt4Wr/AzlE27fq1kd+Maa6j/vHLxTEbaqqXmhlSuPxdGU96xPqEWBuWhXKF6FV5ZBIOev5pPrGdyxbA2XMq1mMH1w6SGASvZUzivSggdYlmR+rnVW6QpLTMgeTkxqzzlcnU+paEKScouWa2UihxmtmTJIDMSGmuspMjCaruWOdwgjGqe1aWzKtQpLLsoE+V9V2rfetPoJEfLCiJDJsXSGV55ZZn2pmmSBWwbItJsZ11N/V4xNzTIahuXKCHzu2oR1wLzxiaczoSAkU82xsREKiu4OqzW1fuyFmmhBYFDauFMRJrtzMy6dNkvrTcpsF8E1XMBkHElcQtt1MyUmQ2g0GqBag5Q43LUv9bbSv91+QAAdfWnG0p9laj3K28Eq0O1zm5vb8/n82EY0A+OjJ3OOIRhGKRpCNDHgIgvPXf7i1/4jT/+s//xH/2P/tmf+JN/6qUPffATH/9o/Cg65/q+7/veOddNW8xMBsDplou5O7K+wu3bT9+8efN//IVf+KVf+h9/4Ad+4I//sZ/9Yz/zk888ffPBvXtvvfGtz3/uX4/94mBv9/T0NEb/hS98YWtryxjzm7/5m69981vb21snJycFYoyyD09EdLa8H5umaZoGSfwwxghEZMhZhyH4SdedX5x3ne3ny5vX91rrlotz10w9y+CZxdiu4yhvv/f+3ffvn5ydL5fetdYYY61xzu3t7QFRP44bxUXGGEFRusVUAqfuxv8/pKBcchqv2PIl8VQYSP0NPsE0etJzN26IV45KKwUg6QRZUlayn2iM4eDaMRoSCSWkYoyB7Mssq5/uXyX9rFJMazGzwZTXBsoRAUrJRvVmkYwZxmXbuocP77/00ksvvfQigCyH/qtf/e1vfOMbx0dHYwzOtgpAUz8OYBN/Y+OgquYEMUW5CInQcOU23zABKXmFy+zptqu70BOuGSLp/hsvXgQbrlu0hZnWwZUigOGStN545Y0T8AolYFNRAFjVtKyaxJVDz039HLJ9sApqRsko07CaGN1JDDk0Srl3AiISaIn5KvuMiASiXkoGkAR5LRiPmh+Y1qK0mk+iF6tYcv36mP29hCvISSV0demqOqR/RxTMfeyJCDDpBYgIYqxN5l2FRLGp1tTTWxSgmoQ2zKyy29XmRpIye2vUggxgdB3K97kfMENWznTtNGOobduunTRNs729PZ1OjTHTadc2E9u4DV3nsodDkkd3NVpmRmaN3ikrLItSLi+fKefQaSpUcfSV4F8IIcSQ8vtyVzST4V2Lb1Dzisv86IgFUd3vSdYi1wJ4tdxE7Evm5VXcsywZsgYnmAOsSvdXYYIi5IqSVG5a9qbOgTFGiCREdVfpDE9mk+3d7UW/WA590xhmjmQskaaZNc72w9A05umnjl96efvx/ff//J//cy+8+OJP/uRPf+KT37O3t9e1rutS143go4ItJ/IAgNwOQccfY3TOfeaHfuBDH3zBGHN4tP/m66+fnZ+89eY3vv7bX2tbe+PmtbffeBtRgGRra2s5jETaPR3HcWybJoSgQQqofHi6b7a2tkTEjz0KlZZQEgQRgCD6Yda52db04YPTj778kf3tnbcf3tva2rKOupl5cP/hu3fvLeb9N775hnMEZCazDrIC0batc24Ioe97TG7t9Gi0jiAUARxjZEABErmyGdi3O/CSsrWm2ta0sX4VVMJ749cree/l03g9iyvtesr2gZ6TwmZgkFAEED3w9sHebG9njCFCFEmShSFhJBQDANeVBh2SLX/UXObbTEr+JrHUPEfqtkIRPD8/f+WVj374wx8mon5YfO1r3/j6a691k6lpHPYyjuOkmy2Xy9pcA4BkrlcR0PIv5QQrqQymDcZavlkJyEpJwzUht1bFW35nliIrcZXvvnKtiAjKinHXHFwrRGJG7KuFCqyPoR5qfc5l2sKNUrnqPibD35TLDa5w2i6rTTpVtXKpnLRMsjLe1J5W3afCIAB1ZrIBAM7NmhAVaCYyc0orQETQUQgAANFmhVt6ZWHJCvLqe0FWTHMCRKOyJamZCYOUURIISXGXJI6WhWj0TESxAnet6aEs2ZWzVBNSkVUiUq/p5T18mfBAZRUmwqj3Xn1btVGIqG26qR6TyWw20xQeY4xwYtkFmqaWvpIdy8ysqMhZWktkFhHNzKJ15PMsdNc8WytRGyNzEureR81hFpEQ9SeOMUru4Ou9Shctg2TEVblaAYopQjpNtZBUzptyThGNkk3VywSfdp8BhDL+zbT/WkEhspSLkq21xV+CiMZQckQnj1jy4GgGXN/3i4vFMAwQoyE0xgZhSXmtGDgOw0A82d6aHB3vv/ve23/8P/6Z2ezwJ37iJ1599dWbzzx9584H9vf3Z7MZAGstcnkJEBCJzIIGHBmBSMhHh/u//du/9Y/+/t/65V/5hf/rf/VfPX3j4Gtf+cJ0MhmE9w92kECrVcg6opTcNI7j4eHhw0eP8v7NggeRIwPzbDr13vdL0pdtrAUAcRBjnE27xWIRQpy69jHA93z3d4tIRNNMd+7fv3//3sN7D+5/+cvfAICtnWkpCmVma+1sNlMF0S+XMUZLJIRGovd+jGwAmNk4q9F9ZhYNVa7vF0aATeb0b3sUTrghwMqOKB/qzX75hCu/ufzr6qEIKKBR5FgBEaIwAYo1Q/DXn33WTrsRIwGJoHEFu14jkivkYAB1Pq/GZuuH1a+6wfo32FYt/5KGQtD3g4h8/OMfu3XrFjP7MHzuc5/7nW+9eXx8HKMsl4NzDoSisDGmBG7TFCDmckuqZrL6sXpuOUSSN2DDcEFEqBoT1R/0voUV6vlZl9+Ur7gBbZF5BFa6zOZz19fvSjqQVcj537T8lzzSBbhAU2mIyOAqDrQxRXmQq9BvuU/hmNUANHq0qtiphsKa/ZJ/EhIUAGYJvFa4vBqtJhowQCqN3fSorzaJIGeu6llEvCRDTlMftUsVAkat9DSGhBBJrFMSSq8fIwMgc6y9x+VlN8zHy5O/8WvZM/VK1b7l+qrN1UwnlFJ1otyPVt+LiLSpatO0bdNMJxNNH81JSUg2eS6MISJLtipkr5QDff0yWilxx6ywQrahjTEg2gl41dhK5Z92G1XDlzO6hWQfz2q/AGnsSU2uMrHGGOSCKorF9V2mUf/M+qKvYx+Sazc5Fa2uU3sV6YCiKUrRO9cAQYs36PL6Js9NRXgxAtoSKvIiEcB2Xbe/v+/74fHjx8OAyNHHaA1a65jZumY5DFuzyda04zD2izhpm1c//srZefxL/6e/bBsaBp507od/7Efv3Lnz9NNP37lzZ3d3d3d3vwQXtJrrYnF2dnZ2973333zzzc/961/9J//4nwPAP/u5vzmZNpNm93t+13d99rOf3dra2Zf9vu/39/f9GGaAfT8Ow2CtfWpnr+/7Bw8eKC2JgHBM4XYiIOrnvTGpV0eIXlGIrbUNuXEcp9Mp+7BYLAHg2TvPfeWrX5pM9+7df/yrv/q5N954FwjaiXZKDipKRdg5p9LXEg5DLyKtdRZVGmEy9A2NIW5bi4aCcF53JcsnYg/Um6hmxYU46i1Wi6Sa112Wo1cK4NU52ZdZX7K6FgBljRUEBIsIrE3kQSglKwkLGeOFg8G960cjcETgyFEAOblkqscyEQGnyrDEWABAxBY2XfPiy6OvhEEJfa0ZyprR8LGPfeypp65ba733v/qrv/b2228fX3sqxohETTvp+95aS2LJuhx6xgIslbRaXj2Oi8UDQITa+9OYtTJHNfkVhQLK9akcZc1ihrS3aWPSy8uuPkglxmpoi7z2xWVXhoqlYbtsqgtry78+z1eOpKazWgCnR69cx+mhtS+hnFk9IoqYssqJmiEKJNC3emCp1XplCGa5VU8UI6JmAwGLtlcmJO2LJxmBIXnEZOUSqFVjWI0EWRiAggjzGDz7GKqMHlFMfGOxaWxLljKukSBGEVtlKSthICXtqhYDG2tRb9ryE1bKFmiLOv2TknwSEeGV5yC/l1KvxFimKIKsSNSsGg1RZprSNM2k7WazmbVOrd5CP5pf5pxDwpw/5QxSiak3NunX+qcxTi9Mi5hzOLVIaU10YUlaXFM7ykJrnnD+yxojunMBoGmacRzHKtZTrNsoTEyY+InEXBmMqeYey5QSEWIT0ZchQfZ41RskW+rAFUkXKqK8UrUAJiLNwS56Q07eQpZowNZ7RERQc4UFwRqDgCxEMJm0TWOnbbO7u728mDMzR68o8TF6RvCRfYx9GC/Ozpnw4uJivlz03n7iE9+hrtdFv/yNz/36z/2TfxICMGi1N+QZA2YggKgWlYGXXnj21s2nPvMD3/WTP/WHv/OTn/DLCzebfecnX/2Xv/SLltA6QsSdnZ0QOQSez+8tl0sR8d7P53NDq3wCVYlKJZsfxmiN91Gz8QBABT8wWGv7vm+a5vTs7Md+5DO7u7tf+fJvvf7eu1/72jcB4PBwV/tiIJoQehGZTNrZbK8xVoAx48gRoHOOBERCYxteDkm/YdQEwOi1DIRAAqJVwAn4tkehRhLgdQEJ+c/CDKFi1BvSFyu6+vYPWvMHrt8TN/mDVuWoUMhsNv2JYww71w7t1nQeRjGoilq9PQs3xnWPbJmRTQFcztj4c+OzJNVbTdgIAJPJ7JVXXtnenk0m00ePHn32s58dhv6pp55Caq2VYRhijJPJrECrgMFc10WlBGJj3OXAhFRl9L+1OcUouMIbAi2IJAFSPNNNZzWuoMPzXNDKOYbZdF5x5EsdGOtf9UtepaGuurwVUQ2Vybsy7DJLqqVsPfMb35efpNL+lPOW8ZdnbQx4bbokgSgRkaBItl027lxujoiSKs7XZJh+YEJDmpcLmWeqAqsdN4XWzXdgRlqh80iqrcIIPPTex6Dd9AKnxsyIaAEBxDlDBG3rrLXGqhkXOARsGoOuxAVjZGNR8/xLWlDJSoWrjloC1XRORIqaJgVRq0qtz68Q60lbzUxFcta6pmmKGarFPJO208xblRlN4xCxaZwawWqJWuIyKl3Ry87wMrFSEj0w9dCVbL5Tjok6qwpirO+g18bKs0JkCyVLBlhT5h5CABEAzB2TIjODgJCq9cLMMfhC9iogEMFUHTUAILIvVEpk5BKrQUSQBBO+Ypq6HXLuIWaVXc/XpPGiGeirqM9Ke60UzPaiZjEzolWtSPtKEdHObLq9PTs/PR2GwRmLKHH0jBACR+HlMDw6PTt7+Gi+WAzBD6MX5MXYA0BgBoD93Z2joyPNz0j7kUEyFEPbtmAwxmhQZrNZ29D2bPqhD76EwvOLJS0uDg/3P/3pT//iL/zS7s7hdLqlsCcXFxdjCA8fPjTW7e7uOufm87m1FgEsGbSQYgcSRAyiMUgGLQrEGNR9aI2LMbIgIkUGH+H9B/f/+t/6W//sF/6lIMxmLSIul4PO3tnZ+WzW7e3ttdZZa1kiBzAIIhzGIUYiAAkxQiRrmFm7twChtTZxyxJGTFSmla+0hih3iS9R3ZQwrqKnG+KppvmaVSYxuZ5J820OvXKD8ERytmgeIavhq/+peEYAAG2A1gf/zI1rdtYt/QJyY3XMmXFKAzFGYKxLMov0FZHssq9eb/1tVy11ESnl8COqXYAIiGnHPvuB55qm8T6+++77b7zxBgDt7x/FGNE4Q+TaiRY8YAYGGscRlICyta7+CsnAXd57nUWNQMznc32ZgirCzIjQOac737WNoteW4Chj0DysHP5RayBWvCZlbxbjNVFPZQFDlagMVfiqJiM1wHXq6tBsuUNZ3WoYm/J1/YYrwOR6aZxzHDblSuG/xWeY0z1U0K5hreSkGA0crOyh/JTkopRKz0CwgrG8IDNoSVJ+btI8vPfOOYgJaYFZxuidc4imVA1Za8cICVdTABEi4OjHcQj9OHgfx5ygJxKUtQeO1prJpG0mHSIu+qVB6CYNEbWuUYkSIxMJgAFYyR5jk7OoSKCVjlUr3UQKarGmGyFzBm0oLLvcuQQyizclhKCe8LSaVS8EY4zihwCAwmhM2k7bGek3inXlyCQIIcIY07K6xhljAMRaGzjqOBEMkVWmpp19IXuDEVENynWwSQaAEH0M2imd9UEFcaUQXkWTqbzNGIMp8xqtbYhGjtE5F0JQV65IcjXVjOySuoOQSoHjhrJSlqB8X6aaI2cAlqjFwzqZcb1qvxwhhDKr2p1Jb6hLAwBAChOw2tcAGitYQ4NputZYRBIJkZnHfvCI4xgY4mLeL8O4XA4X88XFog8cRURwzJtCWLGA+l4Q1CNSXk1EVBKDIRF2xlw74ofL+ae+73ehwDvvvDPt2q5rYtt953d+56/+q18fgw8hLBf9GOJyHB49etQ07WI5NE0DQpPJJOZDN1eZfGIIQ7CkMSIQQEKKMQoZZuYYDUg37b74la9+/gtfPjjYkaAhCBn6ZWS4fu3w4OBARJrWhdGLSNQacQGOPox94KbVjB8QMmZkvri4uNZO++U8CPR9r1AqwsEYU0BcGSH7op9QwCkrLiQisCLFFakUh1ZNReX1iagU9NQUWBZ7nSZXFnPZ4zUNF0ZB6irJNrgWRXGMIjEitFvTo5tPXQw9NCaE0LomO2BWfJ4AgdZCn1DFgOzlYeVNQUUw1+dwTHI3jZWUgumtt9557bXXZrPZYrEIIUxn2ypu+/k84+cZss6wiEjw0ZBlZhYQiZGVvwOSBQaOEgOrLyvG2LYgU9HdqKpukdmqYmjkzFqNmRnKkYlhiEHEWtsk7AdhZshpKSTasAERUyir5hg144b1eFWZqDI59RReppsNnWbj11oklHPKTdaZRaoBlcpNV86Rysiun5KsWAnlNGYGMCEEY0AdWdk+c8aYYVgiYgGMzcRtYgyVPmG0GIyrhNtJ0wJAIFIDzhjTtq1IgtYqHdEFJTLHKJ5jjNGPcRxHdTuPIShcA5BWHwlAtBZt44Cw7/seuLFmNpu0bSsiWDoPZk8E0Uo6EpEmOoGgCqSyXrVipB+KjYuprg2u9O3Xa5fns8Rc084nIjK27F51MkPutWCQtABJe8c2TdO5xllH2UEdJCFUZCPKsBbRxhRuICISkASBbqDiR4mw13XE9CsUHXHlsGEuqFUCFd4Z5QIBa63COOqrWdtE9FzmKqf45lk1AECmSr5bgeuSZqdzqo8SyaXPtIaGtrqw7JpMnJR3be0DkFKppGpBjeiO6zGFwhNXW4y0/mwFxWWMiTwaa9uuG4eBhzEK+8jLoT95fPbw5HT0/myxOD+fBwYkG2JgGVfzn+uMGXQzrhSgSr9Juv6jR48MyjiOr7/x1qNHD4yl60fHW7tnz975wPd///f/d3/9by0X/enpeSRYLoZh8MbanZ226zrXTkCo7+dN0xBB0zTL5RBjNE1jrSWhKJ5FkMGQ03xARoghkHECEBlijJPp1DoaxtEJ9/3oAxwf7Tz//POz2azv+8Vi4b0HYBSWGGP0JqOpK9Yqx2icYTJgCI3r/Wgbp50KQYS0XX3kyAIEvKZuMa47aQAKDLnIJf24HIV9rU/m5jmXvxS57G/OP111cwBtiZaflYu8CHM1lPJV58boD566JoYEOAhrEjivaxjF7wKQQZDWY7cW1mRMcdUm3pr2QKyzqAv1r4r5EPE8nouI9wl4Pca5Ep+PzLxUKrTWttbVWiFUSJBq0pUaf8yBw2EY5heLJCok8Y5KNiTzy3uJUaw1TesQcVj2SuipYyhHAC1cTzPOzBGEDBEgc0A0qsIWNgbrId7LmsqGrN34UP6saWXjnMt3ftKZqxMQ0BBV7SXqD7UeB5XXVATVOVyJWwNVqk4xpJTrl+GVERpymQAYADjJHTJEhFYYQxJjSLYxxmijGxZGY5i5H8dEyO1UBPzovY/9OAzDEAIDgIYOY4yCACERKyJGgZgcL65tu+mkbRoXomhL+SAskY0xgCxCxhikytWZUoV9jFzmeWPeDCJIhFyZpvKAhQ2VJJ2KL8Cqt29elyI/MkK1rBXD6KHqSNu2KKAeIM3NaYy1joxFShiZLKztGaxmH0jxXrpGvXwp22iFf77KC2NmoGjJqQOZV2DIK40tLVulVqr1JoKENmZguPQNWRFGjCqcEpREPoEo9dVh1nToTdVTNakyUcYki5CZER0Bc5UcpyPN67IqdC4b8ErVs7y75p2okV3fs+yyy1uJsr/UGisiDIKGhEvvIGCECLJcLh89ejRfDvPFIop4Hxf9yILGQYzR2PSIwKBIgiIiKVgYlaOuOAALWGAfIhEb6hp77/6Df/Wvfu3wYGdnZ+v+0f3trZ35RX/9qZuz2ezrX/tm03Rny3ME04+DiDx89Gjv4Gj58PHJycn2zkxn7+TkZGtrR7u+2tZ2ronSDMPQ+545MmgXXgNoQghd18UYY/QX84u9ve2+772HO3euHx8fX7t2bWdn5+TkZL7wSGJQkDT4xHUJBTMbskICxo5h1Al31o4SqOSHrqL7EnBVlZCZ2xVwQFcy2EKicFVWs1Sa9L/xeJK0ri+/UgYn5QBQCIHVbBNEDMDBwLWnb4ozRIhxBEOBozGuHvx6p0UUwQioQU4QYEHN0U/F6QqGpUPp+z6GlWairxpjrMtLmLmIc90YJ49PNeI1DEPXdYvFoptNC6YoEfWQ9lXbtmX6Qk7VwcpYVIUCDI0hhItzdd+p/CjutTGMMyLtPFqgdkJga61xTUIzA5QEkqg8K8sJJAZAIPVcciqcrUEtLsHBX6V84fpR5qo+U55AQJdp4vLnWiQjonAqgC7krrujMKNKUaNaUwEgESgcllY5rqhmhD6rbSfFTDHGZJcqxRgUNRNN0utVvG1Nt8vrGNeEEATAjx6RgAyk5GcwrtmaTETkbOl94MGHcfTjGAavCbcmISMSYq4gKwtBRIImCJP3gxr01jBHsYgCSFg6JjWtlewJUKpIr2c2J7b8q7l6sq731HGEei0wx3c3vkTEZG0DEFrjLCJqsCbbsrZtW21ib4xxzjhnWpds2jL5nAu0iEhNVc6wX03TCELxTGDGkCKikqAeY2QQkBKCzWrHSrBtciLMDgBmNo0z5CJGyjk+OR06wU6nkh1IdIVEyJxnb0Xq9bTURF1WEwAMoirqNdfDGkknX5KpMamPtnEb4y/vVZLRyp/0hA6n+apU1FTtBWFMHUmAkAXHIVwsFidnF4tF3/c9EEVhIuIoIYQIAlE40X9mnoYIU/fcvDUQIAMlhjh6b61FsD7iO++89/DRg+vHR7t729dPj69fv3F6fvH0rWevHV9/7bXXnnvuBUC8d//+m2/e3dvd+uk/8tOPHp783M/93PHxsQ+DAAzD4JwLYWya5uLiYjKZbO/uhDBGiBRJkERYAiBB27YKeWYMWkez6dbjR+fPPHN05/q14+NjFcznFyf9MIcYDIjnkBN1RTcycwRISpxBYkQQQKC+783uLoXsM0s7GRCJDMUMC19mXkQAUtFj/eVlUVrTqnL+WqSVdS8yq+a3T7pP/aC0kdfJqZbBAmC03APTeSJikJiwF99sT5vt2cIPgQAIrLXIAimNjAGAUvlSsveQJcUFKk+nbZpOt24IofQwEc7XrFRIAyAxMtHKyVNgbhAREJb9UkCG4IlsM5mOIaB1mrwHZftl1Pi+H4kIzBpAFySGKMw8jqMyNRHROsUSpi2Q35IBoepAiOTqCMq1uYipW8gYfNd1HJmQnDPJKSqSkGYpUo4qAQBA8jqsNu1qR63lK615ugRS2zvJnekAEHI0ZN2FIiKApJwHktTJT5RyeqEVRCRAKblXNQlKPntDMNTMWtNq9CfNGjUmRdAhpwhJNUIoBTaETTORVXMhBkCNLrum9d4zRx9j51xk75wbRu+ca9oOEcdxJGO3trYmk9nJyclyWCz6cbHsc+2pPirqU0hMkb56GGcZwMdA1ESDMYoPjGjU4QagidcUw6qhng6ytoestVI5wnDNGl7To2vtZGO7lmuLBVZOwOy0J7REZJxViGZEdM4pMnDTNM4Zidw0ToVxcTYkVQMw5jyUPP2gd0gaJ0IZm8Vi5IF6YuuFK+JEZ8DYNRlPVXKsnqZ83Dk3mUw452nrvsPsCtDgse41QEOGKLtUyhRpTUOe4cw9AEuFYdlNZQ5FsKp8W81zoVvMcZPVU4ALGZe7KX+gHPYuF9abVKSUYNHaXqv4hsIEkG0s0Dws5svFYtGP4xglCIIPIcbYdV0fxuVi8Bx7DivFQrtBVN26Ms1IMYUjR0SMMfYSQwjG4jR2IvL49MSHwILXjq6/Gd8koqOjIwB45533nn322f/0P/1fPHXz1uHh8d/87/+2gNjGLYeFc24Yg+KcD0P/wQ9+8Nq1a+++8c7g+/nyYrFcoDWFV4z9gIgIiDE0lk4fX3zvd3/k4x//2OmDuzHG4JdAOGlbDv709FSjy4hQqsMDw+hjZLDWFjvfOee67vT87MbWFhGhNUCp/CEyEwkLbdi6l0VjDdgAG7zxqtjfBoWUM/WrK/fslRfW3xRS2aAHADCAEQEIGQQQTCbS5Tg8dXhTnBEIIQTbatQpjTx3FlfvlFHfkHZdgHVDy56enpZNmzRchnoo6pstjIYzzwdgqdKMNfLnnNNmKbp7DTmyOS7CzBwZVk4nxgyKDwWJ0MYwKjXH3K+bsjtRlUrdWqVUMYSgTjnN6moyXw4+giChiRx93r0xsslWkXOt6cwwDGH0WrRCmnKLSGswX5t2ajlq7rnBwespLuZIWVd+crMXuRTKrXkW5shi/Q2XjmB1dDNTVWQhIkDRjFUG0bwMIBAkBpGsKUTWVuVJwCf7CVMeQfCxGjkSkXPOOEttqxFZ670xZmLdwcGBIZeYNcflcqkh2HnfPz6/WGhPu5BwHrQ1ukQkopIQVuakxEGYXdO0hhoGGkcOYRDGGMWAIAhRFIkGKQYFFU8amxKSThERFdlQT3i9UmnzrPseih6Ttqg2c0KmyqObHoEWcE0qEFHXdV2nzQTJIIElhZl0K3ONtWgDQC0DKAMmImMbk+E4SkXchoEOlVogOX1BBVJKZiRXjDyoEgY5leus8gfbtu373ntyzumG0twLGT2RdS7llqv2loXZSvri2n4xuNL3RYV9wQXR6WRmIgsrmwaUdMtCFJmq+8IAph5wUGI3qzJfZtbWw2WnhBCs1fa0VKdbUwVjV26u9GZMy9HHKMAIQCKpW3CIASDp165BMU4kttheXCwAILAAxxITv8zimVPDKJY0WkOEhliQBS8Wiwjt/UePAQjRXCyWbdO++uqrP//z//yZl57/8/+7/+3NG0+/++57IvLgwQNllcr0ACDGOJlMFovxox/9qLX2F37hF7vOOefaaatSn1mUSUqMztDp+bC/0/3wpz/58U+8QgbeW56XBHJjMcSROTRNO/QjIwROrZaCxOTDxGQqxBiZwDnrvbe2GXNyqzJELvLskijM2yr7tXMUoxa6m9dUro7SImlN9F51/rf5dW2bZ5K7PEgAUIMg6iUiiEQCg0RG2D08EEvWNH5gR0ZbT1POty9j1kTgRMYqQzUwDAAIVi3UNClVBwIRASC8otlT3Hj/2gIYx7EAIzSuU9rd4HG8XkErIpACkFB4WmEKukuh0oyKyNHj9PQ0/ZrzMn3UBjUcQRhBCMMQOCdP+jEU69nkqg8Ug7gKL4mkLn2Y6vTTQZX3o7xCEcOSTcYiSq9Y7HW6QVwX9VfR34acwAzCUIRx4SDF4MuzuvKHa3i1BAUBQPvcxYwvRpgSp02FRKb4DxwFAV2TEoWMMdpsQB/atNPZbIaIk9l0GAZE03UdEXnvl8MwXy7m82UIYblcnpyc9H3vgwTPHFNbQhHUZGxZ12dRzRPmfmBrrWVczPuL8wVIbJpm2rXnpxfWUtfYprHOGuecdRhjLJ3qpUJgWN1zfSaJNId3Te0tZCabbFTpUKEZDeQ4JeVmtNY4FY3GNfpo51zXNZPJRE+zZIiobdts++qjCVjAFAJIXFVJ0RA0TRPCiGhcxo5QdsnZgi9bBhROj5IapzLSh9FyivcjoiFiEFulC1hrp9OpKjq2TXa5AaPjTyQtoJnPTdOU9OkNJ4G2ZBDJ7SwRU8m4CGQNfOW5eQIDpcpsrVcNAEiADAXJbSjrq4gkwZatHN2FAErYpa4OwKoLeBSxWdJb6zyzSEQg7cdcqrRZgnqrfPCRGYDJ4HRrpr4E5hTaZAABhe+Hy2EsaywzW5d6SorIOI5NawVosVwux+HR6cl1186mWzdu3BjH/oUXXvjQhz40DsFae3Ex/8KXvoiIi8Ui5lbNzrnFYmEs/eIv/dI4jrZBsqQFQj6MCMY5S2j9MEjkZT9/6dnb3/nJj+3udNuT7uTxQ4vQOTuEiEQhjDHGtm3n8yUYyqIOEbWHMhrbiIAxBoWJwHNARBAYY1A3j5pqpVabn4DCkRmdRknXw736Z8UJlVquvM/GsaFb/9sfGxxg7TdOCxlBNJDFzGMYj29d3z7YG2OQSl0WQgxJ/CbmAKAoqvUwQQu31AVdv3mOcK1YsP5UyoQ2hJ+eqwJoHEdKPVgUI4nHOMQYrSGoj3wtMyfmxwiU060RrLqF6zglaAGUmNzqmUGMNcwcmVFS1YFORN8PAKglfeM4Ztc05V0HgUNrDUfxY1hhKROimAKry8ywsibraC7W87OhWOjhzKr74ZUCdbUOaqnw5jkbOxYqeipPKVm7JfxZbNYN7X6D6VCVwFIyk5mZcjPto8MDABjHEQi3traaptFqCw1ShMBj8EPfl/7qOIR+9OM43rx5c7FYGGPefvfd8/NzRf8ZhvH0/EwdFSdnZ4gYomg3+DF4Zja5zHTtrSszKNFS8DCOzIwAzIqcQMwKy8EJoSyGvu8n07a2TaWqRLo8/3kxr3BFqh+lnFaynYuaT9oBqVT3Zn8fERlrbW6o0DS267rkjjZWc6Gdc0RpyYqZrvdX8IqVV4NZRJbL5WQyq85J6CiYvV6Iq7cREWHR1RzHsYjJ5NIILCImK9YirEggWqmvsepxHInJ2p6ISiNCSfplsFW/h1pGMqDGnct4KNd8g6wykEWSwkVExrSFVjcOyhW9mI31NYGNa3sk682ImOJTms9ljFENvmgkxVYuG0EnUdcuZewbhw0BRw5xNpttbW3NZrNFvxzGMcaIJAaMcZYMBmYDa7AEXM0MigCk7hcEmmmDggRgFCw6MIMh4OjQ1VEDdV3cvn37Z37mZ/7FZ/+nX/qlX/r4x151zn3wgx/8mZ/5mb/4F/+i9jVaLBbOub4fdna2bz3/zJe//NXptJtuzUSUBwsRCcOy78fBH+7tb8+mYex/9Ec+g+JBAgS/OD+nlC0I0+n09Oy87xdN056enkNu/xyYKfuiNSmkQRM5NI0VJBtZxvj48eMXnrvTdd3YL2KMWFdaXuXdzb/C2pIioqRe9084XzbOL6fVxJ837NVmdPleLnkp4Co+zCxAVCCgEVHTMg6ODq1z8ziatN1TaZ/TEqHcZz2/e3kFTNs62/0WTaP4+TqaKKmc2biGmQVABFBLCABE2BinFJZtd43iAKIG6jnlgIigRGesQCzSmojAoD4YbXZhqedM+WDwsWJIwpyVKYgKPgLIIhxigssQIOf60Rtj9AWHYdDhTSYT9Z5phDILVtEE3RHIs3hWexpiDCIiPjrnjCF1WVlHgOjQaoI1ql0iKeAN6itEEmE/RjWmiUhAJ020t55EFpFkZ6/AGVLWJYOmD4PCU6hygVSFbBMeMhtLxhoAkGg1HhCDLC4GkV6fO5s1ZDoAjMJIRp1xzmoFqnTdtKPZMAy9H4XRNY1pOkLjrG3bhgTazh0dHezs7KAFY8zh4eFsMjk7PX/33fcfPz5FMcMwLAZmhkeP5gwxRL9czgFgHKIGOE8enwJACGGMQUTato1RxnEcfUY+Atcve2Nw7AfvvSEyJtVh29wxt+yryiEVOTKHokpT7Md+8LPZrEHgkWnoAcU5N5t0k8kESGzjYoxxHAVIGFkEjLLpXI1ndAGj5O5nG4oOpNLelX+SiKytHb9iCLTECBGFMNXCOTLktrZmerfZpLOua5vWaJUdogFstdECCwo11jCv8JKMazBijNG1beLRng15ay2iAEcRIxij2nmIhkxgTasmEVYMQsnpSH0/OmvaZhsROaQIQmudzcRmEtAlEqBYGjnEMM5mM/VYIBhnnTQYQiCLDTTeDxJtMGiEIghZ4wk4QkSMMXXIoJVzeOVjKKKUiGIMmsekHSyJbPSsZb4ojAgirLpN2zQxRjDZYY6YE0FAmNBm5zmh52hNoxJPWYkWzVlrR79UyapjgCoklKxkAEOk5VkE4KW3hhBhHAO0NDHbs9HvLpfj/ftOUMbRimBg771hSyIBwRpqTaPhEkREYwMCgikqVLHIY4ygdaKCKOjQmIAT18HAZGS61RmmCTnxfvtgOmnaH//RHx6o/RN/4n/+B/7A7//EJz5x584dY+Un/tC/94/+0T9q2xZJYmQOsLez+8zTNy/OT+/duzdGi1GcwMy2w2Ieot+zsw889xIYMo7Eih+Wk6kFi9LK6XiCbuKZp9MpsqcQYx9GJg7Ode18OI04glXuyDM3FTYNhwjeta0QYpCG7ID9eRxt15rArSAZA4QBwINwY02MUnzRjLmd+spIYExdc0CdKFE3JAAmONvMGBNzkHw+V3AFABoRXRmNZUdr55QivS/r+peFcdI1FWNbJIIEQh/ZogEfW+NYxO7M5lbAteMwzshCHwNHsqWpSZa4mnkAbIyBbNkjQsqrMGSxQhioFYwQEu5jicJyxomNVZZBbQ4m1UNV4azImKrStH7z8tCNN69duGU8vN7Wqr5EIQPrGdRhlBoqqLTpYtlojFBDI/qscRyJNGhku651ziFJjBGtWdWwqn6affrMzCgGTfYoErMECZpGqQ5bMBTDyver86CR10SCVQZyKZtUHaJEMcs3iEjkmNlHGf2or6N+/hjj9vY2ORd9qrtl5n4cuqZlFtc2xpgIMnW2cV3giGhu37499kPTNIeH+9F7AD46Ojg4Pjw/Pz86OHj++efffee909Pz9967u1wMQwgc4ezszHs/hiFyWCx6AI4BxnEchsEYQ86q81nHI4JerWZmEfFeMztoYwU3CONKzXTjkJRhAM65xjUmBVYb55xzKfjHEQjFZHRlqHamCkIW3ujgXYhnQ7nWzzGXnmfzbnWAIQ2EO+e0VggzlFjKgTZpHVFSrlCSOKJV9TnHitWsXcURIvsYLRVhI6s0OqxiEIXe1NmgrfFK2lQh+HJysbdYW/dlYaxUrbdqmibFeiXk0F7q4yRIiMgIJuheR03YEQRjV1USRGSyiyMlWhsj4op9r56C0ZBVNRyiQUpYXSQWyfNqzJmrUclHhTIsIp38ZBuwgo3wcrlUTMcNAijcH7N5vfITjIFcS7ksGxG3trZu3rzZNM2Dh4+0FaBr0GRsZwGOMWVfmKp2HBFFACKUWm09PHsiwxytNSTiGock1prd3d3t7e39/d3Dw8PpdArAOzs7t2/ffvbO2yDw2c9+9u/+3b8PCN/5nR/f2dnZ29vT0CERta158823b926dXR09MYb70yta12zPD0f4vx4tvvy8x8/2tv33ocY+zicXjw+ffyIzO7E2OXF3CKJIUOmMTaEcQweM16KzuswHyfTbhiWjWkhAgGaxjkiH1gdPRKDc82iXyo9pKkusx032f6Ve1lkjduvtmT+jDlUl+XUFayjZhn/Njzk8jDqP7MMBc2R4BgNkrD4GJj5+Knj2Ww2qJTUVEHVM0VM5tir+6y8TSt9opygkDqy8ldXbKg4M9fcbpma+SqYDqh4q2IaJM/Lej1l/S9UPG5jOsqI1VG0Jubz3XTvacKIVF3qajdsLYkXi75kSKqcMDkzhDkiovdeIxkKOuNHrcqwCMwECIYMSK4PY2ayhIQMSTfRBDgWQFYcAlSLmDCFVIgIGGJMDdqEtJ4SlSUSETMwJ4Aba/PkMDAyIqp9GatD+WbQ9qWaJkBQ2CWgAZIQmIHato0x7h8cMPMw+Pl8fnRweHJyYozZ29nyfjg/P+8m7umbN7qus5aOjg9ffPH509PTt95652h///W33prP52Pwi8VCJAppklRERPCgHFDttmEYxjHotik1JLoEpVFPvd8u773LdJX/TF/2fR88juM4adxk2iFiL+y9b0xnjDHkwEaogqmFMwKoPVy2WLWHq01b6K0oQJB9v9kgtkXEYkJ5lKZpDDnNWy5ezbZpjDHqiJboK8EZJTfc0/cXbSlhCKt+R5E9QdL/SLTZ7apWG7IuWLg/5paCzKx1+TbHbnJP+LU06bSp861EhAw0rZ3ECQCQARpAC5yImkgQomXFogqEiGKFBWOMMZoojLnsBBGNSbB3qv7rXgOQEjR1zlnrQNOvIrMIJeB3EhHGFejVapyU+1VobiaIEQEhJFRAGE3VbFL02teMq17umgAgl2yVb4qzGhHNtmm7rmnbyXQ6m83Oz8/PF/NxHAEZ0KT8waQipzk0xnnvEamkSpUJx0jWmhilaayITGdN0zSzSbe7t3N0dLC7t91Nmq3t6fXr159++umt3a3XXvs6AOzubI3D0rXN5z73myKwvT0JqU9i104nw+PTk5OzO3fubG+/xj5cnJ9c3z/8nk9817XdgziMi4v5GKIxYKNM2mY2mWzPppOt9vHj+yLSWOca65z1fhiGQQgDR1LMwSF0XQeRu6brz/zOZCt6AAgCxMgchQgFCI2MQ9QaxdQFFZEEMXVUA7gkETfYu6wfcOm4/H3NOuCSQr/+iKtjw4mvwro8r08jRAGyNI59tzVZDL0wttPJ45OTjzz7Ids2S+aSjFm8d2U8+W0QL5Ue1c+yimpWhpu14xU1F5M374G1F5b6OdUHqMRq/efGhfWgIdsZG3sDAACoZGIrp6vdlWXWlPWoWNXMl2L4FmaaszENV4UNeqb3wVrSyGV5kA+jtbYhxJxuo09RwC5kzf1acTTn1FHJLOIjYyoix5hj8+qxDywhsnb00+hYjGUOEz6RtVYrNzBjRIiIrggzKwK2yrmu6wBw0S/H4BvrmqZR6KuUhsMSlsN0irPp1jAMbdvu7Oy8/fa7jx8/vnZ03Lbu0eMHT117aW/vxsNHD6ylGzeu6yscHu4S3bn/4N7du3eb1no/bu/M7t57AABDiFpRLVUG6TAMyv5iFO8HrEHE0upLCLGmjVr3uiwIRQpqZjkh3S1Gb01rjAnCi/kyhLC1tdW29vT0vGkaTQkBAEKwGzbZOvD4Zdagh8bhIGufUil/tfQtNq56SmfTbQ39lpBEjOBcggWOMaIIIlprFcokP50ZElKuEJoqyz2fsyIwARCJAAYgNcF11jELYMoY11HpS8XodS9DTilXMqvridWo9d6D90UkM7OKYcxgIJpHEg2qMS25sEtEAFPRMDPHuIKBVE1lFRpndtaKiDFeWbxzLhuxinxhgTRuqlsSi+nIIpBr3zHD2arvwZrGWjsMA+RQbko3M8Y5p98Xo6RoP5cZVP7e5Y3slF2EEAwJEXVdt7u7e35+/vDk8fn5xXK59N6PmUOmaRHRObStZl+brA+pJCBoEzIzERKZSdtsbc/293efunFtf393Z2v76Ojo2rVr16499dRTT/XL8XOf+9zOzvTs7MwY0/f9wcGuMWa5GJxtdcL7vnfOvPfeezdv3rx9+/Y3vvJbf/ynf/bZp58hz+cPHz9ePJi5FiM/vjgZwzDbnm5Np421ljCGsbGmdYphRcwwDAOCYQ5gLIMYY7wP4+gxwMR14KUxzcAh+sG0nfcewFhnJXrTOHIWEEsEFxFJNEC5GeOXHP5cbfknCKdaPJdvNkTDiodU8FVrD0KgJ5iI3/7Qp3pm51wcvANyzo4cZwfb7e7WGEMkMM4YIs+RRHnLWvenwq/gKlajn5O7TPetCt0itNQzxlxAWbWtZhRhkZjS/RJ7vIKBYnbvwLr0vTytG1N2eabqN+Fckp95YvHwqFHFMQpzFOkr/6263xmANfNwGIZSv6RPUatlHPthGJTDqpPHGROjeK+IAakpVYyiyVnqlYYYMTvPgzACSqk/Vk+7AOb2mDEyANRVqgWjSi2rGDkEVvaULBIyGoBUCzsK++C1KFQIfQixXxKRRQomDjSY3jTW6eVbW9s20un5qY/h9rN3FovFvYcPDg8PX3jhubfeeut33vjWSy8+f/ro8cOHD9uueemF57d3Zl3rRCRGD9IcHux9x3d89K233nr3nbvTaRdjwhk2bLz3xtgQRl5lyoAKYFwlsUu93CUkVmtda7voyd6qfKuImErJsxVlpm2n2tLgQ4s2BvBjAEHXWLIWMYOBW02I0YGpKFxBbhVKKA7SDU5RJFYRw/UBRNY0mmCltJTtYKfbylrLYdSUB+ZAK+8pYeJchIhtjncAgCYx5QemNH+QVb+Bsr8A1IKMnmMDHWSU1pIYrLNHRIrYWmabM26tpjeHEASx6To/n0ulpxIBWWsMstWwvQqbHKlHBEgoWsxrog4rt5lqK3q+cZaDZhulDCmFpTcoRSmps/oxJcVzjOotV0UWJUKElCJanCvMHEXcOuOT7AMDgJJvtXHomDWUq+uY58ob22i22vb29t7hwXK5nM/ni8WiHwfvfZ/TEtUA0PTP7KJSgFV17BsfvG3c1mzmXLu/v7+zs3Xjxo1r14+Ojg6Ojw53d3d3d3e3ZtvT6XR///Ab3/jGb/zGlz70oRfv3bsnIlYTo/yKsXjvQ2DnmkePTl77xrdCCLd2j+/cuNWBOT052Wu3mj28f/++YZg1Xdva6e60aRpE4RDYh2nXIWFjHZB478cYGKz2BuUYh8G3rkGU6XRytHu0PPPzs4VpKSYw97SmEsa2bcmYIJwqqRmF2QACIlcMX13Ul/k/rs9/zQdqHlL9tHkaXLrD+glrGvaGwo3VhZfldwAxZMI4OmOI6KI/v3bjaXHGaycaQ0QkMWZFonbxUi10nyQibZFkhWUoGeX6S4pRtACj5puFDTEzovbAMYU9lWdcqbAUXnB50ms5vTEdhVfW61czoJpRIqLK0cLgNqZVv1QOrqxKt1bfu9PTx4mbD6MxxpENPsagLWnTzZU1cOqzjQAACvyEyDGIcAiRmZumsWRjBkzXPROqUDoiGgPFqWAUIJPZxyiCDBQFBRgRhExkDizLYSSiKBDCWOZQ330EdoQGTBxH7TvWtu3i/nh0dHR4cPTWu29/643XX375ZSG89/DB0f7BnTt37r7/7sXFRdu6Gzefun79+OBwr3VWIjetG4RD8G1Lx0d7164djeO4HIdvvvaGc2ZQt7MQkiXS9BZQkBBVlDCL5EItUGHZXCllL5P+6nNuGlHTiYiQMc65prHF0afSAIA0jV1SlxgBACR1zq+pBapUQWWuYZWBXAw4zGaTMQYzEMpaiNdaYxxmJ3Chz5TlZC0iNo0NY3ZIBo858FHtgss6slp7VvdmNohZYojZ8xkjaAGC/tkYixJZUOlNEZLrPY7ExpSNnDoc2IxWLTmOoF5iAOAwihCiBY7W2khkrSHCEpYSSWo4EYhYAaqXqdZpysoaYxAtExtjrCjAPRbfBnDIJB1KmR5ljPGmadVkN8bE7BqBnGsiOVSviPF+GEzVoxAqxldLgpqf1GOGbG0rPVgHrjHdpJnKVETGcVQdXRlCEbolWpE1Ei5ajrW2mbhJ23VdBwC7u7vGmIODvYODg27S7u3tTCYT51pEdE2Dzr797jsGgShxKkSMws62KuZFEIAmk3Ycx8Y1d+/eXSwXr954bnFy9ujsYoLN/s1rNsiy6Xgcjq8dcgNmim3rppNmOVxw9NN2ykRkIEZW6MMggRGIRIIoHY3L8OKtWxM3a3l5uLX/YPmo92M/+MY6BJQQYwjUNWBImAMLQOprpFWlxU2ywYTXBdJqV9aiF6pv1oRZVTmyJt4A6qyO8rgIYq6Cg15d9aSfRMhQCMGRQcBx7IPBZm9bOofIxpDRFk9ERCQAEiJYFEFmKMhol1527VgLJhX0PsnKr16m8kPftCjFhS5jAnsTRFFl97Lc3ZiUS6wnfeB1fWRjLhBNSY8t816SxYqaH7P1WT4X3qpCN3NMoyq/xo+1V3Hbtnt7B/oUlc2MEHKxrIhof1AdmIKCpigekQAyCwuIQAic27AQs4QoajiuNJtMXI6hMEfF3gqBVekpHkWENFTd4SokhmEAQoVessYKQIgjIlprI+Dgdcmw7XYenZ5cv378/PMvvP32W8a8dnx8PAzL999/9+bNmx//+MdPTh5tz6aHh/uHB3tEgAQscRwjAnAcln0gtB/9yMuTyeTRyWnfL2bbOzxfRkQlGEMacYeY+U7hg2WHlOXeUDPLn4ibcnG19JfoCFEAxFrLIuM4IopzDiElVPsITeOstTEKQAsA1pnW2ci+uoOiKLLm0q9/v8Jw2LBxlWZi9MYYzX92+TDGKbSqntN1nWSXUiqj5MABYoxt2yoiSE3DQGU3rbUxlgTNjcZqp9SkOJqU9UFoaBzHDD0hpfMVAWjAVS3yElcCgGLYcUr0WxXf65hVXE0mE91cbDThaORA1loy6HL/MZ0TBb8sLugQk3FWThARbQsfYySDxpjStCCXDClXZQAtZzcALCHYLMZEJKU8Zv9cLSxNASpZmQSpO6GeRjkurusYc1FlUQtqJkOlHVNVuac7V3WgKEIixpjpdLq1teXDoEWPJdFa9Z7i6YEKgxMRnTNd1ym/0jCZtXZ3b2d/f1ffZWtrZmwDgODcwwePE6MRnk6ni8Wi67rgmXKmCGS9dtJ2Iti4bm9r9tpXf/v3/+7fe23v6P033qXJNu/7O88+03M/2hiMH2VgDkO/MMZYawCtRRrjOO+HwDFEDchDCKHrJjIG8fz+u3df/cirN3afev/du0c7B4u+X5hlJOj7niP0i2F2fGSthRCBkIAIUJPcrxQ5Za9tbPwNAbxx5pUfNmQEQK2ob8rguhLx8h14PSJZjpHjtGniondNM0Rud2a7Tx1Hg6JsigUIFS9BEuB8BdpTveCT5iGVx+B6sR0ilkLPBIWTZZtSUnlAlr4rfUTvVPq61yReyHFDSCMmALN67mqWXU1QcrKXzWZy9xVeQdcSrbtApepJUl5HgfdijH3f932v6RVqtmrqStdNbOp7KsxRsSw8AFHaUbiqd0yPKwCwIfIw+hA5Rt0t7H0Qgcg8jl6yNRxBtN0GIjrQlHkJHKMwIvrIDEjWCEIIPAyembuuG4Pvx2H00RgjlDQMffQoMfBcEf8Z8OxiPgM3mUy++a3XP/yRDz733HPzxflyOd/d3YXILGF7e3bjxrWuscJhMmmZI/tgLUmI5GzkQBAZ4mw23d/fPdjb3d6ZXcwvRAiAWYIhkxkN6/xns2wTgLcIFayKki9vg43VB4D1jbM6VBERAO/jYrGAyNrXz6JhjhcXc2OIQabiBCwRGFQJp/4PCzmb9jILwBybKZZQYe6YgSFVADeN+pxbk/ElCKDw37zxBFFMJhIiIMCgDY6ydlwEVQjaFCTNGOfUd4PkYzAm2mxzIyIIEpIKfoVVYWaFwDTGEIBI5NQzL6VEQQKHsikomwuRnbMxRjKgqp5kVCxmNs4hCoBl7cTO2HQtCpTWoupYjCEJ4NEnARxjqk+TjHijqfIlVTvG2DTOGlfMREAxGcBcJSURQQi6QYhBRCw5Q4bQAiGU9Agi5WMlY0v1JzXlRfOTcxpXHdovZHmZAnUVSv6atZYIwKARAMCU126gsemcpm1KzKjW5JTj2dIemHDWTWKMmqZARCF6ZAEAJScyjsiga2GMX//6a7o/nHPz+bxt2+VyaWj1FABQyF6Fo9rZ2fnSN770X/7Zv/Dic8+f3H0IIshysL27DEPTNUyD61oSEeIQowKMWEuAGIN471lwDAEpoa/E4Ofni9s3n/nM933mrddef/vtN566dmPWtg2a7clsMQ4P+1HInA/j/s6utRYlRJPS/ZGFBK+c3nqXpQ/rTohyJtbm2VVOsg0Jl/bpJRmcNrJ6Ktf5jeQsqbXnrtmErHmUMfrA8eatm2bSRofRB8dSOtcyM4g0zgXh+vIiWDcmoUg3W9ILKcFopNzgckbJa4AcOykvnJXo5BmmVc4IFhFbJ7XXw+JcC8/ZWkpUW42vWANFpgIAom6JFZwNXcpTjRk4Wp/IORxbdnWtpOtO0O/7vldTFRGHYej7vkiUusmoMUYkGGMMoBCTQPBBWQBETXVJaWuL5VK3X2TmnK/BCOyTLRvB6D63NoVIx9HHGJs2KdExyjD4GOMw+HEc0QVhJLRNg8W+VwsjJ0ABkmR/XfPo0aPj4+PpdPr2229/5MMvuwYXi8W1o4Pz8/P5+YUIO4POmZ3tnXEcptOpeIVuEIneGsMAo+8bZyad+8AHnnnnnXfOLpYX8+X9+w/dbOo5ep9c+sEvJtOtcRz7vteaHFNVoClhGIvaWWtjv9X7ZENgX0m1UPKosxbY+9EMBhGbySRGD8QxhouLC5HJDDoRmbQNAY0+zCYTXWIA1HJkylnEqneqkMBsS6nAS9ZPjM4l16tzrkhfzKlMxhhnrUF0bVvkAbAqYSGhQ1CpW0NEVMeG7jJmFlwFZWuFVaVm27Yh46JrbpdWkBcK935ANEXEeu+HwZMx1lqNxrmmKfudstNI51zbqDRgQwiuMUhORPwwT470idG0I82lN9pcVygnK0QHNsZo7CpvGVcR3JWSWpQVNcQBoG3btLlQRZ3x3gOTCNfcSV/HUVJurGmUzRhjojAaijHSqiYQ1WbVSR7HkYxxTUMAirKpqyw5GFTUoKJpxVy11Y+jawy5FdIc5FYxTdPEcSjcj7J/Qp9bOJ661pQjlUmA7HIwls7PzyeTVgMZzGKdBbTL5cVvf+3rXWfm87m1VgTG4J1tRZLVNY5jY1PlFQK0rXv06MFPfs8PfuLVjw2LQSL7fljOF8aRm5pFHFxnlrwEAzH6yN5a0zQNAAlgjHHox74fIxpCCMFLZA5xXI7TZvrgvbth6T/9PZ+an1/ce/z+/mxnCN4i4c5eCGOcbVtjWusWywtrLQcfosLcgcCqy4WyALjK9sUK0lgqYQyVoC0coPxUU9dlXlGzlMs8pPyKlVV9+XzJbb/H5WhbOyzD9tE+O+yjB8rqBYLW5VskqPWASyl+ZQDlJ2a2hdnVb6W0Xrin5PQZzJDLUNWzX5qCFdaSao7l13q3p0FUw6KMUbdhc9SkXN1BX2OFUlnbWCqN6jvAuhEGWU3GrHmUc1S1L4fkwkGucnN0inSbaVcJ55y1FGMcYwiRJazkB7PmrBGzjJE1DVhEWIAIfRiNNrcWiCEyhygsCD6yRVIcHWYOMfoYA3NYDLLuqxBMxK6BpWEYgLnUIhvjHjy4d/vO087Zi4uLl1564bd+6ysxxmdu3VosFpZgd3fbWbLWgESO3mYMkBAic8g8BQ8P9oyxw+Dvvv/gm6+/ARKF8ezi/HQcmFkQnXPCQV1/OocbIrasPKzrm3CVR2hjw2z8WW8bREQ0iEabx/XjkEBanA0cLy4WzLyzNevRb00nKms14YiFJ5OJgtqXrGCp6n1V6Cp5qK6jDRUwZ+gUXq8EUKLCAIAiVHJ5rUVM/N0ZlZpKrqJ5uxylxHGj1D37sIRLOO/HenvX01IkXNmelDKQA4AAcILQzEdRl4sBKjlAWxaodRGlSSKfTNd12tdBNQBlEYqGmLTJGE2ubyYC1d51r1lrcyQr7T5RbFckdeEyM5m1sFF+nZUrWEQIyFpLLtU3E5FxVlsbQqkTy6q5jk3nQRer73sOoWg/mCsFislhqp56StjGrjoxl1dQ8dw0DWWLp7ARzAXBOleJ/kER/ZCQiMhaVYNEvfrGGOYUt5pMZ2GM1sDpydkv/uK/vHXrlrroABJeehTmMairU1U37ci5s7X1+NHDVz/5CWvN6cVjDuN00sahDyJN03gJ1NlhHJhiiAHzhChTHAbfjz4woElbdblcNmh87F987sUXbz+3OL5YnJ6fnT4+2Nk9OzvnwV8/OHREQHL37d/pjKMnpBYXUwcq8bnOE1b1vhvi9tt8eeU36XvcNHMvX1Vvn8T5ZQWKuXF/ZnaNGzkcXD+mxvoQwJIET8Y4Q2IopPeCLJXWhi1XSeUyOVYFKud6X6x0kLItdZYkpS9teo8hEWvJh1h73uWnls9cjaPcSo1RrsryNpSIWh4DlHDsKmOiXLUxgHJ5YbJZaRURNqb0zRVeLwosAg8y49AdqDAFekLpfJcyLxQjvsoPjyBlSmMVfCqCX+3XEIIWcqiDKITUli6E4H2Ikb1PgZ8igIEREVvXiMh0Ot3b2wtx5BCZebFYaDT04f0HH/zgy0QQ/fDSC8+fPHp055lnrh8fTqfT2XQKwAia02SQtQZV63eTqo4i1trppHvm6Zuta0Y/7O9uLZfL33mj5zAdQ1wsFq2zY4gc+tTt8pKNKxCZSRGCNuihXrKNnwg3d9PmyflBMUoIbFJOLAAQcxhDqgTd3d6aL/vWNYFZvFfPx3I5L/Nf5GihLsquVKrKylV6aWzVWkuUWLOzLQBYa4phR0SKC6UyW0QsGUCW3AJPR41o1GWt4jaOa+2Y9HH6U2lRXMac7llRfmZ2bAwaQ5qcXM8nc9A2vuqvrvcjIubWNBiZtW2pSkdNaWzb1jUpubrrOk07UtVBR1s8BEVRKJ5eIts0jUQum0s1YCCcTDoyCctWsr86xhhs4BB1O+hPwCyMtm2q/OTk4NXXSeLTgiYMI6K1psj+tIuJWudqgc0ZFavwh2RdAJhcE1wUGszVjDqxpcit1uOLlmNyK5oimDUburoVEpK14L3fbRpDFoAABMjevXv/7r2zV24+c3J2Vqi9OPyUewELACuAyXx+bg29f++dew/uPn74cK/Z2t3dba3r4zDi6JrGtu4snIGB2EdVIAkJkVjMYtkv5mNkRrIQgoQo0RvXbNnZ8x94buK6eTj147g16Zilde7mzZsnZycT24CJwLy7s2MACZLo04bKinuAVCV/ZGQrWOfqV4q9jc9rX16K3D/p5Ccd8m3t5rWDBRDBmn7on711o5l2FxJFgAAtGWMMUzYagUoItn7BJw1MbeuE9lKkC+ekKkSsVJOVKKpxPSBJPsJc6VE/r1AMVCZLoXLO8qMIVL1n0THpUleDPGgsjGbjWbpJIMd664konKtIBRW9pYq34mxrXAmyJ7y+Vn/yIagJwyLBewBwjanfEQiNgPaE4JjcCfq2unt9DAZtDBxiDAmmAIBQWAQhcIwjlwFwjquu81PtVwIDS9/3muYzm0w1SSTG+OabbxuC7e2th4/u3376FWvttaOj46MDlHh0dAQSLy7O9nZ3rM3IGAJFuYbUbM6w9r9jmXbt0eGuwG0RefDgQQz9a8P7h4db77///nzZEwgawzFoV4+yQOnOCKgo9RX91IoOrruDVq+ZP20oUuU0zqGQEIKivBlEh8ZYi4iDD+Pj0+3ZFqXpN9tb0+Vy6f2guMf5ZZPVi4gls68Qj3bZ6rpO4aWKBayletakvpnWNCUhKLsksSQikYEiaJWThhCisLOuyOCijBbdrpA6M0tkFFBMaUREQ6n2MWd11bsJAIxBa1faAyKa7J0GgOQSBzRgTZPiBSmXzfu8oaBEcwySZjKp+SvZFVT2uM5bHnDMWy7rCtaleSBSWJLlctmPHnPOVMzgdHora20YfQgkEo0xKBKN6cexbZ1zTkenyYwbzLTMA2Znvogsl0soQGZEtoLxazkyAAEAAElEQVSCofX+Y1i8gGqLAxVjugTg9a0RsW26UjGli2JyOUmRsvoglchCBhEtJqNcq8JYoraCbVzs+57F2Bi/9a3XAaEfR2Z2rvEx6BprmXhaIMBxGJrGGoMPHj76nu/+5G984fMo8KFnX7Zb+ybi9t4uzM8uzi/spDGO1GDxcbTWGmMJUARjEC2nCkRalCEik8nk7lvvffqTn9qezt75nTfjcphM2tZZLbjRbmZ+WG7tbt1++plp29BaaRCCkIAm21VySDbF0tX7vTDt+rRK6OJVrt36UPF/5bFBKvV9Ll+BiBAiOBOEpTGz/V10FpghRmMsEWmbQuYg2gtsXfo+aYQigkioNMBVtl61gXV/rialfIgxKoPOs7b2RoVhlXHUAv7KoeQBZX12vU6gKKq1a6i+Q81uoGJYsL6i5SmyitZIgaOqA8aIDLBym8RcXK9D4owx1HadMQaQfVAwZywKR63+q+ELV4kcBgEBzIUxJcpeLLB0K17LMhDOWNPrMQx9i4uLi2EYtmYTzc05Pj7+vu97+uzs7L133tyZTRfLi1dufuja9aNhsZzPzyddY61dzi/CbGqMMyDCApBqKpbLJTM3Ge6YhZmDJZl27eHudpDgx8nTt64NA04mk/niXESiQD+OMrLJHl3YyF3MevEGXdViuKzUBuFtfC4uSsml/ukmyAJamC0GVIlEBnr73fduXL82m836fgnArTPGuMViQdmHjOtHTTDM3HXddDq11sboi/TVihFEbFyj9GmrTtX1sJmZo6eMyaU2WSJmScJVSdG5piY2FYdY6aP1dGlLNxFBBkjJj2spJDVFYTbxcaW/MuQMlGEYY0zu34JUhYgqXfSNfPQckj+AmQVWykQx03FtfblWXjWqrQPQVt9d152cnQOAZkR674dhwCr+ahKsm1M/ADOjMaXlqDGGYdXUyObSZwAAQgNkrW1bTYlPHuaCSaLDLtdSToouNi4AxJQWh8LkbGtNDuVCgpoXkcbYevNWAnhVKb7GXW2XSM5Ak9zARAaZeTH03vvu/8fbf8ffkl31gehaa+9d4YRfvjn0vbdvB3VutQICARZBZNkYDBjPGPzx2GSeH8nPHo0ZB2SC8bOxn58BYwQ8j2cMjDHRVrQIEhIKjVpSJ3VON/3iCXWqau+15o9VtU+d8/vdRp43n6mPPq1zz69O1Q5rr7y+yyXCCAzPPPecCNS1B0BBYWZrEkCJsTMRAWRjME+TEIIz8NVf/VWwc+2dP/dvV7/xvz+1cfz45tbu9t5otJ/kqXHEwETgJUhgYywKCDNS4ut6Op0F0TYnrDrBbDZFEQP45BNP7F/bkdqvDAZXR/smzfrDgfd+PB6vrq7OZtPZZNrr9QBAsRh18xFa3P5uBZos8+SuaIjHGTpy4bAAWrpu9v2RDORVfrt0f/ynyqOxL1eOr1Oe1BxQGAFNe4i46YnS+vxgeeRL04xxM6UN25FVpPloyv2wY/DBYhGwyIKHtvu5y3bjN4cnzG1lXtyeSKm2FbR6RV+OdCBboWPsLokraFNwY3x3ibk751oUEYxP6PLc0EUYaK/48GiLIAmLJ6E2lNLcEENKSzvdcL3WbdBuGwCAtdQV+WQ6SI1CAFEMgAhAiwYsi7qI+sOzLFMFuSzL69evTyaTe++6+w2ve6i+/55nnnnKIKaZO765Qcfo2tWrxpi1lSEKh6quOCSJgvwZ7/1sVhXTMviqdi7P8yRJENBZAnaGQCT1AathniVnevmx7d2dYa/PzNOinExqRHSJWezABXETjjwPUeLionY1P5Cd53TFc1e6I6I2zI43VMGHqjbGZGkCIbxy9RoAbKyvIsYcXQtAGsKUFoUtz/Ner6fuBNGWi0miixBC0Fxr59I5jCLMM5O7seSWzInZQ4ec5npDaw1DC2nunANyvtPvL8435i7pn0ILrWxd0l0rRPU+NNnaAhwTYQAECSINxzHokLTDnfde0VTUMaAKR1xhDQZbtEFrtBQnlyD27VanqMy1c/G+iocrBshZPAAba/Jezzk3nk5CEDWCbdvVtBH51DQVdm19VEweBoCm6KUj4aiF47DGAUCaJM7NE6S50xcBEW2nMxi3ZVSRsWDbc9e2ja00qVt3JE1TpQfdS2P07GNUBfTOVgRDVHpAiUTRUAEBwFhKkoTZl76elYVN0iTtF9PxY489ludNV2Z17UjrSIsMJ7BX2LDxaHb7bbe87rUPlDdeeej1rxWSk6dOVKNSK5d2Zwfr62tTnhljpG4PE4tBNGlSjopJUQJQCCI1G4PBVwS4trJijbl+9ZphROErV19ODFVc7c9Gg3yY5nkAvr5z/dZbL2VJithkqnsR4Gizzt0MrWy66bUka6V1wB7mGK8iUw9fi8IPvPBSTfASz1n6pyVTBh8gbJ46gc5UvhYSZwywaL0zEpJWHwUJHKTTzvXIB0I34QlAQw7UMZrnhw26p7q9uOP4jT+JB6D7YmEUgXlHsqPsUeqcAYiJD3o8rNUDqdaG5mZ3w8PxkHffK50GfEcKYGMwBFx8iCgKtMjcfo0YkNjGjFslmtVxrac6psiq1465Qdo6vCZHbgMihlADmKgKkIGl8bdcUqGDlSg5yuC4sNba2WyWJgkQeO97vd5Kf5Akyf7B3t7ezi23nLtwy5nr16/NJtOiKE6eOGENSuAQQq+fI4u1pOaWdihqx09qDBE1aURInoCy1DEjr/SZc2Uoe3t7s2dmU56ladpPkhDY3+S03ew4RU/GYRncXbEl+on/ZEEOEDDUoVKvIxlDaC1aAOYAKBCCbG/vhlCvDAe9flKWhQqqrgqly652Uky86vf76lFAROW8xmgxkhXRBgQNrSKiMKpYANBC2A6YaOutjbCFSp+al9e4KzsJR9JpaRxrDeLEW88NiiCRgkO19zfg0tzVcbuLGR+r7hxoU6CttRLYGWuwyVYra4xOMr1BTVXFsaK2RiDmH2kmc7xfJNgWtrORmuwBmsQoAlRf9Kwq9R7VG2LX4Yqa0n8bQ+Yx98oYBT40xmG32gJN9GlJe8o0e7woCuz4/3HR/4xNgRw7Z7T1nkvTBjYgccYY08ldh9Y3pi6ExgcDhIAIqPmA1hitWVSEFmYGxcTC0IB7o4hXeB8wxvT7eVV5731/NdndufaJT3zizJkze+MxgzCzs6nWlEXGZQiYrSUaDHoHe5MvfcsX33bbreOhe/MXfdFtpy/WocqypK7LWJgwmo2MMVyyMU0deeIcubQoivF4wgG8DwSQYcLM0+n02GB9dXV4bOXEeHt3Mhqvrg7rshhzKCbTSVH085yIzp8/X5fToihWVvPu2RQREpGw4AM78vh3f7J0w+cuaI/gKjdPxer2Xzr826WMMFWSsn5vZWNdDAEzahMXZFRJQJ9Tu+JWWgF2ev4CgFXVGw+lhoe2pjMKIem4drvSBRdjvSD6tGYO3UBeFEXYqYtfMpEXvcEYeYTW7Mbch+4443mIY+jaCl35h4jq4+r8pMnGard/mQLilEWC7k7D1BAUb0HRcDRnqigK26aHyOLVcKgYYep0i4pj03HNNXHUJjFqly/EU0XafLuOioANzEKTd1qWZb/fB4CXXnqJQ33vvXfffsflelZUVTWZjPI81xZ1eZJq/ovaMXVdK/dJ05QQQ6h12RNjE2vRSl2XAcVYyrKEmVfXckQcjUY7O3tlHSrfto5eJBWZR3ReLfehu2vN56MUF+iIokgGHr2IoBNkLIqCiHpZvz8cWiJflYBcjMcAUNf19vZ2mh3P01SbV0onlhlCKIpCA37qb9R8Kw259fv9mBU1v2Cem9NIKdO6c6zVViqR1KV1gcTd9HXtvXbvkaIoXDoHgsDW8I0jjNbbEsPqDked4WU1Iz/vu4eIRGAMxnWL843F64hoyQAZoqBtl7IsSxJblqW0Dd2oLVlmotj6mspOq10/VyygdYA3Fa5EoS6rKjjner2eiEyn07KujTGpS8g6WoS/QNT4sUdE1zj2Q1HWIqJqk2dVXBpckYbRy7wyOIQAYNI01QIBlabGmKqqsizDRc7QCle1R8FaCx3w0SbkL00EQe9PndM4sXR8CcYYQ069hJFUIqNzxhmLbQ26xM56jNDmlpcQws7Ozic/+eidd96mrjJhREPsa41qVbOSiAiFCKuqstYiwe23356mqVlbe+1DD9T7lS/9lRdeLssySeyptRNk0HuPdq66JWSyJC9FiqKYTqchEDMTGGutJ6p9ubW1debMGS4gz3MUnk5GYACRXJqIyNXr1we9rJ8n1tper9c4TUHzPAC5YaVIHaW5c4QXueOyz1b+LIH9f/pqhNpRXm5Y5D/Nf1nQYH9laPO0RrDWBhYRsYYQEebRHDRIhBhAlp7TFUDxLZGJWUdNXA4A1MspqAxinnHXvkOkI267YgM6rmBtiNu8iZp/YEvg0HIf00LZ6Z3c1MwYibUE7X8BwHuvJbl6RFUkq74MJBp20oAut/g71lrlLMYYYQxBw1SUtGUSnhkAjUlEJIQ6Fu+2BmjdfjbRJlYYBwXb8zXXFIiEiKaTma85TVMEoy2JOpbrPCyNRAZA1fDUJTGQM19k8TFzBREBGVqNmki154Z5KrnHBaQWC0zFlTI7MDSZFf2hXd84gch7O9dX+3k/y/tZKhxC8GgEECupyVEQ0U7PmkCLmOjycuy7bihAQIvARGwRMUttWZarPQvebm4OBTxDoMRUPpB1ZTUjixaJmUnaJLugR3PuWhARQ0CGAJqkXBGRpv81ICBQE/mGjuiFjuBpBVKog3hGB45R66HDdFbsT0Zp6hLr8jwP1mxPJjXi6nCwvTvt9/vOOGiTaxAxTXNW81TAoinrYMP8XWli08TapIdtvNNaiygogOCtsdaGVmQCYtMRT4JBMHVTXd1AO0WqUEFNgOwDAyIAWcPMdfDxGEV5qf7bpszJkDEEhAQgornNtiuhSSi1qa5Yk6vsnLWJYAN4p4PJsqwsy/F4rCpXfJ0gsARBFktSzeWTYnwCkCWjXkYi6OV9EFHUCy84xy0X0Xob3XBmBrIuNSJSBY+IYAgCDIdDAJjNZtx2C20dwt45A5hCwxMEMSFH4/HYZTkRZTYXETRW1aAQAvtAWIuIFm8BgDPiMmec7fVXZrWMptWlWy9c3jz2O7/zOysrK2dOnwRfAQfkMnXWew/WeAbnHIukLk2y/ODggBkIUAL38lwkSPBpmqZO2UPGzMxTdUc7g9Y2yEhcV9qjiTkQoXOpRfJkkUBBhFFAIBgQEBTPiFYYfR044MtXbmhxLAdAocQ5riur7XIZCBPSNqYSmH3qEmS4686LKVUmT89ePPfRD37s0U8+Wo6rOy7dngx703IcHLPUEEoKdT9zPnhMXUW+mqbjcU2WPJeMYJNs2BuOr+zVBbzm3GtWMSvDJE3dWr4xSpMbN26kltmjZ05X8okvn73+UpaYy9mt7GtHKRAJ+AAcCD0wGiOVRp6gExud66AiQkdJ2CalC0EAtJYEFWGqVQS5ibo2Zm7DBxbt2hiko9YabKRS+18UYLW+AISQmQOIQhuatqi3zuHG7va5E7dZawpfVCRCaBMLZBT7kAAIDAAIgu9Iv66UjUy+HRpr9YSIzJNlmv+2BnVMelrSFCI7wBiFboGljljIzjvj5y4bPXxpmAQP+aK7qnEMCdd1LSgIDVpImi74OqJNTG0Ol4iQIWi8EHMUAuwknULMmmEOIVibSCeVIN6jJiO1eHIROAw73oKo/GLbn1hZnmIvcJu60l2QloMvRKe6Q5qTUXsntfjyOuXEGUWB1lwhAJhMJpcuXFhbW/Xe9zfWkqRFmYBWUdDNZY7L1Ro6dv5SEdXU9a86ZmutsNWkGPXSp2mKxGVZGsAQyWbBXbyA6Q0ADE1jhDmFkAC3QC7tlLs3SMew7lIvdNQ7HdJkMhmNvEHK8zxLnTF2PB4bBDPsHxwc9Pv9rN8YXkRknLOd3AXbroPWJqVp6pJMl1T5e+OxNDZuemeQCzkQyAvaapwFM2tpLnbygLDNZuIWqDxOf+kJ8UR0l0IXIcsyavGh1AaNyXTR665iWA3E2WwGALF6XkeufmlIG0rO85wZ1CWAEilW6rqWCJ3d+GnnDf70ip6thh+hQRJrrd4fkbySJJnNZnqcgU0w1vqYxc0AkGWZemiiv1qQyrJEBCJyqY35WSBCRD4UarL3hwOT9j72rnd/9qmnv+O7v+dbvuVbfu3Xfu3RRx+97+67EXxVcl1Vg8FKURXWWgUbAWBgSawzBhVbxjOjQOAAZem9sdbmwx4AkAbCEY1p+BURSOsJiPF7ImpaNJJBREIBIKPHEFiQRLD2vL+//+STT8a1ihH7uOPRT5alLk3d3t4eIZw+fTpNUwMhT7Jer/fYY49dPHtpWs5mfnb8+EblZxiBGUzTLgURa5bxeMygDa9IREajESISgG5KAEEiJMqyDK0JvtbD4qvaGBNCfePGblEUfdeUYy2xdfwc/M+vfi2Ip5s/Qal26YaGRJce2H7DrVIALZs12OBYR1Dk6aw4duLEyupqLA5ClSaHBqJqgVn+ev7XeISpDdwws1UQbVlkcnIokbh7zrHj8o2/i+cNOrJkaSGgw0eWFlS6RDbP4Gji0MpPI3tSDqKdwtA0HNwYQ2SlTSiVFgNERLSAR9mcD1XkldjaT4hWpIbONscbQtuWNW5eXJ9onVMbRmJm7JQtxZk2N7fBWn2CpmlohaUuIMu81V13QbhzmcUk27gFIYSYHcptIDPP81OntlZWVkRECzqn02mer/tQ2xZjL4SgqB3YUkH0diLOsVY4BO3d3lWGAAAwtbNSgKhBXCFoUIuthjuFMWq/LeWTdADb9CQ02e8K0quhEqWiNua1TCTtFXWymAClqoBzDgidcyEQCk8mkxCylWG/rnnvYD/LsmE/mUwmg3ytrINzLkkyRARmp54VAGsb6asiR3WaJYZChHG/GgnUStMmu6pigXmvOtPCIcXQgzbJio/lpk29bXh0x6hdOMwdHE1jFkpvsU0p0i/NYl69IItABGdOkkQbZ2V5GkLQVCwiQmpgtDk0zKJ9UVuw0MJoEzXFao2ArypQtwBhXIo4i6i66UOsSVyaqeuV2iLgtgK+ArbM7Ni0rEYLmgmAqqpSraipRwKFgZtnROp8idCA8TWjIWvtaHv7J3/yJ8sa/uvvf/AnfuInvuO7vudPPvyhj3/sT45trp05dbKqZjVznucamIilRN77eEhDCAJANhFNb6FGsjawZawJIrr7cwCvdgsYDKWuTc7ScD8LIgAKAvq2jUTtw6OPPpompJUIcTpzmidBQWvtdDpdXeldu77/li98rZYd1nVtUE6cOmmc3T3YTdO0rIqt42tlPWNkYTGuGZgEIMLJZHJjd6euPAiJoEFbl3VRFBdPXxgOh5NZwXUNgWd1Za1NsjSpuJjNgMg5F7huFFPnDknellARsUXwP3zDkeZvh8iXv4G2MIekhZZcTDxa+ufCb1/1QhZpS5YjJwSAoq7OnTye5FklrAPS3AUNDiu/Czd5dlcOxs/RZFJX7hxIGRZFZpxP/NAVSNDRuOMTo0iDQzJ4STYfHmiXwzbSSwTadBVsWt7OXWHUwqsG8czsWduuLSRkmTb3QcTHQF1VNyZdNOK74+yyeGwq7oPcfP/iE+KooJMl0X0FaeFYC6uEiNba8XictNCAzCwwN4ij5A7tFQVtfHh3XzDiMRFMp9PZbHbq1Knjx4+fPn1ybW21l+XW2rX1FUvGe5/lGaKIvlQLlMNcIrbT12K1Nlcc0fvGuOxqGN6DNOk/hojqwMF7RGOQg4BK3/kCHpWwINzUCwJoY7tGx1fL2XYwmbvMaEmli/9UdqB5vCEETQIUEUNUlmVhqdfrVbNid38vz3NBM55OBoOB9h4oy3LQy/Tnti32VVvL2oTIBsEkdv5ocR6wUzYTdbUw71BiW61ynhJIHfBL0QYMKthagykK4JiRt3R2OgQ8tzWpE7UpikKNYNOpJwaAJE20zIzbyKUW/6QtdqaaxR1NjojmeXmIoKptYN++tLHyyRgkMq1+6UMdf0VIsbYi6vGw4GuRfr+vvXsVKtIZqy4qIxgFsO6/5jzmWb8l/CYLPRJn0wAqsDEmHwz390aqnd+4cYMD3H//vR/4g99/45s+7yd/8ie/57u++7Y77/j997/vyWeev3D+rE1Q/GxlZaUbJhgOh/ohz3NmJmpczCp02QetODLGMCJKaCXuXP9Qfc4LE4JAAEDlcSTQVJJ1LJPBYDAaF08++eSJEyf8Ys/EVoVtKjh8Xed5vjZcOdgdvfGNb1xdXa1mE936Y8c3H3jowQ+85wNJkgSuKTXFuBCEADx3RQAAwGRajCfTWltd+UBECLg3Hr/5wTf2er3J7n4QDhKKcgYVeu3DaqisKkCsQ+ONF0SZK9ntqdQt1l7OS0e+I4+XqPrwbdj4k+fS99CaHP3zV/k+mgLdG0hAtHaq86XJkqzfK4MPiEgIIMYY7Y+kmAZ6uI98b7wOqwWRsduuIQsdUdQYap3F0rC6b9VVQ4Qxl0R/iE1fvYabtipMlBALy9r5piueY31kI9dbiTgej9UWgQ5uV5IkVY3UtitnmaPeR1HKTTILCoS6boAC4jCi3aB8ZD7Zlj0FXli4eE5ijFntTr0/yzIQ0czhGJCey7DWn9kOyTTBWog8tOFN8fzro7TPStxIXa44weiI1gQiZynLMoWYePHFFwd9szocti0ErMEmP8s5M0/zlyYLggCbNGv2nUbL2PBONFps0JV5pa+DQFHO6roWMglSVdXSQvguED22tL94iQiwhlIDgu0SIcAc+AU6ihp0yDLuo0pBBSvWXPRYMYmIXgCFD0YT55xLs4ODA5ErZ8+etYmtKj+dlWsrwzRN1X4e9HNmTlzinNOkWiASNCHIaDRaW1uz1oa61kImgsYMjUatDql1wyw0n8COdgUAIAQEiETS9DLCjoIcabU73+VTY7WcHIgICYVESMgQ18zISKjtcoUEjMbawbX9kaKVjJo/aG3Ub+IBVOxu2+I9ec9NLayxLSlKPI+RMpnZWIo1C8pP2qLeOfFQB37ZdCAhjTGJbfDnBRpYXC1MqOsmOTRPMzRqnTeP8h2MSZ1XYt1sVuR5Xge2abJ3sF8zVHV97Nixzc3NH/7hH37/+9//0z/9U9/wzd/y0T/58Mf+5COXLl1aHw7qshKRmIntEmOMmc1mvV7POSdI1lokQy2Iguafq6JDbbVVF8AEEYXQSIsN3hwDpV7QpvXt9hIAXLt27eGHH97Y2NrZ2YVF9t2QgYCA5Hm+v797/vQJALj77rudc8BJEDaG0zS/9/77fuu3fjtgWN/aSPNkVs+AQmS6eqQBsKirOjCi8dDsXV2WFmBlZaUOVR38eDohASasfOnFN70fOBR1RYSBeVJXdV2niV1kkoKIfOiwLx/8m4vMKD6OFNULD+n848gbjhSPUcouHMkg0h4xLWLuba5kw77nwEyYaEMTQBZBAQGznFrVPHiJ9UEnzzfOTv/ZSI4F1oYgAL51eeuXUUgvcZkjlyPe0PXEdt/dGfGCasCdjmPze9o/Rd08OqxC23ShtXQ7Xs5OGlRkBDG1u8u7oZF5IIuNLEIIQdQ5umBjxeONEZ+25SPWWtNKWd/2YYVWQVmaqTGm3+9rvWmTAobzCr8uE+/usVkESIoCXkTUKYfAyj7W19fX19dD8NPpdH19XSsx0FCWp977umZEBNKGlnO+Kajlzgtp6ogInY2TToy8DrC9s3Pl2o0gnCSJr7UsB4WbPAndfm4IvgGMiGQ63wgmIGHhrmXfJdYuaWFbJtclNr2oddhmWWZtMp0V6lEEUHhCv72zt7624tLsYDzZ3t4WHp47fSrP86KsEHHYzw01fQOt9hVoZKI1xrLI/v7+YDBQzUlNH5RGW6U2GK8DU3m2FKmRjp9ZGqVWf7hQzh5CYAG7WPcSCQARpS1mUJmKi5Fg7JQX42IxXuikZFs7z9synfIeafMnrLXS9tdTDaOqGqQqaHAQ1VOC2AYCIpKdwXnqRjPyNnk4imGDJEh1XavXQaWv9z5JktTZVgA3KNOIKBJUE9CuTYlNVUx6P0+5j153nVRu8tmsUsZ59epVROjl+TSfXb1+7fLtt/3u7/3u7/7e7/6bn/+5b/3Wb73jNXe+613vGu2NTp06hYHR2ISoqiqQVGPhiJimKWhowFhjTM2B2BO0jjSSNoNFrG26d2CjNDdRkkjw1PpFYqWDQRJBY8wrr7xy/fr+qVNn5rYjCQTQ1VBsGSWVft6rqooILl++RAR1XSeWxLksTzePb6LBG/s797/u/pKrSmqLQA2emAUAsrYu6/3RuK5rJFuXM2sSYJ5OpyePnayq6sqVK+W08GUFAEJYlDMk8hyq4I21UFdB2CSOiyrSfCz+PywJl0SJrsCR4hdlLh3jl0ufETF6oeNfsXNDFHiIC163bg1SVILmY1ssakXE4eZ6b22lCr4MdYYpWlDPyjzhSwgJWyAsEBTEI9BzdSig6Y0grA5tQsvMgoDU2K/Scsa69vHQdscUJV9Xr++u7JKsZZ6z1KWd6P4zypulvKTmMLct3vR4Qxt1Zubo8povsQh2NgA6AM7QWs9dnggdoxMaySCa7ImAIgvSOnK6EIJtkQuJSEuSmiPaPlDt17rtp6bP1wwszQ+KYk/dEUgtCG0HHR4RtUcUd/qN60ZEtkhE2lIG2lKZsiyLotja2trY2BCRxJKv6hI4HfTVv6oOPWCNQbWphjB3D3AniQYAXGvlRwmhAafJlF988cVr164R2sSZupoigDNJ1cbaIXqQlFYB2rJJEGnSGpuFZ0D0ImTAxrlzJwDWlTGwaBB3JYoOr83LS1V1Y+YgbMiWZbm3P9pYX88yur69QwTD4dCmSdKCUJpE85sJWYQEQZPVyDqHaNbW1rIsS53z2LB7bOsF0DeiqKFhIgQwHds3Jg/GvzIf6vvUVtFokHVJADfZSZq51qZK6Q1L9Eyd8Ef3vMRkybhc6qtXvEZo87T1Zu89YeMJV5ePvkuFXAQfjGOL4fC4v9ERHVqniIggWiKDiIDIoWb2IcyVgKbCPnEYyGDDOkLbBhuEmFkyLIqCoMlvsrbp76s/l9YXjYjWZkQzz4GI9nZ2EwuI0u/n1tr9/f3zt9zS7/f/h7/5N9/1nnf/k3/yT/7iX/xLj33qYw8//PCJ48fznmMJa4NhMRmvra0dHBw00R8CACSDSODAaBqu4maT+i1gTgPUtiCkNhWr9hUJEDSxBmMMGASiUJZ14Mp76/2zzz4rAFVVe+9V3h827QxBWRb9Xra7twMMa2trXDcpGsEKAPQG+YnTJ55/4fmtE1uzcoIGBIUIvBciEDaINJ3Ort3YqTyTRQ7gMqetnQeDwfbuzoHsGgHNPiuralxMbeIIzLQoXJYCYV3VxiIaAkLGuTjsysV4WmFR4YZFpr1wAjo/OfKv8b8EGFHwuj85LLY63zcQQV0xHAUStTZsAAFCEVjZXGdCFkShmoPDRMQTkQTWrW0GrLNu25cffnUcQFf8IaKNqnQ8uvpfs4hIFf/atTzil3ogO+7cheZF3AkwR2dsWMSKWro5Kub6QGwNTeh0MdLAT+zONGd8rZ82+oEj1wgRb3kRpLqxvJdGK5o2NZe+Xb4fBbymHOuMVMONz/Te196HFrSI2zJTADAtmm507sGiaIn81BiDnX2J2kyTqdPqQ9PpVM0y9RH1ej0VyXVdb545672vfZkm/aqq+oOe95WC7yj6IADYBjSdDS1kq849H7TQrCJqGLu7+9t7+7PZDBEVz8QY4+sQbxNpIrua4n+UcjyfPjQ4DtxkGR3CVosbgZ1aWOiARjGzwgSGELxnTeqx1o5GIyJilCTJyrLc299fW1sj43b3RwAvO+fOnDxBCEQNSqImQgrXhA5JEJsONidOnEiSxKAgumZ3pPFnGDLQuuixcwS6c9RIJiJKU8CqE2liS845Rpu4lIiEG2VUJ6WCk9omvl1lX2+LeVLYVhDE2AR1miVAR1lRW1YfqM16deyq2DWOKJmbLNyGTpSAWbkfd6AhjEEzBx5hZuNtqL1m2EcYyKhGAIC6bYqiDG3TCw2gaLxDo61GLBpCRAnsa0ZELT3I8xyNAobYFj0XNbwdrU/n3Pp6XtYVCF699sqZM6cMQuKMcy5JbFHOXnzx+dtuu/zrv/7r/+F/+9X//T/++tf/+a87e+HWP/qDP3z2xVcuXjgvgL3BkAB7vZ5OyhrLIJYQQNBZVuMbxQAimXblKS4ImDncnmjqpbrqpCnfJ0AIwSQulLWI9yyvvPIKAJRVRWQZFqSRai0AyMxpmg4Gg5dffPkv/6WvW19fZ2YCqaoK0dShXt/cuPeB+z/79JNksRzPrCUB3/qHDIsww6SY7o8OBCmI8hMnzJZMWZbT6TQ1FgUqDiEEBgkI3teZobSX18GTNZnNx+MD9FUQ1vU/LHWOEoEAqvG3N3Rl0kL1nX4gJDlapC2tDB41gMOvbt6DINBUMXVVhJZnCRCKoXTYn1QzAUBr6rq2iSOywIBoluxctciX3hJnJ23osGsZIuKCw7PL3XDRBu2K5/mEO/JcWvU5fhPfB61J131F15LuDkg6/dHiN1GJjmJbzRrtx6JBzbqui6IYj8cq2KKrDVvPm7KPaIDGYXNnBJHXd3/bHR52bFYi8u2l50FHNRqNdnd39w8OpkWhA9MRaoNhaPWYyKSw1YWxxZoOMeW91RviGJSxKtvS+s5oHKidZzrwYQAALMVsAo0ZUdd1XRWzaOU08+Vmbb33jXtL055D0Kn6ui6rSjtGBAZAw4KeQ5AGN1j56Xg8NsZNxoVqP1XpG/ZvIITaIogE6qxhV2PrGIJH02SXTiKxRfsvKmcRRlGXSBffOdfv9yP1E1kAULAOANjbO7h2fVtxoVfWNoy1mkCumJT6Fk1oIiJABvb6jUZDMbafMyZNUySSRVBoRKy911qOKBSjgiitUhuNNmriKUBtIjS2vYOazP9OrZosmhQqWfUgJEkyGAyoTaEioqaCqMMRvPfr6+s6Ta1Qj7kL8ZA26WztgncVTQAga5IstYlDQzFNVC9NjyBrjHVJmql5oCujaqvepruT53nUDHRVtdeyMSZNcmuS5l1J4torCqfBYKC/AgAgNM6iIZcmZE2e54gIguPxeHt7e9gfIAr7GoGF/cqwf/rUib3d7XPnzly8dP4vfv03fPf/42/t7Y+//Cu/8s577nv8s8+Op5VLcjIWjdX6KO89AhNK4gxIHQGuI2xLnudojRCSs2jnASNsLYQAQs7aNG2UaSAG5CBAuDJcG4/Hjz352VvOn1aVKGqZ2mIongXdEeccItxzz12Dfu5DHfVy7/1w2P+CL/qCO++6oz/se66BNF/dO6dkRsxcTMvZrPLMIQTvg2nwxCUf9D2HikNAKIOvONTC6IzJEnDGc7CJCyKjYiqEo+lEWr4ZWt3wSBNriZVFil1Sprt/7V5L33R5cpctdH/bFYf6VwMYXTe46GGSwADgElP60mT2oBidu3Au7/XEkBAaY7Jezsyh9twJrHSxJw+PBFu3a3Q4LTGxJtcx8sHDArU74SiBusuEh0yTpSd0b4PF2HD84ZHL132L7/Q7hJZ9iCZedZIdlIM0/q5FV3OXicdxSpsiv7S1HKtvQZ3QjccjrlKcrDL9+KHf6wU1LTvZUkuzW9KSoo0iLNH7Zw+51uNvu9sfH5VlWUTBVBuuW8SVpmmaptaQAIcQqAbFxInPiXPXuUCnq4Q+RNnBAj0ICWMIYWVlpZzVk+l1731ZHlhrG4QpCCIkEkC0VwUaQQAmRIg9czpTiyuPiCKh8Y2jxUP+pSUak06kQ0Qmk4n3fjgcalxgNpt5740xvV6vKArVW2ezCtGEEHp5miTJyy9fybPkjkuXEDHNeoRCsZsNWWOcUhOisZ2kMOX4QXxM/wGhbgcCADDWRrhH6Lh2vPeIauAis6/rmtDqDqoGmc6T++YAxbE2RgGfuY2nQOcA6kwBQGVwK8xckiSKErN0BtVnEGmA2zyMRvADR/WCiEQCAGgVUDNBM0fN7Jbs2w4sdnwgtXXntQ/WWjJW+xAIBkdonBWRRgBLLK9oAbEDigglFkPTNpurajAYQBuOCSEoRIWuPxGhMYDkA2dZtrd38PSTn906cdxXdZLYEHyaJlVZEsDm5ube3l4I4e677/j//st//XM//wu/81u//dYv/ZJz58798Qc/eOPGjfvvvTvLsn1E1MZNVps10aCXV7X0+/26rqu6draJlEmbb4WtgyGuQ1NmGgQNC6HClkIIDM35OBhPPv7xjydZWo0mkWwO8RBOkmQ6nSbJSRG48847dYVNA32KNk0ms+L0mZNf9hVfXvqSCKwlYRBRc9yCmGJaT6ez8XTKLM7ZNLXMPJ1OxUtZlmnfCYIHlsBBGK1R7Kfa+1q4qquKfelrAkaiOoRgSRb5MwDQTYqAu9FfWbKAj7pUqHc5ORySeZ/j1Qyj/VFQJzJACCFLXFlXnsGlSR180ssH66ulBAbpRHkx/pYRcDExG1kOS0zoolS1VzQvaa45doZ42CrtSrjuWe3aqd0nHOaYc0Wjdc9GYbOkAXQleqRdgIU9ix+ido+dqKHaFr5jR3ZZdqxpCO344RBCfZxddOIhomqg3FYxWWvV06tq76DfX19b444FHx8SZ9olncjUuksqi6gg0rEUoaPrRPuVmqbrpsnYdM61HW2NMXme5700WupElFpHBKqjNHtH88VU7h9HG9pLl1TNr7hx0OpnmUuGw6HG3rz3NnFdHwOi1oxC1OABmFAAGWkOXrNEHhIgeOEwP2ZdGlvSFJcWSrXv8Xg6nU7VNJHA1axMrBv0+qqpQFvQ5T1r9cvjjz35/IsvaFtyAVIVSrhBsVB9zhqM3gXqgLJF2S8IS764Lg2rCdvustFvQltXFjqpgvpYbvyZNtYI6bCjMwkAqqpSCzX6RVRIqEBSTS7GnrsL1aYWQ13XJkn6/X6v14spBdA6h+P0I+liiyqjwyYiPQLqNkjyTNcnSZI0y2LjZL3i86lTuo1tApeaxdY4QmMTp/8zLjEucWmSZKlLEx1YlvX6/aExLst6RLYsa0R0aaJOizTvuTRDY4mMF8gHwyTvXbl+7aUr20mSCIS6rICFgFNnnHNZkpw6cWJ1deXKlVcuXL68trbxlV/+5X/n7/6PVR3e8qVfevHSbR/9+CendX3s+Imy9npGsiwjAZJ55DuuhrUUXSPY9nJoKAT1vGEQDgKCJEgM2BSSoiGX3rhx49HHns2yTHGhYeFS4TFvvTybTRHg8uXLujVkQETKulb4rcFweP/994/G+7UvDQERWUOmKVU3lQ8Ho0kITEQNniVi7b1zrg6+qMpJORsX04NiMq3KikPNYRbqMvgauKgrAbCJS7IszTObOCV+XCwzg0UB2ZVMC7M6JDu6x+fwrz53oXv4XU1g6xD4ZcyPAyIhnFbl+rEtl2fciZ8CAMHCBPW/DAvjj4M8PM7D/NySYnK1BuJ82k2mCC09IoQFGKPDi7L01shflnYisq3uE5a0m+6Zj0/ARSUoyrAlbVHHqUr+AveBALDghV9619KH7qQaDisSQlA+rr4751wvz0PbTTn+sPvN0gpgx8uNbYIMdxpIRKDKwxQZ1yq67CJH08pVjYsry1Ojx1qKJguoABMRBIGAgIAMzPrsSAaRUJq3t7EA7mT2ImKaJNpWBbTXG1rvmaHB6CYCZKzYYwMvhyCMiEG0ABLJorTRu+5GxGuJTg4T3tL3RFZHbUxDBqLAhNZOJpMkSRTfeDabjcdjAG0abUDQOffss887Y19z150ba6uhLhGFxSsnBfaIVos7TVtf3oRdsTXsFMsAkcgZ40T7OrfIQYgYYwSIGBNZm3UT0jQWzQDTJ6s3whjTcgZaOvzcAVOL4lnfwm0cVBUv/cY5FzyDIAKJsDBY40IIIAjG5VmvqirtI1RVlSEr7JVsuhqzptN3N0IVGmNMmmWCELQwwVowxiSOOGiOknT0zhiB9hJwnqo6r1wx1Eg1lgYgVumwxkpaM13Dvc39zooIoQFLdZhlWSZAZVm6pLe3t+e9f+nlayGAs2lNARNVFmsk6wxWofbeJ9adOnFyZ1Q4Q3fcdddP/PiPv/e97/3//Iufed1DD527cMsffOC/nji2tTLoAZl+rz+djPI8B0MJJeqTNxY1/KHIJtwpzdJZq9MCCEkMCAmACBISs7CgMNRcO4Bnn3tBBBCIEdA4kHlTnMgGENH7ajgcXr169fy5E2vrq3VdW2rqP9XmTvOMma2lsiyMIQ5ACBZNzaxOv3JWT8uyLEult5hwYBKHRJWvK4BQe/UeJRJkKt57lQtBOMmz1BpB2B+PRtNJ0u8BaMnInyEdDSwXDccJLsmIbqKD4BGoHdJJWu7ef7NL2gxyFomGbEuW4JmBkKzxEhhk4/gWJlakAXkFAGyhrfWwQ2fAjWwS7mREzM0J7iQ/wqKUafqSRjAsJRFsQv3zFM3ub7C1Nf+s+S54p7u/wi4SUEdAHumv1g9ROEVB1PzEOO81ZBW6GkM3AiEiwm1REAIgw6HNOrT9ulMSQhR4miIHSIgCRVHWdbA2SdMUkavSF0VRFAWQKHuS1tDnjr89zi5q/caYaHF2VwMWTt3c/F0yyuN6qhiOZpAqdGVZEiUxXaudZktOyADQhX0xIKFDN93xYKeRQOhcg14/TdOdvf08y6Yzz1JPpzNmVq2QmVFYQkgS6yyJoIgVEWL2LA3Q/CLqOLRIsEvE09VC4p1LixNJJbI/ajCJbFmWaeqcs0UxIepvbW1pHuxwMEiMnUxGK8PBlWs3er3e2XNnksTmaYbWtHhPzIxkWfMuYi5C3AIUgPZjszduXnASdzx+QETvg7UW0TCAaE5WB+VfhbzST+vPoIgtiohomih+nvVDCMEHT8EYg0CExjpXV15EOEhd+UZpQO1YTNx2wxWRpmC6qkySqCqpf4oJz9SWdcW8BC2x02fGHYl8MHTyHKE9s0GCECLP14HItlifJmp6UVVVvVIT2dBLhJfSAWs6BVkjCJ6DSxONNaRJVnMIIQAaJOvrWkReePnq9evXT586VQcWgKKs1JEDqI3L2Ht2xrjcMbMwrg8HdV3vXr92+223P/7oY29845t++qd/+ru+6zu+6mv//COf+PinH3/iwQfuQ3JCDslWPgz6TR2EsVhVVVnMdMuiYtSYUKZRnoCsswbmffpINPUv7TnvgejZZ58lgtF0Utc1BzAuei8XsuXVsJ7N/Jd8yZesrazW1VitEEEyxhRFkeYZIo7HY/HBWWIP4kGb9AKRAJW+nhblZDIZDAYaoqqKWQghCAeQUDddWDSFghGYocEr5eC9p7HJsiR39tr2XuVrMkYIiYgXE/LjKV5igN3T0T3US990T/rccXzor69yLQlmlGWDFQGkBTRkZvFeDKb9Xm9lSIkDaBJZoq9OFO9ZAADYSNNYUrkWovDyjOKUu9I6CojG/0xtHS1AxP+fL1xXElCnh273hihmuq+Ejuq6dFa7Zk1X3C7pFEuCX2Jgtb2/qqqo/sfi964CAgACQdr8tCagixIzyLv/bSnjCNN8gYFKo0PoAKbT6YE0xkeSuWgEL7mO48+jFw4AmgqZTqFUFH5HqjjYuborX5aldpjRA4Nt0fNgMMiTtFVQmhcDCCIawNDurf4/AmDToP1QDlQ7wrhWzXyNW19du3LlWp7nZlR4X2i2iI5VUdOtpX6/r4UuzCyC3vtZVYXADIK8QD+ICDI/vV3KiYt55OnVy/umULUsS+89AGsWNDPPZrPpZOKcI8SqLPu9Xi/Pi/EEEdM0HU3GSZLsj0dlVQNQEDbBAIprdoENAkqTfBfVx5bAwDZAZ9S1U9v5ivYcgzZsPD8s2EbHTePTjlayaRsgaqRDpWZk6NAW+8ZoBXY8Il1x1dR/a3Z669nWOyOZVVWVtf+kTp5I/ImSurWWeZ5E3R7tJh4RQvB17duIdXSVzcnYWI2siw9xK5GIWrSymP/VLC8iiATTsEydIbZJiLECIi7CrK6IyLm0mFXGuP3x5Mknn0zSzfWN43l/9fO/4Iu/8Iu/8AMf+IP77rkDABKX+VBRW7vMQQ1HMSBo4NjWxng83dhYu3Tp0g/90I+8//0f+Ef/6B/c/+Brz5w/9+EPffD8+dOnT51wrlFiWuBoyQYDZ+xkMuoalDpTTecGAABCS0CEbXjFNGmqgZKkmEyefvrZlbUhMyQuq6GWTm/ydl8ak7rX6xkDDzzwQL/f368n3vvUIQAqRr0Evnbt2t7+DhmoiioxtvJeyYaMCYJlWY1Go9Q6CJznPUdmXFVKpWVdhRDYh8bVYSwJCIgQ1nXtOZS+5qosqoL7PQ/QIHHOozALEaXu1T2/3UPdCJebthAERORDbRWWHnL4WhI6N3mskIBmriFiQJjV1ebxk/mgF7BFOG/6VzV6AMauRwCK/6xcKx6f7mSpU4XbFQdK8PP4UCNZ4k43L1qQgnOVf/H7m11dsb90fzz82Lpouo/qDkavrsEHnbB2aBMCAUDa7TFH1VAxKhEdsXl6tQdGVSIN2EBTudo+p+WvbQ0fgLJXYFGndOXLaIEdXh/qlH/oXzUo2IrquX4ThTcsUg91wp9dU1jNI0SM+Fz6pZoyaZqmaWKQQNR13Ew8+rjngni+JkGbY+kAYqw9rkOkhPXVYb/flxs7kUgAAEhQEADTJEmdVYuzqme+ZkGo6wDT6Ww2E2bEbq2RIVChND9qkdjixKWjrMRvIg2ofabAgQpu3Ov1DJEhqpnLsoxByl6vVxWzqqpCYF+HrN976rMvHT/+pBZ1cMLaB7bZcYsCgXmesh4DwHFZdFQhqB8BiCi0uxn7fupoiUyYKyrNA+u6dm2Sl28LSU2nGCk6inT5lQI12TgSlfqKo/ta5XTD4Dq97tWYllZyx9TFqCjodPR7NZSNMdIiV3Obvd8V2yEERaeSVgluIxGEjf1tiYiVQgwBNshrTc00IQW1702zlwjGzLUxJILgo4terWGFEXU2MQ5DEJcku7v77/2vH7hx48bZs2fPbN4CgPvjydNPP7t/MM6yZDya5L1Mm29yEMGgS1FzAEALgYmqqt5cW72xu/fcc8+95q67fvt3fue3f/u3f/Gd7/zv/spf/qqv/bpPfPxjn3n0ycuXL62vrYj46CwkRJMmqU+lEwVDRKB5ziaLwp4TGAUUbkkgeADc2d77+J8+vLGxMZtVIQRmwJv3U3fWGoCLFy9CUxXWrLPUda/X994/99wzk8kksa4UQY1YEyGBINZ1PRqPd/dHvV5vMplo0Ho6naI1AWRWzjQjWhAEKOpt3vtZVTbdwBIHElRhH08mx4dDZpaWRAGw245lzmMP41JG7rpo4iIe3Wd3SUzc5GFHrFj7DRJg1wjWFzVH2Bg0EDxvnTjm0mQ0m5pUYWOWbUtqAB87cF0CBKLma2Sb+k/E1q4J3OhAgJqBarv5QdTJ72XWBuzzKsx43rpyOhLWkqyFjhA9Uk4vSdwohGJdbDx13VfEVYhvdG2vVh1z/FW3r4u6WVmDH+3Sx4WTxaxsaPW4uI7dMZsWp1eNqha1J9HDVlUVmiZW2iWCKDDiE6BTGdVdT+4UoiwtZnfdoqiLCSA6HtdmvOR5rmiU+qiqLK1Bm2bOOq1GkE4UpLMOEqBriIe4t3pbXCiKfWHFuNytrKxUVaUQHzwrGoMeUAXhyqC/sbEByMUYC1TB4DVgBsxkAETpyqA2AtMpCwCCwLzuXBZxauKydMUwGqPIRCrwlOPXVWPrrKysjEajyWSiK3P+/PlLly4988wzauTVwR87tvqZz3zmtksXT546YZkIU11hQjHGIAvZVtIs1qp1vehemEJTjRNaexE6XTqIyAsbMtbY+LRoZaryFCl/NptpkTdAGhOaNJ8NEauqSltbmTqpUmr4TiaTEEK/3w8hTKfTbvmQCmwkMgDWWnU2Rukbj6G06RpR1dB3adrOkkKmU22+VMbV5Ao052Xh4Q2HbXcTCc3CIQQRQARNCmPWz2DIAPRazOfo8SYiRnDOfuITn/i5n/s313e2e/lgd/e/PPCGtzzwwANnz56uAn/i4U8cP7Z1fWf7hNkaDHrAYlzjVGDU1l6IVWUNpf18UszW19eTNL9x48b58xdM4v7aX/v2d7/nv/zo//T2B1/70M7OtY995MO33377+TPHpY1tcVlCi4AmrR/LWottDL7VhFpzaT5dVTf56o3rDz/8yD13372390pd18YlItyVF93tm0wmPsCtly8yM7JkWSahDMErl57NZru7uyjBe58Y6+tCJFibAmPFXJb1ZDKZTqfe29msVnYxm83SXu59VRSzqPwRoOfAlSoEbBJHRJWvLaIPrKyhKIoAogBP0HCnua68IIDh6Esk9gs/+sKOO2EuAgTkKIDow7+Nn0mIsUkN6t6gerkHCSG4NF3f3PAcyBhGAGCA6PECUSYM0iLbK5g9iIR5O5SOQIxvjzKOOt5fylzmS49ssnQQPCIlPggQkkWwAhhabAHDDG0K58LQoztrSUxyU17mo0s26tfcSeTh9upKVmZWhh5dUvFXOoAmO1eECJwziNJiGggAxFJgzd0NXkCI0CJZBKP1M0smJsxjh6waCrAACwro//SfBOiM1VyewWAwHA6TJMl6edbLBYFsk40850fKf41JkyTPsixNnbUgErwP3nMIhoBDLewJhQD1fxKAwCAYYZTFTnZqbxjjjHGIBoDQWJcmikeT5UnWy12aAEHlK0ExxjBCkiSGkNhbEAzeAlpACkgBJYAERgnAnqUGoBCkqrwWqXKbh2W0/QCAIUIAQnTWOmvTnhlNR2U92zq+aa0t6wrEBi8oBL7majbM6MRmby0LfZqtJGU/l7Vhsrk66GUpBjFsDWTAhjBBUJBFBhKyQglQ0jRXsC3+KLQJBIs0YwCIGUJomkPrWWeQIIDGuiwNIIDIImmWiYhayVeuXHnpykv9fp6mDtEIE6CtK3nqmRfqylubADV1fsZaQ86luTNWAoe6yUKae3Sd9cJ1kKKsy1ldzurJuKjqoDm63nMIWn+stiBpDq2EOtS1tdTrZc6ZJLGWnDNJdPi3QouMIQCoqorIAGCogzOOgKjtPN8NZOioNANZa+XViJlMJjrsLMsaQDQEdNYLm8QlecYIVfA1By8c480qy9W/Yi0hirVkLRmjLwIii2gQjUtzgcQHBHQcJNS1AXSGEouWxJKQMAQvogEgAkqQcqQcIAVxABmYDEwGlAvlYnpCuUAu2BMzEOqLZCA5SCouhyQzaTJYW03znKwDl1My+PjHP/UTP/Uzd7zmvrd8yZff98D9b/6iL/yv7/qP/8/v+fZf+1/+LdTTb/+2v3rt2o0kzZ9/+eX90SQgKXtFFCsIdSAObC2T8SBk0VJIDJ8+vooynexdveeu2/79/+/f3XnHa37tV399dXj8y7/i6/cP/Mf+9JEaia0Dl9VMFUsp4rKcXJJkuU1SEQw1UzCGLQZDKCIBgIVQUBUQEkGgBMS+/MIVZPDeCwahGkxl2ZJ3BhIQB5igSwMCGM5yc+3qK5//pnvXhgnzNKD3XJsssSYNAYioqv1nHv3swRiu70gp694cq2h1hrayOPZlTbB3MDsYVWAsGETrDiZTMBaAtrf3RSTLMpckSFSLn/lyxlUwgqlJRYJ4tk0m8biYlgD70zGVwZYB6qD2sQNh5NKyALFgYNC+ogGEkbUVkK48oijLZRXhXQAAiH5Y7H4/l2ewIIw0+NU1afQKIFpRwQgj6ysACph6skwB7YzMjAy6jAM5csW0PHvrBcmTifgpBgY0LgFCr2lsBIBCBgnEiOgyNOAdSEymCcBRAzkJwABMBCHU3lciQeUSsycCIrAnTpxQ5Xc2m2VZNplMbGJ8HVBlfIPmi4iNA0rlXFcr0c/RH9sVaarRN/xwMQd4yXbpSsEoiZdesfQh/rcrubtvOXxFKyFu59Lb4VAcWjk1tTgPWlSqfM0Ys7a2lqbpeDzW20Kn8KnrmbTGRNDK+ApqABbqOQEtvrrjDV6YoI4/FnVgx0+o8JZJkigWjzHmxvbuqVOnWMTZJEmTclZYmk+WNStTFMNWRLC7/lHGwGLUP/6JiMjY/qAHAEVRVFU1mUwIEzUIhNCRdTax1hpniQBJyiLozdPp1IdQB0TWvIOldPR2X2jhOHUT1vDm1jBRE7NRTQ6Ae73ebFpEDLWqqmazWb/f39vbc845cqurq3t7e2q2KWxWFPNgVO+ZBz6YGTqVYCEE72sR0ZrY+FsJXPs6UoW0rpG4cYhobQMRHIOmXdGu+fZZ1ti4qtHGGnHWvM1WqTVti1ydtTGm1+spZUobOlUhrfbrZDJJe7ltzx22fpS4pOp5jtOMjnTNWNErbgEz12WJaU+JvtmLlmhJw7qimfHYpposnFbpGHpHnl/dZmhwkcBoJ+bGjIeqKH73d//zyZMn3/e+9z38yU8kibv//vvvuOOO0Wj0z//5z1jr7r3vvixL6rpOkmRnZ0dE1jdWQbDf782mhQaDjTGK56zHWbt3b2xsjMeT3d3dCxfOZ1n2Td/0LT/wA3/rB3/gh++7775XXv7sxz/28K233nrs+CYzW7QsRsjY1HBdG0QyNoTgywrAkDOIhjoTVwcdIIUQDLmXX345BI1QkNIVQgM1jYgsrBEk732aJMHDF7zpTZubm2UxdWSCtqO1xgZTlmWWZV/wBV/w9r/3DzZWN48d23zNXbcP+jY1iTFgSPZ2b9zY3rMmLauq1+upl6Usa2a+fPmSHpDJdDqZTHTHIyWEAILIwr6pNTAAMBqNAggtRglFYmrv4hWYowc0Rt9ayP0uSXQZwud+LcmaoyioHSEAABhAjf2RSF3XxtmV1VVjjCUIPIcw6z4hnpGl74+4E8R0PNIL6oIIANi11eHjj48GgxXmCgCSJAES1L65QdkfIOkC6Tk/ejJ6dJfsYOg0RThS2sVxxG9CBwO5O+HuvnY/LEnrrpDojjOKje7Dl5bjsKSH1lMXrXxuQ85FUQwGg+l0qs7MLMvKsgzd4tfFUUWGFbW57uC74idqCYfHozcigvZuipwXUYiIfagDKqC0CHnvq1kpfccBqsqPYCrBW0JjHHNTT9AO0iBKAOJOPfThZYmyPxIlUYOkT4Dsg3oyk6bqkRV+3yaO0IKgICEYY01dh8ms1NpNIgoihMTMgCysQSUQIUQyiwH4eC2tD3SIO5a9QaduyvvKGNPv94uimEwm1pgsy6qqGo/H6t2UBB2mq6uru7u7w+HKpz712OsfenBzY127E2kPrq6wEVw4hMyMqg6R6Y4qiq5erxdjFlHUtbpCx38bYwqUqDyoqgo72okxBmAhGCwtIkcU25HOo/8pwndgm8wsbQCVGnHYrNtcfQFodraTVKIXL7Zp6pJ3CCFpGkuo6DXc6usSGYdunDAKgkWFt9Hv2xsWoOOPuBABDIB3zolYaNzd8Nyzzz/11FMf+siHn3rqqROnjs9ms//4H3/73JkTly9ffs1r7rx2ffvTn/702traeDxOsySxTt3yJPD888/feuutCinKIoigQrqu67ouiWxZFFmWalHfjRvX7rvvnn/6T//ZP/2n/+xd7/rPX/blX3Lq9PlHPvnwyy9fue++ewC5nFTWkATP3Exb2AsJs/clkx00k9AmVghq5hmTcF0/8sgjCFCWVVXVRMaH0HTlAYn15QawCqHfGwJcu/ee+6xJvKmBQT3xVVUCQBA2SfLmP/eWd/zj3ld9zTce31r9yMc+esedFy5eOru2tmZcsrdfjMYzJpempqqq/f392Wx26dKFtbU159z+/v6s5WaHGakICAi34hMANNOiy8dac3XOvqhFedRHEFHXCyxtbHCJz+v9N6WEm1+HZaFeBAjAHKFi1QYBqWufoKmCXz22tnn8WIni24gJwLIqsMR8Dl+RsRMY6PRHWpI7ImJvvfXChz704V4vE5FZVaRJPqtKYxDBKARn57GsxRaHlwluYoDiIcf9zRboSOF3WEx274kcpzuAeA+1YdSlARw5fuy0XJU2LoVtsFPtS9vpwgQNCBwaY1ZXV4lof39/Op1KG8SVDjaIDu7IPYviFhaUDNV6kDq1QNCa79DGXxGF2TMDmsbe8r4mIpeYqqqyLAOG3d1dlPozjz5x/szJrY113FhZX13xzD54RJSG9gBAi3F1leY1VLKoOUVeHA1BY4yi0KeZU0FFgJrm0zBVJBH0LLPae1/Vs7IMZjIrppNZECayKILAIiGwnvbWNyBNzTu12T1xAXWjwyGguyWyIUBuRVcIsLu9Y48d03TicjbTwYcQyrJOEkHEqvQrg97a2tr29nVmuH79+uXLl/I8oc7lvQfSXtwLgGjMXM/qxGVgQ2ihv00LQaU2RBx/tCeWKJA6SFIsrLFe7iRRd685wbekq7+NECiKlhxprOueif/Vd1Gb7RUpVmkeAtskMW37amojQfo59qiP3+id+jpAJLICXnHQWFowgHkDhwBikGVecKAllgDwKqK3uQggABDZhATqukZrAE2e5w8//DAiXr58+WC8DwCnTm2NRqOXX35lMBisrKxYa0ejUb/fr+oZIq6urn7q04//6v/6y3Vd/7W/9tfvueeuQX84no5EEZsRY4GsMhNrjUiysbFx7dqV2267OB6P3/rWr/y7f/dHfuiHfujeBx66euXKh//kE7dfvrS+vooEQMZaAQniq5o9aCiJBH0A05JpwwMFQmAOo9HosUcfv+OOy7Pah5pdklgiCJ51h4m1LwiiEBhEQoCzZ8+Gmi1SAAIRYHTOAGFVhOlkDCY7deasIKxtHZtNx+9670fCuz6ysYnDwTpLurM92R9Pw9gDwC0XLujRmE6nV65eLYoi5tC0vMXHDRRp2l9y0HI1mBVVWVUOxAtHSH0AIOE2zb/JOZpDQHew4USE28gRLeYGwc1F6dIlh0AYu3+akw6i6noBhAGQAYhJwBrFIeETmxvk7KwcC2GeZhH+83McTFcG6UFAoKiQHL7fPvDgfR/4wAem0xkRWKS27hCJbFMnqu0+tFtxp49NPHtdQdjVl6NCFPlOVxB2pwQ3MUAPv+XwmnbFavfOw4L88GYsLVxnC7uWq/4TnTPWzrv0JEmSJMn6+mqaJtevX9/e3o5+0a5NsDTH0IExaY2GOo4ZEQHmvLXr0F6aS+T7oNYriUHC1h0NAOyDBv/GBwc7O3u+LvM02VhfraqKQIxtgCOg6yMSQkAADxFNsdMH/mafAUB8UBxsEen3+0VZEaVVWYowB6o8l1VNRHXNRen3xuPJuCjKytehVgiCBpNStFkhAEhgBjVzF5D8uprBzTYx6ihEhDiXN8aa7e3tlZWVwWAAIorrKyIaTQheqtKLyMqgd+zYsenkuclkYq1N0xy48foqdwgy32UzByxDbusIQwcmDBHLslTgRlr01lLb+I957p5ihrKuvK80ua9JL7KEwta6IBwCE5vUOmetaPGinYOp6WBUkB8cHGh2dFQHAUATbfQ2hQmbzWb9FsEDEY1zgAgimrIOrc9fpbv+V+9sugKLRKVcpKmAJyJRCBxEEBBCCHFrCAwhIgRg9kYk9twCaDJK/yzxSw2dCGkrRqgbVWwymTz22GN33PWa0WiUZdlsNmMW59ze3t5gZZgkyfb29mAwmEwmWZ60VQNwz/333XHHnVtbW+94x48//fTTt9xyLrSI4oioLuhGX0EkZ+u6Hg4H4/GIiO65544f/8f/5D/9xu/821/8Nw899NDGxtYnP/nwzs7exYu3EKFwAG4gwwRA2IcgiGmzFEqZAMDCAiZJr7x85YN/9Ee33X7ndP8gSRIB8r42pMloonlACIBIWZbt7+wmDm69eBkRi9ksTWzpgzFiEyqKstfroUlmVX11e8cDHExnvvTHTx0H9FVV7Y1muzs7XuyxzZNnzh87ceIEs7927dr29rbCuGInGigiAqHJzQfQyglqsSIYhAj29/crX7tOJg2qkI515ND2i4zM9iZJpoelLx71/ZGXHAp0HhZ7WsjEINC2b1KUGGYOEFyWnjh7GhMLVZs+fOjtUa4dOYYuM0dsq5easu8j4p50fGv9Da974PqNvcR1geKabD0AaMEyAZDb7k9HrMhS7HZpUaLmHj8fOYHul5HhHvmnI2fe/VPXH760cIdleRxkd6i6Ggp3tbq6OhgMer2eYt1F4Pi9vb3PfvazL774ovI1aMEfonkUWgz9sJhlFpmOcnIA0vQcbAMh1AGmiGOLU4uezKZGJbBGf3W/lDNqaN8Ys7W11esN0rTpEdQs1HyROK6VLGqR0jHBpZMuFxPrmNkgGWPW19fzPA91qUabQhSxYM1hVtWTWVlWde2lrP3+wXQ0mU5nZRXmbScEoc0tYJDmydqpKXYsgJuc0sN0Im2OnqKrAwAK5Hlf66TVPRBhF421aZrqsHd2dp577oXBYGVjY200KTS4QERBvb4g6vaIC4WN5efIWi101h1R5cy1/Q1jdFbBEwCg22+ja9Tq2qoYCy2m1eGDpj3+uAUsi7Zv63tAadsiqS1bFIU2AtEMwa6aoiPRp81ms7IouK6BWaMJ44MDha3g1h0XY4GRhk0jF1oNW2P2jEHZDRARobGCBGTaxFGIe7SkXR0+nkddBEBoDIABISALQCAYQqhC4x6vy8ZiS9N0Oiv29/f7/b76n2NKh/ceUc9A+LIve+sv/uIvfvM3f/OHP/xRbNXrxkPQxJ48EYgEInAGj21u9rLs6iuv3H7nHa9cvfKGN77pn/3zf1HV4aHXvSHrDT/y0U+Mx1NAQ1mfyHoOujKCwOKFfSfHockaAparV69WNSdJIj5oo1hrLSC3eDUCLUBBv9+/cePGmz//TVub6+wrYKnrmkTEB+1LFli857LyQejB1z70/Auv1EIH09m4CNs7k+vb043jp9785i96/Rs+7/Tp07u7u4899sTOzs5sNmPmCCmvohdwIfwXqyQ0Z95amzpzY3e/9HUgEgSVGk2ZLItmWi1tH3bYNTN3EWcPn2g4JFNe5Xp1+tF1BoBu80QEQBZnrOeQ9nuYuv3JOIAYt4DhuiRfbj6CBXHTKtbLGWQAQMAEbAnkgQfvf+973xtCLRIEKNQMpCGHaB+1HEfgSA01HnvoyIwlSXPEWrS/PfzlkffIooLTffXShyNvjhYqHNppWUwN796GnVZF0NGqRGQ8Hk+nU83JirVARy5O5FnUPrljYs5LPBux3fnh4b3XxtwIdmltI2QgIoaay2KsbD1zybHNLfHK7LxxibGK0QFBQI0JEVF0uCPJV1p7PQpj37ZyasQn0LA/OL61+dmnnkNqHJ7CDIDeh8l4GkKoqwDIk8m00LZKQQAZ0DRhJPW1tLD7ul4CLIC1zAu6pKOOHHkS4tZga/bpZ0acTCabm5uIUpWlJkExs7UkQMH7ybhQC48AnnnmmbNnTo3H47JU1zGDLOB9RgpBRGZg9oKooBBqK8e8AQBQS1G7RENbCAsLxySawmAMEMFCyjcBtACTtkUVZmaQpuFxkKZ3QreWSQVwDOsSUZIkyl7j4iguSiTsJEmKolANwFpLSYJtFa8+MCZCd6mXmkIvABBjUPvew4JmGZAMwTxpHBo3YCPC25vbDRXqMJh4JDt6PwA2p9vo984lzIwhbGxsPPjg/ZpIZa2dTscEWPkaWvAyPacAkKfZ8ePHPvzhj/93//1fOnfuXAhhOpudPXv2R//e//z61z/0N/7G37hw4cLx48d3d3cBQFc+SRJ1irQq5sxa2thY29/fz/P83nvv/cEf/MH3vOc9P/ZjP/bgAw8eP378k598+MSxjePHt9I8y4mqalJVJTcwuAKBAT2gATKAiCSA8sQTjwPAwcFBXddC2HBaRABBIq6DMVrBEQb9/o0r8JVf+VZjaDYdGwuhqrVFROU18b4QSq5cvz4r6yzvi4gA7e1POISzZ07ff/+DKytr+3vjl1566bmXHgcAIqjr2hkbs3HrujYWDbXYLyoKEMigBqPbjumGSCoJ06LooQleQFvNg+YLA3SCuBR7qzMCYPOnDs/BjoH0Z4u6m19HSormFdyUlUSFHQVAoJLSg5w7d9KkySyUYJCI6rrWGu7IabtDPXKQOnzEiEbU6JrYeDeDroxIE1MjQH9sa+2+++/d3x0hSDWbmgUUi7lLmTkwL5S3LkysVYsiv8ZFO106JlRXqMTryAd27zliNY9IEJgHLLFjOHILoLj0HFkMdi6NJ97crafitjOBZqloJ5nIZLkT21COqdq08tPQmjhdVhjXpJlSZ7O7UieOFhGtm5dm6Ru1101ZltDud57nSZKsr62WZUEEWeIcGYOg+FAAgMCEiMAN9KNoWcSrORigY7voB9U5ksSur68Tga9q4JCkVg0jZh4X073d/Z29/b39yd7+qKqDViZwm2Gopqqwn092/lrqvjGuW3f7lsYZ3Q9dTUvvvHbt2sHBQWylYC0BACFmWba1tYWdFkA7OzsxXdlzUJ6OiEE4ejh0QyvvZ1U1m800CTyWA1VVVfsycO3b/vCm01NIJaIxRseLbR57pNtICKb1D3OnykgFTORW0Uusz1TC0wAEtggt0TMRCRixgUxSXGZoDVxEnM1m3KYWqh9bWnyPSAxxL7qeLYzoBIhEDq1BNIDq5DPz4C4RWWscUdvvQbWqRZ2yqxBT+z9o2TnNv9efWLO+tfnQQw9ev36118uYfRPHJZO6pCxmvTwHkcS5xLlnnnnhwx/++Ld8yzf8w3/4DweDoajL3fus3/uGb/hLf/AHf/DmN7/5ve/9w1XNhrWNWNJNrKpKJDhnADhJ7OrqkAiuXbt29913/97v/d5DDz30C//2FxDM617/Rs/46KOPv/TCy4GBbGYoSdIeM0vgwLWEIOKBgx5GP5s98cQTg37K3NRTGLOQNCqCwg27cM4gwKWL521mAIP3FRnWQhf2QmhmRfW+933gqc8+u7u7/0d/+IdJr3/9xvU77rjjq7/ma970BW8eDodPPvnkww9/4srVF7WGUz0odWhS9uq6zntpzN1zxioVGWNs4rr0ryFLBJjMylld1aGhikb68EK9SbOzrNKkbQfUYvxKm+oRWV/XP3eYI/2fu5rnY7eldhAIYMjm6bFTJ9lgDaylmzFTcvnni2K4+1cBBmzF0BzoqOuUBm0ppt9bX1f9fv7aB+7//d//+HA4rP0Bs0+zvCxLBS3RsxU5o+80so3PjRZJFN7dtx6Wst0JHFZVDg/6ZuoMdCpDlhZFWo07Lln3+66c1g+LezxXoCJGKLQuO24L6pm52ygm+vei1zE+U39SlqViJoQQTAcIsFlDY7CNoJtOg8W4CPonMg2/01dErjeZTHReFWKv18uyhIgcGSIY9LJBr58kCaCEECyxYoxIo1QBsycBJLJELPOG6vFIxIkbY9RpVntvrBWAEGoEBDD9fp5nGZGaTTFvxXrPAQNPdc0xhLm6Y9uG8FU1Q4J+v1eWpQTWxGACIIG607f4sOilQ5BhcacOU5QxpihKDmE4HK6srADw9rXrYJEZva96vZ7msWurhmef3RmPx9rKIknTmht5zAg2TUQkiEC7UxovjMwiSZIsT5QkuM0ZNh2YbmiDtXErEVEVuJj1cyRVKzVaawN7DqIMURXB0LbpVXqbzWaq+enY1Iyz1moTRmOc9yyCxjhhRWaG2awismSt1oIniYZROUmSEIJWtijojYJP6XvjUY1TA1AvIwAY0Z4CGshnQLKIGhBEIgfNqWy9drBcmHT4uEcHX2sgsp4PDj7Nsj//tV/3i//mnRfOnZ8cjICZBGpmAAkhVNVsMBjUdf3MM8990zf9xe/6ru988LX3r6ysAECSJFXprXXKIe+5/8F/8A/+0V133fNjP/ZjW1sbKysr6htoe2+LMQ2EPjOXVbG6NiyK4vr1q5cvXzLGfcd3fMe73/3uH/2f/t5r7r5zNj7x8J9+oqqqs2dO5YO1YjpF5Dp4ErZAImwNoTEgvLe/89hjnzl79uz+6IDZ63Fn9tLQj3qVBLQ2zAckuHjxFjCSpqYqq8BMloDBmaQsw2/95u8GTE6fX/3V/+1XyZjbb798/Pjxfp7Wdf3Uk59V/RKJfV0G8apeMHOofRS66ieLHovIjljbZDEHgipUBI3CuLu/d3x1RQjZBwnBGFPXtbM2tFkRqmIBgFpxXfbeFbpdsw3bMDnOBzA/FF1u0JURSwS0xPY1okRErNjaPgiAS5Kd6ejCrbdTlpQcRCRNElEsfbvQeHuJscAhGax3NngOiPqWufuQQ2iLVrQLJYVQC4eLl275/Dfd/9LL1/r93BijgYQlyafIglH36c68a2d0xWdX6OLiddMjdihKejPhfeRPoGPU3uztR65gN3bbfS91etdr8Wg0VlZXV/v9fnxO7MHQyrZ5AxlpISfVbmbmsqrqtteYNBHZmPZFh4fa3X4iIpx7MqM9qla4tVar+hwZ51zqjK9KJMgSCywCjVOxLot5HBfQGAMs2piv++potCGiQFNNX9f1aDSazWYiIj4453yoE2eGwz4g93uZtfPEHGbWcJT+zzgLhAySZClig1uSpmmWpc5YS+bUqVO9XpZaZ62tfRk6gC3yOVzQpnt0d18nEhHTiqKo67Lf7589ezZ1NlRlP0vrsshTl2UJIiZJUpYynU51sxR3BQCybD417sTCTVuAG6MVuilxETQPIMZTl44Joio/DUxptFl12dW0jWEOaU1e6Cgc0poLOtkQQp7nKysrmqusg+n1evFpatjppLR5M3daC1Pbj2s0GmHbJ7E72m4uoUQHnSJ8KWyhDxwCiCAYIAQ02n1hiSdAR/rqLh368GdepEkqZAyAfNEXf+Hf+Jt/7aMf+3iWp+pqUuVjOByKiPdVliXWwrd/+7d98Z/7cysrKyKi4jlJ06ZSmQgA1tfXv+t7v/ed73zn2trGJz7xp3qs1ItgbaKOBFUfB/2smI6sNSurg7IqdnZu3HffPf/hP/yHu++5+3//9V/P+oPP+/w3EyWffOTRa9d2iZrSCWMTEXFJgoZCXQHR7u7uh/74j5LEeu/REgCweIZARMwSQlDz1xjjnNnZ3T59eu3kqS2QAMhq8YdQG2Py3soLL72ys3Owubl1sD++fn37bW972z133Zk688ILz33mU49sb19XXIhpcVDV02pWSmBnbJ5maZpaR2SaFqUNMQvoeyNlAoC6EzlAEPbeM8DBaFRz0AwAIlKakMDMvgEeiRZtG9DU/WsweBftDVgUbHJI4i5dS4e9+6sjrwBzeUxENQdx2FtbqSRUoXbOaa5W1JVf5VFHDqM5+IvJz+p2buMpbRAntcYArq+uvOnz3khqMkIwBttWfsvMgjrZQNgmX+BNkpvi57imR8j1Rfl95D1yc+l7WKbehC/fVFc4vHbxUiE3mylccNDeq8qktPoIEV3bXFY6LpQl6ai8IDqiI6cGRDIOcSHHKq5DnKAseGysIkZFMdD1zfq69t5bS44aB/Xx41u33X754i3ne71MObjOJU1ytYcAoK7rspiFEFxioKMzxsXpcnm9X61DREwzV5ZFPSuIKM+zXpptbW1meYIN4g+IovW3h6+qZlrE7KuamdUoDCE4k4jIcDiEwI4MEa2trUAHeaM7nu5Wdj+0FHKEw0rNcR18XdevvPLKtVeubG1tnT17Ou9lIpLnaZ7nlgyhECECPP/880QUm9XbNBmNRiGEEOq4QTGgEFOKugsV5bEOvjvObm0uM2uSlBoiEB3pjWuWbKeT7pwwaH4A4xy7VDEcDtfW1qy1a2trk8lEIxR6T8zYUqqGaByEEOo6Ko5VVU2ns6IoFe5K1QiFW4lvnO8LIiKG2lOn0yIighCgAbJCyEgBMGgXkCXh+//f5eval+VwbeVv/+0f1lBIXZUgUhQFIuZ5tre3N5tMX3nxpY3VtXf8o3/07375nTvXbyASBDbG+lY5A0AQAGMB4Mu+4it+9ud//ju/8zs/8pFPhBCSJOEmfd0RWREMIRTFJE2dr8s8TQ3i5sbas888c9vlS7feevEbv/Gbvvu7v/uJJ5685eKtd73m3mefff7G9r6xibMpIrregBlAUUSEX37llb39SqH0vPeCTbkas3CAtiebCd6nSbK7e/DmN795OByABCIAQpukgK72XAcQpnvuvd/Z9MaNnc3Nzf29nU9/+pHHHn1kd+cGh1BMpzvb2wf7u76qEAKSGItJatPMpZmLZKl0lVinUHTR7SwATVZpCLHvggHY2d+rYhZLW5jOtW9CxYgAmmvV8UW/anhx6bqZCFhim68iKY54GoshQqJZKPvrq2vHt5iQmbWDuDKoI5+zJHG6z+wKEVkq5pw/rfXCh5p8XWslzOXLl974hvsO9vcVK7g7H0XxEhFoO+keFpOHBeGStFtapvjwpQ9HCvLu3I7805Ey+8i3LP01DnXpm/hBgyLq3HPObWxsnD59+syZM2o9hAbnYSE8pkaGhhBipBZaS0hakE6a9ywzEaEwTiSy7CVxuLSqUTjpf7Ms00emabqysnLq1KnTZ06dPX9ufWstgHgOCEbIkEtKH0IQPeTOOTQkMHf2dl+qGUChg4XCbRo2AGg1S5ZlzpnzZ8+urAymxRgFrIl9fvQ8EwApfoWmgAGAMaYs636/f+bUWQAoigJZNJ+8LAtr7cqg312EJd0oXksy+NDuN+FDIsqSVA0X9RO8/PLLly7e8toH7l9dGeZZWs6mgZtcp82NweOPP3pwcKBror2V0M4hMKlNyGp3qlGqpNW6WiPYdLWuJc1JFpMhoM02iIJtrrl0ph9nLdDNRp7DteoiKIyRNnxtOhY41zoDF+IL2FrSkVDVfI8+bSUS5nmcRVpfd9SQoO2JhETGWMQWWRWwVRZMR300cV/a63M0fzV9YfmS4K2lg93dD/7RH9x15+1lWeR5bgz1B/nm+npdlgd7u6PRyDnXH/Q++ME/+YVf+IXf/M3feO7pz4LBxhMugkTBe0AEEQ7Mge+48zU/+vf//r/5tz8/ns5u3Ng5fvykThY1kGRcah0JpNZUs2maORE+efL4ZDKazaYPPnj/z/7sz917772/9Vu/nfb6b/i8z+cgzz3/0mhc2KwfvBcBFhBEQPvMM89A60XTGXnvNSaCDXY/G6Sqqvr9vgg88OB9RFSMx6UPpQ+eyQsxuCzv5/3BI5/8zIf++KO/+qu/+ulPf/ozn/lUVRYh+Nm0GI32W79OAGRjsN/v53meusQZa5AMkiVjySTWxa6g2Gb5AUDlS+89BO6ewSxLdvf3yrqSGLFWodtaI0sGRowkdE9r95slUXKkaDiS4d/s53PqUbhKFoWQREQFqlw7viWprSGgIUvGIiHiUo0fHiV3l8YAi8yzM2yeUy8iSGD2IQSLiNooI0vSu+6680Mf+WQ/y0fTQmGuVR/EpkbWwKKE7848vnVpfIfF3uHvu99wJzIaZ9j91c0W9/AaSRtR6P6cFpF94m+pi3PU+VOaupjCurq6urq6ysyK5q9YQlo5pz/XEC8tQhZEY0g9kCq5lRtmWVYU5eKSGk1JCR2giS7XjmmuUe7qDXmeT6dTa0wIdWpdr9fr9Xp5LzPOTafj0Wg06OVIGAQFaFqU1lpC8kFCCKgRJqEQvJA2oiFUziQsLBwAUDl1A0ZtbSKCdR0yh3VdlqVHhNOnT549c2o2q7aLfWst4qxLGAwiLEQg7IXRGGcMeu9PnTgtEl586flemhVFked5VVWE6Cz1+pnsLWS5t6S4pOotqJ+iaPdzAlO6EgAvyD5UzjWvnkwmzz/z9P33P3jm1IkXX3z55ZeuTCaT3d3dUIfBoPfss1efe+6548e3wEjmMmau6zrP06h8wDzpgZjZGAvAKqJUirfjmSPZQluuo7IZEYi0ooZ1cxVgIXhFSmHEBpPctF2SFKJEeaIE9tJ0n9U4a2iLQwBgd3dXkwRv3LiR57mmN4cg1s7rK0yykNgclcK6rplF/a7SwZdGxCSZd0XUiUSvOzPXNSuAr4kdjdpdAkRAZdGaTsUi8rn7mufbDTwX0nNnNoYQfvmXfvEHfuDv3nnnhWG/H0IoAgtCVc3UykeCPEv3d/cuXzo7GR988k//lJn/wnC4sXXcWMshkLHGWl/X1jlq01nyvPdXv+3b77vvvp/6qZ/69//+1970ptcVkwkzE3EIAYHr5lCr35iZYTDo13X97LNPX758kYj+wl/4+re//X/8ru/6rnMXL013rz/+5JOXKMn7vSTPy3KWplkI/tEnnkSEoigr77Ms99yUTosPgQHVnYuMiFmWicDtt98+nRW+LrPEMZks75ezenRw8NGPv+93//O7/uSjn7i+vV/XtUttktrr16+BBBAiIWa2htJeL0kNAFuTtRreHBQIWlaJTYKCQYQQfFVVnj2yWGvZmIAizBCQiEbTalaVmGZxm4iIyHiNeREAg0hooL9uIi+6rD7+87Ck+G+6jvy56jQaUq5DhVly7MypUkIgICGuPTUlzssdgw4/c2nAHYDrprpkqf8TERBiaBpHBSsigCDALnF33nnn+XMnbuzsMiAain2UGgkBRoEaunI+joAOpY0cXoXuf28y+mVDvvunpTsPP6qrqoQOpGX3mZFxLA0P53H+uUlhjMmyTG24LMtUyI3HY0WqU4yFmGIAi67aJU1F+XKLaG+1dyE3aUq+u3rMqKhtcfBRP4gT0VMhoro7EkFRFNooxxg8duxYf9BDgl6vV1ZFgMFkOgXgPM0CiPhQliUD6tv7eWaMCUEAJU3zWeXjglAHZFgFiGZLImKSJF64qEpHxMCImCbJ3v44TZPTp0+maXrl2g1smxiKCGKDiQPiiUyQYAyGEBDgxIkTBwd77DldSatqFkKwxiDA+vr69evXj9SNmBdqE7sWUUtFoWNICTatc9FXDf5lVZVCxjk3noyee+6Ze++9v9cbnD17dndn/7HHHtvZvTEYDI4fLx5//PHXvvaBJElmxUzDYG3cF1Gdq82QuA0wB02GiiFD5nltld4cK3+IFvI7pFlhihNUItRE+sOKo/5Xt4M78JD62yUbWrPJmJnIxpuZWaPBAJAkSUxiIGOsiA+sDsn4fTTioTVr4rDjhxCC97V1Sed4UbtBCk1mpLEGFmA7P8drQfrOL0bEyeTAWtrczLfW165f357NZknqyuCbvlhk0iSdzWYbG2u9Xm9/fz+EcOP69YcffvhLvuyt0HaCAiDrUtUqal86Y421wvzAg6/9mZ/5F294wxt++Id/5O6770rzfDoOzjkjnJgksHCbKp8YCiFgatfWVqfTCbO8+c1v+pf/8mc+/vGP/8AP/MBb3vLnHnz95gvPPbM7Gp89ezZNeyy8vzf6k4987NLlC5NpQWSqqgoMaJp141AbmxmDLD7Lsv39XQA4efLk+tax7StX0mxA5F5++cYnPvHIe9/zX//Vz//C+tpwffOEMWZ3f89W5MOsKqcigoxZ2t/cXE+SxIsHCDZFX85PemunNr5T02BgcdT7Qwja/lajvERIQBCCMUYAyrpWkzESDLIASXygiGjVY3NQP7fNX5LKS9dhCQKvGrWNbe5IAABFQhX82taxleObu9UEEA2gr2tLBhGEFhSFVx9J+3aMLGg+UYkwf5rQpk6jAAA2c4mgKcqKLB7b2nzj573+P/zqb28eW5vOZtLABTd02ZVwR7346EU5rNEcOY0jn9z9U7Q5uo9aevvSuw7vxxLzWhpz/NK0Rb0a3yUiLZrc3t6ezWbOueFwOJvNRqORdBAPoC1O73rzou+lLEt1SnOb1NMCZbh4TxTbzA34X+hUnkjjM9QTgjpkbrrgoUI/aCKG+ldF5GBvP8tXfAivXLsKgfu9DKVxchZF6TlkWXZ8a3Njcz1PnDVYczP+OJK5GtFRU4jIJBGcXZIs98FXVTUYDDY2Nm5s76VpurayMpvNikLBvxo1QUQAhDnoINkLAu7t7W2srq2trpVlaQxZY9QFiAgKrhkRGZdUtI6C1T17S3QYITalLCtLxlhbVVVijLX2ueeee8sXPhR8vbt949SpMydPnpyenCLJY4/J+fNnv+Ebvn4yHu3u7ua9lExjGjLrjjRQ8lEgKl50fHsMTBhQ8jBRgsYUpyggAQCRYsBCSVf9DdHL59umIFGcSwc9Jv4q5s9r8QwAjMfjjY2NKK3j/sZjAqhi0cxhRlppFJ8Z/fZEFN+/9Gr94HQAElFsW6hn/Qyg6Lu6P4gInxsX/jMugbouE+c+//M//y+87eve8+73nj592hkbgqDYLMskNJpHlmVExOLzPB+N9vv9y88/99y1K1e2trYIHQAANonoAGDIAZKvS+tcOZttbm197/d+32233f6jP/qjTz752bvvvms0GkFg7WyRJUlZ19i2V2EGlxhAi2CeeOLxc+fOvfLKS29965f/xDt+/Du/8zvPnr+4u7v76OOPr62tnTt77vr29gc+8Eeve92DV65ctTaptK0nzFkBcKP79vPs2rVr999/5+233Snep3n+/PMvferTj//n33vv//Lvf31lZeM191wuptVoMi5nIc3SsizGk1Evd47McLiepf3gUVisTcAEDrUzCKZpe6XHxDMDoL5uNptphwZjjPbPDqWA98AiJMbYxBoH3lrEplWXepsNgkCrRELbXl2lOzY46hqVmPs7GUTgaOTneLrx5sHdRY38VYlFYQ+YDZECTW6dPOGFa1ZfDjWBIgRqAVxv9tj4ZecIdLVSIVIgv5YNtb9SNgIAtq5rm5gkSQSNQXfXXXcB/LaeUlFHRKO/iMYh0Mwd3PHUvcrIlsTk0s1LE+gyl6WJHbnWN3t7/L77zK5cPHxFG5TaosYmTwoEAKqqUsDxwWBgjNnZ2YkAvxF7wbctYpY0DH11nucq+fI8VyCtqqqGw+FoNOna0E2mgwB0gPUhIqYelRSgq6UHxloyxly9epUM3nfffZvrGzv7V+u6NgQbq2uJ086ptqz9dFbs7R5s71wnosuXLt57910bG2uz2ayf9cs2OzpG+AAAae5UMG1fHSIKwU+nU+MSAHYu3djYYHmCmdfXVxUcuywrzVrS3iiGUASIqCiKfj5YXV390z/90631jV6vd3BQZVmWOLezs33s2LHJZLKzsxOtrrgacU/jRhPNtTEtczpqicQ5B9xoM8wNYvMTTzzxlrd8qbqXy2I2HA5f//rXnzx5/NFHHx2PDu6//17FWrHWBgiqk7XO/2ZG1lpFjosh1a6ZyJ6jitbdQWOMMTYegShotQ93m7rFOjC9TXOhlwhMjVRp+9JoyVB8bL/f10TuLMtawazR3HZU1gIzEIEauEkCIuy9EnaM+EYyBgBjGpDkKKTjkRGRJLGmGybohA/m29GeUYhoBZ2DeOTx/DMvItrf27vvvnu/7/u+zzn37v/87n5/qOZ9XQdgqapKBJxrVkmdCvv7+8a4F1547tixY/qcKH2h5QnWpcI+zTJ1TX/113zN5cuX/9W/+lc/8zP/6vWvf9BUXjwgUVXPnHNVPbPWhRBSa2Z1pZ0ch8Ph9vaNPO+9/vWv++mf/n9/+tOPfu/3f/+DDz2wsrr6xBNPXL16/fnnnxeBsiyNcTE7BKTRyJMkKcuakAAgz/Nr1/mtb31rr9e7dvXKf3nX7737Xe9/33v/gGx+6dLttZdr118QoboSFjOZThH86urqyjBPrQOwCEZtiQC1IDvnjMQs2qZ7h6JFjMdjrfgwxqkHRcPPWZb5eqbtOIkocYlhQGQGmFVldMgBWWRwiGWjLrfQpIBCWlADACBIAgEIheVIVQzbQHL3myXuCjeXAq9yiYhBZBAgXFtbK31FzhKgQZNoIxMJgPIqT16yBKARN82fmr8iIHZNvgXkJYFgIc2ZKJSz1JkQZme3Vv/i17zlP/3O+/vDXkBb1N4YgxCCVDYxhAKQdIVrfHfEi+8OrnsPdmxWakGUutKx647rTlJax1pkxNym2/FcyZpbI5H3dX/eVdK7axdfpEzWtHaAJrs6Z7Is1/RUIlKAe0UqjyIzQlBFedxVJuIK6Ocsy7IsU6m2srJycHBQlgWiAHBde2MMAKoPM3Jz2+nniI03A7nFL20NkaYcs6x8YEiGyYmTJ4uyevr55zc3eqnLAKDXW7EuvXbt2gvPv7izs3ftxk4M6n/26WtVnb/2wfsHg/WZnwQOta8bdzrCwXjU7/eRUYWurm2eOF+DBE8m9d7P6jK1Vkj6GV44e/L5F69Yl2VZtncwMsZlmWlWjLD2lCRJXVVElKQWWbzD8WRPnfzMMC3KldUNsMnVG/s1GyLRAF70hx91DJqgrwg4l2uOiVKiOoJaFgNIElgAAIxlMi53z764//E/fezr//zXvfLKSxtrq+trfZRw1223bAzsI488stJzd955JxjrvW/6SSNECYcElqymExORSOu3TLK4g5X3FsBaK4JERlt46FZ67dlgE2bWfCWv2Jkiouts0BlLbUU1AGjb8DRNiSgI13VthH0dEJEDDYfDuq6zJPXeJ9YRkYIqKIRTbzgIIEDOZvlsNkvyXjWbQVkDQJI5Zfb1dJplGRrw3tvcQYm9vCleKoqizZ5rrHmV7vEUswRjSeqiKn02GABLqGv1b0uQMHeJN4VnaAx7r/sGAMLACDbNoVliVAUFQFrVVoPKKBJQ8dGaXQ8gIuzH4/HW1tZd97/2+/7WD545f/Hhhx9+5JFHELLEZkhgbeJ95atZkiSDPEPEUIUQxJpkPJ7t7B1sbh1jYSLT8hANVAMAACUAYKzxvrbW3n777f/4H7/j/vvv++t//TvvvP2W1dXV8Xhs0sR7n6R5XdfOpcycmAQEHBnvOU9T4HDtyivnL555/qVn/+e///a/+lf/6tve9hdec+edo9HosUc/S8bUHopZZa21lpi5yQMAI0BIhBhE6iAeCJjsr//mf/nZn/3ZD3zgD2+77dLayTN1Xd8YXauqCqYsUCOII0QDvWEPDbnUoqG6Lg0GoBIZDaO2o0UbEDUhIDCAZx5NJsruAgMLAggSIVFgnkyn/d6m6Rsoyr41Dg0R+IwSl5xLNw9u7MBFIbIWQkpmgn6KnsAxq5izQtxq840zDFhABAEMkgiD2sEdtkzzAOiC2IvXf6vozb0pLZTkhRBJdg8OLtx7B673ggkuSB9MZkyNUBEHsoyQhIW3RJa+JFY642myMSTUzB5QBAJwYAmk3BuBEAgFJCCABQDl+MyMCCsrK3fccUfyrvdnWbY3LoxmlyCkLmEFFqeF/jxxTF0pCIfEbXcC0Z24JJi7i4uH3NR46IKO/D5yG7pSVm5iPsaL5qkxGLEAiUgrN1QpjlW88fkLer2ItJCWh230uq5XVlaMMWVZ9vv9EMK1a9dUOy7LUpGJqNODdmlrD2tb0rlaNyYqawaAyWTinDt+/Hiop3Xte73elVeuPv/Cc+99z4dYYDiwvcFKVXn1hwPAH33wD4rp+E1veuOwD63ma5i5LAut+tVqnHYr570EvPdgyKGpva9rn+f5mTNn0KQ7O/tpmooPLkmjia9aWgjBOgciRVH0+/2tra26rouiadZrjPHCXlPbDDTy8qj4xZG7qY2+Y6V61N5a1RBjkZIO6dTJrT/95GeOH9/6ojd//mR0UNd1P0/zPD179mySJO993/s2NzcvXLh0MBkbi71eT3gOwykiHOZJnrogKi+jHmaMcU0eeAxJNB5sSyRtVBgIETBiPc416PaD4jodNkl1bdWovXbtWr/fD577/b5WCoUWJEtEFIvDBwEtdC4KdSyrEFVTu0GYkSbLV6dmrTXO2bqWxtynuvG1Bl1YPTWGTKslkzLW0ODHiffedAKBiELSNDCdzKbWWmcT1I4F3qO1gFjOSmO8a1pbzndQHf8M0qyd1la2XoTJZDJYW7t8+fK3fuu3vv71r//ABz7w2Sef+9SnH5mOpoNe7gzmw2HqbF1W/X7fGDy2sXkw2hsf7I8PDjY3Nwkb51Or1TWHjZlVZugYqqrKe71v+7Zve/DBB//e29/+u7/77je+8YHxeKxgOwpDhvMOVJRlSWjBRK9evTYajW+//fZf+qVf+uxnn/7mb/7mCxcvarK9LniSNJsFbUxKWzurOnVwcLC6svJbv/k7v/Irv7K2tvaa19w+mUy0E4zSs0F0LjMQjLXkjDUJQxiPGhivWmoQyVwGIKEO1oohQgTFGS0mxd7eHrZpntBCeEa+5L2fTcZrg6EYGu3uFNOynydefO0ZPKxaKOvZ5tp6OcKimJC1GbmqnPtjuM3mg8iZjzrLh5n//4VXUGZpgIgCSG/Q3zp2LEmSaT0BkQDc3IBNh7ju1RVPsCiVuwNurNtoCkOUUE31kbTeMotkjTGhqo1z7BWiIVy+fOn+++/58Mc+lfV7DGDA+FBpboghw0eZ/FEwdA2UrgnYvXlJtCyZsF2hvnQd/vJIOX14sbosW72FrWo7/94YBbcS7xlRrDWa5VuWRRwh80JuVFzu7l/NUVhgiDgcDlWqWWvH4/H+/n5ZlltbWzFeCwDRK8CLmTvdRY4SpTue6CgmoiRJNOx37Nix7e1tg4xodnf3n3zs8SeeeO748dV+v2+tDYLT6Ww6K4wxzH5vb+8DH3h/mrpbbz2l+oGqGnv7u8gyHA7TNIdWndIjhC0oBBFaa30I4itr3drqsK7D9es3EGUw7KOxo9FERADRJUkTGTVGAFTRBoCqmMXpA6gTtAXe6mzrEt0fSRtRBEbtBNuCvC7haUCWmUfjybmzJ9/9nt+/ce3a2772q4qiAPb9fj4crCZJ8ra3ve3DH/7w6ur6+tZmCCH4yraAzE12UuuMUe1HRJBE2pYPiGiJjcHWHI96J2vcWlrXDggIamLd/MjoW2yLOgnx5jaipqTi2/Y13nsOgsCRZYcWlBQRNVRpranKUpdIRWld12mWkSUlHjBAIcq8ubJrm1ytTDUJZo5olyqYjclFhGJHZEQiG0JA9siiHQViRanimTAzBxFqdk2xqIAZjEOegWnOQpR8AADIOvuuZ1KaPaXxZDJYXUXEc+cvnD17/vLl25966plnnnnqmaeefuzxz7zw7HMEYowJ7PNeNuwPDMLqYFjVszxNAAWEEWNcgKXtEk8UM2AIAJIk4cBkzL333vuv//W//uVf/uW3v/3vP/DA3SKh1+tpKDQ0/XHnaP4AAILGmIODgyeeeOLixVvf9a53jcbjb/iGb/jQhz504cKF7e3tNHVVVakaba2dTqcxASKEMJvNVldXtfPYieOn1EtczCa6uYiYJIlxhkFclgNAVdXFbGKdeuZEpBYRZ0xVjkMIzpg8zx2Y6XRaFMUtF2/NGV544cWslyNiXQVQ1K1WxdcplNPR5vmzxf7IrQy++lu+ae9gdzIZISKEUI9Ga2trla8rDoKAxpRVpW3+mm26ienyf+fFCCRgAEWg9NVwfW11a6MMAQU0GcIzc1t/JfBq8uimr+DW0FcKF1U4Wi9dixmALEJio+auVF6W1WAw+IIveNMff/RTvTzdPxgbl1hrDRnQJI42vtW9DsunyDKok70Z/xSlS/xSn7Mkb7qviDcvCfVX2cjun6ST1rt0RdYWBxZzd5lZPWbQxobjPTHb5dVHCx2hxW1F6Xg8ds4dO3ZsMBhoWR408cvGmokGVhQhkRFElhdPRdw75bPj8Xh9bWU2mz3yyCP9ft8i7e2Otm9cL4ri+PF1AHAutdZCCFVVXbp0qZoVL774okEBg08//Vkkf+zYseFwmKTJeHfv5ZeuTqfjixcvrqysEFmNtoo0TT9DCGTmjndrbc3MwSfODHq5s7S5uTmdFD5nY63CqwEh+Sbl0jpXVZX6GKLxF3wd6eSww/lzueKCx6U7fIpESAT17zu7e+fOnnziiSd+8Zeuf8s3/aXhubPXrt0QCZsba1manjhx4o/+6I/e8IY3BBANH2DbhAoAQEg6kQJuW1QphcRzCIsqQmRAKMIQQmh7plJQf3IMCZsWFaHbVFik6WyGHa9ACEE1pyzLFHMjSRKyJp5NFjDGpM7NZjMEk2cJABqkLEk1RUSYq7J0rkEOARZf1clKAkTSmkEhBOZ5JldouzW0klIfA+yZ2rYTyPPav852EIAQUa/Xo8YhSUgEIhwCobHWOusAKVRl8N5lGQhICE03QwAwijHdPNnmeaLNFhEBKHhvkuT8hQsnj5+44/ZLL7zwwue94bXPP/vco4995sXnX9jZ2Uldkjh7Y/v6Lecv+HI2m46BPbCWepjuwuKC2ifMTAhkDIcAAKfPnv2hH/qhu+666/u///vruj537hwA1XWtuCWIouea0DibVFUVUPJ+b3tnz7oXH3vssbd+xVdcvXr1qaeeGg6He3s7IqIK9GAw0EJH6fQUcc5573d399S/FfkGIiYuS9M0hACIs9nMzzCEoF6TWRGMicmDAglUsxJAOLWhCDQF7/1kUhRFoURLRFXZAG1Cx8uoqaPXt7fvufO2u++4/eyp071ePqtKz3WaphD8bH/kKn7m8Sc100IQEjLaLHLJMINFztx8AIAOz5SjPv+38gFYlBeISMaIsBUIBGVVnT22afK0qgqyZJEAkBlV8wJE4QUOHylhib3H7xEX3DzzBIi20SoRBU2/ahF5bSi9dY3b0xjjEisQLl645Y7bz734yvW8l1alBzR1qI0xwgEtLk0pjuDw/CP1dN22XYkCi2zxVdSNeP/SPXjUXsIim+vezLz88Ibvt+WM0MJaqVyJIpt5DsSvwibK1MNLETcJWyQKRcLSxnDGmOFwmOe5pjZ03GvzvNalOmBY5OBdDUY6ziJEHA6H3vurV6/2+33vPQQajUaps3XtN48dK4oChPL+cLq9XZbl5cuXP/yhD4YQkqRvDD766KN7B/sXLly49fIl9uHxxx+dzWaJM3VdhzbTuLt3zJ4oaVYM2VjkigF40MuPbW6MDsbTma+qKgm29t455zmwZ9Q6DQYiYhEW0cXsxvWpzSqHo9LdjyQP6Xibo7Yki+qaLKreIkKJTZD2Rwer6+tJkrzzne/8G//DXz935lRVzaaTGSCfOHFib+/x559/9uzZ85ODkSbvhMCtdBRjHbbmL6OkLlOhonHTcVUfJg91C0cfbxyzhIUGmsASkbb0vboyQbjLGrROXRgITc21iGRZpp18XJqIiNpkNk2IqCgKNXxdklRl4ZxDJIjIxmXZ7+cuy5DZOYuI3nubJMrr54tGWvdsotpKbfK/ALJ2UNYSKRbt8dHmdM5dZSJzrI/mHLVCXZEMQwimxRrzvtK3G5POiYEaDA3lbcPhsCzrela5LDPGQAhe2Do6febM1vradHrr+XNnz5w+9eyzT//xH/9xnqbGIAqMRwdDXP3Mpx4ZDofrJ06ICCKDlisDz9s0NRMHarQELyLGOg7BJunXvu1tt95220/+5E/+yq/8r6973f1BpCgKRc4KLC5JVUNKe3luXF3XJ0+erKqqLOuHHnro1KlTTzzxxOXLl9QhpMxB6xt9qDiApp4o8vbBwfhNb3pjURQPP/zJtbWV0HZD8d4XRWGMqYOvah8EjDGzskYAEJqWszRNtzY2xpOD8fhAhFdWB0TkfRUCaT+u0WhE1JSA6zeKW9A9SnVdG4Gv+9qvOn382PRgFELd6w8FmZkN5qtZVo2nWT/zdSk11mUFhOZQaFLJ5Ujd+mZi9lVk8JFyZOmGhXcRgg8WjIhQ6tZOHWNCAEAWtCgAAZtRalNrkPng4SgudJg7tZxfZxRIABANEQCjQFjEurCIyEEA1bqq0NgQ6pXV3hvf+LrHf+U/bh1fDyFw4Lr2iGiTVP0YS7ONMrW7yvplFCTUyc86zAeXfgWHBHPk/ksL0b0ZO5vNh+ov48O6j+2erk6kEKLsZPZdvql/jZHa6Ps9ckhdbUOjv5PJRFOgtTkrd+pZocWjhracqbtWcUbQke7xr2pB1nXdCHXgLMsmk4mI+JpIoK6LLEnX1ze8vwagvj2s6/r82XOfHgxefvnlJNkqisnFixe3dw8efvhTo9Hk2LFj48lsPDro9/Ms6xERGNKqEWxS4bz3vqql18saKYJIBhyhWBr0063NtSs3dpLElnVd1TPrUikbx7LaUmrVGWO49TFGhh5zdujPOmDd3QQA5lqTyzSIEEKjTbVm1oL6AgDMQJldHwx293ZEZGNj6zf+029+6Ze85bZbLwqCQbuxvn7bbbc99dRTGxsbsQ9090AagwpqjSjRXTxP1OpQhUHSqnpsE1yJEoPohWPuRpehcIu1wm2ontp0iqiriQhqwxRHIYQ0TYuiWF9fVwd4dPg754y1IYSyrPM8r+u6gfNtH26TxBhTVTMdKyBqbn9RFINOWxF1VCBqWlmD7BbPEREhGeg0ZQEARNEoI7ZZDtYZYOGACBi/j1YyAIAEIipmhXPOEKAl7oS6u0emqWhChMBZ1svzYjQardsErQVCiwhcAYpzbu34VuJsv59vbW0w84vPP59l2WDQCyGwhJdffvmxz3z6wTzPVjZBAFCCZ2NtlMGRCWjOFxmnCUNkTPDeGHP33fe84x3/+PM+7/O+//v/1okTx06fPr2/f6A/uXHjhlLa9es3BEAErCXv+W//7R92zt19970//uPv+JEf+X/dffdrnnvuOY2vg4izFDzoPmpMQf3bd95552g0evjhT6ZpqqWM3GaA1nVdBs57vdFo1MsyS64oitQlp06crMvi+WefRoRLt14ofVlVlTEogkROKaSq6iTBJEmKWaXdCZVsoidJ4Uvf8sWvG/ZzFFhbW6nrUoCLqkwHmQQGZxzRytrKaG+/1+uVxsymxWE2johgUKuBGZvk58On+1WE7s2O/+dyv8JhokDw1XBzLV8bFqH24lE7TSEIgpV4xJZfsfQZD6n42mmYmiwCERHtskoEzJrYzuJDpChrmr6hAIQchOsSrXPO3Xv3a245//tXrm67tEfWGONCUBlw0wAwHKUgRGkfKTh+v/SNXpG7dYXx4Sd374Gj1JAlKbj0EDxkV3Vt0K6K0BWi3UeFDkZxnODSq7mN1DJzbIcuIlpMHJ8cZU+M23GbOLO0zYdnF1eDmTU4ZK0Fgclkokc3hADGEODBwYGIrK+vv/DCC+pkruv6ypUrd9zxmt3dXSI7m1Xnzt0yWJs++uijz794ZTSaqA9mOFx1SabdAoAlJjEp/629d860yV+MwoRS11Xey1bXhnsHo2NbG8ZNKu+L2YyI+v2+TjyE4MmjgAhqZTAzizbrbNe5K42O3PfDatySt5OZAZYhWbr3z8rSOtdfGRpLk9EBg6TW/cZv/MZXfPmX3nXXnavDFZGwurKyubFx4/r1CxcvarpKdy/UceK9N8aqjzoCjka6ApaOMi0iAoG11Sgzm9bvEomhVT7mEFdKG5EC4wQJG7Eda9YBoKqqJEk0vgsAaZqmaRqgKZkTkTRNqcUVgVZeSgyseK8b5JxzLsXW8aM2lrpzYt2dtKAN1KQpgTHzWE/cINMCdQEwQdIcmcRi2WbyN60QbHsb+LomROecZgUze0SrxBd3vYEjbu3ywWAwns5QmzJhDBsbtAY828QNBivrm+V9991njKmqCoCstZPx1Fr7+ONPBoG77n3t2tqa9kkEEcAGClCnrMNmNeqRlGOLCCAK85kzZ77ru7/nrrvufsc73vGe97z/llvOrqysOOfOn7+gRbQnT54EY9dWVpn5q77qqy5evPjv/t2/A6Bv/MZvfOc73/npTz966tSJ6XQavE+SJp8rJjAi4sHBuKr8Bz/4x6dOnbpw4YI6ORR+PrqjyVBRFAYFhYOf3XLm5Hh88MqLT4cAf+dHfvCBhx545tmn3vve9z751BPT6bSXZeVM1FOtgj/PcxGcTKfdEyQiSduO8E/+5KM/8RM/9d//lW+99dKFYS8XYJc5Zg4QnHFZbzA+c+qlF14CARQgIuA6cjktwekydkSN5i/ZSP8XXEdKBEQM4g0ispR1dcuZU5LYQryIOGMQUBAEGuezMCPNveVdHrLEjrqfueWQIsI69/hX7iLLNu4627iSJHjvrUaMCMpZcfzY5lu++M2/9Cv/qd/H6axESkLgxGUssziZ7ji6E+6KohgrXbq5K3u6Pz8sRA9Ptftl1xxZenX3h9LiBiw9ZolBxAFEA7ebPhN9m9yp0I3DPqxMdOc1t/ZaZEpl5b4F0IkUj4di1bh4Hbk+0Z9Z13XimubEzrkTx49Pp9Mb164MV/qPP/7YF37hF25vb29vb/eGgwsXLr7vfe/f3NxcWVkrxhPn0jzv744K70OSJEVZTkb7RTG54/bbNLjIzMhRmEkbpmy839Y0xVFEgACpNWsrw/r06TowPvdSWVezslxZGTBDWZba303rmkJVa+kktnIIOhH3VyeApVWSNvIqHRcIAMRNPPwoY8zu7u6g39/cXN/a2prs741G4+PHT3z26WfPnz8fQsjTbHVteOzYsStXroxHo5XV9YgxGXNEdcvqto0BdsCWEVHtXcQYrxQEIyKWrOYbWGuZfQhojHFJqrSH7fFR8tAN9d4zCLTKHyJmaR7TApxzRVH0ej1m1ixolyaatywiLE1PzBDq1fVNRayOOOe6gGmaa884NIaC6ff7iDZw8MK5cXoirLVETU1E0tjNlU55Nps1+KWslcQVQGN0aI4zgBA5tAgszMEwWed8XYuIgCBpcqrUtZp0ipc7JwYRhUVUtsjzPCwEL0xg07w/mhQA2HBPACAS75kheF/54JI06/WPnzxV+fDMM8/MitJYns7Kzc3NoiieeOIJk/QvXrx48tQp1CIoEYAAYKKn11pSLVnHFFiQ7HQ6Mxb39vZCCPfef9/3fN/3fsEXvnllZUXjqa0iCFmWFWXlnJtMJte3byRJ8sILL7z97W//y3/5mzc3NwGgKAoOQauHdVvTrDebzYxx3gdr7WAwePzxx2ezmbV2f3+/1+up36sdm61DRYh5r7+xvloW02eefgYB3v53/tZXffVX3nLLLd772289uzJI//AP1x7+5Cf39vaybKgsyBiovdegW7/X087lIuKsjbE5EUmS7I8/8siHP/x3vvNv/pUv/ZI/d/z4FqIB4DRNpfIuTTa3tgaDfjGalMU0MUkpFZIQQIM62vp16BC3/L/n8sIWCQRqDhvHj4EzFESssdaiMUItBDMHZkYwQHNx22VHXU9MlHqI+H+Q9ufBtqXZXSC21jfs6Yx3fnO+l/lyqCErU1WVmkolqSQQIKAFkk2rhd2EgogmuoPoCLfbbv6wTYcbg7HpaLvpwCbARAsKJAYLEEKiEWhEqlJWlaSSqkqVVTm/8b47nXuGPX3ft5b/WHvvu++59xXl6B0ZL8/dZ59vf+Oa128RuVahEkT3AIAIFAIDnyvGALJkjYwDQrkCACCxtdYHuvvcnTQG4GCV9sQA4Cn0YbSx53PuByX1e6x6FVvX1AJuLXUdqe0+8HmlsyOmfQbfZ/lr/LvrT99sCw0b1tQLz+mrsJ0m2r1Ia414DnW5G3VfDsJeDcHuvWvD7JQM6ZLUiZNIY+qqiPR0uK5l7Mlc3DNHC3Hv2LkYi0IPRkCSqXyoh6Mkia65unr88P79B++//JEPffnLX148XKDR1trlcknOJ0ny0gsvekf37t0TXptEkRqNtrc3R6ORMWYwSEWa614NAGmaascAUNc1aRRNRc5tVfkoine3t04WK60x+HqQpUVVAgFwSGILAHXlOZzZFUUDhgvCU7d21PPldFOxtt+6f9sw1GYHiuVmbYsyszI2juPT+bws8zSO/sD3fWr/0YOvfuXLs9nJe++997GPfawq8uVyKbr7crkcjaeI2AWdydKotpKgcEG5KeZoRFStBAXQ+DW0aosiaM3cCHYhMCI24dMAZVk61+S+i0NOdFxpShCzsYHVrMU4KXw6TdPj4+M8z3d2dgJTt6tDa2aIogjIh0Dit5ZNGCdJE8WNjU0QEauqRpTcmCgAo2DTay0JvmJlaaQwAImyVlo556qqTNMMWq4AvRqMAm5AKKlEMgnoXUCjxc2IiNoqBkoH2Wq1iuO4LMs4jooiZ04ik0lAKEloS6NFKQpOW1TaGGPLokjSFBiJWSkkVGLkt1GCqIcM1iRKR1UdHj16NJ/P87IGdRrH8b0HjyqP9+/ff/nll+/cff4MbYacVOrk1uMjQmRVlovlMs9zgQo4PT09Pjmcz+dlWVZVtf/4YD6fLxaL1Wq1XOaLxaIoiuPZSVOWKoQ//Ef+yKMHD4fD7Cd+4u8BwNbWhgQkinOKCIzREjJC5LVuAjM3Njbee++9P/Nn/sxHPvKRn/mZn8mybLlc6hYlTQFOJ9O6Kt5/930m+Mv/5//qj/7gDzz/7B1APjw8VEpNh+nHXn050oqDf/vttw9PckaApu6eo8bKEhRi1O7kRmpEZkZt7O7eIPj6b/y///7Dh49/8I/+wPPPPRsnSQjBRNZTGE1H2XCwOp1nSeyqJpkKAFAxBR/CWRJgu3ZnDIWoJ1R9c9dFIiBXnx/16YlSigMUvrx5+5l0lC2DJ5BcLBRvVSBAYk1gUHMvELpPPS6SaDyvC3GbvqwYQJHwB+9C57LsSLoRQZuaadBNtiJTHNm9ne0/8oc+9dM/80sbm6Mqr6xNA0GfD3UE7htoJ6Gt0HeBEar+fbiMrfa5Y58DwQXOhBf00f5Xly7b2v2u5X4fmLkzufe5+1q3+1efWa413hFr3SKK9CO/+q/u9/PinU5uwJ4JIc/zKIrEFExtrqq1lsBbm4yHaV1WHKp3331bAT/33J39/YPVsqhDgEDGmMlkulwu33jjDReCVWo1X1zZveNcFUd6d3c7S2OtNaBAxQlOVlfXyCBi8ME5LxANjRYeRZX3xGAUTsbD6WhINEvS4cnxQkgJERurEBtUJmZu0a3PTW+nUCKeM2wSnWPV/fstvTibHACA8xjC3ZpWVRVFpigKawbehd/93S99zyc/MUizL/3eF//5v/iXd+7c2dnarF2ZZZm1drGYd/KHMaarT6d6gVRdV2VRjDGS44uI3fw0HUMAUOLkRdSN8AHQWD5bDds0ZWhNU5w4ibvawFKNtXtpFzKzubl5eHgYx3HgJnrLe58NBwDQRM/2pAdpvyN8RKAUozFU18vlUmubJEm3Rot8tRVFzMwEcRy3cdHUpRS7du+hVkkSe2+qqoriyFd1t8M79R0RQWsi9kxKKl0Gb4wxOgKlAEqlFCMwQu29MdbaCJXmZgUVaGgsmMRKaUQVvFdKeU8AipiUUsQKlRKvs/eeIWgTRZmKar+zd3VVVKTsZhRFUTQcjD788qsysUXt3n//fWb23hdlKXWl8jw/PT0Vhjqfz2ez2Wq1EhZ+3F7OBQDQGkNgqaoiNAwRBYTHeSc48Gma/quf+/nxeGytvn371nw+F8nVGMMAqJSx1hojVcNbasmSByUC9zPPPHPt2rVr166dnp7OZrPlcrlarTam4/3H+5GBv/Tf/B++/1Pf9fyzzwyz2NWlc1WaqBACU9iaTD7yoQ+yD1kaf/lr7x4dnuR5jtikoXvvEY3ULxeC15IdBQCesKi8RrW7t/G7v/eV995778d+7Ee/49u/NYnTQN7XfjqZ7l29ks/mZR3AB09n+QgAIPQMEeE8xYbzIvU3uL6ZZ+A8Ie3/hIgIVV5XH7h1w6ZJWc5BQwpnqc+yKxGbOLw1Mv60bnQ/BwANyNAv/QvM1PqfAnMgBFbIzAHYQOPDuKBsebexOX35wx/8Zz/zS0lky9rVdaklX/6CSRkucI7uq04R4ctcdGszCz3fcJ+S9leoz+rwvFq8xp6/mfXoM91Lx9U1uPZwR9+7/vTb6a/Hxfb7H9bavLTDa7PUn5BO0JOa4d0kqBaWazBKr17bI1eXFoGmCum99945ONjPhqMoNsqpuq6dq9/6+terqhqNRkkaVVXlwRvFHsLW1vZ0MopjiwoEq6Z5vVJKGUQdSOJCtfdn8WhKqdqFg4MDQJMNRtvbWwAwmg0Wi0WxKpVSFASSOuRUkKdW1WA6cwGfyVuqB+TSelDOYvrgPBu+eGB63zbJAP1NYq12zg3SbLFYTG/ePDw++s3Pfe77vve7lYJ/8wv/+nOf+9wnPvGJ0SATypgkafd2bCv1AkAITmakKw69ZlmRdelUYWY+y9NnDsymp7h3hRPwfLIvETGCsGFjDCMobBg/t5o3AOR5LlYQ7/1kYyowL1rroii0tkkSlWUZPHW8sxEIxNTTFkmMQcnonPOdhaYpQBK8TH2XCNAtOjOjanqbDodVVVVVLUMwcSRp04yKUTWItoi+7rlmsKlXCIhAxKg9gatDHKVEZJLIxgmhblzAzAwBUDzAYAVEk8DYWBKU69ojYu2rqqqCb2CKfe3yfCXZB+lo/NyLH5CJLcsyz8vZYnlyclKW5cnJyePHj4+Ojmanp48ePXrw4MGTJ4e9/SYdlFEDERijsiyT6ssdFQ1tMr0A6ctbBOmJiJBhe3tbKcjzXBR91cKeE5HUTOunWQtH1Brkz7/9t/82ANy8efP4+Hhra2s0GglW7ttfe+PP/6d/7j/7T/+TF+7eRsWrxVGeL8URwYG9FygC2Nvd+ehHXy3L0kSjN9544969e6uyMDpKIlsCKCJkHS4LAEYw3rsqhDSyeVlHUfR/+j/+pf/df/mff++nvjsbpjqOV1V57dbNd974epYlhXNVUEpdwoPPLoVAvXN62Vn+Bhz3m2HG0DNbamSCoAfJaHerDI6IrNaglWQVIANKgj5yVyviUlbSJ0p9nqiaP5uzrZRgOcuq+j5rYOYQ2FwYgAJoYrRcWe3sbH3bay995nNfHU4yTRRFUfDUNylf5GSXDn6N5z2NW3ftdNz3rFuXVYa62OYaG1trn1sLWyfZiV+q12TzVd/T2h9gx+H6RLzPRNempX+ze6zT6vrIRxd/uDaH/ceoh0CEiFpr0cYkpiayWiyiiFhXVVGsdrY2ktjeeeZWmRcHBwePHz+uq5IIlNHO18hw7fqVJIkUYCDHzIPBIE3iyKo7t2+Nx0NjtQYGRKUghNYaLIS1+Z9RSraUY0Zj48OjJ2+//XYSD67fvOEqF1t188bV4+N4tRTEKw0AtSdmJr/qT+/FHdL/jJdZXPorvrb6XYOtxtystSwaIgQi4ZraRG+++faHPvDC4eHxG298/WMf/bg15ld+8Rd3d3c//tFvEXdmX+QSdTNJEkT2nuM4lrxoaKu6qvMwosxMCEohY7fQCqW4D5+NXcICxPCYJDEzS6KaDFlb4713ztm4sXawbwzX3Y6S/FFBrBSftLWWuSmBrBSIgRM7/xEhIgZHznltsDOhAyhr4yTRiCiuCuHZRVEMh0MmZkaBxhQLecNHkeM4Pjo6mkwmSZLWtQshACitldSfVsoAgOB1EHVmD/SBFAUAIFSRUaiM1RD8fO7zyWTiibVWtQ+rYgENdHZT/M85J8MXbhdCKMv66OhESjjXvhL7sHOuWOUnJycns6OiKJbzxeHh4SovZ7PZ1772tXv37jUMFRsDhNIqBGIGrXEymVy7dkW1ZUaFmjIzMNfOdRHvq9UqnMfubz1TZ3AcWmupL1mWpZRO6fguAFRVJZK0c8F70toKjnrfXhiC07rRpw8PD6Moun///nA4tNZubGwE4vFkdO/ePaVpe3MyGqbAMblSawuxIjAAwOSYcWd7+2Mf+5btvRub00kS2a+/9WZZ1qxBI2jbhNRRAyNxxjZQ6xBClsVVVbhVzSHcuHH9L/+V/55Rfdd3fyKObRZH8TCbbGzk4aisK7GQnR1GhUgAELj5cIFin6elTyOGa99efJjP63jdA5Gxh4vZjbvPqizK69wojQDGGPTE2KiqJLWSWFxU64J+n0+tUZj2LYQAzEExKK2AiIlCaLyN3WNEEEIwUriNpSFGRGBWQB4VEvnNyfSTn/zkZz731Tixnn0datXjfH2i+bSr78Dnngp78cmuzW69+9OnzsclrQ3+Ihvr+wv7z3fNXrqE8lVHZy8+Br3lXxtIXyZ4mhywxra7wa5tlP4rLvaTWmygjtAL0cTW3ymBuFJ8SRKTlFI7O9vPXL+WZVmZF/fv33/w6PH+/r7RVoCxJpNJlqYhePJ1lmVpmi6X8zi2167sjAYZcxADqYgjItoLNVGgEVEr3QwFgSTWQJvIxnVdv/nmm9Pp9ObNm9qYUFe7O1tFURBBXXnnlrE1IUkAoKrPDCRrU/q0dezmufv34jxf+vPuGURUqBBRGN50On708PHmxvTNN98cZukrr7wyHA5+/dd+5caNa9evXlssTweDgcg93a4Wa39XjSOEEvGMpHZ8rr9hemu9rrsHJgXMTQJ6Y1WWAFpuAyagocKNP6ku6gZeP4jxU4cQkiQRdouuZsY4TqUnaZoyc1eVCwAkHAwRdRyHEFztg2Jhw9BWekDERqBra1EQkYD0ioW8Q/IxxhR1iai0NgAS3G6NiZRSRkeYmG6LCqBeoGCUrqrKBUZUJLhYAZiUC2UIYZnndV2v8gartSiKukZmZghE5GsnuTFVVZVlnuel9342my0Wi/l8nuf5YrV88uTJ48ePHzx4MJ/POZzRBIlxli1jjJlONwejoVKKg4QjNEVXvfeVq30I8+WCiKSF7uTKBuW2JBr3DGOdJY9bYB9s0hobF2AURVVVZFkmz6RpKskR3tN0Oha/cghB6cbq1G370KDEN1AbzCy5Z2Ih39nb+qt/9a/9lb/y1z7w0jN//I//0Y98+EPP3bn9/AvPxTaysQ2Ur1YrBk1M3vvd3d1V4e7cfma5WlR1+fDR47KsGYIGCxrEHEVAjZLCwMyuKpIsXa1WUWQ0mKp0C15tbY3+m//L/+PPL+bf/we/D3GURNH2ld0vv39/srF1slisycfiVe0Oq7Cc7gjgeVXtUpfwN+DN/95LARRluffMDbJaswmhlsoQjMDEKLgECKHN1Vxj5Gt9WLuPiJ21GQBQNYr0GutlZgIOxIHIaMTABKCaiP5GGNdEIYqisqyfuXXj1Y88+/tvvWfjbFVWSVu7quMcl3al36e1zxfJ5cWx9QkW966unYvN9j9wz/S99nDfd8gXFl74WZ/ZX3z+4nr027l0+H3e3MW1ivDeRV9/42npsxzoMfiOLIoSIIccOAgP1lovl8skSbyjjckkjuOtjU3c4itXd+8cHRVl/eTJkzzPt7a2NiZTQNJagwuPHj/Y3BhZw1mWDdKEqYbzs91eWmsjCKaBKATWViU2rrFelVWWZbu7uw8fPn733XdfffXVmzev13UdWX316s3Hj/YPDg4eHxwqQGEty7yQF1wcfEfcu6vTJLpJOL8cqr1/Dvr7aXOLbfxwHMf5ajHKBnmeZ0n06NF+lmV37z7PPvze73351o2bsmSmyTjSLSJHQEQBn2qc7gSqrYIgLXcm6G4zMDOiBpDD3UQFMgJ5dr5q1aammrK1NssysSFLkXBrbeBz5X6FVcuOCi2MzGKxmG5uiEAg1pFsMCAiFQJqTSEEAkBghMAEdaWt0VbUUwVKeVdVde0qPxg2bNtGUVmWou9qra3WnQeks8lLBYUoSrwnpTSw8j74UCrlEVEy5eq6DswiKwQEBp2lqVIqACql8qKYna7KsiyKoqhKwTpeLpfkw8HBQVm50/aazWbHRwcPHz7c399v2VJjkxfDLwAAkrF2OBxev3pN5FTBtmvjH4GIyrparJbz5cIYY3VTxlFbwxyE8UstKaUUABJ7ELaAgABa2y6hH3uuH0kDa+HNrRzzDiIUAKqqEOwqbuLdKq31arXa2bvy4MEDY+1oNCryCtGhBoYASIhnaGsAIK4Kib9rQK/K8vj4aGdvO03Tt969/3/7v/8NpYAJ/vSf/uHv+a5Pbm5On7/73ObmFFQgBlYIgHs7O8zsXIUMg8Hg/oOHp6endQhAyEyd06k7L1kaVVUZx1Zr7arSAC7mq/FkeOP61v/zr/+dJydHn/ruT96+enW0sRENUiodItJ5kt4Sk8YfxP1EnW+CufYf+AYPr1H47qrLarq1OdzZLIIjBA2YxEntPQAAEQdCEJkckYSIXA6u3KfD5851+0ExSBy+GJ87507DfUMQDdF0whUwsELFoEAzBgXIITDzeDx8+eWXf+tLbw8nUQBGfza8/iysccGL07TGw/gpOmLH8Nae/2aWRH7Vb6HPLPtTtsbR+5P7NF67tpaXauTdk33GgBe4F7SKLJ1PZ7p0b1282cUWCWnoOLoQDuHI3TNaRavV6nQ2q/Z2t7e346ThB6L43nn2mbIsFbBSCsQJmFf7j9has7ExGQ6HcSIJQqyUkoKd2MbvAIjr7owNWIyiKCJUEaExkXnGbm3tXLt27bnn7kTGUvBXr+yFYDcm0+tXr73z3rvv3X8wO5kj4ngwnK9yZj4nbYmS1FP7Ot1ibeGesmoSDb5egWNt42Hr8pAkV++9QlBoZrPZ8fHxcJB+8IMf/L0vffFrb3797nN3uq0luT1EJHUpZPIlLMt76tRTpRQo027DPoLEGd4LtVWnkBUid/UEoU0t894fHBxYa5MkSbKUBKyUG9dvNGzgtQXrH1tHMiJmWZamA/FSO+dG00nwXhtTtVjQIVC3c4wxyhhgDt4zMxApZbJseJwfMaEIE+LgODsCWmtthbhII4gIoNLBsKqORVQ4OnowGk02phsIioiUtYCgTJNIXRRFkVfz+byu69lifnR4slwuH+0/efTo0cHh4f7+/qNHj/b39+/fvw/MLfqjAgBtDJFnImvteDze3d3t0qCFDIb2ZAVfi9F+Npv1HFskZnlZelA4GAwAQHy02naxFDrLmmJ8TeycRQNRc3IZUKEPQXiqSD/cuh4kMbddkYbcmSbYmIkaDt3hq0RRdHI6u3v37te//tb/5r/4L+69/+Cf/ON/vHflSp6fdPQKFPXy5NkY7T0JtJm4BrIsi2JcFflitZpsbGTJgAJ47//BP/hnn/77P51E+tu//bXv/e7vev6FZ+/cvrG3t6O13tiYpGmssZFB5ZQdHZ14RSogo/jYz5w4vi4QgAIxMDJorXWSOOdKD8/evf7pf/DP7z948J/8x//xVpxON7cevv3ORYJ28eof+TVmjOcf+2Zae9orZBR1Xd964QUVm9yXwftBFEXGOgqBSQFoRGBUqAICK0Q6Z+7t892n9aQlhl5L0Bo01RfWNWAiZkStDCIjNJ6xM26DSIEAII5jD/Dyyx969jc/93D/UNuoi2qG7iffkDvShXjdi8/0mRD10O37nLWL3lob9kXKi60ZeU02ObfMF9A2Lo6i/+Raz5826ouCRX8Und++E4Ww9cP1Xfpnm+8pr+hrVF0kqjj8qClHIzUSGr1nOBxKesNqVUzGQ2YWvN8QQprG4/GQSaJtVaidM/FHP/ZqVVVFsRoPM3FTSYQReU9MwI1bqzFFthEWDR8CNsZkma6dH47GOwGuX786GAwC+WvTK97XAWIUdIXIbm5uv/XOe298/c2qrEU3opZGUi+Xt7+yHa1fm5O1z72lR4AzUKqzRUQpQIpMyhjjqtpYZW3Egaqqevz48YsvPf/gwQOjMY6jV1999fd//8ve0XgyzItKcmclU1Pqy3rvJU+3c3zIt8wcQggApgl9l1JIDXCHJ9Kd2AdKaQUARmsJEdJaG6NlWh49evTkyZO7d+/euHWzOYBSAMN7qyy1xQr7uexC+uPhsJjPBRYRlAp1rbQWdtJNaSN2tDjMWmvQGgAwBGXMeDxOkgSs8mUp+NLSt7WZ71KiDSpf1wi6qqqf//mf//Vf/8wP/y9+5FmG2Wx2cnJyMl+cnJzs7+8fHR1JLMLXv/rGW2+9VTsPqIiprUDTCCjG2slkcuXKNUmRstYSQqidTGxdl8wMzPP50vtazOPWxgJZH5oaTdCtfqd9dp2XDaxMkyI4HA65BTYXriaDEg2YyMv2aTiul/Bv2xHWnmPiDFxWtnOfiBujAFRnQAYA6Xmappub2xS+9qM/+qNvfPXrP/3TPz0ajcryFFtbCPTST7GBFjBy9DroFcLaGIOgFvPVaulCCFrZja290WiQL+ef+eznfuVXP6s1vPTC9e/91He98sorH3zuhStXrt28eTMbjofDoYlsNhhG0XsPHu8DQ2tlV93xsVYj6tp74hBFkWAaI6tA7uR0dvXGxq/82udV8P/hD/4xE9koTny17NO0iwStI3pr9PbiA5d+ftqFT1NsAk03Nz0FZTSFYLV2VWWsCa5mhQoQQZNCAhCeKdGCfX7ErRbU50e9lwIzIzGohl51Q6MuykDGi6hQmQAMyAhKkkzEN8MIqC0hcCBlo52t7e/42Lf81D/6l+PtaE7OGFt7p5X1xErCOxUAsWoyCKUzihmlaknHXfi8agjn2We3ifvjubhC/elYY8PQ8vvOldgRcdUCaKjzMP391eoYW58x99+4xiPXeH/3rjUe0G+//VMjNnZyWei19vtDxvMB4aDQBS8odEqDwPkao4FJISmNRN4jaK0ZFIOq6xBqr0aD2WLpgg9MHHxsFQIoraxWSjI7LYfg0OhskJQlMLMyBrXRyiIgEzMoHxw3VebEJcxEBDZRSqlaExGamKhJ/bRGR9boRGeDOLKxhJvZOKnqcjg0UZzGiR5kMSIbq37/K28wKqNtUVTz1VICymRCLDIze+oMjFDXXik2xrjaAwCaLoVAUj76yKAq8LlQgHaSFYIAUwEAkQ8CH1Z7p5RCwKWrf/fLX/3Ed35HAP3G174+/Ogrt27d8FxXVT4YDBExBAfAiNzhqAgbq6oqihKxEJRlDQDgJZdGEzU2A5TCK61HsKHCTK52zEzBJ0lMFFSLkzUaDa5du/LgwQMAAGKrDSrFBFmcLd1SqloggtgzJSs0MI0mU+fc44cPppNNgiodjQEtsascg4qUipxziFqryNUUpyNgBoVF7lBBliXMTEorVEnMwABBGZ0cL+c7e7tK1wDknNMG0GiSqGylfXAKwTlnFCqgxWLxT/7JT//9n/zHP/H3f/Lx/sHF8w4ACJAN0snW5mg0iW3UN9OJfVWsCVW9you5qNrqfBx7Q2sURJEBEN7mrUWCYNp6vswMAtfZGj+74y9F3lozhiLyiskqAGAKXnA+EQgBQ/Ai6QIiB+qEZg8OlFgQEYCJSZJ3uCm8RkTBKCXB4QDAOhBTZGMissYIpAYCMIvR0QPDaLhx585dJqNVhmDFEqxVjKCMafxW3JaGRMVKgY2kdjJ6b4CAOACrECpQSEh57fOjJSIONrZ2ssx7/+b92Rf/X/8Q4B8+c3X3+77v+z7ykQ/fvn07Hk1v3H7WpqlNYpvE+/v78/lclDRXByDSiAEVUQCFBgwzMwIYxUppshg4zMrndnd/41d+Bwv4tte+NdvcVYVXHpRRWHsgx4EYNaJ26Eix4EYhMirWAEjAfchY4rPPPdrbp5NegWLQBIoBAAIwAQcx5oZgQWmliDkQsUJQuLg1jG9uOleRq6MoUSaSmpnIipgrBERutH4FBGBBEqfaTcvY4EmS76IfJFBOUGYDOQDQBoU8ivXFe6kfr5mZPDEphVZS/wxc5nhDRGIKPkRxUrlqMBi98MILSfIvi3IFoHtKalNLuAPfWmMhcJ6J9lkLnGfDF2WfNeZKF4Kq+lLJ2k/gvITSl0EuXchvIFKtyQrd82tDgAscd+1mx0GVauRfUeO6ca1179KRIjYxtC27FkxmCgGNsWLzCCEQSHk4pbU2gFEUjQbD4TDTGkMI2JMwOsaPnW7d5bOeH533XinjnAuBnPOy/xAx1E4lcZqmnTdLrGrYhiYhKHlSLKVFWdR1LcCDxpitrS3nwuxk/vDhY9BCSSPdVhMKIYgPRqMy1kRJLLZiKdlmTBPW76UOT8+I0hA7PjP7nJeWzlZf2GczUqQzIk30la985fs/9T06jd59993bt27keb61Me3mTbUVkUMIeZ4DgFhBJWhZwpe01kR16yNQPa2IiCg0II5n0XPdthS/vkRLybfb29ubm5vyxjiOFXYkWBtj6rpUUmLB2s4N6b2PbCKBeHVdpyYyxrgQsixjoizLvPdKIXCLuQggvZYBamgSBmZHR+ONqbJ2OBxSkxAMopqfIVkSs2fpW+3dcDh89/37RVEoBMlYFVNNJ+N6XzvngvdlVRHR8fGhRIlCSw10W3WxObrYZH9p7KTYdWm1L8XqLnlBr2kqomJ0+yEwn5mUiAi4lYb1WZhWR506OV7aUkqpNpJOrMMImrip7MsctDYS2NUtrkxX7Vzc2hKY2VpbVX44HEqdtNFgGMJZ/Wxo2LkSQUEkti4eG0ErdaZgyLfcxrfLpwAhiiLvgyQxa63jOJ5MJmmazg/2f/Inf+rvfRom4+SVV155/oXntNZRlEynm8yotV0sFlXpENGYqF0IqeXJ3cS2xjDQ1i7z1c1ru599/Xd87V776McmsQleedfOOSJw6ErMra9gTxmTPzu61z2z9hMJEYNergoCqMbaocE1ZBYRozg+mB3vvfisMtrnVScBO+ccneEXXezV+feueyrP/jxvqcaeitj+udYgAogUA2dBNn2WprX2wUEgCu7ZZ29/13d9xy/+4mdG42SZF50zo+OOqvOoI8J6JeOzxi+9Dxcm+uIUPI3/Pa3NbiCqi6e/zPn3TV5rDz9tQzxN/sCeZ04pxYyd/fbSpi7ORvdnf0V1AxXcgPrWdS2/TdM0Gw4UGqWUorDKl8ZcvbZ3ZZBmGlWUWGwkPFQdTBM0RrPAQax16rwZn6A5coLjo7VWChotzTX1KkKL+y8c3DRhOrrjqUQ0GGbL5TJfFcwcRVEUuygyV67uLhaLynljjK4q9k6ECaUUMrcRuEYOjEqboJg2BzfoFnvZe0ZtOsxtvOC5WJOQsHcBAMpytFvr9PT0l3/5l//A933Pzs7ebDbb2BznZTEcRNDaGGV00LO7IAoCQ2P87zfeX8T+snIb96faGn/dmsoAJas1TVMxDIgIolVzAK3VQs6E6IczdMkzf7pSarFYpNlQa62MUVoXy2XzldahjUCBVgrUHM6cbwiny8V4YwpEjKCjSFRebN0B2Hp8mrA4rY2UvdX6X/78z12/fjUASIwun/OEEQAoRO45wpVWGs+KIzXrhKwUMjZiAVyoZnbx9PUnnHyjUjfSFTayZrPKIBoPISuUv7QC0ZFb9UsabNTfrn2tgm8r/lJHRDW3YGfYOMWDUiqIoigtKA2smM/BpzTCq2qyqpI08seeKIgY14RDK2DGxs5vjGqTp9vN5mS3KIEyBtZaBUVMioiQ2VU1IioADt6RD8HVdXl6epJqPZlOh8Ph7PTkV//db/7Kr/2mMfDMMze2t7cBIE4y58mHvBGCmCVZG0A4EZ8xewSlDHMgorKuru5tfP53vnzr1q2N7S1ltFLgkRCZVSOoocKWvypxEjXHAdYY3hklv4T7nv0L53gwCsqjwpYFBKLARES7V/ZYcM7jSGstN885JrqGLrwLoNFWO5Txbq9qVIy9ACZog+R7xAfa895ub8SOAUMDd9JuLxIVSndeH6XMax/7ll/4hc9orTVgCEFpJQIXEYmlBkC1MS9dm2fOgz4rujib3VcX7198oBMx+g1enLI1Ji2UqJ+o193vmrqUWF/sw7/32343utXtXESi/vZf1M3S0xrpOtZ4C5iZWasmBFqooTCDEILYtbKhydJsEEfbW5vXrl9J03QwGCRJhIK4xISSoidjb3Phuz7oHkSXlH/vMIC01hsbG4hojNHaOudC7YTuy9PCHkKLuyZOyibpGRMKTIHLskZjtje3Vsv80aP9yWTy5MkTa5p4lkirEILRWqGyVlsrgMYS9AQatfektQJ1Xn0BoHNavYLzoNDt9K6nmbXPM7dxH1prhXB0dPS5z33h+7/ve3b3Nsm7th5Jc3WksGNC1Kuo4b3USBdQaAbw7YxppZQnUj0VvOtb15T8SW1lgp2dHURswnYQBbqkrmuiQtArnXNZlq1WK7FAiEA2HW2enp4i4ubmJpNHE8mbJNuYmUGCuqMIGABRk2qgMwF88FpraAtcRpGVGV6tVt7XWZbFcWw0FmEVQtDGGm1BLLYE2pg8z6uKoig6Xa6q6kyTa4epQPKgFSMq4C75mJG5cZMBnNmQWZAlAfnfE/y4RvKU7vTYcz8MLYh3QxOhSSLwrtOkJUtbKCbLqdW9RHDhoMzouwTfNgW8l0sNSoOxtjtQWlmBC10tl5KdPBqNOnmOiF599VUASNNUKR2CYw6IrHWjrnUBXzIECbdWSjEbhcZ5h+ps7BoVaACRffmsThc3lnUG5hqInM+rGpEnG2OllHP18Wx+crqQsCytjY0T5xwRMCCFmhEVIzO3iqYiIGb23idxWnOOiJ7Aajg9PQ0bUwIQXSEwQ2fkOMfnlFRMYXXOHntxNftrfenq9/8M3mtAozUi1hTyIt+7ckVmGxR21BIRpVDYRXjqM1batY+MTaZ4Qz365fIApU5oQ4T6XZRx9Ck5AGEDON60fi6vg8iH4ACQgk9sFACfvX3nez75yq/++henk/EiX4FIW09nmULcFT5VG770h2vsp3uGenl10NlLn67F9hnw2Wx2M9Wjv0/ryVpr/Z/3e7LWz0vZJ7akFgDEK9BZm7kNwqKnBG+vTY5CrbRyrmq7pLmtCtdlXjUQHGVFPtQrtbO9sTXdmG5MjFUhBKPQGAUkmCQkiX7MjK2Dl3qBHqBQbHbOubKs33zz7TzPn3nmGTRWR7G2NtaR0Q2cJBrdmdahF0+n2yxJrXWSxsPhMI7j09PFk/1HN2/dvvvcs/lylef5PDK1p9FwwMy1ayDsjVZABEJ6tCaiyrnONAfMClg3iowCUqU/hxfdqVzqfNT62QA7G+fZKQLFWimllUqjeLlc3n/4YO/KltJ6tVqlyVApZe2Z+iszJrWnhIXAuf2poHdMWnG7eWOfB/T3ZNdPcQZEURRCY71ExLKqBMpK/L6mhctvZWUlMXda6zzPs3R4truoJgBrrYljaNyZgZnZe9QaQjBag3RPVPi61gyj0aisqwiz0Xgc6tpa29AWRGzNEthGWXoXrLWukKQsMHFUHBzaKFmzBwigaWjtQAqlwG47Jz1BuZkWpP55uHhgL06dXCGsu67ajptLf6h7mO2dGQCRiVhrrdsaGyEEsR1RQKMsACgNYvsRIxAARJHx1CQy1XU9n8/zvGJqgCojY2/dulVWeVEUaZoyB6P0fD6/du1GkiSuehLJZW1dVd02xTaQpd/bLvpSa625tWTgufFqQZkA1gYpYM9Ng0pp5z1i479UGlxVGWPysoTFIo5SiY4GAKWtMU0mAjnfxEm2uzeKorwsFICnsDnd2NvbK6pyVZfAhFqh0RgCMxOw1lrSG1piCkzCe4W9Xc5c8bwYfSbMwRnb5vYEJjby7FRbB0UZXcyXH372dpQ00XygFAETUQDm80Gd3ABxnIvB7hPkbs80fRDOCoLR689innrPwHly1PQcBQkLdf/dAAASX45IREXlUHtPPN0Yf+I7v/1Xfu2LcRKVZckAYvkUCgvELJUQzxmJSCBv+p3g1izwzXC+p/Hj7vM34MH9aTovepx7phNw+q1dJIv9n1zKZeGyReq+7f4MLU6h6knTT/sJtvpQR5vE/SSHUHRKZmzDf6LhcChn0hgTx/FwOLxxZXc8HGqNaZxExkaRia2pinxtZqjZukTAgr4rPAQb5RKcC8aY2WyGRo83ptZECMq7oLmJQvJMulUOZZ9JgJJgPgj3jeO0ripEzLLs+vWrABAZa6197u6zp6enIbiTk1MiqD2FsLJJXBSlHHilQNASlYLIKAWWmX27m/tzWJPrWF0ncvZ3Dp+XlOVju+2bFVEKBZ2GFdo4evTwcfHSi9eubMtjjdSsm2aF/IkuIpyyTcgB7+soytaWmAgIQr8KVn+n6TazFs/jXUvaWKf0aHVWByJN066yltT6FT9fWdaJVgxBaysoknEWh7oC1KF2iKisJe+t1UWxyobDqiriJAEAliq/HIoyHw/GADCMhvliIZZ/AGiqTzIDnWeTADUFhcZaWznvHIQQkiQpKye+yZbuNwxbIXr2AEA9AZdbkXTtoLbLd3Y8105l/w62/aHzJ6vbDNiGFp+Ry8YHfCY/Kd0aNFsU7wYgpa3AyMxIChGLoljNV9Kao0DUQFQCgLbADCHA1tbkYx/72ObG9vPPP/+d3/ldv/fF3/2L//VfBICPfexbDg8P49gOBgNAYcZc13VZNviU0+k0yzKpBhHH8Xw+z4tCZrKLEqCLITLE1CsdK1YZZgYGVKAQtLZKqaomYtTWMLMPgcgrUlEUMSAqE0LIy6LxDWmtNVmttBh+dLOg3teeCRGrokzjiIiGgyxK4s3trb2dXWLuDP0MsiCEqACor+3ieZ34IqO9+Cc2MrQ03+qYCAAgKDHinicilpNtMRumrFTwHuWUCRFuQ+r6U3dOecWznl6Q8yRqBAUZppnq3iha+US0m7OIaNnqSqlzrtz+VVVV2tSXVb52BMzB7+1sv/TincePn1hrKx8aTFZC1K0QIJu/19gafekmsT+V34ANrzGni7z8ovO8v+fgfEWHi+0/jTGvtfa03178inukpP9bOfAd623YHpHqAIIv4+v99e7a8d6HwFmWZFmGTSFhmyQouYBd/E4URUmSjAaDl1/+0LUre0lq4zjWSM5V0nhfHAOFopQgyn/N/mj72dzZ3N5JkmSQDVVbt7itZda4Qk0Lyl8UlcQEIWoARQRKGaWc/OT4+FhicwAgz/Odrc07t5/RRlltDo9PnHOx1aAMAESKmdloo9pzId7SEAjII7NWipUSRCcAQPQ9a+eZebnPzLr5ZD6TuM8dQsQoipSC4N3m5vZ0On706FGaxVd2tsX2K/ura7OLfDHGIIIoPZ2/Y40By2HjM9t4kMSkTtiCFq2l3Z/AzBL7miSJBHkBB+HHnf1ZXieADDKcjY0NZU1RFExo4yiKonK1itJEcBDlyTMzkvB+KYOoFCrFzFmcEFFd14PRKM/z2vvxeIzM1rboKAEcBcHzFGBqYGRCNrBarRQ2ZnmBeVo7dkJzGbmFSg4az0pu9DnK2fFEQjh33vuclVuVpXeemENjSGyeaT27RKwQdesRwDaqThndfGgd+VVVee8ePz6MDDgH0FJYY8D7M1KXpdGdZ5+9e/fu1atXJ5PJzs7OaDoZDAbZcLC1tbWxsTEajQbDsUY1HI6J6E/8iT/xh/7QH/rLf/kv/8y/+Jm9vR2lYDQa7WxvDwap1N2yxgbvsyx5++13ZMc1paUJbt26ESfJycmJ974oCmaWbYmIwgvPBJD26mIkQSEQEoVAnplRRRIzIaUgpdCkmOjEAy1YJYLzY4wpvDPGRNbGcWyVRkTQiWYGIBH+mIMydjweJ1nqKdhRFmrnnQ9MrBC0wnOyQgvHgSB1l/vU+FLae4EFNqu/Rjp97Rp8R0RSXFTVzt6uTuNaKLDREo7aD3Npt9g5rkTnNfJOgFNttLZGBUghtKlobWshePHu+xC4VwFJ6FIj4OnWBN18rc48CFITjTlYLYmGUbFa7u3tffITn/hbf+fTG5sjqisGsMbULijQAS4x9cAF/tcei8sFnP7MXqSV3dr0hdb+r87NYy/zuP+5c752/34D7gsXln9tGdaeWRtaf2mVUnVdd3FY1BrfiJoRwfld1YEqdJ1sHUVNkJF4klxdc7ONmqqxkRYkDm2Vjo0FoDSOBsPMamWtVqC8J9SNs+pMg2zLdbTaNiGeCxDTWldlffXqVQEZUMqAQhPFVZFXVZUkmRQlBcAQQlnWZVmKdYSIBBLIOVcUhRSHz/M8z/PJcLS7u2uHg8VitbU9mc2Ow/bW8fExkR8MBmVZmjRlV8o+VkppVMxc1JWA13cBKbqNRkY+K+QsfvbOOnp+sfpLw91qUltogZkDMBCmafbo0aNrV/a2t3cRNKFqVYGuQGzdmRxbZNAGeaPjhf09xqi1wj7nW5N1sLXVS5vMrHVjUaiqSsr/BW586kdHR8KGhVaKO3AwGKQhW61WVVXtbEyl0I38PIoiJAatkiTRxkjZPgohTVP2XmzXIYTBaAQARBRnGYDS1jLz9t5eWRSy8YB8CMFYiwzOOfYhSRJldHCktC7rapBMZ7MZaBBYEh8KpXS7w6Uue6MREILW2C1uqys06pAC7IKn5BjRU+wZRNRTVBr7HraJ8l0QXzfzwlGYuSiK+Xy+XEqZcxAP3zpJQPjIyy9sb29bpaPIMJFSam9vzxijlU3TdGdn56WXXvrQhz60u3sljmMbR4AIqNvIGOkqdj4POUTf+q3f+rf+1t/6kf/pT/7ET/zEw4f3f+AHfuAXf/GXB1mmFBCR8244HH7pS597+eUP/PiP//h3fMd3RlH0mc985m/+zb/5pS99RSpAXL9xI4qiPM/rui6KSmsl+QVtv6nVwFqxgxFCSyoVMnHwXmtN7AGadEdjmrzwjmdL/LMcK4Uox7lD9WpBSuOyypVWVid1XS/zMkmK0Whk04yh9HnhQ9AIqBX6y5EVoNkA5wKk17jjRcZxkXojAyhEpZBE4GBiXpbF3ZsfzsajEirJoZfBOmpqeTUcpI+80diC1wWC3t5r6wzJzUAKGXohKUKDOrNKfyG6oUntkXZ4BMxNiJdkvCmlQnAAEFytteHgX3rphQ+8cOvNt9+Pk9QzOOe0tq32oJh8R8WUUoLxQT1Qhc6a2tdI+mepo0Rrc73GYvmCV2BtkTomx2cGyR4dbImdbpEOhat16909Sb2gm4u9FbqvWpT2tX52b4GegiULQ2ce37MS9C2L1SEE55wYoEKL8SszU9clAMex7SyNXfvWWgikIw0AzteA2Xgyuvvsc2maEpGKlAzAKB34XA+b8TY5C11Ab2ePNcxVURSnp6d5VU6nU4kejHS0WCyW84VzbnfXjMfjPM9FWJa4zSiKAhMjxGkyGAzmp8vffP3zRuOLL75YlnVRFI8fPASAa9euWWu1xiovtNZFcePho8fLvBB8Te9cN89aa++91M/p7gh4vTBpiQMXpRwAmAlaS2N/+VoSf07EptbzTUSECACT4YiIrDHGmDhNhsOhHFRhVNikMUCaRmL+FZYsKyuhy6qtU2SMUSZyznXVCwDAGCPOWuyZELv9Rg2mCjKLotxYERExiZPgSVAhhVt30NxCWRDRGLNaNeYQYTmD0agRT9wZhLK495hZ9pvo2YBYF0UIwfpQ1GWapuIFFQZPrWu/LIokisej6ePHj6eqqZboAlgbAcBilXsPURTx+RMnIzXGMBEi6gYamhA14rmzb4whHyThijiEEOI4DqGWkXbtaK0jYwWpo+MHzFyWdVVVRO7RoyfMXdki8L7hsuKL1RquXNl75ZVXrlzZm0wm169f39zcHA6HaRbv7OxMp9PERsYorfV8Pl8t5/v7+3VRPnz4cGNjIzhf1tVkMtFac3CuKnydp2kMCICKg0Njgdl5stYiArHweJSeA8PO7u6P/uiP/rE/9sdOTo7quv47f+d//J7v+R5mEHK0XC4//MG7/+Qf/aPnnnsOjQHmj3zk5R/5kR/5/d///V/6pV/61V/9d7/8K7/KDNbq7e3da9e2iqJYrRZyLpgbx5Jq6mFjoCZtgRGq2kdR5H2tFQAHBQgMwIQA5AMykG+DVQFcKBvKHIi0auJOHHkn2nYJANoYRLSRNsaQC8ezE2ttOigWWbK9uVUV5XK+GkYWvPZcCcyyakUuhB7wznlKvkbe14jzUy86Y3g++KAxytLpzqZTJMfWUfBMghqrWyCaTvlsyIKYLfny1NDuRawDEbEPrWm9sb0J922CNsrSaN1BljKzZI4RUacBN2IaIShqw5nPD1uujcn44x//6Btff19pBM9aa+9rE8e+CcpvomrPjtEF6zmetzNcOrmXctZzs3OZXaL/ucfhmj/XmDr0NGm6LAG3Y+TdTy7KYtiLFu5IzNqT/ef7H9rH1oOx+bzKLg93bNhaK2VeREXWWqtG2UryPEfAwTDLsixJkslovLu9MxoN0iyJpJQ6MfdC86S/TRlaRKHz1JbA6w0qAEBZlvv7+1IlDVFHUYOA+Pjx462tLenVcrks60oKswfPtQvOuap0RV5tbTVSzuc//zv7jw9u37mFyFVVHR4dbG9tjsdjBL59+9ZqtZKiLsxYVDVzA80oxsAmcFchEsoMSHKOPNa4opXvNphSqjP2dtOLrQasmtRJ358QeYAYg+eyLDcm0+XqlABXy3w0Gk2Vdc5nmZV5kAMGvZDabmHhzPajPAEHNtiYozvjh7xRwqZUWyVaACi4l2TFLI03eVwAUNd1WVTY6srYGMwVIorZf7AxFSNznudZljGhtRYQy7KhpPKrJrgGERFVCGJX0FoXy6Vw7uVqWXsOIXQ1A4TTR5GRITx+sr+3sxvHcVnXiBjHqTXWe3/v/XsPHz4EgLKqEFFiwjtNVIwi1ijvPQfVhYN0opJMiFEa40ZzzQaJSK7WahGwREgtiuL48FgstIhNiYW+jVMp+LZv+/iLL764vb09HA4nk40sy6R+32g0StM0isxoNJJ6z1FkFVCUpgDA3qNSoECq2ZTFqiw35yezYZYeHx6VRb5cLopVzhpRQQjhZHa8XJ3efubO7du39/au6jhFbYT6WS2ePmH8AaBFsgMEABtFw+FwMhm99dZbR0cnGxsbCFDX9XQyffPNN//kD33vM8/cRKPY18ysbLy7t7u9vfWJT3ziyZMnjx7tf+G3futnf/Znf+7n/tWjR48A4M6dZ6Q2w2KxAMUGG2hrUb/FEGLjRCm1XCziLCMf4DLjZZ/+cC/0QbY2spKhNfQcib0PIVQVGqu01nXtiB7Pl4vTw+HHX/mWdDyeHc0IFDFprQ0q55xmCAjMjbwr3ehJyevK0kX6ycwIoM4xCiHXREQa0XsPRi3z5ebNK8lw4JgCMDWhCNSl6l2kABcnpLta3tRYlcX4LEQSROhvA9RlxprZI2ot4qibuSVENACADGsWdGZB+uTmeIqZHoCZszT6yMsf/tmf/TkfPDOEwEQsAQJaa3m+E2MZoC1Fftb7i3N6YWxnT67N+NqHi38+7VcycnVZsPHapF8qc3Wfu9Gdo/L9kLwe6+p3uN/sGpPu9nrXFPZgY/s/F92L2+IBYn5kxCgyABjHsUbI89zXTk0Rx5xlyWAwiG3UvUIiIduWRc46M93JFmEFGpWoI92cVFV1Op8Nhze01s5VURKLhXn3yt7GdCtOMh9C7UJZ1PP53DmXJIkQytls9sUvfhERP/CBDzDzaDR5cniwsbGxsTlBxHy5OjjYB6DpdDPLsqpyp4uV8JuqcqeLeVXk3YR7Cs43xQaoK2QLAMoorV2oVkW1XBWdBtBNNV0I6uELqCy9lRKuzVXljmcnaRJ96UtfGqRRmiXk3TDLVFN+KsgyEQFzC5MEIMUPOt+SthH2oASx5+MU9tmOgsQsgi3wmdx3zjE3Aa6iVRtjbBx1qp74VkWKN8Y0sU51PRqN5vO5tbYoCgpweno6mU5FUACBmPVeEpYay6hW0mZosajkdSEAYmOJafRjAF/X0s5wOCRgZbRzbjgceu/ffPPt3/3d383z/DO/+Vlj9XK5lBE1sC0hSLpU8D6KImttFCXNRGmtle3kGO99WZaS+RYoGASllA9EAGlqiqIRm+JYfeu3fnxnZ/fKlSvT6XRvb297e3s6nY5Gk/F4nKbp6enJ3bt3JWlKayvqQSN7toH0Ym0UR11wpcSHAwAFhyShiKyUGg0yDj6yJjJaAb/77rsc/CKf+9pYaxF4/9FDDiS+1WvXbqAFYAXtS5mb7Dc5Z6qtvAQANoqYfFEUADAaTpigKsvgvHPOGEXkgTwqQDTAcuLRWHPt+vUrV6589GMf+5Ef+ZG3337785//rd/4jd/4Bz/5aSJAhBs3rlptiqIoyzKO46qqUCsijuNYGZPneZxlIQRzVjS+I1BCIVV7mxvqDwDQOHAllVG1/ngGBODARIECa6ODY8jz/Pjo5L23qoMnR3sb0+3RNLEREcQ2LouVVZqZAwWAJrhDkLk7JRLxklJIF4kzNj7kNT8mSi2EQKSVqSns3rhmsmRZl53tRCkl6lJHYNfexudimc5diMhMQEzsJTdr7QFhwNwaX7E1/AiBNW0ImOli8fsjDACKAAEIQIMSOElqqXZZ5Ts7Wz/4R//QP/ipfzEYZ6hAqSakENYJGctGgwvsrc+Q1gSQvqbSf6aviV66Kh3bW7vf7xK26AFPmdPzPomntNP1R3rbWRWwZ9DuWriU8QOcWUS5tRH1X9pTp5o/O2bTvQhaLV/1UvIVQpIk4+EgSSIiGg6HcXRWmIGbNTlXq7HdwwxKAMhZtxICM0vtUiAu82I2m21tbgvdd66qqsIYE9lE3I1KqeVyKdV4iqJYrJaTycTV1f7+/v7+flEUg9FQKgoAQGCy1g4Gg93d3eVy+ejRo52dnRs3bj1755miqN7Qb1X1vYODo6IslEITWV+G2tXyau99WdZJkigTMerASMFXVVUURVeVVliX974Xt3UmJ52XfNeOLsvqOOdUHDOzMaYqnbUxggq9eCilUGzLRATAHVtVbfBOA7CsQmerwDYQT5hclxvd6uIk9fu63d7fcmVZytDEvtqp0eJ6l5arqhLnYllXRVEg4pYxWZYdHx9Ph1vQVM6RaiqaOYC23jkFiFoZHZksYmYElcUpee89jceDvKwk81jeaIxBxd4T1T4bjTJEZt7c3nZVdf/ho9dff/2t9967e/fuc9fvRjaRvSplCToyFMdxXVXDYVYUxXK5Es2h2459qosI165d/c5PfPvV3b2Njcnm5ubW1laaRcPhcDgcbu5sD4fDyCbj8VhCDrU2oujLEksj+WoxGA6bxSWmELQxgroPQFJ5wpgmEhIaiYqUUqgUEKHE3xPFcQzAo9EoS2IAstpI+BscsXMOPKXJYLFYPHnyuKqqKq+IYHN7N00HqlGjJbQe2iNMrR7sBc8uBJ/nOSIIqDgzL/Nco97d2zZWoCo0+Vp2AABQCEobZlaI0+n0tdde+/jHP/5jP/Zj/9v/8j9//fXX/+2//bf/33/8T8Ucefv2MyK9PXnyxBpT1pUWN4fYCrCb7a57SjTINfLYEMnuDgBIRV/Q0ACiNpH5FACQkMExGWu/9u79t96+/52vfHgUpwpBWRMFgZTps/xzds2Ool7KJi5eDVXtEWoRiK21FfnJxmRze9sBOU22DXjUWgsgEjdBMMznzbQA0OaGXKLyUQ+npbu4hZ3vm9z4PP5Exw5QIPGbTkPfWgwAkiTfpGV1UxCAoygiguefexYANHIQyJXQQsfxmWsaoC0Nu9bLy4Z06fyuMcWLv+pP1jdutr+0F193JqFc4J0XW+vR7rP73NOJ+7/qyKu6DJLp4qjXXt31R1i7c44hoAJUrDQkaRRFkXjZlVLj4UCcqXEcW2sjo5BD55hUWhE2GD+eAqNCbJGxEQBVF1KPTTcIGToHXlEUk9F4PBnFgj7vGAXDwRrnnKcQGR2Y8rKoXI1axdYURWFMtLOzNxyOT05OsmxIRLXzWqHEE+1ubW5tbiwWp1WxOjo4WC6XH/jAh69c2X3v3gMB2+qKGSNqRGLGqnJ17W0cMyIRdaFeZVmKXxZa8UWSIrHnd+xtgzPJd43QdAuapgPva8QIJWBHq8FgsLW9Cd7FcSwmX4keElsWtZAjcpy7FJc1524/vVVWrR8pbYyRgHNuTdNEJIRbYrKEpqRpakeRtO+cE94snwWUPxsMlstlFEVorSSk6dYFpbWR4DTvve1cRUQdgzTGAiIonQ1HgJiiIiIK1Or9zEzW2lVdM5NniqLoq7//tb/7d//u+++//6EPfeijH//4YDBI4uzFF1/8Z//sn4/H4+Vy2R0cpZR3Lo7jJ08OieDjH3/1yu6OxAxvb+1sbW1t7e5sbW1JpvhwOEzTNEkSg2owzJRSgAjgxX8L0CjvANxWvgIKJE47hBb0hsl5Z4zxjqy1WinnQgh1FEUKlW41EGLPzBo1aMvsCVkphcq2lkZCRO+cjWOj9HgckOHGjRtxHCujHz16tFwuKYDY/4+PD51ztXfPPff8tRu3RpMpBDE3m9CilTXitAIEBa2M6JxjhvF4jAhVWUcmqn1ttcaGqgRAAtBAnlEpbYJ32liGoLUuyzJJkvF4+MpHPvyRlz/0Y//Rf/iX/uu/+Du/81t/42/8jV/7tc/LGK9c2WDU1tr5chGZuHFj0RkiUO9DB510phi0FCkAqC5iGREZAoAOgYW1SeyjhFQjcgBOhpmb5w8e79/Y2UmMzfPSNKSyn4nUBG8/jeB/YwZ88SKUKML4ZDa/def5dDTIqeYWX/2MxVIjapi+5n3hdWtqoSSM9DuJiIAEkgTQStiSF9AnPt1BwBZu1pwxjEtUbiXaAAEDqwDEzHVZmCi+efPmH/7D3/2v/qdfHY6HRVlr3URsXuRAfd1urdP94fU501oLcJkA8rT1uJQfd6yRz9dpuPjzNT699lj/h6FXb7j7lnoQFmvf9jfx+d6eg4PG8/Gx3U3sApFQxXEcp4lEQQsDFnNiGkdKKaCQJMmtG9c2Njak2q7kBgAqDRJooES8YoXAiKj6BhwNHdtVss+YGYAmk8nu7m6SJKiAm9wnXZZl7ZywkCRJRqMRN8ZhIz5IrXVgzobDycaGKLtV6Xd3t3d3tgWDua7Lq9f2tjYmx8fHJyeny+Vid+/a7du3337n3eWyRH+GuVHXtRZMLGZmlpheAKhrn5fFfD6v66A17OxdoV5hODiL6+4v6Hl0m8s2g8RzOedGwyuuyvf3D65d2dNajwdN/UcJZuqOcSO+dMhKbaI2gZIgqUYhbuPpuqA/eV7URInqkpvyvNaNlZoIBHUuiLM2CtZa4cqIKJJK13gIYXt7ezabVauV1nqyuVmsCjFQB3cGnwAt7wdAT3SWwtT6VIP3WhvdONlYGwPMznlWLDnBURT/0i/94n/71/673d3d09PTv/7X//qf+JH/5ac+9anRYLi1tcUMq9WqcxaI9LCzvf21r7356U//j5/61KeUUrFGsUVDKzWhUp3ljEJQWjcacgiAilwdmKgmBN1mEOkAQTdoGLq1izb0VKz0o+FIrOtaW62bWeo2uUxCCxmEoJABW2+i0EYFiMwASqFWcZzmNh9PJ3meb/itsqxlXYLzvnbGmMVi8ejhg6qq8ip//vkXh9NNZAT2WqvOWHKO6CB6H1arFQCkacoMZVlaa0Nga62yFkJw3smjTKisYfYiPUj+bBeUYBSh4uEgfu7uM8+/dPfbvvXjX//61+fz+a/+6r/7H/6H/w8DKAWTjbHWpqzcarXqOrPGhlESc1EDiPMRRPtl5ra8mO5OkMBLIpzxMGyexgDKoo4Gyfv7Bze295+9dk0UzV6IUXc2mRXDujX3qZf0sz+VUmNbPp/l4lPY2t5GazyXbDW2Zudu7kG8yHj2J1xG9rF1HgFAo7sjAYOSGBrF0CI0dDW5qcWiv6h3CXGw1hpAYlY9Q4QGAIKgz/dDWDwzR5Gpg8sGgw9/+EM/9/O/miRx7ZqiAnz+athPO5A17tuu3LqGujbsb/xVn7CuPaZ6mSdrl+qlfMAFjXZNMoALLtuLfHHtV2vc92kPtPfPNdVnwNwzn8qvrLVpEomCC6qxZ8Y2iuOYmaIoGgzSyWicZvHW5tbOzs7u7nbUZjRKQAC2MVaNYAQKgAHPdknXzf5SAsDm5ua9e/eOj4/H43E6HFRVFQKFEPK8FMukUirLMq2tUub09DQwV84dHB0ZY7a3t4louVzu7e1d3b3GHIxWRjWQzlVVKcQsywAaHe75559/9Hh/Pv8ClIUL4ejoyHu/tbNDRPfv3z86mhsDo9F4MIgnG1MisqtIwAqUUienc4nvbcIsejDF3ZT2BnvRSd/AVzHzYDDg4Gaz2e07N+fz+enp6XQ6FTFIFHRmCk0xErA2blO6G3Yrwm/oqta05s3+7uqHMVOTJrDuFRLXrPdNAWBrrbYmjmLpcBd9DQBpmorIFUIQ0/RyuRwOhwbRWjufzzc2NpgZuWmTmohrxQjWWgTUSgcKzBInorS2wCR7IngpMMDOucYFqNT+/v7f/YlPv/TSS1EU3bx588UPvPQTf/fv//Iv//IP/dAP/dqv/VocWylSK55gidST3l65cmV3d1drDVQDNDVTUSEiu7qsqipKExYsBTZEXkcRILuqUEjB+STL8nzlA2SDsa9rE6fMQaCYm1XmZp+naXp4eJilQ0mZgV5YZbsNhNfrRn8WZtMEocomYdAGKKAyQAzamMwM3ThJMiJAZYHRWnt4eDifLU5PT7MsQ62s1UVVOl8z83N3XxiMRwDKGAsgITiqvwPl83y+RFkIBc4551zgMBxlgAxIQBxYgv4aQxYiiBIcyOmuKiWX4Ml7b5KEq/LWs3du3boJRN/28dd++If+g9/64u/+7L/4uV/6lc8gQmBIU+s8nRM1WgrQMza325UQoMmRQQBg6rM/bIInmo0hzIKYdZzWrk61NVg+fnJwdXtraIyrSq3E0CbkjpnOrMFnM3OBZfTPztlX53vaMlPQWldVtbG5ubGxESAQAmsF/kzqBWh4apOwdL7xM0HkvBMz9LACuY2LFPxWbnUzGQt1+Lj96h2qQbmX9ATDbcxJP1pKGCddwF2Vd4gs+cLdu5/61Lf/21/67O7eznKZdwJ+N7yOBa2xRmwkPg8XvupPwUWGd/GZtRb6n0VHoV7xduyplZ1W9DTWu9Z+/99uCJ3bj9tg1P63fT69Jl32JxNabbL7eV9H75yX0K6ZtQ1uAPa+YuYsyybjcZYlO9s72zubW9ON4TCbTCZWKebgvW/zMHtbrQ/xAiDawEUGLH+WZR5F0WQ0Fn+SjK8si6qqEVGYX56XnkgZU3s/HA5PTk6kz4eHhwLZs1qtEptGUeMTLYpiOEjqopRw3Lqup5vbxhhQ6tlnn3333fu//cXfAaW2trZEq3jy5EkUJa+++szW1tZgNBTNuyiK6XQ6nU6NMQcHBzZOuMXmzPPydD4XdthJxMzcpTh3S9CblnYVjK7r2ihwzi2Xy2duXn9yeHTr1i2BnGwxeHVn/kVkpRqFTN7eLD01JinsBcxjqzf3JTxJ5BXcg/4mlMfSNJXUZ0T0FGQJ2txrqOu6z8jjNCnLUoIADg4OrhqjlCGioigiEyulUGsrVnGlux1YlIU1MRHXdRVFkZTTkoKvIQRqg+HLshiMR865NM2+9KUvff7znx+Px599/fMvvfT8Bz/4wR/4gR94/fXX/69/5a+OxsPBYCDagLiunXORtTLkxWKhjanKMgKPiNRERUidY0VkI2PLumAG5qYoELYZHVEUkXeRsYEpOGes7bZxq8YgoKL2psh5ov5CG7tOJBmbjVlVPmilCRqlQVLVEFhqRrAirXXtq8hY0DpOk5g4hKBQp2mapmlsI/Lh8PDQe+9Lz8zj8fjk5OQrX/kyKHzppZeiJGP2wsGwdX+0qh+XZVksV0rheDwGAIlIdz4kSQLBC3MNrlZaAyr2XlCEtTHUhhxKg0r8CJHmqkDEanEanF8ul97Ve3t7n/zO79ja2vrBH/zBL//+V7/whS+88fU3+7jofQLYWi4vaEHY/COWgVZg7Sib1KflAE2+RR18pHVVVZPR+P2Dg+tPNu32jkWEXhwMtj7Q7g70uGCfm/RZ49qFYqJoObfWVgGXq/LGjTvpcLAgBw2PpM5E3FO+1681Gb1/TvvkgrnNGW8ZqGKEM7CXs7if/mdubUKIaAAtSKHwJjSltawqBMAG2osZEQwoQHCushYZw2gYfezVD/3KL39WcW0gBAVGR7V3iMYoJPbAAZl9OPOD9keoL9T9XZvWi6yRW40QW2UREdemqftVN2B1IbG4i3DpU8Bumc+v6NmvLk59f067m91IRUpq83cRm+ooWmsUYHdpKgRSqqnXLi10Oo1u7Y+9HajEf1fXTRIOUBPia5SKIpMkUTaIdna2NqeTNI3j2BqNAIY58rVryGhgBKUQCUnk1N54m/h48URqbYkqEeiMVcNxOhhlyhoERYD5vPz6195nhJs3buWlW7z/QDyjzrkoiufzZVX5sqwWizyKorIIwLPTWZ7EOJlMRqOR+PliiIyJCHXtq5Pj09FodHV3rw7hxtXtZ5/ZfeftmFjw7v3Vq1dv3X6GQJ2cnBzNj+89eN85l6XxcDDWWj+4d/+dd98TQYSZPZNYXNMkiaJoMpnUdb1arZbLJYCKIsOtjVcWXCmpHxqkxI0ylpEch8AqUvDk8GBvb2eMw6997Wv2pee2t7e9r5ljAG2tNkaFEMrSJ0lGREoJ6IpPksSYiKBvau7gsRhRMsqc9344jKSGEiMYVAE4slFeleJzJQLvSZkQihIRjTEKtUj3RVUCsxETt3PiNa9rP7Tx6elpHKdxlkky0mhjA4HiyEgRmmq1jKJIWytZsdoT+6B8KIvT4WQSqWi1XHmlstEIlEdEZL88XQwGg7qu03SQ2KRYlJzw7PD097/8tQ9+4IUbe3sHD/d/4+D42eefe+bGdQ08m82890lsK8eCU2aNeM0BEZanc2CwSgfOjY4Us/c+ENkoQRVpja72sUml0qpCRgjknLHoCmRPVV2DhiiKqmqRqSGRUyYGJiAEVOI7kxBnOZ+SQyygY13EYnvQRZvpcAS5YQYAAExEClkwWYOomEqBD8oYIEoHmdJkY4gTYyx6coy0v78fQuACELGqnKvqr/zOFw3x7du3h8OhymIfHEBtpFJhUwYqALpFMQuBk3QKDHlZjabxojiM4oxBAypitjZydW2tZaUQSCKzUIv4ogiAABWRMgZCQAZmD8EvTg7n8/lyuQwhzBbzSNd7OwOEW0lC169vvPv+yZMnT4qioLZ+V0PBCAFAm9g5h6C5rXWmoQ3uZZbUOCZJmWMU3tFYrxvNVCMTBR0nq0DpZPrbb7+XTjfHsTG+HmhFwUMg1LaWrFwTMZUSRqEQCagJ8+pguXum0y5lOCJAhlJTZUAxJB4MQ/AuGO0UD65un0Q0ZxomiS191cLtMne6PoJGYu4wsFA8dCBMNACA+GtZofM+sG+jqGT3gFHALLmIWNUrVGQ0EwEBBC+VPJAZulKh2lgbxdqYM3vXGptZu7A37CbElIhrd+eZ2x//6Iuf+8IbW1tTrl3wLcgUolZasNZ7QDDrGn3/6jPObhP0Fce+jLbGaNe4Zr/PcCEhuP/bi8O89M7FPvcZ/1rfLp3Mp8lZ0qsmgdV7RBS/addyzz3ctOy8i9pLfhXH6Xg8TuNI4lYE9244yOLYWmuZGjgqMDIbglpKfeGjbyfoCyvUIrkwc2STOE7FRGy0DaSOj2ePHz+OB4PB6SzLsul0urGxcXp6OpvNEXG1Wh0cHBCRQuMdDYfDjY2Nzc3NQWYW8+X7793bu7K7ubk5Hg6YWVSc4XB4eHA8GR+NptM4ju/evXtwdPLGG29ubm1pGz05PH777bcPjk6IaO/KzrVr19I0nR2fLBaLd999d7msphsTpZRA22ull8tlWZYhMCBGUWOj1lrXtade9UBuweuREUELwrNsmziOjcLgvQdcLBZ7O1unp6dvvvmmMUYKBOV5jsiCTtWl7ULrLBDADdWLwpBhclvxQvRC2RtywrVWpi0x2ctQApAisnVdlbVkoMnSl3nh6rqrVCiKVAgku2g+n0NraRgMBpPJxDmXDNIg6B9as3OSo4KISitN2jkX6pqAQQEgA3lQ6KpKQtahBb6QLtV1fXx8LON1zom39f79B9euXRuNRnmeO+cIWCvbGgOgm6InT55AgxbEFGpuGKW4nxs5mxsTfTDGACoVGV+WWusojquKyrpsfStBxwkQAaiGqJ47blKbqPN2P+08qrV0WBTvKjZWouawgIbmdDTeImviQYbWxNAD2zk4OPDeLxaLNKU8zyVVrK7r3d3dvdtpFJkuagyVYgpVVUnOFQAE56Uj3ntmiKMUtcEGIwlbLHgP2kKTqoIKkCAwgW4x08HXy+WydiX5IC2fnp4y8ypfHR/Pjo6OnhwcnZ6eaq3H46FzVRQZck1RyzzPXe2VNUophaBi61yIIuscK62kqp8QH+ectZFSSsqvaWMl6kKwm6BXgwSRAwRmzQjvvP/eRz/4IpAr6zpSyKiD91pbRkUXCPX6OhFfDNTiVoFWkg7F7ImMMYuy2L6yN9naZETNyD4ISlRfkYMeR7hI57mH2A8ASAyBIDQZvcznzat4Rjy5DTRZ0/rkSTndiAoADV7GvTp222leXec4UOnLNB144sl09Nprr33uC284X3sfgm+scMwUWEpbn8vXhsuY00XedpGJwvkz8zQOuvZwnztebHDtztN8xv0NsdaNPt9dO9vcyyTpjmU3k/0n+7YBPKtxdg7jWpZTvo2jVLWXba4YEbMs29jYuHJld3NzW9x7Qpsia/rz4L0UzGrkkj7QGp/3jzJzaAu/EFFgGgwGWmultScui+L4+Lisatb6+Pi4rmvn3GKxquu6KAohKFXpvPfWkrXWUyDg6zdvYKgn4yloxcRP9g+SKHZpnBhtjRpvTOcnszzPWSlAnQyy3d3dsqwPjg5P9h+vymo6nV67dmM4Hlut83yZ57lSamdnZ3t7m1G52ud5fnJycnh4yAqlt84FBqiq6t69+4NBlqapUkpwoLix8PeWDLGJ/SG2cUREjMoHtzndstbubu9Mp+NyNTs9PQ0hjMdjbnEimSU7oIm/6BrsZ+BgD5NL0gQlhEqWWymlrSGiqq6MMQFYvMv9nSydJyJUIGlXnVNWdk+7zbgois3Nzf39fahdlmWPHj1i5t3d3ePj4zzP0zRVWoHiYrXqeJgxBhGUgsAN0KlqMnC8jaLQIn4LKiEAJEkSJ8njx4+h58y21j558iRN062trSzLiqLwIYh7UoyodV0iIjPMFnNXVUqpKIqkYJFSBhqa49sQqqC0ASI0cahLrWM5AizVLTlGRHGHgau1soDnjmd3GUl7jZ4WL9Kx3pZEAvRyYQlAQnsUi8m6zeXDXj1KwaWBM4e9Pz09Lcta8BpXq9X777/PzIeHh8EmV65cSeOIiRA1oEZkDVyX+ezoUAEYo1igjAGRJStJNaVuhHrIluDQqoMaACWkUgFDCBxCUeTz+Xy5mpMPVb6az+d1Xe8fHh0eHj45PFgs8+Pj46OT09lstli64FxsjU5iSSIvywEAFEVxdHjiCBBhMhlro6NIrVarfpJ9tz/jOCYi7wkxGGO0iUIIDBI0QFo3BC0AA8CDx4/u3Ly2maVVWQJoxUxMBkmps3jUjvU+jdR3lyYIIJY7MCKctDVVlsXyuasfTMbDha+tVhQCIVOLxLnO6ftkvFc3kIn7j3W/pUawlhzURgnsIAq6BKe2cdWxg068lkNt+owWzrOotVfKnSRJXPAhBGKI0+zZZ5+9enWaF05rTSFopRkhBBJTttZnBbT73PEbTGvXUegFOl3k1l2H10Iqug73JZfu204b7r+xa7+biu7+2rv6fbiU7/ZHd05q6QHu9zmc/CT0imzLtz3Lczeos16JGXO5XBpjBoOBYEV576MoGY0m4qSU5BPEBnq46RIDIjsA1WbIMAHDuc549go60cGHFidLqLyykeDbeU/HJ7OqqjyDUsZ7Ojk5BSnao6NVXpaVU0ZrBEZQxiLq2Wy+v39gjRRkDCcnJ0WxQsQkjjY3Rtev7A3j4WAwaDEFOY7j7e3t999/fzoeRVF0MlsUlZvP5/fv3/+93/uq1hCChJDB5sbg6vUbAJCm6dWrV3d3dw+Ojx49elQUpTHWWBtFURzHdV3PZjNBsOiISOvP6oqqN+vovQfieJBqE21ON9LY5vny+rUrzz5zbTabSYNd0YU24AA6+Ml2AltPY0/YktWRZ+q6lsoZUhmNiFArE0dC5jo7EDMvl8skSSRvWFzaSRpHsV1W1WK5lEBi4fRRFDlXJUk0mUxOjo+11hsbG4LFPRwOu+INdVGUZTkej43VWuJrtE50KsRFGw3MgvmsjeS8ZszsvUPU0O6oOI6xrYrWqewSqTcYDJb5qjpdomlD/Jq+OZlea21ZlknSuL/aGZND2mD4gRw9xLr2UNfpcAjg6ro2VsfWFnWBiLoFzQYFDXxEu7TMDEgicrW4gHJzjVGvq79yBwFbZZpbUtPRBKCeB1Gk4dFoJLZuARw9Pp4tFoskScqyzLLs4cOHi8WCtcVQX7lyJUkS0AacB2MU8uz4eDGfI8BoMECCxeI0jmNEkFB/iSdEFhwVVEozE5IHQkBm1dBJ72rDvijyoii898UqXy6XRVHki+XJ/PTRo0cns/lsdjqbLw6Ojk5OZvP5PJAyxiRJg5itFWSpjaIoyzJtoqIoDg8Pv/a1dwNDHKs0TZNsIukPVem4rYYrsosIbZ5CIA8KtYoAAMlrrb1z1sTBO21U8PDmu++8/PxdbUzlvQHQCkMICBrORz+cseGnY2IohqCQEIBZB5YybqDRM8VpMt7d8pp95SMVBQC60MoZte9XQOox4EtjYhCROQiEl27KODE0dSHFvn0OghvObxVjIqUMgGKGS4A4njLS5pL0RE+BELz3165d+f7v/9SnP/1Pt7c3cqy9C4FaeRaIzwGkn3vFRZbWTMR5OaD/1cXHvsmrz8n6P1xjn5eOvc+Pu8fEfvgN3oUXlO9+C91j/ft9OaBNqu6qWfHZIAIZw93vmNk5V1VYlmVZllLvoVOOuRXVGwbQumcIUJy1a8bn/oezHipEVJJoO0lSAEDQZV3NZrMQuC5rwHyxzL33cRwXZR3HsbVxmg4AlOAZVVWVpulqtfryl7+8u70lybtHR0eo+LF+nCbxajGPjd3cmGRZGkIAQGNtpM32zubu9uayyJN6MDudv/POO2+98wARdrY3rl67kmVZJIZba5wLIQRP5GuPiDdu3Lhx48bJycl8vlzluZijoXWrt9LrGf42AEokLjbLoaw2NrGIACE411ZBCD6EkCSJMUbCoIRPIKKcui4LSHwExhhPJKyRmaU0J7UZSuLH1dYoo7k1SnNokoP7FpQQgrFmPp9nWSaafZIkURTN53NjrWjeiCgqtQRbrVaruq5CcCHw7tUrEpPlvU/TmMivVgtjjLUaAFxd2zT1VWWUUrqBsgNkYAIArVSZ50kco1GLk9MQgrVxlCR1XWej0bVr18TnLUJDAE7TdLlc5nk+Go3iKAVYykYSLqiQkyQxBowxjoK11vtKuBoRAWKbc0Vai1OGEZGqSmud51U6QEE4QQW6rcChVTBRFHxQSrcKTO8iFmiyvurWPdM7n0/jwQqABf60kdVak54cQVF6BGGmqwhZlrVSRmsrLhtBbmHmqqrKquZQl/nq+vXro40tYAKCfLF84403xHJrjGGG+WxmrWWGKErOSAcHIAbVahHMwIEZyddKW9S6zOehXi2XS1fVVV0URTGbzRaLRZ7n9+89PDo6qn04XSwPD44Oj4/q2itttUFEjKycDJDE6yRJtra2xuPx5uZmnudVVT158uSdd975wm9/eTaTXCmzs7NLwKvVigklJzCE4IJXSqmmVqaEVnhAUSACKVCgomzw+Ph4Y//gxt6OAvDeIyCHYDRwV6jjItmUab9I9ZkJkTQqz0pse0Zpo/M8371xbbg5zSkoQE1AwJLevUbf+ALqVp939g54V823y/gPotrK6RYtqGHAhEwo8h8ASJEkZkZsYIIQNBMyglnjcGucr8+Z5FulNQBYaxBxsVyk2fijr77yUz/1T/NiBa2zBxvAqdbOed7C3mdCF6ZzvfjB0xhn91vqRYRDj8n1f9s93LczX8qJv5mr32D/JnRKz/lMp9bIfIk7XP7ULUYSAEiKUTeEtVcQkVLYVaaLbEREdV1rjd6Fuq5dVVMLF6wUWGu16WoqiPxuAICgWaNAngN2YYGIyMgEJOXP+8MsimK2mKOxNkoV8snxrCgqZrRRLJUHvfdJ4hEFXwaKogBGz0SAnnixykU5KIqVcCZltPf+3Xv3Y6OZAiKm6UsCA2SjyDkX22g4HGoDVmHu6gf37wVy3/PJ74iTVCYtXy2LoqhrVblSChV776lm55xn2tzc3N7etja2UTSdTsUlWRTFalWIw1JrQRW23R5BIAWA2mKHXslhNBoZY4bZYGM6DbVbLpeS7lxVldSlYGZr4yZEpV3KztAUWmczESEq16ZNNyoLNZnWocX6DsF5r52rEfUZupzCunBxHAvvB4CztCXdFAKRagpEVFSlHPLVanXjxo08zxMbzQGSJKmqKs9zESastaiVC7Wrg0w4t5Zk51zSnpSDJ0+YeXd31xVFmRfD8UjcaBKAfevWrUDAzFLhSnYsEYluLUJGd14QcXt7q6oqZvgDf+APiNHbl41fnIiwxWBjZtQ6tJnNVemyLOMEXelEJ16tVkmSSFi4c04ZQwGMNr16kq1srRQAJFFMDWJdK4TKmVrnwd3VGaX7mHG6S8rC1jestRangxzbOI6JYG9vT+4YYx48eMDMRVHIVBw+Ofi6wuX8NF8unnv2hcFgZIaDvFh97atflSlVSjHDcpGLOGWMgTZ1lRlCCOwJiExkAICDB1BMxEzImlx5fHxYlWVZlnmeHx8fnxzNjo+P5/P58fFs/+CwyMvZ/HSRF84FYlTKxJFCxDi2WmujcJAmk8loNBptbm4Mh8MksdeuPGOMGQxeE//Ow/3ZV77ylddff/3++w8JYDxOJ5PpIEuKsgYIEEjc0o0kxKwAyQellPNeKUMaPQGY+O1HD0ej0TRNlKIQAIhYij3gOVcgq/V8qDNizo1GywDErAA0yQNACItqdffay5xYH0qtNRIDIBlWPfmp41aKoYuZ71N3xKY0et9M2I3tbK8ihEA+1IEcsOYLLFWKDCIiaqW0VdoSsHgMDJ9ntH1Wh5epocLwXVWjVmma1q7c3Nz8U3/qT/7UT/7T4cg6JMHCdc5pqwT8v9/gWsvf+LrI6jr+2v+WL9M118SINa6/Jg3geYV1bbwX+/CNe9t/XScHiFjSCRZ9y3k3hDYSp4nS5NaRTI1u1vBgPFN8K2I/HA6zLOlqhDnnIBBYUErFcSxBWE1/sAVl1LoJAuImHlgyZ7TWrJiIkM4AJajNzzs5OZ2friYbm4Ph9NGjR0Xl8qIIeVFVFYLyTMu8KKpaFD5xiUnFHmutMVEURVeuXKEYTk5Oi6KQrNCTo8NhloyHg/v379+4dmWQJS28Rg3alGV58/q1r735FkN48cUXT+crAnP/wcPZbCY5LePxqKqqVbF0zilrEBE8KqUg0Hvvvffmm29NpxtRHM9mMwm63tnZ2dqCsiwlz7IsS2ul2IMHAGQNLQYQIobgEICBOAQiTxTyYplmDVMRY4PgdZy3aYP3FEUgk8Btvawm5a+vNGMTK8CtR6Cqqtg21QwluFpSEzqVfbFYVFW1u7srkS9EVLta1OLaVQFC7WsmkBgxiZzy3mtjpqNxURTj8TgvV+kgSweZcw5YAZIx2Eq9GkB575wLUQQhkPdtzJe1VPk4jrMkrX1wdR0nCRC98MIL3/raR588eUIt1j+32Bfek0yL5Ex770ej0dtvv8sMP/uz//yTn/wkUAi+SQ0icm18DDNzIKf0WZqWTFGcDso8T5JU63q5XGrt0jjVWiOEEAIF0MzA1GSqAvCZ6ZIlFJw5YSZEc4ld7rLjfJ7UnAVOd0oP9lBmtdZEEEWJc06ENtn5RHRwcDCfz7t4xuPjw6oqqqqqKnfzxu0bN2/OZrOjo6M4TgEktDucLhcioBhjQCRvCaWkELx3ropDJOF10j2xx5Rl6eq6qqr5fH58fHx8NDs6OjrYf3I8O83z/PDgKC+rxWLhCaDFPREYc6tVHEdZlkxG4+l0urk1HQwG1uoksQheK7Vazsbj8cb05osf+PD3fs8n/vSP/amDg6PXX3/9X//Cv31w75HWMJqMBlkKCvM8946MMaiV914i2bhlez6AJ44juyrKe/sH6sruyEREzqASgDYJeW/CqC9oIJdYooWSswTgIQIgceWraDQY7W3XHHwIMWoKREAt3sE5HUzxOYoN0IZ09fEYGLjJvWYAlEwjpZRqsfQ7iicNN1S9r0sqBmyKkaDkTDOeEYWn7bw+J2vFW2DGOI5XRW6YNSqt1QdffCHLbJoleVUzOaVioThE9b9vn5+f3zY9qVVKeG1qoMfSusN5satwPpr6bB4v89rCeYGg/zD0dNn+w+dW67zLvOFw5/32dJbpexHajUWCjmMraTxrQk/3Z3NHAwIiNeXqjFLMvFqtIqPrul4sT09Pk8koy3Qq26ObHwA6M0fLztOoWYvue1aNUQFz48KgtkYYM4s8vlqtbJz4gA8fPvbeK2WKqgoMITixggqHSNNBR5uiKFouc4nvePz48fXrV2UsQhOD89XJaV3X169dXa0KSTeQN4oIrzXcffbO4/3DOEpX+XuPnxwC8872dl3Xp6en9+7dCyFMNydZlknp7CiKF4sFA0vQr5hnkyQ5Pj4OgbRWaToQ+21jpQfl6to5YA6drN1tsCRJJI5mMBjExtpIi6Gp83cKne3Cm6FnBZGrM1G2SEyNd18ScLENiRQ36mKxALKdw5I7Gq+UyCUSKJDnOQCMRiPnXFFWiGitJe7S/6msijTJxJqilDo5Pt7Y3T06eGKMSZOBGMcoQBQZVKYpQhBYKerYiTIGACrf+JuNMdZGZVkuFqvRxtTXNROhUtdu3vwLf+Ev/PCP/KmPfstH7j18IOdInLvOVTJHIgpkWXZ8dPDjP/5nfvzHf/y1116r6yqKY621wKppIEQkCIgISBp1VVUMFDwgYhwnVVUlw1hrTd4rrbMsS9IYFUJT1bEG1FKAHUAgebFz2yAiqrN6GNCZ+p5CiC69RDAQeCdoKIAQAYWolTKoFIBDbOp0DYfDJic7jmWzzWaz0FxOsGKq0peFQ63ee/f95hUA1sSrolosVqgNKIiiCJQC33IJpQQjtqoLY0yRL2XRT09PJY1isVquVqvDw8Ojw5PT09Ojo5ODo+P56WKxWJyczr33ReWiKNI2kuezOBESYYzZnG5Mp9M4sUlkI6OHg6E2KoqsMTpLE2s0AIOvR8PhxuTm3WfvfPTVl//Uj/zwO++/99u//cV//Qu/cLD/JAQYTbLJeFjXrvbOGu1rb7Qp69pYWwcCZo26Jk6y9OHR0Xg4SjdjkHg37xW0hsPWv3Y2/5cJTaIEMzMEZkbQComZQk1u89bVdHOy4hqYAYERmJgD0PmaSxoQ8Iyk93mE3JRj2D/RQExAiGxQS5I/AFBbh5fb+nL9S0hue5QlsEPehYbP89fuxXQ+vees063MLvYuQPShvn7j2vd+93f97M//0ubm5HS54hZ+mgg0XmjhvK0bW/M6NgVWz+Xvnj8AZ77Vjg1f0kNZmwsQa/1n1lruf9tn7Xi+bvEa41xbM2wDbVqHn+p0HTxXXpC6rdW6uxp8hj44Cbfxcq1uavqzBwpd8J6C0QgA1uoQwvHx4ZW9LUkXIfIaU1QsXjRZN4DG1AnI2igIuvNHMgfnnHNVrCwSM6CAMIuGJ8845+bLxXA8IS51ZEWEL11ox2sQMYq0tbF0vhYfc10rpYqylBl78803rbVpOhiNRmWZL0/n49Hw/sn95+7cPp7NVqvVaHRltVqsVisZSF2U6WA0HA7feudeXVajwWA0miilEfHWrVsh+OVyPpuf5nm+yleLxSIxkv5sQoMVqkS/T9NU0Knqus7zvPOUM7SQPNjAJ3X7p7kJIBiBzDwcDgHIey8wxeL3Um0RIWGu4uHu7Mydh9+31UCJyNrIhyDqkTB4AGjKBAXngo/jODChNpG1QrOVUsPhsLE/OYeI8/lca52mKYr/gjHNksFgEJyP41jyPkHrjY2Ng4MjV5Wj0Ui2t9IaQVlQzICMAn5nbWytRWNUYFe5EHiVl6PRyBW5MQZBq2ZKNYdgoihfrLLRiL3/g3/wD/6v/1f/0d/79E+++MJzR0dHiCjGeeecMdF4NHJ1rZXK0vjwgP/cn/tzH3/ttXy1TKJItjII9qY2iN4qBcjkGRDIB6O0iYzWFlAlwxEAa2vY+bKoUWKviESpSZJER0lrQ2aFiprgQYAgGTsAAFWZp9kwBCdqN7Y8r130M39Zcz6RRIVWyIRIFCAQQ0Bu0kMlJRcEA1JrY1Rd+zSNZTXTNN7e3oxj630dxzaO7ePHj4uilq+89wiPax9MZN9///3xdOPoeD4cDKM08UHlRRUnGRFkWcataoXEzjujNGsqq8rXDoDqupTUA8l0J8ajo+P33rt3enqa5+XR0dHpbL7MV4/3n9R1zai11jJRrY6uEXFjMhkOh0mSDNJsPBlmWTIcDoHCMM0QMY7jNI0dhSiK0mSACBBqhpCldnTr+vVrex//6Lf8iT/+x9585+0vfOG3X3/99XffewIAWaajNLVx7KUyoHPWRITgHRmlHCmF5qtvvj155SPTZFCvFoZBt4goBAyBUAAqiJqQCFEM8BwfCQjIIL4LTxQrrYFOTk5fvPVaMFAuq1gbpZSX8EZmgYNmZsXnACE6pgO9Ggx95YdFe2EM4IlIAxJLABp67xFAKKQoMXVdd0Z48U2gMq2LSiNqBMWybTrO8e+9WpZzXjZBVqwQ4dVXP/JvfvGXGUhrdK6yNiaB+X5KsFX/wjbsqPuzP/i+ZLD2q2+m208ZxSV96D53jqu+g7nj/X0p6WKzHfNWvfyi7pneujIzMDcDR0Qp0dzpXqJmdS9Vbf6oPCyl4uI4tsaKITSOY8Hc11o31QI0QA+3RCpjAIAC9j0hwxjFrJmbQCHhuBLQK7RelNfRaBTHNk2TNE21SSSoSrekrRtgM5lKdUGF3LvaucXT+fzw6ChNkixLCHixyKuqsjauKhecHw2H4sSC4PM8d4HLstzc3JydLvPSQQsoWNf1aDQaj4dxmmitozRZrVbHT06ePHlyfHIiceDcRswJP1ZKMaOYVaFNzG2PwLmNEULQGrXW0/Ho5PBgtVpFaZLn+dWrV52rJBSxe75DUdZaS8UhMRoLbkMng8kzIQTnPTMvFgsKLBksssree6U0AEvMvNgwpfHVasUtCuZ4PBaYEe99WdUyFqVUm9LMaZouFovFYkHOLZdLCXcSHcFTANQcArPEDytyjiRSXZu6qrU2g8FQKZ1lA0Tc2NwB2RBESZIhIhpTLJfOOSkW5L3/s3/2z/69T/9kWRUMFGpRl0W2KENwzMF7CiECgH/4D39qa2vz2pUrSikIjplDYBNFoEBpC0Dga2bWxojtwXsvJQuQSGmrrAUDOrJlmfvG+K9YKaUQggsEiBqUUaoBsRfthoMDZbQW/O0+7EbnYGxJTfNHEGrLDIAMxICsFIaaGIJGRVLEGCA4pxVyCHVdS8Fj1SaGRVHUpO0ppZRKkkRO+uNHB7PTE+aQpoNlvioe1NbG792/PxoNjDGb29vGxqfzWVGV0yyVZGQEqupaaxSkCO89sS/zIoTgfS2YlVK3cbVaFWU9n8/n8+XJyelyuTw6Pjk+ni2XSyIimQWlrI1MZLFR7+qNyXQwGGxvb09GwzRNB4PBME2sNmiNWG4acxFYY0xsW/MeEXmHmphYYXjm1vXtrenLH/rgD//Qf/Dmm2++/oXPf+lLX3n8+HFRgI0wy7LY2No7X3lQRmtNxNrYMi/efv/ec9euJ1oxKmYi8lpMuEIqAbCnTV2k4QEwQgQXAhBqXfoKAAajYbY5qThYaxUBEQUFRGwAPYsb+FzhB3w6k6IOaRIUUKNsIAWldYvUQeSDGKh7tL0zpikmFLAOpbRW9ty7FJ5jwOekv6df/e8VAyOjgju3n/nYt7zy65/9nY2N8bIomYMCNMZAWyvx0mb7umY32otzcbGHnTL6DZj6xXb6f/b5OrdAbp163QWGXPxt9+q1lvt962aVz/MhOK/cd+ppN/yuG33TN7ZGAlGRuVWOEVkpFcd2PB6vVqvnn3/uzp07Ozs7aRrJmVFKSYKN5BEykIImK42INAIgoDZdh51zdV1K6i8AlHlRFMVwPJDX7ezsKGWstcZGsbGz2enm5lZ/MhsJtQVWg8t89gjkvG+1T8jzEuJoc3uHGff397c3p6vVKuMsjqKDw0MiSpJEKZ1l2YPf/cpvvv7ZOBmcnC6cC1VVxbGdz0sGuHplM8lSrfXG9tbGxsZgMNhaLefz+Wq1cs6L36dT4pmbwBkxD3p/AcgFEVvLsLWxqALvvPPOt33ra0B+Pp+PRqOyLNM0Fa/tZDLJ87ytFMTGNGoxIkoUtIy7Az6UzC6BZpSI0ybFq1ezTMQFalFO5RIxS5K88zwXZxIRSPBOVVXe+3QwlJmfTjaWq3y1Ws1m83Q4WCwWRVnvXr1azUsJ72Jm1AYgVD4gYtKUNdRaG2NjAJKIJucKRDRxDARJltZ1DSHkeR48AfNytfiNz/z6Zz/72Y9//JW33npLRMZhkmZx4qt6ka9EnrPW5nl+9+4z/91/+98D0Z//8//ZM7dvu9rZOFHsXFUopbRR0LpCgvd4ljYdAAUtLgCAryoTRVEUMRMQ1XXtPSVJEsQbh6ABCMV2JQIfIiEoSJJksVhwG13RY8PQOoYAABAAu6wXeUaxMAJJbEWlUfzBiFDXEo/aoX93ZjMxRIts1OAoCWAtmKouq6qoXG1sFAK+d+/e4/3He1c/4h4+kTNLzMfHx4PhHjBog6C1qPrO1+RDUaykZEFVVYvFoq5rIprNZrPZbLlcns7zPM9ns9nh0fHp6elymc/n86JyAgwjISbGGGOEE9tI2dFoNJmONzc3I6Ol9pT02VjdPmy65D1k0kqDQkJABGTSyKywyOdGmySydnMy/dhHX3j+7vvvv//GG2989evvPnr06PDwMF+WBGCsVa1v2Ds/mozfP3iikD/+oQ8X85lzFPW0k05/a/zf563Qss8JGoexUiowAUJd11fv3BhNx6dUx9ZC6QITIRORAt2E4Z0P71XrodFnrKEJj1AIAMRM7IkImcU60lQFljgZqTRHvu0eQFscRaDKtLLiQgJJiEJE6EVBX+RYfF5ZbG9KebSGZDMzIynWUWQ+8Ynv+PXP/o7SaIwiQXA1ls4XK+7a7DZrd7ObDrjs6tjkOVr5P+Pqt9Zn59zSa6HCoVc/bu3ta9y3a+RsZi7kMWNL3Hvz0HHonqO3yUY4kwC454stioI5SBRoNhikSWSMvvvs7bt3n93e3jRSpCE4MVxpdeaNRmo2n0ZQRofQiguKFRqFbDQCUJ7nHJqKp8vV3EY6ThIAkNOYJAmgAaDagdYa/FkpJ+r1H3qA2/3pJWBtUCthHhRCCEZT8FevXh0OR6enpxuTsczP7s5O7VxdLJkwcJEkye7u7mA4uXlTB0Yi8nXNHEpXz+fz5WKZl8Vbb71PBJPxYLK5kWVZkiRlWS9Xq6qqJFNCKsx0ho2yLHtKPBMzaiVhg01qAVFRFNvb24vToy///le+/Vtfmx0fO+eyLJEBCtxxN8POOe9JgIHkT2zqJjUZw6gVE4jJLEkS733wJGRXSHZfCoRWJuuimdIkiyNTV46Zy6LSDZZWs0vJB2OMc9rVnpklT4kQRqPRKi8Xi8Xu7q4n8lLmtvFCNchbwIpCsFEkLmFJtUClpNjGRpyCAoWYWAtE08lGVVWg1Rc+9/m/8L//r7761TeuXr0yzFIiGmRDFmTjopDIIMUQahcn0fHh4Qc+yyebAAABAABJREFUcPdzn/vNr3zle3e2trNhCuRUIxUFbZPgnAQNMLMgpcgHVCqKEMCAZIsyM7P3QSPUdS1yCTKAAsEOES565vxSAADaSE0OQlF8xVGHrdIrJxDbdJQme0RIvoiwDqViEhM3TgFgDtTmd/XKaSiRn4wxxqpAygeIE7u1vWGsYsZA/vHjh6enp8A4HI6rYpUkyWQymc1mN27cYOYiz5VSIXiQqgxlGeqKlVqtVsHVy+UykFNKlXlxenpaFIV3dHhwfHI6W61WT/aPlvlquVweHR3N58uirqraiyIrtjE5yFFsxDG/OR4aYyaTyTBLQwjDURYbG0UmiiIbndWx5hbunhsrGoLixuEOqICAAijFVAMDcIituX71CjJduXrj4cOH9+7de3zw5GQ2X+QrVzsmIsIkSYu6Go9Gj54cvDN+f2syNtZAHYJEQgL3yqG2NLZHb5t/EZkRiSNr53URRcYT3nr+DmiFNaPBQARNnjEQEZxLFF/Xo86a7ZiCAmYCxgYqgBnb+BipjsltlA8Rh8AhnBlNVVObXIuuKyI9gApNWDsys6HAIvRLReSOMbQfznltoYHXUj3PYoCgAjpj02eeeebDH37uja+9HSWpFLILrn4aX+8GucZm1hjtRY7bJ/H4dA34/9+rM0WK8TBcqDYI3zTL7/NRbOGgu6/WGuls72IN697Y70DHwLTWksaTpqmYhrRBrfVwOByNRuIwtsNBliUIDbJrFxDAzIpJKYWtXImISgMRazirLGaMQVYFFJK0s1gs5L27e3t1kTMzIhOHLMuMPmexQGxgE7ppXBNQmoBqCOIxFepvtLbWPtk/KIrqxvVBkVdHR0fW2pYCgpRJcc7N5/M8zw8Oj+I4TQZZqAUHoEmgGgwGcZoMBjWTAoCjoyPBX9ze3t3Y2FgsFkopgUewNhJAn85uzMyNMsTQaUiCjDGbzbY2J8aY6XT63nvvvXD3uel4jIh17UMIwjil4A/2SguLC4Dauu5E0DbIusWra6JxnMvzPEljCcKSNeVe0AcRKdQUuCwraIskhhCm06nUH0yyATf6feyqktqqw1KfTikzGWXWxFvbwyiKxDzbFIJFSY1twru4X+DF+8Y2SzQej733EntZlmWSZEysjeEiB4B/82/+zZe//MZrr72yv7+PaKqqKos8hMAIRKSNIHQ2gF9pmhbFSsHm67/5mW959SNRbJgZ0DEHIvZ1LRG8IjqI5ywEbgOXgjEBEXUUATnvqaqqNI7aEo3Oe9LKkCEMCG2xElCKvVRRDKCEHTr5rlMMFEBHW4W2Qc80BQDMQTUuCcUUnHPkPZHnFtIVGtjIZlmNMUqD9y7UTiR4qc0lJpNbt+LRaJCm8de//vXVKpdK6refuz0aDItiNZlMBpkqy/LgYP/Os9eAINLKlUVd14C8XC59XS0Wi+VyKWljZVkul8uqqg4Oj5bL5Wq1Ol3Mxfi8XK7K2lMAEZrFIaK1ZgjGqixLRqPRdDq9feOG+KSzNJNORlqJ7m4jAwB9cFwAQK1AoYREteQ6NOwZWAMqg9575ysb6d3dXYYj2t1BptEgW6yWR7OT45PTogp5XhD5EEJJYTgaffnNt779ox9BYzpxWOhJyxc6ffcsUOOMa7ZPRNqUrh7ubG5e2V3VdaQNBCIEpRUEVlrThcCdS8nyOX1JoWw/bLE1gM9UMjGkdcGqDcVat+MiotLaam0RNTU9bmQ/09dE4Sk85ryAIOkWiE1SMxIxMQGH8Wj4nd/27V/60lvTSXJ8OtPaCA+42ODaixrJ4nxw08VnOj508ebT+vwNHljrSecj7K/QxZf216y/it0Q1niSaq9+MFcnNIHk+V3WWzFjdl4lagwdDcyvOICHw6EPdVmWOzvbg8FA/EzMwfsaOERRZIyWJLtGcBONioGZAzlu4fSgkdeAiL1jMTqVZelDLVyqqgpqACuorus0G0dR5AMwnzNvaKW6Ld4FnQo/7phlIEdECDpJEmZk8szI3JSa1wBlWed5PhoPuugkMZRnWfbKK684Hw4ODpbLpY4MAKxWK+fqJMkIVCiDUkbqE0uEc1EUb731TpxE0+l0MpmsVqvFYpHnpRgPGgGlc9bwObwdRCRgV9enp/OXP/SB2clRqIqHj/e3NzfjOC7L0vsglmcZrHBQ7z0i2bZMr7VWGaM1gzoLHkRokM5Wq5VuawN3WwUAgNG7AK3ri7BRoEV9YeZOIR4MBovFKstCHMd1WZVlIZFWAsiV57lSJoRQ1tUgiT2B995G2juJauEGNwqRiVAURmalNRMhMiASU5Hng8EoOI+I3oVgvI4icm4wHFarVVEUk0kqGcAaMYlM7aG1jZNkOasWnTGKTFWUg8HgwYMH9+/f397etgoLX6dpWpYlsU8SwXSjONaolWbUGgDIUwjBOw/WWqobwa6qKmTKsmSQZkopqxUaLT4xZgYFSitAFdCBVJklH2lTOYeIGqxzDpkFsE+JWCqagHDvBoQSsNW6EAEZnPfBOQmHDm1wUGeaUkqJbgoA1loJFIiiSMoxKQXGKK1KrdH5KpB7+ODJ/HThyN955tZ4PDo6Orp18/kQgJmcq5eLU0Coqur4+LiqSwAoy5wDhRAEaMU5J27fqqpmp/PVajWbzWaz+fHx8XKZu8Z0p0CBJzZGK6NRgwKdJNF4PN7YmO5ubw+HmVLKVTUgTaYjY0wSR9ZasT9ja32ROQ8hKBuT1K4HACADoiJDP7deMQ2SlJnZh+FooBVSqCnUTM7ViQIOoIuiysu6LipXFJWvjcUvfuX3Xrz7/NAmIQQJJMaOzHJrAeVzWBzMAr6BjOBrF6f2ZLW88+wtsprKOjVpESpQyAoNKqN04NDX2dYZ+WXG4E7PZGKGIFQUFSMjA4sM3Skboip1NJCIEDVzMBLhKMWVG2y35hVn/r8+a4HLro6CMAOLotH4y0jOvI2y559/7plbV+bLlcj+a87zbkgdUe61eaYQ93lex5nWqHm/V9+gt5e+/dLnqQcAonvoAU9j5P3OP60b/W/7D3QDvHi/G2bHudsWGnFbohaFxpVVXtf1eDicTCbT6TRNUwD23lurI2vEy2g0AqBqN66SpHIkRFRtDTCpQshaI6JqC0KIh1LUoxAcIA0Gg9PlSimVJWkURdOpVMcz/bkSabAviPRnoDelUozIYFuYkqUkA/PGxkYSRYpVHMcnJycaELS6d++e1L/Durp58+ZqtXr3vXestRln4qAqa09EkbHWRHVd18EDwGAwsNZXdX10dAQAe3t7cRyfnJx2ABciQTKHxtKDjAhiKCYiIpVl2dtvv/1d3/ltV69efXDv3fl87pyz1k4mk7LMxawtx68LpJKbjS9TwLnoDH9HQqXk1XVdN5HelWs7w8GTkjR6rcVjpNpQedXUYEEikp6InVaM6kVRlGV1dHTUE8Yb8S5NUyYQg3Acx1r7NkOxOaDYMH4OIWhjPDlrLDEhAqMCrYCV4BITkWauqoqCz7Ls2WefPT0tnrmJVqNSynsl8b0iASSCropS2cImUawAUTEiPrz3/gdeeN4MBkQNpjERgW6qiIqiDwASmA3eyeYJwRNrIBZCv6hKpWCQDQDRVZVmq4wGCURgFEReRANEDBw8I6Kv6sRGHBxyEBlRKwDUgEoUMq2MFFRqcCixcTwqQmaWtAIi1kp3UN4gBwypLR3HIiTJgUVkrTEEgv8fZ38Wq1mWnQdia+3hzP94pxgyInKsqmTNlcWhKbIptoYWGm4bbUvuhgH7RYBh2X7wAAs2DBgNw2j4zW8GGmi0LdOAWjBFtEZSIkWyyBIpsUoiWaw5h8jMiMgY7vSPZ9p7r+WHdc65J25ElmgfJCL/+w9n2MMav/UtwDiONURJbBF8EsVGRw8fPN5ut2maIofV5fnbn/sSEFNwGsAYBQyXq4s4ibpK/eCFA/zy8lLK8Lbb/b4qm7rdbDZlWV9eXlZVU9dtCEFrgYCAMpqZhZktikxk9WKxODw8WMwm8/k0yzJt0EdG5qjzQ/SY4o0BAI0mBNAdc1xvdigSQaVReRN6vAIzyz1EUTTJM6sV0FIBRQa0QWvtvnFKKWMiyrLgJs1+t19fFHk+WU5NRczsMCDxixlfxqvX3fuBgkbddYOifDJZ3j5pOFhjNGAIgRRACBoVEA+0HteE+TWNi/Cc6hkJ587lUEqJPyzO8fgmERk4oLJX2QzoiBAQ0dMVKLlbJ4MyGAQlfnpcl5lB6QFFe9WXFhQB1nV5fHz89a9//Vd/7R/Pl9Ptds/PK5vnHhKvKvPU87W2n6Y7xyM1KKqXfu3feZ4XD0navahWr1kGP0GvD88yXHqwg8azOCzrsQmiuoay13tzjn84nBkAbE9AeHBw8OrduycnJ1mWoRIMSwQd167pgFq99kVEBWK7o1WaUbR6RyIfy/ljK9LTt+2smDBz4xsAautmsVhcrDch+NbVN26evPPOO7/9L/5geXzIo4ph733omxOMJwgVo2IgEnyWsf3IkMcovn37RpqmFxcX0yxr27bWqLXe7TZlVcbGglaSZxUVwkB5kb311luXl5d1XXsX6roFZdI0dS60zgOAb6hpGimb6YAtRE+ePMmybDKZNE0zsAOKHuqnlYZMROfFajWZTb/1rW/9D//G/0ADP33yWPK7RFQUmfgiWZaJr9OBz1U1mUyulqjWga+Wh5YSIGtlEieTicxjFHfSXKEWRkNhN5Rt0jTNYHXJqBpjptNp0zTCca1AJGzkXENEk8lkv9/n+STLMjFNnKslmEHUpQAoMAA5R0qxsZaJhB0FyFVVhRnu97uiKIrJxLWtELxIUQoM9r7Wt2/fPjqYpGnaNJXg9lvv8VpRAwMyaFTOufl8vlwupUipLMu8KJRSACwB8+A8Mwt2fb/fiyGILvgg5+xgGT44E8VpmrZ1FUJom0ZIaYyJTGS7oJFSDCGEoPpq6uDZWut9Ky5+H+J2Mt2ifcl5UF0YTCmFcN04NtqwDk3TgOIe5AgSqRp2uuoLkwBAzg895CWKIiTSBgEoipJyXytldrvdrVs3VqvV9773wV/9K/+xjnC9XjME7z0iPHz4UBReXZem7/G13W5Xq9Vms6vbRrTv+fll7drtdlvvawDwRJFBImKEyHRAsCiKsiyZFNnJyfHx4WGWJXme53nqWzdZFFVVycgLGeVgxjEzIZjeGQjEqLQ2WiKz3JeKpGm62WzaxkXaiLcndDfW2kAuz9Omyat6J8S0VdNGVofA3oOOjMZ0Xtz+wud/ir0zbiOnZSIladGxmB15wINJT0RKoVF6X5ZHb92Ns5QQEhtBIPaBIx28j1ATEeuXV7EOEcrxdMMV8iYoodygjkdWAXrqWeqe9zHGbyqlhKrO2lhrTQGI6SrQJXqHNfkQUCWI0LatNZH33phIIO890Gx0T6FU2ojMYmZEDV3XzBBrxcjvfPkL/+Af/GMkRtAMqAyB4MdCEMQHIhORGIwonWlB9gAQkTFXhc/jgPCgfsZKEftc41gvYo9qGyut8Vjz6Lim8IapHU41VpMwUrQv2lA88jykRQkAgvR140ElK5Ejg/FBPbnouNJXPB657ADO6lYDKgQs6ypN0/l8fvPkBJCmRaIVuaqKhUyYIYnToSmn/F+wcyyUMExBrs6eQwAApftH4FoBxBan0+Lw8Pj09DS0bFWstQXmLI4cBURKE/XZz7zy3o8n2zYOVCttnHOtc6pvQxn6Zj4dgIEUsVZa+7YxxihtxVhUWu92m9de/TxzqHa7WPPl+VN7fBRFuqy2++32jAwQt8EvD+ZtVe92uxCcUibWej7JfJK0PjRN07TOOddYxRU0bS1y3DnXNI6uWkup7XZf161wYYYQOJCUAfcshJoRCVgx+9alaUotLYr5D773Xvkf1Z/97Nvn55c/fu/+l770RfK029ba2DRNy3I3BN/EoPG+TbJCpszGiehsmVaR+7Is8yKLYtu2rUUj9opSKoTAQIDctDUgp2lqrEYVC5e16nktBoHets4odM5ZbQBJYNXeU5alZxfn56tLYHV84ySE1jmntGZSCKiVpuC0NiGEQKQZfWBrYmAAAgxBc4g1QKhAzbS2eZF456QSZrvdKo3OOVDq9s2Tv/W3/tbf+3t/L7YRB0LAyATZxc451FaGBYCErTPLsjSKFYOQapXbHcZxU/XuvkalWQqpxVkvy9IqDQBxHPvW7/a7fDqJ08i3dRzHQp9UN941TmsLjK52DhwAFEXBaIMHReCclxizdyG3sd/vTZ4DkWXWAHVVmSRhIsUUKHCLylqggFYBIDCD1UAUmkZby56C81abIfKMiquqHDSxAuTQcQX71iFi3TRKKRW4rapmV7KOvGsA2Ebq8HhWVhtjC2uSNhhHsDy5RQzr7TkBhcBM6r0PPtrsKrmcEEaWZXl+ft40zX5fbTab3bbc7Xbb7b4sS++D5F9kNUdRhMhGoQLIE7tYzJfLeV5keZ5nWTKdTuM4LuLEKRNH8STLpUZLoQJG4E78aq0VA/ugrLLGoEEAiLVRNhJDVnrnKoY8SRV16Q9jlLWaOUTBs2ZI4yjN0mIxx6QOuq7JtS1wA9CU++2iSN+494pqL6py37ZY+YYiAqVa1yhSCWpwjISA1ismRoUILmAIBlVIja+rREdBwWVbvnnnVhwnm7Zkg4EcaIWBlNaOWQNIt4axOhjk7fhP1YHLmJk8swYDyIHaEIJWymhkZq2wrmvqqxWkBQIxMwUbRxQgBDLWKq1tFCljGDXogQhz+BfNbrfL81zwKUZbUXXqOe//Si0N1tyVHlKdunLOJXEaGG7euvFX//Iv/5N/9jvFZNo471yjlFJdcZ7qHhBHrviVwkP1spD1+OrXBu7TvNLha9e+/2nq8ycfL9XNP+E+AUB0qupZBsfGgbwY21wv3uQQ/wFQg3Mp35cIJwBJHJiI7t69Kzk2pVSSRH2z2HaAvL/0Dp+/Wx7UfPBeAHvWgoSgpaFKkidxHM9mi11VhhC0NrPp4vXXX/+Db38gSGClIEmS3W4jyu/KdiECROirYMW4BmKxD51rTg4Pj46OdrvdycEiBPbebzbb/X4v/A9AvFqtlFLWWsUQRdF+vxXyhlhhZDEBAX2Etm2rttGmjaKo3Ffr3VbkOPYdREzfy1b8acSuuuCl0y05PKN00zSo4Ic//OHJ0cGXv/zlj++/v9lsbt26Rb5l5rquxaTlvtKpbVvhFBwP76B6+1Q6iPMq8BzVp4GHJdEhk3unf7ApQepDjBEqTfkoSRLnGuagtZFYtNbWxlHTNG/cu3v67Nx7H2dZ1DjvnHCYyFUuLy/n87kg0q21wNDUtdKqqduiyECI8pVTWru27ngGKDAQB0zTODRVURRvvfXWX/pLv/yN3/ndNE2bpumhKpQkSds1YIXxepByqa7KKzKV7xofOecGpIM8VFNWEChKUhVFu9WqmM3CZlNVeyLPPrQS4QBWSqEC57spVqiF8lqsGbDWuTaKImY0xgiu8JU7d1aXl8Z01NxisYm7BujkDoui4EBVXUqaQCZa+GoGwhwi8qGVgPlgIWlUMoObzUaAV0LIqhScnp4yGKk1EFrvKIoCw7bcC6lkHMdKQdcYOITZbPbeux+cn58LKk0oPsQD9t7v99V2uy33dVVVwsfO3NlwEu1QCqT/x2w+PT4+ns+nRVHM5tMkSfI8jeM4y7LYWgF1ImJdlxJc6Zb00ACxX8yC5BoW9vi1+AxSvT1EAqIoCr5RxkaoJpOJD2xt7L13Tds2NYe22pYnx8e3bhxZxfvtpm1dcMCIjgGJiZFDIKWBQQscOQQCBKWMAgVaSoCstd4FJJodLBcHS7kT5xw7H0IgjTCwVMLzDvXLNj6M3Dxm1h3umEhIXwYV9HyCcpCgKAgMNKbveCZiZ+SbKRxFQIXTwBB5pRRwlxccq1iA57xs6OPj3ad9SF0SV55dnBRf+9pXfuO3fgeQJKanlUIQ5tvuy4iKGa+Qrp1SVMPlxgePHvXF94eNPY4DjIfyxV9hf4x91p88K8M9jJUxvhBXhysP+/q1YMyJ0WtZHnF9DBcae/DDVYY/RYElSSfRTk5Oyu0ui6MbJ0dZliVRbCNttOl0n3eo9Wg0woBqEG3bvYTA3AUGoihq27YpmxA4isxyuRQ4tIAs4jhmhcQddmmxOND4gQI2CgMxEaVJAgII6EJ8XaAGgTReGyViAO9ckiRtW2s2VdsUedp6Ojs7kwBs2dRPzy+stcLsrxiER4yInAuKiUJXVhskJdO6jvpAoygM5xxCV06m+ton6sl7EZGIERkZhQSzX4QwVMq2wRdF/t57H7zzzjuTaX7r1i0ZkNjqKIqsveJukx6FIm3bDuHVEWBBr4S6KDqzCN+yLEWoyfCK+BtW9TCA4win4MuG04bg65okCiqLxHbt+YJwdUmZNxBpg3VTFvl8t9sKVZbUFieJYeqywkTUNE6ysIi43++gDFmWRVF0evpskudKQRQZ37o4nwK5W7duXF6ea/0LURT9/jd+d70u80khzDAyzrprEM4hBK11McmMMYgsNayTaUFETe3FlPTe1/vSOTebzci1VVUmUbzbrAFAKnDaulqtLrIsQwW7zVaUorU2jROxsTqhH0Lb1M4HMW5Es2qtrbXr9Xq9XmuNgp+SGtKLpxdaa+E4Q2WVRgC4uDwX+Z4kSQjBuQb6RJWk3o0x2uB2u5XXwpQisyb1YMLJLEBZALDW7vcVgxIKyckk77Y5aCFwBQBrY2bYbrcIKEbAs2fPNpuN0hDHcZqm3BOOlmVdlmVZlk3t2tZ774dV08sQQMQ0TafT6Y3jw6Ojo8VilqRxmqaR0WmaGquKIku0FakiqHyZNSFlg1FfhLF0VX3Buuo54OR9GRzlryxFAAAslGmaus1zzcwKMMzmHOjpJ48pzaZZOp/kALxZ79qmadvGY2qMapuwWp8XcTrLi7Zqbd+1TykFREABULFiooCBFCrQalvu7735mbwoLttaxYacp+7Gnqcr/hQWcOpLyERQDpcTsTbwvIooJeqCfGHcbYgBERUq70hZZYxxnoQ4oZczVxPU/4pNYpPQujiKFCvnnImiMCJNHY6xPoCRwgC5stiDgbSxAHD79u2f/ul3/uBf/ZvFYravHLCodTWgv8bnH9QVwKeqt2vrAD5FI46/86IOxhcc6Be/9tKzXbvbazf54kCN72H4guqTKNcMmrGuHcyaa1bn+BGkwsRqE0JYzOaXl5eL2eT46Gg+n6dx1HdSIkSlgPm5U/VJC6ShW+WLzyiGvDFGesYBQFEU2+1Wa+0Da60jTKq6CYG1tjdu3Hjt7q1nz54BBYPoGYSZL0tT6o0GfD7zHUJQI7rv+Xw6m0+JSClo23a73+VpYowp63a1KYnohz/84de//nVjjAa0VjtHUj5LVCNpjxQcAIDSV+eXhKUoMyICuoo3DBJE9V0uZAAAAFnC0VezJgopio3i6Mfvvfv48eP54rMAEELY7/fRYua9T9N4wKtjxwMc9U3FUYT1EMPgvkxlmHFRukopae0QQhjozIYRE2LL4LuC4MEaGy4aet6P4YXcfBzHm82maZqucyLgrizzuCCitm2M0YhKWiTFcay0BuY0T5q6Xi6Xgdo4y3a7XZbZJImAiIPbbtez2UQh50XKvkGtlYKTk6PdbvMLv/Dzs9nkT//0T997770BL2aMYSatOhhtnNg8SQGgbduzs7P33n93Op2ayMqYiASsqqral/P5fJipNE2rfVnX9Xq9nk6nLnQcHXVdalQSPJDWj52ujWOllFC5GWMIuKoqaYjZq1L3gx9+X8J+WZbNZpP9fivEq845RoV9ibZROkmS1epCVM4Q7fChHfbpbrcDALGlhr0sU6lRCRBBagrEctqXrbW6Knd1XVurd2VVV609O3NeAYDkwpumASSxHsqyrKqKiLS5Ms4k4tU0TV21fSp2gJKAtdYYlWVZHMfz+fzendt5nqdpvFgs8iwNwaVpGsVGuPMi1Z1Ta51lmXQoGZTNSJ0/Vx4ietoOnZh79sD+hziE6GWFtz5Y4DiOUW0jo5bzKbt2s0KrVfBus9n4tvFtQ544TeoQPnzy9PRse/dwOsmnykbeMTMgk0GllfahDRw0KlasGb1zaZ5VW3d4+0aD5MnHaAFAAwa8ymN2muZTPOCxQrmmFIiDVOKpq0+ukD3chScZABgYUUuDTuyZ7KQS4Vo/eObOxTVVVSVJ4l3QGkRA9D6ZXOpTHcSru+yJ3LTWTOzbJs/Tn/+5n/1X/+rfWKuhVCLvBRYNoMR86PttXymCsS57UbG99NIwUlrwgtobPuJR+HqwhvpLPwfD/glXHL78Uq0/HKFji3wujY/PNY69fubxlA+H7trWSq72imI6jmMOnsgslrPz8/PXXnv1s5/97HQ6HaivSGC3GiNtOiMUriINIwVPiIqIoDdzuW93KF5g8B3iIE3j2WyCRgupVIe8Z5TeL+989Us//tF7Hz96WFWVtSZWqnaeB3rVweVlkBYiKJEZZmbWCk5OjosiM7bjHZPVyIEArdLqBz/83unpKXaOoO+KHJRq29YYIzXJCBrRoUMXuSi2Td9JN4SuvxMxCeWkLGnVw6y4d8tkiBCx705HUmunlApM3nttbAjwwYcfvf35nxJ3XKqAFABiBzeTVvPCysTM0hUnhEDAg5rkkUGp+xYOcifClKT6bvYiiIeVppQqm0oc36H0UESeeCHUk4b2ycJYmL3T9Hi3211cnB0dnYQQZsWkaZpImzjriB5RKWMEJ+GDZ2MVAOjI1tt95I33/vz8dB4cAMSxJaLLi7OiKJQSlJ8VHXZycrLf7//SX/pLxydHb7/99scff/zuu+8+ffrUmlRGOpCT0i9m3u+3SRIxkHj/g0AXcjFxUlerFTIsl8vVajWdTkXzicOnDJIPg7GCHDoJ5r1MZZ5PJPIh26asK7FadruNaNk0zdfrddyDDaMoimIj5kJZls53jwYAWRIbY7bbrSgz6aYgihARJVctNpbWWpIm/UoD8YwRUaEhHwTiRESAhoFcUysNURQFpuAxmy6r2isFRVEIFqeXS1zWVbdmagFgd4rZGCOQAwBgRIEyMAACxbFNkmS5XC6Xy9lstlwukiSxVseRzbKsaarprGDmNI4UDlgh0RaxrApZRWokSAfhhiOWwLHgGmSv9D+l0N1q01OgW6WtwiLN0FNo6iKLXBXtd9u2qp1zbeNC6+M4PmuqZ4+fPDnb3lzmxyc3t1Uba6OBiAEDBUAjhRwcgiJEtKCc4l1dFQezfDGr2sbEEREB9YJuJFoVQxgJ2rHMH2CD3Jcjio+LWoUQOHgAQCV8dv2vRnq6C/UhUGdVq8AURUmHLgxBoD8AIHxFw9CZJ588ef31113bYhRF1gp/kkQI4WXHWGnB4ENgh3NxTUtgtFKvvX7vS1/67J/+2Y9m85lzTthxe/JV1UGjCHsKzU9Nso6v9VK197xSefkNj1+MbJBrofWry7305z/hEtcOHB2fdqprdzU8yOCcDXIWRouDiKyxRF5rfXF2/tWvfuWtN94oiiJN4zSOtEalQQGj6lTsEPcYPXIYW6zQO2p8lWbmjt+NUakBPJlt9juGDtFW17UPGAI3TTMt8lfv3WZwT0/Pt9t9CJymcV13bYmFCRekqY3QvFBAABN3/ern06lzDoOP84yIjE69IyFJeHZ28eP3PpzMVFPuI62453OmviaHmRG0UgEUArSRE3szSGg9MgYAm6ahEMILrCbQeRIKGRG6AmBEHEqBQ9dDULk2xJP44OjgvffeK8tyOplIuLJt2/l83jaVgJ+FGEEpZeNYWLSJCFEhUB+W6KCwcv4OitwfYiwP4Pa6rplZnDYK3DS12AqigHmESbSRIQ6oEJiJgzXGGGOt0cY65/b7rZZWTllSlrvJdBocl2WZYApCH982kjnWxhjLotWYQxzHqMxkMimrHUOIjF1dnocQRK8IESYA2CR2zt28eXOz2Wx3m1deeWU+W9y+ffvk5OTb3/72kyfPZAU45wyqIs20xv1+n2VZWZaX65VaY5JEg39ZVXuNStpIeO+lxdbZ2TPv/WI201pfXl6KQSp6wuqOA3I6nYorrJSq61rYu6QQHCMFLJyaTpKjl5eXq9VqMpkMTYqIfQhhsViEEC4ut3LDAJCnGSJLej5NU2lC1W1SDXVdy7RKGfR+v7fW7rblYFKIUhc57j3JvQnLk40MUNBaE/C+qkmZvJgTQRRFXS9tAESJNwu8AIZsjvcBIDgXesveIqJQX0vyhRGEQnyxWEhp72Ixi6LIWsMcrLWxjULoynaRcACLDJp4CEhcbRYGIR7hK7nBA9Cyt92VhPpDCLWvfXBMXdBOC/mzUvPZxACdPf3k8uysLMv9ZgtIwfkQQpRmddM8Ojt/+mR1MIleuX0Hla7LqmmaaV4Ag0LPLmBgozQABiLQyhBprc/WF599+8sh0o4psUldVZ2V/+eR18/LZBypVRGLHDwRCUwVYERp0CcXhusowGHSQYlGtZ6uzja+SqeAP3j/w5Pjm2maMiGIhvxz+YJI/Xlh5LwaY0AbQDiYz/7iL/3id77zI2uMQMWGO1BKCVZ29MByN9JW+Xq0eTxAg758UVMON/Piv4P+Hqve8TvXVttLL4qfPjLjj664I/qPxioQUXKyz316LTcsK5v7SI4aYbiYkch778uyfO3VuycnJ/P5NIpMnufGCHdvd5IuyTrilGZmIo/ddZn55cYE9sBy733beAAlvsv2wz0CA2qtddO4/a5CbeIoYe+KInvt3t3ZbPbu+/c/efJUu0iZzt0XYhChF9JaKYWBOIoiQWCmWWKtZvIArE0nqb33qKOPP374gx/8SBszzQuRtsydYcFANjJBzH8ErSNlgjCCRVEEah+cHxRVA4DIPQu0V8p0GEcOiKiVJs+A1CtfAtDIABwGDIW2pmkaRL7/0cerzW5yOIM+VBhFUV3uJAgJAHGcYpdjBu99B2tnYGbTd2eS6RBVKk4w9vgJGbE0TYVwQxys3XY/CMRhPXcLBihQiFUkwFcxjORsjWtjhYgo4Lg0TVncpeC0jrXW5W6XZdmuLLXWdd1kWQY8cOQGZlYmAqWySYFASZKg1lmWbbfbk5MTItrtdrJQkyTJJkXTNCaO5tFB43wSd5HeR48effTRR9PpFEB5kqRpx5NMHIQ1AgDSOJFTOedk4mQoQgjb7V4Cxcy831dxHO/3e9k+klvNsiy4RiBXRVFInyuJPWhrJR+vNBjdBQZCCCUiM++3uySK15cr7/1kMkHFdV23deOcazwPtlG1LyWq1DRNkpSyOyRCCz2MzlOoqkprvdvtYhttNhshPhvkRl3XUloiijxJMueaupHWZ8SMTev2uypJptA3Fb5cneve3ITOFkeJ6AzJFLpqQSbBm05WaA1E1HpX1/V+v8/yJE/SOI6TJDFaAXCWxkP02BjDnnXfDVNGXvLx0IO6iQlHXi8NvYlG0lVQaUOobxA4qNBqS0QQqGanFEKg3XZ9eX62366rqmqbSiy5JEuD54ePHj+5bBaL6I17b7Cn9WZjjdlsy8v99mA2n9rIAAZHBgCUKCAG4gABE3P8yq0WiDR6GgzursSGsCPpYwVDNHrQC/J6yGqPxT4ikgAJkTq6QAgUaOwdDQ87Cq0BARu0vezti8VHbmRntThvHjx48Oabb967d4+IXBui2Hjv4QVNM+gApRRT1+omADOwtIIKIShpHUIeEBn9vXt33nj95qMnl0E6VaDwtEqoU1h4grQEH5Tf+LJjFTvop2v/jr8w/Gp48aL2HX9tuOjggrxUIf3/eozVvLweJga6Cb4CWyFeAb+pr6cS5Sf26ZBOG+Zbvj+dTARFaa2dTadN0yjgKDLaamD23hP52HQ9aPsx90QDB/h1cwyujDJR/x2qiJmlPOzGjRunp6frzU5HsdXKOVdtdoBqVmQKdZxEk0n+U5//3MHR4f2PHhCAA4cIWiOzJmKFIPUMKrFpmuZZ6r1XgG3dJFH3rKIwnPNPTx+8//5Hu30pXf+UUnVTCaOeGsDAVocAioAQNAMLgskqHadRFKmncHp20ba11qrvdiDAJQ+gr+YIe+ZBgA4SiFKu3w2IlAu3wc2mU+/hgw8+uDn/4tABsG3b2XJR78ue3BE9UVvWeZ4rpYfCpCGkOZhi4vMlSVJVlSwPCSBD35JS8G5pknnvJaUtc4GIxhgpGu5CfE0j1oAkm733VVUlWeq9d76Nomi73e732/l8GUXRxcXFdHIQxzEqqKpKIzZVhV1KPiAapXVV7iSCrU3MjHJaAJAqXjER1ut1URTUQbt1lhW7XUnE8/kyuHa3gzRNX3nlFQlytG0bRUbQaoLmCyGs12tZyWmcSE7BjTpSVFWlABHxyZMn0n+iqqr5fL5erwNTURRt08gt1XW735fe+4vVRnpAab3v95QiYPJODAIJUzNjlmXb7d57mk6nu91KaEqH0p2q9ZIwRsTgnaiHEMJm0y0JUbda66qqJIYssLv1ep0kSVmWCjUi7rc7GR/ngtZN1yojTdfbnW8dc4C+7p8YdrvdbH6AgDKVq9WKiNq2TZOoo7FDlIJPUYK9nLlqloqgxspDSFouLi6i2BwulmVZRsZGaaI1TqeFsGeLUat6AL88pkAQJDsAvSmPfU53sM773BYMJoLqkVljFKHW2hOJ+wtMVuN6tXn44f3Tp4+RIbhWIkla66Z2T548eXza3H6luHvzDjeudd4Ys6srr6FqXXN5hsuDiTbIhBw0IwtHjFJ1qA5OjtLFtFaMUZcFUBolQQXSGwag08HXMcXPqbZuRkJnZzAzQxDQDAABiBUiKVQ91sHyWikVJbFkg7XWAMpRYMIhAYyjSgf2wXtvLi5W3/3u94+Pb+R5aowJnmXPDEiZsWqBK8aoDi2GvY2PiFpp70JsbeMDsjpYzH/hF37+//H/+vuHh8uyLJnQjxzNPtoJwxkAAFENUdPxML3os44/ffFrPHJbn79tHIa7v4cO1DCcvDctr0w8eN4CGC43eK7DpcffH/YDjOqRxu7sOLwsJpWE0eSbWZbJ1wbfiIik0iNL0zSN8zyXEgJ5NGutMZqZfPDApJQOwOB9CGG4ERQIfBed7ueiBwYPljUiMhMzMHdSnpkmWapv3KDwZLPbF0Wxutyt223btrFRxhildJ7nqnXHx8cmjnbbsq4bpVRVVRx8iIJvXWQUAEQ2skohcZqmzjVZMsnzPIksB9qtNwBQltWjR4+ddGNUZjqbaKOUNAQjBmTyAXUnFKQqQGsNEIgYgNI4wVmhFMRxfHZ+uV6vd2VXFiyCXiFoq0MAiVhiX0EhlnsIQWGXYBsnaH0It2+ffPOb//ILb91LsjTVMfeFKyaOqrqOQZmu+jliZqV0Le0fNEpNSBzHorMRUUS2JAXlxmTGB8RWVVUhBKOt2CUiLgenfAhzDcaZGAoi/qqmNpFVSkmdiXNut9u1rZ/P51qpKE03qwtRn1FknIMkjrXRwMjkURmJrSmlnGuVUpPJDBGF2b+u29u3b5+dnS2Xh8vl8uLiwkbJdnsujCJMeHl5Gdk4juPpdJplWVEU4uhLkLMPxvr1ei0WjDGmtomEKySG32F2esprY8xuX0ojI0C13e4C+e12KyGNvHWr1QqZBIQcQiDA7WY7+I5KKY2w3++JKE1ziRPIye/fv//222/XdSu559PT8/l8GkLYVY0IQGYWAk4pLSOiJEnE9W/bNkkS+aHMpszsbrvvN6wWk4K7aAdJP6jtvgwhGKOAutqnIp9s9+Urd5uqqsS4kfk6Pj5k8oixGFWDaBK9CyB7Mwz2HELXP9RyR/zZtSBkrxhu3bphrU2zxNqortooNqFvR41aK2uqqppMJmVJ1AMIxHrwrZOtIfNIRKoHW6m+mk519eitfG1wowUnEWnj2bdMsbHB83s//uEPv/+DNIvbuq7LMrIJATPCxcXFg0+2t27mN27caMqdZsXeO1SkwAFsXB1q2u/3X/+pt+MohsZRAAAVmEnR5eX6a+980TMFhMQYH9rOx1Ad5JWZhdYicLdlBg0yCHDdV4tQH8G40tDEGlhrDUghdNpBNrJsvUFfDHk9o6OONkduotdKg6+sGBy51tUGUa/X2ydPnrz++utiwkAAHPWxGRQGMwMoIg+IiNT16ezaxzAyEJFG5YPz3ltljFGfefONe6/c3Gw2zIwoZMMvJJj5CiOODNeuOFZp1wyCsTq8dlxTtKP7h6429Hk/ddCv42v9O49hksazRT0O8NrZhiuKhcGjWCIAyJ6RHNXABCSFK7LVq6oaWspPp9M0TReLxXw+l+hlHGeyPZg8UTBaKWUAoA1eA/fl188Ny9V40nXzBQB6mBQMYDxE1MDHJ4cu+NXjp3mRXlxcdIwEwJEx7AMiB98mNkqWUd16DmFa5Lvdbr/d5ZOoKAqtNfkguifJUkTM08Raq5B3611VVZvNVtoTubY1NlJKZUmqUWS38x4QrVKgdNdsDhEFQ6SC8P4QUbBWzyaFVbrj9D+/3Oz23jW654VQSlndMWMgGGs1cZfq01oDCKGYAgARRmIKR0ny6NGjBw8e3L59G5RO006aF0WBoMWxE2UTQuj7TmFZVtwTBSul9Kg5UlmWw7AP4WhZG1prIY8U50yAvmIfoJTM9jR41nYNoUWHCRWUqLrW+7IssywzxlgbS0k384aZlbXWt0TUkXaJ2YcaFPUcYaQ0WBuBVk8fPZpOp4cHxx999NHqcrOYHyBiVTbz2RIIKYB3NJsubJIQURKpYfUuFovT01PVA85FvDjnBJstTnCeFeNhgR5vaK11TSuvRd5JXrxqOirv/X6/Xq+7hktKKaWqphXUG/SOe5Ikke1MisZ5Y0zbCKAsbp3/5PGTLMuqer2vSq3146fPmLlpg7VWXCZBe8mAW2ul9x8A1HVHKyZkT7IOpXxL4tWtd0opifrL1i7L0vfwmrJsNIKEKChwWTdN46qqkXkXW7xt27jIrLVN1YwFyyD0rtn9UmGutabGiRZ0TVvX9b7c1vvy/Py8rmsEnkwmWmOaRJPJxBjlfSOFuTqy+7qyxshNSvp8CDuLdyuKNvRNp3u0aeciC9J7SB5L6AUAmIiCz5O0xeobf/B73/ve9xjC6vxCdorW2lFblg0R3bo1OTk5obaKENu2ZSIP2jFt9jtSaufotc/e/p/8T//mH/7W7373239apBk5YuCKWpslk4OFjmxsu+0TvDc48n+4y7qx+tQ0oljhnavDnQdMREAtc9Baa4MAmgOFl+U3xwJWioCNiUCroejp+nX7ElwjsMMHDx7dunXLmAwUBrFukAE6B/fqQZ7TiKPTcRfVNMZ4CkophsCeb9268YUvfv7Xf/23FvPJtqwQtESTsiwRKq8RoOzlSnesva4N2U/QvvAyhT2sYzWC1H/aSV481Vh1Yd8nkl+oJO7v/8rCGq7Vb5vn8vACUpcyvr5fXgghSPm/Uqqu67ZtsyzTWtd1vVgssiw5PDxcLhfKGOfcZJKLL+Vcw+St1UpL3IyZkV8YKO5Qb/0GZmZCYNVn5bk3FLq2DQCskINvo9g45yZF9o+/9e0vf+Wr0yLzBNvtvphOQghWmzSK97g1WbLf7xUFYkKmxOjpyVGe5xpVlmWxjTrjw+iqqnwbdpttcM36cgUA3vsk0bGxIrnyLCuKLIqeaxmCiBw63l2llELFChGRY0YFgRg9Bh+MxSJN/HzKzHEcPzs7557GVjSl1abxTUBSqBGA6AoBh6C11oJzEd2/37vFcnZ8fPz973//nXfekdSpNFiV0OKQ5OOOUbkLgYiRJNpRxl9MY3GITS/yRMcMC6BpmuBJ9jH0FFqSfdSmM+GFunssCrEH24sEl7Pleb7ZbELgNE2rqsqyedPWvmmiOC73e1G3dV3m+aSpm1AG7nxEnqZT750BlaVFmuSg9Wwx11rbOFLKuECMCpSeLZb7/X6zOZ9MJjZOAP1isbi4uCiK4uDg4OnTp4JBEzJe51sffNP6pmkEtbS63EomRUaJR4ElwboPauByvYnj2LlGIGBSOBvHcSAvRhUABN9FlTwTEVVto/q4zqDsBeNWlqVQmW42GwCQ6uGqqghGWczQ+TfDNtd9sVldt8OmlsP17Qi7sjGmELpcg0zoEGJhDsyBAxmr67q+vFxJoZTchswddUXeXWEujiJ/Y/E4qOHOcgsEV8A+klXXNM1qtdpsNp988smNk+ODg4Plcl7VbZ4lURQZ42TJEZGOLWHXt2q/39d1DQjUU3N3eRYghbpvDUNKKYLgia21eZp576WvRXA+S1LnXFNWkTYK+Zu/9/t/8PvfzLPEN21kZJVC3ZQESmudTycTpXwIVDWIunUO0BBwAK6dT4o0bLb//n/wy/fefOvXfvXvJ/MJBfShBUIGnh0d5POpJw9syHkFqPpYERIPKoZUl/69Jq5fFPtjHUREqFjp7nUIHbhkWBX9l7tlwITKKmutjiziwIF4deZOa/Q63vhAddM+ePjwzt27r7/xamT0kI7uBfSVPlJKCe84ABBKTDx4AA2oRg3trdKBQuBgTfyVr3zpn/2z3wKANE1329JqZYzy3g81l1f/9uHoa8eLHup4pF70NeF5q+TacS0jO+jC8aD/hOOll+BRxHv4dByCHhT2i8/Vtu2AktXaSuGgtDaSEG5d15KYCSHcvHnTWKWwox02PSOSUmq735Nvh0Sp8wGBjTFAAeAq5DBYIYhIJCp6MNIUE0nx/QtTDwBQ7UsbmVmR/yf/yX9vvd4+evgwzYo8L4wyGrX4Ugpwv9+3+32cduAaYxZ5ntd1e3F27ltnddcGmECVZQmBGIJR2nuvACJjvPc20kWaKK10X91Lz/VRB7H2+9lkZGZkY5TWkfek0IWgjEdtwFodR6ZtrSQImVGKZySrqrVmRqFmMzpSfYPFyEaDMFVKWZtI/5mbN29+50/+bf3XmzRN27Y1JtJaCfRJwhVdJH/EJywXwh7GIs8iqkWQz9BHrmQqpRRH/hygW5PJDPqcxbCuBB8gHHPykacglUgAoIzZlaV4mWVZ5rmGQLGxSZ4bq8/Ozg4OFkRewE1N00ZRIqpduLJ3u+1sPldogcFEsScG8pPJjJmVMs45yXcK6hV6quE8zzfroJRaXCyEAkJrjQwKcfBuFaALrBBa77wLxkQD2xeMfAvVVxIP/rEoiRBC23prNTNLRll8F+8bRJTviEEmrQwNdIMWQqirRkLB1GM+Li8vpUBIZme/32trhnwnkMAputzQYL1FUTSwcxtjiIJSajCVBtAcEaV5JmeQCK1MK0mzKeI4iZrWbXZ7sd4YOIoi55vVapWmKY+KyK/JGXhBxA3f8VIzK9/hLj0pavj8/PzBgwc3b5wcHx8XRXFwsDg+Pr5cr05OTtq6mU6ncm+OglV6Op1eXJ4Lk5dvnSRB6rrWGrW+EmViUsj953kuo8QDrRiA0ahRfeMb3/j9b/zOfDZpyiqObXCehFUXUaEi6prMV3Vpibz3BIoQW6I2gNJWauu/9jM/+/HTx//8G3/43/nLv1xtS95VQLzdbF69+aqOo4ABAckHCkEpxR16DBSxYiAECfZdLwPtj/H2HHYZIlIgYUrmEFzbyhANDy4HAHQtEfpTobGijIlImiANukYsOw4dpZpxLjDT+fnFe++9d3xyuFzOZR2LF/XyicfO5R0XmIpH7H2rlBHHSWvDEG7eOP7lX/6Ff/EvvjlbTIpJti+7ggoZBgJWI8Aw8HNNEWBkjFxTtD9BH4+92/Gnvcepxud8qRd77ZH5z+Fqv6hcx6caGUrPueb8PFGl1lqplDlIf9nLy0thBRKb6+joyBhzfnH6xhtvyK9OT09v3zyJ4/j09JSZkihKUykF7h19UMyeO5d3wG5c2c1EQT0P16cXKtURUSJJiOCaNo6S1+7duX//o+9//3t3796z8ULq/Zu6JueX01lqo8P5vKrqIbFX7vZnZ2fbzb5pGoXBWmvjJIqi0DrvPYVgEGIbB99yoEBO2ziOY2VNliax1VYjcgeP4lGl1vCCiJDZKq2UqrhC7JYuEbWtl/K6PE3qum4ad9XxlyiOY3FTurWguh0oclzoi5kZUcVxfHFxcXx8XDWw3++ZIEkShaptvYCWB+YvpZQyWmGHdh7IIiRKjF223kgGcXBeRXA3TfPs2bOiKESj53kujxBCUMryiCUtBKd1NKyfYQELvibLsrpt0zQtikLSycvZ3GSZ3m4/ePfd1996I89zmyQhuKauFeJ8Pm/bNsknxkSghOfBta6NbCTc3XJvEmiRQ5w2idPkeS7OdwhhOp1uNps8z0Uf53neVLXqmUbkDINeYebY0mC4d88ysvuca0IIMlDWWuHxbtumqoJYn0mSADJxYKAQCEEZY8T9RQRjVKSM914CSESklVksFlI25pybTCYCOxfe6el0SkiDZlWsxJ3FHsIpjrJkhWT5+Y762IiZ5XuWSvnJbDYr68r3nbKY2TOU210UGUD2rfOBrI0l0AV97PfRo0dZlrm2HnbrIPHGyvhFidRLKsWMITCHLoNLhEgBsV2tNnVdn56dz6aTg4ODs7OLONG73U4p1XqXJbGMkjEmixMJ0iAiY9fw2HsvGVK8cre0zJqE2SWvLCMg6LZJHP3mb/7mv/jN35xPZ66ulFJtV9msJNDUevI+BGairlWJ9w3aZF81+8a3xEqZ3XpnFRweHn7z93738PYBJPbydF/vdpO84DQ6vHWDhDUzEPsARKCAmAOSJoXMwF0Xh4CgP2XocFR+onoR7b0HCYz1OZTQV/OPpKgkE/uKFaWVjQZlLCthjPAlImYhjyZmNkmWKaWcbz55+uT0/Hw+nw5hk061KMCep5Alig4A0Dnd3WV600/ieOJaGVQu+Ol0+uUvfeF3fuebsbEX641SGMdZ1dTjapxr2m4k918SQx6v7/E7429+moZ+6ZLtnqtXzNfuarz6X7wTeF4Bj+2Ga482OMGDoJFPrY1CX6UqwR/mILuxLMvJZCKb8+joKM/z+/fvLw/mbdumefqDH/zgs597SxJ7u/0mTzOReoGASRgZ0VMYLTgEgFFnB3lTnvoKlXDl9nLXEhWIEaFtGqXARJY4NHVZFNnd26988uCBx1UaxdNZcXJyYhRuN2tyXqAZsgnLsqqrdlvumZGJwHAIgao6OC/Kz6DSWvoWK6KQxokyurZa267cNk1TCf2xRqWBObBwziAOUQbqYIpgNTIiWx2CCXGcBfYuIGjnwqyYnPt127a26+Ie4jgmBkZC1MBd7oqIgn+OoUzitBJhvnfn+OOPP3799de11oRe966PxJNF+yIicRAuAqU7yioYmWLYH9RnRkGiqSGIvRVCYALB2aZpOqTWZI50r9hFmQ1qAFRnAZgoSpRarzfSImk5W67X68y5xXKpowakw1JZlvt9J6CDE/wtKFXXFTOnaSZLom7bPM9RKWoaNAalAVSSSNBYHDtRvfJcbevLsjYmKorpYrE4Pjy6uLiQFhFVVc1mMyJKXKq1lqGe5IVMroySvB4Elkh2gTFHUVTX9Xy5ED5no7SNzGKxICKp0BXTAQC8C6y68fFtFTr2sY5FLssyhVoptdlsZrNZx01t+jEEoL7rNvQt56DPTBdd+6ZuSbRt29aNRB2U6ehCAWCALBVF0TRN1dTDdCsTV5OtUkoi5whKKaNMIrW/MkqPHz++d+9OCEGpaCzlxiJlMLn6ncsgdfZ41Uo1EDMTgAII3ntQyhiuqsa5UFXVdrdfb7azWXp2dnZ4eHh2dnZyclRkuUTX2rRN4lgiKIi4r0opu+g6DoifSqSU6eqJ+52iepRTlmXe+z/45jd/65//ZpJY39ZlWcY2EkZYRPQUpEc1dHU0LWrTEHk0jnhV1ruq1drGcbzdbL/+zhefPTv93d//vTc++0aNVIWmBq99u7hxND1ceGRxf9ETIkjsWvouKJZqLyAGYnhRLQwS+1qwQTx+q4IaM2D1knw08j3cT5ZxFEtoCkELmfaQUrnSFzRkNNBMJhNm3u39fr9/+PDhK3dupWkMV9OJ/Hwokjl0hAwvC8NqrQFQaw1MITggAE2vvvrqV7/6he997wfWGkAtsTXJOF4/Q0+2AvByz/LTjpcqPHiZ/n4xFPzSq4wH99oGeKndcO23L73rfjM/93QSjRQbv6fmBwF3HB8fK6WEgc9a+/Tp0yRJZrMZAHzrW9/63Gc+myTJerfdb9aTaT6bTAE6QxVAAAU6BNf3WR/s6I78ZFADANzHqFVH08HCYjkssg6FL3iN4Mn5MMmL/+w//Rubzea3fudPvvOd79z/4INXX7372r07l+fnSinvGmnvUddt65y1URrFTeMAlekI0SRKjwZVgEA+RFa3bRuCJ2sV6DxPy7qp9lvhIep/8hw0fbCchiXOzFpjR/+hlNIGQXvHjngymQCjdJLA3kTz3iNa6qgVpCIZjDFaYQjB+9aYyFpL5Im9pMqOj4/v3//oZ3+2SpLERkYaKojmFhTVcGMM5L3nnq4IRmEo6A1q0TQD9Ab6CmNEZALnXFU1cdytusFyMsZIW+hhEUoomKBjwJdrVVV1cXFBREVabDYbpVQxmyVJIjXBRnXwe0R0zpk4JedEB4rvUkwLubftdjuZTLI8h15OVU0D0NkHWiMzS1/3ui6lAnwymdy+fbuua6O0MHsT0eXl5Z07d6R2VqqnnHOKu+AB97MwjjkL9GzYaNT3gKrqUoKB0i9EvHzxROXkiAhKO+c0dgFkCcbk+UQaBgBAksZKqbzIJIgqU5kkkaTPETG4jl1LVLKETLBzc5Ugsck7udtuOhDEWpJIHhHNZpP9fu+p8/7rlos08cGFnoAzBG4cPzu9QMAoigTOHfqyiLFBP/w7HhPZoUN7Ourb5BljADuZw8xKgYgXVBxp471vWw8ATbMNIfSVVJeLxSJLupDA8mBR70sZcKnGjqIIKIwFo/ftoO8FSCioCBmT7373u//tr/3qfD6v63pfVVmSir3IzB0GCBUieudDF4Z0TVAt4Gpb7lrvWAFrDsoAPHn05J/8k3+CSpHGfVuuyp1VelPt3rz5OkZGRxYCIUFAAgi1IzKKGFQvZvsGRy9JF14T+GN9TETKXIXZwojMrks29WUsagSn7byFPhpxzVfkPnYtPzcAIEVjAPzo0aP9fh9FhoH1OMMPahgs7roLIoy8Q0m7cFcjFbSV/g/BWtsyHx0d/fRP//S/+TffPb6xuLhcM7NCe5V5HNQYC6z6JePCPc77pVrtJyjpFx1r7oNdMCpD+jSPeTxwn+ajw2g5DrPY67brce9B7Q06Q+x+idaKXSkcEYvFwhjz9OnTPM+Pj4/Pzs7Ksrx3715d1z/+8Y8++9nPHhwc7Pf77XYbGx3FRtZ9VYHWKDWXffrBD5eGDht2fYhGEXLFTMBXeMvhuYS+v+vyy9C0VRTZxWLx0z/905eXl9XBYru63G22WinvW6t0E2i7X1trjdbBeW2sUVrCL0opIoZA5CloywEAua5b5xpj1GazIeDFwYG1ejKZFEUxnU4lj0V4ZbjgqNHkMIPMjICMCMBKqSSKFUaMCo0FNuW+stYmCQjGEhDbtk2zJLRBR9paXVdOHO44TgCgrusQuGmqKIoQ0Fq7220ns2nbtpK5t2BEcmmtRA0Ma0OcOWbWRg/3TD3Didy5eFFDUFf0sfTBNcYYbeM49p4kwTboJO2VqAEAIPYSiAYA733jWq11kiSNc3mec88jPTmeCL1UaNvdrpEWzoiYZdnQQkBcg912W8zms9lss9sysw9+t9tZa1Ep17ZCPElE6/V6eTDfVbvj7BARHz16dPPmTWHyCr713jd1zcxFUbz++uuLxcI5F8fx48ePX3vttTRNPYXtdlsUhXOu3u8HXTWgsZRApdrOuRTyLAk4n52dJWlsIzOYNWkaJ0lUVZVMSp4UEl7uo6+tFOP2+omsjQcrRwZWKDv6G0ClIM9zra1v3ZCPF26psakXQogig5xyn86QObJxJKqIIex2u8mkQMVN28oybpxUi8AQaAwezi42dV1LVId6OK6Mw9XOff7FsObHokvkCTMLvECcLejqdLtB5sBKEVBodzvnXJopyYlMp9MHD7YCX4+MNcacnk3SKAYAiQdsNpssy5Koa2sh8Z7Q0wyoHu4g2iRJku9973u/8iu/cpDnTdME54UZewBLMwIRueA9hRCcQLHqtvGQEOJmt2dljbXIynufJ8WTJ6cffXA/yqNiOn369Gnj2uniqC2r5cmRZ9LS8cwTMpjYaAgehjgTIHQRM8LrQv7TxD70Tq3WV8a672k1oS8cHcn55yC9zB0sGfG5LtEAIExKzrkQHACY+XJ2dnoaxxH5drNa/dm//ZNf+ou/mMSxcy11vUS8jSIiUFbyvBqkOoUBgIBBIaJGZiZg1EZrYAJm0oAhuARbaNovf/71n/7629/+0x/kxXSzKy0RgFLAChhQ2NkUK2LUml9SgDs8J49sYRi5BWOdByPOv2HJjj/lT4UuXynjkXUJ4zsZ6+/hPGNN3F8xDIpYEqgA0IGe0AxBSyJgbqJoJs9kjAohIKrJZIKIjx8/ns/nx8fH2+0WEQ8PDzebzeMnj27fvn3jxo3Wu/OLVRLFFdJkvths95NprrUhF5IkETfQe+8YAMCorhyNgGRlDncbrti/IYSAEAiIIQytrxmQmClwZFMmZuG7QQ6+ZubjV6J3fuaNH//w3Vmhq3Kfx1kbtNOqQeNszMYE5w0YTco4iDHe8d77oJVF1FZ3YpTYMwTPtWvDyc0bf+Wv/JUf/uDHf/Jn3z0+PAKtnODqhW2RSEmLWaIo6sJZgzmlFAYwzKwsaO+ZKYqgABt8HQrVLFPn08ePN+IqEaPW2ijPOvjGpdEEEY2OvKO6WgsBp3ei94kDWWPAEQGvNuvVZnvjxg3nHGq01go3staaCJxzWhlrDBHJthfNSsAu+MCEgBxoCLcqwLqsFAOhY+YojTh4F6h0exvFWus8z50LEjciIgosgT7nuniJiFrvvYznfruz1paBIqWaUhBGUDZlkmeEoNhD8HmatG0bJWlVt03rPVO73U8mk7Yn4UEGo0wIfLg4Qa1XZxd5nqdRhqQm2TSLc8/e6Lhuw3w+ny0OsmKqtH7y+LFVJOgSEceqmFobr1arxWKx31dxnB4f36jrOk8L731sE41KVKCoTFHw3vsoBF1b8ezTJBOVBgA3b7xSVVWUxEQkcePpdLbbbbTW3nccpQYNBEiTPIQQ5xNmRmhEhUMfw5c63TTJJL6dZ0YplWeFBD+applP50Nso6r2XYi+l8uC44vj2KIAXjqBoE0ku1sW56RYeO+NTgYb15ggRWXAKG5oMZ1vS7eryjt379rY7HYbIp/Ett6tgcIArR+E0pDO6KWTAhB7EpUyTNL5EwHAWp2mKQA1dQ19oTARtY2iQETsyTvC6TR9erZd71xs9OVlNZ/PkzRezheb7bM0TSk4IsrzPIpMHMeZtpPJxMRRMZkgorJWaQzMIfjYGtdUmnGZZd/94z/9R//N3zuK0jrsQwiBg0JQyhCwiSwRUaDWUwgBFEraZr3deu+PP3PvwcdPgofIxj4wa3DoPFa1gvPd5nM3P1etQ3Xp8mS2Wq0+89nXF9MJIvqmJvIsHTydB4Csz2IEBV4cdIYsIIFIZg0Anq7kOQtdHxBAYCZiB9jaiHrIRZ+TVUjMiOgpoELul5Po2izLtYkREVg63qBSAziuVVIcFFwIDUDQiojISBgHgMvSl2X55NnTjz/++M6dOwKxNMai6ksbxZJ6mcf5nOYfCXdRQNqY+Xz+uc997lv/9gfGmCLLyrLUqAhIouRwVaz8EpNkfLmxgnyp8TLWsvy8q8oveK5jlflpb7746f/fx2AlDApPKS1wx0EoiDAKIazXa2vt8fGxc+7s7Gw2m0kTUERcLJZZlomY2G22s/lEmrtNZ0XTNGkcib3GEABAwcuCB6wAwp/n6YZBE+9S2ijByLLRiK/de1UFfPdH759uz4ok11pv9zsS03aofkZAoynQEDF+7hLMwftpMQGgd776tePj48v1dveH/0oCetzn4Qaje8BYvji8amSxCQAtijDLsrpx0+k0BNjtyu2+0lpbbZumMWkswlHgS23jhzVclqXu07dxHEuyYCjJlU0RRdZYy8ELR0rbdkT2vu8NIP2bmdm7roGg7FoZFuccUNeJgZwnGpXhKiW/leBqXdeIXfcIOXmfdmVJXgoWTOIo4o6UZamNlZzFbDarquro5k13vgrODaUyQvOZFdNOD+U5M8uGFSWqTMevaZOoKcs4y1B6TzkWsJjYlxLFNcYkkXLOQaCiKACgglLEU57nRVEkSSLO07DaifxQcSfZR2aWZ9lut0Qk3BTyREqpKErW63WWJdvtloEkQq61yvO8aZxgtSZFGscxog4haKMElmz6btBSBibZ2bZt8zyXTXd6epplmUABkv6grs1tRETCjSNrI89zWYrsWmZWXcyPiFECGLLHQwht20W/uzWvsGkaBWiMCcR13RKox09OEVEwHBJHES+WwnWWoWvSbxC2V+vfdN8n8kpFcWyTOObJZKh08k7CbGL/QV21zGsAiKIosZFSUFWVsfr06bMkSWazSQgBOEg3pziOMxt/8uRZPimWy/l0MTfGoII4jhWDzFGaZPfv3/+1X/s1RGQI3hMAKmUUP++xsHKuSZJkX9WIWNZ1Xdd/+2//7T/8wY+++ft/OpsWbRu0tj44UBTHsWvrjiHE8263y9MIgI6OjmjEz4Wj3hIvSrCXSraxTGaQNDAx+aZpQnC27+F95e/Cc2puOBWiMsYaY7g/p3zSz07XR9mHrplKCEH6uBlrrfN+v98DoAAT7t+/f+PGDWszZgrBB2IA8Cz6l+2ofAheptW4w9x2HCDMVFUVmOidd975nd//g2enF86DUgqF46EfHEQE1OOhuqYRXxzNsTJ+8SfXfNNhEQ/vjNXSp2l9fpm18dLv/+Tj2mRzF+IwYrAPrUxN37q5LMsoiu7cuSPVLwcHB8JO55y7deuW6J66ruu6ttrUdb1areaTom188O0kz5gZGChgXzIGBNgng68P6TVTZrzPBeo+1sEveXwOsdXLxSzPkktUgFzXZRcxluJCQM+sELVBR2CUllwGMHKnMhkZtTWJjap6n8ZJU7Uh8OlF9VmFQ4kIPg9nuBa0GI1qR2QhORoiQqWzDJvWNy7EcbxcLgNfVlUdxV2Ji+i/xrWTeOaw678rAjdJkrraQ0+2J7pESlnk6TuYaPCSWRQqDAQ15Fa1UQPMigOB6uxlqYKFnoSWiNIozrJss98455wLIu4l0di2XmvNHARtK0oF0RKR1kaS03Kq4VdEVBSFjWJmrqrqxs2bH3/0oC3LIewcx3HjWkQ8Oj7e7kpR23GSNHUjiQbJ4hN5cHR6+nQ2m1hr1xcXs+V8u9kopSZZ3rQ1eS//aa0Xs5lWvq7LkrwxKkmi4FpjEq3RWjObTfI8zbLEWmutFqqmg4MDMVmSJNFdYVVHviGFMUmSrNfrvuCnI4ObzWZ5njvfeu/X6/VkkhPRZKJFZxdFYa0ty9oYk08y7MvDxJjrLadICtIE9SboCqVUXZUdh1FPw8LMURQL5FsWofhDkjM2kHrv0XSYMmnZKSuKu3ZbrbUaqA/dAUam6+LnXeAYC0at9aNHjz739ud96y4vL1XPGgajNn/XdO3437GY8j2LbRrHSRRrpay1WZbleS71V865umqhqxFwAOQdIWIbat+0QyR5RzsbGWH+iuO4bjqemVleOOfcg4cnt07SNI1jm+f5fDqZ5IWrymkxef/99/+b//evtG1ttC7Lcsw0DADAHUSrDd5au6/qKIoI4OlHp/+n//x/d/vOKzfPL5Zzq40Ngff7fZzFxkY+VERgbay1rvZ7pUAW8Hw+HYsCHoVCr8U4h6OTbEwAoKGjEEBEg4q4L0noE+eRMdjPo5xQAXq+Ak5qrRV2FRndsulvYLjcIF9l8HuYDqDEQvf7fZIkTJP9fu+apq7x2em5wC5CABecBIihr7mUpML4eV6U3XClfbvsSBN4uVz8zM/8zP/nV//RyfHR+fk5GoOosMdzjW4XP+3846v8hBuA53Xt2IS85vDhp3u6196//nSf4oKPvcOX/nx02x3YpKqkQh+Z0ZiIuWMrlHJM4dyReiQJBAkm4smTp1Fkm6ZxTXvr9g0AMMZcXFxMiowApFBSATAh6G4oCEExACgAYrxKBBPCtTt87sXzT3TtQETNgAoneTGZFIAUx5Y4uKYObcuoOASWfrcOjDFsABn7TD8CsJa8v1JplETWKOLN5WZ5eNS0rQdoQwd8GESz7AQ9yo3BaNnIY8ox6EtglkBuYAyBdvsqjsu2dUajh679izIaWxiysyKjBZhjNMpEiKoQFbjdrmVU+42qnXNaW22lOQwRdXaA1h3LLgzaNLTi6ok0V9YOgr6qhPI6I6IoinygAR+Q57kAXgZzJI5TRK6qZlhmovUlNCIu+/LgUCn1/vvvv/7Wm8aY8/Pz2MT7quq6dxgdx/Enjx7Nl4ddXBQ6P0b+NMYg6t16c+PGDfEFo8js1mtxDZu2Fk6M5XIZRRFzUNawc0MqFxHbuhHbX6ZA8sTyc+ec8C/KaAjjo8R1ZSjEjEiSZL/f654qPEkSaalUFIXSnTMqP0HUQ5G3MWY2m9R1WxSFDL7wpVDP6yTuvgymQCClG1IcWZGMggZo29b7VpLuqqd8EhoQ7721No463soudsUDrERYhY0xhnxXcwIAngKi1Vq3rQ822AAEykbm4cNPvvbOTyulBHgs2m4AFasRewG8IBXHJmlkrNY6MlYSt2I4zmYTRKyqrGka50Kbt+xD0zQldo0lEMC5QNRI3kqQB0aha721tqnb9YqZuSiKs7MLoelwIcSJtdYeHywvnj2b5sXhweLJw0/+6T/+h1VVRVpyvcoHZOLeL1QMFEJog29bL6j1xrn33z/9P/wf/5f37t1brVavv3bvF3/xL/zDf/i7BwcH3tvERh5ahQbRGVTMvNlsoiiqm/LVe7ens4nCK1ztYKm8VF5dczOwmyRkRA1ICthLcg4b7wE6JHzomViGH3Ig8tKBDXqcAaNWoNBTMPpavPY5lSQLT3aZMkmWpiaKIgH6+77/1yeffPLDH/1oNp9baymwMRqNxqEJGo96JwC/+HgBrjAdzBwncQgcglNgvvqlL/79X/1HPnT2l1Zdf0ViFuY/jdez5MzPBWEGzfcTlNz405ce43Neu3l+PoT+k0/y0ndenObxffZPcEWbLpJuMNKJ2JgoTWNEfPjw4cHBweXlpQiLKDZa6+VyKWFDAHj06NFsMk2TPM/zy8s1kT9Yzuu60QiIEWNn8nd3RchjHCArHjV7vhoBwudQ0AhMV0MtKIbBzGRmA7yvGw7u+Pjw+4p21S6O7W69IeeNMY4YEEGpwAQU0GjjmEExMnZAaNBoNDIEYhdibc+fPju6eauqagbYlB1HgQh07qEQwz0PVtEwawMa4gpMS4GZ4zieMNZ1M5nU0j+HmbM0DkzAyIQS5hXfBXrOo7quZ9NC0odlWWaLJEkSccJCCEojszLG2DhumoYZuYdWaaOhh/lI03txqUMIQyshiXNExkrik5lD6xrvsO+4HicpdKPk2raVNpRKKaHolozm8FsxGsRAke43RLTZbF599dWPPvqI+7hu69rJZCLsyovjo916LTHhtm13u51oEanTjaJIRimf5k+fPi1mhQJIs6xtmsvVBSLKUFDHKd1orX3TGKOSJGniWErpRMdLKlcYg+V1HMeTyUQC0aL45VmGahxhxpYQMfUlmMYYiVvKn0p34WUJIyVJ3CUpfAcBSdPUKI0MSimrTRLFglSPbSThfWRYrVY3P/MZqYUNIaRJR7uNiIK68r6rMIEe7Cr9IcqyTJIEoQNIy3qLdNR3wiVmRmCtQFnFfeMEjcq7LiBpTFY37XZfiVB+4403tNbvvfeeVI03PmSZ9qEjUBMDdNiwagRClGMA+0RRlCRRHMdJGsVxPJnk4gFz8OW+EsS4UHtOp76uvZgszF3TeEFRKaVaRBdI/Gnx2/ZVHYDD2Xkcx9vtdjmfzRfTttwr5sV8fnl2+uMffr/c7ZFDU4uQB4Ar6Q2gQiBHHAKbyCJi3bb3Pzr/z//P/9s3P/PWbreL08SH8PV3vvrs6eV7736UJtFqc3l4uNyXDRKEwHXZkG+jyLZAr7xyyxiFNFYEL88zvnA813MPGST47L3XGolC0zQAbLV2rpUmjIMAH4Wjh+noIJ8KzeDXDIJ+eK1GRB/MLNCNKI7N0dHR6elpnERZll1cqNXFRV3vHz9+en5+fnx8jChdz3ygIKCcl2OR+6vS0Fu1Jy/dbss4TuM4rup2uZj9R3/tP/hnv/nbi/l8tVoR9C0QAIbhetG4u6bq/p16cXjga98fyeiXeLEvauUX3x+0Dv9ED/ilh+rK456DuYvcFJGNPQmwRCaFzE/CdKg4io00+Iyi6MMPP8zzfLfbnZ+f37n9inz/0aNHN24cK2XKssySuG09AE3tRDRUlykBUF3TeQZEIBQdTAjEDCO2EAAQNczdPX/qRLimLfLUGMMKb948mc2Wt26+8kd/+EdPHz8LgZABFCqlGcGzN4hEoIABmVVARA1KaTaMGtCiYoTtend+drYt98qAjuKiKLqizP6i9HzrqmE6xEfRiAQoeGmtFREFT31LGU6SZD6bEcH55do5n6Zp3TgKrZxKlCX3drQxRiuQtOjx8fHjRw8ZyBiT5ykA1HWtTUd35b0PvYCGHgMoR9043/dRkAxlmqZ1XQsmdjKZkA/OOfGkjTGKgrz23mvT9VAKwTmHgxskBtyAyRR5yl3As6PYFdfn6dOn9+7dM3GUpilie35+fu/evfV6nRTFarWqdjsp7hTwqvh5oo+ZuW27ZUlE8/lcBPdsOtFGLZdLrTVq7du6rmulompfim1xeDTf7/eXl5f7/V5MJWW0UmoymVhr15vVZFowsw9OG6WNWuSL0BO2iJfMPUI76rssD08kBk2WZWK+LKeL+Xz+5MkTIhB+LkkkswUJYAxJXLGrho3cVRmFMJvNVquV6FTRmmLADbwTiFf9gngUZsiyjCFEkSEfZM1IJl5rjYQEGBgChUB+2DVKgRTptqEhBcgIAOQDKiF6g/Pz87/7d//u7/3eN+/evbvdrCJtxgIHnpeNA2ER9JJWls3gYMlDLRaLxXQSW7Ocz8QQFLj4drsVempB/ldV1TSRWPbDdBCB9w1zrfuGMZvNLspSY9T56tmsyKuqaqoqz6LEmNjoRx+dri4uybe9rvKISKELzBJRCK4NwoEFgLjZbh9+svsv/q//+1dffbVpGpRSe6A7t2995ctfePTo8YOHz/76X//r9z/68I++9W0ACK7D5DvfLubT6Wzim1YSE/3kPhfyfKkO5lEbVpZyHgDuSIoYqNtW0jnGOSctfsfC5wrJAcDcN2FUVoxjelkkVebFhzb0NJZKdQBDc+PGjbOzM+eca1rvvYki5vDhhx+++urd5XKZJImHIEE8ZtZGsaPReQHwallL9I+pR0j391q7VgdK4sQTvPPOV3/913/buVbiSyToPdQdRjfAmItyUJbjh8FRuPhFLTh8X41qLn/C2V7898Vpe+mnL9W+nz7rnQEoOYJh20jm1zlHBHFsh9uuayf+hwzR4dFyaOZzdnamlNrtdpvN5uTkRpZlaZp6R23bZllxcXkZWS3UxEqpwMN4oqQ9WEkgGgEkUYxMPU4K+FqFEo+0bq+GiXoyD/mC1qr1jpCm88mXvvLF8/PL26/c/No7X/nTb33n8vzSewpMnhiM0gxEZBig7+0FCIxBgVIMEIiAlY1CCBfnl2dnF86DiSPRvtdso04cvwwTIGMoT4vIWmttggHjXIiiKM+xqqo0jQ8Ws8v1tm0boXIU8Ivuq3LlJE3TTCe5OI5ZEgskVWuM49goHUIA7GJKItOZO1xSWZaoQCKxTVsPbJQS7SiKgrljZtZa+1bK4nm73ZqeSdRaC4BlVQ2hYPHGiEhYheWQiCv0wCL5phhkzjlQKK+ldcFkMvvkk0+kluzB/fvL5VLKQrbbPSgT+oZ9Q7rUuTaNY0bcbNaL5XK33RqjQaneQwKQUmBjOdBmsxl8fTE44jgWCpEkSZxvnW/zIju/OBPnT1pEdFsYaJIWIkm10hKm9t6nWdK0adPWxMEYo01n3Ij3/ODhx9vtVjLHQtUihfJZlq3Xa4SuRY9EjFXfbk/sXWYW6qsoigTeKMOuNCJDZGzbtuI6W2MUdJwegphDRPJBxzZL0hCCVtboCPDK5AohMIckEbKtK7NVbDWhTlNKUQBZPFmcrNdrIvj1X//1X/mVX5lO57vdJjI2KLdYzLRGIgiBhsIK6LxhCTJ2gVBR4cxdb5WORCLSy9l0mIg0jfM8RZ577zebzW63q6pqt6/btt1udV2bssSmUSJnqqrSGgG0uONijyql9lUTyE3zLITQlGUWWXb1jdde22+3q4tLDg4ZpCXMWFRKVrXtmngzA3rX1m3zf/kv/vbbb799sVohYscJ63iz36wuzi/OTk+Olj/37/3MgwcPFEIc27b1iKgxtG198827aRpLDTr3GVJRlNdkxUs9pUFWSN5XDCyNSnSktdboXmD2Xu+LEn6Ao/e9jzSiQtD9F8L46j60QgZHCBpVFEVRlDCjeuXO7bzIpIhNcjA2TgHg3XffX6/XnruqlcEWvpLLowNAyDavVlsn04nECpalZjUeHiz+4i/9zH5bRpGVrThWq3TNCRsN1nDAC6r0xW9eS5mMFTa8zDwZT9jw5/jprl1l/IzjFy+edjiEwu3ad2BEHivWsYRYRfrLfp7P51mWib0iom273TJ37OdKaQB47733kjyTUsu6bpnZu4CIbdsSozQJEueb6Srpe3WrhF0/BsKXxjj4akCQGUkKzbo3wHunjPbkloeL88uzJ8+eHB4entw4SpMkMtaiZuc1gVYKKCCNUVTCHB96gAxaaxXqpmkuLi4AII5tud01ZYV0RbgxngJ8mWveLYK+xmAAwQ7FS9PJZLFYTPI0jWPZe3H/QnDmoj+SNGrbdrPZ3L59S/xCRLTamF5JDEakfH+320m5rWgd4rBarQSxLN3RVV/SIHgfSdbuq5L7NnyiM8qylKSvhGrl0m6Ui1I9/WToyR2HKJlQuMj4iP+32+3e/MxnyrIUNN/FxYW1Vhrdi0iN49g1jVxCRO16veYQiiyTi06nUyYyxqxWK1eXcWzbtq6qymojnC4qig4OF0ka+dB672NjZ7PZdDqVm0+zZLFYKKMXi4VYIeKqDtECeXZx2eWGhabD9JwGWZZJnjhJEkEvCxOk4HIFLSHxIdk4om/kTYmvyr/Ula4p2XQS5e74QDQy0BAeF19WqY5XQUL6um8NKT6rWGZDeFzMOGEmEWfomtTiHgM8wM24Bzd89NFHiBBCuHHjhlKQpmkgx8zDHI1l1zB0g6wbw7yNMcw9esuHYT0419R1DcRZniyWs1u3b9x79c6t2zfu3b1z6+aN5WK+XMwPD5aHh8ujo4Plcr5czqWATTqOdBhypTwJfwisVitr7bs/+uFyvjCoHj96iMRATHRF3snMoDAwOefa4OVPRiTg9++f/6/+N//rtz//+aenp6hVYEJk5xqlVGKjzXbVtPxX/8O/bBDfe++9yFgg3O32RilZsSc3jq3VSj+35cdibfzn+HUAJnxOhovSIe9E/LJ3kemyV4gI6qpWk0eRNgBQSkvk2RgD2gRgz8QI8t9zt4RdRxDoUwlXYao4jj/3uc957xeLxcHBwWw2Y2ZCeHZ+9uP3P6C+pGFQDGpUit4ZsH30Y7zaZAVYa02cAIAxpq0rBJrk2Ttf+TITxNaGEJTWopnatkUG4DCEjoeTD/gFWayy3AebYDz6wxgN7u9YH48nY6xf5TvD2Yb5G4/1tTm+puDHFxriFeNPB+sYRwGl4ULDBMsDDqPHzJJmExI+yTmdPTtFBvJBzCit9bNnp0SklT07Ozs/v8zzvK7rqm2Mttyn/X2QzS9kx3JpBABQOOSr5LqEEAC5M0evXE/9HL0LAAAjoFYB2CYxAbXegwZW/L0ffDeKzOHhYZIkRimtdRLFGhV5pzuub2RmDcg+eOe8923wsj+dD9qaJ8+eSi5wUhRCysPMEAhC1xu1u1XJpsAVtdt4VIfIoTFGASKyNQqRJQg8n0+LoogiIyCdMbGA8M4rpabFhJlPT5+1db1cztM0tlotl8uiKIR3Xvf9y8qyFPyUvGOMqarKe5+kcZ5mg2gW4LrcngCFJJ1ZVZXAkeQMzLzflxcXF2JmyYIZoLldFpy7BS/VUyLTRQoPpNO678+6327n87kEurMs2+120+kUujqfzvwvyxIRJ9Ppk08+aaoKtUalmEMU22enT54+e6w0tHUpxoE89fn5ORHty21TbmUAiSjSV7ZCnueCb8rzfLlcRkmstRbTKoqMpIZFwgghsyRT8zwXD0zOkGXZYrGQTEQURdLgIUnjg4ODwZkWfPJyuZxMJrKetVHSjVgGpHM+iJxzci2RsFEUFZPc+VY+or51kmhi8a1lbGUnyp8Sc5aFlKapqASZ+i6uaEzbtjLISZIwo9bWmAhAyaTIZo/juCiKsql/53e+eePGiawE4ZySMxhjImMVoFFaS9kpMRAbpSNjNSp5f+iWbVSHVRweeb1en56e7na7/X7vmrZ1dQghTdPJZDKbzW7fvn3j5uGrr73yuc995rXX7t29+8ort2/MZ9Pjk6PpbDKbTSR/rBS0ba0UFFlijdKA5X6rAO+///HPfP3ri/ns/fd+rAGDb3nkTYkeAqCmqaR/r6dQNU3j2sdPTv9n//P/cWD+8XvvVk1du7bxrg0etAqBZdiJ4M6dm5eXl+cX59K+ekCeHx4tDw4OYmOHortBNOELjtk1twc7Ru7nGGGhr67E/sUgt+u6Dtw1BRlc4c7oUR1tqokTYwywEshrd87eAGMIRDSQhxMRaGVMhKiVMgaADg4W8+nk/Pxc6i6MMdLB9/3333/11bs3b960JgrkJSVjQI8fDAGVQnF/e6n83POrPvmstVaAWqvbr9z60pfe+s6fvZtPsrJqxI7APrQyILzHylXeH2vEQTePr4UvUJa/eLz40YszN9bEf56TvPS3z38fAV5CgYY9jBZ6+2j8WiSytXa/q7z3RVFsNpvLy8uOOj9JhEZU9MdsNnv08GMJgTaNI/Ii4j0QAFlrAwGRl6BNUAqCwAUpjHivBuOAELjLJPTYgd5YAQClDLPrhk7pxreeiZCY4K233vqv/sv/+u23furuq68++PBRua8N6rquA3Icm6Zt0caIyCGwAqUk40k+kLJIWgNB27jVamWsUgi655EZDg3oej7RsVGlADvxpK6GVwNKoTmqHvRvCLlhwCLPiyK7uFgFCuKhyvlV3+4Ggeq6zrLUKPXw4cdf/fJXis/lgmYSTgal++BSH3odkLpiNAj2VbKzkgeVHwogSyDWAvvyFKQ0paqqELpuwcYYWWTiKIuhEHr+QsFJKaXEbxtwuYNrJTsly7KHDx8e3TiZzWYSaxWltdls0jQt66ppmsvLy5OTk8Plcr1eA5G4pxdnz5aHh4jYlKVBtVgsgg+ixWfLZdy25IPJ9Ga7ElTgsLZ133QPewCa0ijRzsVioa0ZUpVa68lkQrSFPgYoFonEe2SUJDA+EDTGcexciwrSNEVEAXuLoS+eMTNPJhOx3kIIcRIxdRB0sWCGS3f5aY0arWCq79279/TpU9866FprsDjZuucg6xLJquNvH07LcOUPDEm+JEnKslbKtK3P87xuXJBm6FoFckqrEJiZojg5O72QK9K+GiwAo3VAXi6XdbUbBNRgo4voE/CabHN11RAexeqmbiqhLMvNZpOnHfJcggFWK6sVK1RFFkKIIpPliYQW6qr13jOHNuvwWdNpIcbE6elpUjf73Sax5sP3n/3yL37l5snxg4/uh+CAJQzTbUxPwjLEjWtZaVTYtq0nUsbuy/Lnf/EvBKaL1brxLoqiPE9lqJVSs3zuAi0Ws1demd2+ffuDDz5Okqgsa0RtdOS9RwV3774ipluSJHV1BckcAF8wAr1eE9Sqlw9qCJ6RSERFvh1S/uKm+L61BnCXUR00jqhSVH18ArTWDEoam8ogXNEqh76KSV4ncaatbb0HAMOBYhu99tprFxcXYsV33OJKXVxcfPjhx/P5vCgKCWMqpRRc0ZPKlXgEvHrxUKgBIYSgATl4Yj5cLr725S/92XfejSJbN25gMZWdr/WVlhpueqwbdEc78pJS9JHC+1QsHI5SAmMJPpz/pT8Zv3hxXl/88vMnuR7UfVHf9y71ONOsETtoq8hNjvHyYi1Fn2VZCsGCFGZEUfTkyZNnz86Ojo6a1u93m9lsttnv0ig2ehS5VayUFUouYlIEwsg9UF4xM7DcbYBRWoG571149WhKvgMKtTYAxMHb2B4cLX/u537m7/zK3/lbf/N/8errrz1+/JSA4zQryx37oKQXvQZmkDIs2T1KqRbAKqU06tg+Ozs9euXWyVGWWCWRXkm3iIcRQqAXqEmHqbm2FBWDABaN0gEhMlYpFVpvrZ5OJrvtlr3WSjV1LZEehUghiGdrrTFKxbF1TXtxefb666+fnT8TYIRMk6xYa+1kMpHqiCFNINAq6jGTsdYMXbt1USqCCpb08GazmRYTIWCRsK2YHW3vk8luH04OUpLfITOAemb8wQ0FgP1+j4iD15hNp21ZE9F+vwcAa2JRQsaY2WwmZc1SCDSZTEJwp6enAj9Oi8JWVV3XRVE8evRosVi4ugZiH1pEFNfz7Ozs1TfeyJnFzJfHFCACAAw5LBb0k1ZxbCU+HMexMZVMqzyySOG6rgUjLbcU+poc6PscyNMNOHBR3qKfQgjL5XK1Wg3EKcPaGIaRuqLnrrXRycmJgLaKothttlL9LOteWiQJOkk2I/UYwN73YoSuc6LcHiIiagBkDkVRNK1nQCljdc4xi/3KiEAMu135ne98x+gOp8Pdwu64nY3VxpnBouI+ATE8juT4qesPLfEzNYCBHQVscbfbaaPm01kc10SR1lWSJDpLRX9boCFnLzmItvVt206mOQW4WF1WVRUCI+Ljx48ZKIs0xvHDBw//4r//lVdu3vjk0UMFrICIrspbCZAIHAUi8iSgNmJUAbiqqp/6whems/mzs3O73rjg0zTN80wGUGu9SqosT4rp7Bd+8ecBebPflFV7eHh0dnZmjDFRVNhCcg1aYdt0EPFBIL9UMvPYxepfYG/Yd7n0oeWzueJgDu5Kuw97WfWdRplZK2VMhKABAPv+gZKPQJRef1f4OOxJ8UTDhiCzqYHY37l7+/j4WPIiEhYryzK07sP3P9iu1q5uDCpyfmhdNzxPAOY+u9nHNq/009Wi4d6GcC0CvXrv7t17x9WIBlaeaqx9eXTQp9Shf9rxYoj4mrAefzpc7toXXvrbF0/1k38yBt3By7R7vzgYsfNaxpFqItputxLTW6/Xzrk4Ti8v13GcnpzclEZ1Av/55JNPQKmqaqTxHwBsNjtWWgpm6rr13iNoyd3KSvJM48y0ZHa7+wSNoJ+/0ytUeb8GtFKGCbWywAjE5W5PPnztp79+ufK//lv/3DEtjo4n06nERn3rImtr117tFqIQAoJmbVQUB2MmBwe1awlgt14vJpN7t2/pvgJY9aQZURQZvH4z49UyHtju1rua4KA0WKt9cE3TCNe0aHellDVGd24Eaq2Pjg6zJGma6vjw6M7d27vdjlx7cnhwcLhkoCSNh2izMpoRRJxR30W4LEtxyGDkF4rnKjReYnF27FFJIgTCklMQSSTHsIMG10r0R+j5dABAztnn/zpS5SRLy7JcLBYBeL1eP7h///LycjafC8Wmc04KhABgcNnrus7zXJJQJycncRznecHez2az/X7fNM1isVitVk1VC32b6NfdblfXdWhb6OqIGumvp7UGrQiBEJIsl4uKI5Wm+YC0AqAQHHMAIOZgbffQbVtPJvnwjjHqxo3jqtpPJhPpyzmdTg8PO1JrIhL+CpFxw46WKZCR5B4QIEecRChgZmZZVNK7Qpxk6XokeOD9fj+k1QGvetLhKOVEfRkSAFgby2LIiykxAigGrY11PrROSNDZ+RCIAZQy0SefPPEBhqbIIqNDcHmRMbN4RHL/sgsAQJYKAEgQW9bSIDTG+oaZ27bdbnZnF+ebzUaSIE1VO9fliUXgJ3GUJnGapnmeTyb5bDY7ODg4PFoeLOcnR8eL2TS4drdZG6PSyPimfOfLP/XanVdOn3zi27Kq913JIhAReQreexc4eA4EgZFANc4H4jaE+eKAUF1st5tttdlWZVnvduXFxfrycrVeb3a7/Xq9/ejjh0R05+7di/XFw4cP5/OJ63tje+/FMkuSRPC71yTAT5bS177GvfuLiCG4q92KChkEoDLedMOkq6H3kVLKGgAIDEzABAPRL/Tqz3cdLVlsxDybpGnqfRfAkHI0Y4xZHsyfPXsmAn2S5+v1Wimz3W7LsgYAhdoY4bmNRFUQAisckAbDKDyvYxQBKUattQ9OK2TG0Lrj4+Ovf/3rv/r3/2kxj7333ClUZiJ+XrPC8/pyrNqh94Z5xHA2/OSaTXSlZkZR6+Gc+Hx2czxVL33zxfdfvDcABYDXPr2mp+XemZlZDyFo7qPQiLjfV0RdHx5BZMh0Hh8fC0HBdDpl5rqupRJhvV4vFrM4jtfrtYgPDCwZiDzP43hgDPYA0Guxvu2g3CSP1isrwjC6bWS+gkD3to5p2xqRkUADRsbo3P6Vv/YXfvPX/+Xl5f7G8c2WyCZxFJumKr1rJ7MU+cp3jGwCCltiAlTGPFutPvjoozhNLlf7r37lC7m1AG5sfg0GCjxfgz6Mm5he462IFBC70mtJxyqlfNsqpfM83ey93ElVVVmWNU2FiJE2vm2tteStNphG8e0bJyGEy8vLu3deQcTJZCIlrSL9nXPS+0xSBszsnZdtRX3LlNCzZMgMihkkEyoJzqqqFGDTdFUcEoxFRAnGAsDggcmFAKBpmjiJ5CO5GQFFW2vbqhQ1KbQJADCfL1erVZokSqnlQbJerxeLhRTaOuekwENZS84JFXld7ZOiQNIAcHLzlquqxfJgu948fvz4zTff3O/3y+VSbl5k4na7Xa1Ws2KCiMzoXCDuWhQwc9s2zLxcLh89eiQDiIjSFFnqYYaEujGmJ7kEAGjbVjiZJdIjrrBQZBwcHPzoRz8CgLIsd7ud9L9LkuT8/FwGf8BGiekjug17MKnYplVVEZHgtuI4Ngrrul4ul7KDkiQpy1LaCXNfDDIg9cb7fdjaoiPzbLIr94jaRqauW2MibXXgChTHcaK469z86PHTP/ved+PUtr2OJ/JK2XLfvPqZN+fTyWq1kiEa37zp+6eJ6cDM8oV+yfc7mhUhMKP3/uzszOoO8N+4VqL3svKx7+WsFQVtyFJgqqrGDSuhTtMsFk7QenXxyvFBmsSPH37c1iUCIYVAQhHRbcYAILUPzEyMgQmVbVxQJvYEDx4+KaYTWaVKKa0rjSDza61tm8skibJJsjw42O/3H358f7FYPH16DqwI+E/+5Hv/3f/4L6ZpulqtiqLwZXtN9o7/vCZye00BHXRr9B0F0IYgFcFSNR4ChRBQCaqcCfiaApYLdVEH7pYBMwdgrQxxV1XonUTC/JDbEnCl1AEppVUa2UgrDfj6vVenRXZ5fo7MYqgS0W5XfvDBB1I9JsBarTWiHtMn8Qi9/PyBzHzlSJEMB4TgsiT+0ud/6vh4QUTSR1MAsX7Uzhp7gN9YOz4vaq/arI6PYVcMG2M8Ny/V7vDpVtK193/CGZ7/+ZW5wC+zy4a7vebvDi90TwSBqBF10zilDIASDG0cx+fn54gocFbxtwTeIr7FwwefhBAuLi4IeF921ffee+9IFlPnSAHD8wM4/pOIuJ9pRLzKB4/WYmRiZKVIYWANuNtsEfHWrdtlgJbDH3/vz5bHh7PDJSg1mUwk6pjm2eHRclZMDCpjTFpMDo9PoqJ4dPrs3Y/u69gG4Jsn86986QtFEtneJ7o2L4qHu3puB9IISI/4XB5Io0LEyNg4tsYYq7RYNrKdvPdRZKIocnXTuiZN0yyJJRl5cLA4ODiYToo4slVVDVhcAJDuv5JFhr4ZGXReL0r2XfVwMAkID/FnZhbCZEE2xXEsDX3FtRWqBNE98jU5uepRr0IHwQjD/cuy0X1PdZmgpmkODw+rqrJRJGtgu91Ksmn4/qC/gVmqWS7PL5KiAO9907RVtb28bNsWmI+OjqRc+PDgmAldG7yjcl8DK4VGK7tYLrnPARsTDdkvImHviiXR6D1JVxwZFtPXSeMIFXF+fi4wLtE6Dx8+rOtaMGiSWZeG2ev1OssyURXiVZv+GMIn41iCUgoVMLOE+tI0PT4+bprm6dOnMhRZlklMXsZQMvdaa1QsoYJhNY5NQ+79JAG+BSbvKU7Sum7PLlaPHj8tyzpKUmD1yaMnP37vgx+/98Hjp6f373/04YePJI8+AHyAQwhwfHwo3GGimXAEJhW3WIwGa+3h4aFEZQFAWwOq26qD38bMu+1+vV5vt/umaVwbmqYR6nLXeoXamshoO6AINapJns0m00lexHEss7BYzibT/DNvvFGk8eXps7rcIXhkr1WvegAYgVABK2YMjJ6AAD0pF9gH9ARn56umdU8en16sther7WZXlfumrNq2CeW+WV1unYf1dlfXbVrkp2dniOjImyhKi/zi4mI+z2/dumWMyfNJ6Fs8vShgh7l+Uaoz86iE48pJC32XUnlD9iCOGr3wdXXeQWsRNKLuKB1Ft/cjIj/0Pfek937o7a2UaluHiIa7UJKfz+dvvPHGZrPb7/dSoSi39dFHH73xxhvHx4dSwuhc8Ewc3HBPw7K+pqWGsUBAEC/TB+4dlMPDw3fe+eqv/+Zv58WE2SvotNYQYR/+HXbmNaU4/tr4oi8K6/GUfNp9jifjmkJ98Rg/3fj9/iTqz3OeF+9HZlS2zSBGh8Rhd04AgXcCwMHBgXNuu93t91vnXFXViLjblZvN5vT09OBwUZZlpNXl5eWkyJlZSpucA/IOAIRHArokCA0rZrifLls2zCMidrRlV0dw3hrjmzq2tm3b2EbOuVdfe+0XfuFrH9z/OCkmZ6vLL/3U2xdxtFtdTorcWbvfbBEZlYrjWJlo37YX+/3j09PT9Xo6LRrXQAi/9Eu/uJhO27qM0gheZniFEHCUaLimcccjT0SskDuAFRmrkiRpGiciuI8ikjEKACZZftlcVlU1n89nk2KzXZXbndaago/6WlURiyLioQdZVFWVxgldlQwGgUwYYwclJD+Re67rWrrSivImIgF7dw60923b+j7RKz7c+PFF8mqtCYmARSGJSiaipmkCk/c+sl2u94tf/CKaCBG3m41c4uz8bD6fZ1kh95Pm+Xa9dk3TtjUzt65+8vBh0zRFPpUkSFmWn/nMZxBxu91uNhtjzOnp6WQykfyroHVkoQCAtTYAG6UlzC6uvIgUIVHKsiwEJzR8kmGRSRzYReR5B9C4+Kky72VZ3rp165NPPrlx44aMQwhBjIyiKJ49ezabzQYnVWFX1S3DK1kMIdOWLSb7Qu7ZGNPUVVEUZVlKfF68lu7R+tJP6lvDit2APQJLroiIwKpt26Ojoz/61rf/9b/+1mxx8MYbb83m2NTtbr//5MnjcrdfrVY/+tG7Hz34GACoQ4op6oECSsFisQAkyY4P7ChyLbFaZLrltQQGyrLsGuAoVNKJltAzadIAtNvtjDFZluR5Hlndd2BURVGYni50kJNN4zrpCjjJC/LBKO1b9+jBR/fff9capaSgUCid95UxJjBQYEIVCHrdD40PxkTOt57Z7cs4zVab0lobyqbk2hgTGaO1jkzXPzGKoap3X/36VwDg+9///nq7revgnFPKNK37uZ/92tHREYBsKNW2tdYvl7FqxAMxVhwd1mYUP5PtDyNop9ipDEG4EQZ3BUZ9f6HjobSICICAKK1/uzAhAPQFC4KzkTmVsLOsQ+8DABgf3ID7v3v37mazu3//PgDUdS1mclVV77///mSSx1HknVPKQLi6oUEuX9OOzBJX7l7IYvWhRWJrrSPK8/ydd975x7/x24KzECyJVkB0VUAybBJ8PqgIz+vgF2UuvwwgPZbUw4vhh/hC1Hp8vGhPXbulQT0wMwDDp1hh4+8P2oJHOZvB5go9vcOgj0NXId4VTUrc8vT0dACGyCbcbDbetyGEcl/nBW42mydPnsSv3kVEof1zjsg7pRTgOHDUO46Mw3KUdwKyen4Rj0dHaw1ErDUFZ4wBol3V2iT+6Z/9me/98MdtQ+9+8P6jhx/fWC535+cnx0cUxfvdLo5MW9Xr9br1dL7db7zL8mK6mG82qyKL/8O//NfeeOONui4To/3zhpSIe6VUVVUarheYIUoXsKthl0dQ0hmqr7oeAsJ5msbGrp3XgJMsZ+LpYtq6ZrPZXF6ec/DLg3mRJgKzkvzTYrEQRkkhpBQ+W9lpUu8oSpQQASCKIgGwDPMrtpTkJq216/Xa9+2VhCJDdJLWOo5jVErKz1arVRRFQ/aRR7V53e7rneZBMaBWTdMgqFc/89ajjz76+OOPj45vSM5vt9uJBVxVleq7DAXnxOdOkmi1Wkn0Vf6VFTibzaIo2m630+lUQFJCjn1+fj6bzeq6XiwWcRw/e/JEaodOT0/jTHrldh6tjWMOQboCFEVhTMockiTZbDYC8wYAwRseHh4OtUyDMyqIceo5MrXWkuSWCi6l1H6/l/Tw0LjaGMMEAzhcBlBrrRjbtpXyYtFDUoHdNA0FEfdKbkDISURCXhNxcn7xZriHj4mG1oqyYvLN3/+X//X/8++89dm3f+r2nSwvmLlp3NnZmTFR3a6sjYtpUv34RzT060VRBgwcFrNiOp3utuvOJdVdd1HodYBkKCSiAABZls3ncyLaVzV13Eyd6ybmgjGqqhu93ZblzDnnHUlqA5HFpx+FQyTljDL7EpuZTCZpmt6/f/+D997Ns4Sc994L+KquS62RiIS43RMGAk+Ku2QWbne7KIq9awUTrpRC1K0P5EMI7FxADho7wLlz+8XRfLPZ/tG3//U3v/mdycwAR3Gcek8AcPfuXaVUZHXb1CIeQ0/Vee3A52G8g5LqgqZAvXoKzEzcg8+NQkRJEnWjN8I/D/5eL4girTWI0hUO5i58HbB3eQdrMoQgXb+YWaku+MTMRmvtfGusJiKtk6Ojo48//jhNchun5+fn6/WaW3//owevvvG6stHT8wuBpEsRhfdeGS3JMCbu2bkYERUDsKBkiRkImAKDBJqIkAMQ37u9/Ku/9PXf/t1vz+cTF5CC0TYKVDKSNjqKjAIwBq2OAKiuW++JEAgUSDlczzSKiKHXUkqp0BsynW4GoaRoBDEI/JzDNBbT4w027LFr0nysQXlUbzrYR/IhACDC6PQSs1UDFaVcQsyL8boZbmOYNjFOBxhIHKVxlGZpYXQErIBVWW4nk4lARUBpRnWx2p6ent545S7U/qNnz+q6vkXaMW92++kMdUDn2shaAxgZFQAUk9LCJNxqrZGR2BMHYkKFzBCAQYrlUCEHEAeFSDFrpVkDswGN4B0QRRpCvbm9TP/m/+i//89/63e+8/0HJydHD1e7VRk+/uisDdUQv43jCAwQ8tKa1pXr02evvXr8F37+5165dULcRHlEDFbrwYPsHCOFgCpKkxCCD61VVthIiD0CMiMRKIUAKJFypRRzUEyefRyZummNxiSKfaC6bpeL6XZzuds1JoqICJDv3Lnz8OMHsU0QcXW+evPN1zlAZOM4SrJk61qPoChwVXYt3IU7aVpMkEFMHI0qoHYueE8E3HqnrRnSJQK3aZomjZMospfnl7du3XKtMzrSYCezuKqq1epSa9U07Xw+j6zVWofet/beR7EV4ghA9o3P8xw1ogLR1lrrNE1D66QUlZt6fXmRZ0WkEIFbdtoqAIgiE0UT71sABlDOubqutdZFMZUAWBSnl6vNfrueTqe7sp7Mpp7C7HDOSEpDWsT7Z6ssSdJYGwWtJ6WM7FC5rlJKMTVNY7VRgaUZW2RtbKO6agJxlmebzSaxRuqtkzipqmpQdaJxxfXsIUgmTXPR/YKH2O12AFAUhTRBms/ndV0fHh6enZ0N8GBAFgexbVsfnNZa+MtUTy0OAJJdXq/XzJzGkcTGxeJBxVmeEAVjFZHxPQ95CCFJohCcQeXbJooixeIWIysbFfGDJ2f/t//yv3rlzt2DW3fS+UKnKdqo3O1L587Wa1IKIovaXK53DOA8AQAQW60U8m5Tf+HtNzV7amvyNbLj4IwCYdsSmZblSZraLI5m06TcV0qpkEXIEx9a13ijFLJiQqstMnrHHChJjHfh4mJVFEUcGaWBObCriiSKzDyJLGpjIqva1jknDR+thggQQz2ZJH/0h3/wZ3/4jXlmwAOwUoActIKIAZkheO9cg4jOO9AKjQpMddNAyBTG231j40hbZMXONaCRfcNAzqMxkdKWAKs2hNAyh+0n5b/+v/9xCDwpJrvtbpojcFPudm+8fnz71pKpdQ0qpXzTEiL1SJbrx8u6ISmliFlpBM8hBGQCJPKtc41CQSmzxJ+ZCBg1GmZHwVFwXTAXkFghotKxSVJQxnuPUmnCpFCR90ZrAPQtBecpgAYLoJgxsZlV1nsSEJWojyuSVWMMM8zn05OTk8efPGWE4+Nja+2zZ0+I6MGDBwKPFOada97btedkZoDnFNiLOsZ7r5R+8803f/f3vo0Kql2lbcQhKK0UdplR1dMYIV5VFkq1jGg3pZQecV90GxUR+pQM9jlz1VcP08vysi860AAv8XqvHWPlPX60l41GtwDgBVd7fOlrpxrsgMGylgfxXtpVBkSUkkdxTSSWQESPHj2SlJjgHjebDQBst9v9fl9MMu98XZeQptCnRV1wyo9CuABSTjuu9Hn+Ka4WN125+zDcLXOoqurO3dv/6X/2N17913/yD/7JbxPAwWKZTdKyDCdHh0R0dv5sPp+G4Nfknj07txp+8Re+/qUvfH6+KPI04eDJtfp5Cl/oMxr8fNzihTGn5xhfRzZN0zQAPY4R0NqYLrcHBwdt2zZ1k2SpMWZaTD73uc9997vf+fKXvxxbW5ZlPJu3bVsURZZls9nMGCNG5yDlBYcs8C7ucchyXRtZARmFEKTPgXxHnCdrrfAx1XWtdUtEeZFI4dBms8nzQlLOSikIoQfZWgDwPfMwWJTXQgomzfsE3d00zZMnT4Qsoq7r7XYL0p/c+8ViUe5rZi7Lej6fr1aryWQi9eVlWR6fnDR1HUJYLucW1fn5eZJnZVnO53PvyRijDbZ1fXl5mZycVFU1my32+0oS3oBJFifCZjWZ5GdnZydHx01by8OK4b7flWdnZx27qnfaGgDQ1ljqaKq22+18Pld99yGlVFEUy2VHk9I0zXq9Pj4+rqpquVwKMRwz13WdZZn0DRwgV0opQQ9hDzUSxw4AJKRERGVZDlKiF01KKdUza1byc0k9jBLVqLVxvo2SuK0bpZSyUZREzkNVVb/xG7+htT48PJRRdc61upVypv1uBz0E9/Hjx+J1yfoMgW2sjYGTkxO5eVlsRBRGnoN8lGVZbI0Q9bSNs9ZGxobAGlBAzmK5ympUPZtmWZaXl5c20nNgxgQV7na7NC9MHCmltO6cPNe0kzzjQBrZNfUf//G/+cY3vrGYz325ZxDpKs14gqSqGMharKoKtW6dY6V2ZX10cmO/qeI04TIkaVrWVeucsbEwmDrnEXXwHChoha1z5b7WhqyNQuDEGtdUn3njTeea1fqSCO7duRPHMbNHfYV1/8nHS+KUAETMfdgZ+iIF4DCW5xLG5L5UgYgUdnXk1BPFc1+/M9BJDX72EO4aPIehrSpRp93kOwb6lAai8j5MJpPbt28/+PgRKzTGLBaL7Xbtvf/w/sfz2fLzX3jbWi3LkUb4+5+gdYYP8Hmwj1JaGfPGG2988Yuf+bPv/riYzpwHCoA9uTQQg0YFoBQopSUYgojovQsMHACQhOCwH8fxgNJQsccKQWt9rajm5ccgqcdS+9O+OQz3WGvC8+p8eEcpM14Ww1y+9Pv9T9QwVUMGCLWSNBgAOOfW6zUgTadTCU+KjfLee++9/fbb0q3Bt43Qze/LGhFdGzarSwCIbeIgMDdWG0QgwK5tGPRkZAqRtJCojZ+REKQIuF9qwKgQCQGpB8VrrZNUr9eXJsp+/i/8zGd/6u0f/fjd73znu+/d/xABzs7OZD08efxMXvz8v/flL37h84fLhY1QAVutbBTXdVDYtWIaNhIrVHxFNEEv2Lm9TUMAelhsIA0BEdq2BVQhsFKKgY0xBwcHkn9tmsa3DoiVUlmWvPXWW48fP37t3p2iKNI0btu2bqoo6mDPAqyw1gJ2yNiBlMd7z6FrABDH8a7ci1uMPWRBQrtZlq0uLqUJj9a66z7ZlB9+uJZ2thL3Pj8/Fz0hudWhGlV8uBBC8CQnB4YQgoBxJKgo0CQZooODA0R88uTJjTu3hRJLvhACJ0lS162olvV6vVzOQandbpfnqbXWIgj302q9XSyXnzx4eHJ4oJSKYjufL87PLpIkefLkyXK5lGfpo7IQRYaIFovFan05m82ISLJaRIS6kxhpmjI5MQ6Ggl1Zfrbv0SQ5csm51HVNBBIRlfpp4UuXsPZ+v5feUNLweEicy2RJYFkC3YKjFiSwxPzFrwghxFHXAENSA4JVLorO6xDDS1aL1Exba5um0pEFgKZpojhN0/jDH/34n/7Tf3p08/ZsMpd5z5K0ruuy2onXLnlxBZ0B3VcPo/eeGb2HLgqqukkXHcA9CMD0jY2F59laW+kqtAEZPLHwp3pP2GeLlFIS3/YeqrK+uFjpnjeNgEzUVFXFqJIkiUyslErjJDivUTH6xNg//va3fvM3/lmRp841AJYloAeMipkDqxDIu+BCCI4DUqSUubjcfOlr7+T55Pzyyb5uojyqW4ceU5s2rTc6CZ4QbJrkdV03da2U2VdNmqaXF+cA+8+//WYcJQJAuzg/OzttEOHOnTvMzExoOqdRv0j8NBIEg7Adv6004v+Xsv8Ktm3NzsOwMf4w04o775PPuaFv9+2AbqCbzSASIMCCGC1aJMyyGGxSpF0upwe/qCRLJbv0JOnBpQeXA+WybMElkaJFUmAACAIgiNDoRgPd6EbnG849ae+zw8oz/GEMP4w55157n9O0verUqR3WnmvOf/5zxG98H3HsJj5ijDKApLtmVozXiIl6g6aVMcYCgNKmAz+LlioiasRrTHzUEZSKTdBKSfQmv++9LzO3g+oxRoVtSLi7uzvdGV/MFvLHxiR1XQ0Gg5OTk0ePHk2mg9BJh8pjL9BN+bq/2k59dsvlbNlHeSo0qtFo9IUv/NjXvv69JEmaesMoiZfSqLQITGi5PqWNknRNKYXe+whEMqvVfqAMyCBK5txWd40MDygFum2mokZmVnzlrfl1Sfz2av6wX93IzGCrjd8vsdx0xGtvVh24TFa4/6Bt9w/XEdrbziYy1XXDzKBwXW4o+t3d3TzPV6tVmqZnZ2fL5Uqg0XVdu7paLpfybMfgZrNZVZeKIUmSPM0ALEBIjAUgQgghWHXF+C27BV5hsSZgYa6+WkOFGDUigUKlFAGz99Yorahu1vu7w4N/7Q/8gS/86LMXz10dmqa5uLzURgl/8sHBwcP791CxBlYaNQIFFxkTa4kCxat4BQBknKhfYaWu4d0Q8aqZDddunPxIKeV8qEov58/Mk8lkuVzu7u4y83q9DsEhsnS8fFPNZrPJZHT7+AiQgVi67DYx2iji6EM7u7ndfE3TNPrQ81K1hIKdOCt2DJFFlm/sWh5UmYASwzoYFDJlIaRFArgjIt1RUfb7Rw5OkbXWxNGgkchMnG6SJED8/vvvv/nmm1VV7e8dJElydHQkMzZSShU3IAkftxguZYwJTQNAMcbNZjMdDvZuHS1nc2MMKHXv3r2njz/a3981WgOAsGAOBpqIlFUIuF4tdyfTy8tLcWwMJDNFkv1L+IIKhN1iMBhokzFjVTVJ0tLRa23TNHcuCOGadGFb6cYkOT+/HI/H/XTQYrGQkrXoMYgPXiwWWZZhN0axXC6LohDfDADi1EU9AqAl1ZIdMpvN9namPcZNuERCCBTlKUYJIxBRkFlStFfKYDcdJKv6S7/0K86527dv7+/vj0ajLEmdc7PZzNWNwNpl2TWqz33uc++9/4HEbVJMkih4drm4fXTonfjdthvFzIL66a2NTPFKlr9ZrbMsQ90iEpbLNSLaRMWgULHQcAkPVDs3UXvvomcfF0ttk1GIUqUAYhe81YojpUnyld/60j/5uf9uZzqOMWrAAIqBCSVVYgYIkTx55qg1Fkle1X69Xv7kT/7EYDh1keyAiWi5KkPEsvbzxappYl3XChQRzedzeTg3m02e5xeXF3/mT/7RH/mRzz19+vT733tPwI8A4Gp37/bO/v4+cDuLJTYffjgB1GtfzL1Ovbjy7YEOWdK2ZtD+vh8NJQR1lXTJXVAtJTtAl4XGGAXw5L33zvUHkUhCa+1jVOrKoKGgoLuJ+LaBXBTFm2++uf7G74t1m0wmZ2euqprz88unT58PR2/0/qN1J4wAvTzTttV7zer0uyfEiJqSxNy9e/djH3vwwfsfpfmIGbVuhxqtFYAcalRaa0BSSi7bKwU6aBdC33nFLZ42onain7uWe4/u8963mrhXjBNbBvoVKDWAYv5X1Tp+uHtWr17+9vF563UVtWz54+13bjtvABDYiDgDZgbEqq611s778Xj83nvvPXhwXwj5mqZZLVY7OzsAcHp6mhfper1yzqWZhTnovT1tDUVo2AFQu3RoALAHRbdrxYqJYAt9RQhKKlH9qWppo2jGCNxqu/rg8iKt63WCaZbaN9+8Q06YkKNE8YvFIkmM1qi0UoDeN8RRZ4nWGH2gGAFN/4kAoK/mj9tib796/Z7sfhj7mj8AWGtr79I0JYYQKq3Re58kWQhuMhnF6Ou6FN75GKPSkCfp/fv3naurTel8s7+/X65X4/Gw8UGO1qdWsvcEgwNbp6e2wPliK/vSkbX27OxMMm+ttbWJtDyVUuv1SlyvuGS5oizLQge/cs6JKqI88IlNUQEH7gFWAt8V7Yf79+9fXFwMiqHEAdPp9PHTJ2pnt1xXWmuVJNYkTdNQ8Aqyy/MzoRR+/uzJ3t5emiQxhBC9rqrFYpEXxWa5HIyn0+nuarVZLzebssysybKsrusYXbX2QhO23KyFu0NpLIrCN63fBQAh1Kyauu/1uhC0NcVw0CKiqyDM3rIC3ntjEgkpmqYRpyjrEGOcTCZC0SAWczgcLhaL4XC4u7srKyBDBMJtqZSScUrokki5iVLglUWeTCZEoLUWGFdfNpeHLqEEABlI8mCllGTS1lrJ/r2LWtunz57/o3/0jx48eLOp6m984xvCyeW9967O89ymyc7Ozs7OzmQ0Dq0WOCBioBhj1KiUUqghHxRorPceUYhZLPjIHcmDnFiixU7qJEmM0tEH59xkMhITH2MsyxoRlb56KBARFIZItfNl3eRNvTMeOeeqqsmyomm8bELf1KlNbKq/9jtf/X//vb+7tzMBAI4hxsigIjIzR9EwIAqBQmynkIKrzs/Kn/5TP/nojYerdZkQUZMwIytDYEashqOdqnSz2eLpR0/Kat1tnpCkNs2MAviTP/1TaZr/7m9/JfoGiKy1HD0RfOITn5iMhtqgUhC8k9w+Av//5YMRsUcQM7Nw+XWFxnaVbjhgwWf1ET9DG/t2FU2FyEDc0hQxijxgcF64ccQCmCSJzNDV7baturlu6DlGRtR37tx5cXr24YcfTqfT6XQqhLF1XS8Wi2pdFUXRY3g7b/N6oK9cNLT+5tpCiLm3Cvd2pp/77Ge+973HO4NMYJnG6NQaa63psl5tUKmWiJUhctSkyCiIzDFE0ADMGtEqrZSKDAyMgAaBgYwRVe3w2ip073e3zq9vybympNz/Fb6u+4iI8pC86piv/Nn1v2IE6AQ0GJiZenYTUP3MDwvRiiDOyrIUxKAU9JLECqx0Z2eHQT1+8tHh4WHjXVlX2pp1VT5885FSaj6fo5o0TU0hliXAjhq5oJSPJmqtEcgYiMRBUVtWae+ekK4wM0sQ2PdOhC9adYGouJ0+uNtsNmLgKLg81QTBNbW1FiFxjbPWxlA1TXN4MF0sFkxkbIqKkRUCGGMUMGhtjKk9CZtSv+B9DLcdrGy9CLrR8+5GKADSWlMDSqFwF6zXZeN8lhWuaXu37QwPB22QfNibTo72987OXw6LAQBcXp7v7+6JKWSAPkmSRqO0EqU43DSNAkw6FUXJXUIIshrGGO99YqzAiGTMpigGMlWCiBKDyz4R1yIo6yRNoROek2cbpB/MxFHGCp0UtyXDE4aWs7Oz6e7OcrEKIUwmk9FolOcDkRxM0zTPBhITiMylgOOIg2jxVlWVpjYxuizLg4O9k9Oz4XBcV5X3fjrdjd6F6PZ3987OTgWVaa1umkpquUphMcjlTLxCcXISGaR5llRJX/IlItAIjJLtKaUY2QVPdS1cmNIFz7JsPp/L3RTNYACYz+cSiQpZtCQZ8/lcay3U3JKhSp9YTgYABHMu7WTuBHR7pPT8cjYej72PSlFVNcNhIZtNGskAoLWRoWelIhH5QBQ9gqbY2oTf+I3fuLyYh/j+V7763xJDmtqm8QBgDYQA8gw9fHj31tHx3t7eel0SQU9Fjq32DhweHgKAMSYE1ydb2KpcC3k+28RQB3MBIMmqMVCWpJPJaLUaOeeIrhkiIopRAcSqrGWWLDFWadhUpUksIobgBlk+nYw18u9+9bf/Xz/7/9zf2Y3BOddYbaJ3yrQGS4BKgTCSiYQKCQBenp//m3/hz96+c+dyPssGhWFWxUFdN2lh6so7zxXSixePP/jg8aDIINLlxXxvd6hssrOz8+3vPv4rf/W//+Yb93/lV36VySskCo2yJkssEdy7e0drbYyK3jnnZProh+c/N7u/ANfyTgDiSAykuK0IwxY7AnRlZCKKdKW+oLUG1FpbpYwyRhj+ZVNRIADQSgFGZmaIDFFuDIKW4LLLr/ozZ0RlUJBNW2JJzFQUxd27tx8/ftyjD2Kk1Wr10UdPbh/v37t3j3zUiRUO6xsO7CqfQ+ipK0HyEb7mh0Q9YzAYPHx4/+ho6urKKtRaJdKdUGhFklpxoltCVMUQdVQ6WFBaGSIVDIYQiKJWGhUwECrQSrepWaRikChlXFWjQWMMU+R+SIlec/+2zxC2fG1/XTdcae8PoC0hXOsc3/hD3Grjb2+I7fdsp7x9CsVd7bofpWiH1ZiZWczN3t7ecrn0LsYYe2IgYRiuvVNKiWhPlmWL2Xw4HK3Lqna+yFKpFiSJURrAsVJKdZygmju0XddsYbq2sxkiAxGwBlTKKBURNQBkWSZZV11XiEBERZaFEBRGjREZNPJonK/Wi7xIiQg5GjSYWu9cWZZJYqw2IQRWGl4Bovfrpq6PgIOMmeNNdQ0p4kntFxGLori8nIvEV1nWodN4v5ydxxgzmxwc7FlrB8NC4aHMGibGNq5K0pHRaYwxyVIAkJIjdUy8sk4a2/GPKExkiAJL7oPO4DxHgg5O5b0HYFFPkqRKeBBXqxUiWtvikhBR2FQYSEPrhntkjTHGBS9eRBJoGe+R52t2ORciSTnscDgU39PqIw0GElsPBgPhlVwsFkqpNLWnp6eT0dB7v7mcTae7xiQEMB5NbGo9YJYWQusxnU6NMUarxWKxu7vDTKtNLWqGqqNOlBhFvCAzE0cxO0meRSI0WobKEkoBgIEq76X124/6COxodjGHAbSTF8xZlsmyiMKj1nqxWMj6j0Yj4YcS/kUAKIoCWgBzJjVkqf/L8K6YSIE3yoeGEJwLWtt+GFequ2ISiagJXljZNXNVVcPx5NmzZ1/96u8WRfH1r3/9Yx97O8vz2Gp7iKCQ8jHUdX1xcfH4g6cAoDXkeSqb2RijlWxRkETfGENUx8iBSOJIeWdfbpFk12pjBoMkSazV5+eXznOWpLvTSXB+tVl7H3GrXhpjVMrW3i1Wa9RGaxyNRkxYFB4AFIM12ij4wfe+95//7b99tL8XfCOskqyiUoohKFYBgBl9BCIgBkBT1vXJyeVf/Wt/8f6DBxfzC5sXngEAi3xnZ7c4OXl5cnry0UdPv/Pt78ZIWuFsVh8f7n7hY2+kWZKm6WK9GE3yT33qE7PLi/nlxWa91AiE0DRVCO7Rg8PpdErRR0Dnm95UKqVucP1u2/EbIXtvE3oQssxcAgB0kx2xYxPjbtIvhKDQSMONiNQWM64cWClJc1utoH5aQXXCQiK8G9sKFm7bduZour5LW1i31ngfmePOzs7+/u75+eVwOJQxD631ixcvzs/vHR8fczvsqLHTq3mty9kmzAIAZtXVLAEArDaMCED37t375Lsf/9V/+aWjo0MJ+a1WLZlNp2MjzREiMAhkdQs8AaAI6/W6qoICRm7pS6xNQEPwjhmEBGCx4EQbgyo2NQMI17loPW771OtnKwv2+vuLXVWt96Pd19emmG68offBsOWktz/01R/eOFTjnTIaFIowJ3fqLuJgXr58maZpDKyVGo1Gq9VK4uXZbLYqq6Ni73xzvtls0jTdVKXw/jOzatCHZjIcDAYD753WWpGUZYBVF88BsuoK99xTvcQ+SpCiNCqDQADR+6iUCcGnaUYUU5sE55VSDDHLE++9UkjB5YmlGDQKWUsUhlxjDIDykQDV9nJtB3k3FqrTkHjNr7gFYbEMziGiuENZtLIs15vl4eGh0gMfmqbcrNdLREyzxCg9Gg3mc58YOxi2fExNaKSA2avXiR0XxBAiiq6RbOMsy5r1Stiv2rFLbhn4pPgsWJ6ObbHFum82m/V6PRgMkiTxPlRVNR6P+2dYGy3QIW6J+7Gu6zRNszRdrleilyftW2bebDYcWJrKgoVuscfOFUWBiskTxdiSQocmy3tK6pYAJwSntdlsNkfHd7XWTGyt5AE6y7Lzs1OJ8HxwVqeIbT9oNBolxgDAYrFI05QpZFmxXK5NYo1CuWTnBQOVCTI8xtiUtQCjhsPRcDhEhh7CvVyum6Y5PDx0tUeG1WKZ5q0aWAhhOBwKM6uIJgkeYjabiWcVDmrJMtM0FdkMZpYAUcyXDB3190WWSCpn1toYua5bJhBxxkJSprUWUGTtGkSMMZ6evHz33XefvThdrsrBYLBar2Pky8tLrZQxyqACq40xezu7O5OpAMcAFCBCBFDIHAF5Z2eEiForIlLKRK7bR6xV41AAUKRt61d+LvWbLMt2dnYkXtnDPYnRQ1h3tUh5gpAVAmPT+KqqqiprmmYyGk93xuv1Wg8HTdN8/Qff/9n/8v9x6/CAog+NQyUT7Z4jURR0MIQQQwACBEZGWJer/+n/7H98eOt4U5bFYBwYfAzEfHa+vrh4/NWvfvUH7z0eDnPnKTHw9ttvfOrdjw0HRZraxXJGzGmxn+f50fHB7Pzs4vKcKPgQgY0xWJWbhw8fjocDxBbqmCRXOMTXW+cfngHL3DAyMERmVsiISnXNq96adQ6StWrHuxERlTEmSdPUJKm0KvqEqm85SUAsHydbSHVg/qsuQGeUiKhF0wEAtl3otg1ZFMWtW7cuLmabzWYwGAyHQwHX/eAH7+/u7j969AAAYozGtMIm/anDdX92Y40QNQBFYGmcFMNBVW2GxfiLX/zCr/zKlzSC1soolKqg1T36l3uMVZIkhowEF4hI2lxNhmidWsvMWsFgMIi+qV0wWk2n04vzc2u1czHLk7quESBGKRpc6aV0vcNWNxfauab2g27Yd74uyNP/8EahG69XqrcDsdavbIlZ9ner8xlx+8R0p6EmwbtSSqgKVKfpm6apOFd5UCnCelUS0XA4jKElVzo/uzw8PMzSwjVhtS4V4sGBdS6G4Oe8Bm0MMKMCikmCioECowi+SJ7L16ITAEXsumttGx6olSIj90wpwwxKafkfABAhBH/lIpHQXMNzAQADiN4RvpLy3lhbABANzr4HIrDp3jn1R5bkcjAY1I0Lng4PD8/OL7TGTbnabDZJkkwmk1uHB/O5DSFU5WYyHtV1OR6PdyZT4SCUeoPQcQTnffCi/S6oH4kIJUOCniQWwGgrfjo4L7uXWUTLvZRMe7EdqaxGptVmPZ6Mm6aZFNNmvkzS1IcQvAfmxFpjNBBzJLOluSRhNHd1MymXiXRuURTCEBJj1NpW1VxOVWZnlVLO1z40aWYR8fT09M0335Rjys4hitbq3d1dOXObJOVyJewcgtOu69JY7Tx2KooWsYkx6g51pbWOQIJEk/lvof3RWooFwMzWJnW9FMSZ1rosSwYqslzwxnIySZK9ePFCyAVGo9FwOKyqarq7471/+fLl4eEhdQgpY8xgMPjoo4/29/elPH55eSmOE7r2geAn+rp9nwpLl1reIOwrsj2Y215An7GIEJk8ic65xNjlcvn06VMi+oVf+OUv/IHPP31xcnE5994boxRiCC41ViWptXYyboFp3vu6dkprREYAVKrclPdu3RqPBvOLc2bJfVsLQB0xgJwJAHjvB3kBSMaqPMmA2DlnrQ5BFYP8+PhYyDtd8N57IjE7rYyj9977uF6vk8T4xiVW3z469In58PnTX/nlfz4qcooeIimNHMmTBwDURkXtfCRgIGQmpdGH+P6H5//ev/e/vnX7zmq12ju4VdYNAz59/vIb3/jmN772zdPzS2MQANbr6qf++B/5xLtvH+zt1tUqBOe9n0xGIbLW+u6DhzHg+fm5jHjEGCXZJArGqDSzTVNFCn3mo1sShZtGWF6vVhmhz2sjyc5H6htqLNtSorEewNz7L2OsUirEmCSotYyiteW0NlFhkvkiDex9E0KblIqMtOrobtQWO4qc8NVsDGCfkZOoJL399tvPnj1bLtfU0e8BwNnZ2WKxqKomTW1mk0hR0ArtEQCAX5N/XCVJQuYPbfjJzBSjc/Xd27f+6B/+3Je/+rtHh8cCP8mSbpLSWufqG4mjrKzWmhk1qmC1AqO1RgYiVsDj4cDVVdPMjQKjQGs1Gg6qSi3L0igNXd+aiJhCL+u4dc78qowgvJIrv+6HcVs4aPs9/ZnDdY8OW/57+/9tb/3DToBbAQsQltGXL1/2aTF3QhzcUfr1BcY0TZ0LzoUsy/JNlaTm5fn5/v5eWZZFYvtPaXuxMjnH/TxSO+TenslWkaO78wqUBgakDuvG0AO4mOM2boK29HsJFUOPXsZu2Pv1bXjsas6v6k/D9UexX7G+kFjXGwA4Pj5u+ZyVWq1WVuF0Or1169blxfl6vZb2CjIYY5LUWGsF9ZpngxaZz1Jyb++12aLm76kkZGooS1MKUQZmiqKo63o4LLz325OIfUQsFcvpdCqMsMZq77qRJ4AQvDSr+odZSLWEOwIZeo6tvb09aXxuNhtkmEwmSpk0TafT6YsXL8QJCWQ6hCDCA9IdXy6XWusksZKgr5cLBLXZVGVZEgFqc/r8xd17dxTgcrng6HsZKJumiFhVFaJ6+fLl8MF9RJRyOgAQYD4oFouFMWY0GnnvBaZnklS0jzjEiiruNCrWm41I88oQV2wFiJS1reiylN+rTTkYDC4vL5l5d3eXiGTCuCzLo6MjSUeop+iJUdZNugNiZOXgACDXy5E6aEXSlwZDCKJ0Kzhz7khMvfeCkDZKO+cI8MGDB7/wi/+3e/duP3v27PTsXJtkMBgIu6e11sfIde29r8q11O2HwyHiZrOpjLVVVU2mo3IDDx7cGwwGl2cvjb4ixbuxpdsOZYyo2BjDsW1J7EymQLwyq7JuiGg6nYYQFouVcy3Qkpl701eWJbIb5MXuzuTi5VluzWY5++qXf8vXFTAjsYj69nkeEUWHxKqsK7DamMRT/PDJ+X/wH/5vHrzxaDFf5uOxd7wpm1/9tS/95pd+a7kqJ0WKAAr4z/yZn/zMZz41HY+8r6xRm7UbFDlRGonLjdtUtVbZetWcvTwvNxWiTmwWY2SO3vt3P/lxRFYaMaI0SyX6v1kGu24f+tPeNuwUhPoqEJHItAAS8E1bzR3/sboiEkcZRZLJyxtGBoV7KpJzTjEopsAt1g8Aul7htYJra2OZAK5kaAFaDBEliQGAR48efeUrX5GqFCIWRbG42Hzrm7+/v79/eLjPzLGrfQO+xjMBAAN1Lllxm04CMGitbWIisbXau9rY9LOf+5Evf/l3E6NcXSZJgqnlljPTSQVve+fxFXMyIGJitBZWemZRVxoUmTk6WC7nTVVOH94/3N+NMRZZErmt0ccYfWz7Z9hOR1xNjjKzkHZsb328Xkm+Yd+3XvH6t9cc+Y1sGF7xFn3Itu19u1PCPrJTIHsHiICoJSY7Ozu7deuWVBr7OEu0YqxWRFBVlfdxMtmpqkp6bHmee29ms1lR5FprqzRx7IPEtvUkSISuMAOAQLG7in4KnAGA2pvAzExAwLrdF53/Bo3MhIgEijtYWS/zAF3bAhEQlOo896urjYjA6sauYwSNugsErjDJcnDd6qoqIorRF8MBM08mo5cvT/amE2YmjkWe4d4eUajrejQYSjQDjE3tg6cYWNywVAUECie8KAcHB5vNpi4r+SxjjNamKWtlTc08GBT7+/svX76ULG13dyqiwiE4ebhaUb8YJZCSKrQUq5fL5cHBAfXMoLGVLZE0t699SWTknJdnZL1eyzulW/nRd77DjEdHR9bazWYj4ID1ej0cDtfrtQztiA8uikI8d5pamTM2hXnjjTfOL2aj0eRyPh+OBtZaBTwY5qfPL40xi+Vca31+eSnCSmIrqsYZbRtX90RUUugGAGk2yygR+WCMcTFIs1aYIrTWRqtNvTk6OpIBJJkvEopmCU1ms9l6vZYO9GAwkHkkyTkkngCAvb09UYLqAXHUzX1J4iuBab+eEu5JM1hgaPI2CTL60qK4agkUGlcpzEySeu8Tm6Rp+nM/93N377/xg/feL0ZjbRJpPwNiCF5rTYwxRmAWwLZsoSQxIfgiT43SyLC/twMxABMAyaiqpHr9Zoa+xkMtiIxbtmGlDRaDTCIPTpK9/V3V0cs33kmspjtSbiQ2SjdVfclxfzp5/uTJy9MX1WoZfNOOI7YDv8AMPsQYIwd0PqKxEQAYvvnd0//0P/n3f+RHP3dyehZJfe+77339a7/3C//8NxRCIFAIB/vjv/DH/tjxraPd/R1EDr5WSHVV7u6MyBNo4wKPx8Xpy8ff/c77xLiaXSqd1k0ZAqXWVFV1997t4bBgIABCbPMNAUD9/+KAO9fWxhzEgbs5XcXU5WzkvetXcvsIXVEHiSGxqTySwriw/VkKiOFa/Vkz2SRJWunf7h5tSfbK35rONnYetM1iZReG/YPddqJAFIaJANTTp08ff/DhaDQqigyBqeMiaM8GbriT3m72C6GwU0P2rjE20RpjcLduHX3yU2+vlm39oXNCLV1c7xclq1OAgAqIkVkBW2ut0VKUSES3xJg0Te7dveOcd3WVJbqsQ5IkjBKrcgiB6hqNlkgWABRyBOpCkNbbXbPvW752OyN/nQ8GuHIbXXADLGOs/fuZWaPGroCKAO3X3H3dFQzEFSMgiWDW1WS1PHsBEdfrtdggoQiQ3ybaGKVWm00+HSNiCFSWy9lsJnCMshQ9OCw3dVlWaZqGkLYblFViGRUTAEdWCgJfdYEFaQXQovS3VoClcMPUzjXJTFNX1cfIxKCuj88rgKsFQUQmlF4VXV/b7ToBgga8+pNeyVjAEX28gpJhI0GHdCWGLMu8i8vlcjAY7O/vLxaL+Xx+7/5dYb3Z39/lSL5xaqS2uwzSOe6ewBhCIE3CJSuszoJ/7rYNpmkKsdWbahonh0rT1FotbERCCqE1TiYTCbfLsizLUhCwAtNN0zTLU2ttXdfGCPwV5TQkPJfJYwBSCpVKkyTJityYxIhAggunp6fHx8fHx7eHg6FR2lotok91XU4mo+Vy7lw7DSxPQZ7nQuUYAhljd3d3m6Z1ikmSHB4enr548fjxB2+8/aZbOSLSRtVNzLPicr5IkjRJMmvteDxFxOne3uLyMnTCLXLCAuGWHupqtWICqb3LMhpjgElWWHihEVHqAZKy70ymXZdaD4fDljI6tnNEfXF1PB5XVdWX5WXPSK1OrrQXWfLeS1AiE5j9gy+ps9xoYxQzCysyIsrayt4rsjzG6HwtDT/nXFW3A9bQMchKzAdSbwNDFNMkqeuSmXcn08FgILFXjNEYnaagtfauUcAaryCuzG1zMMZosB+GUUREIUJHER8E+JOaJEmYG0gz3pl4751z3jcILZpXEg9mbuoyTVPyrt5svKtDXRmNsYnMqp9FZOYYObRQkEiatdYuhG98//n/5f/6n73x5tsfffTRD95/7+d//ue//OWvEwMCRIbPfPqtH/+Jn3h0Z6/FXmjwPhgNhJxkKTNHjUoZbWxejD5KXv7LX/31j7/7yXK1joSARisCpZfr1R/44ueLovC+kYQYW2UfRFAKFfGNbAe65bpZZeQtgQBmRoqw9fN+pPCGAe8ro20Wqm2fHXXvou6pDyF4lmY+oun0yuQ4JDZd1nOLosDAa1/IWiut0+kU33jz4a//2m8OhxQjl+UGAPJ88MEHH9y5d3c6fcDMEaL3vsfN8hbO6LVL0546sgQZTVNnWW6MmYzxs5/59D/9+V/c3993zmHn1/M8l8pVv4IoOZh0RLRCpVihUlY4NiAxWmMxSNI0d3V5fnmJwGmWNE1jtTLDrCyBmYM2FKIQR6xWKydMoZGVQYrILUTr9RNWN3LTHxaIve5X/Q5oDwM3de+vPgK2Ng1ejSJcvfoFl+ExmfGX90vM3jRNojBNskt/SQTGtOMxy+VadWORBwcHaWrn8/nR8YHQriFqWVsCZk8oOCzqTltqwm0NX2HLyI0R+obK1aW2O0HAbgwAIDxWwtnCzAqVpLgdRSgwAwMBKARAgPDKRtreY5KJ8hVGGxBFOUKcM8q6CW7EtxUnNRwWzHx2fhljNhwOHzx48OTxh2VZHh0cCmAqMVYUC7q5uJYEQ4DK2BWmutQzikUmImGTkBQqOl/XNWg1GAzEjssoi/e+qipBOwvFxGjUUjIlSfLi9PnJyYn0OOUEhsMhKqjWZV/2lGdBHIOkbT19TwhBeyM0I/J+EVEQGgrn3KDI14ulBvSRUmNNroINRVGcnp7iaOyqer1YjooBMzPxzs4uxAiw1soOh1lVVdraJDFHR/fPT07yPD2+dSRsl9bawWCQprl3UaRqARSHEGMMPmqjZHhdtnTajVR57xWCQlBaxeCNMQrR2NZNyvpI/C0IZKEGk+oOdzWwvb299Xq9WC03mw0iStm/v1ntViTCjgxEng6pEPS7yHTiwY1vFQ+ZRZjIM5O4f+qQ7dK8VxqIgmIQVFSa2TzPl6sFAsjskI+RGVErqdqBQmABqWmJrsgHpRGBB4Miy1Kt9WJ2kSbm7q3j9WqJiqvNCgC0QWYOwWvdspD2+RwiamWZucdFokKlVJIkMlwgzendvR3hQ/Xes49GIZCiEIPzaDC6JjKtLs+1AoyhKTdZkkZqlWEjU4zsIxMjoPYcSCsX3NMXF/+n//P/4eDw9n/5s3/nn/zCzz998txqEO/7p/70H/uxH/vcrVtHSqlYL+t6ba3VSjNEbTQRYjtBp7wnYm2tPTo+eO+DD27due0dOReAEFFtyuqNN964c++uYOalfqok/AYpuirm0FvC3urCVsmw96DUKQy2llf2BpLQcUhAA0KZd4VxaZErAO3gOLSCLrYzRK3ICzMIYhERGQgRkySR97de4BVEmHzxGgfMHQKoaZrEprdu3do/2N2sqyTJ9vf3Y+PKsry8vDw5Odnb2xkMBpEjXPes25/VW97+yCCLh9g2YKrKeyfUufcf3BWRcESUCBRbbLaVH8qf966IiEArZtYoinNtNpwkCUfa25l63zDHLEtWq2i0Go2HISqWkk4CzJEZldEhyGwsAjEapbSMmcuG/qFAu9e+bkQeryZw/de4JeXU/2Tbs8J1HcpuQ1yrrPZfyIONqInAmMQo62rvm3B0+3AwGCyXy/39fd1KTPLJycm9u/eZmvlsQUR5ns/nS2DlvS/retD2TkKMkUJUGhCRGKX3qwhk2DwCSxlcLlNJE1d2JBIgAYicFyDxFaD6leXajiTgKvIQ960QXpMEI/RsOO0I8tbWkqOB9Fa69UFQHIFDCNoYY9KiKAaDZr1e2rQYDwef/OQnnz17lt5NsyxJbZIkien0dDuPa5TSROA7vSMRNjbGrNdrRA/dEy7bVdyztVYn1ph2iE4ybGGTmM1mcnzpTG82q7JEpcytW7eIKM8HMcbFYiFvFi4z1en+dgvFAEkPzZPjMHP0QWVKHhnJ+c7Ozm7duvO9733PGJMPW0VSKUHv7u6en58PBoMsy0JwAHB5ebm/v98KCiEuFisiGk7yl2fnOzs7yqr9/X1UEKN3DkaDQZ7nlxez3V29u7svYz/WZgiCQVPr9bqXju8pmkMI6/VaUmGZhxb/Kj5PKVWWJSIKsbNS6uXLl5LDpWlal5VSSrBRQid5eno6HA6tNmVdiRChjFRh25Bu4dxifCXX7Pu+ki4LYhwRy7KU7UStTGEFACJ+JZQRbeXZKq21AgMdUpI5IippbL/zzoPVRiBm2vvoZQAUWCkMMWigGCkGZ60OTajr+vT08s/9uT+plPoH/+AfGwN/62/9rcOD/eXsAiIVebp0NXTiekQBEUNoC4RiIQWTJVvCao2mNYxpmnoXy7JEhNFgOJ2MmXk+n8slKIUx+roOidLeO41cpAlSdK42SjPIdBEwQ4zsAgWKQkbdkGaGF2cXP/2n//T33//g3/n3//enLy/zzDKAj/C3/sb/8NEb94+PDrRG55uq2mD0CjmGoBATa61JjTGqawqsNpXzVFfre/du/czP/Nm/83d/7tbxUZYlWZrkebpabf7gH/qi1jpSZI5iUrpQA5gZIt3Ij3o7sOVBr8YCmVlJF68j2IOW+ipuJ8e95+5GrttSk9Y6MvTGavuzZGYthoAAMURltLXWIEQiYECtsEu02vgA29TObNu+7ogy12jSNA0+Tqfjz3zmM1/+rd8uioyITF6s12vv45PHH+3uTu/evQsajDEMV67i+nJwZ0O5/xREFiyaMUrYDARBsLe39/nPf/7Xf/3X8zzvIZQdX8zVg6Q6Pn1ZBQUEUquhiFonqRkV+Wg8YIjj0QAAZBi/KGpmHuQFR6pdI96orGuttHAphxbl34bPgAxAQgByw03CVpzV/9/FH9fZifn/C5Krv983fMyrR2Zm6kSstk+pfxsRaVQcSQEaY+bzed/bOz09/cQnPoGIREBEgkEV8+e9F8xeCCF0xPRGqdC9LBttWtFC6Iso0H1PBlCoOEhw/EQkHHUA1L4FWbV/xQCma950ECTuMlaGNjMW2Bag7NE+1LhaH+6kNaQPfHMBNQDFrmnaAsuBkiSRPEy6d8Nh4ZzbbNYHe3tJkty5c6euy+l03AvHhhAEZBUCKdVilIjaaVHZnzZNeqciZyvPuXM+0SZJEkKQiR256slkUlWb9XotIWZVVZPJRMaQAMBaa6OR8rIMiYlnGg6HFFqwhZyeUkrrNrqXeRjotA6dc6ZpEHG1Wp2cnEhBWwZnjTGr5bKqqtFoJO5Zpo0FbUREIqslTe6iKJaXl8IZXG4qcZl37t8h72YXc5lYe/Lkyf7+vsK2FoqgnXN1VW02m+FwiFobkxDRfL4YDAtZmfV6XRRZjL6qNkme+KaVHRSnKDIJzDydTsWDqm4WCACkwSwpoLV2PB4XRXF5eSlZKTPPZrOdnZ0QQlVV0i2Wa1Ft+78lzZWFgm6wRwrFUinVSltrV6tVkrTzEfKkyLNgjNEG+za8Usomyns/KgYAal2Wd+7c+Zmf+Zn/3X/0n967d/tisWx8S8BCTBpVCFEnbQ0gBGdtC5V/6623zs7O/tSf+smf+smfKNLk9ORF4yql2r0UY1QaVMcmJLUr7krlMUbgdgjFGoOGRZYqSZLhCETnA0BlWba7g9L8dM4xgFTUd3d3QuNicE1dYgxW6UieYwdEIiaSP+JAHJlcNAB4dPvhv/i13/iv/t4/jgxpCvnA/s//F3/zi1/4vMIYvStX6xAJQixsGiFoQBeJImitQxOQEI1BrWOIe3t7T58/U9oQN5//wqc/8cm3/87f+YUffO97g0G+Mx3tHuzv7+8zM+BVVtr6FFTMSipf/Loa5LbR4A450X8NnbYgEfeLLH/Yt0tk5AygjZKNSbaPDABCkoiKgSDGGKKTCAkABPOPiJ3YK/eJ6Q0v+WoG3PEcUUBUMicwnU4PDw9PT0+ttS6CMN6dnp7KCHyWZa8txPev/oP7aipAK/dLFBBbmWvxqg8fPvzKV76yndDIiJExSQjysAQFKKS1iKiR278EZqWMVUWaDQaDzCYy0rdarYxJd6fjixlba/NiIlmLMjpJksistc2KvK5rF0KMZNOEmX2nO/baVx8BbbtSefVVr/ZXyL23uOFZtw/AW4kgdDH49sdhh+jro13eam6BmBJ3RctnrT0/Pz88PJzNZswso2KirC6HEsBLXdflpgK4sFm6rspiNKzrOkkSMkbqBDFGozT8EL43RlAAzKrrVSF3mi0MEbDDFyCQTAtey1YVcpR89l+91Nvr2dUI2la6eqXZwVv4tRuBjtYmSRLXTcvkeb6/b8LL8+VyOZ1OR6MRUByNRq5uWk+52bSOLbTADcRWekjW2TkXmYgoy7I0TYG4S2oTGf+IMRajYQfEaDNXGRXDrocnvkHKrZvNhhGyLKuqlshpOp2K2o9RWrgYdafPiFtgYLHyACCUyFLjFZN9dnYmNXBr7bNnzz72mU+E6LVRqUqCj977yWTS7y7pOneOPDSNn4yHMUZU6vDw8L333jOp0cA20cwk6XWMUWtdVdVkd2+13GhtQ2jDuNViMRqNmqba2dkBbHdvCCHLMpEOTPIsekfBByaNygcnrdk+GwYAoZTpe67y1MtT8OzZs/F4vL+/f3Z2lqbp4eFhkiRnZ2fC7y2NZOzqQ/3DQkQy6CK+HDqx4a4+FIno7Ox0Op3KYva+XNjKANu2sbzT1dFaO5s5YwwrTQ188YtfONgfP3/+fLp/AMoJQN173zQ+z7Pg2+EWIjLaxBj39yff+MY3/uSf/Ol33313vVo8ffxhCMGgqlvhrNiiq3TbY4IODiYmvmma1CZKKWw5OgwACGFPkiRCy9M0fjQaSRIvebx464P9fYOwbkrfuFQrF2MIPksTwZ8TERETcWQKRD5SDKSz6WKxeP7hhyIn9Mf++Bd/6qf++Gc+9cnEIkdvtd0sHAIr1MCEoJW1McbUWGZUoEEjMxhlmCjLsrpc7+/veorz1fLBw/t/9s/9uR/84PL3v/Xto+HwydNnDx78wSRJlAHXBOywygwRQXeKBuo1ooNdC6q3GL2pRESKBB3CtLeitCU53xtlbvtcVxrkzIw9M4HCrcpujLEFrmJXsm0HaIkRFW1hXLbDAhQu6CtrCiAtSQSwqJxzEDlEt7s7vXP36Nnzx4NhGryKDajE1iH+3je/c7B/S2ubppY5RgpJmroYlEKl1GZdpmnK1BacAVipK1iWkJnJ8GjslIuUwfEkf+fjb3z9618fDEbIqtqUWZYBiZBcKzjoyQMicaBIrGKWZaiQGRONibYcKTqXaDyYjGezWW61VmCSdDQYKGO0Joplkau6rnJtNhjzNB1mqR8Nzy9neTHcVOVwOCpMslqtHMcGyJjEew+gEZGYrdUhBNWFkCEEZkzTtGk8IqKMr6BsAGlCXvPW2HdzQZQnr+okvZXvvXX/5v7Pow+olVjhPgqWNwSKSZY23g2LQYzx2bNnt27dMlbXTeW931Q1Ac4WS2MSZdLlpsyyzGbpR8+evvHGG5nNUpMrNrWrq6ZJoqCWWuSI9zFJEiBUqoVECileJIrgmBlJPKlsWYUsmkURwDCzgh5KBmJ+iBmQ0CgWQUMEifw6/9265Qh0IxBRSkn9WZ4w6DZ0v0S9SoQ2PTUHKNSKFQdWhJow+pCnWVR4Pr+4dbh7enpqNKfWhADOOWF5DYEYtUmy5XKeZZnW7Qi/WGH5rKbxWmNwsShSCq16eYyxKAxiJv7Pe5emCYAWam4AsDYtimFVNXXtqqqZTrUQ8AKonZ095xxHCMGbTAfnow9G6cY7tDYfFM45oxVT1EYrrUFpIgZQ3sc0TbMsaZrKGBN9MMZkSVoMcpsY7/1qtciK1CQamHYOD777ne/cv3+fG4eJMXk6n8/TNFVyklUteJ8mlDrREZt0kDrnZpdnh7sjt1ocHh6WZTlfLr33t+7emc1mLf4oNMpyuV7funUkZJ8HBwdKKVgtnK8ROUksEI1Goxi5KIYIajKcnFcngpNLErPZxGpTxhiZqNpUSmGWZb5xvnHj0Wi2uEzTdDabjcfjNE+YeWfv9gcffPBwOBhNxhKbS1l7tVxXZQ0AWpl0YCXgdq4hClprwdykacocm8alaUrEWZaE4NI0Dc5rjUdHR+J3xXAbqxh0iG5TksxAS9S1XC4pQpqnq9UqYTAGqrqaTsb/4X/w7/4v/1f/Tr1aWGMDEVIcZHnVlM45rSwAhRiNTQmJUSmjf/mX/wUiI9B0NI4+aK0j6wimcrWKrBgUAxAhamY0xjTB195prSVcixDIBW0QHCm0xhjFZK2unc8G6dTszudLrBtQOBoNNAdD1Xx29vDBvRBWy7MSmQ0H7wISoUbvvZgsT+SJQ0TWifN+1XibJh++9x4R/I/+6l9cb1Z/9I/84c9+5lMAZI1ZzuYhBIKQqyTaxDmnDDpXR+10qryL2liZ+k4S7bi0RtfkTJYwJhmm1fPyc+/+RNyMfuNLv8lARCFGeOvR7TxB750FFVgBM7CKCIBC0U9aaxMVMzMxIffhrHizXnFHI6BWgYkoIkVBFyGicHGIkWCGlhmVaLuwHxQaqyExESAyKESFioEVMqGwzwZuQ3NixtKFPM9tMgokY2+WiKC1m4CAgJ2FV4jYaRy+GkFIVaqHUN+/f3+xWDx9+nQw2MnznIiWy+V8Pn/y5Mlo/HHvwRglCBHq0OH91EHv+fvI4nVRS/ueQZF98t13v/2tb2VJWlUNIlaN64HKfeDQH4eZBWJnUUm1pK8ODYb5bDG31mpjQ4zCJCDXLl0+ijDMiwhgrCqKIt2USimCfDAosKx8lsUYU6UBFKHywQu4NLT8JphmNkaPqBDBOSfD2gKH6SMdWd7IhIg9nmt7BW5keDd+jluveF0Cc9s3S4Cc5/mTJ0/yPJ/cur3ZbDblRtCeWuvp7q5rA+oWTHR2dnZ8fGxtKnq0qkPrOefqynBCzrm9nWmMEYi1QSJSgC3sqe1HIm61sZmZkTsG6WulY2pFSNrrfO3d78lPbqyD2uI67fatQkTZZn3IKb+iDuS/vbA3Ahoi4i6XEqDT3t5eWZZZWrT9HlR6S8VIkk75IFlM2XJ5nitlBG8lbYvYUYRKYsrcEkwKukqGU+X9y+Uyz/OqqqTaLOcpCW6vNg/dwH6e55JnQ9c5U1sEnMa275RMMca4Xq93d/fb9oG16/U6S3ORAXjy5Ek+myulRoOhEJMtl0upn0s6ldpkfjnb399rmqau6+Ccc36xWAiSq1ytRYhX0GRS0m+zf4ByvWHm3b2p1vru3btSAFgul87VSWq8b6IHKarLWpWbtSTf8u1gMMjzXODWy+VyOBwKh2I3rxWsTb33wvQUhFUZYDweS5tZYMyS6DPQer3e39+fzWbGptL3lVsjyZ94azltmUSSDl+fDMnXPVeBMKX0QFmpeAtuC0Gfn5/LXDW00oH0qU996j/5j/+jf/d/+x8MhyMfKAKv16s8zwJFYVuSQCHNrGS3u7vTf/bPfvXD9z/4a3/l38rzVACAANAXtHirKykrJjwq8kpNapQOIdgkres6SdMkSWrvBBDTNM1oUCxjKNerncmkKZej0Wg8yhFhs1oJvS93aQADERNE8i4oY4N3AXSIvnYeUc/nS0/wd/+b//yNB/fXm6USZU7X1DXZQlMTys1Ga12F0qa6LCtWMRI2VW1MotAQIyJGGaDXCTObpNiUze/+ztc3pb/74P6/+Bf/8gc/+N5bbz56efJsMFDHx8cA7XPQ18kEgdv7ixvls956yAPfPlx80+QyM0Ps1ZDkT2krR+rfbIzR2iqlcEs3AbuBXaKglBIYslgGC5ilxXZeRERK6R+G1r5WHd2+Bt0xOsnGzfP86OhIOgre+93dXUFLnZyc9NtUdckHEQUfJQPoV6o/+I2XnIBGQCaOoWmaw8N9Wfo8z01imZkIQiDnggxgGVSKQYsMI0H0oschr8hA3jfaYJqmwtndd4CkGmy1LrJsOp1OJpO9vT2DKBMI4+FgOBzmWaINpmkyHgzGg4HR2miUsp5SQBSyLBNRZLl5SrdmRameS+v6JeOV8+iXmrZyX8Rri9Pvku3lusII4DWnorqXTLAopURDZrlcTsYTsfjr9XoymVxeXkojUE5bLF3vAIQbYT6fbzab1WrVUiSGAACoVQzbW1MpZRA1XvHC46vnDwBbshZI1x0hwM3u+Gu9L27BoOT2CbqnL952s7Cm+3cTKN4eQbfGS0pDEs0YY8bjsRAeCYPjcDgUSZm+iithX/+UiuGOMVZVJbo64lzFrG8XaaRmJYGjiOGI3RdXJ/kTdCAggebKJ4pXFsLIPM/X67XAtWLHLB06JcG+BCK46MFgoDri5RCc/Lkk6yGEJ0+eyBTy048+KrLMKAUA5XojHvdw/8A3bn45k8pqnueptRzjzs7OZlMlSSZJpE7s+fk5EWXjcYzx6Ojo4uVZWZaypHmejwbDoihm8wu5rqapvG/kbMWWee/lkRQqShlAEqSVnPx6ve7763KxMsUu7DHCG8WMo1E7uCWXL4HLZrMRmBsA+OA25VobtVgsxJ+FEOSw8kHiUyVI6kk5ZHLJd6JV0mKTjrJEQi1yWynq0BKxmxKWCofgA5bzBRH92I/92Hw+z7IEAJLEuk6fToB1o2GRp1kIYbNZN01zfLzzg/ee/OzP/uyzZ8+sSUxilTKyMfpnQcRm+g0pJkhssgQlRJTlgxijCH+Nx+OiyJIksUblWbK/O/VNfbi3X+SpQlzO5jFGoACRiAIzE3bWO5ILsfER0CKYzbpp6uA8XczcT/7rf2g4nax9oxIblbZ5Bkbp1AZkD5CORhHVaGeqktTkqUpTgoQh0Tr3gevGodKAhlh7wiQfPnt++g/+4T/+z/6P/8Uf+aM/bhL7j//pP7HWVlW12bif/umfHg6HssidGbmCXm4vyw0bAtfcJDBfUxhs7Zhqr7T7+dVvb5ggYxKtdWesrsyLfCMEz3ILuGsR9jgGvN6HvnFk5ghA11Tit9/Rd02yLGuCB4CdnZ3j4+PLi6XoDVhrsyx//vz5k4+eJW89ShIjZDHCjBijqA+prc+72atTV5PULUM0EWmlAeDdd9/9tX/5G0mS9V0cQUgpogylHB+ZeyHC3sNda5dKdBkoWrFKPmhttGZrW7EUbdRkMhFzyUpF8sFT01QU/M50UrsmSQ2vy82myrNEKVVualSYZ4n3XhdZjNEY7Zz3oTE6kYKt0KNf7QOFyG0EwAoFMdTfDL5ySCQl7mt/261J/5Pt3dZvMnktl0vJRXp0lTBWLv1mOB4bY4QhSGhyEVFwWMJGVBRFUeS1c3VdWyvThIyIi8VyOh55HzmGNKbKtuC93ooBQN8JISJgxUg/hJHl2ohSf99/2PbYXoHW9eI1F97faEmdsasEbNcJrr+z/VZr3THpQJZl+/tWHHPwJOvGzCKzKtxVYoulDyS/HY/H4kTrupbYS6BPYt878tR2HkYsuFRHBccroYA0ibMskwybmdM0FYsvwj4iYi9uwHsv0AQJaqWTKgGQNP6FUjHP06ra9BGblNGm02m5qV6+fJkPivv37z97+sQ3bnd39/nz53lWNHUzGY5kgjxJkvViebi3rwAkRGDmhw8fXlxcnJ+fj8fjpmmE3jKUpTiqnZ0diQwkKZTudYwxku/vCEPMssxV5Wq1SlOLiFW1ljl1MVsXFxfHx8fC/iEbSSiOY4wXFxd379598eJFmmebzUaGjJ1zQrUhpFqr1Wq5XEoqPxgM5B7JYkoHV7JbIRqULwTbJfGorKf4s6qqhB5TeuqCh3C+ZmYZA+Ut9aQu6cH+a2qx083jDz/6pV/6xfe+9/2jo6PVep0aDS1/IfbmWM4HEVNrxdgeH+28ePHia1/72qfe/aRzrnYNgJL6fGsxkABU7MAZs9lsOCxQschhybXU3iEq8l6wWs43o2FeA2imi3IzLNLVctnU5eXZS+IAHQRSAUdmiTMjcSTWNnWOXCQCDoSbym/qZlikv/DPfnO9/o9v374Vox8Ni8EgHxa5UtooLc+RVVruYFWXw+FQI65K76IaFgNlzLp01tpiOFBK1w6//OXfOz2b/8hnP1E37ktf+fJ//d/818LSCgDvvPWm3BcOIjbfd5Su4MTMLM83MysGuqKaAKSWSY9ksWIkIoYIzKgYAPuubas6uO2e29vURf/bk6LdwCMRAQsWvR2glwNp07Lj9cZHKdWfWPdD7gCt/EPmgAEkVO9ngRBxPB4/evToyUe/URRDIkqSNM/z995777333rt3/440UZQ1wAyMSmGMUWvbpvvXYWnIxMywVUnGDvqfGNP48OjBw2/9/nfOzi4AEJQJ3ksSrBR5ra3Aawk6B8eIul0qrZUCVByj564dq7VmREMSSQWZfLdWUmhMUlOVzd7eHnnfhODDwDm3uzudz+d5mhSjqfC7AqgiS2SXKyatFVGUjKquGyKwRik0Pl6NS4kh7F3FdgjU34wt/yr0xVcE3zf8zY0bdGO79K5Xqm0hhKZpFotFmimZ/b0CbigVYtTGrDcbIprNZoPBYDQaJklycnJSZGY6nZZlmVh7fn6+O52EEIoiY+YYGC2zkvJvNxDFvSBa25AliAJHEu/4Ci/Y1k7Y2gCvvqe9TLrypjcCFEQNwIgE1731aw/Yv7TWaDS0sCyd54n40egajiQgIOZYlmvvG2u1gJLko2U7yUpaa50Lkr3RdUwHAAjATWhg5VbKfZEsULLM9XottluyMWut+Ly6rvsgSaRERqNRWVf9Lup3lPc+T9twWzI86pSJldKIKJQag2J4dHQkT7So9R0eHkYfGq6L4cB7X603QOQaNx6Py7L0PpnNZg8fPpxdXM5ms/V6eXBwsHd4ePHyJfkgrJxKqXQ4HAM8e/bs0RsPy7LEdmDBXF6uhVtbarNJatbrpdVChwtSEN7fP1ytVj44gUmOx2OOpJQRwJp47t6/yhUZY6qqypJUyJum0+nF2flgMHB1Y7Upsvzy8vLlyelkMkmsTYxdrVZgLTJQiI4bClFrHX2oNqXUnxtXK0CjtEZFIcYYkSHSFehJggy5+977xWIht6NVv8iyEII2arVaSxIsVaUQ3Gw2WywWSgMzZWkaiVwM3vsksc5509loo1BdDckwcZzs7vzar311NBrtTKZKKZNY2ED3hsgd6IGZyYfFYnF4uG80QpLWdSux3ESa7oxVVN77ED3EYJRSSBpJcyyr8sXzj1xVE5FWKgTPMkjI7UcIVolBOUeECEotF+uXF0uTZUmSMurJcPKbv/41Y74RogMZzAUZBVTIQF2YPR1PrbWHh/vjkZW4anc6KYoCkMejyXR3J0ky1Pbb3/+wqd0X/uAX/+Vvfulv/9//C23MZDw8Pb34sc+9WxTFYjmbTqflehFCkGf2VRsIrxjGG3aGe7AqEpOYC+yWNPS573aeo5TCjurEmlRr8RQaOnYBbgErLFEOUQtIVFprayKTUgpbGiJW2rLzEthhm3NeAb7Mq2e8/bWEe2iE9hoODg4ePnz4+PETQSIAwHA4fHby4gfff/9Hf+yzoA0zM3FvjLb/V1sk0b2BfDUtFotmjPrYW2+/fHlubBpcIALU4j67sAKuhKGgs85ad3AfJbMfSZYneZ5aa13ljLHOOcAoD0CMEbEllDfGMESb6DRNNcCq3GjFWvFoNIqcNJvNs2fPkzw72N/13jdNEyMORsOyXHvvh6NhkWXz2SIw1U1prJWT6aOq7SulK8rOdt/GTlVCXIhS0Dcyu/rqTQq6Gz6mu/Zuup9osVjInlgul2PMjTHCpibLJT4AANbr9c50ulg0q9Uqy7LRaKSU2myqvb2DGAMmyWKxjgwiqYSJCJmliC0XsXwibLnGdq9HCdX77aS36EeuzwuBlnfy9b5++x4Z+INrV7r1tb4Rmrwao2y/lEYZh1NKGdvSjCulbJJkSRpCsCqRKoJgYiW0ksqeXEg7dmJtH8rked7i8kOQunGPUe/upuo9t/yJHFP6OxI2yRt6gQ3xRr2PF8oIIgLiJE+hGzKWWyBZoEzEKgVyGkopgU8zo6jTF/ng8PDww48eX15eHu4feO9d3SwWi92dPUmApBPx5MmTg4OD+XwuSW1ZlvPlYrprJFw4ff48s0mM8fHjx5IOfv/bv//2u++G6N977z3RbiKi0WS6uzt1LpTVWhusqs2Lk/kwz/IiC8Gt10uhBHn+/KmUmmWdvfdWGykMmE5pijqtX0QkYGPM7u7ucr6QXq9oa45GI4EoywmIpxQ6676xLT+UtZJ9K9/2YHIpDkuE1H90GyqR55aRo31tmfUgzSPpHcROzoEjPXv+tFxvZMgCgLXWhTFGlHe6UjxR1B2xQ5IkGkFCqOPjnW9+85s/+qM/6l3ANt8N1IF4EUiBEkb9EEK53ihkq42sJACAwhgYFRDHRCtQmoIbZmkD/MFi9tEHH27WS2BWHXkLbVE9BxLOWWRENLqpmrwY372/7+DZYr0OBMogU/Pxd97a352mqc2y5Pzs5Wq1evny5WK+BoAEVWBSAMvlHADml2eRwRrwr8yUiEXY2dltvH968nNEQVsDQHmerRarj7/ztrE6SYZltWmfF+5Jaq+Moer6wdglwdLv0x0DgBDVgdScuwGN7npjb6L7HtN2nK2U0tYkSQZd2n3FIwmsACMAd40ARCQCmbO4cZJEgSj0lFid/WpFIAyLjtx1Gye2SXohSinoHOp4PL5///77778PkEm3Q8YnXrw8vZzPR+NBe3rtfLpmGdLtLOtrTeS2wWJmjWCUDt7dun20t7d3cTlr6zCxzf7bNZL/ARGUVt3VIhFBCE5pwxxBoUiLt+sOEQC0Aq1gWAz6VebokCm4RmttjLZej1XB0Q+LbDws1hXlWTLI8zTPpqNRVVUaoBhMhuPxpswuLy+tUeL2XOO98VHGh7fkCpiZOtT8FkbpqrKE1+dcmVsys35z9D3sbde7vW6SRuzu7kp5TWplAGCMqap6sVhKtO69x05RWDqX/bCs975pHDOs1+uW9yfj2WzmvW98kGoeBa+1RrQh+h6gd+OssFW9BdWJKMglwhZPVn+x3XpcRX5XO4Sv+sfbW6VzaZpfV0WArR/eeGmtgbGv3ELLaKM1KjHTRqM0yOVb1VH2iy3u/6QdBtWaiIxp59SFMRgA+uZl7+D74RY5N1l5uaei+dqPADBzjF5rXZZlT08hHX0pqybGNr5V7uu3ltA/yYlJ5bavhJdlLf74B+99P8+Ksq6qqhrm+eXlpaShbe+Q4mKx2N/fjzG+//77R0dHQtS8WCxms9l0d08ptVwuxUlPp9Nnz54Vg3wyHp2fNfOzM9FJm81maZoWRSGjt3t7eyLikue5Bh6Px42ry3KNiMfHx7PZ7OTkZDAYlWWZJe2sc57nClTfDy7LMssykRGU+rOcUtNIH1fLJSyXS4FGy7pJAi1Ttk1TaY1ya/oevCyaYLWkni/tA6lzwJbukOyi0I0jyi2T4yBi01RVtclsomwiaUOaWkQuy9K72DTNbHaR5kVZ1kmSAkDjXZsnMShUrDTatlsh+QYQtyhRo+raffOb33z77bclu+p3dW8k5YEFgKZpDo/2RZILEXVudZKaxFoDCFaRNwiJtUDxm7/3/nd///cQkb2LMXrnjNXIDAwRKHaZArW6AMjMkeDO/Xs2yb/2rW9VVZjsTgD1cn55tPfgL//lP5dlCZP0GbmRvkPkxXwVQnj58mXjKlHImM1DWa0Xi9mmXIpcZAgACEbBaLxbu8Cs6k0VyKep1YmOy/LgYHzv3h3naqtaAyhhKyBu9zTF+25bHhCIFiMjq57jiBiQOi6K3gRR73QBiSEyRInpW8eiEJTI4EL7oS2fFcunCD2w940cR6YnBLMph6B+OCWEHlDFbR/hylgZABDqrG0Vo94lSGDedzhijAcHB/fu3ZvNFqPR0BhzcXERYzw/P3/58mWeP9CmFVeMMaapAVAcw7bN3fYc+EomBy0TOqRpOkb11ltvXX7lt41VUqOLMTJgjJGUFB4UYLRKb+1LIOYQyCa68eKKjAKMHBKjahcTa5PUxhhRo9TrtVbeY88xFrzzTS0pSJINlVIKYl1u7t29XbsGgUbDYrNeHh7eTZJkMsqHebapK0QNxPOwNDp1JE0HjgAycUURWRqTvO01SRB8fQbfo41kfyjVSkTIjewxRzf8CkoVnkSdW/WdPwGYWGsbV5+fn7/11ltN09RNyz0EHYrHex8JmLmuaxmd1NqWZZnnedP405fn63VJBOvVJs9zH2JC0bARTNaVL2RpaVxlw22HmOn6yepuNGtrC4IGvBJ7afcDvyYi3PqJlrZK76G3Pe62ndp+A2I7s9+7RtWBuXqnKAxQ4hFNpz0n7UB5p9hfKS8z83ispWUrFlDavfL+Pl0WAyrGvXfnPaS/LMvpdJqmqQjmhBAmkx3p+wpNlex8wRtrrWMZ2LTUV7LI4r28b3Qnk7e3t9c0zXq9LorhbDabTCbj8Vgr44IvyzJPkocPH6426+nuDjA23onYaD4c5MOB2PFVuVbGpHl+fPu2jP3IZGo2HhSDfLGcK8AQwt7eHhAnxjLzZDJCxKLIAvFsdnnn7t2Tk5MsK/amOzQeEceqLpMkidGfnr4oiuHezu7FxcxaO8h3ZTWKohDyegGfS7N8s9nIBcqqeu/H4zEiLpfL8Xg8n8/Ozs4ODg4Qcb1eS/08hLCzM9lsgvSDh8Ph5eWlbGmJOfqlkzsiiXiPVpNgSO4acSDqlFo6sBuKpmxEIto4nyQREVNrhY/FGLVer4dFbozxjUvTlAldDBrVcDiM0RtliCjLEiJqQyWtfeN6w+29z7NssVg9e/Zif393yzh00XxXG0CGvmDQb3hpbUamPDWRIE2MQv793/vmz/+jfzSdTuezhVx4DyaIPjAgMRMjgQJEZgF5qeM7t533AeDP//n/3tls/o//6S9lmeYIn3jnPtDGQFQGlA6jwVBN8xACooa7e0qZGN8OIUj2eXpJztXaKOfKsizrul4uS9fE1cZ99zvvzRcbZY0ymGR2NrtYr5fz2eYP/6EvTEZDYB+8x44SR7UTHK/vLt1wJb3FkLXijvmHO4ra/iUCbF0i1FkP4Y8HrZWVjQDcpcVtpochRKVbrm9jTF27LEuGw2HsKAqkoIKI0QeTtRPn0JHW9ybLXJWLpVnL13IRyRj6SI2ZR+PBo0ePTk+/JLZJsP5lWX3zm9+6ffu2Nm3u1TPXKLjiEFdb2rd0HTDMHfSamJij8w0zPnx4/8mzp4tV6Zrgl0vXNEmSrNfOjIfGJiFcMarLfqLAWaKVbstBQkN/997tb3/nB4nNE6MQAIk1oELFtp00EDVNRKwqxxCHo8JYpU1irWFmq3E8LCY7k/V6PRqNzs/P93amk9Ewz/P1ZnXn9q2L2eVmXea7U46xLMska9t+67JEVFVVaQUk089dVtdb5L5HztdZNeRy+gJ7X4Dq91Z/j2KMgjUQcR7oaDt7X96TiBljxAz1+ZaMmsh4jPd+MBhMJpP55QURbDaVMSZN04vzWTHIWOuqcQq4aXxqEwBgwi41JyR5AIQ8JF75P6UVXLMa3aPSPgxdBPpqBvz6SknvXElS7O5P1Bajp8Qo4iklgO2XV7DNtEUjzJ2os+SUtCVOR1ukY9JYDVvcLPJOiU2FvMJ0ZML9TZQagxhWOY4IDcnTZK3t1ACdMUprK9PAUksUCaPeVYhIgFQ4jTHIIGoisv8BSJhJ0jQVmgUJv4xRSSfUvVwux9PJcDhkiqBwPB674IExSZIkz1arlQ2peHrn3HA4bJpmOp3+1m/91p3j24k2Njcffvjhyakej8fIEGIQzPzx8XHTVNxZrhCCtuloNPro8WMkRora6npVLlcLuZaqqoaDwXg4KtcbYWEz5lAmmsqyRFZSVxN3KyM08r8My0nxxjkn1BxyRwRv6JybTqcChdtsNsJZIbhlRBRabHm/rLC4LmlP5nkui7+9bSQ+k/JGO3Ek3jFJtyVVYksdHOUIJk3SNB0Oh8UgXy3XzIoYrNXOOQUMqBBYKSUzG6bjt9/eNvJZAHB6elqW5WQy2v4tbpEMy/mEEMbTVt7RWhtbVkDerNZ5ql0dv/vtb/13//DvH+7tV5uSpcQMSimFADFGiiwMjUQQGRgxIjJAVhSBYuCAEWxqHjy882/8+T8xHo9vTbOjo6NyufJVWRTZYDAoV5skSZiQOQAorQkEA0UASmWp2CUyOhmPM9eE+/fS4KGq+OJ8tlyu7966nQ2yYpgnySf+/j/8bxHgjTceed8YjQwUg+sDWdzCfhpU3CGZt+Pvzn+xdBNCCJIYSH1dAwYiafwRtSz3sePA6iyMkDS07b8oRmYLriL/IWII7SCMjyFJksFggKhJZq06IQPuZEkRRbtuKz0gZt4q9LUv9UOLePIKIRwc7u3v70sPRuCa0+l0vV5/8MEHRCTlcnGBEgT9K143LGxvqZk5NbrI07u37+xOxnfu3tIG5SFE5hBCXddZVmyLFbXPA7BwONd1fXJycn5+bozRCEYrrTGSD52Om0El40wCHKdu0FPoGwURY4wpy83OznQyGkxGg+ib0SDfP9hVCNboYZGnqT3Y2x8UuUY43N8bFvm9O7fv3DpOjD7c3RkNiiJLjWmnvrfmvqFzJDd7I1tr0tE6XidU6/+XH4q+lQRDYnRiN/LYuwoAWC7XNslkTkMeezHlMuICqC8vW5JYaaEhovcxRp4vF1Uts2exrpqqqmrnJXuOoSuGKySE2AanV11PRESlsWvV49brmkTjdr4LGl+nTnE9VrtSDdsuG2A7KIkCRO8/cfsIsFUYR6lFd6NN7UdAFCJruVnMHMKVflk/DAodkFVCYFk3uRFaX8Ho5KNVi9WnvpDTD+kWRWY6NUzJ/ABA4iFJAeWY/WCopGh5nhdZrkAoFb1YKDkNAd8xc9LJ9BKRRFcSmqRpWtaVSaxkjSZN5LHKirwYDr793e8Q8MGtW8ropmmaphkMi9n8UoDfm9XaCpuYc0RUFJmw8cUYd3Z2pLm7nM8VQPTuwYMHq9Xqg/feE3R0XZdZlo2GQ6XUycmJOMXZbJYk2XA43myqsqwByFqtNXrfbDabzWYj3rTvBcjmnM/niOhcc3h4mGWZQJqFoB4AlsulUkbaBKFj1LKtmnKb4MrGkEep3cwxyiMj9FjMrDSIneGu3ouIEKlpWpGl7T0gTWXnnKvqJDH37t/Z391DRKO0MUYKYNRShRMAqH7m4eqJ6HZpNxoqJyzBhDzy2xayqVqKzYuLC6lQxhibponBVZs1x5BabTR+4+tf+7t/57/aGU/Wy1VLp88tEydFpsjEmgl9oECAaCMjMRibglZR2szl+uLypW/Knelof39nZ3roHYWICpPNplmv60DKO/aemTWCpagia0YbGV0TtI51tWzqleKGXKUwlptlDPV4lO5OR++//72Xp8+eP/1ovVp4V3/m05++c3c0yFPJO+TpphaA1nlA3n6iCV8Zurjhv/pFY46oWnq1/vZR97p6tBUi6CRJimJobaqUUqj11rAlIiAycYt5jDESARqN2nYkU1uFcb55bv3XhEAIV7HVlelHfvXd/StGn2XZm2++KfHFer0GgCRNTWI//PDD1WpV17Vs0HZnMxCgcFj/K3zw9rfMKMmEMeb27ePJeLS3M/6RT32y2qxSq5UC55y2Zl1ulNG8ZVJluoSIIkHTeJGZY45vvPmQyBkNiVW7051WPbtLyDRgL2Nutij45YFXyKNhoRUeHOxba5LETobDLDEhOGNVltrpeLQzGQt7eFFkuTW3jg6OD/d3p+Pjo73d6cRqHBaFoLMRCIEUolbKCGL7ldfWkxb6cAQ6JGTs2Fq2/0RidpmZa8cNsZ3iB1CIerFciqmS5iJ3wXuPVTm/uGRGra1CPZ8t6qqReuZisaqqqm58Xde1d96FGGOgKL7k1S0FXYn4ygWCEqf8qhvu/5YJt13vtV/dDFlkNa6BJq6ix46poGcL6X9F13EWN/5WbT1g/SdG8tSRuki6JpmsZNL9cWJHL6w6xnbZPGLu+1smX/RVjSRJAKhpGhl2ktMT9xk6tkvvvTxfEiBKTL1t/TW23Rn5OMEZyXQZM1dVxVtw+hjjYDAwiZWfhxD2Dg/kbahbgcUHDx7M53MKfm9nV4q6QFEQEkBxb2+HOSoFiIzYti0EjLbZbGaz2Ww2K8tyb28vhABJUte1MFFLtdMonWXZs2fPdnZ2AEDIpaVK3IYmTD3TQJ6nWZbleU7AgkCW007TZDAorDUS4ud5LkAz4V6Vju9ms8myQpCr/VB73xSQTPfGVuStTChJEqXbIoesz2azKcvS14242/7p6wPi9qEDalxNPoxGox/90c+en10oBeV6ZYwBIo2oTTuFIh+67YNvPEqqE96QaKzfuu1GJQapLyrVVC3pqRS0OHgDYBGA+Cu/9eVf/uVfvn37toQIRC3cJ3Yd0AhMBBHa9JcYGFDZRNuUSWSqEBHLzWq1WoTgNuv5pt64GMqmrppaae2jK+uNpxghEgAhsWIAYiRGYKRUk+YQ6o3VSiNoZIhOMTH5nelwWKTL+fl7P/jON7/+tcePHwPx5z73I+PxUCspskXdvaDzvtteo/NU14xhf0Ov7BIF5qswX7Y6X2dMkrqCSRL5LCHfqFwDXUpxw2chcXQNUpsGyBAKM4uGLLKSL7atOrCScF++kH+qDwGuuUC4ntfDtQMRhTt3b+3v78rmq5tGItMXpyez2Uz4KMSf2U6PCQAIUDxx5Nd44quzVJpR6A89cxwNi9E41wj37t75/Od/dDavJRx2LiRJJhPJ1ErBAzNShEAQY8s1AwDVpjzY293dmbimUhgFagjEVrf6YogtNkcKHT14Ukxhkpg8z7VGo9Eak+d5lifMzNELRZwPTTHI8jx1TT0aDsajIk/M0f5ultjbR4cP7t2ZTkbRuzy1qW2tczcwfa2neyNq6+5I7JxxmxBfu6Pq6iAyOCh2VopsumOEwI5tX8wubuXTvVIeIvRgH/HQddV4F6qqipGlp9g03pOUaqEde70u8QFtlR2lgd07XYVaUuHtH/b258aTc+O1/Su4FqhdA5n3v+2/kMeyP4LYStVNLOIrTaOtE8M+cRQfzMy+aThGuX09NFcgP77jvepvUG8yiEhAbWVZSswnPU7xwtsdGbH+kgQLDFsSOxHNFSctHyH3t8+kJfaStrRgiwS9JW8W8gpmrptK6h/S7zw/Pxe0lDCEK0DpTSBDlqSubpxzt27d2tnZQcT9/f3Hjx9fXl7GGIPzTVUnxkowJxGD7qZsV6vVZDIS8mpwTkoyOknSNL1z6+5ms7k4nw2KESI2jd/d3d3d3TXG7O/vSz2fmUWZeLlcGmPquhR9X631ZrNZr9dFPhBt8r7MIy7q5cuXh4eHsnrT6XQ+n0svf7sTL1msrLnpqLwlnOpvPQAQB5u0/f4eAeCc87VUBCpJPEJHyXIV3gERUV3XZVki8YMHD376p3/q6dPnRVEE10jIAvGaXb3+OHQ/p5ZvC7dkicUFtO/vJkHkjovtveoVeg/BI9Nvf+W3fukXfzG1SVNW6/VaKQPSJFJaKQOgPHEIgRCUMgQKGAkUo9XKRgZQWh5z+YiqXLu63KxWl/NLRo4c1tWaFHny6/WyrFeoKXDjqSb0rAKxA3TGAiLneYrMGjHLMmTI0tQarMvlm2/c+9f+yBfOL16Ox4O6Kj/64MN6U77z9ptpZsU+9Y/zVSgPAAACAgd6DbdUG8p0iSVyz7YRO0PFMbbqv0xb6NGu5gzcMvaAMsAKWt68/kXIwpB7pRKbZVma5H1adMOpvXp6W6+t5tm/8n1bf6HbYOETn/iEMXo8mciwo8BYnjx7GjoZTmNfz78FAL0bpq3G540zCdEbhdbqw/09a1TjNvfu3npwb3e58kqpxvvZYuWCrCz1/FAi4suE3kUiUIBlWV5cXDx6cG+Q29DUrmN1FyYE2f19GZC3hn/kogaDgbXaWk1EWZYkVndSEMoYxRx9XSmGxBir8WB3b2cyAgrDIptOxqkxh/t7bz58MBjmqU20RqtVb5pf9R/b67C1MtSnwt1NuzLcttOHlxczSw3NGBOZtNY2SaiTWusHMJTRQigtoNP5fD4cDlfr9XyxcDHYLCUE0KoJfr3alGXpfdyUdVVVdV27xm8XY5mwH9Xtr+WGZ+0uDGW0vbc7r/W4N3+uZHD+RhVaUsB2QmPbPdNVp7l9G26lFzeO37eUuCtob6Od20PJI9u1oLAbwJUvhAaOOrl1cY3yAMvBBZ+V57npRIUlIevhvtILEF9O3dST/FBamLDV2O46jq1hEqkf7IrPsSMhkd2iulExGctBRHFLSimh5i9Xa8FYFUXx8uSUQmya5uLiIhsOL8/PX56cnL548dHjD5aL2fNnT7LU1tVGCC6kCNwScyIwc5Jko9FkMJqs12WapsvlUgQPmLnebGR59/b2Hj165L1//PixiDEMB6O+n9LveWm7zmYzieDlb2/dujUattNZfT12d3cXESX5lhU4OztDxKZpTk5OxHv1ioTc0ZiEjslEHBh0fdx+Y/T8U0KApToONex6DTJrIBjybbIquQXEUWscjQef/exnP/9jn5PxMIm6+k342le72bZ6K3DD9hN1tE4k8YE449VqJY9/CKFIE6Pxa7/9ld/57S8f7O2FENbrUmsbA/kYInfdeu7ruoqYvQ87e/sPHr2xu7uf5gUxoNIKdXtzQIdAvgnRB+d8WVYU2/K4lLWlaOE7QjEJf9vnhTlLkizLmtorVhBJAyoN053RcGDfeeeR95BanWXJ7PJys9kMBkX0viUJESx2aDngrqwKX5mCV53Lq4Zi27Iqda3oG6/on69gJdamyiQAILq/8l75J74/Rt9K/zLD1Xjhq+fyiglsXyj/AK4PIF3bDXgzP5CXMUZpZI537tw5PDwUFM9oNHr06NH+/v7Lly/Pz89lF1JL2Xr12dsngdhmw70zlt8SIOEVDxcyTcejYZ4BRQT64he/ePfOTtM0IRAodD50Jb6AqBkRsatkKi0UOT64cr1ipnfe+dhgmMUYpfDbNI3GdoJItrukMgLDCd20SZpZVDIoyXmeJolB5CJPtVLWaIWQJImEzuPxeDgqLEKWWGTanY6txiLP9nf37hwf2URLez0xQhtLnRd5Pa7v+g3jPhWGrXQQOqshtUdEDCHIVGL/w74Ttlqt5BoDRcmGWyHk7iW2qaoaIoix7SCwwtq51WoFADHGunYyGiEgoL44xnTVu2Il5/ZDt9a2/7tWrN6awOve0pHPIfUk5v3+gTaOidtryB3ACgCkQ6k6MpYriwZtZ11ttQBuOOy4NeLc4wa4S036uQDv/Wg0ih3AcNvzcdc57uO5/ucSz7WCZV0vQL4YDoe6o8wUxyz5JXa4RdUNz/QQ7v7IoRtKlkoGEQlzqhR7JSbQqDQq4XA+Pz8nH6zSGjA6HxqnGJqyqpfL5XL5zd/7RmqT4+NjcboSNFxcnk2mo52dHWGuELIRMT2ReWdnZ7VazefzyWQCAEVRvP/++6enp/Wm/vDDD09OTtbr9cnJye7u7nK5vLy8PD8/n81mTdMISEo2pxiZ9rkG7ldG0PsyLyS2XmhGpNhjOjpuOZpzbrlceu+lhyqZHHQ2VyDl206u/78vLcg7Jb9ULTD4Kp3ol7ofKcYOOIKIkjHnefr5z//Ym2++mWVZcF7qUjc8rt6q8WxvbPmJghZ+f839XMEdenhwy1aNiBz97/3OV3/rS18C4s1m4zrUt7AFMqEL5AIxodLaJonW+vnz5x//xCc+9ZnP5Hk+GA19iNamRECACi2CQlahifWm8U3wNW1WpasdkSIHvooU0DdUl54CcwQKDIRWJ4lJFWitrEIzHu16H5smKtUyBlL0iHz33q2/9Jf+9WfPzwDJB3777XesUcQBiJFa4pEbl4+vTw6vyl3bNrO/rf2vYqtLeDVb0dsc4a7Zenhv2i6llNLtQfrQjfkKb6Ve6WRt37Xu59iXnAHwKhy48fphwYVSINbHGPPGG29Iz0asvzC7vnz5UrY7bWnnbdu4G4FJG9wBEiCjQL01K5mgciE4rXG6M0mMBoDJZPTOO++sK44xxkCr1apq6k7UNvSNeiJWSgVP8/l8NV8AwLNnTybj4cMH95RSVVUhgGCsuJsY2a5E9WfYP1qCl5NrlIkFxPbOZWmSpWmWpKNBkdqkKLJhkSVGaYQsS4xRCmEyGu9Nd/I8FxXFRF+xFsMPCeX6B3Kr7Hxd5kFfWXzokqS+jyUv1QrytAgRtSWcEFsYtha70GbG3fCrAOtjjEVRAOB8Pvfe+0De+6bF/rWgku2T77fa9sj8ttXov9j2rz8sFe5/e/VXinviju1jMkfm2NfoWqjw9ZRX6yvijhuv/qb3z5VqtTIJImVZ0r9BVlvw4XIXuknBG4NSV1cq1lxKyhLiCEiCOuyrZGOy/tKnlyYIM8tAMCJKiN3HENTCeUB8qnigvtAqXkfeLOsglCy9U5FSc1PVt2/fnk6nzPz8+XP59OjDw/sPfvC97x/s7X/60592deNc3TQVcyvYIJXnzWZzcXGx2Wx6dxW7jTqdTnd3d0VgO03T27dvA8ByuTw7Ozs/P1+v15/85Ce/+93v5nk+HIzkQkajUS+xUBTFcDiczWayzjIWpTquMa11CE6gN6I5KPwnPRuRBNDD4XBnZzdJ0uFwlGWZ3EopDscOzNyvKnfE3dvrI9V1ub+LxaKVppCSz5ab5K590G8bazUA9Dyjx8fHD+7f//jHPzadTqMPsquvIRC3TSuA0OG1G5VbuH7/QdtPijEmuEZaG8xc16VUYr7/7W998+u/l2gFFJsug3eNB1AKTYQusEZgRufCk6dPf/wn//hbb73lnIuM1iQHB0figKUGFFxsGkfUNvh87eUfNUGBZk/1pmzKKlRNqJpYO2o8+KiIDaBFSIzkt7lRab1xWglNm8qyxFiYTofvvvvOT/3UH0REhXCwt99FRVeVUWutZKI3HrFtb7dtZ7ZdTO8FdfeGvrLVrioIhWJLzQSsjEmkg9alBzefa6IWfiWf0rMCICIASl9S8bV7vGXqe6vOkgdsSdlcf8ErPrjfc609gnh0dHRwcCCTDzJgx8wnJyd1U8n1bG/ZV48P130PXo/vxLsAMTLsTMZJkuR5Gpw/Pj7+8R//A4tVxcyESkCJ3nsf+2y4bSFLIPzy5cvNZrVaLp8+fXp4ePjmm28Kdkx8khi+HvbCAJHIWCsgUjkZQWEILREiMkR5znsrn6bpYJi34yhaMcWdnR1A4kgcY57n+/v7R0cHk+koT7O+OLkVB9003NChMGgLOtQvF3WDNLiVcvVVUMl+oCU9pj7HFSckiALqJF98bIvbQifkvZ9MJgL8KYoiy4rNZiMNudVqI4Wmvsq0fRNfa1AED7/thl/ng3H7DTd+dWNNtt92fVu3JyP4Z4kOZQG3A51tj4tbfJ+qo3vty/jS5ZVvZcQIOk4G2SpC+ydoLCkP9FDV/oPEKfYTX1JfESspe2+5XMoYvQQHksTIx3WJshVP379H7nLP0iXGV+5gf2k94le+XSwW0+lUyiGSBwNAVVVlWbq6Wczmq8VyOp4kxp6/PLPWLhaLy/ML37iDvf2yLEPjNOAwLzTwerHc29s7Ozs7Oz+VZU/TNMaWkE9Ct8lkcnFxwczC9syMO5Pp2dmZ3AsBasms+TvvvCNqVHLykvVK6NAy2zgnTPrSVWmaRghhlFLOOQASRSkAcM6JGJRwmXnfYsW3w53tkqmsP3Q9C1nbHjItorkCiRCybtcpN8hZ9TTOsNUP2v6VHFDoKqfT6f37999999033njDN1f16ldf17wsXz3soRPe6O81d8Ie/VaRM/zggw9+53d+xxjV9zhkuWSILgIDKACF2sgtu7i4+Df/wp9/5513ZLuOx2NrbYyMWiNoJnlwFEUwqCFSvanrarNZLaty7aoquBqZrFHAMfimKtdVuXZNFXzj6iq4RiEASB+ErUm8jwBoTSqF3DzPy2o9mY5++qf/xK1bR9Pp6Pbt2yG4Vuh+ywj0T9YNE6HU6xPIV30NbgVMiCjEkNsWtR+1tdYStHSERG0TsP1cbjF3IjECSKqDX0FHw7Bto9Qr5qvbckzMV0K0AhBoYQJbpwXIApXu/xIAkDMEE2PUVukkvv3O/dqtZvPz9WrjHQyLPdfgt3///eCZXETibQu49VJMmqKiqICN1laqfJECUFQgOsYIoLRNCFAZWxQFEnP0mcaHxwc/8vbdzWxpm9V8A3W0LiaRNIOOrBiBsC1GVY2rA53P1w2p+dqfzcp7h4Mf/8OfmRQQ6pniGtkBh9QYqcUN0iJFy02EhprVxjJOB/bhvcP7tw+zFClWITZKoXPNYDBwrlYKtEaOLtE6MUpzaGKweeYppsYO8jTVMMmNxSZV4e7R5PhgHJpllts0Ta21xqYWIdWKKRgNkZw2rWAkR0A2CqwCC4REFMgHarr9p7HD1Ck08rQAK2jj67ZrRcDBxTwtsiSfzRZEZJQOQkxKzJGM0pL4Gq1d07im8XVlFQprIADFGMuyDMQuhJcX55GIGUFpY4y2hog8uQiBFbGifsodAEArmU2KbX26c5bY/hOMJaNiVII07ELO/sGjLenDq84FIBEHpoBACllCNKO0URa5TWShUyWK0QvcafsIBlVidGK01QoVM0RUrLagAAJoSNO0GI6rxgt1SE8gIumUiAQDUJKYGL00jgHIGMXMLW9GjHmei78cDosQnHN1CE4wR2LcpY/bND5JMmE1EQ/de33dcYD0dRqx9fLt5XyxXK6JpEFQt6osIVitY4zzy5nojE3Hk8XlpUJIspQRJjvTQHG2XgTkZbXZPTrYPTq4WM51lizXq9F0+uLly01ZHd+6/eLFWVX5yWQvMCzWq9PTU601U8s9fn5+vpjNgTjUTay9JqydL4aj2vnZ/HIyHXtXNa7Mi+Tu3duf+cxn3njjjc1mE4K7uDhDBUfHhxcXZ7PZRbleBdeU6w0yxMhV1eztHeSDYVU1TGBskqRZVuTK6MFo7ELUNml8SPPscj4rikLCIHGiVVU1Ta21Wq2W6/VKIIS640in4Ci4xIiwmUOOgzw1CppqQ8EpoOiiBq0YpCZfrTcGlcwO1LUTR9UnQACAyCIeQJGZAFgDi3iaNQryVD96cPvurf3jg/GbD299+t03j45300xpQ8gBORjFBmKiGKMDaMkrQCtMDGktc+UGMTE6hIDIARiMrkMkQFAaABKF2JQFuWff+d1n3/pqYL8q14EjI0YGH8mFWPvACkN0ja9rVykFlWsCwd/4m/+TT/3oZyPGwC7NjFV+VOidSTJIKctI66ZuVhEa1OCZSCnPXFNomMsQykgbHx2oTU2elIvkiQOTp9gEHwEJVe2i46YhF1UY7OT5UK/Wc1RsTIKk2YHlRIUwyfVf+jf+xL/91/7UZLC0kABpIhVBR9ABtGNmhawQOoRxRIrAATEgKmUIVRCiSq3AICmOFFp9cYhIUTFoVMhIIRLFPnMmDsThqomDSiEqpRLTqvRqrSlahAQAW7AR+0gNQ0REijAeT/Mk9y62uTJ6VhwhBg7U+e0uUDNSNWRmAFYICgEVXHFBM7Ow2782oLhyzBAku+XYVpzu3bv33e98fzqxWVZYa5u6vLi4WK/XO+OR1ipuKbYCckd6xkRtwfP09HQwzKfTcV3XqK45++00azKZuOZcdnxRFJ/+9KeVUt/67kfpGABgtVplB3vOOa0SbRImTxEIUGM7n+OasF6V6/VaPbh1586dL37xi4+fPHn6/LlipRRQ9MxY183FxYVw1kyn03duvb23tzecJHmer5ab5yenq+Vmtlwsl0vhSeAOVKwAYoxG0FsGQwjcJpatio6AuUyaRFLL9aZ2cTQoytpHAux6tEQkwbtE6HLm1wMovVUPIYCWmRMAEEErJGIAJBLV+3YCWEyGUAtJxi9GPG4xIPLWmBN0YB/5VjgQ+vr2arXx+75pmiJr+W+VsgIduRFjbQee12PA14SEcqtvpLxdeMt9qLsdz/JVkaY/4DWqy+2wDzvMmupeVx+N7Wf1CDVB+ggUWeEVuatUKaVH22fD2LHF9R8ti9bnmkIlQUQxhr4NCQAyTVuWpaS8dd3mWFmWScW4qqrDw0NpB8hmk2xvGzAodWMEbJqmr16Kbw4hiMazc248Hi8WK0mmpWwrUGEdwsXFxcOHD2ez2d27dyU0sdbevXv3q1/9qqyG1no4HFbVJjWWBoPLy0sR3SrLUqopeZ4vl0tmRqOapsHEGGOGebFYLJg5T7OPPvposVjcunVLLvyzn/3se++9V9f1l770pSzLBMyCuoVlrNdra1Pn3Hw+19ZYaxObLhaLNM/G4/H5+bngoiX1l9u6Wq2KohC9QmZer9eIKLRuQk82m11I58h7X2TtmK/cL0ku+9yFiKpq0z87MV7Bf1arlfeNMJ/0xeq+MdHvLu7my0PHViaFdBmUstaO96lcby4vL2VkK4aW4VJrTSAj/qiYA7ST+uBcjISKpUKQppn33lhljIoxuroMTW4zM5/Pnz59ulqtyqqSAyZJSq1QfGBudy8qk2XZbLmwJv3rf/2vHx8fXy7nshV93TKYpsDT6TTEGRER+T4ilPVpmiDFv0Y1q9XK10ma2DS1UhkiagufoeNflFVqmhbu6n0TY9b/KkmSsnJVVe3v7+dFcX7xMrHJDYux9aTf/CEIu/5W1ayrEEQiput48rb7SwydbHxrZ9BIfGaMMV15srdLWhu4ElnqOjgtwiZR7TRHW4ICvLK6r6adN9Jx+V+s/LVhkit3S4BXajT931+DCQyHw7fffvu9H3ygtZbYXyl1evryg/cfjz/9iTRLpOqCqrOTKGoNaBLNhGdnZz/4wQ/2D3bH4yERZWkKgXnLkvZJvaBRrNVK6Rjjzu7krbfeOjk5KQGrquJIl5eXeZomSUIIwBiYXCCrGT1WZaOUNIn5B35T1/W9hw8++clPolbvf/h4uVyPJzuTyeStNx/t7Ozcvn1bazufz40xHKmuNyGEyHDv3r08Gzx5/uz73/9+PxYyGAw4xrqurUm1dBGU4hCZ2zFcpRQzxRgHRW69jZEPDg5ens98oG14pGIKwVtMEDHLsrpynXCHbpmjWnGt1+CN+29Vx2rJXQdFnn+xC9LolWyAO+BAv1F6/h2x78aYi4uL6XQqnH99p1PgPCFIsNHOugD1Owh4C72HiKLE0O4r1QsXXXVrWs+KLHw121uruyyN6oqTZNvTd9t1+4G8ItaWC+xXhrvWybYDbp9hIjGOYjj6epe1VgY3qdPlVB3kTYIY2QbQ0Wb1pnwbrtx/nHyE/DzPc2lbqm7cM3YDLfP5fG9vTyk1mUx8JwC8s7OjlJLWgOto9xHRe1+kiYixJ4mVT1mv1xrbmEh6sXLCaZoyglJqOBwuFgtrbQZgbdIHH7u7u7Jo4vvH4/F8Ph8V6cuXJ8651Wp1fHx8gbP9/X1pVI+H03xQ5HnKzE3TaKOyLKsbNzu/ODo6MsaIJJH0a6pNOUfFzM+fPy+K4uLi4v79+/P5XOoBEnYAgASLABBjnM/nUmQm4tls5py7uLiYjEeC3mgZCJJE4P0A4L3f399/9uxZnuej0Wg+n8vKS29Y9r8UoqU63Ud1fV1Bhm773cIdfbRkn/Lq73jo5uB7q9ofUG56CK2llvsi0AHPfm9vL0kSETCuqir4KK1cuVNSsIkU5dMNQJoVQohNRBQcEI2KSV2VRWqtUUmSeFfPlpdCXcKoQohZlvc80jGyUhhCcD5OJsPT87PJeOdv/M1/e29vb7FYIKKEKS5QnuchEjgFoEaDISIyVwK0jJ0KBcXofTTGS5jPSUQFSWIaH63elqBXYnzEPYpTE9tSlmvEkcQoTWjk8Vyv1+vNJrEZAMhw7Y3oGa6/+gBacVujvXo/tQpHKIG1QkAC2uZX6KfzQUgLtNYxUpa1BKgEiNB/OjNIceMKw4WIRCyhs1IKu0YGsOJXEg9EFMnUGw5YCC/Nle28/urefE25iJkRW/4mxPYsj4+P33zzze9//z1EzNJUkH5Pnz59482HMkVAREy0BecDRHbOeRc3m408QjL13zS1RdODVPsPbdtpRoVNsFYjcoxxPBm++8mP//KXfi/PE6M0M66WG3EViVXW2kgspYPIdWKsUqapHIzT89llWuR37979+Mc/dv/+fULY290/PDwcTabee+/iycnJYnEhnf8005FEQ43TfPDGG2/s7Ox85StfEehZ0zRK6B5bCjSUUb+ON7QNPDnEfDRC5fJIt4/TxKbPT84pA6u0jEmkSWqMFvxdaw5a73hj56kO79f3Dq4CK2aEG+9WSlslnTBZf+54QNuIuNu4fZwoEGbpt4nGuwTUeZ4ZncQYvPfGjLCTNOg2hwJAuK4MCFc+uBvp0zJSdPXb3qJB561vHIGZUbCi6gpPoLaQKYAEW+RZ/eOqO33Wljv6FXlH7Pbidq1CslVmFr5DrbV31zjFwpacgyAQpUMpbcsOkXHFfiWpABEh8jY1tBxHSil9mzN26kbyHhmKret6NptNp1MAEK1vY4xsGwmbRJFQ4FdZltV1PR4OpTslGgaSNe7v759fXigF6/VaSjKw2ZDF/qKkM9c2oYVFtdNPFNzyarVyVd00zWAwmM/nxOHi7NxqE6Pf39+XYRgp5CiNHKmJTZKYR48e/dIv/vOdyfTWrVtZUbx49my+XJycnBwfHx8dHb3//kqq/RJqyI0TIhG5wM1mUxRFVuSqm+gVrQhZhLqufdPSWT958uTevXtZlokwyXQ6lSdUgNOC1ZIuai9YIpCcHkFNV4BH6G86Ikqa3peLpC/DzOKcVCdSKV9LBCZdZGtTeQzlitI05fkCETnmGiG1SZMXzEzA69VmtVqtVqvaNURkTdIWMLXxMciEjNxfoLhczHcmYwWkASj6qtqcn58vZrNBnkduWyRJkihlyrIUoxsJ9vb23n/84cffefcv/MxfFJULybllqxSRvffOhxCCV7EYZDIm0xMrUScw07bPo1VKpUYzAREQeSIdmGI0vQMmIp20VN7e+zRNB4PBbDbLssx7FKelO4Vm35H0AYDqtI/6h5puUDBjddAAAQAASURBVE725oVZ99JCghNrtQIjMCOyZMiRYh850RaEZft/bRKttXg9MSMMIANRzIwcIgVRx5Jd2nPV9Xa1zxC2vxacQN/Fb6P/buTJ9H6u/x8AgAWCJ01fgCuVDJnNkhgtaKV8iEmSfPzjH//Od763WCzyoyNmttbO5/MXL15kWZZlCXGkuGVYsb0Atvzy5cv54vLOnTvSvGmXHW90jkkoM4uiaGpR222nch89enS2rL/97e9hmsxmc2tN7oK13iSFI1QomRNYRuTIXPvGMa0PDw/ni0UxGEwmk4ODPaGfLAapa8rZbHZ+cTmbLXxoBGbcuBjKOsuKfDBARGYYjca7u7vf+c53bt062qxWkj/5RqafUem2DakUWm0AORKPRiOtdZrYpmnyPDdJen5+rnXujPPBNU3DEaT5L+GLtbaJ9XW05DXkpCyO2poVQ0SALk0Ehn6aELVE/arrcfYbug/oiAg61LdSqBO7Wq2Gw/FisRK1K9kbVmtWILZMWgl4/QWgRXgErteBiUCpHkOgRO6sfwb6F3f1FeZrVZb2nFkjwI2IpD/C1b6/Wo0WFgHXMZP9O9vcFJS010EpxaABkRiYo/NIDJqQOUkSIojel2U5nk6xK+BLHC3BjbjPjnv5CpsjTMXM7FzTe2WpPIeOY4uZrW3H4WSHCGxK0keB+0pIJAl0URQywzoYDGSXeu9j9LJzpN2AiM45cdjQEVMnSeK9U4pFeFgplSeZjCH5ujk+PhbaSFmZuqnGk9F6fnF8fCwZMzMPh8PNcjUcDmOM6/V6MBiEEIzSwTUi0zuYjJBhMZuHEC4uLgaDPEtSRMyyTAZ2lVJ3bt0u15vT01MprsiJ9QUYAJIUf1gMtNZ37tw5OTlhZmOT0WhEMTjnpL5trQ3OqzS9uLiQAsPp6enh4WGapk+fPrXW7u7uWtvydYi5TK313s/n8z43oG72Wu4mbFGHSltGVkMKD9SBsfu17QsSsp+lyMFdTycEJwhcyey11lOCEILVbYMpSYz3EREHeWETk6bp2cV50zRaIwdSSjFq55ysj9ZaARPTIE1cuXGJ2r1/u6nLJx8+Zgry0Qo1RTImAVCXl5fD4ZAiuOAHg8G3vvv+j37uM3/xf/Azo8m4rmvUqi9vZlnGqBeLhfB+MLMxRgI7uZAY2ip0ZHZNS8ypG12bYJ3TRiXGAkRpJoFWOgb0zjCl7RB/ysyJNX3DQhQ/qSs1JUkSuqJR74MBgNVr0t/tx18uQDGIOHGPbKIOtYaAxCHKiPZ1vinoHS1DB9tUgaNChYgi/UAkI4IchQOwI0Qq8sJaC6BCF22o6yCszl223/agAUZA1RphJjB9xsB8RbN5w8BJdAyAsauGK6VC5NYHRNrf33/77bcff/ChxAKDwWCxWDz+8MntW3ezLOkjAkmdmYEgIigievOtR2V5JOedpqkPLgRChP5UVDsQHJ0Lw+FQKbWYL13TGGOyNM/T7JOf/OTFxcV8vhwOB875zaZUSpkkQdTWmAgRmCOjlgY48sV8I82V4XCY5QlDZAqoOGlMVVWbzarcrENw2pgQnPd+sd6EEPZ2D6rGHxzo8XgCAI8evTGfzwWK1TSNb5wxCTNbY4UlBQDSxGLHsVUUReMqlSTaKABKrVEIWoMCk9qkq1RzXhTC119XoqbAUpntDPq1ebh2G3U90L781QaqCACKGVFhjDFQ7LVRxUxLqQ26wjUDSPhZuTAcDquq0lomtWxVlQcHB9AVtHVHtcivjEbIjobODfY/V0oxY0dtAwAInZD21dOlEAmYFTPhVtEJ8SotRmyLLlvBmbypTYIRr/SDu482vUe/dp7Q4sEEbSqroXqqd1mKqkrTtMPDtwPHXfGKVMfALGmiLH5sx8CU6vCZUt3tJ/fKsuwWhGlrUL5vJ8sYtyj6iXqMVGgljZA/6SfKYozWtBgCocfpvQURFcOh/CTPcxHsE8WOnsxZvKxSajwei7buZrMRJ6c0imuZz+fvvPOOMF0Yo2IMRPHi5enh3gErkRTD5WItSkd5nvrGiS27uLhAJquN6EkDwN7h4dnJSQjhxYsXg8HAGDMaDE9OTuq6Ho1GMsQoIQtzKzWxWC1ha9rq5fnZerna2dmR0V5hDhmPx4LAOjg4ODk5EUEk6FQN0tT2zXjvfWJUX2/o73i/K2KMAqPTWjeN630zAAgxGXZzoj3GuK8DiS/fzpKpmwKXDFtrRDRZYqLGLDGC+dLalnXtvUfU4+FIGtUXs8sYY4xBKRMVJFnOMYiqoGvqLDHROwXx7bfeCL756IP3KQrggKXcB6AUQ13Xk8mkqT0oTNP0+cnLL3z+c//WX/nLNk0uLy9NkimFaHSms7quCUAebW2UJetiIAJtMKUkZpLoOxIGS9FhiRxCqJouyldKFUqj5iBzJBQCIToiEkUQ7xtua4QtOl1aNp5iTycn/bgb3lGJBXzdq41yWtqHq+xWSBHF5yhWxKFzfq195F5ceasXa9MEEQNTS7aBgNAaE0RGJk/Bh1bmBDsl757Vsd8GvbPrsohrxeeOKR9b2mQAA4wdy8drLnL7FAHAAEZBR0vGTZJLcWL1xz/29sXZuXNNlmUCVJnNFs+fPy8GD7VpWRGI+rF3zUxa48HBgVBOAkCIHuJVCYiI+vY6ESnVzXV0X0TyAHDn1tEf+uIX/t7f/6fD4bCqGx8CMSzXmyTZSZRGxEgBIwUOTEprrY2dLdaBgZm1waODQ7LBN/Ui+Kpx6/W6dlVVlbVzq021Wq02m2q13IzHz4fDsVJ6MBwOB4NHjx4B8K/+6q9G7aWWm6Z59MEY45pKIdokkdqdGEqFrdhnniaoTVn7wSBnRq1M7QJz1NY0Ph4dHKDWs9ksBmc6pRTZKgCglFAY9tFM31Bteyx9wtdLGXJXE1Ndn08cRl9x5a1xpsRapZSIPgHAer0+OjqSSloIIU0TBkgSKyq2PbNP72K7TaIACa+XkTt/AwAM0HfOWmfde6NO3VP1eXB/gCsfzFpQ4q/sVJIiwVU02T4M7dVJ9t+/5EHrI+J+6FPspowP6Y5ysj9anqf94quO1FB1lAjQdYl6Rc4Yo1UttssYS0TCnIWIPdOkFGBWq43q5HqSJJH+a57nwhwpiITxeCw3UcB0ACDMFamVedYougXWWjZG9IK4444Wy24SK/XJyWRHittnZ2dqX8GyBev54BSiTdPpdCpXaq2VfH2xmBVFJhiCpmm8b5h5Ot0lZqVhPp9zK31qN6v1YDCwWt29e19mFKuqOjo6unj5UrjilVLf//73pVw8n88PDw+TJCmGg/V6PZ/P0zSV2ul8Po8xboiSJDk7O8uyDIhTm2xWa6nV++CZWdpYdV1Pp9PDw8PZbAYAEr40TUMUet0qhrheryUbRkStMUbqKUL7JEYeIkE+I7bFQ9upUnKr4Guoo1LZftD6WSaxUfLEwRUikkejgUwrtbwoGncmI+9j470xqizr8WS02WzW5UY2YU1KAlpjTLWpE40UfFOufuKP/VED/OHjD6tyLS7NKtM0DZMGrRsfmXC13BDwaDR6/8OP/tAf+cM/8zM/44Kn2ueDERH5GGyasHfQicAaIV9TontRypZIjM6SlJk9+OBbraUYI3pkZuTaJrq93oS1RiDtr/rrKGhERARoGReyLCnLWsbfpd8xm81cE7KsqKrq1ZZo3+XtXdKWZRAc5VVULXNrwNxqOQBRuAbgajMHAO4ySa2NMlraz62xwit4ilGAHMMWYRYiJjYzxgQmJXq+3Dop6Pti/axKRzPO3KF4EIGRO6nWK+WNq+vprvO1L8BWQqTfrPLze/fuHR8f1nW9Xq8lpI0xvvfeB865GAi30nAxgNDSJnCe/38Y+89nS5JkPxBz9xCZeeSVpavFdPdMjyTeGzwAtrsPWKhd7pI0gkszkruk0Wj8G/ll+Yk0EgCNeMAbvNE906KqS159j0oREe784BlxT1XPgHtsrOfWFedkRka4/PnvVx0cHJAAj4PqdxGEaAUh/22Mg5LWTqeTGIPOX0qKBwcHP/rBx2fn17PZjBk2m13X9qvNth1CZElRWDAwh5hS5BSxD7Hrh29fvf7DF1++fvtmvd7qhfV9f3u7Xt1ublarN2cXb9++ffHy9Zu35wySBFbrzfnFxZs3Z2/fvn358uV2uz05PCIijklTKOfcbrdF4NJK1JkQVHI7lhijM6apagMyn00qZw8P5g/unRiLHBPHoWmavm27XTufz701lpBAGWGU0UkxBZoa300EotLDsqCAzsFSnv/TI6SKSYi4P4pa/CJksFsGVI+wET0hde214FmPIDg3nU5FsKoqa+60K3DvDCAYyBPA722kdzwmIggi3HlxuYMzjsNU+8dw/63ef5/sg9/pMANowAnv+GP9eyp3XXyw2uhCuaDmVZ+jJjGc9bLU6erzLZ0/2Euk9ItS3q+db/zI32Sz9JaiclQqQBdQy8LMrORNJvNZrtfr5XKpuK3b21sAsHu8hpBrIXGUILSaLheX4L3Xwq+ChFUPcbfb6JDx06dPh2EY+o5TdJW/ODu/vr7+5ptvrLVnZ28mk3qxWGy32+m0WSwWKlhprf3hD3/46NGjGONms0pDGNrOEliC68vLEPqu2/V9q03r7Xarf6XKKJeXl7e3t9vt9uTkRH2zNmjX6/V+SSalUFXV1dVV13UpxLZtnXM6kj6fzxV+AQCbzcZaq4xXu93uD3/4g3NOEdEhBE2yb25ulPVaB/80oAkhqAAzAMQ46BBtCEFx5jpzXOa8KYtnlASXaNR5LNPYOgZd/rBYxVKx1NJuqXJba4+PDw8Pl97bqqqspdrbyWRS1/7k6Pjw6MAZO8bHOtNnTN/3TV0RyNvXN//sv/wni9nkqy//kOIwrRttl/QxgCGd9xvJkQzNZotf/vr5f/3f/M//1b/6V93Qg1DghIZCGqEJxhhXVdZa5hhCCP3QD10IwephJMXfGGesJaMAkRi5RIEh8dDHvu+7MMQwcjCkJEMIIcagg3ddZ7Kagj4F1WcEAGVHARkb51pt2k+CZa8c/Z6rggzCUvDm6FYAhKMy1NLYDFanPBYk9t8fEYmMtdYab4xh7UpZ8+7vaCQdcjFSjDGuroSMclFnQXS6KzLrkGXmuh8dsNYHgQqj9XhrZDBxZEmJozZfC3vGu7bsboxyRAmlsf4AWrpB/vzzz7tuJ5JCCK9fvx6G8Pbt2Zs3ZypjIoJa+Skxpr7SEHbrTSkHZfbsu54xj01+FkEd6VGrN51OUorrzYpAPv30ewTQte1kUm+7dtu16/V6vdlwEiDTh8QJQpLIqQ9RwCI4Fnr99uLy4na76/oQ19vd1eVN2/bDEPoueF8vlofz+dJam5JcX1/f3t4+e/bt8+fPf/ub33/xxRcvXrzUnpyaTvWyPA50J21pKzCnODOUUW0NhCtr7p8enx4ffvj0SeNdiP3h8uD68vLi7Oz48EBJsOvaN42mN7by1lunrSNrrbN2LEnvxUy6RDpjrrO3wigIJQgtu4GsIWuKc1InOpvNrLaRiLTRq3aHiIxR+sOgtMYiSQibpslh3J2vLY7hfa/8nZ5u3n9EeMcAR+/qHxPdCfrevT+Y8v6Q607MvC8sqH9CGatFuf9dnG6xhmp8NQOWXENWZ6C/o8O1ZVQsZHJ8LaMVoiXIsK9yg5qtao9TRGrvYyZ/sNZqGjSbzZxzmhdCFlaaTCZKpKx9MpUb0mdxcHBQiBX1FCiSVm2cJuKXl5fqpfQKNRrWiFkBw5qohX6YTaYpxN1me319LSKb21tlzEAUAN5mDmdE1K2uU0DT6fTrr7/ebDbMvFgsQugLCRcRhL5fzGYa0CggWT/u9vZ2vV7re2oSrJ91cnLyi1/8QkNVJdFbr9fMfHx8qEoheqb0Ljabjea12gZWHg+tqN+7d282mz1//lzZMUvlRvWRNKzRg1ASVq0BIKKeU/1aC/haSFAVjZRnspULUx2Plha0F6AN2s1mM2R5SvUlYU8lkLKyZOJoCDnFyrvppDk8OLCW6tpPp9PKmclkUlXu4OCgbioCJEBjCTg5gygsKVycXf93/+t/dricf/3VHyUGyiMAemzVuvZ9b33VDaGezH7922f/l//r/+6f/JN/EpMAWUYAGgtCgVPgxADa+JhOp9Pp1DlX+8pXzlqLPA4Uhaxjoc5MvU5KqWv7GGNIcb3abrfttmv7GLTUzAwhk2YPWYpKr1P1krVoocs+ZP1ybQEgiiJMATi3ftJ7X5cvyI0kVvrUZAypk0GRsYLImBXH9UfaaEJE57yIhMje+8BCRBoKwF1vWPT3mbkMAUZmnUZDRGbQ6FxnvdQT75u4lKnO9+P+kiUzMy0WC/2W1t8E2HlbKmzwnddoDQUBwAAWvkDn3MNH97/3yUdaWKgnjbGWRb755pu+D2GIaomMubP7AgwpT5jsTc5Azk5SSvoLAABAMUblMa6qSvkNmFnZKO6fnvwv/tt/YQwhoiO0SLvd7uZmdb263XWtMYass9YLGuPqbTus1i2hJ1Nd3dyeXVyvV20SA8YCkq+axeFRM5kBEFrXtv3Lly836+3t7frq6urLL7/86quvvvzyy5ubm8LDrldonG2aJuX52pE+EIlgfPYqBrJZ3RiU+/dOF/PpbrueTKvlYh77rqkcx6CnySAcHiwOlvPKW2tkPp3k2jNqYUofqC67IdKgCzM6VxJDdqspE/QXbEiSDDIn3IOm39E77EHt45hXZQqIQrysp8W691RN714CVNihcS8vVFKO7+wqLP+/77b/p7zGN4e7geb82uOwzIVEABC+89n62u12yqp4FwVmLT81PQpvURbGzWajhni/75sy6ZvJklMlhlBLXciVOE8cad+rnNJ9TXi9YPWgOtVa0nSFGpVWcfH3agL0ftW7l8hPRSqn06lGhJPJBFhUzmi9XquTVlfXtu2TJ0+Wy/lms1E6yaOjo+12++LFC2t9VVVv3ry5vLxU/JfiwtRz60EoPpgI4tClENrtVnNEteyffPKJyhIzc1VVi8ViNpv97Gc/s5nuUS9bo4SU0np9yxyJIPSDWvOjoyPvvZK1rVYr3agxxpubG2U1UctYmuUKop7P57e3t2ru0yiKBdpf14Z6zKrvkueOZG/cqGR7OqwhImXDFLSzyfTdmMlHd7tdwXkVozc+XDNiKbz3ROidnc1m6oYrZ6bT6cnR4cP7DwQYCYATcAx9V3nz9TcX//V/9Y8Pl/Pf/PqXu/VKRLQeCQBCOA7VWJcEnHPGuWfPnv2f/s//25/+9KcxSeA7fNl+OJvdkhBRVbmmqbz3zlgVDaus81ZHGcffB3gnQ00pxcgaf+x2Xd9p6qtaJmq0ebfrNpudYs2K4dJJNsiAUA1x9hOz916mgED2DjXu1Zk0zx1ZaceOLzMnJS0v7zxCsfLXKttqjPPeG3KQeez3rdwwDCH2GldF5qaZApAw3vFKgggC5zGUfRc7/hOBcjm6JMqaFdBiNtVYQ3OdsXhlUDkx/oQpTKPmo3pfA6j/2+02zPF73/uo63fM0Vo7nU6NcV9/9ezN6zPlThYZG5DMo3ak5rvybsGheGhIXCKIsnZoRvJbGKG5iJIIYT6d/IO//5dD300nTVV7Iuja7Thpx9L2oetD2w+7tjfkAvP1zWq12fYDb9vu629fnp1fCZiqnpDzTT09Pj59/MGHn//gh3//7//9n/z4p23brlar169fX1xcrddra3xxbKMYInMIoQ9DZthnvUIcJUFAW8LaAmyaxqBIjFVVGYInjx7OZ9Oha7vd5mCxbGq/nE+fPnp4dDCfNh5RCFPlLBEYAkfGIhlAR8YbMiAlYCREQ6BgS2aWTFFZSqb7/qnkiPpSG1Fi9n3nIcLGmKpy0+nUmhHSWWpxRPZd6tTxgOXj8j4Ds346f9fDyn+qXl1edw6+VJX3hITvgk007/1+ubD3Ikvcg2Rj5pGGHAjucz2ObCp5ZEWTY81ElTo4ZbICzrNGkrHlxQQgokHUPaBmSFVvKU8y0B4QzFo7mUyGLEqvA0UlG9PJFnW36sV1YoeZlWBLF6QQIN/e3p6fnztjdb7o+vpSm9CHh4fz6QxY5tOZM0SAp6enl5eXj588HEKntQFFdSgcrK7r+Xzetq0Cu0II8/lUc5r5fK51dUT03h4cHMxmsxcvXmjgcn19vVwuP//88wcPHmiaq6HMYrFQheCjoyP9LxG9evVKC0uLxaLv2xQGjmG9vr29vdUau96+ukxFfWpuPZ1O60njKn+zug0p3r9/fzKZLBaL65tL/X2NMnXxS6O3sI6UOEa3t251TXO1c6w5ve4QbVuoiy2RsW4YJWChjNsa4yE3cuCEFIcYNEa31lpDk2mjJeja22ZS3b9/Om1qYDESvcFp7b7645v//X/3z++fnnzxxRdt21prrRujPd28KSUB2Ox2VdOElN6cvf3v/4//w49/+pMhJNU+iiOWedyNqKK9GaStN2utNQaNMVoDt5a0VaHtGOecOouic6ARTEgxhlHTvg+x7xXxFmKmCi9Veo2B9PFpXBuG9N55+ZMvEfnufHA5ueN3MgA2mzeRNBJ5lhBWa2X6pJKwMu2gNYQ2v/M7JWLJrJ+Ye3O68ZhZEEDp6dMdY3+5nnJ5DKOMMXxH5IqZ7cOHD5Uqve9bIjTGalH7u2vxHZu436Jji8TMjx49+t73vvfi5ev5fFlXDZE9P3v91VdfPX7y0NqxyKAlHRyHdFW2eK/xLCACCKhsmfuhxJ3ZNRS6OIQBCJkjIm43q/lsMZ1+EEL4D//hPzx8/Hi1TpvN0Pc9snjrAMBbZ4xFBDGUWISFEvQDd30KcXd2fj2dTocYr69v276zvprP57PZ7JPvffrRhx//+Mc//bu/+7urq5uUUt8HAOi6zqprIUEap8dK/RkAnCNmFhhTebKGCLTkqKmMJohdjE1lJnU1DP20mXhvN5vuwQdPDg+WIBH4wDm33ewEsesjAOPEIIKydyABM5uM49+23TAMmc5MENEajHHM1cb9TSh7e3E/sS5VMvVDxmhpK6KInrfJpEZEja4AAAwKjDg5IvNdWWwAQBkHh+4OSXauQrDfsRUZSalG/OH72+/PhMbvJtkyDg1j2VHF3Y5hwagl+o5qoXMGYERIYc6fJCey2jhU/2ettWRTZssSEZ0mKiVrychnLSTIHrxLyzOa+liiISNQtEZXVY2O7aasjOSc03rv1dXV4eHhZrPRbFgTOx0PM8YwCIOWo1PTNLe3twq/Kl3qggGezWYhRcUoaYOTCHabLQEOiHVdf/vtMwV/LWZzxqSZsd7+er0+P788PDzUyqEu+L1790RE58VVNOLt27fL5TLG+OjRo2EYZrOJxoIXFxff//735/P59fX1119/vVgs5vO5iNzc3PzqN7/WEOTq6orzePrR0dHl5SUAbLdbddXqAi0ZTUb7vl8ul0R0cHCgbeY3b97ovFbkpAzM6gUJhJkfPny43a2JKKS02+2m00ajFnWBWvkUAZ3/KaZT67rK0IKIOndkjEE0OjbWdUPTVPtJAu8R/412wNvCAQJ7IJ0YB1vXQKIfhWTI4HQ6NaYDAHtkHzx48OWXX8a+mx0sfv/71//qf/nXJ4fLL//w+3a3cYTMLGlMpxIgx5gYELFq6l3Xtm37f/gf/vuPP/64GwbnKoMYOWGMkKW3TFbrGqK6Uh7NFJEWVyB3gpih9mMnKCWXeHT5+jt6FpgRBYbB9L0DAKy0lesAgHlkYWzbnkMcJSwFteSz67v1eqNMIKLUj/9JVT/k97RPFUqizSbilACYRqooRhAlm+QRLMyav2GhTFHst6/UMTFoAfnO0YiIIeKUhFHZar2vrfUpjnAbvKtU64JIsV2Y0zOR0dPthwvldyxLtI6cNwroSqKldv2798xgRmxJ4QnZG8QEFoCU+Ec/+vzZt8/btq2rpqqqxWL55s3Zi29fffLpx87vkT/sXfe+ay8PoFjV8gu6aiBojQNBLWszBxFommo+bfqQTo6WTx49ZEknhwfIY5Nvvdlaa8WDc+TN2OYki4nh7eXlru+tNSLSvXy13W436+2m3TnnHjx49ODBg916c+/evcPD488//9GvfvWrl9++0FbTyekRWGuMQYDIiaNoBWy3WxHdDXsAABlDBPqAtU4VY9QZu74P1gAIHyxmN6vN6cnJEEJTHT198tgSEiZOYTKZvMWLzbpNMThfWTdONVCO4wCA7IgS0kk1Q0g0iuWZPDX03lamQvfDd5PjzGzHWHj8TowR8ujLZFKrHWmaJsaAOCEikpJl3mkswp4DHs/NXtXr7vDsnyLdvXp49ioigoqOvrtyARABRCMw+n/9JiABIu1VfkriC3eR6RgPlRKxOl0YRU7YGDceVER1XZPJRCuNatCb6TiRqd5XiS9CJvg0owaR1yNNexqXLLEcQs40n/fv349ZCkmdeoxD27K1tq49cxRJCmTx3mrltm05hB5xZKZ0VV0ctrrbzWYDuXstIsfHx9PptO/DYrGY14uLi7P5fN73/Xw+3+12Osh7fHw8DJ1iiB48eLBrNwR4c3PDzOvddj6f9zEc3ztdzuZ/93e/+Pzzz3e73fX19cOHD3e7nSbHCvOs63q5nOtqbDab+cHy4uLiJz/5icbci8VCH4duoYuLi9N7x4uDH/37f//vNWJYrVYnJyeIqMBma+2DBw+++eYbALh3755z7uDg4OrqRvvB+w3XxWJBRJvN5vj05Pb2VivAWmbo213btmdnZxpOWWN0Mrhsb115yjQsyrN/t/kzAAoARLDve+/ru+29V1h+z1Lp8SczVoM0nOr73lZ+1kz0g8asyBoUHcE3th5pR13kp48frje3q/PzL37/+r/9l3/18QdPvvrjFzfXl4ho8c7wRoGUIqCx1gBQn+IQw7/4r/7l0w8/vLq5WS4PrXeAmIZUWlclHBeRNFJmjobXZBWQupaUUoh6UqxlLcnECTUFewgoANq4NUFC3xljOkRxNMIRhoGcBS0SAABJ5b0YQ9ZSQWbpuFTuvLwfx9+d+mJYBADu2lhEBHskEzKWgRn3oCR66vf+CZqKGCQ1yCAkwgKY55fy7+xBt1IU72ptMo7c9bmqvHeFd4O8glm9jgGzwK7mn7DHUWjPzs4U06vscTe3az3M2f2WN3xPQkvzaBQBRVRrLXIXu8PDww8//PAPX3xtjUtJrPEhxK+++uaDD5943xBZkZSGYIwR4f1GnYjsW/BiQPUeFDHLnIR0f9QF9NS27XJ5sF7fxsgnx0cnx4fb3e7k9Liq/Xq9ztMIzAw0hDCYqqqm06YPKTJ0YVjvWmZ1KlF0itw4ZthsNq9fvzYg3377crfbOVchYt/3z58//+lPf1zXdaFfZObEQsz9MBhnnbHOeYXPqb+xvsIUUgrqpLuuC0H1XC306d7JEQB+8+wlh+CsefTo0WI2RRJj5dWrF4vFwaMHD1+kN5HJ+6qpvPcecCyKKnRWLfhiMZlM6uvbddu2QISEBGyML4tJRLpB3m39jsZIDxUSGYMi6oaJOQGKMcZaqrwnFGauKl9wtjimueOwL8CdjO7daZG96WAYe8law9Ek+C4C0wgyZ7CICH+KiG4fFJ33zzt0qsUOFn+vgaOUuG8vFE17zAnMQc9qSf54b2RLkyH9Qmu8MaubaapUUNOSB1d0q1trhxBLqQ0RUwjT6VT5MYioaZqqql6+fKmOVn1h4Vm8vLxUNkr1/ZqRj5SNzMrxtN1uq6rabDYHBwdd22o3lJkV9gxAwzD4ulLpAr08S6ap6hcvXqxWY5U1hEAGUkrz+fzFq5fl1m5ubp48ebLebj/65NPASckUDw4Ozs7OmBnROIdEdHJyogk6AIcQDg4O3r59eyAHs9lsOp/drlcPHz+aT2d/8zd/89Of/vTN21cpJWMNM19fXzdNc3x6IiwKbUt5gnY2m11dXSHi1dWVunDFQl9dXVVVdXl5qf5e2a0PDw+7rru9vT0+Plag1nw6kmr5ylpr+13b92MVQeukup4aTnlfF0AQ5PKjjDMOVUqDoqlLdaQ01EpHrPwhMwuwtZUyfu+Dv8AQ5xjOWptSSCmhIVI6dxTvfQjbyWTyweMn/7f/7+//m3/+l5//4JPf/PKXr1+9qOva0agTICIpCXMQIGsMM/Shp9r/s3/xL05OTr755psPP/4oCcswjPiePD04dklYiEjGUs2I7y05aJm5YGMQjQoJwCgTJDpTpOdbxrouaihTOy8iOigMLOJMDGytnUxrvQx1vcw8mUy6UMJfRkP85+iuilfLpmCfKitXnkYXCzKWVEszCJAV+VHehEEMkrXWeGeM4QTGWcpKvYhokAQ4iYQsrQuIVVWNhDnG5DiB3vNZoiOUWKrM4/WXXnWxS4gGAG1VOeZ4cXHmnDs9PXWWhpDuUuq9l+x9V/K8Zl4g9N6LpKpywxA//fTT589eKX0jp+Scu7q6evvm/MOPHpNBGcO9O6r9vMR3GvXFjOo97OdVGkEoDmu9XhuS+WQaQuCYlsuDruseP350fX09Xyzqul4vNtfX12/fnAmZwIkEOez6vicanVAckkCy3q02W8rS1iys0ybM3HdtSlsAuL6+vbq6evv6/PPPP7t3754xBvOMAQB473UywTlLWS9lGIIiJDXT8s6Uk6DGWkSI2PvaGh+G9PrNRYUOQAU/pPbu9PjIOH/SLIY+GLOKQaraWWu0elFVVVV5Edbyvq+rGDillDjEwGTIGJMiF3Yb7X/AXnCjq1rqUURkHaEhI0bf3BiDko6OjnS8W2Qk0S2hn3o4wtHVMaNOBUgePIfcuSGwgqXNo9CxMWDM/lsAQHfwfna8f/D0pI3N3zuT9840XclO9oNZRAR5R94R91ijOU9wai2rGN+SZhXIVRgGfdyl7IxZFdjm0W2iUddMK7249yoBdV3XZMybN2/0MrQZPJlMVFoHMwhLRLTAqwyLNk9D6T8BQJCCjGZoGAbNaHXSSa9B0WTeuxDCZrNR6mZjzG63Ozk6ZuaDgwPtyRHR1dXV69evtasKLH3f37/3sGmam9ur8/NzxVQfHR2stptvnj87PDzUbax44JOTk5RS1+2UMEdHXVWnyFX+d7/73cOHD2Mca+C6tnVdW9ecnp6+fv36Jz/5yTAMTVUbY/SaAUBBXvqYnHPX19eqSaxjSypmHEJQJqwCjVauJQWZd12raxtTGLuauThfcOOY5RN2u6vyta5STlnuZnw1G+Ys6IlomFmnDEpirZG6sTo7kBRf5rLIqZbQnTMSk3MmJY3JmMAhjog/a20I7Xw+/9/8r/6zp0+fPv/6q/OzN9Pp1IAojkSra4EFwRiDIaSQkrX2H/6jfzRbzK9vb5qmGWfknCe0YChwgpDxwHEkk8kbMjMj5hnFPAhnATFGJopanQ4hFTHsGGM5pmMhwcBglQTBAhMajFltXRhTkhBaXS7FymhAk7KKOTPTe4nenRl43zGTjHlw9pp3faVx+CiFGO9ia8gBPWbUi82SsmjwDqqsYQWOobky0YpIAQEgGAAl9ivmvwzsCCAUydf9umNxvThCZUdMuJ3NZqrG3HW7kTF4bCm/sxKyJ/ohuahdLGT2i2OId3h4+NFHH33z9bOqqnvuKt+E2D179uz+g+NmUqvByivyzkeICCLtZyf4rv0twAeVZFmtVojIEoFxOmtC37Vtu5xPq8ol4eVyPp3PjDEX55ciwgnQKigm6MzfMAwadtmQlOQTM0DGWtuHYRiG2nlEub1dv379um+Hn/3sp59++r26rrt+5xWAnjilVFVj74SIASCkSIDa3EqF28GOsWeOJVVAgw3CZDJ58OBBN4Tjo9PEyomanPeffvrp2dtz69xsNgsJwsBIUUQFiV3lrKQoiHWtmpSIHo+PD6tJs16vb1ebtm0RvI7BEFFKgbOWhs0jvLDHZaomSb9f1/VsNtXNeXx8GELw3jrXXF9fq4EuRWzIneC8EUf09Z84OWIAU37chMh7frYkwSR7qgySf5p3Bee47U5r4b2Nuu+JSyjAPEbB78WRKSWXBXS10asTnzFT3mgIpR63bdtmOkdEHQ9Ve8rMOm6kJmkYBiKrkJyUksG7sFqP5VhpQCyqZzHGppmq+2zbbcoodEVI6cVcX19rFtU0zWazUZbKEELkmHCEJWovTW9NG8wiopClvl97713lV6vVbDZTH3B7e6vBeByGpqoyV2W4ubl5/PgxIsY4qJzf69evf/jDH+q6KRz6+Pj4enX7+tWrx48fbzY7a+3z588BwFrSOWbvvV7n4YFfLBaqpqDY481m8+zZs5///OevX7/21bTvQ9cN6o/rqtZlTCmFvjfOKZrpzZs39+7dG4Zht+tOT081MNLMWBdK46HNZqNFJq0eazqrxQBv/MuXL72xw9DpJIWOx8AebXgp5uv7a4NfR030g/bB59qtKKAteDdziDEmBh071nLdGH5ldQfnzJCihqE2C17pdte/GoZhOrWPDj7UsYumqYa2BedgZOwCEWROCCgCbT8sl8tPPvnM19VmszFZNdl4N3QdZF6w0PVazlFaAsygBGPumDhRRGlonFNkRkgpZ4G5dqpRQjFlmLlXNdowRjtKtS6mkur3fZ9CgEyToHMBl5eXi/nBGASPydj7jc/3bcj+YX9n2QUAtAGMgDxOJ4R9SBMR5dEPa95VDR+G+E53LBO5p5QILSNrT4RFiIzkFFRxSu/bn3JV2TdrxlXsgLroMeI5evRDbO7Njj64vr58++ZVu9s6Y5mjMyPCG43VQjQDCajKJADwXRtYr5iMoEEgAnbI3//BR9+++JJhe3B0hAZWq/jlV88ePHj0+effTxwAExIYgzGGGGJlK2McI4nkFdwnyEQgg3GsWgshWGd10HUxnw1t0AKsZUwiKJzC0FSOyIaU5s30hm6aqo4s5Kht+16GqqrW/WCtZRaOISVBHJtAja+IjIBB8ijQ9R2ntFqt1jc3xpgf//gHn33/E2/M0G0hBV/PQhgaX5nahBAqb1NKRC5lfnwi6vs2xrhYLETG4qEOI1qryytAtp5U7XbnKB0vPISbWdPE3c1kOjUiglB517arxdT2u3C5vaVUN01DQEozTQRNXRMRS0TEiXNmWs0bqiGYsGuBLtYDpyCMXeys9QIYY3S1S4LIKaVQeR9ib5DqZhpjhJCG0FpnmsovZpPKmdvba2NxsTy03s2mM83s79+fkwDd0WiIek1rDTAVJJGoUUHMvFMMrDoSCQCYQYvTROOzZmZAQbhjCEEwkuWrRQDREpW0+Z19r868MrbvBk0BDaldA2NszJyCmLUotJ6sh1AnTOyoPQh1XavAe2nQAsBsNkPEodsBQO19SknRoYhm1/UGiUePb9p2O5021hKiIBAgb7YrRc9674ehC4G9t9v1rbWj3FLoW2ewrmuOLoWgSVgUWa1WsIfqUttd7CmSSIjoHKKwxM12NZ9M+747ODlq27bdsvd11++msybsdnUzcRaALQpwYgJs234+n89mNZG9vl1r1OW979qha4e+71EodLer6zZ27XzSxMg3bbe6XsU+kqd20zbNdDqdM68vLy/v37+/2Ww2m918vjTGVRXv1pvdevPg9N627SVxXbm+A5C0OJyDka+++Xq1uvn0e585CyDBGuQUAH1iZmHlSK+kmkxrjTl0TMsYozm01tuVLAwAVqvVbDbZbrdD1xpjQh+9tdv1SiesEFFVRBWuvN1uC2USIRLakAKn5MgyR4ipcb6p6/Vq65wTeV+ysChnxBit3eNzFfHOpZT6vNmIKEAAABSMQzTGeAHuAqQUIaBQJ1LXk+12KwCVpdB3lXPkTN+1Mw+1c7/+zS9VgxnReF/HGBEcJyaiISawVRTeDf3y5Pj+0yc4qzdtN5lMqqZBNF03eAbnnMQESCSAeUwOxiYlY9IifLDW+sr2fc/ALFw3HmDouuissdM6ht5Z6tphoMRxUCRmSikmtmgSc0ySUmyoBvIhIfUB0bBITBxCVAy1OFNVFRhY77YHBweqWnt6716MAQBSGpm0/4SjfbfyuheOA8KAwIaYQ0QRJMMxkTExRGFDY8g7zjullKyKpxqxzjuqhE0SBARrRqEUg2p8WBWj1Wij8VU1RVMJJzI2CaMjLSnmUWwAFhRiABylYgn3OPv2y2AiSW/IGLREdHp6Op/PDw4W3pnn33ytdHSRk2bTIiLCSo5JaP48NydgRm0h4mKx+Oijj7766qthGDQwJ6I3b96cnBwdHC6sNSkFEcBcvtM7kZz7f/fNc4UzF/pBJ6ldt+01VlU+Gw1++76vqrHQpOcWeGyBgAAztG1vbVIWBWMkm1epR1Y/Gbq+bdvdbte3/dC2zrkPPnzy8ccfV1XV73aV94gOQBUURDiRNdpPUjio1oi0GDidTodhcG6kjygdJjX9IXHXdUXcZjqZAIA2d9u2Z5Da+Rij89W9e/eIbBfA2DEI1XSQJRJaQ0bL4LpQs3kA5L5vwMZd3+12Xdqmvu+Ntc65oevquha4q9soaEv2xhYpixg2TQNCi8VCrfNsNjNGqxSQJ3ag1C2YWVvCpbFatp3eOGfyjlLwgXdT1f3zVr7GdxUa3t94auwAGUHto2RKDRmlFO4I90v8Xig1NDuZTqfWjphYten6BK21+g4hBC1vUla/UVoJnZE1eR7fe9/3ra5M13UWra6NAhE0NGmaZoTsjpRbVaGp4twnBoC2bR88eKBYKkUVqfPQXFlEYmANI1JKgKC/ICIpiXOVDtFgZkBTHNnhwTGMbFNJFSdViEy5JPWONCNc3W6ms2axWADA8fFxSqnvByLabrcffvihjt62bfu73/1Oz4WIaKFVD0JZk4uLCyCrM0XKEAKEr169stYeHx+en5+r0O/V1ZWIXF1dLZdLDTgKTGEIUY+Vni9jjO5DrdJr1T3l6equ6/Rc65SRJqDMXFVVTEPbtt5YRNFZUF1tGSGxkEQQQFlHXF0JJGsbANi2OzUsalvK4dUzoug5rU/2wyCZ2r20NkpOT0S1U2wwxxhZQEQGosRRCwaTutJzVFWVxeq3v/3txcVFCqPYJeuoOhpm7mMSBE4pJG6a6cnJiSpkVFWlD3paN6WQrntg/zSVYFR3oO6HlAVaKI8k1XWtY75jbQxMt1llaAU752Ias7oYI+UMOCWyVOuRgbH9mdTfhBAokXXm6urq7du39++PQgAiqGMjf9a53FmAO/jbe7VSxCxrynfTR2lvCJiZGQ0ik4H89BMiAIrNiquQXbWuTAo60jK2GBIngJGrMr/u/oqIAO+mHHGvfLv/q/t2zLKIIWeMqevJ0eHJ+fnbzdnaIqnDGH07AKIYoAQJ360Jv28oVQkjpaaqP/n4e29evdaV0c7N2dnZdvvhbD4R0TmjkV5RkigF/3cvt3xzP+qRrE+paZaunSCOB6/vEE1Iabtt356fXV/fGOMsIDNYSzFELWKD0NBHLT8qxLxpGmbo+55jCiGs1qurqysHVNf1hx89/d5HHzdNo2qDfd9qZC2ZS9lkOdiUOXQAYDKZ6NZ3mQNv37eV9iHHpMlH2/q6aWIIVV2LiDGIQFVlojAgKdyMAgCASi+LQFU755zWfLy3VaVKsZUxWFWubVuGZDfr9c2tsTivJuvtZhiGuvFpVB7N3PEwIvQksvobAGCGuq500EvnB2gk4WNJEYwho0/nbuwHdDD6T/FplJxAcrqKWWVMCDH3YPZ31r7f3T9y++9cfoqIOhhmLMVwd/b00egeDcMQQ1APrQGHde7q6koVZPt+BI1rL0YykEqroACgkCibmSCzq6ZyHFJKzo88GCPuxiRnx0q1tTbGYTzYKemqhhCIsHwiAOhUKxHN5/PVaqVBlYr4AkBVVdo2G/VkMuOEdxWS6KeErOG63+/UqWVOl4rt0oqu/rJK5+52u4cPH65WN23bbrfb5XLpvd/cXg7D0A/Dq1eviMbWY0rp9PT0+vr69PQ0Zv5FRQnEOGjPdbVaaWG8rush8na7vb0dgc06J6MldILRgr948WIYBtVH0kvFjCTXkEXRavqeWplQXPFsNgOAGKNy7ekVaiCl/x039l54R4WgBpHHH+WHmLsqBlALSyzRmtGBaT+izP5aa1VgJsZYLkxEMFsnfQom551ElDIpG2sCLiIpOjISU1P5lJIAWyLv7W9/9evXr1/vNq3ut5RSAhHBJCmwMEhiQWN95ZrplNDoVIiGazoyJxnHUCKzfaM6giJZdK/GGNUM7vsJ51xKIw4RAKraTHgyDEPfBSJb1yQC/TCUt2Xmvu9jROvIWqsjL0QkzMXuaaqtjC4HBwdtHljf71v9yVe2BrLfHtWLTSnl56WTbCN1F2fgm947aa6gJK8qz/ru0ISMrpo5Rj3FAOCcU0hmyk2KYlJwlCmCO2uHd3Ti8G7u/l2nCQC2richBGt9SmlxsHz8+OlmtRZgFABgta1IoOJFEhnd3ZzyexY2t0bGAZiDg4OHDx9+/bWKEtZD6Fc3t8+ePZvNJwcHi8ScUtShb+GxQC8qpLd3rXf+uPAY5IaNZGSvtXbXdfp4tu0upSTCm9vb9Xp7fnahd6cLxMwIxhCCJT1IGp8SUd9dHyxSaqqu67rdVt98OpktpvWjBw8/+uijpqli7h6FYfxoJdMv16m5uGYA0+kUMpahBM5lNn90eykZMioABwBar8bcYrTWdkMASETUDT2hOTw8tNt+u92CJBAhQ95bnZIvb55SstYg+hAG5uSM1JV5+uQxIF1eXve9i5ziEMgaADTGJAZgIJRBK/kgxpghxW4YGhFGYCRmtsYDjsJbxjiNM3jEcYwVntJEYWaV3FIEloiM1eh8Sr9ztAyToNwNI/354/fOgSk/Kv5+HHrmqMurEylExClh5ltQc6kOAInqulaOlKpq9CFOJhPlH9bpkfJEipvUjEqxAt5XaEYWQMw4L520gXcrxsYYRLKZykOVjjRrCWHQI+1spen49fV11TTqazEzX2q+rq5FRLqum81mxT4OfSTptOOosGprrebKiIhgDDll9tAQBBFD6IdhODw8TCntdrvXr19/9NEHm83mzZs3zHz//v047G6++ebw8FByeppSevny5fHxcdM02+324OBA4WOXl5eTyaTrdjoKrGGcc+7s7Gy2ONCDcHFx8erVq8lkojPB3377+tGDx2dnZ0oRrHwmOu6s1n+EENtKCUYAYBh6AFAiqlLkcM6VZurY+8wvvS8i6oeWs+Bj2T97Rj/pQ4lDqydXXamO3ChhTgi8b3N0IxljqsoWVIdu8iEEW8K+XL2DjOwr3Zmy0Z1zfbuz1hIwx+Sn/qs//PEPX3zhnCkABWEktCmlqIqjZAUYkVxdW+t2u52QWSwW7a5rmiYMcZO21u0Rwoex7AzF9aoDoDFN53F8kcqaFNcF2ZnpzkTEoRrZP4wxztp413IC5gSAMXDvgjrFqnJJRIt8Ik5EzIAxRsU3IGJKUoKt9yz//qv4AlVLz26C87yJAWR1JSmP+hT7oH+YD6CzrnKuIrIMAECqMEF4p6WW0ji3zczKXJY0ZVLMEO+hPvfiFSJied/XluzxvXvR79uQNALyjhlAZvPldL5Y3VwnYJNRYwDAkkTeeXL7RrB8oTteV9OR+ejpB8+/fcuSEHGxWMQhfPPNN/cfnM7nU2ONSBJB5nFORUQUhV8W+rtPoqQ7JfusJ5Oid9b3PRAimoury6urm27o1dQKYhpSGZ3ScU3ltmQWIqX0xJubm5sbrpzX6q6ztqqqTz96cnp66pxLoTfGMKehH4rLVGicPu+C41BjbfJ4g2SuPv01DZyxlGSZq6pmZuaoWJV9cXJFhAHZruuGNBCR84Y3UZJoQdh756yZNI1WhARBxI6gXEeATBgJJHpZbXab9a2khEAIyDGhICMp+xqOo7FovU2AwzB0Xc88Mq1rxqatYo2v1Z7mPY2Id2dVCkLyLj67QytQCbAKhiDv1ztElqCI4B4K8LvJ9J87qJTZHFliTEEE1P7GPKxJWXRILX7KU0MKVYNcX9HF17vQMqz6y1LQ02fXdV3mgIUxxUlpX+0u5vxAzZwxI9nWkHmh8/hvqOuJtZaTdO1gjKmryc3t6tGjB/q32+22BA3l+kumqLGCMcagxBhvb2/THq8TM4chKd9WTENMgyE3ZDi3c265XD579uzjjz/+wx/+sFptDg6OAK6stVokV9rXk5MT72tFUaleVlVVFxcXqpqlo5yatRPZGKOCtBGxmjQppfV6fXl5qRmqiFyeX+y61jm3WCyYebVaqcS95JKSVoz0pBu6E3XWbaYVb13J4i30oeiycKYDU4en/kM/OnRtqVfxHigvckxJBCFwYmGRO6/DeUZWq9D6KWPhLWfMqmZSYsuUybrLFtW3CjqzxBx5pGbkmJwhg9hu17WvppP6+dff/ObXv2aOzIigesaMiGiIASMnNJY5IZkEEobkPHjjhLHd9ZPp2C4h1bjV8TwWtR5oRphkOUGl/oyIwmOcVyYdSh5fUFfI4shMp1NmXq+3GgtqfSU3s1AEQwimN9YRZsqXYtP0v7PZTHtzktXPShz53TP+3jEXkczSNM77MLMgiQAza06vP9VktCQ8IiIogCN9Qho7yvk9xzdPmEcTCYXNOLDLxcXuDb+QjArEuqpq18qHftc6lX9yobsXAeMqEbHOOecnk9n9B49cVafIMRtNtR0CbJ3ZW4JsbVlIAFkMoIoaGcDKOiI6Pj5+9OjRZrPRw7lYLIxx3z5/uV5vEcgY954lfc+qlkXbz5lkb3BTRJw3SKKSWNY7ADi7uFDIZRaOVor8O25CzgPR+8uRQkDEynnvfeXtbDo9PT398MMPHz24N5vUKAmUAYKZM5uaZhLq+xXtTBlOrIhZLdiWwkBpa5UbCSGoS9MIVPOAMhtakuzdboOI1lCIQ+jaSeUnVV17N5k2KqRKBIvFYrFYHCyWy/lCFe6Uge/e6eHDB6fzWYMpQkoInEJvLBoaH2scQkopcgpprImllESwbdu+H1RJJgrc3t5u1jul4HdkYhhlDBSAqn+phcGYxTz2nuYImNQzzCAMkvYAhCJjWYlxdMOIKAzCo35I+R8IEhrCd2Z73tsq+k0tUQJKCKHvOsnZjNr0IrGgZWTNFPWf2kUDFfvLJPLqJvWb+idjUBKCPrvQD3EYOfo16wohaLqgXeSqqjRMgRyj5A46VVW1XC7r2ouIpnqqmue91zHjzWanabrOIymNlLpA1V0BAJ0FGiIrYYXeXReGGKPS7OnWGtrOALbb9fr2VqVztZQ7nU4Xi8XDhw/7vidrZovlvQcP+zA8efJEWQP/8Ic/VFV1fHysFbm//du/ffnyJeVxo/OrS7RmupinlLTRq+uz37RWwJSGEfP5/MG9+8Dy9u3bxWLx4MGD4qLKymtCbK1VNHJ5WNoI0N9XbGqRatbb1K2oa65PR3vbJXzZ7XaqYKF7uJzolIJzPsaESKUjoL5n17Z6hPMjFiLLQEOK7dBrV6uUu4ksZPR7cQNqHrHIF46IGUZJWvK0ZKzB8zdvf/WrX6UULBKHmIYRqcCAMXJkFqTEEgEEDaBhQNUABqCUJCUJITnn5vO5ZgKYRzdFlHH/jouGs7gI5Bx3yCzi5VnQOOCuoJMRhWOzNLi1RhccRl7o8eyPCxJFH5nq6ABgQZ7PZ0sRIbSSODt7I/J+nP2e93rvVSy5ARRIufgU1LaXgoTsFd6132qMk33FtvHdizjpkFJCYMgdHzUpuf5n3ru20QThaAP3nW4xUPu/v+89rSCIwBCSJRCgqpkcH59eXp7HOHBMUdiOaqwMAASU4B2XaeCdNeKsiaiP0zn3wZPHL54/s2QkMRqazKYXFxdnZ2fTaaM6wbo14c8EC3emfH/Cfe9HzCMrwmbbisjt7frVq1cA5OuKQhSRmPv/MQrkhrGuyxiDEOAYTZNBcpaWy+Xh4cHhYjmdThE5xoCgnGTBGKqqqZotZiaaltRfn42CKXQCUrMQ5UnQyeP9Vr+MhUot+Iwzedq8UerUuq7RYNsGAnDOtG0f+sE5N1/MdJ1r52vnjcEoI95BrZtBFBHleQi7YVu5tm0FYhi6+eLAN00/xCg8DEMag7Ux3iSixBxiBMTtrrtZ3UpiAOm67tXbs4P5KD7ThYGIwhBBkFEQ0Y3aeXdVOHiX+DT3uzLfJCQZ8dF3nH/vbYD39/cefoRIpcZAsuLY/n81auGMARFgpXcAAA0ROLdgy7tpbRmR1BmrHlHma6TtdouIWuzNGoWdEymVDDW+lFv7zKmkgDHGQnwBYDS6QkTNy7WMURbBWqOZ7vHx8YsXLwKPbFZlwriI4ungSowxJQEA771I1Cvpg2smNbQ7IZQo2007nZKIKBkvwah2PAydzj5pLKKN4clkstltV6vVZDIBgLbtp9OpOrzT01MtTU+n09Vq9fTpU5UUBIBhGK6urgpH7rfffqvRJABsNhtXV9pi1JUsU6RKJ7nZ7M7Pz1VDcLFYFEyZ3mlJFJSks6yDvmDk2Q4aNhW4k96p5scGEZjj0HnvlUE9ZjNSjOO+TYwpxSwYHDKIKY4AaR4jY+PUxuuYkwJ/onAlriTNxvgYB8qK0SQAlpjZmDvUNAEYo5s5WTIsYXV984tf/CIMnTGGQ2RmYRAyACjCIQkQAiKDEDlBIOOMc0mwGyLZZIwdiUQYQ0gAylfl7QjHExFQL3PnewTiyJ2io3rjNHOKkm0CIFo9CCEEBWukFNWn1nWd0lj544zhYmYRo6ePwAPE3A4YEKSuvZqpGFEJHWWPcwm/U619zw68d95TSpJCAVVJGkMKyvR/xbIZlZKrauM9GlWH23elwiyIAswaQ+sbVtYaY0Lu+KbAOhSu2F3JMzvlslMGA+7fhXynd1b+aQmtQKqqWll/q3riqt3BwVHXde1uE4YYCZzmbZra78mek0Apmpc4y1orzMXinpwePXjw4PXr11XVsBJXgXzz7NuHDx/WtSeDzFoFyG1geCfZvQt25B1Id/kpESkecrVabTabbdsTUWQJw9hMjWNYIzEOOFb5WXvP+py8HSGLlrCqqqPDg9Oj49l8onGfhZiLycndsauDCCopndYkeVTnNlqy0x6bZKkc732Mo2KM5LC69AJVucFau9lsSpGzlGv0nYfdLoR+uZx7X6vDICKb2QebpsFcRFG7Q1lW+rI/4xguz197a/5nP/vRdHbQ9aEL4fziYg2saLQc1ujyEnPUmEYV0b21q9vNzc1N7T4MIXjnhmFYTGdaw5wfLFJKfQ4VBTgNQRNNPXVE+2MDhHRXEbrb+9n86YszQ5bk+t7+ISxeU0Rgj2RVUQsl/dU/z3wLNGRZBe3QF7OYMgdk0zQpiZZqREQ7uJwRrSNrRHYAI/7WOe+9cz6EoH507Bca55xbr9eKr9aAQCTpw9XwPGV5pfl8rt/Uj0AkRUVVVSVh0D7uzc2NMpBPp9NSgoYRnGliFl/Sjarhgr5hiqMAsO4ZRLx/ekxECkL+4IOP1uv1YrHY7XbKRL1td5vN5qOPPnr16hUAXF1dAXyirvfo6OiPf/xKl2W73TZNo1LBi8WimjQffvjh2dnZt99+e3ywVIPunGvbto8h7FJdTSaTyXa7ffjw4cuXL5XAMsa4WCycq7SMfHV1pcQauakxlnn16Wy3W8rQ3BL3KG9J2w6admy3W91vpWE/mhRJBRiv/ULtodCeinNJ+NRqhyxwlM/UnU6tcVUfdqvVqq4nxhgUJjSI0odgjGl8BcMI0WIGMjAmTIWrAZFFxjAcGdEqp8OkaVbX7d/+7d92beuc04FdIkpoYwiCIGiAAJDIGgSKwgAjR5UIIBLACPL3rmbm3W7XNJW1Vu02ERUR+DxxO+rE6POSzHaSRv6+MTE1xuhR1mpHDOytSymlxAYlIiKONlBEYkoj3w9ASlYjgFqDmBiZYzPRwROj0FRd5yEGrVMaY1T7/Ls++E99k0VY0rja+QGN/WzYU3fQ9UFDImLIKeny6MUIBSQT9gmAjOVeHom7jXfGO5X+VSL9EmfrfijeVHRckln3Z/G17xm3fReGqBIQYhgYiEAsp2CcXR4ebzabq8tLZeQPITrnAse6cint0U/LXYp9F1HuU0iKTCaTH3z+2c3NzWazsb7y3oeQ3r59++LVy8+//9kQemNMIbDWYdYS+cK72U/I4ueyV0URCd77pmn6vl2tVn1IACCAxcwpGRgBqr5HHwZfub2HqiB1OTk+9t4fHSyn0+lsUqeULKEhNojOGe8nKSWFb4TQa8FZ5080Xk4jR2CsKqffNBmr2TRN27beWz35BUKp7A0pBcmU/QCgox02yyeEFIlgGAaOcdo00+lUW9fKg7HdbpkZR2cPtavqutEUDRWogGjuMSJ+/vnnMXJIGKJ03XB9e9NOJyEOzCkkQGCikekpF1e7uq67dlAa567rzs7OjpYHfd8b5WWMoxIc2rH+2TS1XrOxqLZePYRejIgoe3mCVOqHwJKxMgggKY2dNkgsuS0kWbJJvRQjoDEjV7UeAeCUkqQICOqNptNpVVUj0QoiCiSJqv2pj6k49cDJu0rdpzFmGHrlMlQkncZSWshZLpe4B4AqKC3vvYJvtaGVUtKOgE6tvH79uqmq/T5lSmk2m3Xdzhj03gKAem5FyG82G+8mzKxKt7ebtabL6/X65ORoGJJW9nR7AEDbtk0zlbGUUsVxCKpXWSRjDKekIGFdSWut/kibr+v1+vr6+ujk5OXLV23b/vjHP1ZcVdd1v/vd737yk588fvx4vdtqCTfGeHBwAACbzUb5n1Xfd7fbvb04Pz09ffz48b17926vLolsF4ZNu1sul4HT+fl5DHx4eDidNU3TnJycKJWV9uBvbm7UeetWKdwmmOlRQwjgaTab7Xa73W5X15NO1eZRCm+z3hcRbTab09NTrS1r7m4taUsSEfWflDXfODe2SpO4bdtqMgUy5NwQeompdEBDGMWmIkNVVdfX185VrvImm1fkUTZHEJwZRaBZ4C4YzUaYmdmaEU7DEjk1db1br/7mb/5mu9kQUd+H2tcppRAGQUZDLCgIXdcfnpwAmsDJMKIhIiu5+xMDG3IiMnQdSrVYzBQkVVc+hFDXPoRxFJN5JJ9JKVkaw/27c0o5w9trXeuJqKoqxpSSNFCvwjqEUDnXA1hrRGhQLTUeNUVDCERVShJSMiGE0M9mk8yCYEPsE49ACjUIlLHKd94RAO7yPdRJGRzZ1EcWkTRATnbHhKc4iD0/N7JTjc/aGiDDIohAiJKU7dr2feutW23XMUbnbBiGzHOeVG9NHWqpvsCe6nlxSSZD38tdlGUsYYTsJcQ2Xy4wB0BAa+pqkhbp4Ojk8vKy3W2stWIMwEiEC+ihwK/xrixwl2LDO6lr226Xy/mTp4/+7j/+ylV1HgsOr16+eXj/wXw+7fq2duN7vhcvlPfcex7w3Z9qQrBcLl+/fmtdNQyRLA19UKstkgDQWgNgAaCq7diSMcZaY9ApnPXBg3uWTFU7EkAUQ4onF6FxcTnr+qmEqNLVhj1JV2MMAGv5qwBkdNEmk4nO7KYRCnuH4qFM8Lvb7dR6qtIcIm63W06JhWOM3lvvPUhCIItkcQQFiAiwIOJkMvF146wzZE1xUYgOZ0R2sTiIQa5v15eX1yGI9/b4cN622xB6LSoZpCEFEUkxEoGGd23baktyvd2GEF69OQOO8/l8Nm22zg1t17at8RUR3NzcHB0diYgxZnu1NSjqJwDg9PgkxGCt7Ya+rmuVh1K9bQFgjlrUgsyIKXEEl5awukSL5KzJnRh9HCklFqmcZ0PajUYzhrcEWPuq4JW22w2RyVtCcp3QaMlOk1qdDdWsSxMgDeOqqtLUWR+6mmOdQ1MHzMw6QqMI5JR4Op3WdV2PHUqxljQwEhHNLUpuJ5K0OaK3XK5HXTIzM0dVhStZWik1mawJ4ZxTfrQx7Iv91DTMHOK4G5umefv2raaYOnalB+3q5oazD6vr+u3bt9fX1x999NFsNtPf0TAlpXR9fd334fT0VJfie9/7no4AIGJNWNf11dXVdrtNKTTNdOqnVVXd3Nwsl8vLy8sPPvjg4uJitb5R70tEqlYUwjin65w7PDwsosVl6EuNmspOKPOdc9UwdNaSLo76OXUhi8Vis9nc3t7qsqi/HLpdsZsjv7ExzIwEZfRRG5Mpw9YUYslK00GkAai2U1TJUYAnkwlzdIaGLpZMGtIoqCVG2qF3ZIicZlWgNgiJcQT3QQbrNbXvuu4//sf/eHt766xVL96DUanErg9oTV1XL169/Ht/8ZenD+5fXF7fbtZDSDo6hYQ297ZCCGRIfDbRmfFfjaTJwGzFnagNVGepz7SkUhplYu6MGAPej1m14vaTSVXvc8d3dDbO24IwDxyttUMMta9i5GCSYtqPjg9d5YsDQ5L35Y3+1Kukv7ppme/ip5QULwYiIOkubUtZbQKRyN5JoqGxmmyU04R5HhoRtXWFiDGO1n786L0y3L77/O51fneA4096sfJ9C++4S7FkjK8a5tP7929ubp5/sx5icNboI2EAhVyBaFzxJ94XMqJM/+crlyKfnp6W86z74Ozs7OLq8uBgAZnmm4gIMH2HLLBEEOUZlOcn+YQQ0dHRUdM0nUokgdBefUlEAMWMeoisgAIls9Su7aSpnCVrjUUQEASxzjoyKr1V7iv33irjbB8GRLTZ+xYoh/6mtujUVeRseKRWUFGzmAl7tfeW50FphG1nfYWUkqpXqlXSrqQIAQBkBIpzzlnvXWXKy7pSpksER9NZ1w3r1UbQTKfTXddfXFx89fU3zcQBTohotdkF6b2tODFzNGYcGdSmICe4vb01ZFarlUG8vLxst03btpoYBeH5fH51daNSNgcHy5ubG5Sx+FN5651pmqbtemABcSCJOfHdo4Fc8gJvLBKGu7TY7AdkiGjt2AIAAE0oSJSQdQQh7zabIfLBYumM5dwxVYumXT399ZiZ9J1zHIIueNd1iEZ1eBCx6zpNImOMqrhXEoXLy0sVF6qqarfbzedLrd+qRwwheK+lvxGA03Wjwrw+66pymkxgRsLrDIzuFnRIRMZi3fimqdStKnkkZrZhTUf0SMZ+8MZaSzGyLhpLlC41TXN2diZ5bGYYhkldO2OUnLlpmmGIaPq6rpfLZQhJRQx/9KMfxb/7O2PMz372szdv3vR9G0P12WefvXnzZjabpbRWQOXh4eGbN28ODg6qqvryyy//83/81zrd9Ovf/ma5WLx+/XY+n6/X68lkopQjk8lkOp2u1jdadey6TvWLrLWbze7rr7/WypBqTpRyoq5n3/dDPwpgVFVFBEoeov0gEdGst+RtWsmnDJ2T3PHR3GvMrYG1Rq2fIswIYI0BY7oYDAikaADFICEA4sBJQLEmvXOGIk+barvdxtQAi+CY8WCepdHrj8Kk389IBSYcQlJOeAEIw6AckM+++vrNmzNLbrvZee+berrdbgUpJHaTmpm//Prbv/7H/9n3Pvt0u9sdHMh623pLSYCZCY2SSepHVG48+1ppQ6g10NTwEXJGyyMmf1T13k8wirlTC6bR5zB0MQZrCRIgEhjwlQ3Jp5SsqO9PSnqqxjlm6UMZB6DBuiqFoNAwjnfzvkiCop3HP0MD/a7HKsafZaTaKGY2ZQ7X8tL9j5nEyZArnri4Ehox4YmINNTT9anqxnlfPA3lN1HXtP8+5fLwXRqD/Zf8mR+MiCQyIEDMzFpWtkbZia8uzm5vriwZIGUt/xNkJcV57y+TXg0RxZSGMJycHH322We//f0XxpihD13fpRRev3r74N597x2nCAAoIHtViP012r8B3Sv6/pDH9fQwP3jw4Le//8N8vuyGkdUWEX3lUkrAApKMta5ys9lsNpspj2DZcAbEgACAIdWjRqVVMaYQkkgfBjSkipJd1zVNY5xV7KvNJO82z7yXKYUx4t5DX5uMiC7/NHuzSS6L8Eymddd1fd9Pmso6JyJV5bRZEkJApGlTCRpjjMtjnZQlxO++ppqIrKlm06Xqol9d37bt9snjB0fHh199800IYTGbbHYdp8R3uQKlzCPjnGt3PZKklLx3220bY0zCta/Wqy2g0bGT8/NrBawCc9d3IoLAnJxBIkB9H8k4Bchk1OPjTmNnRS9bgJFAY6YixUGav+Tn5ZwPoTfGAEvfdykFjrHrut//4cvT09N6ZDIx2kPVcCdlCgV9h1IvKkW2qmqUK1R/YbPZSIYla8ObMhGuLoJzzlWVtngRsSSveqeG6Pj4+ObmSivDym7WdTsA0Cq3Lq/+1V6NmjV0MMZMp1NldCnIXn24sjcIUDeeDNTeJ+eYOXEo6ePh4eG33357cHBgLBow0ZimaYx3j54+ubq8QcTTxcIYE+P4iefn59PpdLlcXp5fEMHVxTkRHSynm81GQw0iuri4ePTokUE6ODp8/vy59/7o6Gi9Xp+dnd2sbh8+fHh9eaHn5fr6WrvXWqhYLBZ/+OPvVWVBfbCIOGeK1ISiGe7du6f6dPpxugm146PJZfEiipnQBdHDqOkpZFoMIlqv19piNcZYSylZ7dqwZngCnFhQayGqaT1mMSklZw0BKoNs5UwKkcB4iwZS4GQIhAMPvagktSQkAuCUmEiH8k1KKXAYPx2MMCYWIhpCAkkWyRiDKF99+c0fv/iCmQdm731IHFJP1sXARLTd7W5vb//6H/9nP/zJj7UA4FxYLBa3t7cGkHJlVUSAmPLgmcnYi+wz7vT4ssVW/FpSEHJxnGaPaELtgBmFBQciqutaosQYMQRrrXemY1Y8pDFI1ikj4b5fiJmBLoTQeK+9swjDfzrtzdb+nakkolExDxGRRLV2iyMojmPfH+17YkLrnANBEETKvlNRbgQAoEB2zLI0Kv0bRGGAf2Is6j1fvu+23r2LP+t9AcDSCBGw+U1BpV8G5uXh4f2Hj7tux8De2jQk+I7Pf++avntl1hCzRaCTkxP53e9TZK2vbrbrly9fnhwffvbZJzEAmbHSu+/Lv7us+1/oy5FJoPABevLkyYtXb6y1jaEYIxk0lowxljTJNtbR8cFSy4NERHQnYTtSE4AQGZSU0nibZCjGiGYs40jWSCl2MAkryFA9bhkLpndn2L13moep67V5kF8j0/V6LZkLZkyjkZ2rtRaqybriSHe7ndoLRPLeAlkRRILixckWPIIIAqEhY8AAkSVryJqqqZcH0/Pzy+fffnu4nDPzarWtvU8CMTIQ6Qepudf+KDMDwwBDCGFVb5pQpSQyl81uS9YBwDAM6/X64uKqrmuDAECSgqtsHII0sl7fzifTNPQSKzEAKESEBCxJmcXIOWNIhGPOJJgZBYy1jGOjBczd8DSOJGhOYoppBDQNXUcE52/erq5vqtPTbrsrwRki7na7sjnJGgIlqk3F+iiRk9IFaH6p9G3K6KRuWPu+2i9QbR8VlxxriU2jfU1m7ttWW8UiSeHQ+mtEVKgwxkEpAM3/tJRtjDKKj3hs/Vpb0cwMyIm52+50V7Rt2zQz9UYGUS9eeW9ijPfu3dORm6Zpri8unavW6/Xteq397Nls1vf9arWqqmY+nz979uzg4OD3v//98fGhohA0iFGPu9lsKuuGIc7n881m88nH3/v+D3/0/Pnz6+vrk/v3lElba8taS7h37958PlfDfXp6+u/+3b978OBB3/dt2242m2EYjo6ONpvN2dmbqmrUlSqIWiFjWrQosaC2ZiATUekZVBSVjnit1+uDg4MC+9AolsY5e5UHMFq1NkTCY5KqMRAzS0yISChExlVWRATYqwwfR+eodnXXdQDsvEmSaiXwnlYEqQuRqCIi1OQhJs6ozPEMkjBzEkZGEVEoUBwSeDBor6+vfvGLX1RuhDgRWU3UgKiPu6qqrm5v/vqv//oHP/h8t9tZ53a7rqqqxWzStq12nEUEM3cCZs5LGFMXReeRcy4ETpk7IaVEZBFNCMmNucCd3NNdlR6xFF2UTrht28L1iAjOOQ3ZwcS+C8zROZPSCKfKiJmx8NB13f37pzbLwSEivJ/18l7H9/2XiECeuEFEyLTHJq82pyCQYKypcalJaPeX0GqrMTAiotbM9lwMiIiaiCSsZxn3+l/vXRXm0fD33K2IEL5/C6Ml/lNVa0S0d45zbN8CESJZ5yr2w717926uLi4vziwZtA7SHaN9eQv9gJQ5rADG2WTRsnwICs87OT36wQ9+8NWXX+vzYEmbzerVq1ePHz+srLvzGRnNC7m6W67+TwYUKoUUhLXDd3x8/ObNma8rrV8hInByztWNJ6LGV0dHB3uxYUp5dpDHD4JRz1ISMztjk3BIkYQoYwI1rCv0SWW/ag9STbCSHkgePSyXrW5e100xO8biELq27RaLhXpfr+LtUTSvUvDtXscrOeuc9wiGyDKCYhudH1Nw0UE0QgOGxAgaXzVj4ijSNFVKaberlstFwX+tVuvauyEwEsY9KU0t6OnciDGGBYFT3w86JZVSipHfnJ0dLpd93+523cuXL4+ODubTibVWELx3YJ1ReSNmQxRDyKVWRSCP3aZStQMAg8TCIcYkUUSMdagjs9kBa6iRUvK+6Xct94yIKYSu65wzn332WQHyKLu47qJCo6EQUCIUvhMZhKKnlpvE9G4jVmue6l2Ojo7u349qniaTCdGw3W4VJ6wJRAhh2jTqHu5OmrWFrkQncPSzivnTXrgWBlyWj9VqR934oe+ms2YYBg0CNLPPAlyUUgKiEHvdLbrrStaSUlLglfeerL+5uVHxwa7rmmY6mUw0zlPZ2hjjixcvPv/880ePHn377bfrze3YzjejfEUK8euvv9ZbUOTOarU6Ojl2lf/FL35xcnRYEGo6+KuRjbZRb29vJ5PJwcFB46sWttO66ULUpM17f3t7e3BwoNIL+lx056s/LlmdVhS0Xq3Xtp+96f0W++jsKLwzmdQaygxDVoKzNvbDkO6YOlKKFgQBHKEh8BatbSrniGio3DAMxtkYk7U2pFRXbrPZODLIkoRRGA0ZO8bQegHWWpWGjzGCBUQc+mBQrHeKFxmGGGOsvU+JlRchRVnvds5766qLy+uf/9Xf//Cjj29XK00uJWMS59Np27Y7HrKnBN3zxlkex5qRSCFgoLuidPpSZsosIMqy7Ytp1X8WH1zssPeeLScOnq2IhEgAIaWEJCnptSknyegXAmjvWQzi4eFh8eiomn8go7av3BEsf9dRFTdZwhq9RxEhAu0B7v+oFE2JxjaWMSOJKeNdfoJjMVkQKY3SGlZEwJCrKzCUQIQI+Z1Us+Tc77nY9zzrd79TTNz+n9v3/lj27EWwfr5cnpyc7LbrlAIByKhb8/4bMUJRcJW9WS4tFQiRMNd19eTJk1cvXw/DMJ/PjaWUwtXV1atXrz54/AQFTKY+2ne0nHGw+J7fLcyUZAQxP3I+PT29uVkZY7RQjIgEMl9M67q2CE3T2NxZZOYMjhu5YYwxWCASYgjEGCO5AKibMomEECJzYy3GWBYh5dFvbTvFTISku2H/CwAoG9o557zZ7XbWka/sdtMys7GYhlG+ymUScIV0dV1XVVbVtERAOAEaY1EVLEIcQiJtbRpn9c5c3SRAFKwqZ41yryfv6Pr25nvf+7hpmqaZPnr49Nm3r589eyEigKM6W3kEeuy1/VxXTkSYRRDX63VKab3dEZGM3BQUQvKuct4CJ2vQOQcpOueEk6s8IhgkEJCYUh7zxwwsJCJJHNKghQFdn9l8UXaw2XtpGcB7n7gmQEkzhZ19//vfHyWN3t2lY8fBmmLFGO4GTqpcSYYsnV2wS4WvQ8uk1loVwVTXslqtdOhIfd5ut+v7HoCnTaOF0LquNbKmDL4tXyOiBmr6iVVVpdS1batNaETph5YlrtY3gAvVGNArV5pJEamqCngs3hgzmlHNRPUj1KudnJxc7VoYYRakNEbb7db7erFYrFYrXdLFYqEc459++qnWdY3F4+Pj7Xb7+PHjy8tLZNSeGTN/++23ZM3jx49X2835+fnN6lazXucckf3qq6/u3bv38uXL5XJ5fX3dNM3BwcF01jx79oyZF9NZmoZhT1k5Zm7nPZjbSG0hItZ4fSK73e74+FgR1JSbuAqfLvAZbbSncURCiFQkYwAArTzHGJjZIhHSwDyGWUTjzBhGA2i9QURvbVVVzqAxxltKlWPmYMkYU7Hz1qTQ+abmBJhiCEHG8VBBuDNlIhJFSMZ6GOfma0rRIh4eHv7oRz/68g9/0Gij70IfQ9M0V9e3fRh+/pd/9eH3Ptp2rYhI1+l7Kk5NMUbMPMTS9QQ0Y3NXo2edYdE43jmzl8xpyjwiJ4qjFVFOj6Q1Fc5D2PqGo6ELkawxxjiHYEgzSUYKIWhy7L1n7pihsJ4NMXgw09lMZ9i+O0+Ed61T/k5a/P6rhMspJeUf1YSX914CSVt1+c2lRO1I40QZSnGKUCw2AIBQ5X3lm5FEDAEMCiCk8d1UeKb0wiS/xjXcy633c+j38unyIkQoWYVGgoAEACzivZ9MJkcn946Oj9W+lKh8P9aQd1/ls9W2WjuWeXU+5MGDB4XLpmmm2+325cvXpfG2n++/94b7q7//Be/1pBFxNpkcHR3p+bTWTpv65OTk3r17B/NZoY3UldIr19NLGbElMkqFcwbplMYbAECuf9b1BICcrcq8kJpFlSLXwF93rfalSn0YcvlCC2iawaiZ1igS8I6MpjgntVDW2vl8buyoIBtCiGnQsTkA6GMfQhiGvpTgdGJSU2rjXUppiEEjDAA4PT45OFw8evTw5z//i+PjYyX9d8YKJOdHjkm9WvWFmgsCGmWo8L7SsRBEVKojZva+Vj0fdV1N0xiD0+lUmNUp6lWVaoFWF9WZMTPH1Lbt9fX15eXl7e2tzhHtP+5saEbItJrO+Xw+mdQnJydPnjxZLpeHBwd1Vc3nc8W4hRBIQJM/n/W3y8JqmVcyLEufWqE0UWe2Wq1sZr2oqkobKHo9um8LVafSNOo9asiCmR87ZSVBfZUdq2GWCujiCOEBaxVpP6gWISIqp4qSNSofCGemC93PzKyjULmanawla2kymWghXZfij3/8ozFGq7W67dUZK1pKx7dCCPP5/Pb21rqRL/3Jkyc6U3vv3r3Hjx8T0cXFBQAcHx9/8cUXetfGGI1Lrq+vOSu6p8ynEUL48ssvtZJ//+R0uVxqTVJlg5W5+sMPP5xOp6XYI3sS6Lr46o+1WaMdYt2l2+1WZ8DUJeue0Q91Wdxef1l3lA4avBPP5W6Rc25Se+dN7avae2cMAacwcAwo7AylMMS+G9odS6y8nc8mVVVNZ81yPtVykdpxxZCXatm+1RqGQbL9Ubv78ccf63yX3iaR2e52y+Xyr/+Lf/LRRx/xnrBPMU3driUCLb9r+RoyZZUGXnolxYSqJy5JWLHexRwBgDFutP9AOhCvn6i1bt3VJXjVzMFaa41SYqH33ldWAyA9ZVqkgay0rcTpWgT6rh8CGHnj/9yr/NW+9S6xe3FMpfZQAhHI+aT5joiA2UvGCgZ2f8PIu3nmvhXSGBf20l/8Dv9G+dv9/+5/HwBIlWsEBfLSiAigFbYInrCez46Wi5NHDz8g41VrSfsHiAaAooBSlIvge5FL9p8TgxNrrcBQ1/HjT++THYACIhryk/r4xfPLFy+vvZ33Q2IBTgKCnCTFUZ2RABXFA9lH6urp7DmQiyIalYAka+T4YOIoLifuZDk5XEzunxw4BENUV1UMoVSx9AvnnAGwiJW1k6pyKKp8aQiIqG1bnWYJ/VA5TwDOGAIwKHHofGWL58YMOyRAjmnaTCrnheOkqRDFOaMZpHYB1XRqCVotsvqnEILWBjUCBeC+b7tul1IgAkRJKVisiRyKCX3kRN54EYlDgMQpJUJUrcDSB1KzzhyjMJBNaIIgVQ2bqpkdNvNlH8NiOf35X/7o008eWLtbNPPYhlk9RSGDlsCkJNZ6TZ8QMTEI0K5rmWi123V9iokSmMi423Vt2wonA2lWmdPltMLkKE0bX9d1M5kCEqYh9TtMA8Seh7bfbmLXpr5Lfbdd37bb9WZ1k0Kv+jC189ZA4iFxRInICVldlLXW1vUErTHO+8mMfIWu9pM5k+yGNkp0dRU4CWEfY98PKXEIsW97ZxwK1r4mQAQxxum8tAZy6r2YI3NUUaAMVq+Pjk6s9QzEQIKo5TxtCqQUqsqlFHS6l5nX220znfYhqBPVyM9ab63X8RIFFgGQph11PRmG2DTTBAKG2qG/Wa8q3wx9RDDW+Olk7l1tyCEYTfiGrNYCAETQbXeOjCMzqbwjE7o+dH272Q5t50iLt8b7+ubqYn17nUK/nE+9BW9hOW8I4sFiMqntZnVTOdNUzltaTGeQeGiHL377xcXbszSEN29exTgcnRw+fPwgSXz+/Pnt7e2zr76eNbOpn+xut7WpLNpJVX/y0ceL6UxiSkOI/cASj08OX716JSLXq9tq0nz4vY+/99mnk/liu1v1fSuSjg+Wj+7fq6yxSLXz3lgOA4dhaHft7qbtVyHtyCaBENNABlhiP7QCSRkrR0MfAoqgCHGSMEAMLEH1rRHEOcucGl81zgoPBMO0kolPmFYON7M6NGbjoPc4kLQVxdqJN1JZlNiHbju0G4m9DF3qdrxbx/XKDH3VX7ndZc3rR0v7YOkbmyi0BxO/mNQT71MYUKIx2KW+jR07IJuSdLthGzkOnAaRy/Xmpg03bdhGMtOFaRbrLn346edHDx8NWnFMmq+jNaauHKFUtbPWKhm8egL1JQQoiGQtECVBEWy7IbEwY4wsgpoXVFXFLMBAQCkJM8Q4lu547AQrMec4KKUhtTrRADFwAAMhJUQBEiSonCUCg0AoiUNd14gCwNNpYwkJZLNdPX782BgTBUb+f3RKnymMimMjAGJBAEIZxxTzMAWOQGYAScLMKaAwgRAgkVMELYIhtAQILJIYgSsd/EvgbFM1cwYLVOt5scYQUYwDSkBJ3W4DPKAklDSbzWaLOQMCGTHOUUViFainVmK/HrDvUIvVLd9BHP8K0aAQChEYjSO0+Kr/w3f/4O6l39TQ2Ho3n89zRM/a3oc9ukEALgNh8E6cNXbLjDFKWXD//n3NmbSmCgBv3769vLxENNtty1mkr/g22COJ3X9vDXx0o5TAR8EpmgBVo176mK/oNlWMaLkkDnG/QAF0J+kDmadXgyOtUhZCJREpaZzGuWoF9lPz4uY14paM1qYMHinRvWZRLiufaFlSVLprL3Ur8VrJUHOEqxh1raLrY7+jmI5xKNWLHPqNjdXZbHb//v2PP/74k08++eijjz744IOmqabTZhh2i+UspoEl6jCMSh7pmqeUhj5qlKBBvcYQ2uRTbKrepk7vUMbRiEjps2rBVuNrALi6ulJGfm0BbLYrTd9VnDWlMAzDEHp+l4h0vB07Bge6SdSUKNQo7QnDmYye04zWOU+ZPbSErkrUoKzRksHtJUbWfaIPyGTdkRJc6/ZQIiq9C80my8prZVXPlGal+hHOOe0iF+xuXddHR0daWy75q4ZrkCeG9QI0R5GRLCK0basLq3tJxYtSSpo3L5dLDQV0biqEsNlsnj17Vte10qdPJhM9UF988YXu8OfPnx8eHmqreDab/eY3v7m+vjbGKOGzyjAA8NnZ2XK51Oo3AGy3Wx0ijzFqEBNjPDk5+fjjjyeTyZdffvnixYsQwsHBwf17D7Rn/Ktf/Wqz2aj4vGKVFQhWZgpKRUoyvr3k0JvNZoRuMTMzokRODBI5aRKfMkGgAVS2fGuMksk0tZ/Pp42vlBpDN5KOpgCAQdW/Gw+pfqLyd+puLC1qBZBbQykOm81mMZs0lZvUjbdOYpLEiDh0vSHrrE+RY0giEkK6vLp58eJFCKntu9v16vb21tXV/+P/9f/84osv1MKMmXRKClHUJ1usmfd+lOHItdayW2CP4XnEeOcyzB6hJ2s4W5J1yXh7ybQ8nFldjTGaIagFU1NQ6m1jKIAjYVkpqaaUHjx4oHLFxV6VUwzvvhBHf5y7g3d+br/yOib0d9bgrjSb38eMn4Ij1IP3Zmf0pvb7X5DLVMXdyF6tuyTZ+8WM8oL/Ca+sQ/V+KmzzPxCQtRzNzAA6DSIi5Otquphvd6vpbLZe3yZO+T6TCBSeCtrD0exfH4gAapxOzKGqqo8++uibb75VWgktwZ2fn58cLat6JBM2mSkpqyK+09MWEX173is+7607eu+V/acY1mKpy/pSVqaEdLf/9Duq22UznZg2w/R3ME+CxjytK1lQQU0nM0MW+kbUMYcRW8h7ROeQR4olE++VQrfaC/XN+05lLLghIZKqWzvn+iGmlKIwoSVrNNGRXIehxEnEEAMhjG2bEeyGmbMeAOq6bnwznU51NOv//W//Q0zu4aN7t+vtZFJVVZ2EAUBJdlLWHVMv610dgt4iDn27mE1TSsqx0Pd9U7nKaYcvAEDiuF73oVtrPJH3zBhCrdfr+XyujEuKUe/6XeIQJTbTCQAorY/GKzCy6Y6wOIuUxm08GpTtdnt4uHz79i1k1InN7FHFxJRWmfbXtbHattuykfYQHAYxM0uL7Ha7wtVQTAwRaSrGWfNO/RwzQpYjLJGlwvSGYYhxpc/FWqtDYnVTX19fi0hd10MfS5SmUY6WAVkIALTfjIzFEwNA13Vani2lb+0drNfrvu8V6KceS5HYr169UuGH29vb2WxWGDdDCGdnZ1oC1QK4MaSqySGE29vbJ0+eIOJsNjs5OVmtVsMQr6+vDw4O2nZ7eXn57bffPn36NGU139VqpQ1s/XN988PDQyJijvfu3ZvNZi+ff3txcYFoZrOF+n4dOCEiQoNm7NFy4KHtIHE1abRupG5eU7SqdgycmLftzhuLiMY7QiMyyi0AR0tYeUJJkpIFdmQqa8hbg4114/RwjFEgSeIEBgBQADiFvhv6PoRx2NQ5Z72TJIhoyVTOC6XGVwjkDc0nU45JEnd9aIeeBFCAQTjpjJDKdRKAWOvJOl9XKaUkGFOMfZzMpv/m3/67fyTyg08/HIZQVRXAWCYFACSbxlMA1loFwezb9+JXELFkWZxZNiVjDBERQIqd2XcqxfHoeR+ysJslCNl1IaK3TpC6rpM4/j4RKdFviby3u/WDBw/q2jOzNzalO1BqMeB4B8p6z5+pjg4XBBZAtm/5jnjvJftVYmNSYpWLyOkiKn2FDr4SIeS3RSTmSM6avblTyOAeeVf377212r9cfDcz3n+pchPgO79m9/4AiTA7J8RR3NigyGQ6r5ppSqFupt1qheMQyAhX2Y9TynXvX5neYeKRhEU7SW9eX1Su0iorSzy7vDg+OTw4WAxDd/d4cNQH3q+t548YP4Xone2l3ym9Xsgj4fvrgnky3RhDZKw1JRcxFtUB60a3Wdu8hN6U53Q14h5vE6GwZ1gySZiTTrWPs78arasJ0Msrj3O/t7F3ZtBay0yY6VuFBQGQiFnuXHJiEUEWITHGKCSBmWWcXUObOY8NjuolkOMPyK0R51ztag2lq6paHJ1cXFxMp9Pf/Pp3X37zdQjBkhNtUwAo9b+IhJC0cqvVqhh6rZkfHh7ev3/fEId2S0QgPJ/Odl0fQl/76rZfaRyTspaqPtYYk4jo9IvWRQqMXN2z7km9O849Wl38/U6MiJABRcZqnV97kFrG0PfnXGJRgLeO3ucFEXV4OriidDPqsUqANQyD9iwxVy/0HTTFL3RXWhJwzmnurj/CjIUZhdhyc26z2WiIEEJoZtPJZFKQX7oOqq3U9/3p6almYM6529tbAPDGM8cQxqguxpiSIEoIwblKB5PUGlaV2+12IklFkHRaSZHhZ2dn1lr1xKvVSjmzrq6uPvroo+VyOZ/Pm6a5vDx/+/atdmr/+Mc/atv4zZs3WlxZLg8BoGmat29fHx4ePnz4ULNhpXXb3K50qdfrNREtFgvlyzw8PLy6ujk7O/vggw9E5Pj4eLPZ6WEcKaWkU/z2kMYCjPF2xDDGcbK8H9ohdA3VtjJCLvRDVbm+J18rlg3r6YRj4JgkBUkxojiEoe+8Nc6jRSFmZ0xVGeeckNG8ExLLyIM3phA4lhlDjKPdTyl5X1ljLJIhMiE4BFvXzpCROG/qoe+HYTCASZLEZIgEDDIiKAtbNMbMZrPT09Pr2/UYWxP6ugoxnd4/+o+//Lvlsjk4OOhjQBYADEFjGsgehYwxSURxNiJJZBzLSRxSGkUUzAirFLV7Gq8wj7OUpbRTCm9Eo90rmydlcQJXewPIcZRYreuaGRRmaAATZco2YGACAjJgrT04ODB7o03yp+BIACNFtY4bKRmtOpD33Fvx9CIcs+Mvfgf22rGajxljIt+VDPdcKXHi/cqlVlwIDCKCoEEK8o77wPziPfDTu+7vu7e1f4MjVrlc7R5tNJC2gZkZEQQJIpA1YRiaplkeHiDJ4dBfhiHGqINGjGAEUbvIeU5L81MQANVHRExJBUjGGqB37rNPvvfm5Vs1rIiIgBcXF+tHD6bTJoGAVlMtEZkUYtrTDXx3FQQA9yEt+toveEKe2S+rTxmFP2YJ6Y4MAXXcOst0axai6S8Rhdw/LuZejab3XmXYRxyZIYijZo5iL0v12GbJEchBCRFxFgkutU1vnba9FUK+f+9a5NTijIK29NgwkCROmCwa4613jmgMP7FUJtJYL9JX2TckAESa90yn00cfPF2tVm3bXl9fffXsq8mkRjT9MIQQnPM4qkSMbxJCQJCm9iklAGma5qc//fFiseDU8zAq+k0mk6aB7bYl65uqBs44gz1EKKJolVIy7yPvEavGITCzc8xcJY46iHEHdYZSdxqxfkiiKL+HD+//8Y9fHR4eVlWNuXqhf7V/YouV4TK74pxGWkojPEaQKYhgu92GvpeUOEYUAWZJqWma3W5nEG2GN1cK4kKMAAo/0V6y+iFdvZRS3/fOVbrN9FH2u7Z2fhiG9c0toqmqSlLqdru6qjhGgygpiSSyzqKmuZz50UTPQq5vj2NvOkGumxkRz8/P67rWpLaoBmmVm5mbprm6ulLpwOfPn798+XKxWLx+/Vo7R69evdJEs+/78/PzqnIi8vTp0/Pz881mpWIequZUxqY19FRnv9vtvvrqK33oSv1mjDk9Pb2+vl6tVgaw60Z9T92oVVUZg5rgStc2TRNTGHZBvxNjHFW5QtCdRiNNbNc0S185QOn6zlrrrelDH/oWhR0Cp8DAq5tL70ztXe2dVJawFoecYLfZ6JYgQzpJycyQGCVZAmdMAJAUQCixcIiO7KDdIoS6ct4QIHhLmJJDnFrbG+o5YWKhlJiVMydk3kcA8K5+/PTDm9WvXeVlGLzzSlWWQHxT/+qXv/lH/+gfxcAxxslkEuOgw9AafIwxQXa6PIpAcAnlTZ6Lc9kmUCYbKDlSOQsmj2MVa1N+Ry1YPpLYdV0cBiCy1ouIt47QWGt9jGCshqSATEgicnR0NJ9PAQCFYxTELBu/51OhZFZC7/wzuytERGDIRNDALHvjJ8Ums+hcpo5lg7Yq1K0bM472IY0SHbxXq1c3oYSDWUhIHdndRM94PQgCIKzwaQDAMqAMACWuyBf//iSxlB9pz2v/bksqNn6t+sOCRFhPpn3fT2eLuNzd3NyMSwwAROb9Hu2de9fHUJ6fMQqlDtp3/N3vvphOZ1VVdf1uGPqvnz2bLRazeY0szBxDIsPjLWKh7nwPof6OfwK4CwxLjUX2aKpgzN4CAHjvDWAUJYAaQa0UyTpCwLHexVy6HSJikBik4OXU+6YQowp4pVRqvFplbdut1q6LQjjmSXltfnvvDRGBMUgGCQUIUfKbIyq07Z2iR4kSQiZbBgCBxIwEhggqm3WWYoiBkkTa4yVIeTpLXb6IhBj0G2qXr1fXdXWsLKE/+8mPL69vum4AoK4PwxAQBxA9wIICBCjAMbL3brfb/eAHP/iLv/gLh3h1+bbrujipAUCpFRBxt9s0zZTviGY8Z+ziZrMhoqapNpsdIqp1Hov2yADgUemOdS48dsyaGkJmsStLpFjl7Xa7Xq9/8pOfvH37Vmn6Y9aqGyP9KM5WhLauR1UM3WP6tWJPtO5KWURWg6oCQtZdxJl/VGM4bcHO53MNleq6vrm5KeWilNJ8Pi20WYvFAgC6blATqW+rjVgRads2Rl4sFiIynU61Uq2e0jmLiMpPmSMYxNxj1uN2dXUFRaJKRFHuIqIJpU7lAoDi1ZVz1Fq7Xq+1nXl9fa2iC9bay8tL55wxeHJyst1ut9vtbDb79ttvq6pq2/78/HwYBh3J1RN3e3urNM7K7azMkZC4RAk3Nzc+y1cYgw8fPu77vnZ+u32r6A2NepumYY76bjfrlVbIzV0HfJSQUn+via+WKiVFDpGB+90OnDdVhZJAgkUA5m6zbneb1fUVp+i9PVjM5vNpGvrQeSJqQ0QALaKCCr5p3SUFYCBQuCqkFDiN4yiTyYQIUwwCUntjjfPeeRJmmdaeeRr6ARGtd203pL7z3oMhg0BkWASQHz58eH5+fnVzXVfNZredTCZDiiLSNM3q9vbZty+ePn7ifR1jBLIhJO9HGnB9iIyg9LQxRsq1LpfR0bj3Kg6vpH2lgKSPz2YaS8g0W/slRhFp2xHzj4jCrP0OY0aOdnUJOrGjRriu65OTI+ecsp1Ihi4jIjCpP/tzr/d8cDlE+S40ybsrW8vYlxx9ESI6XxtjCpmPIIiw5gCII+a5eKuqqqzx+s7MrBzJ+xfwXr773qXuX+f/39uB7In3hJn0buTu93T5jTExJuecqyvjKxUvU9wHZEoUgTH9LfFC+aRRLTPHU4jILNbYp0+f/u53XyBi3TiWKMIXFxfX19d1c3pX3xZQ+prCV4XISkH+3nK8t0yYUTB7HzpumrLijgzvdWQhB1POm5jZdjQvsZk3HxENYOQxcbfWGqR2nMAZu7Z3YWmeDC4bnXPvRAuw6g/IOaIR979/L7Kn+1TyM1GsTRp5VjVZFE09LCvwSiVihPKZAWOMkawebwvDuLWkPfKxGIAiSYR0ROTm5uazzz779NPv/+t//W+sr72vv/n6+Wq1qpwPIXVdR4qZB6OFmKOjox99/v1/+A//6ujoaOi2VVV1Q9+27WI21RjTORMCAXDhEyhPBBE1YVWTLSLK+V70hcQKgWrj9CoMl7hPHMlgmT7Xs68RutI4X99cfmI++fnPf/5v/s3/Z7fbVdUIgEwpcRoBlgziwDnn6trrAJU+Rz3n6qU0WNGczFrrXFVEAkwWVpIs2Vv2Hma9DTMKsTEzEwFz3O12JycnMcbMS3Drfa3+NYSgsRoRTadTnbTWjEctJjOPshwAiFjYoPQ6daZWL3W5XCrL483NzcHBgWLBFN6lkaIiv5bLZYxxvV4XPG15QBrinJ2d3b9/X/PX169fisjBwcHz588Xi0WMcTabnJ+fz2az29vb4t1PT0/Pz8+NMcp8vlqtdCkq64aq0qz36urKWqvmW/ksJabZbPb27duUkqou7nY7AL65uSmHAhGBqB165dLSxvlkMvHGXrUXYsiINNZzDEa4rmsjHONAQpVFcKbvdsN2e3tzeX150W43lXPOzMqhGztKICySEmgagCTOWEQjIixqFsZckPku7PbO9O1WRAyAgYQciKMAVAa59sO8qWMSY4ElslgD00mDZLbbrTAwR2/d06dP35y9nTROT7pz42D6bDp//uzb2Wx27949SaDaaB58qdZk/zc6yxTFOWfdmIHo0dPglbmI9UqxSO95l31fkpJYS8WsMTOKKFxUW1d933OI4MZRTBi1QVlEnDIsstS1Pzk8skhp1C8aZzSI7iph330xjJ5o79qSMIfQhxDseOhKEfiOaxNyhxEAyBrvPaJBICRULItOsRNBcQr6FsYYLRGpC9esh/dgRu+50js/utcbfe87772++5sAYAHonSI7gWKZmSOi0VPNHMlUzWRq12s7aaqmdl0LPPoYISAp5btxZfY/lYSFlZJNjHGIHGNcLGcfffTB2dmFiCIRrIi8fv16Np/MZjNv1F0hWaMay3QH8torOGRqbHm3EE+FfXtPflUtJrIo8VbOYu/YgMtWSymRNb6uNpvNfDrLkd2dnWVhS4ZjCjwCmtSbKlIaRyku0l8OISgCRVMf/QWFxmBOsjPIEEsqU8pBzGCQR/S5CNIYP+rJH4YBoPSBIIU4DINzwVprrWeM5Ekk9X2UTowxVVPX1UTz8nKziEiIAkAoQhZRnj798PEj6Ibh8PAIyDLDNx8+32w2q9X6m2++efXqFRENQ7y9vb1/evTZZ5/de3jvH//n/8VkUnvvU+g0u725uWmaajKtw5DG6jeAqjKru9WPVrx6iYR0clqBVKqtW/xZjLFpsKoqltH/OTIFtVEOoWZ41tpnz5794Ac/+OEPf/DLX/56MpkMQ9AxWQS03nVd58inFHSIaD+H1rRG2642k3tr6dWYccS84AO0bKUKjDp0q4QSWvkokLGu67wfsaaKqNKGaMrM2Hpt2oUCgNPTU2MMZ/G7p0+f6sqkFEsgpaWCjGYgReeCUnFNp7vdbrlcXl1d6e28fPmyruvJZDKfzy8uLuq61mHrxWLxxz/+cbFY6CD79fX1fD7XZ7FYLJT56+rqqm1bpepU76v9aRGZTGrFlKWUNpvNYrHQKndd1xcXF1oVmEwm6/V6uVw6Moenh8Mw3N7eMvNkMlG4Rtu2q+sbDbnOz8+Pj4+991dXV4iy2WyWy6VGA7rymkPrCbLW1s73fVt5LyHEfjAEKQik6AFCEkMgoRdJod2tr69SHLw18+ns8f17s9lEJzvGaQVrQaTSohpySkkGRlGslAACCyCAUiVwTCkJWZPi0HbbEC0RaDcq9B2K73FnrWcWIzBrGhdjYOFqjOZn08o5x2kg49rBtt1wfHz42Sefvnj5atpMVGWBY6qsU3qo58++ravm+OhQN2Rktu+OxZf9T2QEUggJEcnBfs8FALRxWxxwsY2Skcm6FMVwxQhEjKjUEFw6ZfoUOAEReWOVKoTIjO6aQEUmQgjW1c2k9pXre60V5YSH33H27ziqMbndfzGIMMcQQoyD9Z4ItRO/D+eW3E5yzqU0EjkkENTmXVSvL8wJIHP97xl269UBjwh4ys3K4lbeK69+15uCViNglDP/c693HDCONFA6/vteS2zcjK6qCSSlNJ3OLq4v54sDZri8PM/ML2Mnb+8T7qITVXrRUqE2VFjGKfvPPvvs4uJCxza0HXV+frk8mC8WC/XsKQpARBlZF0pquOd6sahM7/80ZeI0tY8lNRERZwtVWxCRlEBDfmWcR8QUJUWppqNkjWIKNLEwWaNbPd9+hcdaqwB9yHUSZQaWjJrjLLFp8viK+l1nrcqDQy4EZUm7UUhkWjeArAAiItpst/Wk0UKlflNRjkag63eEFlEJRiGEnghCO9bhjbN1XTtjCcVYFEjDwNZaawynFO+Qus77atLMmXkueO/0AQAlkMePnorIMAy//vWvf/nLX/Z9f3V19Q/+wc8//vDp06dP7927d3R05Jxpd5u6rq9Blsvl6uY6pcQpVL6RGJSyLmYWT53SWS6X6qXUEBfEOI9t1+RKTYwQEZUoalKN1HqRx8eq83nW2hjuivPr9Xq9Xv/0pz91rvrVr341ny8QcQhhOq3VrQKANSbG2HcdiCBAl91Mu9stFgtCnM1mXTtIYuNIE3FdefWaIyw59/XLfzXn00BEn3hKScRo2HR7e6vHuPQvY4yKjdL6sz79w8PDm5uVtXaxWISsX6nNcsgYulIAz72uUStpvV4/ePAAcWymqLYKAOjEjjr1ctIfP358fn6uWsj6zb7vj46OptPpzc3N48eP5/P5q1evnDOLxUI3p7KCaD0sxqi0lGdnZ7jXSi/MHpPJBAAuLi70a0fm6Oio67rz83PvvZJtichqtdKjutmsdBKpFCRcXel0Q0g8xHBwdKhcmP2uHYaOEEPXOWtj35Elg+SYJQxOJPKw2267XUsolSExfj6dmdMTjZA0GAKygiiM1vvGqzWElFKsYkpJEpdqlve2DB/mEZ3YNJUxuF6vm9rrejqmGFpOgYwDRm8NkMGY6sMFkvSddZgWs/m9eydDSJdXq5v1egjp4cP7APDti5feeo3hdrsdC2tL5fLy8mC50HIgIjKPJk4bTHqFfpQGv+tZxhhFxr6YNkT1p7lSAilxSUa1LFfSCW27pCGllKp6pHwvXDrW+OnU6MCe4gxSBr1PJ5P1ZiMiu93u4+99pFs6xqH4b9grPL+XLzIIpzusVjbniUb0CfgxOIgpk5hCLo9nU8ApcRL2ru7C4H1NRCGx9pX0AGrzIoQwpAgIQ4qVaWLWTCuV0XINkNusfzJx38/U1UypppGImH3+6neTYH0fLUG/mwTvl6CRiECYAQGJrKsUINdMJrN+sd2uY4jWWuPHEn95k3LdsJe26nsSjzrCp/eOHz58+PXXXyuhPCJuNpvrq1vFGBdP6YwdhrCXhpaixDsPrzhg/WfJLfSV8litUl/st81KGlqWGxE5AaIZhgEnWFCCin2F3LErBefy2Jxzm93WWksISdgAqPnQUpuabP2IksSYLNKZGYzHMceyhqWSqfaarI6Qj3ovIkI4kreRACEnkRjH+ZlBegadl6gq57XNzMwpjGFNisOQKdpHprCQhFgMGuMsIplRwKu5VwmaEILmIl/+4fcnRwd/9Vd/9ejRg8XiwDmnxVUy1PfBWtvMpkPXjhdPIxmQTiCUSoC2NtfrtQbmiEb9q7IjYa7fgoLUnDV7mlFpGIe18lyjKKlF8UkAsNvtXrx48emnn/7sZz9h5t/+9rfWec3VtOJERGHoRjBt3+vf9n0HIMo8VXgNva9ijLPZrO9bPTt58F3PDleVFxHnjPfWe6tbru/HufZS4I15NBxzJ0LjAFVW0FXSYVONQnS1Dw8PVa4gjGhz0ArBdDqdTCYjiXyO2Mrm15RdK70i0jSNUm6pv9xut1qun81mWqMuwFftCGw2mydPnmhW2jTNvXv3UgohhPv37zPz27dvdV5OO9MxxpI367FSCYoy7qwZ//HxsQo0IQuyPPngqfLiKZh8uVxOp835+blzy+12q0m5ttWj8DAM0+nU+mo6nR4eHsYhOOe8IRAJXQcikmLjfOy7LvRxCN12YwAH3qQYHYmS6sznc2OMARTGdtdHYaUR9nVlrYkxgjWAQNr8BwwggdO+xczGlJKwJDAA2+02pIiIXbdTad6h65um8b62QCzIwohUOdtMJ4aYZzWhncwnk+mMgXQAoY+xqqrF4qCqqlevXhPUIjLxNVntyMLbszfTSfP48UNmCH1vzB0Zk16bus8ybod7leSQSTaModJM6fsWss78vuUvEbAxJg1jLBuG5L0HiF076I7ljOSgTC8xmiMymldwSov5fLGYxTgAjCQHKQsSs7yjcSfvljDLapf8UJkn1DwSYUmgSw25pDoAIAjeeWMMI4EQ57HVlJJVkJrEtttpu0cNeFNPx+omiwggqlPHIhpd3vw974bvZsD7LglxHMBFREWpw3dedv+3FfktIAhIRJqMAyChARRrfdNM6+lk0+6M87P5PMa4CStmts5EieVSmFmAWa9bWBPrdxYaIcZofPXg4b1Xr16lFA4PD0OKu7Z7/fr16enp6empmdTFp+7VnyEPhN2B9/bv/D0HXL75XmC4H8WETFtf/lydohLjaWriMrdliZKI3mlFh0xJqPArzRLAoEUD74Y/JZICkRQjZBpbbeAZg2U+Z6xOc0JEb5xIGoZBEELoNZgYC0HMwANSJltHlBSSARBiZusrAFA+mpgCEtTWEmAcQqQYY0RR3y9928UwOH+YktjEQERoDZLqtwAAEVpyMp8+vH/65R9+f3l5Xtf++PhYo1S0xtUVcBxiHEKoqybMpkmSBRziaNk1bwCAXDwH5QWczRbqcdU9TKfTMsxD1qq/lMRBEmLQ8nKZ7YtZ/gUygFm/iWDQp7ZtLy4uROTv/b2/1w39H//wZdM0VeW323axWHjvnR1NkublOmOdMly8hALqINu2jZyytPBd20n/RJ+FThxpoMB54ouItJ1Woiv9fi4dd87NMWswAIDWh09PTwFA6xwq0ue9VWYSNXwFk6JbriyLAo9TSldXV8y8XC77vr+8vFRqGgBQn6rNbMUt611oYUNE5vN527a73W6xWCjo8uLioqpGEpurqyvtTKvTJaIYB2NwPp8rmKvrOp3qFhEFT6lE0nK53Gw25+fnOpfy9tXrw5NjM4rfxZRC30cN2larlWoD6wSgVqF2ux1Zp4iww+XBMHTWWgSorG1n09cvX3a77W9/+avlfOYtWWtrX4npmBUeAWht1/Ua+W27TnPZIQ4iUvd101SI6MDecQ/ASGeokjqjXUFLyq3BoGe87YfIoiA+Iqi8t9bGyN4H5wOgQWuMdcaTt8Y6S0QMUNd+Npsa5w8Oj49P27YPV9er0113oHWaIVxeXg6DCAARTSdNjPHy8vLgYDFtGuVL2PdSdDfX+52iaOZZ1PqWlt+8t8MwUgNx4dvfY3YEABKI+UdapCG0QsnuSZiUmp+1RU8PnbPT6QQRDg4O9OmnTErKzPQd2NU7pjvjp7KJHs/X+CCKPc9sB7JXMBtvXMXFXY3W4F4Pl1mlB0fKprJuIqLzeOrE73DLjLKXN+JemvsnXemffN2FQQXevXfLsO+A4R0s9J1/IrIiQUCMcVVVHR2f3q42Q9e7uqnqlpm7bpciy74yJUACARBCU3Sj9mMcQHHOcYoffvjh1dXVL/7274DM0GuaS+fnl4pPESHKcF9NnuRdkFeGfv0JEEG5/rK9yk8LNN9kiaE8GPeOzLBORGy2Ix/vvvPQnp+OfxhjdH9Pp9PId1q/RGSyxmLJ29Qixxhd8Z25V40jyt8rvTNnPYAxdjMyDLEPyfoRGlbseMqi3+q5NadHHrWVCYRADAIicoiRZRDoEABgbAMD9ENXEjWkQREKToQtC1gSArxDeVSV+/DDp7vd33vz5s2TJ0/UjndhsGK7rrs4e9O3rQi6yjtXrW9vaj9ChHRV95+OMSYlSUmGYafxjeJmFbwjjNbYclhSkhQHjTwwA6Mkg5644NWjeGeE+5SnC0ZZ39n0n/7Tf3pwcPBv/vW/nU6nztnb29vpdAqStIZGRGV+OlcgeLfbaa+x63fKy6FpH+RhRDPKQtzRzOprv7ahzbDCgqsbQFfDmLv4TPNvziKsVVVdXV3dv3/fe79arY6OjjabVdM0fd9XVaWTygWQz8xFT6LAHTabTdM0McbNZuOz5oFqX5a5ZBWJ0ke/2WxWq5X6VGvt2dnZj3/846Ojo7/9279VkPP9+6chBF23i4sL59xqtWqa5vr6uu/7Fy9eHBwcKI662PHpdKptaa2jdl334MGDr776yjn39OnTr7766vnz5/P5XCSpkMNmswGA9WZ1cnq83qxiUoA6Xt3eOOeqqmKQm5ur6+vr48Ojjz/4EABub64P5gtm/tWvfnVzdflXP/+Lw/nydnUtMW02m27bWmvb0Gk9pe2iMebi8rZtW51ZstbOFzMCQeG68e12bCiMIAxJIOicCSOdFqhoDydhQAYkxhgYJAlJjsCScy4GhWs7Y209nThfGVXpmE+9r8gaZRazvmqms3tg+5Bubldt2x8fH67WN2dnZ8CBiNbb9ubmRj3cbrd5+/r1o0cPirejPU3xMqyxX8zTnyKwIozKZuPcEq6q5j0fg7l2GlKkPeioTudDpuozGYZtvOMQdJMXqKC+23w+NwatQSIAScLJEBKa+J3Z0T3PlOeAR3suIgKizSnVkRRgBcqlmMahjLtwBEdbTc5qMiIICISYxSSyWcC9akGZyMCxyKwtXtFk9M9c5/tw6D/plfd/jTU8yOGdftO+9wf5B3ePanwLISJxrloslkdHx2/fvgFB5yp/4NNlGLreWOIxblDvCwAg44feJX9lFBkJhm6op/bp06dff/WsbdvK11VVCaerq6urq6vZZGItpRSTMPEIfi6XCaDldlDM2H5Uol8UW1+OU/m1fcAqj+L2d2lx2cQm81Kpt9MQyXsviYUYBSwZznSPmo+mcDeOVc4AGqJMHK8me6djnZkgovwojZxnY4cY9kbxxvqJtc5ZzUv4boh2pFoUjomzakIm1dNitJ6NlJJxI37Sex/TmPdrktc0FXIKcQCdb2VvxAGwtR5JSd9II9nlcvmzn/3sL//yLw8ODvQaUpeGYfj666/Pz99KiqeHB7Np1TRN3+40CimDVdk6gzGOyG42K59VU/RgV74REU6ACCJCe76NhRUnrB46nxbEPbyicbaUr8d9zxxCePHixcOHj3/2s58dHh7+/ndfXFxcHBwsEM3hwcLkgZamqbVaCzmZHoagSGNmnkwmMUgYOpjUCILACEgoiEAogGK99d4LR0MAkhAwxcGSQWO98zENCGCN8c6BSNM06/WaEIe+N8ZwTJZM6Ac0oP3s2WymOrvL5RJRvLfb7bjTyqMHYMrMMLrhc/+bvK9vbq4mk4kxowBUSkkxqJD7x1rw17mpMhdnrf36669V0eTy8nK5XE4mk8lk8uLFC++tQsO0QsDM5+fnmqFqMHR8fKz1cPXNh4eH2ukvo6vKKDKfz9++favDS/OD5evXL5l5WjcAYJ3pVx0ZVGe8Wq222+2nn346n8+vrq40UDBIlsxuvbm6vkwhTiaT129e/t//x//x008+/pf/8p+vVzcW4eHsUQzhMAwvv01t2/Z9EuSUhiHEEMLNzY0AE4iv7L3jo9mkqZwBiBxgFyJldW1ElAQxhpRS1w1J2JCLMYUYhxj0tIYgzrlep3oAhyH9/0j7015NluQ8EDQzd4/l3c6W682bd6viVmSJEgUBRAsQhGnoe//RxvyARgsYskeaJkcSRVEki7Xdm3fJ5azvFosvZvPBwv3EycwqUpoXhVsn3zXCw8OWxx57bBhjXfGx9yTTOi8366qqBGB5PIb4+PTULKtF5RbOVWAsGYfGAfHZ+ekm8nLZhjC+evVqv98L8zffvYvRp5Tauj4cDgo7PXr0qGSuiJhUP6CqUnqAlus+QUSESWMghKkND/LcxrnlL2ZHM8Fy+5R8QP8ugjNjRrbKZzGTJSGrAnMmPJc79J+YPhYHrD+dUgIQmklQAeqBSTHmmFtnDbk5J2nK1hiKrdCqh766WCwIp7qVet/yEf3Cj/qXD4+2LON7T/J7dC0iASizmCYWtMp9FawYp//l3wYCSKhsW7Kn548Oh8PxeGCERV03zaLve5NnKb93fA8yd4bc/iQiUjfVMAybzebHP/7xf/ovf9XULQCFEMdx+Pbb7zer1fn5qWaiqhx2/z0FI3oIJs9/WkqTXIF8dfYWTOOAdLlLNbeghWrjtFakyZOhB2M0Yi62VVU1+ElxsBCsypVjZuaoqCDllSz0GcoqxKP3gMhT3+eDKk4pe2PmBdR1HTnkGILLMatvnlIuFjIoIsCChCmFlCBGzwxA1CCiq9CQWsbsvCcEKaXkIDJLSkwElCQRIoIBC4QsbMyUuK9WK+UyIEld12bov//++7/8y78korZ2Lz/9ZLFoOUVXV8LRAJXAc70+UcgBEfu+f/func6NYWYE0x0H9QGKCY/j6NwkN01EgqAVAQ35dfgPAHASQmMNgqAhSbkhu+yEQ3f0MdT1lff+xYsXX37x1TiO6/Xae694RUopBH88Hi8vL1+9evWrX/1KB9qsVqvD4ViIG/3QzyNlfcgMktHdqEjJnANvLMZ0Lw+uF06hlOnLOVSuQUQRdo5CEGutal9obLff79WVqrttmmYce02C67oOYSoBElFVNX0/3tzcPH782FpSKNtaawx679frE/2UQj46CEunWm23W2ttVVXaufs7v/M7f/d3f/cnf/Inbduen59/9tlnd3d3P/zwg3ZnXVxcqMIXZRWX/X6vk4b1Qi+XS4XlS1szAGjjk/7K3d2d9365WarS1nG3v7m5qWqnReWQZxH+7Gc/CyH80R//MwDo+973w+l6s14uvPd3d3enm5Pr6+v/9Jf/909+8pP/5X/506Z2t1fXr998H1mudrf77TYmvNt1MXGMcX/s+m4cx1EgNU2zaOtnT59+8uxRWxlhD5xYos7w0T5g3RjKMBiGkQWJJCYJIY4hRQYRlBhFo1wkBggJU2JEUb6tiJgkpvfMTMaISDeEDRg0VUhp8NGBjZFRIhpbUSVOrDP/4k/++Scvnl9fXl1eXgI6FE0ufYy+73tFIObJKOdRXYR2GA88TQ6Y5uvqttSdE2PQuE3HBZaSB8wEegEAWZKIErYpC9JpwctUThIyAiMUB6+vFjOoi6ajnROP7+UkLIFyvew3PeS+vsOc50Po3cHMnBJLRERLxmc/WhywMcZUzhgjAkj2HgwAZhA7HbWqAhszmw6g0rZ6g6c4WWD5wLN8eKi/5fny31KtVzoUT549s6DnnywlEB0xxDo3V4nXQspF2pyexhiBJUW/WCwOh52wMNyrqxQ0A+G+U2hCtgUAQZs1DQsRvXjx4tffvOqOfV23oWlE5HA4XF1drVYLoumKltBs+raHihzFzb9nCucv6RU1mTmlZTO1IHPQRj2Z6hYpNG3JFKNZuj5KxEdZ6S2lxCCQoV1rbQhlSBarvIDeMyV7S3neiNrWqq5lkg2BEhZInmgkmaFj82xt9ZqUJ79O2kYcDN+TxjX/0DquxSrGOAydMa4beiKqKovocrjAtasAEmh4KT5GYQSWSNGi0Z40jHHCSGMIVVUJoq2ruq6//fbbf/jFz5ft4g9+73cuLi4c4fGwjTEGP6wXS72NdQK82lb1KwUX4gRNM52XVuXVTA/jWFJ2slNUG0K4vr5OKSGQQu5TKgwwjkOhcUoGugkNIh4Oh6ZZHA6HkxOjQGvTNClPwlCBHm2AaZrm22+/Vd5TLtN6a+3JyYlOJ5xHSBqeS8aa5mUFmnFkaMafNMaoQIearQJKExHnElSM8fLyUg8Ac2uyZo2an3HWmRmGgVkKKU+da9boULF+p2okxhjt/1GGbVVVl5eXJycnWhLWftyU0vn5+Q8//PAv/+W/1ARXa8l6nLqxEfHu7k5rvc+fP9/v96qRqZdJk2BrraZKBWnX5jpdSZ14qIJZWs1ZNi2gFHp/VU18i88+++zVq1ftavnVV19tNptut+u643q9fnzxaBzHd+/e/fznP/uDP/zDZ08e3dzdbtbL08cXVVv9+3//7199+83F6dnheut9Cine3e72XV9VlW1bS2azWT0+23zy/MnJquY4IBtnDCFau5jIOBJjpo+llAY/cgJBEsbej+M4xshJmADTmJCIkUUkCSNSSAIpIQunQFkGpzLOObdo165eVFXDYGMSEhhDcGRC3ycQ5xxDaprqk0+enGwWj5+cnZ49Oj3dfPfdd9fX1+v12pLhxHc3t/SIlA+v1iYGtkaMmUa9AbAxxs6SOZoJE8WswsHMKcn9PZiryJyp0WVLFytXOkJTHnOiF70gjpNxiFGnoQAwABa2l3yA3MIslQIAFc/SYpC+yhKnPF7dl/YIqDJE+dRM0FcXvHzzfZasB5B1D/V1rTMq5wk+yCFlclcfeZSzKG+efus3++MPk2P4EIImmCrPGk7cfzsYRAGAqm5TSqvVZuz7ytrb68umadq2Pe4PAvdlsOlnpsOaHUQ+KxWjZxBJcnp6+tOf/vTP/+z/cq5WQDLGeHV19eTJk/V66b0XTjMXi7P/fuRCQubczx1wRnc55aKsyfM7V6uVrl2W570fJ8LMyCjGKFxTVdXYD+oY9LPAUth9SVi7QdRHmqzAAACWJrl85ZQ2da3NV4gomfKjkU3f9ynHlepoS2SqVl4zHkVZq2oSVeCsDj9xJcgLYykoQi5DhpAwU3VijEAYI6kAljMWgQVSSt6AgaTRJEpSpgZjMkmk67q6ahHRj6Oei60MC2uy+Md//Mcc0+//5Ccnm7PucFdXzcnJye5uaq9SKX+dGjsPvRHRmkpoShzVb3Vdt91uz8/PSy2g4BN6z6tHscZtNhuVfdaXtDk15Xm3inAYZ0Wk0EBSnnjhvedJaFoQpw6/x48fv3jx4nd+53d2u93hcLi+vtntdkprgqwWq1c2m7CUcr9gvuGn4Tld14lAjBFGjnnoJGf6qJ4FlGIeSYxxsWq7rgOg7XarXcVlCv1qtdIWNU0Qq8oVt22tKaM+9JC0X4g5+jyWmIiGYdhsVoj45s0bY4xiAG3bvn37FhHVg6aUVFhDZ0Lo5Am9Ww+HQ9vWxpizs7PtdtsPHSJ+9913xpgffvhBm4wBSPuGU0pPnz5VRQ7v/ZMnT47H49OnT9+9e7derwt9eowj5D7pEL3Kdsbor6+vnzx50vfjH//xHz9+/PjV998dDoff/Z3f+/zly9vb29vbW50ecX19/a/+1b9aLBaJAyQ+dt2x6/7bX//V3/ztf//kk+e77giAArTdH33iullUVaW//uTx2fnpqqnsOA4WuLJkDRqDaie0vuhj7Lpuv9+HEJJAisKAwqjMwZA4RamcExnJGEYMmYQREy/aVmIaJ4pc1bZtu1ysVpuqWRBaJEtoRTD45GPfCAROIYTFogGAkXtrrXF0erpxbq1zsdq6uarqt+ltiOMwdt4vtcCv0Zgfx0IBKYkpmEmLI3gik0TEOZtSUoxdphx3LHcWFNQtTb1z0z3CrF9LRIGT5g8pC9GkPKClZEra7LRctjpCTe0c831UGvz9/TK30vNn5r6qxBAE9+GCTAqGRIhpJuNIuT6dWCxN38AIquwRY4yjR0MlhyEiQxpSq4vRL5kw/JLozXO8D93Ne294L8h475/lU4hoCUszMrxXcRZIgEJGXeY0yyWMY10tVysZjt1V37fLdeJwenYxjsH7AYmQhYURUTXHAVgZC1O/LikIPc391UKUtbBa1k+ene33e0SzXq8Ph8P27rC925+enPsh6OTElKIUrF9Y1T6UtlpO6b309/5U8v9iCgCAJGqSlBeqtKZCEF0ul0gy9r1Aaqq1iMTIMSZjEhByYh+DIITkyWpndwQQHdnNMXCMxtkwDilMIwKVS6V+paoqHwKoknX2LpoRskRABpy2O9/zelSTkkSQE1nTZN4Nls/qkWugoHExMwOKgCQOMbCqJRz2u8dPn4z9sa7bMYSqbfV2Ekvoas/goBJGThhFOMWqIkZGY8Y0TslK3TX1YhzH3e52tVq1ovUt/OlP/vDTZ08vLy9fPn9urbW2qttFE8P13XZ/OKwXyz54ZgYi61zQ0T1dl0Qis6q+aH+ZRSBDzXJBZPZdZ63RPElEJDGwqGBnShx9GIdeVktDiIYQrYjEIDHGpl3psiB5AVXt0GgmphS6bicSYrIikqIPeT6MZv4+eB+obu0nm6fMT38kX717d/W3f/u33vtjfySYxtRT6cHI95gyhyGDz9N1txLCCDh1Oqm+lfKojTGlEcI5N6RhtVpaWy2XRqHa29tbnfGne14zTm3EBABm0OnCGmfkbNgrtKO3wzD4sm7r9clx3wFL3dSnm5NDNw1A1O7bUvw+7PbOWBQI/ZBGf3t7vd/vtVTvLDlru67bIiKiM265XGoRQfOzlBJa55xrlosQQuDkU7TMKSXlYJ+crA+H3Tj2682iac+HYWhSdXd3l0A2m03fg2bkFxePu25ghpPzszeXb376J//sy9/96tWrV79+9Usg/PLLL59//tnbt5d///d/uz47Oz09j8Px8cnpYXvz3//mv/+X//SX29u7L168cEM0QzgSdv4Q07Be1WcnG2uwtnB+tjhZW4KeGAgFEaOIRAwRPHHcH8cxBZ9CiOMYvDcpgU+eiFjEpzQmStSEGAOnbozWWgLgEJy1RBVHttYS2v3QA0KzWImtzGJ9+vhJvVxW66VbtmIocnCsA+oNxBFCMMzxmMjZgBhDAMYQoo3bBvpPTqsFnH1y0vztcNhumZHG292Ipl2tFam1TZUMDpAgMVmXQhROyhdrnLEGgUhiUJsZI0OCunHGWlVCxZkG0WQ5UwSAwGkKR4TV15I1Poa6rjkwh6h63eohmKVxFbIMw3B2dqYODMkUl2TyzETrppqUyL34hL6JU7TGsIAwI0JKkWNCFGAxiCKQEicRJMeSQggmRSF0xgmCCFlyxrUhJOtqouIRmYSIMKXYxZERUCSE1LRNu1gxA8ukKqqRgogAiTALJIQHSMDcv96j6IYAQOdWysSfz643RwwiksdSGRFWfgACvp8Bf/Qx92eaIDZNs1qtbu+uj+NoHS2Xy8VyqcC6yVDblOHJ/ZdMrhAEBBMnxRA0JtpsNhovL+rNRHtJ8fXrN+fn503tVDhQZpJVzDofSAOH+7CinG355xzfKM8gTt296hpNFqUq4dWU4OZFDyHoODCeFYPn05QhK6QUcrXM6IhEpDJGNIlbcWEk3c+ByJUzZpbE5c2SxapERAn9iKiOVmnYANC2rWaWBXcNs3G2nCVbNTXvus57zwzL9VrzRZhY/hMbkIhk0oOZjmqSlXC11HUIwZq43++vrq4eP378yYtaf265aC7fxbZtT09PUxj1BJXYPI59CEFFx0Ie2KdRajUNbEbmVFxL6fdHRGuNZmY2z7SIs2+YrqWZBCNjjEpKE5m0QpmZp+qRFEqdtaQLlVKKYSy5Y5xpw67WJyEEEWya5quvvmqa5he/+MXbt2/bui1yB5JrGZLrXu/tNJXroywnrvun4GPKJgMArTcr8A44OOdUAUOTeBV40V44veL6bZq8QkZQJM9+KdMsIFMRdcry4XAIIeikXn2n5OrGMAyPHj3Sfi3KQ59sVamSxpMnT7755htd6ZSSHlvf9y9evNBUe7laKUp/eXm53Jzo+Wrr0Wa5gsz5SimN43hxcVEqiyml5WJ1enp6fXOl4V0B3n/0ox/99V//9U//+R/XtfvVr371+eef/6t/9a/2+70fkw5revTo/OXLl49OTzkG17avvv36//V//p/fff31yXqpAzG9RDAkzE3T1NYtV4vNagmSDIgWa1AHv5Mid1NPiwcax7Hvwjh6icIJAEBIRCCmEEIafBxDjAlSxBhZaOKj+WHgqiqFEiJqFq21tF6vV5v1ZrNZbtbL5dItFnpZoYDA4T7ULlXYAqKI913X3dzcxJispRefPq/r+t31TW2ciKTg0VWo/ashihiTU6uyRSVNNdTGVSZPQ7Jtjbl1uFjLchgwQ5gpE611U/kYVPBL98NUwkMEgLquE3NMHkmWy3axWHjvAR/Ueh/mRQ9dw8zXcNZyKcbc5upy8YLFmMPsjtMDVj2i8ouISDT1F5U7fbFYrDcnkIOA8kNlBfIH72UQ4YNc9r1T+8deZYD3ByH/4w54/qXl/jTGLNar05Nz730KAQDX63XfdcPQTSobQAkSiJgHTV36LQgwbbU0FVZHa6tHjx4VY1HXdc98dXX17t27zz7/FFnN7j1nj6eOaSH8bVc0W8npSSLCXA/XCpN2dKjvlDxtEGfSHAVprGtXiHOSuzmLi+LMtbZ54N20vtbqz4WsF1hY05i1XdTZiEiMXus6OIu29DH37mUr+BCWy2XMVcwiDc15ejxkYFZElNO0XC7HMJGENfnW1imkaYiK955oKuTU2CZEmqq/lfejQVM1raZxWuEjlOAHIvr6229/9rOf/d7v/a7kiUZ1XXs/LJfLw+EwdMeJdcmsVXCDmEJoqgoAJIpVXyXgnENASIxGauuEJqZJoSmWs1Nr0g39YrWkrHMSQyr3jLFkxTBPU+QkjwFOyRRgX5UiYgYbmXmz2azX63EcnXPMcHV1NQz+5OTkyy+/HIbhuD9qOEIPCepQtMSd01BAuWalDAH5mmoiq1vl9PRUZUk0QEGceAlacFUBLIWp1V9SZq0r4aWAxiYrljDfL5H27VxfX/JqrS2/uoB6iyXhwiHQbF55eVqhUNjZ1XXTNCpKZa1Vec66rk9OTohIZyowcz8Mel7L5XJRN+M4QmJI3HV9W9XH43G9Xt/e3lZVtdvd6SAmLQG0bXt5efnys0+//PLLm5ub7777LoRwdXVVVdXv/d7v/fSnP/3hh+9evHjx1RdfXl5fhRBOTk5WS4d4fnd3d3NzzSk2TVVR/cO3r/6f//v/vmrqk9N1fzg2VV1uLrU86/W6XTTH45FQTtermLhyNqYgKU4ZXpyIOX3gEELwMcZJlJeIyBgyEKP0Y+hHH6IIYxRi4eDHyMkZK4REhERAAICL1XJlN1Vll8vlcrM+OTlpl4tm0VIu1evtViLy+6QqD4fXgMwBiODhcOj7ngTbtn385AIAhhAFgVM0Bo04YI4pSgSyNWglUUTSpFkLwI2rYoyEoFBfcata1Jg74PJHyfxkknoGAKjqSjdeXdeVc5oSRGZESQnQ0GazUQ7NFCib98ulH/XB5Q2IlFIqMh2TAc9HpVxofYVZ/wYAHYmGRGiMM8aokjMAiYBSf9WSRx8gz5RrmoXW7IhseYO8l87NuEEfHjbOIGi8T3PvG4hULWT+qupCijBmaNviDIV/74+P/xjqHQ7O1aenpz4M29tbH8eqahbLpVpwk1NDEZn3Ms8CDEbQ7nsGox3i4WS9+fLLL3/xs6/NNNY0cUqXl5fnF6fGoGb5RASEOclP71WCP4yk3vNhACBJM7z7UkGJieZOV+eoz6MeZaPAjM+FOOvxyg7Y5VEzuk01DVKFv5QZ+TCrSatbLamnZlFN2/phhBy7xMhqHL337UyqsPy6DpQtJUYNbJXPqT2g+k69bZSXq26AiKqmFm2+0y3InhGrqrLOEIj3gzEGgNiYlFLTTBzRZ0+e1M6tT040pQshiPAnnzz/5JNPUNLhMMToNchwzp2s1ncxWGsr6wKMmvSnlBQCTfGeFYKImo8uFgsFoAAmDovOl9RyrxK4NHja7/fr9bo0B8cU4T7wstkZM5HLWUUPkhBAo4QYY9/3h8PhcDjo+kguOI3j2PcjIt7d3V1fX0+/2I8xT4Yuf8x5AyWG00tWKvcmd5+XLKeQqxExBI9YiYi19TAMqnWsV2q73eqXaFjAua6v26YMb8huOOUvnAQCNRLS7afWVt8sqMNcJxvd971GwOVC3N3dbTabm5sbndq03W5BZLVa3d3dqZAkZMGQzckJZ1376+trdbFae67rWruJNIZrmkoZDO8u36kQtHXm66+//vTTTzebzb/5N//m+++/v7u7+7u/+7umaX784x+/fPny7u4uxvjpp59qSt22SEiPHp/fXt9YZD8e+3H4//yHP9+crEI36JZ2dcUwiQKKj8um7Yfu7vZmuWzPTjfkLBGFmHwIwzBEH9UBKxOwT4GjFiy1cgjGoBGBxD5E71OIEhlUUJdZsvlCYw1aA4aqtlosFquzk7Zt69rVbbNer5vFwtU1zqZ3TNEbyNzOMDPHadSKWpsYp3BzGAZkMMbUzj5+dH7ojmNIPgbhhAkMkNPyvx+JyICwsOgkO1ZMaNrVzjmJSctVi8WC4X464dwRwkN9x3KEuluKIkJVVYkDMCplOsWo0u6S29yL3vI/8XHvdDOXQuJkdVNKmoOVNIansQrTdCPrnLXWkNPcdX4KzBz8MIx9jGzMBBQVa+y9n6t4lltgjk1+9Gg/9MH3Lz2kb5VoZrrQwAYQFIL+R33ww3+icQ5SNGDqRVtXLZqdg3r0/XK51mvDIJWZusJVs+q9xRUQIEECAuLsGq2lL7/8/Ltv36SYNNbuU3rz5u3JyckXX37GHBEF6AHrrOiEzWOrstXgnox9f3WJCIREWN3VMAySEQ9NSkQEkEuC+N6VKBdDcwJtxCyvzn9UH3pv5wHvU0+UOn590EyWoapdzBOcIGc2eK/pKoVtqIAeGac4pFrVQpctX6L2SLJ4NTOXUQdd19mqappmikJCBANEZIAIiWS6fzgmgxTS2PdHaytVZvbeV1Xz6NEjANWdEUQ4Ozs7PT3d7e98PxhjlovGGGOQNHPSjixJ3B0OhUemf6CdRE7mIdE9xYOnKwWzZm7N7ZT4pjHf5H1DCjGUG6muaxUVQdGiAHMKQ/R916nWLgCoVoFkpVx1wMMw+JAWi8XZ2ZkOHnj16jv1IkWKudQXmFlTihy6TTSxElW4PAtSRDSxVt+fMw+x1sY4dW2ykLVWSzwqnqyu13uvCtKr1UovK2V9IpgBhirMp3KbmDuL1L5on1vXdSqOIbnPhGe359u3b5XZVD7lnHv06FFKqeu6y3fvTk5OlOesxQslKJXVu7u7I7qnt+jPac69Wq2229tp8rElANhvd68Ph6fPn2mAojPWzs/PT09Pnz9//vbt22+//fbpJ0+/+OKLruvC6BfLhV7ZpqkksSN88viisvT9m5u7uxsDOHJUzJyZBz91RdfNcrff7na7xaI5WW+Wy6VIisI6M2MYfIpq2UGRkm6IGu+mKMCoEWRV28QcQggh+hSTgDCKpCRoKwBDpnK1dVVdt8vFZrNar9ebk5PFYuEaV9f1arWqmlrzY93VxZPlMpoUEA5mdBb1KzGmxWLZtt2ibqwzvveLRcMciUaU5GOCyMZWxlAC6UPSuh4gxhmkrEZKdx0AVNYoUTT4WCAcmSVwxf9RfuhtUvBCtWmLphIR6ywCGmelgCh5vMp7X/uhm3/vMZ04GIAp6xBmmhSN7kWsJs8qgIRaR7euqpvGuRrQIEKWcQTIchExxhBGjYybZqFt03qmpTF17vKKy/yoi51HJLnhSyD37n6IRL/vnhWPLhnwPPItf8wjgvlXTAft6haxWS7qfR1jHIbBVK5pFkd7KHf1lFQhIBqRe/xWpSiJCA3o2Ao1Ioum/fLLL//mb/5GMTEN2G+2dxf7s5OTNQCzpFkwgu+d1Tx+KYBbiTz0mhFMrSwlaymEQKWkMzMSFXQuBYUNJ+11mAkYAQCi0VI/zAAcdRgySU8nxQnxXl9p8uhp1jWvX9u0057Qvkx5qOFljFksFn2ekQcZSyknmzL1V1Gm+a9XrtGESZd9GIaUpjEVYfR6KyILERlBsiTAyUcC8d6nYPfHbhzHs4tzRdRTMuPYq8o5WgQAi8QS765vvv7668ePH3/22adNXWujqrU2ELVtiwKd7xSh1fTOOYdgdMqbzfoSRKRpbsrM55Tp3HrPlCDmeDy6qq5cPQ7eGudsNYyDUq5U+R1RnLEpJQFBEiQBwdyfMymreD9oTFOEVmwOMpi5aRgATk5OXrx48e2333777beVrWLWGSixEeZhG8Ul00xCqNxWJvcOmSySJXLffV6MS9/3WtxV9pkx5u7uTt28UqB1DbWiX2K1/EMSs074OI6qwau1Vc5F1onxEKJS8bWBTbfHpJLx+o3yzx8/fvzdD9/qaOHtduucu729VZ96dnamtWSlTOrcJO/92elp13XB27apCHDsh7ZtU4hDf7y9vfXe73Y756Y5UcvlEghPT09/9atfPXv2bLPZlHaDzz77bBz7qm289zqzSETImMghhcgST0/Wh92dAUh+5JTapmqqcz8E732MvNvphOn27vbm7u5utVq8eP5J0+r+hxBC34+j9+MYUoQkEINm8BjRJUwJSIi5wLCMIcQQoo8xBp4a9cAgQkqpbVvtFmva9vR0c3Z21iwXm9NTHcdiK1O3ja3c/GZHREFgUMXeiaAw+aeHI1Zd3TaL1dNntl0uFk3bHfZX8YpjwgkKFJSQEhokQoMgJACJRWV8EAkRRECkqethGBTEUgPVj8F7b3VibkbjijGRh3EA5BzUUqZDhxCjF3G5bjXxMIzG3QZEuNzCHz4+6n3nz8/T3+KMOHNilFdIRMyJiGRqf5pEqnMvbe4ABInMqqWoR27vB4Yaa9FaVwoW5UjeCxfgAx8MH40tpi7e9xnNH4YjIsIIFoRUMro43fd+uzwDSvEiSswKj1PVbDanwzAcdnd120QfmqZZrja7u62PAeW+PUsIULAYI4FJ9ggAeALsJxzvyZMnm83Gj6HObmy327+7ullulgYmZbD5Qnx4IecBC0+krdn5E5JAyUHVKZYIaAIV89wummYACqJTvowaVjVwGU1CmIz+pEejJ1JwwmJwS3o3NRrmfVzAzHnVyhiDxtzPfMwUf6XwKLZpALXbUpupigPQZVffXL5NEyOlYSfJcHcOCzQmMICiglrMIYT+2N3e3tpqyrOdsZU1MUbhsW1bUjQixsVicTz2q3YBp3x2dvL55y+bpgERPTXCqeZ02G2nZ2jqVlSQHHLApMZXPYHC5lpmhsSJU6b+Tgs7juMwjJsTo36Fck2rqo2OLSuBPzMLJB3PlbJis3NusVggyvEo6tXUb6kbDiEIqKyjaM+JmuLlcukHr4ehGKzNI0PUHZZoScFhyE4XHk5T0bAvpajDEvQCqYokGdE3aBdQXdc61lcvro6vUI1J/Tma0fq0xbbEKDHGtm2Ln9YATnNWXfkw+sVqWQrVjx49+vWvf73f76umruv6u+++u7i4WCwW33777VdffbXOmffp6amO7tZNpRVi3ZOnp6dEpH0+RFTVoodxe3t77PZXV1eIqElw3/dKeP7hzetPP/30Jz/5yXa7VXOptAbdCQDQLBdlV6SU0EAIoXaGgJ0xd1eXX//615vlKoy+63rtD/LeD35smubQHffX15++eHF2cVpVVVM5ROzGIcZ47LpxDKNPMfIYxPsYEwMAk4rY3NN/TNA+LgzMiYEREAgQiIwxar6t5rXW2sV6dXJ+pkMyqqauqooItH5GRAwgyAKkFrGYrLJD1EThNG+YEZEF15tTWMvFk8eSwu21CyHsdjstXtQVG6TICUBIGHlSWUdlqBIRWk4chfvBi+io4EoEvB8BoK5rI6JEVHWiupfmqRfPK6MIKSWDFEU5vxMbRqMHZraWqmoifmJmQf6mNOk32O1pHRInSAwAZPQwZsL+KR8SMggwCAEQWZyS/ik4BgBhIATBpGpiiOJ9tLZyzunYRFWd9Dx+1F/OkecSBOguLeclIjDzwTM3KjARv97vX5r7VqtOCQB0kPJ7Pvj+rfeHRYiCRCnGlGLdNOfnjzSs3t3dGbM8ERm6vuvvK8HTyRDOfbCmXzFGR8anmFIwZJl5tVo8f/78F7/4BSIG7/UOv7y8fPHiubFIBIIwwYogmQn94KzmIUlOxCcHM+GEIFpmxtw7qxZK9z0gA0yUlrquIa+1doDMU5kHW1Mw8yegZEhqR+btp+UaqNUrWRfmGXDzKK+qKgaGNFGi1MFQZgPqlywWC5XzNVkMRPFYyb2wpZmY7xXdAFiAQCUt27Y1SCUWQZauOw5jN47j4L1SnU9PTzenZ9puYA0liaXYpglWXTvmarl8fHKyttZS1kw2SCklS+3xeNwdjtEHZ6YSuKbmzPexC2T/qpm9xtKgfa4iJNMQKl2ud+8uE7Niejq/HQAWiwXgNDmuAAyQ80v9Uc3bAKCqrDHm7Ozs9vbWWntzc6NCiZq4sOBmswkhpZT6fry6urrnoVRV2VqK4hYhi1JNVC/OzIvFQouvKv0PWb67aWpF0dVnqxE3xixXJ4onKVDBme+t0YnS6JQXPd+QunvVs6aUtBJMBOM4haFarNVhCSGE1WqlIULtqhCCohFlwUMIZA0aCimq7LNKbXzzzTeT0jjiixcvrq+vh2FQMOPu7u7i4mK5XMYQVHJSg4bE4bDvlNb++PHjR48eNU3z61//6vd///f7vr+9vWWQv/iLv7i9vf3n//yf64lrpzUREEHf96pYovepMWYYOgO4S8EhjEP/3//6r7c3t7fXN1dXN977FCfFVmOMNe5wOHz1xWfn5+dVZRnBGhNT6rrueOxj5JC49yH45IOEwJGBiFIWM1DDKZICgFoufZ7IgCaXhNaSAUQWA9hUddu22tZY17WtpvVEM1knIrJEWZP/Xlu3FFymez8loZkpB1u3duyHum767rBYLR+b57aqbq+vBYGsQUZt8WdmMlKRC1ndDyGDNJz0SgFQ36sCT1WywCI4XyL4cidKpsjofrCEkthYBXKM9/7ArDiN5iOleQE+Nh+iOJTf5IOnnSwikiCxql+p+9a7jwABkEm0RM+MzjkQIjJkjDFGeJpxwoI6RVgkMacQAkcPIkQ2q/hpAjZZG4YpAynY7Xs+5aOZ3nuxxT/6+NANTzG7iDIO7mcUcylf61aYOTZmZE5IZMhyROvc6enpD98eXF37YWiaZr1eD2NHeI+yqul3ZACA5Z7KpHe7JmQCbJ1x5J48efTq1avgg3UuxWiMOR6716/fvvzs+dQHxumeFJo91nzV5os4P+H5gjJI8L4yVu1m8aksUeFoxVhCSMYYH4PmCpirgGqpC5Y4obgT5xDmIs9ElAf3Ykr3mZym0Yq0qLvt+tHMuo805xYRlSrVEHvIxFdrrbVuSvsytqzphebHWuNUk80J9DZT3hMApMRK0tHESw33MAyHu1vVuFCk8fHFRVvXVVX5oY8xrpnJuhDSydlpSgl9YJ4G6mVKoRJ8UN3/xMPMIQVZk+IYwiQeklKKMEls6nVRbQHOzG1N1Mpsvma50Jl92kvz+s0bRJz8JbP6QlfZ3W632+1evnyhlWYAEEZmHsdR2XDaHXRzeUVEq5ON4uS73e6Xv/zlarV68eLFarXph+Hy8vLk5Gy1WnnfAcDXX3/9q1/++ssvvnj69Kl6cWOM5g0Fai4Bsl59yaiGWiI9u+KeNTLTOYCqd8HMfX9smgaAvR8uLs6urm5U2qVEabqXAEqYr00mIpnobq1ljogQwlQHdcuVRgY+DNaZw7FzlTHGuMqwRB+G1bJVGrAxCMDff/9tXbsY/d3djY7g7Pt+t91qTKkiG5eXl4fDgXJPRFXXSjlMo18+faKhxjAMh8PhZHMWY3TVxKU/Pz+/vb1Vq13X9en52f/2v/1vf//3f/9Xf/VXT548efbsWdu2zOycWa/XyrM7Ho8/vPkupbRer8nZ4XjAFB3hd6++eff63auvv7m5uem7AQBc3bi6SSm1VaPjGp8+fmSMsZWr6+o4DLe3t8M4qJizH0OKEhKPMTFTYhjGMWZfiyjGGIMCAIZQZgUFvRPJIIA4snpp6tq1ldtsNicnJ+qA1f4QWiJCysQUKeT5qD54/oA83UtomvBhrPGRrasjJ2OrekmCuElJZUwO273eEcPgFVJy1q1pdeiOamGAqK7rMXi9u2OMrqkBKbAgiIQIMg19UVuhVf/5pkVDCpVPtzBMt7b3XFWVRhO6DarK6ohPETFIcaZtWczv/Ez1TpE5JMssia0llUPXTQ44pTopTMSrxKmQHoTRGFosFsZaZk17EjMDmpSSjkn0Y4Q0YUKrzUlVVcIoMOGCMUZTOUwydxzvudu57yhp8X2m94FzLfl3OVN+WJDCTMV9MDxSREAIcioMADJNlLj/TH6zmhtAxKpqQKRZLvbbMcbojKGpxSiVNBRRrLUxJZhpM8G98AowgDBwEk7h0ePzH/3oy//8n/9qsz5BMHXVhji+e/dus1ktVy0RVlVT0DZ6mKnPz/83PTAXUJ1zkNnnlHuTrJsOr641fewR74V2dTtq/prymG71vjIxKSDGqAyjcuVsnmNYekYh16cVrNPdVmjM+igHPPpxukwpsfZozoZ/lY9gboPTlVHfw1rnpqndebFYaBwQQ5qIiyk1i0UIYbc73N3dGRQVf6jrOoQxpSBS7be3tq7aZrnbKROenHOwgsS2IichUZwghAnbnwqWrI4ZAKz1GiWwyHLZhjCNwuUEygtzOr+TH9yuuixd12nOFwbVjMRx9Ij45MkTrUoiouoX7rs++n6/3282m6pqvI8aPBpjQpigC/3+EMIR0SIc+k4PWyWL/9t/+28x8hdffNH1vfdxGLwxLsZ4d3e3vdt98803P/7RjzR0KLcQ4gRCao1No4GShWgqr1lyuToAE8KhbCmf5cl2u127WKpO9d3d3TiOVWW1RVX9XBHhSpk4phdROVwFP9BjWywWGilqdVYVdXTraspewsdJSiwrsj179kyLIzp2sOs6QvTee+91pq9elHEcz87O9MtVwS2E8PTi0f5uqz4bACrXqCi01uzLibTtYhg9CxyPx9PT0z/8wz9k5levXr179y6ltFotdP6SMs91Wtzbt2+///77tm3PTjfG0N3V5evvf/jFP/w8xhhDQjSApu9GXJimaUMIZ2dnpycbJHCVRcK+77e73fF4HELs+pGFfOKQOCRJUZIkTsAsCQURRFggiQjTPR+nYG9kkCaxaGhr65xraqe5r+4ENSyUKVdqWxBIBZmKoQcARClumDMQLSLIAMCAGDGJSEI9DCJydbsAoNVmc3dzE0IMIQx4z2gB2wKAJSMIxiCQIaIkHKYpHZPZSSl5DymliqCgGhqllfMFAIaJy1uO0JhpziYApCSAoridtVakMGMQEY0gsDxEdj/eklT+KyK5FYtVRUudUUqRADmrvKmGBE3qs0hkBAjR6JUjnXYALMwMmFKIo0956jmhRbg/TtFhfSUuQKDZmHn5WNY7/3sKu2dv0JFEepYyK32Wu3Ju35jZFtc7WxqCLEWoW6O4ekTUnYNotKUJ0FgLItXJyUkcfXc4KqzXd4fdbsccjXEx+lL2A2BDRmaZ6PTlCZSRTyAi+NmnL7/+1TfM4uqqruthNNvt7vZ2u1qtlPlcEg75ILWFf8IjewvLzFHY0dQe4JyrG6fZkjFNyiPnyk9wZt/ZPPCVM9trCtPSfS8TzGKfwszSA9BGmmI09Z0TKyonT6U623ejujGFXbQNNIRAhrLXn6IHTWfnv6L3FeEkBFFCsJSSNi9578cQqqo6HDrd90pTUt3NyjpNzpbWucocD4ckEyTunLG0nKKQzDIzpflYASijwBeMRAoq6KFq289yuXS2rqpKZzyUi4OzB2T4brIvWXPj0aNHSCQiqvjofSRnT05a5ujq5vT8whqnPj7GKAiuruZBqC5jQDTG1HXlvV8u13/0R//s5OTs5z//uff+s8+/XK9b5+q+79+8eXN1ee29/8lPfkLTXFVUzFaPsOhsUO5Bmu+WIiGSI7Z7MqeZRuGyNm9QFhvXTEJXXrHcOaWucKzUQ+Ms+S5IDGf5wPtqS2U0CizTL1JKmspTHqmEiF3XqYrvZrO53W0v3NnFxcXxcFAmlEpYqOt9/Pix8qWVUj6Mox6Aum1FLPzYpZQWi8Xbt29VaFpvpO++//78/HyxWGx3dwAwDIPq0eZgC2bLxcYYhQ2dc99/+9312zePH51vb2+v3r1DRE5ibcWSDv1QVdUYk43RWVivlqeblSMGRO/Dvjtud4d914cIwxgT4Oh5DMmHGBliEmZJnCLqwLskwmzYEpIBJFvlblEiIoNE4JxxzjhrNJBq21avGiICTd7XGKO9pBMXVDWOM2iXExvWNqGYVXSKOyCiycRMVTelEdiqbgVS3S5X6zCOgXe7kLxycVMMAGCdUeekI3ZU2oysQURk4RRTCkk3SdvALB0qWKB6ow/9jVobzt1rYAznTEMht2LVdRPKQ/bu/Nve+6P8BPNESgBk0Pw3prImzMwJEA2CEUYyRCZPUyjGbZJDSIjT/SUiKYohnO5Z0ryVMLPQtW1o5kk+4jJ+05Oz+iyUy4eIlGnC/Bs+jpiVsN5fGiGgKXbBGUowX9/5NmKGqmpOT0+7455jqKpquVwej3uJKMjlzVCacWeDoJmZ073EFRk8HrqmbT/99JNf/vJrsCrnCyJye3v77NkTg9LHVNdO8wbJNMLftHKICLnZrkQS+stEucJRTwNlm7bKbbv3cMQ8RtHLaa01SKWkJ7lSklKKccotKCt7qL3WZiHJqluYs/CUG4L1DDArPJRFNnmIIeTCDOasHRE1/IRJEPje+BbwRxUbCHOHVWacHQ5djBENpZSin+ToFosFRx9jtNamFLz3iYMBXC1bREk+dP3BkGNAZl4sFk3TCFskU05Kj19/y+ZJUN77yNMUSAZBYVtVq80mpeRTJENRGK0RxDRxA1EErbE6Y1Xdm/oeMvc5ZcoXaL8/jvFOaWjr9Xq9PlksVoe+SyBrsgBQ1xOyjYiAUy1ccgCrbClF9b/88stHjx7917/6b//HP/wff/qnf7pYrN6+fXt3u/3hhx/evn377Nknn718mSOtqYdhEkxwrgDplLvA9T2Uh1FquKjvCSHoXATKc6a1kO/zOPr1er3f73XjVZWdGAD59gGAAqARkTFINCVbZTfqj1ZVJYZVe1VduG4bnbyrjl+zZG272m63IjIEf3p6Ktu7xWKxWi5/td/r7EJtiNJDVWO93W415/Per09OMPPJAycbuPRn667zMbhYPXv2yevX35dUHjPxSk+trp2p3LJdmPzQk1VKwcXJ5ury7e31zZsfXl++fff06dOhH1+/vtSIGtB4Py4ae7I53ayatrHJh93xeDweu3489qMPEmIKCUNiHzlEiQlj4iisN2LUwEkiiQgwojV4v7f1ahpEg2gNVs6BsLaNtW3brpbW2slvISIRPmxfFJE4y4BJwJDJc5Ow7Mm5KU4pMDNylmTiiIgGAcUsVxs1DsPQpZT8MDIzR2+MaawRxBg5pURgLBk2IBxDCghcltTmGWicx3MpcpN0rkzOfYv1c87pXBPmpGrJRJSSEHDZ6jSjPWc7d+9HPnQiD/zOtE4TwANT98q9hSyfLQklGWdtpYOMYGpMtQKJp/6CqUDDzCDSNMvJQs4MrBDC7DAYp5G9H/qR944ffoNbLadzH3mwotEyTamC4unpQf9T8U8AMJfo/PA9ZRVYOd95SMB6vb6+fHfYHYdhQBYfQgg6YwcSR0AAuRc8K17z/r/MUVJVO5H06NGjb7/9oTv2lFkq2+3u8vLyyaNHIlELt5VzaTraCTD/yKoBYJHLQiVjE8A9tVhv8uRDSql1taYL6/Va31Dl6evqVxRMhtwuYozRklVOlKdx65wpr5RZ7/OrWBwkEalIgq6AvpNyh5J+7TwAUrKbOiT18VrzmzOHS1bNnBvpRFIMy+VSMjNIj1Dp0Jxxy2E4dl23qBtjwFrr/VDXtXNOqa3Hw0EA4hjMwqjaFEvCPDm1QLvz/WetresWgGEPiGjrytaV70YA2Gw2q9Xq5uZuv98TkTF27lp4pgkq8T5k0QjDp3uULKXEDPvuqJXCq6ur05PzR48eKYROZBE7AxP4bG0FACrzKbP2bhFRoUdjzPHQX5w//tf/+l//X//h//2zn/38yZMn37767vXr1wC03e4vLsJyudQrVdjU6gXn+Ws5VJOHnTEzEeZ8YIIKSgCnSaqiDjEmzTKtMXVVCTMCOGtZczQBROSYCJAAkSik5KZ5UBOaVdJrZXKFEFDAGaMdnOodFYWuspZ13/cFo+u67uzsLOZG+eurKx0JvN1u70U/fBi7XloRkbquj8ejOvvg/fmjE2PMzfbO4SRpon1TT58+fXd1qTmic+78/NFut9tsTkc/lAFTerv9/Fe//OSTT4wxjaskdxirtNNisVgvKkK5efd2e3vz1VdfffXVV//p//ufY0qmqtZ1s9/vK+fW6+Vms2ob54djN8bDoTt0Qz96H2JgGCMOIYWYIkuM7BNHTpr+MnPiWTMJgFaCLRIRoSj1ChHRqLdX/o+1VV27pm6axlaOjIGHyRDkHEiY0yytLDaQEVkR8Jl+rb4hlvKTISVOExFZS4RtVTlLBrBtqtur66vry+P+MCZhjgREhlAEWRPj2DqTGEIIWCA3DiKSYBoQrrBKabaEmcsp9q2qKo7l1ZlIMpAWGYmspi4lPH3PKpcFKb5m7lbLe1gi5GI8PHAX98VjXShrK2stgKp6a4GMAdggGEPeez90IsIshHaxWAgil3G7czchggTKfsffnLB+9Jn503PHnL+ERMDY+wmMJcJA/JgWNCICqdH/iIwnTbPZp+MjIsiKyiys+VY/HFMKiNj1RzHGWmNyWJRmrW9TiglUgDhmZmAASjGdnp4+ffrkH372y/Pzc7Udw9hdXl4/Oj+vKhdCQJW6/UD74rc8RCacoVy/st2jMKSJkauVp+PxOAxD1SxwNqNXrSciGqScfDgRUbTMe79c1mrO1FvrgRUVLZhpJigKulwu9fudcyFOey5lWj/n1tj57qTcCpVm6owxjwNTU67ZmOQ2YtV4AgCbRyHp7dR1HWtOzKxAKBBpIXO73T559Pj8/BwRt9tbAGBBAIkxcorWLgxIOcf3/Nl0ZYmqqmKOxlnt6l4sFhIGEdHxt5l/1DtXqd8CgLmEuu6UlO6jPY0VxtHrFlQthdvddr/fq79Zrk5X33/Xtu3zp8+CT6v1YhoaYxU6SxyToogajhBa51zlmpTS6HtV1Q4h/O7v/v5f/MVf/O1//zsdP1zX1RdffPHv/t2/K5lficn0D72jUhY7041UNMhE2JgpPJKcv5bGD31/ZuNzSrGu691upyy5HC7cDx5Os/ZKybF8KWRQ1pXU5RqGAQUAq2EYdNxWeZtuDN0VNzc3zjkfgp7vbrdTT6m5uCpZajChIi0hBA10Hj9+TEQKZd/c3EDik5MTZ+vVatV1nX6wbZc+JmZomoVz9vr6erVaHY/Htm1PTk4uLy8111eG9n6/V03Ki9Mza62SAQ2Sc846Gg7bm6ur2jnnzOefvwwhHI/9s2fPru72d/udcfbkbL3erE7WbQrj9u6q48Z7nwRCTINnBvRRRp9ikgQYGGLiKJI4ZQ01A8CGrDWAJDqKFBEnQVsWRjFAYBBYOCXU+oI1RGScJYW+CBl1rifDVMecbGaCe/cjs+EHzExyH4PevwoCCMwMESSbaxGJKREYRGzbtnLGEsY0orAdY9d1kkYha42xFpk5sAQfjDHWSF1RVU08UCWyUJY9KNCOcRZnyGI5zpIS4NQMYktuuly2xjmY+Z7pbb8Buf2oxUZEgcQS83JN/AfITN5y+yCiISI0zmmc/X4DLUtEER+G6SMsVVMbchGo/Hq5KOWQJvSxOIt/7FEc8MM3U8ZcQb05PKQGl0UzxnxcAYQzrP3RtSvnWQ7aWrtarXzfaUq3XC5ROI6+bVuqJo77fI30Mf3z/glENBbTOI4Mxjn37Nmzb1/9MAyDyDRTqNsf7u7unj59zDEZcpwZ+791dd6PwuYhHuXidErJIpWX9GLHGClGay1aizmsUwdMgG3bKl8A8t2SZoIyOGvJlRlcnzIRWpNRLejqS5pHKnRZsrTyd474JiaFiPR9p4RtnU93v4jGqH3XuFV/WtMXyDKzGb2YmmpSSqt2sT49OR6PmsbFGNvlAhFvb69TSk3ThDgS2uQHAGLmw+Fw7kdbteoUERGA8qpPeBSIiCRtuo0pVFUVqmqi5ywr5+oQwm63VywhO/J7uRIASCGGKSjKdzvhMAzjOCbmcQxayDwej/v9vmmaYUzv3r1j5u+///7NmzePHz9++uzx+fkpCTStzo0YCRCQtRvV2brAsADw+vXrYRi6biBrQghff/316empYh9/9Ed/VMImKPzBrIkmmTKmF0jDIEQcx9E5WwwZ5aYDRTIKn3m6cXN9wTnc7Y6aGU8S3zxhgMxcCPbq/nM0cG8NMc/g0ktjkNSXF+6eRgYFBLbTzIxxGMeiW6kNxGnq2znqTkPEw3ZnrT09PT0ej8aYV69eaRBGRMvl8vGzp5wghHA4HFTvsKoaRAwh6PxKXTdGWJ+evL28rB1pQ1QI4Xg8LpftZrN59erVzc3Np88/UdFpzYGMRWMMRR/GISavvLw3b944Z+4O+/1+f3d39/nnn9d13TQVEuzvbvtufxQOISWBIUofIguNIUYwkZMgJeEkKECs8AIAASGRtWgNAHIGZpRPRAIISRimtCzGiM6CIWMMEBqtAVnDIIiQhJkFldSTpgLcXKCwmAv9p0Vb7sriIcgwCIEwAAkmVfBV4+I5gaQURj8M3g+IaC2R5xSDiBhOSMZUDkEcUWVcXddKrtYNE0VAEtIk06bmgma9Q/NETfIsE0sT3bK8Wa3TYrHSNEyPvfi2uXEuf5eQ/UPTPfOywiw8C1CmBE1vE0EQJEPWVIQEhjjrbLBEFW9QssJ0kNYo4RHMnIElItM4pmJw1P1hht8/GiiUl+ZvmL4EzPwT974g8TxfKo7m48MYEFDgwW+X3+OHpVzRuTMqEQBy2G+JYNku/NBbZ54/fx6Et9vtmPWbEJHwQS0TckhIZEWSSg9GAQ2xX758+fd///ebzWmMXu3amzdvTk9Pm2rSNE8cP3oKv+VRjBTkjT61vlRuHvFpf4uPk3+lHGqoWdRukJREE52pUoukfXXFIqtfKYzoKb5hzqo0jR6DOjwlYKtjLlc3pYRoSuBZhoCqjTs7O9P3KEdGtdHhHq6YKEtK8Q2zUe3KhlH+yMQeqqpuf+jHQXs/NpuNDpvr+6OqAacQTe2cseQqRNnv9957O2H4DgCiMIAQkcUKgBEx5SjE1RUdp4MREZX7j5GV4GqyCOh9wSafgjoYkIkuzsxjnCSXD8fj4dCN47g7HiSLeo7HYCypNb+5uTndnDx99vizzz778Zdf1E2l11oSJw53d3d3d3cpSl3Xz549897f3Nwojv348dPbd3c6Qc85V1XNZ599dnFxsVyuCVN6aBQAYE5lL5cbZ6V6NV564Yhovv3KG5RspV5TYQ9NNLVDrK5andNQSiEppXEMSA9ief0h/af2NenhqZNTVlqYZEAmXZdSplXy13q9ZgS9Rsvlkn1QhtR2O8EMtXU6bFjF7+q6VtT6Xk7LpxcvXjCz6nWISN02ZI21tu97772geffu3bNnz5xzdW3X67XKbCFi1TYhhD/4gz9QApqoRJ11zByT7/ue0mh0fDrL69evLy+vbm9vb/fdEEWbjL0fandxc3N1vLtCSKM0x2OXGEJI3nOIEiKjqYCMKJmUUpnnIgiEhASWcOpAytaPhQ2SIAgI8PQkpBzr1NXU/ltXNLXJznp5mVlYVW4EHqRf82RoDj6XQI1TIGsACIAtTvQLSUkAvB85hRTGoeui98agtTaFmIIXkZQQgJwkss45Y61VebXJ2AoYiyTgc+WiCNcXVycZiqOZWBsZG1NEcz+DS3ERLcHw9P736Uj3nuUhQlv27XsLov9SInNJG4oDM0Qg0/KW5CRN+mQsLACgrYYhBBVFds4uFqsQwnsOXwP6clTlSN87tg8fMgPSH3zkg0Ko2m2YuXyZNcJYAHr4foWFwSDNdbRzlq3tOro5QNFXkQRILNgsV82itYdqCIMYNHXVbhb+Zh99zEgdSEwMIILMTGhwmoSspGsmkBQtEhlgoIh2/PSLi1c/2N7vrVtwhAR4uw1v3l1/9tmLxIMxaEQ3h8xdznsLhLNznAcWJdYTQeeqwGzJsCB7T0TeD01TseLM1ipCqDaxqiyQVM001n4MXWtbsmDpXldL38+5WKurH/KUQO+nEJ5ncpgxsI5BNMZYUw3DUFXOGlL+i0YJMU8Q894Hn2LgcThY61KUEHzKKgQpirYeseBisei6zllLpNXiJoTUdVv1gmM/qFIPSySDBoWjB4Dlcrnf7gjtcnEyjj4laZpFSokQLQiliJjefPfNi+XG1hVzNMY4xJTEIhEgGcPMzlqJxiBFEAXbfX9IzLn5uPJ+YE5V5WIMVVVrQfRwONR10w+haZqqXijbDpH8BPNy3/ddPx4Oh64bkkjwSrJwwSctSalgZBjj9fXtfr/fbQ8pis5s0AiGiPrRHHuMzFfbu9dXu7Efrq6umPnt27ft4lsO0Rhsmqaq3cnJql1YH459d6sBZ7EUKomsZc4QvHOOyBGZvu/ruhrHkUH6cdD5PMWqQuLd7V1VVRITIqI1ImCtC0GLGtb7YLJw1bRXJRqLRBhjcFUjkFxlrIj3WhuWYeiMMcvlUoR02tJi0YQQTjar3W5XN64UmyH3hqlpU+0LnXDVdx0tl411EuKiqkM/KEBijFF5VEQ8HA4n9uyw36/XK2a+uDg3hk5ONiEERLCIV7sbY7FpmsRhuWpBaOiO7XJxc3OzWDRNYzeb81ffd2Tk2fPHlz+8QYGnj59c3Vy/fft20S6bptntDqt2sd8fnRuWyzZY27Y1EazXLe/7umr3t2nfj30/Xl7vuwi2qs+Wrm3r/d27L774zAe+uQvMGwDYe/Bc+WHkJCDGGSBizwlRhtEzCANzZBC2gI2ryUUCdAaJCNWeACVOVNUiKACCFAVClIZMVdlGyNoKwbSLFbnK2ioFLZyAAULVdRKwaAQhJSGaqsFlCzGzCEICtBhziwdl3NKiKUPgRKKgQZHInIIXjr477rd33WFvhEUSjwOiqet2HMdxGC1SjEzkq2VjoUbf13UtkGKMlogcsYE+hiScIleYGDD6gII6iobQkMWUJAZOjIJmiGxNYhA/ekIchr5ta5F0dn5SO5QUwBAo9k5ZQOJjYgyTrRaQSfp6KpATgCbaiEiIkVOMUbc3p2SNCTHGmMAigBC5drkaORqDwHGC+qKQFnNDkigkBEApidssgjA6CxO1a1aNZkEEi4RAkLuS+CGgrfCewq2TqybSHrLsXCckwMzmAxVPxFkXDLJMU8H8/0nzgPXbIMtq5a4TnL0E1trgh+V6fTwe9vvtcrls65qInBsQFb5NAEhTWqBybg8iDMkV55QSWSKiEONysfj000//7m9/vraLuq5FIKW4vdsfL45Nay2i8PsRx4dhy3vPzP9ZtohkvrH+HSdtyEn8uXxEWzUQp6KIy7NdUUexuupwOCjWZ3LzEs1knzUQ0XusFHcL+FN4iZoiKwTEuUZYpKNKbqTDg9PUF+9L3K2f0lwqxbTb7aIPi/PzCWm3FhG1Wl8qQCU6izGqSIJkzMN7j3Qv11XqnYjY9/3+bltbZ+oa7qUhAAhF7nF+k1kCmqnf3t4OY7i6utLiQkFH53m/LniMUV0RAKA1FGW/3/d93w9e82+YsdAlAwwZWZkeIaTr6+v/+B//o86A0u5YXclxHFeb9eXl5XF/UGWDCdT1aAAVJNY65fPnz1eLpfe+rhclrqfM5C/VgYxYYOE0hBQ1q5jHvwawkPsCp3AM1k4SuwDQdR1kH6mAMM5YCAXwN1lFVQc86OWYAzD7/T7GCCKr1arsap3EELJe1WKxgHvGXL3ZbDQTUt1pbfw9Ozuz1j569Gi73VZVdXt7ezwe9VXN2pVTreT/Q98R0c3NjeqeIiKhFZHI6ebmpu9r3T+afOeK/jR+EQDevXt3fn6+22+/++bV+flpSmkcus1mdXZ2drJeDsNw3trr6+sffvhBiyzA99rXqqFtjNvv91pE67qu85BS4pRABCdrCMwcszVDRDIoMIG0VSMkQICAIrmlkBA5MaIAGkQkFAJkjimCCM1Df8nCAJAZJ/pQUrEG0DCnXzFzlmvwabIbBSMRkcBqLgwiCiHwNGgQAYZh6A6H3W532G2Jk7XWEDhnRCoiEEmSEksMPqmTQWNCCAKTilFidpXBpD3HYhBB5/6hECIwylS9ToiTiShcOY0gMCMlkDFRflhYfc/7zs0vzsrD89WQLMkAOeuVov8sgkjOkTEG4N5q5W7Z4p4m06G2Tos4Zho3IEAfOaQPH8UEzY5ZmH+jH8FcSQWWD95wn2GXR0mLP3TApEkwZCB6djRT6IBTVbWklQigQl5msVhdXFxUzux2d3c3V865/jhq9qYron1dRHb61Lx8jaKFrmK7RaSqqk8//fTXv/o2hLGurHM2pbjf74/HY1Wvx5EtZaMPH3GrH1up37gnjKEQJoGFUuEoiO7cpw6Db5oasjWciriJvUwykJQbc0vEJLOGXcido7p9FXM2uesXZzCUgpnF+G42m7mXWq1WJcMumIa+VFpgU9KMOY1jn1KI0e/3+/1+P6kBh6C6IurFjTGKiqcUvIfa6Y0aMPdITOl1ZmQE73f7u83JSv0E0dReh4hETjiKCFlntAjqJMa4Wm2Wy/V2+0PXdZJ4vT7Rry0eSJu11E6NKQ19f3FxIQBd14lICKEfBuVJpZR8ZH0zM6uZmJG5JpIdEWk5cxxH/XL13FPl9YdpzTXsULTWh6G2zjnjnPvkk09+9KMf1bXTuQKcqSt6HRXSUEKcIm+chVmmmDdMhLsStVTGusrpHOLyzrOzcyX9UW7/VZhEL64xpsy4LI5cffzkunJsx1kZn3Ot2hozzSAyBhGLGgYAHI/H1WqlI0/01bqu3717p3MP9/u9Ntjc3t6q2OezZ89ub2//4A/+4PLykoj04ynrKKn+iSodhuAVQB7HcRg71ZcG4N1ud3Jy0jTN69evv/7m1y9fvnzx7NnFxcXPfvaz5XJ5PHSXl5ftomHmk5OT3W737MlTkLS9ueUQx/4IAIe3++3uMI6jTIrlY9M0WkMJIZycrFNKu2OHLDHGQ9cPcVIqIjACwopRitLJ0Y8jxwQsjavWm9WyaQEGAjQGUYRjUEZCYhCBwIkTQ2JtpKjIuYmfL4bAEJAwc1ROkCa+ADCJUchUUi07p/gbLg02U5svyazJDbI/SyCIaMgRADMfdvvDftvvd34YUko+xFqEXGXt1DIQQvBlzCJzZB7GDhGV8WCsnQinZIAlCaAICguLACGhCBPZpCcPLIQAGEJCU4lM7ey67bWEREQiSU2vPKywwMwrF1OsxfCSMOgf6iYg92cWVz1teGbK1l7vBQFQlVDQuEezUpHEaV7SXiwWWW5TiIx8kJd/6BTKM+XX33tpfmqQidDqGefuVluNMbdEl2udreXHWNCzByHwewN9Z4vI2n01tUawOOdS9IvVer1e6x0bhrFdLprFsusOFEgEVeQEc9fze/QonMh1hiVpmMzMy+Xy888//4ef/bJyCxEhA97716/fnpyuY4ymumfD68aef+F84X77Q2ZKNPhwBlEBitWJ6rZT46K2rK7rFKaRghr8vrfzEO+tcMzzBPUP5URQntwwv+SYBR21njEMg9Zi9SALHVcdTFGchhwWAMA4jqoJoA5Smc8x+vK7AGCdUylEzck0L18uW+UsaBRZ1GLLT2CWyO6P3X67a6raLVb5QuQrQlY4EpG1lYgwiKu4qqqLi4vtdrvbHaq6UWqPplO65toRBDmmLnqZ+mTX930/pvvHvf5XSlyqsDTN6J1QiqnrXoehzmIpLRMo7A9ayjVgHRmsY4zONS9evHj27Bkijv2Q2Sv3WkI4U2KJceo+wiy8WpAGNyOLppR8GFiiMXaxWIzjOHq/Xq8BQDUFFWLRrmLdcnE2fCnNpgsrdKHvwawXXSIwXUYlChwOh9Vq1bat0qZKaFLyAxUzGYah6zotOWt5IuWBmABwOByePn1aVdXhcGia5uzsVKVU3r59q5fJ54f+il5ZZW+1bdv1/fn5+fX1tZKzNifrz7/47PXr13/2Z3/2ox/96MsffbVcrbd3O508r5Hr2A8/vP7u+ZPHyOl42LVN5f0I/dEQIKKxdHd3h4iUby5tRzl2ndrx4/EYYiCoJWcLggQiavy9HwVBFR4MgTVYW2MNWkRnbFVVliDrP2BMaRyDNY7tBDpaY5xF1XuujJ3DMFotFSkmDvSPwCnGqE1xORG6rwEzAjHHCIhSLiIRqYaiZPEiIQRm74dhGPb7/f5uJ8ELpxR8CuKtJzuR7WHy90F3aEpBoNVe+cPhQMaoHKkhQuGJ55aSpBQZkOyE1KoaByAJaKN0qozSOzCbLGOxAIFqnHQW73te7T0HXAxIfpIBJKXJxhafp3VlUcma2SxCY6YDoFkXzMQXASjdgJqHuLomoikO+k0tM4LveboPfDDOvOz98/mZDE3f56UPvFsCEZ4FW3lN/lEIevLB5ZimXqu81lLGE5ERTgKk0ler9cknLz7/5S/+oW2WTdN0XYdkcOLyCTMjlFXO1GjQ3FGrtsjMxrqUhIhevnz561+9CnEEIWvtOPavXr365MWT09MNgwAIIX7Yb1ZY3B9f8dlbIUs2auRlcCrTFk+m94n6FcW7ikAg8PQn5QfmUZow00KSGWuOs2xk4b9A7grgLIlOeTiu5NECeoQKBWvgWQ54jhiXW1cFIpbr1Xa7PT89U7KMhsY6R0d/zoJTP9G2rXJ8tECbUrJkyACzykXV+isxD05QHzMO/dXV5cRAsTYrDxTiNwoAGWOhVnb/OFTtav38+YuUvhvHMaR4dnb27bffIpIm3zFGdSSQgX21p0TkQ+j7UWU+mTnyFC5MNwcBMBblVNKmo3zhFAeGPJ8KihABsoAhA4Bc15XVc2jcctnqYC7n3Gq1CuNQ9DrUExdGVeEkxzyXMGWTMQUEufOtqipkSTwdwH6/b9u2seZ4PA7DqJ05egzM6Xicahlu0u6GAgMSESLEGESkbRd932MeqasJsapZaQvQkydPdAawEvROT09Lm5xyjDGPWNc9r/98+vTpf/2v//Xzzz8vJQkiur6+VuBht9vd3Ym6apVc1m387t27Fy9eaMB3PB51Zy4Wi8Visd5sjsfjycnJ7e1tCOHq6mq3233xxRdffPb5n/3Zn3/z6tt/8S/+xcuXLwHg7bs3X3755c9/9veffvrp7u5GRAildtXQd0QEICGE7rDv+56DT2JEkMwk4RRC8F5Skq4f+8Ejqi8AhIlelRiSxCjCAmoQCcRZVztjDSLEReUqa5qmsoYgaYYqPrKzlmVi1amTqCpbV44sGnvf/ldNd2sAoLmQBWMmMc0GxZc6RQJRr1a2K+QkL0zj1CYPFySFcdzv9/3xcNgfjocDxIAgKXhthWyayk56t0EkqUWaInWZPJOIVHVNpkZE4MjMkiKrHYsJBFmCjrlBRgDQHivNQUUm6XulQRVTlpUj1di+32I7977FUsF9fiIyw58xd/HpqzGlGALlQBC15T3LfMJksYmIUggKwinJlCx578/Pz7MhvS8TzC3/dEj5mEXedySTe0ICfHAKc89SztHA+4oI+ig7p3xqcg2/xTPdL2ImRau/fNjcdV/qYwREY7U/Fc365HRzcj4cdovl+nA4iKRh6FOMSOjIBF+avWB+ZFP4hkBEJADGgPBy2b58+eIf/uFXp6enzOycG6359a+++ek/+0NL+cAQROShiMnkg+UDSOQj5zhfXELFb/VTmpiq2ZWsCGNtO449sEB2qDyTCFZvavIAYMgaGphHdBV6YUm7JQPXugjqXEuMrNBoociq+F8prGq2Wr5Z7zHNJEqWXFQqldJlDBrjgCZasjGo6GZV6Vh6l1KKPrBEANhsNsWrFRR0wlRJuuOx744xxrpmIs0GMIEAEqckZIxhETHkwE0M3vXpycl+f3t7OwzDsl1opylk/LaqqtxuQTp8Asn6kPb7ozYgdeNARFrMSymFkFRmz1YmhTQPlfLdPnHLNbNUr4mIbduyxPv8PkQyVu/Ek5MT59z5+fnJ6Sb6cHNzZ7Mom65DYcPpTaFfYvJIjxLaa3miGFyC+9GEGmckTuv1+njsdF7N8XjILhZVuFtEFEDmmb5B+TkNQVSjVMMg/VFE1M4iyMlrlx+6x1TMeSKvMqvzBgBrrU4GfPHihW5gTZu0+6Cqqru7O+bJPprcl6UI+ZMnT0oRerfbrtcbPQVFGhSvbtsWUPb7/TiOf/3Xf/2jL3/0//hf/9fvvvvuz//8z3/v937vs88+u7q+FJGnT5/ut7c//vFXu+12tVgedts4DtaY7fXNOPphDPvtvq7r4zAS2cSMiCEJpSiM4+iP/ShAIIR5MyBZFoycIqcYuMRJhOAM1c5WjhpXLRs0xjSODKqsLwCQtclVhhOExOVWrSpbO8cWUEBSSCmUUFjDaJj19SYQ/WTKQWGJqhOIjv0p9Sk93mKOmDlOYwgYwYx9v91ury/fpRiTH0HEgAhLDEldqNaSdBcBC5J47wERgA/bO0RcnWysMWGMKYqhaQqBJPWFQATCwEpCloQMjBPpR8dJ5SMkbQDR/TMlQJND4glyeNh3VJ7BrBGtS4K5TUAyDKnRTMk9EjPgpPVmjHGuss4ZY4hs0riAYO7Y5lehaRrMCsrzAvADRyD4oXOdO6bpX7MnP3DA+TvzLxQLICKcO51EhEgXSX6LA74vA+d/gio/684AAER5b4CiiBiyCSKiqZuF9x4Znj5/cfk9pyQ3Nzcioj6gqCK/54AFytlycScqt2KJvvjii++/fx3z7PQTc3p5fXN5dfPs8Wm5bCJSKt55aWQejv0m7zudZ57Lq2Gz/lMhUMrjZtX9TArDifV09JkJEtW0+GHaHbMqgkIixXCXKK9cVMyj3VXjXkFj/d3CgFAQby7jUDxK+VrNj5UgU8Y3abVGfflud2gWrQLmzhljJqnk+y3IYq2tap2jN43SK/Mkyha0SDHGMPTBj7JYGCIBAFNmbAkAsHUIYkQQcblYxZCE4eT8zFp7c3Nz6I5nZ2c8tWZN4oVFL0WZPupvuq47HA7dOCDi0PuCpqapCMJ6vfUuhSzPKZIQTYE0ShFOoVE/dIMPRLReTVNsa1V5MEbroyigjk3tgMtqA+oLtcpu8ozIEEKRtkh5ZGSJXUhA97N+dhxG5xwYYua2bWJMTdMcDvvj8ajoMc6I0ylLfBQ0RXeaTrCJIWgKq5ZL/9ATbJtGkeq+71VZWk2SVnlpmqweiUgh8fLry+VyuVy8fv1Ge7E+//xzjX4WizbGoBtewwgAUDGs58+fFyAaADRNDyEsFktrrQ+jBlhN06pjXiwW/bH/5S9/+fu///tnZ2f/4T/8h5efffpHf/RH//W//NWXX35uCYa+/+zTF91hTxKvrq6O/VBydCLQ9DpFgcQ+Dygb2R/7LiSuqipFtqAlCsuAKSYfg4/MIMPgAdhZWzvT1nVVO4NCKJU1iACcEiTtahdEQiQBIXFkjKn0QoAkQEY0MUbvvSQGZIkpwiSVYwC1YUTFN9Q0lMlgc1uh6S/NOjhwmrY7Uee03VEdcHc8brfb47HnFJIPwtERogBL0lBARCrrXNNUlSNzL4xKRHVVIREy6vVq21ZytKfFS2dMEkh47zpUvDwqXZ+suoaUEiEgio5+YmZD77mD9F5/zdyrlSeZWbX9U56jyrnpqMSsIveYPCIiqrSTUR+uU+cllwuZo1YGAUBljjT8h8nJPnCf04EBlURoHiU8PB0pDvg3nQtM067eP+UEIjlDE0mQZ0ROVgg+/njPBwPCVIRQeSyShy9nFjGAHb1HEGOcCK7WZ/7sGHQ1ARQGJEI/jPBeyo+TTnmSaKzhIU+pjIlZjKlOTzZffvHZ3/z3vzs5OWGGlFJdNTc3d48fnWKagGsBUSXPEijALDaZL/qHi6tySxNvhUBr+MoIFRGbhcQ05B/Hceh6zYnV1UlGY3AaTG3nm0bTX80VitvgPB3BZkVWyQU/Ilqv15pVaAcnZ4hbIdlyPCKiB5CyckiJ+/S/iUPwQ5CpdB05GWeBDJqk+GEI4/n5uea41lpdLudcCjHEsV3UdT1diKL1EePkJoloDKPePLrvFQBW6Kzsdd0YAsjM1Xqt+VDi6MiIyLt373CBFxcXb9++DaGvqjqEkKLEGMmaoh+itUy1ZcJT3TfGyKWEoXkDCE0zgrzI5K5UvNcZgwWOy9Cc5mR1Xavecl3XL168UJvS1HXlLAAobZiIhmFomikB1ek3fd+JcKFwcyal67YxeZCtiEhMHrzm9wpIhDixl4/HY13Xx2OHiGdnZyoRtd/veQZ4lr1a8O1yh3vvEzPR1K6mj/1+r5vTGhNj3O12t7e32tut37nZbPQ9IhOYvNlsvvnmG/3apql/+OH7i4sLa+3hcCiV+zdvXispAQC0qKwMF3XGfd+rgJfubc22JY+P1G1mG6uM65TSkydPtne7GOPf/M3ffPXVV//23/7bv/jL//vx48c//vFX//W//Jc//uOfhnH0w3B2cnp3dcnB+6Enoq7rdR1Wq1U3euesj52uvHGWxyGEBCrUQGSNJVsBYYrsYwohRS5SdEKknUJRkkspYSVRm2EECDABWNKSWU5GhY0x1pIxBsSIMAMWXJcAE4eQRA1dQjRgokxlv2mA7UxTgu8pOVBsBQCUacEppcGHNOlTMydIyR+Px9vb293dVhJLCs5YbCtk8cOYUlo4A4ySWKNIi2SNFeskJu89WVPXNSdhBeRYQAiztgZIEiRteGEOCIxowBhgTgwibEya/AKKUtU2m01du3HsER1o2fMhwIso73likntrrA5YbWOp6cBMkUbvUCJSmi8iTt5UUEEryHNEFAFSBXtOCQA0iHzPncnDNBfhnkJUEoa5f33Pjzz0LFJO9p4FPXM3iMiooiAP6r5ze/VbIOgPfPD0w7kY/N67c0tGyQZYgAVW6xPv/dPnL9788B2S1dKkMUYY0mymmw4JBgDlFuUEgojAioxxJLSPHl1U1TTOr2kaBnl3efXs6cX5+WngRJKnyaZ7/4S5HvnBWcz/mBZEPRBkcSjNXTRt0mi98Kc4dxoo4leAR90KahyVeVt4ufpzWopT6Xnn3H6/n1OfirXVbFWf1xG2BcQLIajIH+Zh2qUBSdMvVeTQg08p+eCJqG1a770Sntt2qftguVz2fX84dJqOKIsVgE5PT2P0eo7jOFpL0QctA9vKFXhHWUJo7DiO3eFYGUoxxBiruo06hCfDGDF6EkBHIggRT09PD8c9cLJIACySDrt90zQnJyfqy6uqOh67lEPjw+FQtU3vRyGUKMJYeqwpz7rALCWhYycUEwaA2plhCGoXiIBQGMUSMGLfDW3bbjarcRxFUtuuN5tNXdfLxUI1WFKaGKQ6kff0dMJaFJ9gTsz3dQSadWdNehQhAIDydYmIrE2cdHiGunznXIqqoKvKG6hXQc+6FFa1+cdaq9VWEQlZRBoRm6Y9HA4mT7BWg6WUPQ0RttttjPFwOJycnHAGsYdhUDaynp2Wh+/u7oZhOD8/0/3TNM1qtRKBy8vLq6srY2i5XLRtu1gs9Evu7u60xfzi4kLvAj2v/X6vHC4tmuhl1YXa7/fH/eHdm7c6r+XRo0fm/Hy73b548eLb71598sknf/qnf/qXf/mXnzx7+tOf/uF//k9/+eOvvrwmub56yzEddnsQ8XEMKfb9iGhEg4BuiDHu9we9fK9fv0UiABQkLZwZY3TcOlBgAA2kUkpNXTXOgSSCydYDwBiiJTRgCEQk+ShE1pALHFgAyDCIIBARoKQk1li9SZVgr1ar8Awi+6T2p6S2M/BvZosnkh3OKmV6o8UwQbJ+HFOUEIIqoozjaJBEkMOE1kJiMjAMHjEsl23fjwbRuQXHVLvKkglTNQSSJBU86LpheVLFMWgUG1m0gCuErErEyEAEQgAJgJjBuYnr4Ed/dnZCRg1d5ojNfEzhAzNrM8hUUFMdlbKNtYxSwmJDqAIakqk5+ocaUufqyrlMITUJBDPIRMAhjDFGbQSKnJ48eUJEISX1jsYaziL8IpNI6NyhftRHzgIjLGXK+acwV2b1JcoENAXlJauQZqhyijAy8vEbM+DJq8L7ie69r3rQQfTwZZiR01y7bBf9YrVqlouUQoqeowECmi3u/IOzibDZMSJYJEE8Ozt7+uzJD9+/Xq1WSbhtlyLdd999t1wum6aBxLknpFL663srm6M8AmR4GN3MVwSyXLMCmIZcSql0cUwdfokz8Y+UC1O4AwVH0ncOwyAi8z4Q3W2IqCKUzjll9+jxqE0sfUEKDyq2JvcgDD7ovWOO0/yipGq6i8Wibdvr62sAUA2scRxt5Q6Hw2ZzOoVmzNfX10qftrbiPLcuJdlut4jS1k27qJmnozWWWMdlO2uMqBQrEQ1DbwBH33ddt1hvNPVhBGOcblIisNYScIwxjL5y1hmqjO1RiKCp6kVb+7FXT//o0aPXr1/f3NwITB3SYwxjDOGYFFxNcRrhVzjkAMAhACtB3RBZThOl2eSRVs4ZFGGB4u1SSrUzbe1A0nq1Wi6XT58+Zeb1em2tWSzamJWcOYs1quNMSccqzLojchNFwRU1gBvHMca4bFr10CGOumKci38ioli3BnlF+KlpGlVL1m1TUkzIMMncNili7DMdT79TU3bdS+M4Xl5e/uhHP9LNoM1FiqPorivV8RjjZrNer9c//PCDOtr9fs8sT5483mzW33zzjc4Jvrq60ux5tVo9f/785ubm5z//+bNnz5Quvt1uz8/PiWi73epB3t3dqRypzktfr9dt22632++///7nP//5F199eXFxcTgczs/P7+7urLV/8if//Ifvvv/ss8+cpW+/+Zq9XzTV7u7WjyOiMMhuezDGubryY9BlUQK5llSQyOC9nuJEWWAZtfALyfuo1Rm9ag5JB2eklPrRA9LoGQkMkiVl23FMPTMLEmO0nFQ7iYgYFJEoqLIACmOC3OQmOJlgKAlTvnYzu/eg6AgA6hw4F5s1rgKhGMfD4XA89MEnAPI+eu8JJjiKY2LmBiauCTPoDInKOv0JSyayIDKRTSlJSonh8voGEStXu6ZGNIlZRKlXyrBFEEIDBikyA2p70uTDtIm81K0gO+APXVp5mIfyxpq35BNMCBBj0g0JWROQiNRLUJ7wONk9IGUWExEgGzKagWAu8UzrrEeLoLRwmlS0AOD9Zcd7SjbM//jo42H6iwVnVRQW8lqUSCsnhAL3Pht+ewasj4/oZH34KIeeIzgDwADaus5Vu1iv16vlxvfDgEcR4SiO7qHwh6dK+fD1DHSoF6QUm6Z6+eLTN2/eDH60ecbq7d3usO+Wi3UUn5IYMwl9vA9xzxYOBCEPSSzXoGx30JBKQU7MfaViCsaoG13PuniC4gsLNEpZZbqg1pq/zk22opH6B89UrkpxVy+epst68IfDQRPfUhhWP1SCht1uh1nlQxnjddWqUIuaHi2pLhfr73/4thS5Y+SceTcxRknRGjQe9YobS8aYBImZaZLNswXqsLYax/Hu7u7k/MK6CGhM5Zhj0b9GSQBgEK01yFrRScMwHPZbPc6maVI6ej8YcovFou+Hw7FHRO9jlAgAEwzg74kIMmOV4yy9IEAlZAEwkWnbtrLTiEYlEyn1mrIA/Xq9zl1AXV3XOudDiwXqUGWSUuEYU4xJ0WubNc7KBqZMsFTk32aRZ8kc+JRiidKU/XQ4HIzxNS41fhKR69ubtm6U/AUAx+NRT03TWfjAUoiItVNbtk53ds5peU/RF91FmiIoKU/jOW1sK2URDd2Ox4NiM/v9XudkxOkRlsul7vCTk5NhGK6vrzebjSbWIvIHf/AHKaU3b970ff/JJ58on2u5XFaVHYZhvz/o1jJ5auH5+aOvvvrKWvvmzZvdbvfLX/7yyZMn2tSuv/748eP99vaTTz6RFG8vLzXhQxRI3IcwBt8YyyH4FLtuGH0EQ4nDarUqAKYulCGHZhqkHaOPkkTE+4FZqspWxjprJIYYGTE6axigG0VSBABrsDLinADQOPqQpjoiUdDuJGtV7QkTiPNpHMMwDFVdM07gBCIia2stwoREzigveilJlB4wN1YiUJosQgTvYwpRO8q2N9vtdtt3HSfRtBhAnGNjDIfIzALROQAIAigiQ/DLti6FDwAt0SAzhBhSSl0K1loGDCxI5JwDY9M0FeYjRj7GaCyFEBFFsZDJWKm9/QBwnp3sg6pwoWJoMEqIMpc3nmkw6N0HAMY4Y60xjogETQaFWUAgMVhKk1q1iTE2i6WaYlENEwQAEO2ZmSe/v4GWO/NlxSu/9897nHnuxRIKyEMG1qzWUODY4nf+6UpY+qDCxiqUp/nh5j+wmEU0brFYnF08HschxDGEMaXEMclDZLwcbrnYIiIMUxcBijEGSJ4+ffz555///Be/3Gw2jGAqB1K9vbw6OTmzlkTYGDcOnVahAN7fQ1P1YHaoSgGYHUAiIk4gE2wCMevsAAAAS+KUe+pposJbPVg9ZU1ACxqpLaea4uiYOcWW1QvGmTiwXqrcdWBEpOs6zdgUSwQAdZ/MXAScC70LMmtMf3G322kCpPGYPgMAx+PRmgqEvEQQenTxpKotIu73ncL7iKaqKpfFjY1BEE6CVHj/LELTHaK2u2msH2Pf9+M4WleRcWZWATII0acxeVV29YMPIWy3N7c3V9vtrZ71OI4AE4CvQ5Oub24QzTB49foK7A/DqIm+iABQjCyStKUKs/iUc7X3AwBU1iaAse8AoGmazWqxWq18ilVtrbXDMCiA2TaNAoCKympEpcXUYRi03olIysmKWYLjw8BRMg0EcxMaqTmTzAWL05bQ0ym59eCjiJyfn5M1N3e3mr8+fvyYmfUwNC9XTUrNocvP6TeX3aW7SA+73FB1XeuRFy84YQkpaYatn72+vloul1dXV8+fP9dpgFpz0a5fTZHVMatMh2qSvHv3rsDOb9++ffnypY64IKK+PwKAxg1VVWmUo4Lb33///XK5/uKLL87Pzz/55JP98aDp8unpRnfR3famtk6LNcft1g/dOAwcPAAMgwcgIOt9AKDRx77vGYSI2rYeR9W0mWzl1JadZ46LiAoMaEeBxlJpaqgjAWRA7yOzEAJFHmBSmQhRYowM6tdDSgIAdWUBAFkYwXl/PB5dXS1EyFmbBe9oagZWeqAI6LRrlqKICJNvmMeRMsl1QdKWqekeCV03DMMQQkyRtZah1CAfIuaR9SiAhIIJQ0qp73rQi75s66qq9BSsrRJzjNNoc2bwPhKxtvoQkgATWRQERFYaNCcQVNhTt/fpZu2cE0jamASIgDouk2HWijO/R8okipJs6IMQ0SAzabJRbo2yLCYLPpcMWGZJtp64bmnJyLY2iajNR8Qi5Dh3MbPVnnck37/6T3nMTQF+cFTlgQ8f5W3/ow4YlFsPAIisFbwPD1eyoqlotkd2tVqdnp4d9tvjbjv0Joye5P6gkUAHFhcdGQBAZXrdRyISoq8b99lnn37/+odhGGzdEFpBc3V5fXZ6/uji3BIwA0ie1SzAHyzjdKgZiIbsiYmIOepCGWtKNKD3cIwRAay9j9HKKRtjACaDq752uVwWPipq9yeiWiI1yiUgUuaqFqUw6wmUXEqRZ7V9+j2qNVh2m8m9cSKi9drSKOy9196eMu9BmVM6fW8Ivq7rdrVcrFcqva1nNAxdjNGSodxN6JyTrEWr5h6BBKdbSN0Y57G4mjNZDuJR5S9QMIWQOAAniSmE4bA/3t3dXV5ebm+ufRj0V/w4GmPIOGNM5dzJ2cnrN28Oh6641bquCS20VFVV13VFbCjG2DRVWXkAWC6XXTe16TtjjTG1M4vF4uTkRMURRQStUWLzer1G5q7rTtcb3mzUmUGOujR0zas3TUeoqrnmwP0NXLLhgn9MliImZjYWBWxKSUmbGnhpMmpczcyXl5frk83Z2VlbN8YY9WHaI0SZ4l7yqkJEpyzr2PW9FvJV2Fkyd4GIJNd91UOr4gpneou6Un1n0zTb7VZDQ6WAIaLCjACgeh0ppbOzMwBQTvXZ2ZmIfPfdd13XKbqgua+IaBiEiNfX14rGr1ar8/Pzr776KsZ4OHQ3NzcppfV6KQjr9RKzbILm8ctFO/R8eTzWdb1NaRxHizAMQwJkoJQ4CWt9FADGcVwul8653e4AkkTyZEYDgpBiCtrsB8l7LwB1U1ljJkrPJNZWMcCh9yyGGREYJUmKVeC8E5REyAgwYrLjpJljBRkhhHQ89sZWSLYCNMYxgwEWxEkMGqYeGJkGEj6wSIJQBrbONxUzA4MkTklU12gcJ9EYnXmnKURKPPl4VEEroSQYIqEgsBBWlpJwnRgAhNE5Dpx0zAwz9/0IAIvFwk4SQyNaJxwBDYBB5shqGoGIyBiWREQ6BgaRNGkRvucJF2+gKY1qY8HMVxXLLxmb1UeJTZVYXp601slMSVDovktCREiAcpslsKjMftFL0C5jzhIZH3rWcjwf9xS/4YEfKGTlJz/SHDz3u+W/k3/5Lb/x0YdgHlYtBJAm7UEQ0GHGXEKMyXIRGQBp2+VmsznZnPWHffBD9CH58NtOVXLUktHklOnp6/X65cuXf/ezf2its8YoNeD6+nq9Wrm2neDZKA9rDR9hQb8XrWCefUJEtavVPJXklaZ6ktUdM5m22eBen1Hx4nphtsP0t1QhSO+r0iejtcZyIUsVWd9T17XWtzjPdnWzgXSSk2ZNdAqnWuf9aXiLuRaiKV27XJKzEPzl5dVyuTDGcEwxxexpzHK5TMF7711lmKO1BMKaCOb9JIk5xolwp0iZkrNUUoqZgWOMSjHlmLykCImPh9319fVhe9hubw+HQ4ijiPDk3cV7P4Yjka0cPX36FIF+9rOfd12HIZBA4yptJVIuBpFVJ6cPzntDo5m2bStLzFxZt1g2i7qx1m5Wa2MppRhCAIFVu3BNzcyQ++IcmejDYtnoJkhZY08yNT3OGrvL5sGZJnOcFDFxHqpHiSmlaY67gMA0Vuvm5kZ9xrEfNd1Ugl4RxNAsSuO23W6nImhlJ8z27dRVrEwCk3VJNXABgHEYttvtl19+WTQ+NapLWesgpRRjiDEqg+/q6qp4a8jVSnX5L1++7PteSdHK84q5vU0/2/f9zc3NZrM5Pd2cnZ2pMb24uDAWmbnvRp0RWdf12dlZXbdEpCC2j0HDgnEcz85OPn3x/HDYEcpytUjWftP3dV1DiiGEu25cLBaREyeIkb33vR9FRDvZ+v5YDNx9DjApLoOeqUZmjgwhYq62RgZ9p5DlBBwjCFtEFg5xavBDnMiuKs2BGI0xaEhYYoxd16E1VVNreAcAhFTEnhGV0/S+nbs3x7MmmeJ9U0oi1vs4jiOHGEIchsEPo/qslBLzZBg1jRRmNtYzo4BgJBCDgqMXcQwxyWQK0HtEk1ICwuijprDj2CNK0y6NMTF4IQOQAI0AAbPW7RBAA26lCMQYXWUm1s5seLw2/pVz0dOeKzRwJkOllHjaqNP54qz1jt6Tec8BaEkCdS6qEpB1K+qbF4uFyQqGuaI7lbPx/R4ZmQcH8yvy/qX6DY8PPpiLpyAfrdiW9+tP/w87YAQzrfVM0GR6Scvw+dCJcns3J+GkVmaz2Qz9sT92HOLD457+y+kBsF4e1hlKkkAqsp9//vmbd5ddN6BFImtM3G73h8Nh1bbMrDMrLOUkunRYA+CDlX0/6rmPSsqoCmtTSi7Xm40xRSxNV7CQYjjradg8MclmrfCCWyqhpvTvzjNdyFJckqWmNK0sOxKyvhpnwvN7sIwmLgpW63D1lAfF13WtAlIpTnu6aVqiqSAXRw8ArrJ6gZqm6VKRYxVmTjEUi6wuk5kFJznDpHJUzqpFbhchhGARx3E0OphMUhzG3fb29evv3715o1QYMtA0TUoqFjYll5YopHS1vWoWy88+++xw6P72b/92bkmdMcLcuAYRVaIPSSrlmQMYxEobujhYu6zruqlqg1LX9bJd1E0VY7TG2KysqQYXAXQsVYyRzJTs+r6vmkZ3RYxxt9srCc5aq5KT8NDEq7mkzL8rXrAYmhAC8P19jrMJaIUSpcoYF2fnu91Or6ky+LhIScyGTMwtFCIqLFzIAbrlNCvl3K8c8xgW3Y0mi1lqYKFPqoaGioNqSq156pMnT7QfSSM5ZXhpGKFK97pjNWRZrVZZLU6MMV1/ePvD1cXFxXLVXlz8rmIYNzc3b99eAsDz50+fPHnSLFpNekTk7u7ueDz++EdfvtrerVart3c/6G7cbQ9d13Wd32xOlIvn0zDGoAZX3f/xeMxrMt1cOqyFIVPYEOrKEYGxSICYYU+KMQn7GI2rJWEIgsJgDabkJZbeayJw1sYkMHqJxjrCqmZmNFESo3WL9WhDNZE8lAbLDCKsV5w+kgO85w8KOKFmBIHKM9lbUCb8SSr7gRAREogBM8Zgsw0UwjF4IEzCDKLtJ8wBIIFQSgyYyhZFBeoEmRmR7xV2AAEAAElEQVRFEqBI1PkTBkiYQSSyENFms0HExMGB0WM2mHnAD1ncAADAuXfmfuuWu2bukDirN/OsK1dVGTT4JnrgsBim6UMxS15rKjKXxpyWPYnIJNZUFlwP470766NOoRw8fpD45q968KmHZ4c5gLtHucsV/x+HoAHeY0cXvzV9qcDMB1NVVcH7JMnZerlcxtPTcej6Y+f7YX5AMKXLFNN9zj4HigEgpoDGIuJqtXr06NHPf/5LJOsMpSR9f9xutxcX5wbB+1hVVpf3vUBGr8FHH5LrcyUg0mdWq5VkgfuqqogmMcJCsyqnqWGv5i6ais13Es40sABAR9Drjao9RSLinCvKi3oYWgVUCFoTDmWypDzoQ49Tf1pV6ZlZtX+nBGgci5zyyeasaZrd8YhZCNMYKyYNwyCQ9Ncpq1gYY2JUJhpUVTUMnbWVMUaQEdFYq/mKD4mZyVkWDCH4GKDrXEoxRj/0IuIIx+54c325vb0NwVuqOAWOkCQwc1Xbqq5xmnOc7naH/WH73XffxTAB7KULSwRVk6RyTbkHOM/50mOuqipxqK1bLBYEaB1VxjrnEsftdrter7XCqj44hGABl5tlEYRylen7Xkv4zpijnwSot9vto0ePnHMghO5jtD4AvQrqFG2etIGIpOK0fmrr0g3g6kq7jNTkOee6rtNrquIYCg7rcWr9W51r2XJ8L8AyTd3Q/TYMw2q1mjMMVEpTadX6Q/pVuje6rvPeP3786OTkRH/RGHN2dvbq1Svv/e/8zu/s93sR0Y5hFdLSm0JvAV0rXYeLi4u7u7vT01Mi+sUvfrHb7c7Ozl68eHF6enp+fv7u3bu3b99WrlEg+uXLl1XVpJT2h9ubm5sk/OjRo/Pz8xBGPbA3b96oZ339+rVzrj8Mh8Ph7u7u4sXnZO2xPxLZu7s7/d3FYmGt3e52fd9bWwEAwWRkuUj4oRJ5JmAfETFJDmEdGdf13eFwbJeqh8oIgsgpeGBumqnVxFpLmESAEzOijRYFGAQNpQi2csGnzFGAYkYUvyaiD5PgElrBNDHpXnFdnUrwvZKEvQ+KyoqI9179RUbLILEASEoySkgpgTM4KTZgYLYpJY5A2BiLBiFN2kwxRmNFmcO6LCpT6pwTQIgxCRAJGgfIwsDMYlCp7DkiKX207/EhCk3p48Y2ZR0SnFJeKBVcZZaU7zd5HknZ/PkWAEBQuEgr4rog2hqq25JZ26gmqDKB2JlOtbyfvD7AROcvffQU4EHw9D6Dujzm7K35t+nf/xMZcPaIeD8NEQrgAAA4JZr3vofImpqZTd2aekluWS/WJ4/g8u0bZraEWr+ckkhKAACCAIRWOGphn1AQwRowKUVCefn86d3N5d3dHbiGalzWi29evXr69Ol6vSKj8uWACIlZWBAJhADRGCfsP4xENK4EEGNc1w1KpCJHCQOHaK3VwrRx1HWDc864yfkRUYyTb2bRKOw+G1Z4Wcts3dCrur0gGVdVAmhsDDGlmGSCE0JiTEzGjt5rf7rqFfgYXF1t9ztbOUsmZdRU7bIiQpRJXn3f28qhIUGIMfqYtvvD/tARGc1sQEtGzKtFzRzIgKuMcyal6P1QW5Oir6oqjN6SQYE05XZmHMfVct2ulrvdAUUOxyORNXUzeC/dcPHkBBHjODZV1e/3fuxDCBy8QOqPh6Hr6roy5iQlNoE03THOkXGAtFmvY4zBSTOCsc2b12/RVMMwHPuxG7wyco2hkCSEBBSttQnEEJjK9OPYVLZpqpRCVVlCU9f1yWYFKiu9XMQYnKmatvVxrOtasAphJAPGSp05q0Tkw2Bl8p0K/HL0yA0Yevz4UT8Mo/er1coao2Pv9CqrzLJzbuyPrq3Bqn6vb5oGOEoKtq7HcXR1FWMUgKqpp3PPPtUZgcSOjBXsuq5ZLg6Hgxo4RfU1KiqFCcWQ1TG7qWjHkrUkFcglIuFECJxiXbUpxaYyzgBBEtGONS8iy+V6GHrESflP+4zVzbdtq9od2luvp7lcLsMwHg4HizSMncaFYRyaplk0Vdcdjsc9QfTeX717IyLXcVg0VkTWpyeff/5SRDTL96Fzzh27XgTbtiVj9vv99d21T75d1G1V19XSIVwfj2/fXu52O4N4t+/6hJtHz87t2g/DkuXY3X36aN2uGmsqMHbsjsd9Z6lOEYlIleeZBYAiM6KxYMZ+Z42zYityECOiMAeBFABDgMDgxUhKWkmxSBUYkIqFExsUiDEsDCZAw0DGjJIMM2uphSGluCbyQ2fxBFIEI2ytgOgcRJxucRKWWVIx4aeAEGMkAS2XxiTBx+CFGdMQhq7z3vthHIZhGIaQfBImSxwlaT1YpszaEQbW9DdFFkCSBAbNwUtTWYiIlivnhBgB0AAJ+JCQLACMPsao3ZUkgoQWkHS8vR9HIFwsFn4MCMNmsVxUmvjqzxKiUUAKp7OS7D4tswr6YgIGYcwDCdRyphiIQERCHJUokOKIACAJEIypiAwasq4mU5GrBAyQ+hrUONVLlzjF6AEghFEEz5bnZDOBgywiSmJCEGEDIHCPac8j6fdy6/IQgYmenf1qyZUpd76ommZ2JRocKF6gFdj54Pn3+27+5zLgcnTKUb6Xg84HLQ/PjViStXa5XBJwfzzeXL1Tllrf94Kk+SIAxBiRFL4AzfXzF6tKBmmJ2xCdnK6fPHlydXXVLivtrzDGvH37tm2b2hldJ8pAHyKKTGHXbxkHibPGwTgJQRuCadw6AJSGXUWHiMh73zQT4hRiFJG6VunRKdPVtAZm6qb6Ks0ajYo2J+TxdgDAnEqzpvbyppSWy2X0YYYGM029RvcDWZumsZVT7YiyXYio67rtdnt6eqovtW2rUw0g91NpKqm45Wq1OhwO5+enXdeRwXEctfWzaZqUYVXnXNcN6CaZsNvb27Ozs6qqxmHw3ofgmTmGIKAJgdA0lIlcnl6nMIDmbVVVcQwqhXh7e7vcnOi5FHflnFMemWKz1t5rWunxT3/DNCdqtVqVoFiX12WN3Iw0+Bjj0A9KEs4lNwkh1Dkpt9YGTpJbHkVElbl0q6SU9NgUtuUsXJUL1VRwXcy1BgVIlB9XUjH9IRFp27YfBskzkfRJzr1tZXOW7VrqrwCgmPAwDGQsEQVmxZB11yk4fDweUURR6JTSfr9/9OjR119/fX5+ru88PT29vLx89uyZ3lB6XbS5XLeuwvW6mBr5Tbw2RF2Btl2GEM7Pz8/PzxUWChxSStvt9tNPP/3iiy8UudEeVvXHifn8/NzWlTEmcRjHkQAPfdc0zW63SykN41jX9SeffDIMQwxehRsfPXq0XC+EOPjUDXG/3w/DwDzpyOadX0TLweeUaCJw3Hc/TDZKr4XS34wxBKiorA6z0u8JySAJI1iAXDodERGAjXlQlnrPDErpw8EHUCeyMN6jbswSY0yBY5hgZ218UOzheDxmvbn7PVA+q//NqhDvG7fCCWDmyjm1P3Gm/wO5CqZvbuoFWZNSZABjHIN478lQEjVHSUQgVy4+xG/nSY5+fTnUgqiHEDglRDKGECYxLJW4AgDCSSOz3LPGGMjTlwt2IAIKDYoIkdXq7/RDOgvwn/AoeeqHDyJKOaEtp4YPIesPU+fyfvngShQf/D+ZAX/s8KddNXfA5ciisCECEbKVMOMiPXnypD/uL9+9McZZq0O6YjE0CUSERXh+6JKvYUoJCQWgaZqnTx//6leVWsBx9FVVXV5ePnp00V6cfYTahqy+77ecB5HJzZoTTxXzFB21Gmrlyz4u9Vc1hcrqUDMEGkwgagl2ds8Q0aTXoUgL5hKv5K6nlCfZ6am1bQsAKuVagsfiyHXpVBFQj7YIY00koMjW2tPTUz2F3W5XVVXbtoiIJJqHEZHKZcQYow9NW9n8gKz2lVIiNCJye3urUV5TL47SE5pJgzkrY6iLijFmhQFU95mXyuo/tWNVsw1TO3LWVHV3HF68eLHfHb7//nsVsun7vnRhSW6BcM4Zg1lFxFbOEJFKL7VNpQNuc609NU3TVDUiWjf5MA34jHEA3DQWAFhi0dzGTOgoiHGMsXKOdRilNUVwivJ8w5QSy8RpKoOWlaUiualRr6nOJFZKsMZzkOUIFNhYVU5R3xJ86NG6rA5t8kSQwpm3eQYiaBHXVXrMx+NRBaeUf2eMOT09PRwOBnG336s5896/fPlSG96UKL7ZbK6urhTH1v+GEFTuQxtt9dbabDY6DfDx48faqrTdbpXi8OLFy9VqFZmNRQ0K9XLc3e2urm50OJKSpTXRH8cxpuRTjDEai8mH6IPGOspAVJhHm8dqsE3TLNbLunZAkoQZRQcphpB0s8E9PIcx5yIhRNAZ7zAxegSS3Ava3+clej/6FJNPbVsvVsuxHyJ7AaXjkSHtuhGRaO1UEXDunvAoIkmEpnqWwL0Y371ZK2/W1xBRizhhjH6MKSXvY/KhP/bH41GHRWqYqC7qgYFDFpkaDmVK1SYdDcnCvAAUgp8UoetapWoBzSToOENTOQ900fHGgmq/pw1WN/VivUogLOzIpBSIJk06eYjKyuyhI0pJoEyiwPzIe39KAHwc58eDiIZUVVN1Ix54BGYGRj9OV9baarFY6PoQEeDHBxOVb4AHruojr0KJaWavzt0nPiy/6uXOlcd7q/5h+lse/3854Pufzz6YkFgehAaIBkFQyDpK0RPak5PT589f9H1ft1ci6XiMmCexO+dE8YH7b6ByDYVZJBFVMUYg3KzXn3/++T/84pv1Ygk1E2Df91dX16frjXVE0/mrx5qTHT7COC8PEckmWIimIkTpxDB5JqtaPWtt0zTeD8YYlfLRgy2FZMozc7z31lalcyDl0WlqczU4xTw9Pv+QalxYhRkLaGlw6jwpbrhwahRCVBelgbkmUsMwOFsXiUrV4WvbFmEap1O6jZnZWaqqagy+XS609xQECQkEm6bxMTCDtWa/OxLa2lW7rtMEq8pjfTUIAAADSI4IzMgJpgIzWKst2qBZ49TW3FRgqK7d6enpanPSd8Pf/cPPb29vT09PJ3XurDcigswRYOo1REQCbpql3r117RCgdF4RkTFUW6c5t7VTn7TWjdTJNbXVLL9EQrqY2+22aRrB+/GiiXmxWAQ/ljJ5Cc8VcNZ3asW93GwaBvk8V6PwqPXSmCxAqIs2DAM5+/jx477vtfyv0U/MSiwFNSnHoHoahUVVEIIinJkyr14DvkIX2G63jy6eODJN3eBsDrx6xNPT07u7O0UFjTE6llEjHh01qLXkvu/10ihun1I6u3hsnHv99u3l5aX2KbFEZtaZpFN2KNS27Xq9rut6tV4sl8vFYrE2Jgpz9DFGP4zEy8u373TMnIpja69wCP7Fi09c47rhOI5jSLy92++PR/Wy8DDDQNCgW1iRAKAQY0opMVtEdbo8CZgzsKCII6uBfooSUjppFsvNOoSQAoMWT9PEZbcEIlQsBk50EAvZ7PI9wwhnrqj0z6RSONTsTYQlQQwcY+TAcfQ6MKPrumHwIUUtjKp9nzvg6WvhfoI95GGv+t36vDEOMUrmW2mtIbCq64C1FrIDBoBkQvTJWgtkYoyCwAwxxkfPn5WMIvuVBA/1JTAzlZh57ndkht967wl1tAmH4GPu4i3nVarLNM32ZmDt96ayjDHGFMZ8GMoFmaaOqv7Ge95K/292CWZedvZemaVuE4IIyB+kl+VrP2RgiaiSp37zpA0AH/hg/CcoYX34KBTwh1nvVPyd/JyIIAEikLbzMgChqypEkUS2qs7Pz7d3N5d+LLG8Mfj/Y+5Pm2VJjuxAUFXNfIuIG3d5a+ZLJJBAAQUWq1lksWV66lf0D22Zr90zX2YoI8KekWkhWeyuBU0UUIUll7fdNRZ3NzPV+XDMLPy+lwlWCatAukAS990b4eFhbq7L0aNH53kkd7okJkHxRFWlzNw2NmY2oqZpvvjBD96/uweaF+fgvb++vr5/8uTy6lxNsb3r1SEhM9TuHwtEL+4HL37joNlW20MrnItkHdAi9k1OixPN85EfH6oaQnKNr6GflCOVAQx1EajIJ1mZG58eKz+w5OmHcF2w7wgRkLLUEiacKz4UDmC73Z6dnYHLejweMQwA/kM1k/iHvkWqsVqtDuPxbL3BBR+Px8vLy8N49EXjCZ94fX2zWq00mRP/cHO/Xq+vrq4q/O48u1yzwcRDx14a8SGE+Tih++U4Ta7p1u2aiPrVcHNz9+LFi4fD8S//8i+/91m3Hobd4YAK9zzPzolID0/Qtm3XNCBRqypZIvO+yQduI2agVvoSF4VR5IiqGtMc0zy0A3qmsVyAE6Q8HbjLc8gDQup54LGQ0Q5d44vyFPIMKhEY2E94ZeXoAQY3zLayGWXdaZrI5TnzyCZTSogn6jaTxUxTLgiNqqJUXN1wjBHOmwqD1JeOdtDd8TOmPrRtW0eYAMTD9WCr4EoANniW+/v7aZqatjk7O4sxXl1dXV9fv3v37tPPPsMK/OY3v/nlL3/58uVLkB7mOXz55ZfjON7f31d62tnZ2Xa7ffbs2bDqhmFQs81ms1r3ZtZ1Xev8WCzDuD8ue6LWm+Hy6eXd3V0IiZ0/7u6Ox1HYO8ciIcRkKUG3Ry2RcjIMss3uCyMszUwlE7XyYhZQIUYTNTIRoaZp2r5ru4G9m2JCt7w58EWY2HMRaRcRItZ0UkqpZqTa5PJ/Btf8yHgax3kyYwjazMcZHUfTNI3jPI4z5NtwKIR2a+q2SKk/sO+W01smkkAgZkvSZClFnZtkzjlxqgDuFwR7YXYZJXJBY4pmnKtpqFZU0K58KGzsqTtu4UpPV1Vh8LxRhQAJ4HstHXCpyNYbxESmifgUyagQk8bj8ehcg3C271f52WRvp7WlTE36aGL8Bz9861G/oyBc+yhRXp5nGUDQIhz5+HO5NFP9l2TAWRwqf5hJaRAmZhZ3kscUccSaohJJ03RK3HV90w2XV0/v7u6a4zFZmqcJRkqVxPhjRL6Gk/HUYpvOztZf/PAHf/7nf14Txxjj9d3t9nwDFQgRYYY0HbZjLOfN0FA9/yIgYipy4cysmtDinXmzhUBrhSOD/AaFVSKCq6s4ITOjZZYWgyq5DH7A40SlBFW3YEoJGXDtKqkwuInh/LUsB6NcDXFKaTWsKyAZQmqaxjTXILnQbkMIu/094OK+75vGWy5/NtW5tm3b9p1zDkrLIek8R2YOM/TnaJrCdJwcu13cxxjX67X72olRnGcRKlIrdW3Fe5/qrXRiLCGlcRwxRZjFI2DYbDbf+973fvazn/3v/8efv3r5yszmEsqkxXjj9TA0jRNp2rYVzpJPIQSE81lxuQzVqNVT3KBMs1I1yoTJWAZJVdy76zpyAt8D0D6lZJpjJtyyAq078DBR5YWeF+6mLwIpWHlwmjATEM08T5482e+OlmvqB1aHsYAYJYRviqxaFybSCrKNop0VWdMYIzQIURjGlQOTV9XLy0tMShjHcbvd3t0+IIXF1m2a5v7m9tnLFzc3N3Cuqorixd3dXXbbRkBuBt/BQzw8PMzz/Mknn8QYv/rqq3fvrr33//yf//PtdvvmzTfwx3/1V3+FkQ8XFxeQ6bi5u337/t2bd2//+Kd/2Pd97fRT1eP+cIiJLDnmN2/eIEoAbDNN0w++96lavHu4bZrufvfw/ubO+46V5hhFlPkReolHKSqFEM3YmEQoKcFWOBYzi5rQ+eica1s/J2Vmx2rM8DrzPLPINM9t4zSZkDgmgb6/NE4YfQq5GtK2qKP5R20ndjJi/GgwO2filapSjGEep8PDYbfbH4/H6ZgpGjHGqKe6ciiAGc6vduqKPVkzy2wjZmeM8LeFSeFSnMoWyfLU82jqNXuLpDpNU99zjd1xzVdXV3UmDRXRAi2qBsClTqHGwhUR5XGMVMJZ59hU5znAaNX7ld9evVeR3PHeE3GOPFThTs2ylU0ptW0PdIdMIqkj9wHwkFfsowZX5g+TseVfa82lnsT4w7cs31sf0nryb7lFJjUg+C+vAUtNhY201Mdpia4wsznREE2TiJi4s+3F3d3ddnvx5MmT43G/32HymrRtO84nHe0cOjFzGXqTUkqa4BLgeJ4/f35xcXF3fZfDZLPb27vd5dXZdk1Qglsww8p1fctRPVlZaEGPuBeuOWiFjlVV45KwE2rXB7ZjrgJQ9rKyINDjS81laFL1x1qKT4gHm8bVr1whTbZTdzLiUxQjcfHIvSo6ut/voZkXQmByEEjalx4k9GSD5oNTocjUNA0x+6bhsvnmeQ4hvXjx4uHhIcbopBGx43G6vX9omgYPJFhdSAgOh0PTNDEGjz5EaIOMCkfinIAd5l3jfXLOYVz0erMl49vb27u7ByYR33zxxRe3t7f52uaZSp8Vnn+AV4QuXjZX5hyP+0NqGsgcVaQBrzc6jf6uTA14RHzxuhQPDw+N78hl7Ktt27bNRKoYc8aDlYHnG4YBEDQO5Hn1r1oEmXF3EMPhN8fjEaUBVW2a5vz8vB16NEq1bQt5Z1wDl9oHAj64c6xGTdxpgT9hGyCx6LpOSFOZKYJUGLyBWKShEf/t9/vz4+iIj8fjxcUFesDMbL1e73a7u7u7s9X68vLy7du3zIyWLUDlNzc397vdOI4/+MEPNpvN/f3927ev3759OwzDp59+Oofx+vr6hy9+cHV1Ja5BDA2ggpnbtn3y5ElSFaG+7+McLKaubV/vfou68sPDQ9t2h8NhGAYWubu/JyIS3u0OKanzUhtXIJEmAuadmVmMSdXmENCDK96pqjJ1Xdc4bn1jZlNITEyOWt90jamqsakFVdvv90oWwmRmhLG7hS2Fpe67PPRptRrQRl/dWw0plg4YLpeym2HNjfUkIhZtGsN+f3h42B/3hxDyFNeUkoJLbZKMVT9EMpOZWwCkxT3wMkMD1cOMjIXEJbN5DmbmnHnvvSNVTY7bwtqt2zXvKFXv5enTq5oILZ2NLsRhqum2chEn+1uubZ5nLW27XOaJ4ZHJ/S+MTho8wlkil4gcERPFhZIgZ5W6LHatiRJhVv23+8iPzX79Ov/ZF+NlQqzlhn7wLkMVgai6v0UQVl8ktJhj9I9Bwnp8CYS1I8LHALEUIxIhEVCuXNtcPrma5+nqybPXr18z7duhD+OxbVuxxHLaTFT2sRSyKxroY4xCLMR927z65NP3b9+tmzXs1MPDw7ub64vLbf7uSkmUSyr2GIh4lARb4Tfhn/B8TdsK51QDuwRKQEgFfBlfWCk5ISSwEsAsQGLaNE1apC84OZKnap2ZeUlbJSI0FyGdmscpzqF2+sKZ+YUOMO40AoIQQtN0ZhzCXFBTwl8rKWwcR99kz5RSGseYUipjCY4ppc1mM42jaocRDre3tyklJ839/T1YkbvdbjVsjofpcNxhKQ6HA+DKeZ5FyPsVvkjTdJpsF47DMFByMaT6lbfbi/1xfPebm9X795eXT0qrcXbbZ2dnWB8v0noPsHe9XjtmTN0IITjBpmNWjdPU9x2GgFbmDhbZKNWaOhVVbdgXOAy4JeAKH7ca64nXhlKfVCgYP8SUqhAPsN9aoQBAgiupjn8cR0wBgqYjAhcUxeEO651FBRpJMC4eoZ4vsti8ML7zPEMWeLNe1+zEOWfpJH1VRyxjbAP2fGVcw6nf39xePn3S9/1vf/vb58+fY5NcXV1NhyO46yC9o+QfY7y5uUlml5eXMc4PD3cpRTPbnK1++9vfxjT/0R/90e3tLaS++mE4OzvDVe33x4bp/Zu3Dw8Pcwjb7faz733qvW+7Xpi//ur1q1evfvGffg5soO8G51zSwMzs/M3N3WGcNpvt/jCT+Gk6xpSYIemI1EdTSkoOyRezoCMST3Hf962X6KY5hqgzUgcRaZzOlMEzRzrP42Hcp5Scd957UhUm45NnK8kfNU3TD20HOqH7kP5T0jvO/GEieBQzy3OJgh4P093d/f39/cPdbhxnRFFaFF7NLFFNYcH05ApApg9yuGVDCpEx+SYXIGLI882I2Xkfw5GZieDBUwRcqZjdMq9Wq65rYDeapgGUUreKFZWY4lzzN6oHl2tgtbRIf/HYUq3jZnRdSVHJrIdzC6n85XriJgLmQS9A0zSaiBwJu2WdOL/3MRpsCzmO5e8/Pmpc+2h51ciJfeR9qcYi6Dwv0bAupGo/eMs/igMuSTA54tyVjxhhuXbFionzrWncrLf77T6E8Mmnn+73DykG37Uppg+yey384WWAn4ouKDPHND99enV5eXl7czcMAzg4b9++e/78+dWTrcU8+11NXdYsTKiKl9U5lVeliBpaKQhVjIWINBFoJqhkWFLYxJqX1DIGF2IUiKw4Q0wRrtHKnHaAlrX/pObW5+fn9/f3oCUz83pYgaSKBem6DilmXRARwWRfKV35IaSURhCOcAH7/R5tKqgp4qbEmP1xSqnr2lCm2IYQoP6/Xq2wn9q23T0c5nnebrfim2meiWgaQwz38xymMQCl/MUvfoFBAsR5xvvZ2TqE5L2cnZ2/f//++vZus9kQZqGIMzUSPd9eTs/Cmzfv+n51cXEB0kfTdBAfvtxeog0MJhvA2cWTJzHOh8NhNXSQjxCzi4uLGCMwQC5lKgCYiFRCGaOLbADO7O3btwg7drsdKGzr1dnNeAdfiMGOwG+JKIS5xuxmhsCrRlTVSaMUwgXtqHToys9CXnt9fd33PVJJaPsR0WEa7+/v1+s1mEdIT2tkgGtOKcGZWVH/wAcBNF6v1uM4Qjrj6dOnKKB0TQOeF/YDNE0rw5EK5+X+/v729pacSKHHg4Q/juN6vQ4hjON4dnaWihYNFvb+/n4YhourKwDmRunps6t371Qt/vjHPxahlMKrV59Upt7t/V0MiqDkbL25vLxsh56Z265rfGsaRcRUpfGfffb5//a//btV1xJRbvRiipqmaRrnuWm6aY5kAsrh8rnGo62qlLLljXmuO+ERaNt2PXTBY1TwYXcMSVWoqOVQcGQkogUUaRvXNF7nEGNo2wYTcEval5+R7Xa7Xq/JiQg5x8UauGJVOKWYlZPNVGdmDiHNxzEli0Hv7+/v7x8Ou+MEoWly8xQrkzelFBZAdIGFuaTTygyEncwoiXp2zCUxNtZkzufGtpgSaNwhRCPeHw+b1Xq1WhkYKt7DR65WqymGzomIzGH/+fc/G1bdGAOeo2qfqQQTcuLLnnhYIiIIfkuhJ4TAnGdAMWcJd+fcPE3omG6aBma5bVvvIW6TdemXhZjjcd+2fpryoPSmG5IZVqvG92amC8Zi9Sx1k+Cfy8umx2nx8jXLN8aS9Fsh+dpizN1iE9pin3zo1OgfPwMuh4CGl9l4ZOWyFLAEO2navlutVsdPP/3szevXr19/1XfdYd5VDvUySKkpSD65kRWNSRHp+uaTT16gTDUnG8cjE93e3m63GxYBI8Y3ng08WKv4QL0X+Z+A5gt7ra5p/WfTNKgd4sCugtMSkbbNeY8VlXzsA6SnUOSBI0kphTkxixNpW48kCddpZWzcdrsRkfFw5MIDhPUEDRUBIMa+DsOQ9Te8DyF1nUf+pEXZ3Hs/DANKaK4IGO12O0yagoEW6Zsi0IhowHufVKf93nuP4RpEdBinGKMpT2MYwyxJmF3bdyJyOEzv378/HA7v3r3bnK3nef7Rj34EYabNeoAt+tnP/uanf/SHWAeNSiRt08eWnj59/u7m/vrufrO9/OSTT96/v8FlCHsU/5BTmhk6WGKc8cVzSM6sRYmCc2ODom0GfnGcDm2Z/EjF2UiR19HCynbOoR8xFaI77IVbDFqA78GWwCgheNBU0hTAvFT4z7gLtYSMZYQvwY3uuq5pupQSckQ03SIthnurZemcypuh5KyqdUrg0l4gCAjzhLIrZU5WwNaCwx6GYRiG/e6IPp+U0rNnz6Kpa5toOYmHonhKabfbAa9GKoaIpKWsTNk0DfjMyYyZLy632JarVX91dYErRyMTFnC1WkFWDCuc5sTM7B3a6GOMwux9++b11y9evPjtr3798PCw7p+3bRNCOh6PZ4c+ET/sj+M4xcQxakiUUGshNSOzXOxnEs6tECTixbIibKkQKZcObGY+jDcWIztHwUS4dU6EY9JoViYLEqkRKYuZJVVLeXB47gJH5GfCwibipOhFUO6vzcpPZKkU71NKKU7z4TCGOYUQjsdxvz+O0xjLICASRsxnmKJUHQNTgqHkTH2txVcm/NdZIR4vD3oMADJzjIlIphD9HL0XDC9g71zilBL7LPm36geEp54laeZSJeL6NNEiBay70cxYrc5hrB/qnLOi/0ygX5T4FfeL4NSz8T8lwdUjqmY6d0hpGNau6conEnMuTlvp1tJCff3ASS2vih7nxx/8hj7Kkj2Llt/jc/IXh7KK8XfVOj841T86BP2o03wJ+HKm6YsSsfHhMJ5dXM5hDCG8ePlyHA/7/Y4eJ820uKn8EUoAdW/H1DX+s1effPPNm2mcnXPMzix9+eWX5+dnZ9u1JSNSsqyNgD79cnIq1HTWRESPKiu4DJhCPLRhTr7JcVA/DAiywLIhIufc4XCAjUbCykXbMqXEwoBwmVyKyQotCIZVSqsoHAaUNzJJJMaaedeSLUg9CFQhhjCOY9v2+I649zXJizG2Q09EIAHt93uwiGs6Xmswqup87jQFnBBDcNLsj/uma7O+TKLd8XB9fT3PoWmah909hGG1CHJdX1+DWPuzn/3s4uLiv/vv/jvne0l2HOfbu4f/82c/f/r06eXlE8Qi0ZScd2334x//+Pbm7ubm5vLyCfPtX/7lX755/a7OFAOJF4s5zyM2yWro+r4XsxzipGDqyBrKzPMAV4puVKBAqZg2y5OMPanNMSxgfIuWaeRYdnyv6oBd0XlGOou1mudZyGqCVW86/lldOMKI6+trdNxeXl7udrtxHKNTMMhSSvf398+ePUMLLBEhLQZ6jOADZ5Mi5HJ61hZBvZQDF5NSAiUScwPPz89hL549e/b5558HTfv9/v7+3srsQkRvqfDdnHMXFxcovkDO5erq6ub2Pfwx1Ch3u92vfvOb8/Pz84uzeZ5jnNfr9bNnzzabTdO4yiLU3JeyVCPiEGKcRi1bkZxLZNMYQkivX78dhoG92+33qpo0BbUwx+NxnGZVpXGOqhYTKLjpcQbMaPwTIyF2LOqIiBonuN1937a+SSklo91hTEqJyDfKSYhVE4mSK3oOQiykxOacNE3jhBsvXd90Xbdarc7ONuvNgKiiVKDJyvw7M44pQVmdTEUIFdY4xXEcdw8HDBmMMR6PE4ZemxmEPpBu6gL0ttIYkm8355ZkHMuJ98ZEBeRIKbHQAppWlPVCMuAWzrm1W0VNzNw0jr2PMVIZdg4dtJQSsTPj0hybXb6gQWvBUa7207uM+VMBnGOcNWc1eZ6plumElEnLGYA8TcYDZ9hEF3qunFmZBjVyW+Sd+a+PYfml/1762vKmDyHl332ICGmWArOF8jN0IH/XO1kXwyT+0RzwiYqVY7EqjwW2Wv6TEAHphVMJF1dPRfjp/uUcxr/75S9jbGghz43DHk+AWf6eiMQxkazX688/e/XXP/s5M6Oi9rDbX9/eDcPQ9S2XgXpmj1zsx079g5MT5UKvL9LHMWXRZniy8shlEjJseoWpkV5gByd0yttpTjCVHBoEIuwC7NSu68I0enHwuFRKC7WbBTAaHk4wb5umU9UsAyIMSLwpc2GRk8GOIyHDOSvJogaJTeNSCkTUNI2mJOydc8m0d00I4/EwKdM4Tilp12d/D54w2legbYTca57n+7uHu9v7P/mTP9lsNt51ZO5nP/tPP/hBvL8/PH369PLpk816413z9ddfHw8jSPI3Nze73e5nf/2fbt6/f/HiBU6OO4XUP6UwDEPrG3HZ06jq9myNdDAlEzayJMyNF+Ksb+XLbAwu84twj+BsXOfqk6ll3kCNpWpjMe7OPM/7/f7i4oJKTUtV2zbzeGMR6HZZOsDg+HGXAXXA3ByPRwDy0xi4ttKSvX79GtA3lUo24gmwAeqmlSJm5MpkLSolSS58b1Q69vv90DVYwN3hYMwhxl7k8ukTKFt1Xffb3/52HEfkqUhtUYDf7/ebzWa73SIguLi4cM793d/93ZOnl0tAG5vncDhAh45Mp8Px+u27+5vbpu9ECMj8arXqugEkLOfc8XhcdZu2bUV9VMRGgi8yF1m01bCZpxjmxMwxqCmHlOaQQtBMb1YQU3NZquATTGTMTjjVgbtN06QUmsb1fd80ruu6xruUZLNZXYZzdvKwP8YYiZKQeSHxgilJROaFWcwZN65ZDb330rd+s9ls18N2u724uNhuN03feO/RBIX7Dg8RMbYIslbHkZktpePxOI0Ba344HHHN8zzHUAZ4aGJ2OdO17EKS6SmnMaEyxI0pW9fCdiFHLsc6TJjex5r7zrNRZQJ2qqoxap8omWpUYVAlPHYdgDHQU8wM1f1lgqSnKqkxiWmeSM/E6XEhFts1hEC5YhJQO5AlzJlneDTlUiFn6KCQiAMVYzMjS1KMpJk556sPriiClP7Dil3zR7Tn+uzXr7b00x875voMsprJafJEjYAfOxH58CR5mtF/aRvSdx7MbCacP1WZWXIqXG2HJ58bNLfb85cvXx53D/9n+D/nGFwR/louwcfed/ENlYVJ7enTp5uzr969fb/ZbGE9379/f7ZePR+eQmFQclxcz7yg7SWSRd2+fgtsI3AQiKhpGnGEn2FGkSRxEVIAD77mqeM44gVmxuIhlaWllRN+1JWRDFYEynElFUauWwqnxS9RF4QHrRbfFt3lqgtUpHC+qChqIVyQImMpIt53wDzZIlh6U1ayNOTWzDxPcT8eVfXu/gGZNwpFt7e3AAnCQph6vz/e3t6HEP76P/387fXNdrv9V//qX7189dk333xzd3s/Hid2vl+tt2fn2+3F4TD++3//5//hP/yHH/7wh9uziz/4gz/4gz/4g39/e4uGmarUSETeC/jOYZo5WRin+h0Rc8BZVr+F+buuDM/Q0mgkhULFfGJLNU3DxDWvraFSbX1eZp+1/ydvDMldUrEkDb7MgqwFMAgrIoq6u7tbr9coMzunKMAfj8du6BFJXF9fg6yErNeXCV24++U5El3UompOzAvaRCX61Q0TQthsNs+fP4fI9vX1NdDvTz/9dBiGN2/evHnzBtd/fX395s2bruu++eabzWbz5MmT2/fXKFff3l0/efLk7u4OXwrhxTRNaGLebreQRDCzOGWi1qzz4ZAnGPZ9f3Z21rZt7JSRvjhBTUFESG0cZ9907J2SYYQcWGnTnOYphmBhTkE1BmUnTqDWZOzY8MVRjhfnPVNIpNGIvXMaQ9M0m2FVvbX3nsWNcxznME9xmiYmA6+eiJI5cDtaL0LGruu7dr0ehq7p+36zWZ2v19vt9vzibLVauRP9YKn4nxu4p2l6eNjP4ySWB9/O8zzPYZ5DjBENfiklU0YIaWbMJwZMPmBHTTgzshfOIJtEhlNWJnlcwvxge+AHcT6GgGvWRKqa1GFvowJS4ss2F+MSJSv9Kdl7IebLPnjpaYQoRq08ifqccuHna2EnLQ1+xpmKyuzSC1qenjmHEDBHtR/Wtd63LMcsLefSv36Xz/rYsyxd6be+ILvq0ln2wcL+5z+Dlf5RHfCjEUmnT8lCXlarrcpkRkKy2Z4d97um61599lmM8y//9m9+9au/8/l2/q7FOt17oqSR2TG7fmi///3v393eI/Z0zt3d3r8d3l9cXAhZjKnJM8VwBqFHIU/ZppQ++Kzq4Uq7fRZ5DhN6UxkVL+R8KI+5omEJkwFCY5tnq58+FycPMcPXSLKxIx2LQixGRIrgA0JROBLgn3D5qup9Vo1GVdKYUBK2MssWc9phylUVOhsphBrf4CFJKZkGfGUwfaLobrfrhpWqBk0iEmP2Meybph8a7zDYR4sWBOwEkrkUZk30+pu3/8v//P/85OWrTz/9FMICJu6Xv/y7h4e9c83d3cPP/+aXr9+8OxynP/8P//vr16//x//xf4RUIRazhiwpBdVMjksatptt33ciQ+MFqaHI6YvXpw60eSoA8tIk1eTYypyyxGRqpeh7uk1W2M6IMIC6L2M1Khp+S5sCqzcMAxqLqaTdqrrdbuGKzMy7Fukj+rOB62IA8H6/v7y8BEn77OzMew9yFvabFZFnvAvxQShy4iICah7+CQe5Xq9jjFdXV0+ePEHxkpnfvHkDX/uH/+ynX/zoh89fvkDQGUL48Y9/PM/z119/TadWFtvtdiICte3dbodiOcKF92/fqipuxzi6lCyEyTk3DANmKB3SYQp6/XB98+6mWw1PL59uLy622+36bNMOfYxxf/9wf3s3jqNjoI7knJtCnENcrVYhJDNmcoliipYsmzDnnBKCzhRjEmq8cw4TC1iZWYyRiK+GoR/a9XrdNFltmHLLLA+rLlpE311eQ2Z24oVXXeuceO9Xq/78bL1arVarfrVaDU2z3gx930uDqO5U4IBbIWYiSSkdDuP9/f34sMfGSEmRyYUQQkgoKpmZknA0IlIyjDGsvkeppJvEy1C77kBmpgXlR4nkZNzyhqygESwyvGzjvcGNgSBpaZ7n8/MzBLUwI2GarW3IvCmpWi3YsWBk8LeAlOXyhGghspGj0lz/ouKMa93EucZ771wZKWhSk1cywYqlEKf5KEWDaPl4ghDwcTL6gYemhZuEX6g+dfmuj7/RB7/PX8pImfmR2/r2pPGD8/zjZsDf4oOZiEiJJauUElLvRCRhDmbmvYthfv78+R/98R9/8803NNViQBXV/JZ4hEuBmdVMDADU8+fPnz9//v79jW8bMwthur2/e3h4uLrYmolZ7V7P58+LZbknvV5uXR0iUjsV8GOMUDOFi0JUWJMtq3LW6YR4o5kP5KCQVZdzZY6QK1OqkXit2MVy1GGrsK01wNYi38GlE6bKg6SUQspSlHhZ27bA4HGRvsg66pSVpZ1zFQ5qfKZm4PeasqO6v9u9v34fY4opzx8Ei/sYRnPknHfM3vv9eJznebu9uLu7c9KMx7nrOlX64z/+Z//T//R/++KLLzDD6uz8kpn/9pe/+l//7f+XnbRte319Pc/zZrV++fLlX/7lX6aUzksPUg2KMeWbiMBWRbIrC2FI7xwoGFCWwMp771HZkjIo8ERNz0phkUjgNVWzhfI+N/5qKTtVhwrkBlZjkQczF2XKeoOYGfScu7s7IPNokcJmgJxySokswkXtdrum70Tk5uaGiLbbLTwZhKNLFJKqF88M8PL7ygyoqAA25zzPOmQpDzM7Ozt79erV1dXV4TCCJffHf/zHv/rNr1EAJiLMbcQJX33yqar+5Cc/2e1219fXf/eLX0J19c3bb3796183ZWS1c+77n3769u3bu5ubcRznOaoSKBH4XCwF1gpyaS9evHj27MVme7HZbNjJPM/3+93d3c18zHN/+rZryzwSM0NEO03BOSeNlzkxk4g614iAxpGfQSr2N7eCmDrnoAANdUxw0NrGqUboSokjSNBII0CGU8pK0czsKMvXdH2zWvXb7Xa9Hvqh67pONFVwohr9GorBCqvq8Tjt9/vj8Tgex9pnmEphuLpSJcoekDO8wfKofYUX/njhMDKwhx1qC8jQPsrJWEwYYprk2JxroKtrZmrmnYsxkLiY5nHMHUcppZubm77v126DacH2OLe2BCutVUN4GRzgSFlQKFp5lLBzUoh1QZawhIiA1k52UmA1MxQ+cKMB2lXviykOqloJETXnMTMjZRamR405ixzsQ+/7u4+6AvX2uYwsy98r/S3HPzoEnXn5H+QH9asVCRNJFJ1z1DRMiuTp6dOnn3/++d/+7OfL4KWmaMt7+ejzRFLSxISpGk+ePPnqm9eDd67xZunh4eH6+nqz6kWEDDDsI1i/Ro4pM1Q/5J3TIhogInEZk4xzgF/UMpUBGSoRoT3DzLquC6GUeEWQK9RqMf4LfhBIrRW01DINVEsXEKxYRY/hA5C+WIGac15bIHEtZV3nHJU9V7HcuoFyGUYYoHffod5pzLzb7SolJBb5C8tSIZrScbVasTAGKMWg6GHtuqEqdzZNA75GjGm1Wt/e3nnf7veH9zd32+2267rDeASAaWZdl/UgIWiF+iUzQ4wJSTyA/ZSS5I4kLUkMidA8xRAz/xnLiyuBkBDCFy2letzlojUW+tVQ9908z02Dtc2mP9euknZ9pkHBEQKNcM5pnJexgpbp9Lhll5eX6DzGP8Haw9CFtm2nMQA+6ft+TpnZh13BzAh0oAaFyfPLjYcyx8kKlL5JMPaZGTPqvfe47N3h8KMf/WgYhnfv3u33x4eHh6ZrLy8v/8W/+BdU5MCI6ObmBtvPS8YMzs/Pt9vt2Wp9e3v79ddfX19fIwYCQfrh4QEf9Pn3fvD6zdchhN0ud4e3rVfV29vb+/v7EML5+fmPfvSjP/zDf/bjH//hxcWFkkzT9P7du3Ec5xTHcZ7HeTweiQjaUszcdd39/T22+vF4bPtVjZQRlYpQslPYISLCkp8LMlVtnActdRiG8/Pzvu9jmokGfDUUp41kHGci9YP0rYf3hYQcdj4c8DB0fd+CQmilizqloOzMEnOuDRVUKUstHo/Hw+EwjfPxeAR0wcyZllUclarCXyZLwqfUvD6tJ2atLX3byftWq2UEIb9c+nts03hpYPPjg/KEiEFBloWIjsfjdrvBa25vb8/Pz4f1SlMWIlx6r5oPQAPDzJgQzpIagt1UAsdE5fXpsQJdNcXwmks3Vr8gTF9KSZidcwhntZTkcA22SKN4kfWqaUrJl6FVH5yf6EPv+22v+XZe9AcREv3Ogx+Du/9EbUgAWok5573CuUXAEyWnZiYkrBZZTFmazTzN7C6fvvzJuzdvbm5uICO9Xq12Dw/C3mISZuUSYwqZkQmrGqsXpq5ppimwpxfPn7x98+TNmze+bbyTVT/87d/+6urq6uLiQmMkdmqnlWVmxmNGyXk42rp8p5YyM0tJ+76D5BCY2zWCY+arq6v7+3vsoTkkI0ma+SS+kVR4fvWJpbIzzCyFSESOxbEc94fG+RSiFxejIug+Ho+wQap6PB69NKYkzmk0Ed80zTjOMNZt37nGhxRrTszM8xTJRCSDbG1XdK2N29Y7x0LWODGzw25/dnZ22B2H9cqMTZmE5ziGZDrvphiSWtN4nQKzi+McY+SeSU2U58PUdV1QI6JpPDZNN7TD/f191zTTNHVNE6YkjrpG5hi7YVifnSHCYKNVP4jIbrdrO/GuaxrPYkZpTnO02JCQotkDnX8ts0UNq64fhl7L2Mezs9XhcDQ7jVSzBdnNkjLzPE7DegVumm/bOcaQUr9eT9NkrBjbzJaIqGk758RM0b1zf39fm4IqhsEl3yUINZN4n+0RuniBYWy3m3keh6GbpmPFNqpmHnt3PEyYOVHCFxDNUrXjGGqEt+x2O8t6FxGWCyk+LgMKKkTERTUspTS03evdPqw3fT+gr3q73r59+/4Xv/jFN29ebzabTz755M37dxDF7LpuaDtAC9icQWczizHPrjh/cnbxdPvq+5987/Wrb7755ssvvxzDOIax7fvb29vLy8vj7vjs6QvIeP31X/+1SR5adXl5+a9+/M9+8pOffPrpq8urK+h/3T7sj/e3Dw8PN3e32A9UYgXxfn88PH35/OzyYhxH1ImjKs0p2bFtW9/wFKITp1kn22I0pnxHiJmFRGia1FhUoxNpO3+2atjmtt10XROIiF0iU05t22gInevCkCVdQwjTPJux916ZmsaBi+C8SOvMUdBgYk4kUSIlnkkbT6wmrKqtlzkGL03jWo6H6f5wvD3EY5iDi6lJKVm2iEykIk5VWTyKqI7Z1BJKtmAaAs1KhUqZYo4OhZlZE2tSNDp6TiwiTpOZGHvvmUkLPTPG6Jz33qc4iwiZtCQtiUSVJvu8pHScQ991+8PRSdhuVtFGtvTNb399db7idpNSIiNVB8MlIgq5y2wtofKhzMRkKQJwtnmeiVTYYopFpMgfj8dxnrz3LA0zO9+osLSdSRuVRYTNERGLixaJKaY5hInZQgzi3Gq9Vm40BCP2vlHVGGYuzMdl+svMjj+kYVL2nbKErKoLyz9brtMR1zYnZmYTSpZwL0oKTsZU1CDzUU54yh5xhvqvf6o+4NOxaEyqzh8ZpxiJiLE5Zurai4uLV69excOPv/zyy9evX7e+AfHSKEkjlE5ZtekpfxXhpIqQf06x7/unT59+9dVXQO3MzHv/7t27q6urSJSSfVCnsI/A7Y8PyZ1RBEcojkIIM83olYTaBi2aRD9wAEDhwKbhxag7wCnCecoNTGelQ9fP9UXwSMp0TPzpYb+rMluhSA0TAljnUkpF+jHnambW9Q1CLpclM8U5t91upeguTdMUYuBxNGWiRJK3ct/3t/evp2kaxxForQgNw9C2rVmWSEwp7ff7+h3L3RGc/+HhobZmYVWRVqLnGNfsF2MecPR935CEFCU5IoLMFpfBFTHG1nllldKXxcxEAqVlrFuVREaUDQEKEUHXuIgAJsW61QF8WLC6mGZ2OBx4oZlXN8YSsSCXi3+HwwFqFavV6nDYQa/x8vLyq6++ORwO2JkIj7BD7u/vqdSxsIaVRlvBj6y/xgyoIy70TZHoV3Qdp62vD0WfCz7+8vLyzZs3/+kXf/Ob3/xmWK9+8pOffPb9zw+Hw/v373ExzAyUL4QA/SzLdUSDqHXb+bZtv//973/++ed3d3c313dffvnl27dv37x5g2vD9Oj1ev2jH/3o6tnTzz777MWLF2B4gbswTdM4He/v79+/fz89PIzzVAvYJILVcM6FaQJX6+bmBpRpZk5kKQTvfdd1MenhcFAqkx819+O40io2TRNLI46NhEUAjaBqWBcQzqDiWGyKJ11VhxxmNSacUqi1G5+1SFOM0VlOtZk0mRLl0eZTCnVf4eMOh0OMCgpFTGEBhFp9uku4n8wwSJAAnAozRgwgI8/7kBnZpCmDLI0zSGlzF2JxJJwvA18Q569xpEOOKEtSFccYNVnuuYixb3vTeZ7nm+u7s6e9LtpqlZHxLng5pW9a84BtTSkLE4kI02mWNh4lzi0YudDDhclMxKpqaszsPNLfzD5JSZkZaJ/JaTyJftRB8/c4vqtSq7+jmwh3rnzBBflZl+n3h9nw0tfUUOCf3gFT9sEfpN6c2edeLWEK73q9fvHixf311fF4HMfx/vauxYABBPXCVOsKzMQGxUpNSS3Nc6aGtm378uXLL7/8Eo8rbMqbN+9evXoF37CoBOd1FKMqZbK8amUSo1ovNDPvXX2oVqtV07XoDEG7USlWnQokdaMvyzxwh4CwpBCeudRr8eLa18HM8K+pMKWR4CrZ4XAAoWa9XkP8Ada2nhyGO0YlomEYhlW39BbOOUtGaiAzcynRIf8wZVVlJ6o0hcn7drfbpQjtgr5awxCCamZlE9F6vfZF2b82HCOrw0Sg3W43xwjKNPxu5WP3fe+bCiZntlTf93GanXPzPGKA3Zs3byD7db69BOyPT8dtSqX3gEvFve/7OkvDOTeFWYs+ZTZ2MeJi0HqExTk/OxvHWYrcFSYr4CNqRXMZD+Fee8n+soozH49HZvfmzbuLi4sYFb/HoEPn3DzP0Oh+eHjINzcEaFODFI3ACL+kYsorZ5vL0ZRZT205tPBdY4ydb3D9IgI62G+//uqbb77puu5f/It/8ezZM3wQtM+ur68dYShQDNMsRY3EMleARSSlVhOFObVt++zpi+fPXv70pz/d7Xbv3l2rapoDVgx5ebcacP5xHHe73ddff/3w8IAOguPxuDvs0zRi88/zTCKo32MjYfGfPXv2N3/zNxBt5qKyEmPsuh4RcJhDDb+szN7AhmRmdtw6SGhp3/fr9dB1HZFCzVEtpTKJlpk9C3tXAE+KhathZuadNJ6ZvbAjphTJzHmfThPpg2hjlnA7jLRpmhR0nuZpmsa5BK/MSYhKrweR1nYMZmMmNjVSNHcwE7FUbydmKlalQai0GUXNAQRlwV9yzmHWkwid0PhFFab6SKIk3qUcEBDMYwixaVLXtFHjNE3roTNpx3G8vr5uzy6YWcSfxvAwm2Y8jyiPqbBMV464pymFfMl2soHAEUt6k90YGi5OroOSGbE6M41xjnEmopSsaZr1+gzMUV/mYWupLrvCoKZFsZw/TLQ+6g76HR6sftPyei0MEioV9/Ip33GGx4W/erbfiwMmInQlUWJmZ5TIvPeMRFbJRImIHa1Wq+fPPr25vjs7O9/tDgjrykVbZgIixDBGzIdodBqhxk4xhqZxn3/++V/91V/BpDLz8Xh88+bd97//vaTqHi+QmUGouq7s8pboQpcU9lHktJt3ux06i4BqornicMxj1LhIAxKRL2N9q4eoVFU8OfC7VaYY9pfZiTA8YowRRVBwSmvt04ooHQmjrcFKySp7uNbN89y0zhWdEHyoxoAfYNxxebhTYQ4iEjUBTpnnGUPcYI6plG1sIcqIiAExaQih71eS+/lcSgkOBvOF5jKyIhW1GnDNqHRcLDIMa9u281m7/+7uDh8hIhfn53AMY1JIOxWHmlsm4MNWq5UvI6d8UZUCdRy2u2ma8XAA1I+0T4uB92V4H3wD7uCjaLc8eKCjs6XjcZZSAIZTRxPa+fk5M6M/py511qhqMssUZCvcZe89PBCksmKZdbF0q1h/bEgt7AF8KEqzNzc3mZx/HBEvgrE1p/SrX/26bds//df/PaBvZK4QtJrn+Vd/+3dN01xsz3GFEIiIMYgI7iBaxy8uLqCQFUJCOf+zzz4zM0shlYPZXr9+XZ+Xm5ubm5sblLStNN1pnL33h8MB+rKgbWfJra4zs6urK2z1EgogCEjjNDnnVqtNSPfH47Ft+hrIprTg3HHWP1Gyvm+992ZJxCWN1Zcj/GryyGqpd1nVkKYBPxCRMjeaMEFJ0PyUYTlWi2CDEhERa6IQ4zRNx3ma5ziHlKm2hf9RuTKaSUYkC1svxMLCjr2DopeYGS27a4jo5MrzZVTfIywsp9owESHPkUIC5VxvfjTnw8yIGE3nXdNqsnmOU0iOkzEdj+M0Td633i8AWxOg3NloFjpxzuOZIe0gQiIuxYw/4zWqynyKJ5xzkGFPycBKKeuTiDSmuSadUKmsMpbVolrtdHrcvLSw7R8PiyT6KFutf+XSaf3B75fOvi6yiNRTPj6h8EKfanmef3IHXK/DzKBRRYVYmArB2LWdmc42i8iLT169e3f9/v3N1dXVN199LQaOe1qcsMz4BUdRMpmeiMQosTrnPvnkBTorKor7/v37Tz75BHtsGRkt14I+CojMiBdcvlAnAJrCXp+dnYUUSTDpTCpAilPB/YBPpIXejPSr5r7jdGAIkzP5tlFVY2q6VpxTMmGOmqA8d5zG1WZtAacNq1UPSdUYIwkDwaumuQa5VtiwqWjxZ1+SiBatftjBaKOC/45TmqbJmM24aZoUFQkQfHzlYx+P+e1UUNzqWVFV9d6Dj4NRDURZQGaepr7vNUYv4kVSyI0NTeMcsbAYmWdxrSPSh4c9Hmm0snRtix6SNIccOjgHdTpcG9xYzrnnGUqc6LKFuZmmCT05tF7Xij7wcDjalHUhGCkplRLDMmCXBVUEgqBwpTc3Ny9evICCNwb/3d/fV04ZYjWUV7z34mi17lOK3vm285wy3A3WFe4ObhO2lhXyAc5DRNBnBioLABz7zRWufpnP0UzTxN6LyNWTJ03TPHv27P3tjSZt23Y8jjc3N9vt9rPPPvv666/fv3+POAYD4xDaz/OMqMV7f3f3AKi8aZo3b95UrbHj/gEELqxPSml7cXE8HjFFuOqfMzOGEDji0ebDNDZN470wOxGXkjkn7Jp5iiKe2cWozA7WWESS2hxGRE7on8aTVYHcYnAYIaxZYifr9SBC8zz3QwcRIxESz413w9D1fY8wBUUGnCTEHDQjrlJLzjkv7MWpKllafpyppSQ1J17ESXHKD2Agl6sYZicMo1ib3P5LRGxIfbk6VMM83MJRSuk0HbdiuWZcSp8CxwEjZ6dMhuD/3Kn7Not2mZlmaStOZNMUui4JsxpN0ySkTC4phZCYs6o2M5MJySkmEBFTEKVPc1dV6wDZfKlL8JkowxVq5rxHnk8Z1qbs4k2ThhgjxlF477tuSNGYHD1awJMY1rcaeWauA33N8qC86mi/1X99OFWjvLfmvvWNxd5++6mqKf7gwn5fGXAOJbLzc6eEncs1iaomU2b55NVnX3311d3dHTuJIYLIUzLXGr9IZhiaZiQzmVnquk4Tee9fvnwJDQF824eHh5ubm+fPn1PZi/XCHsH2Hx1LZ6ZqqlmbVxwjXq4RfTf0+/2+bfqlq6PCv4exWJ4ZLtm7lkzUTlnaojqF8klOaoHpmZl4N88zO4kxMuvusMfTXnsGVLXIa3hmxgTWlFLbtuM4juOYUuiQFDuH2ThauMHH41FVk5EqxRiNXYxxtz9gKUCmKDvc4MmaplmtVtD0AQDLJXfE0TQNKuWp6GOjZxR/bdsWCvU4aVMk10tAY7jsly9fXl1dzfPctW0IwTlp29ZiludsGmeWBTcqA7xpGlxSyCqkWcwhpWQpoS+WMa6x2LUaQDjFnJYE3FgWBdf6+FWih5c8PhlBFaoSm80GE/Tu7u7wsiriDR+PPI+yVnMsVToPJRMogmHCHbJ2VDqA7lZPhs2PrBG/CSGAt4UYqB9W0DNBu/YcQtO25+fnZ2dn13e38zyDjI2vs3vYa4je+3E+3t7eqqpncBrMe6/MYZxi02CYwXQ4mhkesbvjGCGw6vPezlJiZvO7d+/fv6+aSkiOEZvO8yziQYmAdydhp863DYB0MKGaprm/vcM9UqOkWf4+JTNW733frVAdmBfDwYoDJmZjobZrhmHwTR6NwJyf667rzs7Ozs+2eL4Am01TqLs3Ft0uzoKUQONIRFiKcc/dHdnvLpIkTkbTNM1zjEbR1J1onmLERpKpzyRmGg0ds1QGwhFnzC8P3GUWcebUjSHQwvviPewyP0O4mFa1ooicg+Z5ns2oKVM9vM9aeImMyz73rp1DAOTmmWPUlELTOmdW+yHNTNiTfIsfquLPIhLClLLwJMHK4WJqTe3kHpida1DldQ5IW8CdMkohhJSCqiayrhtc0wVNIs5/NGXhg/T3Y3v+3caevk3K4lsy47Tgln/gbr/LkSMJ/ijB+71B0ERExMRGaAImKiFDKn3TROycN+2ePH3+2efff/36tbCPNimTc4xWs5z3gmlvQmYspyYwVXUJI9b1/PwM48crDeqrr74+P78YOlnerbIOj7Ca5VEqK6fBFxlzUOgSTJVwxKV7hArLLC2m9qKPs+zFjBsjQYkxCnuludZ6a8ewqmLWiohAuqHGvFRaD2OMUISvQWgNMFMpYqVFw8wwDN7LYZcJU3BU+O6Hw2Ec5zmGkDSEEJIB3kYyB22jesJhGIgULZUist/vwXjabLZEdHd3l2nY8xxLnZULUgRjh1wNjjzTlc1gi6lIoHRdAxS3guqHw6Hvuwz2FpkU5OUQlkLeACQfADvWGR5XC91p6Lq7hwcvsl6vK1YBUjECaE9Sycb4Lm4hg1XjWV+0f/EFod14fX395MmTw+GwGjbCPqYZVOplzR47E7MKwAo+HA5kJ3K1K9+u67q2bff7PaKoCmLXgGOaJkikQe4be8/M1uv1Ybcfx/H8/BxAepxnhAg393dd12F+Bk6IlQdkzUQYPeUcxxgQ3tU2OTM7HA745W63Q8CR11CDL0MsUH1YynhVB4y/IqTDpprCPGVUeYWVqejRxcXFl19+ebE9T0VemwvohbTbDy2CLTw4tTDvnCOnRqlru9V68A2krfOzLiIIDbfb7Wq9xsbufEeEeWJJT97B1UfSTFNS6E0yiVECMaraE13UBZGJqtkcQ0yJWELpYVVVE3Hg4Bo5YlU29MsRqsEOdkUI5LJHMgtcRv3UTKZxvusa77I9zEkqqVlW8pFSMZHSOF5tCIkQZdDYSdM0zTSF/XESEWuaBuF40EoJVFUmZ1yQZCYxIiMhc87JIskrBAIyo5hiWsTl5SLZSLxz3rfMED07kaqYmciSphAmPHfONYWpKmS54mOLCmvFqOjv1yC0sPzf3un6sde04oDtMdaoqszfmjPjjfxBsxP/nkhY9fOQbmY5BRURYUmMu57jVrZhPOx+8MWPDofD/3rz3lt3OOw674wJCmtmAgcsxKmo80zTpAn8vdnEtZ3vQw8gGtG6OP/6zZtnz59/79MXRJlrUC9MzZZkuOXjlHKnQHV4WV5KRHa7nXPnXAZbYno89oQvsla+SBBXlpBbyDJXZ9Y0jXctSxYWh6l1mfDsoTOwXq9BB7XcRFgVAHLdtKbdcDDImVwZqujLSL71eh3j3PadLzMN8V7E/qi7ayL09XrfAAYKITVNR0Sw70ibSsW3n+c5JUP7CrzCkrymqqjt1c7OOoJis1kdDgd9rLEOYBNiT8wMl4NQY/fwsNls8BrPkiA/ObQaExxwN/QaMOgtYD5SpVzldWgaIATOOV/S7lAG7hLkVswwkf58cwYomIosfk3c8fPQNdrkev84jpeXl9gGEN9o2xZY7vE4Nr4jE7guzJQkos1m8+LFC6wGbsEcs9w0/to0zeFwQAaMYAJ7qdaki1ew8/PzGGPlc8UYz8/PHx4e0BI9jiPEq1fsVN/OScdxfvv2/TyPxjTOkxCHEK6vry2mtm2dyLI0Ps9zOuSJ164wtFer1WEaU0rvbq5xi0MIzLZerwGPz2UkF3z8EpUB361t2/3+6Jw7jEcUYkUEhQBQEOYUN+v15dXV8Xh89uzZeHfvfN60JEjC2ElmXwKMQaSFHcgZdaO+71b9YJbMyPtGRIh0tRrOz8+7ruv7FZ4aUASSUUhxCjMqoDFGZpBdjSgRqXfinIfHEhGUTlCEqgEKAiPvG2aHByfG5JwLsCRmSuLY+SYHfDFEVbJEBCVlccyORaBGycR1CDG2nwgXnDx7Mu9917QimY/GGC+wyNKsDAjBX3DxoThmPKosnhoVn2Pl8eiIiIceny7sVdWUTRnST8rkiN1iahwzk5ASVcGNalRTSilFRGCU6S+OnTDlR56ZXek7pxK+GGmMoPikGGPb9t57VVwMVexnmVZ9V5pbX/Rtaeu3e98PDrgDcLOpBBknRmdK3n+7Ay6fLNX18O+VhFUvAi1VzJyHChosrxEJpIjMSPzZxeUP/+BHv/3tb372l//RuyZoaoSNTMRrSmTknKiSF0lF2URPI9MdZkBeXV19+umnb968EZ/v7vv37188vfTeQwEYmVmMs3s8QHuJYOTkGJAQZ48CgwsXQpShFc5jtlhE0AFVA0Ba3CrgmUgRYPRRumvbdre/xzmrM0Du1XXZDlIxNM41qmm1GsB+wmXXwkyJHPOaLxJlBsjsvQfIM4XZe48y8+6w9+JEZDzOZ2erKQaMVGN24zijbFklYbuug0oict/66UXXItasd7/fuyIULIt5QTHOzPzwoPj6MNkYAyWlcYuKXYBJrZ+ILAe+v8piOOeQYvZ9D2V/K2M84B5qaAJ8GCsfgx72Ywn8u7bpYzgIe3EU08xiKUyOjdicYySRuNEpTK3Pg5aPxyMqyjc3N+v1+vb29smTJxgJhccSF6ZFSmW322GhkJtSMdnn5+dTSIfDAem7KxLWoBHBc6/Xa/RQaaHvwfFAGQO5I6rI2AMVpkYIgg358PCAvRRSngQXVTWlpmnGOez3ezJDdihiMcbdbgdO2fG4b5rcJYWaLsKm3W7HzH3fq9Lt7b2Vfneku9j8UiRlEP1g3UIIFZStRRCsybt377qmZeYnT55gZnPTtXOI7CRqGlyPjI1K3gMaP/7bNI1RMkr+dEjbtuIhmmZnZ9vNZoOupDLOFnOFHTNP43w8jlNM43G2gpeGEBrn0D3vve+71jkXUnCuqFASZR1mTfDlZgwpHvEtOZlCikDInBCLa9rtxWXXDsfjceJpfNiJE8d+ToGVnRdVc9jAxCklw3xfO02+sWx2qO+7Kprhi8iMK2CYFG2W0mKneE0IwdjhdphxSurbPGMxatKU5hTTLjVNI2TmhZ04yRJpFYjW0sFlBdibppmZpcSyzMbs1GJN1k/+T1gYYE/jvVclYnYklhV/jZk0RA0xzmNU9d6DhqmqvnNe/BIUrE9Eze8/sOdmxo/7UOuVqH67zgY7Z4t6Nlh5NUOTcrYlKkOLpHnpa8uJhTKzSH/fDpiI2Kg8b5Lh9MeHiIj3FGl7dvGDH/zg7euv37z+0rtc8U2ln8zMVE/3kov4Wg4JldvOO7d9+fL5+5trKqW7h4eH29vbp0+fEgmMKdXb8y1gP/zZqWaMONeIiKzxzWq1ymWkZn0c9znrTY/uYt1z+BnlxppLwRWB1XI8HpkcSl+WVbQCOC+Hw4ja0nq9ZnbeP4q/cG3AJE8Lwqyq6D9piqyglISvgISqqs41ZhRjilHN8e7h4Jw7ztOIOQckFme83fuWSEIY8ZAfj8fXr193XQMP4UtLBhHB6dZNqUX9kYiOx2PXNWdna/iYWA6k/jWmwf1qmmYYcmNxvV+g1az7IVevLaZUJlIwIfoRkcTJOQe/iIRyGIb7+3tImEHBu21b77JAAWq9h8PBipa4lYka8zxvNhtNKXf4WCKN1foQ0dnZGRqualn37u4OnzhLDCEYpZiCc86Lh6PCxYcQvvzyy+xdmuZwOCRjxAoAhIBb6EIOSYueKBYKP8iCfAQ4pLK37u/vu67DgKPdbvfm+vaXv/zl5ny72Wxev36tZKvVCrkUbp9rG4mCimyM0bmsZ5myzHWDBamcr4onE9H9/T3kIGpSpbGq5EsIAUxsLvysfExaIYqHw/758+fS+N3d/TAMpBZNX758+ZM//MMvf/vb7Xa7Pxxx75hHxCVJY5hyqT4jKJ5FRJqViPiG+77HLiUiUmta13bdZrNZr9fDMKDvBckcp3g8HO4edrcPu8PhMI1hHGcS1mSN99M0ElHft6vVaiCZOIkk52X5GNZcCvHuNE1mnCgLyeFOpZSYpO1X2+327PxKRILqFGN/tgnjFEIwopCMk3loSBkZmpUJT/oppE4pOSfO+bbxwiQibdPiMrggvVZk7Mp25Wo9lGmegtlpxHXxLtb3/XSckRGllNDuQQXBzl+EjI1VDY25C2d2+icDQ04paQwhaIw1CqSTnBnujmCYn1ImiovktHJBk1zDTxuRKmEkYPWIUljTfz9fRHTylN+Z/la4a+nmiT408gsf8Zhg9B3FTcuzMtPvwQFnccr6b/jgogwtYAOePJyx895Mu9X6Bz/84dfffHlz/S6FmZw42Ahjcii0GJEjYyZm1pr/4eNSUmJ69uzZ5Tev3717B2MRUnzz5s12uwXdt2ka1fgBHJGPrBle+uQ/Glk1xVAVMACDIG+DdPjHd4tr53VpjAE1RiodehwR3KFACMYsgMfb29sYNcYDeD1chPEArsL21VIrcE4mp4m8d8bq25ZTSmbEnFQ1BBeCX3TN1lKxqipT33fH4/jw8OCkMUag58/OzlFoFPHAguq+xNdkNlyA93k2ADDtWISf8Fh6L3DVAKXxsy99RylTHLvqD4KmRhyVVi4rNJAad0+z1gwPvhPdVjX7pMJ3AyuemQ/7Q4W4w4zhaw3gVvg2YM7A6C4vL3FHjsdjV5jMVKYFVxD+7OzMCjRCRIBAuq4TkZhmz74uNYhCVpTDsX/Oz88xVug4BSxj7W2tSXzFTrQ0fSKzb8rU57rNqMxu4jIkA58+z/P79zc3N3d/97e/nsZglIb1CmcIIUXNVDjnPRaBiaLGkKL3nsySEZnOYWbh4ziFmOW6kAcT2zzPmh5V3dCkYGYu5rFxMc7gE9hCk0jKeOPGNxcXFymlfr3yLDHGVT90Xfdnf/Zn/+7f/bubm5v11t8+3G82G8+CEIqIpBej5BuJKQs5KFF+QFrrVx1qHwhN1uvh7Oxse36+2WyapmPwj5gxG+F4nK5v795f305TGMdxnoJIVm8FD6jrujmmaeha33jvV+u2YfFQyiilQWYPszZN03KorZmy95oSkXjvu35wvp2maX8Yxym0bTvH4zxNIhyDKlnrG5YIRlZIZhYVDGPGlo6gEHdd1zXOg24GUUliYkF6I4W9D1dVjWQ0ZXKsyVjqM8JOvPcirvFdkEjEyqqq1DjnHKZKFJ9HdQ5xNsCOlIw0mz4+DZ4hM4tBY4wp5DYkIhYvpmxs3nt2jRKJeAPmVUcHln7iFI1IMCB8DiZlfG8Ngisw9rt98Ad/NbPfDT5/8OLHkda3vuwR6frj1yydHbP/vWXAshSIXuTBjy4IQZlzTs1RoO3ZxRc/+NE3X3715vXXbOoE5TeEaQircx4sH2mJtc6PYR6G4dWrT77++uuu61LUlNLd3d3NzU3FMWwxPAQhWb6Yx1dfT1tvgIgc58k5EWLQBXEqNEzVW1U3R/2BCnahpTW+hoTOuWnM9lcXIxxgIpmdGc/zDN4pF0QReg5mRs4rsVrlbOs0HkWoOtq6yPM8RxHkhexcDZBTSqrGLNM0MTnE/uhhSGZzjK5pHHOMuTEDHqiqdyGMRTVU0LCkeTIxxBkArqLIV7esiGD18JsaWNTgnTwVtimZWds2634AJbimy1JkMVIRHur7PqGc6RzOX/ug4ONRSY0hh88okKMWReV53u/3gC9rzlEf+xpIwc8h1/RlEsN6vUbzFdxtXV6kswCiY2FZIxypgRRYCzVNwcIiOKgO2EqfJa65Pu24nmmanjx5gp83m83d3Z2qjuMIReL9fv//+bf/9qd/9Ec//vGPwRIHEzglXxis5iiXzGMSsGNijEzSdR0e3XlKY26+B517zvmTOCLCvpHCh8JXW61zDVu8M6aQNKTCIipNcavVCi3aIrLenjli773G9OOf/qHv2v/H//y/UFn/n/zRT3/+s59TIbEXHDX/Nal2fdP3LTvruq7xDcKstm036+327HwYVn2/KttMiSSlME3h5vb+9Tfv3717H5OO4zzPOahl71gtpjDOYY6p79t+6FarlXLqe1ux856NxXH2gng882I6NwzDMkc0s5hQNU+7wwjC+XrQ4zRqMm82p4wYNY6iy29R45RSLJ27zNx13dA1beu9hxCzsYoYLVlCZgayfghL4QiJoQi62aM8EiB8zTpqbITojggONZs4gfZy2XspJbZTsmhmzuVefzsd+R4W0+q8b4GBM3NKj2QTEzQ2jVW1G/raiy8iJhZVaWFGvK/jrb6T7fwR5voBIXf5yuomHzUOLW/it35E/ZOV9Hfp9ZeOmX+/JKzsg/N1nLaIEKV6oU6cWXLixTnftZ988ukXX/xoOo67hzsic+JNc8G1fM8MqfMJ8SgejtsQwtOnT6+uru53DzAcYU5v3759/vy5923KwuKPApMPlmkJp5iWpFgy14lZSHiep5Vf7Q8P3vsUT+Jkyxit7m8AepV0A5Mas8RamqapaXPCh7omRr/BDSDDNuV5iiGEeYqoM7EQe+ecg+dzZQTsPM+ukd1hL4Qn1jVN470jYrBQiWiacpJqJFMI8zzv9/vD4dB2A2L2lJIxSwE/uSCfxXhBzYrm+UiY9EzUdd00TUO/BqK7OVshkuDFrAItwiNamutRKq4MGiwjmpospuLjs95T0zTH49Eowaeie2oYBlNufJeiaaKk5JyYcd5Z4pldSqkFH0qZRcwSas+1Sgokv+szxgC/BeAUy0sFmAIPGZ9eDRaADZCVcM0oAdS3wPdjQCHId078r3/12/V6jZ7dym2GeORSh6taw/xEFV6blWzyBA/mQntsxCFDRWp+e3uLIuvPf/7z169f/9Ef/3O1eHFxAVW1GiUnMshusHcQrupX7nAY56TRKMxpCgnhRQWoU0rish1k5qSaQpbOxjCQ42HE3nDisc7jOLJRSsk1GdDWUq33pSPufHNGTIfx+PLly//Ln/1fHx52P/zhD//iL/7i8vJydba6fX/rFr3RmgVe6gB7W69W2EJOHIB9KGLWx1xZhDMeHmI8juH27v72bhdN5ymmlMe8N9YSUYymKc5Rj1MYQozKMRzx8K548J45V9aUyEoknef2ZDOi6piT2TxNNyER0RwV0dXBLITUOCE6qekFTZQ8M8P7BgWSZQaZmsaJCJsC0MS9q3hJ/vQUvWvk0RSg3IIB48PMYrkuUNewRO0mOe15NANYVYOmRvMgh/r7GCMccH2ZCB5AW3ogIlIyTw6blvOgMyXK3C5hsM1TnELIc0f8ZrMx4xhUfIPPTSktUIdT4eNjb7o07JSN+Lc0EX3r63nRwfzBt/j4PN/qlb/1l3jL77kGfCLSP06CHREpZ4ZeSkGYm6Yjs/XZ5uXLl9fv3s7TcZomKJdWd1XfS4vuJhxm1nUNujN/9KMf/fs//w/eeRGxGO/v71EJNjNw8WgR6SzPQIs4iKzK1BCZAHg8Ho8wemOYpxB4wYl3C5FCs+qwuf4JuQ4MB/6bmztdi9b1WPSY6rgbDAJCRaRmQt77RNaImBlwdXg77z2zmDHUKIlI2JRMmNS0OsK0GGN3PB5jTIfDYZ7iau3RMpBSIvFd15kdUkpeBJ6SiFTjOOZWq2L5PUDy9Xod5gDmjikp5YosJhphJgxSfClT5ev6pzLBmxbz5AuE6Jbg6jiGql6E9enaASuWha7GEdwTePSKTmNjLAtah8MBWRTCgo4aIJxaiNwVE8aBGjYXiTRmhhaHL3MRwDZCCl7xYXw67o6ImPJ+3GMBd7vdixcvGidop4EqNTqJa+8W1gSLT2W2lRbuiRVCZm0rQpiCyAzlgPXqDNi7c+7+/v7nP/852qV2u916ve77nkgdC9h2t7e33LZ5fcbpt19+3TSN9y1uX822s+EmJhMNSVPE3TFKOY3Gik1j6zx4dvvdIWdjTESkMUV3mhEpC7Wmu93Duh+6rttut//yT//V+3fXZKaqv/jFL7quC2EahjzhkYiI1CyRa1zmf6W+XzVNa5a889i3wzA0TQPjY8ZZ0VZ1DlHVEvGc9DiFlCyZMgmxc75NyaJqiNFiiEmjmpIlpTRIUhL2IrKSwZosFwKya9P4aQKfLkfkLOScY9V5nlI6ag4Nq2q0Mjsycs5lLN045uYrheYB7rL3vnXee2cpqqlYS2zeOdQF6svYyJRce6LuoyaYSsgiWeSSkKJKqRCrgWSan0foMFNxqykl5Tx6qAIPmiy7z4X9TKVzLMaI2abFl+cWD+eA7ZlZYWKXntJ5nsM0hTirUtM0bdurqpL6oqakqtWh1DNbGTP89z/4W1DrytXKj39Fv77VZ398hu96Wf3Zfn9a0I+OE4WPiKo0Ry7tkqopCauaeOfVC/v12fbp0+e7+4evv/lSWKTwTZICHaokN4xcOq2CE2maJik9e/bk4uLi7u6u8S3M5evXrzebTdc1y9CMymUxuSpJg8PM+IPlU674G/pwXBGXWUaLtEim8QKYUdivSk2iUqLLFp+sRnO5FLoofNazhSKmOoaZcgwhGGMYYgwAihfEBNi1ygfJF0AnwcVpmkScKYt3fd8fDiMzTyE5l5M8IppCYMvAKaw2lyo4EaE8jO9YJTW89yEG9HTWqYLgbcEVwRsB6cWXWnrZEAJGcRBR37Xr9dqzYMbONB+rX0R/VJ2by4XzgmOJgcO3YR2OxyPKt1XFE75wte4RSQAcxmlxd8D3wdth1PC9NPcp5q6kWvIMYTKzpmmsjFIBVB7mBA36lNKzZ89+85vf3NzcrLdn2CQwsnhltblaCHp1m9VmMzNDH3YNzuD7UcvArEARmaZJ2K/X3WEcieju5vZwODjPV1dXL1++DCHAAXvvd7vdL3/5y6TyxRdfbLfbL7/88je/+fLVq1dM893dHQoNNbGu5HDnqW42BzljhrRCph/PKcZ9JkJ777VMEcC3oAd58eJFTWIKT6pFDeLs7OxwOIzH6U/+5E/+zb/5N5vNJowTWGZZ2hN7qYSDkEtjZpGmbZuaATdNo5R9ADOTMepT+YmLFkKaY0BjUdTYkoc7RxIUNEbTZDbOUQNVlpxj8X6QjLqTc5ZKRRz09Xm+ERIWVvGmyQyyQCQo6BAxexZz5Jxr8oqRmBFy32UG5r0nVvyrESfCTFo42Cfl+ZrAVTy82jBAu2V9xGJO8ryvMTH+asUNh5SSLyZODOOKT00oSyNvljizZdHtXVYPPpJJyoNQ+F+naq6qCmUOWUpJExHTMKyLIeWa/hKblQHetCBM+TJ09ePjW/3i4xd/SJOu6S99B8/rg3N+10fUXHmZRv/+HfCjg4kzFp1/EDNtpEkUSXM437bt2dnZ+fn5N6+/UlXEkmgK9t6nKCCesWEYZc5SUSxpmkbn6Jx7+fLl9fX1amiiqnPu5uZmHMfVqk+lCpWvB3nwouJYpU3h+6kUA5DGee+nqUwDbH0IwUkGxPDK0/4206IqTEQ1B+JFRxNuEqSb8XrYOBGB2lGEZEepdleHwZGPxxHA7DiOsfhsZm4oY9f1UYxF/QO7qmkaFkfMRIxRZcIe8u6xaIwALEVm1ratqaGNFVBejLFpcqnPTMFgcs6Z8skxsNYUjQqXEhWdKhBWuVr1QQUIbGYsVknjzjnHp1UlIjB+YdDH44yVR+dPLej2fY9GHSICMI7ssGLLKBDiMpD/1TQXgUtTRkMCTtQiaFVzX4zHqOl4KY0bTm5m4whF/qyXtFlvUfJ88+bN27dvt9st8H/kiIgV8O2GYbDSw2alXVUL6Rc3lEuNoO46xHNI93HfQwgeBWNymnvw4n6/74dWRB4eHt69e0ekzjkhnuf53bt3zve73a5pupRsvV6T8es3r1Oy/X5/fX1ddyyzobJ+tl0hoc/+w8g5h/2M7bfbsSuw4fF4jGHOG0l4nufbh/svvvii7/s5hMvLy816nYvlZFiNy8vLX1z/Isb4+eeff/PNN8MwfPXVV+fn59gYmY1PZmaQPsYTDdeLIC/TviiHy+JOD7VzzknjnMtgEVFUjTGGkNjlx1ONY5yT4ekji4p74Vi61ndd0/qsmUXMUwxE1DTNer2GevYckqD7tfPOTBOFpBXDYHNqsXGu70AqTI6cqqaikkHFBzCzatJovhHvnXcuhCwDoItZC3hXfdir6H11EiEk7/E0ebNQI/4YY+vzs9M0zjmnKWjhXS5tOJYu0UlKwRZNodUSpnQKIGqeXVPw7MUTkYN21+k6RYSF27bVEH3bV5EwPPJckPOShj1uc/rY45SrtO+YL//h6x+722p27COhj/LP34Vpf3xhf38HXK/3u+Y3/f2PzIvOBVViM3OnkYVqSR1zkiaJuWHdrs7OLi8+iS+/+vJv5/EYNbJQYmfej+adm7EpyJEQWpLQ6K7euda7OM0k9L1PPtnd3719+1aJxDtN+re/+s1qtWrbVuMsDlOShEwNYRUJsRAJ0cT5sDJXEVFkrrU0TTvPQcTFCG3nCfU/Ii4+VZglz3XwfsYcG++M+ThPq27tHHJTVTVIBprl+mKmFzl3//Bwfn6ezFzjpnmGTQHLJTGzNExK7MZpClFVTTV6703YSRvmCOJCmNV770QO+2kYhhRVxGti5/w0TSye2Hw/7HY7Jbt9uEcW1aqIU3FKHJPacYT8chQRz56kTXF2JIcxpyDHaRqGYSoV3xijFwFjHNmb5uqUxjlA2w+CGHGc2r53LCHOmlLXtGbWqKGVZWgada5v2sNDnsM4TVPfrUIIfdfM86yJxjCbMDkZd/sandTQ+6wMIUbyCjvR9T2y7SokiTd61wZKm01ujqok85QCdMHwysPhUAH5J0+e3N7eQmUTUs9w8CKeSJzzTWOV+o6o6OHh4XA4dKvV5vz8/fv3xhznsW3RbSXMtt1ujsfj8biPMZ6dnfV9v99PIXjUwp1z43g0S8zmvXSNsxRqU5D3PpKa2TSPcxjFkVoMFslRtNivNiEEEWrFU5Lb9w87f5ymIzp6RWQYBmaX4vz6m69u3l/vdruU0v7h/nicsBrM5nJ3SktEZrbfH+sgL1yeb9x6vVZixzQMg6mF2caUVLXvXQhRyVT1YXqAB3LOTXO4ejIktcN+f3d7e7k9B5ZOjczj5Ho/DMM3d9/0fdc03jlZrQZNsWsbCIAzU++b8TQXlpmtbX039CbMrVfnGgSOIRERJVNNItK3gyUahnXXDeM4GrOJTXE0sWhRAjEzJtWLiCabFBrsfJyNHBkdnO/6YS0SmtaLOEvaNF2YY9tymNPl5Xaanr59+3aOc98NFkInLmhoGifiVJNjMkpE1nr1PgkZM88qKBh57+cQNCXv/TAMKcVGGnYkLEouJiX2c1B0iPg8gjAqqThJpkgkmC0ZJ7O5CBWwZ2m8MsLoQcQ59vM8W1LyOYf2QjGmtu3GKbRddE5CmPvWqyqnBGl75/wcJjYi1pgSi5Hzc5rF9OHhQVXJLAKsooaM2ERV2rYn8TFGCP4YqST1zs1hjqqHww4eYugH8g1TM8fU+Nah/TeGROpZrOihYu8herZHA+4M5yHm30F5ZmaiRyLPZkxlJEf2AYtWWDoVQBFAkNmpDeljX2sF9qdFxfO/cgb88SF00suGm+n7frM5a4U/+/z7f/fLX7gkIYyJ1Mg7byAXZH2PIpfFIs4RMhjnnAiTycXFxbt375rGT9NEZofD4fbm/sXLZ+wgXL7kOVPJqe1baeVWFIiW6VqNjGr5qtwkTmQxZG5z/priRDCFbXQnYXTKQsoL/XekONBj4sILq75ZVa2I24EUE7OCR2Y+jzpiQwCdc2UIYN0NMFsYHlwbWlDLbMrMRF2IS8NzwGmp5jEZKDFamc+DAmRYaCzXsndayEejY8F7bynh0y1r1ZbFTLnIjbcglj87O0M6DgYvCO1IZNu21TC3bTvygUr9qWtbVb27u1uv11SS40pIhnOtnDJfupZjjL45jTaq2XnXdWW6OJkZZJ6QYez3+6YMgqxxBhUrME3TbrdD+ymwEFW9vLzc7XagT19eXv7qV7/abtdYfNT7h2Gofcy45lQamVwRqqzEMVmIMFihHYSizpiy9GNu+sJNtAJOYtgUwMJ6BmKd5/mwH/FPbIYQsgHymM4hYhagScTMULjDExHiPIecKKyHFWB/X+ZbI1KZ42RmSUNK6fz8/MWLF5vN5t27d19//fXQd8+ePTNhACHYrnfXNwBFReT8/DxO8/n5+TxNWS6tiDAgAthsNkh8Ye+atsUgSFsoiKVy5IiN8kxPY9aULKVoKqIiTTEOVrN5VlIm6GmHMDUtP3t6yZvBe28xQf5XRDxL27ar1QozH2kkznV6dJwj0SQvjtiJGaq/pGgOFCm6PVpyvnmeIRT6wcHMaQEFwyWonmBns2rBTiljRaq5EGvqfyu3A2GoLOplVvro8ANhAUtjUmnipAp9V9xO8pzFPFiTiq5DzTXxubvDgYquJ6TEmLky+PDRSGrqVX2AD9t3oNDfdSxfX37mFFWTfZAElxfI0skuv0tNkb8rHa8n/G/GAZsgvzxdrxGza3y7Wq9JL95NhxcvXtzdXF9fX0vjJSR2nEJUZ8VXGlNFjzMKB/kIZWpaevbs2Zs3b969u27bdhxHUX13c/302RXQlcWyEkF61YzzxMTfRWPD01vjIzwhtKCtR1OJBuMuZQAfQKeUlOikIC2FoTNPse18NTooW0LjECajuI0mhGApEWc6VSVUW0FYKgOrXm2lsNVoAO/NXbBkRFRmJxt6f2nBM6zVWSqqk3CQmuexGAqlMasEOJgPKlN+iXKzbEoJ9tvM2DklYudSeRfOX63nZrOh8gTiK8ApIhvY7Xb4K6BaKw24VqR5sIwAkIHW1splbebG0uHbgcQEPYf6INXlSimIeNwCxBNIoBEnIabBAAxA31DJtiIgig9C7fz+/v7i4uLiyRPMtEcUAv+NdUMNopJKrMDy8LU4Ldw5ETnm+lctZVRwr6yoi2ANF7s9Q47r9TqEQHSaKo3L6NpM16/SXVzEw80M8ZWqEiXnBH2iOTp0rHr6vinkkjlWGPvNe990pzZZxEl/93e/DmEitRQy6+fVy0/wLIzjeCijR/b7B/Tadl1XpcGYqMkTpvM5m6bBSK6maXzToKl36URxhZTUEXuWtnGWAhsoi4nYKKqwmCVhUTNTJs5yFmqmkcjswEak+/0+pJhSCjP5MpeemYXz4Jbz84imhhCC+CZGoGuWUmp945wjVkckjogI1XGy/MymotIqgnlBj8fqnSq7ysx1zo+eOojgX6OU+ugyYahGtzrjJg9UNkjmLR+B6lAX5WSAy8hcgDOfDPnS0zPnPknxTdu2wt6IQNta+s5YxGIRg3ZdBwMEpxvLTG4rygpc2uV5kSN9q8X+Lq9sH7XtAq6vXNelK2WGDuiHld0PvvIHH03f5uP//g54qQT+j3zkdUERgVNNSEWEyQ3DmkmP+/2w3jx9/uLNu/ci4DxripFJUD2uC0FEtTmMmc1STMbMm/XwyacvfvvbrzabDWDA6+vr9+/fP316lZS8q2+1soEMMPLyOmmxlPWW1KWvoV/d8cpEajWiTGWeXbXFvmnrGaTE70ubi9RWMVklVTVXRME5xWGOSZfQx6LlmnPGzEWAd3lyuE9AzZCpm1M0M3gyvJ5KLm6l77YyurEA9Xu5Ih7JZZ5EXaJQtOu8dwgyzAxCpFzCYVwwfM9cpiKGMNdqcdu26/UahhjXj4SmdnON45jIKClYOWbWNnm+Cjy3LRqvcQZk9vVJRq7fdR2LxZhwZi06UERkhvHP7FxbH34rVGS8DNxgLBeIUbAdaEdG4p7K8IP7+/um71arVVwoOYP5BXd+d3eHcRe1/xs3cZqm/X6P7ZozlcLAqoRVkJggTAZMQhwRa8lcgdIzM4sQ5jku9ycvhlOlModVJA/lxUeLsHdCZh5hExszNc5jMVFO9uIqP99KyoU2JNqpc86YcHmvX7++v7//9NOX+4fd8Xh8/uLZdrtt29Ys7Xb3iJ9qq3SMcb0Z8GTt93tEIUisyUkIEZqL6DsXELFK1qWqppRS0hA1L1fpOUnKbExKiomnxswFGSOyxMQsj8T3ERNA94pzl+qjtgwWKGY0wD9ub2+ZiCyRspGSnuyVYIKTESH4S6eguQamzjkxIs41XVyaLZJIeuQmi+til9Io3tfUPJW5hKnMJtLScVB0abIJ0jI9MN8+gd7tY+cqRI/xVTOLaVaLZqTLC2NCWaqYvpM5TWrOucNuR8VFbTZb731IEci/2SMJoKXZqQ6YuXKBHnmZb3c/j/9azpAz+wqNPH77yRt+sOzLiGd5I77r0/9byYBPl1t+41kiKTnviTpbPXn27P3bt0+ePd9sfn13dwfjJWSkmVoqIi7PtszmQ0troJkitH/+9Nnl5SXa/PHA/Oo3X15cXDh3ajHKV8KGlr7vWjhdyBgtExQ0kmfKFguXSjBC+GzcmeGi5nnum7YmiAClkStbGVeHx+N4nLquQz5UIURkEviv82314jVXYz59egWNa++KFRkHdAfllzVdjJFMYghk4l3LyIo0aCJhn6K2Tb/f71NS01DBzArFw9TC0uFKclWmFGVhx1erVZhmYLaolcYspOW18M6apokWkXth7BI45yklQKbQgsZniQgcM84DB1zZZ7vdDuOkQH/r+36apliKBWmhxkUFbq3gbVwMDUQR18xUY0ph+fzjq4ErVHnIVXhkt9uBjYWZCqvV6v7+frVaz/McDinGuFqt8U0xbeJwOBwOB7S6aaE0V7oZtlA1YQSsr1glKiT5GkIhY8ZqZwKgIwiq+MUcrerO+TGHnAosiY1ULHVTyXS4s7U4goBJiJ331XngVFwSaFx8BvOTBst76eHh4eHhbLvZ/OQnP/n8+997+fJlmsP79+/gMO7u7uZ5rnmkqjrPuAA8YjFG9m7VDhiqs3RawzD0fR5cqKoUrT5EbDmAC9OcUmIzU1JV40woIbTYUh5RW9yepZQl1q12B7Vt2/gQ69ROxtAiYW6apm09ZpbEGJiZHZmW+MmRw5BDIY2plAxO0u6PDTusVHW0jzhBdRvX39S6ABekFPe0tjg65wBQ60J5jbJHyZF9LdCwd1VMA5+hqo7Y5IS7qqpqBD6UzbIIsSOmrA5mkjJUp1Zm1DIzZFBxf/t+BUhDwXnU3L5cgr8svnECpWsm+g/En+mUWZ2ih7qAZd3rJL1HfuGDZf/W4+M/cakBn5rDfr/Ht6fUbGRsGG9VfCE71/h29fTly81m9ebNm2+++f8Nw2Ck4qRO4kQUiJNo2RNShB5FOKXU9+33v//9//gf/yMAt7Zt7+/vj8fj+flZSkE+CpIcc7JHEC59tNzV/uKf4hq1U8PcsnBSSylVGAGhepxD7eysSkkxqFqELySSaZo2mw0eDF8m99WG4BBCRy5qcguBYnxiXAgn1TpurVLjOSmqnAquNS6yKku7IrSEeBAVyvpw4uPQiIXvhbPBwlYedV0rLVVtQisFUVrMKkBcEtPsve/6Ri36RoZVB/cjjszIKLFY2/mUklEKMTRNA/6vb8Tm5AAgO4dMuilS2HCcWBMRcd7HlJxzqLbi2gBrJw0gFtUrRDSgpQsIC3U4HMySmTRForI6aSl9WVSyfzMDxgD7a2ar1arvO+ccJNumKYuoYA/A1Q3DUGXCXGkKgBOtaXRFGpoSAYDBh2twzm2329vbW2TGw9A1jRNhIlFVcYmIyYRLNh/KcAusA5v3hVGV8eQSAiJpzlPvSJyTlIySEJtkLgbVKjiXlBq7K5Xm+NrCx2yW1EkTY7y9vvnhD77//Plz7/3bt2/fvf5GRNbr9eFwAHa92+36tuv7/mjWlKIMIhtlspjhitoglOMPycPtKWkKIYaEGCXFmKJhh4zj0bE455Sg02g1r7KFEa3P+9Lud90gIk4ahO7KxCRaZvaxsPd+1fewPCdRF1Jx5IWEUMJmM42YQJXSGLPx0dxg47z3aMp6lGNBRPk7uD9Uqr8iwuwWaA1VW+G9h4NJeQ5bHgVopROk2rqgqVlMj9MSnZQsnCC8oho1nAYAL0Fp5xw02E0Y7b9c4wMizIIzs5QMyBCYz0wCDnNF42qlqVqzb12Bug7f9afFIfU+lywlu164yOqPv/X89FENmIrz/sAB1zPUDFj/Hj74nxCFfnzApxYRL3HEqe2GF5+8GndnX/zw5q/+6q/CNHoWJk7MRGymlDTxaYl5cQhxxdxevHh2dXWFXISN53n65s27YRiYyVilgCqCjgMT+ygJrveg/lA3ARGxMBu040B/yNErimexzD7CMwCMcU6xlz7GKGW4R8kpLUUjZ0QUy1T5uYyUd86fQlp7pPemas5lNkQsjfkZ8S6zWivyXMi9GYOqfbT1yy4fmwq1LZNFKbNQlj/jOinDUCkVuWYia9u2poNipqqr1aomdiFOFSQ/HA596+Dg4d3RAgvDPY4jfCoEpIoKWK5MO+eapjkeDlTA2IpF7/d7KzB+9akVXVeL3uVKIQwBAnAsETK2mhpyKbdrEc6ErUderqqQ/iAiuHArJVvcRMyKCIeAq/Lee5G5FMIRqzVN0/c9IkWgr8yM1dDC9cP9pWKGioPEeB/G6zFUuO2aOrfAezFDHKNwD5XLdjjuoNjVuhaiIkTUtr5p3DiODNVw78QRhtILkfNsRMy+yoDwQnhkCeVxoSPUpMo551xjlhrnN8PZ8+fPr69vLy4uDsf9l19+2Qj/yZ/8CQjnfd+2rVfVFGJu4CZqO0809H0vDaZQYG10DvnAxaRkMudpKPOUXS/qBWFOKQt7pSzHppqMzDQ/zsC36KSLxMziXN+3qANj+2UjwOiVlER5/6SUvMvtcPv9fuia6XBk1CYkz1cF3kmsbDRNR0A4qpUAxd671nko8holoHo1/YDnEEaJV5aai5BytDLDA9X3EFLNrctjbgsYI4hk7UJw3XMsLqxF7I9qks3MQojza1EmzWjhOxnP4iAdwJho6sgTmaq6Oro0hTJiy9BUhq3iXVPWlivG48oMD1vESfXr2EdJ58e/efynk/elUjFcXvkHr//gl8xcpaWW3ve7PhHH0un+fZzrP26iLN9ywm/zdpYfdGna4ftf/OinP/2pc67rOnQfyuPUlUsxtSJ1eAYAE63X6x/84HMYpnmeifirr7562O/zI/vhDXqU/n7wKR9cJ44cUxfuH2Zn1rQPbGG8Xc1iSnCHVtjtVPzco69vRkR1Km11xjnmYl9pCMiKymbKy1s3MVYDV+gKhxaTBggagflRPynSxWKhkB8DZLMsK5GLxFxIVUTUNA1ALfxyWUVG5g06T03j2OWpCTlTpwQH0Pc9jAWuDZ0MzFxmKTok4jVZx3fHb2LRFcGgPYQL4HTgqrgUlfFFVqsV9CyZ2TdCRBidW/FJVZ2m4/G4h6rANE339/f7/Z6ZUYSu1VPkuJSHPnXwx6jgwpWu12u4EJDPzezh4QFZOBLxGCNELKEUZoVovVqtuBDTuEycxBuX4f+iVp0rZMAhqo5KmoNjIk1pDmLUCGMafCOu9QJN/8axhuhZPIsI9X3b9y0oz3V7xBjxRjESI1ZzxI64aVzft0SK16cytbqqbFZPUDXC4PhrcoYg9fnz57vd7n/5n//vRPSnf/qnzHw4HFIKqjoMw6tXr4pPTVMIbd8Pm7VrG+yc1WoAwgFkBWu7BGPQLIBNm1KKQeM813KPb0H+9ySSh8OTMLk8B1dZE6WUNMe+JzIjFGa0iKZlC1IIBNV/ZCIIZTJ50zSoPcUY5zBOxxFWAre17zvvnXPSN23ncyMAHhMpWa9Z1fNY0m4lxwF8guhckeZOyUpQ1eJG8Gl+kqpWM5LpC86d9O+WbszygD6louqd36U54q/xK5W4xDnXNr00vp5GF4B5jSwRdiuZb1qmXAmqOYYUIT9aJMQlIDb77jz1u49T5mqlQlfv7NIaL/9ZP8uYoC1SoZF6I+prvvVUv3/k+eNDHv+vcPiIXMljsT+6YSVNe3F19Yc//aOLq0vLuGh0zllMuAFaBsXUL58/JKmq+sYx6bMnT58/fRbnIMUiv3t3HZWgWZqSirhKT1ieZ7myy99LIU9V+8tFuD9DPYnIBAzVECOLjNME55EHys4TF5IUFfYvnmQiSmWYIMZ+TdMcY86hUzTgclFT/UQqVcxqqbGhXSFcIPH13kPtAcQW+FfUbKqTds49PDzM8wzV3zoKAn2ucC2ulP2oOABkn/isCmsTkXNumsI8x/on55xrfEjRmAChwybWF1TNECQEKaXdbmdm8Hz1FsDB4LIBngPprcApZDSk0LtqtIFU1XuP6b9mhkqz5o6jVDV2uJBmIZShhdBLJebDN8XeQL2ZmdfrNXYFANLD4TDP82azefLkEonIarXqugZDc9+9e6eFkd41TYcLixGXXUOQaZqW3G/k00j6uWh24qZ770OYDoedajRLIoQapPfeeYkJVt5UI6gJrowyrGQlLJ2Wao4WyYhyzck57rqGSIm0bb1jHg8HS2noOi/ElhybpeDYGserru+bFv5+aLvWebFs4ruu22w2zjWXl5fbs4sY469//es/+ZM/+R/+h/8hpfTLX/4SV+W977puOo5EBLeqhYkjiwFfTdf0Q+e8YOtW4BoH5hAjwBrHcTwcMGMR/0R/edu2eaS9eFXFpMtafPHNSU3aMbVeLi+2IoSYiRcNEVZAXXGkqrvdDg+d977tfNc3cxgZIQwlfBCj/aPvuqFf98O6HwY0RXjfdZ3zHOegMSHHrYZIiYzd8nn3ZYBYKvRDKVp43vvaFl8fhK7rmjz1iNyiAal+BCxM27aA30QkFDIjDmas2cmBIaxMRYWD2fm2dW2TUt5RVhT6YOWO02hMJG61WferwUkGk/A419PCwnAZ7SqlPaS6zI8dIX1HBnxyosWYU+kWqetQnXHN6Or3JcmyXqcVWHzidx31BR+QsP4+QPTv5TABtiJGkLBQFkfWdsPd7f788slPfvLT//gf/j0zp3FmPs2eW5qJ5aIQkWCUh6PN2er586dv377dbM6meW6d++abbz777FNlEhIjRVIIixbtUTJ6usDFgd8ghsXABuccLZ6NlBIJp5DENbljpIRvH5y2Qrip6NG43HQUjsex73uiii1HTZknkkwtCYYNVCZFBXVP6XihLTjnjscjCJlpQUSqCRMc3jAMEAjkwvKnMkyibu5aUq0xKZw0iqmwNU0pk1dYsusG7wXA8jiO2+02hOAcx1x/yhEMqq14GJCk1soZ8tp6L/QkAlUajmMWQgK8vNvtcvHbzGKEF4eYJYs5v0KiVu0XvuYwdPihamPhmyIyCFkmHjTpAMIzUr39fk/l6QXCgcey65qUMjV6s1nFqOM4dsNwfn7OzPg4iC9uNpv7+3vU/uH18b2kdIKFEErvUGZI1d2IhLvrGmae51SjByzjNE1QDIVRTtEdj0dwyphb1chMZsksqcZVv25a13dnWHDIefqmQZABMSysBirNq9UGN73vc14FDMAyzSdBD6T1HlkSkqqmadhnu4/NM8/Tn/7pf//DH/7g6snlb3/1a3Cb+35A9fTm5gZxZw19xnEUaZhZF437eCSLWEq/ZKePxynGqOjwKdXcCkc550RT27bsJETEHJ7I0OWMD1Whpmk4Ru/9ZrVerfuz1dqduGZtSrkP3kqzU6VVUuFnMDOeFCH4ktIF5DrULFLMXTdExGJqkRT5X67pyoJiUh9zJiJS7FbnuIDVJCKWzUKeYwhr4xb0pabJtBVAvOiJAojinGuHvppWIIY5S3E5BiIii5kZynLakGj/5VxryE4XWGU11whVQ0jZv1Jm8Djn0E7JZdpE9nJCKf4uVHnpLKuX/cA7Mmc+RH1Zzd3/s360KIqfPugflHTTf1US1ncdWcqbIMpBBh/cNq2RieOuX10+efK9z3/w1Vdf/fbXv7JSMa3B73KhlwGLGRGbhtn79tmzZ33/t2A6tG0bk379+s0nL55Hp60TQ7Ko9sHNqwcvSImnHVZ2bXUGWgSW5xhg35k5hGRmXdcxn2a+lvN8VGnIsRVnr1AaQpg5RZsj6IWY4TWfngoWMgohemMjTTHg8hJlnlooilFhSuTEhF3bzCk6yghVnnVfFI9rRF+3fiqTEKvH5cKvqbk13KcrMlII4YkIaKorEh+86LzCy6xkgZB3xgtc4ZdZ6U4GGGuPkSJmwhmWtwbwbyZjExFRLJG78y6WJssSpOdGWO+FCp9ZizoHAgIqqSe8YMqTBEfnGi06X7SgnlopD3ddnvWEaIBIh2E4jCPy+2EY+rYF5ozvBaQhqp6dnSEomaZptVphMfEtUpnPYQXJr/sTV47fIxdkIy9OiNkohSjEZNp43zZNDGHo+xgCpD/6vj8eD943UD5BFWCz2YAXttvtri4v1HJ1f7Uenj9/vj0/O+wn4Mmr1QozHphZpD0es7qIFD52jLEf2vGYdVc8MwbtqdI4js+ePdtut7/+9a9/9atfXZ5voeXeti0uWx+TbsAJMCYztlIRrKmM5TrLrsZ2pgRVEDIDf0KL5UUY2nY+aPLes5MUJy9OMI2FcmJArEIszKuh35ytzs/PL842F5fnjRPnHBffP03TNGEv+ZTS4bgLYSJSEWoah6CncT6F2bVt07imW1coFd/Ou5O0JABvMwO8zwtGW73daDOjYv0K+JRODtgi1qFt/TAMIlRdWlro/5Rw3IsQE6umUpNG7YOYeUm0WeaRmkJKCeIqYGYJe2ZHJM41OUcyTkosTFQmWVkapyN4YV3XAc8vptWHECBUbJaZrUYqfHJb9gFaWar1/BjCPHnKk3N9pONRH1guAubfdRhnzvTHDuI/e9TXf0zC+q+eBJfJwaiRgOZjkigXEvrV2gk9ff7y1Wef397e7m6vzSxpNCNfdFCpZAPLn3F4783Sqm9fvnz+5s17sOyapvn1r399dXXRS5OSOfAIMEnXnZrEl3cO266aflnIzC5fpmRRUyoNSKc9quqcOxwO3uWUV0mlPLfVH1RiKh7LaZqIZJqC996yPCxXeIdEtFxMvTyW0/XUZBcpHR6z2iCrpUwOILqWMBEMLoHN+h1dERVypccJThQFVwS2bdtqjCg1wROk0lE6TVm9EhmhaqzPDGDDGON0mIdhQAUuqQ39ep7nGJRMyEQ1693Xp2WeZyeCCBreaIm8xRil9IowczTt21Pii1yktMeMw5AVhvE1UVCsj2iF19A1VB0qHCQVg+4yWYEPh4OqmiVUf5HUHo/HaTrOc0CoYZZHs56dnSEbQH+2EB2PR0wRoJL911p4xVTwSxhKIoUXr9Qq59wwDPe7AzM3TbNa9Va6nzebdhiGtm2eP3/mvcOFrdfr9XoVQthuz1TVbJ1SatvGeyci6/Wq7/uXz1/AxaaU4hy8uKFvha1pmouL7TB0eCQB/qdM+2J8Bdz6tstjlVNKIUVVvbm5mefRzP78P/77/X5PpNvzsy8+//6nn758eHiIc6hwcYYfvWNm13jLGFh+BOp2hQOOUSsPbhrn6rcgCk0YEGTWOF71HbNNIbTmQmIm9c5V7CF3XLss8XE+tBcX2/Pz8/OL7aofnHNSZOoRswFBBU8e85gBJGy32xyyS2Zrp0XBEc9vNNU5my/nWYij5SxcRHypelQHTCeJxNNJJFPD1AwOJqLJ1TnXth5SG1x4mnh7RV+dcyzmGJapBrX57FIOZgZ9VVWZjBa8xSVaRrlS4JISpkUhT2FmIwj5jUTinOv7vm264gUbO/FSFUwx97jd44Nklx75V/r4l9/lfZeOowYW3+qlHvOt9OMT/j0PX1t3/qG58z/RYUQMH1y/OJh+RkTkXDPPwTfd5ZMn55dPnj1/eXy4I9AKLAvTo2T1wQ2oyROZet90Xfe9V68eHvb7w9GMVVMM6ebm7tUnL4MGImcanXPK5D4KcJY3pm6C/Eh4D90yEWEnJCetllrfpYLcIslrVn09M3YtlU7WlMezlIJKxqWtaCg2qpRSVFVjZ2QaFTSHlLJIr6q2jbdSGNbFXL8lRn04HFBdc86J5IyzpuxcCFlEVDHtuQyfqYtTM0Ucrg4pU4Vrr+wheIjVahVC6LoWzl5VYwz4ofE+YkSxnVYD4x/g24pNoYoUccn+/SIGqrapeiDvfVIdxxGgqDKlxLW/qIDJKS20gWQxI48X8qJcukvdoge6yYMlTqoFtCin0aKIxcxo8PXe63hExHB3d/fk8hJZ9W63Q6IMn4HIpiIBsczswxurSeJiFiHMJKVJtyZMeeSl96vVCoPwzKzrOmidotiGhrfVqh/H07RmMMiGYUDX0N3dnZWBE/gTcsqrq6u7uzv4177raGtGFONMZf7mPM/OcUpxtVqlFEERypxeteIs47/5N//GObda92dn6z/40Y+fP33SNKCm8+EwPjzsMWOKhB27erPqrpPCx8GjZ5Z5JHWX1kdYMWC+PMVd48/WQ9O6OQY+jBLT7B2T+K5t0L+Vi8Gy2Z6dnZ1tWn95ebk5W5+dnYkQ8OEa7s9zRJQwTdPd3d3Dw4OIbDabpmk2m03f5/541J5LWTqlKmUc4xTGEihw/ZqZseZc63312vUuf2CslvtTSyGz7m1m67omRq2KMRWOymNv6JTGqGrrvHNOSmE4g0ZCvmmZjVnM1IoRyM8mlZEJJX7RdJIEMCNxYMahvT6z0CkTWfKUbiquUSST/CHRLNzU77o0+B/Y/8W/PhxqUJfL6qTFRSH5Wx35R6da0J6/+9XfepxqwPb3gLx/P0cOsbLfNTQ3NOzUuWSJvdOovu1ffe9719fX3/zmF4fDAQpzMYUaClUTSR/SpqDLYdvt9unTp2/++mdt23vfishXX3319OlV13pNyiW3O63sd0ANy93ctu3hOClZbgwqtqCWdZsifAhT6Aop0cwQZGjh9VHGyrIzm+c5xNg0Tcxz0ySlOYQQs1rsiUaYf2B0BRA4R9UHVB/PpTYM1hWIOQjza1swyFlIkadpwgWDMwluVKUf15CZiMBEvb+/r3JaVvSKUR/1RRWEmdfr9TiOXdfVWbypNCwNQ8/MGiLoIdX3Q1HSFboWvhc+IoSwGoZqZPGnWnVTVSkounMumvpcK+2qvwQ7Zp5HcM1wPamwqX0RCcFNR3VZSvMuNgBO3jS5lbYG78wcwjTPhpU8Pz9HJ9V6vT47O7u9u364319cXACf3+12WHkEOkTUMvummctMCKlUZBEsDui+1bsDBMHdxC0Yx0MI4WK7RQyUUjo0Y9M0SM1XXQ8txlXfDl2j0Q9dx2aNc957sAGSMTODRFbRJnw6lijGGMKsmsx0mkbvm9xXfXHxsNut14MZ39zcrNfrh4cH3BdkhyEEDKALMSCquLy8PBwOP/zhD//lv/yXz58/nY6Hruum6RjnNGfBczHTpEqUx1ItH8lyj5SNWt9QmVEmC2rI8qEGi1VEukbE9U3jQ4wicpwCaSKStm37pjUzNRRu/eXl5Xa76drm/Gy7WvV91xJR0ipqm9Em7I2HhwdAI+v1+ur8wjmHTjFEGzWRxRpOMeEejcd871iq5KG5MpI1e8jH1RYkn2KPEuISE2hKMIanyVoA1GNpTUxF2KdyKp3PS+S9x2XUha6WJPdTORERSzqlFNOsqkJszCIO9JSm6ZzzRiKSQ4Fssoym6VjKXtQ+HqFti+FyzIzWB2ZeSl39wz3Xh96XFkla9Re/M/39x+nIXZCwWIn+YXOM/ymODxFwbDviZGrMMSXv26iJm+7V9753OBz211/9xV/8hUguJTpZ0soqqH5KWdar1ThHMnXOvXr16s3bd7vdwXs/hbjb7d6/f//qk0+hIlB86CPJSXp8z2TRDlu8gsH+I9/Ce13hONTnBKEWdrnmsi4ps5XcS1XJLcgOJ+2qRCTwMDElmKFlYGtMRgRRZcCwrmg5UcFFmblOF1imWRhoH8qoRFqg5fAWsQwwxgvqVNe6GlpQaHg1fHod8wD6Lvw33htjbIeevZsPsW+aenJk5KpaUytcFQA9V2YhWylfxSJwDTq05EFVWWupcomP47her1FDNU3OOccG86dFGmUYBkQ+9bZyQafxFayQ2/FLLSOB8UrcTch11YgnlDnB+MF7j/TX5Z7pMM9z23kWu729PT8/B1DJzIiKiOh4PF6uVggIuPBWcJ6mjKlwRZLJzI7HLJqGzEYkA9d1Mzvn2s733YCvibCp6xvfCMKyYRi6rkHYkUOcMCcvMc7oMopxFmnBhMcFI9wZhuH8/Hye53me4GOcc1jzaZrOzs4AY+x2u7Ztj8eJS43fe6+czXrb9n/2Z3/28pPnInJ/f++YiMi55ubmq/1+j6VLpqbZhdTtrarMxuygMKyFv51Kbdg5lxpKqkrGJK44bBGJRL5JHI3ZbdYrZu66uB66lMx7P3S9mRGpF9cN7eXl5bDqHdMwdGhbIFZiJ0YpxXGekNSO4wjvu91uz86362G1Wq0cZcVQZhbHxCzOc8g9CyauAg8pq5d4LroIuIU1nk6lvTA//tmrUbVUvDhSyrIhvihC415jpmqNQblwnQz8LGJVRUiq6BVZGEYRkVyTzuuvVXzDCOnv8lGtaWXMjWEUQkCPe0qhaQY0OKSoeKaWtqviPcQZ1Tthwf+A40N14fKFHhl2WjCKPjpOhc7lbz8IC37HUd/434oU5UdHFf2ARzYiM5LWt0mja1pxEubxiy++CPdvfvaznznnUKh3kkV2zDBhkOmxRsw0TY33Xbd+2B26rnv27NnNzd+Id7DX79/fPHvy1Hm2lJwXTURFhm3pfbmUQquXSqWLF7VVKr6NCoKKbR3ydKasp+G9Z6qYZE55qyEjygNt6oi3aQpTiAYtV1UzhsIXyA5d10G0hktPcNd1je9sMaCJy8wcV6BaK+MTaushrvns7AzODIKIeM7BjKjPfI3xiahyfXHB2+02la7feRzhhlUhMpClNkTk/fv351eXsALrvqMC2LZtTj0pnYDcrutC0WlamhV6/CRUWLgGzlhG7/2uzKWPMUZNMGdlAQ25yHo9EOnDwwNIQzXVbtv2cDigks2FlebLRIQacKDeeXl5Oc9zjCoi0MtMKbUt6sSERiZfurcR3xBRSgmQwH6/r3IxcI2Hw2GaJjQNX1xcII0Wkdq/SIWIkMpcDVirojPF8zyv1+thWNdqCG4Zcui2bdVyDo0MntmyhxBBtRvl5PUwmBkuL6WE4YyQUoFTGYZhs9nc3Nwg0np4eNhuz9cit7e3ZnZ5eT7PWb6RiPq2U8or3zbOpQjeWdM0P//5z//iL/7ik09fnJ2tv//Z99zaIQ7josEpJiKSUiimHMEum4gxB02UdB7D4XCImjUogMljEgDyP3PiaNE/yqISSalrnPaY0LWOMTbOY34XMzvPXdduNmvvvUgORhka0cRJU7UGRAQH3Pf91dXV+eWFpZNxqD1slvlfRcW26ExBhs8556TGFrXySgUgO/HRqFZnsrFSjEaoAbpqcA2kbCRG8LkE0PfC7GTQDsFl0zRQFuOCLNYwbnk450pLcDaMGcs9MUPzF0+KJ7SZ5yL9FqYYI7qqwM0Wdom0AoRSmN6udB+BY8XMuhDcxoGl0O9IXuFK+HHGDMvMC6Fy+l1Z9bfnviX++Ycd3kgY+o/ERqSmjnPYeIoUlqf9J0ap3YcYPUpExJwcGVESYSKXjNr+LPrh1Y/+5Q//2W9+9lf/x6rzjZqQztPkXEOkRsI+E3AcGZNN0yRtP8fYOmuaRhr7/uevvvztb+Dh+mb1/u372+cvXr58HtNsrMbqxdetzx+IXpWH1jRTJJm5a4i0maYpxSRdO88RaK0ZE7GIM8sU39VqhXqYiIQY+4I3ipmGICJt0x6PUzRl38/znIynaQIhIgR1vks6x6RJjcXN0+QaFyeMCfJCxqZxngIlJJ1g8VQXG+YkIn23imHnXdt3q4f7t13XsZOoabPZhBSBgtZ+wUonwTMA8amUDOPom6Yzyzhz27YpBPCMnHPqPZ7nGDRqwoONGnDf9xpiTNo6CfM8m3Vd6/0aw+1jjE3b1mGfcGZWgGIpLchEhEUGJqyqRjRNUz8MWNsJBM4QXJ2iqNp1nWchs+k4rlarmCK8zsPDw8PDw3q9lpOwETMzPI2ZZcg0Qxcmgvl6jJmpgEJg3WA3cFVI+pkd6MFN0xBldNr5BqwrM/Nt+/bt22QWcoMm7FdsGqdxvthuiAS3A5pK3gsA/KZpztZruNvVanV/f+uK4iOpCblVv2YT1ZjJt01zcbGNMarRat06xyGQqnnvdrvdixcvMtQvjbBnZuHkxDG5J1fPzOzrb74chmF7dkZ6v9/vvdAwDHuhqMZs19fvvPdE3HUN80bEmkbOt2f39/dNi3o8N00zp+hY+qYdD0fnXOO7w82hb9pE9s03XznnXrx48XD78Nknn65WG2FR1TlOTZflnFzuDmBmdHvner8lSykLyJT6DtW0DNtGY1BVpkRJlBkoiAhZ53hOnsn7tvWuPPirSomAx23btm3EezHWpmUiRazHzNx6Iw3zHJJOczwcJ+/aJ1fPzjbnjnzQMM4j7iCi3spUCiFMh2Pbtm3bUyKOJFH7oZ3nGX0NRMTsjEV824jTpNG0aVpSBcwjIjEEZhdSUE1t550Ren5UKWpyTSfiSXia1cyCkSVOieFYkdw24tqm7ft2xmhzYhXpVisTicrJHJtrfKOkjojYNM1dO5DOTiSlUJqzTVWIxAk715Bw369EXEyGx98sECkLJY2qcTpClyauV1tTUSbnGpEcM6Ghywr9G0+fcy5pIvdxDvktHStLD6OPmia4BsGxqAmd3iKYzPFBGzGKosA++dHvzdzi05ZZwbcm08zsMcQ+xaLwDhKU/DfCiP7OgwvDfr09+9N//a//5j/99fF4HBoX55nBPzKzQnAXETa1wvIlIkR2QrZer1+9evXLX/5tBU7fv39/fn7mG0lkXj78+t+BSJwOX+amVeB3GZ5bKbrwgvSPZAi1OqQsGVxSHcdZGs8Efo045zA4BWhPfXSlKF0gjSMiS/lDnfO1VrpEjXzTIoPBezFXp+/7w3i0QsZGNuOLeiVwNrwMalDH47HrBhSDRQT8CHzNYRju7+9T0QAyM9gIowSClWZmEyHN8s7VmcfInJCaw9vhN5WyjvvYdR1UEs/Pz2sBu/KngNbCqyHcqSaYi1ijmfmivaeqc5hQt0aMD9rz8jK4qF5475HyhpDwey30DVy5FEFKwOn1IxBMbLfbm5sbEer7fr1ej2EGULxarXBa6B5fbLfv3r3ZbrcoVKMP5/z8koiQZ2c/5BxK+MhoITmy2WzQuNl1XQoRsQuhQmEmIufn5wCB06L/u5ZFUFHGrTw7O7u+vsa+ur+/x9oiEgIe2Pc9MuDVasWuIdKu6y4uLm5vbxFj1S61PMFJabPZtO18PB77dn08HlHgSE3yjRwO+6brNpvNdrt9+uzqi8+//8knn6QU1uvt/f09vgj+ezwe8aBhGjH2OfZtNbJYcFloeAEgWSapH2Ry2RLSqU2OmdFfDgeMU+GV4ls2DprqaWOI6G7f7/fTfOy6brs5g2rmfr83ygLUJ5y2pK3ee22aEAJ6XgHLTyGzr5NGvMaXcZNUSJE1abOFBAK2BxdSVTohx8kU6s4WNRmxlhiFSu8v9kBZMVe7kwtPU1JKYqRiwtw2Tdu2pLE+6bQYoCQCxnWG3Kgwm+wkLx8OhwOuMw8FL9jS6eIXk4lpUfjjRUH6H2qoeQFqVlta/5R/r48A7g+caDWntqBP/Wc/94PDGyVM7PnujJuyNvN/5eOkRI0LdkWb/rPPPvvX//pf/9v/9/+r845dM+4f2t5559U4lRKmJtUFtTWl5D2rmnPus88+/fLLL+ETx3H++utvzs/PPn31UjXFRI3PTUf1OuoSn9adlcoMDVoQH+Z5AofeFSIiEUE4s9423H7n3FiG3sDVsQg0qRrxRCnGSNCATXmXM51GE+L8aExTVbR7Qg4acRmVxv+mzM+B5QLNCncfE2oBe1YEm4jQNoOmCy4oa8Ukiej/z9x/NUmyJGeioKqZuXvQJMUPa86ARqPvXMzOvu2uyP7o3feVnSsysgIMgEHzxuk+tOpUVWZlZhAnZqr78JlpWETmOWhgeoDrUlKSGenhxIjSTz+1uqmSn2Lv/e3tbdu26/X65uYGeUoiauetCEFjpZSAdVJVR9zNOmi11Wqphbt/HMfVfIFyGl8A26CSQGyz67rFYgE2sa7rbm9v8RacG54nDKb3HooqVL1TMHSzbjGWBhjeezBYQZLisZ1zB7iyCCwSKJIQApwGnA9vBjhhpDml5JUx+8AA49YgUsWTQHtBa+LVRARVK4g0wBKComqaZij9dC1sCDMuThOX9J4UNtBxHB3lwZzNZmOcELHAUECNIaSxXq9Nxi0WC0v5X11d4ZFQmR3TSCxoCIjwNV4WTrkL7Wq1iDECTY3XQQxguVwy+wu6vL6+/vyzL0A24sjH6LvubD/0AOjd3t52i/mzZ8/u7u5Wq8Xl5flqtej7fpwG0QSWEhhtsFaZOcaprjKwbYgJpQqfyBWTMzMf17JyCEEq/H+K3iJGQGtj/5o6dwV1DNBTKlx4MFamaYrj5B3P5m3bZTrYcerr5IgpLdy9Xa3QV1Hk0EkCh2ncrH5KNJgKDQsflwg65wzFgvaRuBES51yuiZ/LO6r3ITCczoyBEhFQIWGucw2FRsqMQFRsWTbTp7YtmDmRzpvGey8CccScg6wsKuMYd7tdcN42V6r6YKbKOMBmNLVHX6+ATVN+k1Kr5LkWiIBWTIJCSkrBebum/X8oHao+5Hs+7r+ojFU1ZEIDtOWgA+zI5j07wf+h+GhoOS06WIsTCT9jtlz81V///De/+uWrl58t2rabLzH5zpEoi0jjvFQWCt4upZREReTi4uL999//+7//h8vLSwivq6t3T548CY2LmprqMWrVS8cjXs8E4o2lWOiwCFJhfZMqdWp7RvTQHJcqfgkp6SL2mlIiMageoWciF8SEjUlKiZSIvAilQt8PbwnYq3EcEVesVTKkiRUAOOeYGNBElC2CVISKaOu6rtS2HuHUIEqoUNvAY+ZCDW1uNF6NlZqmSTK5DOByyxJHpSSBc5MJaCAAtrHDERA20Wl44Fga5NlOtoQxZgE6iUp3BKRLEcLl4qXBKURMIoSA/CvMc6hhTIqqet9QCY8jomjcmUXOugJC7h8/fvz27VuERhBg32xu9/v9qm2apoHCG/rcKge1QLlcSkQK/R5eAXXDtvZyFKHEKlzp+ofnbEODafXeN5yZScCqvVwuEUXAvDAzDBoYK7PZbBpkivH8/Pzq6qpp/eWjc4y8KxA86BsAr9q2jUKz2YxI1uv1+fn59fW1QZBCCC9evFiuV8z++vr6yy+/7Pu+3+2vr6/v7jbnl8+++93vPn78eLPZ3G43v/rVr2az2bc/+rCbtft+1zTNNA3gb7L9K6WonSjD2Ww1IpWI+bL9a+rK+Atty9hC0tLdiJmZkyTrcEo22vVyMg1NSZLmuFGcpmkYSRR2IdAPQ9pPMTNOm4oSiRbECiG0heYFjJhT4WxPhUvSl3oK8EJT1WWcCybDlJ+ITEikUa4vF6GkSTUrXaekdIh31mLN3IOUEpWUcAnUO8lo6lwclVLyhZxHyoERTpQbcjA7FXHemVby3ktMsDaEqG1nXYEEciFuK8ObOY64os2y0+ghRVsL5Pq4ryNtf9WfGMUVPXRwTgcckT7RPRDWn6L+D1g4YmI6yuf/Bx1f23OJ6WDvHMyith3H/vHTJ//b//6f/t//ry+IHXswgaemaUpJePTeM2HdHyIMjeOJXNu23/3udz/77POp9H99+/bt9fXj5y+e0qEB5OFQlcMTHR5OLA0MTAoieFIYgyGggZs1fsdin2a0S2gaMEWLCMoBhYlSmqZJiF2FabRlB4CixV2ZWUlUSz9iLt2+yrS6gghtZzPnHEqA4EbgPdCnFsrp7uZWRFarFW5hEU4pECpVZT4kSqnUX7pSfopyF2Phhz7z3msSDtT4kPXEmJsIjeMAWv8kMqVxsZxric8j9wOpagFqCKnMrjAMpdNqHNHsvQT3oO1s0EIpX44xto0HJKrrujHmlhWYJqheOHNEZMqVqy6zUjq7afHRDfA1DHlvo/NH3/eLxcKV/pgYzPl8ycxXV1ewHg6mYUqr1UpTSmlC6B48Vs65EHLQgogQL8EdTZHgTccxe/+YBUwcLLBhGLqmbXyYpmnsh3k3izFOw+jZpSmSqMTMqtH4MD9rb29vldL5xRowtNVqpapw1C4vL9+9e9c0TTNrbu5unj9/7iYiorOzM0ABEGy/urrabrfPnj3z3i/ni+V69fTp45/+9C/6fpQ4pZRub2+TEkB2t7fz9l3zX/6v/5e2bVfzWQHkS9P43W70pdTNpHMsyPyiSA7Njy1kassS00F1zR6LEh/WcG75m/sCYlegH/BhhTunTPhnWgpSG8+z3W4R5gFrmIXHUegsOfU71bPGzDGOTsWeM6XJOXLB4eYgDAHQSVWdb5CAmiYwciNQnJzzJ7pESmk7kApCKiKu5BmdJ3bk2dXhAxFhl5ciETlm75rgW8eHun+qbBcR8Z7tRqZ9MVneNc4FmBzeAaWf5RXwz0QUJV0sFjCas44vcQJcvyklEidekGVha7V131M6FuCHD22ITKgmFcjV+188+bqpTrWQAxH9a0PQKSWEYSTPPREJVpZNx797JvhQPmQH5/pgr5TqFUDMxL6dLX7wo598+5e//Piff7eYdcxOidSxL8h45xyzwwI1+857L5xiGs/OVj/84Q/+7u/++2q18t6rys3N3cXFhfPkg6+NklofqwpXXGi4pwjKQBvn0GZMmA/ysVTu1QZ7JpQwjeVciLFwQBIqLA/RKpKkyknFKxMROyI5wOhtNfgup6kcBXOVKiCJM/PZsI6ptNtzhTICO0FVEXCGHoLahhTDr+aVWl5AVXe73Xq9RugSA4i7pyk2hTQKGUQ8Sdu2nnjRzVB2DC3Ytq13uYM9XC6oHITHiw/qDawLrUYFLYlhR7rUpgDBiVQo6amA2CE1zJTRTBXUcsX4Q4easRxpgE2A13RVhxYqplIsbFxE9O7du/V6vdlsZrMZgOW73S4lcj60TTuO4+1w613uJey9v7m9nc1a6M6bm5sY4zAMXTfB5sBdjAYrpTT2vStoLxhVWDbB+1A6R0E5LZdLuLygN8H4WO2ZlEoqPAZC348ePRqG4fr6GpmI7XYLM2V5tsyVIUR93zdhvt1um8YjZ+G9Pz8/f/z48RSHlBLot7Dq9vt927YUqGkWl5eX4HAehqFpfNPkjpYxxq5rYAk551arVQittVLQQo9KRCllYI4vBNQZZT2bmU8G9YDv8nH4SnWiRN4F55mZnWOD2RLlzLHtHVsMlWw5dMs2U+koQZuiJ3KNnyQRSQgOaCBbbFh7UclWEW6qTBjeNOUOwbUFmcpq9Me8N1xFp4tSJMTaiIkUVkbmRbD3ynrIMWsWUKqaVLk46JC3CB2qYyK2eooYp1haj5tCcs4p+TonYjIKoma33SOO4pxruxk7n6KYyYLZkUwf1J58/UG1+uBR/8msNLuUjRiDSFwq5VKp9tPrHxMV8734Mx3r7697vPDHP/7h/fffX6/P7MmIMs6rvtt/BBrr1BU2HUxcNaQkt1wu3439xaNHP/9Pf/Pxx78Pbcd6QDy2PtgQ20uxI2ZOEj25MSXH/Pz584uLC2RGVenq6urx48sXL16kcUf3TKHK/JHsiJbDNmTbtmnouTQ5MKWbjcriNtkGEKEYJSVr5cvKTqsidEiNNE1EpOxchUSwzZMKiw2VIBPRoWTQFe4k7GHEhxGchBIFfgfw7Gma4Boa7AgulKkfk2Jl/SURMtcQEhauMDS35RpDaY/oMzXV1Di/32xVc9XvbrcjzplR+DGmg7VQpZtjmo6xbLV1ZeQYWgUnDRyLKDoCABlTzZnA0u6CJwRIBOa5FJpcX4hBTP3DLLAsIBcmJshTfB3DiGgzGJFgNGy3W4SdodGhuuxbeHIAeQ6SscQkLLMAA8UVNkH7rt2xbVvnqeW2tqjA7Ii5VtXFYrFarZAwRgQbhtRmt52maT6fX19fX1xceO+HYb/ttwBzTdP0wQcfbDabu+2ma9ppmgDBY2Y8VduFUkgap2mIcfTe9/3OZXs0EkgIvcdcw7OHnAMzzDRNohG9qtCmwrQUJlo1Mw+bAvbe48VTBbmy/UjHUhi/hoOTZxL2QDfL5aCifTVJ4hx6RWB/ktxa23vvHKkmp6yHLaxmDNWWAc6nEp+LpbNZlOwRTmXh2Xamom7retkTzyxlFGSWWuSY1ZGrXWRhZqnezTliPfU1YdGGEBJejVg1OddaAmsqPVipJLBcqen3vokxogakOLUkIoCSYL6WixWXqLIhS2zKAH1AgUgthKWUOdnxoJ6778jWQ2QSgxyzHhITfC/CfHoFUXKH9YDpcPQvKN0TdR7+8R//UVW/851mPp8rsSbxOWvyfwYI9KkrXOngvDeY3Cixmy1CCN/9/vd+9Bd/+fHvfruYzxWUOsRcKyrvY8UpCNHQNF5El8v5hx9+8E//9IvVatV13X7Yf/Xm7WK1XjaeSgl89RRf/8TFa+y6bj/0zDyOQwjwkA4JG4MFgZsmhLYW5c6FpJEyyBnUnGQqnFyewixWCg6IyobMJrmqqsYUga2FryOlew8WOlwZAyiiPMb0CrW5Zx9VKTcqlc3YFdfXN5rzx1QrJx+yG4fXRC4zpTSbdyEE1TSVdoepH5rZTETatoPEFI2OHYyDGGOSqe2aOAlYosxLgzeMXHuuwYjRwJMmpAD0pcI45tzBocEB0gnn3DDEQgeYSTHNlYH3CecPvwLfa8MOGQT1kKrCkvV60bYtCJNDCH3fz2azq6srmBcZDFViyESEc1CVBLjZ+fk5PGC8EYwG8+nt7k3TcKnRwkLCAzelnZRzbhxHHVPbtk3r4dqGEFarFTSo5T5dqQfdbDZdhxqz5nx9drfdMPM0DSG49XopvPSladgwDFdXVyKSxG3jDv0Nt9stbA5m7mYNGPaHYWj6vmlbGGegVESOJqaELvGppGmZPXN28pqmoSkXkePxpGTiY+HotuVqu+P29tbEuq9IGalKE3KFzEppMryVaCo8DwdnyAQovuKYx2HY7ndSOohIBnZpCDlVmSoiHaEjj8o5x47YESl772WKMca+3wH3Z7pBCkW8pX7SMa8tlwSw7Q7OrTjIQE9C6jRbb2TQLXdQBswKsW8DQs75Mkql+oCUknOeORdfqKYkU62A61ECyeA0Re8a1/ixB/zCpwJYCyE49svlkohUGY2HYWFbMMPI1QlK8muOWuHdV5n3/dH6/Iym4SoZcXxl+6RW3k4NeFq886+549cdQUR+/etfM/MPfvCDEIJzOfucLYtyRYRivvlaqfSSw2GG+f/04U51sGpRgi7JGEJQbUTj2cXlj3/yl1dXV/vNnXNMokpCxM4zKcfSkEdVRZIUpiQWRSzlgw8++OKLL2OMorqYL6+urp49e7a8PFOilLRpApEAo5FSygNFR7NCRM7n+iIIytvbW+99jKOBQfAYeTdK7majhVyJ2Y9RCuyCVRWtri28k1QoEfg3cqBGmb0b49Q1rUVQEdNGdz/IX6uUxaPC2UVy0XsP8PBut2PvsDfW63XXtJvNBvIxhHB3d8fMCJ865zabDTiqoKucO/BoOk+oDGkK1RdsnfV6rSlO04Dx8blIxogsciMUdrpcLkMIwGgA+QwBfXH+CH1woSRSYfvCF+HYYTNjVPf7PXKrdYUDdooJcYyMKde3b9/iBU2cURFzBmlOKdlIElEIYbPZZIoAYGS6Dk4eKH7gGUO5qiraL2Lf9n0/Wyygq9brNbw9PP8wDM+ePTFwKTYUbDjU7WQ+isJ1akuraZqm8YhAiIjz3vhhiBjt+aY4DOMwm10iQ4zZn8/nu90OVoL5QyJxv996z03jkcmGtH3+/ntdO9/tduMQxyHutj0zJxUR8f4CC2+z2dzd3TVNsz5bnp2dLRYL8IqIRPYuScPqrKQKkEmLVboDL0RAJSjGBA2qsYZhcMAUK6o3a6lpyvUtZlhoYXcyhW3epBbgffEhPXy+cZgKaN8juoAlpwpdxSnRbrfrh54qakYiCiHbASJCqaJd5AM3+DRNSUqNosvPYynk5XwxjiPOh9kRqtr3OE3OOS0oB+xQKe3LHAp/Y77vMI1MnohcOGp25AojfYzjvJvh7iIym81kmohywQLqCIjIez8OPaCRIfimDTHGGMeYxqn0q7Yrjyl2XeeCTyrImpftgxro2Pe9UjaRmVmEnNNuMR/iFGMk76IKE8+aljxmDVf2Y0pOMxUaP+Rx1v7ryV+lAo1i8dh6S8doTSqgqtoPrk/IRkYSrnDBUlPT3zNHyBDU5SaBPb27vX57/ebb07enaVouliopN4Gk3C/qTzx8oYOHJv4zaV8c98LRSkSkTN4FJhLfBJ/a2fzi0eNHj5++HocUR+Uc2wkheGfcp0RETM4QXaKJhITdcjm/uDj/9LPPUQAqSa+v3j17dA5lpqoi6pxL+TpHnbDs5yzmNEehAUytRbMFnczYtKETISHRnI90RJoKkU2KQsSSEw+HalcVEhKKYrLvRHPs9/vLy0uLiMKXevToERWJYJU2UIdjnJxzqAegIo2YGU6hK1XwKEiQUsAeY1wu5/gkpeQ8kSLWN7Zt56oaTZJko9G2rZI12WWkNodx/+jRIxT5sHdmy0OMbnd3+31vShcjzMV0BYrHqp6gI29vb4GJQ+mqxQA8io6mARAtDIjxYGNwIE2g9ixKaYKbmUPFlAs5iPPxYdu205QMYwz/DMoe/mglLsUV3LVJxrwOReD+YkVN04TBMZh3jBGtnUOh6mRmaHQQoZCoZUxhPyFyQIW7GCYUXhnJ49KPYYGYOTb1WPJ8i8VqHGPwbdd1r1+/hlbe7ncwyHB3hGctwO4LGxfutVjMgobIGffpXZMoTSnG3F3Dp6QWziEiVR7HadgPuIU5oHSPDrZM3FRSPHpf8rpjOE99FMP3wM+KGzUNOEzYGJUBoBn7aYxTqtpZEh1lBFVPyRJxTWQ3VBUtHWOMoQn7zfb29jYW/oCmafpxkAKT5gJcmqbJ4xbHwp0Oaobs9YUUSV+qlEFtKzCz6oEhEoMQi9cOAjWkIaxAIAQXQkD43YbIhlpVE2nwbRM6yCh7a7spSuxUFOGxpmlEEK/O7DE2uVwKGejIp3TgA7QPtXJSv871xDOY9rVBA+jpwa+YDPy6v+Zzij6qZ+Hk1g9+MQgTeffq1as3V6+fPXuBp/HmAR8d3+QE1+/vqqjvNz/3v+Z4AB3NSkKaDQ0f2mb2/Pl73/rWt/a7ze72WsVRkhhH24cCkBklZobxVdYNiQqz++CDDz759DPz+d5eX93dPbFSEJeRXNFe8P6Rl7g6IgI8x3rIU9U43axFoqwMcnCMOUZhZnJMSmnEQ2o6dKo/UEgyOefYOoUZ3sf2qqoCw6wl6ASv1Hu/2+6x3M0RJCIrs3GZxDjHlIDrloprwuJCb99eO+eIsk3NFhAryWYpAAeEl7smt/+DoyYSNTNb5ZPv7u5wr6ZpttutJZm09BoCI3zTNEBrw5PebDbwICHl7dZwi9H+CNyNli12VWE0rAp7d1+oQ/GrPQMGGQrVPsHwGtzMl66IsbRbh1q1X7fbrX3XllbJuDMiz6BxeO/582HYY+XAacYkGmIOZi5GCQFwGDpc8Lq4slVNjOPow8E5QFLf8KUQfNDQzAwRGULO3XIGt3fX19dtu1uXwPjbt28/+eST/9v/4/9+QZfTNL19+/bly5d4nn63h6Zfr9fe+zjJMNwy8zAMZ2crBPCZPZMn2mMZDNOI9Y8xb5r8dMw8DXkYTVWkAqeoP+TSBdkCS1pQyrY4bT+64/ZBzEzkY5wMWGAnWATYdlZM0zAMKZroEyJJJFxqjE0mqGOn5gEfiJHzTgGFgB56bGNhoHgMCrhEL3ImaJomBuNN0SWmVOy0rMZIQdZo8pmZqaC+64BlUuKSUTWnECdgDcQ0DsPQzWZEwuydZ8cOdJhWspzNZSYWbrqmbdsSQM3lGD73pBp3ux3qNbCFmZk8uyY78cUOYxia9vx20CEi/QBc+Rv0jo2D6fhvJpO24X3wIqcn68N8Gd+gv0OUNJvN9uPwu4//+fHjx4laVRUWR8UJrl09Ev4aHVwZU1lO3d7ewtP6sx/1UnPslJWV1ZEEOTu/fP/Dj95dv/3D3btxmjypZxdjFM5doUmV2YN3VHMciYi1YdeP6cmTJ+9/8N6rl1+FEJJK3/dfffXV97///ZSSiDZNTlaxI5VTa4AIWw2PKCq5AysSbFREQ8niJBTwuENRLDxiH+PI3jE5IMnsZV3V+JOZJamSkmOz3VJKSaW1PJAmVe26nKjb7/fILMKCHkp/YsvS4SKIpubhlay2obRMgTFzHfJCMphL9Ck0zjnPwbCaaZrEvDpXmrXhzGHQ0LWI54/jGBp3eXkJiDWCY5Z5nabJKovY6TQmRK7wtKY1YR+YR+IL2QjSiogtG3tzjLFtOgv9mVWEkTFj3AxnaD6t0F4Gb+FCZ2gOAefa69zzxyK3cJHNyun3o2uC903T5CJmLezfUJng4Qql+1BKcblcQhBni4QZyLW2oFcWiwXRDG+tqsFnZw7Xz6iotsVce+9TGrG0EMRu2xYAmVqTed8E14hMIjKfz588eTKfz2OMZ2dn3/3+97ASnj592nXdV199hdqkYZ8Ds8a3FdOIB8bchRD6fowxjkPMzm7hqooVKTHzUXMCG/C8EcqftGq7aZAFjNg0TcweI1m7QffFKBcHMZsyBWZjJbnsqHGNiEiUcRwB3fGeibwwcTrShXZ9YVLHdcGCeXikwP/krsnL5TJG6TpqQ1MHXbSg9E2SMDPda9fGzEQOYV5mRn9755xkBj2Hwg1V1MsclLeICLErpNO+YACdJx94ioPtPlUFiBVBeNEYY+RijHKARZVbqkxJRA++NTOz0jgMMeWmcLkPW4ohtN41tY3lnLOk4X0FfDJl9Yf8kM9af6KZfDtZIZld4b5y5eN4xskP/6Lu/zptjSOopjFK0zSffvrp06dP/+JHf0FqauRffdhIaYU7/bdd6qGjIsM6XFbNVhKR5XL5wQcfDdvt7dXb1199SSk2IaRxwuoSEaennrRzpOocu27mU9Qf/ehHL7985YKPY3LOvbu+RRkGESEshi+xOuJyHT0MF2SW9x44SrD03d3dmVFZr0UqBSRI91KBSmpkg7DS8RRKATmDZr2G8iXj3WSmggmAJ0cl5wooVt/3CGZCN8xms7u7OyKaz+dRMuezcw63QOYG4hgaCKm11WoVMwdTrhqC/a7ETdOgeyuCrqhphispBTEEfYlCZJBUd6o+MFoZYsBRXEjHeyPLQc4uOOSCqiKyGmMEC6OWkmVgmgq+XREB1tJ6YZom5L/HcRzjCGPc2MG44vnDgYuYbkZ7GUuoGzataRpEQWGaYKih3pC+hQZS4ZSSuoyyadt2NltDyKLjAhE5525vb6EA8I5Ugd5VFWhqeyQg6cZxtEGwP+GVU+EncaUpyDRNKaq1fICJhkKpvh/7foQcjDGiHOji4gJ8KSGE7//wB+v1eYzx7du3wzA9ffr0Zz/72ccff7zf79m7NMlqdSZW00meWMZxvL293W49kaAxAzMH3zZNI0lEJucCgufFsJvieID51JuCC0Cp3lO1vM6BJVXnMpOwCfdacGvlROL8GKPKQc7gpv2wB8hAM4BDUqVx62ueiD58oo5rhwtWqRbm15TSrGmZeb8fnHPBB7SvqIW+RWio6tZ1/5VTroZo4Gk452MczTqkyoJROTw2bJj6jsSCEB1c0q7rlMQH3zRNcBxjSjJZ+iZ/g3LG3fsGErvYo/nzGAcEBbE4D/DMWfDeD+Petrk/biJ3okr+DcrFvpJSipJExZjQvu58eggL/XU/f8N97eeTk0MiTRJdIiH9zW9+8+j84oP3PiLLKv/JDFj2HDBCm6Z5/Pjxn1X74jhVn6KiAjvVOeeoac/Pzz/44IOvvvxgv7vbXF+r0+A4MQm5KKllV+g1AKnGDLgUk2+7OE2r1eqjjz76/MsvICJ3Q//ZFy+/9+3v+OAhcTJlTM71PGBBnyjL+Xy+3W65IopLCWD+TFFJpZt9nmzHmnJYUotdSYZDJoc9bMKiLOvsQ0D71moeYUBQCiN6bKpUVdvSaO8wxM4hQjuOB64AxHJjwZDjylAJ3jOirJKTo5xSYlEklZk5JQNMZmCzqkIQp4wE9k3TzFfLxjOgXs45FGwgr8klZwxHNmc6I4POUFVvbm5SVa8FJW0JXSICDSResCl8UtM0dW1QVaSNjTc4laoVEy54AOCVqIT4iGgqtFMYXtwRf/W5SVSGhYsIKIJRZjOOI9iGl8ulMO33e/j6ULFQt1OhpcRdcuGsITALDA11Vt57QIuhZff7DG4fhoE123nb7fbZ8yfr9VpEAMXCkGI6TBMQ0X6/Xy6X3vu+PzRTwiufn59TacsYQkvkPv/8c5SKgf/kvffeWywWm81mv99fvbnu+16V0Z0CK8e4P6lqXTWOoxscu9A0Tdu2zlHbBu+ZSKcpNw4yU3sqXMr4omW+62WPAxFdq/yxdWuzXCskqmSlCo1xpKImY4zOc4rI6Ac9YHYK6JryTlFV5SI55dQPK9vnoDixDgFmbEOjyk0jzExKsDk4owQOKpaZHeD3ptrtT1VRu1RRukTKjoUz2SGXONnJiOV3L+NcdttEmQnSSNSTSIZeYzs7szAyZLrLDyZm4iRmLwKi7L2qKkvTzLz3QKJhkE3omRTCIrEUw32Fd18dHtkQx0d+Zknf4B/e/26tgx/867/ZOAgxpeD9EKdZM7u7u/vFr391dnYx71BfwcAc/ynXshVQv+qf8gT/k4fjQJ4kq1Kvknnnnz59+tWrLzbX16AyZnYq6ryXZLRQzMqSbdUMwCHicRy/853vvLl6C6E29f1XX331+PHjxxeXVKW3v+EFDYwmKoi/GZrGl6oVlCQxc4zoq5NUVcmJIOp1YGOHCW+IGzNjzXLTEvPXQmZpxriWVNN+v99ut+v1GuW/XdfFKdoSn0qLtxij8oHyTQr5O5VCYaQqkWsEvtf7BgIRDzCbzZwnzRYKee/HcRzHfjZbMHNKKbQNe5fbunkPiuN4aDPcEpFpCCq8nng2V6g21uv13d2dSiZfRH0Lnjlklg8KIWy3W1eYN2BOGW5W9VCOCVwPWAOJCJ4oV+A4kwhmFWGiwRmCnWw9FoFVgRey3W4XixV6HnRd9+TJE0BsoFaHfiKi+XzumnBxcQEbwgSNwaSHYYAuLMipvMtSoSvRUhMcSp0SMwOWRURGZA1X2PjC8BVzgrU46Fwi7bFQfCNYstlsYEm8ePEihLDZ7HDC3XaDWEgJrcfXr1/jkRB+B/CKcthTVDWlvLadc8OQEXMoAybOva2mwjumpQTAFIbFmakiYvQF6iwVeIcqPiaMABEB7FO0leoxFQPl8XVKh850RBTTxBMREXKUWo4QHBFFyqF+73xSMZkpJCpqOXh7fugwswiZGbPWOH8gN6VDrtqshEPSulI5eP4iGQ7JFMlL91DYyiWiYy/rOIiIkFQeZ4nne5qilBUu3nuh/L4pJWL1gakyUkWEXSAi5wJ2EBosmshSTTEmbDfvGdEgZhamru2YGTUC9r6W03EVzs7E7H19bJPydWoVf0qlgborWLxvUFV2TboXi/5mBXfyYPQ1+sKllJIIBBl79+mnn37yySex8Pb96cfJKKSUXr9+jYqRP/dxFB7PWThRImpC03Vd183ny8UHH3zw4Ycfnp+f+3BUJ6dStkHegnjm5EOOtDjnZvPu8vLSWrtP03R3d6eqEExAGH7dk5mbS2XC4BzYJrGBMtmd5amqyRrbTrV5axc04Vj23gFzwXzg5jJRBZAqXhfGLCBCqcCm6p/hnRThEsC5H2NErNiX5riWUzQ8rYF3pKq+hWKGMwqvLuNjOcPfkOBxFUNIyv0JclGEvTs0H145hPDkyRMiQu4TzXF3u10qtJfACcNr18KWhfOByIVl0xTajbOzM1cA3tDWNuwIYqPAiQpSCT9DQHBBdeLJ9/s9EGEYEBFZrVaLxeLq6gqlSqHqhY4xBAQdLrj9Wts9yEkvl0s8MBaJRZJFZD6fo9rbpsbMLyCu8XjosWgOqBW3tKXJMcLOCHhAWyNAglBhU3UXxjMggH9+fv79738ffSy2/f7q6urdu3f7/f7Vq1evX7/mQlVNlfephSKRDgnXSXIpbQ/bDssGizPJxE7RSquWs8yM/WX6gypFhTnypXkRUgM4zZdaIMs1ZJ2kOUpsis32YypHoaNRYA8ngRAV51xo8l3qnW7iAclsswks1LFcLpFHCKFFAV4Be7MZCieK09ZP/ZAme80DziqHDl+sh04ta56IjkFnU0VvZ7LIvuK9JxYp7RSxVqXCwYUQAPWqn1kLrzu25Hw+n8/naL6EGUTsyhdomNmFtXn0oDKr1SQd6+n7R/1UXFL+33D+1x3/WhX54OG8cwl7SSYiYU+//M0v395eCSVmntKklIhECSkcAtiv/KuPQyhDS6bt6xTVv+l4+L5sxCXqVEgjswuri4vu/PzyxfPZxTkKBjWpSxomIY7MyqyiCkY1Uq/iUpTGcdu54NOs88+fX6JtAXnvmubt1fXtbjclikLQxOyUOJV/gnywFOgpESklx+odeUfn52t0YIVSEXJTYuVmjAiM05QSOR6mMUoyokQuLL5UonbTNIkmH1xom6SUlIiCbdT5fC6aphSZWYUc++AblezQPHr0CDYmaHWnuA+NY6fOk6bUhtCG0O92++2u8SGgiVgTphRv7m6HafRNSJo9eNtCntlzIomNZ0eSpqFrmnk7DxyQL4+ZPbFjTrNZIIopjiox+Awn3u/3Z8uVJ6Yks6ad+mk+W45D3G4GpmYcpGSRJ4sZomthjPHxk0sfmJ3GNLZdaFrvPDlPSaaYRigeWA/QNCICEyGE4AN3s4adsif2NKWRXC46opLIxwJD8RL+Tyk1wc26Bu3Zm6bLIf1EpA4Rhf1+P00Dq6Q4jsM+TWOaxuD4u9/9NpFcX7++vn797t1V3+980KZl15Jz9Pr1K1Awdk3ThuCZNSXn3MXFI1UWoSjEvunHyCWTjcpmLvweIpLSBECQL11JQYvYdd2jR4/Oz8/RniiWzg21nde2rWj0gWfz1kaVnd5dv4nTfr/bpDS1bbtcrF1ob+52zofZfOFDk6K0TUfKV2+vX3/15u76SiVKmiRNq2W3WLbE0QcV7Z2PiUb8U5Zh6qc07keepBmin6RJ1IyRU3RMwXEzTUmitqFhPZiYDbuxH9DqgERhK1jfBTavl50jzsu4dMEqulaZVSS6qjbBQkdKQqxKwo6UJKYJPWstXt22s7adNU0n4pgb733rg0xx1rTnyzPvAm7tiFlJk5AYjZQLwRmGIId8Go9/7KhpAweepgFWYz9OY5xCcClNRGLs0wifjjG6EDLqojL3J1Fmzy5odlQO6TDYNzFOKUUihTpjp03r2y6oaowjUSHFcmrW+WzWtW3rm8zinqbISt7lVEu2xphAQE/k2rZtu1lMiZjVsTqeJCVS7/04Dbt+F9pGyQffReF+1OA7Vb7b7MhlfwMRIyn1yrUPaju0Vre19qXKNNESu9KCYx2GQWLynElHqaQY7FsnWkc1MStaYOVPzEhzjB+ENGkUSkJJNf8zbYVfjT/k5DlD/QsMjd1u88tf/nL9X9atl1k7YyKDsaVjqo0jRVhZJBB8MKsfPPnPeogV2uWxJqcULi4uLi4uuq5bLc92220XWkJN7Qkkr1hVWTVqDpw+f/78+vr6D3/4gw9dnNLt7e1utwved10rcdR7tX3ffKjqYrEYhgNgoSSQsr8opK4KhVkc1Tzg/KoiGgFNPBTM4XMr22hD45xTyS/lPCG4p6qgXUUOEsFhOEzmre73exjwWrUE5hJ9JXQ7rzp2hRAWi5l5BlDMNdAUF48xDsPeXF4ztFFM7JSYyUjpbDvhgvt+sOpAXMGi33g2bNRYmiA1TYPs5vX1NSyPcRwfP36sqqBTJgjcxp+Y7Tb48BFD4YUvee4cxtzvNs45dh4+fdu24KtarRcFuEvZMdUcRrq9vd1sNsv1ipmfPHnW9z18XMxI5wK0CCLD8Dh3ux1oMTAp9qj4FjLNiFrFUjPKpcM0XplKfpqI0GcQlx3GPfxydFOwhDQ4QKgUaEpJBg/D0M5nIrLZbLxvLi8vu6774x8/efT4MXIHzPzpp58i/OC9b5u8Nna73e3thoibNhCR8w2IruAbhaYRpmmMyr7vRyJqW7/Z7DabzbxtnHO73e75i2ery9V2exdCaLV99+6dGWGWzYXIxtS40oM5paQpC2gEn7SEMU0ce+9TEmu1WQc5bYWfOH9UfERzyMzZ6Lru/Px81s1Sn7e2rSURQyEhA8KAucFxn+KI4oKsZuTQoFAzLaVndIdwYt5tidudhkNFJCWlh/iiXCFzRtc1OibFu+9fuqILV6slUlcg4ENsuXbK7TGAt2iaLrRNNJ7aAnDB1Exjbp05m88Wi4VrQow5ueCIE6HkGoHM7mQY69epn/b+YY44GYb8mKPDxq1W7fX/9fnlkyNs3UOq+uE/VR8e7CQTdEGKsWDhhZjSZ599dn5+/tc//Ssh8VRiofS12vebh+Df7WBmInbOE/vlcn1x8eiDj7717up6HAYiDY2LU94MdG+S8D9aLwDG8vjx408//ZSdc15Soi+++GL1wx+mlLxzKaXgj15NDdd/PAJ25fV6PY7XsbRutZAvGqQzsypDjRGxhUNPhpGZiXRKsSkRdNwwpeR9IKIqB2ydeXK+M5U6HNDZE7mua6FsGpdrYX0B8cJOHGMGX4DFiQv+hYhypJQ5FbpmpBKx1kuqm6bC14iS6Bijq6QSosRpnKgQ8WPfclVBhKijBe5sz8OAkOPUHTiNI4gpUkYehdLHFyAmSyuqatu24zhJSXWnUlyLYAMko+WlELoEd7SD/auKbkUm/iDmMLlJJhERFkTyiQgNjJ1ziPSiuno/THiS29vb1epMYjRusqZpUGTVtu1QuCxSaXJsbr1m+AJpmiAEMQ5QwFY9rKp930Ov+9KeAVFrQAS4NLRAyhYK1beNc2E+n/fDdHN3u1ydMfPdZvPHTz6BiEQgEXFs0TQME+YxpQQoK7FDPyhm8r5hdvv9HpislGQ37jabjWd3dXV1c3PTNM16vf5OjN///vfX6/UwjW3bvnv3Tikhlw+zoECBtMxCsmAp1p5SDjITWNlTkoolquu6zWYjQkisNJnZKieMcRRzKpfpWxzbVq8riS0RAX9tKjQ7dQrPoilYzxYqh7Eou0xHijtKUo2p2K+orIumJEyXgKj1ZPFTVnUZj5pEDplfLc5JTCLqM7FU9CVzTsdgLiomjvce/GVIUPrCDUmGGy2ZVBHxrokS21nXNM00Zq4xDh7byint+r7ve3akkpl8mDjMZk0h+iZhHw4Ziiot/TD66RsOM5ukSvPVGrdWvX+yDlbm3KbowYc5+YodnB2tw1/tzMDMSTPPA1ANzjlN+stf/vL95y9evHg/sXjnRcWxIZAtC/u11Bz2kua5/687ytsagIIdBXJ6tr746KNvf/KHP4zDIFMchwm8P8xKxCezkucpdxEYUpwQuHt7ddu2rXp5+/bt1ePHT58+cR7waTYc9TevDFzfNFBKyQWDYh14AMxSdpxjjOZoUrUIfPDDBDF0MNKnaRJxFh9umsY3jom99ykdcrq+UBJaQpeqqAbgOfC6UICrTOBCktLphZWsrklVXeF1MpUZQmbpgpoBMJjgmqPjLBFSXJVCFWZGltrESiy8oaKN3utIUc8dLADz41En413ThMyhA1JitBwwayCzCnSdZT2h0hDLdZkWNMdpQ6mvnaZpPmvbtmXyUVAufAC3Y7WjdZX3HkE88GI6l6fDXHnLRyxmMyESkeVyzcybzQYVPs65J0+ewFVC2QbCD6k0scB8ceHKiDGO0wDiF1+ayIrIMAxv376FGr65uZkvZq4gS11hpWjbdrfbtW1OQHIBCrVtO/ZTjHEYR+/D2fnlex9+4H3YbDZPnjwRplevXr25vprNZl1oLi4ulqsFEbPzzpMSi9IUk4wTdic7H1U0ST+madtvt9v9fv+HTz6FxlovVxjkb33rW4+fPSUiwAW227uz1Tqm7OJLSSRn2cpZApgTWSKWhGFvZ52pZ0akXWSz2bx7d5stQudcYSIzFetKzborNR1YbyFYq4YDtgjx0nEch34UzqsUe9+2nu3xlCaDg5l4NGBBigfeSpFJq0bxltl1zikR5TajoGomZpj1SqSWZU+VJkCWR6tULu7rSiyBmakUQ4LrxnvPTMANiMisDU0T7HlihVimolR8IUDEroFERUEETHZo/fl8jhXLzMtuTgVUD+Zpk4q1lVOrtFpuf4OPV/stfExEmF3qUoRkgrr+OaswUrqnUOkhPf0vHQ/rwcwCH0tEdEyppeB8GMfx7/7h7/+fj57Muhmeh5iSJO/8g3r3ZCy+YVz+Fxz187jSqkFWq7Pz88vvfe97m9vbzXQ3xVFJ0VaTjmdRS+XZFGO2TKex67r333//9m6fUlKREMJnn332+PEjIR+aIKruIVrw+/Nhc4lKmL7vuyb3x4UtUCT4AakYK2Yfi73k65RODEBu56nBwaopR7qYWZM4Zs2MiblE1VxYBDBRbZnGDIhtS1OdXH+pAheQiGr4T049OscVlxOu2bYtEdA9cT5fx9KhJZXmQt65pmlADAnTwZWYGJf+EBB8XOJ7bWmibH0XqCDLYC6IyN3dHWp4YoyOA3iSLS9oggxqlVhKWi6MY68VksXkKdoncOltbH5PTs+TJMU4ZFuqhlYhkuFD/qIvTQDblJsb4hX2+/3FxaNhGLb7/dnZGSpKkWwmIlB39X3/6NGj29vbpuvMCnFV4rbvezjEphWM29mVejOMG2wRzDgX9JYtLawHIoJDbN7zdrt9+vTp3WY3xu1H3/rO2dnZp599/urVq6cvnq/X5yGEly9fvn37tu/7x7vt08ePvJt1XSfC4yhELilNEX0pRmQHbm5ugEfb7XYhBGJ3cX6RzR3nu1mjql999YZFpzheXV1961sfkuM0SfBeK0AsZSzukQClop4TEaLQMBwRGAAITkRev36tpZbPssu2YbH86mwLvmWIFrsjNikiAX3fT2N0DWDAhxonLpXrpoNrIAXQUXmz60EiQb1NU7LbpUPPaZ9SIgXdOpsmSKUTmqsKBe1RDZoXQoCS5mNwXPn/4BQ655wDA0x0jqwoCF9MMtVLCNbkfLmAiVk0KMuUw1dYVBgN9Bexu2MNN03DrCJIbC1c6RdHx7Unf/qhlT9qIqi+Tm2O6HEA2f6vuK2UiJSJ7pUF/ylHbQ3YCDNz1QBZNSCApqppCiG8evXqF7/4Hz//+X9SYu+CqFTurOm8Qkzx0NP8+6phOxyzY2rbRh9dPvnhD3785qvX//2//x07JtI0HQXSTchChqaIUncOzlPbPnr06Mmjq08//dx737Rht9tdXV09f/4cmk/A+MxE1XwUvXiKmE8pmQK2E0SkEDiHGCdVsEseuvqcWKzKZIsYCHaoDTiLeACE1OI4SYVBKKGmjG+EYkB3hGEYrm+voZ5jjAiHwG/r+36YRpAjzufzfrfHrSGszXg3hV0UmB8GseAnZWU/SklUyyEfptM0Ns5775GNw1tk1IynmEZAqPDwoarF2mw2CCwjomhNchbz1dXVlUGI8SdXYL3Y9icmRVPa2WoJTZsTifBgKoDz+XzuWKcxEWej3jmCEUAl2GgZa9Q4htB6P+AVZsD4FbE1n8/v7u5CCPOu2+8HGDdd4wHexgXzCfO5C8Eyx/BrIaGMUmoq3Qh8hVJOVfdJw3irKuaXSp1VSgnV6haDxSoiophUyc1ms9318Prq7W4Ytrvd+aPH19c3RG6xWL148YKZb25upmn646efPbp80XWTRQ632927d+/6vgenBNLwTdc2TdPO5vDgkafIGZBucXd3t1wu73bb7W7z3e9+dz7vMFzjNDnv9ZgXxfbdVLo5UbGBIN+HaUS0YLvdws1KKSHvGAvHu5YIZyr9IrHebLTtdrVaNXPNFVpvLbmAes/az9gyi8WqaIK8kr0LRDqNhRG2+KYxRlWChceszJ5I4enm4HORLSZMastVVT07rrqJSEm9Za2GShDyuZ0aIxzCzjnnqfzscQEEz7VwnptTYRLecSAnbTtT1RQP4D4ihfRDHYH3vglh1nUp5npRItJ46J+BkBsfe5Ynv9aa9UT03z+ND9nxHAg50Z33tW99X8qfOCISephqkpm18DvpsRtdTvCmF06e9hiHoprHWzVKapvmn371y0ePnrz3/MVs5vSUIUsejEU/8A7/vgczE3mipM5fPHrkPP3s53/96tXLr16/1MRJUlFgR0+I/xvno2ZT1xGHEN577703b94A2tC2zcuXL58+fQpcHDMi0XmRIZ4BuHg997YfmqZFCDRVdQKiBAaObIlLzlxaZkWZ2DEpqahjlySCdgTccloK5rz3znPwwXuepklLk5/QGD19SqWwGBsVyd3N3c45Z3SPVBgzoOqQ3kOTA5gFWcyJtG3LnLkpfMF2WiGBL3zalMUHO+dUZCoQXCiJOthbr5wpDkrZOoTcrAVZjNGAUXgqPGff97NuEWNcr9eW88ZF0ECesjGU84iQm5Ap+MQYHmCjpEJzISJAJzU++IJDTikBLwLrgTLtYrB8YYqaUmrbmY0SlTDmZrM5O7vwfnj37t1qtfro/fdfvv7q9evXZ88eSQGewLKB3nr+3nvIDeMKqdR+YEwQYFdV0Bma4QU107bZEZ9K/2Cwl2jpBo25ADjIvovPV6vV3d0de4eE9Fev3zx79uwv/uIvNptN3/e7fg+0RChdk1NK2+1WVXf9/u3bt+/evdvveghW773zYd52rqAfHPskcT5fqqpzASUA69ny+vo6xvGvfvbT1fmZTJlnY7fbTdPEmhcMDlJFINGktoggh2LRzpTS9fU1eE5UNcbctKOuN8OqMwUmpewYA2KmJE52JTcERxn2ECz5WIBvtVThii/TeFqggHHEeGgg7SqAlfeckpprS8W2yIqkNMpxDrkJdS5YXklVPcDg5TCf0jkmValexCL2zpH33odsBCNKlLNULMy50hL4Bns7sF8hwCYHCwlQMPbe73a7GCMTuRLjQfitbVtQf7c+oITkQFNTFT0bNMRm4UHJb5+faGgpRGMn80L3Ur/117NKJkecWVXAL3j/NK2UNGuqyatUs81UP7xpBFV17N2Ucp5/HMcB/LGOiYQcp5R++ct/QhcU7xwoS6pDqjsd4jP18eBI/RkPJS0851qNjiP2TF6F16vz7/3gR//pb/5GRKaU4fJm79i3DD+Jxw7Bec+a4vn5+fPnz13pYbDdbne7neTvHkUViOXkfet1UC6bwT5SUG+mWlJKcUrmbMUYxzhZ/xkLkLqCgLDoroXLgsspxn63JaLZbCaSXIXD1BzCYuRKU0q7ba+qFxcXXAgf4KlDhSwWi/P1Gbgh6xRUSmkce81c0x16uZsfPOY+x4mZ1+s1wneKzqnjCMFXxKVAAUA9IOKaUlLKreZRhbxYLKBHDXUFIQtDarfbXV9fo9gmhHB7e4tB3u12EJ2wA2az2XK5nKapH3au4KW7rst9D8vw4gmpSo1DkcOksLQfFDMKokD9SIVgC+KVi7/unFutcuMBVU1TtFAY6D+fP3+eUnr9+vWsaZ8/eYqIH5E0TWNNEXAj3Nc4NW3lUNVmAy64FH4o6GYp5RwiecBhSBERRtJSofaC5s2Dp9MxDC/39u3b6+ubvDYuLxDUEWLE/8/Ozrr5bErx9ds3v/3tb3//+9/3+2G9XkP7YvU2TeN941xoms77zNMJyQujB+v5xz/+MVbjECesLi4uqS1FvL4j5uO0hRarFAP47t07LLlpmlLKbTO0eKtUQhdaAPzYXxZeshPub2p7HqNnsYRFqto/mDLDMpbSKxpR2frrRKR6ABhTwbTHqhjBvHBSp7kp0FHG1L7ODjFqco6d4xC8c2xJYkKwhIhUSQz8nEMgYL+ZzdrsMGjmEUKkgJLwMcmXqnbzTANQzwLGzWocMNGqlqhmmLxExKKQJHzMQynHMY96Fu5rTbtvrYyx4+zB7HMTy7Z4anFN36jLvs4IICJWqmDqD1CIaHW4dK+MWqAAVPDQX3716ne/+90wjfAdlURJk6SYopISCcotmPleZfC/x8HEJZV+GpRn5zk0yrxYrL77/e9/+NG3pVDwjylCAGHDaIk+WdDJFACxfPTRR8vlXDIQN3z66afTlNBoWjX3YqJiZ8lJyraCDsFvW6/XIGqwhQXRgP0Pr1SZfHOgIKeyN6hMk7O2oCUUzLkKJUd34e7gfPMCEeRh8qSO1MUph2UsXGnkUzYsd3d3qOq5ODs/Pz+fz+fO03I1R+wXD4C3Q4d5V2oH8eTb7ZaVvHPmQ3PBWCK5i8qo3JfeOetyiGewrFtTMUp2XbderyGv4XWh1OTm5oYLM9QwDEBvYqAQXAW18iH7rgrOegRgodgsDM6l4YQWojEE3qVAT+FxzmYtDA7ouRDcNE2PHj3CyGCU8OJgs/KllQXnCO0WEX7Qd1iPhHEcU5qur98SEQQWjACDowMjjQeGerAcG8bEwOqXl5dcCGEwMrE09oE3hjFBTh25c4CoYaJheZydnY3j+ObNm/1+///5r//f//bf/tu+H2fdAgHDlFJSWi7XZ2dnEMFg4Lm8vOxmLfaRmbyI2ztHKMadzxZwidq2XS9XMGt++tOfgholw569w894eCoRVxOLqFy3jW/JFyJ69+5dmW72PtNiQ+cB90dFFuNDE9ZT6ZxBBRJsQl8KqhkBBqwNLUHgVNp12M7F5zB9sGymCUSeuZ0J1f2LRKjwoJnZgX2Bqm7nnHcNnE4iUqK+1DVxwUJKKeZBcCiVtlH2PGOxMjFgtusxWev1GtRXZ2dnRMROrawUbULMZBERMKqGEJwLIlI4P7J14oPrhz0mLidTCsHOcrFGAgtXg8rH+ncn7CiVRK1Vr6omFVTiCqlV5ZJj9i6pCOmU4t12048DOSZ35IZyhcyaCtM4VXq91tZ2a6o0dP3hyZ9YnSu02Cd/rTW9I1FNUn+TEIv2PlECMeGvfvurly9fSgm0EilMKjqAxOwh/gN08IMAM3hrksg3Mx/ay8vHP/v5/7ZYnznPy9VCSfop812YdYbD6dFQQOVcXl6itUvbtq9fv76+vh7BgOqciMSSmHT3qrRsRs3hWCwWq9UKGhdzn5UxGL0o+9Un6yzPNJ9M9mGCncEpE5risSfWmEkZuQAu4nQgU8S7x1KWkwoFUt/319fX4DMax7HxoQ0N9BNoxaiE5rz3cA+AGeHSrY9yI7nG84HvFzscQqGMTTbbYSJkx7HJs9k0DfwqmBRALMcy7AYppwr3RET7/R53vLu7w32p6LD9fr9arUAfzczohAi9BbGIi0ipKqayUSER4ItAa+LdIYyAssa7YA+jpAfTvdtt0FIQbyEiElOx7pP1vUcKAJOCSqE2txBmpHtrAx+DbENqTGQQW1DMl5eXuALmxTJwWjpJmCOupWLbF6AAUF2IOW+329VqtdlsfNuklPp+XK/X//D3/+Ply5d937fNbLlYz2fLtm1d8HBd4NtJSYcTq2hq2tDNWiNTs0XCpKzUNW1wHtGXy8vLtg3mgGaDWA7mLBV1lQolXJ59OXQ10AK/ijESOe+b2n1ERMR0pBZaLlsAtvuyTDiuR6i/dXKmzVEqXGkYSS6l1ekY8GVfx1SKUCwdCYkI9WBYNtvtNue5KWeRtPjczJxEtKAgMX1t2zZtCxt0uVxaU86UEujWNSvjTGHrnFNNTdPENIoItp4lUwCipiS+9ByDqMRjU6Xdi2JjVQUm3JhZsc5xwa6dGzse7sLBWwTiRL25P62URqtDCmQyVUyl9/VlEaRHavLBc+ghxUwP6WBVNpVkAlvv+cF5aclxMTWVhTVlwJgo093d3T/+4h9vbm6mFFOhsXYM6gjjuvoPUb10rH0Pz5DNEHYIcC5XZz/6yV+8/8GHgIFYxAlhw3qC+fjwTI0Pl+cXtujbtru+vo4xJlItwP1UhAUfe+EnzwNDD1zHVNgoo6SYBEacnXyyEEUE7TNPrn+QEXTY/1xC4jgnWbdwyWlXLmgsbHUt9MhEBJgYLGhkgEIpwiHOtkIap0oBE7MaetkEnKpCICaZ7F0QhWvbFlgPW3hcBdJtOrRAXi06WrjrPNKB3nv0CcfGhkpGDgn4mu12d3e3efbsmSWAqZSQchUbxAviV8zO2dkZUEi1ANXiCpvmg84Df1DTeNXcLLnve3jbuI5lxE3OYsBjjNY3PjSu7UKTuU7l4uLCHfC3EVSUXMX3zG2yBQwNqhUzMPQfZpNLlrQp1CjMuZWFZU+h16G2IbkwX1dXV2CyhB+PgMFvfvObv/u7v/vd7353d3eXUmLy3jWkzjmCTDfrx5QoEfngjPupaUPTBjxGCGGaBhFZruaz2Wy/3yeJ0zQZP6VMUSRbnFTcOPzJqubGcYzjZCYaZ9czx34xoXgqKGAMFxUDzqS23qO1p3vuF99jovi6jV+PgIikNKU01eK46PjgXDAjDIsEM2g5Bc4Y5iORoiVmG0LwFWELEcU0Il719Nnjb337w/mi2+7umtavVqssZIiICPZojKPh523l49nMK/UFKiWl9M5xKJu3ZWb4aFkFMsGY2+/3SsLMbdfNEH92Tdd1KfdzI1Xl4E/y8Sf2zYNK0b5+8kX7FcvYl7ZaVOnLkwven/Gv/+spJyNiziXyfGorPKiD7WpGk62qBy+ejLqMVETmy8VXX331i1//ou93ZlnAZVPUwxDg2lKe79/zOHnhPC6wwpxvVEi5Yd8u1+d/9Vd/Ddyvc040W7sWX7pvnuDztg3rs+UHH7yHlbdYLN69e3dzcyeJxhSp6r5wcoX6Z6nKiqAt8uMqpXhYiOUKB/6dU41LD9sKWlIs+TGQ8Q3MJcfGhfO5wiIdSl1ToZQbhsFsRiIy75BYYJvbLWynceG0wq/QWFQwYuZb4DTQ4FmgHiODFF0IYYqD5SNhIEMNmK+GSDIyvub1UlHhwGoSARdAPkO7E5euOPt+W3r9skXqDOdssowLirgWAQh9I+fNxUGBmry4uEB8GMML0DgRWa4Bf52mKTRusZyh13qhbojjOCJuHNNo2vrVq1cVLJmpoLfwmiapbT9S6TcAKbnb7UxK6sFUYnjneE0LurqC28LPyIkgqR9CWK1Wy+UysEMsFLr/7du3v/jFr/7hH/7Hxx//8e3b6xijxHR7e3t7ezsMg3PkPcMi4SpXqkkccRuaeTfrmtazAy3XMOzbtl2fLWOMd3c3tlaRa9R4YE1n79gXHndVLSlYxCfMLjTXDYETdAVdLpfAE2DwbdtiSZtdVe9fU/P3pfOJxDjapJWrJHIUX7RQc/m6Yz40PIbyo2ImImKENzVvAUV0cszSbK1EkNNxgUProUSXy6ULYX1+/v6HH67Pz51zxKKUc1LMvF4viQQgidm8RUIHpboYHBD1WLzBtobNglmxJsegfYkIiQwiwvWdc6Lqm+CbA+bGGkkxHzo612P7oMqszZcTYUuVH6IV8Wc9L/enrF5UJ+q8OvHEX5WHalHvXf9QDa719VU12DrLt88EooUqlihxQt+33/3udy9evPj2+98yZpny6iwqzE7pX0fQ+Oc73LHudzUqW4i8a7yPXTf/0U/+8uNf/fJv//ZvITrVMeyjdMz76JRSGUTTAT/4wQ+++uqN936cJhH9/PPP1+tlaByXpIcPQap+pThsleBjLvVIy+Xy9vZWVYnv4+Dx5JlOjh+y/qyKsaw9IUI7xlP/+PjK+VtmR8PdKa3lAlS1L+AjiDYIBXaMUOd8PteYc72pFPxhiSFMDUsWBQZTaelj6U9wSkhFjODpcB0pGCs8hnWaQ4wXltM0TRcXFwh6U9lprrQqmnVhNptzMdtjjGjGTMXmgERoGp+zrf0A0zU4z0oS0367G8dxuVxqkpREnVPnsjL2wXtfN0uwnDER4cPtdptS6ppWVdETArH0JNM05Xoq0aia0EiKrCSUKMZ4ff0W2Wu8DnhFuLAccHHcY6HjNq1MRdaEEJA7lFL7a8HVEEKSyTw/C2Oa78uldTE0lojEKYv72Wy2iPHdzV3g0LZtCC1K1D799NPr6+tHF5fTNL1+/bqbz/q+T0mJWCSHPYJj56xLjwKp4D1QI55ZoSpa7ylFp9Q1gZIoTVKkUz3LENmYEdsFzgiqfPZ9NZdKN7b2pBymuWOhu7LrUyW77cx6E51sxiOZrsCkHGlfV8q+M1vZURw7B2/NSG3RgrEEhJi5aTJbSFMIYplBNp2f0BYh9qxvsjGBNtuz5cyhlJ8lNOH5i6eff/bl/u7OM09x6Lpu3i2wMC4vL53PRYz7/c5nkNShdUSJNww2Jg6YLqIGxAZlcBLlIUgpbfY7jMahTN8FpJwQJTIwPwJpaD98Ek007+XrLJ5qSA/a2pVE8lFE7XjWvuE4EeMPfuVB7fsnXjnvQT4OndvfhPLjYjiiJGb+xS9+gXo+d1wZ/ae8z//6Q05+YDjBHIhIybXd7Ozs4q//+q/Pz8+zXKgKzvQ+3K4KK0AAvf/++3uUkLYtyATwdSv+e0hTPhBVbpoG3eLkGGOpquBROjyCgfHc4Wr1TNnhwxEY0hVQHyBCMF2hDFTVXDc4f9gJOEIptzffxTnnfIaY4XMEJKlEPpnZ/pRTvKIG9qaK4U8KwaT5KJC8WSVwRo4YJM38M0uZh0L5671fLBZW1ws9DVAuVCwQXihJAkBMRM7Pz83U2O/3KNqhkqXmwkMJz/gkgtr3/Xa7ha6yyCcUHgwFBMlh0/R9v9lspmnqZg16GzS5t3xCAc92u93tNpxdvQnJYC3xA5RtIMSNvJ1oxuXivhgQeMPmt5k6ASTHl7pYoMfxp/Pz87b0PsLTIu6Nx2tLu2gseLOH2i6XbGHXiAg8LWbe7/q7u7uXL19++eWX4zje3t4CVOGcC87P2m7WdkA/VQkIh4wcq7Kmrmm6ptE0TdOELIMBc6jSea6C+JofRsXuqaWtK1lJ3zZwf433TauAbR1W5QqlcXJrvnec7PGTD7lKNJh3bs9vk8LsbV/YK+AKrtTm2RYzgD2VOu+madCiwlV1g7b34egji2KTi+LyF+89e/z4MZcEAVqwnJ+fX1yehXIAjWxurnMOtoLmiqwD+QbeCI8H/vlaa2JjknccrDV4wpKzuv9+GiE3fMGN1sLNZBp9zfHgdFAlb91x/tgm69+gg+9P94ne/Lqn4ipFeHK1zBZ25GMXrdCGTJ7Cgcf9OD+bf/nq5a9+9asQwvn5OVDHqooqoP9z6OBs31DZaSLiPQvegrzz4Uc/+tHPf/7zX/ziF33f90MfOOuhGI8qrJxSYiK01XMyTSlO6cMPP3z56ivbTm/evFmtF03j4UaP48jHatgmpoiGQ/pquVyC+kCrCnEisnk8nVQmyVb2IU1F+Xb5W2Vliat2SEy5jS4+gbvfdXPViAKGVJXhwqQdqpoHLtQ5JqH2+32/z6gi761uXLtuxqU1AvQEbuqctzWaUgrB416+1NKgiYrFxuu5gwMHu8FUiHNuW4iRvfer1Qpb3aPoM9J2u3306BHaFALZi+gcld3oCmDNwKtcGJG4cAWnCixjxkqWWW2bUgKztBkECNzBR8d0I4SLB0bmGObF7e2t934xn49FNKvqNA2QcXhms9mnaVqtVvv9HrFfnI9wOluCrZiAroyetb+EN1xPfUq5azIMC+DIVPX8/HxfTetms4GG8452u90Yp/l8ziH84Y+fwjaKUTSJd2E+D455HKcYQTUzeHbOM96diocXY3LONcU9pSKkABqapqFt1/N5572PCX0nk6o3s9hViAHbUFw5N6kg8LlCooWubVxTS2Q9ziyatrbn1OK20rECth/oa/wwZq4Fb1N6KmuJQtvCMDuJS/Qblg0Xx302mwEnhdUFfxFLiKuYloiIJkeEgcLnSeTy8vL8/Nw54nAIdUDJUQEkamlq0vf9crl8/OSyK1RrIlkgeO+J2HzrlHQYJq14bTEDbemaUBkrhJdC/IaIuq4L3QF5AABE0zSJNO5jCKFtAxFNKXrf3Fdm9VvXh6qSHmb2xAyqw7RmUZ3o46NLlS/qccTbVsv9c/7Nx2HV5Uc/QUFLdM5NKSIWABJdlEn8+te/fvv2dYzw+dS6N9ODHCH/YcfBCcY7et+klGDy//CHPzw/P4cM8lVPITtse1PJfMBvWywWP/zhD+FszZeL169f73Y7AENgvllw7/6hhxYFAoIIQCH0GMpBxwEK+1zKn+z8Oj5mtmpBM2Zz2NxN2NGWvEHMmcpKgokNYwWft21rqFouWWToG2gpQ0ia4YLglSvtZrHi7crwA0QEpRTIpseK/GEqfIrIB/vCGgH/korhj0ZA8HRjjJAgVNLw8FCRtNaCUUdlFyZxu93CCQMI1gbQJBreJVSUWzbsNoZENJ/PUeIcS89aWACuMDP4wuZvn1NV84aI32w2G8feUrZgZqASMMCYYKhFBEgZLsF8uBHI1OJeuBGkKiIc1gsZoT9L4E2FbMSVJjOG+kanJqwZKcWyIkKiWDO20haLBYbdF9pk7/1ut2N2yIZYeEktzyoHnLnpnmmavM+KEAi7VFoaU+VhEB35NKFwRJhOMk/RFe5VLFf7luWDzeqiY0Vei+YTEWw/u3KcfG6vU2/2pnT1waO640wzDvvV5ANRJmAPBaoNGnmAD4z4zFARMUY0jsT1z87Onj9/nlNsqohkEEkIObCx2+1ms9nTp0/L8/CzZ0/atkW6pB7SE7vBXlBVicQSIrCbbRzsTAuTqCrMaDgqSOLAK0BZHdbSlCJMDb0X8H/Q7rm/QurPqSTRTr7C1WEfnlzh/qXoWEPfP7M+vvmEkyV3oK9CZIcicWLPrSYiYfvSPu7Ep8gTz/x/+/u/fXNzHSUNwyRCmoSVWapaJBKhlIj+jN2AidzX8FkbGs0ReSJPuTg9EUV2SWliZhENfi6p8c3KN4vzyyfL1dp5HtLoA4RjIAoqjaoXdoWsQ4Jy571T8Zzazp2fLYFGdtyw666uduOkvplPouI0SmSkGZiVKImgPCDlPnFTjCOzek6ahrPVbBq2UxyTxGox5c6RzjnA2Z0LRE4jaSRPAZCNspPJswTHjQ9NaJk5ijI1yg0555umH0dXChMRs5rN27YLSQYi8T7brVFkjJGcIxeiSN0LLAMoukXXzoNvxyEy5f0D3UPCcUzBNZvbOxKN4zT2g6oOw6CUAJzGHkOnh5iEnVfi/b4fx4mZhelut3XOkTqpYvLjODrKYChElQ9VQMLz2XLWLfBId7db75r5bDmfLb138/lsv991XTubdc4xOD5jjKIR5TF9v4MB4b1HyaCQuuCFcqc/y0mnlKYUUVCYVMgxTkNGRktJj0XRN5tNCOHp06ftrJtS7MdhGMfNduu8x8IYxnE2n7ddd3V9PZ/PHz16oqUQC7q1aRpVjlFilK6bt+2MyAm5aUx4NtMuMOOwPGBlTklvN5mOCq5Gzm0zr9fraZqWy+U0pv1ukETQ+svlHP2DAc1l5mlM/X6U5FLk9eqyaTrnHDj1Yxzi1IuMba7V9podC0kpNo3rZk5jdMVeydol+ChJKLnGkXPtbIbsb4zjbNYuZt1yvlivFiAX8I7P1ytk6Kdp2m7vYM0IEwcvTI48Kix9CaKa74Wbto0PntGH20mynTWO42azQcDARF+dQMmSUIjJkbKkA/y+eHvZSqtzK+TzP2FKLKKJWNuuwQ6FqQf1Y5YTgvBEglQPesd6zyBgSZQSpbPLs9AF8tR1DU5A5TQ4+EJwZ2ertg37/dY56rpmPu/YpcWyff/9Fyizbppm0c0W3YySBA6UiCUjnEVENJ5frB8/uXSLtll0QsKehURZk0rbtt5z03hEePI+SNM0DUSFE9s7IcdN69qZuCAuTKLCbkziSSnFze1148mJnzeL1WzduEadd00bVSZJkyQ0SkJZpip7DnRg2DyKT0iBjOQ5qtrxulJ5YV6yL0R1XKH/qBhPKDbJdcOV3rVMhK0ErvIFcshOOlUmKf/uQYDhkebnvJfW1Mp9wg+hNjHIzAo5ctVVATDIf91ut7/5zW8e/5f/a2gaTSIiTYNQ9pHmd6jF/ZouEP+eh1nBMDzm8+WLFy/6/fbt2zdd28YB4HuHGBIxkzGAElFuP4KgqK5Wq48++ujll69CaLuue/Xqy2fPnnRNGMZ923kRoWMgAFeBL18gM7E0sn3vvff++OXrtm3jdEBv1REwKm6ZVkf9XhhxVZ2mKZB3zrEDBCOnME2+wMsxK7vfj01pFeAL4JlKXwE4QOAMQh7LlR7PVPzaTL9XGvCh0D6V8kekLbnqawQf9MBH0TSxNFSxpnjjNOIruQTCOwSQTQ5qIZSw/J81fLV+iBhntdYLRFZdA2cRrrOqWkMI45u0lCoXSLAjRUrVwE24fm1Ep5S22+3jx4/Pz8+HYbi7u3vy5MmLFy9ub293uw2iAubdwrIBH0vXzTHgfd8vlzOj/kD4Gg8AH9TyvrYkXMapZdrwtg3OueV8XqJTeRi7pkVgHClzV0DU4ziZOJjP58ZzSTmccKAvbpqGHItI286wJIZhCr61WbD3YvLeq2uCq1iZWMmRiqi63HUbkQNwbsRxCo17+uy92Ww2DP1qtXKO9vs9KrhsfWrpOnWyr/kYOQXFRpXP5IMHTyf8fl9lgsUKOkoPA+99nLLXgF1r1zlxwqpfD0nKWkk3VVraJsuVsjoD7kEQ+8I+a7GBUOhoANHAvrNWXev1erVagTslxgiz79GjR6C0sxVSe3uuCXhQrCJYBimlmOZRhVMO8iO04BkWo/MFrcmcMZiimYheRJzLCESLWuPkerkyETqcxtLnBhNhDjEC47H0abUJslkwAX74sPpTnc7QKgptwecT1XaiYk9udOIHa5Xpe3AZ3D9Orl/bE1Ai9cWJyKFo7yDcWVT1Adi0HLGC/dMvf/H7P3wc41ipAKjpIxjwf7juzeq0Xos+fPitb/30pz978d4HWPrNrDvMBOcCczu7jhdhgX77298OIcQ4Yuu++uplDnyRAzizXhk4LHBnZg3ibM+ePctKmg6Z3drmMuVt66MO0VC1MqyqSgrwkotPoKowe7kiyrBsouGbqFLzyOCaDQjRgHwhFZpG7BwtojaUakW4cVMcqLSXJyJEm3Hlvu9TzOVbOFC0w8wIsqWUwBBies4V0N+Uu6YwBNNUuiabBWBjVdvOvlQBaWnnpwXB5AtrJsBN9i5mNdsI+KpqhUoNNxVzBEoUd9lsNsAqwu+PMW63W+hRLCEAxNbr9W63g1uP2jaI2hhHNG5DM2DElm0JceFssjmCgwXN3XWZscQVpnu8CGLLsWrmWKb6CNbkChmvL8l13EJKkQzQ0YiRwlhRVXSutdUOTZMOjBa5sWs25uIApzD3e2Bp2/bs7Ozi4pyZ4bpdXFz88Ic//OijD87P1zgzFLA3lwhnLctwZA9GD0VKKSVAAVLp9GzfxcvbJJ4IVj32XWyR1zeqRbNtxtoaMNPWfrXntwi5rdXaA8PKhP2B3TorB1YsqD0vLy+fPXu2XC7BXbVarcx2rPcyMFB2F/PFc5HSYqVCY0wmpoiobv2biggyFLfFqH2BX9VBCCy5zCmrCsCpZRa89wZZlULXpVVD+lpWmyrhrzmoAkjbTNm71x/qPe17ctg5h3H7xtNODmauUcD3H6B+5qN1hW9qdRincT2RRETqVDK5AzP/H//H/3F9fU0kLvhxHNXAQSTEdXXUUc3yf8jB1Qw6F1brsyfPXvzkJz95/vzFANPYqUhECAg1EoGdY2S4nMihWFA0Pnp08cGH78cYVVMI4fXr17e3t9aOu9ZkdGw6aeW3QYyGEC4uLqxWx5VU0Mnk1e9STx74t3GLUGrwRSSlCYrW1mgoVXpowioVYzAeNRY2SniENU2uK/RVyKlga9UVqHAcl8ulFpJkvIVdn0s0qe/7aRyncZymqe/3KWWnGRSY1rEAMtcXtKcpPGbOTIoyuZLiqo19izGkqpSZS39GKkCqQqARjZs6h+JjBDwEuhzI5Fo04ItascsaygY6CZ7uer2GRr+5uYFSt6QsgE6QO3d3d7NZHjfv/YsXz6jgZZbLTIwMve6Oc422DCw6IhIxMnWwznA9rqBqIS77vkcZqC0eo+7SUg1slgTeMRYKp8VisVoujZ4slO5VNkQ27MXUyzZKhhwHnrcdxhZjsljMu64dhn4cx8ViFkJwwXddBwqO+Xx+dna2Wq3mbRdKJxjoaROmemyVqmZvAXOEDD0GwUwl2KM2pHg2KTh/+noFXImRU+pgV+VKTRHev47WwfnyA1V9IGzFNqUJJhQYoK/4mUp36hDCixcvAGrJkTPvyDvXBPIukSY6LBvNdVktXh+L/1ACJyrESPRwsdJERCkJUw2UYWZhchxC2zl3oHKyBQAjG78CdpOq+nKL8PmCULFB81WjmgeF3n15eP9zc0JM+pl94NypS3ii7Gt9X59zei8+tQmgNWwFisR6WKAXH7ygLSMxOBUzEx/oLE5eMsmUKC0Ws7vt5h/+6X9ESSlNyjCCqng35zB59e3/eTX8P/X1PEDsYxTn/Pri8tnz92azeQYr1swmsEOVvDq8kMtYvgay4zvf+Q7iKlhPv//972OhqLXDlpGZP7YbTQGLyItnz9MUPTs4wVjo8bhvJVUTUU9zvVxqZxHSH/tTKoZqV9ibpRBEU8UmQaWzEBXf1LwuLYCgYRhAlEhE+/3eTrb9EwprYyjE2q6w+BowBEvUML0ikpsJpqwIa92ghSaTSk9Tw3OJsdFyLjNFbcNJFMukCZmTVH1YO6a4SKpKXABvtv4tqRQCwVmEZEGrOyRogZdR1dvbW3xdhIAUw60xgKDXhnJl5vPzcyLa7XaXl5cY9vl83vc7CyavVitzKLmwWTWF02oce8QnVBUsJXCdrUrKPHUMGiIBehxuSUm99xhbtJZS1fl8HkJraoMFOHmP+ERN2srMua6mdF4yqZR/ddrNmrPl6tGjR1ADs67p2jCfzy8vL1erhXPZvnn69Ol77z3H6JlZAyNv3qI14ZGws211tPsSxUniJLYNzVCAjjE1aU6hbVV6SMrXF68/qR1o+yKuTJX0p2IoGCzIFphFVmq9broZvjIsZtjBKDRAWgFBiLY0LLFpLZ5D3mWI+tg2x70koyCnaToQwrPPKE7lzGeHnTKmSN467mVbtqm6i3LFNdRP4zRN6KyKMIzV1AEySSXcZZDsIhkOgaWTKTb3RkrNdK1o63nBX79Zg94/jrXpEQ73XzrkfhsCyiigulnRUYjbpjvUf2AWotI7KYejT98B+mM39JeX5x9//PF7z57/5Cc/8Y4lF4GpqhA7okITfe9xiejfLTitdJqZJhdY3Gy+9K559OjJ7d27/X6nIsrkiQnEXnrwYh2zKwRsrsmtXpfL5Yv3nv/h4z+2bcuObm5uNpvNer0kclQIwughremq+mksu9VqkUvsk/DxsJzIAvxaxfqEi0/gclMTUNIc4oEhBPgKxhAEIzTG2IQuHpM9ncAQDGFr8VgqUqwt7d+naQrOWwbXZwyRIvsLR9Y5d3t7u9/vsQO9z1E4HHjmLjQmTcxxgWxyhx5qmdiISyN62/ZUegumQgjlCpmiK/HnpmlCk01sNBbE6yMC1hT6IYPXQkaHtoG8cyUr7Ats3lWgaCJCu4JpmvDW6GeA57+7u4MrzMUGB9KYiIZhWC7XuBrQyxiQ5XKJEYMMTym5MkFUrIcSVj0YFsE5cH3YbOJpfekAEQvjN5fcPJbPfL4048MxxHEqjmMgct4zkweCHUHjFAfnXON8Oo7rmnMDqZj9dUrz+Tyw67pusZj1fb8CKZKIalRNAM7P5/PHTy6/973vrFYr5Ok3m827d7foN4VXSGlC3K72LKlSkFJql+0EC5VbmIeq8ABV+jKEIEkN1FO3uz9RpXbfVPqnHSRGNUdSxWZPPEUtPpkWs5uOVQWmDxh4VcU6NDTDYrEwHWwBAOdc1EM5kFah3ax9HYsqer2wd7FPSWTc7Yru1/l8HjpEsIJvguihUQSzJlVyHsRVuSYiitKBvopLKgcmOBzcWPpCTpIrSvBX5CBsiGonW4+tZ33IG6lf0ywPM3G0srnti/d96/vHg0qX77EcKmWGiFrInzwwMxNaOPMDt8bDhwctuPxuWDrCoF2inEzN14oxusB/+/f//dmzZ5fnFwx8HlQIuth+k/kg/1E62Dk/xtS13aMnT77/ox/e3r57+cUXwixpIvLscopfVYkcq5IjrqwwIpI0Oec/+uijzz/7IoSQJIbQvn1zvVwuQ3Aqp1uUKqqdelcQcifET58+fvPmSlVFEpogPWRPZcIp55ryQ236cb2H7U+uEKybswhZk0oHNFc6k+BzUzwnprqrqmsgBaCJh2FolytYylAq0GRU/C08Ule6uyNAHQ7cN7kmFR4SJYILy+Y2MXOpkgQoCRqdszcjjoOBywwb7O5l1ChzWedILJw8Kv4Hro/gMCgIiMhrbqOGQIIrOWBXZfFDKQIOIWw2G9gfVnqBnGss/MnDsO/7/uzsDCwEGEMQo+KRwM1ydnYWS6PJkBsaVkwmJacbS78pvHiMcda2WBWwJ8ZxBI4GsYcudn3fo5gQUvVktWA2XWkbjHQduEREhMn54Cx3uF6u4pTlhs8F2VCKBHp5LgBU51zjOfiwXq5EonOcUloWqs7tdstOfGA8cD/sRC7wGE3p1LRcLs/Pz733b968AYxAK1ep1mcnMg7jMysMD8i8+HKkA4lb5Y6EEDVV6f9SpckPQMDy1tZDGpKrIx2TS9dR0FrYQq9TkTBcU3qBQoszsTmXbILdCzYcQlNGaQe4nBkB+CGpTCkys6PDG+EdJVGSOMWelYyk1nFhCHAuZaSkBWCIiIJvQ1sXWB9wbUhR4TlXyzVeB0V0BvSrw3L181BJpd2XfkeSkA8a8URVpwKpqxXhfS/5644TwVs/2MlqUVVlcQVgdHKRWmErC/O9Lj3lcCYly1XEbL1irSUSzZRQovgbM08CWTb93T/8/RgnuxmC3TiZ5Bte+F+bG/63x5+1qlFWck3TTFG8a548fX7+6PHZxUXWr5RUC6M2M+thKDGpOA2C/vHjy/c/eO9uc4sT3r59O00TuPfoIQ+YjhP4rjDys9KLFy8gK0UTK02F65wqewhKl0pkw/Y5UbbEypk5OGNGPaqTqSClufBppEK1b8I9VUw6zjn0yvWlnhUOHCKuWhwLqCstzdeQxHU+32sqvU65ECPg/FAo5uH7do1XSoAlU9mclg+GKYAxh/lMRFBgKSWlHMRG2sn8Zrjp5k9rQReqKkh229wbrkfqlAtWHCoc5BWI6Zk2wuvARbYrW1DaIplQqERkFJj45OLiESg1bm9v4e0hvqqavvzySwSxx3G8ubnp+/7q6iqWJuSIahRldkSf1HWdxOiIWHU2a03tee+JxDoB932fVHwThDQ0mZXFDB1mRjjBl8JuLDbMoPeeSguK1Wq1Xiy5tNCJcYoxUsraAtq5WFdgwnJdg4x+07ahaZq2aVKM3oPjkIm0cd5pXo3z+fzx48fwjYjQFyhi2TDzfr83BhLTavd1sAlNLoGWaZq22y2yAFjwyHAbjqGWFbZnza2v/1QftlNcWVy1fsW6rZ9TjmGYriAbzBQ4ERou9/hziKlAq8EhNj+eCmILIEcjCc+X8s43wYWcIZKSPcFmiaWtSNd1TH6ME74bY4QTjJ07odqwgoUSe98E5wIRgUWHS/xZSioEV0YzaWwliI5Qiu/R9CVWHO8Ws3EVyuFEeNZzXUtIWxWp8B/USlcrW/Ob9MQpwqaODZ8aWDkaf8/HrJeTqvXXOSRNTk4+8MjIIVlylLEw58kCHSawVHWM0xdffPbJJ5/sh76ck0Qju6P6mW94yj9NE/8ZMFw2Ac433gfy4fz84sc//vG3v/XdZy+eR0EDdmFmPLwrcJK8LDw7z7A54jSIyJMnT1AGAKn9+WdfwluKhaYYhy2FVDinuLTtDCEQy6ztLs8vEMLVijr8JOplurPW6/XJahG/CnEQq5ZzVBS/BWnt5zzKJRximDIkQQGshXYBxyxeEELZIrGIdm63W7i/TWm0nAq2K8bYNZ4kBkesybO2Xc45ERGAV+ZWmgbCOEBemHXCBXetlJLkAqqSesy8u/A1mRl+J5KmeB5waSEMAG9MS4/0/X6PTC3khVFjYqyg5pkZYOCavQvzvl6vYymjspSVqr579w6ujJTKLvh2zrnnz5+bVQQbQlWb0h/QpKeIjOPYhjANmVN6HEfQgjLnHRpyjVZ0pX8iAFOh4MMxyEBWw6SAjQUK6yZ0mGJQafZ9P8WIZdDNmru7u1DYTL07xEi5OHzGeUk5IQKCs7BcLmddJymqymq1ZGaAw0MI2BltGxaLxeXlJXiwx3F8+fLlP//zPyNluN1uP/vsM4yAKw0Wcbtatmap4hg9Yn0Tmg5FUwMzlNHB5AAAxcRJREFUr9dr8LfUaHAq4WULzutxWpeLi19LT3dIcuevq2rjfOAD8EfvOU+molwph7PnN8yHKZvDxncMUJVrAoqhF+tV6FpURQPhPKaYSNXxmAqughSdZM0CNmtAi5GdcmE9q/JsNluvzr33gEOa0MZqTynFWEJovjGZJqUPqb3dMAxQwOr4/Pwcd0cauO4OUktFLl3UUiZLSPUSOjFKHrSHpKBBjdNeS7TSl9IvOU4fmAllM9uULia1gYujXir22CZ40WbYJs6uaV1DTPLXV7bTDgRvWgVMTOHlNc1HWXGT10OcvPdC+rd//7fv3r1jn1nFx2kahp6d6r+OiuO+Gv7zI6gxYeS4CV03Xz5+8uzZe+933axr5/v9fogTIn54RzCN2LAws/OlukDTe8+fffTRR0Xu+6+++moac9wpFEIZLhUyphHN+DgYKJSWy2VKkyu4ifu2kn0iFULBOWfPIyLGKERoDEwpyVSvHqrN2EpnmGlVbyGD+SB6bB4DVIWhSe06MM/B9W/r2NSniFhjJPwKJzUUnvqzszOL9HIhmLUAHR0bwly4M6m0XPWBfWAiSjL5wKg8sbojV4gMIfiAakGpNCqCUlUxHEr7ZLikwzAA2YTk1m63gwQ3OWLbCTisGCOUHFqfmnOsqvv9AIPAOQdXeJqmJ0+ezOeZJhfIPvtZK7zeOPZUfH3vfXBOSikXvFVYGFg80FsiAjPC9BNewWIDqooYO6LTpcY3e4fDsOcS53SevPfOk3Ou9ZlWpSmlbvg/xiiS1/l8Pm9D8N4vl8uu69omiORU9Hw+98RdaBbdzGlOVcCNRlOp/X7/+vXrf/7nP3jfXF5eorHxYrHQJG1oRASsnIBDwtLCynHBs0euxzdNF0KrmtP2TWko6QoW2hRt3s7HuparECLRqe9rnx/EQtl3eJH6oGMvKlQV6r5Cftntailvu5JLUROOUPFVaeU+ElHm69BDXBPPZtsH4ihOkqKmqCUVldEP3ntJih1hRzHug/cZKtG2M/IulX7AqfRSA0VPDhTNOteEUFjtTrah7X2p0vAPeqj1ONSf1J9jX0jVoOlB4UnHMEzbvydx73rW7s8+Mzt3pMgJuGifs1TG8lFfn/n0kXCE+pb1n1VTnaZlJ0TevplKkzgY9dfX17/67W9Qi6aiIQTKeqh5MOp+X8FUx//igiUG0oo5eErh7Oz8g/c/+uLTT17Pv2KnEtM0TUTOs4ceVRIipyrIIzOgGUD9pPj08ZPf//Z3iO+No7y5ertafAAGqxit4uhQgK+Zt9mpAuDGoJlZrRcXZ+fDFL0LMabStMMRPYDOoEqbIr3tCoTE1rFSotK4m6riEDWyGMnxXhNDXGzJWDrtWPADiVVbUjCKsXWD81Lleoly14Ha4EDMDD/4cIgrxMLzALyS1RCbeoMpYDrbHAtEX2GeYx9mPc0CgNh2u8X0oaVPkgkIUsB5iOjs7IxIYozTMKIKT0TgKFPh94fva3aSxDQmWZ+tkS2GqrOkMu4LTg+E9WKMjx8/ns1mNzc3CG+0bSuSu1asVitVvbm5KanfVYw5g4vhNZcFasa+qyVQT0Rd0+y3dym14ziiyfQ0TU3jxzEX9cJxgfsrhYLUlwoxuMKH5pjqRKZaN7RtzixMMaPZ/dwzEzsF8tM5nqZJCH1syGX+yzSbtUQSQrdczl2BxjSNlxjXy/m7aeiHHRGtz5azWZjNZtM0XV9fE5EPQVU/++yLi4uLn//85xcXF3d3d9vtdrVYpim+efMGjbDAvoJXMNnlfKNVnAmjgTCGryD9+JmrSlYuCQhfIdeo8tWohI5qOWli2lRm3ghJhXOdit4Tf7h4LD03bQPa2jZrqSiYA5+zKV3TOlKRemp2Q8s+JfBE5WRwqLqOSsq5RBHBqiNkDUpzXyqeKPpV10a/sGvbzreNLUL7AVFlZlblxWIB1Jh1WVBVH7xJm5oo1EbJdOpDYvt0MLUKx2KB+aouuday9TVdBYOtdap8TY7/vu5nPhhMJ48KNWyYofrh8ff7yCenpc2FHdVfpS4RJhZiUREub9g0DerMFqvlJ5988uvf/bbvexc8ERmFSv7Wv5T9/nc7lBKVnlQpavDtk2fPPvjWt99//wPvmr7v+2kkIp+reo5GP1+hGqWzs9VHH32Etq/Oua++enN9fW1bwibbdtdBVZR4hfcsU2ya5sMPPzRwkyV46Lhhs6aa8fxA3wH318BNztPJ3e3h65yCGYNSQnDwrrgEl+AFVmGojJuIVVN61FkCLsTuQAVXCy/mg7SC82R2sWWIU+kNp1W+g6rEgRSya3AUWODLSpax/eDjIrZvJRxmHSNZQEQItVGBjmuS7XZr1zmRdN57Rwcha3BiSwBvNhs8KgID1iDh3bt3u10P0Wl2DEThbreDYzcMA/K+yJty8V8RsgO0DRfs+957hrFiE8E5SZxTazGOMF8MLjuVjs5gngqFg0lVAbpOKc3nc4CcpbBYw4tdLpdN0yyWM9guuQg7JcRambOxpRG44kOhC8rNi4YLXdetl/PHl5dmssPfWq1WoFdj9vP5/O3bty9fvvzii5f7/f4nP/nJ5eUl5ne73bZt+/Lly7OzMyREsGCkpDlijEmPgkNS2kalCluLw5ScpfNtJZhetCG6/0WuNGutOepdxnLI+0oVP7MlzVVkyHSwXaqWM3rMFmI7i+9Z5LZTqk8UWJxU8AqqhyJpjA9Tzg2P/VCK7KPkYvgYY5Qqdk1FlxNRilaDfjAcMYYwqYkI8WeoSTM4TAzWo0GVM/CgstDjwz6shapl02w06h/qX22KqdL69qeT8+0gIqvOrf11Wy1SwDf1dajS4pbetSNAtTKx6lGsrzqEyLMokxJpVClUdiM2T1LlJKr8m9/8Zr1Yfve733XEItKEkFJkdCNhoQxF/g9mx1ISRx5YZ+ccu/b87PJ73/1Bv918+sc/KtNquYxD3O12bTtrfJjSUZck1UTklVJKcbGcxxi///3vf/HyS1eqXa+vr8HDoAUWDxlxfwHZHI/j6MWfn5/P5/OhH0MudnRCykfLUUWV3MESdM55Z83Uyq5mCXyIqAC1US+4+z9oSeFwZU1nY7nqRSNVNDgUNoM4TmCL5BLiq3NpJTJ2UGbTNMToEWKFFYwgMBfXBDXBWqKFcIJNJbsqdopXq/Pu8PB2ux1YnIimGGNMY0NzKQjY8naTSOucc+xMl0MAERHeCGobXwSc0O6OhJMZTDYa6Hu4WCwAqkop9f2IWLdWprRJwGEYLGqnqqrDYrGCILNIeG36MDNYedHgj6qQhqlwe522bVUZFhJVAiKW1guQEYg5e9eM49h1naXu4M76RiDXxrFnVpQhnZ2tk/L19Q0RKehKyiLx3ofQTNPUhNB1jc+tojpN6fz8fLfbMGcYY0rp7u7uyaNLOHnztpvNZo1vb27ebjably9fwp16/fq19x6tP8/OV8G3q9Xq1atXME0w4ybB7QcEUWF2uIqpzUS/jXAdX4kxepe9QDnOGtoP93exbY1KTB+Z6Xqsp6HAptISw07gY3ew/sQ0DQ65l5ex/Vvfzt40t1zDQ8lRRnMYBmJlJXM2NIesoohoKrg28iqsxIA4olirnEyIWsGoTSldXl7iE2wNRIy896kY5VrBleuXrcSs1iN58kN9ZgiADSbvERlOqlK224kuwzVNeNZjlcOTdPAWbEZqqgwyTepKIxMqbkYZt7wI3XE43Wa26Nn815xII2ZGoPUegpeInBIVJ8YMZC2Na/BqsJF//bvfzmaz73zr2/APQghqdkouB/r3K0B68HDkqAToQ2hUxfvmyZOnf/mXf/XpZ3/89S93MUbfNihA2A99iXFhOhWTAtmFoMdsNnvvvfe++OKL2WwGjgUD69qg+9JBHc+glXdIhz2sT58+/fif/+DCgcaZH4q95E9YSQ+WeAg5HSsaVb0Zm0BIUiU+ygUPMourylH8GgoVrelRLuFWEfFVkApiummapj00UdGqcsM5RkdCPjjH0XvnPYO7qe8Zop9LKtF8d4OQmBOsJYkFLos8p6UDnSsVVmSkInyAnnrvRXICGE12nXPTmAFcQOEi14sQWSxNjpk5jrm6CTXERomVvQQR/ElVz87OgK66ubl5/fq1c24cI4rBEOythYuNGBxWhIKBjjk/P0dVMV5kt9uBMwtirmmatu1CRbQSY5zPO+gkUye1/Y7cymrZadWYa7FYTNMkieqQPkwHSM/Nbg9FBWSy4Vwuzlbb7TZGWS5nXdfd7e5chs5lBbOYd23bEqmIOKIxpWkaYGWO4+gcte08pXR7e7s+P/Pe73a7x0+fb/e77XY7DMPnn3/+6tWrVGp2r99eLZYzrGfj/LJAcdM0yq7G74hIInVNaHyoV765pLbsa1cspcR0+FWqksIT7Ws/87H3Y5dlUetlY0IZqxeBh6mwqNZb++SatU7FYXK/ltK1ZrL4U1FyZKexkkpuJ0gFBj8NO6ZMU5wlQxL1uUpeExwnBpugc67tZuy8FOi73RfuBy67XC6JnMgEOzhW/GK2Z40crQ4AmNS6J7YP43Ni4iC7zKUu+b5jzVXgoR5V+2t9/dqEKr/iHbMDef+79kkt9E6MCVsz1ZUVtUlHRGI46sgJEfmscg7PZKuNiMYhqrAD1X4T3r59+/vf/34/9CGE6R6YqJQD/RmzvP8GXS5K2cChMr7z+fzpi+c/+9nP3n//fbxdkpjSZEgfOg5rEBEiqPP5nJmfP3+eoqQoXTu7u9u+eXOVkobQAraQkoqQcwG5YWZfYAeB6Ih78tGjRxC4zjnBUFcFFYBY1RvetqJIVvkWGaaCSZbSmtfihya5cAVX+tu7Cp6Ai2iBO3Gh1oLKMayKbUIkQfEtCwKb8WHpZF8alWhxIlNKItHyvr6AS/FeU+E6Bt64vi+CkLiFhbK1pKIt5OUrXLT5taj6hYjhAvmOpbKWmdfrNRS5lByblrpMjLl5wKlQPELtwb/81a9+dXt7++jRI4CcYRZAr2A0LAHGzCADMpeUSiwUj2flsCZ6kJxmZu/ZAF/MjGyRmU1Q1UB0m6wHgcl+v7+5udntdre3txCaln3AWxiEmwrxC7OCi5iL2+ScQ56vmzXL1dwVzqa2zY2lIUwNLjSfd4hz9Ls9TKXlcn55eYn4v6peXb373e9+94//+I8xxh//+C+6bu6cQ+nRF599/sEHHyDJvd/vr66uDCtQQiyHccNimORQpcMHry7ngAsu5hCY4eK1mNyzRWjfrUFJdtqJ76v3DrtU/Rj1n7RS8CfHyZPY3jw57Z7OOGK9rmOhIiIxpSlKTBJTSmm1WgL6h72JbYJaXpxgL+i9b0PA2kD5TSqMJSBfw8+r1QoWGMJCCMBogSVD6QLaaavdXlDuhaDv6a0jxYb1gGiNzR0fVx+djHM9p/cnq1aC1dRHE7ZcGVJ2L7lX1izVgU/ccUCbiKAHnSpX961/PlpApoOxUXMAwTWuoCup2JJffPHF559/joezapx7y+t/Xgd/XXfCf+FQUpXoYNeoEnMIbbdYgXV2vV5Dpx5G07G6o4GzIYaAaJrmyZMn7733Hnpop5Sur68NpsSl4shVyYl6cWgxZbqum7UdIDn1A3OVmThZsu4YMV+rUtMZ1XyTScmmcCNrwaGYjysVVSF2jmUNqWgI2zxSUi8m96FdEBqpF5ztDV9KI+zilAP7ZMo+ltKXUPryclUrYpeyIDDEoitlV1Bph+xgVf1lDwbPz+pKMSnb7RZKd7fb4UNoO2vpisrpmgykPL8ys+W2X7x48erVq9///vfTNH300Udo/IwXjDGCluvs7ExVsX6mQkBtMxhCuL29tZcFKxYe2CBUVCivcXEIUBFB20cMnZbIsFlgUCQAbFMhciIiXBlSEgXKGDoIOMrxA1FVVJWgjApIb1vbzrmm6WCNYdDW63XbZghY13Vnq3Xf9/1uf319fXV1dXt7Cz5C6IDr6+thGFTzAuj7ETvoL//yLx89voDZ9Pb1ayosKJhiQ4OacVkZpke0UFTMF1gttZzFEtJ7bB52nVpe10pajrm0TsSmXcF+loKuOEiAe+fb3qR7asO2s21J07L2qFxVvKCmgwsFdBO6UFqGw8FNVXvm+hWkYnzEfRvvg28tmaJ8qJ4ahmGz2UAsIPuLlaCl+RI2JhW3uy1t1kyN2d0fPE5evDZlICWga2Cufd0V7DpUfI8ayfGgI6oGGi+rS46Lcqky+2xz1WKKiu1iIporD0pVHWlSyZgBrXoZ2VWiCki9hUWdemKNOcmXZFImoK6jiDKT4+To//d3f/v29l3TteQB90VUgUU1SlKipA8qYHfv3zcc/2KF0sNfZ3LOBSVlJmUVndRT1DQJf/jtHz569v7F5RPvPbNKGpkHp+SJnQtMwUnDKagESSxC3qHwY1wvFz/+0fe71pPGMJu/vbm9ur2bkgplLZXSlGLvOJKOKfaO1HtP6iSRJ3bMjnUa9sz64UfvMSWRSDJqmoIrIRRRkZyByMpVXeAAnFTbzob96Nmxkgp714xDRPHufN6NY4+uO4tu1vpASZzS2O9Yk6Y09j2JdE3jmdFkh0oUlEQ8M4k03jfej7vtsmsXbRPHyRGTaJqiaHSeYhp91cKdmRvn0YuUhWRKjQutb1g0ihNyU9JhmJqmAwXxfr/v+13T+EmmMY0lQBASk2ucb/2YxijSj6OVTPmmAR6x6bpuPp9SIufY+xR1vxscByrJG2ijruuGIaOXVbXvx7addd0cAmWapsVs3oYG7DHTMA77fuyH/XY3DeN+u4NbFWP07BofWEklekdIPjWND8ENwz5qJE/bfitM89VyTPHTL/7oGvrpT/9ivV4y636/jdPknQveLxcLlTj0u7YLT589RtleHKeuaWdtF5zfbbZpys0ejOYajwHHIqWJXeqHjdIUGodaXi2wZyvu4EKALBL7YVit1/PFYrFcxpTarpti9I0bpt43zjcuyjTGYbGaN13wTZgvz0I7HyPfbccxctKgHMh7Cr4f903nmoabhp88OvOc5p1vWm5avz5bXlyctW2bRK+vbza7vh+jKs8Wy24+c8F3TTsNo8o4jbt+fydpaoP3zMv56vz8fL/r90N8e33b71PbzL/48qv/8Y+/Hvr07nqz2+2wJjUlVu2aRkS8Z5KkKaZpnKYhpcmTNo4bx7UWUVVk0PHvoHqjqBApqzipmr2yb8k1SV1SJ+SnRMMUpyTKjrxTx8LEPrAP5LyyE+IoOiWJouTz3p+mCTpahJDoN7BbrVqqvs4co6iycwHZ8dr9wjO7KgHJzGgAjH9UUYiE3HPJtW3wnmezdr5oupmfzUPTMnFMMiQZlCZ2KTQOAaYoOqY4ibIPyi4pJSUlv+0n33USVROlSTwH7xv4bEDJDcNgbbZB+paSDnHwrU+UosZUWLhFxHwAX9VTMbOy3PtH9g+due0FreACs8ylkrAgKg5mxOH6xwlWPXiYB+eKKrcHVK+4CDQxslQppUOooHAHjeOokkhFUozTOE1jSjGlGOM0DH2ME36OcRJJIinG4ZT40J7y5EP7AWRmZIlJFqSpnXPI5Klq3/f/8A//8F/+5j8vFgslDT7ElIIPnl2UyESe3cktik79DzvgCH744YevX345n893d7eztoOpVHtyqopAeghBNbePHcdxPp9/73vf+/jjj5tupqpvX795/vQZEY1DnM27GEfvDtZuzvNzslmHx0pEXdddXl7e3N5571XJ0pCADNS2vD1M1sf+4IWYywi3htmHwEQkTOqYks3yoTwUUKbFYsHeI8/XtoeGr/D1tbAMuqoJNvZSKMwehmqeJInIvO3MAUVGU1WdC/PuUPsBQhx4eKG0t0OwtN/t2xCcossxtwXlOE6TaxpNqZvP2xBYtQ2hDcE7NxKF0p94nHqsSWwYy0jFGKdhbHxAs3pXcZLYS6GIAuatRXFxHThecDJCCWBAx8dh3BasFnBD68X63dt3aUwvXrxwzn36ySeqauTGxoiJaGrbtmkk+MRJpela3+ToQv5rga5Iod0Yxky8JSlHRLT4/arkvYfT7wr2Sgv+Cy8ohRWk67rNZrNarfp+3O+Hpmnu7raPHj168uTyzZs34zjMZt1qtez7/TSN2Pqucibgj8YYte+bWbdezAE43+23KNR2jq6urlKavPfL+WL2uCUikJOoslPyTViv11PKyIbXr1/jgf/rf/2v2+32O9/+9jD0+/0+OLJiKu+9FICFCfE6LEFEVDUyh3QtPp8/+J9iktfx4TdVjVpgFlJV68GqyZfVU+FJVXawfJ6Qfso3uyfu7ev2a61fiWrRka98DIA4OIgn6BMTFL5UN4RyuEx/cQgDMOdwPSmpKmilscGlSkz4Qi0upZU1EPshBIQPY4xgVEXSAZkmc8qbprFOprUErn16uuf643Al4UUlnmSS2b5iLkp9cdNrdYTjwXPqP9VxOzxA7bJTld23vI9VfDx4i5M3pQMIq0pWSwXMoWrtHnx/JtA0lhcWVUqUSwKmGJum+fjjjy8uLn7+Vz+LKQYfnOOYJu9D8CFnglnBw3VPDf/7HTavhNYfbfv+++//8dGj7ebWueCC18Ql/X7UIhDPjFUFcdl13fe+973PP/9cVOfz+d3d5urq+unTJ5SgBZnoEBAjFqCpiRgthL33zrGyND48efLk+t1Nob9Ilne0W+eLOFZVdgfSXSpBZsr7UI181QJuRARsiAutVCBerK2mabxvUsq9eIloEmFVazMgxFQqX7GdMIr1Jjc7VFUntJhwLEyoWPPECZ0vSqO6OE4SE4KxXTd36pzLaKxpmrh09XFVZZeU3sZSShtN7qBoBwp4GnMsdLvdslIck0h0xMO+x/RZ1BcESWiiYFAOC+YjqFtbPxDWGPYmBDjx8FDHcWyargsNxBZaVcYYP/nks/VytV6viciy6V3XISI97Ps0xZTSGOV2c4elhVkwdYLwcizcQzB09n0OpvU0amn8rDnAdVilcDvMOEulPQ4faLc9kev7DMBW1Qwy7wcWnTUtizqlwG5MY5GG3ATvXYfMcdM0w9gbFC6mKXg3jnG1WnRNM47jfN5N0+SIk0x9LwhXxnHyTUDGFkzX2+12ktR1HTg73727nc3a0DQ3N7ch+GGSMMb5fA65M45j23ZtO9tsNkQOAMYiLokL+zcdQFgEm8FEqohA5RKRalSMmmrJSWbpJLnCpEQRuRjBQnQsZ2tjnY7USZIKjWUnnIijWuMejP7KFDaJj2+dKGCqtO+JbqCKdQQLgIgQ55cK+qSqkoSIVPLDeBeICOmGJAfrH1dG4BeuCEobiChvOmaoagdulnZWZuHAVl2PADOf9M6pDaB6NCzf5AoXZq3I7EXq0ZBSAVWPFVW6lioPx8bfYkhcumJjSGGRa+H5qQf5wZklOsLi2edHTFgnx4MfpvIZYtZ2uZSS85RkUk2qiUh++ct/evn6q5TSvt+XFzvkZogIBFv6kI3w5zj+RH/6wK1PRIvF4sMPP5zNZt1i3vcjGMyZmUishKveXdM0TXEIjRONi+Xs2fMnXAIjr1+/Bv1CVn6a8YfMzFbVwwfKOi3m1dnZ2XzWAR1TrwM6pnFhZrDNWY5WSLlCDOEhx9KRlyoGf1JQtCuC+IjljjEOBa1jebi2bbv5HJfNXfkkJZVUmq6gqkFLgs0CSpBxBv/JsKzix6eUSB3i2MzsfeM5eA79bp9TcTHO2rYrrR1SBYf23sOCphzeT7APYG7DDwuFZGfWdpqEROFhU2mDA3JaVxqe26jaFkKB0Ha7Rf2r7f+2aUJxg+xk59x8NkPh73w+B/lD4/zZcoWatHEcZ213d3f3+qu3y+Xy8vJSq9IRpJbn8/ms7eA3SIF64YGh1RC+CyGkBL422u02GHOcg2A7FQ+JCwGh+c1l67kYBf/GMU5TYvaIHyLjC62PHoib7a0PHBqnlPphFxrnWbvGo62vFmAdBPqs7dgRk27ubhE8d44QJSaSWdt1behmzbt3767evJWYgmNLRavyOMZ3tzc3NzfMHmL95uZGHYPKn51D6gpB+N1ud3Nzc3t7y8yL1TKpUJHFVAmuOpdZhKw3cnw7x9awHfjVqbiC1zSRYir2/vn3/1rLdKr0sRwf9cl1svDkqGV3fS9XHfUX69uZHEil5h5byQoCbedKLhgmVUVdFtxWSLNUSGqx11BHhz0FLxkLA8sVuef5fL5arVDDjXPuD+DXHXamnW+BHNuzcpzslwIFkHuQugevT1Xs8GS06xQGVbAbvH5TCCx9oSfzFQ+gaW77wW5ktu8JE9bR85mdYg+qqkSZIUsptwmy5bXv++C9C77v++VyeX3z7he/+B9/89f/GcHM2Wzm2CWJeIByUYEyqG+LQf5T5ubPcjCzqguhJecuzh99//s/fP3VV3GcPt1/kuPt1bsnUs5gZkbkMBaCXxH54IMP3ry9IaIQwtXV1fPnz5um8QSjL4mI87nJ2cHC1oMxrqSSUtvNnjx58tvf/d45J5JizNhmTIccs3uyd6G07WRmUPGZ1c/F2MxenTCpU9FUynC1oCSYGUQNkAVTaTwwmzEzA+Hnco0pYyNDBzvniD07j3SNSnJlqVm8mo4FB/Zw17a7YT+VVgcG7ph3s9lijmfo9+N81rahiaX9eBbrxHGKwXnrEQJCq/P12W4/mHTYbbfjGE0E13QfkOCr1SrFyETB+/ls1oQwORe8V9Wh70vsNFdKxCmzbBIRZyTagXOfiFRkuVzCFEAQe7vdIrZ8dnZ2++6Gmefz+cuXL1eL5cXFxdgPIwm0JpL0zjlhmi0XGMB5E5rSAwp0mNvtHaYVFU2bzcY1bpwyjb5wbiIZQuDgmRNKcUQ1qrTzWVQkF7NDrIWc0lrJwnrAyCB5iTKe3bA/Pz+HboDOU00Id7SzNqVpHCkEF4KL0UGRLxaLlCbWzI/ovZ/NZv2wY+Y2NMjCijglPw7DlDID6G67H6OeXT7aD31SGaZxsVh0iyVWLEDd4xS1tI4fx/Fusx2nOI0wpFyKkgpbggpKHrKiPRGIZdbysuR7RljZZOoJUZzKN6VMg4PqlFq/mhow8VIrG9MWdEz2XsWBT6FGqqcObi27Tm7BxTeVCpRrl8WRCsLfOQd2yRLQPjwPLhKTzLpGVTP4ufRP4xKV7fse2H7n3NnZmXOu73vg1bHNJwS5SacUoygzBx9UFLQShL1E1c+Hl8scPoffS+bLFTK+kLnED7bswYaoOMLqI1XtI08miO/FHmxeqLJ48HMo6bBUoI550vVwvh4nI06ugB+OiZbuJSRw3LMQRQi5+SQa8UO+NPx0ygUkf/jkj3/4wx9EpO/77XabJKWUHHN5PAzAg1gqoX8BZvXNQK0/NZ3MnLUpM08phbZp57PnL16sztZt00U5QqsnstFIIqj+PmRWmqZ59uzZ8+fPb25uoNvevHkTYxyHWEqPju5rwHIuoGuPUjOVp0+frpYLyqBBdc6BXd1WA5dYinMOj2/BJaHSNhH2vgvON843PrQZyFDR4kD+GhpwGIZhGqMc2LKQ4EE0KalS6drrfe6Php1pfpsqKzl0o7SdKYVD38zSaZqSSEpq6EFzSbMCSxQnQTcVrrpQyAFNk/B40HC+kHjAD87OYtOIqpBC3eLWsDPm8/nFxUUWxt6zc/PFwofQdh3+zReL/HVsJOd8CE3bIpKYioeqVe3EOE1EhOsDFwo3GkLKgsMhhJu727fXV77JoKrtdpstht0OkgU2EzNHlSFO/YTIsMMFaykADxKNvVOpzPaFT9t7tvKtkPs0CExGqjKaPnd9VufCYrEahmxmSUnwW7jeZBxek4im0lsphLBer+FLuYL8cqUCGwh/nB/ahsgBrFfc+pZcduU3m80fP/0kRe33o/f+4tHlarVCpwERud1u+mnsp3GS1E/jOI53d3fX19fyUG7PRKEcY3pNmt1XkydHHfL1xE4PTrMklXQU5KyPWiXXHk/t9Z7I2yy5Koe4PqcW5fbJfZdXDgmI/AC+4o7mwrIOZlYYMaUe7ygDmoFpVPBoaPmQDlH0lNJ+v8e2YmaQmlkNvRWAYMehQMAdsNkHuX1f9RQw2unUmF600avjw7UStQVpt7PPv0772jVPZi04j0AdicLVcMSshLia/dMk+ZPjK9evKQ9VsgWnpIe88cGIO1mgZCFsly0WOyeTGrOGphFJMaUQmjFF8m4cx9///reXl+fn5+cxZj48OmyMYg6Q8jepzH8td8e/QvsWS4SJvHOBNHnXfPTRt7fb7Waz++qrl/1u75QcH00hHQwlBRVLcUP9j370o3/+539GkcZXX311cXF+cXEBEkEiIrX8hzI74MNVkW1hU1Tz+fzFixdffP5l27VRcq8bqGEqqRd7BhGxOm1TBt57KR0gTO5AIAJjgpihCXEp1IbkfOMDeSo2bKZdhJOENZ1K/kMLNZUZxXyw79wUo6j6nKoprZmILfymRCG02P+Zj8l79i5OkmLOQ8dIMSmzI/aiTOyRshVlZZpSbFRYBQSo+6GPMQdd4fJ2XRfTiL5sSYW9Q7ecNCZV3Ww2s3kb0zgMw2KxGMZ9TNElUtVulsfkKKpJxv4jqioG5mdWotVqtdvtFovFZrN5/vy5AdRhaU0pojJn32/Pz8+LOlQ0n7+7u1PVdtbCDLIAsqXnu9A0jUeVRSyNdFQV8rNpDl14MZj+mNaYSrcoVYUKh+lTNKWqctOE7XZ7dnZGJYg9DAN713Vts2vevXu3XC5Rv4S7IHnRNg3OBN6NCllH3/eqlBQF1n6xmG02GxV2jdvv98M0rs/PyPlh2HbdnDmvjcVidbvdvXr5ehpT0zTPnr7wgZUp+MDMrgkhtN43u12PspOmaVpRDo6ZNSllQgkAZLKpdlBIjpyrGKPwAmxzeJpDrV0cO8cTkeTwLB27NQ9ehI61u1YZx+Pz889S1ZvKMVdUfbXjOOfhXqbdLUKLM1NpyMOFMLWA6g92CVcGLg5EzlB3p6qTJkeHFClqynFBtIwUEdi1QHo751LK9nERszlo5CsQuI0YEdExNXIlonNEVlVh6+PZsLkszGbfqqO+Nhf3b6cVsMsVrlkqwWciQqWWFtClzYKdcHJZd0BjMRQrnkKPebIO80jgg9YjY5AeUuD1kqrGq2SYC7rSOScS8QF5ent99cknn0BAT9MwTcNmu3HsWDGT95XlgynhPx0g/a+DUpd94bEmoshssXj+3gc//OGPnz57FprONpite64yLrZD8O4ppbOzs5/97Gdv3lwBYnN1dQ1q3/urgZnBMFyvD0RXSNLZ2dk4DSKCFS8SQ8V0SsdM6DY7WNymC7lqg4PD9uFUegMjJ2r5UZIDCSJyxrbVy2LiYZrGNJrTLAXdIwUS1TQNWlBM46iFcwNSYIhTImrnM/yqRGPMxWzkeIyZcMosaBjacCJ3ux1cXnBOIXUKfQyvzns/SUqUaTowp/CxsJNDCHjgKGmYxotHlyicyPZNhZIw+msEb9Bbl4iEkrKwp9B6HwI7JyVUMAzD48ePnXNtG7bbu9VqsVjMJpmICOUKU4r7oR/H8dWrV9vtFrpht9tFSaFtFqsFNC7aMKuqtUlYdDMiMYIqrfjCMqh7mjBKmCOzshEwTGlSTdM0hODQkvkY4p49IVdSa5hEU/bOORBQw0dPKfXjmPKtxbnQtjMiN46xaXJuHv7QYrFgn8nxnQtTkmEaU9TFfNWELk4SJwEwJxayNiyVcRz/8Ic/INYdfOucU2VwRKCr8d12s9ltxzhBCmNO4RBLwa4DGVAXcULeHP7d8zG+Rjgctptz6IPrWBA5899wkVp0mMQwELIlC+0w+5UqxfzgU9Xat9Y0VBGPpML2bBQ6Ji5iKcFHGMPS8LE0SDZljOFtMhTDWcQ0FSSmJXqpFALM53OTeFpMdqq8eTv/XzzULN2CnYMANK7Wk0CCVOW5J47myWVx1JLzxEjC1w3CUo+wzbhdvP6TVOlns4SKhHTMrrbHnKt6JXpiX+gJUyHfsb/WN6gvnY5q7DJVoZBapa9z7je/++1nX3weJbmcmRCkgkQE7D2qIlqnNvVr1HB93I9OPxCvZjqF1R39Mf/LT4pAsfd+jNNyff6XP/vr1flFKn2fpALRmblkmowKjY5I/OC990FAwszv3r27u7tj78k7osyTDIkPAqAEzWMdreMInbGcL87Pz8dp8KV3EFEmF5Qq/ELFWcHX8bSYHXg/mZgwJk3S+AAGHFca7lIhzqUClXIlYkwk6JAISl4qmKApDo1np3k/zOdzsELGcqSUNMk47Lu2RXbTOWcEI1CcIYT9OEQV4CeZGQ0tiCiWbsrb7fZut0XtkAlBqLrZbMZOU0qIr0IH403xJNt+75pA3t1ub6eUFosF7AzYMah6Qj8iM0Hs8cx4hz5D9whfSi9sv1UDRVTISQAdguTabrequlwuzx9duiZzRvb9br5YPHr0SApIjT2KoMQ5t16vNab6H0DIWqDgIQRweC2XS8xa4/y87Vg0sEvjJFOkJPjnlPDrcjYHhjkOoxZ4M0IaIgL1bEJNVff7PV4Bvuzd3R2MntlstlyfS+705Zh9jHGz2ex2O/ZuSrEfB1XyPjRNG0IDUdt08/X5JWwLIqfsbjdb4PgMNz6OY+vD+dnZNE1Q9t57kF5Bi+z6fYwiIkx+HKIkGofoXeN8I4k2d7t+P45D3PXjdrtHB4Htdt/3Yz+NiOFv+/22H/opjkn6KSbiqDJJAs9BVIkqwqdRXxNrQIlnQUcaD4KQ8KeUlMhNUzr5OpAEphW4xPzdcTy2socOQEszo+vDXLFaMZgioVIKAeYy3HosjYchse12uMVsNuu6A7WOlGgZBEipGsrRFCxvVP3e3d0h43B5edl13X6/X6/X0PemkywA7qoaOdNb9cua8jtRhzhMvhmqtOigTJCABVmmyeaL8Sf7h/MLp4Kv6rM5JQWfFb6FL0pVgW36zoaRi5MgVfiaHtLZU+mrZgcueChDUgUYltXIH6plVGstOlZpmH8YBvXJaJU3ycTMf/jDP3/44fvksuoax5GJunaOp29CQ0RJonen8ZbqeDAQLdX//+Yjp/6VqGk6rAFm9+K9D/7z3/yXP/7216iPhHahwht8Egc+DGOS2az9m7/56e9+97vlchVCuLm5efbsmST1pZHImNvFtzFGuoeQ9MxE1HXNsydPP/nsU9GkBWvqfV6IZudCkSfNhUZExFq0iEYRYVKzeX1VeRILUahtRUfkmb3n7NRCx8SoKTUhTMMQQtA0pdJsJwEctO8lJlby7NCaUNKUCgraLDPEk5um2e52SdM05U4+iNJA9B92F/nsv5KHnnakKWfBE6tw4GGaYoyzxSJ4H4eBvQ9tq6WVUMjtGYbFYtGGoJmsTZxr8TCWeyZNq9UKIWiMBgJucNmJCLFxV/rI7vseaCAprCPz+Rw1WlRaX9zc3Dx//hyFNJMk56j1zs9BeMnB+xDc5eXjvHunmFRWqyW8ZCQsoPsNz9L3u+KFEFUmMkQhEUHOAo+KWcYEWQ6eigvShkZVp2lMzrehCc6nlBazOYlev33Ttm0b/Hq5iOMAgNs4jo6DKjsOcTqY/LD5gJQGdADvjjIwX6giuy6nitFZHovfOTebLbz3wxR32zsf2ta57W6vfFjebdtut9tXr15hVEkTSsViUrP2siRhnqYJo+e9d0QgJMlemgDrnoiobTMLW3HLmMjFOKkqLLkYo0aB8cHMUPmqTJTLL6XQczKh+JiIs/0KteecQzCACvRMSjMuqFs9LkAynar3POlagvOxQ6z3Qqn3/3rwsY75lqkC98YYgaAPIcC49AXkbOG9tm1T0hAOnZWhYtHxkwtJOKSBVDx6uAiw0/YWrmSsnHP3n/nkXewTe9lU8Py2qr9uHB684P2Bqu/18If/yiKdUvB96Id48grlFvmyR0zlOQcsJFVCvj6YWbOqOn2Nr3tVIfLOvXz91T/96pf/+8//Nx9y0eo4TU3TEBMlLX0aSFQySeTDxzfo4P/Jo1TTes9E7JvAfHn56C9++tOf/vSnv//97+tK2domwpc9MTGD5zxRYub333/xySefuNJd7u7u7uLiTCh5crY0cQWm+6tEWHkYhvPzdftVC+UEJy+RejoU+TAziUbNLATTRGmKJJqJ0VmYmelANJOfXHWE9sUOV4VsHitaY2w2iHUc49gzdzHmQdDS8G6ceqDw2DGxsFOR3JIssEtTlCkS0STqOkpKpCIxjaUm0pHEmOBJqyogVICvYojiMGpMiSaNyBtJiimydk3DqvgyiDgaz85RYOc8zbuGJJJIcC5lc54RNYD27bpOJDJR23WQ45vNBpYKgrSLxcKiglxYTVS1bcPm7saxSpriNM1mM+9cYibV7WZDqpeXl2frZRPcNPZt41tuh90+C68QgmeROBSeMrihr19/tVosZm139ebt27fvXrx4sVrOiWi/3wfPd7udpIRiz3EYEHZMMcIaYKLN3R26MJEqMrJxmlQEZlrbNPvdbhxHSakJYbPZIDzLTrUUW7tSPj6O42Kx2O/3Z2dnXdcQIcjRpZSa1o9TH9g1zivntBkzhxDOzs4+/fRTick1bSZUKeXLgZ1Mse/78/Nz7z1YrJnZs5uGEep/uWzYhZvbjWtyQ1+0q/W+6fv+448/FpHlfDGbtzFG5xubCyLCBcdxhD4Aq6UvfklKKVFaLtYpTsMwMGWzQBJJ6mezmWgOoppnI1M0BYxbCGAqTN45UjcMQ5/Ab8MGfXDOqWjf98ZwwoyxaYlIJFGVZcwS5/gH1L6f6Ax3j1CCisVsGRNmrhkMbcOa/nZVGYL9VQpFvHNwu3N5Xizskr7wB8xmi1RY5X1zwPFZ9vfs7AzWs4HssiNRcZ7XD1//zJX7UVsVJmDrUZJCuYxrIhxygh07GSi650A+qHTvf/dfPMx7sesf60GnCmTOw5rUjlDr53og1H1dVoNNt9NxfvvhO0DTOPrFL35xcXHx4x/8UCShvAeADucpxtH7xjsvmmFkXx83rtXtn4rMQruP08/q9yJy7JNGIieanG+YOSVdrFb/+T//l08++ez167cIGxp7sEWgnJJ5sarKzpHT8/8/c3/eZMty3ImBvkRk5llqufe++1a+B3DpjU2im1S3KKplJjOZTBrNZ50vMDYSZ8ZMUrNNYBMk0ADZALE8ENtb7lJ1lsyMcPf5wyMi81Td9wiA7O5Ju1ZWdW6eXGLx9ec/v7n5yle+8p3vfGe73YnIZ5999vTprYiJFmoFsxIMaI+1XkZmGYD6vr/a7T978XnoYoCgCITEtesLASItfLYAwFgoJAtzBQMidsy+ZSVnWZnVLY0EAGYiqRCOZ0/jqamJYEY0UJnHBGbOFGFmfewAMTCaZgBmQBA1ADEoKSk1yzJDaZDSvLe1dYxqoWNV0CxANo+TquY5xRiJSUH8QQkRzCRnFem7QBhM1L9CgOPpmKYxhACq03kMIRCaiUnKAqBZpvM4z2PrWIAGjWoQzJBck86MgKaRCY0kzfM8S5qxdl1kZkcYI+Lr89GzXK9fvyZARpCU7+/vb29vA7FJns6n6TzmYQMAm80mqxBGETocDsc5eVcJH+FAPM8zEsQQ5mkSke1mc4inu1evY4y73U6zzOMUiOeamPcefKboeuWMU+EfFfULBmLxLAOgiWZLQ9cTYBeiieY5BeIkcyDuQsycmAmBc5oIu6v99tWrV+P5OE/TNI7DMKR5nsfJSTo3/YAGMZCBTPME83kYhtPhLue8Hd7vIwdGBlTVjnk6nebzmYhOiKYqIsfDnWtKrLzEXqx4fP1qnmcVePHyReyG8+kwnaec1USnlM3MJOec0zRv5y0HPI9HrVADEZvnLCLn48lN0mlKMZ6ZOWCp8OaOJZun8B0ZkJKEIF545Snt6+vrtrWdpCyooKmaImLr/DONpVv2NCWzgjhDVH8vv4WI7PfQDwWnJisSRBEJgVqkCi51w9qlazIZa9lMkwztZ0s0IuIDJ6TJ5PW3uJZr2ypMbWY56zQVDpli5Yv4n56noMCS1JPmIOIBNg/SIOJ+v3dT1bd5Q4T4aDt3W9PBD8In8AUqc5HRl3VBUsFQAOAVlYilqyA80qy/jEL9onMuFPMXOIQGttRI4Uq76AONS1/uIl5QUV68sC4e9FrbP07O6gq60uR7e0NES56ZS/KNb3zj9vb23edvOyOdqFKFw6lmZCbEv3vYVnf+5U9tGv2RJi5/i2mLAYdAZpxV2LoPPvjgt37rt77xjW+IiKutGGMpbzNDvZhDRPSI8XY3fPDBe3/xzW9dhaBmd3d3p9M4DJ0SNkHgKxUWpBxatZrcyjOz29vrz1++sFXVOaiheXMw1JrKKIwWgaiyKkLhikoYa5w5LyUE/gzMaLIkkv0gA4rBGRtTKnkXL/UpiXCylAv9YUopdARVs7acltsHCqKSiQjBTCXNU84ZiTypiQCEJYSOSHlOAOBvx0hl3Zs6T5ZqFpHsTSJFAREAYyjV2ItwUfXB8Tz3dth0XSDsd7vd8XgMSDpPoEpiJBaBGJnUcpo6YksZmDvinHNEIjUAy3nSuUC+Y4zb3W46n+5fvuy6LiKdTic0G4ahCzSdj5oTBMqFQxtE5P71q367ub+72/bDs5ubw+Fwf/eakZ49e3Z8/fp8f39zc5NF0jjtnr99d3d3d3jVcZim6f7V6+l03m6358MxqYQQTocjIprANCbVyd/3fD5PR1RVoQK3yVPp/utBxZyzzAUtrKrnY8nmikiex/FcuGMbxUeaprs57Yb+8PoV6lUkPB7uTLPjnNPMACDzBCp937vhcvv0GahpFgXEThHgar83sxDCbjM0petc1mrZzPb7/X6/924T7z3/rRDCmPL17c3pPH3+6tXru8M0zshsFUMjIuM45nlWVak9wVTV0wSOU3WTxUOjxSs16LrOEr548QoArq+vc9a7u4MHObqu++yzF+fzebfbVHUuInI8nJ8+3YBRStl5ctzTEjUzOxwOd3cHfwWn2SLC4/H84sWL0+ngcAFEjKFXVdECY2zKJoQhV+KXR3KyCJALqXV5Gq4M/dUXEWCpgn2D6FvFQh+rHF9LLSHVUF2+nYftzlr+i2LO2ds1uvZ1wKDnIPyXBlJZgny6vuliWDwUwpfu74M/YYU6ZvL+YFpNmTeE7h/88qsej+/++Pg7TzBrGvrLdPAbqCiLiVSh9hfaF8BpQh/HRtZntqshMJApgqmEPrx8/epb3/rW03/z3202G8mZQ7Hjuq4T1ZRSF0u31BaU/s9ykIMPClCtkLVqDD0ivv/++3/0R3/04sWLTz75xLWLiMxzDqEQMiy6s+i2DIbTNF3dXH/44QevX931fT/N46effvrBB+/FLsqcRORLnqYwS6TJrJDLZJWcs9cBayoQqlIKohI5xC6mlIgcWV22ESKoapqKJxcqpaXvQ6Ka/S0qHIjIEImB0czMU9GSMxNxjF6RDM1UN2BH0mYBszwnRDRRJWFmNQk9ARakYsOmSu1aaGaMkJgdFMhY8tCgJik1MQMARFp6V6iqB6AK+BMIkJDIYDyeiAjRck6BOmYmQFOxZHNOInIWOx5eexLXLRv3OBGRHP203bVM1VnUwGKIIqLehRzRAFBN5uQ++nvvvEtEh37wqw1dH2O8v78/H0+scLvfRyTT3HPI5ykCoYGkHDk8ubqe5/lwd9f3/ZTz8f7e18/Pfvy3r+/vd7vd4XD0iN/5dHD00zAMr44vFEs80EuBDeDzzz83s03gvu9zSi7ZUwWET9UhM80pJXSBJUsRsEeVY8eqStyllJBsf7U1RffRmfn99993cBwihsH7+8LVdjMMgwuHlAqk452333KP8PbqFlbMySEs+jLn7IXaXRc6ppv9jpnVs3qAt9c3XTc9f/rs9av7EGbHfAHAOI5o4sFMEckVKOo/TRQRGyGRR1YLYk5EEXKtMlfVly9fnk7nrus2m+3Lly9fvXq13W43m507r1w7nVfXFlVxmuYQAoeSufzsxcv7+/ubmxsDPJ5O8zynefKbdt2w212FUGjAC+YomF0ykUklglh7O02RrJVlkQZvcuZW0sZV7/Ld5uPimxoXtk+a0d+KOJoR1p6qG3pPCbfUtV/BJ5GZ3ZBypet1/FpKjxZ2xnZ9rJgyrQiGL3qp9Z9NDrhSbxL4jSPzwFhZ/74ez/UtHj8G/HKaGx+1/n3gytplEOKLrrmwlovIeiLfYCyViy1ucQlyP7JBwKgofzStWPYkOgzdxx9//B/f/d7v/bPfbef7yJbfTQG+BIf1n+4o5hoAAKKBgbrdFxHx6urqvffe++lPf+oRFQDwpuKIi+faFj2UZgA2DP1v/uZv/umf/qlDM168ePHkyc0VX1mjD8Wgaspmq6az6+lUNTHr+z6djqKJQoTWZ75VI9jC70oEVD9HRGY0M4Il2uPX70OEuBBG6gOeHVGpxScroxhEUlsYUvlozAyp1BhwIPV+36AAMM9zIHYHpWwhESbSLGIZ0TgEA8hmjJBhYe/K85Q50mVRUMHdzMnM+r5XrBninM1sms9tTHRWh4q4vHZffzwd7u/vveedZ6pO8+RFWQTIzPNYOvW6jGBir7tHJNXSLOx8PH02frq/2f/Lr33t5z//+cd/+7dd181TKfCQlL2vsEzT1dXVSY4uL8QyqKZJZjPNgohMgQOnaW6idxzHw909Ef385SsOcbvdHk8nKTyUvc/4NE1m5OyYPjtSqTQR0aHm7s66g6KFzhqHYfDiKBfN1/2Vj1WLE/oo3d/f86qNgXvMMcbAV45UBwAnWACAGOM4pXmeI+Ew9PvNMFWD5nD3uiF7U5VEagUe77M2jflwf1/sjPMRgFLOigTI7hYj2na7ncYkIpuuFw6iyW/NsfOQSYhct0lZYI6/k1aLoepKxSttxnE6n8++SFJKXnjtI/bq1V1K036/Z+abm5sYo+PIfIH52kuSP/308/v7++12O/Sb169fH4/HELrA0LKSKSWAuN1GL1ggovE8z/Oc8sTM7qATLTuxSZ+1DCFayJHW0mClqpejnmZtv6zPeXCFJjTW51At0M+P+nw7LKN8XTGr8/JC4yvd7XauaK+vr7Fysze9zkv5xmJYrB9s7bDBm9TeWkcUnW2FZ9dWbRXWsveL7BV40/FGZfzLaN/H17HquzZaEACw5b8I3pADNQBY+D/9Tdfj9fj1zAyc/A+XwDcCgtnir1rJ+T8w8RDQmaS++c1vvvv87WdPn4pIIAbAnDPXgoSu2zy2Fx45xBdx3191vN54EJKBiEjwtCgxgaFpCOGdd975wz/8w+985ztWi8+cushhTO0d/TohhJTmwDGl9OzZs3feeed4OAGAl5P2fR+dkUPNyS6MlrHyTHWzbZmjitze3mYVr38VEbZSsedOLYK1BIwHxtvXYyy1CmRAjj90/kvLzYq6NNO8PoGCmyJqSEiArnCpchshFv7eUNoAo6O4YJWjcr6YJhGoQnNDCJZFLYcQu65D89ImBdGApcNuzpmQAodsmYgwFBu8Ge99H/0TV7GA6j3AHQMMVX9sNhvnomJmlbQdNiGEeZonYg/ZhRBCZKeiHCufrSt7j8qEVfu/EAICmOr5ePjFz37+4vPPx9OZACVlrjUhDnb1PuYm2nXd8Xg/zpMrgDbFiAhAMcbj8eh+AyICguN3xmm+v78fx3G73f7is09PpxMAcQjPnj3re3j98t71opl13TBN09XQMbNl6ULcDRt/fQ9Hm1kIFJDIQGYvsA6Oo3Gj8HQqBVdebnQ8Hq+urohoGIa7uzvPjHYcuEPnwkQtdtL5fPaCqxjj6/GkqlOac86n0wmEu64DVF2xT5NBswBUc5O/8zzHoe+6DimM47jZ7omw6wNMcJ5mrKBZEUFCN+YQ0Kr8ldrDys2FZjW2hIinIQFgmmafRHfp7u7uAMANppcvX97f3zur3X6/v7q68aFwQy3nHLqY5uRAaxf9TrxFFOZ5xo5zzk4slWtTJpHsc/Tq1au7uzu1fHV15dFpoqV0uMpq37C2VktvVCR1ny76tV5qaSSwzjG3JQ2rRCGsRDrXXpx1akotjcuWzWaTUoFfmaGDLu/v77zx+TAMV1dXHtVwPzjVMMwKXGKq1uDfD4yM9Ut9idpbdBstCFartRvr6zx+wcfXWUcF3vjdX/L4wvPNi30fts95dC9XohBEEsBDCwUuJwzAauYF3dpcrx7Xrh6a9g/KpbBoajBz8ypSHHW8O9//6Z/9u//lf/6/cQwp5U2/ned5CDHNmTGslGtBTr2plvdXyHI/HDgIq78UqiFhoAjg2tetFzNgDDlcb5/t3jf63d//2l/+xZ/FEOQ8dmSsAoro5Qo+WAwAQGoBGEQBbLuJv//7//xP/uT/41WwP/35L66vb5ljH+M0nRHRTEEW9GOSjGjIaKLMmPMcQ9j03fMnT37wgx9RYAJKBB5bzmlmiyGQSU7zGREddYXG4KGHWbuuMxFDQVAECrxOEHiSv026geNioRQIVd3pFPfEzAYaA7P3ObCslojJFFyZNUcE1IBVxOUvEIEH4QAgpYnRIcMoaYYqRNRDYWYixohIIipIYCCoYllFsjdYXXvGeZo1z2ZGyKZzMGMAjKUzgWnuuxB4A2AYXGPFGL0eMQIAB8o5D0NsJguA2wrEyCIZlVQVRJzwwlQ75pzzN/79n3kb+dPhHgCg71JpzdYBIRq9ePkq50JfoGpyHLdbWofmAPRwPnchTnNaGyieywSAm5sbM3v69K3tdjyeTqfT6ec//4SZQ9+dT5OLyCdPnnQxdj0TURc3zBwJAAAVttvNNBEiAhMipnH0unxmnl8fvXFhqCSRYDTlhIgDhfHuMKeUs2aV+1f3m81mPJc6H69qExAASCnFGEKISTIRgREzj+dZMnpwyHeWqNxe32y323Ec53nuiUQTVeKIopBMN5vNq/vD7c3NsNn1TH3g1/eHly9fTnNK3ge2fiVDRhNCBARDZKx0wZojcxc6rtwUZqYCquqGAq6alLjdFkI4Hg9m6qQuwzCcTuPV1Q1gTmnGAAr26tUrojBn+eyzlwAQu36a0/3h7Ljx8/m83+/3cdhut9M0ff7ipZm99dbb0ywpiVq4v3/16tWrzXbQjNvNnjAwRVSBqgnQ29EAEaEVyjBrm8KXunmtAC6BZQBXk2tMFqkCuqDEhepLa2itaeK1JvD/YkAPxXggkgKHGIhos9t7TbNfRDRxoCzz4XQEwhjD7dMbBRFJw9A5SapqZvZmxqvGaIiqbhxQlTxLUvwN8hnRkwhuiyMwU0wpmSIRcXC9jmaAuLBrQfEDG0z4QVWulXVS3Qw/fOj8OS/iyev2BHihXNY6vpn77buICCBiuSgxK8qECL2k2C7j6uQUsg8uDav4e5vLZbwuVaE9wnBdvt6F3eFqGAB+/vOf/+W3vvn7//xrHQcvih3Hc99t1kEMA4U3qN7/rAcippyJ4ebm5g//9b/63ne/Q6izr13XEi3bAQaqYorWWNDIzLbb7QcffPDJJ584Nf/d3d17771zOp2GoWMsuEcPJNZVW0qMtBL6OJq/7/spzYFjMGDGwI1RhwKBWVCA1lupFCkW9COqLi0lHk/W+kOA0jMclu2xbNry0zePgoICgOaLokMAAFD3mB+EvgEAzYzddzEjXUfb2i0YqiVRA+BZMhhhxlzbP/hpIRbmprNoCIXwq+uGFntsstiqw+TjSUQGCmIhBCdPtwrOXINFRZOZ5VR7TgAgYuBwe3vryrIVpzFz3/fO1hQCevGuiHmfA4/m+ZyGSsi8HTZl8KmMsru2CCQihoUw6Pp6L2LTNOVSJ2Ov7NU4jgRKoJELfWmLT4iI12tRKbgsLaScfzTnjFaQjg7Pzaqq4NhXVUs5E1HhFEtpmqa+2/gsxNAzs4DzMzjLKYcuuuqNMd7e3qa0QHPNjAD3+31rtmOwtLTC2jSMwUTEOb+6fvDWFEMX/bQIaqFLlQ6QV8yaD8R3qq0yVgp4LRaXpewhd3fftRbpAsDNzY17cuM4dnFoAZXPPvvs1avXTg8yTZNIoZJIKe33ewD4+c9/Po7TZrt1hlGnNnv9+jWAulvstBjMzAE1KQAAk1c3FRLaVTB2vReaEqkU1G+gPAQAs+VbRAyXekJXdDEPthsiGjV51QAt2KAS/mfLWzszvFvbIQRGSmZOleMFhFA7Hvp9Qwh6WYfTXu2BslgfLdzlCtyr5taFYWa6UpwX2KP1XVa3+LKEehvP5eugHuuGqra/+Mrw+M/Hx+P3xerxBqlq+uIfIpTedghLOBsAHKbrPMbrpC/ACg7elHd7ZzcxMkAXIgCkcfrGN77x/Nnbv/3V30xJ+ti1FJFezJD9chHmX80J/mXGqP0+DJucE1L8yle+8o/+yT/+xp/92bbvz8cTxaBYeFFLDbN5N6PWWgQc/fHVr370ySefOJrm5z//+XY79DEi4vF82vRDLkQBrb6tMEy52WjOgBDCzc3NZy8+BwDmoicCEjuNPiMiJvXWvW4kKSNqMc2syZ32auuF2Kap/tlKm0oFhUu0lGeoYauqXNVo6YeBaGhoZs7CcUF22BYVIkBhkHCzuA1+OwgJYKHIKdYlGSFHQwEnsClMmf4iWTJRaFuomXGl8mgluQputnKKDcMA2rzSwtJX5EjVZ2Xc0DlMIIbQ971U9sSCr6lC30k8CEsvBBE7jUcf6kAsBrlC8K6u920KtBY4tr1jamkWMaUy26hqXR+HYfvs6W3bUzlnAuFCfex4JmW4QN8gIhB69YiCdTG0AfFJIQEFCxBVlVMwBGelKKdB3G63HlImAo8Yz7P3hCCn4M5J3UZEZHc3AUAL90UHbulHUr1ADxUQHCEiehJRK7FDjPFwOt/dH8ZxNERG0Lpi2+Z6IEl9vfmqKG/tfgwta6Au7MVlkUoKUVpIIY7neRrTZhdTSiL28uVnr17feXbMfUQz8xZewzCM4/jp61fMvNlu53l+/foeObjbbWaRKcaIBLvdhpmdu630DK2v0BKZLfy7qMbytGjmab92kJtPqz8Xnb1qC3Shg5uWWt+l7egKs2Ko3FgODmiTxZVF3L3/7XZwoR1C6PvekXruszqzx8OH+KUPrHa8VMauNYRt9daKeNHK71INE9JaU5ZeT+u71KV44ZAsgw+KRbK9QWGvtduDTx4sywefP9CJABfx2Dcc63laa1YzXevgdo/1L+2WRIv3nVUIsN9ujvenb37zm8+ePXt681SzhBBSypFDU7j2q8Ghfw0d/CXnF/vDJZCqEuJ2s/9v/ui//d5//O48jb7NENGbNHjRrNuSXu3TXFgyePLkydOnT+/u7kIIp+Pp1atX7zx/LlIae1kJjLRdsehLIhIo6+/29vZwOp5PY+BYEW4AoGRFX2rO7iAys/vjiJ7BXUg/pDYz4dpFuO2RqrlNxar0XiWTGFsEqfkZVZELFBWLTbVj5fxqsqbdCBEBtAC2Vx4qVQwaEcWqONfGe3kmD7Mhdl2XZTazLsTS6KI0j/K2P0ugqckXD4sBljjkgylvtyvuYy0gWZSiGQB40iuE0HVdzjnLjMCeIJznmTkiE6J3iQzOzAAACDYxt8ZTADDNY60IX14/5yyp+O4iklVcPBIRguV5OswTAsUY+9iFEJiIeBVjFBURrqlT7njwbBwVglhYCFMVdGEz7WXwgVKwnKrgc9Ej0A8REafpXDxsU0S8uroKIYiomAbuAGCeZ0R26EDzg/2Vs8yH47LwoCpgZk45AQDzjCH6xrnabU7jfHV1ZQ3+CsSsYutt8gZJ9+AT73XSFFs7p3lXa2JRM/OQ+OvXd33fp5Ry1tPp9PLFK7dIpmnKeVFjPuDH41EVVCUlOZ5P+z1ciRyPxxcvXtze3h7ujlfX+2fPnt1e33DAYfAapAtGqmYQrBVwexEFMwEiAvOI4HJUzd3M37LvZMWSu74RrPKG69GAWnpERIgEhK5TqaZpm4ngnKDeHejZk6dutVxfX4vIOI5cCZ9jLI28isqEN2imL9HLflrDcqtoiQy9AbH1sKEFPNJEl4cPyNKqdf1I60VVhFVpAP+Fz4kP3ZiHpNPrK6+fp51mZuucK5T7uqeL2OYIV9GJdovaRwEfXPqxMtZakwCeXzRAxO12+/0f/uDtb7/9X//X/437TSEEUWVaGMldIQHSL62D4ZdQw7+knl4MnNBFwkAMH3z0lX/5L//w3/4f/3sMXUopeN2yYanDA9Pa0oSIUC3GmGYhoo8++o3vfOevzazrwosXL95+6y0iYo5mYoZ6Qa5WynXAUzhMvr13u90wDMfDqWxaQjfrHMZUgC1qTET1yQMR1XwnrqjVfVK5srO2EGhRMLXfcJHptcTwcqmtVqotA4Urnltyap/VUePtFz53E4jtjlS7u7SgSL11gUG6+pzT6HeJMfZ9iSUGZisrtfQAWOtUJOj6+OCmVmOzUGNfZpZSovpIrrbNzFdOSjpNU9d1iJZzRgBmy6K20BlqDCHnPKZZREULYrmBuczMeRsCMVe6hhCCdxfW7Ao4oLezIHTuMGRylisCRMQYOASOMYqVxCEBWmPWBfCqemamwIgopu4F+BqQVRNTVTUszpmZcaAQAjL7SokU3cza7XY55ynPHux1trXT6ZwkSy6lokTB9XpZYwEBwOexGTFUGxKU9ROC62kwmKbpPM5Q6oty3/eANM/znDXnjKrgnrg3CYWSwGtLca3Pyk/F0HdNhZQZfxT7cXiRE0qcz9N2ux+n6Xg83r2+R8QQw+Fwwgq59xudTicz2+/3U5pTSiF0TgT94sWrcTwx8ziOn3/++f5q1/de/MZEvviXPVjkUdW+RKSwKOP6Up7vxKLJ0InyqXbiWtKCJWO0ymW+Uc/5dtZ6QAsyITrco5UsUkPAIbpb7/Gh/X7viG7PjjXmcP/p1O5Y+W4BL9ThpWx/w7E2QVQ15eT8Zf7h4/dqlv2j63yZnMcLT5IfPM9jlflFjwoALvyXb32BwgZzV2t1IwQzuyDigEerE1Zm4wOTAYqWreOrZnjhXPsvhEFEOGCzzohonnPXERH9h+98+/33f+O3v/qb8zyb6RrDvbbUAL+EG+vB8WuGo79ouHPOIfA8nUF0M+z++e997Sc/+cn3v/fdSAyIompWextIsixgZQTIhCioTZLk+fPnP/nJTz7/9LPNZnN3d3c8HjebTZNKWkqelniUiHjnhhoGAWmOrChERETiorEcYWCEAEZUGzNYWZpcyoUXDhrfVNM8MnOwErltM8tUhp0882BgqnYRnFkOgwJwaDNlDTdQxUz9XOp42hJeRg10Adp02S2AoMUEbnFmrEa9v3uHndeJam6hYiFEWnVtohVKAglUluaJrgBEJHJApIbaaMIx9j3VgD/UUDMiBrKh6wEAwRrTnmd/nU36cDjsr68dWbrZbJLk8/kcQuhCseJT6YVeIntY21B6mtAkNQRpUZAVKFfScsBW7RJGQw4pJWf+ai8utY6oxTytrm7HWykY1TXvA9L3G6ieR92AgIhe2OoPM2x6TAWA6hfvum6g4XyeqGSdDWEBb/rzt2yiR3G9YIlqT3WyQuwAHA6Hw939cRgGhQKJJ0yIGBkZec7ranvvvSoGBAB6WQlZBlARAPKqofoDbd3ElKput9uc8+eff06lznW+vzvc398TkRi0YqRlOVU1nMTEEFSJWQGmacqljeYcY3zy5MkwDMTQdUEqj3Td4NCuVuMTS5S4TYQTaPtyXkv39vCLkPQLropzHiin9Vds5XZXy5gM0QGM6+HS2pbDW/8S0e3trX9+dXXl67lxDLT2D+tHWh8P1M3jo0kqWAzo5TUfq9vHKrk8+aMr13MWU6DeSB3PtRZiX/KE7UnhclH9qoffKDz4qF1OL1DQS4Duwfub2fpl2xXWJo+HUFQAGtNKoJTSsN0cj8dvfvObb7311m63y9NMtZ3ZWgd/8YR9ERXlP0BKuB0hRAQgjkBAQE/fevt3//nvf/vb38aenda2pLIYQUBVA5fla62jCEsXh9/66m++/PyFiPR9/+Of/GQYBm9ow8wP4kiuxVTFd8kivMTcjQvEMcbgHcIBkYwoAFrKJW3v1FhNvNoq+IyXeL+mcpYxr/mCpor8/BazXVtIAOCp4vXz55xNBOqeJHLASLUkVt1Vm4I0W9wm/5aBEmIr7ViuD6pmqjXAVYYZiWgzDDHGqUJem+fqOkBN1u/VpKEDjgBANTdJ5BYVY6mgaEPEzB5ctpIjcB3DHIMH6Mxsmqa95kBAgGgSCE2ygmEMANa68Pb9pvn3WtFqqhqQHshorgVXiKgpI5l7O+7HIkSvWacV0KYBxJr2xTqA57k0mVi3uAYA73zVEhOqCtb8WjJQ0QxYRiPnvNlscs7unM1z9jWB5qX8ZSV4+H0ZT2bXtQ7YcXxyx+SkxKEfEHG76Yk9LB/S+ZxzBqucR5pNtRu2DS3c3pcMs0pbnD4CORUTYX3mA1nZArYhhM8//3yappub28PhmEXO57MZns8ThRxq57sWQ3ItdT6fQ7d14zCESATnaQJ1qKm9/8F719f7foie1c6SEFGlmJJlf6GbuugW83ozFtxZdUrMDL6A6uHyvcgMRUxX2eD2uk3CrHd3TcaTIYVVd3CtIGo3m8ZxZKT91VXf907n4nXhVpuC+0Jtir9okEfC+5dUV1K7ATKzWgYAp7VvV1iG5QuOx4oDEWtA98EY+qJdwKrlBAOgB7fQB9+1B4bOo+dZi8fHzxa+fDgeWBz6pgbRy11sgWK1e/hUlVXbORdr6rouYDifz7vN5m9+8L2PPvroX/6Lf0kxCAhfqlVEBDB8qGv/ThboX1kHv4kvGgBAVLDkPrvpdHz+/O3f+72vfesvv/k33/9uxCL11ATEnEZx3bUGJHdd7+Lj/fff/8lPfvLjH//Ye+mcTif3n7JqJDJb7D4v6nIxDUXBc9NbiMjMHQdmzzoQOY6RChLDkzleHWSqUt2sFglk5tiFZk6tzdXmYq6X+Prw12pmlpk1LtZFfLsFUR3QqoCLjFgnX0t2VkoMFqryaCaCe/j+/K5TwQgRY8ceBTUzj+TnnLUy7+AKKdZekythSDsHADabjXdwyjm7n79EZc0yqNd0uI9YHgnQcSgeS0gpEUmP2IVIRPv9vvFM+QmG6MSHfpfG6JRSCkTBO6WbBSJGlDxj39fn0RgjMIHbNJ41iCES5xVxYK7PRrWsyN89pUSBS95Hq/seQqQyMj6kaBCIgex6f+Wvad4QhcArpbWix0MIqTb8JqJxHJkLoYe7j81DgtXeNzOPSfozO5WNr20OoUO0NPd9v7u+aasuiwGAGA7DsNkVHtDz+Xw8nc3McLGr2tI1M6Koqv6m5RMGooiIwQPXK7DFep1jDUucz2fvqQcAajbP2RtL+GDCyl5kZq+c3m63p1lCDDlnGcdC9Qx6Op1ub69/4zd+g5kDsVeuU6FwqluJ0GzZd5d7amm7S7iA5tblMet3L7u15KSWWqa2Z1cyvJhubYMvA0KIsGycNrweSc45S8oYwvX1dc6ZGZ3DJNcGcU1ViyTPxUxpZmcXXkmYN2qjB4ef46aq04aX4D9pIX0qrGGERBdtbNuButb8b7zXY+Vdo3SGWOwhQFBQrCvnwdfXT7v8+Xc30r3Qqgt2dD00b7QdWoSwrdoHchlrAhnW0998xNo/1S9CaETkdYr//ht//vbbb7/33ntmpqIxRMdeGZiqepOGL+2S9J/w8LuDISBhiAD25Nlb/+P/9D+//n+8fPXqlUmOzrwYwpSyf6UJO99ELogR7YMPPvjZz37GzH3ff/r5y+3++mq70Txl1cjFd6RaTxJCyFbJF0PIKTvJbRteM2diJAATEarJeN+O6OlpAKvdQF2SlvFX9GUtlx3roArrokGh5rNX66E5hdUaW+ri2+u3n75YVC9aHzYYjsvxd99+x3uLNoOg3U5ECAp4xxTFjMgQcZ6yyGRu0RMzF24QUYWK/9IKNzOHrXaF6cXhnbkSSA1d72om59x1wRtN+rdi12nKrb7FZTFSeZFmnrtg9Qyiqg5dn1Ly5owxRozFiW9gzj5GF99uKYVAIQxE5HA5H0XmCGRJFNW57EFECU3VQs9ew5uzMkcF8VZIzW9uIUSFUksaKnFpm0oiAjWVMlzezkGz5DkZApfOr2cz87ygquZqfPvy6LouZ6l5wdLDrsUGmsZ1DshxHLuuG8ex7/vtbldkqxkzd4Rjym64nE6nLEYpHY/HKRVyxKwFDYBgm82gBgQaqBPrfY05CMjdF5/KMjUU1w6l+w1YWVPac7pCffXqFZXmhvTkyRPXvj6YYuqQfdXF46xDNJNXqa5yw5s+TtP8ztvPY4zX11cxkKoSF6iH5EaSXK7jYAm3ydqKtdY3V2eHiHspTnvslhfwETBFD9UWfvVqAVOF97eNvP5WEwhZJc+p64b9fu/r3P+3WWDH+wMROfa76zrV3Pe9QxlcQ1drmyhEMdWc6DK39WBrr/XWA93mjTo8g8OVHxRWSq6tYURE0qaCynTjYlis7/vAhXigyFrElxoNRDFryu10ZSqZmXmRG1SaCrW2MB5E0f1bvkOx8oW1kNXfgYJuL9xGCr2m6BHYqiUEDd/8XUCFVeu9pLmL0Snaz+fjn/351/+Xt//vABA5aC33LC0HTAnXbvcvqYl/HScYHpkw7U9VAyMI2PWb26dPP/zoq5999vW+j6qiSV1GxNi1iS+/eA8RAxF5+5233n333Z/85CchdC9fvnznnXe8VaqZZSs5OVtFh4hJcgmseYNYx0z55kQgTyu69+zIFCICMy1wAMw558qbShXi5OtAsppemFC2An9BBWUAlEJZXpV/rIPYzWXUFRVRoCWoDuDtr1voplQueRLUzM7TmOfUjID1ICAiEoiIZFOdFaHJFFRDMkRU1qBMJHC5rx6Emk8nC5GdIsoHyv3103gOIWhuIKnCiEtE42ix44DUmtWnlIAx1O4uWL1zEWkdghHRG8ECaM5zCNy2IpoRADNvNpuc5/Xgt7eWmmJs6WEvVDueT+M8oYFbKl497y5aMReiLwbzQiAHZFBF2rskRcQuRI8AFw9mTm4suucHQODbT8vc3Z9OTivmr68Vm2OVkS2lFCO5unLN19a/y27/0KkfRVWPRyLq+96jAknNGypP0xRjnNPskNpNKKHjyKGFHMwslla+viU1ibk1U8qfKsjLrDLNXEp8MnAW62WD1w3rCzjGbhzHEKNfk5lz3YN+ZsmQ1Au2UEeeJ2Puh2hqT5/ebDabrgshUmAKIXjBoq7KgQjQCqtX/eSSLqOoQA4V9IdlfxG2ZEoxc3WxbgEQgBB19cnymlgISbBeXtzG8gXvZc1QgRdcuSTH09mVsfeUVFXHK0jFZ7VUDgVe2+sPNOvjT954wpL/vtSaD77VYuyPL2WXXunlXLfo2pL/WjRxA0hdXrZdvI3nsm1B/ZpEREi1n99ynSYn20pbG0ZfqIDXT3+hgAHAldujCidHbcDl+i4P+qi5YQ0DKnAgwB/+8Iff+Ms//9rXviaCAOJ9NA2MkAzs1+3N8Oskgx/3TSIkq6BYU6AQr29v//E/+6ff/e5fz9M5pbQbNvf390M3MDfExMXTBjAjUtWPPvroRz/60WazM7Of/vSnV1fXPmimgLSQV7iMozZzCIfDwbUgMYYQmJhKsYcQQdd1nmFyIIeZgaF3RF0bd3CRZi5lcKYOtSpt11xSPD5ZKwMcAISwBE6av9u8DSJSAM3Z19lax9QoHABA28Dn8xkNvKqn+cfNwCeDUovllsUqaU1GgQwRBdEE61x7N+x1cJJc/XSxJ2RakVe4JvBgAJ1L94K2/4kIk2c/ikQLIWyGjYfdPJrqnYWY2RMrTklotvBROKswVUy4WwCIGAKtJUjbnFZVb4lPxFJX2nUdc8m6EZGXxKiCi+ZVvME8WBOrxmVmMW3+d7Pk8pzWVbNYCp2xiXUFawFYDxucTicirozQc4PLSm2p4jl7Bxh61w1V3Ww2/tPNQV1R6jNzrK3gp2nq+s2w2fkJChWOTihEre7L5ZunjUVEKlTe064istS9VI+krecHOng98m2Rm4Gq7q+u3CO3Wge/3kRmbbF5hGlUVSbigGRwOJ6ePX1vt9v1fR+Im1Rs56/3F3lvswciqK4KEQHL53MR2T4dMUaveijq0xbv0Jrx3UYACqtdEyxQS9F8qfsC8w/LRlgVILkmPh6PABBj9DCPT67ViJ1HlTx0hJfady0GH8zCWne0t15EH5Gvn7YmHzjB7Wh69LEyXo/z4xGGSy2LWLzYx+c0sXZxwaURwOI5MLOAYGHT8DMXPgMXXMTY4ClgFmh1v7XF0R7NqS2xPWgdCAanqsLLL5rb3u2aBFXQ48VAMLNKoWtwuM3Xv/71p0+f/vZv/CYimglhMDAPPv86ILNy/PqArLppSp1NeWZVVem7zVd/87f/q//6X/9v/6//5367TSp9N0ANZbinCQ/AAvM8me2vtk+fPhWxGOP5PH722WfPnt36ZAiUrkzgeRdbaM2nlI7HY6w+Qc45RALAnBRMuu6iIF0BTMyDKLD4oBdczeUFHxmPZubfQ0QEUikMAL4TvGKpiST/ihY/g70qtf0PhcBv6sfSbkoURCZXXa3MwGqErT2PAznWmWM/jevhRcNQY0dS6vfbQl5m3/UNG/uSq2dWFGhgFPTiLuZitbiTx3UiiGickpVE9UREREGzHu+Pt7e3xGAIWYVg4a1t7OxN61jlnmxSo9k3ROQlxFCFgrtiTtarWm7ti5IDcwgiydRUFSrIkS0Q0TRNLXjerJZAXDAnWYAshOCE+9OUCoWIo9BLH1wbxxEiT9PUdX2MMcauScmu61CNmWG7EykhhzHNmnMW9dVLtdE9EXkPiZQzM4fQufp0XS61q4RP6zzPnuhthVtee+3RSA6xsYnVERMXRDkrAKjkquSW5ddW17ILaqCyoSv8mjlLSskAnMH7/v6+3wxeO7cW8R7FMbMQmGgIxNN8TilNKcdI77//ftcFKl6viUjzrVp8qz1PW70E2NQnVepA4AAAKUkIS4rKc3Nl6V4Kx9pQpu0gAJSmg9dLrokm3zWNOWe9UN0wcsU/DEMIwWnbXTe3xEe7oxUoUPnkgQ6GS427/uSBnmty5MHT1k8uUFHrkay3M1vBUwCWW+AqlXnxLTVoWeEyiFUMrgxlddoDKiq2bQe4kGyLTdB+b/q7AF/Mn5AWaCu86Vi988NRgBofX6/LMg1grvkREWtu0v05jx8jotX0gxtiXur+53/+Z29dPX329JmoKGTCICpAsBq1XyMT/KA+uE3el7vUTcHMTKX8g4kQsxnGoX/+/Pkf/MEf/M13//rjH/7I84giogJqSkRQuFmLIHCy3M6Qmd97772//uvvurz77LPP9vuNl9Mse3WVS/AhOp/Pac59N6hTwKc5Eg999ECjm8bmBpUZIlm11hERiNfmNxAZmpk1vOta+6JRSgkJAVHB0MAKlzoBADLBKg1WRACYqnpPoTKz5s8f1HHgdRVqLXZcC6DmW5RXqCKAaymOLSIpIKJ5fGVlqyYxVLXKDdSqIDhwCEG10D537glR1QpgABhil3NqMrGBsxAxBPL6CjMZz6Vj+TzPpsULCSEcj8fD4RBCqKwRJWAgVrC1QNiwWsUfrcoYVpGotc/NHNKqubKpAcJm08cYS3Ew8zRN7lACgLfcaJrG3f0Yo7cwajqm+anQrDGxptgASvt6Mc2pEHYiEwDM4+i+LwB4xrfv+92wOR9K211EdFYZrPlFM9sNmzHNbWavrq62260BNd5HrlU97777rqre3d3FGPu+V1UFuru78wdzGyJ0vY9Y65DhHNp13LLjZgooXavFaQVO0RbShRlK5bR1wNNKZLaAG2NkdwoBgDmuT6tXo9jTdrPp+/5wMJBOTZ4/e+v6ek/Q7CoQEc8cwZs0kB+lkkfVdz0jgVrOmbD0SXQSrjWexsxM0eOfzXD3Jii2CmgjItBD4mJbFSX6OvR0mO+ONpUxxrtXr11ceJ7eZ9OyTGluU1mK8d4UpcTG51NTqlUWwVpvQjnFxJa90FSyLoQ8Cit6RLskyVkP71pzLQ/zBS4yNPVMi1Zun2PxdwEAfHVdvGC9xYOxffBUTVHSyjdbFDA80sHrP7/ordZO8PpzMgCsvApVd6L7x1UfO0Wcb40YgsNYPvnkk69//et/9Ed/dHtzKypWu87BP8Dx2BW2L9bBi5pnYnAn3tyhD0CC3Kvm3f76j/74j//mu9/rQkwqIXRrdiFbwQEAHV8zK+AHH3zws5/94nQ6A4B3lysFoCDWrJNKagNAqnA4HJqmDCHEELbb7Xa7BckqCaD0CnRa2TYR5AIpdrYGa9TEVQ1hFK+9hV7dciDCNd0dIkqt0mnWp8fBgNhDlq4adUUap1WaBKw1hZbd+XMLer1GG8CnqQoXR+1qhvkiyq0aiL1Ol4i4RoRcVWBt2YSlMgpSksYBUlCdHjiNYQHRkFFtN9vCRDmrGELSk4yqioE77kRkmiYAvLm5LdYJYheHrvf+xyNUfJZCibq75iCiLoQKLXlTZkilSBm3CpAJycyc3dB7PIzjuNlsXNe+fv2aaryuWjUjr3DjIgqAMfYevnGaiALnyQIAkkE1RdMQAiHHnmPf+Zv2fX8TgpnlnGPsobb8i8TTNPV97zHtaToTATMxd9OU3Kve0nCaxtbWt4uDd0zy+Z2myWfTex5vt9t5nj/99FPy3JPbMZW9BHkJCfjcTdN0Po8NsqBibTnV8L+qLO351odfiowUS3l32yBQMyMG6oP85MnN/fHU3MEHkhAATDWlaRi650+fbbaDpHx1tZOU+50/sxTCHC0prfWM14tc+DZtMTAzY+nP4duBKv4jhKACXqwIq7wpMxtgizpQhX3QygiuKYZFGHoGN8aYs7aKXhcL3sONiHa7nacSfLFN09lyqW90y4mIagnMG1xeuzQ71nb/+kwXG01LtTVcJarYJQ4cEVfpHVvd60IftdsVQfIo/uzuL1IJ/q0Vp4K8sZKqTVazyaACS3HlFrejjWqTjf5nWD/KA3uhfdLWx/qhv0gvls/dbri44MWIt4o6IgyRp3H2ufze97632+3+4A/+wLGdwffeG+8E8CtGmP9+4ehlYooO6rruo48++q/+9b/691//s6vdXkUDRcCFOIIrCT4ipjmHEE7H05MnT/7xP/7HX//6nw3DMM/zixcvvFVZ23iImE1B1RQBLKX5fD57Es53b9d1wI4kR7u068HnmBqgrHzSZqFFrlyg+3+pas6zW75dN9hS8w2NmctJ3q3mIPxgZkNq18kqWPEIbUuIiFfzmBmqQdAmC7Bmd0REoLTqaxYMM/k1PefWljiv6mKxUCN5/toAdbPZrDeAXRQ6MzMxs9c2TNMEq5BgE3lYE2YucQAgxmjZ2Sp6DGxm2QxDqMXaJZwwz/M0n0UEsaBkVTV0g+ODuFJRmhlUZDIRtUJqf+zopSyK4zypqs/1NCZVVbCGg3Vyj3mc+r5HRP8JdYc7IEsrJar/l+eeb25ums/nTs/5PN3f34sp59JrEgMfTmeHLquqtzNypPrxeNzv97th4+uQSlK/hOtFxMPLx+Mxxniaxvv7++vr63nKt7e3p/HsVP5uVHla4e7uzvFfx/M0jtNuFzzw4FmJ7XZrFd7cHrsIfZGUS3Q9pblC2Rd4oF6CdJbd0RwsWOz79WmuS7KknAvoV0R8EtvSwpqT4oDM3Pf9zfUVAAhSjHG/33d9aMuvisSSgbbKKrnWCs2FbcvVF+o4idRiZau1wo5r09olF6vvDgBq0BSwv2aMkbA0t7Ba0uZyHQCICQAcrN4ew58qpXR3dzfP883Nze3trQckrq6u/b/amVgBnlaG8Q1e5mPDZT0jTZmZmdrCTtNEIhTte9F3aG2vND210utLpNNWx/or62d4QHKytg/Wq2g9QevHfqwiH6jOte6HlXr+MhT0+g0fjNTyiG8MTTfdi+iQh3a0q3mYK6U0DMM0TV5F4JmGb33rW0+fPv3df/a7iGBgIuLsFv9Jj9b68Es/p0aA0veDd8T84z/+4//wzW+ZWRc7EcM6x+X11wB3oidPnqRZnj17ttlsxnHcbvaff/7pW2+9xcyl79VF+KKwGaSUutj7iHmv9WkapxD3m8FA5nFiRjOzChL2b5e9KotG9L3XftdaLgUARIE5Orqq7X8iB9sZGjiXRd0D5rQVRGR14qByPEmILZYLK3fcd6mkiVZYM6i2NjNrlSaV1KK08VlrZddkjT2DABHLsiYDAyEMHodrd2++bA0Fe+Q2Oh6q76I2sJJIoAW0bJVhIOfs1O7znGDFnuHSzVUv9l3PMcTi97SLpJw9w+IYopZgcz/YNynWoLSqQk5uXQ0YRQu7NQxgiq6QRGToehFx+6Aben+SvOp472PlOsM1mWvQyGGkPM+ZmYeudzpJANpd7Zl5PE/39/cUw253BYix63b7fRdj3/f39/ebzcabJF5fX2vKjhDWUrKcXevv9/uUxJsZA8A1wle+8hXJriNT7EsNdIxxt9t13XB3d8cxvv8b++PxeB37d955py3L0+kkXoyn5rUurrldXLj5SFzKx8/naUoppaQKIlL41VWaun0gfH15F4DBQ0kqIcQ5JwA4n8/jOMZ+aDuo7Q6sztl2PwzDcLXbxhin8+jJgq4PDlDPeW6ryzNSbkp6pxQEEM/8rDRNyxRACQjPPg7rDeULSSuHRsvXiAgaeSSrvbjDozyM1zZC2zUIhohd183z7L2KreYsfNbcWnW7NhLv9/vj8dhablDFczFzVVmLi/l42OGx5rtUeL6GW4TMHwMRfY21gaKK7izFgSsFvJ5Tfx7/nj5KgTXBQoie3H2gets5689phSTFVclGO81WbvF6tdgjK8TMSrcsM2NGAFQtVrZHl9vZRJ5iAQ9TNBnEWKPj9YpF9il4wi3pRcibiDxH6Dk2l9pIJKoiSkYIyh19/c/+dNh0X/3Kb4lITQCvPfpSCmAgBG8kBvmi4wtTyF8UjF5xgGjbAoAxq2z2z7NGDK/++L/97/6//+//tbveqY7IT0SS92Qmg3kuqSyKIavYLF03KHT/4g/+xf/2v/5J1/d9t//ZTz+7vn6CgGrZEKb5TBTASNW6fvOLT17OCbo+UmAF3HRMBn3XMVE2DcgcQx87EVEtBXNJhYg4sCiUeGlKMfbICEDjNCES8RA78jkLCEYllRu5qOc8Z1fPzIwMWdKyGRDNIIvKnGLomNkJOCMHDEsjowaWJlrYnZyZaKErUWeepFZI5zetVdSmurCIq6oR1jBmamnssrgdE0SYRUUSM6MYMfQhNo+hbcWUxhi7YRgCFq6Ymu0rzmvVjsjMWWVKiZBjjFDDfb5xzRQAQ+AQghE6dlfEiFBExnEkRSJCAZmFiWOIIjLlZCaY1WkxkNTLCwr+M3QioiabzSAiYjj0Wx/8eZ6975OrVQCgUNriAmFQB3aByBRj3Gwc2iaqGiP2/T6EkNK0HQp7xqtXd9fX1wo2DNtpmhDKNBGSATIximbM3kfEx3Cz2Xz++edeKeTSP3a1E8M4HcYpBgKgPI0etunicDqdnPauGRAiMs/jOJ4AoAt9ms4myVTPx8yMY8pXV1fb3aCWj8djDP3QxWlKSJTUEy7SDCbJScVUcyDKLviYLKspouPAV/JUsUCjAaCVjrT/rVKZkmQEcusfoFR8EVHfB0l5TjMR9MMwDJsYYwx8vb+62u+maepj2AzdZtPHyCLJUwxSy3b9Ikqg4NmpsmihyvSSBlJEYBFBYAT2Gq1xHIeh8+6xLWzuKz8lcb8/Z5WsybmPCGOMIRIiiGmIxYI9n7NqdgyRVrxV3/eq0HVDzhmQDSzl3MfueDjP89zH7upqN46ngPTk6a2U1gvRhSkzGzkIwLG36ySv+y4Os6+JyAJe8zOb54Zu06eU+n6jTuLNnWhqRiRjuaKrGC+2UTV2u7xdtB5rLQtmuuruUG/qD2GmhR5lWQNrTZnNVJEImwAhLGWx/vj+2iWLjaS5QEC9Vq2GYYiC15Ejotb2jioSqrhcinTXP5vmXwUoqK2AJjEBgJwHnx5SUYJnYnBJVCB6JsOaa2K1zFSds1cVEb/3ve/dXD959vR5Hd4v4aT8L3AgYs7iuavf/u1/9Lcf/+Cv/sO33n3n+TiX4I/mbEyBozfysyyhi2nO4zhutvsnT57c3NyklIj4fD7/4he/eO+9t72GrOs6EQPEEHgcx3EcvT0qFBAKbjbDth/meSa0GKKBS9iYUgVOV842VEu1BXohQeRIpd/ALEJlyRJCmTtqkqKNtoiYaCOZg7rKHYgEtmhcKCqzQDOabdg0lq97uERle+SZuQNIzfRug0zEawvXGxQAwDB068WDiAQYQnCYUfCOAmRuthORK2wAAKAlCSoiUrvXVXAjVhKVxjDVx66PnRY2t/6B7KZ65JxTmqDuGmbe7/cBipWtVqRw3/fdZnCWiRBCVwsRqlQtFB9YioAB0S2GxMwx9q6Gm1+V7hMzl3LECh/FGnqxav3UsMespesljuPoRSZZ5Xx+GWPsu6GWGJG3vzVaEoreLCjG6LwQTpVQao3MiMjpgudpUk03NzfevIEpOj0yGoTa4c79m+1269PqIegQwjzPnhTzWucQwtBva9xCPSXpcXgPgbhpm3P2wfTXjxyEvRODikhceR62cjTbmLffm6ADAKhU6gCopm3885xijM+ePfHx6ft+6DvVXMqyN53nxQHAk0rUAJL1UFGrbRkREYAcgOGzxszZsogAAjEYiItlqdQxzfNpvpcTADAzIU95PjtVmSEihshce66sd9aDQK5bwyX2UJ2ueZ7P57MpDsPg3QkZ0IGH7QF8FhTLZl/3NoYvyxuWkW/7N4TQImrUynNhAayo6nboacFylth72/vrKW4yqingutPFajxg7YMWXVg12jrCjIgMS85+vU6a0Gj3Iig4ofpfa2g02ioZsb5aaJ5uu3oTav5L29JWw25aicdWyrtwyZqVXKnnAwyxOs+opfN5YX9o0m1tqpjZlKbb29s8508//fTFy882m03XDWbORP8PBcj6+x9ECH03zPPpd37nd/72h9//2u//i29+489TSkSdGaBVunzmaVYiImTJ7kGypDwMw0df+fAv/uKbbz9728w+//zzt956Bmi+z1U1J+37jXPNMLtBHYhonscnT24dYCKSj9Pc9SGVms5iqmtNbuWcAUqpa4nOPVpM7rAhIpiilXlsm7PsWys5sBrDsLr51Xvf0qpmoC1BWtE66mIJLuKvzCYCICbJaNj8zmbjq1pD7pgZBXbHep5HXFU3tuqLtrtiV6yQSEXztdwSoouV7Xa7nU5HosKtIeJywULpox5DCMEluJELsnEc3YjhVQEGMyNZZBQpHj9Uzac5ISLHEBBTspzznLJWAgdEyzWEI5JSShgWfgOZK31Sxcd5S6g2QURETMwcOSCWbriI2FdMFtRZafPlYAJVNcMQAjL1sQuhxP0AwBtCJ5VZsqdjXQV6mtnr0X1NEtHr169deQAAEvmYnk4nDyBPY7Ek9vu9iDCSMzZ3XRfjbr/fmlnO6slOVb27u2PmXYxJi9dIVIzLrmNXQhxXbNUObaPAzOdpbGu7/vIQct9KhOFS8lwuXTQtZCQA7mUtopmZh6HzFeh2gOdotEITuq7r+9jUMFZZ5zbE+l4AYIY+LUtIExiglPeUHFCFf6VU/P4WnmVmf4+UkmT1mfLAgyLM8xy7wP3QJPY8p/YYWFMkXeh9SYDHUBx+aPDixQsH3O33+xh7AO02gyl4XbvVSBUGppYZXclHf0MrGu5xkHLBeTmjhCtIZhZNda5aafJcpr72NKuVoYiXEePV1EPTX2vd3GJ4jxXheuU0rYxYQlzazkQArITngEvKEr32Bx1Dtr5405vtFutRCqAFE+B5WzUDNXUkflXAHof0K+U8tdGHVUbEzAzV8MJAcBtiMWpWpVHMLClpteka2Q0Avvz8hVt2f/VXf/XB+x+W9AmAc2ktT/93GVn/QMdj6BYBgBogKHF8+tY75+NxGk+/93tf+/jjH+6udnlKSXWz2Yg4JV4wMyTUlAyw77t5Soj4G7/x/re//W3PssxpvLu7u729JhIz67rONOGKwNlNkHkeQdLhcHjy5ImNJwTGQOfTKcYIBg5sJgoNBkcE7qPPYxK4MNwKbMfMQ+UGgsgI2Pd9O6e8LZFq6TvbPmwn5FRoEBCRKvTdapEDrpIf62s+NqTMU74cQlilUsyhntqkfFPAIktcxBVws+7NnCTLA+Banb9C7uOu6uFwmOd5GAYGECmIhK4LkTmlNJ0dS4VpZX2WZ7alww8RFTpMkPk8e1hYCit94cUlwJTOc55qrp1cP1NN1ioAAThuCwCyQhcjVQpJqb00vAxmHXxy+UWASsIDuQ5zRrQWHwaAAnBfJd1zLkWcCga5BP1UrCp+OFf8FzM7bMov7v2LoKYbAaBlZ1U1huCwvRjj3d3ddrt98uSJU90Rkbu5Q9efp9Hv/vr1a6dX9KXYIqLTNM3znM2du8LtJWIhBHcrXWU1SaoX23PBEEEN9y1SGC/E4gPpXISyXkh2M3EPBRE1J68+KLCMPo7TmcD6vu/7ngN2XbfZbLz/YPHnGImREkJlxm5SWGRxywiDu8WODGsiu+u6lGd3oz0uEqOHQjVn11spxsgUcm1Q4SOZDQA0JwkhhEApKxNMU3Kd2gw4ABiGAZFzTi0G6fzkx+NRRPq+9xK7nNPt7W2a5qac3JXU6qohYmsAAxdqhpa9s46YEgCo72nXiz44OUtTfkRAxESlWBwR8bIg2y5Z+R7IE1vBWluMqn1l/ZCPP6kPQIUIsKV1yWXmAvHDSxhBe45F0xuaOkEKrkxiAgMEDHmafbc3X96NrPMK9YeXcPByg1Tgvt6Ivj1BWwdVcuX10De8gHeZqLcoSewYI2UdxzFwh0jf/va33n///T/8g3/lpBSG+IASi34deqxf43jEJuF5MlMiknl8//337158/tXf+u2f/exnp/s750p1JI4qeK2XqobQsRkAeLpx6Prf/s2v/uiHP1VVA/3FL37BjCESAJiiC9/D4QAAXdcpWN/3IdC2j8MwqObdbici4+no0TARYWFEBFrCKS4IAne8ibPknLOqeb4zpYQAxIjIBOrFElhhKbACZ1aL8sLzaDCrwDW/VAEdzQLFlX2K1ak1g6Z+2o1gFRZbKTyPKHa+2/3V2sYYhq5qFkUDqk2KqFrbXgULoB2XTqWwgm802dcIKIigK2AIjzeUMx1k0Bb2nJauO8RgVVy6uqrDpQRls3DAcRKRtJiqhO5PL4OzAm2JAtVw7mazce8BER3DDOTZHL87M7MbRmXEjIhIKkb6wvCtMuL+/uhxYwBgQqLQ9/00TeN58qgyAAEXUiQ3U5jZw85E5M3qPZGPiLe3tymlw+Gw3++9NLnBtYZhiKHnSqPtCYvNZuOgYgDwkfemOl7uYmbuOrv/Z2YcgkseT3MSkZkAlGbP4u0PVQOncZ6qBU8h5pxJUhIrOeAqx5YtvNa+fvgKlCyNa6iuUvGtdJbS+bKNOagjqAwANpvN1dXVdjtwxXj7aS3w6BRs8yQi2jK+kl1JXEB7oBa0dF03zcXf9WC7xwNC6FRnH/nNsHWEVFPwOeeApJpPp9N8tWfezPMMJtM0iZiDn10UhxBibUBnZqol9PL69WuPUF5fX7uscBaO0+nkVqYDIZ1QrL3jAxLiB8P7UITWHgGImGU2kBBdogLXNFObhRACmgurqhdrSdX6gg9+byc/0MFNLmF1D2yF1Gtyr1yq9szwZC87L6MBWOG7Kjtr8ZCpou0IDB40kVypvHKENM/YWEYRsVr38zSFyuGeayeTrguA3J7Yt0TAwIBeUwj1gQixVHRQmwwfAtfTpSyyjUWoFK8B8Pnz56o69Ntpmr/97W9/9BsfvvXW2+YNb///JQQNAEAYADQTI+Lt02eb3f7Dr3z1P/zlN50uYJ7nEDrf6S4XzKzvu5xnIADUOY0ffeU3Xr44HA6Hfohe5vHs2TPPbJmhk90TAaKhahf43fff22667bABgB//7Y/Oh+Pt9U3O+XQ4cKXcI1uyRERkirvdtuu614f74/GYczIzKaUIzgXf3AUDXaa1LZSiIKX0V2+ruSljWKT8YlHyZR/iNmhmoIhrlWAubgBgtWfq7kXPrhXdD8u28ccu58N6vy3OjZmZiVQwuW8/AHDOSK1N1uqjOVVy8Ztd8QRigMXpBABf/1AotxhqI+EGU3J5NGeJbIiYZyni2BE0NU++HmFRJ3tTESHuETnneZoScwwBkClwYR41MQD0ymPXQF3vSX3MOQfmfXetDqGb5yZ6eFVjfXV15bZdCEGhZIKYws3NsNlsfD79IQFgu9kPXbi7u3Pupc3V/vr6Wmpl6qeffmpm0zQR0dXVVc656zovGL2+vj4cDvf39+7d5pw9heyi3BVtF2IIYbPb3t/fn04nRPRco49tCOF8Pmf1ioN+t+vmeXakLhXIvc6zAAAydX0cur7yWGhbD2xFKxTJjRceQvu8LWCrxVGpkG+UcAvWRrlmRozOyMhIzukdIoVIm83Gi7N9fXJYAj9tCkSEuZShq4AYJCkhrtJCQ8vTNtiEAxLLoppnEdntdsXaE1MxJyFPqYQEVABUsGNEnqbpdDwTkTiCspjX2LKhrVcjEamAmfZ9L6UVoyHi1dWVP97V1Y0jDwIHVxbep6uJC4ASnbRHgVZYI3hQPZNoZmqZiaF6faH0Hr2gUsDSEo3xkYpdH48/fyB22n+F2qkFqjmuq64ttnI54E36Umu9Ej1Kuj1+9/b5+nkeXDy88847UIGFVIPDtuIk852LFZsqK2YZNzkXp3YRjtTAMoG79f2aaUOtKwAs4Uoz895hrjlubm4Oh+Nf/uVf/tEf/dFud1Xz3G8oFvpPeTwg0iofGlhOc4xMGADy9dXNe+9+EEL4249/lPLcd0OuXS9U3aaDWigSUppyzmYyDN2TJ09ev36dMyLi3d3dfr/fbrfMXseZWhwyxtgP3dV+t9ttNkOXc/4n/+Sf/OTjH//4xz/ebba6IqxoOgyMCNndaEQ8nE+IGCNT6fpSx9DbknpZgi4QqmZmQktlJREt8sJFACKaLq2jaRWDWkdK/ZMq8gAuV6Efs69As0jF0rXqxLddIVrYBorardaAZqlZUi2R2BX9UyQ2s76PzSl0b9Il2jyN7vlFDsPQ2Xbr47MdBlVIpojmtUkikrNiLGBMAHMaBK9QOtyfREvNhm8NCtHMArXN6agoAyPV0pKvDYsCoKEZSs5iCoSb3bYfBn+LeUrNZeJahVUsY2r1JzCjOfQMRL3MKYQAXGSZfx2Jzufz4XB48uSZIbjR4GV+KaXTaWTmOPRERBhijKAFLaWqAUlTBrMEqdlYvpGdsPD6+trFqC8J10nn8/l8PjthiMt05xJxs/7u7s7j/95T+XA4cCXA8s0yzrOqdt3gytUnv4oLETFQtFKKXdi8m/WJKxuxLcIvks7+S8u4EzXwREltbLaD5329Ia5JJoJh0/knfd97fxFmDpGbpl+rYQAIIeSkCVUhj+N4PJY+B10nXdeVWh5kZDPRpoNdLzqcDQx1U4jSvLfp3d1BKtlLIFIIzbxLkg+HQ86ZGUPtCYFI4JynXclrmKLWqq1pmnwEbm9vh2EwNTfyTqdZxBDFCDEsU2+r6qw3at+HB5ZscZMwrlyq3hX3xhdR66fVlMpavTFz5cYqwsSH2U+s57sgcvfNvNRqvTCaVmrGxFrotXOapIIvqKWp/7/wCqzlWzvnwc9wtdtjJR5rUs/3uVdMepioadl1ctsfUay4yE37NhMGVr5Uk8hUISouyotJkkp5+GzihH0GOs9z38Wf//ynP/7xj//pP/1dQEVjKx0HyuW/bKb/IY56s4eZYDQIsRdJzAEAN/urp289B6APPnjvP/7H73l4bQnsi7qMOJ/Pbj6D5I6DIjx//vyTT38uIn3fnU6n4/GIyJvNBgDP49jAL8/eevrkyRMHKQzDtaNdjnf3H3/8sdcdopaEMTR7tvoxPmt9iALmDH9tcRBoqZgiCsS2ArG3NQcra6nSyi7SLXBsGwNxob+Q2uiQVqXrtCBfZG0SWk18aAWkUI1RS6MaQEArtBuIqBVK6lmhomxq1N0qdUNKyTVSjL2vWWYmYlUQMaLAIV5f9X3fE2NKydCLceJpnPu+H0KnqtNc6qeJec4OV8GKKnVT2ocL2FCTWJbNJhKGOc2TTO3FFYiIzDRnDcH3PGANKSMTISPCPM9g6Fzc3r2urSUfLm9nhDVnKVk5FqTrNE2bvt/udp5edWsgqbTksSmY4TQl91yJKSeZ8uQNqn30GHDoeiP0J3Y8s1stfp3T3anruo6DAgzD4C5djPHVq1fzeey6bsqlyZKHlKuCxBijN8M2MyfLFBFvtZRzJiTH3PqLMHOMBSKEwGPyrpqlO2HlgjA01hqhFRFfzEiGegGOhSqn1+JorSr895zFzBBgGIbr62unyM05E2MtqIWUJkYKfR9C6PrgHTiYi/TzlQyre7WJQ0Qw8kxKzvlwOL18+dLMvKEyETkqlpkJSaqMDbU9yel4Pp1PbvLu93tVZY5mY0lPeDEYBxHxBqD+8IdDmsbzkydPitAmG4ah7/uuG2LsETil5KYkAJzP59ev7+c5M9Pz588d/L8Ztp6FNbNk4upAWivDnNGjU7YM4y9zhNrJgy6yTovt0kQQro4mAeoWWLq0+Y/au6CpifbhRYUOVmTiWs78nc9fJery57ITHZ1QqUAZmIi8OAGqmi8xPDKobQ5C8HI0QDRoXQQiIwBgWPpRNFtAURGhkWsgIgPBqlbExfPqK6rasr+oqi5vCJE8j1g7ePgyBTNv9O3TA2abzebb3/7W06dP3377XQMBYM8Eiwqs2uT90sffX2cXc8ma4WxI3fDhV756uD/+3r/42g9/+LGqv5PzxnWahTlO89gSYJ5jY8Snz26fPXv2s5/9bJ7nvu/v74/b7X7OiZCnabq+vk1pIiKXBWby5OYtLwX50fd/cDgcfuu3vvryxYsYO8tC1DWx0hSYj48HuOZ55u22Lhpr6THEgDWQazWps1aEzQ8IIcgkqoaxBLIEZJEyps37KcHAVS4HAFZuH1FpZlzC1G7nQY2UtjXtTrlWROtqB5Zi9AZtlVJwWRZ8Sv78wVQAaDzP7RVCCFTWmjn9iHevIA+vISBTTjPnbH2fss45hxBEldXYS64NiIhDAE/u+po3m+fkO+00TWOaiShNk4tCItIC7AzMXpoFOS/9mFOaEZGYPa3uuBs311ratfVJRSzBeVXtqn3sgW4E8Hyq50EAYJYyKTlnR8188MEHZnY8HsfDiEBA2PgOPVnrGtfP2W635p1uNxtPNIYQvDiYKgGIz3I27Ti47uy67nh3fzifwopEwgffFXZz6F+9erXZbGLHaU6ImOYRkKuLfGaOBd0NjIhzLrVqVEHpZiklOZ4OkgQAGpuzWuFPhksF3OT7AwVcV2nBwDvS+8mTJ8fTwbU7F2CzeW7Cs2Y+XPv9vu+74itbiRE2EV92GRAh59pr1QzHcby/O4jpnPJ2u+UQNwO0lWygoAYUEAgUY991Qz/nJKYvX7+aUvZ2uaGLXOcXwTUNeKI35zxNmNL06vUpdPHp7RNmEJHAnRtMADClueu6w929E4iK2N3dHQB0XQdqjXDUDanmKTZMctuqeOkjtvHElXfXjGmoNrpV/IcVl7H4Zg/mxQxMDEoJK4ChqrkA4VCgVSuVDG2yYBWKazaf2yvN04VV77W1cYYVloHVXcaaaPeirLUpYDVKl0SJEcBhurxeAC2U0h5ARMKCYXNpS/77Et9ba3ho2IRQzQeghrNog4tQKt79IlpZguusmPMatiu7kLbiTCysLgCu6XNO8s1vfvO//++fMkfX14EDEeGvxsLxax5rjxsAANQTuoAIxFAqzi2E7vrJU7D3/vBf/6s/+ZM/efvpczOIMR6Ppxijk/gDgIOzrNocHXUfffTR6XT0PnFzTofT8e3dfp7n4/GMiF03hECgRmCRwzAM58NxiF3XdUQwT+WyXnH0wKJvgg9WoBKF7Ij3eqqWzhlQFHabbj9k1Wt9scYMEUqfqnUIGi6TxM2Abem0OaeGZDQzKMAqMFPnhHFNUC02AFVQhEu0Ba2qgJqkMzMymHVuBkF9VLcqEEBFnGPLIkR3s6ijgsMyYkAzyyo55xi6fthMc0ophRhBUUXElKhxDpe6TCauO7DAWLiMG4ADH42mMUmh9CqPHbsOgEBNsiGiaPIF3/V9qEQ3IuKJNw/JQuU2cQ/SxUGMkYlceTuhoKSEiGaSa3tHrpHG4/HIYfCeCtj4xQxdEYau9Rykddr41atXze7xXdyITRosyEE60dAVtj+/f+i35i56u9liW8zd8+fPASBEOp/P/iTmfRJzHsdxux28yRIiqvZS0eBX/SbG3smwnF9sHMeUTsMwJMqQZlUld84EFcUpYqzCT5ssXou1tdpwb0FVnzx9utvtUp492zKOY1MxzdHRVenqhUtdMe3ldgoqtX0voZnlrB5vSJIbeKoECUpL3QKbbVKbmZ2JzJFQp9Mp11ZR6y1QMVZLkRURxYifffq67/vNZoNA5/P5+fPnCDyNiQK7SeejfXd354i52+sn7gL1fe8UqmAk2Zxiky6Tjw/gV22fAoC1rOkqFWq2cLXCSiUTEeKCrmoyRFXRFljo+iuNJqXKwOVbsgLAt+s4Ae0Dc8Fti/Y8Nc6RzExlIXb1l1DRMU++GQOSijqwxsckpSlYIKJcO2G7leb3clMSFjSoBRFxYhFEVHPW3qXcm1bBfX/n0IcCBSQKIYCRi4mF22hRAFVt+2+VB8R/F1h4q9vPtb5nZjSS0v6JXr78/Ic//OE/+kf/1BQUzeDiqf5LHeixDkQwDN3w1vN3Dq9++uGHH7799tv3d4dtP0wTeHq1uYPtF1cSku29995Naf76178e+8I6e67cet5adTwff0H44YcfDMPgYedpmq6vr2MId69fq+rxeIzFFGghFPF6BtEEAOZd7SW1J19bAwBAgLoaybYWXfq3bYO1jrZsAyjtn4uZWRpsAAB4Cena+2n0EVZl1np7IKKnPNrd3bAbCph54ZFvxqK5f1w9YDJQAKQCkmoGH4hq6bUeiYIT7jQdn7MmFLOsVnRSwBJVE4Npmghws/E6V1TRPGV//SyzyxEiQgPvb++VnbhK8zBACOhufgilwhsIkTRGxkCaxcE1Tvg358lpKBwj43AqFwH+ExF3u51WSI6qjtMUQymlZUa38Z3n2V0Bj0iFEJhizhq5lLG6MhARryXzeRw6y2qO4drtdo5pc/9bTK3ixrXiyNqbutPs7qDXrzusDBG7LpipVyL5YzPznEYicnvIIwSe7CzcGtOEiICll5TnDlwR+hUOh7txnEMIm90uBEoJzMzRAG2tmKitiBS0sq09lhvtRfyXGOPTJ7f7q53LUJEEUFoFqyqoCZf964Pm6fVFC1Yb09W/Xhzoe7zrun7YuCs8TamhgrbbLaEjcsDMgMqmE5HA8ebmBgBev77H2pei6YymhJpcrW/KXTfkfH754hU+pe1uo4DDsGX2AmVRTd5DzCm3AGC/3V1dXQGQagohHA4HWXUetFYEXHKtxddqQ2orNwBcB192GbIVM0YLkpmZKSqYO8ZE5MwHTkKpj0LETUr4/yI6Dz1I9V/XUWUprSodzIxqmqsj1EKSWqk9veODVu4BWPmvbYpdxKW6eIhZVOd58vsWaEUIiJjmnJMgLZ0//JkdNhG8Uc96OfrSWa9Ra9WWK3he2bEVkrM2TJpP2+TgWtGu3kQNwHBJ+CFwzuKuC1RG0BjjdrfJSb773e9eX9++8867DKxmf78+wf8wh5PVIYCZIoWbmyfvvffe6XT6N//m3/wf//v/mWu3c2dGNCsLQlV9VxB5pD2//fZbVzd753k+3B83wx4AEFmcZi+EaZp+/OMff/zxx1ebAQDuXr32K/sY7IZNtaOXjQegpticVFjFdqzwQi8VNVwbR7cwkR9E5MJXL0FV6wtqY0njhbKKV4zq68PF99rkgurUmpWcULsL1QxTW35QG2qWHkRWNiTVRg5aq+C1dT+ttx7H0Uxa4BRQEbHrN8WqgKLFvZGXZ/JC7bLgEkdEkAkQiSFgqNeH9gqBl6SUH+SiH9Hq9qZQIkatdpMAATXLfDpX1icz9/C4tnAItUmw61T/YolLT2ekrgPIOSMyx4g5ewQewOasOhdX2C/iPx1UDADDMOx2O/er3A/gnPsYEL0FpKmqh759Ts9ZKLC7rVAFIteedOfz2ZXHbreJsdBgce2f0fd92mx8ZDwJvel6L/BVbPTFAtDf3t6O45hSskqQ6RU/KU0i4qninLOIYfGlwAvwvI7XsKwiWUehKwDQVsm/tbiziuK5vb1xC8Z9GqkE4PWLAABEQcR8zyKiWWylHGiYNLflvV7/85zcUvHFsN1ux3SnalNOdi5T4E5wCMFMmalULojknLvY7/f7nPV4PJadu4pVLHKpHlpj9ZvNcD6P9/f3h8PhrbfeSimpelEkuOqNHByuBQDb7dZT7zH2ufYNI+9RkTMQBorI5Ht7rUH9l8fGzYXMNO+7XGSjmUHtcSQgmjSlJJr81RatLwpQvD5aQYPbmz64fs0XFJn2wF6seNhSguxxl3YpqUBjAGCOIuq04WbmxHlUYEzYdHY1R+h8PnmOHBFVQVU8qe+t4zwQIrkQPorI0ufrweTBii2sidq2WIuBaWZ6ASEDgMZ7Ur/7eDIuS6/UAFjN1AwxtQRMCGG72bhhcnd3h0DznP/dv/u3/8P/8D9ud1cuexvF6H/2o2g239UIYB5oYb65faIGXezffffd73znO2+99db5cCQKnvT1AdTK4z+m2VXbMAy//du//Rd/8RdEnFK6v79vHMiSshfad12IMUpKXdeR5y0AYmQiHsexJlDFizHagxbTrzYdMjPEJR1gZqCGRFq7F7RIRtNh/mcTQM3WbjqySS6mpURvLResgjmxRjJhVZKLNW3jez5nJzisen2VjGmaPuesyq78ylNZRW7zwuyIiAAlEN33UUS8zS6xfwuJiAxUhBm60NW1nZnd/5AYo0npMekvJTWH14VYX9yIiNCYiSiaGUgLZFnf90kFC3OZEFHXRcTSpCjnOeesl0GgYRjcRPDgVd/3Xdd5Q3tXPO41AsD5fO6HzlWFmcUYVcVVoA8LM1MdPXccQ+i40hN6jBERJacYil0TaxtHn6xYu020WJ+vok0/cAyeAfUz3aJ/+vRWamu8ssDQsiR3ju/u7hAx5zxl8ZjBfD5BrQlW1VmydyokIg+KepgtSWmh4b6FR9EdA5yzKsA0pSnNKSUvuW7VLMQLqEdWLsEDdQWL/jDm4OlVbysJtShg0aJqXvcFAAZpvZuaQF9vDTNTdf6TpZHR6/s7BBqGLby+Q0QwSilV2pPQd86Y6OCAhAhuinmF99OnT10fSy2lbfPyILZkZtnbPjBdXe1F9O7uuNvtACm7iVb2gnnpec55O2yurq78srvdbpoSIiOaGARkNWGkB+Z1UxOwiiW0P31sYJUtas4rAktWM2ffK5Tv3iOkhWq9qtNLP5pKypULzMn222WthoKnqZg4zOxBWPciRWZa5ZhxBRy2VayirQcKJfDjjdeyM9d6wl2gebSgklKapiknxXFuFy9ZJMvNdPBPnKMJnXm1SaumRNvzXWhKD7boqnk4ReCSCqpCmcG5gaqxWRLS1etl5qY1q/zlVvxHRF0/5Jy9QLPvuqurK0cH+HIRsePx2PWbGDtVJbzw3f9THE5U9CgNXI5i4QIgsIIQUL/dfvTRR4fD4cMPP/zOd74zjiMwYSl2zTWoosxMMdg8qRUmqWfPnu33+5cvX3exv7+/7/uN589i7LuOW3sTd9Q8UuSdYUTEg351AS3RHreXAMBKJB9UyxZlXEJzTfrbigAZVpGP9rJtw5stkbGV7YXtUg0kiSv3Qh9RsrWvUM071kWCZhkEFJRWRzu/FCpYkzVliYrM9Qkd/QHOe+AuIAd0VU5EIZTGRGpCULoYmaipcgjsoDQtpU2IeD6f+37DHtoyJ2XDwthvZmBkRFYJnACc2fs0jVK5zNRjoCnlfAaARogx59nZtv39PPzrktcdrBijc42+fv3aFWdfU8UO6Oi6jmNgxtbfbB1/s2LEkBmm6ayZCQMietslhzv51BNx13XQLCpmD4D78zguCQAcHeZfTJLreAaHPTOzI3dSnltBUc55Tii5dM7Z9H3f9QAwxOAeLRFN04QzNiPgfD7Pc3ZTNUp0BJlHBdxMd+NAxLLqOM4NW0CEwUwZANwWArjUsmYPw3tt/fsV5nlWza7/PLzn0r8M5krX5rykgZsT7EPRtIWzwDYkOTMzo2PdHR+DwICgiud54uNRtfeEXQhBRdwam+fkztM0TSF0+/3e8eQue/0QkbrfXW6BQOn34BkNTfnJEz6dRlXlwB6xd3F0fzj4Qz5594mzfbl15Z0lmRkIQwii4hpxvbpgpTLeeKz/t0W5xON7pUVbQMz1FAqhcyPVSi9USrP4CMSutPDx8SEMF2RnAIqqApvNxnNepgiAYJaTiiTRaS3cbBULyXlJTkGdYsDo0fIGv3Kx7C1GfPCbfDgej5Jhsx1845xOp6liMD0I2hA2PghZ5osuV1ThM+uxa4LPK0bvDycv72NmJ4pz87DrOkRuXEVNUHLVxH6pELp2R7ediULpSO+4zZSvrq5c+qhkANhsNsMwMIV5nne7q81mG2NEQCb+ArX4n+Mwx74Clig0anHKAd57773j8dh13e/8zu98//vfv7m50ZSrolLvKS1gwQiBmTGlBDD2ff87v/M7/9f/9Wc5Z+cdNMWU5y5EyYZk6r6tyJhKA6vj8dj30ckmYYGWaEVCoosnqB5wU4FowGFB6AFAhXaDN8+By4xO08SPde1FzkmlWaPrpdbkGqwUg8uvtQnfuCNkLllnAYjdwr/WtrprVlV15hAiam0EY2SnHquhbAAAA9hsN9Vcxa4bYlfAQZ3TKMYlbqyqsQuluEK1484LYd0TzSmJLIk38JLwmsVEUO9n094dkVqNI+IAtd1FSkk0I2KI3PU7UEtpmucxxoFqroeIvADUa4paY1o/fGN7bimlNM8z6NLP0ZVTq/Tw+Y19p/MUY2QWBCYpWR6pXBCu0T0hHUJI8+x+qssR17INbtn3/W63Q8Ss0qZ1v987NgoJjsej47f9Cjnnvu9NwU84nU4+iqrq8oSIYheAioWhhSllzjmrQGnAEIO7+I3CTMRevn59fb2f54yns51tFC+bVhHxHHDRnWu8wqOjWZZWWsOVDbVoi4ZCxcrJtNoFVuE8LYRQBT22pS4iWbJpyaRM41wpL8oDSPZEiUnKKaV+6CigJxrO56MqMIVxHHM+tTUpVf2UUBNQhUQV3wAACAMh56SAcHNz8/LlyxcvXj158sTEKDAyetDRMx2OMPDV1dpgGEJgxlW9n9Zia/hS7Vv/68Lu8V9y0nqR0hseoSCTA8dm9/gbaMW7ETKgtkFW0aIsas2nz9fr1699JftFfJHnvEA98LIBQ17Vtq1nVhTad83MhYDVPK6IpApBd/2aZ1AdPcpoCkwBEcHQC9lTEjco+74v4sexiLACtq3HqIWq/Iqq+tze8c0JAI2hvrw2cY36rKqSbHkfZg6hJBRrP1FAxM1m45u/67osJVjadV0Xg5/m+9ZpS507TVQAgOk/fwhaHxcytZ7BBgahC2Jvv/22qv7+7//+T3/6U0ATVJOlhFoRUEsgOksuyHiCt95665133vnkF582O1o0OeH7MAybPhIRzIUsRkQiBx9PT7BRTRMAQHtIXy7uRfm6iRyEFtI7M6tOvv9eNnNJtFZlgBWa11xVqkjXxaKEFThgRS25Hqvm5jajtS2zgoImPuczNLZqXNgx284x84iCegvCGGPHJZlCXetRSsV6NBCR7Xabc4FNARZHn5lRLHbBC3sAkRhbOEcrBAEAVNRVUcE5MwNAH6L/b5Z5t9liZbhEXRoJx/3ealxhGAYgnOe511IwVgps0GIX+t7BlugEy46/80XSuAZ9YbiU94i9gbbqoO1uy0gi4jSlLkPNzPnYswoRQVfcWTOLoZTQAJdkmEulcRxVzR2dECMz39/fNwkYQri6umqLrUUj55x8uFT1fD77ILtQa8aEmW02WyJyJYoIKWUHpsYuJE92pnQ45O1262VUblxyYFMyM4/0NCEzjqMZ7DabKSVVaLYOETEbUUwLh2VBbuKbymaaCkkpDUP39OnTGONpPIcQzuNxGIYQyWnAEZFqt7Hq+kKLuvtQu93jI79OAQPAOI6m6GLNiUdU1Rc5VdQLImouEIFhF6bJfN5Pp6MLdGb0YLU/T3UiS/MPKFv1oojWXXkR8cqi+/v7YRi6rjNRRLy/v/fJevLkSeH5Yu773qlwc85aq7NCXCC6XCuRmjf5WFzWPf5QSf/iF784nyYzG4Yy0TkJInoCAiohhuNGmdkkt5WmVjRizpmpcMgU0HJNeB2PR6ppL999rrM4oFZsR1Oc7dlsFcd2NceK3g3d229rZXopACxaSyRXmi7umitLDnRNcyYipuAdd1rgMGyHXVOQtKS4QVbsV02bEpHRwrhUPldTNeqW4pP1T1sF2dvSR+TtZglfUFX+BhQ5IFPXEWFApIXdLRAgE7MBgRmRB2PlshAbYOW6fcHxBg36JccKUUOrn+5aORJ4OQEBzK6g3/XXYT/Z5uaTf/b7//zff/3ru+0WI6lByrnvY4w8jUmSISEqOiRynmcke+fdZ5999gkHINIQwZRV86aP7ESegHHjfXtk2G7neQ6hxAlbKT15XH4ZBBYRJjYVAIiBiZDYWf4hdp0vJvc5oISptXrGRbPGSnzTxdCWOIARAlW+e0TAshs9+AyIUBwPxNY0zYBzzgBGgGpeBYsiZQ/nnEVTypOIAOpms0FTyQtVctPHZXVpcS9mLBlKTgsqhBk8WQQA5+PBm46Iy25EEZnmhBCINGWt9RvFYmgXyTL5dprz2QhDKtWQInmSorRE5E7u3elstQeqec5zPnhslhFR5gSOgBAlLwZVMAXXmm4kqeVAxgxmOmw6Ve3iQERjmpndGFLH3RozIZqRiRFQF7vIvaqYKSKHwMMwiCT3JKZpGvo+pRTdIAPoqhmNHAqzB+Gc0pRmA1PQNJ2ChAGHAAHQkADQJKcuxpzmBs/uht6tDW9i7/fygOfQbyTI6XSCUm1GOSdJOZudj6dyd7AY2FTybJqViDruyAJmmOZpnue+6wD0eDyCB+0xDl0/zzMa3N/fm5mIjfOcUlJfsIRipgAKliWZrYgd1EyVAKxaD2vti9WXff/939pu93OePB5gitNYIgpEBOiYGu8PqOuI5TRNqhGAmNXK2r5wp1TVLIrKnPR8mgGoD/F0HOPAzQBCJBETMTERm3LO2/2Vig391TTqq1evnLAz50Il5oZy27NYyjiLdGVEBPRmYZpnJmIOsfaFrLamSYZpFiK+vrmdUyaiYdMfzycFyyqI1nVdzjMzd91Q5UxVvcCIhLU5jplzJdeoJ1qSHLArMGYDyfbXf/XdH/zgR434peEuzSzNGVaKkGulNVQrp/nf1f6bsRRPMiLEWPJoV1c3Td00HamqZGARH4SCTZWCq2FzhncjzEYqgAoGrAI5aUFMktsBQuS9maCwd6nnO0LKBo4BNAVQBFPJTAAgtUAXNZe4RSFkbvqVW8eF+knTnVUBLxns8jkUbDetjnZNfQS3wdXRBoiIkJ37Ht2sqHpkObP6VQtPGP0XA2F94WFmhNh13ZMnT54/f/7Jz588f/78/u4u5+xxlZQSBXbyZ6QSNGh41Lfeeuu999775JPP9rt+nudQQYDFfw3BLLWqMqqFB7bCIjbdaxWe2kZPanERrLCgfqTatbQF39yCBiiR3vXaaAKriTAiXFYOAIAb8otYgdoWtyEMkwrYOk+pHmqzXPO1znRYHYhih1WnvHwLvRtpuTUiBlo6kbUB4ULcw14f0gaBmQM79qoGGAHAzF2r9o7VzmW3lurOv2iypIrzfGzxqzZEIllVpqmksjxW4T4f1fYsbQ+aGWBrX6QtREYYTISjg2wTEZoVzARUW8GFglQCzmEYnPHUDQIrIHDjuiW7rguhU9Vcv1VYP7N4ts+n/nB/HIbBCXoQURDUoOOgakicRfPxHGNEJk/Kc1dcQHe+AcBdeQ8JrjDDJVTjwD0R8RZhMUbDEjwAAGfwjjECUwjhOE5dHDz35kk+50AW1Zz1NJ6nsQQ5mpNhlyDkclSzua3Y9j+e/pymCbnQUiKi47R9r6nlEAKF6KAzqMkOX65tefgIVFtNWskQEXn6sFB/EzmyrESVVjvXvWGLJRizjoV4rTasTraVD1cFwIWkbRK4SlHIOe/3ezek3NP1nIKj27RWnxNRSs5KVth4bBVJ9p27Hsl2+DlJUozRvVtEFJG//du//f73v//06VtYkVNloAxVte/KLZof7BZGU8krU6Y6nSvZ2yrNWmrsgbBSRMvWYIzLBq8Ssmy91jtOl14shuAkeFiBtB4MkLKmSkKlVucoGiCar/CGo167f+ggLHx0AEBgthUj4PIOlzVFZEXku83VZqgp3fUVVivmUoX7CWoG/pioAKXRW01brhbckjW0VQeO9a7+u45fzQn+lQ50ChuAq6urD97/8MWnn/zinZ//7Y9//Pbbb89TqdcGNeAWFgPPq/nqHIbh3XffffHilYiIGAVUy2gcAqNYntOmZ19engVUZVgFfssUNDlT4W+8IhBtj9oUG6+IyGGZdGyX1bxqUFq/C4XGkqoOXmCEAJBSjrEAarB6eKrq/V/rFZbC6BAiMwekzMJQYk0558hFu1QFfIG+1sKRtYZ6XXJQ187qnjX3/iTuSq5eH31v8RJPgxi4SWoEF+RihN1mKBULWFq1M5TrEwRkgVVNCFe2ZP+EufVSFNdPrX5PK8Dbm05BUajqyV1VNcXdbsfDEIm5H7J5KEz6fmAucc6W9FJVotLSoG1qRz+l2naQKDk+AJlijIQ8z7MpbIatX0rBQoiBq2vi6DyvfWNG797q7kOaZRRV3W63ScUIxzQ7qtZ1jPe2g5oQqYk9iDGmecpJFAwIDSirmWXJlg26rutDV24tItlEBPchSS79yIg8ij/nPE2F/DJNo2fyCLC2FrzA6BKR0QInXK1nAAARcXrI/fUOABpvgxsoRDTNknPmWu2Gj4iT2oy3263cNcfwFQCzr5AQaTyXxGR7Eqz1AifLXdeFrodLS6It4CZy13rxgUSCVUmLVk60Fy9eeP7ofD7P89x14fb2NnIYU6YV5LtdpKUaL2SsVVrAL0gDM0UEDoEamu+TTz65urpqaHCpWGVXwG061opWGpHqys4o12f2Au2V/Der0K0mlNbarXkd7b2gVm8W+9XW2r2lKnxXFhaHKQmiNqvDFphLJk9VmKsqqmug4kYvmuhikFXdJ6wwMu1Z23NbhfJ7ONTjDE6D0FZDWz0tVw+XCr4uCI9wF5ayNtxNzqIZkCIs8ssFqMs9xxI/nuxfTvv68ffXwfrGTxFRHdYIdnt7+957H3z26S/+41/99TzPzLXfNbJmCcRZJScniKCc5yllRLy6unr27Nknv/jUeRiIi/rsuDhPbT/TqtNt35dcr0jpuLMec1gBm6l2yGlLv5FPWanfKFHuNjsAsA5ImInqEjWyCj+BlfioMN3SBEYFHCyybpmCiKKKNZw1z7MiccDWzyCEoJLb70QL95ZrFy70pcviCStbDQAUUTV7n0QtAssYoVCTiGwHUiXx9sKuUSRzZeQH8IQaeSYF1BSLAQEATuHpKl9VPTaoqqKpGSuaixaURtMNSDy45KJLEiURcX4PqAQ67jG77uSAXhobgtf/RGanNlys3tqEERCXi7fR8Nf3M0UkZ8k5w0pqr2U6AExpjNttygJS3DVFOI2nKWX3IeZ59uchIgETwHkePeZ8dXU1TdPd3Z1n6H0x+0UahoCIsB9yzkOMqjDPczdsvVopWEhSRKQYmFkg5Nh58x9VPZ/PaeVXeX2qiMzznOfZq9fIUFZIXWiRM0C7tBebCAqVQlwVEG2dZ6Wa9ZjT1Iq/20+7sD6Th7VUl9VStonmEBnOAAAhkE9s7Fiycd2t5WlVzOycMjNvNtl1sG923yCw0kNtytZXaL+v/9RakXg4HHzuxnEMgbquu7299XQYMU7T5PM7TedQ6cz6vodaylUFCz6+44OjGQc5548//vhHP/rR9fXtNJ2a3WwKGcX3cePnuwh0rbDKj1/TzJzMr0nFlhi0S8ydVRfZ97X/kkUtm4U2UEjITUwhxhqLUgVAICsBX3+ApvuKouQKWF4Hhes64fUze+FGsJzyZScGq8aamcEqaFwuZGitAeRFucub4XBSubYf6ANEWv/ZFMOyT1aDuJ5yWhOQ/r1Q0Eua8x/2QMQQY84Tc3j27NlXvvKbv/e13//T//PfPnvraSTOSTBiCJSSOIg3paS69FGIMb7zzjufffq5qiJooI4Lz77EGJlYVwgpq8FnEYXaVwpW80JL+e/FFKxFD9QNbDXF8njhSu0V6hfAxd58HN4AAAghmBXaoyyVjZKd+X3BRecsYAVj8thoWHvzqgqo3ipPK4yinIkG2bWyYqQWky9ZRmQAElEiDoXnz6wWqjJFH/y2mB3f1HCtawMSSpimHCGEyIzIZioizkMCAISBKQBASompRKtcAM3zbAhMce2pI6IREiIwDdy5VoNq07SZIqJSu0zUTLGkiwHtYU+uFCLegAtXtdeqKgLO7CEiWdbMBgArSj+srhudz0UsZGkFGIjoBpBbQtfX13Ho0+n0+eefO1TZtex2u/VrdhwcAu3f8ns5x2EIYZ5z13UUg/N1mxnHrswOc1Y4nU7zPBPQZrPTPLppkrM2Nmwcx2HYHs+neUpejnEax3nOqupcWmvr0IfLCKtgvUBmtcC+qg5Dd3Nz49gIH+1WpGfVOABCruApK5tIpzQjEzkiPq+4MAHMIIQQu+BGAxH0IZJBguSJCD/XZauv0vP5nFLa7XZU2WDW4vexyF1vW1u5xetZ5lpVzIyuYq92+z524zj2fe/mtVszbkxQa4xYSdS9BKYpHnikfa3a5UTkdWuHw+Hjjz8ehrIq/Fg8QwAwo8u8JKyCCmWnrCzFi9tBKRa1EpDjhvxq2s1FxLLpVkGF5cpY1EoRUG6xAhhiOxtqTHF1f1Kn5yUwU1ADr0Tnh4MPK+0LAEFVGQXBfEW2Bdq0Xb0NmWGLdqy172oEsGFcm5SnRVLD+uIOz3owZw8mD1Eb27NeFNKAN0I3MG+XXTXxr6FNfz1X+M3urx+OR1BVBNrsrm5un7777vsffvjhy1cvwrDhQJql3/aWZZbMIS42ipUG3zc3Nze31y8+f9l3jR1Czqfz7e1t06YOpPRIg8tcRCRvt1wlS11z0naIrYINzRGB1gljZUo37d5kcV1zikjtjqrLMlg0JYBa9SqQmd0yDWagULoO18RnqZbxLHJAQiqt1lxzRKYaMiW1QntLXjOKCFKggmbmxSrExT/mWk4KlacJmEiL34xIXj6kYEnUOR9SzliksNc5uBWyJKVUtQLNihNZeNURmfl0Oo+EzOyQbKgoWRe1iCgKbosQGjMrgqiaasWwABErKDKjKnMptDBkCoVBzO84104GABBCrFNPNYVtIjKO4263i7FrCB0vtcpSujG2Dd48bC9eaqk+XxKTaggBRD3lgYjb7ZYrva373F6p/+r+LufMYLe3twDg5DAfvv+BTxYAOG+G0ywQ0X6/P51Ox+PZ0dHjOIo4G+KcJJ9Op8AdBnaAa1aBM4zz1FFhwC6f5yzejRF5SnNKOeesBoEo1xSPH03Ulr+lNJShFauMr6VaXixm6BUjPuPuFAKA1RKGEILDg32Em9HmfzqmgWpll9vW7gaFELq+0Cc4RUxrzWtmJmo1UOnT5xMaY+wCz9XDhsuMb/s6XGrl9SdWPVFVTSm9ePFis+k1C8fOOz33fY8EMosjk5nZE7TOkwoA6FyrNY24Hr3Hh9WmZM4w+vHHH7948cpvFLpYgr16EUJoZOnryWqzs9YsD2SaiwvXwe15in2+kk6NmGUd+m1OneGje+mSJ3VN3URr04CICKBMHDgQmJoBFhHnsJO1GeTPBRWwFpYLqRmBmbSoc1kBiLDyndubPDB81pLdT2tywc0PBKBqdGBJYhcXvoryJdy9shfMHxZX0U4zU801ahhWM35R1fpLH7+qDv4y7VuNKAvcGaSu63e7q91u97u/+7t/+u/+7TzP2+121jlLai/LhavFQGAWQbJA/PTp08P90dd9QGQehj5ut9s8n3a73TAM9/f364FqsY62OHTV1dI10PpD38OID5NhS7gCSFVNTc0Vqiqbijg1Zl3ukvOSOChfZyLkKsRjFfHm6czYByBPwxIRucMEZl0XiYgMDIqzUgAReVlaImIg7V5cjMyCDvNI/trmQMSkItkMIU3F83bl0fS0ZJM5hUjOHRE5MF8S65d5JVMzxaQSQoghEJWEqJmpWR+j5KxqRJDN8jw5O2bOS9cXfzYjVEFNCRFzKTTyULaFEPohUIhe4JTn1KbPmWdCiMOwaTY4Ip7HuSlU1RxijDEGka4vOcvj6eSSlCYiRsmqK+69op5F3CMfz5BSnpP7kXh3dxf74erqqsXktR673c7VKq+Ibfu+J7BXL166fCeiYRj81byozPWW5+0Ph4Pzitzc3Mxz/vzzzwGg3+xU8zRN+/0+pXQ4naZp2mw226H3SmIxnaZpt9lgbdKVcwagnHMXIiGboeRERH2IEzjPG61Fh1X/gRFtxV4O65BmbZzFtaWmhw08fpNzNjGMZmZIC1QCAEr6edWcpingEAJTNFxYLTmQKos4W2GrXEKsKeS1idxqaRhBmdcBqvWBbuP676v4or+I7wutvZxPpxOBAcD19bUDr4ZNr7XSRlflrdjIkIs8vlCH8Egjtgfw4drtdp988skvfvFpLbWg0n54yV83dU5QW/jpKur5YO5slYOzVeTML4QrJ7DO6YK7bPPbHrI9OdKyGKhU2K7DcktNeUrJfQ8iJxHkYrrJ7G+BiByI4EInPpysxoRV3kENwQwFgFuUw8xQDRzMfllJvV61D1Id8GgIoHqxuAp4AqhX+jwwdlaHeqZt/ZGVlED2hfumV/tVw8u/vA7+Mu0LAAYeRUFkFkkhdDdPnrz1/J3Xr18+efLkxYvPHM0/TRModDFmMUNwDgpfQiGENMvNzc3N9WtV7fseVbuuG673Hol69uxZjNERLn5gSxl4QlFKD2oiUlv0q1RwKV5aTuXFar/YtUJtK7UZSSYFkAUAgFpyzw1ZoMg1uJRzA/QC1KQmVqZlUO8ZHNwacNmOiJGXTmGImLIAqJMwEBFS6d2tqrEmmJsuaZ6x30KBQAxAzExMkZC9byYTE/ddICIxGE9HZu77ngxKBlp0qGV/TaOLqRnEEL2e8u7ukPM8dP0aWMQV2KwVcuVqNURnFnSjmKlUc3KSLCJMzsGZmXlOp4AEAHPK53EEAFDruu5wvPNF4m69b8yUUuwGJ2pOKU3z2V/EzFxnIKKTPF9dXTl9436/jTF6h8GUknpjZgNfk4joSqfoS8lIfHO1d32MtR+wX3k7lGa9qorXV+6PDjWw7CKiFCubMfM4TV55IpU74untk5SSie63m9vrr4rBPM/jnK/cfuLwJOfjefLFNvfzZrPJeT4cDg5eI6LQdRtVEbcAsmWJMQLhPGWB1HE4pwL4WscA139qzfY1x0B1wSe7avd3zzl7HypmjiEg4DzlflvcXA8JtBi1r3wfUlVVATBFSBTYrRmPN0gufrOZuBVO4BlG8zoQXcGsmoZm/DIx5GlUWzl/AOBF5FoTwKG2HfPgx9OnT5HA+dX9diISY0Q0VXTbdJ0+WMv29lS28sHaJx4jORwOL1++Pp1OvjiT5Nbo2ir0qcScSzEZWOV8bfpPKhSrWdgNGAQrDw0Rmw5uFnkrm22ChVe4VK01zUBLvsZvGqg+jGLhE0QEBO56EdGKFDYzRmNcbAEoPMvl2WiBOi0WjDn9va1Ay+Au76r6VVW9YYOZgSjFJUfYJPUDq6d9USs7Sbt4sVygLHoRQSfWxAv9ratWssUOakmpVdDDzLwey+/8YCEaSAPptYt/kTHySxx/h+ptp3nm3AA49hwCnY/Pnz//6U9+9OFHH7148RkRiWQTbcvYn8izMojoerDrOmYax7HjEGM4Ho85BTN76/23X7x44ZUMbQrayvN4kdc8+JyKZqiZmPKJiO9GqYC7OpIqIl3sXY60DJ9vVKqx6H5zFQLlOQGACghmZu67/nhwZgCfVvMahrpOitBp95KUAdQfNaWkIqfT7LitNCuihlii3FnFzDgi+jKwBU45plnmlFvTPWand4gxIvCUZJpOIoIcQkAAjLFreDcFQ6bzOIbQiYGpYRbNgrN1nFX1/nACVM3iaeim5g11v9/7Kur7TcrzeDc6P46qCiLl7I0KNpvN+XzOYu5beOM8RDQEyaX0aJY8z7NICYgx82YbPUILAKVnTt/nXOiTXL7nnJ0IKed886QTovM0ASLGoRs6UTUwQ+J+mKZpf/vEC4E2VxER5+kYzIjo01ef7DZbEGZGyT4dYKZETMTTdM615+vrVy+bPwQ1Td4gC9RaLTEzYckcns8eyD3e3fu7T7VKuPkuVOD3mubpdDxadZiSU4dCyYwc7g9+RzB6OY7zPDpuyGo0NWfFQh5pqjrPzo4JvozX8rRJgAvPSUtYYu0g+plu6/hOub+/dwsDak2gyFIagNVRa1gzL20gIgRW0bYHK5NX6daMBDFGr86C4gQ45nF5Wl5Af9isgbX+aAcANEo4MAMzAV2f7+CGFs/Y7Xaq6Xp/1Q9dzrms4aoOzSznUgbms2mrlGqbTf/E35cr7WiDdACQJ5u//e1ve+7ARVN7KShykM3Ap7IqFDQzzbmNbbtjE+MtjLwED+pDNlXtt6BaD0arvPJ6MZTRVg9rLdBuQ6+uLjQB9ZGLxiEF97YKHsDU8dRgZlqCGc1YgWLnwep3C7ZyaqHFjUFttQIMxGPfRGSaAcDJS40W/6kucTO7yDW2d9MV3XQ1EpagNIAAoHvD6y8CAKI6o+UDO6s9cFkT9SnK63kwGOSX7hn8JU7wL6l6Hx8IQMQxdMPv/KN/cjodnzx5cnd313EAQlBIKSGFPM2+yWOM52lUBbUM4Iah5JxiDEPfd12Y5/nu7jBNs6+rpoCp8UvXhG6snW2GMCyrDRCdw5PCGkTQpKHvCscKtSyvmbWKQKjl8HPKZgJqBpzmnObs/E1e2cK1E4PfJaWRSw1Dao0K1ldDKDWXxZsHFCFmRcTWLJaZPeqrqqpTi9F58szvKCJiKIbmta3g7arZ42hAdJ4mJ6YDUM96TqnQhPWRVTUQb7dD3/cRCREt1JR2M/NhARUCaNdtAzEzu5IrccJmsqgSECCHGAHJgJzPNpX+DqSq45wcZdPFYbfbSbbNsHPaHeefAqMY+pzzOKdpmosByiGEYEj3xyMzx74HgHEcs0KMvWsOIgrdkHOes3oy2Mx6yNvdrt9uNpvNZrd1plgIkcPCko9Q4vkAQGBV2Gat8sOkaFNYYQV85UznuSkDADCzLOLtRpoE0JpABQAMpKrTNJWWU7Er8tSJZyhM55N7MA5yNkRVHbrOXXZVNWytbMqOYGbHQznTMj0iNn9siJMBGWgFYXnc2y1aRBaZ+76/u7vzJT3Pcx+7UNmeYYV+aHdxKyqGHqvr1u5e7eDSTXLJATWALlyUqrRjPbAAYLBk5VYOyQX1BwM6B0rza5tqdPdxu93e3t6aGVeG7ar10RU6V7AnrhJeD+6LjQMOkWr/NLcVPE/xN3/zN97dy8wQaJ5K3bO3EEdkosV/a9cEAMSAaIy0wN8egrCa93UxZg80XxN0sFLn7S5QZWn7ipnVlvYKAIAMte2pqzlVRbBCWFF7sqEqVEX0QP2tJm5Rxqoamo1weXt/b2p7TCH7jVTdOjCCgAp22Wx5/UpSMdrr52jvsOyE+lBUKmgfoMv8u4ZVveOqlpwqxBeKDm7TsFLhqGa0GrsvOd6og3817duA2QgM4A2L+t1u1wV45713X9+9/Ktv/4fj8bjZ7LAWesYYz/NZK7I3pZE5AgMzb7fbbT90XefxUisxA1yDZYiWdVkMlMt+9esXb5KCVujo5uB6ajCXBmTu+fm+NTOP+XtHAW0pZAOv0BAHYykoInIMjiVqYS6qXU2IFlHYpE8MAXHJcjUzi5l3cOVh4foKiqpowLEIESJCXYxfhVq573wOxf8pLcnO5ynnmVtG2YyIfTC7UISjY4lTSgEesqMzcz9coLVptdJatYbWIK2Zidr5fPaG58AB1JAs+ggEBgCkoAIqCzZ1t9ttt9vzNHIMDMCxQ8T99c3hcPBpTWLIkUJEhZySqniYfZqSiOWs85wRcbPZAFDOaoaqKeczM+f5/Pr+LnIQkWfPnjnGLRBnmckgAyZNRIAclVnMUJSZpVJ8w0pEttxHGXxRRorctRyBVRysk2yIad/3jYYJEed5BjAKMdY2R8ycVeYphRCQAwDst4NYKWIWkeN59Di2T800pikn9LgRAAA5Ta2ITDmllFyTL9uzPnnbMhcR1GoYtUyNt2UTkePxaGZOx9Fm2e0kWOmhZo6oKlP0obBaK0g1hudz7e7g+tkeiL4LwYIPM4BkC+v4stnrXriQSCtWcK6gPN/su92T6+trM2smu4sUXTEhNm+bkbJe1Km2G7V4SXNSRcRDX5999tkPfvAD3/7zlNpOAQA0QHS2riUV/GAQEC9oy+iix5TCkmRFANc21c4zayHhRTxePvkiN6qFxCtWHDND9Vy+IWItuUVw7iv/+qoWB8ndzyUOvzbL1tNkFU4RWj5m/VhmyfVde9s28S0IbF4SLOo9VYia264ApG094UMzbVkrK7uj/V7Y2B6CtqXUQa/cvjoBGWoLCFgG4+J2NSnwyxx///rgNdCNEIFi2O73R0gffPDB3euXL1589v3v/UBEYugCkRXSKGTm4+ngwTPHAQGAC6z2IiEEk0JZ1cI4VqNhubXrEm2Uqg+Wmq4OP5+rbPVeN75tfCiqimocWF5uMeuSFOCUHaCETDGGGMMSiAvMiNYAHQAaAnEohpcrciQIgUMMaUoV8F/SKkSESEZoyO7Ucu0kY2ZUF7oDqRDRU8KzZIVUVldtfGkIOWcC9M5uWFhXycwUAgCgiYh1XccInroL1LAc4OwfiMhMeU7MPKfZZZazO1E9fKe4giksBwnmeTbiNXLVvPIqBCLqOuj73rtaubRzGmQcYdMP3dAzc856Pp/P5/OcCwAEER1Z6oLVp9tlnIt7qRW3nvH159xsNia83W6Z6NWrF69evt4MPaJJyojI2L4YqLKFeySt4epDJaZtm9pNFgKE4JE9aAvGKjdICIGQDaEFM33iQgge5AwInmhkZkPIqSw8Z9Jg5vE8I+KY5v31zTr6egojn8/AlJJDw03BSMFDysxRatkbvEn+tLdoMtGL6HPOP/3pT/u+97RIAzE0VRroomrAr9y2Us4ZgZvk9HrugpG25e4tU46uGfw6LS5lAADZLhS8rexsAEBTW9VzWq1RffBqIIbhgsauKkhsKLkQKOfZaY6IyJdrqAcagJphIQZo0ltrGwOoPe2tJDSDixER+fTTT92o9SIxvcgILCp8edpLh61ENS7lN1ZHtnpw68ldaKcMgFYJ/vUCaFco31klm6kU3FbZ0cafbE050NYMIgIqgacTce2xWQuDE9bm5WX8yobygW6ZjBYkEUlkQcPDuW/j0oYJRD13fXFmI+5Y1cC0IXgQ07j4fekK8HCrlIFbrfs2Cg5h8IjL4+LgMmy/7PH3rw+2UkANYAiBO+oyXl+pPb/56e2HH354uDt++unngaNVzu6AwcsMmPVwPIXQjafzOJ5C6CwLAExcvEDNs6/7sm2XPEeNMyN6Ez3z5rcc27xhsWE8AmY5exSXiEqyNmdtxePNkm1WoRU7FBE5xrJgZpmZo6iims2ZmVWzqvZ9zyHkNOWc3RUsBPRZPfboOewGqEYiDkFVc9X6xPz/6+1aeuNGjnC9mkONNJLsxIcksI9JgGwA//+bk/wKIzk4wMpY2CvJK82L7KrKobqbPWMBCfYQniQOh9PdbNbr+6oKqk0KAIjs6AYgJGZ5fzgQ0SDBfO7mTgSFTEYRb49qaSjkYGNpgpuFaBgkEER3n+ecmIRWIkJghFGwoiBMYVMDkKrqPGFXDwgRLy4uIuYWaamFZIQQ2O0IzIBB/vTOTqUojOmRqueSKOu03e1/e7vR+fjLtN/v9xBJLD4d52m/PwaEjJzGcVyv19FxITyPSJW5urqKYWAtSQ01th/G1vF4REnzrC44DGPO+fkpsxC6hcrVki+rOWdwYmbTSPUp7YSLjqx2Rgv5hgoJgeWVat6eMhLFbgqHbxgGp9JCICSymSFWIUgcSivnPGt5sof9VECK0oDBEXGeCivneJhVS0HKbKrZs+Yi94gaqAlVHDVp1ivm2O0shXj88PBwfX399u1bItrtdtEsKPq2BTY/jqO7U1poO3Dqny33XEq/AdakgNBb85TjCVrWCgHH67oQJjqx8oJURDAEdFgUSX9N+0qbePsj53xzczMMMk2Hy8tLZg4oOkYbbBKOvAAkwIqqVoHbbts4JcU0kSEy6OL84/3Dz1++Xl6sY0DUFSd2XxbKqoWHTP002yyIFo5V0yNYHN/vj24RTj/wU2e0rLa71GdkqtBpOq8XAACV4Z9HUt0dgRwRIo+pwB+d2iIEAKvNU+uTQgAUzRnqb7ewTLsvg2khKlvtbc7dCNRtKVvYvtiZcWAdcNu2ftO4vQdcttR3Criq9gVEASgJ19ZRtc0M0YlkCQgAtXS683X/78evdoVLVKQExBwQHTmRytXV1Zs3b3bbp9/94fc//fQFulK3h8NORLLO7969+/L15/v7x/1hGyGjDIiIprVxJixThu4Nd/eUBijNbhdbL3rz9YtstbrbmT3YbK92JRHVrpzY2irExe0dU3Uib4ywlBJ5QX+bFk8pEUfakrnmCC2iAwEyEpjPOpsW5dRC1mY2z5oSA6i7VWNrQkRXrSV0HDueIRFlc0eOrHmL0ElfWI6qTUoUP5eYEHGVhJmj35SZo3sU6Y58vrLrHBFxGMdpmrCABSWeFoBfkaTgh+motUXgPAU9G9Bdc/Y8MxETJWEuLRGBmdc0bi7Xqnp5kQCADYery3jXZlOikWApasg1WhglhYl4GEpTxShdFOJsnudxHEWG/X4fYPnz8/PFxWqz2Vyt10/fHi1PLLIa0nQ8lMpcJUYS0EOk4gzuTmbC4DY7kkE4AohM7tmyWml4HDSVKF2HxyD2E3HFGpxw0uzuTtVNLOQpsyp8wlAAqF3Qkc2MUMxsHEcR2c/WrsFWrTPP7qBVDzphFGzRDmrxLoOlOQC9LikKpuV2E93d3YXzejgcEP3m5iYaT03TJMRBFEcpnIkWTHZ3hJIX19jj7c3SyvYnommaorQZAOCptGHgcKWYXqaw9DITEQnBcSm72F9TRmUOvGyhCD9cXl5KaTqeWkgMggx4cVEEwmmFpap/l+BHbC2sTKgIxYUxqqqfP3/ebreRq5JSivOECIa2aPPl0PD4TxUtALhjvIFtJPXz8+HVT8Pjr+5ovRGC42lvRKjvtnshPkWKVPx4BMBjAIbQkGDQEzpwfN2yL9EZWryjVs8STkEcABBTgPhAQuZiKPm2ynFlpzWhrT40yNP7mRcPuPzrEYw9mbPXBMTvQs0vKOB6Tz2Dh7H+yjJPxGjFUzQ9LCoKwLDUevvf1fCvD0djhQYRIYo5TdOUBrn9zev7h6+vX7++uro6TBMDM/NutyOC6+vru88/3t/f//mPf/rbP/7+/PwsIikNiZiITOcwWiNTrT2CNvEAxgDArBSqtdLwbikisyxyhAtOc9KhSrT4u4E6TX6Fw2oGLTxjZsSCxKbuDuYmtUdzxFTdslk2k+gjEz3FqmfpsblVNZtS7dxQ9T3EXNw1R9KRFBpX7JxBavkhpN6YUKec8/F4PE7zAlIyDZLafFNKq0FExLImLm0kqFatAUBH0zxHRg74AhMKi2kJsjEzuGvXqh0RgZCrPcTM4zhqPoZ/HLHiVqCfKCqBgEN0NWZGkMTRAztMn5BHaK6WVZURgGie5xUtMOQgAGYNtkL3QeR4PK5SSoWLh+ggjAD4nCfV4du3p+3TkxCv12tGByCRJEgi7O6as2aL9LCW5sEslBJVbldY1UmkiVrBItmneQ+EjiWYm1JS8NnUEAhxHEer60OlTUjRhVjCehrRqmnK7i4ipsDMk+ZwmFYIl+t1EJJZpHDgwc0q+uYGZqoaVTlSbRzrnb3eZxaFW992vqSheMxIz8/PHz9+jIG9f//+1aubKMC52Wx0Lo+JZQHg2ssYhKZ4Jb2ifaGh1S06HGMX/3O1YJHiidgrbBt4SR6eCRx3B1fAcw9quRcRtDxss4hRjeOoqkE3i+4jgWXEeSKKSHuTCW0bn42njSo2beAOAPD09HR3dxcudQTAYlNN05SEEMCtFEe10lWdorUBIvJ306x7/OWlaLZI+xcRAbiLY73ghp05If3f1WAq3CNwr/3SEAAUAap6pgofNOnq7mjgrQnjEq44P6SZHlAKcbgZRopIUeTmVlUylooafYWs8ymF8l+QjKKnl1BAvwrnahugrxXcL1y790vnF2+42SNNtTdLAk6B4f/jgWZGSdxnEXn16tW3h19ub2///enHzWajpZ6RzHnabDYfPnz44S9/vb6+/ufHfxFKe0WJKN4KxpISE0oXamoK1raXEQ5smUgtPtZvUFxA1s5M6/YoFlQeAgmGDiOp10SlN1ytmIgOTAyVzMWYnPtoilpmS4CFb9x+sd1TCJlK89d5ngGKZS0iYcOqKqCDRX6UunviMRL2Aw32BWspbDLmal8jIYC6CUq0T+iTtdBLLDSKArCQlHZjORLoAMDNIKhtCIpLqepoHTPXjC+v9jvXRM9QISHdgtjFgmqac2ZZ58ofBIC4mJkZTESC7WJmhKyg0/4QrRGGpryhZUl6GCU5z09Pk5Z8MyCiAIkb8dXdh2GY5ywi06x72+ec0RTBmECQVqsV1uqhka5aPFd3SgUpsFKCzil4q3U/ZLAIIzbaESIYQq6FsokYK5ZhlQXNnB4fH0ttMl7abanq9fW43W7N7JhnYFrJKhof+UkHqqJF9tNxCn1rnnM2yG0DNOPSOu5IYOfx7jTmRFyWD4dwBw+Hw+3tbXjDZrbb7S4uVsGLVFXLGujpbBN2mQje4+Jdxktg8KqaVkNU1gxuV3w35yUhcJEaiACQ8wydoOsDVO1YziBxpSzF9b3AbHo3Tq7X63Ec53m32WxivmNEdxCJaLvdrtdrGla9wHT3XKGNs2FIaZ9VhFWQxj99+pSzpkQAHoVIm4eNZ/ks7oiVyxMBAzyfKRG2JS3SrIqyM3XQi7uTk2aR6wwdJHomA5dVNUciL1BygVcAnDr1HFdqjRPAaR+2ZUitFCVCo23G8R9WkirDTZpHzwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import urllib\n", + "\n", + "from PIL import Image\n", + "\n", + "\n", + "def load_image_from_url(url: str) -> Image:\n", + " with urllib.request.urlopen(url) as f:\n", + " return Image.open(f).convert(\"RGB\")\n", + "\n", + "\n", + "EXAMPLE_IMAGE_URL = \"https://dl.fbaipublicfiles.com/dinov2/images/example.jpg\"\n", + "\n", + "\n", + "image = load_image_from_url(EXAMPLE_IMAGE_URL)\n", + "display(image)" + ] + }, + { + "cell_type": "markdown", + "id": "7e3240cb-54d0-438d-99e8-8c1af534f830", + "metadata": {}, + "source": [ + "## Semantic segmentation on sample image" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "49226d5b-83fc-4cfb-ba06-407bb2c0d96f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "import dinov2.eval.segmentation.utils.colormaps as colormaps\n", + "\n", + "\n", + "DATASET_COLORMAPS = {\n", + " \"ade20k\": colormaps.ADE20K_COLORMAP,\n", + " \"voc2012\": colormaps.VOC2012_COLORMAP,\n", + "}\n", + "\n", + "\n", + "def render_segmentation(segmentation_logits, dataset):\n", + " colormap = DATASET_COLORMAPS[dataset]\n", + " colormap_array = np.array(colormap, dtype=np.uint8)\n", + " segmentation_values = colormap_array[segmentation_logits + 1]\n", + " return Image.fromarray(segmentation_values)\n", + "\n", + "\n", + "array = np.array(image)[:, :, ::-1] # BGR\n", + "segmentation_logits = inference_segmentor(model, array)[0]\n", + "segmented_image = render_segmentation(segmentation_logits, HEAD_DATASET)\n", + "display(segmented_image)" + ] + }, + { + "cell_type": "markdown", + "id": "de40012e-a01e-4e73-bb71-3048f16d41c8", + "metadata": {}, + "source": [ + "## Load pretrained segmentation model (Mask2Former)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ff2cbbbe-c53c-4e5b-977f-c2a7d93f4b8c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/private/home/plabatut/github/patricklabatut/dinov2/dinov2/eval/segmentation_m2f/models/losses/cross_entropy_loss.py:222: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``.\n", + " warnings.warn(\n", + "/private/home/plabatut/.conda/envs/dinov2-extras-conda/lib/python3.9/site-packages/mmcv/ops/multi_scale_deform_attn.py:209: UserWarning: You'd better set embed_dims in MultiScaleDeformAttention to make the dimension of each attention head a power of 2 which is more efficient in our CUDA implementation.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_ade20k_m2f.pth\n" + ] + }, + { + "data": { + "text/plain": [ + "EncoderDecoderMask2Former(\n", + " (backbone): ViTAdapter(\n", + " (patch_embed): PatchEmbed(\n", + " (proj): Conv2d(3, 1536, kernel_size=(14, 14), stride=(14, 14))\n", + " (norm): Identity()\n", + " )\n", + " (pos_drop): Dropout(p=0.0, inplace=False)\n", + " (blocks): Sequential(\n", + " (0): Block(\n", + " (norm1): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", + " (attn): Attention(\n", + " (qkv): Linear(in_features=1536, out_features=4608, bias=True)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=1536, out_features=1536, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (drop_path): Identity()\n", + " (norm2): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): SwiGLUFFN(\n", + " (w1): Linear(in_features=1536, out_features=4096, bias=True)\n", + " (w2): Linear(in_features=1536, out_features=4096, bias=True)\n", + " (w3): Linear(in_features=4096, out_features=1536, bias=True)\n", + " )\n", + " )\n", + " (1): Block(\n", + " (norm1): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", + " (attn): Attention(\n", + " (qkv): Linear(in_features=1536, out_features=4608, bias=True)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=1536, out_features=1536, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (drop_path): DropPath()\n", + " (norm2): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): SwiGLUFFN(\n", + " (w1): Linear(in_features=1536, out_features=4096, bias=True)\n", + " (w2): Linear(in_features=1536, out_features=4096, bias=True)\n", + " (w3): Linear(in_features=4096, out_features=1536, bias=True)\n", + " )\n", + " )\n", + " (2): Block(\n", + 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scale=6.283185307179586, eps=1e-06)\n", + " (query_embed): Embedding(100, 1536)\n", + " (query_feat): Embedding(100, 1536)\n", + " (level_embed): Embedding(3, 1536)\n", + " (cls_embed): Linear(in_features=1536, out_features=151, bias=True)\n", + " (mask_embed): Sequential(\n", + " (0): Linear(in_features=1536, out_features=1536, bias=True)\n", + " (1): ReLU(inplace=True)\n", + " (2): Linear(in_features=1536, out_features=1536, bias=True)\n", + " (3): ReLU(inplace=True)\n", + " (4): Linear(in_features=1536, out_features=1536, bias=True)\n", + " )\n", + " (loss_cls): CrossEntropyLoss(avg_non_ignore=False)\n", + " (loss_mask): CrossEntropyLoss(avg_non_ignore=False)\n", + " (loss_dice): DiceLoss()\n", + " )\n", + ")" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import dinov2.eval.segmentation_m2f.models.segmentors\n", + "\n", + "CONFIG_URL = f\"{DINOV2_BASE_URL}/dinov2_vitg14/dinov2_vitg14_ade20k_m2f_config.py\"\n", + "CHECKPOINT_URL = f\"{DINOV2_BASE_URL}/dinov2_vitg14/dinov2_vitg14_ade20k_m2f.pth\"\n", + "\n", + "cfg_str = load_config_from_url(CONFIG_URL)\n", + "cfg = mmcv.Config.fromstring(cfg_str, file_format=\".py\")\n", + "\n", + "model = init_segmentor(cfg)\n", + "load_checkpoint(model, CHECKPOINT_URL, map_location=\"cpu\")\n", + "model.cuda()\n", + "model.eval()" + ] + }, + { + "cell_type": "markdown", + "id": "53c0309f-df2b-4912-bca5-e57d8b3875b3", + "metadata": {}, + "source": [ + "## Semantic segmentation on sample image" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f4abb13b-0e5a-4a40-8d44-21da4286ba7d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/private/home/plabatut/.conda/envs/dinov2-extras-conda/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1678402374358/work/aten/src/ATen/native/TensorShape.cpp:3483.)\n", + " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "array = np.array(image)[:, :, ::-1] # BGR\n", + "segmentation_logits = inference_segmentor(model, array)[0]\n", + "segmented_image = render_segmentation(segmentation_logits, \"ade20k\")\n", + "display(segmented_image)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..da67abd8ceabe6d427a96e5d9d4f04b25aebcd32 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,29 @@ +[tool.black] +line-length = 120 + +[tool.pylint.master] +persistent = false +score = false + +[tool.pylint.messages_control] +disable = "all" +enable = [ + "miscellaneous", + "similarities", +] + +[tool.pylint.similarities] +ignore-comments = true +ignore-docstrings = true +ignore-imports = true +min-similarity-lines = 8 + +[tool.pylint.reports] +reports = false + +[tool.pylint.miscellaneous] +notes = [ + "FIXME", + "XXX", + "TODO", +] diff --git a/requirements-dev.txt b/requirements-dev.txt new file mode 100644 index 0000000000000000000000000000000000000000..5cad34c34cde3a182b616d68b168588827eb9b7c --- /dev/null +++ b/requirements-dev.txt @@ -0,0 +1,3 @@ +black==22.6.0 +flake8==5.0.4 +pylint==2.15.0 diff --git a/requirements-extras.txt b/requirements-extras.txt new file mode 100644 index 0000000000000000000000000000000000000000..2cec4873b4ca4ea6e71ff055bbfbbad480786e50 --- /dev/null +++ b/requirements-extras.txt @@ -0,0 +1,2 @@ +# mmcv-full==1.5.0 +mmsegmentation==0.27.0 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..89bc6da84bb735c211509965f6bf82c3de46e8f1 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,12 @@ +--extra-index-url https://download.pytorch.org/whl/cu117 +torch==2.0.0 +torchvision==0.15.0 +omegaconf +torchmetrics==0.10.3 +fvcore +iopath +xformers==0.0.18 +submitit +--extra-index-url https://pypi.nvidia.com +cuml-cu11 +gradio \ No newline at end of file diff --git a/scripts/lint.sh b/scripts/lint.sh new file mode 100644 index 0000000000000000000000000000000000000000..b91acaf762c4be3a0c9d2a162210bfebfaacba08 --- /dev/null +++ b/scripts/lint.sh @@ -0,0 +1,28 @@ +#!/bin/sh + +if [ -n "$1" ]; then + echo "linting \"$1\"" +fi + +echo "running black" +if [ -n "$1" ]; then + black "$1" +else + black dinov2 +fi + +echo "running flake8" +if [ -n "$1" ]; then + flake8 "$1" +else + flake8 +fi + +echo "running pylint" +if [ -n "$1" ]; then + pylint "$1" +else + pylint dinov2 +fi + +exit 0 diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000000000000000000000000000000000000..3cac0c045434cde205eebe91fd5a2c35a1226b4b --- /dev/null +++ b/setup.cfg @@ -0,0 +1,7 @@ +[flake8] +max-line-length = 120 +ignore = E203,E501,W503 +per-file-ignores = + __init__.py:F401 +exclude = + venv diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..daa9d6322fc3a451d2a07038ffdb9eea709e96bf --- /dev/null +++ b/setup.py @@ -0,0 +1,88 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +from pathlib import Path +import re +from typing import List, Tuple + +from setuptools import setup, find_packages + + +NAME = "dinov2" +DESCRIPTION = "PyTorch code and models for the DINOv2 self-supervised learning method." + +URL = "https://github.com/facebookresearch/dinov2" +AUTHOR = "FAIR" +REQUIRES_PYTHON = ">=3.9.0" +HERE = Path(__file__).parent + + +try: + with open(HERE / "README.md", encoding="utf-8") as f: + long_description = "\n" + f.read() +except FileNotFoundError: + long_description = DESCRIPTION + + +def get_requirements(path: str = HERE / "requirements.txt") -> Tuple[List[str], List[str]]: + requirements = [] + extra_indices = [] + with open(path) as f: + for line in f.readlines(): + line = line.rstrip("\r\n") + if line.startswith("--extra-index-url "): + extra_indices.append(line[18:]) + continue + requirements.append(line) + return requirements, extra_indices + + +def get_package_version() -> str: + with open(HERE / "dinov2/__init__.py") as f: + result = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", f.read(), re.M) + if result: + return result.group(1) + raise RuntimeError("Can't get package version") + + +requirements, extra_indices = get_requirements() +version = get_package_version() +dev_requirements, _ = get_requirements(HERE / "requirements-dev.txt") +extras_requirements, _ = get_requirements(HERE / "requirements-extras.txt") + + +setup( + name=NAME, + version=version, + description=DESCRIPTION, + long_description=long_description, + long_description_content_type="text/markdown", + author=AUTHOR, + python_requires=REQUIRES_PYTHON, + url=URL, + packages=find_packages(), + package_data={ + "": ["*.yaml"], + }, + install_requires=requirements, + extras_require={ + "dev": dev_requirements, + "extras": extras_requirements, + }, + dependency_links=extra_indices, + install_package_data=True, + license="Apache", + license_files=("LICENSE",), + classifiers=[ + # Trove classifiers: https://github.com/pypa/trove-classifiers/blob/main/src/trove_classifiers/__init__.py + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python :: 3.9", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Software Development :: Libraries :: Python Modules", + ], +) diff --git a/upload-file.py b/upload-file.py new file mode 100644 index 0000000000000000000000000000000000000000..e9ac826bfa798701629d62b3e917229af759f79f --- /dev/null +++ b/upload-file.py @@ -0,0 +1,11 @@ +from huggingface_hub import login +login() + +from huggingface_hub import HfApi +api = HfApi() +api.upload_file( + path_or_fileobj="/home/vishak66/.cache/torch/hub/checkpoints/dinov2_vitg14_ade20k_m2f.pth", + path_in_repo="/home/user/data/dinov2_vitg14_ade20k_m2f.pth", + repo_id="Vishakaraj/DinoV2_Semantic_Segmentation", + repo_type="space", +) \ No newline at end of file