diff --git a/LAVIS-main/lavis/projects/instructblip/caption_nocaps_out_domain_vicuna7b_eval.yaml b/LAVIS-main/lavis/projects/instructblip/caption_nocaps_out_domain_vicuna7b_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..87f383228c23ec93051fc8788083a96d48cbf3db --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/caption_nocaps_out_domain_vicuna7b_eval.yaml @@ -0,0 +1,82 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna7b + load_pretrained: True + prompt: "A short image caption." + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + eval: + name: "blip_image_eval" + image_size: 224 + + text_processor: + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + # prompt: an image that shows + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/instructblip/nocaps_out_domain_captioning_vicuna7b/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + + img_ids: [2, 4, 5, 8, 15, 18, 19, 22, 27, 30, 33, 35, 41, 42, 43, 46, 47, 51, 59, 60, 64, 65, 68, 69, 71, 72, 73, 77, 79, 81, 85, 87, 88, 90, 92, 100, 101, 102, 105, 107, 109, 115, 120, 124, 125, 126, 127, 129, 133, 135, 137, 139, 140, 141, 143, 150, 153, 155, 158, 164, 165, 167, 170, 171, 173, 182, 190, 191, 196, 200, 201, 203, 205, 208, 219, 225, 226, 228, 229, 232, 239, 240, 243, 245, 250, 262, 263, 264, 267, 272, 278, 283, 284, 290, 291, 297, 301, 304, 305, 309, 310, 311, 314, 323, 325, 329, 330, 331, 333, 334, 341, 349, 350, 351, 352, 354, 358, 359, 363, 365, 366, 368, 371, 372, 379, 381, 383, 386, 388, 389, 390, 392, 405, 415, 417, 418, 420, 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4195, 4197, 4215, 4220, 4229, 4234, 4245, 4249, 4251, 4252, 4254, 4257, 4259, 4264, 4265, 4266, 4267, 4275, 4276, 4277, 4282, 4284, 4285, 4288, 4291, 4294, 4295, 4301, 4302, 4313, 4315, 4320, 4328, 4333, 4336, 4339, 4342, 4345, 4346, 4350, 4354, 4372, 4374, 4375, 4377, 4379, 4380, 4386, 4388, 4389, 4392, 4396, 4402, 4404, 4408, 4410, 4424, 4426, 4428, 4431, 4435, 4436, 4439, 4442, 4446, 4447, 4449, 4452, 4455, 4458, 4460, 4461, 4466, 4469, 4475, 4476, 4478, 4488, 4491, 4494, 4498] diff --git a/LAVIS-main/lavis/projects/instructblip/caption_vatex_flant5xl_eval.yaml b/LAVIS-main/lavis/projects/instructblip/caption_vatex_flant5xl_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..14484ddfab86f3f0440831c9c6a4787cd9ab87a0 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/caption_vatex_flant5xl_eval.yaml @@ -0,0 +1,90 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_t5_instruct + model_type: flant5xl + load_pretrained: True + prompt: "a short description" + +datasets: + vatex_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: vatex/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: vatex/annotations/cap_val.json + test: + # iWNXAYGh9cI_000004_000014.mp4 is corrupt and removed from youtube + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: vatex/annotations/cap_test.json + videos: + storage: /export/video-language-dataset/data/vatex/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "describe the video" + length_penalty: 1. + + + seed: 42 + output_dir: "output/instructblip/vatex_caption_flant5xl/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/instructblip/caption_vatex_flant5xxl_eval.yaml b/LAVIS-main/lavis/projects/instructblip/caption_vatex_flant5xxl_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cf210113f8120af1559d3a81800940b3805baf04 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/caption_vatex_flant5xxl_eval.yaml @@ -0,0 +1,90 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_t5_instruct + model_type: flant5xxl + load_pretrained: True + prompt: "a short description" + +datasets: + vatex_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: vatex/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: vatex/annotations/cap_val.json + test: + # iWNXAYGh9cI_000004_000014.mp4 is corrupt and removed from youtube + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: vatex/annotations/cap_test.json + videos: + storage: /export/video-language-dataset/data/vatex/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "describe the video" + length_penalty: 1. + + + seed: 42 + output_dir: "output/instructblip/vatex_caption_flant5xxl/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/instructblip/caption_vatex_vicuna13b_eval.yaml b/LAVIS-main/lavis/projects/instructblip/caption_vatex_vicuna13b_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e7e51bdd41bbb043f2ba4f1e69d86f4237bc4fd8 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/caption_vatex_vicuna13b_eval.yaml @@ -0,0 +1,90 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna13b + load_pretrained: True + prompt: "describe the video" + +datasets: + vatex_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: vatex/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: vatex/annotations/cap_val.json + test: + # iWNXAYGh9cI_000004_000014.mp4 is corrupt and removed from youtube + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: vatex/annotations/cap_test.json + videos: + storage: /export/video-language-dataset/data/vatex/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "describe the video" + length_penalty: 0. + + + seed: 42 + output_dir: "output/instructblip/msvd_caption_vicuna13b/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/instructblip/caption_vatex_vicuna7b_eval.yaml b/LAVIS-main/lavis/projects/instructblip/caption_vatex_vicuna7b_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..70559fc8e82380f93ec850cc8274119da1c5d599 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/caption_vatex_vicuna7b_eval.yaml @@ -0,0 +1,91 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna7b + load_pretrained: True + prompt: "a short description" + + +datasets: + vatex_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: vatex/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: vatex/annotations/cap_val.json + test: + # iWNXAYGh9cI_000004_000014.mp4 is corrupt and removed from youtube + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: vatex/annotations/cap_test.json + videos: + storage: /export/video-language-dataset/data/vatex/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "describe the video" + length_penalty: 1. + + + seed: 42 + output_dir: "output/instructblip/vatex_caption_vicuna7b/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/instructblip/classification_modelnet40_vicuna13b.yaml b/LAVIS-main/lavis/projects/instructblip/classification_modelnet40_vicuna13b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1891a79e03c975c2d3c94923bd6e4615ee608065 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/classification_modelnet40_vicuna13b.yaml @@ -0,0 +1,101 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna13b + load_pretrained: True + prompt: "describe the 3d model." + format_candidates_prompt: " a 3d model of a {}" + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: [pc, images] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet_images8192 + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "describe the 3d model." + + max_len: 3 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/instructblip/modelent_classification_vicuna13b/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/instructblip/classification_modelnet40_vicuna7b.yaml b/LAVIS-main/lavis/projects/instructblip/classification_modelnet40_vicuna7b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ad3906b67b8f4fb8c54f65c27593f33eddeb1f92 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/classification_modelnet40_vicuna7b.yaml @@ -0,0 +1,100 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna7b + load_pretrained: True + prompt: "describe the 3d model." + format_candidates_prompt: " a 3d model of a {}" + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: [pc, images] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet_images8192 + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "describe the 3d model." + + max_len: 3 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/instructblip/modelent_classification_vicuna7b/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/instructblip/classification_snlive_flant5xl.yaml b/LAVIS-main/lavis/projects/instructblip/classification_snlive_flant5xl.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6f5647064047988c954e39258efb73938e4c7411 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/classification_snlive_flant5xl.yaml @@ -0,0 +1,94 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: +## note flant5 has been trained on snli + arch: blip2_t5_instruct + model_type: flant5xl + load_pretrained: True + prompt: "" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + prompt: "given the image respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/share/dongxuli/data/lavis/snli/ve_train.json + storage: + - snli/annotations/ve_train.json + val: + url: + - /export/share/dongxuli/data/lavis/snli/ve_dev.json + storage: + - snli/annotations/ve_dev.json + test: + url: + - /export/share/dongxuli/data/lavis/snli/ve_test.json + storage: + - snli/annotations/ve_test.json + images: + # storage: flickr30k/images/flickr30k-images + storage: /export/share/datasets/vision/flickr30k/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1. + + seed: 42 + output_dir: "output/instructblip/snlive_classification_flant5xl/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/instructblip/classification_snlive_flant5xxl.yaml b/LAVIS-main/lavis/projects/instructblip/classification_snlive_flant5xxl.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6aa66f63fb1f7fa625fc85c465666438607d133f --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/classification_snlive_flant5xxl.yaml @@ -0,0 +1,95 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: +## note flant5 has been trained on snli + arch: blip2_t5_instruct + model_type: flant5xxl + load_pretrained: True + prompt: "" + + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + prompt: "given the image respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/share/dongxuli/data/lavis/snli/ve_train.json + storage: + - snli/annotations/ve_train.json + val: + url: + - /export/share/dongxuli/data/lavis/snli/ve_dev.json + storage: + - snli/annotations/ve_dev.json + test: + url: + - /export/share/dongxuli/data/lavis/snli/ve_test.json + storage: + - snli/annotations/ve_test.json + images: + # storage: flickr30k/images/flickr30k-images + storage: /export/share/datasets/vision/flickr30k/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1. + + seed: 42 + output_dir: "output/instructblip/snlive_classification_flant5xxl/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/instructblip/classification_snlive_vicuna13b.yaml b/LAVIS-main/lavis/projects/instructblip/classification_snlive_vicuna13b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..92681c9c51274cb7868beee62a465b67e0c80c15 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/classification_snlive_vicuna13b.yaml @@ -0,0 +1,93 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna13b + load_pretrained: True + prompt: "" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "how would you respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/share/dongxuli/data/lavis/snli/ve_train.json + storage: + - snli/annotations/ve_train.json + val: + url: + - /export/share/dongxuli/data/lavis/snli/ve_dev.json + storage: + - snli/annotations/ve_dev.json + test: + url: + - /export/share/dongxuli/data/lavis/snli/ve_test.json + storage: + - snli/annotations/ve_test.json + images: + # storage: flickr30k/images/flickr30k-images + storage: /export/share/datasets/vision/flickr30k/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1. + + seed: 42 + output_dir: "output/instructblip/snlive_classification_vicuna13b_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/instructblip/classification_snlive_vicuna13b_test.yaml b/LAVIS-main/lavis/projects/instructblip/classification_snlive_vicuna13b_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..93be7b1d4ccbdd887572c09069cfe63fc15481de --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/classification_snlive_vicuna13b_test.yaml @@ -0,0 +1,93 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna13b + load_pretrained: True + prompt: "" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "how would you respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/share/dongxuli/data/lavis/snli/ve_train.json + storage: + - snli/annotations/ve_train.json + val: + url: + - /export/share/dongxuli/data/lavis/snli/ve_dev.json + storage: + - snli/annotations/ve_dev.json + test: + url: + - /export/share/dongxuli/data/lavis/snli/ve_test.json + storage: + - snli/annotations/ve_test.json + images: + # storage: flickr30k/images/flickr30k-images + storage: /export/share/datasets/vision/flickr30k/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1. + + seed: 42 + output_dir: "output/instructblip/snlive_classification_vicuna13b_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/instructblip/classification_snlive_vicuna7b_test.yaml b/LAVIS-main/lavis/projects/instructblip/classification_snlive_vicuna7b_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b938d19b1ceda5c0c7af0df99a83005829c583b8 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/classification_snlive_vicuna7b_test.yaml @@ -0,0 +1,93 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna7b + load_pretrained: True + prompt: "" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "given the image respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/share/dongxuli/data/lavis/snli/ve_train.json + storage: + - snli/annotations/ve_train.json + val: + url: + - /export/share/dongxuli/data/lavis/snli/ve_dev.json + storage: + - snli/annotations/ve_dev.json + test: + url: + - /export/share/dongxuli/data/lavis/snli/ve_test.json + storage: + - snli/annotations/ve_test.json + images: + # storage: flickr30k/images/flickr30k-images + storage: /export/share/datasets/vision/flickr30k/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1. + + seed: 42 + output_dir: "output/instructblip/snlive_classification_vicuna7b_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/instructblip/classification_snlive_vicuna7b_val.yaml b/LAVIS-main/lavis/projects/instructblip/classification_snlive_vicuna7b_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5427b6a5329df2e3953b525646a3ed38fffab46f --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/classification_snlive_vicuna7b_val.yaml @@ -0,0 +1,93 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna7b + load_pretrained: True + prompt: "" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "given the image respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/share/dongxuli/data/lavis/snli/ve_train.json + storage: + - snli/annotations/ve_train.json + val: + url: + - /export/share/dongxuli/data/lavis/snli/ve_dev.json + storage: + - snli/annotations/ve_dev.json + test: + url: + - /export/share/dongxuli/data/lavis/snli/ve_test.json + storage: + - snli/annotations/ve_test.json + images: + # storage: flickr30k/images/flickr30k-images + storage: /export/share/datasets/vision/flickr30k/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1. + + seed: 42 + output_dir: "output/instructblip/snlive_classification_vicuna7b_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/instructblip/completion_modelnet40_vicuna13b.yaml b/LAVIS-main/lavis/projects/instructblip/completion_modelnet40_vicuna13b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c8e96151e19640db5202728ac6b3cc0e8be81c40 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/completion_modelnet40_vicuna13b.yaml @@ -0,0 +1,101 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna13b + load_pretrained: True + prompt: "describe the 3d model" + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: [images,pc] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet_images8192 + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/instructblip/modelent_completion_vicuna13b/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/instructblip/completion_modelnet40_vicuna7b.yaml b/LAVIS-main/lavis/projects/instructblip/completion_modelnet40_vicuna7b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ccb0ccb62d15acc6be8b221bda7379b63bb2c82d --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/completion_modelnet40_vicuna7b.yaml @@ -0,0 +1,102 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna7b + load_pretrained: True + prompt: "describe the 3d model" + predict_with_gen: True + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: [images, pc] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet_images8192 + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/instructblip/modelent_completion_vicuna7b/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/instructblip/qa_msrvtt_flant5xl_eval_test.yaml b/LAVIS-main/lavis/projects/instructblip/qa_msrvtt_flant5xl_eval_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..491de9bc12b4dd43084479ec796e1b67133871e2 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/qa_msrvtt_flant5xl_eval_test.yaml @@ -0,0 +1,92 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_t5_instruct + model_type: flant5xl + load_pretrained: True + prompt: "Question: {} Short Answer:" + +datasets: + msrvtt_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_train.json + storage: msrvtt/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_val.json + storage: msrvtt/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_test.json + storage: msrvtt/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/train_ans2label.json + storage: msrvtt/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + instance_id_key: question_id + + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + length_penalty: -1. + + + seed: 42 + output_dir: "output/instructblip/msrvtt_qa_flant5xl_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + diff --git a/LAVIS-main/lavis/projects/instructblip/qa_msrvtt_flant5xxl_eval_test.yaml b/LAVIS-main/lavis/projects/instructblip/qa_msrvtt_flant5xxl_eval_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5dc726fce9efc02f4caf33f6226ce910436a5ab1 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/qa_msrvtt_flant5xxl_eval_test.yaml @@ -0,0 +1,92 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_t5_instruct + model_type: flant5xxl + load_pretrained: True + prompt: "Question: {} Short Answer:" + +datasets: + msrvtt_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_train.json + storage: msrvtt/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_val.json + storage: msrvtt/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_test.json + storage: msrvtt/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/train_ans2label.json + storage: msrvtt/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + instance_id_key: question_id + + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + length_penalty: -1. + + + seed: 42 + output_dir: "output/instructblip/msrvtt_qa_flant5xxl_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + diff --git a/LAVIS-main/lavis/projects/instructblip/qa_msrvtt_vicuna13b_eval_test.yaml b/LAVIS-main/lavis/projects/instructblip/qa_msrvtt_vicuna13b_eval_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0467b44b3438fdcbf3cded844831f44485de0c5e --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/qa_msrvtt_vicuna13b_eval_test.yaml @@ -0,0 +1,92 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna13b + load_pretrained: True + prompt: "Question: {} Short Answer:" + +datasets: + msrvtt_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_train.json + storage: msrvtt/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_val.json + storage: msrvtt/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_test.json + storage: msrvtt/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/train_ans2label.json + storage: msrvtt/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + instance_id_key: question_id + + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + length_penalty: -1. + + + seed: 42 + output_dir: "output/instructblip/msrvtt_qa_vicuna13b_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + diff --git a/LAVIS-main/lavis/projects/instructblip/qa_msrvtt_vicuna7b_eval_test.yaml b/LAVIS-main/lavis/projects/instructblip/qa_msrvtt_vicuna7b_eval_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e60a48f23739791120eff0cbe4fa0124e7dc3a2a --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/qa_msrvtt_vicuna7b_eval_test.yaml @@ -0,0 +1,92 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna7b + load_pretrained: True + prompt: "Question: {} Short Answer:" + +datasets: + msrvtt_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_train.json + storage: msrvtt/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_val.json + storage: msrvtt/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_test.json + storage: msrvtt/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/train_ans2label.json + storage: msrvtt/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + instance_id_key: question_id + + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + length_penalty: -1. + + + seed: 42 + output_dir: "output/instructblip/msrvtt_qa_vicuna7b_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + diff --git a/LAVIS-main/lavis/projects/instructblip/qa_msvd_flant5xl_eval.yaml b/LAVIS-main/lavis/projects/instructblip/qa_msvd_flant5xl_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fd2f9d6d66adeb3a3aa981704dc1fbbbbe0abd88 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/qa_msvd_flant5xl_eval.yaml @@ -0,0 +1,100 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_t5_instruct + model_type: flant5xl + load_pretrained: True + prompt: "Question: {} Short Answer:" + +datasets: + msvd_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_train.json + storage: msvd/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_val.json + storage: msvd/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_test.json + storage: msvd/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/train_ans2label.json + storage: msvd/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msvd/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "Question: {} Short Answer:" + length_penalty: -1. + + + seed: 42 + output_dir: "output/instructblip/msvd_qa_flant5xl/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + ques_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_questions.json"} + anno_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_annotations.json"} + + + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/instructblip/qa_msvd_flant5xxl_eval.yaml b/LAVIS-main/lavis/projects/instructblip/qa_msvd_flant5xxl_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2d3ecee13d14945465711d8ec63adf1b38d4c50d --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/qa_msvd_flant5xxl_eval.yaml @@ -0,0 +1,100 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_t5_instruct + model_type: flant5xxl + load_pretrained: True + prompt: "Question: {} Short Answer:" + +datasets: + msvd_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_train.json + storage: msvd/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_val.json + storage: msvd/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_test.json + storage: msvd/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/train_ans2label.json + storage: msvd/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msvd/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "Question: {} Short Answer:" + length_penalty: -1. + + + seed: 42 + output_dir: "output/instructblip/msvd_qa_flant5xxl/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + ques_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_questions.json"} + anno_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_annotations.json"} + + + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/instructblip/qa_msvd_vicuna13b_eval.yaml b/LAVIS-main/lavis/projects/instructblip/qa_msvd_vicuna13b_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fe5fd31f0976701f93b831cc6e81d8c8766cce7a --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/qa_msvd_vicuna13b_eval.yaml @@ -0,0 +1,100 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna13b + load_pretrained: True + prompt: "Question: {} Short Answer:" + +datasets: + msvd_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_train.json + storage: msvd/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_val.json + storage: msvd/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_test.json + storage: msvd/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/train_ans2label.json + storage: msvd/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msvd/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "Question: {} Short Answer:" + length_penalty: -1. + + + seed: 42 + output_dir: "output/instructblip/msvd_qa_vicuna13b/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + ques_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_questions.json"} + anno_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_annotations.json"} + + + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/instructblip/qa_msvd_vicuna7b_eval.yaml b/LAVIS-main/lavis/projects/instructblip/qa_msvd_vicuna7b_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7f0c4617343bb1e58fb03d7952489216b3b2349d --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/qa_msvd_vicuna7b_eval.yaml @@ -0,0 +1,100 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna7b + load_pretrained: True + prompt: "Question: {} Short Answer:" + +datasets: + msvd_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_train.json + storage: msvd/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_val.json + storage: msvd/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_test.json + storage: msvd/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/train_ans2label.json + storage: msvd/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msvd/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "Question: {} Short Answer:" + length_penalty: -1. + + + seed: 42 + output_dir: "output/instructblip/msvd_qa_vicuna7b/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + ques_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_questions.json"} + anno_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_annotations.json"} + + + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/instructblip/qa_okvqa_flant5xl_eval.yaml b/LAVIS-main/lavis/projects/instructblip/qa_okvqa_flant5xl_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..78e1f61b360a6471d7d2931ef666d67aeebc6028 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/qa_okvqa_flant5xl_eval.yaml @@ -0,0 +1,90 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_t5_instruct + model_type: flant5xl + load_pretrained: True + prompt: "Question: {} Short Answer:" + +datasets: + ok_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_train2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_train2014_annotations.json + storage: + - okvqa/annotations/okvqa_train.json + # - okvqa/annotations/OpenEnded_mscoco_train2014_questions.json + # - okvqa/annotations/mscoco_train2014_annotations.json + test: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_val_eval.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_answer_list_train.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_val2014_questions.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_val2014_annotations.json + storage: + - okvqa/annotations/vqa_val_eval.json + - okvqa/annotations/answer_list.json + - okvqa/annotations/OpenEnded_mscoco_val2014_questions.json + - okvqa/annotations/mscoco_val2014_annotations.json + images: + storage: /export/share/datasets/vision/coco/images + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + length_penalty: -1. + + seed: 42 + output_dir: "output/instructblip/okavqa_qa_flant5xl/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/instructblip/qa_okvqa_flant5xxl_eval.yaml b/LAVIS-main/lavis/projects/instructblip/qa_okvqa_flant5xxl_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b0cd6555c53f8f101da2578119b3cc8c61338975 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/qa_okvqa_flant5xxl_eval.yaml @@ -0,0 +1,90 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_t5_instruct + model_type: flant5xxl + load_pretrained: True + prompt: "Question: {} Short Answer:" + +datasets: + ok_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_train2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_train2014_annotations.json + storage: + - okvqa/annotations/okvqa_train.json + # - okvqa/annotations/OpenEnded_mscoco_train2014_questions.json + # - okvqa/annotations/mscoco_train2014_annotations.json + test: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_val_eval.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_answer_list_train.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_val2014_questions.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_val2014_annotations.json + storage: + - okvqa/annotations/vqa_val_eval.json + - okvqa/annotations/answer_list.json + - okvqa/annotations/OpenEnded_mscoco_val2014_questions.json + - okvqa/annotations/mscoco_val2014_annotations.json + images: + storage: /export/share/datasets/vision/coco/images + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + length_penalty: -1. + + seed: 42 + output_dir: "output/instructblip/okavqa_qa_flant5xxl/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/instructblip/qa_okvqa_vicuna13b_eval.yaml b/LAVIS-main/lavis/projects/instructblip/qa_okvqa_vicuna13b_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ccddfc32dc36abe664ecde70e47d8ccc14a8f9f9 --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/qa_okvqa_vicuna13b_eval.yaml @@ -0,0 +1,90 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna13b + load_pretrained: True + prompt: "Question: {} Short Answer:" + +datasets: + ok_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_train2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_train2014_annotations.json + storage: + - okvqa/annotations/okvqa_train.json + # - okvqa/annotations/OpenEnded_mscoco_train2014_questions.json + # - okvqa/annotations/mscoco_train2014_annotations.json + test: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_val_eval.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_answer_list_train.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_val2014_questions.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_val2014_annotations.json + storage: + - okvqa/annotations/vqa_val_eval.json + - okvqa/annotations/answer_list.json + - okvqa/annotations/OpenEnded_mscoco_val2014_questions.json + - okvqa/annotations/mscoco_val2014_annotations.json + images: + storage: /export/share/datasets/vision/coco/images + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + length_penalty: -1. + + seed: 42 + output_dir: "output/instructblip/okavqa_qa_vicuna13b/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/instructblip/qa_okvqa_vicuna7b_eval.yaml b/LAVIS-main/lavis/projects/instructblip/qa_okvqa_vicuna7b_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0cb0b202fbbe4c981758d4f4ff8073798da43bbf --- /dev/null +++ b/LAVIS-main/lavis/projects/instructblip/qa_okvqa_vicuna7b_eval.yaml @@ -0,0 +1,90 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna7b + load_pretrained: True + prompt: "Question: {} Short Answer:" + +datasets: + ok_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_train2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_train2014_annotations.json + storage: + - okvqa/annotations/okvqa_train.json + # - okvqa/annotations/OpenEnded_mscoco_train2014_questions.json + # - okvqa/annotations/mscoco_train2014_annotations.json + test: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_val_eval.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_answer_list_train.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_val2014_questions.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_val2014_annotations.json + storage: + - okvqa/annotations/vqa_val_eval.json + - okvqa/annotations/answer_list.json + - okvqa/annotations/OpenEnded_mscoco_val2014_questions.json + - okvqa/annotations/mscoco_val2014_annotations.json + images: + storage: /export/share/datasets/vision/coco/images + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + length_penalty: -1. + + seed: 42 + output_dir: "output/instructblip/okavqa_qa_vicuna7b/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/pnp-vqa/eval/gqa_eval.yaml b/LAVIS-main/lavis/projects/pnp-vqa/eval/gqa_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d8c97adfdb987426a8fc7f2f22dafe8c91967788 --- /dev/null +++ b/LAVIS-main/lavis/projects/pnp-vqa/eval/gqa_eval.yaml @@ -0,0 +1,60 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: pnp_vqa + model_type: base + +datasets: + gqa: # name of the dataset builder + type: balanced_testdev + vis_processor: + eval: + name: "blip_image_eval" + image_size: 384 + text_processor: + eval: + name: "blip_question" + +run: + task: gqa_reading_comprehension + + # optimization-specific + batch_size_train: 16 + batch_size_eval: 16 + num_workers: 4 + + # image question matching specific + block_num: 7 + + # image captioning specific + top_k: 50 + top_p: 1 + cap_min_length: 10 + cap_max_length: 20 + repetition_penalty: 1 + num_patches: 20 + num_captions: 100 + prompt: 'a picture of ' + + # question answering specific + internal_bsz_fid: 1 + num_captions_fid: 5 + min_len: 0 + max_len: 20 + num_beams: 1 + inference_method: "generate" + + seed: 42 + output_dir: "output/PNP-VQA/GQA" + + evaluate: True + test_splits: ["val"] + + # distribution-specific + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True diff --git a/LAVIS-main/lavis/projects/pnp-vqa/eval/gqa_eval_3b.yaml b/LAVIS-main/lavis/projects/pnp-vqa/eval/gqa_eval_3b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..933691c2cb1730878c7c05fc3e3c507047977fdb --- /dev/null +++ b/LAVIS-main/lavis/projects/pnp-vqa/eval/gqa_eval_3b.yaml @@ -0,0 +1,60 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: pnp_vqa + model_type: 3b + +datasets: + gqa: # name of the dataset builder + type: balanced_testdev + vis_processor: + eval: + name: "blip_image_eval" + image_size: 384 + text_processor: + eval: + name: "blip_question" + +run: + task: gqa_reading_comprehension + + # optimization-specific + batch_size_train: 4 + batch_size_eval: 4 + num_workers: 4 + + # image question matching specific + block_num: 7 + + # image captioning specific + top_k: 50 + top_p: 1 + cap_min_length: 10 + cap_max_length: 20 + repetition_penalty: 1 + num_patches: 20 + num_captions: 100 + prompt: 'a picture of ' + + # question answering specific + internal_bsz_fid: 1 + num_captions_fid: 5 + min_len: 0 + max_len: 20 + num_beams: 1 + inference_method: "generate" + + seed: 42 + output_dir: "output/PNP-VQA-3b/GQA" + + evaluate: True + test_splits: ["val"] + + # distribution-specific + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True diff --git a/LAVIS-main/lavis/projects/pnp-vqa/eval/gqa_eval_large.yaml b/LAVIS-main/lavis/projects/pnp-vqa/eval/gqa_eval_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..537936aa4c4f95e3c94138d48ddd2da09600c9f0 --- /dev/null +++ b/LAVIS-main/lavis/projects/pnp-vqa/eval/gqa_eval_large.yaml @@ -0,0 +1,60 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: pnp_vqa + model_type: large + +datasets: + gqa: # name of the dataset builder + type: balanced_testdev + vis_processor: + eval: + name: "blip_image_eval" + image_size: 384 + text_processor: + eval: + name: "blip_question" + +run: + task: gqa_reading_comprehension + + # optimization-specific + batch_size_train: 12 + batch_size_eval: 12 + num_workers: 4 + + # image question matching specific + block_num: 7 + + # image captioning specific + top_k: 50 + top_p: 1 + cap_min_length: 10 + cap_max_length: 20 + repetition_penalty: 1 + num_patches: 20 + num_captions: 100 + prompt: 'a picture of ' + + # question answering specific + internal_bsz_fid: 1 + num_captions_fid: 5 + min_len: 0 + max_len: 20 + num_beams: 1 + inference_method: "generate" + + seed: 42 + output_dir: "output/PNP-VQA-large/GQA" + + evaluate: True + test_splits: ["val"] + + # distribution-specific + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True diff --git a/LAVIS-main/lavis/projects/pnp-vqa/eval/okvqa_eval.yaml b/LAVIS-main/lavis/projects/pnp-vqa/eval/okvqa_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f29e95db6c75bef1219b083b23f310840507e72d --- /dev/null +++ b/LAVIS-main/lavis/projects/pnp-vqa/eval/okvqa_eval.yaml @@ -0,0 +1,59 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: pnp_vqa + model_type: base + +datasets: + ok_vqa: # name of the dataset builder + vis_processor: + eval: + name: "blip_image_eval" + image_size: 384 + text_processor: + eval: + name: "blip_question" + +run: + task: vqa_reading_comprehension + + # optimization-specific + batch_size_train: 16 + batch_size_eval: 16 + num_workers: 4 + + # image question matching specific + block_num: 7 + + # image captioning specific + top_k: 50 + top_p: 1 + cap_min_length: 10 + cap_max_length: 20 + repetition_penalty: 1 + num_patches: 20 + num_captions: 100 + prompt: 'a picture of ' + + # question answering specific + internal_bsz_fid: 1 + num_captions_fid: 1 + min_len: 0 + max_len: 20 + num_beams: 1 + inference_method: "generate" + + seed: 42 + output_dir: "output/PNP-VQA/OKVQA" + + evaluate: True + test_splits: ["test"] + + # distribution-specific + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True diff --git a/LAVIS-main/lavis/projects/pnp-vqa/eval/okvqa_eval_3b.yaml b/LAVIS-main/lavis/projects/pnp-vqa/eval/okvqa_eval_3b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3a0c2dd26cb0e46a7d8ab990dbb23153bdea136c --- /dev/null +++ b/LAVIS-main/lavis/projects/pnp-vqa/eval/okvqa_eval_3b.yaml @@ -0,0 +1,59 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: pnp_vqa + model_type: 3b + +datasets: + ok_vqa: # name of the dataset builder + vis_processor: + eval: + name: "blip_image_eval" + image_size: 384 + text_processor: + eval: + name: "blip_question" + +run: + task: vqa_reading_comprehension + + # optimization-specific + batch_size_train: 4 + batch_size_eval: 4 + num_workers: 4 + + # image question matching specific + block_num: 7 + + # image captioning specific + top_k: 50 + top_p: 1 + cap_min_length: 10 + cap_max_length: 20 + repetition_penalty: 1 + num_patches: 20 + num_captions: 100 + prompt: 'a picture of ' + + # question answering specific + internal_bsz_fid: 1 + num_captions_fid: 1 + min_len: 0 + max_len: 20 + num_beams: 1 + inference_method: "generate" + + seed: 42 + output_dir: "output/PNP-VQA-3b/OKVQA" + + evaluate: True + test_splits: ["test"] + + # distribution-specific + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True diff --git a/LAVIS-main/lavis/projects/pnp-vqa/eval/okvqa_eval_large.yaml b/LAVIS-main/lavis/projects/pnp-vqa/eval/okvqa_eval_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a55d7f25f6175c21bb6ae29c5544e0811e34d4b5 --- /dev/null +++ b/LAVIS-main/lavis/projects/pnp-vqa/eval/okvqa_eval_large.yaml @@ -0,0 +1,59 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: pnp_vqa + model_type: large + +datasets: + ok_vqa: # name of the dataset builder + vis_processor: + eval: + name: "blip_image_eval" + image_size: 384 + text_processor: + eval: + name: "blip_question" + +run: + task: vqa_reading_comprehension + + # optimization-specific + batch_size_train: 12 + batch_size_eval: 12 + num_workers: 4 + + # image question matching specific + block_num: 7 + + # image captioning specific + top_k: 50 + top_p: 1 + cap_min_length: 10 + cap_max_length: 20 + repetition_penalty: 1 + num_patches: 20 + num_captions: 100 + prompt: 'a picture of ' + + # question answering specific + internal_bsz_fid: 1 + num_captions_fid: 1 + min_len: 0 + max_len: 20 + num_beams: 1 + inference_method: "generate" + + seed: 42 + output_dir: "output/PNP-VQA-large/OKVQA" + + evaluate: True + test_splits: ["test"] + + # distribution-specific + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True diff --git a/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_eval.yaml b/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e6f26b6e3166372314a300aa2e2c8e437a2e93f0 --- /dev/null +++ b/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_eval.yaml @@ -0,0 +1,60 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: pnp_vqa + model_type: base + +datasets: + coco_vqa: # name of the dataset builder + type: eval + vis_processor: + eval: + name: "blip_image_eval" + image_size: 384 + text_processor: + eval: + name: "blip_question" + +run: + task: vqa_reading_comprehension + + # optimization-specific + batch_size_train: 16 + batch_size_eval: 16 + num_workers: 4 + + # image question matching specific + block_num: 7 + + # image captioning specific + top_k: 50 + top_p: 1 + cap_min_length: 10 + cap_max_length: 20 + repetition_penalty: 1 + num_patches: 20 + num_captions: 100 + prompt: 'a picture of ' + + # question answering specific + internal_bsz_fid: 1 + num_captions_fid: 1 + min_len: 0 + max_len: 20 + num_beams: 1 + inference_method: "generate" + + seed: 42 + output_dir: "output/PNP-VQA/VQAv2_val" + + evaluate: True + test_splits: ["val"] + + # distribution-specific + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True diff --git a/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_eval_3b.yaml b/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_eval_3b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dc0db0176caf98f24527808752d16533b510144f --- /dev/null +++ b/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_eval_3b.yaml @@ -0,0 +1,60 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: pnp_vqa + model_type: 3b + +datasets: + coco_vqa: # name of the dataset builder + type: eval + vis_processor: + eval: + name: "blip_image_eval" + image_size: 384 + text_processor: + eval: + name: "blip_question" + +run: + task: vqa_reading_comprehension + + # optimization-specific + batch_size_train: 4 + batch_size_eval: 4 + num_workers: 4 + + # image question matching specific + block_num: 7 + + # image captioning specific + top_k: 50 + top_p: 1 + cap_min_length: 10 + cap_max_length: 20 + repetition_penalty: 1 + num_patches: 20 + num_captions: 100 + prompt: 'a picture of ' + + # question answering specific + internal_bsz_fid: 1 + num_captions_fid: 1 + min_len: 0 + max_len: 20 + num_beams: 1 + inference_method: "generate" + + seed: 42 + output_dir: "output/PNP-VQA-3b/VQAv2_val" + + evaluate: True + test_splits: ["val"] + + # distribution-specific + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True diff --git a/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_eval_large.yaml b/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_eval_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3e59eae9985a3ca5df3bc62036c9fc267f4e72f1 --- /dev/null +++ b/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_eval_large.yaml @@ -0,0 +1,60 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: pnp_vqa + model_type: large + +datasets: + coco_vqa: # name of the dataset builder + type: eval + vis_processor: + eval: + name: "blip_image_eval" + image_size: 384 + text_processor: + eval: + name: "blip_question" + +run: + task: vqa_reading_comprehension + + # optimization-specific + batch_size_train: 12 + batch_size_eval: 12 + num_workers: 4 + + # image question matching specific + block_num: 7 + + # image captioning specific + top_k: 50 + top_p: 1 + cap_min_length: 10 + cap_max_length: 20 + repetition_penalty: 1 + num_patches: 20 + num_captions: 100 + prompt: 'a picture of ' + + # question answering specific + internal_bsz_fid: 1 + num_captions_fid: 1 + min_len: 0 + max_len: 20 + num_beams: 1 + inference_method: "generate" + + seed: 42 + output_dir: "output/PNP-VQA-large/VQAv2_val" + + evaluate: True + test_splits: ["val"] + + # distribution-specific + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True diff --git a/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_test_eval.yaml b/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_test_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a5f3d387a6f419db69f77fa36a01ca3f00b13734 --- /dev/null +++ b/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_test_eval.yaml @@ -0,0 +1,60 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: pnp_vqa + model_type: base + +datasets: + coco_vqa: # name of the dataset builder + type: default + vis_processor: + eval: + name: "blip_image_eval" + image_size: 384 + text_processor: + eval: + name: "blip_question" + +run: + task: vqa_reading_comprehension + + # optimization-specific + batch_size_train: 16 + batch_size_eval: 16 + num_workers: 4 + + # image question matching specific + block_num: 7 + + # image captioning specific + top_k: 50 + top_p: 1 + cap_min_length: 10 + cap_max_length: 20 + repetition_penalty: 1 + num_patches: 20 + num_captions: 100 + prompt: 'a picture of ' + + # question answering specific + internal_bsz_fid: 1 + num_captions_fid: 1 + min_len: 0 + max_len: 20 + num_beams: 1 + inference_method: "generate" + + seed: 42 + output_dir: "output/PNP-VQA/VQAv2_test" + + evaluate: True + test_splits: ["test"] + + # distribution-specific + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True diff --git a/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_test_eval_3b.yaml b/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_test_eval_3b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..799ad56333158b4bfb73775deb48ed4d81d1f9c0 --- /dev/null +++ b/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_test_eval_3b.yaml @@ -0,0 +1,60 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: pnp_vqa + model_type: 3b + +datasets: + coco_vqa: # name of the dataset builder + type: default + vis_processor: + eval: + name: "blip_image_eval" + image_size: 384 + text_processor: + eval: + name: "blip_question" + +run: + task: vqa_reading_comprehension + + # optimization-specific + batch_size_train: 4 + batch_size_eval: 4 + num_workers: 4 + + # image question matching specific + block_num: 7 + + # image captioning specific + top_k: 50 + top_p: 1 + cap_min_length: 10 + cap_max_length: 20 + repetition_penalty: 1 + num_patches: 20 + num_captions: 100 + prompt: 'a picture of ' + + # question answering specific + internal_bsz_fid: 1 + num_captions_fid: 1 + min_len: 0 + max_len: 20 + num_beams: 1 + inference_method: "generate" + + seed: 42 + output_dir: "output/PNP-VQA-3b/VQAv2_test" + + evaluate: True + test_splits: ["test"] + + # distribution-specific + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True diff --git a/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_test_eval_large.yaml b/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_test_eval_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..10aa447ed472a13fa77014698071053334ddaa3b --- /dev/null +++ b/LAVIS-main/lavis/projects/pnp-vqa/eval/vqav2_test_eval_large.yaml @@ -0,0 +1,60 @@ + # Copyright (c) 2022, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: pnp_vqa + model_type: large + +datasets: + coco_vqa: # name of the dataset builder + type: default + vis_processor: + eval: + name: "blip_image_eval" + image_size: 384 + text_processor: + eval: + name: "blip_question" + +run: + task: vqa_reading_comprehension + + # optimization-specific + batch_size_train: 12 + batch_size_eval: 12 + num_workers: 4 + + # image question matching specific + block_num: 7 + + # image captioning specific + top_k: 50 + top_p: 1 + cap_min_length: 10 + cap_max_length: 20 + repetition_penalty: 1 + num_patches: 20 + num_captions: 100 + prompt: 'a picture of ' + + # question answering specific + internal_bsz_fid: 1 + num_captions_fid: 1 + min_len: 0 + max_len: 20 + num_beams: 1 + inference_method: "generate" + + seed: 42 + output_dir: "output/PNP-VQA-large/VQAv2_test" + + evaluate: True + test_splits: ["test"] + + # distribution-specific + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_caption.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_caption.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a1415c883cdf298f332620e0180ab9f346c689d1 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_caption.yaml @@ -0,0 +1,176 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "question: {} answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : [audio, video] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + special_qformer_input_prompt: "a short description" + prefix: "You are given two inputs. Select exactly one of the two by referece to its relative position (first or second, left or right) that best answers the question. " + predict_with_gen: False + use_caption: True + use_describe: False + enumerate_inputs: False + add_space: True + + +datasets: + audio_video_discrn: + # data_dir: ${env.data_dir}/datasets + audio_processor: + train: + name: beats_audio + n_frames: 2 + eval: + name: beats_audio + n_frames: 2 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + data_type: [audio, video] # [images|videos|features] + + build_info: + kwargs: + total: all + shuffle_modalities: False + balance_labels: True + dataset_name: audiocaps + ground_truth: False + classnames: [audio, video] + raw: True + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + storage: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio/val + video: + storage: /export/einstein-vision/audio_datasets/audiocaps/video/AUDIOCAPS_32000Hz/audio/val + +run: + task: discrn_qa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 0 + max_epoch: 1 + segments: 1 + + # inference-specific + max_len: 10 + min_len: 1 + length_penalty: -1. + num_beams: 5 + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["val"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/discrn/audio_video_caption" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_caption_13b.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_caption_13b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cb7bf927ef97382b921cbdb61ed57a10ac0a7bbe --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_caption_13b.yaml @@ -0,0 +1,176 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "question: {} answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : [audio, video] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + special_qformer_input_prompt: "a short description" + prefix: "You are given two inputs. Select exactly one of the two by referece to its relative position (first or second, left or right) that best answers the question. " + predict_with_gen: False + use_caption: True + use_describe: False + enumerate_inputs: False + add_space: True + + +datasets: + audio_video_discrn: + # data_dir: ${env.data_dir}/datasets + audio_processor: + train: + name: beats_audio + n_frames: 2 + eval: + name: beats_audio + n_frames: 2 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + data_type: [audio, video] # [images|videos|features] + + build_info: + kwargs: + total: all + shuffle_modalities: False + balance_labels: True + dataset_name: audiocaps + ground_truth: False + classnames: [audio, video] + raw: True + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + storage: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio/val + video: + storage: /export/einstein-vision/audio_datasets/audiocaps/video/AUDIOCAPS_32000Hz/audio/val + +run: + task: discrn_qa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 0 + max_epoch: 1 + segments: 1 + + # inference-specific + max_len: 10 + min_len: 1 + length_penalty: -1. + num_beams: 5 + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["val"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/discrn/audio_video_caption" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a467a5890bdb89a2e0f11a9a109a069ebf11c437 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe.yaml @@ -0,0 +1,176 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "question: {} answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : [audio, video] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + special_qformer_input_prompt: "a short description" + prefix: "You are given two inputs. Select exactly one of the two by referece to its relative position (first or second, left or right) that best answers the question. " + predict_with_gen: False + use_caption: False + use_describe: False + enumerate_inputs: False + add_space: True + + +datasets: + audio_video_discrn: + # data_dir: ${env.data_dir}/datasets + audio_processor: + train: + name: beats_audio + n_frames: 2 + eval: + name: beats_audio + n_frames: 2 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + video_processor: + train: + name: alpro_video_train + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + data_type: [audio, video] # [images|videos|features] + + build_info: + kwargs: + total: 100 + shuffle_modalities: False + balance_labels: True + dataset_name: audiocaps + ground_truth: False + classnames: [audio, video] + raw: False + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + storage: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio/val + video: + storage: /export/einstein-vision/audio_datasets/audiocaps/video/AUDIOCAPS_32000Hz/audio/val + +run: + task: discrn_qa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 8 + max_epoch: 1 + segments: 1 + + # inference-specific + max_len: 10 + min_len: 1 + length_penalty: -1. + num_beams: 5 + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["val"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/discrn/audio_video_describe" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe_13b.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe_13b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..766b1ad7251fc5650571c96f77f49877609bc94e --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe_13b.yaml @@ -0,0 +1,177 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer_last.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "question: {} answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : [audio, video] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + # special_qformer_input_prompt: "a short description" + prefix: "You are given two inputs. Select exactly one of the two by referece to its relative position (first or second, left or right) that best answers the question. " + predict_with_gen: False + use_caption: False + use_describe: False + enumerate_inputs: False + add_space: True + remove_start: True + + +datasets: + audio_video_discrn: + # data_dir: ${env.data_dir}/datasets + audio_processor: + train: + name: beats_audio + n_frames: 2 + eval: + name: beats_audio + n_frames: 2 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + video_processor: + train: + name: alpro_video_train + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + data_type: [audio, video] # [images|videos|features] + + build_info: + kwargs: + total: 100 + shuffle_modalities: False + balance_labels: True + dataset_name: audiocaps + ground_truth: False + classnames: [audio, video] + raw: False + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + storage: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio/val + video: + storage: /export/einstein-vision/audio_datasets/audiocaps/video/AUDIOCAPS_32000Hz/audio/val + +run: + task: discrn_qa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 8 + max_epoch: 1 + segments: 1 + + # inference-specific + max_len: 10 + min_len: 1 + length_penalty: -1. + num_beams: 5 + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["val"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/discrn/audio_video_describe" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe_nocue.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe_nocue.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6068899b1678ce8b43c14000b59bf174d1dc94f5 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe_nocue.yaml @@ -0,0 +1,176 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "question: {} answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : [audio, video] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + special_qformer_input_prompt: "a short description" + prefix: "You are given two inputs. Select exactly one of the two by referece to its relative position (first or second, left or right) that best answers the question. " + predict_with_gen: False + use_caption: False + use_describe: False + enumerate_inputs: False + add_space: True + + +datasets: + audio_video_discrn: + # data_dir: ${env.data_dir}/datasets + audio_processor: + train: + name: beats_audio + n_frames: 2 + eval: + name: beats_audio + n_frames: 2 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + video_processor: + train: + name: alpro_video_train + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + data_type: [audio, video] # [images|videos|features] + + build_info: + kwargs: + total: all + shuffle_modalities: False + balance_labels: True + dataset_name: audiocaps + ground_truth: False + classnames: [audio, video] + raw: False + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + storage: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio/val + video: + storage: /export/einstein-vision/audio_datasets/audiocaps/video/AUDIOCAPS_32000Hz/audio/val + +run: + task: discrn_qa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 8 + max_epoch: 1 + segments: 1 + + # inference-specific + max_len: 10 + min_len: 1 + length_penalty: -1. + num_beams: 5 + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["val"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/discrn/audio_video_describe_nocue" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe_proj copy.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe_proj copy.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f708c43134bad765627ace195af7e70c987b223c --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe_proj copy.yaml @@ -0,0 +1,179 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/audio/20231115194/checkpoint_65001.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "question: {} answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : [audio, video] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + special_qformer_input_prompt: "a short description" + prefix: "You are given two inputs. Select exactly one of the two by referece to its relative position (first or second, left or right) that best answers the question. " + predict_with_gen: False + use_caption: False + use_describe: False + enumerate_inputs: False + add_space: True + projection_only_audio: True + projection_path_audio: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear_768.pth + proj_dim: 768 + + +datasets: + audio_video_discrn: + # data_dir: ${env.data_dir}/datasets + audio_processor: + train: + name: beats_audio + n_frames: 2 + eval: + name: beats_audio + n_frames: 2 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + video_processor: + train: + name: alpro_video_train + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + data_type: [audio, video] # [images|videos|features] + + build_info: + kwargs: + total: all + shuffle_modalities: False + balance_labels: True + dataset_name: audiocaps + ground_truth: False + classnames: [audio, video] + raw: False + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + storage: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio/val + video: + storage: /export/einstein-vision/audio_datasets/audiocaps/video/AUDIOCAPS_32000Hz/audio/val + +run: + task: discrn_qa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 8 + max_epoch: 1 + segments: 1 + + # inference-specific + max_len: 10 + min_len: 1 + length_penalty: -1. + num_beams: 5 + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["val"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/discrn/audio_video_describe_proj" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe_proj.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe_proj.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f708c43134bad765627ace195af7e70c987b223c --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe_proj.yaml @@ -0,0 +1,179 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/audio/20231115194/checkpoint_65001.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "question: {} answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : [audio, video] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + special_qformer_input_prompt: "a short description" + prefix: "You are given two inputs. Select exactly one of the two by referece to its relative position (first or second, left or right) that best answers the question. " + predict_with_gen: False + use_caption: False + use_describe: False + enumerate_inputs: False + add_space: True + projection_only_audio: True + projection_path_audio: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear_768.pth + proj_dim: 768 + + +datasets: + audio_video_discrn: + # data_dir: ${env.data_dir}/datasets + audio_processor: + train: + name: beats_audio + n_frames: 2 + eval: + name: beats_audio + n_frames: 2 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + video_processor: + train: + name: alpro_video_train + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + data_type: [audio, video] # [images|videos|features] + + build_info: + kwargs: + total: all + shuffle_modalities: False + balance_labels: True + dataset_name: audiocaps + ground_truth: False + classnames: [audio, video] + raw: False + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + storage: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio/val + video: + storage: /export/einstein-vision/audio_datasets/audiocaps/video/AUDIOCAPS_32000Hz/audio/val + +run: + task: discrn_qa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 8 + max_epoch: 1 + segments: 1 + + # inference-specific + max_len: 10 + min_len: 1 + length_penalty: -1. + num_beams: 5 + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["val"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/discrn/audio_video_describe_proj" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe_rand_init.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe_rand_init.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f1381580b75c5adc608615705ae66ad7552bf433 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/audio_video_describe_rand_init.yaml @@ -0,0 +1,176 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_no_init.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "question: {} answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : [audio, video] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + special_qformer_input_prompt: "a short description" + prefix: "You are given two inputs. Select exactly one of the two by referece to its relative position (first or second, left or right) that best answers the question. " + predict_with_gen: False + use_caption: False + use_describe: False + enumerate_inputs: False + add_space: True + + +datasets: + audio_video_discrn: + # data_dir: ${env.data_dir}/datasets + audio_processor: + train: + name: beats_audio + n_frames: 2 + eval: + name: beats_audio + n_frames: 2 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + video_processor: + train: + name: alpro_video_train + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + data_type: [audio, video] # [images|videos|features] + + build_info: + kwargs: + total: all + shuffle_modalities: False + balance_labels: True + dataset_name: audiocaps + ground_truth: False + classnames: [audio, video] + raw: False + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + storage: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio/val + video: + storage: /export/einstein-vision/audio_datasets/audiocaps/video/AUDIOCAPS_32000Hz/audio/val + +run: + task: discrn_qa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 8 + max_epoch: 1 + segments: 1 + + # inference-specific + max_len: 10 + min_len: 1 + length_penalty: -1. + num_beams: 5 + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["val"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/discrn/audio_video_describe_rand_init" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_caption.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_caption.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e2f611c4095ebceb7982e601848eae933c3a55ac --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_caption.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "question: {} answer:" + modalities : [image, pc] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + special_qformer_input_prompt: "a short description" + prefix: "You are given two inputs. Select exactly one of the two by referece to its relative position (first or second, left or right) that best answers the question. " + predict_with_gen: False + use_caption: True + use_describe: False + enumerate_inputs: False + add_space: True + +datasets: + image_pc_discrn: # name of the dataset builder + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + data_type: [images, pc] # [images|videos|features] + + + build_info: + kwargs: + total: all + raw: True + shuffle_modalities: False + balance_labels: True + dataset_name: objaverse + classnames: [image, 3d] + ground_truth: False + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/objaverse.json + storage: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/objaverse.json + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + +run: + task: discrn_qa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 10 + max_epoch: 1 + segments: 1 + + # inference-specific + max_len: 10 + min_len: 1 + length_penalty: -1. + num_beams: 5 + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["val"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/discrn/image_3d_caption" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_caption_13b.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_caption_13b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7ec85d2d848a75d510a95d157f4ec911ae4f6819 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_caption_13b.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "question: {} answer:" + modalities : [image, pc] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + special_qformer_input_prompt: "a short description" + prefix: "You are given two inputs. Select exactly one of the two by referece to its relative position (first or second, left or right) that best answers the question. " + predict_with_gen: False + use_caption: True + use_describe: False + enumerate_inputs: False + add_space: True + +datasets: + image_pc_discrn: # name of the dataset builder + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + data_type: [images, pc] # [images|videos|features] + + + build_info: + kwargs: + total: 100 + raw: True + shuffle_modalities: False + balance_labels: True + dataset_name: objaverse + classnames: [image, 3d] + ground_truth: False + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/objaverse.json + storage: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/objaverse.json + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + +run: + task: discrn_qa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 2 + max_epoch: 1 + segments: 1 + + # inference-specific + max_len: 10 + min_len: 1 + length_penalty: -1. + num_beams: 5 + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["val"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/discrn/image_3d_caption" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_describe.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_describe.yaml new file mode 100644 index 0000000000000000000000000000000000000000..125202de1afc5a2ef87ebb22acbf232b1dafb0d8 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_describe.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "question: {} answer:" + modalities : [image, pc] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + special_qformer_input_prompt: "a short description" + prefix: "You are given two inputs. Select exactly one of the two by referece to its relative position (first or second, left or right) that best answers the question. " + predict_with_gen: False + use_caption: False + use_describe: False + enumerate_inputs: False + add_space: True + +datasets: + image_pc_discrn: # name of the dataset builder + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + data_type: [images, pc] # [images|videos|features] + + + build_info: + kwargs: + total: all + raw: False + shuffle_modalities: False + balance_labels: True + dataset_name: objaverse + classnames: [image, 3d] + ground_truth: False + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/objaverse.json + storage: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/objaverse.json + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + +run: + task: discrn_qa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 10 + max_epoch: 1 + segments: 1 + + # inference-specific + max_len: 10 + min_len: 1 + length_penalty: 1. + num_beams: 5 + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["val"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/discrn/image_3d_describe" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_describe_13b.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_describe_13b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9212af039d5616d358c4875fc36da178db3e8199 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_describe_13b.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "question: {} answer:" + modalities : [image, pc] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + special_qformer_input_prompt: "a short description" + prefix: "You are given two inputs. Select exactly one of the two by referece to its relative position (first or second, left or right) that best answers the question. " + predict_with_gen: False + use_caption: False + use_describe: False + enumerate_inputs: False + add_space: True + +datasets: + image_pc_discrn: # name of the dataset builder + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + data_type: [images, pc] # [images|videos|features] + + + build_info: + kwargs: + total: all + raw: False + shuffle_modalities: False + balance_labels: True + dataset_name: objaverse + classnames: [image, 3d] + ground_truth: False + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/objaverse.json + storage: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/objaverse.json + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + +run: + task: discrn_qa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 10 + max_epoch: 1 + segments: 1 + + # inference-specific + max_len: 10 + min_len: 1 + length_penalty: -1. + num_beams: 5 + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["val"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/discrn/image_3d_describe" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_describe_no_init.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_describe_no_init.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8b8a0cf28320c8135ea407ee4c0a916c4b3eee56 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_describe_no_init.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer_no_init.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "question: {} answer:" + modalities : [image, pc] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + special_qformer_input_prompt: "a short description" + prefix: "You are given two inputs. Select exactly one of the two by referece to its relative position (first or second, left or right) that best answers the question. " + predict_with_gen: False + use_caption: False + use_describe: False + enumerate_inputs: False + add_space: True + +datasets: + image_pc_discrn: # name of the dataset builder + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + data_type: [images, pc] # [images|videos|features] + + + build_info: + kwargs: + total: all + raw: False + shuffle_modalities: False + balance_labels: True + dataset_name: objaverse + classnames: [image, 3d] + ground_truth: False + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/objaverse.json + storage: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/objaverse.json + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + +run: + task: discrn_qa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 10 + max_epoch: 1 + segments: 1 + + # inference-specific + max_len: 10 + min_len: 1 + length_penalty: 1. + num_beams: 5 + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["val"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/discrn/image_3d_describe" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_describe_nocue.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_describe_nocue.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2a7f17bbe4e1c0c83e25582dcfa2d85ba0595755 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_describe_nocue.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "question: {} answer:" + modalities : [image, pc] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + special_qformer_input_prompt: "a short description" + prefix: "You are given two inputs. Select exactly one of the two by referece to its relative position (first or second, left or right) that best answers the question. " + predict_with_gen: False + use_caption: False + use_describe: False + enumerate_inputs: False + add_space: True + +datasets: + image_pc_discrn: # name of the dataset builder + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + data_type: [images, pc] # # [images|videos|features] + + + build_info: + kwargs: + total: all + raw: False + shuffle_modalities: False + balance_labels: True + dataset_name: objaverse + classnames: [image, 3d] + ground_truth: False + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/objaverse.json + storage: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/objaverse.json + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + +run: + task: discrn_qa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 8 + max_epoch: 1 + segments: 1 + + # inference-specific + max_len: 10 + min_len: 1 + length_penalty: -1. + num_beams: 5 + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["val"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/discrn/image_3d_describe_nocue" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_describe_proj.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_describe_proj.yaml new file mode 100644 index 0000000000000000000000000000000000000000..17031af42bfc600fae52e85081365bcde576f4e7 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/discrn/image_3d_describe_proj.yaml @@ -0,0 +1,157 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/pc_qformer_linear.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: null + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "question: {} answer:" + modalities : [image, pc] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + special_qformer_input_prompt: "a short description" + prefix: "You are given two inputs. Select exactly one of the two by referece to its relative position (first or second, left or right) that best answers the question. " + predict_with_gen: False + use_caption: False + use_describe: False + enumerate_inputs: False + add_space: True + projection_only_pc: True + projection_path_pc: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/pc_qformer_linear_768.pth + proj_dim: 768 + +datasets: + image_pc_discrn: # name of the dataset builder + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + data_type: [images, pc] # # [images|videos|features] + + + build_info: + kwargs: + total: all + raw: False + shuffle_modalities: False + balance_labels: True + dataset_name: objaverse + classnames: [image, 3d] + ground_truth: False + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/objaverse.json + storage: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/discrn/objaverse.json + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + +run: + task: discrn_qa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 8 + max_epoch: 1 + segments: 1 + + # inference-specific + max_len: 10 + min_len: 1 + length_penalty: -1. + num_beams: 5 + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["val"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/discrn/image_3d_describe_proj" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/audiocaps_captioning_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/audiocaps_captioning_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..06faf3df2e00667e59b4a324b19621c38514f102 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/audiocaps_captioning_qa.yaml @@ -0,0 +1,159 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_qa: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + build_info: + annotations: + + train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/audiocaps/audio_qa_final_train.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_train.csv + storage: + - audiocaps_qa/annotation/train.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_train.csv + + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/audiocaps/audio_qa_final_val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_val.csv + storage: + - audiocaps_qa/annotation/val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_val.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/audio/audiocaps_captioning_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/audiocaps_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/audiocaps_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3146bf11318b70eb928f0678304bbf554fec2916 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/audiocaps_captioning_test.yaml @@ -0,0 +1,161 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_caption_instruct: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + prompt: describe the audio + + data_type: [audio] # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/train.csv + storage: + - /audiocaps/dataset/train.csv + + val: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/val.csv + storage: + - /audiocaps/dataset/val.csv + + test: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/test.csv + storage: + - /audiocaps/dataset/test.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/audio/audiocaps_captioning_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/audiocaps_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/audiocaps_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c0aa0cead02e4d86889bfec2c7359116aad2f4c9 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/audiocaps_captioning_val.yaml @@ -0,0 +1,161 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_caption_instruct: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + prompt: describe the audio + + data_type: [audio] # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/train.csv + storage: + - /audiocaps/dataset/train.csv + + val: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/val.csv + storage: + - /audiocaps/dataset/val.csv + + test: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/test.csv + storage: + - /audiocaps/dataset/test.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 4 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/audio/audiocaps_captioning_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/clothoQA_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/clothoQA_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1c5d1f2589a5fc8e9a305c562423ad4fac55a012 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/clothoQA_captioning.yaml @@ -0,0 +1,155 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + clotho_qa: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] + + build_info: + + annotations: + train: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_train.csv + storage: + - clotho_Qa/annotations/clotho_aqa_train.csv + val: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_val.csv + storage: + - clotho_qa/annotations/clotho_aqa_val.csv + + test: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_test.csv + storage: + - clotho_qa/annotations/clotho_aqa_test.csv + audio: + storage: /export/einstein-vision/audio_datasets/clotho-aqa/audio_files + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 4 + accum_grad_iters: 1 + prompt: "Question: {} Answer:" + + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/audio/clothovqa_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/clothov1_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/clothov1_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4bb7720365b2a1b1e3f96502aea31f1ca58a5fc7 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/clothov1_captioning.yaml @@ -0,0 +1,147 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + clothov2: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] # [images|videos|features] + + build_info: + kwargs: + clotho_root: /export/einstein-vision/audio_datasets/clothov2/ + split: evaluation + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://zenodo.org/record/4783391/files/clotho_captions_evaluation.csv + storage: + - /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_csv_files/clotho_captions_evaluation.csv + audio: + storage: /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_audio_files/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 4 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/audio/clothov1_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/clothov2_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/clothov2_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ac9afc5d38c2fea98eb08a1a50e33d41bb594c2b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/clothov2_captioning.yaml @@ -0,0 +1,148 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer_old.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer_old.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + clothov2: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + prompt: "a short description" + + + data_type: [audio] # [images|videos|features] + + build_info: + kwargs: + clotho_root: /export/einstein-vision/audio_datasets/clothov2/ + split: validation + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://zenodo.org/record/4783391/files/clotho_captions_validation.csv + storage: + - /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_csv_files/clotho_captions_validation.csv + audio: + storage: /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_audio_files/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 4 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/audio/clothov2_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/esc50_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/esc50_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8f2dd12974565ef16c40dba72a56dc0609910828 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/esc50_classification.yaml @@ -0,0 +1,151 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "describe the audio" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + format_candidates_prompt: "an audio of {}" + +datasets: + esc50_cls: # + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + build_info: + annotations: + + val: + url: + - https://raw.githubusercontent.com/karolpiczak/ESC-50/master/meta/esc50.csv + storage: + - /ESC-50/esc50.csv + + audio: + storage: /ESC-50/audio + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "describe the audio" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/audio/esc50_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/esc50_classification_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/esc50_classification_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..06724ad4e802bd245a0ed73da295d27b359a3ca4 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/audio/esc50_classification_completion.yaml @@ -0,0 +1,151 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "describe the audio" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + # format_candidates_prompt: "an audio of {}" + +datasets: + esc50_cls: # + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + build_info: + annotations: + + val: + url: + - https://raw.githubusercontent.com/karolpiczak/ESC-50/master/meta/esc50.csv + storage: + - /ESC-50/esc50.csv + + audio: + storage: /ESC-50/audio + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "describe the audio" + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/audio/esc50_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/musicavqa/musicavqa_audio_eval.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/musicavqa/musicavqa_audio_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..627968a90267c77de7d18c5efd8571098a0db1d4 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/musicavqa/musicavqa_audio_eval.yaml @@ -0,0 +1,182 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + musicavqa_mm_instruct: # name of the dataset builder + data_type: [audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: qa + modality: video + eval: + name: blip_question + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-val.json + storage: + - /musicavqa/val.json + + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-test.json + storage: + - /musicavqa/test.json + templates: null + + audio: + storage: path/to/videos + + video: + storage: path/to/videos + + + +run: + task: gqa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 0 + max_epoch: 1 + + # inference-specific + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + inference_method: "generate" + prompt: "Question: {} Answer:" + + train_splits: ["train"] + valid_splits: ["test"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + ques_files: { + "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_questions.json", + "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_testt_questions.json" + } + anno_files: { + "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_annotations.json", + "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_test_annotations.json" + } + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/crossmodal/musicavqa/audio/" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/musicavqa/musicavqa_joint_eval.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/musicavqa/musicavqa_joint_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c0add441d3df3ab1d301223e2226300727bcb3c9 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/musicavqa/musicavqa_joint_eval.yaml @@ -0,0 +1,181 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio", "video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + musicavqa_mm_instruct: # name of the dataset builder + data_type: [video,audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: qa + modality: video + eval: + name: blip_question + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-val.json + storage: + - /musicavqa/val.json + + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-test.json + storage: + - /musicavqa/test.json + templates: null + + audio: + storage: path/to/videos + + video: + storage: path/to/videos + + + +run: + task: gqa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 6 + max_epoch: 1 + + # inference-specific + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + inference_method: "generate" + + train_splits: ["train"] + valid_splits: ["test"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + # ques_files: { + # "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_questions.json", + # "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_test_questions.json" + # } + # anno_files: { + # "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_annotations.json", + # "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_test_annotations.json" + # } + + # model specific + k_test: 128 + + # misc + seed: 123 + output_dir: "output/xinstructblip/eval/vicuna13b/crossmodal/musicavqa/joint/" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/musicavqa/musicavqa_video_eval.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/musicavqa/musicavqa_video_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..55e37e9467a282ae3cf77a47d1e8bb25ada519d3 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/musicavqa/musicavqa_video_eval.yaml @@ -0,0 +1,182 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + musicavqa_mm_instruct: # name of the dataset builder + data_type: [video] + + video_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: qa + modality: video + eval: + name: blip_question + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-val.json + storage: + - /musicavqa/val.json + + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-test.json + storage: + - /musicavqa/test.json + templates: null + + audio: + storage: path/to/videos + + video: + storage: path/to/videos + + + +run: + task: gqa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 8 + max_epoch: 1 + + # inference-specific + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + inference_method: "generate" + prompt: "Question: {} Answer:" + + train_splits: ["train"] + valid_splits: ["test"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + # ques_files: { + # "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_questions.json", + # "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_test_questions.json" + # } + # anno_files: { + # "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_annotations.json", + # "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_test_annotations.json" + # } + + # model specific + k_test: 128 + + # misc + seed: 123 + output_dir: "output/xinstructblip/eval/vicuna13b/crossmodal/musicavqa/video/" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/vatex/vatex_audio_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/vatex/vatex_audio_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ac9d76f3d9a9fb964c1cac3a1bf2790eddc9a77d --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/vatex/vatex_audio_captioning.yaml @@ -0,0 +1,183 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + valor_mm_caption: # name of the dataset builder + data_type: [audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video/vatex_joint_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # load_gt_from_file: vatex/annotations/cap_val.json + + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/vatex/vatex_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/vatex/vatex_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2b16a7539bab388f26287c8a3fc6e94f541cafdc --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/vatex/vatex_captioning.yaml @@ -0,0 +1,180 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "describe the video" + length_penalty: 0. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video/vatex_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/vatex/vatex_joint_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/vatex/vatex_joint_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2f4480da049eabb3365371df3b1943781c8767b0 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/vatex/vatex_joint_captioning.yaml @@ -0,0 +1,183 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio", "video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video, audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video/vatex_joint_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # load_gt_from_file: vatex/annotations/cap_val.json + + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/vatex/vatex_joint_captioning_interleave.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/vatex/vatex_joint_captioning_interleave.yaml new file mode 100644 index 0000000000000000000000000000000000000000..54d0f0c6b7ba75987f6ef7483123ce4c3eca8f91 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/crossmodal/vatex/vatex_joint_captioning_interleave.yaml @@ -0,0 +1,184 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio", "video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + joint_video_audio: True + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video, audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video/vatex_joint_captioning_interleave/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # load_gt_from_file: vatex/annotations/cap_val.json + + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/coco_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/coco_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..93577d697f977f815146f3be76c9b66a635ac115 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/coco_captioning_test.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image/coco_captioning_test/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/coco_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/coco_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..db31d6b2542c3890edf033807f45f5153595e69f --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/coco_captioning_val.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image/coco_captioning_val/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/flickr30k_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/flickr30k_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..561a513f1c5303bf1816446af4304afcc823b63f --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/flickr30k_captioning.yaml @@ -0,0 +1,150 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + flickr30k_caption: + # data_dir: ${env.data_dir}/datasets + data_type: images + + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + annotations: + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_val.json + storage: + - flickr30k/annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_test.json + storage: + - flickr30k/annotations/test.json + images: + # storage: flickr30k/images + storage: /export/share/datasets/vision/flickr30k + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image/flickr_captioning/" + # annotation_file: /export/home/.cache/lavis/flickr30k_caption_gt/flickr30k_caption_test_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/gqa_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/gqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..867d8341ed5f457a1f160cb3515a1500d536f5de --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/gqa_qa.yaml @@ -0,0 +1,148 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "based on the given image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + gqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_question" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/train_balanced_questions.json + storage: + - gqa/annotations/train_balanced_questions.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/testdev_balanced_questions.json + storage: + - gqa/annotations/testdev_balanced_questions.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/test_balanced_questions.json + storage: + - gqa/annotations/test_balanced_questions.json + images: + storage: /export/share/datasets/vision/GQA/images #gqa/images/ + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image/gqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/nocaps_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/nocaps_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1c3896d045ac8b61f1d433786cf6087a9eb85040 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/nocaps_captioning.yaml @@ -0,0 +1,142 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image/nocaps_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/nocaps_out_domain_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/nocaps_out_domain_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..52ffb0722a9ea176d22ee6b13699ce468a6801c6 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/nocaps_out_domain_captioning.yaml @@ -0,0 +1,143 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image/nocaps_out_domain_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + img_ids: [2, 4, 5, 8, 15, 18, 19, 22, 27, 30, 33, 35, 41, 42, 43, 46, 47, 51, 59, 60, 64, 65, 68, 69, 71, 72, 73, 77, 79, 81, 85, 87, 88, 90, 92, 100, 101, 102, 105, 107, 109, 115, 120, 124, 125, 126, 127, 129, 133, 135, 137, 139, 140, 141, 143, 150, 153, 155, 158, 164, 165, 167, 170, 171, 173, 182, 190, 191, 196, 200, 201, 203, 205, 208, 219, 225, 226, 228, 229, 232, 239, 240, 243, 245, 250, 262, 263, 264, 267, 272, 278, 283, 284, 290, 291, 297, 301, 304, 305, 309, 310, 311, 314, 323, 325, 329, 330, 331, 333, 334, 341, 349, 350, 351, 352, 354, 358, 359, 363, 365, 366, 368, 371, 372, 379, 381, 383, 386, 388, 389, 390, 392, 405, 415, 417, 418, 420, 421, 424, 428, 429, 432, 436, 441, 443, 452, 453, 454, 455, 456, 459, 464, 465, 468, 469, 476, 477, 478, 480, 487, 488, 490, 491, 493, 500, 502, 504, 506, 509, 510, 511, 512, 515, 516, 520, 527, 529, 533, 539, 540, 541, 544, 545, 547, 551, 554, 556, 559, 577, 579, 580, 582, 586, 587, 590, 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4388, 4389, 4392, 4396, 4402, 4404, 4408, 4410, 4424, 4426, 4428, 4431, 4435, 4436, 4439, 4442, 4446, 4447, 4449, 4452, 4455, 4458, 4460, 4461, 4466, 4469, 4475, 4476, 4478, 4488, 4491, 4494, 4498] diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/okvqa_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/okvqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..29de2b041cc1e837bcabc2edfbc9013e25899310 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/okvqa_qa.yaml @@ -0,0 +1,157 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ok_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_train2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_train2014_annotations.json + storage: + - okvqa/annotations/okvqa_train.json + # - okvqa/annotations/OpenEnded_mscoco_train2014_questions.json + # - okvqa/annotations/mscoco_train2014_annotations.json + test: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_val_eval.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_answer_list_train.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_val2014_questions.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_val2014_annotations.json + storage: + - okvqa/annotations/vqa_val_eval.json + - okvqa/annotations/answer_list.json + - okvqa/annotations/OpenEnded_mscoco_val2014_questions.json + - okvqa/annotations/mscoco_val2014_annotations.json + images: + storage: /export/share/datasets/vision/coco/images + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image/okvqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/snlive_classification_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/snlive_classification_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aefd757b53009947dff29e5bcf95a5529243a810 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/snlive_classification_test.yaml @@ -0,0 +1,153 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + # special_qformer_input_prompt: "{}" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "given the image respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image/snlive_classification_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/snlive_classification_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/snlive_classification_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fe7b84ffaa7cf2af452625a1e6b6b547b6a627da --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/snlive_classification_val.yaml @@ -0,0 +1,153 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + # special_qformer_input_prompt: "{}" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "given the image respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image/snlive_classification_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/vizwiz_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/vizwiz_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3e88e5c1c609d1fb3c4f471e9b338cc7442dd7a2 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image/vizwiz_qa.yaml @@ -0,0 +1,151 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "based on the given image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vizwiz_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_question" + + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + storage: + - vizwiz/annotations/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + storage: + - vizwiz/annotations/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + images: + storage: /export/share/datasets/vision/vizwiz/images + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: based on the given image respond to {} + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image/vizwiz_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/coco_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/coco_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..acd6d9baf5f208b0a2140087be3ae669bed2dcc2 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/coco_captioning_test.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna13b/image/20231027210/checkpoint_40000.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image_coco/coco_captioning_test/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/coco_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/coco_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..084ea1c1e4b8935bffdb4a6c53121ab801434217 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/coco_captioning_val.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna13b/image/20231027210/checkpoint_40000.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image_coco/coco_captioning_val/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/flickr30k_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/flickr30k_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..37f849463ce769396e6381587b7e2daec311e88e --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/flickr30k_captioning.yaml @@ -0,0 +1,149 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + flickr30k_caption: + # data_dir: ${env.data_dir}/datasets + data_type: images + + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + annotations: + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_val.json + storage: + - flickr30k/annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_test.json + storage: + - flickr30k/annotations/test.json + images: + # storage: flickr30k/images + storage: /export/share/datasets/vision/flickr30k + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image_coco/flickr_captioning/" + # annotation_file: /export/home/.cache/lavis/flickr30k_caption_gt/flickr30k_caption_test_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/gqa_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/gqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9548f9537f1fefaba69f925b2a1c234311d8e732 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/gqa_qa.yaml @@ -0,0 +1,147 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "based on the given image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + gqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_question" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/train_balanced_questions.json + storage: + - gqa/annotations/train_balanced_questions.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/testdev_balanced_questions.json + storage: + - gqa/annotations/testdev_balanced_questions.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/test_balanced_questions.json + storage: + - gqa/annotations/test_balanced_questions.json + images: + storage: /export/share/datasets/vision/GQA/images #gqa/images/ + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image_coco/gqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/nocaps_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/nocaps_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..eef7d122da7ea161c40d657f475dbbb10cdd0ecf --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/nocaps_captioning.yaml @@ -0,0 +1,142 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image_coco/nocaps_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/nocaps_out_domain_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/nocaps_out_domain_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0ba01f2f76ea9e3b589cf83f41328093189870f6 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/nocaps_out_domain_captioning.yaml @@ -0,0 +1,143 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image_coco/nocaps_out_domain_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + img_ids: [2, 4, 5, 8, 15, 18, 19, 22, 27, 30, 33, 35, 41, 42, 43, 46, 47, 51, 59, 60, 64, 65, 68, 69, 71, 72, 73, 77, 79, 81, 85, 87, 88, 90, 92, 100, 101, 102, 105, 107, 109, 115, 120, 124, 125, 126, 127, 129, 133, 135, 137, 139, 140, 141, 143, 150, 153, 155, 158, 164, 165, 167, 170, 171, 173, 182, 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b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/okvqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..be366b663d6ebfac5c5b3dcfc261b58eb9ccd974 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/okvqa_qa.yaml @@ -0,0 +1,156 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ok_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_train2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_train2014_annotations.json + storage: + - okvqa/annotations/okvqa_train.json + # - okvqa/annotations/OpenEnded_mscoco_train2014_questions.json + # - okvqa/annotations/mscoco_train2014_annotations.json + test: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_val_eval.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_answer_list_train.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_val2014_questions.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_val2014_annotations.json + storage: + - okvqa/annotations/vqa_val_eval.json + - okvqa/annotations/answer_list.json + - okvqa/annotations/OpenEnded_mscoco_val2014_questions.json + - okvqa/annotations/mscoco_val2014_annotations.json + images: + storage: /export/share/datasets/vision/coco/images + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image_coco/okvqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/snlive_classification_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/snlive_classification_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8d1e1f9987bbc73b32ab03fa42c6bb42e1ab7805 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/snlive_classification_test.yaml @@ -0,0 +1,152 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + # special_qformer_input_prompt: "{}" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "given the image respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image_coco/snlive_classification_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/snlive_classification_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/snlive_classification_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..537298f209d139e3b536b5c9fa5647140271d50a --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/snlive_classification_val.yaml @@ -0,0 +1,152 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + # special_qformer_input_prompt: "{}" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "given the image respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image_coco/snlive_classification_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/vizwiz_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/vizwiz_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0b4d5163ecc113798656109d0088e06b2379f66e --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/image_with_coco/vizwiz_qa.yaml @@ -0,0 +1,151 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "based on the given image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vizwiz_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_question" + + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + storage: + - vizwiz/annotations/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + storage: + - vizwiz/annotations/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + images: + storage: /export/share/datasets/vision/vizwiz/images + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: based on the given image respond to {} + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/image_coco/vizwiz_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/pc/modelnet40_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/pc/modelnet40_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..80a0647b76937b5b64d0a2ba772d484b84c912cb --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/pc/modelnet40_classification.yaml @@ -0,0 +1,148 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + format_candidates_prompt: " a 3d model of a {}" + special_qformer_input_prompt: False + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: pc + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 3 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/pc/modelnet_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + prompt: describe the 3d model + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/pc/modelnet40_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/pc/modelnet40_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4ca7df9172f26442891f9db0995d8e805048c69a --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/pc/modelnet40_completion.yaml @@ -0,0 +1,147 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: pc + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/pc/modelnet_classification_completion/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/pc/objaverse_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/pc/objaverse_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9b7ed67fa8356eda9b86f78dbe73976d4cc09a94 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/pc/objaverse_captioning.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + + data_type: [pc] # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + storage: + - objaverse/annotations/train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + + val: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + storage: + - objaverse/annotations/val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/pc/objaverse_captioning/" + # annotation_file: /export/home/.cache/lavis/objaverse_mm_caption_instruct_gt/objaverse_mm_caption_instruct_val_annotations.json + + amp: True + resume_ckpt_path: null + caption_key: 'data' + sample_id_key: sample_id + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/pc/objaverse_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/pc/objaverse_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..964bc04e6ad36ba4d1cede5ec9dfcfbca5b5d479 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/pc/objaverse_qa.yaml @@ -0,0 +1,166 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "based on the given input respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + objaverse_mm_qa: # 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: +train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + storage: + - objaverse_qa/annotations/train.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final_val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + storage: + - objaverse_qa/annotations/val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/pc/objaverse_qa/" + # ques_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_questions.json" } + # anno_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_annotations.json" } + + + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msrvtt_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msrvtt_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c3f3388bfe0f4d95185fba80304b23d67d07b531 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msrvtt_captioning.yaml @@ -0,0 +1,159 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_caption: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: videos # [images|videos|features] + + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_train.json + storage: msrvtt/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_val.json + storage: msrvtt/annotations/cap_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_test.json + storage: msrvtt/annotations/cap_test.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video/msrvtt_captioning/" + # annotation_file: /export/home/.cache/lavis/msrvtt_caption_gt/msrvtt_caption_val_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msrvtt_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msrvtt_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..44ce319e565b58f9264a0fab8bb1d0905a837840 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msrvtt_captioning_test.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_caption: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: videos # [images|videos|features] + + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_train.json + storage: msrvtt/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_val.json + storage: msrvtt/annotations/cap_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_test.json + storage: msrvtt/annotations/cap_test.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video/msrvtt_captioning_test/" + # annotation_file: /export/home/.cache/lavis/msrvtt_caption_gt/msrvtt_caption_test_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msrvtt_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msrvtt_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..78fd0abd2ea13138afe4bc9af3ee6579b06898df --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msrvtt_captioning_val.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_caption: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: videos # [images|videos|features] + + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_train.json + storage: msrvtt/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_val.json + storage: msrvtt/annotations/cap_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_test.json + storage: msrvtt/annotations/cap_test.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video/msrvtt_captioning_val/" + # annotation_file: /export/home/.cache/lavis/msrvtt_caption_gt/msrvtt_caption_val_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msrvtt_qa_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msrvtt_qa_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..529aa8eddb30204933d68f52ccd3979f2a4f7a81 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msrvtt_qa_test.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "based on the given video respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_train.json + storage: msrvtt/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_val.json + storage: msrvtt/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_test.json + storage: msrvtt/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/train_ans2label.json + storage: msrvtt/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given video respond to {}" + length_penalty: -1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video/msrvtt_qa_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msrvtt_qa_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msrvtt_qa_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..18310327f70682d0429f4b10daacbe210be27a93 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msrvtt_qa_val.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "based on the given video respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_train.json + storage: msrvtt/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_val.json + storage: msrvtt/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_test.json + storage: msrvtt/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/train_ans2label.json + storage: msrvtt/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given video respond to {}" + length_penalty: -1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video/msrvtt_qa_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msvd_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msvd_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5b52df50b4487c8cb211bed6e4f282752dbdaec8 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/msvd_captioning.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_train.json + storage: msvd/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_val.json + storage: msvd/annotations/cap_val.json + test: + # video f9_bP219ehQ_63_70.avi is corrupt + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_test.json + storage: msvd/annotations/cap_test.json + videos: + # storage: msvd/videos + storage: /export/share/datasets/vision_language/msvd/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 2 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + 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-0,0 +1,168 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "based on the given video respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_train.json + storage: msvd/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_val.json + storage: msvd/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_test.json + storage: msvd/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/train_ans2label.json + storage: msvd/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msvd/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 2 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given video respond to {}" + length_penalty: -1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video/msvd_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + # ques_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + # "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + # 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b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/vatex_audio_captioning.yaml @@ -0,0 +1,183 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + valor_mm_caption: # name of the dataset builder + data_type: [audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video/vatex_joint_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # load_gt_from_file: vatex/annotations/cap_val.json + + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/vatex_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/vatex_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2b16a7539bab388f26287c8a3fc6e94f541cafdc --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/vatex_captioning.yaml @@ -0,0 +1,180 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "describe the video" + length_penalty: 0. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video/vatex_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/vatex_joint_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/vatex_joint_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2f4480da049eabb3365371df3b1943781c8767b0 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/vatex_joint_captioning.yaml @@ -0,0 +1,183 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio", "video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video, audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video/vatex_joint_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # load_gt_from_file: vatex/annotations/cap_val.json + + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/vatex_joint_captioning_interleave.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/vatex_joint_captioning_interleave.yaml new file mode 100644 index 0000000000000000000000000000000000000000..54d0f0c6b7ba75987f6ef7483123ce4c3eca8f91 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video/vatex_joint_captioning_interleave.yaml @@ -0,0 +1,184 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio", "video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + joint_video_audio: True + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video, audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video/vatex_joint_captioning_interleave/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # load_gt_from_file: vatex/annotations/cap_val.json + + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video_image/msvd_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video_image/msvd_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0e5c455a2c2dd9f6e07c1575b323fb1cc4245ac8 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video_image/msvd_captioning.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "image" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "image" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "image" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_train.json + storage: msvd/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_val.json + storage: msvd/annotations/cap_val.json + test: + # video f9_bP219ehQ_63_70.avi is corrupt + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_test.json + storage: msvd/annotations/cap_test.json + videos: + # storage: msvd/videos + storage: /export/share/datasets/vision_language/msvd/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + 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b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video_image/msvd_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..68a9b60a2c609cb6bd0e0b728a710fa77c57edc2 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video_image/msvd_qa.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "image" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "image" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "image" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "based on the given video respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_train.json + storage: msvd/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_val.json + storage: msvd/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_test.json + storage: msvd/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/train_ans2label.json + storage: msvd/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msvd/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given video respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video_image/msvd_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True 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a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video_image/vatex_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video_image/vatex_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..df4d643a44b1fcd2a7b7e5a7be0b806d4c7aee11 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna13b/video_image/vatex_captioning.yaml @@ -0,0 +1,182 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "image" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "image" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "image" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna13b/video_image/vatex_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/audiocaps_captioning_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/audiocaps_captioning_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..709f8772a808a91c8173a193cca9206b7d5911c0 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/audiocaps_captioning_qa.yaml @@ -0,0 +1,155 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_finetuned: True + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_qa: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + build_info: + annotations: + + train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/audiocaps/audio_qa_final_train.csv + storage: + - audiocaps_qa/annotation/train.csv + + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/audiocaps/audio_qa_final_val.csv + storage: + - audiocaps_qa/annotation/val.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio/audiocaps_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/audiocaps_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/audiocaps_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..060ff437ad146008ae93167e4fdb068499c7cc6e --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/audiocaps_captioning_test.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_caption_instruct: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + prompt: describe the audio + + data_type: [audio] # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/train.csv + storage: + - /audiocaps/dataset/train.csv + + val: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/val.csv + storage: + - /audiocaps/dataset/val.csv + + test: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/test.csv + storage: + - /audiocaps/dataset/test.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio/audiocaps_captioning_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/audiocaps_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/audiocaps_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d52e020c1a0e65de30b0a60cff8e58e1315c2c6b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/audiocaps_captioning_val.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_caption_instruct: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + prompt: describe the audio + + data_type: [audio] # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/train.csv + storage: + - /audiocaps/dataset/train.csv + + val: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/val.csv + storage: + - /audiocaps/dataset/val.csv + + test: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/test.csv + storage: + - /audiocaps/dataset/test.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio/audiocaps_captioning_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/clothoQA_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/clothoQA_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..667417460704a0996f9210a77ff3eb4cab4f98d3 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/clothoQA_captioning.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "{}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + clotho_qa: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] + + build_info: + kwargs: + non_bin: True + unanimous: True + + annotations: + train: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_train.csv + storage: + - clotho_Qa/annotations/clotho_aqa_train.csv + val: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_val.csv + storage: + - clotho_qa/annotations/clotho_aqa_val.csv + + test: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_test.csv + storage: + - clotho_qa/annotations/clotho_aqa_test.csv + audio: + storage: /export/einstein-vision/audio_datasets/clotho-aqa/audio_files + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 4 + accum_grad_iters: 1 + prompt: "Question: {} Answer:" + + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio/clothoaqa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/clothov1_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/clothov1_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..90e2fa56007dbbd9f43798316b56c4f399d4dab8 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/clothov1_captioning.yaml @@ -0,0 +1,147 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + clothov2: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] # [images|videos|features] + + build_info: + kwargs: + clotho_root: /export/einstein-vision/audio_datasets/clothov2/ + split: evaluation + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://zenodo.org/record/4783391/files/clotho_captions_evaluation.csv + storage: + - /clotho/clotho_captions_evaluation.csv + audio: + storage: /path/to/clotho + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio/clothov1_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/clothov2_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/clothov2_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..739baa2d4fed6778f54c13bd26824ac62da4eb0d --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/clothov2_captioning.yaml @@ -0,0 +1,147 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + clothov2: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] # [images|videos|features] + + build_info: + kwargs: + clotho_root: /export/einstein-vision/audio_datasets/clothov2/ + split: validation + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://zenodo.org/record/4783391/files/clotho_captions_validation.csv + storage: + - /clotho/clotho_captions_validation.csv + audio: + storage: /path/to/clotho/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio/clothov2_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/esc50_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/esc50_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6196d67f3543901d8f6abbfd09f899b6ca98a3e0 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/esc50_classification.yaml @@ -0,0 +1,149 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the audio" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + format_candidates_prompt: "an audio of {}" + +datasets: + esc50_cls: # + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + data_type: [audio] + + build_info: + annotations: + + val: + url: + - https://raw.githubusercontent.com/karolpiczak/ESC-50/master/meta/esc50.csv + storage: + - /ESC-50/esc50.csv + + audio: + storage: /ESC-50/audio + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "describe the audio" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio/esc50_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/esc50_classification_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/esc50_classification_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a4edce53a6f44ec5b8e992e5c8ee27a0d60b6c61 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio/esc50_classification_completion.yaml @@ -0,0 +1,149 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "descibe the audio" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + # format_candidates_prompt: "an audio of {}" + +datasets: + esc50_cls: # + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + data_type: [audio] + + build_info: + annotations: + + val: + url: + - https://raw.githubusercontent.com/karolpiczak/ESC-50/master/meta/esc50.csv + storage: + - /ESC-50/esc50.csv + + audio: + storage: /ESC-50/audio + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 4 + num_workers: 8 + accum_grad_iters: 1 + prompt: "describe the audio" + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio/esc50_classification_completion/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/audiocaps_captioning_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/audiocaps_captioning_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..42e46bf5f793f099c688d2c541740654b87f1084 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/audiocaps_captioning_qa.yaml @@ -0,0 +1,159 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_finetuned: True + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_qa: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + build_info: + annotations: + + train: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/audiocaps/audio_qa_final_train.csv + - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_train.csv + storage: + # - audiocaps_qa/annotation/train.csv + - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_train.csv + + val: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/audiocaps/audio_qa_final_val.csv + - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_val.csv + storage: + # - audiocaps_qa/annotation/val.csv + - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_val.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_no_init/audiocaps_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/audiocaps_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/audiocaps_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..43cdae19ae2e71e9f24a821bce8eaa88853e28f1 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/audiocaps_captioning_test.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_caption_instruct: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + prompt: describe the audio + + data_type: [audio] # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/train.csv + storage: + - /audiocaps/dataset/train.csv + + val: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/val.csv + storage: + - /audiocaps/dataset/val.csv + + test: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/test.csv + storage: + - /audiocaps/dataset/test.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_no_init/audiocaps_captioning_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/audiocaps_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/audiocaps_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..635eb639bbdad7197bef82b5d29ac2e97337814b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/audiocaps_captioning_val.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_caption_instruct: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + prompt: describe the audio + + data_type: [audio] # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/train.csv + storage: + - /audiocaps/dataset/train.csv + + val: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/val.csv + storage: + - /audiocaps/dataset/val.csv + + test: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/test.csv + storage: + - /audiocaps/dataset/test.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_no_init/audiocaps_captioning_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/clothoQA_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/clothoQA_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..affb2c2b4323613d58a80ae7dcce1578fb852a4b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/clothoQA_captioning.yaml @@ -0,0 +1,155 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + clotho_qa: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] + + build_info: + + annotations: + train: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_train.csv + storage: + - clotho_Qa/annotations/clotho_aqa_train.csv + val: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_val.csv + storage: + - clotho_qa/annotations/clotho_aqa_val.csv + + test: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_test.csv + storage: + - clotho_qa/annotations/clotho_aqa_test.csv + audio: + storage: /export/einstein-vision/audio_datasets/clotho-aqa/audio_files + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 4 + accum_grad_iters: 1 + prompt: "Question: {} Answer:" + + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_no_init/clothovqa_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/clothov1_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/clothov1_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..773bb28696cbdab59ed05c3bebe363eff207578e --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/clothov1_captioning.yaml @@ -0,0 +1,147 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + clothov2: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] # [images|videos|features] + + build_info: + kwargs: + clotho_root: /export/einstein-vision/audio_datasets/clothov2/ + split: evaluation + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://zenodo.org/record/4783391/files/clotho_captions_evaluation.csv + storage: + - /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_csv_files/clotho_captions_evaluation.csv + audio: + storage: /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_audio_files/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_no_init/clothov1_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/clothov2_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/clothov2_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9c4a8b060d68b8faa1e6e346396602109bbf461b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/clothov2_captioning.yaml @@ -0,0 +1,147 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + clothov2: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] # [images|videos|features] + + build_info: + kwargs: + clotho_root: /export/einstein-vision/audio_datasets/clothov2/ + split: validation + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://zenodo.org/record/4783391/files/clotho_captions_validation.csv + storage: + - /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_csv_files/clotho_captions_validation.csv + audio: + storage: /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_audio_files/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 4 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_no_init/clothov2_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/esc50_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/esc50_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e539b6f73c00b55c66ca2219cc0623e30fa2147d --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/esc50_classification.yaml @@ -0,0 +1,151 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the audio" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + format_candidates_prompt: "an audio of {}" + +datasets: + esc50_cls: # + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + build_info: + annotations: + + val: + url: + - https://raw.githubusercontent.com/karolpiczak/ESC-50/master/meta/esc50.csv + storage: + - /ESC-50/esc50.csv + + audio: + storage: /ESC-50/audio + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "describe the audio" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_no_init/esc50_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/esc50_classification_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/esc50_classification_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e5017610065f70ea2f579a7c205bfbbf4620f04f --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_no_init/esc50_classification_completion.yaml @@ -0,0 +1,151 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_rand_init.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the audio" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + # format_candidates_prompt: "an audio of {}" + +datasets: + esc50_cls: # + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + build_info: + annotations: + + val: + url: + - https://raw.githubusercontent.com/karolpiczak/ESC-50/master/meta/esc50.csv + storage: + - /ESC-50/esc50.csv + + audio: + storage: /ESC-50/audio + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 4 + num_workers: 8 + accum_grad_iters: 1 + prompt: "describe the audio" + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_no_init/esc50_classification_completion/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/audiocaps_captioning_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/audiocaps_captioning_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..671b233a56fa95f08e83acbf0f4e5b6dee8c36f1 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/audiocaps_captioning_qa.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear_768.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear_768.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: null + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + proj_dim: 768 + +datasets: + audiocaps_mm_qa: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + build_info: + annotations: + + train: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/audiocaps/audio_qa_final_train.csv + - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_train.csv + storage: + # - audiocaps_qa/annotation/train.csv + - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_train.csv + + val: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/audiocaps/audio_qa_final_val.csv + - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_val.csv + storage: + # - audiocaps_qa/annotation/val.csv + - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_val.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/audiocaps_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/audiocaps_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/audiocaps_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f4858fd3141f419b114e6b5b634d614254032541 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/audiocaps_captioning_test.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_caption_instruct: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + prompt: describe the audio + + data_type: [audio] # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/train.csv + storage: + - /audiocaps/dataset/train.csv + + val: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/val.csv + storage: + - /audiocaps/dataset/val.csv + + test: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/test.csv + storage: + - /audiocaps/dataset/test.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/audiocaps_captioning_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/audiocaps_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/audiocaps_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6a0ec12a7707666ad0ecca75a6b6fa600e316c6c --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/audiocaps_captioning_val.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_caption_instruct: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + prompt: describe the audio + + data_type: [audio] # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/train.csv + storage: + - /audiocaps/dataset/train.csv + + val: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/val.csv + storage: + - /audiocaps/dataset/val.csv + + test: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/test.csv + storage: + - /audiocaps/dataset/test.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/audiocaps_captioning_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/clothoQA_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/clothoQA_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..81693b05dbadb3fe2e6fea093640845f6edd2eb1 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/clothoQA_captioning.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear_768.pth + load_finetuned: False + # finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: null + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + proj_dim: 768 + +datasets: + clotho_qa: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] + + build_info: + kwargs: + non_bin: True + unanimous: True + + annotations: + train: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_train.csv + storage: + - clotho_Qa/annotations/clotho_aqa_train.csv + val: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_val.csv + storage: + - clotho_qa/annotations/clotho_aqa_val.csv + + test: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_test.csv + storage: + - clotho_qa/annotations/clotho_aqa_test.csv + audio: + storage: /export/einstein-vision/audio_datasets/clotho-aqa/audio_files + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 4 + accum_grad_iters: 1 + prompt: "Question: {} Answer:" + + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/clothovqa_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/clothov1_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/clothov1_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ed6ebb00caf706db51a08a67ae13570f8ee98767 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/clothov1_captioning.yaml @@ -0,0 +1,149 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear_768.pth + load_finetuned: False + # finetuned: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/audio_no_init_continue/20231115024/checkpoint_60001.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: False + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: False + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + proj_dim: 768 + +datasets: + clothov2: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] # [images|videos|features] + + build_info: + kwargs: + clotho_root: /export/einstein-vision/audio_datasets/clothov2/ + split: evaluation + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://zenodo.org/record/4783391/files/clotho_captions_evaluation.csv + storage: + - /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_csv_files/clotho_captions_evaluation.csv + audio: + storage: /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_audio_files/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/clothov1_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/clothov2_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/clothov2_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3095a218b1bc86abbb99fb6b83fce57d5410af8e --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/clothov2_captioning.yaml @@ -0,0 +1,149 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear_768.pth + load_finetuned: False + # finetuned: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/audio_no_init_continue/20231115024/checkpoint_60001.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: False + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: False + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + proj_dim: 768 + projection_only: True + +datasets: + clothov2: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] # [images|videos|features] + + build_info: + kwargs: + clotho_root: /export/einstein-vision/audio_datasets/clothov2/ + split: validation + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://zenodo.org/record/4783391/files/clotho_captions_validation.csv + storage: + - /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_csv_files/clotho_captions_validation.csv + audio: + storage: /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_audio_files/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 4 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/clothov2_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/esc50_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/esc50_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2e2bcba0b28b66fceffbe74b012c4a5897e6ee9f --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/esc50_classification.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear_768.pth + # https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_finetuned: False + # finetuned: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/audio_no_init_continue/20231115024/checkpoint_60001.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: False + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: False + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the audio" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + format_candidates_prompt: "an audio of {}" + proj_dim: 768 + +datasets: + esc50_cls: # + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + build_info: + annotations: + + val: + url: + - https://raw.githubusercontent.com/karolpiczak/ESC-50/master/meta/esc50.csv + storage: + - /ESC-50/esc50.csv + + audio: + storage: /ESC-50/audio + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "describe the audio" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/esc50_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/esc50_classification_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/esc50_classification_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0bd5972dbd05bc3cb060408b5c15eca8212d9fea --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only/esc50_classification_completion.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear_768.pth + # https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: False + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: False + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the audio" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + projection_only: True + # format_candidates_prompt: "an audio of {}" + proj_dim: 768 + +datasets: + esc50_cls: # + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + build_info: + annotations: + + val: + url: + - https://raw.githubusercontent.com/karolpiczak/ESC-50/master/meta/esc50.csv + storage: + - /ESC-50/esc50.csv + + audio: + storage: /ESC-50/audio + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "describe the audio" + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/esc50_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/audiocaps_captioning_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/audiocaps_captioning_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..67eca723d8763ad64738c55624e5818bc0569eec --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/audiocaps_captioning_qa.yaml @@ -0,0 +1,159 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_finetuned: True + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_qa: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + build_info: + annotations: + + train: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/audiocaps/audio_qa_final_train.csv + - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_train.csv + storage: + # - audiocaps_qa/annotation/train.csv + - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_train.csv + + val: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/audiocaps/audio_qa_final_val.csv + - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_val.csv + storage: + # - audiocaps_qa/annotation/val.csv + - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_val.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/audiocaps_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/audiocaps_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/audiocaps_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f4858fd3141f419b114e6b5b634d614254032541 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/audiocaps_captioning_test.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_caption_instruct: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + prompt: describe the audio + + data_type: [audio] # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/train.csv + storage: + - /audiocaps/dataset/train.csv + + val: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/val.csv + storage: + - /audiocaps/dataset/val.csv + + test: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/test.csv + storage: + - /audiocaps/dataset/test.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/audiocaps_captioning_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/audiocaps_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/audiocaps_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6a0ec12a7707666ad0ecca75a6b6fa600e316c6c --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/audiocaps_captioning_val.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_caption_instruct: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + prompt: describe the audio + + data_type: [audio] # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/train.csv + storage: + - /audiocaps/dataset/train.csv + + val: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/val.csv + storage: + - /audiocaps/dataset/val.csv + + test: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/test.csv + storage: + - /audiocaps/dataset/test.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/audiocaps_captioning_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/clothoQA_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/clothoQA_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cc411ae332b334c6ba7c5c1f9339a1e1c06ccf9a --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/clothoQA_captioning.yaml @@ -0,0 +1,157 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear_nocue_768.pth + load_finetuned: False + # finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: null + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + proj_dim: 768 + +datasets: + clotho_qa: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] + + build_info: + + annotations: + train: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_train.csv + storage: + - clotho_Qa/annotations/clotho_aqa_train.csv + val: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_val.csv + storage: + - clotho_qa/annotations/clotho_aqa_val.csv + + test: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_test.csv + storage: + - clotho_qa/annotations/clotho_aqa_test.csv + audio: + storage: /export/einstein-vision/audio_datasets/clotho-aqa/audio_files + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 4 + accum_grad_iters: 1 + prompt: "Question: {} Answer:" + + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/clothovqa_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/clothov1_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/clothov1_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ed6ebb00caf706db51a08a67ae13570f8ee98767 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/clothov1_captioning.yaml @@ -0,0 +1,149 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear_768.pth + load_finetuned: False + # finetuned: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/audio_no_init_continue/20231115024/checkpoint_60001.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: False + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: False + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + proj_dim: 768 + +datasets: + clothov2: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] # [images|videos|features] + + build_info: + kwargs: + clotho_root: /export/einstein-vision/audio_datasets/clothov2/ + split: evaluation + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://zenodo.org/record/4783391/files/clotho_captions_evaluation.csv + storage: + - /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_csv_files/clotho_captions_evaluation.csv + audio: + storage: /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_audio_files/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/clothov1_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/clothov2_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/clothov2_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3095a218b1bc86abbb99fb6b83fce57d5410af8e --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/clothov2_captioning.yaml @@ -0,0 +1,149 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear_768.pth + load_finetuned: False + # finetuned: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/audio_no_init_continue/20231115024/checkpoint_60001.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: False + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: False + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + proj_dim: 768 + projection_only: True + +datasets: + clothov2: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] # [images|videos|features] + + build_info: + kwargs: + clotho_root: /export/einstein-vision/audio_datasets/clothov2/ + split: validation + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://zenodo.org/record/4783391/files/clotho_captions_validation.csv + storage: + - /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_csv_files/clotho_captions_validation.csv + audio: + storage: /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_audio_files/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 4 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/clothov2_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/esc50_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/esc50_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6c2f2b444cfaf5adf1723ab04661062a747c9654 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/esc50_classification.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear_nocue_768.pth + # https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_finetuned: False + # finetuned: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/audio_no_init_continue/20231115024/checkpoint_60001.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: False + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: False + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the audio" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + format_candidates_prompt: "an audio of {}" + proj_dim: 768 + +datasets: + esc50_cls: # + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + build_info: + annotations: + + val: + url: + - https://raw.githubusercontent.com/karolpiczak/ESC-50/master/meta/esc50.csv + storage: + - /ESC-50/esc50.csv + + audio: + storage: /ESC-50/audio + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "describe the audio" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/esc50_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/esc50_classification_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/esc50_classification_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0bd5972dbd05bc3cb060408b5c15eca8212d9fea --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/audio_projection_only_nocue/esc50_classification_completion.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear_768.pth + # https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: False + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: False + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the audio" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + projection_only: True + # format_candidates_prompt: "an audio of {}" + proj_dim: 768 + +datasets: + esc50_cls: # + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + build_info: + annotations: + + val: + url: + - https://raw.githubusercontent.com/karolpiczak/ESC-50/master/meta/esc50.csv + storage: + - /ESC-50/esc50.csv + + audio: + storage: /ESC-50/audio + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "describe the audio" + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/audio_proj/esc50_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/musicavqa/musicavqa_audio_eval.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/musicavqa/musicavqa_audio_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ab3ce2ef42f5825f11901777102d0ba3defdec05 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/musicavqa/musicavqa_audio_eval.yaml @@ -0,0 +1,182 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + musicavqa_mm_instruct: # name of the dataset builder + data_type: [audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: qa + modality: video + eval: + name: blip_question + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-val.json + storage: + - /musicavqa/val.json + + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-test.json + storage: + - /musicavqa/test.json + templates: null + + audio: + storage: path/to/videos + + video: + storage: path/to/videos + + + +run: + task: gqa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 8 + max_epoch: 1 + + # inference-specific + max_len: 5 + min_len: 1 + num_beams: 5 + length_penalty: -1. + inference_method: "generate" + prompt: "Question: {} Answer:" + + train_splits: ["train"] + valid_splits: ["test"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + ques_files: { + "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_questions.json", + "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_testt_questions.json" + } + anno_files: { + "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_annotations.json", + "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_test_annotations.json" + } + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/crossmodal/musicavqa/audio/" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/musicavqa/musicavqa_joint_eval.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/musicavqa/musicavqa_joint_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8c36dd4620eeda1eae6f1d5464132b252a0ed9f0 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/musicavqa/musicavqa_joint_eval.yaml @@ -0,0 +1,182 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio", "video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + musicavqa_mm_instruct: # name of the dataset builder + data_type: [video,audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: qa + modality: video + eval: + name: blip_question + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-val.json + storage: + - /musicavqa/val.json + + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-test.json + storage: + - /musicavqa/test.json + templates: null + + audio: + storage: path/to/videos + + video: + storage: path/to/videos + + + +run: + task: gqa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 8 + max_epoch: 1 + + # inference-specific + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + inference_method: "generate" + prompt: "Question: {} Answer:" + + train_splits: ["train"] + valid_splits: ["test"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + # ques_files: { + # "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_questions.json", + # "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_test_questions.json" + # } + # anno_files: { + # "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_annotations.json", + # "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_test_annotations.json" + # } + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/crossmodal/musicavqa/joint/" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/musicavqa/musicavqa_video_eval.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/musicavqa/musicavqa_video_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..46b1bfaef2361a6b87d3aca64030e3473b55a0e8 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/musicavqa/musicavqa_video_eval.yaml @@ -0,0 +1,182 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + musicavqa_mm_instruct: # name of the dataset builder + data_type: [video] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: qa + modality: video + eval: + name: blip_question + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-val.json + storage: + - /musicavqa/val.json + + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-test.json + storage: + - /musicavqa/test.json + templates: null + + audio: + storage: path/to/videos + + video: + storage: path/to/videos + + + +run: + task: gqa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 8 + max_epoch: 1 + + # inference-specific + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + inference_method: "generate" + prompt: "Question: {} Answer:" + + train_splits: ["train"] + valid_splits: ["test"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + # ques_files: { + # "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_questions.json", + # "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_test_questions.json" + # } + # anno_files: { + # "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_annotations.json", + # "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_test_annotations.json" + # } + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/crossmodal/musicavqa/video/" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/vatex/vatex_audio_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/vatex/vatex_audio_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3aab1f5be5cb4bd20ccb7e7d260ede4649887a01 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/vatex/vatex_audio_captioning.yaml @@ -0,0 +1,186 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: # name of the dataset builder + data_type: [audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/crossmodal/vatex/vatex_audio_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + load_gt_from_file: vatex/annotations/cap_val.json + + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/vatex/vatex_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/vatex/vatex_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2e01c301b37c3db19037a4171ee26f0d2e9175d0 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/vatex/vatex_captioning.yaml @@ -0,0 +1,185 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video/vatex_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # load_gt_from_file: vatex/annotations/cap_val.json + + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/vatex/vatex_joint_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/vatex/vatex_joint_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ee28c5f3249f951c03ecc8cf6a72d407a09de91d --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/vatex/vatex_joint_captioning.yaml @@ -0,0 +1,186 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio", "video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video, audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/crossmodal/vatex/vatex_joint_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # load_gt_from_file: vatex/annotations/cap_val.json + + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/vatex/vatex_joint_captioning_interleave.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/vatex/vatex_joint_captioning_interleave.yaml new file mode 100644 index 0000000000000000000000000000000000000000..afab9e1c037b6267bc16735af653964544c106f0 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/crossmodal/vatex/vatex_joint_captioning_interleave.yaml @@ -0,0 +1,187 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio", "video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + joint_video_audio: True + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video, audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/crossmodal/vatex/vatex_joint_captioning_interleave/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # load_gt_from_file: vatex/annotations/cap_val.json + + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/coco_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/coco_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6c836829a5ca841a17f15708e5ad627d3b16968b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/coco_captioning_test.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/coco_captioning_test/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/coco_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/coco_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..553aea2fc42c547cad83d0c539eaf0b0f2d27403 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/coco_captioning_val.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 1 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/coco_captioning_val/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/flickr30k_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/flickr30k_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ed6095528a8845b3f01d5511a582049e49b31d2f --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/flickr30k_captioning.yaml @@ -0,0 +1,150 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + flickr30k_caption: + # data_dir: ${env.data_dir}/datasets + data_type: images + + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + annotations: + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_val.json + storage: + - flickr30k/annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_test.json + storage: + - flickr30k/annotations/test.json + images: + # storage: flickr30k/images + storage: /export/share/datasets/vision/flickr30k + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/flickr_captioning/" + # annotation_file: /export/home/.cache/lavis/flickr30k_caption_gt/flickr30k_caption_test_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/gqa_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/gqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..81ddfc64759a9c3446938a3f34bd669ef4883c18 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/gqa_qa.yaml @@ -0,0 +1,149 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + gqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/train_balanced_questions.json + storage: + - gqa/annotations/train_balanced_questions.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/testdev_balanced_questions.json + storage: + - gqa/annotations/testdev_balanced_questions.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/test_balanced_questions.json + storage: + - gqa/annotations/test_balanced_questions.json + images: + storage: /export/share/datasets/vision/GQA/images #gqa/images/ + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/gqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/gqa_qa_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/gqa_qa_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..359e849bbab4e068a53dc5e1369797ce80caa685 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/gqa_qa_val.yaml @@ -0,0 +1,149 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + gqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/train_balanced_questions.json + storage: + - gqa/annotations/train_balanced_questions.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/val_balanced_questions.json + storage: + - gqa/annotations/val_balanced_questions.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/test_balanced_questions.json + storage: + - gqa/annotations/test_balanced_questions.json + images: + storage: /export/share/datasets/vision/GQA/images #gqa/images/ + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/gqa_qa_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/nocaps_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/nocaps_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5ffb512a572d4b9abb5b74602073b6423cb7f02c --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/nocaps_captioning.yaml @@ -0,0 +1,145 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/nocaps_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/nocaps_out_domain_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/nocaps_out_domain_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..38275999bd30746e59caa8c489ad721dcee19dd3 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/nocaps_out_domain_captioning.yaml @@ -0,0 +1,147 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/nocaps_out_domain_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + + img_ids: [2, 4, 5, 8, 15, 18, 19, 22, 27, 30, 33, 35, 41, 42, 43, 46, 47, 51, 59, 60, 64, 65, 68, 69, 71, 72, 73, 77, 79, 81, 85, 87, 88, 90, 92, 100, 101, 102, 105, 107, 109, 115, 120, 124, 125, 126, 127, 129, 133, 135, 137, 139, 140, 141, 143, 150, 153, 155, 158, 164, 165, 167, 170, 171, 173, 182, 190, 191, 196, 200, 201, 203, 205, 208, 219, 225, 226, 228, 229, 232, 239, 240, 243, 245, 250, 262, 263, 264, 267, 272, 278, 283, 284, 290, 291, 297, 301, 304, 305, 309, 310, 311, 314, 323, 325, 329, 330, 331, 333, 334, 341, 349, 350, 351, 352, 354, 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3620, 3624, 3625, 3631, 3632, 3636, 3637, 3638, 3640, 3643, 3651, 3654, 3655, 3656, 3657, 3662, 3667, 3668, 3671, 3677, 3684, 3686, 3689, 3693, 3694, 3696, 3697, 3698, 3699, 3700, 3701, 3703, 3704, 3707, 3708, 3709, 3711, 3712, 3713, 3714, 3719, 3721, 3723, 3726, 3737, 3741, 3742, 3744, 3750, 3752, 3757, 3760, 3761, 3764, 3765, 3767, 3770, 3772, 3774, 3776, 3778, 3780, 3781, 3796, 3797, 3805, 3818, 3819, 3820, 3821, 3824, 3841, 3845, 3848, 3851, 3858, 3866, 3870, 3871, 3876, 3879, 3880, 3883, 3893, 3896, 3900, 3903, 3904, 3908, 3909, 3913, 3914, 3916, 3924, 3927, 3937, 3940, 3942, 3943, 3949, 3950, 3953, 3954, 3959, 3963, 3966, 3969, 3972, 3978, 3981, 3983, 3984, 3986, 3989, 3990, 3991, 3999, 4000, 4004, 4005, 4006, 4012, 4014, 4016, 4017, 4019, 4020, 4030, 4035, 4046, 4049, 4051, 4052, 4053, 4057, 4061, 4065, 4066, 4068, 4073, 4074, 4075, 4079, 4080, 4082, 4084, 4086, 4090, 4091, 4093, 4094, 4095, 4096, 4100, 4102, 4104, 4106, 4113, 4114, 4115, 4116, 4118, 4124, 4126, 4127, 4128, 4131, 4133, 4134, 4142, 4145, 4149, 4156, 4160, 4171, 4174, 4178, 4179, 4180, 4183, 4186, 4190, 4191, 4195, 4197, 4215, 4220, 4229, 4234, 4245, 4249, 4251, 4252, 4254, 4257, 4259, 4264, 4265, 4266, 4267, 4275, 4276, 4277, 4282, 4284, 4285, 4288, 4291, 4294, 4295, 4301, 4302, 4313, 4315, 4320, 4328, 4333, 4336, 4339, 4342, 4345, 4346, 4350, 4354, 4372, 4374, 4375, 4377, 4379, 4380, 4386, 4388, 4389, 4392, 4396, 4402, 4404, 4408, 4410, 4424, 4426, 4428, 4431, 4435, 4436, 4439, 4442, 4446, 4447, 4449, 4452, 4455, 4458, 4460, 4461, 4466, 4469, 4475, 4476, 4478, 4488, 4491, 4494, 4498] diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/okvqa_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/okvqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8e7fd77e86837ca8d9d76c43638420115e8139cf --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/okvqa_qa.yaml @@ -0,0 +1,157 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ok_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_train2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_train2014_annotations.json + storage: + - okvqa/annotations/okvqa_train.json + # - okvqa/annotations/OpenEnded_mscoco_train2014_questions.json + # - okvqa/annotations/mscoco_train2014_annotations.json + test: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_val_eval.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_answer_list_train.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_val2014_questions.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_val2014_annotations.json + storage: + - okvqa/annotations/vqa_val_eval.json + - okvqa/annotations/answer_list.json + - okvqa/annotations/OpenEnded_mscoco_val2014_questions.json + - okvqa/annotations/mscoco_val2014_annotations.json + images: + storage: /export/share/datasets/vision/coco/images + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/okvqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/snlive_classification_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/snlive_classification_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9ec4d85490ecabd2f2d2c4cbf5493a6b96e18487 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/snlive_classification_test.yaml @@ -0,0 +1,153 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + # special_qformer_input_prompt: "{}" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "how would you respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/snlive_classification_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/snlive_classification_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/snlive_classification_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..af18bd6fff2398da20388bd99db330e7c7ca2bd0 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/snlive_classification_val.yaml @@ -0,0 +1,153 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + # special_qformer_input_prompt: "{}" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "how would you respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/snlive_classification_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/vizwiz_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/vizwiz_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..edc88cced43681661e58d85da27f9ddf23780750 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image/vizwiz_qa.yaml @@ -0,0 +1,152 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vizwiz_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + storage: + - vizwiz/annotations/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + storage: + - vizwiz/annotations/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + images: + storage: /export/share/datasets/vision/vizwiz/images + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/vizwiz_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/coco_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/coco_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a589d6706f9d043d38644b0e1073351fcc426fba --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/coco_captioning_test.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_full_init.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/coco_captioning_test/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/coco_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/coco_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..553aea2fc42c547cad83d0c539eaf0b0f2d27403 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/coco_captioning_val.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 1 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/coco_captioning_val/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/flickr30k_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/flickr30k_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ad577a4e330180e3e5fbbf323c6582a30a23f05a --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/flickr30k_captioning.yaml @@ -0,0 +1,150 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_full_init.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_full_init.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + flickr30k_caption: + # data_dir: ${env.data_dir}/datasets + data_type: images + + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + annotations: + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_val.json + storage: + - flickr30k/annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_test.json + storage: + - flickr30k/annotations/test.json + images: + # storage: flickr30k/images + storage: /export/share/datasets/vision/flickr30k + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/flickr_captioning/" + # annotation_file: /export/home/.cache/lavis/flickr30k_caption_gt/flickr30k_caption_test_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/gqa_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/gqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..55bbeb61747e03a0cfe9857dc0e9d4b5ec3e14b7 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/gqa_qa.yaml @@ -0,0 +1,149 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_full_init.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_full_init.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + gqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/train_balanced_questions.json + storage: + - gqa/annotations/train_balanced_questions.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/testdev_balanced_questions.json + storage: + - gqa/annotations/testdev_balanced_questions.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/test_balanced_questions.json + storage: + - gqa/annotations/test_balanced_questions.json + images: + storage: /export/share/datasets/vision/GQA/images #gqa/images/ + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/gqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/gqa_qa_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/gqa_qa_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..359e849bbab4e068a53dc5e1369797ce80caa685 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/gqa_qa_val.yaml @@ -0,0 +1,149 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + gqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/train_balanced_questions.json + storage: + - gqa/annotations/train_balanced_questions.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/val_balanced_questions.json + storage: + - gqa/annotations/val_balanced_questions.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/test_balanced_questions.json + storage: + - gqa/annotations/test_balanced_questions.json + images: + storage: /export/share/datasets/vision/GQA/images #gqa/images/ + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/gqa_qa_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/nocaps_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/nocaps_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5ffb512a572d4b9abb5b74602073b6423cb7f02c --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/nocaps_captioning.yaml @@ -0,0 +1,145 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/nocaps_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/nocaps_out_domain_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/nocaps_out_domain_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..38275999bd30746e59caa8c489ad721dcee19dd3 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/nocaps_out_domain_captioning.yaml @@ -0,0 +1,147 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/nocaps_out_domain_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + + img_ids: [2, 4, 5, 8, 15, 18, 19, 22, 27, 30, 33, 35, 41, 42, 43, 46, 47, 51, 59, 60, 64, 65, 68, 69, 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4475, 4476, 4478, 4488, 4491, 4494, 4498] diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/okvqa_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/okvqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8e7fd77e86837ca8d9d76c43638420115e8139cf --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/okvqa_qa.yaml @@ -0,0 +1,157 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ok_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_train2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_train2014_annotations.json + storage: + - okvqa/annotations/okvqa_train.json + # - okvqa/annotations/OpenEnded_mscoco_train2014_questions.json + # - okvqa/annotations/mscoco_train2014_annotations.json + test: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_val_eval.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_answer_list_train.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_val2014_questions.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_val2014_annotations.json + storage: + - okvqa/annotations/vqa_val_eval.json + - okvqa/annotations/answer_list.json + - okvqa/annotations/OpenEnded_mscoco_val2014_questions.json + - okvqa/annotations/mscoco_val2014_annotations.json + images: + storage: /export/share/datasets/vision/coco/images + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/okvqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/snlive_classification_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/snlive_classification_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9ec4d85490ecabd2f2d2c4cbf5493a6b96e18487 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/snlive_classification_test.yaml @@ -0,0 +1,153 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + # special_qformer_input_prompt: "{}" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "how would you respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/snlive_classification_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/snlive_classification_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/snlive_classification_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..af18bd6fff2398da20388bd99db330e7c7ca2bd0 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/snlive_classification_val.yaml @@ -0,0 +1,153 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + # special_qformer_input_prompt: "{}" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "how would you respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/snlive_classification_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/vizwiz_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/vizwiz_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..edc88cced43681661e58d85da27f9ddf23780750 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_full_init/vizwiz_qa.yaml @@ -0,0 +1,152 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vizwiz_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + storage: + - vizwiz/annotations/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + storage: + - vizwiz/annotations/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + images: + storage: /export/share/datasets/vision/vizwiz/images + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/vizwiz_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/coco_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/coco_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a589d6706f9d043d38644b0e1073351fcc426fba --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/coco_captioning_test.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_full_init.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/coco_captioning_test/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/coco_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/coco_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..553aea2fc42c547cad83d0c539eaf0b0f2d27403 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/coco_captioning_val.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 1 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/coco_captioning_val/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/flickr30k_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/flickr30k_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6cdf8fb22598e6176d2f70e200ea1fd9bb9b3bd1 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/flickr30k_captioning.yaml @@ -0,0 +1,150 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_no_init.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_no_init.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + flickr30k_caption: + # data_dir: ${env.data_dir}/datasets + data_type: images + + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + annotations: + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_val.json + storage: + - flickr30k/annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_test.json + storage: + - flickr30k/annotations/test.json + images: + # storage: flickr30k/images + storage: /export/share/datasets/vision/flickr30k + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/flickr_captioning/" + # annotation_file: /export/home/.cache/lavis/flickr30k_caption_gt/flickr30k_caption_test_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/gqa_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/gqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2bd02f71c1ba51db7eaac00db31b08f0a607e14f --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/gqa_qa.yaml @@ -0,0 +1,149 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_no_init.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_no_init.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + gqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/train_balanced_questions.json + storage: + - gqa/annotations/train_balanced_questions.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/testdev_balanced_questions.json + storage: + - gqa/annotations/testdev_balanced_questions.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/test_balanced_questions.json + storage: + - gqa/annotations/test_balanced_questions.json + images: + storage: /export/share/datasets/vision/GQA/images #gqa/images/ + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/gqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/gqa_qa_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/gqa_qa_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..359e849bbab4e068a53dc5e1369797ce80caa685 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/gqa_qa_val.yaml @@ -0,0 +1,149 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + gqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/train_balanced_questions.json + storage: + - gqa/annotations/train_balanced_questions.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/val_balanced_questions.json + storage: + - gqa/annotations/val_balanced_questions.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/test_balanced_questions.json + storage: + - gqa/annotations/test_balanced_questions.json + images: + storage: /export/share/datasets/vision/GQA/images #gqa/images/ + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/gqa_qa_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/nocaps_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/nocaps_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9dbcc25cdef24d0458e11137030a718e14091e0c --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/nocaps_captioning.yaml @@ -0,0 +1,145 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_no_init.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_no_init.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/nocaps_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/nocaps_out_domain_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/nocaps_out_domain_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..38275999bd30746e59caa8c489ad721dcee19dd3 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/nocaps_out_domain_captioning.yaml @@ -0,0 +1,147 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/nocaps_out_domain_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + + img_ids: [2, 4, 5, 8, 15, 18, 19, 22, 27, 30, 33, 35, 41, 42, 43, 46, 47, 51, 59, 60, 64, 65, 68, 69, 71, 72, 73, 77, 79, 81, 85, 87, 88, 90, 92, 100, 101, 102, 105, 107, 109, 115, 120, 124, 125, 126, 127, 129, 133, 135, 137, 139, 140, 141, 143, 150, 153, 155, 158, 164, 165, 167, 170, 171, 173, 182, 190, 191, 196, 200, 201, 203, 205, 208, 219, 225, 226, 228, 229, 232, 239, 240, 243, 245, 250, 262, 263, 264, 267, 272, 278, 283, 284, 290, 291, 297, 301, 304, 305, 309, 310, 311, 314, 323, 325, 329, 330, 331, 333, 334, 341, 349, 350, 351, 352, 354, 358, 359, 363, 365, 366, 368, 371, 372, 379, 381, 383, 386, 388, 389, 390, 392, 405, 415, 417, 418, 420, 421, 424, 428, 429, 432, 436, 441, 443, 452, 453, 454, 455, 456, 459, 464, 465, 468, 469, 476, 477, 478, 480, 487, 488, 490, 491, 493, 500, 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4288, 4291, 4294, 4295, 4301, 4302, 4313, 4315, 4320, 4328, 4333, 4336, 4339, 4342, 4345, 4346, 4350, 4354, 4372, 4374, 4375, 4377, 4379, 4380, 4386, 4388, 4389, 4392, 4396, 4402, 4404, 4408, 4410, 4424, 4426, 4428, 4431, 4435, 4436, 4439, 4442, 4446, 4447, 4449, 4452, 4455, 4458, 4460, 4461, 4466, 4469, 4475, 4476, 4478, 4488, 4491, 4494, 4498] diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/okvqa_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/okvqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8e7fd77e86837ca8d9d76c43638420115e8139cf --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/okvqa_qa.yaml @@ -0,0 +1,157 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ok_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_train2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_train2014_annotations.json + storage: + - okvqa/annotations/okvqa_train.json + # - okvqa/annotations/OpenEnded_mscoco_train2014_questions.json + # - okvqa/annotations/mscoco_train2014_annotations.json + test: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_val_eval.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_answer_list_train.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_val2014_questions.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_val2014_annotations.json + storage: + - okvqa/annotations/vqa_val_eval.json + - okvqa/annotations/answer_list.json + - okvqa/annotations/OpenEnded_mscoco_val2014_questions.json + - okvqa/annotations/mscoco_val2014_annotations.json + images: + storage: /export/share/datasets/vision/coco/images + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/okvqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/snlive_classification_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/snlive_classification_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9ec4d85490ecabd2f2d2c4cbf5493a6b96e18487 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/snlive_classification_test.yaml @@ -0,0 +1,153 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + # special_qformer_input_prompt: "{}" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "how would you respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/snlive_classification_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/snlive_classification_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/snlive_classification_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..af18bd6fff2398da20388bd99db330e7c7ca2bd0 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/snlive_classification_val.yaml @@ -0,0 +1,153 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + # special_qformer_input_prompt: "{}" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "how would you respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/snlive_classification_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/vizwiz_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/vizwiz_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bce0849a708d53dde98a5bf405f6506c4ee2ca63 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_no_init/vizwiz_qa.yaml @@ -0,0 +1,152 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_no_init.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vizwiz_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + storage: + - vizwiz/annotations/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + storage: + - vizwiz/annotations/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + images: + storage: /export/share/datasets/vision/vizwiz/images + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/vizwiz_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/coco_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/coco_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..349f72091b1eb8d02519ed96f3d5c9a664baca3c --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/coco_captioning_test.yaml @@ -0,0 +1,153 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image_pre_coco/coco_captioning_test/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/coco_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/coco_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fec2843d65436115f4ad03d360d2764b79fad185 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/coco_captioning_val.yaml @@ -0,0 +1,153 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image_pre_coco/coco_captioning_val/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/flickr30k_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/flickr30k_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3d5f4958e45143f86b89c5cbd6624de230f0d7a7 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/flickr30k_captioning.yaml @@ -0,0 +1,149 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + flickr30k_caption: + # data_dir: ${env.data_dir}/datasets + data_type: images + + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + annotations: + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_val.json + storage: + - flickr30k/annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_test.json + storage: + - flickr30k/annotations/test.json + images: + # storage: flickr30k/images + storage: /export/share/datasets/vision/flickr30k + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image_pre_coco/flickr_captioning/" + # annotation_file: /export/home/.cache/lavis/flickr30k_caption_gt/flickr30k_caption_test_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/gqa_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/gqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1ed2de900ad37492a689df7558408466bb2fe73b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/gqa_qa.yaml @@ -0,0 +1,148 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + gqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/train_balanced_questions.json + storage: + - gqa/annotations/train_balanced_questions.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/testdev_balanced_questions.json + storage: + - gqa/annotations/testdev_balanced_questions.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/test_balanced_questions.json + storage: + - gqa/annotations/test_balanced_questions.json + images: + storage: /export/share/datasets/vision/GQA/images #gqa/images/ + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image_pre_coco/gqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/nocaps_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/nocaps_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..415317ccee4f52d9d438b523c30d0c269bca0667 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/nocaps_captioning.yaml @@ -0,0 +1,144 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image_pre_coco/nocaps_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/nocaps_out_domain_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/nocaps_out_domain_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..22c4d02b4691c80692fc56228a7232da817f4823 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/nocaps_out_domain_captioning.yaml @@ -0,0 +1,146 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image_pre_coco/nocaps_out_domain_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + + img_ids: [2, 4, 5, 8, 15, 18, 19, 22, 27, 30, 33, 35, 41, 42, 43, 46, 47, 51, 59, 60, 64, 65, 68, 69, 71, 72, 73, 77, 79, 81, 85, 87, 88, 90, 92, 100, 101, 102, 105, 107, 109, 115, 120, 124, 125, 126, 127, 129, 133, 135, 137, 139, 140, 141, 143, 150, 153, 155, 158, 164, 165, 167, 170, 171, 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4016, 4017, 4019, 4020, 4030, 4035, 4046, 4049, 4051, 4052, 4053, 4057, 4061, 4065, 4066, 4068, 4073, 4074, 4075, 4079, 4080, 4082, 4084, 4086, 4090, 4091, 4093, 4094, 4095, 4096, 4100, 4102, 4104, 4106, 4113, 4114, 4115, 4116, 4118, 4124, 4126, 4127, 4128, 4131, 4133, 4134, 4142, 4145, 4149, 4156, 4160, 4171, 4174, 4178, 4179, 4180, 4183, 4186, 4190, 4191, 4195, 4197, 4215, 4220, 4229, 4234, 4245, 4249, 4251, 4252, 4254, 4257, 4259, 4264, 4265, 4266, 4267, 4275, 4276, 4277, 4282, 4284, 4285, 4288, 4291, 4294, 4295, 4301, 4302, 4313, 4315, 4320, 4328, 4333, 4336, 4339, 4342, 4345, 4346, 4350, 4354, 4372, 4374, 4375, 4377, 4379, 4380, 4386, 4388, 4389, 4392, 4396, 4402, 4404, 4408, 4410, 4424, 4426, 4428, 4431, 4435, 4436, 4439, 4442, 4446, 4447, 4449, 4452, 4455, 4458, 4460, 4461, 4466, 4469, 4475, 4476, 4478, 4488, 4491, 4494, 4498] diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/okvqa_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/okvqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..48aa02284dac518280f30ca8030ae67b0971c6d5 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/okvqa_qa.yaml @@ -0,0 +1,156 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ok_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_train2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_train2014_annotations.json + storage: + - okvqa/annotations/okvqa_train.json + # - okvqa/annotations/OpenEnded_mscoco_train2014_questions.json + # - okvqa/annotations/mscoco_train2014_annotations.json + test: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_val_eval.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_answer_list_train.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_val2014_questions.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_val2014_annotations.json + storage: + - okvqa/annotations/vqa_val_eval.json + - okvqa/annotations/answer_list.json + - okvqa/annotations/OpenEnded_mscoco_val2014_questions.json + - okvqa/annotations/mscoco_val2014_annotations.json + images: + storage: /export/share/datasets/vision/coco/images + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image_pre_coco/okvqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/snlive_classification_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/snlive_classification_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a4ccab2225f36343c42ad7d41bb85f848c5a2cc7 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/snlive_classification_test.yaml @@ -0,0 +1,152 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + # special_qformer_input_prompt: "{}" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "how would you respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image_pre_coco/snlive_classification_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/snlive_classification_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/snlive_classification_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9d883ee9e8a2125823f0b51581c0c6539edcaa44 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/snlive_classification_val.yaml @@ -0,0 +1,152 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + # special_qformer_input_prompt: "{}" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "how would you respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image_pre_coco/snlive_classification_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/vizwiz_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/vizwiz_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..551f5c813b8ecdac7b3cdbfb708f233c21065103 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_pre_coco/vizwiz_qa.yaml @@ -0,0 +1,151 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vizwiz_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + storage: + - vizwiz/annotations/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + storage: + - vizwiz/annotations/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + images: + storage: /export/share/datasets/vision/vizwiz/images + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image_pre_coco/vizwiz_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/coco_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/coco_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a589d6706f9d043d38644b0e1073351fcc426fba --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/coco_captioning_test.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_full_init.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/coco_captioning_test/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/coco_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/coco_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..553aea2fc42c547cad83d0c539eaf0b0f2d27403 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/coco_captioning_val.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 1 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/coco_captioning_val/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/flickr30k_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/flickr30k_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dc69e95bdfec48b1923796ed15362f374acd6c4d --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/flickr30k_captioning.yaml @@ -0,0 +1,152 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/image_qformer_linear_768.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: null + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: False + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: False + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + proj_dim: 768 + +datasets: + flickr30k_caption: + # data_dir: ${env.data_dir}/datasets + data_type: images + + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + annotations: + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_val.json + storage: + - flickr30k/annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_test.json + storage: + - flickr30k/annotations/test.json + images: + # storage: flickr30k/images + storage: /export/share/datasets/vision/flickr30k + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/flickr_captioning/" + # annotation_file: /export/home/.cache/lavis/flickr30k_caption_gt/flickr30k_caption_test_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/gqa_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/gqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fd4e5fbb8abaddb7907aa056fa86d126676b62fc --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/gqa_qa.yaml @@ -0,0 +1,151 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/image_qformer_linear_768.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: null + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + proj_dim: 768 + +datasets: + gqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/train_balanced_questions.json + storage: + - gqa/annotations/train_balanced_questions.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/testdev_balanced_questions.json + storage: + - gqa/annotations/testdev_balanced_questions.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/test_balanced_questions.json + storage: + - gqa/annotations/test_balanced_questions.json + images: + storage: /export/share/datasets/vision/GQA/images #gqa/images/ + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/gqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/gqa_qa_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/gqa_qa_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..359e849bbab4e068a53dc5e1369797ce80caa685 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/gqa_qa_val.yaml @@ -0,0 +1,149 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + gqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/train_balanced_questions.json + storage: + - gqa/annotations/train_balanced_questions.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/val_balanced_questions.json + storage: + - gqa/annotations/val_balanced_questions.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/test_balanced_questions.json + storage: + - gqa/annotations/test_balanced_questions.json + images: + storage: /export/share/datasets/vision/GQA/images #gqa/images/ + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 0 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/gqa_qa_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/nocaps_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/nocaps_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c05e24257ec99e154444402e42c0d764b396b5df --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/nocaps_captioning.yaml @@ -0,0 +1,147 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/image_qformer_linear_768.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: null + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + proj_dim: 768 + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/nocaps_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/nocaps_out_domain_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/nocaps_out_domain_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..371f4c8df8b00b2c6ded43d2f1ba6809bd2be1fc --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/nocaps_out_domain_captioning.yaml @@ -0,0 +1,148 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/image_qformer_linear.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: null + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/nocaps_out_domain_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + + img_ids: [2, 4, 5, 8, 15, 18, 19, 22, 27, 30, 33, 35, 41, 42, 43, 46, 47, 51, 59, 60, 64, 65, 68, 69, 71, 72, 73, 77, 79, 81, 85, 87, 88, 90, 92, 100, 101, 102, 105, 107, 109, 115, 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a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/okvqa_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/okvqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8e7fd77e86837ca8d9d76c43638420115e8139cf --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/okvqa_qa.yaml @@ -0,0 +1,157 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ok_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_train2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_train2014_annotations.json + storage: + - okvqa/annotations/okvqa_train.json + # - okvqa/annotations/OpenEnded_mscoco_train2014_questions.json + # - okvqa/annotations/mscoco_train2014_annotations.json + test: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_val_eval.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_answer_list_train.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_val2014_questions.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_val2014_annotations.json + storage: + - okvqa/annotations/vqa_val_eval.json + - okvqa/annotations/answer_list.json + - okvqa/annotations/OpenEnded_mscoco_val2014_questions.json + - okvqa/annotations/mscoco_val2014_annotations.json + images: + storage: /export/share/datasets/vision/coco/images + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/okvqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/snlive_classification_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/snlive_classification_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9ec4d85490ecabd2f2d2c4cbf5493a6b96e18487 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/snlive_classification_test.yaml @@ -0,0 +1,153 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + # special_qformer_input_prompt: "{}" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "how would you respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/snlive_classification_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/snlive_classification_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/snlive_classification_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..af18bd6fff2398da20388bd99db330e7c7ca2bd0 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/snlive_classification_val.yaml @@ -0,0 +1,153 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + # special_qformer_input_prompt: "{}" + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + # prompt: "how would you respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/snlive_classification_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/vizwiz_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/vizwiz_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bc44dd182663394fdb00d899befe63b39a35c902 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/image_projection_only/vizwiz_qa.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/image_qformer_linear_768.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: null + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + proj_dim: 768 + +datasets: + vizwiz_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + storage: + - vizwiz/annotations/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + storage: + - vizwiz/annotations/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + images: + storage: /export/share/datasets/vision/vizwiz/images + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/image/vizwiz_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc/modelnet40_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc/modelnet40_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c6c777303e4b52f59f2a481a320cc8192abb5c77 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc/modelnet40_classification.yaml @@ -0,0 +1,148 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + format_candidates_prompt: " a 3d model of a {}" + special_qformer_input_prompt: False + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: pc + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 3 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc/modelnet_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + prompt: 'describe the 3d model' + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc/modelnet40_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc/modelnet40_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a83ddd6340f8d9af257a9890a953f4637ef6edbf --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc/modelnet40_completion.yaml @@ -0,0 +1,166 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: [pc] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc/modelnet_classification_completion/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc/objaverse_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc/objaverse_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c1dc02a53fd03c5846f5f3d208fadaf52c6a5f7f --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc/objaverse_captioning.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + + data_type: [pc] # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + storage: + - objaverse/annotations/train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + + val: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + storage: + - objaverse/annotations/val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc/objaverse_captioning/" + # annotation_file: /export/home/.cache/lavis/objaverse_mm_caption_instruct_gt/objaverse_mm_caption_instruct_val_annotations.json + + amp: True + resume_ckpt_path: null + caption_key: 'data' + sample_id_key: sample_id + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc/objaverse_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc/objaverse_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d6b2a40e1d5f5c2fb545e2101e8e6e30dbc4380c --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc/objaverse_qa.yaml @@ -0,0 +1,166 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given input respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + objaverse_mm_qa: # 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: +train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + storage: + - objaverse_qa/annotations/train.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final_val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + storage: + - objaverse_qa/annotations/val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc/objaverse_qa/" + # ques_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_questions.json" } + # anno_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_annotations.json" } + + + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_no_init/modelnet40_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_no_init/modelnet40_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..56de5ef591581e7c801c81db8117559eb6ac5dcb --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_no_init/modelnet40_classification.yaml @@ -0,0 +1,148 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer_no_init.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + format_candidates_prompt: " a 3d model of a {}" + special_qformer_input_prompt: False + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: pc + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 3 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_no_init/modelnet_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + prompt: 'describe the 3d model' + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_no_init/modelnet40_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_no_init/modelnet40_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..55f05b88efc4db7d3ebb41903908f77f47f2586d --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_no_init/modelnet40_completion.yaml @@ -0,0 +1,166 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer_no_init.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: [pc] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc/modelnet_classification_completion/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_no_init/objaverse_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_no_init/objaverse_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c1dc02a53fd03c5846f5f3d208fadaf52c6a5f7f --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_no_init/objaverse_captioning.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + + data_type: [pc] # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + storage: + - objaverse/annotations/train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + + val: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + storage: + - objaverse/annotations/val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc/objaverse_captioning/" + # annotation_file: /export/home/.cache/lavis/objaverse_mm_caption_instruct_gt/objaverse_mm_caption_instruct_val_annotations.json + + amp: True + resume_ckpt_path: null + caption_key: 'data' + sample_id_key: sample_id + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_no_init/objaverse_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_no_init/objaverse_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d6b2a40e1d5f5c2fb545e2101e8e6e30dbc4380c --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_no_init/objaverse_qa.yaml @@ -0,0 +1,166 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given input respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + objaverse_mm_qa: # 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: +train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + storage: + - objaverse_qa/annotations/train.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final_val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + storage: + - objaverse_qa/annotations/val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc/objaverse_qa/" + # ques_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_questions.json" } + # anno_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_annotations.json" } + + + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_projection_only/modelnet40_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_projection_only/modelnet40_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..eb00715107cb7a3bda97f3e1c46908ff7e4c92be --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_projection_only/modelnet40_classification.yaml @@ -0,0 +1,150 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/pc_qformer_linear.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: False + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: False + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + projection_only: True + format_candidates_prompt: " a 3d model of a {}" + special_qformer_input_prompt: False + projection_only_pc: True + projection_path_pc: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/pc_qformer_linear.pth + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: pc + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 3 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_proj/modelnet_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + prompt: 'describe the 3d model' + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_projection_only/modelnet40_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_projection_only/modelnet40_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ab48d80c401e213c66ad8811de239936af0b103b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_projection_only/modelnet40_completion.yaml @@ -0,0 +1,168 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/pc/20231115104/checkpoint_65000.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: False + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: False + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + projection_only_pc: True + projection_path_pc: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/pc_qformer_linear.pth + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: [pc] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_proj/modelnet_classification_completion/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_projection_only/objaverse_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_projection_only/objaverse_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..75b2c607dbf89cb6a0c65f6560e2f86f8c45370b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_projection_only/objaverse_captioning.yaml @@ -0,0 +1,169 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/pc_qformer_linear.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: False + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: False + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + projection_only: True + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + + data_type: [pc] # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + storage: + - objaverse/annotations/train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + + val: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + storage: + - objaverse/annotations/val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_proj/objaverse_captioning/" + # annotation_file: /export/home/.cache/lavis/objaverse_mm_caption_instruct_gt/objaverse_mm_caption_instruct_val_annotations.json + + amp: True + resume_ckpt_path: null + caption_key: 'data' + sample_id_key: sample_id + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_projection_only/objaverse_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_projection_only/objaverse_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8d0beb1b3bf4a782db77c1231ed9f13564a9cbc5 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_projection_only/objaverse_qa.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/pc_qformer_linear.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: False + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: False + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + projection_only: True + +datasets: + objaverse_mm_qa: # 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: +train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + storage: + - objaverse_qa/annotations/train.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final_val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + storage: + - objaverse_qa/annotations/val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_proj/objaverse_qa/" + # ques_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_questions.json" } + # anno_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_annotations.json" } + + + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip1/modelnet40_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip1/modelnet40_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9bf2c747557f4a748a6670e1d34592a515fc6a72 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip1/modelnet40_classification.yaml @@ -0,0 +1,148 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip1.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip1_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip1.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + format_candidates_prompt: " a 3d model of a {}" + special_qformer_input_prompt: False + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: pc + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 3 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip1/modelnet_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + prompt: 'describe the 3d model' + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip1/modelnet40_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip1/modelnet40_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8a3d2ba91037ae02f544803085f610384726867c --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip1/modelnet40_completion.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip1.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip1_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip1.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: [pc] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip1/modelnet_classification_completion/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip1/objaverse_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip1/objaverse_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..abe99fcb20517393544714394574777800e57486 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip1/objaverse_captioning.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip1_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip1.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + + data_type: [pc] # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + storage: + - objaverse/annotations/train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + + val: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + storage: + - objaverse/annotations/val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip1/objaverse_captioning/" + # annotation_file: /export/home/.cache/lavis/objaverse_mm_caption_instruct_gt/objaverse_mm_caption_instruct_val_annotations.json + + amp: True + resume_ckpt_path: null + caption_key: 'data' + sample_id_key: sample_id + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip1/objaverse_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip1/objaverse_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..27c143be7ca8d11311ed460634025652a4c719ad --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip1/objaverse_qa.yaml @@ -0,0 +1,166 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip1_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip1.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given input respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + objaverse_mm_qa: # 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: +train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + storage: + - objaverse_qa/annotations/train.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final_val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + storage: + - objaverse_qa/annotations/val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip1/objaverse_qa/" + # ques_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_questions.json" } + # anno_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_annotations.json" } + + + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip2_scaled_up/modelnet40_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip2_scaled_up/modelnet40_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1640f95653df3e52ced4b3fd452e3f076d0c32c8 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip2_scaled_up/modelnet40_classification.yaml @@ -0,0 +1,148 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip2_scaledup.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_scaledup" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip2_scaledup.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + format_candidates_prompt: " a 3d model of a {}" + special_qformer_input_prompt: False + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: pc + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 3 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/ulip2_scaledup/modelnet_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + prompt: 'describe the 3d model' + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip2_scaled_up/modelnet40_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip2_scaled_up/modelnet40_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..55b5013e7188e5b25060ce24b945264a8b2396d3 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip2_scaled_up/modelnet40_completion.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip2_scaledup.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_scaledup" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip2_scaledup.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: [pc] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/ulip2_scaledup/modelnet_classification_completion/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip2_scaled_up/objaverse_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip2_scaled_up/objaverse_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..59683ce555fd0f05c0746e147237fc900bc7c99a --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip2_scaled_up/objaverse_captioning.yaml @@ -0,0 +1,168 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip2_scaledup.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_scaledup" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip2_scaledup.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + + data_type: [pc] # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + storage: + - objaverse/annotations/train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + + val: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + storage: + - objaverse/annotations/val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/ulip2_scaledup/objaverse_captioning/" + # annotation_file: /export/home/.cache/lavis/objaverse_mm_caption_instruct_gt/objaverse_mm_caption_instruct_val_annotations.json + + amp: True + resume_ckpt_path: null + caption_key: 'data' + sample_id_key: sample_id + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip2_scaled_up/objaverse_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip2_scaled_up/objaverse_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cbbc9ce12461c7e04258bd42a9d5bd90f6cd18bf --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip2_scaled_up/objaverse_qa.yaml @@ -0,0 +1,166 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_scaledup" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip2_scaledup.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given input respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + objaverse_mm_qa: # 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: +train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + storage: + - objaverse_qa/annotations/train.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final_val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + storage: + - objaverse_qa/annotations/val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/ulip2_scaledup/objaverse_qa/" + # ques_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_questions.json" } + # anno_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_annotations.json" } + + + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse/modelnet40_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse/modelnet40_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4649fd47d791f5bf3f80d19fecb8a69e7d509888 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse/modelnet40_classification.yaml @@ -0,0 +1,148 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/pc_ulip_objaverse/20231021125/checkpoint_65000.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip_objaverse" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/pc_ulip_objaverse/20231021125/checkpoint_65000.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + format_candidates_prompt: " a 3d model of a {}" + special_qformer_input_prompt: False + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: pc + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 3 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip_objaverse/modelnet_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + prompt: 'describe the 3d model' + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse/modelnet40_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse/modelnet40_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..38b718be42a3c45eacf8df20f96610011a054656 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse/modelnet40_completion.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/pc_ulip_objaverse/20231021125/checkpoint_65000.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip_objaverse" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/pc_ulip_objaverse/20231021125/checkpoint_65000.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: [pc] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip_objaverse/modelnet_classification_completion/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse/objaverse_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse/objaverse_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..037a8a834327326722e3bb5332707ae8609bf18f --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse/objaverse_captioning.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip_objaverse" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/pc_ulip_objaverse/20231021125/checkpoint_65000.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + + data_type: [pc] # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + storage: + - objaverse/annotations/train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + + val: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + storage: + - objaverse/annotations/val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip_objaverse/objaverse_captioning/" + # annotation_file: /export/home/.cache/lavis/objaverse_mm_caption_instruct_gt/objaverse_mm_caption_instruct_val_annotations.json + + amp: True + resume_ckpt_path: null + caption_key: 'data' + sample_id_key: sample_id + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse/objaverse_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse/objaverse_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0e5435e6ae8f07efbf1f391d1299e148ec97b6a3 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse/objaverse_qa.yaml @@ -0,0 +1,166 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip_objaverse" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given input respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + objaverse_mm_qa: # 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: +train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + storage: + - objaverse_qa/annotations/train.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final_val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + storage: + - objaverse_qa/annotations/val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip_objaverse/objaverse_qa/" + # ques_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_questions.json" } + # anno_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_annotations.json" } + + + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse_shapenet/modelnet40_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse_shapenet/modelnet40_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a82ee3522bb8e9a717ccb7988bade8b23d2be55c --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse_shapenet/modelnet40_classification.yaml @@ -0,0 +1,148 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip2_objaverse_shapenet_k_1.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "objaverse_shapenet_k_1" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip2_objaverse_shapenet_k_1.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + format_candidates_prompt: " a 3d model of a {}" + special_qformer_input_prompt: False + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: pc + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 3 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip2_objaverse_shapenet_k_1/modelnet_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + prompt: 'describe the 3d model' + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse_shapenet/modelnet40_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse_shapenet/modelnet40_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0340cf732baef8d77866320843f123c39811370b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse_shapenet/modelnet40_completion.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip2_objaverse_shapenet_k_1.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "objaverse_shapenet_k_1" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip2_objaverse_shapenet_k_1.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: [pc] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_shape_names.txt + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - /export/home/ULIP/data/modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip2_objaverse_shapenet_k_1/modelnet_classification_completion/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse_shapenet/objaverse_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse_shapenet/objaverse_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..38f28f7c531dc3700963832b430194301589f375 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse_shapenet/objaverse_captioning.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "objaverse_shapenet_k_1" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip2_objaverse_shapenet_k_1.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + + data_type: [pc] # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + storage: + - objaverse/annotations/train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + + val: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + storage: + - objaverse/annotations/val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip2_objaverse_shapenet_k_1/objaverse_captioning/" + # annotation_file: /export/home/.cache/lavis/objaverse_mm_caption_instruct_gt/objaverse_mm_caption_instruct_val_annotations.json + + amp: True + resume_ckpt_path: null + caption_key: 'data' + sample_id_key: sample_id + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse_shapenet/objaverse_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse_shapenet/objaverse_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8def462a80f26b3c75766cc04942a7a14f3d41aa --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_objaverse_shapenet/objaverse_qa.yaml @@ -0,0 +1,166 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "objaverse_shapenet_k_1" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip2_objaverse_shapenet_k_1.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given input respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + objaverse_mm_qa: # 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: +train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + storage: + - objaverse_qa/annotations/train.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final_val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + storage: + - objaverse_qa/annotations/val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip2_objaverse_shapenet_k_1/objaverse_qa/" + # ques_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_questions.json" } + # anno_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_annotations.json" } + + + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_shapenet/modelnet40_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_shapenet/modelnet40_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8a5e3355522a001552ce23e9c9cb65d016fda72e --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_shapenet/modelnet40_classification.yaml @@ -0,0 +1,148 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip1_objaverse_shapenet.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip_shapenet" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip1_objaverse_shapenet.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + format_candidates_prompt: " a 3d model of a {}" + special_qformer_input_prompt: False + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: pc + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 3 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip1_objaverse_shapenet/modelnet_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + prompt: 'describe the 3d model' + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_shapenet/modelnet40_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_shapenet/modelnet40_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..21cc9d9690b473549f0e129130802fd1485240a9 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_shapenet/modelnet40_completion.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip1_objaverse_shapenet.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip_shapenet" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip1_objaverse_shapenet.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: [pc] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip1_objaverse_shapenet/modelnet_classification_completion/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_shapenet/objaverse_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_shapenet/objaverse_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5173fd5b2ce7dfa36b3fea698f737c886df8116c --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_shapenet/objaverse_captioning.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip_shapenet" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip1_objaverse_shapenet.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + + data_type: [pc] # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + storage: + - objaverse/annotations/train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + + val: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + storage: + - objaverse/annotations/val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip1_objaverse_shapenet/objaverse_captioning/" + # annotation_file: /export/home/.cache/lavis/objaverse_mm_caption_instruct_gt/objaverse_mm_caption_instruct_val_annotations.json + + amp: True + resume_ckpt_path: null + caption_key: 'data' + sample_id_key: sample_id + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_shapenet/objaverse_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_shapenet/objaverse_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..33cd6ce0f6e2fb800f2db9e5ad3d38eb14b7e043 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/pc_ulip_shapenet/objaverse_qa.yaml @@ -0,0 +1,166 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip_shapenet" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/ulip_baselines/ulip1_objaverse_shapenet.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given input respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + objaverse_mm_qa: # 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: +train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + storage: + - objaverse_qa/annotations/train.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final_val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + storage: + - objaverse_qa/annotations/val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/pc_ulip1_objaverse_shapenet/objaverse_qa/" + # ques_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_questions.json" } + # anno_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_annotations.json" } + + + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msrvtt_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msrvtt_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9d2dcf1b7b4c76c6adf53f7fdb5e6f810020820a --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msrvtt_captioning_test.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_caption: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: videos # [images|videos|features] + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_train.json + storage: msrvtt/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_val.json + storage: msrvtt/annotations/cap_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_test.json + storage: msrvtt/annotations/cap_test.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video/msrvtt_captioning_test/" + # annotation_file: /export/home/.cache/lavis/msrvtt_caption_gt/msrvtt_caption_val_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msrvtt_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msrvtt_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3781ee90f63334e4057a8fe0fca9bda621e82c5b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msrvtt_captioning_val.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_caption: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: videos # [images|videos|features] + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_train.json + storage: msrvtt/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_val.json + storage: msrvtt/annotations/cap_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_test.json + storage: msrvtt/annotations/cap_test.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video/msrvtt_captioning_val/" + # annotation_file: /export/home/.cache/lavis/msrvtt_caption_gt/msrvtt_caption_val_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msrvtt_qa_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msrvtt_qa_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c891465ccb5739f0624ea50e0ea976270bf22653 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msrvtt_qa_test.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given video respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_train.json + storage: msrvtt/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_val.json + storage: msrvtt/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_test.json + storage: msrvtt/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/train_ans2label.json + storage: msrvtt/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given video respond to {}" + length_penalty: -1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/video/msrvtt_qa_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msrvtt_qa_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msrvtt_qa_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7efc8ec1e6a82668f31b74d4dd5cb3fff9f732b2 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msrvtt_qa_val.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given video respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_train.json + storage: msrvtt/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_val.json + storage: msrvtt/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_test.json + storage: msrvtt/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/train_ans2label.json + storage: msrvtt/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given video respond to {}" + length_penalty: -1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video/msrvtt_qa_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msvd_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msvd_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9964e35e9a4241c5a8f0cf2939332f17d753cff9 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msvd_captioning.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_train.json + storage: msvd/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_val.json + storage: msvd/annotations/cap_val.json + test: + # video f9_bP219ehQ_63_70.avi is corrupt + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_test.json + storage: msvd/annotations/cap_test.json + videos: + # storage: msvd/videos + storage: /export/share/datasets/vision_language/msvd/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 4 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video/msvd_captioning/" + # annotation_file: /export/home/.cache/lavis/msvd_caption_gt/msvd_caption_test_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + img_ids: ['ghynaoVNwZc_1_20.avi', 'fEsrO_poIUg_161_168.avi', 'jCplbayVbtw_10_20.avi', 'pzq5fPfsPZg_29_33.avi', 'fcvW1vr8hAs_104_108.avi', 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b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msvd_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1f0011dbb942fbbbf7d5bddd5c247b4e028ba0d2 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/msvd_qa.yaml @@ -0,0 +1,168 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_train.json + storage: msvd/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_val.json + storage: msvd/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_test.json + storage: msvd/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/train_ans2label.json + storage: msvd/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msvd/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 4 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "Question: {} Answer:" + length_penalty: -1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video/msvd_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: 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b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/vatex_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2e01c301b37c3db19037a4171ee26f0d2e9175d0 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video/vatex_captioning.yaml @@ -0,0 +1,185 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video/vatex_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # load_gt_from_file: vatex/annotations/cap_val.json + + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_image/msvd_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_image/msvd_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2f67b6f0aaf51bc188d62384fa19be3f3ac364e7 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_image/msvd_captioning.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "image" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "image" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "image" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_train.json + storage: msvd/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_val.json + storage: msvd/annotations/cap_val.json + test: + # video f9_bP219ehQ_63_70.avi is corrupt + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_test.json + storage: msvd/annotations/cap_test.json + videos: + # storage: msvd/videos + storage: /export/share/datasets/vision_language/msvd/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video_image/msvd_captioning/" + # annotation_file: /export/home/.cache/lavis/msvd_caption_gt/msvd_caption_test_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + img_ids: ['ghynaoVNwZc_1_20.avi', 'fEsrO_poIUg_161_168.avi', 'jCplbayVbtw_10_20.avi', 'pzq5fPfsPZg_29_33.avi', 'fcvW1vr8hAs_104_108.avi', 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b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_image/msvd_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cd5d0331adc9535910f0afe67a2bde426fa35812 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_image/msvd_qa.yaml @@ -0,0 +1,168 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "image" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "image" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "image" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given video respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_train.json + storage: msvd/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_val.json + storage: msvd/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_test.json + storage: msvd/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/train_ans2label.json + storage: msvd/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msvd/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given video respond to {}" + length_penalty: -1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video_image/msvd_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + ques_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_questions.json"} + anno_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_annotations.json"} + + + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + + # img_ids: ['ghynaoVNwZc_1_20.avi', 'fEsrO_poIUg_161_168.avi', 'jCplbayVbtw_10_20.avi', 'pzq5fPfsPZg_29_33.avi', 'fcvW1vr8hAs_104_108.avi', 'gp8XjWSoP2k_0_10.avi', 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b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_image/vatex_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1f6be0ce6bd4593a8ce4ab99dc2d34df9e6035fd --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_image/vatex_captioning.yaml @@ -0,0 +1,183 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "image" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "image" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "image" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 5 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video_image/vatex_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + # load_gt_from_file: vatex/annotations/cap_val.json + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_image_pre_coco/msvd_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_image_pre_coco/msvd_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d6d5bde5c3e1bb538bef247aa61f599afd62ee12 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_image_pre_coco/msvd_captioning.yaml @@ -0,0 +1,157 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "image" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "image" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "image" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_train.json + storage: msvd/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_val.json + storage: msvd/annotations/cap_val.json + test: + # video f9_bP219ehQ_63_70.avi is corrupt + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_test.json + storage: msvd/annotations/cap_test.json + videos: + # storage: msvd/videos + storage: /export/share/datasets/vision_language/msvd/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + 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b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_image_pre_coco/msvd_qa.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "image" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "image" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "image" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given video respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_train.json + storage: msvd/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_val.json + storage: msvd/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_test.json + storage: msvd/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/train_ans2label.json + storage: msvd/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msvd/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given video respond to {}" + length_penalty: -1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video_image_pre_coco/msvd_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + ques_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + 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/dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_image_pre_coco/vatex_captioning.yaml @@ -0,0 +1,156 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_pre_coco.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "image" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "image" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "image" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: vatex/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: vatex/annotations/cap_val.json + test: + # iWNXAYGh9cI_000004_000014.mp4 is corrupt and removed from youtube + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: vatex/annotations/cap_test.json + videos: + storage: /export/video-language-dataset/data/vatex/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 5 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video_image_pre_coco/vatex_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + # load_gt_from_file: vatex/annotations/cap_val.json + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msrvtt_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msrvtt_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1d452adbf0aaa9b841f4cfcda36d0a0e235d519e --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msrvtt_captioning_test.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer_no_upsample10k.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_caption: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: videos # [images|videos|features] + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_train.json + storage: msrvtt/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_val.json + storage: msrvtt/annotations/cap_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_test.json + storage: msrvtt/annotations/cap_test.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video/msrvtt_captioning_test/" + # annotation_file: /export/home/.cache/lavis/msrvtt_caption_gt/msrvtt_caption_val_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msrvtt_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msrvtt_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0c984cd7c70c7e5756d87b893a492b63f4d1502e --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msrvtt_captioning_val.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer_no_upsample10k.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_caption: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: videos # [images|videos|features] + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_train.json + storage: msrvtt/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_val.json + storage: msrvtt/annotations/cap_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_test.json + storage: msrvtt/annotations/cap_test.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video_no_upsample/msrvtt_captioning_val/" + # annotation_file: /export/home/.cache/lavis/msrvtt_caption_gt/msrvtt_caption_val_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msrvtt_qa_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msrvtt_qa_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..24577ba88121a92722a5d488f4909558c03a1ff1 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msrvtt_qa_test.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer_no_upsample10k.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given video respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_train.json + storage: msrvtt/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_val.json + storage: msrvtt/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_test.json + storage: msrvtt/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/train_ans2label.json + storage: msrvtt/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given video respond to {}" + length_penalty: -1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/video/msrvtt_qa_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msrvtt_qa_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msrvtt_qa_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0105d8b21ca4d39674041068a26930a384f3fe89 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msrvtt_qa_val.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer_no_upsample10k.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given video respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_train.json + storage: msrvtt/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_val.json + storage: msrvtt/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_test.json + storage: msrvtt/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/train_ans2label.json + storage: msrvtt/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given video respond to {}" + length_penalty: -1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video/msrvtt_qa_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msvd_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msvd_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..59361237e1c24d7b7e64d18ec5b34ac0aebb83ed --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msvd_captioning.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/video/20231111090/checkpoint_10000.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_train.json + storage: msvd/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_val.json + storage: msvd/annotations/cap_val.json + test: + # video f9_bP219ehQ_63_70.avi is corrupt + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_test.json + storage: msvd/annotations/cap_test.json + videos: + # storage: msvd/videos + storage: /export/share/datasets/vision_language/msvd/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video_no_upsample/msvd_captioning/" + # annotation_file: /export/home/.cache/lavis/msvd_caption_gt/msvd_caption_test_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + device: "cuda" + 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pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer_upsample10k.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_train.json + storage: msvd/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_val.json + storage: msvd/annotations/cap_val.json + test: + # video f9_bP219ehQ_63_70.avi is corrupt + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_test.json + storage: msvd/annotations/cap_test.json + videos: + # storage: msvd/videos + storage: /export/share/datasets/vision_language/msvd/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video_no_upsample_up/msvd_captioning/" + # annotation_file: /export/home/.cache/lavis/msvd_caption_gt/msvd_caption_test_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + img_ids: ['ghynaoVNwZc_1_20.avi', 'fEsrO_poIUg_161_168.avi', 'jCplbayVbtw_10_20.avi', 'pzq5fPfsPZg_29_33.avi', 'fcvW1vr8hAs_104_108.avi', 'gp8XjWSoP2k_0_10.avi', 'o_mWZWcm2r4_10_15.avi', 'hXn7D6-AAMA_0_9.avi', 'g36ho6UrBz0_5_20.avi', 'n_Z0-giaspE_379_387.avi', 'hSgGBHbJrmE_0_17.avi', 'nMBSDpB3WB8_5_14.avi', 'lrZxpneS6Gk_0_12.avi', 'v4_AzQSnmY4_40_55.avi', 'o4pL7FObqds_72_78.avi', 's0hwEUC5emA_127_132.avi', 'vRC9sBNt9vs_10_16.avi', 'xxHx6s_DbUo_49_56.avi', 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b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msvd_qa.yaml @@ -0,0 +1,169 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/video/20231111090/checkpoint_10000.pth + # https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer_no_upsample10k.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given video respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_train.json + storage: msvd/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_val.json + storage: msvd/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_test.json + storage: msvd/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/train_ans2label.json + storage: msvd/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msvd/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 4 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given video respond to {}" + length_penalty: -1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video_no_upsample/msvd_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + ques_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_questions.json"} + anno_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_annotations.json"} + + + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + + # img_ids: ['ghynaoVNwZc_1_20.avi', 'fEsrO_poIUg_161_168.avi', 'jCplbayVbtw_10_20.avi', 'pzq5fPfsPZg_29_33.avi', 'fcvW1vr8hAs_104_108.avi', 'gp8XjWSoP2k_0_10.avi', 'o_mWZWcm2r4_10_15.avi', 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b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msvd_qa_up.yaml new file mode 100644 index 0000000000000000000000000000000000000000..99d09c0645ceff254bfee5a9add8816aef6586f5 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/msvd_qa_up.yaml @@ -0,0 +1,169 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b/video/20231111090/checkpoint_1000.pth + # https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer_upsample10k.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given video respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_train.json + storage: msvd/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_val.json + storage: msvd/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_test.json + storage: msvd/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/train_ans2label.json + storage: msvd/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msvd/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given video respond to {}" + length_penalty: -1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video_no_upsample/msvd_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + ques_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_questions.json"} + anno_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_annotations.json"} + + + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + + # img_ids: 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'inzk2fTUe1w_1_15.avi', 'lB1UPJ4leqs_0_6.avi', 'vZa13vJugGU_0_30.avi'] diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/vatex_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/vatex_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3123ebd28bebfb53e4c1e3fe0606db0abb30681f --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/vatex_captioning.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer_no_upsample10k.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: vatex/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: vatex/annotations/cap_val.json + test: + # iWNXAYGh9cI_000004_000014.mp4 is corrupt and removed from youtube + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: vatex/annotations/cap_test.json + videos: + storage: /export/video-language-dataset/data/vatex/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video/vatex_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # load_gt_from_file: vatex/annotations/cap_val.json + + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/vatex_captioning_up.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/vatex_captioning_up.yaml new file mode 100644 index 0000000000000000000000000000000000000000..980bf1426b2d446ffad5f4ec11d63d8ca86a2504 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b/video_no_upsample/vatex_captioning_up.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer_upsample10k.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: vatex/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: vatex/annotations/cap_val.json + test: + # iWNXAYGh9cI_000004_000014.mp4 is corrupt and removed from youtube + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: vatex/annotations/cap_test.json + videos: + storage: /export/video-language-dataset/data/vatex/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video/vatex_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # load_gt_from_file: vatex/annotations/cap_val.json + + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/audiocaps_captioning_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/audiocaps_captioning_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7e12cc8a6d400322c2fde13882436c00cf9ab7b2 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/audiocaps_captioning_qa.yaml @@ -0,0 +1,159 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_finetuned: True + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "given the input respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_qa: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + build_info: + annotations: + + train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/audiocaps/audio_qa_final_train.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_train.csv + storage: + - audiocaps_qa/annotation/train.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_train.csv + + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/audiocaps/audio_qa_final_val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_val.csv + storage: + - audiocaps_qa/annotation/val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/audio_qa_data/audio_qa_final_val.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/audio/audiocaps_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/audiocaps_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/audiocaps_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cdc278a1eacf3e78612f730370482f28fd8ee008 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/audiocaps_captioning_test.yaml @@ -0,0 +1,161 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_caption_instruct: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + prompt: describe the audio + + data_type: [audio] # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/train.csv + storage: + - /audiocaps/dataset/train.csv + + val: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/val.csv + storage: + - /audiocaps/dataset/val.csv + + test: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/test.csv + storage: + - /audiocaps/dataset/test.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/audio/audiocaps_captioning_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/audiocaps_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/audiocaps_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fbe10c89f0674981b70e93353cf1eeeabb0278fe --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/audiocaps_captioning_val.yaml @@ -0,0 +1,161 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + audiocaps_mm_caption_instruct: # name of the dataset builder + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + prompt: describe the audio + + data_type: [audio] # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/train.csv + storage: + - /audiocaps/dataset/train.csv + + val: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/val.csv + storage: + - /audiocaps/dataset/val.csv + + test: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/test.csv + storage: + - /audiocaps/dataset/test.csv + + audio: + storage: /audiocaps/AUDIOCAPS_32000Hz/audio + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 4 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/audio/audiocaps_captioning_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/clothoQA_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/clothoQA_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5a3b114104556ebe3c50317e86755a09165b5e5b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/clothoQA_captioning.yaml @@ -0,0 +1,155 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + clotho_qa: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] + + build_info: + + annotations: + train: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_train.csv + storage: + - clotho_Qa/annotations/clotho_aqa_train.csv + val: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_val.csv + storage: + - clotho_qa/annotations/clotho_aqa_val.csv + + test: + url: + - https://zenodo.org/records/6473207/files/clotho_aqa_test.csv + storage: + - clotho_qa/annotations/clotho_aqa_test.csv + audio: + storage: /export/einstein-vision/audio_datasets/clotho-aqa/audio_files + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 4 + accum_grad_iters: 1 + prompt: "Question: {} Answer:" + + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/audio/clothovqa_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/clothov1_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/clothov1_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d9e4a3b70e709130da0ba73bd37e444489029034 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/clothov1_captioning.yaml @@ -0,0 +1,147 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + clothov2: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] # [images|videos|features] + + build_info: + kwargs: + clotho_root: /export/einstein-vision/audio_datasets/clothov2/ + split: evaluation + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://zenodo.org/record/4783391/files/clotho_captions_evaluation.csv + storage: + - /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_csv_files/clotho_captions_evaluation.csv + audio: + storage: /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_audio_files/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/audio/clothov1_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/clothov2_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/clothov2_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a0fd44d2e983be83deaf47a292ba8f02699787c9 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/clothov2_captioning.yaml @@ -0,0 +1,147 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + clothov2: # name of the dataset builder + audio_processor: + train: + name: beats_audio + eval: + name: beats_audio + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + + data_type: [audio] # [images|videos|features] + + build_info: + kwargs: + clotho_root: /export/einstein-vision/audio_datasets/clothov2/ + split: eval + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://zenodo.org/record/4783391/files/clotho_captions_validation.csv + storage: + - /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_csv_files/clotho_captions_validation.csv + audio: + storage: /export/einstein-vision/audio_datasets/clothov2/CLOTHO_v2.1/clotho_audio_files/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 2 + num_workers: 8 + accum_grad_iters: 1 + prompt: "a short description" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/audio/clothov2_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/esc50_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/esc50_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9b30082dee5f84dd5b1292efca62167be972e424 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/esc50_classification.yaml @@ -0,0 +1,151 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the audio" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + format_candidates_prompt: "an audio of {}" + +datasets: + esc50_cls: # + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + build_info: + annotations: + + val: + url: + - https://raw.githubusercontent.com/karolpiczak/ESC-50/master/meta/esc50.csv + storage: + - /ESC-50/esc50.csv + + audio: + storage: /ESC-50/audio + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "describe the audio" + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 0. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/audio/esc50_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/esc50_classification_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/esc50_classification_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f94585f01e8a5762ef73aa5f109e044dc8540058 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/audio/esc50_classification_completion.yaml @@ -0,0 +1,151 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the audio" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + # format_candidates_prompt: "an audio of {}" + +datasets: + esc50_cls: # + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + build_info: + annotations: + + val: + url: + - https://raw.githubusercontent.com/karolpiczak/ESC-50/master/meta/esc50.csv + storage: + - /ESC-50/esc50.csv + + audio: + storage: /ESC-50/audio + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "describe the audio" + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/audio/esc50_classification_completion/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + caption_key: captions + sample_id_key: sound_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/musicavqa/musicavqa_audio_eval.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/musicavqa/musicavqa_audio_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9ff66379d7e95fa22f82447783efc79074bc93fe --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/musicavqa/musicavqa_audio_eval.yaml @@ -0,0 +1,182 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + musicavqa_mm_instruct: # name of the dataset builder + data_type: [audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 2 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: qa + modality: video + eval: + name: blip_question + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-val.json + storage: + - /musicavqa/val.json + + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-test.json + storage: + - /musicavqa/test.json + templates: null + + audio: + storage: path/to/videos + + video: + storage: path/to/videos + + + +run: + task: gqa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 8 + max_epoch: 1 + + # inference-specific + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + inference_method: "generate" + prompt: "Question: {} Answer:" + + train_splits: ["train"] + valid_splits: ["test"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + ques_files: { + "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_questions.json", + "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_testt_questions.json" + } + anno_files: { + "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_annotations.json", + "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_test_annotations.json" + } + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/crossmodal/musicavqa/audio/" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/musicavqa/musicavqa_joint_eval.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/musicavqa/musicavqa_joint_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2446849ea8f1487064a3854e81cb9ff2e31a64a6 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/musicavqa/musicavqa_joint_eval.yaml @@ -0,0 +1,182 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio", "video"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + musicavqa_mm_instruct: # name of the dataset builder + data_type: [video,audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: qa + modality: video + eval: + name: blip_question + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-val.json + storage: + - /musicavqa/val.json + + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-test.json + storage: + - /musicavqa/test.json + templates: null + + audio: + storage: path/to/videos + + video: + storage: path/to/videos + + + +run: + task: gqa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 8 + max_epoch: 1 + + # inference-specific + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + inference_method: "generate" + prompt: "Question: {} Answer:" + + train_splits: ["train"] + valid_splits: ["test"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + # ques_files: { + # "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_questions.json", + # "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_test_questions.json" + # } + # anno_files: { + # "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_annotations.json", + # "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_test_annotations.json" + # } + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/crossmodal/musicavqa/joint/" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/musicavqa/musicavqa_video_eval.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/musicavqa/musicavqa_video_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d7e8db2d0792d00423caf3332513e804b12641fb --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/musicavqa/musicavqa_video_eval.yaml @@ -0,0 +1,182 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "Question: {} Answer:" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + musicavqa_mm_instruct: # name of the dataset builder + data_type: [video] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: qa + modality: video + eval: + name: blip_question + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-val.json + storage: + - /musicavqa/val.json + + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/musicavqa/avqa-test.json + storage: + - /musicavqa/test.json + templates: null + + audio: + storage: path/to/videos + + video: + storage: path/to/videos + + + +run: + task: gqa + # optimization-specific + batch_size_train: 8 + batch_size_eval: 1 + num_workers: 8 + max_epoch: 1 + + # inference-specific + max_len: 10 + min_len: 1 + num_beams: 5 + length_penalty: -1. + inference_method: "generate" + prompt: "Question: {} Answer:" + + train_splits: ["train"] + valid_splits: ["test"] + # test_splits: ["test"] + + # distribution + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + use_dist_eval_sampler: False + # ques_files: { + # "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_questions.json", + # "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_test_questions.json" + # } + # anno_files: { + # "val": "/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_val_annotations.json", + # "test":"/export/home/.cache/lavis/musicavqa_mm_instruct_gt/musicavqa_mm_instruct_test_annotations.json" + # } + + # model specific + k_test: 128 + + # misc + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/crossmodal/musicavqa/video/" + + evaluate: True + save_freq: -1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/vatex/vatex_audio_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/vatex/vatex_audio_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c13020bd1fb853176da5a4d613b32235fcaef2ef --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/vatex/vatex_audio_captioning.yaml @@ -0,0 +1,183 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: # name of the dataset builder + data_type: [audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 4 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video/vatex_joint_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # load_gt_from_file: vatex/annotations/cap_val.json + + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/vatex/vatex_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/vatex/vatex_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..07ed4dd83037b6260e2394eac573fc91a9463be3 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/vatex/vatex_captioning.yaml @@ -0,0 +1,182 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/video/vatex_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/vatex/vatex_joint_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/vatex/vatex_joint_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aec100b1edb7eaf14c6805ac98e7f8c952393bef --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/crossmodal/vatex/vatex_joint_captioning.yaml @@ -0,0 +1,183 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio", "video"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video, audio] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b/video/vatex_joint_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # load_gt_from_file: vatex/annotations/cap_val.json + + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/coco_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/coco_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..786602663ccf7417ab5d3d9d90ace67561fbf068 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/coco_captioning_test.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/image/coco_captioning_test/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/coco_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/coco_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a5b53d91f53478cf49ca63754097cbf0a3cc2db4 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/coco_captioning_val.yaml @@ -0,0 +1,154 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + coco_caption: # name of the dataset builder + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/image/coco_captioning_val/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/flickr30k_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/flickr30k_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..55a17c949a9705454498ff9391a250746159ab32 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/flickr30k_captioning.yaml @@ -0,0 +1,150 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + flickr30k_caption: + # data_dir: ${env.data_dir}/datasets + data_type: images + + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + + build_info: + annotations: + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_val.json + storage: + - flickr30k/annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_test.json + storage: + - flickr30k/annotations/test.json + images: + # storage: flickr30k/images + storage: /export/share/datasets/vision/flickr30k + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/image/flickr_captioning/" + # annotation_file: /export/home/.cache/lavis/flickr30k_caption_gt/flickr30k_caption_test_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/gqa_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/gqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9fc2fc15d765925bf6e056f2ccc9d27bb1164844 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/gqa_qa.yaml @@ -0,0 +1,148 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + gqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_question" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/train_balanced_questions.json + storage: + - gqa/annotations/train_balanced_questions.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/testdev_balanced_questions.json + storage: + - gqa/annotations/testdev_balanced_questions.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/test_balanced_questions.json + storage: + - gqa/annotations/test_balanced_questions.json + images: + storage: /export/share/datasets/vision/GQA/images #gqa/images/ + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/image/gqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/nocaps_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/nocaps_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6444976c799e3e85927f5ef073b436c1464603c2 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/nocaps_captioning.yaml @@ -0,0 +1,144 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/image/nocaps_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/nocaps_out_domain_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/nocaps_out_domain_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d887040b87379a81fd19f8fe76babfecede218bf --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/nocaps_out_domain_captioning.yaml @@ -0,0 +1,146 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /export/share/datasets/vision/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/image/nocaps_out_domain_captioning/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + img_ids: [2, 4, 5, 8, 15, 18, 19, 22, 27, 30, 33, 35, 41, 42, 43, 46, 47, 51, 59, 60, 64, 65, 68, 69, 71, 72, 73, 77, 79, 81, 85, 87, 88, 90, 92, 100, 101, 102, 105, 107, 109, 115, 120, 124, 125, 126, 127, 129, 133, 135, 137, 139, 140, 141, 143, 150, 153, 155, 158, 164, 165, 167, 170, 171, 173, 182, 190, 191, 196, 200, 201, 203, 205, 208, 219, 225, 226, 228, 229, 232, 239, 240, 243, 245, 250, 262, 263, 264, 267, 272, 278, 283, 284, 290, 291, 297, 301, 304, 305, 309, 310, 311, 314, 323, 325, 329, 330, 331, 333, 334, 341, 349, 350, 351, 352, 354, 358, 359, 363, 365, 366, 368, 371, 372, 379, 381, 383, 386, 388, 389, 390, 392, 405, 415, 417, 418, 420, 421, 424, 428, 429, 432, 436, 441, 443, 452, 453, 454, 455, 456, 459, 464, 465, 468, 469, 476, 477, 478, 480, 487, 488, 490, 491, 493, 500, 502, 504, 506, 509, 510, 511, 512, 515, 516, 520, 527, 529, 533, 539, 540, 541, 544, 545, 547, 551, 554, 556, 559, 577, 579, 580, 582, 586, 587, 590, 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4388, 4389, 4392, 4396, 4402, 4404, 4408, 4410, 4424, 4426, 4428, 4431, 4435, 4436, 4439, 4442, 4446, 4447, 4449, 4452, 4455, 4458, 4460, 4461, 4466, 4469, 4475, 4476, 4478, 4488, 4491, 4494, 4498] + diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/okvqa_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/okvqa_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0d58530c6fbd46b9910a1b6c821c85b60377cbac --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/okvqa_qa.yaml @@ -0,0 +1,157 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given the image respond to {}?" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ok_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_question" + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_train2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_train2014_annotations.json + storage: + - okvqa/annotations/okvqa_train.json + # - okvqa/annotations/OpenEnded_mscoco_train2014_questions.json + # - okvqa/annotations/mscoco_train2014_annotations.json + test: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_val_eval.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_answer_list_train.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_val2014_questions.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_val2014_annotations.json + storage: + - okvqa/annotations/vqa_val_eval.json + - okvqa/annotations/answer_list.json + - okvqa/annotations/OpenEnded_mscoco_val2014_questions.json + - okvqa/annotations/mscoco_val2014_annotations.json + images: + storage: /export/share/datasets/vision/coco/images + + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given the image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/image/okvqa_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/snlive_classification_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/snlive_classification_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..be458e93859f096198604159a90075ebd605789c --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/snlive_classification_test.yaml @@ -0,0 +1,152 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + caption: "given the image respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/image/snlive_classification_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/snlive_classification_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/snlive_classification_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5ed803013d66f58d8eb67d346f7fbea05077d021 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/snlive_classification_val.yaml @@ -0,0 +1,152 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + snli_ve_instruct: + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" + caption: "given the image respond to " + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_train.json + storage: snli/annotations/ve_train.json + val: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_dev.json + storage: snli/annotations/ve_dev.json + test: + url: /export/share/dongxuli/data/lavis/snli/annotation/ve_test.json + storage: snli/annotations/ve_test.json + images: + storage: flickr30k/images/flickr30k-images + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 30 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/image/snlive_classification_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/vizwiz_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/vizwiz_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5cf91aa3d55848696e7dc82cb1c03ebefe925451 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/image/vizwiz_qa.yaml @@ -0,0 +1,152 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given image respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vizwiz_vqa: + vis_processor: + train: + name: "clip_image_train" + eval: + name: "clip_image_eval" + text_processor: + train: + name: "blip_question" + eval: + name: "blip_question" + + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + storage: + - vizwiz/annotations/val.json + # - /export/share/datasets/vision/vizwiz/Annotations/val.json + test: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/vizwiz/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + storage: + - vizwiz/annotations/test.json + # - /export/share/datasets/vision/vizwiz/Annotations/test.json + images: + storage: /export/share/datasets/vision/vizwiz/images + +run: + task: vqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given image respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/image/vizwiz_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/pc/modelnet40_classification.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/pc/modelnet40_classification.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0877592c4eee19501136238d682a904e0e3058c1 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/pc/modelnet40_classification.yaml @@ -0,0 +1,148 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + format_candidates_prompt: " a 3d model of a {}" + special_qformer_input_prompt: False + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: pc + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + prompt: "describe the 3d model" + + max_len: 3 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/pc/modelnet_classification/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/pc/modelnet40_completion.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/pc/modelnet40_completion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2260091e041ccee184a9ee41df40345efa47b890 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/pc/modelnet40_completion.yaml @@ -0,0 +1,148 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + special_qformer_input_prompt: False + +datasets: + modelnet40_cls: # name of the dataset builder + data_type: pc + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_train.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_train_8192pts_fps.dat + - /modelnet40_normal_resampled/modelnet40_train.txt + val: + url: + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_shape_names.txt + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - https://storage.googleapis.com/sfr-ulip-code-release-research/modelnet40_normal_resampled/modelnet40_test.txt + storage: + - modelnet40_normal_resampled/modelnet40_shape_names.txt + - modelnet40_normal_resampled/modelnet40_test_8192pts_fps.dat + - modelnet40_normal_resampled/modelnet40_test.txt + + pc: + storage: /export/home/ULIP/data/modelnet40_normal_resampled + + images: + storage: /export/einstein-vision/3d_vision/3d_object_datasets/modelnet40_pc_img + + +run: + task: multimodal_classification + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/pc/modelnet_classification_completion/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/pc/objaverse_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/pc/objaverse_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d8f3aa15c30032ec0f56026f4f0ae42f65759800 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/pc/objaverse_captioning.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + + data_type: [pc] # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + storage: + - objaverse/annotations/train.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + + val: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + storage: + - objaverse/annotations/val.csv + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + length_penalty: 0. + prompt: "describe the 3d model" + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/pc/objaverse_captioning/" + # annotation_file: /export/home/.cache/lavis/objaverse_mm_caption_instruct_gt/objaverse_mm_caption_instruct_val_annotations.json + + amp: True + resume_ckpt_path: null + caption_key: 'data' + sample_id_key: sample_id + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/pc/objaverse_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/pc/objaverse_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..405a99eceb2aa03da00ee3a557289ca50f3b58a7 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/pc/objaverse_qa.yaml @@ -0,0 +1,163 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given input respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: True + +datasets: + objaverse_mm_qa: # 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: +train: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + storage: + - objaverse_qa/annotations/train.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final.csv + val: + url: + - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/objaverse/CAP3DQA_final_val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + storage: + - objaverse_qa/annotations/val.csv + # - /export/home/LAVIS-xgen_mm/projects/xinstructblip/data_aug/3d_qa_data/CAP3DQA_final_val.csv + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 32 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 1 + num_beams: 5 + length_penalty: 0. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/pc/objaverse_qa/" + # ques_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_questions.json" } + # anno_files: { "val": "/export/home/.cache/lavis/objaverse_mm_qa_gt/objaverse_mm_qa_val_annotations.json" } + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 + \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msrvtt_captioning_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msrvtt_captioning_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..957632b0cf452ceaea4b3189877b703b090a990b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msrvtt_captioning_test.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_caption: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: videos # [images|videos|features] + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_train.json + storage: msrvtt/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_val.json + storage: msrvtt/annotations/cap_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_test.json + storage: msrvtt/annotations/cap_test.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/video/msrvtt_captioning_test/" + # annotation_file: /export/home/.cache/lavis/msrvtt_caption_gt/msrvtt_caption_test_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msrvtt_captioning_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msrvtt_captioning_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b7d8e574a2a7f7bbd48401c89521dca0ba4a4d56 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msrvtt_captioning_val.yaml @@ -0,0 +1,160 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_caption: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: videos # [images|videos|features] + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_train.json + storage: msrvtt/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_val.json + storage: msrvtt/annotations/cap_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_test.json + storage: msrvtt/annotations/cap_test.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/video/msrvtt_captioning_val/" + # annotation_file: /export/home/.cache/lavis/msrvtt_caption_gt/msrvtt_caption_val_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msrvtt_qa_test.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msrvtt_qa_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d852af54884b7f3de21ed00a8709df6d3f0b36d5 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msrvtt_qa_test.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given video respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_train.json + storage: msrvtt/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_val.json + storage: msrvtt/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_test.json + storage: msrvtt/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/train_ans2label.json + storage: msrvtt/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given video respond to {}" + length_penalty: -1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/video/msrvtt_qa_test/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msrvtt_qa_val.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msrvtt_qa_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..189af74bc80b7348111e8d94a4ff41c56134f4bf --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msrvtt_qa_val.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given video respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_train.json + storage: msrvtt/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_val.json + storage: msrvtt/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_test.json + storage: msrvtt/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/train_ans2label.json + storage: msrvtt/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given video respond to {}" + length_penalty: -1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/video/msrvtt_qa_val/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msvd_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msvd_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8a3f41c47870cd193a29f0c8e05d513d586bd070 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msvd_captioning.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna7b_nocue/video/20231101140/checkpoint_10000.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_train.json + storage: msvd/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_val.json + storage: msvd/annotations/cap_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_test.json + storage: msvd/annotations/cap_test.json + videos: + # storage: msvd/videos + storage: /export/share/datasets/vision_language/msvd/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 4 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: a short description + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/video/msvd_captioning/" + # annotation_file: /export/home/.cache/lavis/msvd_caption_gt/msvd_caption_test_annotations.json + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + + img_ids: ['ghynaoVNwZc_1_20.avi', 'fEsrO_poIUg_161_168.avi', 'jCplbayVbtw_10_20.avi', 'pzq5fPfsPZg_29_33.avi', 'fcvW1vr8hAs_104_108.avi', 'gp8XjWSoP2k_0_10.avi', 'o_mWZWcm2r4_10_15.avi', 'hXn7D6-AAMA_0_9.avi', 'g36ho6UrBz0_5_20.avi', 'n_Z0-giaspE_379_387.avi', 'hSgGBHbJrmE_0_17.avi', 'nMBSDpB3WB8_5_14.avi', 'lrZxpneS6Gk_0_12.avi', 'v4_AzQSnmY4_40_55.avi', 'o4pL7FObqds_72_78.avi', 's0hwEUC5emA_127_132.avi', 'vRC9sBNt9vs_10_16.avi', 'xxHx6s_DbUo_49_56.avi', 'hM3jzlyNIpc_0_10.avi', 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'kIZanu909lw_67_80.avi', 'xy9LLUUZ6ic_50_60.avi', 'fVWUaH2mCt4_1_7.avi', 'rq2p5ML8-WI_63_69.avi', 'kEGmZDpZ_RE_295_330.avi', 'lR8RrUBhCQg_5_15.avi', 'xCFCXzDUGjY_5_9.avi', 'k8l4ETsylVY_9_18.avi', 'me1D1WZ0yNM_120_124.avi', 'mtrCf667KDk_134_176.avi', 'rwHT2SuNOi8_240_255.avi', 'uy0HNWto0UY_18_25.avi', 'rQuNYxNmA6M_0_4.avi', 'q8t7iSGAKik_57_74.avi', 'zSPBC8EO6dY_122_126.avi', 'klFyrnrUSck_79_85.avi', 'mHv4iJ9Yr1g_10_16.avi', 'gqxpGOHUH9k_113_119.avi', 'jW77z3-SrO4_56_63.avi', 'sJSmRik2c-c_1_7.avi', 'nlU3crMsbWI_19_23.avi', 'kquB3rIgfGk_537_544.avi', 'inzk2fTUe1w_1_15.avi', 'lB1UPJ4leqs_0_6.avi', 'vZa13vJugGU_0_30.avi'] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msvd_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msvd_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..97260545f1d5f1d3e9d027cc0c4b300a1d480f28 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/msvd_qa.yaml @@ -0,0 +1,166 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "question {} answer" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_train.json + storage: msvd/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_val.json + storage: msvd/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_test.json + storage: msvd/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/train_ans2label.json + storage: msvd/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msvd/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 4 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 5 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "question {} answer" + length_penalty: -1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/video/msvd_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + ques_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_questions.json"} + anno_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_annotations.json"} + + + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/vatex_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/vatex_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..07ed4dd83037b6260e2394eac573fc91a9463be3 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video/vatex_captioning.yaml @@ -0,0 +1,182 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/video/vatex_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video_image/msvd_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video_image/msvd_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9977da87fe6e8d791722302da9ea0dfcf43d53c4 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video_image/msvd_captioning.yaml @@ -0,0 +1,158 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "image" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "image" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "image" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: a short description + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_caption: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_train.json + storage: msvd/annotations/cap_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_val.json + storage: msvd/annotations/cap_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/cap_test.json + storage: msvd/annotations/cap_test.json + videos: + # storage: msvd/videos + storage: /export/share/datasets/vision_language/msvd/videos + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 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'kIZanu909lw_67_80.avi', 'xy9LLUUZ6ic_50_60.avi', 'fVWUaH2mCt4_1_7.avi', 'rq2p5ML8-WI_63_69.avi', 'kEGmZDpZ_RE_295_330.avi', 'lR8RrUBhCQg_5_15.avi', 'xCFCXzDUGjY_5_9.avi', 'k8l4ETsylVY_9_18.avi', 'me1D1WZ0yNM_120_124.avi', 'mtrCf667KDk_134_176.avi', 'rwHT2SuNOi8_240_255.avi', 'uy0HNWto0UY_18_25.avi', 'rQuNYxNmA6M_0_4.avi', 'q8t7iSGAKik_57_74.avi', 'zSPBC8EO6dY_122_126.avi', 'klFyrnrUSck_79_85.avi', 'mHv4iJ9Yr1g_10_16.avi', 'gqxpGOHUH9k_113_119.avi', 'jW77z3-SrO4_56_63.avi', 'sJSmRik2c-c_1_7.avi', 'nlU3crMsbWI_19_23.avi', 'kquB3rIgfGk_537_544.avi', 'inzk2fTUe1w_1_15.avi', 'lB1UPJ4leqs_0_6.avi', 'vZa13vJugGU_0_30.avi'] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video_image/msvd_qa.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video_image/msvd_qa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fb88ccec5693533a2152839ff4717d8be9764696 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video_image/msvd_qa.yaml @@ -0,0 +1,165 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "image" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "image" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "image" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "based on the given video respond to {}" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msvd_qa_instruct: + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_question" + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_train.json + storage: msvd/annotations/qa_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_val.json + storage: msvd/annotations/qa_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/qa_test.json + storage: msvd/annotations/qa_test.json + ans2label: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msvd/train_ans2label.json + storage: msvd/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msvd/videos + + instance_id_key: question_id + +run: + task: gqa + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 10 + min_len: 1 + num_beams: 5 + inference_method: "generate" + prompt: "based on the given video respond to {}" + length_penalty: -1. + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/video_image/msvd_qa/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["test"] + # ques_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + # "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_questions.json", + # "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_questions.json"} + # anno_files: {"train": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + # "val": "/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_val_annotations.json", + # "test":"/export/home/.cache/lavis/msvd_qa_instruct_gt/msvd_qa_instruct_test_annotations.json"} + + + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video_image/vatex_captioning.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video_image/vatex_captioning.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3cf057c573cc3a8f57f56191e25ca7ffd2f986e7 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/eval/vicuna7b_nocue/video_image/vatex_captioning.yaml @@ -0,0 +1,181 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "image" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "image" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "image" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "a short description" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + vatex_caption: # name of the dataset builder + data_type: [video] + + video_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_caption + eval: + name: blip_caption + + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_train.json + storage: + - vatex/annotations/cap_train.json + val: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_val.json + storage: + - vatex/annotations/cap_val.json + test: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vatex/cap_private_test.json + storage: + - vatex/annotations/cap_test.json + + video: + storage: /export/video-language-dataset/data/vatex/ + + audio: + storage: /export/video-language-dataset/data/vatex/ + + + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "a short description" + length_penalty: 1. + + + seed: 42 + output_dir: "output/xinstructblip/eval/vicuna7b_nocue/video_image/vatex_captioning/" + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + # annotation_file: /export/home/.cache/lavis/vatex_caption_gt/vatex_caption_val_annotations.json + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/original.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/original.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ef5e9d16dc8f97cc8e57aebb6b8330c7a9a68c0c --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/original.yaml @@ -0,0 +1,81 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + prompt: A short image description + + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /path/to/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: A short image description + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/prompt_variation/instructblip/original/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/template_1.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/template_1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3766e0a880c95a8c24b5d1675ef312d5ac44824d --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/template_1.yaml @@ -0,0 +1,81 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + prompt: In a few words describe the basic features of this image. + + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /path/to/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: In a few words describe the basic features of this image. + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/prompt_variation/instructblip/template1/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/template_2.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/template_2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5785df5446be1e2e84008d2ad8841ad12c056fc1 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/template_2.yaml @@ -0,0 +1,81 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + prompt: Give me a recap of what is happening in the picture. + + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /path/to/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: Give me a recap of what is happening in the picture. + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/prompt_variation/instructblip/template2/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/template_3.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/template_3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..925bbf5dc69755cacdd8551d4cf884e75073a5df --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/template_3.yaml @@ -0,0 +1,81 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + prompt: I'd like to hear your interpretation of this image. What do you see? + + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /path/to/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: I'd like to hear your interpretation of this image. What do you see? + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/prompt_variation/instructblip/template3/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/template_4.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/template_4.yaml new file mode 100644 index 0000000000000000000000000000000000000000..70e085d7d7c061eed2a7e850e5c209c93e1dafcd --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/template_4.yaml @@ -0,0 +1,81 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + prompt: Provide a verbal snapshot of what's happening in this image. + + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /path/to/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: Provide a verbal snapshot of what's happening in this image. + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/prompt_variation/instructblip/template4/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/template_5.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/template_5.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cb79d08a74131137f5ed14e4e50c3f5efed3dbed --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/instructblip/template_5.yaml @@ -0,0 +1,81 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_instruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + prompt: "Please articulate the elements and context of this image" + + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /path/to/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: "Please articulate the elements and context of this image" + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/prompt_variation/instructblip/template5/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/xinstructblip/template_1.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/xinstructblip/template_1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7d1c53e8f0dd5f7e26189c0203730511fb915943 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/xinstructblip/template_1.yaml @@ -0,0 +1,145 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: In a few words describe the basic features of this image. + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /path/to/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: In a few words describe the basic features of this image. + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/prompt_variation/xinstructblip/template1/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/xinstructblip/template_2.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/xinstructblip/template_2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6a9aef34c3158a351efec0ccea385d673e9e8093 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/xinstructblip/template_2.yaml @@ -0,0 +1,145 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: Give me a recap of what is happening in the picture. + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /path/to/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: Give me a recap of what is happening in the picture. + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/prompt_variation/xinstructblip/template2/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/xinstructblip/template_3.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/xinstructblip/template_3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..178b54765da879d3e3f681fa6a4d73627d728e68 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/xinstructblip/template_3.yaml @@ -0,0 +1,145 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: I'd like to hear your interpretation of this image. What do you see? + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /path/to/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: I'd like to hear your interpretation of this image. What do you see? + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/prompt_variation/xinstructblip/template3/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/xinstructblip/template_4.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/xinstructblip/template_4.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c4e0a3328b94ba3fc43d2a1f967113d55119e5f1 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/xinstructblip/template_4.yaml @@ -0,0 +1,145 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: Provide a verbal snapshot of what's happening in this image. + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /path/to/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: Provide a verbal snapshot of what's happening in this image. + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/prompt_variation/xinstructblip/template4/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/xinstructblip/template_5.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/xinstructblip/template_5.yaml new file mode 100644 index 0000000000000000000000000000000000000000..531c62e5d2eac6566c8ea64fb78014b618dc0ea9 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/prompt_variation/nocaps/xinstructblip/template_5.yaml @@ -0,0 +1,145 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: Please articulate the elements and context of this image + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + nocaps: # name of the dataset builder + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json + storage: nocaps/annotations/nocaps_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json + storage: nocaps/annotations/nocaps_test.json + images: + storage: /path/to/nocaps/ + +run: + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 1 + batch_size_train: 16 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + + max_len: 80 + min_len: 10 + num_beams: 5 + inference_method: "generate" + prompt: Please articulate the elements and context of this image + length_penalty: 1. + + annotation_file: https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json + + + seed: 42 + output_dir: "output/xinstructblip/prompt_variation/xinstructblip/template5/" + + + amp: True + resume_ckpt_path: null + + evaluate: True + # train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: -1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/audio_training.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/audio_training.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7b492ff6e825d36d37debf2f35d796e10202465f --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/audio_training.yaml @@ -0,0 +1,218 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "describe the audio." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + + +datasets: + ## CAPTIONING TASKS + audiocaps_mm_caption_instruct: # 38701 train examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + build_info: + kwargs: + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + + + audiocaps_mm_qa: # 24158 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: True + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + build_info: + kwargs: + add_binary: True + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + + + ## Classification like caption + audioset_mm_caption_instruct: # 14141 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + wavcaps_mm_caption_instruct: # 297341 examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + eval: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + + + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 2 + batch_size_eval: 2 + num_workers: 10 + accum_grad_iters: 1 + max_iters: 300000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"audiocaps_mm_caption_instruct": 0.19355908897689844, "audiocaps_mm_qa": 0.1529265758175233, "wavcaps_mm_caption_instruct": 0.5365125368594943, "audioset_mm_caption_instruct": 0.11700179834608398} + + max_len: 30 + min_len: 1 + num_beams: 5 + caption_key: caption + sample_id_key: youtube_id + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna13b/audio" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/audio_training_continue.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/audio_training_continue.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e8ebb9a149488056338e3520ebb0ef30350deb19 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/audio_training_continue.yaml @@ -0,0 +1,299 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna13b/audio/20231027135/checkpoint_240007.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna13b/audio/20231027135/checkpoint_240007.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: True + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "audio" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/export/home/LAVIS/_pretrained_models/vicuna-13b" + prompt: "describe the audio." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + + +datasets: + ## CAPTIONING TASKS + audiocaps_mm_caption_instruct: # 38701 train examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + build_info: + kwargs: + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + annotations: + train: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/train.csv + storage: + - audiocaps/annotations/train.csv + + val: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/val.csv + storage: + - audiocaps/annotation/val.csv + + # test: + # url: + # - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/test.csv + # storage: + # - /export/einstein-vision/audio_datasets/audiocaps/dataset/test.csv + + audio: + storage: /export/einstein-vision/audio_datasets/audiocaps/AUDIOCAPS_32000Hz/audio + + + audiocaps_mm_qa: # 24158 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: True + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + build_info: + kwargs: + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + annotations: + train: + url: + - /export/home/LAVIS/projects/mm_instructblip/instr_data_creation/audio_qa/audio_qa_final_train.csv + storage: + - /export/home/LAVIS/projects/mm_instructblip/instr_data_creation/audio_qa/audio_qa_final_train.csv + + # val: + # url: + # - /export/home/LAVIS/projects/mm_instructblip/instr_data_creation/audio_qa/audio_qa_final_val.csv + # storage: + # - /export/home/LAVIS/projects/mm_instructblip/instr_data_creation/audio_qa/audio_qa_final_val.csv + + audio: + storage: /export/einstein-vision/audio_datasets/audiocaps/AUDIOCAPS_32000Hz/audio + + + ## Classification like caption + audioset_mm_caption_instruct: # 14141 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + cmu_dict_path: /export/home/LAVIS/cmu_dict.p + eval: + name: blip_caption + + data_type: [audio] + + build_info: + annotations: + train: + url: + - /export/home/LAVIS-xgen_mm/lavis/configs/datasets/audioset/balanced_train_clean.csv + - http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/class_labels_indices.csv + storage: + - /export/home/LAVIS-xgen_mm/lavis/configs/datasets/audioset/balanced_train_clean.csv + - audioset/annotations/class_labels_indices.csv + # val: + # url: + # - http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/eval_segments.csv + # - http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/class_labels_indices.csv + # storage: + # - audioset/annotations/eval_segments.csv + # - audioset/annotations/class_labels_indices.csv + audio: + storage: /export/einstein-vision/audio_datasets/AudioSet/all_audio + + wavcaps_mm_caption_instruct: # 297341 examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + eval: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + build_info: + kwargs: + json_data: /export/share/datasets/audio/WavCaps/json_data.json + + cached: False + cached_dir: /export/share/datasets/audio/WavCaps/beats_features/ + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/share/datasets/audio/WavCaps/json_files/BBC_Sound_Effects/bbc_final.json + - /export/share/datasets/audio/WavCaps/json_files/FreeSound/fsd_final.json + - /export/share/datasets/audio/WavCaps/json_files/SoundBible/sb_final.json + - /export/share/datasets/audio/WavCaps/json_files/AudioSet_SL/as_final.json + - /export/share/datasets/audio/WavCaps/json_data.json + storage: + - /export/share/datasets/audio/WavCaps/json_files/BBC_Sound_Effects/bbc_final.json + - /export/share/datasets/audio/WavCaps/json_files/FreeSound/fsd_final.json + - /export/share/datasets/audio/WavCaps/json_files/SoundBible/sb_final.json + - /export/share/datasets/audio/WavCaps/json_files/AudioSet_SL/as_final.json + - /export/share/datasets/audio/WavCaps/json_data.json + + audio: + storage: /export/share/datasets/audio/WavCaps/ + + + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 2 + batch_size_eval: 2 + num_workers: 10 + accum_grad_iters: 1 + max_iters: 300000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"audiocaps_mm_caption_instruct": 0.19355908897689844, "audiocaps_mm_qa": 0.1529265758175233, "wavcaps_mm_caption_instruct": 0.5365125368594943, "audioset_mm_caption_instruct": 0.11700179834608398} + + max_len: 30 + min_len: 1 + num_beams: 5 + caption_key: caption + sample_id_key: youtube_id + annotation_file: /export/home/.cache/lavis/audiocaps_mm_caption_instruct_gt/audiocaps_mm_caption_instruct_val_annotations.json + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna13b/audio" + + amp: True + resume_ckpt_path: /export/home/LAVIS-xgen_mm/lavis/output/xinstructblip/train/vicuna13b/audio/20231027135/checkpoint_240007.pth + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/image_train.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/image_train.yaml new file mode 100644 index 0000000000000000000000000000000000000000..195d8e7ce256a636fdf4d5111c320f42b9f3d652 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/image_train.yaml @@ -0,0 +1,345 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "describe the image." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ## CAPTIONING TASKS + conceptual_caption_12m_instruct: # 6029862 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + + coco_caption_instruct: # 566747 train examples + dataset_card: dataset_card/coco_caption.md + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + + capfilt14m_instruct: # 13873136 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + + + vg_caption_instruct: # 821774 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + + sbu_caption_instruct: # 859739 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + + + ## QA TASKS + vg_vqa_instruct: # 1440069 train examples + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: qa + modality: image + eval: + name: blip_question + + + coco_vqa_instruct: # 658104 training data + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_caption + + + ocr_vqa_instruct: # 1002146 train examples + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + + ok_vqa_instruct: # 9009 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + + aok_vqa_instruct: # 17056 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + + + ##Dialogue + llava150k: #394276 train examples + + data_type: images + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 8 + batch_size_eval: 8 + num_workers: 10 + accum_grad_iters: 1 + max_iters: 750000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"conceptual_caption_12m_instruct": 0.19438459253859763, + "coco_caption_instruct": 0.3, #0.05959403162103753, + "capfilt14m_instruct": 0.29484615861022884, + "vg_caption_instruct": 0.0717603173049719, + 'sbu_caption_instruct': 0.07339922359647665, + 'vg_vqa_instruct': 0.094994793467885, + 'coco_vqa_instruct': 0.06421779912617889, + "ocr_vqa_instruct": 0.07924532498245215, + "ok_vqa_instruct": 0.007513571880526308, + "aok_vqa_instruct": 0.010338243231923557, + 'llava150k': 0.049705943639721646} + + max_len: 80 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna13b/image" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/image_train_continue.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/image_train_continue.yaml new file mode 100644 index 0000000000000000000000000000000000000000..248caa8430894faad5a63dd659c8ebe85c455f5b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/image_train_continue.yaml @@ -0,0 +1,542 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: /export/home/LAVIS/lavis/output/new_training/vicuna13b/image/20230915215/checkpoint_880000.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null # https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: null + pretrained_pc_qformer: null + pretrained_video_qformer: null + pretrained_audio_qformer: null + load_attention_image_qformer: True + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "image" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "image" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/export/home/LAVIS/_pretrained_models/vicuna-13b" + prompt: "describe the image." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ## CAPTIONING TASKS + conceptual_caption_12m_instruct: # 6029862 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/home/.cache/lavis/clean_cc12m_org.json + storage: + - /export/home/.cache/lavis/clean_cc12m_org.json + images: + storage: /export/share/datasets/vision_language/cc12m_resize + + coco_caption_instruct: # 566747 train examples + dataset_card: dataset_card/coco_caption.md + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + val: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + md5: b273847456ef5580e33713b1f7de52a0 + storage: coco/annotations/coco_karpathy_val.json + test: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + + capfilt14m_instruct: # 13873136 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/share/datasets/vision_language/capfilt_14m_new/annotation.json + storage: + - /export/share/datasets/vision_language/capfilt_14m_new/annotation.json + images: + storage: /export/share/datasets/vision/coco/images + + vg_caption_instruct: # 821774 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/visual_genome/vg_caption.json + storage: vg/annotations/vg_caption.json + images: + storage: /export/share/datasets/vision/visual-genome/ #vg/images/ + + sbu_caption_instruct: # 859739 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/sbu/sbu.json + # - /export/share/dongxuli/data/lavis/sbu/annotation/sbu.json + storage: + - sbu_captions/annotations/sbu.json + images: + # storage: sbu_captions/images + storage: /export/share/datasets/vision_language/sbu_resize + + + ## QA TASKS + vg_vqa_instruct: # 1440069 train examples + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: qa + modality: image + eval: + name: blip_question + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/visual_genome/vg_qa.json + storage: vg/annotations/vg_qa.json + images: + storage: /export/share/datasets/vision/visual-genome/ #vg/images/ + + coco_vqa_instruct: # 658104 training data + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/vqa_train.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/vqa_val.json + storage: + - coco/annotations/vqa_train.json + - coco/annotations/vqa_val.json + # val: + # url: + # # TODO make this order insensitive + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/vqa_val_eval.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/answer_list.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/v2_OpenEnded_mscoco_val2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/v2_mscoco_val2014_annotations.json + # storage: + # - coco/annotations/vqa_val_eval.json + # - coco/annotations/answer_list.json + # - coco/annotations/v2_OpenEnded_mscoco_val2014_questions.json + # - coco/annotations/v2_mscoco_val2014_annotations.json + # test: + # url: + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/vqa_test.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/answer_list.json + # storage: + # - coco/annotations/vqa_test.json + # - coco/annotations/answer_list.json + images: + storage: /export/share/datasets/vision/coco/images + + ocr_vqa_instruct: # 1002146 train examples + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/video-language-dataset/ocrvqa/ocrvqa.json + storage: + - /export/video-language-dataset/ocrvqa/ocrvqa.json + images: + storage: /export/video-language-dataset/ocrvqa/images/ + + ok_vqa_instruct: # 9009 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_train2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_train2014_annotations.json + storage: + - okvqa/annotations/okvqa_train.json + # - okvqa/annotations/OpenEnded_mscoco_train2014_questions.json + # - okvqa/annotations/mscoco_train2014_annotations.json + # test: + # url: + # # TODO make this order insensitive + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_val_eval.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_answer_list_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_val2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_val2014_annotations.json + # storage: + # - okvqa/annotations/vqa_val_eval.json + # - okvqa/annotations/answer_list.json + # - okvqa/annotations/OpenEnded_mscoco_val2014_questions.json + # - okvqa/annotations/mscoco_val2014_annotations.json + images: + storage: /export/share/datasets/vision/coco/images + + aok_vqa_instruct: # 17056 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/aokvqa/aokvqa_v1p0_train.json + storage: + - aokvqa/annotations/aokvqa_v1p0_train.json + # val: + # url: + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/aokvqa/aokvqa_v1p0_val.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/aokvqa/specialized_vocab_train.json + # storage: + # - aokvqa/annotations/aokvqa_v1p0_val.json + # - aokvqa/annotations/specialized_vocab_train_lavis.json + # # - aokvqa/annotations/large_vocab_train_lavis.json + # test: + # url: + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/aokvqa/aokvqa_v1p0_test.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/aokvqa/specialized_vocab_train.json + # storage: + # - aokvqa/annotations/aokvqa_v1p0_test.json + # - aokvqa/annotations/specialized_vocab_train_lavis.json + images: + storage: /export/share/datasets/vision/coco/images + + ##Dialogue + llava150k_dialogue_instruct: #394276 train examples + + data_type: images + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + + build_info: + annotations: + train: + url: + - https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/resolve/main/llava_instruct_150k.json + storage: + - /export/home/LLaVA-Instruct-150K/lava_instruct_150k.json + # Be careful not to append minus sign (-) before split to avoid itemizing + images: + storage: /export/share/datasets/vision/coco/images/train2017 + +# laion400M_instruct: +# data_type: images + +# vis_processor: +# train: +# name: "clip_image_train" +# image_size: 224 +# eval: +# name: "clip_image_eval" +# image_size: 224 + + +# text_processor: +# train: +# name: blip_instruction +# modality: image +# task: caption +# eval: +# name: blip_caption + +# build_info: +# # Be careful not to append minus sign (-) before split to avoid itemizing +# storage: /export/laion400m-data-ssd/laion115m_capfilt_20220817/{part0/part0,part1/part1,part2/part2}_node{00..15}_shard{000000..000118}.tar +# # storage: /export/laion/laion2B-multi/part-00000/{00000..01743}.tar + + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 2 + batch_size_eval: 2 + num_workers: 8 + accum_grad_iters: 2 + max_iters: 40000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"conceptual_caption_12m_instruct": 0.19438459253859763, + "coco_caption_instruct": 3.0, #0.05959403162103753, + "capfilt14m_instruct": 0.29484615861022884, + "vg_caption_instruct": 0.0717603173049719, + 'sbu_caption_instruct': 0.07339922359647665, + 'vg_vqa_instruct': 0.094994793467885, + 'coco_vqa_instruct': 0.06421779912617889, + "ocr_vqa_instruct": 0.07924532498245215, + "ok_vqa_instruct": 0.007513571880526308, + "aok_vqa_instruct": 0.010338243231923557, + 'llava150k_dialogue_instruct': 0.049705943639721646,} + # 'laion400M_instruct': 0.1} + + max_len: 80 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna13b/image" + + amp: True + resume_ckpt_path: null #/export/home/LAVIS/lavis/output/new_training/vicuna13b/image/20230915215/checkpoint_880000.pth (Cuda OOM) + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/pc_training.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/pc_training.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e42a83571a5257b062c3cae6977a68e4827f4824 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/pc_training.yaml @@ -0,0 +1,170 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + prompt: describe the 3d model. + + data_type: [pc] # [images|pc] + + + objaverse_mm_qa: # name of the dataset builder 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 4 + batch_size_eval: 4 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 355000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"objaverse_mm_caption_instruct": 0.6174707510524334, "objaverse_mm_qa": 0.38252924894756646 } + caption_key: 'data' + sample_id_key: sample_id + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna13b/pc" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/video_training.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/video_training.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f00b43b1a965edcbc6eae2b4feb746458c0e0bb4 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna13b/video_training.yaml @@ -0,0 +1,202 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: True + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "image" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "image" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "image" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "describe the video." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + + +datasets: + msrvtt_caption_instruct: #13260 + # data_dir: ${env.data_dir}/datasets + data_type: videos # [images|videos|features] + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: caption + modality: video + eval: + name: blip_caption + prompt: Describe the video. + + + msrvtt_qa_instruct: # 149075 + data_type: videos + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: qa + modality: video + eval: + name: blip_question + + + webvid2m_caption_instruct: # 2m + data_type: images + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_instruction" + modality: video + task: caption + eval: + name: "blip_caption" + + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 1 + batch_size_eval: 1 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 25000 + iters_per_inner_epoch: 1000 + report_metric: False + train_dataset_ratios: {"msrvtt_caption_instruct": 1., "msrvtt_qa_instruct": 0.17864922797406588 , "webvid2m_caption_instruct": 0.6543554630478173} + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna13b/video" + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/audio_training.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/audio_training.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f751dbee721a6cc8a06a59d5ea7816d11b91991b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/audio_training.yaml @@ -0,0 +1,215 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the audio." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ## CAPTIONING TASKS + audiocaps_mm_caption_instruct: # 38701 train examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + build_info: + kwargs: + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + + + audiocaps_mm_qa: # 24158 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: True + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + build_info: + kwargs: + add_binary: True + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + + + ## Classification like caption + audioset_mm_caption_instruct: # 14141 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + wavcaps_mm_caption_instruct: # 297341 examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + eval: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 8 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"audiocaps_mm_caption_instruct": 0.19355908897689844, "audiocaps_mm_qa": 0.1529265758175233, "wavcaps_mm_caption_instruct": 0.5365125368594943, "audioset_mm_caption_instruct": 0.11700179834608398} + + max_len: 30 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/audio" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/audio_training_improved.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/audio_training_improved.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cbbc53a2a9432d1f37fb8ec04decd8d1d688da51 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/audio_training_improved.yaml @@ -0,0 +1,216 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the audio." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "USER: " + postfix: "\nASSISTANT:" + predict_with_gen: False + clean_tokenization: True + +datasets: + ## CAPTIONING TASKS + audiocaps_mm_caption_instruct: # 38701 train examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + build_info: + kwargs: + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + + + audiocaps_mm_qa: # 24158 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: True + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + build_info: + kwargs: + add_binary: True + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + + + ## Classification like caption + audioset_mm_caption_instruct: # 14141 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + wavcaps_mm_caption_instruct: # 297341 examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + eval: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 8 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"audiocaps_mm_caption_instruct": 0.19355908897689844, "audiocaps_mm_qa": 0.1529265758175233, "wavcaps_mm_caption_instruct": 0.5365125368594943, "audioset_mm_caption_instruct": 0.11700179834608398} + + max_len: 30 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/audio_improved" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/audio_training_no_init.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/audio_training_no_init.yaml new file mode 100644 index 0000000000000000000000000000000000000000..db4ae5a2f332de5128d3657d1741b179f67fbdca --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/audio_training_no_init.yaml @@ -0,0 +1,282 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: null + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: False + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the audio." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ## CAPTIONING TASKS + audiocaps_mm_caption_instruct: # 38701 train examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + build_info: + kwargs: + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + + + audiocaps_mm_qa: # 24158 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: True + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + build_info: + kwargs: + add_binary: True + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + + + ## Classification like caption + audioset_mm_caption_instruct: # 14141 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + wavcaps_mm_caption_instruct: # 297341 examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + eval: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + + audiocaps_mm_qa: # 24158 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: True + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + + ## Classification like caption + audioset_mm_caption_instruct: # 14141 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + wavcaps_mm_caption_instruct: # 297341 examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + eval: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + + + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 8 + batch_size_eval: 8 + num_workers: 0 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"audiocaps_mm_caption_instruct": 0.19355908897689844, "audiocaps_mm_qa": 0.1529265758175233, "wavcaps_mm_caption_instruct": 0.5365125368594943, "audioset_mm_caption_instruct": 0.11700179834608398} + + max_len: 30 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/audio" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/audio_training_projection_only.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/audio_training_projection_only.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0a207ddaa36797d51c7149f60ed6dd6b620b0384 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/audio_training_projection_only.yaml @@ -0,0 +1,218 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: null + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "audio" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the audio." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: False + llm_text_input: True + modalities : ["audio"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + proj_dim: 768 + +datasets: + ## CAPTIONING TASKS + audiocaps_mm_caption_instruct: # 38701 train examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + build_info: + kwargs: + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + + + audiocaps_mm_qa: # 24158 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: True + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + build_info: + kwargs: + add_binary: True + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + + + ## Classification like caption + audioset_mm_caption_instruct: # 14141 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + wavcaps_mm_caption_instruct: # 297341 examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + eval: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 4 + batch_size_eval: 8 + num_workers: 10 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"audiocaps_mm_caption_instruct": 0.19355908897689844, "audiocaps_mm_qa": 0.1529265758175233, "wavcaps_mm_caption_instruct": 0.5365125368594943, "audioset_mm_caption_instruct": 0.11700179834608398} + + max_len: 30 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/audio" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/audio_training_projection_only_nocue.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/audio_training_projection_only_nocue.yaml new file mode 100644 index 0000000000000000000000000000000000000000..58c84f21cfd3c40da5afdb93ca1a067c4fceffed --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/audio_training_projection_only_nocue.yaml @@ -0,0 +1,218 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: null + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "audio" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the audio." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: False + llm_text_input: True + modalities : ["audio"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + proj_dim: 1 + +datasets: +datasets: + ## CAPTIONING TASKS + audiocaps_mm_caption_instruct: # 38701 train examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + build_info: + kwargs: + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + + + audiocaps_mm_qa: # 24158 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: True + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + build_info: + kwargs: + add_binary: True + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + + + ## Classification like caption + audioset_mm_caption_instruct: # 14141 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + wavcaps_mm_caption_instruct: # 297341 examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + eval: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 4 + batch_size_eval: 8 + num_workers: 10 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"audiocaps_mm_caption_instruct": 0.19355908897689844, "audiocaps_mm_qa": 0.1529265758175233, "wavcaps_mm_caption_instruct": 0.5365125368594943, "audioset_mm_caption_instruct": 0.11700179834608398} + + max_len: 30 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/audio" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + caption_key: caption + sample_id_key: youtube_id + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/image_train.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/image_train.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dbb8408050f3a1ebba86b369ba868ffce302db9b --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/image_train.yaml @@ -0,0 +1,346 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: True + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "vision" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the image." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ## CAPTIONING TASKS + conceptual_caption_12m_instruct: # 6029862 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + + coco_caption_instruct: # 566747 train examples + dataset_card: dataset_card/coco_caption.md + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + + capfilt14m_instruct: # 13873136 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + + + vg_caption_instruct: # 821774 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + + sbu_caption_instruct: # 859739 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + + + ## QA TASKS + vg_vqa_instruct: # 1440069 train examples + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: qa + modality: image + eval: + name: blip_question + + + coco_vqa_instruct: # 658104 training data + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_caption + + + ocr_vqa_instruct: # 1002146 train examples + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + + ok_vqa_instruct: # 9009 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + + aok_vqa_instruct: # 17056 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + ##Dialogue + llava150k_dialogue_instruct: #394276 train examples + + data_type: images + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 8 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"conceptual_caption_12m_instruct": 0.19438459253859763, + "coco_caption_instruct": 0.05959403162103753, + "capfilt14m_instruct": 0.29484615861022884, + "vg_caption_instruct": 0.0717603173049719, + 'sbu_caption_instruct': 0.07339922359647665, + 'vg_vqa_instruct': 0.094994793467885, + 'coco_vqa_instruct': 0.06421779912617889, + "ocr_vqa_instruct": 0.07924532498245215, + "ok_vqa_instruct": 0.007513571880526308, + "aok_vqa_instruct": 0.010338243231923557, + 'llava150k_dialogue_instruct': 0.049705943639721646} + # 'laion400M_instruct': 0.1} + + max_len: 80 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/image" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/image_train_improved.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/image_train_improved.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6633f8508fa23cfb866caba212871e2975682df2 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/image_train_improved.yaml @@ -0,0 +1,346 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: True + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "vision" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the image." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + predict_with_gen: False + prefix: "USER: " + postfix: "\nASSISTANT:" + clean_tokenization: True + +datasets: + ## CAPTIONING TASKS + conceptual_caption_12m_instruct: # 6029862 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + + coco_caption_instruct: # 566747 train examples + dataset_card: dataset_card/coco_caption.md + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + + capfilt14m_instruct: # 13873136 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + + vg_caption_instruct: # 821774 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + + sbu_caption_instruct: # 859739 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + + + ## QA TASKS + vg_vqa_instruct: # 1440069 train examples + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: qa + modality: image + eval: + name: blip_question + + + coco_vqa_instruct: # 658104 training data + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_caption + + + ocr_vqa_instruct: # 1002146 train examples + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + + ok_vqa_instruct: # 9009 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + + aok_vqa_instruct: # 17056 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + ##Dialogue + llava150k_dialogue_instruct: #394276 train examples + + data_type: images + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 8 + batch_size_eval: 8 + num_workers: 10 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"conceptual_caption_12m_instruct": 0.19438459253859763, + "coco_caption_instruct": 0.05959403162103753, + "capfilt14m_instruct": 0.29484615861022884, + "vg_caption_instruct": 0.0717603173049719, + 'sbu_caption_instruct': 0.07339922359647665, + 'vg_vqa_instruct': 0.094994793467885, + 'coco_vqa_instruct': 0.06421779912617889, + "ocr_vqa_instruct": 0.07924532498245215, + "ok_vqa_instruct": 0.007513571880526308, + "aok_vqa_instruct": 0.010338243231923557, + 'llava150k_dialogue_instruct': 0.049705943639721646} + # 'laion400M_instruct': 0.1} + + max_len: 80 + min_len: 1 + num_beams: 5 + + seed: 22 + output_dir: "output/xinstructblip/train/vicuna7b/image" + + amp: True + resume_ckpt_path: null + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/image_train_no_init.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/image_train_no_init.yaml new file mode 100644 index 0000000000000000000000000000000000000000..896082d69b00ac6a277868c5aec155aae56cc73f --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/image_train_no_init.yaml @@ -0,0 +1,346 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: null + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "image" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: False + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the image." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ## CAPTIONING TASKS + conceptual_caption_12m_instruct: # 6029862 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + + coco_caption_instruct: # 566747 train examples + dataset_card: dataset_card/coco_caption.md + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + + capfilt14m_instruct: # 13873136 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + + + vg_caption_instruct: # 821774 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + + sbu_caption_instruct: # 859739 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + + + ## QA TASKS + vg_vqa_instruct: # 1440069 train examples + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: qa + modality: image + eval: + name: blip_question + + + coco_vqa_instruct: # 658104 training data + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_caption + + + ocr_vqa_instruct: # 1002146 train examples + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + + ok_vqa_instruct: # 9009 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + + aok_vqa_instruct: # 17056 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + ##Dialogue + llava150k_dialogue_instruct: #394276 train examples + + data_type: images + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 8 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"conceptual_caption_12m_instruct": 0.19438459253859763, + "coco_caption_instruct": 0.05959403162103753, + "capfilt14m_instruct": 0.29484615861022884, + "vg_caption_instruct": 0.0717603173049719, + 'sbu_caption_instruct': 0.07339922359647665, + 'vg_vqa_instruct': 0.094994793467885, + 'coco_vqa_instruct': 0.06421779912617889, + "ocr_vqa_instruct": 0.07924532498245215, + "ok_vqa_instruct": 0.007513571880526308, + "aok_vqa_instruct": 0.010338243231923557, + 'llava150k_dialogue_instruct': 0.049705943639721646} + # 'laion400M_instruct': 0.1} + + max_len: 80 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/image" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/image_train_projection_only.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/image_train_projection_only.yaml new file mode 100644 index 0000000000000000000000000000000000000000..39ff791dffd470f7e85c86f1a31588ac60207a52 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/image_train_projection_only.yaml @@ -0,0 +1,347 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: null + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: True + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "vision" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: False + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the image." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + proj_dim: 768 + +datasets: + ## CAPTIONING TASKS + conceptual_caption_12m_instruct: # 6029862 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + + coco_caption_instruct: # 566747 train examples + dataset_card: dataset_card/coco_caption.md + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + + capfilt14m_instruct: # 13873136 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + + vg_caption_instruct: # 821774 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + + sbu_caption_instruct: # 859739 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + + + ## QA TASKS + vg_vqa_instruct: # 1440069 train examples + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: qa + modality: image + eval: + name: blip_question + + + coco_vqa_instruct: # 658104 training data + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_caption + + + ocr_vqa_instruct: # 1002146 train examples + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + + ok_vqa_instruct: # 9009 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + + aok_vqa_instruct: # 17056 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + ##Dialogue + llava150k_dialogue_instruct: #394276 train examples + + data_type: images + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 8 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"conceptual_caption_12m_instruct": 0.19438459253859763, + "coco_caption_instruct": 0.05959403162103753, + "capfilt14m_instruct": 0.29484615861022884, + "vg_caption_instruct": 0.0717603173049719, + 'sbu_caption_instruct': 0.07339922359647665, + 'vg_vqa_instruct': 0.094994793467885, + 'coco_vqa_instruct': 0.06421779912617889, + "ocr_vqa_instruct": 0.07924532498245215, + "ok_vqa_instruct": 0.007513571880526308, + "aok_vqa_instruct": 0.010338243231923557, + 'llava150k_dialogue_instruct': 0.049705943639721646} + # 'laion400M_instruct': 0.1} + + max_len: 80 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/image" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/lora_training.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/lora_training.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5ac85e792cf1d395dc9a2c504b59d75a4b720ccb --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/lora_training.yaml @@ -0,0 +1,926 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + # pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: "" + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + lora: True + modalities : ["image", "pc", "audio", "video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ## IMAGE + ### CAPTIONING TASKS + conceptual_caption_12m_instruct: # 6029862 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + # - https://storage.googleapis.com/sfr-xinstructblip-data-research/data/cc12m/x_instructblip_clean.json + - /export/home/.cache/lavis/clean_cc12m_org.json + storage: + # - cc12m/x-instructblip_cc12m_annotation.json + - /export/home/.cache/lavis/clean_cc12m_org.json + images: + storage: /export/share/datasets/vision_language/cc12m_resize + + coco_caption_instruct: # 566747 train examples + dataset_card: dataset_card/coco_caption.md + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json + md5: aa31ac474cf6250ebb81d18348a07ed8 + storage: coco/annotations/coco_karpathy_train.json + # val: + # url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json + # md5: b273847456ef5580e33713b1f7de52a0 + # storage: coco/annotations/coco_karpathy_val.json + # test: + # url: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json + # md5: 3ff34b0ef2db02d01c37399f6a2a6cd1 + # storage: coco/annotations/coco_karpathy_test.json + images: + storage: /export/share/datasets/vision/coco/images + + capfilt14m_instruct: # 13873136 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/share/datasets/vision_language/capfilt_14m_new/annotation.json + storage: + - /export/share/datasets/vision_language/capfilt_14m_new/annotation.json + images: + storage: /export/share/datasets/vision/coco/images + + vg_caption_instruct: # 821774 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/visual_genome/vg_caption.json + storage: vg/annotations/vg_caption.json + images: + storage: /export/share/datasets/vision/visual-genome/ #vg/images/ + + sbu_caption_instruct: # 859739 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/sbu/sbu.json + # - /export/share/dongxuli/data/lavis/sbu/annotation/sbu.json + storage: + - sbu_captions/annotations/sbu.json + images: + # storage: sbu_captions/images + storage: /export/share/datasets/vision_language/sbu_resize + + + ### QA TASKS + vg_vqa_instruct: # 1440069 train examples + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: qa + modality: image + eval: + name: blip_question + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/visual_genome/vg_qa.json + storage: vg/annotations/vg_qa.json + images: + storage: /export/share/datasets/vision/visual-genome/ #vg/images/ + + coco_vqa_instruct: # 658104 training data + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_caption + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/vqa_train.json + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/vqa_val.json + storage: + - coco/annotations/vqa_train.json + - coco/annotations/vqa_val.json + # val: + # url: + # # TODO make this order insensitive + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/vqa_val_eval.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/answer_list.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/v2_OpenEnded_mscoco_val2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/v2_mscoco_val2014_annotations.json + # storage: + # - coco/annotations/vqa_val_eval.json + # - coco/annotations/answer_list.json + # - coco/annotations/v2_OpenEnded_mscoco_val2014_questions.json + # - coco/annotations/v2_mscoco_val2014_annotations.json + # test: + # url: + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/vqa_test.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/vqav2/answer_list.json + # storage: + # - coco/annotations/vqa_test.json + # - coco/annotations/answer_list.json + images: + storage: /export/share/datasets/vision/coco/images + + ocr_vqa_instruct: # 1002146 train examples + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/video-language-dataset/ocrvqa/ocrvqa.json + storage: + - /export/video-language-dataset/ocrvqa/ocrvqa.json + images: + storage: /export/video-language-dataset/ocrvqa/images/ + + ok_vqa_instruct: # 9009 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + # TODO make this order insensitive + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_train2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_train2014_annotations.json + storage: + - okvqa/annotations/okvqa_train.json + # - okvqa/annotations/OpenEnded_mscoco_train2014_questions.json + # - okvqa/annotations/mscoco_train2014_annotations.json + # test: + # url: + # # TODO make this order insensitive + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_val_eval.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_answer_list_train.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/OpenEnded_mscoco_val2014_questions.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/mscoco_val2014_annotations.json + # storage: + # - okvqa/annotations/vqa_val_eval.json + # - okvqa/annotations/answer_list.json + # - okvqa/annotations/OpenEnded_mscoco_val2014_questions.json + # - okvqa/annotations/mscoco_val2014_annotations.json + images: + storage: /export/share/datasets/vision/coco/images + + aok_vqa_instruct: # 17056 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/aokvqa/aokvqa_v1p0_train.json + storage: + - aokvqa/annotations/aokvqa_v1p0_train.json + # val: + # url: + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/aokvqa/aokvqa_v1p0_val.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/aokvqa/specialized_vocab_train.json + # storage: + # - aokvqa/annotations/aokvqa_v1p0_val.json + # - aokvqa/annotations/specialized_vocab_train_lavis.json + # # - aokvqa/annotations/large_vocab_train_lavis.json + # test: + # url: + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/aokvqa/aokvqa_v1p0_test.json + # - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/aokvqa/specialized_vocab_train.json + # storage: + # - aokvqa/annotations/aokvqa_v1p0_test.json + # - aokvqa/annotations/specialized_vocab_train_lavis.json + images: + storage: /export/share/datasets/vision/coco/images + + ##Dialogue + llava150k_dialogue_instruct: #394276 train examples + + data_type: images + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + + build_info: + annotations: + train: + url: + - https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/resolve/main/llava_instruct_150k.json + storage: + - /export/home/LLaVA-Instruct-150K/lava_instruct_150k.json + # Be careful not to append minus sign (-) before split to avoid itemizing + images: + storage: /export/share/datasets/vision/coco/images/train2017 + + ## AUDIO + audiocaps_mm_caption_instruct: # 38701 train examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + build_info: + kwargs: + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + annotations: + train: + url: + - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/train.csv + storage: + - audiocaps/annotations/train.csv + + # val: + # url: + # - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/val.csv + # storage: + # - audiocaps/annotation/val.csv + + # test: + # url: + # - https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/test.csv + # storage: + # - /export/einstein-vision/audio_datasets/audiocaps/dataset/test.csv + + audio: + storage: /export/einstein-vision/audio_datasets/audiocaps/AUDIOCAPS_32000Hz/audio + + + audiocaps_mm_qa: # 24158 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: True + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + build_info: + kwargs: + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + annotations: + train: + url: + - /export/home/LAVIS/projects/mm_instructblip/instr_data_creation/audio_qa/audio_qa_final_train.csv + storage: + - /export/home/LAVIS/projects/mm_instructblip/instr_data_creation/audio_qa/audio_qa_final_train.csv + + # val: + # url: + # - /export/home/LAVIS/projects/mm_instructblip/instr_data_creation/audio_qa/audio_qa_final_val.csv + # storage: + # - /export/home/LAVIS/projects/mm_instructblip/instr_data_creation/audio_qa/audio_qa_final_val.csv + + audio: + storage: /export/einstein-vision/audio_datasets/audiocaps/AUDIOCAPS_32000Hz/audio + + + ## Classification like caption + audioset_mm_caption_instruct: # 14141 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + cmu_dict_path: /export/home/LAVIS/cmu_dict.p + eval: + name: blip_caption + + data_type: [audio] + + build_info: + annotations: + train: + url: + - /export/home/LAVIS-xgen_mm/lavis/configs/datasets/audioset/balanced_train_clean.csv + - http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/class_labels_indices.csv + storage: + - /export/home/LAVIS-xgen_mm/lavis/configs/datasets/audioset/balanced_train_clean.csv + - audioset/annotations/class_labels_indices.csv + # val: + # url: + # - http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/eval_segments.csv + # - http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/class_labels_indices.csv + # storage: + # - audioset/annotations/eval_segments.csv + # - audioset/annotations/class_labels_indices.csv + audio: + storage: /export/einstein-vision/audio_datasets/AudioSet/all_audio + + wavcaps_mm_caption_instruct: # 297341 examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + eval: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + build_info: + kwargs: + json_data: /export/share/datasets/audio/WavCaps/json_data.json + + cached: False + cached_dir: /export/share/datasets/audio/WavCaps/beats_features/ + + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/share/datasets/audio/WavCaps/json_files/BBC_Sound_Effects/bbc_final.json + - /export/share/datasets/audio/WavCaps/json_files/FreeSound/fsd_final.json + - /export/share/datasets/audio/WavCaps/json_files/SoundBible/sb_final.json + - /export/share/datasets/audio/WavCaps/json_files/AudioSet_SL/as_final.json + - /export/share/datasets/audio/WavCaps/json_data.json + storage: + - /export/share/datasets/audio/WavCaps/json_files/BBC_Sound_Effects/bbc_final.json + - /export/share/datasets/audio/WavCaps/json_files/FreeSound/fsd_final.json + - /export/share/datasets/audio/WavCaps/json_files/SoundBible/sb_final.json + - /export/share/datasets/audio/WavCaps/json_files/AudioSet_SL/as_final.json + - /export/share/datasets/audio/WavCaps/json_data.json + + audio: + storage: /export/share/datasets/audio/WavCaps/ + + ## PC DATASETS + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + prompt: describe the 3d model. + + data_type: [pc] # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + storage: + - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_train.json + + # val: + # url: + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + # storage: + # - /export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_val.json + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + images: + storage: /export/einstein-vision/3d_vision/objaverse_captions/images/ + + objaverse_mm_qa: # name of the dataset builder 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + data_type: pc # [images|pc] + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: + - /export/home/LAVIS/projects/mm_instructblip/instr_data_creation/3d_qa/CAP3DQA_final.csv + storage: + - /export/home/LAVIS/projects/mm_instructblip/instr_data_creation/3d_qa/CAP3DQA_final.csv + # val: + # url: + # - /export/home/LAVIS/projects/mm_instructblip/instr_data_creation/3d_qa/CAP3DQA_final_val.csv + # storage: + # - /export/home/LAVIS/projects/mm_instructblip/instr_data_creation/3d_qa/CAP3DQA_final_val.csv + + templates: null + + pc: + storage: /export/einstein-vision/3d_vision/objaverse/objaverse_pc_parallel + + + msrvtt_caption_instruct: #13260 + # data_dir: ${env.data_dir}/datasets + data_type: videos # [images|videos|features] + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: caption + modality: video + eval: + name: blip_caption + prompt: Describe the video. + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_train.json + storage: msrvtt/annotations/cap_train.json + # val: + # url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_val.json + # storage: msrvtt/annotations/cap_val.json + # test: + # url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/cap_test.json + # storage: msrvtt/annotations/cap_test.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + + msrvtt_qa_instruct: # 149075 + data_type: videos + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: qa + modality: video + eval: + name: blip_question + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_train.json + storage: msrvtt/annotations/qa_train.json + # val: + # url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_val.json + # storage: msrvtt/annotations/qa_val.json + # test: + # url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/qa_test.json + # storage: msrvtt/annotations/qa_test.json + # ans2label: + # url: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/msrvtt/train_ans2label.json + # storage: msrvtt/annotations/qa_ans2label.json + videos: + storage: /export/share/datasets/vision_language/msrvtt/videos + + webvid2m_caption_instruct: # 2m + data_type: images + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_instruction" + modality: video + task: caption + eval: + name: "blip_caption" + + build_info: + # Be careful not to append minus sign (-) before split to avoid itemizing + annotations: + train: + url: /export/home/LAVIS/webvid_annotation.json + storage: /export/home/LAVIS/webvid_annotation.json + images: + storage: /export/video-language-dataset/data/webvid2m/postprocess/downsampled_videos + + + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 16 + batch_size_eval: 2 + num_workers: 0 + accum_grad_iters: 1 + max_iters: 100000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {'audiocaps_mm_caption_instruct': 0.04838977224422461, 'audiocaps_mm_qa': 0.03823164395438083, 'wavcaps_mm_caption_instruct': 0.13412813421487357, 'audioset_mm_caption_instruct': 0.029250449586520996, 'objaverse_mm_caption_instruct': 0.15436768776310836, 'objaverse_mm_qa': 0.09563231223689161, 'msrvtt_caption_instruct': 0.075, 'msrvtt_qa_instruct': 0.04466230699351647, 'webvid2m_caption_instruct': 0.16358886576195433, 'conceptual_caption_12m_instruct': 0.04859614813464941, 'coco_caption_instruct': 0.014898507905259383, 'capfilt14m_instruct': 0.07371153965255721, 'vg_caption_instruct': 0.017940079326242975, 'sbu_caption_instruct': 0.01834980589911916, 'vg_vqa_instruct': 0.02374869836697125, 'coco_vqa_instruct': 0.016054449781544723, 'ocr_vqa_instruct': 0.01981133124561304, 'ok_vqa_instruct': 0.001878392970131577, 'aok_vqa_instruct': 0.0025845608079808893, 'llava150k_dialogue_instruct': 0.012426485909930412} + # train_dataset_ratios: { + # "audiocaps_mm_caption_instruct": 0.19355908897689844, + # "audiocaps_mm_qa": 0.1529265758175233, + # "wavcaps_mm_caption_instruct": 0.5365125368594943, + # "audioset_mm_caption_instruct": 0.11700179834608398, + # "objaverse_mm_caption_instruct": 0.6174707510524334, + # "objaverse_mm_qa": 0.38252924894756646, + # "msrvtt_caption_instruct": .3, + # "msrvtt_qa_instruct": 0.17864922797406588 , + # "webvid2m_caption_instruct": 0.6543554630478173, + # "conceptual_caption_12m_instruct": 0.19438459253859763, + # "coco_caption_instruct": 0.05959403162103753, + # "capfilt14m_instruct": 0.29484615861022884, + # "vg_caption_instruct": 0.0717603173049719, + # 'sbu_caption_instruct': 0.07339922359647665, + # 'vg_vqa_instruct': 0.094994793467885, + # 'coco_vqa_instruct': 0.06421779912617889, + # "ocr_vqa_instruct": 0.07924532498245215, + # "ok_vqa_instruct": 0.007513571880526308, + # "aok_vqa_instruct": 0.010338243231923557, + # 'llava150k_dialogue_instruct': 0.049705943639721646 + # } + + max_len: 30 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/lora" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + # caption_key: caption + # sample_id_key: youtube_id + # annotation_file: /export/home/.cache/lavis/audiocaps_mm_caption_instruct_gt/audiocaps_mm_caption_instruct_val_annotations.json + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6f97ce1c20dc76555d3a20accf414ee80775274d --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training.yaml @@ -0,0 +1,168 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + prompt: describe the 3d model. + + data_type: [pc] # [images|pc] + + + objaverse_mm_qa: # name of the dataset builder 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 16 + batch_size_eval: 16 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"objaverse_mm_caption_instruct": 0.6174707510524334, "objaverse_mm_qa": 0.38252924894756646 } + caption_key: 'data' + sample_id_key: sample_id + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/pc" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_improved.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_improved.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b875871bef506e602a732e1859ff3d0fdd7f71ba --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_improved.yaml @@ -0,0 +1,171 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + clean_tokenization: True + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + prompt: describe the 3d model. + + data_type: [pc] # [images|pc] + + + objaverse_mm_qa: # name of the dataset builder 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 16 + batch_size_eval: 16 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"objaverse_mm_caption_instruct": 0.6174707510524334, "objaverse_mm_qa": 0.38252924894756646 } + caption_key: 'data' + sample_id_key: sample_id + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/pc" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_no_init.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_no_init.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0806643474b0955705291af5238707dec1736313 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_no_init.yaml @@ -0,0 +1,169 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: null + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: False + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + prompt: describe the 3d model. + + data_type: [pc] # [images|pc] + + + objaverse_mm_qa: # name of the dataset builder 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 16 + batch_size_eval: 16 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"objaverse_mm_caption_instruct": 0.6174707510524334, "objaverse_mm_qa": 0.38252924894756646 } + caption_key: 'data' + sample_id_key: sample_id + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/pc" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_projection_only.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_projection_only.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e8a1b6f6bfec79276aee052c33886f556b1b0bfd --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_projection_only.yaml @@ -0,0 +1,171 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: "" + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: False + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: False + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + proj_dim: 768 + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + prompt: describe the 3d model. + + data_type: [pc] # [images|pc] + + + objaverse_mm_qa: # name of the dataset builder 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 16 + batch_size_eval: 16 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"objaverse_mm_caption_instruct": 0.6174707510524334, "objaverse_mm_qa": 0.38252924894756646 } + caption_key: 'data' + sample_id_key: sample_id + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/pc" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_projection_only_nocue.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_projection_only_nocue.yaml new file mode 100644 index 0000000000000000000000000000000000000000..23e72ade0b3147afae30a159131bf47cb5216f63 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_projection_only_nocue.yaml @@ -0,0 +1,171 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: "" + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: False + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: False + llm_text_input: True + modalities : ["pc"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + projection_only: True + proj_dim: 768 + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + prompt: describe the 3d model. + + data_type: [pc] # [images|pc] + + + objaverse_mm_qa: # name of the dataset builder 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 16 + batch_size_eval: 16 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"objaverse_mm_caption_instruct": 0.6174707510524334, "objaverse_mm_qa": 0.38252924894756646 } + caption_key: 'data' + sample_id_key: sample_id + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/pc" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_scaled_up.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_scaled_up.yaml new file mode 100644 index 0000000000000000000000000000000000000000..487659cbdb25bab4d6acb2d7912857ccd75a1153 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_scaled_up.yaml @@ -0,0 +1,168 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_scaledup" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + prompt: describe the 3d model. + + data_type: [pc] # [images|pc] + + + objaverse_mm_qa: # name of the dataset builder 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + data_type: pc # [images|pc] + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 16 + batch_size_eval: 16 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"objaverse_mm_caption_instruct": 0.6174707510524334, "objaverse_mm_qa": 0.38252924894756646 } + caption_key: 'data' + sample_id_key: sample_id + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/pc" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_ulip1.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_ulip1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9b17f1e90b771af9e850f65468d587fee44c8072 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_ulip1.yaml @@ -0,0 +1,169 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip1_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + prompt: describe the 3d model. + + data_type: [pc] # [images|pc] + + + objaverse_mm_qa: # name of the dataset builder 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 16 + batch_size_eval: 16 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"objaverse_mm_caption_instruct": 0.6174707510524334, "objaverse_mm_qa": 0.38252924894756646 } + caption_key: 'data' + sample_id_key: sample_id + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/pc_ulip1" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_ulip2_objaverse_shapenet_k_1.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_ulip2_objaverse_shapenet_k_1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dbef54bb6727c0f9ded80b217c404d6e1c2fbc6e --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_ulip2_objaverse_shapenet_k_1.yaml @@ -0,0 +1,168 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "objaverse_shapenet_k_1" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + prompt: describe the 3d model. + + data_type: [pc] # [images|pc] + + + objaverse_mm_qa: # name of the dataset builder 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 16 + batch_size_eval: 16 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"objaverse_mm_caption_instruct": 0.6174707510524334, "objaverse_mm_qa": 0.38252924894756646 } + caption_key: 'data' + sample_id_key: sample_id + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/pc_ulip_objaverse_shapenet_k_1" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_ulip_objaverse.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_ulip_objaverse.yaml new file mode 100644 index 0000000000000000000000000000000000000000..133eabc3c8c138ccc2fd742483762f928b2c7d1e --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_ulip_objaverse.yaml @@ -0,0 +1,168 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip_objaverse" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + prompt: describe the 3d model. + + data_type: [pc] # [images|pc] + + + objaverse_mm_qa: # name of the dataset builder 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 16 + batch_size_eval: 16 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"objaverse_mm_caption_instruct": 0.6174707510524334, "objaverse_mm_qa": 0.38252924894756646 } + caption_key: 'data' + sample_id_key: sample_id + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/pc_ulip_objaverse" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_ulip_shapenet.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_ulip_shapenet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b23eb1934e061b3e9f72b8cf6bf591c4808d4480 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/pc_training_ulip_shapenet.yaml @@ -0,0 +1,167 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip_shapenet" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + prompt: describe the 3d model. + + data_type: [pc] # [images|pc] + + + objaverse_mm_qa: # name of the dataset builder 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 16 + batch_size_eval: 16 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"objaverse_mm_caption_instruct": 0.6174707510524334, "objaverse_mm_qa": 0.38252924894756646 } + caption_key: 'data' + sample_id_key: sample_id + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/pc_ulip_shapenet" + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/video_training.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/video_training.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1da49427fc2abad104e056c0a4083786044bd649 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/video_training.yaml @@ -0,0 +1,201 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: True + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "image" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "image" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "image" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the video." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + + +datasets: + msrvtt_caption_instruct: #13260 + # data_dir: ${env.data_dir}/datasets + data_type: videos # [images|videos|features] + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: caption + modality: video + eval: + name: blip_caption + prompt: Describe the video. + + + msrvtt_qa_instruct: # 149075 + data_type: videos + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: qa + modality: video + eval: + name: blip_question + + + webvid2m_caption_instruct: # 2m + data_type: images + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_instruction" + modality: video + task: caption + eval: + name: "blip_caption" + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 4 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 15000 + iters_per_inner_epoch: 1000 + report_metric: False + train_dataset_ratios: {"msrvtt_caption_instruct": 1., "msrvtt_qa_instruct": 0.17864922797406588 , "webvid2m_caption_instruct": 0.6543554630478173} + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/video" + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/video_training_no_msrvtt_upsample.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/video_training_no_msrvtt_upsample.yaml new file mode 100644 index 0000000000000000000000000000000000000000..deebd0773ed2a1a7471389480714d7bb5c4ae9c2 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b/video_training_no_msrvtt_upsample.yaml @@ -0,0 +1,202 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: True + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "image" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "image" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "image" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the video." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + msrvtt_caption_instruct: #13260 + # data_dir: ${env.data_dir}/datasets + data_type: videos # [images|videos|features] + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: caption + modality: video + eval: + name: blip_caption + prompt: Describe the video. + + + msrvtt_qa_instruct: # 149075 + data_type: videos + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: qa + modality: video + eval: + name: blip_question + + + webvid2m_caption_instruct: # 2m + data_type: images + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_instruction" + modality: video + task: caption + eval: + name: "blip_caption" + + + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 4 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 15000 + iters_per_inner_epoch: 1000 + report_metric: False + train_dataset_ratios: {"msrvtt_caption_instruct": 0.060116940686215065, "msrvtt_qa_instruct": 0.17864922797406588 , "webvid2m_caption_instruct": 0.6543554630478173} + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b/video" + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b_nocue/audio_training.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b_nocue/audio_training.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7f1e6ad75850b624d2194dd542fd674d05b1c1ad --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b_nocue/audio_training.yaml @@ -0,0 +1,217 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the audio." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ## CAPTIONING TASKS + audiocaps_mm_caption_instruct: # 38701 train examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + build_info: + kwargs: + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + + + audiocaps_mm_qa: # 24158 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: True + + text_processor: + train: + name: "blip_instruction" + modality: audio + task: qa + eval: + name: "blip_question" + + data_type: [audio] + + build_info: + kwargs: + add_binary: True + missing_ids: [2sh7ZkazyO8, 966jA2-z0mQ, 52RlolYyjAE, HVAc9hm4jjk, 8lPjqvYWNyM, eXgPnnE3TuQ] + + + ## Classification like caption + audioset_mm_caption_instruct: # 14141 + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + eval: + name: beats_audio + sampling_rate: 16000 + is_eval: False + + text_processor: + train: + name: blip_instruction + modality: audio + task: classification + eval: + name: blip_caption + + data_type: [audio] + + wavcaps_mm_caption_instruct: # 297341 examples + audio_processor: + train: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + eval: + name: beats_audio + sampling_rate: 16000 + n_frames: 2 + frame_length: 512 + text_processor: + train: + name: "blip_instruction" + modality: audio + task: caption + eval: + name: "blip_caption" + + data_type: [audio] + + + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 8 + batch_size_eval: 8 + num_workers: 0 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"audiocaps_mm_caption_instruct": 0.19355908897689844, "audiocaps_mm_qa": 0.1529265758175233, "wavcaps_mm_caption_instruct": 0.5365125368594943, "audioset_mm_caption_instruct": 0.11700179834608398} + caption_key: caption + sample_id_key: youtube_id + + + max_len: 30 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b_nocue/audio" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b_nocue/image_train.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b_nocue/image_train.yaml new file mode 100644 index 0000000000000000000000000000000000000000..447735a8d9f3fc7c7b1a6879f563131972722840 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b_nocue/image_train.yaml @@ -0,0 +1,344 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the image." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + ## CAPTIONING TASKS + conceptual_caption_12m_instruct: # 6029862 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + + coco_caption_instruct: # 566747 train examples + dataset_card: dataset_card/coco_caption.md + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + + capfilt14m_instruct: # 13873136 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + + vg_caption_instruct: # 821774 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: caption + modality: image + eval: + name: blip_caption + + + sbu_caption_instruct: # 859739 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: caption + eval: + name: blip_caption + + + + ## QA TASKS + vg_vqa_instruct: # 1440069 train examples + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + task: qa + modality: image + eval: + name: blip_question + + + coco_vqa_instruct: # 658104 training data + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_caption + + + ocr_vqa_instruct: # 1002146 train examples + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + + ok_vqa_instruct: # 9009 + # data_dir: ${env.data_dir}/datasets + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + + aok_vqa_instruct: # 17056 + data_type: images # [images|videos|features] + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: blip_instruction + modality: image + task: qa + eval: + name: blip_question + + ##Dialogue + llava150k_dialogue_instruct: #394276 train examples + + data_type: images + + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_eval" + image_size: 224 + + text_processor: + train: + name: "blip_caption" + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 8 + batch_size_eval: 8 + num_workers: 10 + accum_grad_iters: 1 + max_iters: 750000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"conceptual_caption_12m_instruct": 0.19438459253859763, + "coco_caption_instruct": 0.3, #0.05959403162103753, + "capfilt14m_instruct": 0.29484615861022884, + "vg_caption_instruct": 0.0717603173049719, + 'sbu_caption_instruct': 0.07339922359647665, + 'vg_vqa_instruct': 0.094994793467885, + 'coco_vqa_instruct': 0.06421779912617889, + "ocr_vqa_instruct": 0.07924532498245215, + "ok_vqa_instruct": 0.007513571880526308, + "aok_vqa_instruct": 0.010338243231923557, + 'llava150k': 0.049705943639721646,} + + max_len: 80 + min_len: 1 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b_nocue/image" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 \ No newline at end of file diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b_nocue/pc_training.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b_nocue/pc_training.yaml new file mode 100644 index 0000000000000000000000000000000000000000..41ccffa12b938a1355670250f0e5b760c6f14b31 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b_nocue/pc_training.yaml @@ -0,0 +1,169 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the 3d model." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["pc"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + +datasets: + objaverse_mm_caption_instruct: # 651576 train examples + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: caption + eval: + name: "blip_caption" + prompt: describe the 3d model. + + data_type: [pc] # [images|pc] + + + objaverse_mm_qa: # name of the dataset builder 250070 + vis_processor: + train: + name: "clip_image_train" + image_size: 224 + eval: + name: "clip_image_train" + image_size: 224 + pc_processor: + train: + name: "ulip_pc" + eval: + name: "ulip_pc" + text_processor: + train: + name: "blip_instruction" + modality: pc + task: qa + eval: + name: "blip_question" + + + data_type: pc # [images|pc] + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 16 + batch_size_eval: 16 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 65000 + iters_per_inner_epoch: 5000 + train_dataset_ratios: {"objaverse_mm_caption_instruct": 0.6174707510524334, "objaverse_mm_qa": 0.38252924894756646 } + caption_key: 'data' + sample_id_key: sample_id + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b_nocue/pc" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b_nocue/video_training.yaml b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b_nocue/video_training.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dab97b8be6e8f5e9c3fee1a067cac2217603bd44 --- /dev/null +++ b/LAVIS-main/lavis/projects/xinstruct_blip/train/vicuna7b_nocue/video_training.yaml @@ -0,0 +1,201 @@ + # Copyright (c) 2023 salesforce.com inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: True + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "image" + load_ln_type_pc: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "image" + load_qformer_type_audio: "" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "" + load_projection_type_pc: "" + load_projection_type_video: "image" + load_projection_type_audio: "" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: /path/to/vicuna-7b + prompt: "describe the video." + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["video"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + predict_with_gen: False + + +datasets: + msrvtt_caption_instruct: #13260 + # data_dir: ${env.data_dir}/datasets + data_type: videos # [images|videos|features] + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: caption + modality: video + eval: + name: blip_caption + prompt: Describe the video. + + + msrvtt_qa_instruct: # 149075 + data_type: videos + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + full_video: True + + text_processor: + train: + name: blip_instruction + task: qa + modality: video + eval: + name: blip_question + + + webvid2m_caption_instruct: # 2m + data_type: images + + vis_processor: + train: + name: alpro_video_train + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + eval: + name: alpro_video_eval + n_frms: 5 + image_size: 224 + min_scale: 0.9 + max_scale: 1.0 + text_processor: + train: + name: "blip_instruction" + modality: video + task: caption + eval: + name: "blip_caption" + +run: + runner: runner_iter + task: captioning + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-5 + min_lr: 0 + warmup_lr: 1e-8 + warmup_steps: 1000 + weight_decay: 0.05 + max_epoch: 40 + batch_size_train: 4 + batch_size_eval: 8 + num_workers: 8 + accum_grad_iters: 1 + max_iters: 15000 + iters_per_inner_epoch: 1000 + report_metric: False + train_dataset_ratios: {"msrvtt_caption_instruct": 1., "msrvtt_qa_instruct": 0.17864922797406588 , "webvid2m_caption_instruct": 0.6543554630478173} + + max_len: 30 + min_len: 8 + num_beams: 5 + + seed: 42 + output_dir: "output/xinstructblip/train/vicuna7b_nocue/video" + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + # valid_splits: ["val"] + + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True + save_freq: 1 # save epoch every xxx epochs -1 only save last and best. + val_freq: 1 diff --git a/LAVIS-main/lavis/runners/__init__.py b/LAVIS-main/lavis/runners/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..38494f1cdf68b0a419c3e0c401871b653a481963 --- /dev/null +++ b/LAVIS-main/lavis/runners/__init__.py @@ -0,0 +1,11 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +from lavis.runners.runner_base import RunnerBase +from lavis.runners.runner_iter import RunnerIter + +__all__ = ["RunnerBase", "RunnerIter"] diff --git a/LAVIS-main/lavis/runners/runner_base.py b/LAVIS-main/lavis/runners/runner_base.py new file mode 100644 index 0000000000000000000000000000000000000000..8eaec8e065b1952b27133b782f59193d309116af --- /dev/null +++ b/LAVIS-main/lavis/runners/runner_base.py @@ -0,0 +1,670 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import datetime +import json +import logging +import os +import time +from pathlib import Path + +import torch +import torch.distributed as dist +import webdataset as wds +from lavis.common.dist_utils import ( + download_cached_file, + get_rank, + get_world_size, + is_main_process, + main_process, +) +from lavis.common.registry import registry +from lavis.common.utils import is_url +from lavis.datasets.data_utils import concat_datasets, reorg_datasets_by_split +from lavis.datasets.datasets.dataloader_utils import ( + IterLoader, + MultiIterLoader, + PrefetchLoader, +) +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.data import DataLoader, DistributedSampler +from torch.utils.data.dataset import ChainDataset + + +@registry.register_runner("runner_base") +class RunnerBase: + """ + A runner class to train and evaluate a model given a task and datasets. + + The runner uses pytorch distributed data parallel by default. Future release + will support other distributed frameworks. + """ + + def __init__(self, cfg, task, model, datasets, job_id): + self.config = cfg + self.job_id = job_id + + self.task = task + self.datasets = datasets + + self._model = model + + self._wrapped_model = None + self._device = None + self._optimizer = None + self._scaler = None + self._dataloaders = None + self._lr_sched = None + + self.start_epoch = 0 + + # self.setup_seeds() + self.setup_output_dir() + + @property + def device(self): + if self._device is None: + self._device = torch.device(self.config.run_cfg.device) + + return self._device + + @property + def use_distributed(self): + return self.config.run_cfg.distributed + + @property + def model(self): + """ + A property to get the DDP-wrapped model on the device. + """ + # move model to device + if self._model.device != self.device: + self._model = self._model.to(self.device) + + # distributed training wrapper + if self.use_distributed: + if self._wrapped_model is None: + self._wrapped_model = DDP( + self._model, device_ids=[self.config.run_cfg.gpu] + ) + else: + self._wrapped_model = self._model + + return self._wrapped_model + + @property + def optimizer(self): + # TODO make optimizer class and configurations + if self._optimizer is None: + lr_scale = self.config.run_cfg.get("lr_layer_decay", 1) + weight_decay = self.config.run_cfg.get("weight_decay", 0.05) + optim_params = self._model.get_optimizer_params(weight_decay,lr_scale) + + num_parameters = 0 + for p_group in optim_params: + for p in p_group["params"]: + num_parameters += p.data.nelement() + logging.info("number of trainable parameters: {}".format(num_parameters)) + + beta2 = self.config.run_cfg.get("beta2", 0.999) + + self._optimizer = torch.optim.AdamW( + optim_params, + lr=float(self.config.run_cfg.init_lr), + betas=(0.9, beta2), + ) + return self._optimizer + + @property + def scaler(self): + amp = self.config.run_cfg.get("amp", False) + + if amp: + if self._scaler is None: + self._scaler = torch.cuda.amp.GradScaler() + + return self._scaler + + @property + def lr_scheduler(self): + """ + A property to get and create learning rate scheduler by split just in need. + """ + if self._lr_sched is None: + lr_sched_cls = registry.get_lr_scheduler_class(self.config.run_cfg.lr_sched) + + # max_epoch = self.config.run_cfg.max_epoch + max_epoch = self.max_epoch + # min_lr = self.config.run_cfg.min_lr + min_lr = self.min_lr + # init_lr = self.config.run_cfg.init_lr + init_lr = self.init_lr + + # optional parameters + decay_rate = self.config.run_cfg.get("lr_decay_rate", None) + warmup_start_lr = self.config.run_cfg.get("warmup_lr", -1) + warmup_steps = self.config.run_cfg.get("warmup_steps", 0) + + self._lr_sched = lr_sched_cls( + optimizer=self.optimizer, + max_epoch=max_epoch, + min_lr=min_lr, + init_lr=init_lr, + decay_rate=decay_rate, + warmup_start_lr=warmup_start_lr, + warmup_steps=warmup_steps, + ) + + return self._lr_sched + + @property + def dataloaders(self) -> dict: + """ + A property to get and create dataloaders by split just in need. + + If no train_dataset_ratio is provided, concatenate map-style datasets and + chain wds.DataPipe datasets separately. Training set becomes a tuple + (ConcatDataset, ChainDataset), both are optional but at least one of them is + required. The resultant ConcatDataset and ChainDataset will be sampled evenly. + + If train_dataset_ratio is provided, create a MultiIterLoader to sample + each dataset by ratios during training. + + Currently do not support multiple datasets for validation and test. + + Returns: + dict: {split_name: (tuples of) dataloader} + """ + if self._dataloaders is None: + # reoganize datasets by split and concatenate/chain if necessary + dataset_ratios = self.config.run_cfg.get("train_dataset_ratios", None) + + # concatenate map-style datasets and chain wds.DataPipe datasets separately + # training set becomes a tuple (ConcatDataset, ChainDataset), both are + # optional but at least one of them is required. The resultant ConcatDataset + # and ChainDataset will be sampled evenly. + logging.info( + "dataset_ratios not specified, datasets will be concatenated (map-style datasets) or chained (webdataset.DataPipeline)." + ) + + datasets = reorg_datasets_by_split(self.datasets) + self.datasets = concat_datasets(datasets) + + # print dataset statistics after concatenation/chaining + for split_name in self.datasets: + if isinstance(self.datasets[split_name], tuple) or isinstance( + self.datasets[split_name], list + ): + # mixed wds.DataPipeline and torch.utils.data.Dataset + num_records = sum( + [ + len(d) + if not type(d) in [wds.DataPipeline, ChainDataset] + else 0 + for d in self.datasets[split_name] + ] + ) + + else: + if hasattr(self.datasets[split_name], "__len__"): + # a single map-style dataset + num_records = len(self.datasets[split_name]) + else: + # a single wds.DataPipeline + num_records = -1 + logging.info( + "Only a single wds.DataPipeline dataset, no __len__ attribute." + ) + + if num_records >= 0: + logging.info( + "Loaded {} records for {} split from the dataset.".format( + num_records, split_name + ) + ) + + # create dataloaders + split_names = sorted(self.datasets.keys()) + + datasets = [self.datasets[split] for split in split_names] + is_trains = [split in self.train_splits for split in split_names] + + batch_sizes = [ + self.config.run_cfg.batch_size_train + if split == "train" + else self.config.run_cfg.batch_size_eval + for split in split_names + ] + + collate_fns = [] + for dataset in datasets: + if isinstance(dataset, tuple) or isinstance(dataset, list): + collate_fns.append([getattr(d, "collater", None) for d in dataset]) + else: + collate_fns.append(getattr(dataset, "collater", None)) + + dataloaders = self.create_loaders( + datasets=datasets, + num_workers=self.config.run_cfg.num_workers, + batch_sizes=batch_sizes, + is_trains=is_trains, + collate_fns=collate_fns, + dataset_ratios=dataset_ratios, + ) + + self._dataloaders = {k: v for k, v in zip(split_names, dataloaders)} + + return self._dataloaders + + @property + def cuda_enabled(self): + return self.device.type == "cuda" + + @property + def max_epoch(self): + return int(self.config.run_cfg.max_epoch) + + @property + def log_freq(self): + log_freq = self.config.run_cfg.get("log_freq", 50) + return int(log_freq) + + @property + def save_freq(self): + save_freq = self.config.run_cfg.get("save_freq", 5) + return int(save_freq) + + @property + def val_freq(self): + val_freq = self.config.run_cfg.get("val_freq", 1) + return int(val_freq) + + @property + def save_last(self): + save_last = self.config.run_cfg.get("save_last", True) + return int(save_last) + + @property + def init_lr(self): + return float(self.config.run_cfg.init_lr) + + @property + def min_lr(self): + return float(self.config.run_cfg.min_lr) + + @property + def accum_grad_iters(self): + return int(self.config.run_cfg.get("accum_grad_iters", 1)) + + @property + def valid_splits(self): + valid_splits = self.config.run_cfg.get("valid_splits", []) + + if len(valid_splits) == 0: + logging.info("No validation splits found.") + + return valid_splits + + @property + def test_splits(self): + test_splits = self.config.run_cfg.get("test_splits", []) + + return test_splits + + @property + def train_splits(self): + train_splits = self.config.run_cfg.get("train_splits", []) + + if len(train_splits) == 0: + logging.info("Empty train splits.") + + return train_splits + + @property + def evaluate_only(self): + """ + Set to True to skip training. + """ + return self.config.run_cfg.evaluate + + @property + def use_dist_eval_sampler(self): + return self.config.run_cfg.get("use_dist_eval_sampler", True) + + @property + def resume_ckpt_path(self): + return self.config.run_cfg.get("resume_ckpt_path", None) + + @property + def train_loader(self): + train_dataloader = self.dataloaders["train"] + + return train_dataloader + + def setup_output_dir(self): + lib_root = Path(registry.get_path("library_root")) + + output_dir = lib_root / self.config.run_cfg.output_dir / self.job_id + result_dir = output_dir / "result" + + output_dir.mkdir(parents=True, exist_ok=True) + result_dir.mkdir(parents=True, exist_ok=True) + + registry.register_path("result_dir", str(result_dir)) + registry.register_path("output_dir", str(output_dir)) + + self.result_dir = result_dir + self.output_dir = output_dir + + def train(self): + start_time = time.time() + best_agg_metric = 0 + best_epoch = 0 + + self.log_config() + + # resume from checkpoint if specified + if not self.evaluate_only and self.resume_ckpt_path is not None: + self._load_checkpoint(self.resume_ckpt_path) + + for cur_epoch in range(self.start_epoch, self.max_epoch): + # training phase + if not self.evaluate_only: + logging.info("Start training") + # See https://github.com/salesforce/LAVIS/issues/449 + # if cur_epoch == self.start_epoch: + # self.task.before_training( + # model=self.unwrap_dist_model(self.model), + # dataset=self.datasets["train"], + # ) + train_stats = self.train_epoch(cur_epoch) + self.log_stats(split_name="train", stats=train_stats) + + # evaluation phase + if len(self.valid_splits) > 0 and (self.evaluate_only or cur_epoch%self.val_freq == 0): + for split_name in self.valid_splits: + logging.info("Evaluating on {}.".format(split_name)) + + val_log = self.eval_epoch( + split_name=split_name, cur_epoch=cur_epoch + ) + if val_log is not None: + if is_main_process(): + assert ( + "agg_metrics" in val_log + ), "No agg_metrics found in validation log." + + agg_metrics = val_log["agg_metrics"] + if agg_metrics > best_agg_metric and split_name == "val": + best_epoch, best_agg_metric = cur_epoch, agg_metrics + if not self.evaluate_only: + self._save_checkpoint(cur_epoch, is_best=True) + + val_log.update({"best_epoch": best_epoch}) + self.log_stats(val_log, split_name) + + else: + # if no validation split is provided, we just save the checkpoint at the end of each epoch. + if not self.evaluate_only: + self._save_checkpoint(cur_epoch, is_best=False) + + if self.evaluate_only: + break + + # save checkpoint according to save freq + if self.save_freq>0 and cur_epoch%self.save_freq == 0: + self._save_checkpoint(cur_epoch, is_best=False) + + dist.barrier() + + # save last checkpoint + if self.save_last and not self.evaluate_only: + self._save_checkpoint(cur_epoch, is_best=False) + + # testing phase + test_epoch = "best" if len(self.valid_splits) > 0 else cur_epoch + self.evaluate(cur_epoch=test_epoch, skip_reload=self.evaluate_only) + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + logging.info("Training time {}".format(total_time_str)) + + def evaluate(self, cur_epoch="best", skip_reload=False): + test_logs = dict() + + if len(self.test_splits) > 0: + for split_name in self.test_splits: + test_logs[split_name] = self.eval_epoch( + split_name=split_name, cur_epoch=cur_epoch, skip_reload=skip_reload + ) + + return test_logs + + def train_epoch(self, epoch): + # train + self.model.train() + + return self.task.train_epoch( + epoch=epoch, + model=self.model, + data_loader=self.train_loader, + optimizer=self.optimizer, + scaler=self.scaler, + lr_scheduler=self.lr_scheduler, + cuda_enabled=self.cuda_enabled, + log_freq=self.log_freq, + accum_grad_iters=self.accum_grad_iters, + ) + + @torch.no_grad() + def eval_epoch(self, split_name, cur_epoch, skip_reload=False): + """ + Evaluate the model on a given split. + + Args: + split_name (str): name of the split to evaluate on. + cur_epoch (int): current epoch. + skip_reload_best (bool): whether to skip reloading the best checkpoint. + During training, we will reload the best checkpoint for validation. + During testing, we will use provided weights and skip reloading the best checkpoint . + """ + data_loader = self.dataloaders.get(split_name, None) + assert data_loader, "data_loader for split {} is None.".format(split_name) + + # TODO In validation, you need to compute loss as well as metrics + # TODO consider moving to model.before_evaluation() + model = self.unwrap_dist_model(self.model) + if not skip_reload and cur_epoch == "best": + model = self._reload_best_model(model) + model.eval() + + self.task.before_evaluation( + model=model, + dataset=self.datasets[split_name], + ) + results = self.task.evaluation(model, data_loader) + + if results is not None: + return self.task.after_evaluation( + val_result=results, + split_name=split_name, + epoch=cur_epoch, + ) + + def unwrap_dist_model(self, model): + if self.use_distributed: + return model.module + else: + return model + + def create_loaders( + self, + datasets, + num_workers, + batch_sizes, + is_trains, + collate_fns, + dataset_ratios=None, + ): + """ + Create dataloaders for training and validation. + """ + + def _create_loader(dataset, num_workers, bsz, is_train, collate_fn): + # create a single dataloader for each split + if isinstance(dataset, ChainDataset) or isinstance( + dataset, wds.DataPipeline + ): + # wds.WebdDataset instance are chained together + # webdataset.DataPipeline has its own sampler and collate_fn + loader = iter( + DataLoader( + dataset, + batch_size=bsz, + num_workers=num_workers, + pin_memory=True, + ) + ) + else: + # map-style dataset are concatenated together + # setup distributed sampler + if self.use_distributed: + sampler = DistributedSampler( + dataset, + shuffle=is_train, + num_replicas=get_world_size(), + rank=get_rank(), + ) + if not self.use_dist_eval_sampler: + # e.g. retrieval evaluation + sampler = sampler if is_train else None + else: + sampler = None + + loader = DataLoader( + dataset, + batch_size=bsz, + num_workers=num_workers, + pin_memory=True, + sampler=sampler, + shuffle=sampler is None and is_train, + collate_fn=collate_fn, + drop_last=True if is_train else False, + ) + loader = PrefetchLoader(loader) + + if is_train: + loader = IterLoader(loader, use_distributed=self.use_distributed) + + return loader + + loaders = [] + + for dataset, bsz, is_train, collate_fn in zip( + datasets, batch_sizes, is_trains, collate_fns + ): + if isinstance(dataset, list) or isinstance(dataset, tuple): + loader = MultiIterLoader( + loaders=[ + _create_loader(d, num_workers, bsz, is_train, collate_fn[i]) + for i, d in enumerate(dataset) + ], + ratios=dataset_ratios, + ) + else: + loader = _create_loader(dataset, num_workers, bsz, is_train, collate_fn) + + loaders.append(loader) + + return loaders + + @main_process + def _save_checkpoint(self, cur_epoch, is_best=False): + """ + Save the checkpoint at the current epoch. + """ + model_no_ddp = self.unwrap_dist_model(self.model) + param_grad_dic = { + k: v.requires_grad for (k, v) in model_no_ddp.named_parameters() + } + state_dict = model_no_ddp.state_dict() + for k in list(state_dict.keys()): + if k in param_grad_dic.keys() and not param_grad_dic[k]: + # delete parameters that do not require gradient + del state_dict[k] + + save_obj = { + "model": state_dict, + "optimizer": self.optimizer.state_dict(), + "config": self.config.to_dict(), + "scaler": self.scaler.state_dict() if self.scaler else None, + "epoch": cur_epoch, + } + save_to = os.path.join( + self.output_dir, + "checkpoint_{}.pth".format("best" if is_best else cur_epoch), + ) + logging.info("Saving checkpoint at epoch {} to {}.".format(cur_epoch, save_to)) + torch.save(save_obj, save_to) + + def _reload_best_model(self, model): + """ + Load the best checkpoint for evaluation. + """ + checkpoint_path = os.path.join(self.output_dir, "checkpoint_best.pth") + + logging.info("Loading checkpoint from {}.".format(checkpoint_path)) + checkpoint = torch.load(checkpoint_path, map_location="cpu") + try: + model.load_state_dict(checkpoint["model"]) + except RuntimeError as e: + logging.warning( + """ + Key mismatch when loading checkpoint. This is expected if only part of the model is saved. + Trying to load the model with strict=False. + """ + ) + model.load_state_dict(checkpoint["model"], strict=False) + return model + + def _load_checkpoint(self, url_or_filename): + """ + Resume from a checkpoint. + """ + if is_url(url_or_filename): + cached_file = download_cached_file( + url_or_filename, check_hash=False, progress=True + ) + checkpoint = torch.load(cached_file, map_location=self.device) + elif os.path.isfile(url_or_filename): + checkpoint = torch.load(url_or_filename, map_location=self.device) + else: + raise RuntimeError("checkpoint url or path is invalid") + + state_dict = checkpoint["model"] + self.unwrap_dist_model(self.model).load_state_dict(state_dict) + + self.optimizer.load_state_dict(checkpoint["optimizer"]) + if self.scaler and "scaler" in checkpoint: + self.scaler.load_state_dict(checkpoint["scaler"]) + + self.start_epoch = checkpoint["epoch"] + 1 + logging.info("Resume checkpoint from {}".format(url_or_filename)) + + @main_process + def log_stats(self, stats, split_name): + if isinstance(stats, dict): + log_stats = {**{f"{split_name}_{k}": v for k, v in stats.items()}} + with open(os.path.join(self.output_dir, "log.txt"), "a") as f: + f.write(json.dumps(log_stats) + "\n") + elif isinstance(stats, list): + pass + + @main_process + def log_config(self): + with open(os.path.join(self.output_dir, "log.txt"), "a") as f: + f.write(json.dumps(self.config.to_dict(), indent=4) + "\n") diff --git a/LAVIS-main/lavis/runners/runner_iter.py b/LAVIS-main/lavis/runners/runner_iter.py new file mode 100644 index 0000000000000000000000000000000000000000..b65d6cdddc949a2446ade69e70bb80c3a98f9731 --- /dev/null +++ b/LAVIS-main/lavis/runners/runner_iter.py @@ -0,0 +1,341 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import datetime +import logging +import os +import time + +import torch +import torch.distributed as dist +import webdataset as wds +from lavis.common.dist_utils import download_cached_file, is_main_process, main_process +from lavis.common.registry import registry +from lavis.common.utils import is_url +from lavis.datasets.data_utils import concat_datasets, reorg_datasets_by_split +from lavis.runners.runner_base import RunnerBase +from torch.utils.data.dataset import ChainDataset + + +@registry.register_runner("runner_iter") +class RunnerIter(RunnerBase): + """ + Run training based on the number of iterations. This is common when + the training dataset size is large. Underhood logic is similar to + epoch-based training by considering every #iters_per_inner_epoch as an + inner epoch. + + In iter-based runner, after every #iters_per_inner_epoch steps, we + + 1) do a validation epoch; + 2) schedule the learning rate; + 3) save the checkpoint. + + We refer every #iters_per_inner_epoch steps as an inner epoch. + """ + + def __init__(self, cfg, task, model, datasets, job_id): + super().__init__(cfg, task, model, datasets, job_id) + + self.start_iters = 0 + + self.max_iters = int(self.config.run_cfg.get("max_iters", -1)) + assert self.max_iters > 0, "max_iters must be greater than 0." + + self.iters_per_inner_epoch = int( + self.config.run_cfg.get("iters_per_inner_epoch", -1) + ) + assert ( + self.iters_per_inner_epoch > 0 + ), "iters_per_inner_epoch must be greater than 0." + + @property + def max_epoch(self): + return int(self.max_iters / self.iters_per_inner_epoch) + + @property + def cur_epoch(self): + try: + return self.train_loader.epoch + except AttributeError: + # pipeline data (e.g. LAION) is streaming, have no concept of epoch + return 0 + + def _progress(self, cur_iters): + return "{}_iters={}".format(self.cur_epoch, cur_iters) + + def train(self): + start_time = time.time() + best_agg_metric = 0 + best_iters = 0 + + self.log_config() + + # resume from checkpoint if specified + if not self.evaluate_only and self.resume_ckpt_path is not None: + self._load_checkpoint(self.resume_ckpt_path) + + for start_iters in range( + self.start_iters, self.max_iters, self.iters_per_inner_epoch + ): + end_iters = start_iters + self.iters_per_inner_epoch + + # training phase + if not self.evaluate_only: + logging.info( + "Start training, max_iters={}, in total {} inner epochs.".format( + self.max_iters, int(self.max_iters / self.iters_per_inner_epoch) + ) + ) + if start_iters == self.start_iters: + self.task.before_training( + model=self.unwrap_dist_model(self.model), + dataset=self.datasets, + ) + train_stats = self.train_iters(self.cur_epoch, start_iters) + self.log_stats(split_name="train", stats=train_stats) + + # evaluation phase + if len(self.valid_splits) > 0 and (self.evaluate_only or (end_iters//self.iters_per_inner_epoch)%self.val_freq == 0): + for split_name in self.valid_splits: + logging.info("Evaluating on {}.".format(split_name)) + + val_log = self.eval_epoch( + split_name=split_name, cur_epoch=self._progress(end_iters) + ) + if val_log is not None: + if is_main_process(): + assert ( + "agg_metrics" in val_log + ), "No agg_metrics found in validation log." + + agg_metrics = val_log["agg_metrics"] + if agg_metrics > best_agg_metric and split_name == "val": + best_iters, best_agg_metric = end_iters, agg_metrics + + self._save_checkpoint(end_iters, is_best=True) + + val_log.update({"best_iters": best_iters}) + self.log_stats(val_log, split_name) + + else: + # if no validation split is provided, we just save the checkpoint at the end of each inner epoch. + if not self.evaluate_only: + self._save_checkpoint(end_iters, is_best=False) + + if self.evaluate_only: + break + + # save checkpoint according to save freq + # if self.save_freq>0 and (end_iters//self.iters_per_inner_epoch)%self.save_freq == 0: + self._save_checkpoint(end_iters, is_best=False) + + dist.barrier() + + # save last checkpoint + if self.save_last and not self.evaluate_only: + self._save_checkpoint(end_iters, is_best=False) + + # testing phase + self.evaluate(cur_epoch=self.cur_epoch) + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + logging.info("Training time {}".format(total_time_str)) + + def train_iters(self, epoch, start_iters): + # train by iterations + self.model.train() + + return self.task.train_iters( + epoch=epoch, + start_iters=start_iters, + iters_per_inner_epoch=self.iters_per_inner_epoch, + model=self.model, + data_loader=self.train_loader, + optimizer=self.optimizer, + scaler=self.scaler, + lr_scheduler=self.lr_scheduler, + cuda_enabled=self.cuda_enabled, + log_freq=self.log_freq, + accum_grad_iters=self.accum_grad_iters, + ) + + @main_process + def _save_checkpoint(self, cur_iters, is_best=False, is_last=False): + model_no_ddp = self.unwrap_dist_model(self.model) + param_grad_dic = { + k: v.requires_grad for (k, v) in model_no_ddp.named_parameters() + } + + state_dict = model_no_ddp.state_dict() + for k in list(state_dict.keys()): + if k in param_grad_dic.keys() and not param_grad_dic[k]: + # delete parameters that do not require gradient + del state_dict[k] + + save_obj = { + "model": state_dict, + "optimizer": self.optimizer.state_dict(), + "config": self.config.to_dict(), + "scaler": self.scaler.state_dict() if self.scaler else None, + "iters": cur_iters, + } + save_to = os.path.join( + self.output_dir, + "checkpoint_{}.pth".format("best" if is_best else cur_iters), + ) + logging.info("Saving checkpoint at iters {} to {}.".format(cur_iters, save_to)) + torch.save(save_obj, save_to) + + def _load_checkpoint(self, url_or_filename): + """ + Resume from a checkpoint. + """ + if is_url(url_or_filename): + cached_file = download_cached_file( + url_or_filename, check_hash=False, progress=True + ) + checkpoint = torch.load(cached_file, map_location=self.device) + elif os.path.isfile(url_or_filename): + checkpoint = torch.load(url_or_filename, map_location=self.device) + else: + raise RuntimeError("checkpoint url or path is invalid") + + state_dict = checkpoint["model"] + self.unwrap_dist_model(self.model).load_state_dict(state_dict) + + self.optimizer.load_state_dict(checkpoint["optimizer"]) + if self.scaler and "scaler" in checkpoint: + self.scaler.load_state_dict(checkpoint["scaler"]) + + self.start_iters = checkpoint["iters"] + 1 + logging.info("Resume checkpoint from {}".format(url_or_filename)) + + @property + def dataloaders(self) -> dict: + """ + A property to get and create dataloaders by split just in need. + + If no train_dataset_ratio is provided, concatenate map-style datasets and + chain wds.DataPipe datasets separately. Training set becomes a tuple + (ConcatDataset, ChainDataset), both are optional but at least one of them is + required. The resultant ConcatDataset and ChainDataset will be sampled evenly. + + If train_dataset_ratio is provided, create a MultiIterLoader to sample + each dataset by ratios during training. + + Currently do not support multiple datasets for validation and test. + + Returns: + dict: {split_name: (tuples of) dataloader} + """ + if self._dataloaders is None: + # reoganize datasets by split and concatenate/chain if necessary + dataset_ratios = self.config.run_cfg.get("train_dataset_ratios", None) + + if dataset_ratios is None: + # concatenate map-style datasets and chain wds.DataPipe datasets separately + # training set becomes a tuple (ConcatDataset, ChainDataset), both are + # optional but at least one of them is required. The resultant ConcatDataset + # and ChainDataset will be sampled evenly. + logging.info( + "dataset_ratios not specified, datasets will be concatenated (map-style datasets) or chained (webdataset.DataPipeline)." + ) + + datasets = reorg_datasets_by_split(self.datasets) + self.datasets = concat_datasets(datasets) + else: + # create multi-loader with the provided ratios, without concatenating or chaining + missing_keys = [k for k in dataset_ratios if k not in self.datasets] + if len(missing_keys) > 0: + raise ValueError( + "Datasets with the following split names are not found: {}".format( + missing_keys + ) + ) + + unexpected_keys = [k for k in self.datasets if k not in dataset_ratios] + if len(unexpected_keys) > 0: + raise ValueError( + "Datasets with the following split names are not expected: {}".format( + unexpected_keys + ) + ) + + dataset_ratios = [float(dataset_ratios[k]) for k in self.datasets] + self.datasets = reorg_datasets_by_split(self.datasets) + # to keep the same structure as return value of concat_datasets + self.datasets = { + k: v[0] if len(v) == 1 else v for k, v in self.datasets.items() + } + + # print dataset statistics after concatenation/chaining + for split_name in self.datasets: + if isinstance(self.datasets[split_name], tuple) or isinstance( + self.datasets[split_name], list + ): + # mixed wds.DataPipeline and torch.utils.data.Dataset + num_records = sum( + [ + len(d) + if not type(d) in [wds.DataPipeline, ChainDataset] + else 0 + for d in self.datasets[split_name] + ] + ) + + else: + try: + # a single map-style dataset + num_records = len(self.datasets[split_name]) + except TypeError: + # a single wds.DataPipeline or ChainDataset + num_records = -1 + logging.info( + "Only a single wds.DataPipeline dataset, no __len__ attribute." + ) + + if num_records >= 0: + logging.info( + "Loaded {} records for {} split from the dataset.".format( + num_records, split_name + ) + ) + + # create dataloaders + split_names = sorted(self.datasets.keys()) + + datasets = [self.datasets[split] for split in split_names] + is_trains = [split in self.train_splits for split in split_names] + + batch_sizes = [ + self.config.run_cfg.batch_size_train + if split == "train" + else self.config.run_cfg.batch_size_eval + for split in split_names + ] + + collate_fns = [] + for dataset in datasets: + if isinstance(dataset, tuple) or isinstance(dataset, list): + collate_fns.append([getattr(d, "collater", None) for d in dataset]) + else: + collate_fns.append(getattr(dataset, "collater", None)) + + dataloaders = self.create_loaders( + datasets=datasets, + num_workers=self.config.run_cfg.num_workers, + batch_sizes=batch_sizes, + is_trains=is_trains, + collate_fns=collate_fns, + dataset_ratios=dataset_ratios, + ) + + self._dataloaders = {k: v for k, v in zip(split_names, dataloaders)} + + return self._dataloaders diff --git a/LAVIS-main/lavis/tasks/__init__.py b/LAVIS-main/lavis/tasks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..212029ac5b8ee42c99f48e942a3c30035c353221 --- /dev/null +++ b/LAVIS-main/lavis/tasks/__init__.py @@ -0,0 +1,48 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +from lavis.common.registry import registry +from lavis.tasks.base_task import BaseTask +from lavis.tasks.captioning import CaptionTask +from lavis.tasks.image_text_pretrain import ImageTextPretrainTask +from lavis.tasks.multimodal_classification import ( + MultimodalClassificationTask, +) +from lavis.tasks.retrieval import RetrievalTask +from lavis.tasks.vqa import VQATask, GQATask, AOKVQATask, DisCRNTask +from lavis.tasks.vqa_reading_comprehension import VQARCTask, GQARCTask +from lavis.tasks.dialogue import DialogueTask +from lavis.tasks.text_to_image_generation import TextToImageGenerationTask + + +def setup_task(cfg): + assert "task" in cfg.run_cfg, "Task name must be provided." + + task_name = cfg.run_cfg.task + task = registry.get_task_class(task_name).setup_task(cfg=cfg) + assert task is not None, "Task {} not properly registered.".format(task_name) + + return task + + +__all__ = [ + "BaseTask", + "AOKVQATask", + "RetrievalTask", + "CaptionTask", + "VQATask", + "GQATask", + "VQARCTask", + "GQARCTask", + "MultimodalClassificationTask", + # "VideoQATask", + # "VisualEntailmentTask", + "ImageTextPretrainTask", + "DialogueTask", + "TextToImageGenerationTask", + "DisCRNTask" +] diff --git a/LAVIS-main/lavis/tasks/base_task.py b/LAVIS-main/lavis/tasks/base_task.py new file mode 100644 index 0000000000000000000000000000000000000000..fe96a2869509563604526ed1b2da74fabd54cfbe --- /dev/null +++ b/LAVIS-main/lavis/tasks/base_task.py @@ -0,0 +1,294 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import logging +import os + +import torch +import torch.distributed as dist +from lavis.common.dist_utils import get_rank, get_world_size, is_main_process, is_dist_avail_and_initialized +from lavis.common.logger import MetricLogger, SmoothedValue +from lavis.common.registry import registry +from lavis.datasets.data_utils import prepare_sample + + +class BaseTask: + def __init__(self, **kwargs): + super().__init__() + + self.inst_id_key = "instance_id" + + @classmethod + def setup_task(cls, **kwargs): + return cls() + + def build_model(self, cfg): + model_config = cfg.model_cfg + + model_cls = registry.get_model_class(model_config.arch) + return model_cls.from_config(model_config) + + def build_datasets(self, cfg): + """ + Build a dictionary of datasets, keyed by split 'train', 'valid', 'test'. + Download dataset and annotations automatically if not exist. + + Args: + cfg (common.config.Config): _description_ + + Returns: + dict: Dictionary of torch.utils.data.Dataset objects by split. + """ + + datasets = dict() + + datasets_config = cfg.datasets_cfg + + assert len(datasets_config) > 0, "At least one dataset has to be specified." + + for name in datasets_config: + dataset_config = datasets_config[name] + builder = registry.get_builder_class(name)(dataset_config) + dataset = builder.build_datasets() + + datasets[name] = dataset + + return datasets + + def train_step(self, model, samples): + output = model(samples) + loss_dict = {} + for k,v in output.items(): + if "loss" in k: + loss_dict[k] = v + return output["loss"], loss_dict + + def valid_step(self, model, samples): + raise NotImplementedError + + def before_training(self, model, dataset, **kwargs): + model.before_training(dataset=dataset, task_type=type(self)) + + def before_evaluation(self, model, dataset, **kwargs): + model.before_evaluation(dataset=dataset, task_type=type(self)) + + def after_evaluation(self, **kwargs): + pass + + def inference_step(self): + raise NotImplementedError + + def evaluation(self, model, data_loader, cuda_enabled=True): + metric_logger = MetricLogger(delimiter=" ") + header = "Evaluation" + # TODO make it configurable + print_freq = 10 + + results = [] + + for samples in metric_logger.log_every(data_loader, print_freq, header): + samples = prepare_sample(samples, cuda_enabled=cuda_enabled) + + eval_output = self.valid_step(model=model, samples=samples) + results.extend(eval_output) + + if is_dist_avail_and_initialized(): + dist.barrier() + + return results + + def train_epoch( + self, + epoch, + model, + data_loader, + optimizer, + lr_scheduler, + scaler=None, + cuda_enabled=False, + log_freq=50, + accum_grad_iters=1, + ): + return self._train_inner_loop( + epoch=epoch, + iters_per_epoch=len(data_loader), + model=model, + data_loader=data_loader, + optimizer=optimizer, + scaler=scaler, + lr_scheduler=lr_scheduler, + log_freq=log_freq, + cuda_enabled=cuda_enabled, + accum_grad_iters=accum_grad_iters, + ) + + def train_iters( + self, + epoch, + start_iters, + iters_per_inner_epoch, + model, + data_loader, + optimizer, + lr_scheduler, + scaler=None, + cuda_enabled=False, + log_freq=50, + accum_grad_iters=1, + ): + return self._train_inner_loop( + epoch=epoch, + start_iters=start_iters, + iters_per_epoch=iters_per_inner_epoch, + model=model, + data_loader=data_loader, + optimizer=optimizer, + scaler=scaler, + lr_scheduler=lr_scheduler, + log_freq=log_freq, + cuda_enabled=cuda_enabled, + accum_grad_iters=accum_grad_iters, + ) + + def _train_inner_loop( + self, + epoch, + iters_per_epoch, + model, + data_loader, + optimizer, + lr_scheduler, + scaler=None, + start_iters=None, + log_freq=50, + cuda_enabled=False, + accum_grad_iters=1, + ): + """ + An inner training loop compatible with both epoch-based and iter-based training. + + When using epoch-based, training stops after one epoch; when using iter-based, + training stops after #iters_per_epoch iterations. + """ + use_amp = scaler is not None + + if not hasattr(data_loader, "__next__"): + # convert to iterator if not already + data_loader = iter(data_loader) + + metric_logger = MetricLogger(delimiter=" ") + metric_logger.add_meter("lr", SmoothedValue(window_size=1, fmt="{value:.6f}")) + metric_logger.add_meter("loss", SmoothedValue(window_size=1, fmt="{value:.4f}")) + + # if iter-based runner, schedule lr based on inner epoch. + logging.info( + "Start training epoch {}, {} iters per inner epoch.".format( + epoch, iters_per_epoch + ) + ) + header = "Train: data epoch: [{}]".format(epoch) + if start_iters is None: + # epoch-based runner + inner_epoch = epoch + else: + # In iter-based runner, we schedule the learning rate based on iterations. + inner_epoch = start_iters // iters_per_epoch + header = header + "; inner epoch [{}]".format(inner_epoch) + + for i in metric_logger.log_every(range(iters_per_epoch), log_freq, header): + # if using iter-based runner, we stop after iters_per_epoch iterations. + if i >= iters_per_epoch: + break + + samples = next(data_loader) + + samples = prepare_sample(samples, cuda_enabled=cuda_enabled) + + ## notify model that sample is empty (error occured) + if not isinstance(samples, dict): + samples = {"is_empty":True} + + samples.update( + { + "epoch": inner_epoch, + "num_iters_per_epoch": iters_per_epoch, + "iters": i, + } + ) + + lr_scheduler.step(cur_epoch=inner_epoch, cur_step=i) + + with torch.cuda.amp.autocast(enabled=use_amp): + loss, loss_dict = self.train_step(model=model, samples=samples) + loss /= accum_grad_iters #TODO: not affect loss_dict values for logging + + # after_train_step() + if use_amp: + scaler.scale(loss).backward() + else: + loss.backward() + + # update gradients every accum_grad_iters iterations + if (i + 1) % accum_grad_iters == 0: + if use_amp: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + optimizer.zero_grad() + + metric_logger.update(**loss_dict) + metric_logger.update(lr=optimizer.param_groups[0]["lr"]) + + # after train_epoch() + # gather the stats from all processes + metric_logger.synchronize_between_processes() + logging.info("Averaged stats: " + str(metric_logger.global_avg())) + return { + k: "{:.3f}".format(meter.global_avg) + for k, meter in metric_logger.meters.items() + } + + @staticmethod + def save_result(result, result_dir, filename, remove_duplicate=""): + import json + + result_file = os.path.join( + result_dir, "%s_rank%d.json" % (filename, get_rank()) + ) + final_result_file = os.path.join(result_dir, "%s.json" % filename) + + json.dump(result, open(result_file, "w")) + + if is_dist_avail_and_initialized(): + dist.barrier() + + if is_main_process(): + logging.warning("rank %d starts merging results." % get_rank()) + # combine results from all processes + result = [] + + for rank in range(get_world_size()): + result_file = os.path.join( + result_dir, "%s_rank%d.json" % (filename, rank) + ) + res = json.load(open(result_file, "r")) + result += res + + if remove_duplicate: + result_new = [] + id_list = [] + for res in result: + if res[remove_duplicate] not in id_list: + id_list.append(res[remove_duplicate]) + result_new.append(res) + result = result_new + + json.dump(result, open(final_result_file, "w")) + print("result file saved to %s" % final_result_file) + + return final_result_file diff --git a/LAVIS-main/lavis/tasks/captioning.py b/LAVIS-main/lavis/tasks/captioning.py new file mode 100644 index 0000000000000000000000000000000000000000..1e2a8b9b7d8e3f2dea8aaf8bcc520aff02d2544e --- /dev/null +++ b/LAVIS-main/lavis/tasks/captioning.py @@ -0,0 +1,260 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import json +import os +import pandas as pd +from tqdm import tqdm +from lavis.common.dist_utils import main_process, get_rank +from lavis.common.registry import registry +from lavis.tasks.base_task import BaseTask +from lavis.common.utils import is_convertible_to_int, is_url, cache_url + +@registry.register_task("captioning") +class CaptionTask(BaseTask): + def __init__(self, num_beams, max_len, min_len, repetition_penalty, length_penalty, top_p, temperature, evaluate, report_metric=True, annotation_file=None, sample_id_key="image_id", caption_key="caption", split=["val"], load_gt_from_file=False, img_ids = []): + super().__init__() + + self.num_beams = num_beams + self.max_len = max_len + self.min_len = min_len + self.repetition_penalty = repetition_penalty + self.length_penalty = length_penalty + self.top_p = top_p + self.temperature = temperature + self.evaluate = evaluate + + self.report_metric = report_metric + self.annotation_file = annotation_file + self.sample_id_key = sample_id_key + self.caption_key = caption_key + assert len(split) == 1, "Only support one split for evaluation." + self.split = split[0] + self.load_gt_from_file = load_gt_from_file + self.img_ids = img_ids + + @classmethod + def setup_task(cls, cfg): + run_cfg = cfg.run_cfg + + num_beams = run_cfg.get("num_beams", 5) + max_len = run_cfg.get("max_len", 30) + min_len = run_cfg.get("min_len", 1) + repetition_penalty = run_cfg.get("repetition_penalty", 1.15) + length_penalty = run_cfg.get("length_penalty", 0.) + top_p = run_cfg.get("top_p", 0.9) + temperature = run_cfg.get("temperature", 1.) + evaluate = run_cfg.evaluate + + report_metric = run_cfg.get("report_metric", True) + annotation_file = run_cfg.get("annotation_file", None) + sample_id_key = run_cfg.get("sample_id_key", "image_id") + caption_key = run_cfg.get("caption_key", "caption") + load_gt_from_file = run_cfg.get("load_gt_from_file", False) + split = run_cfg.get("valid_splits", ["val"]) + img_ids = run_cfg.get("img_ids", []) # evaluate only subset of imgs + + return cls( + num_beams=num_beams, + max_len=max_len, + min_len=min_len, + repetition_penalty=repetition_penalty, + length_penalty=length_penalty, + top_p=top_p, + temperature=temperature, + evaluate=evaluate, + report_metric=report_metric, + annotation_file=annotation_file, + sample_id_key=sample_id_key, + caption_key=caption_key, + split=split, + load_gt_from_file=load_gt_from_file, + img_ids=img_ids + ) + + def build_datasets(self, cfg): + datasets = super().build_datasets(cfg) + + # get validation dataset name + val_ds_name = [] + for name,d in datasets.items(): + if self.split in d: + val_ds_name.append(name) + if not val_ds_name: + return datasets # no validation sets + assert len(val_ds_name) == 1, "Only support one dataset for validation" + val_ds_name = val_ds_name[0] + + # get question file, annotation file and anwser list in COCO format + if self.annotation_file == None: + if 'coco' not in val_ds_name: # coco is already precomputed in dataset + self.annotation_file = os.path.join(registry.get_path("cache_root"),f'{val_ds_name}_gt', f'{val_ds_name}_{self.split}_annotations.json') + if get_rank() == 0: + os.makedirs(os.path.join(registry.get_path("cache_root"),f'{val_ds_name}_gt'), exist_ok=True) + convert_to_coco_gt(datasets[val_ds_name], self.annotation_file, self.caption_key, self.sample_id_key, self.split, load_gt_from_file=self.load_gt_from_file, img_ids=self.img_ids) + return datasets + + def valid_step(self, model, samples): + results = [] + # run_cfg = slf.cfg.run_cfg + captions = model.generate( + samples, + use_nucleus_sampling=False, + num_beams=self.num_beams, + max_length=self.max_len, + min_length=self.min_len, + repetition_penalty=self.repetition_penalty, + length_penalty=self.length_penalty, + top_p=self.top_p, + temperature=self.temperature, + ) + img_ids = samples[self.sample_id_key] + for caption, img_id in zip(captions, img_ids): + # not all img_ids are ints + img_id = int(img_id) if is_convertible_to_int(img_id) else img_id + if self.img_ids and img_id not in self.img_ids: # only include specified img_ids if specified + continue + results.append({"caption": caption, "image_id": img_id}) + + return results + + def after_evaluation(self, val_result, split_name, epoch, **kwargs): + eval_result_file = self.save_result( + result=val_result, + result_dir=registry.get_path("result_dir"), + filename="{}_epoch{}".format(split_name, epoch), + remove_duplicate="image_id", + ) + + if self.report_metric: + metrics = self._report_metrics( + eval_result_file=eval_result_file, split_name=split_name + ) + else: + metrics = {"agg_metrics": 0.0} + + return metrics + + @main_process + def _report_metrics(self, eval_result_file, split_name): + + if self.annotation_file == None: + # TODO better way to define this + coco_gt_root = os.path.join(registry.get_path("cache_root"), "coco_gt") + coco_val = coco_caption_eval(coco_gt_root, eval_result_file, split_name, img_ids=self.img_ids) + else: + coco_val = coco_caption_eval(None, eval_result_file, split_name, annotation_file=self.annotation_file, img_ids=self.img_ids) + + agg_metrics = coco_val.eval["CIDEr"] + coco_val.eval["Bleu_4"] + log_stats = {split_name: {k: v for k, v in coco_val.eval.items()}} + + with open( + os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a" + ) as f: + f.write(json.dumps(log_stats) + "\n") + + coco_res = {k: v for k, v in coco_val.eval.items()} + coco_res["agg_metrics"] = agg_metrics + + return coco_res + +def load_gt_file(file_path): + if is_url(file_path): + file_path = cache_url(file_path, registry.get_path("cache_root")) + data = [] + if any(ext in file_path for ext in ['csv', 'tsv']): + df = pd.read_csv(file_path) + data.extend(df.to_dict(orient="records")) + + elif 'jsonl' in file_path: + with open(file_path, "r") as f: + data.extend([json.loads(line) for line in f]) + else: + with open(file_path, "r") as f: + loaded = json.load(f) + if isinstance(loaded, list): + data.extend(loaded) + elif isinstance(loaded, dict): + # assume that loaded data in file is the corresponding caption to the key + data.extend([{"sample_id": k, **v} if isinstance(v, dict) else {"sample_id": k, "caption": v} for k, v in loaded.items()]) + return data + +def convert_to_coco_gt(data, outpath, caption_key, sample_id_key, split, load_gt_from_file=False, img_ids=[]): + gt_data = {"annotations":[], "images":[]} + if load_gt_from_file: + print(f"Generating ground truth file for evaluation from {load_gt_from_file}....") + data = load_gt_file(load_gt_from_file) + for ann in data: + captions = ann[caption_key] + img_id = int(ann[sample_id_key]) if is_convertible_to_int(ann[sample_id_key]) else ann[sample_id_key] + if img_ids and img_id not in img_ids: # only include specified img_ids if specified + continue + gt_data["images"].append({"id":img_id}) + if isinstance(captions, str): + gt_data["annotations"].append({"image_id":img_id, "caption":captions, "id":img_id}) + else: + gt_data["annotations"].extend([{"image_id":img_id, "caption":c, "id":img_id} for c in captions]) + else: + print(f"Generating ground truth file for evaluation....") + for i,ann in tqdm(enumerate(data[split])): + captions = data[split].annotation[i][caption_key] + img_id = int(ann[sample_id_key]) if is_convertible_to_int(ann[sample_id_key]) else ann[sample_id_key] + if img_ids and img_id not in img_ids: # only include specified img_ids if specified + continue + gt_data["images"].append({"id":img_id}) + if isinstance(captions, str): + gt_data["annotations"].append({"image_id":img_id, "caption":captions, "id":img_id}) + else: + gt_data["annotations"].extend([{"image_id":img_id, "caption":c, "id":img_id} for c in captions]) + json.dump(gt_data, open(outpath, 'w')) + print(f"Saved annotations at {outpath}") + +# TODO better structure for this. +from pycocoevalcap.eval import COCOEvalCap +from pycocotools.coco import COCO +from torchvision.datasets.utils import download_url + + +def coco_caption_eval(coco_gt_root, results_file, split, annotation_file=None, img_ids=[]): + + if annotation_file == None: + urls = { + "val": "https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val_gt.json", + "test": "https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test_gt.json", + } + filenames = { + "val": "coco_karpathy_val_gt.json", + "test": "coco_karpathy_test_gt.json", + } + + download_url(urls[split], coco_gt_root) + annotation_file = os.path.join(coco_gt_root, filenames[split]) + if is_url(annotation_file): + annotation_file = cache_url(annotation_file, registry.get_path("cache_root")) + + # create coco object and coco_result object + coco = COCO(annotation_file) + coco_result = coco.loadRes(results_file) + + # create coco_eval object by taking coco and coco_result + coco_eval = COCOEvalCap(coco, coco_result) + + # evaluate on a subset of images by setting + if img_ids: + coco_eval.params['image_id'] = coco_result.getImgIds() + # please remove this line when evaluating the full validation set + # coco_eval.params['image_id'] = coco_result.getImgIds() + + # evaluate results + # SPICE will take a few minutes the first time, but speeds up due to caching + coco_eval.evaluate() + + # print output evaluation scores + for metric, score in coco_eval.eval.items(): + print(f"{metric}: {score:.3f}") + + return coco_eval diff --git a/LAVIS-main/lavis/tasks/dialogue.py b/LAVIS-main/lavis/tasks/dialogue.py new file mode 100644 index 0000000000000000000000000000000000000000..2477bad209d1b14f8a41b33e6494b8384b9f9671 --- /dev/null +++ b/LAVIS-main/lavis/tasks/dialogue.py @@ -0,0 +1,127 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import json +import os + +from lavis.common.dist_utils import main_process +from lavis.common.logger import MetricLogger +from lavis.common.registry import registry +from lavis.tasks.base_task import BaseTask +from lavis.datasets.data_utils import prepare_sample + +import numpy as np + + +@registry.register_task("dialogue") +class DialogueTask(BaseTask): + def __init__(self, num_beams, max_len, min_len, evaluate, report_metric=True): + super().__init__() + + self.num_beams = num_beams + self.max_len = max_len + self.min_len = min_len + self.evaluate = evaluate + + self.report_metric = report_metric + + @classmethod + def setup_task(cls, cfg): + run_cfg = cfg.run_cfg + + num_beams = run_cfg.num_beams + max_len = run_cfg.max_len + min_len = run_cfg.min_len + evaluate = run_cfg.evaluate + + report_metric = run_cfg.get("report_metric", True) + + return cls( + num_beams=num_beams, + max_len=max_len, + min_len=min_len, + evaluate=evaluate, + report_metric=report_metric, + ) + + def valid_step(self, model, samples): + results = [] + loss = model(samples)["loss"].item() + + return [loss] + + def after_evaluation(self, val_result, split_name, epoch, **kwargs): + + if self.report_metric: + avg_loss = np.mean(val_result) + metrics = {"agg_metrics": avg_loss} + else: + metrics = {"agg_metrics": 0.0} + + return metrics + + @main_process + def _report_metrics(self, eval_result_file, split_name): + # TODO better way to define this + coco_gt_root = os.path.join(registry.get_path("cache_root"), "coco_gt") + coco_val = coco_dialogue_eval(coco_gt_root, eval_result_file, split_name) + + agg_metrics = coco_val.eval["CIDEr"] + coco_val.eval["Bleu_4"] + log_stats = {split_name: {k: v for k, v in coco_val.eval.items()}} + + with open( + os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a" + ) as f: + f.write(json.dumps(log_stats) + "\n") + + coco_res = {k: v for k, v in coco_val.eval.items()} + coco_res["agg_metrics"] = agg_metrics + + return coco_res + + +# TODO better structure for this. +from pycocoevalcap.eval import COCOEvalCap +from pycocotools.coco import COCO +from torchvision.datasets.utils import download_url + + +def coco_dialogue_eval(coco_gt_root, results_file, split): + + urls = { + "val": "https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val_gt.json", + "test": "https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test_gt.json", + } + filenames = { + "val": "coco_karpathy_val_gt.json", + "test": "coco_karpathy_test_gt.json", + } + + download_url(urls[split], coco_gt_root) + annotation_file = os.path.join(coco_gt_root, filenames[split]) + + # create coco object and coco_result object + coco = COCO(annotation_file) + coco_result = coco.loadRes(results_file) + + # create coco_eval object by taking coco and coco_result + coco_eval = COCOEvalCap(coco, coco_result) + + # evaluate on a subset of images by setting + # coco_eval.params['image_id'] = coco_result.getImgIds() + # please remove this line when evaluating the full validation set + # coco_eval.params['image_id'] = coco_result.getImgIds() + + # evaluate results + # SPICE will take a few minutes the first time, but speeds up due to caching + coco_eval.evaluate() + + # print output evaluation scores + for metric, score in coco_eval.eval.items(): + print(f"{metric}: {score:.3f}") + + return coco_eval diff --git a/LAVIS-main/lavis/tasks/image_text_pretrain.py b/LAVIS-main/lavis/tasks/image_text_pretrain.py new file mode 100644 index 0000000000000000000000000000000000000000..218c535682b4382c8fde54887dc2e55107465c7f --- /dev/null +++ b/LAVIS-main/lavis/tasks/image_text_pretrain.py @@ -0,0 +1,18 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +from lavis.common.registry import registry +from lavis.tasks.base_task import BaseTask + + +@registry.register_task("image_text_pretrain") +class ImageTextPretrainTask(BaseTask): + def __init__(self): + super().__init__() + + def evaluation(self, model, data_loader, cuda_enabled=True): + pass diff --git a/LAVIS-main/lavis/tasks/multimodal_classification.py b/LAVIS-main/lavis/tasks/multimodal_classification.py new file mode 100644 index 0000000000000000000000000000000000000000..84a6eebebcaf051db14bf76504d713da909cae3d --- /dev/null +++ b/LAVIS-main/lavis/tasks/multimodal_classification.py @@ -0,0 +1,133 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import json +import os +import logging +import inspect + +import numpy as np +import torch +from lavis.common.dist_utils import main_process +from lavis.common.registry import registry +from lavis.tasks.base_task import BaseTask + + +@registry.register_task("multimodal_classification") +class MultimodalClassificationTask(BaseTask): + def __init__(self, + max_len, + min_len, + length_penalty, + segments): + super().__init__() + self.max_len = max_len + self.min_len = min_len + self.length_penalty = length_penalty + self.segments = segments + + + @classmethod + def setup_task(cls, cfg): + run_cfg = cfg.run_cfg + + max_len = run_cfg.get("max_len", 30) + min_len = run_cfg.get("min_len", 1) + length_penalty = run_cfg.get("length_penalty", -1.) + segments = run_cfg.get("segments", 1) + + return cls( + max_len=max_len, + min_len=min_len, + length_penalty=length_penalty, + segments=segments + ) + + def valid_step(self, model, samples): + results = [] + + argspec = inspect.getargspec(model.predict) + # check if model allows for generation arguments in classification + if all([k in argspec.args for k in ['max_length', "min_length", "length_penalty"]]): + outputs = model.predict(samples, + max_length=self.max_len, + min_length=self.min_len, + length_penalty=self.length_penalty, + ) + else: + outputs = model.predict(samples, n_segments=self.segments) + + if outputs == None: # missing data + return {} + + predictions = outputs["predictions"] + + if isinstance(predictions[0], str): + targets = samples["label"] + indices = samples[self.inst_id_key] + for pred, tgt, index in zip(predictions, targets, indices): + results.append( + { + self.inst_id_key: index, + "prediction": pred, + "target": tgt, + } + ) + else: + targets = outputs["targets"] + predictions = predictions.max(1)[1].cpu().numpy() + targets = targets.cpu().numpy() + + indices = samples[self.inst_id_key] + + for pred, tgt, index in zip(predictions, targets, indices): + if isinstance(index, torch.Tensor): + index = index.item() + + results.append( + { + self.inst_id_key: index, + "prediction": pred.item(), + "target": tgt.item(), + } + ) + + return results + + def after_evaluation(self, val_result, split_name, epoch, **kwargs): + eval_result_file = self.save_result( + result=val_result, + result_dir=registry.get_path("result_dir"), + filename="{}_epoch{}".format(split_name, epoch), + remove_duplicate=self.inst_id_key, + ) + + metrics = self._report_metrics( + eval_result_file=eval_result_file, split_name=split_name + ) + + return metrics + + @main_process + def _report_metrics(self, eval_result_file, split_name): + results = json.load(open(eval_result_file)) + + predictions = np.array([res["prediction"] for res in results]) + targets = np.array([res["target"] for res in results]) + + accuracy = (targets == predictions).sum() / targets.shape[0] + metrics = {"agg_metrics": accuracy, "acc": accuracy} + + log_stats = {split_name: {k: v for k, v in metrics.items()}} + + with open( + os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a" + ) as f: + f.write(json.dumps(log_stats) + "\n") + + logging.info(metrics) + return metrics diff --git a/LAVIS-main/lavis/tasks/retrieval.py b/LAVIS-main/lavis/tasks/retrieval.py new file mode 100644 index 0000000000000000000000000000000000000000..deacce9ccf53d020dbd7f95e5c45e91cc58b6492 --- /dev/null +++ b/LAVIS-main/lavis/tasks/retrieval.py @@ -0,0 +1,107 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import json +import logging +import os + +import numpy as np +import torch +from lavis.common.dist_utils import is_main_process +from lavis.common.registry import registry +from lavis.tasks.base_task import BaseTask + + +@registry.register_task("retrieval") +class RetrievalTask(BaseTask): + def __init__(self, cfg): + super().__init__() + + self.cfg = cfg + + @classmethod + def setup_task(cls, cfg): + run_cfg = cfg.run_cfg + + return cls(cfg=run_cfg) + + def evaluation(self, model, data_loader, **kwargs): + # score_i2t, score_t2i = model.compute_sim_matrix(model, data_loader) + score_i2t, score_t2i = model.compute_sim_matrix(data_loader, task_cfg=self.cfg) + + if is_main_process(): + eval_result = self._report_metrics( + score_i2t, + score_t2i, + data_loader.dataset.txt2img, + data_loader.dataset.img2txt, + ) + logging.info(eval_result) + else: + eval_result = None + + return eval_result + + def after_evaluation(self, val_result, **kwargs): + return val_result + + @staticmethod + @torch.no_grad() + def _report_metrics(scores_i2t, scores_t2i, txt2img, img2txt): + + # Images->Text + ranks = np.zeros(scores_i2t.shape[0]) + for index, score in enumerate(scores_i2t): + inds = np.argsort(score)[::-1] + # Score + rank = 1e20 + for i in img2txt[index]: + tmp = np.where(inds == i)[0][0] + if tmp < rank: + rank = tmp + ranks[index] = rank + + # Compute metrics + tr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks) + tr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks) + tr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks) + + # Text->Images + ranks = np.zeros(scores_t2i.shape[0]) + + for index, score in enumerate(scores_t2i): + inds = np.argsort(score)[::-1] + ranks[index] = np.where(inds == txt2img[index])[0][0] + + # Compute metrics + ir1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks) + ir5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks) + ir10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks) + + tr_mean = (tr1 + tr5 + tr10) / 3 + ir_mean = (ir1 + ir5 + ir10) / 3 + r_mean = (tr_mean + ir_mean) / 2 + + agg_metrics = (tr1 + tr5 + tr10) / 3 + + eval_result = { + "txt_r1": tr1, + "txt_r5": tr5, + "txt_r10": tr10, + "txt_r_mean": tr_mean, + "img_r1": ir1, + "img_r5": ir5, + "img_r10": ir10, + "img_r_mean": ir_mean, + "r_mean": r_mean, + "agg_metrics": agg_metrics, + } + with open( + os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a" + ) as f: + f.write(json.dumps(eval_result) + "\n") + return eval_result diff --git a/LAVIS-main/lavis/tasks/text_to_image_generation.py b/LAVIS-main/lavis/tasks/text_to_image_generation.py new file mode 100644 index 0000000000000000000000000000000000000000..ab58a2e1e25cf0a9f132845f1ca4a111a915a53d --- /dev/null +++ b/LAVIS-main/lavis/tasks/text_to_image_generation.py @@ -0,0 +1,23 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +from lavis.tasks import BaseTask +from lavis.common.registry import registry + + +@registry.register_task("text-to-image-generation") +class TextToImageGenerationTask(BaseTask): + def __init__(self, cfg): + super().__init__() + + self.cfg = cfg + + @classmethod + def setup_task(cls, cfg): + run_cfg = cfg.run_cfg + + return cls(cfg=run_cfg) diff --git a/LAVIS-main/lavis/tasks/vqa.py b/LAVIS-main/lavis/tasks/vqa.py new file mode 100644 index 0000000000000000000000000000000000000000..fa7c155c0060569f65b94f298d071ce430417859 --- /dev/null +++ b/LAVIS-main/lavis/tasks/vqa.py @@ -0,0 +1,479 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import logging +import json +import os +import torch +from tqdm import tqdm + +from lavis.common.utils import is_convertible_to_int +import lavis.common.dist_utils as dist_utils +from lavis.common.registry import registry +from lavis.common.vqa_tools.vqa import VQA +from lavis.common.vqa_tools.vqa_eval import VQAEval +from lavis.tasks.base_task import BaseTask + + +@registry.register_task("vqa") +class VQATask(BaseTask): + def __init__( + self, + num_beams, + max_len, + min_len, + evaluate, + num_ans_candidates, + inference_method="rank", + prompt="", + sample_id_key = "", + ques_files=dict(), + anno_files=dict(), + valid_splits=['val'] + ): + super().__init__() + + self.num_beams = num_beams + self.max_len = max_len + self.min_len = min_len + + self.evaluate = evaluate + self.inference_method = inference_method + self.num_ans_candidates = num_ans_candidates + self.prompt = prompt + + self.answer_list = None + + self.ques_files = ques_files + self.anno_files = anno_files + + # generalize to non coco data + self.sample_id_key = sample_id_key + + self.valid_splits = valid_splits + + @classmethod + def setup_task(cls, cfg): + run_cfg = cfg.run_cfg + + num_beams = run_cfg.get("num_beams", 3) + max_len = run_cfg.get("max_len", 10) + min_len = run_cfg.get("min_len", 1) + + evaluate = run_cfg.get("evaluate", False) + + inference_method = run_cfg.get("inference_method", "rank") + num_ans_candidates = run_cfg.get("num_ans_candidates", 128) + + prompt = run_cfg.get("prompt", "") + + # generalize to non coco data + sample_id_key = run_cfg.get("sample_id_key", "instance_id") + ques_files = run_cfg.get("ques_files", dict()) + anno_files = run_cfg.get("anno_files", dict()) + valid_splits = run_cfg.get("valid_splits", ["val"]) + + + return cls( + num_beams=num_beams, + max_len=max_len, + min_len=min_len, + evaluate=evaluate, + num_ans_candidates=num_ans_candidates, + inference_method=inference_method, + prompt=prompt, + sample_id_key = sample_id_key, + ques_files=ques_files, + anno_files=anno_files, + valid_splits=valid_splits + ) + + def build_datasets(self, cfg): + datasets = super().build_datasets(cfg) + + # get question file, annotation file and anwser list in COCO format + for ds_name, dataset in datasets.items(): + for split in self.valid_splits: + if split not in dataset: + print(f"Split {split} not found in {ds_name}.") + if ( + hasattr(dataset[split], "coco_fmt_qust_file") + and dataset[split].coco_fmt_qust_file is not None + ): + self.ques_files[split] = dataset[split].coco_fmt_qust_file + self.anno_files[split] = dataset[split].coco_fmt_anno_file + else: + if split not in self.ques_files: # precomputed and passed in task builder + self.ques_files[split] = os.path.join(registry.get_path("cache_root"),f'{ds_name}_gt', f'{ds_name}_{split}_questions.json') + self.anno_files[split] = os.path.join(registry.get_path("cache_root"), f'{ds_name}_gt', f'{ds_name}_{split}_annotations.json') + if dist_utils.get_rank() == 0: + os.makedirs(os.path.join(registry.get_path("cache_root"),f'{ds_name}_gt'), exist_ok=True) + try: + convert_to_coco_gt(dataset, self.ques_files[split], self.anno_files[split], split, self.sample_id_key) + except: + pass # tasks like vizwiz with no gt answer + try: + self.answer_list = dataset[split].answer_list + except AttributeError: + # if answer_list is not provided, then set it to None + pass + + if len(self.ques_files) > 0: + assert len(self.ques_files) == len( + self.anno_files + ), "Only support one split for evaluation." + + return datasets + + def valid_step(self, model, samples): + answers = model.predict_answers( + samples=samples, + answer_list=self.answer_list, + inference_method=self.inference_method, + num_beams=self.num_beams, + max_len=self.max_len, + min_len=self.min_len, + num_ans_candidates=self.num_ans_candidates, + prompt=self.prompt, + ) + pred_qa_pairs = [] + + question_id = samples["question_id"] + for answer, ques_id in zip(answers, question_id): + ques_id = int(ques_id.item()) if isinstance(ques_id, torch.Tensor) else ques_id + if ques_id != int and is_convertible_to_int(ques_id): + ques_id = int(ques_id) + pred_qa_pairs.append({"question_id": ques_id, "answer": answer}) + + return pred_qa_pairs + + def after_evaluation(self, val_result, split_name, **kwargs): + result_file = self.save_result( + val_result, + result_dir=registry.get_path("result_dir"), + filename=f"{split_name}_vqa_result", + remove_duplicate="question_id", + ) + + metrics = self._report_metrics(result_file=result_file, split=split_name) + + return metrics + + @dist_utils.main_process + def _report_metrics(self, result_file, split): + """ + Use official VQA evaluation script to report metrics. + """ + metrics = {} + + if split in self.ques_files and split in self.anno_files: + vqa = VQA(self.anno_files[split], self.ques_files[split]) + vqa_result = vqa.loadRes( + resFile=result_file, quesFile=self.ques_files[split] + ) + # create vqaEval object by taking vqa and vqaRes + # n is precision of accuracy (number of places after decimal), default is 2 + vqa_scorer = VQAEval(vqa, vqa_result, n=2) + logging.info("Start VQA evaluation.") + vqa_scorer.evaluate() + + # print accuracies + overall_acc = vqa_scorer.accuracy["overall"] + metrics["agg_metrics"] = overall_acc + + logging.info("Overall Accuracy is: %.02f\n" % overall_acc) + logging.info("Per Answer Type Accuracy is the following:") + + for ans_type in vqa_scorer.accuracy["perAnswerType"]: + logging.info( + "%s : %.02f" + % (ans_type, vqa_scorer.accuracy["perAnswerType"][ans_type]) + ) + metrics[ans_type] = vqa_scorer.accuracy["perAnswerType"][ans_type] + + with open( + os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a" + ) as f: + f.write(json.dumps(metrics) + "\n") + return metrics + +def convert_to_coco_gt(data, outpath_questions, outpath_annotations, split, sample_id_key): + if split not in data: + return + questions_data = {'info':"", 'task_type':"", 'data_type':"", 'license':"", 'data_subtype':"", 'questions':[]} + annotations_data = {'info':"", 'task_type':"", 'data_type':"", 'license':"", 'data_subtype':"", 'annotations':[]} + print("Generating ground truth annotations...") + for ann in tqdm(data[split]): + if ann == None: + continue + # if ann[sample_id_key] not in img_ids: + # continue + ques_id = ann["question_id"] + ques_id = int(ques_id.item()) if isinstance(ques_id, torch.Tensor) else ques_id + if ques_id != int and is_convertible_to_int(ques_id): + ques_id = int(ques_id) + questions_data["questions"].append({"question": ann["text_input"], "image_id": ann[sample_id_key], "question_id": ques_id}) + annotations_data["annotations"].append({ + "question_type": "" if "question_type" not in ann else ann["question_type"], + "multiple_choice_answer": ann["answers"][0] if isinstance(ann["answers"], list) else ann["answers"], + "answers": [{"answer":ans, "answer_id":i} for i,ans in enumerate(ann["answers"])] if isinstance(ann["answers"], list) else [{"answer":ann["answers"], "answer_id":0}], + "image_id": ann[sample_id_key], + "question_id": ques_id, + "answer_type": "" if "answer_type" not in ann else ann["answer_type"], + }) + + json.dump(questions_data, open(outpath_questions, 'w')) + print(f"Saved questions data at {outpath_questions}") + json.dump(annotations_data, open(outpath_annotations, 'w')) + print(f"Saved annotation data at {outpath_annotations}") + + + +@registry.register_task("aok_vqa") +class AOKVQATask(VQATask): + def valid_step(self, model, samples): + answers = model.predict_answers( + samples=samples, + answer_list=self.answer_list, + inference_method=self.inference_method, + num_beams=self.num_beams, + max_len=self.max_len, + min_len=self.min_len, + num_ans_candidates=self.num_ans_candidates, + ) + + pred_qa_pairs = [] + + question_id = samples["question_id"] + gt_answers = samples["direct_answers"] + + for pred_answer, ques_id, gt_answer in zip(answers, question_id, gt_answers): + pred_qa_pairs.append( + {"question_id": ques_id, "pred_ans": pred_answer, "gt_ans": gt_answer} + ) + + return pred_qa_pairs + + @dist_utils.main_process + def _report_metrics(self, result_file, split): + """ + Implementing accuracy computation for AOKVQA, see + https://github.com/allenai/aokvqa/blob/main/evaluation/eval_predictions.py#L45 for details. + """ + # TODO add evaluation for multi-choice + + results = json.load(open(result_file, "r")) + acc = [] + + for res in results: + if res["gt_ans"] is None: + # prepare test results for leaderboard evaluation + self._save_result_leaderboard(results) + return + + pred = res["pred_ans"] + gt_ans = res["gt_ans"] + + num_match = sum([pred == gt for gt in gt_ans]) + vqa_acc = min(1.0, num_match / 3.0) + + acc.append(vqa_acc) + + accuracy = sum(acc) / len(acc) * 100 + metrics = {"agg_metrics": accuracy, "acc": accuracy} + + with open( + os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a" + ) as f: + f.write(json.dumps(metrics) + "\n") + + logging.info(metrics) + + return metrics + + @dist_utils.main_process + def _save_result_leaderboard(self, results): + """ + Saving the results in the format required for leaderboard evaluation. + + [TODO] add support for multi-choice. + """ + result_leaderboard = dict() + for res in results: + result_leaderboard[res["question_id"]] = { + "direct_answer": res["pred_ans"], + "multiple_choice": "", + } + + result_file = registry.get_path("result_dir") + "_leaderboard.json" + + with open(result_file, "w") as f: + json.dump(result_leaderboard, f) + + logging.info(f"Saved results for leaderboard evaluation at {result_file}") + + + +@registry.register_task("gqa") +class GQATask(VQATask): + def valid_step(self, model, samples): + answers = model.predict_answers( + samples=samples, + answer_list=self.answer_list, + inference_method=self.inference_method, + num_beams=self.num_beams, + max_len=self.max_len, + min_len=self.min_len, + num_ans_candidates=self.num_ans_candidates, + prompt=self.prompt, + ) + pred_qa_pairs = [] + + question_id = samples["question_id"] + gt_answers = samples["answer"] + + for answer, ques_id, gt_answer in zip(answers, question_id, gt_answers): + ques_id = int(ques_id.item()) if isinstance(ques_id, torch.Tensor) else ques_id + pred_qa_pairs.append({"question_id": ques_id, "pred_ans": answer, "gt_ans": gt_answer}) + + return pred_qa_pairs + + def build_datasets(self, cfg): + datasets = BaseTask.build_datasets(self,cfg) + + # get question file, annotation file and anwser list in COCO format + for ds_name, dataset in datasets.items(): + for split in dataset: + if ( + hasattr(dataset[split], "coco_fmt_qust_file") + and dataset[split].coco_fmt_qust_file is not None + ): + self.ques_files[split] = dataset[split].coco_fmt_qust_file + self.anno_files[split] = dataset[split].coco_fmt_anno_file + + if len(self.ques_files) > 0: + assert len(self.ques_files) == len( + self.anno_files + ), "Only support one split for evaluation." + + return datasets + + @dist_utils.main_process + def _report_metrics(self, result_file, split): + """ + TODO: add other evaluation metrics for GQA + """ + + results = json.load(open(result_file, "r")) + acc = [] + vqa_tool = VQAEval() + + for res in results: + if res["gt_ans"] is None: + # prepare test results for leaderboard evaluation + self._save_result_leaderboard(results) + return + + gt_ans = res["gt_ans"] + pred = res["pred_ans"] + + # if self.inference_method == "generate": + pred = vqa_tool.processPunctuation(pred) + pred = vqa_tool.processDigitArticle(pred) + + # added to ensure that the ground truth format of answers is as expected for non-gqa but similar tasks + gt_ans = vqa_tool.processPunctuation(gt_ans) + gt_ans = vqa_tool.processDigitArticle(gt_ans) + + vqa_acc = 1 if pred == gt_ans else 0 + + acc.append(vqa_acc) + + accuracy = sum(acc) / len(acc) * 100 + metrics = {"agg_metrics": accuracy, "acc": accuracy} + + with open( + os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a" + ) as f: + f.write(json.dumps(metrics) + "\n") + + logging.info(metrics) + + return metrics + + +@registry.register_task("discrn_qa") +class DisCRNTask(VQATask): + def valid_step(self, model, samples): + answers = model.predict_answers( + samples=samples, + answer_list=self.answer_list, + inference_method=self.inference_method, + num_beams=self.num_beams, + max_len=self.max_len, + min_len=self.min_len, + num_ans_candidates=self.num_ans_candidates, + prompt=self.prompt, + ) + + if answers == None: # corrupt videos + return [] + + pred_qa_pairs = [] + + question_id = samples["question_id"] + gt_answers = samples["answer"] + + for answer, ques_id, gt_answer in zip(answers, question_id, gt_answers): + ques_id = int(ques_id.item()) if isinstance(ques_id, torch.Tensor) else ques_id + pred_qa_pairs.append({"question_id": ques_id, "pred_ans": answer, "gt_ans": gt_answer}) + + return pred_qa_pairs + + + def build_datasets(self, cfg): + datasets = BaseTask.build_datasets(self, cfg) + return datasets + + @dist_utils.main_process + def _report_metrics(self, result_file, split): + results = json.load(open(result_file, "r")) + acc = [] + vqa_tool = VQAEval() + + for res in results: + gt_ans = res["gt_ans"] + pred = res["pred_ans"] + + # gt_ans = [vqa_tool.processPunctuation(g) for g in gt_ans] + # gt_ans = [vqa_tool.processDigitArticle(g) for g in gt_ans] + + # if self.inference_method == "generate": + pred = vqa_tool.processPunctuation(pred) + pred = vqa_tool.processDigitArticle(pred) + + tokenized_pred = pred.strip().split(" ") + for ans in gt_ans: + if ans in tokenized_pred: + pred = ans + break + + vqa_acc = 1 if pred in gt_ans else 0 + + acc.append(vqa_acc) + + accuracy = sum(acc) / len(acc) * 100 + metrics = {"agg_metrics": accuracy, "acc": accuracy} + + with open( + os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a" + ) as f: + f.write(json.dumps(metrics) + "\n") + + logging.info(metrics) + + return metrics \ No newline at end of file diff --git a/LAVIS-main/lavis/tasks/vqa_reading_comprehension.py b/LAVIS-main/lavis/tasks/vqa_reading_comprehension.py new file mode 100644 index 0000000000000000000000000000000000000000..254f464b8ccf370f6263febb0bc73b9639f52d4e --- /dev/null +++ b/LAVIS-main/lavis/tasks/vqa_reading_comprehension.py @@ -0,0 +1,248 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import logging +import json +import os +import torch +import torch.distributed as dist +from itertools import chain + +import lavis.common.dist_utils as dist_utils +from lavis.common.dist_utils import get_rank, get_world_size, is_main_process +from lavis.common.registry import registry +from lavis.common.vqa_tools.vqa_eval import VQAEval as VQATool +from lavis.tasks.vqa import VQATask + + +@registry.register_task("vqa_reading_comprehension") +class VQARCTask(VQATask): + def __init__( + self, + num_beams, + max_len, + min_len, + evaluate, + num_ans_candidates, + inference_method="rank", + **kwargs, + ): + super().__init__(num_beams, max_len, min_len, evaluate, num_ans_candidates, inference_method) + + self.config = kwargs.get('config') + + @classmethod + def setup_task(cls, cfg): + run_cfg = cfg.run_cfg + + num_beams = run_cfg.get("num_beams", 3) + max_len = run_cfg.get("max_len", 10) + min_len = run_cfg.get("min_len", 1) + + evaluate = run_cfg.get("evaluate", False) + + inference_method = run_cfg.get("inference_method", "rank") + num_ans_candidates = run_cfg.get("num_ans_candidates", 128) + + return cls( + num_beams=num_beams, + max_len=max_len, + min_len=min_len, + evaluate=evaluate, + num_ans_candidates=num_ans_candidates, + inference_method=inference_method, + config=run_cfg, + ) + + def valid_step(self, model, samples): + answers, captions, gradcams = model.predict_answers( + samples=samples, + inference_method=self.inference_method, + num_beams=self.num_beams, + max_len=self.max_len, + min_len=self.min_len, + internal_bsz_fid=self.config['internal_bsz_fid'], + num_captions=self.config['num_captions'], + num_captions_fid=self.config['num_captions_fid'], + cap_max_length=self.config['cap_max_length'], + cap_min_length=self.config['cap_min_length'], + top_k=self.config['top_k'], + top_p=self.config['top_p'], + repetition_penalty=self.config['repetition_penalty'], + num_patches=self.config['num_patches'], + block_num=self.config['block_num'], + ) + + pred_qa_pairs = [] + sample_captions = [] + sample_gradcams = [] + + question_id = samples["question_id"] + for answer, caption, gradcam, ques_id in zip(answers, captions, gradcams, question_id): + ques_id = int(ques_id.item()) + pred_qa_pairs.append({"question_id": ques_id, "answer": answer}) + sample_captions.append({"question_id": ques_id, "caption": caption}) + sample_gradcams.append({"question_id": ques_id, "gradcam": gradcam}) + + return [sample_gradcams, sample_captions, pred_qa_pairs] + + def after_evaluation(self, val_result, split_name, **kwargs): + result_ = list(chain(*val_result[0::3])) + result_file = self.save_gradcam( + result_, + result_dir=registry.get_path("result_dir"), + filename=f"{split_name}_gradcam_result", + remove_duplicate="question_id", + ) + + result_ = list(chain(*val_result[1::3])) + result_file = self.save_result( + result_, + result_dir=registry.get_path("result_dir"), + filename=f"{split_name}_caption_result", + remove_duplicate="question_id", + ) + + result_ = list(chain(*val_result[2::3])) + result_file = self.save_result( + result_, + result_dir=registry.get_path("result_dir"), + filename=f"{split_name}_vqa_result", + remove_duplicate="question_id", + ) + + metrics = self._report_metrics(result_file=result_file, split=split_name) + + return metrics + + def save_gradcam(self, result, result_dir, filename, remove_duplicate=""): + result_file = os.path.join(result_dir, '%s_rank%d.pth' % (filename, get_rank())) + final_result_file = os.path.join(result_dir, '%s.pth' % filename) + torch.save({'result': result}, result_file) + + dist.barrier() + + if is_main_process(): + logging.warning("rank %d starts merging results." % get_rank()) + # combine results from all processes + result = [] + + for rank in range(get_world_size()): + result_file = os.path.join(result_dir, '%s_rank%d.pth' % (filename, rank)) + res_ckpt = torch.load(result_file, map_location='cpu') + res = res_ckpt['result'] + + result += res + + if remove_duplicate: + result_new = [] + id_list = [] + for res in result: + if res[remove_duplicate] not in id_list: + id_list.append(res[remove_duplicate]) + result_new.append(res) + result = result_new + + torch.save({'result': result}, final_result_file) + print("result file saved to %s" % final_result_file) + + return final_result_file + + +@registry.register_task("gqa_reading_comprehension") +class GQARCTask(VQARCTask): + def valid_step(self, model, samples): + answers, captions, gradcams = model.predict_answers( + samples=samples, + inference_method=self.inference_method, + num_beams=self.num_beams, + max_len=self.max_len, + min_len=self.min_len, + internal_bsz_fid=self.config['internal_bsz_fid'], + num_captions=self.config['num_captions'], + num_captions_fid=self.config['num_captions_fid'], + cap_max_length=self.config['cap_max_length'], + cap_min_length=self.config['cap_min_length'], + top_k=self.config['top_k'], + top_p=self.config['top_p'], + repetition_penalty=self.config['repetition_penalty'], + num_patches=self.config['num_patches'], + block_num=self.config['block_num'], + ) + + pred_qa_pairs = [] + sample_captions = [] + sample_gradcams = [] + + question_id = samples["question_id"] + gt_answers = samples["answer"] + + for pred_answer, caption, gradcam, ques_id, gt_answer in zip(answers, captions, gradcams, question_id, gt_answers): + ques_id = int(ques_id.item()) + pred_qa_pairs.append({"question_id": ques_id, "pred_ans": pred_answer, "gt_ans": gt_answer}) + sample_captions.append({"question_id": ques_id, "caption": caption}) + sample_gradcams.append({"question_id": ques_id, "gradcam": gradcam}) + + return [sample_gradcams, sample_captions, pred_qa_pairs] + + @dist_utils.main_process + def _report_metrics(self, result_file, split): + """ + TODO: add other evaluation metrics for GQA + """ + + results = json.load(open(result_file, "r")) + acc = [] + vqa_tool = VQATool() + + for res in results: + if res["gt_ans"] is None: + # prepare test results for leaderboard evaluation + self._save_result_leaderboard(results) + return + + gt_ans = res["gt_ans"] + pred = res["pred_ans"] + + if self.inference_method == "generate": + pred = vqa_tool.processPunctuation(pred) + pred = vqa_tool.processDigitArticle(pred) + + vqa_acc = 1 if pred == gt_ans else 0 + + acc.append(vqa_acc) + + accuracy = sum(acc) / len(acc) * 100 + metrics = {"agg_metrics": accuracy, "acc": accuracy} + + with open( + os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a" + ) as f: + f.write(json.dumps(metrics) + "\n") + + logging.info(metrics) + + return metrics + + @dist_utils.main_process + def _save_result_leaderboard(self, results): + """ + Saving the results in the format required for leaderboard evaluation. + """ + result_leaderboard = [] + for res in results: + result_leaderboard.append({ + "questionId": str(res['question_id']), + "prediction": str(res["pred_ans"]), + }) + + result_file = registry.get_path("result_dir") + "_leaderboard.json" + + with open(result_file, "w") as f: + json.dump(result_leaderboard, f) + + logging.info(f"Saved results for leaderboard evaluation at {result_file}") diff --git a/LAVIS-main/projects/blip-diffusion/README.md b/LAVIS-main/projects/blip-diffusion/README.md new file mode 100644 index 0000000000000000000000000000000000000000..20de17574feca8617360b32e661abe0e8e7d331c --- /dev/null +++ b/LAVIS-main/projects/blip-diffusion/README.md @@ -0,0 +1,133 @@ +## BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing +[Paper](https://arxiv.org/abs/2305.14720), [Demo Site](https://dxli94.github.io/BLIP-Diffusion-website/), [Video](https://youtu.be/Wf09s4JnDb0) + +This repo hosts the official implementation of BLIP-Diffusion, a text-to-image diffusion model with built-in support for multimodal subject-and-text condition. BLIP-Diffusion enables zero-shot subject-driven generation, and efficient fine-tuning for customized subjects with up to 20x speedup. In addition, BLIP-Diffusion can be flexibly combiend with ControlNet and prompt-to-prompt to enable novel subject-driven generation and editing applications. + + + + +### Installation + +Install the LAVIS library from source: + +```bash +pip install -e . +``` + +### Notebook Examples +- **Subject-driven Generation**: + - zero-shot inference: [notebook](https://github.com/salesforce/LAVIS/blob/main/projects/blip-diffusion/notebooks/generation_zeroshot.ipynb), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/projects/blip-diffusion/notebooks/generation_zeroshot.ipynb) + - inference with fine-tuned checkpoint: [notebook](https://github.com/salesforce/LAVIS/blob/main/projects/blip-diffusion/notebooks/generation_finetuned_dog.ipynb), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/projects/blip-diffusion/notebooks/generation_finetuned_dog.ipynb) + +- **Structure-Controlled Generation / Stylization**: [notebook](https://github.com/salesforce/LAVIS/blob/main/projects/blip-diffusion/notebooks/stylization.ipynb), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/projects/blip-diffusion/notebooks/stylization.ipynb) + +- **Subject-driven Editing**: + - editing a synthetic image: + - First generate an image, then edit the image with the specified subject visuals: [notebook](https://github.com/salesforce/LAVIS/blob/main/projects/blip-diffusion/notebooks/editing_synthetic_zeroshot.ipynb), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/projects/blip-diffusion/notebooks/editing_synthetic_zeroshot.ipynb) + - editing a real image with DDIM inversion: + - zero-shot inference: [notebook](https://github.com/salesforce/LAVIS/blob/main/projects/blip-diffusion/notebooks/editing_real_zeroshot.ipynb), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/projects/blip-diffusion/notebooks/editing_real_zeroshot.ipynb) + - inference with fine-tuned checkpoint: [notebook](https://github.com/salesforce/LAVIS/blob/main/projects/blip-diffusion/notebooks/editing_real_finetuned.ipynb), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/projects/blip-diffusion/notebooks/editing_real_finetuned.ipynb) + +- **Virtual Try-On via Subject-driven Editing**: + - the model can be used to naturally facilitate virtual try-on. We provide an zero-shot example: [notebook](https://github.com/salesforce/LAVIS/blob/main/projects/blip-diffusion/notebooks/editing_tryon_zeroshot.ipynb), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/projects/blip-diffusion/notebooks/editing_tryon_zeroshot.ipynb); + +### **🧨 Diffusers Support** +BLIP-Diffusion is now supported in 🧨[Diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/blip_diffusion). +- Example on subject-driven generation: +```python +from diffusers.pipelines import BlipDiffusionPipeline +from diffusers.utils import load_image +import torch + +blip_diffusion_pipe = BlipDiffusionPipeline.from_pretrained( + "Salesforce/blipdiffusion", torch_dtype=torch.float16 +).to("cuda") + + +cond_subject = "dog" +tgt_subject = "dog" +text_prompt_input = "swimming underwater" + +cond_image = load_image( + "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog.jpg" +) +guidance_scale = 7.5 +num_inference_steps = 25 +negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" + + +output = blip_diffusion_pipe( + text_prompt_input, + cond_image, + cond_subject, + tgt_subject, + guidance_scale=guidance_scale, + num_inference_steps=num_inference_steps, + neg_prompt=negative_prompt, + height=512, + width=512, +).images +output[0].save("image.png") +``` +- Example on subject-driven stylization +```python +from diffusers.pipelines import BlipDiffusionControlNetPipeline +from diffusers.utils import load_image +from controlnet_aux import CannyDetector +import torch + +blip_diffusion_pipe = BlipDiffusionControlNetPipeline.from_pretrained( + "Salesforce/blipdiffusion-controlnet", torch_dtype=torch.float16 +).to("cuda") + +style_subject = "flower" +tgt_subject = "teapot" +text_prompt = "on a marble table" + +cldm_cond_image = load_image( + "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg" +).resize((512, 512)) +canny = CannyDetector() +cldm_cond_image = canny(cldm_cond_image, 30, 70, output_type="pil") +style_image = load_image( + "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" +) +guidance_scale = 7.5 +num_inference_steps = 50 +negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" + + +output = blip_diffusion_pipe( + text_prompt, + style_image, + cldm_cond_image, + style_subject, + tgt_subject, + guidance_scale=guidance_scale, + num_inference_steps=num_inference_steps, + neg_prompt=negative_prompt, + height=512, + width=512, +).images +output[0].save("image.png") +``` + + +### Cite BLIP-Diffusion +If you find our work helpful, please consider citing: +
+@article{li2023blip,
+  title={BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing},
+  author={Li, Dongxu and Li, Junnan and Hoi, Steven CH},
+  journal={arXiv preprint arXiv:2305.14720},
+  year={2023}
+}
+
+@inproceedings{li2023lavis,
+  title={LAVIS: A One-stop Library for Language-Vision Intelligence},
+  author={Li, Dongxu and Li, Junnan and Le, Hung and Wang, Guangsen and Savarese, Silvio and Hoi, Steven CH},
+  booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
+  pages={31--41},
+  year={2023}
+}
+
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Please import from diffusers.models.attention_processor instead.\n", + " deprecate(\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "from PIL import Image\n", + "from lavis.models import load_model_and_preprocess" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n", + "```\n", + "pip install accelerate\n", + "```\n", + ".\n", + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n", + "```\n", + "pip install accelerate\n", + "```\n", + ".\n", + "/opt/conda/lib/python3.8/site-packages/diffusers/configuration_utils.py:195: FutureWarning: It is deprecated to pass a pretrained model name or path to `from_config`.If you were trying to load a scheduler, please use .from_pretrained(...) instead. Otherwise, please make sure to pass a configuration dictionary instead. This functionality will be removed in v1.0.0.\n", + " deprecate(\"config-passed-as-path\", \"1.0.0\", deprecation_message, standard_warn=False)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No ctx_embeddings_cache found in /root/.cache/torch/hub/checkpoints/blip-diffusion\n" + ] + } + ], + "source": [ + "model, vis_preprocess, txt_preprocess = load_model_and_preprocess(\"blip_diffusion\", \"base\", device=\"cuda\", is_eval=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Description\n", + "This demo shows how to edit a **real** image with a finetuned checkpoint on a given subject. It works in the following steps:\n", + "\n", + "(1) load the finetuned checkpoint.\n", + "\n", + "(2) run DDIM inversion on the given image using prompt ``A ${src_subject} ${prompt}.``;\n", + "\n", + "(3) extracting BLIP-2 embeddings on condition subject image, using ``cond_subject`` and ``cond_image``.\n", + "\n", + "(4) edit the real image with the subject visuals, using the prompt ``A ${BLIP-2 embedding} ${tgt_subject} ${prompt}`` and the DDIM inverted latents." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cond_subject = \"dog\"\n", + "src_subject = \"cat\"\n", + "tgt_subject = \"dog\"\n", + "\n", + "text_prompt = \"sit on sofa\"\n", + "\n", + "cond_subject = txt_preprocess[\"eval\"](cond_subject)\n", + "src_subject = txt_preprocess[\"eval\"](src_subject)\n", + "tgt_subject = txt_preprocess[\"eval\"](tgt_subject)\n", + "text_prompt = [txt_preprocess[\"eval\"](text_prompt)]\n", + "\n", + "src_image = Image.open(\"../images/cat-sofa.png\").convert(\"RGB\")\n", + "display(src_image.resize((256, 256)))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading fine-tuned model from https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP-Diffusion/db-dog/checkpoint_40.pth\n" + ] + } + ], + "source": [ + "finetuned_ckpt = \"https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP-Diffusion/db-dog/checkpoint_40.pth\"\n", + "# can also use a local checkpoint\n", + "# finetuned_ckpt = \"../checkpoints/db-dog/checkpoint_40.pth\"\n", + "model.load_checkpoint(finetuned_ckpt)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "samples = {\n", + " \"cond_images\": None,\n", + " \"cond_subject\": cond_subject,\n", + " \"src_subject\": src_subject,\n", + " \"tgt_subject\": tgt_subject,\n", + " \"prompt\": text_prompt,\n", + " \"raw_image\": src_image,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.8/site-packages/diffusers/configuration_utils.py:195: FutureWarning: It is deprecated to pass a pretrained model name or path to `from_config`.If you were trying to load a scheduler, please use .from_pretrained(...) instead. Otherwise, please make sure to pass a configuration dictionary instead. This functionality will be removed in v1.0.0.\n", + " deprecate(\"config-passed-as-path\", \"1.0.0\", deprecation_message, standard_warn=False)\n", + "100%|██████████| 50/50 [00:02<00:00, 20.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['a cat sit on sofa', 'a sks sks sks sks sks sks sks sks sks sks sks sks sks sks sks sks dog sit on sofa']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 50/50 [00:08<00:00, 5.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==============================\n", + "Before editing:\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "iter_seed = 8887\n", + "guidance_scale = 7.5\n", + "num_inference_steps = 50 \n", + "num_inversion_steps = 50 # increase to improve DDIM inversion quality\n", + "lb_threshold = 0.3 # increase to edit fewer pixels.\n", + "negative_prompt = \"over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate\"\n", + "\n", + "output = model.edit(\n", + " samples,\n", + " seed=iter_seed,\n", + " guidance_scale=guidance_scale,\n", + " num_inference_steps=num_inference_steps,\n", + " num_inversion_steps=num_inversion_steps,\n", + " neg_prompt=negative_prompt,\n", + " lb_threshold=lb_threshold,\n", + ")\n", + "\n", + "print(\"=\" * 30)\n", + "print(\"Before editing:\")\n", + "display(output[0])\n", + "\n", + "print(\"After editing:\")\n", + "display(output[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(512, 512)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output[0].size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/LAVIS-main/projects/blip-diffusion/notebooks/editing_real_zeroshot.ipynb b/LAVIS-main/projects/blip-diffusion/notebooks/editing_real_zeroshot.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a71ebba689b4555a98476a2fa51a05e3a3f16808 --- /dev/null +++ b/LAVIS-main/projects/blip-diffusion/notebooks/editing_real_zeroshot.ipynb @@ -0,0 +1,301 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install 'git+https://github.com/salesforce/LAVIS.git'" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1690878779.956453] [sfr-pod-li-d-a100x16-2306-2:7659 :f] vfs_fuse.c:281 UCX ERROR inotify_add_watch(/tmp) failed: No space left on device\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:Pytorch pre-release version 2.1.0a0+4136153 - assuming intent to test it\n", + "/usr/local/lib/python3.10/dist-packages/diffusers/models/cross_attention.py:30: FutureWarning: Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.\n", + " deprecate(\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "from PIL import Image\n", + "from lavis.models import load_model_and_preprocess" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of BertLMHeadModel were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['bert.encoder.layer.0.output_query.dense.weight', 'bert.encoder.layer.8.crossattention.output.dense.weight', 'bert.encoder.layer.4.crossattention.self.query.bias', 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'bert.encoder.layer.4.output_query.dense.weight', 'bert.encoder.layer.4.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.9.output_query.LayerNorm.weight', 'bert.encoder.layer.10.crossattention.self.key.bias', 'bert.encoder.layer.0.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.3.crossattention.self.query.bias', 'bert.encoder.layer.0.crossattention.self.key.weight', 'bert.encoder.layer.4.intermediate_query.dense.bias', 'bert.encoder.layer.1.crossattention.output.dense.weight', 'bert.encoder.layer.6.crossattention.self.value.bias', 'bert.encoder.layer.9.crossattention.output.dense.weight', 'bert.encoder.layer.6.output_query.LayerNorm.weight', 'bert.encoder.layer.10.crossattention.self.key.weight', 'bert.encoder.layer.4.output_query.dense.bias', 'bert.encoder.layer.6.crossattention.output.dense.bias', 'bert.encoder.layer.6.crossattention.self.value.weight', 'bert.encoder.layer.0.crossattention.self.key.bias', 'bert.encoder.layer.11.output_query.dense.weight', 'bert.encoder.layer.4.output_query.LayerNorm.bias', 'bert.encoder.layer.2.output_query.dense.bias', 'bert.encoder.layer.11.crossattention.output.dense.weight', 'bert.encoder.layer.4.intermediate_query.dense.weight', 'bert.encoder.layer.4.crossattention.self.value.bias', 'bert.encoder.layer.6.crossattention.self.key.bias', 'bert.encoder.layer.7.output_query.dense.bias', 'bert.encoder.layer.2.crossattention.self.query.bias', 'bert.encoder.layer.6.output_query.dense.bias', 'bert.encoder.layer.3.crossattention.self.value.bias', 'bert.encoder.layer.3.crossattention.self.key.weight', 'bert.encoder.layer.10.crossattention.self.query.weight', 'bert.encoder.layer.0.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.4.output_query.LayerNorm.weight']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n", + "```\n", + "pip install accelerate\n", + "```\n", + ".\n", + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n", + "```\n", + "pip install accelerate\n", + "```\n", + ".\n", + "/usr/local/lib/python3.10/dist-packages/diffusers/configuration_utils.py:217: FutureWarning: It is deprecated to pass a pretrained model name or path to `from_config`.If you were trying to load a scheduler, please use .from_pretrained(...) instead. Otherwise, please make sure to pass a configuration dictionary instead. This functionality will be removed in v1.0.0.\n", + " deprecate(\"config-passed-as-path\", \"1.0.0\", deprecation_message, standard_warn=False)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No ctx_embeddings_cache found in /root/.cache/torch/hub/checkpoints/blip-diffusion\n" + ] + } + ], + "source": [ + "model, vis_preprocess, txt_preprocess = load_model_and_preprocess(\"blip_diffusion\", \"base\", device=\"cuda\", is_eval=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Description\n", + "This demo shows how to edit a **real** image with a given subject as condition. It works in the following steps:\n", + "\n", + "(1) run DDIM inversion on the given image using prompt ``A ${src_subject} ${prompt}.``;\n", + "\n", + "(2) extracting BLIP-2 embeddings on condition subject image, using ``cond_subject`` and ``cond_image``.\n", + "\n", + "(3) edit the real image with the subject visuals, using the prompt ``A ${BLIP-2 embedding} ${tgt_subject} ${prompt}`` and the DDIM inverted latents." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cond_subject = \"dog\"\n", + "src_subject = \"cat\"\n", + "tgt_subject = \"dog\"\n", + "\n", + "text_prompt = \"sit on sofa\"\n", + "\n", + "src_subject = txt_preprocess[\"eval\"](src_subject)\n", + "tgt_subject = txt_preprocess[\"eval\"](tgt_subject)\n", + "cond_subject = txt_preprocess[\"eval\"](cond_subject)\n", + "text_prompt = [txt_preprocess[\"eval\"](text_prompt)]\n", + "\n", + "cond_image = Image.open(\"../images/dog.png\").convert(\"RGB\")\n", + "display(cond_image.resize((256, 256)))\n", + "cond_image = vis_preprocess[\"eval\"](cond_image).unsqueeze(0).cuda()\n", + "\n", + "src_image = Image.open(\"../images/cat-sofa.png\").convert(\"RGB\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "samples = {\n", + " \"cond_images\": cond_image,\n", + " \"cond_subject\": cond_subject,\n", + " \"src_subject\": src_subject,\n", + " \"tgt_subject\": tgt_subject,\n", + " \"prompt\": text_prompt,\n", + " \"raw_image\": src_image,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/export/home/workspace/LAVIS-latest/LAVIS/lavis/models/blip_diffusion_models/blip_diffusion.py:331: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\n", + " self.unet.in_channels,\n", + "/usr/local/lib/python3.10/dist-packages/diffusers/configuration_utils.py:217: FutureWarning: It is deprecated to pass a pretrained model name or path to `from_config`.If you were trying to load a scheduler, please use .from_pretrained(...) instead. Otherwise, please make sure to pass a configuration dictionary instead. This functionality will be removed in v1.0.0.\n", + " deprecate(\"config-passed-as-path\", \"1.0.0\", deprecation_message, standard_warn=False)\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:01<00:00, 25.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['a cat sit on sofa', 'a sks sks sks sks sks sks sks sks sks sks sks sks sks sks sks sks dog sit on sofa']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:04<00:00, 10.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==============================\n", + "Before editing:\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "iter_seed = 88871\n", + "guidance_scale = 7.5\n", + "num_inference_steps = 50\n", + "num_inversion_steps = 50 # increase to improve DDIM inversion quality\n", + "negative_prompt = \"over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate\"\n", + "\n", + "output = model.edit(\n", + " samples,\n", + " seed=iter_seed,\n", + " guidance_scale=guidance_scale,\n", + " num_inference_steps=num_inference_steps,\n", + " num_inversion_steps=num_inversion_steps,\n", + " neg_prompt=negative_prompt,\n", + ")\n", + "\n", + "print(\"=\" * 30)\n", + "print(\"Before editing:\")\n", + "display(output[0])\n", + "\n", + "print(\"After editing:\")\n", + "display(output[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(512, 512)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output[0].size" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/LAVIS-main/projects/blip-diffusion/notebooks/editing_synthetic_zeroshot.ipynb b/LAVIS-main/projects/blip-diffusion/notebooks/editing_synthetic_zeroshot.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..62b7b01705af222c9447fa5e930a743204e82e70 --- /dev/null +++ b/LAVIS-main/projects/blip-diffusion/notebooks/editing_synthetic_zeroshot.ipynb @@ -0,0 +1,270 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install 'git+https://github.com/salesforce/LAVIS.git'" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1690878873.307132] [sfr-pod-li-d-a100x16-2306-2:7657 :f] vfs_fuse.c:281 UCX ERROR inotify_add_watch(/tmp) failed: No space left on device\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:Pytorch pre-release version 2.1.0a0+4136153 - assuming intent to test it\n", + "/usr/local/lib/python3.10/dist-packages/diffusers/models/cross_attention.py:30: FutureWarning: Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.\n", + " deprecate(\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "from PIL import Image\n", + "from lavis.models import load_model_and_preprocess" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of BertLMHeadModel were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['bert.encoder.layer.6.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.6.output_query.LayerNorm.bias', 'bert.encoder.layer.10.crossattention.self.value.weight', 'bert.encoder.layer.5.output_query.dense.bias', 'bert.encoder.layer.2.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.7.intermediate_query.dense.bias', 'bert.encoder.layer.5.crossattention.self.query.weight', 'bert.encoder.layer.10.crossattention.output.dense.weight', 'bert.encoder.layer.9.intermediate_query.dense.bias', 'bert.encoder.layer.7.crossattention.output.dense.bias', 'bert.encoder.layer.10.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.1.crossattention.self.query.weight', 'bert.encoder.layer.9.crossattention.self.query.weight', 'bert.encoder.layer.0.output_query.LayerNorm.weight', 'bert.encoder.layer.3.intermediate_query.dense.weight', 'bert.encoder.layer.9.crossattention.self.value.weight', 'bert.encoder.layer.5.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.11.intermediate_query.dense.weight', 'bert.encoder.layer.6.crossattention.self.value.bias', 'bert.encoder.layer.9.crossattention.output.LayerNorm.weight', 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'bert.encoder.layer.8.intermediate_query.dense.bias', 'bert.encoder.layer.10.crossattention.self.key.weight', 'bert.encoder.layer.11.output_query.dense.bias', 'bert.encoder.layer.8.crossattention.self.value.weight', 'bert.encoder.layer.2.crossattention.self.value.bias', 'bert.encoder.layer.1.output_query.dense.bias', 'bert.encoder.layer.1.crossattention.self.key.weight', 'bert.encoder.layer.5.crossattention.output.dense.bias', 'bert.encoder.layer.5.output_query.dense.weight', 'bert.encoder.layer.1.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.4.crossattention.output.dense.weight', 'bert.encoder.layer.6.crossattention.output.dense.weight', 'bert.encoder.layer.7.crossattention.self.query.weight', 'bert.encoder.layer.10.output_query.LayerNorm.weight', 'bert.encoder.layer.3.intermediate_query.dense.bias', 'bert.encoder.layer.6.crossattention.self.key.bias', 'bert.encoder.layer.4.intermediate_query.dense.weight', 'bert.encoder.layer.1.output_query.dense.weight', 'bert.encoder.layer.7.output_query.LayerNorm.bias', 'bert.encoder.layer.7.crossattention.self.value.weight', 'bert.encoder.layer.8.crossattention.self.query.bias', 'bert.encoder.layer.9.crossattention.self.query.bias', 'bert.encoder.layer.8.crossattention.self.value.bias', 'bert.encoder.layer.6.crossattention.self.query.bias', 'bert.encoder.layer.7.intermediate_query.dense.weight', 'bert.encoder.layer.2.output_query.dense.weight', 'bert.encoder.layer.4.intermediate_query.dense.bias', 'bert.encoder.layer.9.output_query.dense.bias', 'bert.encoder.layer.8.crossattention.self.key.bias', 'bert.encoder.layer.8.output_query.dense.weight', 'bert.encoder.layer.8.crossattention.output.dense.weight', 'bert.encoder.layer.7.crossattention.output.dense.weight', 'bert.encoder.layer.2.intermediate_query.dense.weight', 'bert.encoder.layer.8.crossattention.output.dense.bias', 'bert.encoder.layer.1.crossattention.self.value.bias', 'bert.encoder.layer.11.crossattention.output.dense.bias', 'bert.encoder.layer.3.crossattention.self.key.weight', 'bert.encoder.layer.6.output_query.dense.bias', 'bert.encoder.layer.2.output_query.dense.bias', 'bert.encoder.layer.0.crossattention.output.dense.weight', 'bert.encoder.layer.10.intermediate_query.dense.weight', 'bert.encoder.layer.9.crossattention.self.key.weight', 'bert.encoder.layer.5.crossattention.output.dense.weight', 'bert.encoder.layer.11.output_query.LayerNorm.bias', 'bert.encoder.layer.1.crossattention.self.key.bias', 'bert.encoder.layer.7.crossattention.self.key.weight', 'bert.encoder.layer.0.intermediate_query.dense.weight', 'bert.encoder.layer.7.crossattention.self.key.bias', 'bert.encoder.layer.6.crossattention.output.dense.bias']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n", + "```\n", + "pip install accelerate\n", + "```\n", + ".\n", + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n", + "```\n", + "pip install accelerate\n", + "```\n", + ".\n", + "/usr/local/lib/python3.10/dist-packages/diffusers/configuration_utils.py:217: FutureWarning: It is deprecated to pass a pretrained model name or path to `from_config`.If you were trying to load a scheduler, please use .from_pretrained(...) instead. Otherwise, please make sure to pass a configuration dictionary instead. This functionality will be removed in v1.0.0.\n", + " deprecate(\"config-passed-as-path\", \"1.0.0\", deprecation_message, standard_warn=False)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No ctx_embeddings_cache found in /root/.cache/torch/hub/checkpoints/blip-diffusion\n" + ] + } + ], + "source": [ + "model, vis_preprocess, txt_preprocess = load_model_and_preprocess(\"blip_diffusion\", \"base\", device=\"cuda\", is_eval=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Description\n", + "This demo shows how to edit an image with a given subject as condition. It works in the following steps:\n", + "\n", + "(1) generating a synthetic image, using the prompt ``A ${src_subject} ${prompt}.``;\n", + "\n", + "(2) extracting BLIP-2 embeddings on condition subject image, using ``cond_subject`` and ``cond_image``.\n", + "\n", + "(3) edit the synthetic image with the subject visuals, using the prompt ``A ${BLIP-2 embedding} ${tgt_subject} ${prompt}``." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cond_subject = \"dog\"\n", + "src_subject = \"cat\"\n", + "tgt_subject = \"dog\"\n", + "\n", + "text_prompt = \"sit on a chair, oil painting\"\n", + "\n", + "cond_subject = txt_preprocess[\"eval\"](cond_subject)\n", + "src_subject = txt_preprocess[\"eval\"](src_subject)\n", + "tgt_subject = txt_preprocess[\"eval\"](tgt_subject)\n", + "text_prompt = [txt_preprocess[\"eval\"](text_prompt)]\n", + "\n", + "cond_image = Image.open(\"../images/dog.png\").convert(\"RGB\")\n", + "display(cond_image.resize((256, 256)))\n", + "\n", + "cond_image = vis_preprocess[\"eval\"](cond_image).unsqueeze(0).cuda()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "samples = {\n", + " \"cond_images\": cond_image,\n", + " \"cond_subject\": cond_subject,\n", + " \"src_subject\": src_subject,\n", + " \"tgt_subject\": tgt_subject,\n", + " \"prompt\": text_prompt,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['a cat sit on a chair, oil painting', 'a sks sks sks sks sks sks sks sks sks sks sks sks sks sks sks sks dog sit on a chair, oil painting']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/export/home/workspace/LAVIS-latest/LAVIS/lavis/models/blip_diffusion_models/blip_diffusion.py:325: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\n", + " (1, self.unet.in_channels, height // 8, width // 8),\n", + "/export/home/workspace/LAVIS-latest/LAVIS/lavis/models/blip_diffusion_models/blip_diffusion.py:331: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\n", + " self.unet.in_channels,\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 51/51 [00:05<00:00, 10.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==============================\n", + "Before editing:\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "iter_seed = 88991\n", + "guidance_scale = 7.5\n", + "num_inference_steps = 50\n", + "negative_prompt = \"over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate\"\n", + "\n", + "output = model.generate_then_edit(\n", + " samples,\n", + " seed=iter_seed,\n", + " guidance_scale=guidance_scale,\n", + " num_inference_steps=num_inference_steps,\n", + " neg_prompt=negative_prompt,\n", + ")\n", + "\n", + "print(\"=\" * 30)\n", + "print(\"Before editing:\")\n", + "display(output[0])\n", + "\n", + "print(\"After editing:\")\n", + "display(output[1])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/LAVIS-main/projects/blip-diffusion/notebooks/editing_tryon_zeroshot.ipynb b/LAVIS-main/projects/blip-diffusion/notebooks/editing_tryon_zeroshot.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6dd3723cca3e7e6699b04e05418a4647381da3a2 --- /dev/null +++ b/LAVIS-main/projects/blip-diffusion/notebooks/editing_tryon_zeroshot.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install 'git+https://github.com/salesforce/LAVIS.git'" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1690878977.004777] [sfr-pod-li-d-a100x16-2306-2:7690 :f] vfs_fuse.c:281 UCX ERROR inotify_add_watch(/tmp) failed: No space left on device\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:Pytorch pre-release version 2.1.0a0+4136153 - assuming intent to test it\n", + "/usr/local/lib/python3.10/dist-packages/diffusers/models/cross_attention.py:30: FutureWarning: Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.\n", + " deprecate(\n" + ] + } + ], + "source": [ + "import torch\n", + "from PIL import Image\n", + "from lavis.models import load_model_and_preprocess" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of BertLMHeadModel were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['bert.encoder.layer.9.crossattention.output.dense.bias', 'bert.encoder.layer.4.output_query.dense.bias', 'bert.encoder.layer.1.crossattention.self.query.weight', 'bert.encoder.layer.0.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.6.output_query.LayerNorm.weight', 'bert.encoder.layer.4.crossattention.output.dense.weight', 'bert.encoder.layer.5.output_query.dense.weight', 'bert.encoder.layer.2.crossattention.self.value.bias', 'bert.encoder.layer.7.crossattention.self.key.weight', 'bert.encoder.layer.7.output_query.dense.weight', 'bert.encoder.layer.9.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.10.crossattention.self.value.weight', 'bert.encoder.layer.3.output_query.LayerNorm.weight', 'bert.encoder.layer.7.output_query.dense.bias', 'bert.encoder.layer.4.crossattention.self.query.bias', 'bert.encoder.layer.5.output_query.LayerNorm.bias', 'bert.encoder.layer.9.output_query.dense.bias', 'bert.encoder.layer.10.crossattention.self.query.bias', 'bert.encoder.layer.11.output_query.dense.weight', 'bert.encoder.layer.2.output_query.dense.weight', 'bert.encoder.layer.3.crossattention.output.LayerNorm.bias', 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'bert.encoder.layer.8.crossattention.self.query.bias', 'bert.encoder.layer.10.intermediate_query.dense.bias', 'bert.encoder.layer.1.intermediate_query.dense.weight', 'bert.encoder.layer.4.output_query.LayerNorm.bias', 'bert.encoder.layer.1.crossattention.output.dense.weight', 'bert.encoder.layer.5.crossattention.self.key.bias', 'bert.encoder.layer.4.crossattention.self.key.bias', 'bert.encoder.layer.11.crossattention.self.query.weight', 'bert.encoder.layer.8.intermediate_query.dense.bias', 'bert.encoder.layer.3.output_query.dense.bias', 'bert.encoder.layer.6.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.4.output_query.LayerNorm.weight', 'bert.encoder.layer.10.output_query.LayerNorm.bias', 'bert.encoder.layer.5.crossattention.self.value.bias', 'bert.encoder.layer.9.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.6.output_query.dense.bias', 'bert.encoder.layer.5.crossattention.self.query.weight', 'bert.encoder.layer.1.output_query.LayerNorm.bias', 'bert.encoder.layer.0.output_query.dense.weight', 'bert.encoder.layer.0.output_query.dense.bias', 'bert.encoder.layer.11.crossattention.self.key.bias', 'bert.encoder.layer.11.output_query.LayerNorm.weight', 'bert.encoder.layer.9.crossattention.output.dense.weight', 'bert.encoder.layer.2.crossattention.output.dense.bias', 'bert.encoder.layer.10.crossattention.self.query.weight', 'bert.encoder.layer.9.crossattention.self.value.bias', 'bert.encoder.layer.6.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.2.crossattention.self.query.weight', 'bert.encoder.layer.3.crossattention.output.dense.weight', 'bert.encoder.layer.10.crossattention.self.key.bias', 'bert.encoder.layer.2.crossattention.self.query.bias', 'bert.encoder.layer.6.crossattention.self.key.weight', 'bert.encoder.layer.5.crossattention.self.query.bias', 'bert.encoder.layer.5.output_query.dense.bias', 'bert.encoder.layer.9.crossattention.self.query.bias', 'bert.encoder.layer.6.intermediate_query.dense.weight', 'bert.encoder.layer.0.crossattention.self.key.bias', 'bert.encoder.layer.8.crossattention.output.dense.bias', 'bert.encoder.layer.0.intermediate_query.dense.bias', 'bert.encoder.layer.2.crossattention.self.key.bias', 'bert.encoder.layer.1.output_query.dense.bias', 'bert.encoder.layer.4.crossattention.self.key.weight', 'bert.encoder.layer.2.intermediate_query.dense.weight', 'bert.encoder.layer.10.output_query.dense.bias', 'bert.encoder.layer.0.crossattention.self.value.bias', 'bert.encoder.layer.6.crossattention.self.key.bias', 'bert.encoder.layer.9.output_query.dense.weight', 'bert.encoder.layer.5.crossattention.self.key.weight', 'bert.encoder.layer.11.crossattention.self.query.bias', 'bert.encoder.layer.0.output_query.LayerNorm.bias', 'bert.encoder.layer.6.crossattention.self.value.weight']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n", + "```\n", + "pip install accelerate\n", + "```\n", + ".\n", + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n", + "```\n", + "pip install accelerate\n", + "```\n", + ".\n", + "/usr/local/lib/python3.10/dist-packages/diffusers/configuration_utils.py:217: FutureWarning: It is deprecated to pass a pretrained model name or path to `from_config`.If you were trying to load a scheduler, please use .from_pretrained(...) instead. Otherwise, please make sure to pass a configuration dictionary instead. This functionality will be removed in v1.0.0.\n", + " deprecate(\"config-passed-as-path\", \"1.0.0\", deprecation_message, standard_warn=False)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No ctx_embeddings_cache found in /root/.cache/torch/hub/checkpoints/blip-diffusion\n" + ] + } + ], + "source": [ + "model, vis_preprocess, txt_preprocess = load_model_and_preprocess(\"blip_diffusion\", \"base\", device=\"cuda\", is_eval=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Description\n", + "This demo shows how to zero-shot edit a **real** image, in a virtual try-on application. It works in the following steps:\n", + "\n", + "(1) run DDIM inversion on the ``src_image`` using prompt ``A ${src_subject} ${prompt}.``; the ``src_subject`` can be a noun for the outfit, e.g. dress, shirt, jacket, depending on the ``src_image``.\n", + "\n", + "(2) extracting BLIP-2 embeddings on condition subject image, using ``cond_subject`` and ``cond_image``. The condition image can be a different outfit/clothes to try on.\n", + "\n", + "(3) edit the real image with the subject visuals, using the prompt ``A ${BLIP-2 embedding} ${tgt_subject} ${prompt}`` and the DDIM inverted latents.\n", + "\n", + "### Tips\n", + "\n", + "(1) Best results are obtained if the shapes of the clothing in the conditional image and the source image are similar.\n", + "\n", + "(2) Try different ``lb_threshold`` to adjust the area of the edited regions." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cond_subject = \"dress\"\n", + "src_subject = \"dress\"\n", + "tgt_subject = \"dress\"\n", + "\n", + "text_prompt = \"on a model\"\n", + "\n", + "src_subject = txt_preprocess[\"eval\"](src_subject)\n", + "tgt_subject = txt_preprocess[\"eval\"](tgt_subject)\n", + "cond_subject = txt_preprocess[\"eval\"](cond_subject)\n", + "text_prompt = [txt_preprocess[\"eval\"](text_prompt)]\n", + "\n", + "cond_image = Image.open(\"../images/pink-dress.png\").convert(\"RGB\")\n", + "display(cond_image.resize((256, 256)))\n", + "cond_image = vis_preprocess[\"eval\"](cond_image).unsqueeze(0).cuda()\n", + "\n", + "src_image = Image.open(\"../images/dress-model.png\").convert(\"RGB\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "samples = {\n", + " \"cond_images\": cond_image,\n", + " \"cond_subject\": cond_subject,\n", + " \"src_subject\": src_subject,\n", + " \"tgt_subject\": tgt_subject,\n", + " \"prompt\": text_prompt,\n", + " \"raw_image\": src_image,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/export/home/workspace/LAVIS-latest/LAVIS/lavis/models/blip_diffusion_models/blip_diffusion.py:331: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\n", + " self.unet.in_channels,\n", + "/usr/local/lib/python3.10/dist-packages/diffusers/configuration_utils.py:217: FutureWarning: It is deprecated to pass a pretrained model name or path to `from_config`.If you were trying to load a scheduler, please use .from_pretrained(...) instead. Otherwise, please make sure to pass a configuration dictionary instead. This functionality will be removed in v1.0.0.\n", + " deprecate(\"config-passed-as-path\", \"1.0.0\", deprecation_message, standard_warn=False)\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:01<00:00, 25.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['a dress on a model', 'a sks sks sks sks sks sks sks sks sks sks sks sks sks sks sks sks dress on a model']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:04<00:00, 10.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==============================\n", + "Before editing:\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "iter_seed = 88871\n", + "guidance_scale = 7.5\n", + "num_inference_steps = 50\n", + "num_inversion_steps = 50 # increase to improve DDIM inversion quality\n", + "lb_threshold = 0.15 # ncrease to edit fewer pixels.\n", + "negative_prompt = \"over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate\"\n", + "\n", + "output = model.edit(\n", + " samples,\n", + " seed=iter_seed,\n", + " guidance_scale=guidance_scale,\n", + " num_inference_steps=num_inference_steps,\n", + " num_inversion_steps=num_inversion_steps,\n", + " neg_prompt=negative_prompt,\n", + " lb_threshold=lb_threshold,\n", + ")\n", + "\n", + "print(\"=\" * 30)\n", + "print(\"Before editing:\")\n", + "display(output[0])\n", + "\n", + "print(\"After editing:\")\n", + "display(output[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(512, 512)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output[0].size" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/LAVIS-main/projects/blip-diffusion/notebooks/generation_finetuned_dog.ipynb b/LAVIS-main/projects/blip-diffusion/notebooks/generation_finetuned_dog.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d50716585ebd02e57b77fa464b266452040c62d6 --- /dev/null +++ b/LAVIS-main/projects/blip-diffusion/notebooks/generation_finetuned_dog.ipynb @@ -0,0 +1,317 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "WARNING:root:Pytorch pre-release version 2.1.0a0+4136153 - assuming intent to test it\n", + "/usr/local/lib/python3.10/dist-packages/diffusers/models/cross_attention.py:30: FutureWarning: Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.\n", + " deprecate(\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "from PIL import Image\n", + "from lavis.models import load_model_and_preprocess" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "from PIL import Image\n", + "from lavis.models import load_model_and_preprocess" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading (…)solve/main/vocab.txt: 100%|██████████| 232k/232k [00:00<00:00, 6.02MB/s]\n", + "Downloading (…)okenizer_config.json: 100%|██████████| 28.0/28.0 [00:00<00:00, 192kB/s]\n", + "Downloading (…)lve/main/config.json: 100%|██████████| 570/570 [00:00<00:00, 3.98MB/s]\n", + "100%|██████████| 555M/555M [00:07<00:00, 81.6MB/s] \n", + "Downloading model.safetensors: 100%|██████████| 440M/440M [00:05<00:00, 74.6MB/s] \n", + "Downloading (…)tokenizer/vocab.json: 100%|██████████| 1.06M/1.06M [00:00<00:00, 15.2MB/s]\n", + "Downloading (…)tokenizer/merges.txt: 100%|██████████| 525k/525k [00:00<00:00, 77.7MB/s]\n", + "Downloading (…)cial_tokens_map.json: 100%|██████████| 472/472 [00:00<00:00, 3.07MB/s]\n", + "Downloading (…)okenizer_config.json: 100%|██████████| 806/806 [00:00<00:00, 5.47MB/s]\n", + "Downloading (…)_encoder/config.json: 100%|██████████| 617/617 [00:00<00:00, 3.56MB/s]\n", + "Downloading model.safetensors: 100%|██████████| 492M/492M [00:06<00:00, 78.2MB/s] \n", + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n", + "```\n", + "pip install accelerate\n", + "```\n", + ".\n", + "Downloading (…)main/vae/config.json: 100%|██████████| 547/547 [00:00<00:00, 3.03MB/s]\n", + "Downloading (…)ch_model.safetensors: 100%|██████████| 335M/335M [00:04<00:00, 77.6MB/s] \n", + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n", + "```\n", + "pip install accelerate\n", + "```\n", + ".\n", + "Downloading (…)ain/unet/config.json: 100%|██████████| 743/743 [00:00<00:00, 4.36MB/s]\n", + "Downloading (…)ch_model.safetensors: 100%|██████████| 3.44G/3.44G [00:47<00:00, 72.4MB/s]\n", + "/usr/local/lib/python3.10/dist-packages/diffusers/configuration_utils.py:217: FutureWarning: It is deprecated to pass a pretrained model name or path to `from_config`.If you were trying to load a scheduler, please use .from_pretrained(...) instead. Otherwise, please make sure to pass a configuration dictionary instead. This functionality will be removed in v1.0.0.\n", + " deprecate(\"config-passed-as-path\", \"1.0.0\", deprecation_message, standard_warn=False)\n", + "Downloading (…)cheduler_config.json: 100%|██████████| 308/308 [00:00<00:00, 1.66MB/s]\n", + "100%|██████████| 6.95G/6.95G [01:04<00:00, 116MB/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No ctx_embeddings_cache found in /root/.cache/torch/hub/checkpoints/blip-diffusion\n" + ] + } + ], + "source": [ + "model, vis_preprocess, txt_preprocess = load_model_and_preprocess(\"blip_diffusion\", \"base\", device=\"cuda\", is_eval=True)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load subject-specific finetuned checkpoint \n", + "\n", + "Check out ``projects/blip-diffusion/README.md`` for guidance on fine-tuning." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9.69G/9.69G [01:03<00:00, 165MB/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading fine-tuned model from https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP-Diffusion/db-dog/checkpoint_40.pth\n" + ] + } + ], + "source": [ + "finetuned_ckpt = \"https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP-Diffusion/db-dog/checkpoint_40.pth\"\n", + "model.load_checkpoint(finetuned_ckpt)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Descriptions\n", + "This demo shows how to render different renditions of the given subject using a fine-tuned checkpoint.\n", + "\n", + "(1) Load the finetuned checkpoint;\n", + "\n", + "(2) extracting BLIP-2 embeddings on ``cond_subject`` and ``cond_image``.\n", + "\n", + "(3) Generating on prompts: \"A ``${BLIP-2 embedding} ${tgt_subject} ${text_prompt}``\".\n", + "\n", + "### Tips\n", + "``tgt_subject`` can be a different subject from the ``cond_subject``. For example, if ``cond_subject=\"dog\"`` (and you use a dog image as condition), and ``tgt_subject==\"tiger\"``, you'd expect the model to generate a tiger that looks like this particular dog. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "cond_subject = \"dog\"\n", + "tgt_subject = \"dog\"\n", + "text_prompt = \"wearing a santa hat\"\n", + "# prompt = \"in oil painting\"\n", + "\n", + "cond_subjects = [txt_preprocess[\"eval\"](cond_subject)]\n", + "tgt_subjects = [txt_preprocess[\"eval\"](tgt_subject)]\n", + "text_prompt = [txt_preprocess[\"eval\"](text_prompt)]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "samples = {\n", + " \"cond_images\": None,\n", + " \"cond_subject\": cond_subjects,\n", + " \"tgt_subject\": tgt_subjects,\n", + " \"prompt\": text_prompt,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/export/home/workspace/LAVIS-latest/LAVIS/lavis/models/blip_diffusion_models/blip_diffusion.py:240: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\n", + " (1, self.unet.in_channels, height // 8, width // 8),\n", + "/export/home/workspace/LAVIS-latest/LAVIS/lavis/models/blip_diffusion_models/blip_diffusion.py:246: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\n", + " self.unet.in_channels,\n", + "100%|██████████| 51/51 [00:05<00:00, 9.98it/s]\n" + ] + }, + { + "data": { + "image/png": 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y2dxroi9q98osF/OjosPySsS2lTVI+6yKKMKiWdD6xtxbMhoPLEzZKu4/6mZFGKUI/RRmK80vLm0Zu8olNAWOVkK3Fr44IuVM+hKCIlovdyRciyCCrrrVuvOSK/Xw6+Os5GltXqqL+XOKCDzQY3WsSKT6fGJStfHL19YoX/RGqM+sKKFlbgrE4r4E4Fq78cWaFfE3WnurKQUSUwuZNTNCR7ND2YRn5Y4NEyilSadhSljPyUbKPUVPoCj2LfK5lXVZEnsx2lVG8M+WrDiZC4fPr6fqu3whcyszSRMT81RrNtZ/4wgVvtFqpJVArrEbqQQNxcpmN7K0OMlkLRiISpVXgz2EnlGSSvihVYP1qSwYy1Q1Dp+FjKgyzrmAObEyClzBLor28nxzrCVpa/4XylUCRaYiAi2lzpAMQLw510MWYyfkQn3UudckwwQsecoHSm8UgCDWHX7y7geFJ7VwtaYjinFe02jBlhVVEM8hAK1KcjN/KFK1aGPiboHhD6FwMSrF9oupp7L/uV+tvlcgGDlfm8kzjS1BW/GmVunsmVv6JzqSrFYRjsT2bK0AVI5ItnIrUiLIFKtSLuJD08yd5GxlDGrkywSstWN1Ynbpczng3A1D41MLKd2TALeoA0TmY8q6JiV9aKpZzauihzOTp1WLrFuaWFnBViZrXtNMYgWgFSOXe4qZLFynWdGq1S2Qp54EMjAXuFbrY7z1k0StRI8HDRHeJDwPfE777YWCkhCKya2VmqcAcZr52VAPnE2TowdOz0QXwF5cqh/SWJJj89pFBSgEtbCQWTeSTKzHXy8zyjWsJ1CoUYKfZHc0SrNF/BqVtBDuaokfPGLzMzYtpYK1YS2TYBRVWEuheuNYZ2sTJbgUgPlSdqQQZWI2ausw4DnR6zEr6D/HKoqTiX8XVciGIeYqdWmfo5yV9j7c/9m4xTIvDkISpk+YSGLWG1Cr2I8v/RcVuqU8RAOaLGNJVAG+ZXb6vGlNaRT54g4t6aFoySP6UxyBYq5k9v1jtj3NlQABOILlfJppWQYmXqipmKdL3iLJNc2hp+J7NhHrmej5qhZSWiJpbhBtgeZ1WKA+7o2LXBYVtz1MRDFcbBylpUi15yurJGhJexkSrgoCZ6ZnHzXOtOo+tyzmqqmzNFx5GShTYmnLlW9XekBtiqMU1hSeiiMCKMxuwYOHTmSbdcaXPO+k6enkGUWFkSrs7AMpr7gODxjBiiJd35ZdshUMpH1vCgstXcgcRlzgyOaZp9TVOo90tkqxWSap0viEABnw5u8crfNsHOKyFmNA125mRUFhaeLo6e5FYbLQVrJSN1/3W3Ra3lR8esgsafGBygV4A6KvTMkfZ3/O7479lnG3Zc+VkDmvxFx7WGQaMkCXK1qtUO43X0q3abUMyaBlQTujNTZbTuQI5OLGLqnkVdjyLBS8BAqTG0y6lBwtpZG6THYxEEKAkPJCaoofE7PI7HxQJpejfhKSvZAF6WMWlLLzWCbCy1BNyUJaRq4ktbit2tiv9hyRWcXfBy285r9UnKG4QJWMrYEl2dM3wFa+rQj3VgJcB7LnD8vcInSmaaf5JvktJxDtbBSMBJUZ3GLcrp5Gig0XQRisvP/IDkTOa8m4albZWqGYwhlfEN2OOIsHnxRYczJFGB8YC9kDqxW4aJzZUyxXHlsfwLCCfSU/kgFfhNSWFGTvZDlZ4GiFXhU2xovnjDhPay6VqVkEShOySMlD0l49HFqmGaLlqnoiEIjSLriS23pdz2h+aDUzF6LQlMyoUXL+ojlq+QA2vGGopATpaq6EQi1nBR6uIrFxybMSrpNleexsBQplqEutHhbuko9nteCauowPh2o96dlgRTFaS/VqkPmG+ClFlM53LbFUTGiJ+824oDMacuQYgZggSV+ic6QCMihrEYiKd2gtwqY8z1AjOJ6zNccel1PLOaoYGgPVyaYuYfZlrfKaFQa2GuZs3DKIUqF/YlTJrRz7X2cA6sXIjM+d4+yhhiI89NBiP+R1FXtXShQvSZjssOYsdzntevpzs1VNZNaAEvRLQ5rBeMXfKKFng1TfH6CF6n8SIYl1Ebc0SXxhuSrgLvUz9R5BoNpU1TQWTIj4n81bupT4RQrYjJDZZCBvZ5OpK/otzH7NkpVRL0mcp5UzomezKJzV+qbUrOT1Qls2WAn6M6IV5ZUKUPLjir6qtV9P6px1hQgug6xrN4CShAobS0HTxK+Ca7UlSfdVJrL4N3dXxscp2r+cUtcYgqxkm8qZFbBEqJheHOdIcm5QU8sF+ovvhS0rhF3LcfKXxIU5klhxJQMRKMopqwqDQDy/1XxxuCLPmXnxO1hBRAwiqOgcERIVAsct5Mwimt0hIlJdelTIzMt6z5Kpiy4TohzPUkqp15IZWvA7W5Q468rqFYueOV8p24y9iSDKAHi2iNXupz4SwqyGSCCeVyESRFHVNeYMVkSeuXN1ixVEVNOtL9UIVs1IoUW4KgLJskhFDGJhcrFyi5QWZjkpEVAajJLZdEZKYW+zwSuVaFnkQopjTuLBrlbVCIvUR17OWqQrPmkG7cVyxolTKRwV94jmHEASW00gXZqgGmMp//uQ7s+ieO7SJTmfU8UFc3KTtHQFWDzIIY0uVhaM7D0VcSeKWRRK/dc0JzReidjZoQlZM+hXk6cKnGrj8WZmlbc+eHbNooqmdKZA8tnUlf1F7yjS8qbpzlPLqftinJUppHwl/ikMwUJRYnluQ7MQU3FHtd/AKj6ezATFtG2RhcgiQiyK+f0MRqGqzLPzrxy7YRVSZRAreA5FipKCl3IcVoYSvKhCgwJQifUMusSREEOTpJBIaf6myQCXhp5QRh3iWVXOW49iMnkpFu7F3HtKhlfMKleu/Jj6rS6sLX4JbkW4Pg4ZS4cy6ujSKKYyakSINU0RmGL6r4hwrXSkPlaRwKgzumqUKa7+RRbTmHVdlDbuhtajUpo5nbE20fkAJFACjYqe1Yfqc/KD4gPhhMpBfsAMLOuoD/KL1nyKeJiKGhLtRRlV5hfSFZ0fBIsjU7QFi2ymRa/tYEp5U8mANJvIuxU+xh1C4vhCfu0OVi7hWmxSxDiOsgwzd0gUpTy+OKW0uEVP85k3QGuNfNm8rHUJqxqtN2Fk3c+bjpK9RYdlcLTuai0Uuh6aUhEjcmZtvpIs4ty6BOPSYKUuqyB1TWA1WXpg9uWdmr6vF2LxbbKFxUJQEqzFyZzFQ4iYoACT6hz0AVQNyIIswSoZEEkwSgbEAHuxzBawACvIB0NEohqCEpQ4NEYtCRsxEEUgOIFTDcCkCFABZH5lmSqBZ4yNoB8jlDNApqmtFj2KiOZilPhvNouzTMW8zmzOs7zVR6FyRVC/YHW1ABVYlTJFxR1anKuHy2ubFjR7a0UzpsVUI2HAgxG+TIVCo0tYwWrxObG0yDonIiIMpAnkJGLxfOobxv+E44/XWX3z16rl4q3Or+hJ5kbP2FwgW05h5461+F6ua355H8CZ9ui3xIf1tbwH8Q345XMAmut9K6FY4HXZUieDn2+kZOUokra4EzkNQZkRsdsE7Q/YuJoriaLao4h1pAtJmR5dkn8lYQ9l2FN5QFUysLJfGZPWkhnjJUUB0Eotz+eV2pQijwKJCzrOmVF/Tt1kqaK0CSzTd9mAaaodwrJOhfGMfm4KEEaSNY5W5WcVqUBxPlvEHh4waZGaZZAUKCcgesTL9jA+HTIDu6IsPyJSJiKSmXgRJt4YbtTu2DTgjZANsE6NYx4dTQGTZxeMCLugpxOGSfueh8kwkZvCODhS3W3x6Kr51LthZ3XXobHeWG1oZPGszpIjHVU98ShBCV5C5FDydZfZyjL1RHxkI6U1SUCFJdAU70iyRIiPWT38HG8tJnP+I7/PNy386q4sroUAatmSil5LTcjaUUhF1e/8d9545EDEG1z4gviy+DmjOJUNUiYgI3vqK04nxaCKlEKab3TWqJjbIlClYq8g4AGqC20llLmQkl8LWyNOxr3UAhYZWBKXCvhJWFrC69niLxJXbCQoVioUxKdwXk7SLwJScKp6F1CyQKUfgHg6UbYKlFesWpB+qdRYqigQ80bLWlah4bi9pbO+Mk9zz/OMaRVBy9APqplwrgQL39Zu8XJ/JThvPpLipfaKakeQlSaRFylezbGAz+JbGbU5k8f1dLL1eKD/+UqW82qU1TxpcQzS6fhvuausui63srQwIc97BuziTi1uRTTliqxL2SpmoJnDWkRMIlg2NQKjaJg65ktjroBukvY08d1ANyd+daKXd3zscX+vx17vD61qOB6NCPmRhh7ew0+GARCEHIhaE7oNrq+CNX5/QY8e09V1++4Te2HD9YbfeSS7ZuqsdNwb40kHhoN60SBzSdJCtgLLe/Bo2fFnEEzSpVF740XNaxc1LFXvzUnMFQsJEZjjS1KKkG+5nJGHpRTEBGwhD6Wo0gNiuuwac1atFoNUFZmKpx5qtr6rdis0Ikz1uvqo6590FOauuDNLEpAjU/mewqwqSpErRbPEkZkPa9VJUDffWjE/L3Dut7I4cdjCgCZrMNuxJEPVAqaceTX9ROSCqaQRlKpYBSixShGfBF5QumRriVfZWysPKu4seLvsyFLSYpbibDoS2p9j/gqQqPhQQkf07jP+UlKiRfbKnrLDQ4nnCwM19bGmo4K7OH46eWZUCtafN4jDFsb/3L5kMEzzfIi06ljEp4D/B7iXZb3u5wGzUjB5gaNcskhIehQ99wp1Cs8zs4kKHxhAsvaLl5hL6RYII1LS+aXmxBKreogQQKQUJBhVCzTCezJ7MXsnu8PYvv8CP3iJ773Pzz/ml6/pcLJDT97r2Kt6aGAllgDVMJ7YsGmNJQ7OCcHsrkxA0+zD/dF99MIEp631pI6NPN7i2V4bI48ehesr/vR7u89/dnu59/vO7Trf8gjyTBOpV/IKgQZAUgpqsWkxHEIqkh4vl4U5JQ4t1mPhji4WhGrRW2lpynkl0Jg1HIX6LlY5S5+WS8RalAlk7yza3rIEq6rQKKSqcleycJUbjXN5i+GEiERRbVeyrPU/kQcpXJhRu6Cppqako6IpgrRmItLLDQFUL9FYSMhoXhqPyNCC5LxVIqQVyjU0C6lxnWhB0aLB4vpkH7+a1Iyn59a+nHFidOwvdT6DuhIR0svgwGmjnbIo8aa45SirMbRI/CeBij+wsMB9RsX8yoeCm2uDcw6s8yosuWksYeAk4BGcKisSHYuKNZHykoC4XnGC88dYE1BRsgLKc+jUOJ1FiPKW+iyDWq1aEZgrm2V7th6uMPcrHqd/C3o10pDPlYa0shlR1RMu5X14Mey6TCptx7KRzHclSuP/4zfKl9KsJN41L4AsCR+GqDCJgCFbpks2j4y9mMLF/dT84CV9/4f6+3+AP/ohnr+i/mim3ven1hjD8N5rCMTKjQEoDBMANmCB9uJhghtVAfLto6e+vzHdRXN1tTXi+hPJ5PsT9WH89kunQZi0bc07b/mLvW8309WW332nfefp9t1n9OSRazd+0zpjPMvEPEGcqAcEKsQSRQLEi8ZwrALCHOmZq0QZUEjCzdUmsHKLyyXUbIkjXKcmWhigmYxkKKLKLttynW+lXLA6kzCDVhGbWsmipmbl2q61DeffVocmTE9CMndZ1uzk6Zc73mw66jtWSZOEWOmNH1n2UyBZs9rVpk5XjKfiWu5/ARMg9lRncdMypPVPveQISWENyyLR8yMu3koiUgY3nUDaZcZ1pHkKRFDYbC+12C/kGEtyHqjmSRFyr6lK0FJluisa/+RHFIz4ubCERFnyigKfvINdjERegwJJH37PbDQrD9JYhUmT8EdpK+N2xY66eONrBsbln7K/YsJn6rMmhupz89fV3rTYHdbTia5lapm7KU1U+bOfVDqFVZ+EysOf5So5EUCxeEsSVysbErtJ2QdSURDCfJKgKtBgBHviPdvHsFcjXbw42e9+qL/1dXz9D+iD9/l0Q76HBpAP02g1CHMIquJhjDLCpKrExoh34qTZbsmon7xyUPFyuAl+NGZrWcPt0F5fsQEx8bbZXO22zaOb731v++RpYCP37u673+NNCygef4+udni0B9r2nXf3774rb7+lT67Gfed3zWSNszqyOsDNc2AKoiCIzLm3+Ov10V/R6CVGZpb2uZT/Ah4X3mUpoljdH7detFiEjAbRxieVz/2m6GkBLlDwIkwRHM/FtkTS8niDIq2ONBeK+lKRW3UZzxRqnOiMHUULtFLwenceE+xriKLin6QA8dQ6VoOVo4NEuiZzRRFHC1+nojfdXWF9Mh5vOnL+LHWmaWXjRiaNsjQr2Jfgnf7eL31zXc1CqXEhgrMblwP2SH0u865fbJn4mPH0Icj9xKO6LekLrSRjzYNSGCp2rcen1V+gctip6ibthB6kMX+LxqcYr25CxSK/2dSUkPvgkZkc3bTqjjrkp9WNZehQI2PzZTojSVdf4uS0drKWyHW5KU8SdPZu2cXLWDReYzSOlZUgomRYRRm6IX3UNE/VXB2m3fdf0e98k7/2B/jWd/nVc+oP5HpuRIIPIqLONhxAqgg+qIgYViJmI86LksAzALBaGBgVT4YliG1aCui2e3hvbGP212iV2m2gprl87E+3opvm6bN+6Kd+mo7H5r1HMgQn2m0bFjTbDsbKfjtuOnn3bfnsu/L2M/d03+87v7OTtY7FEQ0+eFBQkLFBZFkZUhWhecqcojn5VQuF37BazGLJlXIcui4wVJ3fflHF9OL3XLNarF/0hBMpMY1X6VdBzBv0u/ym5ye1smKph/R8UD3o2hJUJqeQt/S9SCMnv1FXN9QwlqzObIdSsVwttMWEqi8FAqcuNU4pNqPsNp3RUxiUhNDlUZMdqzQqHtarE204xT9RESPty08FZwOQbFcRPCxhjHKX1VJoJKkgOc4xP/JbbYwenNaDRwG7BUGZdQmnV9C/HuJsoLgfKhfy/G1xyOi/3LZa5wpuo5oVnVJBeGqftzRVL7VY/8kOqnpatDzxOk8hdxjTP2eKUGv26tBlMK3PpC+JA+mS5m6jHEZPX2LSct5mCACQADzX9ghBpGXqoHtrHhtzPWH/3Rv9jd+nX/o1+90ftjevEXqSHiboNFJjfPCiXlVgSImdF0EQIBDPnrBAZqIMW4gKAoMNMQyLBCga2xmlhtT4YGyn7cZeXjUXl/19b6jRwOHqSrrG7Bqwa7/w7uH5wXpujNX7Qwgg58UCRqeG3HYTHl+F/Va+8Bn69Hvh2ZPwaD/tO9/akTBA+xCCkjKHIMQIKinRqyq06OysQotaSbS3XCxdJbspD0vzA26pFkLXLyFIxXEUZWVZ3JSTiIoOVSYGRImL95hV7sIaIDWKSYX1paBRcbK8ASV0Rz3RyjrE83WwpJbHxc1NXx9G0oexIvdT6G+MF2VLnMPZNeTnGzXixmrw2nCd25bKli1YnBy8NYatQOShcyV+FyyPryEnrUJAD/CgMg31IxVrCxxt9nkpT50LqgapRKmafDKyVK1JERhNclL3XC9gJHddk10BciThARJjlcIC7NHJLTC73rNmigsvvhRjaN4drKUwGUv8MccsljOElrfMtKIU+1wgl24mxK1c1CStW5f2SGMWsmyGpGcPLmEcpfAp0pfl5W4KktySlImAuSbfELbM1w0/teaRn7bfeyn/4mv+X/26/eZ37N3rVnxr4GVS8cSEjQlOCFDVAEhQIQnQCcFBRUmgE4JAFGrAVoIBC7wBQ8l7BAgQrmFIhall2xARueBe3/rDqEradN4FMvby8Zc83QfScLi7+NQ7/Q+eDx/fXnz50+Y0ou95GsLxXl7ebS7244sPcbEZ3//e1DTmrbforbeaz30en30nPHs27uzUNCcNvQ8OpApVFiWZCwaJAV2eY0lqU0CPRI5GH56KxY71u0QS75lrQSjmitP7d5ese5H91bTGs0Br+RBbKuwoQi7xVHZoKgFNNJewn921tJtesnqxvGBpn9Ch8KApKpFi9W9G9NhWF4sW0+m5O2S+ZmEuhHf1ISamKTOpxqjy3RdvgmiKg1MZ2S+LN0pKFgWlmIJPWKFlz/SAfSnnUi6KojxTZelyFVCuQEPBy+wiUNF9weY8RlGHWszknGt/kiM7LDOmZZHIhqKArtIjKMcsgCcdyYc/MwklrGs0/UXdcx0VxErwK+uYSKXsPFQlBekzpThk0pYVYQ9YhKh7+oAZXFn/rNdL96Xly1Rkpci/DZ7MyUP2G+fiXrgfRRQixqVLwzwjlEIWtIICokqiW+JLYx8bvpxw9fxAX/vO8J//s+Y3fn97ujcYjXUqYRIAQoZ8CCQcgihB2YI0BBGVCTqBB3gPDUAPrwgEErgGwcJ0YINmwBQQBJ4gzt0/onYMDLCxjfcnSxzcZNrN0N9tNjsML6fno728Fpbx7r79kS2j233uUjcQ5+Sub46HriUOTf/yBVywV9uBvI7eXF77xuCtZ+76Kb70o/bLn2+/9Nnt1WbctBOHQTGJjqJeEYKACESiSswCWUEegSStXKqASE57/NFgmd2NWI0apV11fkwoFmLNqVBJLkT6GVxUTg0VkaLKQ8mnzmAs6lIh90lbS28/jRCNQpkTmRvNkkjQWCqWb6fYffzf/P6OPMEsjivNfkCSVzJdFioUE4+dxHbz1NY4oNAZnZM9WiNH0U31ffkQ+ZAyapmbReuzNEci9aFFycpZD2rP8SWbunLea89+3fVCUmEpF5gqe3+I4nV3xddkIuc+y0cREKV8bh6BpRjoTQZnJX/ZDFe2oxDAN7E5zzMC8jz38pYohXGAaPszz1IPiew8aJp62t6f4XwqR9PCq8r2KvZPuQcAiFW5VE2vCvHknfeDnCxrMso+6gpcSr5Ezo6BBcLEsR6UVcBsLOlFx0/Bj8Zw+dEr+2++Z37lN/W3/037wfudm6wNooEZKqQSFAowgSRIgPMKVZoQAtDDeWBEcNAB4qETgocnQAAL3UI8bEDvIAJpmUcJBpNVZWBjt4qR4EQh6tSNbbtlHcPdzXDzgdgGF5fUNeHDLT96Zzi8wJEunj4We+9ffOy0126zawyReHdqug0uL6fTQQTj3c3Yfn/61u+533oin/ssf+VPdV/+Uvv2k263G1sehSaIZ3YhBKFJIKpCxDGCqssSEqXnzjTxl0QVCmae41xc/LTN8tZrjc8jLWIQUwEUa8UXQzLD7LwD0fS+XlpEOGWqz3f5FRpUGkaF3JY+LxW3EmLVQMR+LXynYrRy2JTXiKMk5KkUljI6lJfWYl0ieu61miHFQQtKMwCse6Dy7SCzHiczHhmaxi4cTa37rojVxMIi0hIfAIytst5ne1lNtUzdKSxFlCnIechuRB0u5lozK+0OsxXNWF3GJN4IzVV/xbfUoSI6k0mS32xUKhOe1iLRtWqYy3WKjVq9hTmj8VxKZq3SvDRA0sV5bIrWoHSjHpw+lSN/MsuS1BcUlUJ/PkqR3ipxPto1IkpBpnqcVVcJ1mc3jupA8TxZVSbInGIhUlUGB4KCnOj8jj5yckH8lM3Tu6n75of0i//afv1bzR9+m0NPU29aVg3qJAhURCEKqBcPFciE4BACMMN9Dy/ACPKQAeLgFfAQgAKkBRTUwwGYg0K9YAR3kB5y3TQKjO5koBYNwSEABmK3sMKu9zLI6NrL95rrLV3vsCX/R8/D7Ut/d2fRkPVmv3P3B3d/5KsdB9hNp3KEoWbXXW7a1/d3+M7r4f3vDl/79emdd80Xv7D56Z/dfebT7cVF2BjH5AyPoh3YBXXEARAVECukfkIv1USQAsSkSqFYx6QvNDuRKkxsmCzR8p5UKABRDaqyKKcQlOOGbAl7zruQqAs505yDP1UsEcXeOdFXvRCuMCn1mVqZkvCvRa6Cj6yeb9aPUoXqmEslqlp9fUAtK/Ar5Dt1lYukNFknpIAxlQj5AJHpfIGi5RzWehdjeGdzp3rKNZynkTL+2FQtllNDDx4FwMxfI8wkrtXBsPUM4p9qdskmvnkJC37P37OMZGeD6n4fALz1+WKhE3FUkV36zNmHqE3lOYsXmSRan4n6kfqmwtUpeq1CU2+MrhZTiFC9wvozoal/FvABdr/xcjadWk6nLCmIScO8qdDluRYiIknPNBGIIEQqUGNYuW1MJ7r38q4LT7//If/K78iv/Vbz/R+0N6fG9yoTWUdkAIEEgQhEQQHw8B5hRBgweogHOdCE4CACGgGBOoSwOEwsIA81wAgESAejkAbmhOAAQ+yVlGmYphbCQIATBIXopDyNwt7rSXSDprv4kS/a68fOj+7D+1asPU3h/kAsQRnHIfRD022UW528H18ZFXGg4ATtRuyOm9GpnPTwrT9yf/Q+fvO3j2+93fz0z2x+5sc2T96auraxVowO7E/OTWS8qidVYpPCvkQEgQKqpMREorQkfoM2DUOFSRuihqkhso1acMvMIuyDCohY2QYmT+wRPNMk4kQCqYOo0uTFGo6PIc3PBuTqkRhxydqmWIyDEuZfYSiCHrV4v+HQtW6dCWXW+xJ2UTyEkDpYY2cOQ1RK8YAqry49hIbFQMnvyoPmCD+jfEVUIhYpjZKU5GyArFAr6goUS+8/Q3IvFcgbusIs5iEU0UXL4GmBonZl9q8JKY6RqXjAOkYAzDa/MDArXhZMemCd9YHeqztqaxeXc8lNFXUHDy5ktlIaObdcKBeghlbEBqAEq9WCF8u6fC2Su9leLBvARQuWRauKNEuro+lsxe/aF4qNymROXXlRLMgyCSSDM0sSJY+rrNp8A/uRiSgiw8WF2QjNCcdILYNUdQ5HMLEsDwFwgApBmUm0hew9nsA+CfLkG39k/tG/0H/1a+Zw15kA3xsD544aFM6oQuEJRmEU5BF6uAkYEBwwQAQ8QieEABGQBwgsgAFPAAAP9SABLMTCeIgFDxABDQis5AAXPOvUggFrwAAHqMIccSciG7q4/PyP0ON3myef8a9uTVAeB55cf/PS+FGJFD6chu1u672GSdgIjiezv6TdRk49BeyevEXdlvupefrWDs3h+Yd6783dq9OHv/ryX/wmv/Opq7/8849//Mt+SxcbO7bmbnI96ARxHgBU1bABhDQQgwJ4fn5M0DAbUGO5UbRkG1AL5knboMYFPZz09TG8fG3v7/l0DMOooEAG732a330S3n3kL9u+67wNg5FRvDWshDB5EMXqIBIoCRJQLDITA+5JgmaNySjzwFHCdyniteuD2h9dowY9cFekhJDUZa7DzzvT5CudSXlhYCqVOCf+4Vkt5GiOcyUwKGZJ8UrUwwKKSgh8E+/eNDSwmlqh/bNCFqCjxQf6v//yN7MDqwJKj/8tLuUa0R/gQbIEVAz0UPxEM3REe0HFchUwvXRRp2o13ZZylSs2rWAsDV7sSSOEFY00lT3U9zx4RMh7k5Vbw3jVVyHS67+Vya+r9fOnBNtn5x6Y+kJptM0PFQ2sVydGh+evRZHHLAXJWFUeW6WmqdokhurmeSgWM+AEtm1UqBF90tCjCU9vTs1v/l77z/+l/b1v4eOP2g4qLpyOTKTwSqReABaIBxxkhB8RTnATeIAMkBFOYTw0IAAkoLDELgBQgCokgAKggAG14BYEIAATZIQahGewb8E8g9mCGEzQCWpAAdrj1OLRsy//6csf+YlwuZXDUV4d/KtbHgeEib0nQTieNpdtmCY3TmQbVSIjpmuEG20tmjao0MX1BKgQ7S91f01NqwL73qfuP7jFxVamQS4ad7XVzz22v/Cj/aPL2wYnEm/MOKkG7dqGiMR5AxgCB7TMlmACWoUN2ony4M1xotc9v+7lozt9/sq8eikvPpCPX5mp30rQceQZdozVq6vjvnWPn4xvP6Of++npT73dP9mPOx4b6r13XmSehg88/y6mSHIdkD0LFDpbVH5kCSkiRfPZQqzOSzpprU+1sC79nz1kmmInaWuNov0i3brWu9VIbxw79fFGXH7DhbMRy2vZJYv8KIMZtctXjv2grpeX4qdK5WOUKtkcQnoZ3ILMRSB8+VBmDIoBagqKHda69qcmkcq74qSXDw9th7JsVOYtOeUar33ikWM0CY8rzwNxKf6Yfoo+HirAqodAxapYh4fMurJhdBMKr+hhgSpkSVcEv8GtUSjmp46WXJNmySh0I9+tBbEae1j8olk8uRytYOlSJ5tMztyZxt9m8VAFgxST2zJfwTzup8vnp+43vtH8y98yv/WtDqMngSCMk0AkCFl4hQICHxA8qMd0QugRTggedoB4SIhmKeQEiywvfsb8HAALIEt2Bx5kQQo4iAd5UlUdMArYoWnQ7NA6+BEjAKJNo289/TN/7uKzX5gmxXEML2/l9qD9qbUkQkREDYF16ofgvQXIGNcPpAaW5TSgsYJeTavTwZoWu830w5f2cUDbBCGZ/Ka56NjTfitBcefHf/3t49e/jbffevvf/ln53OUpeB/Ygmny8DAgQ2Cv7NWK8ORxUr056u2RPn6tN/f44AO8/5F5fdsMYzuNMg4YbtSi6zpVz0GDeO0Dt3a6eb4harZdYxv3u1+Td97a/fmfvfi5H5vee3zaNKdOhqDOK9gSROdfVJi9L5Wq4iCJcfp3EZ7k8ZU73QgyWt6/FvfsFi5QVglc3pYXdxTBplQbUUn4uS+bISAGBsrE6R8HLOlIUy2mUnS7fF8/DAlkJmhlT1de3dnxBl0vprlgT+WD1txfNNYW15JxngknYI4bPPDEQm0qK/BOdqZMN0cLUdWqlACcRlXUFnxZDC3Zs6qaWQz/GZERDApCSy5UXKvX5o9xBN54VESv8LVMM6WEXrGNSZMuk2YFJz7BA3jg2/qSoq7vP78nlxpRoi22jZm9eZ+qKDi0zGzFMQWW2D9AxETKBK/Gmha4ZHpKfNWPu2/+4PAP/mv88CN7e2/GAxvfdvC9J1UVmjBRMEokhKMODuLAPUKPcESYoB7ilwcV2IAAMDi6gsyguaxSgAAN4BDRiAEHFdAAUaZJtEVQWAEF0ATv0BPUgwLM53/ip5X2u3c+JVOQ49EPhy0j6Kjqppf3prHT/ZGZDHO33ToolKb7vnt0qe5k4YVURawluJEUoz9Zt9vblg6vp2FsNrvw4qPN42dyw94DxjZvPaPB0Uenq9vA229On3/LhiCbrt20EA+v5EIYxnA/mmFqh8m/fKkvD3z3mm9fj89fmKlvNXDfG1LLxkKDHyU4VXGhD2NvG2Nao52admOmowyBHLp2g9OL9vX19NH3Dr/ydvPzf/7qF766+8zjWyv35B2pV1IFkwkiRJizMjNcpurRh7y7OkCbhC+rQwTtxX1IcFF4wQ9Jd43OGhe99ooWEDw7n/Wt6Hmd/vxE/6v4HglNncZq18Kliu5vxLLInsXoaNlfEQ6vp/oJ+h1RJVGiRUwjPwexMg8L2Py9X/4WpQTA+c8mZh+7MsOFvVlb82oYSlZ16exNsa3UsKqkT9H3shZhbZrLED2iZFFc9DXWld4p5UXLTdP4Dwz1xx1xCc7FMFctASiSEbOwF3mJTGqkS8/gdTWvc22oriERsIhCYWA1We+F7PiaoIIzxTZQCxUtZ5lYOt/PadEDlAwvjy95tdALa9+y7eOXg/m975h/8s/tb35zM027Djrchbs7YgQnbHhyo8OgIAfxQI+ph46gAf4OfkSQRZ+4yMHPL14jhQjAoAAEkIfODwQoyMa5MjSACRSgAmmhe+Aa7m1sDYjhgObZZ7+yu37y6Cd/anx5R96E29P06jWFabNpMQ5mGsP9PZiZVCbPagICEdu2FVKREO7vqGGz2Suxl1EOI3c7gWl226Ai3hEzk8EUaLPxU9DW+kBot4Z56oNebvDokWwvpo0RMrxlQFUQBu9fv2Y/dRDc3/qbV1bFwJMf1U/MyiJta8ma0AdmFnXt1TZAQu+axpJq0Im6zo9KUCV1Yep22+MwjYOGbndsuvHJp+Unfuryv/dX9Sc+c3i0PUp/UpociEQEqqIIDJojxhR9hPwESZSIAkeiKNa+5NrTW/lJS8+VbCexzaBSiP0KPFc9UYmCtCYGGeOoULw/6VEpRnVUTxiViJagNufUZwpqZS3eyfuJx1kLrT7UdgDLc4M2tSsCFNGrKxF+gdUSjIoVyOHzGAqIJ6vnzvJU53BytJzJjJZmOO5BEpzPElNWFaQSW2AWifQQ5ari8wHTXZmGteiWsvv/w7HOVS/2eJlEYUfTb84u5M1VlOUKVh5JNqdvOGqOFPWu9VxjEizzZfa7CqbP17LVkiwEaQ+RRCDOc5lUGmoeRaGGlwdNrGEj4cqYdwxffPe5/0f/Ff/uH9gffLhz06YJ/uYeIty2kKDwk3MBLkAcxEEG6BHuBO2BCToBHjxXK9o8HBTzm40XEfBQARS6/J4XEoNVoQEUIAZoQApqoB2sg3sNvwU96d65/uKXn37xS+1+rwoRyM09T3676/T2QIdTuB1lHNiAG1VjgkLE2aZVFa8BGjQIdw0IzM3YH9lqc7FV07nRqeuJyDLby707HdkI+wHiMVFjDY0HADypvjZy+xGaprHsvRd3UhFRNGwRnCUJw2gM2hCsZVJhC50ZIMGwIRC3JOobZgnB2Ma0xAI/DNSAIcaoTF6Ds4b86X7X2K5F39/yaDt3Gv3NdP/94ad+avvX/y3zmUemlTvxXkhUiIwGAS0vGqI57Zl/MCQ6wKsCxChwiLJc+Iwl/BWSW3pUa2l/IyDWmruASoKxxV495EjmOxIcrWzRJ2Jwxvaoran3dcCqQP3zy5pgb8G79Ht51d0PzPlNJzSyvTLFC+jZktDUSCvq0201lqcGsR65vmNxLpO1PjPkycksTCFqRicnuXifQYWSlVuxTPJBmCztysMGpCbxfL0W9C6aJfOWW+aNbXEzJbKSqVr6W2Iq6VnIiuDE+HOKCn9i9axaOYX4+NCKtVW/81MLmqiquVIxJOdpyhBcJINiXn75aSoB2Ngw+9gqDell2zxycvF7H57+03+8+93fu2rMZmPCKOIn8pOxjfNBRby4ACcQBx0ReugRblye6qIJcAjRHTH5yViQLm9KIEAFKgAgAaIggSpgsLwjIWYIAsWQkUe4w2QRNs3jL/7kTz/53FfcoLTfhV6G169o6BvTudtXjbH+5GQ4GtugIwLsZuPFN62VECSIm6ZuvwOxnya2NvjJ4QTxLMa5yWwb0zH5cTz2s7WQ4OC8VwLE7LbiJ3c8gGG7DZEJzvmjJ2sh3iB48d22M9x6P4QxAL7bXkyjZ8uhHzWIaTtleJwsNsF71eDc1GxbGfsmCBEHH+bXxvlh8i6AKIQgIoYAISEYqzYMlkC3H959/dXp+985fOd7z/4Hf/3Rj/+pttvcu2lgDgKoBQXSeaMlUYxyJKgQ/FUVaHToirOa9bxwQKN+FdhQOKTVUWLaSgnK5mU3NSCmB39yu/TDAAVq1hhcPIhaHMn6nR1ZaQuq50EUmYCyGWVPfGFUPYsHjNIZmGWLW4HlPG75k5CF+VqbijccZyZUE0wuYyXWP9BRXQD5hl5XqF+YxQcMUV3VWSwmFZSVnr4mnHtT/jm3LLyC2LOu2tXCrslClKPOl1KZtCI9sb/ixZueGVmuZuhd9R1nvuijRm+j6HOZ+SKoy9haLn3+kHZ+URYX1YiGZQk8zrfKTI5CmQw4QFmNabgN/pGlt0d/8c0fDP+nv3/5Bz/k49BcN3ZDMvWQgUbvPImqGu/hjugVNCCMCEfoALjF8Z+DOcogXuL7OicaOLJBlh0AKWT+vXYGDETnFDRovsSgBgyEFvYe/QZmh+2XPvcTX/jTP757+laz2+jHw/j8oKex2TTwrr18rOEkd5OOU5gm0wRjaRxOIo5M648DNS2IbNuqc945EU+G1SuRdJutSGAvBjideqbQdm1wgcRJ3xMRNQYMmU7eBeYAQ2ikP56YiOdfxLEg27EwgrjxFFSNYRFyTkUYaIwN8BNEFGi4JSIgBPFgcs4xsZtGCSGoGGODEzLwGkg1MHzw+83Gee9EQCSqGHtjvRnvn8Idf+cX726+a//KX9v9lf8mv/1MaTrCO6iFCaqWQSCuAsVUi2XccEZtSSiUYH7Z2J8JcwLHSi0eKv7L7d9wsoRuKv8s1ZdJuOfWkqaR/auzx9lWcBovFThDRevzuWj+8yAW1kZx/lwVkBdIW98TdXTVNp2KtxFRfBlc0bR0cXNJbBkP0+rPQhmyl04PTG49tSVIkYAvOQWJAEU9+6U/KqrJVke51pr8j2pQXbcuskfnjKhKYNe2KD9WWBXvJIhEsthlvYRGxqde6u1fbbfz8zRRqDTxbu4wy22aQV36VSN5FMxFgnXG69L+xGLYqJ50xp4F+yOHsRT5LJNmEgUZmkJggWVsQnjc8tMh2H/8a/Krv331+z/cbVjZtBsKdyfSQAG064ILEB1cP2Ly0Anegx1ohAZwD89gB5EF3LH8Mjug0Dn3y4ACspyZDzM/CNZACUGB+QU7BmaEb0ACNyEY0OeffvErP/3nrj/z+Wa3GV/e+xc99aHd7eV+5PujOxyn199RH2TsDRuz27rhZEyz2W5DCOodGzZdKwG2aaDifdjurrxI0zV22/qxFy8w1p363cWFGwcfvDUbIwbNhhgheNvsvT9ZVphuGAbIZNqtseTHaZyGpt1Mw2QaSxbQkQQe0rZb70JjWjc5DmGzu1QJhlSCTv3Elkmp7bbDcGpaq2QUgyUWJkt28iMzKahrNxuAqXX+xIZdmJptM/TTdtfJROF+kGM4Hb5x90cfDV/7/at//289++oX2k138E5UvTPLI2mY8z5SyjZFWcpakVxaLAahcN3j1eX1FAWCFW5Mri8pvLpP9lPpjV+SMMdr0aHRRHMBReduXNWnll+ApEeFg1i8AUDTLzdUSJaGWwxiAgMtgq/x3JqGEvHTXkbzLGK8uQrsq+ZfBCsGyMuUByuxPQJZTc0b2bT6XsZHUhlAApDSTM54X9quNEzB8ER+WYFKReO5oWZrVLAlex21OKV/CkReeegZ+zVNJd0Qm+si24mkspqpInJRGI20Fkuuq8brirIYf6nZtvAksnSxJoW9ygMU0pntYeRVluTMRxAhvq8Qc8FlkckmUbJstpZ3AdfMzybHv/6H4Z/8YvPhq21nbaM6Dnow4dTT6JwfyRjvJ6d+ghswTsAInSBHiAMNIA8OkVjOZKtCzTIDmQP9vPzcIkLcDQTI7PIDaMAOwsAG7OAaNNd8/XN/6a8++eznumdP3CD3H97g5tSwodPAu724Xo/HzRTC6eju7rb7PYKquM1mE9wkYtzobLuxW9sfjk3TuDCFyZFldI2cDsbwcLhT78mwqIUfw8hQ3nQX4gbf92ADayEajr0ySBAETbtRYtM0w9BDyJBRYhVPQYmMODRdFxhumLYXVxCBCzKHcrxnJlU2VkSUbBuCMpql+FOgTRumsdt21lDb7YLq5uJiPIxh4surJ06Cc9Pu+nJrb5Rpv4WfpuPQP5Jwe/xh+JX78PGHzX/nv3X9V/6cubq+gwYGwYgPIMyP+80pOFIQkS46vcC9RiRPmr7ChSjRCezKXGj2bgpFeKjwD5941Ipe+mor1c9+F+VTuc0nmp5zK3N2rXyKqUpLF7peqPgq6BAJqDzUgqy6kCfxNdXkRuOwhIASHGjuOKJYOb8anpM7qcXVxLlsBh7iRhVr0GodYovFBKVeyy7jCkVKShrKyVB5sp7JWX6/ELCH1lQLWC5uXAlgaRcjH1LadbUmEZrX3aRlL0xXeRRyt3ScNCanjamef91PfDTrIQ6kpwRmjV5fJ8SliHMixfwyMigxh2gb2IWtxTXRE0vbV/f8r/9Af/l3rg9j17CZhnB/0sOJleHHMI0C52VQ8Iipx9QDATQAJ4QjQoBxEAJ7qIne4Fznk7SCQQqVOCkLEiiDAjxAJtqMgMBgC53gFHqB7uf+/F95/OhTT770RQL3L3ojZCbpLvc0iBxv+OTp/iSvXozeW0tta9QNrp+YVImb/daNwbadCiRos9uomxDEtAiTn/rBuYlhm7Y5+r6ljbGsanw/AI1XiB8Vk+EOSmxMCE6C2rYhEYCDD6Ijswne2U1Hwtx1UA3e2860m50jtcpNt3HjUdxkrZVpEvHWtk7UGGNaS7YFqNu1zrsORMaA7HB4bexuc7VTGL0/kF5sto/kwhpiA23FszGGPSvatvHh3rbWXNFextHJ/e99/fTxR83zD578t//q7nOff23D0XlRaoxZfrJgTrIs779Kjlh8Fx3S2QpFo0IoldoX87SUn5F94Chya3XU4w1H8n2TzJdhp4j5K5dwEbOS6DhqfraNCmoqND/zHZcOo7IuLcunVAt63hAKVqx4UtUUIXl+afuU+sm/WT0DyN//5W9l3zMagFyroyUVhc9ZUlhRSbnpWbAur/dSH5xuid5pYXrre6pifyrtUIVxhV0oazzrg+qWcUbV7KpgEc1+SSEomjKeSPi+8E0R/R+KcnF+LDVzSUWgiXN52FxNVVJWrkrRYcq+QnUOLFHmURqHNG0zokoW4rL8akoS7sp9yCyL+bHlV0eW3bswB1FjbeNlS3hi6LEPj17cT7/8m/qLv2O+8/Ltp1dy+9J/+FHTtEJDuBu8uiCTw8lDHHDC1CMM8A40AUeEESrLux8kBv1DerJonp+FmQM/AsIS3+cwV6ACE3wDUogBDXAEFUgAffbis1/9mZ977+d/Rkc6vbi1ZN3zO8C3qt12N7z/kb68Ya80jDreGYIxPB1PDbdEytb6yTcXGzdMxtrgBSrcdSoujBM3rGrYNv50RypBydoGLG7y1jYgooDAGnzf2JbZuMkvWxdSbo1XEDMRu3FUsgF+s9uOg2NmKAXf25ad6ObikRtO4qRtzHQ6dl0bHFgD5nc3GG42O7Pf+XFSJnVitnsNSsrGsErgditBIWK7nRsdEzvvm00XBCqh21mMTiX48eSHQYyarfbjOGk/Eh0eXbgvfqH9O39bfuGr95vtzehckPhOb1gCVCjJVBGzLCoLI1hnTy8Ks5Yym3pB5VvpuU6fo2Tygooeda1PDx1a3FsASPKyEl3pwVqNk6u82nJv8YYhP9lklYWAsa9F92oLk0ZNlSMKXZqWJfgRmTVqkKQdQOyGkikpQnnRKBSIUHiXXCJwSXAsGi0KTIt1RcnjDK1r37z2EbLElPiXk79VxUAsta24mKNEVElDBsaFC0WMiIrz6Qaah6tOp9J+inWfuvIwdLVFOBPc4lQugaoMbjyXf7W3aL+QUpZkAmVyujhf1ZmW3o3mlstts+wkPWYiWV7ERAHKxngVFTARj+Nl015Ze3030Nff7//V13bf/6F9/x6n3svIx9fdrpXRA6rwQab55Q0TQo8wwg+Q++VlPkFADAASwCYuyJzUnd/sRlBeinnIgCnW/kdKhUAtWBE8nIAa8Aabq831s+tPf+lnfu763bfH52MYpo55+OCl3vatqLjemdfNNBlD/cuX3WZjLnfudIDq5vrC34/iPHdbCaPvvYJ878FEEoL0OhsXhm13Y39Skabrut31eLqRaby4vp7GaRimzmyZlKhh00xjb5uWyIoPqjqNA3EDSyFMUJjWklJ/6PdX1+LFT5Mxth+Ord1Np5MSifPO+6bdtJu9tAqZ1KuySgjihYOyaYe723a7b7cXfnL+OHXdZRDfv77fXF7DUvCyefKp08cfhJsDXTJ3G1ZlacW0wGgv2vaK3enoJRiMu83jjZXw+pYP37r9wf/l8u/8jSd/4y9unlzfw578OAXSScAQAZm5ci9ml1LtT1TRWouKVOsKjrKQJmk8934ykBbanr3hDD8LYCPBWaVxhbynzs588oI+ijpE5U/4RNrjZX3AXJVzLKeg51ephK1IQE5APjDoKtgdu9d5i7wwIM3SlpMvHf1o1aJFKLOBcTWroWPLbCviI71MyTtNyB9LQxeELC1n6gj1yUzEmnsUOVKX99LinEfbrTlJQFTkltKkEpujQEYSIz/LsbV8b068L3cYHXzVKv2eawtKDq62RAVVmbjECS0uVj6ErkzSyrjQ+R1xSZPdOpvjiiJdOlaAmMzyojeFKltjOuiFbd6xzeb2IL/0jf7/+V9f9b1+9MqY0G5hTi/87T1fXEpQN558OCkAuB7THcYR3oMG6AhMi5uvM9YDZGHnsv0ApaWscy7lBC0BHxWEBraDGSAMctD5jaEbdE+we+edz771znut3b79o1/QwRBbfzi21h4+fs37TTdMzb71z18amaw19z98bq1t2427P9CuZdOG+3u63AhT07YBzG1jjCEVYe99z4aZGm4MifHTKOEYwrhpO29Y/SHIoDqNzgmh7Rpj2/54Y8h4qDHGNtZNzosDwMYwkbKo88StnxwZahrj/egnJ25qdhuDzhgGWBR2tyfvu6u9d4EgxrZCCsXuagc14xTYNPun7zk3Dfen7bN3JNwMp17EkSXatv72zrR2Ot7wfrfpjOEuTIHYjMcjK4lFY6y5eKoem/1FkMmdXPfe4/b9H7ih37+68f+3//z4re83/+5fvf7xr9h9cwq+VzBZ5xcrrBIfflGNXlTaEWj5p3zWSTNI5FQpMupUfl5G4bWsr12rpe/KsZ3JrI1FYaCKK7pgTHJjC2Cet+pa+EiZRM23F9Q9GBl4kODibOnwRrYmCssMRX5X/rwRiB+A+nXtBIq/CJZ0OzJkscZ5OtGbX1L0qxD/YouiMUlxaaLMRcoRksIwR/YgghcRYkYgw6kmA145ttDi6ap4rYovxl1PtpeI4jU/EhrfaFxNJzEIuRvN5ObBykWjSGG9QeNigivAzYc+8DHFQOfThfgX42cBjXdT2utVZrSwlgsZVIa1CvMeU3VLo5jIS5V+CiViUVEIG55kfkkkWtWrhh+1tPnhnfz6t/p/+ItP76b2ODTB7S5bd3Pj70/N1qooK0iCYvJAQDjBDwgDVEEj4EATdH7NJS/vZsCcwp1fBjfEl/8YkAE5SIC3UIZ6eAPuMRHYgD/z6NOPH79zffHkrc9+kTeb7noX7o52ux1OQ3h5S6N3znWbZnM0Mga5PYXXB9Lx5P3u0SVUZZgAlRCUNAA8TWStMiupWiMg74UUxnaby/3x7qYRq9zAMLFsmr2Qk2HglgyUui0xWWYVce5ojGEYYoMWzguI2s1OFGx4GI7sTbfdinBjaHI+uMlsW5lcs2sAbLsN29YNvtl0CERNp2arY28223Z75WQK90dqLsIQup1VNj4It0aG0d/d2+1O0FtjSanZbciP7nAy3G4eXfjAzI2cJnKyffsz/vVzy+SOo2JQZ6cbH6ySY/dCt/t3NxeOj6dhOoVf/p3jdz+U/+5fuv7rf7a72Led7V0Qy84H0PxGZC2kKyp/EWIokGtRnsIWFN5g1Puo6oVbVWpC6cjWkh81pLQn+QJVzt7qiKUexdOl6dPid2oqg6Cqw+JMdZQFhpSeFa1apFqQAt0SRQVWLVqsaczY+ey5K+WINSJlKWOg9Pf+5bcKAkvYK6B4NXY9lTXhS99FBOoh25Z936LnepxyFePaxF81XR5Bj2X8WagWjpccWgRkMdFp/SJaz4h5TiPT8l7z2VOpn8IohQ7LGpfUk2IpilhdyQ8GP8yOKBgl9ysW5+40ZqUzdxa21aWn5xJdOkDnn0sRSmTJUsCBpHxE8CAoNY3ZOH3cNU+h3fOb4Z/+avf73/W//p23dht8+NF+A6NBxl7GA7et6x1kmjAMOB3gHPQO7hZhBAZAwT3gEQAweH7bzwDXYruBnYNC17tH15dPxbvtdrvf7/rT8fj6djwdLzcts94fDtaY7Xb/uc9/ZbvfXb/7tt3YYRhs27lhcqexvbiQfqKThMO9HgbbbUI/WpFwd9sSs/rh7r7dbs1uG04nA4IVCcpQDSoBCEKGvRsVYGOI0Nhumk7GGGJM/bjZb0WDdwp4A3g3mKbxXtmwErHlMDhj2uCF2MAIIN75drsTUYLCGrAq4PvR2NY5Z1jFzr9hjOl4unr0iOxunIbt9bUbJ7Cxu304HZmMkuGmoXEE2zBMpuVmd+GdBxvpp3a/c6NH8O1bb48vP2LTqFMyBCcwKgra7Pzh2DUt7y7d6Q7ToGShBraBU2rJdls3TGDRzgrD++M09PrW5mPDpx//3PZv/83p8+8dN5t7kYMPEoTUEM0Oq9JcHcQVqiVJA2JJ8kpcE0ZHJdBFu6qfmUktNbmt6bcqyvqS9fsjFqWpozSLxpRjRgOU/LuE2oSl2EzT16L/ovapprR0xM+OiHRafH2gbbZYpZVIsezC8a/uVM1x9FQFRHGakQFrLhVDFt4x5pBGgbfnlbKF6xmpTatcAuFiCTILi+fyCBQHz8TFtPuMeFyZkpwBzV0UWEizIaCy2hioZlbcET3m/FDEOTtWTJt/7jY+2JeTppRYVgTxUqqiZFVBdhqg8IoSSaW8VitYvXniQU2Jnc+pXC1nkLNZM8Wz1x/r/QGALIuqerWsmwlPus0jRfv7Hx//yS/vv/v95sMXn/rsI335WqcDNR1CYARqLMCQnkCMJoBGyBHTAb6HOUIE9gaeQCNCQDAwHnK12T/tnr335L2r7aPWN7vN/ur6umnbsT9sNxvDJvTD8PhOg9/td6az0/G0uX4EkfZyPz7/SO9Pgh2Pcvj4ZbvbbLudTto1G9rJ6eY1qeJ4sm7y/ckEb/YX091h++gyOO9u7oObYDvbbdSdTNtMMtmmmcJooNw11rbivYpOQ99ebMa7Y3txaVvxPlBr4Z2xJkyBqGl21+7+TkUhaptOjRCRaRofJnVKzNQ0ykZ8z8aG0XWXF5Mb2LBtWzDZbTMO0/76046PIkLGitPWdu7UgxhBAG4fP5tub4msOgRlY62QI+D48ctm04E6VXGT0/FExkw3L3nTIqDtOtgumFM4Hq1tzHZrCDpOw6vnYHSXFwoOh76xjTaY7m4nUeYGEoJ32/fedj30ODR9w5ZvfuPbd9/+u/4v/uyjv/WX7dMdb81pJC+KsMglg2MKbPFRalc0qkD0mVALbeF7RWEtLYCmaAgViB9fYpXxN99Suua15qUdRLJHERi0bLaomeYNh5Z0Jy2LPlM1frJr+SjRBCXBOG+bzxRl2Qnqszebujtz5GmZONHf+5Vv50qhKlZFqyk9fJyhS1yIomgy7/kiFCdn/az7lce6rFO2LlphZSaPomGLM6mmsU7pcpKZpeei4iZ2keqIlFRzwczcPpOYAnxpxzU7BBQfl6qMSobZyl2gesVLu752FvQTlyNTmCxWNnLpqYUkv9moVhUHxcc4PdVYOwQSUjAFIYg8bvmJ2TwjK9/6+OXf/S8ufu8PHhm/naY9Sfjhc+NGFRgDkIc6DTq6O4Hcwd3jdIDcwY3AC+gJOoGO8D18h/bzj955dvXsYvv4U5/7XNd0F7tdo0ZOjnrXdNvpdB/6gYhDP9Lktld73trp5tbsd2a/0RBU/fyMLHVdfziQmO7JYyE33RwbEJyXw8HfTobt9PJ1t7GQQApSgXjuTLt9fHz9ccMskxCRSjCGRcU74W3TXmymcVQXZArtbuvv+/ZyBw1hmvw4EkgtS9Bm2wDq3dTuL10/tl3jT33wThBMu/OTZwu72/lxbLbb6djbbqMq4hwZDsExEYyllomMIihbe9GRU1JDgae+p8aa7QaWwp3ny0vvxnDqu+tLVWl2Gxkm9d7fHjb7najh1vrhhBBMu1Hm5uKKmOV40tPkRMgSQajbAGS4Q5h8EOpa8dpaG/p+PJzUOxUnQU1nTEfN/jEMaArTMPDlTlo+6qnvuhc/9uXmf/zvnL786VtuDyEEYQQiFSZSFV5kUlKUuZL4rA0PbMm1EMioS6prNal9p+pxdSq1qgat/BuXGVMjfXnwVfcpzF+kM1fl7CmgWjhUyITNuFjOPsFUPenUT4LSZXMBzVcr0so8QFU5lFw/As8PgqWJl5HuSBylBVnI18J9TZQmk0uoNi71o7fz51TAnZfx3BKUnZfCMdNZGLIlzUERktMd6VcZKc5koX5hPnEsK4m7RV0C3tEGUbLtpb1OGwFNBGR/uba/BfRStDd5dcvH+2h+aUEx0bTYMVZXyklplR+y0Kn8am0pFLEWW1GVe2YGV/fMFhcs86/FqgjATB4gRQhuE+hq112K6h98ePP/+k3zG3/4eGevnu7M61d4fWvUs2V36qFQPxG8xzQiOPgJ7oDwGqEHv4AL3N7KNGBk2M/sP/1nf/YXPvulH23tVu+HhonGqSE73dy3ZNQaf3fwNzdN17Ll3VvXej/Cez14a1o9TuLh+95Q8IND23TPdpt2H/rBffx6++wppJdj7+/uaRhb0xKxvbxk0ulw2FzsXX8b3KiO7+4/NK1RBbUmHI+m7ZwPza5jHsnyMPTtdue079TCtmYjxm5G3xO02+3GoW+alvZdf7ilpoEgAO1uKwEu+M3+wruJmtYYA1WzuVa5IVjbbRCwefJsPL6WcSS2xESgzdX1NExOJj2N3GzG0be7nWgQQWNb4oa3lxru1Du2xlxdmsaGYYQP7W433rzurnfT3WDajbubmk0DY2E5HI7TNIAa8kJBrbWm3fR3LzANlqyTO1GIeOsacTKEwG3H6uymDaT+eCLjXD/64y1LY5udqgsQ+2jfeNpMvfza77z6+APzP/x3nv3Cn9lsL24l9CoSlro4BQiiMWyuUFaVGBmqBa/+HIU14312vUpASQpVNKToIWKlFIU/igK6MsoXhdJrxx4R34uOyp+Cncux4+gl9M83lP5mFSkvHO/YYfG9zEdWQFN2TyVDZr9Y0wvZkjOuquW7gIpYmS7Z2+Lxq8RnyshCxeBajDvfQsX8MtNmwCcozasXX9JYIX7MGaXnB1MKuso1x9x8ZUIBzOH7lNbUbBsKq1vdkbYj9S+YZqbFJOwcySx/nKBE/5y3SfQWC7M6gdLNWWQkj6a5TSQ4Y7UWrk1ck5WazPOcH+ha3vBZDZ7SuelSYV3SihSRNImFAIEgAmPpUpsnu/YZePraD/pf/Hrzu9/cvTpeP3mEH7zwL264P7SsQo40aBACHIYJUw8/IdzD32IaYF/DjzADwgBPaD99+d5f+8v/9s4+3uHCf3ww/aAMFplc72+P6BpjWIfjxrBpyE9HOUxhmPQ0mbb1IZims92Oj6MgkECdczevfD82jQ33x+H+qEExBeOVTevGkcZj01pxYomDG9g2YsRY0yoJVIn702lzsbdtZ7wPImAmQdO0Ok7ENLmeBaQy9aNwCGMwDasA1A7Hodtd8rb1bqCgxK0fjsa2olYaIlDb7cI0hONBPIfJmc6qET+NKioCJdNcXqE/aWD1bMxGjAmTNHYDwXh/3O53qgqWcLylbWeCikCcgxhjDLkwTffwwauSNdw0prMYx8V/DsESAz54UcNKPoz31FgmhrBKryA456Z7JiMSVE9q22G6t+1mCkfj7RROJGhMC4I2MFAatBXSSR7vqPn++6/+o78//PY3Hv2df6/51FsvmumoCGDE57GXBwWiBkQ/LOFAgal6LrM58rmS6awxSZ2rfPMDjUsQfcBnLW5b9KiMMxRgGwEKS8nkupo9JSIjQKxc24SxFR8qfU+Tq6aqCVQrz3OhJO2WKg4szuRMsy1Qeg0jVAyTvmnifWaPJsK1vrBcLrclQHq2L1MRAS1xa3lN8eyBptlRVbyJ+IRn0UlemhgpKsoN6ixHpLu2nhobUjRzlVteLEpqlg+taKjue+B0eWJt0qHr71RcWH5cfSlkypmMVE4QLX1kioAoPiibiwZQrFwUgsWQUyZ7GV6gzCYsQU9pvDzqto+tcb/9R/6f/5vL7zzf3o/797ZNP7iXr413DWAMq1PTsIiXIBP8gHGETAgjxIFfYQKaCb4Xt4X96S//9Jc/8xPv/shX777+veBu+b6Xj+/tVWP2W4yuY9bBK0IYB0gQN4TjSYnEEFk7jYMB04b7Dz4i52nf0WZrDdHg9DT5cIKIH4bOdOK9G8ZgeXO1H4ZX7DEcDnbTTbdDu9swEVS919N4v91dNbbprh8NtzcGpJNrtm1/d8+NMaYhg+76Qj1N90cicsO4vdgFUXEn07TkBgSRcbLtBhzceBLvjDX96dDt9+3FhRsHMUbGkbYbFda2peXZNTKbDZOBaTxZcb7ZbohNsEJQFYBps980+8v+5lVDGz8cjagLgsnBhbDdEpMQ/PFkt61OU7u/9v1gbeMnb9uG1VLTeD9KH8x+56eT74ftW2/L5KmxwfWEoALneoZH2wzhID0cAGg7mkCyYztyGOUegduw4ZE32NhptHZjthvydEGNvP/67r/89bsP+ov/4G+1P/GFl5twNwUilvnH65VLdSwEsRC5qLWFgK/2BQ/DeuXCURWtjZCCBEAoFH2xRskKLSgRi1NntcrlPw8cKRQcETYB6zo2tAaDNJuHsKP4W+NE4eqdExN3+2fkUnFWYctYfPwJ88TuMtseTXUePC9ARMbkmqYBkh+5/DJzXt+5licCjhaTJc1dUEX+bO2KsFpO0OT2iRkxiTNH+DUXjSaDFLulZatQsjb91EXkzeKGa15i0to8VkKdyYhjVueKxyoQtzglE2p5XGq5Cv5lGS52LXWKfpnU/AxG9lDy6hTdoaCPUspo1jxe7hdSKBvQnvGsa98hg/dvX/7DX9n96re2Qk/fanDy4ea+aTyJ8/3Je08ANARyDkMPd4DzoAPkI/jX0An2HtJjIvjPbz/141/+qf3+ndMPbvyx1w8P283Oto0Zx+AGDRo8LIuIaxq211d6e2itGV7ft+1OTaMW4f5WbsWQMW0biHlrefKH959v9xdExE2DUdj56dDbhkg9O2Fjj4f7trXUkOWGaCleF5H99pK6jYZ+uL2TyYVpYGOdR7fdWGud8+PhJM4rGmqou7yQg2933dAfms5OwwkqMhxJ2vvbF932Qtxk2o4MtWwswQ/D2I+mteg27eN3w3AUHcPgVQKURB1YMQzGWlIVDRQciE+vD9uryxDENOz11Fy04+0Nk6EQ1HlD1L7zNBwO4jxx03atgljB3MDf+35QEaEQ1BmLZrtVeBcctWS0EagPA2Tqj/e26xRqWlbFYboxxqrpwuRBctDJq/touHGAhQb4PabWtAh9M3hqfNtZDWAy16Htbsbma//m9B8f2//5f//Jn/4KWhy8qpKoqCrH1FKU7SSMcVebvTNdC2g+tIKH80Ox0odzL0xLVFtUsFD7lZpgsSJRLTOyr1zB8seRl5bL56hbDxJMWWnz97Mgx8plLnj1AAeqzJ/W3Jyf3KbVPAtAyeYRKNPrn2SQF3OakKvkHGKV/gzkROXCU4FPc4PFhiwWJF0u4/tITw/EaS0pitKiUk0tFWyLswTAeemjG12UCaW5r0x5RUosmc+BoYKCysmpjPhiaCktTKJKo1cee1r6rzPIiHvOxUQsm+VZDKNaZQGoKujyOiX7T0SLy7/8BBSIPMDKIJhJrzbNNTXyhy/u/vlvX33zo/3knz675uFu+OC59UI6ueMB4hlKRL32Tv2AoUcYYW4wOdABuAUc6UlHRfjS7r0/85WfefLk0zrJ9PGLR5etGzfMzEbczam53pimU4Tx7gZTb7qOmnZ4ecem2V4+JkPj3dFeXRJbY1s3jGFy/t7xkf00bS432g/dxW7oTzo578V2DbMMh3v2sEzNxc6NPZRtY0+3d03TElkNwavaziipJSYm7DbkfLvZDqf7aQrKpt1twBSG0Wy6aRwsYbi/CyG0rVUoiAE7TafdbqMQ6hqdpNlcjMNLp5DQWGtmCzvdvdbg4B2gRJYaI31Pqn4YmstL9SzjKThvdu1m04XgREQmpw03m862jU4uDCNE1dBw8zIc+m6zDf0EgpLq5IiOCOLHE7FtxLjxBENhYAkI00T7VgikBkanfjDUMe+mcFBSTxghNrhDcB1f9uQ+CrcjPEDj8kI9O5DbBLlg24ojp/pKWrtzAHebi13n73r6+jdu/3f/afM/+TvPfv6r3Pob9d6DmbC8/kKhVHpkmv7JyEBKEv2jUoOzV7xktRbDUfmmKPUiwkWhvUUl4lJQWIeBky+c1CW6XmfGZZVYLnGh/FzBrVakRvUvoZpi/vChg+LU0gQzRQlFEklp27O4o4z4Ouh5kOShU+673EFVxYHlVDX1ujiry78a+RLJiw9G0LIDWMJUyUNNqE2IRZOg6udkNDm5y4sW5oucGDGnDXS1blpSTXVvOXCS2IYoXbS867wIp6DIjBQcKOxs/PSw0xJHKS1U8YRJkiSKwpZ+xShPKLkRxTCzcVmSRstmJne7/JB7KdtL0p8Qd3nzO50prSkFVSJWQABjGIoN6HLD73bm4vXdR//wnzb/+g+3H95tg/f+rmk3XWPM5PzQc/AKdRi9OgWO6GeEu0F/Ar2An2DE2LvQC8IVNj/31Z//1Hs/KsdRJxc+uh32YFjpvY6T3XS83cvpqPdDQy1tGVPwL163bWts6+6PYIvJ67GHg/hJpqBhQHBNs2UhgFSCP/WNMXzZurujNSTKrW3VTeJALTfNdjgebdu03Y4bAyHTNaEfwumoYVKFl8AAqZxevmgvL4IEJYiXtntEemhtM4XghmGz2TZtN5xObAI1HVtLGw6TJ+h2d+3afjrcMTfctATTXF6Ox/tw6i3gx8FYbnY79cHajdMjERliYxrnJhVttluFCgsCmrabjkPTNjJ6Hcf2ch/6gIa5IXF+07VMLPCqamwD42U8qFdjGmrsNPaqgLKII2VuzXQ8EuM0fhiMMozZbO76l7BEGoKGAEwQB73Tm1fwrzHdIwDKsB5+h7BXswdZXBAbJ6E15N1IxsjhSEQXGyu3t/j2Nw//8f91x/9L+vkvujbcSRBhxfzDz2SWfa3UxR0RKSlpx9lz99F3irnK8mz0uwvtz5WSc4Q5Nyl9zgqbs67WalzcVhQMVQYqKvfKW6w+JtOTnbfYO2HxcrMZ1DyLug9kA6rp33hXZXySwUmQp7AR5dMPKqYqEapRruBEqjLJUfGI4BrJOUfbNOd4ffkvon/qizKh86sAomMf+6EEhwWSpj/ZjKUkcrIAVNASrccaoZNIRJNFy/tnynlV9UEUeagxlJXSRVWHWbpqiUqof0bLYkzTxrEmodCZxcjWhi+tdfE5GrDF8MedWjHecj29318VxKziO6+P95vHXbN7frz9R79kf/cbT0b36Lozx4GOJ0xTOB0x9j4cgSCgAMfABO8RHMIBEsC3cEfoEf4oYYB/hO2PPfvydfvudDOFF8+3j7e7z12ZTWtPYfr4tr3oIOLvD1a96KimE69hcu2u5abxh8kIA6RsjQCKIM6wkjLaZjr27cUjuR/IeT96AvOuAxvVwNwKg9uOyLNthnFkpd3l1XA82W439QcZ1NjGtI34oCoaPJlGJt9umuH+tL2+ElEXxjAdyPJwvGPTbi/2rh8hypbbTev95E9j++gKYB368fYg28Z0nW020zRYa0N/MmTNo8vgAxlsL67G04ECQui3u72GEDT406CMbns5uQNEuutHYRjD8Wi6hhvi7Xb8wZ22TRgDBeZmCxYRH0IIk+/2uyDe+0BwdnMZQjgdX243l2130R/uocIQarYBh8a0R3fL0gQwiY6YTDDKNmgYADT2Y9/3LH+o9yPCCNnAAqEBD1APAcyNOCVrgA01yspMCC4cj8GZC9vRy56H79z87//PF//h/+i9P/+jzW66HT2J8aIkIDYqgYiplMMUbC8f4ypzcMkw1CGGjHdl8eWi/stzObnEodoXUH50LGpq0rYC5SN6xshrdhzPfMhEbOo162bcSMSv1cakqBGt/UxkcEEZrS8RP5uMZcg4HGbaJXJx3vDY2Lp4J0JpEc8ns+BpWcGidZPIiWh/43YuBnzi/QV4KdGyD4CmuqNlJx0RL+34ZitSVgNomuTKS6AqaRH/VJTHcEk5iRxgT1MAp5dMzI04PnKilLP+OWaWJC2GMZeqzhK+Y1fzJiOX+VTOiaY5F4uQ7EBOVyfW5bcs5WkvFj15EVq/VmnuidLSLAMokUCJG1XZEj3Zte9uu/bYH/7Zvx7++a9c++Hi8YV5eUfDqOPgx0Gng5ejwGF5EE49ZMCk4BF8gr4C7kA929cy9OoV/sff/cmf+uLPPnnns2E8adhANBxHen10w2Sl8ZMnN8nxxhtr9pumu/TDod1vxTmyDaP3/Wi6rW1bf7yRKYClu7j24kyz13BsbDforW0aKEOD9L2KhyHxJxLP3E5QkO6eXE43d9NxgGroT0wEI973wY2m60Qnu910+/1hOCk3zdaqm8b+1HSdugHBMpG6aZyEbWvalhvy46BBWElOjkDG7L3rjTdMRsUbCca5MDlu7DCEzfV1s2nECJGxBszQoH7ydrMNbmrZSnCsFCbvjj2Cl8HTthkPJ6OmffRIvTYbmm7vBeq8a9q2aTtjKXiH4Btjx6E3naql1m4hGE6H+afznEw6ugAJbmAws+mlJ0HXXNy7Owl+wugQJoQbE74h/Q+tM0o7bkfR1sOpNKABCPAKDkoBLI6vsG/Z2LYzKuy8aRud/Na59lvfOPwf/pPN/n/6+Ke+EtrjYVISIwisIGJaVFCj8zt7UEkTCiTPWrE8vZPBLulU4SfVWpPTzLOrk+u+1yBWIWlK9BV4Xp5YZfhyR0XBZmHIMr2LG50KC4su0pYl7ibys8FLzjDVbJf2KZmGcuaVDx4jMzNNtvAhl7+kc5WWosShgvDaqiG+6z4tS54DxRh0guqFQF3qWHhe3oWiaAeX/UHcVWTidG5crFlp9AtwjBzPtqr4VWGK9r9g6TwRaMq0pqLOckUynqbt0oKUuRMkezBXYSYWpoz6MkdN7KKC9hRrygaX8lRX9i1b1WhbIn3LqqQwHCgOM688L9MtqyDiCLK8PwQe87veQjOFq6192tn9/XD45W9M/+K3n3L35Mtvm49fh7s7PvYITkNQdR5+TpULRBACtIc/Qm7hb4EP4I7APfw9ROF/4uLzX3j2p55dfVpugx/6cDc0rbHemrsevZN24raBOLvdqgO3m/HuhZ4GYzeBCOGIfrTGsLI/vA5jb4nRwJ9eh75XL0HD8PoFsYJURscGhIkYAmk6o9IMp4MakpNXBQyzNX7sw+QB2KbjphMNYZwUnj0N/rDZX5KxFJwfp82uM2YzHe+NRUAQFQNuOjP2p8ZuTWshis4SzT75ZLetjKOydYehaRqPAFXxoSENx/sgavZdd7H3wxiGkYxp2w6WyFgdJz/0tmtp04bxZK3B1oKVhHA82asLFwY/uvb6SgGeBgoiGIkbCSdDNPYn2+1kcMJK3LTb6/vjR8FPjdl7yOwOGttKGA9yUGjAIL47witUEQb4V869tPp+Iy+ALZEX6QKMAtAG5BAEdIeJAIuOMHRojTQGTKYhd2Rv2oY4UDgc3De+9sH/+v94/b/6D5/+5OfRnW5CEIUQMUQYvCStNO6jFyzKOp4cpKgcpTeffPASsbKHmHQ/6lp0GZMbHbfHufsFglAOGemIJyL0n4F/HDTaiXlu82ldd0XAWrEr7Y+2IkHumWeuEQOWv6UJzEaIyr/zwbRkYxM7NSYkKf8giD40x/R9XkYFLS+iX+L7HOE/5QCYiJl5/oeY5iNDHGj2tZdpRRBXgaqKzLYSC6wtbvP8SAFosTxxOjmJTgQi5UwVZ6M0U1ymH+bnB6Jpns3SXPtDkQ8otg6E2Zhp7BxE4LSJmX+Fdj4/g3VeEyVNy3i2+lqyu/TlIzXVyiyrrpovpTzvzAyihbeEvOBZ8DRJIEVbBWFmY4jJBrnaNG913cUhTL/ye+GXfuvyh7cXjpuPB7x/S8eRppHYEyTAC3SCCwgKHuHvMRzgb+EPCPdwE/gA3Km/wfAWHv/kOz/2mUefC6/d+NGNtc3+8bYlyPPnFAJ2lkmaR1djfyJrm4sOCrZNd3VFzDxOchg2u52qD+7gT3etgRuPJEpk7cXWNGazaUm9Dl5Vw3gK48kNztjGdttpDNRs7abtNpumJYQpeKdQspaYiNHtdxKEDHNrbNeB1RKpF8CId0ZpnCYfPDeNSGi3l812p4B3rmkZbvSHQ/B+GnrnR7vrzMZ6N4AIhprd1nSd6VrbbsFsjG2Maa1t2MBNOnkN3gB+7M3seAY1BPGebWesZWPIsE7CpiHv5TiE/sjehzG406Hb7ogQTsfxcDf2w+RG01q2nZOelPrpdHf3fAyjIgyhP+E0YvTwJ38YdDJoRvhA/FpfTPAnhFuMR9ZhY37A4WP1aq0HC5v5XdwCCHQCRuAAOUDvQQPoFn0vgw9hGgcFxtMdQhBLrI5u77d/+Psv/zf/0f47zx83241VZVZlAauQzGGbxbnN4L6oqVIhunOwQBeRXn4mlEgWzChyoktQekahWeMowwKS+gOLM1XkJguFW4CxxsC4ZY//pZCvLoNmWFouUIwlJPWbh63szsoaVEMmVFuDMUXbRtnOLOiw9vDrb1za1ZnwyDjFzC8tJxmxMg5FUCJiKAPxv4Qv8V4inu9QzC0JWJ7djVyNFii1LzApmgcgTm4u0UyLlxi56mSGY0LF74jviwugETfT36WNxkXPS1Jai8iD1QfNaaQ8g3I5l5RHxGVi0My3unWWhBKyKV7QJY1V2yfUe80imJYWMV3QSEcy2FG4ll9OJyJRIfEblked3fWh/6U/9P/iG1cf9k8eXWymHt/6fvj+h3wcuSG2rDQJPMAe6iBH9BPcuLj/02tMH2McWe/hvE5Psfupz33lx77y05ef+hTC6eKdnXGjMZ0q2uudvbpiY0Xd6bvf2VzueZrGj1/Lqzs+jDqqPw0UtLGNO52m44ktmn0nzLZr4Lw/3KtX3/fT7UGcpxDCaUJwjTXtrnGnE3tt2FBQwwYuiEJDaNmO93cSAqwJfhpP96SKIJvtJbFVZdPs4NXfH3TyzvfsAzmnQQ234pyO3rZbNg0TwQUCqYSmtcQUxMOHxlr1XkIgNsF7kgDxzCx9L+Mkw8BC4gfyzrZtGJ2B+Ps7d3MLF5itjs4dTgD70cng2Fh/mkA83d+TE5kCuR5uGm7vxzGMfhR1BHGuV8Lp+CLI6GVUaK+HQQ8TnOPJYwgUDhg9xMOPkAkcGjvCBZIJ6oGpsy8NfWR0bI03RokDwUMdwRNPoAk8gBzMCXJEGEAD3An9oE4ABwSQEzdNYxBiJby+81/7ne/9b//uxfc+fme3u2gUFJaYNhGREY24qmAiTj5liVop4hIBbvYIKcJ99GMXtc6Cn/4m331xH7PaRi0lAs1Qlv7lBUyyl5nUlmjGPTCBiYjBNLen9CEdwJJzzV/TlTPDtJxcwD1Bz1koIO+Sln0HFVum5OBW+k5END8JvHA85tkpBQ009Z2rdtOmJ0FtbB05G9MkRSYn+ZlzNCYSFuF1meNiErQIdaSfUlx+ZBQx4FKwgLAEhjRxgJJnG7cHZbSHMreISFVQ3D23ONvuaOZrxfCijgbFD6xT5FD2yJf8QO5aF8lNbIgV0QVbVk3rqJQmmVh2JBoFJBK9WMxlzJiPoOVUPJ1rq2aTQsTMqiDFhundTfvMQH/r2/f/2f/n6evTxoi5vdO7e5kOTE5o0qBwQTQogkAI8AgTvIPcwL+EPwK3xCfglsNJ/Bb8M48//5f+zH/jwlz0P/gjf7qn6V7HEAASsGmG01H7u91bj5xhYvVjT9NojRkPB6U7hBDUaAhmY5pNq+pN24yvbwlkNxv2nr1XCTJOsNBA1HXtvhMVDZPKICOLCFSYIN6rCDXt5Nzmcq+A+JFtpy602900DVN/Z4iY/HQ8qB8Mod3tRqdwXsVZ2wbXE9SSdcOp7XaqIYyD2VzAKBmrYeDJ+dOgXWP2ewP401HCyLYhagjWNC1UrTXucKMirEZVgoxMLCJN0yqbIJ6sJQ1hmMQHw0agUGe4UYbdbMZxmPyk8N1uKxKCpxB8s7HTcJrGWxh7Cn0HEwwkKIMmTCKDMU0fvFBz0qDwAdojkJ6CUQILAlnzIkwfGXrB0jNdGKsmhABhdnOQcPbeMXt1oYcfgB2sh47wPL8CCsogC1ZMHZtWfHvzqv/VX3z1n1xf/y/+/cvtleNJlDSIkJAqM0iSGz5rhGYN0IgyUXJjyXxCg0V/Ehzl8MKiVZSyXWmjXdVWLKpXvcehsA4ZFQn1XVjC2qkMMil1HDhSPb+OLO7cCyUuukvFn3HimvEzm6+lZUVhpGqp6k7cymixjKyqiK+CyEaF1/0g3V+AUuT3bEWUCuYWHSI3mhmUgnSU37JwbsuoHGkhPe/sEtty5oSKnU4FnhRjhCnlPGdAY4tEbSlbBXeXm4tNXAq4FNTGNYzQmjz3kmWUVmDpSfOuMjE6tU8cr4U/PRJQOvOkiBUPlTzmME+ivb4eD4lZHNZZaogCKQubEHZkntjWfuv17T/8Vxfv311u2r3VMN2Hw0H9ScaRNAgcQwK8RwjwHugRjpiOCLeQE/hD9q+tuRE/iGuhP/X483/hR37m8aNP9x98LHf3W1FWpraRY89qxR+NaZUxvrrtHu+n40GmyViMw4EaGGVPghBUJ9tcuqmHN8NhaLsOXqBKlqehtw13GzvJBNHgZNPtQ+i9c7ZtmUUgSt6ph4iyzgEWJS/eqQumaYIP3o1MJKEfpqFtL0wDGGNAw/HQ7jaBdBoPrV4YZucGgQNhOt0zU7u/8JPjtlMV07T+1Df7bv5NgaBiLJOyMDGUNXDTeAlkoP1ARGAJ49RuOogQkQ8TiQnBQ8hstzKMzKxQEu2u9mE4+jDqRKpqLZt2G8S7YTSWbbtx0ntMBk1Q15iN12EUHyAEnnAyMMZeOO2J1QFBaJBRoKpB4YxRCZMng8YedXKkbNAQKyQACmJVmh01AOAADdARYcQ0YtuCLGQD6xEA10AnaIA4UQAb4tP9zfN/9s/8u08u/r2/GXabOz86EijABBUhNhDRxSOUjNyI8KRYEDolDZNOLG++pxWglzgbncL4OsjFmiWfUtdKWKlh6pJi0wp7Y+gqx2A1G6XS88wjrZ3N1BUlbxsoWpbVSiXSJXzJUEYgzHU/i5MPpHYziyzR0iQGj6hgd6qN0QSlJX3RL08ucJxpvGtptGJkXMH8Oa5Q8v2jS5oqt0piZrNeGeW0ZFSSXx1LoXwcmlLlT+FVxA0MVQamNhbVGsXuNOfsF0u1InmGby36S2MW3ZXJdF3NISK8np3MRj2JFmExC2m6C/5nwjLXYrKFADAzeVUiYyztyLy7M/sPnh//6b+yv//dK6/U30kYQt+TH1ic6BTgFUGhgjBhUMABA9wAeQk/wL7mcGeae6JXIWxVvtI9+4VPf/XtzXvug368eY3Tndp2fHFsH12zJX9/211uYHiYQtfo9OJGxrExdjqd7G5jt9vh5WsmIgtDjZ8GGSdyagwg6l1vuQPJ5vrSDcfgRoWypU1jw3CQ4KHiRKSB6ZrgRlWYjrnZODcaCz+d1Adr2iCh219M/VEhqrDGCkRlattO3ECGgvdg05pds9sN9y+tbTD/2C8bY+009CpKofHiLbVirKUt2LExwQVRonaPEEzXhslN/clu9uIn27UintlAJvWOTUdGiJmIDJOMvjGt1xNbA8vB+elwA2ZrrZKwhR8HoFVjPRwEogjBMayCRpl2u4vjeLINiwse6mEs21GmSbyIH+CbpgUITaOQEHiY+oECsB01jKROAgmTOlINSg7aqhgVXpBWHNQAPfwd+g7K2CjUQPfYEGyAmxVkhHeQk4ZOMXz4wf3/4//dXX/26q/9WenaO4hzviUboLT8vHN0fJNyRK2lAlQXx7BqR0CxuS30BLoEN6N/TZJ+KWX+BSKdcU2z5SGguPcMWQrnb/43BX4LZzVhd9TuxeEs4XohvmipyxMLmpsQCoCIvinNpTSF/udqodnDJkCKM7nOZd4BzIinJaUZdWneSpTjxiHir35FHsfJJTKKKa/wmAojUQXoozNP8UvuIyL7Awi/2GKKTMvucdniDaa2qpCcJTUB8MrmJa4l8YosTVC67DCoKinIgye/IBmbbJGWlacoI6uirzoJlSgrt13Ikh4jnPF08keKiRQrNC8IEZHXAGYgsJe3LrrHh/H4939Rfv0bb3eWMemr124ajCGyCMOoGA2MgzBYMIdw/IBphDtAHPABhoDNczeGRrZKf8q++2/9yM9/8bNf8R/1h8NzdsPFpy6DZwPtdhf+9lXD0NNEXWOZWYN66WwHCbZhOR6ncbKGIcGY1oeJoKa1qmobK96Z1kzHG9PuxE4KcMPGWtf3ULjJsTHUWnKTkNq2YRlNw94LW266netPBDhGEIWSO55Ipd10ME2YXCAQ4LwPQbnp5mesxIgn7a4eiXgfhJrGT37TbXhr3DA5IAShFmK7QQIDfX9quo5g2XIYVRSwliWogYAsqN3uxXuwkcnb7V6GCU0TvG82nUeY3Kndb5yEptsI9/7Qt81W2y6Eqek2IYxe3Tj2bA1IFA4EBQ8YPXByI9kG6gHyEMCOEiYRDz9BA1wrhsgSLkbug4o3PEgAxIXg4RsrTAw/v6aOhTGCIaQqrEpQu/xIpx4xXoA7WIswAS2oQzNhIFBYMsZ+AI5erszl8Ed/9OLv/WfPnl4//gs/QhtzJNIQCBQAnV8fLWDwkr8SRGWDimTRnhUtFdwUikAJPnIoaYkUJzjIehDNxnw510Ikz2/ljmXfa8a3s6Ax1R/WHmnE16IMbyE26ylFlc4ufNbdpcovs4VSB+sKkgVNlrsqJ5QsRbSYGbAYRy16ByJrltRH/E4AYi639OmrBAJFhEWkJIIoZVuVXlSGFMtIC5AKoAq+JOuU2T4XWHLy9MvIUkFBZceSHdbiQizdzKCcg5CLWFDEaUq+9hKYS9nperHS9WL9C64U1FLmQWEckqNRgr1mohBlaC6NjuQjLykQd3GldV7eEzQnwpgoACCylnmSR619WyG/8nvTr39t/+HBXl66jz9GfzRgDZO4o/M9MAEdAI8pwBNIwSeE1wgHyC1RgP1Q3QlTcP7z2P+NH/uFH/n8n2ZjfXfXOafOTyexetV0JhwO5ALbzXDzio49iXdu1BB8CMTUNJ2GEeoVYsjI5MM42G7bdDvnTtPhHgZNt7GbLZFCJogPpI1pbduoimmt3V1MGsga6Uc/TDIF2loB372+3T5+EraX/aHfvffpUy8BZLzs9/vD7atm353cfbPtbLs5DaNt2LSbcHKwxpLK8b5pWzXa355st5EN957EWLp+JEoq3rM65w1k07Z+GkY/sbEsAp2z0I19dHn/4sOrdx5Pt7eGggg3m61zJ1ZMIXjxbdcFKNqOicahVwL8oCHA2lE8hiCqID956dquMdZNE5GX4CYJIGPRTXoKzilmvgTADOhbWA9neRNYRt+7ME7QwCcvOikassyyEWM1PCG6n3iU4BAMtDOtn9/gy9QspTuqgIA8iMEnYA8hNAF+AhjqIQYEGA+vsC3khHBBejGdxj/4uvsH/2j75G/vf+zdiWUUFS/MTRAxhqKLi6W6bZUMzqCfdaJU+mVbsLj6swolFzzFe6LWVwoZsUqzMi7ZgsK5pDxchtyMLm8+1kPNlomQf301TSWZiQWiS4xcjxVDwTFvvhoxPqtUJFBIARvLXlR1jrAlHnH0/RMyJuYgfYxBuIcrYQs3M+Z4l3XUlOQoXeO0JgW+L0SvbUBpJAuHvEid51Yxk7TKwMYxSNM6xNhgJhsLd2Kn9WYmG74cLFsxIAlQ2j0kc0PLhiXlaMtFjUnknE1C3KIWb0eMtgnRWlFmciSmSGnQWjgUSiQKMPz8onYQu3Bp6C1w+29+2P9Xv7a76TtD7MYmOCL44WisjP4A9AY8YWTAww8YPWiAHCAvEI7Ax5A7Fc/YqF7g4m9+9a/86S//DE4IwfM4ji/udpe7zlwMH71U78PtPW8tN5dq2bJrqR3c0bCHteqnqR9hYRuyxkyHe/VkGhr7g6qqIZXQ7q6obex+M93dhGGi1vjetVvTXD3p7+59ayaBt9vHP/azx48+GIc7d0PgjX18ESy/VuPbzj/pLr761bZhfnY1vrjvD4e7b31v++xyd319/e47Nx/fjh+8fPYzP9G78fT+h08+8+7w+qb/4fO71zfXn3s39N4+utxcXH78/Y/to83Fe2/fPX+l04Cd3ez34sKL7/7Ain/y2U+//Ob7+ydXMgV3vJHbE/lRHytsM5rRNB1TS8a47W7a2Gn01FraXeg0Dsd+9/iydxOp7jYXw+EQyNhNM9zfGjJeRGA8NbDGWkNqgptIxSmadtNKM/qRjXVhDPAgNGS8+IbsKJ4AS61XNaQuTLN8EpsmGJB9xg1RmLT/UN2JBcQixDOACgUyFqS6/MKHggQYIQI1aAEEhNknIBgPEJihivCO2QQ/PWv2/eSHX/+N+wuz/Z/97csvPFUSJwQIgUTB808HzD/fRIVG1GoCyjqQxDr6VNl3m6NHc6A3e6uUVHTRq6QbEfoQ9/KL7hKi10R0ZoPOfk6Dqs+UP6Yg7GLiKKJVajibv7n3GC6I7SIuJu89Y1/CydKY5VT44p5TfN2cTSylDKOaq2dA1faq+Jhj5vk8paGLDUQB3ohO/UxEurKOrxcrMROe+RPT/tFwpklW/E6QmOJtc0kYIk4musu9S83BBWaz95FQfKEpDhUJWTge+0i2I+efl9DZIoe56qlizTKPKL+p6moxVqqKwnQ+FEtM27fi6bfKYFB89dwSsSQoAwo2BuI7wju2efLybvzHvyhf+96V2dhp1Jev5TAYK4bgQy+YArxDIDQG1iMMcAH2Du4IHEEnwgF6h1FEn6D9C89+8ovbz/PU3X77e7ZzhsPV46twP0zHG9JJhgOzZzXTcDAbhmC4fQ3CMBy67U4NEZHZb93h3jkLKNQ07UXTBNdPipaaVgjucGx3O4eudxM3W/vo2S3RNLI8fo8v92Nw2mzGrnPvfsEf+92P79HuDl6ffPnzYThxt7l6+/q1YvvO27vPP9UXt/c/fLX92R9vG26ePBq61k7mkQkHiO/l4i/93O3r23A3ypdHGvp+123feYLOBEPXCt5Zbs3uMBxfnuw7l3Zr3MG1z39iuL25vbzs/uKfo+vdxW7jXrx4/hvfa07T5aPt4Tt/9OwLn1aZDs/vh+PYXW9f/OCHz/7C06bhV9/8XgOEMN3eHLY/+owmfzwe7O5RP3nVQLt9w8Ydhv2jJy9f30ofHj155Ke7aXCm3fbjZAQiIsQuTAxmmFEna6yCvUiAGKUttwoV4V6UoLum0SBds5uCXjX7HcR5HF04WfEswmoFXpUIBsRKwuw0MMirNAAhHMCPsG3RDZgY4/yeLocQIAIw0CkZKNz4pGlvb1+P//JXT08f7/6Df1eeXtwG55yIKikFFYLOxiBuvgscQin9yVHO3mqqu0valaNCCsQIe4qlpNuQkoyUEDgqES3V1w/FfNJ77ldOMsqvCQwyUGcQLWC8aKLzDqAyT9kGpNuzxkeTRHGqGRhnmNfkG5JF3BSkYFS1zdE1NueN2Rw1XixQnnrFAErzitxYNip52zM304TMBfxFrqRg0dm1ipWZwUtQRiNO52R26bMjmvRYf6lFjznVTuWUFDF1u9hFjaa6tMxpsUqTny3n/Eert6vWshJz7oWLEXk3D5ZfGBuvZbmpbpqFJM4k60uUZLAikDJTEOLgW/WPbfvkzst/+bvNb3+7eTnyE8jhxoyBGzHKAoiMDlNAUEwW8JAJboIcMN1DPoI7QV+pM4Y3gTcwf+mzP/uTn/nqdfOYL663jxo7eUuNnE6GLZEXCdKwcKNu0BCo6cI0wYJVu4sL8Y7ZiAY23G43Mgi3W7Wb3ok3TWitg1HDxGwePz7wTnbWf2q7+/Tbzu7ts2v1ofvMW8cQdl96215uD89fd/uLhnTzqavt5Q77S+enC+Lm8RYN7MnrfnMcPH/m8unPf8E0gCg3jRrRCaYhJdWPPT9uN4cRoo213BApuLGi3oew27fUob8bWo+LTacMEh/uhkuyl/BuFHu5N40xOyM3z65/7DOt5ZZNcxq2b1+5w8G+6HenABMe9+Pund399973X3hP+mn39FJf3z/5wjuvv//+9NHd7ulTNny8vX/8eO/6081vfgcG5Prptn9NwfgruTx0F914cx9uR7tlFplu7/atWgQ/nQAGKRNfmCaIjGHaWHunzoAV0goINriwgbaD33at8O6FG2/82DO8FRAbZq86KSwHFRj8f+n6s6ZbsuQ6DFzuviPOOd90x7w5Z1ZlIStrQKGAwkSAEEiQIimSEkUZKbVkFCVrPcisu60f+ql/Spv1Wz+qm2aUqSW0BKkJGgkCAkCQAApAYawxKzMr887fcE5EbHfvB/e949ws9ZdA3W84J07EHtyXL1/u2xzEMIdP4Bsse6hABXbAdIKNQmZMBywKKXAxPwPvMIpiYy5P94f/5V/eXFxs/6O/YRfyzMyWtlaJHE2lndxGM6HNmK4I9NNpuyNe4hj6BXRvigs/2sirdB1YoVwyuEeBRrML3g1At3Q/bJOOjFN765EZPbItHet2O+ovJi3Qd28OwIqSX0S+/cWtz/YLIHy1AUgZ6LEl8cYv0As49zie6Pef1rv7rRfTnv6pcaBGo/U//xAh0/98/MDrYKxhTE/QrNxWu7nVcR6rhvqAUUz52lHCm4M9GsgVdffpXeeqpwTaj6lbDrvc3k89WqG2yo6TNp+KcV4YMXfo6lrWUThe4i8ONMjNj6RnbZa6W06PvS6nbGDEYA+X74Bhy3R/u3tt8e1v/P7hf/nX9L1nZBNdTlwPZgo3wKteLdgfMAPq8A3kBvUadQ+6BH0MvQJfs+8Nk04XkJ89e+8nXv+xl175zHJ5Uy8/oGK4methtv1ByXe37mN+Nt1cqWF7uttSWaabenldCnmRzbCpy1wPB7P58GghHvbVzi9uXSr77XuzbE7effPZ4wUX48Xn35DbJ9dPp93L98/fus9nu8OVy92znYDOhs1s5eWz8dbmwm3YDWUUsCvgDtkMLGRmpj6WIeoCy0hRlwDQUt1HFOGq5ubl3incy8tbBrmpGoh5UaNx49WnKOo72TBD1ax6dRnunhBg7oPzUnWa53nD5eWT3ZsXOFQ14sLX5ixnW6M6VVaH66x1+85Lw36Znlyj2gmBX744efjG/P3ny8Vue7alm0Olyoflzpff2X//0cmm0OOr3SBP/uw719/68M6bL5+enD7/8OHupdPp8bP6p9+s18/0+RPHRqtLYXfUapF9m6uOtGFgdojShgvABt2418NyMfA7fLrX+pB0cSxk7ijkSlINY6xlAsAGU/ABfoXpAhsC32A/YDCogxYsI2iA30G5VU42PspmGPfLtCz26OGjX/ln9Nbbm1/4ivC8SNsORsRJl1DIdJoB8O4Wjgxx52WOLFfvzHJsOR0tGl/RXuc08o/drByZ2Rfteu/C3luVRtBBwJEU54W3tG3dKZDc3Z1LXz2CJ6BvL4gbWKkdSqC5plnDUx19d0Rwp4NpD99up3S1SrPo69HAnbw4NoHH2pwXoWs6xz6ix6MeN8ErkdNHFN1qvvC7/K5PQ09sxjWbWfy0LfzfuDq6XzuG622EHOsAHa0avEDutxixtdjwdJNNW7D6j/gt9bUFmOepEclB0Zq+T5fTV/JqtNNmH5+MA8AMRP6pu4sBsVyztPrkdOM99knX1LA/2OEwOBEvqoORwDYutxba/cmj5X/4Lf7WR1sxcCk+6XQYytbMl8PzG1wt8ApVwIEJhwV1D7sBfQJ9DHqK2Z2Begr+ubs/+lNf+vm7d19Z9nzz/sNCxjb5zQ14GE+35PP88Fvuy25D82FebozIVWcWNjMclskBGWbHYdbJq4zbm/HW9Z17/vLL9//yT+5eeXn3pVfo4XN+4/b42r3qMxs2F+d0NtKIEyUiGkfY4iMRiFgI7lrVCnl1HtnN4aYEKgyBWWUiq1YB3jGYzBzKxK5wGuLMLottlzNkADuMwA7JdeYEEhCBCpO6koHJ3Z2MB5KTjS21ksMWbAjVucDM3c3Z6IS8OrkwiyvbeSkPTklJBjm40d3d7c+/6kokvoUPA9ebwzjb5umNgG5L0ec30+dekx88O3twmzZlN9fTO7v9+z8o/+b15et/7u9/byM63VzxQPOzJ4JNhSpsBHYYZ9ftMKAaGxOk8GY77G7qMpTxvtFbttR6/YSqClcCQAOous6EAlYQMRdgsjrB91AGF2xmXC2oCp0xEWxDOPfh1mZ3Mmxw43aznEDKUOZlv//ed5/8k//2zst3L959xcpczcXImmUkcjMwwTvUaRsIR0s8Sd++lZq9aueZ5o5LQmU18bnL6PifHzYix56EGoRtVH6ayW5AfN24Ryi//Y5e+E23dd52NWg9EqZB0yMHlBzWp++yCzxXFqc5r2Mqp72MABQH3NB86xGV0W70yCp2gPrDX20OgqnpJhYJtTsePsp0pm/qbHU09e/OvItrvFFGxx/0Kb/ah3glk44b/fXZTgY9sx4rj+8Z+HjOQefRnInd4WYguGEljZqNTzFUBhbxwfCQrbUY72hFua3BQAua0l1m5mV9Ms8rt1++SMi94DCATn51F8nd3TZnFLdEnB6M2R3qVph2G96Yv8R+7/sPD//kfxr/9Jub6Xo83enza+hBFCTM8Bu7mXGYAIAMNkENNmFR0DNgBl1Cr1GL4wT+5e27P/6FnzqRW1Zs/8GH5dTpyZNNGazo5cNHutuCVHTSmz1ONpvd9ubymt0OWh04u3P38vGlLa4iy+4M56+NX3h998Zr5eSevPVg96XX+aU7yvyUfPsjD+TOuW+ljDKwm5JVd3KMTACx+wg3mBo7k0BEnIyZAAeTmxO5afYgcyEZirub5vozRE9ycut4htzA3MCSuUewQEREBmhVthBkGAtpdXphyRCJuLWFyjAzAqkRszCTMbl5NXMIbwqIbIExiMSJAbKqgTlnVRrHBTPdPV1mtd12uLM9v39+bl73k8hWhDHocO/s7NZde/MN/fb7Z8z7J08ff/s7PDw/Ob+lk988+XBYrkmnAcvVcnmCkYWLyUCyLCCj5VDPx83r5WQ2P9SrmXxhJypi7kQzkTMVJ3Ny+IaHS5tPYVeY7mEzYKgwwlDhAM7Gk7ub0x0GqYRRSGFLFcWD081yc03f+fP9P/0ft//Ff7J9cHrpU43darHxEL3A1q7NAX2t2bijLF0zYmmpc2fHa/t+aWRPZ/O926cXLEs31c3e+5GZt6PvP7Vxj777tME83uM4asyb+Pr4dCcc6Vn7FSkCnbT0nzIwWH9cHQ85H/9AzfwDJcjkFhutrNSRZ22PcWR7qZmangdOciFBcRp9XzX9R2/1IxtLR/9YXu7IeR3xXP7CYPYn9j6FLa8epGG++chleXuEuKu0+UeMTprFyA15v4OjF3aP1DBgDjeht5NoKd58RrQTGfunrAY9bEEb+Zy6TxUQuFv7e1hzOopOX0iBr+6mfdD64xGPmQOfzbwAMLFAx6ovbcqdy1n/3781/PY3ytMrGiv5UNh8gYyjO9NihmpAhRGqgwx2herAM+gz8FPQFWYCtuAvX7z3xTfeu/vyG/uPDqW6z0/tcDUsk1Jdri95mGtVYaZhwDgv+6nKYTaroEtwleHhwifvvKMm27ce3PvSO4fT04u/8p6cn/Ozpdy7Ra/e8d0gJ8NmXmikKpyhu4MllwWJu6UjZiEWNlW32DDu5kQk7C5wh0gMGrvGVYgD/rtz9nHJ8C2Xk+SQc3QFz/p5VwPchAhwzvFGEXGYmQsThB2AQd2IICKxmtycEKclEjGJCIyyeoeINgDIzUDmDiM32ChiJKo67HZEGMB1WSoxRnEiPZSl0nJzU4y2D85P75/bZ18arn6kXF/R9548/6P7w+Hw5uffu/r44dV337+D+vQbfzw9+mR7ZSA3JycBj8WKQYR8s8yvbc/U8Xw+zG43ogQSR+UizjVO92KvbtVxUspBdfbZMRJkwrJFIbBiZmAo24IBcB4KAVzLst/Pzw6D6snjy/m3/rW/+97F3/l5HWSuruaMdC1H+nbvq7v/tNpMOkbX4UNiJIng9kKlEXXM13O+6Fv6yFDT0T/NB+QO7FFHRwefMvhH8H81WscxwbEH6WmH9vIjTqJnFR0vuIyjb5yOTMf6p7xQx8NodsAdhYnSkcXSa6PSyOP1iT4V5qzBCK0/xjbk9b0BdI+9T7unI+1mh8ItgMthXiHTcQTXH8vXIQfwKWXvsffKycmbSuPftFWf8tKEzset83j0o9PqsZySM/QXX/ppj58+2o+Ch2Om0tfvHevybKvseLRtjRyajyEPnVz4Tof3mCG8kbsTO6dEwt2ZxIkt2sFJGZi35ncGfslp+KMP/De/zo+fb0Tr4aZOB+HCRiCfr35QQAUmsD1uALuB7uO4KHgFfYTlGr7HdAfjT97+/Duvf+kz735+c35enz/X65vT8+IGfaq+0O7WbrPH808ej7uTSedxu62DTXu7ctd79y5+9O3l7p1azl79G1++fDidfvUVDGIs+vJ9Oz0bT7YQmDARqvpsNLoIC7HXquSo3haeEXGLddyZAJEV5BDcYdqKTnsvrsAv5p9a40hKrvPDuR9iGQXNbCFaDC7Zk9VBdPszB1NbBHAYCSGOWwh/BGfhDMwIeWsAEZl7kFfV1FyZmQtLO9PBqrk6FZEBQkV1MQcVss2gtPDZON3saSxaCg98uOITObGXzy/evuuL35yMW3kD331jc5hf/ezbP/i13xwf3vrww2+fn55P11WdXG03npHN7DaC783yhm6v7XCJWosezM0FTIMBcHMXkMM2oGuve1SFF2xuMBEWBwMiLlho0Zlp1O0wnGzcdBzMl3L7hnZTtR98fPVr/2L31femd+4tZE5upg43ctgRH5Nyn47skLo2UGAm7i/sgoqeQID34iXHupvSvMY2+lQ6s72A2jX//6DjIzr909b5RXPeYpiVm4hf+rGxxPFbWhxwZOa6+fLWdb57gBVHenMZISIEUa9xJhCVo5vhI+fQ7yhvgbpZjFECrVuh+QWgJSWOB+HIqR1TWKt8qd2v97+15z0ana6Z/6GBWSfJ109YPXHelfd/jvLqzXI3xiptbksEIaPCvFS8a02qtNnry7Ah/+6ACMBRUVnedtehxrVtHe+2Uq3/6KtPb8NjaIg+P6tFGOtDdHUEeRg4h60KIo3+2sxEcFVebEd2Cy5/8RD/4nflw8cjm2sVOGkU+Y7iBEwVh4q5oirsCssMeg5n0BPwc9RHWBx2ivGn7v3Yl1/70u2XX93curj53gc+Pbcnn5RqPh10OdxcH075wguXuxcPP/zIheowb9589eKtN07P7p/94pc2f+sLixYuBbc28r2ny0t3sN1ubp1UJ4pWAcxgdlMaaBwEINXaK/MYFIltwBFlUoxsobPysg2yOR01l2nLz9v2wAuxbAZjOHLfnTlsWaCj9R8zyPDM0OSEZ0RpeQqKZT7zCNy5G9zMqnnm/MgyFjAhdnNigsJc61LdHAAz2QK4wRxuNlevPk+HMg7bW2fEtJhXMmzKDCv3ToZXb5nSYhXjSPfHp9/7eLd5cFp+5unX/3wct7rh/cdP5eZGiu3Gcibbw0EHkXtD9WK1+rQsj4kPFGeI8QE6eDQIQjE/kI2gpzjcx8kOBNCEhSAGWrQyDbIlrSqzOqqT0wKvjsUGkk09XP7h15/9979y8r//Tw8nPNHMRMEOG5zh7hY71GBtm1KCpvCtx/nYbj5etNetdyMhIvQGa7vNOd6y3U3gyOR9ipdZv2IVdBXfigi7nBHHH/KCiLNncztT3mmUFxzKag0a7stHbb4iHFngzgxXGj/ScWV+aDkaw1Uye8RBrXobHI1IYN8mKs1enEQtSe1wSgo637uORD5WM1/NN7/o97pTW796ZvVovHDkS/Jytg7Ui0Pt8XZvoOs4h9LdXF8r3Ub3lnxHCl5/AYo0Pxdj0J1C7yjUSP/moLvmqJFKR4q0PizNGMQTdT/0Qlliv4VjF3LUQiJjlRgR6n91d2YxAoDR7VTodR7ufvJs+ue/VX/zD0+mZXc21svL5fJA2407YPNhulYsiklhz0LlCZ/BnwAs/CH5VV024HPc/oW7X/rRL//E9uTexXuvLd/61vTs0aiX85NHwsJ+2NuNO1/urxUF5VzP39q8fuvk7c/e/4c/eZhHLxv50ut+8RLGzbQssttsv/QAzCRs7OzsADuiEskljjkmc4/0bg5PQujM2PARYgAAi7e3dXe0JNtkOnpe72jprFjBWkaqk37tMvES5miS3pZPJAOQ55jGpcxAZH1rxaU5+8STmmpVc2dQSDat5vuYhYhMzc1UTVWZmJhNDRZNv5yFAGH2k7IDSxmEHHWuNlitSso6FCviBVa9wvhkYy/deTaTvDfwndu3fvpLxZS++dHHv/U78uSR2nS3OESe39wMQzml3etFr6bZDQ9hXGQmZ2ZTH2JpMynRnu3K7AbzlsbRh8e4EUL1OnuVUYQHaMUCPdyYa62HMpaxbGoxriiXT/e//i/Gf/evn3zp9aulLqYFZGYUPcvMGTBuR+ZGMt7bnObW9rb9qA3wCw1imhV3AEd4ODft0Yo44luxbsFP234i9A5FBPd2Ee/GrpvQFYM0w4mVQTp2F6vNf8F1HTmp7gO6rmONEvrr/cWPPkLsiUVKtyJ0lC0Jf9XX96oL6hkHaw4l/8uoarWpnxrHdeSPHu34ufpPMQ85oe1KnvzJ6jioXevIPLZHQU+p9yH0Zm6BsAztjtoYHYeT7V099H9xaLxhPnQr8inH0fFCILxAKO3p7GiuVruP9YmP7qcjy27f1gl9YQkcD+hRyJt/zDFU8j50Vq0wlbpclPHi2bX/iz+kX//G+NGTYYPDk+cMG3aDu/gCuMImAa4xf4zpQxyeEH3MdsXmwzibPTkcLlC+KK9/5vTNr3zhq5tyb9ze1u99Yvs9H54Q5vnxcy+b4cHZsD251jo+uP3q3/3Lz/9ivvNjr29/5r35MNt7r5STCzkZebepG9bFhtsbV3MCmM1z+iOeMcvBJ4Env6JH8rk+f7S6b1tTd+5ohY59qrB66aMR7dRczq9133682HI1oNkZs0Y4d8dg6TR6GRGQ/qN9bNwdg+BuutSwTm4wqkzk7mZKzG6mXVbmLsxmUUzOplrnCsEwCDFBqAzFzVGdhJi5FMFQym6gSJkwo4ib1akOD25tL872Hz8vr94922zGrYzvP8LbL938wR9f//4fDzoP7LO5LgqWwctLw+kny+UZuQsmcnUvBHVnokpgwoHwFPNDXJ85CRjA7LXC1HzYnBC4uCzzNeBCwuMIYV2mq8MND2OZZ//wg4f/5Jfv/J//0ck5LoMJUzZzRuMyc1JsnUgHEAqhPqEOpiak9GasVhD1wle7SFJLDTkf2etuXOn4h6N9G4sm4Si1V66b/egju2lq9rRxTMdscluWq6lYLcwLuWrvK/cFn9EBcjM/3fim8yEiKtF3tV3lGMO2XyITJVk41zwX9XXfIHy37s1Vrma4cTjeAXAmUY4+O4ONYPs88Sqt4dTxEB6Nhjuy1Qe6Pe4o/4UxQsYQ7s1HeGdquntq7i8DjuYDj8ieyB90h3HMCObOXO/0BVlBAyptsuP0O7cXzEqyeQ4cUfstCkmn0zx0f+QXxjmpqHUFBh5KDQszE8Mg4BPYK2V8U33zr74+/+pvy4ePfH9pBxdfsLgyyTgwYIeJYRPqBD/AnsCvWC5FHtar882Ob/xHcP+L52997vbr58P9u2+/60+f1ve/cbh+simLLNP2pMwnZ0+e1lsnr5SXX7/3c/d2v/Quhu3+7NHFL31FX7nDFyd2a2cmNKACROANg9MgBgtuiRqob4gV/yE3ah5uZ7nwnILfCU2AoYGUFzZYS4blBOX69hUstE3UGh6ssbu3ddDctLegmzuxuHKL5mjYJj4zvYll7CvtmChTYyYDrKqalSLuMDdz90UBH8aBhd2VIHVZInvhrm4ug4hwHEEiowDk1UyruVOhUgoXZiogzMtSxGUcvCqXU1TTgWW4s1wfpkEWpu2Pv/3yy3dvPv/G4wcvDR9/OP3ZN+da52U63Ww22J4u9PbI2/lwPesl6iJk5kohhnUyPWW6NL90TLATlFs4eYK9wSccLi8f7nwUE60zjFzY6oRBUDBUUl926qfXh/mPfm/3xz+3+0vvXLMutYoB5s5u5swxIQS45Q5Ou8MrQEYzf9S8NF7YTt0dNNKgr6wGP9tufRHxexPqHG3IFj90PPjp/OTxinsBQiRUw3rJo1Cl5S96jrm/oV87Hn61dt5tRV41/3UQOtPV7oTcqZh7l4ZENpgalukWxtGGkVYetXM0vkYLaZe8DUqHoMeRyJHHbmRcmt/MifVReTHndjxo7VLxyS0MXEfF4XnDiG2WUh1vDsDWkpG1X0ILKNAWUbP84YjbHXm/wf5hOXOebEEOwVoEliYr78+SOz62RXREK7U/WPOZ7ba6q2oWiUC2RlV53zntuZBBAJnH0avO5NWsoHBdzkReKWXze+8v//O/9m9+sLHK20GWg9CwTDOiiYDNFYcFhyssz+Efgt5nX3ipRuc23LqSN3H+ldufOR3Obt+9f/uzr+HxE/v+Nweplx9/PG3ZTrbKou+8yeXW3f/Tf6bTxu+WuivzKd/+2nvznQvdCW9HE4JZrnLLNe1H68XdyRktACZ4vDymtA0MJe4zApw5xmPV0TYP0GbMVq3DOuHWF21yyn603dq6hLtHnxp3UPipdg/mDrcUb7U5cculEQeStmUIpijhYwBaqye6N6u+1EWEQQhCCOYsXETgptXgUFUSErCpOYELM7ET3FRk4EByTOycS4hIGKam1UvLAKmqg+oyMxc+K5uzC2eer6+djR/szm69U+5dnOz1k3/xW89//d9ODx9Wn3k8mR4+KWZv706u7PC96fmVLQvcoCTiyLOGJ5HHusyOM2CDYcA0AxXu7iJUpz3InZiKk0OrTjpXcFUtQrd4vP74+1f/n1/Zvvtfbi9GE8SQrrvQIjveBOZo6kNaQ7JGt1vbNS2L21ose7f3PwQCqaUCu+FbLWe+iI8VpXhhf+eS6ZhgZY/bZx1ZYXQTt37A0UflN96sIq1vjGW6Wgf39a/HNP+xL2oroVE+Vhrv2e+osdrdzDU405GpIzt2mzcr0548GWh3gLRtgo5I+30n1eatJQTW6o2M7j25thVF0YtukzrwWm/zuDnT0dzkfEfOKHceqAP+9fmbdYA7EfeI3/tluvlf3Vp7XCBFIC1KApy9nTXn+Y6WOV59iLf5QJqTXL8tmHCKQ7aQAZa35dWmvbX6pv7hhBeunkFBZIPVHCQCv1B6Xf3en35//p9/rf7Jt7d13p0O8/XEalQkInMiuE1AnTGD+UZ2j5d6PdBgcqvuHmD3Nm69t3tAdnH64I2Td+9hevbsL77H02K7cnP2Srk4vfXFL9bz7fDWW2/8zBeWB7fUZXywmamWLfuJmIELovcvJLFAQO2Ii3ILriMalHiLzt1do0AUybCnszhaF0dLgPp05nqMQY4J8VTwJLILSWYO8roy1wsC6FnhZoTgXrUpkCmwfDqrsBmgOPwcHsl8kHBXU4TjAKNWX+aZmMdxo+ZLrSJUhoGYXK1WNTMiDMMwjMOyVFMrIlKEiFzVRYhcF0UISw0gMBOAWqupAWyqvlidKyFSJk6CCmchqELK88trMhuK0MtnC8nF8HPPLm7Tt79Hz5/V589lODvRyRzl8PSN8ezZ4fnT5eqASoaF/AB7zrYlfqj6PVyf43wDGcHXoAnLrHV2H2RwMZ0XAjkxSCGsvlRbqnkpu9P5sP+93+W/+JvbH/vsjVZ1sxBsJV5vMf9qJQ1OSslUJK5zcDO5a9Vu643fpJ9NupOtgdqcH1mQ/GX3JO6A9p4UAIQYnRdu5vxoB/asYcPpx4AOPft37AfSULfQ4siIt69OUaY9XOUmn3rlml2kTp61/8oPX7DdnPd39hvyF9MPcA+ZwzoV3C1vJuZzU7ZXcfN46U66h1kvmnfSlHnHt95nZdWxou/5nMIXbjoBePxHDcMnSjCs0aHnevAAheRHz9pim/i/I85+fdbV3/jq7Ly3LffW9si127X0FAC5eYgS2wMGN3QU8jgokrndKoWv427EiLIxN/wotZRTYomZDUSFUBeueKWML3370fX/45f5O987QWWaXbWI6mFWdxbhUtQqucum8DIe7Pq57RdcD7Ne0Mlny0vvyvm9cnrnwd3y9mt49RYePv3BN/6Ebannt+Szr7/041+bzx5sfuI9P9vI63frvbHaTIo9O41bI4sjX1u6Z8VXsQ85h5lg3oQFUcdDWTACz+2amXIna8PRlhMFc7Tisr6DsTKIbeFnzxkHjlZVf2dfN201ZYcBN8v4M+Uk5HBPuwsHlqUmbyVEFKc5uqqBwMSmFpuInNXNXW1SM5PCzKJqy1KZfRxHItZqtWo1JUIpQymDq5tDipRSKPRDwm5W1aBWq2o1AFxYmFXV1DLmpgxazJyMWGQ6LFIEbqb1MM9lNwqBCTwMddLN5+6/cvbj1x++Rc+vnn/ze5vLA8318ccP8fTh4fpZfX5Sn34sy9Xe9+K0EVncDkxS+JNleYTrV3F6gs0lDg468HRnd8F7nZfKhDrNGMgLLfuq5A5jx0DkFfzo8fwv/9eTd968PuU9LLeFwTihQuwASvPuIRACusEGHcPypus6oja6qaS2CjqZvIJKxFuoX7cZd1pxiZHHQvVPvb1/341Ft15tka4wsOelXiQDXtBl+vFrKB/jGMP3wPUoaqVmtJrNJASUgpdkIWMDtqCnu6tuBJuO6Mg9xYL3vNALmKhj3R5P9Uh73VHtu46/M5hoRjyKZ9qoH1V/Y93nKwogovXssyO6pCN8ai6aGsqPbYsjvUBzVs29rFZ9LQ4LmmE1DX2GjhjAFohmZGpNb5Y+0bxNPbCyyOskGhpHvD74uqb7vFKW9uZ8d8WzRwmFE/eQAjGoRGSQBXecXn12bf/Tr+kf/wkdbmQ3+mHWg7obn2zdweBhGP3mWvUwDFzZYHiCGyb/gp//5Ktf+OyPfA4fX81Pq13WH/zbP6dvnGPkZ/uy+8xn5atfffCP/9b8zr2xyH4Lg+tAGMyVXRVuhYVEmBwk8OB03YMI8aQqzdeUWFpnouABOhOQU2Bt2TWPvrp6iqKmXA9M/ZLmqa2iNtBH+CMys82vtjd03EBtjTR3kFQEgcia/ARE5mZVg6EtHMeDOxxqBnJmBmBqqupuLOzq5mZmRMRFCKymzC5lMIBUl7pUUxYaZGRmVVMzh5EUi4IPN3Nf5lnNTM3M3ABzdlFAqzqcC5VSiNlU8wZc3amU4ubVl2VeSCCFXZWKqNs87ycuegI8ONWzcv+1O/uHl7ieDn+xKzd3pufPNx9/POxOL598dL5cP94/K6oVNhuuyR8xvmd6DhlQzrF7hpvL+XD/hHg7QvfMrEOhwrZM1Y0Ku9EI2YJH82GZ5A++bt//qzeff22CLa6mmuKv3Iym3gLAVoBJiLbm1DKofR8daevCQFHfh2svnyP7SmlJOryj9CLND6QOrbND9gKixWrWvYeffQW2Pb1+YGMFPuUtcHRH/T4S6+f+9m4fjt62mqhjpwSgmetoaVLiSPT2gsbypyX5IcdzDKHa7xpw6o/0YrDjSLY088AhmTqS8aR5Pq6JONr4zWn3ePx4uI5/CPEVtSYM6Ea9JfaOdu0LQUuPd3w1BbR6v7wfX6+A5mA9m6E3ia+1IbTju28fAWqAtX9yIFRrKywfoMGD7oVzZLtbS46H+oTE0Xare6Zo8hMrjh0hfhVT24DPQK+oDf/rN/T3/8wfPR/vb8oJLZOhqpHzOHJduFqtN1rVMSkL6fQQTyfav+S7L966fU8OV99/OM5eVfnebXtch9OL7Rc+++Df/Xm7dYYvf3Z662LiWePBCxFABnIUYReGw4PDtxrDmyODBux7UsPbEsyp8XzJukLy2XJjhUdsQ0XduTrBYdCOvNo3qzchp6CZPXg069ssw8GkBCOW0vXOck48u9dzaBfMTBXhcp2c2B0wN1NC65zpZm7Vqpuh5iI3cymk1QFjJgibO6kFe0PMIoW5MLF6BaFIcYfWumh1M3MPfWie7YAQidZlrkQgYfECh05LrTVSqG4GCFSru6oREwzLMouUeZm0WoXB1eB8OpipDUWYfD/dLm8uV9f12dX53Yuz84urRxf754+HR588vXk66/PFqlH5WLRaHXHzNs62dMKul/Nca92WnYw7HoU3XG/2pUhhUcjJUEbmgcui1QnjRx9Ov/NH27dfLyPt2VGNPXvCcdoV8yZ8sKa4TYPS2JM1/dns76q8bjux/9RkKSsuQzsooEWbzYlQ7mqgX7D/0zZm37Medqwhw7QB/QaOLPqnCZz+9YJr8rbT8y/etI9H111tRV/HuV/ib+xBAa2hbrd41Lzf0Yc1cc06bEfQqbkmELyVSnvjWNuWa89+NPhHz51b0Vr6poXvub+O3Fi6KG+P1y/ebml9Hm+uYnUp3j0c1gxilN+4N6VTHw3rI+s9CHD3AJ2eCRB3Zu4mybw9y4tPyEehYIQFn/Jhn57wI7zfp4Y+/ZQ9NYOjUQ16IrBqtCsDHGI0mL2C8tIPHttv/LY/erS9GGHL8nyvpg4FsdsM5sN8JQOjYCZ2rTPmG+joJyPk7Eavnj1+hOevbt8sL7958u6PnOC8fO5zp3/pC/XureWlnZ0O836ywWTLzAQWYjBAwhmxuzEY5A5BhO3UZmtVSjZXdzQc3kbkGCcFso9ZMzgyNAccbj0Sy8g/UTxWKBbnC6KBjPV4E28KnQD5cPdoS+AWMWCm8vPKlg/CxKSu7l4XNWAcZRhKrBg1g7sQmQFW3X1ZlljzrtbnTquTgIgja2Wu7em9cIGh2oJ0NQxAF1uWudaqS2XJHcPMRYRFYL4sPpTBCe6mVetcdYl2QgAo9EdmRpkYNl0U8KIGV5sdRtUWmxSuUqSqKow2Ire2VCCFh+1QZDi5OHv6yZlsT+Xx4+HpJ7v58sqXyegh9A8w3ZC8CmZgwTJPB1UhCJed4uCLulOR0QFiEiGCFYE6D4fr6eu/O/ziz5aXN7ZUKNSVelCb4x+zf2xGA61nZpgzN9hFEa2i+0gRQz0I4EB2ufsaK0E92Xm8JNP+NiamLbPVFHW4QUdvI6ytzXK3NiuBNeXctB/rxm5Wr5mKVdPpR8nqtlu66O34VUc3ndcu64LXHJHYGqlpS6kJjs1moqsjX9Cs+qriySRbbNFuU9twef9Tc8X9YkcG7sherqCvj5mvA+yrZz9yA3340O++36s1O+Bwcm7HUK/BwZHnSIPkoUNoaw5rtGcEqLbJi6drd7yyjCFgX0FEI2fw4h2vHjNVoD0waeKeHpBmpVljPNYHjvdr1M3AicnMhISX+bz6nU+e4//7W/ydj/z6hrZe9zebbSFnAi/ThFnH3dkwDKaH/TyZ4Gra3z2/uH2pB6jDn/FZhW1PHpQf+fLJe++Of/knyq27/O4r80unMxO2bLwMA0EKjRIbq02qk4NCEdma5aXzTdCfv6GmoPAXprM9ofeFl7+zdPF9tR0tykZ2g6DqDdhESx3L5eWwyNg308/rGbTksCBTCFAiaxKuDD3cVzMQ5JWpm5m5EwpDWGKptPIPCnrG80XuBF2sd+oTKRKKR3I3Uw1UDgKGcezVAxonZY5Fleuy1GUBYRwLgUDETAQmJneYGohFXKsttRKR1upqxIBLLkuv5kZELEQAE1RRtVo1NpC7zmBhIanLxOpw4iLjbgsW2m31ZCNEJ+cnZbc9ubg9ntza7Hbjk4/p8pna5YLxI8yL31yzvVHOB3WPAg6n5WZaDldBV4qhagW5OkkpYymz+uiE77+PP/vO9t57G+FJNYbbzEHZKiMFoW2vtAKltKKAR4XcejYqWgrkRZOUX9aAZgfu5iDOvRyGhI/fSkdrsK24zhMAqw5tNUZpGV4AiMe3cWTc2or3Zl+O4NGRM8AP/dzxkR99TFpnbtvIUcysf3p+dj4mN7zk3S1YmnZqg3h8f2ixGB355BzK1dU2d9McSFyxy2XQtl4D+m1TrnPSXE172XpjL7iM9sFH6NhB5GaxhXKizDlYl6zWSb8fobH3AUl5X/ejzVp589zkCObAHbRGWUeep3k7b1OVfnUV8bZHTXqhVWi0oe5PH56O1uvHtXoSMu6TmA3uxG5GMoj6bafXp/nWb/0O/c7v49lNOYEws/D27OJweKTLtBmLqfuyh8NIHFRwe1d2n9w8uwRVnL7Kr8qdz95+/e2zL/9Yefszw1c/t7x5t14wXaAOQAHYmQsxRw+csCZANlLtAbJ7g2EM8khm+4tePn9OqjIGgGn1wW2UOkpfF/nR1j6qAgARuWms4DAIbagyhZy+2axSwz3wbA+XWRlDNsxikJOFT/e2QwCY1qDmnYWIh+CiTdVgBFJXq+YwU/Os7fKIyonAIE6rrVqrmWXyphIzazVyLKqtiAAwhxARyjjEkLv6XKs5lUKqusw1TtiKxSRFYJ6NSA2OFv44VdVSBOk73NRMDaBlmU0rK1iYmA0w2Oak+FTNVdllW+BjuXOKk81OeDw75XFTttuyPd2ePN08+cHl9OyK+NKnDzArXd0zv7Tl9m5kJbZDBckwzHZwWCmmakIssOUwF7AIho8/2f7FN+cff8fZ1aCkhVcnTymqima9zryqXKKTRqjGEiA2lJ8aM26bpgONni3ti47brsO6Nlt/lR5TrFg4LVsDyEFVrYt+BbkN0b9gwJux7Rbv6A8/7C9e+PphBuEFl3LsBtyV0EBqSVLyKILxtuDhGXYf+Y7uy/oNRTDVzFo7c6TZ/u6HkEbLkfA/ds7qHtZ7Pcqmgtp4egw8VqC9xhJ9zlcf6/3TrZvKo2ZS8WgBD63F89SagLlR3/Tx2Lkc0lq0dZKkRcR0+oLv7c6xueAGgpvC3LO74YpYeqzTgiLuA+1HAoMGH47cqufwdFUwmktjmAAGQfUb3HJ68J1P6q/92/HRJdzKbofDXrab5XCo0zxuSpFxP10JDMNGxu3pdvvo2V5x+BM8252++YWf+stv/MRP+MUbZ1/70rwb7MGtw51SR9gGYAcTkbNk9UMDxC1QpdbzKBdaD1msLbWGnlq5IXly+C2GagPblFh90R2v/k/FiUH8Jm0cQW6j71rcQUQcuDloFm87MRlmAgfahIM4B7qzCRJZn3yBN5jATMJFiLS6ubkbMYhgi7lnjhZubMQiHoOHrFwwg8O0VjcHOUthIlcsVs0VDuJEBEtd2ImZpUhwassSnalR51rNlnkx1yGz7uKmiyozlqXaogCImVo6eoGbaSQo3E0G0kXN1NTMvLDXxSHEzuYuG9HKZKjmVBi7kYahuG/OT8pmuz09HcZxkLEQttfFrx8d1J7axOAJ0w/2j988eeBuy82EQss00eAkVkqh6tsyiDMqWGh/sA2bfve720f77f3NjVe0PJq5RYV0zGXyFG27HecavYP63JjESLvDfZM2iGhNZZz7yDqv0Ex2M+Np8PquPBYVeXc47R3eFucxHF4/+ti4H3/CCmwcKw/ecV5/sBdXPh395ijUyLVN64XgmQMIy0QNTPUfvfH4zZDlCK4XI6CFr7FtunrTWtKhvSE289pto0Vt/anXwaC0cDmSjZLKkW7NtTrl20A0rcPQ2Zi4mHs4bko55pG7zf/xZIy7b/CG7hJxJunYZyTTBV2Y2CwagOjFT37Efh1Z+EbZGJqvzeS5v+AR3TNUaw9yxPYTPIyWH9fxUWcCMyYwsJsXFsx6utS39ov8y9/Qy2eQA0mxmz1qhfFhvjzdnvGOYbo93V09f1TcXLbTrNHffzp743P/+X/4zl/7G/Xsorz86nR2WjdMW7LiXMACEBgOpla9GxsPyY73hRueMzeT5dO0JeY5ON3gNxbQ+3LzNjsION7e1L1z+3N7hzfrkCkcV6CB+OPwifoxPXm1VrGb24WIiCUWjYUN95QGRac509a3O/0KA26ZLyA3M0dVjXUGb8CEYGbMzMzRzoGIzauburuaEhHImAsoKSEgYgozYNwUN66mWis5K6wwi4gZzHWaJq11s9kUGdR0mmedFzVz10hqUGhTicwNjeBwVwZxtNV2G7dbr5UhVivMiZlIdVEqYk5UWNRBxUTNJgzkRXb3Tusyne7PQQpehpOBN2V/+WyyGx/9eV3ep+ufKBhJzAphYRANQ3Gq7q7mTvOhSuHhdLd/9mz0Uh99dPMn3y67d8oOk2ksEnKDWWtfDET2UAPbt8mzlnlr2Hs1amagCJFz7sNsMce2zUqz1TO0LBIlrqB1R+ebO/w6ogliwZpH2WKLNrrTwGqc2wZZPUW3/vERDUnh6OsIO3aZ63q5HpkcmyE6wvQRAVhLcrey3G6yAaQADujlE51x7gETZYAfF7XmeI+cV9+hjXsLSNvTf3jhttFpn+NDzbtv6sA7njt41Ciw9NVRdnuRt3TsX8KvUP9E76ulOQ9AU8mj3ibDW0+aHAZHCyp9ndT2DH202qNbiNJj0KwPc3pVdL5+HWVKZTtaV7mjLw9uLDYsEN0lwyjGo7kTUbSOMbd6NteXfbP7rd/D1/948DqcDPX5DTO7QcU248aJbi6fCzEPZdyeLXWW8XSentygXt45ffe/+A8+81/+Q7vzipWyH8TIMMR54ERExE2D22M3j9nrGsssO2xrBTCLtFubFiZuQXtzZs2at/OK+mIKsZQ1l019bbywZ7x9UZMNucfWBrETBehu71fXEJIzZzuBxIopM2l7KSWiRCB4ITY4E5s5wSw9HsJLQARgQFu+2HrYSETEDHZmicQxiOFOLLUu5kYMdnJmNyfE2WRmaiQsRGq2LAq4WwGTubmp6yJScoDdq1a4i4iUBvLcFAZ3KVy4BFgKhO/qIhyz6eG4tC5zaHYZBoOSkYiYaWT1CdjudnWZ3dUrEGcobAfMUyUtJ8Pm7pnsmAoOl5tx4KsyXN88fab7a+GP7TDTLBONQths6vWNwav6uClOpItjBDtNh/35/fvPb57y48flL755+t5nHokpq7mLCBGE0ryG1ir2fucwkNK8qNnNvci5+oyasQ2wggYx3TMg7Ga8lfAkovGuTFkD3MSmDaJ2LNhDxSPk1rmBF0H9MYp/AdQQ4YVL+BFn7Gth16esf+/ag3VLrh/TXunuJfxZRklrBNBG5DjM7m/2FjI1ULo+i4UYIwP2Hiqt+QL0C7YbRdrFPnx0JOTA0aB2g4kGcnv4AM3PfeFtzUcft9wBqBXm54ikcQgzHPklW/2CHWVt2yf2xLX3Gz+6t3SNbZEcUViEHmbE/a3Ji1bG1Z81Gl01fEPMiLppajroRkocLbdGGcctsFCtSiCu9SUr7zy6tH/+r+jqUsbdvOyLzyij62GgjdpiUs7eenP6/nesmmzH5fp6mp+BnAbC62+++Tf/ur388qEO5aRUNgGIFeDIhkVKLL5/IbFN67e+LgCDZWnUCkXy3Neex3O0TBTaGrQGNXJ8vHm/fIXBWmPHRuYAiE5q7h61sN5kGqZuMBCYOTM3aJV7MXneY4To0xmXDtrYCBRnCzDARMbOROrqcF2UHMzRwTjzk2pKBBZydRCImNgkuB1QngDgbjAiFGEidhjInJElAuZmxsQQDr6jSAFQ6+Ih3ySwe12q+0LCTLLdllqrmym8amWik5OtqXOLfwCQ1pvr/bIswzAUkWmahQWAWdXFC7PWaotDTZgZRVhMF2ap80wxczAuZdgYK7nWw37vDDkbRzqRgdxsGEohPtlsr662ePaIGTfTfIBtWfywQNknXdScnTebw2LVfbfbYVpsf73ws2FkefLh+KdfLz/xbvnc68TFYTUEZGZClti8R5EUgIIskwCk1jdkUz5F6NyKNqjRsmlsjOAWM05r5flqPldo2WxjuIHAtNTbIFMupw6eM/3SdjodW0Zv2LHbAQe65qOFsWg+Kb6xlTdoZqPvs6PrrIFy21Np7JA5gJaCNV8BediWRp+9sLlfAKPdMHrTcTT83cxvR7V+RJkdQdj2I9C8dnsZ2mV67/3+bf7c52B9eWxlX61w38qevSObeUgKIPs3NpuBxFDWbcIat/Uite5BkeD3aNTbEqMV9vbcUsSSyIs21s0AUELPKGr1zKcn3IgK4nBf3L0OuQUTlEkVN2/17w7yqg5wXcZqD2iov/K/bD/5mMXGi5P52b4ULrdPh/Nb83cf18UEvjy7Ih0xT9f7p8NQ9HB1Pp7Kbjz78ucWHv3yoCNT9bIZQNQ8Vlu33l1eX4QtSoOjyaqR1rnVbeXiaDJLc1+Xv8UwMrK7cmK8RuvHpsxF1qcu5ynSKqtHja7QjsCwdozMohmikXMwQ30Nt0R+29CZuQbSZyDCgSg6InBQ5+aQbJESoXDEYELUWvsxM8EhJc5iouCznUAMMqJSAGeQgR2VSDxq3dyIiYnDKJVSAK9VM12rzhR9J7yqcmFhISJVVdUMd4RMfRhHIszTUpcpxKAg3my3da4O7Ha7Os1uzsZcRIShyq4apBRMpACsuli1UsirEYhZaBiWWmEYtjtX9UEBmLAtthlGUVuY2CpMaU/T8uQPLz/62e2bm80GdRKhedrvNqOpl7tn40bKNQGT+6w6Ow3zZMv3vrv88Z/IK/dw68TcGKSq8NYw0NZ4OOY6JI0KZyciRp7anKsF5EEyB8XcVRaJ6Y9RvRE1QRqhOQV2iq7i6P9DjepvB9ccJ0kbhOzwu7HtzS50E3mkEKU1V/ailev2vn1iWsIXzP+6GY42aYPT1OysewnU2MkPMKdtRI7OmjLo1/D1hlM9ncFTM3U5GtR3a2trGn87umC+0z2hbjPK6Yi83W46E4/7WeF83rVn0abRmm3s95mQH+7O3Y90TqBZd1+HKW1p92qtVutohOPFrVjILYkG4oTt1IbdM2O7jjHQwrhMl0SpAYXcuz0W54gC/YQ1ODFbrUwMEjdlpujv0JLVZF7DiasBRQrTxstLxK9858/mb/yR1ZvdyWk57He7W1dXz/15rQe6WU4qCrmf2Oaw1OpWsdsNp2V6zjTuH19PB1MhnydnGbCBC1Ph8E3BhR6ho6MGFi8u41zaUeyLoz67ts4FVnvaPBw8s+7NzcWYWgvS2u6IDKr3xRLrLW+scXtMTk5MRFGTnP+FBJ6Fs0Vg20PhBjh5rLXNiAwpAwyxpQMMzt4P8ZANE6lZRpshZwBAZKYiIoO4WWv6QQCZGQtnXRtRYWKONA8Wr0ISxHRMtxmqVmExM6+R3IpmfyhS4KaL1rqoWhmKu7mCmcpQ4pfEMm53GN1g02GeDoehFDVb9jOD6qJCJEMxU2Fx9rIZrKqZzfUQJ9IMJwMt5hJn0cx1qpZMOGkUNDsUVDaDeS3boU4jb7Y7O3/ldPfosH92uKELRp2rTosuW9B23OzruPv5r2E0/NqfTs8vVQ80lEfL9GS2J/X66qPvXn33zf2br2OgYTuUIlZr9Adv5scz+IuV6AS0NisewXhblPFTwmCKQIGaQoXWtkEAtfz8qjVxVXDfky170G1RhHHAC1mCzB/QMYBe6Z+0lgn/2l+bj0Bb6WgGqq/vdev0/qT9+s1ireGIr0jUmyEsasbrK9zMGORrH4f1xUk2BKGRrgTE3rQ9SBecw7r6rXbSWN4yNS+yslOeXtH6/edDepqINTFI3mmU+IBG5zDBG5/TB4xSA9AMjDZ0eoThU0gADzzna4TQ9IJtLNvNIptWxzpLKO8IY5BAsRkQNEo7P+yIKGwfgMTswFHP4Z5Lp1R9xMHYRsQgZY+uYbYGUi1aM/C0TCTb5TDbsrzsw+b5Qf/Zb54zyp3bboBvn3609/MLnA2+Obk6G+tu9+oX3pp8ubnSzekoy9Wj9z+yP/gOz/M8DiCuKCe77Xi2IWHA3dSZ+wrv4U1bzP7CsyWeiB80p7txfOklW6DgkT4/4tuC8opkRtI8ObK5pjgprzDS7EHt0LrdibmXt5NTCPucSyrxkX2Umlw8t6g39GPVQWCh2PQM8siFZlSRyeWIDQAjklgS1SpRrsvABnFGZSZNnJhLA4LsZq3HOjk5izCYiCNeESmEjFTNvJqpqTAjGswNIwheLc8McGcR98VicasKszGIoarLVMsgrs6lsJBXl0LDOBYmd6oz4CYicMzTMg6DViUQFSogU120jszCbFXdnYjNXIqYCJmyyKKTK0jBzOZORdxRdruyVKmH27tyc7jCya19ffSD/ccPZhJyGliwNdoNr7yObxe7flafaHV6vGA6kY+3Fzdvf/b+3/zxk7fuT6fn3/3g+8P5+Z3XX9I4/ayqEKkpJxxT6lmkmPWGS2HWVwUzZ9qsbU4yRJyUhoX6Oo0ckDULnjjVopolaeCWBG6Horg7E3tjCKJRUQ86m4HrKL69B+ufj7ZRp11WIgRH7/QOMNYNmDC86VQ7JGqxdzykw0MF1EV4uUePKJb+1Wx1ExV5u2dLHGjZnzdGPt/eNkkawmYX25Ot+Q0g+yIQjqL3NoKexEnzSW6Z2YkD9SiUGZHNyXorb+Awo47s2ZAUc3cA7t3iY7WhawTQsHrM99H8IcjguLzn2lqbaWeUluA0Tbk3q0ZNLNvnxVsU0LJPgeqjESYRUwx0sO6ukcx0IiePQ51jREhTnzKo0v6mbsQPH360u3nGH73/4Qcf3HvlFfjJU9pOb77x0l/9sfGdO1fF7n32reHNB3JRyP3s7Iyk2P5w6/HjD/7b3/jol3/14z/4hlw/fuWTJ+dfeEMZRTgZ8QhPGIgVgBam9ejveJHGTFDPmHXFQqSrY5yY3JCqDrQoFO7J7bd5a1snMW904jmiIYm4rJAjjGA7S9nzV7H6mJgklkqql2xFIIHuY1GEzc4V5RZoMWY0UtHp0pijLJngWquwROirVUHO8TZKpRzMqXDq1s2jCzU5WIRJiKDmRKG+d9To6+NCTOxUMQzCYFWlgZjYzZWyK1FdKmtLbxCGcSwscKp1qctSZzXTApmmCiEpQsQXd3bLfnKQqbKIs083cxmKA2D2akRC7FBjsDuYxVHVTKSwsKuKsCrpouPptvKs+zpPM48kUrzKdFhowzQMh+VgxNuzu4fr/QfPrwflwfbbsqG6sc3p+MorTz54Snp9M8965/z64tVb/94vPHjjle2Pv3f61n3byOnHTz/6ja8/OTy9fsYnZ7dB8OoQImO4MafgV93dAqVT0NqJPIPQa6aMOc0Yt8ouNUNGAwG+iNyZg8ltvGNsf25mtQcGSPYpFmgTVrVdHya3mZAfknWs6o++adZooX9KyHVaX5wOvNLedDoIzaB3uBuvbsXmDT07HMVXvdFa6NWyFrHbcjDaTu78zSod6pvA3LmXacBd06kem9x+643Cbd4rDMKaNU06oZ2nhA7ts6//qus36z0jbXVl6a482Icc1daDpQcrae5BrYt7C53ax7UwPh1WC8kawe2UQ+uJHHpzvKxUpHQU8DyYYHV63r11WpdcMM03msTxhlkpBAKMDICachLkCgSyRNQbLUrVaUGty8154ffee3n61e9//eEN3T69+4tfO/viu5svfna5dWf8zMt+TucDAALxDCORvSpU/dboD15++f/yd87+wY/JL/+zb/72Hzz85INX/MuAOgnIOA9f6YzL+kzUlkiupHbb8WhhHbPlTnPZuR/QIp0Wy7aLxB7IFnweBER8TCvUWz8Yjt7eLYx/wxIpPuJ2pFTHQYnrCK1lf85aJGbJQyafwobY3A1XhMgzrhLnsOTCIWJmuBOJOaEQ3ImppQAZIBYuRWI9KyoMxMwAS2GwAQQ1dxLhSH2bEcDMcBoGMJOq5Zg5qmnISWMdzdPEUphJykhcqhmZz9Ncl1pEEBSaEymqLSxsVamwLQomU2ei3emuVtVFhUuMj7A4ubMz2NWkFFTLnW0AIKWQVRbIMNiyDLsNQ+v+2kxlYPfCu1HJmFlPh82tw0fP3n/j/P5yeZjrCbZv3v67v7j5j3/k4S//+cP3f3Dy13/k9b/3Uxe3bm3efdNKsWHwIgR/ZXPv79z6ue9+9Mm3vv+Dw3w5z0YQGwdYdbgZyjAQM5ObxVmhoYyg8L5uzmxEqNGUN8vOKSRBzU84WUQJBM+AL5mAlhnuLEaDOhkErKi51yV4iybCVjYP0WA9cPxtt/hY17UHcEbDXA0C5fpGi4YbcGnOAEfcff+Q1L01congXlrtSmc4sDoPd6Zu8K3HKWmnoywz2jSCTN3IhNpxSM2StlRa2uGW2oS7xwkfq29A+IYglvuu75GVN6Ij2u06M2cIkNRKK3FsCDynwBJOh7jHrNkIdwtsnw/Vvve8YF4DcHi0TgzAHwoNIQEhkuceWiEgRBHIJEhMPrWS04gm0ePTNkLH4Nm7G48BVBg1ON08TR4Q4CQFUcevsy+sBPi0+H7ycrodN3L/tZf+8o++8YWzi4cLfaTly3/rq7e/8l65d+anXEBVSl2UjEGAukNQXaQsOtfDovDNsD15990v/uNX/fy/t+JYnEaGGxlRSS/diFZPf02IX3UCP4assz2ewU6SZ+aNLUNT5MEj4ZJ6Ho/p48iuGACSaF+/dumkNILdGhE4VkjAvjUL4Q4NfJMwhFfxla/pt/5PWyRNrJqBNRlZCPuxpvMAXyXTDiIKioYcqdCNniNqzCKRKnIiQLO2i4WJwSwca9rgcXwbAVwcYAETWBzGDHcmUYoRskCtZibg9kUsLASvqmZWKxONwyDC2812mev+MNdlJsZQZO9KzCzMzMthMeZxlOJiy0GrljLAvKo5oOZuNRF1ETJ3VU8huZMICQ0b05lkM9hByTEwL6bQykxUBpMiWoYnm3NsfnCYLsor+Nw79Y0vjP/wZ56fsv7t8dWXfunWOy9rKWMpGKTOtmgtgLptRn5wb3vv7ltf+tybHz188t3vffzxJw+vnl6enJzcf+X+4vTxw0eXT693t87cIEWSnGGucCZ3dzX1RvXEAcuACzvCz4eVZo7Igdw0gvAEBd7PoiALJ53KmIYy+gJK6Az0vwbZ8L9l95vRPcoJ9D820/GCNToSf77gOpojaNgmZRPhOQBClh7FxoqWlfR//6e/2b1N68SWCogwwi3DsfI1jfNq3i9WobnDQyHc8FV+am6+5F+pIV5v5AisuaP+FiJKXmUdBnL3Yy6g6y+9o/Yux3FvPcIAc2IyD+BJZg7qbRADUqbAvyd+4T2zEGGNmlvGFk5MDEaRIToldbcY8J/apEUvqsQWzeJY+qg+mQjjFyECmI5ayWUc1OAziNkoSQpzG1hMqxoO0+XudLg4vSibzfvffPT8avmZn/3c17789o+8dGtgLyB7funw8fQM5CqoatVcWILxCNhKBF0qxQG81JYbk4D96vqDP/vz3e37tx88oIFBkFJ6GX1EkGi1NEdPZ8gzvNKdtWk/CryaXCfhQVrdnjbNHxsj2AMrh7pZ10I1XJMAuwWClElRR7uXHoBn23fH6tSpPbYH+GtlBp7BfYB7NKDYZq9ltRpSiZWwKgHRF1hOoqeq1N1JopdE8k4EYY8oid0s+v4Tk6lWM2EOGairq9a4Ca211hpJNlV1zdOvatWqGj5TCgNEESSbiwgzL9OkaqrVDeY2bIuIZImEGcA2KcDm8EVlLF6V1FndqzKI5pmcrNahCEzdzedKUFSzZfalQqtNU72+mZ89F7XDzfV0dbMsy81+lvF0ubzE+x/6n/3Z6dkrX/nbv/jg7/8CXn97+Pztq+ur4eU75BsqUHXmISrfTGscnVakxNpw0DLb1fX05MnV08eX2OC1114+PTv54OMnv/k7f/DxoyenZ7e3J9tac0QiT0lMURafQiwuGaBzY/UoUluN0gchk8AUkBftr4wOy9NIrqngoB3iN8HYoMcC6Sma9ex+wLuZ61bRm21pdAQl4KC+lJqR6jHpkVNoVrFtvtWQplEmMMxLVW1kR9sKjVuy5g48aZpc5OlVcsgAkLa+WkZpBVKx0LRTbedZI0OM0mSkZDv2VdA6fYu1PyVv0OJtuCVpE1mRCAJyJ+dzUAZGlijT4nSB5j5i2+ULrJXw5GvSEyRX7N2/BAvA6kpMaoYu/Ek4R3AN3YYTDM7MDKY+VRQe2LvRiGknb1LHIHKAZEMCZ4crAUEM5AaoWxGuSyXTm6snVOvP/+LPfe1H3/nw+0/+Ynf68iu3vvTeWxcbMXeoujufb0DFnJyoqqmTMNcazeIgzWuxSHc9EfZ45Qql7fbVL325HvYQdzdmyVtMTWTjVRuX33NdPSlCaK01+vJrMWjsn4wI0JZMS3Y4QCQgN0OPVEEUp5z3RvzpQDJX7mh7VUpiMBf0j4pBL8RtWpEppBYgx+2FXMlDQRgP2JqKxNq0DhZyNwQtRnEjq79rIIDa79PlxR6JqZZsJUEZOhoJDRSlYKyR6QGImVOQFkc6EjyfuhMRpu6mxFWsoJFtrk4sQgR1VTPNMRQetS7sVKc660HGgbgAPm4GMpmnCjUeiquBXOFcmBwCMi5ELjxEiM1xEuTitS7CQgU6z6Zaq0LocDiQRIMjLzKq2cnZ+P7VpZw+ePs//du7//Cv7f7yO0+eT0+f74ezW4PsQGzuMrAuttTKzIVajzrzRO/iZfTbw+bi9vYzb9+fFQoT2Fv3b5Wf/spv/d4fffjRo+1Wltl55CKlqrO7Nv4XDHaYQFjcIUwaTpudsz0qgaJCjw3OCZFAFH1/W3lhR8ee4Xs3n71tQLAALSZoi9/S4qGvuLZp3Hu/x8QFaIYoFrqZ5jJpkNw7Jie0l+YWWw1mWu7GLsQ+dS9atfmMhN+G1bPg6G+ZQu25iwZo+peZMRN5qOACSDdTvuKnlpf2dkRjv2Fqmwth9dA45kwGdvLK+601HgCt9S/yML8ch75J8xVwi1XgSQVY78vYMgvRimV9U+viCIcwWx7fQZR6WdCRY8/oBYizAM2dTOO+PbD20Zg6Mu5pTW+YDBRq0DzbPAbPyMnZUdmlScJB7rx/9IlNT37pr/zsX/nKe27+7mu3PvfS2TCyBKis1QkaZaXiph6guZA7UApDAfdoQAaCsrkZzEHGxKAQtDGcTExOtiC2pSIUimBfg8O+QtpodEVL/4M7Zd19zC7ivMyk9ttVsvMbcW8DFNNHPbjqfpfgkmvarBULhx4/GZS+VLzBFU8qBmaN+gnNG0telFsTj6RWQBRcn4OZVkzQs2SBL2Ld5vGDYQQQ3fwIkTJN9JZIjjIUQLCsADhq1pBSj1h0zETEEo1BMyICgQqHMEBKYRgckXhzeCVVdaZSAGKKAyOd3KupGuBMQkAZN/GEtN0eDjemJsJG0LoQuFJ1c2GOPngkUuc6jkNUsamajIXcrFY1d3cBSBiVuEg4TpaBRh9PfLYqVaebGxc4bL+/sc3ps08+rm/f+8n/5G985e/9++XOnQ8fPXbw5vz25myTcZg5WEE0jkOJSglrcTncXVEziy4EZ2wklwuLvfngYvOTX/n9P/qLP/qTP90ven7nLsk4bLZ1qTSKqbow1AAyV2cHSaRP1J0k6iW47T0BGyVZ4rRCOaQIrG3Rhpwy5dCtI62mGtRATwsOm8Fq3zaMkqQ/Gg3eaZRmynoQjWabVnfT4HkT57XXdtMTK9CBWDlF3dfktDelEQWPk5RK7p3epjMcV+t/QxkOBKw9CkyaKUendNOLez5kgsDW5K0JdlpU1fZ5kgQ5XL3TZoyXZX4045fUZSQM5KN2jaSWnVisEf+eB0yZarQJy7KdZtkTqFurwjPVmBpTuGkbeqOMAxLvkkNzMBAbNTwZWdO5pqdoQY9HEBA6n6OeG8wAZf4xqRmAyNwXt4Fx63z3V/+Dv/RTn/+M3uxrnXcn28JC0S2m1qo1HB6zAA4jNSUPkAchNrGgK5D5VHJAmMCci7eaqynSktcaihwFIEN4c2kLx9aYOevyw1un0fOeFutahSMglajZ0Q6vdQdbS8seV65G1T9xXDO7+edmdJCwq8NB3nr8qcV+bakhz/YV62YDASyhGLEIJSNbLeB190QMhvzgpuZt4tFMzwLIvm4IxjgnstVME9zMNfdlrIL8AxExoE7H0DEC8rZRIpAlJmaO5CQ7PJBr8NMECLFLGPe4vBXz6sZWHLpUgOuyLMvs7gYN7+8CeBF2oVKXOu2nUsaAe0utwyClDA4wyCQKUAqg7CCKw8EVAJcixF5nUq4gEqZxIBloMMc0jlt336o+npdnUn7hv/qP3v3Fn9vzTg91ODvfjBvesINF4KoROROnmDKnPXQ1nvxDCKi8tmysGdwOS4XT3bPtT3753Xfffmuvfnl4/v2Pn7z/wUeCEwBwW2qVgc2ImdScg7TO/thWSnG3JILM48/cCCSOs5eI3L1VDcLdqXvv5iqaXLItW7QIM5El+hpEU7t0wJ+23bzTPm0dtePw8n9btN7jhQaojm19sg0JpNtHZkbbi+ahpoGWrMGYfILmDNpzdvx+9GUG4giQV7InWSuiJqfMnZZteYi8528zEkBnjFraxeNk9riidT2/tscluPcoNxL0FOYfbW97x2wtMjDrjdjdVHO44uazAqBTTUD2C2wxWRuK2N09+MhXMqXdTLyXGahGlVGngFa3Gs8R4aBlMZNFrhwMKFEsOKZoU0Ni5MTERhebzc9+7UtfeOO1wmK+jNshClDhZHUJ2DIOBUIAg6i6kyE60LQ10OrIYwF4tNbiKLMiNyJxV8n6DBIGiE2tmjkiyWkE50aEEEdnPItVEtxcwykdirfwyrumoKOptIQRnnIrqIph9ZZB6dxd9Cag7KHUVmkEdmox2QRiIWvTxFHHm6RuPjjF2m623szDBRI3JqrlOZCuBP2pLG+c2yZNSpAJ5tL7rre3EBFDzBwWZ281KtndoUjbH3vGjJLmagEBEQu3FwVSzUOGnYNWchGWIPyraVW4CZGxA+zq6igMIq7mgC1LLUPhoTBhnhUBRKgUiangIhYFE3BIKSBlIh6KqQsxD4RajQIbRn0sqUv1UBOR+8CbHarvTk8Oy2F76/wGzoJXv/Tem1/7he39+3ulUnzcDa5GzGTmSpErtAXUSQXKTeKO3s2PEmM7uXaRNgFmSmR3bg8v3T9hGZhfOyz1V371t77+J9/a3LlnChKebioLC8esmTCRcGSCq1ahlj0mspSuBc/olp0hPA0ekqBmR9/jDbE2Y9GMd9/3wBrMHb9k3SXNyLeGwYZ1lTRTT2h/hmN9h/vqRALjpjFawXrjnNxgXurSSiIzF6mUeogQUzvQqtNe+HCgBbrE7LXVRjevGIeQ9HDn2Ah4Y/wR56ZGZjyLMDvW81Ur2yX51Cu5KFK7LSBKjZ6BYucH0so8HjKbl4LxiCKitRayQCPV4h7BSKsehLu6pqyl5yT7/edDpaMjYiOi6N4VhV1OLHBtD08RdB2tgqhQ8kAAIaoM1OdpGRJuxvmxA/lsFYyx8HQ9yeB3T3Znm5NlXqJJ5DAQEZjaYcBKoWB3M3dy1zJwO/eMgLRPucIAblGcgcRg0eWUw6iGC3RhdjBZ8FKp6vJk4fN4aBCIBO3CGdGiY55csNHYqC/jDIs6wGiAxTTQX2RzkvWPtxJnBiiS8aampj04ZmJkVsPbqncKbbj3ldiDMHbArDUTSikROWmPQhO5900Q0X90cktiKxwcubtleNdIMQKs61OJCVyyhVL8Liuk2zIlCrlTMsqcxAEBUHMGwKuWOQ67zLA19A7VtNY6LaZZ18nEZmaLanGQbXYbwDbbrZnWusRW1GpaQw4kqM4scONSoCCCqSWvF2YmnU9bA+ZF2FWdhTcDAJ+RSQAaDsyQzUGI79356s//1Ktf/LHx9u2bvXmhTREQQYSInLNvTk5NkBUR0CfSdeIsiXSWXCZOBQiCFcx1nkUEBK0LGAzbjfh7f+1nHrx875d/9V9tNrdOLm6FoXAiNSPAnARsTNGgW9kYCDEQuTJJ1NpwTnUkCz3cBzUlWVAXRGiMLuVgRajc499uPtEyvGsqYbWy1KVnze6sx9DEHmvb1rt5AdCKumKK0OCz9zDC+29yREtVpb49KRpDRKp2lcVbVuNTiz1ovWsHJKgO817HDwqmxlMn2I8/zK3u6YCQtBWiaR8ny+EegilbQ5gWdrdwACDqHfgbYtdm9hENEjXMD+BNtWfmazMgo/ad50D2RwaB3IzajUr0D8DqRDqyS3SwBn7NwBMclbqjoIwAkDxRFJenJh25cjyeOrP6cRvAYipC81TLuDlc7XUYX7m9+bmv/eibrz4wAhZ1F1YYOxRqRsyDkIu0kYDVSsyAEDVFPZIuD14i5EVGzR9GyBViGyEmVzVYgEgSMEwdpg4RAhXmTJ11mpuQp420UDXRfrOcbSn19b6iJ09yxBN6x6RGRX6er2FZmWUd45iZWcRfbpk6Cvge3Lc5iTAQOZae2aEINKkHuIibj0p/M80mp4kCHOGwkN1nO7Jqu1dd4yi2wAT9dITQ/kQ1QD6eBC4JLBTFbgQS4cBECTmj+tdNIE5ARGkxbu6RxMqAFsbMXh2AV6tL1aqqCnVTlyIAjbtNrdWqRutbliLDUGutixKJDEQsIGKHx7FkFm0WOcGWOWc8Se7QIIUckRJTUzTNPaTYCK+gQXAiLLvrq+fTrrz37/zMa1/9iXJxsTiEjEsBwwGNfReETCLBLvMLHOkeMnppuz6iOgMk114UU5fCCJUXk5lNdYY7UH7qy+88ef78d373Gz5vl6miUFUjYcBY3U3ADmJhjoIMMAkJMZiciYiYDSycbh8Mc4rqRaJsVpCRWAazSQGgK2v8yM7nnm/m2fHin3LpxxvNI9ZZ3UO6kMzX9jXoQG85m7be0bio5l1aJOIAzErVyt4oSHiSM9602AnBLRXvCE8bRGrrAqkN9kRpLrfPaSr+niWLC8d2RbANbinOcnB7SzQCy33iiZw6YEt40Ix2/m+igqbkjG5uiT1JTc00a/atBdlq+S61EJlZL61wj1onb21BqwWHFYGO9U3fdm/Oh3tyQDGinsqe8JbZ48DTAppb5tXDCiMyvYkmybxZYTALuUJtvnz4WBZ97bOv/vRXvvjlz7+zHZ1NaShqFgJbXYIjzmBPq4NdHTBnil4K8aBERM6B1t1SFhssQx58i5A15AkoYFCoZgl5xnAcGWtKgDg5FXYQsTfVW0x8P2irQ5C08glB2gd7onm0woy0sGElWTiEvEH0RyvO6LSTwUG7t4hvwiPXOdP7YZbc3U1ZWM3ifC4yYmaDMXocR1nfrJrWn7IsONxB8+8NCWqckUIRggRItcT9rhFyJG4EAVqVhGILtBx55ssdDnYCySCJv1rgLCSe6SyyDDGsLpbgxk1r1eyNECJUMlVdVJfKIALpUt2xPSlDEfA2RHBqprUSpBSutRIPUiJeopAAygAYw2GqTARmNUTdrcehlcLCA4FR1aJG0TJrWuFcynC2w6RLnfcncvcL7776ta8NZ7e80IYBiAIOY7USLH8OKtyNBG5xhjSZwzXBWbTrbpG/O5xdogTbLfpA8LwssWbYTasS+OrZUwzl57/6xaeXV7/9O9+4df4AI8s4VjWQDnHSJ9xclUzidAYll9AIuAKEOLGhtUD3qLJQ5sjAI0O4PttuSNk7oUH1WAQdIlpfSQ3tp+kEEBRos3QJnVsSoccVnjF12vXcLoG/W9CUhG+qOPP6HtpCR6mquWfzagHPUtkZFw/vzMRwGJSyuZsRRyegJmGLg3ZSa4+Q0+SUMtC7w7cbaUeCaM91uDetkrewKomcFi0d8fPh4pi4sTvBpsA9jnENFQcSBTdpULzR1LBqRV1VPXPjbTe2awb2yVtWb8xMwnSO1vJI/5jb2C2qeNy95VLMoJ7BACX1EWnReOasiQu2gABSz+JqErKqDru6/sGrt279O7/wcy+f3f3CO28NQrqYM7mqDAx3NZgZk8NZDW6uFtxOYIZIgLRoMs7eSJTujahwNXU1Z0dgKCVXd1cCe0S8oPUCZuaQSgaYQspQBibmUPdywAUKHJGumdr69q5ca40xKMMwmDpaDgqgODfRNOYDZBohpxvqotkvNIm8zNKDIjFpQgAzgFJKyujIKWEqoeWQKLqw9bRbLoS40QC/mVbIhI4GQiQQmUZPt0gEJiOclWhOceRqgFlmRuSK3d1Qa00RLbccISTsCFEkHJ3jBHk4QFY1M2Ha4ZDHcV25WMzrtFBhidMFgHEcOc6HUV3med5PJAQnshC4egwB4EMZQERG7qR1yZ3sEh2H2ZvaWZjBqEriFImT6hFMsgQJI1oPcSBanQ8mZIUOW3vjKz/9mZ/4mfHWbWMGTJny6RyEoNrR/LKl3WSn4ITIuqImAOkKfBMiE6qFl7cArOYQJnISVvXt6S7W3Y9//vP/6l/+9hP3O3dfSfRCDDN2gzoxpVt2YiINZjrsNjsTk3m0WeUVtUdFWLwr+vAyTGMew6U16x5hP3tHPvEV2cv+MkrDbdQFhIGf8mgbbxifViKC0GTr2UCmXdwbHdQcSDofhzNgZkVV4yyjdn1O2BuKHlov7a6x+cMnODLJduTNCCFDsC7LD2lOChjQ4hQQzBRrvVX6n3anRiBrC7/TVumSju2/GTs3MLCqXftXbGBvDiB5BXdXY0LbRBmXZHCd1skyiR9/4i7WyEmIL3ON0rAoajiqfkBkLYHsXOlHXSp6vZTHBoQSk6WKqecTSd2ERZeZB7q+fHRxuvs//Nf/6OWzi8Fo2OoyzzBvnU7ittXIRLh5QAVARjwImMzcaqMf4uYjEUKhBIEj28qDyDU5UIequRngioboWVhbdOfRmExE1dwWeJFiLNKY+gaM0pY2MU/7hXtfmekckU2lwmJKYFVrYQszaTUAVWezrEKI+4tigWWpTORwM5MiXAoP4k6W3IxprXDngRlsjqpKYDcJxA6KuSar2nyEE6CLZWlV6wXt1TVtOkHd1MpQMizODEfQSIFmHAyrTuIiZOamCgrLo8JsIU9ieDvTwQ1mrf4c0EUjua15YqSbKaVIFPHOGqClqtrCcXZM9h9SW2a4mTopubkvaupOTgMD0d7OhVjV3cElWnaH7t4laKvkZ9WC+CeQcDQHAdxAyWIRyTBI1N0U1bJczf65n/3aqz/21WHYYTBoXR0/g5zNDO4KZ4r8V2vlFOmGFjQizXEDEeQACbMIgSIlAZ3mqkrMIlyGoubm1aqxCLmrLm+8evs//8/+wf/wP/6zzdnu+mpa3GgoVedY1cwsTBByciViA5VINkW3PhOWxisAFn3oPB4jYJ07BepKHIcVoSMcGXcLFi6tgeOU+SJ7KzTV/xE1pJnQTTqI0riFRVkromCNwkGGHf0Sa9rZU0zjpdY8ngMW1IaFTQoiFDn13QrnPQc6897sLYBsyBJcUxiU6qO810TrKbWjMN+97NW7aW7VvgREYWqEtcF3BmRrIUhAJEltXfqGrGOOQLJRRv0yFKEBIaQDL3hJRwSvFCm7vK80rmtS3ztMzEAsx3klpNL3aG+z52sRdkKWjO5DhRkkRjiA1hHNI8VTK1CmSa+fPvuP/8H/7v7ZXfIZBFOSyHDBCMgjvCOsInZNVxovCs6ACcbwalkJxgTy8Gshi09vSFFNRK7RdMjCdxPQ0iouFnppZpCBtCoWMwbZoqajSwnDRD3DkZA2hiwdQTra8GHxm5iExjqhcfVmQahFKjIyUrrUoCEEcPXoUG8wuC9LBRMLF4Et7lVpFJ2Wuixw02oOI4JWc6ahbNyNiEspzALCPM9ajVuhogylqkYtsc/GAxBQ10zVFChDieNY6kGzET0RMzk5F4629aYWCQF2LLPCnBiazg/CLMKHaSGCcAG8zkv4RTdjKVG8YppRLLOozeHqqFJhqbboEqEGzF14OOwPpTBL5NU82DOHw2qkFWLxkZpnPARdapTd5XzBy8DRLdgs0Uw2xSNQ5AIKvJKpMRMzObMtlQgWcH4kHuj2y6/f//yP8HZ0A2kc1ZJggEHaon7O3nu5EZHWzq314ghoGyYgEosZNgUuIEDNzIRBwUrB4WASFxD8MC/zPJ3wyefeuv/GG6/87h/+8dnFK9uLk8M0i5DBba7I/MUQfSFEmIxFiOJUgYb+suqCSCnC3EyjOpH3YpAG9VrGNMn4YIt6zosI0ObNEtq3fuINt3ugc+9RT3QeS8Pj4f6PPKVbK5qMMIUIrUlUh1mNiveyzHPonMA9Rk8UDW28UaDyLm3qMU9TY6WH82S7yOBknf5PmxjBSbOOgf1WVWgE4xkHtRy3N16zuYekCTLjElKVmhxCRCYrcZXYLRq1ZaRPbmaBy6hdyrGS8OEksn4tgUY4zvDo5knvctfVrmg2XDohsmYtnEk3waldR6tRQF46EQWxk6Z/U4MFfWjmVRddvMjp008e6+yMgzrDS8QdQmSarhtwFhkKK5ws+iAKHLqYR2uTMOhmQhwUnzDDjFsvOXdzhScgI8A1jt+LkVIVpqqmqk5cwNGmDu6VzBXEcLVpIScVb+dmEGfMHu0ZPCOglpBH0nCek17ViBHw1RmubmbEbOYU2V0L5+g6z1JoqebVuFA9TItWMKK0m5ltLtvdiUFRl7roorMDMJqXg5sLMOw2RDTvJ60qg4hLjDkRkUTKCKUMUVAug7hTnZcyFmI2N9eQujEcFvqZ0K0zU5FqymqGTI/pspRh8IjDzEGuqgYvQzGr8xT5DHU/MJiJI5xlxjTtYx+ZqVYV5hivZTrookwMsFslIicMQ7GqalUKWmt6AxKZxZk0IiAW44wkRBKpNhmzw7RvxIE4GFhkczRkcwUDhACQcNRcQMTUScSra9U4v/6wzG9+4XMnd245k7Aizm7hMJMcH+pOiExJ2MYmBErIRavSJl4IMie0zGyC31p1XhYzFypEqFUJgDAZuddqVph9GA+HadzKL/z0V//tH/yxzvM8De6+AIASiMyZyWoVJhDHCQ2mRMLZwcnhZgyO3ccUfaQzLRol41mSEfI5T9sNSjVZFoMHBdyMeOJIaqSOcbMrjfEwaha8wdDuHNat5C0DHP/k3iKiNDopqgIAc2eQuZWqFUbg7F/iSJnIGnuEOU1fQC3dnVCkg1ogCESgbXbOrEJT+ZmlsAFhqXvpLrw9UKNQmnPriJpWcgsrOxw0XGMVWliUhteOurNmsVhkk6mPHefpVM2qe85bI+56tINwkTmH4R1CudLgClLj3bxKRi3JUXrL83ic7tKGMPL7lh66HQKPMJRxkp2AebvdPH368J//6q/93Be/9NnXbwfhYE6OCMBBrWUZU8olKfNSAZ1jEDP3S0WEYAZmzrMQyDVAtOWRLDHhcZa1LhoGUaQ4mburWVV1Awtl1YznhUHkyzKpljKM24GzN347FwUrughO3ywbLUR+xNThJlzczGFg1rma1TotDtrsxjrVOs8Q1lqXaSoDu7kt1fc27w8kDIoIwBlMZ6dXzy+JsczzsqgzeBy9qhPYjcswTbq/eqpWrSr2ImGWmANNcOJjZyIRsb2JDOZaKyN5TSJHnedYI3WpwzgSk7nXoWitkmHQGqwTYOa61GgVZW5mykS1RhWA1apSpLC4U5zXDYZWjWNknHBzuHb1wBNVq1ZlImYeRoFTXaqMJYksB9qJYaGKU60kcVplFJ2RBIfmZgYu0u7R44ibiB0iSxNCbWKGa/SoQNOwMROMzCsJm1YnJ4YQGUFOTsbzC0o2Kqs3Up5hnb+Ij/DEy6GASPKt/ccdz2YpKHHAqoygl7kutbqTI0TRKIMgwh+HVovefNNhJoy3zk5unZ4dqJL6XM3ZWuqWShzATCA2IrAxM7EJM4ygzDHazJQH9oCYPbpHhWENqMjZJ6wJPZiAzHl0vIwXvyiNkAPWCiIbrk5Qu74l9T9NmxMfkv/rSJ4wTyTlVRAa1/QIntndyjLX8Ebcq1aimwO4SePzYyMNZOsTuGcnn3QW1LvEuNtaG9k8gmn6dGqVuCtr0nxBusCed43Lrs8dToKbUYtjHHIZtggqaQeChSTO0rgEl2euhKzj7QxrxipH5GLWiCRnlqQlpw8ION/FhO34UOT/R1iQcRU87zEozWwrvyqkWlgAWApgXDywtbs5O5dhtlrOT/e2LAtQCrwW4gigMqJkcoMQCQsccW54+n73quq92VH1MrIDXKLPLQGhp3FDShFLJF0j++aWLSljqjiONKRqqrbUxcGQUgrB4WoQESdWVQfTwgUMScRhrZGzN2VntCEpI+liZsrC5uRRqB1dB5ZFl8rs+5tDYWa36TCZ6nAyTjfXusx1lghcdJpvrq62w0Y2onWeVd1cBlE1rRXqBgeLLlMsvgJ3LHCuS3XDsixQjEMxIhGy6qrGQyg11Z0KiEuZpmfDdhRiAHGu1na7U6/RYc3dtVYwqSoLAa7mwgiBmroxy1K1pc1krjML+aIgr9OiUXPkDuVaF3Kqh1lKcFDVtAaegPsyzyKkhlqXYRjGzXaaDosuRaRstiFfJjU4Qc2XGqxyYQJFE1tq1oS01jC0zCmwJyKvrl6D1CWCVs/Dnwnu2hLIFuRWnEbATCREhdwYzrosBN3f7OXW6XS4mYcT2RZvUqg8mDVpDyPJCiAA3mrSG/pP8xGvRp7O2dhxR+Rx41RMc8CsGkoZiCmSV1V1misLV3NTOjndadXz89N/72/+0n/z3/1K1bEGcBOKojMlZxYBiIPGNBEWBrELc/EcVBYqTWsXtqiGUjmOJSBIyxW1REbmstdEI6XyIt9PoSgJ0A6otlYLuUkbE5a8Q2BSd0VT8MWoZTOzsCRwYopMUh/OhNpm4RXKsswZp1dNmxT+pJPbDsQBp0QJ36xZ7UweIgsFWLtTCqm2eQpjiGCmKVrKeztmm9IPHBNK1KxpPycsYx+3KGJOON54szw6KtRzwVG1hl8xfk5spJG4pGZ8Gdx5O4enl2vsHLjNVlT4U1a3RmxuxNRCCm4HEqAx6RQnkqf/yuVrntq/WOrhl7IIjjyjhWimRMYkVk11IfDp6cnr984JBlVTp9ICpihWJScm4TSwuYs41JMeRwbCEadIwtvJma5wMvcaHTHc4RAWpOApkpCp1TGDe03Sj4kFZq5qUDAZhuKuy1yNCzODeJkmg48WqVFHPFTcdGQ8AFVjQl2qLgrAqs3zDHho/pZ5MrUivJ+my8dPh7FsNsPN9Q0TtnZy8/yp6gIzqy5DqXNVrbQdD4eDFF4O89mdi3lahs2oyxLJ0mHczsuCajeXV3AFYRxGJ6lV61KFSQhlM0ZiXISEnYpoxVLr1c0NkQA2boqMhcDMLkUgpFMdx9EcJMKFrWr03Oc4dMwVJCy2KWVa5t24WZaqsOVwkMJMgxRZ6lJKYVcGq5kwmaEuC8ikMHRmwjiW6+vDWAo7iVmt6qqbcbMZBjcVVYgXElpmNtQ66zSH1SSOU1+S6CEnaRvVPLSNjcGOeNFS6UREoMhRE5iyJt6ihK01zEI6E4vGydGpiclZ3W3YDB988Mn42uXdi3vM2d/KurAmdHcRIiYcdF5lM80jtE9YWYmWpIvGXhxCLObiqO4iFAiUQYvpMtewnnVZmAjOWhdi+sxr93a78Wp/wLiDwrUGZDR2liArmCLqcrcoERC2CADIxZkkeluCzcKUW1b3cAN+4P4UlER18vdEaGRK2gODkaZ1pCbraGnfMOWONROAlPzTUWAduCr57hRAI1ZBa2SdvqbreqyoKkWihVrGIOKUlsFraYCUaYaTSDfAllqdlhlqVprdjViiNqdnNFoQ01ialU/C6p6a0Q0NRTqu5g1SzBNSC+uvcXenJttJK9wSHQR0DRsQFfZEaywVkDZZjOSQWiUeOR2tyyj6dHi+P3qRGRww7j1siLTVvzbesgU61Em+/AwOHW1G3unYHdA465GpiJh51br44Xx7BwXqqTQGIShQMydGFDmFWLyau2engRgFMxViKRLHCLtGmGym/TRKX3sPmau5qkblrGWlfZBLlRQQIiKRoma61GVxrSqFCaR1kcIgcSI9zARwkehGYUvL9EZhFnxZFnYn5mWaA0ks00TC082NmdV5KiLV7fr6Gmbz9WG5of3VdRnLdHllVud5EuGxlMhNDZtiboWJycfC2804T1rrMs/TvJ+G7ZaLLIcD3M4vTufD4fTi1HQRklq1jKciUVyLw80MeCQ6RAiKs7OT+y9d1Gk5LPNQyrzM7EQOnf3wdO9j+eSjbz9/+vzk/PTq6ub05OzlV16+vrn8N//mdzdnJy89uGfKddKf+skff/L8+urZY1j94MPvf/PPvvlLf/1v3H3w0tNHz2BUBpncq9bzi4st5ObmYPMM98eP9zA/PTvZbMbNZmRAl3kQ35TCPDo7s1rVDWOps2mFY5lnXbKfgZCD2bUV6oPMXGUgZnBhEWKuVYlICntN/QhTYWFKxX0YLG+NPYxdgjFORBeiXGKYUpQks/FYfDYv9YPvfOuNn/pxIxtIQO5k0tKfCRTd3KMaLMweRQFSQChiCn1faqJt1dqRQc09GvCC46gcgZGDpTiwzPM0z3FGnlWzamZ1LAKiZT9dbMbdZvzB42fbsmkHSiNEDe5uZMRO7lWyLAAgEakiQsRMrFRZoncg5QHOEohRiInbKTMJ9oL59aSOswazbbu0QwnSkMQtrbbfQckdNb17e4O2+qfOzUQzM0vxTriCI5bGe2lTSGytqCqT5AeZoRHeidHD4jN7iixbbRSRe+jT23+tsTPAcA02OsF9Q/hgRuvO1iFH/Lkl2FNh3uiX5u/dqSmx4A5FDOKRU4mn8+7GPPXUnURsnpgyy0AxrpFu8fYyijCEqPM+5FExS8QZUjSepxNm5Ix2Vi3g7c3exMEdBKAlRqKuKhswtHAcQPCi8YlkoX5kXq701c+/8eqrr925c1FGWvbRribrRMxMiLQaC5FnzywGIdvKe8g0Sylpe4FFlbzxUO7mVIQjG6KenRdZOLrxuHfJFDEAiaxA5MHYmOuiBK+GIqVEtpacCAbXqtN+3m4HjsOvzNyhVd0xz9O0P7gq3Of9vD3f1urLYT8vMzmksNX5MM91mpd5VhCsZlnspLbU7W5LTDIODpqnRQjsgrrM+8NQ2KvWq6ths2EenAfespRis448HPaHab+/vLraXx2urp+yYSwDiG+mm/319TxdXT599OiTRwocbi4vL5/du/vanbv3bvbTMG6fPH1ahD/+6IPry8sylmdPHl8+f/YTP/2jv/Frv/fs6vKNz37mW9/8HrE/eO0Vs/rHf/Knu+1me35y/95LD9+/+uznP/vaG2/98b/51ye3xqur/fPnz//1b//6l7743l9866O7F/c/9+Uvnt+7+9/9P/+btz/3I1/68peGYaNTfePNN7/953/yG7/+q1/86lfffvNz2/OL7TgOm83Vs6fbcbw4Pd2dbpeqUJ33N7vNOG5GJq/zJFLUZoKYmZu6zmGnzAAW1YVl4MHhwWkTubuySDFriT1TVRNhFs58lCUAhdWQCkYv8IjvEzLMlZ1cwbKp4teXj19948Gd27ekiC4LQCwN8VLI+dDKqfMustQ+0GSCxqYQWVMOkXw1ZumaS3jkkyI1gKo6HaalqgOurqbT/sDEMHOhLblV++xn3/ruR7/n1bxw1PgRgUMlGB23ADY2TjpHzUo0DmVmIitOShR7mQFWcggLuXK0K6fo8YrIv3FLScbLXmyQkjoTb9C0Pf6RiDqpZW9WkRGZgGNMmTSyGVzRfIVlrjGsUx6w2EmXf/x//b81yUHYwbXmNz0F2seveQTqNnx1NC3tGAw3UutOR9r7DEHQDKMdcT4ZKbXkBxr7HlR8ixe8D1aSXsxYv9wdrYo7bWpWN4MocxLOqzfoxE67GnUU3h0AMncfkoOkg1pJt1uLi1JWFIrmdHltZ8E9JUiRugBsVWplcNCaAuRdxLlF5salSJF5b1fzJ1/7zGf+63/09166v62LuSmD3S3yllHgG5U4ZgZq3kW4mjKoFHFzBmbTuiwACHngeKay2uSZu3DGcjFroby03GmcwvNYdmpVdZnnKMIk0DgWKQMRcZFYGCJlsxmYJfNjxMs8H27203Q43Oyj3mK+OZyc72q1eTroPGmtcQBEnabIAMswqFaSwiWMkshQVKsMZOrLsuyGASBdDnozbc9PDtf7s/t3eJRnD59fXV49efrs5rAXGaerw5Mnz32ZL2+uyPkP/+D358NMxa+vn98c9nWaBiGtMyqpOxPVaT45Pzfzaj5PVRXDINttQVUw7af9WAYf5fr6UGQYd5tpf3B3FDnMtYy8LFqYz8/PL59esxQ1W5ZZCplglHHZL6OwOi+L3X75nMvp88tLqO3OBiEZN8MwDtvd6eXT55txc+fll4aTrVm9ff+168vLOh8ePLjz4OX7N5f7jZTTcffaq6/cf/mBgAphKLIZRgEWrfO0Z0AAYQGJg8pmI2WgYYhmoBEEhw7ewdF1MFERc4gpyCk6argDRCLMYFscof2GuanVanNlp6VWuBpNjx9+8vYvffnuq58dyom7sWTDkNR6NAY88Fioybw1Psr+knmYTjZtCesYt6c1wC7Sf6m5aRkHJlnmeVE97Gd108WWGkeiLQIqg0zLzDKcnp79+h/8+T/9ld8edreVxZaFhIDqblEyHm1SiYUBihw2c4lsz5oHpvBFETBx1oV6fJuHw7pns3Fu8kKs3WEjI+CZyeMjG5uGKEieZIRWQ4nopruC/SCPrRH8RNl30lOE0cl27x/oDvcC4paZaBxKdiyhJFsyKmhJ0vUKLTJkglHIuDJOTEPWDX7nX/pDe1bZ0/r6TiiutBm8OZ6A59SIoBaopPQ9P4ybBQ+A3D44m70QdfUOJe6gvJm8QQLa3FAvvKOu1sqSjQhLCWjsqaXEHnlYRDxzBBKZbMj4klpbbI/7dsvOGUSMoOEMKWomCMX5XDx7na/ns4tTFA6OdxCy6vk0TK5EFJXs6WKIOdrOCHOenek4zLMzmMUJVrNUhIhg0OoB/UUoohq1/FI1InKDuWU9XKpazdyZqJSiSma1quJgMmDcjBFtErNXW7Awq1eFMMi11uVwM91c21Idjqp1ma4eXc1LjaYGpkrsXtWqSiFhuC0wYxEhgsHqUsg3hQ9XV8tS2WHbDTvYdVrqx+9/+K0///bp3bMnzx5960+/vdTp2bPr7333/ZPTk5vLq2WyYeCbaQbo8vnlyckowrrUbGxSuEg5O99NZrfOTx8/fiLkVHxw3sgm+MOX757t9wfTero5GTbb65v9+Z0LcKnzdOv85Pqw8Ci7YRiGUU3VdBS6fTaS8Kw6TWUYh+1m2JbNc3+22fBmc3o9Tcv1tZfDS+dnh6sbOeyJiatcPjzsN5tZbffSg/f/7BvztA9ebj8vgwybzQDXWutmuzk7u1UEL7/x6ul2vHv33unu9LXX3rx7evvunVsX5ydTnVzt5vqGIGUcALdpIhkAN/NSBgOKjCZCxEbCJFKKuUnwoBYMuJhpahMU5ooMNTU0rYyoAzACaq1uy263A53ysK1Vx5LHrXR8BSD0KYm/stLashDLj7hl8xa4B9x0T+GCuwNCIGIBl5EYZuZMy1LV3dSqKsHDMBNRVZun+eR8o2bvf/87m914MKg5k5BH98bGN8EBjxaAoZMEaxzKxu28T+HIj5AYsZQwxRLJk2hEGKBnyYZFaMX/gYS8yVUAfxHLNpRKa2Iyezt0Ai0boATuh0frLQ+Wzj3qzZPP9oY1G/BH+5tZIeZwJtScEjfv02602fx2Ax4mNRxPIH0WWkvRIl/tKX1JFE3Z9jMZceKm8eoOAMg8KFavyMziaFRSu1QrvE0Nf64mTy6JsvgfFIPa+KXA4NnVqEceifXzM1u3yHCYUc1G6WConSUa3qqFEc11Efr0dsYNRMSGptLH6m9ghKwgpPTO4Rs4WEwipij4PyzLJx8++a/+j3/7r//Yj26M6jwTDGN2chNh9xBT512XwgCrxoFfHpnkKPoFkTCpmdUUZpYicHJX9TgvBnBxs2rmmQZwArFQ9M6I7hHM0XTOTY0yzyhwg5u6slavcb6gCFutqpMOm8ENy/Ucosznj5/M8xQs4OHqkoVtmeGu7oBR4ZCbFiFiV1WvFgnh3XBOsOnmUgBafCRn1Gl/8/TR4fr66umzp0+fPf39P/jD7373Axa5ur55/vTm5GSs1Q7THJ2ST8tGq8BMNsO9++dx3IoXGQbxqiIJNLZFri9vzsZxIGciluIsY+E6zTTvz4WchMdi5ON2ECkAVxcW2kAqSMoIxlzhzGSmxYeBadjqokJUylCn5d7pDmpnW7l/6871fqpzfePNB/ur/bNHz4ahkFDd7bbb3WQw0/Nbtw43w1KVJToYFZ0Oda6nL90dtuX5s5uHj55cPXoqbkTuzFTk7HT35S++++UvvHdxcX+7O9mdbM1sO5yy2c1+HseRnbjINNswbnU5QAZzcinDdgt11Fkl2mJLaPfBTG7hhRH9Um0hI9XcY8xUGabG4Ent+tnl/tl8eXrJXMqtrcR8u3ok1SJWz/FmIEoPOavTG/oGR3hNRDAjjSrHlDU26setFAlzt9R6OMzzUg/T5A44mZswE0uYsf1hKuNmO5784C/enxdD2RIVhUa1GyPL4lqIHk2jksaPBl4AGVucKxn1/v30HCbK1rI1uYZMLC6p1lg5hs5EJFRuWp2V9EDjrKNMp+tyqKUEshWAOjwgWppeWLYWRzf/2VYhkTt6p4NC2dEk4rJm4dNNNSvGx8C82avW0HIlppLO6k/e6SsP7iDEiH7cBCk4lEwBNZLMjxxdGy1af9lZHWpC4mNb3izsyui0N2bilFbCB4w4/mR9vPxbX15o+XoiIFqdZUgW2dhwzKuyqFW5rmnm5MHCRiNJPcqqm5YBaM6l9TeDu6s5F9kWKT4dPnn20sUpV3v2+PnpySZuztzIyFQ9JUWWe4mdnaKbGS0zMlKNehFq1h8snDSOqZrBjUncbVGP+s+4DXbyGuGfuflsBngcqO0U5+xl7XlUdrMaEy3zzCJmfri8Xq6vdxcXRDJNh2Walmk63NyozrE8dZmXqRZhj1YY5KxGgOvsZqjwZTEzcuNqde8ihdmnm+v98+vnTx49ffz46eNHH7z/wSePf/Dt731/Py821WG72Z2d1Hk5u9huhrHOCxHOT0YCbm83h8VPZBhPdtdXVzpPxDEK7qp10c1QwM6EEe5TVcdmV1wBUyhY9WQ3mlUwLcsBzANaOx2Bqoq5LV42gDszpnnmoezCSRRR8jorue224+H6YChWuWxkN56MZ+Xm+b4UOT8/OdwctrvT8XS4uprOTk+qqtb60u2XZBiWuW7Pz1TrRvj5s+uraV8nO9+evvmVVw77/XS9Pz8dn1/fPLu+1Onm937n3/zJH379/PYdEnnj9c/ce+nBvfv3bp+fD2XYlk0RKmNRB66fM3i72W52J9W4TldswnAIkQicpYxMQlKIGBqlBLmhlDIFaeZWlUiIWL2S4XB92JRhU8qsOh/mYSxliGpRNURpFUDsLXMJjxpKj0QRsxcZwpIxJ4cqLJFFlAD7ilorCKa6LErCy1LrUuuitVrY0lqVCkSkzvOy1O12U5dp2c9//+//3f/XP/+NHzzxamaRx3N1IwDs2WsAyQOAAKvuUa3AzICaG7kQRRejyBQnE+oO0mwTDm80MgNdgOK9hrZjf0pTmWYw+XgiitKsYFwo44owkyEBVYdnuSEhWzmm+2jon9KmdLolgg+zkrVaBCJZTSe12/RgllIhkx0mQlqSPRDh3bpGokgyt9/uvwcylOQ8hJDJ4JCgdOInA5GUKjeT761NXbP1LT5CeKB2lgd9irMJoE5rRNN9GzqpEywKM0WHCXCwj97+fOQsjnwQC7VYStpksff8sccUvWDu2wiEB8otg5V+as4npaPUTgfxSef7L91aDvr80XMh3u4GGdKdkFKceKW5LIVBphYCiVrVYYMUJ1JVN7catV7pzaLnWiD5WBC5rM3cFSyAq9pca94Tp3+L4l6NMk5Edw1DtCUzjzwwsw+MOs/Xn3wy31zV/bXTWK3WeZr3B4fJQOS+zFXniQszkZrVZd5uChWeLq/dK9yKFMKy3Uhdpv3l1f5arq9vLp9fPnr4yYff//C777//4Q8eXj2/3u/rZlugKINsh62IfOmNVz46u7qaDk8ePx9KuTWW29uibmz1tAwmshz2dwZRIxmIIcYYqdA0nV9sNtsd3IfCTOyLFiGlovDtIPubaal6enE+T4vCNrtRq11dHU5Oh6vn++3uZFlsfzWdnp4a+f4w8e2T6v706lqXQznZvfX6ax9+/8PNdrxz795hv9xcHXTR3WZDzNvNeHN1M8p4enfLtzAvy2a7E9qMu80wDNfXh0Hk/qsvXT294oFPthvUyotenGzu3Luz6KGMrPOO6gJdbm3ozXsn8zxdXc2PL68eXX28n/W7f/Z9JVCRs1ub+3dvv/bq6/fv373/0oOTkzNxjMMwEHZn5yIDURmpEIm5bbf/P7L+rFmWLUkPw3xYa0VEDns4e5/hjjV1VQ/oCQTRYhtACiQFA000SkaDTKJMkslkxke96qfokZKJoiiJkGAUzQiQItEkQDSGblSP1VXVfWu49547nWFPOcS0lrvrYa0VuS91zO5w9s6MjIjM/Nz9888/78wIUwT0RM68QyDwDZgi5WFiAERIQojZbMlQANIcp7BpNtuVjIMhIQU1SKLsuc7v1nQ0G5YykUNJhuhSnAHzCmQr/HOdHMqtbTfYMAABAABJREFUuKp9N6AsXLayyi9JiimJQDalylZ8aqKaS7t5mtrGD1Of3PjND1/8K7/xl/7Tf/CHSdR5rqIINSOF4ieTRSEIxYcIFJWADKBuitSqWxEzRMzMs5lh2R1Wvu+5ZEHERQNrWjNlzSbaS2YthJyz+0oY5CdDnastHLpB1kSh5G3HAADV+SO/ZKWNrFop5vTaEKAcW91i51BkjBn6Smu0TODmjjYaaBFzKxIvkFsIlkzEKEAefl4QskSiit5YKSWqaAglqJzUMLXlUHg4QMCyOrWyYOVxBgXvM6SX7L62l2r5VZsHcIoGFfxLpBADIoYlzuESgWihK61WNLhk9/nyS7B+1KwoXj6FJqox8zQAUHkzyusWK/uVj5qrSATQPJlpzNFkGpIZb9YBwPLGSQATSGgEBphXuRGqqCYlh4TIjhEYACRJinPWCKsoOy59CbXqOQqYc5PSHFKAbKoOSVJSza4IxMye841SUEmSVPNdYSJkh4jTMKYUj/1+tWrZYX/z8Orjv3DMcTyLScAxMYqoJTFhVAMwR+gI0zymOQLBPEWKFBzPUaY5HafDOAzz3I/Hw+5hd9gfPv7408+/enVzdz+OKQs01l24OD/z3susTEiAc4yfvXl9tuqaEPY30U3T5nx7sWWnbncUS0ln2ba0aV236XygtmubVXBqnFLb+tA0xOSarO6jruuMG1O7enq+ezg+vN1vnqzjHIEtpUTsh+OADlUtzjL3k0IY+3lWOfbjw/74/IPn7xp8/NHHMsY0D7/6a7/52c8/9W51/mKrSXf3Dwh68fSJRzedj8R0cXXZrdavvviybbo4y9nFuUp6+/ru3ffe3Vxsdnd35+cbk/jw+u35ujm7OOtWYYpHBR0e7nZv3xyHXbDREXWULs/5olkPCe/6eZ7jrDJL7B+mn93e/eynPyeE0DYXV5cX283V+cXT6+fXT66aZuVDu3JN8G693gwpIvIwK7uWyRlg13TEvWMyVXSE7MEQGU2NEKIqGAgIegzQptk0JSELTWBvgZ3EwrMpGOfsUhS4cLuAIhKdy5SyLibYluVuxZXKUnbezkWAISDM0ywiyGimTIxggtkmKxLnOiCpKTlSBe/CPI2e6IPL87XHUQiMkMlEVMuwZqaYsaTlnL8chlC3gsjyxc8zUKWZmbuDNc3LLYvMVKEaIdQJ5wJxWBNMrTw1AGQfHagpeyF3oXANamXFbjVVy9uAFqUPLit1KgZlGTeUfgHm734GJ3UZrQGq8LZq+bW0MfNQasm3i71lVfYAYF59BUvtkFU1BlVoZPVESxivuJlpIThZZhgAlc4PlLOpsp9q0Q62+D1klCqdhkrtVDgvzQDComK2+rBlCKtyPqWzjfm2Vl/vEnaRqQYVy2dkBpRljktMW1jMgv4Z3qs8CwyyxSSdIoEhZH8AzL32/HKIxcQh03kGgKiqmiKha7rNb/5bf6U7a810HqbgWECBcnfOAICYTEFTAkAFRa1aZBVTjTGqKgCKqIE6cNkQSWqlCFlla/XNAjBTTdkAGaAKyVSEkITEACQl0YTJIHtmeQJVIGzbAGY6JWxExnTz5Wd3X73cnl+ASjKh0AEBsc+MVBIxEUSQpHGculVIMWrS/f3D8bB7+/bVF6++ut89vH57u9vvj8ehP04pgUVxjh3zpuvaxgeC4Nkxh8ATKCh4T63njmkc+qcX69/+lW/d3u3vjoeHh+S95+BfPF2db7q24eHQb5qQ0JCwaakhRLWzs2693Y674fz6vGk9Gm+fnIdui0Aq8dnVxfy8D5sVE/XTMBym1fn50E/zMG2enh3uhpjUgB5ujsOUDtO0P/bNunOrZtNtv/j5yxU2a+d/81/6K7v7gdu2aVdPrq6J4OLJRVZqAeF7337/cH9cbTZNaE3pbLudp+md9+b3v/P+eDheP7varEIa+qvzzXoVzreNdzRNx5vXXxzMb6Mb2Elqp3E8xKmfxEU7C93V07Ui9fM0qapIMhummERj0t0Xr1+Pn89TJO/axjVtuL5+dv38yfl68+Lpi+32ScOh9V3AsF6fOUeH40PwgZEAzTs0Y3LBdyuDhOwob4WJ4hFiindffOWvthh8nKIkDY1jlwfTihKlpEYZWKIAaXCuYAdS3uOtIoaYbeMxL45UkSQIwMzO8zTPIiJJJBoaee+iJpWUuxEZoESTmbm8vEbRMzoEnGT36o2/ek/QNKnlZTBmxoXwrd9ttFzmP8IPqH/NmeYiZYeFsSjTUQuHVKWcGfzMKItRqEQRqPJGtFMKauWYGRlrPzY3HqAgeb6PGYW1QrNVTiVzDFXbSVBfMp+oq+eWk8JytjXDBcjGbrlcO/3Kyhbq2hC2E4n+NU6j+tphScqJT/eRYbkjSza9HASW2LPw/lgm1ZcTg5MlG37t6VievtzPpXY4zRecujBl4iG7O5R4U22Eqp29UplHrrTVqXWxjHchZE+syhrVT7dkvsWWjkhtXSMWg61KtmH+cAOgqmT6FdGpAnD6T/6v/8Uv/fv/86ebxnkf45S9FgzNe0REUYWcLWneb56tniWpZlsuyxohFSZUzQM0eWNBLTstqYLmmca8glFSvlVEnL+ehJxSLiXVysx1YRJhjAbqHLVtO4+Tc6zzvN/dv/rkZ5BS6vtooJ4BAYiZMFcPkqKZEVGc53kab17tH25v7u5uPv/00y9fvXpzc3Nzf6DgANAxmSEhrVtvHrvWOc+t9wjmCBE0BExxDgiZuujWLQDADMMwPrnqfvV733gY+pcvv7i9Ow7aH3zyOkDnh2OUoXed9wyrzeZs5T35NPaXq8t3f+m726tLtDQP0a864o45aEpGNPXd6slFnObh2MA1d2fn05T6+0N3fTFsZwVq19v728PN7cNhmMYkBqgqV+fXT6+f7d7erNv2/V/4zjzJMEloGlFxDSPik6urOEcFvbx+vtqM7WYbQmByzjEaxJSevvd82O9DF+J+F+ic9cl65VaN7e9ubr+673TH3G/OXd+G27c70enZZdOt1mp4TDZNNiTxngSQFKPigQnZA/MwRQOao+76kYmmYfz85VeffPzSOwuO191qtdk8u3qxPbt+7933LzYXzKH1zSpw40inwTlm7wUde8fkfegMHImJzul47Pf3L96/wtCMYx9CiCO7TVcMHPKuU0SkLD/LG1CK8YtM0YgQSCRp0toAhcVFrHTRTNOcCImIlUzniGDzbCkmVUt5yAkhxpQTa2IfpxnUNOgwT9fPzkHGFEfwnVGeJs5qmkxQZMNWzBV+kfQjFoKE0Aw0E9FQG75Q6/5qm1oCQ637TwiQjR8AADD762GRiRd4qBljJfexshZQBnIBF0CjUi3kHeyIkLPtilHL0XDB25q+OsBi6pS5ACp6xHwHMlSC1q4nkJkJaNn4isRQra4ecTKY7RsXBgeACwxnFq1SKPk5tamKANWIodpOFHFM5XHKNJ0tj7QFzq1WMAan6eWFkS+lSilOCtcGlY6ykwYLETG/c1omktEAiLPHUe5gl7hFlUXKjRer5ib5DV9Gqb82HwcL7Yn1gbVQQMz2QqWtzlQWCBMahvEw7m9ev7m/f7J6jp5RiREMyECZCQxSQfuy5QcJ1EwkJVWwovuMKTJXL3szFc12MdlOe8nxszYjfyqISM1UBBGdK/aSokrlzWRAUxE1kygI5tDPEBmhXTUY5/1hd//2jSM47HfH/dFtOwPsNps09moyDaOiHXYPu4eH29u7u7vbz15+tj/sd7vjPMxgSIym3PjGecfIhOiJHedNsIYAjBY8eVfMrdvAEiPlzVVAjWcOQUC+fPvmzc2b3/jVX/3uhy9u39z99Oefehk6ctuOW4erxnddu9q0z56etwyrjd9uz70P51cr9gJM22dPDHwSNkUWRHbbd6+RcDoMrmu5aVzbrZQvXrwTRdbnrj/OwP7Je+f+/HpWQ3KiMI3jcDiePemv33kPnKPQPn9x7bsuzlFUEM13LSKZWLdZs+fu/PzyxXsqKik1wQECiK6269V2FRrGqzMHEiim453092xD6zRcbcLlSmQaDrvL841zbr3pxsPxk89fN+jnWR/66GcZk6JiFKC1m5KKUcvAiBp825GJwrYRA0sJDGJSVXx4vXv98mYSVId+3XZd9/zp83efP316tmmDWzGvwoq5aVwbzHXtWoW6roEA3qRxtro8G/sJmYlRkqWkVI2nfEDi4p6WV4iX1UxE6F1OGh04KVZrqGopxZgkkwc5BVGJimXCNasdRDSJqJkpTDIDgJoxk4oQIBqnlCabVWJo/L/+t/613/n+J0kRXPWCx2XqtRjnGZaZXwWlwuMDACKX3C4DVn56UfGUx1SMyhP7NU7UYHBKCpGq0iYzLFD6H+VhmvEciyy+MNUZmkHzSzsqHFWeuGEwMMubz0sXFmrgQNDC0ThDLHzLwrovcnrERYBly4ZIZMNsGsZLPVIrBs7ukkhcQDgbZ1sJPlZawwWkIHsh52qisO/1l0WpT4vgstBgpSYoeTeUfiqeUB1rfKt1QDVgpVLBFG0rVvoka4yg1miltsjoXgbWHl9jKXpQl1exfH/LgqWC8kvH3ZYSweojoVZUJjUS1KAAQGSmiJSyJJIZlIib/uH4D37nn3/r3/t3WAWAfMMxiiqo5sVMWgRzYmCmqmVZl5koZNLKk1O0NIsUmkeTGRtBzL1bK7OHCgiaiy+D3O63THzlOcNSrdTrzIJkNQPQ43FqfNg2TeP98bD/5Ocf/8Wf/8W26whd03kcmhgjssvK0N1+d3PzZv9wuLm5OxyO0ziP8+x9ILLVdpXV1t1K2bNzLpNTDOAcOkegymbBY8OAJp4YEI2Q0DlER845x4Yqslo1AjocD3/2R3/0y9/7xq/+pW/8+nef/ujHfx4Qz662z9995pkQ6cnTp2dPzixN3Tr4ptFkjuZhd8dAhIwBHTnkJk1pOg4m7ELbrMmvnAJxwJSSggvUcrPunvgkaOja2cwHIz/PomLjoR+H48Pd3SQxrFbXL97htonDrIjTOK4uNtM4h9AAULtpECnFFNomTbPz5D033o/9MTStQ0VBSqOzBDSn6b5x0/adLYctJ03jeNz55+9f+66djuPbz+Rb33qBRAagIJ9+9vr2bojRhgg+WYgSVSUggSVTFKPWgyojpWgWMXbk2KdZyPGhj8coh3m4292/evvmo5+5sArdpjnfbp+dXz9dP3nSnK1824Hv2s3hzsiDEB89v/OXf7WQhyISI0AnIskUwRKod54DLdmtCIoIWl7zWPWIanUQ05gcECLm1ikk1TjNIinX5magZjGJiFldxCgihNnk01KaEVBF0HQ4HNfbi2+/+87vfP9nCsLsy3LXbAWEYERaP+dlu2POaWlRheT/UgXMGoNqm7MsJcxJbV4yXxPwaveJBnmppGFlrWurso5uWfGhNANEyt1pzeizuDTWwiKjpqkV2py40hWFZCiVAgAygZoTK7besFQfsISJ0quBZUgMWVWRuMqRyBbxeyasqOTUACekXgoPxLrRuUTQfAfgxM0XNeqil6rXVhP2HAqWqFqaBCWwVqNlyIVa/XGuiarZaZ3VqqVBvdDa01gIoiWq5R1FiMhmJwkN1Khkp/KNausDyycGSswRNVpqmqJhgKx2LlMnBoZ1EDz7iBibiIEiiGvd+bOr5NtoTGAqCdSQyee175ituQARSys/y/4LXwVgQEBZpQ/Z28dMkjATgImoSHXmBWDHCKRaumHkMIBPScAgpqgmCMtAs4GVYRQ1M0uA2poXifubMU79D/7oBz/60Y9WHLbnZ9zQpPMU03G3G/qxPw5zVp+KAWDwPoTARE3rHREgJVUgBlLvmHJFROCZQ+MJjBWIrPUsKYFpmtUFR0ZE1DQes90YqCdjk65rrjetSfz8k0/3t69+6Te+8+u/+Z3dw+7JRdN02Hp3/d6L1eVZmif2KyY1SOgUPXWuQ7I03qOO1K0lBWyblike7xBa8wyAjE6GHoARPXkDaLBpHHVibnWxjkkprCimFA2a1Yafra+f3rx6Y56h8auL87SRMtOP2KyEfVCRtmuSGQX13iFRaILL3jzz6BsfyFBF9zOMDxQfvI2+I++zFFGZ+OLsaZ7Lb9ft9qxJKR3udrf3u/1ufLJddSGMfRyiHcY0RBlnGWeNMU19bJogMrcuGIBnSqhoqqzcUvCuaeiZ493RD0nHOQ3zdDz0Nzf3P54/bVxz+WTz9OLy+fbyenv54vLZBhxFPbu4vH94+8VPPr784AMz0CQESVRME+VEx0hEbbbqSZdZZVQxSTFndsDoHGlJ64As+z+bYxQVTSlpqXkVDFRT0phnf0WSlOChImaiUQCRmNmx93R42F9eXq0YV+wmAQFUyh7kWZZUs9SFfkLUbOYCUG3rsWRDRatYO6CICCgAiGxLJYDlSAtPXV4FalMYSM1OjqGZP4C8LR1qHox52T1A9qOCkmUWHrku+iECAzWQYk1XEt2aOWPZxovksslcFf3jwl8DlMU7UE2utQa4/MyqcikrJIr9dW5MP4pI5QRrep4lO4tC5sTvl+zdavWAJ2DGSuYvjH/N1iuHBo80olCYn/y/UBrtVjkhwBrhirVUWdu1PGNZ1VKBPp/80qQBqMPqUIPvku8vlwNLeyi/sbV5ZGAKugTkQvrAMhGIkCf6CA0UkNEIgS2J5+0YYRBZtQ5ByCR3/0E1qSJBXtuEgHmMa2GXwAyJ1UzFRCWKUHmXMAcq1bxwtS5LEIOshjD16PKOAgQQTWDCiHn+J4+WFfORrEhTAQK11HYdO7v57NPDw+3Lz18+Obu6PR4EJOo8T5PE6MgBgCNq1iElZcdmJmKIyoRIaKpoykibVQhMzGUFCDMDoSUBsDQeFVfec2AviMzAhCbqwYAoxeiIVsETwiqEzYa7dtsf/f2rt5/86Y82v/nt7373vak/SDx2m3XQvR4nAJ6TWRLftoiGhm7jERRZNU4wJlPnLi4wMLFL+wdIXgy7szOQFPsYuq0ljQ8TrS/UG4St7xoHwcg1a69IUz+leea2c+vNMBx806Hj1XqtWfPunJl5H/KuRwfg1g5AfRtC8GyW5rFpmsDoMLLNokeZHpxO4bxjZ1nppcMDtZvuYmOGFuOQdsgppRFh2qx9Q2dvb2/H48iqnecmrKPocZCoOidNFzZO0Yiz6GOKMEZz5ASRvEtjRDBJ6ayhNvCR4HzVxO1qdxgOMe6GeHN3vLs//hRfnp91H37jw3fOn13Rpu+7/VcT/fAv/tUPPhynSSH54CXOpMqIhiRJlMQJ+eA1JSNIonm9qBrk/hUmaNuGmVW1BoZs7guQMx5VKVM4mDQlKZLR7LErUD6slL/HhEWj7NzZ2UZ1fv3FF3Eeodkolr6tFqxDzJ3K+t20ik5at3pg4Woy9hMsahSropLMe8BCqRBWXNJFDYhlGy5iEYAo1IoIy+tWiFuAlAp2GZihIua5hZLGGebpijorW0BmAUio9DWoOSsd4ZwDU+E3rIJi1vlVb7tKWOfNWIW/znmmfX0iannJbOFTIwKYLWBnS7Zd/wpgpc2tsLj0YIXKuqZyMUAFKH34Ig2q/WaolE0dMSs1XKkmS3Ggde8pPIJyIsy5BhlqLs3AsllDLiSsCLRqFCm4XhxAC2dUIkFVEJRew/IbKqu9AbDOHCyFodXIUxZoAiWRIaXn19fbs/XLl6/4xcW6YTFDMHKkio4RsmcLmhnkPi0Q5AEB5DKAKSopSubLVVJKCZ0XTQBlMU5WPpOZJZW8sRZNs1V/rSiQQUXyckJJKRvEqyojOc8pRp3jNE6XXWei7AjZK4KlOKZoBL5p121HBN65qZ/AjClvloWA2IWWDMgTmTGjc0xikmZSREJmAlQENmQibbdnYBA8M5NHCg69I0kaEFMS13gw8RZZbO35vHHB48XV5jvPN55V7u/2r/Hpk4vNhy+YwaYB5h5cE9ZrDisgB0n6N7dBWuoCe5d3jbMKDPsxUXNx5jedTJEYU5qIqL1YpWNPfu0QIB6RPCinnqG9QOfBETvfNY1McRyH9my7iufzFMkHdD4En1Jx62NkEQEAESFHhuaICUxiIububAPT3mKf9m9tuGeYMaDzQdOITePbEJ5e4CwSB0027vYgRtTM/U6SOMREsl23q7abhnnfy91+iOOMCchs07mkGByK5L3f1ARolQ04qQ7jDKaasskBNMDGMMWpJfCd3zjauOZhSvt50iSv3x4e+o9+2r687M4v2/VB4vf241/+a7/VrbfjJGA67PZM5EOjCOSI0OXMGglN8nIbZGI1EiVKqqoSJSXNyzwliog6diJpjgIMQAgCktUOapk8N0NDFLEkymhmKoCurjkJjkHNhwDEv/5Xf/0ff3Z4+OKW2BkBMVtZ/Jd7gUhgsDR+M59CJUF9RBKUiFAq+9w1qEx95XFq+llRJ/fZrHhuYspDpLk5nAsXKMmnAtRtUdViv6TEqFWfagBWVtqDlFYjFv/UAlmF/bGaqQKaKxVERScoS4tres9ZJFPIi+K8l8uZryFgVdBb2ZK5MEqAJ3gtt7XkwifarISByg4tzdBS14AtlFYdhYBSaOXjl6EBqtqd/PaUGqe2D7B2Xxfch6xfWoRGtaObrwdyHAJAyRdWev1WhvOqJ8mjYqPItpbeAJbIsZQHi4qLS1QsEcsAYImxBCimeedGUkD2F0+351eXn331+h/d7uNv/sKv/8qHgdnAiBCQjAEB2ZMkEVFkxrxCHRA4U/TZuRfzeIajbFqgWBnS7PZVYlShxcAMJEUxy6tITAARRPKuCQUwwGJOy1ASFWaajsMRzPpRDIlD8EHQQsMu5RMAQiCyOM/sslwYUM17REDHLClClNCwI0JLZgBRzGd/InDEBuYDeyJHxGgK1jIBggcICOSdmYEkiBOztZ674C9X7vmT7XrdgM7PX5wR2/7VV3dfvW6mKQTrLrr1xcZAksR4eItNh81WldkFVPYQTCSlyKGFpNP9A2AQZrfuEFzcH2HswXfNmtnB3O/IrWSORMzOaW+SANfgkNB7RMYWW88i6pmVowvOuUCErmkQkZnRDIUAEFICBMdEiHlMz3vHmEwmiAeYHiz2RMYhGCQTI4/kAkhSJTBO8+ibJmzD+HDYPrm8vL6e+vH+7d1x3+93/TSbCm1WXQh+HNIQ4xwTGLCpiRITOwzgWqIkME5z0zWwJofuYb9XUQV1hAyakiJBsrQiEg9RWZ3n6Idxmsb+9rZvlMV0Sj6NI19cslAeEZ+TEpGIrbZry8ZBCM5xdhoEs5QNzTNgERmAiJhqtq0TgYzEaZq54bzI7ThN/TAye2TKZqWZnqQqqMlTxC7nsmAWBSTTFk4TRzVfBBiQzXy0LJ3MX1PUmuVXSrkU8VBkg2hg/DgkLBluxRksSJWxNKs1clpZNDyItR0IhXvPwQMXLEIseFIPrJUZyShmxUK0IEulVRCBMoZaleuUNgCQKz8FW/AXYOGvSyO2HihjejUOLRqdcpmZY1kkOJUbOUWLUmZAzdahaOAX9qaQbYb1XhQqCAyAaFnHmw9fXz5rk8oJa51xO3FBy7nUC8rqHa0+zAbLEF55P+s7hLV2KNvv6gHx1OmtkSN/TLVEddTi7YlVZp+dDg2ttuMBoKy2poXZqgJeVDNkyvIdMFCgw353ePv6vWfn73zv2be+824bHEMdkjFDQSAwUURkx2SQYmIjYxAVEy2nqMaOoGqomImQQPK+22UbFGFdVCNJ8lVKyrYnJqKFwyske3nXsnO8iaoKOwrONezmNNztbrPv6TBZtmJHhDhF5jw7ZCoWHLEnR8iNjzE54sY551hSAiABCOuGCX3jmHCekmfnGFtfQr0HSnHumsAmMif0rvU+EEPSbReuLlar1iEZpYeL7dN3P/zG4fZm97BfbTab800ITuYpDjYEMwMXggseRSHORKG97FzopuORGWSYiAgdsw9xmKU/Gho0jV+FtDua9RHVhzV7jf0NcWOjSRoxPEFNYKaKQJ4CAyJ7byRsumpC9hsWA8+MiMSMCOzMzLILDjOJiKZEACF47fepv3XpHmIPNnPjdTwImw+eN2uYRrMEKilFJHHsxuMByNrtatodxkMvUYioP4xvb48ueMBgDqZZAXDTNUlQNPWQZJa89y3FyMido3GaCQkona3d8dATO0QM3g/DHBVCF46TokMh3PVz13rfsAFOs3pCJDdO42ef/rzZXrjg5mnOApukRgSKqHNKkmCO7appvM+u7whgqMzI4EA1JUspAZpnBuSa0xp5hkwVMCESE6mJRQOgPPduKpQJDgJHRIggQmBIut2unHPr4H7+8vZhP3IIBJjUjDBli8ZcnSOU9Vcl1QTM/AQAQG7kmhmKWvaI5LIld0kkC2jgozx4yYBrjlyz3pITZm42twW1JJ1lxQhWuM5pbskstcK3ZW1L6VkgFGa+nH/mjqoqBQHJRF0ZtKt8VAlftsytntL/era184CVD8o/rIlsTdxPSX0RsT7quS6h0ZarL/GlBECtFc8SIfJMrhSpVakeSgJdyoKiey25vy0qn9ONN8DsZE4IsnA0UIazlndjqUby87VqjvIvy11BACOp+YSVc0UtBUAJIbaE1mJpC7W2qHsRMMvIDEutAIAgqkqmgArAzgXsJMXPfv7Jk7/+K5tV651HiBoRwMghghGTmUkRfSIzpZTyXQ4hzDEiQYqRHDNhEjFVyr2Woqs1qMJWATGFKMmyXRSCYyrRpmwAKSUYARCiWBF3mUTHFNoWAeLYf/Hy088//RQAJEZmdgwmyUy6tgnMaKJJfeuIkBnQYBznNvgQQi79fNMMw4gIXRs0icxJyYLjrnUMRUeuSZLpxbqL04hg7KgLPnjbhoYgEEZKh67Z/vJf+rBtwnDYDfevzy5Wzz+4ohDUUorj0B88q02jJAARJlJDnZXclKJ363Vo/Dz2jtn6ESg471FVNMLYkyl457bdeHOr41Hc3m02TNE0pvsj8Ro3idpowMCcAPz2gpqWHBGzUgtEpjDNyYfc5y59Iis2tOUPIgcfVJMOu/jwBsd7kJ3p5NcOCFSSazp0DHGylGQaTK1ZtSqUxims1o704c2ruy9f3b25H8Y5GbRn7QViP0RTQSNN8343GvJqu3bePb08Hye53z3k4c3giT0rOBFFk6ZlorYfxmxj4BmmYUQM27ZhM9/AyrvdmAYxFxo0CGTgw/3+/j//z/7+//5XfoOR+t2+XTXkvKq6ttEkapJiRMY4gSN2jIZKDrKuDQBMIUnKy0WmODkiYpdSEhVEFE2mGqM4pq5rhzGJgaimlGJKoOKY0DFTLhjVRIjAkwuNZ0eK6dPPvvz5xz8LL56GJjCgmFDJGZeZnvrVh5L/LU1hLQNRCyZVa7BHLdIl8835MdaoULUyS2J9YkUq+NCCQgUYczOhmnoZZPFhsfYCwDqYUw5pUPuNkJEHT4cBAFBAc5KHw/L0KWjRn1IhBEpKvuT1hSo6JdW5Xau1I2sA8Iimf3Q2pz92qgEe/RKzEtbQsvlfNn/KwSGz71BYLSzti8dkVIny9Z3KP3mE2iV817u+8E9lpAIBpEYJPMF8rVCs8DTLznc6BSBSMywGICWW1SPVcFklwDUi5RKkOOlZqSOx3ti8JgMEioWcpKgKSdWIv/29523gFIVQiQAQVcERIVPe25W09Cicd2goJpKUmVJUzy6JJJC8STyEUEyA1AC1bEI30VSknvgobD5uWOeGQpZG5X1EqmIKjKQpNeuuC+7Vq1f//J/8s08/+YS9C0wImiStmrZxwSGBChm4wN4RM6YYDWyzCtlmkpAlyX7f+8Y5pOE4EEDbeELwhIxC2SzVTJla79M0tZ7PN+1mtZrTEJgQZk+4XTeXW4+QfvYXH3Wtc2Yiur3arC42vmnaVUuEmkzUxkOvam3XJkIDc+2aXROPw6hf+bNz1wRUmd7eY2OeEQma0CCKzkMckVZNWLfT/Z5TFFUTMCObBuMxjQNfpDQmionOwJp2nsWvOySHTEhspl0XkOp2cStkQmYPs4wXAUAVdUr7t173Ot3F4c57xubS5nted+wI1eL+IU4ptI3bdPG4T+OUh8hif5yHGRxePD+/xoDEAI6dv7s9vH27P/Qz2j5wuzv2+4eDKt3wzvvQrb3zbppSP0yG4L1Hh2RoYuywbUOaJQA54M11Nyd9GGOLDEk23kx5Ok4xzkltmmTaT/M8ffzyc+db00jsfPD9caKtZ+dLB0mF2ZlYilEkqQgFBkMR0ew0ZcZEoqpJgDklmeeUokRNImmeU4qpado8o5hSzMZXuZurmte2KCipJk1z45znNrORivzi/Wto1wZeEc20eKeXFkDN6/I3GbHmqSeiJBM0jCgACEaQLTGqCrGiSClZKrNCp+Q0o1BxaMjccml8gi1TTxUlMl4UozozyBbuWdQPtZlcAAwrzmXIq5Gn0vOFjnY1lJ2+7TVglIteoKzCFNZ4YvWvp4uFJQxUDqfC6aKzqfB/ku7Y8o9BNvUvi0PhBJoncU75K5dMvtIslaSpIAsA1VbiEVGTXz6bemYOozYzlnfW6q6iJY3PWXLW/tsSJwFrbzcf5xTzSuJQxtvQzIoatyw2rUYRtdLCGody6QdI2Zq28EUEmqxdX6jTP/qjj3/hf/hXEGdGBgbO77eVT6qAGapjVytURDVCSyVMGpiJiIqKpJTyjRZcUAcsb36BjOqlUW21PbBcllnZAl0eCIjECAhpjKJi0MRou/6IwbVdQEiaUhM8GjhiR+gdeQLPqGoi0jiPaEzEjJJkmmdTbL1jpODdtvHZoL1xrCqYzFQJsW192wSRiLlfAHbY31+cb7qGVm33/ntPN9vm7vWXw34ETauLzTvvPfPrdpoOoWsMkBhJjbuWkDm0zGRjSvNspjLM0KBHJ8PASObYrzq/WR9e39k0ua4DEmBEE9mP1g8W2HnW8YhxJtciN/PhQewB3Epigu7ACAkgmXFzZh4xtIiOwKQkjpQlhbakACV9oRQjAaAkm45ODjjfxfs3nma32tr4oDIzM8zztHsQnV0XCHS63zHharNGS+P9/dz3q027vfyWa1uHLg7z+LC7ffsQh0nTrCluNoF8d6kXx346HCYxQ8NpnodhWm26J93FPMck6XgY5pgcYbfu1k1jAUApOoki3jlF3E+JRGCaO3RbT4c4mwEZCpgGtnQwnZpuM4+jqjnvQXUe59AFRjTnsi35NI2q6px3CIgootM0AyIB+4ZR0HvPjmySfn90gb1z3jERzgzTOKhCkrJlj5AMVQ1NTSG50nBCYmLnADTFmYNTxYur87NnVw+TBAEjUlOqvHLOILP4pNL2gHVkigAyu7rI08GAyBDRUBEIT9s+KhAbQDb7AjRQRDbQPCMAAEggVnDSSv93YWWocAZFk0laV0SWxyMsCx0L1pWUt0IT1nT8FMMQzdyjHvH//58yBPAof7dCVy3JdXFOWkiyzDbXFBoXousR4VUjC0JtX+TPPEFRMy18RG3OQqmDTmdP9bVKnwRKubEoh5b6AquCKpN3eS1B3hOQk+iKn6eKrb6jYNmnHAABqTRSysvp0omxWkjUL25me+oZ51qlzNtljgjAgNjUjKwyfDWolgKEECHnMMQMJILYj/Pv/eEP/63f+vVNgKSal0tyfphayt64WHZVpCiiCRGRKDg8xikXdqiQ0xxELB6CCpi9BdUs79UDSCKl01sET6dBibIjQaHuCMNsjyiaurONis3H4aMf/+Tt/Vt0YGrM0LQtGjSO2MCZtU0ATXFOTRMYiQmRICUZx4Gda0MwVXaOM+kXYwguO+565zwzmQXPhDgOkxF0zl2erzbBmyRiQbJnL55uN939/V236jZn7XrVgOo4zbzyzXotcXJdOx77pnHdqgUgFlMBWDVtG+I4aDLQmZ1jbGQ4mkEc+mZztrm+iscjm0Ic58PMTdN2bRwnGEdmSCYWIxnINDqAcTgCJ0uGiNQ7kURgyG56ML85426TYUVEuGwugqUKrBIDBTNik+kY779s4Tbdf9E0yp5lfEAAt2kRNPYHmYf2fANgNh8DeQCc98fhcIQkTduqITHLHA/7uzTG+TDe3dwdHnqZZ5M49OnN/VfrzRYguJat19c3N6rabtp+nmyeyQiJLi6exGmaY5ynFGM0I0esBs7BOCfHeLlpusBHj8coW/RG2B9TIosar8+248Ptf/P3/v7f+rf/J6Y2HOfVdqOih8PhzG2YIDQ+qZimzLrn2Q4kFlNmB2YxzjECGpAnADeLNisvAoiQF9rk9WFxjpA0r+JSQ8xdWTRmQkSR5AEdORJlREdkSRjsq9e3h8MeN1vJHbecO2tRPOZ2Mlb2JstMyugTgJX9ZflLDQiLoDOn87DsH8TyjzKSVLqnZFG15XmSFVVEM1tk87klXmoGwOLEC3kal1BEMk200B55Co4qMQVYs9eFeTIANJdtHhay6REzUz+UVd9S2qLlAVZZoJOdz5Ly5+Ll672EKsGp4XSRbsIp/GBle+qx6217zCBVlIdCspeUf+mtLqsjK5TXGiobwxpk+ogQi942v5FFWpQz8iV+WXGuWCyWyrmZUTV4y8mCVe0YqGKRDy35QraJOGlSS0K+7DQuLWUq4cEeRVxEVSEmNQBqPv701QzGzPlWa0qQ9dGmYMDIuWmlqkliJoUYOc0JwZAAktT7ioykee+iiJlh9ngHAzQVVZGiFkPAPHpjj4xetbi3EwITWXWBNtPQNjjLJz9/+clPftq27Fv2zKjqiRpmx9g5BBMEaLvgmJHITFNMqNqGAIjzPLdd65kJYY6RAFKcQY2ZQuMbx96zY5zHOftabNYrx74fJ5FZZXr3xZPxsP/os8/W66a73m4uttuLDYigqMyzD6FZ+ziNjAYyz7sHMLUEho6bYI1zq2Z86FM/mPNu06IxpJkUdOz92RY96TiSAiHGw95vL1znbU7xcNAUCTj1R4uCLnjvj/d33CSLI0xDuHZzsjTF1fMPrEdDhNBRCMVqQIuLYqkpK53qPaNN4+6N7r5SfU3DPbQkCXzwTCDDPs5C5NbPn8E8xOORiVSnOE3zcAR264sNuy6OQqTH+9142N++eXP36iECPb16Os3zJePxmJp1+OLVncyjMV9dXnWr9/fj8YtXb9Ne1m3rXQDCCLFrm4ZDUHKO0Wh/OExzQiRACkiHYUDEddsAGzlh7wOlZCj3Eym9ub37u//R3/nrv/03Lt55cvv6ra3yR0xjiojsWgdJJSYDBaVJEiNTcCF4Yp5T0piSSQhhPPYjQmi6tm3neR7HQZIhkmtcGxo0iLOkFJlyozivi0dEBFUGYjJn6B17JmZynpHg9Wc3d68OF+stENsiCFl0lgUWMpI84pyzpw2A0aK8yVBHWTeaAUWXFKokuGA1PbXSYsipVWH+MuTAgq1fg9VScJRf1lTYcgLOpYNoZUU8LrXB0jTOqF+0J6XDgC5juj5C+3qSpRdqNbXNjehFtFPoKihYq+WDW+H2BOzLXauM1OkX5UVP0GpgeZypNncLPFaSqPR6oTTgc/6+uPDke0NV/ZMvH+tpWWVgyulbbdnk45QMt0Q5q10NrmeiJcen/KvarwbDevfy0grArPSHXMZabUZDaQst1y7LyeReQm7YWFWAlQ8ImlnSRMjNekM+vfzq4fLDK5OUI/o0zYaKuHxuLakBgg9eROMcU4pzjJbpnyQypxr/TU2zvrp8dg1VNMsniMDE8mcZ8lZgs7zyYtk1QWaO8sZwJaLG+Wk8Tmata775vW90q3WLSIpg2jjXNuxACTQmbYIPnoNzhBhjUhHPSOTGcUoprVcrh5bmSRVUxTOhITs8X60MQUUUYZqVmZ3TNgRRef1qp3F+cr36xrc+OGvt+LB79uLi6mqDADKlrz7+Io3TdDist6uziy05AI3oaJhH5zwBaIopanu+CetN2G7CapOGGVE0zq7zzrcyzkBgSWgVzFTn2URMROdDUuvWK50pjqOAtmHVH/axt7DehKbt93tvIgKDMm6eY5xj8NSdS5xxde43Z9w0iHXiMtO/Vp3Bzcyi9Hd2+BKPr6T/Uofbzp2jDypqEfKYGAWn4zEedoSoChLn6XA0FXY0D0eTY1idz8fY3x8VaHt2Fdz5Ybf/4vMvjn0ShiR6/fQ6tOu7m8Mwzre3N4ZOEX7pOx/udsPr17ez9dyElGSYo3PkmPLm+M1mjcd+mqOIMruzTXsYp1lT0zTGKCCIkJSB3DirMz0eXn3/D/7pX/s3/qZfdUlV5qlbtWCqADKZxHkchqZpwECjREktAnZBNTqEmYjVRBSQhmF0PpgVswRJkxqoBU/OEBDUERooKfi8dQCNmE3JUmRE79AzUk7qGe8fDrfj+OS9d0QZAEWtUghYv9MnLClfsJqbVdi3k36mom8GnaKUwxPkZU64MgeLZnQ5ItSk8fFh84ENsuy60FOnbHJJwA1zd7hm8wgL11R5c1i22oLmHcXict5aCJzM9daiZUFrPeloytERSZdIVPUzWnPhfK71KFjFPjXNP93CMl9bVZ9QGIWM5HSSAVTGCGtnJI+G1rzeSlZboNkITnhdKIxHZQ2VeWnAk6IKl3BXom3VYuSZ7/zOW62FcLmpFUvrPQQgxLynbelAZEDPV2BgakR1yXUuDqBE+3w5VtohRFb3tmXxkge3Oj/74cef//L7TxvkaZoRLIkAmXMOCdOczMBUnedKJuX7oESUYSVZkfETSVVam5nmtZuahJjLPoO6GVxFcvC2bNhVClB0zoOppIgGzE5ENOqc+qsXF+88f7pu1lO/N2e+9cEjoiURUg2eCYwJU4oSUzabkqhR1DlabddMhGBJjQDaJgTnQsOENo4TA52drYnAkiQ1Rywi/aFfBd9s21Vw82736s2wXQdTnaMQ2u5mTwBdg2cvnq7abhoH3zSbF1fzMM1HhKjNZsWBEAAJNJocelqtmnaF7KjxBgq+hWjTcfDAqGjscMMmc2jXpoYxTod9ON9I7Ie72+MYfdsNNw+WjkYeEefDAXD06GVK8LAHAHf5vsYYiKD14Flz1IdSXptZviemYmnEeY/HNzDfxfu33aV3DaX+QKs1Ba9m1HiIMR57nWbqglqKcWw2q7ZrDGDYjaByvHtzvN3Nczp/+nx7dk6Iw8Oh7Zo///HH/XECpVef36823ZPry7c3d3e7Xd+PCE7n0bXNs+dPh3Hcj+Mq+9aBiOowimeeYzxbNd7xOMs4JwXoupZmvT8OzL51pEkNrXGW5vT8yfnL1zf/5//T//EXvvdL733nW7vdg0dIcUZwM6FjFjEm9MFPx5RSNLU0o04enYvzbJbMdOhHlRRCk+JsSRDAO0erVZxmkThNs4F6RyKiCuzyqlRUVdSkSQnUo++8Y0/ETI6JQhP8wxDbwKltDAQRRIWIMwtUEHGxBi54/oixg9LJg4XbgMJLlCSxMLoIWF3MYMF/KCnnwqA8ylYLx3Ta0lGIkLKWJme2GVtqSWAGRctTJDxYOr3VYOaRsiUDuCKAy/ZsurBWC3ddslY4qVpg+b/K3xjkXlaNdFYg9DF1YzWMLXRQvfIFHwFO5UrtR55udlFWVf4qUy75RldZPWXKFAgN8h77Aqv1lpcoipY99tgKz7TUWDXIYo3SVjf5ar0NcGLY8NGFlfe0UEkm9SYAgJnWyLGUKGilCWw1uJbLshIqAMA0VzaYdwmgoDikCLbr0z/8J3/4b/7Gd6/WlNsMzpWCAwXVDCQ3vqTwCNktUYRt+cBk+wcREWbOq50J0SEb5OTTcngrKghVw7IN1RSZ0aEzFQRgxCSKAAzIKTJCF5xN8/3Nm/3xVuaDI/HByxwjYIzWOGxb1zXBMwEYIYTAYiBRIkATfNM4Boiqc0wm0q07T5xjZrIUvGOmfhjmMeYN22dna5V4cbbertdNSw3LcbfXcfjOd95/crHdHw5fffqFR9CYthfN+bPrhLp6etFtW5UJUcBknic8CDWOXWBPMicHPo4jdwGiOcw7XnrfrTvfqSQwFRG/uWzQQ5plluDa+eFWpujalXfDeOwdB98203Hg4IwMJM3D4Ma1iYzjDQdSU3/+DLow3wmLunZNTQdYzGwM8qYgQUyO4rj76vDZRyu+T8c9XlzE3YMoOk/EwEygAmI+OOGVoZmmZhUAOc6zGqgmQvbOnz+99KvOAO4++2R/u1djR+5v/Gv/8g/+5Gf/4g9+Eq0359pVc3F19u1vfXO/39/e7frj6KMozEaIBne7/Xa9JkfkQCIO0+wAdiJtF7rWg+G+nwxTEvOGwzCR43VgAWKL3Lp+mp52zWoefv+f/MNnH7zbtGEe5845Zk6SoqolsWK7kFczmko67vdN26GjJvg5JY5iCc0MDaZpQjNmVhAVTXHuD31ompzQMWJwPE8x72MGU0b0xGyqIs67ZtUhOWU+7sc/+8kX9wcOV548aB7HK2lyFbhklK4gWOj+KmnRmjs/aj7WTLeCS22fVaKDKuaf4gNUegCgthRLAlfQIxNKsBDIUjmiCmVQUL70BtDyjsNSTRTyBjP3ocuj0RXIrhKZhb9eYpo9opEK5bNQ1wWwFqU91rmFfKcMDDLEFEkjfC0tr8XVMplbQ2u9hVpfu7holjw03+zCp6mVWqr22cvWgBoAlisBqD0GM8v+DlhnPU6EUQ1EWGi8mvbXIFYPibU0W942s3KPCfJ+dCvrHWu0yynewnaVCkShsk0GVmg+Wt5XNQUFJUxobArgH5L+i5+8/Dd/7VvM3PfDqvP5WCmbZzGSmCQAMHbITGmO2TlFoyASlX2tpdubRyXLEgzTDPeanYOKGiF3OwwE2OWBL0FRQtKobACE83Eg52Kc21U7x/iDH/7wH/xXf3+e5i44MCFWMFy1PjCsGg+mKqAaU0xxjkTExG0TfPCisj9OYOYcrdomMJPj4LxommYbpjF454jaNiDA+XYDYEJ2/uTs+PDw5ec3q4auL9ff/N632s32i5dv3r6+74f+6Xn39Nnl+dOzaZwunl1tL1uRYeoP86FPKYaOEcEkATB477dB5pmYGVwmqQkxays0eI15aYhJNLe9koc7oRlRqW3jPPvQus2WZ5mnGRhMZU4TEx/2Axke796CO4hw/yW1SUyTinTP3pWdkQkiYrvKu7/zF06mkSnOD6+n15/Em6/sTJqzAIQm0ZIStugcWLIosZ+5ddyuCCTNQ+yjxRjaNYASp2yXqUjJhIBc27SrNA5TmqY//v0/HWd+/913/vRHnyhRf0wq1nWDb9zV1UUT+ruHnTObwbqmVQu3d7vVqnGBzterhn3f90M/pyk5z23wtGomETXgSZzzt/dHF4IpbJyjMTWi3fm6N/nT3/vdv/U//dvd+ZpUgXAaJ+/DnCKoOiaJkuJMaFFlHpNpkijd2QYZGZkJuQ2oEFOK8wyWwMBUk6ghekcACa3I8zTNjAiWHJKqMLFDIwPPHIInR2PS/hj/9JM3OwXarAwFiDUJey4br6h+a2tfQDMTDFD6jICIWKSbGXNL8g6V9KiIlmnypXZYiOzymIIkhF9TkVMBlXKYSjFgpqeLb0DpE5/S6ULhYLWMznzMQrIYlBoBAQ3EwGWKoy4KK2vGiq6/9gBOAQ3r2eS6IXcua8u6CNog6wat9sSXYFiuY7nkem1l18rCxsBi8XOKm1BurJWooqXGAsNCdlR7nRMin867frGgAq2CYt7HtbwnuSlSH5QhfVE81RgOS6heiKWlkihckRlkHEcQK7k9LqeDuGikCuNb7AXLK9dCYtGckWH2FYEJhQWnQf6j/+d//cvv/K/evV41rRfVOE7k3TxHVWsbbwCgaqJxlny7Caku1svnZWoV2swwa9DM8ukCIFbhkoEgGKKCgs9z7qqgRgBUVAkqMaIIe9e2jXPw+euv/qu/95/+6Q/+JID3gQEUGUPwjjA4UlAHGDPjIxpC6IJnh6o6DIOKBs+rNjjvcohYBRfHMZowYte0q67VJKJxu1mbyuHYK+r41STjcH2x3Wy7Dz949uLp5U/+/OM3n78liy/eu7o4ay7fOWs7T4GQpjcffzX2B3LoPIWubTdnvgs2zXHfyzgqKAevMo+jNOs1OAQAElSLkIS6FtUBCTpQS+q9mKCBPzuDYQJTCh6JbE6ri2fTIco4QHDE2N/cr6+v93c3IXTi+PDV3IGOu123btTPSoAcyAdDVkRQJSBmtLmXh1fx7aesvU5qgef+SEzNxRk3Qfq9pAjE3G65a+I4aFJsV14ZnBFomsd52CMjkPdt55s2xbQ+P7v64JvTm4cvfv5ZoHB7uJsi/Opf+taXXx2i6jjGGNV5IqbNuvF0+erVG2Kf5nnbNZKsPw48uakfz9arzjd9HNIkKDAIeM/ekQK07MY5rZ+e7Y8jqHQNOqJvfvjs2YfPb5P/0Re7n/3Z97/9a7/l23B4OK7aZjz26InQREVnUxJG9EaxH6dxlCkiGPsAjrnsDwft52F/VItMiIAK1rQNeZK0rJAE5z1lElyVOReS5px3zqnaMEZebaBxv/OHfz6xNV2T90cyoybB7GeDmKlOqHkf1pQyf1VpIUissh6ZGsEF2064/AiQ6i9q2b9Q4DnhKpj4yGO6pouP7BygwlJlS0rTuZ5epcpzllw2meAjCMdsdKbgVDWvd4cFDAsAZZDSBZaX+JOPv5QlBifaBOoJneCsjDMvMpvHdwLtEQ2PCzGydMcX0gQf9Y8BylYZRMjpag5BWG7DaRoi34Vyo5Y3yKzk2EshlEOa1khQ8LgsA8vvbuGpqGzkrDVK4dPKyWrpeOSzqoSfLQ82WK7Gymcnnx1i3dBglV7K5nL5kkQFgRDJBcewluP8409ebdv3mcTSHAiHfc/Boco8GFEmxDSb/Hh2M+ROuRFTvm3EiGQaE1SOn5FQDUyWgYtlARuaMROYEuapX5OUYhICbBpvJsBIltDw05++/Mf/8L/++NOfpSl1a08kKSXHjXOOGXzAOM0iFpN4x+uuaxpvKnOcp5g8u9W6dewcU95o772PIqrWeIdEnQ8xRUfcNDxNceynlObVur1Yte3FZrNt3n12DSg/+cln/WG4er69PFttN83zD67X2/Zw/7ppm7i/S0O/Wnvyvjlbt+1q7JPOQEjOO5lnz4jMs2i7WbmzLZhOh53E6L1j34KaovNn25RiipNvGgEFVWMiDDD2zfkzm2Q3vN7fvmm262mcx+MRHTvfSIze0e7+fjUnaIfQrSZ4GC6e8PlTEfNKSMzdppTLgAiKMI+HL9P9F+tLJw8PfLZ1SNh5ZKdRTNGt19S2Js187EGQ2xWyCascDsMwzkOfNHVPrkK3DcFLUjRSglc///z1Z69uvrgxwsuri/3Ht0fpL8+bm/3QDybJAGh3u3+4Pb777pNvfOPdTz97RSZo9vzJWb9uDvvBgCypb/nd50/2+34a5nmcAdx20wHRMMyNB0RYIV2sw+XWbYN/etXujm/DAN0s/9n/7f/y7/8fvv30G+9bTINKSvPabYgZEVOKBAwGzMhEZjrOo9zJerMm70QVAM10nmaChJCbJQBmmJKJOkdauAVCTUyO8/xfTApAotw2HJwBqjFQePn64dObB+7OXUODmqgUop7IqsTfTvx0Rjh8RBPYiYJfkty8taBCXakUTnRQTRAfPaU+BKsKxSqP/YgtKQljnfWt+beWdLwQEo/JmwUBofLlpstOXkNAMkUDZ4YFzgsDj5mwKUr2wmCcAtpyJcW/oJIVJbDUp+QRxpKSI9ZbWRsWCCesg8qOFAqn5NdLGWWnpWtlCa7k9crl6MtF1jjy6NIL4C4IXKLnSfqa72MtTE5Ynfu0i3mb1QhVufpCQ9WaoJ5ojpSWSfx84aallVtvU+WUio6oBOuaO+RfnXwp8glQtmXNb/n22fXv/OEf/8p3Xjw/66ZBiICcj3G2pIjUNMFSinE2IjNTTQamKpxXaEvK416gqiLLSxmoqBTJHCJzptfMypLrnJSaAagmMyEUByizQEpt4z3B8eHu+//0v/3H//V/kcCvN53zZCbeESPIPCfCw5yyfDl4v26bJrgU5ThMgBqc69rWTIdpNjVQW3VtiinTatS2qy4M/USACiKTahJCvLg4Y8b9cZyja9rw6adfnl2c394d+of99dVmfXFx9fTi4snFq5c/Q07NTPdv73zryDfeE4H0d7eaKMms02zIzaYZhzHtjxTYN20aRupa163QDSIDOAYD8s48QVTHngPjmAxEFF3gOCYVcW0Xuqa/2XNokFEHg6Rq2B9GJexW7e7+vt3G/RuelEO3CsO+vXjfFIx96zy2rYo6ZpOo44Me3+h4B+yyp1Cc53bdsguAaGlSQaQWzftg8zDGMTUhqFq/32uydnMWkX2zQYPxOIER+ibOevb8/ee/9Gu3H3/x8x/+MMb5/FI/fvlmjLNvmob55q4PXXOxvbrf7/78oy8vLlYG/uZ+b/fD+fn2/GrbNuHu9mF3GPb7Q7vun5yducCo0E/TfrcLbfv8yfn5xWae+7h/WDv5xnvbF8/P2NGXn91YHNzFdvj8zac//sHT956jw74fVqsuzrOqC21DhPMwZvMxQ3XOTcOYLEqM7B2I5NVFIMl7l13+VVJm+QmUAZjRkqmKc9kkTjWP+6j5xrmGokRs2ma9mtj97vd/8PkXd6v3NjFN0QEHZyq88Ni1Hj/B2ymle5ROlky8sDAIILq4kVUcqs+2Eyw9OmDeJFUc2hdCn06YiwZASyQwPQUmLYcrWAGPuAaApbkASzDKHLiClf23Zi6neWrlIh75PBeYLg3t3EIo2aktdyEftZyk1W3DizQmXzSeOO4FiO1UDlidUQN4/KiFtDHTBRprR9tOFUGpeyxDGS5HwYqeS2FTQkOZvF0m0CrZVdJ1AAAUtRrPTuRdJegM68eglBzLNZQ3scTUGm7qS5azRHt0k0+1zuOi7RQXsa5mx0rM89Gar47j3/mdP/jbf+MvX6/aGMc4J+dA0ThgShMjQgZ9JlVBQFMVNEsiMRmBphRNRdQ7BsKUBMxoUT2AMWFejSpmhACmEoUQk4rGmTg3ekzGCU3J4TxOP/vJDz/77Gebs3acdewnBa9svvGeAUzVUOZ5s+7axneN98RTTOM8O89t14Hp/tAjQPDeeweIx/7oA3virmvaxh92AwEAUdMF78jUkFhUxqHfrtcX59u3t3ed593+q/54eOfp5W/+1d+8vF7ff/nys4/e6jxevH8Gabx4erk630z9QKDj3ZCmKU9P+xB817puFce4erZJfS9xdiOZCW9W3Lo4jQjKTQAA9MG8GSB4127W42HnGECiaTIg1zXkgwCkOZqCJE2iKWV/DYTgnPfDMALvlP3ui59vAYkbibEFwrbzaOwasDTt7uzwdnx4DXGCBMCoMREhqMh4MHAUGnQBjUxUkmkS55zOCWJMmlIyVmLfHt7cjf1hOEyTRpF0cXn+4ju/MM4pXFw9/953f/jPfl8Y2s4PY1SgpvHPn1999sXtob8NXQDX7of0/ourp8+u9g/H/bG/e3vLSJeX61XX7fb7OMvd7rDZtAbWrRsSThJ392/n/u7pmX/v2r//4YvzJ0Hn+e5mfPv5zf1d+uRmujumv/ef/t1f+1f+B9vz85giMUmKYz+64JkcsxummUARiBy7QAQUU5LDEQlijKCAiOQIs0GVJkY0UHZZTZSQgCkvHJcsbnGE5J33BGaK5hgV+KOfv/4n3//j7ukTyDuNTUAF1IwyMOqSvi8YggW0SjN1ISRqgnwawio4+ZgmeJSkLhl17ZzmrLAoKAsfYEU4hJA514pgtiTcNXgsh665Lzw69gKmmcjIqksgkPJKkM3gyrKX2jdAABA9Hbe0BGr8KutyT2GwTCA/bn0UYmypKuB0OpUqX+Ada/sElk74kvvnYJlb4UinxjpUaNUlwJbQ+TXhPzyKtqfw8/iV6g1aQtDC+dTiomI6Pr6bea8Alj2gUN/JE9mz3KG8WNrKRmko7h3Ly5pZqTfrs6zqweuRzSBvpGNCFtb9PI7j9E/+5Kedp3/nt35t7QEAJCkjOcIpSmnkxBkUGZ2hkBlidQkvXJcxAoCqWLUCNVXN24TybVRJYKAKprG4c1gCFeeIGSQmi2PXBpiHrz7/6Q++/0+/evmFmbWOXd6pggBRkiQfvGdab86CY1Axs+M4zlMiYmLuh0mmiM6db1cotut7MGs8t41fda2Z3t0fz7crmRMSmWpKNk4xb733wYU2jP0Q2ubhsIM0n5133/3VbxHHP/6933/v+ZUjXF+s281muhvAheEwoMowDWlKSRKkvjlf88aF1sU4h9XaAMbjGESRVIe9Dveu61y3sjnJOBgF0B5Cq0nkMHhPYbXRcQBQ8o4SMHGzXrO/n/aH9fl22B/jNAMzIkqUaZriNG0uL2/fvGnWaxVTNW5W6Xikpl09eyGxJSIEYovj/jY+fMUk/Zsbv25N1JybhtEBISOiyTR5bECdCx40gAiklI4D+8YY2fmhHx4eDo7QdxtHBoGZ6c2XX63OZbu+On9y9u1f+oXv/+4fXDxZ++BvHvrjUQT4+dPLw3GcVZE5Cfz8s5u2pbYJ3Xp16PfoaOiHVdu1VxeHfp7m6bDr2dFoRio2T+uGrs/OPny6/sYvXAcPfd/HQecBqNmE1XSm4YPr1cvx7vf+y//Pb/9b/7OuW3/15avLiw0RW0pioKZMgIAiyRG4rosxphgRjRCRIHvAxSiEkJIwATEzEyOqJlMlQkB1zGgoKgjI3jG74L3m3ha7UfCf/eCjkV04vx5mMzAQyeiuUjuQAIj161255QIVS0cQF945p9hLjbD4pxWOFwCqeUpO/giWBwGYZU93KkpgrXRJaTPXtiosxcQjrh9rcv0I/ZcgtqST9Sq+Pltg4OQE4VB5mYWALmdXlgSYZRPlAt1lLy4s5c+p9FiiYj3lJd8vyGZL2v41UH6Mx1b7oZYlOzU41lw7v4QBlB7D14JQve7So6/V3KkZYKeCqRZxywk84nqgtETt9J7ne5Tt6uBUCdTj5RBaB+uKfqqc0iLOfzThvXySoAqEqlJoqQjKILqB5QVHSOTa1nX8j//4xzoe/vb/6LfbbrW/u113TX9/VDRkYMp/UC2hgsQZidWUCWOc81ANI1s2hBNVUyREQNHEyKigYKhJVImJAdVEkjjPRBYINc4Qp86xxPGTjz76/d/73R//6EfDcfQeCdQ5VEQkYhI2Q0LnOCVJ87RqwzxFSRraoGLzNCNi14W2aUW070efFwWAqdhud0wpbTcrAwvBrderFOd+GNs2EJGqAMLhMKZ5Oh6PwHJ1tvrWd77l0P/g9/7onffOJY5G8vSdd4ab+7kfQyukaR6ncRq6bdMG14RLv11zEzShbxpug8b5/Nlz0+g8TP0YGGwYDYhcG/tefYQEYX1GjmAe07hHSDZPKsIhUHD6sO+evbu63T8cb2UUpsCsUdXIGQkATsNMbd90fhp7x268eY3vj3M/Nee3Mu6Em2bVaYoSj6m/dzbL1IcQgg9EjIaWBBE4OJmjW23AUjz2BGBMKGnYPUgSVO/R33/1MM7qeOuadjg+TPMwQ1ptu6Zd9bvjtrskxcbTt7/3/udf3E7TtF25wyEdhng/jMg+p8LkeBzmdIQ5ivMuRtgfj61zB5c2Fytu3arh+TDopOvGsQfvw4q0RfFJ5vuDNZSGePd6/PzT3S5yVDBnQab5dvi7/4//+PY4/M2//b9cd61jRjCLKcoUo7jGAxAZoQAZUOMSDimJmjEiMaooMRBQEzy50mOUvLaBSA2ZXNa5M2ZGnx0HQopx8ut1t7346NXDH/75zyycAZhfu5QEmQTUIeICexm8FvGmVSqjistxsfBakLzCgC3IdiIPzBasyexCIbFLQYCYNxRC0VXCAhSPwfXUMc0gZlh3yEOhk+lRq1czzljdX1mTYS3bAwBMXa4ptCJ/AWtEq90NIrITXirUbTSF4K8S0fyqy6Xb6TQyEudMHetEgNXWMNSIVG5zDprlfOoJ6fJ+lHdDKW/LrLibeYqlRllIOzOlR9ESMpll1S4iR5jyl3Ku+PVwZiXTN6svjrV+sMr1LVRPrQCw3M56H/JRYJn5fPRmWuV+bCG9oIrNls9huVg0AnZkaEOcU0w4xd//4UfvPT//1W9/42K9ZgeEXZwGM5uGOS/kkhgtGQGqiPdORB2wEmDWkhmoaVYHmZmqIKFpkqhqIKAEQKoyC5o03jMQEJGlNParJsTD8dO/+NE//+9+92cffzIcj9654EDNRlFDUlUw84G9c2ioMXXrhhmjqAEMxzEm7VpPzID0sD+oEKO54JixbTjFJGKb1UoNxmFehfDm7d00DtvtKknEZN161fnw9mGfzMjRe+89e3617Zy+/MlHnrFtXJwe3v/uh1N///rTzy7fPWdWQ0C082dP2nWrk6hKmjX2vSMfdYAH9KFRGIlw1shMoqKThGaTxonYg4CoyDiTD6ICADpHGXsDAfCubeck6e0b80ze9fvD2I+ATmQejoPrvIqF9WYcZmJdbbo4Tyy8f/Mpri9oOsCwh+5c59kRoiQGjfMI09Q8v0r9iEHyqlpTTePk1xsjs6l37E1EY1JNCNa23TTKzZd386xg4fZh98Xbjx7udwnmJ8/PvvGtd995fn64n19/8fLFu++cn5/dvr3pPIxs4lAYUoOYeEgJAZOCc3hxtjHVfT+N89B1oeU1qMyGdw89OWwa//47V3GYCdJZg2RTY/O6wcvr9brrhn7oH+LD/XSI8GY3HcQeZL47qiZKNv2H/8F/uO8P/+N/539xcXYZZ206Gg/KhAIW54QAKaYMD+QcShJJKASIzHm+FzFvTTIDETTzLq8HIJ/3BBA5RBMIjfNtAyDtZktNt9v3//nv/KOXb/f+vffmcQIzQWA2NgZdxDy101uS5lNemPt/aHVseFFPYiVSlnTWCinwKLOFvHkPH8ssK3IVMC38TiF6MpxVzimDV8HhysxTwSLLyFHoIa2EAxmAgWIBrVI3WFll6apJ/SnfXxSWWJPYR+w9LP+fRZJF5Jj97OqtWvrGWhWQC5eSy5slaa6dhVMJU/9V3Y8oU2ElvT7Je2qsqPGh/n82wagYfYLR2oWwWncsyF6vCxZOzh79LFt7P37BiuhQOzFQ65LFQsKsNvWXmES1qKi3t/y6LJewsuY0B3yrL4/LB8jAVAhJRNBR8L5brTfPnuD+/j/+e//tb/3Kt/7df/2vv/P0LKqJEtjEKIiQptEMTBIwEKKJUN4mDaDJrJCiCgiqmhtBqobFTxFkTtm4MTjH5NmRTpOZTod959mD3rx9+8M/+P5f/PmfJxHHEByoxuNkhh7EiMB73zY+eHYAoXEK6djHeSrmE5t1pyoqcrc/OuZ151brNsuvp5gAoFs1wzip6Kpp+jkSwvn59jgcrMd1143H8Wa4MzVje/fF88uzdX8cbj776tn19urZGpy9+847w/3d608/v3xx/vS96/Hu/rifnMOmcXE/HG4eussz1mZ3t3dNAw7JhVlnjXGzumDfzWOfhknBFIJvVi50yFGmqGPPPpBrxsMNaUJEmyZJqR/FB2YSRziNvQCllFKKYkjIcUjMXhWYm+PDTbfZqIrKePf5p+ff4OPda3z76uLpe6ZKjtM8Dod9igmNh+NkU+IVOOcADJnZBWqCTRO5BsjMsQ5R04yAUz+MgyaTKcbXr14/3PeKdPXk7Pxqe3F9tt62+4ehP/R3r9589sMfvXj/abcKoaGz89X2jN7YoX84mgk7AtEpyhAtBli3zXvPrm/2+2meDaw/Dnk2tls1YPLZq3jW+HXLYHTmOYTm6mqNpnevb+9e3z8c0+3B7nt9+foYm+Z2gCgA3p1T+0C7v/ef/L/6/fjv/W//PdTtweNq7Vsf+jSBifM+RT3u7jaXl2DkQiDmnHORKeXZbTRAUkkIwJ4RQUWdYypoQuQwtA0aosk4Tn7VJm7/xR/9xe/+6U/a63dmBV51UWZCzirJnJVnwWRWsjyCvjoWlj2AsK6W0gVMMqP7COtLF/VRSZG/8mYEJgvyLHqWr3EUmTV67DNhmVsqrEFBcrCi7a6QYblhXKiESjFkq/H6nGpzAIAuw46q1qmCmv0WoqUa7WBZHFLPszo1IBYvS12EK19DsSVoPE7RT3zKArrLY61Q3+Vc66QzlhqjkuOFoz+h+QnJH6mSKr8F+Og1DEtoqs8FeNTAeXQeAACmikRaq7dTaVPjNWarDKw0W3kE1eKgdEGSap3oe1SqnLijZci4EGr1BmEVaOXiyzIPOk/TNPRv+oMXpQO/enX4Z3/647/2a9+9WHfBe4DsCaRmkldk58K4nI0KmCGqJq3phqFVe1lN7BgBVTQ45wAJ2dTYzJkdh9EHDMw+uIe3n//gD37vpx/9hDBtNp1JmqcpqfomKLIkRTDvKO81BIBxnkVEY0LiJoQmcExpGCZEXHfBB+cdxzQzkQkkEQMYxymEsN10ZnbcHwlg6Pvtprm4OgODwzAQ0/Zs9cG33j/sdi8//xLT5M36ka6fv2+pv7u5i/30/Bsvzp+dvfropx5Ds14z6XS/G48zBzf00/1Xb6do2EbfhZvbL9uzddu0Dw96/uQKjAmDaJweDrYl0Vt/fo6aaDomUEXwBHnHiM5zGoZmtUkK5Bog572fD6Nz/ng4snPO+f3tsD0L85zCqlVjnU0sURKYBpn28/FgEk2TzGMCZ2AATOzHIbJLrCazaFKJyXXJWG0cLEWVZMBAnj0ht7uvXvV9UrUk8ebN7WF/PH9y6X3YnG9cQOdIYgJ07OzJsy0M7eH2/oNf/QUX/A/+4IeH3ShomxUD6G5MofGKaFHu7o9xk3bHftUGYiKzF88uxn7MZh46CocwjAMM7Ff+3Xe3V1ertsV+Pz28vuv7KK4hZ+zl+vrsZp8uGr4b0qipnx6er8JDTP/0H/x/X3350dXFVTj/4Nd/7dc+/PDF9TsvyPshRcdutT3La+3AEToyhSZ4nZOk2DaemFKMjom9y8skVMUxMeX5PnDei2jjwzzMpoRd+9Xx+Hd/959PzRabCxEBLaP3+VtqxcK9iEmXor6U6cu3EgwMkhWr4MIQ5/mePA1EWWdeSwA8Zaj2OGssmbJ97bgAWUaEiEWgX9WdOUcsqf4jCuQR7MIj9ASoWqKSqNMCywaGeRKoTgKXdNfKKVYQslqG1H7oqRoqGW0laswUlqomnyucBmfLaxb27LFWZ2kOVMSuk1tQCbDT8MVyMrX3bY/+/RjiH90WK6iNpzfQlvLg1L2F05sN9XTKfa7PLWTdo7qv8j+A5daU5+dEH7LZp5UmNlXOJ6+nr9dntXdRglcVHdc7UPMDAlQGtNrmAo0RmbpV98vf+sYH717/8Y/+dD7s/tV/+dcvVg07mvfiPU5JibKrEDgOqlFizMFGEQBkSaPymAkQmLq6R1oRGQlBse38uN/F47RqG2aIMn/18rMf/Ivf/eGf/WCe+y4EiYmZnGvI2UwmQ5rmebPqgiMRjdMMZh6BGZomz3nZMMwpiXMewZidY04ikiTGBICB2XsX2iZPAwzDdLZZI5qZOOK+n3YPe/Z0eXnerrpPfvrxw8Ne5+nJxfo3/+pvfvO7z1/+yR/fvPr83fcvnj27BJO7T1+uVxszOj7cpVlUGcm33eZw/0DN6sn1k82zF7dv3+o+PQz68st7x/xLzXbbtklG17bj4Q5DSFHAWDmAHklnQrA0cmhFIgE0nnme5ykCJ+ewWXXjYQLRrmnGaKtu7f00DtEFZym1qy6lGJiQEC3G/b67SOPuXuPYbL2pknNoIFHGfvB+Y0SSLE0RMVmUNO2sa1wI8XhEcuga17Q2z0mEcn+J+exi1Xbr0K2QHbKFlpEwTsP6fH357Emz7RyyHMc3X33VHx8ury/m4ebwsOu6DomUpkM/rbqVZ2bimGK37jxTcDwN0zxN59uGwIajaNIWZLNpzs/abQvvfXD59OnGOT7uDs3KTaOMs57NFI7xGpqf/fz2Zh/VaOqjSUqiGw63w/Gnf/rDl0SJ6Y//u//i3fc/+F//7/43F8++uTpfxWkC5wEg9r0Dp2DMzMzG5sAMMCVFZBcYAVKKzERMzvnMTjSrFolVdDgM3Wrrus3tmP7v/+XvfHkY6eLdyGSIKokLyDxiKzJXDScmGYsGpPg8V9BAyIQ+ZhM0qt/lOhm8rCiHgqkKxfR+wbev4UlF1qVBuhQWj/AdzKrnWmGQHrvnnLhsO8FIhrYayAq85UIEnGlu8VKlTU7wafVkan+6ylcq1mLlsKDKZuDrST0uafDyvBrxstEkAVnF+VLU2elGQKVB4GtHrOXE8tYgPT51W/5bzrPWUPjoDLBsFDjJ72Hhd4osqR4TAeC0MbJMYtdXw/JRsFMDIH9+yr1b2tyPWbX8cyqzx1ZnmuH02Pq+ld8jGCARIDKRqRk5Pn/2fNutW4jr4BAEiH7y889lHn/7N37xnafXofUqiYmYYLZEqKoRTYHMkjCzD2E0ARUEVEtM5FxISYwBzOKcTJU9+xDiNM0yAIszdU4O93ef/eyjP/y93/v05z8b+6P3LgScR4kxGZMCxFmOw8COM8oTWBt81wUGQ1MAHMcZABvvQtPk/RvseBxjjAkRmLjrmkCUTEQ0xlFE2qbZHw7Bu4uzTdv646G/eHLunRvnOWmvSS+229/4lX/l9uGLl5/+9PNPf7hy+hu/9WukYxqOYPNme5b6+fb+HpF8aLDptk+eAFiruHnybHX+1Lij9cXZN3/xZz/84adffDyNw/nlwT1f6wxrT67ZyKxReokxbC/AOR+CWRr2h2ZlMgyQZvbOZgGJBl5NCa1bNfvbwZSZfJoRjQGp67r7u9vVukWwaZpCG8bdsaMwXR1cmkySSiQkNYtJ+91A7JEImTUli+a3QdOsUyRP4pA8Drtjs3ZKaeqHeZiikvPctO3+7mAmq81KEYjMOZQ0ucajA3AOAMZxTAKbi+tmddGuX039dDzEGEXVPLrO4Rhjy251tjqOUVSmqAy0XjdEgBY3q/D8Ys2S1o3bdhzYHM3Tfn877wAgTgnYxUnnWUdBJn7z9k41Bg9OrGMe5lnBjVF8YB+cNzhrKU0Pxy/mv/93/oN2/ey3/42/+c43vrO5uEo6O14ZKKgBOZuTJSXLnISRI2bnvXcmjsgAnHOYFxupqhqTD9vtYcIvX97/v//hv/j9Tz/TyxfarKZ+dg0XyhgMjTIvkOn5BbOsSh5rvY5qmtd1ZG95ZDKwvAoMa1a4cPhQyX4s5QAYQF59uKCXQeZnMn4/xpUiEK1BaAE+LHBbfrokwLZAcTndnE6WWd1y4CwrAi2Pd5WaOcHnqW7BGoygsPUZrLAw0wW+yo+WfBipCp8K3mVepJyNGRSTVTjJQxf8rRhoy80p92l5zPJ4rO2HOmVVflTu46Phr1JFLH2Iwvzj45BXLqaGghysl8qsfh7qJVq5rrIp7GTgf3rREhGQyBZn18pNadnTA5WkK69qy/k+ml8r/y52GQKmzqN3wQHAuD/sjjc3r20UlvSrv/jBV7f3v/8Hf/bXfutfenp9zogmqjI5h6YAGk3VTLxnMANLjoCcB1WD7M0pxJ7ZAYDEBJJAKU2HQI4dxQSa9NVnn/7FD3/40Q//5Kc//sg7dIyg2veiauyYiaYpTqOQ4za0RKwAbeND4wxMTUwhxtgE3zRBUhrHmRgdU1JlR0YuG2MywTTHWVL+AIbgxmnadO1mvRaJDw97JjaDKc7MYGCiEoL7k7/4E5mG50+6X/vlb55tud+9DuR8cF3X9bthf7Pr4/Tkvfe2188uP/gQmG5//smm3W6evZMmOO7ePLzd3YxC3fq9b37jk08+++gnn+/uDt988bRtkdomxQFsVrBp3K/PX4zDmMb74DkddvPhIEPvHGXbJLOBudGkCDzNMcbYbs8tGaiJpaZlsDSN42rbpFkdGxhCSjAMwXNRPyA1qzOgELpWxibNkZqyukjmOclIzsVxoJRc27bdBoEJ3Ob8LA/5NWH1848+Oe771dlFs1mDpZhmJjPitu18aJD8fBiH/fF4t2s3m261vvrwveNuvLvvb1/t0ySXZ2dHkjT12WD6fNXc7PazaBIYJV504WrVIgjE4bz151u+Oms6pqZdD/1dOk5mpkbBAwhG05XzGhPPQyAYiLsuHCStHI8ACqpIgJ4cosWz4JLZq59+OqeXn/zoJ7/x23/1t//mvx26FXsfGm+szpGIhNYRAHkPBYHJOfKZ0yCwGPvD/thPk2gyl3zDZ9vf+8HL7//084/f9vP2WaQVzIreAVn2NC7e7WZU9n8teHxKGRfGpjQISq7/iK5YEALpsaN/AT+sEvYCQwtBUeMMwEkK+OhPRrhCBBf4/BrR8bUH19Tcln8v9QyCLVQG5JAFYOagpNBQlaJV2bKcaTnREx10QszKFD16/KM0fXksLuVO7UEXkw1clpohUt29lRP0+uTKuj/GyKXgWHiY+i6dKJXT85buB5xazacCorxzthQitYwrMS5/QGobAWprJFdMp2qkmmPXe7HQO/nDVN9XfWwmVQSq9eyxkmpQInp+EpWHKCA4ZhPz5HSObz/+LB6PlsY1ctt0F08vPvz2tzcufvXpz7///e//1l/+jcuzrWMX5xhaF6eJ0EQBk7jOoVH2LWUEY3KOJ9UkwgAOQUTJhAOl/tA2oVkRmQ7H+88+/tlHP/jBn/3RD/YP9xbFdcHlWo4QiRPYOM1TFDPzIYjpNFvwJEkTRnYUY0KTtmmd43GcTM0Hl1eTzVOcpwQIITCB3e9inOYQvKmu1m3belNDgjlNqNA2LTL2/ZDm1K0alUTkjv04Drtf+MaLb3/z6fHhDge7fn7uUA3s/vXdYbd31Dz/5rfOvvnt1eUz9Qwpnn37u9PNzdSPh5vdlOQ4Ts4FJPzg/afbdfji0y8Px/5+f/SN34BvzlaubZEVjBWijAdGicfBpmSmIkqE3CCo+uDzKMfd3c43Pu/gPOx3Ibio6bh7aBoWtcPuwI6HKcUJnn7wzIWGQXUamCDFGMdJBIDDNMqsg/Ns5jUmAHKOKQSdkpmT8ejXZ45JU4xzjDFN/fjJqy92+0O73Tz/8L2hH6NIt2pFpdt0xKRilsR5f/b0qtuux36c53Ea5qfvv9jvjlM/zWN/f9idry72Ax7HyXlR06vNanecD5r2x3gcxnH2v/ThsxfX51fbdtrfHnd77IKKdV1wZwGdk9lcZFlTGEUIY7+/3nY+uHSM86jHaZ6QRkNFENMxjUjhaHYYxpVrus49PTt7/fbt7/2D/+ZHf/LH3/jF7z5/8e1vfe9763W3OVu36xWBoplH8j6oqqYku0Oap/7m1jMhWCTXJ/3oq9s///huF7oxrF8N8W0f08U1dBtIFpM4YkkTmQkaFpchWsZ/T8pKqKkwlP1eizwyo+ACqRn1cm/xMedRBnBPBMUJ3JZG52P2wpavvpnB6ZywStkXtIOlEQz1ZQAAcpn/aBq5pt35xTOg5Z0wYJlMy9SRLWVDqU9qTl0T8CWDXTC//ri+0ilmLp2UgswIZllcXlrQpwy8+DyUYz3qCzxmjJYDGgAsrPzjP3mUru7pOr1/UA13TidqJcicGgpQazYEK0NbefDpFInK+dV7kGuwmgIg1K2Otfard+MUCbBYcdSYnD8WtlCIJaCU+LskDKfkIL86gSF1m23bNDoPAXEdqHU83b39sz/98ftPwvvvvvvTP/ujf/bP/+lv/MqvPn127pnTOKOJoXlPzA5MTBRMQSOCoSHmTZeaTFGTWJJVcI68MEoajje7r15++uM//aPPPv3s9edfDMMUPDXbzTQOwCQqAGhkklTVkBEMp2n2zjfZ1NMBmk7D5BCaJiBCiomIyUESFQEAk6TE5Dx7pnEaHdJmsxIVJibCaZzA0DtixM2qU1MVXXcdrWkcht3+0LWOTd999my7avZv375zvb66OvMrb3F6/fLzcf/w5NnTZ9/7pfadb2C7lSig4s48JsUhxLc7gCSivglM8HB7c7wn6lr2tpunl1+8dl2Yk55juw2t8w06lvEO5tlvm3lK6ASSThoVBJEtCa54eBh2uz0ysudhPK698x6TRBfINxyjhOD7IY9q+0Hi/WF//eyFmjA5IDaT7uzJ5vrd6bOVOZ/6YRonYkfkgkKcxbGS8wY2HY7cbsb9fh4jsosxHY8DOff02fPu7Hwe483rO9c2TM16s3GeEXB4OO7e3KnG9dnKr1pDbtZn8+HtPPXP338+HMfj4SUkfHt/f77ZqGl/jIG8gGy6leBoaLt+fNinj754+3q3f3re/eI7T1ddQ/PcrJrtE+eCcGCMCBOp8d3d/uXLm9c3x9l397O8fhi/uDuOisdeRiXw6BqPjpxDU0TiCcRmtcO+CYzO7l99fvf6c9V/tDk7356ffeub33vy4rLpuoCw3mzI1Due9vctwXaz2my6zeoMDGZe0fZJ6C7Ci7XQ6ic/+eLIrKuz5Jo4J3IOHYpEBxWbiwoC7YRwAKdJ3ooalmUYJ+Kgfs+huFSf6ARbMvHMteCjQLEAVmEQHi8vr9hXyYaFyMYFUU5c8imJhUrPLwLO0ky0U4+2QqlB5nLIAIFcxiarzpdmljeK18dXqMfTSZ2u/QRusJxvuVdLMp7NhIt2h0w1W/efWtLLk+xxHVV/CacW6Ynez5TbKZtf7u2jUAvLgUrOXqN1BuuykRNxgdxSJtSNNNXRFE4dYFxuC+SAcqL4y517JCy1Yq5Q3uol1pfeDp3CqYEtyyfqs6mWN4Sm+cwQLaYEZsRujFPDfn1+BXHUNPfDMQDub1990Sukw5PzdYz9Zy9/0vIH11fXLrDMCRARxDmWNDvHCOAMGC2pkCFKtDiGxiGKaHRsOsf+9ubNmy8/+egvPvqLP3/9xVeHh+P55XbjGhNTSVS2yasPPCdFsKi6G6Zpik0XAljXNW3rQAWNVy0yZ0+57OYSRZS9c4TjFEW1CR4AH449qJmHOKTgOHjHhGDgGNddqzH1h4EIACmqxCia4tMn54gQSDerQHE6Ow/r8zPv/DSmLz/93GR+/ovfvf7g2+23vwXQjG/eypwMk97swPFme7XqttNxoJsdHff9OIvq7rBrbPXue88Y6e7Vw+3Dbv3+lducJdV0HEODaZ7ifi+pdc6jZ5kjALrQ+G4z9DsVkmTO+/ubgxk655DAe5yPE7ITQVVtg28M5mn0bWhXztTmeZqmkT0TIjLHGZuzJ9xe7h/+nESCiypHkDYGDsHLMSnO3eXGBS9p0hl3b+9xtQqrlQGq6nEYpqTA7TzbLCk49ZQMuGkbblAFLQ02Q9RpmlNaabfdDFMch/7q2ZO3X93fvtmtHEmaAyOuwjxGbsBwaBi7dXvZ+dvD8e2b49s3x9cOHt4evvPO5Qcvnmrw/Rixj86JKqZ7Oe6nL2/uD4p4dnZ7P7x8c9z1EppwTrRqbUw6K7L3EQFQoyoTRklibo4zsq3AvPckIpDS4fbN/vb284/XgRhhRbhaby7P1+fnZ7/0l37xnRfvdxfbtmmG/RSPI6/D+PAwDjZ5vh3msW33ac7rt5OJMyBQR0YCkHfD4okqX8AcoPih0SknrlBTUkAAsFoQGAJWQ+JKfy+8si0ky38PME46xIV3KUiBNccv8wX4dV37I3gs/6oMcs1AF8JqebmSXS7MNoIZuKLzRLIat2p2XSoDrGEOs2nRQivlJ9SggKd48Ch0Zu9MfAShReNi5VqXuLKUD0uNYTXwneB86QkszzqB8umHX6uTHt1zW+7TCaYfnUYmi0piXx5nyyuciptlpk7VTs11qFPhtYttZcUjQI58thgCFfroNHRYHldXOTy6qnxrDTQ/h4iDc4GdxDlN8xxHkDQPfRqO19vw4Tffs/3rDtOHT6+vzn/hzec/29+83npabdZe1XVNnEYEk6F3wTu0NA8pKQKTp04xMKTjoT8cp+NxGu7ffvHl6y8/f/P6izev34z9aCleXHSiUdRAkLI+zcQHJ2ZJdVIYkrHzjWOPuOoaJBuGicBaxxQcMsSkMSZVJWJ0PE6zJgFCZpqTzv3ITI133mHThcb7JHl3vPeeVdMcY+MdIzvmY0zBc+jCOBw8YWi4tbBdh2fvXnXbVZr6Lz/9mYH8wr/0l89/8ReN2zSq3n/J5JLMSrh+cs2rNUwCBs2T1QrcGKfGuy7KqBKnydr19TvX8zRT6ybTcR7J6Ml7T2N/N+6SJDu+3q3OV9SgJVEAAD/0Y0oaR5n68XA4pGRd104etpv1w2Ho1s0chQkQ6fxiO7/ZMXs1Cm0w03kYQuNVREQ4dGrmL5/71XOj1fH+iKKXz5rhGFtqTAyTohM/ikwGKBS67ZMnX756y0dBH+73D6HpjKhZr8Z0iJOyi7d3/fps/fRFc3F1LgYq6TBpI8rMce5Xm83Z87MYh/F4ePK0G4ZeRomiXeCYZu9pOI6hdcRoAtt14912mMTAdJxfv9mnfnz9+ub6yfr6cr1d0br1oVlp0PBke3V+dr11P/zhq4dBV9u1BjEHb+/G4AkBnGIkk2jDMCJqcM4RT2LeFNUmktXKiSgAsHdr71pvK9SVow+eXZ5dna/X6yfPnz59/6pbOWp5nEZuvMJqFh37OB3jmze7O1yZY+e6GY1VEAEsMZKZKlaYyWKWKrGnR2QvLpDy38v5C2e8QPVpAqC2j6utVy738VG/b3nko+Mt6Xb+bxUCVbqjYHcdmToxGCewezRyVkAZyz6Zwi0sV4uVGjFTlwe/Slqa+w2EjyF2wcGSAi/Xt1wM1nz4a/SQ1cTcso2HUT0DWLJyqxNoVqoTq/PB+dhLUMaC1EvJ8bW6AZfaZomhSwR43CSor/c4ei4RCMsqvlPZYEugqEEvvxdqiKCi9RineKpoWBr+RkVWDNlUNJ+FPK7JEKHa69XCr74MlNuHWBmq/BKqllJK4okB5dj3joBVmJiJj1P/l959Nu/f7l+/tIev1g55iLons02z2lAy702Ou0Z6d5jT8cHifNzt94cpbDbkW+fCbPj27auf/9mPD+P+zRevH97cgsfN2eb82eV+30eJOguaZLsgQmBH6DBG5VXQPoYuHPuRDIJnz4QAziEDImMUkUlUzMyI0ABiSmqGjgHMCCRJ03jPhKAGQExTjCmKd0zE3jsTdY5jiuM4EpIPngglTihx3babVQjeus6fbc/G2L/8+Kfn2/U3fvPXum98M+7usOMkfrjb+bPrzV/+d+X4kdx9DkPP4HJF2G03+MafX5/fPBy3T86O972Z256Fp+9cHI+DmCqZQZrHKc6SokqkNqyc77ZX5w8vv2yazhSJ2DXdPMd5FgMnNs/z3DT+OAzeg0aIKsQcmI0cqSHRNMXNZgWOvQuhCYR5szQksabbbJ9/u3vy4c3nrx3aft+v2pUMyQFtzih4L/0EYNM+csfNevPk+vnHP/ucmJt25ddn0yDzlH7yyVts2wM2Znj71afPb3Zm6exitd34i3UgSjrOnuWo95eXT86fXco8PdzB+dbfjolM0NGqofEYz9YhmRJhH+M4Wrduz7Zd8HzZtjev3oBMJAJpnnpOA/D1k4fj4fZufHV77NVuD/2ru/H/R9afxdzWbulh0GjeZs65uq/Z/f6bc/7TV9WpKtuKUWIHMKQiYcWhkRJChIBLJC64RSIgIcEF4s40NwYhBUSixJgEDJZjYTuOUy4X9qm+Tvn0f7v7r1ndnPN93zEGF7NZa5f3xd77W9+cc73znWs9Y4xnPGMMVddlEcOiVoYxQ0TBIWSrVNBAlfqi5s17jh6LYLaybcERGpJk9ViKATe0XFfLTbVacbNwdTC0VHKLvUNgDpWBbO/SoeuWsfro2cPjHpwhZW2RehWH6NhblnEMFQIA6MAITAVDisNMyNkdn13rE98+edYjTJ/5gTBL4fWclTh9vc/5lNE9PWUMzq45/t9OepAT538O9Cd+Gyfgmp3biWE4F5tMVyAwBHM2FqXazLaMNz2VKpxTICfoH8KLoW3Y2Dhz6FszxT0DazGsEk97OORHJ9M7hiFmY2XWyfefrY+NaptTCHYKT07e+Rw9zJHOvNw5oDlZ6NNp490CjCPLpknCJ9OK044OPNsYJ9lp82EwVCaAOE9eABgn/I32a2L8z3gonJ/LSNvZdLdTmdrJ+ZiCSjPoUsKiVQib1bJar7Ckq6a6Wjb3X/1UM9we248vN2voG2fXC0em3N260IaqQAmEEilbesfHW9zepMOx3G2l7W++LF2nYXXZge0Px3K8O97t1suwXDxOKt2hK6ZZipqimSccUtuOycRKVlFrj6k75F4MCoToHLGZqWjKpfIBCAyRmMnh0NSlT1mHSQ4AWsR5bpqKDQqYRwTD1AsC1HUVvEewts2Ss2lxSCrSLJrNemkmx13vmBxBs/APHl1eXDV9d3z78rMnzx8//+Vvu2cf6btD2bfe9dancHHJq+flUEgkWIbjtvvi3a73UC2OXdq+3Ve4frJ+9u6w41VkdjEunn+8/OLTz53DnAVdo1ZpaYmquFnEgIf7t932rqobMgAFckFBc9vv77rDPiNgiGF/29WLUC3C/j7Vddw8XL/cHZjHYDTUfrFZdX23uFilPnW77eoxoyMfAmj2iwfNg4+Oh99MXc60dE9WVFgAOKOXQJbRSn9ooZPcITWLprq42d1fP3+wu2tffHkr3P34J++uP3z6+nhzfzheXT84vMuo/eGLN1C6X/3lrz3ZNFcXi8PtqwpyXS2Z4sMnT4mR4As5lq6zUpSIsPKHNglBAfYArIolOwfWlmL9Bw+b66vHy6WTktv9cXd36Mrtvk878W/3/Zdvtm1SF7yaVNH7GBhJQfuSu6RGuKp8l3KXNIkmMzIT0YOIiQpYFUwdrepgaqYlNFHUstnd/hAXMUbpdjtEt36ADKTojodUCvRtoewu6uqR8QG877Ics1kCREBiVR3oH0KdhTVTTDtEAvoegE6IdYKkMwCZUWVyiCeZyNkhdnp9Aq3Jxz1ztU/OLYxASSe55YmMP/tzhkUzvXNCNztf8fuXmKkHcPYedzK60zb1Z7DJPZ21k9Ox487Mb28EM489NpEfatXm6AmmPsgjCNN0O5MCZ14fTmTJaDbm1wHOzMR8MzjZ3vFZnuzr2BluWvBZ6cT4qE5WcZCOgk01wmfPf36n+W7H/DwOxN8I47N/AFP58bShNhYdjIZ1NAZmMHzSBhXCbB2GRnw2b9LJHiIRsmfnKPX9Fy93TVVF5xxKU8nDB+v9yy/ffLWve3/5sHm0jk4OVDrKfbVoQttzqCS31h9ge0NpH+UI0NFCaklv73ft293N6y9ujvmQFOr66vLi9m5rhKnPvWratypWipgqIoZAgcnAkqqYqVFKqWRRAcfgHUbvnCOUEmP0jkylFFCdC0mMnUMbqsmNQAnYDIqZqooIATJhDCFlETNQKF1nKnX0AhDqCGj39/ft4bBaxMUyVNE9vN5cXq2Z+i9ffn65Xj765ifuYg1379oXb/jyUkQEkZeX/voC7GV581o++5QkFFutnj6uPvnk7hevt+nuZ1+ku23at93u9k7b/dc/eVqvwsOHT47bWzbOBbpdAgx9u094sJpNsojlIBQDKljOude7u92719u+5EUd+mNXJMemltITmIKVVHKWIpk8NbGp15feeUMpOUvqQnCoyZSLluBcvLxaPvn46uPvvvzpD2UrwsflyiFhK117SE2QyrNrmpxxv+0pV+LDw+fPj6X8we/9rHB9sz9eP33+8iAv37zo2b39o88JqG7CxUXTHm8Th00N3/vk6VK6jUf9/OXV9ZPK+apZrK8u3r3eipXDMSk6Zlgtqy6lbAYMF8uqz9n6duFpXcHTh5sseXt7PBw6Dr65fni7717cpq+2+32bbg7JmT1c1uv1crWIXcqEmIsW4bZPatQnqesgS1JQBOr7UkS71OcEvQhJQcXOJHpnnhjRCLo23Un2DkvXdnW9KSVULlYFXJDs2mMmDNFt2ny8dJw8VgZX11VP9KZPb/f5kCSLoXc6fNeYAIBsTAifGAA700SCTRPA/hQtfQYvZ5WgJ1CfgGdu8z4UhE5gNXAAo2r9DOdGmBsdv3kNU94RR/Jgxk+goa4KAMCGQuLzZOhEdYzrHeXlYGDgAAkRdYK70TTYmWYGJsZmdJZPkIjz62dxx/xnlNFMzfenTTk1NpovZRNMz28wdmibzzmB8MnWTr7xFN7MKv5xWMC44zM7NVvoUyHX5OwP9g6n4GGG/zkOmC52EkuNC54hzeZUx9gbYl7hzFDZvJ5hn1WHij4DHXqLzm+C04Oazht2W0spjtG5sFjWdUlSusqLHLdvdrcSyocPLpYavRxTezi8vXu05osHjQNmKA6OmlvSBLJD35fdDrot9a1t+/Tu4AQbp33XBZVW9c1X28/br/zFGpmEqGQ9tgmKkJUYQwjsHJmqZmAOKnJo+zaVJIWIvGNPPIwQcExAkEqxUnoFFRtUbWIy5N4MkQDZIaKQwNDungHYs3MOCNs+BfOOqKqq4BnBGM05BpWUUqxCDO7jD57WwVaL2N/fGPQPr66efuujEEJ6fVu2WytGsfb1BYdrqIixBWkPP//szac3Dz76NVkss9/cfy7/6B9/8ePPb0UDYmr8+psff7fcf/r27ia9/uqTb33MUKfsCHmY1S4iq01kj+1eEBBTQjUT7o+tQnU4yt3dfrOsKu8l6WLR1HXsW6XaiRErISMCuuhX1xfKNQqvLi6OKROjaSft3q0bACuqtFqvnn349Nu/tt/efPn5Z30hCJdAsO9aXdWqut0eQwZz0VVLUsvmSoYvXx/g4jrlxZt3r0Oz/q0//BMKrpMOIOzu78rW0s9fIciPvnjz699+6rn+9oNVtz88uaS+O3CM/e5oORuIWK4XfL9t2QUfHYW4b7sicHc8rivnSJ2jvutub+5KyY65Xq2z85++3n35dn937O4Pqa79k4eXtSdHQJDvbg7eOY7OAzAZOUTPvIqi2BdNGdHhKlaIkHNMIklEDRRUxIoqmB26vK6DAkix3d1Ru0KXFPh4//LNct36WIuFGIKjYChIdY26DO5x1G2BoyDu081u5+LKEwM7IQRTsaHDCg8IYmeqR5slfjbRLDiD+RmE4AxfI5k7fmFPfvdoHCZTMaPyyLDY1FXihEnztXGSRJ4yhiMKnvnzUwNrG5atOIHPjPpnYH7y7xHATb/EcZbLXLlwymfOIDRi06x+x8nHxln1OS5usiOjeZiraE8B0UhYneuuAKdM6bDvs9zpxAcNrv7Eo8wWaDJC03p0SrHO58JMRZ1ZgNF04PiKAdg4bBlwDhamGxkuMb//6NMj2CQEGI5RUBxsAODQPBtHaeqoOToFW4SqCuNbz9M+gcZusWhmwJNbMLRkiJUjQNOU24adj9UykMsUsFv6ED1+8ODxJkq4/bK8fd1ny2Ed1zWZGQoxImWgVuFIlIFVNHNJgbTvCpWy8O54UK+0DNFQUp+PovskKUlJOXq3XoS68uwQFY3RAA69tr20SY5tBrIYqAouBmQatyqLqEjJWQSBmAFkDA+tiCEQOVIxVXHMZuoc15UjQABruz54P7SSJiYD7bvEiNp2kvrVqrq6Wj+8WLTtYRmbGFyoF46ry0cXlnKbkvVtSsfq4XOOddcdY32Jou0XX+Q32zdfbPnxt2/D4ssXhx/945/+Zz/4o7d7/cmnr6pY3x/u8vbwZLP6L3//e19/vnrzYtvqFxtPTx+t+TqUXJpVWFxeLNaU0sEMQuXBCIF3t3sfV10LdzcdcYihxmJMDAxq0h9TbMJmc7Hdtavlwoe4vg6Ly6tcHHOoYyiHzI60b1WS5MzEwMSLpnn0+Prb37u7f/vVq7d3+yJv7xzyYsFAZUs5qDZOlxfx5m5n0kuo6uXFm8Pb3Fy/vOletnDUw5vtsZOegt/d3Tp2Dz94Goq8+cVLTfif/aNP33xx89/8C7/69NKvuIfd50+//vUnH33U9/vrh+tc7mJFXSqqkDW7GGuOqcsmIEUFSmBz4D797OVqvQhN098fXt13X7463Gw7V3lGWjRNE0MVoKmcmbE1OWsuoqgMGLxDQiQC5IVBkWEqFfVZACA21QCPqQgTH/tU+hQdFVPnKoDc98U7PrZdcIxiDCa1hHqlWbOpkV1cXrU5l+OtM4QeX9/c39507AKLMlPbJ6qD82x9BjEkVTCDoc0KTLH8iBOj6z1i6T/P/7wPsbPYA84ChumMyV0eDMKJsYBB+Te5uwPC2IRyA1CqzRg6j3Ef7Q3hSDbbyDyfwyzMxmN0/Ue6CNHM6dSbHif4n7WJ5xHJdJcz0k39rBFOFvGEtefs9nSlMWTAMcs5XG5winHyzGnOpExvOR0JUzW1nT+eE1U/rdTO7/cs9kFEM505svnyY7XDuUE/TxKcGd1xdPqkDJoMhM6hx/jycLX5lnDIBr0XpEy7Mpn3gSgadwmJQAdbh6Yw6ceGxAJ6R1pKbrt9KevapaSe7bLmhw261JVtV6hdy/Hyweqi1jqCpuOgKWWKgAZWEAoFLMcibQIpOIyjUZWiIGXwzFDteDzuet33EqKrggveLepYBT8MJECmJKWo9FoUNHgmh3WMwRGClVwAEIlERVWKAJqZaJuySCFGA2Dvau/UwESH84jBM6eSUaxIYSJkUrNeyn7Xecb1ckmgKcmyaYJjEt3d3V9vqkUVo6NQU9NEhnx4dS+lC5tYbdZu6a27D4uV7b7Md0d3ANPKXX9y61b/5Hd++rd+8w9/8uXbbdevHjw++PpI7FaXQu4++h9u726g+yt/8S+9/OpHd9vt1QPfmXNFiWtkaI89OY7Rs/f7m50PRr7aH/rXL9s37/Y1wvrqot/elnTcPLwyKYtNMERk7I7tYr1Usyff+cbxtqMYl5u19TkCl+O+O+7t/ma1eoRVraUzorjerJ4+efz17zz68vOf/uEfv959sdhsrnGdg6NSXOkfxuX9i7usdH93Hy8fpv1bCRcvt+kPfvrFi9ftZ69/aiYQKB321cUy+MrFq//Gv/1v//AH/58/+p0/aG/wx1/d/Z1/+qP/2p//3kUdF+vFfrfL3SFU1XqzOuzbu5vd8iLe33eoUFJaLlZHbUEB1CzZu7d7vF5fPn7y6mbb7u93SW4PqeugruJ6s4h1aCrXd6lk26bEgIYanGNPLCODIWI59cMHv5jknNBHZrdc+KKWUxEDjwAASx9aM5VCCvu2bRyE6MnGWYbsQMvRCpHW5AMhM4odX0uPVlzbys1Bc6Gmqh9Xq9c9gPQYq1LMcmEVokETTiPSyqhjxHGO7TkZMghGz/B+go6BNMIRPnAmhk452pMDOtLEowOLYGO953Qh0JmlORWS4lAudDoaJgd8ajw5vctEhU+gdNYgedaATn8cvoezw3VOvMdwyszaTFg8lG7hZJ8m9NfRsR3d6rHp/FTCBAZTWe6UPDgLiXCKCEa6ZLixsRLNcN7j0TAOMxhmwzbHIyfInhSnA5eHMAp0pp2YTPrgdONZhHV60nPgYmBjAwcbMxaow7i58+Nhnl082eDzUGZsOgKnUrthDwxxpO0Q0RDIZAxMpvQzEqEZoBpY6Vr1BJEZ+w7bY0l9rz1fVx88uHx0tdT7N3y4IzzWDTcBvVP2RIhWsh4Oko6gLUo2KQoCWMgpkgAWRO0OnWaULAhAaHUIBsYkxeGyZlAAEyzsPQNh28vx0B66stt3SS0GHyIzs6kmzcPt5D5LlqHCmZhMBAGd82ZCRGwkIggWvJdSFNErEhGzExNCP3T1Pba9qVaBfQz3h51kWVa+oCy9Q9LFsnIOunZfFrS+2HTH20N3iORdw83FNa0qrisTMGvzi52oB9j0EN8S/t/+5j/87T/6+e/95PNqvTp0ZS+vVSvoYHW5UMcpcP21j37+i1/87d//4V/87gdHdC/vDseS1+RC3Vw+XlPZ9ce+qAOFuGyOt73p4t279JMfvY4LrNaesYi0jrD0raTu4sMHJamaNMvIi7q+vFo++6C9/3Tx6KElOfT3LkYsHXR7xAxWVESVRTQ2C1cvl48ff/Dt72637f6f/fxwlF2+fWJWefK5uH1HsepTNh8/e3PXc/Ouz3/02Vefvnj3bnv0gYlqdViKLperY58/f/Xpf/Q3/o//ym/8V//kn/0i3dwgx9/9xVdNbX33/Ptff7a7u3l0uXCMwH61Xu63LaJbNdV+2yO59rivnO97UVN2gX3FYXXbWwfhi7vdvk1E7KPfrBsDbQ9t6pFQhdEBJjVC6lNvZt57ZiLEIqYCpWTnmAmr5QKAwQ2dfgC9y6JZARFE1TEbE6hJKQUZ0AGiiaYulYolq3JXwi46q2qKQG1/RweLbrHxAS+XG4xPMRythtt0p6C174sc23HmQNJRaW0TCzzyOjiTIDgzHGfu9InnmZkdGjFrwBA79QvAGapgLiOFcRofnNHDMLc5mJmWwU3GGbkAaGybNkYEOLP+ZkOLwSGXOxiJwcGfXNbBgR+MCbqTFZu9+7Oenyd+ZzhodIeRJtIcxiFhp2q2kcg4BU3j5BWYhO6Gs1G1eTNP55422EaSBeYWzFNmYmTr50eCE4MzrW/gZ2zcNoT5oZ5On3M1c6B3wl07T/Sf8ien1tQzfJ8df7IGIxM4LWM6ZmK3EE7PdnwYNAY2BkM63Qht+GiM1t8MRQSI2MwsXy7j+qKKmFZUrql7EDT2u/Ti5uGa6w014CS1u912tXGxcuYYzRiUqAyTI4oWK1lVSs6ai4rmnGHIUvbSq4mxCqQiWbIZdWR19MtFvQyx7bui1qa+TX3fGyJFR9F7UCtZcim5ZEBApGF0+2DhGJHc8KEnQqcIKkLT42AiICxq/bEDNTJ13lFwuzYxWRW9oW4Px8BURa8qLnpmYgLnsF6E1eUi1u7+7et2++7y0UVY1M3TB9b2VCq7PwxtJ/LrLjz4JOXF7/345t/77X/6//i7v9MCcdXcHbrLB4/XF4/fvbm9vT+mmvdvDwA3v/j8JYP96KdfXjbwMFaGHIttsdSpNGW9ffOl6hFAS85VqFObsuJ2p7tDWiyWF5ta9VAFVdFF47Ljelntb7vueKwuLgrF9dOnClRdX8bFuoM9OQaUZt2A9Zr2ubvnukIiRETmsLlYPrh+/MknXZey0KeffbY9pKx2/eDSutzmfWzKerk5Iu6yvry9/enLt//s0y/aArFp1o+fvP7yXeo5C7/56kbYGOzFi/Q3/vp/eH35cb28s64/7NIf/Oz1N58+3PZwRfD29u3lZpX6BIiri9Wrl3smHyIUIO2lLRpclaF0fQ7VqjV3f+xf3OzuWjHjTbNktLaU7piqGGrHyGhoqkZEqgYGcSj0QDSzwOQaDxBVFYDURM2klCKWckmiuYAAiAIZDt2GwTQ6VIWUcg/sQEgKWeGLGgHMsvXJp0Rx4bi62FQN+EtfdVTfS/zxTXff50pwGZpDVywVUvWeCwARytDrwWzw5BBwbsMwkSIzOE0AA1NW8H1J/hlWnUiXWQuOJ3/bzozMlFccYXZ2kOGMoZkd3JlEgclgjHA5dq2cfGuaV346HifyHmFoBjc2LIP5ItMtzHzP6LSe6BadCAuairpgtJIznTKHM5Ns1FBxLACmkflAmEvNhjcjIECdfjczMnN0BHNWFCdjfc5WjcNsp2Z7Y+SEOgcZBFb0ZCRmAzQn/M1OyeQR/U+5aETEUad+Sl3YmIKAk9M+2fMpWJoIp9FiGw79AHVIgg6TXlhNvXNSxFTNgJCRB13p8DxNEMGspGxajkwP1s31sroMemGHOt0vrDxc+atKr9ZLTyvt93Bv1u/UCkUmT6ACkqBkk4KmQw90MBhGakgWUcmiSmAGKpIKoCMC1+UCGR5crRd1TZ4l99vd/u6Yt13uMoiYD1ByXxTQTEFBzSE6R+yIEZjHqm0k9kwAIKIC6II3AykgKr1AMUEExxyYCEkMtl0vYo6xL9kTOCIrojlv1osQXZFCLtaLgASGuj/urN0vVot6sfCrlSev1mmX++2RqgYthuUiXD3+gz9+9df+9n/+93/wJ4lcyiUG/+jp41//i//Wb/+dv95ZA0C/+l/6N5999LXf/d1/+ukPfvv5BZZ3/+w/+a0/+Ff/7Pd3lvq2XK7qpq0ep7UKHXbHxYaObatWwfKqfZNevbpdLFzjaREdyBasXCybUEdmtF7ZeUa3fnRl9bp6+ISqpWtMAcOi7rfZheAou9qTpWC9aAt+geCySFhv+t31+mH74Nnx/u3+sD++/cmnbYHb7m3l3H1lF4J37U1PbqvVi/vdz168zkAcOS6XNze3d4fyZ//Sf/GLn//07tVX3/nex5///g8tlbe3L/t9X1OVSAXDu337g599eb2ou3h4vA5tlat6kZI0Da5WcNhlYotIfTIEd2h7ZKIYjzndvrt/26btIZvz682i9GoATqhugnfOrKRORATVHLkYOcRQVCVlZhe8Y0eAlouYAqCaAhGxp5T7XFTERCAX7VM2ZARk78CEAD1D0kLFPLNDS72lFhiUTIKrvGcOLKaIyUFXAFDhfv/uCmtFOgBrf2wTcsLry4VfhZucSptRpxbBf0oBA2cu3knIOIHCGNJP0frAUozO21xV+p51GEEb7D1IBZg9WANApJG4OesYNjE7E/hPTYgNbGLkRy3/GYcw+ZqjFZiXjEPDG4cT+6LD1LBzldOc9pg2YnzvIa9pp6HGM0JOdzYi5MTJT276JBSdNhEnQsZmOzjbOpvUoSPGzzVcNjUuorN3ntK5s3s+Wa4TMYSA5w2tR35odt0HkzZaBTyFgSdDPrrjRGN8ckonT8HO8PjP1KXjWacrAMzK0WHdQz/k4e/gmHzo+tZkslKEhsNkAWBAROIqQLZ9X17fHZwyRwyUgpSu36bS44KymJGwgY/mm4ULhCggCoqoKZfe2qMcO5CMCDAM1RtMrhmIEDGqaVFHXshnBWarg4+h9ux7KX2fC9CxVzVMqRiAJTNRYPTMkb33uKwDMyIAIwKo5uyCA0TJUnJxzOS9qhY1IO2zFIAsUqQ454diWSavZkDUlRIcMSCSD+TqOi4WESk7RiBXirmqOu7apmYSInaoGhzc/fRHkrR69Jxc45oNW7Xdxj/845d/7W/857/zi5f+6mrhQ/dqe/3k+b/+3/kf/Z/+t//r0FSu2cDlr/0r/8b/4F/71cf/7n/8yf/u7/2jt74sMfzBFy/A/+g71xePF6auaj9/Id39ZuFX1w+P91+GUEmohS9+9uqnd9v09efrj5+sLN8QlhCDq9zyerW/uesOR4ir1cOH9dOnvHgQ1teGEaqMaIjs68Z5ZKDl1bNsiGWPfWXA4BdADI4uP/rQJG22+4sHL57un+0O3ed3d7fblsj1a04kpomq+k2f3x2Ox1QK4XK5Tsdizm2uL/67/5v/2V/9H/7PX3754u19i8wUoEmk0lYXi/51UbZU6OXt4fO3764/utr2ifatr5ZN/SDzYblyKW3TsfR9SSI5Z1/VinBIfZ/K/tDv+4zsHHA6Zu9cFXyI7BxAEUJfED2z98zAyMhESL6ISLFWi7ZZVdlxFSOzQ4dm2KaeOdSLKKJDz2gF6lIiIBEZBkaXIkIgZplAHZtpTjmGQEAAlLseKbBH0CLSak4p7bi3BRXxVb9aOw11QueD+nBfZFtQRJHcINLUU3nq4IYjDDobPFO8zIqVCS9sLtlCGJjq05d+bpuAJ354Cgzg5DvamF84waJOiDheDc/OO8UIg2gSzN5rp3/GUtmcFHgfi0DBDf8MCDm3fUB4L9Ex2KI53pnmHMAknjmB7GTB3qNQhivRfAAOJmo0JzZdHKaup2eoCfPwBDxlTMaIgOxsN4cW3SPTNTX2nGze+Kaj/hKHnMApOILJWp3e10xP9m/wC2wknSaybgjDZv5o+musq57swukNzuzakNafDcwwmQENipSqdhXGdOzFFIzAgAhHX52QEEKIYRkp50O7/Wp76F0fV/DssXu6Xi2zlftXd6/uK2/Vosba148vMSAAQFcAM5gwmkghKZKzZpWiuYiqASEM9JKayhzLWBEBBAoeiTqx47HrsrRJui4fumxGDlGzkmmMoYquCrGJwTuMnpwnlYRGCE6HRqiRwYKYIVMuDABqkIbpg31KQFm0FRVVdqCigEoMRYp6V0TEgavDvu83TWxzG5U60WKwvriy0ioJ+9C2qf/sBUqCLpXt1mqj5qIv/MPPbv/Df/iHP/jxixwWh670nadq8/zJr/3jf/D3S39cXNWb57/85Gu/sct6d2i/9a2vweOrrn3B6hPQZ2+Om2b99PlHO9Wbmxd1lK633iqnVnqEVN7evH17d6wX4foyVFUhgYXzqeuZILcHYIp17a8f8fpx/cE3VQL6VdulsL7qtu+iD3H9yFUe1OeuE47U9+AKqkAp4IKaCVJ9+bDZ3F8+fZ76viup/YWVt/tjKu/e7d7eHTm66iLseri52RcxUIrVRd/eAtKDZ4/evHy1efwQcne4ue1L1xBQHSHB48un5WDIOXd4u0s//eLNJ1dNveZ3N1vvAqILxIxYV+HYdanIse2ROaUuIe673BcTMeecD46Jg2cG9IF8oNJ3Q2UtIvrgQbWoGkDXFwBjGv1O55iZESCJUAFDS0WHb2pOkoqmImLggw8hmFrKGREMtIiKoQjkQsVcMeyShCQxqOTUSjHRsFpzrKsmVOi5h+AtdMVbyZQduicXaxf9nUh/kFIyIE5DbY1oLMBCG8nmE8FrE8E7CG9m6mCWkU9gM3xzx9Nt0oQiAoyvz9eD6dt2RjIN6D7UHpxAZoK7cREzuiDAnIc9ESqTDnJ6r5NjfxLCIDiYeIpBrDiyORNdDedc1HjoxMRMDNXZOnBUJ8Ec34xmAHGUQc53MAVI51wRzvdz0uGc0faT3JWGiOu9DZizI8PvzkwlTLkSm3AdYZwhM+2sjZnrKZWAp0dhoyE5PX+Yfxof25k5nc/F6eXBKA2XGhufjmtBoonhQRuaBZVikjX6wBW0XavTCB6iMQx1gKhKZhFR0UERsD7vutJEleIqaJaxulg1K+cJfYggWfcCgQhmvstAdag90KKpTZJNjYYm5kosSUXVDAWpFFExRTSFXHTX71PJbau7XSdFo3PEFIiIVAWWqzo4qkIgsypQFdhAmJnIiMkMRKSkMgysyqLBIZDz3mUzBN4e2mPKx74c+6RAXZfZkQhE5KTiCD05A9zv++LJVBYB725uuaRV9Ptj1+32yzputxLYIO9iRKfpcH+3qK+7ln7x5vgf/N0f/J0/+KytVtnFQxIf3EUTS0k//9H/r3KKpe3vf/ziB6/+rz//m1/+2T9/8eTXHi2etrKFdH/RrJ3nL97eXjTNJ4+vVq7a970Diwergy/CVvjl56/lePzgUXW1JGetc6SS6kWUXJjIoPhF5S8uLKyUGqo2KRkEN8iiNg8fWLM0ZAVfMlnB0mYKQN6QCRBNzdCFzTJsNldPn2puj+3uayVX4f7t7f6r2/tt10KuWLtd0tttQlcDQBEz1MtHT+52r/79/8X/8t2L27BaYmkrJkZAIHVKrArEPlTBR0vvDv27m+0HF0+N5fbdTeVoXwjJiRZDzAbClLN0XUpqGbgMvfMZmAFBmRwjSk5tr0gQmAe3reuLqUkRNXPkDIGwOKYQmZiPfQ8A0oopiOkwKkQKiBkAFkUDZUcDH8/MIlkHcQKQAAhiMcqKVLTP2XetgsWqKa6H3d71BVaEXj2HTRPIuapwYD6yP0jZau6O+bbLyRjJK5KZ0dDeHCa/2gAHWTmcEQwjouIJEmzGuREX5vQfjd0+T4nV0XrMQphZojNBw8w3TD7qiT04+zWM1DDA2Kh6hO3h9eF9bDIts1WYAN1wcLjdBLEDytlEhJ8uR3OUMV92du9xyAYDgk3DkYeF2mSkJtM0NdXTWXIzsVcnnJ80muPenSKcCasns2GKNPE/wwFjz38bHfvh5qaMBs5wPJJyo1WYc9uTkR1juFGYP8czk8c/PxGcG0Xj2A56EOsMaz3pTqcnfkptD00+Bys0WHcZzRMSUUmp71oVaYKvY+xSkVKGsnRCjMwezINSzmayCvhosXkU68e0j7DnUrgUF7FahFAjAZgDJEdUgSmUHnqx9qhtN7gjSA4os3PQl9TnlCwLAHISycVEsYAMNQyBSETb9kCiucjh2PeHYkVj7araewYGbJqKiaJ3RBCYvWOEYSCxOiIiAMQqhha023ehaYLzfRLADKIeiTxdLCMfbb2sj13at2m37VMRQ+q77B0WsUylZHMEfQLJJIHWFacC+0MKzBya1CcU66zHvvMPG6o3YfkAqost+v/oH/zm//cPfvH2KEal73MpJdDRres3r/+w796gR2tZb3+0JEe5+lv/3j95+Oz7m3TcrFa3dz0zo9L9Nr24vb+OYX3daM7JiQtLpLDf968+fXHz8uZq5T547NZVLoc9suvbLtTL5UXNwS/Wa3/5KFw90uYx+wUuL2zXOmZJ5eHH3wMHFEAIHYbUqV+uOTRiqCmxV/SspkXVsVs/f9o0zCztcZv6lHsJMXZm0PWv7raaUVxT1IE6c3DY3Tum47s3/WF7ePMq1FWNWlchl0yERhbDYtcmNk3qpCRDvDvmH788Xi66rz9f1a6vHbR3fRJJkjspnRVkdzx2uZigEWMVHBMSmneIilpSUWC2KnhRQzQVGKbYqlgVAjMTopqJGoCWooc+6aAqBhRVZmYmM3NxrE5F9MSD0wJFTVTFUFWAUQEFTIz6LKRAbB21ILkefXBCDERSukSG4FCJqmUTQtVg01F4sdPbQ77JaV/U3Oj8IiEAmSlN/M8YeY++JJvJUORJE3jYzF5PEAMnUBt5JPtT86XwFEacYoATEsEMegOKzKbDTjbg5L3iyG7PFmegdUcfe3LicahwAhww62TO3GxPzggRwKkMlZFs3IvxvmZx4rklmrz9KRUBI+hP3BMSoY3+97j+c9UMvh9NDHcFiPOceZyUPGioQzmpEY43c2pmMV1yusGTpZxCDpiiEJzdfZis3mmnR24K5lBiPIrAzj3+ectw1hydGaqprGOKAQ2GMGNMF83TfseuD6ZqTFxUqBQhqmNgYs5kYCG6JOrQaqAGMDJ4Rl/S0vpFOS5D2ji7WNCm9k00R8lU1BRAKRiqQMmYE4BhCJx6yFSKSlYwVLWcixmqWhHokxYAMUhFBKxLRt6xDwbW59wfekBIJVW1W8fKO3Ye0dQhOzLH5kgRLTiqIjGiKREjooEqMDNTc7HBi4v9oauaKrC/2+27vs85g0hTVcvF5phK9NzE0Hi+2XVZLSmI6v7QHRCr4AgxkoH5IYZaqM/iX910waErqXZaBXRIuTTKdYhX26P+wx/8/j/8/Z/dt4mh+tU//y//8A//ULqbCFBSl6WodCikEfP2vnJEqcJd1789XITLxx9+//bVDwRdn1VSvst5l/tWYtfjul4YLNX77fbw+tX9MuB3vra+XLt8uDUx6brlqo7r2q0WymHx+AkuLi0uICxSAs4Y15dmAJQkZ6oqUHWhSl0Cb/7ykSQ0Q3IegIdmIUgkhFjVy2dP2/vt9ZO37aE77A/9Yf/8+kq3u8M+3XVoqL/83V99s3+9P9x2XQcudPttyUfnSLNAFuGY+gPHWNeb4OJue9el1jtYVmHl2Fm5ObSfvXl7cfEku3QAIYVt26dsXZa+SC8KbIxMzERghmTGhIxoZE0I0bGZpZy845xkmDWdU3FMBohAuRQxNVBRzVKKKhOTQwIEIucdgDl2wTtEAsBStIhI1ixj4SA7hzJxsMSGqGIKZkxmpCJSiuQkPkvJ5Lx3yI6AGb1DT+qMqG/7vNvnNwe9PZS9UvZeQFUBQHlgQhBRZgoBJ/AeYItOxC4YDs1D8QyzZ/HM6T+j+GVmBUa/+ZQPOFPcjInVIeaZXP5TRsFGCgNgFtNMsHPGfozJCYPRiJ1UqhMfNbIjbvAGEYYZMkOx1HgwT/0lJjjECYjP21oPgDspNXGqaTYbrPB8MCEpGdjYJhNUh34AUwQyoOS8DVP6FufVTyViM2JP6DyZCTjz7N+LnU45FGSYHwqebfxUu3wyrDA9y1NuwN7f7OFkm1c/hAMTLTTaI530sTSkoIdjxqcFREhD5AtGTKboMKhITplUENkzFRFTCEzStm3KIpnJHjXu4SJesX9+0Txf10vdPqzLsiE0tFIABIgoeHBuWIKCDg08DYeuJ6hFS5bUFynDLBtwxKKixcTAEItqqDz6ICIGeDikvk8MVnnnmJermgAcEaI6QscEInUdmFHBGM1M0UDNHJEhmkgu4itYLOp6UXdtL6Dr1dI7zrkUKaVvfbSrVaPgDm27rLlpfNuXYy9tlzPCoStKpmBiKiJSQspqdEwFKrY6uFWA1Pdg3gd3UJc6dFv66U37H/29H3/2Th5+/NFf+Mv/ra/elatHX5b9W8mtEyMtHKpcMoF4jkV7O7beJyf7vutv3nrpjeOaJXOIXYIv391c+KYOChcbxSq34fbNvUn+5DsPLlYWrd/tj2zWrKKLoT8kqpduc8nLK948FbfWuIphba4SdehYGUWc90v23piZCnpVcOiYXUW+Mkcqys4DYu47EDjk4jdXy0fPHnZ96VvZt8e73fW6IQrxvuyFvvnBr1/kFz/++T/puv3xkGJdL7hKpWMAJGp3d81iQb6pqmp3d5/a2+AXm2q5IaS8qwAa4vbQv3r7RhbBUgne749wf3c8lFwclaKqQI5i5RnRjAnNMTgHbnA7syBCdIzETJyziBRm8J68oyKScq+GWaSoqKmP3ntvyCbCRDkLMdKQ/iEE0LlXPengxoKKjnyoWDHJiITiDQuaejZgLSZctCQpHWYrKTA5IMHSsaH1JBD6DkpyuYCaNlWVEWHsQEUINtYZDYoPJCIyEFQcdeAzxsIJs99zo6eY3ybrYeeYMqLJn3aiR5yyMcU7dUezEfxtVpafn3lCYRtnLI7/n4zKlKY+8fsGMDZCHaIVB2Y6dA8iULUp1zstE6eM5jjod0p5ztXOg4GYHNuZVJ/IsJHpHnzciRc5o6nONu6c3YJT8DAeOLRKIJznKU+3iRPyn65wlrOY8yszQzMcjzNxgydDe9rM8co2taw7Gbw5Qw8wi5nGp/feJU4ma7LbRuNxA8E0BHfI4+ZOz3NYmIJzLCWL6jBFxxMv6poYqetJUtnf1w6eXNFDjwsrK5/Z+tICcwdYuKkwhCFiQVPziJIhKaghOyA2QmBQFVXJOavQoLwGRGAEAfbM2QwRxFKfe0mBMTiqvK88O8YYQLMyavCuDr6uRrEREeach/gvm3glclYtKmZi8rmU1OW6ccH7nAuArVYLAUsptW2b+v54n33TLILV3jc1p2Rd1uMhHbt0aAsym5iagpUuZSnAALkvq4orT7niR+t622bsoOMcmuanP/jFv/+bf/TZbb54/l1ePPn4m//CD3/yt9pSHlxfW3e8O24rF7elW8Tm0KVYQ3B1lxMqS1FivX/zymyx2Xx43G2Phxfbm/19CVffe/ZoU7lFc3N3fPWTH/bbm29+tL4IqQkkx33lyNTiKvrGUVPH5RrrS1w+SVrnUrvNRXF1KeCbSo0VwC2DACNXHCNiQSrAjYFRqA2dGQISEJmaC76odjvhEDbPngIU5AKlgy/8i/s78XSsse/Lz778rfrq8sn1s9zntktFWlBDxVySJ65cvLp+VF1tvvrJz47HAxpUzSL4uqT9Auzjp9dPVw5Lklzu7zUEj556VXFe1aroOyzkPSHQGNwZgTECG9S1c55RtT2mXIqqOBfquuq6foAcBRUpxGQKi1BlFSIC5mFSqYsVEcrwIoAomMnQKEu0SNEh7lcYhy4yIhuAgYoVK8nUoQuFQogArIYApEULprTfSs4ub8ih2EHAJWzQr6uVf9D4vPZvjaveOnQKrhfr2qyDD0wIQHMVv42BvgGSmSKizsTILG2ccH6G/xHBTkL1CQ4mPLGJRDo3CJNzOUHJ8CY2VMCeTSw+yf1Ptggm8sbU5nWMidApHpgh0XCYCDZEC6oDVTUyHTYKa4hITU9u7kxxIJ7eFGHSKo3u+zBuARBFFYe4iWyoCZl3aLrPsxPnPThlG2Yw1yFAOdvX8X5tGvqIo5HC01bPDrmddv/k1J/sz5SbGbPNA/zPNeA2BjbjMoeZ9gZANl/v9AHAWToAp2SPIYxdZnFsIqp2ls0eAkSj4fONAmgKVfCgw4BeQzHvYbOpmiXZ7u2yAHU73pXA7LQodLx2YVETeyUyzzY0Wysy9CQCZFAwBVClMTIDkzLEr6ZqqsOsLiY2EEeOHIBql3pVRdWmrhdVqKKPgVWLYwpVYARCQBWR7Gvv0KWcvWcismIAoKrHQ78/tM6Rj365WAb2phi8q6uqlFzEvOfNqun7Zdsd+jYhzTXeRh5NNSz9qnbtUlWRkHKfATSXVHkOhKyas7DBXooq9NvdahWuafXq81/85u9+dn+XDOvnH37j6ff+YrWkf/Ev/Is/+pPfe/fm3cXCA2AWIcXd8eijzyVJtlwKAYWwTNtibvGtX/4z/5e/+X94+/LL//6/+d97+/Pfae/Si8/vGl0Q6ZvPPt9++sX3Prn8+rOw9n0Vob0vjl3Y1AC8ePjQP3nU7Vp1VLqOLh+GxbVQ7epLHxZAXHKJbgHOawEOAZDBKQcicFA54mBABgRTL0UDIOficm09s8OY0qMGPelyfXH54s0vXt5Q6I6vbl6/+fLlmy+6rMfjnuuA6hZN7HPb9eo5xFB3Kb3+4U/vtm9Wq9X11aPL1bUvCYte1/7jh4vHF650oT0cRC1lTdIfe+00V00kRleEvANVNKwqz+gI1UzBJHjq+4Rgy1VNSIbknO/aXorkklMWQmB2VXRZVFQ9OWTIqn0WRCxDq1gcWlMCDb4jmnPORU/eRAyJimjf59SXBOoJh8ZUQGSEWbWX7DISI3IwG8QJpW+PLFAycnAUaiBTyIZ5GRYPwspX4RL0Y3NdLttk71p9i9CRtoIGZKhETk0GqKLJ7bM59p853pHRmeFqzECOcE+Ag0LkBOozY3/CI5hqhQcYG7LQNAEGzN7qgN1zdhYmEzItbiSWTpnUE6RN+IqjgVNzJ6U8Ip7m1syae4ApKYun9eJoqcwmodNIE803iad7B1PV4Zbn9O7E48zByXvEy3u7M1moOb1gRnMKeL7abDxwvmGbiLCRhKJJNnRijKY6Bjx7JlNCYixgtpNjfr6JMDa7Hs4ygKHJBMzphPl5DpThOEt6CCOREIgGPDNDA9VpDJHC0N3EzPI6BO1Lb95ANXUVh5WvHq+8+QVv203VLGjP7X30ttwEFwEpExuAmhgUQ5Vh+jNIgSxWCpih8+QEEE3EeccMzjE5RZgLLc0TdqYOqZh5wso5dtEzRu88E0jxjHXlAcAROiJGAADJBRAdDzlfw0hg2uUCA5eoULIcD8flYukdee/VzJKaKWXoU46L2kF9sV4e9oft3Z0qOvYli5Wcut7HuKljKYaIEH3OWS3U0UcCFQMtANal0nZ97aGYf/Hy5ery4l//K3/hwOu/+3ufv7p58d1f+c7nf/y718+e/bk/8+f+07/1ends66pBs0+eP//Z5z/Zl9ZUGdGRB7P9/r4r0jSP/8//8V873JevPn3xS9959sdvfvqdqyUe24avLAscj9/7zpNf/tbl1cLsuEt7NQBwtvn4MTmXkhy/ukV09cNV6ZSNibyrNlStgIKSMxOsSLNhZCUyA3DREM17IFbk4UsOiGYysp/EHCMQEPiVwv4GLp8Vb7HmuqqXl/d77+vy8zdvu9ZV8fWrr0Lyoa73+2wq7LwhJKO83ec+NXHx6PrRxXrhSnLSOyofPFx9+GDT+HzTdqrYdXm5DF3OqWQz7bsEiKpS+hJq752rQ0ACJmIA7zlGt6wqYvIhgGExyCkFz/X1MqdSihW1UvTY9QOeiakW63IWNVNIRUbnk5CJgIFMiSl6cd47JkRQ1eG77x33SdFKAvOOAF0BAcm+AB57QjZR51ojcx6RBDWg9NpmSaq+7n1M2mfpndtWWMcQD9K7XHIry0K+Wb5KKZVOCU2B2GBIUOPIbEx+/wgaU/uf2Q88w9gJOk6CSJyhBsZU8cwPzIZhZprPZPnvM+4DSzTD+TltAlO2YkTAiZo5CYbmo4dfORu6sgPCKPc/KXfABpgDmrzrEU6nd5uKr+CfC3oARliEodH0NNnAZnocJxMBM8118tvPLKThHEadIH682RMeT746zr+ZBD/zfp6CpDHVPOgvAWHatOkG7MT303xDdgpbcIrCCEHnTkyk0zGEA/s9L9Qm6zomFWRwwwc5FNFQV22DNMeTFQHTLpVUy6aK9/teQIJzHuF4e3/j0wdX7iqsY3cLUppNs37AwWVHPfRHSQUQ0DmkgMCABKKWE+QEJqhqRQEMHQ+mkskTZ0eGbEzIoEzACM4sSQmIzjsf0DkfA9fRE5ophuic48HspVS0FFMhx+xYBlfOQE1DcFWMgObYMSMym4mqmooZRe+ratl3qSQpWdvdFgAg23pRe7i6eXcLJosmLBex76uuT47MAhJAFo2eFNC0IGIVmMEDCtdNCLyIoYqeAa+u10+/+7XkLz75/q/8vd/59Lf/n//7q8cfvPniF/+Vv/QXuO1/67d/Cw2/9Wf/nO5v3JtXoSNPUFKvqpaEVv6Tr/+Z//G/8z/5B3////1X/6d/9XrRfnDlf+NXP/r6g2s4vI7YY1u+/80nj6945ZP2rbW9EZRUmlUjmtB5v7zwceVXF1atPG+guVCqVQAFRMwIiKMRYRgyZWxFgQmBkR0Aj1+3sYBoiMQNzKwUETPmsFrUcn339nZ5/bA/ZP/uzvX9dU0fXC1vf3ZXXblf+d4v/cmP/sRSZucUrG+Tj5WaGrhmubm4uHj0aLVkg7aVki9X8cPr+tFF7PsUHMXGk+Nj2xuAqPUpeVeZKhoulnWIxESHY1sFFxfRMcXgHJOLnoi6theFw+EohqVPgJBy6bOgc1UMq80yl4J9TiKpSyYgolIMgQgJCYlxKEYZOsoKgGbpUwEEVZCiJZfhYDU1IDUsCsQemcRQAPqijk1E+twD+SpU7NBEVCEXpYDI6B02Whwkh9z2+bbVd62+7WRvviUQ5MBBkMVs+BoTEjHNMG6j1p1m8B/zqiMTMXE4Zw0zRxd7ZoImMHsf1eG9X9jgxE4VSBNajnByliAFmy+AU4p3vpq+R3uccx8GAOCGC+mMbWdLGEzJfB6eCeLfs0jj5c888dPHV8/vcqL9Cd67yGQSzvAS5rKKMaMypLcHYwQwBxGjVHfe1LOt1TFGwqlI73zlZvMTsVNd2ynomYM3OIsaRqqKxv6cMKZYAM+3brK3k9E4ezaEgISiykQ4JMOnk0XVEA3AI5JjRAaFY9aPF9VCy92hcz5UjtZVvPZ0tQgfL9dwJxcArkpdu0PskTpiAekoMqo3MvQVAMLQ+MEZmFkSADVVM0VGZAJIg2SXAdiUwFDBEbWpeKIk6sDqUDvmKjARSClA0Pd96sE79o6YCInEpORsCuwdeNWiUAxUfcWmIDZ2+CAiEck5o5kVIU+iuW6Cd3Xfdm3XH9rDXcqLRfPo0cP73TGbmFkMTqU4xFQKMkNOwTEAoloIWAVPZDnberNsKk+mzcJfX19G5/rtzeXzy029+iv/hV/5W7/9J2/v77a3u7/9f//rv/Yv/bdf3tx+/vLViy8+fffVl7v7rZZ0b50HC/Hy+7/25/7l3/jX9oebf/d/9e9sX7/5ZL35N/6tfzUcX99+9VPqt1dPV5QPQXTF3Tr4ygv0ckwpNuHyg+u4aHJXXEUYHFSV+cotNlA/1LCEsCRX5SKAjoMDcoBE7Ab/aCj1QEMwBuJzyeD4cTKwopqk3x3JgXMY6+XqyTNvqb5+WF2t6Z/+flwcW7Hb/vIPfvLZk29+7Zd+5VdffvVF36ecxHOoqiWiJ46XFxcLz54gH7Z63K4xf/PJ8uPHq82K3/YmIgCYBY2oFBO1plkRckpds6zJMPeCAZeLKjjyTMGTI3SOkcxMECHlbr1aFC1SsYruD3p1sa4WTVHIUlS9845SNkRyxQkQIAgVMQVVM0EBtGOfiKlGJkbnGREcAHhT9jkXRCQkA82iZMhg4knEsmn0VhTbrgWIjlgjgbELlRE6t7DQRP+8uFQDd1Ci9scE20N5e4SbTJndEXqgkJC4cpNbqkQ4fGFlQJ5Rdj9OfdKpW+ZEOUxP7sznnzn+96qf3kfPc2XNyCidocp8xZP3PNY22elTAnD+q5NPjlO4MbI1E6Fh4KZIE4AJRRBPTA0Mda2AcE6OT/g4+MkwGaTTumESusPUgQ4mjn2WviIhTMNxdNbg4KmACqadPOWCJ7IFx7hgImDsPc3/7ObPmwaj6ThRP3MYYSc66zxYGM25TXHPcCtTkDPlFgZuFobMvdqgD7bTaEsAwKk7ERgBqYmZ0lgGjVaEEYBIDBlIZfB8CJkYzRC7rr8/2rcvqk0oN/sW8vHu9t4F9Xtr1u67z9cbWsLhnfeOytFEtPQuIAKiqWnWbGhoooiCYKaGnsEILA06MkKgIVVgKiqARIRkpil7ZATlQLEOMaKaSUntUdizZyREZgKFnAohhcCNr0RK3+XD/lBEnENGEsY+peWiAiUEBCLHyM4xUxU8AHTtEcFSzkUlVA6TXa4vD227vdsjkg8eFMFAcw6eUK0KpEUUIHWprryIlK4IkxRjZ2BdTv1qUVUVKvRdysvVGlFdur8M8S//xV/54iZ9/mr/xc32/u4Hv/Ff/8u73XG/2/evf56RmuaKvHmiUIXK0W/9p//Bp//0dz642vzSw+rXf/nJv/SN+vd/8/NPNs3tm/2mrl9/9rI9vvvGv/BBzT2lvt93q1VdX1ThImSVar2pri7MV+I8uJgLMEUTLF1S7mnhQU2LoPfj93hw8wnAYGiOTZMHZypgNvTvA1CRDCYheC1F2h4c+NVGu7Z5tnq+9A7gx7/3+yn15tRX8Nmr15vHH37jW99Qo6KachFwJkzk6ipKum0P++OLzx82/M0Prr/78cNnz5bekvcuVLE95lJyERKV0CyYqN21sarYERqoWup7R8bBE4J3Dg2kSFXHnDKCLRcVADSxiXV1PB5Wl8ssWlIqYqWUpNp1XQEanDLyRAZZSzZJSbKqoQENgTUfUkYrQ206Ezpix479UFJkCFhEUY3QYQYC9M4lMRYgKU5JxJuIjtkFVrJScpEvgEmxNuSUyyFZ0ijkODjzsWGfFBVQVM49S4ShZy/NcDsh/dhUBwxG+b1NlmDOIeJEH0xk+3uMzsktnxiKGTDnAxQAx/cYiahRPj4ccOa7T5T8DHQn9mSadTlBLQCYO7dWQxe1KWCBWcOCp2OGrOU/5/+foPYMXifbBxMJZTMtck7gnJDb5ldPWYc5cTvlr+30E0x1uzAzNVOK286vPSVBZus3YfBp9fNew2kwwmxwR9s5jXaZdhknUmiKuwAAdPoMDLKfyWCRmhLRaCSQCBlUh1v1BKA4CPBVzIbhWGCi0ElG8h+s3NrXfWodunL/KiLave7d/XKhHjo2jXUgQkIGKKZiIkgAJgN7Z2YIgjgwWiKYtSRyhIjMHOpASTxjRK4N+1KUHRtmQ4rOeZaSi5gBBudC8ORwoHIQgBAVoORSsuQsiFjXFXsGUGa0oqVNB4M6VgjgCGOIoYlqJqIAtlgucs7HrpNDPoAiAhI9fXotvRwObVdSRb7vkyhWIQKAmmmS6D2CFSkoILnklJo6iqT+cFxfXyyXy4sH6+Vms7lahrgwioe7Hec9HN4sj3ZhVD2++vGnn/7BH/3R5eOnvnoUa/8wXnz24sXPfvwn+/vbi4sQ+uPx/vCtZw9+/de+Jfe3Ty6adnv/9NFmd/PVRx+uolnru1/+jV9qrMVjCyV7hnoZOHpjXlxuwsU1hEUpLBg5rGjxAOOFYBRBIWMxZAJRKy37AJjROUJCdGMO9EQej99VIjQ1yUW7ZGpMTGySAYoER53RYXuQLj/4+vPrq/izP/6Tq9v7ZeMg7V69+gJ8vHj8GKuqTwhY5cKQBHLf7m7L8c3DBfzZbz35pecPP3iwWISQWgHFotD2qUsdcXQ+AKJkXa8XPlJJfS6lqUPgkfkJwWkpJobEvVqsw2qzdI7AOPXd9vauqHrvvHNaRFI2MyaMVfCISKi5qKoOSrNUBr9x8LCYyTmfSxEZi1ZBgHBIkKGqqqiaeodgQKKePJJTACSfRThLwpTYh5CJsgJzxQDALCptaVWoQFiCryLXzWq5yd6ru0+IzrGCRxKDXCTlIiYmgMiqMtUfDUBDo4TFxgTgSAa959xPJPMYDdjkOs7e+dkRCHYSnrx3kclZnrzh0dM8RRwTYfH+WRO44ZmvPhq0Kbs7TQSzueUyTqKkCd/wrGD3vcjFEMlApzYDk5mb0HE86IzAmtiYcbMm5T/CxMbPYdW5N28T0XXO/k9t3fBP5UdO2ZTpXg1nczGvfxDmzJZwguzJXp3lm09GZiaFbNYBTMognVr/6MkSGwyZf0McJsWb6TgLy4zMwAIyog6Ddh1XSGwioChqgYhDsAym3bvD0TnM+/tKuouKH2yu6XB7SZna9tAeKi9w39plVUWMC4Zig2hrzMAYIA9Gi0EUUjJJiMB1RZilAkPJBqEtVYTUK5pWlUOFSFwMB8KoiJKBjw6JckqWhhBv6GZqdV1Xdd21LTPFOoTA43dBDB1oEUQOni82K3BccnYESC5b17d9d5TlcnG53qTUl5xVpO/71y/eOGZgruuqSKlC07dde2xLFjFFIFFdNGHlq5Izg7ZtqgJ5V4OW1WpRV6FqFvVqsX7+pLQtIkv26dUN3u3kXdckf9N9EY/lkw3fv/lF2//4YhmKw8v9/oP8bn3ZPPvwyc9/8eP40WZZ0cOV3PddbLr+/is9vrP+uFiBtvvrS8a0L6WV7uhRmkVTQHObLz94Ri70GRDI3MKtHtL6EW4e5xytvna+8a4C8oCOvJOcDRHUbAi+YIIBAjUFBRvIOjAwNR26fRFoTl2bj/t8vyMGF5xzFJA64C6ZrzfV5uEjKiSXTfPtH/7wq1f3x8OXnxs7jivzvF4+Alc0twHL1aP1hxeLbz5bbRrzjhBIFICcY/aIPno1E0sE3lD73KesBhqZCIEJq8qrynHfIlhdRe+8EQBYuz8CoogCYlPXLng0vd/tF01YLGoXgpkd+5xz7nppQ8xZjodkRcEMiQyGlPegtjQCJCZ2nkjJyExNVRGZGYlAiogQgYioc11JgAxgdXDkPAKqABqR9+wcGKIYEzKTAvaIyQqAgfOozBT6NmVwvaiIEYOKgCEzWiYFUZNxLgcg4EmpM9Iudsqzju7iCSZH6D0h70wNnx81/DzagBn6JuSZQ45B5TLx20M3ZpwHAo+i0oE0P6H2UCisZ3ZhpHlU3AloJ3/dznBw9MtP9sXOF3zuhI+QOomCTugPE3Ey78p5vDBuIsH5tQHgfDcnwzqRXacAZwLx4TA7XfLsTk//nMixc6ru/YsMxuj9ReDZZaaJazaEZFOaX8FQEWxodKFzX73JfBkCEFuJ3jvEu75XQjBsgk8Ih7YUzdF5530vGZXIaBniarOS/dvdrquX/mFd5bv7yvvrdVwu1pUg7W/Wi7BaxYAVc4H+kEUQjD2TD+P9OQZkKAlMhk1H79gMBYTER9f3xUoJjjyjR6gcqAA5V5BM1BFnM2ZnRMiUC6hBKVnVEKSKoa5r53h/bOsqVHVVpHR9MhEwNQXn0TGHgEXKdr+vmwjA2/tdFoneOaacZbfbBx/rOqw2DZqWrIftYbvbe+9cxBh916cquOAWNzf3JfWlmKkNc+Z9cC74VSDJGcEvN+vN9bXkLu2Oua5uPv/KOUeu63b3eXd7tVmVticom769lVRv1n/um98+5mO73X7ro+//v/7e3/740QpdfBjqr1L55BtPrXSlvV9V9GDlcvu2tO+++Y0nrhzbnFdPF8uA3bEl7JvVpu1670OzWQGF3a40j9YFqrh6aM1lgQp5yc2lVJfgaoPAzGZWiqph6jN7R+N4UUQkhUGNjUNJzuCK6PDFJxcWHkAsVt5XHcV02JecAMEzOESuqrj0H61/PX66rBZfVk0Vjb54edMpv3zx9n7/JtZX3L2KsaoZw2p5ucDLII2TiKlasGUwICR3PPZFFQiOuwMa+qhS1DN5RhCpF9WyiblPx+PRMxNYVcW6qQ1AzEoqfcpVjJvNulo0AJpz6vt+fbFg50UHYjNDyWRaefKL2CdxxAYWq5BySUVL6oWQEcyUnVPVnIcaGgXTIglVY/CxqqoYyIBMGCyLDMMEIDqTYiUvm2gG7I8YYuNqB2BmZMCI5tjQGUUG8kVWDrapw5QU2TBkVRIGwCTm2QXnRKlIGVsCnbjiE8U9QYXBSVU5emA6SklO/iyevP/zi0wO6YnpPgNWGP3lAd6Qp4TBCdxgtjHjN/2EhHhOrszwCKCI5qZE9txwYgpGJtSbKSqzif8eDcbME03rPZXkzo0WziKB0fIpnM6dIp/zDflTP9mE1FPwhKf4eEwnz1txcr6HwGP8Jc6WCOad+Of+2BygzOXE85HTfZyMxqhNMgQewhcDAJO5UmFMQRsOjhwMcl41IFs2IZW867UgFo91qNDgsD2Q5KpaVdHl1FMycBArfPDkyd0XX0HWiyer58/W7c2Lcn/DNawqt948iK7zaM4clMzsmMFUJCcwNSOOEQShdGAFtIztQUZp+dBoThwTO3IOg8dayQBJqRPs1NBzIdRsWQ0RSrGuS4ZkAN67RbNyhEDadomY1LRPfc6SU5IsqgURfU+hCd47IkgpK8Bi0axWS2LqUtKcYx2kiKm0XZtS65lUMHi+urgUSblPbelKka7rYwjr1bKpqz5lAixSwExzSVpiDLEOVaxCFURKs1jWiwoUndFivUx9H5tq/c2Pj7tulSxeULqNX9x/hkf9xc/3bd9uVqv/5B/+zQeX1f5uu6rg9u5nlxt4cBFLl1xqg/eHt6+uL6lesOPkRCILYikHsZLWl4vUdnG1uvj4MS/WfebV1z5QjdXlU4hLrS5El8SNYm0W0deoQ0ErkkfDQDBohIl8QGZTneZDTKowGNwdNEQdG0OyeePFsq4qXiz6/UHKHvo+d2V33LkUllebJ9/+7oe/9O1f/M4fSvLbl29WC1c/WfrVU3S+KFuyjx994+7mD9l2TciXq82qIh+obY9d6nb7g4IVVACo6pCTmqp3zATeE4WQczoc1FQdoQsheg9oh0OLRKUUJmia+uLqEk1z34pIsWKIxOyDI9GUsuaS+5RLKUVFQQxzFseIBIasBgnR1PbtkZwDyOyZwZmBoRFTcBFUBDSXbMaVGwSGwEwmoqIpCTASAXVJRc0OAKiqVbOkAMpRxYiHBkbYMF84D+g6oTa6Q5eSFjTLYMiOEEopxAwARAyEqmBqjlkRVBSHkZSjk4xmhnTifQdcxkm1YzY75WOi9pyyJsCTVhxhMjI4cygDACICTD0lR+yd8QxPLu4knZlzCTNyj3z1wL2AoYMR+AAm5eRIfE9gN3wY5x/P33IKF86M4vT6eMLsPJ+Zhgmj4XyZ89VgDhreMwQjQ3b6aayVnt3xs7BgXsNkXMaLTbTOVKUBU3QFZ4ZqvqTBVE0whznDj2pGk/5qYFsAwGTo5SBTCsAUBuIOodBQ0W4KUtLSLVt2xQPm3GdAxKebhQR69+6GelvWniou2uOxz9Imjd/9xkd3X/78cLun1eqXvvVR96rI/RtIimGoXFfLWfpOtSu5uOCG6bvIOFopMuiLSQZAELUhzwBAztkxmakbVHGRFZA9cSHO6pGPxUAtoalKf0yqhI4dUR2rGCMQSs77Q08A9aIKnlPKhFoFh3VgRHYuEAkamTkmZDazw/HAi0XdLJumLqXkUlCNCEwk98mKHnfHkvNi08QYlstaVMG0bVPqU4iOXZWLlFJMLadipTChR0K0qgqE2B2PzhGL0wy8UwTzdeVjfbi7M+RqQXWz3uHua8+XX3z+ehEWIWK7vXmw9lcXTVfJi198VYfw67/yvFlgz3po91VVf+e7312sUNvbYLrf3QZGZdV96wNhgPXFBddXEBadBvKRLx7qXjt1rBXyhVs/gMW1hSVYZQomZUi7ixgA+hgBkZDBUOdoV+dhSzh9pfksAEcgb8FAiYmqqinHWA6sopb74KImFY+lp6uPPkZ2AN1PfvQTv/EFJOWy8GFxtbD204vGsE+LCOuFj86lvu26w+FwuH13c+yKFAEkJkcVqhgzIljJOUZf1ZUnIrT1cul96NsjGIqpdIkYV5vNYlWLFAa6vbsL3nH0i0XtY3CODbDruljFywdXIpKygKKK5VT2bWr7VFR3+xa2kIsAYl/EzCxLKqJqROAc+MCeaajAdERoGPwQQY3uJSIWla5TEAshisjx2OYk9bL4xpx6t7pSDBmicV3YVy7UgusYL9SSD1Fgn9pDX7RoASDnJxJ7IF5gFAGaOWYDNTQ1G/I0M0adO+cwyUxGyeCoLBmrHecnPXmpkzkYqetzyZBNnvBUUnQOeOOBNkhATifBbIOm15B0Tu+auRPLjWjjVN+za574nWkJZ4g53JZN2emzT+kJnP9UVdTZqmDeqvG0CZFPcH/imGZqac5q43jxYYnDxSfh6XjNaWzm6X2H/842hs7WMpcSIMqpd9L5n0FVKjCbyeF5DoXnDOwcGnUlDc2qhqrn4X6GD40AFhWO7A8FiiGHVLJpf8fyaFl/LT64ef2G+xSrWIXgsCAXO+b23r730ddvX/7kxz/4YX7sfulrF6urD/Lti353g70QqIPOmSAz0jgIgX1AYjCBXEyKiQEMs8wQQA0QmRQRiYnVeQ4sUDuu6NgZJABCQhKQ3Kn1RWScDeEQo/fOh5KLmIqWWMXoXUllvz2UoiYFAFygi4tFUzfBOwBkwuAdEtIw5Ndkv9s57+q6YiZDTX0vOZtiSaVulgaimo+H4919YmLy7urqwhQOu2NOacib11WIntEiIpfcNXUtpYhqbjs16XN3dX1hZPleQDSp5K5TEzXqtzfe0ofXm8fLmjTsDu3t/cHVETDXTZM3i6dPr6oIb778hWOoWb73vWeXj6vbz39O0Buhr6F7ta0sZU0mCBSWX/9af2QJzeLxRxAX7QEKLOvNh+7pByUvJF6oBLAANjSSMSmFkJDIEIgZZzny+HUyMxAdgGX4mJvO3ym1YkqASIhEFAN4Cf6K6orjgl0s+9vueMiQkWi1Xi0ePn/2ze7Qpc9+9iOntmqanHq9OdAiLIL6zeLB9bKuYs6CBcnFnA7kApKBWlEh5wgJ2BxTFYKVvq5i5b2knkNQsy6l/bEtOV1u1j7EuGyc42W1uNttk6S6rqo6+lgDaupzd+xUzcWwXK0BKZVcASFA6WW3PUQzV/mcigEp4LHrFBAg52KlKABNDc3J1ASEwYBAcy5Eh1y8c44hOCYm710da8viyBUh8C4XAEQ9poozcE9BeFm11HQQlOpjwkKGppdLH4zvBN8e0fR4zMkKGJAxGDERgWNUNVEDHFx9QMJh3uD7zAeeCwgn4BmeJpxSiAMQTZqfEWEHKJ55n5FiBoS58BdhngdpcGJmJud2hr+5IOB05ckynWGbO/1/8pNHcJzTq3Z2f7P7/35WWWH6AM+/m//gHKecdgRPHfPmw96/+tma7FyENDYymhMBsxgHxjHIZ9s47fSc/DjVNIzXH641xmqTWbS5qPksBsF5C8d873gveqaTUvEhFi0qQ63yqYv4IBI1tJRzzuVytXh199bIgMABdX15K4dHi/jRh09u39y2XaoM1gsfAkppd198+tXNF1eVW1z4fH/76hd33QIval1uaocdmQwdetgTWNKSNCuyMyAsWSfmB8mhqoiAGRKLJhXhOnogYmbnsmpKoJAziBJJNgYjNB9Q1XkAI09MzrlcMqkZoXOeHYmqmA2DPTx7YoxVNNOu7SWV1XpRxeCdUxBRJcYQwqB6l5I9O+diWC3bQ5tyhhjblJuqMovHw54VQ+RYVVrSxWa1Xi9Kzru77W532N8dm6byPgDaol6FGL0n59kkqUrtiCUzWoiNaYGSScQ5iMvF/m5vIJ7BXa33dwetXSlcNJtBVPj46fVys8j9MZJebeonzz948uFjZr64vuy3LwnS3d2bfH9XP2hiqBSsuXjU3XW2fFhdfwDxooSNqxu/uhJbSGosXFpYInn0XrOWoqUUFzyMhnCssVGDoTzchnZXA1d61m54/BojIUM4jcEbfE4Gb46X3nnnfQq17G8tHXxAcH7x4HF72H34jY/fvfz58d1tym2zqEMMjtQhLuMi1gtVDE112B5SBlUypPaYKDprS3AudYmZ0UBKic55ZgAjJnaUulyKlD7VdXDsL643QGCiL168YOZqUV1dXyFj22YTi95TXXvvFLnkUkRijKGKBNhxT4jLpRXTXIqvDj5g3cdYd7e3x7bLTCZqzISIZgVhkHti8J7UwNQTISgoApjzxI59CMqKAlm0E/M4tJZwbUFSv9uJgpVFvM+u7aQgoQf2eCyiZtpDje5isbTjXqGYARIVFUMCAkA0wlyEEQqS6VgEoDCVgk08+XkYgPSn+YkzamNEb5sd5AGFTnKWmfmfQO0MHwGBiIa61NEpHTnvGS/PaJQJdMdGE0Ml8LQEgLGn/z/v+xpMTRPwjBaZ0HR8baJUJr94vv+JAjvRPBNdg+ddRSdjM7r7ZxGUncKNsaWRTdA851on8zFefKL/7SRIGjLZeOJ9puKMqb3G8Oyml3WSf+IcrcyWYLIWg1EbOvgMjzCGoCW1qR+DVBWkkYhSMDMrAK/v77719OHC47Y7so+u9nVVaz7uu7Tw9cPrh69fve23h1QQXVk4WXhYWXddVc8fPI6yWPk+2MFjS9qNNQNIGMgYQJVMiQEEUFVVARB5QBZVE0MgRhg8emZT4DpSCBSKU7VtX1VWCKGXApIVgzkIHrL2qQgYEeacdOhjNOQSFEVVsoRAwQckY0IDi9HXsQ7RocHxeAQxAzTUGHzqjAmdZ1DY5lRX0efAxKKqkhF0v9sTDRXIy5yGxpF28za5qnIhPnz64MGjR8fdvaTSt71zvFhUMQQXQ9HMIW6u1iCdaSkp32xfxeXaN5GYQuWbZlm6vt1uKUvfptT1zOFiGd+8vQWzrlgVOR31uN9abh89f379wfXx7m30a+dMK99vd1Ww5tHSTNi5zYMHvllljfXDp7B+UEplYQ3Ng1Kt0V9CdUH12oBVbCjIALBQhUGwgUykBkOTh9P3G0/fzylpN0zRmILoWVVhpkNDTSMiIADvYblEM8x9FYhNuj4r4MWDZ1a6737/V//Zb/12ezg4CItV1XWdi42QyzlCBDMPGNvjrhdNBUMVssCiXiBBFT0zExKDRh/8kKgAYiJEVNPFsq5iZaAp57Y7Auhy1URfVZVLfZ/6lGXoAyHIHOtYNU3dNI0PKafD7kgEmlWkiFnJoqaeyDGbJDBbLKKICqshmejoFZsxMzsavsGeOYbgGQkQQBgRmYiJPZMRGQENejS3uLjOuJC4Dosn7yTc73BHaE29T9bmwqG0WQ9tESMOgZxfLTaEXZv6VMTA1BKak1nyB2AgMgH28KzgjDnAM6DSsR3mOC4QJvJnZH4ml3wCzen0KVyYiZQJdMdU8zjLfU5PggKgqQ4FhdNVR7yeDcFZlgHMzM3ACTCXG58bI5hQcS72Gk3NfPUhJLH5BDtZnZNhOPE/I90ywOx71unsDaa/hnP/NOU1vOOcr55u673dGzf4FJacXfxkneC0/lM8YaO1sDFGs1P49Z7GaXpJQU3RLJe+PdZVSCmpKREaoOrYFMLAEE3Ibfc9OXxyuTq8fs3Ipe926XhRB1QBAOfsYu0LZpa2gv6i5mcPlwtIJvvdV2867eKV4wqrCLFuvDPpj4hE5E2yZUNilQJFhmEcxIN3MdbaEQ/jgYvhSCyT96ZQTCwrB26Y5dCnlFFERRGdCYAa2TjQh4m6NrmhDIyGbhAWIqva4dh5z1oE0aQOJOD8YlE33vMYhTCZKDL0bdseOkYsJb+8vVsvlvVqsWgaq7yKSc4qxh4RYNk0XepAVSWnrst9f7i7Xa6Wy80yhBBDKF3a3W3bwwH7DhFXTZBS+kNXch+DlyRmh5wze1e5Zn97d9zed9v7erVGwNv9gXwyxE3jP//8s1CFi9UDR9rvt9/+7tcef/B0/fBR862KdV/u31iXt9sbkoIEaH717BlVS4nr+uFzqS77jv3Djy1ucHHNywulBtCjc6BD2yoCAGekYoTzXDssRcZP4TBiW4cc4Phxxdn5AB2+roNDMv2swIgyfQ7ZERGqclpbC0Xa/a51Xb9ZrK4ef+xKp4fDj37vd5eLcNhuV5dXRCyFSk+E0LeHvusPbXvsOlUhdpEQEI7H1jMjgyNo6oZoFEko6GG7V4PoXVjVOefFojke9gDmPMYQ2XHX9TkXAAEgI65i5ODZO0Lqu9S1iRidcwgWFzE2MRUtuagpsxvGofddalPCYX4qgRkWEVBDNFNRBQQCHrXzYhCDI2QCJcQipXKVD8EhAQyD5kMBq5frA9WMzrtGsrvZJzRJLty2bX/IxUAFidiZYrEQfVMFQZEiYKw6QDEh6NCTeHhlcmxx6lUw4ftEkcyYO8H3yEUMgHuOU+eOuuEUFExpSpyPswmzQQarMIaOOkHUOZaOeYcZrqa/Jxxzp+XMhmEiNcZYYuZTZmg+sUr2Hl+FM2gPHveUNrX5yOnkP2Ump004KTgRpjT4sI6zHPPYIAXOIB9nSn4KMybiaVrCZI/xPCaAadtPd2WjKG9wxea0s03PdGZrdegOOKRPhskJ6tvjYbV8EJxv+xbUDfGZqgANEgYAR5Lk5d3u6x8+ukv329sDFejbLLpYXF1In6p19cEHj/JxW/fbhbClbdjvm6ibxjXOL5dVXSM7A+0Qi0riwGjFIIEUAFAFTYJgRAwMJsVKsSIqijDUIigMdS2OOThENlFWZ2giWZISKJqyI1+AwViBTJs6Iod917ddYnbOU4wBAXMpZpZ7YcbVomFmMIuVj8Gh6e3t3fb+fr1ZrdfLuq60SDbzzq+uLts+AwiYXV5s+i5B0b5rQ6hSykTkIjANPp0ysPdcbdZq2vfFtEhOue+ZsU/q2Pngg2N2nHLqU1+VeHl9lUsyle6Qtvfb0PWry5V0oiVpUa49mj54clXyUbK9eftudXn5/e9/G8Q0Z7Dyre99+OE3P2iuN9XlgshK3/fHY+7aEKjkAuqq1VqpwcUj9+BZChfWPIkXz2z52KgWbkxrA8cUNKGIGSkyjTJPHqu6DEzExgaRZmBqokVGwSciEA4t9lDMAExkzBkiE6KqmelYi0GEoGhQVNWYqK5FE/S2WCIi3O23FSPXV+snHzx8+7Ic+hCYycAkcACxbt8W6e932/2+UwBkCESpK7kvlXPOEYJF78jEBHotnonZBe9ijCEwmJojhRKjj1XwMThPRaTr+77vHLPzfrVaL5crHfo8i6iomUpWAGDivvSGNHTYNYMYPAGWrG3TZxExaFMRVTBjJHRD5YQJqIsMgKqWcnECh5KbOsTKRxeQEZid901VO+dMxSiokRoy+n2XE/RtAfS1kMMQA7OJkoAWLX1JJSEpqsa6WjaNdumYEtIwOH50PIeUzIh0OmKOTpXAM6wgTLPjzaaJsJMnDjD/akLQs39HDIUZ5qaWNZOMdLiQGRLh5D/j5AnPha8Tbp4IkzlIGdDRjQnbs7KyCVBPqD/dxWxFThzN5EXbCbynuGF4k/dsDuLUP+MMcqf2GDZpaae6imk5YwiAJ2LrFHcNNzRIS08NH+ZNGwzaQOFMVnHc8umSp0K0M3sNU+h12rHJis880hRl2FDopapCoMd2d7lepJteihJyUR0+pqNxASDn7vep7+WXnj37afosHQ7Vgtub+9vjYRV5leqy9OtImypzf3SQnqzhMuTNpQewWDvnDcm0F0QFAVElzWYCuagKATrnkBm0WClaBFWRiAy15JISgrF3jkHVKHgQBQTfeIFCCciUELxnr+YZvFEvOThOau1hXwCJqapDDC4VaY99qEIIjsl7ZkPIKTOhCh+OrYj44JsqEkDf9X2XQMR5V1LemyiYlhRiqOq4WaxLlpRy3ycw649dCM7X0TETGtfUpy6lPkS/3jTkOAQ2MyaKMYYYLx+spRTv/c3r10Ropsf9PbEH4vXjq2YZPBMzWbcnogfPr1z9ZPvyTiw/fHaZe3v4/EnX5Szl/v7Wk6wvFsvrRfNg4WsX1r5//Yqka+/fHm9vHQOTa64uw+I6ddX66bfd44+0fgjLp2K1hbVSMPTk44AQKqYmRMhgw4RwmFpaKgAQoaKoalGxIYw0UR3anKEA0tBy4xQ2gwGJ4nsuIaEBEIEZmeQ+SS5lJCawqKD3LlQu+OucDndvXv/i54zWHfZ1teIYS8lZyn53r4zGlg5t7sVMHfvYxBCDSK6iR6Pj8Xg4Huro69WirmNOGUxz1nZ/8IHrRb1YVcQkJn2XRQ0JYx2cD1WsTGB7f3DeG4EBiBYVMTAiLIAAmIsQMxHmXA67gyGLllC5mDkbK1jb5sEFc0TO01DrRACOcWikOIBbTjkzeu88+2HsJAAiMgUP6DnUgqjkVNHA2FNkS0Ri5om9iwqkpVgEVUspZxSRjEjBc1e4FB09wlH6CWPvF0Mc/E080xTOUDgzBzNqE44zBmx0c2c0G+Wkk4nQ2eWdsXhKLsPIS88VwjD54XZy5mEilWj2nkdCY9oZMDVns7x0Lpw1mz9fIxF+WsqJjJludkREg9lcDRTKqQ53LoEeRax2WsLIPY2rUh2KlkdTcKr4mo0Pjkuy8VlMgiqbwohhebMsaT5mXNoUro07ZmN3B5s2ed6F6TDDEzM23Y5NjhuN0YWaoRoxm2rXdtfXV+t+sd/tAQlEAACBhuieiJncoe0+++rlJtCHD65y5S4bhit58/L14zrW7f1ysXi2Wl7XFZVcO1e7bsFgZR/qyJA0ZbDi2NA7RFApUgTMyDuHHk1AQVICLYRKKGCmKloMBNj7KdtTyHtEAgZiLkUHoJEiCEiITOgYLQkClFx6VUPKKYcqAMBudxA1cuS9c45KSttDK2Kx8lz5UkrTVFXlF8uGRtfIGEHZq+mhbYsU5yGEiIB9m0oqLvi6qrq28845JFXtj93t8VhVHpliFURyKSimTml1cdkslnW92N1tRcU5h0Yu+uunDySX0uebV2+k34kYO6hrdqxQTHJuLlYimdqKnWeiw7Zl7+qmcaFsb29WtVtfrDaPH4Slbx4+Rjbt71W62y++gj47Cun+zofol1fV9UfLD39JmkctXnP1DOoHmtjUI1fOR0UchSIGPgYimsPgAf3HgBZNTUSsiAKQqKoYIqaiQ99NAEPVEB0ygqkREA4tZBGmfB8gqKlqAQMtQgZqlkW1FxAxzYh4bI+LEOqHTz/49q9Jzq8/+0nla4Qk1nXtoZS27/tj1/dJVEog9jEgooh2h11VB5DSdV3lw6MPH/vgEYZGRpz7nh1dXayQ0UdnVqRgzoKAPvqUFY3rZU3AVlBU+lbEVMmqqmqaBnjSGBoF0VKUCE0RyW23u/tde7fdJ8DDoUciBRgawLUmhDCwXYQ4lBOyQcpGzhFAyupSAUBDl/ocvBCbD+RCQOIMKpacCxHxynkPfg/QChbVhNqZHNo2lWJExFSFoCGIaC5JbTTTAGoCijTK6WH0Dc/onxMbMj7oyVeFMwHj/JrBRCwMiDbKz+cE4xm8I8yEC0zsBA/GcLzUSLoM/chgDh5OZ4zHDT44GSAOOYATxM7maNIxzWzSOWkzLX2EzFNualqYwWTKziKPyXc/cS7nzPwUQoz23E4QP1xi4pSmBUzLHb4KZ3rZ2bzMp5+UQgP4zWloJDpty0RRjWud1K2jabRTjHW67eHJj408zQCQvaK0banqqm07KSUwjepPAAMapq+Bwru7fcsM6/j84WWN+vG3rr7/yYN+++7Rurqo+cECGzs4YjCzIgAKhJr6nFKIzgVGEC1FSzYzdhV6JgMoechIszcABElQLOc8pmgIVGXIKnKMg2NA7EBtGFdpos67CigLCVMvGY5FVEVUzRDt6nrF7PZtCjF679kzE7Zta6KxjoTovAOQUrIUAvNFxBFmKSjKTHVdg2FBXXAVvBcRMy1FJCtCCYGD80iwXC4NAESC9zf3tyWn1aZeVI0phOgdc2qzyB4MF+s1EsTKp7YvJfuqQkzNalMtF7cv35SutaJ1HWPjNBdNfe57VHRZq9XKLxaltAp8PN4e+1Q0Pfn6h5fPHsTLC1HFKuhxR6iYu8hmbMc+MYT64ZP48BldPLaLZ7D8wC+eZKvBKgqBKNjoIA6dzMyQmFDNAEgHgfogsdQBtUGKqo5jb01MVFUVEUxBxcDMO5a+d57Z89CSvajyoD80EJWBG5FSHHsr6ryLUKX+mLKAWnSRULuyA4Cmis3DJ8++8R2A1N7e9Yf2cH/P3knpEaQKDtVcXSFSFaKBdV1axkWsI4gs6+C9G+QDOWdE8M4tlxtCFCmOGRFLElP1dairBonZZXaO0UsqqkpEyUqoAhGRd2iK6LxjQDTBugmpL2DWLJaL5Xqx2Ptw0+VyuN31x9RlY8eIGDwbspk5AkdkZioKiH6IQgkcganlBASK3lKX99iakREpClFWS6pHIw7eGWZlyiJdyaXoXW8tWGHXmbR9R56XAK6oGiQFKYKEJsOIvBHVCGxy/QfWYuLgZxIB5mKlwYkcfcrBYx3nI5rNDMVkSkYicPo4jY44zAB2IrX/lFwdR/oHYUyvnlmf4X1m6cqM5G50ewdt6+kGBgCeDM6J/lE4O9/OPBGb9KOnarQJzeH0FjAdP/TQHG9ufJdpZ2ezgTgqhQZF/VkkA7MlsLEvHsGk5jnZJByM7SkkmrZwbm4xmpkplJjURpN5O6exJspo2p3pjJFcRyRAA+yKffXmVe3rKsZODypiQJKNiBBB0eqaIzZ5p8R8d9tZe9jU3G/vv//1B19/dh2tpP3rQ9tyyLzAEAwIPTlSopIRQEsWMBz79IgZsg+gYlo0Jcs9aEFVNLEhFYyEgyi1CKgiOq6DipgWRGdAZiK5aC5qBZn7tjOiklIquaRMTLEOJIbelyJdn4mc81xUDrujyKA1gkVd1cuKHTly7IZuxqXvpBcpaqCCSId2D4qxqQ2BzCPTql4cjq1oMaOupCrGInm7u++7JKX4EC8u1t6HUlKzXjrvUtfnLN27+xCDiqwvoV7UilhfrIE5OEOh+7fvfBWfffNrSPnw9r60x37flpyrdbNcL8CoqipDvH3zRrRfXj9wGdZPl9XmqlotedWAElpGKiIGkkCOHlMR+f+z9R9dkqVJliAm5COPKDEzdw/34JFZWVmsq6sLM4M5GHIG5DQ2mIMtsAW22OO34D9gNUtgwHp6zkyT6q6uriyWNDKDOjOiqo98RESweE/VLBqIPCfSPUz1MVO9InLvFRGtmZombPbu6hV98GN4/rm4V+auCCOSM8W1swdZ1cAUiS8T5PUsZqmaKGitVayqmoKscVxNLRfJuarKsqPbMQMgmVVRKtX7ZUISOVpW8KhWJKA0ZzQY54kQzEiNmrZLw4QVlhndLjRpnoOPzH3/4rPn5fi2/MJKoZwR6ygVQH3gJvSGioAmmuYUnGu7OI2zd4xIkktFHIeha6OPcdO33SbWIiKU5mQKtui3BrkWAyHHvmmmcTreH5g9O95d781MREqax1IXSAghEjNl77yXIlYyErVd0+f+hT7LWVKyIkUFECCV6r07mzKYCLwntFUERWPvPC5SAZBzzjlGMNUqUqmKQDYhY6TgnbgKriNQDrXKUGUacw0Bmib6po0MIuWU5jJXNXNOwVQMHod9GSzj3xSMVgTXldUxOztCH1mSlStZ+XnAFbgvO0OeOCrXP5w5ihV97ewJOv/snLYuB36SvNuK0Y/Id3n1heFfMQwBTN263WApVs5zGpbzPsrWlyz6abg5R4G1aniMGCuyP8qtaww7FwkAZ5flyv2fGZj1MuCijaxiwBNcv4Se85HOhOilu+38HPAHd//05v+DcHC+oDXq6uNV0qWKWqix//BRLmHykgEAgEkxUzGTWQo+228fjicr6hkQgRgQ2ET6XXTh+nS499Gdhofx/f2RNM536bp5uYmf3mz2wbMOaLMHIBdAEyoSO6JlNWgxXbdEEXswMAFQAVXnnSmAiKZScyUGJk/MKgKmxMHMJFfVis7DIj6WZKoUvI92vBuRqKRUqqiK8+S8n5UgizISOrI65VLmueQKaDF4x9S1TXQhOt/3DRGqCTGimYgYGJMts84ABADmeXKMIOC9L752fZdTmueUUklTCsFJFa211qqARXPQ0jWh1OJD3F1f5WkmapomhrYhYudCbBryPE/TeH/SlMtU0Kzf9a4N1Ea02gTvo4t9s5i2S8rj/cH1bfTt/tVLcI3bXnHsgEnSaAgmYnkiMyiTc4KuDONxvpuvf/oR9c+kucHmueEe/A5dB8pEZEzLN87UEAGJYWEG7dIKSEsao2pVMdcqCjnlnEXEiKkUnacZEE3ErDh0gFSnRI6R1HuvJuQ0gkcQqMvuUFBR5xwAVlFCOp1OntE5F9u2TCJzcc4RU9800zh1sQ3bF/vnn6dhmh/uXee0FAJD5wDATAiM2Emtm03LzEyATUgpqSoaIOHz59cIysTRc51LSok9NU0kRwg455JyyadEjpq2z/nove+3vQ+R2RFgKhnAmF3TRVnIHMe+aZEYmSi6nEueS0kFqtWhMLgyVxAzxFIFCdUKE3rmCsYALhghpFRqxcAEUrsumqkZqVRlYgRVlSrkamyCIWMMwm6UUtKh5ISuvXIddNF3uzvkI2NGEIU85SwigAqgqrra6MBQwRDOc+yBAIAUFG1ZZLrmpmpGj66XFXGfwjGesevpX9fc/CyoPlEHYIU8O7cHwjmDx4VEWkEJV6T6QWlgT7igJ/9aZzO7xeW5/Phi14eFcXkE7QV+17Ot0Ql/cLhztr6Uv7ik8GdcvZQWy2nOgXIpSxbiCs9VDS0bAmBptVuLBn3ClOlFbVhMOXahZuDS9IVnmDZ4GoQuD3i5XgNYzbNwwXNdVZZVhLBzDXT5jdl65pUTWuUGhaVFk51nU621QpbjWG5uru8fHmhOyExMpaRqMKf04bMbREu3d198+OqmebFrINSh7QNFHdK0cRjYgfJ0OKgkT+qZkJcdX0ZgiEyelgWeaICAImqgVUynJFItl+AdoKoIAkqWtaIkUlFAZu9NQcpECNSEesilVgPTKiI1NjFK7XubK2ityFhLGZNkASVgJhddbKKJNE242m289/OcToeji960huhjDG1oicgzk2NEMJNpmuYxee8cO1CcxxQb6Lp+v91NU64lp5SIEFl3XUdI5PB0Op1y3W9dxjnPyTcemYuKpZTynKahiY1vY391tfvoUwogw1SmPB8f5tPofIgftAxQ5qmKIZMBNNvd5uWHGLxrHG2vVJH6fc0TilETAbUOyWNFl/IwnN5+r9PoOH70xy/DRz8Kr35qL34Prz7TcAPUmjIi2yNRCHDmPpcPHREbmCxFgVhOVcxKlmpWipZZUi4muszHLrWCKTlH7IjZDHKRPM9M6Hw11XbT2oZzNa1lMSF6ZmBULUxOQJ0PKiWnakTGhLR+ZgQMHQM7dC5uXzX7h+76O9AkafCtn6ckWcmRSc7DqLpMOKHTNBtC0/jtrnfswCoHb6rOrz3dfd+oacplmqtUrWoAEJo2NsHQ2HHwHkP0wQPQNA6n00lNdvurlGYXODQNgOU8ex+QAoA475wLzFnE2jCRDk2IpSRcltUBWFVbWFs0Q6hFDcwzq8qUkgRSlcaHdQVtqSFGI7AKZVRD5OChpFwwV/RhI6q+YDXsIlETonNBcRAtRqntnS9jzlZVzYAYwM492QayVv2AYLCMBlA0UEBDoJWzhgsGPZnv+eQD8mTI8pJuX1AM1uWCdkb49Z9Vaj7/7RHi16al5S+PdMolrtgFvi/gBkBmAObg0UJ5ptAX9KZz1FqOTBcMv4QeW4meR4odH49vTy9B18t/tPHY+SmtRNdiqjkXSstbL3d+iUK2liLnZ3n52dqMtwYYeCxdzlH0iVEUn/7g8Xd0ufrzNRAu7bzrNdKFdHpkhfTcIwGrQg/KSEhcCUTqME2Np1fPrt69vZ3mAloDExJatTSXfdcdh6kqxnbbBNt2YTq85UnAJr2rkWYrE5WDw9JG2l9tXYymQIGQyDkWU1NREQAErUjORKxkdt6FwD1oKVKraU1TUlEkIkIzMUT2HolAKy3rweZ5nuaaCzvCapvdZsyIQ9WqiA5BRK0swiYYIznvnHMGQuwM4O5wQjXf+uA4pckR5ckWMRk5UGB2jggRPTjs+03TxhgbFRMpWmspGcSzI6CIROwIQB07QjgcDzfPn8Xgp9N4HMYmutD4/X7vPLvgYhMNjE1j62s6PQz3YMTekfdxt28ZRQSIOJA5rvPkusaHQE3nN3tsm1wKNBvXb0CtDrfsIocGCHwyG47T++/rfL/4VSD48PwVbz/A/ce2+yjRFsEjOVqWbtraCHjON5avMtrFx2GgYqpW1EqpJekwzqXUupgiq8ypMAGyc4CogIQly2mYcqm5JudcCBEZ7h4OMXhiJ3XVFx2SCeyv+hidY2uCQ8d1PnlyFRmcA1JcZscwKaESY7vb3rxKLz5+99UvtYiKaVFCsFy9c65h7x2opVR9F5u+6TYdgB2PRzRAgRgDIgKZlSrLtm+zGBwEcsExu2qYczGiTdvE2AHSNM8ppzwn7z27RmuxqiGGkooBimjOijAjewIIsUEmCm77bL8ruTjS24dhSoGXcUnLTH8EAO8cL+3UhMEF9B5NHDsAi02Apd1CRYsAq/NoJvN4rDDP4os53KDFbdW8226UYZLigCuGnGs1ci5UNoBatQLSBY0WIl1AFxhRVViW+53tIQhoBOfFwXhJjh9h8xHrL4y2PVLjT//zmtM+OmB+gIkLIK5M/CMgru+6kFALUsOTiHTp2UJAA2dqyw0+DSuXaUJ2cTUpPPLu6zHwbIVcbDFnzMdzV8O6qAsUAHRdRUN01rPXuT1r9MKlzfrMzKOeBwytQekRzs+5/tLZu5hxyEAJn+bq8CjQnOuBC3lz4fRtrZvw/AvQ9d5olWAubqLlRs9tmYYrgWew7gMCWJQKM0QyZOdI0WtJh4cjM374wfOvvv0WAR0yGHWhrzlVcl3XDOPh2+/GX7x/vaP8yZbClsLeO8SArOyeP/+o75idMVZQ01LNwIzM0ETqlAFV1JiRyJADEbMjqFlKEjFCEkVwzJFBxMxQlb0nNJ3nZQV3rakmBQD2Dg1iQ+Mk8zgTU2xcTlXFcpZSzIxDcM55AUspEYPzrqrULG0MUAUdX+93TdsxgAESmaFZLSVnRQOQpmnM48PhZDAgWJXCgKCW5kzEPnBsQ0nivVOohNR0sZRMFZq+o5TzPM/j28PdXWxi7IKP3PXdsw+eTWM+HY6MBkQE1N7ssG2AnIhoyrlQ3Ow2H30Czk3j4PsdbrYA5psO2IuonE6xb9AHTSPUBHXWMhEWk6y1ypT7lx/i5ga6G2luNFyzvwJqJQOQkqPlE2UGSOtuR0NQU1NFYiAAwprldJpTrqXINKZpTGJWaiamxbtRs5BD710ppU4JiUT13du3zvvQt7cPR9UaomeklJILTdu0oJiGPM+T/86kpI8/+eDmant13ZHzqaRUpM5p13gxVVH2LpVCjhg47F5cf/zTeZxOb3+rs4AZEbrIDpE9SjVRaRoXo/eNN6liut9ufPDs2Ba3gEjK6zQp1DrNA5Ev6omKkXOOQ2xE7XQ8VgMTUNC+7WIXnXNpTohQSjGpgORCdN67EGsu4zSP4wGIqlipKXZR7+7ZIyZDNc9MiECoVZePPSASYa6VEJvgEAAIgTnXGoNvQoSFIDDs/d4cMHs2JICxWhpnwNa37en9u0ko+T41PcT+KvSHh3GQKTMr6PIhXsTCR6P8ygRdGBjFdTwdmJ23cdjK5Z/l3wvWn1PyRzBf2XN61ELxcrpHIfRib3mSr589ZfD0n0sucv6RAaCaXfJtOPMYaOBWFRrOJPalJDmj/ypQXE6zcvtnc/wjNl+aks+BAM4Vk9n5/WdSFJf8CNfHSMukLLOLG+lMG61EEcJCftk5oq1Elz1exPr7eKI7P40AuAQdON/LqrKcSw1DNb0UbriaqwRsaUNUReNl7yOu1q4lGCxDChmRiE0XG54RWK3K7NC3hvpwfOis/tnvffTlN6+nsQo4qyWGVmvGWve7ti0p7to/+OKjH79sr1y5CrULIIfbSFvUnOtMolBmZnTBcQjEKFJFzLUtWgUERqppUlM0tKJQSs3FRATEwGLfIGhNedm1jcySZkQoqYhkNEhzBsDQtElzPqRpqN7HU5pPw/hwKmPVKkjsYxOBuYiqKLMz05Jy17bb675rYxsDMWz64J1fVDg1VZNcS1UBVSYuVfptF0M7j7OIOOY0p9NhqLW0bWtZ7o73Ifht34cY0nDaX29fvXpVUpnm5Dy37RZMN31btapK13ZXz24oNqGF5qoXtW67ZY5qho4EYAGFnFPY9BAbcL579lJzUWJJM3mEMisQM9RiWEeYR0ZFKICVCKqCZKO4jfsPXHtNzz6D/Uvze+UO0RMyIhkgEuI68Hv5NqKZIgAyq5lVG07zOJXhNFSDNKZxSDkXIEJPUKXUSkAmdhpOg1nTdkz8MD6Uqs45Ufv6q6/7bjuOw7MPXjycHkzMFfz6d69B+eH2odt1pRwt6/1w7IP78z/9o2cvNoiFcZySHmXp/GJEJOJilbwj3sarVzefnPJ0dOycd/PhgEQEWIsAQCm167ntIkevpZRlxIBVMiBCYgZddstZNdu0bZQuz5nI51IQsQldbJtUdBgPc0meXYjeUI+no3cu+OiD7/utalW1ac4iZrX6GJ9tNlJlnlMtVaqkXDZtczwObml+QzNAVTPRUjIzBSQX2SMxs4j64EutznGes0fMRDF4My21DsOt1953wZHvt11vfpI4I+YyC4JGV0jQEiUDgJvtdj6cyITAmMDO5164mWX5ykramCKtncomK/29hIGFmT6zKnYmDy4u/Ata/4DnWF5znm56QSSzy2sfqY1HYfjxCI+0/BpsHl980aOXz+j5pQ4JTG1RtVdAPWMmrAnvmYJ5cio959pryHpqN4KVp1n/sMSWx2p4IeoJziQ8nvdpPvI+S48CrA0Bq6R8voEnWTnasqHNHsuSS+/AIizoeQa3XcT5J2Fw+d2sx4ElEoGKAiHq2mIHBkCAy+NBVH0cvrFKD+cQRQgALGpMjLiuxjEgyfD+7f2ffPrJ7rPNX/38y9OkYx1iKoHk+VXXuwzptL1pW09vvnn3q3evj9/+7rrDL152H73c9Q23EUTEhxjbYCYiokYE6LuOHNV5AqslC7EjNKxS8gRFDYmbuDwSckHTSU0MwDtni/Uyl1KKlGIFOLBr2jwkURVFdu74kI6HeUoyT0UQHTuKDhlSyVVNzcy0beJuv+/aZrPt0SxNYy1V1ZWSmZ2kAojLnLWmjV3fOfZiqlViE/u+r7kCqOVar6+9cyWX2/tb53vnvGcGUcd8OpxKlja2RBy6uN9v1cSFgAw+eECZpznl0vSbzYurEKKoAtC6aRNNkMAH3uyLCKFHDugYAaRUYtBaoAgxgRQfA5iDVkFVbt9z2Gi9k7k03S7019y/0OamQADzJo69A+D1i0TnxMZsad69fDlEBRDznHIt05jGIaWUD7cHJWqabphGrx5U2r453I1vXr/puib4kLLcHe5UbX9ztb3a/uKXv2mbqGbv7m4P4/Dtb7/udxupxuBfv35rxcxbiO758w/uf/Wbh7dvakr/+f/sP2n6KAcA4nEalUITHSKQKSgUEEdIcbP74PMyDXdf/6Ich27Tt42HWqtKLaXf3TDDNI42YWxC13dt17nIBCgqeZ5LEXYGaKqWS3EhsNNcivNht78qVU6HAcm37cYQ1TTlPByPCtA00exEBj5434S2aRExpzlgY0SzytJkbAB9352GKQa33fW1StKyfNkcEXuP6E2EwEiAAsXgEQ2RuiaAApHVKggZzIJ3YDBJFbVSNG72GM17BmZG0lNqmH2332w/MO/fTNNXd+NBMygCXeRYNDNGqqqIZgKIqArMaMCqYiQGRISASzOwrTnkKhOe+ecnPszHdtgze0JnToP4DNK2OMyfsDMLKXHB5ye9YheB84JJl5aBlaJ5wp0v9AsiGppbznFOjh9ZnHPpssItnqkjewxHdqFnbG2KeixqzqTQmv0/8S4tD/XM1uCT6HYOesslrjFjrXZ03TN/8facpYL1v1+KLF35pfUp4KMgYEsUgLWAuCyhPA9gMVwMB2CmAsZVChmxAwJwxAKqpgqmAmfZ2tSU0Z3dS4qE3nOuFRe4XTjOubLl3/3mmx9//OlPP3r59eu3dRivCHbRnnM6vvmqb+l5bE5vXs+H+12gf/xP/viTF81Nq4EtkDKba7zWDFoJ1FRgsZ4SSKlIZOZd4zGA5qnmiTxx0yEiWLVagMCqLVHNAUiaJeeac51nACBw7MkAyljnIZ2OqVSaklYR77kjh8GZD8Ms1Ww4TXOtRhib2Dat866WkokG0Oh93zVd1xBbLVWqqiyjbVQNks3TaSQi5513cT7NyIim4zQSgA+hVi0pN21sN9eb7cY5rKWAmIpJNcfsG0+Ex8NBrLqmiX3khjf7fdNFci7lrICxbaBtrWR2AZ0DQi0yTyMpsO8wOEDSkjUlTROZcfDUNTAdtCSQZDWN9699aEK7ye++mR8mdG13fUP9DXQ3uH8Znn0s/bVaA0QmwMyqamqLsR8QTAnQlt3MpgZiVcpwPA25HO+Hu7uHlJJUFXTTmF0TDvf3jthAv//uW0Ayh6dhuD8czUHfNPfvb19/+5o9OYd/+e/+6uVHr463h4e3d6eH4cd/9If/8Jd/U9Xm4TRMx0L51z//GRK0HP6f/+9/drXf/vk/+ZOmb9M4MLNWNQ8AXmpSE60gnkP0jq+3H33hvA3RyXQgqI7JBW+izAyqcCXOMXvHhMMwYcVSJdeyeLQYiJkBwbEzQM9RFH2Ih9NJVBkotD52se3bOScE9Y5VJKVZtJZcpGauNJwGCtxte1MtJSG7mqCqplRqrY4pp6RpWXCkc65A6Cq3jW+bQDG4xWxWSkrZEaEz5BiD42UgsxoDAAKBgZGosGmdksrJnEDEAqBVGS0Nh3EqJbQT+FNKGRgwqIISKeHSMaOoRKhLeYfoCMuyHQaUjNFUDJ1DRAIFVb1gzuoLBTwvezvPTDA48xVn86QCoIKtqyfXBHJdM06w+hPPXWJPkvoV+Fdeys7ktuJFjFjw8UnX9IUKcvY4KuGcOa+Xp3BeNWlPGwou4WYthM4tWucDnLWHixC7VkuIy5b0S0fYmUS6xLCV9jln2HSOZZfbpCdV0LphZ5nDgYRwXrm7vu18lEV/vjRMr0zUY5wzpOUpG6gpIRpCF6JvujzP85RqLWbKtCwZAlBdCzRb2B6QWvCyM9OEAE1BFTw7QseMSUrjIY/304Mbh4c9jF/89MUeIXhtROOPP/z442uUksUCSsBM06FxgiDH29uWVS0BWLtreZnlaYKki16IqCprUWJTtlIpRARjRs1ZagEQVLaayRGIlGGoYzYVNIxdDyo5SRWJvp/nYR5STiVlqMUAKYZgArXibFVVpzmLQAiOvev6BgzAtGlbT6wi5NkRaSm1Ws5ZxZCwDTE0kRHnkmqpYMaL8iNSs+ZcxnlKOZecTGyz3TRdY6Ljccwla60AhgyEjgx9DugIpHJkqVkyj8ejlTIyIWGz6Zk4PRx1SmnO7c2Vc0GKAYW4b9HR+lsjBGQkdG3QlAkFooNThZJqTlBT222ITB/e13lA71xoc3WheQbuKu5eAPdWEAiW/Qoq5y/kwoycbQZIy7AfA8L5OOdcH24fvv/m9re//u31B88N7HA6bTdX+f5BofzoJ1+AQR1TBt1e7/I0/+yv/vYP/vjHGGPbbr756uehcXh9pSndv3mtAt9++7v99c0v/v3P3r79Js+zWCklixWRqlVutjd1PPzz/+6/f/XixasP90bMPmrNKlpzBRUiICJTEIfouHn2ITOCwvH7r8iS9945riWjIwINkVFtTnOeU7vpaym1yniaS85d38SmzVmm8TROkxqwC92mKyVLNSSaShpTcsMpdC2hK7UiERN3/bbftvOUpnkucyo5Q4VxnNoYXWjyNDdtx96b6TTOtUjwXmolQCaKIYiagZUqmLNzXCvQ4oxDXBS5eZzN+yb66AkRpSovvs3l20gUm6jApdSsw1Sn4J4DsqgUmFJV4bZt/VRwzLUaVQeGqAueqsqKiErIC6ATcxtbQJrGCRapHwwQmXi1ji5uw7Nf/CIXXvwoF31gVSAViEDVbG0wPePKYyPthW8hu+DbOe3/oWwLT+PDmgA//bkZgDoiWlCfzu3IREtGu/gtH/mVS4x4dC+dG9V+mPovtc6lnfkSrGz9w/KmRXJYX/NU4D6rEvZkLsY5rhhe1sEvFfijg3MRbQEFz+zQmcs3M3kMRk8OeRbczQCAbNnFjWhZJWD9+IMbZ3QYjncPw5TmUnQhxGLbiAg7h6rMLKWKWtu46COgdhh27dZUkGgYjlZKKXjN9ulVvGnFV93u+p98drNx5JgAZfz+23/493/1/Xffb5t4s912Hl9dbd5bmqM4p5KH4MwH9h7ZeQQDYERBRi0VqpoaO89LtKNlMJJqnVWBXVDLUIrmpJqtZjRod72WOp/GMidVMwUkHoZTmucQHLFvkMZThUnKpNNUxlLHonNVBO77yMEjYS1CiOQYTInIe48Id/f3IlVVQ3AxBgesqNN4NFuCMqhZTcWgBHaqpqJt22z6tkgNntumYe9yGquYVl2Sa0SYc3GINzc3jW8//PxTjuH+7p2ZOHRpSHkYFWD3vGgW7vt4fbV/8YEymy67LllBYc6AIiWbgaTCjlwTOAKUqX7/jc2Dc448GjkV1TJrmQEUgsPYdbvP+eVP1G1Px4L5pN5hG9jYBVJBUyXHyLx8RUwVbOnHEAMch+l4P8ypDIfh+6+/MbP3b9+ejsd+v38/v3bsP/ro+W9+8Ssi6p91myrbNgyoP/ns1bbr+rZ/+/bN17/99UeffPyLb998/eWviez+YfjlP/xDv9u27W6zbWvOtcxh05CSqsTGlSHvXz7/xd//4m9+9ndXN/+RI76fDh6UyRruHBOaqppzpApFIMQ2PPt4x77d38B8NM11HBkVtaKWnEdH1u+2ZFprQdNCuNltmHmap9MwllKlFmQM5JsmAiCT2z7fqhpN8zQM45SqWtdumiZ6z558zvl4f/I+xtB65wFJTYgY0FRA1e7v7tk55wM5EpMQ/Wa3zfVQi4TGI4GIikieEsZgSOTYBYcAjlCkOAIi1FrHYi5w1zaE6ByjmYmkcVDB0O2rQJ5nKSJ2dJuN624i+9k0iOziRpiLlaJYmRQAicis1mqg68Z6E0du3/VX+xtQef9wt+SIokr+PDf0TJ3LY58TXGBzhS8FpMck3RbiY135YE+S30f+HM4c/SP//+Twj6+8UEHn9B2eMEJnptwIwRkA0uMoIViK9rMvZtlOeqHn1wQaVzYLH0kZe7yGpXvrCSO0Xgw9Ni3YmRNaLuYC9Oc36CVwgD1hcJYrgjN7g+cyyqxeHoOey42zM2s5m643QJfyBdcNnEstALrMGydEg1rK3ft09+Zt3/a7vv3Rx6+w6inlcR6Hcah5NjMtkykAU2S/3fbzMJR5IKKiPI+Tg7pp+Fnrm76++PCDzz686ftgoHPa/+ZXv/of/l///NmmvXn5wevf/Bqm+//Jn/3Bn/3hjx0qqUqZZTrku7GSdS11m93mqmMCBHXB1ZxMFZGJmQCNWGsWraoKVs0EQAiBDciRlaw1l3FgQnLL77paLWWeFyXNDIg8gLGnbbPJU+GKjDjNZUxjFYrRVwLXODilCpBTTsOkCD6EEHxEPB1Ok6P9fhc3/c2my6kAmI8UfJjH6XB/X+acayUk8oEIpZRabbtpu36z223YO5ESYggxMPHhcDwehtNxKrWaKhO1TbO/2nZdywQh8DCcWt5sr6+7be+Ixod77TcuOENjH7Y319g0aZzCZqNSyDlCQDUpWdJkJQGxY3ZNhPloWVAFS6kpT6cBtDZ9G7oGaq0zACr7EHc32O9UoaiPu2cWbxQ79B2SA0QRWVbE2WIEZ7PzDAgxrKkAkIi9ffv+y998maFi5Lfffn91/fz+9q3U0vWbr78+NaEVov3+xf3t/S/+/h9+88vffvSjj3/+D383zelwf/+3f/f3X331G3bE7OaplDz95A8+f/fdoW3Dtt+8OX6lICWjqea5iAmY3N6TTNNf/+xnf/AHX1xfb03uDFVyVS+ZgU199OiWPXaURR0Fd/0hNhvMUxlPEE+gSeZTOt0RRSlTISGyUrKJ+BBErNbMxBiMEEIbWrBaZJqy6Nz0/ZzmGNum6bxjVUg5q+U01TJbu+n7ZjPlaRoH9o68C8ETNXd39yHEEMLWNXOe5ykNp1OpMszT/f3D8ZRqqQaQS3GOAcA7bnww1SYGE9MqRJRKdky5FM8OmFXFsiaEGBxEryLI4EIAFpE5uNYAa66lJD0WNbX+eUubbBXLyYWm8yEpiKIAAFOtFQgQvJggUWhD33RNbKyWh+PhNB4Rg6k4F0TVAMUUEQlA8YlmaYv3H37onVy5dXuUL8+CJZw10QW28MLxP4rDZ3RdMfWMqGb2RAJGWGsRWieP0gU5ARyYAjDiwlrbWTt+stVrlQIMn1QBF7/OCstwYVcerwEfr8n+//zrsbrRixHzErXOsQqBlgLjPEIPzhwOgBk9iWmPP7Bl6QY8abR4/LFeKKhLkDw/WDQzwoUvUjBWsMNwOp4eXr99e3397MPnNx+/vPaoeZqGsRzGU055mAbLp76H3//kJs3l4eGBhmkfpue7TX8ToiepeH21nyFND8c8HEvKL/fb3//PX05v3/Yt/9l/9ecvX12d7o/f/+arUifMk2O92sSPPnu+v27ZqbMEKHWeAaWmGQh94wiopkHmbGDOITGdBSFENVwc56loSWDF+0CsWKWqmEids6k5T+KQgA2YELsY5mnUUTnENKXxdHIMWTBXyVWHMg9jwuBytSpKjpmxawMziTkf/HAaTw/DZuO3u22IEchSSj74q7CXUtOcpSoxAWKz37vGTcOU0lQkbfs+xNh3nRnkXMFsf3XVxdYQgndMTgEIoe3i8eE0vDk9f/aMyPr9Rst8KsUxd1e7pu9VRIEUOfhGS9GSOUa0qnMqwwiqLjC5TvLMWPPrbxmUFKbhRNGLiG/bdruF4CEPkKFWcU1PYSPUacEy5OaDDvu9uR1ANA5AbAYuOMJl0KIhoIosqpqpIdrp/jSlPM7Td19///AwjFMqKQfv3r//7vjw/uVHH/7lv/iXn//+j37yJ3/w7/71vyOHr799c7h/6DebX/zyH77+7Tf9VSdFvvvmW0fQRJ7SXIt453IJqun4/k0e782sWkHxkkWrWp5Dt3l/++7Vi+df/vqXv/nlr/Z//o/zOGNE7xgITERMqRCaASMRVVEFMBegvXLt3rXZlYkt58M7QO9s9FBqGhnFEakJiM1TrlJynlJKhFTSsngVQvArJAjMpzk0QZE3u26jMNdUxjmlpHeniaZm0zH7UqrVMk8Te27bOIyzqTb9tnUb4uB8vL+9A9VSak4zIBBCLVoUlu1eofMOHRE5JqmVEX0TmEhKqVURrGk8I5VSGWDC0bMzM+DqYscEWjOpOURjVyDPp4eai8Qr53fBrAeHMXKI90UnqVVVAByxZ981W+8dBAbHJefv37xRNVEEMu+9LmXukn+irS2vZ4lyIQl1ybJXXHpC6MMqJNklQX+SeS8QtfaHnTmUS4p9/vlaZJy9jXbmny4dZOsvS83AgBHBxC2ZsJou+T+tq0/Oo8kfGZpLPEAAWA4EYHSBU31sd8NzqLoEinPpc+GC8HyZl7oBLmHvnPTbeazd460uS3dsJXkUcHlqK6m0PukfKto/CJbnQ61PaqHBVo7NkNDOLT0GRkxGQVXV4O3t3fv3t87Rpu+e32ye7XYvX+0dWu+YTKfjgUC3z2P/492Og5o8HA+iAqo5p7/4V3/5/Tff3N+9CWo//fHH/7v//f/2D//k83yavvzrv5WS717f/vbXv923YeOgie1mHzfbpu98aAigaDGoBR0iILJTrWUYCJkQfBOJ0GqSmhGNlv10WmsaNCUmZmegtPTd6JxrqaBmiGrATE3XLh8oRjdPYy45tF7FVNV59oSarJRai2q13b6bsxhY2zfOuxCc1JKz7a52XdczIhOaFjMruZSc0jRPw+C8D4F3++1mvwUzAFKt85ikZEIC1dPpiMPwcLxvYrff7l68fPXu++83u33btEXS4eF4Og3saJqGvusCc615GueUZkaKTYPBp5wPb9+Frmk3OxNLp9E1PQZhFZhRi4TokQNortPoPEOtgWG6vxvvTqHrQ4zoQvvyJSiAVC06HU7sPIdo3KtG4zb0z9IkPgIQSq3AlaMDMXR8SbLEzm0yqy1N05yR8M33r7/63e/meWJ2ZUrDeMzTeHWzf/f69Smdfv3Lf/j7f/ibX/7y547dcDqRUdv1ZlyKzAOyg03vNU/vbk+I5LxLKc3pvmkiAtY0oeNaSyljraUhFpW7u1vHoQ1tKcevvvrdn/yjn3rvEQ2dR+cZuJZUq5gqO6dU2TkiX1WZgjoC58uAuTqIV+6Kdbg1SA45n25LLYzmmExFpJZSEV2puVYgXDrYzMSQyDXMHMZhZBemcWyapokxErdNd3f7nqiejtVHH7sup2QGOWViCsGdTlORyiE6DqbmXOyabrettdrxOBXRGHwVlaoGcDpNTQieEZwzMKnFk+8a50JjUkNgBAGztmm0VjIkQs9sUq0kcg7RahWo2YSbtkcTxamYV4yuvZ58u+P9Pasju69wn5NjjCF6o9bRYcrjw6yOay0KaIgGSkwCsK4JNjiLryoLqYxLZxEigl5IkEtb7hnEnxA95yx8MaycM3c4b3dfVsCc8+9HYXnJ9M/Z/fnPKyd/ET9X8l1BycCZ6lph2GWLCsB5UsRFLFihlM7Ey2NYgjXP/g+5qrNRFeH8PPDxv5zz8sc4CJfbeSJpMCEa0iKrKZ7XFD92ZFzg/UJ0LZO0LkfCJ8ddnuyTruxLkFhKkIXvWh7COsCf0BCN2LAWtSxyuLt9ff/eAzTOR4dXbfz0xW7fOlfkbkyToyH44MPrd3df/+6r+9tjG92L66s//r0fP3v+T3ZdYzX/7N/+9S//3c9+/JNPnr98cfvV13dv335wtfng2W7rpzaY80AEWmcZMpISCiKQJwAwERV13oMa0WIVUEAkouXjbrWYZEZzrYdSVQqYMkLNRUGBVKtyE0NH7KPkbMssCSRmRgJEynMysNg2dVTQ5B0HY3CQ1JDYAQASMaVSpNSmjWZS8wzBEzJ6WvZ5AUATYhMjgbronXO1ZAQg5IoefycAAQAASURBVHEYzLBpQte3RdTxMmZdieDh4f7rr792zm0327uHaTid2rb56JNXnh2Y+eDabfSO1eB0PBio1PTig+fsw/hw6HxrRfJ84q6pNFkBrx7VDLRkrimBaQgs1dJpmA8PVSoHFzaN1MqxB/NWSp1zHeYQWnSqFanZeX9d/JVAGzYvAIMaudhgaIDWHWcqusi/6wpoFas6HieOHh3MKR0Ph9dff+ub8OLVy1//w8832z56fv3mm/u74XR6+Oaru5pERYrTJgRAGMcTABu56f4OEEHLw8M9qtGScRm0gVVyNUAmqGKmChVNpKhnj0AhhGlKVeT127uU69Wz58PxXkSlKnpUAKlFECMRsTNccgkUAwBCitx7h2C516nPiHV6QFXzHaiCVREE4tD4KTmrEmMMgUoWds45V2rNpaZ06LabEIMBMLFUkVykVOf88xcv3r1/TwhpzqmW3X5XqzjytWrTNc6FcZwpi7iCao5Qcu2aqLuNCYJOCtZ3UdWWDmowm3MuOTMjM9VS5gR96wJ7RoghGCiIImLJhdAEwfsWatLC7Ns2uJQy1FKmk2uDY57qVPCAwKBVpdjk2EcCQZOm65TocJjux3Eq2rasonMpRMREYnV1+5isOuWSjuqCgmqGeu7yejKL+NGgY5ecdyUkViR+jBC4MkJPWI2nAumC0gvvvtakYLZQ+OtfEfEsP9s5RzcA93g6gMXEtpQIQD+Yp/CEigeAZeE5LiENDdb55ecrNISL5Wh902MS/3jJ8Mh72fkWz5iM64BSQDATJpayBqhlE+Zyw3a2htr5jfZo63w82Gqp+iElZY/nehqMzv9HUM0IsQJUEWYiR6a+ocbUtMop1bvD/LYcv/zld96ErfSR0aRvm03wr55tvvj9n4ynh23fbtt261tGHMeD1QySjsfTl39/PL7evXq2v35+c3O1Z5kbFqYBsZoUqFVLBVQlZWYVNamrc4CdWTGzmovkhMvWQRSsGa2C2ToWVY2IJedatZaiIuxc3EUk1lIMUAGRmdVMFZia7aZWc7P4xs0pSVUFcM6VKVXAWq1WI89N9OiIFX3busBmMKcM8yyiaxaiRkR9E549u266aKBaxUSIcJ7mELxzPuWpSo0+UAj7tpMq4zRXyVf7nYGK5pzzbr/p+j7GaFKnaTwcE9+j805UY9MQiGMseSolO8+3726lVmDcvHgWCZqmJTAjJA5SCoGZZkmY51RTZg7dzQ07AsLY7qHtwUAV/PWNx1KzSdGwv1G3qeJyNYzelOZj9jtzjFgEHSDSYsxY2//BbBn6iRBCqIab693rv/vFPM2H06nV8O1X9v72fhgPJvXu4fbh4Xj/cEdMzrntdjOOA5kVETAxXIYDgRk4AFNgEwDTikRUalVRQ2RgMgayXJJzXplSzQRUa3l//3673Xz79Tf/9t/81Z/92R9yCEyGYKjGxJXEFEXFBMk5ABUzRC4iouKdJ2LBCHFLbRFRzXNR590WNElJBmSAzrssKkBmiJ6rSp1FYCGC3DiMMQZRcux8DMH5rDbPcya+uroexlGtgtlpOPX9hpDFLM/VObfb7Uutec6l5FoKezcd55wLEXZdqFWrCig4hNgET4QY53n2jqpUIksJyAbuWhAba44xxuCIEESW3xSCaNWSJ0OMod9sNwZjES3jaFUck9nkqfNwUEDKHAzZakeoWUbJwbF5anrKWsdhFlNDBiRmd8YuNLDFAYpq570kC3DRKrPqOTNfCeh1MtCZI1n4jIvLxgBgkYov2SqcORTAy26SsyEN6WKdObNDa6Ggj4kwLf0NiABq7pzmo+l6SXYWLi6ZssElSFwiB5itx1kCxCIcGz5C8RO2Bc5lyg+x9smfnp7riRpsy2B0IwNCU0VYA8OZQFoI/zUenN9/VtbX0144tfVK7emjv8SD84/MFJEWO9fa0I8EgKpaREmXESPA0YcYXFVIeenGuk9Dz3bFSGXaba4/++Q54IuHh/mrL391eHPrqt3cdNvAH3+w+eTHr1yZXzy7bgN7qEQTlKPqZDCCZdRKSN6RLV2GS/FIDMtiwWWztFQE9Z4RUauYVDAhQhNRKVoKqBKZgdYyO0fY9MRMQFZBQKWKayIS12mqZugclAIKPjTowrvX349jyUnmKiVVdY6Q+q7h6NjRmHJwbCbzlL33xL7m1QzZtNFM1RQ9Hx4OKYd+03nvjFGlGkIMfrPfjUc01Vo1sL5986bWxVyJTd848qISLNRqw+F0tCOYMCGYhdBtNhtyDtBUMhkNt8c8z0a42e366/3u4xdhu0mHo5Z5eBh9412Iruv9fgNlSoeDj4FiZHaubxCZNz3EDiqWafRtB1JUUM37plPolDo1T2GLvhd1zW6PISCzGWg1RMVlvrepVUAEgQpq6L3r+N1Xb03k4fbw7u3btg216pe//rt+193f3ck0HofD6XgyUWUYpmnKiYlUxVSLqgIZEiiAKZBbhnKa6jqPmBEZi4gJrHGHWESLaohNIJcndYDs41z0OIwpz41zp9PQ+CsOflmyJrWqKqjWVNDMlr1AyM5HMATwxqriLXQSeoyTQwskLStomo7341FpytOUVRWAQgxgmHMFgnlOQMzEgNx18fQwpHGOTRub4NkPp2EaJx9c0WJiJMScu9g69oioRc3UIfuu2/Td8XAyVWkaKVZTrSJm5hwRoIiBWqm1iX6/7UzFha6WvAyOryUjIRmUedZCXR+ZUaVqhQo5xlBzIiIjH1zXttHVWsdJUwGfA0dH0DY8BG66LrftDbsZLCMPuVbih5rezfMplWKGyAiL9VDskUI4QxiiggKQLhmCnr0/KxCvmHkBP4NHyHpEspWCUV3LhEvOuya1jxrnkuqTXmLH+pr/IEgYGMjyfjVDMGeqS3SyC3F/zqlNARmeUEkrztuZdlczAlxtuHBmnVY+CBdqxxYm/2xtusQ+vZD2C+++Fj1rgQJmQItssvSe2ToYmpbbR13rmTXFt8v4JVsosvOd6JOAterUiOc17edHd6678GwRWgdxnCUZg2zFFMihgmhWAEIkE1MRBkVHnWtChE+etc969+GmcSx/+S//AkS32/6zZ/3m+b718dOPnzUeNhvqSCBNwZPmCdFUs6dUyuQZtIojY89ECKBaRGTZy85oiEQoGUHZMxKCFCsVHWBWMNGcak5W09LVbCKIFvqOiAARFGqqWlSZnPcUu3R/p2Y+NACSUyVHlfTNN2+GKadcx5SrYGw8x7YoqEEtZU5iQKd5rmLEGIK4WK+2O8chei6iWvLuuvPB1TEJyPBwPI2z1sqM3aZ3rO/f3FarntAFPJ3GOefguW3bru+IWaRAgSaEWvX0cCSipvXdpg0x9n3nfDStwzj0fb/punmaj8dDjI00Bffx+P6B7o7TMJrW/vrK+1iKQC75dCwpo5miuabr9jd4vYOUgKNkrFnYUMapjgctyXlfq4oKAPmr5+Hqw1Q8uw23HaIXNakCAC5GRAAiE1UTMENC8r6KmGLThLfff//dN9/d393HpvUisWnMaJ7m27e3Kc9iBR2VUmVdEibLJ30dswkGpA6ZCB0A6DJ73pCNg6+1kik5Yseg6JhMRFCIEAhC23344csPv/js7//iL+7u7kquMYbYd6paa3WOnfMAa9OalEqEIXpAqov9kLDqMvejhWXAVOTpvWQZxru7OtzP0yA5Abjt/ibN8zCmw2H0zquC8y7G3mhphdCc8v5qP4/T4XBIxTexC9E/HI+pwP7Z9TCOjl1Jekhj3/dkJGIpJxOtWhStpDKM4zBOiuoDV3WSilQxAuecFDHEnBNAADTIue87MnFGUisiEJpz5JhrLhbYAdRaEKAixK5FsFpnBGKk0PUANJxmM2G0ANkseZDSorZcmy4jH8byrsjb8TTlcpzn4hw4MoCqQohMxLiMTFwYaF3XMJ4z6SfIvjJAF/HzAvV25ndgxUO8ACFe5iqcN8uuWeyl2Xc9vD06XM5nsPNPzpzKWZ89v84tYUUvIx/AzsEG1oGg50h1tss89sGe/7cUvwiXImOtR562/K5k+8qy27lGWOHd5HFHy3LNl1C1xpG1r8sU6DwW4txjd75qWOcdLZrF0iL8A8H5CclzrriWYVGPsvv5ItZo9Bg+ERCrVAILTKEPWgpodlivGrfj7uWOP9w/++iD3bbh27sHtPJP/vRHOkxRxTF9eLX94Pl1E0BLhmmUPGlNyoCkwaGDwliNgADJxVrHejoRGaqaqXOOnAMyIgCrCCa1ACFChVqJEEGMUEUB1XvCEM1US2Xva1IrBtGZZE1VC8myIhi5TKMxNG2PCmWurmvLPB8PYym1pgIEu207ZVXycyoUnAFJqSlnH30IPhKFEACs7fuujewjiEQmv2kQ5PgwgAoySa6NZ9c1bRdjGxsXnPfjPFatAK7rtk0XffBmAKYiWjNoqne3dyq63219DG3f3Nzc3B/ux3HUOhBD7BoX/TRPwzgKWLvpXIw1JUjmPDt2FH3omnFOruvJN65pYynD8eib0O+foef07l4U/bZBF+PWQRnK3bHmmciqacnYXG+LsJLXWcD3PnZAfmn3x8gACmcXMgIS0mrwM6tJpdbplN+8vi8q82k20znnvt2EZveb8UsjUqbhODtHHJzzTIDEuGwFQCARsVqXHgswE2MCEFVyBI5SKVUVeBnyKQ6XiMG5ZkZQMjR79clnX/78V7e3b+ZxFDEpwg4VpGT1PgCI9x7URERVJYs5JWRcNsQBqgIBILMAGQYf982Vwviub/zprZCnPPPpYZiGRIrb7d5Ux3FMOb2/vW372HQtESWQ4GKV2jXN1X53GA6TjW3T+ujTPN/fPXz48Udv3t3u+46c16pVsveRnC+laEYV8567TRznOeeCBI45BqhVcq7I1jYxV1UtucxERI5LzmwQous32zRNhKqiQGyKdawcmYlNVElVlNBKrQELCBG6Lu4IwlQU0EAmHQVKZc0IJFINeTzN37+7f1cqbHsfXarGxACoUh3hsp5HLzCupGdNcrWTnJPnBUWfINAPPO4XDsTOYHXhOZ6O0lmyZliqiifTE5YMn2itDi60Oz4pP/B8AaaoaATq1gqDzorzavKBRa7Wcxha3mkGTPgYv9bY9PQm1sHRa3a/lAGPd2cAT0LQekFoqku7nF2OuRQOsLqI8HzSc8q+xhS8UFAXi+o6q+mR4T8Hh/VlZ0bKQC8xDADOCvgjnbVGFKmVmAkJibya1mJ1rGnAmnqGV5vNH766erHbPr/pG9af/+I3P3v72lIhkc8/uv745c3L/VXXdzJN8+FWUBCLh0qmgYFRQSWAMWSsCUVFxAB98G4bkVFTRjq7bbWCFK3FVMgze2dl4YsLzLPWZKDOEXqzKgufoLoMYiTLWecJBJF96Fo1LXOCSLFrBKtNAsyAMJ/meZpLqkjUtWHOVk1TNRHRBFXMCLtNy8SGcHO1n1ISE7V6Og5Ec9PGvm3ZsxX1bqVKOPht17jgwKTmckp1GMZhnjjQyxcfcmAXXEo51yypHofTdBxNrevaPvoxp4gqWk+nwTsXgm+aJrQBTB/e3uVSpdbd1TZsNiXn2IRxzLfvTs1u49uY3j9cffhh/+IG1PLxMA3HZr/1TVPyLBNw28e2x6Yh56EMdTooYLPbq4IKba6fYdwjtYYRIAD5Mhd0BIhqxNEj8fLdtVWnWleIShVQjd7f3d7d394HH9KcKmsF4S783d/9u6lMFbJRidGx99VqSgUAnXeO2NCBikNEz+un0NARLwIec1Bj9oQqc56GPJVSmhBvts+brhNJZupD++zFp8N0/P7t9yLat30TGgbMwxjNuybmUtoQkRGZKRXQhEgiqkspwbz0g7J3Zobs0MU0z4auFJtTyqWmlHOqU5XDNFs1OR6992TkfPPsRTtOw3iamrbZbXszmMY5pdQ0bQjN4XgCwrbrchGpdn9/fPHs5bfffbff7pq2n1NOMgKziuQqaZ6JUdTIMxS2XNBhdCFI9cQ5VUHrWgfg5pTAzFTzNHnnZ1NGC96tRW+V6J2BmSpHT2CEBCqgFQlVS61Sy2jcc+id4TymtmtjE49pmt6VechDaE7gT8DMyMoiGskNkkiE2ROH6LCULAYCaAaqqrAMhlgRFy+W+pVhf+Sr11TXHnFxHXm5zudZcE8v737ac2VrF5Mh4YXTBntUQNcSYsmg8QyvZ3MPnKOTAzRcnEvn67N1ysP5Elc/6eXEhucYZAiX5ZNnbDbU1YmzDL8wvEzltDMlT2dF4YkOfoZtBFTTlftZIB4eyan1JuASys4B7QmS2/nJXoZswJkbutQLCGuf8yXunrP/NRCeg8yyLR2JDLSgqlPpiV7u22dhf7NxL3e7q00TYvjdt+/evX9bU3l+9WyH9MG+vemxpdqWeasctw3UmQECGQNaTtPpKKBtdI7RsZmKSGEDDAE051NCExcikAcgUyVDrRVVzNTEas2oFUERkJiZokgFK5rERJAIiWouRBzbjeYRYqsKRqSWpaoRNm2XxxOS85tepT68vj2+P06HUY1CdKma9w5KAgCK7IjLWFTNKQBB2zT3DwdgitGhWWybrmmBbJ4nnQTEDDVG3/UNMKnaNBVGEJWSUwxhd/2y3e6C90Q4p1xSmtN8f3coaY7sut3WB8dAiCw5gVLT+q7rAcUIpnGWUrzzTeO8Z2BKU2Gz492YijT9Lna9iy0yAsbp9lRLGW7fxy6Womk6ha5vr6+gadSYDCHPlicC5S6CgilH3ycL+WH01zupoJpDF4iaUquJcWgkV/YOiJaRFqaIDIxsCEgYggcEkapsf/PXf80Nf/ftGwEbUypQyGGtmubJ+4BEDr33wdTMBA0ZPPoGJJuJSgUABEcYEFRURSnEoGgPp+P7u3c5TaqVGNM4vfroR2KkKb94dn19df3lz//mdH/7/OYmNG2V4hDAtIpS0dCSgqExGaJjVieiIoagjoGJ9WwWYSYDjwbkOJ1AoBkeJihchO8eRjArVUsp8zD54NrYapXNdnd9fSVqIiXn4tgFCrnkTDXE0Pb94XDYX9/4GEqqDw/HELub62e393fDPDexqYLjeHLsAAmIT8cBEB0zIZc855xVkZlC8I7dlOZxLETYtVGq0DLM0aSKTJNa8EQUPLPjJQFeHgFHJgIpRcVwmdsDkvIMWJyKc21AL1MJoe9i5QYNgFt2zgcIiD4KH4oEpMDIIRhAFZjTXLNUQnVoQKKyzL587AR7lHgRwIAWwvqS5tsZ3c8IdqkSVjYDV1IGV1ADXDmXtSEXzi+DxxLjQtRfYoedz3/22Kw/c2ZmoGsivR7xiY1mxeRzSr1MW1htpZeuqqdTf8DMzISIgM5Ar7Z0r61kO649Zeu/lgtaGo3tgsQGy2St83pINYUnPct6PuH5rg3WfocV/BHs8brwDOkrNwUKyxYHI3hcvwZwjpIooMsVwLLlVYqQmIHujFuW55Ff7Ta7rdcsf/vLL7/5+vXpNHtyP/7ow31z/SdfvIB6yA9vkybJyaZWVWEea54DSAyMJDc3+/3NVqaTpbmSghQzYR8AVM3YB+dZS5FpBjQiBoJlex8DmBVaaiQtCFpKXibsgAmiAS+7tisAGQJI0VzynLlpCQ2YnfeujabmuDEwyXV8dz88nMZhMNUQvG+7YaiHoTjnppTNbJqzj7GLTakiog8PR8eODOaauk2LBuPhmLUyUfA+xBgbn1KaprnddLGJUuo8z+R42+6c976JaDKc0nQ85FpNsZTiyHWbKxcQwcZxmIfRhdB2rWMITRQtzExqpZSSpVYJbT/cD/2u7TadpGK1isBpnLnbVKubfW9ZsuRpOPm2aXY7qRqawCGqKIoQsZUsKeXDQa14H8kR+65UVPTNbgdxy9iI6wBcFck5h6Yn7xBoEYjWbnUCZgZAVTFT5/18mpoQ379+/9VX3948u8k5URPHcUDU4EicExe9d7XmoeQ8z2YQfWxcoyYeARDJSIEFlQgRgJErc2gaY3w4vPvmu2/MChMZgKodh4erNGy6m2aLu+3u8Pb7YRyI4JMf/Wi3vybD4Dw4dowM7DggWCnFGXny6P1CXhCiidUiyEyOQZe7AZWlEuj3zz9pnL/79jc61/3ug9u3r5vQBnItN4fjqWIm9MNwFIm7/S5NcjqemCGExpHLqSK7GKK2/TzNTdtNw0PX9m/evL26ut5vb97dvZvGqek3hOH+/uCCc4QGNJxOiEaIMQRQyFLBMKUEgG2Ic8kmMqcU2IFZ1zZEkFNRk5QzM9ecgw+h70LjS8lmqrLoLIhgaMYEzkdCLDNgzUQu+pCgTMN33PRdc1Wxm6ELIQSMzvz1LpoLSaUiH8bhy/d399MM7MGxI4pdLCrjmPXcvnS2fF6oarN1rdbZhwJPmfQL7fAUdxc0PzM/Z43U4HFf7ZrXPrG6r3XG4plcRYEF1fEHmLsEgIXksbObDWxxA8F5htFZo35EeLic7RKoLtrxyvQvNMwiWZyFhYvssTye5RGsa5Ht8rI1nj2WOpd+3nNTGpzpnLVZjZ48snOBsqrl+BTX7RwWz3uGTRFR12psvZrHW1oIIADRCsgIIFID0o8/2H60CSnL2/env/77t1i13bVXL1/86R/ttj62bC82EeXQe9j++GMHJknu375tWTf7zsrsMHdtIOdQ88Ob7y2dQGfVEryPm87AKcKSD0oGInKBrSbTrFUBAIjNqtWMoAvprLkALg9JTY0cm4gZrrRErSllU/XbzsUWNJuBiJYsbIQuWEnp4TifBhTZbjvckUKcssxTMuJSqqmCgnfeuQCIopZz8U2M3vvgNpuu7Zrj4aQI3jtCbLrYdd1SknkfpJa3b96D6na/bbpm03VINE7z/eFhnsd5nJ1jRtf2XWhaUU3zeP/+vandXO1C2/abbrvduqYZTw/jcUinyUXvY399tU9ZvG/INW9fv5+Og2RpNrsPv/hMC/rgpcL7798iY9O5puuNXIwBXSzTKY0zgDXbrRrVXJAb7xvJ4uNWzVUlv9kLBhEmH9E1oiDLbFMREWVaUgoCA3RMS9IjUvLMzGKopqHhJoQ5zb/78jf75y/nOpcpRQ4Zdd/2nunbN6+Ph8NUk4oQoqd4dfXs5mq3ZE+IjKhkaADKxogonpir6bffvzYrACS6rE4FqaJi++11lVm0HseHOc8xxKvd1abfvnvzuvv8Y0eMiOyDFAUyNcm5Apvz3jsyIHBkimrGZkRoSAhITJ5cnU5pmu++/gplVnMPhyRlQt+hzt750Mar66vj8cGTyyXNw3w6nK6u99u+vz8+TPN927Sh6eZpcruGY8zDmHKJbXM4nXa7qzev3948u7na3dzf3Z4OR3KBObx9+54dPN9vJDan4xEMGbGJETOJ1FREqlRXvHPORyQLxKaWcg6Ot5tWVaWUpeZHUJWqDLEJWisRMZipMS0Wihq9b9ro0MwcO68hxuCT5jTda62uBclzSWEuhP21q2MhP2dJFKZaLGvNlRrfNbGJERB0aVAgRKLLpGZcMlMDW2Ya/4D4wEuGfiG4L2zHRY20NekHvEyTO2MlITzNbS8weDmcXbwvlwoDUZe5PAZq5lYgXjP/M3cCK3YvePwo/l7G9JwjDV7ajBfmBFZC6NwpfI5lqnqWji+Ie64tVs+N4WNbwDm+nPXq88sWjmqdNveEETuTS0+u5QlfdLmI8+txnadx1ipA8TwqAogAz72dhqxr7xo656Dq9+8fysnnUqfp+GrXb65237/7bjxNE9WbF/vPP37xwc0WpiFN493be0IepglyDa0fc2kCt/2ullLm1JA2mw02zvGyBZbNKkFlQkVVUZMy52RSwXRZlOi9VxXNBbQiAjsCQGRiItGsqkRkKlrERKWIIdDiTgkRPYNWWJpiHDO7chzUrJ5mScl5BojmwYc4p3r/MJBDEEilkGMGTFWd4eF+RKamaX30RBhjo4h3d4dSC5hd7Xd918ToSxWRyoTTOJradrP10ZtVMxnmMQ1TStk7F9i3z9oypWIqWk/3hymnmkuM3XbXxeAcs4keD6fp7XuRgqZd14UQwNHD4YDgQhOPh9PpOAOwi943YU7p2QfXrmuOD/fkXewb8iTAvetLypaGnJKAGBKG4nzjuw1ChZrDbieKxRw1/TCL6zyHDTcbNTJkMPEhIhGoiNQl9XYxMrDBgsPmXCCwaZyZ+dnL55//5Ec31x/87h9++fJVHE8nKLB9sUs5lFK+/Pe/ur1/e/m6q5lInaeZn/s8lyISmJYEhZDVqQHG0MTQTMOh1Hn59K4T5QGIfd/sK9jxdHrz/l2tWSS/fPX5p59+1rQuBjaF02kgIn4WA5BKYUdgKKps5kIAIlFd3UwIKMyOYGl/MWu6fnq4/+7N63/4N/9iOr5L83x/+/aD58+eP7va9a3cn/o+NE07HI+brvXe51zv7u42fb/fXT8c7+ecyDlAd3h42O+vapBxHNt+44N7f/vm2fMP7x8e2A8uRik0TbPzTeD4/Zs343FoYgSjeUqMRoTkEAG5bdM8G9g8J8eESIVK18TogqqO4xy8c945QqmKaLlkQDVjIpJcyDlmv7h0UC2n7IJXVNFa6qwghH0bG48upayn933cqfOC8N2bh3jTn2R8N2fx8VCkGjr2xFxy7mOTa0k5LUijpmbrhpALCX1BTjxj6qqDXlhrewwOC9ziD/JtW9F8xTA86wBnMF7B7JH3fgwCiyi8SqarIYjAAME9EUbtCdY+hpH1ZE8Ot0a1i/Kwjl+DS3myKhdnFgpXIuvCWF0qnafngXNh9HimS6zBC0sEbLBO5INHUXvF+icXenmHXcojQlx3xj/yZohmZ9UZHu/CENcxL2C4jPp1JjmbvBu1FrvebV8+2+jp8PNf/20o84urbt/1H77YAdivfvVVv2s+efncm/vqd18xun7XyP37KuNxfpBnu3672+92mmaTOfRXi3mkVjGDMg2AVevaN0vOUeMZcRGsjADEiBkdriM9xNh7MGEMiICMOg+LM4UYbfk1E4XNRuZxHk9AzE3jQpScnXfT/dGkOO/RdQHUKqYpne6HWkwKHO5OsekE6HSamrZ7OI3BB9fEEMM0zf2mn6bp9G6IMWz6drPfdY1jomlK8zRXrYTog4ubftv2peaS6jxMCsAG280GAbS64zQZYN9393eH43HcdN319b5pGx89mtZSqkots6q66LsYQhMRKSWZa0WwYZaqOg+zEjXspzoohyqvFTT23SY0xKGKBeDDw2E6Hu/evN1c7TbXex9bFxojZ0i+6WSCw+FYksarl2Ds+z13e+BowGpIjtm5mmsa51KK8xy7RtQYVAugcwslig5N1UdnYoz82eef/yf/yX9WUnr/3fdhu3MSX9y8/Ff/5l99+e2Xp8PtJSlbYwBUJNx01w/5fa6AxGDGCA6IbZltX7SmXKYffD8AAGC7ebbZ7Ybh4fb9e2SQmvfXz/fbXRt9mqYXz18CWtd3paR5mkxq2wYmFlFejIwiDMDEFQx5SSrRzNg5VRin43GaY2j+9M//41fPr7/+9d+/e/P189vt4eHhePugKV/t+uE0jcejqRGAD27T9U0Ih+NBam27zTCeUpW2iXPOcyqx6Uqqac5t252G+fb+3bPnL2/f35Y8cohVdTodmi7ut5u7+/t5Ss5x44JVAQBVkSrsoG2DAXqvOSfVCsBpno2paRsC0qorNgdnIqBWcllWOxFBrTV4j0jkPSEagHfeex7mmqvkrCBacvJx0zSBFKOHwub7vds3U6BIAad8ENNc0jg2q/nT3r37XkwLWqnq2laBEQGXHYJnFMSnzpQzop/h70LJPEIkGpzFgjOo0UqhXDLoS4BYUE5XffXy2dIzkYOLF2i1iuKZVzFzZrDOH32S1V9atgxWzuQCzU8ZnSUILHToY7PvI5O/9givg5HOuu5aDsFZPEagsyJwXlt5zuPX84MtvTDLPDs45+267vCFC+d/uaJzwCF8IntfZN6nHA+uNdm5eZqWmRirKmIIQEsJ59vQQ7NB3pjk4fbr1/et6U8+fPGf/tnnH1w13aYfj+Pf/M0v798d26vuF7/41iFs2vjx82vy8Pz3Pr3aNcd3b6bhNFWwYw7Og8LhzUMbHEqSNFlN5MAHQODY9b4JHLyoMIqqkTJCBangyaos69IQoOYCNSMTM4OCCiI5YAYyRF1mtNRxAjDXtMiOvYcsMtd0OIkaO7ZqSEjoptNpnrIAmEHO1bErosdhVoNpmEL0bYiCmHPu2mYYBgBou9j1bROj1XQ8JkklF2EyF1wuxQfvkI6nY03ZMQbvCVBEx3GUarWWaSpt37x/9zAM43bbb9q+6ToAS9NcawWztm+23dYARHWc8rvbUy1GTfBNe7w/jimXIs77zz/9mIGmKb17e/APc7/tuv1VFssPY7PptdLdu3evv/tGq+6ev9zsX7i2qVJNNGyi5pTnStx0Vxvursw1gr4UBRWQis4zgKrmUoEpUFCTeZpjbAiImAFBZF1wnedMwYHZ3bv7aZxfvXr57KPPv//223a3B2cE/Mtf/zrlAYBcjFKSIUMtAETUfv7pP9rdfPT+9k6BjFEUJYn3ngxVQFmyZa2yjEVZv4sEXbP78Wd/aObev36TctY6vXj+6sMXrz764KNXLz+wPNc8Q7spWg0sBOccq6mU2m03qpqmWbVyIO8bQGy6VsVUq1ZTVUL2oS1DOjwcT++/qVPZ7m/Q5NXV1eH2LdTCjIFcu9+Pw3A4PNwfjohY7d3L5y9824/jybiE0GWpaojIw3Dqbc8u1pyG09y13Wkcx2GMTX843rc+uOBOw2kYp+22r6Xc3x8ap7Oz6Hn5yiNzzZUR0eG2b4r3Oc+qWkslsNNp9EhN6x2yCFBWUIitdwiAEL0vparZlBKzQ639pmfyKhK3fR8YJ5Vci1opmOcHjp3feMUsNqXpne9vMnbjOJRSS9Zus1VErGWoZZzmTdu03S6BPozDrIK6mGEWcsUIHwcELS1jT+zqix3lcXbnJaf/wYiFNau2RXZaI8dZBjjj6oq9tnqB7JwePzkwIODaV7b81J2JHDgrs2sqfYlDj9Fp5YLwcerbwvtcCoLHc8Eje4VrzwKs9cEju3P5g52j2dNjXEKjXcD7rFDYEwLsEojgHAsNYN1hD7A6+ddz2fm6LnskF1EbF3bOcJ2ov4weEhUiQiS0SoqeoiOzmk6n22tXP7xpfvKjVz/5vc82+66c0v/n3/76/v7+k49fXrUbGCckvuqaLz57fn21lWk+yvzw+o7Jhe2NY5qn0+E49BHa58/rw7syp4ag2fWxj86RERKT1mqioFpBFhVdqxIyLU3BBAAGCopGoTWpJc0qhdmZAbEHVICqJpqKija7HtRMpUyTzElyNjSO7LwnMBebdDzlca7ViBw7Y8e+oSroGJHI+VAURCSlurnagdWm8UR49ewKESXXcZjUxDkKgZkRTPu+c45Oh6Nj3nStC66UNJzGeU7EbFVzlXbbLi3BV9dX3nsO4XB7rwZtE0Lru77x0adUUq61SKmmHCnwMOXb169TFgMKofn0ow/evT8dDici/OTTV7tnVz7G93cH4vH5q5fTaX7z7Xcu8HZ3/ckXn/dXz8jHPI+G4JuYp5xPA/vYXd1we53mrODmuYipazm0QWqZT2NV8F3Tti0xp5RMFdmZmpGJKACIiJS6FJQpJTS7vtn+oz/vv33z5stf/I1q/b0//v2f/ewvqyXPmIEsF9NlLh8g8Rcf/8l/+b/8r//yX/4P06TLpgECIsfERMQKVVWJtY/t1eb6cLgXEAQIrnvx/GOB+vb7b+d01Kp913/28WfE3ky+/NUvfvrTHznC2LQEUmvp+gYJptNA1NSUDJYWJhAFtgrIJorMaAikhFTFUMC7+OXvvvv5X/yPw8N3WE7zPOy68OL5nhkPD0OZcwjUb/r9fjfOAwBWkPeHh+BD1++rZBPxLihi1/eHw+E0HPt+yyJFsoga2JwLezOw0zj0fd/328PD8fb9PbN78ez6cDip2jowCs17p44JQIoMdXSOmalvo1Sb5pHAilQqZuRiiGbgHZdaY+MMoNTCzgmoiCIrKpSatpu2CIynRO3Gd23Xx2qYstZSKyAYMpIRgJXpdAt93YQuF6hzYRw3XVsIplJ2bfPhs5fXV9ez6G/evfv+/j0sftNlEdWiFz3RaOE8KPQJ03LJ2S/J6kV3fUKY2FPoPKvJZ6ReaY1lJO1yLD2/DuwHTMkyswEBzBzgWSN9cg2rJHyuAs4UyUq+0+VeLlcLT4qYR8l1SanpByFoFYPPlp+1w+sC4E9CyIUmWkHclm3JALR0BSCuOjDBWZ9YGNlLLAAAM8QFLXVtdVn00rPfBxZLt6GZiSniyvkQMBGrFkRyiKgVyrGkyZvuAv7pH/3ox198cvNs8+77t//s//4v3767f//+PRB+87vv/+iLD/7r/+qfNF2bpqRFvv3+7fE0Ho/HDhlNPNYmch0PONcPnjX7V88f3r/urnfepNm17I3AwETRQFBNaDEWmwEAe8/OoQlaNS0gIlVArapKKYTMgdl7qFXVxMyFiFaJGA1URKZS81xTQTIkDF0kZpBKxGUe8zhj8JbzdJyOp6zolk+Mc67tmgqYTnPVakjzNJaSkdyzV9elSJ6nkqsnatumaWNNczVbHHAhtI7UTGvOOZdxHmqVtmsRMGN2COMw5jn12569WybOI2ETfIwe2YlaPSUxy6lOKQP6ovD+/f2bt/e5KLiw3+6GrL/+3ZvOx7bZfPLjVyE09+NYxnm/27Qh/s1f/y2gff7Fj9q+IUfYbIxomiYp1TWhFim5+KZl1wq6Os85W5W52b8gFwQDEBMwB9MshMie1+F7Dr13hFxKQWIjMzPyvHw8Ywzxw/b29s57/NM/+/Ff/+yDX/71L/7op3/u/9z9q3/7P5oZAyAaI4oJu/h7n/2j/+P/4f/013/7N9+8+9YsETtHjGomYKQArAoqVVR8jB+/+rhtN2lOztF+f+N9vL39/mE4ktarq6vPPvrRT3/y0znPDuhHn33xe7//k9vvv72/u/OBrq/36DCwc/ut1JpzBqLYNMSUqxiQ877USqohBjSspTrnrQqq/d5Pf8Ry/Pt/fbj97u3h3fu7kmv64NnN9uZqP53G43B8/fr7Xd+3fXMYhm7TR/YAehju9tsbBS05FdXNdhtiPJ2OFCM7J/OM7HyIh4f7mw9e+hju3t8Wqd6Fvu8BYDydJtGrq/3D/VHNmEFELEvXNqbGRMv+Azaa59Q10fV9nidgK0mAxEwDOW5CE0KpxTFJVebgPIFb092ayzAeQ7wqxaRm64Lf9d5trFTKICKqUpPFpiV2jsIM1kJp+7gJcSLKiL5pfNs417686n773Zv303BIM7Mnx8jOVGqpCIrnqs1sdULq09k+ds5gn/A9F1P+Wd18gqyiFwIG8cy46BldH/n2VZRdKW14POEiItFCdv/h//n/goDLQvu1m2w14J87th5DjJkti8OWNemwzhNar5nWhuVl6D4gLMP815fZWr+chwU9CRn4BP5/oAKc2ZtzIFpu/EmPmcLC4MPSPXAunp7ExNV0CghGgEwMYCogoAZr+xsaLoO2dZl0tXT1GAVmQpJayFSnY7T6sgtfvLr64uMPdl1zeBh++/r93d3DcDpOD+O+oestf/Hhsx998dFV2377+uGhTCnb9bMdKb9//fb0+raejlcBPnrR/+FPP+mjm99/S5Y/eLHf3OwgT0BqeSIyREFDJIemYAVVARmkAACgkamBgGatBVEJTaSuGy8FiKHOoyEQOSRbRk1IKeU0qRiTEaExhRi46UxyPR2tisx1PI1VcDqVt+9OVVQxHIaSKk2puhjHLOOYBLHtWzBTpBhdv+nSnKdx2m23u+2WnN3d35nK4vZZaN+cqwGUaS45t31zvdvPkqdjnnMiZgAgYgA4nU4hhujYOY5N8M6rVkAix1VUjeas331//+7+dHc4qlG/u3KhzaUAkWfcb3c//vGnTdceh/H2cP/i1QdtiK9fvw0hfP6jj9k7R9Q2rYvhar+vVqvW7Xbj2x7BXBNLVuLGXKC4cd3OXI8UclVAQnaAdNbQWERMKsfgnRdRQEC3+ErOuReh5JxLMcPD/Ukq/Lf/t3/+3/xf/5s2tC9/79Pbd9/+83/2z4739wKgkLsYf/yH//H//L/4X93f3/+Lv/gXD/e3TNxsNwHNctKamMl5D2alJDAkx2JmgOM0EQAzK2AtRaBeb3bPb57/9A/+6P3r19urbaTwj//xn3z6+UtCZLTtrr+63i6zhJeh37UKE/kQmF21ZfBfIFr2jCJ5kqq1Sk8h3x//+t/9xd23XwWf6+HbMjzk0+l0PJ7G4XrbP7u+IsA55cPhgRmqiDmMIWy2vYoMx+NHH30+Dsc8T+1ut9/tfvu73xGHZ8+fP9ydUp5i193fH4DsxcuX796+m6aZiKXabru7e397vD+woyb4YRgYzDlyRMG7xjlypAq1zqZGiNF7x7SsiFWRkpJnRMAmekfQtg2BAbGa9W2LTGDKTGYmRs415K8w7rTZqt9o6NE18yyAOI0nBSiIIfqu3YmLg8BJ7FTN2n7k8N2QUttmcMNweHd4GKtUdhAacE4Ja0kmAquxakFGAOSVf1nohzWvXeRdfAqL+kiBP8VVWNmjs79zdQItbyQ4tw/YU4HB7ALVS7qNCEhmaOrwwq6cVdT/sPRY4NYeCfZlSA+dE/2z2HseNLHaTdcKAOmxFe5is3zyzw+5r/VO12B2nvC/0llLRDQ1ckSLkm3ne78oLJeqZa1g1uLGDIhQUREQmNZWNwJQ0EeKaC0xTABVTaWCOakdyrNd93svrz95tmXSr7/97r/75ddV5dOPP2VDD/Rn/+SP/tEff9JHMJ3fvH333/63f3l/N85iLjbP+u39/fGjZ7s//PHLH3/0x88Ygk06HsaHN89v2u1mHyLLPKgWZkAG06J5RkDg4IiXLWpoYmBaKhIgaNVqsvyZVCsYVFUmBIQyzuSIlylCOZecyjASYWw79h5AJM+u7zy7WopMk1bRUvJpYuRaZToNLjIKp2JqomZtHwE4HwciRM/O8eF4aruenRunSYps+gbI7u7fG1rsu6t9x0QEOE1zypUdgpp33LVbRHz9/m3JQshL7plSLrXmlIldE2NJqe0iEddcnGdFSFMa5jImyQJTsWnWXOjlx6/GuXz99pYRr25ubq5uQmzeHdL9V2+++v67P/7jn4iF3339Lvqwufogq3+x/4AcRU8huDmX27v3H3360rddyYWc02QiNh4OLvTN9cYKIEItRQ2RQNIM7MyMvSc0FUnj7NUyFikSuuCXDdGwtGVozVVqPR6PBiSiRvDHf/SH3/1nh7/5q3/9/W9/N5Txn/6v/zdZcqopxl3TNJLt737xt7/+u7+dcybWdrNr+54VSgVEWRwW7JxWFatg6lyQkh0hKKgUQO67JvjYeA9qr99+l8fjixfP9vvt8XiXTvvYx1TGrvOlZBAttRJ17LyKoIKwEqsZOscEoCLsPBEBgPfBkX7789+9/e03t6/f/vt/86+/+frvv3j17PlVe7VrN35HzO/uDymXrm+23Wa3uy61bKKbS1aV+/vjxx99ROjf37758IOPXs85p6yATdM+HE7DaQxtO8xTPQ3M/nga37x934RGBNOUhtMp57zrOlCbxsFMg3emRkTBkwnMpVCgGJ1zvpakUo8pxeB8jGAQPHXNtuaZAGrJSFR98Us/J1DONTaBnXfegRpV06rgwUzYnGLzcMjcM7qG2fVxO5epTvPhYXo43HLX+G7j0dVpLtlO5I1DnuYhl2EeBawAAqDWIrWKGXvvo0cDXKavilxGzegyrcyWltkFry5i7dl//4iJtgA9XqoAOxPma1PYYw/Vf1hELBh7QdkV4s89sgZuzbQvXp0foPOZLzqn6nDWYG0NKqYARj+E9YsH6VzvrPzV/w/6rxTQeRvAD7L/M5v0aHuCNQThGdiXSoUeA9yFNTtf7sX4unxHzw0ZBgLngLy2pyGpVjQgJEKqlk2k5tSSPQ/8Zz/65Hrbj3X+9Tfvvv72+2/fHPfXzz754oOPrts8H3f98x99/MHDOP/133319z//zfGoDw/T/urmRz/6aBzuT+9ef/pi/5/+R3/aWarH420+dZh2QZ7fNJu9D6ywuj+F2SOyiecmQFVVAzUpdYF4A2PP7AhqRavoPYJZLWCIZuzYSilzQoBaTfIsqco0hU3XbfbcNMwgJakQhQYMaipWSp2yVrFS0ZHkOh0nJty3/eGUxrnkXImjih3HgRidD1Wtam2a2HVx6aV2zBWUVWMTvWdwpKJtaHPNwIQMtVbPjM6J2XAcACE4z8F7747HYRjT8kHc7fpa82bTdV2f5+JcULA0pznJ3f2o5IpgUXTd5vOPPv717775+ttbH5vPP/u4Gr8+zmEow9ffTWn+9ONX/f753f2xaXYIdnd3im13+/4htLFpfb1/qHl+9uJGFOY0N6EZx0QMsdv2u20BmufqdI77nrw3Na1W1UwLE5damQzMBMxy8i46v26CRwAwRFRVc8yE2nVtySbO8jC/+vDmv/hf/E+R4Jvvfnv7d3/3r//Fv9xebXNJBchKdj6UXE1g123I+/7mehgGBQMxEQA0FQGzXOZaBRF8BCTqus4UvPdaBRmZecgTeoaHh32/K7l89qMfXe22Qx7Z2fPnz9FqLTU612z65WvpPYsa2uJ7RkIUUUQSEREhZimTZ+6a8Pb1b0/3r/+rf/qfvf7y+S9/9pfffXs83jX9rtl1bXQvpmmcTvV0fL/b9cycpPrYggLk4ZvXb7749Iu79+/u7t/HLh4Pp26T+n77/v7h4TD0PTZxM87j8Tg0XXd4OB1tvL65LkVK1TQPIOqYfQilpBiCVCGAaSreucb7qrWm2rTsXYOmVWUeE+XkXZBikS34AKYEAGY1V8EaGnTsTGXZMg8OAJkI1SjliiZI1QJSbJMQkgUK5Gi7eeG7TO00zRk8u9D2Lohv3o1FDYqVLAgEcbsFp+VhqkDOs1RF57xnMkCAUmU2VUJbunf0glN2TpfPjkl7zGJ/SIdfaPqFXFmdNfYUr3+gwtoPY8D6gjUgLOMnzADMrUWE2rkCuVzXKv8+dQSZ2fqpN1tWGoMp4roOe52usCL2wtas8WVVji+qB57nqJ9LFjgXNEtQuNBjeLaWrgHorN4u6I5gK8FzfvMqrdvS7Xc55HpNy77mlUFatfL1vhGZANVUVUUy1LwL/otnN3/w8YdTOfzFz3/1/d394X5sQnj54y/m4+nh9ffFvfjsi+cvnu2Ox9v/x3//V+9f3x4He/78+T/9L//zr37x/euv7/7xH111H7k/+v3Pv/zbX3PXvLhqXnxw9fHHuzrdO50aD5pnlcoMSIRgi+tf8tL8r1bFwMh5MEVQKcn0HHMRmQiQtYoWrSKWitTqfNQygdYQHW9ufIgIaCh1HqVU126AVNMIRXTOiLbMMVNCNXNN4BimudRU85SZuIoWzYiIQMMwXe/3xcBAUyrMVEpFhO2mDz6q1rmkQJGIh9Mw5Sy1+uibdtOEUGu+fX+PhN4HMHTO/X/Z+q9mS9IlOxBz909FxFZHpqosXVfhqu4GugcEbDhG0Iz4AXzgI/lIo/H/0DhG/gXSMARJo80QhgGIARotbl9965auSp1HbRERn3J3PsTeO/MOmWZZdTL33qHOSRdrLV++We9u7tbz2cwae3K2VJFSiyFKY1KkyZwqZtlsR3RBmHYlrwdtuvkv//DVLtbZ8uT07FLAlMhXr1+RQR/cyWrZnZ1//d1LFZZ6u769fefd9977+PsPHt5PdRjH3Ww2a89Oz+6dpTha24zjyIxEphQWMrab2W5lw0yNFQERSFybJhjrkVBZxmFMKc2WnbdeBLgWyZmnbwSBcqmlWh+4aC0VkQxCzhHFnizbn/z0Z6cP7lX0u20ah22MmUs0BG4+CwDa0OnZRfCrHQ9SqrWWDRi2SGIMchUFrVBQDHESMEaM895Y64wV5lm7BNo5056fPwxE3aS7rRXRKtIYh0XXWkM6WdQzc2XjnDHEWrUCWUvOIaL1nuv070esmdwIKxr9h7/767/5tzcff/zOe+99mGLcbW5QMKW6WnRd52vmkou3RlhTqakwAi3np8M4Pnnx7OHlw/XNa2sAiTbb3eX5fUCz3mzJNpvNZr5aprrRmBFMGuOLl1dN6xer+e3V3TjGEHzwlrnklGetL7kaY2opu1rmbVOklk1sgnONDd41LtRSrUEEzCk3jUcwwTpVLjkDIteiLMaSBcNcMCFZZ3zXGF9prtRhtyjOO9NUAEZbiIhMyqWIireAJjH3MZY63MaYve/RbXKJxmPTVAYwnkwWFlDIpWqpsWfrLKFFQ9baUngf1oxB5gnkF9aDqmUPr+8HWd/G7OEt4wOkwxrJozvO25H/MM+0j3qHTuJPE4O8NQxs94GYUED2tj1/oqqRfYWvb/Cj6YCE0+dQdELa968d7wEADiZyb0udDorNo5hnr8DcH/0I4CPAAb3fj2zpfism7PdlIBKRvN0k7CfJ3rQ8h67p+A48amP3j2BqQCYPV9BpjyNqOVnNf/ru++2s+fXnX3/z+jkCALju7P7JrLl6/fIE689+9v733ru/i+nf/+LTb757MWzBd+/9i7/8cLYK+fb6xz88fXz/h1/95pfXr2+/BL44mT26aE9n9vJkJiWqiIIdNmtCsQaYxagAgNTMtaoYADXGIO1ZHhEFAVWUUkAUJ+QKAbSqKBnkLIDazuYKBCW5xQkgGmtqLlqLlIwWXBvIGdWpvqtIYIwRqTGlYTsqGoOuT2UcchxGItM2fhTQApGzc/ZitmAVwwDOheCraC1pdjLzPnCpudaua08WiyLCXHLJBk3XdgSw3W7jGJ2zXdcN40iG+t045hqCQ6T5fM5VuFQR3ZXYhOBbm2vZ3A0xsYAh0222m5d3uwr+m1dPx8xItpstBfDFzebl1bUQzhezs3bZtCfPX21fvXg5xiGn/MkH73fz05vrdepj5hiCmXX3re9KARG7vu0RoV2uuKhUsbNAzoOSqHLm7WYtgLPVwhhHhlRFUY2B5cnMGIdgnAUQrrVALUKEAClFJKNVrDWENK0IDo1/+fx1LJjK7t3798r3v8exfv7156ckTlgk3169FBDbzEaJt9cjUAUoVZQs5ZgMayqyWJ46a6XfWEMTuxiH6FwQZmFtfGPJdr47XZ2iyu3tXfDElWO/6+bd6vSkpn693iLh8mQ+FfjOeeuds44VUk5WkZmN9WQMC09rvIDwy99/9eTrL+er87/65//TL3/z99c3Ly3WB/culnO7vevHOHrnvCWy4IyrtbBg13SRa+G6HQcAcIovrl7du7y8vnpJLsSYgGi1unj16stuJWPmcrdGYzNLGpOKOLHb29182c2Ws7urNSEpSxeaDLFUXs5n/TDELEjUj0PbNYQupZJL9c40rQ/BxWGw1hpDCNp4Jwyiuq+uRMAgIdZajPeqgqreNaZdWHeaxfWs/W6dXEnG2nYOzmW0peIubYA0ARcERRwJsWttaM5ms6EfY6mVS2VtjdPFDIZ+TNP+Z1NFMisRixooWgUMkiKRKhCJiBxi22EWCeBo1nAoVOGtX/iW/fOfYkT7RHDclHiwZID/n1+TcvKNZtNO5Oohp+zJWwXYB0mcGON9NY8ALIxIILqfSjgSxnJgoWE/f7WP74iTZxAer/ktPegxVL+52TddyxtAa/8qTjuVZS/82Qd4OaSL4/H0IGPaW6krKB04AUQS1YOXywSuASor6iQ2MIYas3x87yxy/MPvvnjx4oUJ3aP7D07mi7wbHMuD99592MnFwv/q6yd//OrV67v1ydn5yb3Ve5eP8hjH27uStvfPmhfffbY68Z+8972TQD/46HHaXNs06HCX8oiOnDPtYg5Q67jjklkqgCAiWe9sQ2BEMmoVEUQw1otUBFIh4Ko1q0plBqlKiiDWenVOREGYnFelmmOODFWJlCyZYABENQOXEvsas7GGhUschav1Pg45pSIVc8y5ctN1u6KzWXt1s3HWkrUijEQxRiVCwJhyt5xZZ7a7UUpZnizb2YyZc8y7vu+6xhrDopXFOeecLbWstxsiGmMCVWdJ0C0Ws1JrrbWWGhofgqvM/e0ml4pkBQGd2w3jth9FIQsLQcx1cTK/6fvdi+ubfhfatlZ5cHqO6L588uL6erPdbYKn0+Wy6U5WJ+eb7Xh3u2m6wNqAaYyfbbZDSTFzPTlZlQIplqZrvPWiVGKyYhnBehe6zu7n0SSXXHJpm8Y4n2OpeQREQgVEYwgRcypV1BEYpJSzsrjglJUVbNvUNNzevXre55PTs8vH9796+jx44n6X+91idjY/Xa7XW+MCzcvLp8/nq5NZ28Z+k3Iccz9rw1yWrms6rePQO+eJzHKx9L5BBd96BarCy+XpMPSr0/Ozk7N25pxvzu/dv7l+tdns5l1ogpktltY5UPFoKhcyhlXIOqtuAm1Fas0EaKaYsr7Z/eJvf/3/+e/+G8mbDx9f3Ftd3Gjud/kZvzw5na0uVmUM682ma3wIHlSryhizqpC1SKQIXPn66qZrZ6tVXZxefP7HPy7nyxcvX6H3rmnWm904DIoUQjuMfde217fXJye+CvZ9IgOr03nqE6hUQkPGIowpOmsg+FKqQUrDSIYWXYuApVapkjG3bVNLJYJSq3JFNNaaxWpRcgGuxppJ2K2izhnmUuuAGpgHpZkggRIZWysoa8lFLQnYXoiVS5Xl6QrIvbq6HgXIYC3VzbrQjxiMJQ/ed45qqTd3d0OpBWwwQRh178R5EObQoai2BMIAcmBxkSYLnT2fui+Fj4g+vEUJ62EUCo9dwv8f6P5QraseXJ4PLgn7eShEBKuHOd7jkQ50LxwY1D0IhXs5xD45qYhOphKIghMzDCyyn0cGfRPe3wboj6F9n68mlOkN9v+GhH47FRySy2GEa/+Ow8q1YwLDN5lF9QA17VHaPVLLCvhmWYABFGFURgRD2DVd8NbE/OTpC9AsKhfn5w8vLk9Xi+GuXzb2wcVCRW6uXr/8Yv3Z0+vC9vHDd/o+xvHu6+323uXyZz96d9bcf/3sGx3Hywen907aB2eLV19/DWnbaFSDaGA2bw3aUotU3uNaxhi0aC2QU4BcEtRCdqryCxCBqqigiCiDMCATqRigiRSuBdECADEb76RmYNZaEMh2nQGQmlWErFEWQ7Sf/B3LhIOllBXJejuMA5CuTjoW8iIxJZzqJyQQGPoRrUGgXIsN1lhKMefEy3nrfaiV+35IKRGBd843IccygTlVtBRx1ilA21gRoVzdbA6IY4qlJlTw4Kd95aJMxlZm61yf+Wbbj7kWhpxLP6Z2PhtSGRPfbPvIjAEe3H8ojE+3uz9+8UXwTnL56PGjs7PLF6+uDLl+HE5OT376zo98Y+/68fZuvRv6+/fuLVanaH1lzAyNC6oGlaoIgtgQgrVkHTPkkipzqdVYzxVg5MIMqNYSobXOqcKu3yESkqlVvAVjKITGWtqOfYpp1s0A7Lvvv/v08+/e++Ch9eHl81cvnz7xbVMSGUPr7R0D5n693d32cXf54MEHH7z/1VeflpeJOXHBKsUrGWsQcBjjbNZ1i2UTOgAuhXPMJyerUkobAmptu8WjR/fv1rePHtx78OBBv71dncxOzk9UNQ4ZDARP1jquQmRqYesCMxNRZWbJqqhFnG9iP27X18+efle3Wzvuuh/apm05oXBd3+x2fnh478I4t97c1RiDDS60Yy5XV7cuuMYH6cLjdx+mUl6/fG2d/eQHP2jni5evX19c0HCzAbJoTGGJ40g21CzSUrvorm5uTk9WKebgTTFiG4vMpZbOO2BFFRZpg7PGiHDOoqy3m+28a9vQlBpb5xC1aUPNWUByZeegJEYUH6yQNcYYY0phBog5Ox/iEAtvtCVq3Wy+MNTVcGLYjAzbWEeApFkIgQwbZbBNtwrztNvs+jjGlDW4tmnmbbMZakyFASxC64MAGPKGkFRUQIl4ikKkPJnQTLukEQVB9qNI+Fbd+zYZetBN6jEE4rRf60/4WdgjJgeaEw9wyCGYT8DSvteA4yiXVQSkgyBmz0kcSegjSXFYeDFNdclhwcB+Hmkf6eXgdQQ6gdr7fKZ/GtT3F6v6FtMAhySmAAAE+zmng+AVDgTGWwTIHmd6g3LhAe4/zEJPDwLh2H4cPqWIgARIpKwVgMlg48Nq1o5j3N7e1nGoJd07O3lwfjkjyKm8evrCsM5OFq/W62cvrl6/vDqdBVJ3EprLbvHDhxfvvXM683aMuYx3n3/3cm7Nj3/w+KQNZ8sZD8O9hw8Cncfrq84C1AR1zGOPBmwIxntAi1NW0r3vmAojSo1JVLTwxFaDMKIgKKIaY0WSm3WSR7XOYgBVqFWR8tAjIrP6NhjnAKSMebJS0ZKUBdSgouSctjHFDEChaUsq292gorNlWwTSlq13dRsR1VhS1TEm7z15E1MBNGSwH6KydPMZeX93vQ5to8qz+cwHY63LKY99dKHJqQhKCKGWElOspVpjfQjO+TjGlDNX7rrGWjPdfgihCkrSPua7bSwM5J1EHksKoWUhrbWPcZfG1cnp6cX5kPNXz169vNvUUivKzFgufDJf7obxP/3tL0xwP//ZT7/4+rvL87PF6p2mDWj83c0W0LGiKrgwI9dVwLjp0TZohKZNf8Cl8Bj7pu18MwcFVCzCLByC9y6A6OQS3s0XRMCZ05DGMRqL1lAcxn63c8YUrlDrxXL1+e4PRsrFvdMPHz+YB3v14sVubW/Xm1xrzRlIoMaTbvHJJx8Y5Tj0qgzKY4ws0nVzb0vnu2GM8+WyacL6bt0t5iE0woRqVsvF7u7u9vW1s6b75MPVsvHeXJxfcO5D01jn0piH7c45S0jBh5wrQPWNBwUik1NuQoPOlViQsIxjf3dzuuz+0Q8/uvn2Ged0+/Lq8uEpa+nmc5SyG4bvnr84PTtdLk42m/WLu6suhOC97xquutlFI/XLb75brGbPnr38+ptnSd1yuRrHPI5xGEuqctHN225WsvS70Tu/Xu/mi6Zpy5gyqlbW1vkYd623IIIERFBzlSrgbNu4mPR0udhuewOYU2HmZdeUyqRoW9N1jdSidtqtDKVUQA1NwyxawVinVRQQjEUlFoGc0TEBeeeY0JJtfRjqQICVuQh71wwp5fXm0nbz2TIynvgmCmzirt+NI9A2jtfrHaOGrrk4XS0Ycqq51pTT5JNgrNUpcO/N8RVUgAHAKAEByJ7cPYpd9iH/oHA5xvk/QbePu9iPwpdjrD3i34dQjocwPqHw+3fat2rw/ejUoebe9wxTDpA3fOzUzExYiiAesg3uZ24VgHBvewpvLTPGg670zUmOkfsQ4/cAluwvnmgf84+8uYIioahO4wjHav8tkGj/OODYAuzvHg9uTKCopApoWCshuOC7pjGKd5tdHIaaKwpcLk4+eefhibXrTbzZpiJMIHcvrriW9d2ms/aT9x/wkJXof/LnH75zsfzlNze/+cPny85SXj8+aT98eO984WbI119/aZil85hG76lBibstQela59sGEVKKKELGEB2m0UQBGUDJe82VgtVpU4mzoCKS9+olZ2uKAEBkEEmYRcBYgwmAjHdEzmgtysU4QwSqoKwljiCCpZSYVbWdz6VwjDGPGQ3NOlcR48A110K8T7wiuQqrWoQYcxV1nlilFm68B8Gc2Bg7DqP1JsZkzWyspaQYmpBycUSrk+U4xpQyV2mbzgevzNt+GHdDrdUFT2RKZSkFjc2lsmAR3e4iopn8vHIWMt6CrcKRmZmXJ6u263Ku3724utpsGNFZw1W6VXv/3uWTp09e325247g4WVzdbZ48e/ajH3x8/70LMu03T15YY8gHtKGbtc1smRNbdb6dobWANO76lEq7nIU2LE5PEUhVcqq7XW+9s75VMLWKcB230TjbdA2QsRa1A66MqmNMSCa03a7vh+0472Zlfdc17ubVK3Dtuw/Py7Cti27czdtZW0uWxMtlh4R3t3cXy5Pbq5vt+taSYQoEEJz3zl9cPHz94mUQmc9mtVRjQKvYzgarwJrGZJB2m83HH753/+Tim+dfvZQnwfjKLFXyWMhQrdk7x0UqMCBaT84FBayVrbFCpDkbNK7zL/74+eeff/7HT3/zwQfvdVJnnX/x3XcnqWnbFkDnqzlZ+/r2Or143bXz1WJl0L589YqVZ90shKZpzS4OkFVEH7778PPPvrm7uX76LH7y0SfCeTdepZLvtltDZBtfCg+xn7Xtdr1puzAOEQWAOSEvu/lu2HbO5JxnwfomiGOjSoCkUnLsupBzLlycUkppPutqjjUyefXBMbERMd5WLlyFS/G+UQUiQO8AsORM5F1wahGlUq5iVaVUqdVraCkzr3zYpRIaH4u/W2+dXzeNn6GVyqdhtjD0PI5Xu76IGFAEtEqtazrvdi5CirGkyhXVCLISkUyDLyiVlUgmf2gyIjwp0PfYjv5J2H4TLvGtaKlHzOPgI6F7/vOtN72hbN/CgA6kAQAC2kOukQNMhXh83yHCHjaSHZH2vQkFEsIkuIH90JkeE5TuhULHIPImyr914Lf+5tiyHHD8KSGKTF8c+hqZnHoU4S3Da9zfPey54sO9TzPXk7ABJtD2cEwRYWvcvG2Dt8MYt8OonBXUWbtqFu+uFj7XL1/fbmLMAqpYctYsLdHj+w/euZx/cmkRQjXhu9ebf/jDt998+3Rp9R6tPnr84C9//D7VfP3yxYubmzSUi5O5xLxoQje321dfI8B80VnnRJhrIWsAlFMqKtMGNiIjqgJsjHXt3BKBsKICV5WsURVFceplqlYmlCIVmUWYC6PxrvGgBZQ5F7JGGBBRYkr9CMigTFJRRVXG7U4YamXyZuHbcch5W5XVOKhFpLIKVoBaZTZrU62EZAiEgUshY8gaLtVYW1TJUNM21hrmypVTkn637uYzNGa76TfbnTJbR4YoT+4OtajqpAklJJkEdMaqQK1cijZtN27HnLIyWmOsghgT+6GwON8AWWPs7Xq33Y2IkxmSRZHT88sYS0nl2fPn7WpWUnz64snpbLE6XRoKT7+8un599/5Hj10zVzQpSagsCrEfbK3Ot4oSY2lnjTEG0XCqZIgBWKr31jpvrQEABDGGZrNWEIVZq0xIb60VmFkFEZ13ZjSnqyWhjsPm/sOz7Wa9WPqGgMf+wb0z0NIP/bgZKsrl6UmtYi19993XyobYCXBO8vjRw4cPH6vq5u5mGHY1J2Ftm3D9epw1nQVoZou+39Ysl+fnHzx+dHtz9Yc//Pr87HRzs77qXj16eM9bt1lvjDGL+YlxBAA5Z9cFcm6yuJaKYtEAqmAcxtvX25vr10+/+/yz3/3may3/7K/+ye3d9cc//Xh7fdOKW99tK5eLy1MFuV2vN5v1drdddO3l5b3t+m4YhpQyohXlYbOLJZ6erx49OB97vr3ZfIFffvDe5FSRN9veWiJrLdgUR+ZqjUFVbw0BomrNWYI7W54O/doaSLl6g4v5rOYiXIK3tVSR4j05cTllEtNrHyxVZsoKoM6RgAJJYy3oNCIlxjpEQ8ZNTpqKWkpsfRvHXeUbUDDdhUfLnK1xXfCjSGOBgB8/OD9ZzMZx5Cia61g5s0Bw1ntgzmNEASRNcTCGXGhab9U0lWuObIJXQDYowtNGHjaoKjQV8XLU+ByoSdrzqAR7PdD0+ziie5wE2KvY4Qh5vIn4+xcn14f/UQaZfEERUdGqCBgAoLcq8P1Mwpte4Dh1dijm94W56uR4t8ef9pgL4dG7HwEBRQ6WeHhsLY5BWw/yosMbjqwDHoL4vsYn2J9uT0zg4VL3d3Y0Pjrc5f4hAB0ez0SkCKISgLU+hEZJb+7uYhIAsGidD8vGnQWXSvzD9W1fRYxrrDPGBhPOzsI/erCYBfvVi5tPv7qZ3zt/9vJF7Md5Ax+e+P/yL773/sPTeWNTTt999o2ieNt1pxaNGuRShnjdLxa+WcwJSZmBxVAQKWAMBdSSRWiy3kEVAEfOqEKpQojCFSaHWWsVGABAKyoBqCijSOVKCMLczGbWGi5Fc0aDxpKKcEqSsrVGQCRnYWEF6y0ZM6x3XGtoOiAyxlmHFdTkwqlWZgFQcCGYGJP3Hp3fDckgVRVvLBEZa1PMLLpctqDArKgwphLHtOhmypq4pBRFxVjjfWCWYRwNWks2araEBl3Nte3CAbdEay0ycknMkMaCxjob0JrNWCxR410l0w+xzyUDtrM5pzHGGHxzuuyctQry9PmrZjabzRaI1IXFj/7sL67uNv/1/+H/PNyu/+f/8l+07RLAPnn69N7FvVSkmc0EsLWm874yNDPfdJ31NuUUx8EYB5acNe18DgqWzF4PbQgQYilamRSqMgF574lwu9nFcWCWpm1LH7/75vnV69cnZ6uuu/fkuxfPn75Qlkf3H3LmHONtHE5OThfL02++/SyVMsRUorz/7sevbl9SoabtoPL9+4+/+OrT1Wolkm9vXi0Xyw8/+OjVq1dc2Pvy6MH9mMcYdzPnHz189Orl065r7z+8/+zFs8fvPGbAm9vbexfnouLQAyqgWmsdOSE0asLMlZg5ixauUb/5/MnN7UvfwCffe/THv/u7V0+/WJycxCEvTk+8BUWOMd7dbubzTgxt7ta77W7cjbNZc//8PAx9KgUUalEgO+5ivxl8F0zjAM3tzd356RkAjGO/OjkZdv3MIHlbHRKpstTMCBqaAAppKLt+BOXVbNn3d0RQQfp+u5jNYyykEBpXakEW710wLg5xogSNQQXlzKhqiECUEYKfpvoFFYwzhowAKaAocKnDdk1hyXWA0arpbLewCspStbbOxn7ABppZh7Nm7DepJKlSKtv5LOYchYVZFCKXEsU0BmtMIIZtEzz4gIGCbzax9DUzGiWByo6wVFWYVJRCSFOlim8seA6Snzf4yEH1gnvEBt+g+Qcw/DCVMulKpw/sy2AA0Ol0CjDFaERAu9/uBSoqeKR4j/j9m37jILg5FO5TkplcdabgjxPQANOG96nXUDnsrtwDMDhNfh0FoBNOpW9llzc3PHUQygqIinIAcN68Fw4n0v3l7BsdhcNYnb655j0uRIogxoXWNyx1s91xFQUCNIJqjF+tFobLdb+JhOy9E+PJWXJOZOX1BPnLl+vnV3fdvHny1dNhvblYzX/w3sO/eP/0g8dnxpqr17d//Pzr3au7YNxJ5x6sluQ5l23Tyup0ZgOwKHMGFgBBAhWtXFCAjLeWjHEKqlytdYiCwiyVmVGVpgxIxgIisBbmklVEVQ1MBG/1jUVSqVlyVhEkwylrrSqAhhBVi0pMUtW6tpZcckHApm3U0LAeS4UUuYjGsZQiJriOzJAl5WqMRzJcKoimkguLcyGPpUiUKovlzDbeAKRU1puts7Q8WQorqoxxSDkvlvPTk0XO9eZmzcyha/p+TKWa1m132ya0osq1ApD1YRyGVCWz5FraeVcFVLTvx6adRRZmTALrYQzzhW+63VAZkNDMuhkqSa3rFIns6XK2Olmhca9ubv71//3/Rogk+ujidHFy7+ZmfXe7Fax6aXYxog+ubURh6Hs0fr6YG0tcOY1RAUSqJW99gCqVuUAxRKrKIlW1CR69yzEaY4iocNEsAJAr15QL1zLEm+tr761vmy8+/ezXv/70ybOXy8Wy/9Xfo3O1lop1tpzFHDebO6msZFXUNv7y7KKxngiMg836rmmDb721c0e02/XL+cpZ17WtsXbb377zzjubm7uYx8eLd15dvcg5Od/OZ4tXr58tT84NkgjklJS5XcyMMUiQSqqjuNAYRRcaBBz69Iu//ttvv/3yyZPP7l90jx6cm4/ee/n0yeX56na3UzHd6dIHhwDjmMc+tcv2/PTMgtlst+OYvnvxou3aUqohDI3nyszirYlD7ObLDx4/eP7i5d3N7fJkOetCv9s5AhLJQ2y8Ea2KYpAcGRCppVpCJCo5b7gYhVJqO2tLTjvou8anlAPitHBUhAlpPutKLQYBUQwiGTKOvHMIx42a5L2XqaQhmCS/xnrnrKKttbhWqyasO4vtspn3RQzZiHC6nFekUnLOlZAIxVq7mC/DvNmOzAwJYSg8Dwvrg/OOSUuuMcbd3YZM283aPsbCoKy1FlFpgzeWyGhKSUGmoIz4Zir4oJTcl7PHrbhvB7JDHH5T9R47haPm5YCy7CWmtLcOwn0MVYBJBnpQ6+OBhz1ATxOQcxT1v3XmfbY5ENYECKKAAoSyb62OyeNISh+r+2k1gR74EDhmi8Np9pYM0x/x2ATsr+BIDR/SzKFxwWMC2d/MGxpgD5mhKoB1wZIZc0458lQoIApNJ+DUjyNXNG3bNiukE98ER6WmIcqrbf/d7Z1UKUWY0rvnq3c+uf+Td89bT8M4/OrXX45c4yY50g8+/LgDPjvrZmm4ffVNSznMO84FlUS5llRyPDiEGBcCeUdkRQUMSa1gPRgjXBEZCAkNAgAX2CuvRLlKqSKTwREoq1b2XdD94vgqla1zADzNl0lV69AaVyODijGWSy0xMhjjvCAiOjSp3/YsVIsKi3PWBz9m5qKq4KyNKbOqKKZUjTU44WhFTk4XCCRVN8M4plRybXzHVUR4s9tNuzydc/1uGGMBgOB9rdzvRh9czmyMMQZrLaWyNWaMsYhUqaVWaw25dtsPqdQmeBvau12soikzIhFZxGmxoUMSUGpnsxQLpNq1jRYehmFMOab07OXz9x+881/887/86IMPhfDFq6vz1eri/sV2GIL3LFLHZG0nkldnCyIT+zim1M074x0AWGuUJaY85MFaS0TOBSKDylyrFCQi6wwoQOHrV1cMMF8ucN7eXF1/+fnnwZvVyb31zfr5kxdnJ4vLy8vdmK5evVy0jR9cN5vd3l7lXDOzRby4uLi9usslrk5OVmfLOA7jWG7jiw8//Oj27vr08jxYX/jFdrM5O79gKdb43eb2en2TS35472LX707PTs/Pz1++eDVfzp48ffaj5co1DRCRpVKrF/FNW1lc8ItZqLWOw65xjVR59fL57377q1/+w9+rjLxZffTBu5fvvtundLu5uby8ePHi5escz85PQ+N55GGIOWUXXDPrqvAYR2HmWhUg5lq4AkosSdT2/ciCLOwcgqi1BkS7EAi45NJ1XckpOCNGay7WGGtM4/zQ94BCaLgUYwyzxFLaJuQxts53bVNTNqBN29RaOVcXfNuElAcu1QRr7CTecdZa5knyTQxKzrOA1GKMc9apoCiiIUuENXftPEFFKQq5MZaUQMHbRpuwjYNDc746mwfPCqq4SeP67nadCy2Xi6a1TQhd2EXpUxKEpmlCCEWxz7kfRkVrneuc99aSNQq15EiAgCAChmiCOOStSP82WH4gfI+hGPa17oFRRX3TABzK573HgQE8TOgivoGZgJAQ1OphyftxbOywiGA63B6UwQOIcjj/AbERnYB52iuZ8DCohnpwmANAESGcnCQODnFwyDL70YM9wn+YzD1mi/1FHAEfRDooYvft0HEkWv5HOWC/4nqfRqYkh4gqmkopUlgAEY0FACA0ztjGUyqlCouSQ3WkWSSlMtQ4pqilSmYL4MiczLqzxey9B6cnZ6Hk/Nnnr3Y3u5TF1fpP/8mPHz88S7vN7frm6vbqctku5kvAWkTSbsicakzGGQI7W63crJn0tSBKioBqvNHJCWLaqiMKysCCyiCsOSGKAQB01jlCAa2qYpxTqYSQx8E4a4NHYo1FagUVRNUKaYglZRWc6iDnHQmVXKy1OeVxiCrcLdq6iSYYVRiGPCQ2znnX5lhjSqyExoTWzRfzlNIYUzebKSiq7NbblFIp9fLyjKxZ394VlqYJ3sByOQfFONQUs7GmiPS7nfUW0YhWA5S5Skpt0+ZaFQyrsEiplYxlrUBgiYx1tRZDkMZShQQJWBlFUUClbRooGcTFVDpjDeJuGIdU1/267dqHp/c++PA9Er25ev3yxfN+t43vPe7r8NFHHyuYzW4X2i6mHNo2i8Ttbhj6ZtaBIWYFBWMx5Xxzc62oy8UC0PabrYi0s3kutbKExqMyiwCRdVZzIcA0Fuf95YMHkAshceX5vCMfSpXr200udRhGVJRcC+L56cn5asmcT04uL88vX19fac0+dPOLxdWrV/O2RdUffP+Hr65ejsPwzqN3JfFmu7HOnl2eL+fd1dXLWop79z3rSbCprLXUcTf+7M9/NmxTzpEQQxOa4ELbKJFWJjI8Wc0ygNG+37589iSX3c3NVY35cr6soyDo2cXpuFtrraeL1c3dzXa9aZvGe59DFoZ+N6ZUmsayt8oISG3jC9eaM6ucrJZTSgCg+5fnr15fMUs7a5fj7G69uzw/WW92CkrWpZya4H0bCECkeuu7tkklCzMXtpa62bzfbVDVWdv34+nJYrVajMMoXBrnk2LJqUpunAFAAnXWIoC1aJ2zDoRViqJ1xjTMRUWZgAuHdiaqzIKAnKLwtTshKONurLVbCqqQyTLmXGJJrOJ9u81jYQ7dYpfqoLLT2gg7GwThdjP0Y+nzKEjOu8Vq4Z2VfgxF+hgtYmja4EKuaUjj1IoYtEgqoFNAp33g229TP3jpHOPYERk/jF/hPnbqtH7+OGt7gEXMFCX3sA/BtAvgrQ7CTslD95/eE8HHY0xx+c3Xx/UEB33oMTADTDKbwwH2qWmvDkIzoTF76uLgfvd2i6DTAhc4xvypZEdQUAI61v/6huI4Fv9wlD/tO6EpmdKhd9jPQpjJtKIyy95viQinETE1gKQYsxgkVgPKlbmgGcogWgSEQLumee/xaWs1Df33Lhex5H/7N79MwsuuHYf40x98srIk4+beveZ3v/1VXG/vnS4fPbi/mrs0rEU1Vy5jts6ExXK+WAISEClYmaawUYlARUVQlZgLACI5Q6SMiEKAWhS8V84lZ4NK1gIZKRWNIUNSgTkDCpEikuQBpNSYRaq1jpmFi3HIYCaISVk4J0JTY05jNIRd1+QqXNV732/SODAag4CceduPIpXBBGOa0KZUt30viq5krgwCSjD08fLiNMYUhyQExiCrdk2LgONYci7kbMqlpGSdsc6NMQEgkVUGQGBRBayFSxVAIjJKpmQhxBACGpfGSQFVC6Mho8JA6AxZM21trnlMVGtxyqqlcNESfDhdrhrjL0/OfvV3vyaLNvjT89PK8hernwXbpjG7ebM6O4Fi26bjyMPQk0VrvbLmFAmRax3HkVQvLy9McHksWdk5KyWnWmezuSGz2/ZpGMjSYj431mzXu6uXr4J37z16cHt989kfPo9jvPfgQRJ9/t3zm+vrcRiaJixPlzc31yer+U9/9qObq9sUx9PF6XfPn7zz4L63oR/Htu1yP5ycLdsm/ON//PN//+/+bezNo0f3uRT7Gufz+bOnL5ar5cPHj18/eXZ7t96s4S//6q/6fsuF5xcz5+eb7U1NQ9d1SKZtOySngM6Hkop13hhCQ9vb9fWzZ9ubNZB+9IP3vvztF+Ow5ZIaR01wNZlt33dNYyxtNj0iOueRDNdM1nKtOcmsmW2H7TCOOVfv7Gw2yzEPMc66UKqmXF68ftU2zdD367t1O2v6fljveutsZVguZut1EWHvjQEskccYnTWNd6JivEsl2ZlbnKzG3RbYONR+t+Xqu64Zh5ryqEAiYgtW5Pm8Ya7KTN6CAIiGdqZouFRFD4Ch7ZhrGhMam3Pp5qvKtaZKZEGESsLUI8yoVDNzNeY+92Pl693Qreavrm9eXW9P7l2eezc/W6yc7TfrXSnW1N0IMZchFkFoGocuKOtYU2Vm0qZxbdO5JsSh3w2jEoCqsX4/P3sYs5WjL9rb0s89TAJHq0uFw7pJONTs+yL3EBUPWp03hPAbCGeyoNtL4e0UkKfIrHIIv/twexiiOlosHKI/TnPAAEB0pGHf6P3xQGYcuQrZe47C3kru0NXAsTTf56QD4HMs4o8Oenu46Zi+RA+o/0R344T5KOi043d6txx3ceJkXoGMoAYJ0CIiKiHq5BQnypGFAQnVEXoyMRejBZlBAVXDon0w73ZDL8JPt8Pt3e1myCmWze0AAr/7/ec//fD+Dz968MfPv+7X28f375+tuhjHr148X8zccuZJyDcL34VuObfGImitpZZK+2WsKKIiKIrKQkRqPQBzFQQ0iMAgoNOCAOtD8E5KUhFjiQC4Zi0FDTjvkUDrUMeeUyJjjDeckha2zgqqFHbBcE5lLEAGVaWqCgGCocAxeR92sVqyPlBhralsx0xkEc398/NhKJYwZx7H3LXN5BQkwJx4OW9jjuMQRaCbN8G70HhCGPs4jNE6Rwats03jiOj6ek3GGmN8CCnlyuCDLbkAkaqUyoBUqmZmVkUiFZ4MdxAEpHrj8zCEdqEi3mApBUEJfC0FnEnMuSYbWos0C7O2cy9fvQDCu7stNVYJtcrmdv3V518Msfyjn/94FmaZJA5D329j5gfvPnTOIKAhJMKcUwhuuZob71Cx5NTNWmMss4a24Vq2612qBZUNOgQsY+p3G1Q+Pzvfrbeq8v7j915fXXMu2g+vXjw/nTX3Ls8uzs+G3e787OSjj967uDg/XS5vb+7eefBoM6zb2ey99z/64vPPrLU//PF/dXt7HdOY0nB2ee5m4fL++YtnL7/3ve8vT+aI2Hq/Hneri4v13dXp6jymIYTwyfc+uvfO5a/+7jfk7GIxny2609NTQsOVZ4uFKlcFFSg1Xb9+/f/+v/7r7559hyLvvP+Y65n/OKahv7l98f7D+7ZpODc11Rq4a9q7tOmHwXl21vnghr5HoN0wsvKsbcaUt9uN803m2jXNcJu5VjSkKpbsOGYWDT4E50LjSxUEKyWOMa7m87vtmkQQmADHsUhwwTsEiLWgwnqzvTw9VV+xZmvNPLSFZRxSG0LBDCqshKCoktPYtoFFDSCS5czSAllnbVMqAhAbg8YHmtXKVXSIEYnQGsnApWi/KbuBlvfZhDL21jZN25ZhPDs9bS9O4GrbnZ5XpCL1uh/A2rBYSFUGkn4cS2EAF3wWwVonJnIch1LK6fLUO78dt7txB4jWuD1UclDpHPlN2Tt7vvHUPAT6Y5F7wID0UIAj4GTJc5jvwgNMP1ny62GnCMC0VxL3vvcCdh/d9xH3KKmBgx/bm9PvC3qCffo54lGHUK7HZHP8u4NQ/03wP8wNHHqHN0kOjxZwh5Tz5sU3oR+PCNMe7zk0Ffvgj4d8ITItYlMFIsTJa1kPTqyERJP3J4AisoiqTn0GCKtABVA0LJ1xi2UXDIrKWev7nG5yvUl1xgZo2Z4u77e2rm98jQFl1frNzdqj//jP/2zZtVi4bNbvvXu/xu31k29OljPbhDDv0JjKYggR0XsrCioKAqK0/zaQVAHlKlItoTGWOXNNUgsqGGeRiGGamFNCKzWBgrEGyUgZSIRrQRXrrA1OclEBcr5q4RjJErBKFbIeANOQ4shFKpAb+7FkMX4uKrtxRGtUcTsMhNZYawFzrk0bhjGmXLzzIYTgPIDp+0zOoKFxHFnFe4dEgMqFudaSS9MFALBAVVUExhit985ZREtEpTKiUSBVUkAg4lQZUKqAqDGWjCmVY4pTn2yUAZQIENDbkHJGRGHOlZu2yZWnn4TgHSqq1vVN3O3WubKxjjOvr++01NcvX653Y8zx4aN7jWvBB4PKgudnq8Y4qKhQnbOiKiLBNyCQ+rjd3DVtg+hqrSIAqiIChWchhEVTch63w9OnT8no6cnJMCZrCTRsrjcsZdevf/3L3ywX/uL0Ykzp0YMzSYsP3380my9KET9v3nn0zvXL24uLe4aMsLJUrJJSfH19o1qeP3n++N0Pnnz9ubf2/uVF13Trzd1PfvrT0u9M8Dnn33/x+/tnl2Mc5vP5dnMryIvFvNTYNW3XdWSJmRtvUxq1iiNrLTKZ6+vXN2P/d3/3y7xd/+gnPzy7twrz9t692Xh9fXPz+sG9kxDaKENO2bc+JBdTyWVwxswXcyAz9DsE3KyThcW905ONd/04jsNQUzGGRIkUUyrZyKxtSsXXV68++f737MamlFbL5VUpcRyNaRzZlMfgrHEUGltTIZCmCU3rcsyezG67O1suN9sbVtnE3qBxFQqCMcSF285wrcqgVUquoQ1c2Toha4EVHPpugWAEDAsZJLTeVFHVNO6wiCGyLbmmySmLFN3dMbqaOPkG2mbWdetUTcVuNs/AXQiblAsAG5ICrm0Ng/aRHDmHwiJkUWCM0Tt7vlyBMbnEftiMMRF5skRoEEFAhUFE9jjEhKEj658M+B7ncveYz5Q29NglHCrnw3TwcdLsEN/2+xHxTRgHPcA2YA/J4hCoaeJYDwX022c4ovNHh1Ddr17EA4qzP8VbswhHjmF/A/vcgIdO48jgTv+lPXd7wPL3mQYBZD/2hoebOdIeB+0P7k83AWpoAIForypWERGZJE+gCFqBiZEZkXQ6AxGhJa8EgIoqTmXhZ/cb887M3sVhm8u63z27uo21ZtCtDFSVSuHW/vijy5/+4EFHVjSNV+tnL28lp/PVCUnWOKaeWsdnDx80BgiKllzEWKJc6qQlmYwEkIjQwF7lb40hEWPJK+ccB1W2aN20cFEEpKhU4AIEtVbgYghUdYr7JSfNqYzJN14q15QVFERABZFQkUux1qWxlFxyqkIUQiiFFcF5O6S03QxVgHMZcmqa1rkQSw2+9Y2LpQprTjm0fjnvSuHbu7Wx1juTS865tG04WS6UQKUq11LZOZ9TNsaOKSuAc5RzBcBCdTnr+j4yK1ksVQtLZh1jmVajWYvGhsQ85gHRuuAacr4aY7QKAABLnZavVq6WUAEEkMiMwxBccIakSkpxZtqxQtzF5dzPV2e3t9dPr1+8vr774Q+/d7pYfv6HL66vt6Dw+P3HJ+fny+VcVVIa8zjaJpCzxhgV3e02tRSWIhLSbkSkXAszA5Kw+KYpOedUXr18sVrMu3mnCq9evl527Xbdf/6Hz8/PT9t2dv/B/Q8/fm91cvrX/8N/RmPuvXfxxWdfXt/enZ2c/sdf/OLPfvLzbcy//s2vnXXvvPf+3/zN38zmHYD79ttvvv7m67/6i7/45tl3T598082Xf/bnf/63v/vFYj672tzmUj788L1HDx988PGjtm0dmpTz9asrh/jwk3dfv355slykcTQGl6tTMJD7IXh38/Lqiz/+QRgE05OnX11erjak3371Led777/zIDTOLJep3+2GaBxhAmDJMU+eH8MQo3BVmc86lcqloNqr6/VsWVazFhFjjKWW4MO2j13XuKbhWllksZwPfb9bb+bL1Xrz7K5fW6tj5DjGRevFtKXWWjl458hwrcKsoIYgx9yGsO13s7Yb+y0iGFIF1H2HTypsLSGJAhoER6jGG4NN19TExli11jUrhgAMXBmMc3OnhZ1rtbLEBKTeB0uDMzaOkdev5nPxzjJYdq6Klr4XUfA+5yKqFUzKJZfaD+skWFHJ2VKYnEOEkrJ3tgvdrO36sR/GlGthRO+ssci8V20qoUXaR7UDlrKv0/doyz7aHcaE9UhxwrGJgD9JBQB77JwOzC7SfvRX5cDEHlB+e0gbEwS1f+FtCc9RqfNWiX8I+W+S0NvYzGFbLx4YhEP3cshbEyLzxsv6MKU2mcPjnzDOb2I+HKGiA/z01v9wvxDy0FuIIhgywTkEykUVSZSRjCOaRAW8F/CKoIKiMR5E0BACEJJFaNS8O/cE+Lvb7dBvFcEabJxbhhCctQYbURMHknhzdf0rzjNjPvrgvhjfdME2rtZ09/Ll5aJF9KcX5/PGah09EQhPz8c4DwB7GyUyioQiXFmVAaCyiCgwSxXvgwELUpxREFGtAipcQARADQJZpzVyTVqycYigtRQTPJea+silOh+Mt458NSYPvTAysHDlzM56g6Yyxz4jUq24Xu+Ms0HkLuZ5267OL++2WwOum7e73TgWrgzzRTufz1LMd9vet828bRRZGE8W3enZqlTOOStgyVkAxt1u0bUlVULsZt0w9Ggo57rs2lhy5YJEItAPqapUVlGRwrZpiEgRc81WlYW981Sr8w050FwBTRVUVLSG2LFUUUFnp59Y7z0B9iXVISdISGbVzD588O75wwfPX7z4+tm3d+v13/7t33/04QfDED/5IbfdLKfPfvjj8M3n36zOT30TpJTtOM5Wi/liLqkQqCOcz1eAhKpxO8SSVTS0jXekJY9jyoVPT1bdbF5ruX19C4VfPn3+5NsnF2erxXx2e3fzz/7pX6Khm9u79x4/evrd01LTH//waRzLhx999B//49/noq5tP/v6m6EfB4bf//7T5fli1p3/7nefvnzx9OMPPu6H+PTZt62fP3zw+A+//e077z5CcLvNVkrZ3tyen578/M9+Ol+dS4lp2DlkYHl07xEY4FS7pjNIlszs/N7d9e1uiH/9H/76ybff/OQvfnLv/OFnv/n0B598EndbS0KkWKoPrgycS2yCt5ZqYWAFwG7WFq7DUId+MABd163TmrlYa7fbXYpj04QQPGrONTfBlFLmTehHVWDhigY3m+18tfCNi0NvrLHGbLZ9GmNwZMAQUUyVCHxwpZZ52zCXVHEYYhcMWXOyOBnGbWVVZUOIKrO2BRSRahEJwTqjKqHtwFhhDfNGkUFVgMC3RA7VKBqyDjuqcYAq2CappYAiWiTkWL01dXuHtVgVlLrybUZb0V73adSUVJqzk1jqsNswmiJofVMYEKUKI4AzeLJYtD7cbTe7YVdVBA0RAKLwHq9QATpuWEeafOFY9lsXD+ToIdIeMHWcSM0DS/uWj9o+YUyFM8GxXgc8aoz2NmiHoS1Ae0gnk+kETcXwIbLiUW2z7wH0WLQjwBF+n4ChI0Wxn+46oDi6d4LGQ6Z6A/r8ae7Ag1+R4GHYeH+0N4IfOWyyeQsvOmYW0H0PoQqI00oPthYb14JKrSysRBC8RzAMqiKlVqm11gxSRYRrViQkrIBk7RfrPpU8pKgpzw11wQqpM/zu6dkPP3zv3VWgWtfrzd31+tXN7Zeff/f0yat5g//lP//Z+bKRzfp7H1wuGoc11X4rQ2lmDrRWEWvDlLcNIBmj06C0KkspVQFIFa3zBlilKJFqKqUSVAIArlqycEEVMmQJCbjGHmomYOOIy5j7ERSN96BqyPiZRyQytubMpULVWjJXQbBIOk3z1lRFVQ0NQ/QhRNa4jaenS9eEcdylXFYnq1xK4QKiTRPIQIzjro9NcC4Y0FpzDd7N5g0ZTH3hypW5VvHerRazYUiA2DRhvdkMYwzzsFzOAGTYpXbWjTHVCqzIIlUAEdHs3WZTSp6MIDjbcBVCo9PQQxE1qqDet6xYmQGNWgdEzjfY75Qw1iqgpapyhKL3FqdG4IcffPzP/ot//tvPPv23/+6/vd3c7vrN7S6Mkp599WJuA6EjS3/+T//JuOtRaXa6kCq18NXLl7NZ13RtHGMIIaW8226XZ6uUqrMmBPf6xStA03YzARzH0SB99eVXZ+ens8Wymy8+/NEn/+6/+3cP3nnIJF98/vm275988+0fP//ig+9/8uru+ovPv7Ezf7O9+7vf/moo+dWLZ6Fp/+ZXfxMluWg//eKzZy+fBUPPb1+8evLibnt7vuK/+Zv/fHd7k9O4PD3DCtdX1/365oX1BgkUnDVj2v34hz+ZL2fb9bC+uX3+7ZMPv/fJ7/7hVw/ef+/j733yr//Vv3r18nWW+uLV62/+m//nxeXlg4vLsd88enDuSZ0WEss5GmdKLpNpJRpQVakFreu6hktJqe6GnlUaH8Y4WgJRO8RURSbbNUJi5lwKkc6aJtY0lVsxjs2smzXNJpVprcms7VLKOcusc6iEAJXZIDDDbjsYa5eredqOOdfb7bZpXWNdLcmACsu0gstZNEjGIABzrc57HxqyjgUUkaxFY8B5tc50S6CGhdTaYIO0saRMyjXFGkcwBbiauYNSCZP0Q4lP7SrR7LRZnQkSzlveppvdZluuSnCBPBBaFS2VqhoA60zbtN4aQ7De3W5TrArWGkNGFAwZUmVlViSUPRSjMDGrqkBEbyrhaZYXjuTuW5qXKSIffO33/nCquofnJ3/7Ka4c8R4ksz/4tArkkAD0eBYAOEiQQPeC0WN3cWgc9sdCeAPJwKFF0Lfi+7E6P1zu4SaOsw2Hezn0EfuMsc8XeLiuN+TwQeWDbwSwcEiT+94FD2InVdVSigpULPtLQIg5F2HnnSHngwvBGgAVqLXmwlxrVRZV5jpkARVlRcB50wYALEW1xiz9Kn31/NVvPtt5FanlbN6dPrh3N44rh+/dW26fvdp+3T84W47CZaurWdPO5rW/G9cRLfn5grzPmVGk6H4iXRRFRITAeGPIEiKIlKzEQIRqjAsGSGuSUlHBkAFlY0ClcCnGEKKRUqSkGvM0RyGllBitIeObmnLlgpXH7VBTatrGOCqFnbciWDNXRePceh3L5AgZ6+Jk1szaXLFyOlnMRDHH1IWggVIuwphjsdaEYBrvxiF7Ml3TeOP77ZBLJqQ8ltXJwhqz3u0ATNuEIaZhTLOuMcapyBiza3wudTckZjDWkHXBYspiwEyG0ohQS3amEaCZb7Zp6JzdmUooiFpqDRSMJetNrdI2HQtXEbS2C23i0pfRCojCog3vXFwsmsXKNeft7ONH7z//4Pt//ff/vuzKK3v37fPnX3z15MQ25NzP//xnL549d0hIrlu0CHT94iWXiG0rVfMYNZfdblit5pyzsw5KrsySip9bR/jd189OL06H3bhaLkIIsc9tN3v55CUZe319c3Xz+u/+5h/uvXP5y1/9sXD+6ouvttvhi2++8e38ZrtbjzsAZuEauZQIBvs88u1z7x0ifPv5l947o9j36zhsfWN3my0qrZYnm+s7mYfV5ez5t0/vrp433ezHP/9JMP5v//rv/+EXf+u9b5v21eub//Df/wfg+r/63/yv//2/+Q9ffvvFxx9/dPnuw09/+7v6/MXFcmkVT08Xq8al3bYJdjsUqOIax1wNQUnJWu+cHWIE1bZtgGLNNacY2rYN7W63NY6cmpyKtZI1hya0bQA0fT/Uhr11onK6Onl5dXt9fTOfh6Zt4m6wxqBBZ7uc02Y7Ns42bfDOlVJDcFYpxnG37bu2IUsljjXlnGs36wCz5OIMlRRbPzNIgOy8ExFgqJW7bmGQWEDBKoBF8LN5No3ahkyoVbL11jYQKqiQlDJGjpGYIWUoVfveUOKceXdricyy2YyQMi3ni2pWay7JWTdzCQCHrGq8gW45r0QFak3l9fXGGNIi1npEqIDWIu3/vSsBIBqeDB8YDvU3qgoea9m9mP9YHh9g80Pcm8LzcReLHgpnItQD+n6MpVOIpr0Yfh9a7XQ0nT40bQZQBZqWhh142QmEelO+H1wX9FjuH3PVtKlsagsORMKblHAYVYbjr7e+fCsDTIb/tB9Se/PZKSVMGQjfAFFw/D2RJEjIrFPHVWuRN+tfUJVqqsyCUHbChAYJg/chmFnTWTQIyFVzHjiPqZai1dnw6Pzk8bzJJcdhN8RxjOOnr2+uru84p5lxlyfLDy5X88b+5c8/6gBunj59/PidYEwt8fTsRDW/fn1jhLsQFqdLBU2pKlpAAgREIyJKqGCBJuNnFkCVMuF4iIaUVJEFJFdUNGSkFkTVUjmPwMWQTGJiqUpk0YBWzv2AomYW0jASYMlFSzbWWDNDNEaVQWI/km0mee6wTSkzOc+szru27WrlYSyTr32JsW1CZWGuhJRTUlVPfjFrt+sx+NC0waDkWHIqwDjkeH52kkvd7na+aQBhN47DEEPXkjPW0nabCGkY82Y7ACKSceRVeEyViIwx1pha8/7bZtCgYamOVGv0qERCiMACWL1zu2FTa10P21nTDHEQkaicuYSmkZINoyW3dC3UlDe3D/7RT/px9/j+40/PHvRxGF/e/n3/y3XKcb68f7UWCH/4zW+B4fzy/J0PHqMxVy9fXpxfDEOM1zez1bymklMaoq9xPL243N1sbq6uLu5dtovV5tWttzTebTebzeJi9ezrJ1Lxq88+iyWjpadPn64uTn73298V/V6q/ad//Dy0DRnnGvv1d1+7xhhjuGQR9capolR11pEAGLIGCIzD0NjqrGtC450pOXmwmsoosSHisb7ePpcSMdbOWde1//pf/V8+/8Mfa+X/7f/+f0d+9sc/fqpV/k//9f/x2TdP+ji+ev46peH87BKkjv1wuTzBWoiMtQBcfONqHHNORFCLqOoYBx+8NWYYBjImOAciKZablEPTeO9jTl3bFlPqVEkVTlCQsGubfhjVCRIVvgvBbtZjTjE0oaqS4rgdQTR4p4K5cC6DgnSdjxHnjfcuKNeSqnekxgozGxnHftYF1/gSR2uw5EgukAUEDG0LgkQmpdzMT2zT1IKqhhksoAutmKDOoxquDOiQLKAGmlEoaRhKPxg/V67k5y6OMmw4JSuR1tddWFGYJeWztjHie6M7IeXiKgvCyelpqYoI65hv7q65SGs6a62qGHKgQCyIUEUNgiiIViQzhU+DZq+sn9xoQffmzAewRt9GPA5qnwNhChPbiwgiSvtITbDnFfSQPw4emrr/IABamnyBph5kH60PPhIAsK/3DwX7W9U9Hv13pnxyGACbwiztIzYeqvzDmO+Uk97ARQdC9+1scDjLW/No+0zzFllwoEcOONcRiJpQJzI4LbTR/aCDTplFVckaIJzGBFgUhGUsu1EQ0Jowa8KinV3M5ypNrZxzJQMNmrtdGoddKWPjjfON8+Xh/bPGmvPgVxa//9Gj+4u26Yhq+eBHHwHnfjss5/N+GMd+s1jMz+6dG9CSEwJWyWgtEiGZg4EGkkJlFs7GTFUACRjUOi28ASQFQCIiVa6Iag1qqYRgvAFRjllKAtVpSWfNxVpHzoFo0yxy3BCXmEpoOgKUyn2/q7kUBkPSnayuXl5nqT7YUqGqtO1sHOOYSh/zfDG3oPPFfDckrWINDbtBEbu2UZXb623XNLNZo1KFNeUiwqXk1XIuoMMwGueswSLKjPPFzPtQS75Zb5SsqkqVWqshs1gsUi79EL23jox1VKUysyFiFmdNFVYtqIlASCsBE6G1WCR71zZtG/OtpAjKRqEMQ9fOY45aKykaRmt1TMP5/CQPcbjbnJ2dvPf4wV+OP9/F4def/X4ToycYa/rqybf/6e9/vbl+2RqwTXj65JkwuICsEDfrOPSz+YxBuNbderOcdWkch74HhDyO8vp2fXUV5t1nv/90u9m8Dx/9/te/uXl9fXr/8he/+NXp6erpy5cPOH/37Pkm9Sxac0UqoOyMBxBrEBHVGGAmtGSsOml8sGRrqSporbXGzmermtPp8qzkZLxtm44rL2bzy4sLR+5kuTo/Xb1+/ty7xbDuP/vsq7u79XJ5+m/+zX/6wV/+03Dv8tWXX8CzV3/xz/7x73/ze+ba+i6Ow/17pwVq11mj7EAVNZdEKKGzXKoaDI0B1DKUlCKiCd6PYzLGWOskyLiLiNm1rTUuxuyCdYTWaowFp9hGtmlCGoe2bVEh52o9IVG/HWZNaBo7605KZjK4AOqHngD7vu+3QxOa0ZRFE6RQrRlVbLDO2H6z8U0oMc9PTwhBOJVaG+8RSFmJLHmjWLUSCNiwoNYWBnUBEK2zFV1RRLOHZZQNoQGDhMa2Rm2rlQ0Ahghjgz4wF5Y6Zo55Q0GptGgqIHqkk+CaZr5p8qCKwW4DvX79apciFwm+USJnkQUAtDGWiyiiI0KgIhWIFMBMjgBvMaIHxTtOIeyoqTmA/Mfqf18HT+FtH4vpGKN1X6zrfupq8pDbj48BGkRUtseUcgyzePSVwING9Ej1KgAC4T7H0N4DQvZjbFN6kUMaADyOAh8i/qRYVTjegSIck8M+A+4zxH55wdRYCCAiEakIIhwK+sMTOUJRR5+gKQvhIdVNqyvh0M4gKoiIIgKQklpEIBBVLbXc3uX13c5bc7ZoT5q58Wa7u3vSr8fNpvTRW3r/0T1q+GzZffzgYubMOyfdw8Us1wKV/+6Xn2LJjSfM5Wwe5GRhpJ7fO10sF5W1lCqV8xCbxdyQA+dAVA47lqUyEDpnkAtzUQVUJUAkQkERAWZCUGZQsQZUKoiQIVCtudSSoVbrrTU+70YERGNqzpwL52IQgXF1eW8cdsN6m8aKRLZpSUmr9uuhVAld4AwxivEWpJZaUk7drHXBtqHZbDZxZDAmjrFpG9/4zW5XVbtZ5wjzOIpqLjnlSs42bRsaf3O7qyKosuujAjZNEJTNesNVJmkWiw67SM7O53PmmktugrOGnDOqexcnJGzbFqZJ/spkNDgIBZwBztFSY41NJVljGxtK4ZSztySod8PdwvmYqxV1ZAzAen0zd00e++fPn3YPFot5e+/k5MJdjKi//vVv26bZ5d2Lq6eb/7w561YL45x1q+VpEbj/4LzKVds2s9Vyu90464dt37RBunZYb58+/e78/AyD++7Lr9Hizeb6xYuXMQ7wxP3u93/4+rMn//J/+S9+94fP5idBEL569l2YNcM2kjWOXHDe2TDGYa9zA6hcpdbq2AIF11gyVGtjjbBYS87bHFMTQtU66zpl9eTdwjXet80MS53PZ23bXTx88ODRu8+/fRb7lDPnIT1/eqPdN4iGwswY5JKbtru5eq2Cwaqk8eSkm89C40m5gLJyqTWrsHIdU63iQuN9sGksolmNtdaM4+ib0IUQyA0xp5KscSJUC1trLKGbuRwLEjKX4Jw3CxURllq56zyISkauNcaEqmRtSjUY27UNIcyaNpWEIGnMJY6Nb0ihTD8Sosv5oqQUvB2GftY1MRZUKTU3ZA0FZSZnrWtYCMiwggsr63xk5ApSKrZkyAsiGYcKwsJVKovz3lkrsURNhgiQwBjXzDj2hIQx6jCWGFnyeuDw8ALUlFKd5bOmyWN+eXN7ndImZ+vd2ekKwSFR8NapLTEW5kRGAIXAVEEy00pIqYxkRGTSn0zF3z7s03526eClrLDH5/eVMCqoKBHtRfQIoLLHT44ojyJMoJAqyp5ZPfQVYOEwobUX6h+r/UPtrnKs4RGnuhnwLVAe8JBUDjp82P/pzTaDN0TCnsyeLIZA8TAKdqj4DxgRvp13EEin8eHpRAJvFERwmGx481RU93lDJy8IQrNvPlSUzN6sdH/BCABCSKhYp5zmrXBlrXEcrzN7BCPaGNfNFuBDS+AlLzD/k5/86Hw1r2O5G3YvvvluHPPdze3c0/c+fK9Dc3ky8wQ19o6gXc0ZqeYEhZ2x7fkZGmutZUVBOcBnikSWQLiIKIuSAgGxqJSCNREUAwoKBECWUPZreYRFSkJl5z1NtDGrdbZkVmHNWUolQ6hkQqhSasopZ3LGhcYYk4Y87EZ1tm3CWDSWZBtHxu2GQVUab5cncwSz3uw2m34267bbNFsu1OB6s8lcl4s5qcRYvfNTARU8VEUAuL7eiqiqxJgIrQJyqUV4KglzLioahzKft7PVvFTZ7SICkEGy+6XwOUYAcsErgEgtqXApjXdjLsFRyz6OyVs7tXqpMBmjOYEis5B3QxxI4WS2yP3QdbMU0zbLNd0KSxL+n/34Xz56/4Nh7J/fXgUhANUiHl2s+ebqVfZburj32RefuXm3XQ/vvffw/jsPL87PHjx+uF1vrLOeDOc6bofb6ytS0Cx9P379zRfnZw9+9/vfnZycXV3dvL69vb5eJ87/6T/+Q5Vyty6+Dcbgolts7nZt10kRADDWchUyqKrBewBJeUvaeBca37TBxe06hBDH0TrHysaZ4H0wlhCbeStVLJmmbWsuVAUApAhZ0y6aWMqYonEUOnt5/3Rz9XQeGj5ZpqH/9S9/1c7nlmzOg3U+OLx3vlo0bjFvSTIIlCxAZIwvKkVqHKJUns9bQtr1g5QMxnRtGMZITXDezgzVXEGBEFQQWNEaRHDeVBFL6IPxrt1uhuCtUxAugLCYNVIVGKd1Xw4NC+ecnHNt2y3bOSBISTc3dzEOwTnjXRsaKZnINR3FYVj5tsZxMVvGcYMoLMWTI1ZnvXHBhY4VlQnFuO4MizBYEIM8Ga4Qq06OUoZUFBkU0LjWMlGthYwHMr5zhSin1CyXNrQpc6145ouftWZ+8iLGzW7oC29UhYsFXTQBvffOK5IIgEqRbJ2NLALAKiCABEamGC9kCI2VSaeuSnu7WdgLPfcRVo+4zN5E+tAM7MUSB6gEp72SbwXPyWcC3vixHXRHAIBo3yD7uK+NFSdyepLaHJsBxGNe2WeMfcuwT0m0R6uOgNMhhB90n/pW2jqkkGMyUz0A/4BE9GZqmWg/RXxojA6Z48j7HhLBxHqQICIBCojoZKq976kQpzPA3mT0yDcgCIrqVJYCojhvjKAQca1RKqncmy3/4nvvOAtDvIu7m1nw8fbuDy+vv3z6fCypH3Z/9cMf/C/+q3/6cNkiVK0Sd+uxHwxCs5opmiFnAmpnC0NECGhJAEVV0SKRTDmWlIUrAwgBWURgripqjfVuJnmHyggKzKgELDUXAAEuIAVBuSRWsWRqLrnvwTipVauG+dwgSmVjLCIzFxv8fLkS4XEziqrtfE7MVYdtEqBlt9gM/XrTG2NPL89EcLPZpFRD40vh5dkippQGzjmdni5dCLvNtm07Y2iMaTdmJCBnyphL0pTr8rRjhnFMs+U8xVS4ouJYCgIQ0upsZQ2UnGMSBHCWvLeWqLKmmNGQMWQQgEgMBLVjGgnsrAnGEZFgxYwEgAZJUB049V2JCRQ+/PCjm+tX2806G3O6XFkiY3R3PeTxNRAu71/ELOfLMzUYVBvjTdbVbPbw/fd+9/Wnd5sy7qq5T2rtbz/9LQq9ePX03RcPv/fxJyr126++mi8W7zx+7F3waP/46z/85M//0a4fTD/0N7uXn//i65fPP/yh+/1vfm98uL5ZL87On313vVgsXePHcXTBsqL3AcH4prXOKJnZfFGZScRal2pxtkMyIbTWGlV13hOZk7NTZmmcN2QNiCECgDwmH5qm7ULbAohr2iYEBr3/+FFYdJEzczXWgejF7IT1quT04Oz0tfDtzU3KZdHNFs3Sky5nTWPBoYzDetE1oTEgYdz1qDjrGrQUh5hT3qG2bei6JsUkUsm6tvEpF5HaNM28mceYiTwzq7AzNAWyQAYI4jBwKGcn82EYAMD4ULUQY9M2wkpIQ8y5ZENgSEXqbnPnrW9nTduEi7OTfrcxACWmit4Ym1KeNc7NOhYGlVKyc46UvXdci2sciKiA9QFtV5HYOaliupPJVUsEsCJ6o6yiokhoDYqoTH436rw31pcUWWtfommCJQKE+Wwlmy0lyMbFIQ/j1aBayRpyC+9aA5fOUeu+udkOKZIxAAgsVSQKgCFnLGbe+y2TNtaBQQbNXPUIkxx0LG9KbNizrQfxzwEp2aeCPcy/HxObUA/YqyX1UN8TofA+bB7GAt5AQG945uMbDrQyHCmHvdIIjnFX9zU/Aep+h4HgwYBnsmzDPey/5wjefP4Q+RUAJi8k2GeFw5bJ/WN6e8fMgYZ4w3gft83vLYNwn5b28JPAtLMYD48DVY+WEIfht8kDabpLe8DCBLQAFGESdqjF1NGkJNp48/DDd3Op2yHe9f1qOfvZux9Ywffunxvm2+vttl+nofeWZq1frZZoIMbk28YYC8YyK6BqVVU13k+sBSGAqrAAApAh8iilpkioxlqUWmvSWpWTxL4JLucEJYbGay2q2MwXJY2corEupzHuekJ0BNZ5t1iAMTXumBkBuBSy1jdNLSIxIRIZEIZhSCkVBWzbZrPd9GNazDoyzgKlwjkXYwiMMc7nkvOYFaBrQtN2u75vu9YYNwzjbkhTmYCsOfF6O1ycraTqOKb5ap5SzrmwSk7VNYbIEpKzRhXikBTEILZNENAxZkBywRprQKkIA0jJJdfaeidI1jonpDXLohuz9LGioDc+W1C0Waq12Kf48Sc/+vKPv+VUCsvMN6NmDlbAbmvZDXnz+i7QkwWF601vC5/6btF1D99758nzJxvYMciT69vvv/+DV8P1+vYWK9yu+8vTx199/Z+CxctLdjbMfXhdv1vNglS9fv4KSX/zi1+RMTH2f/8//A0ANrPVGQRAOl2ZXHIz74RvjSNkaQIgWWUozN45CoaHaEmJnPdtKXmy71ZVFmhCh6DWBsLivUWwKGrJMQtZ07StNQYVyJi2aUvVMPcXD+4DUmW21jprWPT566eR426zrsEbR6cXJ6VWS64LzipLziUTWzdsImq2BpHAeJt2o6h11uKsJcJxTCjadcHN210/5lSct97tfVHHOiIiWbLOETphAVRrnG8cIJRst7t+kF07a3NKCIIKwjWJSgWy6Lyx1jEXVXLechIAKUM0lZtZaNxqvd6iwGa3O1l0FlEF0BHXbBBLTG3rAA1Xcc4CidRkdEaAYBtyLaM1FFjQzZY1VUEqLBbRWqqHOEDOKAuzElFVIW+CaZHM2I85V+ssIiYWsR5YggsiHJDQuqsx+s4DmT5nhhxLROVFN6usKY+1ChjjHJUqwIIGHRnvHAApKIPWUoRFq4ACGjOpgPYS0bfWW70J/m9hKkcofELg4ViwHyvrfZl7FNMf6QWYOgU7VfTTQC/th8QOGP0b6kHfQvcPaM+hiCeYJhNksnGmqaSdYuybeYXj1b8Vew91/nFgGd8wx4oHuGviAA6JA+GQwUB1Wkp8QI2mO9pvigfRg7UdwQGQmuYL3mY0AGC/LnHKf4SENLHNVdhaQ0rIvB3KX3/+DeV0uep+Zh6uVl0ZYynp3Uf3Hp6snFLKMY7l9dX1vbP5arVatP5kOQeodUzdYqEAKshAaJFU0RAg7aVWqHrcxABIxoNmrslaa5CwRC6iLMJKasJspRxVxXczwEzKaJphu9aaEW3NuYzZOe9aj4rCTJ7KGPMQyZlpc0MzX6Bo7rMCGKI4pmE9sigZ0wa/2/Z369E6vziZM3Mcxr5PXdOOtTaNj0PmwkRojOlmszRGUnRkYkxpLAjadm1lXm9241DPz1cCWmMkMuOQhjF3swCpGEsWCRCMQZBaMnOtobGzrq0iNVdL5KxDi1U45eSDA0Qw1lnLgCWL99ZVwDZYZ7j21VoCsmDVeFNq63wuiXO5vbt7/73vffntH7ZliKUP1guJShGFYbO7fvJaxwwpzWxYWfvu5cXr7bop9OPv//Rmu97uhm2Mv/3uU3KmH0ZJ9e52/f/4t/9t6+3M+g8++iCOu5PF6tGj+5th2Nxtueb//v/1b7bDDtic37s0Q713+XAdS+MJVMEBoHHON23Dyt45KULWOnRjjo0LzIwtchVjjAgYMEBqDRJQQOscGUBQbXxj0DrnHTlSaixaaywZY0zjvTNoyFhjiYir9Jvh5esXwTtjEAF3fV+xsOB22xtHs8VcVWrM/TCsggWGNgTvEMjmMYG3IoKoLrgcC6IqYTdrHdEQc0oJyHRtyyYrQONMLizMIKAIYJEMWYtF9muliJAsheCa4HIuUmvXButsLYUrS2YwyCKgwsrWgrNBK7ddEBYVAJHc975tLs/O13frmlOMw6z1ZAAArTPAjIoi4KzhnIE1NAEQmYuIGhfANZlcBSKyDGTaGQGwIldFg9NmcNlraYxBAESDAgpiDBhLzikaMMDMjArW2uCBVQrnzCXWOOTNdlinHB242bwA2BAcKIgMWRTYeasKhkDJd8F7IlaItQ7DmEtimYyTjZniqO6RlWPJ+xZsvy/Fj3KdY0ic3oZER+nPBAcdoXI9iEZR9qXzFM7tW0c5GHnCMUa/tZL4mFLeFnHuj77f4TuFbJwGgSckiACPqwX0zYjZXur/1owvAIABOGJPuIeg9jnr8L4/SW94IC2mM8HRRWk/vnbcZID77oYAlKa722eG/YuKQjiNyxEJMOwFmMGFxjmn6rTUGlnri9v+9fNfPlyG04XFUjEv827cxpxrWcza9x+/c3Y6P2ublKMwE4Lzgabln9bKJPUlYkAypESqAsJ7YTACGcM1g4jznhSkRJ1eZSUbrAvACST72ZJTX1IhUGvAhdD3W+89CVnvm7YD0jJGAE0xQZV2tVIArFVQRSRtI1cGpDRGLdJ4R4JjrLt1P8Yynzfdcp5iZsaYivVOEUJweUw1V0A0CMY5VAVRQ3B3t0E0ITiLhqXe3m4F6PziBBCENecKFvKYZ/MuxQwC5+enaRirijUmRzaGzs9XxmItVQVab9U4rRLHJKpt2zhnqjATqEjTdKExFQmHmo1SrU1jWSuRy4KW1XszX6xizevN1aurpy8Ks/KQ4zw0Kceai1Uzl9Sn/ne//939k9W2v+5C+8HFfUOqzyBurn/6w59/++ybP3z5uVQZ0yBJhyGBArF+8+wr60xA+2Jz9YOPPnzn8n7XtI8/+OjLT3/X92uQcv/0YhbOHr3/0ctXV9o04/OXSqWKEHKwVMeKaj0Z6wNUCG2HSiLS+KbmQkJC6K11xkuJqYwWjSOrhYkMAIQmOGetsc46o4bQGIPWGEfGkYUqAMYAGTQq4H1TY7m+vq01mzC7vbpzrlFvnKHZ4pSVrTWl1pLyzOD9y9MHl8v7DxeN0RqHkrIwI1Qy5Kw11qYxAkKOGQw0neXC1gBwIUPCogDWUuiCMtTKxhlQVRZDUEp1wdWYAHHWNV3XBO9qzTWXKjxv20KSNRNSFeZaCa0xAKogWnPx3iAIItXCdUyuxbPTk912x3kc+tGAzmatt61QAeZaqrPOWOOsE1Y0tmYOgMDg5nOxLfsg5HAySCanzG8FSlQRERAUY+yEG0tVQrLe5cLCGRDVTkoRTrUatLOuFcs1pUvfNUVtToNKRDDGZuZtzlxZJlsBcsZZIiJDwrzr47Qgm6uwACEh0UG5L4cKGFEnWBoORe/e83M/DQx73EX3C78Odv+HuIlHHniqzffIOwIdEHEyewhoT0LqEa6HQ/0OuD/VPrXAUd7/BgKCaZ0wEYIC0R49mjKUCuJU4OseaIFDEJdj8J4uhw5zXm+lm/394H7eDd/kimPzA4dMAYikiCBy7DEOkwJ7LdLkGkB7dAz0+EQOTIAKEMgkxrBkGmdUpR92FvEk+NViJb65fv3CAszb7oOH5+/eP58HF1nH3VBL+ebbq3p+sWzdtlZQAUcAQmjQGEXIma0zaKwiIeIE/U9ZSgCRDBFpKYYcGQOSpydUhKVKCDODqJBAKhpfJYOiCa1TTuOa+63xAQxxjr5twFAZB0VE4wgIjQoix8g5W0tEJMq11tiPPjSuCXWMY59yFSLjHHTzFkTHIbKAs16NBYSUay5ZENDYpmkBkUuRWlnFWWKgWmsBHofUhODbJteqVSpPswmyXHS1MiLO5s3Yj7kWH6yU2rahaUJK2XufRS1h2zWbzaiA3jsfvCj3/YCEoQnWuipqnCEBDNZ5s6CwjPV6HbdJhqIxx5lvF4uL7esvibDvh1IqCjRti851Pow+xj69Tv18Fvq6/eKbl98++eZn3//Rj77/42+vvrl/vuwMPf328/ffeXyzXd9ur1lLRVLEKrXxdtoNENMQr1KO/fbhjVFYnj1ANnE9/uCjnxrfedOiuiZ0SXTRdSbFmLOdzWPJjqtTVzmhkjdu+hE0uGeF2tDmUlSZgKxxQ9yVlGaL1lJDqsuTVU4RWAFg8k5waA2RikAWF4AUsapDCwwl5jTEtptzqX0cq2I787PVfJys9JiRZByGWoohvTxdXpwvZzOvUsEgGXSthUqqppaChgKhc10tAipkYBwHg1QLA4CxaKwVZmPNRNRZSwhgnRFhQLDkWSR4H0sZhiGI9961TVOAaq0pRmudIVBhqygK1hCACCsZg6AoAqSq9fxstd3tch4KOTJQCRBBRMd+hC5YQlVxzqqo70JOCQoExKZbSJ22g1lCA8aL8wKIhkDVBTcVZCL6RmQ4IbEKaKyxWEoGIOd9ZR3jiGREWBSNM6CUVR6dnDe5v0vRZKYmZOde3W2TQtESc0LAk/mJ9QEBWKRW3vR9Pw4pZzIBENE4Q0BmMi9R3lvYE6iA0t7TGEGnxSoT0L3PDfsoekCE4K1W4fAVHphh3CvNzWSeuecYAEEJwE7oCx3UOXumYYKTpkwwBSyESUXzNoazj8J0IHfxwAQf+onJa/94XUfe9uAO8SbQv4nreoD6D5IgPLDax9bo8DdwSBA4bbeZGs6J432rg5jQLUAAmaREb2WQiVSejJKmohUQDVmDwLVQBYeIUgSgIiKn+8vFD7//4U8/frhs7RDTere767ffPXl2tppfnJ2enZ9I5Zd3tw/unaG1zBJTiVnIGu+sdZ6MmZbTT2JbmTZsGUMKKtVaB8ogDEA5V66iYMJiZQCgFk5Rs1gggx6dAI+5ZiKjvmnaoFw0x1KKDMkHj9aIsrN+3A21ZAsSmqbEIcVRQUW4WbacSuzzrk9d12HkVGUefMl8t163bWN8SJVZOQ4lM6uoMSZ0rYDWzCpcuZK1iMjMzBJjds5183mqWRhKYlU11nVtU5iZeVoVksY0mzch+OlHh2uZd75UdgbRmBST98ZaKwosXFJZLZdgUUVKFS6cxihojHPLeZOVWLHOnWBRq0NCcvb25sndzWsRiTGG0OZSijBktc7P5nMVrllf3d6sXPv0+vltvo1Uvvruy8++/q4L7c9/+vPX2/LH3/znv/rhn12Pt1d3r/747dciUBQdIVqPgCISq2xK/vzl05T58Xs/WM0vT4fi7YyMF8aqYsmqauvbnEvbdIIGcMdsjNQpr+9SnYWgQvNuTkTWUwhtLXXMAzgq3Mr6JQgfNBlUYlKR+awDBYcGGIwhZHXGeh9AocRsQ0g5Gkuhseub2+Xi9OLifjtvh01/8uh+ydlbx1JjHFJOgDk4M++a81XrJJOo1VCzgEoITohZ2DpTK9eap4VoIqiqXTsD5DhkZfDeWOdBtHC1RMxSa7XWGEuGHNdqrS21IsCqnZWYg3MiWjktF7MUc05J/r9k/VevZFmWJogtscURZnali3ARER6RkaIys0RWZnVXK6CHnJkmiZ4eDpqYphgQHAIEgfk1fOEz3/hAEAOQAIfT0+xqVHdVdVWXSp2VmaFd+/WrzOyIvfdaiw/nmN0bNR5IT792zY6we22J7/vWt3J2gIqQSqqCLyLjWJyj4GkSRjMTml5vr733qc9t65MYiqF3Y04cA5g5ZHB+UtEaarNoStY0puakRV+XoiTFNXVCVwwMjQ2J6JbtDRWTyf6AmEUlZyFFdhx8XF9vigo7z076oR9T8iEi+VjXV9dXz4YX3rtFCL2YV1hvRwckmhvH7WIZY/TsBfCi215ut0POqqAKRIHIEe2gCsJpsStOY7o7GvjWGNRcbt/y2NyF3snNGMC+8s3bAf0GAgGASXVoajvZKLldmTz9su2kOrADUQD3epubwau9Dh/m5Y3zt2yflnaNwz5N4O30tIv6O2e7man42//bY1uzLnZ+mu6THu7dIiYLiBk4AyCkmQvYqSzns+7aAtoRA5P9NpoooqkBGRFNTdNBHVexXsboQG0YgveaaOHx8Tt3XAxdST/5+JMXL18eNtX7Dx+89+jhalEDgObcHhwJ+S6jZycgOUsTvAuBQgQTICJDABUFQCTnAECkEJGiaTYt05IvRPK+9kxmZcx9PxsEkZdxy0ZgpEaMXB2e6Lg10ZSKCyHWTlRlzOx935WwPPClZ7A8dFqkXixyKsQOkM4v1sA+Rq8ifZ/6lIm567YHBwdmOKZUxAzNQBnBmHx0WtJYTIuqSYjeh9Bthuvrzlfh4GABiNebdco67Tj1wccYci4q1tYxFyk5HRw05FBKAkMEZqYJ/JmQLkBkQnYEqiLWNJVqltHGMQMTs/fg0XsfIzGhWO1RAw2JisGqqUbDraTN5XVzsKzro5KTCeVJ6TFuK79q67ZA4hjOxu2WoFquvjx7+Wcf/8IET1aHz56/enXehUDd2bMqelZbulC8Wh4Rp/02SpEUYRChRE9fv/zs84+f3P9wdXSU+1x5LiOomOeoOVOhSBVVnA20ZHAgJg6x5HKwWAYfjHHbiUkhIO8QgYZeKfi6XVTNAgGij8RkJXfr7uBwkcbRk1OgKkRHnMehqCIgs3fOjUPy5HnlDOji9ZvTkwfvP37yO7/1937yH/6cuPIUTk5O1pvLPHbexapeBm+ny8Wi4eOD9uTOAqFIKXkQy0pMSAhghBR8MClVjCKahrEUIXKLlc9DTmPOZfDBTR2tI9yNWmouGZHGPC7qtu96TdkFHtO4WqzUynq7OVotPVPX9VZKDEFNVKQKns1STkLa1HEEJRBml0vSkshh128W1YKsGfotMZdSGJui2jSVpZ6ZTM0FRwgEnFOKzQn7aAoKYM4hOUBWQwBCxWnaFgmJeFLSqBqiI4aScknFiKqm7vohl9IuGnIuFFG16/VWNx2Aqqh24tq6jhVVlRv1chgtLDIAIKjI226zGcdtGntVM0aaOj4CgiKKjlQVYFYq7jGRWcyCZvvIavuIua/wp3Bot+Mq4l4uuYv+M6mLBkAwgf8w4U2ICKozB3CDqNz6aj9VNuE/BrPXz+6Cbs4+nXhKVbtH9tLUKRjvLe7mVLNzqthH+ltxes5lez76dprYi0kB8WZnwJSYEKZUeHMevKG1d/oeQNw1HYqTsaMRopnx9FMQAURHtPD8teVqLPLy4lXu1kdtWzO1FvrL67958XJMw2bYfO3Bu48e3D05XLrgS5GU+ufPXh8er0LPAJzWm8XhclF7X9UcAhCaEAEhmgoiMSKoKagAMyJIyYaoOPlSR0eBSill0CGBi8zo0EnaGhKFCoZs6MCjFJFk0iVfL+t2MayvRJR9yEMJTTQQU+yHbem6WFdpGBEJkYdumHgFA+nXI6IdHrX9djw5OlSjMY9jMnRsqkjoGaOvhjGZAZYCgr6KhDB0uRvzctkQe/Jufb1NqYiIDz7GyM7llBCoXUTJxTE29QIASirBMxEREjGlsZiZD75IMQPngpY0pOIDK0hKY0rZgDwFMG0XDThXRIJzznFTkXfZxzq/us6M66vr64vLO4eHjx9/zdV3f/w3f4E0Xm2v2rYygIvt5tDXLjghHJ2DKhZ1H5+fvx3Wq9jea+s1pO//k+//+3/3R5/+8rNNv0ZH0aBF3qQMyDKpJdSaugbJleNuGP7Nn/7r+//p3ePFXQyOXQBTzhy8qULlg4teECyNTdUqWMqDZjay6EIIUcmwaa6urtqqij4wFFquujHVoWb2YuKY2TEiH7YHqpk5OITgQvBeywSOlxgjzBsuQEQ319dN0wbmt2evY4j/5T/7rw7j8R/9yR/YwsLmSouUnGN0AHawOljU/vR40bR+u117Ju85VtFEgQBMiR0ySFGdsV5j59RMSklZvHdN9CbTphFinIV3omImBEgEjCyam0U1dEMMTkW23Xq5XCx5cXFxWUXfLtuh25pJ9E7ZzLRdtrSFcRx76xbLZR7GUhIYGmLwlMcyjpvFciGaNCWOfhzHOnoTqOqqpJGRVbKvF0gODIpkX9XADgyZvZFTYJ1ov13cmPUfiKrmiBHBe49I/XaUsZhqE+ueymbokJ0PMaXsferHQYoagQEO3eDN1FlRS7kbM4GPl9vtNicxHEsG4sjenDOd9hOjqRHZzrptgu/RYAqzM0S9B8xnXx74ao0/h9r9QMBUrM9hUG/SxBzOCed4STtPZoLJ+xRvxdX5ShAQeM4fNyTwjirYC5EAiOaovCv6b2Wdr+SS/YXPTnM7OdEM7s8p4SYPwU0TMR3Ebj106znz2XCu623ONDuoaa+KBbwNOtkuP4GpAdG8ingue9BMIABQ0Z89+0T6DkBWrg5iB2192ASiAiBtE+6dPjlqG3Lu9fnli1dvLs9fv3P37t2jIzAm47PzS1O73y5Ojw+9ZwQDM2RCQpN5emIi4g3JTLOIGRA7QiNzbI5Qxn5kRI4tSrJSSgYCCsuV9JdWDH1EEh23IoIu+Bi3641kDW2Dpbi6puAslXE7OO8Wp6el25KPpCCgphpCFIU0ZlVtV40ohOBd8F2Xhz77JhqCFSPvkDin4gG34whIdRuymhbYbruqrZumHce8XXdjKmlMRydLh2FMSSw7ZGRMw+C9DzGYQhpTXQdTATDRMiZh76MLKRUACIFFSxpGYBr6rIBM5Mm7GIJ3VdsgQRLxTGCZwNjVi8aRhwd3/LOXZ2uAwxjJcRn63/3+N772zcf/6t/9wfazjgEsSSY5y/mkaUfDzebKI6iUyyEJ0L2T+6vV0Te/+83Bxr/5+PPTw8Xf+d3fjISvLi+evzn/k7/5RXGgjsW0inXJmQxSgVx0vdk+ff70mx9+gyhsL9dcOYGcWRmlitWoxbREH5xzQ987ZAw1Q1GVdrEYNXfarRYHJqJJY6hUzCwh0HJxmIZtDLWjoJiCi6DEjjSV4ELkMOTeEYfaa1JznpHJURFh8gSIwBevX8fFwZMPf+M//2f/5XYc/vxP/m2ftvePjg8PDkzSoq3unhz7MRFR0yyq5hjJQAqoWJl2ISGilaQlJaKAMG+TzWNGAxFREBM1M1BFmjo2cZ6RwEQRNI2JaI4zzaI2s4NV22/77WYdQ1itDrrtRnFYHi7TkExFRmHHANA0tSMsktPQtVWTMpVSpGQAv1hUQzeKlMWi7TsjBCAoOavnXMx7Ni0gzgR8rMEQBFSBXDCZas5J7w+4r2hn1eIc6AxgmgwA4BB8spL7bAXJu+jiNo2SRiLfLBvvXcrFVFMBRFOhIXfF2AU3ZN0M66HkVEoRQ/SOvBKbY1VC0UnargZKONmjKigAMU1bvQBsh1/s4Ord4NWOtp6AmZknnmviKa7ufJdhh/PcUtLj3GJM6MeUYdwuyu545V21bDNZcJthsP24LRDs1KC7hAQ7yGdnLQS3+pA5MN9uV3Zkw5RBbrCf29DQDR5ENwlglwOmC8Dd0W5PQEx5dK9qumkgdpzzdB+0Q9bQJis8c4REXDSVUt52Vw7ktG2+8eTx3fZACW3otZQR0vL4KKsoV0/fbs4v3mTpakcutAX83fuPHLtx3LzzMHp2d+6cBmY1mQBHQhQxmOwAEQkJCFWkgAExIZkKu0gmlm0cRx8qsGTZclaHjOzYNaX0bE59TaSaewRHPvp2WfoOyZEH9pXa6HwNZlIGYOJYTZ2vWBmH0VQ4OBRKm15KAVAAzaMAQc4yjqOvPDISYCYDIkAyK5OuXJCQWHMaRgmVPzg46Lu0vu6KKRMeHrSLtrk4X1fBEbtxTJJK1VRVCMysaPXBYhgGMEs5qWhVe89oVpjRe59zGodMTJJLjMH7EKoYYkDvZmdbhNqHkkspxcyGosMIvmlXq1DK0TDKKDoM49BdGa6//Z1v33v86P/z//p/nr16fvnmDXj0Vfzi7G3wsa6iy4ZgBeDR8eNvf/M7133/7OWL/+4P/+3l2dtvffjb/+nf//4nn738+Ivn63V/WLdrGNddR54LFTEV02Ho27pRKS9ePK/bVdePMUYGr5kSJ2xalzOLRJAxZwX1wNu+M3JQBmCFYnVVpWFQ7w3ZDNpmIapHMSaR7VBvt5f90LeViyGCgmPvCCEwAYKoIyeWwdB5X5Ky0xDIu5BSkiJE3sy0bNcXb8Ji+c/+2X8hRf/kT/7VJoZFc9hW9dGy9VpO7xw+engSqxDbOBF5JgVV0tiNaSwpGRXyXkoxUQBjZvKMiA69FAFTBAC1cUxGaFqQUFWJsYpVu2yGblBRBJ5WXSfRED1gSCnL2DXLpt9u8zgcHB72277gWLISGJMtl61ISSmJlhAcMwqjlGyeq+hLKsG7g9Xq+vLKgyoZEiARO2Oauc2qajPXQA4MiCOw28PNyDPtN08IIRCRiU44jKrlYQRkdoEZhi5dX18agwteFQCwSMq5EKGNRcGIfB4Hjhx8GEtZxvru0eLp5bV3aSGWRbJA0lLARIqZEgLaJJg1AjCcxCDTNJcxksLO3dNuZIy2s/mcpnG/Ej9t6kx39miw13LecKWqgFNFv/OfnlVCYA6/Gmv3MAztJDR7R2r7yvfnYL2Hjgx2qsp9l2I75nZiAXZULsIstZkQmxsa+xYtfOsc+4PBvi3aR3Pbv3zf7ezGfucv4GZwAvevmA44CZNg9mCZBEKkIJbMsgeN3p22Rw/vnXKIZ+O4HbqLt2/fvH7jkBbN8t7pcVXZ808/6ddXD+8d3XvwIHjMefzlr3+97jarVfOD3/7N46MVioAYqE1qL0RgBBFlJgM01UmDDIboprJk8nhWA3I+kIqkMozJsdv5QoOU0o+FXTDNoKiDuNCIqhqlrPVyaejBewi+bLpc0LUH7Dj321RAAVwVzCA43lxcp6E3laatRTFGIuLr6yFEXy0qVby+3CCiY+6GsWQVM1+HSG4Yx24z1IeLqmlS31+cr9VIRNplU1dx7PvAwEwXF1erg6ZtWyQC0JKtaqqx68cxAZhzvGhrMxmGEZCijznlPBZmRoKmjqGOqAiICkogzrs8qpmJiKqxCyJIgC4AkTrF48M2A47EV1fXYxr+/E/+zR/+uz9IyA/v3H98fPpL/Lk53PT9kMfmoD1YHaS+J4G7dx/+1re/k7utOP8v/+0f/eqzj+8fn9x7cOcP/t0f//jTX2+GMqThsK5bapeuVQcvr67XwyZSTQgC2PfDm8tz4xg9pM1oWli0Ql88MjJLGXNGAmRAQzQokiuOqYyCBgLLetWlAQKoqCfXVu355ipUsQntuSIgLNqFV0LRg+Wi765BFBSylehjISZyznk0ZCRSdt6VMQ1jT8bBcVu32+vrbdfduffwn/+v/reb9fnnv/ir1zq2SG18Jxy2hyerZtGGquIQABRMMAQE5Sri0EsWlQxqkpNKKWOSklSZCIyIpt9eFReorVoTVYlFioFqzl3Xs+PV0SoNYxmzAUHWcUjmqK5CXS1SGlTz6clxtx22m03TNNxWaRzzkDUnw4IszmFJmQDQLASnjCCScmlCLWqmUteh324XwUsuzJO9rolpYCiQqWow1mZcikAgIFYzVFMEJNOpxKbZWoEIJ/tNH8JkDICohBgDI9ZiknJRLb5qAEiGwSFXy3q7Gfqha3ys2lX2tD67uH57eXHRFaYy9OD80XJRVPuUx6wFgBhLEdMiqklAzQxgUh/RbgKJAcV2Fe+E7yASABroHOFtt+px2s+7K31xz9fuYvguQDPtIt5MnQIaEoEpuB2fusdXdtkDaTr3BJ1M8XcfbO2Wqn+SBeGuXYC5/AaYXzh1BTfPhV0vc7uk3/MD+/nc239uUKN96tmBSLe5g31y2PVJAEB7c4pdBppRP5o7rRliAwRTzSaiuQF7/86dg1hJyZeX64+fPgcC7zhSqJfHw9hnxCHZxdszxvDo4ZPu+u163X/w6O52pOuL829++P6T9x82MdiYpaiakHOe3Dx6ITKV+6CQdBb6AwIVA+8JwUyAHLMRWOnSOIxE3lcBZETUnLZD14eqIUYYVQ1cuyIEGztAjqsTrivJZVpzBsTVwTEGzpvLfrt2Ica6RUmSx2Gz7bfb2MRQoypbVnbc98k55ypvwGVM7LyoaSkpJ6LgnXPs+n7o+nx458DHuOmG8zfXCuA83zs+UTWRkodMwfVdd3K8MKLg2QxzluBdGsdhGENw07qukrOBMZMLYdoExszeu1hHAS2qLgR2TtUkyZB6BTJVF2LTNs5FABJyCkQ+FuWiGBc11f7N6/ji6Yvr86u+5H4cv7w8X65OvvH195fLk5/+4id3j4/+43/yn33x+a+GUtZnZ1/74Gsx1H/1o7/KuYjmD99/8uTxu3/xk5/2r8+rGL/3ve9ppj/7yx++//7XMToF2Kr+2V/+2dB3GP31VV8sr4f1th8bBS1FCzokJla1EJyNWkQASUwiIhA7JGNBhC4NgOgpLBu36btYhVKKiLZVA54Olquzt9Eh1VWT1l1EHrZb5xyAkuPtdlsFJkLn2blgqsRUVY33LJvivU/bbnlyl4C0WBq2XXN5enryf/yv/8//j//7/+3nP/73vvZ379+7e//OyaNH5HBU4CSIwExWxEwJkZz3PqqpiSC2KgpmJkVKySlpyVaKTGyACRkoGDkOjtghaCW5jOPYbbuqquq6VtNhO/gYypj7fqwqbdq2iA4p1W1tUhAEFJ0j34SSKKdBBYoWzaIIVV2PQxerCrRYkXHs66pGROdcjB4Ii+TWByRCsMksw9QQyTWL4hs1Q2IDIHaT1HIGOVAR2XSSArJzpCJmxp4tlaEbYl1DCDn3MdQh4Lbfas7Mzpnly2uJgcnVIXSlvD07KzE2wa23/cX2LVRNVk3ZRtkYGjExIhqwYUVOyQ2SACnDHNR576OzQzUQZs+bqYpG01uheYK+5q/ZUHcK+1sB0/ZizinB2H48C3Fa/Dv92XcAUwU+P2EPt9veSX+K6DtAfTdENRfT+9GqG+75VgiftnbqjCABTUsRdA7Gc8bY0wCwv5zdP27Aof1Bd5nO9nd761JuXrc7ov0t+gQnSnrvEURgE5wpUuoYT+pF5UIu6Wocri6vrq/WItA2dRWC91wQgFjRiPH08E7tEWS8OL/60cXFo0d3vvut37h7euCZwcBMUs5VFTgE1QniNND5xywmYjDm4h055hACIpiZ5IKA7F0Z0jCMZuBjFFEz8oQpFdcsXTAY+lIUMHJTl82FoeO4CNXSwCSvOQTNg/lAwZmIAsflgQ8hb7ux65ktpyIAPnhEP25HH7yImZiLTnIpUnIqMQRDPD+/BsC4iqbUj6mIHp8ejGJDN1xdbMSUmA8PF4SQcxEry2Vzvd5UVZAiq8N6TFmzhRgQQVN2DkPwjjGlkrMAAjN1215EYxXaRR1j7PoBHTdN5ZxHAEMSUV+3oa589NOkhwkCMzIDcCZtmmY7pGB6dBCZDmqCVy/eLpfL9abPJl//6IO+77dXb+8fLd/7xjdSf9l3m5zHO6dHbev/w5/88bOXT+/duVeS3D0+evPmxcXLF0ft4vf/3u9/73d/8+O/+fyf/Sf/i9HQ12HJcWvwra999Pzpl8nhj370s1989uPucmuQ85h5tiVEj8iBhQwLK7GCFQXLKYIbxhSa6DkamKglFSJaNEsxcQrex/PzM8ehic1qseyGreVytDwau2sGdgDo2RQX7YImqksQHBLx9FElQF9FzcURVYt2u91k0GbRrq8vLy4vj+7c+8//+f9+GK51e746Onr0wdePH9zJaQCjyCRjKqlXziYZQAxISjIjBSNACp6ZCLHkXBmmvkODPPZFiqTRpKBISSMRgZiBsvdtiCqFGBUBAOumUVPxKY+upLFIaZtlSlxKqqqokpGgjMUhNm0c0dR88D6HklMCKU1VFynec2GUIqUkj56Qog+EorlIKU3TMEciAzAVIShKBsEjByAHgOScTZvBAQGRiBVADVSNaYZUzAyZiaTfbK+vr9r2gBRefvGUgg91JSX3UmS9tSEPb86Wd07AHACIajZojo7vvbOw86vzdc/EiFAkI2KZNUaok3kBIbFjUilF54BKCKCz6gf2Jfccokx3wPguIBsIGBnZtATbgJFsHhVAQ2DYu7/tgCIkEN0V5zYtfwE0NyeKORDvmOBbWPmtkLyPyjcP4P6ycFIH7QPuTdSeXeTMiGlafkhAus9Be4zmZh7tFvNwkw/+VlewC/23O5/du3MDKu1W0+Cu+ZnK7dspc2pqmBCBmqqpvUdzn7y+HrrtOI5N5ev2IOUx5zF1W0fMZErOE4bot1fX15ruHa0qhnt3Dh/eO75zdFA5743ykNU01JVzXkUJkRFVsVhGwKEfUxER4Xn3iWfnVGUeYEOUIjllJGT26HweOh9CkZF8cD6glVJ6pIihVkIFxhA51gosuedqBWaWE/gKiUEyhZopWskKGBeL/vJtznl5fBxCM257LaYGY98RowE44qEbiCjnXIqyYybyQKkIGjWLRVHo1kPOGUSD93fvnkrRcRwAoGridrslBGZ27MauLwrOOZEsxRDNB08I4zAaYF1HUemHwTmuYmjahplzzjEEjh5Ah77PuZSUQ12vjo9iHZCo5NytNwhMLkCoiJkxkOmiCW1bN21uPD04WL139/TsxVsn5e06f/HJrxar5avnr+pFnTaXf/gv/4eupLt3T8Od+8/HIW+vf/BbvxGaxed/84nlctQsuF5+85tf/73v//bV5TqPqQ3L683l6/PzD598+MH7j//Og+9cnn9jTPhbT578X/6vn2pOsQKXSRQCswqgD+ogi7Bj1zQZZOw79K6YedcIqOQcfUwle+fHMiKaJgl1PQ7p9OhkO6xlGgyVwsBSEiIF5+s6jMMICMY8feCHfgxxqhCBjFOXjNHFWLf1xfnbcUhH5LSu03bUojlsl237T//Jf/Hs+c8/+toHj7/9bZOURUxESwohBmtMk5Y0dJ2WzN6rKoE6JphXlgqAU7BqsVIpHKNqATPJKY9jSaPkhCbsMDiXc1LgaVcwAE1Zwdch5DxsnYoM3aZZrQyb1G8AwQVHjJKKirKj3CUfnaEi+bHrquaICklJoQ4FDcFKzt77ZtGmfgOOtBTNysy+bjUVcpOvABkgMNssV1FkQtWpqlQxdLutW6BSpMg0OIbkw/Lo8MXT5/31i7un9x8enz57+So0zem7T17++tcvXrzyWcDk/PWbo4f3q2Ut5oHj1cVFfbA6XB0Y+N7M0tiVUQlEBInVJgCzOOd4ik5EaDpjJBMSvCNxdyYQNtfShoio0+gv4K5VMFOjaaBhooxpF/gAEIAQpu9OpqQzr6wz0EIIaOZ2VME8mzRhJnuN1ASdq07e/3vPtpsADHBDoE8vsWlwbTdjPd+HGgGA6pwl9hO/eJMmdrf/VTR/X7vjTcE+V/k3CqVbEtlbf+/lpTd0xCScsl2emibAQM0ElDxzFWI/lLeb11oEioHykAS8HbcLT8pG948PQOTu4erOqj17c71B9S48eHDvZNGsFlUZt1lyQ6HvhiIa68jsp/1sxGiIosWQTKwf0jAOpnZ0cux9cJ4BAYmhKDvGorkbc0rMxN6nYYzNkshkFAq1WtY8CnquHDGP3aWrjtBHZZeHrfMtEg/X5y4snWMEGbZvOTQCkvsNMqftpqRMVawOjyBlIwPC1A0A4ByXomOfg3cKlosWUXa4bFbrbigFQoji+fLlmwkFVLN7d48cw3a9AcQQ4tAPTNgsWjUjB6kvqiZok8aqqgISdet+mrzrt6mUEppYV7GqQyky9gnJGUAeVUTV0HGoDxc+epPSXW2KKiC2iwNybAbmIyEjOccoCFLGgxiW908kY1otKqLVpnqEd/oujSU1jx8cnh5db4bW0W9+61vf+dZ3Ljfdj/763x8uF3/nd38nqW5fvW2a9uDg8KPH7/7df/ADBvjhX/344vJSy8fvfPDB4qD+7//lv/romx/euXvfBybyi2r1T//n/7M/+Td/vL24vLc4zZ3BUFTUO6JIOEiWgiYMhoB9P5JjH6oup0iOkMuQ2TPFpk9d9IGRVotF1w91qPoxeY5j7gGsjCl6770vWaMLOWdEniS03Dg0ct6DmXN+TBl18hcrUkrqxm6zdZHrus39mLfD+vLq+PDO/Qf3D+7U7JukllNCIucrLVnFREAFXWzjksBMVQxUS5FcQITBUM1sci126BWkgKmvGzeOWqSkQfOYhm7bj1Ud25olydBvQ4wqqqZMPlQuhCr1veQ0jmO7WGC7lLEvInVTF86aMiA1yyblsaqqXrqqqcxKiD5ZMS0+cu4zGpmIY1LnwIEPTopSLuwrcuSqFjiaIbuQgVQEA0kxDESMCDTVparAzIY66TIm77U8jky+WSzeefDOs48/e/HZr08P7p0GfvarX18++/Kdu48lLC+ff+69nBwuYP12fVGG1cr7B17C9uK6hIoJxyErgBmqaBIhVGSeGDUCsJyymjFPa70N0ZREy4T0TOHJdh6Ws20O7GPYjZxxHsOaa/x9JNz57MyMMM1yyF3wnHsDUwBzu4iOuIu1OKuRcKcvgtlWZ6ev3xkpw+y4sCu99zC9wc6JYU5uhjSZxyjqTP5O6xB3Jb/tkpbdRHa7yYIwC5rmZmGP9uzHEXa551angDe6ppuktXvnph6AEHVaGzY1gwKbvqgZ+ICYs5WIiFpaR+/eO7x/1JDS+4er6+14cXV19rYsF9U3ntz3dXV1dpaG8ay/PD0+dM5drjtPXDcxVhUhaBZ2BAQpZ1MZUz57dTb0XQhxdbCMMTI7YAAwSZmZ0SCLmgF7Nw0JuthQVUkakOM4jt45hEBNpZLNTFhd2xhwMTWPypxTD7Gl4BVkuH7rFoeAmLbXYXmcrt/0aYwHx029ULFhe8Xel7LWkpmYiLSoC4yEKVnKhRhCjH3qhrEIu5r56vJaSqqber3Z3r13zzF0247IQhWHfkSVEHwppa5CPwymEqswEVXkWCX36+SCDyFK0VxyiI69A9Dt9dbF0LY1EOUsZghEi7rxdTWvSjACNBcc+qiAzntAzqJoQJ6dd0YM5lUB2Lu2Au8WR4uzs3MwakP7/MXL589fvfj8y24cD1fVer35f/+3/21Reefend///e/dO2j++I//AsBiFX30b9fXP/zRj0316atX193Vb//W7zSN/+sf/eLxhw8p8B/8q39zsliA6u/84Pf//j/+B1/87OM3T189+OYddU5JIalvIgZYVN6qME0LjcOQt3k0JVIdhoLqqupwtVIH3dCRYaxiTgWZD1cHhvr67M3B6ujt2eDZ1U2cPFZDCCUNTVUPY4q+KloYmdlbUTBTX5Dw6uJydbDMMOacEa2MY9oO27fXdd32TT9eb2vH0g9NOMU7R5IEiJgQzHyo0DxWNaCUnNS05ITICIYcQoUmKiXlMZUiSNPObWF2JaesxrEiLy56yYGDl3EEUAVDx9VikVMKde2cN1WT4iK56LvrNakOXd+uFoXRVICBPZhoHaOVQo5BtVm1qR/KKMtFTWBD3/nIJQkjolnRUrd16jsgBGcTnuNiRPKuXgj7DABIQDz5PE7V82S/BbtKkplVRQGKSHTRiPOYKl+3B4vTe3c/++FfjUaP3333/snpr3/1q2c//PGTB+8fPIJx+7bbbgsMIfruIo+Gtry3PLl7kfJQEnrEDG1dKdq0CdIMRFUmGAjJMWad9SE6eycj0VdqVbhlhwY3oQ5ml2PbZ4c9Ur6vuHfhd7pTgGkea1+5T0UwTjuBp4uz3QF2slKb/5qtN2H/2O7a5sp9yjk30pvdlUyXotN5TAGQkKbWY0/f6v5Yc0if7hL3i2R2GWUHEe344Fv9w9wOTOSI7ZqDXUeAex56bl/mHGp040Z6k3zMBBSISYwCY2A+btsHy+hzvrpYm4Of//qzYRgJSQ0e3jsd1Ajyl7/+5O7B8sG90+V7B23TfP70RXd1/d4Hj9pFqyrKhgwl5yGl109fnb198+DRIy9Vu1weLA98CFMqNgMixw7LmMBETNUM2YNZaFszQQ5j34WqRVBAJyWbC2AKTa1VpbkQGVAYhw4dkq/VqQw91ishTMMQFgdpfbYdczh+x1UrBszXF6E93L594RxwE3OSrhvMMFZx2/XdkGJTTR5Z3ZCqqkqG52/ebsfx9O7J+cXl/XfuKli37TRr0wQB1JKdc6oSPQ/9YKqxDoSUUiF2WrKALdtohFKyinrPsQo6zWMGQqSuGwiJgmubhkN0ISigIk7kIzpHSN47z66IARgjFZWS8uTXiIYcg3NhTElKLlJCHYAwm57eO1ksq1fPllfr6wz2F3/502VTvfvwYYzhx3/5Z//D8zPw9cnDh+88fu+TT/7m5Gj1k1/88sWzp0+evP/17/6GuvT/+9f/6v7Dh//0f/O//Nkf/SV85yPK48npPele/+F/90sSdBhjvfCW/UFcHq8ohvZey4H7t+t60cim767H7/xddk0UhY8//vjFs+dvX74aezXm1gdC5OCauhmHnHMB0tXy4O36ohu77faqXh2PXR+dK0n3hVzOCZEAKPeDr6KIlFRKKlVdqUgepaSMZuNGsORQxdJ1yTtU696es8OX28+rtg4rH5vQd72hai6gxRGqFVEQMVUyUzSYZtSRGSF6ZJrIIlEpWVVivSglM2LJI7KrYkjMFmMees3ZrNTNMtY2ptEcgRGzFy1EuDw6HrotZsm5EBEFTwxARVTIsa9CHss4DKvDxcXrN2jYd5tYN1IKSIneaS6IACJ+0U62yhycdxEUpikwcAHbJbgafWUGJYtvakECkUn7rwagNpeWBCDmmAUEFDjGUgqzW909XR3def7TX+L5dnl6Ulv94ulnH396cXh8cHjn0MTOLq/cMK6Wq6vtSEs1K867oduOgGUiRR0joYEqoqqMktnIITni4L1N22HAFEBhFqfuzT73QXCGsOFWObsPwwY3Gpq9W8M0k6s6C54miSvNFCTsmgkyc/uW4paAaM9FT42C3Q7st7EY2ONHO4J2Lrr3e8BscmUwYpqBF5t1+rMbzm1md88CI+DMRMw3vOthdrNwe54A9pexR3puE+K7f92aH9ifYd82TMlhmnErKlCUzJrAx4cHx8uGRhlN3lyv193aCMpY0JCB7h4uenU/+uSzvL341uMHJ0cnTz74yJC/+Oy1j+Hd9x+vlsv+ehOWjYKOYzp7c7FZX11eXK8ODwk9wXBwsAx1BELVae8aEVNOCdRUDYgIAvnAzqNjKzTmgauWGCSNCC6lzsXA3muR4tg0gXelrAtLDAvVnG0UVHRIzAhRpBuV3cHd+ui0dENKg18s+1dbIvZ1KH3puxS8J++ur7pcSt1UZoTM19d927bbfugH6fv+3t3TbsxNVYUQN5suj6mtIyJbynWMY0m181dX18771cESmbbrvqoqAwBSVBhTKiLErmlbJByGEYmRqKpj1dTMzOyQWA1yFsnFAIYxMXNctHXbFBFJWVEBUU1NkRwDkIoqCAN5w3FIKgZEaNI2NQdvSv6IPB8/fPKeKXz5+dNuu2bk7WZ7/urN3dPjO0eHA8VFvRz64WqzWTT1ycnpZ598VlJatYsf/sVfkI7f++6Tn//xv+vPrn0pX37y/MGH3/jhH/3pWMoPfvP3/u4//gf3HtyXPsW6rY4bIuClM5Hl3QUi6NAeALL3wGgJ7n1w+vbLNz/607/++JefDsWIKLpYsvgYrMKh696+OT84OThcHZqoSBYT56mu4tiPVYx9P9R1k4YkpSADk6t8GMxMlABVLecBg3fMUIzMUE2GnIY+92V1crS9WDeLCjK++tmndz+6Ew7i8nhRFK8vzySNptkxE7kYgoLJXIeISpnHVh0ToxmBKQuLiEjhEAjBM+WUiglVjeZxqmkdOjFxMVbBF8mmBsyshKaxiuxjHgYzYUdSivPenKsc65hcFYD9FKIXh4fDZmN5tteFwlYUUKu20iIqJbStjj1x8ItFzurIYYiaC6JH5y0EE1ZVU0NGZC5FiR2aigooTX6Mk5QRgIzAENKY2FvdVPc//Ojq06fPf/bz+6enH/6Dv3e6OvjlX/3kan2dt1fv/+CD5mpx8epZlWH58OhNiBdvr0pTVaEa8yhaVD0bEGgWETAxdcyRKBAjkpgWgMmLekL1pznZqUjdh9RZLjknBZvqZtwFQ9whRbuYt4NADAFpwuP3Lsm7YLiP7+i+UkXvdfZ7kQ0S7kzZbsXNOfDvc8ZuG7vd8Bem0wA3gk2EFuy5jJtMtrun6cb24P6tyD2zI3vb//k+8Obd2XcAtuO94ebFO3JiGmXfNwE0a1vVpiF6mgbzVA2MHQXnDqtq1dTbnC8ury6u15YLIpujVXVwsmo98kHjx2F7vtm+s1y99+T9rz1+OFxff/LzF8Nm++TJo/sPjtOYiwqbdZvu/Pzi2bOnWsrXv/VNUDp7+fqDD9+v6waZdEqJyEyU+gENcs4pJSL2oQqxKtMmOVBg7zybFCPImiG2FqOyU8yICDHkkgow14eCCoVVtFiu6xqN1bqh77E+rBYLZDdefdksl7otOSd2jhBzHpu2KTkrMBEdHq62fRoHyYpVVfUpbbejGty5fzz0IwAe3Tleb9Y5jYtl7ZC7bRerqu9TU8XN9aapoo8BwLrt1vuAYI4JyK83Gxd40TYcYhEBxXa5CDE451wVvfN5V50BMKGbpANtjHXbiMGYMiCigmgZhyQi5L0LMbbRpkUxLrD3HEmKplzq5QJd8N477/phcCHULmTV9/h9F3jcjNdX6zdvXv7yJ79eHh0bYlV7xgyl/OWPfvjw/un3f+97bXA//g9/tr7sfvD93z3/4vXF27dfe/ju44/el832z//1Hy6XJ//i//C/+9YPftAenwAplkLB2xRYrFjRMiadFruiDdvOVR6LssBR0/zeb/2OE/7y6bPXb98MIIuDVRVDqOuS8uFqFX0Nso4xdsMW8JTZlVwQsO+7WNXMyITFcuUDkrteXwFi8G4ce0Zwjse+a+qmlITkwAPkBNkEh+3bCyeaEFzxQ7p8ebVu7y8f/OBb6KGuW/EeUTVnm6ZIJyGLiSkBOZhJGZ2LUucMMiEiMZhMJWd0jeQMKmhIwOY59T2oqBiwc9FJKYBIgGQkYOB8WDgEI4I8DEU1VgvQJIpjynW9NEPJ2ddOtYybXtHYMRiEEIdSFBQZc07N4WEyASQ14roqai7UAIBEBmRI5KuSRUQnUSbtNqcTMez9wZBBtYgiIYCGqp5sSNrDgw9/8P0r/is6uz77yx+vfvt773/vWx//6Cd6ve0+f3nywdf1st+8fIbV6/vffQfYXwa0EBgWuN0qofPOTNUsqZYsoFAhokEGSCrFCiDqZErDtPfP2cH0gABIYHtlOyLsx2JvWandFux8ZeXjbgzYDPabhhERFdEMTN0eF9kj+HMA3hXnu8r7K/F/VhPd/nNzeXAzcjDP/yLhvKfFYDd/tesmJujlVtjHm1syA7DdBPE8unerF7l52dRv3PQAu/yAcw6Z+5xdmpsy1Lw9x3akMBETuaYKtfdQ4NnZZjNs1CCDD94v68ZFrplRxjLmTS9l6N5ZLb794btHy+WwLq9enb86e61Zvn/n2IpeXV0fna5evXhz/urs5euX3/3tb28uLzfrjcP4+Mm7zUELSCqmmomZHZnYpIExNR8rM3AxCJKAOWJA9bGexGSGCkzgEKYhKGAAQNGiGV0NjkoakEIeh7A4NPap61SZqgMfTsD5bnNe331Iw9XQd+TIiuUk5BgMREFU2JGYTauJDbGUst12nrlu2yxgBkfHi27TyVhOT4+GPuVcYhWGcSA2Jte0NQB4HzabbYy+bqvrdWfAJaXFQRurKEXErFm2VdNMVjLMbkylCJB3gGzEjtm7gETMPucCzulYQqjIERuJQayBAxUgmOTc4BRwzGnIhZiROTaNqxokR45FCjtXVK7P3jgXqljfv3fv0q1DiESgvaz74fThw1jHn/7sp8+/+DTGqnK4qKi7uqAs3/rw3e7t9SLyctm+Onv9tt9224sH79z9F/+n/+bRd36LQijdwMRSkq8qI7BStGhZ98jE3gEqV569jeuNbFL0IV1cRdd841sfqZlr3CdPn6vIsNkqM4Ip6NBvm7ZFxb7vCCnGSILjuEWgqqpSGoauZ0IwGseulOydt2n/BHM/DN47SYWAmCiSG8dRsxBTf3G1WrWYi4pmIulGTGN5fOXfORAkQfI+KPIwbmUszhMRysT5qqrIFFGmrGxFiZ1pJkYVlSKIQGTArGC+alSSFRKFPI4IhIbRV6ONTJP5PaGqlOwc+sCSCgZDQ/LBcTUawpgEkGPtXHCeHDkr50ysKY/9WLdNSimNJUYmF1S1WZ7klABCdXBnHDUJ+SoSM/kqKRuDq+pUbNo3Sd6rymTDCQiixjiZ5SA7mipFA/XRE5MR1ifHb5Z1evNGPv94HNK7/5N/vDxY/PLf/MH6+RtX/OHJXewuuxeXcPj66N3HJcSCrKCrWPUl5ZKLAjEzMjIUzaNaLjKqKZmBopFOLKnarIefwPAbc5wpmNmeD5gEMggwyfF3dmewD8GzmnMP4+Ok4pkyA4rtwHaalsLTPuTuPSf2EfYG758ERF9BVezWKC7cTNkioE1OawCqOi+muekX9q/fWTVMBfqOt57D+a6y3wfzm/PArifakc87NdAOBbNbl7kfZdhNKkx2SLZDi+bZsBlJcv2oXd+ZGouSr8BgVeFxEyqSvO2jAamQ2tGqObpztFgs7r5z5/L84rM3H//oxz/9n/7H/9AZvH1z1raPVotFt96ev377+WdPH757t4zy4uz8g699cHR41NaVJmGvkwxp0uSKiJkAgAGZiQ8VOidFXIg5JTByzmkWJDJU8mQAQD6XQrGxUlSSArvgTBVczKmnuKDJsYQjehTyyFAEqTqkYMPl6zIWX3m1oCUjQR5H51zu8rQ+1LIioeaS+oxmzapWs2EYm7YpRcauXx22opDGsWqrsUsqsjxopCgyEGOW1DQBGdfr62bRIFBA3yyXiCSq6ILzYWrwTZW8r9oGaVpp5XyMzoeUiwKUVDgEYkZ2zpEWySmroAUHhuxYADTbWBJ6F2LtfTRCJaoWSyRvAEiTnXYmtdVyZUDb683by8ucU3OwMND+8lqNbRh++fEXv/qbj0+O2u/9zm8dLJsf/dWPjg5Xj+4dby4vUKE6bNqDwyGPAUsTm//sX/yv3/vt75KrZZswl7TtCNVCo1wk9VoUwVztrUhOCR0AoyMoaTCD9qg9//Q11+7Jh+/6l5Wr61dvXg25oCGbNU095CzdeHB4iDILt83MeY9qaRjNLLgwPQxZ6lipFEDVnBVh2HbxYKVFCBBZu/WamcUkosXoTfLYD66qXFU5gwri+S++uFN9hC0Rc9FCxDG2ygXQiMCQxAaRMnGOKjb51COiiLJzqgrkyMGUKZAg+FpzAmVw0UUDAJHCQKIW60VJA5EzBfNEyIBqRujQOwdA7LxK8TWKGy1nRPWh0pK5akM75q5HJh+DIfo62qDTlAz56Ool8oC+oriIVRRBpZiSYOPY10MSZnDeFTUkBZV9xTqVqVMoYO+kqEghnly3DcgouHhnUd178OUvP/eXV8u3axnH0+/+xgff+d1P//hPLr74orF1b806vcHXF/zOAxc055yU+rHvRUaQsRQiduwB1BAELKMWUzICA6EbEJ8Qd/C77T0NcLYMu4lS04Xv1Tz7gnjWyEzRfzqM7qDxWW8DgEhApmZgaOhuCuk9wvSVsvqGcZ2tn/fPvF2V78VIN0/e+QjNyA8goOpX0gfuuoiv8Mc3mWfW9NgMeO30q7v34X+Up3ZdyWRRcuvhOcns+4YdK3wrxQECISCIiOE0Mo4GoBIIlkS2ubrabIPCyf2jj957dPdo6bwXhf6y/+SXT3/9858/fHj6O9/7rZLGx+8+YDVU6br04x//rIAujlbL49NPP//yyQfvHx0dtU0FZZo6K0WAidBQZKbD0YicQ/Ls4gR0iYIhee/RABitFHNhunsxBB8A0EzVgH0kAkBJKXGoJ8tRJIfBjUWQI1eVDoMLfujOUCyGQOpLctPeXRecZDCTeXDPzHk/jIU9B0fM7vrt9WK1AMbtdtsuIiF2603TVJoLmta1d4SAKKZoXEVvpsOYTk4PshggVYs2JwHCenngm4WoGiCR8yEgs/NBVQ0YkYqWoagBArGvvRFKKciu6zsTHYfBxejNiUEeEjC7WJM3rqJzEYh8iL6upyn2LGqSDKFpmjSO4zgiO99U77QPF4uVZ//65Qsws8+fn78+C2jv3T16/8n7D+/eef75Fw/v3L33zsn9g1U66N//5nsm5fmbq3z5vIH2n/43//X9b/1Achwv+4DgveMI7Fy56oyB1YjRwElXpNsYqKK56CUXyLn0Mm04w27Qrrt3uLr36N6v/iZ8/MmnfU6ALDmVVNqmXlTL66u3pmoEiBi8T8NYSkZVci4NA4MyoZYyDmNVBzADNceMgGqgIn3O0bOKIICaondmoqmMMPjgVYzUp7P19ScvDn/3CTu37bcCajLPAxsAEniOrnYqRbRIFlBDNO+YiFTNeacqIqA5mxkTqik6h0CmmauWkMaxn8KJmbGPoopMAMCRQMQIiRx7L1kwBNQCmXysxm1nknzTDOtr7yKFilIhUEJLY6kWbRlH51zRQs6DI8QaOAxZm5NTUByTkjIjMpEPnIshTWNDbDoxRDuNyy74mAgzA3ARATUjSKJYrGmb4yePrp997e3btSsFPv6ie3F19+vfvnvn8eUP/yxJqd7/CB8fPb84C6/O3JNHjICky9XC+l5LyqVAzmDgnANCQgZwTKqmBXVSSYIBI+0inNnfJn1tr87ZxWDYF/c7ZhS/Ep/hKxwr7uz+TdVme30CE7fDTqbq+eYVN8H+FuE7/bVX2+xKapyW9M6B+W8xr3gD0eN+XuD2bdwq2O323U3XtMN/btLJDbwEu7NNBT2qzj3PlMZuJgB2B58hLbz1pu1HD8wMjRBRhGQyvR0Dxdb71lOzWh49uHu0XCxqNobPXp1dX61fvHjdMFWh4uABeBj7r793/9Gjd7789Mvnz15su7FL4/Hp4cndu69evHr8wbsHJwdtVeWkJY0IYOKQiR2ZATKCgAqKKLDzPiCRlDJlTOeCmE57HHIxdI4mITigqBgaIJLzaGSgaRy5qhmh5EzMZnnsRw4tOwKEjNkjOBeUSGCaIiFDF5pY+r7veyBk54ehVG212eYyikVu6naz3i5XNTncdH3TRCDstn1dRwPdbrt2WYUYTVRLdkyIRoyA/u7RQTHzBXxduRA4YLM6BHbKvm4XoW5UiylqKf2QCHlanxS5ImJFVUMQzSpAmFOJTQuq1WqBSFpM1ACMnRMzF6uqXYgYsuMQVAyZc85q5mMkojyOKlY1LQLHg2oySWTn7tx7JyUh5tVh9fzLVw/fudMuq88//TgEd+f0FEp+9eb8xdPz7Zh+83e/dXLK+fn147sfHtz5gHmhV4PrRvahlJEjqyGkDFLykDAYLiMMBqqhCo5cue5QBVlLGoOrINrli9frt2u3aA+bR/fu3B2G9PL87GK9DlVQRPahrZqzV8/SkOpVDcVyyqYKhMxcxsJEoBCbquQUnNMkAKBq3nkEBhMCmmAAdi4PY8mFkTwisjlGULGcx+urXAZBWX7jYWmn2aDJmAxEBAvANMlrZtOGCsdSTHIZJbNzZjaNYUwfYNUCagpAAHXdjkNnJfvYqpmM47SBV8w4BJynRxmJpz1S5LyhGRJ7jz4yaiSfug2FytWCALFdoSqWBMyig/e8OFiJFnbexagUQlMXAfNVQq4Oj/M6izpSMmPEaZCaVQQAVc1MkBjM0DGIiRYzIEdMQMxOpYgBYoihFO236+Zodf/rT9LHz/KvPm+q0L99/fRP3ywOlhWQO7/Q5sv4znurdmUCYnJ8sJBt7yovCDKAFu3ToCAZQGHy/WHnWGeDNFI1mqEO08mmHg0U5g0nkxcQos2ox07qPrcvRnOG2MPbe7BjT/vO2iKbRZm6W8o7uYHu4j7ucf2vwDV485jt1PdTdJ7T506+c5My9hNssAvIMKt69rNb8/3ueOc9SLRX/Oz6jN1MwC5L7fOa2e0sYHvDh+n5u8Rza0HC7gXzBRnAHjKaMC6bexQUj1o18d7R4UkdaySqAzh5fn6x3my6rmMWNuRQHz+8111vurfn49B/7TtfO1y2v/jRT8U4OLfu+3a5vPfo8euXr46Ojpu6aas2JxmGflK5uhgId/JcNQMsYuQcOS+mbCaGCuadn+RTTE5UkJkYyTk0UFViNtN5zhAgp0w+IpNqQUcCJtMYsGNCS8OWQ0TKjDDmxCF6Y6GhXi5LSiUrOopcDYOmUQV0HIuvQ7Wot+vOM3rnrrb9oq2ziGaLHhHt6vLq4GBBjk1KHpNzFJpA7EStapqi5kIMi9rXjSGE5UEuCs47F8HFMWczNFMpyj6wo4mHn8tXVUBQBTMgdlXtcx4V0ASQCRiIKCzqqm5k2uVshgA+eGJGwDGNauBdMLVxGNiHxVHD7E0xpQyMIVQIkHIXYzw+Pqyi7667g9XBxfnlxdvzw6PDZ0+fLduqZved3/hwsayuXr199+tff3Hxs4O/+2F7eBcKpS/P+HINGOKqxTtt2mzTekAoWnJcVXxM5WproIC6fvW8bDauaRRyfVB1L6+69Wbst/UyDnl88/kXEvnkZPF2fU4AsarzJOFQMjADJSSkYiJEKEWFFABNFQDGbpxWsbjgxuveV+xCVDUwk2LsER2nXNAABLxzNiae3r4xxcrrMDJZue7TxQZ9RY5MZRJ1mKFqAQBDnfAd0eI9OzQEzUXGoSfvoAA5b4BA5DmYTvPslqyQ90asZWQXVBQQwTkyNDDng6gRIigiGBAXNXSMxKKlig1oImAnakhhsczbbXt4JCmnlGOIXmzousM7967PX7GPqeTlwV3yFbvYFxAkjEtGRY5JzKMhsIq4gAYsIjZDLMrMmgV5EpjjxMk57wFpWqQxjtmxJwjbvmvuHi8/fG99dnb66G53sTn71Y9p+2YZGm4rvHq7Fly++21drQY1Jmx8AKRVCBNjz45TzgBABgCqMk9HeZ64B2Nknbydd0rfvfJlip5me8x7Z5eAu8A5GSIA2N7EE3fEJ82qmWnOGL8inDdEcFMktv2xbmMqu9p4lqHemEPclM/7Inwfraej7LiJXS7a5YldpN8/vrsfwN3swg7h3xMMNuM+e4Lh5n25qegBJ0ON3SXu7nN3DbtrneaW5+3zuwSFNGNLTKBqMfpFWCy8857P+n6z2Y79uOnHPA6HB40j9OZOjhaH7cFytao8hXL67Xcff+Oj9zXnp8+eGvAnn74Ji/o7v/3t1I9t256eHh0s2nEs47brc27r6JlVlYkRwETVZkNoIjLVaVWpigYfANFEHXszUVUXPCIQUlFDZkCDYmV+y429RwTTSQJsZMC+EhFyXHLCaU1LXq/X16Fqah+HN8+zYHt8J79+vl53sa6zSCmiZsOQ1NAx5yGZSPB+HNOqqbphiDEoIYKlcVi2FTEyWVEx06ap47I25SzFCB1xvWhcqLMaxdacD3Xlq0bUUpq5KDM1IiQqakVUVVTBsSPvEYw9+aodx2EcEnJ0gR37IjKMY6wbV1XmvHMeCNKY0IEZlFSKCAC5EEQVEX3V+OiRUZRyHqe9usBsZkAQKn/27EIFnJNY4WIZHj48dS6cnJzcOT2uIo9DGa4uHzx6+PSnPzy4e/j+974LjuX1hl9u5IsvylVXffdrkjvBki96IAt1pdHD63X/5tqvqrQdxs02ek9J+vMLD0f1sr38/LUby+reEXfjJ7/6zC1qauvDtt5eb8axR4A0ZocOVB0yM8swxhhzKQjinU95JDAFNC1pKwSIwXvvmRy7mPoelcwyGOdcUNV5j2h5HFGg9qEOtW47wIxAoeKrzQYBfAw5D7NHpSogThNSBqgqxDNGagDsPBFlHIvMM3vMXq2AGaIjpyaqakxshoZsSC5EJMTJt9TAiGMVSi4OHewgbmRCZEBS9EgIjBjmeTcKAIyubsvYIYEacqyTGIeGggcKFGoFCss7LhXwbQKG2CAHxqCKhsbsTAzQcFr8BChqzDZrBIgmt2dT1aJE7NhyUVRIefAuLJfLzXhe3111Jwfd0B08uVu6O3J21pcNG6TzLQ6lj8fh7kKTvz6/HNg78YjsgdjQe8eOVYpHErOUxQBk3lY1n3kyJYDdosfpwcm54TZevg/hO5AG976aU/Sf6lu13QwW7l41573ZhmGyH3K4i7G7hmKKxbugOh1tLshvQSe7AIu7/+aLsH2I3sXWXTFvt161m/X9yv3gDoYChNkqbtfp7IabAWBynf4KgHTTCtzKXnu5qX0VV7phyXe5hnAejgCYZMoEyEp01Y/Pz7dZcinCgj5UTVUvFk0et0PfSdaTw8O8uYa0/dZ7Dz58cn/stp9+9vzyemPOW+TTO0fjdjh/9ebRo/sheAbaDr2ohhjIOUR2jplI1cyUnNMszKyiRKxgKsqOgWhKfmqmquhoWtGs08w4EYApqhEQsIkBOQAAVCCHBCoipuyDgGWRRVMDljH1sW6ZSxrXXC+Jebh+c31xuThc5VxKn3MWAANQdkxk4zAiIXpeNNXlxdoH5xwR8Xq9Dt6HKuaceZr+XVQu+BCbbtuvjo9SKVWzMOSk4OtFXB5B8M5XYxYFcIvGRLOUPJqLjskxI2dFJI6ekKflGEiUxVxE3ywI0NRUxLFFDr6uENiAtWjWAghEhAg5FTN0jtTUOe9jNANV1aRZxIUQq4q9Q0QpWq9W7fFysWpfP32mZUjbzWJRba6rB+89vHPvnTz2AGXVHoSKnz17vT57/fXv/KPlg/dDCHx1+fbf/7T74tdexlevnjZP3l18+6Px4oLbBWAumw1tlYrgwOV649mcYyvFX6VBL9sHR+3Sb5/35x8/d0ftYdNcdtuScozsQxjLEHyzOX/LhpplHAbJyRMFH2WTDDB1fUk51hUKlJwRUbJYFlQgABVBNCID76WMoYpmc4vJxFZExjxCVwUmBFTLKcVFzYHNTEWAmB1qkeljCNPSegYzJXAIlnNCU+ecocmgRRShTOy9loJMCKQmpmKI5BjBi2REdN4jIDhWUUAC8uQJkNAIDUQEiJA90YRFILMjpDL2hubaRRq2rl64cTts1uiD5lxEVnfvX1+tY7MsQMgVuAoAxdfqAvpahIwcIIsIkhEzqjKD6rT9EaUIOSTikjM7b2YlaRkKO3bBT6Bb7sfB1u2de23drh1xW1388uPt09df+73/6PzHf3D+xZfbK/PObS/fjtWXaX2kd+pxzG/6awFoV4e+jiF6yCBg5lzOoxo4JkNSLQgoE3KNAKaISGBoqDThP9M28z34AnOhN0t44GbMd4JzbTdKBoqwm3Hbx06ai+1d/W6A5nAfLW0OupOh2h65n2Mq3CAztwL5Hsa5AfFxtmDD3XXiLeri9iHmbmSfN/aZY4Igp2aAaGdjsQeopqQJexJidz834teZO9i/CufUt2cudoeZ7aL2DkSIBIhURC/XW5OcS1FDdhVXMTjGPPTjkDdbT3DQ1tuLV/l6+50PHn3zyaOm8b/6xS8//ezp0dHp02dPH73/7rJpX79+0cS4XNRF5Go7XG02l1fXD++/09ReUobKFxFPBLMCDAgnH45ZT8HMhKim02cQwMAxmBKyGhIhTkCJKjs/YSUIk4WLIfGEFrpQgalorpoletFxyMNohi5Wjmi4esuIOefmYFn6HrON3QiEOZe5QhBDsqqtVe3q8jrEaVEMpDQ6IiRA1HbZpKEPnkNbs4tq1h4sOVQxSFguSlbkyKERJATajqkUcHVA8OCQSJt6SYiTvSVHI6KxFCCefjxSiiGSCwggomnMasbEcbmY1qkWERXh4B2zIYx9j8jeB2ICdsF7VRWFkgszx1j5Kk6Wq8QEZtOgXFwsHzz54M79h+u3b67Ozh59+NHqeFWyDGvfVJzV4iL2HsrYP/76B/Gqp8824x/9ZPjJL1q+uvjy2fjLTMMP4tGRI6VSnEZ/suxfvvFtZUOSrh+vt/GjezTk7fn1gpfDy4vgIxjWsQ4YP3914Rbx8uri/tefJPRweX5+1bVNg0axqlUyEzGjjP2U7nHytS9JFaUUM3XsbBJMKyAbFCXmMQ1aCjt06BwRgKVhZANDNiUjTH0Jwae+P/n6++3J4cZGpJ3Set6wTUAAoIxOTbKId0RKJla0iKj3gU3T0JFjj4F9kFJmBJZ5cicjds5HycPUOiA7cigik+aFnDNDU3Dsp1cRMxmYAE7uDikRgquaMmb2QL4W7Xx0FKOMxXxFESg0SbFuV0qenMeqNfJAHpARCIkMUFX37CARAqCqIFJRmdaDlZwAyDFt11tmAm1DCJiyIQzX3cvNJ+88eHx091ge3rv+xc/HN+evfv6XB/e+PV6ut+cbaVmdV9KSemSsmzbkTZ9z1w9l6KnyTFO4Nue9FAVSMkLyakLossgE2sgsoFeQPVRiuyCJNMMhhqqIdGvk1XDnRGtgUvbCUQOdbZunpyPsRoWnMGh4ywxuHzJvl/DT0WGnxIcZikG4hansQ/pejrlLHje08O6MN2X9zUtmAH6HON0kB9qLhcDoxhxoz+POlkEEsBtm3u2FR9vlC9slMLj5B845ZpIX3bQ3CIw0TUyYgiGTR+e8Y09gUqSkkRAIrY1+e3FmqN/58IPvfOsJSvnlrz5//eZ1VTeffvqFq/hwdZhS2Y5j9P5yPQARsY7DeOf4mBE1lRijisQYwZSINQsiiaiaESEyEQDxfv8mgdn0CwDkbLIxmYfYENipCQIC8ZzpDVQFJ/5AJCdFYvJORdHQhQqG7JcH6fXnEKJmYR9kHKyYpOIilWwIUFfRDIcxte0ii6ShMGPbVrmUlBISVHVAxBgCOzLvQtsYOh9r9I6rGomr5rgYUsBqeZgLjAJp2/tm2Rw06AIii4gPTI4MTLOqgQKkXAwIDNFRzpkckVopOaeCyC4EcszERuyITFXRYqyIUNTSMHhfkXOAqEXApBhOJiRVVSExMKsaEanK0A0K4Lx3IVDtY9uO2+3i6PDhhx+KKUAZRvFVrByNwyBjukaHtW+aRTi/3vzbX3R/+iN9/bS39cWbzxTQ/eTH/u69w+//xvXr83HbLQ8cqlEqMoyVZ0SM6M6ePbVcQCy/umrfuRO9767O1+e4jHU/FFcoX67r6LmIJ1ABEGGc1HMFiRUkeB7GgZAkF3KkIswkBRAxD0PwYfqgO+/S2JMD73zuk69Z1SQnIAx11KxmJkWqKpY0GuDi/qk5MiMiUgDJBcAUpqV1SDscNjgvUhAYeTKdUyYiYuEgpRCRc5GY0EBFJs9jnJBumEWiwA6QyAfDxM6rCCATOSMDVSJGdsRsOxLRSJEjTn5PVUOFQtV2foPBaxJjMq7akyOLsRRu6pWaNwqA3oBNDQlmyTvZDb9NAATEZAjTahtTJSQDkJSNnfd8/vyNq9bH90598BkBvD/75Avdlve/8UG+vOzeOdLubPPmEyY4efKPCv/7fHWF0S2XC3HIIbSxdj6lvgckYRrXWx8CAQASO3TMolbEAIGBPGNkJwbFJImY6o1OB3ZzTTeq+ykQI06yFzCCiYuFSRaMiEA7hndWhO4QFIDdIWdfNUR1k45zsmbe0aI767ddMTDjQ/vyeq8WmuPvnlK+idq3lDq7KLvvJ26MLW4cf26Menb9wH4swG4u7eam9rezL/xtgnumBgl23nV7wvor5MCeDJlkYbh7nhkYEKjOi9kjeXIMZliymsaqOqri9z78Fkr39vnzFcKTd06H9ebTTz794umz4LyYCLm/9w9+v9t0f/hnf/EP//73S8YXL14/e/XWefzGh08c+3EY+s02OLr7zikimqFNMNtkVU40yZmQdrkSEUwM5uZg8g0hJrXdjkQEAjKazTNMBKd965MZCJELHsCYGTRtN5vI3Byc5PF6c70+Xt4ZLp+boRVF5FLEBd/3WyIOoeq6PtYVIpVxTGM+Ol0BgUpRyTGEqo1mFgLnkkJbxWULxr5qjLkoxLbFUJXtGBatAgmCrxvPHkNAdsycUiFiBZSxTFvNmcnM0LFzXqTkXBQADVU1F/E+ELFN4yzESDQOCZ1nR8gzZVIvFmAqalJkIttSST6E4CMyK4CqqpmKICE69kjkfZJSxAq52CwcQU4FQBGjw1Hz6vz8VSU4pHx89/7B8s7w4rq/fm1Pz/HqIvdXqFcKQwdQDz2N4/jyrBTNm216VY9p6+o2l5FQSS2frb0jbqIHGq67l2e/ZhewwPZ87ZY1szFIf709227EhNXSmD3wsmpBjQzBRFJy3rN3WtR5L6lUdVWmAUCbJ/BzTmCCNq8HIMfowHtGY81AiGSAhIZgYoQoDM3BweLxvUKaxwSEjhCNTcEkGxoCGUBRJUJ03iGVkqUoIRNC0UJEoW5yGmAiMVV58rY1NQRknjZgIzuASXnlzQDJGTAQInqcQB+aykMGYjDjWVmILqqWpMBG3tde+t7HhjyTRyxFyMV2ZaGWUQs472tTQmNANp1m1nRSeE+RhszUDKdpSkADU0URJWQ0kzEbggu1D/782XMQObp7J4ZailGks88+cabHd06X33hydn1VLs43Lz+Ld5bN0QMp0mWl3N95cPwC0UDbZZNIt0MBdsQkCkVFJTvvsgMzSbkgKk9bwploVkOZZZtgj2lcWXWydL4Rhu6d23a2cXO0tcnyB5BnyzVUw124nBwHDMwU9IZWBnC4o3l38wM7xnX6cdhXAueOG6AbRB5vQCbDXfid2eAbiGYXdW2iOnAffG9RyLuYPg9uId6E8blN2aW53TP3nMTeoOJ2Jtode/cM3d3DbjnArC3azwij7SRWSMjgiB0RTRubzUJ0x83yo6Oj1K917E0VPJ+fX/7Fly+//PKL46Plg/eeSBp/+/e+l6382V/99ZDzly/eLtr22dPPt9fd4/v3AHG93q7Xm+Dw3p0jJkeERRQBEKFknVCgiRNF4qlk2XEhaAbMPImGEZGQRHUKnbsL19nbfKekUlMiJiJ2DJpF1IXacxWq8PrpJ4f3HslwAQj95TWbiJiL9abrp7TRbTsX2Hm32Q455+PjQ0WVNBJAiK5ZNkSMjDklQwh1heRcrDAEFQpNhb5VgOXx8bYf1YprltwskQMQS5Eh5fldRjQGnto5ICQBcKYmauTYTS4izD5GEzPAaX5FDPKYfQg+BtX5k0yOFRRU81hE1XnH7CgQIqY0GpJzjsiJiPPOeUfsACAXcc6zQ8fOOwZEcjYMOQRHLsQFh/Du+uzN4Wq5undve9lXcmqfvh0//rX044vLs9VpTLFej92jJ4/i4zvuaOUVmmXIXWfK43qAAAZFtWxfvjHT/s116WVc96Byvb4atuPQJxn7gbhLXV2F9fl5AYjHxyMUx8ZIKoJgaRhABL0PjvskaEZEVV1dXW6CC6DKng3MVBhRTbWIiTZ1U9IgJTOSqgBTzqVdLBmUlCxnDHD4/j1etEPJhqqlKDISGuJUZOz0mmS2w6VnYhGRTIsxMTsGQFVlAzREQ2IyBSBGJGMDRYaoUtA5dN7MyBjZaSmABOQQyXa+vEAMokA021eqmshUyCChABt5QI5tla/W6GJRa5an4pIAO1eREHhP5HQHDcNO6YeIMq24xukXCRAR1NAMUEUslzJcXTerZXt4cPbsy2c//6UN6eDuiXd+def082dfDj/86/zg0aMPv4lX46s///P+anNWfsxcraqW2/rqOq9fv4V7DxzTauGSldHGbEbAjmkcDRAUUVREZGpyxDIgkjLSPGJnAEjkAAqYATABAswDVDtfh5uy1faR0XZ9mk3eRgDz4BbCtIxL1QBwZze0U2a6qUuaqAGbDZ92A1QzrrLDdb46B7YjEnYIEO4GzcAmOc7uGTfx3fYH2CWUGebaHWiPR+3PcBvL39/nPNEA+85klxR2pMMs9NSb3KIzJoW4603w5gi44y52HQgaTbtxpGRRUSHVtoqbbvPnr18eElYeWrLvfvDu1VU/Jn3/g48Y8su3b9978BjIffzJ5xjc/aPj50+fX277R/eP/uE//LsHTf3yxdnVZkOo79y5g8yqCsYGQIilqE0cJpCRTfD0bnBhfpuIdreKZJMkyIwcqRlO9I2CmdIOCFIR5z1ORZADGQwQQlOTlsvzs/rkDpBodz5s1grKzGYJkVR1HMd6FU1QQUvJaFZFX3REQynZedcsW+ddySXUjUqpFq1vKuQo7CnEyJWxVwFj1ycRYo6taw4pxJwFEMUA2SMTGAz9FnkK9VRkcsUyA/AhEmJRFS3sAiNnSKYAAMQMaJXzgCgiKmAmgAgKJcvk+lDHSMxqigCqBohERMxFxHnvYiAmKSZaAJmYJvQwjUlExnEkhO2YECmG4JbhpH3PrCTm6zweWe9Pmi9//DNt8BVsrt+uBXJ95/3Tf/T36NHdsqhgm7Ctc7fFGFglbbdx4aRovli74NwooGvoB19VFWI/Fu16bCrQRKbrN6+9mqpsLt64qhFDK6XkERAcI1fBzNg5JjQiNZCixFDGBAAcnGj2IcC0qteR2awfIyIEZSIRdYiqMmlhiioDnXzjPaxdP2yI0QANBI0N1BBRJyQG1WSK+UAEQNNwNaDOOD4SMk9CczMx8Dg9czapZ2QgMmJH7A2ncdcphTAQTf3czG0SA07ktBAzqiESkmP2ApBLUiRwQUzFgm8PgAJXKzFwzQqMwVcKxruzTCIYVYVp37bt5v2RCFTBwICIp4WXTFy3dX959faLz+4+eO/evXd+8ekfXVj2Orq6DUwf/sZvfP6n/+HiFz9f5a/de/y4vHq56b+AkqEM10PXnt6Vkvuzax221rRItFzUBXmTctqmLEI+ICiYggIxk3NgNvlD64QD4MR60qxLt+m/ye1ht7f21iDVDRE6267NkY3mWv4WYg4wRWWccsP01pjNC2FudDQ7eH7GeHbaoNv86i4swS7/7BPGdADaxfpbKWQK4Hv/zVtNxU3pPX1pc/iGndkF7ILzHOB3Zfr+cnfvC+BOV7rXMM39xy7g7/PIzprObtCq/b3RnASYeRJaF0YFYqQ+iQ0dDD179+G77/3+7/7G5Zu3v/7Jl5eb62rdlbx5/O673NZ//ctfXV9dPnz3vUVsfvLjH905Pfz2t79Vt80Xn7344U9//vjB3feefHC1vjq+c0SOisxFvMg0m05mRszT0MfErk910bRjR0HnDZ+TX9oOTANGLQqAu88hwAzgwrR9U9VEJLiK0FvqMkK1OIbUjV0/XF/Xy9W42ZDzfXeJZofHR1lAWT35NGbnAJxnhL7vY3SxrX2MBADREYLzrlouKdYiyL4KzSpnIwq+CsOYimpoV351auQnvhWZplpFBUTFx4qQdFLCTR9Mk1hFUStF1JDYEbth6M3A+4BIIuLITcEGAAzEwJgcmJHnUMUJAFMxk8ndj4EZkaY1pAaQUymlEJMPnoBEFAAMFc2KFB88gk3dj+eAREMeU6LKx6OH97uPL/LJ45erZbbLAovc0p2vPzr85m/6rz2yo+OkMoyjmfDpCgxIlY+jb4LoaxobM+2vOwTdbPpqIcuj46uztaW87cceC7eNldxfXkIT0PCq65uDY+fZijBhrKrucu3IC2RHDM6JqZbiHWsyVVMV51kkWdEyZnKsUmBqA1ENzQUmY3ZkuZiai62aNAeL6vQ4TfOyjjxyLuNNJ8xTkFJiUtUJXCWa5lgQgYlYpCChc87mScy5Rr3hA5GcZxUBMgA2ZFOJsRIRYJoxnzm1AADNUlGdQ54aGDlFcr4ax1SKQghglJR8eyxJ6sVxEaVQ5WyenZnppJCYijszwwn42SG+szIGCUHE1ITJmWTVTOyaxerZLz5xFu89vHvn+LR79lkR0aotpkdff/Kd73/v43/3p6/+5lcnh0d37z0MKVy9fJ62PYxbvXghi0VJg+WCBJ48E2DL3jsppUvFExXTYgCmtMOgkWlCaqaiWfdVnxpMyziR0BR3NOztjYlGuo91O8O0Xb0+C352oe2GT4D9VoAJ4Jm9gHZDYHPhPbdIUw8xV51TIb2v9/dyIbtVtO/OZHbra0PFrzo/72Gem2x2K/sAEM3M827Y4VbKsD328xV43/aJYHej816y/TlvGppptZkBoumthsPm3ZUGxlMxLdO6NnJExKQlDWqmnuLR0fHF1fpP/+LXn33x9sGj44NVc3L8PgT75Wdfmug7Dx6t2kby+Lu/85sPH91fRf/q5eVPf/43CPLee+8FxnB8dHB0YABSChHmVBARJpk0k+27J5tWwN1+q3GaVptuSmeVLcI0UzO9+9PIB0y5QMkMmUrOLlTOA+R+03c+LH1TWe7TMIaqkuFSzcZ+6yvOoyIymsSqyv2oWcwzmuRSmkXFHr0nsKLMddOmnKvlAp0HckDO1Y2hJ8euirkooPPLJcclxbYUVSAMznSa/BE1FTMiAmYyNgDnSUWHQWzIQGyzPywWNXKeyQGiqSI7RbAsWQRMEcE5nprbSYxogLkUNCTvCRFmWkUlqYtBwbQU7x0hmZiASCoKyMQixbKwZ0T00QFiBpUsPsS6WaW+Y8IU8Is3z5/8V//J5uXr/nz78JuPDz94WD1+t3540I+SL9fMTrT4qkFPLnp0RS8ujb2Z84TMflhfsRYGHS4vLGUV6bcdBB5kXbB4pGE7SiEfIhm11fJqfUmABFjVlRVDQ9UsKaNjUIuu6vIaEJyjECotRU2AZP5NF/XsUBXB0DkQZSNIQuiMixG09+9yHQuMxNN2WHPk1WSK8NNg5AwS7+o0JEIjA1TIU4ja1RvKjCo4PzLXWjs9x0Qly6Rdnot9NkPmHdPMUyAj5MmIAkCRHJLTUpBcziP5YBSQrVocqpKRh2jmKvZOyJMnUTN0OsckmkALm0GsaW4C1GQihYmQgEvOgEAulGFwHmJdLU4PX3755cHR4em9d198+eXw8lUMTbdet0irR4+/8b0ffPxHf7Z58Wy7TdWqWt4/unplakPfX+nhYth2tO1L1w+o7F3tnXha1gHRFIWRURAmRY6BgBoY0fTmkJgAoEwN61wYI5gSTDvLb1fKaDN/fQPE7+PklHx1DxZN0M6cIXAKLzb9XA3cHkffhWKaC/tdmwZ7u4w5RcxlNSLoPmzvJsB2Zfb02zKDK/sN9vt2YMJ8pn/iztjadhL/uQC5ecWN3Gk+m80UxS2NP+5yxgT9zGTETcrYEQvzSPSe5rjlbmeTlnY+/ryrkxFBNScBKzG62h88PLkjkv/8T3/+8WfP7r9/790H71xebonrZy+eOwchhs8/+/X5xbnm8o///u8bwM9+8cnrt+fbrv/ooyfbbrAY7t27YwJZi/MsUubOZ/qJ7nCpfVO1zwXzj2CamsE9rWU27diZlwrt3pf5nggJkcwZTICAFiBfOU+mZdhsVIuU5AiYkKMnMEIxs1jHnIqqxSYgMzu+vrqsm4o8kfN5TLGui4qva0Dn2pUocKi5Wogih1oVihSqWl4eI0cFkqmrM5jwK0OHTkIMCEgKqlJUAKmYkmdiBgOYVbAAJuQC4LQ/iBDNRIsqoLFjBEJHYCACBCBFRZSZyTtTQ0IxzamgofN+arFd5KKlZFHdPdmxgTpiaryBeQ5AmJKolLptgvMqufRpcXJQPfDd+vVH//wfptfb6+cv73/nCS2WKYSRLdUl92sK5Km24PyiNcu6TeRivWovf/kag5Mxpa7zMeiQuXEyDk7NEW02W7esVAZUCI6HnMTMQEKIjGwKuWRm1iITRcSOx5SEGA198ClnU8zDSEiTnNxEwXAc+hC8iaIKGnARRgyhwrGAcPC0OD1UxsnfR8HMRLQAgqp65xRpsnq2SaIzEQlEOHvpINPOMYhRhRCIWGwyniS3+1UkADCdpGhT9Kd5hgWBiAFpT9PCLMujibVTVEMiFwBdyR2qAgVDUt/62I5D9iFmoapZqCJ5p0bo2BR0+ijMrJohgpgxkZmhERgQkqkazObPqgiIKkpEzeHR2fPXzz7+4r0PnxycvnP5ox/jwTKa6tMvvXd8euedb37j9a9+Ul69Wb99eeed+0f3H3TEegVIsWqX617U8HKzLSbtagHR+8Athm4SXMUwOT+CgQIImpoWU5la/f1F4rSrZq6XdxEYZ5gIbQKNbtos2K193IVanKvDSaSDtm8JcE7niIBGbq49d2yxzV4Is/fO1AEoGNEue+y7jHlWeQJTYAdUTD/CqWrdB/Fbhfb+/6cindDm0YabIh9uvC7mFK5z6pmyzs0C+t3vDEyah32nsNdL7U96M/m7a4du2o6bfTpT64XT/B0BESAxWtECJTi6c3B4/+hoxfj84uLzt2ftncOHH737y09ebK42n3/5a0kjsTrGYbv+Oz/47Q+/8cFRFf77/++/DpX/rW9/63u/9V0Zy/mbi/Pu6vBwkXpXrxpAnLAPU2PHAKAGxNN7PZMUNBl37xqd3Ts0Zbyp25vHqm/yHU5oCs7lthgyMXrK5XqzqZqqCp7zBrVIHk21OTg4v3oZ28X2/MIAmNk5Hrc9groYVdRAD44PQhWLZWSMyzY0zdD1vl6YC65ZypC4Xhp68kGKZC0War84pNCIkhgCMxHPK/uQSskuBOJpaYGYTX5HhkghekDSUohJVNSU2AGCitoESauZTd7d3jlW0yKCAG5Sf4oSkU3PNyhFgMB7TzzFLwBRFRPTkgsiEDExIRIBGaIhBh8RQRSQyDMzwtgPiNSsDkoqm811319fbdaHX3tw+uSuHbbqWVIeh55aju8sIElsFxwjmHI3pMtrQPZHh829E05lefe4Xfj12eXm7NyI+svLIUvKAlrKdiCnDCBFK+bRQEthxKoO/XaziFUeMxVzzEJzHUcI2+3GVwFL0SK+Cigmoj76NCQmJjMwy+NQxQiqBBCYiR2xgaqvmvb0UCSpFSJGMjNCRC0JkSdfOQGAaQ/A9OXkVYMOwZhZiu4+ZAiAE2kMgNNHlMmJ6vQ7aYhMDIgqE6qNszyR2GbWc4odCNMyJLQi02fdIaMhMvC23xp5DBH9IgOH5dIAFUim7sGHUhRnt98pfUxFHsyV3A1daKqyjx1iBgDMXksxhcVqeXLnaPPJ08tQnzx6cv4f/uL64y9OHp9UY5OffnZ99sZO3jv95nc29cH6V1+sL8vJ6bFfJOi2w5iPHz7YVs3lxSaRH/J42fXVamEeAZDRpZQJp8KGihQEDIgKTIZFNYneaC8nXercDNgkXwKcVsFMlZ3ZrA6f4RubK6SJzrwJCzcwvcG0gGwKFFMqdlMo3KEl+5A6oWezSwPtQz7uf9i2f2ft5ldgl8ZxkofSHLHmTZf78Hxj/gC3WI3pdRPpND15P+tM8zf+Foa173tsLo1nOmOGt3bngtv08yytn4uNKQ/ekBoTNjTZXxEYE0IpoFoT3jlanTYtmH3+8rXqOBK4ln/9xdOzy21bhzLS6fHhMnqC3AV3enB0WFc//9mvpIwfPnr/5PBQc3n+7LmUfHRymEuJwSNSGrOIAIBjni5gT+GaKdxc2pz+/8dplHBn4Qc2UQjzDc1D2VOKIFNFojF1LlY+RCDZXl2OmwvH6NqDXEbXLkq/UZFY+ZwMzJwjMWBHZlq1wYUq5SG2TckSVwclF1cvXbPEamEcMERzNbA3g2JFicPiEONSLRgiEPBUoJAisqkye+eDiQBiLtkHb0hg6jgAohQxpEnJ6dgBQhGFKXdMqY6I4P9P1p91S5IcaYKYLKpmvt099jUjE4nEVlujl5niNGfq8IGH5/CB5A/gb+QbH8hzOGzOdLGr2FUACoVErojM2OPeuKtvZqoiwgdRNfNAR6GAiHvdzd3UVEU++eQTEUTClEVVkYmYzdzPO02NXvfDkYnYEJBIxFREPe1ixjHGGL2NEpimLIDYUmuq3puVQyDirMoUkFmYpKfF8e3wFF9/98eje3f59pFINgSIFKHFCDjZY0Ektt4gCVz3oadIwcgmB3u4WUU46IKtz25AbXl9RWrS9yDAAbfrTTNvTYVYAjeT2ObUtRQ6CmQuU5cADGoE2IaoWVUV1diryhElZdmmGIKkRIySsqmBCQfS3E2aWQxoKtJtWybsJTI38xm0UbslIIHjMWZVAhEzcDmhGalkQvJ0OhgSkZk45+g9oNXFc1TEE+gVWJ6JdWBKfo65piF5CPs9CKAyvNDg6cnYAAEAAElEQVSTfGgln0BqgNyAKwJEMbRM0bBBDtTORFRFpVAW5K12oPD8oDXPZwNu9W2IZK4ADcBAmsXJdjSDgPv7i83RyTK+efXlb25iePzFT6/++NWUWdY3V+fvV1mbB93e4ye3P/9ZjO3lP395dfpy1nAzneWm+fDqVXjyyDB2IoqsBMvNhvqI5IIe7FNiZSAAhJxT0e2TLwRykXWj6phMGcy4G3vaHWbr1r/wxGBchnB5ryiriz/ARhxwtNtFskIBWVECFUNem0hUWFkSEBWODi4CoVQBO5lbLPjQSqK0/7SSPMJiZKG+YEDiA3UFgOQDGYCqHR85mmoMC/1vNe7xLzL6rtJ9Gqw2hi69fnYZ/2JWrcScOx4IIBCZKYhYToF5MZkezmfTaTw7P+9Svrm54RCg4btP77588cEibfuO0dp28j/823+f8/af/vm/qqTf/+brr775/i/+4vPAdHlxlVNWgpNbJwd7s+m0AbPU582ma2KgwC7xZA4iQoyqRUJbCihqTYOVUMk9pydYClVURFTDsCNfWAoAqlkokLcG59iIqhpMZlNtmtyE7XXXTCm0k+3lB9XMTWhj3K42FCG2EzGdHLTNZAIU2HhysOjXgiEyMLRznMywXWRBns+AGoyx67YZYpjNeXagEHM2YAocDEBzQmQPVbiJqqYGKtpMpkgoIhwbVBMRQMc7BsAiqqXRPBNhDFGyh+3eCRU4BKDCCqaUY4yEJDkDIZXcLyBS6rNpiW8NLDYNhwAGkrNvKA4cKRJjSlmycgyMYKrIvN12FBmCNNNJ2+7vHx88/3/8/fmffjhB0DYm7YwbNEVBYNQYsEt2udXTVX7xoWmpN9X1Np1fRjTZbNPNylKXt5vt5UVYHATmCNb1W4CsiTT3YRbARNcrRIzTxvqtdGvhwIb9dtO2MzTKeevxUzud9NstMzGH1G8DU8qpKp8wRETVSEG7LoAGZO36pnUJVFbCDAa5M1EBQaLAjaFAbUoswgboHH1BIYaiVgSa6J64Yvey7zyZW44veXtwQjACZECvEDYkLsQ8kgt9Ch4rtKeV5mVAYshmOasBt/ODlCxM5tRMsxpQJASE3pQQUIZcKQICKhnUEbiOiAu7Qa4BNTVDUQ6EirnvQjs1w67fTufT47t3rvb2rt9/WF1dQJKnjx41E9a2ef/t7+WmW/eGm3Tys1+f3LsLn51dP3+/Wfbr7TIs7m23trxcyp1byCy55xBNVcAY0KQ4pyTO9qMZqXrPDAIEJApY2GdiNk9eFBNVlKxEVdVaoe+QMSxshjMrAEDkqUEt9lGtMkA6anwsDISOVcNSSPsds1ucROHsq50FNC2JOmKsVD+CF4APF6u6/2rlq1THBuHSuIEK5T9EC6P1r5TN4IwGfzBoN2tQUhID1azXft++gDXlUL1rcXNQiCYHEv7tmTkwzqbz2bQxsRfvP4ho6rZmkQmfffY0xsnbi4vF3oH0ttV02a/+8OKrz+7e++t/85er5fXR8fz/8n/6hWn2hMSG+88fP+7XN6vV6uhoX8xuLq8phKalnHLbtuCjPpl895egDtBMfXjZ6KsG+199WU2616IBZwfKfkPmQISggswA1E5aTtvcd9ubS8i9ATaL2Xq91JTb+dwAZdvHSSAGZYpg7d4iJ+EQ2sUehAmGbEg4mWIzx2ZPwxQD02RPFAAUMDR7c2rnRhMoDDKKGiE4SEciUkLArD0ihhgBwFTbySTnLGJEXII4IBOXSamjeqaopkAgPkOPCQDEj5NCFgmBkDCljpiZyReRkFLKoAqAITAAILIBqKiKuUyIAzMHBdOsZhbaEEMkZlUw09hEI86iBsBtjGF267NffPWP//mX0u8/fExH82wImXDdQ5+b2wd6sc1vr/L37/Xs2qYNqK5+fG2bPqutb5aR7eD4dr/tL87Oc9qY98UBkJSzpRhit9kaTIxMtpt2Mo3MadslXTXtzIhAVUGbJq43W1RzhVcMbc4pSyIK0/l8fXNtqk0bwSxnQYTIpCpZZEK0Xa6ni1Y0N4uGJ9xZBkY0BFVjRQUH+QgoksEACZlZRKwkn6hE7uUAeuG2Qe1ZXGEqaWEiqxVxkO4InIOvfj2FDh/JzLDo3ADcFBKbaup6FWvne8aKsQFXBABiYHYo62zPcJ1iPWqJz2CCnBZHMCTArGqWNDQx971JDg2lTKY63V+cPH109frN/GBOZx/evn91ZzE//jf/u5uz19fv/miXa+olNYvESVcZp0iLthG+SBuYoei2aZgFkhgiQiRQr6QrZnUAtUSAxkqmLt4v1IgT9v4loVo7KGyvPwKFYjyx5DyLtaysj2l9UGOhAAzmokikDBCxVAIXo0kwZkV9xUpQNvzB8jTdWddQCwHLE6uFS2NiF5CHHLLVcKPa6LotYOh659+vfGUb7rPa851EB1ZNar053Ek1e1OcsVO2Uck9VY4NwQxtpM4RkHGgqSwwTprphBEYbzab7Xrd9RKaZjqd2cSOjuaffnrvd//yozHPj6acppuAGOFm3d1067t3765Wm7fn57Ft1qsuMKV+O13MptNAKk8/eTyfNqurzWa7jW2bLlJswnQ2q16wtKQuLt6sjK4cbrU8dhxXw+qEBnQnTCXbQ1VUTwimqe9VZTKdmWYTkX4bCJvZvNuucpc0pdnhrFtvAgcEbCfNerWeLhZqxk2r0Mf51DimPtN0rkZhdgA8wXZh2AhG4FYtazKBGKd7QFP1LuccSrVNaWetiGyQxbzjfZQsZhqbVkBUDAOLqqkws+accwLnCDgAmKq6Mos4EFKxIB4WqUZmJMpZOHCIwQBMwL0FIRqTGy0CImIxFVVvv+onU8BMgQgUEREyGOWsBuhtMAmbySQ2jYJuJd36+TNL62///h8+/xuYP30EFMBU1x0uV5A13Czh9Gr7wzvr883LJQltP3xAxZw3y4tLnwG17XNWs5ygmaQu+dkgYskdNbGXRASITX9zrV2OM9aUEvVNbEgROUq3RQQkIwbKGELISSwJzyY59xyabruJoRHJykSEk9BGQkrahtAwo8pkGmf7c0HLpgAYEJOqSI4hkEaBDSApmogwMRCiIRJQKSPFgkYIEdgPGxETYiWl2d2Cp3MJyMj9AwNZlWYTIXr7TyIyA5Xs3J5XuRB4i2+vYUIBBmooGseJgkN+Y0IA9poPc42954DKzJLCDRR74UQVkJgwk7ef6rdbimEyn65vbpA4Nq3mdHl1PTs5vv/F5+nD2QTh7McX6d37NPv/7d1/tnr3evt+2Z2+f7P6T0jctK3mjmeLe88+I5N8fGDTqSDOp21GXEsyRCVDRHEk7IIU/ajoiJw9J4OSoC6mAGuBVgHUHo15JxiXD5U4CQDLczFnSusHDdlDIqqFTwCAoOBUSxhcw2AIfUK6W8IacdAQZ0BNJltB/0CIqurVXIUCGswxDmEDFNRQDHhR+hef6NGAGSDSkBowHQxdMfk17bsTnoz/U92U1ZAJBnte73pwD1DXZvAdgACqhozEhGZNiIi2yblfbcVMBYgjBMqiKunw4O4337z+/scf9vf3Jm3cZsF2cr5avnv94vTs/PjofQNwcufk+esP52/P9k9mj27fCoSzJt4+vt00vLxen344n7QNIV3dXN+5dwvMgMOuEzXAKtca/VvxoBV6FQ85+MCCCQaHiQhFPwaIIbBqjDFul1vptiBCiF2f5vfv95dnFMgghiySEsdAsV2cTDLgZDbrttu4d0ihtdhqWvN8j4TC4lh5AjzJQtS0ECcqGyMI8xnwDKgVSaFpUt/HEFUVkFLKHOOwNzhGFd+ZIGYp5yY2qkYEQNBvtp6mI2KO0VRMUTVTAY+aRU3VxIjJzEIIHIKqcQBkNjCvtUT15noKRE6hIXuBBMTgIQIBgE8IMQNAdn1KzgkUkXg2mSIHYuLpBJENRSHZ/ODRr//d6n169/rV4vpmPj+eHsxg2U2aaf76zertWX5/tX19ni5v1mcfUi8cyCis+ov1aoWqLLjpswURg6S9oIplI+pTjg2LZOf82yl3Cmnd85GJauq3DNjEySZ1IQTq+xAbIARVFUUARkRGMyO0ELnvNmTGgG3bQGFVLWWZBEbRdj5tjxfYsFlyHMYEIjmDNk2A1PSpQ0AKqCqEjIxIBGXUKxX9jKEZKAADFhGI2yEKfgoREZS0kjwF3aEHgs7xlHQjQBGjeD2ZhyEAJgomBsQY2SiYqhEjMaoikSv6CBkCFvLbKWQ3TkXAXkpN3QB4QOlnR80oNquby8lsr53NVjdXIPnw6LC7WX148/r2k2fd4cmr3/1TOlysN6uz7/50dPDJ3Wd//f35P+I2rc9OLW0XhwdtaDY3y7x/d//+wzTdy01zvunVuIlBAHrNVgpRi4pGTL3CfdeIoQEjKgzVn+WHQxq7tnWonIfn2Wufnx0W2x8CmgGSoaENmW+shqXAcUOA4LDRO8xU2F1gc8kvII3tFQDM+4NgAV406D3xo5xq/chiznSEaoi7Rq14OSwVEIWvqzDXhqWoSkmrkN53jY3mEGoFlF+zvqT+sLbVM6uqqKJiqumEEraSKSBRyjmpqYlbAY7ooyvEbNrEFuzd9fr45CS0vLxebYWyQBOa/VsnOaU//PGbvcl8tndwfHSElh/evw95GwNPmxgQl5fLi/PL1XJ99PQwpYxMMUbiUv5nOtI4QxhbHox6rgULOrAxnV1ptgJ1YNf9EYaAINilFJqYs+Uss0nbaRY14Bbjfp9PMbbIRjkbQjObm6IzJjCdEXKYHQCRGMW9NswPRKNODxRaAaYm+u4FbhQpThYAMYsCsmRhZgVUBSQkDgjohf3MBMAiyUBDiCl1jAGJte84kOTsndGJKMRYlgKUONTQByUnQHCtRGwa9PYpTGrqJUuKdQOoUWUkOAYCElMzM2+mrVq4QSJUMDWVDDH4uziwgUnSNjZghIRd7q3bxukk8+wn/8f/cfnD6/N//NeLr3939+GjJqE2LfT96vnr/PpDurhavTtdX152vcC05dlibVdd6iKQ9bJNPVHbp5zFxMTITxFnb+9lMuPperOZLI40p4CETJYyY5rNb+XU56yEhCLMjTKhqhqoqXSJAzdh0m22FON2u523baRIKmgIgtxQCIw5dV0vDVkwLfNIEcpymEihayTnGIOiFlkcQOV/AA3RNdKWoXDvaOZYlhDJzzCgF7gTIo/iZEQD78WGCF4Mz6q5MCEqpgCGHIJoNsAkGtuWAhqwEYlq4AgAtRLVm+kaM4qVgmKfrOKMk1fMWrF77qW0GgUIgQK3y4sPe4d3Fvv7F+9PL8QOb91+//2Ld8+/ffTs2d1nz97fXGwuztfb1ZsffzedzY9vP+mvPvSX201PN1fL/cVct93l829YYbK312KgbN16Y7M2RgKNOUkGFTM0NZe0qALtMCuF+6g5VlfljMp1gIHNMnCKrAIpgJrphWLT0AjV1LmwkjJ0NsyLIQfxqAEBBJe+DOa2gur6Iqynb3h0VHke84GC4JrbygtVlF0JuCH82n1B5XNGtsf7H2GJPgrjQ7UvXPGHUOuMYZDJj0rQGjABjj0rij0s1FAJKj4KUMoiYSmkUjMz0OzjoEEViLgGPsCqxHrv3i3wvjFtu9nkLNClnHoJGDZqy+3y+M79zx48+Pwnn4Fl7bqLs9OTo71HD25P2+a7758zkJjeuX97MZ2frs4BOQRGDjklL9xF9Cy8ueoRxmSwJ7MBvarDoT+NYL+cQwO3b56p4xDAVE2BmUKQ7RYBNMt2tTZuQnPUZROKzXQOgppFMYfFXu4ViXgyUSReTJUaRTIM1C60nYsGbPazkgFhbCyrGGCchGYqXufCRKBuQ9WMmB2Aq9+XGRF7gOfaf0AITdNttk0gkZRzQqQY2S2JeFcUIiT2cbVu64mIKJZRU0YAmkQBfBQCEaGqWVZmBkA1pMiAlFW9UaWaK6382t5+QE0NiDQLMGFgRUyqbTtBQs3ZFIkDTOLq+no2w7RoDn/+ZO/BrdP/7x/Of/e1vb08ni0o5dUPP178/svU99vlVU8oiImalC94FpV1nURAKMImLTMYUDYMRAxkzMECgWXCzMjabWGqs2ljIoFi7jqMmDQzB5U0bdrNagXagUg7n4loUkIA7TXBVnPOYHvTuYkEBcgGObWhQdHk/aVjmN4+UpRs2dMQZpkIQVWyeC2tQzciVqkKimKFCptRWB1A1/wUADjmB8CDemcYSvTuB76QFAOsBZf2+lA+L+b1ShxDoNAgRSA2IuJiRYmcRK/hPKA61zeqOqywF4OF8M8xc3JYxcX/FJv56eu3/erlnSePm6a9ubgAsbufPPzTf/ovp0k/+dlPZoF+//LVKl+/fflyHtqTvZO9e89umHL/ett1q01iov76XF99v4nQHTfTR09nFFcihLFhJAMUB/Ou7VQ/F4hYKmRhbHtQyY6C6oY+dloFQgMdPhhbV/sUbtxAseLy0d0WD0GeakA0MgRCk1DbSrvZLY6xNMzGcdlw4B48yhukh1bTAjgIcB2DV11KDXAAoDQ0hhGNF5KuOoTBoSFVLqmkvz9KQ1BNjQx2D6qRt9ovyZxkqtQJOFnFpaR2yKQb1Dz7EEeAkkuREAmByMUDQobZ5NbBfH86/dOPb86vlhAdkrAZNMgC2ovszw8+e/L4Fz95cPtw8cevfvjdb/84n0zv3D3Z9ll6NcA4aTT3GOKLl2/fn5/+7Oefx6ZRyVB6VSqzt1Iyq4XKZoO62VcLAUqGZ1jlQSLk9+oVbUjIhKrUp23TNoTU5TybzTYX10kgxIUxim6VJkYmfc/zQ94LEKJo1y72aDpN294oGLBi4Mnc2rnCBDhmmigCcDQ1Y5JsGCIBEYesQhhUlYhdwcMhAgCYiiqANm2LAAqiYERskkPTbjcbIsiSU0oq0jQtEIBaVi1kI5HkXDWF1IRoCuJtXghNLWdBQg6ltEVUVYGYTcHMKAYiKooxBPWCaqwnSlTMPPxyxSo722DIgYxKm6as2SwTRQxsoKL9Fq05nNz63/xqfefuxT/+/uabb6//6btws+7Or67lcnZwcC19B9CHTqNGorVskIgigWmXExoFygFZ0BiROW76TMFajkhxHtRWfTRkoqP5ohNrjG21ig0aoAK0McQQ0nIZERMooOXcT9pZv14HhIAYmU0hbTYRMCJPmqn2WxJgpnCyzwezpFlBEJjAu6IJeP04KAIRq4dQAzB0SGUFfXvSqQpzkQDIC9qpZgeLBLx6hWqt/FxSbQFawnwVrRQtARACebNojGBIZshIgKQi3tXDj2vNPiMAghoGAvGDsaMrqSOf3KwSIhKgoqkiYKB46/jeN7//p2k7mxwsVlcr7fvZbHb3k0dXP7y5eXsaFe9/+qvN2bv044+6WS+zhkB7Rw8sG10tU9r2226+P+m36/79B7m+CaoBLSCKGSgGZCIDy9mKShbAAqKOHQ9qS/sdOsex0rA+XO34APWGf5c10+Fud8klh4QlAnAIXJMKpuoqoOIgnfKv7qIC58o2FdO6I8Hc8UKFErLh0wsDM8SBVO4JSz7Db7pEIlC5ICi7aYwSdlj+6gNsN9Coy1HJorpNoYqCnMly4Fz8ku9bz7MrYKUcy3dlZKhBjP+IEIiAANBo1kzfvr9crtYUGZFT1l4zcsiSIttn948fHu/fmc1I+n/+lz9889WLg6O9v/33f32w3y5ms9T3knPq6datk/Ozi2+/f/748d3ZbGYGLmY3gcDBTEW01AF4SOTG3uuEd2qsy2MYsgO1HAyJagqHzECyNCGGGNK2JyZTXS+XQA22jVmWPrWLY8w3JtJMJwoh9x3Np9DOLLSZgNppzgjNXCcLizOzAKHx3nVApT4FAyBHwJBFibnsGRpLUUyL4SjVQGqiikQAGEJIKXNkU8mSVQWROURVMVA1Q0KioKIASEyqQhzMSSJEIkYEsRxi8AsCgGYpyyCqhswcKIqrawFtUAcWAtVUlQwQiQITIWJgIkBUIDOTLnXJ1IyYYjslg9i0cRoRJQsAAu83k794cO/x8fp39y8TXf/2Wzo+yqfXr68u1ouJTNtr7AGMZAOQI0UEgCyKXq9GHCxJhzTJuW9D2HbbFqaSNkeTeYd8lWWS4Ze/+On6/c37F2+WN1ucRRHFAKnriQzZ+n4LRSqF3XZjlmMI0ifh1FLDkPNmvTc77tarJhCA5l727xyGw3lHG1UlItHsY9OAwItSkAi8hNyJaCsppyGd6C1Caz84RiQocQCNuN4PKDEg1oQgVO0oVabSTFVd/ODqXyQrlKdhaMDAm1MYEgByDFVCigBDn4EC5kgHCUqxG4WvAqtzNVBBQf2HpFkxyGx/f7F39K//+A+//tv/ePv2w+urs67LYT5b3Du++PCOzq5bCLd//uv3ijfffK3rVXtxGibHodnbO5l3m/P1h4x9QhFOW1hv1jc37cm9Hm2rPWMIECKykSJa1ipSH+3uQDFgqeSpTEAJ/Qs2BRqYgGJ6K0FsVhE5AFglTmAwjWUJtFDxlVAxIgyO3bXyJ4NZ/ZiJtmrTd15QX1x3Qw1lSs5l59kMzhdHCGHgAoFSwjB4BfxvTf8Q8Ox4wPrN/GUG+PHSQGnsY+X7afVpWEaMWRFDeYMQLzSjEgc4kTJOXkZADgFEAxMxb7qczfsaQtMoQg4Ei8ne/cPDf/P5Pew2z5+/fPlO+77/9NN7d28dr25utqtVt78+PJg2k0nbNOcXl2/fn+7t7R0f30nbFDGEiEXt7khUtbJYTgIpcdVuqQINpGtdC6hFvwBEpTzft335CbP2IimFELfXF5qzhkDTNq1uJtP9tF2rBpgcWDsFDFmup/uHORkgQkBoFxDJmpnEOfJEjYzYvGcLOGwGz+jWHmFVrWoA5jpANDAxJUYkMjURQURmBiDJyb24iooohxhCzDlVBhOYox9wIlKTECISiSghslcYiCAxMSqgShHFqWYr7ctAzXItWnXoqkVxANWsN+gF+uD4ENQUDEMbiSnn3FtCoNliRkTeYMOn90hOxGgKQhaOef+/++Lp7ZOrv/9j+u0f4TcTef3jRlJab5p5m8gUzMSY1RVvihAZATCjYWSBzBj6ft0ST6Hd5C6sMy1m88wzg6ePHjT3Z895/vrtu6Vuue96k8ZwyiFDyNuOuAmAbdumba8KAdEQJoSEAjlPQzQVTVkFgQHQDp7dD4sgq0zMhGSmCM6ImaoCEHM95NW2YKGFnESvRAWSVQWG5/OcGa4pLD/uSPU8lmtYUZgUKGNlvIUXL+NAfqMr2lld+Yh1YrYZcuV0YLB9WJy6f5NqEytNUX5npsSoLraJhAj9Zhva6d0HT998/fV3v/nnX/3t3x0e3jl997pbbgLY3tH+m2+fz0R1Cnee/Ay3283L532/OV+/MKV2dnJweGfWtOvlRUTK3VaubkLuY8AYQtNbNlVPaCEFACD1Xl4lfwG2M6Vx1Ma7mRro+rq2A71VD1lZvuHucKBqbECxTgIhog7gsVgPNAsVRA6G3UU+OL4ZSvw2uOvhqpWMKSh7uI1qqSurUq1UfRkMlNcO7VPDl3o3xZB7nmDkxYocqfy8AkwY8qF1saAkhc0jvppzL/dmOx/hG6mi5koFFWlWYQicZZw1k71mfoE3vi8BMfV9w7Dfxi8e33l4dNRv9bvnr1+/f/f4zu1ffPGTluKH06uv//XL+Wz6H/76F3wwm7RTVD042Ds+Onr1w9vtZtXcPuhT3/W5iTGGYOB9npWQLHvKosQxoPUmcbxX9PnAldDyn3vTfCqdT2ofWMTcdxTYpyHSZEahpZCyroUiTfcQlZrJtutpccfCRHSbUkfNwuIeWFRuuV1INiMyRCRWdPNJAOABQeHUy/HjKsFAHXYGoKh6fOZt7yQnYgAByd6YgUITTXwvE2Kh+kUEidSUOCKiihEyQsnxIgISqpY+XwCgKmNhEgcg0pIj8oSKIYALyEENvZMZGBJxCIDgxj3ExkX3agIAoWmBKIswU9r0YBhj07QNESIzAZloau3k10/37x/cPDsOs5j+F51cX52trmwDa6I+mgIjEqhQmZiFEFgBxBRUUfKEsOVGtWcV7Vfczpplfzxb3Hl00qQJXD2at+2L129O+5Qle+edJkTLRl4EKKiq3v0tUNCcJ80EkDWnLq1j4EnkzfJqdu9+e3LYp14gQykz0UrYgLmrctoZDAol62gCsEp9AJA8q+i0GjISlzi6ZmcrF4A7XQkQK/VvNdYvMNMhDij6YD4vLjAzKlMcARCH8GKEvDiQAgM3UqnpwqJUEriq1N36uVySkGIwlXZvfnT37vuvv3p76/7Dv/jVYnF4vv6Bzs9PHj1+9OnnZ7/7h6s/vJkePrn/0794bZubN29C19u2t6008Ghv9iBM2gSa1/3qzYf0+EaPtrSYxhA0ZzVjAKh9nUs2vVjSwSDiaDKxMDbVEo5SeCvyVtslAQo5g1i1jQMDUj6wxACgMJLzbsY1lPRXZdaLvXQbWmz6YKyrYbRqGXeI/l3FDRCMmZeK9+sDHpiLAVeU54H1gkPKYgDwgw0cKP5Sbzjok8by6DEuKDijOtu6kKXfUU0a7PSqK7I2q5csj6nOmbSWMaVuu+29z+Bm20+aeGtv8vTO7dvH+2cXl+/OPrx+986ynRydzJq2X25evHn13cuXD27ffn9+fn5+try6/uyTJ3fb26/fv33/4cOth5+HZtJt0unbs8dPHwCB9AIAIXLaZgBDcmEqFYLP3FVR2dBmagpaW6xXNRbWE+q7nJg062a9jjFK2vb9drvN09lUBBRj7i3ODmLUtO02KdPiRHrpLGcjwQlPD4QbpQkQqzEwFmWFdyuhqjJD9G4TpgKAUspyyA8zgPl8KA9NNGdmdoVVfdDqFScxRjBQUEMMHFQNipwExYQ4uBkoAuha2osAogqGXhsmKqamokgcQwOE2TUshXc0FWMmRE8hAIcgWVQthuDduQEw1D4QmrahaUJsjGC93obYTmYhOGFlAoLEHAjRFCL22TpM8f7eXvz5dDKHLWx+9+WhvqUgPeRL7deiGSGLqSKQAaMpZI+eUgrGwSxCZiOmGJrJdr3sEzy592jy4KR7t53dObxvQp2mzXqz2VqWfrsJTRMw5C61yKAaDAI3TYgRccJNt9lMmJUZ+xwgwjY1xPtP7rXH+9dpk7QHs1hCYEQwNBQvToNqValG7uanhAAhl4ZtAKiIZd5Cbefok2OGVDDWKAKhoLG6O8HZeVfqlANe6QsUU3TnDFgPqsNjzwND/WPjwR8t3w4p4QG/f3cr2w0IAcErS5BJU5ZMd55+9v7bf337r/+8f3RrcnRwfPvkw6v3l9+/f/jZp6uDr979+HL54VobPXj0eVbdvvpxpsikq6v3y2ubHE55Njk5Ploq2mWn62STCQSkQJ5Y8RDeFWpWgyMA0MFGV1RXCRcHUnUFsToBN4kjkt0p0wZPXIKUX5qaQWmgAoCF9hAVA/Qp4sEvVK8wEi87PnW0/zvymsrpDRKUGpBgvcxoZQdYX++PqtcYAEH5DdZhVrXFAQ7OBmqlw5B/qg+7rs6Y/HDjrbV/cr2Fmt0Y3zECi/LGIQMBQzk0qikANIGB8GK1mTQRlZfLVcP44NbR/ZODRYwv3nx4/sNrDnBycOvBnZOffvpkc73656+++/bHHzXGZj67XG3zZvX43p111/348tX7D6effvJsPpv/6Zvv3727/MVffIpU2hMyUe7FkXIgQiIrmXpDAGI00UJaOTrzJa4VOF566d7WRA3AVMGUGBgpb3W77qYHB0TYLW9CmAC3FiyBZm6MhZupaZfTWqnFZqLNXrYAFIyIKKhKIQypTpZAKD2DndglNu9WzeTJQ4PSzK88O1UoAQ26hD+nHsGYAzGqCAAaYAjBW8s4YlHNHGLZV6WmHYZzXlpUEhloVpOcTQyJmBkZVYEClW9hhogUQoEBReJiRERMPqeJODAHQBRVVcFQv4F62YqI5pwIiEm64NycJe+7A5GzmgCEW9P460/vJVseTtb//K/49g3kbr9Pa8Gbbc4GvQpFskBClFUQkTJNgVhhgoFEt4jcG0H3k4O7n/z8c4tBp9weTWZ2EJJsby6Wy6sMgByQKPcS0FKfiAIbTOKEAVumbrWeNA0BEEBsW85KYBzbW7/4XKfUaxeIQNVEiwTRSTFTtBrfI9VDjuiUTJGmo8doBgBeWkEEtuPyS9CPu1ah/AUqYB3UH0hIoDoYQRxw4cA3+NvrCa42ZpdqcLBci4qKVhorQV1tAxoAlYGQRB7zYJzMX3/7am9x+OiLX/7wv/ynppl9+h//hzsP7mxevu1+fLe5ugn3HuZvvu3PL+0P/3x8/4u7D3961q/lh1eGsli0b9+8vbw8nR0c7X+2d3D/3pXlnHu3R4ykqLXDGQUyVJT6dQ131VUw0CCD1bWB5xgsb939tXfBf8udIBfCv+Q7zUlHdOtf1LEAGGr5xGihkUo/+p1e0+DIvKBMACyt5gD9GqVtR5XtVwuK1fcWDqrYpT97qKPLG9yDO3zCyu2UCxh4tzv3fjA6JxvQAI6boXgGqybeBgfjaM1Ay2as0KYq1syBAhKU4cCEDGCMvNz2mHUyid1y2TI/vnvy6f07y77/+vmL86vlzbJ7+vD+k/sPHp7sX1/ffPX9n377/Ju+tycP7vNkks2efP5pyAqoy+XyYO+wnTZvX73JQid3D2fzGRNt1huv2Ul96rNMpy0RU+nzXPh1KzxQYT9KUDUcOixxOlgZARcCmWrf9QQs0qe+b2ZTiqw5Y5gYJIizbJkDAQqAAUWxLDjBaVCaYJyZogF4VxPXIBfJrBZdAUCR8Q6xZcEmCGA4rm1da6d0iBkQTAQB0ccQmgF44S4heTNncj4C2bWG6H3c/EJa9c/IPkLEEFBVyCcaIhGjP0Hw3mJl7agSqMockMnDPlUBMwrsnXD9ZigQh5DVNG2btuWmYSLI0uUtxTifLYg4ZyU1MUNmH2/Vb3tliPuh/dvP53ePLu/eWf/nf9Dnrw4AD5LdCQZivakCKiAoZ1REI5KGQoNo2DQUaXG4TdKHycG//cXJF79INwYfOnu3se1mYngymR5S6A0zhpwlAmw260ABxBhoRm3uOkz9jAIlCxFVNAJFJhU5eHjv+C+endpl168jEwCCIhAYSNXu7FjgEq4X2EVIPk3XV7KcH0RirmwuEpJCLS0o8g/vdYNotatwUQI6KCitb5HYKuNRQU21jVpsXD3jRSaIO3m7gXt2lG/1HYAfWQAoTIfHnU5cSEN2cOvgw8vX9x588u7wD29+/+XJgyd8sECi7Xr54+9/e/vZ5w9+/jev/vP/R98vN90PaHgwfyC3Ja9WGja3H528/uH18vomn32Y/PzRyaf3+mkjpGalH4aBmIE5ewqUTdyoYkG5I1r2tYHhENV7syqK9Ri8LmCVYREWoa7vbAc6gOV9g9qqkkXgEx9UAw2tpusq+fG2Ifbyr1JkqIOrrU7JbBDnD9+4Oo0BdFt5bMPELSgOqViM6m0KgTeYdxr0plBzBIPzcK/nZ7oyQTbs2Bq1DJvGSozlKMK3oksWtHT/HMLNolRyKT0iUMDSt8zMsuY+E+idW4cP7pxcLjc/vj87v1jGEO7fPbp1+6i3/vd//EpyRw1++uhZy/GTZ0+hT/1qeXq1CiqSuncvX/7k2aO+y9Pp3nK9me8tAnG36YjRwPqu71PmEAIzMZXikEqBYX06pgq1FZOXUFJlgUxUzTwbDAgqqubIHbJImEw5hK67DE1jvQlPOaiamGYj3qStICsHbqdqTNyCD9Iqn+tKE1LQStmVZPlAOsLAuBafbDh06gMFb8VF5S7ABYRMLuc3ICRFCv5QbYA7RODW32eLAIg3+kNEJvSxf2ZOPRGxKhhAVnMGTx2DeXs4A1UjA+JIhKqaRcyMI4cYXUMNAKCKgGCYc5askRk9oWLW9z0yTRZTipxEtM8ljWwGamLKgZFI0PAwyK/u7d/5u+bTu8v/1z+kP36Lp2czQ05gCkSYFFQMA6nl4K2NelELgRc8OZ4/OmqePI5/+1NpML84jT+cw7srS12/3uKHiyOgnvl8s+WGur5vYiAgMEAj7fopMYpGwEU7uVkuiZQsNe0igd3+yy861kSZEBQ0YAAANQErmn4/JrZj/r3mtqgVPXT22Vtuxc1DA/K3Gfl06mIpxhPvsh+oJPyOqTYEc4annnMAH3Mx8BzFZPieKBeGHfl8lUTsiENqkdF4+gsmKfEHk4n7AlxvNge3DpdnF1cfzu89fPLqx7c3P35/55e/CkFXm4vt29OZ9vd/8dfd5fnpb7+0q6W8/OHO3ccbncSAaXs9Xywef3b7zbv3efvh5tULfvxob2++NRXJmThwJCjVOYqFPK3wpUQ1xTxDXeCySGXlKn4GLfU9A3FTz1hVve++BwCAANSH4Q2FBxV9q4JBcMG/VQTvSO5jAF9Xu/ZbdluD1R3V7zCyQVBR/46p9n1UGZt60aGX4I4Br+S8VRapEjzFYRAPPKENN1/+F3YCmoJGnWeooQYglnIqgDKwoCQsrLSvrS4LEAEUsWQ6fXSs9NJ3qseHx0/unlyuuxcvXy+3W2O6dXx0OJ2dnZ6vbi7SZvPpvUdfPHs6Jfz6+esPp+cnJ0c4nX/34lUE65dX/fL6wf2763X31dfficr/9L/9766vVrnb3r5/Ikn6PmVJ0/kkUCguEU3B2BU1iFDgrAvxcFhkMwPQXGAuGYKZWdKUkpoxYsqZYuTAkiS0U1JJJBYUI2nOSh2AiZBSa8TAUwIyIKNavkFozh4C1FERIx3nkx1diudOSU2R0ESLgFWrY6+1FWWjBDYv5gIFRKbg6lVgJAAR7/1sTvsSohtZA+/gS4A09I4kZIpoUikjJPMmEkhO8mDR/5ghmloScXMQmsAhiIiBz0VxTRYgIjNzDO7rctdn65umbScTCtFUk6rkFNuWlIPn8EWcepKkBqlnoLtt+3d/zY8fbv/L75f/5XebF8/DZYerbYMYiNypcduIqiFJaHBvxrfutA8e4NP79PAk6xK+e0PnF3i+pq7XLuPVepp0kWydIRF22QwYKPjURRJrEBqzGbfrm2W37o/aFqetbJd5u473bh3+8snGVtvtjUfHhZA2r5WmQsqAkrfMM7fuSCX5in76yatUfPwiuWYfkUhVwMhMhixC1XtjPZ2Fn4Qx8MZqB4tTQI8XasFOsSoDvBys3vCXevphQLLF6BsMbx9LARDAmWTjwJLVJwSff/iwd3TSfbiCOOGA7/7l9/PZvenR0cWMcupWf/p+c3j/3id/ublYbl48N9Xl1VWw5ubmIkKi7ubowZ2Hn0xPb1ar65W++xBuP54s2o1Zr6qgiEBsJAXFl4DXRRouOEPPUHm/otFe+R36rAYDz/YBQm124+tVU8jFYpeBC+iJFfW5YyU9A16ODNVeB2/TrUNEYQilEBWqnLMkIlwXUxXe9ZHW34/wr/6/uwisx7ya5fqAKmk8PMkxwTs+Jr/D0dcgFA5oMP8GVRXpVOQAIooL9MR3wdBQ2z8UbzcGWaMVLVwnIIOZJ43EgEKMIStw5Bab2LTn63R6drURa5pmf77Y359dXizPL67VbG+x3+7Nplm//XDz7Zs3baAtgWXIobm6PNsnOrl9u53v3Wy3b99/+OInn519uDzZ359MpyLSJ0kpxRiZY3FvXuZeKhGgFGSMGHsIxSowAqTCzwB4jw0TJJaUACA2EwMzk9i0abs1ZOAWmPokk2a23a7EGBgoTAGIQswps7dRM4GS1MWKRor6CEsP20oZeuA2HjdXi1fcYb4py+Qj8WdRNQOVHCCvWNESO8Kww914OLGjPghToUjI/fJqAsaRmUhVDQwJmAmICy3mPI+hTwxGxBgjEuYkBup1EiIKhsQUY2DiXgQAzJSImxg4UqAgXRaUIho1QwXpxcDipEE1QEFQMQVDI+gOuPmbB3ufHMz++5+vf/OH/M2P9O7Szs4DMSaxELjhZh4m9+7p8R49PMH9PdufWpOvvn5t5x+iQWSi22xpYefr/uxqfXYaAh5No0p/ud2K5pTybLLPIaBpIOacIXcLIhCdgW22HfaSW3367/+yuXe8lncxNgJCiCqlXaAfI5PCtohPARvCefDtpN6pvVgfRyT+T2/jCmR1VKA7UDchhcgdqOIK1ctGMe9wMOxn3AH/fpZ3SqSwsj1uA6vRqFrBigNtVD0CDPKXkiRwhG0GFMJ2u23ipOu2p29fnxzvw+Y6hObmT89f/M//90e//rd3Hv/0x+/frs8uP/z+vz76xa+f/exnX20vN9cb21wd4RwMluu+t22vcuvBT24fHekk3Fxep4sLntyJMTaE2ax8NpdyKhrauDszNuR9i2us+L8GK/71B3qk8OD2URQEYwEBFDUemYmH7vUjbKi+UiRElVARegn26ofhgOcrw4IFwVN1AJXwNR3FOON3HB/VYNDLLRT3RR85dNt5Wf2JjTvP/MHuEjoIpeK5+n0rXgLHaMqvMGiOi++oKfRhSa36iyGicR1WZcSYkKxL/TZJNphEpia+u7y4Wi0Z8e7R0cne4mq7uuiWfbBpnE1nszbwv7w7/e7l6/V2c3Kwt972fa996nDa3Do5eXC89+mzp6ev30EI67ydzqaT6SSnvF33OYuITecNGlShozKR1gnRgCZltqknho14eBglxzE8QUURkSyqKv6c1IyAYpwYZCTmpmEAUYnNVCQZsjFyEwmjqoIYURikXgo+aZ4GKX0l8UbpFNRzXvalYSGLfNsWUR4MFVtQICUZgKkQs3MCREFNHFi4psQhhYrP/8DBqBSsQsRE4DkuA2YqJQje8pN5iGbRRxt6aoEwxsYMJIl/E/UJM8TExMyI1CcBxNhEf6NXGq9WS0JuJlNkRiYEzV0SVmY2VUDSLFmyiXEgYtz264RMC+NPDxZ3/932F4/gzUU+vWSB6XSOTBoB9yLvT+hwAi1qi9fv33bXF9vJTXgUYTrJW1BuOEV5NxUEnEi4vGk22yNgFJwqA7EkgWQYGLediUbGlgAotgaKeiMw/+TJg7/71VW6WPU3GIIbFfP6qmzuPgtsKjklq1Q17UwocgkYgYuVzXz8CyEpAKgRspKXfGJF95UhKJxPhXJFqT1E7FBxWzn05b1OCQz4YefXMBx1qJWe9chDOem2403c1hUULaCkxoGbpgHUxcHszR+XvOmPb929fvhg/eZNf/rh/He/PXr6H+9/9jeXV//r9vTDxb/+bv7g/v3je9dyOWfQm6VA1thu+s324rrr/nh078Hto2fTvaM/rfq8XNl0wbPGlLOkmnsqBpeAvLhhTIkgAFCtjDIYC72GlSk4FatHLMeqYj+rhhIqxvfYqC6HeSlcCRQAwCyAVX10DSeq+8Gaia1gn2jIBPimsPotCg8zJgMGIqvsmLHDM+749nplLdrwMY4YX1hvD3cCjOpW3D/B4ObLvyszNIRSWH9dLlV3x25hXXnr8JkVehc9kqiJpSxt27aTyen59WbbMeODWydPTo5Tlu310kLTUJiEdtI2Zzebd+cX25QnkxaIFUgxE8LBZGGq9+/eajm8fPUKUe+d3Do+Oj4/PV1drx49vZuzNk3rgArLTEcSVSql9paTDPve87Fo9atWYOPdPpBRs2lOpuq9GPu+byYNASTpwcCIPeY0FebY9x1QQITQTNSbI4OV7kNYNGXknQEHJOZlfzVSrw/e/I21T3mZyeC3A2DkQ7iKHJ68Dw2qgc9pAkWmsR8tMhCiuNpHVUsvXCvdhMnQQH2IoPPUAsR1SxoHBqSai65KBDWAMoHH/L6YnL1xexECe2/6lDIzUwhqYDkTs/aaFWKIwND3WyZGZkQgVMiEk0lOYtlvWwOTiYkmNi9MU5qwRArxJB1g88UxpgxNK7lPKkm3fVppd23rftOvARWC6L25xCYZYYYuNJPpxO7E+PnB5Oze8vc/2pfP4Xx1wDonFKOM5MfSw2FJKUY0tU3frzU39+7+7P/8d3ki2/UWCIAkUGtqAOILPUaV1RQg1L6PNdbz3xGieSfOwtki1gqCkhke49LxQINnOz1vsAsUB0vmxn7Aw+Xlu9+qvqEoEG2wNeMvCzNQf1UVjmAjJeHl60RkaiklDiFLDsy379999fuvw4kePHmyOXufvvpu9fxPQZqHv/or+/zZ6Tffwum71G/29w77nppJixNtjdbLS8JIIlfrc3ktUdLk+OD25z/XEJaSIQVgbzXvdITb8iJIq/7JKs4eMO9A7Vpt92kVnIKv7zjjt5bSVjQ7LosLxq0iqMIcmxN/gIChijOw5Dyr2YaKh3ceodWz5zn5AV7X6KHEMCNx4/vIhcNFvzlUbhX7XuOYepECNwt2H/yIDZ9gY7hgAzlkRSVg1f9pdQjw8c4Y76asZv3Y4R6t9gnwSGdwBUA0nQbk2PU9mDDj3mR+Ml+I2unFRZfWqkhAIbAaLDdrMWjbaQi6mM0Xi8X11XXXbaVPs8O9g/ni7es3Zx8uf/Lpo2c/eXp1uTz7cHn/wS010CzUROaIjKqeuQEm9laVnrs3sNJe1sBcpYoI6GI8kpxDCP5EvYU+AjDyNm0oBADsc2YOXbdRACZWMMaYpVNgRaPAhCFbRu+CT+D9UqxMfKUSFdX5ajVO8p0GZopAVCStUDxZiQBcgEZICBm0GCrvXEhqqt6sCrjsDN+BRGDDDKOa3lBjZp9vaqIcopOS3kwJkbyXhjehA5ekGLoWCEqxNHGIOWcEQ8KcSwNMROQYAEBUVSzGwIFTFjWITUACFa0DLjX3PXCYTdsYMKWMGLJmNs7mrUhRXfuRJWsiIGLsNpv1tutXNwaJCEILqV8jQYaUdKsgOWWxjGTIaEzoY8vEIFoT8k2+BsrNjBePm739n+Hh3uo3X2+er7HbtHHG221soohqloCkLRrAslsmwtnj+z/9v/7v+dn+Zb7aaufRJRkpZCYiYtHsZqhIvQ2dovXgDJzmQqcg/amCKY6I0eOF0q3WjzsMRmKA5CCloUwBbh8l3GDAXsXR7FILsGuJHJjBGDjUGNj/PiA/23kbYhkEhkiDpoI5ePfYzfX1dDI/vLV/MY+v//jlkydP7z/7bPnh8vrFm/7DD6t3t25/+qvlxSVcbkBpeXGO2359kwnStD3CxYFum7w2onyzXcLb10evXjQ///ne4f0VkCKZYlKorStggLQwqN4rk1GSGYPHhYpoq7cc09ojdh2DofqHdh0L1PY7/jGDVtM/KAw9IKoiHmpWehAjVYtZnsLoh4ozc3Nvw+rb7veph3m8Fyp23GqpSI0AC7Ss7Hx9goW4GZ/9QFCNOw2rlGp47eA2oBIUaDVA3Nko5SsOrskASrVduZXBp5kZAuUuOUfcEN3bO5g37dn11XK16VI2Iw40aZuIoZnOKDSW0mzWziaxW68uzs9Ju7v372XJf3rx+s27tw8f3P702bP1anP+4eJwfzZfzAMxtdy2TQwMLkx0ys2flQ4P26CmjsyEvAir6O6Vmc3n35hlyd6QOafswFazNk1jWRCImRDMNJcAlJiRiVkMkDhrihTUMiK60tNMvKkVYuVycHwqI4KD+r9Wm0RV+wFmQGxABoI1b1zKj8xxJKtZdS7DjlQg7xKsBkCAzAEAPAYlKnMfvZ8wIhOigQBx+VX9Jp7AJgTvXGZqCIiEKkrs7wIODKaqYKIcAzL3OSNiCIRgpR8RFaUckHFEUFlf99xEQIMEvRkSMnPgoAk0S99tKRByMIB+uzbNvXSKSqCJMGEnOflAAkAQBEM2M0IzYBQAZiATsx4EUTVYSmvF1O9P579+Nnt8f/LHH5Zf/yAXN+uza0oJRKFLmWCzSjBrcH/v0d/+1f3/6a/hweTNzYeOEwcUUUJWtSIwIUJG8MXzgLI+uBI01ZoA3CnEGahsL8U2NY/JCvYAcO1YZXiGoN23sFmFecMGGXaO1ZwO2LCf6kndMT2FCRwNarWtVK2PDScGvf80ApU+HKqAKJqZAhO27ezd8x/uPXpy99H9yz9+9/7HV588viWPn4Bpf3p68e3vj/YOP/3sr1/+yz9RM7s+fwvrZSRabm6a5ZYm08PZzDB9sO4ghPeb69OX30++/wEW+83BIUJZVU8AD/ft38aGmODP7Gu1Tzsetlq3sTC4eoMdYqxerhw4GIhz8omD9jFHDwGhqGKcYx89av1SNlzVhqYgY1xSv+XolnafVU0su+AGC0b0LYOe867BzE7IiMOTHrhjHP+7Wncb7X197n+WRq4udaCF0EpB4O6LykXKA3GXM9wC1C5ChAhkqgiaJBPYyf7BJDRvzz5cLpfbPhtC28bD2WISWVNGRUbc39+ftWF1dfP67H2/WU+I8mY7Xcxv1uv/8N//zetvX52ent00YbXcMO6D6qbPmG3GU8/4+4kavRmqAVoWMxWxkt5ELlsKAUvNJrp7SKn3QT2IKKYhBkAIISBaL9lPsGQJMeYuIzIGMhEwJGKVjjEAurgYmVGzYPXD5SlXKw+4e5zL89DSdWuAhwN856Jf8lyCoQcBPoZlzGQ79nfsDkXz6gbbu8p4lEalEY2/CB3aWwmJyDMNajqsEiG5Yl3MEJRcI6vq04OJPIGJasIxAFLO2VtcqBgSqVlsQgiNqYqK80nb9cYAkYE5muQkSkzUTiiiqqTUSe4RWyPtt33qtmY5a+LACJwlJRNDAyoqQYpBsgC4rhX8eBJAGSpb2gKGLovgRlqdPZ7uPfj54V9/3r04Tz+eXn7/SpbbnDuaTOaLZvHzZ0d/9Xn4/M7V9vTy+n1iBDUwc44MycTDMC1DegfzUYy+oT+gWvlTyJkS1BVm0BBLctighIB1sIm/iGqerQJZBXW2oiQDBqwGg3mvvEc9h76tsNK/Vp1IbUZTfu3/0KIhwpEUtqo8Kq/1hnQGKCKMNJnPAk/O371dTMPB09ubH08vz2K/tb1b91TS+YvrF7/7zSdf/Ifj44fXp6dHJ3fP+811t+4DZlO5utBufXQ4XcwWa9nM92cfzq/7s/fNzQ3OZtg0gGDesqXucBzuuFrRalBrRDRAfCzes1r6j+B1yX+Ukld3J4YDDQj1MPl7vdsYQv1vALBQFpNgxOJQ9sBoJgd/Mn5sAdNWv4MNzHw9wPVRIsJHlzIDj3sQEVB3ExqIdS2Kg7N6AzVog2ET1k8BqF2sYYfpH90WDCFnCVNw2FaVLxvpoOqU/SzsJlecNxNVM8uTpp3E5rLrb7pND6pMbcvTdoaEN8ubtM0594tZQ2zdVk5vLs3y8cH+3qSdLeZA7fMff1hulrbu7t+/c+/uSUppud5eX66T5DvHR6Om36sQaidoF/+IqndFRkIwUhUARAYAUzUKfgRMRcFARGIIIIgEij7iV0VEwbgJpkqlUxt5jpUju6LGkMrQokHwhaSUeThOakSsmnfwGg1PF6BO3/TwX7VsJ2JEVJ9mh1TnM/jqk3l1DJqrgkrvQ1TVYRsjIgOxqdWOAjDgU6//QiQQEBWOhMgiQuhT7pSIXLUiYo55vf0cMjKz73hTTDmFwN6Y0XtyiQqHoCKxaQKTSPLBBsgBicE0xAgGOafARhQkC7Yxd32WlKVXE8kd8URSn1MPoExsyCJZ1XkxAu9hh6UnqZfUklQ4q87nmSIQYhZo4iSjrTV1/XZvfjB9OJ08eLb3N5+cbH5tGyFqLFLej/mArvL16fk32HACNQ5kgdFUxbkvBlasLRLRAKjmEBGJYcBh/kjLcS5Yyk+caQ3g3VQNPh+QkP24WXHRoDaof3Zw3EAa4BAJjDTOjrEb4OOOEVTw+K9Ak+oKrIBkqGd9B8sOfJQZUciSRRQJ58eHL//w5cFnz+5/+sk3L96dnZ7Spqduc/8nX6T84+W7y3c/fr0IM+yMZ+Hek5/86fkfcidNG6aR18t1ukoni8MG+TBSIjl7/jL8/Ivw8HZkyGhEKGVqMXxsbIb8ZbH/Fd0Prm5czzrFcLTSozm0ai4HLzu6ho98BpRMuSEagAUbADIUCO1HcrDZWJdz0ByVHeKGAOrBhJoRAnM0586dxkCm3In/wHRoYG2F0isby5DAxj0DFXrsmP36zXYcf7X4OKhGd578GF0Ua+5bier9FfHnji6pkkA1NhJfL1Lr55Pp4cG+ol4tr3tTYNqbtJOmDUzrzXqzWUuf9+fTu7dOUORPZ2+Tpnt3bj05uR2nk81y9eLN267LP3z/5iefPnz29Ol2efP85eu7x7cuV0s2De2dktsFMzACZCYAM1VTSSmrFmoIBclqj2gDAEYuCWAzTSkZWAwRDFJK3qI5MBtRXidCDIElgQGJiJgqIJc8s0pO6AOsi4cAkWxQqmtVs5m5slNtSEQVo+81YwagLhGs9B+OtdzozqOeWHORtAvPi0FxHKBFt1SmXmNB9QV5EmoWZib0KSJm3iWpNJpGZq9yR1X1Vkq+nmAQYlBVBMyiQBhjBELLlkFFhJgA2Xek5KwAoYlEAQIBk4qqWznE2LRmwDECooggABoRUNMGBDDN/WZT8l+izSR2axFTUOEYRHxUpSIiKop4eRwDAEDpi+BcrpopCBgC1cKZAD0oE4tZCgBpfaNrlKuAzBMKs2i2WvdJE+jSbtabjNpwa6KB2bKKZHfQHpSXRniuHgN3yl5qSh6qeeSGhX/2wbVogObvG/T+FcYiDIiyGmN37E5k2hApFpBVXMRQ7+gCs3pgRwQ7HPpRFIrDsR/RYNGL4e6bRxs7KOiJDFREmDCrcsC9w722nb38+k/37t+78+Du+XcvTWQym5yfns9vH/WS16uzlLFbLq+2F/v37j68/eTN2x815TCZTPbam+XNxVWeRE438PTR/cOHj9YpaxYIFkIQgCRSyYaBH9FyBHbG8I7uodprGHyD1Rv86NejQ8TxDZ5cHVs2FEfh/U/U0YShaRjxcYmwrFZvl6vXwKtcYsdXV9AwGM3i1Adzb0BoO05oQNg1oVQphMr60PBNcbxmJZtq7DaY6erpxnijbJqBdfjozwAT6m4YKCWsVFZdxdrtpPoEBPBBCjCfzCZNm1Vvltd9FuIwC6GhCQOs1pvVZi1ZZpPmzsnxfpx9d/G2S920mXKIqz5R1q7vMsNkMnvw9MEXnz0Csx9/fB1ivLpeZc0/efo4TFrZ9iGgiI+yKmbCzERERVS1uE8zoEBIosI+tqzw8qBZiRDAMbyFSIDIFAzUp+O69k9VianvewSOsTbv9E7UiIiON8nUapTq/bnIpPSJJPSqEi8hNe9ZNMRwCOyOnIitbARD8wGNqGIGaGpMBKhqSMhqReKJYB4bqol3MgG3794EEKkgeiTv1ORCKTTMJkgQqEHwqfFaqo7LlLRSTOG/9V6jhgSiBqCqTMjE3m7McUFgYmQz4MCEpJoBIMQmhEgxkoJnrDhwCCEEBiIKQVUl55wTBWpiowLdeuP4g5lDbHK/UhM0APNGed4OAmoPhbGru9UumUgBkXxiIiKBIQInyB0aReptWyu1KW1zNo3YYGavcEZFJTbJzlX5NBz/KFVgLpOQvXtT5fdG3h+QEAk8iw+1GtXLN1waUOxWsd0OwNQGubvVbYxWnY1b/iF36JuswoXxxO5a/mqFKnFUhZE7R9cGFgABqykrp77aSoSqPGQKCEpgBKgEi+Oj6w+X77/+0/3HT5rMF6vv9o73zn/44eL12/s/+QSDvf/6e0lrWG+3yHdPbsejB+fX59Egdd2EdNVfN2EhKa8uzucPreGwNgoUk5YEpNaBrwZgqFBJvsqBfIRARxNXmAobN8OAmHfN5E7auBpVMwTveVNIUgTVYRGBEIOfJhhYqYENqY7BdsWSVOko2zXGf/atC3weeCuoAqbBlXhigspRLJmRgQ34KMbZeYnt1ExYwe2w8yk1XzXeyOD86nWGNYKyVfwOnKTEijp2LutrZZ6JDchNExFsvdp0WUyRmEBJTFZd16etqDLHvcX+YtJsJBnZYn9/3jRJ9c3FhWXr++2McLVd//Lo6a1bB29fnD9/8fbk3v4mqW1kvjc3BdGcBSwLY6A6ytz1/GagGVQzB2SOIJYlhcCE7AbS1NQ0pwygxERIkl3xxmY+rgOJ0dv7EHPOvdWmXibAkRWS0TjFzwhVxJw3ZK74rgaAdYmxynxL2Zc/8xpSEZMM3SmITLU8LK8tJxQtT79AzvH5udtAMIwcaxbcTBW9ZN0KoimULhq4cpQJAHwWGHJpKMrMWGsUfPiJN59BU1EBgBBKz34mzlkUIMTIzH4EHI4LABFNphOiaGqCRqAxNuieknnogaCSQwwimUNkNJWMZrFpyUy8QsynZZmqH1KkYjDJ7bJLZp2E8gCPnYK0SsErKUJQ4l4yMBMDECdRa2NgkqwIxm00URExtEixPLNhvIRb8nLomQCMtBx7GHiAoY5ysMKjWRp+uBOw1xjAwAlAMC/WM/AYkAtG97RkxS07Br+KJWE8vTWCH8UixZ4MesliWMb9OULlUWvoTcvdIKkpCRlKUlAjis10PjmYb398dfYjRgrTCZ+9fHPv+N7y5eWH71/c/uz+/v1b16pykbbrm/e5j6HZwxlQt1jsr3rqUZaWl9xBuolvvw/f79nt+zRfEBIBcmmkUYPminoRXKepBsNSlAzJYAKt3lmxTYNLGwrHSieN4lVhvP+BFXPMW/IDFSQ7GTs8112nMvxzsKLVySOUzh8AVoQ8WEROhE64IkCZ6obovGt9V3EwgyjKCrr3QHP4r8LWwmhxCou8o/LxkrQyoaPmpepXHOw81C2KBdSPd1lueowPhy9Lw7oQEhNx4BhjCFFU1v02Zed+AM22ktb9RlWYuAlx0kwY8eZ6+/biQrLM26aNU8K2U9qq9TkByqN7x4H09cvT3371NUQmbrKkvcVsPl+kru+6fr3Zes7W55ubt6skFpHcdSbCRk4smM82FBFRn7hopmBOR6AKiAgwxhgAwcxSTmoGyH3KAKVZtKh4431VAcPAsfScQCokfnm0WKFHjbD8kdQuJDXBVagDdxDuw9xkl0e4658LgVthUP1jCH4kyoOhj/KK5SHjeBUzK9MIwRWMDE4HFsln7ZtiDiW8PJj9hz5+pzx/EARvbY1cxg4TAPjkGQCLsWnaligECj5itWlb5ug7BYE4BP9ORAyEITTMwTsgmQFj4NiYSjWt5TwSEyEDlhWvyLB2+GNnIwXR/GgAQBZXPWFKCdRCiCqgYipGIRqQGRAzAbnasGh3PdwlP1fVftejUw8EETGUaGlYVz+5A7czoDtf1Y+YZhdNIA7lvjujJZmKcRsM+g7/AIMb+PMAHsaQv/5rJ+sHOwe+YOyd64w1UwWY+DZ0ShCBkAKGq9Pz1HW3HtwF0LMvv5y1k/mt25nx5es3s8UsdtuL336Ll+v79+/EecgkV8vTq9Vp191s1h3gLMb9tj2YLQ6woeXyOm+utLvqr88p9ZYT1WZJpG4Iq7kp5qjIusvGr0YVyLFHmY7s+ZSCXnF0d07I+dYuPUwGxqU6T/R1Qk9uDlUUECpZVgiZ8bEOtPxICMEu3rcdkECFu8eqFMBhfEPdEc651++OMFYJVVC/q08qFmK00DjclQ2/HbbhaOd3WKoKEgBr1chH+6pcioDqHqrlFjvfekiIlZsCTZJzVjCMgRGsoF3vYAOmIgi6XK0uc5/6ngH7GKcTSRm6LkUAMuu266On945ODl+/erfermPT/suXX/3i889Ojm9N2rhZb169fv3Zsyd+WRUBFedjQES7vl+vZ7NpQNSs/WqDgXjWmgKgUmAwSym5AzUzST3HEELIWZBZe+8fTCYWOVQ2H5iZibIPF0DjAh6QkbS23/QaYxUwEzMdmsXulNr7V/Y412OCYqkd8pe9WF25lbUmBCCiaoWHHTRqAQkDIlenot4ltNSdgrmkgYjRK7kQiAMimYmqYvBxV8XUmDcpAreSdawCmMNxFSUvuVBBRA4REFWAQzAAjwNDjMwxcKMGgQLFhomsclhG5Ap5FTHPTHPwE5mSM3VomkUEDJBYTaAw/8WIGqiqGAAymRkjI0BpVwggmomD40MG4MCpTz5oKpsAs5kZgaGHRKRmZAgIRFz2f4mGCSomhgFoIRKQkreWRYRS9u+P0QqAGt5SXT5WN1CkHeUh1syca2fLafSPqWIeg49kLTYyBXVD2Ud8cL0BLPaqvKrgkcoi/JnvqMamlBy5SqZSJWqmokTETZjEdr06nd45bPb2Ir8y7ayZtPt7y/Pl4cmxWcLzG728Ecu37z5YXV/1W+w36bJbBqBumaZThkRZ5Nnh/Xd2IV1qILWoG81g0VP9BD4ObDBQVq1oSZ7X6nrfr6MhG9cAy05xo6YFdfljRxt6LNQVKSOaDEzBo4RCeniLCIMAIwQun+Cm33nJ0ZCO1tZTPVgMd4lJCgQsk7JqVICV8bcBllv11rR7SRfy4E5sAtW/+6NDqsFMRYCjCRm/4p/tifKjgYqoDnMn1iw5EoSSiwLDmqUabsDATCyDuif1MAHQE5IAZUoS+rHPkjTnQDRpW8+YrzbrlG273jRg0dL8oJkE/vBh+er1u5zk5uaibSYnJ0eHJ8er9ebtm3emFohzVgSDnJNKZNKcu+WqX3eT2ELKRoRClhJhALPcd+18In0yMO9hAGpJMgUmZuk1tgEAUjkPqGoUQu6Sk/wxNpoFDdWAiQlJoCuMPACWLr0OHczU049i49msEXqtVXDFMJj6eADvo0rMzhGXxS9/6gPebW1YdoDvGwbCQiiUZ0JOgCOCivcmAiihrQv0nf13vT+WCXxQoKFTEd4YbudoOYDx0gRARArB/07MhuBzFGPThGbixhTJO8wFp92YyQAZ0G2KqSF7GXLwMJmYmTn3nYmagc8HRBRgUq9OMgVR0wE+qVdBg1bao+bWkEtPuyy51NNZsdpqAlhdipUw2vvwqVoBnsPQUD8BxIBYYX6Z6lDOETpPNcBN+GjRSluX4kqsahEHK2aqZd6LloGOJcL5SIuOg00fjHc1CiOWG4gPrNZ/eJPteAmrNqLaj6pR8JcQ+rD7Qo4UNl77lEOgyXS6vVkvJ6GdLS6y/ek3//LFr//q4t2U29AsJjdnuNmsZ20jH5az4+lidue6z1s1CJRRVttrg9BQAwq2yo9O7r26uNEPF1PpV95zB9CLegY0OaYyFQoz49a5VC3CmPws58X3eLlXG5s/F2tb3XFVRVddqAOLgrqtHCwsD0FDLbmq4Vf15wNXQl4HhPVtg5st+q/RGdfHBNXGFmCvdYPAbmBRYYP/nGrQZsNFhh0y+JBqbKyWqFW7PqjJdoOYyogNe3VwFFiSFOB6i/GzdrFIgRVjqKMGaFRq3cmADNQ3GQKYqoEly0g4nc6OFnttDNuu2642680qdV3eLkn06Pj488+e3L97/OLH9+fvz42pT/rTz5588tknZPDq7Zu07Z7ev51FyQQNLk8/HBzsmeZuefPh+eu9xYLnoYlt6PT8zfvmaNHOIhOJCoqCatd38/05EffSuwEAMCYmxK7vRZUDgxgH1pzMrJfsbQ+QQCEhISj5DkRP8A5NxQGI0Ifm6oBOtDJzMEB3ACwqMB/DZQjOuXoY4ztrkA/5s3DZq4qW2i8dSVsALJ0mhw3jVWuAWm2NO5PS2x0sIIKhajYcMCX6IJ2yEbHIHQoIhAJaCRGZsIzeJHVFagg5ZWDgJsZJS8RA7EolCgHEDL3WHbGMKxBiBEUwVMPArFlVjSj45kuSmYOpqWZVA2Qf3VDICeKC1jAQoPl4Rihcmdb5nkaoKlKa4kkthVMo7sO8/TUTVfWTEhMM6cFy+34xH85e1D4l0io2xFkg0J2TVc9ucUsVgFkN0xFrogVGu4HlpJvVjgw+3Rd2r1ih/WhGhiM+sOPFDthg1Ov7sUb/VKxB9TF1YED9+GofDQwIKQQ0NDFghsXe7PSHV3dv3z7bm8n7D5vTs9A0SHL94/OWuWua9XpFov2m39s/2G+OKLAZbXEdWu23XU9rDvG8z9NtCLMWe9le3VDXUTsRVcGS4efKjFVzNKITt/o18KnMxyBzGtDzsPa1GG84TDtLNyBjqzgZ0beWGagP84YwGGQrsK0sN+5gaixHb4gUKp62qubd+VOJv/JMrcSLNeAc7XAhXWwE5gWRfxzzwHAx3zfjvY2RwmC8hwhjABPlJTB+62F7jlRPxQo7Ow9g3Cdlng/U0KAmc6B0+dYyYEZiGwnxcLJYTKcAnLNy7JocADTS7Pa0+eKTpw9Pjk5PL7/66rsk2k6n9+/uf/bTT1Ht62+/O5g0v/rlTyKF1fVNDKyqTYy5T2+/ez5pQouULq9n0Ah1ab2l3Lc0R5N8fdPu70mXekntZELEpkqGgsBccqcpZzCNIRqoqEZm8R5wbQzEquqTZwDRUM2UCEtmxufzgiGiqBdJubS3rp0ZMO0uG+DOSgOYF6PRR5gRAb2xaAVyMLIHNgaJxV5RtUcFpLNB6e/gYQojebcOQyAKSCSS0bmV0gqmCFoKCwCVPa67AwCIqXxpQuf9icmtDYcAhBwicVQDAuTAplpyM1CaFDEHM2NiJhLKBsjITCgIhAQMqe/UhDkAkfS9AXrqJVv2yRem9fhg6VTr5hiK1teYgyN919R47EPIVXjtS1yhOiIQiSohGjDWyUj1V/UBDi+ucW9Z/tqDr5K7ZfcXImWgZGuaphqt8VOcP4SdypudQ1tx+sfCocEV1BNY/zVUClWkP2w4g/owSzHnMEK8snsw0uD15QZOb7oWVjMyp5xSn69/fHu8OD65/+Ts6vL1776+8/iTg8Oj7vyliD772S9/+Oq33XKTuiVc9/PJPjEGbFugGGZbWp5tL3vVLufebprFwfXlJWy2tt7AdI+5xDMDhU9oqgoKVjU4/p/Shry4swKFFa2K5gezONq0UtNeTFT10kNEUNamegmtDsYQgcJA02B1ElDqX4Znthuu4eAtBqNbIDrUlNJ4poYrDIFM1fDYWNP38YMtP6sGfHRCuzdf6L5ix8t5rt+47v4CAgausO6WGspC1RpbDTFs/Egbfj54LgMFQjMsXYh95cFPokNWo9DEJgridbeVlHLqkmQgPNjfu3X44Bef3Dlp49Xl1bc/PF+n7s6d2yfHJw8f3Lp9cvDlH/7QIn/y9AG2vLnZ/On757funuzP9xDx4u37zdmH2cFifXFz5/6Dac6w6dbn10ef3AlN3HZbIEzddrXe7t065iZoNtHcpa5p2xCb1KWmbSWlnIUCggAzi+ScEwAG8j6ODjmRmXJSAhQjTygBgpkyB1UxNXOuQ8VhPDq7Wgsvh9gLBmDuXeMRiBl9lFQ5lQBVuVYqRQarMG61gj4cqnk/Cu9eB2ZO5QMKITnaBVFHtC5l91beXCeFjRFiIZSqhSidDsiNqZoxBWIGA6SAgCrGTUPMMTb+rTgEcyyCCISedyu7DZECa6nUthgjIqfchcBEhJk0A4cIgNkSEQEEQOdgUHP2mjfCICredakEN7kSIBTVFH2IvI++EfGMN9jIMEABYTTU8ZVquILpSyLXD417u3LGcBg3XYuOvYXCeIIqwzzU/Nl4iArEdKbChpeNr68PHgZhESCW/tJ1A9SArER7zirWBR9Mvw2v3AncEYagcIT8ZQu5tLfeKJkpKKhmbz3VddsmNnF/3ybh7fffHR3c4mbRzjbTeaDJUffmff/h+tV3Xzc8S5x67VZpvZI1KE3b6WIyAYvH84O23V/122xpq9vt1Q10CV69bx7ezPaPLMYOqRMlDqIZELRUQQJYQUJ+gJxPsIpIh2HKNphNg8G0VRtVvcHwf1Z/XlBYeaMLLwftEAIEGNFwjS1KVr1EccOJxfEhQfl6Q1hePU15DOPT3LHulcmEAbSX5zwqWm03pVTBAlSL7FFSzQ2Cjb8uCci6LmUz736JYZ/seIrRZpUVo8HnQvVqUO++CNYQa1bcG9aU6Nzlc2igfZ+SZdVkWfu+V5FJ2yya2JItgKdNk6azx48ePnn0+Gh2cHV90xJcfbhQzT/72WfE8fUP7/p1x9NmsbdvSC+//Y5XXX99c3l9+ejpJ3MOenoZttsFAqd++cfv4icPBO1mtZodHXMTA4U+5b7r47ThECxL0zZQT2MgAiZQ63M2sRCCqjVNEBHNmdn7ghSfJ977ExTAdT6OGcjUKexqnjzkh1JgOeLqXcc71v0SgBKiD45xgZAPQ8ZquN0T+YYvfsXn/2F9Glj9jedRJTMzACCzqhbFmYEZIDMCoU8tN7DayG7n/AyFdBiY1EB9WgAwEiIzmAExheDMjzn2t7IbfW+MAkc3fwCqlrM0TaMGmpIfa80imok5xJj6RExqYAocAmowFUStVFgm4ipUxTGm9TOaARjAkCnUgSflFpAQAaXwxEX/5H8vrsRbORWuB72HQ/ktcRHTFI1+ObnjKSuGX+sPq1d1bDnELhWVmkEdGAaVZfoIIH7EHFSEjrvgcngtwu6x9g0JI7FcTaRbqfptEYe32Wh/FMyMmFwF0G06S0hNmzZbBYmzSZhMrn589dlPPw/bTz/86+8v3706uHVbpwuRs3bTt810cfvBh4tX683K0JJ1/XZt2mSOJnsc9ifNNMt0Svs9IM4m6Yb60+3VYhPBbiYMIhmLixIFYrc5VIYSQmWxqqUZVt/DrWFjlzs2KCraYpqwzIkaHk+5GuwQIoOxRQMF1TA8j5I2ALDhGdT1HhZ4yJ+UvFRx2QV0jGSNmZlJJVlGH23VUWh9UkOgUh1+McDVkO/EGPWXVf2Ku0EIjDBkmEY3xBujrS+fVnPTwy3WGAYBtbRgGKOAYbPhDkAFtDL/ym+LEBBUQSybqnkzfeYQIgBdX2/terk5v3py7/jBw4eP796/vlm+ef8uBjo6enT64fzB3fsXq+77r/54uDfJOR0sZtDS1dnN+moVVls5W9769BF3snn3yr55J5v1/X/7BXcJpkFSd9Mnms0newufxYpkTROJKBAJADOVkYeBXWgollPulVD7fjqdq8oQ+gnU51mJAM0+WhIAMYtVaDWe7bKnRmp1iNoMauvfYexo2V1mNlQbgROSULxLhSDqdV6ABkpQScWKSGzwK2BIgQZ0iYDMZuL0ECDqYI+qAVIY+A1PdlD5aKe/qQBndxVIbIBEIYTIFBSAmT3zqmqhiV6cBwjMgYgBLaVctisRgDeWiAYgmkU0xihq5qIPDozcNE1OySE+cSAEESGnboiRfPwNEKLrgD22IoruMWtn3iHiMgQwcvoLEdl1n75eCOjsAwyNf4diLDc4FRsNZwJ2WLr6ZB3OlyrWISvpLqbkhyoILdhffezrzmkbrlR+OKjJ6of+2et2fgUVrA3WBsu4q8FeujUqN+5hjlsZKOjDNx8Fjl23xhAn7fT6/DJGmM6nm9y/+9Pz47v3YRItpe7qpp3ObLGgdGWQm9wdH5xAn6HBnDOB5JRvuvWWe5Ur4kmkJoZ20kznFPLVlXz51UGvN49Oru6dXE34SmFt0KlmU1REn2dlOGZEEAC8y/mAPwf0O5jxsquHd7g/cG4HR2M5NugpPJs/F4NaOw/BKrarOqlq/d2qf+Sl/bgPsHlcaqjQuTK44xOzaiHGn/slK6U14v3hJbtXQNjZCzVTXI337p6pf6kmaBc2FNLZ6itqwkoNULW6m9JSaIhB6qcPrhOhFLnUHVQyM6UgtgQXiECG3ktGkQgBU69vr5dpER4/fbDNenV6dX5+Roiff/Fs06fr5YoP9m4uVp3S6eX1pI2HkfvUL2+W1+fntwKfHC9ahilDo6ZqfO8Il9vN9bXd2++vr3lvERbz0EyYqEtbVQsxUGDNEtvWVHOfiSm0UfscQpP6wlmF2HBg6817sWkWdKmwloZA5pOFDABQRNGht5RDWOxJXaTdjbIbmbq1LSrJ8UyjWoXQvoOxqFVwSE9+HL5hKderV/DLubEgAhmBnwcSUPEmMZkWytHHR2KhMWqneKJhHBVxdM2+jwoAA2SiYtwRzUvCKefEIbjaxwwIMTRRUwbELMKERGxqornhQMw5ZTNgDjE2CNCJmAIxc2AiAsgGGGIU0Zz7AQD7BGMBAytlwICKAN5Iwz03uryw8PWIoFAIn1qOg8MsR6qwvviD8paBidkxN/XvNmR5AcYUoaNLUwMa4DqUJh420qfFT5e08Pi8axznz2tIXXxsH+q5LdapWpodNm8QMjkGHg3UeIUhl1mwqaezg7Mu3oZps95MOWKImjMAzmbTzTxcfP81p3zn00/Ov//u+t3buJHbx7eFmovTH9P1mqmdLmZg1DJ23TZpj5h7hQwpyU0UmKQmbiJeX8Q4wddvm+evjx4/mXzx0/mje83dw+uZ3DARUjJBry13Z6w2NOP2+nZw71zjGxhOy7DkNljWuuRQueyBVCorUWoxwON5LGn0UP32aI7BSieP8rk6Vtgi7DweN3dWWdSPbPzgnQFK4t2Gd1feaHjR4FfKhoCdD6r4YNwcdS8AWJGc2+im6g4ubmeIlqyopsq5r7ksv8pQGlI2b/lgw3GVPw5NAWo3ypLSUUMwH2DlPyVEQyMMBoDAholQJs08df233796/fLV4f7s04cPXr58/+333925fatPutjfu7y5Sf36zsn+ZLrYLLfX788i8eFssiBYxHZquv7+ZX9+dufe09WLH86Xq0nzSTx5Mj08jIdHRGgEIspNYGYCElImSjk7NI4hpmSSs2nmEFLXT2ZTE/MiWGQkYzVFAxEBAlMoDSldyO8os951welW2sLUEwYVx1mZBGumgGZWiooMq+imotbafAoR1JSIvLICgMY0Zdk16OX7JeGJPrzQrV3hoHzwhl/PFWBEZa5OkYIg+pBjQDArfeaIgqr69ENiBsAQ2AfQIxMyI6GqEQMimZmqugS0/J1xmH9gKkzIHBRUAQgwNLHvk5iAYWwaIpacDZCbyCEGClq+HCIHyKkAaWJEY/ZBnojE7pbAEIiRWV3RhIgQhvFqCABEWqYAMniSwPc60YDSa0iL5Skh1la4w0HFiq0+QnOF4xvcAI1QuxxL8x4TxRpXm+9OxYqXghpQQzVhH33GgL0MwafOQTloI8UNw3m1AfO6n6qh+g5WcAtVXq9mCIoIpsCRQXPkeHn2/vbdR3uHe+niQrp+uthbvnp3+pvfPfu3/y4u5g3h6uvvV+s+Ru7F1t0K4aqlZjY9IuMJtPO2ZcjrPiWIAIaQEMBgE02g3077lra9Xl1N372b3bs/++kXl5/dP7s1u2xwhdSbgaGIMOycLyvUAlZz5D/1DjXOgeu46AXUj8u9Ewq5B3EbVV4Do8zCwELFyc4eefdgqvo/f91ufmZX4PNnD2xnv3gkUTLZg90uL0AYbavb+yE8QaxU0vChxb2V9RiuMkQFBaa5yfEhVKNHLH5g1DwP913pMVedlMaT9dqe9holxNWkVd9SqaVhN5fj5z1rShcZABLNlk1kwyktJtP5ZHb29vLVu/eEuNi/9/Lt6bt37+aH872jvclkenmz/HB1OWl4MV9Mp02/7rWHv/qbv05ffTlfzI0tf1iuvvrhwS8fbN++vfjy62uy6a+f0GIajo+NQy+ChsgcQ0AiU2EOHjcSIYfGFCAQJOHAljpmRgwKqZc0bVpvVk5EveMRJG/i6/3vTWQItgvrODxORhiWl+pWq9Gsn0OEgjexNAsCGM6w23YiUykd6KqcBMogbPcrlcX24MAtChKNQ+j8+IgnFeoe8+5E5i2dEbE0vcCyuZ06QiTiEmAjky8ZiFAISIRcWlqqKDEiMoIBoWanEoxDNDEV8RsPgVWMiIDQBzszUjYjxhBby14FgKFpvTu1ZTPT2DQqapAISDkYAGEwr8IFA6DSzEOUORAF1VwOJhO4oyrZEk9k6VBXQRx86ILVURzgSZmCx4sjqF6hREVUUqZUjGlhywgAVAu1pgBFsQoAUCo8dk/qkC3bNRU4yLg/siS7KK+crlHOMx59rFRzvWQ5j+MHfcQNWI0UeHB/3rcIzcRUibmdTC8vztc3N3uL6fJme/X63VGcdtO95ftX53/4A89ocfuw+dlnp3/8diE4O7i9vrnebk63etOv0gwnszgJGvrUI+gcvN12QAgZAMEQVGATxXib9OX14ux1+/7t7M2z+MufTx7cPttrrjh3UIytFW67WGconZ39INng1oqBLPmWoussB3BM/trHC6KI/hR92AWAGagiQBiJHDeZTugObn3wQeX69TvuPjSnf3c8+ZD0/fM/1cU5Ki/pAK3tw6rNqKySw8dxO0BlHOs/R4mPFWnmR24GirZ1hAj+Wh9wirUCoLQ7QKhN64ZFhD+/g/oAhmDGqaTB41ZkBUSYs2jqCWzSNLdvHT29fRgJ3759++TZo0f3bl/fLL/+5jtiuDWb9aLvXry4uLzaiu61s9t3jjZd+uG7b/eNN9cfWHtqcLHXnP2/fzsjaicH7//w3fKyu/9/+Ns0O2ruPcL5fLPZIMch/amSvRxMchIp8139jzPRahY5EmGvfRsbIAjAmURVFYyYAFTz4DNB66zYMSJzQWYZaaQO7nD4fQ2sqgKk7FFvie7rXfxEZdwMqjeuMIa8dsaoctsF+fkYGfCPB0OoY4ex7qzBDBXSqiQsvJOoFaTlO5g8i+0dMQ0BfVQWsoIBAREFDohU+l4SFaTknLUaMzOimEg2MwsxghoiikgTWibKWSQnwDL/wMA76VHTtprBzLJ0oWlCjN1mQxwMlIlVBInKBHY0RGIKIhmRiOPOccKa8OWSkfKAyCs7ysBeouI7cXyb12njYGUL/B9UFBVl7+AiKGef0Hy0DlqpqhsW3gmj+soR/o/OHnYzjOXE7xD+nlCG8aSWuyuhQ02I13cPl7bxJ5X9h48+Y4cY8lQ2E+UkMcQY2ybGs5cv559+ZklpK5vT8xnPeLa/eXN2uN+evrs4+vwn0/tPrl8+x27ZEO3x0VpWAGlr65y6xqKAEXCGDKAGmiERSAc6BQLADtJcFw0Drnp91U2v39H7N7Of/aL92S/CSXsWaYPZCM2MfMldX1fEtmUkSQEoY/LbjY8nNUqXrOqCqwFDQBsEMrrrJbE+0WBQ3IwNvRn8AVWCtO6CIeFQ/W25xA4Yr/qw/8a3w+6FdmKCIdYcYHz9agVu7+KCeotYIgas/xzAgv9z5/arXS60oQ2J9PHLjBvT3V/NXg33iINjqQto5UlAiXP802wnZtHUq4E1TPuLxe2Do5O9mW23bz68B7S79w5Wq5vf/O5LAVvEZrlec6RmMlnsqQodHh71Cc7PPjx//frzo8M//u7HZ7G5d3D3+r9+tfr737cPbp396zeL2R4/mth0Nntwn+Z7XZ8xNrFtuu02NA0zpb7DwMScc85ZODIxSxI3uDkJIFIIknMhAYhNRcWyZoeuKobohX6FlfSGbsXkFiKBsKrZrI5kKMXpaIRcrXNx+YWbHCvGnCPw9F0peB2eiD+nYvPLI8BhiAdUb1FwhEHpMINjsznztqODCB3BJ7ogUhXyIqIaDP1BrfTAUataRkIOUHx8+R6iKj4ZzQzMiDCl5CNlCNCVASn17XQCdQahmHEIpqBQSr5CjJ6ocN/QNq2CMpF3IAIADgHRIIuSC/gMiFB9emMQzaX3FSKqIhICe3dJREJ2V+YNvYtxLmFNXdwBR9X4HkdTORzm3VPrZ7P4Ps9if/y4ik69WHBf0RFEjjBuxx/UwB1K3FdO+E7ecaSKxgxofc/OH9z961BL4btktAbobSB8axgxa05qHAJzmPTLN6y2ONh/s16Hy+3N6w+PHz/F2Xp5+gJX16cfLo6/+AXduX+x/QbOl3fa47DlCNsr3S4tAVgARFQ2i340TLFSyQzUQkTQBpsmxj5ruLqx7pt4eYk36/iXv4wPD9+HvLEs5pNsEJG8AYwNUiuAav1H52mm6LXfMLJA1drXkvyB3/HbLn0e/DoGPhGsPIGhDXdBC4PR21GFDJ8POO4R2P1RfU1lYGB8AOUbObCCijQKKVW301C3Vz5/CEpHtwE0un23/eZbfGdHlgigqk+GiMQqJTHsm2KwhrZ6H3ut3dsrDQf8SJQS+GqMVABBVS2rABrHcDhb7E2mwHZxc/3q5YtuvYwAP//0gW7777/9YbGYCMK22xxPjk5u35I+n72/zr2cf7hsiL5//qdPDvdi26xfv9v/9OdznH345sV0Mbv1738SDh9/+F//Zzqc49EBHR6KklimwKYynU5U1DQTYWC2LGKCjLFpmFiThsgqSURi08YQt9sNIMYmaFYCMlMT9SoqUEUiS+WuSkTqkQSoKwxLxlbRQBWMawtRRSUkBFQVK4QP1eNdcXqleaAUEBTZm0NT/79SQIBF9TWYFX8YiENfcHNKw4+AW3k/8ESMiIJaZtXscEhYmneAATCzTxImDg5JUsqAgGpMwdPXfnkREREi1+YYMuScASjn3DIRsqgCQGxicWFqKfXeWJAIDSCrhBgNQESJUExjbBRAs2VRQOTQqFlg1pySJSTvYKoGiKXCGP2OrQRHjOQovsQK6MmSEgjUHY3AZYz74FT9xHiUgAMzB5UAcAq+hmaFmDbzvg4l2CgKsPIRRUbidlttoEgBd8z/iBHLh4zWo0r4R+RZrJgV9mn40RDgDTZm5KYHKeGYZwDwpkijkTDRDICqxszT0LDa22//tL+3N91b6LvLqLa9vJhgQ1ukG+HQXf7Lb6b37pzcfbSE19fL9aQNlKakGRGIA4BgpgAY0BsECAEF0AZCA2GCISBHgcDQUACCdtvP37+L63+Ybjr+21/D/cU7s54gqQGBiPgGK6FPpdX8YOjAihfj7KEbOoT3QRdQFx8NrKhDC6CuDZ2tRgADhq6RIIy2utjwytyMNnPE6vUFUN0PfPxe2wHcQ7DnG7Rcog58wno7lZzZ8XXlArZrmndwAALCkOX1mKYgzF1IM/wp91LEDDWeAgPXJg05rHGvwXhrWJFF2XVFDqoqJoAwacNsOp82E1G5ur5ablciulmtQPJffPLgp58+2WzzyZ2Td+dnm3U/m80Wx/um9vz1m8vVTUC6uLnBhg7354uDQ9iuHx7cmfX56u9/J5fXJ796hCxn/8//2+mL93v/8a9xMVncPsmBLfc+0pYDpT4bWhMiEWdNKkpAxFFyBiI1lSxIEEJ0dp6JA8cud6LisbZkUbAilvPFkGL6saJ5hxpEbjcBrGQMhhEh5nrrutqF6amx2PDDssgjR4c7zxnqNijDGYYN5Fi+VoYZABASIinm0uWyaIhoR+xYvtiwmV0SI1QMng+RJA5QM8OIGEJAAFdMABEhiogL2osMGgnB+m4bm4hIKmpgzOx1ZCqacybE0EZJhkQ556ZtEDAlaZqQUyYkMAsh5qyBGAIXchbZyIiYOaqqqbi75RDMFM1F/OD0Dlg96U7smHsbNKjyDS0//gizA9R+HL4etXs2liSPOvPnSwNVPQvFCyDRkIisXfsK/KtHxx+5VYZ1B/vXUzji+8oBjv8e8LqfOSvQbUf6M6aaxlih/K+zZw6RAYr997IKJATxgjcmSXk6mcQYGcPbr77rT24v9o5O4YWsN83JCTfT3GnepNlM2w5u/vj17PhoRottOFuuNoEbJDyICxboUh8Q2NiA0JKBNcABsIEQAUsnUEPIakgUMAKzmayv9Mvf7i/aHP8yn0zOICXIqgxURjUI6Ng1YnACw1NwvGWqdWISmFbYPhjfodlJobcRsHbiVDMrA2HAasA9rG21rCNAx8FOI46pmOELlmc6uIuKvWsR0XBZt81SGpiUqrzyAIfcVL3lOiB89Ab+wxLWlpCutG8ZVc3FctvwxYZMwmBKoLgsZ9DKDQ7gwgHP4HbRqAxaN2/lwYgVEnnHNG1jO5tM20lrWa9vbtabpWXNWZjjdLYP0onA+7OL5z+8fPP+HcbAYXZ0fOdkPnvx/uzq+hLJ+tTtTduD/QMSZYCjyfzAAn64mcfJ4U+eyPVy9Y9frn94P1nM+dYtvn2ylSzrTZhHN3upz1lzGwiZzTSn7AjQO9QgGAGIKCK1TVxv1ioWI6oqFKSnapZViIyIxYXJpaa3uEWXpBN4DZdWhR+AlhKBaiQUymTzohdym+K1RyMZDfXMFmBOzlkCOAwF39nkTQWgRn9Eg+CwbFgszAPVgg8DZOd//N+ICIrEFY+WQjQ37IQogGDISGomKl5+hchudkyFGXPOJactgADMDKop59J30yfaILuuhphzFlUNgTX7/HHvW8eWtWlbE8lJKHAMEQnUgGIAM1XlGM1MNcV2YkDQJ2bMkihERpScEb23NpXi3mFph6DIrSChy5bAz5knxBF8FQtKN61BOAAgkbfvL0qnghIARAzAgNBEvQmYzxMttbnFpNQEQsWDA2tcKMMKKXYN2XDQYbDpNubZhhdYJZSqucEh6B8ob6ugoTy0MrysqtFKx72asUQjAgXLKTOHvVu3b56/3Lx82XVqis3+9Obqcn7YTo6Ors7fIfE8ztZXN93bN3ExPd6/e0UXq6tLIGyoneEkGlpKYphAFSSCAnBpvQcUKHDtW8/EhtrE0Hd5j02vL/t//b3Opv1f/aLbow6oB8XABp4tGwOp0XtbQTVu9saw2sA8HwZ1hUfYXtO0Wv9i3gvIgu22mBiYmJ3Ft/GjEbHO5xgXfjCau8/yowvVT6zRHxSxcrlo/Z5WbwJ28jrjRQfvtGM5fMfYzg+q/R/Dg93r+DfDAVAMO9F2XmoGBf+O27QGO+YN5LmcPAhMiEyIIURiUpPrq8v1duOlYAyBYkQklKxqy9X67bvu7MP5tuv2Zu3R/j4xfPn9i3W32Wy7JnDbNnfu3Lpz6/Dszfl0umiXN82l7t+aL9rY/7DefvknWG24aeafPuy54fkBcoyxYWZjAkXV3EYGNAIwRR8l2IQpMoFkBEgpmQmHkJNIyhzYuWkDQad6ABCMiaG4fT9nwyZzKtm9ilX4WfqO1U1Znj0N837d2noE6uzLR765PJbBL6D378RqQHyjFxzqJNGwJwsK9k2N4IW4Vn5Szz0SgZUJlTpEMIWWQu+5riohNmKCwM4MMQWFMqDMVEHKF/eLgq+wKJp5wTAHBjM1RWPigAAqyoGJgyTx6YOxbQlQ/BKqxBhCYOaUe1XlyK7TcOdazTcykyGwqVduIxIy+gz3EtP4jdQWzr7kzgV5nFT6AIxnEnEIYXcOPlY/IJK905BzwoqkkomDDa3Bi7yrHstyrmsKsJyZ0lViOJ7+0cUFl/M6CEbLbwt5VzbCeP4qPtvFgfVNw6Eejiqi7yIYASRWLkUAEJG8/RsF227XseFguJjM882HqNbszRFs8+ObxWShhLw/BdWby+U0tpu07Jf9ddfPbx1Nw/7l+fXm5rLlPSY2wt4kW0ZQLIAfAnCDIWBEJPJB02ogVHxttjnR6uxs+69/XBzfPv7sZNvANZqoEqN37sSCnR1h1S4INbQyMCrqw5Jy+G/snmHd8Q7tiyK6mkEq0MygVrWUD4RCwGK9nNXRhDBQSjDA7Pqx1TH4y8qQG483KkLE4QGWxzhws/VjylCD8U9tD1k/2oGLqqqW72SVo9RRlzZGQmUP7fzLygoWp1KDJf+eNGxoGAksqLDRM79qACEGDtw0TYgxSb5ZLa+uL1ebtZlnIwOwFyBYn7Xr0ib1Z9fbOJs8ffYsYuy2m5c/vD49O0+dtBzR6KfPPvnJp882F+tJDCx5+/r0ZH/v5NOnkWD9L1+uvnkZGz355OH8/snesyfx5FaYTDGgiICZZCEmMUMgIso5OS4kQhXx0q/UZU9XptSJZmZGQ0lZszs2UJXoA0wMAIwDsbcKJa4YrgZ4JQ0PPoAXSk6krPQO5nML/tHaa5UD+eMuZL1z5OD8tQM4qEhy9B/kUSOBmiF7yS6izzN2F0Xsos7SYQfJCSIkMkQmwpKb9SoBHmauMRGIoYmJNMwmAqKMmPseEUCMicBqbztw6tINnDExmIkaIjZtE0JQ89EAbgQh5xyahghVVFS8ys0LwcwMAYMPbkTm0PhfiBsMkZgwRCSi0PiUdiAijkTuB3zSDSKSIfB473XaXpG4lh0PBbYNJ0txJF4UwEDVTCUlySmnHlTLf4pNUAQzUDcfiIN1Gq5Yzlw5kdVK11it/mfkU2HHytdwzv9ruFT9a4EYozMZL7Jz1k198KlVoQiggolJuY7bEOf1VEV0fbPiEBsiubqG6+Xjx084Jb1ZTVTTxft0c7m32J8sDgCUAOfhwKDNqV+dvm8TPrx1cjRdiHSa8zKvs60BEoMFQAIIwACg5iPRGJCIW4yNEphSjE3EMCE+2Gz3Xr3c//Lb47NuT7D12FnUxBtNOsoB0sJGFuZBwUzRKr6SUts7Gv3yn9ILqFrnAq5L03KDUE300BV5cOk77tcNX8XplUUdbWv91UCt4KDyLNwBgHrOuqI3tJrvda7vzz52wCuF5ynGogqDh/sZws4SmZT6xsGC77in8sYhnwElXrBxM+FwRoqJq16HfJSIMz6ixGymferRYAsdmIkXTVkZY+ZRsSoYmZk0TYztfhN4MqHDxXw+m+ak626bTIDjZNLmnj578nB/f/4v//TbieH9+/d5k+/N92+fHMDp1fnf/6F7d3H09HDv4a0uCCDh4SG00Vyy6I1mVQIhGsamBVQfORJCIEbLhoR520lKIsY5SxJzHhQsSw4hpG4rWZEwtFFFvd0bMln2iRJeOw7EjFDLTotWBnFI8fq5G7K5Bi57oVq3PzxTNwROzxDxgE1t3FiFzXQ7RbUw2P1/adQDaj7TjlCljn0vPcyK/TM0JLIsRcPuQ8mRDJTIJ6soAjCzxxmpTwSUOiWOAJK2iWIEN/wpqyqF0jdZRUoNGpKYoBpzcA2PSFaTsjUJAYyjpxPQrX/WbJVAdGyHTOq8BHPpoW8aYiPJvAtfbBswQFJHakwM7vus0vMjDkMwJWI3h14q4UtdTqIIInrRH5Y0o6+1qgiomoq/xs8XoKpvl1H7U2zLaFOqjcFBylmtR40dd3K8NgqArOpW6supnk0bDn6F9VTfNaj4qiplx3QYKJaemgZI6nWOYKZKHMB7TwEQs+bs1iyljBCmzfz64oXcrGzbt20rDa0vP2iyB598tl2vlS11fQzEzCp9yml9dTlfzAIHQcmEE552eR0NokEAjMABmYyKWUVQowzKGAAlq1CvoIAQp0TT1XLzzVf79+7cOv5sE0DADCBX8FkGlFAxss7DYF0WcA0eggfBw3LBAG4HAqMaURvobbRQGbfyi4oIbJdwc3eKQ+SnRjjKRnV4jMWoQvU6biBgTL5phQM4jJKsAWVB2e5dFIZdUb90sSADYWXjNjEdtxtUJbJWIRQA1PKKemPDt4CSotoJbMvNVL+AFUlg/RqGAJKlros7IQ8Lyub1QgswJDZT9GMGgKuU54s9Jjw9P7+4uWFi4jiZtJNJu/j/s/VnzbIkyXkg+KmqmXvEWe6WexYqqwoEQBIgm+Q052Xe5mX+9oyMTEs32SQIEACLKNSSVVm53PXsEeFupqrzoGbmkZC+AmSde0+Eh4e5Lp9+ur14efdw//Vvfo3i//Hf/sW//g9/fv///Yf13R0+3+v7m/RwR7aQpvf/+Pvj1eXlZ5/vs7gAMGpdHSRJJLqsmKyYE9R0N18I8UnXnGR1nJZl2s0wqOluv2eQ1ppETGusd0mShQUGGqi+h/bWcq8gZ7VCRAC717ZHxM4CAKKYgxEPiM+cK/UAfUvtdiHrD72RP9xlMrr2mwChnzEFn9P+uSMK7+QGCGAiNQCIVufWCcVcy0IjWUwxXTSWaNLx6cm0zLs9gngxUlVhIsoEq1VZmMGJZa0rDCSsBqhN0wRQqWU/ZWYpZa1VJXFO07qcSq2SBHC1Kom1al2LpMTMcDCjVmuKZBREYmw5Fm6wTzgF8Va9wVfmFCJsZGPST2Ryw+VSq8lxc3XTtpaLKDD+WOXhGrPsnGIehhat1cxcK6ccdf5u5DCnrujcCqq6gmwwamhte3jDmjSN7Q5+vKf7gPanD6xuYGGsV+6Qtu0z6fFBBCLeUGI3lB0g0pYY7mtp2JhFLXa1EaeEZWEWK16OpRZXt3J3rw9PcL/4+NXy/jXX8vXvf/ni+iWTrDCuWrHuZCe8W8qiT0/OUtyghyu+uJJL1TW7z0h7pISUIAJONE/5ono5retS7t1rbKKfIIyZhedaL27f4lf/eP3lp8++vDgmrQI4OQ/c6t5ZMHReqB1VqI/5OLsztqSdWovUrKVT4xFFd99oBEMnVwiwrrXxzMaHtWijOSPvibjujVupANNmsDvwQ/dj3rin0O8mRjFNP94Qc3TPPMoZju/X3RxA8xAgUBtFQtupnOWEm7s4D5HaPwxKyrsr7ADee1gRhznOrj+T7iHgDga1bqMWIm0xDEHIoNU8idweDu/fPK7LaRKxnAiY8uQi796+ORzur/bpr/79X/z0p1/+/f/2N/zL3/zkxSeXnz1f/vZv7v/h93axlJkfb07XP/vq6svPsd8bc/hTyRkOYzLXnCcYVBWuwatELrdqPZ0OBM950lqZSUSYaC0rEwUVm3La7XeAa6kiiYFaS7jteK5tL0vwShSfAiZ216GpRGGGWgB/5kHQS3wwYq5AFy2FhQ3sBZIb8YRraLaRZGyvic/yKGClxknEcGYdUci6rGlKFnsSiWopDBJmU3UtcBh7WRcSXpZTXU55ystyYBYnJi+cEhi1ViIydckTgFKLu7OQm5FTmiYAqjXPMzO7Wi2apiQitazRPJFTjsVhBFY1EaHWE6BurtUkp5bS6/aQRUzd3UV6IXRr9XICMZOqB7gPl2ttQyYa4d+NPIG0apCynUI1SaLaTr7WGh/n1bWWqMqLZkDVSBU3t2ujfbIttR0PdUsTohuChri85yjP9Tn+3lMT8bL4gB7/BbDtJUkdQ7QkTF/qHj6chtq1a1PcgJn17hKPAzFSTpJY3M2qpmkSlqWeluW0Pp7KYXXQzfvX+Srj7vTy2SeP8luezOA39++v8l4oT1m8MJOQY2YstqacgRPXAqvslJAEdQYLOINjYjizFNPidlseih+oHZtO4AutySZ22q+n8sffX/z+q2cv/uzpWiCsatpBffjd4Sr9LMLqp9U0YyNqmh3sMZl3G7hZZsAtGcZnNIolRCVaSQZlGJxOvNmtTa47I+Q286qtfnDYXKdequ+titl/pOhnTaaD4xshyUhCoEebXSZ+JHDj5x4zdFA40srbnZ6z++OU2tEM4qobeIzz6mK2wZkIxaKJlJre0bh6dHR0X+LVHG5lLVYxT/O03095glZJcnvzTg+Hzz9++e/+9Z99/Nmrf/jvv37/w5t/KeklLvU3d+X2sPvkYvcvrvM8XX/5mX3+U7x8Rfs9ZzECS6ztjRgTQq5mq2o1m6eZjLSoMJ9OJyKSnJmg0YjkZjaWLRIR9VW6FrN9rdY2X5gJsbC35aGchVsJPTXaJ+pBOykM6t2JAXLb427YkPrTbIaijSMNW9+2kcVLYg6omSkzE6dQc/Mo6Ww92K0Znt1VmcTNCDBTM1UtYBtzoKxW05qSeC3CVNe1lJM7VPX6xfVpLWG9RdI0781hMCGvy0qcJAu1TolKwvG9iSjnWRKva23QB7QsJxDMFO7LspiZ5GSmVt2sBmHjgBZjSWZetTIB0KjQVVOtNTTFzC1KksxcoapmhlZbNVqLKRa/SnA+vd2/kVpmDnJToA2rcDNmcouuleA043MqiMwqzCmGzYG8Mz/dxId0t2/aoWTsfKb+642WGWWFZyZpcBRd74b1x5miBtLzUYcxdNCj3QE/KuWLptg2x9extRapttNrQE3N2FngTu7GRMKSmNan08PdLWfPORnh5Rc/e/f27x7efbebpnI4QHWxQ1KrrhPSlGI2rrmpklo5zZIYM6k5aoLMSDtkAcgrI2eZ5t3FUcvN8nDvB8Wxwhi0hzhEYHBLyBfqdvOu/P53z//ks8eLl6t6VjMzl17HpJG36DVbTaGCxWxnOoza4DkQvw7apx10IzPcjB2pxxfjGfSQgEb/CYDIy7ZMen9IGM68E3btI7t9bfVKYY9bSd+Gxrup9811DYO9eY5u6h3bD+OTN/k5QyCBKEa9QReeDjfgP+qocz97e7icvn427oiwbbodvqZ1G8TJ0DhzagPh2z6neA5tCA4nIVJw3uXdlJ9dP1+X+6p2e/Oe6vpnP//y3/27P0sq/5//938pS/34xfzs8YHubk53xo+Hq89fOh1oWcqip8f7CZ6nDBHOieCuzox1Kfv9TERWFW4MTinDjRNrKetytFqn3QXAppqnLMJaKhFZWzXOKSXqhZS+LQUDWRRQwkgN1oaAGoIJCElrS13GOTbnu034OHO+AWVBo6IBY7u7A53EiKp2N2sZSAuiP1IsTCC32EtvZoBpbIOJzzQNI0CuVmuIhCnVdZ2nyeoK06VWq7WWBaBpzq5Vy1pq2V9c7i52tVopNaXs7knYTMnInExrksRMy7KmnAkUaQMAeZrIsR6PRMjTVIuWupRlAfO8m0x1XUsbuiZSl1UkwU2rxnpeLQqY1nAPBoeqmioAKDlQtZiG9QextLBZzSPcaXpoMK1mrgqrUcoJJtPCLG1VvTuIvXgseG6srPVMTyc5zYykkSj9/yOrEnVcHab1Ao9mXrrRoU0jm5Kio74wH97GT27eof+ltQJ1Onb7taMnFs/QXMNlHt5h+Km2QMiqTVNetTI7c6q1gjTnyb3AobW4KTGV9biWw6svXz4c7urtzbJUT3y4vbnMTDItuggUpOrrqerVvD8uy9U0rQ64ivvsmXgWc4YJPIMFJDCgJt4JJld+Kst7vXvEI6AJMaTbATCUQRMoOU31OP/x62d//PnTJy9PExagqJs7pM/y69+W0AeodXfajG4XHnRz30x/HFCb8dutIAB4cvS2jd5k0//Wydn2w6griExufHCjZcItxb/0jG13JjHVD749UBoePSSIhuEYPEDcw+b7h//vKaAGRYb4xGf3XpdACN3kt64w69L5I18R1+0+aFyupxCo84y9pnnEH96zxF0KqcOj8SHeXAXg5ObFwcRTnhPZ/d2H49Otl8Plfvcf/9d//9PPX/7x+7d/9ze/+u7b7z7+9PPT+3f/Cvzqi5/Px/dP728PDx8sn+r6pGm//+mcXjzHft8PKkyw55wki7V+chJJeUrrqUrmRbWWKpKzJElSmN29Fm0sbfC/5tM0aalglFJFWCSVdZWcRChqrsAklAJeg8gJphbW3wE3Y+FeuzxOuYkhev2nb3ai1YM08XV3750Hw6pY23lOINNKThBiJ6hDxFQBF07uTjBXVyMATCxAXU9lXQGUdbm4vIxAzXT1Wk6Hh1pW4QTTtJuZqJ6WspxkmlPKxHR4uieWec6ZyazAaD2tebdPkglelpM7zDQKj6xWZwKbVau1xgjPuq5lLczkZHUtIC/rkpIApusiIsrEVR1Q05gJqrXCnZMAXktxuKmyMBzEbFbbX+MYzQOwm6mImGn4/lor4K51KUpAlqzFzNSjm6wZjSbyptaj1q3jHuAh1TQYTeqq2EwAD7UZmKjzp4gUYN8j3DHWdolhtTuhumV7u93vNMFmLvo/Deqng8O41siCwj16FASuplZLJY96BWVJVqrNBjiLMEvxtVqJZT9rdZpmd2b1i4srvb17Wo4zoxJmmtMkuuajrbMZmGs1gzGIYeyVXHLr/BKGMWqGGGiaL6aLi0erb9ebD7h3WC8xJIMbvAIGc6jCE2i+/3Dx+99e/uzL3f46w0iNiCzC4nDdTPCe4R41ooPe8a7TG873HxlVb5WU6A47IXCrb1zKBqvPVbg/IELvue2/G5aw5Z02GxufOAxtd/GDTemouXUWeLvUj4ilboXPjOpZ0dFAJ4M26NYEOHeSZz6mfUpDrBv91B0W7EcIg7b/jpvv+e/Orw06axB2/cB6POzGYGbKTr7Wp/KUUCfUL3/y6Z///GfzzH/9X//hV3/4Hkjp4sW7m4fPZ/v8q5/Ikz799tvTtz9UOlYuBfX5X/7p1Z//uT97thIrCYftZWf3NOVWmgKQI83ZLTYfqZYy7XYsYu5QDVbSzcOkg1HLaZrn4HOOhydhFkkOSzlF62m1ysLuYGEtRoPGi0ohU2qMIRF1GroJOhxRrYYeETbHHVPYhtR6c/Des4bksVcxGsUJXisAMnBKbm5lAROLmNWoWjFTFnaDTJOr1rJqKWDKeRLh9XRihpbj6fHB6sptiLQJs5ZSakmZUk51XdfTEVZ2uytdT17WUmo1v7i8IjfTGv+Qd7MrKIvWWkqhxOXEXjUgWI0+i7KCiSkVVcDJvQ3VM1dYlLu6GQkpQUu1UjmxlXhFdRi5Q83Mcp5UK6KOrmrUr0LdwtwTXBXMrtVrISattZaFJSURc9Vawsdv9Fuo7VC2VjjRCZdea9eKbtuQUaKmbkFs8j/DX03b+4zFESeca+2GpPDP9byr0sBWGAzwjxPJGDCrsQtEoG3QNIi0qkCI2L2a+un4dHl9XbWCGgUt0casdnh4un/z9mp3sdvvXv/tP6an5Tklrrrc3tlyXA83ry4u0y4/HZaqrmxCcq9lzrlUZfcZ0vE+pkb6+wSZkISSgOe8M7HvD2++KzcH1B3IwA5ykAIKK6AFxqgJzEhSFnnzQ3r9w/7j3TxjEhyrWiIQxz73kekZyNa7Zw3b12m3sxCgW84Gzdx7tA2glYF2OvXc2LKNZ7uhXHOndiVyKDVdDrNqoxPEO5xuILVtFowVTGEkO3XTq2vGR2BkNAgIUH/mP854qPYWHj91FuLM/3Tw0cLS9o/W7XoLgEHBdqN3rQQoGe2m3Wf0L9aL2UbGyqNigcyH8ceZ0YOQZBZxwNbl9JSgF/v85Wc//dd/+uV6OvzXv/772/vHeb/jdLna7T7zPuGCp4mTHk9lWdKzzC8uP/2XX8lPPikX5BOsnYKbeazbdXctSpFUZco512rCvJxOakosOae6qLlxEhCZKxEZTEuVlDglqB/Xk2qd5jno4Zwzgi0VcRhB4CZJTBHJVqDROcFywdzJzSyK8uMebXMI7dxoRHUdzaCFVtSJ7PZFgswRSaoV7m7KwtYCYydjd5OUq6lbTSJMXLzAqtViZYF7krzb7VyVvBLZ8nSnZUlM0zw/PR6nee9qpZzqWthzXe/ztCvr6mQ2pXUpADGl3f6CgbqegFUNxCQMYXEt61pBNM8713Vd1iTJFVpLrdXU0n7WukByWU45T6WuDjfTvJvJ3Ny1VM5ilVSVzK2EYKnW6u4i4kTkZrpaLcTkqnDEejFEq5aZVXVTr+5Ru6lGMK2FWazW4HmIzCMI6pqwgbhWs9uzfdxAdhwyIqtP3cqgE05n729wtP2qgTnzvjo7GFmc26Zu+kPnvNHFPoLEjhW2lzXm23ulUZevswBSTXuAQuqtrUlIdLXj4SgiamW3v0CbbYUkIkbH2yfelSnlmZhKWW9udl++eEzOZERZzVPmeZ4OtYIgxCdTrAB8DxLkTAQ3BibQBGLwBHa4OZ7tP067i2/W1786/vEdTgwQJLXlBJ5ACkxgby7BAN0brfd3Vx9uH49LzjkTLQxFx0dhfTtlE/52Y2p8MHLoRsvdezVUZ8haiNXtfBqRWg/LgkrpMMGxCQRaitj7oCig76qPZ2UgsJNHvnqg7zY5vFFRI+jsEP7MZtPmxoeMtfvqMtsZqi5/1AMW6oMnO0poiZJwlLR9wPaBPS6C+yhq9e3i/RD63fm4XfjZm7uDaB55eJ3+LYhoEoGWspysnHxdP/v8459+8erzl9fv3r//u7/75e1hubi6UqSb23vW+uzj3Uuxz4Tqb759ev1h9+lH+198SrI+/PHd8scPeJqu/+xf8X7viZzJSyxvcc5satXUXEXE1QCrpS7LomaZMxFDzKty+y0QY4TZQWBitVJLuby8fHo6EXBxfSGS6rqmlK1qUTP1NgCaHa0+pAGJ0LuIN3nbKhsR3sjMha8NNNCjv4ZA3R3C3N/k3gbygITdFabu5l7XU0xtYxCTuBDXuropMwnHkizXWswKk8WIJHc3reRaTgeBU2Y4CUs0SdRycjMmz0LrUo5PyzzParqejmVZOE3zfs4px6KuUi3laco7K9VjtFIpknI5ejUzU+HdcjytyyklIRYy1VLqurCbJ16PR+Kot6nqcIfWFZQHFDEzES7LoqbMzOTq7qbJspaVUyIBQK59sDsckQAxdTiZQwi1p83JNQY38zlli0b6eS9xCyTTprJjAHNqDF2QztzCtlbZgIG4O04fUD2U0oYiAh1RDSXs/7QBzDHDY9i2/wsrv90b+u/O/0Q/UWgoMTGxVT2sj9N+frx/vLq+fnq4F5mm/bwupylnq1qP6+H2ab7SS8fFRTr8cNKHx8P7t/M0P1mB13VZoVMS2SWp6mYK10wQdQIniDgYPIMvMCXETFpL4F2+dsjrx9t/Kj+8xbGAL0BHUAIUVIE9vAC5hQKewnKYTsthevdmvv3J5bNPHtkJpFrRx3LE2tJhgNpjRTNK7Zjs7MD6y0aG9uzYCBEBbJnlRql0jHx28t4t6oao47k1TN1lqY9l9jaRdKvkHffhaF0FGxLpd9V9zhnp3l0ZbYHFgN+NQNhe1OEDjWKc5mfirltte09tnDk6NG+6XX04qmbQB82Gdmt9FFkrjhp0UBdcplb1xKprLWSVbH1+uX/1yUdffvZiEnr9/u3vv/7jzeP9dPXi7vAEmq+vr55fymfX+efrTf5wZ0+ni09eTPNFSly86npcjnz16Su6uAA4tpBJEmZiQdD0sYg5TUmrxmy4WitLYqa6VnNlYaAthWAhIjLiPCUiVCsX+z1YWGi32+12c6zlcq3FK5OATZjMTKtpK/ZqlqKVqPUsiMFjLK87mIbIorOSjUlgkJONGqr+HExVqR0tMUytRv2Mq0UtZMzXlJRjd57D2alqjei+1uJWDK5F93myqmZV12U5HaGaEudpV2qVHDdtTEqZ+9R+ykl89eV0zGmapuyuZXkCqKhxygzTshAzC58OJ2JiRqklynXqSet6qqWQpzTPy+GJJEHViXRdHNWqCU++rk7QSOoiqRYrGptmDHArWgpPk1Y3MxBKVaANwIpGedPatxnDrUZ2lwioEVQ5ETElUMD/NvK68ZFBKIfixXL4pszxWDYYQwI4vGHWLc3Vjc7Ys9fGa8cTtEGRNrXqofmmWzQMPBoaHWa8lfYPhduuNOpMvDV8YMsLxDusWbhYzElCoIe7+1cff8JwVNN1rYdFL/ZZEhSSOUmqj4sju5crTy5yWB8O7/zZ/pPFjQicsrmVteyn/Up2smVmmplmpQTe88xWGcEFiYArSsJuynu5vr6t6z88ff+3+vYRugMUiAxBWP8Z0ukcRDrB4Rlm9ZRv3lzefth/+TInZkBESjumbdgTzs/0LKCC96be7VFtbIEPYNZZoNTtWv9VN3Ptit12jv/69rtB+jt6HEJAYwZGVNFfHnZ4S636eKQ9J9T5n7DKjZWiQIljgnkXp8FD99v1M2DfxyS1T+L2PWwks9AQyADvvfyl0VS+xSLnTqZLvgV4bW7yx18xmp2JPTLJ6oAJLJP/yRefPd9fLOvy9dffzhc5z7g9HKtJfXpUS1cf7a8vLl5e7unpg3y4S4d5epbk2Xz64f2HH76tsvoeF1/+yfTxjmdXUk4Eae2tzu2mwUhJtFQiVrVSK4iJmUjUjUkkcV3bkkiRVNYCB4ssx4XAeZ4Px6c85Wm3kzSZuiSP9QEOTakNFWi82FksOpTYo5vHg5Qy8Fl3TxQFRd/B6GVpA8yaCQpzzm0flcNMXd3UoYhpPpLCvYbIMRGTm5OZiiTilgVxYteaUhYWIyPoejoSueScp0mLrrWmJGoVpqaVmMGyHI/Xz1+0FJv7vJ/daK1FVKsaJ0mSzEotS/RYuCtD1uXoIJjl3e60HEwrk7MgWnjRqZRaC9zdq7mUsoZmMEstq9Ziqokz3GvRWla0rT6p0fcAhM0Vaq5BxMbmTK9VIyAAHMRRzwN3MBusj3Glhu4jeYszlrXbfjA5Acxjzv+IAM6Be+c9vf+4tfG7eSPlG9j3fhF01DSU8kwNB9Dstn0w/s0GjJvpyG3cTENx4bvcmdmqgSVARE45Sbr5/t2UpovdTms53R+48tXLZ0Zupcx5z+Bd3j29+TAz0cNyvbuspXi9Ox1PM+jkxkZMZFasJiEIPDknJQIypxkCuIAmTEBS1IS0w47y/l15+vXp7m/0/WuxS55JfTUAlOAZDrjCbIP/mTpwJDW5u9t/uL04nHbTLFFPbgRBj7QDXjUz2Cn0Db520DsgdZyre9B6W/UdAEQFxbC+DcX1t8f06GFie3k9NTzQWJZuvXsrVHus6E8rcgp9sXBMdiRCqzC2jcFqvmtD2x3ye8PZ5GTk3I1O9//UpdHRqS3aRKQfRjdRZ4EM3OHmxqDS8WyEliFwtH2PVoviP/IG3Z2N6mUAPf3e9oWTRaXmfrfb5fnbdx/evPnB1uXLzz++4KkoJKVnz59df/QqTXmmVA6P+ubNxWr7Tz6mev/4/R8O3/0gdTmUJy98+XPmLJgzREIj3J3BMLdqxCBiOLmZzFLW1dzBPOcZDq0KQWwvMbNpnludeIBy9zTnqPG/uLpMksij0ZfVzOEsKTGbWdDTMeAu5o01gwn3KPwPftAi7odZ9KW2fB2BotugFfxE5WwbQtkMmakSexSuMFBNySEiGo1JDlNLWeJRVlMmFklEVGpJMSlaicDzfu+OxHh8fHJ4TpOkZGbH5ZhyrlrJyaqCYykrXV1fmilAbiYpAbwuy7zfr+uipol2dVncQSymMT+UVCtLCsErp6PW2mWBtFYAsOgJADOBySusmtWK5rsNbrWuRFzXBcRWCwHWpjdpFGCDGKoOBrX9zObu1YhozAeLldShGdrGRKJ1g3nM0w9Cv1nmjhd/lGJrfqLZ9i0miAq77sW7ZtGw663EruWHW1VOZ42btej2GkPPunnqWtR/03nFc8LnzOwPpeuqCWoVBeQx402krqsngtA88cO7N1dfffXmD1+j4Pb+9bNXL+aX12ampeiy7ub5ePuhrn769vVlkj1nOR3qephFp9Z64gK3Wog5k2SXmZlgM4isTKAoijIUgczYy36+m8tv9P1/s3d/5FKSJGcGOZSABHewwxVe4RXGEAcpKCY9uVta1/nu4bLaBaUdwUDqpO7exyaPwvNOiIxz9O7PB+XTXYM3Q7yhduoOIKxpjw+aeeyZgZE1GKc9Ht4mKh0V9ie6PajB5TTv4R1CAN5rgdHftLGS40IjOoyvTCOmoO5XOvxHN0K+HUd8TI8qcFaiM75QoH63LZIag223lAn6J245gSiccnfqcCcIqD62zgkUBXVmlKds8P/5x+/K6biWKi5v3t3nW37+/Plf/stf5Cy3T+Xhqb77/vVsx4+Xpz/9yc+nl8/xX79++uabp5sPtHNlZuwuX360u37u+10xDS/Fib2aqUpOcPUKgPI0mZmammuWzJJKKRSVxOZRLcPMy2mtWvNuAijlbO6mdd5d5DxLSrqUec7H0+KqxJxEUpLT8USxHaw4I7bCjGAvrH87I5JAo8NgjEqOoJVpS0kFbg3jYGamDrNqTGBmq4WJ3JxJqle4E/E0zXCklE+nQ84pEGz0QKWc1rW4QdIMMDkODw9aijDvLi6W47KelmAihMXWIiwOIxIzlzyvSwGMmHeXF6fDKU/ZVE015UTEqgW9A0uYq2qMMgKBnM0qASmlKF9yM+Lo6A9ChqoqEZWycAwoJTZVJnJV5xgjpQNmSWI1c3PJ4h6YvgXyzGzmzFsBhTsRCxiu7fiZBcwwZ+IenRNGpU0Lw9qTabC9V//EK9CxUKvvQi8bOmNsxojpYTfQC9E3kz0czIZRh/lvsK01jPRQcMD+zvCAzgs5Oq0xtLPZQwIgy/E0XVww83I6xc19/9s/fP7FT29ev3/+4uXTzfund+/z9YVWPTwc6uNhVzyl/fGb3+mb94Xcno5e1uQlADOnBHcCC5zcEyQjkfoEIqsJmDElmaBg0A67yrixx1+fbv9Pe/dP9qQpifrJnJxyW5tKW2VJCwIsWsMMcNQK9lro/iE/PO1fXUzEJzhDYihI2N1teWPzrw2tj6d5xtMQRT1Fc+rU2ZEG1FOb/rAFWD4YXADcQ4Je7ejeLPpZMtpjCoN7r/aOxLJ3096fbP8Ad9r6SRqk7img85TSSDRsxrxHGxQtjkEXNjtiw8Rgk5uzAOYseowrtwW1PZIaoYq7d8dlDbR6Sy6ga0gD/c2nnUn7cK8GCxtdYVZqcdN1nfLu2XydM7ysZvXq+fPHxW/e33x4++7x7qh3p+e8/OWL51/+9Kv6t7/58F/+dn26NyupJMly9eKTF//x3+kXr05MJNyAm5p7m4Dm6m3hBSGyj2CJdjCgd82aJUlgKmsxjxWEIsxVXMsqKeVpTiJE5CIgjRKgIFhqURYuRVUtVk+YOQkhBhNSz88DxBTjMOPEeFvjvnmEM+ASj9n6qBFDxAcE1zY0LYkUrRFMzLsdk5iZWmGRqEyVRLU6A8SieiSmNGUGSl2DUQGonFat61LWnBMR1bImSaaqZpJSkuSI+iWSlGKxOwvXtaScYhSzm6Y0SeuBULjTyDNbdRixuLctZkSxfcndTdLkbsHfW60y79yN3OBeS0EcI3pXvIFzijYu7ZilcR0xmyce6AZyiGTMLTFCTKBrqbRmWIPnHHw/RlEbNTDfnhsZ2ZmRbtVcXZk2QugcZhEQiwfafJkB4UdSoYfk/wzSNxFqg7n7wsZ2g6Onx1v3b29E79oaxBW1peIRWgrsVKHGksqyJGE3q8txuXnYsdTD8fnl1eP337/6ky94Soe396m63N3J7cOFTG8fboiQ1SemTBN7XUEwZ1AiIQIb5jRPzlBN8AydIQlQXQgyy4520yOO/4QP/01v/tGO614uRObVZ6XJSYAEZ/T1cgCBAakQg1R4BmsIcDnx4WF3fMr6QiRPLCuzRYbIWxreKRjyYdN6M2ZD2M24EnlfD0ne6b9h/wEkd4O3QXOdvfOxzinKdwbz1KbCdaHqFw1EDLjHauN42LY5bN9M5TCi1Fu0mwPYDHyzwe1NdC5n4+1xWXNn4t6X1tH/MOvefcHZRUa0cH5tx49QfBuC1oU5wiI3o2hk9viiI8HdB9U2MR7hBkUsDxALA5xAieWwLljVVZ3r9Hj69t3Xdw8PCTSZzNN0aeWz3TN/d7r/L7+th7vdq+ud7qY0qUzTy1cPv/sOf/oTYpFpMjcigaMfIxxuqokpNn8Js1Gb1N+MvxqBRKRWq7UwM6WUc4Y72nZUzjmJiLqJcKlViC0nMo75/74qzGspkjhKiai5+kA3HmVgQGh2lGgzMOgCa2YJhN6ZuE0ta3JHRAxXopB3tqqFFIDkJCkxSVVLwrWaCLsNyGDOXGqF+zTtHViXk0X3AJMbltPRrM5zjnlo5J6SHJeFhIWlDxqLSfpsppKSVo3B+m7R6ZZYkqm5u8JYUkoxvBNmFcQS2fCwv8wtpSHiZh6NSaYRaUWVpMX8Kia0kRijuJ7c3MgcfVdyB44dERH+mUwHDmMGwMYNWodV4O54KUrhW7m2n12hJ9XObENTOmruoSO+8w/t+YCzmueGNAdwiwe6ebHzkLpdcCDb84s1T0Pjqv1a7H26zGg9aW62GnPW+nB8fLi8frEentLugtTK0+M//vV/+fwnX97ePoqb19PDmzfPvvrSWetysIdD+fb1J7vdY044PbCb2pJZSEhiGoebw4k4E80MKiagBJpb4T8nYvLkiV/70x/48H/oh3/ipyWnBPJqE1Jyy0COKeYgdXbAQAYBsoNWeAzdjrSNutnjo9/e5vWzaTed4BzTrhpuDi+sHgV1QYyD+hibMEu9OtIJ2+ycKASIA2bAiCiZg2BBS7k15BVXwzDWPedK1Alf6p6aOrb31pseLG9UiQ96sdWExM9m1P6hlYOe5X3OwXTjn8g2wRtD6ponYdLgPr0FiGGKQ1OoH4a3zO5wKs0JtgzVCHx7FILBhDq6/kQEoG0MN6iRcWdBaQ+xvfE/jjE4QV3d6n6erVaHFRgY1/NFVTwdV7c8XexeXOxeMH3ilz/77JP5+9v14Xb14y6zWcmyW2utx0d7ehJkSCZilpj3a8wsTGqqaszMzFprbHZMkmJGSlD9KQkChrqa2byfYZQk1VqJKSXOaUo56VpjjBUTGzmAeb/XtbJ763tqJ0spZy1rPJtowiLi2DEWFSSxHBGR6WmWh7Yy0AFAtyfYTJZFkZGTmfYpL5imiTmr6pznWFrCnBwuibVWjXIX95xn1Ro9k8vpKIkAVqum65Qmh6ecaq0ppbWcnJGEnSlRcoObpyyxSyQkkDkCAhA5WNzdXFWNhFNK7jGGwqrZNKVeZx/uzYhIWMw93G3Mq8gp1aoppTgHJmm8RyNYOsSmLnsg7+0uPlS78y2tiRKxHawtUFNqgDroz9im4xt+oh8nrfrnegPi3cB3eNkIl+EYek0GdcTjTT3CevT4Z4TE482dHuxeYpQTOveEWgsFNu8xsNXmK7rw9FpFIhizVK/MOcv0cH+7m692uxlAhkmpN7/93Z//i7+4P958/MWnb7/+td4/olQ2Xw5HvX30x/vH+ze5Foayq8FWs0yJGAKoeyJ21QzmVRmUQBkpwzMmg008W8qPO/0dPfyn9cM/2OlJaAfJkAku5jvQBCQ4Gs/j2tYvSAUtcIcKYt+curLkSZentByTlomQKLJoaLrjPfPdwaz3YCuet/UKvUZmA2P4zUjkNNRFlAKUe5/fd37UwwLzZg8xzH/M8e8TngO2myDGMaijT4tH99DdrsecnFgPgAYS2+ywRjzReQ8BhiSEZfUujf0rdojUxRYdYPaXdmTR1edcrB1jTjnhTKb6LTshRqcjotuQ+daQR10P+5HFz/0R+XglkU2SP728KrDl8XgsVchfXuxWB+X14mLezbPr+rQ+fXZFn331fP7m7cPrd4c3NwXLtJsfLxPPU7l/mNPsu1mmeUxWYGFyMpi5p0TuULWqZmYgFskglOMCQgKnPC+HI7Fr1ZxyylkkMYvE0QonSW6QlIldSwz7FGdHxOlqqtWsxq7qmGUGkFPkA1jJiKBxNAyGBLitWqhNEukPGt0QuLtbw/r9nz0m/1ilqELgBIKk2Z1NXSSpWVWdcla1SIyp6kAbqspEtZbT8QgYcyYnSkw8l7JwSswCFCMqpeRpX0udMjtTLTVPGcRQS/NkMVmCOMgbIhZOFjEdsaTJWyumqrqwmDoloR4vR/WVNyvNar0HOxr6CZFFH3UWPTaKExgFCNIM7uiBaivqz4H0MJFhZilm8MEjoGDaot1gTa17215DEw69rfA6g9iBGVvA0EF5K4hAj/A7O3/GyPcwZlOPQRo4cGbeh78h9BRD+2atZsS3kvRh/9t/GsvBzE1NYYClebcelvsP766unumx1HWdp/nm7Xsu9dnFhZdFj8fy8IDjKVdaHk/Tutpy8vV+UjWQCGk1AquZEMHBBCZOzJNZBs1IAmbQhElIxMlY3uv6u/rwn+TD3/njKSVKWcBegOrJmdA2NwsaOeMAIApXuMMqFCgOT8CK9cIAVzoeUlW2tgtMRFqFV4OwMQuWgq/u9mrwq6Pktz806kFBc78EZjdL3vviOhVjjVDuKX60mfa+CaK3xM2YiNAu35evdbKpo4Sm3E2MBgwx86AHXEPhfXyRZg9a4LNFCds+ioY4tpinQfeBNHrQGGmqZrvRt2f0SKK7CKfeBxBBFYbPaTWLfTQdOqPe76f/U7iskFicYyaHkUMm+nB8Wo6H5bjC/fnl3onf3d8el9M+5XoqT6fj/vRYlQ7/+Obbv/59/eFBgUl2l598RtNeUtKrefriFa4vvBX6OhwQcjJXmBonxMz2WgsY836mmCcbIQIstnyUWiVxnmaCpCiTd+eUYpu59YlRBlfVWkssOCTzZVlBnnKupTK1wQlxWpKlVk05x0JdtLXtDDN1Y2ZiNwvKridU4pFYy62MjEwjuA2mllOGcyUj55QScYYZMdVahFOtxV1TnoKTkcQECXNlWk0rkeX9TA6t2pbemEWjgKRUjidhAVxSCiGUJGouQpKyexxpAggspppTYiIFiJgzg5jBxYpZlOOyO3KECNbGq7q7qYHJe0M0s7gjShVFxEY3BbWm6JbW6iCN0T2Eb3a3lbbFPNZWdzMMYy/xR2dvuBNvozYzLh9S3y11l9cuvNQv0My1D/vrwwIPWzZiA+9kQ7ubcIW06WjXOtp+O8x//wLoL3WcRUSbdxm61cMJALE+jtdlFUnMfLh9v7y7+eSzL54+fCDVV6+uf/mf/vNf/a//j3dvv716dlVu7rLZcv+0/vDO37/BetDDU4KLkDjtZTJd3V0gTq3mkE0ZmIhmSmxuMCfQlHia3tvpN8vD/17f/4+y3BLDaUcEEnGbSFhRYQIiSAUEFKu6i+MIK1gAS6DU9FkJvrqyrXMCaWVyAQmoWGeouRXUed+Q2I+GWqztwxS5O6wb1iYeAXS9yVPyTkNaK93iYL4txmZs7nzzKN4rR20TBh95gYgH0TP+PqKUjhiawR2gvsvNOTTw4fGbNYfzsPh0Hgluwk7N54yY4YyaaoFI/4BIG5OfNS20GiF0D9fvnHpZ51bs0m+yr7dp0XXXE+pQaBweuflyWk9Fy3qyalMSZn59c/v25v3ls8un02FKF1L10uun04zv3lF1efXi+ZW++rOfssz2+HB6e+P7qc4zSFwthTFgEJFW9WqJ2d20VpHAmBKBdBh0sLCTqtZa05SFWVJKU2rdIQ5OIkmoJ78JxCBzi93qar6uq9fKnGO5+TzPqjViJ0nJAWEhEJyC0xBJ8JjY2QdaRaK4LR33M4mKZHsjEeNvcJeUwWTViHjeXRBxZD8ZKixlXSjW4wKuxeFCSatJ5upWlhMnJnBKuSwlorjjaZl3U57mZTmxtb2J7kgpRQbUHLG4lYU1ii8leTWKAXMpqxocYJY0I3xVK6kgd0iSGFXtDR94TMomAG6RWNpQDiECOPceYjebOSweOnw6A9jb7zqV32NZEI8AIn43Lomzxvl+Je+k/jkr3z9xvHj7vAa60ZFOU8MxpH7o62D/qeGkYd03M98KASK7OxBhd29+dptoikrMbYRDC5HQ85IgR1UFQ0SWw0m9PLt+8f1vXr/+1e9e/T9fPN8/v7n55vnli8OHOzqtYnz17OWb777jU8lLkacnenrc5XzgNCWHVjKbcqrQaGMVYjdN7jtJYkguiQBJrpUya0p3iX6v9f+nH/5RTo9ZpnliYwFQYQ6GrFAiJDA5KkGa4yNVUzgDGShQhzIowxhEVi6EVKBo/fBuBsgwdHEG1gxUg63xQKINsB11w8n9+Wxh4zhtTrGkoq8DdiYyiiC+L83egs2A0N1UjjRq98VderbP6mmJ8xxrR9QUeIO2ZVItMKDmIxps6fPULKaUjAu1q3qXPNq+Gm33MOKtYO57uDKI+vbFzshQ+Dg1H/e/hVbtl05+Joxx+B3lNALZx0BjF2BdjQy73UVmuthlV6tl+eLLz4p73l1R9Sn7zzJdlafH1x9+8fknenNMuPZLe/jw3emHd+Vm4cs/uSbJkgC2Dt5UDU4SYyaLqmqM5s85E8jMmJFECKgxtV9IEotkkdS2NDCSJEoixKomKbkakVerTpQl16KmRm4OW9fV3fI0sUjIDDEzi5s7Q/v0SmFuHqS6O4jRYkY4bHQtoSHYbZtYV/1Ih8JVjViSpKi1rLqknMhoLUtzPKpWq7VwGGnKVhctq7tGzsPV1S2LlGWZ5x0zwkVpTGoTcWNOU2RJJCVJ2dRV3cw4JaYozGDmbAYzM8eU50APWmtcrm0eBjNYo7o5hs15DDTSWrUtaXewUGsjp25V/SxyPbOD3g6oG3vvoL4lPpvgc8dkRrQp0XjrWXBAZ5/YGvS6/AbnM7S1BSLnV+jwqX8sDXVFN9Yj0O+/PfctTa/JN4viPt7sZ69BD6PjSjQWUZF3b2FOwuHZAgeE6Owupoc3N7u8y5wf397c/OF7U+hSD6V+8ekn7//wdX52XR+OeUX54Wb57h29fZvXJRMRKXlsQdAEdmEUczZVz0zZaSYRBqmqGSFP025Kl8c9v5bT3z68+R0fnyZSoTQLTj7nTLaC/OBlRyA4O5W+09FB8ERk6mCiCoebwCIXNEGcJ593KwM5xKqfVj/VETR369eTjoFaYqtWW+c2LOG5kY38K8EttTk9QEvkmHmzsxtq7894sOk9BdVCkYb8se3/22x6e8TWI8bzf2w8kw9zOb4Zhk3tZUItX4weKgzP1jh/6u8Y4rn9mqgBtbYkyYzOqKiGM/r7ziMW9xZtj0+NjzHvM3M359dVB74hmrhx91YhTwZ4dX9a647os08/OZiGVAgZwx5/+F4Sff6Tz/bv1iM93fz612t5ON0/pizp8io936XL3XJaZ+FYEMgdOXp0+JmDSWtlDidh7kbgthdeFWHjJEtKBJDEppfYXU4AwFGHQrXW2KzSj92cSKuuyzLv5t1ub6palCVNu2xLlZyW45qmCQ7OwkS1FGLu8hPtcOgEQQRfthHA3sxNnCCzmGlLKcd6FnMzm6Zc6xpJhthTT4RSa4MjBIZrLe41uCSeUqk1RQeZA0xpmstpJUFcWiQhMYgbQ8MJkdeiMOHsGrxNUordidY2s8Mt/oGcSYTYOUUFMFHPi4ZaMLy2gh/uO4Ja6wP34oeWD+2WMqqD2tuZOoZvPoBpU4juRofSDIlsGo6RMAhWKXoCRlXmOdszcove6eJzYongbZtHp/yH6I/Wj7bzlTbr3/xQqFCzOS2cbox01+Z+793UhAFiJrNxY1t6evgAR2wyMHevVdnp+Pjksuipuurbb36fPOdyen519erVR9//9utXr/5qqflyno/fvba3t3xz/yzlx6ebBHdVh5EboM2KqRFAisw5kWSRCF9n2YHnu7p8d1j+d333S7orLxIlKlXFdD9Pu5xhZras7kyelCpcOmdfAEBzW0zp7s7cO7vBiSfnVFOWaWr8d3gA7nieAkCEBCGY08D+RIjqmzYssslFN9veegCo12QS0BfRBZne8y2bLKHXvPTTDzS8vWyEFd4f52b+zwLI7he8v2S4FdpiWQxMv9GTQ9K8TwAdAUj8vIH0fmnCZv0B9ILUcZ/jN2MZWTPmRKM5bWOSuiMbMUGrYRlIh8YpDBVp79i+PaGqkftprVbdAVxf0Vqf1gVEp+PTjvnCj8+Jr5+M7FBf393+3R8Odw8X+7yXa9+l688/Wz9/XpjS5V5yjtSYKdCXp2upVasTJGURYSY3mAiRxyKRKBPL0wRzJmJmEmk2xDwlVnemVkpCoOqmWhOSJHan47KUWjgl4cSEYm2oOjlznqpWmZKkBKecUqmVRbQVO3KUJfhIOqIbSu/pgI4lYrUmCGAmhwjneVersggzmVY3LOsp55Ryqlq1FsCJRUudOa3rcVkW1ZJS662PkWm1rO4QkuivFVCW7ASiWBBJZsKxa8DJLLifJJzUVCQj4JEZseRptuok1HJBgHACERONBFV8MTNj4bKqtUIACnPWpIpIWFosyX2QIQCiXre9ca1NEVpA2v7fNlvYKg7OHSpi9XlrCOBR5dH5gU41dYVrP1Hzzl1RepzWRXmArHM19AZ6NkC1vb592Qa+hmvq+gInkI3KvIYOep1q/6ZEg00K1x4eJQFgAZA4Va0ME+bl8cg5Y610LFxsAn00TZeqzJaXw3R8Ojw9Pn/+7On3f0xvH/HhjpOzVjYlp5xTtQrXLOKJqFgCZ9hM5KYB1ZiZ58tl5h/W27+rH/5O7++uzC8mYUwrGETJK63TxPAEaKm2AilYK3WCn+CVyNwzWSY3hjgHOQQgSbKc/GqynQSgHsj8PIZr2DtYjc7Ft0RnX+jQCxy7bWtutweHIDClhp66vYqcs3f8Gn6hFZturFs3Ow0b9Ge5WeHNWZyb0S5tg37E+aubocWP/uGf/23I7wg7mvUdFRTtlz+SRO8lFluDwfY5vWJtkGXeQoEW3owXe/dxPj4GoyarfbHm+2i7XxA8UExKIpw8g9mJ9emoq6vG9j6zWfjKZGd0+ckr/e5h/uh6nuhqnw/3N2bl4Y/v+dOXOad8dQEGp+yAq8XE/4CjMJckBBDziF0QM3UiGSxC7mmamGPHa9sHysKNk4g9H+ra0qot6i2n5XQ6mevF/ipJMiBJKixRB+nqwgImlixMcDCLmrk7i5jWeGKMXu3XUiwDfbZpBLHrIp4KE5Nw7MVlFtPKzLVUwHNKKcU6rVq1zrtdLZpzjpHRUckSo6GrVTjg0WbpIhxhBxOTkDnMPE/DjBARm5kwW/dVphb7cNBXiDhojNBgEWIO/CXuBDL4qNckQKu6OcxJhKMtoJnjbcoIjw10Ha2Fuev6tekufLBGIzgGnb0m7MVo6O0JM4AoBsk11TvD3N6j3sjKMtp2wU0Z4iC2cX7NM7VBdPFFHYhMxnlAEV+vtwduhH5EIfFRfBZvtM+NAMhimlZgslY8FkPpoiiPHB5dFE5OJMxGNze3l9MOXpe7kz3UdHB+Wl/+yVdvvv4nv398Su8uLp8dbt7o0/ry53/+4bv39OEDDocDFbdCbgK6SPnkalphyk7MlMwvKE+U4JyIZcou0yHV76389/rhr+nm/prsItPMtlR2z8nZNLamz7sctrcUO7pl8xxVXy0gUjiMnEAKCFjJwbywyU5wMfs8m7SycgeICbqBeZw9wZ6N5dblcea80fXLcZaMD3+KVgbalNG5xb5RsI1B0vqGMjZQi/bcO6hoD9q8l5yBMKrPzgzzZtR9mFY6k60mzed+YaDr9u19FPJs5U+djm8VJoP4j7PqXqKTSA2s9GI6H/UV3t/TiOle/9bdDTCOZTuKcavhp3uCPvxNnLiaM8CcDEZuWvSwqhoZk4jAWdhtXfzp6Rd/9a/3L/Pt//bd8eFNOhxvHrGuRybTNGWSKWfEmAS3oJnNNIbmg0IhSaKPF+4GFoZaTN0M1ldSJulgoJ1+qyliEYS3gprZPE2uqGvRsp6WYynr1dXlxcUlFEVPT4fDvJtAbGs1wpQTScpp1lKRiHyN8FndEYmlkKpG9zeoQMSAoZufzvSiIR0nJrFYPgnUciKCqc0Xc1mqo9aySBK1GPvMVrUsi7qmnFPK61I4CSWyagFfiNmqikhstTCAWUCxWdclZyvVHQ4TkoDRxOxmVQ1wmQROMe0nvohwYmJ1YxaHq1vrGgtJMGulTcKtuwHsaM2PIDIfPdJknWRsy0T7xvRGKA1oPMSNBsxrUj2QNfXjjbahlmwLfN2F1vrYrQgOuhve2M4eeoMG6hx4bUh9s+ybsWktu63wAmj7ZEZ9yPn7vaOveMN53rLlOBp0Fbbu7ZxaWZS5u5Wc5oggE9hd62n54Zsfnj27+Pbrf7z03exFf3g9ffqvLtLO736web56/pk+Pun94+GbP/LTwR4edqLH0+NFnhaAYQLepakWjZlaiZIQLkjYRE0nntN0XS7kW7/95fHm7+Xu3c6XfaJEDjPBBBEmVq9ahBPEpn1WrmBoUSbASBjZKYEYRoC0gl04nJgs4wS72CWbwZM4YG1SA2E7kUhsWjNdPWjrALXb5JGrjUxoPAsO2eBmv93Z258zX44uCe5dYxtzB2/z5NxiVldAIWslfqMoqV2ig+lhPc+YoGYG2sd01+IO3+oIwtxvcHGITf8LeYc0gDeLNj4dzeu1H7DRDRg3CW9rmrz/p3f/hu/zfpb9wv2uz7xwuODOrYYIDycR1yVyEVa1dVnWZV3KutaqAFiYk5uXU3n68O7VpXz2Jy/Xb7+7/+WvE06rngoX2rFMknJKr6Z8tdcY/qBtKJuZMqNUrUXD0HCSpkK99J7CaxIHYmXmKIAJPNVahVtM0JjasL/CQoTT6XQ8PqWcpulCcgZjLSVlTikLJ3WTnChNwhlEnJObaRtJDAob2iUYG/lLwW53SgPoPC8Jh4jEjrOUxE3LsriZmqZ58uogWssiid3hailPtRaHRjmNpGwWmVsCPCqbKSY4p9SGuhMTC0lyQN2ZE1NSU3djliS53T6RwYmcmQns5kwcoYZIopid2Sa/Ojqcj3JbwGPuHjPHt2aO6lgwRXaE4hE0TxyK3fFWfwzNSrb/dp6kI592cv1B93d5f6DNjtN2zsNgb1adwLxBrEYQRf/2IFN7esyHLe+XG/EviHhsZR0636OBdtmz2HiLAXvMEa02AHqewzQGmjb9I5BaRWttaEXXxJRyJuK729sP374m49NhzVc7sbK+fufr3YxZ1aQcL6dMCl6Lf/iAx2MmnpIAlaH7OTtUakHVveySc3K6oN1M2UzE87N8DeIF6/d299/s7X/G2zeXdpqpsFZyi8GIQgQowWDm6u7OkDnxTmSXNIvNpAmW4Ikss05UhRZxE/PZfUdVHM+TXSe7yJbFhRHjHZv5JIKzg6PstdvYdn7ULVR7PO4RpfF4BETCW7c5EROlLhIRnhpIojNgc+wj+gyD6GNaRAsUBm3j3nEFsIn1gMfD/4/EboftQyxGPNocj/VsVScMtvB6lLD1Cjo073SOWDbJC4h0DkJ8ZB58C3mbGhBRR1gtFBg3h075jLB2c21bpBHoq4EghxCLMBmiT5aEUxKWSYWsOIGc6OJy/2rHy5ub2//z7x++/3BJ6em0OvzZ/hJMcrm/+OmX5UJoyiQU09MoETuVdY351MwsKbUg2i2gYzRZOWIsNAuPiC4OlNFJ/7bAQWOgHUTEalXXUtaUZNrtp5xhVrQ4IGkmolprnuY85SnPbsRCVdXUvLfSukcGhkJaOpzsZKJTNNSFqTa14VVjLaWqkpnWWHhiU56FuNRVS82S1Ey17i6uTM1UJQkzMwvA8VAs+kscIBcRBHNgAIMlEZg5sYjqypJiwj4TRJIBrdG6l3XGBAgwTJWoL1iJik+OETojngQzq6qZhiLGX2lMrYnA8KxeAgHQTEN+iEaZMg3ZdG+Ts4eG/zNlCheBng04+xPhaIsDe+zV/PBZvzwNhikourZFoIOc4Yp8vM07jNzYpQBA0Vrkjef1DvND55qvIaIoBJCua96v0b5LTEOKVonAeeYOZyaUUqLJnCBOpGYAT5il1A//89f2cLiX208//ZPj17+TDx9omtZaXhZfPry/unpmfDsttdw/JqsJ2BOLW+KceIZSNiIYwxJyBqvpRDnnKaV5zcsHuf8vy+3/offvZug+K1tMLqdt6U54ATK3qgVsU04ijAxWkLsYeYWauTa+PodBETi7i8hVsgy+2GnjG4lYuBcAbbWHtPn2eBkDbfG1Q5qlwiDbbZORzQcTOEWhyBnf0xchttCCGvpvrAwBPfggdKqyeed+jcaa9IiCBnHVHvtwLZ3AxIATrZSj2+XOhnaJ75Ie36sHkR1tY7xxs8cDiXTj3O4WZ86r+cp2Vr1ZydCWClOnVHvd6FnpxaZoAygNT9HSEAYidfeiDCbinCmlJEwGruox+2wSEkyXkzx9/d3ju5v9T5+nOl/dzM/2V+J6enrMHz0/vn+QikRwuAibQ0t1j8XsnnKiCMhjJr4E+kXENZHvZeE+nX9AimZD0A+VmKrG6Fk39/W0Omx/dbGfL5mSWXXz3W5n7qVUd+z3F5xkmuZaVbWG1WNQ1a1Bu+HcHpcSc98O2u+hEQYtHhWRlJJWZeGynFQri0T5fVWrtYDgxKqRoaVlPcWOsID/IDYzNcsitRgIMUEIRKZGHC26DBLiZObEAmYtVYjB5ODoqovbc28VR6rKkI5BiKhRE0zSQtjWNETVVbU20oUa+FL31GIe6lim4Ql2dgQtG6ViHV+3mp8uYD7AOQ2RAxDzl869go94Ypj3rmgY+YNGMTUQ2SLFgbZirNdZxNHf1TRqc/DtFVFY0mKFnkMZ8L79PUBjx1ABKCM+HP3G7R+FBa4AmWrKeV1WZnbTHjxBT0sVSdMEQ5qTlhNVP9zc3Xx4/9Uvfv77v/mnP9ld7abp9W//57Of/OKTn39V371+vn+WVtspdkZp3h1f/1D8JEQE43Wd874sZS+TuqmVWTKBd2mfmFOeSsK3p8N/wtv/zA9v5mwXWckVaDlsIgMSIKBMMIm9S+ow1UrCROSiUWIAgSrcICCPhRlRCSyc5skzeBbaZc6TkzjIWy96r7Ft2aazJ4pWNBEGM4oDm171XU1bNDbyBU4EJGp2yxvUp25bmylrHoM6CO+yROi3M6I7dA5qAIazPy3vsAWuhOFmfoRrumH2/lVbVml719kdhjRHPRXcG7s6YtJxu/0v2xdAl9pNZdwbqRS8k2+eqem3n4s9utnq7mYwWm3mU9xiu5BXi5E8Nuco2lerWs2suIBNZMrJ6+OT8sf/9q/4u/uLu/Lw9E/sIobd1WWdJ76+opTLccmXV1EjCYbXqPUkAiWWuBVmdmxr2sLmRukjhlvujAH63DbADV6Lmrq717Ws63JajiQyyW6eL1R1XYvCTH2ed6WUaZ5ZeD/vY+lB0CpYrdZK1GZFO0mkRuDOEmGrU2sB7hEBqK9PAIGJRdVFuK6LqqZYfmDu5jVaNLPUtRARC9dSmIipxZ7MCZ1aARELGSjnyYpFKl6EiZM7pZSY2SIJr0ZMDoikxvc0gY6eBjLrI/UD7UQlabd+MYM5NqebO8yYudYqJEly279GUWPVzNwgf7qQEQDt65RDQEOpm6xR4z1aPsB7rNmBDwes76rf2bU+0SH+03Zg9wCwiy0N1W7z6bY8QhfxdkubVllX3hZODC523FJXbR9VCU1lGr0TWmbuQt1/tZOJXopwJG6uVSWJqno1Y5cIp919LTxN6lZLNdCcd/awyGHdpYud25xlnef69sP1n+YyXR/sg6zlePetlFPBW1FiFiuFibnafsqZ8m7OpmpAhkQ31PX1Kwid/PCm3H9tj7+m+pZpuUg8c9GVnBOzEEdYR+pAdUhKJAbhFPP/SGiaGB4tMkIVpM5GaNNnHcJxDnLBLIl2iSdR6Y/bPUjbDvk9ZuA5SZyqR+ncMPEO67N+2lPtMuTUaTgGOciQ4vF3P7/ROd3KxT2MB9+T9t2Q+IYfh5htxreLxIDy3Za3YsNzIDMi32GhW5zZ2L5ej88dWjSKyaPTvYH6sy+xwfMgl8f/AF0Ku3VHk+j2iwZF45WtiqEF0s3zjLxD3Mg23mZjurzHBN0YMRFlTgDWtbLpqaiL7PKcKE+ktC7Vjrtnn1xcPy+/fXr61df1zX2Rec5Iz3enBfnlS03MOTuczEhi0oASEXNycBuw2itaWvoj8Ccat96cLNCsfw/dWlWjWcR7zFLrqqqSmTnnaRdHo1bLulxfXcNonveSZTdfIKb2JyagliVCEgKIWRWImMDRMp6mTtRiyJHG7/ITu2IAIia1qh7LXnIthSVVLaZKTNBuQSwaYBF5mMjBgki15pTRWEl2MBLIKLJ0FhFESm2PpoO4V0mBnRBjUjiYwFa40qXVwpRzN5cRgDmzgGAaZenuMQCDGp6KH7xtJR2sJTWxbjyONwEyD4fWqrJa/VoIYoz5bLPT3ADuw2gb+hnI5lwjzz906MW5ze3iax0snhVzDNXfQoyOB8fl6JxfpUZCouXvtp15OMMlLUZokK6pZ2AAITb3KJNlZjUlZiJK03x6OPBFJjJi1mWV58+ZsK4LiJwwuQvLi1cvHz/7aL0/7NJ8VM92Yk+ye84V9fbh+mpfP9wrYt1omVAnnqc0weki7w/1sKzrTvakmCidloNyeVve31HdX/ALTZcGJa/u0ZJJFE1G7K3v22LVqgjIQbE5Wq3CcuLwbXMWZhd44rYJI7ZbpySA8ZSYBDw7ZyOOHn4BcfRXuhGEolnLI77caJHmw7ltOOk1NuxAaz4M/R7PlTg1GN6TPYTIaw1bRpu53PAGDSK8yc5ZGDCcyfhPs5bchNP7LTeXFXrV8BbR1ia+vbd/3Fkh9Jl930gtGjMFfRO5iJBCKxpM37zOphXUAVTYwR/l28brm8KOb7pxUtuH+RjpO1ijKDlvGUIzghVQ2s8efDtpWdbjaS0z7V7uDr/7w/G3/93f3zgs52zzdHP/+Ej5hdDuas+7SUshdxJDG6fOgItwj/rD/gBtLoiF9+GW7sVmE5rvas+QiapHfYWxMAilFiHZ7y8SJUdELJ6miSlxFi9lnueoEU6SSl3NqqqWWgBjFsAdCmqEQ4hl5NlHkoXGMgMM05+YyGIzMDEnqMUqY3VTECVJtRR3ZxFhic26aIcgLZBgZk6mlZycOAYHEcEh6p5SJkrsXLSqqUiCKUuCx/QLCclr64aJYc4s7tq0VaTZYnQZaNS3OxzWDEGb1EHk5pQkBh1ScwN0hrIpYohW7sHhHqkZ/s5mRmRKHK6IrKe1RvwUxoiJO0+/RQNhzuPTtYUg3Eu5AYdz2yxGzSdvocaZjtFwVf1bNz6KWpWX+xCrfuFx72fBuFNfbdbaRNyIEgjqzu5JRM1oEFPMelrzNNe1yJxA1owm8/r0ZFqSTK5mYDBfZKnrKVWDiz0d95QruHx/y3KRHMcP7+XpNF1dH0G2PpX1sOO845nVajFCOVWvKKwG6Jz2THMhvTk9vMeBKV9U+Xf5VaLdP5wO79xOyacMU3UnQNmpqjFFzsicKTMBrPEQetVFTPlOCS2TRFAzY6IEzgyDZKYkPE0WdsgNEIdLjM0Iq98JRIcTc8yRDm6nddEQWZ/tHDavVxcEVCAPM2VI3Vr6mHWP9qh5JLW6EW6PlnpcMCxk2wvU8M1md6mD/xAc7kF0CAKdoZLhWbYYs0OPLdV6hiPQkUpPEfSMALzZ2xbghtcZdf/UHZWd3V3/Z+9v2axkV4QezgZAPlcNR9eydk/90Zz7gIb7wnXAQXkSSinmtrq5Cxf1mnY1X5Q/vLn9pz/M06Wy7+h0PJx82j372Rfp6oU6rChNSZi0ajDpFNrAoUhbyVXXUGnVxr26qnvqUXwSYugWi4XNRDj4uiSc57yb9+uyqtZ1WYR5t9/BCKA8TSllU8RuAHZei0ZEEte1WpjYqIb3A9pYbmr9qW29xVnIxoGaW1qciOAEVqtM0OhkZjEzdw9zBwBu0dZAoChJA0wkt/IvApForVGr4+YsSVJmllqKahVOhABs0KoyTa7qzOj64hGcNg4cw9a3kYmtx4p63VM8dR+60RxwD7t6rNqeQ8/rNiocnbzx3vdEHXQQM0aFvzffzjSedd/c3iW4PdqG4/pndpnumoENsLVojDb124DV5kc2zaBONG+Fm12JOs7YMOE2KYxo1HQCZiptFq2jDQaKlmkZlKAwCydzrVXnHanT8fb2+voyzdOhmhYHsLu4uH19Iy7EuZxOP/z6f1xf7o+Gy6vL1bA8POz3zCukEmVaH+4uXry4++btVeKJdhL5aWJ3N1Ks5dl8mdIenI/r4a6c3uNeQcnTdJpyzf/LjgvY1rv3RCYEdldLUTPF7NTqNt3hTJKIOn1vjpxYCIyYv8IMY2M2FSHJzFmoFuLE8+w5G7NHg06YMwJcYZBhdQkjUGOQb4XBhNDmDa5294Ozpx63Qd0GwAZh0KDFSNjSAPrBdvXc4bDSGBRgc04jYugY270NF+tR/8gvnMOMzYRvkz96gNtfEDjXOtQfl2iOpkMqELURY/FoIo5uk0G96+EGwrrj8WhA6fYbMdV/k+TmP7fAuX/hXkKx+Ut0jaJznTN3ZjALO6x3g09zfv7qY6yP64e723eP9OKzT//v/369f7v+4fXkRK9e1s+fHe7fz6cTDgeCEe+cyM1ZGKAY9gDuOq9O3Qc6rCNOpl5RQtRPdxw1QU1VldriQT0envJu3k2zmTl5rSsYDNnvLutaAKQ5CQuRq5nWyuJVtawrJ3aFmVpbxILM7AY3DZWO++gVf3D36GoXakEiAepteHJsmGiZAyJm1lpCuZgktsH0mkWHu6ESMQmrqpoNlByAi5klTQR2b1xEG4FnZuoxvjQ6ldQ14GdwPh0MOVPqvVLwrcOOrU3OQNVWu8ac2ilHjV0rZwxfQB2nUA/XiJijYlW9eosvHN4qvjtMG3uW+syJrj9htTc2wL2h8257B4baSNyBqbjxsmgkwdD9UQ8ydJOGi4jaIu/zWtCfwZZ+6K6kUQjxZBtEAxGNJhA1yym1TZruDG+HbuQwFtZaiVhVd/N09927/bTLeynHpT4+rbK8/PhVVuX1aIcjwe9+/83P//I/rGxC9eJ6ziBf1uXhbl9Kynx4ev/s45ecxKGLWyaaOBMRVLVWBxLPKc8Pevxuvbn3Y4XuMBuMDbzq5Wr/t6srcvtHOt0SLRLPvRXYk5E5XAix/kFIpD2nBhvISAjcyIGUSCBCzIklCYRpyrTLnoSTxEO2jikNg9ntgDL86AB9TTIGsdGKut3hBD/bKjEMXBp2OyK/6JlsT3vIEwXGPovtBvjv9Dx16HvmnrynvLbAYQjhhm+G6PomNC3t8H/harr4dgvev7f3M9mMdW+J7lXDLX5pBtypK+Cw9Y7tZIZS+fnfmuFsd959YQPRTT/GPQ96pVnigayEWzmuxP1ZzB/Z3d+/PdZ1/+rjZ/+vn/Gzy+vDVbr47PiHXz2dTpho/+IamQ6Pj/vdHPk879rdHi0TTEHSCbMx+sMh0rxdf4LtBQPkukHN3BKLKw6n07SbUkpEbFbKsrghpbTbXYgkzFzLSiS1KiVigESW07GsKwkheFutLAzXJNKOr9Wqprj3OCuPKRXMROibgWFaAWdmrcpMAJtVR2Qm1FrxTbDhwS2BQ8pNY8iZA0ysDmIx99iTY26csoP6Si4TSZEYb+aJOEynuzMJEFsAOPw2Is0b8sUSlQLkYOGB1KP5i2IZTszIU0UY4i6s6DnXHgFHgBp4pDMfw1JTG/SI7iyaYrnDjFLqtAu1/Ae6D21XbLaesJFOEXDE7IoRp8SrvHmOEeUEhtsGx226QTGGBueBQg83eq47vlN0H3UfROjNf0TmrrVKykJkZillraWrSdNYAoRzqQtz8upV7f7924vrPSfZ7S9ef/31R59+jufPTo8PeabpMqV9Xj/cTiZY18Nymy+ufAHVE9UTXNfTwlqXu3tmL27k1Yvv9jt3JlOYzjyva7lfj6/19h0OFT5hztgZtMIZdY+UT+kvdpePRU92spk1O6K0zVlhUknNE7uwuDqEKbGQQ5sSVHcBDM4s1IalM1gAkZTTNEnes0xgasUItA3B6b7WW3YrrHy3ke3EPDwxbai/5wHiaQ6TxiH94SWYYqsQuP21ja4K++JD0EBhnalB50YqdZyFEfH1V/fqJWrbngKcMnGIG50Zx+YEAuR3DpWHgJ3BnfGqBq29y5e0sqohq8zbHYY/DTO+xR7ewo44D2bifiIDOI/fbH0UG0E2vNC4pY45iSi0P+6dQUwsrVm0DRPWNhl5qYVzfrw9vvzqp8//9E/pqdbvbu+//sN6OE7P9lc/+yJ9dLkcT3k3pTk7E6X2jSRFc1GMUW72q0V+BCIJ+7o52+6y2h2j9SLWqlBnFiePm5znWavWWohhgEia5jmKw2XKBLSsaSQdXM2sb5wXg2WR3ntABJibMHM0trgTx9+a5LijyUefaG6OaKGyGOUW2SnAG5cYLE1zao7WRRQ9nExcVWM/YnRshaxJnoQTQGbqALFwW/Ll7SRahNjFsaUiGz3PkpqpRYP/6LX5IQ9mFi6EWSgyL3H2rTOrhRGhfBhy0+WVmQES4jPh9EYPbXVwPcuMGEk7oMfQR+rtMAM09XiwR/Y0fhxatREL/cNoaFqT7q6orSOsPTlsGG1cKu7VzuBTS/5sns/IwWCtahpbyN3diZNBo6yCmTmzO4gx7/ZlWSND/uz5i+Obm9O726f3Dy+uX/7wqz+U4xNqfXy8v/joZeY0rTbzbt7Ndz+8Pt7cJuF6/0HqKSeZ8pwk8bJe7/eubtAQG8nTWmue5918uSR/rU/f4nAEWGaAjije8KQlWKrlswV/oZefLplPhpO5GkcHALHBFa5mbt5qhd2FkIWEnckZDjciM1hj9pzZhURkmnne8eW1yUxpFzVrEhDR7SysDx86Cmu6VHm3mdzo1LBXYYWYiAEGEpFwqF+zTkNKwjRsPj1QUYhyXI66f6BmnWlYzO4x2osY49XjhzMzfoYpaAgfMLJV3WATnb2DQqfOfzFcSguSm0eKl/P2TnSmtaUJ+pu6/2uWul+3f5P+Kc0Z+rjr/qvQDqbtVd0LonsCcPhXIrS6Aai7mqvHJq9J8myoBdVZ5ucf46mmb2+e/vofl+++radDvp5PP7x9evOYdhfz1SUxuRo7sRCzEHN0Pnlbz67U3VPTRR4HHqE3us0M0iQat6KjMkVLd84p5UmrWq2qZkBOed7tiRhJGhnNbdKkm9Va1mV1cmEBqGrJaQ5fCoK5GzyQTrRuxjP0LpSg4P8bMxOPL9Jmrcu8CRATEYsAgsbakUfZa/dnEWEFg8wskR2Bw9zzNAXBUUoBkOcdQG21pMhoTNio/HicLYjq4oKAVw40rTCPsdUxMqiJMvUJE4HBOzzvwhqlS93+dytJ3imjFmrSqNHqhppasVzU/vaodmB8IKKEEYiPS3cf0+FWE4ktJvznr91KlQZAwjmw6go3dJk2fBGSFpxjVL92PxHBgTs7u6EWNcPp6RiTzswsiYDIzeCK2CcaJcNgW/34cCyHdUq773/9zf0P7/XD/e7q5cO7D09v3sFrnuZ8/dyVM+X6tFxfPdsp5uP6rEzZZMdThrA6jLWusts7OxFVcmIxW6acKtN7P35fn77G/S2qSBYlQTKYwk8oBZWYMrAz+ZlPf+rTswqGCwm5k5u4uwBEBjKKMjVrVokpMwtT7BIKi2SkLEQCT5QmkSwkwrvZha0ljhu12KOBBvZ4s6UtwOwWDELMxEKcmBPF5SEEdkRDPBNJNOCRpF4a0oOFLVI4KyHoFoS2ktEme947jd2HuW3px6YyHvUBnQ9sc1TQbGGnhVp82ox9DxndqQWqIW9BpPbPD3luCtDnh3dP2Iyfbxiql9GhS2t3HI1JoxYeNErmXMBBQNeqpm9Nefs5dMvf4voWL/XZZuED3MECCWovRlAYpTyBuayFhG5PT/P1y/Lm3fLff19++atyf0PQ9OI57659/+zqi099l4nJa/WUrHr0cbcCwqj+adOczmqoaDwUh29DvVoJQUyEczMzYpZYZaEmKSdJZTkRw2oVkSnPmbOHvyCXaCkmzyxrqbWsagpCSpOpJeFhFZipmjLIk6DpuYoIOcy1zzgM0Ypda5FzbcXgDaoLwcRNI0MIEMCAoRceECF670WabiVOUQEU9UbzPKsqA1qLk+c0xUFobVVS4sl7KpKIopWZWZoEEYS4wYu2icmYE4gclYnjHFskyeKtLQDViogEtKcu5g0/Y4Q+7ajQWy+7LDdpbOnuPm7tLIsVT7YNHYqPiCVjTU+oJV37OJ1wLN6v3cH9oF47s7x507DpPeRuujIqCMJm9K4kHiVMXUG880Q9WpPuY9zUXY1c6rqWMk15clfKMxMDakAmcmbn5ObkPud8Op5mYj/pzW++T4/rbk71J189v9zfff2d7Hez01rteFhezEmf7o9v3qVak+nh/e+5lpyF3Bi+m3ZCbEclIyZPPLHQ8e7p5fX1Gxze19Mf7P4JmkhgnDEratvaDBdwtTKDJ+fnNf2C92/Nj6fHk6izMStzYpA5wOQAuxuZGhdDZggjccvbRgKFmA3uDCR2ckrCKYOZkjhv6Lfx4Y2nYCdncvK27c3gAnbqTfdwPqu8jYF7w8IHHBmxXN8yH7007UcmtHkxLUZwUJRKDwAQnmewJdTnlg+xokYohkHfQLHZGWr3oGKoK/RwLc1IMaHXJVDHznQOO0bwP97TSKSO7huqb/nRtumgI/d+6xjhSwPpLGEFYrYA90PqUQ93aNs/Bc2knkXhbZVoo3wAcggjSUqUMjc2KUvixFXVyNWW/YtL4un0P35z87d/fXP7vWbOz1/OH30pn/3k4l/8iU+83D8t726OH+6sWl2LNxhKBMAMFqC8By1BMozQq/Gz3rgrcsDdbIDCuFuDSUoibFXVbC1rSlNOk6QMCetgKSVqB8Wm0e6o7pZYpBVBZ456h141GGIYxlRSG6vZzMrwFt6yA4SWWmxBy5h64kBrNgtxo8iBx3k3PxBPJnZ7cTBUzpLcwSTrusBtP+9BXItuhGOjkLx/bg9Ko7WiqUcLO3rU19wqRWKuZ9dbTBbPxF1YYgVw18iWWEb3K+Gvm9p1S93iIg8o1akjeEsIxwOkaInxs/KjBp26FPaXejfz5+F+gzXt8Q9M1L/diLHbY/AO/TqSH+jLt7NwR+vps15418ljp94KaarOYAKZaSmrmR0fjxH8makwaeypi9QWk7FzojTlu/fviAtBrxKW779Py1Jfv7lK6fv//g/L929eXl2U+5tnz/YC1Ls3okXYCIryROWQhdXqbppdQRBWz2DBNE/T6emOE32/Pv7T6f3fnD58n6vlnHlXHQo2iLXgjAhkUEDFTYr+hPd/ka4/R0rq1UCSARIiJ1IiJTESIClgTrH/F9SaCLtBY0k59tCRc055miaRzCkJtcjTOfaQBUOI4MSEhWMSbffl7iB3mLE3FjymBglcAAHYIUTsFI0FiShtBPsI86jR/51C6sxPE4rQv4354REldJxLG+8zZJiG5NkWf3bNHSi16xl31xLTLZjAQx0bSdP8TaRJ2k0GqxVt+4hKJjS/0Qxu48Q264/+XYdiDLJLuB9D+0rxMX3n91CW9h3ahzT/0luA+tWFmZiS5MwMh1YzU3YkZlODQbXWUudXL9InL28PDweUBbXM7i+e55/+wp49q+bl7m69uz+8v2NOvq5RQRjGqzs937I15/alpxR73c9wlQgCJx4R98p3IobBzNwsMggpT9SMHSgqjrQNnqla1aq5JUnCIiJJOE1ZhFmIQB7F0U7VKmDERCzhRkEQGjvyugE9G0PWtk5ykFyRjZQwju5RQ0kUs9giKc4Ch/WqG+Fk8JTnCE5VSzwrc3NTZnKr3MSrIV3qhjvaw8Yp5ZTcRwlYr8Bp4jua9CmKTYfUoLNAHap0uNDSuTEBAiOTgZh00/90lN5hYLzbLYZaU29+hjs38AFva2cCkIzrtE/3ETV0t7/9HZFfHhzR5jnC755dqNeWnPmSdrPdY0RaJYATWn6FYYpeIdnXbtB6KiL58eG+1mJmpsbx4MxUtZEHTIbqUp8/v/rw9m3xw8c/+0hv312C7v/wzcvdVbo54PUHOZ2uZffs+npi6LvvP716vpsyWSUvsHp9/VzcUBeqlVzXsrJTTnnaiSfwfv4Dnn6pp69FjzkVzhN2GfMJWuAaPA5QoAJyWAbEdF7spzb9ie93C8iwVKtVYxrPWmx1r2BrRKsZanOS1hqHiSnniSU3jUgpp2naX4FEGpAhMBlBCeCYOEnEJGByi6ytgBOxOJKTOImzOKAmYdzVY8gGE6UkQpSYEygZibEYJ+rURX/UzfuPR7uxix0VAcMCYjOc3ex0Lqc961Z/2TqTG7G7ARH8OKYYxUCDwWj+ImKfQUB17QMcAjg5+3a5XnHfb2v4g24fSeJTudcndT3uQLT5avQvGOt6mChqMpzPWtKCRYofuTeBjWIk98C+kf6sZlWVWuGLxOKnAMbTNOXd3ihP//YXfn/78E8f9nPe/+kv+KvPTrMsp8PpXaF5d3F9KVNSV3KVzgE1s2Te6EAMN8D9W/k4aEdbyNWtXpDxHOajfV+zmOqTJIskbqaWyGkIFEZdZlECokqHiTyl8H6qlYnAolqjl4oJVpWbD42Z77rx5ETg1lHZyj0dIuLubSO0SMQBBu8xWKNJqLexRKlcgIFaStT5kJCWNabMi3CtCnfJqdZKTsxspu0caTSBu4gEBvXoMHCPyRJBfHp0dll8dHexHUONeHSIXFjRQbY06fez/6UeKhPgHn6pu/aOpHtxZ3Mhm0KOSQHWHX7rPjQNBfoROAjo4B0cBD9JzbP1IrqtHaH7sv5Z1CfQD13s1CNGIq290cfsAADQWiDsZs4ShN/h8XG//8TUDvcPVy+e16XkaU6Ici/LPCE7qTmlQmtBPdwfUqEpX9LT4fG331x8/pOrf315xbn+8e3p2fXzq6sDp5u3H57lXUpXXCqjMjwnATlPZNXAKM7rerhIMqd9sWWZ+Hf6+Pd4+rWcLtKFcXLFyRQgQAU8gQ0+NbvhHhNkAa7+kcqf5Ytv7HBfihIx81KMmYVcjVQoxZh+iu23xkRtGJY7PBOQOIvA4SQgEUiCcAUrcZ+SDQYSMzm5W3T8CyI5Yu1Zx4QW7w+UJTAMc0MrTBEjUItCKACjpSA1olOsV7hs1cQt3KMfhbAjUj6DKOd9gw2VclNrMAkQ7F/r199ka7iBcys/ItZeYNqFLQSxuRkALckOaje5WbhzVNJmRYQdDA2nEVkHQvHmtDCwXBRadmfF7QYbt0kbTgu5b2Cs5fZG9i+2qVHE/WRwhZEIozV0Whsi4XNKu3x1f/d0nPn55Uu8eIaPvvzoX/6bi3/zZ/TiQspTvb/d/8VP0mcv/dmuenVkL4qZXI2TUG/7jENxiyKKfq7bU0PT8X5STk6G4L9icDHgZa1mFv1YKVaZcNIwkW7mzTK5AzDTqlpVVZJkycTMcNMK5pyz1nX4CRJorSRCLORe3cwtDrhTSSE5HA3TQHNLwjHBf1SlAZ0kaYkqD3NM5s5MTAz3qiopSZrMDOruxkmoDZZJMZ2IEPdi57CjW+I2WiOeq8VDJ2gkzIUltLZHVd0+ukiKUMmjH5ibqd2C4jarlUfYEIsBLBD2mSI1ofczuN14MjNT5rPC3mGd23N1mFNsB2pNvu1i/SKBW4aa9F/5sOve3Ul/BVqoGRwEnVG147Pb/L+IokyjJMbdue3LRCnLfHEZoVgiIZbT01E/Nq11Oa6XV+qAK/I0VVWrVtcqmV1VrZJw2k3vvv3+ZZ7rNx+ur64PX//hxauP1x9uZxO/u93dPC5LlcPx2fWVfni4K99Da0rJ6zrPu+PxKNO8lgcXT+5J627aJ/Ah6Xfl9Dfr3bdSfZ6dEyyTRf+FG9yhBVEf3waAAOxRQc+QVb+g6c/Ss/f15i6Bhc2UYyCUuzGMkYnB1gCuOSRMLbtDzQwqJCyJUyIRgxqZRUU7sYAzfAKgzoCwkDuDIg9mcKtKZlD1GHQuYiKOyBKQ9RHvfV6OcxsT33JoqYWq6GRQhyBh1bfMbK8i61xgJ4Xao+/ovNV4ta5g9AUXzfuEI7Hga2NZxHkYEVRLWCnHBspj8llHRh0o9UKkkEDurGT3SqBtlBxvBFe3hh2pj5rNEOUWCjgMfXgOMALnuImw760FBiPfFTGJe6Rp0BaZeA/23Yk4SQ6bQREeMRdVIpqmiUSPqyy6q7d2erO8/OrP06uPsVR9/bos93xhUq4Pv/stVUmffJxSpiwwpcRAWLBmdHokRb2ee6j8gGydB2psMxGh9Y3DrXrVGhuHmUlybsfSAii0VbwEKOpaaqlqJinlKQtnEtZSOCVdlxC7hgGYVBVEOc9wr9pmObiZu3osTQsaXz22WcMp5pg2d81tv4G7NRF27+jVuzz2bvNIGolEYFTrmlLKU1pOa5uZIayqwmyxFbnNEGnCStQX4DHBycxSnsw0YJADQuyAmjmMmQc67hCqeQXuoSYcbYk5oe8FG6LozS/EhINAFJFRAHeo3iU0pM1tuJPGtMeIAWrV3i3NS702w7UvCwvwFnE5/YgEOpPk7kXaUBjv6u3usfqmfVOYo1dARW2uWYt2CACpVSKO8R9MYGE9qJmnlGtZhXd5zu7+9Hg/z9Ptuw8vP3l1fHqaLiaZpzzPx/KoVUicmEF+Op1Y+Nnz68O332U9/eTPf/bHP36f7dGPD5P56eFp/eGmPNzvbAXSybADESVbTvtpB2QthQiqVWSaDNPuwuA3evhGH/8eh+925rv9J/sLPSxcSUnXSB/BBKiwCWIIU8JRIlKBncMULyh9RRff2ONxPVVq1d5ksKgViHifEsgNRr1cJwICg5lWSiIpI83OZDHngyzGqEAtgybzxOYg1AozMaLY+2tqZfW6oqqaCYn7VD2BxcENEnbcASd0ie2mF6nVFbWg1QfI7jhx/E/YVyImcu8ja4eBocaJ9rK8DSwDxDHeqgFS4lCr6NRttd/YZJzO28lA8D6HcYMsBOmQHv2mNycyrBxFJB1oZqNCeoiyBdE9xggtICaCtEAn7nWE7e5o1VlnAQxhFGnFVFL0kGnDVUQS3UMWJfPOiYmFM8jNtK5rdVw8v3la6w8fntHV/PwLO/jhf3xDdFimul5pxWH9/KvnX4Epancs8EmHZf6jQwhLxh08ts5pp7E2oluTcABGLsQGr1pa+4KDoks27GkITzwMApi1rCTki8c8xJwmIiJmZ661SM51XcxNo2EqBid4S0zD23SdIWHM0tfqhIlztIrPQRgDAcM1ItsurPHdmxhwLJQHQCxEyczUNVL5p9Mp58nUmGVdl5RTLNXptEfzht6THUTUu2oYxEGMIfy7OTjyw33qKzHcRqF04CaFUotLBG34EXcnTcO8hvW0rga9SgfW8xyNR/Uuuv2GBznjjbR0IKw186g0bUVE1nBMe22D6u4/UoSucwOC9ZbdFgC3V8TIdGrkmCZOW0ICzsxWFMJQ5wiT3JgzswFa1mWaZsRCR/f9fr757odXn39a16K11mU53D9NlxdTzjVPtaobsbBwgoOqziyPb+/54bR78ZLXY/n2e73+XA6Hua7LN9+U2/tf/If/5fs373YXF/V4EiPhfH35/HhaRNUSwQ1WSGjmixt9eOunX9HpG6nrLq+ukxYhmBedUBdT99iSYeAKmwGAKuoEqcClJLUKV17yF5z/Rbp8V+u71ZB7Ch9UVRNRllRDGTmKCnwSbpoXaX0Cgae8kzSRE7MgWqAZyX0iF1BsIbVSSNWWSqimqmVhr6jqVXOeXCZlZknmpsrKrUOeekAaIDU4pehgSSOsJu6j0M6NemsMJgzz643jxIgiw7A3M9zKuhts7q4lOgoHfe9NULpkobuOBpeGbd3cAnpqIV5L3HYqeeymCZr/jJnvwKmVRDTT3zmycAu9KK6/p7NCTU0G1G2q2n4Zl6Bo4OhDGs8joqat/ayp1aLDHaZqZmBKDAZcLaVc1kVVPcthWZcpIeMnf/mLzPPht9/kp4e6r/NffbX76mr9/CX/9Kc6T7YWiJDkNAu43y1662Z8T2qlS63+Dx33bVh1hC3QqgG0tai3/EQfigAnkRbbU0tr11rJDExa1OAsLJIoFnRQ20zhWsPSNELcjUCchZi1akPEHuUz3DgLs6ifcXWixmuNYuK4b1UnGuFkm17ZjWaPzxwiMT8RZhq8f1Ht9V5StTYETR0LxM9MPnwQbxZNUnZTeKvIjAbr7hyGzXRvfgIg7lMOfjSD09CSgByDu91akc+IMXsHZQMvjB4BtMko6HEYRjx9FtjFVpLwE95gB3XCswOjiG46sBvigE4ShgxbG8iMrpCMEOrRbd/j2jh381hk0KAXJwl8Yu5EoqqpDfWZTofDNO+JUGph83m++N23v/z4k48zs61aSi1v3129fL5/fhG1COtSUxK4+6pU/GrevXt62jkvb2+eX17y3b1+/8dLSsfjoz0svBZ7dC4VtdSyzHlKOXPaOa9Vqy4GtzklwG/94SbV3/Pp/aTz5QXY9eFY6kKUTFCJC2My34Fzn2NZQQnO4PC2Ry07iAC26lWin/P+Gxwf6qPniZ1FxODRP1y8woSNUu7kP0NYQK4wpqqUSGLuJ4MZiauv7d1qyVWqkZnVUteTLicqi5aT1QqvDBWAU2a6Mr6stC/kaxs/ZBSxalP/bso5hlAagNhRQRx0xbCeZwN/2IeF27JuDeSi47eW9TknRAYMDwnphGer8YiWEB+Bw+ARqf/ck2cD4fdQuJlxdKPaTXKjNNoLN3Kyez2KWe1A5576x45Qp988YoZwIKvR0L4BbO/f2McwBuoOKpQ1AoLuTluvdqyFyixBkJGChV2dnCRPkHpaj8f97vN/9fkLfl7/4Vf13beqypfPds8v7XpnyZfTMZlXVVSbUhQ8OTo50Fg8H7zW8IPesOoW7nfNJ8TKciAWy1sQU3AX4d6F5H3gRMD2TnRYrI6HpDzn3CXBVauwmJrWSuQiYqrmgLCk3FsknQnkbGRErKUCbQp3ONcWf7evNNopiJnctJ9+cwZoZA0cUHdhYRFVVVMzSylFojfx7HCtlQScuPbtvh0iNJ7dh72zCHg4jH61GmXP5pSYVesIjyPKpHGm3oG8O3GUJ4yLjzc08z3YlS79HfFsOhEz7MjVY9KGd3d8juZHbNt/NXS5oZ1gQpuY9/PsAV6HXuNa0dcWPg9tw0jjCEAhzCLiquxwDTKzpKjdi2FJzFYrAe4McwgBEElluWMSBqspzF11SvL4/t1E+9PjI4PevX790Ref7y73JCxCppWcBZyJAOxUXz5/fvzV7w7HAy0GX9/9z1/+5E//sqphXYr5/bff6bJwKbPxzKIG85ITa4m2A/LiByn3k76Zy+FFknx1dTHRqR4fj8UqMxbX5JkMCS4gHkl+sAMO1vbvFEeaAVn9I5a/kOfva/nhVGgH7cBFqxeOEW4sSimxxzT3FPMFIsBjJKmwzOQiCnMm18WOamuRWuty1HW19aT1WI8HrauVBe5ZUsqS0y7RpdtabXaowaUZX7fRyBGSAA9D39ga99RZx2bah00fAGkspOwy0C4Y/mBA7hYS0hlabr/ZMFLH4j21y2dESntVtJgiDFpXSTq/IjxCB+pguweqHUzREOb2NRpvS50vbh8UyjdATpjqDqe2RDJ6ZRJwduENQLe3eCeielkF0FkXMBFMjaMeJZLLPZ1mVsiRiKE+7+d5n7/4q58vv/yBf/+H9eb15Zefvfz553Q1Hb57eHo47D79KqoP0m6GZGI2M5Z+Wo3U6Y83qhs7Dm/YsKPk/hjDrJOpmZq7EYuwRKqXR3ERkbdHDFMjoqpFq0YPbc6ZJTmcXDUatRxVNaAFN1uJlBKTGDvcRRIzqxcmMdUGS1R7mCLBirQUq/fwqhWTAZ3s7lUSUe6iUY0b2dcIelLOAKyWlCYjqFYwmMVqpTbIM3w/BZ5tNk44TBYZKMpGYSICwFQlpY3HD72J/4uWD2rJiRaQNU6yK4IPRNJlhaInyqlvhUR3CNGD7702pxlljBB5APrWmTaAlLuxpI6uvAUbTWp76dcIPgbwiwjfrbEEsRymKUd3+472c+/gBpFqndI+eqLjq6lZIhZJrkpMarbWMs+7lKbT4VjWNU/5/vYpK06Hp0nSux/e5Cr7yxmS1sPx9s2b6XKeri8ddLx/WB9PV9fX9bgs7x4uLU0rv/9wr6VeU5IKEd+TPyxHtmVKOz0++OmU4DnP7hCzTMTpoqAyk5ItpB+ovt3p4xe8//jF86r36ykZ54vdcjgQoagd6iFTNvAKMDi1s3IFBDAwwAIoFKAEr3X9CFdfkvyM8r2XU1QsqOUphcVR08RwZ4I4YOqIMUHMIsmEFepMxjCy4qZWyulBi9rxcHp69PWk5eTrqZaj6wqzRE7OKU+7dDGlJKLqxoCakam4mYc0oqcc4fAYRrNZ1k7l9XwqDQgxcHEz27RNJiJEoTtv7e1nr4uafeLeWsZjHkrgN4rXDI3eDPDmmGh0oBERkbRt2jyuha09gdugnvZa7n6hXWQY8a4xBBImaVN9fLTtxnv7jY4+L2rXby1dneIdwUC7ONBOo38Dp+7jJFyuCEuv22i7juM2IFmymi3H07y/eP7ZZ6uk4x9fP/zwDpNcfvRx/ulP0zQ7T6/+zb+5/OwzokScagxw6ENURp1Ho6woVklRVCp6I3o3RNleHJ0EzGEdGvyXjhMjpewAoGqNfmhbbQ2IpC7ylLOkeIpRbMVM7tba/wX9yUtKEwhuxilxiq01DGrEvzdnzaMTqv0rqMeJ3Bx9/5Yt9duNEYhFkjADVEv1JtwMgEVAHuPbmFvJkCNSqV0K4pOiAzL0BCCJdkxWswZoOgiwVkfb8Lu1JDyh14wPvNMs/ibsW6DpnWSPv3divRno9sdHOrfLMI8LNGcRPHtzPE2XvPF2TN2vWYM21pevBa4ZhFK7HsbXH8uYbAseY9iUWRuPxyBSUzMnpmrmHvvRKHg2c9RanLCuK5yime70+JSnOU8TGVj1eHP38Ucv3vzm9/XuQFW51qfXb+5+eJPznIKUK2W/29Pqj998sA8qD4fnLum0+vF0enzYwdcP7yZOSfhid8G1eFF2cgOpC9N6eFqXYyUYhObd/aQ/7OrbT6l+8Vz3NF/NIgJRn5B3k8GPh5PDwTGKmQu8wBywdmAc4x0KVAGAKhlg9bR8ovJzunyliRZ1tUk4EbJkIYFT1G55G9cjpkQkaG1CwmkGc4VWVV0Peryv9+/04Y3e/+CPP9jjm3r3fX18Y0/v/XiX9DSRzskmrqJLgs4ChkJXtkqqqA61kE7u4WcDwt7WqAdISmHWuY8qaO2awaa0oqczpECDHNrEcQSuQAcT4197jNAoyDMZazPce/Sx2a/GeLbP9OYnWpKgbZLq/b1E1CvPthRCn7Ll473d8HVOgdD5yhZveIt/mri3v2ywDQPaD9BH7UidCJHNPoNRHsoMllYC0CkpB5lrq6PvpRxmlUwTp2m3P9D89vvXH909+O3p5c+vkYv94ffHy2f4ci85L+9u1+niVOb5aqdrBXOes9Ya4yVitncgUJKwsGjKPjIkXePbDxGmBmKm3jXcea94Zh4lodS+ChGsupuzCFzbqDVVYpiamYZPihmcpVRDYaI+/cQd4CRMpGshpnh5nHGAfYDBMVmBQM69EclhAfnbg/NmkdvZRk1RtCBZHzfUmxsi+hmc5ZjQ4T0OG997iCHC8IqYuaM1XkXzBBxj4TuhjzZpWGh0tpO1ANGjfbA/mpCkTo0OR4CmPSH8WwDaQ8UmKhttGtsUMP5E/wQ5EBMee2xN5i0x18tJm//z7oqIDMbeXLJ77ZUMIwKPOn/v6hplrCNcIIuiz3OXBHf3TAlEsRFTVWutEUE+3t29+uwL06rrSQ/L3Xdv9nt+djndf/fd5emjHae7b/4Ix8c//ypPSeu6HA/Hu3tWPb1/9+G7t+WH73k9GQmZmhV2nO7uuInkpHUFXB2udeJZcrKqVk5mwG46pHJ/idMroU8uyqzLWqtS0FNwlFV11f1+l4tUpcIg8ysidqZWBTRoaQAIN0AOBqjqVZ3+JO9+Rrs7vT8quRgT50RW2dyLFoZyiZZJMGdVmyQJS+Kc8hRYoPq6nI4nfVhLWZZ1eXoSV2bKXFUXEqOqcIdGcWdlygKzuhLPDEOtia2aCpKbsUukhoZKtx2hnTPt+wDQ6QwaT3kD553MbrzRMIkYdr87gcEvtrfHOzYf4gH74nMaid+4iybPPb/XTdXYeuFohGkvLQ3/1FLMvZKhuaEO1TbQ1nQ0Ltg6E7zTqS0K2LxX/2zQ9qX6d/Zm9xsd5uhJopZGdxAlZjBHHkxAIBKQmsFdOLnFdoRirm6VQZeX+599+vlnH13ffrh9en1zfPvw5//2y+vPr4+/+T7Nn8rLj9KLq+V339++v9M/+5fzv/kFOZmZMJkaCceQpK1OsJn1oAYioqdeWNuigWY8vc9naCM6hGK9VJuMGafkRBTDgjzCBLhpZWHhqS2iESZydSBm1wh5cdUaadU8TTHWP+YFiQhp3Ja4VwY7w2KZpTepiRxWN6doz6dX+3gPzlo5mlvvzCDT2tgqZmayVvnADg+CJb5yVAh3AOEjNTW0291ZUrj6+O5h6YIRAoG23ppzU0lghsYBDuTRZ+MMJp06bxRQrLk+OvPPXQitfXVm7skJazP1ztQQ5w82rPOmCc0hNRfX4glDm9Ic8rv96TWdSbUCbUqzkXmv7Q59NUCIvBfyhUtgFlUlFsCres5gErVqXhlsWhmcWY7390IENQbrw8MXL589/P67Lz76hO+fDvdPL7/49PHD/a1+Ux4fL149z1kel2N5fEpLTQ+H5Y8/7NfT/fHukmdgEoDV2FbmZJJJPbNUTkSAacoixMhJy4o9P6b14Xldvsp6kXxHq9mxnFTZnKppWZbMdnW1w8GKGlSD6NdWShhjPq1AU+uEYYUx2lzyxPClvOL0Z+n6tZ6+KVWmHJHZ9P9n60+bZFluLEHwAFA1cw+PiLu/jXxMksVcq1jVVV0t0jkjIzOfRubL/N75BTMy1dVdUl2VzEwmd77Ht9w1Fl/MTBXAfICquV9Ox+O9vBHhbm6LKnBwcAAIGxErYNVUlbgWV+YsKTr4ElFKAmYFaq1lKfM0l1rZcb1JpjYfj+QKq9F8EQpjIatJgn86iUfBQErlynnJMoZ6TN3NScmZ2Lrl9DC3DgLSqv9H0yP5Gg+uihE0eHZ28asWrcFuWvF350G6w8C65LufiP+CnumSjn44NPU+4NyKG7t8s2u9XeJ8KNBHt/INZ1Pottpm7imvbv07vdP1Tg20nXfqejS0oMLXT1iv3ror6R/TkVVsu67caBdpce5hMzx4qeIVCjdNQuMmbzeb5zdPwOmff/Md7t78cJ6+/NnL2/Ryfv0mP31eh6zjrcw6v/n99osf8WdPK1OZJh2QbIzZoHFdfabg6pXNratHOoEQnpLREnvoxTtnOqgZRKJeUB1HCwPUgGYMUOyEO0CSuC61rwBe5pOp9mlooYumaDgqSIlz1QVEZtq4v0uBqfdgZW1pRYTzQovnZf1GO7riyd3JLQapM5hbVoB7JWGL3bxbWO+2Nri/VQLgIRYgDvOHlYu3M2MCEPeWPlh1Dt11tIXcY5smplq/I3Zas77osWGkz3zVNbW1vYrfYj9erPd+ETGLNpL8xiwtfQPQiohWZglo/1gvHk2qZCF5oHaF5soitRR3mDb1URyL2dUaKgpKlCGmVVLy6LFNUtW0akxEYeFyrA5RU2GXJHdv3sKcrT68feeHg2jZf/Xtu7eHn/3rf/Ph7Tu6vt4Un+/vTm/eDLebNMrh8PB03CXyvNjp3f02+Y4pqUI8E5Pp1XYzTacsAq05DcrJqWbOSbKbupsPdBjK29v68CVOz4ZJoQSjxDIA5lXLMhnq5H683w8Ltj7AaXLbcDKDxywuuMErlODSsEfkokShyeFedzZ+hvFf0fbBDieDVgc7C21yokpmcFe1Simp1TZVgpGE3ZE4VXdAmVTYQWAGg5ZFGVp1IvMmzoMncoJxICVdHEcnF4gNJ88bCvIfjRmMVSArY2mrzBmp4QPvi4o6EObWaIo6rdFdxZpGDdB7ATRWIvG80rpR9E7ReMuQdkcQmM+Z29nFbgla3+h8ML5A5QE1I8CPNFvPqxIcMYzW0UDYBfXddg81eqttntgE53xBD8IjyD6b//5/TXsUFuMsoO7GKtLY1EZENZfhQKsPs1pUBInpKo+bnLbb7eZqZKXXr98cvn37fKAfPLu5vsrT777aLOAh15vt+Pngy7fjtdDLTaE63d/j00/SmGKIKgiU1uk2Rt3e+QrwmqKvAehw0dE3Il5yEeC1CKaZh8ZQ+WptwiDGK6L3UTQ10VrNNUb+aikB/EM8M4wbB2Bq8JQzE9WiImLWNPLuq8e+dLYRjDbQ2p041qtDd+3rybf17t56TVtnKdug+f7yvio7VxfXEFFO3Bx2d+nNrteH7zGQUrXtB0e0oPDzsmnbwTsw6Cu8L6pYfoEfvRWK45yQc5yTxi2M8V6msM5CQE/o0JoS75SLmxGn9ujQbpI1Je2axm97nwJEeQd0vp4mRWSZEivYXdX0nJxoFHBDiBRdNCKisAbLeCCvYCc3csT8dnn88MBEaXOdJJXphDJRWd7/9rf27k19uPv3/+bvfvH/+l8fN0+GlPLd46th9/7w9u6Xvx1fPEMmgj+8ef0U24FweHjkK3t5feNl2boU1NHpajOeHh9STnWZQ+aShAU0z0cinuGPUt9c690Xm4drXmypBlfKWWQc61zUjStdbTf382F/nDc8wk0g1zmTwmEKSyAH9YGMFJDKoApyIIGqG8N9rk9Ifiy338H/OM22i74ZFQmbMVkl99o8ucG0koibciIRVlOASMTNzCqRWy3TPNVlqvOsWjh0BkmEmMmZ3a0iSDgzFifNVMY0bBdbyFLUEaojxTyMGHXXYU4szSYDje3v68jGdQ+eG0Gzr0b9DH/PMTM13HyJlvtu7dQNVmaoBQcRerYhml3DHklfAhCUCnXX1Da7Ocu6z1r9gTRlautSuOYFcF7mcS5roWyLhMMQtLKGs2T1I+B/NjorSuzpAedoLHGJT+PcguBHAqKnSCIO7onHlHMeshCB3cs8H+7ujodTTunTn/xwnKby/vEP37793Deo+9N4tXl1w+Ns372xXJmqTY9gCDknAXHoMleAFoC3qTWwIlKsXqrhTQCXEnVvnu4y5GvGfcWa1NkDi4Hv6LFFpzIcTKTuxIJS22LgFODaCCGOBojE4j5pn+4daYh2TIqyZG7pVTTfjubAaFXvUA/x1s4cEeZF72XvfWy85eqop3XQbWYzZ+ut6GRJFK9Qm5FKZxrQez69H6cZeO9yUV/103Eu7q0MfYX86/3tXuvsAdCjrgauVsJm1e+eNV1hanusGccLEO8wh7RXxVkEj9EQe1sR/V73lD71G71CKVMlYotapyF1/q0vhNbWO0M1cV7qYjAtCrhU8z7J0ks1teU01XneP+w3kolpPhxtWTDP6XScv/8wvfl+5vzFqyd8/zBsr9M0LadJ5n359ns67K9ePb3ebfzdoU71asyzkM/L7vbJaVFoHYFNSskpC7tBS0UyId6wqNtSK2Xek38Y6sMTLE8EW5CxuwqxLpZSygPGRZ+9uJkLiQzke6o8VM4LqCWoBBADC9jhCkRX+t6C2A21ggyUoyyuyAvKP+bdvc4fTnUmGbaZTZ2wGZMpuTZLpqXymFmESBwA81yKLlrKAnV1rcukZTarKXMeMvkKXpkZrgr30CnAJ6+G6iJZ8uA5O1zAwmlM2YkUBHCFCdTQKrfNNQlTVH2G9e+oqNnpC0PvHdjAvduUjgv4PGK9G5sL2M1rWratLaLWeb9B6L7NOzjB+n5n7sX1axJbWiaLuLNALZyhi53ZDTQa4I8935wBnc/0EuV129jf0KWSFxdEzQ51SwF4WFvvpFbsIRZmEhEOUYywhDCHiBLDVYuVWpbldLSlMnh7dbUZrk6H+XRY7N3+R2k43d/v75Zn/8PPxn/3N/7f/2H/1Yftv/+LdPvSaNzsbpdStRQSERYYkZNr04qI9MoPuPdxguhEQpedcH+KzTY1IU1YrHb1hDaTNdIM3hghBF8UxibiADeNUa5g4mpFq5JE8X+KeCp6tYqIG5IkrZWIhAnCVZsmisMTRdLynAPylvu5pD1aBNACQWpECYEiJOSo/3I4TDudQ71OBI3riIzuhZzRVEWSg0IOFlNlQiLXM+keA23WLeEOa2MJGkJas1jew0fqBShAL7f0dQ9dpp7jbYFL1rBmVVsQeo9lJqgDUZZhtbt3ii6hvQV/e9pObrDmrr1fOxADFWj1K0wM1iA2PKI6ZU4gqKqYtOnt7Y4DiPxTxMBGTLWWKIAzVQIsGh+akWM5zaf94SZvtCo5Pb2+Lvd7vzvI48nvjy92u+3sYNj9w+bJre8fjt+/TdshHY6nr78aXzz1edZloiPbfr8duB4WXWZWzdUHTiPJcjwOYK+aAHYXYYPCQZmO0P1g+yc67bKNJixKvJFsqoqqWoYs40bSyHnCcpqfP7sWTTIZ3c9mZgxVFDgDAlN4BiucAUbUyWs01yeIwwk61HrN9LN0c0D9h/mDii8y5yTm6om2Q1Y1mAubMIhZmCQnIlZ4hZWymFZX11KsFiFKObnW6HZDnTmxPsejLOoswgKvQsT1iLL3ZQPOLqMRVRVrobzB4d6G/kYs2yIAa+a3M4JrhLgiFu/wiSLZuRrGVdJAXeKwYpL2XzMoTXfgrduvu8Oos5q0SlBwXqPc1zBfUks9eInAghrv3jFS2I1er9iCmEjyRnYLa4zQQt94S6d7Vq3POV/X9pevTqATbOhvoBZDua+nGK0eWFhitoCZmVVTPS5zmZalVgKuxmG82Q3bMRHbyZZSk9bZ6Qh+7/zpT342/vRH/Pu3D799u/uLH2++/Ovjnz7Qs5dyq/W0+LhwGqXCCVYNgKtxYu+FmlhDHWBt53vpztZrDdFrY5+phTHUmI7+OAMymDWFbB+pHOqx82ONW8qEmEyUs5YqzO6ccjJzEjBhmedmOVZc2bo6o1vO7kv7U20piAD9aGfWfVvHDX3sdV+0cANLpExb/NAAe4sbYku1AmBiWRmfnhph7nPPW3dAaqDBG2UUVj3uVlt1FL3+ONqwndHPaujXy4oAyvqp9bNeFzLae6nBpQbLools1Jd4bC2n9vNmIsJBhhNEL5dDz+70fdv3mlrcDD7PuWwaNbfoDGuSUm+q5U320GGGu5taMAmlVGY2AzmpKTngCjNfFqhpmYUpk+y/f/3w1Xf08EgPDyb+8OYhGYZhFNDx7Wt9vN+NLx6+/nr86avnx9NgVuZlm272xykzDj5vponLtJXtQBnqWstA2Yg4MRO5gUmwuZr5eKJpemrTyzRvUetClBw+DJkYe53JvZbqWkV4N+bNq2fTsRz3y1KXtMlWJium8ArP8DZZG1G/zWuw5GDAFKbwDcnsZVjGV2P+S7p+Z8dvp6Ox1I1vNgIziG1GYYe7C4HAnDOEjdyjt0OZp3nSqnVZQiLBhGga5HASTin4fxcmqKk6QcEiksAzlr3kTaV7QRIeE4g2qRhVDzoCzKA2iBbE6JM9GqTqXZ/QJcAX8LmH+xE7UoftsTu7qu1Ceh9bohl/Pq/wtqO6q4itjB4A9LgiAv62r7rJPv+qL+CLfdN2dvMNzd6dDTpW7UtDw52Tis0o3L3IKiw979n+OS0AWJ1lJ4/RXUTHfA6qpaC1y6qIRjpEwgIIy3AzXjl5AjbDuCzluMyn+9PTzfbZ7fjl1csvZH56lbZpTE93x//11/zFS/zw8+m7h/r16/n2sOiy/OhzeXJTp3m42jDE3MiptUtbLSeFHBMdIq+38AwH0UmOi5tGTVVFveUBgazFW2ETY9ODICSAmanDWdjU1Kt1AJHSoL0RRWAcWInOUPFwVVtqNMbkubk0Es8AuFmsjfNloVOC3ax2MLBmXJ05dQsaosl2dWYd26y/bv8XK9FjWbp5lJyYqlunbhzEpObnRQl0nrC5zj6yolmFbsgJnRDqkUFUmHfPEDX5TdjTLLzFVu832Vebg/XQYdwbu3euAWzDN7vfdMBj0HfbndSD+HVzeutHay16JnJUuBDzUueRhUVKKap1wNDyBC0o94YnQURspoJow2pwwE3LkrIgdM+12nw63X345NWz45sPMtX7f/nD1UTTw3Gc5l1OoWKnUoc6vfvmqyevXg3bq1SPfH/QD++uOL35/g3dZj/saTrtkLLXkeRaUq1uy5JFFlNiuGs1g7mBj/rwmMr9M9Sf3hTRoqVUFfEsiVyHJNebsdYK0xmFeQDbZrgaOS3zctTpOE83nqhpGN2gACvgAMMNrqgM4YunLZDFTQjkOhyXT7f5mn0ieAABAABJREFUb+XpovVurjmxFXNwdR0kuAEQjBIhRkiaNgoQqrostcAUbmjMcaLUb626w4UpnnerPYzFpAXLwUmSM1HWtDFQ5eRpJJbAK2aI0oBoGZ1Yort3M5S+Slc+QiHtVxdx+FnGHNmv9cVtU7CHIr+TLaulaf6A/SOb3G37+rl0adjby3qX9XPwQaswox2in8EK3nHGw50kCJC5ch6XxNWF00AvRe5guHmc9nJqSsKzMVr3Vuwlc3fTGNVHvSiJQSmJpxwTDq3Wx7t3ZZqQ0icvn//gyfNnVn4w6Q9z2X71nq82p++/rzc+/MWrumD+339l377DX/7ww8ibn/zAzVJOxKxToUEEcDlDYqKm61h9FbzzP9H9uhm27mo/ej7d11NH5/25AdEjsoUSDnc1ImqzikJ3FDXylJrtjwvnxASTxEzzvIikWpcApEwp1mVUkIFao8Mz9YFWitySEN0R40Lfg067BK0fraLW2KyHNheXE8aWW6uc4FWJ4KwdE5C7hZY3GlRT66zL1j8lEgiqxnIR1vp5oQa35p2eumQWw5JQt9wgMrdzxYCvr4v8WItZ22rtqYv+XMwj2CFRq30FehxTOK2buJWQUaee1lRRQwfGLGpQrZkHIjEzkRRUg5mziKmjuUlqt4KhVd10Lst2u7VqKtVB87Q4fHN1VadFS12O0yhXOp3q4/HJ5urxH34jS0p3p5fPnr/59pttHsxZF9OHxysaPv+LH3/73X1im/705v0vfuvbdJ2H8njvD3cjdMZyvbny46ymbDykrblqmTgLiL0WU/eUjnl52C3Tj2+na3FmKRE+GYOkksGGIWfmpSyb7Xaals1mnMu01FqtGFk1ZUlJOeZcGlyhCcxgBxk87Ie2WRTrv8PpOtV6XfKPxt3Bj/8yP9hgnEXAEvOUyCWl2DcsQjEfz5Uaz2CJyVPkz6OehR1AIriTOzMzeUyA7GvK3Q1Qr5MZYEzOLBvhnPNGVZgklLxNPR/q7agDCPq1h6UEeOv0Cnfv9EsDzdTHXl02Ge9AamXX2y67jMY78myYaCVNusVqq54abQnvwUG3A2sWbfUzF6FyM80rr9p3f4OF64v7Na7ehuJN3GzHav26W+X15M4Gfv1aUXN3H3TxDQFgaR3ZW7d6IIoq3ZmczZb5xK6fvnr17PmTp9c3y/tDnfWvfvjy2R++ro/T1Q+/nHSfrl+kFzv7b7+dfvWbMVVdnmC43dxuJ/eUBAZ1G0hAUQAUM6Eas7A+IPc2JGv1qx2eXtQHxCUEv9uTJS03gIs7I4B1547IgZC6RveeGKechlHnSpmsmKRoIE0G5yRWo/7Lwjiu45cDjTZt3Vq86uhlJHERBPQSpPWeB9vi6zLqBpRCGdyfDPVn3YK5la8jxCyEcyaY3VyjtWcXz7j2groeBjfKhUJ3y+s2jEC2CwOoJY07bxgeovWkYm5gP7xpI3p8Dc7QFZstsiV0rhXtaHGt4RfViKXWIim1HcC4WLqB2Lr+ry9V6uUeBJg5szO4+DLAHVbVkwxJhlqW5iQCVLAQGRGVUoecnRjqdVlwdUXk7gpldtKp0Ebr8WRLvcqDHh7nd+82ysfXH/z1h4dv325rSdvbXcrCZCxcNekyMOd5znevJQ/5UMofv/FtvpKr5f2bwep2m2zhxKzInAYmERmW6YHIWUiNOScZ+YHrnSx3z2m5Jk1e4GnMABFMCDGdkQAe0shYio5OBoybdDotTs6ZJKd6mItJ8sFWg7JubTiaBrQFQ3T+LTuZuCVNL5f0l+lZhXxz3Fc2ScbJyCW0c2kY8pBTyiBmETX36lZ7y34hhxJL+JtYBg5nJm8jq63BocaVu1ZNQtCJ6MAy5M01ylXN28QZ5CBWANwgEbkHBXRpZ9suWi0tM59N3BoON1xM6wu9o/JLBogu/lwYX5w/5mJ1ruhodWkd1p8PcBGOdC6gb5GVliV0s0/rm9tOPBvDEI6vcfNlfHwJg//s7wsJyTn8758SYHdFd4ATM3Ub1ji21hqM3NRc1crtbrO7urp9dotiv//d1/pYf3grf/wvX9Nv//jjV58oylKXq922/tOvyz/+xvfv6zN5Xz5sP/ubvJEZ0LIsIM45bmB3f+RkXqNBfHtI5rYCRm++tFUPXDLtZ4PhzZmvxW2t+4303vfeLZ4BHH1szBD1aGxqaRya3JjbZE3VVrdhsJTYjZ2j+lwc8Ab7QcQGQ+cXO+vW/Vi0aWsm0QhoXm1diatwlHrtMXWbiYs1h75W7ByENocTBt4dIm6IhksNAUWD5lgp68ogCrq/3ZceDfvFplrdUPOR5Ihudz35QNQwOy4XK523yTnqjQ1KftHau52Pw+HqLg1+ENtZFozLIzf0uD5rkLqiBTYMd1UVTmWZh7wBEI2iBOjE0zqTssLMVd1Ua6lzGcdh//iYZTCzWiZ/X5eHPbsLZDk82Ol0M95MxyN/uMP+w3Z3bbbklE7LgXJK0Onh7fW4sXcP48PBt7Zd7N0f/jhc5+c//w9vfvPw7Pl2eVe3kupUdrstV0myqTq7G5iqViGq5jOWu7Q8vML+JiPmEaA1XnToYn38uDMR5zyYl1oVTqWapJS34/GwGJslVFA1S85EYoC6Z5DDL5UpwamERXZwhbo7gblolvzChp/IrtTDh9l8VBrI1JgykyehlBKLcPPV7AZmTnmsZTHVGDRlZo1viOJymEEDibXrcALIIpEDc4B18eVEyxHpKOONwNQ92po4wfw8k7ODhUukhI4tfPUNZwo54ih0YiwWOffFtb4rwoUG9dsg4B4DXFr/C69wuVKbu22LmlZaZgWqXdvs67/Wdze06N67+K2cTXwwd9r/jPlbWL0aee4fHSj4rB+MgYB97zR0v0KyFcg1DuEiw9lq+dt25eg2KsO4215df/ftu2V/9Movt9sxlen4nrzul2lzKle3T+ntn+p//dXx6+/Tzt8uh/SD57c/+YmDqVZSUGZXM3OQp5iTCHfr/f+J3KyJYLqSxi/T3S0d0l1Cayz3Z+4NLXlO1LvodFIkbrvBzcyt8YtQERGmgFnElEVqn7tSamXiqhVEaiaSCayuMTcvFiiBXECtSLLNMvJ+w70lff0CX1CXshJT9O2h8/J1dN6mL68O9eHRGsh7vOrSpgo7iIVYEXNyKIxIlM/3W9YmeVHsANL2cOnScLdlTBdhKGBMycycPGaCtgEDUUbgre9OK/09+64LAEIEJzcDJwDE3EZrMJkZzFbF3HmHUW9Zgeid1eYgU2hCwq44Qq9LIrXqOA5TzIWmGBFaHEIspi5ZYkIcMRk8iZBjf3d/s3uS0pBEyrwMOXnxw9t3I4sXzUbTh4f8yUv4kg7zrpbTfDTXe7nf7W61qphuNjlX2g7piV9pGpdSn5OUh2O9K+NPKr/Zbx1zNXbR6pvb62U5OtUFxRkOF8mMdOLD/eDvbrF/eXXKhMXyxiSlzGLwNqesLiklM2cDhPOQzLHM1c0M5m4yps3tVlDoCBAzcdAtBqtA6gbPAGnYuKPJvhYNZlDMx804fiFXe7qapsc6FGxk3GRBDEzmUGCCxZoSwEFerVQrMLTadfVefkqE2NFO5i4hNORIxUgWbg0bGW4Cg6mgilfSCkFrFRNIg8EGAnrGjDqI7nz5GldQE5Z18xsv87NtpY+AfrsZDW7SmnFt8M27Q2kbkHpEvSK0i1d4h03901fv0PFe/02zBtTx0Xq6HYQ11xN3wNaTW8HThf/rmB6ONkoBLd529NmQ51ign2TzPS2obhCsDb6B927ba3IWjKvbXYX87quvrFoaxifXO4bP+zvA8otNFud573/8QP/86/Lbb2hDeHkrVp/+7d8OT2/fvn7jT15cDXI8HmQciSlvBmL2yBC5uzknaTVW4YtXeEv9HnYoG+Vd3cl6uI0mz3fEnF2E1qgZQKIoPHYAUNV2aF8PLOGVYsC3msMhKc+nY05JVbVa80rMfeGvgB8W80SDmFyRqjnQuoi0oKqXd3WX7ueyLFofasMjtK6jOGLPiHNTNICIwoqqa3xswP2IJ9w8oh/veROs6zjm4HoDJOiE1bpC2n5Ay6u3HoBmnXp35sgBck/LO/lHIUu0+gjAwr0tEvfGDEStZW8EBH6xUWJRnk8ba8LjctsDIIumUS7m5mpDGqsauVetQXCZqqpxzMU0dXPArKgkEKiW5d233336yecxIkfLLCJJ/cMfv/ryJz+ph5PO8+NXX5dnLzKc3z3gfv/i6nry5Xg6KIowbSG04OmzK1rs8c13vNhm4HRY8n4WAn1/GBfgeBo4H23ejaNWA9O8TNWLu1PORHS002Oqb2/r8QfjMgoyGcxMExLMQ4fm7sZZ1eAe8nvAOAnVlkGmRJJlgZei7qH8ISJS9wxckJDBXvJ5pYEAshgbDlICeclFNjL8ZHx6WqY388EqmJJEL/ewD1FPyaS1Rq5lmWatpSFxCzVyTMFrjShDeGdRhQQDg/PQ1ww1iEaAVUE4RyeylrhC3wtEgKe+bmKVdD0/t1dw3xsrM37eQb2LWmdTPg4l1n8R2qDuy5XZElFtRXr7Qd8tK/ZspAaoYfDzMVo83HQdnZ3yzgy3ECVivQ6E+i8u2tBdGMJuOOJnHSN6vxkEIGimXh91YUgR6GCVnLTLX1kA7g1khCUEp8yY5vk0T5RkvLkZZDCyZbGFxHhz9fTJWJ0fF/rtN+WP3wD06j/+/Pe/+Qd78YPh9kV9ONjhuH36wk5LyimNmYlY2FuZrmtVFrgbdZrnzBS0vKOtAZe59ciH2pV7U8I0y+doB49IKyqtGna2aPIDkMayMEpDZiJVMBNLjCxwJiplsZ4fHTebZZpSSq2NM5FHD0kmALyqq7wlnvuS8DNUiFeck/NwuJD059wjxmbsqAXKKyIAIcT77mYmbc7BhUg/Vj4zrM0u7qexrg9fF+ulre+hqTdr3PnSQCPuyiGebzGKkbfcSdvd4RXcgAxqTfkbZQQP72hdOXpu4kOgaP2hFn0n+ypf+ah+jwCzlrX2PgyBmaxq9M8zs1rKJm/J1N1LWbJkAqqqmMWikDbsF2aR0aS6FFddDsfMoqcyP57yFdnxNCrh/jR9/84O04Y29c3+4eED3t1dkQkoSyZy1GWp5cnN86TEhWyugGUiXerx2++GWsC2/+Vvhmmudw/Z/EZGIdF5BlS9EkGGROC9Hfd5/u6qfng64HooVNQAJV2qksjAbJqyxIzo8HmlLCykCq0GchG5vb7ZjGblodDESeoxoICF1tN7+gsAwbn/uwG7UAqggQB3EjC781yecPpZfuGq706VtoaNpUGYXQSR+G+cpirMBFTNTNURnaIQKz2kbEzEAiZhpt7GmQEiYeYURaIkYzT0cuttvqjNHjjrPEGO3gqi55a6FLKtuY7L4oKZPG5ACOrJL8zd2W6v2LobWPxZo60OxNveWVG89327vsA/NqcXJjpe1jxn+Kb2Z90OfA6Zw6et9tg/PmbLiq0UV7uA7sZW/75GGCvZ1S85vrj/eH2ca6scEPiiWs6JzZwJV5stJAsgrl7drI6b8fr2aSqabdbXb/T7Pxrm/OXt47vX+cnt0//x5zIM0/1+l4f53WNZ7PaLT8ESPZzNozNQs/puHk2xQonkdNaDhvuNJx1BgTuoN3sO6+8NZVgzQ/3xYQURaJ8VbgL9cVAbWK0gEkmmJsJlaf2Z3ZFS1lpAxDEemwy9FxC1rAKoNdvxmK0cyIRorXIAWgV3Tw9H87roD0dtAmI75TXCaF/r6Z8jD7Q1EAGku4O5d5Bu0F0pSNd2TKM2RYlaGWdDMRG1fIzBWyS1op+Gdizm4nDMG3AhaUtz1eA3Sp7QgowO/FadXj86ffwxHs0708qb9ti96z778uYewQLRpD4RmGqpscsTC0tGaHxL9cHRIEHDN8SYjqdNSjBzs3m/v31ys+z3yVFPJz0eNqDj27dymOTxoMfF7/f85qG+/TBAp2kad7vtMACzed2m0dVEmS3l7JRk//3d0yzlVGRMuJ/L491QF2EsZpkghKqVRdVZ3RZaDrx8uPW7ZzzdCLtVU8kZ5kRSF2UvaTPASFIiIkVT9daqSZKJLNPMxDAnhwBDzpQxZDONxEcUOlDDrui8b19FXZNM3BozusMMUPPkOii/ytuJb0u9LwaGEIEFzJ2Hdos8UINlZuGgae1C36uvmCgxASRCRMxMkhK3/vjMLAQhEudk5hQjCLrdX6EsUTBMnhquX4djrAbwwkzDWwUj9SnX3ex11O8rxXKG62gApN+cvntiSORKF5zxeQdZsbTO33YAhwCwPRgJC4t+os0GraFE54/Ws3WsYroexXykL2wf6OcdirPIf+V9edVnd1x9/ghvvsm5jdeM0wmFUY+WTFWSDHnIRPNSlmWu5p7yhnGVZSd8ZVLuXpfHgxwelnmmJ9vN0+fffv319ud/++Tnf1fJv/3Nb3lz9Xqanv7d3xE+iyGLacwgr7UAJEwOb7161h1P3OvzcQFcopF9s729BjBsytrnmJjI1AJARGTQrKC1+AsEN41OD32mO7Kk7hTJYeZKxMOQVWuLKhwwWwsOOwIm6z1xvJll6xYOoN7jiCKxwo61MRw1Y7cij87MXQSeiPi3XWgLqNdmcGiynRYmeTTx8zZr/mKgEFGsvbZS/BxDtf7iTKq9YXU/d3RmFT2j4a3h4coanl+KltYgAE7OTCHx5/PSM5CsPgsxhoEFMRCW05mFRc+jrQNemgG3flvI3ErVlDIRqaqqCnA8nm6fb8o8M6fj6bC9uombL8FWEaEqwdy0LjNU2R1LnT/cJZLDcUrLkhb1x4e8nzZIup+fPM3H/QPVAjjM9HgaWLebXbFh9JFIs9lcCwCCXI2bsbKBrOjAs5cyAgofUrJaNVGtC2Ux8JyWfSrvh3r8Yme7BNbqS1kKOVJOapoo1bqUgsyZFcxCiQGay1JPZdhuNptxyJv98fhw9zgv5m6UqWpx5tzSJ47mh0NFE/h4RYqXf8Og8XOBgKFWMSNTfjXuDrA380HVoJCRKFqqqsJcXc090BDAxAIYC6/Lea2VNZAwd36BzLxr2PrqESYWyclBAmJAiAzU6KsIBAEwpZXkXr8aq4jzAVssTs0HxAr29cNwtv49Tm83Y7XB3tiTzvuc7XpPJXa2t7kDXNCgZ0veTS619F1sRWtJ5o9i8Xb8ix/1zdhT1SuivXjPulkboF134HrK61HDfTQb+9FnYEX6+LOvoMIl5yGJTPPpVGYnGoftZthcjzTMj2KQQYbbLX14e9jf0+fPNp98efr+myFfbccXfLz57r//L4e7by2P9uLZzfVOp0ndNrur/kwIgKolZiaYurv3IrdOq3hrB+gWFrwv48bBNPveuSAikGuT5fDq/wjmbm7ctZXgCLMoAmxmYiZzY2GzCkBYCMQs4SGiN8Q8nQJPxWwk09oWdSxYalg/Po7bSCxaEUrL8jC7WYjl+xPuK9JWy9tCWlqffU8EcLR6tjbAzXszDFAPwANTt7vYSEuOHnPR+K9pl5tTMVN8tHpp9Upt7ceqYmrC8Rhy1zxvoxSdzGKmB194sOA11+k0Hc70gIyYOXK5IKjWJDlCfe5hOKF1Omq5B+3RvpOWghHEMUPCOKfpeLp99kRLBfnxcHj6zAFYx6eAHR8fbSnp+lpInt08YfW7b76h03T74tPX37zFPKWDvf/l73Y0+LAdl1K+fT+cJhppOWoWgS0vNle10ijjgExuvlRSZREqJtX1WNxK2mQ7HrjY4sWFtnlU1VJOYFJOezsdB3187sdXV8dd9sRaVBjDOLA6DClaZIFrqZwI8CQDErFTRi6O03GG0Xh1dbW9NnU8HklVE7Dh5VgUKUIzjhGGkBUrrwaDOhQ4GyzAURUEwyDJzLjYVRpe+G5y2xdIzsQCFqMYqaNqam4hpiOSbuK8gZL+QQ5iQk5JuJNH8GrGbmSQFHYRLIyUJEl1alolATE5kbmhqcRbLpqoKThpPff+v/WDgyoi6qm/S8Parr/vG1qjgb7u+6c0VTmocdMR4faMABH6bK+O5enilasnCAvkvcp03a1nQ+v90XRKt8G2j38dwbLb2tb3HNqhCa86JdvNfMN73dgGeG0orH/fv+3eAqAwiEQAhJOQlzrPpTDzZrhKaahe7u4eptO83Y1mNKTNNE01wZ9u0vMncN/mrcy7t//pV9Pv//DZ9un+9396en29HVPdH5gAIa3F3DWGzhNJ72qwcgDE0bfdLVQEbSRQl4qaA8xCgV7ReQyKNJSbRUhBwc00/B9q2jZPBkSAuYZ1YGarTR4awyOJechD6IWImCTVNpcRzCKczvuIuYWR3Cb9EkXOk9clBooOFg1CN2HTuo5AIF6V8+ua7eu69biN2VgERmjzfT2EhNkOp93krV1gGpMrVuRvK4vSnRJaiAJ0XxJrEe0qIrpYCwt6k5YOnSIVt5b09sEvvubhvE2VaqudwUyMsPLxPEy9nV0QEUHSed/TLYRdS3mcGlPs7q7mQK3qaq62zKecsy5FQHAz1ZB6W61lmpN7gpfD8fDmbnBsx+Hdb35T371LttiHD/Jwqm/eyn7Pj490/zi46d339f5DJmKybU6bIS2HY6q64yS1QmudJ3h1c3JKSYRcCGzQZQ8yEAugVrRWkCPJoywPN/r+Fvsvro/Xg27JEnFOJNhthzwMTcXsbVlpKbXWWquQ5JzdPEvaDKM76bwwbBzzmBNHwRMD7NXV4RzZVDhg3GSgDtQ1FGjxY+N/uHlIEEiUCQwty4bl1jc3dSM2EMSJq7rBnRGNEdFbJEhvaGhmqqpq1VzNIk/sTmZQuLmr61JKKaXWqrXWWkwrqhI8rD15da0xltvRxgZpaCyIUljaJlTr6oAL4968XMfmq2VtkcNqeZvZ5L4BYiV7cy2xHPua7YtzNdqEHsEjzuZMTHaUHlEBWleui03VHEr/sAugHzlZP3vkvsU6CPQzU3wOF86xzUrIXZy8tzee39cu5CIaXH1f+2bFZ3ARMdhSvNTCzCzJHWUpp6WMrOnZ9rs/fffFY33z4WE8kN/cPv/0M2wlTdP7u9Nw/w1d6Rc/+/H7X/4uT6cnt4MfDun2CYu4i3kVcncX4TyImhI18X7U74EaGm70hjkAYXH0ksIzpRENBWICTNs/Me823t4a0EdSzKLRZJQ4eBStxgRNEJipqgVrlDiD2MlrqSLSh4e5iABezZlBkN5x2pi5J8DQdZzWIivvpa69kehZJtd5rhXpU2/U3chWoE/BdIf3waZrurwdxtxXBp2APpeI2tOlddV6u11YP6+RikS8dgLvOwYIBQcRtRHEgd/RHFNr12XMRMLQSHi0xittDberPGN/an1VgVawBtdm6sMB01ru0Nd2ixccGh/Xwaa7O3lOSZeFt/nqajw+HJ69eF6WyVRrnbfjWM3Nldh1Pm2G9PjwuLna6OFAZcmSP/zhm6fjWJ7f0/GIaqnM9vBeecs3guM83X/YiRMNaUi32/HD3bThYTNsyUmXk2mxWrdjrnMx1sSkBJCYwUmJORrER5eOmuRByn6L6dPt4xOyaxRTh8jABC8FxWwYc10Q6ho2gQDgWgoxKaVx3F5teZpmCzrcpZbqbmnIYAxjnuGRyY3JTiHv4XWXN95uddwIP9EiToRIKIZktnW1nMqQ8tbS6FbCZ8ROi8XdhtxZlAc6w81Ckwyi1JqKEQNGXrTCqDmpTmqqazUQeBxAMaJZK0zJtU3cjnEeiNZdQBQLtoXYl1NYMu5cTc+woqeYrE2NRMfL65d360ndgnbAfs4orrxjt/f9de1Vzd505I7+px22hwUtcumd5tqJGlr2teckvFvp/tXUhBFP8DqruNNrF98GNRJ3vEUv6189SFiN/SUXSBenGU6xnQKczMzNSlWzwGbRFLSKJE7Du/fzcdm82cvdQ57o5u64mZedzFfz4zwJbb589vRnf1He3X/43R+/+OSzl59/evz2+zLPxFSOk6nVUt18zfS2KlMmhp9hp6mHVfRgkW3thvpRirVJ6Rt2arDV0Kb0BEfgamrUHqUzSSwwFpaUQrAQFb/qzimJSDRRyHngED6DJAknqWrDOIikjtZAzH3Kbuce+wqgTv1RDyljnQWzsT4T75jae0i5xpax6nprNw7FZ8ME7muFZ2/AgPAT7t6HOTOvSzzKr/szX41zs/mt4JBW17NCAVoPTq1IG80GeL+sNhUD7mtEcvZSIO3C/sYRuEfvkegPE+nKdhcahcuBz4gj+IPHDTNvS3FZItNTyjJPJzOty8xkuhRdlsRUF9VqptXdT4fD8ri3af7w1dfz/f273/yRjkc/HneJ6GHazOYfDvQ46d3DmEap2Bb2h8etaVJ4LbYs03S6HnfCyYtep01mIrcxZ5FEUSoPd4OZFyqKClUwMkkhPyV7s13ePaPDj7aPnw12myfQ4lpUXeuQaBwSXBUlj8zZ3bXWGtZRJJn7PM9LKSxpGMeUhIlPp6nUmjnlLClJqSUlrq5KMIGSM1PXXV0ox1cLcLYJDTpEA0NvmIXdnMFSMCCPMrCI5MQkfdWBiFR1XuZqy6JlKfNipZWoMSNy+AQjOLl6da9WaxhI7qQsuQNqXuBGVgUm7AJjV0T76DjP6B9snjqiocYJNKIeHbN3j7ZS8b7a9m5f0SlzwgVmvnQN4QXi997o/Qs19ro7+j7HxXZZw9Q4qxVm08rxNCDeHwP+jKO6fFYAenqDulScLq6gUwQXsU7POjfEFYdmOr+hH7pdVHc63ggz9M/qMLT1AXAHW8waZzLYcVHj7C92hzlZuhmPp/2H9N3r/e42lZtPXnzxJN/cTL/+av7q7atnt7fPbve/+O2bh/2zTz4dqppa3mxqLcOQ0pCsVCLSUiUL3DkneDQKM8BZzmigye2DMfRzo7eY8d1gLAUKpigsaHrxziOgQVRpwSWBOApoI3WsbflzAgWuJ3fWUgjOTCnnMpdhM1BzOkQkWbhUZWK4iUgMJMHFSlnNv0ceuDd0WuO3tvqsO2SsPw4s3LkeEAIjg1Y2Lyq4zWytekMjG5vXDO9F6ArPiwXTHz9FqeW6uuP/mNBTEgjfTCHtMyMWavGkUwzgbFUOvWdK54vQg4XOKV5gLIdro5601pQzUYt/GsFLTMxejRObRse9WKzmplaU3GFE5ilTXQoBXrWcTqeH/dWT2zLP/ISYhul4xLzA6v3r1zadloeHDVUp1R8Py7uDT8v977/ZmKdFfarXu2f7091Qqiw6ZKnqKfMgaTmexrwbc/bF9o93EDDDaqnwnJKTG3zRxdyYWEAiPFudaTmwHzb2+CLhx9fLbSq2mBCCZwG8mCZIEltUa5GB0nb0aa5eSUkrhs0merOXshCTiJi3dDozwTxJGjeb+ViUFxKpc2v5EavJ4I1bbA0AuTsAAqyLXsjgUc5iUCcBCUyrmZqIRafgpCwAWVPJs2oFC1jAGvWIBA7ROJxUo8dx1GBHVbazkQQCIAraCI1Xd7iKMCVmaqPeuQUoDXrG8k8rImmWqkOU4H06EUL9n518Dy+yru/+1wX86SxMBAarTQ/ceU7YdWrl7HdWv9qI2ssthNXqdudhzbGsf11Ya0TLjUtM1s+pX2ajuuM0Ps4UrA6oZwDo/PG+Hr7/ovuNi5CgBTvn38DN+ogmcGxYIoZwwOQT8E7w9PPrNw/l/fInzXS70M203/3ks/HLHy2P98M07dyU6jDNb/7hF/7sVRLRaZor+cDjmMOOmzlgECb3GLQL95hITMymFi0oQUGUg5KsV+xVe0N+UC9uYBYQeVRR2tomCs2AOcxqSMs5uKZqkuWCUCcWbu6kRatoM2RlsOTM4qpKDmCMAcIwFq7V3D2SXa1cIMxxmwHZHt+qDfMOA6hHrqvRjFXboBZancE5OUvdKIehR+/90BpJnLu+RIjEF6DDeyGxB+i7dEG9d3ZbenROBzfU1fn6GOsR3RrdjBKix6pfLMhOPWENN9A3VMwnifheJLXJzQ2RRejiQm5mQckyqFoNmXJL+zum6XStlVNSq2ZSy1JqYdDsvrvaSJLjw8Orzz8/HachpdM8+2l6++vfP33y1O8fbvPGHvcP33w3ffgwpM3y+n0+Fd0f0rRIktG0HO4YxZyFkMknq0wsBF1KsDutPi5VMgN7LXN1d6Jh2FRdjHy2MiU9ij5e0fJFqp9c+yspplqJmAehJGRVrWgEtjnneTlpLbLJeTv4NLsrlKwsadxISkTi7gxLSQDn7TAf58eHvQpZtdNSSq2SWJjJLBiKyMtE4MRYH657q17sNx1G3SU4QK7gTCQMXkoVDqONqgaONg3miCnP0XYX7k4irkYtGRB72hrCJVc1iqZtKlkSS1I14STM0sY1RFdfI1eYkhuZRUvb1kjIHUBaLTdaw5qmjA6THrtpTXN1ZE0dda88vqNBFTobyVjv6wuau7g4kp8zCZ0gXd2H9zir7+d18XvXqPQm552XX3NxcXv9ozigbfD1XC/8ycWFdR+wvn5F7h9zXYH71thiBaZty1GkF1pWrcE4Xw8etpUAkKBLs1MWN9uX+keWw/P0ON9aPf3rL18OZE+W/RdfPtscRv3jf8b9h3p6+LDMdrO9/slPr4btMi/Hw7J5sss5E7mqmiolTgHqEWofWxsXoEk/L25+PEn2QIX9JpLBoU55pVnYYXAj8oaUw5WYEpNrn5lr3rpjem9Gyj3LCjBzgbk5g1gyiDgJOTsZASwpoIdIigS/sJCQ1ug/aaS99CNC1qZvCexhWJ+B9zqyNTC7CGadViWMtRUem6LLndG2tV3Q/aBVL0Xsri2A6JYcgK/1Yt5i9ou4s6ciutfpYN6pDdXpjJZrFIm6tW3Hcu6/jd7hLa6xD9pr59sPhZZhdmPKSt7GxKON/SZqFR/h/61WiXo3UzeFJQId94ftZkNwXcomZwMf7u+H4VqXYsuihPrwWO736TTzcCiP+1zx/Vd/Gj7Dzs3uHzfjk7o/zR/eP91e+zLTciIeMmxMWUi0nrzWjSSYj0O26lbN3ckofKEjKhYKXOpyNMakWsQe8rK8GMvnV/XlgJ34AJttHHNKWRct7ourw2uppO7DMA6beV6oWBrzsN3Mp5PCUS2P5GYsIkxjzotWykIswaDMtSxGV7vN/mFWWpyb7DVWD3el/zqT+qwC6PLQdcHEo6mmmUeAweJW3QiJq1vRIImYCDE6R6u5GiKaVvPWpr53Zm8rOtyQC0OaQs/UKxHzKO5gEjMzNy+z8wwpJAtDSYubgzjCPJIEt9QM0ipsR4O3K8PRbC0aq72CWe98avcffmF1qWcVVju57s2zFzmb2RU4fwTy+9ZbQXY7EYf3Ehbys8VqwtHzRo83XgYE/TSwxh8dN67n3wxEc2508VjDx7VBIQGr+vbvJ92MSNuoAcr65fZ/dF7NzwDUHYAQI4kW/VD0u9NcZfvJzz/70257enf3N3Jz+/nL6+/e1MfHvCzleKpkejV+/rMfp4FOx5NIzpK1aBoIhqq6GSSWpcLZA/oRM2DNOjALdV1vWKrGCXjLK3pIZ0QipjTX9hoKmELa9P4et6UlS7jPDvOwqkE9o4EJEVOFQ63VD8ORJLta6EZTRIYsSXiZJorpFWCQJyZ3UtQwl62BRYzuiqCkLS9CiDJX8Rx1SVN7/K1fyuqe1mVB7Tl5f7rrw4xHbxf7otHq3kPaThA2Jqa7oeZdefVABAtzfJ6w7d5BA7UBDsQkRSsRx9MwtZ4IiUx+MEUN0fSn0JaSuau7RhsZOEJlRNRRAFRVJHL18CYNcG5dL6jlxdTHzYhqdS5yzUspZX/avLhGVXKjUlLRD3/6E5b53T99/+z2djPu8OH+5tWnB0oodVfpeJjm+8Pm6pO3+692ULcioIHJzUx9TEMmcWV3CFN1pWoqNSVR11InmBF5IqpwBz2mctqU+ZNBfvaUn24NS3H3ykmEyZMDTEY+jsLkSynVDNMybIc8DqUoscmYNle7ZS7EXEphEQK5sMFERJdatBDzcDX4xPN0zJJTFk/kglo9EXS1FEDbWZfcwkfriHDuEOfuCreYqEEZRdV5QGa4OlhNg3qNZhumYXjY2rTBbs1W28pMAcIcTpDYoSByaC1EWGhOAq0L1xmpwGaygjoTo0UH7mB2qwRK0dobF4SFA20SCHo91gqUVxS9RgDdisYherX9atXPZq+zQf39PfMB/ygq6JZ4Dcr7L0J/3b6l9cWrtmOFbR9LdzoYbJagW+Lzqx1YB5+3EKbvZVqzCT0e6d+2Jr5h3/xM0jbIHc6CCBQz+loccD6vTgX0bcxhGMJ6CZAlpTzudjrg4Umeb26O2f3+faaUeKjm1ZY8XOd8bafT3fdvb//2b8zUixPxMpW8yWHYq1aSplORlOCIKDZsYFj/pjV2AM020WoXaY3hHK0uyjqL1xSBLQfFLRxrJpAan0dEImKtcTkTXN2jNtjMJCUmUlOYCzNyLmUJXmSVBxORm6WUrJYQ44tIiHfbb0MAdLbV8cgtZKRNRHF+AbX4zQPNdHiP/t620pqKYCUIG+9kHpOzYr+s9P8FXGqxqLeCZHQgRD1G4RUueZvq5+TglKxWb96qtXUTJup2PUrVIi4zRDGAceg3Y8uaMTOgRALVBoS6rLA9lPjQHt+oAsEIMNytlGqwWqtXK9OcmMs0p0TCfHj37njYj+OmTsd6PNX94fjhw/L2w/Vu/NWvvn7xb35WT5Tm+ftf/NOQNpxH3084zrucT2VP5SRO4uzRTsqUjIREZDBTcq9mlMSsEKfiKm5ELkwGmXQqREfR/ZXbl1v6wRO8GBTVnXJK6iA3Jk5ELDCJMlVpcknVeS7jOKY8qBkVS8MgnEstVTURqxlrLYvnzZBy9oK5LupMwpKEjMZxrOk4ec1MxhyqtdaZuS8m6mJD6vx5DwGIgOjj7zA3dYeamRmyOMHdilZSq7USCElcTVXdASMGzDlqX8yVzEiaYC+qAkN0Lyzk5E5mXuFQBcBRVW7V6kK2wArbklyra2uEDQBkBCakMGfr6Nhz1OoBqVbLjw5r2pqkZhTavnFqQeWKtMKCfgTncd4nZ9jdLXV8G25tdTkrimrJ43B11j967YUSW7S7o0u6Zt1CH7mY1fgDDZx2vLhW8LQQo6fmGvzr5qJ/RGuf4N0D9dzyustXQmG97sb+8DkICgTe4SPxdne1u95oLQ8VL8btG63jt99+djh++sWnx/d3yibjRp6+2P/p28P96/zFq3G7LbVqrUkZjgy4unLU+sG1VQWbahgCIe74uakaYpAIebDWcc1Nxxm+KrqVxZIIdTKBSMRL6SwIhdEhoj4ypT/hfmu0qpuxsJoOw0DMXjXlBHGYlVpSTm5IQmbKRM5cS91sxlpVrSUxm3Y3gHLkxVz72uux4go+en3Wit6o7dy+xkIx1ZNQwf57PHvr8hvqBvTiwbbt0uvL1nXXgiqw96XfWFVC67yzRh2RfXUFAFeHhaxbJMfTAVyi+U4grUZ2dW0o0G5yu1p3Vzd3agPFglNyaHjotikRpAOrOXFz1aoGwzgMHOOXzW6ur6iqmDPIl3L6cDcdH+zZS52m7XZ8+Pa703evs+Fm2Fxx4sWZrDw8Fgy7Z9fuOu/37sYp7w/vM1SQ4CoyVDVSy5KZyYpFq22r1YTNbQCWWs2qCAmLsR2pPIqWV8le7fSTwa8B9qpazbk6gdQMjHJxhyVRHsTcZqtW6wxstpuEJCK11DSkRMmisNG8LIUGwlzSkIZhBPPpOMPJzUqpCgWTEdRhTBHxxuhHBhStUoh79K+AAMErxj+wzo4P4pFhbiSMBDVQG6nMVdVqUXUttbprVWf3hquMzN0BNSJSb2OuswgQUTiZeQr+MCCLmpvCXUAwS9Gxy4pyVqANuo8tKZz8vBV6AnsVXHiPZlaicbX3sbXWYMBxmataaZUWDXvbK2f8//FXX5uOtcP5Sqi0wsu1LSetegd3p4u+JmfG9cywdg5phfrtCLiw5at3WKMU6oC/E1wXLNJ6yuuVnDMkQVo36rvf5H4J/ePogjVq1+z9I9ZLcMLD/rScyvXV5mFffv/+w6fT4//jxYunn2L646+Hm5f5xTN6fkV1Wh5x+/Svrerjwx0Jb292m91Gi3s2BsWAeDNNSRozA3QDjdYpyGGmQMywbaBf3Ti6PhgcrqoOgikRw1xNw8xprTCHMAlHwVKLf7qNblR44z2swW8WhktKrkZJ4BCRKItj4ZhTrqXAQUyb7cbMmUlECFA1JnY4c7AdDlDryQM0i4AzZF+fWbPU0QlrBQJnx9CZIpyfh68PpK2hdknelQkxrLKtG2u6y9Ze1Po+uljh3e/EHwNipGtNkuPwqmpqObOqMhGzxK0Lp7zK19ZlhHVrxjA151oXkZEANW1YY43ozDyIiD41Pnacmgqj1DKdjkstkiUPfPzw8PiwGUZ5ePshL8vx+++vXz7TaRZYebz/8O13y/sPY63vfvX1SIZpZsGGicry5OZmro/L4UGI8/XV9ObdFZO7p5zchExTyqJMJCzs7KakNGcZVE2YuFrmvLcycbmn+bTV/fO8/VfXdrNdaLFaZGEZspC6hn6+qEtrnKChSHARliSsOs+zmC6ElDPnzTDkUipnDhmiQZlYrZIRjFJmx1CSlWUBUxry9bNbPNbCxm6i4eaJIVETQE0sAIf2Hb1iZKCh014ytpLoiSpDE1OKnicCQB3W2r+pw9Sqm5uT6Rq9OXl0gW0En4FYZFU8WujSzDk1Hx8UH8HdqlhlVIHGgiWn1tjNLUWXxwAvfJE5bQF9YwZAq4qib6zzJoq6UvePtlM4BZy/uwDBF3a0BdqgTjrhLA89L/RmQKlTP4036uT7R7bzI6jfc2TdmYDo3E1svdCzjaC+Lfpv2ol0qBifd447Lre3nxlgXB4GnR5qk876ne1IssFWZrbmemlZFgOlcXg4HvUwodJi+Z+rPX/5dPM3Px5o/Pyvfvr1f/9PD5jkL/9mc309zbUel81ug2LuXGu52ozNTdaWPNKgCiI3aU7CYQ3NzGB9lDaFe+BeuOwx8K+ZHYapR+XXaiXdpQ1p6a1yHNxqVhr5r6ZRxusdeTGLMFdrcviqSmAQYgahaQXcCUMetWr3TGRmKTp5aQ0MC1e0ug22LiMlolqNzsmtdTkFYWdrQNCjk7bS7M8ASnfWPWZoa8R9XUM9OOjBHajV/hKR+bpw2oLxPju4pQ7g5NFSLsYwmJvF+BztVJizuBlzdIEXwOEGim4zTOjMpFkSMa1JEgxVq6iipX9bKGYxor3p2T0yM1WVk2hZlmnaDHmIos1FSRWLDsMgU6mP75+Pm/37d7fPX9FSysPDUO3uD2+un+/yZPsDNtUe3r32YxUerjc3y/F1nn2zuXGdrsCofr25MYdCmZyNqtfsbK2XqBElGFIa5nlS+ITlOOo9pset69PkL7d+NVgCyyZ0DULIQ/LqtRQ3VFWIMyADWcGyGJiYOUv27K7uRasjJWEmkRR4CF2oGyjbYVWRUt5sdgY50Tw9HpZTQUU2FosSEF7cE3DWzAFoDW698z/rrBh3IGZ7ubvDHKJwFa6JK6xq1GQZMbN4osTCnFIps2XSWslNq8LQMnAccuoIw9nVq6oQCSd2R+tpoNHMBeQMIyiRshVYJSvslVABYcnOHG2sU7PdMbOwqezcOwLvzIZ3ot6BeG3nNteLD6O2yoJWDN+31IVvOGOwtcZx/ZQ1fAi7SBcIH1Gzcrara+yL3gxsfedaCebe90jA+i7dPLsV9ENSTzET0MqPLp5l9+79WP2Uzv7GHVG4v16fufd4gy5uaay9SzfSJL6tH7cRQZjcVNV4yMiDXeXfTnd/+/z2xz/9i5v8xff/8l/K/X77o7/IP/zM1R7ffG/sdL0p0zRcb3JKDFZo2PTY5+FCCRSlof0+ublGldNq/deVbeZq0dYqxIoOM63abuwaUAh5bfwbdadM7n18mDV+xp2Fm7KIuFY9J1zMHU4Mso4tyCWnpo9KuSwTyCWJpOTmZDH5oOmaWh1Mg2gAvHczXemWvr6aqB79b5z/vjT87cl3aqeBjX5m3pdSz+uiMexnPUgclXidOAZv84F7fsTJzELb2ogkR6sdNpMkpubqnJqdNzNJrXk12vjGvq0c7s5OS6kpD7UsZV42myt4gEKnVtbQwmY3d46aM2iNkSM65PT+cChPZne/++b7293V8rCf7u5ukGip77/+02E+/vCv/w7LfPf7r/T94Zro+Os/8MyfPH1Wj6W+eT86ZSeZpjQtmXlAOp2mQe0mb9sQcAUAY8ClkAFgU7UqJKVUYprZquA7n5Zcjzecv3wxoyxc8sKbtBFODnH3Uqq4DDmRAEoodZmLe81jZiJJqZSahCtXJqpuS1nYExWBUE4QQqRCU0SRqmkQtZp40KWkPG58u9vqBzx8ePtBTv7ZsMtLdRR3qqEMc+pJfDK4nleQ9wrBtokUxk7RlpmF3F2JSuLJfTE1di26wgwmBgMpM7FLUtUCjbkFagZzIo4RsBxUHbkrHBrvDFkb3AgSzCFqRSkQZa+wQqakxiJMpCAQOSHF8AIDGK59HoBTTwqcjdlqyzsiWnOy6uhJELSNgmbKWiDRTOD675VQdbMOkNd8a7PF8N56kxrLzlELdwnTus03c2qKIG9QFN5qBFrZWeww9N4Gjj7t0unC5nUv1y++7ejVR6xZjY99W3cUF1bEzsnn893rsYH/2Yv/LFQSETWvppyEW8UAy5OX+zE73di9Hb7+47z41c0t73aPb9/r/vj8s8/UagFR64BopprGbOam1rXB5B3dIxiU1sDJGL1MNExbjBU2ZWFThTubOcGiX2G3VnBnYRiRNNcSt0uYo4zJHUH6x/KNRHITOZjmYQA8EnYsBIhaJYohA3CznHJiqjWUIAwi4WxeKxyASPSKsNYWsNVFsZkC58z/edmCY/bWWqO1LuIeuXsD5mu6+KNn1MmUxuVjrfrpkKl79RWhrCyBOTEbIvFGfSpkBGVIIhGzN5qUo5+MqWkTk6H9Fl01EJW+ESeZqpCYK5lbVVWPXDrHmAEPcUJzAJENijhY3eFWSh2y1FrGYXz9x2+//OlPshpOsy/+/he//uLFp/Uwl3d31ze7bKh3jxuzD6/fnN69+TSlN9/dbzHU/eRl5jo9fXJTHz5sXYlpPn5IKAM4m+hcbp7c3h3fVfjVMFRf4BFp0jaNDp9onpPd2/JuUx/zcvWDG3l1nW6ucplrmTlnd1SticjdqvpAXFGC0GdKrpjrMs+zg1gSEUotKYmrkaEqWdV6mpjQeFG2693NNJ1IuCrKUvI4qBVQ0nmiYdjudi+ev5ofyv3dmw+Gp5x2RuRe4IvXjJzQkWWrAGjN1hpP0YymMeQcPTo5cRVaNlhYa60VqsXhEJFWCmCt+yALE7FIrkXNqph4WEszVyUjU+PERJDeuUCooy+KeNFMK2uBLtDKcHalVQhMkTq2RN38xKLkboXo/8+keQM6DTK3i7c1DdVgUJc+gc7IuxvVM9+6MiSrls299zyhdk9XoVycz6oAvYB1PZI/f1KH4X1zW2wqX4/UzEE/3f62SyN8Pu0/w4YN+n18Y/zyr843NE+4hiv4KJhY78LH4VHckyigrdXUjViImZk2w5B3Q93spg9/+vCL/1q+fXP9P/278dknVsCHw5OUff+oRXafvkKpGHKr2Io++wQhBlyrSu5lKG3UrVFP34ddCRvR4qkoxgccbqam1bUNh5SUnJydECWsHjS0A8TSw7YWSfUn2A2xJFGzlDLQVAShyNaqKSet6tF5UYg5RAoFzAKQi7BorUScRBZdyEEkDQcw2pAyR5BPUUTq6Ou6J2Y6UFlV210Y055aD2JAvT1G45KCUSSm8O6+qvBXjhLdv5NH+RiIDOgNgBju6hZ62aIwUwkuTo2ZvFogQa2LWrjtXk3B4MgBxvZcl6u5A7UWci+l6lI24wbmKSUzy0xwrlpaBNb3qlqNn4Ra9LQ/oJQhpbk8Hj98ePjuNd9eE6erk/H9Xt/f0X4adrsNEwOnN2/3f/zT802+e/dh3Awp4fT+oHXOKY3jeLr/QMs0bq+1Hth1QM4puwLFwR7zD4lJ3JzMnYr6kXWP6R1PxydkL6+uP3m+eX6NLZ9Oc5Lh6W7LRJxIS4EZARIYAeSnBcSUeLPb0kTH02zujiosQx6qWUoJbk5WJ1Or7vOYBjBb0WmetlfX83IaOc9VVZUIeWQi1KJ5SE+e3Synl3qa5m/2S8ITHnPxwBYGL4GZgfzn2C92EvWYkQRELGYwQOG6HcpglayaV++jVNW99Qc0b9MgOCbRpkG8tiNHishaU1JwirZc7kTBqUpM30arHic3cmMYuaGos8OcEq07wSmGBlDPUOASDDcDe84Ed5LT+x+s/sDX5R9+4oxwiS4dxEdGE1g1auv9a5bossMPPCKSrs847+IGV1e73Gi4sy1dU8cAsDYhQicH4sTah1xSN6vNXkN8dBKspznigjrNRKsfPRv8AJQfZxn7qy7jjUupb7glcyMnF0kpSRPyMc3LMu+y1Vq05s9e3f7sZ/nVrdpMx9P07pt9ls/+p59r4mUpvt3M87TZDXFFMY/MzVmkdXYgiiZuMUOR+9woMyNp12JaowOow8lNtUYDCauehoGFTZsOi4hB5m6NVyaO2YGNJKImbIR7tMgnEmntFpsQsqFSZoTei0gksXDoKSWLOZl6HgcCIiGsrtEmMkJuEEWFmEX4Qg4w9TH0Tr01W8SWfbrZSqD0/NNaVuLwNjpuDQovoQCtmdh4ZrECOkd4ZsM6hdqhhjvC6GcH3M3MRFq/NlUrtbDk5pCskiRTjTxuiM7bPu3LP3K75MaAmhH88f5hfDUKMRNrUYqpbbWvsnPRh6919gwfhrxMp9//4p8+efWKy3LNeZj85hpvf//tp3+1rUWvOZf9Mn37XgXHb97fgOzhuAHVZXn47q0v8+5mN2a6Tpu7/btXV08KMrmCLSMVM2YqqJJSqbWgoCoJVbbFtRDepeUO8/7ar37yMj/Z+QY10ZDHZFjmyeGUJA0ppUFL0aqc2qC/6qZqooSUUh6viKd5sdYQ13sSyoRSYita6lJPh+Nud8ObXEuxXMY8Vq05odbCLqaV00DupZ54k6+eXz+dXz4Um95Oj7RUw9VK7IAY5lBCJFZNwASmNveJDMYwIDFnBwxGsIl9uRr3shzqtJCbkxusGvXB8gFfaqlRMy9JGg4lZ1BKbARncm3lxJKI4OwkBJALCzMzE0uKKTHw+Axt6HXtMSUCBeDJQwbcsctKgK5bI9iSdRmvscEFk9HWdy8E+4jtX20leiJ8JUy6kVy70p6t7EeQ/Fx8hpXSbyb+HG6d8xDWLfU5Grl0UNQqi9C/xYX5RXdy/fR6tOHdzrf7Emal/6tdcsOJEdn024ezpfgY/McePgcl7Q3uTgYX5iiJVFOWNBXcVzw8nsriu89/cPXlT4Qzvz/O335bvnsDxw/+/t9ZQT3OxsN0OKRxw9HRzgtF+RWchHp7SGhP5q96EmvCWm6hqDmIrFZTq3UhczXVUtOQJYZUUJRhMYBALw3zWhufQmgzu8LUroO+KHpNOAyk0fydSUthlsYRBcIH1aoiyd21KLOkPMAMhUAU+lxhaYLRtk2IiBXurcqpP+Lw9tQDVGrqvf6su4qhPaSu2mpLCv8HX6soNB72x+Kiy8TWmYI61xM6EVSr1ipCJDGLSVSrm3FmN3MzJjJfZ45H+Z5GozftdaFwN4WVmiU9Pj7Op+nh7YcvfviDeVrMLA+tC0gssUgjW19t5CDDvD+SEDuV+/1mtle7J1cyfPP1a8rDMMjxj9/Lz37Kc53uDiKJi9aHo3+452XZQpYiP/n0b7//9l9utpvFfJx1efd2a1ImTQMRYGoqOaoTTqdHkI88ZCdKUuEzcEf1PpfHreLpOL643nx+a/DTPIMYzHkQR7KiWpxgOac0SIwryCIQglYn16pc1QFJKWUsmE3htRIHHhZ1G3ImYC5lOiyM4y7dsmCZpqub3Sjjogsx1DyZuWnKqRR4rZtt3j25thfLfnq7v68kcuUJRlELo60vkHFDyNGooaGGWFcc4yRjbo9DB0w7m3Kdy+ySicgVquqqRK0YPiCrFg3l9rogI5RIOcHUZSAA6izE7kKtSAXi3MaCCbNwSk1eA4AZ3MZ0RN1iLPLUaYuwky1Ip/O6XcUq6I2BGyLuAT21bGlH1hdvBVoVIppuolubSzPZqMz+jjV1204xNuw597sa2otM7GqXsdrjzkGszmhtx+a9u2TrA9AN9CV+7wBuvQHoFvriZd37BBaLH66zO7pD6J/5UW3CmXxq7rIXyTVD3KBeYBl1EqDURUv5p7vTT3abVz/8Qbp79Ndvypvv9Y+/m3711e5vfroVefP6fXl+W2XYDc94E5A+IKe5uySORQNCwOeWkxRuqZjGFMFUwcRJrLQMIUxhsFoAl+jraXAQx+yxbvsi3orpYn1EAHm00uxNF4IBjzF3tVYRIaaQwqM3cWXmcRjLvIApJalaYu5dkqReQMQSM5bJ3FLKbk6A2nl86rlLaFtT1GLBvjh6r6MWQ3NI4i9W7ply7AXD6+roS//cEfCS5+wPPbQJXe1DbfhilI2EQChkP0myV4tmesx96Iybas2DxH2LEhGLet0goNCuiODCkllsXk6H45glSVIzSWyunWaNkQPU5vqBSikisFo3Q0at2f39m3fL2/fz7rv9VJc377eb7UMpn3/27Pbm+t0//aIup9vtZ0+vb7/95pvtqfhcpuPMsPdv/jkV55TyXEdGZtmMT4yoTqdMadF6c3U1L6fMKDpvKY+JlzoT050sd2n+jia8GLafPitbsiSFwMKsXK1QJUjajpsqpSxL1UrAMGYZc120dbgTIXEVim46VmvKmZlP0ww3tSh6cmGqRXNKTFyWpSxlOU2bbYZwnZftbsdpM9eZq9WqAA2bnMe8zNWsDtf56sW1Lss0fdgXFWCHvEFlSHBADAqyOmIpbva6lYBFpElODKls5Uqma0ywau5aoTBqnVo6jnIi9xpQmt20GUvANDqui6CvaECrgimOMaYUud8GLAVOREkojUijpcEkGcdY0BYOwj2t5EwY3WZebdU7UyhZ4he+2tP+FbNt0StC+2+9MTTeLPclpe4AmhQvUhF8kTbpNvYSE7fJFR5ArWeou6Knb2l0N3KGONR457N8Z8VlazhxeTFnJur8k4vXd+u9/tC7pGQ98mVbkI8Q4Nn/oN1JxAPH+UD9HMismnOXoDCTkKqbyDLm0+2mGj786je3h8fT92/scD8CdL8//uGb4S++OH54c8Bm9+xpzokIbhYQXESCmrcmDwcxmRqLrIReuMw2kAiI5rFqpS6zucZs9GEYOSVzmBknaaqhNlqAEUW83ehaa0PdyiNi1VqIGVrxNYNDV2GSkru7W9ToA2RuSTI8kvuU0hBHiA6m1YyAaBPpCJ6EHdqpnlDlUL8YBF/paNbfO9YAAsWAvRf9dQDQ/9Efcuff1wfceZT/g1XTFGWdAgsPwkTOBHWQq1XVKpyYWGHkTu5qTiLubqYwb5NqzCO0+Wj9EVlzE2VM6fDh/v2bdze3N1mGMk2bzbbWur26YhZVa/wy9xanTKZ1yLmWOqZ0fDjU48kPk78/bn9A+nBY3nx49Xcv7795Q7N++Md/nt+930imquXdm7Q/5Xl59/rtDz6/9UMpx+nLT386H0/lcbGypO31Uu3JeP3OjsJInN1JXL3qBgLjUsox6Vuf9pt6vEZ9frv97CrdbJZlsepVbTNukrvWotUE5gl5yFWr1cUpwdjZKYmqwoyZWcQczlZqjaebZUjJpnmCg0lFxKPgqsEIslJPhxNDc85wr8uSNsOYh8lmTqLuZVlSGjQ716qq+Wbc1BtbtNLD6Ui8KBvFcEqHGhgQQEOKaCgCCX0WNVPWcpklebnlgywLak94anWjIGRatY2Ru4dGx9y5FfQZoLCUkqBVl0U1cE5inW41N3M+F94zOROlgcYN8sbTYJKVxILwZxCQQKmTPMGNBshda69a/OjexK5/ts7b8sYq+emZgRUp9/e0lIhTHzS7mkzqmbtW4dXFly3BcAGomiE5/5MuP6uF15fVaWRhkInIW012u9qPbbr3rF83wE5rpgDrx7sDa39grL7SLl60eoKP7tIZiq8BRg8LvGmXCDE6JNQahh5JsboJi5oSfNymvEtloIe7x3z/nj48Pv7219ut0PZKkmTPBFnePgxP2JaiRYUFoFI0jxlEYZnbowwxKIuENM1j3m+b6MtEQW9ZXUyrlkW1MEnKKeVRwL0MhR0cYtH2KP08N9HUOIVZs2iMjgt8HquVCLAo+mViMlU1I5EIRJglzi/yxjklONWqMqSylMglErcZlgQ4O7Wog0NT0RQvFMPl+3ojDpe1RppduE+h6Is+ZHxhcAPqN4AUzuE8mfcMiohaW1Jqi+qMAc7CAApEZXAqy5J2Q28ZFSkZYqJaNZr/sLA3pq5xOElS0dJygETVTED1NNtcfvfLX/7Hv/97Mq/TvHv6/PB4uHn6sta6wjvuk+UBCHOdluVwUtdy/3j/h68f//k38jA9/u7r47u39GG/OeD964fP87W/3W/nWoo+vbrl+/3x11/NHx6fbEYc5ulw2mBDRqiLeOTus5l+mO+qK7EJo+oiwFzLTdree1XR36Xp7VCuvtyNn9wMm2QDFmYDc460nI1DKuRWqrrXyptNzsYwMljVmiWJcFQ1hNFIWVDdRUoxW2phZWFmWHVHK7WgqLvN7M5W1ZalMGysykh5rHNF4pwHVkWFFpWQ1DARrFqVXdp+cjs75tdTcttULhrpX6+wAnUgQQESUM+VUsLQmjmTVJnLi830Mh9kWoojdLgW+sngKsDMpiHcgDuZK8gjwgvzqTBnT0xw1xIBI6fEgEmbTetwT0mYGSScM+XRhg2GDYatp5FEiIlB1u13ikUdbUlWbrvjoI9MW7N3YR/XVW+dCu80BnzFxyvTsxrpZtv8TIKsB+lEwoqJzx+6wje0cBhN8NkzuRdAfsXjqz2POAaXwUE77gVz33/ZXUEXNKIJvIMo6F6qo8o1DgB6Q4g1OGlncVkcdxk/NbsRm7PnKr0RDkDryO/mTLBSk8hmGNJAe1o0bV9+9sXhq//9KmWpNb169eLv/8f56fX0eJ9OJ3n2YjMOAtdSyDkNQ7+vDnIKzoYcTiJk6i1aAxGRltJkoVVrmbUuuhQ1dbO82YDZQc7sZsMgrTAQncUzY0nU/HMbJoyYDWktS+59hnC7hRQRJJGQqcGdic0ciR0emgarhZgSjyy5LNOa1HdY+4hIdZlF6wigBYoIk3deQsbM6P3cVnxCJA679Ns9lYAWwjbrHwWPfhmtnRco1iWNJk1oP27sZMNP4UOiL5sbizBzjdkMpgRPEvM5nKIfELFbq+diTkzi3qJzAGam87Qbxvev33/9q395cXMNp1KO4/iZ1Xo6HWIsj1WzWim6XBDXpRLDqh3f311tNuXdyR72D7/5o77e/+D2lX/zenn//ZfPnsqsT4me5O3vfvMHlfnqajum3Xf/8C/0cHq23ZgudX+4zturfK3LUawaoKhKSbmU41FYzCkNudR58XoCZq4f/HS31Xc7yy9u+NOb/HQ0aF1qStmNzDWPydXVbdgMs6laXSYfNmkYBlMvc1XTZCkl8QxHjRyUx6C9BHO4el2KKUG9e14lsCTSxWG2HXJxqbXUpR4P+w1GHoZxHIPkZuacGcA8z+N2U9TSmBdVUx9utmRUKi16OKhBcKuJ4A51xAJihys88vUGOFSQFLaI2i3hh1fHm7q4swvBfA4k4HAzIiGYgZkj8IMTRWTcx1Mp3CqMNRKE7OTuZs6Jo1+sualWa8OuyN1FMiS7JMvZRCCCJE7cumeBAKQu5OTVkoZVWxvReSc5G9rxXvobBq5HpH/Os1zQHwAa9xlGYHUUayhA55+0X/eCCluZqehScjbWXY3a+fs159tidl8trzfb096GVQfx0Ta+KESI31hn+du4KI+ZcB37nY+9xgh915s7bE1mrOfVPsi7T/zo3VgtBCNKiuBeh5yYKA/jkAc1+/71Yy7zj569pIf6xctX4qUeH8cvPilmlMZ0/72/e28vP7dS6v64aB1vrjPEDU4ukf+pNcgYToKmuDUQhCVEIyxUp5nMibxMcy0LCw15AJGqDVk6Bd+bDruTUMxZE+ZaaudGuA1sjARwRBVoihbqNX2E1k6vcYXMA4ukVFWZJbHMKEKchuymAESklKJqIftvekJmtxBbOnNUB5E1S966fkZJHvVEhffAFQRYmPtOyJHH+CVbAQk1JRUCj6+7B83gw3vKiM5PtGd9I7gg69uHiYS5liWnQauyMBGVUsw1c/Y2twch7au1Sogno1bc2wg2JqqlCLPAWfWX//iPP/+3/xYkb95991c//x/+9P03m81VVOgxszuY2tAFgIRpWTQtNoi9++0f6XB6+N13Pxh3d7/59Y3N6TB98lev7HFvH47A0UoZktA4bJZpW5ZJNVmaT+VJ3hKl62ErLEdVuCUZ3CtMYcXhS1UCKMvJyhtfZpmP17o8H25evShDWXyhBdvdFSmBfBjTfCim1WM2RinbzTBNUyllOkw3T27HDTGXutR5mSpUksTNYckUHHoI0gxMoqVqNS0KoTzkmJMlmVGcGdthWACQL6eZEtI0pSRD3pnXqiVGq5DaMi2ZhyHrkrRgnpaTbCS/uFLV2WZMC5GPnjbQDOqQggIjGyxBjOFWjXgeyV5sji/sgfVwLIdpWubiTiwUuodq1VjCYESX9cAvF5RyFHtZA2/mBHJzIzV2AkgoghZ3U1S2QhgUTllcxFJ2Zid2avkwYoI6RTfQBnOpRannnKS741zQtRIoHSx3J3A2XmfUjVUF17VB1BnvFW6ft5CfPyCMBSmom2pfo+3LT7uo32q/6ZQQgZwJ6D3Q28mtzuwyvOicDH3kD9acRb/ohvwdABSgyy7sdI4GqMUL7cV+tgbUoqA/8xx++Yn9FrgbmMl1SAOxgMBEp9MRal719VL/4c2H/+uXP3r66tlpeszPrzcvXi374/L4+8OfvpLNJiHhNAlIlxOud26GqPISstIVxUzMUHUmZmc1VVMmBgwFDCtlLqVYrVbKOO4k5cN0un36XFLS2kKwyEWyhHVDtHgjQtWQdYKI1JSImdkNLM2kEpG1iedRR4Y2ZlSYwJyiP5Uzs5oxwUWypFoLMbspoXcAJYqEs7qi1WY16UdwKkRibs1uejsl7w/NNdr9Ewv35tLkvYq4Y4mukItAlwlNbdqbfTaP3l3HZeuqvtwi2A0NHJkRC4AkqWrlyD6g9W+PuodSSy3LZhhUC8HNNWpB1MxMrQmuaDruN57u37x9/advRqWbm+de55snNwafTo/PnzwnwCwaUFZicdgyL0yohwXznNQefv/18v27PJWrRT9/dvvbD3/Y3Wzy+HxI2/27r7RM99N7HsrR9eVw9eYf/zkPpMsC8O249Wq7caeLspsXJ6FhHLUeNnkAi5kZ9GSnudhjLnfbOj3Jz//u01EoXw/L40O0cxWXRCYOEtbEzcCowQUb3myv9HA8naZhGLdXW8/MkqbT4ovCaRgGSlRVOWa0qRKEXBmsSnBSdZiT1c2YHeZFRQQKEYxpOE0nSdDTsqQp52EYPcto5tWQEtdqXsAJ27ytudTtUJfjNE+bXeZXV0hUXx9PJ2MtKfJMLbRkBwnEiKqbWiG4D/n0bHN4zkcuS9a8SVJZKsN9WQonp2ilG6SrmhHCZ8dggFo1sTO3EjZyMtNoE+vuakrVSEgoOXuDXGo+dEJdBqQBkiklp2hN2uYJEIu7pxZOuwfva6YGUCiQ/GzfzgbyDHTD4q22knrr3dWkrqY0bCgu7PWF/V1pom6Mm3Kije5DGE6iNbPSiJeevQCidnI9we5dvP/o4ncXBE6/GGBNC3QOJ/ru9vToeoHo7z57wSaraG/uUqiLg3XI2QAn9etpl95vGlPvFBR4HSKJiFy1zKW6WiUhyYkmSb9+f/f3n36yvJkL4+bJDddcfv/1/s1yf3zc/ce/3V7f2Pfvasrp5Y0ttSZJObhMBZpshkX6dEZXrSCItIbOdZnNljrP0/EI02EcWYbTPKXNOFxtYVBbqHt1jplDZmGhajWHswTACODJIJg6WvqXqE0U7hVBYIcCBiYJSQDICcxctTAxSRKomdVaU04OuJe4rymPSVKtRcyUmn1nFpB6a5jj1EUTkTZzbzzOKi8TFou0xxohoGWserwXDifWSATriHbsISTtsSx1dst7hNGWRegdPCp4mnozoI7LkNw9OoGCwClBayxckdT6PsN6fQW5KjGj1mrVa00itpRvf/vbZzdPntzc/vdf/ref/5/+L2We5+k0Xm2b6zF1dQgILGDyOt3v6/1+ef3u8Ps/1Tdv5KS3ml//6nfbCjv6p59+gvvj4zevP3n1yfHwDrDnuyejJa22G8dCnEvdbQZFfnzc54ypmjCbA7UK2/40i7ATFP7o5bix+kT4+vr602u6HmqZRDBud6fTo5kuZU5JaimcJWVydbBXK4CLiYhcXW2Px8N0POU8pJSK+/Zqu5xmN6+1hopLrSHiAKS6FHhMmEEthbIXpiElSkTuTDCrwnk7bBZdxEmP00y83V6DeEiDoxpYQItVK8jjZjvu1FA3vsz7RWcfNT1LzDt9PU1H48UFmtEALXc8ypwKqW0Iz+XxU3rcLMVUa0osu6sNQ+Z5DoUbmTHcYvIdk6mpOxE4GFpC672YkpkKc0rCDmZ29Ri6Ye7Vq6RkBBZ2N4rGLiwOuDBYYhooRTdzbupzJ0QE0NZkcwSttxyDWmVP2MBOXazUzWrKLy0iEPqb1f55p33OxvFiBzasvLoBIiLr5TiN20EQSHphOftXE3uuCuuG71vWLrZfHw0b17wa9NULXR7v/F2jvLB6qIB1RBefQ03n1esDOpI/uxVQvzkX1QxneRLWi15TAo0cYYfHhGs11WpEiViIqZLYDb2Zjp/dv9+y7b9+t/zm/dWXn/lRt59/efO3P5+/fv3wy1/Yi09u/2//AaZWa9ptmcjgWqsMiVv/HPQBFDUPmUBWi8HqMtdyOu33dZ7GzTYPG1U1K7c3L8hJrbZnbS4xmMXM4cJk1aKSnVOiGDUS0jBzYlaNkle4Oayb+Uh2WCP3YrKxt/6jlVlcPQ+5GtQKJwlApKpOzpxSEhExVQN7Rzxxz3v6JZpSOIFJcl/hBvSaXjS3cUHp9GDNKVqmo1VInH3eOXjrWABd54PzzrgggyJRwmStCyubalmKSAo6X80cYBIGG1BLSTJEt1R3iDA4Bhe7VgV7rRVaMsnjmzf7r79/+ObNv/o3/1ZEltO8216fyuxmGxkYVGsx1eCUGLBphpXp3Xt6PM3fvcVh/9U//PMn2xt+sOlxfyN5c/VklzffvvkN2ZLs8Li/22TalOX+q7uE5Fmebndel+N+SuNIgrJMBFRLu+Hm5MfTvLj7kMcT2TvWx8G3f/kk3SQfyDe02GzmMJdEKQkRylLztYhza0hPTkycuGjJnlmRNsOGdZlqKWWUbeJUas1DrqpWLQ/JJRWdgxBmppTFiy21ZpFSqruXakw1keScXGtRFTCTC2SbNuZVl1L4dHx42D19mjcbMSMHC9lciOG1ZpFRhjkNJ0mqVl0L2+ZJlnRl7/X0sKCCi++iWhUe/KoMueyG+oQPL/ywW2ZYKUB1JzDRZjsQmWrWmqyqqqq1HiXkBHejUPogip+ZHK6mxA5KweOABE6MmOKnUBgTmQlFjRgnSAzVECM2IkQRWadSDA6nFJMrvLXy9AtyxgAyU6zMO52NHfXAN7Ag1mV+ycR0Wx+WsA8bOTuSlWLt6QduqeCg0zq07ta07zvCCqRXJ3A+FjWF07rzeqlV4wi8QfXz7uxntfozXyH7Su024L/mOhA5htBv0EX40jBknE2jBn31gT3kOHsJ9ya47b6wMclmVR1kZiychuzuQxKt+nhcxpz/sH/41y9fjOnHx//2e5kPfHhkSdcvXwwylF/8b/LPv9z8z7ej2TJPnBORqJYoESEmdJFMZHcHSR6DiGoFmS3zcjyUefJSxifPzHGaj9fPn+U8RNFKDCoS4ijCUq0yDA4yeK01Wk0xeM2dRlVrNCOKmTLRRTlAMUXtOpycKAkRgaGqwZWnnLxnc7Jk1Qp3MyXmnHOIRNMwzoeFpc1QFGJlhlF0v0ic1ataCSNPBmZxM0QhGDdONOqiW21MPEBqTzIWUHMUK1BBD007BGr/nfFRXzE9+Gy7KGCqR3RLfT6TuxtRYqFa1LSmFJoYZlLq6Y4wA2PicnJ2pCRvvvnu9O7t7bNdnZavf/fbn/3VX0/zPJXD1dU1JW56MhARCYkVw1Jomvh+vy38L//lH6+hn336vL553N89/vDJE8z56W43Pbxejg+7G1/84SohOYvlLW92t1eZ0uPhUJciJGRILJUlGhrNdXJBTfXoRldWr2ifs38ylNvBss4xiyyasrglJhbWYqBSFh7GLAZFUTc4ck5aNIbXZqecsmeUWrhI1DfVuoSfr6oikrKURU1DOAUIDNXMN8MQkaMRV6oixMxDpjobOEp5SXgzV7allv2hbDcyjEMeyzKPOZeUpnkBOKcxCY/DOG7H40GR8lyPxl6vZXc1yCHZVMdCKDQa3Fky85DK4GX0w3Y+ZStGJkSe2K3VYpvmIdGiQxpK8Wk6srlqVx0wWMgdZtraqTtULUkyN9XIm8W0YA6UQ4rQDoTzAAvnDM7OyYid2J3NI0sC7+2fiSiZW9OFrKnLYMTD+XCjT/rg6GbPLvBuZ8NXZqeh5TPAxWp/L83equpsWwmhorczTbMeb90va6rWVwsbpmR1Cs3axtaDex+A0D6cGty7IPwvvlZXE5+Ps+1vbW7oo47BfvEHvRRurQmLv/n8Qv+zD1oDJrcVLTYr5JHRtEvbglpnB+VtPtT6VgZ9deP5Ec+38/tv04mv//W/VdngP/9v+P/856vjXr97Tfd320+fFJiVUskSEZI4oKrUMlWeMtdpJmZhcq/T4Tg9PsKV1PJ2m8a8zIXIN5stnFStLouMI4WCyNRUewLWVWsoF4PBtFbL6oDHpHiPOq1oM+cejXrQn33w9+1RusWosqgbUCtDGphYQWZKQgKklEWyWtW5xPwmh5qjuhFBRDzIdXdi8aVQZnJPKZm7mQmxxroX7tNXWjvrNRqwbuLP+oYG8z0YGaAHpQ3yRA8374m0C9YTXVvcCv0wL8vV1Q7EprWUxeEpD6bmZoDlYYDDYcSRTndTI6bG1NWSmJb7vR7mP/3z7/7P/8//++9++dX3373+93/9r83s/dvvXnz6RXUbIgCsy7LMm8043z/Y4cTHR7s7/NP/97/99JNPyumxPD78/s2bl+PVDsySBek4fyA7gobpuKdaEm+1WMrZqj9M+6lOo4gptCy7fHViMqpTXYpPJvwo+jBg+NF1fZp0TBh1vyxhqGCsVplJi1JOiVPF4uxqWgtnSZzEq2m1zWYoubqrQdw1S/IcJsQEAviYx5Me3SxxJoCZWUwtICtxIiMULSJpO4xzDF2otEy02WQAObFOVRKTghMGSnPV5fGQN8MwboarGyMlwjDkaS6lLuQQzimVIQ01lcU85bzMBUKebNyQaHaSCiruLfjkeqplgR1d1eCFxWTIEjM+yUmYoZaEzTGOTNjMcxFx1SgGacVcDbJ5q5RUrSmzu1dzBhhJmMHRHIXdNJRAkdciyUijSzYSYzYmC26bwGAndiMzJKw5y7DcraWXN1MXXVPQMgLel/QZIK+o5uwOwm724bmruMYaAG920fsxVv8QKXSsRr+3zqcz6l/xVE+2AuztHq0+yTuxRC3p7W5+znNfUFNrtL9qWIF2kxx92kPH/D0PEn+duZ1GGH8Uo3Q/4dZcQGtAuUpF+snGhflZqRo/JYR2nuHupmYMMrDBJYPS+EH4u4QnT6/p5RP6Pebv72j7p/Hn/wa//K/0zd12h/rd+92+LKflUFyuti6uaixg9OJSNRFA3auCVYHp4bHM03I4kKvkvNvdWKW7d++ff/mFsBBR1UIspjYOI5hMS3XLKZFT5BoDmMPJ3KJVZ+vkH6kCgMiJxLSXpcZgCSdrE1NBcDXrQ7lg7nVZYkafmbspCKCYHMMRgbFwXcfaAlbdzMCUiGvVnFKtVXICINEFPnpOwAHEdAFQrxxrColW1dixQFsJAC7Zzd42sT1QPr+rLZeWR+hoIhAJR+NPcxFOedBS3E1NySznwWqZl9ngLOwWc8It5cwQI9VakkidFy3zYjbvD2+//tqWsv/+3bvvvvnxv//3VQ06n07Hm2fPybzWKpKO94/MbEuRUqbvv6f9w8Ovfy8fHkzx5Cb/+vV7MWw4jRg+ffmDt2+/Vj9d326nw2nL2AzbOhMRpZSq1mLzZshE7MlLrXfLo3CuyR+tnEbjF9vT9ZV/8eRwJcWLMpVS3S1DcjQwMMC9aM05ESMnjuk9tVZz5yQLF5hr1XEzTtMyJK7ViBKDiDlIbDepWnLOpS6lLOAxJWk0RPWIEcqodSl1UYJlzhoZE62VOScRZsmEqiRChsxpKYtVW/aneXOQNCTOk5aUR5apLPVkJuJCPOQ8scAkp60gT6cJg6lDhCdZsoikVpPi5rV6KV5KTL1UU3dbUkrMAoCTJE6qalVjfW5YSqnCsKRO7qbiHiR8Q5YhLNXW+7aqCrGQEAvQAgtGDBFikmQgShl5JMnOyZnhkQQmaxaXiaIS2K0ltNxd1165AfwD+FLfES1H21B5/AAfm8U1ROgm1oGzMjr2VC8cQyON4r0rlj5jZGq/gTcj2jFxdytr1cL61dn0ntm42L0X4fsFvU8XzmNF/f27HuKvxBBw7sV4GeZcnsE5RdKP0+MU717wHCS06OTi5Ol8JG+FYhG7xAwXl3Rw/GE//eUPruXlUyPW93ebqwf5p/82/f7r3bMRu5vt5irdTfV+Hl6My+FAV4lljCjKzFWrMLmqzYtpyZym+8flcCBTNq3T6dnz5zB/ePhAia52N4AsSzFTJhBLKIi8FhYGSItWKy4knEFiph6Eh37s65jgUWl84cXhbgBH8pZqRBWhUgIiXkmDmFow+2ZgljwMIqml/twBmFsiIWag6ryET2eRaJMIElOjaGDXMoHOzCQcvgfaynG4S1cb0wmPVd0igzZjrLnxlbFb5w7D2zidFT30/dFXbGQ4rI7jRmslgtZqqonZAVOdjnvJKZaO1mUcRjjUa9UKN7A8vHtzu9s9fP/2u1//dvpw97O/+ev5/f3p7ri5ukZOb9/84fbmaRrHpUz1oON2tGXJwzB9uLN3H2Qpb//hX7bLfPXiyZtf/2n3oy9O7w9PxjFVef7ith72+/1D2haqWZTzmH1GdWP4skzVZhFygboX99mLjENZygH1cAP79Gb8/HreEt3kpS7TsQybQXhQc1i01XMwHK5uc12GIZcSZXfMIlorp0zC1YoYEuc0pI7mgkQ0FqleiQXWEVXEnUAekrurLjFXWyTlnIu7m5Wqqk5kiXxZnDGmRCxA8C1FN8PWEw6TlsflNO6Hq+043sKRMYxpW8qDubuqEJNQGhOWWdXHtLGBljoNQ3JY1JZLkCzR2Z+EoJKY1atbNddSF/UhW8qi1YhlyAJiJVLVlMUgtZQOQySmwhERDEzsDJgDFoq7qMx3pySpfSBidyYnDtqHWCAJLMQMZgfHSFhfFZREyS8I/TOA7rC3TbFu/Xy60iJwm0Vcy3ZhtaIri6OB6gigA4uvjEbbDGetaWQ0EEXvDYCvZriprM82v+dLCedqq2ZX12jiI7vcbLW3hHHfkk31utr4NZJZfUD3guc4pb+5pzhoNeYroD+zOeueX48Qd6yHKEDjs+xjJxL+oEVcLTCiEKcAXtRLmR94/M3p/u+HT7cvPrPPf7A9jS//1edYHtwOu514UpkOj//v/1Th+T/+3K8AojTkWjRkiLosZLCyTA8PBKJhsPlEy6Rl8Vqvn9yWMtV5ef/ddz/+D/8uxELunjhRYiZh5rLMqpVlgJqaOizLIInD0Uv0hKDIbbYESwjD4lDUuP/G+AUsQrQhggMQFgdMS8qpSZ778xVJOaUAxe5etDgwDhuLYkYzkuRuwincZZB+w5BXb+y9KzoRCdisBvphkoZcuoVHG5/Y2caPlsG6DPsYkBhr0wBRrxlsG43MzMmJDAh6Q4iJDcs0wS3lDRlM9Xh8fPL8lTC7e5LUnUplbtINLCpX7tP0u3/45fH1u3/12Ze//tWvfvijH0NgtpwO+y9+9AMiZk5kLiSuPu8f82G5vhq/+e9/evPLf7lJkpb82eev3n77/ej+6W53KzdE+n7/mqQ6RFy2486KzfP8/yPrz5olS44zQVA3O8fd7xoRmRm5ITMBAiBYBMkqVnVXd0u3SM8vmN87TyM9/VBdNTJFVpEAQay5L7HHXdz9mOkyD2p2/EI6EIK8cf24+1nMdPn0009zhBa4MoFHMJATGHhFPpZ6S3q3ZX9nM7+3e1sOEcRLZSnE7Op9n6WyRTLrkK0t1Y2RmbnV2pik5LN2ZIQINQc0IASCPkK+71N3Q2IUYVUXJJgzCCAILCLOpuoAwExlKu6tNTA3x4hmyOjkrt7CsnkKAtBQj0shLjEdD8f69rCcH6b5Mtw92iSlyGxLa16NhZgnKWUquq+LwTTPjubuRNyqBqMHcGcmUwQQAaIEKTRrKVRdzZtSQSklhRC7chxhIMxzQQStzdwjG4PdOqXOuoh2pHavB+OYB4TBySZOrE0ESDSAEwcHCpJsAQ3AAHLIknnWpFAGbJ016I5g/lmUOpLgNSgf6z5D4bHMR8w/4u6+7mNA5xjDQkZ236+c0Qzh0kpEnsrYgcOgj6QasUdWK5i0Au8jDYnB0hhYfIz5HmtV4WGIPazzuISVhdr/xsPDuy3rX52HjzDw1NcQw+BH75N7AJGtN3HU3HFEM6eOtp4YnTgtiF1U3iMAuUzsxrc8f/Xy/m9//Jm8vsX9P7bvfth/9wKWW8KNnm/07b7+4Y/7o27fewc37+H1Vj0im3KtYYAtS7QFXEPdTOG4HN68nWaZiKbtfP/2TXN89N47TJMbBrqpI7nQRAitVtWKSCzizcKVihAykfgYN5b+OYmSZk6E7tn83k1v1qsgS5wdKurgGzHmgCKEpJoCInpTVWUmFiGSdP/WrFf+ikBTVQVATFkiJkTss4WZaBKvCpRJFEaylZDMm5sBQHKa1vU4PPmqLdJphqdQpNejTqBPxy2RE4CMLgaXr3hvUA6IMURTiFQXCAOzIpNqPdb7wlK4BICbEnNEGLTIbgAHPR7RPKrdPHuxJbp47ykxhbbr997Znm1f37282F7Nu3PEbKmIw+2NH49nu+nm5bdo+vrrr/Tmbnry7tnZzvbt5Q/Pzs+mi/Mzrv78xfe1NgDgysyCAbXuc+5MuHk0U5s2m9tWF4I7qscN+iXvz3f7XZFzsdKaKSFHzYZTDgNBjN7kl23e7I4EiEhmFgDmgLU1YmIOtyICqQfrjoFcpMvKIkJK5wNCQJmnTEQTKoVRpWdhiwgFIhQRLa7uKAAKhKSqTOQluQIESfICiiUCDIOtmd3t6/3SLioS5aSwouV+qYbuplwKM5XCi3CtyoAik1kDCHNwD3IUDBbg1FGLLnglE3mzVp0C1QxcVV2miZEBIQWiUg5gO08V8Lg0wHA3Yeli6RRxmngKDJhkl4jk1CEzcc4FI+ZSEARZUEoABaIHOVCcmO0YfVRckHvOVk7LE2v5FNaAehjgzmnI1d31pfMf3oOf6GFQ+CjzeYRHlrZyklT/lXlY7yPt/4HoBKjoNWrsCNFqhqPXAGJg/KvNhrX5Kv0TruydB035vfUUIpmI42NW8KV7hsDT9j+lEt179IEN0ZnsAKczhJ6teh/skI9mnCScTrvbd+9dqONXw35Ed6zphqkDyR5uruqmbmqqBu1+U756c3ssbNfn+N41byavd+14/+b7H26+/X5/PFjV+rvPv/3//Kd2c8c0eVAEIHE0Q3M7LvX2pjCQqR/29y9e2uE4bzcYgEC66NtXb3dXj0qZI9zdWFJkFgijLrXVlrUkc7MIYSHkTOEQIodZ5rPxLvffvV9Cmj2lSVbZyrdJ95A6zj0pRTMbfefZRsFTmXO5EfHAk3BAN+YYLDLP294amXVdZEJxwBSQiAASYZKwyLtMzNhVuyJ9Ul8Na6BwWonRz70HSgiBhLxGLF0TOzO+ke6ZaniEBQG6e2utsARErUttjYWJyVTbUolEyoSA4L1A5KoylfAgRN3fw1Jvvn3xza/+9Pz337z36NHd21s9pj6K3r5+vd2cRdN52vhSC8vh+es5oqgevnv5wz/8rn775mK+mtpM+zi+fPPo/Oxqez4x39y/PUR1hMvNZlemULtf7t0DCIOBJiwbgUluw+42/sO0vDiP4yfn+LPH/uGZncEBvSJKmXJh66GCO4YHOQumUhkEImOQZ8YYARBEgO7QWove9QNFCiMHBBKZewpAESFx6uGAQ1hvVeKI8LD0JQBBKa3HSAHMXAqXwkVEiIiRWcLBzZkEDApvCk5sICDQDFpMMYPC8eZQ9wdVc/cIDGYuZN6qNXcH4VKEhQNxaVVVSRgZicgjTL1p1KZmnuxmB69mFl6E5nkiQBEmFHS0pR7uDnVpTT1JaG7mriJSRMwALJLZlEEVMfiqDk0gjEwgPSRO5StkYWQmmWSakCbA4sgeyZ4ADTDHnAoWFEgQ6ASrVU0rH91ejZ87ceFku/LgNLknja3Vrg2DPt4Cw6HE8DTjpW4TTgj4sLzDGa22eY3AR7vHMJ3jeH9wynH6gP4VsUZpp8h//fPggEGjGqcPMRSETnWFNWyHDGDHZXr3mf0+5I+4WvXTn5PFj9XzPvQiq1t7UGHojweBqZNoEGIhelbbm8Xh4gI358eXtxi4v7+vSzVv9VCB+f7ZM3v5gt1cNQK06XJ7iOZ6fzi+ek5uwoTgdnvvx+Nmu0EqZZ6Xmxs9HnZnZ7vzS5JpWY4ehhBFJMzaYbHaAKBMk9VmVWUSZEZicz3dg7TmOV8M86Hng8Po2gsPGLaBnSKEY9JAACB4OBMDgKl5ODKyJFUiIMBaQwASopSnjowekBP9zIpJADGJSJhlv3G45UAM6f0KAR2f7LH+qvWBJ05zUEeyICsBCEAPiWJjUfrpAa50BndzDU03rmF1OWI6DvNa6+H+7TxvwMFdtbXCk/AUEAGu1twVmcLdmy7397EseLSbr76bapuCLs7PXr98+f4nH067Mw6sd/Xs8uL6nSd1fxDmevO2vno9uenr13F7aN/ebA70d7/8n+wu2suD1baZ57nMb9+8cW3oyIHhHm6LHpHQEAgnQlKIm7DXZK/O4tUV3X9U7C8u62O+keXOj4YGEULMJLPMYQDu7m6qqprbwsMdnZgDApi8J7xYJgmAcFczyPkBnQGA2bGhptnW3Vm0mIPkLTCQEQLcwtxV1cxFmFgQQ0pOgE9tfJhKtqsjALbm1lp4tMMRQwhKKFJsYglfwA7QlqhHNXcLC8JCRMThgIzmHg5SiuSkJqS6LK0qobAUJgJANVf141Gruoaph7mreTMnjjKLkBCyBamFqdWq2sxT85fYLbRVIpwnHmWwEObcMkxpAyHdHAFlnjsWOnChFMmgsgkphqyADpR5QCQSDwAEHu4QjijDhgZ0PbiOw5zoOH9mNwdrvkequUvyFQfocWsvh42Zjn9W5ewDe/tHRcRDtkSauT6eo5cGMABw4OFrxB1r/S8RKRx9Bw++bvUhEamA+Wdqbf2gcW2jEBwPOKWj9o3Qu4D6Fw9/MC4XImUgVwRnQAknEKnr06cDWPvJ1hvT2UHj/afbfVLqgEwIMNACathNte+Mf//85t9szuf3P6rlt6YqspFzfvzh+0eEb755Xh0vr89ZZohoi4IZt8oafnOvb27ny20IlYlev3559/LVJx/9tLXqbncvXsRud/3+UxY2My5TmZgAQzUQWqtuGgjeWjMPhDJvEj1n5gAA612vhOSjTRoiPGzF+tPC5ji8LkExNAYJiQAtLMx7wSmzKYBkmeLo6LVI6IYp0LRldM8sBBgezGx9MhSxFF2OSOBqKZFWpJAwO4fXsaqzdXnUw3L1eF/Vg2YW0F3U6p1HfQYBM6dGDIeIYGaAdYxGIGXjCLRWz3Y7JqrL0bSJbErZukfT5mbT2SUxadOEkohFikS4NdX9QUwPdzf333775vvvP/jgHTRtx3b28dX55a7Mcvn4enN1wfP04uvvn37wzrPPn91/8dXm0WXs94cvvm/fvX7/6vL5H/4kGljbvJmnAn7Yt2Nlja3gLEwR1aur+iTIYuh70PtSX3NbLkp7Mh0o8IIruoOycSCgcDQPU2ByB8nInSnQ3H3iScGtORKauxSu2hBRw8SBpBREVwU1nOee6GFEBCM3bZ1bnNQWYkAM1zQ2WeSnTkTuzZ/ERA06+o0RYYjgiFJKVFNTAFBTBmQvZo1UGMhqDQWtrUIt5+bNXdW8bcoOkIRLmcp+WQyDkYFg2s61VhIxtbo0QhKWEPDWiNDdAAjdQtMwUbMghlAVKYRUAlE9gkybmzVwQAEAYSjCQWzmGATCTXtcg8hIQEAQThEYOYYbu1YQAWNia8hcRArJFDwZsgZpgPbOMrTwbDHLnhKIGDWABxluFutP8ejAwU9l2PXXPRKmnp7TapFzX3SdOVxD/PzsYf5OUVKcGnRxJViuzTXjRHCMbk1LnNBwP7U1dl7LsN3UDkM7gvcRa6+oDayUnhgX/GeJAvYI7xTg54cgRPQe4xEn5uee4IvVyMP6GaMRoZ9zV0nrv4cBWa2hMXXFnUAARlppVoFgQq9j85tnz3/ys0/03ev2+Kx9bhfvPykz3Lz+7u4IvOOLD3+0bHdRxGsFognQXr453twff3h+/v6Vvr3bnW+XWg9v32x3E/J0+9131x8/NfOLi0ebiyvebg3Qq0EwMam32upyPAKxsFgzdZ/mmZjB0cMRARLn98i4lYgjwCNrgF3DMkEB7KHGerMokmdPPeeFAGH2ZFakUBAgIBGhO0Q2qbgTpsCBIWKRAkCuwTOBR1gwCyGFeQCYayr4iwiTwCggZczvf9YTn88y5aSGAuCQ8xleOzJu6PNLUxIDKcIh1aTDAdBN1TRdo5l7KCIhc9VWWz3c3Z1dXqNw3e9Ds5GCc2uISK0L80abWm3teGi3txJ+++yH73/3x8OLt+/9/Pqwv+MiKHysy3Jzs9lsStndvXgzq919+c3y3YsLJ3q7by9v7KuXn7773uuvn7376Low3UIrAXC0tr+XoBK+AWMgbRUw5mluEHdh91yXSzqcy5uZ6sUcG/AAFHADBEoRcCEOGSI2geZBPIjXEeFAQNAlQ0Awya8OAOa6mTeMWMOJSNUmKcTdQKeTNTUkQgQ3YE6RcAr37PtDAC5cqzJitmgLoTPnwQhATKpORAFABR3CzdSsEJHMUTMrQ1PVWs20uce+uQIHL8sdylGKMKEwB2BttUgRJCIQ4UOrUri1thwXn0IKA6FnbQM9DMKIc3olmqkzo5v3kciMZhBA7g0DAN2coEhS05CQwoAck8gOCIhEwCun3JM5l6NUEAJJCPscNeEy4byFMjsXIAoEBXAHp94Mm2bE3TFAMhkbKrnwAPvMZzBM5MpjjCFYnSY4OtEia5QPTV437ScYJwaXpTOKTjY66764xt7RR+wM3jXEn5nG7Lk+VZVXo95tcHcla5SGqW26HvDQwUT3Jd1i43ri+H9Pf8bZZbQH0EWPu7E+eZQ8JvOfrvDzwBEMF5OAcRp38J7KeE+MgLBrDtDpiaGBA2IaL4/YI71A/L4e33nvye4vf3Lzm8/bYY9Ix8W0wk0APX1y/YvP5GJzvL+TjdBttWfP3vznXxP49aO/032FxfT+5vUPzz7+7LN6fweLb+SMS1HTad5iyqILEZOpmXs7trrUstlAwFLrvNvO2zkFqpJYGTn+NIWsExhPn5DF3njwjCPCHYjW+jbm/oYEwH3oLGEf/gUB7gy8lo6y+aCUydSGJcYsHoJb02ZuTJ14Gm5pPyJimiZCcjVTzQpxqILHmAEQBNAbvbF3cGE+Ghyp2kgDc/F7OEYIk61ankShZt68q0gguiNCmE9lBkBwrccjQGy3O/QoxIdlKdOUUu7gZhoYgYiF+Xj75lymtzf3prY8fy3V//qXPzGFF1+/fOfDj/j87Pq9R3eHdn11NRHX/f0Z0lf/9ddwc4D7/WbiV7/5enNfK908vt7pmxdvnr1kDif0epwB5/CZUQIADadYICq3F9CWHd1uIN7btg029gUMiQBcsl6e9EAIMAMEVzUoOE0I5uEF2ajPuirMzqVVDfdAxqAyUdLNIVLzVMLd1Ayo7GasFVJ2j3gEbgE53TYCEc0sIFjY3QGgCJtquKkCFxGRo9VAKJOoGzYztUIFBSCzCwCMxMARwhHI3d3h0OzobWOh6mJAwK6GUiAwtRK0qanO84wAXAo31eZSpFVFbYAgLA3CXd3CA5jRPAvCkSQw52BiDCRCCIIJzCBcQw0BQToAgYhEOdAe3MzCmTCFEjhbfxHS5RFi6gIRMQNHABKhCE1zbHc4bRHnAPbAFqEehuAIBh7eDaKs5rBHor2/sYfy+CAApj7DCwCSs5F2vb9xxLujN368D0ZtYYArMPCjgZMgrLEXQNIH18kVGCuV8gTVnkCcFUBaQ3XopKA1YR+1xvHFK4ADMEzzkAU+tfV3I5xNuacGgBXHyk/rYuSJEAxwargNXPOa9Hjd83VAI8XmVnCog9eQKtzDNfVgs3+kEwKvLMII9WhId1P5083NT54+4qtH8/WT17/94fxAy/7OYbv97EdX//t/nH7580YYN7e40O0//AGf3dKfnmk0+tlPdtcbffFSl6UgheDx9u7x++98+6//cuf+o1+8T/PUagPheZoAyOpSl1qXY0bT2ipNIkWIxNUzcYTo6g4QAeBEko+698WO8k3mCd3nn4C1/iD9hKMDMUGEN3e3ABARYjKzHLWqrizsFoNPhgjIRcx674cwmVkimizsi0IAEROXcG9aVTUjrHVwGAz9kThlitFpxthn5oydgjGKWwiwNhlgjrtRjXBrLbJT3yEAvGmZJzMAD3QE981mV0i0tjDjiO3uzLVFa1MpORRGayNzbDoJlxZv/vDlRuH25d3f/A+//Of//N8++OST/XH5i59+Rpudv3lx9ck7y6u38HZ/vF/e/OrzrfpuMyNCvHx7vSvtcEPqt6/e8GSXV5eH+z0TkdGWEIUXoMb+CuMNWz2nt4x2QX62xa0YeVVHYfAgJATI4cwErglJEyi5J2kTIYACoxRaqpk5E7FwtjGlogAiZfXMTFmEWRy9LYsgublQliKCiR0sPEDQzIFXGgqEQYBJmv4IYs6BmaaGCEW4NiXCIqLFXN0RmLk/rGRnuXPZtEMNt6p60OYIUSTLt7VW9QAztAYABFSoHO0I4EvgZlOICBgFyF2seFUHdkAUnsMworlDmDEiUQkkw6W5ZkLJJAljogMzGiCBuIW1FiKIXoiCSKCAUI2VMEKdcgAZ/SNScM+LAR1p5t41hojEKBOWyUMaUENUgGRYP4BdAhEFYVAfR0z+59HuMLRJBocICILkcA/TH2PMyANi5zCxI2Yf0fgJD+qRFQFiMGAEYM7rWFPqB7H3Kfle/9v7DFajgiuyswaZo6z3f8NjTsUFzCgrX8pz855Y4Gj/6adysvfj7vUAkMa9Sqc33oU9dD/NWVs16sIBOAIG2QSQsmKSxjFo+NlRjsRxzrD6BMPYm76I9q3yd6Hzo/f9/Mn57iKWW4Ojw6Z88P72w499nu312w1ZWe7bD1/L98flixfXP37ff7iBA9k5h9V2sPNHT5b9HTB/+et/vf7ZT2WeUaZ8lojsrdb7Q62LHpdpuwFVdXv0+MptVPAwhmBZD9WRKPGtLkUGAx/IxsaeLPZlDam2DwDjnvQO9OghoGeyhT3LVFczJ+QI9LCABE2Zcsg6qoW5W4QTCbiHu7WsInApBQGyL8fBC2KY0aAV52J3NyQA608HBiw4go2VxBvryJkUXk5Oupum3Lapuvs0z+DWk2pCMAsPbc1VN5uNagswcttM83J3t92d635//u7l25evsAhF3L9+La3p3X17+fabf/49t8Onn777+qvvofp8dqHG1+//6Obu9ZsXbz/4OSxvbqeDvvinf9Uvvo9N2VcDInx7kMeXE+Ht21dTGDAe7t5yxIbkrrX9zFrspS3Hy3J/Md0VW6awIjSTUwBpdm0wM4IjOAFQPjtCAUIIJgSSHPkhLMtyDCIWFvGe5gkRBUSYOhIF9gHOzRUMy1S0thwv3WplBPVggGbKjADgboQp2so5M8vc0DE8kCCpMggQpiJTVyRERGLhKKV4DbPI0b3OBM1cUwglANDMs2bgBLKZg1ktrDVHqEv1lEoDIGKR6XjYe1uSlYRI5nUqYm6GvhxrzEUEWcTD0S0cbdHqziIp/A8BFh6ujMJCZOAGhBQALEmPCPdwCmYExAIEUcwMw9ZGWOoQuiMQIxAROiICIxGLsDAxMgcLIgdKC1RHI3CIIALEHCaTJlq6RcNT/Loar57frnF1YkMZpg9rOnKCbtO7isHYOAPthxVhCoAxOA9wVF7TvvXvXeOr9WNifWH8Gb9ZN2iMCG71FDHOeBiRFUCCk/UfSM8K/uRbMTKaDcRTRjPKCQiJDSNEnEaGDEis+5+k6+atGn6hf1t30NnXg4DhMLQx89JpPT5jzxNc1DlaHpDAajbBv/bli7uXH3z4ePOLny5//GeuHMAL4PT+U5ym5dsX+2ffv/vRY7197d/d0E3bXVzQUY+/+6Nt8LYs9PT8g0+ftuV4d/N22S/7Vp9enzNLsLTjPUsJbeGmterxiIC7eXt/2J9d7LJ7183CTCYG4gh363KhSAgYmgEVojD/2WPKlUNIueJO7QHrkkjG/AADEZi5Z2+IYH25jmgHAICJASjCiMgi8o4Js3WsyAGglELElvI74UIYEcSkzdLXJTmCRmEmxvIhQADKLhUA6mXpUa1ZN01ERCgCEEYz9QTLswciQgoTYjMND6t1d76zaq4N0I/3t8eb/bTdYXi9v6d3L7U2Bm8Ben8/kex/ePnDb/6Vjvubl29//Pd//bt/+u2jsyt1/OSv//L1N983219dXy439357+/y//+aHf/zVo60sb29K86PF48cX59vzFz/8kaqVWXCWm5ubMlFlOmxKfXrp15vjGb7BGrOpLliSLUJhXRoTEQuRWYATkEM2KAElqt45FkCJ8TCzqvHELGJNHaIgOeXQY8dwAi5SWlUIcNNwKcIRRdUiHBinIskLambbs6lzux1I0JN54qCmrlgmIaSqxsRIQW5TKYTUvAUACYVBTFEXjXAB9ETwAQVY27Fwub3XIHYIRyhnW5OyWMPFcSpEqGpOLkQEQYjmqFZhwd1uN0vZVw3EqUxq0by2pjmHVLioo4WqORgIOXIJgoguQOXmTCmwghBq5mYA2Yli4dTjJGASwAB3gxRhHFNOgYaisJAgZV6bzCAmEQdEEhRGKGDkgBoRiOEYSU3IbLs/ubHMV0ZPN7c4/MJps62GuQdmGHDq0YSuMtc1JzNmw8GSGJXSAZJApPprSpZi//yA1XL37+lN3z6IhB6QugJ0sv4jZcc4uQkEhK6lh6uFP9n6EctR54uNc6U+aRAx+VU0DhheIC1Leq31DNIT4LD+3Z30G9mfZnYCYbYLIiAGYeT80rwH4FkExTRAOJxjquZ3IlwgYhAAMih5FL4B//rw5qV4+fe/mD755K6FwuSb84tPf0qXu+Pr19ul6udf3/3Dv56zHN4e3/n5x/V3X+hvv3z5f/zDOWxQ44PPfrx/c/PknXe//vzL86cfXD/9uOx2VhcuguF2XNrxqEt1rUhwe/Pq/OKsbHfW7FBbrU1KyW4Sd8+WkCweJI2mP8scmkdddzMFlnNteMeJuofuNSH3h3gdIiV6g4TgYZFTzFLmIVInvVNIIcybRaaSHBgRNojSJKX0klyqyLtRLw67jxFnf7agAgCBqCupQkAOau91+5U5mvIsrg4B2EXXI1zbkm/0CNVKxBER6KbLVKgggrbl9i2HHW/ffvm7X20LxfG43W3qza3VhRmXm7cbJml6+Pbb9vzV7bPXH37y3nJ3e/9mf/HOI5inyw/eWd68Ptweri+vudX27Pmzf/zvN19/fbbDN69eTjPum27fufr88y8UrVxMbcLbqMtuWh5d8S9+cv2///vt//Z383/8q/mXn8yfPoHH53A241yAASgAPcLRjSggnCmYAQGIB/yMAAjmQcIKTb01dUACQlODwCSKWwSxIBF1KUBA4gTxkVhVzV2KsFC4qykxT2VKYFDVBDkzAFXrhRkEClS12jqsl0a0y5VjqqKZQrCQZJcKoQ+tNCIEN29tr8cgvzneH6BWNpg5Zr+rd3fH/bEezbS2ejwemyoBpQhua3a4PyyHIxOnFAozFWFCVo3WmjWNRBpJCKlVXaqqKpEgFkRB4F4UiJS/ToEgTPijGz23HHLBhIWlMINDRGAAozAJJzGKMDBSDA77zC92JCT2nFrBCELAFEgOGOFuvS8rGfsy4uc00HAqq8bJTI7Md6BDa2hNHWdJbAhXe3gi1KyNw/hgez8IyVd4dfBq8i/lCPM/C/nHiaZpTVLrCso85ND0KLGTlB64LDiVOFYXAbDycNb0/kFS0E+ecqw5QBDCaWr7uGKMtRjePyMvBCw/fkgeD48R/f4mogq9MACre+1WD2CFpPNG0eg7JkIDxwhtsKA8O9y99OMnP/lQf/FX9//tN9vp0eb9j6qyHHVG2u7Ojr/5Nb262f7FZ8fnrsfD8eXrMzuPvVG1i4srs3b96KqFovv540fz1RVtyt2bu7PLs9ZarYe2HLUdEeDu+bN3Pvlke3Fe3d+8eLl79JiYzZWIIaV3IYiZs4DtvY6acBo4IGGMeSyrnT2lcIhZPY5e80idToiI1HAOCATyaICADjIVMw93tyBGIlJVZvScCJVNdGpuHhCUZMLeiuvuDSn1GSx7ANJm4whmEqfC4X/748DxcBDBe+9HqrVnMFtEBNEA6nE53N1DxFQmBAC1CCMMCBfkWlvZ7Pb3d/cvX3rE1cXu5tlzu182Rao6R3zxm988eu8pBfjhfpbd/pvvX/76j3p7/+OffxwgP3z/4uz8Es52H//sJ6++/vrll99+/Iu/tNv7V7/7/vU//Vrv7s8eXby6PwZC2+3OLh770yf04uXx7Vu4KEfz3ftPLj74AB4/2v3og/nx+d7u7493m8PrDbf6tp6Vs3o4Iri1KIyOgeCMIsyqHu7MScf0DtYShoMwc2FzNzACGqREzDaOCADzXt/iQAjPIjBCGiUHEmJCcjIIWpa63W1KmVqrrmEFUlnWzXK8RMIHqaKPAJhQEUQANLNpYhZutaa2RM7grM2QwtM3OGUIbdqag0Lb+zHK5GzGdGz3E5V27Ko7Cgatb2pCnsp8OB4Ox0VEisx1WZgKkRFSC2+tIQsB5/iKQHKIasY8uYFwiXAAwwC3KIUiAilY0GuMCmEYeAQwYkbICbNl3EzICMhETATgAE7oAITMSAIigWzITMUCNciRgsi9B5COGOCdco+E7rICPh2ZH1AL4gMzhL2OB73eCQPwhmHpM4ofGkMDM02WXze/+GemGE4md2h1jiMRAGzsRRwBtQ9TTyPoTjn3EYV12x0AQ56hG/IHLmxkNav5OTm3LpAxznR4nsEaSOGBUdpAWCPF7kOG9X/otgarJPrN4d52BBB95HLOno/BTIBOIsxU6ISKEGWlMbx3JEGgk4dHmIcS3iz2/d3+7oMz/uW/ufznP+iL59PTjzaPzmYNfXuDL1/i98/koARwpvXFf/7vmx2pt3c/e2KvXszT+6yoy7Ganj++2Fycby8utaqUgoGux7tXr6bd1NoStZZZiszH/fFuv5d5Tua7NaOZ3N3CibAImwUiWGjadQJ0C5Ysk3rOooMVXhtQ2hrwR3hO5Q0Id0vGiHAZbiMiQjh7iExdhRkDa6si0trRVCN8mrf1eMi7nOtLRMJd29LqAhiMYDZEgRLv6fTPQEzuUF8J9GdA0EBGAVKvghnNDSKEmQhNm9WlHQ8BxqnyEn7c3867GcPNIuqymQuZ2uH+D//03/7i3/zl/s1L3d8W0HmzWV7f3N2/ffP9tx998iP0KGbL6+9f/vZf92/e7O9ufv6//NU//Z+/unp8XR5trn707u7R9sv/9Ovd9vzi0fVX//hf2/Pv3vz28+li5u329bObH/+Hfx/by4/+7pdvbl7MhHp3ePyzDyvR9uP35PrcpwlmXNqyv93vFzURKKXMUwCCG4a30GhO0mkLaq0n8uBuQUhlEtVwNSTUCJGpaRvPMVcxE6NpczeEoDJpMwhHpmReIWSvH4a7O05Tac29mTu0ptM8e7bduyMLEbZFAUFbQyREs7BYnJA4Rq6eXsCBicrEZo5ExAGM6OhuLIwNs8kNWNT1GLZg6AQ4gQs10OYKTtCASqOgAPem87wVToENnIos+0Z0FJ4R0cCFeRIGRzNUNYhaeGbhtEaZuxTiCCcaU3YhzIz6PO1Ug4heQnRDyKwfKYHN1ErCvBXUwQnM/ktmZBZiEZjmQAmaqrsDtdTCQ/aIoM4wXFUaku0vw1g9BKIBBkaer8UDw30yeh3TOAXnMSzhSvYbmgyn1AG7de7GGdf6bKxf9wCx78fFn9nscfsQaNj3GCB7P4a62ekapw9ApW53uq8bbiNOsf/psnse1KGr7rLwlIWsPgtHYaTX0fN1piQ85PgTAMi5hwiwkkd7ZxCt15vkrICA4DQ9yRZI+nO203ZBYg8IBrRQM/d5fn779sXN6w/+4oPd//rvjv/8q/mTH83XF/r19/HF935zc767vn31Lb28Xz7/lp4f6PHu0d/+xf7Vd2+++eJq+z/WperhiGZP3n9anr6PXEKNIJb7Wz3eAwaYYq3W2tXT94GoLYsdDvPuSSnigUjW065EQNdiVgAlNBuBfeEDIjJSn+FL2EUNR2JkY+h5mgY3S5ePyEiUDabgfcBADnFhImJ2cwIwU22ai6AtCzFZre5OOXwYsGlVrYSASK3WU9DQMyvviWQ2FllPGtNL5d5I5B/7nAnLincPUxTMGrpBjnI7Ljxv0EytoZsguSqhtOOeeLp7++a7L/9k+7vtdnP/9g2AXT2+liJtOSx3t7PQdi5LbXZ/uP3yy+dffql6eOfTpzifK+GTv/jYdxebd5+o+atn3z/9j/+hgd68+H4Suv7bn7z/k58u1ZbqT/+HX6rF9rMPDs/Or37y3jRP5XwjgD5PlbC2IzGoQwOHqQjaPE9aJz0oM4dQOKs6Eas5ZU0HEZFd3cxSTE14ioJLrRTBE09TsZZCs+HmIJFzuBAgLGhiYdNm5lGYmMhUQ4Mn9swDwqZprnGEiHDQptM812XpVoEQCNVNtQVSYUTEFkaDyysyZSylqkSUVMumigwkGM0jZV+FI8iBAcGF98dF2XESmIoyqhlJ0YBwpaWlzmi4Iysxs9CyKBIT+XKoNge4IwtiFKEUfFrMWwBqm8qGmSM6fTaha0BgBrcUmgBwhpQ+RPUuHZ7EaMcUa0AoVAidAtRqRESMZgKCwsKjFxiEuGx92jiKoaiDBmugpaATIgTYKtM/TJ3ACr8/nEG/Ijg9QOrBd88HKHG208txemOPEsbBsabOPV0YlbOeDqxGdAjt9gotrg3D41sB8spHEI4DS/FRh44BvqfjGTTKjEKTPzpGga0YUfcQgxHaFfZWJze+pZv/kWvESI5iHP/Q6wH0HDhLEl23D3rv8sPrHvwhWK+U1huS/4ygBGMTUc/RGklACnQ3IWRkmvHA+uz2xbvvPt399Yd2+2z746d8Kfpf/qX+0+8unp7DPcGLV3f/+C/LH77bzApG55988Przf734+D0Iifu7x+cXb1+8vr85Pvmbd6kU02U53Ov9/nDzctpt4XjQ+3t14GmiiHqoal4khb4MidxyiKMIszZl4bq0wOAUbIFg7KxwIgrrDbrpmM0dEEoP8CFyjLt5DsNLchGvFH2LQChlAouqDdyllO72EVw1PMyViIqgqnkW7JmmUqyZazXTcGOC1BmL9Q/0xxhD6AkQRpt9d/Sdz4SQyqPuTsyduOxGhO14FAzyVu9ul/1xFgKMu5fPL995tCwLByDoJAyt3b1+8eKbz68fX5pVANvudldXj1QbQgtr19fXwNION8f71y++/RaFt+8++eRv/+b56+cf/bu/uv7Fp7Q902b72L/744+u//LTZd+e/N2/2Vw/4d0ZFdmieBHdzartmS/tckuPz9XMmRf1wCAiA2qqboGlCMwRLtOmTLO3hq0CoUycj8DDGaiUSVWJEBg8SFiy5oVAIsXUwoyYmYSQgcPVwhyZkdFdEVm1CbNhy0SYiJwJINStMHdEDaLMk9UGw1aUKYejIeXTcESg6IwypuybBQMCJSuFIjyQVA0RkYGDQo2FuHCERoBroBQ3tIij2UJ2pJivLqAUAzQnKuzhFtpUAYMQVC0C5+2uCDPzshxT81NrDQhKwRICKRQh4mGhFq7ehIT7CkFER1BGSRvBCVdDYqWW/GZwcHQSRCThkimpYZc4RZqgNSR3a8Al9cyJGVGQBcpMmy1Ol8rbiGJGzakC5YAkgz53qsufQMfI+3zJxHhoxc9XW55o52qqVqYQntCVFS5ZqXJrdH3yCvn6ml48+JYOymSoPejw+DDpGHYXuyxIxtErHAs4NIIwoNNKcD2xvLZ0HMOLZcx9yjgeXvCfpQcPKhSnwD9GYWDNbHD4vZUsdTrxgKA1qg3qOUT/0JVtkuhfxvvrvUh4iwKJgh58eEoW5QcZQCHEiEUtJlZy3ZTzz96fPriqf/xT/d0ftrd309MP6ptn9PbODrG5kEcffPz9n/6lfvMNMuCj8/tnL65/+uPleH98/WL70x9P261FhLnV4/2L55vd5uzs4v7rb9vN2+uf/mS72Rxv9sfbu6tP3s/+/NaalKKqhCTCfXpBOA5urZlzke6nESGnelE29Iabdbe9+nuk5P8gpWRJJG8aENzWCVlk0AACiYTZzJCi1eYdFA5C9ADTFskXSBq+m9YGYJhnSKStEWKftTHgneh5qlMnZOVpjTQwHGOMx1vL/QAR4dbCatW2f/nmh2+/efTOYw877m+yMWB/f3M+PXY1ETncv3r+/JsnTx59/LOfL7WV8+0OcfPounnFmXBTtrsLC7jfv41Cl599dvFvH/tRt+++y+zvffYRnZ9N27P961eTyIf/j//Fri7jwnZPr4m3IKRhffAxgyIAkhdBoaU28kBkbRa1zvNcQhQiqAC4sxJTmSbT0hp7S1tcWq2ACAQpWRwILJJOPYb4voZ3Zqh7MhiQybyZtymEiZq1qYhq80ARcTV3E2IONIuwiD5CtIdsXAQCTFtKEKYIYSAzghOwgC4GjiRUymY5tkBPNidSIUcsGGYBEY4iAoDuUaaizZEyKBMmXlo7aFMwmArNG2dZbHFEN6DC4U3VAIEJLMJbA65CWOaJWmtLnec53Jtbxh4TT4pGghziGu5hZogowplKEoJhFPQAYCQH7wVDxtCAbAL3iEwWGNRViNWMCDgFbg2cwdyYCfOOISEwcUGaUDYxbX2zc9wYzYbFQBwkkEf7vQeeirKp1iU47HGG4L3/NHoDauL+/aW1oRUDRkcHxAqCw0qF6Tupm8oVMD39ycnap+YDXNFfcAjCMSuwUwFXK4sIyDDs+oOkBDpOgg6+4jg03jNSfDyhWuNM8ohh3fFBg1l3QxEPytgxXETywDvB88FnjZQBOyKE1IP6YMy7Cn3QCQTiOlUqonvH6GY+IpEUBkRwThnv0Zjna9ZFDIThFk3v2vH7N28+PttdvftOfP61vHX84cbfvObl6F/8fv/mDahu3r3gze7Vt19fvfvuza9/Z5dl++hKZr7/8qsXX/xx86OPrz/4GAz8/kBk0I6vP//iw5//JTZb3r5mgfPrR6b6w9dfWsT1Tz4kobo0ZnI1CsTC2Etwpdbq7lwKRKdAuYe5cc7DAoRIxbDeJts5PAADWzjBoX2EvXuuBgsQInKoHuExzZNaQDhihLtqIyTmCbK11A0whISkuJl7ddcIE5EAcrfOPhi1rni4MnqpOuv/SUkaatYdzRotY24ApK2Ru7gfD4fvv/7q1fPvn37wju2PvCnTNN2+eTWVCQLctJnX/e3ubHf95Ek5O9/X1+cXVyRb2uycGaZ5upCyu6imWGR+9OTsnU+60KbQGX00/egDrV63U/gZbc9i2lURlbAIJnE35NJqJSI1xd6bnToboeCuSkTgOAgXmHX0Vqs2A8xpgsTCoSiM4AVcEcJDhcg8pPAQ/iKrjaaJEBxAzUgkM72+UxAtDIlWyoqbCgszuBkwsRCERupjY7iZzLM7mlYus6la01TtBgL3FP7E3Cp1qR4TF5KpLMfjJNRM42hTKSRESObqpowFhdkiwIk0080gsAAlP4Y2Jp+lMTbXw1KrtmmeN2UGIA8zIzcHILUaQFEKEk0iWrW1rJNZTulSMJKUewCGkvqvqYHMxAhBAD463hEh6bQRIUDE5BYEAMxhTowpnWzoiL3/DRkLikKEaQB6WIJcgcgyybyFsoWyM9lY2TWfFScHcmSL5KUFI/qQD8rOygCQEygBSJgV5d7n221fktOHDTxZcRxB/sBVHobPvWL2wEJ2+zl8yMPZSgMXOhGrc/nEQ6O6NoitcfwAUk7AeyLI4xMwoCMAvXAHAGhj1GsmCvggHTnhPcO59INORYAHeNbwBOsFrF5i0ERT+SwYkJi434AcHpS+ofeSYieIQheQQYDO+UUEoEACyCQgYbTUm6d0Q+45M4zLdLvsD7bg+fnm3W39//1D+82XOxS5vrr94quL7byX4EcX8OoO9S4qfPfFN+//P//Xyx9/9OrrV3e//56utvPFoyLTq999Oe+2sin1h2eXFxdYFRf15VB2F3p006Mdlnd+/Bk4mUFtbWYBAA0vgGbAws01DTcxhnqqL0CqtgF6BBAComsOwA1iIWZ3ZwRmVm2DRtmJA4g5xxgJEJgjorpiRCkSAa4NOepxCXPO2QJIZs3CiTlMiRkgluM+EwJKBksiUb3+Rg4w9A87+RT6KspbvrZ4ZJhr4eE53T4AEF2VIxDscHfz8vkPr1/9cHG+OTvbtsVlkuVwiKa7R08swMIKoWy2jz780fbs3JHns4uyOQeyEAwi2ZzJWUFHR5TdBUohFDcnxvvD/fzeE5XpUPcbYtmd8+5CNQBFQamwB5oDAwCgmTFxQDCJGyRrrl9ULrrecR0BsD/uyVuZxJRKkYbsxMFEEVKoNUrubiERJnMlSTsFzhjuRaTLeAJEuFBBRC7SWsWmUgSAAZAYLUjNtmWqARFRSjF1DG9qU2GMqMe62c7u7KpcRJua1jCTMhGxe2vqEWAQCqHLsoENMUmZmlYhaFmOIZqmQkyqDc1yLFxBKXNELAJMiAZu4BWiUQQCBS7WDs1arcg8aQnribnlFwLVpSEgCxEzCZkljZMDIcmvXEiEzTT1NjsNzomZCQTCAdzViakbJUy5jCAEZgwDIghAStqBR2AOyQFwIGZi5ODoJgLcggswIbLQvIFpG9PGeLKyMd9oTDXYsCS/GfDPBk8N8NuFaJhCygg0B3PHahBHFtwD667gtiI8adS7C3gQCsPazZlflhFVr5ZCp7mvMPgwwqOqMOxtbrpI8x8nCxsAvZ+sY4U5Dw0GlWhIDBE+sMnd2kIn7KyX2OXqo7uNXtHoVx+9PNgr2v1AXOGiFcjCTiMZSEcy4RGQEQiAkryfSVPOO1/ZS2sFxW34oKChmEwRBIEP2oYDoBdUPbxnoBGIB623XlvhzdlmOryalpt2OOLuyu/3NG/k4nqzfXxbX7bmy5u7s1/81faXfyXns98cX3z+7fyjd/Sbb+D9d6lIvNjfv71BbmcXj4SKv77jBtPZ1TRt9m9e6aGqgzZDVkRw9ao6z1MGFjn8IcJz4KqPx+zuzOIpCoTYU10ISKWEACQyD7eGRNm4JVMJdSTKuB5TacsBAlQrC3OZdKkRbqnf2+q83YBF5/WbI0Y221irntKS0Fn6ed8GsNOzOTw5gQAMJMZOzMsn1ScHm3l4ThQMdMOUfqyL6fH27at6uJuEPv70x7vt2dvl1szM2jxvsEi7PwCYI9O8lZ2QFHMTPgsuNIuDayDKjDJ7awHhKGXaujU3J2CQAtMEwDzNEchlE9ilZQAxLBL/dNOOmQN06IpRl0rJu+96/DnNgZ1F5u18dlZvDq01Nw1zZsToCY8bMEszhaGFFw7g8ZDbEEAyldYUIkopESAsFialZP3GCd29TMW8RUT1RowRaGZTKa1V8L5nI6LVxiKmChFSWJu7u2pDABYBbRrOIux+PByh1lIECYkl5aeaKTSc5sIkRbw1wwhGVjDE3s7vCNXa0c0nxiIgxQGW2jzgcFTiVkrxCHIHEURS1WSG1QocAgClTGbHpdbtPJmDqxlYxUrEqeAohVUDwiEUATtByQKz7QcAAAjQMxdDIEDOqhQnegGJhyAFEUBnsgkzqUEHshBLVgKokGxxPrfpAuQsaOM0K0yOkxFbRAb+nVu5ct0GCBG9FasXujIuPuEnHWXBGB4CRhwOa2E0UsvlBLJEAjs40uS1fJqxMYy1Sb2uiacTgNGY5pDzD7BXlRMCCYbeSJVtKBTAlG0rPVpEQKKulj0w9+jpBUHnMwwHE+v1QYfXu9fJi+yFEzhZf8io9GT1cfVSCeZHYAz9HwTGpJEHumM4YwiBQHA4QTCEIAgCYzBAISzZKwLIEAzB7gxO2YIfhpGNlEbgDI5hjCE5SAfw6Pjy/rg39t0V7q5eff38cHuoVQG5Niu799v9EReftrPtzj76n//HzfnVza++vvntszgoMV9cvVvf3G5oc//H71/9l/++I7h89OR82rz69a/bohePP4D79vr3XwigsJQ5haxCWxMmJsrelbSTnpoQyegPX3FBd+trrU/IQmYmxAB0M9PWGyYiWBLqC20VEYk71z7CPaw3Wy59onqzBm7EQ0XCNCKmwq6OhFpr0+aqOEa+5JzHDoJA9jOeyjkwCLhE5F2KLuOxwDR3Hh6WcYa7aT2Ca7Tj/c2bw93d43efvP/+R08+/KCZO1MEEhNvt6kGRsRUprI9QynBDCzA4gBJ6POI4BKEweyIMm8BAFHUQ91IJiobEiEpSEI85XCrgGyTzpngfb0TSl5KRHjyO4gAMIjULACbmhGACG+nsjuX+RzLFpkdkKSQiAFQkRTg7tgdRZdkNnNzJpKJU1eBkIoUZA4H0yBEIUlMV9UgwM0BUUQ8zCyQmJkiICCnnKN5pHPN+ZFMDB6cfd4IrVZAyLkrbWmtOsuELEtrraVGIgaSari7m7eqAEDMQWhaA52J5kmQopkeW1W36hbMOM28mZu7udeq0zwdjlpVLVzNOjiOYOpNTd1b0wDIETMRWlUnEURUVVMzS24+IARTriUPMEREQqY+vTcIkBEJegsqAGAQpxkMxD6vClKXMKJnAe7UXXtgBBJ4KDClqBCUOWgC2TpPLiVEjMkALAE/guyaigybsbdhCvXwOq1gQu+Quo2j8DuC/zRpg04DgNj56StKgytCkyWCMRKvR+UZrw8fMKL31X8A4CjwDnxojTJg9RndSYz4m1boKLViEhbrmgsnVH/YbIxuYXpCMCzCOGrFpNJ/4aAHrT6vHz6w/v5Sz58gtYERGFCQIFwQeJR/GYAR0E9KQUMJoWdGCf3n52J2hqdH6ecTA6LKWgn0CVZhGN5MD+Qv7+6e3d5dzTt47ym9+971xXS43dN2a4Iixe6OdnNHUOaL9w4v99dv9PD7Vzf/+tXF0/PL3cX9N99ebDAqHX79r/7DC/rf/v7sfHf85jtY6qOf/ljKdP/9S3x7c/6jJ0TOhethMXNvdrbdRoCqTXPxAFPD1DQ2BaYswgegmZJIEn96hzBRWgGA8HBiNHeMWFtpDQIRmTmb8s0NCTEImVtdco6gu7prCmMk4dpMN/OmtqXMJcLbsqhVRJjKZE07CtRLLz2j6pBeGEREOCBSLzaNBxwB4UhiZh4KEYykqgDBGN6OWvet3jPz9vy88Jbmeb/czWfbpkrzBP0yCZkQuY+OZSYHHL2tCOTgqfmdwu9c2N08NMANiESQOZAISzbQB8AQRs7pyohDTc/7CMWHsGkmzZQaPZ761RA07YqzXAuV6d7b8X6Zpg1XxWrmDszAjr3wDeY6Ih0IiCKC3eIzoE+bXd0fIrzWJlMRJvMuAesQ1pQLJyCeI4ag0+LIwVLDmSl1Xo2ZU1CgiKiba5h4QIpk1laNAqZp3re9qiLgNElq9kRAbcrcNtNMBBOrW5jXwhs0QsIQ0OqHZakRxmEMblrNg7jp3iFmKU2NiUJd1KYigRjggVi18ZjjK8zOpE25aweQeVCO7e0sk5SscEArEgSIwrFaYERmcoO+4COCMcB6/YtW9CW6mliQhWYvNiIyITMDcgAjTQESwIbiSA5oTkZoAIruAMgE4Jgpx6mUmfYKAsMRTkFrR59HOJ9x9TgfWHv8VnGEvl2HhR3hOvalPLYRroa7x8sx9tWoRsGK7Z/s/cB2giAJkZFaVJjj6hAJkZEY06ECA3JOAsIxfLDH4JjN3NRPYxzR841cpONMVmB/9EMjjLsB3fj3imz3jf10+72CHObj1F0aCKIgMRIDCiIjMAQjMED/JwJBMAIFEDhBZN7Qf0mwCvASBEZ+cgAAef9ZiCzivrVXy9vDBfpfvDf//adwvTne7+lqO3/4QTve3H3x5eHuPs62u3ffZY27X3+pXz5/78njD3700et//u2mwdken/+//g/7/sXFhx+Us3NYWvv8q5mlXDxS0Mn0ejPPZdrsdr1/LXzezCTsHlIEELVZ3pUwg25yyNyyKXpEzalx65SShuERRpw6PACIXBgQXA0gMqR1NXMLCGEmpuTSuDZtFcKJiAQRwNOyICRDlAhqXcybW2PiLGphxwCiBwOjFP8AxYz1vz34ioiw3IoQZllcDQdwAif0thzv7m5K2Vy/897m7BI3W0MOJPNQbWXeQM9XkUWAODkfvaNg/ICESIIDAF2XsJo6AJIgi3sm5xSBHmNfDjJBfyRDrx1WtjMiEUGgsBASk2SG72bqEIEk83xxdfHko7NHH5fzx8eGwBOKMJdsxWAhws5bO4GrAczEhQ1dvaVLmnczUJ+bKsKIaBAiFBiqGh5cikOYm5mJsIM7GBfOZIWEA8EjzIy66BpN00yEoZi5NBIfl3rcH11jkmlZrGmrqkIizMQSAaptOR6YiFk6IIAhQmWem7e39f7el7fH4xGsQWuuSKFuzdUsNCiAA6JZUzVzpwTZINxdzapW9yBiZgmMRSshKiAghQcTIQFxV8zMJBMcEIBTBCwlZpCISAqXIkTEwszDlA49mJXBMgysRyiAI3hX/gxCYJQ5sFiwAqpBkASREyqBZ8k8RqLxwF4lSEiYIEx28QxcBXrguQIv0BPMwXBPeRzKi0jjm38znVsj5mE4aV3hA9FhHDPbIH8JhMg4/p+wuyKAfoaQshZUEIVIEIW4EBZiIWKC7h4iq6YJKwA6YDww+okRjWQmC7PjrLowQ/o+6gKdRDCeRJfy6WhV3px8tXsgIk7hoPz2gBRwpm64IQ19BlM0QnsExwiMbNZ2BEcIcie3RI3S+eW7ICKTuWTgo3u4gxmkPiLSEvjmePfWND56R37xI7++js0Gr643P/oU7XC8ee2Cu/efHl49gwXobonvXp8hoNIMcll29sfvDr/+/eV2uv70w5lB//Q7ff0dFaRdmQDg7u72h2ft0GiaMQBc797eQIqdhQFEa8qFmHgt0yRIMjwBIHYJFGJiEc5WAPeI0GY59JuIwBwCzJ2JmTkgzDTCc/CvqxGDtmqq2pq1JiyMrKauLSVfwo2LaG3WavIliNFNETFbYWydRB3h4AgRqxBQElaSKpoIUfbrIZqpmXICvalrRuDazJZmcXZ1PZ9dLs3mywsA4iIAOM87FAEiQJRSkLgXuhKQOaWVGLn/AnJseaIA6uYeKELMfTt6l9cgYkSOgJxAhpHNVI7ZTNrdaQx4NSKlLYgAcoYzphSNqgJScMHN7vq9zx69/9N59y7ANpwDuMgcFoRChQHQkiOL2FyzWZqFuJCZAjj0AQASYzY2EgKgWjCxm7dmSCRSwIGR3UNEENBdgUJVPUIkWUbRVBERiAGAiN2ttRoOxDKV0ha9u9sfj42Q7++Px8NyXBpxYREpYu5Va10WIUHAMG/mwRxEGrCo3rnuoTY3YCKm1A5yDHWLsFaVkD2BMochQk8IaGZqvqqR58yi5g6ATc3d1DQ61oDCnfTnVvv0pxE9Z0GChSnVikSKjL6ubq86NQ5zSQb2UDBGOIhEKAAMpQSLIjlwC6wWClEDLLB5aKSUHAz0vxdxcln3InAkcyurXO69dIqZtT4IgpOOOEL9h/R4hMSYTlF8J+f3JHsI9NPad9tXZXob6JXnrHx2ZH2F5vPjEICygSiRAVyBnf7uHg+t9aRs4l8p//2HAR7FKgLfq9MeqRQ+ov2BWnX2UTZ2wYk0uiYAMDzlyAK66WeItPvYLT4ML9K/dASdmRLGgIMSZHOELrPdMbGuPeyJwGXBD8Bz8zCgezsc4e0RXt6+fu+9p/PPPm3f3U5PH03nj2BbwANMp+3V4W6vN2/rv/52WepUyZ7fuNHm8hJfvTx+/T0c7qPg+Y/fO/z+t8cv/gDvnJcPH6NTvLq7++I7OjvbvfMoTI+3d/W4P7/YUimhgYAOkMB9V3bAPv89B0dnRuxqY80gEkUKL+ez8sgWsHymqe+GWYn0PmtaCunSsn+41aa29Gongqu7O1BizUGIplbbMZdYkWIeOWHYu4WN3F0e2pdM9FoREZ/wvoyjPZLsmcmLTBMARqgwmWqtR4/YXVyWeXc8tmDgqbRjzb4VYAbk1A8FJOQS7rmlAaHLTPQ1kTAmRARY5AHZa5zTX1kkcsxeykgmYNtHcLtH77NJSnFX145AxnBEIA9DyOoZuhkTA3JgIAkQNQtCkk25eu9Dq8099ne3oMumlCize5vKVL26KTILWrJvXZULC5MzIoabQsA8z4fDASFCfeKpWk3TgIRu7ho5ySRL/fM8lVJqa4hBQGFOpSBiaiCbNSmzRxDT/ngAomVRYiGRsil3t3tTLfM0STkeqzAW5jJPUsDU1a21ysQsxVpjLuoIIFjE2Bc32hQnDEDzqAZNzQ0AqaoSszt6UFVj1SJiDpCpjWOgm5mnxxYRALOIcKbkq0JkmxtlUoAQ4WEOxCnc7EFDGDKnFGAAM7tHlukDLXcBIQdYRtpICOYISBNhOHKCGhgkDkwyBYohGZFGOJAjaWAakbT8KTIz8olApCQg5ZLo83GSSclpaqGPwoPOh3PqL2VgDmvwPgzkSHT/zK6l9eolXQwfKFEg5lIATugmQ/hM8yJSAoMAGEmIhEl6IhS9ujuK1WsMnoE5YcZba2qPA715gDgNyKuXySBjTyDstYQR4A/kajXxa4UYRrVwoDHDDZxgqywIQ+LaPd4HPOFa/SQRHHPMW1hO1MUwhCDwPIDAKUYpuMN0kUhRry4gILqb19Zevrl98fb2xtrtPMsv/2L68afAAi+PelBCB1W73z965wndv5Ifvp2azsivvvj28Qc/OaNzff4qeHv2i8/mMz/+/p/2h1dxsYXzS6y2/4ffvP31v9C2bN9/D4miVt0fmSdm8pQJRiQirc3MkmKP2O1qFgAhI1MACOAR9yWalgAPMfdgrbVEAwgJU+8nXAqHGiQbSJtbdTUiEuIwV60Y0Qd9EIN5q0dXlSJMycdL8BUDwcOQUmVGATKzT8x8JAEB4IEAGfJ3qq6bWUvutblpy0kHrk2J5Pzq2gI12rw9g0AWCQBNFUwgZAFkzEkgPQzHFULsUcYqEOKAwojoZmaa7iKlkLo/gsRiRvIQoWa9s3pwllLVlhC7pB1CRFg4J1xKHH0MDpp7C6fCjtDMsZTH739y9fSz3eOnFrxoA0RzIBIWMVN3R+QiZURIKCylSDqkAPDwaZpqbUutyCSF3a2IIFKYW9NwYCmBAR5a1SGYOfowGdDWUueVMpgwBQgS2u02GKitLcejqRPJZp7DsS1taUosrXlTdQ+ATLYIiUzVAVprTduxLftWoRScN7SdjUg282JeVY+1mkfZbNK7mzsgIZK6NTU1A0BTzZS7qTVt7tHUIIBZujd2Nw8YGAQzMuKUL0O3vIzAwgiYcAETSyllKkSc846YONdYn5rUu8+hVzLJmUFKYRJAAGZkiiAnjjIFCVDxxIIgLBmEEAFhcZJd7MYXIDIDyPAt45y0KXnUmOjXzRutb8eTEYwR7PdA+0FSkOXT/CzuCUGKWeMIxoeOw4j5e2TT04YeLPd4GbFD68M6M65GplvfWKlvnXoSCIP/3+fJZT7SL35QPE+hfKYGMNC3gOBeM4HxHHD0YwWMGhYiRbZujWSkQ2eRBpoGpp8vpaZYrNeYX4QwnHRW/xEhHL1r6+BIU7pnjdw+/mfsXveAuD+2W9/d1cPuTOQvPy43++X/+uP+qx/c2uOffqoH2z29lip3r19r3XtTwN3uyeOy2b74//6/3/zw1eP/8W/OP/n05f/5fy0vX/rji1uNJxcX+PW3h//y38wP26cf0Ha7hO2P90SxuzgzjzBn5owWzAyiU18xwDwgchJO5GjGTJBGRojZDuYWq1n0CCIapizMvauNmpspF9baWl3crBTBQIdQUwBHJKuVmbMFLLwlGAVAnpsvcOyoB2lhl2iJsUH6zcwei9SvIEKPUG2MlCNHMIKYmllom7Zn4U4yu1qX7soOtwCZZkT2AESKpKqM/Bj6HoqI6Lgp4kgGMNPrHFTLMonMgBhAmRAJDmubMw7dErc0D3NztcQUwsI9kIIRiQTAzJxKh7Opg1E9AowAEmYSU+Wz7aP3PwI3Ir97/pW6uftxqUQB2Z5NSGFIaO5QmxQJZlNnIWvu7pyrlqg1zWpBbcZMquatIcS0nUOK1ooB5IEEzNJUy8TukeNFoe8mEKbeW05hZgbYmhKhzGUbm+VYrSlETGVz2B/LNDMzI4GgA4QQoRvC/f7uqL6vzvMWy4wGfrTDYjLNpg2BuUSYY7I0I9LzRZBaABoBphpVAICTGRJHWChaESGmgHAD92DOsQNZJIHI7rpkVXaRMqBSIgKRAbNXuS9OFkGIVj0sAlygL84sFFNCC8zE6TomkplkBpmCJ5dJiStzRVo8GoVTj29jtTNr0DGCY2HoGEaM5qmsnKYOb4+cO7LTI93o/Bka5n3YJTjRbZLakRXRh9ZrRNeZlnTbml1hODgLA8mJsQJiAMiDIxRZGMC+hfpp9G/M9h6EYcuT5JPzExDGleStxZWDk6ff8Z1ePOuvEXQaYwxO6rDCfbMOsDhdgo8bFSNLHzSTiEgAJ/mto90Xe/NkjP6HXi8lAApfQ73OqR3fbomMuOaT9Qg312bOcOP+purjy51dTvHkkh9fww9327Mncn2xfV+wxKv/9A83+1siq3a4xHJ5eV2/+p3etac//+T6f/pbU737lz/e15vNx+9vn3xYKtVffb5/9sPZ3342PXoEGGCVIudWIGbrdmEC0tYyYBDM0baB2eefxdY+Gg1ECABM++RGrYqMlFhthJtlwbFDk9oy4wz3cG2LumkXkAjon5n2Vhsxg1l4M1UIKLNo1Qw0OEVjKDAnfa2PLOumeWM9KGmpHgAQZh2bcQeMMKNZcvZXp1NHmMW8nayFQ6gZl9IlXRGBiKS4ByIhcVinxsK6CnPBEAdAjsbJRcjEnhMFXZGYswUIObsoWCTrd1mSNXcPJ+QVKXM3gi7Uk1+FSISBOXrBOwxk7kSlE6U7Wk+OgcTVqmyn648+gYk8/P7ZdwSApoUYp9nUMACBAjz1NQmAiBSaRQCjqgFwmYqq1Xo82+205zbEwlabKak6s4SEm7k7AaKgIIM5EKoqM0nhVEeApDsxEpJMYs2XemRCmedpM1Hhul+IUM0Y8O7u7vx8R7MwUmTzOeA0zbf3y35pN/dVFA2CZHI6tuaH/b1bxhBhEQQoXLyZurq6kHiENkPEIpIotJpr+IZnw2buNKZhE/WueAhzQ5E0KVmJBETAcCQmRGJKQISIAYGJIkCCUVzDSxH1Ft5nta8BJTEyITGxMMrE0xbK1mWD83lMFyZbk61iUWHvAzjCIAb3pwe2a/CZ/kBSwZnW74GhxgMA4OC9lQq7BY/oSikB4T2WT/eRB0W3m44w/henasEgMmaBF2iNunpJZLWW6STyTAcJMuEdHGYaIBx8Hd+bScNwBSd/00svI+LCcejwRsPNDSe2NjOvGxRGYSNP/hQ8Dlt8amoeNfyOxvZUqX+6A/TofvjSGLpj0XOyVFgJx3WQTXgMhUrshDpIo0UPupa4u6cQRBS6a/rN7d2Z1nN8PG0InlwcwFkVXhxQ4vW3X989f3N5dv7D/QuAmKdNWfzNrz+Pw2G5mG6eH9853884HeV82r2z48v2u28Of/g6QC8+fDpf7w6vXhzbkbbbs6fvkMZ+f9heXWJ0WCTcSYiEsufds6U9H1JPHBERVdVdA8ObEWNfrn35wane657jf03NvAGAqboqEoY5RAAjWoA7RBauvNZjUICHTBJmSe1KuDMTx3AH7xPnV0gQESP62IbubJP4TwD5DCilfdEBPAyZXQORymYOIC6k3np7Tp8zDTkCBQCACSEG1SAX3qBKJL+vh/MAEAi9jpLsRiLiUrJ84GoYyIXXqcnmnsOigLvXcs8CsAHxCbvs+0vcGiAyc9WGQQFOVALAMcwdyLwlWU3Mo2w3V+9+HM0A6PW3fyzEwBAV1F2EkDBboc0V0t0TqRoRE5OqTfMUEap+PB6lzFobBQgTiLhFq4oFipTFvbWlg0iEHugO7saM7hGQnlsJJZBEGIkc1CG0NSBm5Hk7TTLd391pUyfQY2OBIjsKZCatbZpnJpqm6Xi33B812v00bV3QHJE4muvSkhCWExYZQxFVNcyYC4KbGyMO4DiBXlBTZArrc10Y2cERozUVTuYlEnV1d0Do+jYRGXXnIBcEICRmSvUqCINgg/BmSbofhNAgiE4zYUYWogl4xrIB2QSXEHGZnAWYzcCTeoeQRqPT2dbYf/1vgJyoSqvqYcSAaToogtB9yINAlDCbybvdglWKE4b/GCVXGlRngM7CRIi1sXdAln2vQPQwH9J058aGLqC/9q+P7+nuZFjytVkzzypzAxxntYKmSNFbvNLkRPdgaYRG/S8jqIBRjotufPGBXV/pmAB9kG96wQfMIsS13jtmp+VdxlS7jNX0Z9NZYNLmEwLKOsTIKWi4L6J+K7r4FgS6Q4SGTTLdqz2/v3+H6YOqfHVOP6bpdilf3U1IUP2iHrebuLt/0+4P7168v91d3v3pq7Y/bK/mY5VHV+/d/9Mfl3ueri62Z+/w3T3+/mu6fU4X281HH2yuzl599eJQF7272T16VI9LFEoysdqizVR1M20YWUObeS/NI6YOaD57M/MwJLBm3hoIiRQWJkRTd3cpLMitVtMGCMK86MHNJLUfILQ25pIjpVprCfgRkrbFvCJ0VmjOPuok6+zKd+gM9F4Kgty06WGJcAx89AgnXp0EuFkpHAFuFhFgY6EBQUBQhAMxQY/we48KASkY9z2MA+nLTRbd/q9uMXcpwRivZg4gneHMAUBMlFgNUc+3I8wMCR2ckNKPZlU4N4WZMwcAEjGRh7qZl6mQirbKwIBTXiBRF9zuGtgBTizz5tH7nwSQq918/0VtlURmBkxCIKOGQngzEykiYqrMBAHAqE2ZBSK8z+zt5kOkNNeVKyNcHAAsHE2KAIGbIYC5I7OrY8E8Nwp2q2WaqCoRavXjcZlKiYNvd9vzs93t7W0EiHBdap0EJwogRAHCIjLN8yQNWS3w2FQXvT8ccqAiEfUxKRHzPHs4YTigRWTXYYQHsoenKjsxtWbmLkkHC8vshawjeeNZnsCM0ZgdlDMi1mCvF2woT4JKgcw2iZKqkMB2Xz9ZP2KWUoAmnGYom+Ap68CGbCzA0qMZpgQXEHLEbZysczYEY97/DmJA9CptTxBigEXDHGOa/RSCyCQAEYZCfQyhn0Gx6Yub+vzoFUcK6GQjR0i5xbX7DNbblgF10ANTj72om/E1AGDyiXB4nHwPI0A2xcCAm3JM+WrW04F3ow9jF578ytjY4w7gig/jeN9IE6J/Hg7Ln/HiWOzrDo9+ZStOBWt7V69ID0yp12mypjsWUAx+UceSY1zt6vkyWHZ3QrTWDm43gq8P8v2bt3B2dfXRVbFPYPkSDvX46z/qq9vwiohPr55cnZ3HIQ77N1uaYiq828JB+fmb3eU7+7JsdpfTm/2rf/jH29s39Ld/T5/85M2rl3e3NzjPu+trA9Zay+UFErm2pq3pMm03AGipPhOBCMzsZuvSswiIhhCqzdQjTIAzt0zjxanlaRZhpss0b1pdrNZpM+dh1hoQlknCo7XmYVLYWsuCbVaakNiszwGGdL25dPqkOYQ08rAS0TKLD+xDKz1rsB28I8zxZEgUpgGO2XNL7MOLJ7PFkKLzLwFx3Q/rwl1jLxj/wtOrADkfPKvNZpodvwCA3DuLEu3pSzLrEx2R7BygAEfuw3nAkYXRM3oCZALEcI/AIiUlIVQbcQbwvcLce64RAtADZbu9/uDjsKbteHz1lZhNc/GqQEn6EnPzCFVFQhZpzTabOQcyajQu4tCaLogYHsJoHtMkrVbVAISpCMtU68IAblZEWkf20HIscPXNhsOdpBCK6Z4lNZ+gaQtwiHJ70y7Ozy8vL29v3ro7oNRWi0ypwNhqY5mmIttZ5onfvLpDnoHR3Jf9ghCcenkEm2lS16nMpjXMAUldmSSAMrPK9IyE2NQ9M0OAcFMtIsxknk2OaWY5dS97v0luV+Jev0EbTfKAkB2TECBgrhHE3LR1KDkgICdHIVBKDm2wnIHsopwFb012LpMBOUrKeGYFkVEUtIcjaYK6zYwMWB1ceEAqK3jSG+qHuUzRIByq+wPkCEB0z7IqJZMaoofZMUQchlHrDixtWY8EsAv1gGe0MmCb3I8OgHiS/wGgQSRFJBzNnF0TMz86huXGUavoeM3qOVbk61SSgIHOn7Yl9he6pcjZIJFMR8QhnZR2gwAICMD77kswN3o/3YM+sgR5oM94W3E9WFuIY3U8MNwz02jQ7r9dWa7QFY67Kr0BdUQYMcBgaYud7d4s+mrj11M5v9qg6XT/FP7wjBglrB7bk8dXqvP93b3pPYPA2eYAy6f/8a+X3/7q5e+/vPz7f3f24U4OfvynL+uLO3uyu/zrv5DHFy/++C+HVy827zzBzdPjspRSpqmgq2kzMx8LjYhVR3HCDcClTGFpk50JrVVtDQBZyN1nZkJUNSIk5JwS05YjQLjH4e5m3m4Q810VCbkUCAPAMEUKt4YR2qpDzv/jGDIURMmkzS7fLigNfeykjzw1iDgGB7jH5idkcNjtDDQyv0wOcQz/EZnM9i0T+c9eHEp4J/reWcOQAVpif8oYAMgU7pDid4hEQsSJgQcEEoU5DmJPn+0Kke1aQSl8SEQImlpLnW4QEAFISCKiahHBxIVFQZOvlWxwRKTUawIgYIsg5OaVt/P1h59a6Euy+vo5UMatDIBFBBSshUxsZoSkrrU2IW4RhD2cD4AAc3UECcCIKCLm3rRR4lyErbaplAYqRVKFqEdXgaoKwAYNmaxZq1WETSTC3cyIAPDt7duzs/OLq8vj4YCIRGwWcyELa0cPMZqmeVOmSURov9RpM+82W2lctSEEA4pwRnFNVYQBwZuDdw2QZgYAzNQjS+6NYWOsK7gpEpNwdIlbLMIBgcgI3qN8RkAgXmUZLBJqhMjxk4gOSmBZKQDwgN5/1wXkkBl5wmkDsg3ZwObMaWvTTnk2KkgFiDAcgdhBM8vogAX2mHQswWxAJ4TobMsAQSidrYZdbwcAMRiRCRhBCIWwNz0h9J8xut4ZBGfkHs79n9GZlBG9S6v3rmWH1DpWuk+P7vaud6Ul1yV6faH3AAOGd5AVYgVb+NRl1pGW3lEFnX+ZLbUwTgaic3IeAjUndukKLsEw5ZiHpRvpSfIgiXYgaG2IY0LOHJl6ftD9G/Zs5/Qg1sI6PMSoVsjoIQ+qVz4wZeagXzXk3TCnJMICmDVQv7+5bQ4HxDtbFrAqZpcbvdrE5ZmxtGOL1nTRV69f39++Zi611icfPMW3x9e/+r1QOb+8tHs9/NPv73/1eQ3f/OjTy1/+G7L9/uXLN29vy3Z7cXZGRFImwmzwWawuwkyAzGJNR2yNrtpnwQOEG4TV42FZFldN+rqUCYlqa5h1YiLTdjzc5y2/v3vDhVkmbc2amjcWjgh1V20Qlr4nwrqjxays+/DKkA3CAFmBT4pAt9adnTCeRtKDekkpC0IxMsXI0YQxHhtFz9T64+2JXpcHHYHIeHg4gqcBCY+AJFFaiFj5URGW0mPESAREyNwT1n4I9bTRM5Ue5MuUCyMmFiBSt4CIMOjs26RIc2DnOBIxZFk4bNSwANFHc565u5OrhwViKZfvfnz++CPcni/qzogIwgzdSEBtjYXCEx5pHl5mcXNvpmaFJQADc26QZtFYpCBDU1VVBBApqpYgGzMBYKZc+bjNzd08nIVVrR4rEsokUibzAAT3qMeDtrY926UIdhACUyAu2vbHZakuk0xFznfzdi5u7hbTNG2mGZHS0RqgWphZRDByYRmJWxbdQ60DegSdTAUADhiuyH1dUIrzIEYgs/Ru2cwQc7YBZ+cqjYghe5+yjMKllKwY4EkEoYeBxMJUAgtQwWlnsjHaRNkpT42LsWRbA6J0DjBh9hDwak4iW1Sxw/NA0nmiHeboYAtklywM4sxaDY2x6CENYWDGyD7oNmDdoGXjGtBJXg0wVou2RrsjA15T41hNb5+p25GENa5P+9iFhR0yLo++gT2fyQi4ouc7w/ut0E08jLrzArtsJY4Pi1MdY0hb9w4n7ODvGp7jyBhg5YASnPoS8k7nHLUHcWTSQzv0T/1WrzEpnu74ms/gyRlA8jmCCB3DLQhR3ZglXJsrGMh8BmV70NirbjYzncf80RP79u1UXW/u6qGW3dmGtgjIu3l3vrl89+nxt/96sXs0XWzr7784+5u/sc//25vn39kHM21309nlyy+/vX355vzR1fZshwbzdi5zcW2qrTUDJGaSJA+7m2lGLSwS7sB5912XY6gDmJsl8pAVYxFOQMZUtS3J07dF3fTs/FKPtVmrbaGORFLiJOHWxecAkCJxfDdHpACDrOV6ru0OMSFC19w+YeWrlklWjCESHPIEkfp7OlQ6ctj06g6dYNGteefsJUcOARxJskXLI5B8DI7raxmHEcm1Y25uZm6YrBfs+H13Ug8Isu5qKahHiU3loLjsm80+yN4TTBm0ppoEiZGHGzIzMwcnbRfcPCBlKjwMs0IOFIg0FYjQoHl79ujdT+r9zd1yFGarS2Ida/uZNUckCifoNB5AUDMMUDNiiohe4QAAJvCgrE0gqZoQMvfGzdaUiLL7Lxio95ajLtVMPaI1ZUYuE0IQcz7aNDGtLtvNhBCE7E7Cwoxv7/Z+sLOLy812y7eLiC+HViMKTizMIa4GiNo0wqeJJUfoBgCQObBwLlkJspTtFAbz8VwAkNxDcroKBgUxUVhQQUQC6jsUEZmEiCOwP0nv6F2m+8wEDsJcu3sJwiSmZkNUISksM5YNlJ3LmeMEtDEXczYgz34cRAxkQnNYYUYMIOyofbffgAAhCDF0gkZsC70Y6gg8DsUHwTMOwhACegSvVrTjmGsBANMN+wnHwA5zYyI/A2fKiCtnq66pcfcoaVJxHR0PuTSzoWz95oCEehJq74O1YK17pN1eWxo6M2o9Poa3AOwf3N+LI38dlzYCQATouX16ox4FPoB1V6xtAArDnfZLW/OMzhvqYBT274JBN4qRDTyEp7pFS9cCHOBZFghXQNxt5sfXj6dpe6h2a5Vt/0g2m/e2vkNpH5THu4J7uX1V747T+cXhZn999Wj38TsU4W/udhdPysXlzTdfluuLl//wXw+tyYc/Pvvrn+EO6u9e1xfPP/nlz6bt+bI/nl8+BmQHC/cIF6YiMwa4W4TR6HS32hLUC9WmzcMhNCB6Np2eLiubAOpqqmEKQnZUAy2lpKJyq0ePIBYIcnBtVduRqUuqYwAQMXMAUK/FRsKGkTwCDwin0+L2U1moS+UP8HZA7LE2lI/gJBBzbPDgU8fw0j1+jghM5tzDOH84ccx46M98wJrXISL0oceRvb4SvbwUfUAHQjajZkKCA6BKoCmDHWbO6WzuBhRuhkSIkidCiExk4WZKwiKSLcRhWgpnz5Ewu7sTqFk4IGK2wrVqzNPZ+ePl7XM/3pZ5UlcwF2YDqMeGmCEtikhtDSKIk9TvtTYRnsq01IUJm6lIQfQgCe/QW0Bgl/3wxJ17mBWITAjkDpSyEIBTKV0lDwIIihCk2uBcAMJqlc0mzCk4AFnmpvdv9/fNcHt+ud1u94sxR1UldkAq86ToS6sdsvYw8MITuIcltY4IOCLHjgVn9hOpa9L3KjoBIDND9pYMu5QyNQSeTVDERCypOuBA4WGWxahufYmJGJmoEYTFiDSyg5CQhWRC2RpPXnZKc/C24dSAtPcqo6e4Q9JMoUf9adICTtT+rOMK9jUYIxLBMZNkBR46OJNRE5zi677m1gAZkLolh16dhVHW7Zyh7L93yJGPSX45oaHrnoBxUgNhpUQ9R5rQcyLoalixDjrGvh+DRh6/BvQpfA09yM8ZSTaYPScnlzvOYxj+1QbH2Oe5pXvIhz1XW7Oibv1x2A7swSNAH5ie2kyel59JP4YD5simGO6mLyB/MAYgXSQkqzBHovcptRru2YetHiR8ff1kd3Zl5ntrSK0Eb7bYznd4QfPPHsElTvpR/EttbyvIxaOfvOcQyHH37HW99+lxuXv1dv/mdfnTF+oqHz7a/vQX7//Pf3/z/MX3v/tTq4pQXj1//cHPfkKM+/1eNsXUwwMnBncnMlPNUVwgKSUW4aFmqm1ZUv8LAjyVnUrpQX2Eqqo2N0VwDgow0DZfXWq12hbos1wCJUuMFcCRmYMiEjF/0ETm2aZk4L27wvvcxwyOY6SJ2Hu/h6TCauoTNhkr3sdajBio0ZqRrgBOKpx2ABIhS/XZKdELQAAjLoIVPepWPOcMD4syZLZ4xCSAQDmaFbMjIjybMAD9dCJJvh6naKaUA3TQIwKRHLN8FAZGkLFv0VYDctAgujsgEEmASaCHujsx1aUWdA9H5nlzvrSj+R7dIIAJKAgQzF1NGZmFEMAsIExYjKwtjYkdQsoEbkG2aJW8BwQQUaYSailk5CmVQchEagogbsFSkEUKSJaAO76KEWCmgDRPpUgxa7vNNtyttZTnhBbGzDzXtn/25u1jKpt5I+VQLJr5sizzXNAZCQuxWhShiCBAdyMkhZYTeYEALGMvB2AklELWAoncLMuV5i5FsgkYEo0YdCwoXQ8WgLviCTkxWscWDSBYSnqAFIbjlP+BAIwuQRoBRCiCMgdPQZPK5LJtIA2KIkXaNMauFqDpiWIQ7weR/YQsoOCpJprW0PNi+lpN63+aDDaI/NBp6NQX4NgqmPB63ytrnEurkY0TIAOx1oRPsX3PQzA/fmQAPRCKk6XtBYNh+NOBrVzVwdoZafu6e1fcqv9mxF/j8jrGs7qC1SJ0TzHC9n7atIK7OHxDv5qO9nT3ACnOMoC3HCqIIzXJjodk9mdak7fyQRcYICR8gYjmvuYMGG4JB4WTkEfstuebzXkNvLu5IaufPX0MkwTT0fYQhgXpauKP39PvbqbPNpvd2Xx9sfzxC923w8u7crYr59f15uW7n372/A+/5bPLJ//+38rPf64VXv/p67vbt5/9+79EmRnKdHbWanU3CDZtiJjRt4e5aUAUlvROZgoYZq0eFyld/T/AiaTMMwKEObGoNW3VTcMaFbbaXFXmCTzqcZ9r2JvNm+JubhVcs63EzBKVi/EQYkAuOQN1td1px9P4n7LHvvJihPAOozkPHvjiWBO3B/naWNQrn2w9MtblM5gLuMYsI54YcGtfrZDnFgCjmpO5Cw1kqa++MO9FLEpU1dccGgndEVOMOKcGRrpDR+RBHUvPk22UJCzuZupmlg1rZg6oCAgM6Ghq4DhPsx7uVb1pBJLMW1sWqwsBaossV0Qkeu5IU5a2Vc3IWYqX8AitJoVI2NWbtvRwNIrkJNx38UDrgBEimqoEIzjLKLgQmBlAyMRM2FqEqSuU7VxQHJyJvKt7YDPFwsAMzEttL1+9ubp+NM/z0myey/6wNNUcgMNMRTaqjdCIUdUTBjQMd83Yz8y4sFoV3iBRkCNiZOtfN5ZADETs1sXLkJGIM9SIzpxHJg5iw2zlsD4cFaE3kiCzSBaNMSJhdOpjWySwGBaXjTE7TcrTwqLENaWCiXPQajgQch96Bz36X9d7X7gQQvAAAxn2twe/eXwnpSBCStqkXfdeE01z34OaRHGDELL/M50Nxmq0T2yJbu9jWMDoeOiflQZWY9uLB92gI+JqqnE9z062jVg3Zxb0+naMDMcynU6PRfHA1Z0cwdjWAIjDc63Fj+G81hNeN/0ABdaP7+TfU7oPI9Xrfq33JkVPOE6RPyJGeL55VCTXdqFuVHJKLhIPQBE8YHt2dn55GQh3y/Li5f3lrjSeY5bguF+WQ9Wz4ADnDePffCrN56eP4E/P4uXV299+Va62V59+qC+eEfnhh+deY/7Ru9PTD88+eXr/4tntD9+dP94Firb63k9/ZMejegSCqQHAZp6JUGvLi5eUMzEHDLfWCYsU4dBlFLlImVg48RIPrceD6gLujMFYDsuehETK/e0tgCOGq0opiOCq2o4eWqQk+pRNN2P7ACStPKlWhJmRerJFALN+0B0t0SnpSn/VkbsUUcAe2kOqao2cth+1pq2di9Y32MMQZ32gsA5kDFjzCAAI8PDUdQn3pIUwESL3fZGgbT73LkUwsoTcj101pGcdSIA2lpCFoaEZEZIkq2ZY/6xvIyCiyBTePMzMGLFLRGTdGICITBtzQaJjOyhYrY0hmEoQC1IKYCZcw0gJ0AMAEhVm8wCAIsVUVRuAlLkwCSZ8MGipbiaFmZnIkTnUEdEjx4hCBGaPMzNPJXWTUZuB2cSymee6LB7eVM82Wy6kRyVmRKhuh9a0WnMq0waaHlX1zavNvM3pMttNaeZ5DsAYaMIEns385hHM4JA4mQ+4OfNFFxKeSm+3suAkeJgCCxIKUJqMAEARIga3WG0VYRe36OFp/zvSrxqQug/UtcMpZbwLUgmeAicN9mBDbkSNuTEasQMQdNGrAVOjdaucceJqVNNKJgQ0zCx2uzwCkh6m+Br+jtX7IGgZn5XhEeUmROjPbw11oLcJZHSWPqeHUjBQ2n4KTmt2Pe439uW+ojBr8v0g8ALo89LhtL3wFPGPN5wiuLTR6GuTXH9x7WjA9Ut6iH/yDfHwZxwvn0oGIyEICIegHtFHVsWxu7TspUBABE/P2ycBeXd6OJC5AOgA1vB94/4TeHNAdIR5nne7Kwc+HupdVUOoEfd7fbuZwQ6lahzvnwhTkc0H0/T+Wdyp3murqoGb9x+dP30HXPc//ECFoB5hF/DBY7y6rHF3/+rZ9dX85PGTF2/efPpv/37ezrdvb33i+eLK1SUFdc2Svk0IQogAGqatRRgAgjsiAYG1ZMsJIbmGFPSw4/2eIHKiNhepxwXQWUSrAjgjNDXIiYDh5ku4sQgiuSsKp69Nzm4OpAz3XnzPIeBd/6fD9yva120zwIr+OASYeUQMwOUULeGDfw3zDSNSOGWR64f3sv1YMDF2WpwAw74dwiNSBN8RsSuvr+9MM0G9NbrHc5gOL1cddvGhTDNTTxsQANzcyCMUiZAJeiCZGqOQsDsTZuNBhIGiTNnzHJiCaASBYa5BUbbz/Y1NF5vj81fklUXCjIW9apgTMwlbU9U2TRMi9IY2NynigWZu7tCsMCCxm6lqiud7RG2thBMTMaCwmefEMJZCgMSkrSEzcdd/t+phXjyo8LzdLceDBJj7zBuYAEmqtmp6c1gWdSwzgjgISiy1qTkxxxKFhQiW2jKVJu77P8ByHJF3s+eBDu5Ipe9yBy6UVHpH90Bz5wRezIQEiBEQiYmKTDMlcNmW7ANAEiROBXpEpERv3ItIFiF7o5BD72wiZiaSGWUO2rhsnCanosiNQAmN2AkzYB3hAkDmlDgi827DO3Cfm0FoBK/rmu4R9MiUcbVzaX3WXtwsCD9gNnZEc4AqggPCT3AUORupPAx83XsrCvrnlq5/Co6oCyArGp3RFiNiXyP4PL217pvBBfTyxeq91jICIkJkwMKYCi3DB8CDBCRd18kbxfrael/WlOWE1nSvubrNYXvgNIun/z/2emPXAoIs6CWLsSczD5GlAd9h97gYqTXlptuzs93uXKbp3vz1/f5oAYzV8NX+cH61BZt1v2ydzye5tUXORMKFNvbqpd3sEdvu8SUQte+fxX6JLS/1MP/dx/yLD+MdMTxsfNnf37eyPHrv4+C5tQgPmSYCQAIqbK1mjIEEpRQ3N2umZqbhRkRqikgiCEwBME0FQgBCtdbjAuDamltjIWZalmMmhXXZQ5gSuprMMyOqNTfDMWgsY4vwzpeBvtpGPDHuGHR6aBZgcGUmjLwsIjxo+IlcXWsN6kFiFg8XwIgj8qe0pw9S2IE8rkWosb+wL2vq1r+v3vA+Qgez0Td6Vp7QZhCM7YcQGDzQqt4vn7osA5K1tKTO7mrWiOZ0HrnhONjDzA0ipqm4A0vhbCqGCGsUDMSQODhRkUnbgoTT2fl2d3Fc3koRaNXVI5wBidFtFVtM3+EOwIzkXT0DA8o0mVqrSzBz4a7nAWjhAY4B5k5MNoYMajUWQDTIgZSZnght5qm25h61NVcKZhLabndmDQHNoshsCN60Nl/M7pbqR0NmAGxNp2lSVXBnRDVD5nkWszBzCFfwCCUTYrG2To1ARnFogdmM7ZDnLMJBoIHdVQCGMxcwByYkAiaaimzOspFQsFf8ACGo6zhh/zvEDrqsZCBBjvUcY0YERXDaWNm57Ix3DSanuQYpklIqTQcBEYwOZOzeLE7rDwbg3CN5wcFoGDh+AOBQx1qX7upXMkbP7QQdg3mwEXCYtJOV7JAMrAO38ht6BZi69R6aPsPVdLglcicGBALDWjAA6Bv4YR7emTXrhurJBQAg0Ir8r7DraRvD6mTGXRsG+hT4nUz76RRWmDc72iJWf/nAccZIj8bNHBZlXAX01Ke3GZ6Sre7kVi4KdTgCUp8yYaCIIKLNtN1uz6FM9+av7g+3LQkThedCm/nmqM9vbn2pH15u5VCfCNFSsYh7AAZtqYK340KvXunbuxBV9Lbd0vUTfvea3tvefPP5H/7Tf37v008++sl/eDk9PmqDOw23zXabyJqaa6vTPEd4Ej+StxNZDx2NS8ipGBSlFKISgFoXC8sp424tcfb9/d5dmWk5HNO9mTaep3STOcydmCgy2oKAYGIAwBH858NNobTM70ZNJToROCAw225HPzD0hsQkmwNgarpl31+cnl5/WrAm0SN+Ob0cK+LfbfiDVZdxCI51lHE9uMeQYBg67Gt6DA/y2WwLAk+CZt9luC7fk+hJjj5HojAIM2MjJ0SOCCaObgcQENMxI2GRAgFmFgH/f7b+tEmSJMcSBB8AFlFVM3P3ODIjM+vY6nOWummGmnbnw9D+/4/7aYh2iZqmj9mqrsrKzDj8MjNVEWbg7QeARS26x4Miwu1QVREWZuDhAXjgospUzIvS0hBLcT/R1bssp6cQ3/cejspUqXgwvDS1lWCESRNDeGDUAWlL27Zt9JGV8VDJuesRQs8ZvNHWVqhQJUeDIctJVdzDckKompkaWwQZFEJNW7uQNJNEzDQfe0AWl3HbO3wEsfcB1XVpex9U0ikUUTETQIcPYdaeUlWpwnAGBaYmDOdkMAJBRBPJVwZI98O6W7M+upmKGaTZemnL2ntXEc0BGKKmTUQYg67Z+EaGKiREwFToUaWpqTZRhTbqKZYHnh/Gcul2Gmg7zCk16lAyVCGm7kRMCHmPW+8EeUW+bTaipmQjpEbHE5ku4z2KnUFrpdw4ufjpNhKUlR/MzT+r6RIHx/RB0wbKYRzxpkT/bZJW5puD8CO6OK7lV8RqJXzrax6cbRDVvgfMXNy03gDorFM5+bgKTA4i50B9d/w3r+u41JnFfutrp72YYD+V0UyUEjNJcDg91N9nonFSjZO/ylKWmbXOMMwpCC7ndnp4imY94uPr/nVztxbOIXx/etDT+aevry9ft8uy/PHltrV9Pw1fYCrvT7b87v3Cdnl82P/rn778//5+vHzRhvb48Phv/w7/9t+23//Vfn35L//7/y5n/PW//Bf/5b/8w3f/69/YetrHfn73pMEg3Hvv27IsAkjG5n2MsZMu00dn3nVp67Zt1qwtK8C+7+kkOAZyBpEg3MMHhCM8fCzr0vtgSFuW6OF9j+Ei0qxF93wEKfcoyTYgYhYTZxJlbscMNavdPmrrTUCfHV5CemTgPFu/5Y3DLs/8JgY4DPTcW0d4OctPKxQ9qMGZkppphHvUnc5JEqCx5sTw2PtHRnlu3dmMkEcgwPs0pKSMw7018yGMQzAo1BpIERXJjhlGeE6zk4CaRXggODqWlusTqX0tinBdFlkW2OrSTueHfrvCOYbPNiloa0bPIF3Feh/WNBC9j/PpFDHGGIvaoPc+1sUiKKSHNmuBgerwYAlgAZnXdKeZDncwVEHB6bTctt3UJTj6EBERW05qKqom0C3YAztDtC12isX30bfbJia3224P52VdYgQx3IeJeYRDxBQBkUYxiomGijkGABMN1MQFy9LjCApNZWnaqRrMYTdBFzulNldTs/Wky7k9PskI206+X6V3NQvdRKtIzMPVfWktr4IgTKqpry2tLdoWaSvbGeslTu/89OT2sLuFLi5a3SvTNmHmwwTUZDfI2dtRNWEi1ffYULqSVZ9TJwLI4IEH7J8+AwfiRg5xrXNSxD7fmvIjT3B8e6ZHy3znnpbyTXOXHxT5vfRhXt4M0Ase3+NzmYgLb91bKTnnf+Q4xaz3S8NrgKe6Fo6Wn+wIfhPH3AN4HPeC+4mri50BTR3vg3/IoVg15qEG0grSD9wTemDl6nlUitS9QgQyBVZZjlmVKqImy+kmEs6Xff96GyEt1Mbop2WltS/X/vLab7fxchtXjds6RPXp/dMX37hfv336Fn/9yPPCcP/xn7t/Xi+n/uH82//nvx9/+M3Pv/z8+fV5v/bf/U9/9//5j//x+//H/3Z5/8315WVItG++gSh9hIepAGCELeZjuHfm7FpVqIwIKkyau5tpawtE9n3rezcFGKQzG3Q1t4aPESTbYmMMRrRlzcWJmiakZBb5lx2WrKDnqHCvHpqwhJ9Z+KaqNQ6vjLJbICSTB45ScpoPkvMxH4drGuS58w7sX8D6MNszWGQdpvmGzGbe3MDhxQWBd0+RqnmHq3n7njLD7Pl59+0+kZ1kf42KECoaiBLqsYWMPqLZKgp40a4crk11UYWIa4wBqxQtxQnS0cyGg4GHd0/749PL9ct1GwGjuqjGGBwzrhdApfswWKkPQYLct346nZyDwUTTEVRR9yEu3UfSPCGUENJNUyggnMIAI9Rad1eFiQpxuZz9JXx0iqNzWS85a8VUoMoRu/veR2TAlROI9t2DIrht4/FyUTPxcN9zYwlSb08hoWIqGirAyNLcEn3C0cqd3pHahKpLaxFw5rg5qmpbFmHYemrrRZZzO79vQF9Ptp/9+hxwmC3rGmNLS5VdUKmZmHbV1EKttWZtEVvEmp4fuDxwvXA5uZ2G2g4Js6iMwR3cTpa4DGFlm0nkWJQidwRAy0SGvvUf+WZy7xlLMubASxWe1ogxCjWDd/Ioejm2usw4+Ri2iOO/PELjeUrKMczrrlNEyoxOcP/+PELlS+R+FOe509leexyOg4yfL5UkYXVWefCoJpyO5Pgzsyj5ecjNIG8v5Yjr08GUwsQRdiEOn0NIzriHTNfMmUxJ0lGKsZAJhUr4DjHvldX+arfQ2L37uO7uIVhaBKjWzmcHP325vrxu/TYMPk6r8PKq54/Dttuzni6Leftwdnvw/XH98ntfvD1d8MPv4rvfjMvy0z/8mYa/+3f/4VN/3U7nb//Nv/Pb7fXrT5fvfwuEjzFKi0WzbJ30GH3sHQI1E9Zkg2VZw0mEiAHwvXvskoVyPoiIGMlz+kixTYpKeOowiy0NBMJzJyiEadrcqy6Lkdr06YbVWoTPJcoUvaYZTXBTkz1nOBzTEqbUXtlXmRAlyb3Z33g39rlna5+nWahOsGP3AzOULFZzxnqzIAKQFHSqMBkUtXx5laPORFrMTxKxt4QicyPeQ9NirnxUeDTGyJrILLPsPrLESNWqnpguVAGbLmhCxmBg9NNygaH3QUYfLiKEmp2ePny3vXzp/WV0YvB0Xnpw952AmWWBpkD2sTeayqJqwRgRsW+n1qJ2OPvo67JANYImRf3TQ1XA7JkmsmCa0iOMEJHbvi/rWXdH76fzukXE6ILk7mwVhciIGO7D+23bHOIU0UVFHs6P19sWiLH7Vbbz5dSW5tHGCNMWEQg6uEy/BSBR+GRSRJEa2k2xJBtLhDUVwYqlkyIZS9KsMbra0tYHPT3q+UnFuF64vTKg3Ll3W3pbB9xz+CGRTeygZBMKW0vpiCZt0bZiOeP0FHamndAWmJFKEYgqjy52MKmCjLx5WK4DxJf3StzUDqRyANoJZ7NMJpvaa8/Lf1ciOqH2NKfFVxw5gLvXmN+YJvzATcCbv8/YOv+5Ax25H7cEZfndmb14+2bTbt/vYILCgzo6qoI4GRfIfRjwMfnrv3M4B7QvlqvyG9PH/JoKPhDqkRWcOG2uYBp1HNeTvctzvY5XzIVlCvxVIhAQdQbVPBh7hOneOdigLUYAaK211l6u/fZ67Z22nBziy2IPl9GWv3z+5ffn09aMi3Tz87entvw2+n7+5tKe3tn7b1/69vxl/+Ff/Std17/8w3/5+hL/4n/7X0XPLz/+k4ufHh5EhIrYE9CpiEZ4RI5qQZatjdFFdV1PARLBCFHrexclIiAhlByvXfY5Inww3KzlJhVIW9ZU4YmMzBQRocWZQMVABD0rO3Mrskr46fSDnGFUsewRz2WpXIRXPFnl0MXFvd23k1ucsdp9N88AdW6NtztaMOHHEQ8c/OkM7OowsAjV4/gcbP5857klM/c7a4Tyg4KpjXgPiERgaqOPuhBJps2lWZAeA8gS2NSy16hARKxJQxu3W4i6d21NVMN7NirCVLG008Nyeff1pz9TTyqx7S6Yqi8BU/MBSFgzoYwRatKWZd+7u+/Esi6RUzkphFjTfdshsmjVXHl4yUaoAKipueCo6izp19fW2rJYv+6tqYtBmOyQNc0jPMgIOmN3UJWjK3RZF4i8vt7EUrzImklb1u5bKh6Fh+Ywswrgs2hc3zZjlkC9KOCpeisqBmW4LM09m68IVYaKLXY663rW9WJtxRhoK0huL3J2j6F9J3raKB+x5J2TQp8VQms7naWduZzZTlhWtDV0oRhkpnNmqbDyHmbyIFUwyzfvBioNPAVoOIAQJyVdQP8gdEhiJhcKcUkBj5koLXs7uc37uUgbzmn4pvcp0v7NeXlT11MQTKsZuE5M4nMcfzihF2emd/qxI3srJRmdNlilIPu0FBOFVW3I4ZfSOzJ3QRx3MQkqkXlWdV5YLTInNJO5JCjbnhUpR09F8vuY3G/dUZmZOXve6rFUpKQ48gHKoFA0KM4YAx50KmWJHGstWYoz9r7vA7CFbVFRrm2ofu3jEnajDtENfe3hIrro8j/9/vxvfserY4v95fbhNz/48D//p//09br9zb//t9/99d/uXz9fv359+Ns/mJmIprhnW1pifzC899REXGzZ+w7RtqyR81zCmylAlRDIiCGC8MhJsKIWY0SEjwGzDHzAmjNMYowhQjNlUFUU8Mmah6cWBQUw1UxCZhsX6rnLrA44/O7E/nPrHEh9HqBJRB5f6/Gte+j5lnlB1XRNs38Pbytzfz8p+a8WIgsEp6rPkRs4NvB0KoE5Q+ZINeQt5y9wUprFkjItvHoEQE2dSYY4lAjP2oGW9yuUEd4MWUTYrK3ttI+tj26EmUmTSOVuD2tL6GKnJzs9bfuubfX9GmOotrHvojWX1IswUXcGsSjWZRm9i8gYviwGDXqM4efTaW3r7baLtNYUJhLi7tWjms3OKj682XLr/Xq7Wls6R1uX1U83f7WWtD9ItKWNwett30nYsi4xOBzovgt0Bc/nFcC2bwHcbrfTulJkPZ/61utYkhEuqjknQkQ0pJ6YKpia/BAFPVKNtC0GFTWhw3KOddoea9pWW852fqfrg6wXDW9xEVE386AhRr9lz4rHaCEpYYASjjNTtfVs5ye2C5ZHLA9Dzo4lZAlYlYtOYlnlvj3mMN4awjft9xvec9q+Vu4ClOIoZgukyCTxi6CvWKJOSpXPH/abx5mQI36YdnDClUOdfbql+BU+LtNZ8YS8xVB4+7d6/UTkx3fvQQKQYDFX5OgOmzjqLVJHVRyUOzkCCsl454g63gQdR1ZhZt7KkNdayfGK+60X9stXxR37lz1CJm0AqbF8b5IfuF92OfgIETUnnRghtOZkQAlJede2LK0tnnUPrTVbBAoTVd18fN17W5fbhh8FWHZnf1hwWVZcck5nuPJMHz/+t09//6fbpv+3/+X//uFf/+vby/PHn345Pa2Xx8uyLEH0bSOppuFDwTF6TJnJ3jsAW1swRh9j7K01mRo9Pjo45+bmpLNUpKlaRjZrqQWvZiTDXZAF8rlR1bP2w2bhT+EJK02QGT2IyJy9JbyzpJn0Z0lGV88Xjp3A6evvi6/HZqlHITNfdYD9WdBW3CmTvaiNhQlqDhSfeyVLOY/4Y+6gO/XHua9/fUX3UKPuKI7LnO4oIUotGr34Qw9V85xzkM0BgKdSW3pKkkRrFjAPkFTCsjVMnOTug9pC2unpmwjfPv5ZoQxoM4qFBwKmTU2SOFIRoWYPcFuWbd9X0+02LufVxcO5bdvptJ7O69h7ap5l7sK9ylsAUVMSe98hEoFx25s1MW2LNm/eh4ktbVnXE0T32L+8vF5HoK3aVnVEuEJG9AE0b6dLg/K27ZC43q6n8wWOdVn66KYIOil02qJBN0UMaqU2CpcgZf4drKkuMBVp5iPgmcoHBdoWXVdZT7qeZT3LerIIYWtAjB7LzXjS9YJw952MCA+q5IwAE6VZW9rpIusD2yPbu1ieYnkYtrqai8x+gaDqLPjXSlQnj1+1DbVH7zi8glGAaGW/qsLsMH3CAA9Weu5KTlkIRtbOHF1fZdvmzMfjRQdBetjcuTk5FXdqsxenIvIG+CNdD391GjEB1gRTk9GR+SECMMXeCIrM7PJ8g4nBjsOCN8ED3kQm5bfmEawknRwXJOWlBMp73rDqVo/P4dv3Id5guIKJsytOoTJr2HPFjowlKhmZ660IkUgZbyPFJCcYSarDeIon9hFB0WYBEYjZ0iO2AcP6ZXO/bp9ftucT/uZR/3A+n1vr/urDg3sz7l/++PxPP71+jR/+w//yzd/85vXLL8/DXPj47W+anSASI4JcTg0OBX2MlIBWq4fQliWI6N3dVcWa1hYPPwIiptHXBlYJIQDTlj8SbdYsR+6Vyw9m1d3BD6bEW0F2vnnGtcEhooemwxRzngUEqJxA5ZyIZJnuqPoACCBmhvOodQAmEplHSo67qvNWsGbW51T+jXc8wawyrLSiVLHa24/nMcqm9qUW03tUt+ZP0r8dTT+zoowgTOAS9Jw8WLNuUp3bqakeGjlg01XaiGHWDM334XTNCQuqEhgYhHRifXxC/3a/3WgnCtH63oeZ7e7pWJvZ5iNNkKZGyKAY16XFcJJb700tA+F9H6a6rC3CmS3CKiISI9AgYkj6bnRCrBm79+6DXJZm7QSKqp5O58v50WxBeA98eb5J6zC15QRKa+bD+j56vylbW2yVRTc6BDFELSKaGTUw0hoiqNYknNYgQpEJKapLT8QMR3eFigTVdASRozJhtp4pC2xFO8FWtZUKhYuIXR/W8+axaVvkdMYWAhcC9CzoVDVtZutJ10s7vRvtMZZ3sT51O7stoSaiLLmJQv7JWPHAj6gR3LXRJqV/5AVSXaiRZEAnB4KJk+ZGnFZsJqNQ75DeUO7E0YTRbyw9M5w5aCUptMupvFKG/U0gPYmcY37mUfEzI4Jpqqe7qjs//kxjX8dapjWYwCqt6JurrMhdgMlMSa6DHFblqNY8Lk9keg1WuJPDBGclFN4sBEHiDT6sWyCQ+mZpCecPjCybNP11vizDjgyvSu1+QIoLElHLenqECtQ8oruXNqwaTEWyLFK3MfoYLz4eDQp8827dlvO2qu22gMtCfvmiuD484t2/+Bt70o//9T/Jv/4Puo7L6eH09GDLGh4RfjpbLnffPQeYEFA1UMUIkj0nMkK1ebgSPrzqnA+WJH18clxCUQXUIyBireXAG+Y4SSBAlYS7gfwbI8+AHi6+NlJmfbSM8GGp5dinR8x6Bz56X+x63OXvD9xQGfiQSXPOwJpp348gAfMVkF//XSZsrygcBKWS/sev5c7Te5zA+frZ8HX4iftn8CifQzVe5OXPQF3u+5JCOl1QpC6Fw4eGqNLM3LtZU2O4B0JqohhUlQJbV45xenp/vr6+fvq5X6+X02m8XkV0WZeX55elNXesbRl9F1WnR7hoa9oi3MwG4B4mmmpoPlyM2iy1LrLlwEyzbC4bsptq5CS0PYIeDI5QoVqTqTvbzicSt+Gv122LwB7LskBDVLSpNbFmo7sIlDhZ04VdFNkbQe4xmilT4U6E4TBTo3rKiohMBTpJYeAZMAVjsQUibPThsjQ1FbN2etB2wnLWtoo1WNM88qSeLt5fpV/0dFEDvXO4lghbyvhnBv2k65NcnrS9oz55u7gtITZy2IVaHMaRB74vvHOHkSUXxRnwTkIUFQGUjGJCyAO+F8wpvuIN/zKTAkc2LFKE8G0pRB2xI+6t03cchkL7h0PJG6jeLRFJVUKR2rXHYZtu5I6PptdJnzAzDhOD55HMEV3KY6DWpFkn2J+2f77+TtwcZNqbBB0jq5KOCfd19dOJiqSFuHu6pDIreCppDhEciV0I68CmJ7CZopG758RxugGBZ0CuWq0FIiGMcMmhTuRwRACmqg0taXOqqDjDMXwYYFyu6/KTnuj23c730G8Wk022X57bwMNvv2nfXl5ft355v6Bft9fH735QUxHzYFYqjuHhHhNat2VJdh4Qd48YompqKcrvtVYS9IIRQhETER99hlRqqoPJYOsYI/uAVBUZHCV5Q+p0jUeweGh9T5Cd1jt3Y/COmin3eqx78Rwxm8blyPn+yp7XntIZczKv5aBJDyt+aM4WuYdZXIxq/c1lkKlKiuNSjzIBuUMwiRQGmVdVroypb/qrP7VnWHa/hlTPo5i4r8RGIIKaO9aW1WMgwglqaBOF+nBVEWkz8Eyt4+Y+lnXdX19hbXl4Or3/dvTb7s8eaAhRtaX5cGkYw82sriokKpQRj1iXJZMKBYgEHhFDZrqXTL3epghiYDCGUtU8vCoIwOjhJqLemqb+qIoONbamtqwKT2gRDtEx2AzraV0WH52mGi7SBNi3282jt2UxyTBaIjxrsTQVGBVmk/nN2Y1Z5gBEwN05AqSaacAWqAmtBeR8Oklb1JqoQizfM5wQQVukrZpFoujaLKd+aaqiQVUFbbXTo16e5PwEexJ9op1dFocExGeuMNvXdSashAfKTxfAmAmA+nb9oLZEq/p6AMj5bVJYRqZ5m0ei7NMdOrE+od6Ab7DUAWXKYB1k/0T6dUzTaqfPOYroZ4/78TnEYX4xe4DxBpAfZ5UVTdcZqwTuPOwJIP8HhHa/orzRCRYrb8DpCSppfP8AzAV640jevP8RchRdnGxs5MVPr4f6X9a2o/LEkU6s3rnyKZzEdgA5+QGw0BzULI5s10FTSw/YdM2mVglCHCJBHw4ZXQE1tZN1aT93u34eXy1+vwrU3y8P7Ye/wfU1pO8Ovn9a33378eNf9N375XyydYWp+Ahyv+5tXZrZQNZuQ4Je2aRgROogChCejSUhKt5HaXWTkj9NaJKj3FWjJPSkZscTKQefWRKSNcqRpd4myIKWeyGwUIIid7+bq5ivmltYCmr8KjkLyXZFioJ+N+lzi02H/GbHpOcv/M1jJ3CSmvP37sehePmkDuqHCbFlhq7HURIg0qEcd1fHM+ItbMozL/L2NB+BY3qBGRCAnP0xFAwfUCnxqRgOjc3NljRxkULlMktCqqQB7XR+vb1iOZ2fvtlfPo/rdn7S6+cvJtpao8L7UDXvw9oiCluMjr3v2bNN0Kxlk4eZEYxBDAfEzMKDcKQoqpo0EPM6s4vNVDxa06rWNG1mp8tF1MYIoD28e3/1Z++dmtWcVNUYFIlmRiMY6+kEormo4rb3zDm1ZeUIDWVaeBHJSV5WZ16n3cp+AWQXMFCDWxSmENOgSlNti9oCbWIt9Z3IHD7aKA266HqStrCrqqI1UdU0QFRZF10f7PIOp/eyvgt7DHkc7TykjbT+0+6KqBDHbJqopqPatlFm//AHGW8f5QNsZfNqS012c5L/ue1yt5GR3fM1HmvGoQe9f4fyhWeOHX4vUjt2JacZrFfOoohEgpl/lzfbnqW1USfmcDZ1q1OZYmI4mSmE+7nJeJdHedER7LzxSmTRP4cdv4NwvP2EssZaVMPhYecVTyLqoHOnjVE5DhVQlmzSUzXSpt4/DsxXJqOCOwkyJxCFwOeZTh9was3UOjP9A6WM4aKyLrKIIsLDT6e2tlUlsNimwoFt+OvYX/orvz/38/Ldb98/kLHfXl+f2/tvr9vYAz/8/jeXp3e6ts6x32772M+Xx9aW0buZ9YgIOlOFUZjyGFrV81WCkusrEBU6E7aAAk9dU5yWs0eknoRAfPRE7jrZFRUlPAmiSQ8e6B0TAVR0lefteFiH/b5vqbll/i9Cvvng5w5PgMNjP2DOFKoM00Ty84nPTZr2eu6lOzzKw5lURl1MLcb89IPHDcnd8iYuIbPK0HEUhqEilNlwcDCnQLYOKoUs/B9z7bKBbrjTI7XMnCMGFzZbQKpaINugoLVDpS2LCr0/PG/bw4dvw7cvf771/VXaur2+ns4LqWLNuwuAYMt5awa6uHtrNrqnWEUKJWqqE41gEIalLYPuTvRYqto+wt0ZvbuWSBFVQdJUBbqczm09iWj3ftv7NoY0hAN072EUUyxrEyKcZogAdCy2qK8MqC7btrl3EM1aT6mbwnQ5xlFi5LaKewow5Y6jTJ6IalPzYhJVG8XQVltWsUUtB9oypLayWMNQQiim1gSpqCoiQl3b+UGW93b5gIdvY3lyOW9y6tKGmIs6EBSnuwim5IMfjUZx2PvpCGR2IWWWEDNeKLUh1syuwyLP2YeTrJ/8IcoUzlNX3/oVJDp+r/QUy1kdzIrI/E5tWUzz+dbiSx1nOcLpaSWzD/atYedEeUc8XsZ8XjCn9T08ISaxOy9s3kI+niCOoQgHX3Y/u4eRn3607he/Si6IpmMuNdVCeoBIzBmjByZLDHv/PBERnXmbSopzKhpV23c9GbD8P01NtDk5K3ihUEFoRgoeQTcVaU2aEREq1OXltsVtf5Robflp4GT24d0Tza6vr+N86o6X4Ls//PabH34Pbds+vr5cb7fr+fFieZK837YtoNa0WUtvxJBgmJqKesSRVwqGqtw76PJ/qgg3tWBO8TVVo5dxTNX4WbhSju0I3Yovqbcr9v5Yolo51njEOFxDRZWFQ+ovMmub6ySUu7hHjAcCx7QNnLmxeiAynxgmqNDj+EyHg4gE+KVaURf0huE8PiUqhr1HEIy58XlceW123htnWAF+1VY5BOElEj2nJ5FBZ5De6TFcVQPqPqDiPswaHWrZID3PeNYqmND1fH64nTa/PT9+85v95evnP79IyHJ5oPfYESYUaHIoIaISdBYbRQR9UAkzyy5fa0LRcLqHqjVbeu90DxUVpQjMhnePMdyXdQnPaVoMSBOomralh79cb6/7Pjw8ICZFThPB6L2bNEWoiVo9gGVdKdAeAlz32lQ5iwdOMRWFoqkpPdVssytgdoqJAOJRrbNqoo0eFCjVqKptFVsgKtoSxhjUM6ZQC6ZjFDUTtOzxhzZdz7K+s8s3+vDNOD358tC5dj11bUNkEAEOempuZD/o0cyIzPRlednxrCmSIyCClcfKaw+2whBllTNulYQJxwjEg8rh4SgmvMH0hyWthbd/JDUVoswsBRJH9ahgEuuzooPzICcWnjZZjzBWwP+OaILcK0mTTJdp7DOSPsr7Ds80rXUFN5OnKTr4uI5ykJgedBrc+Y38nGmRkdc534Oc4s2FDQ6zMdFioJ4IMaMbmUUdxTJNxqMI5DIiFfGLRLFFiDnqUvOcZV1jYpjgaosKlbRM9qk4Y3OoIpxjv6J38dEkbg2vbOPyrq/nq8ny+HiG3L48r5d499d/cHrseHm9fv788fJ0OZ1PQh9bf3l9oXA9XczM1hYjGIPCtixZCzijJVZNMkBKcKTufOoFp0AuAYVmN0vQycgW4zLWItkRVvEi7h1Zc83vGOWOfhPfCJipTCkjmhFi9ezMd5hxhMx9PR/5AXYOcHOwhZUGyIBTSmpCZI4CwoxCRBXZM6851X0C/Kz5nnCizk1eTUyOCziimHwvwVvlAM6Z7/dAdC46OBEYBZk3YLY7AUSEqrh7uKuoR3d3DVVrOVvUWQ1eTlbglRClaZPTu+8+fP15j40P33x/e/368pc/wmRd1r7fkok+SozyslSEJllwlOOpAmGw5H4tq4QDfYxlWVpr9EAATQTZHmBUbvsw5bIuw0d66NLKN73t++fPz5+fry+37hwUNFNTS8gpJOHaNBNiqRjhPlRwWhcRdWL4AMGAmDicgaWpiokItEg0RXa+i0nRWSGIcLGTqkJDVbQ10QZptCZtKR4pu5KyCitBZlukNaqJWtNVwBigGpYHvTzh4T0uH2J5Gnq+ud5UukoXDqRXzW1Zxbt5PI69PO1pQZkiCpPtjsknE2AkBZQbpExM4Ue9m3pMs0eggqPJ80zEdHwiRGaHrhyyziKY5UAqB99z9xPTNh4e4GBL83PmLOx5uokaWzZrMA4qvl54fzPM30rrWSO2E19zfu/XQfr/ENccjfzz27hnPu66bqiusXpNzTvWWQ1OpAMWqXT1na1VBY6nV/A+zf7R23cQGqVPOQ0Ta/qbCtDEUi1HzUyFAW1Sg2XyKkXpPiDWEIB3Z9+N/tjsdDmpAetlw/Kly/np3XI6q7CdH9fRt9ttv+3r8rBdr+dFnx4vKtFv2+vz1wFe3j2pNTEjQaiDoipmGRlHramihnRhOjmL8GnQDCIISLPwHkyhGEtGOP1nNgDnGTxooUmbTfuXIAC4g/O505Nnn9uSx7LiVzvx7Q44mKdp1n/lWgr3x1EfwOMx6xFTzD18QHVWfJAcH11k6uoeFwAgB1jO1x+B4OGOpLBF1L1xxqF6lABmHFaT4yIoWliemAsTAdCHb/vOGCIYw1Ug1oKEOwp9oyqxtYYKDvcseVzO5+Xh6dPzdT0/vf/NH65fPo7tVRiyqImsYh5DhOEhEFNLkRxWrooQuodQlmVh+sJWQ+rHGK0ZTDwix0l4pPCMmmof+9KaLkvsXVXF1KwF9Dbi2v1l692DmmNHlaCEqtBCcrpLSokEJPX4IWlgzIm4Xve+Jwx2UGIYl2QiSFJCVETSA6iaSFifWT2oqFkOZWE6dTVYq8bGKh2FHAGrGlSkmZgKmlpLnlR01ct7Pb3D6clPj0PWXdtG7Mouvme9vtrB7NdOjpC5AXOsEObXNf16Uh4Z6kYwAWIrC1dpR1YgPbV3RAy8N6nMvVZfH8UUnL89bafc2Xggc3EKDYFwTnadukoZOtS7vI1opc7rkYibtvdX2YGsvZk8zP0sHa3188Tmoqf4af0acxvmAz7SvLgH4pMok/nLb2KPlOqcB/hwl5zwUapihceiTDMllUchjvTM4QwSf0xvdnzYr+oSWYnTaSJShxVZ0dei3lRVc66piGhWH3gqAwrDiX0gooks0tbzqmofPnwA+4+fvqjg8XJZrJsghHvvL8/XtqwRcm66PjytJ2lN//jPf/n88eOHH363LotYC6+iTVVTM03ZIo+c3zqhMabkZTXcE6LW1Kx0LbMqNttaFFkMBhGG62RsCooEDjHxaakPkDItZfnMSimhkHHtUID37m1yXtuxiziDrWxWO+LOt2GuiKSPn7mDhDpv0sFp7+d5mmEdg5GKAsLAlB+YxdqoG80byGngb6Zg1M6pwL6Gwle12vRYGS9InmfWO9GPOcfaQhAeOamZEU0snwayQTrCxGbFX8UfDlc1NQ3ChRR5ev8BgY9/+hPXp+/+6l9dP/65f/4pxsgSA3YvhAWJACh9uEF0abkNB4Yz6H1tqwez2JcQBOgUEwjCGcIghg/PkcUQBm1RXRqAZmtbzvvg1+vtZd967yGaFsQjE3UwyfORhMAsvss7mnDUVNvS9v0GkVJeiiADagRM7VcxnYqZhbCxmhktq6aWhZQR2taT2qLLItokw7+owK7oI2sYzWyVtpiczIRBUZP1QS8fcPmWp/eh5x22QbvCtSX548iByzMVAXjkPgWBggVZmMfSbZNq8yIRLC1icUKCraamlGXSA9Ln3p1BJya9PU3hhMS8/3X+XOq06IHsRQQaszYSE4ZPJPa2uuIeS+id5ZbDsvNtv9U8kzMAKD6p3kvqYqbJnuwWyisc5pfzLohsf3tzm3dKZ0biqPxJftSb2GXGGfOOikQmmFWjM+pJkSAvLDiHMwjeBhDHqkxDhizVLb8CBGZa9cgYJAUn4mlqU61WhKojZ/OGR+aME7hGAhW11UZwvZzo3INfX145/NxM+gUcY7hqW5eTCc4K9JtsvHz48Pzzx359Pp+W8+XB2upR2TGIQEzEQAIuqRE+0gjI5PZSbb5IMAHUmg9XM4aLSDizSTUzo9NmpwqLVIvtG/PKwub1pO6g/g6/ZZJvFX/dIUVC+QNR3PfFtPXJ4ZX1vifDcCTIDmRQ26++WXKlSTYJcIzWq9kvTOClpvWMc8eJHp4LEz/VD8tAV2Sjkho+mEeSABFVvlcXmhECKTn1iEg5Wke4uxB969vrtS1LH7uqhDS4C8WsALhAUiIij3kijwCTNQro+vB0+ea71y+fzt/9cHm8fDF9/fSjMjuiQiBqNoa3JktbanpdeFsWOtrS9n0sqt27qdGHmoU7BBShU1uDiPvI6vWICA9rqQsHNVUza6u25cvr7dPL6633QKlJS16AuIjl+ZvwGJkFQE3yIBhtWVdiRFNTYTEkqpr5cxOFWZbJBCD0jFeQxhaEKCGEarMRAYi0RbSpraJNtLGQjZbEu2r2/Ko2PT3oSHOnEQtOj1jf8fTkdt516aGd2sk+wkWc9HAwH0o+Tjn6YWSiTtSQGTBbB+eWjkoYS4CqKmQr8FA77sgxlpU/zNsBnN+kqqY1v9tZzEVOIqjMbn1mDbYXyL0ZoUDQAXU5rffdcNcrys3Mk3F8KuV+HdMfFDs0sVJayTopGbBRcKhklOlPmHTcxgH/357w6QuOoTr5ZsHDP1Z4NO1Jwf9ZsCI+j2jyZFO54O7/5FBYmj43FyJlbDN0iMO41W1TRYd7ar8E6JlumS0VqZUcrFFDWhFaUkfWg4vp5vHl+hLjJt1v2/jp54+3ly8n5dPD4+ksGvFuXeP6y/XT5/e/fR9fIf22oOPy/rSuQnXvnKVNagYoEImKfEREP6YvZzhS5F6FJ1oRAZAQD0IziyCgNQEmp+ZyAFIx0qTEp53nsQ3KYlZAVshfjvIe4M0WmhurokncewHk+J3KXR3e5cA9RddhEpbVE6NvIshCXgILuqoCJf+ZUNysyRRbnKM8xNM/A5j6M0f0qYetPxJ8R5YBhXNqYVJqKfniqg5CTtRR0Rg5uqXftusYQxUukNZCg3SlgiLDs+wgZxdOOwEiVTENhK1LRHz4zffLur5+/hliy7tX653Xr9v15XxqngZZbXi0BlV170JjSwpW1nWNEdBZ7xTRWup/BES8d1sWs9Z9B0TFIlPjQkZYa2ba1pWq131/ud6uey+WS0SE2QmR2u41fH4i+AMdSs4yFmlmptKWU3QPhDJfq5DshU6jlnUKNc3etOU0SFGFSEBMLQl5tUWXVTMBIBppFojh2a9jAYU0WU4S50pUi4pe9PQe62O087ClwzbiFtycXQo1MBwUwPM2al9GkAVYKaR75t8OXDNryyk5IQU1h6hh/sIkQCrTehjSu92bKGCejPs/mJz+REwyGQ+5b8+D1WHh1pStqB/OQ3tEDYclnkf4bewteczu3xY5YiIc0Hn+hcU2lEc4/FqNKKsyyjeXP5HcXMD0ZPVJ81ZkHkKZriSZ53yTmEcyDX2+9qgwOVglmR96QP/5EMgU+wAhzCASAL1Q9v0SxdQBqgTLq6mZs7LtWQiSiCYCKdAv0BVNoMNjXRpVv2771m/q1w+XB9HldXeJzsUVHpvZulyvbuP13cVW/yyvrq/bh8tJP7wXYOx70GGttRXBLP9kwEwRebALnGQB6xTvATO11oyEmmbRCkqnDFlll8SMaNKGEqAkLrvbaf56w8i0hjJBfG0Dmfu/CrTeTAPDERQk0X0ECHdfwbnDy8+j7uAIAt9AlqITa3NJRQsy7TWrK0pqSpcc6WjJ7PeEcsn4ERBqQniZSGW+6h7yvMEjMvlGks6ICFV6kf7h4Lbflrb0fR/73pohZrojCj1KhDNAqpiHC+zYr+kMIiKre21pEDzoh3Y+ff3556cfpK2n11/+dCNHXJ18uDz03m/b5u7aNKgkfUQzdacYdFGSWvKfaT2VER707lADsbR127pp85I+hYilxbbltA//9Pz1y+urj6wbrkZSAZWEzIBSAJY/EKFIkk6Z4hgqKoh1sW1kWewQQQQMbQrASQV9hRTMrEWjR4dqBNratC0SbkvTdrJ2UlvVFqhiThxNmAAYICIm7aRkqPnoS1vVHuT0xPbgsg7qRt6G34LDkSlvp8/nkl3uuUM4TdGxmWPKjCPtzpSevOescoM0omrT0zCSKH72oN6T8SiC4jCxExEIZhUp7qnbMm9ybMs3dvlIa5Y9zZi0phnfqzPfeAMctaJ1mmJ2FrDi20Ngbf66TE7sKPmXwnl50shZC3JAp8wk6ORMZ1zCGQ0cd1KGOt9SpxBFWlsQcXiQdI0qqUIjWdSOA3HyIIrukdNkww54mXciM5aR+2IWFFOp3oX5mDN47UQAAHl4SURBVBMNhECkZg0ie1QiJRSg1hRKynA2043i+zj5Hu5thH1oA0ahLeu6Sox9375ev+62jO//8KHFZ7528XfNm67fd4Z7d0b3WC6tWRvwDNXNGo62FBEVuIdaK1+bPS+ZMBCt2sDanRVehcR9V00+7YAUtZvurKxgsm61ueYzOzasYK7wDAZnxIAaGip3gFh7YgYHBQdFEBPtT6+CSdEUkNEZciAy0JoUVs3NKgE4ZqfTwU2RWaw/dwMPpyJHMFq0U8GOmP3mgCC1rwRv/mScUBYbHL2rSdAZeP789f2H96+v137bzw+nCJfQVOrLSIyQ0OT1lDmZXGpFFRISaqLUoNdJMX14elIx7B/Wy/v18vT1/HD7+Y8cL5uHmrXWwGhiyaUQ7lOgW7PT22ePSAJV1YTKqTo2ugMywhNe+Cjuy5aVgpfr7cvr9bp1JlIgyBC1SaXOZckPAt706XFOwyLFCW/GXXzKaGZeitOk5dzFYpBFhZJD0CjI7ku15UTfVBddz3o627KKtvy43Hp34aqkiQgRBEBdop1pZy6PrithO7ExtuHdfTiyKS/VXHWmUFM9gklNkZglA4zJZE7oIjMp9OZIAIw2ScsDNaMAOu9IFffCnsMUlpniPEITdSmnlZ8OqmBLnYQDxh+fyIMkLaANHAeaQJYtFbdyvJjzyE1LOu+xLMLMXnA6DoJKVG215OMtEeHD+KLOFvUAhHqY3Xv8wcn01joLa1D9NNvlH2YkVV4JgAZmxvoAbgAwWbI0dVV6wVlXO7HLEYCkazvWSOod0q7EfU0kEKzNLRSNHCsSiGwQMHUhh29j38eugg+P7+kKg2oD7Ha7qe/n/jrG5/N3J9mF20eN7uOm9k79ab9+eR1f2B5PH75FJDejzmhVvslc5Er5WBOVGAMQrSlilNbCqZZNamGZBiRB0IOzITPhP6eRz5M0x7wctvFg4mpLcHpizvcs/5ELWwhhulyZW/hOIFVf3q/29rED6+3fmtyjZJms0d9FVqmCQLgHc7QBchLvcdhYbTDCiBx5VmB1bnGZfQz56KcXLO9SoRMSxLvAIvXwATD7v6X3Tg/f/fV627Z9ed2ev345r6fRXepBOCgSri09UUDENSQk1KuoT2suKSnOlH729A8wbQ8XWZfT49Pp6XF5ev98fvz60z++/vKXs2lbVkOIyKnpPnqMziakNzNUhzzdk40pBKAqZs2DELg7gwE2a9bW4ZupirWcLvC8bddtJ6FmKlZDijIuK5EEUVKgQuTooTymqsIqMkx9qRBpIgJ4dYLlmStkqzl1QFQpEkEzVY2ULaIIVKU1ba622LJqSwrIWAnYaeYymFMFTLJgm6CPaGfRc7RzxzJCXiNe3a9b370DIEOkBUKy/ZMis/aHB4IQHJzEDAQnepAJinAEyRRkH8CscwRYthvHIWBFr8dqzCN1lEVMVJvrfaD+Wd82b54Hbpp/mwVwVaufEfoRk6eZTRJbNeNlOe6hQGFMiysHDCyXE7P8sI5HAuN8BKqHQZjgu4wmcHjJGQEgwKNL6x7QHGf/uKBco6Q37n2EcjczeefzpObC6pufFpQjZBKGFUJEyTlNU5EhUJrBNyYAUl8m6ElOOcVlZ0QllqmYkIGU06H7EIeEdgVsoQgNQX9+3R51fFjt5PJubZeFsn1mfw6KsFEt+Am2KVc8nZoqstVleFtMpDEV6wBpppKFjZ7sTYXTdY6U4mR4RM4eZ1CopKtYQjYCovTwiEipREZWNhyYQGYAcGw/pGWtA1en4uDlyojPp5ZelYefLZB/+OQai1Q77njWInrHNPMhlvWfJ4hk/Vr+OCKvMztaWRFbkKFqOHCSZDj8JiQ5gFYdclTCOGmgbF3MbQ5oZMGihHuEu8dtf02n9/r8+tPHn989vLvebn3bp0AbpAvYzCxlD4gQajFvdg9x5AisMyOaeXlCzAjRZiFC8OH73y4P754+fH/57oeP//if+6efbs+/XNaFPnJR29KcMTOxcM+0UAjV6aoqRDBUGzPih2XjmjQhsLSm1pqtsp5vgdfeB0XUIsRMrDD7bNKuw6aakWemVYJi6bZMIKLq4WaNoiKcg0+QTQMZCHp4U5kMNwZpgLZmotAGW8Qy8Ru21hAYbSdRIyWTcEcWnZQgRBdVYXQ6Q0JspZ5ClhGygTf33XePUfyMu1hOPRDJrT5lbotLpYBQVAVQnYQ33DpmCckbY8dWucUZB/ANvj0sdXqAX8nYH5HvG9MFSA3OOIiR7Hw4to7M8DkTKtXXmGEyeFxjpdoBOeqs07Ye7iSP2dHim1JTAiCKZ+PsQhAGofc54BUosBBj4vSjdTamx+eRNHnzkdOH3iu777kDnXrwco9UZiX5mwJTIq92pkmQXjsyxLpzvPXbNSRpbuP5/Vrs8iqHL6PE9MxRJ2rCY8kaL6n+Ea1KS5YCs3jIMH0ZcXm4DME2+hJsbaW/PqzytF6aXlWGq6ouoobesT2jUdkRj7Fv9nAuqKCmOc2mDyEgqk3HtkMUMmuiiQyfUVYxGNRmBFU0MOSo1lWle4U6xRtNPVFSFNn6SBTxLZouVPm2AFdmMqB+CmQuOvW0KuQ8wtgjrCrIkSTRvIb59FlVDEmDHkAoARPnThbAw9WsbOgdplSoOEmy5IKKJEqG4u14g4wG8lwkX1Y+Zu4QzMATk5Mt9e3w55fnED6cLq/X7S8//eXl+vr+8f3Xr18/f/zF2rKuhk6zNgkqd3e17OFKeiQbCd5YlTqWMFVmU5pwjBATMe3DtZk9PK2PT+dvvn349jdf//T3v/zD/7F//XlRkZxM7z0iNCO+EguBqmRrW6Ib9vpUBpg1b/k0I7QpVLPI8vnrly9fn/c9FRwyty41DjZhaO5KUUlzL/P0UAQqWYAjCnprbRsuU/JTzNRMVEUbOar7RBCAE6eWCj8mrakuIk3aCltg0HbW9UGWE6xRcnJLzgoO1mBqJQS60IzasBgiXJSyhuhO3nzcttseIydeaMVFjtyrs1KzTGiBPoWAHnNDayLEMtCVA+N9KxOkNzkMiZTli1wdreD6bYPSRDozXpYZL89A3FNePS01p2cQOTi1wzgee/qIUSZnlCdkAnhJN8CKEMp7ZQGlTKNYRvjN+xRCSg2APLFpzmPeCQEiJtEikwHApJom5zShv0IDOIxuHDI1ZcNSFKTSFZot0JPhZ/3K4VpnPec9iCrbrvOA1+Iz5prJkZFkFZYet1s+CXovSJo+NR9fZkA05JiCEgKYZGONhg+B7uBr8NphDI0Q2XbBRV4v36xNhy4SprQV0th79Nj2l65PLieHiFweHr6FmEhU7wizExXLegpPMfpZ6axCD7WmIh4lUZCP2qx577WMqNZJmfFcAiidNE4GM3mjcgRMx56aZl20phdNFKRz/73ZzlX4d4CQmTKqLVEZMb4JL95s8BkwcJY81Jto7bCCnFFZbgDHrLpC+CH3QR7T+vMtvEKFu0z9g/oZWYnsPGZpJRk5tBaDRMR2216v13VduvXPnz9+eX7OWd8///zzl8+fv//N77x3bQ3JTYmIIMLdBwUUSQXoNClmNgFn6fWCypwBNgJQpVBorYUgGA610/mbv/1XD9/+8P43f/vjP/zHlz//n/35K1oosKhlxiNFB6wS/qJyhD4gOaKcWa0MlBHWTmrN2tqHb1u/3vbUvApGJyy7q9O0pa6sikpVm6V11+rhyZSAAqrSqLT8skJGEdG2nsyMlKxCOMxgEM1axrLSFlgTbTUO53SRtoougGWuoG50HlcSEA0xtjPD0VRID0DXgeb0PcbuOXImRIWM0oEBtMx9GqdIkKBHnFNbkISj/GfFxqzTdIhuAmA78otH/UruZ+LARImn5TgxdSSOPVrdxXIY7Hp4YLZ7Q3EkqDjN55tc2VwViMx6u3tWYe443vtEMFlQpuB7XgwKy8/RNrMM6jixrJs5mNyJwQ9cQ852usJu5WtkOsKJxOPAb2+s+B2T1QPG5K2nExC5l3gegJ6HuUveBgdTgcMETDCYBm0e/rQ506egVqSClgKuhQaI+2/Jr0YhRuEks51xIz5fX2++fX/SZenj1k9PulgoB1tjC6HGGNFv/baPG4Ypzzpu10Ynh8oqtuQuYEBVc5qGLS0DPoQDQhWFackfiaSfNtNZ3sMyNyUl7SUb2SL8SP7kzd2dPe4xWbq66danjZ7OXOV4xjM7KGWzj6Rr4O265p6X+2Pi/5BhKB9weGUciZxMcBVHP4t/1LRyQUxGqFJNFUHfN+h9u+SVR0xwgmkAmFFE5NisQ3kq52vebq+fP31SwX67je4///LL7fr6zTffvV5vf/npz0qO0W21CnEpABik5TuG5kAFlnApS6S9BhVw3qGqeAAQj1A1iIzIzlh1qqqeP3x/efrm8Te//9N//vbn//P/O57/sjRrAAapKoxyyhEgVRuDYirGEYzZvzzcrVnEaG0RqMBsPe99PL9cr7fNSdPceDlOUubGSINfFkpFq0VCj8q69Ldi2kbfRcSskeIxzMzaotZsWekDFHBIFlgwBYiaahtOiFFM2gptCth6FltlWQ95hYJh8+GKKMSoFmI0jaZBxmCIdeIWsY0+YgQcAlZFf+rJH1msNNnUNybECCcrjV6WJHE9ZGah6VPPjgDZZvQ7W5Q5w9kDW8qRc5o4n/N27qX3dUXTcFd37vztN0XYca9uSPBykCHIFuY825FnNYEesidw1OjXKnGbgUXBqAMpE1N9esbUCMDyriRmhMwqqC/nzLzmshdZMlOXyrIbBfr4JliJ6W/fWJRpwnmEXUngT8+Zj4/HatbL5fAfR+q99CblGJ4G3O0B64EWHc1yesVKcD4tJgjNqx0p3e+qClVRcZAi1lp6i72Pl5dN1K/o7xdtTc/nFXGDqZOgKUb43vdb77vIaTGtgv3UbTMbvZsacwRY3gu1LbaNbJEPmJqoNiPp0ScYEoWEH9kyqkhrFjEd3UHIV9czmXoy85Br7dh6BDP2rY1dJV8111eQO3K65nykMjdhXQ5mfDU39bT+94dZxOOMn2dRZh3x3PRaDTsHc5ktSUoyIugBS1KpytQnyhIQ2dMXlOm3mARvcU4zPkhAULyle+o3xPB967/88ssY3Z1i9vz85aefflzXy9LOv3z6+PXLF1O9vr6aXECu51OEIyCqMdyhBkGz6YMyBas5bG6ehKTKdGbLQCKcqlTVCORAim2E62iqy7ff/+3//P9aH7798T//v8dPfxTZBRAPUYhquOfWjayZC69BJdmzhqq4g0JNxNSWNUQ3933kkGMwNf61ouOcN6agClQhmczIhvn8HypKyxUPespSQVVSMU4VgKpZWxww0b7niDRRbZXLt9YMIg22iJpqQ2vL6aEtZ9VGKLKtrNqIk0XKHdtEjdlnsC7wcI5B7IzBCOJIhs/EIpD8E6F8gxFZErD5e5p8GRMcCr2wCnU2+cbc7gAYrRJHmSM5OPU6WpM6PcoS5ACUB6+Coj1qK6YbICoWSUMXdxNXnTaz/EaOQX24l7LGhG6BuQeKIp8Y6nA8MgnwuoTAFA/AsUHvx2RS5GV5axmlzOjMRpDHXSXYLGKs+iFx+PL5yTz+g3ktZVWmt5QyAhUoYD7N46tJ8ExO4PhW+a/psCsAOwDbpLMmKHhDsR362bWYxZt50yVf5aS766pU5GQX3108Goe01ZqdL2trEDE9icROJ8Jjj30fGYuKhvqu0hHDWiOR4y/ENLvpQ2JdLLVW6KO0PdsCCsPTl+fIcndPGYI8cjJNnarGGGQql6F4JRwVBAfsOGx5OdZyx5KzReTNExPBXY6KR2asti0wH2W+uR4GPv/VuyWv4tE7Jnh7AQKwpGaYvEo9RktcEzUFwQDKvfZJ571EKvmgnmlETMTB2pYqifajTlpK+DhMlNi3/tMvv3z89Pn9+w/X6ytl/OOf/qyij4/vt2v/+MvPfbiutu3jfA5V6X2cloUMlYbKJBCTj6oIqYl7IAmszIorhOJVnDqzZvV8JBwhLiKj1sfWx/c//Mv/2fv+8+79yz8r+7nlQFCYWLWsZJcEJ888TVvC9tz02poty2Bc937dt72HqFFB6Bs4RZmuGhVlCVOgOOMyrR1CAKKUENXwMElVn1bBAVTM4C5Wo98pmoSP6SJiKko1XRaIwUxtlbYmKQTVKegqhB5yTKg8bguk9B4o9MHd+x7eYzidc45LTfHBHWKkMpoUuGZ9lYY8g0RRJRgihXYTn1TOD2+qfloO8j7eJ60ghSESESIyE08yjUru/pBDB42l6pNXA0wELnVCcIBXClIQdJrGqrMtVJxXMk/e7PjwGp1xnAVOGMU75V2+MFelsnbTJLDqd6exPhrkeOfV33q9+Q51iVkhniJCTBW5SX9hMjQ5xwc8fjX/WtHmva6jVn/6naoNL8xeNEUCzmLFKm6RN1c5L/nwA8AbjkKOdtEZjUx/T4hgtcVEIrxHUGi6ANr3Do+FMka87tu7x+Xy+GG94HQWlZ0SgoHYSfdtv123QQthjfDjxjipgOGa2LYmRgjDW2sillNS3QFTRbO2hnuqkWUfQwBiGj7ytkSEOdBKxMdISXpTVVVGf7MA0+ROLmjuhYn2KzbK7s3aVCJyKOtNKyMVFOMex1YQdY/hcPcqMh31jJinjzhqZPTYIDFD8YgkaizpWCeDbrpA9Ig3OdPV83zOURwBsYNHIln64VKhReAO0QDEGHh5vv7zn/8YY7j768tLh3/65eMPv/vdsi6ffvn45cvz0tZ979fb9enp7DEhwqS0SJhpuGuqFwhMl+wpGRGNloRxeAjCWYpF0/xLWZWaoAJQYNrHoDU9n7/7u3+/3/bP/+fA6yfHLkFtVtu7xA0lnUEE4z7VKGsikAMXxZY9+Lrv170HEAGzSiUVQpiZAC3xWKkq3UodoX6ceWBVSYunysL/BlFrS2uLaQsNkKbN3TXPmmWhgUqGMNqSSs1pX1SjWDkxUimOLMxlZJ15M4gAJmjQrFFUh3SPEZF9XIJspIkql6lQm0EeAsPByG7vSg1ntjwV4Hgvx/cZRGfH8UFltIgMMRGD5StESnwKIAJV4zANam00Bu5OaB4YsAipPESlV5H16VpIpij6TG2naHURmeC9rrMigbSj6QOmT8nvhVQlQvoOOYBzIeG5PrOmaQbOITMfqHVjlIO5KjAl00bzjb0tVn0ibEx7U+9eDzp/UadpKHh4XGbd2d2dHUsqUrrZch/1d/dD9RT59oMn7JzM8+FqA/ewhwkbMzBqkIY3DcNqorrdOmMII7bhzst5vTw96NLW0wqMfe88n+kDjfttj1v0rsOlNZP1TCj6ALp7Dw+rq6ta8Sy5E9AHi1lh2LKKKNQxwszK7kEYnuWSOdISoFpz773v7uN0vujcaoeXLDgyfVtZ7rq55Glm1uswqzMtktYeM28wH/kd+M9nf7B6h/8+fuVO+ad9eeu0J0oXyRYdL/dhYgSCI8sTUtSWdXyYItIESRdoGkHIxDOJ8JKDlpRHzaOUT9ndAwRVb9v1x5/+/OmXn3/44a9eXq4vz8+vY1OKteXl5eX6+jr2rot533vvHsGI4W4qosqoFqcgc6gDBjQF/Y79eleeEdaXTP3XlLZO661qQUhIIOgCscVak7Z88/0P/+Lf+/Xr8z/+H75/XkxEhCOOIVwgBt3D3b0HIbQqeQ1Rqsl6OtnS9tfbbeuD3HfXVSXYFIIahpnl/vWMEptojcXRmSZO2RLVxgxORSF2WlYfMXqIqYqZNVVTaz6GmIiXrHi4U0RKI0JSCBTWtJ2gDWopEFSOvc5jgVSmeHvOiRQTSLC7YPfRY6TckhKMLCANyewuMyVS+yX3slYpS5qvOEShmWV+CQ3fQqZKJCBAiaSAZu9ciDAbE6RAzT22ral7c3pcStmk2Tl4ljLdLGbtiBzn7j3MFUmoxGGz7z5mHu8ikOubkiHcXEQIklU7bCrIMuh32yD3MhHMSL5S1fOo6syZT3/GOdhA6lShzH4EdTbZ88gGR8G+MsdH4ELcv0prVdzSWwQ53Vb91tu6j4OWYMw0L4FK6Oj99W/SnJxRwyQy5o2BKuruKV7oEsM5MFSUwL73cJoq3CFxuizvv3laHk/d+PH2iv0FZxob0C483/x5bGpYl7XZ2lJvbhAeHn0vw+dEMwJqLfEJoMGhJqQB0toS1cKaCJfWmkE3dwisNYZHeGtr3v0Yo8oC1QR0pBhEDeLDjKKAzBvx4GpQ4msiWVSOgLwtO5ssaO1U4cE8lklVFKJN1ntCcnKGGZVPwFEpV/ZxFoCJqKQGIrL795gEEOGBMFvzEzhhRyoTRPkGjb1XQ5kaAEiMcICixkL+HOEFuTIBYDa6vzy//NM//7fFlibtn3784wjfe3//4f0Y/vz89fr6qq1RoIKeTUaQI9mKeael+5agOKImj+SAgOzPIFWmJE1yHKlVlytGrTRNVtaLArJHBPxhWd/97vf767/r1xf/ZcT2klU4QU9RNUKDiODu4UEYTVUtxe9ElmbrArO9j9fX1zG4LDYiKOJwFRohYkyjnj5AUqLZMvErkDvRD9XWSMEIUQWkrYtunWqSjJyItRaZHUdDc1UNiqhKWnmRIBdLCSWVZRFrolXxfG+j4gxUSarBTLXVrGDCBU704aN3Hx6jM5wRymB4BZLZ5Zk100cXYUTBjTnvLSkHuaPGbF5gTClYzFdIRIuKCNK5SOmHVtdCxQNkGv5KnJX3mXmgO+1cGyHbHiGkKO+sutRZmjhp5kgLKxVuPwAVmT1vdzhcKLs6nFI3hHerOZtvkFuu4L/MGF0mvZs+qjTXAveUwbQc824PkkdQc9+YXohH1iP96XyxTIyJqLrDdPU4wpMcS/KWPSofUeZ7FqIcUHIyFGXSRQr21odWgHFEJdNt1u/MNwupshN2BhFCOIiROeZwH8ZYV3t8OK3rsjs/79ttfw7ZNdQdnfHdhWOspu80INJiYPc+xqBYp1oVuEXm2OgBg4jAJMbQmqVESNKpI4soPFybichIbWSKqPqoMo+IwTFEYEsTKER97LmfMZkbFNsTFXgSUnXW+XZ59GYtAb0ofOarcqvLMUfxeOQyt309bH2DYGYggrfPai517YKyolqnnZG0ar7avSNHgNcg6Lklc2RT5CcagxHu4a2tIhqM0UfSAQKrKt9gdaGHx3BRDeD2+vrPf/7T6/P1r/7q7768fH1+/fLh/YfenZC999frtrtDkKH/Pvrtdj2fzpO3EfIoeJhQi0Fp7q5ZNI+ZSIdkzEIoKfDU2Zk3RYBxSJEnbFHRIDf6erq8+93fPH/8y+fnn9B3gQNDTb0PShAYgVsfu7uqSoAtIZkxh88t6xb+vN2eb7fb9QprJlnlAAVN0kFCRMysyjnT+Isk2MsJjRJozdRaUFQd0CCWdW3LMOtMFVIIRJstrowYzVYgrAoS0lsKFLq06lxfVjGtHJUedXZztwIQhSqQxFEeDAbR3bv3fdtI+thjuIARLqnhwoLo077UViwc7AQpQkRIlVtQKR4hSRZGCZBEAIecLtnyCspaVKw5iw4n/4A7IY67pu2Mi+urO4PNWYRDSZRYYGyO6JsANmv0Ffe1eRuGy+ymlfnJmJFM/T0OhqV4K06jkMmDnKrBGeWzinYq6MYbCFf5uGkCeA/9i4OpGtJyB0KpNjy54815lotdKPNQj35GbIcDKvNeDuxu0Q80Nw1KHfNyLuD0OAf/IDNgOIKt+m8JfGXlXpBaAwWDUYiybpgKaSrvL4+PD+cx/LZf+fr81HB6XFcucNme99fbWMGzLY+qdA4f221AZT0tQ5qSqXbJRF6W7TYl/BmSlbOqKnSS0j1ERMXMFAyIi7acktiW1Zr1rTe1zlQLWlpbhndLSmHqQlRor8oA6DKTH0nBI6Z0hEoWG6RhiwhWerYYDKkItKwwpyb48bjeRKJRNhGHx31DCsoMP6ZPn+Fq7W4ISPdwSR0gaCX4q+8s3ZKrqhLdR8b6qhpZ5B4RWU4iIlFRfETAaW3ZfCyyjOE/f/745z//04dvPqxr+69//8+n9bycTv7peQDwEU5V2/qeZ7Xv27bve9+X1YAWHpCoSvPUT0ssRQZDaE0tguFOQpuVBkrGVkV8hmQF0ey+yFKOMYaaBami+4A1Xd9/981f/ev+6Y+OsP5S+gZoY4AOOG4bX7d+Oi+nxTywtkZFO53b+tDOD1v33bkHBwRjWGsAhPQIT8VDupXOoAFR+7HOKrM/VESQ+V5KM0eWZy6LtUaw+pEV2sw9qxLhwGJm2kQ0glpNDxBbEaAsYou2FaJpBF04+7WZTIOqEgox0QZRihHRGXtEH8mmOmKHO4jwoQBSaCWpt0y1Vo0nK+/NFH8tWuCYYKQQellVVpJAGLOo2b3d20UVUnMbWDu4DOTkE1AWkdPiT8MHHD202bRVxlK8mM1C31l7dydjc88IJFAz6vLEAm/I9pI6k4m8Eq8fe2sG6On6BDIj1jdhQP2bHSW1iG8st8hkcLLdqIKQ6ffS0JKzRgTAnLRwd45v/9wtwoGE5IhwSm3oYCIKwVeUUGuY4dabdyxkeohZ3GOOooyPBfrvPhzpmAXZgzD9dzjzQNBVYJDH82Uxe3m99dt17C9NVNd1a+9/Ef1yfT7t+4nX3z7ad6fmq2n4GE5tJuaQIKPvCE/yrhopRZEDZQCIMiKnKIWPjC5FYGpq6t4JEdVwJAuUVtu9D3fTZta0mSCQE/tIHGLXk3oXyDT4UEvKkPWFTHImpR+BGvKXPnTa88iw4E2nzAzzZtVjZfRxjzTrIeSoBk4XIJhCQIlCEmt7hFpOvEqMrZm+g8jUptHs30ZIH33f9lDocYmMMYaoSGukQyw56CRmxnZrau7x+fPnH//yFzi+/e6Hz798pPvy/vz6euvRxZaX16sznEjFR+rSRx9jjDFY218QEwUVmzML6oJtbe6j6iBcZqQwCwtpUhA70T81swJ21K9jhIvStO2M02l5+s0Pt9/9zefrF7xstsjYdigdsft+ve29x6BgH4sZQ4JUacvlUZazy/q6v4at0k7LBf16AxrohJvaDNYYHjAiS0IJSRWYgAhMVZDlPk1lAaBGa8vwgNiyrq01eNfSDjUxscbwDgasobUAIOqkaBNB/pdQ2EKzqK0hB3b8lXWZpUfJRIVzjNj3vffN+44xwOE+hMHhkZ14YHYC48DkJHNQW1qSxPioLFjWFrOkiCdLdOBDgCNAtmTrZ81XaPa6pUxSxTeFoA8sQGbd2vwjd9+Qd1gUTmlnFxAGi0jVjMdBFI9PADJVy/KXdTJFmW6YhPzdE2V0neq1VToXyeyVrZmcRBFhmEXeeSVxkD6qgcGysQLBLCGSWWpbW7pMcoCA361sme1C3ZMyxsTrlWwRKDRwlLfhyEXU+s0cTxntY0kLtk/qmZkeIQ+sCsZMxOHuqyuvz1kpj9n6MQvm88fRVODR2gLB677t19fttlHi8fEc53efROQ2xsvr4v2DUU8tmu6x9nGVsJOo7f2ki50WAuG7EFnHJaZBaKmeiCgNqqbhwXDJUR6qyGaxUCpJStC0AeI+AOy9Q0SybFREIH2UTKMI51yD8phQES/LZqoRzCnkJTqX0nGVuckQTt5Y9Kg9AkALwsjxqEq5ccZLOOLMI/Ir714VL1V3qsxIPUgiwlUzixkEW9aqR+TEUNEsEJxqwR59u0VwBE9tFUBU+ujp51QtuiePGPQRI/2YR+zb/uPPf/nLT3/67sNvR+9//vlPp/W8nta/fP6xpnSqDgGEapozJIKxbZsPH2Ms1pjFNFmtnRkuk1m7KL33tqyUqBLJFCCSjKwFlo1jafwhohGjWXOGSiNL9EvVoOoeQ7E8ffjwV//29vXzrV9j3Giiy4NE31/66z6ebwMSRnOPrlhWWx8u7fKOy9NYnjaNaI+P3/5e162ftrFtiO7jCvbwMUBdIJ3WcoBvjaBTZIVkVEimiIjzug7PRoEGuJnZYstiI3aIoC1izYAxqMtKAs1CS/8ncglUADE1s5auM2KyKUygOo88wAgVZjNgelgKRoy9b/129f0qHpLCz3SGK+ZwexKkHsMleQz4okAQLqD75Hsq/WyYFOeUb5p6uwyQ7VSq3EWmVM+ZZgGwYvL+hWzmCeKkjdIx5VmIeSbyR3rwXpBqQShTWbUDkBmVHGg7W3sxr7BI8KhyGZmOtOimPI+H16j3RRrNwnjpALO9WOellSU44oN5zSwPUW3P916HmR3MMGHe910NINE3jotK3DbNcZJO+d3pwWK6DxyveuNUjkmIfPs7IaLZow/ApbI9mFkmmVJTqJucPh9C5hCJ8v3zpgjCzATYO3zvo0fIoqoDp2tIf+3b89fY92/OS7A9tqch+Ofnse1cxN5bnLp+304fnh4cWfGOagCQKpen0yPMbC41K4wT1WrVh5pmSaiotaZ9DFUdu4NQ09aamkUM+jBV95hVtrinzZOyh4iwWePM+yTLlJNGijSfZaAJ25HO4MiZvEkDVAw4fekdDLAgwqTiIPNNWfUCM1nF42mTEdDU2A9RySyumSVQy6g0wpNV6D5GHzCjB5d0VBzDg2zW8mozNzu2XSDX6/Xh8m7r48effvr546e1ndty/vPPP269f/vDb758+urhoPaxe6r8p2JDMIIjvA8Pph7dVFiVwr0oDYbCnyTCh5ih/FoiRtEcB52URBKWqhEhaszhs5QyOyQ9RE1MaIDYww9//W2//qTi16+L2vnhW3u+ff2H//zpx//49XU7XRoRGONhWbme5fSoj9/K6cMrlq4Xu+BsT+vFX7++jG0b28v2gr57BjKuYYtGJN/BTAebqgBOSZkEOnRpSfpswdAGUlozb7asfduygjw7fklDJjwQoYu1FW2ltiBNmsBEGmCQNrvNcKC6bAkuGGwCVYJW9Lv4cB9j7JvvO8fuPoTOSOEuj7KD0+zFTFLhziIkypjFPFk6TEKZ+iCiLGaBxUikQwlvylAT8RSSTAQkBGJmFmcR9FHJxDeA+jB6xymp276bNUALcMVhb2d3wKRfDzeT4UVF9lFSOJM7ArJHkwdHldeo0xukMQhHFeJIMdGaI55n2uKAWjPSTXav0q3z7cuKkKhSK5TjkON46x0mTjsNTGCJw8SCoMyyWsgsQpLD7RzdbNOJTPdUYcfUk5GiZo++gsP7TSIjr+0O++97pP7C6SpBRASyTBPB8DHc2ayZmPXgp6+vhjF6v7R1tMve7E+9nQLXr2Pb0ODvlH91frrEw4XagBi7WoUXMhcYgqUtJeCTWU1GM1GxRL7Mmh4zGdEasulPm1BCTBUwW/DGcyYVTlIbNIN9ECVCICJNTJnactX3X9HZtMuCbGblMbKURAiQjEYqzR0B4d2Vy5urODrpj3/l2PlVokDOhvNsAUgYmOGQtcyFBitxFUFnVWGOvW/bLSJUU3C4CeDhe+8QqDVCRozkP2/bnuMOPXy/XX/8+edffvn57/7m727b9pe//PjNhw+i8vr6EoQ13XtPdq5skEhjC0Tvhw/IcWwa4Utb8hZy06kqSDUNRpNWHLogECrKCJEGkB6cFHGq70AEI7x5ch151FiDM2KIrqfL01//q1gettcvpq2dHi+h2+Pvlhfu//k/qbCP0a9tfXzi42/k3W/tm792rruPaNRzO6+hKrZcbq+vY3tQbe3Wxu2TYjBYxaxBXZupiMQstKIITQwqogY1aefmYu3WhweltdN6uuz7pm1RbVDTtpo0BQ0SEdKatFXbWVTpTpiTAhNtgBGqMFJAlRzOauRkRZABibU5wkGC0ffbdn3ZtxeNTh9TxY2IsJwlU3uNWuRtmZXa20ihDgQjC8lnXY0AkHvB9LQoVcEZbdFsI6BFkUYUDGbHU0LVJL95B5UxZzxN1Doh2f2aMpzApC0Oh4X5++CciFjJ68nUgoz5Fe8ovTLE5ZfSPFfs8WvwnMMS0l2Qqa42hanzBMcUhJvt/4JZ11HHes6dm4fb5/CyibjzOh33zO4b7h2HP7i7xVmKy4OCm5zMW1f59qbvr0h7VCWndQ0zi5Prxcz23FuGjz/1Kf8X1zb/4jPMcBE0hggZEtxGF47Luq7ni53P17E9P28wbtcNjkV0qL2X86etf4OHffThQ8vvku7STNLaCjSU4e59jD3r74SQpVVLXlBFQwNAjNHWdsyShVhbGtJxgHVpEQBVVhEd9AbNWzA1tZYLxcoEHGh+BqN67EKmCnzy6Ypsns8EQBpsJBWXFv/g1jC/NR8W79Hk4Rby8KlmlU6EEyAzZaXJoWdAIFF1n0Ff18sYPrbter0+nB72vRNZUicJz7VZIqSIQIjHcB/e47Se973/+PHnL58/ndtF2/rjP/5TE3t4evf68jpYtTfVG16jsY74RUdE7yMoXnA1gjqF4e7ZjsRwqhbhCWenpArFJE9VYhuIhodaslzZvkUAkT20pBWfCXePttjl8cPv/3bbrx5sWBdrt67f/vT52c1fP69qD+e1vX9s3/5WvvmOT791j+35ayyy2pmAmbX1sa1f9+2qImqK8NG/BrsMb8ua6FVFSGVQWxWwWhMxhaiLma66qqxn6ZtDVlvaslpraqpNxRZZVpOwNfpga6ImYotaU2tEF1uc0lRzCo1AKCWjRCa5nWi2ok5VAzLDrsPd3bd9u12fvd9AR/jkUyhgOBF+EOyQSQNjGojM3LI0gKfiNCBtpmoplJIQjUMOh2A0M4GHZZJguANpNWNS65nXrU+jwENQnh/TTN3LnOq0JTCXSQJXqUNahzdHJ89lElil8TBlQBPzTzL26D9IJ/XW4NchlEirf1cYE1KzmyIcEMlhipyXkowAph4tJ59DEULeiAcUvZW1zTgSZEiILnh7MSJyB5CFtzHDnPsVT8PCIyrAxO1M34OJTuuPzMR1XkUlV7LYCZTgAEWnaubcKHUhSR5XED6z8GWxgoAnQ2bm4ZnnkexcglLNIRvjOvrL64sZxuD5tIS0V8Y/vlwf18vLCKJnGp/uYiZTP6EAl8aI6H3rvp9Pl2YGGDI3GO6REbpGeFsawYxaE/yaWkR4uKgIVTByWnxmFOYDDhGBaoq0EhSFmXlPYRwG+GaATG7CI9bK0lCZbBkiJqdWz7+eaS3jfVlneDcfzaSFmNlFhAfCfTjDtOUbpNcJMvedCD3CY7RlSfr/y5ePUAt6H9vp/CCAx9j2bYSfdQFzQrqbLdvuo3cxi+Dz68unTx+vry9/+Ou/+/jLT9u+Pb1/t91eP335QkJtzvaNEJFmrVCDAfTe923fx75zWUqfIqo1yFor5ylgCsaEZ4igZqqmpZufmzRth0ltVkHW8uVZ8xAqNQhI0JZyxSNcVexyOZ1O274hxMXau/cf/upvX6FxfVmEj0+X88P5/PS4PjzF2vbrbWcbEtqy09cWWx1mp7OK6tq02f56iu2Z7MhUFNiapWMWyWGlhIi2RdcVukg7qSznh/d97B6MpnY6L+cHtQW2QhdtZ2gIRVamqmgJQrRVxQgJ6KBYRujHmZ9V26nhUZGmVf0oVCFKj9HH2Dbfb2O/qoJjFOeTfdggwgkkkQpV96kExtrFnFxCPjo/RCUlglmnC0qaMZCY9Apb1tAFsu4kFdLz6ihUoWQXGiD0KNBImIgcHad3a4Kqlyi+Cce960TspeiULSKJxPUOJ6aZq/AYhIgyZiN+Avg5JluOrHEWt+aSRFJm2Z/NgtwZwB5D2DkhHAifAXs+s3Q66epqemUyY3OhOUmww6bPRDflHosc/5XJl91JHZSpOeKDw7zMr6fVPsDXsSC4//K9DOst+zx906Ev8ca5JNVx9Bpg8muAwCNmFFSZ6lA0aB9+lX7zvfu4jiGO0+kcy3IN+uZ9218/XK7beDyvQYnhQFMTTnioYtnIPsZ+vb3Y2tqySls5axzLwaoAUJgIxhhBoKkgR3EjIkxURUbfVXUMttUAqOrwLklJqsRwVWW4moo2IIfei3uxbWoq0xxMNWaf2paHROLsF8suYtVZUjfhyFvfXUmDyrNU53qh7DyKEfTcI+6hplU7qGJqDI5s+ydA7Ht/eXmJ4Grt5fn12q8P7z4A0n3ftquqBaX7PjwCwhju8fzy/O33v933/vOPP3758vXd04fTevpvf/wHEf3Nb37zj//4x2CkhnNEDmyDSlXiSZ15HWO/3bbhgWwlKzG1nGdZ+21uqYzCHQBDp6nPkGeuWhmuPPeSfxiTQpsLSSJGWGvdPTkliKg1Jyni1tYP33zjouHNcFpsPS+tNVua07v0nRBr0poKtS1CnKX5fm62rqfzfjptzw+3Lz9v25c+fFEpUX6RQGRSikCECFXbKm3Fuhqho9v66GMLW7RhefggELETbA1baDSqOkHABKIhi+kCNAZCGl1CNQgv8bfClEk0ZO0+UdI+kpMGID6877d9u/btSu9BgJ4gD4PJN0oGkSK5kXCUVrIEgoiq92OpTDGqHDMIK05bMhjB4ZQioiE8B4u5e8bj0GwJJ2MknYtDJo5Z5ENhHZf8qDTdrOBnnpfE9VUVOs+657XM4LYI9YJT5XvEUIH8EXGXNaUUn1WcCJNCzU+7VxERkSnf+aVkj1/5piNOr8yMHdNumDSxIK1/HPeSY4hZSLKU/jl5dxEAMUkbkflsBGVQpzPI25GZxJ5OrfwBJ2XB6e7uDWCC6RrrJxWdJ3Ex1w/zKU2iX8iYXdzgTD0fS5feA0AWouQFOWlKpZLew0cfRfhZEwGt7ZC9795jDY4Q2KrtlEBCTKjCUIHY2gAJj22/ffr0i4/tN+8+rMslS1lTMwiDsVA8SOhiDC+gLy3tL4OpExmSOzvqGQegQo9939bTYraMvnnIGP3p8cHF3Lu1BjERR0IPmYIzFXlGbTcAkvNjndmOO6sI5uOvOor7w7ljiDrhdbTJzKB6OOneR1ZDeqSurzACVroxUIxt72Msp5OY9lvft72pEfL6em1rW9YGYnSPEboYQB9OD4B99G27ttaEeH15/eXj5+vr9V//y3/z6fNnHzyfH37+9MuIntqcENYgSk7slAINleeTre+9j3t4Xbg+x4EpUKxCDoohaGKKHEoMAhStuiCkeJGSFFtQ53fW0c7gPmK4q5pZJYcKmKVbBEya0Fp7fBS4iuhiFHhrhEBVbFFdgi7WzER1QZCLkbaKLeu6LEtbzyKK1yb9RRehNkcWBFNoCFNT0RWyQFrY0paTaTvBusd2fQZDlrboCYK2PoqtLk1E5GRtJtFVlLqGrSUeoAukBcUJEy0rDAISmUtHcYwCESopoIgaSO/eb7fRXy2c+YOMF2fSs+BnDWk+WEqpGH7ajQDo2S9QFhIouR0SVPWJlGOC2Ra9e8DDNQUHSXja+xzVXUWYacUFVUvEu1rplJHLqKGu+zBteXGcoz1YUVjWpUyrfYAMlHn0t29QPFCZVCFmI8LkWisZcSDiAKGiXj5RhdWRLxURad0OIZjhbT6O/MtMYFYWpRpL59EHZk3WNMBQOYIUeXszc8BY2e4DQ01EfsDJid/fWv+MKg4v8QZ3FsvGN7/5q7SkzA8v0qIo6MPnTIecUgmpoVErXb8S5e7qiwzuAljUQqQ79+ErRFqDtLZetn30MeeUSAmt1GA4kUE5Pz6el+/Olye1RrrY4iO1qEWh1c5oVSXBEl80ECN6XmSEB+sluTJk9LH3vreljd7H6HvfF2siS/gOoYoV5kLJI5q2ICsjnLAgpW/SNkt2pVVWZD6dY+u/ebKoB1acLDBDQwDwGCxZ41BVBsw0ghEuauuy0gmqjzF6f3n++v3lDwy+fv368dPP377//va6fXn+/O333y3t7Pu4bS8jfGGTbGaOiHAffn15OV8eX26vf/7xTz72h/NDJz99/tisrQ/L9fkapDbDHCFZjZVmnoJCmjkaA9H76KP7CPcR4U1aMMxayo6pZStSmEqEN1syjRFjpByCIDAHsCcEFdOKpTmPvEzVaAZzmjylBxYzYFJzAElrupxOoeJmCqHQTFJl1sdw9gjCTCJELKetUChNDeJ0kbY+PMLUYw8N9FU1whDL0ppg9BBRVaqGLG29qJ11Odtyhi1NbO1jBGPsJrpEBGGnB2lnaoMpXWRNQ0tpGtaoS5pUiqm2KLmnY0eQhOlRDQI5xC6Q2Z0I9771fXulO0ko6dmum9AzCrIl4M75tPneUsWEnJgkGTzOz2J+LzyIoIQjMxOsdBCDaO6dLO1dMLtnApSAMqodgAJVrbK5OdWoOorSyeXWV8naUp8jsSrrevAbtSylcyol3Z2ITsq+FbotinYaLaZZofPQzmXF6fdMXW62Y8vlIQ/PZF4yVllflD6oavRDZlOvHIG+TKQ4Df8sQ0r7eZj6aZbns8jgOs1zMS01f+pgjeR4g3wvKR97WOe8mRmo4c46HKb9/v8yRcd3eLxVSQXnx8vkKXjP/tXxlPlzZN67kgsMovLO0+BKiA8CzkEXqJo0wNpyve1P7ySCo7saFghNW2sVdYm8/+Yb0W9lVs9ISI5eCp/6Paz+dE+Il/2/akEXSamixDHRmuXD99EjuO9bU+17jrXf27JQzUcHYLqqLSRNxOFI3elmHJ7lN5kzEJHsqKjmi9wex11PmFJLGcfDK5auShPSrDEgqWsP0N1HeOQoNEAihjNWawKlMmLcXl+ut+u6npfW+rb/9MuP3bst7eePf7xut78+PzZtt/G6XTdkF7UKB8KHj9H7LipOfv3y9dPHz937b37zVx9//ni7bW29PH9+ThaCCgZmYzSP28vZMhUEqwzvt+tt7/vSF3rQqBVsJjjI1CJm8IX7SDJQyOxWmcsiwZAAjBHM5F4Gt1pVQCn9rRRCZTjNQqGpihAiqnI6r+t5+fLyEsgCsRQbkXDv+77dbvRA2TZV07YsMejq6/kk0aIZ1BgOXdBvi2FRXde2LBr7JqRl2bEtXN9jeZLlEe2sy2mx07KHdodsZsYgIqStup4HRUw9QFWohucAYaGukmOsRCEtYBTxiEzUhhdGZeHIglWiCjVAfHBsY9/3fev0IcLwsArwsyZOCm9mn5dKjm0gAwcTURY2iaK0mgLAs0cAGCNQ3FTaiQzpGM5G7549+BGcRpNkUEWUVdR9JABwXFdioAKPaUiKnUgf8ga1l32uBKwc9ksi5EhXljkiqdkaMf1nESZSUxCStCWnHAOkqFepUk4Ww1nnuSB8TRNLlyQiNYm+vIVMskgmpZOzoCmRfmw+hjSi05XhjuHfeIZ6N83gJGax68Ty6QIO2z6BPad3eAvky7Ec3nG+/RFwTdrn/u6Hs5j+FPVUIsdo8u1bAUFRhVDAWWE7fQxniluLA6RBdXi14I4YrnjZbz0WXR63rV+37eHkMLHWQM3Ki7UtITPFmmkqM3CgpsAEcjpSRsxCNQOEUvWFZi1kRDgl1KCmY3RJRQW6RDhdhF8/ffR9/+Fv/tYHx9ihauslDXfMQkZtxvJoImrCCCeyJifd8KTaRAwyO2OnB2CCpkMlahq8PJmSGIKle8mgu/sYy6KB8BGgt7ao2OibtrbdrrfbFZTL47vu45ePP+/bvl4uX79+/ennv5wul8vlXbiHOyPUrLUVhEf0PvbRr9er6vr6evv5l4/uvi6XcHx9/eLkatLY9u21qTHYTHt4ym5zcvXZQmjNfAwGEHG9Xfd9f3p6DB9gK9lhUAzhOZs3Dz/6GLlQpsaQkBAz3Os9pOYhRVSQRQmkQmciM1UVCaEEI9QaCWfkmJWs4Tuvy3ld+tgF6gRk5fAQuo/ttvV9IBwkRojJkkJJWQdMVagsQipJaavQm6I1Oy1ra9K3GyIU2dh8EjvhdEF7kOVB22LidhptG2ynKlwenWohCwUhAkOWlJBwUaimsmim4UI1IAHVe1l8mcOIVGqtwJNlphCI7mPfe+Rs1NmnOZlx5piczCJmeXNO1K7GT5GRfb8icVBpJe4NJg2QH0RH1aGlCidDKMYW3rNUueT+NK2ygunGLEtisum8CCmZfOlhSCYGZiJpTps2VyG7YclJbWBKdMvUFkrrMMFmWduKmlhOq2wmi8Io06xlXqmFT8odlq2HlLnH8bI0fHebWwcjZUymS7ib8qrtmUz7UZIj2awwbTmAGfYQKIaZ6S/l2BDy1lHwMPgHpn+b9ajQ6qi2nZZ5BgUZY9YaHW7l+LTjU+vlMeOxOXNSePx41mbNRcpwfua/c7NVCjmb1Lq/Xl/i3ZPYSdrJKS/X23t3mKI1SJtRXFYFTz9X7CYEVmVpZpJnFWRATb1DA6VclHI3U67WNJsiOfbOCFsMCA4Her9+BdEW27dtH/tpvaQmIZlFADrliSohpGoRWescWeRO+mT93+yW+lYCFK8N/CaMqi6puRNS+1MIH+59FzFGtKWFw4Nnaw4lukd8/vr169dPv/3t71T1xx9/+fM//7jfXr/9/nf/+A9/f729fvv9t6fz+fX1+fr6ko9FJctF47bfRNvzy+v5dHl+/vrl67OT33/zzcePn0b38+Wx7zsBheVGTrnQmGBKAJ2z8JCRutCht9tt27YxBnCaUlHTH2rZKhGYNQ9nsK1mNcmkHm5QoMwKK4Fm0htAIIKhzJHUjizPC6+JHhEqEqiucAoNaku7nC4+vPu2rssY0VpbFDH2ve/ed4yedZzqOsQT5HmOrhSkHoOdH/X00BQSbM0u53Nr0vZt9K7IlPPCUD2dtJ3EzlAThC7Rzh7REQFGVvhQmtNBdZBN6IzsrLJmqtlvldpCVO0+4E0m7SOio+TQKIIU1BPRpNLdYwwfY4++KwThJZ9fEVi6jyruEQFyblmROCh6WSSJ/0TI2QODtNAAMUfL5ZABSFTsbSbRUteJ7kelEkUZUr0Gnv0NhrtCc1UcHeNpDhqkrNWkLrLqaRrNNOSVFiiGJBtMpq7L3WjdP6oMVH4qmemWIKYWkBDa8sKr0TipRhFwSgXkZ87S2HzLlE+QGnuQ13YkbA7LeXgrYVFGef+skfSlKiS8pwFRbLpMF3Vkz/P+OWUe5hodYYSggPy0zdPoB3HMhpNJMnHGFG+SBDMPQB6KQfV8apvUcparkGnwqck3TrZNilzDTG5DVUf5VJICJ1yM9vT4YT2fAaGr6gkwtUW1SemTpwnN3OgR/GWdHAQSJsyJgPmBfRRpviAiYgwveZMsXKlOlwgffW9qCkq4IGRsiKG2mmj4Hm6SA2m7l31XUbXCPZn2KM34PJIScCA1z2M2daX9u6d/EbUwx86skZClW8IRbqIUjN5fXl8k6XHG9XYL94enp1HdDPL68nJ9eVHI49O7GPzy6eP19eXDhw/7y+unT7/Yoh/ef69iL9eXL89ftZnJuo9B0scO4MuXjyJy2/qXL1/BOJ3Oex+3/SbQyPEspFROLx92TLRQXyskEKo6vPDsbdtu+54lWD7cTucgTSVLxSA5Eb7WpTURaARL8SJHuZqoKDTJpSgxpvAgRoTBS4kpAiRNghIRWehVfsJFAF2sQR/Wk5k9P19BE3oXH03h+37bou+KUAhEzS3LfMfwbEoWFVHVpghTUTNZl7au69KWdbE2xr5dkxZr68qQUBFtVKM1wHVddZy9a3afZImWizrTiigFkWUzhIn5VK1Mca0x3CyUAYdKtkNTDtJDDGKS3yIBuPvu+4itdr67ZPMaQoAYnge5irUJsVQ8mdasupomaEtT6UzGKd/HJVlzMWutrW05ref1fLmcLg/vLtZ8DFRAMe1D+qAIRLYFCVlB4JElDEa1EuCgLNKkzxodQiUzAbjb/QOBlxOAe5HOhYzTjGqbg/Fmw+G9dSsBWDFqnHQTyzoKMIUn7biy6W2mnZnVH2WSptUT3o243JEfZlKgDHUyqhnN3cu87uSN4P5hrHKXevFcsfztKNfCNzw97jJA0z8crjFte3VzzLHHCUTzlZxuRmYhaUUD1JzfkOt1CN8claLxq3VgpVlmICcHjxvHb1JFT+d3tp5ULZS3ffTPX/7qdLZlFZYceDAO3dlamrodZuQHyYkc1S3P1IPTJiX7D4hoHpjwQTFTQWyj+77RlKFj7Ijut5eIfT2ffXgf/SQP1pqoEi5lD2SmqtKw13RgiGS6UjwT4oc+eAVu9TjlnqSRWjQGHVIMHWYbrZjEHs8vz4w4tZO7h8u2b+lhfQxpOvb9y8dftr798MPv6PzLX/7y6dPHz18+Pj6e/vTLz3vv33z34Xe/+53H+PL58xjjtC6Xx4e9u0LHCA9crzdr56/PX7bbdr1ef/jdN58+fd5vW1sWVd2BHHaSDZtE1a8f8bWKFFiGqBkjRBe673vvWQ0KQGBq0iwbQhghSyNDxKpyThViicOSaOYgmgK01pgNuKkpkCEVK47L9Uz8FEHL5juSwTA0QYRLO52Wy8P6/i/9M/2mEq2t0clx9eum6k6nmEaLiD6Gk+GRHSdoms2GUB0BtUXWs55O0hpNrUWzlibDlkXU3APSpCnMMgix09kl+0FGorhQS448iKyryeZ8Qj3q1BIpQQqLcoFUIodCZ2G+0KCRk8/DW7Phw2OMsY1+A50MPepHvfgUAkSEp6mnjJIXjFlmy5LaVuQAs2W187Iup7auy2ldzu10Oa2X0+Vyejw9PF7ePVweHx4eLuezPZy+u7Tmo6eZOYx0GY9geIqmWYTALHF4de+WAvs0vVLVQZVhjcR9NRF0BgdvuPAMbO6MzyxrKVIobcPsxYo7PxGM7MGYJA+nWZ5WeZqYchoygXRQxOaXE16XAU2URykdPVT6er4xMp9WUFDKYsu0jvJGyQETLlbaIM0o7+Aa89dkMvhSpFLVYOaVZMkgINlAMW91BgV3SuhtQembi2E5U8gcJy8VgBRvMXPplZmpa6vXTY8WQKaBmgqyQBNzdBdC29LaQsHroL9+/qu//sPycIa2iMhZdMX5pm97E5JMtHJc2pE6r5rx8Oo2T8FGuk94UAHAGNuCBdZ87KNf99eX8K6mt+1FADM1k6ypsNYq7hVhmkXeIfwUsQkkfZfZjrdhmRyE2yyIAzgnFZpYyq+EOwEFhg8fXQQBbrfr1sd2u10ul/PpYbte1/U8In7+8aefPv70m9/+7t27D58/f/mH//bf9n17fHh6vW0v19d1sQ/vvzldHr58/rLdbiF4v5zNFmzD2VXk+vy8tvX1dvv65Uvfb4+Pj334dX9pSxvuEl4igIyUzTi2uxQgFaQCSMg8lKqmw+X1esuVoEi4m2myoqy+uVy9UBERCwgzekCWgeuM/oPDITUCJubeDg8A1qxoA5JAlkZBNCP7vHJngDyv6+P5cbvtI/zycAq/Cp3jqmNAqIJQDN91YORwi6BQWnOgtWYZiIg2MRO1PjugCJHllKOsaBaZns3gD4AZWpOxqEWIS7CqyQlSnQPFWIBQwFiNbZAqmZUAhw90ycyWiHgwSM8xZEH2kEzCEwyOCKdHuKjAJaoMJzFJePesHz1CYTEVbbbYsrRlWU/n8+l0OZ9O59P5dDqfz5eHy8PpdLlcLqd1XS+n9bKczsuyLufT6byeTu20tKWZmUlrDdFb+JihITPRH5QMODxAkUBIa/TZ953MbDX5Jh5NLUOi3Hth1INun0zULC9GpQSPuvkoqFl2SFQjE7LTeiRHnw4mdWZkouTw1Peg5PscTZ6HcaueqOTGJCVWygRXMaVMpqQ+MWsByxGITPeWQ8mKAeJE75XZqcJ6eSMmX5TSUSSON38qtJlmrZwo6uHz7huqvvTglZKPqiXlsdZvtJRQRU6cUcXsxawHkW1jM1yMpCWrRmqm0ZUmOa8cyFjFRKMKS0zAdVkbNIabLN1v317e/f6v/+58+WCyYLLNBWAObmn2wxV/WI5sOoUgI9wdxZMc0CCtVM6h8r7vEZkDNLqP2825b/u1b5sIx3YNF7Xk98WamulIPQZHWxYx9TH9rFhqx0ZpNMyRbyISWd5QscJ8xGlDPU+IQn14lumJiKldX1+C0cxM9Hp72bYd9KD2bTtfLjkK7euXT788f4LgD3/4IYL/8Pf/8Pnjx+9/+P7nH3/sn9wxHh8ffvu7PyD6L7/8xYNUiAl8bNebxxCTz1++PH3zzdeffx7DX15uP7z/9uX1ZXS2sylJd31TGHKggzyJmo11yBIvVvkKhNAIXLfbbd9HxAoJYjELyam/Uima2ntgeGZtVMy0+G6I5WxJWmp6x0hWVa34WxUPD6hBRMTEInwMFYSIaUBS9Viw+7629v7pIfro26uym0BNMLYxuqk0UTVxcq9JLkEPDYyOWFdyVUt+WUfECKqMl9tQ0eW0tNa01YhIOj0gJtJMoCOCptT0ChJiQBsIhY4YXgF8OLNLRjxbKSrxx5riOTyVk6FqpTwUTkcAYjIcHKkcCioZHmMbt+fnzxY7t92qwrVZa+vpvC7n9Xw6XS7n8/l8Pq+n8+mynk+n0/l0Pp/PD5fTejqdzufT6byu67qeTmuztrRmpraYtpqIrKpa1ZBpUSOCpmuLIvCQ3EmNnASO5lpCOSLn15TRL7opK4HoB5fMQlKqqZB+4OGyVVW6MtEe31YiopgIVWEwq2Vl0lFHxHAUV07bImWvMY0hy+BIvrXOzS/Its98vxnIT4XgQtMyIwpkSFWTjEWY4gYTjcOzSghZhTqTxoe/m+6J9yBEDoq+/IFi8iJHymE6zvKDfLN8xeBPGe7p5o7QqK6AmEVWMkNtICdyEOm47hnueuQZsaX4R66ZCoOqVR4vwvJ/s3xQFQ3B/TY2+fzzy8N3p9/9zd9999vfW1ujmq7Keh8u5+757gELZpSV/ikZy/DhkFCEVik9OVIBKqKPse/CMNXwsfcuiBjd+5aDQHz05fTOzIZzWWBifd+1BjSqAIjQGfOoZoqSkBwVkCyUTuhSvh+liMn85QjP0uyIcHdrKsHw6L0LoGDftq+fv2zbdjqvw/X1+aueL6f1FO5fv768vL48nh4+/O7DcP/TH//0/OVr+Bhj3F730+PJ+/jw/rsP7z788ssvL9fXvvdvv/9eYS/X29Y3APt1F7Xr9brdtiAfn56g+vL8YtaGDwK7j9YsBg+UFTO8PSLv3HdWP2JBK7Vt226319E9d9HwYdpyugYo7nEouFb3cjW4DpEmot1HriQjEAins0Ntpsy1ZEoiEsAHHNoQEUHN8oF0yyIRY1nWdx/OT0+nP376s+l5gbVz49jHvilopipiy9KWk1KCUXqyWsioLaG2BESivVyv1hY1UVUMEWk5h46QEIeJWJOUlaAzKFiY8SODIsP//1W9yZIkR5IlyIuIqKqZ+RobkEAulZWd3ZU91UR9mv6AucxhDvPhfZhpoq6uBcgEEFgi3CN8sUVVRYSZ+8Ai5qgAUSAA9zA3UxXl5b3Hj43QBEDUiNu0aTN0EgNQZXfLMUIAJkYSVSJiF1YT1FwhkFiD2tUqxyRVDKqZKFbEutluI4zjdRjH6WK7224uN7vNZrubNptpsxnHcRiHYYgxxpjiEEOMIcbIkQMHZvfl6JBDg9k7V9g8PLwkaEwmoY/7Qqi1OLyEjOZtWOOsu3C+nRqB5hHiYdBMu0rOq7x+sMzA5BzFzg+R39ze5zcVDnhMORcpCGDuS0raFULnURt0W187B0eg85+bbuo8FkCE0IwOsDlC+3ttdXeP+n3ip/cKHXVp3aIH7Jam8Iy8YNfV48vlaW4b59Dagiz+Ot55wOsYGHRc6fw1639o//Yk9u9q/RcgqKUed79rhITfAnP6Ds2lKQ1KaRPo0OxE2zshI5D2ygZmaGKkPjxhyIZQEdRRIRVggwQWELCulpeMp/T29os3X/7m919vr64QuTEp2HH0s+CodzPecbUGRMxQQRUItNZaBUwBxGoRU5UKvum8CqEi6FLmWlY0RZOcC6IiVC0ZHXISiykqQS51jBBCrMuynE5EzMzDtMG+XsVPpY+2MJEpGEqbgW8H+yxO9mBnAF314CJoNVCLHMys1GwGKlVrrbU8PT2eDnPaDHEYlqeHavni6l1gWMQe94+m9Ysvvh43u/fvv//40wcGUIBPnx4Na6m82exev32X1/zTh59Px3lICRH2hz2FkEuZ16XmzGn4dH9fqpYsFzfX+/0B2S2LsRSJvnIZ3RGyzbLrGXftjsH+AGLbn+F6CRaT03zKeVXZGLetis0jpm1QA6e7XeROiKoqgCYFmDgE60dOpWK3WEKwKpXD4B0JmZRiwSGUX00EgQFUYXZFAlTV7dXm5vbVd99+W0dhtroKmri2RUQEoEpV1QBsJr3+o2qtUHe5J1QCJcxCTMMwCEg2IQiUqOd4QiIgYoqqiFXFcZmqZqjIRTKAlpoVJElDjxS0lBwjAJhVATAmCsxSBQISRgVAZkCoqkWqVAViMEQKMUSsuZhEJrWy3Wz+8Ie/vxz+cntxcb3dbLfb7XZKwxBTSkMKKcQQODD5wmWEhla22qrFAoDmYeXtNXbc129aQyfOG9EQPZBbxVBL8bDn6UkcEnWEGqnhM+RQF5pPjDlMb+4Y2ErTxsWatVFntV+VvF2H6Xa0RL3y67s2OhvZOpEmEOJf1cDYqntra0kB0KB5gfWS11r9rq1odZHPWXbSY6cHJOoJ8Qwm9TbFK6YuPjXEbnx3Dswd0bbzWesbCM5cQI+xPRf0gIvnaH7Gms/J6ay7MbBfw8/9W/qXEaBhs9DGq9GaVA/AfF7CmzUCF++iiYKal8nI3rSpOtyObXLbVBDDyGEK4zDFcZqGIQwDxYDMyMRklFwipjWQXQzx9cX2t1+8env75tWb1xSCiJxXL5kJvHRbfk3AwfNOo4KamKpJ9cUXrhwHE6lFajatJlprRdMQuJSc10XqilqqZNGsUhEEtBACxaBSmcOyrMNWmXk5HvO6iEjJZXt1iRz6mUR0z0hDHznzDUIuEDqHSn/QnKcAcE2Emgl496yK3gSYqm/uE12W093PH0+n+d27t5evrh/uPu0Ph9tXby62l6rw+fH+86e716/eXVxeP+8Pjw9Pv9x9kFUfHp/+8b/9l7/+z3810s3m4vb2zf3dXV7XvCyXFxcIYT7tQxoO+4ORxTg+PDzOyxxiXOYVEHLNbrsPik25YY2CxXbNW6njHX1bJ2S++QYQPfUpoKrKaT6tOVeRAbzH7zM20PbbYGuFlXzpeavw0PU3hljVVEVFFJBCJAIxZUIwZWpVMCCCqVSlEIlItW2oC8Q+n+D92Hac3r19GzabKkaIDBYYCUmlaHt3WssaiYnYoWMjRhKxUkqNcWQBqYbAIjNTKKWO4zgBCycoigQQwLENAEZiIqySc5UlZ7HKznkQlZJrLSIVwWeTCdGqVKYoWnzHUZPJJAtpAIAQkwBIreuaS1mrSS2CGDjGYZiq2jgNphyZ3r169frq91+8vn51db0dIyeOgYFc3e7oeEs6+FJ6e0x3FIM6pAFdo9aeQWiMH770edj3/CAAEqoFcRmoiQ8uWa8azjHREF0A7QOTCI4YgKqcRUDWfvmYQVeLOYRESOaYKhKhNQcgUutMLvW326MlGiKQ+WRExzMa89qcL7B/QOsqZWz7XkzBu3gAH/S1Xhl3stmnoRR+Hbg9Qp/rcTTfxdUhF/X+o1MCLSYjdt2R3w+0NjHgREljETzp+AnpykuD5ubXx/Q7QIR2/sjnZOR/3VqS9X97enG/4b5M9Sz1q2AqKqCIHrIAfNyeEWOkGDkOYRhSGtI4pjHxkEIMPFBIHDYhjZzGIQ4pxiGmgEzARIGIjYLv/QYLASPTNIbNNG3GXRiSVQ1e97eYqR2L04YtQhs/xP5O0RXOKlrFQFQqAoCK1lXzggAiRXM1M4IIICoFtKpUqaXWorVEBlQNQBAHIi5SmcMURzCYD8chcZEKQCEEAEQC4mjSW0MEAFSvEZDaOe6w+Jl3R4TmhNN0geLWLogktYrUqmqgp+Nh//SUaxm3iSOeDsdf7u5S5NevbjGkv337zYeHO1PZXWwPp8P33/717pc7MFvm+e2bN8vzKRe7+eL6y998XUu+v/8075c4pFp0zSsgLvO83x/GzbQux8NxX5aigCHFWqtUiRylTfL0h7WPiZ7PG0BbamHWWNuWpkGtG8sghTWXeZ2lFoOpV48ECIzsGlMnzIlDq2QITN3k2Z0DzrkdAVwN41osjsy1CjOLqmMvZoAczPOoql93cmWRQrUaY7y6uErD9Pnp0+12R1IMMSIBoamIqtUqBoZGIfioh2/+JUQOYmpRNSgRE1OQqqqrqYFA5IQQOTERI7lwBQhDCGZgVWRdFiBTQCGpUqsWkdr3DTtHBKZaa1GtgEZejfpgqwFSMIRa6zIvyzKfTkexSoERAtdYiw5DFSnDECvB7e3bN+/evHv7ZjOEFLwA7nMb/Xd3FmrARaf1GpbQEfhWVTbEpAFBbTSsYw1NxNAKfAXEIG6+6HBRu3u9v3DFPTqj3fu/tm5GzNrtNutD4l7U0kt8RGxj+OCbZoAQuhDhXHFrU2V4PEVCROspzpqBu7qI41zpYvtUat0cGltDYYQum3LY29A3WXoqAG9eocmn/A/etzSiuF0DbFV8e3awBWfDvkYEAcjOEqOG11PHsbD/49mLzr0aITYqFdqL00tz09Kuu0W2W6VA/Z2ZKpFv3lWrgmhiYmplzSYSCJDA0DAogKSUttvddtxNaZyGYTuOmzhshzQOaRhiimEcw5BiTCEGDoEiUWIOyIk4IAaiwC6qNh95IUQ2OsNjhmZgRDSMU6CkgtSYUYA+HeRZ1iO/O5h71WaqZuIrqE2kliwqYKJSEcFqLetJam4ou2ggVLGSF5WsUiQvIs4Dq7lJIpGqGmpdl+HiFlDXeZaaF7F5WS5v3nrq5sgGZkrYAck2TIOkbUCpm2G0sIbQ3NV7Ve1RVg2JpZac83yaeYg55/1+n9dV1CLxvJRSluN+/9V/+PMQp8O8fLq7O572796+2+2uf/7l5/c//bjOSxW5u7v/4ve/eX54urm5vNxcvHr15peffliX1cyGYVrWjGkoa356fEgxSpH96ZBzVsSAEGJ4fn4Cs2qlxQdvfP0BOc8n+3lz+18zcHUeUIsqoOYjrhSEw7oua16tlSiqwmBGgYDApF0JYtewIHbFCiIAoSvlwTBAMAIDFFFAK1JjxJJz4FirBAy5qqEBEqiZzyuQIFHVSsxt2FUlAVxebnaXF9/98LftOG449IKIxdy4HlE0ozKUEAI0AowrGIuYidQ8mG12V8QBwHIuADWGuiwn0SHCMMbIyMaE1qKSqi3LKZeVA4mZoZpJXletoipgawxJEcxqKcWbamKCNmwSkIJUyFis1DXneT4ty6mUCghYFaAySg5iaEvNxxk2UzouS67qtLCB4LnQ+3e/e2JpLVevjLvjpwf4vsLBJ687l9v4zg5Fu5TOix4Dg1CbUAytav8mBI/74KhGA2FckKTipie96Ac7/6SWjKTFT89lZw7A19Vwi3itvLKOciD0H6RkLTZ4p9i+6qUlqiGhSgNxO7LTf/SLNJQ6fNRwsQ6aQZtFfAm7vW2CnlI7PNVbDfe2p0aeqim5e4Fq17V46jrPBJs1ObmXWGZdz4SAgIxk4sQjqrm7qGPN7U1R4xMaSu6NIIJv09VghqCoGhnHIcY4jOlit9lebjfjNMQhpJFSDOM4jkOaYkocI/GQYsIwxjAwxxgCUfRdqT4T5ffE0diWuBztMz9/2Clm/01bduM4jjEltGDgFFivUKgVMqbqoDDhOdv7JnQgcumzqIpVARQEI8RqxaSIFJMqRRAUMVpVk+Iz/37e2iolFRGsUhEBFesq2xsGpFozsT58+mwGt2+ZKFKISIBqgtUU/DPjWUmNBGji7xabd0Svb6CKeMkp0gTigLqua8kFEWvJh/3++fGxSkFE5BGAfvnww+XVxfbyUqp8+PDTw+PDMA5/+OPfV9Hvv/v+8dPni8uLUta3717XJRuF7Xb77os395/u7j9/zus6bjcimksdRNdlVQUOlHNelqwKMQYi9t2ZZsrEJhUAQZyZalt8vZMnRhFTkNYtqwGgmFCbX22gLhoYclmXec1VnRQE9KkZF0S4qh3cOw9KrSFGJCSjUiUgGVoVMRFmBmQz8esc2UfwxIjArGp2ma6CWs0KwCEBOv9flUNkZqa1yFLWIYWvv/rNX7/76zqfxu2mrMWCEoAhv1SDAspOUbqkIZuBL/omVKQw1Ik5cOLECQGRqapqXiiyqpoFU4vjiMwq6zwfj4fndZ6Z0F0e1rpqEdVqBqC+dAVqraDglkQmgsTiK3VyMahy0lqkapUqKsYxBmZDEJFaCigcno8WgMxOR8hlrlLXnN+9vr3cjVOK6Ko1h1jQAIEbSXmObx1HcGWD9Rq2l5H9OezQQk8aAB2VcNQDIYiIv3IblcVeXFvrIwzJxYztpVtB5IMybiLtYc8BmZZwuugcG+6BfvxcUdsmALAX2q2DcaUld2wSXYvSNBvWZsEQ1JuCLq6xXs6Bw+Hauhfo9bi1c2y+8K+1FO3ttdbBwHwf8lkuDAaoHV+yPiWgbioJ2J40v+KeWjx09vxpgD5ii2bWUDpzY6BOAUgzUoVudeCJVUUQgREIMRANiVPgYUjTEDfTMKYwpTAOaTMMm804pmE3jpthGsfEzCFQCBjZf1Eg8g0V7szgi58J+qVvjLn1zQ69l3KzEvAmGAEUjXrj2SROgRlSjCkRBxPAF60/GkFHCxwVVPTFF6ptyt25RzNRUVU0QzJCEl+gIhVA2Y23rPplExXTYiovwKADGmJqVkUZMa95DBMYS5HtbvNwd7/fP1/d3HIIPCRVaXvmzCcxCfAsgMF2wKwRFa28UgUEKcVUq9S6rp4ttMqaV1ORWqXkU54fPt0fnp6Wdf3NV1+/+/q3//r//8+nh+e//MNfQPTbf/vmh59/UrHdtCOi+0+fHh8+LfOx5pJSury8zqUEim9uby9ubr79p3+xIlIFDaTKkGJZ5nXxEV9YlgzaGIycq2p1zEfNqok7AZiKiztFQU18i3IrnP0hgNYBO1eAgEgotVnziOq6LO4l0IozDGZgIj6vrWBkbcRGRFDJDGJIgFRrZSAMUdWInDg2NSUKBijiLVW/1iIASIFBVcqKPLTdDFqMIYsgcc2lFk0hXeymw/2dhBDYSl4JkZrWGSiwODwufoAVAUQqcjBRC1ByLnlhChTHzXBRygIGUgo4gKYKpcQYnZystS7zaT4dci4psY/6KZioIAAxK0AtlRy1Zo4xqSl0+2IELcsCSK5KJArAYRiYYwDAWosapYFLVRAfXbCSy7qupzn/9MPPt29e//Y3X7x7fXt9udmNaYjNRZAAHKdvjmXQ+4A+UY89FjVw2sPWiwzPobxWlmPHPRxwDl7XQxsTak1wD5uNTXeSx/Vkqq0cbTnAueBz/lH1N6RnVMW1hYBIAEhnqShqp4Qbj+qIOChUX1rh717Nw5EHzbOun15AfdCGyVEnF027cSMikKJA70HcHkZbzUMIHYgBBFAkkHND4btxvKf1FAFOiZu1+E5tSssMvH1s5ona8gkB1jbrpG2c2gc/xJ9bIiSEiBgDMXFKYUzjmMIU42ZM2yFN47AZ02ZI05jGIbYpjhhT5CGlGMKQYgwxBeIXKsVe7Ij6HuXmHWi91UJo+7784+tLF+advzck7u/fToPrXhDMpbmACjikGEJod9a7JUU373lJ2qpESO6y0sRK5BblDgWJGTGxAKL6/JGBxTQiYSVGhSqL1ApavW1lIj92vt/DFJECaFa2U553r94GDiZWl3w8HRRh2F6kODbozwAUyK1Y2CXYAoiEbCLWOQoEAjIr6jGulqJWTUSkMIeayzrPooJIh/3etH7+/OnzpzszjDEA4o/f/bCfj7/96usxpe+/e//tP3+jpLvt9re/+8N8OH3/r98cH/bH42m3xVevvzgcFgz0+s1NiPH9N98uyzrPJ0KUXESVAOacRQSI59OplmwGxFxKJuZShRCriKEYmCNU/gD75AMiaBVkr+OsikAf+W5bz5zMACRGLerd4LospdQzhIcERFRr4RgJQauaoVhlDABIyFVVRZl9sZwRIyp6vWwAxMQAYgVUS1YxYw5i6kk4lADEKpBqlhBtGAnGGCKqUUAAYrLNEGxe5sM+DzHGRD5QTWa+VS0EhwRKFQQDMyJX/WGtArayWq6VSkYFEcNAVm2aJiJU1eV0ijEiwLqsCsvz/smJHIPqnJ+LqZg5UKQQRKpXCYE5pBiIkbjWKlqRgxoYECLHEJgYmKQqIsYUATCkUaSWKqgFAWqtIXEIqeb89Hh8+vz04y93//btX29vb97c3nz95duv3729udymyGgSubXrDgp7qYY9MIH54EHDirEXch7se+vuaESXlbTYbkFUABCQ2pyz2VnBbR0EJ+TGkDb2zg9Yx3/MzKvjTkx4LWym4OLDXpK7ASx6pDBAcVgfoUsJHJIkxZeGA3wEyFtR7D2NAqKJ+IYPz3J9C4K3DorsHDMgkKOKCOjcK7myG6FxvAhgrQYm7FOF7k3bbHF9DIKBCI3MFJhEBM80iO/WEGNy2yWTKmgqUkyrqhACEwUOQ0jTNGw342Ycp2kYU5yGtBnSOKTNmKYhTjGOKU5DHFMYYkwxxMAxcmAOwaW7xHTundpWFUfKGtp13mh4vu3Q+8ZO8Hie8u86c9qt63o5KX4ju0iXnGBp8sFhTCEEl5efBzkA9SxCUKlOFLUfc3aq6H4Uaq43b0fS4RcDY2IwTGlMIQGSHOrZYQ0Mfae776vq1Qxw4Nr4TSslT2OaD8e8rikN24sLClylEoMK+AYVfzjMgBjA2OcNPPgjIhBYqU1ksmbHqeqyIqCprvNy2D+P43g8HfIyL+vy6f7+eDxM0zbwlCwel+O7m5urq+vvvv3b+/c/r2UmwC//9B85hH/75l+++/Y7BVz2xy+/fLvWIqADMYdwOp32+8d5LjEGQ5qX2RCrgLMgMUUVVTWRCoAKtqwrIq3rwiFqFWAwVS9PzM+oC4EQe28ACCA+8NE7gL6lxN2e/PmEKu5OnIdpklpCSADQESdecxZVqTUOAyEhCIcYY6i1mkouq4hvaDYiZo6IqFGQiACNDJVC4GBkBmteZ5lLEUQ+HhCIYtpcXl/UUtI4kEgIgVFfX128ff36x/ffvP/5299+9YcEyGhMAVR9NKGIlZpNVYoAAMcQAkvOqoocYjI8HNYiiHEcpzgM07gVQFAt68w1i44CtpR1Pp6eD8+lruOQAGI7xqohRQQgihwiAOSSCdmrRw4BAJkJORAHVcXEbjGLzBxiDKi+fsGUmAOnXBcOkZgCRVEF4hCjQeUYBGSe6w8/fLy/O3z88Pj4u/nvvv7N2zcXl9uEPlDWLOedYuvsaQfuW/XfOczWzrb/bfjyHy54Bvf5DyLdrvFsIHOuFR3yQb9zjt40nMeL/XPd0WvL1kS43MbnifQs2FHTVu97fmhYU6dVqTcw2tYSdQEeKJrvaWofzituhLMyp31eaS6GBj6/jIgG4vW4L3BHX1nQh9JaEwyAyKaACopOwSuZMw4VAIkYzeuboKRG3PKukyLNqUJRFBADIBNGpmGI47CZNmm7mTabcbeZduO0G6ftMEy+tSiGGMIQOYUQA8dAkTkyBaZASIzenp8RG78d+MJ3tKTc0JCG3Z/jubXL2/7j5SQgukbQM8RZydVOTD9CrUzoB+HMpiCghRg5xA4U9fLffUM8mXvoF7f3b2w8oBkBNPd8dZcC6GPuTUBlpiqEEWKMNEbVUta8zqKixTlvVCAgAhFvs9GMORooxWhqIfCynD7d3x3X4+svvrq4vEZEUAWFELglTqYzFApmaqIiatpXHKlIRbBa8rqe0KyWXGvxGfmPH36ueZ7T9HD/mVD2++fHu/u1zGMaI/HT/iGl+NXfff3Xb/768eOHh7tPCridRgP7p3/6p/v7u7yuu93u7/70h3WtQOXycjeMk9b1cFoOh1MIkRDXdVlzIWZhQ2TRCivkZS05i4mBozpScvYrLGZQTau6o4CzdJ5lWzmIpt4FqlX03ajt8fDToi71AGQkACylIDFz8JJOVVzAo7Xmdd3vn4n4Jg0GtuaFpZRCa8nz8ejWeygwTCMTqEktVedZwScNAnPUZRarCEQcAgdLjIYhRDFRmR/u5gM/7q6v0zhN211Iabfb/pe//NfDPH/zz//fh/fvxyEkHDa7KSCbWa3VyLQacXCGP2A0oFJX5hjTOGx2MY4xDqIgVQwKwlJUfXVoYD4cjhwCEpS15JKJwxiHkEJec6t52yEOhhhSGDjmUkNkAAT3AqEW75jcAUWcMpQqbh3BFJZ1IdGQJjM0UGbmEIsIAlRRQuY4jCkFRlOpZf386bj//M/f/fWX/+Mf/vSX//yH68tAPgbVkWJEYGouHPYSIbxQ9gcfe5fw8oR31OgFuQ7SJrK6gqv9//aCrWR0yzb71TdBI/hM1bHFXkq/xJ3eZfaOxAtBp3CIsA8kkR8OgOYyAo296npyn3RyYZB/NLIzloFnNgH9Oe+nmgAEmlChmp7Do/rPVQQTcUjIQxa1QlkbUtCQMQBA1QqGCqgIiiAuVDILRJE5Bk4pTMOwScPlZnuxmS5cf7OZps0wTcM0piENYwpjiAOHQByoW5QRUoOivBhHaOYDZ4WRdfgOvIr2uHWuq8k65eP8iP/FfsXPjFG7AU43NAXsOQufW4DW4WH/Mpy/CF1TZkZEFBNSgD4/4Gm7+cd1Bw8wARPE4OPHvX5vmaYtWlGz6ioUBwW1Ss3LKU07MiKOw7DTnGvNde1Io3986L+8NGIMaMQpphGJTqfnp8fPcZNubm4Dp1ILYQiRrRqiITMgqvhgOalVE3NRIRGJimqtZTGzvJxAtazLusy1Fgo0n+bn+zs1IdsfHp9ixM8f7/cPD7UKXMt6OiHB69/+5uOHn7//9ttac8l5mrZ//+c/n/b7x/v7+TiL1JyzYCoq76534zCua16z5FxMrdZSa2VGQCw1sxlaLblgwJqL1OIfXWphwlorAWipft9MpDvUoamjPdRd6O2MBZNBM9j2rSC+4alfkBhjWbN3RDWXmBISiCgCiOnpdDw87x8eH8c0BI6UYlmz31cwI7NhHAmJmTmwO6szBQBr68GIOPJ6OJ5OJ2bcXF6JgCFFSokZKOa8rlaWvJTPdxcX18ycS4lAf/enry4u/q//9e7Lv33/r3c/vV+WZdpOVTUNEYk2m22IMaYUKZkaU6xaRXR7sQEOzJFDaG28oQGmIYkvD0BQ0VrF3GIZyAxiTACqokwM5G4fwByIQhXVCoghRQZADgyGVYoaEhJRVFVQRQiAEDh6lwOGNmCIseQczAjcTQQYGQMRIIEAA0VCUOZoomghjcN8Wj98fKj1XyrKf/rj725vtjFCAIA2KOHMp53h/2Yu8yLegeayCN2T65wkwI0eAAHCOXx0Yrc/0l1LAISA6A483ZkGoJlGKKDbPDl71mhRh/wRnCBxZWeHq8CIGoZuhog+huNrngQAlXwrNRj4SFcDGbw7QUCltmgCPFwTNdMwRes4uH8BmMChfDDrk24AVGoFYgUgCgqgYoxBHTM1NdCqtdf3PrWAzDEGHoYxjuO03WymcTu5sd64G8fNZtqO42aatmmYhrFJLQNzYCZkpzoQybUnBo37bZnSH1DFrkzVtibHzl/V81IY6C0auLjUrFP9L8W4NZ0GtnHr3i5g++2MD1pP1fjrkAptR6S/Wjdscw4bEJFi4hCQsPlRNHSuvYDnRgVVEAM9j3lDQweb589ZR3a+CGpSRebDPq8nCqNIjbwJAwxykeual9mQGiNBgBU6+2JtHp2QiAnweDg8H58x8avXX1zsbmrREEOtUiv0+8BqCoTkew1VSqleujpuLjUb6Gm/X+eDlKpaj0/Pta4q9vnTp4ePH8fd9PTwYFVyrsfjnIZ0dTWqlI8//7jbbPOXb3758efHh0/TZri82G42u/l0uv94//zwuLnYXPzhS1Q87I9fffFFSglQj/MRGE2MIkutUk2AFJGIVaoUBcAlz6UstRRKARW0ZLfHU1Mk1loddfQuWn1FCJxZOcW26bc1WoDQVpud4cFGexsAMLEzBhitLKtzviJqUtaiZrDbXiLQ0/MjAQUOWcs4DNO0iSnGEEvxrXEamBECkkAxAMDIiIiKZnY67muVWvHi1Y2JLHkuNcc0ckqJsZYqqsfD3kCmzaUajLvNu99c767/z9/96c8//vDd518+aJ6ZIQQmCoYWYxzHzW66LLrWKvNpkVqZI8RgBlUrGFBIxMyUkADW7HYlwOTIGIcIhkhcc41jQKDq8VRA1BKnkFJdFt+bbmZEHDACYgiYSwZgMCRkQRSVttKKMHDMpdQiFJgg+EbjEL3gpMAMYInYfCGpS29R0yYgMRItJT8cnv/7f/8fnx+e//M//P2Xby63YyRC8jELaFXVS/nm97HBrN1l7KWKO//RBfmgAKGTnm5j1sIRdDrVwKx2q7xfRRgHFVTFsHMR1vgB74ba3D+2ANawBXTxgHmQ922sgKR9iArQbQPp/I7b9NZZjYoI6l5sBtaGEs/5oBu7dAxcBRo72jSyjooSabUqZnU1Q8q5UsZSCjEZAEdGhu12O07Tbnux3W6303a33W7cmWOc3GpvGoYxpiHFFGOKIXEIgQMSN8OUFmCt0W7WaPV+gHqZbJ236ffHRy68tfIqBZvatN/GnsNfqjq/LK0vgHMF0KJ/KxPa2q9uhdPBnUYmvLx2OyRnoqn9FDNDImIOKXEIvSMEAGMgw0abgzmP43+bKATsZh/YZsgVOpqIAEaIACoCqFLLWkstpdbqfCzFIY021rye9iIFBEHJTHrPioiMVp09BsSSczVjwd//8T/evHojgnEMtSylFIDEw4BIyAzVfP+gZDFTMCEEEDPTUpeyrut8ePj5p2U+UqCy5tPpcHh+Wst6fHqSKvPD8fPd/TAm5jRsUymaazl8umOEcH319PnzfDow0u2r1yHEWux43BvCMKXtxW4tpWq9vL6aNpMhPj09jSmJmYDVKgDIEWsVJKpu/QwwzysilFoMrGQF0FoyhmAGUgWgaBuuVhVz0BPBMU7xKS+3nsRzB6UGqOCqcXN+C8x8ZDfGFJd5qaKneQYEN79jjoEDkewuLqHS4Th/+Pizmr5+/aqWTNutAdWqKhkclDNu3o8YYoQiqqJS3XU/GIVlOZg97i6vQ4oGIqCklTLGISESq1aBeV7UcHd5WebFjHebePXH376+urh7++7jTz+aiWipRYAwEDHRWmaDtm4FmXIWq4YEnAIASilsVHVVVUKgSIE4pijFHafIdaVpE1SUnPImJJB1LYYkYmCEnsWUfNOuqSBTTMkbAgKyUgOxmW8wA2J2p8zIQRkMDJlQ3SaOXCZLRKoKqEQsIogkVTkyDxwgKsdZ8t9++KmK5D/94Xdfvd5uIzVCDqE5pHnEfrEufKkVEfAc9706dzVHU7NgULevbNG51Wk9vmt/MTvXmtDBfhWF8w7BFusa5tgGbMAbP2ua6/YeEbzrJPXjR3SmYNGnKdzVE9xVooEeHnCaPqlN8oC25qKxtWjmikNPbuLMeK1uYq6IpFpFTXw7cojDkMZpHMbNbredpnG3m8Zp2mynzTTttptpmqZmtToMKQ2O2jMTMyMReWLq4A34M9dL8bPCstvNtVjNZ/d9HwzuBAmfwbLm59P0cm3siNqoWaPKrV9cxJeeqzdvjSJuoBe8vL3eKjYIEHvj17EgRLBzwd8rhl6hGwExcowUAuCvcg34LkXfFHb+PKZghtaIG2xWBG7bp6YKSu3LqqWaiUk9HfbrciqHw253rSXXmlMIFOOw2Y7TZc5L8STeDJ3aPTAjAKXALo8Jcdzths00lVyGXVrXw/7zQwXdbi/TMBCzYVM6VVXRqiCihQDLuuZlAZS6LJ9+/PHD++/Lutx++eb7b745HY+rrNvttH9+ZKJ5yYFoGodps5vnJQXYbkeRenV9S0T7p6cQ4vXN1TRtqurT/efLm4sQ6PLqcl3WmNLV9RWFtK7L89N+GEciK0tZloJMZjqfZmRChLIWIhLV4/5ABBSDAdbsxX4d2SMsqEj0jfPubg1mRqrq5gpqpkRuOYzBCTDTKqLqVj2IWLUgEiGYChFxGtIwFikKJFKHNMQhILGKDuPEzEvN+8PT+5/eX19dl3q53e4QIJeVAhMgcUDfnmiBG7kMgVEqMKEZliVf7C6X0+n58Hz5/PjqzTsYUi5zNQHAIBA4CqpadfIh5zWGcTkeUrhKbK+uLiBLmZfn58fNsFlKHcaBDEpZTXXNZRiHcds0GiJAKaQhefoRM0BkaiNE23R9Ks+goCJIQdVCjNbnpU01whBSUGJGJGKpCzJwTEREEAwACMdxMAKt1bHpiEHMtJpC27MI0ETPwzCoKQcgIQ7MyNoaa6xVUwpqOgxDrbVkQbOIySKe5nUYNqr6/v19PpY1y1df3O4uhkgQGBDbxnkvgLG5PvTn81zc93raIfZe6KkCBDBBYHDIw6vW86gvmDkS4kGpaSsdb3LMxqzX3J4ffEoCGqjdylqr2hCD/vp47igcMKh9YkBBG7PnUGYrVptXlH8YbEbkXtoDIhCDSWOGzcB8sAlMVNrehgJqqxRFmzbbd1/8/t3bV1dX19eXF5vNdjNtppQ20zQMKcSUYmBHfMjF9F5RdLC8F8svttOeahFRG08KcDad85zqvZobCjRMDMAcn32puL047h5CBl3U1deE+i2wDs+3Zqv9b1/U2MK+D5M3uMTOtYDDgdCxJg/U0FN6/xFtaK7dQ2yfjhCQMYaQ2mV/6UdavWngA4YKpqgCou3tuU7M1PsatyYhZCcJTGotq2opy/74/GmZD+s811rycgqbTa2ZiUIaL66uSp5rXqS0GNaaXgMkApEQopEu8x7WWUpZy3L9+t10uXm6v3t6+Dxsxmm7NZ+OreLvA0TQVLXMx73mKnUt86xW7398//GnH9d5z4Hvf/zbLz//EBDefPkupvBospumMcVxHENKIaSL7U5UiWCz2XHkh48P82l5/ebVze2rw/7IId7cXKTNhgCrAi8Lx2hmJa95zmiW5/m0l5IFmbEt0NO8LCHEEIgRNdfNmEpeB2Jg3qTkDgjDODj+xYHVjePNDIw4EHMpFRSIqRYxAAOtog3+Fy1VREVExBUcKgYqCCoiamON4zhGjsOYQohw3njFBkyH0/r0/PzX99+C1OvLi8iJY5qXNQRORIoIYsxcVg2szCnwUC2DmpAyBTAKte6Y1uvrw3I8LadrESZG4iJltrzFaQBOcTRdFBQQlmUWNo4BEObjbKhpCje3lwZSSh4HChwNNEEqNU/jGKeRomgpUkWkggKIE9DQTOMBa6lqeFoOSobucVlKSKNW45jEqoq47DtwMgS1mqUM20F9ITYjU6hVfIacKVAM4tZ0ZsyxLTVAhFodxVBVCkaGogaEaugzGGYmIkBWfes8Jg6Wa1GFKkrEiYOUGkMqufzw0/3Taf7q6y8ud+nqcrsZhxQpBYopuESQmQHAQ5Yjz24R6pEA3NrGsJWnSEYYwEDOi4fbMueG1Sq0urJZEvm8z1nQ15//jv40hOfMQbaS+Nc16svolUKT2TbJPwGAIriu0eNLUwedwajGJjjD0LAgRDDsc2PQWFQ/OGC+CV6hKhgEefXq9T/+43/9y5//w83t691mjCkOKRBSCIEAmBi6RfAL4N7BqJbR4Iygw5mQ7AnAzBol2xEg68U0etbDdhmaYBWbcsbtrz2QNngLz0xOD7VnLVXPmwg9yfqfqN1C+NV34BleOqszG95v5xzWAD9vr17kP/6a1lhdQkOiGCP3l/NRAsSXlV/Q9zaiGaqiGRGDWgOADE2aUwookK/XUANREKllPTw+HJ8fT3k1qaWoxgpVFDLGaIhhutjdSMnLqT4gsmohpFrEOQAFQkBd5XA4PD1+kgKvv7Qvf/dHEXl++FTWdbfbRA51FSZVMWIqa0arZVmW49Pzxw9WpeT5+Pi4rk8f3/+0HJ92F7vtsN0fP335+vry8uLi9ubp8eF3X3/p8G8IIcRYapFqx9N8cXkzTtNpvyeGN++uU0hmNKRBVDeXl+u8+vCiiipWslBKlpp94QYjK4hKUUEOITFfbK9rlRgiI11dXIpBrYtUReIQooHlUpgihei2axxjzVVqNQCOwQBwImKSKlKqV3UUg4m4faNIFQMxFbNaiqjmXErJ6zqb6evb22mMgcgUVJQ4AJNUUTAtcpqXH3764XB4vrm4unn1JRMhhhiVidScOAVDiykgkUIVQ2JSUPIqkyykYKa7i+32eSNWlnXeEANCTANpUVCBylbiEEUqRlY1RTWRp6enm9vb/f5EQ9jeXkAK+8fHvC6MaMjZlCkhI4Tgu1hYYZokIBuguqsgABOCALIxo2FtZRtRYuRILmV34m4+zb4rghg5Bc05xQEMVFp3HoewHmfEwIljTMhVlhUBxCpxAEIFMVcto+tgQclATbMCKZAyMrHjAsHh6zWvYWCHWA0VLHBkMlapwLbM6+ku/3T3PAx0fbUd0zCmEAMwcxojM8UYiTAGDowhxMCcIhNhdBE5B2aMSOQXiQNgDVmAGlPU1p6ISicOPZirVGkgO7XvwQ4GuU6sNx1ef7a4Br3BaWUgIRj4cj5GcnzG+V4E3x6JQAoG5th33xaJTik7udELcCJqTDOAa2oQDYiwuf0gIplVAyAysHxz+fb//n/+3z/+/s9Xt9fTyADqLrAArS5o4FUPZnhW17yU0C9lcwvlZ9jFGVf4d4nxhfbGZv3v3K/h+WeZ/eplO4bmetVOCrzAbuda3RpY0zNsC+0t055hpXah9Px2oNfjAH3LcAeBujqnp/beZrSrTUCAMVlgiGzwK+SqkRvmDonQUp1WtgK+btWn+sBHy03F20qTqiyqpegy5/3p8Phw935/eEzjJi+gJkVxlRKZwJiYOQy761vDKrqc6oMFMFUMIG4aBahgtZyeHj787W/f3L774zXop8dP+PR5vzwPcUpDEpBqGsmQfa4Ey1yO6/PDxx8+ff4YqD58+PB8f7cenx9+udtdbK+/fqUoGwu3b68BAqFOE08X47oUEeRAAJKmUEuJYhVlf3xSzcfllGiAMBCpBVtmgXllBlAVEUr0+PgkCmayzmscUowhz3mYBqjKgccpjNOEzISYc0XAEMkEIA7raUYiRDZQLsghckp5qUyEzOu61gUoMoXg5QoTE8N8PCjgMA7zvA7b0X28pKoAIGPOSoilSs5lWZdLvMin5fXbd5evrkMYa61ioKayiqrOeXl+fn7aP9/dfyi5Xr+6ScNgYEUFiTCEXvMhEVIIhFBFihpWRGIVMWLTWrQE5GGaLi6vf/7x52mzMyYAG6YRGU0F2ZRBpLhETq0Uqd4YryXTgGYSY9ztKJDtn7CWVQ04EBsSEQUOgUBEVOuqIRIYWWAKXPPqow7c94VTiIEwL2UYx1IrGIfIimAC4zACIQdy1wcEjoFLrW0GxQAEx80wnxZi1mo8jQbLutY0REN1tTMyoSkSmioDm2FIY9a1VKmLBlaO7HWUqEg1iAwEYoYECsZB81Ijc1mruzRrDPvn54e9zPmEAEMiVEOElMjMQmAzI4LATBRCCDEQE4FZHCJACCFEJgKLQ1TICfP/Bv8kmkkH3f2cAAAAAElFTkSuQmCC", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_output = 4\n", + "\n", + "iter_seed = 8888\n", + "guidance_scale = 7.5\n", + "num_inference_steps = 50\n", + "negative_prompt = \"over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate\"\n", + "\n", + "for i in range(num_output):\n", + " output = model.generate(\n", + " samples,\n", + " seed=iter_seed + i,\n", + " guidance_scale=guidance_scale,\n", + " num_inference_steps=num_inference_steps,\n", + " neg_prompt=negative_prompt,\n", + " height=512,\n", + " width=512,\n", + " )\n", + "\n", + " display(output[0])\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/LAVIS-main/projects/blip-diffusion/notebooks/generation_zeroshot.ipynb b/LAVIS-main/projects/blip-diffusion/notebooks/generation_zeroshot.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9731df8acc6f85aab21cf06ea230772f8eecdf49 --- /dev/null +++ b/LAVIS-main/projects/blip-diffusion/notebooks/generation_zeroshot.ipynb @@ -0,0 +1,276 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install 'git+https://github.com/salesforce/LAVIS.git'" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "WARNING:root:Pytorch pre-release version 2.1.0a0+4136153 - assuming intent to test it\n", + "/usr/local/lib/python3.10/dist-packages/diffusers/models/cross_attention.py:30: FutureWarning: Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.\n", + " deprecate(\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "from PIL import Image\n", + "from lavis.models import load_model_and_preprocess" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n", + "```\n", + "pip install accelerate\n", + "```\n", + ".\n", + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n", + "```\n", + "pip install accelerate\n", + "```\n", + ".\n", + "/usr/local/lib/python3.10/dist-packages/diffusers/configuration_utils.py:217: FutureWarning: It is deprecated to pass a pretrained model name or path to `from_config`.If you were trying to load a scheduler, please use .from_pretrained(...) instead. Otherwise, please make sure to pass a configuration dictionary instead. This functionality will be removed in v1.0.0.\n", + " deprecate(\"config-passed-as-path\", \"1.0.0\", deprecation_message, standard_warn=False)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No ctx_embeddings_cache found in /root/.cache/torch/hub/checkpoints/blip-diffusion\n" + ] + } + ], + "source": [ + "model, vis_preprocess, txt_preprocess = load_model_and_preprocess(\"blip_diffusion\", \"base\", device=\"cuda\", is_eval=True)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Description\n", + "This demo shows how to render different renditions of the given subject in a zero-shot setup.\n", + "\n", + "(1) extracting BLIP-2 embeddings on ``cond_subject`` and ``cond_image``.\n", + "\n", + "(2) Generating on prompts: \"A ``${BLIP-2 embedding} ${tgt_subject} ${text_prompt}``\".\n", + "\n", + "### Tips\n", + "``tgt_subject`` can be a different subject from the ``cond_subject``. For example, if ``cond_subject=\"dog\"`` (and you use a dog image as condition), and ``tgt_subject==\"tiger\"``, you'd expect the model to generate a tiger that looks like this particular dog. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cond_subject = \"dog\"\n", + "tgt_subject = \"dog\"\n", + "# prompt = \"painting by van gogh\"\n", + "text_prompt = \"swimming underwater\"\n", + "\n", + "cond_subjects = [txt_preprocess[\"eval\"](cond_subject)]\n", + "tgt_subjects = [txt_preprocess[\"eval\"](tgt_subject)]\n", + "text_prompt = [txt_preprocess[\"eval\"](text_prompt)]\n", + "\n", + "cond_image = Image.open(\"../images/dog.png\").convert(\"RGB\")\n", + "display(cond_image.resize((256, 256)))\n", + "\n", + "cond_images = vis_preprocess[\"eval\"](cond_image).unsqueeze(0).cuda()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "samples = {\n", + " \"cond_images\": cond_images,\n", + " \"cond_subject\": cond_subjects,\n", + " \"tgt_subject\": tgt_subjects,\n", + " \"prompt\": text_prompt,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/export/home/workspace/LAVIS-latest/LAVIS/lavis/models/blip_diffusion_models/blip_diffusion.py:240: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\n", + " (1, self.unet.in_channels, height // 8, width // 8),\n", + "/export/home/workspace/LAVIS-latest/LAVIS/lavis/models/blip_diffusion_models/blip_diffusion.py:246: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\n", + " self.unet.in_channels,\n", + "100%|██████████| 51/51 [00:03<00:00, 13.61it/s]\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_output = 4\n", + "\n", + "iter_seed = 88888\n", + "guidance_scale = 7.5\n", + "num_inference_steps = 50\n", + "negative_prompt = \"over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate\"\n", + "\n", + "for i in range(num_output):\n", + " output = model.generate(\n", + " samples,\n", + " seed=iter_seed + i,\n", + " guidance_scale=guidance_scale,\n", + " num_inference_steps=num_inference_steps,\n", + " neg_prompt=negative_prompt,\n", + " height=512,\n", + " width=512,\n", + " )\n", + "\n", + " display(output[0])\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/LAVIS-main/projects/blip-diffusion/notebooks/stylization.ipynb b/LAVIS-main/projects/blip-diffusion/notebooks/stylization.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f96a20e45535e8d043cb98950a1426d65146bbac --- /dev/null +++ b/LAVIS-main/projects/blip-diffusion/notebooks/stylization.ipynb @@ -0,0 +1,326 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "WARNING:root:Pytorch pre-release version 2.1.0a0+4136153 - assuming intent to test it\n", + "/usr/local/lib/python3.10/dist-packages/diffusers/models/cross_attention.py:30: FutureWarning: Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.\n", + " deprecate(\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "import numpy as np\n", + "\n", + "from PIL import Image\n", + "from lavis.models import load_model_and_preprocess\n", + "from lavis.models.blip_diffusion_models.utils import preprocess_canny" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n", + "```\n", + "pip install accelerate\n", + "```\n", + ".\n", + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n", + "```\n", + "pip install accelerate\n", + "```\n", + ".\n", + "/usr/local/lib/python3.10/dist-packages/diffusers/configuration_utils.py:217: FutureWarning: It is deprecated to pass a pretrained model name or path to `from_config`.If you were trying to load a scheduler, please use .from_pretrained(...) instead. Otherwise, please make sure to pass a configuration dictionary instead. This functionality will be removed in v1.0.0.\n", + " deprecate(\"config-passed-as-path\", \"1.0.0\", deprecation_message, standard_warn=False)\n", + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n", + "```\n", + "pip install accelerate\n", + "```\n", + ".\n", + "Downloading (…)lve/main/config.json: 100%|██████████| 920/920 [00:00<00:00, 4.60MB/s]\n", + "Downloading (…)ch_model.safetensors: 100%|██████████| 1.45G/1.45G [00:06<00:00, 241MB/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No ctx_embeddings_cache found in /root/.cache/torch/hub/checkpoints/blip-diffusion\n" + ] + } + ], + "source": [ + "model, vis_preprocess, txt_preprocess = load_model_and_preprocess(\n", + " \"blip_diffusion\", \"canny\", device=\"cuda\", is_eval=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_canny(cond_image_input, low_threshold, high_threshold):\n", + " # convert cond_image_input to numpy array\n", + " cond_image_input = np.array(cond_image_input).astype(np.uint8)\n", + "\n", + " # canny_input, vis_control_image = preprocess_canny(cond_image_input, 512, low_threshold=100, high_threshold=200)\n", + " vis_control_image = preprocess_canny(cond_image_input, 512, low_threshold=low_threshold, high_threshold=high_threshold)\n", + "\n", + " return vis_control_image " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Description\n", + "This demo shows how to transfer visuals of a style image for stylization. It works in the following steps:\n", + "\n", + "1. The model extract BLIP-embeddings from ``style_subject`` and ``style_image``.\n", + "2. Extract canny edges from ``cldm_cond_image``.\n", + "3. The model performs stylization using the prompt \"A ``${BLIP-embedding} ${tgt_subject} ${text_prompt}``\", with ``cldm_cond_image`` as structural control.\n", + "\n", + "Tips:\n", + "1. It is suggested use prompt that is aligned with the edge map. Otherwise, the model won't produce generations respecting the prompts.\n", + "\n", + "2. Using condition images with clean background helps to better encode the style, especially the subject in the condition image is highly personalized." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_145309/3546753008.py:13: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.\n", + " display(style_image.resize((256, 256), resample=Image.BILINEAR))\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "style_subject = \"flower\" # subject that defines the style\n", + "tgt_subject = \"teapot\" # subject to generate.\n", + "\n", + "text_prompt = \"on a marble table\"\n", + "\n", + "cond_subjects = [txt_preprocess[\"eval\"](style_subject)]\n", + "tgt_subjects = [txt_preprocess[\"eval\"](tgt_subject)]\n", + "text_prompt = [txt_preprocess[\"eval\"](text_prompt)]\n", + "\n", + "cldm_cond_image = Image.open(\"../images/kettle.jpg\").convert(\"RGB\")\n", + "\n", + "style_image = Image.open(\"../images/flower.jpg\").convert(\"RGB\")\n", + "display(style_image.resize((256, 256), resample=Image.BILINEAR))\n", + "style_image = vis_preprocess[\"eval\"](style_image).unsqueeze(0).cuda()\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Canny Edge Detection\n", + "\n", + "The quality of canny edge detection is important to create visually pleasant generations.\n", + "\n", + "We generally find using dense canny edge maps can better control the generation.\n", + "In contrast, if edges are sparse, the model will find it hard to clearly segment target subject from background, causing styling effect to extend beyond subject silhouette or blurry generation.\n", + "\n", + "Two important factors to create dense canny maps:\n", + "1. to select texture-rich subjects in preference to textureless subjects. Particular art styles are usually not texture-rich, such as anime, cartoon etc, and may be less ideal.\n", + "2. to experiment with different canny thresholding. Any edges with intensity gradient more than canny_high_threshold are sure to be edges. Those below canny_low_threshold are sure to be non-edges, and will be discarded. See section 5: https://docs.opencv.org/4.x/da/d22/tutorial_py_canny.html for reference.\n", + "**Namely, if dense edges are preferred, setting low values for both threshold helps.**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_145309/2682440837.py:6: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.\n", + " cond_image_display = cond_image_input.resize((256, 256), resample=Image.BILINEAR)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "canny_low_threshold = 30\n", + "canny_high_threshold = 70\n", + "\n", + "cond_image_input = generate_canny(cldm_cond_image, canny_low_threshold, canny_high_threshold)\n", + "\n", + "cond_image_display = cond_image_input.resize((256, 256), resample=Image.BILINEAR)\n", + "display(cond_image_display)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "samples = {\n", + " \"cond_images\": style_image,\n", + " \"cond_subject\": cond_subjects,\n", + " \"tgt_subject\": tgt_subjects,\n", + " \"prompt\": text_prompt,\n", + " \"cldm_cond_image\": cond_image_input.convert(\"RGB\"),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/export/home/workspace/LAVIS-latest/LAVIS/lavis/models/blip_diffusion_models/blip_diffusion.py:240: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\n", + " (1, self.unet.in_channels, height // 8, width // 8),\n", + "/export/home/workspace/LAVIS-latest/LAVIS/lavis/models/blip_diffusion_models/blip_diffusion.py:246: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\n", + " self.unet.in_channels,\n", + "100%|██████████| 51/51 [00:05<00:00, 9.54it/s]\n" + ] + }, + { + "data": { + "image/png": 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", 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X31NpAkoUxyMpKZPyMSmQiHVGaT5V1H8I1RbhU01pcEuuXtEmTpXDNwXcYBjD1bZ4WhPh3K0Jh1QspI47e0AwlIZ0vqgKCYhK+ZMy+GQ3aa1YO7zwLlzPxDKjZoIw2R4FzXc86kcz6OwL84IvUIIbWlqFE1gQDvX3HFtqVWnMokWL/xo/ybcRRPgWCwIubDAtchGAkPPYDqs4T30GcF9n8R5lcfYT6ciKJmFRXkkXkYu4xEyMScWx/EkWh8pMMMyBSsZNGKpeKcEKZAHwaoJylmvh4IhdFKa0CO6rdppBlJTWtKn2GRlBpypgYp6eW0yAxatz/E0kIxARss8t81I8NTNnQq6xjtWl/xV+EYUY3T+I9FuLJTUazMLquGBZuiReZkcK08v2oHySUF8CKcV16ZOwGBnNDoKzogXuqAaRNqO+kIjP3a3/deGmRfDBzFEzEROIbEvRKd/shVYv8u5CmLEClDhWwCR9EQAivqD+aSvwdi01Mxo9hhJ/IYWm2shuZt0MJGhmRorZXKFt37XwmIMIiyNgUwGkqZ/+0XisKq37GpBGm0P5IsxBgN3MaEJuTk7asBWKqJjlJndGjahvlNtkHTyElTrhm6ObYWhNzQCIaouUmljYNQIZU2xNRZqIO5dZ+Kuy2kPkOShNook2tLRvrj6motIUJiGx9LRmVFSG2C81tgKBga1pApZYOvQokC3h616qY6LKlXhRPPoqRlOIDA1wA0CDaGEI0xeFiIOgEKItRZkVSon5ixBYWBETs1gjDEESXdKuRmowDwLKZKduwoowBxtKaHMLu5TXRLgWJfmxpytUTLwHV7RYlsezAFazxDhAXxMeq2gzcRNJWoqJYUHnJcqP1C0RDS80HZRFH1BlFFgorwiizKOsbbK7GAF9P1dksAxDmjO4bfIgHmjIcOfacoc1lZVLFbuqaRFXC4uaWVbQ+zKYce7dn6aiCnXMmo2qMht77wREmguMuTRAnZAyE6mScSX/WEWHIkJoTvAtBuQs3ZkP1OuhVbTkMjm0cJEgAoDCOqV5fBUQzN2a6jzPLigOws6Fyao0F9LU0xuZSpUML8RyQbNQmMiyL6GY9fIJQMwAOgDzaAcy+BrIasGuPRJF6wDNutOseHpWAUX4xWtrZBlyzMGACCL53gX39UIjBk12M0NBN5LsFqFnWg+B8a85HLcm0iLGjE6KsHcCvc8gzTqNFCJfIQKyp4EM+WuqOgyttaG1ptK22+C2LrkWEmpCqEIHC1bUgrOQ1vtModnQdDvo7uy06abPFBNa7zaLUFXzZIWIqLY2tOZGDuDUzXrX1oZhaDoE7KG11giqesDJLVBEhKQpSPXPQyxon/sUINHNpAHEMAwaFYjUTN0kXbL0pBWqQiVo4aREUMUM4co2EasQYLBiAyGMaifHM2er4SaUskoc1AoLH3rU3KmFqsiQks0EpnAAwitT9RMuSYHEDzggwChdlhXHTCxydksyiKoZm4/fKC0nI+6um6KB0CY0xnG0cP/SDISZRVDa8CpW7BR0K+RZXB9Q+SexWAmesV2JnC+AhKR7QD/CxwXMY8iORA62SbbTqchAYOwM0o9O9yHkgJnfTnruupu8MzEvmbKghhFDh1u0QuZicWmbFmpdrkZVBUpFSx0rmOibssIc/1L+kcpb3tmCqASBydjJjVPpTYOKkcQ8E2ZmjiHmD5gNkpWNSROC4YbwuQ1IOxCSCVA64pOSGGTLqpGI0KioLKk8JqXweA+CAE+ZAATgWhYfYZ45Kt4fy5jHU8oHc2W2wlfQ52ixAYDTZqLDAD+R2Y0AO4PF0nyFSOu9w3qfzUD2yX8GmgBm3Wlibm9sVUCLeyl+Zj8GKwTZ06H23U4i7onpcBnM668iAkPGZrlAqqTbLyLSVGQjbdM2Mgyqap3Wgda9qiQ7Aziqo3JvUSFGD/GBnSIybJpAI9jWdGgKVUAzcRiHp2uKTbRnjp0C9u7+lNFI2ew22oZdG5oq5j5b9zm3pgrMvZPQoak2bU3JPvcNzSVHh40TStVmc3diQXV2DPbus2gb30UZNoPoIG6OYgBgp5lBBWKiYiIktEUsLIoTMv4Z4KDp6dL3IRw+oxnFOiGCrsjnCCLSl9CW0A1zwROq64UfSIjPJ8sRiIfz0BzejJAhca3Sg8H+QvUcqlSZOVtA3HxI+rvxd4cyI1oVea9dghxxuCwhpD5IRJGBRxjVkxCJ1iDp2bDydssWYkVzMlOSGioL1CFPlkXx1YLZ9RlUNGRBX2POk6nuAMQQJ3QsQ81LLjSRHRnpWaxMRXYK8uJPGdQi/PHzQpkyQU5eMgrn362xrVA5SH6+zAhdviBhHxZLiNWAi9VHkid/UtYxDEx4IbHLCk+7Gdj8gaqqgt7NDwabTdZp8xKZzcKJZR9yV4I8kBHWRRmpkGRjVLylL1R8I1diJR0rN03yKJjPAALJk66LMeaClwitDDeC8KrEqPyToCd+wC1ko+eQzbJEyFz5PfRE0LpFbCTi6xGmJ815Ny2chQbJOIBABmclgbKtiaq0TdMISoShS6302VuH+OkHJsoncYFrH0iK57RUJZ3fsML+3WyZEZxLVVobnDuqNPbOSHGLSNPA2OYwLSrdqEPUXPVpFoHN3YjNtin8UClF/DSTRmWaa1Ocz2CsZ2y/kGoAu/n35z4jg89KmAADYB4YURP0bjNpYKO09DtnYe8cRJoqRekxExrEbO4C6aSZiWehARUl2c20yUY8NiS0br2712dzuE/N7VprQtFZRaX3jm5RBiVNSZppUzEa0EnMs7Gb0gww9wfgXXVEmkB1LqjLmKOAHhCyKC/zJjpA7GIkJTTy/WuiyJkRPycGZuTSf+VqYAsAkYC6C4kq5BJxsp++TER4RKKCKogTS5EyZUEaonA6Rui67gwvObAKIhoTiBVsUAqByvm1IOGSik9WqtFhjpHjKYOWBMrfQWggbChGxaMXjM5aUqQ1Yjr38DM4cYCh3K2aUkIrA3+4Us7goZmwLQQsep5RhQrzBNwxBh3LE2GNxQcJYwERh7soGZcMbZRHE5QfgecSFmNh9WHI3ZtfbFOQYkmpAQix3q2bKQmKcLPZbrY7zLOQQ5OmNk7jdLw9HG9smsgJNI91mAU7dPwNhzJ4rsd0mQQe6Sh5wS5UNQiLRDhVs3AwTpkLrPtE/HS+Cqpxle92g8B1je59RyWYh19oZqphBayKE2g+4Ook46veGFmjAG0BZAAsD+8KVKwneQtnWwRRJetRWlDSJcwoYFoIgXC2YdOkqbZBhk1rgycETcxdF2fQbvhVm1goU5TJxfp5LR9EYJaxbKSueYyhCWna1Al72gDXdA1L1KQZpvEIbdoGQNrglRzuWSgEGxUI+mxzn1UU3Tang8pGm2YmCRlSTA7GclDjvyre5WULKsAweHBis2mA+G5Z7+6U+f6G7W4yeDTct7PTSHaCmLuxUWZ2CZ4uMCHF98yoxi5GiMWpdsxTH8eZogZwtj5PqjQAJgSlE8JhO6g29YMtqiAGgVm3snKCbkazCXM3kT7T5ogs+AzNQc88h2y0cHYSAg3OKhyoTAza/WB0S5g35/7MQwQRxukMMkPQOCD0ypMPizeAZKOS5p+JpxG3KZ6WoCkiVUXrXzFCDVAHWHKhV77PJnRxTwfV+Qm9NNj1w9NxqQpSY6syCoSbr5o1s+Lg6n8RWYAugxzRYoVLSDyBnvn94pB5VDI1BK6HUQvLiHp5uKVMIDKh6vaxyHw9mcW4M8iVU0mkX4B2IetlQZJ5l21YSHRsNfI8QX417VMa1wrt1BJwvRK5vGGLltEJ1tYq7TtJYpymoQ1NVUBtMmy23Myq0KZG2+gI6jweeptpJux+cJfqMlpzlIhSuFFSrutWkC6kihCqqhAVlSZNmwqaiEqDAAnssvqqCkThzonkfopXjnuZShwEArvHEKy8rxAzzYwT1ZZdABSu8KIZuQC8JkOhTtTcSigogtqC8qAhmlUfgJiba3fi3R8DOykUBwkdtIkOg7aWHVS89BVmND/h5utjKLc21yEphzHNvXguIKwDCgfIWElBZOfp8VVPKIoQsM3uxAjR5q5JfNvNjTcE8aM20A7xJClUPSUQlMO0g/QcRBKNaEnpqN9KWTzIGZ8TiMc/hBDPItNUxbMIqW+S7XNaHtzy4ieyCZ1JE2KRt9coCBNzPTbfaJFsg8a5i3ShoFsDOFtr2fuhxcHqwXNbQNT2r3LhkyfslSCN7F4o6rFEIDIlDOYoOEZXHHSzDsKb/3gplcVZKsIK1jRF1cysmOZCr9z6+SmWjmEdTPHQTSj/ggaObxDJE4WI48XaVIolVqZswTLAoyXxt+RECO4v2jLT2CT2i4U8HrzB4hBAk6G4eEUKMR8YaJbKtZALp5ARVhbJiFa+MIfqlU5JnbL6PjPDmXH0cz0SPQi9TjrfG5qa0f2wUiXPK3xmxlkY4eb8UWAHc8FXgZnlLSy3INhjZWaRp2ASghKgWbmF8PsL6Bcoj3+ZO5ErUyDpiLxgZFABBAGa2Nw5HsaT7U5EaNZEdtvtRlvvcxMxRWsn2rab7aaPd7BJpHteoh6Y9gXrXHx1fBApRybqLIAW1XTB6RtMAlgINPeB3QjTIp+GOM/o0wylMKMRUVDPXl9w7xBexROb4geJaaJeP1kL4hk9lfBSsFSLOkY5dqa61YpK+rDhyQPCkDFWfpgM5wCAQRxkFVninc6bG05jcHsGU6haVc+LILqxsaNLHLDwtK77AiU0FsXsVfmUIuVtsqxbOo6C7nuXMVbPMaQX1fvs3FwsXDSSs5l0Iuu7FIKmVdjvJs+6RcsKfzRNuoS2Wq9KsCZCp9g0cWLs+X0rE4MMN5oZu0fbyM4kOES3HhDlZkuEhPXu5NUPJViEp4wR+ItQHiZf0qgU8shExPfocXf/DgTovZOkluyxW4/aqWAg0W7PH+qlrGSnzTENsx7vNxqhQuvMWFl60RahHCwHQhGn3w0eFOkyBF45PXyhkmeJJxamhH46bfLAPbBur9JUGSKFjNIwYVsYgR5PYrhyiacWjFAkeYo3Emkx4vTZguwMKEBWelTpaqJfehtId6GYtLCKW8JJlwTpELSg2P6ThFcibWQuT2pyhV5QhmTBT6QtTyBPcr+ErIllxrHpUi258wcIigpZ8blC98VHWHF5yaetRgskt34B90NPhGAdVHaWKBmEXc9xmVu+HyJgp/XeWwtUUtG2HQZs3J/DwKFttps2Tyfsc3wv3YxgdknWQTGvRFzNIAusRFv2ENeqyhcEuY4QkjOvHD29F2KUmxnCS4gliNxYdrpyEu1WP9YyLfWyH659afep0rA6Cb8MOZYVKL+sYq4S5IkOOJrKliRXnai6ekD83BA1A6oCqLPUhdbDXYHFd3DXT5N4IHA9cEliF3oUg/ryBl5QkEbHT7wly0IiC/IEuApgs2kjewxIQjnRzeLUlRi6zRRRNcK6iQLqBVEiiA5KHvrvVpkSQRzXcLHI1SndgQg8wGKOp0aie7LJM/7+IyfgloE9grTeSw/MOgFVNTMhlOpnfEMIpb4GdqPmf/r+RM9kkHA4hkhkc7nUf/uTPVZlMbuwAfUHgGozi4AN0tdhnmlw4TOz8uRd42mmdZgXSMdZJI6Dlu6mhAphNsSXaZIwncxTWP9LqKrRxERUzUxbM2NrsbwKStb1SYj2osvLi7keVnobJD3TIlRoghnpoViPJCafDRT2PevRpjUgPmMuJX6hDmXeJKAyXJYCz5W2Bn74+/w8dQpAkfZCPsfMFWYhHIslb5H0MHi61RrnK9Mo5fzCGIfABJbr4vHWyyRrgMoQZJcApAaW0cMqaIO1a7IM4AUzGc7Ewgb8OfHpXLEQVrjNiO/13ntXL3NuyNOIColGbEqnP3LweCaMJZChaL4HKkqN40VeqwiXWaGwd2M2XBTvuOCkueKPJNm7zVSsevekEWMW0CVR9h2qdRKVFn3jsiYVGYxMFxmuhd5PIs68pPrFw5gRDc8YmEQSOJxalebhGffbWp65CcPg2XL1LkYOIrXF6Qhmtsyl3QElnTyAZCe9PZkB7C7Iplltaj2E3t8apyAJr2QUJ4zBXmmr8thKkHhuU9SMMBN1GIfvGnrPw9VBUf0QtWvr3GcAAbPdoiuU6yk8Vp8rKHE0FhCxOLyYZj72wdh7Fs5a/KEX8HhtKv2RiEAIItBvVXjb3XapOnX2Svso4grNiP20uYeIlaxZp3/X/9hCZOu2E6wpFIUwybhHaikjMqdpas2f6rEBXbgWMuASaBOR80KUos5O0UvLk5YWOHGoGmFJS4KKekc2jLTwpr3M0i3yYlgIioMA4bl2BjaFC4Hlz3IO1K2LK3JUAriwR34hYut+WlpzfiwCRUQrm4wdpfbEO93IqZccMcxE8QVyWSxk2GeJrUtg08L4feFEs7elJBRmQnZh2izZSHBnzXexJknnkYuBYNuh2Miphk++jossIZxys3K3o3annJpk+pLlQ8io1Gpb0qtA+j4+c8edXOBYp/KpHamjzgoigrnbcRqH7UabxnCnmejmU5EmBqgaONM4z2noM+jvvdxoknhrIYa0eTYgihMUBFRaBH6WHYo4YV4jwCgjWhQsDEx0wvJfqIj3kyTrdLMkfwixCy3NdI8gC3B1kWcR9ZGJk1bWttNPW5lCDSQHJHHTOiO+pOjNvDg0qsHT5fBdib32GKY/JF4TRErgWW3QHUbSzNQ1yywxlgY/ZeaBf0FR/hgLsrtTDJ8+JN8uf+HCfA2AdStB9pHQzK8AkuxQHxlYfxZtnueQ9240P7qF3rsojd2sU8Tm4kH05LYfioCK1aic/vfZY0YQ9cYMJNlNRN3q+NxVoo2+LMkwz4vCzJqqDs3hpHzwbOYaJw5VvN9n8jzXUB0IaJzvdigteK9auoJgKVSUpKsLA8qq5fxinspy+U29L8GmVyJU052imBnlkIzLBZC4IkscBxsqwlNsGenhaVP4iS3NUTKpc3LogosccZKVFfB55D09Z9bEfUZGNyfi0b/g1bVuEb9Mmhr9T6IPRmpxcKBQFcteC4mM2XElMC+Bl5V/A1azY/4iXYgE9sJ1JiIspDKOkqOcDKygu4wzi0OiIlSp1TWgxMOanWC5iyODM2kbfCOCKudNRWmGULsmiSAxsVLWHGe8ywLm0ydAal81jWDMf7FAyAlTwHmesjAaRpvnuU/H2WZCpA1CEVhX3B2PNh5UOggvRwnKaaBX8EUmxx9sTmXnniOSyAPHLXTO0yOLox4l8cElO68QoRP2FleBREAbnklARBoY5yJj3tElInISkozYoc7Cs/HEMkvvkSkTMiIJZoZs02rFxkQyFWsiHR5TMLNIUjq0GytYs3ikxX7c68iwjtXmI8IhBLQbHWIZJ3V7/K8zBsLZWLLjrK+HeWY2Yx2gWZiNDO+bzYD0eS5yzYgvMYLyjCCMMY870ATo0+SrHi4SFYT1LipeHSbhp0mUsTo6UBAnyAtTgrNllxFTdw1F4ccANAkMBPJCo+c8fwMRmHtLCeJENPtLOHY2Saf5rhwqSonGmUyuyp4jD3SkqHhDKt9HiUp3hRXQJTwokMlF+uGlInBIqleEBpSE/kymSmpsRCyjTjL6pGeJjRB+Fg/i3UDTa0BYeS8cdEFI96XYcdp/oamf5hUnW9E3gly1SiibJ4iig7xbjTmgoucRfPe+coGeQTIRlW3xn3H4M2KYzgGzs03c/QQshoR5ynehg4IIzRrS3QlLyAokB4Ik22PNIxPRid8JtVnuEyCRw6dk86wQwTX5xiowlS8q6M/dZ7oCUv7ZYnvzOdkpbO2yMN2eWOjg/kHgV8Yvp5JbUZLkaF9Nkf1jWP3n8ncvwbDj/tA3HYLe52l/mOdxHEcz0zZsNtvtyQl1oMjcZ8GkUSbCiAiFP87cQ/c83eMHENWfBED1wL3V9RqKlC+J1jxCVUCbhHuYZ8PYQ4JTyVU8z5m46hbFQ7PdVrsTMRY/l2uezItERBVIhKgHFwbJbhWdtiDhgdK+sWYUdFFDn9mNHRX0WqA/Pc58bmcH4eYFHqzIuHm81t+a7bHNK9XNT4T3CotH+Mg1vzMDK8n5/f98BJ6V8IotKwejUyLa/n1CkcInEbOXqFt03VJk0dQ6SoI0obLULxfblEgirqxnyGPpe8piPCp59Epkfb6SKpkgpd4aVZq6i1DqgNWI1//hiUnnoyRVN95GjDnseB+NkEH9lg/XLYGugVGKeLqER/1TKrHbHi/qbOkCuq5IHOyIUKYskwmF0Wx9lcZD0jNwxfJmcFwYlyydi/xrmlOOFFa0jZL1AMPlCdnIU11ZdpM0kRG2jfo+xvHl2DMRgZhhge9ln0OtAj0TxgN1maCpcdVEgLUwzzBD4qgc4UvvbM5tm67EBWnaU0q5+GG5yj5f14rVCGnLoFEeUNjVzM/l4YaYRBkPUEyifEukHIK6GSa3LEeayJFDTcYsQB0QWD6bmpjuQpSfEMgMfelqWexosyrLb0uMSpFgZDpX9Vsh5nHq00gK2W2exdggftaoQQavFjx9dNQN+yywjLMmPe3drMP739IgVhgEGidKk0EbVEDn3eoablkVDTIvACB79I5PVp6ZWIBSBw8tWyRInEqIsLKBZO89s3QeqoDQel8IaDAvv/eDpFi3OSfl9yKw98gmWpxQEYHBgzJmZl5XbjabH8R1b4GeQmQCnIiXvTjO+7vTdjr9hiBKW4KO50nkoOaIstM12wkN1GI5vs/VHEYSpPIv8BLcUJiVbEvJnKQwlbRgRTSCGgGSdDgeHkTMMz3xJquSjUr15IO5PNvNczhEq0/I6qXLbDLzxBqU3yfgrxmyvmWhQRlVhIm0gIY8U4fy+QOSIFUYT2bVSoUiUL9B1BQXxORMmS930r/ap0VhseL7CTRYUddapgKBxZ4lwmieBM5Ha1xDmlstpKmqeVfO5syWXBXYrADT3YXmQ85Wz/EFocYtZh5QWkl0QRNBbXVfKGXV2D7ND8JnS5hyQRepok+Jyi+NdfQP20rCgtMgUoOL1Kb0J9QvP0rSsYROuPovKW8nliHf5bAkMcjFZsV8l4QlimqXBoSj4Rsfli9Mh8cj8zRgGGPUJi4FTkhyFG5UDR9Y1ge5BCvbamlK0sS7xcjEQ3ZpZS4bkgyICLwtYYiktu1WgS3p+9mGYRgaoINq2wzsXbJZbkqW9bnTOmFz76BZn3ufzTptkkYdim34xXtNRYwwdBoi1RdnkiGIMzCWwsAMc0A9vQl4V9QI09CsI4bhoZgOs3nuUflnEavufQ5rTlrvEC8eFRF00ro3I/YGOyy/IcjwWhKTsQNKM5hBxYtiWhxx8MOyKt42oYICUdAk0iRbRIc597svUhCY/uUCAy6iK2TAWjqDo+THkmpISnORlhCxkhGmDKQ6YJG3qq8L0gFx566+uAqESsInsCBSIk1olIDaxMvbI3riU87a2eDCZYiS/wYxa4jIduAHJQP9qIMnyJiP76vVGkgUvCBDeb7cyZtZg1+IGxO4Cmbiwi+fv3pbDi8HY+EWI6mUG4dcH1en+nHCf+5E+Di1w5KF/X6CryBFQWYZaKDJEnwINKFgdeSj3hDI2wD60UmImMiQpDKms8KIKDAPmXTe1GQFd8gFhet2CVmwcEkkdgdaa+ZANnirBi5A4mscUi8rVeqX4Jrg7I6VFAdZEK2C4kmYIwzBNfSF9V4vUVSQJknUOA4X+x+fypWMTZQkOsUBUiN9jyt9EXYPVY+3tl5l19ZfZokQl6VwCPL5lkUJeaqVqanl1kqo8or1FPETyJChRigiBC/qVUAhlaKtbVQDjZsn+MIwJnZ6tWKnWe+9d7M+s09mfe6zkLC5g5RJYGbW0fvUQfZ5NvY+d7IjzAEjCALJsEgXSLYtQ+/dU5q99947vOzI66w7Sfa5wyAazRyMZr2rqjYlracZE6hfsETzyJVkc6wehE8j0qJ+Sat32heA8FMM3it3BgltYm3YyjCIDCri/FTzCEuCZoolFsEpgeUitFikIUXFN4vp7EsVHCx/SmS4KAEk6VhqTUlBxQ2Y3Mc//ULIFOkwUSs8EDJYKrUyTIAT7lgnJldxtdLs+RUuRSW7JMxUIiBB1RZIsQRDEmFMiLg9pBA1/ybI4EhRIt9bAB6+ctuzooQLKwvHqOYWMQlZdEugolH8r37fAr2AIMtzlskh3AlJu+vuji7WUfLZrvKJrvmkqmNbfPYhHu5TtVqFsOEeYCofb9lG5icFCDH3qo2GHKt/TmpBc1UIKLIzqC/oyu1gzRXlpyTESL46+YpkF3DfV4viwCIzDPEKsS3zEDsi9ZSwapaZ1pLgjDwt6SDUIeOVuC66GPti9JXwzgGoFM1qcZkqFV9BIqxloX7Gn5avpIQGd7dVnDItXxGfmgJSMYoLBg44FyjLgUUQkvMv5lAWCZfwAJACmtjvPGJxtuK3ydlF1G8NcFUl/NovCL3Zgpt/HVox1wzD29yt997nae7d+jQf9iTnaSK72dSn2dDnaRZhn2YDbZpZ6U7/exSWVzKWhk4TKPuykS4bTXVQ3ag2haoMzlyaCiM+S0BUm/etCrR1myVzxxG9g+7C+sbnIU8x0iP3fuZYvEWYdLcW3WiUruzU3lS2J01lJ7oFIJSmanEqSNJlSkvtwpmdjgJUl+bJwCIJGR5JPUlkAeIJC1VYtL0gJyj2IkLF0fI9ZZAWDzKxG1g+51Q7tdFFxHvWIlurFn9K6ixpMUKonCNHEjvprS5nLYKJBfZJMpuggWEapfQpgSSguaK4C1vKsKxvQWhEmYOV2iRWpGYt9N1BNZDZiVfc71mek6tUHPYIcpZTj2dq4UBBcqknJN4XZx7TDkrtVWGat4KQfBUI0DuR6iIf8MxqrEb+28IToXB1AyJJyYVz9DQzaRCT1hppWpeK1VwjCh8HCiQPeiHuL/NYRGzbUqwJb15ukKVnTqyGVTIoBqgZhkO0Zk0rknAYeebFchetzbX084jxbpfnWMo8DuqUJOiJy4kV0S8cLwrvyOfr7q37WHu4bAdzLHDRshhc0IoQKzdLzHUMMQhxShJUFrFYYBrwSDOWHvqSJEVa1rWmUudP1+5uDlxeeHhmCHzxva3iikXkP/69PPQbk0mhgkijt3UYqARVtAFDA3oXAtbE2DbDABAKIzDUKTL3J3xCWQskUdsjomhx/FC8a4LX84hm4EUE4jffxeQiaQ0RBdXYAZJ+AIqdRplp3Su4hRrtjCRKQSnM6L33iBMS0hVeqohOYAaIvc1Xd/vnrQ3UwW9ZgLFJq34wwW8KwkvO6JUCoeWB6b6wniez3KHENDAJUZbllKgkYGbcHJkLLSkuyrGuM05xKZ9iiQUs7LKwaQHJ8i0rlJRCysA/t5Vpj5xKZFRweaRGSb5LqU9dStUl6LwgK8IX2idR4eo7z+jXJQsF7Exas4h8VBCn4of99QWrWL0Do6VhLuNRJ3q85X4EjdNUC+ClCj3DF5IBMQYsZFnRmgGAWXOfdDZ3pmioiEjc4/jCcovWnvuaMq8ilaxZ6HEuTJZwfggAwwT4O72Ntiyxp7jzRXMMZqYyoHxX5A2Cy2UsZKV2llpXRFA5wTTDOgbLe1Hzc5oenwBUKcAp2EEuo+NFJWoKi3KTF9iSekJiZsIkgSx8DuXIdQvJK/q1AsokV0gzFe6zG7www15fvorExUrGTQPid7yt0w1cJpnx3NJt/0iM2lbJ7ixVXOa7WqUXnMAgA8u/ayUWiM/Do0HRWEiYZmEhktE1Fznu+McPQJq3zuqGziYynO3AITXPnQTflwCkvGw1djZCa9LCcc3KT6ZIB9ZRMjzprn2tfQgT09k2c5FGRquiWsmPaplZ9j+miFkC1bJyXpXn3foooKe6vewSJhDr2t++fPa96/0nXTcUMYqqgVQM3vYnjtubLQ0rUuRKzIjsf1NhCFmONMee+BoV6KYAefdcBE0V0WAoREps0idZYUAI9WIEWFYpjUOsx8LBQ0g1A0ru3QSbCh4gyG+lA7DGumjNgkSqbB6cUah4nUW7AZLIC6GbhEelEKpXEiNKkwn1XgkBkgpAG8LmejAl4uSrHFsAKZMmFYsTgfcR00B+xAIjDWRZLJXBkMeiADDqlo3OFLOuPVQKGm0/09DKSgQAjXZy+et0UwAMwRJZPWnciGVjn4whuTIk+a79drQqaWAWheQuAQH6ISoRxF7QTyS8vjaEna9j7ki3q/4zTVjhLasqwN1SX4AXwzuBfQtLQgoyk+EAmWvP4EXhwhK+QypIuXaZxBWsGhOn3q1AI83RC1ojmWcAX4invwDyaXvSIGAxX/FDLo5lWM1VcVYaHNZAy1At9q2Er0z8wuMknONlFcITja8tdMf/RWShk8NyVvckI0rsXODBCzd8Z6JDcqxwYjkZ7fXd4GWlBjAkN5J1BFZSiou++smyZAmV1VzsczI2V6W0kguPW1EoJxmAVLB000AR701Pivdr8/ZpGgeEXaFpCA2LQl6PuJpbdhgVjYTRmqB35Xb70vaL9v7xBt0wqAio1Yw8eKPLEUrck69IRHwi+aexQenVJSRDqilSwALyzEP6k4lUbvek9j42oZAgrLYs0dUIDAGFVzGgZJO1qCn/Unq3qnmJz9DT/o20lfxGUINLTQ7KuhSJTtwTbTniGr07alk1GZGRZGLV2MAFSeKskkZIRvK9aQkStRAyGTsSK+lv0LzIPi91XNX45d8ilposUKLGPQovpKJ2eSV7ttVJI7QgwMp+QCkeZFsYrYgOqF0sz8ajl1J5F0iei/GrbWR5j6Rhy9czEySC8GV8HVkBNYLe+scXNqg84+bu5DIuPQHrmUsu5wWoNEoCUDoxUXnl1qagMcWsdikschmbpRqOC9KJxC15uTXVak2QP0XJWZnXABVk6C5k2XJrk6yt4L1sBtOK+c9Sr91uxbnchT0xFVTqV4GaUhYC+aQyNAwzmcqX0oL8I8sv0nLHOc/FxiHcraBrHnFmwr4DdxbWrC1BAiwWFV+tZgFuOUaLrxv5uyAahMf/0rC7h1/ZmRULSO88tTQeuLJCpYEIb8hlPMpAK1eVt2m5crBP7oJSlarh1g9swrjyYAlGxpqal68ktDpN9HbiFrkBmhlFJmIHtpPNo5feufvkO7JtHR709HishL6T3sQFIYiraWnONSm6g8eyAfB10jQJae3pFSZ1hVFFcTKI7irJFBKXzIUUlsAm2UjRlpA9enOTLODJoDzS7EAzHi5So/c1X/bJEUZIxp3qLHlfkQGG2q+7/4ogUgeJJ8hoPjJpx2L3pR4O+bos1YpHSjK6heIVw8QiATHFXO/gwkygqL6I9aOiImE2xGUcIhWnCaRFRnFyUuHlRyuOYgeBSKFRAIaI6cKVyz1fT0InG5TC+XUilGBaoojdB4x4+rpSJJ4OFUlTWBtYZqRO54T3S/UrBVmBybBjBovKogjblT8gjKR5LHHcGaXJBxwBNNITBSwZEktDxJLxUIr8QbFy4Uoe18YiNSq97Ny7+CFTFRLkoo9jeu4lf+uPFXL7YNTVLjn4avQRCCqLVP4fwewvmt5Nylgpa/6xdOV95szwBnMCOdvVb51gB9xX1Cuj7iV1+dIXCqViDNm1G4lUSKEo2oll2GHWw5yFSc5NrqlXaWCuYSJg6iiZSsYX1iCm62Kn8dv4R0DCTMQRl0Yv2zPZbnQzSEefO4jOxUT681QYTNPnQ4I9YC6YB8NViHa+aMA0T9q5PT8/3Z3d9WNrGyiM5gTUc59x60FhvsQZ5eBo2clKlmLy4vDMTAA80hi7xWRmbKUkqkraQuOSoiVRKF86WUfEW5h+CgVJUo1xUS0Zbb+StsThWNcyb8wpgHijPnF8zImQZlEzY9RgaekEJM4kREenDywOPePcX1mgZDle4++kjXlOiFWcmVHqgtq4CyQwQJjNoLzcoc4OLOmUrE1MgxFeBtPNihEuYfiVWmXAu5huEYkXKGsp1wJI4WxJDDKZJiCQoTQk2//4ZqAMfuARliVO81q2Iyy3b01C45JQ9z3OM2kOeTG0MBwLJ6JAjVQ/xZXwXm8sDpLKHWomEhYhwqs+YjJ7iJZ45tLlKqTzhDTF8c7U4ELSeFkhWDXfcIuTNB+1FAvClk/jW85USCnISfMswWmCyQQLErP0n+gkJVS+OlTXooQ2Me8TXkmCj3mhZKyjt1EsL/D6xbKMQdwF4t2tnHXSb75wKVm5BLk+QkQ9bvy0/NAUxIXj+2oJg7mn4SVTvDIogJSipEf1PE0+VWHZsJpEBB9LVByNNHmhSN04tnzCTZ6/ssJprqVOTupcFiC9m/U+CJoqRAZvdYewjd4iwh8fx1sCbeF5sdWsRODNByGiNBukGW22ebvZnpzu7m4PCKKL5AfmWQQ6oEjVm8UZPkhcVQf37c0bxUcIwQlenaiPrFUoYPzx7gWuASotk6XudUTi1Mu2vRdNel3ic4sSfqSz6UusubMFRpKWsQIOgGSRPDLol+oSxXbaBtKi3F8gAmU0/0Ad9UyvJDrcLuBRCrEKZThqMxKqgF/7DsbiFGuK7zqLVr+3FyuUh+eWuHqTi5aaG9FMuKwK7pDl30Elg4SiDlc7WXT2mffCc/XBRUMkwTE0MUC/uJgBWgEvBTksnl9nWl0pDF1AKgDOhAL1E5ixZEino4x/GozgqRZXaTM2IOJymTBwwCUpAqN318hdMULd53GRQi12CGAiRygysArmFLlYTaECnLGfAdnpA4kU3c2FXRZ4WcfaKmB1wdmaVSeWZ91u7jJQWiSJl4s4ohIFGbd0rVt1YoiQXFASJ4OyEFVLR5fJ3aUah0VbzeC1K2rvbNLSwCzqiny0ZUg83BFfZAVMCnJcSCU2PRciVp5LH5zMdywR2uz6GcuskSdGGo+0B/n0JCXMICMo2TsnCPVSlxb7EgAlZajXE0zg8RXhYnqReuzVibaUEUlcUmjo41HIYXOCpkBeJxDuUdztvTaSTjXiDMQygjTFXpVuJpDZ+qy2Oz/j3ZX3TDQvvjP6KqVaQ00dqtDhKYhMKllIqNPnlDIo1QTNFteu+HN6goM283lTIGxBXygi0uK8VR2xEYVEc8ailFF1RaR4BoiskDSdUsK8WENFudCdkBwJihA1gXSs1/JI/J0G0XBg0u7kxCRK/QtYMp7xotJLbW7oj2NZSudSWZQCGCrrM0jAKWld7S0EaKLhaOVN9TkGSdMhxQOLJIloYkEuWzCUxb9AEK1sUpTugeZlGvmAVT/RFMUBzLZEiVQE/XJhAmLUoSU9AxAXxZFxrCVc17x3GEvOe7FSsTIwQUMCVA7Uk3h5Blh1sTQi7gqFyaiaAZTPy7iRJcJWaXFiiZIaZrNiZr1X7Igs8peQxiwWrKVYpW4lXdHU73hxBXgkPWgXwUT2xMHEkoJ11J4v2uCaHeHa9KYQoBX4mxjN9cOjA4xU7/iID9J6zWSF4774+Sgfgi1w6H/JywWZo8iJQwjUNa3hDAnKp0AugEQQIqE/vZtgKK76Xublm5DfiaAya5axDHXuLVlGbFG+09Vc3WjFXnuphU/U8kksfy+mRmA5ReNdIuIVnvlPm5scjRSRhkazPk0TqZvdlqJQWp+F3tEsblcMMLKMnceVX5J8gtEozmPTMHpr5aYUyGYQadJUoKoGAM2MBu/EDPHS92D2zadagh2gknImyeXcaPtJhuh9VwRO0kaptEgvReaWWvKfweW8NUdC2dyzj+i8ez6ptrltDnre7NX/eKPU0rFIBXv3NIGfkXIFSmpQKqVhOh2O6hp6JqcKlFfE5TEFCLEOLg+SjuHy3SJ5LOj3LuWLCK4CLAk/Ae4iHs6GyWoYKhFXR2WPw+TkYS6JXjiSQSqmMZH8uUgkg6QylA6JmRpMogyg3Je0WxX8SLgZXM1Q3lJSgcRzyYO+qZtYkid0P3uxHI5+aUbMssc54fWdDtlaqArCYBog0qrvUCBNk3xNTD5XKglEWIXkbGGxV5N0A5TREQgiCV5CUBYy3J4Ew9z/qPaLtTOPDUvSf4HfP+czYRikhfAJpHomxHWb6RnKuh10jtaBN0oaE0nDQJmsAwD1FheCgsAyal5sE6ZTjERP7Cp3jrkzBavelMGzDc6/fI0qUObuZeSTc/EZ34r1ZQqzLjkCIM2zMX+L/HUt9pr75MsS54uv+dP88GSxsbyxtT6lYGfubiQ6Mz6+7EI92TExqRORHa0skb/0KkmYJwFURbWBxnmcgbY9lYEiG+sz/WJT79Xm0SjJLokZvnPdqkHBoyfmfQKsNWmis4hsFE2tB7lQEamMVaCrGMxvGIi7wEu8BaRp89CN+T21RjbPYHt3OmFGfVO/BF4KkiAYTndkigLga2ndeJhULCOpuSuaVM5goSz1ifDwWmb5ad60OdAwCotstbt+61TKDAivkHEvwZumeQPjhZwloRcsNthFAhUwSNPkxQ2IDdEU54gMlm8bj1m1I3cL66EeRtmSBmnxT1naQqnMp5GU5VRtKD1klYePEtxUodWUNBYt5lBOMhLNEiXKsoQH6LBA4SBSB3F9LwR58SlL3i1brLvee4anSfZHhxRM5bZq0gzJBiChWtYhzct1SYt27G5RsquqL4cQRotbv1EHuCJTFGoYCUKPOeYCptWU8iZSzJLABoSVoXc8Dam2ChPEMi+cMSWY8QQu8T4VTY+xkkYlE6HYCTelCFk4A2G2UPWTbrZAYFAaMspHPG4SaB+VODlv53xa9sAtlkOwAOxgZM7ClpPrjkTB4aPPhZQ8erwibYBkjZ0rTVq7LJJZyDiKnqR4xKCdyAS3T5PJtAE5nyy09AAP0ktAlT/kn/CV8v0r/zt0LL3S9LFyGIlfJdrrExEI6xgMLr2D9DkonhgIgdfZuoxHCobdVpoqButdPQW6hAi9XydEo6tHhVDzHAFF/PZBgXV2jsfDdjucnGwmbGXTaO7vWDMzdvE2Qk7ELEBJ4OWSfr0SSUobzLojpogYrakK0Hv31vYkTaM8hVyQJ1o+uG4gSvVyXU2zGh1egSwtWXwijANmAr3LWJE/ybqXRN5cJY3NXzhptGslAKUksylpkRAkAQDVFtaIENXgI0nxnTqHDIWhXKiAqJhFN+nVYZ/kUcBKvJw0WkUXPQzp4F4nAwQCzS7QyN6zKdMCgd88RkArZE+mSKe2adxY4YNRBUyY2edaaSSsM41CxqlywA55mjAPAYY0H0SCJgyeoc/wNeNcZCaBE99pZs1vBQsUTRuKcndZQokIFAqz4iRcbFfayqlKIvNStpzZ+UzSxU6UrUMGiHxbSvMlXIQwbrkK4X8FWqYZ8JUMvJZ0zx1TKhUWkOeQ73xMBkWyT8Eqr+jn/2MsrGmGg5LBBv+NTyeDI1G6mNfxiUg0t4zfhrdRJiZKCAEsOsxE1fQH86dhwCy6lsfIEKfIEl/Ve7jHx1c90PPJi244Cyv/JJIEOe0C/iX6JIK40q4AHsjjbIr06WRxf53ORPtLHxVT1t0umlneZhVPwnLDaRK42EQaMgRUFCB4AgmwU/Mon6RNyS1zS+PeGGdSoHHfrzb0mcejdWttoGYRUUUJc73Yu6O9Z13S4ISljdocpYhw6jJsBqPeXNlwkOGibR8MJ7rdbLdim9ZsPvbOaewEbK6LsOBEVADr3mUJosY++400om6ATDVrQN2vhSutn6737kZCcbk3R4S4fs0jEECikgOpG6GYZhaVhPO0blLDEksW7EsV+GcRqiOWhzUseU8kEn03HfCRvlDttV+KkPu5WIjkLvneittA4B4E8whx4KOLaFJCi5yHQKJPnHeOy5CDx6xDXl2SmWiT9Gfx9SJaSyySKItnE95KKnyUJqU8L/UAvnahyBmbkSVAmn8rU8pMYfqThuLmy96kYYmsgmfkExVL18MM00Bt/pkkaR7GlKZkna0JIkTmFQK59nDbGbmOxAhIHpuJVEa6EWDmG+tnEYTJxxHeq1KY2aLYQgbxLwc5cQmLCV1ZiVpeJwsqCtJgIqLeGtWMnDkF0tG6pBGhVeQOLjpu/0FE6FM8g43wb12J0sjFpnh8PDDYkSQu8HBW6x9jDTU6VjiWhFKFO5tAHJdqoQ6OS1KzwPds1+CORzKrcDvFOx7EsngYedn4ME++oAgnJs7+pc0A0kLJUluxpAEgVhdYMxiouylR5IishkearkgzkOhprvJXy/wKf2NP6uHJPWuEEXq1VIR8YEyTcc2sJDeOyxG906H2Ps/sOqs0FTTXnlTY8NYpjESN80AK/JfZwTonRULasD3a/lf+3//3D7773dfeflvvvbS5ePnBw5fPTs8vLh6enp9st9vtdjsMu93udLMbhs2Q12MIGF2tJ7Pe+2zzPE/z8djHqVtnn2MZQBGYUoTqJNaPQ3hqAeh+AKwhi1BAVPqX2VRBSUhTr/gAK8OZbApU0VVMZqmCcZhskY5wOM98crbdz9hCJaUg6TpWwUrif3wLGnLspJNFARIkF5lM4StIXbiscz5HbSULYCMkhvAvI/BaAB1GSZDZ1bouOblpaGJ5vQk+IQzFc3OE8eRoJIR0bII1hrUMHyJXJpzKSKglCUrFpog3GgzEaRnvkwVZJFIrGgxJmPU/xawd8+tvWA3CKXjlVJlL4ycHYJ5FjFozE8nb2kQyQBPkLqhKIfViucOek4TCSwjSKWOaxriTKHyDvEOn0LkEh0AV1jGFuOCO6KRow6Y1pc3TaN0i4s2Co9xGCZrj9Aeu3T1Ezb2nem1tZMZwAnONPYICLhRZ6yQLeJHMOKYwO4GsJCMNasQ2dCV8TBIVtiP8UERxQl6mWA6phHebzwyXJiQVEnnM0JC1tckAc7IPANFMATk05/KMq56WZ/sKqbBn9rYcGlDWqRxRr2b1yOFKTuBXzoV+5Gx9YF7H7f5WkMuFLQXPzV0pE8oluOrXIsV0KUbRRnh/ICDcdoFQegXH05GNTVwnLSn1bAoN1jmZ4tA/ee+z3/j13/ji737rxkBVUzNrF6fnm/u73Qa785PTi/u9n1zcfzyc7HZnu91uN5w92J7eOzs/OTs9Pzm7v9nd353e25w8OH/Q2tBBWp/msY/Hwzz24+Egoxl6xNo9ZxHtLOgNNCyLjvz/C0FS2NLrDoBWiucOq8F7WGpZImJAMluG6fZ4EwAsnW0kWy/5XV+lX0gITs6U13M6Dnv8QiwuXFssRh3mSihfqAmil0TGQpxtpFZBRKKaM9QsalhCFVO4Q63cP4kQq1nuf1giEcRlslHOlNkvJn+KdpkRJfSgkLL0DeEjwAK3PWbgUQfmyWGa6JIrLhR1zw4qMlQnzjQkgEilutfWWFWRTr04rgUI19aH1iVgkKJefxx6Z3RXsxZfkBfBv1BB6m3gmgcDNGNYi6FnWqklbpQxwYAEYklnSeIFJC+eXHz/fE5BYTmGGbcDIJ0mIk15vtsKOB561FAHOifka1aMVSc4RNi3OEX1I5LiwMHly38Kehwz6fT4YOQy8jMUeGcRSgpFqlniU5q4ZLML6BY3zwsq4if54GIcaSYz35v5DGGeIxBEoZ7LnZvu5FJpKvzvkSTkejVSrIEi7vH6zM8JCdEWDM7HqnnXszGO4wbN9uVssspsL4ZLAsItQSjYia6ECBGyLKQrmUtaqBEj1LQQ4Se1EumcIERKLTIgm94GWYcEcurOxBkHVwjZbNswNHDz7OPx6rnx4ealPh/u9kfre9Pbq7vDh37ZhUnTcTRtmwlGoVi3bdPddtjh9Gw4v3dyfnHv9N5Lm3uv3n/40r0H9y8evnz/wUsP7r98fu/e+f3z+68+3si2Gadpvru9vd3v+36a5i42d3SFaxdUvVPF0MVgkEZYnpytRfPYhJhW+jRJg2M4AnXDOEOl6rWk/DUPRKk4T5KmuU5SGyLM12VtTK3umtnlg13MIrjntiFEzW9zzMoOGjWPEqUBCmkgCY+bhe47HJt8/+i/j4THhepLYDqGg1ASCVyKdzl8+i1j6YT6QMn1scmAJ832aViFArKdfnryxZrCbCA4k9mQWg4RLqXAeZVznKETAWpdozxLKRUqLd1C1XKFJ0tP7Tq+BPnx6sJo9RTpz1V5FQEn8kwfK8kgvVKuOmC4sSBCzUzcZtR812zRxa+cwGD3ktCM5eWCagGIZH7+wbOT04H9eJzmqVsHG7qZptzEnvjFiO4uZVvBJdALxG1/lZlxvlessOIbiBQuVCI9mb8oxCdhKE/CR2tRbuT9vzTDI2nMnInAs+dlLsJmxlZzlQV2LKsL/xK0Mwrlv03ozu9qWouIpGqUu2jJX+WI/cR3aFiUKa8scMQamW5F2Q135rBcY7VAeNaMacQywq1LExmRvHhSFrGsHHGks7AAiGI1nSCkEtZAl7X2xZA0uEkkHdlXpQhS84w2Fcjob7mEUAzsxIC2u3j86pd+cvqd3/noeT9rbbs9sz6yQwa5UGm0861Kn7a6uT5STrfTzDYdR5ul9/HGeMn9h7jr73dpHTpsGzbY7jabi93Z/bOzl8/OHj46f/TKo5e/+Nor77zy6pcevfz6a699bgOFyOHu5vrq7u76sh9HsiuhgyFhQQQmltAWYoVo+e2bVdq2KhwI8u5AhQj2Rkgso0IiMAttqt2vPylXgRAZzMk9Xj4ZaOBUObwIREDId8NDSHT48wtivUs1/bIhRmUKUzVYgQEGxiSHSg8gwnhpnyBoEYIpIStXFKWdEMmomkAyIgITpxghHYKyJKyX+m99Ud1vSCqb/msGZlfLwlDc4QViTauzAwjLlAE0DM5wcwucozF2K6g4owVJDG5xI0CoisR55liPKPKl0SEjIAyRfshFCgDIfqcBA14ZVQQ1WINzAdR84z/yiHlJj2QoDUvJkDrPiSCNG1RKZHKtn1+cnux0up365H3rJTv8Rts+SWjAwpyZ7NgPsIQBqpyqWy6maeYLspWJf6ckEoyJuTNxz1V5BXUvVE4isNh/50ERQepJrFW6x7k8Wdzt/+tLZ9bDZPrjXQ/y9w6g4c0CWUMsNTcfVQt8LBr0Isj6rNLr90VylbVE7sD2JA1ZSJP8Zbk+riaDiCD7GzIsJqU9ER8u85eTSQuQirHgeKy9prMfsSvJyEQxpNQfRAJTFlvDBQAlfBbnV/HIqNFQADRrqucPzv7Cv/eXfu3XfnnA9eluMx/uVGTbbDxO29M2iJphUPesTecuc1f2+wPOtm2WNh2Mwq0Mg9hhgrZ+NPC2T1fH2/evnzdOoiLSdtvT8+29x/cuHjx+/PYXXn/rS6+/89XHr7117+HrL738mFO/vb29ur6cjvs+zyoN6GZWh6X9ShWol/BVaiqa2KSftNjGzD9JKJuGi8CkNtn9hgnTKTakilTdcgkAgxFmCwGUVAQYaWt+91pBWOFe0oYsbs3Tsk6TQYh7lAt5dikMecnDDwgE8M3NcyMq36fszGFLqiTDTajFcUKXgY+M9SS9qOhsRNfiFeEuyHJYhkKVlk59QI0TrkLIIXhijDv1TYJLZVsmFcmjFt4oNValyCogYsamjGanuVtJ+ULyV25KXOIbv0d23ss8hOtMMDCJn1TcKbihJw81e5cCWBQ34Lv6Gy11ykm0YqVkZa9WB1adw3lUeQDPt4ppnue5R5drk6jdcjuAnsdFc4OtbECa23il6sKR3Sp4UXmyXx+dSBTY0KiIpsIWPMtDg3HWPr0IYhV+AFl1/QJ2/wDFMzDxCYH4af44Ab8EPCOOKZRwVkNbXeLKoLLaw1dkNzlKeOuLz4q0/GG4i7VlZaBAm3hWtUh7lSoFg3TccIluqS8Lp5Z6XwCMamy9E5BIfqQSFjeKuUXEtjzxWIv0SXIaZShCWyzZrsNQckEyPTqGvY6d8o2NB3L10BLGYIs6ztPzq+NXf/gHfvALX/3Or/83917lo5cGbTL7xTnjROo4DH3uHfzc2xcPNry+vrs73Cm4ER276dD28wxIFzToxE4Va3LvfNdhMmAyng5Kcn93d3N5eYX33v/6139DVHanJy8/fOntr33lR37iq1/94Zff+Pwb77zdp+nm5urm8mY6TMAsoKCzW0sZJ6HVsjUpczhheW5cIMttgExxX25/DHUs8TErRECGPyVXU1weM3viSGsJ/QtFZF7ikBC+8K3a/yWdISsgcYaSkXDfTL8o142bRQSpnlPy7Px4pZFcXpa6kyoXvmwm0TSWT2JVV5xWFxqWpiDE3YjwfVlGimlTAQYhLW0kOHinBlezhdEzfSxW7Sa1bqTLHj5mNrSGCAcyL/VCBsUropeJUGNLBI96UoRXT5KNUhwhPWRp6dTnfouXalkkIcq+hNgVoKRChSWTiBnKkkiUxCR/Y+CKu/fVs9el9my3VR3GPs1zJ2C9g+LNLsujCm1m0BmH98rxRZWbWxmLXGdwWMsONRkDF5V0kLx5VI9FMoNCVK3PCCvDhOaqKSthFaIDS2mq5wBE6mrJ8PHgR/DVwzUtrVQzM5Xm+BWB74WHkaAf6osONqmWYREsi4VT+JIXS5K8DKNEMxks6pG/XhBfQlLWWOkujsuxil9GFzuQwBqrIOVkxIAkyl7rj6VnLZSoXGPYBK58FQeDYFmSZN+/WzsdfmedXsgIXOQKwlmIn+T5vrQpRMYEHBZURfHo9ce/8PM/c3L5+z/xg482uO6biToMu+FE5OpuvBtHDNp2Jw9ffvD5Rye3d3cffnZ9vDtwHK/38+XYx27S+zRNYuiEDpu7m3G6G8f9BNU+zYejieiwbbJrYjIMbdgOM+X66Wff+uBX3v2n//Uvn108eP3tH//5X/jhP/iH3nrny6+8+s58sOurq2eX1/2wdzLk4UeR6FThkCsaQRURAZqJKZr71VJQnx25itO6YLpeRbYkLWmQiMXPL1uOxbQiA1BIPFt2P+Lx9R7/4nK4JItiKrYjyZlXwFquePTpCr83/IASFWU532nea4TJx0uaAVnajIfT4N0jkicHeZYijkGpmC8XBF1hlmClHkUIaxEzBhMZIgTRV9cACCWvbHEx9lwPvRunJM2i+3fprSyxC+8OCpJiZMtyzhW5Qti0SIUwborPBcm6I8DrgqQcHJ8ly+A6UYobYyoEB4g4rc7jFYiK9nrIYp4Iog6Hs/ImKt7jF1AlRBpn7aQ1sU5AevC7sNusJrZZu5DdfxlJQk2hQVABNzopYhkLBuhlkREezrPujk0qhFmPDi2x9B6IymxqhV8tjlZI7Wx2T5FI+xvQRKsbh1ewkNKUZoAMzR+1ckwylKQiUSuZBVp+7MixUkXQkqL4VD28E4ieHLoKruB5c4ulTN4mUrXSoc4ACO+uEGYvKnjcTkkJZFDtRBhhBLJQVCqxtkxvcpG02EynJ/B/ARwiQq8h8MFgnHNVSUHKqj9r2fs4ZQx6QDOkkS/ED0mP3wp4fXNtNl09f3IY7o67h8ODC5W+P4zb89N2dva4d3nyccfx/muvnp3du93o9qX7r7/y+fE4dd7duxzPbi7HebLpeLy9m6dpHvs02tn5yYATETncHub9rB37I0fitmOGfno5Sp+GHbXx1Qfb862OlH75vV/5z/+TX/6//aeP3n7rR//Qz/7UT//hN1/7wqNXP7ffHy6f395c39pxJNnM6LcaOw1P4h/rFyevkidiUQRZVpdRx4mgnuVcZoP3jJrV3WpIZEk6lN+IzfUVrgBOPDhbWQYrSFuSuxPkmsnMaozu7ATxacq8HSgYEi2CmcxRpetcBQNl+paQu3O+TChEEJJQ0fAvKj6cEpwRREoaFlkKfpAhZS+JXxs9gdSNFBwiZqCpB5C8PwPN++Et1I9af0ur7Gjij3ZEzH3wkHoCAD0glbH+RHRntapVsQM/9oUc+jqMkJhdFhbhmsX5VibBi1pl181o/Z3fYE2o7IGHFFkMIfXdgqh1m3Xj13gDRjGYGEJ5DSgYQDH6OIoSfoAQZA+hW/+Jvaxzv4tDvJohy+prLnCO3u8TKdlnXpIW/lDoljTJRUaAa5yExAudC8MlFwilafau8BPkYbyhDMta9D8ZjzcaRKWokwjnKzKn6x/OBHJITqq9pbdv0S0+v1C3OCUoIM134f5Cc6QquAg/qBVFtwELZHqKCNcgm2nLC2CS27BSZSRap4RxuaaUMe84GwYwu5pHXjAdhPj2IsesgaRrJu4mCvxoK/Htb37ro2effve7m93l0HbTRgd09LuuIsBuutk/55O77fVGFDTZbqWdyomKnl60M9nI5p7y/giOZv3mej/tDzZNlPbSvfN57vNhOhxmAvtJDrNcnEzj3Wxqxz49f3Z4Mo4D9P793Vdf2vXWPvjwW//w//Sv/9H/+f/61a/9wE/94h/94o/8xONX33ntlTf3t9Pl5e3d7R1tpHWyo880gagJNbLDiU0hpWKWRxpjjRAMzNHbKNVsJkwnAa/FzBhpCD88PlGYU7CdmLQE9hxOSHrjWoatrTaiqDr0pAiUxUAVECV1W9A59d0oVQFaI2AEWLgSsTVrAbKTkTg5jrc0r22RdKZVvMiQoEQJUpy0I+m+iFMHkVzSkGEvM1vETgRDrQrhxNDHGV12BR6I9xBQJt8KJVUIq57zEVQA3IlKA0w/FSYLx2YeIq9kBInwyqsvkqEOBwWUJnFNpQw2G+Dx4sb6GkIkWYAGKrj/ktailiLVmoJM+ln0rJC20+12J9J6v7OpgwY/5Zv3nMRUmOvsx2gTwetGEpYVimH6X30Cq76hElkLzSJrifSyZhTBETyd23TJUOG51Sl5klBAG0lp62l7oM8g3thSyDi0JdGLCKAgy5m4YH2x9MTHNP+Sfyls821M/sxqRudUpbiDQgywHsfnAr79cpSMCjALnxZ74+FLFfOT8TmOjE0B9F54ft1vXkeXkp70Q4DqRRxdtqx3503RacEnJGKkSkvbJUB2NkprQqYrQSk6Xx1LkGHsIChSG8FaTMShiQ4XIjOI2Njn/REzdX882ch2u9GO+fZwaGPTrTVpw8V8mHQ8mghpvR+13Wy3rckgFNlu+0ZOT86med9OTh+dn3K0Ph6Ph/nubj9stifn5ydTH4+H3WxGPry308nGqd/N/bPr+fbQ7w7jh0/lu5/c3DsfHt8fvvTmvdsR3/qXX//1f/Iv773x+Id//Ed+7Cf/4Je//JMvvfT2g5ceX149u7m+5XRUHcRm9g4zlwONDU8+waiFhRdE+Qov7pM4I3anOhdIuPKLI1RgCRJY8/bcNqkcfgntguF+eDLOEAT5dVYnq0MnLxDQknInY0smzsoxrXcsmhCamlVS1csV6SYkn0G0S0u54/JXpP1jjEZSfpiCqABNmy413xEnCtWv9La/d6iVdRcEibGEIM7gQRZ4ogMTElyWVsW+Hf5XIczQmjCcd79swddBkseuFClAWaSlJQkvIpYol9W1VssREM9oSplWya4JFBH1KzTFGWdRewma6r0QtKmIttaatgZRPyiPQRTSgGEAOoeLczt2m0mz3s1g1qPzcsJhLpePJYuMIJUrz41GRQz9bEVKYjWqD2peIUJCBC0qWBb6EHEFSSiPvwJxEjrzytHaZRUIi7c7camm8D6yFXo7167atVz/FMsS7bTES9IMWAUzlsGlvLOsYcTQLLA3ZAOGCMgHXISuBepTLIwNRbwdDjWO/YbdXYI2WBDEhUcWwA2QAQSWt3cRnd1BKY78ZOgyzg9mPqZX3DCa18ShzywpAMBoQryuPApDUfYw6VSKvHMrhUasz6gng8m02Q7nw8nr79x75fHJMMhh6oe7I1Q3Wx22Q2unbKbARqUb+2jH4yjWMXSFTvPB+jBPn6nSjjNlOB22AzfDyeluOL+9vRFAhrbdbg9jh80nWz/5gvMZ9x7a4ThJ788v91eHdtPtg2d328+u79/ffPHVs2ucv//06pf/n3//n/7D/+qdH/jyj/3UT//QT/7im5/76kuvvjre9WefPRsPI3snDX3M2wN8kSzcvsSA5ncuUBMSVxU71dU4MMbLKhImEGUskrmVhVemMBIveL2y7D3zKjmnh5rCLNlDQ1JbQvgX0oXsShCAErcYkMFZ69XwNEH696ELdecLgjpgCU2klpaHkdbLveIg1/GqpQapYlwLNw6um6vxff9JrwJaFCe0EgIatenixwSpTqcCYRUkOGx435U2lKzw8VdpZHUgKlmuSLGqK82GAWYigsasNloKB5bRh9sodOeEIKg9Srf8wh4XBLft3rpFKaqq0GG7aaptaKpNNyoa7a1EBEKjgZ39aP043z3b397yeHe85Rd+4ZcwjLvzi+PxGHEVHRxEI3bg/Soy4lGB+KIO8a88YxLmDTUrQJAyB1lSFmFc4vaV9AYs4+BMDC7cSXchfr44sEmky1sITiAgozVPWAzWfnPJqAbEhYxEjwoH1HRm4zALIjbmVremH5ubYXEAoCw/JOsKVk8epeIJI7XAhbeESXF1jUrDhFgysR0ECxYgbhc9fhVeorvQoXsZAVieVfwzzAnN0s9Ifx1+SQzCAbQQh+i8HRuYXn/0iE5CmZ9HRv7CBkRHIHNy0+dOkf00Pnh8+vDNhwOnuVObnt4/bcOwPduenZ6cnG7Zms2AzBTpk02H+Xg4grMAx7uxm2xEWufU+2w6zzetNQxts5GLzfbu6rgR6dp3kIlqnHXQYTgxne6d6b2jbKW/tpnG3j496LPd5snz45Mnh2efXN97MLx9cX7/5bMPP7v5Z//Vt37jn7z72hv/xQ//1A/81E/8sS/+8E+++soXxz599uz5YX8U7oYNbRxBmjdB8ViCCju8Z6Yskfg6qJH4GMIoEt5WhEMKyELFEoqSWTslZNnvMhohSLmxijpmlewme06u2SqwhFCSzQlQewfAU209a0OdDgaQL7ickswoO1goLWtaSQHLGMi6QhKh+avAeKFM/TLetziYFaVIEzJ494UwRAJv2i8LjqyO1oa1jU3Id5hIy8S1iXophnr4f6X+YmatpSIbRFtmm2P7ynDTGLc6uM8Y65UhZ8tEbRX2eP6DqWs9r2KlDJvWtG13281m01pTldaiLarRyGkee58P83Scx/3x7vrw9KPj8yeHm2e3zz4+XH769GCfffL0/v23/6Nf+BNU2Zzuht1p7+Yhag/7KyzYvBPMsLHpAaSwYPl72tlcwpjeEk7ShMm4NnDZ5ugckxwo4d+jPbF+bk1Yz6AA7IRkg0Mm/q0IMyJuuGYgYe+lmgCzQvbMODgEgk6XD2Fdzpdn6GWhu2D0Jc2IHSP4GiAPr1mK859MiyjuBOWJvwRpAN7wuTqQMYmQD57rTr0EzcKxkFIRCqNSBRDvJYRUOccbB/XEiziju1gX/6enfXAFrV3JaH+27svAhWWhXh3DTgNGIA9YuwkgxdCb2by/vrk4He4O1jZtM2C7acQ8KHcbbrVP42itoQujVENPTs83Z+fzfq+wk7N709i3rW8EM3QezcYJxGwmxPnj3aNHD/bX1/M07fuEbvN403ku7bjbDl2Ak93cZzmxreqDHc4uTl57TW8P8/Prw7On00cf3A1b+fzZyUvbsw+e3n739y7ff++f/PN/8Ftf+vLbP/ULv/gTP/dzb77zFZHXLi9vbq6uySZC6VOfu0XbCRVxs1nSm0ngjDz39ONTypk9tCVVKpEaEJVuddOARz2USUlZ7L3wn8XIRGRVqZGfKbVloJmUiLyo1UnGzSIznK0XbBlq+CJLUqjovCIFw3MbLXA7SuEl51fjEjo8umauPdv0Kz02bJEPSJXzl+dJ0LwSkoRX44EeY1KtuQKsmstkRyRaMlKA3aS13IeimC7JeQVnBi28tjz3m+EGeBNCM7SWvoaQXrlYRjMSNXGmFPAl1qQJPsCm0tqwO9lsdpvNpol02iR2PU+H4+FmPDyf9pf7m6vx6qP9ze3x+mp/e63j7d3+7nA43j6bbm8Pz+/mj5/f6mx31g63hy997RRmhA7b7e707PbmjlkTLFhMOJeGyGtwlxIer1dBCW3CXAZnEGDNKC3zPQyTKRm00GAo+n0mI816IAgqrBhXVGWoJq8lWo1PJBlrhBsLlAi/uiRLjNNiwNE0HuE54aXtKJzixR/v0RZ4JyDymPeq0zo1SvQNeXIr9CVe+EIUGO6s+Yr55y0rpouZ1Iwz7REphGgu5vbZ+wSZJOy6UyRRLr2wJomPpTlxu7DcohYqDPMbbNFCtQ1R3hZNo6zME4m8wywz536nFYjoX+MZcetqfX/Y3+6H7XR1ebj32pkOsOnQu3X0eRwPu1l0S8Ug0mRoTSli06E13WyEJiY6NNlud0NrJsPh7jA8vt+GrfSZ83zYj9Lag4uXVWwaeXO7vzs5Pxxv3V5P83ToAoAnu+PRNqfDiXQZ5gfcPbzVt15Bn86vbkZO89nNfPLyyZMb+fQWTz4ZP/zo3V/7te+88vf+Hz/78z/6B3/xT3/hyz/z6iuvPbu9vXr+DH0YmrbeiW7dg7VOozKfn5scbXGjSDph2iMDvsIi6em7dIlfLiYiUS2WfcIr3Jfx9/QYVZdfe+V1dJ9MQhNurmZTUgKrZqf+H4VAkYB7QYIzhOM/iy6h4kfjU1nD4LljannOMdG04tv+T2QdJIop0tIwRSkjB7SIt6zqNVyo1RuaRBWQT8x9ZQm/zIdsRlUt3YZInBpY6GZ64cmXwMoNkEt9JwXRVHqBHkZbazdewnovMzgmxQvSb/H0LPNwlwlIbYOq6LDbbja77W7X2kaUfZ6vj5fPDjcf7Z99NN5+cv3848vPPttfXR9v7i6v9oebg8yzCZTz2Ra387w9OTnemMrucDurqcIebkxf2uy2t3260+EC8IA+vfoXNVaniatsiFg6ArEwksGInH4UGTL8wJRul8rcLsbzdHGnAmw8z27IAFn+uuiMg9RicYJbpXUKBaA5MBtQ4JmAGRpC0lfZB5Mfig50EaT3wH00maukS0iDC6TAvENGsAqHN8ayWK4P4fUYFYvymWRIgEtyG4ioKMIHE7dLS0pjmUIFvaL8yOix6FgumFhkiSIKjIgLJXhL4sXipGdkIOJm5XjEAhRD8PdYdbqIaIDfaCOgdY3TbxC1oBMUmtEIMcFmPBoVZ5vNK2892B9uOsXG4zRNmw3PhqZdByFNVXWz2Wx2W0J7N86zs69ho00N6GTnPG0ah0HahrrZ9q5bgNY22622NuywPXt0/+U+T2M/Xk3jNB6OR7N5Hsfj8WTDYTDOptCR1BOdBmtDu/9wS24wYn/oxMnlzfzBp+MHT+erCd/79icfvP/Lv/qPfu3n/sjP/8Kf+ONf+KGffP3lz189v3326acTKaLKOSGzO5yvO0XnSSmp/0kamxRs4R6uLREdCOxI31Glrgpz0A+yI0sdkcObm39AIKKWx0MEoMX5TVda/0sGs6Fx/zLWLMWVWlONkSwWFcUqnct2KrKq30cEpleeR5ofVbEKxYuUKJaRyRXQ0JewWn6ctBRHIRhifpKYbCTZWgt/KncgNMAiM15lzWWm41KeRLKY8sKiPG1nYoJWiWXNzjGxB1pAuM7cF1GNNfRovYOx6tA2281ut9tuVGSCPbu9fHK4en//7L39R+/dPXu6v7u9ef58PBymcby5GU+2OD873Xbc3O4H6O1RLi62J6fAdDarnJ/qMPKVzz/enOH1+0127aDt5m5jh7vh/kNah3Xrk2WmOfHOFyfQM0XTmQMEQCcdhiRLRZPpxJbWiSBPYUTtTZ14lzQT+b4USzptjovf8pC4D4RJpiSZRugA06Mg4hy7W5MQvQiqJ/wDyyltIovXIYibj4LPJCZHKCmPWYOU7IUYL2Sw9+WUfGBzdU1nUIcXqjPLCYhUsYV2SNEQBGbkGCXXDpJBWBDRwCzsYJJ9W4Zh0RIj2T2AwgtfIBdrofUoTXUy5hYVvs9eyd5MDH7ITbKwvfTUD7KbdmE8tZOAsXtj5t5NgM4+HcdGe3AiZ5yfX7K9eg7dnJzidKvnw6wU6wab0bvo0HYnuhlm6cbehq221obWbezHA5oMIIfBxgO5mUz6DCG2u+3u9HS/vyPQNsNmd9pwIXZ/GkdVHfvYx+O0v7q7udmP8zRNx8PUjGxoGx1tHhRtJs5xdn/DxpdmfP6tzXwl3/t0+v3Phvefjx9/5/Lvvfdf/oP/1z/66Z/+sT/8p/7U1772iy89evmzq6eH8di40Ub0GcgL1ovsFRqimEACAGrnkatNCf3JtFXUELD51ZnOiFTMumRIwiPaGReK7MuKsFfQA5nCDVyOYYRUaCpWuANFXXyoUT7ACMuKeKO8JbhZVimsjiTNl6Wcr54fHRcrkBDGphggnEnL4v4kQEme3HK+bVRgiO8bReNaonB3kR3cGO3IoHnUKZlrSn5wdMmGMJ6cTS9LpApEPEnAuIDMnYnEuGolGe1BX8gD5XxpJvB87rDZtGEzDFvVNtv09PbZ946ffvtw9+HNx9+5eXazvzpyf9hfH5ry7Hx7sRnGEfd3ev7g4pP3L89PNyfnJw9fvne0k/OXd68/3spm13XXb2/vX9hmtzPFdrBpnPp2t5/Px/3l8OBzorLZ7gTXC94kDxfvN4DwURxK8hhF1ByiQKpylgnWCUaASXZqi+4p3p1iqZZww8KM/2TdiPsAUoCa+xMUe6lEzDdJGBgT9z1Xwc/VTRpknIsNI8SMR1kGYtLhoBjNHWql2HI1gUNrlXt7P0gCgm6zV2q5rKC7K5DVXL140cK64/sB2n69ViqJAdX2ycs+Q+klqqZ9GgLmncE+HXEDUOtGJJqHG6XRjdVXGlA1+h2UBnYRTQPMGqCIUQQ2A0L0rDoVCtDh9cwxO4uds0hYMh/VKehT19Z6H+9dDK++3r7yxVflSxf33nirbVvXe/PUNryD9d6tjwfy1iYddm13cWFtGE7uCbT3Pk+j2djHIznLfDceD+PxTvo8z7OMpq3pYON0Z+jTNNH6Tru0YRh2J2cXxrYxHY/73cnFvYfzofPu7vZw2I83V32aucVkdn1516TP1mVQAbX1QefTR+2HH+zeelk/uxre/2j37WfH68+O/+Tv/4vf+vXf/tGf+9U/8xf//Oe/8iOKV549f368uzVpAMEe1xJJjyRTuLwelvEgcY/wwyo7WDuVlkNTq2TJFkTmL2KPWOmDA1n6uOIee8XqohegU35t5drF/2fFdmaUvXlXAKRvrzd8XmrfgaLJopLE0AeYeW4uNiOJTwQqRUMfJK1L2YaghgIgi/vqV1J2Ke2XKroNCa4SU4/8qRRJXGrvM8zlFin89aUlg/9eAzAMGVOiwpv6o6l6ybbzOeGq6BcZfUqTEwZfFBZpnDAcIpvG3XbetFvtn85PvzU9f/d4+RxXz5599ik4z/s7ucVLZw+ub45vvfPKu1//ZLtpLz3YPb26/tKP/0A3vvXqy9uLs/Nzpei8OZOTttk1kbbZnh1vr7a73kXG8QA72nyUdjtov/zo905f+wOS3dLp/dXKxGVExPmBxYXiGpQB7AZJxzO2x7i0MmYCY/HDwMygCHH+1rM5IYta4QuCQg0ccQ+5LjYKhpKkk9kNoiyF1QWYVmWrQN7UzjDsiEO8SdHyVmeLaYcewKP/1hfFJZZ3MybufpC4BTJJYCbclbGYnflYzY8LmXtXwQgIaNb1o3SHhLAbMs5S19iFL+WRAmP2ttfcDDKSFhLSWBcb0zc7sr/hkJsfi5CM95rngZPkMK4uD+PjxjDNg2Sc2GJzNWidTzYKXIUC6502j53EvGsnF6eP37h/tdvc3+rN1fuqapud6gkG1WGnJ7vN48cir7ThRMVkQNve1+2ZDE1EZRhEAD01CrgfDze8vZ32n8z723l/0/eHqR+PdyNH9D6qDNN41yl2OjbbiKpSd9utnpz3Pivk9P7FfLy1u/PDdDyOx6nzfHfRj9PheJzMpnGaR1JlaGiDPbrQN75w8aV35h/+cPfex/33Px2ffHb8x//lP/nn//zXf/pnf+xP/9m/9NbbP6IXm8vbK7FZbCOcSYgorXvf3ypCzNhyymSVhmWYQaIcN9DKcQzJ9ZGmXkSzRitK35Htia28C5MF61ARqML8vKsggdDvso3/LDD3d0Y337BAicSMdtkV56hYVHAQpO+bQuk3DBdrDPbpZgnZ29E9HbdhmlmNiG6WL83FtmFwkfZkcpw5dsNDQqnasjwEguyPEbE6qaok0IOc7j+3MDkZAgsVRdDHFta16luJ7DwB+Fkbj75ph8VlYIbmkrzR3WY62T3jzW/xs+/cPfloHvt8ddn72HQ+uSc3T49i7VQ5bLeY+34//tRf+NG5He6fnb6K7XA2yGy7s53t5yZshqMOHLrZvrXtZOM830zTbLA+TXK8m8dxmDHP+/d/61ff+NG/CEDbQPq1Wb5QbgOx9HSxZAUw/4lF4E1oHfQL1p3UgJ1553jsjgHeO5mkF0dYrpVQYOYReyvXzs8ueeJ4VZxj3gfOzXO05zWS6S/4x8zNCqpOlOI39TChO7HaT3Em2C7Hn8PgJIEI41jHxJegE+v/syYy6MPyPRFhD7wmmX1Apc/xqlI4AuhJ1zMf5/cr+5OlMjEiZHQzDc9K8kCade8pG8E7r0P0RQizHpYEyGBm7DSKBGbcLBYrPtpnb6lUMQRzM2yFVBknXFWwUkJWaK7K7HPcdKkbabTf/Pq33v3GN1VHTKP1SQc537XT02F3ttk0tGE4PT1vrZ2fNAzcbk/bdrs5OdNN256e6MnDzb2XN6cvndx70E4etftvnr/0hdkGszYf7vp8ebx9Ol4/m28uOd/c3T7F8UBOdryzbiq6u7gQ2e12J02Gaezt9CF2Fxuz3e3l4Wavp9gMfe7z3WG+uzkeb3eHeWxK4yzd9neTnshbX7336PX+paf9ux/uv/Fk/+Ty+lf/P//st3/rd//iX/5zP/+H//Qbb75x9ez5/u4o3LY+e//DIPV5FSikMqMZQJXaGD9G49ESLBWfdHDLgEWF71VYN1CJd5MUeq8zbwAQYZcUc2+VmBbFn5BYXC6Ikx4GeUdUKob3nOegE+JlYRj+svAMqrfoApiS+OlGEO4P0QTeV7hiCenbpEwCVFGLJt0eLkjhDcuEIXEk6az40+lHlAzWvNFbNu0plu7+jKouk0oilqHS5JzR2S6KupIlBepUw9JYRZ9zBxViNGFron3aqp0O3MiV3v1Of/L1+dPrqyfX48EEfT/Z47N7V4e7fs3j/tH+6fiDP/P2sLt444dPOeu9tx+hk60d7vbTxGEnh0GP81OMXQwT95DDIH3Y7fY3+7tnVzKozRxv78B5GPTknhyubL992g/XbXOPotoG9NkpOswAB6rYzcBPcVpHFcnbyq3iPulrmrNL/3JEAgNMvbSwzryAZlFaE9YigiISRYOSmBpQZN3ZO/q8VNV4cX7yWIG3qiqGZAaoGwUHfqiwp0CReXWRVsAp7F48ttEsjo14358E3MwgeZTM79CKOH7mwxQ0b63PuJPV55WtPEK3LA6JIPCZxkwHhgvGquFBOpFlcNyLrJNE8RUssTRJ37V6JsbNruHK+yskw28ZPtalr2zk4Mkw3I5NSJfFUUU6UbfrehqfZkT3d5mJQMxmo/Spb7cbG/BM7j767Y+3p3o6yFZngMJu49gFVAxinG2rA0iqHNFPTk4GYrcdVGW30+1O7p1tz093j1+6OH/plUePH128/MbZK2+ePHhtd/FouH+2ffxVGvp+7NPzi6unh5sPp8snx5vn09UlbRrvngOt7U6JnbTtZnuum4thnk42u7OT/fFwozhsVIbN5vxkO98/O47j7e1+vLmZO9mV3fowbc776y8NL7168oWnm9/51t3v38mz92/+8//D3/uXv/Ir/52/8j94+ys/hnbv5vayyWDzKNGfLUq2FJn2jNSbZ1cTKh2CJKs4V9S9bLos1VbutkEapLXsNhasH5AmaiEhSb4zNg7v/LpKjJakLVK+VO5FsjltQ8hJVRT419OViNBrilZIvU8jsnj56CD+S4Yg3SNWGCyrBDPTFu2D3YUKGk+JHEAm9bzHaSUf4MHTOPCcHq4gLW1CeZm8SFXEMHJ8QXK8VipSL+pHAUT8xJ2qkd4AKNLjZsremm0bTk6O9zaf7Pp32u2HN9/7qE+T2sS7cUdpOzx7tr/8aD+8fnJycda3mx/8k28fP9mfPH6401MbNjb1yQByOhzmu/18nGS77XdyuNrf3e6b7KY+Wr9tmHXYiMk8D9Pl2NnN0Da7WeTqkxth43C7v/303ksPMJvHy51BhxvmzaElEC5sgQoM3au9GKFqb6zH6nofzN9X1m0DHf8BmCOo+5u1KbWLkgyBVRQa0cZwT4rbIppWBH+Ze+Zg0l64OTdL++OiSb/VNq0O0TvgKuDAaiEXQeE7EUuTY/M2nwHAkg47IpoZaSJAYJ0g5mhwwiyDJQXe6i/R1Gx+gfkx45CSDnbegcPC36XYw/VsBoCMfVkWBoUyynJYITSPi6sDiQrXwITYke6mxr1sY952H4xKol+ERNvocKWW0io3AD2vMhcKpAutizTrnWbzPF9+dPfJkzu0AUOXTjPdim0UrbWjsKkOUWPdRmLG0AQnxMnWowF9gG3lIGa7s0/b6Xv37smDB/r48ckrr9x7/c3XHr795sXLr589fGd38fbJxcPTh6/em798vLuabp7O1x8fnn1yd/n+eH053d72zmGz6ycX29PHFNnsTrdnj7Z2pvPx7uqac98OvW9x2mR3/zDfnR/ubvd3NyI8HinQ+Wba7OSl19rPX1x85Yl+52P7zY9v3v0X7//vfvt//4f/7V/8pf/gP3z7wSvPrw7Trdg8i6FbD4IBg99TX+2ERaIMN0LEGR9/IUoS4f2F+WbAG02ZKF8CGphGCKMBkRnrqgkXIa/AkegI7S2JAsqdYFtWjQL0KsriUBGYca8kapsjoZq0xg1ZYkISlFDKchc8ZgispCwZS+pBGY/0lFGOq594E4ioG4BiQy7xko3aEPnLcBp8YVrUKMZr0j9JHkuJy/sq+hShorClHudFA2gwZROJuk4CYs1oKm2j3OH2fLO//2C/nX7XPvm16fbp8U6O16OZwM6/+63rAbY93Z5vLt75ma/c+8Lb25dPYKq9n7wJg/Su834/jdPxMDbBNB721zfoZsNmmub93a2xT7af2cmR0zyNdwIVlWk/moj1aXdx0g+gtJ22/e2TTz743YvHX/TQltK6eQoj4cw9eQ/kLqWGEcpnVKlb9vMxxOXmsoBX7IQwb/UiSajXkBDJy33Fe/eEuccySHM7a3PWxiTgR7MbD7h4XApEj4yB+y+68GBCNA/igdZZJRL+cWk0E7QgIlj8z8y5JicKfHZDkPqDyIcEIKZrwUgJ9GiaaiyjkyWqQch8qQiCCr/Rty6xIGF+c7MvZEJFVZ96JWq6WnHLR0CAdz2x2C7JIbLH1gTfSw83j0STzFY17uWa28AQjOhlxriWIjp8i6qA3Xx/zdBUnCD6r00GbWiNhs12Q7PjbX/ztbdefnTy+htv6xboMkLUOqLHAqZpz/nYj9PYp96neea4P+rEaepzR28dM4YT9pnHjRwnXl3bhzfjyZMb+caTB7tvXzzUN18+e+W1x6+88faDN790/7V37r3y+bP7r+pLXzZ+pe9vp6vP9s++uf/oo8vLD+bLy/143F9dieyG84uzi7Pz+2c2tXu7R9qUhz5O03E/tc1Gzs6MD/q4H8fx+ZPrw83RtIlyGg+bC755Jq884KP7Z7/5/uHpYfyv/7P/5td/5Rt/9X/+7z9+9YfI7SwCjipK6zCHPDYd3BLAqg4YooBQsrosIUkYhWSZMci2hlgSbeK9W5C5tohRqoqZVzWIKCSr0SRJNCJxpi5VIh4UBla8xTm8ZmTDG2B4LkrCbCV4MG9LEgg8YOlWKwVVkH0DXN+W01GRZo0Ab96dzrQuTi+zNgeyGIq0HvJ3/9Zfd7lWb9SKfFt4Lj5BadpCAT0PKyJQURVRVf+hAmhNIdKk+RXz0vwQgfjHIGiqkKaq3vanafP1ldZAKlobeLK1xxf7+6fvnW4/u3n3X+HyGbjvGO+e4+Zgd08urw6nurn3Q3/onfufexXtZd09lK6q6POB/XC8uTvujzDaPKnZ8XAU2jhOh9s9QMVmnMa7w3G+ud5e3OvG43zgOHZru23zyo7DobeBw2aYYSK9kbcTHv/0X/3Z/9b/xO7Gp08+ub297r6CkU2Kmxm962SWG0bELGrUyoMKEJeoaiDDVqdjy1VUz0XHMS/3k2EhwPTOhAuQedA/Pxz+qyGVJc8vMAp8JEGHmecNI+OfSqoAMevZfqNa8SBgXGTVnkU8dhUWIk/cSBJhr4Uw9rWnzGiC4dRBGFlkRsDGQTwr/Fh0ZPl16MZiRBcSKOkk+YKYpRsVG0RU1apft8D8h66mVlrPjh52uEsO0UNa+S50300REZZ2UAg0keXYSzRONYh0cQPgNNVMIkMOmoFdTGXYbge9fv3Nt0Q2JDn3jKKZmU2T9OkwTnfjYT8dbqbD1WH/fLy5Hm+eX10+e351+fTq8vrq+uZ2f3V1O8/zYZoG1dans/OtkOeDjsfDDvP2VO6dtYePdm+8/ujlt15//a3PPXzj8xevvX328K22e0wdpv003n16+Ozbzz9+7+az7/H2ltDNZjMMQ9tszx893J2c9iNEh95J2nR3S6BhmvrxcH2YxvGwv9vf3d0e72yc5rv5+PxOT3Y3R370oXzzk/GTq372ePsH/vgv/uyf+JP3zz/X+zhO89DN0COcUSfgUy88vm1V6RBWmhl8SXFBMO4gyIvAVM0k4xpXjxsyu9ax+FmVVYcGF6cBJUoBUeIZ0hDJtiXtJ1Edh4WRO/mOUYYzQNSV0/6CqLeu2C8zALb4vEj/PbQS3ucRYYzCB/EMo2QVBOXv/M2/Vp3HBFJsj4B6sRqogf7qBkDTAKg2v4bFr1sSbepoL9pUmjYK/DOuqNocTrRpU784zC9cg4BNISfKhw+n+/eenPd38fQ3x5vn83Fso1ze3fZbfvzN/aPX7736ww+3r/0I7r199vj+ZruxGfPtOF5f8XBLscPV1f6wn28PUFq3DvA4TeM4zTZPM8z6rLTeOdt4aG3DJr33eZxciqfZ0KTPXch57hyUnBQ6HqfjSz/zb/9P/2Od9PlnTy4vn0496tKV0caeyJZzqimHjmga+yeUrAX25c/gGywuClZAo3/mEt+TiH9m/CRdqS6ihGT0IUI8SLsRnl7Edoooe2KaqxrrdJMlgyARjAIyLcY4a9OZjuICsFq2YP3eMGhkRIryZZosy6V8cWY9HhO1r4vHKekJOZRz5ZAitTE+DZXuzChjTUESfWYMJ9yi/IZC9Tyt2exxqm4z3GoxVomI80nmd5RjLl9Z48iGI72KStMmaoxLFhRp7UL/RcBsjZoWLOshAGHcbBCjFBqlCUwB2d/efOlHf3A4eXA4HmDdD0y0pjq0pk21tWbq2uXJxTAfvc/zPM7T4fbu02fPP37yyScfP//g2+9/8t0nH3x49eyTm5ub8UiKDeDubLAuwLzhuNv1tsP9i+HVV87f+dyjt975/KvvfP7eW187femd4eyhtJN53E9PP7z+5N27j967e/6s3+61bXZnuzbsdqenu9Ozze7U6wfNZNzfdTta74a5z4fD4XB7fdX3t+N+f9hP18/2c5uBdvhk8+ETffeTqz23999++Et/5T98+8t/4Hac2fsAtT57Gz5xUPXcUMLaAvK+5fClNIl7tpZzUoK4riTE0FHX/WFNdC5VAxlnYPxX/vOqSU4BDvffSzDgeFg0zpjt7kOuLQ5XJQkREauT5/4TBJsisQ4NeWyJNF38AwhWN9FmBoqe9g1U9yxZ4MOid44df+dv/TWfbBiBdI38QxkW0mB3qv4/7seqRis1BNVJd0DF/4WVAfDvi0K1ATo0v5XATLQbm+Hhxfzm4+uLk99tt998+ju/e/HwrEGff3x9/d3r8XCQBw/f+aM/cfrKW8P9N+TklT6f2jjKeMC0n66fT1fPjvu73ruN02Qjx3E6jvPMydp8OEzzTNg0dvaRbIMv9Tyyi25FxPpMd036PEuT2cwmzNM8YSCmrci4n9/nq3/1f/OfnG8eP3365PL5p8fj5OCmS9F4xS7gltT/qtJiIyMWk6yYCbYLHVnCIinQnk9NjhlQn+TejQPALNoMR9bC2wOi0MbMRKLhqLmnLH4e3ADnChHLyxrq4O057MCs6H8Y1zRmYWWdDyAZZ7+RChDQHMSGCYU+fzIJEUS8XQ9T7Bain4EcpAecSYEwskQcTfFocGPvYVcjhJoJ84X2Z2zKcy02E6SX0xuV2ROXUKpQxG80bsEMtfk5IhVRpBMivirCpYklAxdM05a4RjJRCckfEcOMkzg5YS8oBWQ+3r70hbdpmxlm7LCepxDyTtLILoZutmGIkzKbdrLd7rab7W7ThgY2HcfxMN0+ef7ku9/56Du//8H7737vg29+9Ml7z6+u93f7aZo2Ik1lmrvuWhO7OJ1fe2iff+Pkc1948Nrn33j81g8+futrF698YXPxsmBzuHl28+T9u4++c/v00/H6KefedLM92W3Pzofd7uTePW0n86wKm6YjxETncTqOt5fT/uZwczXbeHNze/n0drrm/Gw+e/Tw+Sfya7/zwbODHfT+v/c/++9/5af/6OHAcZrUzKwD3QsNQAAmfjs9KX5zdUY/U1myxpLhGbPIt6w+hvVN1KmLpKhY1ixkfB3JjfJMn++jBsIiE58r85GwHSfM66VghnkrJBO3GdPq2HJUEmTOLwHev5hVTNErNe0Q4Flf9yIq1FN/8cH5sgggf/dv//VVuhmiHt6N8E/guyul99MUOLjT76vzAIGqNmlo4Ri4xVDvvynBTbxgyH8kDQpyMDHBrHL3yv3LL7z88W7+jenjd+8+uZxvd33a3X042zA8+tJrL/+xn7WTL+nFfZEtZph13O3n43W/fMo+H2+up9u7aZ4oZtNxOh5snPY3d8fJKI1TB9h77zNsOg7brS8LzYTY7ITWx4mzwQPuM+d5tOOeu+1wmAVqMh7vLo/Phpf+8v/6//jym1+7/OzTTz/5YJpnxmkfD/d3OJQig2eRL2fwZefWFmdDFHFkNQMGDs40Vh1D/PGazKAhS5VDvKLTgggLnOSjrA9W1xLBywDY43SxpA8b4qBxVRNWHu9S1mBgi5gRUvQX/p3cJV6Nyk4D7quiKqNY8uewFXbCqh1jtnVhvT08iyzTLEsQpjL9ApJVx0xLbz/VcEW5IlSaNrAsm6o6FVM4ZQEAsejz4r1po2BKmBf8xkiYdyIu5/N8gpH5r3AbhEo2H72CECVhUIqJOti0mIUnPM1smnUnj15/c5ptDNhwsx0lWEkSWQYuNiCsXO5VkzYMJyfb3cn52dnZycmJbpT74+HZzZPvfPO7//o3v/3ub733/u99+unTq+sbULCBykCb0KfTzXx2YS89kMcvbd559f7bX/rCy1/62kuf+8kHr31pc3Laux0un+w//M7Tj761/+TJPI0gNpuT7b2TYXOyvfdgtz3Rza7TjJ1KdLPxth8Ph9vnPF5ffnp1tx/3n/bL53cnL2P6RP/Fbz7/DMrh4qf/zJ/8sZ//i0dpxi7stFmMUKKwPBh/tuHKiF5x+QBuZFVNgnswsTgOWQAflhcAFCqat7eioBxFieq7q7hS2YZscSbsPTPCQVM8VhQOSKbHiqfHk3O0ScdXVJHRI3PpTbR4LZL8BkGALJVDUnNDOuEBB/m7f+uv+/sy3bsOq4oA0nwi0elGRLQF30e0zwdEVVSbZ3LVO242aaLQNmQzZlFtIFQhspnNiI3qfHHy/LVXnrxy8a3d3QfPv/XufHP36XvX0oaLV04ffuEnzv7AL+rDd6w9FtuQXW6vZLxu0/P55no+HMeb2360Po3d+vFwN882jrfzONo4H6Yjx8NM3UR5V/Nz7UYImplEkqSBfT6ONhOEWu/TOPW572+O1pTYHQ+3Oh21D/ODR3/yP/rffv4n/uzh6vKj9787Tofee/fqKusCMyM71IHeOwQwDW5KjyOGQL1lFVYnStxtZ64yIlK0sAnxzzPIY2JNkZeM86Us0yOGRtAbd5etWAQiNShp/6Iz6U0iQjH+fclygpVW5PiWctLIM2CR2Ax2Je9imYiaA1kfiM8vzYLSw6i6ofUEyewIXQYB4qk8uoBCnLNrVG+3cGTVp04CnpNIHz2sDk2ohKMJvUXbEsL1Ap+8DpCdrEMZSe3NIM23fk1bkf/QS4DC/ZbaunSwCIB23D9+5zUZzvfTLBHWYxYvEWarhUaVOyW8ZETIYDCjGQ1GE22iw253cXq6Ozs73+0gw3Rz++mH773/zX/1nd/7+ofv/+6zTz7e7w/720m1bRW3+2mj2Ozk0SnPL/jKKxdvvPXGO1/48ptf/tLLX/jayaPPNQzT8ebmk29evv+t608/mA938zydbDabiwfbs7OThw/bdkfZaNupbuzYm/Rxf2vHq/lweXd3e7i5vbm+vvnkhtt5Pgzvf8RvfPPJ05vha3/2F//Yn/sfYbPt4wSbwV6nXgAyFzTSYB7dTnvvcGYZCO/dg0jehXBh/CtnNyStPFDJREPooFcEBVAm11nAOXUqRNJScWwh8YHTKcLlVbhyhcdSroIHiChAVPQTWA52xgZn8MYLzcrAhMMgpVBRipIBWhF0yt/5m389jvK6hhnQKiURXZYC9zUb53vcX5pq3SmbQR5IU8XgzF8aRAb1bEFe3iMiYtzOM042fPni+VtvfHD/9Hfmp+9N7332na+/f3el88lLP/7v/1v3PvcV3v+aDS+ZKSfT+cjjx/36Ce9u7HBzvL6dZrPj3Kdu03E8jvM8TsfjNB7mqZPo7Oijc2FOndbYrQv7DDPMRxo8VGDTNB9up9nYrQ2DYupUpY0n56rYnJ8ML7++s7FP9x+ff/GXfuRP/y/Gm5sPv/feYX87zcfeTajWuzsDdKDQOogNoqLxZb5zsSEsjz/YS1/JURmAEMOwwa7Z0bwzeuwweFAhu2NUsJ7FNQ7JScsRkiQRluGKVqfRSqYc5Gph5Jlriiqm5B8o8Et3BPDKdkk+uiKqpT9m2loFurK6P7yAzEPnUWpNRhLtcJ0uiRt3bUqIwPNRcTWmtKRowZd91fNeZx+jD60O1oMkrSPDCEEY/NxcHAnXpY+LW00y6rZUhdEco3khE7zlae2jb7rG5mrIgSAP1xtpMzBPD155fPr44f44d/PaOwcUZn8nX/K1FagodeFgoNnKO4HQus00dqXKsDnZXVycX1ycD03sdvz4u7/70df/6e9/67c++O73bi6v5343Hk02w/5q2m3lZKuDdtnw/glef/3k81947c2v/uDrn//yg9e/evrwpWm6vX3+yfOP3r15/7uHZ1ew3na73f2L7e7k/P6D7fmjtrvXdNv7rDrYeBhvP+N06POtjM9vry8vn9/sbw9TH64+4z/7xkfv323f/oM/9af/1H9vc+/xcTxE19DuHUqZBRIV5yuZg6anFMKWvDYg0kMs3lgi4rLJcrIqzpXCnSlVjThqPDzVbaUgWJhR1pGx8s7ifBzlMqTnzjgQFc4MyYgXxbdYuaHFbCRnM+vedmKZtsO2P7De40X6TJc1HyGE/N2//TeC4gXmhyX0xUqWJAAyFRz03xdF3VsWqDZ41U/4AB7vad64p6mYaIMZhk41k3sn11987ekrjz8Y7IP5o/c//pfvfvzuUz0//6F/9989/eofaY8+P+OcPGGn9mvuP8X+Sd8/HZ9fzfs9x2Mf53nifDweDgdYt3keD9f9eBTpvXcdGv3erpkzlDJPd9ZnOx7nacZ81/tovfNwmAfVqc+bTdudtO3FxYNHp5u22V2cDCc4v78bpNHGk/N5d3J6ed0+/vj+j/7lv7W9f//Db373sycfztOx9zlRz1zx0hskRKx3cTFcwtoSttciApKV+1zIWoJpsWzPKMazDRmCjxYDddEyKkIeMo0MdwgA61EtGj153JZDdVBvoGsRwHCTkUe5kMW8dX2ND1BJz48hw1FZbZByhAWe0s9NbZTyNr1xSgxGBFTxajk1/2LcL4Q8LS4RlOTKE3fbJJ5h92uBFqPnGZFouUNGvy93lME4Ma3qMTJ2M29QHFaq0t7V/hogxKyze5g4P1xOUdNwVozRib7WTWrYhROVb0sRAEmF0cBB5eLR49P7D/aTmc2InKavao9/22KPg1DG61eklMErUVdcYvU+eAPqboSqnp6cnp2fX5yfbdp8vLl+8q3vfee3//kHv/8bH73/navLu3mmdc5TVzE7GYjpbJhPdtPDB+3tNx99/p233vzyV1753BfPXnpDh83++umz773/2Xffvb183sf55Gxz8fD++aPH2/OHu/ML3Txout2dXnDq/epT6u2gd4fj7e3Nx/PYn377s+N4vD3oP/6197/9fPixn/3Fn/mT/93N+aNjnzlN6AS69S6e1WIe1IrzJeVeljeV8dhVG9GE3TT5uYHBlPPLhb65Z76dUgtewBwVxILlmy7kPW+iEKmrntOnjyozySLhtWijfN/MLmhm+QMkCNJElZ4RyuHBT8CJcMlqJ6tzOfQCZaP83b/9N3wlkqHGZIL8V4YrFFRFo7bB20SrqH/dA/2qqp4ji/9sDRuvFzVqB2lK8vWH1z/45d8/236zffzhZ7/73nd/4+r5gT/4Z/746z/35+3VHyIeKQabZvZRp8/s5nt287TvL493t/N+38cRk03jNO2P4+3BU6TzeJymsQ3o496k73Yb7XqY98e78fbIvj/eXk/z2Ptoum069c1ud3KyOT3bPrx3f/tgd/HwfDgbsNmyUXUwtt7v+nzHaQRh03Wf+mE/fPZk89U/8b964+f+7OUnn733e9+8vf2s94nw9nVRxRHRZwPUy9MY1wFlN/xCZADm164lPfeddborUdLra1/gp7DqlxtORtL9pDX5JQhFGrg4lFK4LER0DpEqG02kC4ZDM4/+IaOWi/C7rYjPpwORYh9uTnDVxedI9Aw3MBN4XJQvY+UgDTMqFklolE/VCF0lHJiZQysMzgiZX+ropZzMMHHJuZDdG5r2ImpJ2ryyNu7SAAB0z6WH2klcAmEVY83ET0wqDF6ZVGQAOS1k/eMWK/z5sIRD22xPT0/u3xu2m7FPc2d4QlY8t/yWclgQPkGOIb0uP18GSl4OaukkhO+QIOnO2mxGUx22283p2enZ2Rk53X32ycff+PV3v/EbH3z3vf31s5ubw3E+bnZD27AJm019nszGi4vhwf3hnc89+srnXnvzqz/y8O2vbk5PjreHT97/1tNvffvu5pkqd/fOTi625/cenz58/fT0le35w93phVgb959tYLbtNh90GKary+vvvfvd3/nX18/Hf/ru5ftPdl/7xZ/7mT/yl+T+w7Eb+wyadPO2S356RBLYfNqVpE3UJul3k1MMgjwkWR6A1L8gydbpiywreoUFS1HEfXHDF90kUpjSCxOsRKFYeHwOmQxIeue/1ryrw+CoG0xxdZ2QAHWFIIP+eE+LSh/kyJcXQUTZTf7u3/4bMSWNrjZIcyDVB87Dz6oi0AwB+R2M2jR+5eleeAGQOwSDiAzaiEb0zmFW7mT6wuf6V7/64e75P/7sW+8//cZHtzf788//3Bf+3H+7vfzjM14TnNgM7bfks3b4TPZPxuefjDeXfRyn/d24v53ubueu82Q2Hubj3dy7yIbWvRKiz1Mb2Odxuj5Md9Pt1Tx1DDAItifb07PTe493pw8uTh5caNvQ0AZAZdDJpmmm9fE4j9PYOR2OON7ObTjcTfc2qvv9NA1Xz8/uzn70j/wv/6YOu+/969//4IPfNxtb0zo8moCheUAFKRxQOHBrayraVgehkaW0yRJUMigsKTEAvAQrRTZqGzVKHZKdY11nIAn6Ue0vAV5ABTkl+UYZ/oooLV5LBAddiKtev5ShUDr+J2PTbilMFh3k8tUomUvGBcAYPSbCZ/V2tAp47b6oM+vwtbAKnVWuWgnvrOxfTNbvdNxHInTmHnjgzNh/61V32WvXnTpj4atFbEXq8pwgh8Euq0VA2WNkW+nwi1paX4hKk+Y3Ubdh0DaItjZ47bSKqg4DVUHOvZuZZ7GE9H4yS9QqIAMLiAQkvHBPukTcIzcirV2FMDIgkZtIAc2MJHXTTra73dnJ+TDsb68+ffd3vvuN/++33/uNZ58+EbVd29zedozjxb3d1c1+OB0ur46q9vDe5nOvnH3pi2+9/bUvv/GFL51c3L+5ef7xt7/12bffPR4PJrx///zBK288evXt3dnjk4sHu3uvKgbOs8im7XZ6+qqNn/H4wfPv/ManX/9n3/347p/+5tP3PsWP/pGf/4W/8D+chov9YcQ8g6bs3jYpTlv4cRGscsBFeF1cMk4ZoZEA3QyiY6licBKw9qNiGyn5NX4fqKZHGxc/lOebkuqZ3mhusdjehXkhw6NZBM5KThS3Cm+4dtu/oFl9lIGf4Hpc+B+X6SG9a3AoxyZOoC2YU8ZKucy/kpDeR2spAxQojWwm0lwJlYRqN9LYtWGeH5zPP/hj2889+Gj89Dev/9XXP/7Wp2ev/fAP/vlfOvnqL3W8BZw2DDRrcs35A7l+z26fH++eHq6v++E43x3H/U0fbby7NWzHDrBzmkw6O0RnUhQDiPHJ3bQ/Tsc+YH7tlYcnu9P7r15sz05sN5BimOd5NI52uJ3m2cZpf+x2PM4HTrNN+5HW55lz731/3G9Ops5Nn3bsGxmOV+NvfvQPzn/8v/iFf+ev3Htwvv10d7ybDKZEa2EkY+0JN9GsdklpSyMzM0QtbRuc8dP9K6QLkG303NYu1MO30s9khRYn6DBlKD4eQZS8icvSb02DIflBrvouh5Qt13uxOLz/tyS7D1PkEIqE2uRFyShzJEmIE2GYueJg/tENMwFXINILkUxErM/OcBxYLTvSif+WbmSD7Qo0auiiRNdPZ1vMLsInfkoBXklrPVC8ypMCHmsqQcmcYIVDJoiaZ7S4c1qbiDQdmgSai2pTFZFBRaHwEgnVxqZ+PhhZwUPSV6YLabMFArlXFEmKWBPEzoPRjmCxrxF6sKhPDhyMKqTQ5PS88iflkwJJHrUJDNZtf3N7c3tzudncOzt768f/0Btf+wNffP+3vvNbv/rt3/vG5fOnp2eb3Ssnp/P4+P6JsD3YDJ/dzDc3+O3nt9/69r9+5RvvfeXt3/jSj/3Iaz/wQ6985UeHexfvfePrd598Ot2NNvbp5vLB41fx0ivHm4/P7r+sm4u2ezBra8PJsH1nr6ev/tQXz197++x3/8Xpve/98q9+61/+/X+4F/kz/87/WM52NzeTmHQ4hVYI1FskBgcug7im3hGokYh/Lk5zhijL4MsC3xIaFzZ1yb4GyOb3JRh67YA7xwnTzqf8AKykViBFyvxYe7i1jHvK6tcMranIJiq7Fydngj3GZi8xpPCbLfx4F4kYtlAGhMObb84Ig4RTkCmzxSgwAIhQhRlVWXTNyW8XUaO2KGHvbCrHN189/NDPym7+4Pab//yz3/pXl5eH1/7gH3313/oP+ubHZ7wKbmlm0tUucfxInn+bVx+NN9eH28vDYd/vbse7eT7cWQet2zy1CH5JawLhMDQq988v7fk4TLZ7sL3/xqPd2ck8i83DZIf99c3xsxnG6ThPdpzHWY3zaJxtnMgZh0nnicfRCByO1m3uU7dd388zbRbYzbOb48GeP+cv/1/+01/8pb9w9vD++fkZx4M2SNF9ySxltP1iZkg9fx6mVqKyUEOG/MNusaNuUCKBy5JDQrJEIHhbKLMXRv//2PqzYNuS5DoQc4/Y+wx3fnO+fDnPWfOEKgBFFAYCBAE2CYKUCGODlChQlLWM1iYzqdskM6n/SBpFiS2zlmRsffBD/FF/qEVac2hJaDYHGEEAxFAFVGVVZlbOmW++4xn3EOH6cF8ecZN6BCvfu/ecvSM83Jcv9/DwKN4BKU4ndqnujWaBp+mL8RiEeU4ttY8mlUIjpyigzWTApUlYA1NFGzs1E1ArafwewbOzTfHwOXvyRSdqzXVZJOtlHsrGzRDUZaFjaeWbEpriott4IuuBla15qlbD2I0yTpvFZYbtATvdoidYou11BePsITSxbWJsYhNDiEGZexMDs4QAjNANdu8haV7NTgloaoaEZNTCX5yZJkimbHDUeKL0zqRMpirKGNn9AlsOkp2ikt0pR6CUeFZJBNlXg3pCUjJrMWgTG6KU8vnFxWq92d+f3XnpR5967uuv3vv+D//gX7z19u+uT0+akCZNG3u6fXX32j49XPSrbbPt0gefdMcn99+7f/by+x8/+8or1568/fLr33ifv719fLw8WW2X3bgZOHft7mHuV+1sZ3Zwc7J3M6S9uP/Mzu71nBb7z+1w3pvP3ggS/tVvvvGDf/1vjub7X//Fv0Q7R5vtkhKJ9gsCPnoezdTSuLlWQhXAhTp7VExGw8gTtGrLdpiKKumKwIIEGSOj3uy2Eihk0GmECORJTg1YvfGnSA6GBqK7cTkDxBHOEoPx6StxtQtUy0IMnZ84bnjZHo7Ucwi4fYqJpHHIMHrD8GsiOEFKRBRK6GkEV/XPrr8JIecUYlSJR2KRmDiQ8MgSxounbm0/86Vxcv7Wxdtvn7/5g1WaPfuTf+Xwy784xGeI9jg3kkeiTRhPaP1xWn0SlqfDxcnydLXdrPrVmvtBqMlDSDlJplYyU6ZJjLFpmDLLsOrW55sxj7Ojtj3Y50h9t13dvVhupFuPaRj7Po9DFsqyGrYx5n5MMaSUKIRum8fM4xBzTuM2BG7TMGGWttmlvNdM2maS5wdXXnj55uTGM3deeWl1tnrrD7//1Gdfn7btZtKywYzo9fDBFAQxmC+7VgiykHUBpUSJJFh1ZxIL2jV6yqAehB0lIWsWIxFOoECyhqsMHQW303gzOhAhE2MRg+WOREtWSONT7WMMzAJTZHgLZCaNqiQ7xIs2BlpBiRt3xUiomZhG5vpvu2Mjk5BoUy3t+iKs4wB5z5JDthJOLXnPWnxjSquxhfdvU/KerXenaMsjy5t7X1VkZW2BIjFxiMwhNjE2TYyTpp3GSdM0MbZNaGKIMTR2iMWMgw0wkFkRseu83KuQ47l7GJN/rv5hVuu83hHfmomC2Ru3CIzaqkxWZ26M14qlvI0SaWdvjXMYDtXwArhvJUlCFLVvIdohENmmiw6ciaUJUYRTGh8fn56F5e7O/u7tz3z11usvfu2td773r9/+g9/7+OReK+H6bnP9oDm6uXOy7E/PtsvNsFr2H37SPXr0xrvvfvLyKy+8+JnPP/fK1x/vfnR+74OUNxdni3EYdg6W+1cOd/b3aLukbjEZupTG6c1Xqbm63baHr13n3dtfu33j2sH013/3gzf/+b/Y2T18/Uf+7KydbdJWSIRyyEJRfax5SUCuNj8I1maP5JIOEGTPHOoSWsS29SqGgOae7i403wSubuvPxoXryl6GcWLnx4Nl5wZmGWpRgfzwihkiBQ644hGMheGTjIrBU2nbFLK0Pkbr+7xqjwr2DRiFV3GrAlhQb1cQmC4UN0hZDwRrpTXrYTyxvp7ErH3HhDIHvnjx+eXrr/TD44+P3/je6b1Hk1uvfeabf4qf/tmebhG1kkOQjuIqbO/S+kFaPEgX99dnF9uzk9VyHIc+jh0Ti4xCzCkxcWjaZidKSDIM63W/Pt+sVtK2eX6lGbvh9MPT1aIb+yQp9xuK88hZeBLTKJv1sNrmTZIuZw48NtMUZ+18Nj+4enTtxsHhzaNrt67uP7d3/fru4WR+dL1pr8bYNE2S0HLTJA6B42wa+z4//vhuO81XrhyuN9sxjVF0F15D9aSZebsIRrXLEM3zLHr9Uaoa2IrgrmfC7qgHmlnEbF5GLHp2esOgceIwJ0YeSEZff8J5b0baxrhqJibtf0VZP89WAU9S+hjnEXefBWi9gpTm7rnAHDMLB7GTSnAZtjeQ0YoEKJi0kCVrlwbmoBesqI9SlxM4a509MYkkFsaVwnW6Jtv1Y3bxkzLfQGxHV2LU5iWK8k2cNDGG2DYxtqFpQmxiE/RAi7EzFak1GZOswxLb6ckZLNlZNJv9K76Y74T/tiHpJqWVA1ocJkRM3tHFZMZu3c4hTSQWUlKJ+MXX1x9j+xzOENFUWZdIT6J4iYdI1s4WQVjQtI4FOXICujGHGNu2zWNabBar7Xo2m+5ffemLP/nKi1/4xT/63V9/441//eHJ2aZv71yZPnFz//r1g/Vy/fG98/PteLHMZ++dPjh+8/HjzWc+99rh9aems3bx8JPV2cNlv+g347DdSr8NB7vDajPttpOUJrt74ejldnZrm9azJ38kXNt/fdIO9Buyeeu3/9k/ivP5y1/8+el00snIKVLMuv0mnBl5G7MM9v+AKMMqLSowxmtQqgvjHpnQGtQi3+CxEUJxe0IFoUSCPIxDNlYb1+jBTxtlN85klxhZFwfcQ6CHjQtHJ9g4MjyMBmDIDjBZu1/GGrIdMC4hjxBxY+Sf/KFQWSy+paahDUoZLBNlyWhhsp4+KqI+EzGPQ94J5y+/fPHs7bW89+b99z+8WMtTn//jN77xy3n3pUT7LA3JSLyk9ChvPpDF3f708Xi26hePV4tuWG3GHGSQGELKqe/HcTvOJuN0Pg2tpLxen27WJ8vtahhZwk6T8vj4o26xSEPOKQn3iYXHjjPF1ZL6JGPftNO9uHvjyq1b1+48f+OpO4d3njt64vbe0ZXJbMZNSxz0xCcRk6RERJlyzp1EFskykghx6Ls4jfHKUzcevff90DST0KQ+Z5IsKY+ZKCk6BCUHIBJ6hNMIRMCFUNqi3OkBIjGnkG76VPwGlRXz1QKlrDQNDMj/wUSinYurl8AyigaQ84BCUAnc4lOqj3iYMAABBwGVtVeRf4jUk+Hz7hep9C9E3IxBFClmHGVBC0dlMsSaio8xcohNCG1oGiPwTat/CU2ITYhN5BA4apEV+5a4Yby6EUQSiH+wGmLsj43/C0NdjEW6WZHe/oGp28FS8owNEg3FZ4vTTsLJXlOJskUIWLAsgdspmauz3u3ww1gFbNFon1dQClQskwbwdgkPsWi073prK2lxBasHjBxzIEp5tV5s1pvpZDo7vPZjP/erL375m2/+7r98/w9+5+0PHt24kZ987tbT1w9uPXntg48vHtw7f3y6vjgd//A775w/evzFr33+5u2nD29PmjZsL06GIS3OumF7Mqy3uzubbns27y44bXeF+eA1ktlIEqbPNc8cvPzzB1vJq3/+xm/8V//V7tWrTz33jTxp+77jHImzSGKKFtmyWImEeKITZESPpgfC1c2YKKiUfVSs8YElW0ShGjtJmhpxKKz/2MW2/mP3D1UEAVLkigOTdaZufzWDyObIcFGSY69/nUnQfZrtmjMClhdLZsGLApPw3/s7f8O8CfyGS41R+219P4mFODITW6Mf1rIg3dsKTMSReWAiikPK0/nyG5+TJ/fvn7z94OSHH4U4ffrHf37v9T82zp4VmWUJxCnkE0r3w/m74+recHHRHR/3Z+u+Ww5DzuMYc0xJUrfuU4pt0+5Mp9OUu/X2Yntxtl1c9KkbwiRMp9TnvDwbV9ucQlyvxybk9TbJduzGnX62N9u/evXZF1760hdvv/S5a888P51f5dByCMIsOeeUc0oilHIST1DnlIkoZyOmynNz5ralTNN2Opm22836N/5fv7671zfTXRJpYpQ8mo0qV0w5i3X1d0Oukvpau47EAQLJWo0qRqG8zoKIAviX1c4/b77HFIkRTwYunsHzOPiG2bol2dl3o2070RuJunICZgyVDJG4FBoRtiDLZ8k1XF8F/be2nBTYmkcxUTBvgM5SgWPQ1HtsQ4wxNrFpYoyxNf4emsLfL2m/ezwG0lMVPpiAqwIbKjUzGpWzReiePxOcwjKFYey4IKTHg8SOJOVMeryLwKolW18wG40n9JHPwt9rz44BMq4PdrCwNCBXp4SBCua5zSPlLOwaRP7JEkVSAf8yI/sfVEBV6WZiycLNfNruznezjI/vvfP7//K/+fCH/3Zvp33m+tWn79yahNmj0+X9jx9/8vBsM24mTbxza/fzr792/dZTzKvNyf3l2fE4CDM3QQ4P9vavz6mR688+Nb/1xd1nfozmz+Yh5ryStNyJy+VH3/69//rv/8Nf/+755Klf+Y/+04Obr2xW637so2TBVcFa1kWYpoG0R6tgIL7vZdiozUCoEl/xvww3DfOF6LGHGkqNPxGRXmXqft3c/6XmVwJn46evMsxBCi8z78VIL2F14FB80XykBB3ziEaTrXip2qo+DecAbGvDHk1MRCHiMiat/tSkl5/bCYFJQmiYKHIIFIViDpQ59KmfTlbf/Fpza+fe6o9+74P3zvdvfPG5r/8Cv/gj0hySRMqj0MB0IZsPxuX7fP6gXz7enK2Gk4txk1MWSg0PHHizWZ3kPDZ708lumwNtF8PybLFedN1qHAK3MRPz6qxbLIaLi4F5sgnhYhliQ5H3bj7zxM2Xv/nS179+64XXdw72ZDJhjjlpiU8exzFnySnlnHEXCnwk1+yHJIsedpOcdZefkzTtpJlO+/Pxne/81tnpA46BouRBJIlwlJQ5hFKIQ2RVnLbDrnvCwbm6cVhSBMRODBLOVUQKXNJ9f3f9iArJ2atiSCZtc6tMQyx1DgrgaVBHaMe/so+JGFMrVtT9uz24EzF6QNYDigMFRmFrYPIWskGria0tA3sHQa2WCSEi566VMwHHCJk5BnsPV8Pg4vQsq43/fwnUqAoxLFJxwi7OhWtMdEl7EqT8EH+xb3soVJy1uJUCgBANkNP6CmiVeoutnw2N4SQqp8t6RYnPjuxH7jOwc4hzyxmtI7JVi1sbEiruw7HGN94JrJmZ9RJvgIZl4ahiKoEs755yihxns912Msvj5off+e//3W/8N6t7j64/sfe5l5+/fvN6n/v7H9179937y+0mj/lod/L511968pknY+w3Z8fLi4X0xDmKyM5R2Dmk3b1w7ZnnD578ys6dn8izOzk3Y3/eNHnCm/Het//7v/9/+ye/+f2dW5/9xV/7j5vm5mZYi4yShCiZ5y8C1qxcQOk/oxNbNo4j7h9c2IBSFI6qkEzChQ6ZezU/wahUxh8xrdH9s8wxuISpnDRWl4B4gIsTAbdAvZ4US7cTXtkPmXr5p6tKINL7P/SshPsFZFeJgjD/vb/zNwrlq8bOBlPk1/qawXnDTwBD4KiRQZKQQ0h5iNPHf+xre083d0/f+u7xvYvbL//44Zf/XDp6fgx7lBuWkUNPwyPq7uazD7rTe8PF6fr8Ub/cDts+p5bSVNI6b7djP/B8nF/ZaSKnzebidH123C8v+m47xgm3O5PNcnN6Mg6ZF9s+rZaJ53l+7cpTr//In/gTL3/tx+ZXbofJnALnod9uunFIwzAGyikntmvPQRExZ1WGgNRCEQb2z3SzX4RIUoyxbecNx+Vis1lthn4QGSRbhpozZRnVjK1raojWKlVJboQgkajGf637Kv6vJvoGLGxaQAYsrm3F4QAhLGtRcRr7u6kemILY/jOcEspYcckjqduRWr+Bw+UgQqlXIPFyU/NyVV/BgqggzkaBdeOYynac/jIjFwPoUyu2kKhgsyGtl7sW34BdMCsfYjf1ksGCcJiMShfYg9z1f7P9AMCAMnt9okcUbm4iSPpoJv0Sx2dvYmDftzYyxSXpd0BU4TLEX8fseG0XnGmJSPkMviB+4tTVBo/LtsNsndFs/8bqgxUxvaWJ+wAyOINXzkmIOXKcziY7k8nx6Qf/7l/9tz/8nX97MOtfeuHJp59+eu/K0dmjh9/+wx8eH5/1Q96fNl/6zPN3nnuiafPmYrU6vpCRaQxjWB4e8nyXdg73j5564eiZr+4/9408e1aEZOgkbWfNIn30nf/n//U//Re/tXn6a3/iZ3/pL8n86GKxzJKDykQbcIRLXh/U14m1sVu0F7yMf/gggjK+pCjQOf2JZvZ9lct2sq4PMc7oWTyq2nXpHgL/NhSrcH3NwaHnqPoENZZgezm+a1RzG8NrQfYZwSkjJxsoS6P2zmCPhE0GY3kiQW+UZLVuRAsqXWu7JEw8MAnFPg8H882PfuPw6u7y/Dt/uNzIs9/6c7Pnf7qfP8syDTmwbCgvpX+cFx/0Fx+Ppyfb87Pt2WrsN/2Q0xhyl8b+PIyLmIfp0Xx6tJdTWh2vTu4ulifdqpchSTNvuyGdvr/oMj065lVqNqvJC6+8+IWf+hOv/Pi3rj39Ou9MmJuxT91m22+3Y0o5Jc3JZd3G4BxCrKIkrGlJh1c8wKzH7xTXhQw55S6vtxKbWbu/tx8ocmO7h4Q4jMqOU6nXcBCDTohThQrKkLarcgHlP05lqkx4Kf/FtDygIXQMLz+tnAg7qHqKocSV9f9cyhEAUYu8/OUuN1xcQ0IOaeDoTtNdwNVEFe0ZduiBS4FJGErFRrngvpSFMqvCe3HHmlU6eZdSrpZBisPDYMruiri8yoQK56woGDzM5Yw+IWJQ8cN7+zZuySq5NNV9+lUG4HuMLh2E/cdg7aMNeJTJqDYGiwYIl+l69ljHYN7ZvsJkVzH7pIwoXEo5s2axxACJcqQoIeWUNpthSOPhlRd+5s/+tVeff/Xf/X//4Q/e/ODiePXMC88+88JzX9/d++H33zl+cHx+3n/ndz/IKT/14q35/iH1vHh0JkKBpqeLtTTT/myd5F2OPJlNpjdC3nmC4ySnsZPd9vaXf+Yv/y/f+fi/fOPf/OaLL77w6jd+bmdntlxvMnPI6sAy5+BHpbFDy2wt/GBarFVwku3+BhIh3BtdmoKAk1gdjtmR4TML+8lcto+K2AlPwlaqLifbEtsdIQTIEQKRsvSrq5VXJ5u1+zREKxTUr5dbp0QbwEDRAjkgadpfjMip2/57f+dvOp0heAggIaAOeuO939ierNsOUZhGCYOkZnf1Yz96dGdy//yHb6bt9pnXf4ae/+kh3hKZc2aSnmiRNx/l5d10eq+7OBmWi83p8bDqiMZhSGM3pH4VqJ9OeO/qvNmJ27Ptyf3l8YP+4oJFRBpOOfdd7kdZLPNmvdqMezdf/txP/IU//8qP/Pjk6HpOYdgMfT92fd/3vVdpcOCcUwgRJ+OL8bgFCWUsMPgdgBU0GGte9tOdM+k56YA4kQinqawrh7N4ZhOv2P3qppmXvEJB3E+hcO3M+RKY4LuubxU2+NpWbkaAXGbgnrlwGlF5KBSaVEzikoss8sLvKj7ihAa0yCNa4CLJpb8pq8I+NdVC8HGx9041K9LhezO3MkgL2PEDKU8o74ToLv0T48R4qibTnj/RJzLWsfZo5QuXJHVpMJiOwE+bC7vMEeogpOSRytAAEA5qVXZbPavlk0pYQYRu1bXjrklAxmWilTvN/k5XIdBJ9SnMTJk0YxpjiHu7uzs77cXJu//iH/0/Pnnjd452Z0/fvv3qFz43Uv7wg/cefPjowSfHcdJ8/jNPP337JstseXJ+dvowTKmZENFw5XAn0nh0c37r1c9ceeGPtTc/l+JVGSnnLlDf5tNP/vWv/+//1n/+aD37a//Z37j65Muny3W3TSQ5kPXKDrpT6swmkwTtQkJZsvd0g3egnOyCMKhgtcSKmYqgHschEmUrkLH1o2JZpegLqSJfW/G3Z3GMEadv4orvOAXGavgjYqfN7Sse9JKZj5TTXGWdtekDR8qZ/8u/8zddKSytGqygVTksk96mSewpoRhJyLrViN153lFO9OjHvzR94Rm6+O7v5Lz31Fd/Pt/6kYFvhTQhyUSbPJzm8VFafpzP7venJ9vFxXaxGBcLGQIlGbbLvlu20+3u3mR25SDHsLq/fPTR4vxi6Ifc0zSEFNKwWKblWbtkWq8mz33l5a/9qV959Ud/LOxc6bcy9GO37rbdOuckImxXraISUZxxFduBo6vOzpV8ATuxK7AJukglSQCWoJCOAMrdC4En++Jd9q+2XFINyqG44l71x8WfUv0OYX1RE3/2Jd9x6QmXkMlDCqqEVBiM1AMof2e6JKHLzsEfKKadOkJxhKpzQPbCovQFT92/CF2G3vJ6ctj1KXpkjfkVb0JCdQd2SEzKrh2j37oWdeH9GV3haqy3/16WJ/3/+QFVvtBjuk99x0Xtbvdy/AcBEu6YJXYpCNmJE0jX6mVrj5yrOpbip8yVWC9MHMmuEh/oW44V83pRfa3dFGQkNegxhBy5mc125vsHPK7+5T/+B+/8zq/vEd148uqrn3tt5/D6vY/ff++dD+8/OJ234c61gydu3ZrN5t36Yrk8nk1jSpxyOLq+M52tbz1x9dbLnzt85kf46PXcXpEUhLrIOSzPvvsP/g//x7//T5/48k/9h7/217fT2fnFRlJmGclaSKnZIxKwPBzYD9s+nZ5dUfuUnHGOHKjpO8NljRjQT2wfs3DA0CaI5BJsKYDWzxERu9geTh+AYqOxuzGw8eDEVOBNgreUILZb0qKOFp8HflOVIRJxjGPK0jggFKKI+iEDzDrFoBki75PORCQp5yy8zg+++mp8+epm8eb7Q77y7Bd+Lt/6xkhXKE9FhPJK5JS6e7K5n04fbB496hYX68X5sBmkH3PPuRuG9aqd5/mVg+le0/f59MH6+O5ysRg74hCbTDR0tF3lfkhDnuzdfvFP/eqvvvTjf0zaw/Vm2Nw/X216zkmvwwlNw2nUwhfOOldB4syuyeEKm3DCAkVghuEAdSmCJJAeyyZiSYQoqhJpAClCVvlH3vrDMERpuUGmb+6bx1X7UaJVW+Bl/m2W6IOFQpbflJQ3whEClnDZ2apiB8aM3Xe4Q1QqINUnpTgaiAtxo//QC9LxGDb0IH8LjiHhN1BtiB4frQh+9XdIiB0YGecTBA8kRMx4MhFZVRPOQoBouXBAeeGTWMiJXa5EJjg7g68UGK4XgRBzmA5xha6ilfe20N7WDll9BXrBapJ7O7zLr2YrHJas+MWROaApIRF5IsuyDlj2CEHbA0miXWroMkZHGCwMs5770XpSEj2xrTtGTCQ6r5iY1t12GGVvf/dn/sxf3T3c/+5v/LN7p8f5+2++/go9eftZbnemux/cf//+2x88PFt1Tz9x5drtndBc6zYLSULEp+fr62H30ccPYp4wzfbDJFx7JU+OJDWZAs2vfPGX/vyf+953/uG3/+23f/u1z/3En5pPJ5ttR9nWU5gpp8ARclYiUvaAVH8CBRWOgx4pLOgyaUYlw2T1QdoU2vQHZbhKlAH6oh3LXZAmVaSjhCUQWz9BcxIiMP8QMRyVf4k1iNnTjhy0/Je0yAwbbAoILCIBB3cEGg6AY2Jq2KzTLaRUO+n9vgRXAhDILNFzIVmky3FIJ6+8OH7h9d3N97+z2sye/NqfGp/6itDVkBuSMctChrvU30/n97eLh9vT0+3x2Xa1TcN66FiGsV9s02o9PwiHT96Y7tD2YnX2ydnjh/2iI+HcRB576ZbSZV4td3duHHzpF/70N375L7RXrq/WfXe63SzXKQ8UKTbm7SRlLYgmq2SxnrksnDnZ8mGXkaAJ1j7FoztxjFQkzUxBAus1irmYnJB2ZSuHSoyX4cgnMAIWjIDS7UlQuGeYKQU1TM6iioMJGTiKqYFUxWxgqohJSir88mSdmNvnP/UxB0LDz3p3yj6FCUj1O2fUpfVMGQ1+C6mR0i+GA1REBvBm/1RZx5LfESnDdD+MDZFqgJ5g1a9o3YedkcY2KNu2vvEeQ3/W8TlZgH3CDbNY2sBpOWPTAlJkz+ra+M3va0pV1bIQCy464rYKT2RHL/2QdpG1kRVFMr/rquY3BjliLgD3SigCiM9B9VzP/HHhfqwPJhAJZYFEmjAFpKo/EIVWFgNH9WoxDf3m/HyYHx58/ef+8sGNm3/w6//1+b3zt/Nbr7z62pWDG/FpbnP85O6jByfbhs/bKV25cWsyjcN623djl9Oj47PrV9vjR4/bnbfjbLo/m/DOC9wc0BhzyPLUaz/zl/6jd07+89/8b//JE5/97PVrL1BO3ShpyHoHcwiN7pSa3DKubzAbKsG4FZi58It0UFbv5AP0g6g66KUqwmYRvkwmLvsLM5cbfXUA5G6dhFkvC2dhJs5MQe9/cIcvFUnSc5/6AzvjGbBLRER6GByYIkTWTBRmxUwNVJdq+y6OX8dbUUgia3GeiUhSGnnDqztP9t/43LT74L31cPTU6z/RPPm1Ua7k3ASmlBacH8v6w7R6MB4fby/Ouov1sFrlvuNE1Pf9RTd224OjcOP563kyXZ4sHr27PDvO2yGMTQgT7tdjWku/5Ny0t15+7Y/91V974jM/slnI6b3T9bYXkRCZmBqN3XTcYE+qyWYbmCIRMZV74Lgy4gIfBLmyI5suk++5+wfApmD3SLd5poIKBQDHdKi2QB0QoA+uGaWNxIHIfyT1wNk+A5SC/sHUqfgfwqDAOsro2eOf8qKSJy7l5e4Z7WEKJhBheT4hgjEnV/Tdxiuu++QxliqaYBpElxyZFBfC7P+GmMWbrWPuZYulEqhPnP1D1XPxjjrsEHNfxaOXoVU+TTBQFuAA9AH8Eo6VPa0GPRE0+uZyS7MngrU0SHeqlPmZl1IQD8xUNp1MjLaRiwIS91CarkHjGnYV9CyIfj1Xm2QWkZiDVHoZK88mObMdFfL11TPTIUjIMWShzbILOXzuq3/28PDKb/zjf/DhyfvyTnj5pS8cHFwfbneTOT06XlycrO7dXXA4unnloMmTlM9jT5F3zk+79mjy6ONjDt/f2Zs1tycUnpbmQIY4hP3Dr37rF3/h2x/+3//Jb/1//umf/HP/o9n8aFzmHHIQVxFQEQrmqpgoWJSK2N9NwU3cpheYJZClBOuFBzHAeTCCKsOFokELsdNu1sYnZU9BnbrrmQlZ4P2F9FiZDgkXN2pXUdu7h9nBzRflKPbGxBYY+vYyaxWQwVsoK485As1sqiSBAlvHA2KhUXhIw3xy/qNfuJbf/91xvffk538+PPXFng9ZmkhjljWNj3L3kWwejcuT7cV5f7HerNYy9G3K4zan81Va90d39p94/oAkPHiw/vC908WD7SjtdDYJ4zBuw+KcU0/EN774S7/05V/+czw9OnuwWa26lHuKrNcaBCHOoikYdpxEhtlWgzQEBsKZoNzwK3LmsTu2g9l2EokEffWMFUsxEjza5AjgwRLgf8TBEH7VrgJHDpLKk4pXEWBNqfzAc2vo1R+7l1CtKJpNVL2IiOrr4wHdIvVcEBxJ9USjRQaOgaH5fvFZlWKovCyROdg6NjMOZgfiQDaqvFJxHcAiIC1V/ybcxVoPk7QEgpH5qBwUauMufRwCs9UUoANe7pMyLLX26WV+xTUI0gJiezO+Q8BQOvsZew2O5RLKB+t5mm7COQsVR4D0MXluuvJ0RNonGSlg0rwTkEZvxiYumm+JDsJ2lsWl2tQW28DkKSurcEAqU3/HZcRMWSSwUEi5X29kJEpPvfjjP/Fn5v/6H/0XDz884eGN51797PWrT+bl+fzWbndz+tFbb9CHHw7jwc1bR3GY0LCSJMLNg8fbW2Hn8Sd3Z3O+3rTN7Z2RW+JZlp1xGl77hf/wW9/94L/7zu99/JmvvfClH5u2k3EcSA/HKYBSwi3pgmjAyZMXPAkooqV3BBhSVrmya2K7z4mYc9ZOgCS4fMxdpimp+xTt607BcNyMgsRqPcmSREzWV8ciTYw5C7NerysCT6UFmiLmEcROfrLdTOmWDAUFNUF2U58LO6aapCpsEomeByFmyZSyjJIGSikuvvBq29x9e7WQm698Kzz5pcSHOTciKY0XNDyW5cfp4t54erJ+eLE577r1pt92w7q/OFmdPDhfb7obL125/ZnbLPL4w+MP33p0er/P3MRJHPtu7Mbzs+02NwfPv/Y/+Nt/+yt/4a9sh72Hn1ysVhuJWS8jiyQNS2RhlYsIWJgQaadM2zj0sJo1DaBqn0WTl3qvTaisTZEKZ7nEwMmP45Peqaw50Br2oP1CbDExQFot3mN78XWB0lUpH/08ibBNyN0MEVmakAEZ5MMWaJNZM/tyV3BHeHjBNEN+xEkk2ByjkvTEGMzMi6/z7/onwKsMyQTIVrZkhbIP1emWoXbxT2KcyD0aiZWhc2XELMIlUNUB6r89ZGGsght9jVX2FhAHJnZvXCzABlk9lt2/iiM0xGAtBIyJ2BB8hJCCHg50TfBR1kNRY8Q9aLaQ2h+PiSgzInU7rcnMdnsa+d1B9haXi8vWrnN1R42A30arLw96BQg5P9UlCmgGb1pvQQgz4VwL+ptrM9cceZS0XW1Xy8WdZ770rT/5P6WrVz766L333vpumOSnn39+s90ePXXw2o9++eR8+dFHjx4enzT7TTuZ53HQOyEW67Q67x5/+PHZu2/ks3cDLTgSEyWhcOPOz/8Pf+XG3sVv/vN/tF48mkzbtg02eCKFLnEgDUoKioT9tluIG2lYJ1m27jAmLn0SiLQiN5CoxSnZBgk09FQBmW7p5oQe2QDQ4gl2DyMzMccghfYFLKA1nNSMEPrOubXaH1M8J6j6dusKC5eid3sx1pZ8PdU52AaPzyOTJZ1kFEqZUl6/8pkrT/R3zx8+2Hvh6/L858dmX6RpZOB0Rtu7+fz9fHF/PD9Znyz689W47vOY8qZfnK1Oz5a5pWe/8vz1F251Z6sPvnv89g9OTx51sZ1PphPJsu3p5LRZhFuv/pn/ya/8rb978NJnL06H5elqSKNe4cEkLMyZ9KwrsYgkJMy1e4Mw6Z4KaAuwLttthyS4ZAkJFSIhye5zgwQu8EiFPcOjCDFlo8BG0225gbAKfAWkXb7YV9WFIhF0pPHhYiMPwyaosV5QLlJNq8ZIG6FY5E5kV2YTPql/t3vGdHkNnj1Hhgm53ZR5SHmTU0IgqoBslzhL3OUy+x1hDCeN2RaZlnEWnl/5VrWsSrOpGlPxHlKNn+BJ1a7MNIG94m/WVXEROVjC/sHj8Bp7iJ3ZBm6iVoL8IwhTykzIyb+bbaEEbnQYL9iKgbi9l4jKecyKISCMEGTwkU2ynUMRCj5kvdFPPxaM0nsgBa9ARQjuIAwhAhMHCnr8xb4YTMYJN0HotKIIcUpjt92ulv357Ve+/mO/9Gvh2q2H799/8w++2x48eePmnYdvffDEzauvff6zi8ebhx+fbbeb+f500k4ls0g+PV2dbYaLZX/+6MHq3vd48QHThvIoFHra233tG7/wk1+5ePedj3/wnZC7tm04sm5eS6bAkZmZg6sU4Z4OLls1hA1Prqgie+LWrkAMZr367cCuDhCdyCXHqvK3E/4C8YCYBDYqSVX0xkRoJu6v8IOiwto3QDU4+ICZccJbocqmUC2o/0FfW48A3JuRU8ri78W63WuH7SQilJP0L33h8Mru6tFbb145fP7g2a/n5lrOkYTSuJDxPG8e5tXjvFp255vuYpuGPg99f9ovH643F5vdA37lS9ePrraru6cf/dHHH394vtzG6d4sNpzHPKzSepXn1678hf/1/+pP/pVf68b9R5+cLharLvcSRXC7Ehuj9D1pTYBkIeFyu5mjCZBF3TQw1a2p4AgbngtKxfBV59ti0bTDIwi7AHDYwMR8CgCG/QECfgsUt1czCXEuZFxXqd6cKNhFRstVIVk/ad/isqTV2tpLdYzZmTlklV1MeEJ2r+OeD5JWulI9kAsQOaZWnonEdxIVnSrgJ8K1wyVZQ0UC7kAZGOfT8dMLDvekM2LviFmxHLDhXGW1PEQz5+1MzV+rtuRJ2Et7AFipomXmKcXUj2Hb+rlcfdHFUqYfmArgWCQAiYg1byYwDY2h/O5MZ4uOPGWO7uydugr6buPD5B2/ir4UENGPsAInoeRdIKqiHrgEQdQ5cLnwXjiTBB5T7tdD33cvvP7Nb/7ir3ZHkw8/ef8Hf/Q7N5579nB/743ffeP5z37xpVeeuXi8unfvYmfezg8OsjTDQDGGnKbnj/rlyfr8k4+7Bz9ohgfcjCxZuJWdo6/99F989fb8D//lP12ffDJp2yYE4cxFKZlYtAmJQi9pz9cA/0oU7Px7WQHTELv6Vow2KhxDQ5gsSEL6roC4pc78+eSCJPaCVNVQ+BFdQiHk1lwVSONmZuxOIUy1mAPQYG6FiRilPIWP+R8RW0a+/FMdv/gf8qvxWLk1JxqGdPhE89TRdv07/5K5nT//eTl4OvNOlEx5If1j6R5If54259vF+fZi0W3Ww9Btz1aLx+fLzWJ+OH3mM89O5juP3z/7wXfvv/douaJxvt+2IVAnmy6dXGQ6fPFP/2d/69kvffPk3uLuB8eL5XKkXlhiFs5ZsrAETe6wbbaJVLpviODAUQGirRHu2UWLxAq89DPl4kDgUTBxIZNEsKd/X7o1Vaw9kSgv0+/DZZATUwF/ljIFrAlfIpIV+7cv+UgLEn5qWAZ/XIcBBn64LAOfd0IaSomp4IFIJ5Th4b2qlDpece1F9kvpsSNFcRAWPeBB7tVsLsgcuf9yTsNcLYXfkso1fMPgoNZ+pQGAucTPQMbsBNpo/yUOYWqFIbHKU32D7+JWzNzsyJk4UaWqlcJmJxgoQxPrLO2ADmWxgznae0ndFpEdRmRWg9D7yir6J0TuddGxz66rIw7KMkQoBORxbPJYNfVGYrftEOpDuB6V/X8iO1VLEjKRCGfh7KUYRMPQbTabbuxe+vLPff5n/sI2y8c//Oj+++/devallvIf/tv/7qXPfebaE9ce31t9/ODh3vWdyV5smiBBssggzYcfH5+fni3uvZnP3mrSOUUmoZHbyYtf+flf+mU5f/DB7/82j9tJM40hwKxslOKdSBTN2QyjqH5h78XPC5StREdst7p6fsw+4P04oSykkbUIYcW8GEXl6q0LTSfUwKgAOKzDUItNvEbc7I5Utq1+IiIPdFRbUZ+IqZGuEKMfGdQJ48aD1N0RSpFyZspCQx5lvv78jz6z+O1/N75998YrPzp/7nM5zCmHlFapO+HhmIZT2l7054vuYtFvV+PY98vu4myxXK3nVyZPff6Jdmf+6K2L97//8Gw9Jp7Oj/Yk0Dim5cX27qMUbr/wq3/3bx7d+ezd97vH9y96ztyGGLkhCsTRZFzvngvIixQYE1aCSlJHscBil49ydfg+8s/4v7GaUiq/ywKXhzGTKheyE2xkF8W5Tq7B73U8cLT2E0II7+zW314WCCScys8YelriTkdYJJ6ouJMygSqbUsIYQHnZ8XC1v+xgqPzd3miTFkyF/H2Ij+yFjmt4BHQ9l4dCK32nxUG1WkSH8WqNwY+Lc3WbC4QKP+bqCjbK5Upwtpu94CwgNCKqKK2irT2WC8wXt2Slr8boShYeboUE+XqyLwpof5m7UVLyjI89yNZOVPnNLRe9si9XI4SwqzlqAVHQha7v/wgkzAGzAT5oshvvMMkzfFsVTXDl5KgipMKUJSdhHtK4WazHLF/5sT/92te/fr64+8EP3+i79fOvvRSG7bf/4Hdf/sJn9g7md989WQ/b/f1JO6fdnel63RHFYWw++fjs8cP764e/T4v3iUdqIjGNk/3P/9QvfO65K9/5N//v9YOPZ22cTlqOgQIzBdL2+nYAyzOT5gABguAsAffH2EzLFd+4MlF1mZHNE2gqaTtbW0dxJ2HBst5YKpr3Cc5aQIZ0IyAgZQTrZm2/qVuOARcQGoUxffQLi8huHsa3bflQI8TWg1HELw1yfDHFVmsmwm19WS9OpTwIrYf1n/jxZ45O3nv7D/7o5pd/7Mmf+aW8e4ekobSV4STIccNL2Z4Oq/Nxdd6v1mO3HVfd+mJxtlq3u9NXXntuf3f38Vt33//eh8v1uuF0tD8jkTyO2/Xm/uOuufGFv/Z/+T818+sfv3//7GyRA4UYojAnoiySsuScZZRAwtkIKG6FEiG9zJqoGCFZIrBsgLDYBq/5U6tuZr0sTZc2cLFe89WFF1cJfMNXXQxjp9ZmzYixIZ6798LmKiKATxbx1wCgCyfiR1WrnTsPMuDc/NBmjdLixopXIehwJcGzwDjEPafYSU9zM3zpCw7N2nbUn0PlG8iIFAjHLyoZAlAwAvYHmyzcbfgY6sezeycpyahCUPEgF4WKlYXYgnS3OAGyla8wa3qtAgjBHQ+VazSmBy9qUTxcQjCqAejB3K2Cye4ZxryMXqoChoJOZFhPhgq4IBrGD9HpVMX8RmHxrvD2xJLEYCbWdL5pH5dp2d/tjfbpADpiEtD0mHaHq5fMcppCdrUgh0icKfXDdrVeymz+Yz//15/44jcfPz5//823Qrv7zMtfHDb5B2+8/cwLL687fnT34vD6PuUxS75ydW/bbynJxfH6+PHpxYNP+sdvNvmYZSAKKcz4xnM/9jN/kjerN7/9W3FcziZNjEGIR/XPemdnabpLlVCYyDcw4E1hr8wmYiM31heZoKmFxqvkVKrak4fZ7v+wm74q6daqGQJRuYGSK6Fil0FdMPifLgbUxHxATQiDJXiAPEY8SlEMVSci8Ec5UAhkJQS6byxMLDnnMXfj4oWXDqbtg+/+s3+8f+PZ53/2j4f5E5Jn4zAMw0bGixi3srnoT8+607Pu4mzcLtrNlterft3v7LevfPH21YPZozeXP3zj4SYPs9l0bz6nkdOWN+fy8O56/+kn/2d/9z/Jm90ffu/hcjvQjFPwI7VkKK2jzJmcbgNMKqmaQ/AMm+dna7MGMddNRQkUYO5FrdEs0ACba6X3z1CdiPHVM79D/ovCsIEiFcI4lAASBcwVxT9EWvaKklQ3dbc402hfYHLfZY+xzQlHIl9391b+EIYclcaYqyV/MxlLcB9V8N0XQPGwkNx6nNBOU3gpaRYpS8CMiALa714cHl6NA1W1ZXk8EgPPqwRCJk9jcMVv2YAEkyz/a/VIDuCKcJ4YIngD98d4ItKB4hlAeHqzSIzZh2gnfsTl4nIFeDDVQvREYanyK+S2TE0dJG73sM1wgAvjDJG+CpQd/hbDRYxSUghWtSJ+A4teagUZWJESTuiYTuQUiUeirus3i83k8M5P/NL/fO+V2x99/MlH7719eP3q888+k7fdJx99fP3pWw/unu5fmV69uT+u+yHlvYMDSk0Iu+eP5OT+cvXoPbp4L9IFxUwhpunsua/+9Fdee/kHv/cbZw/en7RxFpsQKQYNBKzQyet9GJf6FB2o5cagCKaJjPmqG6jQV5XV7klU+2JhvVVYCoQxM0c91+v9cQuuiUQO5Muivw/lr8Uva/6bMkNh3BO5mghJwHcLGria63ih3XAU1uZMBKUtmRAwSxhy2ovpc8/N7v6rfzFN/OWf/Zn5M18T2qUkIQ+RN+1UJG26s9Pt6dlmueiHTRj63HXd5iK2/MKX71y5Nbn/1r0/+P0PzyTvHs4mzWwYwrBKqy2/ebdbzV/6i3/774Tpjfff+nhIiSLlNDRMDXOkwJlYKOesPsxMBPkS3Llkq2hryszWjsPAhf2qP3hyy2cSEbFkuy+E8RCXmxiqGpDDZj0NU9kxlVxHSfPrQlmNQeWZYMCSfSYOvfYsW1QDBAFqMpPnry/tAthkgjFivfLIXMJlvCMgumMP+xyB9MimCGCEq1SSnpK/lFdyf6e2RohLyOZILkTEbF4zB56jMJ99CBIqIwOiGcZXml0TGfdg9j6AsBkAuwmEmtbbrcPiMqbqjLUuKCkGYvG4KgEiq8FwyK18qZMwg+/AkBP5z+FH8WBL+IaiD7Va6gaF5gUs52Bc3ysL1esQM/ndE+wNL4m1dYwUUVIZJVxxSQPB9fokwIXhg1FdpZ8SJGRtSlnYr3UWIwYtk+Rhu15ut+ujWy9846d/pT04/OCd9x58+MmTzz1359atcbvpt5tewve+e/fpF15q52F7sckxTXfnm8242dLdD8/Ojh9v77/NmwfMAzNL08Qnn/vqz//SrOvf+YPfC3mctlPVa70XQy+/NH0trK6m0m5kFS2DStDlWKokGzQCy0hNmmJE8X0GhA8wRXfV7gc4sN64GhSKLUln8S28FkgDEvjBAF5BBZVCZJzD9BBkw3gGl3DELaXYlvMXJdqkFXSJM4fuc5+5Ppy8NZ7ce+HzLzz94z9DzbVMUYgDDRw6Sn1/dtqfXXTLxWazzmMa+7xadTJpnvzsk1efODr74er3f+/DxN2daweTJvbDkDdytpB33luu57d+7f/8N3f3n3/8cZdEpvsTZpnEEJglZbbgUnsxlLkZDxQ/9uhwULhXYYnAUwM78BPUBVsu71LYa/6zlEeqsy6o7gZfVaOr18EOnstXAL7+A7K7aIwxMDNubSG6dMBH/wn0EhH0m7B/GhGw8bpyOVz6H2fjJocylPId4LBvKeFb6jLBNYjI2uMUsDXEM1uhkjq4hNDqu1g5lSspls1zr4zxes95kFV7JJYK6Oybo1BiHyz5f3Sa7gKrv4ivb5kO+wq6kbgXYr0ARNUGqucvYRyuxNrDoRsThgeyvXRsXrEmH0tmxhbHwg1ynNKmlYx1VCSTT49C/RXwyy53tQOOxpsgzuDUVV9tXtf1xDIgASSamJB01oVhRxN7QlkqxrqrM7I8S84jC41pWK0u0nZ757lvvPLNnznNy+99/428PX/2lRdvXNvt+4uRwgfvn3etPPXss3GStuvzuJO4GYdVv+3yw48fXjz4YHz0TpOXFLOwjNOdF7/6I595+sqbv/+bw+nDadNOQgTmBVybbO5ThyXm/oLBebUt6/IXKKX7PyaDfVK2L+I3H0XrS0WBm8Ah2l0xTLqvZtvsOhwomXcPRv6QPJYLTHbKF7Ur7oL8H4H0al4ivf8mFGW+fPeUkWOvAqKy0WG+jaxdIBNRJslEkvNIeW8v3tnLxx98f+/G3rXPfzXs3MnScM5Rxjwspb/I67N8cdGvN123ycOY+rzarhPx7ReeO7p5dP7u6W//xg+X2+0zT+5PRdZr7rqw2qQP7o7Haf+v/t3/3dUnn77/4Ph8saK24cQxWxcf3V0V/B/VJ7cZ9DarZhWnbSExE7rNwK2JgHuTCFlLcynwJ8bk7KgSodKDwPzdNi9Viapt+b4CkxGCkiWoQgT2ERnDtrYIOE3FuvduAbPkSyF+qTGFH/Ijaz5vXTkxZGOffUk7fhoEoRcYP1M55cbkyCLlDRiT6zaRVVg6ygqR9jIh25C3P2zPZD+/ZCQNqwdXVFwlRGDhAwn8P0ZWqKyNMxv18X7cdggBdI7dJ4nxaThP9MDx0FDKwP1cmtcWOMISF9GV7JDNSRwKA1V0Ae8tTZ6owKiUziLwfFXeTu+nQ/RFKOm3fJ3es0ZkeGZ6WBCMETbo11kEhf0M4LPb/4rzFu1UaPk6rqkKYfOFNCJxQqFbFGQQJkSZBZc6B6LcsPT9erVacbP72ld+7umXvnJxvPj2976fhtkLr716ONvrt9u+T9/7/TevvvDF69eOJkL9enV4MBcR6sNm2Z0/ur89fS9sPuHcCYUcZ3T19je+9ce6k9MH73yvDeOkaTgwSpLIKkFLKt12Pip9c0OwNbVI0Q2b7DSAPgL1T04TWEA0WayqLIRYqBKZ12EKFo2VPWl4TfJdffsWM+sVA3BL4hTItcMGGUIVsZthYK1Q36UfNDsXjz3tKaoNOYteGJ0pj6l77aVn1w/epfPFrVe+uXPz60I7OQVJ49iveFy141pWZ2l13m9XeTtINw6rFUm+9ez1KzePuvvb7/yb7y83Zy88exCpW27GTT9ZrdOj+8vtln7lP/5Lr7/0lYfvLU5PVjLNmSWQhKhn+URIJNthr1yddCi0zSL9AnZYpELeCQkHnKFU0iTo3elIRqBIcIQwZdZ7WHVVQbYKvOv/BA8PwCgJfta4bSlGJCTldeFK5T8yWyhZM6u1Lwh4p60wXs1QCsEJMgZMG3SS+CQhEeJiqgTKUz5OIK6eiNQXO5i4J4YKIclWvKypfM1rRFw13R2be/XlzUTAcPL3VAPGS4HyvghFMOQzlhKfYUAO9G5R7uEgAvaNDwxA/KQQV++zH5VYUH8KEVGReXEztkImR/gMd3h4i6KSM0T7rmFLFrbm+3iH0lx7i1WWWW4AZbJAD1FKS1Ae9uMmSMIRI1lk/yMuOR8yG3kxjNLv28TNIusQGbIisSYK2jGIeL1eb9ebo8Nb3/jJPz156uCDjx589/u/N9mfv/jyi4fzhrg7O17de+etJ158bbZ3kDtqI+/v7crI0sfTB6vF44fj2XtRlhyEONJ054WvfOvFJ29897d/K28uZtNJEwIF2+FEesvPTgcqtMcLbPyGF0VIoDBWAwhTh3kmirLO8BfqgD214w7d1kNXx1IA6pyZCVlc4wa2bFYCqSuNnX9YKRGpp7HpoR6EK8MgbIOAi5g3Z9dwpd2SSUIIOafAgULYbeTW7nBy980rt3auf/azvHMzpUyyJdo28byVs3H1qLs42242/brPXT92Cxm2V+9c2X/iYH2y/IPfevPu6fGTz85iM6466TvejnTvwfbeWXz9J771zV/+8w8/Pr7/4IwaIpFG55VJ6/1tRaB5QEKbk6OJCp5KTwsBkDl9xXZthsCExMsy1RuGwrjgg52MCapyK5kiT8cV5gEhMBDcB0dMdoVewGyEJYtkdGBhP/3rBkYOs0BJ1wWsKAbkHFNKEE7ONI0vfAoEnVFT+YtKK+BDSjVMotUy1FplGcvClJnYE2v2FWTLdfSessNnsFJuakS+ZFaEA58B9xtM1uLa4OrhIwE1E58gIF7Kh4rEjXr5oA2CNVZXvEMCEOP0xK9PvH4smdHXE6+HBkWxpQo+CPiw6lv4JqihWbAeQQjs3NzE6zvjOjDxRSDr+4+JBDKtU3A0ShtImwX4rrWfgwEhICb2pkFqQbbCmcC12DFTdZklk1BMxp+Cjj9LWq+W/TA+8cKXvvjNP9PNmnsf3v/k7fePbh2+9sK1WSvLi9W77/6wz/naU0/PZk23Wc0O4iDDuhtWF/3x/Qfb40/C9iGHRCQ5xubpF7/6kz99/OjBww/ei0RtbLxzGqo0YRjAXzNBLZpyTbTODJgYQ01NBUwDlYAF7CUQB7ZITzxto3aU/UWatbFvK9JEi8IEGqgyDBYiGMUKASptoUa0ff2yp2tg4IdBdAZI6zlEIDxjj+bMLYcQrDEBS6a82SxeeeXm6d23hrG7+cKr4eB1ibsig4Se05lsHqb142F5PK4Ww8U6bbfSb/Iw7t+6duX2rbyJ7/7RB3cfnly/cXVnd7YdQjc03cDHDxcPFjR/6sVf+Zv/m/Vp8/DBarq3w01sIiuOB9t5zXbGoaKQilfsOSvVOQS5tk4MgyQChuMZ+AUzM4vruCXj9bCI86AqsSqIc4uFFi5cuClQzxw6nmP3tQIiHet196f48IIh/nmNFSpGDmQquEfOwASgj7ka2wtsLFVdSvk9pGGzhMMpbFSl7XxWXyI+zuJ18HvBAyAYuvxMQ3K3q/IYjA1/N4DzB5CFsHCCTFyNhFB15UwTWTLju7pt7i/1CZmbJ7e7aqLknSdKTT2UsOiav9exuyiIeLdFJj/hidOergzuyaBm7tF8tCauECqujuwRiKH6ESOwVq2I+pcKtzAodjux+pNA/krzmqZP4jsQxjaMyvgSXDp64c4UqTTxl3qiyRySpBxiGFJaL9fMs8989lvPvfLa6Xb79g8/WT44ufb0zcMru3lIp2fnH37w9tVbt64+cTVyGIZh72CWRxqGcHK8PHt8bzz7MMiGIhEJ7e589stfOZzzt3//36ShmzQtuZ1ncvrhe6gwKXZ0LnxFB0n4DTwaVsHXBn31USZkO/iX/D36cAgcDwGdiazDn6cTyWrGTE5BrP8SijLchzllg8IDkGBX3gNcDI5QDOVq7uhlyJNzCCHlHJg4ctPkFw7p9P6bs71ru8+8FprDlIW45/E4bR5xXki3ktWyP12Mq0XuNkM3TA+u7d15OnC8+87Dt9/5eGc+vXo4l8TjmGSQx4/k7sOemqNf+7v/CdH84w+OU0qZKGQKwgHpcqGcKBOhe7uQb21q+kySheeergZSMHmjLq3rYCKqGpZxZb+kmGOopDGEGmig6oxgnfMXzwx51Yf9mOoPQO3ZvSxMXLxixOIOc0h4AJAAFdR8qT7MQnjntOL+S5EjF/2o1rR8VCfPBVaIkDIQHBGwq+xQ1afbER45cCWDQqYrasUlEBHEEKK9DK2uLngqzbMlgjyxu2p32dUyOA+GKAp9c3cCb2SalO1ADoGx+u/QMsJXrmREfS0NvCHNGmrxHICm2AlFAu8KhZ4QOTrgwyo6BtITammIiILFYIKlEmCDWCV7pdVVXKJr4iaEha08O7PSRrAZYJoqNAnplpJkJm3nAHGpwnqNqEe37CcY8JgqiDHnUQWjQkQUMnFmwiVskvO4Xq83XTe/dvMLP/LHD5688+DR+Vtvfzj0zZ1bz00DDdvlyf3Th/c+ufrEq7Odo24xhraNs7gd0tCPpw8ebk4/jOOjkPsceGzao2df+vyLT7379g+W5/enTWhjQ4GtuR2HSOjlwKz9jBg2iJ3dSu2yBEu5kC9p5QsUhMRtnKmczHBNAVe0N9UnvTRmMDXS3VkUc9lYColiIqvl5aJJIA3MIVSiNr3BgU64BQatsOS0IRF2g3VekomFU6Juc/H8M9fO7r2Z1se3nn1ycvCsSCthyOk0re9FXoRxJavT4fyiX55xt6S0jfP26u3bs+n+8Qenb3z3Q8nx+sGkoZiG0HW0WYwf3Fs97ua/+r/9X9y4+fqjj066YZBIHIQDblg2rxaC2CaAcSFcQmF1MAGoBM6ooiRyb6nfK8xfbdwtC97fYRItVcXB3jVenYL6dl9aItgVIZwqRIK4eivVG01AE1MvMuTFKEAtuM4s+5ip6JWrF6hLPZcCarb8oGZlylxNs6grnmNyYKkfCYiyuNcYom64e97NoEWhX2kKlxvJEcaZ/ppnAmrW8oGDRgjCPnk3CdifDhB10yCZ9sOABBcX+dVydti1VcB7yq4oO4+gOlMGWRCuVTI3gD9q9F6p7QsAosYhFDJChPYMqthlaLBphr4JHhy8q1nwMRcT13X33ofYnVUv4vElwhkDCKXGQVNDAjlgVXH1EXtazPgEVs9XqVhY2TpXxRG1wYBmB3lMMXKf08VqMeb81POff/Ur3xjm/IN3790/Pp/u7x9dP2gb6bab+3fvN3v7u4dXucnL9Sq0TWzDtJ0sz1fL07vp/L3Aa6YkoaGjw8+8/pm4WT784Q+ayG3TBLRSUKQ2z1c5cFce9np/LQgIOAJiXhS+2j7L+AsqdsB+PAlmKMPqW2ujE+wjwAkENStoHRuI210sWFf1JSHgEHNQ7BffzKtynDq14JPyJwdi64pncb5k8EjNvVAMMeThpSf27737g+nk6PDOHW4OKW04n3P3ONLAIef1Yrw43ZydjH3HMlDk+bWbh7dubi/W3/69HxyfLPYOdmdtzENYjZOLFX/88OKTi/FHf/kXXv/Jn1qcrU6Oj7P0gUNU1ila/mhsn9m5DMCFdGyV0fu2bM4FX4xfQbXdpgUpEdZ5MzvpM58qZYl11zkL6hW1bAg7uqoDud7d4uoNsEaYQq7oLMBHPD+sV9Ir7w44OFNO9epZkioFpEIxuQiK012zEEOoobqKEQpCxK2+5JfxACTL6BLm+ntNV7xW0+CsbAZWXQZd7vVCOC67Oar0xCqG1GtINR9GqQK8L/L+7vDgKUVwhkAIURrAurgKRawCVeKOoVADRcmyp+2mpRTQjg2WHKAEZlyxUM/RFkpJIPlrCl7S5e3B+g9YjfkMAclwTwOdEPJHU7WAzCUWobKQWLqqzZKBSInGrKsbLjUU0ujF/TrBV4aCSh6MmV7UXpiKYw0hoNRQsipuCJKlbXjbdatV10yvvPqZbzz93BPjOLz/zoeP7p/euH3j5o2b6+Pl6mL5+P47h3du7O7vDutOMsdpk2Rcna+PH9/vT94N4wmJZGrzdPfGq5958uq1d974Tsh9ExocoJIsdc0TEXMWbV0qVtIbTEoBm+NMDp1aiuP2XtAfM68NUU/JZd1mKXX67OJiqXynZaHJXVFwmdpmQAAbKxahbw5ZhCna8iNKsKjFzItEEIPq4Ny6ih0QScocWMU0jNvbt64Pm5PUr3f392aHdyhcyynL9hEtPqJhLesxLbfd6XpcLyV3ferb+d6VJ58fm/j+995++PGD3XmzN59IDluhTU+PFunjs83N23f+g7/8F2mQk8erQRJPgt0xDy0NHPQEk6W+vZFkRj7BzMOIJ5XEGeBJSo6ZBYyezeWJWFsFT+VgxbBTKrhQ2u0KybiCFopnfriMCRTYYVEkI/3kOYRM3sLOvQIRZ8rkl4gaUXIKC/ZRYZarHQAQn3QFxJedszsxqXAQpuncsmbIzvoQacC5gQvlmkzB5fruI7mPcT5Dvpo1m6/FaWAiiMUKcrmzUIXn8nz7WKnWhTl68oKIPBFkbgeicR/BPgj9jO672MmfyrUy6zYRiALqNODaLV2JNDKwkMDg2d+vq8xYLiZGJ4/KyxO2drXusyyafk9Yrz2pnAcT4TIA1R1mQmbPvmk/De7mjXSVEnishdhbiJBVdQ+EGIOq4EOnznA9RWMZQma2C8+JScruvmZxA8d+248iV67d/Pxnv757tXn88OTBg0/2D/f3D4+a6fTx2dnjh/d3Dq7eeur2znzWd32badwMaQzLi+789H4++yByIuIU4/6dJ1994amH9z9anR3P5vMG59r0FIUIKvTKFWyYMdaShHxn1/7p1+NgaYwRVI5O60rNiNWPuBsnZP+ZGIe7UFxR6gJUmipL7O/bajpfd+kzs6e8A7NfLINORWYaFmrkYtC437HkcElEKJBQDiwUIkl+/onDi8fvE9H+zd1255CYJS1kfUIkLINsLoaz0835KqdRxm7ksHvz5uxg99EP3nvjj95PxNf34iTyQJy3abUePz7bPtrM/oO//j8+vPXUg3unF5ttaCZI+rPdviza8PPSXoch4GVbNOLp7lQuG4d9nUAg9WABDL5ye/gPQIQKg6IKd4wKFiqjJVO2qEYrwWelDJPgnzA4drsHMmN71pmmfcIXBpO1OACxAqyrxhT3OPV3seVQ+YLiSyqfYqoMRLEnZU9iXzpB5kDg4vOvVO5YmcYlh1NcUYE0PIhdt0tggTH6B4zCl3UWsIQyHeBT4XmXHD4+Ucmh/PGfl51t/6tksXNY7kxMAy/FWz4wnxymXMMFVEw4ZyMJNVo4OS8OpiLVOu9QvlHUlar/LV7H/uISqckP+8IU+9IvBHTPvyQfeEquxe07m0FNg6sLyMhHiG9p9UMGryPKxHnou2EUme09+eoXXnr9iyR08uD04uSsncwOrh7SSMcPT7bDYr67s7O32y9X/Zgix2FI69X6/PysX96LeU05CQXaPXz+pdfydvPwwzebJsQYSA82IW2pp0pDAXiG+8QpCSZE3t4NCPyMUFbDBU7Ak6ySHqsZhJnQ+F/VHsWebmtM5eSNKgnyE6aHeJNXMTChtI5JKKC5p30NGuF+AD/CIARHA+Ha9S+BhVOSREQh7c1op1meP/5otnc4OXiC4kHOY+qOaTwLQnlzMZzd35wc9+OGacw0TA4Pj+7c4Yuzt37vByfL9dHVg/k0cpC+k/V5tz1Znx1vXv7sl77xsz+7Ot0cn61yDKS31occGLzdxqJdsrBTVigl+t4zMU5xfRoHC9ziNBZsqXoSdgIA6M6OHWuK5SCrYEiWcVbA0RVf8KyTsz9Yud3GSmBHFWW3G7g8f2VbSapn5T4AWKaTUjg9cQrq0AI7d4oHpam1DX+kEq74fyo3wJWEK8aMz8M9MyTgU0MA5ShUvQn/YECRc5xSnuYgrnMu+F1l09SWHdr8dfprJEORmb2Ezvp2lHk7SmJUFcb5Umo9FUJ1wp2TzBw4GHiG6jlk9omoiSFuZjTSwQsrj2yrixSioAywOB4h8hQzIXNb4NiGisJSKzMKpd7PX6nsCqIksnJB0xaVXu1YTUowPJse+yYIANPUoyJCMD0mzk4I/NxCCCQhSkgpr7fbJGF+9c7zr3/+ylOz4+XZg+PTyWR64/bVZtKuF6vN4nh2NG9350J0seo2Q2rbJmRena26xSPaHIdAWUZqd649eWc2ad/94RsNpQlFbKoVpwXDZ6021OUGYhZdwJxrURjKmxzIDvfCC7AqnykwpM7wpKVgs3L3tSao71Q9c+Nlu2PBvuBGoIM3HYDIkawUaJEP3fZNA+rfdaNVRFjsqlNmCWkcnn7yaPn43W55sXO0N7t2VZjyeJHyBcUdkYa7bX9xslotMtMwDs18b/+Jl2Ocf/L2+2/98MPptN3fm48iaaSLRVqv+PGji25sf+HX/iLx7OHj0yGnhthyjTlQtuAE/JSEOGUzA/IDwLZT4YhpqXpXSRVrht+GS+DCGbHjj8e4CpiaiqDw3ZdbLUirV9QtC75vq1XwxMC3HiQLaskudX4nNregIwwFbNgWitDE5JLhFmZZETNbfiPOuUrzW5kvOLwfOavIKX/aLEBUKlk7uFcM2sUrBZuBI+Uz3tqnUCD/Mrxd6UNfGL2+TP9laxlgCVSO9EntCIWwjVMZk1qarrGbD5Yb4sNEoRHuoQv/AH6IZ4E9B265ddLSaSQhqXK0bHhMpUwTCugTVcUh7CywAYUCkFFGPTtySR/gB2uqbfilrSh9cdhh34RPJGKZbs/tY7mhG0zuotnfZDpjzX2gt8VXVn0iqDhr21v2A2zmSoSzSGZKSUKIfdd1XeLJ9RtPffb2My9Nd/c++uDR8f0Hh4c7TzxxbdON997/JPN0d/egje049mHWTg+bvtuuF9v1+Qmt7obUCUsKzf6NG08cXPngh+9QSm1sjOuzWG8LnWO0yg+uTn0Xq2JGCIttAGaP08yrB9i/7pB7/s11EPycyoawm261OcTMIRIpJucQQrRDHnAaUFPPUDAAHcBjJ7EZOu2FinihA6JXh5J16NCuK5IzsaSQOeQrR7OL0we5Gw+ODqc7ByGwDBfcD5LbPKRhvVyfXYw55zxkbtv9O/P9G8vz87fefLfbbq/uzaYxU84XF5s+hLMkHy+6r33rW699+cfPj5ebTrNXiUPWQ1+gIrkQSRGtpFUzMX2ql8cMRjwt5pgLDgk0cfpsmEhgTGZhzKS5VLI0DFA8O0ZoPYAYnwP0F0Ja3gSUdBqRKfstu/h0laIRm7DOwcCkgCmXlXYrxoPEkaL8CFboOQup9cffztXrnagBU4t3Iw+nPOlRe0opil6BqT3a4d65pKC+wQcMToRecbr0mNAlUlxps4MkgfkCr4hR8mTOx6kco6iFsHq6xoygXojIEIrLhwt61V7FB2UvkpJKCo5+ZFVqqMS3F0BLDFj1OYV3E3HN5f28J8Sog3RPqTIMTl1RiFKQV3ysEKgSdtYjBZW/tPnqwFwdiWo18JHAnYCgmjguMRXrkFMOw1mHSbMgkCwXXWQhGYbU9Z1I3L9y46nnXzi4src6W66W53mU/f292DSP7z/Medjf253thKE776XvUhbKq3V/cbHq1/eYNkKDxGayv//UrWvnxw82Z4/atsUdFwRzM6xRiPZm12R7swSOgqXGuhJ5+gUm5LBqBs1VIEDGXbBnoKAtpPeSOTdAN2p9MW4ZY9Ka4BIukH0Yas+uRFAUXJeFHkPMltyGE2AmIu2gRkxIwhkAZ2LmSNcOpm06X508nE/avat7MTQ0rnl9FoQ4LfPyeHN23q82Mo7jtguxnR/dCEEevvPuD997ON+dX9kVGtYpj5tlNwn8yVm/aW/8yb/814gnj0/O85gis14OHgy0k9E3TYaiYDuj8ZkgmPeInphzARuCqLFIgCeYIrRXvGk+EIMQZOcCK4Yweg8DOKknlMriQ0vcv5rsCeBiv7tsZlAP2LmBchVYFC8nFYErjqoYtVyK07n0CXLmWv4Kbfv3qVnxZIKfmV+FC/JxOBAGd1/k6s4+K6niFn1hYCLKNXt1F03wfbn0yYF92mowoJG8kj7Xa0FE2AsrnUcRJvoKuQuVuvdnKZG87GLJ2V4RHgZU/GlGXt9YTCn7g9+ywfnvyEGvcGbLLAEHKvdj7FKj4eDr4gzURQEbtgkr3ED6RbHJGm15lbH7BfubHwgrURZBUr50zhvghIppQH/0pDxfIhvMjCtPdEvToysbsBBL33XDMPJ0/9rTL958+ilqw3qxaqfN0ZO3dyZNHvvNxUVzZWe6207bdn2xOXl8Po79uBmWy+X2/D715yQ5MzW7O0/evJK64fz43mTW2MY8qid9TSVbBSTicJ2pNQliP0MrsHhdfKuIDcSosLR5c0F+XQgy22FUsrvxi7ikbekZwGF0BjqMDzD7OX23rcofgC6H6pwZE9vNF3UQ7yO2jruSJWet4MjDsLl1bdavH0q3PZjvtpPDdr439N04dm3Thm6b1ufdxRnlRONADbd7e/O9Q1mt3v/eD4ch3by+33IaU1qvxqFL/bo/Xw6vf+3Hn3v9s6cPl+tuoJiFMgchVjHqzm8qkTcwF13RIF0trHKTgv3413QdnZM6wruoDO8UA5CXv/THTc4Y67/3h1lwc1VVqmko6UbhkjbTBH0TXwZHaIsB3A6RgaqrIWvfQ0TuXQQ0Rvz9te5hZOyfNG4AXPUpXwJrqpALTraOW/EtOF2iKn4mOBZ3P+qzgWD25uJL2Kflmu2wDuoIMZIXVlWLe5nCkv/KmKsQaQV2IatEle6U2KD8hbHDZBOoi3rIVYiY0Fkh27a92b7YQwCpXMnsUk8uZMsFSlk9vkLOOpPrXK+CgYovOCMxr4bEt6F4tZJi60yVOGzJXbZMhFseRQTBhj+HfEFxbarWttsIjVn6SQ6GYmD0gSPGo53sKIQ4DuNmuyZu9g9u33rqqcOj/Uf3Hz669+E4bJ587slu299795MQ4+HhfgyTxWnHwxgTh5zHbb86fSzDaZSBWHgyP7xxu2E5/eTjtokNB2bOGXczI44iP6BlKhrsxyj3rkpxGD36/OiFuBsz5oPDLyVcqAA/hFABOAPlk5qaOepA7MlD7PS4VlSmIEaFvTWMkB9Bgx64iVvJaeXk4S4y3B+ziCQRiiHvz/Li9O627/au7jXtPPU8DoPESZ/SsD7fnD3oVhfbbiU8hGkzObzeTpvTjz9+cPejvZ3JZDIZE419WHc5hHh6vlyNuz/15/9sGtLx2RmHLJws9M7MmViP6LnOqxwjEVny0bvAibgFKPhiu6n8xxVNGKl4KpndyxyWBJ65ID0CPSGxzCjSslQexgAnX/OMFAe2hdzROIliriJHcgblJlyQrhAvcysFIz2Nw74pDZGBTZCf1rews0QtxcKdOLiDclQxP2Va7L8RIHn9HDgh9kGXzJS7HwzTGAoXk6s9iMqjOEUPIMrc0etJH5fxBSfR1jel0n8EMezzw7Emb0dUhK8VggqdkDnsTXI5Z1IxX+f0DN7ofsuCR5Wm/jy4K7cjQTbrWm2r6ZRTV+phXVcsdvdx1nAiYJwCzNL4ABkBL+THcIPLFq82PavVhsu4xGNMXyZDJk+jY4WxDrZ8JkzJ5JEXCeFyp8zaTFbbNWy7vkuhmd68ev2Zdroj1KwX29OHx9fu3N7fPXj4/j3KcffocH9/xilP55PtNsemWV2sVoutrM5Ixjz2FJqdq9f29648undv0jbRztkFNE9CmU6BTJiYCjWgy4WihAUQVKbpMF0wh5Xm+1f0tzULFPKmUqy1LoX7m0x1hznATdvzHfyAJP4HFb3WO9rXrNQEFSXHi0JhN2VkRJEp5J1JinGxOn4cwyTMdrM0mXIIgSh0F+tuseyX6zQMKSWO0rZxtnO43Sw//uCTcUM7zZTGTBRW665tOEs+W/TPPvfSK5/98uJiOY6JcqacmLKeq9Ip61aAmUPOFsYCatjgSJhYe6jpbCUXQ1J7rasnHf3JgRaOknE0l5yMw4mD3Qd9FZgA166BqcqbSYU5CIR9PLAort7mBs41javxDt9mOBo1DBGprb368qewzGMdWJrxhU9zPaw91w/yJ1X7Fu7z3AM50ukPMwZmV8G4OJ0LmayMg/jcbZ/HH6lDBeZAUAhsuQzStogwEPOAVuFQpCGmRYJ+rt6NtcRJQiSi9d3MXF3L4AyYqrfXp+p9gJV4yQOAshyFhmFRFS4ke0cgL9v2EQm4g4ldvxrYW0VSzmJuxtyDncnQ8SCFR8gQkPi8K2Bxd1+IOVcqi22wMjQmEpgP4BPvJWveRXrwAmegzT0EuJxS3OY/5EAhkCVoYhxT2mzGEKaHewdPPvXEbG/n7HwjIx8cXNnb298sO+IYd6d7V3fm+5zyOFIQbrutrDfj9vwkpHUMJEy7146uHu4fP35IIpO2LdSKcY8pw9iKJUDX9V4/X0er62HSpnKAEyYUytbHgC8lapAZcJZGBd9d55G6gP3CIfsJZS3zZ18LC1KtvsGXUgXqR8KLnRZrIqa6AyxZL3sWChRY0s1r+3G1oH7c29+Z782ZMnMbIvG45v5iXJ2nrh/7YTqdcTtp2lnbNot79z754H6I7V5I1G+2625MObBsun7dTz77zZ+a7Rycni2TJI4hhGgt70VEkmqBeN2UIlcWzUoVqzIxifkAL+Az2AKQqB8pOFhDGgkOxbiB2RNc6GY24iZt363xGQilR73YY2pYgjE/Gw/8UCFNPsLKddQkQFUGWWap9mwVHpVBsn+QCNlLJVNEWbtLOZ5U/saDGqcAGB4X5S+6axW5tb6bKMEQXV5VTKPUo0RgAp9dx1J69wLwpU5JCyZ2SexcLJaxnIQo2Be/CosgIc9AMOwLqOwLZySg6sJPPsVQDMke76V9CI+cq6kcIEgwgLKuYpca6beDh75GwhDWlr0H11ISkex3P7gmmpJX7hhdmrXMEkMXa1tbxwymP/Y6016u17GyC0K4XBGT4jGwNGisqz9j9/WBSY9Kq3zY8UngZe3gnVIuIdr2W6Kws394cG2n2Z+ena671XkaFlevX+uH4ZM33zm8dYeE5m273Qw0pu16M2w22/Vq2JxRf8EhS6TpvLlx5crF4nToFm3UPT1b9mDXJhSAENCFMntHGC5BK4IeFlLe4EXGBgWGRrY4XISGA1n4p2C7xbRb+1XYMxn0HZXFAo1ErYhrb2V3DgNYYLZ5sf9KJ+gluLAWYr1zMVLanTWr5WIc8uHR4e7OlchNoMTjGKXP3emwuch5jBxykCHl9uCGpP7hhx8vlovpJAQeSeRiM6RM0sm2H6W98tWf/eNd1y+2PaEVuCbgc05ELJKtMTNzRksAY9pe4ZNxYwgSZaroRkg8YnUrKGBQEKrYTTFl+6BmovSlpdgEeKiD8GMk/n2GSZvZF8l/Ku6oPAkQVpLdvVkhC6AYGMFW41VvPVZIhEIdKhoM2zepEdpWwAcI8oamXSZLNinW0gGAFcR3DcNfwHdc+UrMJW7YVE+BUETLxPW91BWNJf8A2TlWyDP7DIIvK1Vn3Aq8+/gxSozKVtcJq/gv6xUjc+nMiHjco6hgrTkHw9kgCwyhGhKQobmNGfX7RbU8LemWbS0qK22Vyiv7wqqvYieXtpaCT11K7xUjcPCutFMVxAgFoK0mTXAiKHU3y3PRYuSXvsWMREfZtcGLTQIOo8XJQXaBQtP3achhMt87PNqb7zXNrD07Xz744MH8aD80s4fv38/x9nxnjyifHnfny25z3k+aMHRjt9mmzULyQJwns/b61f1t363PTmY701A2Uyxm8pw8yi2LIvgawm1hkDhRwe6E3YkRQ3XNHIqnI2M20HGTiwVqwX7MthkOUSAKgTOxBYUv0sfbTWsWWqmNwwuRh3fueDREQO8fs2FBPenOLMxCvzx5vO36ZtKwrNupSL8e1hfd6mJcb8auG8fEMcZmGmcHk92jfrm6f/f+MOQ43elSGoW7gadNk1K+6PPVF1649dTti+VqzL2dK8RtBFb3Siyi0nJSWWVEqvMbksXJhX0SGxiQeLWCLnjt8WmJPyev5eI+9hSBRa/krSJsoVHo4asEKHKjR8xgX2KQGiK4Hi4vFx8t+nTaInsM5I7dyLKvX7FHYlRJMZVaFOuJUBMAwtCtNYW4i/Sh+UcQO9kjsvuEao6VLVd/yhtDjUeV0sPkqWCH/wESl5E4OjjtdKsk2CNwlH2exe2IEJptmJEUn4dMnXsmxgTBdpm8yqjysr50thgCSXn3BcdDEfHmdACQTC5yZjsPRSCXDoiEVJCDrE3Nx8wVFsBzkxhvIOCFiiKEIEELmI2oChUJmBfDewkhV3BpWEK7uKSKf4jZGC4CgkSLvBjn271PctEnv1YtICFCIeDIn0qsHzLH3YOjG0c3jvqUt+sxp/Ho2uGVw90xrYfxwfza7mQSm0jzWUtJYghjnzabde4XIQ9ZxnY6vXH1Sh7Gs4d3p9NJ4OCxHhNZ2WHVGVnbsbgtGG649sENhIA9DvD8Uq8fEE7pphRQpZJdyU8whM9+A3NFzyoEwvOZ0bHUwNxZoZ7oAmPE7kZgrxEikDB1PtYQ1e8OFKIYo+Rh1rZB0uLiot9sOVJKHYeYcxr7rltcdIsLGYml4dBIDpP5bpw0/WZ7fnIeJUjOMUz6IYeW+yzDMPbCL37587OWN8tNzhkt5xKAMtuNX+I7mCapwt2QBVKF1o8XECurUBQQjyBb6VJBoWsPVMp2Y0O5tsUV13EI3rh29rBvc+vi/gprx6jtADoxIUNXaLs7KXykti4VgdjRbvxWQAsZFuhJ+1JlpDaJT3GFCaYvUDHQ6E+Zt8in3oNZq81Cm0ssBJYEZAde1N7Y49xQZluRI2dXvgoFiJWHF+9Blv0rYiC3YV8FvELFaLJyxmbkqHA6NTexE3+M7QodT5aqbV/FZp31EUosfCNDpS5FVjBNDEqwDwoJuQ/z9XISXTyl/wRLW7ld8AP8jp1JBQSShM8IZlQlF8o1kKHeGCAi45li3FaXwi+3UIMKHl3gHAZZXxr7iACEGLbFBAcKJ2lf4sAhSGAK3TBwnE73Dvb3d3cm0+163K63w8DPvPZCltXxG2/sHB4yj23glHkc05DyOIzdZpP6jSZRwmS6f+WKZD55fL9pmjZGMw+uMnswW9WyYB3LcTTTDEeZNc5llAPwBrVOR7DIeCC5GFFZEIoc3B0WoqlbJEVn7MM44GgUHstrfoaMq7mIjeuaRynhN3TATgKXpY9BiCjE0M6n87Hb0rbjPHKIiShzHIdx2Gz6zXrYbHPOkilEJk7TncPUDyfHp/12iE1gkWHox5T6jtPAXWqIj1766tf7ru+2HQQPghAACX7eFf8pUOeWJDhEgmlLTScAueS4Y2ajyFvFE658BNaTHXj+vQ8RTnvXgGjy13EVVkgFktwxwUj0MzYgUEQ3XafD8AZMIA5qcaWWizzEUB0tJEsywAdsTH1dLoQQpI2YteuSCbKwzAKKFdKVzYbCdghgRGXJSKp6CeLCXrn6tu4olDVCPyVUhhte12hLyKIUZ1LA1z0z1q7izA4+DtSo9yaU85kfE6yHU6JiXWpJ+nYsccVUTBMcRvUjjtgF3qEOjoDmZoiEqqOCzqSJFBpDgN9mAh0kAt/3I7euxAW2oPVGTrJ7XOsjXTQXcnIHWrtx/0R5Q8B9bOBpJW0qNjbQVbSsEAHxNWpEBqfBjsk72YAGBw79IIknsZkeHuxmThxktdqcPHx8cGV/HqYP3/tg52hvInz9yjz1iULMEod+SN1mHDcy9lmyxGbn4LBt25PTB8wyaRqBljt51Fl4m4VPEXZfeg52D4xzcCrK5rsaUDy7GOCSz6u8ZtFVXBprSXk4S3ujEx2tRClQoYGbwwxTNSgqCgjbxxiczXIoWQUOQnokmttJM5vFNHTdpj+6ss9McTJtY6Sxk36ZNhsaJWh5JEsIoZlM+1V3/8HjUYRCOxARSdcPE46Rms0g86Mnbt56ruvGIY3m1FBmUVpy6vaPYi1XbNe7ZRajNs3E9CqGxUUW0CagDhm/0ycUsu5fJ6wmDFVAOR15baxuZK4g0BP7lLH+ogkYuDhU+FK4u4Dyi2WEytc804H3gENi+iXtbnJ1OGPzAeU0gb4HPcnd4AQ24OQCQ8cg2V/ONkhID5yjWo4C04L/4EfmJzxrX/kesZEK1o1gd/rrwjCdAdsSwaHrf6Xsrgf2IiAnx0XuDDSSKmnLmCPUgEpdggucyOJCIq57/AIn/fg9QdDQM/NV9Rg02+SCZx+YWEpUpJivO0hCN69sK072afNoZSFBXTxz7VvBBDT2DhmXMk6ecaj8lqtpZXf2m+KAiUjvYCCoI9TLc0H2aOiF1xSpl6PAFIkDh5RSImra+WTSHB3txlkzDMP6YjnbvzqdzU8XD9Omi/OJCKeR+oHOHvdMMY9Dv14Q95x7ojTfmc7b5uL8ce7WbRMhZGK2MMiVTjmNukhsFl+6yUMjJ/snU4mChSs7dbkVt8n12WmyaUK3WGMeJxVFGkRUvZHhcAMb/hedsYW9TPWLnjP7iQ3opKbkmOxOGA4hxBBjCFFSv1mllENDIYZmtpdzknGd1ovUbTIJUQgxCkloYwzNdrE5O15s1qPkNA5jn4a2bYV4HFKfxsPbN/f2dzfb9Th2cJ0uPWv96VqlNT8la4YsW/YdKiiZKGUybgTs1e1jYL1xF6WrJZ7FT8zCjZgaZoIb6OeA1kKoL7QXGtT71z59Ar54qALoLNVvq7CebAhw4xViuUcqxNbzvcWwjYhw0HuOMprvqM4FLr1CQjXOolBGcT/dYsayD2aoyEVaw6xiNoywghxelQNmEb+lGTjo8EbVZGw4ASqsb8F8USRkLlhPuOu1P5XvQR9lbH/ZsQzf5SER6/pedjVESvWqKlVZN1RuANA0bijejpGK8sUWcd2rrN+0BXcMetbMMjv166DFpg9KSqHz/h52patwubgpd1T6aQQPNSyUoWFGLkFWnsmOK7AErreFnZhi68L2HkyjIAlFmXpDm4jtHBYIgRYBc9VSyPyvedkQ4pBImv1I0/2rO7NZc37eb7v1uFru7B7s7z/96OO7k52dg+tHgUVSSkn6buw3WxkGTiPHKESz2XRn2l6cLVPXtW3Dpu4sSAfX4Fv4Ve0ciorarwszhZmix5gHqO5KqXwSftoUK1xasgx3IiAu5MUF9pb6WBKsj7ASxeMws7U7qhalImbKB/AsZU9B6zKmk0mg1G9XHDKzTOZtmHDO3dithu0yDQNzmylkyWkYwmSfMy9Oz5fL9bRtGgqcpB9ySjSOMsS4yXLt2Weatu37MQsunNehGw91GuaGKsUaxbezLVLWH2XrHWS5HRggF+EHrKUYWSO9bVjs1agBKMzQ5Gp9hFRJslqAWYZUwwTm2Qszcv1u81SZgRULZTvkhKYG5MiI95P/nz8EhiQFSNmZcUXE8A0cs1JyWDlNkiyMrCVUUXzbkKy4ymMmWxvBSpWeNJVBuHTKzDFanxfA1s3LY3z7Z73WWAwm7azlzzGTKkE3Bxa97UqoepvhLyG7LVhZDE0ffemUia1n7f00bWokFpgIelA+RebtDOWdtwn4CnvTeSG6xBLI9p6rkanwaxqOuViwZesLKqL+oIRv8CNu6kQYvM+9NGxQvBCLF8XO8TIOwfrp1nr5XMimyJRJKOvxbsQf5i6o2BZqf1Wf3LWTjR/DwWYokW4DMDGnxDmFdjKdTZv93eboYMYimdLp2fG1O7efffG5iwcneUy7Bztt2wxj1/ddt9lmTURvVyQ55xwnzc7ubLNajN2maRq7SIsFDeGEsM93KfY2NavbB9q+sVuRWkC5ohGo4KmDaueWME9vRHFJzoKVLVIyzA9C7CdhYTVK59BkKatBKK4DqcwI7f+cKrveo48hMYUQOMQmcuC2YU7d0G9DTNNJYOrDOORuPazO+9V6GMaUiIKEEEMzb9q59ONqsUpjiiGG1EeWENsxs4RmyM1I7Y3bTwuFYRgk680Vpj9e/exSAGAyVSpKWW+0K8bjwjNVtgyiOYlP2aYTUoMK57wqwjqAYDA7UDTVEh+xPVSAbrVVqdXhilQMocAvghUuC4WxIckA8lPNwYy5wlx/BjnSkcEZw/PbdxH6INivoF9KGxMBt6wYpUEk1fB7uZcRPIcDqv1UgcEzFkTCHEqnHEJNTzZ8VqUXKpAnvqHglMpHVVwpiT6EDaUVnOE8EP/VXkfBkYuKQKroMAqrMGpWCTwwl2vGKhLljtWyBfi2QYpqkC+f5xZqHsHQlktLjJ8QNkiMpJqluPLqKIylgpESgf2UzfZyOhdPIDg3Ytwo4GkQo1wlLQbLwNknSBmLYmFKKM+HZ4Wa4Y+wnUdmX1qwQHa1Nj8mQIkxp9C000kbJO3uzoftIGNery7C3njtzhOp3y5O1hJo7IdGaNqGJvIwdH2/kdxxJJEU27C3O+m322G7iU3EYXyn8wHen1E2UwzJOZY7M0igrIMbKYdSaaa/FcCL1IuOJ3oQKCRWO4SucUx2Q6KajeX3nVhUfxgkugrgraoLv3K8KTNjNBYKIUScZeAYQwzcUKIxBQ5BuG0mMUi3uBiWy9RvOOfAnDOlJM1kMp3PJY+bxXnuJU6aGHkUkhyykEgchTg2N564k4n7YRA3bsnYIsWpS8mAJw9LdJPSiEOdMoEksTVZ51s8I1eZGfIT+I+IKxhO83I5Pa9fQqefKqjCOmHRuXq1/g7epJBWZXBSqZOP1rRdHgABAABJREFUzRwYc1kqTf44a+fKy7hFge0a0uhTLzegV70jYi0MNTgxq7cUGVAgg9tAQAAHm4sLk4jQFcMH5jzT6Q2GJQ4P2rpOMJBLc4HD40pJC3d1exGlRFRwpRqAiH+0nKpl37WmavwYl/ls8/6CmMleXKgulMcOLPg41ZYrPiYoB1MNIRcRVbOz1ztwQG8uFW4wo7GwZSkDG/uxRKPzJPMN7jjZvJ25bfsAubiQvajlAkUu0awZC/uKMMKMIkcInD1mJSv+cnAR1PQ6iEIEApcIX026uELWuKXysfYn5sy5nTazeUpjM2tu3rm6XXfL0/PzR493n7wmPL24WPbLPJ+28+msaQJRHrabsevzuCEZKIQQw3x3Zzv0fbcKIYQQbe0F2AJMF4v3ism4mJDgq9bLfTbWmrwwMFSFpUojIN6qPAAPqt2N/7LaKPJFhX8oXTZUuuI6XZ5LVv2p8yqfdsF7MzgdRQgxxhhCjEHyOI69CDfznRBnkps09P12PY7bEFkkkeQQmGOTs6yX54uLizGN3ZC7nFPO3ZC6XojCmKndmVy5cSONaRwGYkFpFXpvIHEpZB38M9rlF7QuCS9m5Xs5X0J9PAFyg2HYiogfgwGQiWTLL4BtXX4YV6YiqBvR4YrfZuqRd/FE0AXE4dXC6ajAdvyAZgUb2RyStUkhoipLBLTxzUVDRHjIkqwgoGqAApmrcx+pHU0F19fiAWr0Pq9Cj6hormlqWQ97ILJTQuwb+567rw8ol4okyBAiUvmIeOsrk5h5Sr1KU6APNlURCV75YPTakNJ9C10Ogx2ixNHYvY1LWUWnt4IT651VsEOTonkgGyzb3Bg47ujg6TTQQryX/X914hZiIPCCSOFxNVCSzFQK6chtWXxuiGOgaTAi5PpcM1Gzrh8A5nOw6vAiZx+zIwt01gAahkLYfzPsZ/MfbGklz9QF91fuLvH02l8ys1DOOYsQT7iZTXiyXg4H14/6XrpuTL0088mVK0ckcbPcEoXQhm5I220XAqU0pLFnEgoUYpxOJ92Yu64PMQB9bODM7FU9nk2Qyzs+VARcKKrv/6uJ45SRwRmwFxd1aewTAvyk8RdBAsj1UAh1U/5iJ2wAkboHpZ5IKJk9LL6xFFHr9gyRf4+DlRVx0NJbBZzAlMYxjWOIIYSY+tT343q9GvqU8yS0MwpROBOl2LYSZLXZLpZdCG3gIDFYTQIzc0gpz/auzvevppTGcSBr9q7zFWJs+WGbyG5tJVIcwTihcIXIQbcB6O763CfrA1Hcb04eKGHLpCJ1V3FJwaHGHnO5PYYyADM90n304jGsT0rRHY/0qrGRgXOhsNi4tfQpoAMsy2YgeIff9QGfV0xfi9lRGVLHDlJKMMTF704Ye7RCCHXqrW94iwL9l6Wt4nJFNeoCFc9QRF/RAiYCz+jyQrf28pmKjblBcNAIw1bP3GMoTJJJ/NKtiqS768Xhs+C3rJke6DxNTiwc4B645GfB+GxKTt5Nu0tYVjDU0R9yVMGLr7OjsvuRgCOdxRHYF71AqOii3ugBjwjNJ7os9gAeykTu1I3Dg4xfUmB2WgPUL2tF1Q0EvsGADUkhYaYsmXx/Ds9xnhXq8THkYcScQ4giwqkPTWh24npxkfo0iXE2n+d+M5nxwdUjorxYrmc7jSamIzepH4hz7peSO85jE6VtSHIax0HhntwBY8o+TcMKrWIqnto3FUl/SyVlZnJVpSs+uPbRRkpcgWH5Rc7syQWQJfeF3l9AHOIMtISc9MCHQcEslHFXV5870dhAgh7sViXTMC0wRyYZO8ljjJE5CknqBsqScubQhjBVZeBAsWlznwbt3M2chiRDopRTP0SiGDhn2j26MpnO+m2fc67OpMGiMng01EGHLwVtCL4SgEKkpwVha8Y9KmsrhBz/VTF5ea8rPyCe2DXPiad7Awje/QYVACFvRVfsw+C+onBGLTBBoKfUTk4X3ypqMnmTO68vRAEhwWlWzpyro5igzOr9bZcLzNTjT5dXIZslhEAPBmffiM902AWKAfbMdrwbI2QmQTkTuKUOVKjE2HCa7gxNpIXsXHb6TmDsgwJ+CndIEEpVXJGFLj1JiLk6AaODznALdleHPx/RSeF/EBpk74tYKBm+bc90VS64jRfj4ZgcML388WqQou8EauJ1xeXplS/RaTOVptAEaAjeJho5JSHk690TK1ASgJJ8sSrQE4RHTKzXlsATQBOIhAIhTiiwi/mzaYUbnUCxbNa6noGb2AYO0zZOWFgyRyYO3XJsd3YGoZOT5aYbU4iS0jAMKQ8kI9FIlNK4HdMYgzBTkhxiqEzfpW9LKSReN1YvnGuDlPgcDqNy28HO4lY+EKqDlamysE7zLW7Ahr/tzMP2BIwPhf7Bd3+DsVriSpy6AHXHIba3lUfq07DJxll9XAiBOUtOWXLiECc5UeDZ2MvYpZApchDJOeeUiZtZmEzT2K8Wi5SGtmkCh5zyOJJkamJMwmOW2cGV0DbjOADMVWG1FAilS95UhKpmDGLpwow6QpxBRMUfXZqgShao5sZmyiy6/xe4fBeEyv+h4M1wuWaHDJbCxcQqPm9P9yoHVxzCkpMHE0WVyJw/u+6xNhOiXCqE9LcAIDc7/tQ2kNOQywGe7bFk0akbqMPySlIIPgnqzF76BHA0iCp/kaJGhimeJLP/VZGILXUZjqBFIbb5CQeA7IyfOj+nmTVYlHxYhZsGTzAih11grPoH5/72mexnllz8wsye6/M0DRVHazqpC0c4EMtYbZuEcbFCQIrjwVxIL0DCAiMFUBbTtUJc0ExMuEeh0KT6gkz/Gf5V+Aa0EOebuCxcsAFbnbkUxiNCHpLaP4HJFffk2l1UioJxqniwgVf2mX1YJlmzUyLyulX/oI21ZY6zdjKZtEc39q5en86ntO3W2+VmfnTUhDZlGVOYxBiDUBopU+oGEcmSQtOkNDBziJzgEQ38AvIz5giYLftSbn/0+VgH6XqCmt4yhCVED0K439ymKHiYCg9JS7eBop2m4w4dIPBe5CkcuLq806zBT7sgOqzXgk1RnXcwvEZwxQuRQwiWn6KQsxBzStJ3/Xa7GvuuW67HrmciylrME4gaypGExnFMiSKzZErSUmglNMKcM2cO871D4jAOgxApoWCD4kKmspNBqKBVaV3q80wMjXTlIihIyX+5yIgtrwquRJcFzlRRXrYuRMhkcAmeytcICCC2VcfAm2IG8BButw4iBbkEpFCkWJsEbI4QGle6/TKXskVb0OLhsCdE9kXnM0qO0XDWmZ7GXDhyCMTynhaQEBITICtuITZTZ3BQUFU+Ng2rLdrOOLrzJPRjJldjh/wyMPITIYT/5yP0yftMHfNR+hz0R8onjWfW5/IrVTGoJl9GcttRUuo0kQuq2XMEzBDdVopWupo5SFtAIdl3BFH87v0F7I4OrjMtRIT2KQIDhvAQ+rgkBL3MMTEhp6gSCi0Mub4Uz3I1xl5BQzlUBKVwrDIr0jSDEHpIEBM2cYjQ0rIWb21GCmt+jt1j70sBkM4sM0vTTgI30+lkb3/31pNXJjHPp007n0xn84bj0I2BuQk0jWE+m5KEPNLYJ0p6vyEHTkQiEjhEL7PXF4gQSmCh6i7OXEkcNgHerWuISEJAPsxdYwUFBmU7jnoKr9LiSrdswdn/x4idOgVi43IqZCq3RMMKGEIFzLnWKBlxqqHfCZKT45cEVLoJZeEkedttu223XXf9tuu3Qx6ZOXAWyjkQNe0sxDbnnJI0TduESaTAFLKQZMo55zQG4dl0l5lSSoAGPxdukjFar8gIANKNVobkCAoCqxBSnauZuMULJm8P1ZRr6aLaJchipsvQLxHhsiHBRHZHhVuR0lJ8XG2UEUMwDA3UmMsHxVvWFLzkWo0Ih7Qu6T02mpwsoRrE0ZIK1jNWP6BQqQCW19eTqwc7eLjkRErVExYEr7dQlAHfcLeFCEFGFMBJDajcdUpl15b4qjm4uZNcj6ocFbYHOi6zw3D5rJuThwnOvwr0CJwZOU9nzKV4F6NThWhQtTp4tSG5vxXF+LYSihoAWJ0jwQuZlkCFQZBBNxl13CVIK8uGZQE1KNIlGBY7cabCD1QPAzF24cSKR1W4LOoyvVqt2KUOS60z0OXiE32+/jUjRlCCl8mdNhN2xMAhWJvdaLhX9qJsoatFFdaUQMqhaZvJjGPTTJocaDbbm04nB0d7uwe7lGjcptVmzEKNNl/jMPQ5Z2KW0MZh6DmTpEQ5xeitm23UUhp6QXUdoNnnK4IuJWS1y3YMRT/uOGwQb6l5mzFWm12lAPXMbjIO4lT+AD1w+EBMPsHdqmEHQdeVVpgu6XpV3iVgOmYLIdlZWvZ8EhOThHGkceiHbS9Dz0M/bNY55UAhUNRECYcmhshZUp/HIcfQxhD16GfKqc/dOKRhGEKgtm0lS06p2KeaSoDqF0plLf4lC+uRYMCb8zEHxcL6CTcgFymaCFyZUFfphX7w1Ww82znIJUZLlho2s1QjKajrrlVMV6XwvsrHK+RluGEpKiZURu3BgDXCloBzTNYjFsEQyoFreYg7yEprcdzfAKjYGeYNwdh07floEoGzrKbzxWv4NXhlhraH5PhEhQ7hX/YOcH23dxI70C1AZMpiy8eltoF8xsWRQI/hhtlXkE1QcMzALPCvYmxVq16D2Gxn+rh+K/lg7f+oUIEyGPuceXSfD5fv2GeY6vgShfyE8MyhvyIGxWPZJCFHKlcAlpwD+wS83aapoZBQuNTjjLD2xmRLtkBIbx4QnDZyE4P+EOHKGcUQO5vvaoMJ43HFAuxzHvK7Kqm/gatAH6vEtI1tmO5NhQdJ43bohUKmLCmkgbfrcTKdCrOEFCITsSSSUShlEomBUhrHbmTiEKKZKTNy3sr6ASdYHK4GBkfghQK6oQjLYnUHDOF4oGWLC16rNJSL7mjm02wFYOTyZwMcPBZA5OjveTILOIqHVQx0XQcYKuoHGzRTSFkP5ARmtAYlEZExS+rHhrkNDcvQ8DBrY9NMiKOW9xAH4ZCybIcxJZGc0tClPOKWF2HJ2lmpCcHqe20qoF0+dqMQZniOKoHhD4gQHGAjV6BWKoksOJheTMzWzd2i+QBI1tBA/IIKnA2BwPWv2Es0LBH1Ih7AWrkOeaRC7C8pA4AyecoH8FuAW9czuCVhJAwybl/JNfP0NxVmAdkilkJca7qSUcSql9jgFiO3PKi3Dd9F7QRDKuQpcvaH+9+lGhS7rtp2v87oUuwGLJOMAy8ENCq+p4zEJaBhMBU2wOVjYuITC9IvrYvtQRG7G6jl5tSDsbiuBow9cHgsNgriPe1M1SoACQZq7q6cu1ymATXigExYmMyECAn3pjoikxJSnyDbuQQG3BaqEhh7SkDB+ip5EOFP/8WEWfvd7PoCyFayEaDY0Cmq/xQbLLvscGquRBqdUWVlgYgkbSONu3uzECWPaRj6lKlfXwwb2q62/ZiIeOQseRyHTlKObSBJJJT6Te6X42aRxk1kiU1LhOKGWgmprHIhEdA0ESU55RYzYAg78FR2XbM/WyTsEzCzhQ4mJyUl7J4ebISY/XwGDnU53//0HyFcHepclsAqTM7EGppdCt+bLCRs58BIJKfMMQYWTimENlKXUxKhhpkaFpJRUhtIQsiJJOWhG7ar7XazFRkySxJhDoFjE9omRiYO2TmFKZwrRnEK2U2JgHg2SCcqJeY1d01F4kxie7BmvVnM4/hr6kVWywMC2gI4UQLpIkv+6bdDsBjbvD+W2bDOtu91HuX4h8AdIMYMCPZFPL/hqkdUuhhl+0Xx75VDU801P2+b1eK0gHBPSWllrG4na+FaYR9UBAPuWN5neseX+lthGarec/gCidZOCPSu7CSz4YXrrVhVOIdL6WpmuoQLCpriiZMqEnZq40GGjhmpkGK8oJmaH0OJPWk+RNdSEa2MweGfJWf/CTkGwgxNtGa2tsXuK0uu9iaxbAzGHCKLCO6Ss1xizr48dmbYF9K9Z6V2gk8LXT6JZtJQrmA7E8JKz1VsxJaAIT9qhKwi6JYqoR961A8HLmYCExFYIMZgtRQFY9Vw9OpKslGHLGUNKzkUrgiHS0QSG5aciHITwt7OXsxCJFdu7M93rxNxM2mPntgdL8ZGmENu2kiZQ6Amch56SiPllMd+7PtJCNNmqgcLxPOoLhUqqCGU7TY9pmBlZmJF6jjRXWIl2JpQ2UU3UpONW+Sq8yxoGxcT4yJIaHQdE5iuwQwJK2JMAG90J+0SBFqKWa/3+dBPBMlZzx6LiPhZ3EyRQ+QomTZd3nTDMI4keRz7od+mMcU2tm0MRJQy9SNnjiHknPo0kgSmyDEKcyaWlNKwLZzF/StVW3zko2WkeBA1wYZ1CbiyRrdwlYdu9uoNn8HIjvt40RoPEmTy4awFvtFVHxhSMeOiFgAB4D+oIcgu/LjAnPzSSizZJaYnoGZmK1jpgGQftKQoqbg/RddMhCKmF46DPn5oA+O6GMizqoQBjkCmIgr/GAIUj1mpLhdyV2cwjeA7AIPXSx0pEZHkLHpCGCdTbJkAbuz1Kv7Asi3hlLnIk7h8kS5JFX/BpBzc/TcEp1J0TsMCPzYl5RXwTwTKAeXFb0LZl+IiXiMdWEPWAlvrveP1UdYWDcslVQDk+m7o5MDt+ujhb0kRaNkQBFKMCok4Yq6qTk1tkPK2j5rO2ZamzRvphMpXMRPhvIV6gnLJpZ2RYdcTOxxe3G0ooSW5RF0BVLCRhGgctrv7e7uHu7GRvcPJwZOHzeFOOydpcqaxbWkcRMbMQWIT2zZQHqMkGvp+O2z7RIEn85meIjX2jkpl13Lzi5ViQ9/KWrATU8QBuoZ6sZjjk9jesvg0DMy4mJXzBN+eBz4ANqBETj/sEVxGYdYHfcTbKigAQgm4m/4oSEosErS2KNjtmBQoByYKfc7rzWKz3q63/ZhISMahF6GmmTFPQzMlu7c+BIqcQxMmkjU2oG5MYx4lpX61kpRDDASbZHY6U0AhSxZUvyjtNJUo0WopXccykHsMyFV8MbSqkonYZkUVl66cPvyE/b0yOKlfBrBBDgoWWC8mwTKlmByhzJHcb+RKmxSIcgV3oF1AKO+KiIDV1r7OUyNczuZ7VI08gnb3oD7SnEhgKglGY2bGtAm7fobFkIDtGHmQU3alXE+zV1IyQFXvvMU4TXHNYQsMxc3J95zZZw3RAhorE4JnIOyMQNjk1wy4lAD9sOJLDFC8Msl0Usms+XKEFu49mHDuNxSvQuIyh5KTD7nIGRobynDgWOx7uQLogKHrMLITQ/uebZPA3XnwRzAOWIYnJkxzdKWKGbqQHXjF7UAQ6rgyEHMpQjUXjimWkwUmM7ck97jm4MUH6A5BG3Zl0Zy00sRA0k4oRIkNT6eT+d58ujMfUy8sk5s3NjzmIac+99s0bIckRDFkoiHldjoTEpachnGx6jm009nuMI52K1X2oiagMKiUxU8ELleZuXlp7JW5ycAQimtjrAj5Xq6emHPfb/4aPMMNCfpDfugPqw7xi5MTWxo2T8auvgwfIlhVTBB6zGHoeskGCoE5MpPQkEU4DimncWgjd6ttv+0kSjtpY0PMIlZiF4UjxYZCFOakSGVWnUWSSAoNddtFTn3bNE6XoVeAVPLpkaKY4UUAtcq2n1nK/N1k2C3btFJty8zVd/Dh9aSy40LLsL2WS7RNynVI0FdL8dPYgVojYL6sA7tm63dgBFhaMQ/hBsLQIbYgRGCA+J4gZQTHL5LB2SrGVKYPFBQ7yig+Qia3UcK72BtmerE2QTa+MX7p8QiejR+LGrxzZICkRUIuJDzVV68euuFTcdGhMNP6m5dmzQR6ULQJXdSLK+fClYpEHSn17foT0x0IvDBEZ3AOAmzQbx6tbpGGegMSVEaGT6+Rvc8bzXpZMODC7iMjEdE91pI+M4uGOwQ3Yl+gArWEJAxBw+wJtqzs2xJUBIV3gGqQ7hm6PFHICK/D1RgEC+hgKiS2S+x7bAUafSmL1mcRCiTmWkwJs8ZVYydZpvO9yXRGYdLMd8YuDQNLipRyQyFSaGJIEjI3TDEGjoGJWPp+WJ13y9V6sdibzuJ0PqRRqWTwc1i4Jweob1DqpK0ecEbCym6Ggc5CiyuBm8Rw6thVxLZe8Bkhq98LpWSW5FI3IeOFrmbwmkwghFigivVXSsn4WHEQTERNymkck+C8aB6z5EzTZsxtFp43zSwE6cc8Sh6lmTaRJYRImYVZKHOgELhpYghRJAWiwDSKBAnDMLRtbkLYri9k6NrJBETK9biYI9yfFtdznX9nZO0xTUiQ0Cw4MFKTrGbFjHuyDCA4aOICr8qu3UTkZ98J9NMjDY8wctHfkoIhPMOMU6ts2KmrDRLIy8DvYiJUvl3HmCAONef0fX/BCQR9taubPawwbBKRrLleG57A90AZpSRd3DDZOSSJMAf82j2ow44xDPtJBmsspK48jxFNwLlVBZ/oEoOYxTZUwFjMHIU9hUOIY9XlmgPTj1pmWVOuzJrmwqt8yASaDg8dsGvqAFbzU8bykINjsVMuJ7k8D24byoyAxz/vHFqIQggCTXMKb/Bq+1j2LhOr6wnua2TNwYsWitlsxFUteLt1ytX3i+4F0vOfboUYBLbEXUHdHZh0ieycQsFGRyJVBvuVfkeYSHDsAP9r8y2GnnNm397TzUm2hjoxJh7WkWM7PWhmEwpTCjmGvcAT6eX84cPFYj2fTqaTtg88JunHxJSbSWQSlixj2Ky3F4v1tRdutNPJekxZF8uLd13SpmAmz0pHCEktKVckF0yHIjG00WkJAEZg11Stsuuy6g9OhwBBciZG+RFaURVUr81dPTqRL5yrnP2kpob4OhOFlEaSMedRSHLSoFCSkDBPp3PAS0h2Fpc5NsKseJiHIaTUUo56djhymERqMgfOEohjGylI3i7PNouztm0YVAuYIeAc4jWUOWPrEI7YjNvs5zKjtBvErI0atJBdNi4e8fJ2BpuupADexJdWkYmI7QZvxyML0suOnz2CqiSAQYGtpNNQGEp5cSFD4q4dr2OgP7bTCEEqedQhwDEuj+MSmmBiZVedsm+6+h0APnAza6lGaYjF8HaKp86pbaWoCkSwy1Dpp6ksQ/8EIYT9olz24nIqv4QEnC4RjEjI7s+DMbgTELhOEQ6hGiBffj2OfZSvEpwEYW5MZJ3WibH1XczboL4alP+C4MNESilxYWpVkqSyVddIayVVRwb+XCI93e3/9IBAIEO+NM0Mql4UzwVWkEjgo7lom0Uh1UY9TEkll02LypEYs81qa4FAovB1fbZmm02WOQuR4MIcCMrPhTCnbhlkbKfTZr4/3b8yO7g12d3fOTyc7d8kyTx0fT/O57MguWkC59wEmswmzDlOGkpdWi/X622XZPfgVjOZp2SH6lhf6q7c5c9OhMjKOKFZLj5m3clxdXC/r6tf8Ks82ti3kTk/PG8qYSvtfgYAIURW6YtzHl7bVyixaa7n70w9Lmm/fbRYEXMYhz5L0sXmSBw5BMrjGOM0NNNlN46hGYj7cUgpEYfQRO0ayiGkcRzGsR96ymNm6TN1QXIjFEna0Eyato1N4PXioluctrNpE6LZj9LSTMjBsVUOELHVKgjMnhCzGIkkZDBVFVXz2aOsWre1WZNzG+Pyiinscbd4bYRTLTdL32pzsGPwAi6i9IIipupyz4IGhLBQwybz5I7SZRWJyOqRYXQMIyXCKsLw2d0jWDC+4MMz89VgE81H62jRiIeDmuC23mr7S6dn0tAdreKGhUhTBKBRGB+cEtl0sA4Mf1CMTT+Ws/kYEmLKMLaKKBa/d8k0sLxYsnKfmq2BeNWQU1Q7jQLf45hJUIla9cqA3T0x7NVpMkiDsWIviOV6tuL+EX4RcIj/85CxbK4AA4v/gy5AqlJKdZz+uFcnGxe5axeibA5B9QN7H7iFXGm4oKKfiHF27FOLRkGjN9wFJl7Yw1SFGhVJAgZKLToAlgdSMECopuQmrCeT1E6appmEpg0xxuksc5pdvSNjunj48PR8m3LutkJjjiyTlmMMJEGCZrXzcr3sMh/cuhPiZBwTM4cQtfgFQyQOloMoQ8VciuOXy/27YLxOYNy0K/UJEICAsgcrS3FtwjMC0h5F90yR2ccJ4AAm4DkkQEe/UjCwoaMTwvJSYqIg4yBp1MMIgUMMysKFY5u4Gfpx7GUcxn499mPOkjkIUSLmTGHMNIyJme0UNYUgsWHhkFiEI6eciHm73azPHsWmaZtosI6mU5gh5mWUsWizQNyAEYNI8qRKwTKSWkmzsx6QdRTTColkCYGR/2QqFEeAWs4CsfCgSYRdStVSLmlM/B5PYiwVnJKwCEhtsaV61FTl+t12SphBVEwRzN3wrIqM8F/zQhyYcjZfgvsFpUoqVMMFgPt2qCm5Y5OUxzoKg3ta3ObQQvDs/ha+NJciAkIOFBk/kMMCIm6nzFUmEQAJ3Cu/con6Jgck4wXyLtPCagnbJFy76upxNV7Y2K2yonKiHhMw/q4qGgidsk1czC40byWN1whVpMTVzTW0IC1QoICsI2ktCPg+oSwMTw8uwERM2bBY2Yc3DmNVF2Tv3KMBFFUCRFWRnrnC6ron/Mjk7StbYm+sJwmxcFV/y0TDTtO3s0Ymbdid8WQaJm3g2M6vTK49zw2vF9vdncmkiQ1zyikNiYjjpJ3MJzKM42LZrbdn56u2aW5cfzpLGPW4GglRviRdgQ9Fq51imCpDQCd+LpJzWbQqbWmrZsAift8ka22xbzLwJQm5TplWMBPaJtR/gkUbblxF/xl2yvANuoyMZp+f+tPkcRyHYRzGdhqJgibKmZoQJu10Pp3OOPeTSQwkOaWh75lDE6P24BWlBtwKTTPFEChyaiRnzn0QFuqGRKFJNJ4f3+VAbdNQR1BslExLYM5OH9jz/kIBXXGKTwwkWQJCLeVFRhUNkvSy+gKxVn4rwiFkT6AjSVtMjtBDuJSYE9bJYcvKMDi6w2XvlsVO30DgDOovnbUxo1ciIG7yQFfrJywYAKonqcJe2DHSYoSUS3EaTKLHAYg46KlodFrz2Qpeh70KcnPHyJ32iNMM+EYh8owZQEZPOdQ13fYEgxnWCykxF5+V1Vubeaj87V6Xkp8i8duhy/OFyJobgKy6DQl8YbU6Bojub6WSmbKHytSKL2fCqPAGoDTeZWM1EDP9oUA5s+8Q1J7/kmTKaKE8wf0OXuYZBSJt38a+LHix5xOcEKnLyXghPBvBcyAABevHuNQ6TKgMycFq9KSeL5afozQqlzP0szBleBGFrHLyizJZ8hc6ArolITBl0nLjCQ978xSY495Vme1Jl0JsJmk2jK3MDmjzII25nUz2p+16OwiHEEJsQ2CZzprJJHPXr5arB6fnze78xpN3hlFvNCxL7QJjYr9kzn9z6TwQ1s4kIGAMWF+tpBdbhcrqnUKyn6VA0YX4GIiJNUmrfMjyhK7VIcBai+6YgsGzeiBqC1hiBTPiiqKqjoqkoRdJrI2gQ6QQU85EzXS+HyaTrutDkJyGceiGfkgpZSJJ3ThuUuqkSdTYLnATY+DAgUJLkxiYRFJusnAaFw/vSc6z6RSKUNTZUAm78GwwL0S6225VYeTFy9goN2dXTl+qaOxoikC/PaWorgZMsOTZXeeKEqP6r0YUtwPHaEHAAhqo6gpDZgMOEik3gDKVwQNZyDewXckMDclpW/3HWYapq2O2VJ8v/EpKCSarlBTMjfhBD4uHqZMWeBJwy/0R9AkMjog5m14WCiMQnDlOAx5yiHJNLN5FF8AKd1nr01QpTMcFnI0RJ5A4jxb3vvgBO5+6NC1MpMqYccF0ZIU8ZeRnHVhcPgVB8LwShyiS4iJc26mq3JH4kuGrzjmqiJBVAqAYZbLuQQvE2qr6MnkIdhljAERUJk8EBl4FDuQnG8i8QsDyI22i/jK4LIRwvE7siIA9woPF8mz9/6HKuEL4+mnviJEkT8K2ySuKTZ5eo71nePcqT5swTWH3dpbJ9u7Hjx8vZrMZxTYTcZRmQk3kdtaGSTOmftiu1svN+Wq9d/X6wY0ntkM/5gTArO+yZ09Ei9mmr0IVCVQTYS+jcrcakGSBzJWfSHkWO2fQ4jlcSmx44nyfXSuI6gZMBve+/cWub/pe9qURX274chgku/8LlIachqxAEoJq2JBS5ph4PqR2uc0X664f+3Hs8rBVDMySWHIMFEPgSBRz23DgLJJ7Dpmb0MQYYhNi23KT6fTRJ7lfTmdTQHCA9gvpDhLiemwAqIyK3tpURHy/14zWKboUJkRgH+7xwLgq1EEFOhcKXkwUJK4eZjEIKCr7ZwmMHrdsWaqL0DBEqijevZ+wsHsgC5xr7kcVi8XrYOI2UuykYWiOhfaB7DuAlglD5wNXJvhJAi0TESuvKsFBYZv+tiKNfx9eSxWaggfpjAXqy3AdBbTsBb5Pq5phaAuq6h+3iRCQjeF7AfdwTya6ohviAYQThwzNsM6KiB5qv4efAKAhVZ8xgdhQmXExS8ZWIoZX8cMqo2UuB08w50FM9Z105Ptd/uJiIlgHJgo4+czBxUPuNGBHJd1MXnZQyaeeNUPjMU72KysE4YVg/4SIGVdfmYPBtwnDZNJwAafP3Ag12oBBTIbzNuZAJDKT9lDa3ZEpNVfba69xaDYP7i9XfUq07bosPHYpcJhO2yCZ44Qkj2N3drbabPsbN5/b2T/qhkErTUzWAUsl2HgLZr9caTIoUzV6CxnhOzi4rlt+H5rD5tS4CEDKeQ43dlN1Rwmx7IWaincfZrICkbI8VftVHQnBxtnfytXwi8OiIERjSmPOYxLG9j4TxXYaJ3s5zJedrHvZdnkYxq4fUpbYNkFCE5ppO2slRMot58mkTX3KWYaexhxyCBIixZCZcpaL08f92aPZ3jxqUGow6e7Igh19d3bLrqHRNZhd+CRerAYMuSRNEqHsrNPNDG4V0ihH0mEe4jvlgFp/AsQHNmRjKmCACrHiZx3vKqrhfsDgqNjvJT2x6BjswIetHzKkMxpX8WpwVBbUxcP8LNApeAZtJuCGoSJ4A0RWQbAhNz7L8C4wfCGqzlvgK47FLNjWcaZMjntVxGMOC2IA8qo2a2mhJ53xYi/DUAPRMlapIoAiOMhQ3xDI34qA2utHAa+FEpQZ+8uLcuI99kl2h1pjqNtmrYkE7SN2wfoyXW4YWZ5WvKLVMBYvZMEN8uu6jkWHqECz/iAUpSfXbRuQqpi4hniBaW3FPiSobDUrLChMxlZTf+M+g4jt5AUOaeVhOW03k9kktOcUmXNPTYhyzoF4djPI9uSTd9bLbj5vh74f+j6G3Ex5stNOZ/PJbJa2m+5itVqsNh098/yroZl2/ZgryNTr4FXTTAJ6pVqo7v+CEAiXO/pgqUgY+KLMxZWthhs2k3IpFfAhQhNnNzHxzyiPtFsKKiWDLtY5YBQiX3Itl8JrrImQKv44pL7vc0rqdAJRjJzSyKGd7lzddmEYOY0ydHkYaUhMNB1Hyikl2Y55m/MgNDZtQxSYm5ykiU0MMQkPQn3KzLxenZ7ff38yn03biXo/74Hn4wkcFJlCqC5zMoCG5AqIF1PwiNgA0Pmjri7Z9hqVeq/6vUYPDeW5dDOWclWZ6Ye40D1ZbHTHCbWNhMSpsrD3tXddwnfY51npiYgDuyA95G6DqGwqerhgoCde2VlxtwL0AiEFYFPpn0dEOJRrB3Cq+VbHDsTuV/ADM67xcDGgVAqzNhedPvpK+pw9SmMgKxi6/RMjAx5BRt49VOrQA8kiYux/FEgqob1jqPXdzB48Ipy313njcP8DU3WxwlRruyIISgwKqt7xVvyJYaiiwi7RRo0rf2+haBkVlfXCekCS9ljgJ5VhOZTj3bZUUsm5BKhSng3U06XBsNmNyVyCC0mHJ9U4uWhvuX9CBchwc/5f2IZgd1OIaEIXRwexiS3vvE7hkHnKMeZ25L3XuT2Q7ePHH32y7UbJue82szZSHqZTjm3kJsZmxhL7bnhwvKTp3q1nXxwldOMYhCUzZqfdOzAjiAzcqr5aTqzVjE0qkPjmOBTZWou6m/cl8dDeiKGtA3L9Hgi6bniNqX47VL/3Ei7y2wgBkkWkntsUEE3nHGzyZ+YQYkhDl8Y+jQOxNG0TmyAio+RmMpnMD8bQpJRIxmE7dl3qu6FPXcpj1/frzXax2mz6nkIkDqHRbvDNrI2TyJEDU0hjnrRNt9o8ev+HxDyf74oIg2HbqquG1UzD8JjEt6Scp0Bba+PTj+qxCV0TFULOmYs2apbGOjoBh+3gCYN1lAcyQIRdiZmcVVmkau6gkBz39hmcwH9ky+BWUfyaY6gDGrvdOuAQlX+WMJt9wIWbg5xWzzIWDA1TCy9skaQcWPVNCf2UFnQR4/5hfIH9M5VfBcZaWYgrKkt9hwa6X7nPpktLTH5WigtXdnSWGniLuLhgM9SnsFFdOSp+lWBmTl3JJmuqWPItVG8AgFVxsWdcdOPDwaB0rcFFpCoSVySxN0JnbK8R91ARWWtlQg0owxOgGNzQWYpWeZOSUDEGc/21wsC8yrDt4UVZ3S2bftoQBPplmqL/snwzm+nhUYwVtjnDBMwPwOjdp9hobPeSOSTZ7E+WTTPIdI92nsrNUY5zkUjxCk2fSJxk/ejx42NuJkMSaeJyu2kans6bNtJs/2js83bZLU7Ot8vlwd6t67fv9GkcUxKREIiDrzMHwk04xMzeldH9Xm2b7Npk1Y+hVkYsCvCF3BAAedA+W6IQjHSKZCwKF8OSIhRLe2Aoug4BgtYBFc7Ceu2dBzowUJc3k4iEJgYOYRzHlFNAd+wQAzFJiLPDq3uHV1IWojAOecyy7bZjt2qYKYSU8jiM0qeGQxu0/2duo7SBYhOEpWljG2Mbm7Tu737wtmzXe/v7kiXnnCUVcTEzBdN1ExmjuB4H23TE7GKmcoNg/ccIhHm5atcXeCfMQU+31vahzy3af4lAgZj6arpJ6H5kibx8DIzAuIryim9wz+eIwCX3wf7i4gngjC6hOjuTKorlvqLyOlBW6JSBoHimEqzPKUqtzyzilyJA9YEu7ETD19JfRwWViBERmOr5gctKINDTQgx1+KXmWgA2tmzkPo+qFbIHiaO4mY135cMHLkWBxOCucKFwz+TuoXpF8XnkZ1l8bOAQxc9jrfGhAvplObES2Wm6GFXEMAWewE/PiEeiBWmxxoWq+iJUYvTtWSInWABseEQfrGskFAeOPkAULFyVG3F5pv4PTtDZhgO2HcytIrgsrVB1xJnylNdHO6OM62Z+Lcd94piZSTi3+9zsclpsH79xcnw+9oOkMI4ymzVx2kTOMcZ2vsshSJ8WZ5vzVXrmpZfnB1eXq22SbG/P8GVCwhICe740+Ng4UF0NAq0IwdOXQla7LAXdK8O0c7xwCMwUgk+chLxU1JQZIQVUG+rtqo0lrvQHWuohtT+wUBljGyg3BMHSOyAlpzGllHMOzE1sSIIIt5N5O51TbFdD6jN1fV6t+iQ09COFEGMkyTLkSOHK7s5k0jQTYhkD64XB0s6mRKEN3HIOlO9++PbiwYfz3b02NiW1rWPHyS/2uL7ek4VKwyJRZQ2s8byBw5rpaAmaREonghJemIzMQGHVjvFlCAXiikWRBbOMLKr7KnUZphzgsPUKkk/cNZ4qzunmWsAUlM9VBApqEAenU0yXgEc+cgFyGz0MuNyGEBCAThLcjv0Segi35yxDIAREXJVVg7Q6j8R6eKGIWU9FrpiJhYW8A0rFFt091Wsgzuh92y1LNXQi0HCDzipGgVVL8QeeoKiWsjjHy+teCZn9XSJ+ua6vJeSOcNH3uIo7rkWhWw6lohjzclcEwRcdI/KjWK7DQOZa/vZfSNHoOgpAzI7E5w79Lx6gin/KsxBHsh8EE4i9WjDdjnLWYi+AL69clTk2IaE87jbLKa13r92k6a6trGxC7omfD3GnCZuLDz88Xs5i2BmSiEiM0nBqJ+38cG+6M+OUt+frx4/W2zR55uXPcIjdOOidVZRz0RKHXdNiXwv4/lLpVMnQOB9jmULl/oH7BBQg0iyOgEthtSswZyrnLRQEkRoqOqBIGEqGVooEodHusMkbSCCbh7NQBD8bQgjayXkcBhJijmRxQExZwnR3dnRzzE0/0pjS0KXtZpvGlMaRhJgkp15IRg5h2oRJaPcabogmoZ1PQ0PEA4Xcbbcc+Oz40fF732/bZne2o2JA8xgDEueMzBAmDDNjMbxDr1uCphapHCMQFGxfOm2oul5HCyoERNmgNmRL7iQNu4gFAMBlKzQ2lUYEwGUN3JWYjpWO78zaiqSsjgImDFU/Zr0DMTAnGI57Feraawp5Lt6h4pIiJe1dJFpmp+LNlq4CexZh4gqXbaXcPXA1YwEs4w87dcEM4dcZmMD+eQN6EZ9B5UrYP/Upn8xYG0aWz/Db7ZnEGLShsTMjv+JDmP1fvpRSv8ddB9yLmbgaGV/+qGuoCkic9QBuihaZc2cSHF8nJt3IyCDR6ALgXJ1KJshW20UnrvBOLBi3x1IlRhx41vG6GhWBazWuENk1q/ZTH7mKPOj1y4ZHZVBGy0BdfCCuBMWTmkizHwTkHGIYJ/3Z/sG02d3tt5tMo9BFzh+l9bvEO1n6sLh/99vvn5xRO52MJNS2zHln1k7ns2Y2pyZKGo8fPb774OF8/8ozr77SdcNm0zNxDBHmYJtZmUX0ekg9HxcCEQRMMAfxlWGYtBmjPc3g3GHGT32aWkLhWVImEgoCBTJnazykBKSqWPoh9i68pRJSYINM5LVnubh5xh8bqbb6gcyFKIQYRLKkMacx5Zwlp5SFiAN3QwrNdH54PU6mQ+IQGsm5H/Jm2203WxLhQNu+61IaUgrMsW14MhWiYNduSk6SmYVSw9Rv1x99//do7A8OD1D+Lti6VKzxGUMnoGVBzVigq1AjcWFbdO/kBgX4HMqEQYvcmIv6m98vqX/4eLRAYMbHET8wSXVpMPuKSZE+ldcVKATEq1UDAoicB5J6frxRsOTqeGx1K49/OUYp3qqigqxlbxWCI5gnZicFRK5hfoOrIwG59ykEEP/jWy5iVzmafWCjqXAerbrxg7Ly/yPrv6N1W477MLCqe+8vnvOdfG549+WHjIdAAARJECRBgCDBTDCTCiRFUoGmKFmWZdljeZa8NF7La42WJXu8LMvjUbIskZJocSwzJxAgMkEADw8PL92cTj5f3KG7av7oqup9MJfEffecb3979+6u+tWvQlfbRBsdylBuaCtgrQFjEeaLjnMGbOaMxaYkCl6GkAza+SC1RO7ocnqqvpzMMOcfLmBWlqC8zvpNW5IMCWgBD7W2toJK0cygOolIIWLqhKeSp2/Gtmq2JKCWNi+QegHGAbPAGGNNfqFqVseDsZyH6wiA4ViilGY8tKuT9vmzDShpXfCrVhY0rAviHjmUA0CZkQnJ82JtCIPN7ejWoL+GBTOf+OYAikuAE0er1Y2vvPTCoXflpBzHSDEEV7hyUPQGo95ks2ma5en89GR6cjK/9trXb166uqiaSJTkxQmtV+bOYp6l+SuzGjVQWEBdMETUY1pleVAXRmdNr1WLmzffEKeddJ2OMap3xgTUjhg3MpLJ2sA9sz6THPsFGrlKN2MDPiuaF6FEKVpEjIFCUzd1Q5ERkJIlCMH3BqONXSzG8yXNqpYQFov6/Hy5qiqG6AH7RZF6bQwKP+j3Clf0B0MEBMISHQYGhuGo7z04xpdf/lJzendtc1L6ghmIUTapKisSpqg6aqDJHS+xE+tQ1tmhEwLrotK27bCDjqKlFnrvWIJsxnXhjdhm9UnrpoQn89zO+kM+q0U5rbAu4kxObQ06D8UMEflXwMnpw4trKzOli6znJ5u9N5aaJ4oyxgrOqNyo6VJjrL9Tue/sFjZXxICCL5xnkC2p6hKAro/No1NkAzXeZs90HpR9GUzqmxj22Gh0/c1iGfTpDYW/of5TZ7FDwHUS5PLsGYEBbGfh5IlmHy9ebBKqIG9hSxFssPW2S8S02M21W4oOD7V7aPfdU2beWQWhMZLORTYuGUFi9iwrpJELEgg2cyR6ZFDfWROz/aJydlp1J6eNGkjpDtb+K6sqNk1pFgBIKgEckWsOh6MGehPobUPZx3ju8CHwfRg+yhQ9HJy//Knr9883x4O2mg/B97wb9HEwHkLPDSdrJVJ1NntwY1HVo9e9/ZvYD+bViiLLsY4JW+Q90pvZEe8IWhJmS9jVTJNnyCIHcgz8BVhOFCHTOLAGGx1upZIiKt2ldQCgtVqsuGKMw64QtJSiE2ZOpw6kx16QOxBmbAZMSpwAgaFt2hDaGAMg+MIDuHIwAF8MhuPx2taijgCuqdpQBYq0WlVN2zDEwbBEWALEstcbj8fjUb/f7/miCBSLXtEbllA4X/phWZSuODs5uvfFT/cHxbA/Yoig3KtLJ9KUGtqiMi/UN0I9Oc+MhE1C+k/SN+rIp1K8DO7pGsC8MEy2CJnQZY6fqA1xZk3yML2bSQvAhXsIC1AzrOuNHfgBXVKVKjNOubl9XmzDKlb2ZiVAXSi0iROYz99OmJPv13kdJWvq1XZmzMhx/htkArkDeQCg/pKNVmdMnXvWaU0aKGWpDiWnqMQcWG9s+Ip58xd3pjY9OyfbMnWz/F2yS8o9UwaPQA2kekP6C3l9WYy8kKgTqAZGeQcolnUottkStumxuUPLKpOuQL4kEZEkLSbgasCsQEteTHd75a196R7MLMeBmbHMsqT8x1xalgJ36Khbx4MRwSBzbZU2dWyfUKDufCnF0hfvKJ9s4Jd2QbJFP2qPUyZ0NB/D6WTDAUKMXGL0dOaX9zBccf1NLGusD1785OfOztt+v4exxdj2C15b7w8n5XA8pPo8zo5O7x0cH51u7r3u6jNfs6jjqmmj9D0TsWVOcR/WVTDZ1ndwuvFb7JkacNEwBgDbVsbKIRTDdTYUbcwkstYxYxY40EbIRiZZ3GYZgY5Q19kstQoEiDYJHeyim3gT3Fnx9MdZgoGImqalqIbfe+dcVYdiMNm89JTvj9gDcOyXCEz1olrMlk3TlmVZ9voxNIBuOBz3i74j6PWGyN71Cur7lkP0SECD0sfV8suf/hjXi/WNTQceZUeYnuao0qF2UD1xXSOpprXFMm4EkvpjiaoLjJpjJe9M5nMyaBQYdXnsPCmzG0oFzD6Jcivb7XAalRadbgndGhdi0SgHzLY5EtKBRGqa2ULV+gIWqU6/V7/beL9ajq4JQX3hjgwreoi46eM7vLXj5IKwc7CpE/zOQq8QLKdWgQ43vSSLBqgvoJ1UZB6ysCrfkemirnKB1lMDd5FNfGKxYuqvoH4g1zmtJ9QVo1Rgh5oVkmUTIpjHRFkxjO2ZO8ZMYriYzG9LXEp0WCZaDbDl81JSgthIo067MUt9AnzVK6dx6XlEAlBOG3sQAIC0IkPM4ItqrZg7+wMyFpuHmCUAIc98Z6XExugXZA+SpLGkqkzlQRkcGBHN8pIl0yo1xMB05FsVOlmgkk73dqhXYrOqHcy5Ogxnx/XcU/F6grKAafXqnz7//C0cl7UjAhwUbtgv+oPB2sbGaH3NubA8Obp79+B0AU+/9e299cn5bBmoZWSXNigzQloiTTfKOueqs8z11GQ60Y2OwwZKWdIHrH9EvjovrGuPmL1bLbHr2mlbDb2PIAIrNFl/EcU3/VjKSI3PMdhWMkhaac83t8QZ2jBRjCFSlIMhGSJBZMRitL57bTzZPD1fMXNdharm2ZLOz5umAo+Dwg+dd+Cd90V/0PeIw35ve3sttNGV/VXjqir6Egb9wjO8+NLnjl55fryxPuj3AZGRkdmjZ2aFyQvWLqMcZDambDODtZ1PlD5It0kaxeaDuZx/5M5mOfWIRRKTqWGZWTXUmk8D5qTx3En6KVNioWxWAnjR2AIIz01oYmTKGIHQXaHAcqIh2utb3+wkXFoIaG+tpFU9EAMkAQQUWDZKakCfKUbmz6qMxtXsZTOOdEs60pdRjYzYLgUP1s2RF76B1jkDEBVfsksH6tR1KZoIBHfkQ4LOOgwis0tKhUCDVN2kmRzJlLX0q1kWiO9od5d5FdzMtiH90nwADS6Dqv0FLMwLquslOogSO7TJRWMCLs+6IVQahjMTjmmXqE63yBvYC8p2yDy5uYiFxYKkJcyFT6oVamASZ1ILpK/DyaOy0LWMk7PmdTHJUpXiGjkwlzUJGzG3YT7w062JW8wX7MpieQoHd5b3bqzqBoc7Dipf3b7x8Y89OINLW3uzVSCAoudG497a9rrvlcRM9er83tHtOydube+pd399C9BSYGKUYuIU3U2ZykyQAJRBMxuTATWp8iqWjknayPm9WEOL8rImNE5j+ZICY0BkRMocSp1vAEQkWVDFdVluQZXcbBHyCii+mxLIv8S9wY4qWWNKQGaWIwMZmYiaugkxEjOgA++ICTzWoXWj9f7WI21NqzrMZ/V81s6reHY6n86rQFwUPe9coBA5OgRXuOVqMRit97xfLaoYmJgJwUGcDIpqfnb9Cx8toF4bDhAY2ROjPDHjlZ5kxOqkqpKwWFkFVrWqZjvy1Cud6fAcu0jmRgytMXpQHiPgIqKK0N1SkcQHEmFkI6Cq6gnNbJ8bWlBJbXQGKzQyzt1rDCoNo7NVZLZzboVS6UsKCqgUdN5IaVpXazk7HzqRrI5OZwYNEs1Q6XPzvTBPsJzaxXbajHmd6LTk1Mh/enb3uEQNzF8wA7pqOZcjJRCcw3UChajyoI5OXnAzW7KmgtmsjE+FJa2FzaJKk91I3JDsiNnnNgylbdJvufM/EZL8hyHRAZsSMdGdCbDlsEVXz8klytAxvhKxRPs/+Z7VFOT50MfZ7Jh8gBkQ5Qi2YyB1Pseu+Ra/UimP1RVfHP2FR5iwIKAeVIYmWUm+Swg7W6N6Sf2t1/YvXYK4dMc322be27zi/NjBIt7/yqc/8SLhxAH0wTvyvZ5bW+/1Bm6wNvSuXk7nt29P7x0vr73hrXtPvvZsNq+qhpP/ra4bo+Q9umcAADDKhgAUmdTZNTuRlsi53I9Np0h00pYrH+uB5jMB6sx1yrgQOiEmp1CjWWmzHRfErDOvyvWlUiiXMMhCd2VbJFTezCXei4hEkSgQxSRETIzoi6KMISCWk92rvles6kBMHrGu6mUdF6tVXVfM5IueLwqm6Dw470JLDdW+cN6zL13bREbfL/x6URaBrn/pT+vD++PJuECUXj3qUqudtCraC/Q5zWhnSpVoMwOJy60ErKNEHYVUhdfHqfSjiH6mzmDky8TbzITGb4VOC4NgTB4NKISLRJnJt34EuiIE1hk8c7aLbNEEz5ROJLPzCxmr6pTNFKpK2Z0u+CKaHsoiZBuaO3AoomtAk9+rI4hqNowsoprwJILcPZJKsf/CXKilUD/X7p+8X4Uzy1yqMCir5WR0Adn2QnZuJxqXOBN1QE5NpxEMsAR/d3bNooqnmHTm4pXc8ei6b6r3lndEtfJ23856KQrY66OuW4ZjsGvSW2FnlOkFRbCRDV0SJIlOWRC14wdnEy7GpXNKltxcuhwL1RXI0Uyi6pqtnnbMZRULJXZsxsLmSTaFYOqszuCo7dF5z01He9vF3qPNvKLjwxCXvpyUkycJgoOTW5/445dfnW7vrLXLWLDr92iyUWxsFoOhL3pA1WJ+OL/+8ryBR97yvu+GYrCsKqYIzHL0h4T7LFPK6DowrhIEoOcjKVsThMFk/Tp6InPDWVUlDoHKMYGs+TV3/XoG1j70cu98a9UApVaJCjEjWzJZ76actSNd+l2TgbwM+kU9WkBeIsQY2hBj4BTwQoipOSf6jf0r67t7iyoEpsKzI2rmFTRNrOdtW/WG/cL7ovTD8XBtsuYQKYRevyh6hS/QF54DRkdchrW18ujujXtf/uxw2B/0elqXmhrCIyZQZwtyYWdhOsqmEELWvgYZECgSsGQMbPchy7xaOXLHWoMqmP1DzYJdZ9J7UXBRDH1mAFZaruqEaKAHSrW7qJ1eWKERLyBdpgnqB8g8ZJnLTENZfbZ3mP3QdAc71dtoW5dNgFUymF7qadfQBSyV2xwvMLYs3pDcNMGNHT4j8ELquiGAbXBlPbBQBqhyjTnbqTYuo6XIdXeR1JNLU2Cvl2K2QqnUSoJOKuuIus8x/yBNsASU0mfEhquKmaLJgo/ZvxZaq+uQnqFJKVkL3f9ugk1geSpQqDWZE4mzDecGRbJMIpdCH1QcQWWdSYvA0uYSyE1WMhsCSHvDZN2VODGj7BdDkPlMv6ELqCf2jr9KCI2Bigh3VknEzAlWIhIMYr0/mm2NV9yetNNDqu5Cc7xs6+Lq0w4nHmZw8OVP/94nz1sc9MvIYYhhfa3c2B0Pxr3h1gb0cHU+vf/qwd07y0de85Zrb37HbNm2scF0cksyWsR2Grw6Ih0KprOn75sFQ6BTJTnbd4Wb1Os/BTOJ1QPAdAUQSWC3a/nzOsncK2E1m5Csrnq4qHFmTvfPg0tPZHFNOuJMmujuwIL8xlEk83NCG0IIzJr41gNgnfe9wdrWpSdaD/NlM1tWZVkQ0HJZH59Oq9WSYvSl6/UL58H3XG9UANZYkO859lCWPlDbhgbLuLExaKv5n37yd5v5yWR7x+oL04EAgpPASqWhI52qbKKtwNamkYGhU13Zrc0BVhaszj/qfGcDnlxvxWd5cNfwqKJ1TXciYsa5wKBfbqtSlFyTDmaY75IT2tnipxE49dAxdwJgeyf9X1fBxYTYGhttRD1iDjrOfDKr3BWyzN7y05K8m56oxiCARjTNrKFiA+qPrLZXkdGQMw3OaVy7g5D5bbKlMVWz6VX9MNvNan3S1MrCdbw5q2rl9IPCKyKazUlSJivPds5EViTgbO+y5dHBi+jml+n8lAkMJ89B0NmYTxIYtf+oBlLcSnNms1iiEAZUnFdfn+115e3RNMKU32YTlC7o5GEepMSvshqICdU3EuKcYFuDSVlh9OZ2rBh3pRVEeTuGlSKmKo12vTy7ts/t/HC1KCEOBvuA7fGsgWLzcQCG6vb9T3zs5vX51mhrXjMiD8Zua69Y3x70xj0CqBfV/fvnL75wOxSjt33rtxa93mxetSFEeYIe2pHIOQixQ7ZsSjcIIRbQpg41ggZSu6y1+pilE1MgIC2Ey535WGS+40wrsHU4uzwK82yKGJFaqbR6uWWyCo5NqfTI6ZqTbNtYVQyTjLlkH4AgWYlIgWKkyObiOeeaJjg/3Lr2xsFov6qbUIembl3hFm08n7Wrpq2blfMe0bmiYKThsIcMzAEwxhgDxdL5puImEGGYDHt3X/nyrec+PlobjYYjZkJ0BAhWIYNiAZLUS3duogTzpknmkzIwgnA9UoKJ+S11HtOWHwuWoAFBks7sMcv05WXRjYuCNWL6rT4jr5nRCHHNE1Yq/IBuctcisGxO5OtJ1CSSfpEmQGe0am5yoKyD3JmI62hNHNiE0Z6s76wAq2qen9ydE1kCEiVHAD32r1OtLF9CVrOcrAOqoKdHSCgfO4ZW1E25VWcIuuad+6PuUNWRgDSZsWsv/s+cTKV1xgM6pqRD+LpelFo+NXodQ6n2L7sQCeKy3TAzD0DQqelRITUjKhuS9HrUs4/YFsmAVzxk6K6grkKaebX/kM2FPReyHOcJ4SwsnZyzvKN8LmURqi3oQPLOaDIo+qsrRTnTboeRytmX2daKChUc+33XbK0TN8fccFut8Og6PP+5k4fH5e5jRW/LwWHzyp/8wa997Oi0WHP9ahE2huX2bm+yXfTG7EtfQKjOlzdenL9yt3ny2be96VvedXI2XVYNpLCWNpqWd3CyKAyqc2m5RKuyddea5DR5rEqqCt6RXgCQxgVsLyfQBM6KmEXRGFJcGsE4uMqWeiG5/Sh2nE7O1RZyN0BwoKwLgKOdm3JRi9IaW/1hQtf0e2DgyKFp5UgWAoBUVwnM3IYwHO3sP/YkeIjEpXPDYkhcLBdhMV81VRWwZhfLnusXRb/sDQdDqtkDNwtaTrkm53vDagkxgAfHbfP5P/ptmB9t7255OWDI4ILZQroACE5PiEE2lBGM0Bgum2YIA2TIbo8AICV6JUhufj0rxUGbVu1mLJQZLygD6goAJktrhc+YSWKyM4ZXrEE+A2c0u96FB0Ml1muyTBmwGQFXBc6andpoo0QepD5Pv6YWwNxykyX1fXK1WKfbpL60fkeQUF0G1L6embvKhMnyicbp2PNWm/z2Rg0BQLr8q09iXprBtv0oXxGCJVIKRoey95bNs5pCpUEoOnzBfmdNScuY2Fc33o0qLPJT51FyBbGZQuy+QZpi9UzUL4S8tmz/TYKeqnRUJlLoBtREZf1GmdtspZDBIeuGC5U8c3BQjTcySyMuWXpFJYEfEQWlTOr6GDdVqc7SJIZH9pTZZHZMVsJFJ7VGkQkAIlAbW0/zSf+4WH2srednD8+Yp4Uv5q8uF6v18aNPOZj6+a2Xfv23v/LF+XhjZ9U043E52e5PtvzupcFwOFyu2ma6PL4+fenlWSiefO+P/mzEjfN5o6qoqyIqw4nmZjcEWBH7q9ZT9jHr3hQDE1lKbbGR4/uY+g2nEJlsNVdNVy4pBicXumjPEBAsUUFRg8/5W6g+mt5J5zdJHFtaThdAGwgqvdAJYW3Bmp4VYgwhxBgi6b4oxhgjOyKm4Iq9q28ab105mbbTqm1j29ZNaJpqUderdjlvGKHoF+ub68NeOdpYK7AY9Xv90qH3rUPoYTkaRgL2PBr0j+5cv/knHx2Nev2exxiQdAoEtBlBNoYLUxc/Uw2gU4ggBlRObXprr2TwjMip1YiyQFYJB5V20DQUpGK7jth0UBoNup1zAJw8ReF7hlGozCyvtFEtABCcA4BUBwfJ+mk72AueWlY5VuYEyrQuwoeuLXTInbFJ0DJ4kxWdLkosBbIvYHfowBIocc6zZZOcL1fczvPF2VfrODWoL2EGI5lLlBJt5YU5lJYZGXGqxAeZK7B5wMRMtRgpk3K5eWZNHdovNuzCnj+7ez7aSVtw2Px1zmIz7pwWw+lCK4TqNTKHto1AZ8RkxqwpK8yD5a5s8QynsikH7XYqo9MKgK64SFo4TRapkZI6eNbsG5gwiFCS5tBF+DsThwCoCWMBQS0DyvJjVhuAgQhAS4TTbxw4YGTyjuJmebBb3sej4/NbR+NLl9aGxckLn75zY1pefX1//7KDB9PPfuxjv/X5aRP7w7Lfdztbg/HmeG17a7y509Toyv7x8fzF68cPzuBN3/SNV9/01qPDRdVWMbbM4NCh9X/TcSV1JNL0BstUmupDBktjf8YJme2cNibdnQrAKNEaEzjj3Qa/HR6iepcqvzv7RVStUvWVME7x0QARmYiZEtMVWSJtNiB3ImZmIqLIwqfSz2x/nHOOtZKaiEIIgYgZEF26PTosfBmJCXA42dvYv9xwPJutzqfzZtUslnXd0rKN1AYAAOf7w0FvMvLeDdcK5DgeF3UIq3lLyOyoWUZGNxgWYTX72G//Sn18d+/y1aIsGZBEn6Tpgcp64nZ6eowSziRfmfTlFcULawYZsBLy6y9VoNN8yhYzjRVn1ATocIRUkSW6xUbQIKEoGwmyYm02lFPZ4I4+iFnKwCHONJuIWQLAEBcSPza4Vb5gqQ4zG6Ksih4KNeI7ZlInKK10UVHOsEX1BfX3qNRV6Z4+PcO7vR4kf/liDBqgc46g3R4VR9lAy97LjFyeK7uT6xInKXRh2evTsRpoS4YXyLe8qibbGazmIzkN6qBodAmVfut8quCZf56tqKJEFijZsZjdSoneqvuYj/1S0bGWYGyqD1KHk/t0pjUEMJ/YTKKSeHsrscTyFtidE0C093KANpiU/gU1T9IFh1Vw7DbyF7I2AwI7tlOcLMVcpSYMEJEDMGNEnE/G00Fzvlxu1Kt28uwVCMv7z30ZR+XGm17rYVW/+pnP/F8fff5OuPTolZbDZFLsbo3WJ731nUnbcr2sw6p5cP30+o3j8e7ee7/vB9qAy0XFIRIHBwTMTkVCpTtNKDtA4elO8wKYMqrG/DjzIl0OrehOuKv5NURgQkDJITFw4has1oZ101LiAcScIxp0YTXl2wCMHPVgaNUTIikYkK+gGZi0JNSRvE69Xx6+WIe0E9gRQwzEQDG0MQTmSFIzgAhIEDEx1KJ/+Yl3DNb3Y8NcR4yxLIvlql3OlxBWcTkPBNAflqMRlLhzZXsw7JeDAgFCS2FRFS33ekXbthTqjdKd3rv1J7/3q8M+7exuIBISRGZiSpMm70ZA5tAYdxMDSaiRH+NiCiiSopGGEB2XWTIBF9hT6gmaPQzDA123TPQ6FQ2g1qVjxJXL5ouMZlz8t2ksWO4BFTWMfgF3QLPzdrqxO3fgge6osgClHr+27S+XCKfLcjM7Y5ndYXdmI0mf+SPMOjZkYmWMDBcGK2ZXgasbhsY85K5XIKFRkXvULAJg/tCGK2Vdmlfg7AuzwnoG4YuTIzkMeW2n72gHisntuq7PV61vxwjpt20A8m31JfnC1xNxs3HKbHamVd3YRAQshZe2taO1cEksQwFF99tZbFolLdE6XTCS+8uxgrbgrM8FrUyXjKDwDCNVyJCbyIDMUxpnZ5K5O2My/UpzAJFRtzAAALoIBYB3XG8NT7b6i7OH9xsMV966C9Nb8xde5g0cXHOjCcPJjdu/89HPfe4hjneKAtd7fOXK+mCNx2tFf+BaIu/i+cny1VcXh+e9r33fd+8/9eajg/O6JXDOIzppa8qAlELyDJj2rpFOChMrm/4qmdGOFzavxMDgUqPJDBLG72y5OWOwzgyznDYj0IwMnRZvJkIAwEzYSU0AMDABUXffkvJ4ZXCCcgx5hxrpyidQJ91JxIDMQGkfnlhwYo4xNE1DUWqJAJmYgADQofMEMFi7dPnpN86bs1lVOwcOuGlj00CMFD01VNdNFYG9Kwdrk70re6UryqFfLJrVImCAuonoimrachMH6J7/xEcOX/7C+ubG+niNY4sJWMAxyGk9RtzsNTGjAArps9nv+uhEwHbgswEKm6qBbZI06m1ekmiMQEmOp3V6syj2cCfMAuByyY+suaViVFdB2ajSRVO/vD8bFG3thwvYmskmml3LPEuuSe+tnXYU1vSLyifs3ukNUWVc7sidGRMZ1AuEKyGazHZjvnmKVGf0uBDKiyUYgfqwxKtQrAWp32CLkolYHo8pAuBXvb8UKkH3fXTm02mzwJD2uygcpndBsFieOkmc72rRGFBPwobBqDSLbVZNcvOi6JisqS9nQMhrK38rqZc5JhArKRtDzXhcMHJqfFG+qFKniiJLxxfKjlRixBhKPM2GpK8TAfS5nHYNs/F7tO7lXVETMgOISELc0iCIADBSrOpRuPvY4M7y9qvLmi6/87XFI286/p3rR9fr8f6TV9/+ZqgeLD7xhT/92MOH03JrZ3Pg/ZOPbJZlGI2KyVbfAa+W1WpaH16f3by+2t5/43t/8AdX83a6qhmYKAJHBpIYKyETqXesjJmBKYrcprFakY8sMStFZw2eARNpjDVbWtJeOqpiTBdaSTMqFJhtB1A2nuIcwDHZIW1OLQO0SuskOvlLnGgzp+AO2W+JKPV3ZkiRIiJIFyVTR5y8IrBwOjCEGGOMJI0IUvPSNGRi5hCZ/GDv2lv3Hn1DFRwMirJXePQUeL4KgSJBQ46Ga731rTUHsLmztbe3vVaUDrhu4Hg1H272y9J7hP7YD3u4PDz87G/8Ks9PtjZ3S9eDSBIFI12YTOWUr5h2s8K0CW6HxXaoE6JMJiuHymvQ/WP99MDoCyrq2U+oXgfnZGMmUnTB+kMn1IDmUqCCmA4bNdGm/W8gq5DRyGx5RNtFVRGMf8tr5+aaMowOxVZQT7COEhPQi9K0g/7/RSpnL6mnZoKEv0Aokjr4mGvZdC3kHS+YqI6xhe4fJs1LgsAUyIKw4ZDGUCQ2jbpz0wqY0/s7uSEZLxPTDqlPmt0n23uxAvr+2uCg03IDxD6hsbmkOcm7ksSyLpClUexlSb03c2mUfaj3o1MHufonz5BJhbEUtdJmY9RRQwALFYqVIuMfadU60MV5RTQg7hzm+h+z6SkKak0sUG+UZQOlIkUxBcSyktaueHDMGAE5MDbLPt15Zu9ggPfa5uzSG/dKzw9+4w8f3l0M9h579OvfUAxWs49+/I/+3Weff6WhcvTI1c0re5PSubXh+sbeWr+Poa0oNEcPps+9cHcRe9/xEz86XNs7PjkP3AIE0UNRWHFFEgLKPKvxTVeBRoMtUk5ERJx8BgBgIAJFXBDYUbHlDmMQ0FZFyBoGAKD5uCheB0FWRHa6qYiZtTsqZCBPRibDgeWENZIolkBpicpKplBsb8ZOVAMRXcoqQGibEAIoQnhps8NExAjkXX+y9dgT7wx9OD5bnp4tS1cg+mpFy1nNde0hur4frm8UPQSi/f31vd3h+lp5WlV19KtlzaEtC1jU0B/1ioK/8Jk/eO4jvzrohZ29XYa0vyQKyTBtUA7KCGLuKPO77BMYDAKDth1no35iM8AkMqOqQjsAONcJT+QouSweitAL8Zfjp8SsJ9us4pTjuQCoO2YyTVa8VDAEJ5sfVLUMbUS9rMeZfYaAoB1IzD5BVs4s2fYYucZ0IqEb2oX2pQ6IgyIgaQ2SwiPmqA2JK2amxwxJx0PLdNVQ3KauG8bK4zFAk2exISPoILPjgdbSMg1DWIFzZk1NXNQjAyWDqNcrG75AqFlDCJr2Q8illWi2RWRPJBW7rposUSqLQqPFmd7o7POFSBqLz5UdNSEHF62mirh+VVxV0sPX5OpO3w2TK50tK8w3m5/WNNvxzENSCYOWoiMQpvZZOgBUdWCAFEy3XDMTMQERQowMoUU+fc2TMNqpZ6fnO09fcs3qxh9+9vZLB5PXPLr37iu+WC6+9Mof/OoXP/PCYhEGzzyxf2m777gpR+WlR0bjvq8W1fz8fLGoX3jl/Mb99t3f9qHXf903zaerqiaOETkgsHQbUoknEV1QBDdsZDOSir7GddjMufy/VUSB7jxNplQ7y4jVAOGF8s+0Nyy1FWTFBJns7BNL8VCyECSgL1/I/oTkgBkgqkMgPoMRqwTcTBTFLYh5C5tcabtxZIljCE3TpoYQaRdDjADS356RKQYK4DYfff3lx982nTWrZWgCMpTzRTs9aZpVQ6EOTRso+sEQPK1NhjtXNiZrPY/F+SLMz1fguL82cARV0w56SFX9+7/2745f/PTm1mg06kNAYAzEhEiaiVEsZ+UbkJOxZOFWU4xkkUnUXNVIwanbzAmzZoKqn0EFA2geJF3o7HPoKAaDEP+0e/b/f+syi+5doMX276xbWiDtnEIbdhi9klAUfETLkaYnWIuDDLkdU6FPsb1gCnBGTYXYGjqwcELry4Q6D2j1iEZIxcTplKOZU8xzKZeLJpmVYlALwsx5jrKlSN9E1m5uSlbB2lPKC7K6LwZ5ZvbBHsMAajBtfTUNIK+kZFdMJeTy7Ty5Nhj9lZk5mV6VpiRsavmSPZSErP5GxVRFUufcTAij3kqeZ/05Oy8PSh7Y0ERn3yyzzk3+02EhaARZ2WiKmrDJtIiezpBDndEkRak7qVQnJrjvMC49qhMAAxUcPNaNXx2+4XG3dZVWt+8X1PQbePVjL61Wze61/u5bd5hPTv74C3/0L7/w+Ver0xBf+/rB47tQtvPxxmjn0fXhxDX1quUQWjq4P/vSi6uNS2/54M/8xXkDh+ezuq4YwE5b78pgmgiJtlDuJChvR531ByBphZCulkAMqFNEkShas0QGDYeSHVckRRCQfIZkQ2S1MA9JyCgQUMSOMWCrH2UpkUnflTBJumlySBTYzVrI+oqLapuouFPPRsSkO/WYU/CKmNLxkMzg0QE6zd4xYmqlwW2M2B8+/Zp3jDYHs2o5nc4fPDheVW3dtItVs1wuAzfoQzEsBmvD/rB/9bHLV65sbG5CU4dFoJa4Dlz0YHXWcAuT/nB+cvib/+K/j6evXL6yPxz2GFiTUELak8t2oS+ENOXLuAKmf4rZuorc+T0DJA8XIe8aAxHhLhHIdzZMQBNh5TZCzUC1wL6RZEtUQVRctynpSU+ZKKvlV8DXhovaSDmLJKqwZb4v40DtJ3LR3NkzFJE7kpkvUc6ncQlDVQSbSrwwRRpnyEa2M5w8fx3w6VyUQNg5nUoW14h1IvnCV9iYeeeWIFVxhkloDwTx3IwX2FPUfiZ1y0yXxRnJL6Hall7d2ddFDC68bG5xITYStCrKMtWUW40KzWRZEZnhLDYp7pyB66vYBKg4ZsKvhoLVncq7imy9TKpFblHtA6nHAsxEybZBR9wAMBWcKFkWO6pz54QNKCNBVN4JqUrDgUMmZgQNdkAMAZvZOt34mneeb77m4ermF+uTU9duPvfHN09mbv/NVx/72mvt/QcHn3rwid+8+9wr9aodPPvmzb1dV+Cyv+bW98qN/SExnc+Xq8CnJ+Ezn7kb+2s/+As/48rJ4eF8VdURAjOxhpQT+yUm6p6poq5AllpQ0g5MJISZo8XXOcOpsG8GKSshlQERXzP9RvvN9EpFo/J3XSf50Z5DFufPTJ4iWRRJV5V1/ewB6uiosQLu7CYRowDyjg4wnTSkwkwUYgyxJaYIKWOPal+AmZwHYK5jGO5cef2zHygGm/Oqohg8xWq1mp0tl7NVNTuP9aoc9te3t50v+uPRo09duba3udbH5TwcnqzmyxpLHIxK7/xkUm4WcPDySx//5X9eNPPd/V3vCiaAqBE0VhDRBbDlSr9HRE3GC7wYasiwE1AZt2KDCQA2cyoYjKi7QQwUlOeobFj0n6HTfFjTrXqxHSQtMXrdHJasvdFPVeP00HTEsZgtJ0DE0Ml1g/ItHT9aLN7C2sr90GLzoES+Q941mSF3RTVlhnWGu2khnMb5UVFbH6rf1THkJIRKjt0//euizZAZc7qVSWJqZJqaQyxofET2YXQeyGL6Ob2bmVYBRznuCrjTWNRentWXEp0hdHkulABcYPkCwgi6Z1E1z6SPRFASaMr4BfZFelQr8+Zb1OMx1EdQPbaQpnESnQg2I6Rjy86vrQvIr1BroNMNkjigVknodBknAnUa0VhPx9iCABnrtBoYiUmnrh1kQCJHEXw73S5PX/vGYbnf8gtfnH3uC8j9l164u/L1m973yPrYH989eeGjr370j6+/fFAtG3rzm4b7O8UAcTAcbl3Z6o98pHg+m/cKqKr4mU89vHvQ+5YPft/T73znyfHRYj4NsSVqgdMGTAQ1ctgpKiHqVMpLARDFKFXzMl8yecrmFN/F2IPac1bjYSF6ih06zlETxyn1qrX7DAlwYydj2x1SCvXHmD6IUcr+IxHFGNMvKUaiGEP6Z7pDpHxN+g3JJ/mqdIImUSwUrJARKTXjaJrQtjHGHkizOGDHQBAZUjWow0AcnN+/9sbTB6/efOHzfV801RKomE9dfzToHc/Hg03mPveotzNxTdy9ys/MVyeny7sHs7rB4RCahsthD5vQrtpRb7CM9Sd/7zeHO5tv/56fuXL18t07DyMHTDkKh0iUCr5TLZxQEEFiZpaTLc38ArO67+kDZ2IovgCJr91FIVQoELPDkG+SFckOTxVD45zRN840HKXVYnqWHjqRhAlNurpqLl8B/YreAW1YBpqgCMGQDj1OdxGlzviOWkMJxhg5hyMMFdB0WVoqs5o8sw1gxQvpI9Y0KIBOencKM8FSimsQoR92eL1mPkWbEADYcdosqksM+Vv2aAV8ZClU031KKh+ZF1ubKUTQTQiQZ9TERl4j1YwkBEuzrKhnbV5sWsFAtWsZQDdACGp3jigQLGUAlRBFEfkGWv0Sg9lvELxItkQqlXSNSBaBQR2UHDUySpMewWrqdPkBWLYEs62N7MhmuY/OtHXO1juDPZJ0wkUBUoVVGmuyxxSZi9A2A1ju7B4/+nS/N4yLz7189qUHy6q/WJ1TEZ5515XeYnH40tlXbs1f/PL5qsGN7cGTT/fW1lteBT8c7l7dbiIVw2J+NqNQhbq5/tzJK7erx1/3NR/8qZ+s6950dp6C5i5lWROi+Y4idEt39I2J2DiL2NpI9jZsC5TCPDaPxiRR9Y7TCgEAEpDaWgaGmDYH2NzpPWSGJLGkjF02+qB5l904QyLioLihREC/AoxaP6w3l6OwRK3FgAEAMnEhS6piQoGi57YNFIkBHDj2HNMGPmRwDoA4RnJuFZu1wcYjr3v32YNbi5PZoI9F2atX8fRg2veT6nxR9ObFsPD90gMijzcu7T7xxHw+b6ZnzQwcrcVJyWXpHRXDoceC29PFH/8fvzLaWH/zB37s2rW9u/ceytsRg3fyZgwsp9ggACJTiiSYJsoUO5egBAHQpZbruZG5sUzoiKzT6c8xkA70KgkSCBJwUTZh2SBDf9AwBejMiQhalQgbXrKOXyGDAdHMTBf55aBwWT+BAUY1iTJEcUV1Pjp9N1liuxmasvOefV9WELE37hgMuXXCn+x7yMXd2ciDAoECNjRBpfLK0+1eWsLEmuHIN0lybu6UrQaA+Yj5EcaUEzza+yoOZhjNXoKIjNxSp17jbmDTmAEEnRLf7pxrZbgJjQ5L9xmZGKGOkzsvo+8j+q+fKhcRAFDioHJmtpCNZqgGaHlKftBXcQ61Rul7iEya9bNvqKmXUQFqEhSZmEw8svuCyYoTJ3x0MSKG0A+ra3uwf21YhAfxT5+7//lXCaAG7u+Ptnc26+P53VdPXnr+9N4pNKG8+kh55dJaDyJGhkF/48pmiIELZOcDtaH1N1+sP/m5o/HOa37+v/klHo4Ob82qECKRlbYp7YoKtGnsnKsZEKUcCFR0mdnqj3M1moGnSqymxymfz2PwrAZTbEya52RUMW3RRUwWIt3M9JmTqcwzLp+bDIEVhqokKAyIQHZUTs2Tjqcj73Z5MgAJEoQsOBdjqgNqOTJ7QMkDkMgZJeV3MXLtef3S40+98e1f/tTv1NXgjBYbG+tLiou15Wpt6meDtWITx+tI7JA3diePPXa1XlVf+tLd2XJZDMZV1fiyxxFjiOPhuFlBtVr+3i//67Xx4PFv+IFHH3vk/oOTGFuKMa0YJvFE1gOxMO3FQHHXZbdjZt+Ge5lGobludjQIYm65icLDuoqskQs1BJixpqPwbFuKkFnWWnU+25BsGBQ207A7u0k0EpOoL3TZuix5AkDWorokDIKoqEwlzUA6qskAXkhO7qnXtTEGzVntZT7tZU2jFBpQSWuyzwho+M9wcfCGJppYzYYtu1loIgryhsKSktS6PFZ5cfXMGBgp9Q4hh/hVkcLunzQ5VgBk6Kl3yb/DFBwhAq37TAtInBoLp4tAQwIiO6z+BZI4TgrzZullHObvgFKCrnmBrlOhAmb2Ln/O5rnInobOl/NCMJHLhyZnyDFY0JUQ2Uh1DaI7SgwS6DM4cAyki6JzY1w1PVAwClFq06ntc3PtEdi/smoPXjq+eevgzlk9K9a2iuEYBuVwea++/cLxzXur4zM32e49ujvYW49MjYtQDvv9wdAhsiubSIuj6cDT7N7qs38yi4Or/9nf/78NNh99cG+5atomBAZykIgXE4FD5NiJDWQzi4C5EbIYQpITalHMoMqC2nI2s6gVgTbBGna038jeXAuMpf2+yieQORUDKZ8DgJQ9tjiTjUyfKl5pB8LNSHQlHTP6g0pn5pHcoYvIVIDEAME5F9tAFB1A2zYhRCJCV7B0Q3UEJE1IGYiJiZs2uqJ/6Y3vOZ2en9543tXYNlXR7y2mNO2dOSzKXuH6BaKLAOu7uxiKatXUbbhx/YiaQA65iM776Wmzvou7W8MTHxbLk9/+5//rNxM/+a4f2t3dOj4+bQIgRGSW+hgA1cDkuyWIBxCaCQhg3fMZNWQB2NEJUP9Lp+uCAyGEKf06BRhAt1MZ1cxIzjKFdk/LkrH5yWkOWYiZ3oaVwBmhFh6gnlz2BvjC2PURhuz6+4yymukUe5goKaeXUZQ2qyoSZJRVJeUierIhY6qVpg5fpgsD5I7Rs9ATqLll1Bg4iz1SnyMDl3oMGmXo8OQcugNm2anXCUwxajz8QlRdOJ28ZzeHofQuTQd2vpkWIC09K/sGCR7mUbEaXjsOCNFZrlgewOIl5GXNEptuwWrQuEvlZIVRJjn9UiKeTk4pzs6twkXyLQhyYACViGhQSEWAxR44kXzIZl3cB0iEVrcUsxlJttwZc16T/NbMQIwhQMFh4BaPXwnrvTurF74wv3f04HjOvd5o34GL85aOrp/eu3V2eK86W/K1p0b7m8VkjXkV2gbKcjDeKocDt2qK5WoV2rYcD07vHXzqUzeO2s2f/du/uPHY6+/fnU9n1bJuiAiRU71eBjptj2y9SZmZOfcRQAUEk38GVvjWShS1iJkz2O0zgyfDGXGv5DyAjhfOWoRmMtSl85psYFRLbBZbVYq5s9qqLAYBiZikPTCISKm3vDM0QwQ59ZsBPUChOCLKmWYphNA2LREXmgAEgBTujsypZA8LT8RVTeVw64k3fuNq/nL1cFotg+sxEDpqfNnz/T47XNvcLotx1bSTq3uPeoaiDu381q1ZVQO1MFjzG3vD+fESt/zm9iieNsvq/Df/t//l66Znz37bj+1f2T4+mtbLCjAyRUa0BoVCv5WYAWqoWBEnM3Tl8vJ3gjon0Y/0FXtJ5cBg4KW37ITjFbIVoWVF0Zic6SQDADt0bPbJaZwHlMWDDpsN4BK+G8+0h+ooSfv4ieSZT4EAnR9QT+MyIBE/waTYdFZNYZdI6C9NwhDkTOOLZEoUKE2peiRZPgHMSgkssi6EGF/Wv9T/AZCGkQyMnWO/0nDRmvjaYnUYT8fA2qMyUU8WAexbSuw0KGWjZWBwqOWLHd8QdNzihaKCHgBIwxnbCiyPlbXT6XL6FeF0mWtogA2MYJpWytnhDFJwyRZBhjwqi7+pVctGtcMUNHigD9LXlXkUjAImQCbADHBJy9LpYCIERGwtlISOMKcGLpECkEeoN/rLa7vzYnXr+PqLJ/fuL6oIZdzdcc1yfnpYPTzG2Vk8m4UY3Ovetj4ZxDEEF3hVOy76kyvjXg/b2Kyqpm2b4RAXD08++/HTWwfVh3/2+9/yDd9yerCaz0LVNAQBUY8SSdmSRN9UNpyJgZisdAgAEhFasS8yJ99A/k8Q3Dg5M8sOw5gJPyDk4Iy4QQCZ4KuspyujhVHtjrKaqS0dMAMZgJhdEqRGTYSmOGH+A9ZNH6zEDg2+HDKxNAxOBbzgHECR5dNUlGJs26ZpYgjQ7zn0kUN6H0lwpA3fFBkch1AFHO0+9eSz3/HS6ver1dwv2U24ib3T4wX70vlev1zDPgJQG8Lapd3dWD3eVE1z6+HtlfdF2+OmX403e/WsxUlcG7sQ+5Vfffzf/+8nB/fe+6M/v7vzyKnj1SqAjxyNw8k6puSAxgYyejkx3WrAcyBIAFA9bYFaJn0vgfdOqB46qyeMi4U8msIZdugsKhKnxrwK910WmcFL8U+flpbQfMMEuxnh1Yox65m6uWmloSSDkghpvsIImneyIL+wV4tH5wpCfR0W7onyNWukk/Gky0O4+10BVVMBYMmMJf2TqcbOdAoaEwBKkK4zTZp8T+vuHCuzE1VCe45MtZHwr8oBXjRMhn8iQTonoIKmy5sQAjG7/+5CbiGtFNuc6sgQHWfAzGIqfFwtrfJsZrRzDjomVsOJ+sK2OdngwxYUdAZFPo2qdjA/zYZGKDQojvbiaiGy0ZAlZoTMCslogwmBbiePEajlHrZ7m+3+8KQ4/sLhzVdnyzCbz4rN0WBQTI/OT86q269Op/MCohuuDS8/sb4xCh6AG1i20Ov3168O+r0QG1rVbUQebvWWDxaf+vT9T31l+sEf/K73/vhPHB3Ho4eLRdMGbpNLmoIriJRi1bnwgCiKsmOqf5fOEAjAqXJfeRLooYKg0gq2fAAAHI2824pyxutu2KfrxikBFUCRo21NwxEAnCsQcovipB5gX0JEwATuUhOf/rhkF7ocCoSZ2hvYg+w90HvEIrkd6ByHiCiMJ8bQtnUIIekYInBMh/cmUimN8tABMbdtDB72r72zni9e/uxvTOex7I9rYsBQTJf90Xl/UAzcpu8VEVfIw61LuxiDq4se37r/YFa1RVwUk1GJ/aJu2AMhuj6OsN987g//w+zhy9/yE7+098hbFs3adDprmwDMQIEptRxnAK+hl1RajgZd6lopb+M842oITB/tvAqNqks9j9BDY6baya9byQgK1NlUgxxyYMCnCnhxFbSGR3OJoMM3gpbMjWXz7JHAYHtMZNAAKK6/gYdwQCb9QGUVOvOkxRqGt/IKxj8y0Mv8QP5lt7qGL8QaEcDCqRKFkxlQqquvRKzHlonnDo71pbIWYU6PJUwn0w222QOlscT5tEXu2KUsC5ZB0cIa6FwjnMAsgc0GIIJE5DQ/kZZbKz4675VyIoBMEURp5U46qyodoPPTNeE6HJEaBBLrdYH0qxGVn/TXyZxqkEu9AZmlXPWcEaojXfbYRGFsZ1fK0suLg516xUCAILlBZIrELrTBcbs2bC7vxIm7GW4/d3D9+vmyHe3sTYoQOVRn8cat6sFJe/KweOzRwdZk4J2fjLhEAoKq9cWwt7blB2VLVZhPmxZif2O4Olp98qNnH/382WNve/b7f+GvTKf+/oPFsqpbjgDsJXuNwEySgSEw7q0VdhpqyQVRzDk9avzEVMSiN1k5OrOlOmurKccxWrRRBVaBWfQBJFRzYamtP0ln2yiKGciMsWMSUDNPOjxU5mrCrDwXsxVQ3XfIWCi3Ze9dG1MBLMUQmqYOTUOR1CY50vgUoktqQxwQXYhcNXU5HF197fva6uj65780XbaxKCIxc3AeCyYHXK6Py8HIldQ2uPXIVU9D73tl/9bDh7PVgrhdFT0eD0ZEntrITdtbx/310cmN67/xD/7uO773J576uh8otrdm8/lyXgMiQIQI6JGZU9u6hJfdGZVlwO5bI1hFigRkDJ6V8aczRrQAwOJN6t2mOwk7VuddV5k7MQ1TUT3FHhT7uOPCmChlE6G819ChSxKTHQI9KQE6XwZAtshIukY6qaqoZbfGSYIMJRokHotRF0EQ0Hdnk3DuPhNzWAnU38x+hPJJMznJ8mkoRgNXaI4zGHFKXK3Dl2xR9cbpd7rDitWJkC8Y3naiYxemSqmurIjquHOpNSwAyMnm3HncV1NyW0QVi7R6pNLFoEGndBWxeZKolN+IgjgEHROVvFKxQ0YvOlZWh81qDlmFUdZPA4hJNiwIxubEsmTRhBqbF6T3j4jO2oIBABClABZpXSMDOKmzQWCMEYC44LA5Wl3ZOR/BvenNLx++fLtG6m2thzKen66Wh6tb91azpQvEjz3eu7RTQrvc2dosRu7ssPFFvxwP+hPsDWIz56aN4HzZK5bHs+c+P/3EF05He4/+4n/9N1ZN/979w9kqEhAgOAICcin1iowoLQJUtIXREwuRVdLy1cJl5WUASquT4kq/TlQYMYlOq5foXMJjOSEhT73oFuYv5lVCRXWNWgvSY4fW5SdDHoVghVZuZ4Jr3zTmJFbEUA6F9jjEAnLIWCvAGJi5DaEJDRM7dKxDBN1H59ARsJPa/CLEsGxXa/21Z5798HJ5dvfVL7DbHgzGbVueHi16HtG7iXfOu3Kt74qirdutxy4XZelc9HDzxr1pE9H7/qKuh/2yGBbVrK5mjAO/vbN5cnL4+//0Hz149dV3/fBP7e0/XW/i9HRRLZdEDROhYyYE75AJ2IvyI2YtMTDLYt11dS/YDJZEolkLZZSqmEnJbV3VFIhOde/UWWDTcCnw79yYzbSI8JGZ6bz2MioVOb2hAbrUh7AeVCR4oYeg5dfEXOPSeTuTfR1RFl9U1m/1JyIKGTLtJl0a3UHcTgk8dP6TPSi1JB0CC4pJF36XFlVOYTM7Ieujay65FrkxpWCQZdgNvlVrUAvYZbE4x16AjQ2IYy9Dzdxfv9dxQZByV15A0FQeamtf1URJt3JXftRXEbvACbXTq6TLSAoaM8Ww2It+VUEeAFKAXu8PwJ3SW7JJNQbEwGDBoMyO0+tQ/qWmnllem5OdSxF/gDDwze7ucmN035++cvzgwenh+RJ4vDtpZqvTk+rO9cXhvVVV93uj4mvevOuKxfTwcOfSeLxWzpdtW+Noo+iPnPdEITZ1W7cNeKyPwotfnv/xZx7i7v7f/H/+F6O1R6/fms5XIQABsQMgIECOKacSozFfsBmXlQJ2ylPs+BpQoEfpMJNeHdGhbTZUaHbq00tthku43kV+pFSaKDPU6cdranaBpWfxs0VRpwQQutXD4jpwinYwSBxYTv5O9c5i8LxFVr86t8WAznlgQCDQfQC6A9wXHkIkprZt26YOIRQ9jw4haoUvp+wcATN4QHCAEQhC0y5g1RvvvPGdf66t/9H0+sli0QzIceTpyXwwWitmNUcYBPDDnuuNI+Bwb/tSfDICV3Tr6OFsed4W3pc9j8i9PobWrc7CcThZG44XVL/wmd8+uPXC277zx558x7ft7G7O58V8Og+xRiuNck4JpUQcsLMDS1ypNBOmuUnRnWpSh1Ln1REfX82GlsYZLjEwgrtoYuzMB2AjG8rgkoOq0qZ1raalGjERYmxJQTE9cNElQMUn/SnZdjt3FjP0dqSBVX+FLoB69Aiao07k1Ym5SNrQYRVsUqpPYPF+dA1kahID+yprnN/G7nVh9gRozT1JsKY3T+tkcRjJ0gFiOlKZIeGYnFab1c6cDFkzZeeICsfAjM6lWkCLloAGt/ROiv4a8BG/BlE1RB3zjM6dwGs2ItnbkamSIWjW0Wxgx3YKXltnQZfXTxGEFdIJAJwaJEDgVDMHDN0iaW0yA2xehkqYzXoijUSsopHfjVk2GUUMEQqi8SBcvYyD+kZz9yvT04P5bLEEgEnv6GQ2u786PuQHBxWRH2/5J5/eZFhxU+9f3RwUxenhinxx6dq67wFCYHLLZYjgwJfnJ9WLL88//plTP7n8n//9v3P16WdfefF4tuLonGdEjMxJrCMxO7Dot/AVhw7YaZA8cQGHkJKgCSHkYGttrKtcQtUx87gkq9k1BGZ26EAaeKajh8GliUvba4Xx6bdzQz6FU2XtyeJnK6DKKsuhJgRR88CQc2sAeggSSXFjEg/5cpYNTfSlrzosko0iZueQkK32KMbQhKZtmz73VPfEWdQvAwAQk4upXQQ2bTurZzubV599+49/bvrLh/de7Y8GV3Y3qrP2bDBjKLyLPc9FMYRUgOrLjf1dR+wIev7GnbvnTXRN4zjiYNDrlwUAzA5Xy1HLDrYuDRcnNz7yz/7B7S9++i0f+tGtx960Ptmbnc0WyyWk/tX6mok+eXQsvZ+VqJMqh2p3kg6Fpex7p8WRTZ8k6cSLGbEOpAJwNiwMTgp+OyqfzYUCP6griUb9slRoCSkYQVMES4gMrLsy8ogR7CsuFxel5C2rQ6NhEsPg/L6gmYCUPb1Qpg+azc4hAsM2i4MpyVYsY7OdCnlp3rIpFJZt97aAUZ5jMy46Nag6qRn19Fqg3dPUsl3wAMWUdFwrfaTevcsQGZ02ulQzbMSCHUNnddLXxXilGIwaDOWSkN/AehaSnKqiIZc8RZl/sGk+2FBsAqXOSKM6Fk9goFQzpWNm3frHitd5P6pNAGi9Aug4pAwaZLcJa7mn3AYxbS3lVKyIyEQQwbu4Mawvb54WyzvzBy/ODx9WMQbElmh6d3F6UC/P+HSKo/Fwfb3YXMe1XjUqYdjvVaswnzauHA/X/KDvELiq4nJRgedIMD9tvvLc8rMvnQ739//mf/uf7j/+lleuT49m1AD6iKVD9Iyp6ZD3BXPao5D2NqcNoc45lRxgACX1WkuThE+FAZMjn3iEMG4RITPJCJBq4TRwB8gADp0UCAhOWi4XARC9GF9Q6w6py4Fqh4ZHxatI3Eu9U6GvrIk6eZGk4xJ/EoFxCNlWQY5dmMbryyaVKxjkdDH5yANHZobQhrquQwiRyDtp/CeDEG1yLM3kUGkrV1V7hovtR5998zfR53/3nywOZ/N5xf3Cny29h6IM/YKKImK9ciNyvY2IvHnlcumLctAfjm/fv3te1S0VRexDE5aDtQEgHh9EN+I7Nw8v7fa9b176k48c3L3x7Pu/93Xv+u7NjW1fFNPZDNrIMQIAOpfS491euBZmUyBP/1bnVetFE6FLeOnQSQIT7CNRJqcRBufQDgK16LmSVtNkgCwGoqQ6HGfwJ2psUQXjepk6qHrmEEcK87CenIwagDezIaKj5EX3JFzYjSK8kkElS0ctwIHahtNwCvQ6G1uaIlDM7oCvhZWNL6t7y+a4dLmzXQORNY8GDALK9kS5J+o2d0RTIf3IJkB5bP7JrJFZKnkHlyaEOqU5aK239GA/tndls+XKhrOdE5uE+khZTcGPJGWsPgCgmGqzEXYfmyeHThJT0iAXELrP44QOLCCloZ+OGe8KpThsaicQUCdSh8TmBnZsfUI3yiYrMqSKm1ERN4bVRv++mz1/fO/ecr5crGoY9Jaz1fTh8uThrJrB+QzHG/3LlybDIuzs9nsFeXKLxYpbGG0Oe33nC6C2bmu/WjWMvlrWRwfnN24un3u+HV259p/8w7+9eeWp6zdPz1YEw2KA0KMSHVgjM0A1eASYk2SY6Z0Ar/J38yPJnIbuJIkVMbAE9R1EnJLvoNekX6b+VOJRGM0SrtZNGaI6q8ZGhYBJBF/JfUfX0UkBqMi+RHWdIBpIdjaxwLzoiMgUxYYZJwM1AGlWHCIBOOc4EjqHDETUtHXT1kTknaC8VFKRmAINpQm34AIYsWraaTnffeIdb3nv4lO/9csHhwcbk2F/VLgZMoKjwIFHGxOqI1Djin6INLm8Vw76w3HZ6926fetoVbWrKThkHPBgs78Rl22EuOgdH7bDMW1urt2/88LDf3X/5gvPveeHfnpy6TWjtb3T02m1rFIGQ6Y5HVYtbpgptDMqm6YVUCos2dbK6JRFzDXUmWaQcuqYzcgrYTcpy9wdjVqalRY0JVSXLQcrkNXCM0D23Tpwq2lbAGZ2Tgk9i4CZPGWAU/7DRJ0so4koGiSBs1tJQluDHKJIOnLREFa8yByDpc4nWwezRnKS7QUuKREFTcVnc+hyblaWKztKwsFRRde8D8X8tCaoykeCAjb7oJhrEUIEBoxEEssjZg21I6LtnBa4ZJT9V2YVUR14dQ2VcBo4CbUGh6kAMEVxhJKwWkq5lYyN2Yg5UsZh9Vrt5gblqOIKCGAdYOwm6UPSr2iIgXXPayqGRLtWr1N0YwRI+6LRpQ6KgdoexI1R3B7Nyni3fvhqMz8+OZo1gQM1YdUc3q4o9pZThBAm6/7qI8XmerN9aZ2aWM0awIH3o9EuFkUfQuWI6lVb1W3b0Pm0ms3aF16qb94/33vqjf/Rf/tfre0+cuPV8/mKIqIDKL13HlDslssJFGTwmGi0+XlCm0jjvU6ji0yIgIVSMUDghOmUwkHJvqCssCIJ5zxTWuakwda+ReoHJBnQAXQEZ2dqIqeBELNjtCL/jB6g51skSqo0NK0KZnRThc3EHpMvmHQCAdB56A5NJMUBQaGhJlFLQHDOxUDMFNrQtm0MoSwK9B6ZHXpOJVYsvj/LnIFz4BgiEod6sQIoepfe8i1vCvHzv/0v5yfV6WDJAIhNiQ0SEhTlwPmSi1H0ZT8ELMejvScf86UvesX926fLaRPYL1aN7zWD9TiIflC6NhZtE0+OZv2BK8r6hU/84f0XX3nPD/3Y69/5/q2dzWmBi3kDHK1/fWpuq7xYlAaV4wOoDaa0MGAxARTfixWg1WRDh5WzUmjhdtI+C/U+kJiFQzMhXUavEGarph+yYqbBB5s6KnEV+4BGOnPyQhmkiYg4nnJf3fzWxSUdgLw86/jTO8sSQ+c3GXRUldh+J/bEuK3e+kJmtGtpTRvYunSliA3Z0PI/DNXlEZlSm6lOUO/koVY3Cp0LzNaKC5je2yE6rXhhBERGBoqUB6FPYRt8XiDmVMUKedXkzElElq27wKx7bW0zBYp11qiLDikRcBK/kyV8KRuuyUK96bkIkNMYKjEsA81tP6BTkCaXqP0WYdORq9dono1IJQOnACdBiMAh9LC6tBk3+se8eGV2eHd+erJchsXprFo15ZBm02Z6WiznTWx5Z6fc3FvbWh/sbK95DyfnC/R+OCr9gHtjqKsGQuQQl6u6Dm5V8517y+dfPrt5q3n23W/7K3/v77rNq9evH84rJuc9Ys854FT9zuwRI0OOtxiiMSDqpjtEBPCKgk73NWeyhSYT8g/IciLyihJDwA5lEycwsyDjY0nVsm+aLqV0CiODKaiT1ERXu0B4noELmAkR7QANgwr7V6cnDdWhz966oYG1QMqvhoVcJlou8QHnXCQKoW2aKoSgbAs1gKCSighA+kJEhADIzrUxzhfLsufe+PXfNyh7H/3Vf37z9uGsXj5ebpTOEcxbprWNsDbZhODAMRc+ki/7vf1Hr43Ga+uT27du3D2bL+fn0GdfjAvvnPexH/3xCVFTUqQxwNW1ydHhnV/9B3//te/6ww/81M/t7L9mvL42PZ1VVYMMwBEJQVzEVLdqyGNRU0szqkeveRKjuoaB6DLNVxuq0qHEl+Uf5lOkn/Pycg6SXFjtrHpqZQCMGOsjIZMDReUOPmNH9tI6a8yEdZyydnxBDjQMpShqEKbDyg/sXN7REU2OmOXAVBneLZ0U30h8lzQP4qQ7vcwBsBxjrxAklzuxyqoIgknGq2TfkwbHzMBlw5CXTZY6j0mYncyT0d1M7cG8t/QBaWkmqCfZEaEELozZ6DKnuHxnExyoAROLJrU63BEANS5pA7BIhzg6GfR1lcRNSwZGxTTLFYi9VkLTHS2IJUANhIi5Ed4BZriTh0IATBwiMdTrPbq006zD7fbk5aN7D85n8+nDs3oZx2vOFcXDe6ubX1mQKyZj//gTve29YX/YH/WH6PqrRd3vFb0RFEUo+g5iLJiXVXt2XIUA01k8Oqs//unzg9Pl137gW/7y//1vuMnV69ePVw23ZYEAfYcI7AARHAE7dOCF3xjqYYFaFcPoUTm7La9uH2KGtAknRXKY0XswEJAbsnOoGSfolJSJbujPAAjd/LBOsnEm0yarL1XFAUtvZdEV1qjRCmclSyrNss0kefAeidk5qVpD1ays62Jq5O6CaKkKCKXjPzmH6HxTS8laDLFtm7apaTR0zqcqV77gVzE6F4kdYqR0upioYtPUp2fOucUz7/4B7A//6N/+T6cH5663bCtcWzVNtWqbhimu06QYDfrr64V3sWVXFpOdvXI4GG+s3bv54LCYT8+rukLgOOgjEa2N+4HhbBa4Lk5PH16+tOagffHTH7v98gtf913f/zXv//GtzZ3pcrWYLSE6RAImIGCgnHwXFztBCyhwgtkEQKXbCYkQNUhi69qJKSN2GVYCRcMj8+REuY1c6Mqke7qcCco3R0OiNN3EdvavslZQe5WkVN1ScUrshWx4ymW1bMZshw2UNbOanpiflLEm2wf1mUW2GUBKrm3vfeK/FgNPY2D7vkJuxln9mKFTypDwWkBKOXiy3WxmTjkdINiGKZDgT3LMmfW0TvXwmIUT44WpTj5iIuyWw+COJaHIAGAhGk5kX18ljYQkN8eKSglhCdHJ9lMNIYj50ZmU9lbp/BADDyIJ75IJh7oU9hqSJAREYKLOayvPTdfL4SyS3ZEpJbWEYJFDdW0ZOcVu0xl7hDEEiLQ1rq/sVkX9cnX24smD84MHZ/WqbheLtclg2A8nd+qDh/XhQbt9uffM60ZXLpeDzZJDSVUMTd0blb3xgAHbSCE4buJ8QaenTVO5s0Xzysuzr9yaHR34D//sn/nwL/50zYN71x8s6ghFUXos2CGwZwfA6MCx7xAmqWIQ91g5sqm64Lf4N2z8Ir26U7YOKgJOasydeJSo8qD1+uo5GRrIkqLyBod2aF0uYMgRQgDIfYhFopxkLikdj5HizV3OAQkx0sukUCExYEo/MLPkgdPCOq1rElnL2Q6ZlUKmBRLfJI7snWNmR0hETd20IVDkZH9U+iRqqHYyeQdiitIMeY/McXZ+5gt+5u0fXF8b/+4v/78O7t6Iq/ZRXAd2FM5DXbdVtb67B67XH5W+349twIIH6xvXxqO1tclw8uDo8PDeg5PQgu8NvOPhekHOEVPdRg7lycNpb9K7vD05mJ/+9j/7X1/50+c+8Od/evfJt66v78/PV9VySbF1SS1IX5DklIgE7CLmEnFOV+SkKFiuRRiR/FrXj1O5oXG6NMkKvZql0xtqgWZSSLE+Fty1iERXqFSYcpQZbMEEmSR7qy6J/jFg7v6KO/CRyQgDWsZSUUexL8cRWV8G9OtmHBQp0yNMH615Z54iyX1iZ0QWx8FsM4R+5kC/sphEfDjzfWYJ21DObDt0aSsQExKn8DmomWOW1IU6ImJ2kv6iWit9KOhlIDYWWMemtZi2yqD0WV+VlQ3oxLHsMsum1dqHsc6GIbsIjcghkRnkJCS5vkfmX0+LzRVgercE5LZYYqlsK6/82FkScXX0RQgAWkIXKHJkhGpztHr0UkVnz1cHd+8fTO89PIUQLu8Pyl2+88LZSRydzLFhfuSJ4VvedW13r7c9cYRUh0ix9sPY7zuP0DQcEaplmB3Mlytomnh8vHr5TvXinRVT8R//N3/9a7/rexcB7t+f1i27snDgHKBL2T1Gl5Q1CbPvGAFEccIBFLMsDg8AzlTbeePfFhSQsAoDpxIS16kQZTlLENXfAv2qOfdJbLQ5sVYYiR5LrwHAbJBEE1C6s6UGJ8nZ9cKDnCh5OipKIg2ivJy6gzjvpDMjICDL+SnppC8mSB3xk9h4uZ08HKFQFUUJTyKDA4wIADFQ24a2aWJoi7JARKmSTqSA1IxoMhAJEQkBfUqNc2ib1fSEiPjSG7/lO/7C5h//yn9/8OWX7/FqcxS2dnpU1Y48BQp1HTebwWidXc+5HgAR8vqVy0+sT9YeTPqj8cO7J4vTZVGUbh2Qwu6lQTPnpheqBbWz9nD1cHNnQhjvfvmPf/m/fv4t3/ahd33HT25sPNEblvPpvG2aIpEc1r0CneSFoLGe3wy5qED4rTnkauBs7QX1VQENcHMNlvBo9RLkREvSj5L2qTspq4IKzJZ04jycTHb0EUoj8y+VIetCY/cTxX1FeQVG0G3D9hjMXo4ScrFPOdJtVkhslOJ2HjBfsGdsPgHgVw052zNwAFGzLZpqU9Jq89u5iWo4qPWUx0cCZGfPYBN8nfJ07KiGUjmSqLosqVn17Knkys5sCvOyJHMi++SFNJDWHYAE40m4knXSZD35k1miNDZilp7GQlB0lsSGMpGqHwNDZHLqN1rzdpVcRfVOSgDtXqDVq7oSYs442Q1mZkIIIcZQ9SFe2lrsrp/XD56vz26fPVjcuX3qx4Nrj0/qxeLwPBzP/PFsWZbDK5eGTz+xtbY53Npd73s/Pz9jQj8mV3BkbgO0EVbLZn5SNys+Parndfji84sbh9XGpcf/9t//Tx99y9cfH62Oj6ZVBOyVJboCEdg7YJaTaYgzR2FhykmVMQVAKLl6qUcaa45ciTt3iJySHgCNSYrxB+PdyFLhI2qLWVeE2afZc/I5A7jk3GdFNWYI9mxTA40gO3TmMXfigVbogOLbkdwumZ+LxxFKbYi8vfr5mhdnqSZiBIAiRyRAuSozOnAOI3OIbd3UIca+vKRKpx2qRAxePGqlZSD78tCxwza05+dTBrr6+Du+9+f+7h/+yv/wlY/9/qrHCOtxPHK9uqrbpq45EG1Qb7yOI4+AMRA6t76xPhwMd3Z3Ll86uPPyncOzk6pe9crSFb4/JMByvDW+f/0hNnh+uLi0O+y5/snp+R/+y19+5ROf+fof+anXvPObxld3q0WYzxaxbTiG1PJJjgMTsJFGgAhat+sMahQ4xAnI2imdgpQyJ7qHhkJsHkQnpo+yiqlCqwsmCdVQn2k/6nKi/dbgRjET9JEZipwV/meCAabcqAQ8iaX2lRS7I1DYjfxkBRNToVlSdbUlvtV1UUyu1WVGdYYu5BfM0jAiamdvwR+ddB21OgmS/EyxIQ1XQcJrHSITQ4q6pwCURtGFyHcFXvGdge3sBNtQzWYz9W+LOqV7GLtjltANADDr8d/pA+x0HtWVI5Juk52pY0jep5z1JHacjZoYgidf0k4xofTGEaWFpcUnyV5PXUU1HTkBagubzZx4HxqJZKBI2FKIMQzL5uoOb/UeLu98eXn6cDZdnpw1r337I2He1mdLWvQPblQPjmBzF594qr+zMdzYKgYDNxj66rSOVeM9ofeMoanbULeLRXt+vJidxnrFB0fti3fOTk75Hd/03r/09/7WeOvq0fHq+GRVEUO/dMieEYAUR7XZMQAoxjKDQyDi9LNEiFIjXmJI1QoqLMb4mRUJUyyIWTQI5PA16/eHWlhgStXBcSVWNvcSDMmYYYXF6VNxD4UIsrQsQlOd9CFC526gTIuAJR2AnWeqwdIftKpb0yLqE8lGB+NJ+Hf+6l+StUe2wyOZuA0hhLbf623t7Fy6fHV9beKcJ6YYYowRkKSmzzlwKcWMzmE6oAsL79EVvkBXOHQMiL7cWFt/5NITFM/+5D/8g+d/93c41uvj0drEb+4OJ5uD9clobbI53NnubWwV5QAdeiwgpBiXD3U9Pz67f/fB4YOH0+NFZOoNCmJ23rXEYR4P763KNQyxHvQHq1XbFuxH4yff/q5v+M4fv/L0u7kcLVaLal5xbB0zcUSHSidZ1l2KNQyLc/Rfsceoo9hCiw6JS6d8AbsxfV2hTDQgL73Cqnw3sY9cWm4Xq9QKL84BQUXSNE6lfWwWQHHHmLgILUqfmQSdmO2dMkOTvGyA7ApD3EyYQWZQv2gYgijNFsTqsCF+2qgh8fr0GWoxAwG67t4uHZXNhzgALnV1T5v11AZYt9K0m0vmoMPZEbTwMZk9OYVDl0NnPsN/h61bFBjMe8jEIJmHzjaLXNujvNveQhDXKcfMXqTNZ3qHBPBk37Y3k8mXrWeksQGbB7N5AMrVbBHU2ujxmHlyIVXWgCSeOCK0kUOsRq595pFmFF6d3b9+evTQ93B6XF953R6ExenN6fSoPj2A2axhT0++YXzpynhztN7fHRDTcgaro6ZwddGDuvVV08Q21Ofz+VlzNg/nR/H24fKlm8s2lN//s3/ue3/+J2K5cfhgMV1UNTE479EVhmopSith3CTZKp9EqF22OZ0BeyF2KuVqSRKygisOdmgRZKkTDTCqk2lcuizH0S66FeJQdRdUg77qq4haiqx1oRxEx7MyWT8+LzEliiR2T+qYLbqrQSdLtUmMU7IhziKHAlRYpCGBM90C51wbQwp2RKI2tG3bEpN3BaYWQt7FmAKEjFI7nJQSAQnAOwLwCQciM2Dh0cFstbhzeGN/98q7f+I/33jyzZ/91X82u3eXW0AMQE09nzeL1TiuRnU9WtvqTTa4cOwoBsfMDsrtS1fGG1sb25uH9x88uHE/1hUBMLu1zc2mh1sMq3rZzsplU/fHblz0z6rFc3/8uy8994U3ff373vP9P7519TWjyU49Xa6WFYYWOCiXBOVEMnlJBJxzWV9Y0yoiE5lYp28jWjU3ZFucJrajWQqACmCcGTfK4qUnMUDeeZAJQYYvsQ3p/l0+YU80EDPfFsySJFdXi5qE05g5MyabRqwBauiiYrJaajf0N2xz2eHmwN3KHGnPm7DXSb1uklENazADEwPp0VqsMJ58LNRz7ZkTdiKkA1hZAALRGpZJhxS1ftldSXKK1tYmIUkiBJmPd6cHFJbBuLkMrsObc7oo/VZ2p6tDoBkL1qpPI/LJgSG6ENNjbfHG6Zzwi2cBQt5RmGJBDFGq0OTRoFa4Q+jNHUg2zGwP5HFL2x8iYIrgm1hBDGtr4Q1XF73l9XsvvnpyMoWiuLK/6eIprVpXFicHy7PjFori8mPF9t5oY2uws7VW9tewcbPZYnY6jeixKClUVUtNXZ0czZvT5eqMpg28cH1147DurW/89f/yb7z9/d99vqTpw9m0IgL0hfPovLR1jcDonGPkFNgTvVOHib0DpXCy9Dlc7tLbO6fBXwvnih5LCpNB259kIw5opZ/qrhkB8XLYuNyF5SAg7gxF2WGusTP5l8ARZo4oD0t2Lbk5IOcaCJSnrxfesyiZxgmc6HMqfUFG7emS6mOFwEgS2CnWpSogAKkiFs6F7J1L4kgxtk3dNHWMsewlckvIaaNsBAOZxC982ubAxOAIIrJzwD5pPAHzYjF7EHkbL73m635i75Fn/uif/w/zl750cLcJRBvr/dCe1qFulm27vhq2q3K83h9t+J7nJqCjSG0x6l9+4snxxtbmxs6Dm3fOD48XVbtYNuDi3m5vVRVnbu7daDabA3GPuHS9sJw///v/x43nPvo13/59b/vGHxlvXimG/eVs2q6QOUBklY1UbQ1acJhPjRfkFwxji9SnRbeMsQK/cTxQj1M5Nzi2PZQKL0l/UbEbIVN7MSsmpUYTpcpUnyhCgyphaoU0U9qhBizwx2ZUQP+w+kCaf1DAAoN+c0ON+yvh53wX8zdlNoFSdx22PBQiyhlTUZAwiV86pcJh6R1C4bxzgM4556Q+Hwk4EoUYQwwhtEDkU//0wiX5JYocQ0qFMScDQWD9oDKMC4U3b/tCDX4yah0Tqx4OJ03mRJw5TyFD3nBAifFYOZZxOjEwxNE4JzOn43RlaQHtZBLW1U5LypRjSmIGcupY1lHJZ4fEJDdTXVSZArPWmT2nSBMCyBlZCAyEsUVqmpap2p+Uz1yNRf3qyb1b9+88mK3C29/1xPnhQVn2lofV2en03q0GBv2nnt5+9Oqk6GG/Xzjv6gbaarmaN4wRnavrGJu6bmh2fDw/C7M5HT1sbx7P7z8oH3nTO/6jv/e3Hnnta05O4/nRsgptRO9cQn/JmTECOaXbabdet5SGbc+UBnJJkrFOwVr8AUDn0A4+M0xWsp6U8EJkhTtXOmf+tsvegF4CndEpR9F6s04IXyVCPmBg6ztryiSfJWskvb24G91FtSqZXgpgpW1lrBEluxeAbYBj/QUD/p1f/EsqWeLYhhiAOUZq28BAg2F//9LV/b1Lw9EYAGMIMUSCqIYyyxwiok//c+jQee8QvfeIDqFwhXe+AOf6/cHW1t7W9mYIx5/8P//xqx/5rXj2cH3N7e9v9AcDX+L69vp4YzLc3BpvbfeHG+mQBI4EWJZFD7CIbTM9Orl3/eV7Dx4cP1w5po39McdyPlsh8ny2bFfUrKIrC9/vo8clrtjhpSfe/LUf/PDTX/++0douBb+cLetqRSFQbNA50SjngMmlTnvS3BU6um67qzp1LVqog4aR2QcA8chYf435H1apKYtn2GwRm0RvUz0vqV6bU9tJZBkC2/86et8NEhoEau2Q7jjVz425dpDEYD67zMAkRT7cmSBmO/1G/BAQpkvokKKe7AXg0Tn0RVkWZVH2er703he+cCk9hSAgyxyTU5DPNQMHnh16COhThS9EdphCl/WqXqyq2FAILQEgEKZgJSuT7wRzwMBdC36IOdWV5PYSFmqX/1jwR70WYI13KWQDAOhGTFbKDZqcSCfBZmOaQAQ0AoScy4qTAciUIauamRU7aU51HTvD7UYgdAU7ToAGG9P+HQZpyMEEzNQS1O2qWS0evere+vTg+CsfW53dOzw4PTo+f/s3vnH+4E47a9p67eHt2d2HRxTc69+5f/XKxsb6wA3LpqZmPg+B2+UyUmDEplrVFa3m9cMH81DF82O6cVTdPY51rN//oz/yAz/3C4O1S4fH89WyaYmJIzjvgROnFfCDaE4wM+TdI+nFOnrQdYil9E/nUDyGzszkb4lqicKmnRmZ9MiliTegqR6aNmbtSSFJKeHPc58XzhKJuYw4fSblA90IvVwmRcCoSWGRQOOXHS7SCWshQ9qR0wlUmaaS7gr9r37pL1FUJ5CZiVIrs9CGNgQi6vf7u5cu7e3sTSab6F1sQ4yROLJ2EEoiiIjoXDKMzpXOIZgZcM65wnvvnUMswWPPDYaTyeb+Xn/sX/2TX//S//lPTl98pRyGca8/HvDm3mg87g221scbk/Fou78+KQcDX5RMwAGL/qDsbxBQs1oc3L5//+W7D2/eCwhVpAAcmmo0LNoFNTUT+GreYsns28GwRx4Z+7uve/s7P/SDT7/5vb5cq5tqvlhyEzg2QOy8YwQ9LT6V5WoCRoIzutwaEkpMz458suIVMd8Z67PfCbk9siQTDIlkMZ0SEfNhIdPVtI4s2wKsVY0qgUGaWHmNlmjJIzpkQNDTVIDzs5k7g+gIvqGRJUhVjCw8lLQOktVMtIgoJvKVApLOoUMsyl6v7JWDXll6X3qEyEBELbVVW03bxaxazJr5STU/a+ZHy/minT+ol6vQLmPLyFQMJv3R7vrupcml1/ZGu0xNfXZwevjS2ex8MNq5+pp3XX3yraHXXyyXy8WqrpfIDnx6PskgxcHSrZTi95Ied6GmwXJ3ZCeKKGBDCvo7ix51ThkTyDaLCYrvHdvMFsKXizkvhU08dv6VgrEiVE78TXERVakps3xdyswU5LOcN2IG7UUBIHZRq2sdcQxAFDk20+1RePNTkc5fvPHJz5zXVTnxTz59pVmeH9w5PXzlrA0e2n5VNVuPDJ988/540B8Oxg3H2dlydl61VVu4xjtezWNd1YvzcHK4ODupTpft9VfqO00c7Fz6ub/xc+/64Pc0oTg/WS6WdXQAznlChx7lpElgkPy8rpqBvEiX2OlsAITiqLpgCmppCF5Mn5nLPO2dkjBVT0gVjw6d1FwpMnRoN9gKinmQsTIagUDVYU1rJdCXaChDHr2BuPI0AN1/xSDMLf2f7h9WZwYZrZpRmJcaB1aLgPmATAZ0jgkKtWkMAEQmOeKRM1AIoanrENpIVBTeORcjIQA4gTI0y8TMzOidAigjIDKk0SInEYse++TiqprzA17b3nzNm75/79Izf/of/t83P/qbp+fnzVovAjPHVdtW01Wz1a4HGk7WB2vrAAVhqOtFAOyNNoabW1d7axtbu9tXt69ffzkcni7P27LXw6JwvTDoYSQHHKuaaAlAi+F4DXp074VP/trN5x9/6zd/04f/zP4jr9kdbTWrdjlbtk3FJHue0TvjhcbOEhhTWlsNd9sH2SvPVExCHjKfaivBzgTWkDyCCmMSgLRPCFOBn5ZVpW0pYsmlBVXmFayAoVKiyM4dGWCwjKy4bMCgypN3xl0gVMY9VQKh80dfN71OKv1lJooenUfnvSuLsuyVRel83xcuEdYQqtPl8eH87OHy+Pb58f3l8Z3p4f3z49PptD49X86n1WxZV1VcttxGCpGZACIWjsvS9Uu3tdV/5NrG2sZ2y+7swfmNWw8PpqvIcWN7730f/NAHfuDP7D7y+rLXPzoIITQ+OkoxIbVclOomFa5lyvWwI6FBCdbBaL6ZbDEATBFAqj3TZl2FWgVWNZYaNVT7oI8QcM72FlKlt0OUCnQnpE/1UzCDdOsAWts6ZMeJoojfJOehpsUV+6HtlRQKJR7OnHoZa60osS+I6gKb/X145hLg/Oa9L/3pbIXbTzzz2OseP7v1ldk01lU5PSc38Jf2/N7lq4P9NV+w87225WpRL6ahajHWgcoYZtO6Lk7Omnu367qBgwftw/PzRTt49mvf81P/5V+/fPWZxYqn56u2rrH0BTCm8vxk6hAYwIMj6fONzoGeSIKYcDl95Dq8S5HYgDfxoMyQUCu1kyoJZJvDj6rCoiRyT20pIbeVAuVc5tdNKQsX5NyUTdS825ci/ZEN8PpW+tv0Eg5NdgVgHSO7hAzyQGtA5GRRQY0IMpHUg5omy6ABWFgD/p2/+heZUQsmEj+JFDlGakMIITiHW9vbl/b2t7b3e4M+E8UYYwjMxHIrlg6cgmQO0aNHdB69c+icK5zzzoHzyQ/w4hdw4Qb9tcnW5sZe2z64+Xv/5Mt/+FtnB/ccwsZOf33oR+OyV/Qme9vjzcloa2swWCsGJRSesFcO15zro+97V1Sr2dnhvTuvvnL3hTvnhxUOCnZc9j1Fcoxhyc28gbbmksth4Qd9GBVnwfnB9rPf+qH3fNsPr28+0gReLaq6WsWq5rQdQzE7AbGE9Ryqc20ZIYScuc3i0o3tqGiy5smzI5ljPR25SLPIzBJxhA4f1/sY/HaAWGBacV8Ox7YGgSaJekSwfBPltvYQhlyok8t7VS5zNEJHmgTYAWKkiIilLwaDfn/cK0qAUMd6WVXzxfTg/PDu+cNbpw9ePb17/fDB8cnx2WK+XK54tQqrhts2MvnAXJQFInh0vnTM0Ct8z8GgLB2yLwug6Dk6aF3RA4e+JXSee8WyivNZs2rj1iNP/PQv/a3Xfe0HGqqPH55EaJmQHaB0usscLLu9Ag3MUTM6qNkJBkApn2G2jvpS6JmmQk6AYRLTYKRbWhGAAoXeWRy/BCPJdfagpIsBbcu+7uan1PNLtp6paU/jo9SrihW7xBMBzIoPeXWzyQatPdTFBCBichzBIy+31/g1l1dluHn/85++d/PO7lOve+zZp49f+spiUWFTPv+FLwHR7v7m5Sd2JpNt7mG7qoiIV7SYrmaLpmXkdh5WdVgszk/bB0fNwSneub86PmhGG+Pv+DM/8+0//ZNutDY9X62WdVvX7JjQOd34gpIqItbYBzAbgcZ0RFpil6woqGXEIs8qpDlQqrMkWIwKtRrNwU5MNScBZDblWBhGsuBJF/0NYa0dXEf9L/yCM/nKSgvZ4U+k0YHSCCXskuZFrXDSIlQp8WA1dmxWh82HELqTiYxsEncAgP/lL/5FldjUXZyJiYkoctOGGCMATzY39nf3dnYvDUYjYKAYQowck9/YsauCX86hwwLBOee8cwnxnUPnS+/AeV84dM4X4LwryrLojcajrd2dfr99cOMTL/72P7/3yc9Oz6b9XrF3ZatXFL31/vr6YLQxmWxsjLa3imEfy4KxdFD43npvbeKLoeNmdvDw7gsv3r11596dh1WMvV6fEYuydDXFJsaapovGOyoH2B/hcLJxWtcnK9x97DXv+/BPvu5r3luOd7il+XQ1n84pVIgeUjtAhxw5pXwQMbXQ67jnahUyfVZ+IeawE7tRYwDGRvL0AeSCVJOKjLzyrc6doSM+F/6wCqNdprHobm1ZAr5OtsvCBVliOwPJDEV7w+vTZPSOgAF5NBwNRj3kpl2czI5und57aXb/laPbLx4/PDw6OJ2e1auKqmUIjMxcFA6cowARoG5DwlgAZmp7hRtNer3CD8tifVgOez1GV5YD9L5fhmqxbFti9o6h3wPnHVHkGpcV3Jouysn2h3/ub7ztW767bunwwYNIUdYJQA6JVWQAQ/Dk0aXds4n3aRtoNRXAtreWKKVlNeRDDLrHF82UI2jzFUQnzB7FUuqWIsF/gZ80iIx4tnxIJJ2sSEbDF5vBIXTbPzIxp3L4xOdUwgT9EoSSZZsAUmabCFzLETlsTNrXPz7Egy/w/NbhzRsrLh59+9tce37ypZeWoXzli7fKXth/YmttY7i2NukX/cixmi+Xq1AvquW0itQ0bWiX9WoWlmfh4LA+WIRXbtVn0/mjr3/dT/7VX3j2Wz+0XNFyWa/qEKlNRVUevDarUxsHgmloBVQmcckXd5lEZZ1U57rr/aZlYMv2A2tdT6JEFwyoUB+LcGr+VnTKMp5qOS7oiyqd8gpUu5IuyCqVKZWmD7kbZDJyllAiKzkipxpLrVzSjJ16JbbDSAIByajk0ds4tBcQA0gptFgiEjPivAsxEnFo27ZtYozMGj0DGZ1sxtO3sTM0AVXiRXeAPaYOJQjJ5WAHDBxj28xnTQj1xt7u/lMf3P+zz7762K+++ke/cufFu3fvzTcm43EIcbUKqwXX8xDmg8m4GA4K3wPoc9sAxP4IXG+wcenRYW9jsr2/tXP9/u0Hi9msjcCRA7d+UJLHjd7aYt4sa2piu1gcbe1v9EdwcuvFf/Pf/T+eePvXfuDDf/bak29b31j3vpxP5yFUEAgcUCRwmnMSDRJ50tUC+Kq4usgMCudKbWE0WGic3jxJsQiZtkiACIwLZB6HKub6aOOpDjXaYAtsEJAD9yLZRLIwyuJZI9pyZUd/oNOBJrf5UV1y4CQqRrCxMx4MeXn88tFLnzp45fmjWy/Pjw5n08Xp2TmzaxoquXC9AgMAYLVsi8ihbn2EAuPOen/Ux41xMVkrNiaDfln4sUd0vuwV3qWKnqrGBrgo+1QX7bINLRE456gJDNG7IozHOB6NH56f/9o//R/L4cYbv+HbJ+tbJ0fH4COjriNriAxYIqqp1p6j6i0CUCeAw/a3VO2wHqGh5jypkzN8T8LvHKZTQsDZh2AOQD6JiJmxu6KpxiRTN2TnHYsgpbswIaeIbbIljBqJJU61M2IppJ0xAEgrIgG/5FyKQCFgBFcgQwFx3IuveWTUo3vTw1vHRwduMHrizW/p9+LhSwdH9w/uH5z3x8PHn7k03B8NiiFE37QN1/Nmvlyeh2rVhhipWYSGzg7rk7vN8ZKu36uPlsvYjL/1x7//e3/uL29fe2K6CMtZ3YQ6CnNCzymjlRHYei8klUk+UAqXpASYU3XALogmjqVRUHQovZg4i3YH0mXDijA5S1mBmGbD9WQ1HTJokqDDyzLjZtZiT1NCxVs2027aj0rYQcIGzmvLAOVcaWxsDzNLJXgudlEtA8hxqTJow+ROMkJur4kucAXkrJV6MYiUivSkSSG3ITZt27YtJzEnkWqhQV080x8xKRKrr1kwQmpQhTbbwJHYgSOHbtWs6sOHk0W9sXXpmW/7xZ23fPPkI//mxY/8+uL0uF2GsN5HiHVVNW09btYH48lgNCoKh7GKPA1N6A8n/eHaYGvrSn+8NpmsTTbu33z17Gw6O684oB94jlQMvXeD89myaZHaEsL5aKf/yPbG/fP5cx/53Zsvv/JN3/VdX/O+71nfuFb2tqZn89V8hkyI6FjnPgmAEIA03Wkuc92xTCRqNZ/JGsuqoAo5G2VTK58ZRjYLsifKXAyWRC4ggAEzMiiwKakAo0aW1ZYnCMGU6wQEc+QoV9XngaVBqdyooQNk4NR1hGLoD4dFLx7ffe7lj/z66StfXp2dMsdh4XFrbRVxtqTjozmEhqEpHQxH5fakN9norQ/Lzc3BzkZ/fXNtMC766yNC9pEKDETURggBOLSxbuqmbRtqCACIBhAG1LRMgZipDUANtg1iAYNRUfQGL50+/I1/9z9ffuq1W/tPVat2vpoyR3XPM81MfJ4BgCIrpWfVZi2mT0Xx6geAmufkDzqnRN7ZX+n4p4T3cv6U+PImC1IgiHqgj7I3dbKQnYUsQPELE3siRofE4NF5vRUrPUBO3oFD3XShBo8deGCQOncmOcWQiAh8GSk4gPEgPrNVjeIJLw4AfX+8t/n6Z2Lgen5+9uD09HS6vj288vje+uZaUfQ5tG3dNG1sZmE5r6pITb2aL9t23h4fNvNFODiJNw6XZwu+9Mj+j/yVX3jrt303FaPTszbUbcsRAb1D1Mq3HCoFSIcqgslkJxCCMqXKpaQwU+NgBkZqYjusyYAyJTwZ1JqaCjnXtcbmOOkpwoCQcjNaQGX2BCylB9YKWFUROqZEY1nd1wFLTJDIFetystJ8w2ftVME6DM2Mo3oMaLJmsQcGyF0goOPiIEAB+sKKH0Cg7YDSGJljDKFtQmgpBl+U6DBGtRqYeJXuO8d0dwJ2zJHJIzA7clKcysgYIzvAiOwt7EDg2HHTLtrTqppvbO9sP/Ls1/7wa59+03v+5P/65eMvf+rkaFk1zdZGD9yircJgPYxG9WCdBmvAIQa3isslTzZ4vO2H49Glq/tlMRgPD27fOeWHp+c1NS0AR26g5O3tolpi29CqisuzOKuOd7c21nvl3ZPbv/Uv/vGXPvvx93/fjz71pvdvbG9A4WbTk5Kd2FWERLDMS1PKrZEczDl6KT9HNI9M/SaWAkzNVsne18RezMhwdmoZCLlzGKTU23S4CuSPZBmzA8ushfCo9sOkKXEbe6oxADRSilm+L0aKQIGSGDxGACrWNkcwff6l3/s3pze/NMHVlSuDquHz83B/Wk/P2sKXTz+yuTkpJmv99Y3R5sZgMhmON4aDzWHRdwUiRW5CU4cAgPX5vG7a0ASKEAMjUVu3HCMAlw4YOARyyP2SIxIjliXUFNGjL6AsuSxcxNHNey9/6vd+9Tv/7H883lhfNDMKSJF1v0d+BUmLiwtsRoDNtzJSIzDvHDCic+kwcDEAAIhems6AMUMLR8hCss6nKD/L8kFWcQCQDmQyPElA5aoVdJ7TOSbqKYo8sQgXghcQMF4HakTSRqDUGgEYmSM59EDsgHHQo2e2m43icH7/wfreZHlaFZvr435czQ4Ob94+OTwtNoaXHt8fbm4U5ZhD3TZhsVxWy3Y1DU3Vrpbt/HS5WPDxWXt6VJ/MmnsPeBVH7/zW9/zZX/rZ3de+db4Iq1nT1G2klll2c2EakO781qq3ZAxIYtwOOjFv0HAZSM4bVHcg77BVTM+OlBIrNRH5bF6BZZ2kC2uBliO2dTNjDJBS0EmNu7FbJeCJGkIegVg6ToZCoF0Jem4LCkqnc9GdQIDF9C+Ep9hubY83mZC3Fl9DTZrGJKDQBwIiMhJFEX/ZK+EcxBhCbNqmbaoQ2rIoAVHKugUiFCx0XBYfY2DG5K1GhwUxAcfCoXbgST4CEaeWqwUBQAinh0f1crmzvf/o279t73Vv//JH/80Lv//rpy++0Cynm3HU1BwYqAlNiyGE3mDkiwEQL6FlDCVsOlesb+9ONrcmW7sP174yPjw8vHu6bJsmMnBRDnr9AUHhRuvrd+6e9NvhWVhd2ivd1vrh8ezVT3/m8PrL7/nOL33Dd/749t6T4/Hw5PCEQwNEUhaVD11mFUSdZcnwArNsrGZgC9lwBnVQp1J8Pf1isp7q/6XbqjxSCr4pphhHyc6bzj4oxxFJdXoauAqbBYoE5VkNCgg3NCXotECQh4iodnYKMBBTHE/G/eLs+sd+Pd7+yv6I1yJhnPbXBuPt8SYVW5uPTEZro96wHPV7/X7R6yNT28bYNm2oq/N5bENoqAlV3dTA4MkjU1gFImnu4l0JruCkaczeUeEdAEUSSfcOQuTYxli3pXfba8X5ovniR3/vG973/VvXnplPB9N6qn1RWA81SfZYkBFMudlgQaI56b/gUlgHER04676Fit/IIMlCxSp9mlwn6KMqkheOdX04W1hBH7CCVNCVYEBAD8zaORKsfjE1/lJ40SJz0BAEJjsibTCYCch5HymGEB3GRx+ZjKsvr87vFMPy6CtfmZ/SU+9+mpYPZzfvTB8eIPgrj1xbG5U9P6DAzXK1nC0Wy6aeLRbTenG2Ojmn5QxODtqTNtx+2NZVvbl37Yd+5sc/8GM/7vuT6axdLOrQthEIELzDVHuqXMiYh6KiYKftvLFSyLxCBtBpXhFBm2+jBpI4uWrSXATMAIv6ssVXjcArnQf9g4DgrFgD0YqqETU3wMnPy7H+nCMDDfvorVDYo44aLHUtcWDRRGbQClBQGbMv5b8kXSBR6uwHyCGjqt9KPhUn9G8sEosVCmiShwBp9yETA8cYmqZtQhtDm/YMImA6A4DV+Mp+dwcpRoIufdcTAgB7AEBKO22JCdkDkfOOiBlT8RIBR49FKpxcLBbN8vZ4sr62tfnst//Fa29838t/8G+/8vu/fXBwr+jjPhahrvvteWjb/qjqDcZlf9WDPvo2tquiv172xliubzz6xHBrc3L39ub2/Qd3752fns2mcRUawOi8jz7sXlqbL3gxDfdoVvZpd2vcx+Lk6Pj3/+3/du+lL73/x37+yjPftL23f3p6HOZL74kI0AtUJ9EleVlUom8ByE5eydxHVjugWanUNV7NiFpy1lkFVvOgOpzZY052yZJxFroU7NVFB3TOoE0P/zO/uMNzQXkWiEkAs+UmNnofTnssSbaWl+Px7P6nD1/+0zA79cVw89H9rb214c5ab2+32FiDQFA5XtYhhGbVNItZU9XVqo5t07RN2zQOgFqOFKBtiZFd4REwOZcILEkIPYGSCRicR4pYeGRmZlcWzBwBIgCij+sFbQ7c0f1XX/nTP3r3U68dDNdmi1VsG0DsdGeTeKoukoAJAoJziA4c+HSWt9PjAQGVspsNztgsi2YQ3nEDTFZY0SXroFHHDvVEvZcxYj3fGhBQS3/yOBBV7gDBsctqqRiUwglAIC2BgYlSn66GIDI8+eSVndGLs5tf8nsTXk7r1fLxr38D4Pz0lRfPHt4GwM2t0frOqA8+trxcVdWyWc2a2bRdnNbzk9VsVh0d16cL9+AmH1BTL4tv/OD7P/yLf/mJt7+pqv30eLmoq9BGQHaAqeEusvY4ZUYnJc4a5jbtEZKP2pHfYqgpmKGIqwxUvCKJhulG1SzxtiL6bzTN4qwGoKZAlcHCKRqWQ03midEWzZYxKE3PhkTvla83QUgjJ91plKZGIEK9+WQl5JxzBHNnjPKbmyeRH85DhnxX5YDqCCFILyBdB50B1q0q6XbE1Ia2aZoQA4N2E8X8DPUp1I4yM1GK8AEwgiMiIOcxbexjMXoE6Mlx4VCpLgeOjr1zriQX58tZ2zTDydpk/3Vf82N/8/F3vu+T//6f3PnCx2/fPN7aHF+5gpGAmQNT2fYCtaGh3hoNy6JAVy1Xrhj31rYuPb0+2d/ffGT/7o1Xjg9OHt45DW1A16eKBpNxMfKnp7Ploi4Xvq6mm7vDfn/j3uHxlz/xx0cPDt//I3ff+A3fd+nypfn57PzwyHny5FLoiiLr+yljNK3GZAWzQisTZNTFU1S1zLD+rU4AqGhkuFCNFi/SuDnnviMJudOqqaAjK/NENk8NWPmA3NIYi8IMaP4+PTrFJdP1qDX/SljQYTy8/kKYnj1yrf/G73hX/3XXoCDgCPNAp2dxWdUzaGaLZrZoaopNC8xt5LQP2ceIAIVzxI7KXiQm9EAEBWBgZEp7Uyly2jbmEICic8CB2LkYY4xt6vSKBRcOkRmpHhc9ni1vfOXz746xP+j5oowhkIa+NeRmgfoUM06M38kmOzlPGgVfoRvDUZOogCXlttnPVuc8mVAlfXhBUtSWKxdNIuOwgwG6Szc9AhILlp3qin9yMLQwR0yxAtb6NJEsRkjNBDDVrWLyyAnJhcnecGdyvHru14q9q4PXPjv96O+V4+1i/+rq7u2j6w9qdmvr64NR6dGF2CwXYbasTw9ny6PzxXlYnq+Oj5bnDd8/qs6ncbUsd65d/rG/9rPv+c7vcaPt87PVcjZbhcAUnWMnGVMEZkKRUVS7q2qSqL1ONWvZpc6dUHLFU6PuFvpOmCQLoF38wdANVMM0ZmP21ywFd9QhPVIWWV0E1puIm5X2O0qtpsmFsrB8L1lMtiNBBUJY27QBSExeeCYLqReVliCzsspUYKLx2I7ykmaEwLoEYn46oyauoTC/B1K1mexrjd47joSIDl3qEhpCDG1LFF2Rgp2IDnWvvE4sODmLFzCFTcRwOQ9E0rFIwqHMwByRgSOQd46IGNmldhXUOvYIMTb1/LRpe/V4fX37NV/3oV96/asf//Xn/8OvPHjllaY635oM6512XPXHmxMmim0bw5JpFcdr/eEWRFxMl+VgMth8xI03+tu7m/fubm3de3D7YHqyWDWwnDeuxzs7vXMPHKCpwvxk0V8vrmyPTk5XJ6+8+pv/yz88eXDnG77rZybbjzLw7OREpsjlyLkuofqdImZKCJg57zExxpcXQy61TxXkNW6bV1YpHxr0ZMGyGi69aQ4oJVTS52dHD9U/yPw0i60CvaySaiUooyR5eWZmLPs9Xh7z2e3x9uA1H/im/uu/HZr7NH2xfXCzqphdv0f1/Ghez+dURcAytDElSUJgQkZXlN5TYCgBUtQ/RjnKRY4gTEe6MDr0gn7I6YSUQMSRY+Q2tiECYunLXlnEsumXGJt62R5DaFI9snPeQQGIiNEJ1EvlPaJGfFKlNaKmAm22AbstA9N0ouXdQCDfmiqyVvNowh6NniZvpiM8hm3MgOiS8y2xQKdJ+04MFwH0eJBsk8SF1CCUsY8UF8rrD5y8dAD26AL6kvHatUdW1/9V4UajJ7/R0UkZl8Orj/eAbz73/Kpt1va3B+v9fuljW9XVajqvTx/OTg9qmtaHB9Vs0Rye8e2zdnpabIx3PvhD3/MDv/Tnt558cr5oFser5WwVuNbDV9CxiRGjhk0ZQM5NUHDVKQcFuo7gK3PhPPHdiUmYjOotglQ82Y1Q5R4AbDUU97M/LdkCLX/WNRR/Rb8N+nR9FU1kdF0SWRoh9KJlxtvZCJhYldTBmTu37qIACoM3i6jyoeMG23EGygWzmyGPVjcLAdKRkA6ZrAdJxyKkOXEOGGIIbVs3bUvyhkCpzaLLvWmZmB1134eI0DtiduK6E2BK+ZTE4JnAIQN5cEDAjpy0sIvIBUME8tGxB1c3q3jW9KrxZGvy7Af+/NNvfd8XfudXPvfr//LhvQfT8/7u5Qk4LoejIrShds2qGk0q2oLRGnvXp1g11AIXW1uXN8Y7V/YfufzIw9uvvPrgwcHB3akrkEZ+vDFczZpBr9fM67AM65Nyd23jdF4f3773R//+f19Oj775B/7S5tXX9/qD88MDCi0AgBenhYhS6UdKWYn3JWvbUUVJeCMgMxE6C16a35g0gc0rxWxJMtdhC1lksdBIqviJctu89V8fkCJ7Knl5bBYwtS+n1dd+nInwdnxe/QpxBPAIPlbLMp6sr/WHg41450+r6Ufc6Grvyns3dt/RcJ8ePudXH3fLL0PPVxVhIvshyil2gCFyJGIEShFGIIfgwFFZloWnGIFjjNyGmJoUEMnQ0KMjCBSZ616JRa90DBQhtkUbcLUIw8kuYAyRApHzDl0BgIDeIUv3OUwwqyEV18FKabudw25OKCYq3RZ6KFOaqRTaT7ZpXH6vrl4KU5BFDjPBJWGutnIIShWZ880RENCBuvwWENQ4YWdrd0YiuTU6Bud9GyITrE82ysVLcPKF0Ru+mf2wuXu/KDb85XecfemzJ3en25f2NrfG5SC29aJazWZH83t3F8f3l/MzF2bNg9P2/nn98JBqcG9+1zt+7K/81LMfeH8T/eHRdDZv2rZFj8DOI4KiFDIAOALKMMcAGWgNxjl15FUHlwF1OXQ2sq6lySHBNy2z0JUTOmPLlKEQsvGwNTQTZfFbrcPXJJ+waPuOeu4aa0qwjgCMBODUYTSsz3xdxmuQKyYL9WhP0FB+hnKh+JjDyReHnT6jPDiU26UGVRboErkrWDEjcUy1YLpSgnHctm3TtqFtYgwAfUyeMkdm7TBnfzC/Jdqj0Oy2hJUdMzhOHkok9ojC94Cd9ql37BwQEThXBGrD/Gy1mk8mG5tbj73jw7/45Nu+5hP/6n+69YVPX//KdF678bgar/v1tVG/dkg1U83VaW+y43sbgH0IRbUqeoPh5v4To63LW3uP7N24+WB88+ard+bzGuKKORTe94Y9ijw9pHIUBqVnN5ifHHzud36tOp998w//td3Hn13f2jl7eATQIDjW837SHEOiN8ln00Ck1pgzpI3spMSFQQqANPCTqvs6MUOLuqAZZelEKY8RTNGrJKRtZkNuw0oSVU47HqnJYjooVrOUnGsPs17aDUGaNosfSxSp4X5/PkVueXnvpNwY9rffgI+9ncuvbeAqY9nbf2w0L8P8fnVyTBx8fwAtEDIiUAj2VE6hHCJMjcAcgPc1RUxVaRyJIlPar4AOEUsEjNAyesayGIx7vVGvWcbz+6tYF4cPF/2NS298x/uIeLZcLpp22Cu8K1MYxSHL7vVkAAwJNNojSo4A4ORY3JzWEdool6X2u/Y1+ZeG4QAAKOOyKSADoNRnmXUV2ukULlNbEMrb/jUY4uwusoBOYyOWzHeYPzWvE7trjwQ4HPVGw97hC5/cXRt5bF39YH7zszB61FfxxpdeGO1ubV/b6JVt25ytlsuTh9MHrxwe3KPpgo9P4vH5/MbDtm781auv/4G/+hPv+cHvHm1tz6bx/Ohs2dTE4NJRIAnQ2GEKR7BuJnLac81IkMp1gg3RFSt+UXPLZgXkKwo8nW6ZRvn12vTyhJpyzCSYuwMw+osZA5MYmGayVf9hd7hpn7YaXL1T2qqW0j/q8YNdY/2X8yfZ62dL3aslk4FhbuTEMhEunaTRYZm6L9DMUvdBHdOp5wEAOOciRXE2GSiS6+x3IKLQNG3bhjbwEMA5jsliQMowsRWsau+BHNVKUSun4StRdzsjN5WUBK22Bz2UK0kwESNDcOjRQUQ6n57WTbW5trX9+m/+0N9+6+c/+v/91L/+/9x96aX1SXHpsXWOcX3YI2iX57Px+mhcU38jFoNNhyMmrhZEkYpyvHXlydHG/vbe5a1LO8994YvTk2lkR30Yrfeg5f4aMABUVa90WKwvpvMvfuoji7b54E/+0v4Tb5tc2j4/PELHzsw+MWAHJYyrA0glQ5JFUqoC2kIyda6HxHdQE1iCzcbMs0SILFjCIKO6qj8bYTRPmZWFWl2cIFFK/mRBgcxQ0FRE1IO0SC8Fl/UYEzHtVRwN9h4/+9KLJwfXL2+9pugtWywRBkVbAgK5Uf/qe/oPPhYWi5JWDEgI7IFC8ByBI5BLYyl9ivwRpbZgseXAjI4oAKd9DwwAkcEX6AHalpAgotve37p0ZXtVLU4Ws5Oj5Undq8eTD/7gn3ni9e9e1TSv295gVJaFY+8AwREiGcPjdFpSckZk0wUycQo0W490UVvLv6jXDyl2ZLAqBN8xaEtxRi3fzvZFCWQ3Ng2A7MArjwMidpCas5uDpwZeFwZVMAiyhnd6nYnMqPlGpeAIBM67sj9qZ6/g8nD96afm9++sVaf1wo0u+emN3ylG4drr9j3X9exotVge3Dt++cWT8wf16UOsVnTncHU4q3kw+vYf+vbv//lf2H/NG9qWDu7MV4uqjsEBe+89aDJMD1nT8gTQmIkxHgaNVCueW807gFg++RgNB/NbWWhNfvvVUCsoL3sfdAgdKLab5YeCsHPVNeXYqk0onN50xPTKwk0KAp1P5UasSw+yVBJEZvuRLS2cmZi+bpIgti8DOJdjiglCUbmADEZeWAlUgnUoUN0T0tOskICZUGvbvfcYMTKHEEPTxNgCUWrnw7FLMBIhIZRxaA4eGMGnrooMjiWozMzEHgFSZwWKAB4LIADHFFl8bEn2pEQLIaMjcIB1tTiumrIarW1svO0DP33tte9+4V//w89/7CN3ri9GY967PNoYeQByEWN4WM4WvcliMNwerG87h9Vy5sum8uP+eH3/DW/p7Wz3t0cvfuYLd64fTo8ihOD6gAWN+0UYFG3Dsaq21obL5fzec5/4rX9BH/pz/8nWtTeXo2GzXJZlTt7K2yoYs0KweuEsUkF6JIgKvf3JjDvpuuwkSLfUYCnm6y4IXtetNJRXuikxDbKofbbNqNVjag3yxhOrAU1+YVpdgZdIhomMEEKNOFp/ww/VTbj5whfL7eO1BhzdLh95vYNdYA8EPNjobz85P76F1YqpRfSp+TNR6wEImQOBR4DUUx8QHUVKzUkcEIAjjugYwcUYy573iBRDbzgp13tPv+E1fWqrw9Pze8c3rk8fNuubr33Hu9/7vY+/7RvdaKtpmq3JpPClQ3CMMcbIkSgw6NmNMqFiOy2qkLwr58UbsJS50TwU2s1yzrMunPjsirsSvOeLwM1yDrPSJgQEPb1SJMfOeQULeii4pS9RujeKRoucOUV9uMCGUdoUozIQV/heWfiDhx9//VNPx3b+8m//o/XH37H3tu+swimFsPfkGvllc3JaT8/v3zq6+dLR/cO4nMLpWTw6q+Zn5eUn3/zhX/q5b/yh7/CDreV5fX42XSxXjOwRPSAyaINKQsG21C087auRk1xyH09G1qI4FTSwALal1DB7xslgq7qBlmAbg8q61TEzGu3Erg4phMnyGqyzHhIoc6t3NTXE/JhMrrp7+vQx3LVKmrFLhFduy91wolp6Bnnx9AMaUFjRDVzcwWCmilkSUDqV6ghIKIlE3YsLfhDYvcxuJ7rgACCEmE4HizGURS8lzlhcAPOVENWOMQvRYCSXDuMhYnSc2q67FPKJHjwDy9lNydYnXipwhVpRmlIfjhwjuIAUV4vYxHYy3nn0de/9L/67Z1757Ef/5f98/08+Nn3xwdakv7M9ogjFalXOl8XZdGNnGanpr09wOIzQcjPH1vnBZPOxZ9YvrW1eXt/8g8/cuH7v7vE0NuVoMsQ29Hq+XxTgXFuHNd+v59WdL3/6I7/yP77/J/765tYTZ7GNoUWQptfpaMOOvCWk7GA1qkx0ZA6kzaeEcQ2rk2QnEqDSA6oLYAdFiWnId1VXQwyOJmrV/OdYJeXYT/ZtZYgq58p6iVQBzMYjcsp4oEhzoMYXu5tv+IkI/+7g5c+u9pq9nVHfXwt0FXkCDEgwvvzo/FbJZUERkBAcoy+0rym4NkYSYxNCYABkRzHoM6Pz6UgdAKBy4LmNzns37m1e3XHz83svP3zl1qoZ7/SfffvXXHv3E29/z/r2JYCyDhBCdMiFg553zhcOC5bShHTSQCTWQwfs6AEJUac2oay4izJ9HVOdNM3O8U5eEoJmgxmzcmkAULUWiZQ3mXSoaSGLnuo6QzZNhu3KAY0bIoJXAExIJ1AlkOYQiVMs0hFgvywozGC+wuFkfufW9T+5+banPrS+//XTux8ter4c+fn5yeJsenTj5ObNxfEhTo/9jZvnZyuIPfeeH/2O7/mZv/LE29/WNnF2Ws2mi3pVk09HQIFTHE67VDWxe2FOWE/hQi01MA2SVseseJp8BdbptfIHTYXqfgK12mLyjPqrU561QKmNfKT6qGsMlrrpBJ/MILFSpqzLqFDfMSAXdCvjZPJ1soPeMSF5M3+6f8JYG5zaIxUUISEqdIB6u44Fy4E/VFeCsuNFXMiTOXk6pPxCch+gs42AIYS2bUNoKbZMZce8KTKlCZWjFpP4iUdMTAjOM2Bn8oS6omA8YyRGIHCO0gQRoXNihtBBZMbU2FFTIiE009OmWlajjY39x7/+w3/z2S//wa997t/+46Obt4+ms+1t2tntu9Pl2kYvrKrF/Gy8vTnc3h2Nt/prWxDn9SwWm763+fi1d13e2L62+cef3Hjh5edfOjy8vxgOextbRYFVf1SWEefHdTEEWE3v/ulHv7h75e0f+guD0cb8/JyZfDrPXE8IECmSrVIArL1CJdqie9yT9Sd2yhNUwpXHqEerHDKJB0lyWJ1eTlBmRDGrux1ypwZBRMcsvIqenjwIqCKrp68wm0yhBpdAv4aSj0AAgti2VHK5trH/7A/PXhgtT7708IVbffiC23+cJm9g6sdqTrMIJTpfBqKyQCgKJA7UOgSMjj0RBSAEROeQiIgjcUjkxSE474koNqHsF0DkEPy48CWv7h3evz+jvWff9Bd+ZPvyG4vB2BVDJNeE0FTVfL6cTacUY6/s94pe2euXRa/oO1c455yDFFlMs8COyQ7EcNoUEZJpEKjPnXWEf3VYoMxGzg4qbigiiEePciZa7geU3XtBKFUNMQjKCyBlK4j0dAp5hFaaGNe1xbIoog5KNogRIEDh3Gp1tqi5V65OD6/vPfbetatP1vOTtWIVS6iWs+XB0cMbp3evz6+/XJ2c17du06wpt598+rt/9se/8bu/d3PvUrtqZufL88W8jdEjOgaf4t6sG2UF1URkBCnQYpRGpaW6SjgIgMwj2zsaI1FUljcFMTH5F4Z02SarKZQ5tkfAhdkxuwFqRgQ4TXU6c2yfpiDwhXowjf6qhel4Bqx77NG+3kkkAJhHyFmfFbA7Y1JB6fQZAAQ7OqaD++ZidPc7mEVMVUBZgjvJeAZwDigggHcuOcwhhKZtQmx7wA4xgnNIrEHmBBe55SSi4Y2zvbF50cSRYyJ0AOCZATkCYEI6lxqdMDlm9B6BMZ04L9hFTA6R0PsQmunZabMcjidrz77/J55429d/4ff+7Wd+45cfPLhzdgrbm6Ne3y8X095quVxO187Om92d9Z0wWrsCBbbtgtqhL4bbb/j6t21fW7/8e+PxZz713P3ZqnaDDY+wNXYO3foa1Uw+cJgvvvix31nbv/a6b/rh4Wi0WMycJuBEyHNhB2evHdQmghUzpFLdzM7k9EQtCU2+MaXcgLkRQmEAiFNDdwUHzPcR7FYJV5KT3bLM8TEdZoKa+JEwkDJPDbqCGRKUDkgS3FTcQY8esGTHbm178oafmL76+7MHv3tv+XuTZ8rJWwrsXXLjsHz1/uzwqFc4dJg6TVHTAkUipBCJYoiRYwR2UqDjUzcqDx69w77rV00ohowuUtu60ZAdLu5XL710Gnfe+MG/+J9tP/Y6jByaQJFj3SyW0/OTk/Pz6ex8xkSpur9X9Hq9Xtkvfa/0vZ73ZVkU6TQy7533zvnU3gEBvfTUZ47MHJmISPuApkPYDUS0JQCZlhmaIMgRBIgu+XsJmRwidWCEtTiP2UBBMJ65E4hIRD4lBQChiyWsTgYqIlrBmBkqjZAwAhYIyPXinB17Xo3XiP29/rbj6o+rs+txcfTw4cnBjaP7t2Y379R3D9uT01WvnLz3m97zwZ//qSfe/s6iP15Om2o6nTUNUfQOvMT00zBJodSQGHRECmNa0C4/uQQXZPOXYFtOXRWLpvsiMyon0ARWgVRymTl6xhoLo+SMmKKnDlJJD3Z9K0QzYnalWH/ODwYwPVXzZkaNUy5fbmu4rLZKAENFAXSZlICzYIU4Igk1EPPspmFLNUM6WawzEabDCGCZgTTDhc0+56IiQIZI2g83jdphDLFp2rZtQwjM6eCXaEWFGoQzi6N9c7oFj5C8YGIPzA4oMnq1nek7niOx9+kdJCRFyaV0IlZE0nkxCQVFBGBsmlVs29VwPBpvP/a1P/zX3vzN3/65f/9PX/n9/3B453S+CFub43WOcV41g2l1elwdn4227o92rw43dlyBPNyezcNg79GnvvV7ip3tMP7I5z5+4/TejIdDKPzGWigHMIYSAkPhFkcPvvj7/+7Ka960+chbm6qOHBzKAeBSvsVqtC/u41dAVcHreEGAaQlyDDRdiZBOUybULRQZdXNsUYkCgjSsTyHR5ExAxxPoSCqAlISy8AjB/JQe6G6WzY0qIEWYhZqkbbEeENA57yBSzVR46q0X+2/59tWjT55+/t+ffvaj16rP97e/ATafXNZHNOhBCCm0TwH/f1z9Z7RlWXIeiEXsfez15nmbL72pqszy3VXV3dUelg0QQGMB9JQomhnOcDRrSVya0ZqRNGskkjNLlBY5dDOgAwmCIAA1CKAduqq7q8vbrEqf+dI8766/x24T+nHOPvcVs35U5nvXnLPP3hFffPFFhBISlCKFoEBppYhYQYRmJ1YJQCtbk1jEUiPjoCRZ5SowHvSS+zeOPrw/OvmF6UFXN1sJEalEjMM4DHqjME6icRSMpEhFnCgliLQGYojIObdszi3kzOKcccu2bNt2uMW4bdmWY9mObTu27ViOY1vZi/K4zoQIeYiiiEhn8zDAaPCNASkeFysGyOavKDI9E3YajK2mwiYdS8JnT0cX8sdP47tjUcmkN2T+PqJcb2VyFwCIqAkYRyVVkoxtrrQe2Ytuc+2U0ocYJePO/nBjd9TVO5vq5tV08zDVojw7c+Irv/rnX/jmz5YWZqNIRaM4GAWJiDTxnI5GRI1IQExTxjMCFNDTxDHHyHcszHwOkg2LA8YITWzp5Kdo/jcJcgo7P1kDEyGYagDzXcbeTnh+cwBN+DDxVYXBn4QC5uEYOFXIi4o/zPwq9wCTfIDBbuZfYFYHTcCWF+zl12vGSUwwfnFlYDgAIxs3K5YHPZnlzGQOpvCd8tUAPLY5gSwsVpAmCzMRBRJhlo9iPMNoQgopBZFGtJAhyVyxkC/3hEYyzxSK9pE5CQKUdU4mREZaZYUCSJoB01ozixFpIpXNWcwfi9ZmXFK2yEZdkJceSIZ21mYiCkIlRblUa88/8ZW//vee+ZlvvPfv/829n7zW39gfD+x6zWtNlakzDEei1BtUhv36zKw/ndRmESszQqaWV1+78sV6sz5T+9Eb3796Zzg83CtRm800HZEqjzO/Voq78c76zTtvfv+FXzrvl/wwDIzhRjgOfEwckK1EPoShoOwNSUoFwEHTd4EVeyPfgIiYFeXBp/5kG9KQ+dnZYcWjPcZ1GqhhnkeOKMmcnmJrYgZLc+ub7Rg9IQFNZJJHhwCsOJqEgGBbDufIkMABb/r87LPTw6v/dvzJf8TGlnfhS/Uz5xxvV+7sQz/UGhSziKWkWFYMwJDbjBmjkaMJzV1gFgEpLYAxbiEyxlxLMh2NxObt7r0H4TD045i2bl53tUKXJ0IBc5Br1+ZS8FqjZXuhShIttUjTRAqhtJJKisThknGW1ZhFmf3ijFmMAWec247LLNvxPMd2HN/zHd9xbc6zcAEJORDPkgRZVYIilU8uLmIsyHVhBETsmJXIjCEr4N8xYnqCOtEceaOfN+VguTee2EPM9B6GK4eCRzIfeMxcYhZeY3akSUlSsYUoZMupnZhZDOy4Nz7YfXhjm8aw9SD64dvbj/Z5yfefeem5n/8//JUzL7wggQ376XAcp3GqSAOxrHI6vzP8NCgBzLdPviMLA5obtQIjZ9EQcuPzjmPqfIvlcKrY3Gg0mgXRY+6Ujhn3gkiZHCnD5MPkm7DIqBWcWX4Yc278OHBCJFNk8SnlnHEELHcPxx4TFt9tqP8c1RaaMfM4wbyBCtx/7Lsxzx5RDidyd5ZVt+fdDD9NJVGOFSbMDprYMXu5lX1uZpaNWgsx07RSNmUuB9uaSKlsGphUSjE7n/miiwxtgeURgCjLOGWBnc7zwzlLlUdsBMjzx6BBU9YnQmdED2kiVITWcbuX+RKmKR+Yl/2UMSBSTGenQMeRSMN47DilSrW2+vKX/tYLz3/j/dd/4x/c/eiVzV7UT1i77tt8pEgm0Sge7Fb7u/Foo7pwttRaQ7eFldrUyedKzlS1Ulfff3dnP4iO7IEQtTIXJL1qaarhb+707r33kyuf+1P21EmMkBQQ00gso2WyrU1kUivMPFpGx/aJ2VQmo6XNPE/T5DnfGWS0aCZayDaNzh9+YSbAeHLzssLrGh42f0dOWRavLCKLnJjOY9U89Z5nMMwho8m+JtMFFhEYA8uyLJtzzpiN3EZFTNvz1Wf+7PhWee/2q9Xk3Rod1mfK0bCkxpGSIJVgCAQaIBulormVkfAG/xAQB8ayml/FLZJKACFolCF17/T3rg3HfT6/ujY/Mw8M7z+8v7d1GCsxOzc/NzfXmqomZHGLV1seQ6ZEmkTROBZRnIo0Ba0ZEeeMMa2JpVpIoaQQKAEJEDCmMTBkyIihZTuuX3I937Jtz/HckmM5ru24tm1nEQRoYporqbVSWRIZ85RdQeNP/K+x6oXNIpgwJ0Ua3yAw+NQGKAKAItlj8GO+WzIHbxAxTYwt5EILNJ/DGIIQoIVWltB1GwdcpTCQPIopxfW7g9feP7h5f9RcPPnX/tv/87Pf+BW/WROJCkbxIBilQmf0nEWcZ+7OgIsscVkQkngMVE9wyESE9ukbKgZcF4zP8a5pkw+d+JT8XBSjVPLEuomejfU0ERVQnp9jaICRiatwUldAuYU211AMUs3N7nF2KP90nDzQycVnAHbS0XPyPvNwJ4aZjmd8Cpxe+Lf8SU6S+tnZzpdcFZ/FsuGOWUv/PO2QRSJZVeMkD5H7LKsgiLN1zWw5ZNUyWfCgJ2lHKaVIUyFSJYUDbl5/gFl0rLN7BlPNVGyJY708zBVA1kiYSBNyINJEnBnwTABaA8sKSDK4aTYZ8nyDU9ZOFhGPSS0hG+/NLQJItVTDwTgKq37VO/mZr/93T51663df+c1/sHHr5rgSLi+V+RBjBkokQXfsHhzVe0F7ZVifP+82V8D3/eXT578GdsP64//w2uFuEI5LqVCzdT/uxmtr1XFU7exsr3/y1hM/dc5znDBN8uU0T4zM5syjnky6w0wWwPgzEwSY82Hi1tw7578zu5fM6uXVvJMoDYwHhxydmIjMxB9gIkkDQADoWAtbxKIbpnETQFkmhrIhtKzoW8cYcobIGOMcsxYLnHG0ctoXQQqlUq0QtOKJXMLSs0dsW45uu3ceqA1GidQhUywDUBJBIeeSJGmlFdM6m4DASBFqjURaKVISgVIZx1HCOCdNgwN1tBkB2hcvnFt77qXWmXMHSZqiP+j1Pnrn3VLJu/LsMwsLC835OYuB8rhXr1T8WmS7oPsWaWWxrNzMsR3PtRFBASgNSZJEcZwmIk1CJaWQAgmE1gyZ63u24wNntu3aru34nuXYnl9yPc/zfce2OeOOawPYpLSSmqRWWmvKrZrxmpnBy/tNTOQNxg/nAZyhnI8BQ2PpJ9yFKTvJ98ynIFEBG40FoIJay6Fdfjw1iQg0ac002kqoJIyY5yyvzN67EyUMmyem/i//w3///M99M3bcJNZpLEZxkkjM5vAiKcxPJuZkRdYutwA4CKanDxmHZIAM5js422rFT7I3suLk50VYxaoYQ569sFgfoxycmB7jCA0Fn81zoYLpLmKCfHRaQcoci5lzQw35bAbM78GECHkSAwyxc5yWMm6Xjh1pcxfHHm3hLHJwn70Ui/Ui4/7QmIDJ7yBPI2UBgckg6oxKPobx87cXBPSEdQOAYyogNDeTQVHK7UIuB8cMdqrsXAilFWmNgIyhUvmgJEAofF7xQPMCKcw/mTM0WShAAiSNmgMrhrsRgGbEMrBPmOmASBMwTcSBUdad19wYAQEwQk3EGCAwRqik4izLOiuVqGGaWJHllconX/6lmUsvvf/b/9+f/M637t0ZlWtsdt61mWZMMxaP1APVPRrubLRWz5YXztulOk4trr74ta9Xyz/4zde6m6NulxPgTIXGSXR6ZerWg87W9TfPfeEb3C1TkkKOnrOJkVDQ6pP5nNlO1BO2D0wR7+RQ5DGdWT4y0yXo2IYyW6Ow5majTbIPZmsVz9b8gExwCdnABsr67UIePJlUG+nMtyAgaM0YMgBmWZZlZcnSjKohpTRKkEpJEkITKZHIOEpkGqOGMFauZyF4nt9a/cJfU92Pdx++t3XtmoNRDNJp+o7HOEXc4kqRlkJrjchBklQEyEjoTKGZse1aJlKRIkYIQS892ErH6E2dnJlfPbO4tDAKo1vXHy4+fuXSYxdPTJc37m9effPj8ZnhC3MLaydXdna2Nzf2KRZxEIMMuOeibyMhaQqi0Hd5yfGBQ6IUiWwnMtctgcu0JkAKxgGBVEIpFTDORRjZNk/HnHE2RkSLMcdzPM8vVSqVSqlcdRzHdh1UTCslhNRKIWnIZEVgCIQMvGdl4CaJxxC1NhRibiF0brkmRL+RWxyzdACQFxJPzH/2ctRmLxV5e8aQABkhZ6BIcyKpUimGGqSMe5qX7YpXWbSee3HZctj7N8ci6WmmkXSahnGcCg2ZkpsRMcgJZgQAVFQ0GTpmtAr8S/lhmOR4c8RjTGRuC00YPLHFuRswovZCkGCgsVmGgijK79gQL+ZSqBDcGHOdHS1twhAiMFSQkVUdO1/mMJqTNWn8YC6VjtlSMBeRG97cDmf+P6v/yNfkWF1xkaEwLguKH+Rn+NjuyE0tFEeViLIcYPboSVNehGrurfguXfSio7wS+NPMJea3STl1lF06IOOktZBCSJEptfP2uZjNassJgYKGQMDM62ZKR0JimRNgkP8ss975Y2CYmXCzFlmMoEmTzhqKZK5FYT6aLevwTcd7pgPoLENMQKQUcMY5BwSlVDiOhBL15vJX/sv/+fGf+dU//If/050PXuvfHc1N2UsLtZLQQW+QSqDxcDjYn0lG9bm1anvBLrXOXnmpadVe++3vf3B/fxCRg9o/4JefOre3OxjsPoi7m/W5ywGPtJZo9glM9gyBQRFmh+dVkYa7yaKDYvHNHjL7KtudWY+ObF3yWkqcBAcmo5iHcceybLkfyk5LkZOjfCdlfsBgwmJj5xW/xAAYY8ziPLf7HDgAaU0ijpMoSZJxlCRBMoqlkAQIGhgAJbHl2iXP9qwyS6hxouZU6hZafObr7twX95f29m6/PT76sNzdKfEOjwfMtbUklqbcsbltaaWlYoqAFBCRIFIkFTEVC0EYAY/DNAytMU15c4u6NTOeXr13FN65t11bnl5bnJute96Z1sXLV6rfe+3e5j3O4+pU+fTMOffB7u2PPoqPhimA25Rlv+ZbPiMcHw4/vvfJxubObtAvuc6ZudnV02cWlmbbjbZXrTVmm1pDvxcc7e5YnFSspJQMmetaSRxF47Em0JzFUU+B5rbl+36l0SzV6pVas1Iuu65bclypQAmhpNCaaS31sZpBwgI25kLoXN5j2Iv84WkD8o0NwmOgMicb0YSSBc1gsGXuYJBYliUzR9QMALcJtIiPiFLJYs5dCuPWyuzKY9PJYHD97oM/+Ff/WHreM1//pleqasbdICWttMrJeIKsDQDls6YoN4xYGGKTkEUCQF3wWmAutbjC/Ajj5BxAgcQZmuQoTKxLYazI7HCgwrtkr8oTJJN8gTkg+fegSeHmSN8cKDP127iL/DeTrmf5e48xXZOHZiC+eRx5EGYiEUAyUouJWiS7onw75JmcPB43FFchriREUBooaz2DWikCINIMMVOO5SEnEgAncxUESEXoD+bRZSqgYzcBxTJB3vc/v2XGGAIoImn+aCmZ5eTcVyZRz7dk5jkxU/8b5j+LGnPTZ4Iw0qRRY1YlRkiZUA4zbSKhxfNbNipsyMQYOssWgKmHQgvMbWAOq7McCyitWUZJkUzDtBemXrlSP/HkX/z7//zmK//x+//yn27efnAwTipl8kpYL/emmjoZHEXj/vTyljxxtjJzAm1/6tLlr/7Veu0Pvn31vfvjoVRTFcsvV+vuxt6O6O6whSfMMPE8LY1oSm6geOrGQKNxbzRhAWHSvt1sNSq2NplMFCCaqa6Uy0CPA3/zWOlTWAbyg3NsiwED0DnaZzlxl59PzHpXMYacceSMM0AOqElRnMRaJFEwjoNRkEiBWluAtm1bdrneqHi2pYlEHHOCesO3uRr3YypV3ErN8xwhpdTKquuF+nR75WtB8Pnx3hYbbcrO7WA8SIc9lQ5tKZlKpBBpqlOFcQpCCYsxsi2BjCuXwCW/wcsO8516ea7cXqpNNcH2hqPhxS9eaZYaYXf//atb27evvfZg26v6X/jsU/vbO877TmNxznZx9eyZUTvYeLAfBaltK88Xvu+tLDYpXtDD0FVOPBgmbHjAdi1J3gn7xKXVWsXvD5NSlbdFFUG53NbAx0GsAVip4lVrUgoRpaQpjOI0CNIgGPV7YFluya9UKs1Gq9qaqdSnbcfltpXGKRFlHa+M9QENugCtAMceIhT2kcA0mcpOZW4+TTrUoLOM4TW+oWAOjQYMc8pv8gcZKikZdxPJlAyAOZZlkUq0SmyvzFRsu7hY8z7ZvPdv/u7f+fjDm7/2X/ytUmVOuTYkMgGdYU1E0AXLafYY5JYZsND/5Lf3KYmTuQ5gxQocx09oHJzBN5P7ys7J5Gwdc5DF6k24GDBYLE9IGkknFu+YmPQ8m85yAF6w4wDHUiwEk28uQL5hVrG4HPzUHdHk7tFIQskAucweGu9J2QtYroIxLzM5BSLNOUMNLmOW43gV1/VcbjOSIh6LMBJhlCgFBASoGcO8MarRl2vjHTPDYhVRlRnwWySms98gQ1C5MgmRQAolpExTobTOM8gZI6CLWKWgHXPQMaHqstvMLZMGjchza0dZrM85gs4kJgyQ8n5xGhnPPkMrwmzwdrF4xqChwdVZlX2WSM7aCmVxNHKutQrjoUwTp+xf+sKvnnr2Z26//sPXfvdf7u1c3Xk48h12bqU00y4He8P+0cf9Tm/m9P7cymPMrrRWlz//za+Wmn/QeXR01BEbdx4tLs3eWj8MeoeIwBnPBIEEx2LzjFs0G6vgZyeAvdishJ8aBwEAYASdBmvkvVfzcE7nnGvBBRyLRunYKcxVcGZzZj/SoE0rVwQgM7YckSGzGWdMM2SgpVYqSVORyjgVQmpNqMG23NlmmzuWTVoDCECNDnIblfAJpCTPd/ySq+Me2FZlru2VPCmUTlKpFCIy2ylVK+VWvT3TJH0hGb8Yh3EUpxBHmIQqHkUJUDqSQkgNnAPjxLhiee9jN5ZaR0oCCub4paayeTDoUskadEbb97Yerm/c+nh989FexNIzK3PvJG/OnVoZheEqRvPLc7NzLYdXHu6FLApdBjKMQhVblj59qnX56c97vseADQ/HW7tHe+sHvQ3qTJXiWuX+w939vYNmu17xHe26luUNO51UgV8pO9xlQlvIq+VKpeyDVqhlKmQQp0G3H3Q644ODcn2vMb00PT9bb7dctyRSlcaJkCJrcWFAAOaRsJlYhlToYZEITeF3jm+Md88PQCFWOS6WAFa03zGww7wZMgipc91IQkwJAqFtrFrefBTuO6UK+LMYD0joc0vVKvceRcmH3/+W7YZ/+q/8V5XySeXyNM1wat7PIaOyGCCZAfWQ5wOKzMVxqsT8pLBDuYXMYXT2fyrwi4HDiAVPT8V/xxPORTRFZkWM9c1Raa7VhsxGFOwGTD6YMVMFaS6MTOVB8XSKb8yNqCGTcguXBRkmqp4EFzRhpRCL9E32pknvzsxRICKgqSMm1DqbnJE9TKa1zZyaX/ZqJddyXZtrrbWS3POa9aqQLByEO0fdNGFaC9AaKO8Ml01hzO4+p500WblF+rSFKsIhnV+8NqENSSnTNFXZdDDHRcZIKSzu3Sxc7v+1xjyqzZMwAEA5T2PGamYRRGbFSANwBALKxxrBZOcAAiEyyCoJsrqRPAmVeTDKmLEs26KzLo6gQWd7EUFKhhxIqzQOZJRYkee7F7/01Qsvvbx/9/p7v/svP7597UH/cHers3qu3Ky5B/duytGB7PWXzj8jyxVeW3ri61/avfPx/e/dwYiopFPSo/4RQFp0Ms9958TiktmV+QIRTDhOMhkhOq4ENP0WCkSVW3dCZgYooYm/M5+XT/hDM8bHRCEThJgXZwICFk14sw4WLJ+Eks22JQ06StIoiYVIQGoExYghaZtb6DHHcRmzGBFpnuhEKSWJlBKaQMfpsB/75RJpNnzw0Gt41RMnXNcjKbXSUpHWxBigVIgaNVkWJ2Y5Ja8CoLQGJVEpmYokFiqJVSq00qQ0gCZSoGRGWlQUJaMgHodBmEadg92t/ViknPEoGA+7O2kiV9aWXvzyS6uLddntxoNeVHK1VOEwdizfLdsNzpdG84MgcFDEvdHeo439jYfRQafm2U89c3n16dNU8pNI7+z3WaCH6raU6rA/TCNVcnv1quP7HCQeHe4d9oftuj+zvNyoNhy3XmpUmcviKFBJ6CAHsF3XBamTNB0ddML+eHR0OLWw2lycLvmu7ztcWkJIUkrrbKxq/lQnYJnl9j4zgWjaaB77g4VHMFvPuIBjlPNxQwXHcEK+CUFJ0koxoS0hLcua4p6THo0sr4EwlUZH3LLLZXcc7l2qNB9t7F374R+1Kvhzv/a3y/5MEIYoGQGB1sSRZZItsz0LEFSQIRkggizonDROMkfBbFY0LgBMjwuT8TBh8yQaPlYbVhgfKlhXNKoT41mMgzDxMZolyf1D7m0mWdfsKvKw3kBbs+CUu5D869G4r2OyDhMA5TSs+QwCdkwXlJcO5qkBjYDAaMKu5ebSRmAMbY6O7bq+51dKvGpZmqW9eOeTa9feefv6u2/duHNbVazHr5z95V/7S6cvPTfTnto+2AWRdefXAKjz0kVjljL7D2QVrgrzVhDFxssvQRMhYyAlQ8YQlVJCCiFTIYVnulXkM4aK6Kvw5cdjJk2YDfBjJhtBOk+wAxGpfOox5t8JhDprJpGxSTljphlkfTizmSIajm2FPNGAxCbb3iSYlEJkmmnUpBARmZIiCoRIXa/kLl15dumx57886u/e/+iDb/3z/U9e7R8EZ85XfCF279zXis2fe9LyFzifW32swo5gvBN5lsc0BL0DJGnbPFWoCkxhcApOoHi+6gaRTbAbmoNrzoHZ4CZoLFIIWQVqwfUXlsM8sk+BGsqLOVjxpAtYZCFyi3HkkBl9TYpUGqVBFMZpwgF8h1dtxy3Zlm0BgZIyTiWg1ppICyFllKQUp1oqy+IcdHTUC/vJ7Lm5mXn/8O7OcNCvrJ2rNJukQBNo0MxiLIuspZIaGcks/8UdxjKfjExpmQE04BZZBKgybAFkaeIgBAlNCpi2Eax42B0dHNoiblVqcRxaOl1YWihPTdVm5sv1isUlzbaRc+C+dkrN2YbveFqhZYMF8qPv/UTGEaXUPdgKtrZdqRdWWp+8cfvejQNRKctUe/5Uc65Vb/r1qeqztRpQOh6J4GgQxkFnHCaBUoHsyL5CUrPp3LLjVuqJsOOwnyRJxff9iqulSmIBAFppZjsqTQ82HnV298q16ux826/VXdtStg1KCimzUugiN0AAGZtqaJzClOUowYCe3BwVe6AgIHBi0rKQf8LPZFsl2ySZFUpFYnsOpF0oK+7aKIdAiRDdNAkk4JDZW+PK3TCZqdj9weF73//WqfkTj//MX6t5fidNMuPMcnxs+gMaG5o5tSyOOZb4LSJdRgY/FzFAHg6TIZHzOb65lDHLjxtYn/NO5nuy03+MiJ/4ueJqzCnMc2j55U6gl1lSMD4iW98i6MhzAsYuA06+pgjoM4+FRYRhgoDieRSPyxTzZ79gZC68UHhn3+vatuf5lZLvVizOUKKdhmnvXmfn7r3bP/jRnTffG+z0ehGUqfqgv3/nze9+95+9/bf+r3/55/7m31iann+0swMISilEC7AY76HzddJAQNYkdjGjXvIqXMyZ7exukeeaI6VUNshVSqG04pzntaPHgKxZxsIO5ilfImDMyIwQiLRJC2W5YtCkeV7mlXdKocxLZIEYY8XOmHxHli3PzKXOSy0ns4opC3aLqQyUV0Vq4sg0UZpGaRpzy7Vdz/Irp668tHbu2e0PXvvjf/j3Hm1uWJZUVrRz56Hl68UzruXNabF/4tnntt78JGFoIwyPtknHHLyJnQdT2EhIzIQC5gznm1dPQBDkC5NtutztU5Enzlop5hseACETimTlnWSQX7ZIRQ0XAiMCZvJUlIdWwLKaVoaMIWjQKlaJipI4COJxZxRpaSusVT27VSuVHORM62zSIsVKklYikWkqQSrSlo3gl9ySy3k0ipCvPrFcb9hb33lld/do5nNfaK+uaUSlSSiNDC3byuJ6ZtmAjKTSABm5kcM7hrbrWo5jS0cpBVJrrZSUOpFKg8yiASlFkqYaRRgHkkW2Z/vl/eEQo7Q6vVhvtUt13/Y9RVYapkkaK0DLUikPahWnv7m/u39w1B3LVM1Y4cbudiog3O00ZhZf+vLzy4szMhJKUZSGjJhbqTYWG8IWo0SGaVwicqsWt+t4pEZJcurM6ly7jrYVY4QaXQeBEsfiDAiQW7bjlX3GuJPKOAiUkJrrSsXlYAWj4c69nZ37rD3Trs/M1Kbn/FLJtrgSSkpFWhERFv0QwUy8MXti4viN8CHHGTnAzlQlppuYsf4TZogKe5cV6wIDQtCkhF+qOcE651tWRdt2D2UXIGVaMWaJFIepvT9On131qzHfO+r/+Lu/v/b0l732Y17gCC0VacKMYjDETw7+8kqHwrQWQUiOVgx/kvsMk4TKj5Cx3HpyI4XxpAyaT1B0DriLJir5GSTzWRPrXxh/wy8V6BQy7WmOQSjnmNE4zBzP54LL3H9g0RQ9D6qJJjnszBdoQxQVZxjzfiAMADP4l4NbM4+LA1hg+77rlx23Ytk+KE26E3SuHRw8fLB+79H2R3f27z+M9w/0YVBx7OfmWtWVU8//d//ZHPf/yd/7X974/h/96//uf7q79+hv/ff/Q6Nc7QyHpJFQGjRJWQfZolqlkIFSdsM5FEFGpLKbwpzez8E+aS2lEkIKIZRSnFuADECBYaxzz4Wgtc4npmRoN68PL5ifLCjIWz5kNr2o+sjjMUDAPP2dKUNZNqQ7k9Blnp9Bvp+RwLR2h+JQFHuTmfUnIiSGXJHKPANjTGql0sgWUqbM953TL33tzy+svfL/+c9vvnnj3NMn/QZjMk2DGzKs+tWq3/K8WkkNAhepf7hL6diyqxiFZG4Yj51byJNJZuOa3XAs8DdbxBxROLbzikAyXxNNWEzSzoJLkz6i/BGx/NRkTVW0BlAAkLU9szgHi4PWcZqkcSjCUCWpkmRZ7sp8Gx1uEWpABSqIE8aRAKRUqdAIUmvBpLRTxRlyQMaRxoN0LEU6rsy1GnV38+on+w9uzL300tqVS0qRSoVUYDFm2VZ26NDiDDBNBeRJI6WAlFagNGHWoQG5zZ2ym3FVWmuSJKWOAzEaBmkiuG3bJd8haFv+bMmRccSkqtkuIEiZKErA5uCWba8M4Xjcjz76aL1URz1O712/MwhGi8vTSyvLi0uL5UYVmeVPVUqVNgLvjMTuJ+u37t9BShZWZ6dnZyI2DxW3Pw6SYdQkJskahMH+wd4oippeZbp0anF6Pi5NpUmcxkk8iC0PZudn0XFUKmScCqkZd/wKH49H0WCkE9FslMqlcjAKe4NuMDos7z+stmZmVlfqU3OuX7FsWwoppNACgaQR6eW2KldAGoRbIBlEI94qqPDM9GRn4lMYDIy5NbhYAwEn4GhhkgxDnaRirFnV4lUOqcXHnqNtAhGSi+rKHDz9fNtN45+8qT+5euPmO288+3OXSr4/CgOVX1NeSGKYKTLd+wvee2KucxNYJDFyg4+Tow9EBUtd2E8DyIvsd0HN5NyTYeLRnDUTB8CEXy0OIQJkZAwU35p9Bive+2kC61OMah7M5IcRSBMzrwJj/s1FIKHpC5lZX0QAzBrsak2kldKKESKAY7llzy/55VLFZR5SHAe7+4f3HtxZ3/jkw4/Xr90MNnb0kOqKWshWlqee/uwZ4BW3in/y/nryysc/999+8y/+l3/Tu3v3zf3bf/ib31k4e/rrf+6v+3E4iBIOjBm2Obe7WdIUs0Iws98Khtq8EhgyQsXyGtaseYMUqRCpkEJqbThpzlEqAszazU7I7sIOQ94bL9ufWao9/3UW5hrajjQRJyRCzYBpBrZx/sSAZ9x+QRpmWxk56rzhCSrII1LMpk+wwpNj1oU64w9V1r0ZGdOodTakQksGUnPGyG/Y1TMzp5544t61W9z1F88uWr6lEzXudV1/jrklr73LdGS7zmDYT0dHdmORc5QaDATB3PYfg2MFwV8ktbLr0uZ3gKYWkCbbx/yoSKwUuXowjxIK9YI55pkXRszItqy5QVawSkqkaRAEYTxG0haB4zheyXN8x+EsFjJOEyFVCtJ2fBGlIkohlZRQEkdJFFQcZhEIpbhQWsXDhztp4Jz701+rLzde/3/9+92DB4/9yudPvfgys60kBaWIFAAykoCMEZJMhBKSIEtnIVqMESEDsLOQLMtskxTKIEAEJFQqDYJoONakwWLIba9sKRanaeLYTrVZdbniDDhzU9AypeEg6Bz0MRDdo/T8hZUTJ9ofvHHL4vzUhTOKq4ODjutiJMW423n2yVNTq3NHPfHgvVs3Ht2+e/uGTqOdjc1U6Km1+fbJk3OLM6fXFqrM8sslz7NTdeFoGB0dDa897DzcDUuNul9GBpQIxXhpbtmuztaccimOkuFolMYhcvCrFdu2knA0HiWCEmLatiylcDwIgvH6qLNTbbXbiyut2VW3VGO2lbJUS0Z5kU2eJcrKEdAIBwteGgBMuy0wxhUgH6GRH6ain51BV1BkkImAMW4zLqOkZ2Fvo18r14Q9qyCGRHG04jQKVFKu8fPnqssz3AL/M5fb7/7h5g9feeWpr/yy59XDOJLMhozny0TZuYIxByOTbVzY+kluCw1QPEblkNHfQ5HgMPGCOUwFzQzF12UUC2asTG7Oi3SIacpQOJHciWa59aJ60Xw2mVgbEJCo8BwGlk3WDyYKJZa5rDwO07mpBzT3hABaU95jkFAjkAJNJKWwAXzXa1QqlVrN9RnqVIyG4b17e7fvb1999+bVm5vr25tDMYwinqZzlnvBKp+abtcqTefUqce++lPOwbV/+1u/HQG+9Z0Pv/yrf9qdnj/35HOj9/r9cP9b/+4/fO4Xf82rlLpRJDU5PAOURJCNVSVFGoDlQh5D+ReKAoMXtMbcUxLL5vAhU3kEIJVUzIz3zPcjmAUscpl5YJBzQIYkIsgmxWffrDUgAzZRLCAWBk0h8sx4EuRdt3I8kTPkuac17gQnD2zyzSZFnO8uMhodnRttRqSZBuKotbaVpN7eo/d+8pZn217NrbdrSZr093aAyomIgJ+zqtsY92ZKuDEeh73d9uxTFmJMOtOnmvOYB+VgUi9wbOseu7gJYDhW4W5QTobui2ihaBeBpktPvheZJgTSirSFjDHGGFqcWxa3LAsYU5KiJJTjUTQapqArJd/zPJc7tutwy0rCcBylYRxGQkhNwOFwb2e4sQVEHnerjJUbZU/JqBumcXy4sRXtHziztfapy+effXH56dP7V9dVqE8+98zJZ19gVlkpIATGc5euhFaQdUjQWeMYIkIipbNZinmCCBjaFs8aQZPMsQgnQIvVp2rtxXbQGadR4lhcCemAHAy6EiAVsVWt+RUHmGvbCFymnVEQBMEoXT41d+HMUnd/+8KltZmV5VTFvXTkcKtUqUqwuhsbB++vH1zd5Y7t9QeX1pYvXFi2S/5g//Dmh1d1Eqej7vhQPoh7LBHlSml+fnp+dXVhdbk1xRZPhJ3t/WAUjPthybHQAdvRFgMdS6UspVLb4TbzozhGJUo2MtsejwKhtQhTJNv3bZWg1iIYjsbjYedwtzm9Pbd8sjG77Hm+crRKhJSSFBBJPdkIWeQ7MaI0KRkGzNCrwfj5Hsn7dWNOIEERKwASMmCADkcbtTra782PK1MtRirRvhBhLJNUIkY6bc16jRlf2JxztXLSn2v6773/4c6ju0sXX2BEpBVmpZmYNQLIFTV5GeSn45DMN2A+BMYchmOqiFwvQcUxzXzdJJlNE1x+nGGnCc0wCb5pYtW1sUI0CQ+y42KYiSLoLq5zAjERDBcBAFlhAzPdYCmPuTC3T0QwuW8qVKyIgFnbZNRZ9yilEKBW8VqtZrVaZUhyMO5+srN3bf3RRx9f/+Tj+4/W9/o9HMdtcObAv+y1KmV3rjm9fHJ1ulJxa5UHw+3vfOufiOER1qlstYRMg+EOiHbq2KtT81sb0b2bm3evffDsF76wsb1HyIAQSQEiaKW0ypCjJm0VTEXuCGly74W5AsBsQHOGX3VWDiaEUkIpxRhDxkDrCejPbzxz9GbRAfJeCJNACUAj8ny9C9BLJmbNL2BSN5PVsJne+8bp5v9jhbAySwvkz9Gg7czq5gJbyEejoYk/CICYRoWgtdZgP/jk9WS0e/npmVMX21qPVJyoRFlVqdSj8XDRb9RkWp+bbdy7u9s9OJi5yG1uMUgN0DDXm/NOx5hQk4rJTbix9QVKyaCfCfdBUd4Qz7jGPF+Sewqd++Ss5YCNwB0HES3Oucs4R4Yox1EQpakkLce9rS3X86cWF3zXZcxmjMpVXyLb3Nne2d5H5IRMpQxCIba6EIz9tluvTlXkmLY2rTh65btvdffH7fn6k7/09XMvfraxtuY0/cHu0WBv6+Q3Pjv72LLfairBtRYZzNCasi2T9YUHhkprUJryplOACMCBEVjI0eIWI60o5weU0lpFaRKHErjNCCkR3b1DRrpUrVSqXrNxMgUSURSPwl6vZzmMlUvIbL9RP9GoNet1VOr27YejflwvlQcf3hViQAjc9eZmT3CnhocU3n6499F6rzOMXGtmae7ky48vP3cRLXbus0/vrj9E35PDUf/gsN/pBUEUpbxdr509d2bp7IlKu7201NbQJs5IpIgoQY/70XikLEsn6VjZ2nN9AEzTNE7iNI6SVFqua3ueRYozBraTioilLE6TYBwn0b3R0W59eqG9fLo1s8xtj2OqQAJaqDVpBdroAnIZIxQIyPzPmK5PIYcCSpA5lDrfdMA0ATDktksAOw93zj7ZjlyNJafSWOToaK4V56nWVsnjFU87XkLR9LT1/Nnq9T/p37lzdeWJFy3LAhHDsSxFbtfNDjedeQrsnZv3T4keJoIc1AXbXhifYq8XDBJMsuDFT3ESaedoKceaRQBh9DmZPdM5cmfG2LAJ41QoZTKHYXpyZ9RTnnyD3M+SuX7KwUxxlPMrKZp+E4BSEohAK9d2apV6c6bq+paORqNbt/bfub7/7tsPP7i5uXv4qBukOnGYXvXLi+X6mjO9XJupzJypTTHpeAfjzsdbH3fHW/dFuLGdal363Dee+dKf/gWLWuOtw+HhjRvX7zea9lJr+t5mcPuDa8//7E8RgtDgelynOg+bCLPu5kSGAsKJ7c0WSRd6Fa1yKS5i5giYlDJN0jRN0lTknEz2WJBMI+Fs1bEwb0B5Q6BsUYx3yUpcTbuPIk9BmogjEiInIlbkg/MUDkLOhvNsexHlc6UwR91YcI5Z3yfIQ1ITjxWRI4AGzbLrJyJUDBG4GwX9/r1P6jPlC8+fmLs0tf3xwXC/z1xslLRMd8Oj242586p3d3amLj7eODzYOEvEuWUqElheqGZoNKOBMFC/SIdkSzBJ9lHhhU1vDTCsmM5THKYXVfakNCjQqEgjgGtbtsUdy+Y2J1CEcrg3uH37k97GA8etrjxxdqE1jTPT1WrdcUsEQqYJAUQCR+MxcHt2fkYEg6DTj3tjS2C74lXPPuH4qPn0ox/88c6Hr99cP4hn5n7l7/xnp89c9pfnnLILoIPBsLt3UFpqlFpTzPJIM2AWIGmlAICASQQtJDKyENCwbmCDw3iu5APSkoQkJbTFM9inlZaIJIUgYOVaJQzCNJEyFmmqN27fa89Pr56cd5slh/FUykCJo93DVMaLa8uNqekTJ5c8m6dhKuNo/uwSPdrtHwT71+4Nbt7DoO971p2Z9pS7ZLN5uQOVnZgJGY3j3vDafQhUOmicv1yan3n8zEpzupUM02E3Hg17e3sHuw/u7dy++dF3v7f7Xnvu8bNzp5ZL0/Nea6rcnnIq1TRNo8Go1x2Eo34y7miUke+5ftWyPV72bYtpV3LH0ol2uGVbmCTCVo5UKYuTJIqFiMbjcDS6s7+/PbN4av7U+WZjyrF9IaVMhMKseoeogBTHJgwXWO04aCtgXOERMjF4ZqQ1ESIjQKXJ8+xOEo2TcaleZrForJyQUQQWJemYu1JB2pqZefKLL23fftezbYuHZxa90nhnd3sLSHAEJDRayRyygElI5JClAHSGysRczpdvZjD4kIzZNcgdTSoXJ5beWPDCu+VGxPw1e7WBfkVioHh1JimkQhtY+IxPxQ0TkscwUzmSz829hkn7uf9Ua5sZWGOrlCYi0FoRaQ3Ks5xWq11r1TyN4cHhzq31rXdv3nn9k/X120cH+yJNOLAyeKu16lrNbc/MTdfn7JHFpBOPhtthZzMNb8UH3aAbjbsp96z62ld+5Rc/+4tfd0tV9ajz3u9+6+GD9fHRoRRp2bIbYN95/xpJYblIKQCSQgKtSZMGBG4BaKW0SQIXC0VFwXPuxRgDrfNAMwf7iFlXOKWEUoJbFnKGpAFz1sxkdD6tPzOVKZnGswDHWRMrrTVHnuP5HCDyjOXQmlhmvxmYqsEc2E98Opncbw6UGJrgMLc7k01SbMNsJj2a2QnEQaPFLJvpzo1w5/b0Yos3K7Z2FcL+5sH8SgMF54wOOw945VxjutWsu7ZWu5v3QY8tx0JEqTXjjGW8DxaStWOBarEk2epqnQdJRSCZbzQwnjDfZop00Wo6c7pKS4YMEB3Ldj3HsZnDOQOSSg1GvTe+//qrv/ndKEm+8ed+9jNf/JJTKyud1O2SDINBFAVBlEhZb7Y4t3zPt4QVR0k6CNwgXJips1glu4Pheufuxxvf/d6N1lLt5Z99+df/62dPXr7gTVWJAG1bJWk4HKj+ISaBIl8EMXkW2QyB6USKVACRJCJAzjlHyiRl3GLAiTEE1KS1kqSEVhLiRDuupTShloBEoLnFLNu1bVtLyRG9ij+UKiL1cP3ozrV76YtnV8+c95rlKBHjRCpmuV6z0xk06+12pa0wHnZGe1sdyWh6aYaxAxIL+8Oj3R9tqsHQpUct9+OlUiPsxU0Kz5XK9Xp9wJoPbnf3tl69N/+uvzp78vMv2s8+Px7G77z6apJQbap+4uLJEydOD+/d2L778PpHd7Z3j2bW9hdPrFbbTadZr8/PT00v+mHjcN8LH4yGewe0D47rMLvSnJpqLc9KgDBOBIsU0FSzFgYiGo2ZzTi3bduJQw5SpGkUd47uH+xuP7qxdv7yyqnH/GrTcuw0jqWQaCZWAnJDshozlf8ra5lQmMFil+VHPOsQSUgMQSnFgTObg22JeNhIBBzsY2ON11Zl/A4BAqUOUw7Hdqtk1Ry75lpWhXMIsSMtcsAiobO2QJkNznZsJuDUWd/DzApnDQdxQl0apscUNhh+MzfblAuXJrH0xNFRkffFYwYXcgRbdMwwB05DHj5PsgDGXhARZUKSIrTOR6KbI1ikWcBgR8iVKCaAP8b7TFLc2YTXzIplZo00cYJqpdyabZamPDZOxrcf3nn3w4233np06+bD+51uNyadWmBPua25crPhzU0tLNSsEBn1hmw03AkHh51otGcHvbHaSqXF3ZMzS2vPfekLf/nXZleWeTy68R9+sHv1kwd3bx12u40aH3ZCt+TPl2v+KMYkOHdicXenT5oiUBIgBQIEqRUDUlpnA2EoB+6Yu2YweFubuyPDQjBkiExKmQqRpKlSCgrlr1nhAn8b2QIWhpdMU738ORTqUZPsBJ3N/8q/ThNxZqJcIjRjtI57luPcORSsHTPbv8jOTDRxxjvlJynz/5qAWTZ3+LD38Q9sFc6srjVOXdKipyIRjeM4EiIBYHbJtQZ7dxpnym7DtzkcHDzSScd2FoEUAoLWGWFrpAdG+ZNDpGO8U7HXjfnPjzNg5nKzF5pB3vkJydLXRNq2bdu2Obdcl9scGeeIBEkyOuz+8PffuHfv3ktfffmJZ55efWzVq7lpKtJYi+Go3+13RiKI9crJOYe7SRLEg4AheCTrzVqp3hbb+/c+vH3j9Ws3t3dSz3vqpz//l//uXy+XqtLWqCkIQq0IlEjH41E/aNbZ1Fw7TkEzS6UA45hkkISxGiVChMxnlWbNK5UZdwCISDFAqVKtSROSUtmYLamZ43HHRimV69mWZQHnzOKOYxFoEcVSCQ682Wpo4msrJ3/4O7/36ua9C09uXnnpaX9mbml+Lm22x8F42O90u/3WzJHtOMBZqcaP9gfRsGshxjKaf+Li/MLp0c3rRzfuBPs79+MDBoyD1w1TFcf15sIpb+p6936302c3H0TvXrs//wdRzR8notPtVqdnx+fWpk6caj9xce7KUyQpCUdKJoPB4XB4VBpO2b5jEdiWXynPWYueZzeS7a0ojEQsYycVkWqtzLeQRoNBKuJSvdps8P6RNRyHyiHkUZYML5OXBs642xvv7d7qdPoP1lcuPTO7crrkl7Ut4jRFqWReE2dKArCApTmNUtjAgm0povoMm4I5f1opjmi7XhxEozAa7Y/b8xUqlS2nqhJEYakYS7bbbpSq8+V2dy086OztwLffu9ecnb3w9CUCKbXIzjMasGjANxo4Yxh8KGJyMPzIBNjjxIsZSYghiAru/1OcDx7zAWByfpNqq+MEMhjeZmIdirCB8jneGWmfiad1TmVkNoxyu0aFksh0YQfMCFigXLdv6hQYkxnqJyJNHLFU8ttTrXLNx0h239ra+eEHd3/y9u3bN3e7W6MgKglolWtV7k5VZppTJ6adisdLIuh3x9gP+0fh3qNwHykYRioES7ul5YWTz37lpc+8/Ezz/KoA3Hvttat/9Ce7dx+Nkng4GtRqNjHsx6llOW7ZryhveKPTvLhyYs0lAIjjQSiGozhMkziJVSp8G62sL12xdcjEA2ayoXmOQJhz+NnIVkqFkFJIIbWbtfBnRdAJAEV6Pt915r2Ze8ieChEx09AmZ4kydXK20AzM0FTQGhgz3BFOtjcA5dx/PveGgXHf2eWzQjE9iRANSfcpPRkQgGaMLKbGO+HePZex6dU1tJeHkQqkFTtuHEMgrFKlMj13auf2tgxnKjO1RsnZ2T0Q4X65fIIBKlIESIyDNlwh5bVexjnmzL9pN5WLprQJjaFgi4wPLtppZNftWJZl22Ax13EYkO3YNkfOuQIt0zAO+kk6vPy505/52tMzy3Ou6ysVBYNOEohg3Au7gUbbL5WmZsqNaikNw3A4UGmKSpYssuLo9vXbt7//Zj8QHar/xf/3fzN1bq0y1Sy1SyqWTOtgFIz2D4WKMJXk+l696k7VmM1ZFMmRiocDNcI4VdGIkoS3ZmsLJ0qeq+I4SUWsgQFaRGAxBM6AEC1Ha1JKubbteraIU7/sMs65w9NYjvujdDQCkuQgt7jrVbt7e9EgWLk4d/LM3Pvvvnew07cSevwrn6/MzTCfcY+nIo3j+P79dQstjVa9WZ7j9u7GjogGLpOL50616gvpxTMPq298/Ad/0AGpPI4aYqFqOih3Nuv16ZI1V2NBIsL0MPGd5NKzz518+unE4b17D4aDgxsf3IjPLp6+cG5mdsZy/TiIx4NRd9QLw3Dv1u2oWnbKlUSjW63Nri7RVDPqDoNYlUpeyS15jKp1r2LBKHRt17Ftu2HZdNgJo5gYcYuhVp5bV9UEiJeT2ijsjw43PvnhxubcyZMXnm8tnHBdN2WpSomUylIqAPkAGmNAi6EPuXqMDBlUnMHsbJPWDDgikWZkVcEt3T86jBiHchlhDBTEoxhUGI+TWt1r1wF2O3Ekfud3bt2/Pxwls9/8a3/hsRe+Mh5EUsv8ZGd6a4P5ihOVtUDPv7ZI8xmLX/Avhj8xhJXJWhQYH02HSzgeGeRaEvNv/LR3yV6rDbgkA7mIIMvgYp48y3PpBGgmMpFBldnZzJTlhffMQJ3pygOYM1CGMNeklEIghswvl9rNVmXKhTg6vPHJ9g9ev/5Hb+3e3djpDgcirvil+dbqij21tHS6VK5KrhnXh4e7gwfvHybdI4giEQUiHKepBXaJT51Yu3Tp5Zcvf+Fz06cX7biz/8G9D3/0+qPr73c3d1Iip8RKVbBtPo5SjkxGabNSX6z77/+Lb92W+9BSTz735bPnzjWaU6VSKRLheMzG/dj2Lau412zds/SILkhpNKlW076TYdboTAuRJkkiZKKpwrNUZJZKYGawQ8GNUV7kC5mU1PT/ySmawnGDmWeY0U0FcNYZaUA5Ys98i84bDZrHBMhyzJ97HQMRskvJEhSFBDjHIsz0DSHSgAwhBeVEh3HvoOzbtVorjQMSQoDwy45KhIwBGza3HA5J0h+0avV2vXy70xs9uFNffMmyuEjNaAYoEA1iPsq9QGaZh8u+PaP4DSoDQ0dmIh/OdNaSTxbn9tIAAQAASURBVAPnyJA5rs1trhRZjmNxy7EZIIGQUgjSSiUJt6xGs1FraYssVKlIlFZCRKOgO0xSUapVHdclxkhoNR5zYNVKTYhAJ+lwv/Pqv/ita/fvn7188Vf+579Rnj7h1ZtSqlSko05XJkpFcb/b63bC5nyrvTBj+57llDRwnQimmBzHLAyiQSilIu5OnVieOj3thj35cG+004mHI1ly+VSbV2pureKWfGBcJEJIzW2HIYLWXtnlNu9vdg8fbu/eutO5c3902MWKa81Vp+bnVk6ccSz28R9/XzZbT/z8FS8R3/vOt6+/Bu2p1tqLT2PZYQSe4wQi7nd6vaOh0HxpYWrp/PKZ0qn9B5szCwtRrHt3Nql3FPttLDeVJNexQLIQSArVp/ho3AG7UXbsiltfPrP69H/116onGtEwKs21l688GfWOVg8Puv1DpVW/36+3uF8r+7VGRU3Hg4EYDUQYjjqbwaDPS/XKwlK51uS1Cqpxd9CPxEikVdWqK0mCo+26aHkcrVqrZY3CNE3DMACpGUfG7VJVkCts1yHR6w8PH934yfonH56+/PyF514oNaeQ23FImpRpRQ+THssTU5pvwQLvZGc4nyNMBvxqZAgcbdcul8l2bMbLQDJSaZSOeDSOur3QrtZty7r51kd//5/c27ofVxbW/t4//DvtM5dSXQqSXqrIDIzLcGOReMjcQJbu0oUwyAQBBfuTn9csLZmF+wzZhFTPAF6mspy8x1gNRHPOCw+Sf6Q5a5kjKjLENPnMHGBpPKYvhSwLnfMZSPlHoSajpQUEyiT/uas1pZfZh4JUiqRmpH3Hr7UatemaRTS4tbv+xnuv/+D1Rz98F46OqsCWrdKlxsLy8qnm1ILDrVDLeLw/HB/ujbthFA1UpyfHfRIiSFyQbbe+snLp8Z/52cde/kJtuhUPxxs//vGDN958eOfabq8Xi8h1Yt9xXA5ayq6QkhxO2lOSpWk5cjv3etv33t7pHP7wH317aqly/ktPf/mXf31+Yb5eKtVbbRUmRR1AJoc3cN+4czJEOxAAA62yXsSolRapSNNEiERJyW2ODFFjJvCAgsHJ7SrljyHzNJqQgSbNgOe2FxhlQ96Lh8SASJupeQSkQTOTISi08cfkAgW4MCpfnOABQEBttATF4N6sDQdBPkKZALRGO+XDw6QXudMNf+/++nJ5yvNqtuVVaiXspTwVLowh7Nu+kxx1dWW6NdMaPjzc27mzwhOH8zCz63qyAAW0yRdUF4SmQT80Wa/snypTICNTSjMAi1nc5q5rWzZHztI0tT3XymRHWkajfjoM4nDQ6wxSIRCwXKn5labjqlQcaonEuRYJcxy/3LYAiKRM9agbjHud809dAoAwlOFwPO73pq889ef/0p87+5mn3WaLM1JCBv3hYGfd5UoxT0oZx8Ipu67l2mgxodI0VAQ0HKbbe4PtntOoVGfqtTLDsm9VUXWOjoaD9N6jwV6nMlVrtedks8rrVaa47I/DSCbAqrNNv+xbNmekRX98/5OHD1796Noffn9/d2vQPXr+hefGo/jBYBtKbr1dffblF2dqs9/7zuv751Y/90tfdy3x4Eef7P3gtSq3aGXOm6okcWg7TKbctkp79w72buxEnd4zX7xy6tzC3fdvPXrt5qO37oRb22k0sl1nxvZJqzRVsdYRKQ3CUcCUqMdYApyeWW5/mN559V8dPdyhU6unf+HXrCY0V+Zrc/UoDLt73cPdHcfzS7VqbarVaDdUnESdoHd0FCkexkm0tZ3MxNOLC7X5tozHnb3e7nZn+1HfdhxtUaMVtRek67lxmCQicRyraVcUciXScRTbtapMEs9GJOZWuKVhc3v3ld/9t7evvv35n/2FmZOPe64TxkhZSXhGmSIA6BxAFKWsGZli2kMZ6UqGmMDIqTlw7lbcMxenFi4su/Vmur+TBJqUIsCoL/s63nxld/3j3t17tT/zl/53X/v1Pzt1ajEYxWEUCClAa4ZZBabOIboB6qYZlUkHHut+k2s9jOBjgt7/U64+f3UmiMhM+eR3RUhTgDnDskJBAeXv0TrnGLB4NxZEk5Ed5Vd7/IuxGBWT23qjp81rigiQADiybJqhJiBBJdtp1Or16ZrtU9jr3nnn3Td/749vvXP9wa3dOfAed6bXyidWFi82F1bVQnV/+4Oj9Q93g8POeNhXQ+WgSLXyrTiUoJ3p+vxj586efO6z57/yuerqST042Hzl9Vtvvn9v/Xb/cDdSIfPI5qrECIGCWMUaA26lyH3f8mwcAPtk445TcRY8d67ZEgoG+/Hd33rz+u+/2Tw1u/L4My9+/c+W2mhplU+qMnpNNANjC1UKKq1yuE55CRlDLqVKkiQVQippaQcNFsn5nDx2y1G3Id9z5o+O1cqSJsYzY54Hs5o0y9hDnTc0MA8NTKBncgwG6Of/yp8z5lbWsIwEx4epZYLlLDhFICKlDd2uVMKk4FNPtLbeea3q80ura0yn7o17TnWcDHtoSR6NebM2fXLu6L1X0rTp+A4A7N67DzLyXItHIPPIaTKLAc2pNMEJ5UQQATJUmgoUkv0UAYiB1MpizLFth3PHdTnnkrSQCoEBIQcAmY629gadTrla6Wxv3762Ho+SE0+dbM/PlBslZnEcpsNQEHfarRJ3HY4+SDHoHsUDub83aNV81/e0VFFv5Pj+kz/9JbtW12RpzQb9QIUDFYfxYNB5eKdUr5SnFi3fnypXNbppEAzGQ9sBKWnQHSsgPh4Met1yMq6V7TQltbsvXL86PcU5OBdPL770WY85OgxKjZJ27aATj3sdIclrNTzPR8kG20fD9f3dt96/9erbu492DzvdXtzTEJWS8MVTa//u1dubGxsdgKONe1/9hV8+u1x7+Nbbn6TJV3/xi9OD8c133t0fdSpnz9YuLjTnF2bnl/xKKR12g43B0Y0H9VGv1I9mTs6HN7f3Xnk3OehZIq5CnCbCSogAONip63C/WrWtniCZilEUa1Lr658Ev/HfHKbj4Xgsb9zYPHx0+Zt/aubMOa9cBm5543g0HjoWcofJOESpmOU4tUrLt8rNWhxGSTRI4xEFHa9Z8mcW69Oz+zvDo4f7KghQxfv790YPt7BSJpC93X3XY7MLzer8guv5mnGVSO7bQklKuO9UlxcZJ9dRe7eufXSws/6Fn/3FC09/2Su1kjQuNCo5BNIas76cedRtEnug8q4ElE/bIK00IAMLNFnMbsxNz37m2frMoiJfRaRFgoAURYOjwcO9+MTzX/2Fv/2lv7n8fH15ijT0B5GI4yAVqRAIkKWjyMhoACGb/mpIgElRDhTEq9ELGVk/HAf3RiBsYhmEAn3nrG5u2QupCpkcXtbkJq8UyGer6aI5D4DRjBx3MIVfIuM3cRJgQNakABAp0wpCxpHkU7Ez96WANIDWkhE1auV2o+HXKyqJDm4/eOu7b/zod7698/FtTNnj5fkvn358rd6USUtTiTqH9679ye7o/m501MOEmIhJk1ZKMIqpVW0unX7iqa9+7uSzV5yl2aAb3/y9//jwrfeP7q8fDgdBGttW6ltogZaKSONIWQljIyUS0L5nM8ePOAmtZBz4Ml6o+KdPLpfrvmfZCbeCRL9548ad3Vfuvfrm8mfPWIyxY6kOADDDeXPiGrUmhiyjzrJZUIwzTZo0CSHSJJUiJeUA8Jyt02DgdqZ5J0BUWjPGiIBlH5f35qHiEWcXoYgYARa8YfZsst4ZOZQxxpIAGOT8f0EMYd5GGo6xdQUtVOSNct+jJ1kAyJl5IRX5rZPi8FIi3njs8efJXwAee67vu670PbfkAi970yelD47PBr291oxTskvrW5sUdZzSNBswUqCBkAMSqCIKza27STqZnLsh3Cb8J8NsjAFYtlXxHERwHJtAC62kAqWx4rqOzVUUPPjko+t//MPFJy8//uUv1uL+dGvorbTmTi9w1wmDsUxCELGFbjjuDpTlunaSaLSsRt3ntg+2mlmsM0Zxknq+y11Haq7DVIhApCIcjWQwjHrDarU2f+GKzWzmVZmLFoMoUokYk++l4wFzvNmVZcktgNBeCkaPdg7vPtj96HZvqzv1pc8+8XOzvF5lwk971uD6NZ4cVc4vsalZciru4hTvBRYq0T/s7I13/+SNvVfePLh2o3N02JyacpteWVSCEN795Pqj23fnfc+eaqWx2N8affDKjy49dv6A4qM//MFHgj/x+c9v3Vm/ub6zvbk5+17t3HPn/a98vdqcGezsDx5tTzcd3Dt8sLNjTT9x8/2f9La3xqDqluuRO2t7TU3MtZxy1W83FLDbu49G44QYUVlJ0nFw2NnbBWBHwA4fdu3+eOqzz9WWlj3yXL/anqcpnNFKyiQNhoNxP5ApWb5ba7Zqs1MNi0XDUdjdH+7vhb1hc2W+Nj3TrvCuTLp3rruuXav6mlO/F6ao+4edw4frjao7f/bswtrJcrPmMsZcNyIdqSCIEofcmcXFil+zfWv9we0/+lf/fHB49PyXvuk1WkEUcWaKDwFNB6GCPTEBcC47Rg0KdTFIMh9X4djy5PmLFbvG7H1KeiQ1MMdKx3I0UECN5XM/+0v/x9ba2XGcRlGiCeIwSUUkJWVUEst69xZHi4xGnhku14BtKmzsMc4lB23H7H/u03Te8y63SpjLN4/H1Ub2Q0BF7+v8OzKwnuvLc/TIcsBPBAAMWdEqMUOeGfmR0Tu6SBgUiDNriGPcLRIjyvt6CiKtJUecmmpOt2vos/HB9t13r77529/64E+uym7wuDd7/uyzT546a1mQ7G/f61zb7BztREcD1eEiTJlGgnIJIQGI3bnG8vTihZXPP3/mhScrJ6dFMrr747c+fvXtwwf3kkFfq1SicJgskVTKStAOyBkrJpgDSjLOW47LHRe4n2gplNCxcjnFpPtWKnUltS20IBZi0S41betgPLz/zocWRy61zFpr5owEM1wZmpAyk3IB6qxyFgABFakkSZMkUUoREEfUxBhq4PnMgYJZo8J1m+XMJhJmyQDKp5FrhKzvLYOcdUPSpEEzbjphm62MeWYnt6uQDyOiPCdfpOwBgI7NJM0J+DwINYoI03IwZy61YA2qnZ9bOatHG1on3G+7zaYXPcISS6Sd+OVe56EQwaAvUbDaWn2u5vQPj8JH151zX3YsFmupFViMZx6qCKuwCHbzLhTFliqADAPTUcW3Hd+3OZJlWYqhECJJ4iTVLne1ZQfDYOP6zX/7G//4YH3j5xtzT3JVnyudqz2exDZPVdjr7jzaGm/vWdXq/OnTJ04uieHo0e0HMSG6lnd+2a/UESuO67kOLy21cL4ajsNwnIQHR+N+n6SstFpLp1bTlBSwSqlCaMeR1GqsZDI+OgiFrs23eLM8HgYKdbXqWOVqe7UknjkxvreRpglONT1eSrsRpiB37sd3H/Q+umO7CLc+imemncuPlU6fYc2KDsNxOnp479bGKz+i6zfsTrcGAeuCqlVTIMfGkgU0GnfH41qzUWo4kkTn4e5WtVSvezcePopf+7GIRqsvvDB+/9rdjUcH44O59/igPK1PnepubEC8nw7pzvYmS9M9fpOBYJA6AJxSxt39OFK+I4AckrzT7fUOkyQsA2lup5G2mPY824oRgZVAC2D1tZn20jKzHXAsjWR7FWZxnSRSkKZEAktEpBj1OodplFRaDdvxvcpUMEoe3bi7/dF64+TqyrnlWt15PxwN7+7Oe1ap5Vl2nXyb2+U4Zh9cvT69ub16emtueaU+0yq1a6VqlcvaOBVJpDCVjNlrJ06XbevdD9/6o3//W0rRF//UnymX6lGcMpu0UjrnSxEQQOUmOeflIU9YHssEIwGCYgx12XPrC+eBl+TmfdW9yqilWClKBvEwQdc79cwLjeUTaaLDURwlKYJOpQRFpmNOjqvznGxuwgvMTZjBR4ZgdJRkjuUEdxd/K9qiZBhpQkNnFqRoNzDpl5m36C9YLjBSqElLPTCSkJzZz+wYZv0pERigGdKaWS0CyBtX06SQFwh07nyA6azRPIKSWmtKVWIzPjM7254qp2my8+HOtd9/9UfffvXoxsY0ll4898QLJ05bfmu8u7m5t/lwtL0e9jYgCLRmGJUdZinLYbrE3fZiq7321PkvfLm1ctFbaMl4ePdHH9754P29a1cHw54SMbCUoWaaGJKSagzeyOZdkMDKjFlt3656HteUWKC0ZKhtG0qW53vcAejpJBwdIKGOxUDLQMUUJg/C4bzTtMrl8igY6yyMw8yMMsPZQ+ELCVCTZmiaqyEhoExFKlIhpdKaWwCIoPOnZ2JO45bB8HiFt0fMoH1BBx7j6Yq0Q5HKyolOgxJI5+PFWM7y5dIznafss28AgAn9Yi6iIBzJEIla5zAEUYPmDL3aSWiujXYHIkyZ5zjT03yP2Z59tDdQTDXiysb9neBh11m0L68uPHFy+sPbh5vX3j7/+Mu+70apSEgJYhbjxnkaH5hLjRllud1soVimHM2JWwsty7ZLvossKyPUIhX9wTBNoqpfdTlqkRxtPnjt23908+PbFbuunXa4E3ELooNRSphGIxXKZq2+unamVJ+Oo6iz27GEPHHlAjgoExkNxukwOTzoOK69ONf0qnXuWJbjW66twtiuNJBpjXaqba9dI6WTNLUscGq2TErpSNq1atvhtmeREDaXg539/fc3XSWarQZvt2WoZ65cXKy1o8Fe8vDe+N5edH1j9GjTazXEwvLo5r589x6/+eDEr/+Sd+Fx7dglqDRWRvfQrbZnltozEI7Xd7rDkLjLIkHjNDzlVGoJqGEqtTNn1ykYDa8+OvnEqbX5pTvbm+wtXD1x8ezcqdHuMLDrpciOf3x18N79UITpweAoHNd0HFIaSHnKa0671d0kiWW6AzoFPZIateBChHHEQFQtmzQxi9kEiNxBy3MdJQTXYqHmP/ell8rVZipliVW5y1ATtyzu+2XPGzl2pVZzPTfo9Ta3DqPBoyToV5tNv1RpL6whuQ/ef/fqD969+9GNmVMntdt458Zb3uGjVr1c9bz5ZrW2cOXihSuzcydGw4M4Sa+9f4PL5OTjaysXTlTbVYuaAQ91mqYB6VTOLK1eUYM33/3oD37zt8qO9Zmf/4sCmAKyLC6V1AAs665atADCAl0RAiBRPpwDQCsNSjsMHSxpyUFAMnjIU+b7WkgcjWk4jMltnHziCnAvCaMwTuJUcNRa5bWxSBwhH9tkknOQ8zlkeizjf9IfOIM3VChtMnqUijAdgICYNow+5LyysUaIpo8FGUY1I46zw8OYwZf82KdhNvu6UP8RFCARQSlNgAxR6xw76iJfoUAhIalc2YiZEoY0ZZ1xSEqttaz4/sryXM23Bkf92z9658e/9yfrr30oe8GVmTNfuHLlxMwc9sI7d6/u7tx/EO09hGCPwjGRQ6zNnDZvl6dma0vLJ84uLj7/ROviJe35stvbff/19Tfe33p4e9g9SGXIXQKtQMkIeQzILEehNyJKFStxq8qV5XpNx7IsBgAOkbQ048wmJNQRiDFR0o+lVDpRTIvIJ0DrYDDS5fnP/vSvW/V6Q0gVRqPCSBqluaFxsmyuqQ3OpLosaw2htUiFEKlSGqysbqAYEZf7gdzv5xCBqOhLp3O+CLkJDhDzMgrQpIFxnu+SfIA5maZAoEBnHY9NOhXzbA3LKb6J2kcZCSiDTE1UbCzM2aksziRARkSkiBAUrznzF8XW/xb1d3j5pFeeStGKB1G8291NVJfv9XphEKhlrR2PFqYa164fXr9x82Qc+tWaHYSpUioPYliuocpRxkQLl3ONBvtnFKdlWb7r2bYNDJGBVCqN0+6gEwRpq153FMqB2L13c9jfrs2VP/fiZ0u15ae+8gKreqBrlp3sbexw152an3IqZdepeY6nkpF2XWemjC76Jd8qQeJZmw82g17nIIyH3cPG1LTveW6l5FanK62261icoYhlFKbEIttxkVlJOCJkSMxy3SoyDYoJBcBKvs+np1NNO9eur793F0aj1nSz9sTnpx+/Um6WR+u73QcPe6Nxn6x2pXL5G798cP/HD//jd4c/+omcqp5fWODVxrDTXTx3Gv/zb179R390dNiZay3Mukedu+v1lLUtp1Kru7FsOpZfcaDigs/bbKp/1D/YHbzwM19dvfnBax9sDDcfPX3ixMlnLiNretHO0QdXE+hI215qNmdWlgd7u/GoP0qDKE4YYxXfD0AJJqDkBoCkpZYygtC23ZFIEtB2whhoh3PmsVDSSMcxyEZjSZQa2vZrU/MWYww1cxCIRBIDIHf8pHOIttOan3a88v3rH+/fv5s0m43mrNtqzyyv+jYn9dbGB3eP7hysPHHxl17+0oc/+cHB7v7e9v4e3F9qbV7kn19+7jnOp1u1psWie5+8u3PntkiipQsrc3NzgJXRYMR8psQYLGvx5NnLofzBm+/+//7F7ywun1l56suDcch8B9BiQMTM8ctYWEOF6tzeZtNYQCMSKdfj040WAyZ77zutBzYv2W6IOlBCDcdi3B2W62cb9UVQlErQKi+dZBMQTwS5e4HcNuZ1LpAzByYiMEm4IvNXtDjMETpMkGGGE/F405hC4YmIprMu5JkD817K8VMuYKSiZ3N2+HJbBpAnLIqsA+WMPqPs8Ge4ML+/7D0amJlTohUwAOCaSAmpRdJo1JZOLbqadj/efO/b777ze688urE+BaUnl0+//MKzHoeD9fWth+u3h1s7arCJ8YAioWUrxaXy1PLU2uzcmZkzF2Y/f668PA1l3t8f71994/CT+73797rdwyDsczu1mSYhQbAQZMp5yACBpCbLZnPMqltWlQF6gAhSa82YjUiOHWutkQmpI8RQRsy2xloIR8YjkSapW/GrJ87/8t/8G88+tmg1as1USKVEHMcaFGlAxoxdBUDKih3yMCrX8uRiHKWUSJNsPjC4gMiQMVCF/TWMS5GxzWJB09KMMQ6gEXiG9bNS4qzjJ+O5Ac/K6jJyJM9JADHG8udnYj44xjUimamSxshmHP/xzZgDgnyOBQOArM4SAUQac4ZQOx/IxYO738PKWnA4DAOZUBqM9frOboxdPdanTrfadREPgrNX2m/86OEH1+5+obtVm7riu6Uo7Suz+TPAAkZnkG1WzGSt2bgb4FnzW8dxfd+2LRsYEGNpItN4fLizn2pqN1uu5Y73dg/W1zfubFx88dLLT55df+/+E1/6POeujOPubiceJ/W5mUqjbFuWRtbr9hCDVtuvTZd0GqUHg+07m8lwxByn3G6fOH+eA1ek0yge7fe1krWpaXSYSuM4VlIq0AA0Vl7qOJyAgv5II7m+65VsrSxSCYIC37bKjlOvt1dWR0fDcHdvtH7n0dUb0UCWPbbxvdcrS42Zk6d5p5MkNpuxp1a/magj+Qf9/kd3D9//cP65K7altu+vN1enln7uyYPv33ywte0yZ6G9MOjsu1KD0mTzYawiQWutqZOzy0GlJuLxmx+9e/37P/nM83NCrfQ2RvHh+tT0sjN9dtjbSR1EZAvNxsL8qd7BfT4Kmpa16jeilLYiGca6fnZt+qlzUdnqBD0kiI5GNSE9zx4dHon9fhorEnKkVE/KSCQaoNKYP/n5L8xefrI0OzMOQksmoIQmRVqCTIZhIIUc7+3Fnf78yTMzK4sXrjx5cH9j5+7tzTvbRGyqVZ86sXD+6UulSGx8uHF49d6pc8tf+cpP3/n46uaD29u78m73aPTqD59yobm26JJeObnw/Be+GoT9rQe3924+oJGoz7TtEucWAiunAbmlqXOnznUe7n9ybes7/+73fm3p6XLNk0K7DhcaMtEEqax8kxkWGzFHOjqba8iJkNmNZoUYhA/e0MHvTD17Bm1fDANSlCYoUyeIvNrpk+XqnFDKNP6lXO5jzDczfaqNEdcZk4KMGREQYA56jN4nEwUWxS4IkyuEnClizMzzmITrxbdkZzk3yAWZC3mnNdLalGDlEVAWhBvqK8N6xhwVgiMinSWvs3ub5Iozm0f5j82LKU2FSpOZZvPE2TkaDdZ/9PGb/+HHn7z2UbwfnvHmnnv88SfPzgXh0dW79+7urj+K9jdl2HOTMFEty1tzZ05OLZ5Yeao+t9I+uVpbXUhbztbuxsH1W0c3HwTbj6KjfpiMgGvXUg7IUGLCrIBUqmzQ6BLnSDXLbtp22bKZ5YINKSMltXAAkBSwFHXKVCSCaKzjUhxFSarEOExBxiitU5effPEbP3v5M09Pr7RkElmO59erDZGmUulEREWeBHJXnP8tM7VamSALWTZlLElEEsdKKSJgnGUjZRXJIgQ1y3mMVDJZ36IEIxsbxTgWGyJX9GTeunhOOfuW83xADJiZ/UWEDDO2R2PWZwKJNObeW2HOYhWRca5OUoAAwrBWSBqRNNpM8LnAWquP9jgeOCWbx6ntO6PRoL8b7jJ3aaZ89nJd7d4Ua0uz56eXKvLO5sbDD15/5qee8H3fCQKpFJBVMJxoxvhmmCLPQyDLyuOAyLEdz3UZAJEiocI4GXb60biHCMsn1hzLi4fx0Z2rnf7e0mcurjzzRK3l2e4UaR0Fg+7WgUh1a2bGqzla66AfxHFoWdwrM840xbEWWghtuw5vt7x6pdJuOaUKIGqlojBsLU1xTHXQxaTELU+CZVnIGEOZJr1RLJVdqdiukyZRPA5BuLZjAdpSURoljKPFXa9aciqV5tJ8eGYlurfT+Xjj5rdf5UI0rpxaePaJNlOdN98f795rXqDlr/68E/Dw1sbgoxv+fGv++Sfdcm24vVWbaccnpq0Se/SjT0rMmZtf9W19FItRGEYO034pPopuDe5UyxUN4dAKD/vj3/rdXd+xaq7laarWS8tTIpl+rNN7GHSGMgk7O7f39/fKAExzdMvc4TI6rNSn22fXSicXKienVqcbrfmFSrliOy4ForvbHfZ7YTfUWkdxEo/GUkoPncWzq/Mn16qNaqTkePcg7h1iOIrHw2gUc5KNlXm3Va80Wp2t/Zuvvz3YWps5faq8sjTt8/2fXL/zo/cq/c7c6tzM4+dt2xY86u9uqyS8+NlLT/3Us+17tdbHN299/OCgH9576/Yly+kfdLYfPZxdmZ47sbDwxEW4v7Xz8CBB3V5q2yWHx66WzNJYbafnlpfWH25ff//j629993N/6heTWI+Hh3a5yV1HSEpJo+bZ4CBEpiEnQTUBaCTO/BKWKq7jU3T3vd4r/4/WyyvKjjWkMDqQsiwjndplNr1Wn7/k1edkgiqrqSRFpAlUTrtkGCZThecaOwYAjLNjkCyPtBlA3o0aCIqBIDjJABuGBSiPVxgRZfLKSWY4N8RQAPQiz0c6w/F5V8RsRHmW2MtHExNpbRSykEPFvH6BcmuEhjbSBIZtzvUkSmd5E6aklEInaTQ3PbNwdi45GK3/+zd+8ns/vP/RHZTq2dWVK6sX55fn+1vr1+5/fHOw+1CMdzEYWbKsrMuV1qX2idn2Sm32TH3+BCs3FMLm7bu7b+9sbz0Mj7Z5GolwSBSio7QmTjwV6VhhwDBWzCHXR6dkWxXfrVquzUExjCwWWjIBJAsV0ynIRFFCGKcRqaAfj0fDEJSu1KsnL688/uSTT734udULl+ySo5QilSYaLW5xt+RVRV1prUdSiKy/G+Sj3nnevDWTazFGSk1aWGoiIUQ2H1grxRlDROSIwIq8fLbYOmvWUwj4M5udAZYc0Rd8D7CMMFfEWfY4kfI8sEbgE5IRshlgOQjJ2upAEWRmuiKdqy1NZAqTsvU8NWT2QCalRoYEQuvEqkw//rPDD//ueOM9pz7HAV2fr640auhApdRaKLXj8VZHnT39LLjJM09Of/Dtgw/efuWZz3+jUpoKPEdHMsNBOYTIVKFZKTNjnDFgnHEEIq20xbjlOsziQCR0rIeD7t720e5+yfPm1k7Vyq5O8c7Nm4eBOPeZz7SXL9huNRwkSlEaDKLOYNjtV2fmHbeSBFEQhTqIKnXbTlM9juMh9NM0ipN+fxCTrjfqDc+2owhti7slq+KWajU5CsdHe2F3xK2wVKtXmzO87CSJEsFYhqGOpVI9p1yxLRZFyXA4tj3Hsh3bsVzLRs4YR2SI3LI88KeXcao9uzJXGm/uP7zPu1vJeola1bI37v7ht3o/wvrJlTkHxrVK7/5O+v6GXD1TnmnawKulqfFHG3qq1HpmLRWivTbDRnK4u1ceJlXuxjLavb91MDgMQEqUSCiBlQDKEuu2d3l16aDbuf7DP9B+ZblW29/rxL1+B0CBjABKEqOe4JCedptzM/O33/v46hs/Ui3bb9dbF86e+twLS2cfL7dqc6dPTWGchtpmUPJ9BNBK2dwhUowhpWlwdDQ82OHxWI+GozAOB3rK4WykphY9VZ9XF+1773702h+/rqz3Fs8unrt8+syFc7jV2X5t8ODtR4/We1KkcTiMB8Odh4fhaPvJLz79xHOPn76weGrhvXd/fENFI5vk7MLK7UePbv3gzszK0onHTsyfXLV4f7A/dN2g2iLgtlspURiBV507Pb+wMXftzuabb/zg8Ze+0JhevP3RBw68u/rUZ12nJSRISlkW3JIizGcqaQ1KYa3k15uasy72Pxhe+/de/bB66csaFVOHQLYIAkhU1fPjelyemxMCB4PxOBgLESMAz8nTLL9qOBrKBfhZKwXD1edTR7Jzlx3FPBbP273mADxrh5WnhzOhIIBJmBmkRnlNUZFfNDoiIm1y0ZkaKLMxRNk0ciAkTdlsKzS9hNGI/3Jy1uhTKf8845QQgBQQapNWB62EkJzw7Km16flyvLV747deu/r7P9y+fn/KqV44vXr+wsW2V9l++MHtG9evj3c33GhXJ8jZrNu6UJt/Yvl0uzylvarVmhvjaHi0O9zbPtrfDYJ+IhVqaRFxRQiaWZWhCFJiqaSxSBPkZcdruNWq59t+CW1MNIakYpChxSJGKVM6TmQqUkvHQkZax0PpWLI2vfD0Y2cuXH7q9MVz86dOVRpltEGkSipBiAqZV7ItxtFxnVKlrLSUMhnpkRQiz+Yjak0TWVUhZQfNTBMNKWSSxCJNNClEO3tiWXYIMhCPWbSYPSaDGMB4W8z0XWRUt4y0JmZBnoXIPouKZDQRYFZrRRmhD0CEHIwmKCMjMXc6efiYl8NgzgmBUSRkUQGZJIcphyYSSnGOEc0LRfHRdzl8vtL0LR9m15rd9aFvwf39wf3t0fzJVffUnLj+KK46q0uNG29cvffx66df+oVpq8b2R4qcNBu8pjUCMSTOkSPPllEj4wxAggDkls2IS6WjcXKw9TDeXk9GPcHc9vRstdokift31gfd3oWXXmhOTzPGo26QjLtBEA92uyBUrT1tcy8a9RVqRwO6EoNRMgyCQagtVwBaFXf1yprt1Dp7+wePNntbW/W5mfrUrF8pu9Wq69k4veQ5tTAYIAMSoRaInKHrO3VL2gElQTyOuM1d13FdJwmTOB4rDrZr254Drp1GYTQay3Fka1a1eProXrpxr//aRzvf+fHU9BorN6RfPbp7t6IVq1yliiNkyhu8kgRp21396c9CypyW9cRf/dre/t7JqVKqLC2G0VavfPUGT2XgNcev3woe7gsgDmSRlaJ7tlpvNVqPz86VRL+5MH9tf/+T/QctaMw02s9/46cHO0eb+8P2zOne4P7R9uYwCKehdKk1dbC/30KdRHB4OCSnc3T1zvCVH909sVA/c2L2qecWL14utWZQYxqEnuPoVCgbNFEiZDIOlRZLK/Ou5SKD7lhEQtLGI9x98HD0qLlyoVJvrF64IFL79d9/deMn7xw8tvzkl1948Ze/FF554aMf/CTUYatZ2ttY7x7sj8Ows/3owx8MVw4v1M4stp8/dw7UrbdvbW5uzT125vJnn1rY2zs67PQP9iyiExdPl1veaL8vy5xsYL4FZBNKv9k6s3ri3oOjO7durz+899m1x5Syf/Ddb52//tYTv/hXrdoikp2mAhkD5IiotCRArUW9wmvVgHf3g5tvho9+PIp2Z55+gjfWaLAOIiDbtWoV2NxVQsWDXr09FydqFCRpkmiSFmOZSifDggig1DFdR3FOiz9YFPYYzEV5tkDn4B6AQCudN+ZBjYwfS+AZFnpi9cnASEMo5P1ekPKh4VAIgSjzOHrS+DOPIoxBAjDTQAxTm6cDNeRfoYsZOpnOUCepcC126sRyfbo0vLtz/Tf++Pp33jp8cDDl1R8/dfbSEycEqeu3Prrz4JMHwcEOlx0lXdtb9aYvTa+dW1jmpVogNDp2Z9gbbu8ehYfjg0NLxw5jJQYqlRSOXbQU6USlQmGQjpQQwNh0rdysN0tWCWw7ZhCATlLU3BHIIlIaIYkThiKScRRGWkNjqnnh+QuXn3/67FNPzi0t2SUfEGMh4jRRiSbIoiEiTZq4RaAZY57nAlW1Uop0MB5ppbRSyBgiMxW6BigzAjUJyrSmJEmy8ZB5zR0gAlNaTXg+LLx2ZsFRo0ZkxHQ2v52y4i+NhIpxy4RmuXHONMBogEXWviGj0aHQdeYZhkwJmqF5Q+sUVHw+a54YM9oDyNVxef6KGJDWWqMGshXXrjX/U4PN79h43/KxujA/e/ZpXr+Z9DYffnS4PNNcu3IaNu7e/OG7WxsHdbvU2zz83/7Bb7y813v+s5+L+9pvL1u2I0kjKQBSMhZJnKQiGo3HoyBOIlJUdipzZ8/blqcB42hw493Xtu99Mrj/aPnk+ZlTl1qzi1qozRs39w+GJy+fr01PS6XiwXDc66XBIE2U5dn1qSlUGtRYBYktJSoWDPYH3UOpZHN2VpFONFWa047VEBE1Wguzc/OpCEYH3cO7twVgpdaYPnnSazad6kxZTCeDXjweyUHIXNcqld2y55RckFWdRFrrUq1kWSwaheEgjMOxlIkQioVhNByPj/qjw/5ou0fgOkE04ovqMV8Gw81x3F5ozJ87KZaryThIhEONUmWmWV2o15fneaO69e7dzTdutq6szl465cwsDfq9/sO9rVd+6MWBW8H21KIziq2Zivzpry9H4ozj1TlWuLXq0rh38HDj7o27NzY++ggsVoa6a7f86nTqlLdGe6LRPPEXv7rssN0bHz383hvjXu+1wwNbBDVwnmg0U9tPLCaB9h4FnQcPjt69f/t775z6+Zef+dlvNpcXFVjj/pAUcSa4y0FJ7jC/VK36JYtQkqrZ0d71je0PbnRvfDge9Cr111affWL+sccuPLO2uDT18MfvUGcv2NrdKc+snH7ixV/96Q/eeKNam3nsc5cP9jY2b11/eOv6w53hje+/07jaml+cnlpsnHxiYbR3eOu9N5Yvn5s9tTK7OjPu9VyLjfYHi+fODEs7g6MeJwU8tkpuMgDbKs026isV/3Z/cP3DWy98iS3Onwo79MP7H5an31h49stOrYUMNWmtFcuSaFpXXHu6pWnvo4//5A833rulkoOzz622L7xsQ00hD8IYrLpdarDqUX/9MADFq+1Opz8cj5WMGSltZWPByVj3Txn3fL7f5MCbdppU5L4KEWoOx3J/QGToFiQli4wBYl6ygMXQmywla+pCtanRzbKDmKf1cl0HUGGjdIEi8wIyBKLcd6Fp/5DpSXLNJ03YrTyPoSmVwnf4mbWVcpsffXj943/zJw//5L3Bg85cberU0ulTj13sqe6NG2/c3Xq0Hff7paQrZQVr55unnmiuLs4vSi32+4PQBTE6OuoMwrArlSp5vOk1ZD+UUWDFGsjSEmLgwzTtUkSpLlu16elGq9ZMXTskFVAqVKosrl1LpaQYkIilCtIgGcvUtryLT125/LmXLj59afnkiVLN0wxkIgKZKiGFlkpQPuY9L5kD0MrK2GnLslzXrdaqUgsl0jAKSFPWkzLvhpGzGYyyOYtEiMAZKqWSVCRpKoQgz2Oc65xAYjpb5Tw4M1LPbPJ7MR+SNEMrS8rm9B5pQkDiGTuEWS/MSY+mjBfMTDZnRkOT7aicWzfPEcCwfnmH7vwFpEADsE8NgwMAxhgyYppxDgiOZVkldvIr+7vfr3TXvda837gkrOemVqubm9slYK3pdleM+9+5d+P19fFIuIL1x/zowxt3d47u3n7Lb59eXn5+5dQJy3KkSqOg3x/sdg939h7cPVy/n/YHSRSSVTp9+fmfO3uWMQzjZPve+t3X/2j48F65fqI0s3b64hMly966faPTj1cvnS2VXN2PxsN+MOwlMkGltEz9cp2JnhwMwqDngA2ajUMhZYJ+2dUQK+14jqXlqNfbuHonImft7ImZ5blyaQrn/N5BxyJNthOPhtxm4NcZc5hbksPReDyAIfObqV+r2a5HrssYiSSJ48TzHKtc8m3HUVUAACWlSKp+pTq7MKt1EMrBIKnU/HLD5w5Lh1GUJHbLt8t87iCUroXMq7RLfsV3GKQDFewMd+49DPbD6Ld/cCv47d7Dg/DRLgUxBz69PP/Yi2ch2um8+sne5mGpOXvqzIo3fcLqdh/eXf+4vy1Rbgz6Y63Ljalnnr9CWFl+/ikadDe3Dv3FmtrpH169MTs/S6XK1C988ca3fzLtTz1Xm7/24dVkHJYIfBdnmisXVso3tja3jw4H4+79f/59Oogvf+Mbc5ce88uN0XAg0tipNEq+naSpVnI4DDkBQ+QIK0vTzpXLDHD43tWbb93aWd9+4WuHJz/73NKlM7Mrzf4H7w139vevv7f77lvNVl2Pxh+/9c7e2szC46fbZ09R21Mf3zvcHyeHg5BZslWamV0e7t69+dqHG5t7l1/+7MUXPuM7/rjXT8Po4OHWybk113KH/SEpXfLLQrtC+U55c6leftTpHT66KyJVbVWrjZmd66/d+ck/4syb/8xX7IoHkhQBKoEcXd+bajIn3Xvwne8+eP2dPurnf/7sqQvn3cYpDYQOUmjZ1Rm/fF6Jh8GQeO0p5NUwSOMkZiQJFJAi0LlABBGpaPKCCECSAGEi0jE1tQU/Y1oGYWF8C1DGCDLTYrKuxgqDKaYnmowL1BqQFcqhjInWBu0RQcEwZXYu7x9PWZYwZ5fyzLAhIgp4OhEnZeVgGQrVWkvp2Li6Muv6sPmTu9f/9bfvv/1JtDWamZq5ePHizPxSJ+xevfHOrYP7B3IobJYoe8mqX5k5c3b2ZNtrjJPRwdHRgZJhWSXDYZKkHocZ36865eHRQAQxyZRJHSMPddrBNNBSc6fdri1Wpr1qRVMylslASG1rRM0QbUuQGicpjMdjJvu11sxzn//K53/hZ1bWTpcaPrNRSzkKI621kKlKhSbI0rQZs5NNeSdkQimLSGUz1W3XJvAruial1ERhNM7T3gwKNkVpjUUCBQGANJFIRJokSkqtNGMWMg5K5T4/6wyROQyGpIk0ITeDeMjQNkTISWdKTGPBde5ozNPDwncBQ54ngZCZH2VfV7TxztoQUU4p5qnnLGxAROCADDhjLCvWMsQUQ2LEAIARQ+Zw5azouSuH6z+YwUF0eFfhqywdhkJUGiWy1ObVzsObW4+24lhaaaJK3N3uy0Hv4Lu9d5z6x7X22y8882S7XU9RBOHesLPZ73T4qI8HY3cUtzg/iDisnq5NV7UkTNLrb/1o7869Rlmf+szXHnv2mUbL27l2/3C/v3b5YgmYOOoODw/TOFIWOI6jVFqyOYVRv9eLe12vVhqkWqWiUm9OTS8Ph+OgP6JUd/v79cUpGUflqt12qmJ3/0G3N44lrzjT87PTCwteyWVapnGMoiPRRQSr5NlpJQlHcjQgmzuWrSw3QZ7IZHg0YKRKjZpTrtquY3FU0nIqJeQcgCwL68BmSYdhipq4zfx6uWIhadQCnak62rZVdywux/vdnbdu7b3+Se/uo3h/u/vwgPWHGtIKOFUb/daMVW2vnV+1VPXdf/f6o9HDFbAvKD2/P+re/cn+aBDFh11KhlofAJu6+OxXf+blxoXV+sk1dSSGH7x58gufff8P/3Dc3/7kN//RJ2E0tln7yle/8Of/wlu/8097MzNnV/7rV771PzYgroYi0eurjz/19EsvX75+u3/txv3D3sZvf/f1na3zf/pPLT7xJOdeKlI3tJBUNA6Ektyr244FSnGUXsVeurhaWZyqLi7KcXp3e/utN+4c7faXL4+Wnz9XPbnaORwG/R4Owls3PhGMB8P0Zmdvvz+ozbfmTy49/4WF3v7Ac+29mAkKx1r3+E73oJtA+/BRPz4bTi0uiJHoD6JB0AtHablsa87TKNbpWABCuToqV2rtUuURejSUMmQ2W12e37V8u2epg+t6+FnLswhizi3bQsd1JGMQRbs33l6/dx+rR59/8dT8Y+XKks/cHoGDo12LoYp3g25fBFE/cFae+QyBnYg0y+SSzoSSqEFrAsZQE6DSRqifk/y5b8hpeAQCTcqQMEDZkOr80OenPwP2LKsFKqRyBn3nNakZa4+FsdZUJHMzJlcbzgmAsBjOlaUlAFAblwU57Z8hRJa3gtSUxxZo0sNgmKEsqWzbzqmTs24JHrz60fu/9ycH79we7nQXZuYuPnmhtTCzexB8fOvtre1H+yJOykxJNuO0H194/MrCqtL2ftTtdO93g8GIpeMQXAV1u1x1K7akbn8Ux0l2nWOuOjocUxICOCVvvjbTqNTIsvtSJTKKrZRZaNmEtiWTNE7EOAg743h6dvHFr/3csz/18yuPn3Jci5RSWidhLESSCCFTqZTWWisC0DrvzpG1rUZFyBgwq+jBhgC2bZdLFS2VUkIpmcQxAYCCzHbrbIJKlucxI2IQUEiRpEkqUk06y3cios4CgQwEZMCcJm0RihxDngtGBCKexQkakBsNVmbCiwFbkO0hVhSOGbEp5NghhxbIEZDZjCMSsbzzFQIDRI55JQDmo8JMmir/eNCkQINGzQgS2/Zac1/sPPjJsHOz0jwiyxJYtdxSre5Ho3HUiY86wKtlOdCCqUaNkaBBKPQw6faindsD6+hobrZEVsIhqnlqhquqFVM5npv2pbLTR9F0u6kUONK68+M39j68ypV1+tmvnXv8uan2dG/j/u7G1urZJUaqu70V9YIwGjMHS7UylwoVkAr6h73ROFhbW01GcqdzVJ9unHjm6bgf7fRGaRL3Dwft09Nr55btcmVwMErCaHiQbt98wP3K6vLi7PJiuVLmnmMDWsFwOBogk26p5JQqrl+JhmUZDpMoIiW5XwVknDuuZw+2DkadfnWqVW00bM+2HBshe1qQRsK3bcdmwGA8CqQU6Hvc8y3bVYmIBmG1XbNGcuedq3d+64/CTzb1ZkcHiQdquuTXlh+befZc6/QSn2pqKqubW/rW9b33P4JgtFKde/nEWSeRwUFnJ9zp+6KjRAS6cWrxxae/+Piv/nSt6bUWG8FR/OY/+1/DR5uf+z/97ZlLFz959R0/jcZwxAT47353PNf+2jf/92/9L//P88+svPh//2dv/+O/3d3Z6MSHB9957TPP86knHuO+Ld++Peysb7z28V3p+K619PTz1VZbxKPx0WE6DGKGth9rZCoNB52uVk5tetFvzZx93mvWqiduPpDdBzIc9e7fmj3VbD/xOGvMvfPH3xeptpbmBacp25axJr8kLbsXsYrbrNWc+ly9Pb221w/W79yvXuAzCyvN9vTMYoOEci22euGCvL0xTIJUxDiK0eLcAosjc5wUERwHOLg64STTOHQ9ViuxqUqtUrVPzFq+HAw2exxTqJU1s2NkScwe9h6Jhzc3ku5Tp9uLc8oul93mvJaHemcjuXUjkaDTSAxGR0djuza/cOGpVJMipUkxlDjJq2FhhzEfBzNhUICAGan4RJVh5JoZJszZ+Yweyj6h6EaM2SQnMpm6Y383yDw366aKPvM8hTfIfk9FhiFzPznZkHuXLHWQeyEwmWWDEbVhjbKXSCEc11pZm7Mtuvudm+/8ux9uXbsrj+Lp+tyFx56ttht7+9sf3Fx/eLAV0ZBzXkqsKadycX715NKsjOODwe72sD9OOiOdag6O5VUtr2m5OlLd8SCOhXaBMxZovQ9RlwnuQq1UnmvN1nmJCMdRlJACSzKmHdtSJFMpxkkyHPe8sveVX/7ql3/ll1bPX7DKJZ2mIo6zUe1CpCJNlSYllcqH0mS2M0/ggAZNGhkx1JZSmpAwazfMmePalXJVa9ISSA/SJM4zI7kb13kYl+tpSJMWUsZxIkWqhCDbQcS8WVBmVE2YmPkAE7khMkY5O68xm8wF2SgdJE2AGhgHk4DJqxlzW57vHnMb+RVhPkuMOELWVgELLVNOYTGN0mgFco2SAgLQWIjKiLTOAIZGQMf1/dpZe/653sM/rO2XmR3warvaas2sRt2NQ9f1Hddi5ARBICUre65tUw0xlNpjdsq5JZiMhe1IJgWNAw0J+qrR5vVWOU1IPxqOesG4N7774e3v/eFvycH22TPnVy99YfH0KdHf3rp/t73S8Dx2sL0eDSOSxF1uW9xhVhqHkiQD5lQq83NTe5u7u7vj6uziuc88BXq8192VnHekPv3spRPnloHSh5uDasWK+geHDzprl08vXrrgV8vc4QiYTV/UDISSKk6Bkc9d5jkO1UQahYNupMH1E69WtR3eaDZqnh/2RykpEUZZ9kgzyRgjZAisv9ePpLRdnqQKiDf8CiM82h0kceA1Gq3p1sbbN977+/+C3Vy3JZNhWCq55VK9vnTGmX++NN6Kf/R+/7CXaj4YBazfEyoque7ptYvTjcaDWx9sh0ebcrQzlCHgYy987rE/+zMzl67Y1XKprHoPt/71/+1/FRtb4X6/+09/45mf/9zTLz9LG9t6/yjqDQgG4urrpecev/Ts8hu/+V88/eu/9tW/8hde/7f/YXD/2pbq7r797eX358+/dBmXlsudozqosu1bwmPIwLJtr8qmbeKD4PDw4MYH4d6BBaOUIBjIUrVZXVxpLM5U6+VLn7lQqj853Njdeu/926++O3XYb58+eeby+bvvfMQrlaV2uzQ/601X7VZT26Wwp7r3Hu2t39q6G7pzd8pra08/d9EtP2YJ6aIq2fxg6+DD9z+ZXbrYWDxB/Uc6SlClqKXl2MFoVJmZh1QxLRw1qNjSR6/klkU8Go9CYk4Jebr/6PTnMDkKtt76sfbLkldj1QuCA5GOo3G/3Wo352K3MefPPQtuCfv3xhs3peD2dEkF6eGjzjAez517bPrEmcPDELTkyMwQXlN0Y2TiGUPPDPdqgu8curEMT2eyESLCYspslmLVmAtJjWaPito1OG7vIYObiESUNS5TpI6z/FiUqeq8n4pJNhg+SeWtvnIBIWE2q8OImIoEQ+aLJqSQUopxXFme9ap08/fefOe3/uTo9o4cBVO11oXTjzVqM3s72zfvvP1osD9KwrINdsynrcbZpbW19swoiLe6W3vdzoAiCRGzy5VSs67tCrdElPaGQRLF2tPA7I4a70EYoHLRa9Wb89WW47lJkiZJrEFaTNo2MU6BDMahOErHpPTFZ5/56b/wZy4896RbK+tEpVGURHGcJlIoKYTQmqTWQKQy443a5FvA8HEAuUTRUkoDR8xZNEJA13fLqixTIaSQQmZDiDI9vRHkA0DRjhaUUqlIE5FmWYb8qU+2wySDk72V5b6ZkDOzkYBAATKCrBUoy8q6iYhhLv/P2/dk6i4EAKZBM2JmsGOe0s3ugUABkZaQe5eiyybkjsGEerm2ACnTk+UJZG36BGkhsDRTW/nK7Xvv+v0tr16ruCWr5JfKalxKbcdxPM6ZbQ9isi0QyAB91/7/U/XfwZKdV34geM5nrk3/Mp935atQBgUPEKAFySa7m2y2Y7faqaWRFCOtdmdGillNKGJHit3ZmNjZXWm0sTErTYxsa7qnvZsmm2w6ECRAeFNAefPqeZs+87rvO2f/uDcfKPyBiqjMqsqX997vnPNzRysSEVlmB4RWKok7NTVs1M3cnD9/cqE6P6MRo73+zJaQXm0Qpdv3Pmiv3zuxWl84d2ruzApIu7N2r1T1y42gvb0Xd3tSCseRWiKwHQ96IHRrtgU26h4cHdxZ295ut06efeS5pxHE7Q+uY1j2q+FC7Vxjfro/Ttub+1Y4Bw+2Kw157ieebM4uSddBApOlGaAmZUmQIddzhA9ZksS9rpBllEJ7vuhhNI7ifoSCvcAh6YRTtepcI0tSM84yRiGENXacRMisXCcFtFKwsSnZ2YWFUhDcfO9Wb5zOLky3VhePNo6+9d/+S/He3RnChdnlPu/12oPRcNzv3THX7ithIOkS2CHIGFhC0gBuSHfQPby2vb7VPnzIvciVuLT8yV/8ydNPfrJ+qmVRJv3x299842v/6vci228EpWFDd/r32wdnz/7yz3M2Tg8HdGsjvfsaxcPdd/5yfmF5GZ27v/1b8nFz9Rc+v1u5YA97G7/16r39vYN3Pnj0xSt++cnHzk6Xzp2tLLYUsBkMhRDKdb1aVcdgcX+4v27NwKm6ynH3b9/beuemrJRrqwsXnvlEdWq+Wp/yJO68/MrRGx8OD9pTp5bPPP/xw60HJhpH1vhOZW7pvAq90TBKo+Haa73D62/QKJl97Onypz7unTzrVbQXuIASev5oY+tu98OlM3MokiwaG2tYC0AQvrImk6lRSXc0GljlT82cdCulwf5Bvzvsg9Nh597djf5rf7zy3NPNS8HunTe7R5vm4A7GQWmudf7SMvRlML0Ynvk4V2ft+Hq0dj3p9ZRXziIz2jloHyaiNHP26c+AdGLTQyBCi0ATqL4A5GVuzGQsNjDB8RSeP1q5VeDHJNZFahd/1GxPMmCoiGODiS9SHPf7k3MYjpUeliZ7H3mSH1NgDIKP3z+ZDCYzfXHoFZPHsQftozfk6QNMhBa4aGssgWUreWFh1vXEjT+/9sbvvbR9a90ZQ9MLLp1Znp2p7e+tvXfnnZ3+ZkxRzXE8o8tubWF+ZXG2OUjStYPdrfbh2CSRtiUnmCnVEcNyYsajuDeO+iaFgITQR5npZMlYi7IXnggrzbCGLMbDUZxGqQIQViMaQ1majuy4O4wrc9M/89d/4WNf/ELQallr0sFonJg0TlJjsjSzVBhamZjzhJs8doYKIe3k7J+gZszKGlts0CUSUgqUjOx5ji2HxhprzGg0spTBREY54WVAoLBAAqWxWRLHSRQZk1kiKSXkWlD6sRTAYyYYoWCEjvsBmICLWCSGQkE7T+6FY8ahkH4BUS7z58myrUmwX55IndM3RAC2WGyWv7cwnnDhAWY8lop+1Hcc3y8IFhkyY3HHq52cO/PJ3ff+2JF7s9IrzS56TddrqzRKp6ZU74jKnhwDkABBSAgli4pEL42POvv1qfCpx0pnV6aq883m6mK4sJyBHN5Z11GmlOeElXG/t/Ph2wrF1MLi9Kkr5fpU/3BvPBrNzU8Pjvq9vSMtQStHgQVK0pQz4c4trfpyvPNwrbvXGfVGs6cWl8+f8j3ZOTgAR7aWppUXJLFN4tHh2sb+tRtyvjw7c6q6eKK23FKCGXmi1mJAyrJMClTazeKxcHQ2TOLuMENRrrlBs6m9OOq321vbbrnseoFJ0qmZKccNtEsuQBpH8TiJong8HFSq1fpsM47MaDicWWh4Wqyt3eeSu7o0Uyl5STJ86X/4t/Er76yiKvluYuacxtW6/GGajDtZZNJ0h1MB1gHdlZRx6lFSA2I76OwkO1n7CCJ3bubKT3/2kRc/763OBLWSS/HWzRuv/LtvrP/oXTPqLs6XsuSgN+ixtjsba8HJs1NLtfLSIl45xfa0uX84enBzr9te/snPX//GGw/feX36nHvh7/1MZbm6c/rsq//+e6WTc7XPPC8OO1NLjep0y0iRjKJhd6yqlRKGqHRYD2fPnp6q1smk3pTj16aGD9eu/9FfrN/ZZiH6M3fqJd8t12ZPXpkK/Y13383qfnlp2dYWeowP33xv5wffWzg5TxTNL59kR82emos/93kVlrv37yWZbd+5f3TUqZxaWFiZr8w1/Vbda06N98ft9b3pxVJvmAy6R7XFOT+sOmUHQi+K9uTh0SgiXZ99/Es/ISUMto+2Hux0U367M2hYXnr127o+WD13ee70iwe3X9Gd2EmtG2ioHI0bU7PPPCPnFrN4M7n/fvvBIbs+GpVsDjqbo86Rmn7ys/XFK/0IbEa5LF8wsshlOZCfj/maj8lCSvixg5258OFMMNrcaYMTiKc4nbno7SbLYOAYI5ogukVzXrAFnCczY+4+Lc6ViT5lQh0D5IhOsRaQcqkIFTE0x5xvHo5xPGAgAuXrgxksWiGQyAIDCnni5HwQyBtf//47f/TDw7U1aRM/KJ1cXjpxbm5jfefdD99eH+7GSNWKJ1Lfdcpz8+eWWrOR7W4eba33NtM4AxS+X2uUGwoxS8WAs24yanOcuaAlD+LoKEm0EAtupVWZmnI0ZzROh7EZZZxooS3oNBtnCMNREsvkwic+9ZW/+5tnrl5ENFlK0SAeJ3GUGptlGTETk2VgIM5DO4+rHgIzooT8WGQGBCIQTACorLXMJJVEKYo1ygIBwPP9EpHJUkPZeGwKd7mQxa7GAphhAAbiJI6TNDEmI7JSqeM0zvyYwR+/rIUljMVH4+SEgs19AMfC/GK59CSb7nhYKCo4wXF5mAS7gUCiQqpEbAt/YE5X5OUei4IPeVbzMXaYk0j5V8PH2bPEwONoyDJoPvIbw96tza2XlCL0grBZayw3R+sHOKc022QAUipKqG+4l7GnYTRM3ezg4vnW5758+cJzJ52yT6UFFcyAljobjzeujx8MaQTZ/sH+B9eywajamCk3LsyduORqfLi15ZZLjls6XL9rEnYrnlYOQ6aAUmGqs1MCs8P9He15QmK9WS3PtrxQx2kklajVa6VyaIyMs2z/7sb69Ru1gJdOrEwtngqqNRRsDBNYJgaFebSf8hwphTHa9gaGEqECUCY67AI4zYVWuVaJo+Boba+/36kvOTTsrx0d1pp11w+JMU3jOIurjXprvhV1hvsb+yjV9PKci/b+7QfK1dMzzXQ0AlLj9aPR628usZyWDrJM2ndM8mE/G0ScxGBjCYBsGRKKRpJ2uDdHbkU4roGHdu8QhicfvfjUb/6N2WevOn4ADkTt4e5bb3/vT767fWurNBjMe3pFyX4feuPEqUPA/STeHvWMp8ivV6rNi3wW8L16560PW082Lz9zev/V913dnxmNArNoT5268relP9WsLC1V7SklWbmBJDYJuYDCUblM0FE4c2KOZutRt++VgmBmcWpuOt59mIxfH+8fbV67CePO2CtVK62mTGiQHG3dG/f2lj/95UeefrYZzN/I9MHaB6//b38wvdo4delCuHru1KMXl0+sbFy/tnfnIVXDzNp7H95l5Vw5vdpaqNhhekDbFHXjcdaYqnxw47v1pRlAiy5wNhCdveGDtXEfGxfPzp89F4/GH7z+5t0bN9BVZuj2baLvbO29mSy3vMojj7UWP9u5Bd4gLhs6TI8aF56TM2eA+xhtY+/QiiwoV5LdztaDTm89cWYvn3z6i6jcaJQamxFaASqHVAtWN1/Qkbupij0DkxaKqGj+ESftFOfZwkUQS+F+B/gxX/AxQFDofHJXbjE0FNRxbing4mEu5nzmQqmTZwkDCi6kR3SsFsnP/mNvV2EB42J/C+bxAQzHFKNAJDJkWWu9enIhDPSdb7zxwR++3L67xuPUk3plbu7KY8tbD+6+/t69u9G2lHauVrMZCBHW6gtzS/Npqu5vt9cGvdhKn2xJe45b80hkmTVkxsn4iAZjB1MBFKejJAuVWK5VykFJIiSjOGU7oCwG8CpBnBllTTRKekxueeZLv/6lz/7GVyuNijXJcJxG42QcJUl+8udJFpbJMgHnOn2YuN4wvzD5K/lckFOrIMCyyn90spaJhBRCSIEClRCGfc+z5YqxxlqbRDEzs7UAE7VUrp8BBAZjKE6yJMmMJZV/06KweH3UWU+Awhx9y23AIAQe78stWntgIIEy1wAQk4Ccg843BCNZkgLZAkiAPP4zb/BFHgtaDDhclHo61ngV+ONxXhZPkpmL+xcwj5HNo4RQCBAEFkgMuz05++iJJ/8P97pbGzf6Ke62rKhMT9nGCLJYBNqSt3F3IBjBOnttI0XsHHSe/8zcL/zmTy58/GlyNRFbDiFjavf21h58+O6eNgJdqNQ5Gx+xlm59evrEpXJrZtQdJXES1JztjXtpmpVbzaDsO47juDqjsXvYS9qdxO6X/JJQ3szS6ngw9L0QAJV2ZFmbLADQxth+t/NgY708NXv+qUcbC3MQaMGYWWtMKlCgFIKBrGGQWrElQ2Sl5493d6UvnHqp7rsmTsb9WBA6bjh/biVp1IdRFE6X03HUPjwA3o9GWTQ08xcWS5WyVEoAjuOMyXa3d6M4q9ZqYSPstgdhqRY2w967a4sjUdMlZTiLbMaHMccZmAQsA1krJAIg+5zZFGYhFI4vAbbS0UOIZlurT/y931h6+gWnWskGvfHazu3vv3zzBz/q7XZLrFdn6qvlarlWFm582S9vxkf93f1a3LdqJjapYGoPEr/k1y5fsWGTrV2aroQXL1BkR6TT7XZm9YlHnxCem0VJPB4pSU4WEwEJJ6h4UmkEScSu9lFqt1Gq1oPe3l60+8AJ1PJnPqZKweGtjW43vvfejbW9w2EWrXp2TjcPOu3q2np9ZqE+N3PisRNzc794/82FB2++svvu7c7Gg+XH91c/9uLUifna0tTUiQedvUEajbob+50DxLFanKnDIKU4O9xLGJkTOzTTiBpdz/dkHEU7D+7dX9vHysILX/6lSmNq5+bO22/+UGI2X6vEWlRXVsrY3N88uvPhrVNT1eVzF1uP/sr697/Zbe9XLnxGLTwhxL7t3h7cem/n/nY428D+6GDjoN9N6ucunf/Z/8qbO59E0sZDASwRmSxMUHKA474cJpk7gHkaP06yzZGRZfFHPmr9ilm/gNyxYAmAWSBCERdRzPk0eVhhot0pTgyY5DvkvPJE+pEXFAJgPg6PKdyfYpKBIT5in/G4guRHkkBRiE0QichY40h1emUhnHJu/uWrb/3Zt/bv3LfDSKM/V5174qkrG3t3X33r3RtRlxV8drG5u8OjRFcbsxdWzvgSb+/fXj/ciMwYjXB0UCo3hdA2yTKbxuPxIOkbSMYEQOyRLLvhbDXwfS+xPIzGHCWxQqx5MsPYWCm4PxwmY5g6f+Hn/sHfeerFZ5Qr4uFwEI2Hw3icZmlGYCcjFRkmYEssJqlFH41lxXwzGQgKsjOXU6n8y8zz+RmQmSyAEIRSSBJ+4BtTTpOUDCVpUuA5k/ZeSkGWDQoiSpM4M6k1hikf6gRgkSrKx6kQx/9hcRMVABAxFMuVOY+RIGQhBHOh8p+g9USEQsi83xdcBOkAQl4Gch6DjoXAVNxyVGBFuTYJfwwCxOOXPiKDioHV5jZBAcKiGQ1vtGYvLD7zX9/55v/v3vU1y7FTuVBaXE4GXTmITl31WjNq7c5gty12jjKU8TNfPvELf/9vNC89YoSb9na5fxsBaBzvr0fv3+7dH/CsF5ZOVK10+oeJRVVtTNfnlrWjxj2L0ppxZOPe1HS51Ji2JnW0Uy5XjXC6OwdHN657M9MuktDKdSozC2fGNmOSwOhWQtMdpCPT2dod9Y9WziyvnDhba7VAaGNSQxaBhBKOdCwBWyJLUiEbS2yZ2Fok7aTDYasUypkpSkzc7w3bfdeT/lS5tFCFjkxGmV+pKi9IkzTa7VQrTliuJsMEIM6srTSrGqjXH7uedlzNGZUrZa8Udvf7o2vXm4dtnSZ9sDGYESQJpCMAA9aCzW88ZPLBuOBakH7KAxgNIW005y794pfPPf0J9P3h2ub62+/c/+Z3N258iFYsTdVac1OPnrwwf/bqO9dfr5adxcTtf/sv12+uy5kbsxfO1xaXoiyLh3E0jIIgKC/NmzgG35293ExHaToa9MeRZVAEArSQYJLuYDwOwwCFMjb2XCes1N1SAGSzNM2ikWmnysZi1P7wh+/qUnji0csLVx8ztSbe2RzeNjLa6Ozv3o/b0l0rqaA5VNX332ldfcJfLnuzpdWnnzCat67JcX9/NOiZuO27M0aXWpfOy1Y3OdgPFqaDaj01GSLU5kvduB7KpL21GfeGq+fm3HqQmjjaHR6s3b7/+rWj1H38mU9e+dinknR8/9b7NmtfvnKuUff7aebWmlV3Cq2zfqenW3dCP1RB3Z3392++5bOoBoFJj8Z3P9i7/aDcbDSnZzfevj/ujGJWFz73qeqZq0kScDLWWlqbKzgVsOFJ/1z0WAWOAwW0e0wvFhmHhUULChNVkQKNk/O6EFryREeEBYRfuHUmRz8cM3dQKFE+wu8n1WjSvRVPL1HR9f34HFJ8vuNAYwAUPAmEwHw7GuewgSGpcHlpLgy8e9+5ee1PX9m/s5H1TSi8uUbr8hMXtrbXX33rvXtRV7nquaur/fe6NFB+pXn+zInZkn/71od72w/JDLVNA1lqlqueVgnbkYkG4ziLRwOKh2RAytmKX1EeSuG7YmSTfpxlBlxfJgaUsYGmYRa3h6PU2sufev4X/8//xeqjp20aD/u9o+5wNIpjQ9YyW0YAtDkCkkvvbW6IJUsIgFQEXRb6ykmxKPhuy8Csjr8jYmYyzAJB5gItRFRK+r5fLlestWQpM8aylQJRCLIMudwG2VpKoiSO4gIFkjL/57mAbfA/vVjAFvB4J1ihE5tY74hZojheLZR7hMVH0wNbRok8yVku+oFiID0uM4WbePIv5mzTcQ5gXhKLiZWKvDlGAGSwfLzvAkgAIEiQBNyN0sapz5x90V373r89uLHmhDszS43W6srW7fvWxq0LVafp+w9N4tiZldbnf/WvNa98loBtZz/e/kBlD6QO2puDtXVIVPXUhYXxzsHDu0dlmhpbidrzy7NObUq6TpZak6EdDjWR4/mpiZO4nxjH8XWlFCqlIx0m49gtxzDKYtNuzaT1hVOkIM1SM1ImI8Z02O75zcrc4kK51lBKZjZDYgEAQIKUhcxYIGIpEEFYY5M4FlJKR5WbzVHnMB31ykFZVn3X0+k4NvGws92RSkityKTjAVRqLcczQamulWQyg84gybJSxa+UQ6HUTFCJ4pHNzNHOkdJa+sHOdud7f/n6UrQfoo6ZU4AIbAScAVmwBBABAYAHQkEtBN0HJ8YkYpidO3nuF7/06Jd/Ajvx4VvvfPi97996/b3uZrdU8qbnF9zy1PzzH2889bGxH25vv//k5z6/HAbsD7/9Z986vHnv8Nq7M6srqhSO4iEBpSZL01QCEEgLQirFyk3seDAcZS6WJCrtVpvNqmXpSGIeD6LBYS8acHXGluoVsraztTO893Bv4+64v6vK0we3t7JRd/aRy9XpR6RX8cPwrJb6nn97y1vvdrYh2h2omfXBQmdUO6UYrWqFc49e0vWgu3ekSnUZNMnIcTTIDAW+E8y25h1tlJBapEKAF/gVpyrrg+7R/FS9sjRtzDAd9O69/YNbb77f3U1PPPLcJ7/669Vmc3t97dY7r8zNeqeb02QSx1AirO9SoKc6Q7v97pEjHsydZMHDcDFIe7dGa8N0kO7e2c4ipwblzVsPx1mvVhN3d5MP7r7z/GNdKQKhUbuOVk5ibL6JEQm5MPpjsdcCjk2+kyIw0dXkp7EoIHdEnIS/FKlcBdxf6ETyHo6O/1j+2OZvYwLLXOyAOoYR8o61KEl5jj9O5CTH3d1kSMgLTN7h5StfUODEoHZ8UCAwsyUp5JlTq9Waf/el93/0x986uH5vtN+f8mpL5fLVR88dHGWvv3lrc7fvl/WlE43d64dJH73KzJkzVxbnl9bvvL62caefHSUglVMuladd4TDiMB71omhg+3GWjNii0qdn6oHntYdjqSEmHKZJKhhcaZD9oJLaZBDF+3HEfvnLv/bTP/G3/malUU6jfrfTOxr0B8PUGGtBChZSoM3FMMCAllHysbQF0DJJZqBi1ik4eQFY2GiLqUpBDpow5RWXkIRAIgAGgQoFKqWCILDWmMxaHlljOV8oAUgF04xkKU2TOImzNCWyUsh8de0kWWNCvU6uhpjoAoobBECAAEAikjKvWVSMDoJlMdFMgqMLBojyvn9iFrGIgnNyeMIRMRMUPX8BRtKP+QtxMsxCsV5iwiUU72YAKaWSWoPjycBDLSxS6/xnHb9y7+3/af32dTPcXz45U5tb7exvJjaZOzcrvVG5kp5/+pGZM2cNVynqpEf76dGecsuDnWF7s4vO3MWVxUalkkzLWty9vb9PQvtaKUdXQh/IRmnSPui42KtVAwMkNST9mKOREkaKWrlWLc02BqOBtSAdvbl+d9jvg+OW5qal9pI0jka9gJ258wulRsPxAmCRZmmaZkrmulthLYFFAJQoATGNjcksggQQ2nWYyZQqaecoi9tSVZXrCx2miFGU7u8eaCa35HlVZWyiHUf6GhGyiNFxHFcLpdOUQ0eBkq4qx6OxReA0jdqd9LBvLN4x0EITQyyBEuAURAb5agdOwAmgdKZ5upfEM62V3c2bscrqpy488sUXTz7/VOfh+IO/+ouDrZubd9cho9bSyid++jMXnn5ctxqdyAZTte/91e9GLhtlh65z9pe+Enveve9+f/f9G9MnTsw8+XRYKo/GA88PlFBRv5/ECQOjECilVyqDdoRUxjCClcpVWiipWCKCR4kcHHQPkr14HAnXz4Te2+0c3dsejvamTuorV87uHx1sXH9/6gy7lbq8cPbU/HR57+LUrc2ZNz/Y7XVdJ6006lprraRF4bhV7Tpu4E0tWDd0KyXXEHheSVNKKLSqagEGLBMY1l49mMGEb/eXT66WalWIh6P99sMP3/7w2+8Ok+yRFz71+V/+W3NXzoyHyZ3X38iGB0urM6FUwyFLjFxOObUMouo3Rt2DjXfWdBw0l7E81ULez7Zu7T+kwa713XDv4Y7jc2t1qv3h0eHG/sa//d8P7vlf+D/+E+VOl8v+cBDSIMpsOpHSI+ZsMMuiPYdC7FkAAuL4+IVjtCX3HeVCjXwRCCLmywoKedAkoPO4p2eYdPt5xzYRehyHNzAUDoHc4QQARBYQhcBCAFJ0fsWokcPDLFDm1UagyGErAYygUDJzlmWOlqunl+uN0torN3/we99av3F7fDBquo3p5tKp1dnOQfvN9/fWd3u1Ep+dXji41zbDLAhrp1bmzi8vbt2/e/3+g0PqJ8KS45RLTcfRqdW9qN9JR92sO7ZDg+igPLNQrzvuvU4/0uQGIY2zTEpQEki6SlobCZDtMfjN1i//w7/xmZ/9KZKy3+332gdHw2gYGWstk5XIxMIW4XuIkJ9XpghsIFGIZYqY04lgShynEk8S9xDUpFgWBzURsDGIiEKAJADWjrbWBkFgMktMxGSNzW21cIzPAaRpFsdRmqXWWq05r/bImCOFxx5xBChCJY55/hylx3zHV+HNA0Rmi0KKHB/MEX0iQCQUWDh/EQBycgAKDnkiM8hFUDQR+eS0D+QhIBaLUIi8DBYrreF4YR6CEFJIpV3PcQPp+cJxQEk0NskScKi8cvV89R9vvPM729de6fe2G7N1BMERK0xXTgZyeKiiQ97dlM0T2Xg3Ge0Qh/vbh3v3tlW1tbC8MtOaV+5iIsLp5dHt/jgdD5UiX1qprQJGhkG359YwSSJry6CGQjkSs95eN0nTWqNaa9QsGUThuu5Ua3777kO/ujPnaOGGhikI/VK5GjTKiMhSmdRkxgBasmCsRASQmGN/QjClzMxKCQC0WZZFkfadsFqJJbR3tsLSUDqBVWgTStLIC0JpIahWvCAQUoOQlkBI4YRBvVSyqekcdvqjTmaM9jylJSpZazSGvQ4no1KAT/3sC/uGkp0Du7mVmUQhlxZacqrkhOH87NzC9LK5m7x3+72tUT82Y9MKWydOLL14OThZ+fD977zxte8d3bvvInqV6srZsxeeeS6BaE9lc9BGzP74z/9oc32jsXz69o0PHlg4d+nq6a98OfbxwVvvr739jiqF3vJilIzjPlQajepUPR0PrWWlhFDS9bzAssmyLEsHvU5mSDvKdZxSKXB8v9IMMTWpMMTI1gbNqaWnrpYDt/fwQTaMsyFd/MQLvb2jnfu3w9qUX58ptabmzqxeuHK5e/Hy4cGB5Wzq9EqjNZcOTZTFriN8z/UW52mcpWQlkuNrqdHRmpjIGEqzNIqi8ZAyUEdOlnT8kq9rrm3vjm5eX3/ljR++d9NY9Zkv/9yzX/1rc5fOjYfja9/41s23XymFQeC7wD4E1tHE1miyaRajYl95o+3BQ75jZKO1qkuh191IttfHkqXgsRQiqMru/eSD29EoLm+34cZ//G7Xzv783/8vfL9WqTTi9NCMLLPBYtl80TlC0VgWSA5gseHux4B7OA7Qzx/uYlCfaPZ/jJmDSd4PEFEh1MvV2scqQihUIVjEvuTlg2GyG6xglO0kfG7CNAIjCpxoDpGBJsZ/VCgNm/wjWJMGSp44sVyfK6+/8cHX/+3vbrz/sL3fWWxOzzeXTqycOups3f/w+vresFZKH50t7x/1xoMUw/L8zLnLpy/1jw5u3HpvO9pK0Eqhm+F0VWjXYDfp96P+Ydbt8cAIKvnumalaPdAP9wZjpqDhGGMskJSaBXhKS43ZOO6No7A+9av/4D/75K/+1Gg8PNg/3D846o6jxOStPguSwBaYBDIj57pNJJrsWcnnNQREznek5zUSuSioE008MDOxgkkXPFHYMBELKYCJLQAKIuN42pINwiA3mpGNqcDjEZilRGOALMVxGieJMYYZQCDYApD5qDGYbGbOEXdEpJxoOu4hio9SiPXzF0Rh3D0+nyHnjJgYJPwn5pSJlzuvTIS5ADbnJAqhT77DqJAq5zUjv0XzQAwhlNTKdR3f136gtCOELhzGjmS2GTFh6tRPrT77T2orb6/96H/p3n85cI+m60sdYzy/FA3TB6/dtFlj+umGwzqWot9Jt291KqeuLD5yZao1o1FkA7ZUobCB9XQ4aIukVI4TJAtSeo4L1nb3DqGqa8tLSTySCrUTZjFu3tuSUpacwFRqSRRXPX+6Na9E3Q2CODaI43K51FicU1qDUNZSFifGWIlAjBZQoAQstGJCILLQEiwRUZaNsyzNdEkpK6XjOF6YVRtH7U463AMSwpFTsy2vVXc8Tzq6QBQLUE5KJRnFsDdiZA04OOpFo7YX6tpUoIQEJs9x62cXVs5Nxx9/bLBjOBoIFqokE+aMTJpGUffo2p9//86bt7vdbnW2Uj1VU2m68OnZcM7cefMHN977QJlo6cxMNazUphpho1mvG0eK7/373xpF7fZo1PajpROPNadbiPLWWzeG4+zxTz198vnPODp8+Mrr9+0Pz37m47Wp5t7uke2ntdnpMCwbMrkkApGVIIGcjkbRw824P5RSGKFKtVJ9adrxw8wMo2jAPWGkUq4f1hrLzz1jzp/vbO5IheXqVGNx0XHk+tvXRsOxhKVK3Vk8u7BydoEtpgbJ9YWjYmuREpNa0iVXu0Yzjq3QQivt+o5AndnIJsPhOL7xwW7oWccfc0KeTMKqd9TZ2/rhD67/6K0Hm4dB88xX/uavPveTnwzq9Wh/eOMb33r7tddOXjobBDLrtNMoNiZBMsCZzRIGotRoQK3dw81tqw6txBPnnJ3NbH+P6yXFDjuSD9aSW/fM9rZ1Sp4mv5+Yv/yrv6pOz774s7/iBx4SohRsJeWeIoSJ+CdnACbanI8eqeM+vuj0ioUdkxyGPC06Lw1icixMkIEioZ+Z2OaqxUJslM8FRefyY3w0Q5EixpNHPj83hJzgOwJz7xgAoQBEyQWAJAxbIQVYIms8LVeWVxrz9f2b29/791+7/dat8UE0WyudW55emG71DrYf3r6xddSeasiVVqN9sBsPE6XV1MLiI499Eoju3n1nd3TYF6nCoOZVqrLqk+7Fw8M03ckGPR5agSXPX52ecpzSem88IHLrLoAwlkEIJnaF9jVElvbTtDzt/eLf/83nfulLvWi4sb5/2GuP08wYRNQCLLPN1ZI0+VpAAlChxcolWkQscKK6nWzUZAYoYvhwQneyQFCT75cBMbfvIiJZCyBA5kUbGUC7jksU2DD3haVJQmSJaYIAYpZm43GUxHkBQCEEIuUhEpObomDwJwWBiVngZN1aoQkGnIj6uRhg8jWkyAyE+YJ4wJzBKCIssOjgIa/wohgWiI95j6J0Ft4HQhS5QhTy7ZL5DyAUSqU933UC7XvKcVApFAgsRDFKFMNJzKkBix6Ep89eXvofjvY/7L//Jzv33zq0w4POoe+i3e/Hpdv+wglZPWNSNW7L2bMvzD7ysaAeAAnigXQbqY6wPBeEbLI7o07Hr+8Ox8NpuRhOzfj+7OHaBkKvYUa1sNLe33OmtF+thPFoa3N3utlAJaRSALLaKCuvRMopVcLSTEO7rpBCSJUlxjIBg5LIZBBAKS1QWzKWrBBSKYEElmxmMoHsln1fldhmkJEFA4xhZQrAsWVC5rBa8UMPhZRSWmZCTrNUoGImjYKMHQx7aUrVRoUSe7i9v3NvvdfvLJ5sLZw6EYalcZIO+/uOzYylrCxlSdEoGvWz8cFhsrM+vHV39+1r7e2+tY7y2V31t8WOUekji+dPvvgszl8JLmzKQb+KVgqMBkNHy04KUzJbvnL2rdfexcGo2Tj79LPPr1w+Z0ySZrxxb690/+HZk2dOPPuJIPAfvn1t991rC49dKQtx/4PbR+vb82eXqtNTnuuCUvntaSFmpyTqzYrys06PR/HhZnewsdVYnEEdlKuhP9WSXpCniUlgMddonlrALNVVj5WaPntpTLLXGY+SpNseOKXICWSl0dQCCZSUguPIGLaGsiwzAEIgQRZHFqVF8rMMdh+sd/c2HLfJ/dQt+b7WLIZM3a33r3//jz/cuXXH8YMXfuIrn/qlryxcvZya5PDmvfvf+rMPbt5bvnD55MXz6zdvDS2QiYgNJIm1mQZAIVhIkxGjdpR3+KAnMymOTGcjc4wWRg/HCQAd7Sfr+2I4dtxAJiLRQkvqv/Stv2i2Zh954Ser5WC4G4NESwwAIgd882YMsTjJBR4bjQqBIBaofU7EFuM+T56f/E8DWJjA+kIwEAHnM8AkF5Inp/4xlFMUhJyHwBztzf8GiR/9KZx0/ZATf0UlOFavEHNKGTNIkGRsxQ9PnFguNb32nfUf/Me/vPvOzfFBb75Wf/TM3MnV2s7GwebNh0f77YWmbi3X9zf2x4M4A9evLVy48sml+fD97798/+DmAXeR3YouN1Q5QJUm41402jLdIx4KIcu+d6perzn+UZIeZbEfoI+YZoyMFsjxpOc4aWa2xz2n1fj5v/OrH//lrwzGw4d3N/f7/VQQglBSCBQSFaIFzlfjwgSxxqIbzkmPYoPZR1afAtUAYAYh8u3IuVQIkVHBseTqeN1bIdEhIpiE9AGiUFr5vmeNtZasJZsR5XbAQgsKWZLFcZJlhoEFCiEEmOKKT0wcxyMkHK+T4wkxnAszmQUTIYqJn4NRMBXMBjEhCAskoYCLsChREzEpMzOQncSd5gMRUHH6IxMQ5zGl+TsRAUTe9vqOFzpeILUWUhWyMc6DKfJNwdYyZUSIkILNFVeO47aWn6vMPTvuPvzef/xXvndrqpTt3Rv46Pe3tuRYhcpbvXBOT53zymXqHcT9fmRJI0quhc65xWbpYfNbo3092NKHuw/mzpx2y8GJq49uXX/loL3t+TfOX3xsdrp1NI60dptLJ7rt3Z29Tn2q5JbLTjVkEjHFfuiHU2WtFQtpDVhObWqIGQXnV1JJVAItWbYZIyqJwJRlKSBoLbRWIAQQWyGsIDAZCiWVqrWaIBEZkQAEkgVjMkuWmFAgSJaoKLW7+/vDdnvh3MlSGAwx00HACofjUbsTNDNjQbKhLLXReJh02vHufra5Zre7dqhGD/aqMJKDQX0wOLvUjLH80Kb6zMlOd//u/d333vj+6c+9cOnjTw2HVzAaYdIdtQcH6w/Dkn+0vf9hZ7R08fKnn/o4Sj0cRZWZWnl+QcjsPOrEvX0QpfXM1Pyw/viT1gs7u2sbd255tdbcdG1nfesQI6BVv1wS2pXaAWBG1NUgdGeFseVBnHQGYTcd9Pa7B0PtJ9VQkiUXUbiOTZMsSxHA9bWqeBI5sUJXwvlLF7yt7XQYBZUaazEyWdZve145ScfaVYKMlpQvgkiMJQbH9zyGdNjrbO9lBg+291MrSxytrpRLHg/G42h37YNX33/j66+0O92LzzzxqV//1XNffB6Fu3dna+fdt/Z/+L3Y9M8/ffXk01cP13uDQTKMRtYYjkkTukoBUTHPK2RglCBd0R33xMNMjJUr/KO+Gad64yg97EF/JMoOplZq5rIDIrWjwdH3/uovnNr80oWL9cx2ut1cls1MQkzkFpMMlpyJRUBgwkkAAGKBA022wBATEec7eAk/mulzkT8VB8ExGlFoPDFfqj2RriAiwrGRQIhcr53/PuKkXmBelyagARcSf+YiQ86QQRAC2aRpxfNXlxfLjVLv3t7rv/VXd195t7vVb3nh5dX5Rx9ZvHN9+/6tTn//6MKMXpybfu/B9nAYJeB6Uwtz55+48Mwj7XfX3799cy3ZJ0fVdFDzy4H0ozTbT4br0WEXRixlzS0tVRp1peJx1jcZaKGlA0YCASFoXwa+p1EfHHbJq3zl137zU1/9uVGSPnhwdNAfWeUqBI1WCNSAEgShRUAkRkAiKmiPwuSbf+EEwMAqnwokYvGKhNyBdayOQkYhhCr0upxfGiicEcyAbC0IKYo4ZiAhhHYdzxAZJkNk2dKYqQDuwBqTpUkap2lCllABohBSENkJp18gNIU4pyjkRfJmLvgBBiZEORHzTHiCXCwkUDIQgsjD/I+XBDAT2KLbOK5n+U+Rfy/HU05+mNs85gEYpFCO6wYlPyhp39fSRYkw2VJJhYOsAE6YCYCFKKZIFMLarN9LxiJyPLfUnP3Z/9M/qrh988rvf/utO5UsSW5vDrxk+dIj1ROXRGk56+23Nz5c39gbpU4afdCabjQXzi0vLFSyr/7g8J37a9sP33n9/OPPerWp5Uurlz729I1v/Pn1N6/HOr2wemG63BhkKBzplSuO47qeFlKS0pTZ+mwtKJellsYYIEhsJqRjrQFkRKmVAEAAYS1bYywZIRRzlhmSCpWjpFAInJnMpJkx1lgLlqXjSMdxhBLFanFiyzYhS5askVprJQVKIcR4MOwc9sNaILUwqRVChtVwZnXRr5TqjZLnesxIEoVyUlU+HHa3P9ilgyMn4xKw16pWlTauO3/ubF36L71zLXXKJ55+4eJMbf3/+d+//Z2XmmdXPv+rvxFUKlRXAmp6JqqdWopGA1OulpJFr1YN67XGzHTOS5ExhGJhdZFJPNw9JOkm0kGJ5YtXrR909zcHG0flOefkM4/Eg/G43+lsPhiPBgLcSjWszC6Ey6vSCSi2XhhWm80sTqvxcjyOzWAr2dvLhl0xNevNzAvPQyQzitkQaZkKQcIAYlCpKD8YHnb3tw96azth2anXQt91rSErMHA1gAnDSrVS7sfp5vaBlP5ss8akunsdp6KrM1WvMVXR7NmD8TtvvfbynbW3btx/0A6bi7/6D//uY7/6YmVhPupFN95888H3fmA27pUwaZxcbayudLrx5tp6v9txQ60pAI9tYpJk5AoSOf0qtRM6laCFPqJ9YA53eu1xx5ibOyYzJgVmK0s+lH2hpfKVmQncnsF+1DncXHvtW99g8Gqzi67nxDELJKAcmxdC5gBqbhMmUBKLSAeRlwSaSDNzzw2BnbTiE4Fo4bkEgMmOllyNTSyFBASBeXKvYPkRUyAK7SYVSP6k05/AT8fgBjIS58mlQMyI1jKzZSSyOT6S2azi+stzi+Vm2Hv48JU/+M5bL725tbbpiODEYuvZJ65ev3P33v2do4P45KL/scsnX3r12iiKM9Jhaa5x8slP/NynHUy//erX3+jeQTed0bVZVfYhNCT24t79+OAA+1bSTOislOo1XbZZ0o9NLFk6nmSRWR1bq31ZqoUA1OlER8Z96rmf+MKv/0IG6f27m+1un7RSUgrLWqJGKXOdDguQyCJ3sQlgEBIYIUfMABhZ5FiPZFlIXo9Lo5gIrDD/ZoQEoYpBCThH26kQXRV74XPZLwEKgUKgJHRcxxrrp2FqTTZOjUnJWiALwGmSxHGUpqk1hj232Bo2QfFoguBP6kD+uSaXboIgIgKBFSCPsUGcZIUUsBZzjt3nodKS86WPOdxT2IZzmDKXlgNzrorNT3RLBhhASMfzvFLZL1Uc35NSIwrCQgaaL5fO54+8O8kTB/MYQmSR26gFSt9zmQQR2ESUW3Vfq3c3N+qhDAyN9vY/WN/ya43a2WV0Fhy7lfVuRombpcYL6qMoc9P+wvITQav+gpw+/P3/9d2XvvXIM8+f+9hnnLJ+9Ms/bWjw5je/+c4P3oTB8MpjzzWnT8aIWmkQgpDIRATC8RzHd5XwUkPEFhUjQJZlmLMZCNaSVIryDow51wNYRsd1hYMS0RKbOMtMaoxlZKWUdJXQWkiZO+YNWbTWTpY4KcdxtEIpwOaFnckklKTZaCRDjVKEob+0uoArc7n+SgplbJRJM4jMQLjB1Quz9acdg6oTh2HL3ruZffDBVGvhlZf+ai2xi4/Onn/6HIvmr/zD/+p3/sX/6xv/8bekxE/+wi+5rSmWpCoBpU5YCtx6A4lGw0hqx1iUWjsKmdM0McPRsFIqnz1RMiSEwAxcE4R6cTmwMBzvQjUIpqdLc7K7d7B7d33rvXfZ9hZXmk52uTxdrdWXhnEvHqVO4LpN1wHl2Jo9hMP1dHdtY9Q9mvXdUrCitGAXzGg0Gg6zOHWrYaXkCKMdpUvTLZBaWx4NerGxTj0USmYZj43RjkIlBRIMOtTe290cwZkTQdmr1Bpe2dOBk0k7fnDn1W98551vv72x2Q9K/se/9NNf/Ae/2bhwMsvM7oPNe9996f71G7Y7LJfLTnOea612J+1vX+92ukHZcbQGEpbSjLw4i1KTlH3fCQOvHGi3xKouHb8FZm3naHMrOsjSuG0RuDalXU2hC47rsADpCLDWlW7JelE6PHx448M3qpee/0J9rhENotFwSAiCgQXlOCoKCwxKymMKDiZT5yQ0mItjqTiHJ1wgw/GZzjBheRkAUAoxEY7Kid9U5D7k4pjPNYMAQojjdIcJC8zFVIHIFmhieZqk46DNzzRLSFxy3fnZ6Wqr3NnsvP0nP3jzO28+vLujGZcXay9+6mPrD7e27u0c9MZTq87Tn7n6yjev9YeZQVGeWqgvPfb8L31+Zrrxl//y37567+2UsxnhzYFbMZgJs2/s3eRoHwdG43xQO1NuVL2KJeinpk8GpHVRg0FmQE/4ZY1Cd4ed3UF3/pGnf+2//FvKwdt320fdASuhQWhEdIQAQsZcwwOIOFleX4w5eeLOR1wJHHf4OWGKxXOf4y6Y8545lM6W1PElKUxbhRM2p1ILK12uosqjN6UUjqu90E0pMDa1xhRTHzCTTeI4jseZyXJ3IOZG30ntP9YMHY91x6IdLpy/P4YR5lEQQhTzDbAlQlH8zo8JNwuHV65w5YkDIOehCZiIkdmQtUTELKVy/cCvVIKg5Pi+EBIFFopmYlPoEJiOGxUGRhZ0DFLl9xBTESlh2QKxSG12tDnePnioguDso61ud+xXnZeu3wtPNJaubAfOEpMXNq+c8KP+EIbdrJsYmWRVit3mcvPJ6ifH+z/62tdf/+PfWZhdCJZOOWXvya/8CrmlN//ij9969dagEz33oqwvnvLqcwmLcRozUJKMOE2M5cSOMsMg0PMDoQQZi0oCWCahtEIGa9mmBAKklEJIpZSUEhCZyKYpgxVSukqilEKg0poBiZksW2a2VNjmJUiplBAsJsI6oiwzSuGw0w9LVccLQDlCO2HgSCUdjZTZOMoo4346nJ6pt2Zbxhowma/ckue1d6L1e2tZK/zute/eHXX9Sxce++nP6ah/58GN8vLq3/5//L+/9W/+2Wt//qeDXu/jX/xS/eSqKodSKoNC+coSlzwfUebarcSwkI5AyFJav/dganmhVq+aDJSWloQDtSwery7PV+YajucBWbfG7uzJWVFCjlnBSFbrUaKrxnGd7lG7fXhYrZfDZsnxdDLdCqXvHnX27tznMFwu1ZxSTfsOQDaMxr3xCMaRmS6HgaNLoReGvtPSSbQ3HBNIAhl4JcjseBA70s/GsD9sS2GrXtld8iuNoFYNRJqN+4PhzRs/+Nbrt19+fX/3sN6a++Jf++rHfuWLM5dPWXC7Owcb195c/9HrB/cflGbqYroazkyzF/QAks1tHI2CiqcdR5BlLaRwGa1vUzOKMpE1Wk7Q8B3PHadxe7Q+OBrtZ7VSi52xmfNiYDPOsiwh4aFg1I7DBKwcLyyHjMjhZnf/zhs/3N4ZvvC5n1g8sQIBRXFMZJgIpQS2DLmj/PjgKbKBeJLGAMXOxaJHnzRzAvM0UBQ5tpzDPLmyI9/qm48YOEF9sAB/f4xDPM5uyUtJriOEIoOY8mF/shWAbDEMMAjBgIyh687NzDRnpqLDzjtf++Gb33n13rWbkIilmcoLV08lR8Odm/vjjfjMfO3S505ee/m9g4N2BFm1uTR9+fGLP/v8uWemb/zx2197+7tH8WCmVJlHVcukI9JRMlxLOzu2Swi10FupNBt+LSPopeN2lmQE2tGS0AKxtl7goOMMbLY9HGFt/tf/y787tVzb2+90Ol3USksAyxLyxYcIhfEJ82IG8NFWxknqcnHEE09AMpjQJ8BEJI7b7kIJVLxf5SDcRDDLzFwk9xfYSQHCYOGuA4mgteO41rdulnkmM8aY/EJlmYmTOIrjNEuIKd8XLlAwFpQ1TM79Sb+Qn9b5SlCaVCcqZhXO7xA6XguRBz8wsZDHYp/cwDzxc8OkBBAjExFbmzvYDAODUJ4XBOVKGJZ14CkhQQgiJiLKnQ0foYYTs2POnEy+zAm8yQCgUCKAtYhCAAoi9fD2zXOLQfOFK0drr6ej7Nzjsz/3sfbBg/WtG3+54mTCX6mff9brH/lHQ213fAHkuP32VjOslObmzv30V1Xov/Xdb/3V7/6rT3z512qnzkjfe/bLP7d09vSrf/j7ezdfezMdXX3++YVHHvcrC065MjZo+5iitCaLxzGg9AJtjaVCCwEoUWoUAGRtZkFKKTVKzJM+2JIhQ2TJUu4pRKGUQiGUwlzOTVDY9ZVkxuPHkYnIshBCCoUIruuD0FGajfr9+nSLUpNkJkXMvb1ENh4nnKZO6FUrtVG71+sPBdkxR6RlMhqv7dwfHj7MzLj6+MUnf/Znw5X6zu7m2oO7Zmv9hZ/9mZ//b/7pO1//o/feufHt3/pfzjz1zNLly7WFedd3QWulRd5QKkBCIQ0lUUT93nB3b/3+ptVSOY7v+RIR2KaUzp1bDBs1ayDqDaPh2FhYvnjGeewSZZZJMKVJdrS7tya9hqqFaTTceHhU7vUbc01wQq9UWrr8qMtp5+GDerPROnnRK5Wto4XrsVY793big251Opw/teQHpcxTzVMLwpV7mwcpO71x6vsB2ThKDcQmHseVZm3q9LwMHGuG/c3Og5ffeuelH2zduRNlsHD6xOd+/T979Kc/Vp+fiQwdbh0e3Lmz8e6N7v5m6KSLFxbCailxqik6lpVJxwosln0lBZMlYIUIytEgiI0Jhsl4tHN/MzwMg1DEGlNVNmK1efLR1idrmNjB3uage9g/2j1s7/U6h5Y95BABletp5ZnUsBLlUO/stgfm9luv+AgvzK4sK63j8TizwtqMmVGAJc7JPAZgQpEj7Tl7RzRBJfJfhIDcN4AAiFJMCGCBhREfioZf5HBGHv1SHAaTLvOYNwa2k2aRGWSOYRcZAMTAbKnIoQNLxMhKama0aVr23NZ0Y2quEXdGb/3pK6/85fcefnjHxul8ffaJC6d8dG6/s9m+37m0XL18ceXN7z44fNgbJUljeWX67Nmzn796/mOnB2tr3/izP93t77uObDjSA9dImRCsRYd73Ddo6jo4E7bmvADQdkxyZNOeyFxHlYSyBCwBNThCmjTuDTux0X/9F3/+0jMXRuN4ffsoQpRCKzSsCaEQ8yOApYmBbnJa56xq0a8CoxD0n+60hxwmP/bY5rNBwZwWW14UF4rMSUtekCaFZSB3WLCF4jIBCiEUsKO0dTwTGGOtyUxsbF5dsiSO43GaZtaSOF7bm1d2/jG/IE7qwWTskOIj4W/xS2ExoUl8HwpAYha5/Q8ZsYj7RwQCEnkaVP452LIla1OTGWOtkNL1w6BSDcMc8FEgkQHIkiEim5fHvFgWpUYg5AotIQsMqni1QLVyVRGiFEJJRbLX7kw3/NVL88mtH4y7I18Fei779BdOXPv2enLt1Qf9SE2fnj55RirttxaXGovjg/YoFah9zBJVhsb8SuWnfrPsl9/97ne++zv/88e/+HONC5dUa+rkY8/MLZ9+75t/+KOvf3vnD/5s9dKty88901g4J/zAc5VQYRTHjvaFZGWJjVVCGGbt6DD0hZBJRkzkSkQBQjAKQOA0zYCZCMlaYhBCOUoKrSQiGc5slvsM8ikhnwZzUsaQFSCkUoAspWAL4Ktyo9Y73D082GvMTJdrU73RaNzptw8O+93BMImY1exUY+XUcubGSZpYm2ZZFo+SB3tH+5t70yemWjPn+bFH5y89MX1iWYostTB7Pnj3Rz+6/sPXn3nx+ce+8LPTp8+tv/3h2ivfu/f6mzOrK4uXzwflhl+roEQARGMpS00UZ2kUd8b31/fcaqBdbzwYJlGKSqGQju+HzaoUcng02Lr/UCtwK9VSzddSSVTWykH/wKSj3uaRLQ0qU83K7LyNDgftWMu+W2Pfc72Zlvfcs/WHd/oP9oRTqs7OaccNPIXNQI1rO7fujvswPDgUxqhqRTjarTUqFv2gmg4HpXKl0mywVK7vmgxAYnw0uP/m22/86ffvvnk/6UVTM1NPfv6Xr37i0fnnT+npGRGLzs7uw3eurX3w1tHBhk1HZMlfaXB1Kqs2rSVOETjVYFkIQCBDBDm9CholSJBOkLl+FA3tcNzb7wowJkvT1qnHPv+JK5//XGkqJCuizmh4eNi59+DBgwf7Gze7B+1k1NayiFdxXOU4rtJaoLN9dLRx65rWLgk9u7IQlitJFCUmphS50FYWIm4UAIxSAuenff6CyMOBitDPogAUpCMjoJACj6f2QprHx4hO0R9ONrwUp1PuIpjgOwCCDTPZggAoaOniHYh5IyoFiCiJK44z05qamqmnnaNX/+Dl7//Btzc3H0JGs1OtZx+7PFtSd995uPUgPntp+rlHpt59/cbG2mEvi6cWTi499qnpyxfOfvGpeP/wj//lb79+7TWbpLMlr2ECya4pBfdGvbvUPdRjTztL5ca015KE3WzUzaKBTYXLnpbAqEikkgLfI9ft9w632skzn/rkZ3/pSyx4b+cgShPtSrCMIn9kOZe05oYvmDToxY83mXDyg4ktToL5CtB/Qq3ABBrjH2dc8pfU8QQFRYxajqTQ5MueTHjMRU4fIjMorVxyMuP6WZBEaZKkTCmTzdI0GkdxHBljHMcRQhSaywmtmxeu3KBRzBZFog8jMwjBeBzhh5ZZCZXz0yiLyG9iFjAZD4tbR+RLvDi/W4gtWZMl1mQE4IRBWKkEparrB0IoFIIQ2BAhiAnCeDyS5NWmGFVpwk4c162P+GsomGmJkkWcjNAenbu8jBCP98fG8+MuQ8d16u7q46v9t+5vHb0O7oPo5a+1nnqh/uzPkXV1Xct+oryQWSNIsNafql/80q+Wplbe+NrvvfKH/+bM05868dzH/bkTQb3xzC/9jTOPf/bP//W/uPHG+9ffeufpJ86fevL5YPGUG0ypwE+FTqJ0lKUmsx4YRyultUQNJBEMCAFgmMhkjIjWAoo8eRGEUhpF/lCypZTJGmuMlRJBSGYWObzLgICWCFGiFIBMDMSIQkjGUrlUqte2793f3tg6ValJIaJRN+p24nHMBMJ14yxdu7UmXV2ZCoWWQmnfVb5Jlipec6oWOEKEFe2GnqMzykpC+a3pZ6qVg6323sZBuV5qrDxSWzq/++DB1vV7d9+9feOtG0ElrNdbYa2kpEqSYRTHw3a3Prcoq/VKqz53ekEITKKMAYPAcx1HecpEaXu/v/1wR2rpBr6rOTrq9/rjZDjSng6mA5Y6A1a9I4EibOrahbmoP0oTAyCAhZRStRaUV5aNo/b+7qg/nJ6f91xXI1bKoT19mi3FBmCcBTpxUWrpzswtSkeJqaoBMECYJe3ezv3XPrj2o2v719cHw2EtLD/7qWee+PjT06dOuzNTqozR+Gj/veujjfvbt+/vPDxoj7rxaNSou+F8VdebEDYNC2sJ2Yqc1MoV8wR535extciAUliTZJZAHwxtZ5CiEmG1VSrP9OPhw/dvOJXqOE6EUK1Ga+HKM1Nnnmhv7ezce/jwwzeiqJ3FPWsz35HCSiQzO1NPob/dPrz94RuposfkJ+aX5/0whAhTZLZ53DozWCFzayhAfkwIKFp0LLi8PP43H+rzZwgRhRACBSALQMs5zJwzdgU2NHnS4MdOsCKalJmLrSFAREUoAQKwYAEohLREUipGkFJlmcnipOQ7c7Mz1Ua5u9l99w9f+qs/+NZgfy9UqlkNrpw7uzjb6Hy4trfZXThXvfLY6lvvvnd3o9O249Iji5e/+FlvdmX1k1d0xXvjt15745Ub3Wg8GwYn0KfEmmp5J80e4rANRpNaKU2vBDUfcWy5lyUDTsjhkpIuAAIYwSiQAADNVmfg15d/7e/+nVKjvnvY748TkCCYc5wVGCUKYvtRpLZgtkU/jSB4sgptoluxWJzXBXtKkGdoA02yD6DgYvL4DQZANSE7+ViVWUxbRdXlfBTIvdlSoM3FXgKkUp7rGUNBmKZZlmWpIWONzeIoicYmS5mDnLkXLMhOkoiKE4UKzZiYJDbBR6Qri2LzXA5fQb7KN69Euak4DwwhJDheOJwPoZwx2SwzlAKj44VhpV4qVxzfF1KxwHw/GmDejwgq1LNc/NMIkO8PLRJj6aOTHyfpJADIYAEECqkVomBr40F/aWkmLJcgStGtZm44ancO3h8FF2tG67TSiu/EvbVbPaw4taR+lY1TtkkHoiSx4JWqZDKwAjMhAn/hhRfDudnr3/jzB69/b//Wh9MXH1+4dNVfPFE7sfDL/9f//sH77735zT/+4ZtvvPfmjdVLZ889/tT0qYthqRZUyhmVU5sYsiik1SpDFpyCzQAREIlQKkchCiCUSGRBTwYxImOJrDXGElkEFEIB2YxI2MloKUHkeBxZZLZEbACVUgjK0bVW63BnZ/Pulh9Ug2qIUupSZWF6utGse24YxdH+w+2draP+aDC72NSu6wR+fUZKKbWjdehpR0sUlhgI0RGKcfbkQrlRtyYdjsesRVirzT1xpX7y1Mlnnuxu72xv7LWPOvvrR5FJBFhUgtJ6MqifP3NqfrUhNDCwV/V91xVSAnESZ0mUDZOkuTJbLpfBpFEUD0f97t7R+o2dpXMLJ2an3cBrzFay0YCJzWCoNJYq3mCUjgdjM45Rl0olD+u1kAVkxAJtiqPxWLkeKjV7clliZgmlkiik4zrgCiJBSdzvtw93Nt/67ptrNx7Eh52M7Exz+uNf+OzVZy54rVkduCRMkpj97fu99dtHGx8c7fdMOh6NkywiXaqUy636bKjDsvBDkA7ZBNgyGztRtxQSu/x8JIjTRKJwLMZSHnT6hyNVWri8dOpUc3p+aq5VCd3R7uG9N6/du7fZbx+dPHvy6gtPLZ4+NX/xVGN5fvn8ubXbN/fX1/qd7W7vwNVQqvhS6qWFpl8aP9ze37z+DmrF8Nzi4pLjeQiQYmxNWszxk4O+QE2RefJw53tjJgRw0c1jrvOZpEMzF6v4iCwxC8bjsJ9iM/jklChY4+IppSLSgPLHP8+6ESyACZRSLARYBmJBoj7VmF+YcrU4vLv+5h/96PU/+/Zge7MRlpq+e+7U+ZXWcrTe7e2nM6u180+u3njrnfs3d4dJVj6x+OjPftFfmFp85nztdH39pVvf+e2vP9jbrjh6QXnB2I5db0C0ZfpHZhg72WIwdTacVsJPgQcm7tskFawd5aFUFjKyoJUb+FZQt93tY/2Xf+GvnbxyOk6jbqdnIXWEkCgAjMxPn0lPj4VbmvIQi0kvLhCJUcDEXwcTbW1BtjKKya41KBS1hAVVA0IgMCjmCXtetPmT/+eXqcBGgJkLASRbRJUPeVor33ezxI+9JE3jzBprbRTHURSlacJsc/9FcbYW4SD55/uomcZJNMSkyZ7cQUAC5PHkU9wpxXHGAnNbn2WLKARnwAjWZIYNADhBEJaqYanm+r6QMt81BsVXVmiJigaKiQmOc64mPhZmIpxoG4QsFolRniJXDL1ogcGYNM2mW6VGo2aTRFGshzvZ1u7+zXEd3Nm0bptet7vg1s3UzFT94mO1q6ezLFXllH3duXEgF1t+6BETG87YSmmkp6YuX31u5fTGGy8/fPuN62+8vHvvzZnzT8ydu1o9ceb0k0+vPnZlsHX0zjf/bPP1V7//b357abp+7olLtStPeTMntBMacGLidm/gSrApaEcHvhIslUTQmkEIYZjZVZIEA4O1xXSGApWU4Gg58dMQs5Lq+F5BBiYSovjqLKWUphYAiLQfTC0sHmxtP7x/v16rBtXKyQsnqo2aQmQGJ3YzwU6jHI2SKEodz0OmIPCsMZasteCgzLKEAdgak8Vaac/zRBPiAbNAKaRJjSQKy165trBwdvlkZKMsSXpjZpumiSZg4SK6XqAkQpJZA8ZzNIFAhCTNQEjUIqyWlBJCodCBFiqUWijtVKbrUxUhQ5TslcrCqYwHg9gaiqBcdojSztEBYyz9RWMYpWJLfrMpPTeLE+1o1xGgRbleURJtCpk1ZO1g2Dta39y9uXn7zXe3NjeGo7F2SidPn7v6M1+aPXsyqIWWrBAUD0bbaw+iqNM+bEdx2tveHA/2hOchShFWvJJUbhhWWn7oStdHqcCSNUQ23/Oa34p5HlcxwFtA13OTOB4klqxDYWvlxIX5M5fm5ufCquv7TqXki1PO8uXkfG/w8Nathzduv/Htlzbu33z8Uy9WZ5YqzeW5Mwt7O0f3Pry7+d41yV2DXYGm5LkClWXe2dl/8MGbgowWn5hbnNelUCYYR5ymaWEN4VyZLnOBihDH83PBp+VepGKjb/7b+bqqnIezNi8BkMf4Ev94g1gISQDzU0QUZB/n/0yOIE9k7gIRpUZiZssCCZhbjcbMXENQuvbmg3f+4Fs3X3lvtL27FHpTgb+8unz6xOzoKDnaPCojXzp/8va1+/dv7e7HUevRk8/9yhf0zGz5xMr0+aXRzugb/+pPbt3/oCFwWrg1CyMlR1ptp4Nd6qWU1t3SqdpszQmilGLknqXEaiHBR08DogWULJWwzOM4OuwmZ84/8TO/8gXLdr8TdcZDFqwBpTgOpwHI1ymAKLYv5wpNsJM2umAAEIptCmw/0lMyFFt3C1MEFoYpAERRrDxhQjUJbYKJKpe5aKs/ktXmUXFkLAuBgIQWhUCJEoRDju/7SZbESRRnqbXWpDaJ4zSOrTUKlcjXTdLE7wGQy1fz65kLVqFguAsEiiTI4qoTMUqRWx8mHAEjQBHzkKvcrc3YsmGLAK4fhJV6qVRxA08IxQgFMcTF8vkcYSLLfPx34TE3ld+vCAgw8SKgyAex3DZQOOgUCgKyxEKg64lqxUNi4bqQsVs3FdO9MxBvvd2+IhfKaYtml069cFUvzrvTLSdGe7Bn+lLXF8szI1MumSwTIKLEKtcTCi0wKtJl78QnPz915vGtt1/ZeefVm1//s/svf2v6kUfPPPWZcOVEZa71mb/99+Ov/vXu7Rvvf/1//9rXX07/5AdLy9Nnn3qsefkJrzpf8nx23GGJGNBKLUAIFIiGrGUkKXIpFZJlraRFq1mjzPeEWSGkJWsy40iJAikfAnKDCTOwEALJ5iWTLDNbK4DnFufLlUo6GDKgH/gSZZakKWAaJ5k1nu85jq5UjEApJGqnsPnn7EIWJcjMgk1mBUrH0SCFAhXWaoCIJOJxRNYAs7GAruuGUtmgVi6xFGANZCYPKGRiIFCkCLUQRMxJakFL19FEmKYJMOWecKVAlZzQb9VnwHMkWxqMIkSFShjhgSThlq105+enytVpk3SlkkFYYc9VEiRqq5CMpYyjbt8MR8Pu0e7m+ua9rVvv3Nm7Oxz1R5gNyy7OLE4/8dTHzjxzdXpu2mpXqCyJ1OH2/qjfHe7uMtjuXjumYWpMZu2g33McxwkCFgqFUtLXTsmvlrWWypVAlNkMjElTQ7kKXgKyJCDKf0OiVAIyQlBag1dbmnrkmersmelWQxkiSkw8HkJWqk/5081grtGYmzlz6cL67Rt3r9/82m//9oULl8899UR5Zm7u7Fx9oXXmwiMbd2931m+x7dio45l0pVUJFd3b3d364C2FhPDC7NKq65cYBGNEWVJ0kRJF4cgtuEbAY9SUJ7B8obErPAKADESG8jWGxzmdOXYwASOKvyNHkgQKRhAMKIvNw5jHoVnK9YpF00gMTEqqRr0+1aimSXT322+89ocv735wOznsn2g0mipozp88ceb0sDfc3TpkDc89dX7z1v0713YPB6l/fv7qb341K8vSysrC5dPKpPe+9oP3X/vWOOst+cGidDLpx1K0De1gp0tdrWGx0pj16zFBDBSbLOI0suQLxxUaiI3ATJKWLoE8HERjG/zcr/5CY6U+6ETdfhcFSJmbcVHKgjpBBkEwkaIXQRBSCJyoeAgLdiBXS7GkyUSIk2g+JrI5vFOUTGAsgvsEYL4QZhKf/RHVwgXDIH4sqIcRChNUwSSjEEIqdl3teZ7ruU6SmDTNsjSKoyiJjTHa0ULkBo5jmv8YZsnb/wmXg0IWQVEiBxbzypZnt+YyJCEQi0WXIBgQwUJuRLOM6AdhWK2FparjBzLnHiYjhgD4sT04H92BUHyyfEgFRFGsBipEpiwQUQhglqCOX2EhiIBACAVZMq4Eru/7BGgz5fgVOT0TLs33zHpWXRxdvNR46hPnHn08bJVMbxx3BvHmQXKwpWcWp5570lvoZVraFJgyqVykjFIh0SdrSABKWZ5rnH3xiwsXnt5669X1G689eOetg2tvTl96tH76ycXLT7v1xszHnn7xuaev7uzcf+WNu3/2e6//2Z9Xv/O1R66cOPHUp8TpJzxvZpTSsN8fZZGnfEerwNWOkiIP0mMhJICU2gFjLBkrlRLCMdY6WitNNjVEFgvAFQRKQgZEQhRCMFrFYMGiVJIBmMuNelYOwYKxPIztMBrlATJaSaGkRJS+BiahlEAEAVpqKaUh1lpKrbM0ESg1keN6QilHurmVI40yrRztOcREZNgiWyZDQhXKACW1ENqYFKSyTAJAAgsBjEhAiFJIAVmmpCOk1FprIUAgIpDhJMtSYwHB9V3p+VrJ2pSQDmolBIBA2SxV01jb1EaxTLNRnA4gzbIo7tzf2h+0P3jtjffeu9097Fo2jaCyPLv03BNPnT5zfuZMw23VwHUNQpoOjo72+jv7mYktiTTqDzp9G8WMOB5nVpJfDTGxvlWeK9ATjKik0jLwXM/1PC0FSjA2IZumNiFIQMg8FpeICQClICSUaImFcquVmpYqrE37rVk3aFCU7u8dDI4OwwBn5po2iQYdz/W9MKxMrcw0Vlpnnrj61huv37j23t0bN688/7FTTzwahBXv7MzU4tTh7qmtOzcP71yTopvE/Uap6p2q3Lu/ff+t1zKTPI6fm19ZrlRKEuWYmU1WBK4A5eqICbKaG7FyBRkTUT55F1LNXNNZ5DzksL8ofEiF7m7y0BV6b/jIgwxFDkLO/AEgCEk5mGSsYBJSeJ5XbVQcpTtrex9+/633//zl7etrHvHJufmGX6tW6qsXT0Vj3n3Ybo+SJy8tbB+137x7uB0l5WdnX/zbvxo5jqo15h85CQjrr9/+5r/7/cPtnVDKaTdEUJkTDlM6So9GOEDN00FzqTTtoNunaKTYpFlqMq1ESQidf1wFINFQlowG40H83LPPP//Zx2wKO7udKLNCCIEkJMu8T88RcOS8Y7WUl0rMD8kJQJ4bw7DIMabCQQG5gZqLN+QnGOeANkMhoyXOd+3gP/8n/w0cQ0cFUJJDNBOsrkCESKDgyY51VCo/HJk5S+1wPG53O912d9DrElGjOXXi5Ori0mq5UiFim2bG5u3nMYdaeLcFipwNFkIAoBRF1RdCIaKQIrd7CClRoCz0YQJREBCTNWSkUE4QVmutcrnsBEGejXfca0zYbs7d6jQpQbnRK2/2ReEpLEDHgkSZYD10LFLLmxNmlCoFspazLA41LU83kcEajsajMDCi/dLWb//e9bf64dXPP/KTLzZOnSOnNu4cDu9u2vV7gR0PEU2psfilT9vOkDiODwZM4FYrqBWhQO2AElKIlFk5khgkI1seHfQHa7eT9185XL8esYVys3LyYu2RR2cvPuqWS0Iqsqa3t95+95217/9wvH6v0ayevnRqauUCVRcS9I4sSmlk4AopQt/TrgcChcpjuUXhu8zNNYbZklSSMhKCKZeRQm6QJks2b+lIMFtG5IkuueCYiDi3J3JmGIo3IwoEJMD8EiihQLCUwpAFVNrTZCxZm6Sp62rX0TnelCu5kigVWiNZENJY42jNNgcHJAokYiml5yoiQCVzXieXl5O1KHN4WiBTHlEokTlJUsvErH2XUQghpINoDUpFjDYzmKU2i008TLqj3lGn2z0YHx4ebLb3evv7Wxs7W0fpkIgArdC+WFg49fzHP/30x674U1PooAU2TMkwGfUHg36nf7gXDXtZv8siA+3lP1WSpARgSFglyMqwXgalKdLJcKQlgSIpheu4nhs6rpPHliTZKM5SYzLM2+T81j7eyYvExEJ41drSVL1BgkjqamuaE/Hgw+s3Xn9ltP+wWQ/rDX/h4im30nCrDaXCylSj3JiRnkYh2g8333v5e5s3b82tLj/5ic9MnTirSj4op98bd+5tDnbXumu3er1DcAxKvHbj9pj01KWLzz3/2VOnH9GOHg0HcTRiotQYZhJkbe7Et4QiN2AWOHI+UMJkPJg8cbmfp+gV8wrAE5g27xyLp1PkmH8RR1c8zBPCAApihIHQD9xqreyX3Kw/2r+5deMbr6+/8WZvo+tYeXJpuRSWw2p1fmnFWHNw9/Cw256+pMNodPe9je2uqDw6/9V//tXb7+2q5szZRx9rzlaTg/4f/bf/4x/8h/9NmnQldFfcOoE3DEp3envryWFX9+qlyiMzF5pOqDMeURyD7Y5GoywrBW6gMBerswRwdZRkvX7HuLV//N/9d5d+8tnO7uDO+qaRQgAoTQpYF3p9ztUukGN9zJZo4v0i5kKfmbfzLEThu8q7d8xxtknrPolFy7/I4siDSXP9z/4v/+jH1S1ME41mPnlNpPFcCHkYpQAmECq/TigkWYqTpN8btjvdbucoiaNSrbK8vLyyerJabwgAk2UmM8Q2d3NMsiUAGOSEDEIh8qpQGMGFKDQCiCCElEV5yD8TszVMAOgFpVpjqlyre0EghcQJVJa3GQiYb0cubCkTCqGobcXWyKLfwNx7WHQaRd9Bx98BTlbOMBhAQ2RMpMEszzU9xyWDQNYO+hgNs527R2s77vSqc3LeC4K0Jzr3x931fR31ZqYgnJa6XOlkXnD2hDVZfNiNDg79+Wl0HcdzECUoKRwHtbJMADlwAUJJAAUKZcbU3szu3YhuXevurR0aEc4uVFfPVy49VVt+xPGRJEiC8VE0uPne/htfSw/3lmtQeu7zlSsfT93muDMa9w7GvaPBwb5NB6EfhFMNFYR+taKcAKRgkBIlARWgIAiZo7fFrE7EIGReoplM0UTk9jqUKPP1sQIYBWXGmIwyWwQLMgFKk2XM6IW+STNiiuNEaEcqiQRCYpakDJgmqXakkAKYrcnS2ISVsnS0zEdUS1IV8Sb5yX68Z1YIFJOEKSEEATLkSi4kJptZMkw24yw1zITIxKllJiJKs/FgNIoGg3G/00l3tsYH2+39ne5O96ATDdoDYtIZVEqedOXU7NKFR5898cTZ2smFUuiA8eI4NUl/vH8YDw6yJIlGoyxJMzAZSJOmbA0Au74mKSxDRpQComVwfc/xi9YtQ2FlEvWIY6WF9PzQ95XSiGDIWKY0y9IsFUxElqylYh23KFBgwUJIxwu8sFYvV4P6HKMqV0rjg9F3fuf33/7uX5QwdrUWnFUWqvUzp5snVqYWTgWhV6k0w0qj2mr5QSUat2+/9871V18RZFbPPrp6+bGp1VNOKUDArB9vX393b/Pezv17No3cunPn9tpuZKaXTz336S+cPH/Z97zRqDcYjchkaRYLREskpCjSVGyuUwQiWwhXciQWJ4K7Cb2X8wf5EZajtnCMcRcadCwkK8dKQYF5jBwzALFAIaSoBKVyvYKWjrYOt964uf6D9zo3H/JgWPKri61FJ6w7gdM8VU/H5ujeQdqLphqQeIOd2w+7w7Tx6Jm/9f/922+9/2BA3unnHqtWpzwQb/zud/7Xf/x/v3+4fqJUOa0UgAvl5oN4dGewc2CHwrePz6zO1OfZcpaRtamxtBeNQELF144QmWVAYimso0aj4X4//swXfvFv/9O/x4F/797ufu/IcZQCoTVJZJWLzEVOfCIi5UlKeRWYcLJFCzuRpOcoSzEVCITiiOfCDp0DKjnkISDvnnNxDaoJLFcwCZMiUkBBRAXvUKApgGQsTzSSQihkEog5cRcGQTIem8xmaRZF4yRJLFkUEgTSJNYhXxhJWOh1CVhMZg+BCCjyy5rfHvkCeZHnNjNaNmQptUag8INKtTldbzS9UiCFyhcDE7MFZns8WeaHA+dtg8hz7cTE9/bRIAlImLcURVkosEvMzcMTBhkkoiFOrbGUSYrnp5u+FxIAyAzHI2gfxWtHvbZTufKiKOtk57CzvdvZ6nX7WKm3VKisB9jtCuTS+eXxOHaUN9zYHYxHYmZGEUBmpSsdBGsMIgupmawSCpiyJCFKpauEUP7yaXf+VPnZF1q9/fn376bXXt956RsHP/oOV6vlldNzT32icfoxb8YPZ55tfvLJo7tb6996q/3N3fkbv7d8cjo4e85fWG2eWNGKxsN+tLtzeOfO9s77bhgiOK1GVVRrblh1Qs9xXeF4oBhAUC6syAV7AgBFwSQVhBMLBplLr4VgW0T+okClFWuejJAAgG7+i5LacxCxlHcrRSeBAoEIKDOMkNvrAAGBjCFkaS0zExnLAiSglHmbwDmvT0xIBGwxz+lLMkrJZBllkBm21iZpGkdpFsfjYWc86I4Hg2hw2N7eOdjf6e4djg77MkFiKQwTCRbKC5x62X+kubT4VGNq9fTSuVPezAxWlTEhQzo4TPburN3r72ZJ3D8cUDp2pUUllYdJxCYxKigFzYqSNUoSk6YGiQRmyISgWIJwHC90paeRx8M2ApCJhDRKKO37TlBSCgCEtWlGmbFkiWRhjJJ5b1N0NoZBSMFWOlo6QBxlGKCSjvZ8pcbj+PDhw1H7kKVVlepwGO8ddOXt7ZkTreWrV+fOXYjHsd85bG+tTy2v1qdnHnn2EzMLqzd/+PKtN79/98Y7Jy49duaxp2sLK37dO/Hcc/76Uqmycu+td9Je+9yJU357q7279ca3vj4atC8++oIblDJjRsbmsfvIzJYQEYiYSXy0GFIAsJDiGIsQAgtDU97Z5jq9AvAHKM4CyEdxspDT38c1gylPKkEQ0nGcICiVayWFtruzt/PWnbXXbw7ubeBhvxKpam1+bum0Fa4s+eVpf9w/3Ht4iImdn/P1sPfw3uZRyuGF+d/4n/7zD9+9d5jqJz73qcCTUpqN19/+/f/xX9w7fFDWcDr0S1029eqBNRvZ/oHoRU66Wp2ZqjQ1QWLJQCoAx1mcyST0AiGFJchjLVgJK3BsyajqZ3/up3TJ2d3vj+OhdpSSUjAJoNx3k9fG/EhHEEwghWAmEnk9yEMZPlp/lif15ErFnAAWE8hdyEkeX3GgMwAKIXNrBiCrSa708d5InnipciMfTvSgOTHwYxYLKZgMgMjN3I5WnuN6XhCncWZMFEVRHGXGaE8jCyGFJcrBunwihMLPhbn8XoginjSPWCBL+RADUuawnrXWUEZEXqnampmpT82G5bJQekLrss3rFYhCJ1zwC4VfDJlyAS0cTwJQrEljIJ7k2RKxnQRTF4MCM5NgIAvIbAkwyxIlspmperlUIaEArR5G2X47Wm/HYyg/dk6TGLy/1d3YiXtd47jTS8smoVG7UyYT7R/sO7uuO+1dOJv1esONB269wnZEXCKDQhEhghRIZG3KzCilkFJKjywD2nQ4GBy0vUB5jlC1lvvcDJxcPWf2s+1Ne7S7t/72/Ttv7M6eK1+4Gp486y+cq6ysXPq1ZSI2OxvZ3Vc3vv7v5PB+Y/FC+dzjunUeZ2eWlxdOyVLUx+iwvX/jtYPNd3uH+w6n4ACAqlda4cyy32ygG2q/pKtV5XlSoWAhmAFRYx6hRAwsJTJBkdYNVBg4WRwTgYV2DZgYhJSQ55vkBZmYGUzeIziamfOddGxtLnk3bAQBC5QKGIiM4YwZmFJrrOUsS9MkSylJxmnSHw2zdNRLBt1xv2sHSXZ4mPSHg06vfTSKhnF32EtGMaXgK+UrrNbCxWqjvnp6qTZVLZW4XPGbU3JltbRY03VHBX6qOUnTqBsfbe+P7xwk3WGWdKLBII7G2tVaKTMeoxCZUK4XZFIa16bCF2EDglnEJDMQW0AtJGRARrAFkFprh0FRggIEczpOUQvlaK20G4TKCZAsYYqGgXJQhY6TllkIEMxIYBmQiAiRbZqyo5RCIpMm1nEcEA5lkHX7Mo0IeKwiDeAb6B6N7g+yKFUpOScvl42FJGr3o8Go3WsurTYXl5770s/5M+Wbr7/6zsvf3nh4+8Ljz5959Knq7PzCqTPV+nSpMv3B69+PxruNyrRWo1HavfHD7x3tHj3x6RfL5RoDjRHAZBkYjcqyEco1NmUAWbi68rMoF3CLQhxUULY5FSAAKKce84cVi9MoXy1VJLrnkiKylMs3HO2EYalcqwjJvc7R7vUHd165M3rnjh6kNZSeqrj10vT8fKYCpXVp2h21jw42t6xI56abJhqsr+900pG6UvqN//mvb9/buLM7ePbnv+IHDnIy2Dr4+j/712vX3/eBTpanWtIZC5Mh7JjRoR1nnJU9f74xrx2fMmPYkMmstRmNtQCpIOei2DIoJGZrzDAePXL5mZXLK3GUtjsdC+QIdAQAsyw0OgAAIncpFWe6YCAuPNOWmCUXWivm3CALBRkOx18yCwkCckURH5+4XMCGVJQWzhfCiIKuz5U5RDRBSY475AkQx5y/ggSWDYAAKfPGWivl+m7oe+M4sPHApGmWJDZL2fUmUx0Wk2sOqNiPHH0wMQZy7v7IVaPEwJDZmIy1lDCosFxpzSw2WzNBpayUk7PFNqeVOM/AYIETaD8fWcSExMiRH2LOd9TlR1FOCxNNPIhsbR5+RwWZxRPwEdBYtsxERkA2U69N1ZrsaOBUDWPc6EZr+ympYGGKx8ZuHYy2dpM4dWe9mtaH11+/eW+0NFOpJdnh+7fXsuzS4x+bX5rZ39tnk7llTxuDhlgxCAFSIQAZW6gvqSBiclbCkY7wlQRKj8axsVnGlrS7dM5duizjgTzcWdp9wGsPo3e/k91+73DuQunsxer8rOsqf3UhXP5F33yl99ob7//uf7j3T/9vJu1yGFz59JOzT3186okXatPz4ewnTglkj9LxKO6q3vZu59rbD77/0tG4bbuDOMq6QitHsyO90PFq5XJQc8KparlSDuqgJQRuENa0A46rHUegECikAhcBAAlQIjgAVOR6iHzKFyKHdgCZjEVjUpvP8tKkTHGWJjZJKCNpkzQjQ5lJktiMkzSN4jiJo/FgGI/7o8PDg+7w4GjUPhwm4zgZWLYsmdmKAIWvsB64QSloVOorp1cfbYalcnV2ZbHami3N1mSpgq4icFFaJGkhzSIzHtl2pze6fzDu9SObpWlkkwQoUWyldACtAOGHNT9UMk6t56YsyZWZkKAVgAArYiNECkRgMmBHS3QUSoAEUQnHAWLkjAwzs7HGArmOqx1Xa8dxPSGYyBDZLBf6WwZCYGJRiGQQEHMfIyPkcw4RDK0jWHphkoyccgW1FK7jg2NYjyAxMTtg5qTroTMwMHrQPXLWWvVT1Yuns1HU2dkZHBz12gcLZy/WpltXP/Ol1sLZtTde2bhx/fWtP9m6ef3C059cvfyoVw3nnzrrTHvv/ei19uaaI6Xgrqj4O2vX/vJ395/69E+dOrUUOmGcjb2SiyDY2CiOs9QkWcpE1loyWc5JCgQh8j6fEFEKPo5zL1R5x0nveb/JHz2kOd6PjK52gpIflkte4CCZ7t7+xq21h+9eO/hgI14bNt1wOqxJdhTLoFbO3BI66IReb2u7u7UH2rZWg2Rz4/7a4TBm9+rib/yHX2uvDV5598FzP/3VSqXMPE763W/8m9959eXXLNhZ5T8dNMaxM1RpDLRHw76JWcnpynQzbEqClJmIGU3EsbWJ9gJXCLLICEILQCSwWZJY8F548UUdBvt7RwaN4wJaoYQAYIWc4+GAICgPK8gpXxAoWRTlsNDLH5MjiJDnU+a6ztx3wQATX0aBBiHkNovjrp6ILbEqCkQxpheKTD62bR1LYqBg7yfOjfxSsbUGWeTrNh0lPc/1PTfJojTN4vE4S1MuDHu5u3jSfhfs8kT3Xyi9uFCgk8zTpIwxmU1RYFApTc8sNKfnS9WaVhpQEudL53M4B3miAc5xQ8uAAnO/67GbMMcZ8huLGLiIDinQRma2xnBeEfK/ZIJDChRMbACQQQuebZRbjYbwXAvWPRrB/f3D++2BUu5MK0kN7e1EuwdGWHdK86i389b1tZdf02eWlHNx5+b2+t316NTJ1Y9dtXa4df0O65JTDhEBkCUyCmAUbG0xwQABAErMvX9ZZgHA8T0hJAqHiShKhVJjgxqkVg13ocmts2Z1GLR3nMFOLUgVH2a2TBgiJKw0oK489eQj5y9e2Oi89c//w9r7L629+ub+jVf8P/n/SO3rsF5aOrH6ic87zYVw5vHK1ebMk1cucsSjnup3aKs3uLW+9/57R2sPdtd2Bp2bO3GUSCSXpQckRF+4BNJIm/M8UjkKtC8dhQIctEJbIdGiUkIgcKCElGCs40g2saMB2KLjcpoyorVGGguGGZAzYTgThuJBMkiSUT+OkyzJsjFZYfO20TpCKhn4rjPtB1Ot5tSFqZnZenl+pjLTaqwshI2aqpWU72jpFVy/ZErBxCYepmY4jo66o+EwGg6ZrEmjKEqSNNWElBhCIRyHWLBQjkItCJSTsrDCKoEkBepYgAdWsQDhlFhaBcSZsRlhljRWZ6wtJ0f9JIodFAI0CQSUFiywSSkDZpbICtySlqiUo4UEIGuJTPHACATLxycfF48JIjIis2EQ+Z7WzNKYe4Hnu+XIZn0Wpfrs1PypszdufUAmktqRBm1Gs0JVpXc4zvr3t9uLa6sXLrYWTu1v3ey0O7GJMmtnhiebq/MnLjwyuzh38uLlG6+8svvw3tHB0fbDO5ef/0x1bmbu/FK51XztG9/cf3iv3+ubve78qROjfvrS1/7SPP2x2bm5XtSvTjVKVT+s+vVaSUghlMxMBsAms3GcDntDsia1RNYW9ABbIi42GWLhNsm7NLKUGQOYawdQCRRCeI4bhqGjHRCYRuZw/XDrg7tb1+511/fswUBHuFCfrZdq0WjErGRYBj+wAGkUHezvmWG3Wob6TGPnzt2Dh/v91M4/e+kX/8Vf33pn9/uv3nv8yz9VnWsyZVHHvvpv/uJ7/+5PbTepg3Ou3ihb995obOulgzTujUeIVHXcldJ0SWrKkswyMCNBbDlVwlMOCMUoyVghRK5zH6WR31h+7BNPRlHSj1ILJJCV5DzlQBRcZIGA5kc65MhIHoCdH/AT1iRHMXJALI8DKRx5eUZHTllOHE5EwJaIrGViYhDMINhaVYD9nOs8ixF9onthRMG5Gh8wX+ZZEMuMDARIxCykYsESpRDoONr3nFGsrLVJkpg0Y0siDwbJNU0wCS6dBAVO2HsAALKpREGQkOEkSwXIsFZvzi1Mz86Ua3Wt3JxNLPAaO9F25p90kkArEGQh9ZtMScfCIyw2IxMhoxWcexSILec2FKBiRXE+FzGAQIkoWKISLAGmy35zqiEDn8HIXt882O7fPux7FTlfiYYRHR3E7SNk6+rptLuzf/vO1odH5376hZnZ6fRu+0H7IJ1b/tx//Z87jcrR7bti3K4sT2kntCQU5Bh7rnHLA5gAWKJQQgLnH0vkoh1rrSHFjML1A0SRWQMAsWVBDDKQjSqU5wejvo2PJCkRZx4PkpRYKtBSudKriOy09/g/+1tXRn/TrxsxSMZr67d+/3sHP3hr7827w9ce+i3hBq5qVKVDU1ceq5677DYWzeUV/+mzdfNFwUk2HiodSxaURenYxr1h0u7tPezxyEajfppl8dgmJooHkR2MBoNhlA0H/QiJk4yYUmUzo2xkDMcxoRPHcdkTiBk6pYwyRVmSKSRlhGttYI1UkiEjS4zCc/1gdq7calXK01ONWsmvtaqnZqenm8FMU3uucHJWC2zeYKQyi02cpMPuMNkeJL1OlkTRYBgnkUkSk2XGGjAsURlEoZTnqCSOrUmFcg0K45f9SqiVSlJr4xREJoQlVEwS2VokqaV0lUdCkRwn7LoOOkJKpBIrVEY4YQgSgoFNJZJCAVZYUJZYsEziARI7vuuVnNqUwwaVlISWgIhNYokAwIJgNvkDqLBg8yZ5XxKlFFqANcwmS7PUCsLe7o7jO34piOOBW65MnbhYrf6g394IpAKyCVCcmbpMM7LJaHxw98bR3VNzV5+oTy+ajOPRcGfjXhz1k3Qwd+KkX2ksPvZUudm6e+2DtevXPnz5O3c/uP7ki587+/RTpangk1/56de++TJnYmv7/vrtmxcvP1FJeH/79u3XX31w+/b93W7z1Mn5mal6q96am2pOTddbzWq9HtT9oFytN2tCZam1w3EcDxIzTnMVECLneVVk2VrGIqmGtKO1q9DagjNmZBAmsUfb+3sbOwd3d7dur4/W9zwjXHYcrlQazUCJXm9gCXXJJYI0Go77g3Gvo0p6er5Sc+3mB9e31vfHAs+8+Phn/9Gv3fzOWy+9vfH8r/3K8olVhNQm2fd//y++8a9/q7vbDhFXRHDOnbnXjq32DfBOtN+nETnULNdrXkMQGiYgyzYzNoutYdcH5dr8aFOCgQglse1Ho2c+/VR1oT4cjglMrpGXnG8hQSyyULmQnxg7kSoyQ85TAkyMTVgc2gACKLdQUY7f5/FrOYMLzDbHzZjzKpsLiiwiMiHZ3AdwzKvjMSBTsO/IdJzKlqspJrqgfHkLI4K1lnITjhBaC891PMcdj4dJkqRpYpmUVCITCAIsF0bcyU+V09GFWBWYjU05smSIoVytz8wtteYWK42G1g5MLBH5NhiyxefkQqlU1LDCflhUm2LGyEGhCb2dFwOSkC9Nzv0iLBFQClS5Pb2gzimnuoW0TBqxGspGrabDwArW3W78YLe71TO1QE/76UE73WunR1tkk8Bz6cHr0c5BUCm/8A8/GSQer41g+3aUpZe++sXFZx9Lo/54d79c94OpEot8MafI73/BaEUOiwqQAjAPfi1YGAFMhtkyS4EShZQIIEExU777BRGJmBV6jSbI6cwYk/SjkQE2FhIzLgg25WjH9YO6I5WElqOrC+cuP5X2h+iK0BcOoB2M1IO9nW//1cF7axs3b3oSksEwiaibgNOoB6F2Ks3SzCm33NDlmqpU3YXZ+YUArBTSc3xfO0J5gKzZWksxYAyUQZYatJQKjlPtjtmqeDyi1DWcyPHYZGMbh4ORESa2oiY832m4uuI6Jen4SmtP+KR8QFCK0YAyMWdZkgyj/nB4dHi4dff2eNDNhiMkFlqSNZoSIAI21hhrLbDKMoOoooSU6wkhJCNKh6VkLZGFDgObpqSl45VYysyy45UcL3AksZIxkIHIFQmykCSAU0eh8rWrBKBEUEH+fCCAEkCgtQSpeDwWjBoYfQGkkHRGGomNjT2vpJWQSjqOZiVZkTHGEAHkXZpBYGbDbDHPurXMQlhgzLeeWlRSSiENGTSCEW2cRlFG6SBbW2/5Fa/a9Ov65DMXZ169OOxscpamaZIBpAJSmYFEy7SzvvXeKy95rebM6RMC8XBzozsadLpHcsuRUpYb0+Vqpbowe6VWbbQad956//qNW1///d/aWL/3whd+prEw+/wXf8J3FLwbP7h/8713r62cPvv/Z+q/ozRLrvtA8N4bEe+9z6fPyizXZburvUc3bMMDBL1AypHiiDKckZkZaUeamdXOrDQ6a6Q9Gp1dacUdDUUOKZKiFw1AGIKEbXQD7X11eZNV6TO//NwzEffe/SPeV83GOQC6+nRW5lfvRdz7s8eXV8rJ/pU3X7x6e3P9wvkrWTtN0Daw2Ujby4tzRw5nM/OdztzyyszCfNu1esFzKD0RzMzM2MQJV2osOeLS54UHkcza7kynMZNKyePBaFiVVVHkw4kfh/7t3f7168PNUXl7RJ47aTbf7ISSrcMqnxShQLImdcPBPilJKNQXaQPbTWP2N6/c2tjf6kOCT/+VDz/09IMX/+NvP/vO2v0/91fOPnw6wVAUcv4P/uwb//aXYHujZyELdLa31B9BPwj27EGZ71VVZfJW1lrtLaeGhJFDCMBqNK8qVnbo6jQ8UUJFg8gwES82feyDTxuLeV4CqUUiYKw1hlOjUgBCBZSYbc0S5TlaU7hIUCeExmuAJMqkYt/HdI+oI28QmQU0BK3VNrWI3hhUUENEaO/ofCJ4Mj0g43cvEpWUcEdvGpnbupw5iroiO69U831JkqRJWuRlVfoyL0PlE5co1vE99fkdR/9YXAcAqiGUHHzlS0Om1e0trRxfPrQyM7eYNhpkUKZ9EhLTG0CVp1R5TOtURIrJQNPdKKJBdV3ZVAZVn6W1HioKdAHAGSNAJBqzCWukDWtTNoE6NN3MzfU6rtlQUsqL/Ob2YGuf53raNby+L1c2/a3roqNWo1ne2h7tjxurR5cffh9qbka7w2pwtdxMzt77yH/xE8ZUOxfWBtv7ze4Cs1XFQJqSkqpUFaFDRCSS+GfixauIVnVFAxoiMKmtyRKqc54AgEMgQLTWGgADAiUgmQQRG96XGBDQGhX0AYgMWJ9XgiwEQAULWFTrgFQ1kDeW5uaLTrd94pQ6XGpmYTQZvn1h+L3X8ouXbl/YneysF75CCYCAFCQjTRqVQZM608iaWaPdzrK2c412miau2Wp0W5S0jbHY6FiToCVrIGksBPbOeQ+VL0aTQjD44XgIWAAkHcuZz12e+uCqsXLwfm8/H472trfzvQPvCxUGFTDOJlYtOQMmSOKsRRMGYii41BnTMFnmg2hgslQGYdVsvsHMpCGUpRjyREFFyTAyNZxNm8xWVUySpK1WmjrE0mIjcQghq7iPykQ2S1pJao01FO0lihYQlQOzqDCKAkPwkwMecqAGEpgopUIkUG8ya9ESgE0IHEoIItHRyBrv+BCiElxIvARVQCKvwmAQwRKgxBcdHSVgLKALYMMkL8vS7/T52g2XzXZaM9lSduqRx/tvvzga7SjBBHCEIVdtkq3YD0fFjXfPz7/+atqdnVs5xKKyeYurcjzY31nD4f7e7Pyh9vxss908+tDD3dmF9uLsW6++8cZ3vrF78/bHf+KvHj135pFPf4i6LYXkzbeuvPPKW/v7W73Z3om7T++uD0yoZgwbNTzWwe5g78rmNXxbCGwz7XVb7Z610ACbJbbpXLZ84pjLkslgj7qNVmbYVyEoiJDi8rGjS3NH+7trG9e2h1WJRdEyCakNAz/Z25VCmyZrNNvdRi8fH4Q8jwdjI2swSH9nB8CroCFsdzJHdr+/dXNz2w8mrtv88A8+hi755i/+5yvro0M/8iNPfOyjjUYi4+rys2/80c//qt/a7Kg9CMOT7SOJaV0bFZVFIroZRttUVFZOt+dnml0OHmKosELFWjETkCHGGtMQMkZUBaEIVe/QsTP3nYmSZIsgEfOfQi+CU+1lRF3qIhtRVeVpaMGUmWWYmkAwNuMYiZkavk45kEiVWzRgLFgBJUIyZMnUjQFIJGDxjuR2ysGoamz0QUCuzXoaw9tEVWM3g9ZXQMzxERVAq1x7yLIknTgbmIuiCEEA0BrDZECDAitArarXKNxRCd5LxSyNdnt59cjS8pHZ+aVWq2Wci+iQaC1IjRyJgkoU9NRvARDoNGAUVCXmxGnMftOpsin2nKigChqyYGLNWUwiJEVKrI3GYwCI6xgDI4NwI6GZditpNgSBJpNi7dZwZwjthkU3vjmuru+Ul66GMEy72cHm3mRHZ8+eWnj0BEjlAOz+9vqF7x8cTT7xz/9W0kr719b2126xwYAZBFuica0kXvogyCEQGjAkwDA9EBSUDKCNjS1EVKfhA6tqxIGRwICgeggQyJAaM9UAKyKCsREyI2cBKLDgFC5EVdKA8aoXLX1gUTJGVRWdMy6EIGmS3n3m6D2nj0EwYKwG1gqqSge57/fHt7dHmzvDwagYHTDHUNhyeDAudvpcVUF9YAm+QhUAQQPoUpOkiA6VLFmwEJjUoiUsJ+OsbUza6rY6rt0g07JIFhgEQx4MWmZuJOnMTMNYh43EGsvCqgDGOuMUNahNEdGlYpzLOgzqJyNijVG1AAGTRigqK6U4BOsCBwBut9sG0LoUXaPMQ1Vws91ot+faCQjlReE8YVGMwDXSzBBZY6iWK7CKgChHnTszq7IyBxUO7Kvgq+DAxJw4IKMsCEqkoEhkFLCqfK1/QAJm5gASRCqImg1UNMgcIktW45qsBu8Iu8GChQzRgEOqQKrJwe7N69YlC90eNdKVR04uXfmA//63q/F+SuSrgoW9YQQS4Z3d/rvvvLxw111zq4vzS6vCfrK/PZlUe5s7nXnDsjkajVKbdue6s0urj3xy7sTd9772/Lev37r+u7/07z77o3/xzNOPP/Tk+1pZt/RfO//st165dTt11OX0/nP3799cEyVjnMOsY5LQGEox6ldlWRS7RT7aI2KwmFl0SZKVO0MJPOlvGefSlm0YkxkH6IRp9MbOteSdYnBQTQJau7Qy00yS4EU9NmxHMm5QAlXY3b8RKm8dGjIuaUyqMB4eWPbOiCgl1paT4d5oOB4MIfjOTPvMubO3Xl3f2drrB99430P3/uhH0hk37IcLX33xC//mF8Laza7jcjKZhdb83ML6QXkgpe01dni8mw89+XbSONZdyIzxLBECIaAQgkjlKLOEZIhYEdEQiQRWLQLfe/fd88vt4IOAV+A7DDfU0hiozaood4xdAoTIcYYFRGPM9KyuKVtLBkAlCKiKgHBEgmrfEykZa21Ev7EO38Y45ZKBGAanUENrWkfwRY53ygroNGd1aqqKCv077mEVQUQOwQABElEUICQ++CIvfVUxazygFOu4IgGGmGMi7EMFwmmzNX/s6KGVo7OLi+12N0ldFD/V9vH4ehAQkJKIAmHUKt1ZXATITG++ugKujnoTQYj+MjKIhETGGYPWGGNNlBvFTpgprQ4EwAoUI3a56qTp4txso9UUZSjKcnOz2DvArAcS5Ma+XlsLt656P2z25kf98d7uZOHEucaJu/JR1VYNobz6ynNr2vjYP/nvsqUj+V5/vLsDWbPRJcsJqEW1SUbGEhgjohCUCalmJVgVCI1xNkkMWQI05FAVSIEINOaGx/ocRY4yMkBFAeFIXrCKGlBFVIpVCiH4+PSwQtTWE6CzRGQ1ts4JSgigCurFW68sHILEFFjrFYw1qM5lTdNdtMeONx+WxVguxuILkZIlqGoIzGGc+6KcUlFMIoiiaBxhCuArBu9DVapxBp0pBxy8B04IncVAkjMCJolB4IrFqXOlKCha8GJEDAYRZFVCMi5WUVM6Yyx4VUYsGYMUwVgiQTUAiJQqpZ3lOSO+8JWotQhh3HeIJGotOWdSl1RmIhqC38jVcZUXRVWVwTmySWIEVAIzAqKGOHAxGQRAEWEOwgrCEcclQ2nTIch0hIE65JHUkFVSEVFCL0qgDhEkIHhmDzg1+tjI9oIAk6BAIGOjJY6QfAhkiAwaspgQA1nsBWAd7G9ceKvdyg6dPtc70zjxgafKkMNrr5XjcUoMEiCELHVCbqsqb165fvntVw+fObt08ni7yot8YEIlRVEMdq3lKp+Md8dchBPnTh2/79yR++/vrS68+t3vvPXC9/7gF/9/nxj+xEPPPHPivuMYPu32xy+98L3tzfWK3HzWPTK7vD8e9yeS06SXGGcas6k2m8kumCDegLWsDkyHnBXuTXJB60ynGAdbJtlMt2tbZJ1zCbMfbo9aWdpImy5rdNP5fDwo8pIrtpYM2Emec16gVIklAwgARTEuKrHCiVFCFtDh6CCfDDhng3a2055tNjffuQaTQhU7S73l1RXN81tvbL77Zy9+5+d/GfpbXSp0khPg8YWVUtOtfGK7Ti1eG+3tJqOS/Jn28kw6o2xZvHJBIMrqQxnAWOsUSZSUxBhTSEAELyEgnH343jSFg6GPFz4y1GbuWnqP9T0gqAQxEQ1JQQ1AnBiiJS7yqaLTfxOVrEMVcsJiSKd52kBoyFhDiGjqoz9WacbpG8mg1fdsOqpwB1iP+wnge2Gr9a1T57EpTDvFQAFEBBCDcLRtOGtTlwSWvMjzvOiwgCVEEhXhoKCoEkIIXLFw2sqWVo4dWj0xt7jUaPcSl8TXiQGU9c85PWOSkUS0f6pOqstqAYBIVYFFlaO2c3oPaOSE49hmiGB6DcZPT41BIhLRwKCgd7J2ObCqtrJkeX4+aWaCaqqR398fH4yYkEd5cbk/unRD+huOqs7szN5+sX+1mn/oVHr8cAimmZXVzsalV19eN/zR/+kfdo+d86OQb+6KqGs0RLhCNWRaWeZSNKTCAAKVRhcYCgqCUmpckhhnLVKkwOpstijWqq0iCArKCMoKyioxMYlYp9qv8F5Fat3xFitGleLnACSqzFzLTRUgIs9myjgqGFBhDqEs86AcqsKDok2TtJHFYM34BY21lKExiJpkBmW2TfWOGykcUZVIrCeEHMSQhLJSk6Ko5cLnZZ77LBHjC1BJGcF0s/aKL0eVn+TlGMsqBAUvVRB0lhqapQasQ+OMtWQbQEaqSTkaF1WF7G0I1hgREggORARcimnabDhnqqEPiCpB2sV4VBVV0ziWsU0Ta02+u5cPtUotJagsSQIIVE0mhSgaQ2SEmT2Lgk0M1ak0ADz15iigMRQT3EEilUZk4sppCAisogoogydSEjSgtfAMWZA0BrUGIDSEyhwIlEQJxFgbn11jbPBx71YyVh1aYxswr14Hg9vvvPBcVVSHT953/P6lUD4oid9/6yLsiPOa88iXvpUkPZvs7hcXL7xz6MKb3ZVj3d7CeGcnH+VJ0pIqjHb2bKNlHRRjf/n82xqKQ2fOZLOtxz/8sVaWfu9b3/rC7/x6Xg2e/PinTj6wguUzZdh/7uXtSX/cKE0oxBI1M90f9QMXWdK2zmTttAuN8WTAngOrsi8ts/iDsc+yGXWZo0bimmnaFAICFS7RmXYjiUWwVQgHu5u+LELwFhDFeCYNDCjWkoJUXoPkVVWoCiOIemafBwlSGc8Nl3WTrIFmsj8GZmecEjhrJ5c3zv/i16u1g2svv2TznbkOmLwqfLkEveOtmed3+9AyWUZro4N9XwYJc0njeOdQB7OJoHgxgoE0hKoUH0ywDskZIBAFRgAiFgkSssbM6bvvJQMMYozqtEwtDuu12xQV69nfmNqwikBRNmlBJILdtbNGQKMdDACECKPBBtUpAhLG5uRYy4vxFI0lLLWnEAFE7ZRNrqf56aEugPUpXzsM4kQioFPjWO3P0zqcIWaw1F3qoC4hU0FZlsVk7MsCMQkhBB98WaqwqAeBtNlcXTm8evTowtJKs9OzzkLd8KBSdzxIzfqLauxlV5RpLSZCTZtHSVTtQFVVRAOGjFGcFusQGUKMEXFYa2hFgaVG2kWEVYRVFRSBRYQ1MHdb6crSUtJIERWCD5u7w7X10hsoJ+XV/cE7t4rtvYSqdGF+cID765vd4zOdTo/KqtN2PBpf/P7LV3bHn/jn/+Pc3fdqURY7e1U+sc5aJc8kjoDROCRDIuqD+DKos84hEBpjjbUmdcYRAHEQNEgOa+8EK7MCqBJhrMOhqNiK1IxKYBZGwEoiOK01VW/EWIJaNIsKrAIKUvexxcBZQDSoAMpS7wTkUIxxgBwwdRwCoR0f5KoVMJO1ZEprrbKQwXjTCqA1pIpCaIxT4OkfksbwliowB1ENIXgUBVELUoJKy41RDKqCViJFNeGDm9VYfWBngWzTNWzikqa1asCmQERCCOjIIAgbBDGpVGG0PwzDkWoFCZHNTJqQZR6UkmBVljaTrJXhpAJvKE2hqipyoOIL74MqqWbAFYsXC9YQRd2cBDKpBSRCUUEFRRMfREEwca0mJFQAUjCksQZFuY7mir6g2I4YgxyELVhGRWA0YBPjxywx0pFQvIAxBOzIAsRoHSRUa9AY50smVCEUDjEHDQ0asqZtDRljuX/91oVvPAsDXjhz95GHTxVOqiQZvXwh7G8nJUx4zJXvJLYE6t/YePf731s5dOromTNJew4OhsVkkhngwvswmZ/vdZK0PzjYunV92N/KZmdXjpw++8RHbGvm5W987bu/+yUew/t/5JMnnjp7UHx4WI7efOVtKYUV5rKkAzozu3hrnPu8HAdk1WaWNFzHi8+hqEDyKhiUfDSmvCDTAHKapk6bYrISDBjj0qYjACk0APO4nASrbEUSl7Ln4BNVQgCWitUH9sHnqCGh6CMsi2oSFBK1DZu10QHzpCoNYJKaoAgOfF4dvHOeX31zNCiUR/OJNT7n0ncgO720emFvXCXYardKf7CL+QRLdbraWFhszZcsLKiqQsBqcvFBAtoUAQ0YVEAlQQyghFRJmFk+dPjIiiqwryJkXVe7xMzraZtXTDiVOt4ITIxKIgQFNVCPaCAEqKaWhAlAna5gCRBJkaL5eIr8AEZKAPlOvidrZB9sBJJqarQmmqeyfJjeSlMhkU7vij+vqJnuD/HsjT0CYMjYxOb5ZHCw1+vNiGSV5/F4VBQjY22n11k6dHjl8ImFQ8vtdtcmKdRcRD2oikYMBjjaFUQISCLZIBHkqrOjarUwxJsDTDzxpz/4nX5RiABbRN0AVbmuqIl3cBCO4lZDMW6LVRoOF2dmktQpKJZluL19cH0vlEE0+Jv90dpGYsbpkmNI9g92h3njxJOrs3ctKc+5rBfO37h648Ie0If+wc8deeSx8mBY9vfz4YjZJNYQGmNj8isZAhblKuS5N6lNG84Ya8iAQzJIBCDAwmSMsQioJMIMEjh+6CwC0V8xdTigIgeO3JQgKrrAWnlWVYOAAsyK9Szw5+3QtSpXa3KJSOvbNaoFlEBVEmvEGIJUFHrdmfg7C3AIjCCYGIy+dADSqCFAEWQtRUA4gAgYAkOgiIHZswqTNalTi4gszqI3RjEVk6h6a0xDwFeAFroNl6VOQRAAgWrDPCmzGkIyZKaaOVZQIIMO2y0FJ8wmyRrtjqpXCqaVsUo5ETKq4IQrCGAbbUOAEIKysIKCS23aqJ/+aCAEMknskSQjwEgsYJDIOSJDdYgVGDSECmAAjYU6jk6EAygaIhVWYeWpJV4DogTvwVBijBJORgdFPgIh22gLiCoykkOw1qoCx5sejTGOUi2Lwhrn1UvtazSAiIbSdtsmR4CTjUsX3nnu28fDZO7UPcceudv2Fi7b3uClV8POuvVU+RFWRcslw4KvXri09PL3W72ZdrfX6szsTKqiKHpZ0rCpQtLpZgzl7u7ecLDPt7Z2rg9OPnbfybsfXFief+FLf/LcV/64gMEnPv+jj3z6o5Ljzl5x4+13mPhod9kOqwPxR1utHZf7svK5JH7ccZl1LZ3p5hom5bjKc5AJgJJWZTkallISJdYpAriWNY0EbWIIBUU8cN5wvQShKMpShCVRZWEWqUBZRRMKKQEJeuAQJqw+EdNQMYEDFQzkENUZISKIvbGVSMnjQcrVgjUKVBRVE5K5rHV1Eg5MmOnMJGxuj8Ie5BWVDUyOdBZSk+aei1AZQ56xFPFBlYnAkbV1xKWoUQDFQODV33XsxOxsRyUWfSH7Wp+C07SxiOhoTW4C3gHyY2wO3hHpCAjGNR8lJkKLIYqLQ5STGlN/C1pbqbGu5Im+6jpnAkHB1r/JNDznTjRrrdKsY7fu/EI9xt2hjKM5Bad3g0iQCFEYTIypECfD/kF/k6vuQZH7Iu/Nd1ePnT56/MT88nKr3bXOQh3FEAfwmvUWAUWGGsVhZjVGKZ5KGiOLkOqwuDuRGBhXnrgcRMY4fvPxP3KHLohMdt0GVttsKBoQFQ1oUEzJLC3MtbodQEEpw+7e4MZmNRT2kt+6Xa7vVHnV7CbK3e3tndzZhaXZ5dXDgs7mON66vXX+lX3lx3/u51afeTiMJsXu7mg4UFZrEjQJEUpgsCYGoUDg4EOrnWatlAwpkERTtCU0pBqj8UBYkTWEWOUbY5lrC3jUfREikAFVIucSw96DQhD2ZSVCNrOxEQwwpmLg9OGAaCp9jwOZXpVEGmeDGDuOSooQiRTDCgadIVFlRWtNzAvkwIogHHXBSiaq4UCMEtl41TCIL32YlBKCTUw1mRyIcmBDqiohSVqdJiEmNkkSl1mrGuVKQGhitOEdwoqiT0oBQsUqCBhYWNRa21mcU/bBB0XIshYlTrRszVmufKiEfSWiSmxTi6k1iAJI1oiCBu9HlSbqHBFhlE6TMYjoKx+78VRBrVinNrVJYskYtNYa1D+XbIwOQQkN1rG2SAhIBiWIL0thqcoyH485FBgrFT0rK3qZ7B/YrGFc0xCUVUADajQxVlOgYIIEFjWkzmZIUBSlBeODYHyIBeKr4dq95GTWCpPbL56//MWvHTy4tfTkwyunl1vNj1xtz9/65gvlzmXrJcAEfJGZZr6/t3HhrdunTh87d2/W6CAeVL6s0mRpdnGmt6BWnM+Z18cHAwA77pd5lZ958P7D99/7iZ89/tzv/M4rX31xb3f4wz/zXzzw0Q/cOL+9dn5rfXT7u42Ne2glqzSYkJlELGLg0XhSmiJz42arnWUda5dCMpZykpcjtLaVtNmP2fPYCwtLsYdoUIUMAhgBTsi1fLAR5GU1ZDEwISUGHZIlQ6gaeKK+FK/KjqwzKaExajwRKrEFiIlR4onFS44iDTLWkQBUVdWyjQ4mw4pHoO3erCTpQV6u6WBPx6p+oTXTbcwzkIfKKBiFPHAZKq+FGI02FEMYZxBAMQRKGAweOrmaprbiipk55thF4rdubo9kQNSDoirXaQXRmisMFE1fta4lsgbxpCRQ0aiArPd3uOP+rbVCMP3Sd+4ERQRFsFOMV/EOuTwNgZ7+AgKI1vcPTH8pRnPc0dyrKkDduAKqgkCW0CgOBwPW0Oz2ZhcXTt394PHTZxYOrTSbHaq3Wg2gEBMO77S0RMi0VidpnchcJ4bFFwpqxSvWes874s/3/udOc3vErjCKg7TmI+/cX9OEaCVExBACCIHo7Ey302kDKHERDkaTW/t8UISBjG7vhrWbw36/szjPrruzs2dSe/TwiU53Tgur/dHk4q2LaxcHFh7/b/720kceC4OivL1RTUYEhA6NSYmskMGmkCUUxOABsTnbzmxCqCzoWSmlpJGgiY1JqlIzBCyg0eMEBBbAGVAArjNSFNRYg1EnKsLOAQCMRk4KajTQ1WoCmF7k8f/HTxRA9E550HRQmJItgAiGDJopT1U/bRClyrHUFcAigYmZ7LbWktXWu3jFxL9EVdWCD2QUIFReBSaDUVl5RBVQHI8tQZI2oJG4NK0h0el7IQp1RquKMRRTykUlvgsBFMmQEqYWrWVOqAmo8QsHRFJSk1ogNakR4TomUBEVLZCSWFW0NkkSnfpdjIJNE2YBEtKGicCoxvMcG2m9E6AxQDGRBDkIBwBSkAAGbZokxhm8s6cIcxsUGHgyKXwxKfJqPCxMJb3WTAqNrctX+xtb2bm2SRqJQqnegBEFIqNGydjAahgaiTWmJUjeF4goIoQgBg0QogXANGvj8rGZxf7wzbdujV4o9/uHnn5sbuF054PvbyW9K8+60a3zCaejfFAWeUKNwfWbV994s7e4uHLXyng04bIKAYVce/aYySoPk9u3jJYVOjRJWY72r50/T4hH7j/3wb/4N3rLf/SnX/qzrzR//cd/9uc++9M/eOvidX7x2SLs7en+SdcxXkborXOjULjUVWWO4H2/wCRvNtrWJjbpWMDRpEgaiW0khS2ZxRIEqVSYxTOwIBu0BBqk9KLAkJCx6I0hR2JAFKESrYSDsIAYhJRc02YZGFJUJVCNgQoiQVSEPUogQQ5CFi2akQTXSBNrhpMiF5Nk7XZjbk8n+8VkV4qRGc257pH2YjNJVECDGGUEAuSSh0Eriw4FDdVnb+2iEiQSQTl8+EiamWoCEDOcOILXIne8rHEIBYomLAGuCQIREFFGVYxBnjo91OPkLgAGMAr8qa77wDu0bZz14/+NX2+aKgWgYOWOs/w9IkDqqGioUSHR+BZFKXqt/om5mVDTslJjCRL3fvWhFM+VVMLcmVm4/5H3nbn37OKh1azRijCLqHCMB6nFrndyACHWwsQwPAKMpQFmOi7X0EXcSuoYsbiqxyVKUUAjuh3pBK7hNBFRjeCaTj+UOlkj1llHllRC1eu25+dnrSWFUO0cHFy4EfqMRTm61R+vbcjmnrYxULJxZcc2zNLS0XbaVq9+OCj3Nm5eeKvfaD79f/n7C4/dq4GL29vl7VswM5ukzWilAGtNYoSCxHZoi2nSIJuAauWDFzWZs00HqBxEONRqW1YWFKF4EBAag5EoF2WW6A9CAgA0CKRo6kDNBJoCIVAME0LQ+oeP9DFFwh2iwQ+nsRnTSz5+3AzTFQvrJ69+oBRiAWs9ptRKBXqPJ4q/p975rAEBiBQ0a7tY6anC7TJ0Wx2JSuPgvWdfsGt0DGUABiHEZxAUldQYBIogJnAI0e4SXzdlrJfjuNIRgiFRQRDDkdcBiHl1qGQA2Uj02AOBItQ9o6QqSE49AyIqIZGgko37Z2xEU0YkY7LMZkmKij5MYafozDCoQdl7VTBkQI0xBhggBFUCgzazBCZUod123Gg0O9pd1HIkWpn2ibkjO3svfvl3W7O35k6esc4EUS9CYJnVuriAqQathLOs0XQ0HguycGBRsIiegzFgEudT65eXmw8+OFvI+huX+/vvmu19eXxn8b4H7/3YCdfxF782Ht6+2VKY5INyUg13+zffemP1+OHlQyvzK8v7tzfLvByUk93BdiY9xFanOz/cGaOGTqOZNTIRuP7OO9V4uHL/gw9/7Jkjh5ef//br51967e7H7v2Rv/ej+f+2f+PtN3i7HCXNcpi3Ww1FyK0tqgkmRoCNqM/HY1+kaZKYjDDNnPPjwnSyZtqqvHdoDDQ1lPEOUAJCwqBQK8udi+MqKHEogQNyYAmgCmABrWKC1sbZABCVDRkEFuEgqsIMDBI71BLV4BVT55ppWpXFgJmbrWanMxaugPvc38KxABxqzs13lwwaCWIVgIwPPkjwGACBCCyoRYOxtgtUVJyzzII2m19eNsZylI2KKItATHqI8Wux3BJrtoxUAghGiy0ri4KZ0rLR74sgRA4NxB4VE1F/oqkYvj4n66n4DnwfV4D4qipMSeD6qI9I4p2TH+uZH+PopfU7Xnc8Y1SZAGBMf4HY88MsysGrTvIyaXXPPfXQg48+eeTUyWazgWSVRbyyijALAAeOcd7R9jL9/RENmrolALXGc96bIHGaCj69OpFiKUZ9J02RsPrnjbPjVNiKinX9TMTZasAtai5FfKuVrByaMwkBBL9/sPXSlfHtjUanI5XNi6q/V40n1JztTHb3G9paWT3cnHNQehgNq52N27dv5MfnPvRf/vX5x+71lUzWNsYXL/qk0UpbmGXA3iQGiKKBzqEAITGpePFaVsggScvZxEzd1YIIJOoDqyprTGhVVUKTYD37q9azAwFE9p7uQGJo0KTkQiKVVyTB6Y1Xf6YAU/J3mqJUB0PpnZl9OkgITG3h05AMnD41CtPcKYQ7LU931kTUegqqw1+hxiqjvJiQXGZto4sAKAYUOGioQB0BSs1GGLSALBx/vunjWXNoMA2oirsjgE5BPphedNEliWhqv7UA1OOAAgkpChJFX0RtrIvhJ9OZJkp8sP7wVQEITNZoZIkhMvm48CEYa6g2zQsIoELWSiOxRSKcV4AUKo8OnHMGwGYma2chBOMaZRG8r1qpnYzYUvP04x+++fbL19/9/szSIdecDWAUKKgkxqAQmbijBvYVpGnUp40GI+uM9xJEgrGu3Wku9uaOrLikMzxX9s6daXz52d2vfW3n4uUyz7kYHnv/h+596j6T4IU/++7wysUWS1XlxagYrO9cf+Pt2ZUjR04e73bbe/vjg/3dST5JXc+6CilbWD0yHO0iuk6n51rdSVmur90YT8L8ypJptBZPdL/7xT8YHGyvnrrncz/71/74l3997YU3bu5tivIJtIvNVU8kyOV4TIkB0tQZUZ+XXGiJNkkoFVE/LCBxzjhlZmUL6EwixnJMPhUmFWMIgFi8IrCGwKUH0ejEVkzINsBkagwaEQwqFNm2iPyAjwcwKBg1KRmM9iVLiUXxVZH7YOxce84SjqtiL99dL/cHMJ7H7HhnsZm2AjN7IUBVAvQVV4FLNgCYWOMACFSZo74ElSgwm2a2fOiQgg5H+XA4whhkNs11qyNxBbkOIoug0PQ5jS+hCNI04iYmMEM872PRaqxBgvfG/+kVMD0Opmc/1rN/PDrsezNfvQDANLAH7vx9HdXzHuUbp1IQnaZMxIgJZvGhClU5Kkqyd919/5Mf+NDpc3d3Z7qEUSWtzBxY4vA/DTWpix7qxAZDUZtI04I4Aa0TSKccAU4rauO7iIhh+k1Nf0qYkpF4hyYgjrNTffzVjAsZiyiiAYi5ShK7srxkU4cQfH+n/9qV3bfW8/39cqZI0iWLxhtJF2eSVgIsM41mqoL9QoehLHZ3Nq4VnfmH/t7PdB+5hwWKtfWdl17zInMrq0mzgwkBW3AgngHQASgre618sIkBRAZw7dQ6AxDdEmoNaVAVNmRYGCXEPxVDjlBUgKOoHIBiADhrjeUpgIqKEhpAo4BFWZqUIoivqAhKPM17iiEhUNfoxF+LEMeUXIlYkdKULpZ4jdD0n9y5KXBKJEP9EQPWgnaq5Wsx9ZHCNL1PAABM5CSMQzBgAVIBCRy9f3E/I6saiENcnLGWrtVh4gigUTRW32JxH8RIeaOCTpshREQxCINSPO5VVRkFVVjr7z5uoFiHCMY3SlA05obUjQhEpFUYFaWC0RBENZQVkJJaRCVr2s22GhBU8awcAiKAmsQiggqol1yKZis11rrEttpZWVbV0KMPeVlmCwsPffRHbv/62ta1i0fPPmEtiAUUUhFGBiRrrQJ5H8qicknampsRwMmo6M5kIDRBmD282l2edc45m6webTVXHrWnjzUaxaWvf2/r6q38YDIcr5/++CdWTy6QfvYm9vbefUXdaBIm+cFw59LNm0eutrtL7bnZwPlof2uwtR6qrNFqdJe7M/NLM0tzxcF47LkdSsqQgh0OdyflSEq7s7W/ce3y2totq2nv8MLdDzzZ1t6b3/76/nBzg0ej/vaplXuu6oUhV8wcRDMrBogEKgkUyJMIBGEEX6GP9hbxBGWNT4gwmuh6VO85iKpSNHp6VDQKFjDBxJFLpe6oYAQko3V2HldaKXtQMGgcugQIAiipMtsEGOjAT4bke82VufbsHpcjzvvc3zF9Ub6rt9htzpBEI56QklcOwQcRVitklExKDuqTGWplDGEFmM50Dh2aFfbjPC+qEkFpOijFTHSC+lTnuvUFIg6CUclJEFsRMda4TPXf9eRXTzp6R6RzB+So4XGtX8/aNgDvnfoWFID+nAoIovhDp9dBHdoZb4KY1I9YQ1HTcB5RBS7LylfD4dgbOXzXicee/uj9jzwyMzdPZERUWYOqBPYhZryDSA1TEYAxaAypxsjrP7e+gPKdn0lrJSrX1RH1RoISRY135tH6B4s0hqEYABpn0HpGVQBmVkRrDIIGAVHw7AHDoaXlRitTkHJvMHj71ujaTjnMq1HliiI0QlWF3mJ79nDPDfOyNKbkcuBL7ev+cHO0Wy73Hvjpvzb7yD1carG5Prx0UQV7q6vZ3HxsTcAUYnQjBRaRULFUYtPEmbSsQtKw1iGIIhEaBQYeFtWoAGcoc2QRUMUQEoIxAhChrQiyg6qE+GgoERGgCtj4Z2eMqlZVZW1KUZCuHI/eGrwBEWCIiYPTUF6Ip2BMZaw5AYolmTWYo6qMkY+FmkLXKcE03Q9iOwCCAiiDmphmb8gQGRtFSyrAgdmzcGBE65BibY+KKgUBY6hOSgGMiujaDUMxzQpBAkCtHZh6DpCijVY4/kq9K0bM0EQzRXSOq9Q/kkYeRAHqEgMArVddraHWeoMkIgOAVVn5qqQ0c4kN4wqUnTVgwRpCY33woVSlaA5AY0lVAcVag7Fcs9ISK9cQmbB6MQmm7dQ1M931Krh6z8P3PPHhi9//6uzyVnN+EdWWEEQ1Zq+LiDHWGSrLnMbaay2unL5rsNEn45yG4tb2+ivv3PShqMr27MLSofnu6pFeq5P+1E8m80ev/sKvbr91qezvhHJ04t7HV2ZO2yceaki+fun1jPI8LzdvX0/ffnPl5InDp1YP9rbziR/s7DWz2fbi3OzsTG+2k7hknBZ7t7Z2NkauYWzbujRhItvMVsxRLmBrc2Nr7TpXhQO7cmYlSz/6xgsv3rp+qaRJc1dXmrNVOZ7IUCjEcjNL5NAwe0TMCIKP6UaggIQa5A4tWQtvCSVokBp8VES2iBaMAbJoLCRGiYAlFoQhKRBHnQQED0xIBMaAI0VhrVUjhhhlVA6GviA3szSzKAEmUu6E0S3uj7TqNppzs4fJNZlVxVN9RmmhXIUSJRasRh1wnJhtfIiVMCDPLyz1Oq1Q+rzIfVVYU9eWxLcEMfbcG2sQUJmjOakWusSqpNiQCHXzHcaFewqJQy3VoToJbgptT8+7+KLX6H1c2BURRdTCn8PRoV5I4nFbfyWt50IlQmaOfi4AFCAAYRGuvJdyvH/Qz6tDh0889aEP3//4k0srh6wjiNobkcCBWSUEVuU6rJOIEARNlG2CAkV9dPQuRQFQ3FeiO2l66EeyMi4zNfhTL0q1yr/OA6pHvEhycA1uQBBVRTLgrCEiBVDBEHwI+eGlxZluR0GLnd3B6+dHN7fC9sBpaDSQ8klxezix9vB9R7oW+7f3sOJksefKMPST25u3/OzcI3/1Z2aeOIcKfq+fv3tBc99eXmrMzSozIRpDAOJZJXjxHDwDi0mcs6YKFbrEZUnsUTEOsKj6V7cvP3fRIB3/yL3tXoshAFpyJuIcEjjmIN3xakeePJ7QUjuKGNEYRO+ryXDQTpoAGPtDIlABABrBj9iO9t4g8d59Wj9VUZYWH+w/Py1Q7QmPFg0g0DsYTdT+ELFoZKcBIL6QpIisgAxKNX5FIKxe1QcwokTRiRZNkfEWiipjjc6/ekUEMEDonC9yAACiiOvUheKghMhc740AIHdsK/WswIgWowdBQCkKNhWn0rHomUSI4SdY646JBJRUQJGcS1NbcSiCZ/YNSHqdjgojYekDYoREQVhViQyqKHtFZ521Iqxq0izhEibjiY7VOttsp80WjcY5dRvnnvrk2pW3br1z4dQTXZt1kdBDrIRUowog1hGCKfNqsLPnWu1DZ0+WZciHe+nNa+9+8bv7V25B6Wda7f3FWXt8ZeHJh2ZOPdB69BOn/vsZ+Ve/sH9rs/9n1y5fL098CGYW59wTDxEerF291AIdctjduLp/+9Lh08dco70/GIz2B82V5tLi8uricdfCsQ+uSWkrGw6GMFGxCChJpy1ospnuAx96eLzfHx7cX1TFsCi9zQ+dO9FaXHrj+1+/8vyrlyeXF1qLiUvAtqpQgIZKAgJkxoFBHwIabLkkFzYEXBt/porlODKjCACiEKEwGxWrlKIFIhUitKQYA9KojgkDBp7Sfz6OnQmQUYjiIkIjCmIhD+ORHyq54/OHUtPc81wpTHgygCAWjndWFxsLGmLjHSICgwZmFs/ELGyVCK2pkVkXj7H4bHuhhSOriaXJyDOINcZi3U0V4wtqO5hEbXtcFOvRByLzEY98mo4+MN26FRkBQIyiwDRvuQZB9E6up8QieaijRGGaxgaq9o70D6Ce9eP8H2lhVIh1bvXfIiiIigbVIKq+KqtqOBqN/Xh2YemZxz786NPvP7y64rIsBnaKQPRYKUctq7EIjmLCfZyE6i1DQEGYtWYaRTGSw/VZru8lP793U0HdZU4A0bgUacZochOFaB+LChmR6QxJiIDGWlREITCgIj6U893W7EyPAcr90e6r10aXrsuwhNx3u81iaKr+7tiXK/evzMwmBy9fGm7vt48cWj66Mrpx/drlW7LQfvAnf3jpofuDh+LgYPTW27C73zmyao8eYQ8KYBLE4IMIoTILhwCq5MgiVcErUta0wMzCxtjRdj/c3txdH+Qld9qZMSmpQ2sEysAKqBA4nkRQH70wDRIHUOQgdeq9kjFWGMvBsNje7fWWTGpCPbkgxaeLIizGgDWHPJXaRCEWoo2KgTgI1P+5E+IXp4NpRrnWWGFcDGL0jzCAGmMI0SUJsIw2dw+2NnhSmE47yZKk1XSdtnGOCICF0GBsmyaaAowxeZZUAgFK5A9APMeGdOBKFVQ5iuMU0bAIiIKBOvoJ6gRAjNYGjdE78UxnValfh/qRi0UlSkQgcQCJeRsogEjE8YYgIiPOOUBNXeIWWuJZWQVR7xDVJrb3BfYhUsEqoCTM4uMFqFAFMIZEkZWBgy/RJonzRny5cHLlgQ989Nt/8Ks769eW7jqXZk3kMPaFjbewxmZuVISynOxfv0ZeZs8ec6kbrOx1ljqDS6IHRbE1xrVtvrhevPbG+l2rMx/46bl7H33k//xfX/s/fnH9nWv7z78hrEeffHTpxGmAR0ZV4NvXC39QHfRvv/bKoWN3NRbn1XvliQJUZSFVIY0kQFVCqalN59tWAQwxSzkZZ62uV5VB2W42ESUJacpclIWXg97RuSc6n/YjePeF5zxurbbmnbesyGLUqFewok4RLAVRJhFrAkASlfoS8/Epbq0QvU8YpR7g0DglVYy9iTTVu8RXnQEBNGiQmEOlQAQWjVN0qpH79xoCQhHyvJx4kKO9Q53O/F5e7Srs+WrgJwX6eeqe7BxukquCDyIWQQypSAAupVSVOEsaBKTYlw5xsyBTW3VXjxwH0ErUxBUSTY3KRGosih2j7CXSkrEniWBKg9bzPEBtzkWIQGfcVFEQMPpV4u4awZ87yk8FQIh06/TFriV/9g7IW/NyEW6Zgkc6PS8iDsCqKhxEufJVqIb7/a2DUXdu/umPfPaR93/orpMnG400HkmswoFFVOPty1FXRIYgWhZi1b3EsOMo4qsdviCAUzAaaLrj1DrQ6foft6c7uqApwV2vOBEIElCSqU+srgpTFDXWGlSDpIgiwCE002xpft448IP93Xev7V+8IfsTDji/OKuMB2trB5Nw4unV3mx74/kL5d5k9ZOnj82dvn394vlXnh1g58Ef/cnlj74PHfj+qP/Cy+WNq+3ZVWrPihhIrMvSMJoQolGtykn08hhCA6jAqmgalkH8pASjbKka8rXv31x65vipJ053AKziYLPvgZNZZ9I0rmhINdI3ZYSIgFBBgqIFsqBe0Vkk1JIng4NyPEBfgm3ER8HUM0UdN/Ze7/N0Way1xDWTjvGmQaybHmt2dBohHt2Md2ZrrB9PRFCyBomssca5MK5GN66/+ydf3bp6Wb1Ao9ddnGsuHjry5MOdxfnW7CwKlGWB1kaDh0XLIcQ8VxaII3S97xA6Y4y1yuJLH1RxGoHC4kHewwgFUEXq1SDSadHwrAigHDhSxPFSiUupvkdsxCenBg7JAFENrgqgsc46Z1ODiL4wZeVN5ogwgASObz1hTFQxEHe1SGiIcFUxkSMrPmdMUAWqkie+KBOb9SBNTRkCWr378fdfv3Txxtuvz8ysJllmjctQg7AGBgEJQmSNMQKSF379+tpoOF46vbJw+Jj80Cd54fD68xfy9Vt7e5Nqf6+3zwtre6M3//n4wUdPffzD9/zs/zh78c/e/r0/vPnG9VERTn8QZo+fPravYeDzQTUaDneuXbv6+itHH3t8YXnRVdJoN4dheGtnrZFnkqSVMqRpwzkILBxQmUFY2FhbTaqdsUcEyqxxhOzFy3C4Nzd/4rGPfna4tb95/eIe9me03XRuDIAKnlTEp4qApCSgwgAERhEMGqAa1Issf838KFBtlERWNYDGWBslyAqsQBTnXQFhgWCmbGGi6JSobvwDAWQKBZd9GTP5o9nSbO/Q2ONAxSe2rMo+HaDo8fahnu0qKyqYSH8KGyARYR8YBcShiWk3AECqhEJq4rosxroTJ0/5oKUPzhkDCYGDmvMUQQEGUIlwTzS3mKmuQkRqgb/WAGytAroDHxGKKk3Nw1Crt7WG+e9QW1JL/epRXyJuCza+qSJT/lclQrzxKyGiioiwSNTaga98Vfl+f78/6qftmSc++qn3P/PMidOnms1WZC8ZAJiD1L6rejg0EQtiFZVIeUcjPMffeKo4wTuneC3mw/rbmJLf8dvFaa0ATq1r73nRAO7MeVNWADHyAGCMQRsvD4p5u8EHUD/XW3AN54eT/bdv7711PexPQN3C8WVtr66/+uytUf+hT9zbNa2N7716MNGjD57sJnM7L7zz0ve+vZUP3/dzf/nUMx8gssVoNLh4Zfjm5cC+dXypLJPEZc1eOtoacRkseKkKDwypIQUCVETvWZ1BBAnsGol4cSYLnpfmO9Y2WgsmGdLlr768vT1affSulYW7pp03Nc8dkUMkAAKRiGdrBGuEUI0xilqWo/5WUYyUAxkioJhQUr9OCKh1Z9ydVbD+lGNYUrTn1UMV1qRDhC/r/RIRLAsTkdRlOhDbgQOH1JKziVAYjfrb33n9xve+vd+/WVaUMGXGzCytdo6vtuY6aTPxIaBGEX0AQLKGQJXIsxeJgX5ASMaS0jQl09ngC/aCAmgIlESC1hJskaAIpGoi3a4cFCJ/jXGsqF+o6dOOQKQGSBBApwZyFYGac0MWBgVjHVnTaGRJkhii4bDy+6O3vvoaNPX9n/+wWizykhCExVD90bokiUYUtCYuKmRRhH2hwkKcxm/Ze9FQoLFpmiSpnQyK5lz3qU987vqVd69du3jvfNemXXVOKmQQz8FhyozGEiEJACsP9nb03aK3unD00bsXHnpw93O7u9c3Ln3121e/9moy3sbJMJls8datm2+9cvRTnzn65Mfn/vFjb37hP1741hsTXz32THLq4bM8OvAXStlZ729tr73x6tyR1YXV1VaaJKkLzPv9/l7fuVbW7nWanTalVBW+HI6tJZcmymTICDhtsUAVilJLdmTFiFgYDDZXzx59/2d+4Nv/+T/vbN+gBiXQTsEECqCsAAFNpihADCwgJECAFJuekIBVgVEC1cscAJg611gRKUZistQdJWjUAAdAFlQAQSUSNMYkiAYACRi0Ug7KOVdjHuWi88nM/OxhcO18wh4gr/JtPxjKeLm1fLy7khrHoiIsEFlHjKo8ZmAX3fIWAGLLCCogoQAAIQfO0s7hxRUARYJG6oQMgoF4vEcs1mhNbt1Z52s6tka9o3spbtqKsV1bUAA5VphM1Q/xmosy0RpekTtn8BQUgilIixQvgFrbX68KgADKsbUVAwcARDKqzCFUwuV4cHtrG0326Ps++NjTn7z7/nOdmU7E8AMLcwisHHydZVgTiCpQBz9DrfqMLadTP64gAEYTQ20bqoHnuPm8F41aj28AWofhA9RjIeh7AC8QRHRNkZCioOWOYKRmvVERqiBVUfa6ze5sm4vJxvnrm69cgL09B9ns0nLiZtffvbixNXzgkw9ndv76N14Kmhx+4uhMZ3lw+eI73/rKxt7ooX/wM6c//ynXzHxVTi5cW//G9/Nb46WnTtuZDmW20XHFeDw5yJM2FXtDCSFZ7KpRqZgB8rwwWdaa7RCQNSS57F27ff27X26wa5w5unJ8UY08+69/5a2Xrx554r4HDj+NaoU5RvzF57yWaxIBKAuLGIMAQCooSjHVVMYHg51bmLQ5KFYsaAAjXKhTD5zU2F4cKOrLv4Z34E5ALBIBxT8/nBIDEgtMQEkhki+BGRjBICkYTJzNmHl3c+el3/3izguvtGc7pz/0mc6JU67TaTfaJnONZhK/jAoDkTEIBgFYgvA08A5ASUUFGISMRUvWIjAYSoOv/vQP/mj1+PI9jzyO0yuNmadTkMSZIypK49ILihCthSDKGN3kioJgapqx7qZSlTtgWpzjQZFSZyExu5c3G4eyalhiPv7Cv/ytiy9d/2v/z787s9AaDQusGy+QRWobgSggWkKRWrYqDASCqArKibXW2OCMVlVREnMy08IkI6PgdOXMiUef/PAL3/x6f3d37lDLGWLruGJADRIgKBFhgoaIVUS0HFVb1zf3969ho3nXQ/cun31o/q75zt3Hrn/xa/LupVEwEgbtmxc2/8ONuT/64l2f+Kn33fsTJ2Th+1cuXnrltYcOLS8/dubW7nprPNgej/dvrm++8erJJ59aPrpajkdVCJOi9BWRD42sOXu865rZaFgMREOep4lttWaLPA+GTCMJVeETW+0HrsSXZdLI0MvBYP3EYw/sHeTf/ZPf3dvfDSk0JSUkJ6bigA4L1SSKYZQAolQfwxT5NcJSv72GFE2dnwaEBoBYZUpsxeVUImqJEPVBYMikZI0KqgaAAOgJSi6HOqo0LNLMifkjrc78XghDIwbZj/f3q31qNo91jzWzGQ4hslkIJggbJGb2UhYQAoEBUjRIU56qBipRRQNqq92Zn5thH8q8hAgRQr1/E0D0cCPcEW/Gp1RAQKhW6dU0LACRkRoUFRAlUPSCtS+ghsPjw40KJmqf4rIQL4I6HQHu/Bs2Iv7TCS9CUoAEHFM2DYmIcGBWX/rd8fBgNFg6efYDH//0g/c9sHjoUGIIFIVVmL0w+8B8B82J+xrpVGMOAios05KWOucIAEjNHSVUDYMBQJ2pdUdcWPvgajWTEsbsOajRIqmJzBiPO/0ksP6acdp7jxIBERFfCoT55Tki2T1/+9Zzr4edvGnt3OFFMrPbN7bXruzd89ixrmntf+9NY2H59OlGYsPbb195/qUhyUM//bkHf+yHkLKyGJW39vM33m4l3H747OyZU6aVpZ1GmOTFqLAJJWSH4ypI2WoukrWTMArCSStLWy0USAwl1k36e2tfefbGC6+1Tp196MeearRn3/1Pz37zi2/MnZ2/99OPmVZD1BAgWEtWJGrxVdAg1v31KqjWGERQUWMMktFqMt7dUC+9pQWgaNmrn7/4YQJAFPwjQQwIgff+UkCN5sGYM8caIrAicQSJITlRy4AoqpnNGq22zRJPYK21AC5Nh3sHl373GzdffX359PJ9H/7YyqmTbqZLxhgy1loWDcGjoWmQQhx3VDDaAhEViSyLB+XI/pN1yogKSOTS7mQ8PP/K9XsffKxGOvXO6R8RgzsvlcIU/I8/DkCM6on1eNOJRQEBmWNKIAhpNCwgM7qk28vStHHl5atf+1/+7eJHnrnx5jtn7j168dV3hhNvkmS4NzgY5C4hUIcqcT8XldjmWS9Y9drP9aKNFhBcaoIq9/3mtfVWKwOoWrMLjW7bl94l6ZMf+tSNi+dvXbnamZ9Pkp5VTRLnvQQJSEjsjSbx7QmqCuiAtMqpxRe+843UNFZOnbr3w59YOP3Y9RcuJ89+s7z+bjXe34Ltra3XLv76erI899j9991/9sHrt26uX95Yvv/MkXvOjTY3rOvv70+uvna+Pbc0+9gDrpFV4zEaw5JXpQqGZmJbWRq8cGYH/eLg1igs8uzC/GSMPgSXNYA0a5a+KgVgMhz3urMSZDzZfuj9jwz7my9982sH+ThkuoBtEaDEVMIJOla1jEgUiAMgglR1XC3W6WVEAAYRhAUB0dTD3FQnFuFJJAVAEUQUdCqGKEFECEEgCCtqQPDgKy2VdcHNnJg/1uvMDzjs+jI3mfh8J18XE040Vg+15iyAVwkcjDEgAARCKp4De1SPQuhQVCxZpfpwjSklUbA4t7TQ6zYVKgUlomnOwZ8HXN8T69zhOlWVWe+ccPHf4phYWf+KIMA04jKGnQkKITDFG8Vg3GK1xjnBABEgGOQQIqhup+ohqA/S2INIU9YVtGIvRTkpyp29kbrO0x/7sQ9+5INHjh9tZSkhcojnPjMHzyxeRGPJbogu36ledepei+tMTIsz5o6i6Q5tMQUjaohZ3tPgQeQ4WEURTbSfxdOoVolpVM2amJY6PfanDEG8JhAUGDQEYZHxaHT48KFmZvrXNq5864WDtY1WMt87eihp9/qbezeurh8+10lNsvvCJYt06Phq4nt84/K17z2/vTte/rGP3ffXPu86M6Ufh1vXRi+9ub++PfP4SdNa7a7MeeEgwKPCJORQdTIa3Vq3M02FAOSYxWaZy1KXWEINu8ONy5vl7jibzXB1+eRfuH/+dG/j66//wa99efHxu3/sf/ixzuxMwDDZL/xo1JrpNJc6xhpVVq7xPNUYuxmNrKiINkmAhUO5u3Olu7w4u7wi4LAOUYjPJtVTUt0iQHoH+I7ylQiM1P8VJacwNSsCCqBBJTKJS1tJo9F0PceCFWu1J3s3+9fWbz9474KRhu01Gplfaq8+9mOfPfHow+SpGI2Mc0AUAleBqZbfBIplnHEvlppLiKeytVa5fpJRVZRF1WrpWvbTP/6jb7z+fUAAVAmiikhU2wjqc11RNYZEAdZqn1osIQqIYmqFqNa4aNysEAzceWwQ3cLCXLvXevkL33j+F7/Enl79vZde3Xzj8ouvzzuZm0lf/c3f/s4vbfYevfepz31y6dAJk6ZIqIRVXpEla5E9++CtNcIcxztEJAOqygLOORWcDMb5/k5myyRrmEZm06Qcl43Vucc//kPf/qPf3r+1sXCiiZRalCCGQIU1EIMPmCIgkCWvwUEyv3IEKVw9/+6l55+7PvhtXurAzNHV+x6ZfeQHOfxtv/762ot/cvnN8/n62vbm1oXNW8eOrJxqzTZvbcLx5aP3Hh3s3DcOPFzb7O8Ul9++2Juf6yzPBDBZKxXRMRtVmEwm7GXYHxeTPLG0ebs/2Mu9VHOzC0W/AglJ0wXfzgIXlS8nYWd/e3FxAb0vYfepj31osLv/9ovfHReTJIOMrUEShKBlVKEYmRaeaXw+yWCMuACKH5lofXDcwXoVDJFOTYEMChoCoxG0CoaogoDR7UEExgiWAxlWnC+ZmaMLx3vd+UHQ3bLUBIz63dHaHkxm2wvHuketOmEUESKNpSgKdaVJqcGbgrBBqnFtQQCLdeNKHGXUwOzysjNUFVIF8Z5peqRFSgOIMEbU1GdVbVqtvSv1GoNIgkgxwrKmagGUAGMqjwJH1Q2CRTRokExMV46hDkSIdTIXqqpzFpRExcbZj6VOFIo3DZesBoIXVV/66mA4ylnveuDBDz7zmXMPnJvptmIKmLJ45qr0VRXq6lVWEA4qEkIIgVmiSTlqV+MfIBkwiAY0shLxAIepHxhqlYcC1t03Ma8FCAnt1JKgU1a8XhajyD9eFhrdP8jTtaimRKQ2igILeF9x8I1mY362N7598M6XXhne3neUzq/00pnOuL+7fmPtyNnuXDvbffsSWttZXbYa8NblW+++tMuTo5976t7P/4htzvpyXG1uHHztu+svvNH90JOzJ8/a9pI4Wx2MvITEERnUyvNB35dlxzSKtT3b7bICKziyLDBa61//rW9k7dbc2VONx+5++sP3d0/NTV7a/ZP/16/NLjZ/6H/6S63D7UnfD97avP7im6Y8WH34Pte7r5E0WUgw5qSqCkTEB4FEGW0KCiqhGOyOimLhxIOuszwuOWgw9N6TE7uHKaLeiCrAXKssIkIeH7/aNoxAiBp5ASSfF1naTJOGbTrjzGSvvP6dd9bevlbtHyRrG7sXdq68e+vg8bvSudbhH3hi+d4Hi+3hXPdQlluTUC6GvWICaIzFGpifAp5xedQY+VRP8TFFBw3UHn4CA8FXBQZH2mh3T951r/f15B91rFFHiopEqhy/a1LkCEvWcxfHu1BjF1/NBCOxKJkawKmzc41ttTtNk732u99+/Xe+lV/mz/69n3KLh/7p3/n7mlfHl5fdYH/z+e/dzjeT81elzJ/4zKc7y4fb87NaymiYN5qJMTaUXiUQkTWkBGVZ2SS15MiYEERBs4Y5+fBd1c4u+hDycTHMsm7XOvTqT547d/W10zfX3mrMzHUWlwCMJYMAwkECMIghJUcqahB9GRqt1CrhmK68cpEPbnZBCsB34DfSZq/x0P2rD93ffOYHHvnsD+bX1s5/8YvXLr9+dS0/gNbKcOvhTtJ+8IGzT9w32hqWG5tlNdi8fP3WfG/F3EO9brPZyOabWVANfmdzhz2N8wpJOu109uRKf3c4GRUumQCa/u5wNukmNpFWOwQuJ34yrjbWt48dOaoKYMoPfeaje3u3r199u2CfGGNZAKSyYhAVgMFF2YYQEoBRIABTK2RYIRo+AFVQUYUxCoSEADHU7JYosKoxagBNEI5PkUYJGVQHMhiFaj6dOdo70m7ODAPuVGGSOINhsn9t3w8aje6JuaOdpCOiTKAsCAIGVRnIqnIh3isDOIRoLYnYtU61EwB1o4ourC44R1VZ+1GmE5YigBJClMLUK0yEuuuxNwbhxGSbeopHjuGVAhBUiVFQRJCZPXsVRgSKiezGJWKyhAAgBhJCzX1FB4ISABHZGC6qUMtbVVBYBEE9S/D5uNyZjLLuwmc/9eknnnpyeXEhtRYVBAJ7KcsiL31ZsfcsLKohSEx2U2XmqH8HQ4gWkaaCHlRU0QAMkbQBFCWDhJEGuKOfgNgBWceAIdTsPxisXSE1/1NfBXEMmIJCEhEh1SmHLSAgMVUJlEsfDNE9Z1ZAymvPvzjZXHdgl+ZbM52GDgab124tHW0tdbsbL73jhecPz6dC3B/cfvPlreHO3MeeuOcnfqC5shJUqvWdjWefv/DHX+osLR89e4LSnhByWYqoc7G7KVRlNe5PekcPGw6DFy7q6kp239Gs0TDWFvvj67/7rb1vvJQsLoVBefTHnnR3z+U7o1d/+cvQbXz8H//43Lm5fHOy/p+/e/Nr3yz7w/nDy5PZhexTj2EwZV6YrNY0Qqy0NATKQGRShwJa5P2Nm+nsUmfxcJ4LAdg0QVWMu1Otdtep9lejPQ/qwBEFQkGMELyZwm6WUA0hEUjqGo10viMh/7V//kuv/dZXGjd2Gpw1wRya6+goh2rrta/d6MOk+5UvfeDz/6fuIw/efuM1P9gcksl9gYXOnz6VNZvO3OkxqJVbLBDtL7EBLko5FMBatJRURckgULFBci7xZbj49qU0Cb2lRS4qAQAWBFGpX5MIIUaZoGitJIztB4gmigfqBwhRkFhBCRmViARiXJwlJNdufev/+K0Xf/n3joAsDbsn7nlw9YH06btPXnr3iofk0t7eiZVs3iyVB8O13/l6fvXqsSffv3z3Q52ljtOkKIeJnXWtlAtQ79NeI0hQSMgYAAks1mA+HOej0dKZBVxZKrbz/sGeswVmrUaSlGWRziT3PfW+G//5xsa1m53Zbpo1BTPJJ0g2CKsXQp+lDQsALADEEtoz3UN3rSwtr+QHOzOQN8DlEPYn29Vz39p47jmcbXQfve/Up//mp/67By69/b1XfvUL4/3Nt26vHXzxa3evHZz6xDMPPf3+/OabG3sTPxhsvH0h7c7M3zODaueW5xeM2dnZ2NvdCwWMhjqz0MZea3aumaUzDIzgWDiUMtwat1qZRddstnUGi9HtMCo2N9aOHjsSFBqL7slnPrazvzfsbyFixyROkIBQkOs3XaOwLVr6CGqPItQgblwXpVYIKzBIJQJReY+17D0F06QUEA2QJatgVGFCw105yCU/hN1TnaPd5nylZsDsnUkd9vc3b4RdbXeOdo+2kg6LcghKqhhAAEQDMKkFAFH2wABoAUGQLCnonayZSDapClnbne2Rgg8sIIoYIxBqOJpjdpnWqpZppUoddB/lQPVaioiIgiaimaJOSFg8C/vgOVRVEV0vRJRlaYYUU9ei4pvqz61WU5IhVeAQrEzVkgagrugQYQnsq/1hXjDede+jn/nhH7n3nlOps1GG65l95SdFWRRV5aUKoszxEEEQQHDWkHNRMmIith+j1qJRiUwNgKkAYJTEmNjVWCeJ1VAs1Zzk9HyYaoHgPc5iCiH9OSVoxN8U6zplrVnnuv3KWENqHOrK4ZV25ta/dzFc38yC783NzvV6JuftW1uLM6HTtpuvvru/d7Dy8JGskZQb/d13Lu5OxvMPnzn7A5/LVs5Wyn63v/On3339136/HPVPfuqT3WN3mdT5wKAEBK5pSSQUYdzfw9S2jy1sfO3F/S99zz949q5zq+QMC0y2+gffeuP291/qnD0xd//RbLYVcth89ry/fvX0Dz06+9Dx/gbe+r/9zpVvPVds324u9vjU6okPPgzXD9769gv2niPHHzhlEkMAGlRiHhkrJRYBgEN+sFsNRwsn7ksa7TKM/MQ3nI1zisSk+Rrnnj4VCmhN7cIwJtZJRg9UUEGAEIQRwEHhdXZusTXXRmv+0z/+5S//f35lFejeVnMh6y7OHbaNVjXemrsl28XgEkyGB/0//a3//YnW58bJ3lu/8gt25ujH/tJnT9x/zkThRmxlBIqZO6xcqzoAnTWRr+U6OcQkhshaX1Wgqqwt60TQUZg5chitUywNINeBGHV2D9fpQIiAIgIYMXlENIKiLGBIgo9iTyWjGLOmKEiIZkUfeKY350f+5V//s/Vb77bAHIWl0YVnXzv02A5ICeXarevzmH7gR3/s1Dz87//iV0eD/NZ3rmy8cXPh1PdPPfNUr9kzphiunjj2oSfarc7erc3tW3uEJus1CHgyKtrOWmeQcHRrPzOm2Vkel74IYERAg4jlIJWFhXtWTz/44MUXnj/o786vpKm1nCV+XCHEvjANeZVkGROySlV5SPXQvSun7z9x/cL5BIYzkM1Dy0E5gaBQ6f7O+Nk/e/Pdyyc+8NmzH/n48X/62Mtf+f3bf/z13cnm6y88r2U4/dkfvP8Tnxt+5Yu7e1vb25vphXeS+W6amcAzvV5vnpYuvfPuaMSNRtuSMSoJUbq04MuSyBaTYo+FyIVKRQXVJlk6M9Pd2RpWOWyu3z6yehygvOveo0/c/tCBn397AAEAAElEQVR3vv6lCZQGqENZooACgUQBWCpQdQBJTdjEwO5a/iHiWQRAEIVBFIzU8R4IYBAtonHkUrTR1i1oGZAVJljscb8M5WK2cLxzpJ3NF5jsSxhaBGtGBzs7g12Tpoe6q52kiYpBgxILcK2FQAE1igjMgYNoMGgNUIyLQgWMxUwRzRdVK0F5dnEWDeZFKD2DqCWql5Spng+nQry4m1KULcZNQqcIOYKJzY4xt9ACMzEFRIgpO6rKvgrRJW6tGC9EAYQhnrIxppCsswYRDACQsFqNC7ioj28Zh6qohvloOAndhaM/8IOfe/p9jy/Mz5BCjKcsuSpLX+S+rCpfSWAvoihoLE65BDDGEER1em2qRFUDNQiLhAhGQUlqHj8e/lNqI356teFNZHqyY4TvAaVeAClOdtMcyqlKEaa8cm38iU2FhGgNkjVgHIbQcJ25pfbg6trNV97y/bLbbs7OzabQ6G9sZg1OSG9///zWdn74/pX5uZUw2tq9dHH/xpa55/jxj32se/Y+SBE2b+9/5/m3fuM3+rdvnfjIZ47/yOfS2ZmyKoOXUFRZJ7MZVYMyn0yC+sUjC5oPilvb4xs77sRdZDBtNav94dtf+c75Z7+qMFzo3NNaWK6G5fjS7bXf+vLtnB/4+H3i/dv/w79f++I3u6ntHjnU+8iDJz7/wdFo/O1//aXkzMz7PvYUgiUgYFUUVrbGACJYAwpSFoOdW9Tq9pZWCWyWpVz5qJOrfcNIdxwXhgyQNQICWowrg0CpoqJNrMN4hqL3XphCihmDMdybn22mSX9w8M4XvrHc6j66cmbRZ8uHDqGM90vePhgtLR9NXT68VoQw0OGt9edeevy/+qnReHTzpeskjYX5FWvTMpQqgoaMIgupsjHGUJ0cHUNoRZhFVFQ9M6BJkiRx7ENRVqJsLN3zxJNBJHix40KhmqpBIjkECIBcC0MUlTQ6MFWUEZEcRvURIIgqc7DWkLUCaFLnjJmUwRO2Fmbh5kEjNC10GzON7f7kT3/pt679u194fX2tCeX7588sFeH7v/TF7j/68ZVTR99593IzIdPXvTcvPfyZp9XCq3/4jRK+//mH7rNzNgTINwdlGbz404/fI6wiAanhWslEi7eefVmDjoGPPXiqm8whWCR0STI+GGSt7OyDj66/e2Xz6lpvbsYkXSsKjUTLSllQhSVE37QEHwRGO+PO3OyhBx/tf/ei3zgfgNq21QuUQMjBAhhTVPna9au/+R9uvvCtJ372H370b/2TS489cuHnf3V/Z//tl9+pfLL62EMP3P/+N779hwF0a6s/u7az0OvtXd8ADmkrzTrN4cH+TC9daCXV8GBUDOdOnWvNd31VdFrZcG9vPOwfWjlcVqEocgEyqcva7fGgD6T7g712s+V62UNPP7q9dvutN19AWyCiE0y1jkg0gQ0qAAgQGYqzSFSoiEodj8uBKMY/WBv1YWQIrTUOAQnQMAOrdUYICq5GPL6Nu57L49nh1dnjjWQ212SMPCYNKeTj7d3RhjfhyOzhdtYGNsyq6slghMcF1EAkrFREQlSbYd0HGo9pNEbJAgoYEFZFShrp4txMVUrhKxUlnfLVhBanQmSEO9anWj/0njojGn3uKJYj5kEKQjFWxYgxkjqXucRngVkU0RqyRCpcqQQffOCq4iDiEsoSZwxaZ0EpVGwhCAMGZlDxZb43GA2GeXtx6QMf+chHP/rJw8eWLcb6FGEOk8qXRZl7rsqAIVijTYuIVuONFeGk+q4EiG8VTs/m+pfjTwCqMDXdUMz65Lrna6ofVdWYO/+eLBFYFAggAMZ1DlXrWySKQIXjnqBTIWz8BIFAmQXKSkQ9Ms8fOkSK1557e7TVbze6rcWs2c6q3X4q47Korr97e3tncvT+1SN3n7bbfufV9c3za3R88eyPfar3yCPSSmX9xv4fffP1X/u9S+dv3vXkuYf+9uezpcPlZOzLUI1G7fkeGQrDspxUAtCem7Wz3eE7o47IfvBpBnPLPZOYd99df+t3vixwcHh1debYKR4l1774+vU//sqttfUn/ue/2pudO/9T/99bf/JtbdCxv/ah2QcepGOHt1698o1f+V1/aPlHfvRHuwsdJxiGPhQFNCxbNPFyRQqBy4PdctSfOXrG2DSSmwLx0avbJwg1ZhcbIrQYqkAKMi7XXnmru7o4f2wpzZrsNVpF2FrXtMO9YtCfCId7HzuTpQmoXHj2jYfmuzZZ+At/8x/9ybOXX3juN8blNayUxpPyYLh897ls8dChDa70YPDC16/ee+LM/Y9vfuG523/27D33np47eiS1LniWGOHLAVEJSAPHwP2SQ9I0iSVfBF8GFDVI6Mgl1iAEz6pMZBszDS51eFChIlqDGJhruWVcDKU2L0RbQJ1NEm/AWkuhiuQazWZVVBo8iLjMWmedMa25xUaj1eolOgitoyvtte58c+Zaf+vgxuCZn/kg/ocvKVQraagOKi22/+Tf//4DP/6Zi+vXeORnk4YE29qcnPyZT1z9yh9ffu78hWdfvO8jj9y8uHbw/HNz7Z6fb4/PraRJI6YaGuNmjh+9enWzKnnleK8zO5s2UgHwQW3UeAgsHz186uxDz7/wpwube4eOdZxx4oNzSeBcETwz5r7ZzJQ0qC/yUUe7h84eunX2vv7Gmocqtc3DthWEypDvyq4ClqBjKOTKW8/9r//8wb/59+//1McXzh17/l/9wuClq7evXUitmzt++NTxE1dvX6uKfGN9/ehD52ZmWns72/aAKEiSpi5N0dGl16/l+d4H51dmFpZyYyzq6rHVC+cvDifD9swSow3VJPgBoAkKkyLf3d6hBZNZWj429+QzT/f7GzfXLyli12SpNyrqwDgEBq8ACsDiBQgwCIqgMpJFJEwatm3IWXBEiSETg5LqlExg0tjSqYWEypd901+ngQ96sn34ePtEK5sJ6EqFCYpYyScH/eFe5Xh59vBMY07BclTETEUJSDX9SIYYVCQEqUBj6hvWkrwonIMY4SwEwIhJ1uzNzCCRjxkmxtw5/qJcCFHrgpMobNXoboI7phuse59qAGz6MCMgkSVSdUbB2ZCkMWZTatibfQhlVfnxpAo+L0pWRVRrDKBaa41LVMCyVhosc8jL8d7B2LXnPvLMDzz19NMnTp7MUkfAISirL0s/yYvRpMrzqvQVe7LI1pnEgbPiDFEUnQpKLVPCOmv9jgpzeg9MORIElKnzWUWUMIa4x6QBkQACqgFEuLbtqfjAIcqIVInsNB8PQUGFWQOz3KGUyWBk80JVRrR1mHOe56fvO5ll6TsvvHPz2kYnM535TqOXcjXx+f5oa+PGrb39kKw+ePfJpx5IJrL55ovX37ykJ5ce+us/kD14P822ub+19bWXX/2NL7/1+putM4/e83f+8uwD51iqMs8h5GkSpd+iomCNRZOh0dzL2sbepWu3t7eOkncplYN869JNGU5OzxxdOnEmXTy0fvH8xa9/8eb65ol77j7+wfc/91f+1do3vyPzs5/8zX/QXr6LX7y59pt/+sbz72T3L/3AP/v84UNLZsMffOOdjVdfha5tfuTh9v1n0RBZCwA6zkfb65SkzfklBGUO7L1KmCp9FACVVRSMjXXwUpacpWmSNRZPnmk2s3K/JPAmNaroS5/nBYqGAcu6/51f/s3/5j/83aWZxm//y994+de+eow6d5/IfvXX/ueDtf2tvRs2wSSXALjtdePG68sz80utORzl+4DXv/ydlXuWTjx+981vXLl49+v3/5U5VjTBFeOiv7fL+bjVbTfbHWNtVVbjvRGo9FZme4vdxGE5ykMprW4DATWoqqaNJMq9RdVYMhYFrTWE6LXWxNUjBxEBYoj5vJaADQBJlMQKACiLD3tjxCJtLmy98EaSH7TSqkjH5V0P3v1DP562Us3HZSelc0sHz7lJgAJkLpG75n98/tPNl779hZ31UTCuD8qba2vfOzix9OT2wbcxpxaQ/8orl/bDCs8Xs7n280Q8DkeDV9YPtt/44D/7y7YaubQZY+E5aKvbOXX/fQq0uNhpdrI0bbGPZXEha7VCWWSN5MwjD7z5zmu3rt5eWDhksowscmByrvKlMUa8cAjGkjBWlR+Ox2k3Pf7E3ZOXvzkajW7gwapZaFO75+Y7sLQ5vD2s1gVUIOR711/91/9ydOuHTvz0X/ng//KPnv8X/2b/+1vvXrm0VO3OtuePNQfb+/u6tbuzdv2uh+5xw+b27WuDkQ9gbaNVBnn7nUvb21dXz50+dOpkmmUhlO1eZ2Fldm9n6JrjRrPjJ1ixFl44yGg8dGQoaFGMycDJ+049cvupnT/dHPdHzUYajKIS19HuqCJkCAgVDRmTgEFjkcgZYyEx6CxZAsP1CSMGSdkjgAGjWrGEUsuhTPq8v8uTBjVPZavHO6dajR4CFIgTrQKEg+Fev9r1KEszC73WDKjhihkUlQkUgRkABJCmxixEDyHClUbRKE71B1HRw4QowtaioKad1vzyrEg8mwTC1HNj4pivyIKEaKJiASMuGsGMGJ8bx+ha5zlVCkV6OOq3xaASOGusgkwbMr0E9BaMdWiTwFnmg2fPla/KUBYBC8CEK7bsOR8Xw7ziBB754Ic/+sxnj504nrkECCUEZimDn4zHg7wcjYrRZBLKACqI1EgsqhlXrCGoiDPWpS5xibH1QqAgJiYVT8Oq65+BYkSfxnoeFmFRiSUJCCLgOWgE+US58mVVeB+isThw5DlAQywgQ2PAWmPIEAKzKIiyMotB47JUkfO8BGQCzCve3Z2sHJqbm5vbeOv2ze++0U5podFuNxOXh3xza/Pqja0bu94mZ548cdcDd3WLfPfl23uXbjYP987+1A8mp8+4mTSM9offfu6d3//C9TcuLJw8du5v/eCxD35ADVclhHyUdrKk0UAygT2XDCAEWBxU5aWNzV/65tqL3x+ZFNI2KBlLvWZ67vjh5e2klx15+6V3L199a2/vmib24b/w9Ks/+0/fefHtxnzvJ7/x7/DI0cE/++3+H3xvbdK3D81+8O/9eLfTmVzv86+8sP9rXxgObm+v2MOL6dyDdxMiGauBq921Yn+vc/Quk/UYIQQf8gIBEK1IqO9LgFjKAyrstcqrRuI6S+1GuymB3vqD7++xnP3YQ6Hfv/Dylf31/sxC98kPPzHev7T18tWNFzbe+U8vfOtf/MaMyNyqfvlLLxH0GzA6ZJcaJkkPQdPNDPv9i4N+MczPzDTmksW+FI3i4OIffOfsz/11337uu3/69avbVwqnKwv3nXnkzOKpFS5C1R+Mdncmw2K0Pz7Y3evMN7GBzdkOKCOha1ibWUMkROQVwCCaGne1aBwimSgWUqVYUxRdQ6ogElQBSJxzSOgrD4hkkxCCISg2N8ffvrL96lbj5APZ4r233v16/8Jr+d5N88Gt2cffd9fZM6bT4kawWboFxXYfFTxOJlvf+83r17cRZ3ZxRM12Anwz3/Uv/9kDR+dmm4euTvoC+vrVt86dXs1D1ew2z33wfm1lm9cPbDC7N7ZH37s6326ERifp9kII7Dmx6amzx5HQpVYUyJhQlSEEUJVSLJngy9nV7t0P3v3Kc9/c2tk8tHI0MY6VlYnIArMQe19mpumsrXwo89y108KGUJQD2MvzSQ6jnpvtlbOtueOHHrnbVWv28juDwe4BFEM/qP7w94brOw/8zZ/5yD/4b7/xv/7S/ovvbN4YctY9uXg49bi9Xm2/cmPt5JV7nvrAaH9r4913aP5wu90Nm+vb77x25WD9tRe/d8+DTy7fdYxDWUjV6bUL7/PJwHSdayJaZvEHw36ZH7SzLM/Hs7PLw/293mzy0NOPX7t59eUXvjMSnyZJwxvyYhNrrQMyIogGkIwjMmQUDWHsvLWIhmprrKpyNLKoIAQfJFQyYV/0w3hfD7ziHM0fnTuxki62szlGzdVPghRcTqrBpBw6gwuzC+2shUosIYCIQQO1EjUe7nFaQLAi4jl239Z9StOmCwJE1gAYOQlTlPny3HxqbTkux6OiKos0SRkEEICjfEWNMsA0wVBrLSNJdDvANIerxkG0dt3o1LylU0gp3g1IIEgGEK01akU01SwTQaidWuI9KyqqFBUEn9vt7YlIunD8zDOf+vgDjzzUabQRwHtmDUUV8smkGOcH/eFwOBlOKhEW5GaaJqljDyAhqHjvq7IMhbjEddsddMYzS2BUb4ictTaxdciFRUACY5BMzIhTxhAq770PIdLblfcVh8p7ZmEOXJRFURVlCcZGnS9GmjqIqloyWZa02q3EZdYgC6gyASXWpYmzlgSwM99MGg0vsndQlGHc7czsXN7Y/v5b7bJabPdanUbKodze3H730sbWMF2aOXH21JETp5ulH7154fZrF4ctd88nn0lOP9w8vkza71+99Orvf+XNF541SwuP/Zc/ed9P/wiB8VWQvFBWl6ZorYCGSkHEuaTcK4vNfHTpxvZrbw9Hu/PnHj79zBNJ2hrdOKA3b5qN3Xxvp90era9d7O+tZWTveeiB63/47MYb75Yw85l//LPNTXr9b/zj4pW1oTdzf+mJJ//bjzZWm3vPv7L5899uvn7R3L65CQcHIVuZqDWpEABgORqNtm/Z1LUWFo01nrkqKl8ESMzUaBE1L6iqGhgRlAOFoCpFVVFCjUbqsdV/4TV66Mjg4tXt77402fHansme/niniQtd89X/x3/YfOtyR6U300WmQ92WijbC4pkz9xxbbu9xdimffPiTH1n6xi9dv75TjPM9q2IzGB6Mnn+z9V+1Pv7f/91Lv/hH3/p///u+KU/8i7OzK8lgZ2vjnXVfjVvdTnOhtf7Gxa21W/fNP5J1egiUjwtESFsNSwaRQAEtoiAaFFZQ0QDR/YYEaCyGUMP/qoCGlS0aMKoi7Muk0TA2KYuKoUHtxJgSrtzef+OC3+GFD8xknzk1/8Rq8bvNm3+4Nf76tS/9u39//09+2piONNLxtcuHIGmR8UADGL/57W+VYp2lrjENnRxbnlkpwbSa69evz0KHshko9vdBXn/x7aee/khx681b33zl7N/48NJhfbm/pVT2335na2P38F//yPz8TPAelYwjl6UGkUVJAZRBAISZVUWzbpMLTwYfeuTR2zcurl282ptdTLMkQVs6JI2pNxWLZa6MTcg6DqLWzd210rjnVPrmrRzyHCrvhxPYaKxfbuSHTjz2+fkTD91ee/nGtav94UG/qna+/rX9mzce+zt/4/0/9xdf6f3+7WffurlbpF23euQobfSbanpW0BWCnENYXZpvzfZ2Lr+Z721Xo8lr333h/vtfnTt0GI3LGu0in8x0W4PhsBoPVLXjEk7StVwomMH+3naj2e72bOpGk35vceEDT39se2Nn/caNIfrUJC3TcI1UyRgTQ5sZrAUrRkxEztFojP/V2OPEguATQvFeuGKuJpxP/GgoOYtYaCw1Dx3uHJntLpKYitUbnmg5kMkkPyh8YVM7155LG5kw+qiWISBSVBSKyYZQR1NGrlc9SwgcNEab1TYsikQmGRRVsiAK3uPM8mKWUH7gx0WpLKglASgKTlXVBGAtsipF7wsgIUld5BL9WzUAHt2uqFQ7Wqd+HZzCKqp1r5GL5XRqFBSd05jPqGktiqkxJBQR2z506pEH3/e+9z3YWZg1akbDCXOYFEVZlnnpJ+OyGhegXJVjLksOoWCfH4C1LrG1RJOBQFVDcFXCZVkGGed5WeYEAY1JrLWNNIY+JFmqoFxnhykKoRjmKpQ5g5rEIEAIzCEwi/demFFYFYxNyZog3oeqmOSh8oTk0rTTbTebzV63bY0ThjyfVEVFxpBBz1JU3Ok05+fbztiJp/HIZ8467zZeudzcWl9c7HY6DYNQbm5tXFjb3Bu2D88uP3Bq4cRJmjSHl65fv3Bzo3Fw+MMfgUfvbp8+pMVg8PZrV/7jVze+9v3hePL0z3zsnp/4i+TSSV7xaFgV42a7Y4xFQmFBZXKWQf24qrZGk/VrYCacdVaevn/+yEr/YFBtj/3rV69fudxtGChHu9ovHD987GQ2DOvnLzUh3H3vqrm+/8L//HegHMLhoysfe/rE3/tkXpU3/6//aeM3vubzkjpsYYQwCZxk8z2wRJZCVQ23NqrJqHv4uGvNCGE1KYuijE0wtZxKBYgiAcDKqGCMsd2UQIth6UFxIT31xOnnfutPfv2v/bPTD54+3GtdvnlzY3z19ptbvePG7oz2r707C7ZHVvu6vr9LCWfduWpUvX3+7eF6sl8UeWv28qw5/uSPzx27uvvWa9vjSShGIMJQbr/41snHnjz50Sff+fWvd/Z2m9eKt379qxef/e5gO2+cPvyxv/X53uE5m+hsxy2ePNqZaeXjSTkeZd221sV+MZWKIOoDpwGfIqwQKAqgESUm8WOMqYgUEzNXDFqqNrtdMq3Ko7Vp2mry0VV/732t92WH//KZZKl17N7egnyu+pOvb+U3wxde+fobN/NU2tCd2UxmsROKAwDxIEMZAGQcxIUCKn+wJ1sQ0KET6vOkkcxp1kqKg/H+zpXvvDTT677zG1899KHlw+87/ewfcHfSuf7ixTy75p46Nnv2RNaew2juFvYCPjARQmzXE1FRYygESZvNcjiZnT/0+L1PfPVrv7l1+/rq0dOukaogqPgqxNnXo7eJazaagQUZl8+ePPVDn51s7exuvZ0ATkACVGMoXH8EL/zWygc+d+6Jz8wcOd/f3wkyOf/ma1sXz7/8f//f7vvZv/DkD33mxszSq1/6+sHWTnN+Dpsw2d7bvXGje+Yutdjq9Nqz83lZ7W7t7UORG7y1tvGVL/7hyum7zj32YJK1yGB/Z5Mscomjvb1qPFleWbhx89rObp9zzPLh+u7W3NyChmpne33lzMoH3veBLw+2R8M9Y6XXXDZgEY2SAaA0aTB7VUIyQI4IGEE0sCpxAFZlRggTrliKUvO8Go+0EmVnmnNpZyZdnO0tp64JAAwh5zD0xSgMch6DSK+Vtro9Sy6w1M5bg6ohyocUVYBEFWLeIcTERQ4SyCCDAZxm0wOAISAFQpVpap2lQysryhJEog04ClRAVFkBAImUUBSBBUyEzFFAKWI/MFU81sq2mOFWs1s6/QdxI5h6vghRY9pvrLy+k9wFU245Fl1EzZH93A//8NnTZzSU/b3BpByP86IqirIoAbCsyrJQQ+JcEkJRcVlOJoPxqCyq2FmIwsamLksS55I0dZD2Rfqjg9FoUFUlSCCyWZKgJV9MysAubQCwiA+sSDY1WWrbYNiXhaIkWUoxwthZqPlfARFUCixaeuHA7JEpc80kca1er9HObJYFBV/m+WhysN8fT/IkcUmaqEoQOHpsZbbsViT52APwyaNzujaobt6Yy7jXTSxx0T/oX7i4tTdu3rWydPep2VMnLXaq89f67767zwdLH3ig8/5zs4+esR4Oblxf+9Lzb33lO1d3Nu/5C59++Gf/uuvNVD4vx0U1GnZ73aTRVmtE2PuyzCuXOFatmKUamf4woF26//77fuAjmhe2MOMr62//ybcHMGTq8nhtNMmfOvfATGF23z0/T3jXytF2nl/5d78IUGazDx37O3+7+Wi6//VXzv+b35vcutyAfs8ks4tWcp4LWePcPdmxwy6zojoeDIfb62naSBcWydnAXBUFe4+IGPMigBXRIEiMWEOwZIyzIOqrELyMi6o92zt078IHfuyzf/hPvrz/ym57/hDs7Ye83Hrpze23iplRPwDNADlhKicMniozHu+nzYRY93cOBEozKi58aefN7PlDq/MnD/9wefubt3f7sSXj9q/+zlq2UCXZ3Kwth5PrP/8rB9UOwKAHDbc7pI+JHCrOPfI+tbp4eEVD8FVgk4IiMArGqi5EA6CKIqx1aETgwMCAcUeoNRmKCKwgzMGHfAga1BoItjSm2ZkjBwKVM82FUyfv+5F2OQHRLhSDfGxSxeZ8e3a3W9wa6O2dLT9qnH2kMXvfsfm1jZ1rFhICr6ACbIAspLlyHyYemD3lYJvgi3FlG2mSpFSVe8ObVMxy5b/5X//62X/4Ex/64R966Z/+fD4pcAKTly4VH32i1VsK7DHUDB578TFIxQdBiakelQ+EBsgGTY7d//jq+fObl6/Md5esXUicVUfMwlKJQPASXUuGKJ8UictXHn9oNJwUvwvr6zcdBA8sEBTM7cF2/t0vLPYfzA6dPvPh+xqL2fFPP/HWr/7Z5tvnX/m3v3biBz557AP3NxazV3/vT28Nht4XxTA/+P8z9d9hll3HfShaVWutnU7q0zlNDhjMIAyAQQ4EAWZSJC0qUMGUrOyga1uW/Z6ka/vq2dL1tXWvZcu+tmzLlkhbgZREiaSYIwiSyHkATE49nbtP9wk7rbWq3h/7nIFmPszXmK+753T3XrWqfvULTylYnIqaSayBuMh2dpevruz2czA1JbT52plnv/6dY7fcohtxY2Jca5Olu+nmpjN6fXml1jp86MCBrfWdvs9qXZvVs0GeEXK/s4OT5o63n9peX3rie1/v2XTb7i40ZjWb1HlQBoQYAgInaBQgV17D4LgsxRVgrUPnvc0lLWyZScHMISataHIsaCW1iShMdGCsZwtFwUXPpQOXlS7XoWlGY0kQIumyog7QEPSp8jEqCguPGuwqLwiBvLATO9zxwVCTJhWzB7RUJAZET4KBmd+zTxAdSLOZiLXElWapMuIipRRQJclhQMHKJqjaLAMCEA/f8wY5fsh0l9F+C4fysYo0j8PJQYYhujKyyZChkICGK1gRRFKEemJmZnuru7JyZbvby63N84xdiQykjHfOOSZFcRjmdpBmaZ6m3W5v0Buw99ayAMRJXGskYRARkTEJgPY+t0IsioXJyyAt08Gg3N3RjbBW89ooZ11RuKxX6DCqJy5IIq1VFIVKK/FOa23CEJUCInYWrCty66wzgY7jujGkldJaxbUoTmIg9Mz9QZYNBoP+YHuzw95HtVgXllEVgNMO1zt9L16TXpye0oXduPJGXXYbUzOURPluf+3s5c72dmvPnsmbDzT2LkRJk8+suXOXB93VxsF9zVtum73tVg3SOX926/MvLf3pU0tXLk3cf+/9P/232nPzJUuZlojSnpgwtYhBeeaiP2B21YYm7aUOS0h7xcZW/cTM9HvuDdrNzRev0ZHxr/y3P1rzncSEHejVSvNYe7K5urO8vj4G5czYhAV1/tKlFHwQzN75T3+mMMWFf/bx3qtnVL6eQDjTbu07oLvne9Y5okbt5NH2vj3aBHk/TddXfZYm+xZNaxwJfVraNAXvSauRFw0pRBCBkbOW0gYAnBPvBZC1EW8LSRIcjzYhC3f6gSWfDQKgy1/6fbvb0dBvgGgQBPTgKoNoJWAHZc0YHdRt7g1ACjtlLtcvZpv48ageJ2A8UJ3Cnc7Vr/ybX3VgCPI2KAXQhqIAnkZy5eDCZ35v79QH2vtvjieaxOhyW+SuNhZXWUgiw8xjIpJh61+FSgoqqlI+lVGoFHgmREYSKGyW2u3dIFZCXPZSUSrySCoI6i1k8IUdm5oID8t3PvXEix//fH3NNpJa0dmwnjNfelcoKNqgpuLDe+cffe21LxVQOIA6oIfSAQpoAuhDaYEE0IEqQAIAgLTMvdOaDdbtgEtqqFp2eYv//BrN5Cq1IfR2AHZevljs5rIgSqmhvt+JVFwlJyyitQIiYec95F7iJALP0Xh09yOPfeYPryxdv7g/iSI1pgi1MdY5IPEgeVYIqiCuGW1skQf12uF3P2wmG+FXnrQXL1J/4Hq7CmQD+tu7V1afGowfuH7iwPumTuyfuGvv1IHDL/z3P7n29RfOf+4r2+trJz/87gc/tu+pz37GXryYw4WNtXzu6s2495ATAbDZoL+7uw2gazqY0Uby7OrZ0ytXrt62/x7nCm20Hph8t1OvhcroteXVPYcPTl6+dvnK5c7OWntqnL13bC243c5Ka7Hxjo9+YH1n5eVXn9sod2p5MhVOR1FUOvBC4C2QoFgAx8ieS+9zVw7I2ZLLgotSwKMnCgKoNZJ2M2g3k5bWoTKxF2Z2BXOa9zJM+z434Bv1OA7rgYoFwHtxIKhBEMWLAkCqPHIqp/ohcMogoIiFnbNuSKSrKOsiKKqqvwToARiUQetd0GjMz8ywCCAkoeKKei3VJSAApBQNuXmMlc2nVItfqLCm4dUjABXzvdozVPOAwqF+oDI0HG4qKjMJGmVEDaVR1RRcfaobODCCkD5//tq1S5d6g3XDLKhUEBIFtrD5oCiK3NpSm6AeJ6XtDdJsULBzDJ6EUTybUBsVc4E73a7zbIK0PTUxOTGpIs3eecfsoCisSXa3QE/tm5zfO+tzn/cHeS8bdHKlwma7FTdq9UYcBYGIA/ZAIAqYjGchZuW9LSSI4lo9Io1aKQIMA6MDA8h5WZbWhoGqJdH42Njc3LxWw8jBohTrGBWurnSiQC3uW9AUd6+t2vW1iXajPtEuU1558+LK8tbk4YPTR4+25hd0M7aXr/grVzs7F91krfnAvQtvezgOIF9f2nnlu698+9uXrr6Rt1pv+8Hvm7/tJGtfprn3ZZwYFAUK2ZZlryjSQmvUxgiL845dwXnho8kD7zk8fuvBtS98Y+m1LXhsT4rbjbmx3ko2n0weiRqy1et30zaU9bDey4rNTqcAqrVuvv/XfvL6mS9f+PNv40anqcQQTEy0525S2y9dz/pUg8nm5Awcva21d0G8LXv9tLMZRFE0Ma2jGltn05RLi7pKWQTPo87YV8MgVlwtBkbxRqEXjHTg+iU32Ax7CexaR+AnTWLyVFxaByYgC86DeOAQAkKlQFDA27RvS4HCALUhSkANQEBy6XslToGzbEMAAFHACgCALVAEGAHEIhHj8tmV7lKvDNcnkTw6hVGcRDo0vvQgxCLVk64YAKqM+mG8OzIwl6VFU0U1SZVO5p2z6faOFq5NzDgAHmx3ljf62ztjed7eozCqOwull/p4e6rVvPTCM25tBaDmNGy7ogdbGrQDUaCgL+ee/6PNwdoAbAAKADxoDwMC6oItoNCgG7UGx63+IF+zKTinpfC2JJApqHWgOw+EDNvPfWXLdjehXwI44J3OrsJQa1WW3rmR2yShFwDCIFCI4IUZQAc6NMY5jyjKqJlD+0/eef/TzzxRH1+b1bGuBV4bo5l9wczWClmnA0ZkZZTXHNSTw48/tHDHnd2Ly+mVK4MrV1bPn985d7mXD/pSFqs79Mzzc3ftj8y+6WOt4z//EdeKVr/94tZ3X3xpMDj1t37k0Y997Oxn/rLz3ZxtanvpoL8TxioKo6KQLM9zcO1AT6DulX7t3Juf+cT/1LVkenFKJwEQju/Zv+UvH7zpwMULy8B6fv+htdUNm0mvl9aSPAwoieuD7vaVpfO33fngh37oR3Y3B0vLl1eKDkIclNqJYXKKHSqLvgBgr6QsnKUSi0o7jqhqkQmSqKZVEoCut8YBKlNRlUFe+MJyWfjC27RADrVuBrUkTAgDZmEAXxlPV6bIlX0mC6BUzkEwbKG9Z09KiXjLloGHJvYoQylVZcrjK9GpAJJF12y3JtrjIuy8J0Q0ChmpomIP41QrqSswV68FBdADY9XdwKhhr3wmKqPooSZgWPVHNMuhDmrkY0UwYj8LDF+gjII/qnSsauOsn33y251uj32xuNgOkxC00arlpbS99fXN7X4/a9QaOEHNVn2iPa2iJIziJAmUQvFS+LJ0Ls/KNE2LomiO1RvNhkIUEEZ0yCV4dJ4QWlONqNboDWx/t192tr2Veq05P784szBTq8fKQKAMorArbWkdVbOXQgDlJQjCWi1BAkDw7BCVUuSl0vJwFGqlSCkjLKh1HAZaKS/ApU/76W6aLa2mh2pRK47dxkbn7Ov1xEzsWfA9Xnnl6urqTuvQ7PydJ2oz07rWcNdW+Pz1rTdfz8I4Pnly7pF7I4LBWn/wzWc2/tdfXfve2esObn3/O+54/J1KUZZZhWgCpRBJhFNrs0GZOu9EKYMqBPFSujjN+qDmHj01eWqOvrZ8/bc+n7/7tnv/7tsP3Df9hZ//b/nK7jsO3nLltRcGthBFcRitOdspnIfmoVP3H/2Rn3jtk797+cw3x0pXm9CB55qiuKYvP32ttDAezrRaTTp2UN9+Kxoqi7K3vg5FHk9OJ2OTAKrMsrxIq7kWhgh6leSCcMPcrXJFFV9RdUOFgmidRcKJPUlQj/P+oF+m+0jHwll/hyBT4F3lIg5egzZKK0TvHYHNoCwgdWCmJ/dOHNjXXeqGW93C2rFE5/kOeo9DnbYYAAEJQVfBQCGEXchaOBaYwBiaO7In7Qw829pkM65HQOABKoAWjNJEUsW/YCUCFBJQCsGyDySIjCA4z2g9cmGzAQhHjXoQJ9pEosWlfuP8apc344lJE0ZktGPOQVpH9tUTTmFHgYvCsU0tqaUAGIACr93Wq7uDTQd9BWChLIeGj8xgHXgIoz0P3rP3tltlbGJ3s9fdWr36zRd3Vy6H4AA4hQKAl/0O+MzmXaF0AJkzNOcbjrxj8E6s9c7ZKq6uCiBTgUIUbx0zGK20MYoACYusJBWYWnzbyfuvXr20fu16rTbWCicDbVzgbW6JiME7y64sMlStei1IYlDkgaPpem3qiD51mJzrd7o71zfWz1zduLrq2E7vH/dst89f6Q9cP+PxO+4IGrWNLz+z8eKrL3L9wZ//6MO/8EOn67bz/Gt6qxfM+sbcdNJqD9Y2gcIAgiRotn0Zeur0+k9+5QtvXD19+9vuufnWUzcfPNgaG29NT7VWNw/ftNf5weRYY3pm6vrSWtrrl812rdFShINd3dndXr1+bf9tt7zng9//qU/9QZruDKirMErzsge7BhQ566hvtAKhwNRCjJCBVBzrwEMQBUEYRYjKoEalrLPMzjNntkh54F1BWBoTjJtaFCYaA6psPwSYUIArZzQFo184tMoVYAAF4it1lggzsPcWhkrVyqYBBYlQE+rKpk4QHUrhXHt2rlGLqxwlo8g5X2mhaChVqvg9Q/UrDFNOKoyHq81zJYUFAkWkqLI+xcpiuXIvrLr8Ibd0lNE9+iIAgABYbqR3y8jpUCqpMutC2Asolq2tLMpIExCUwFmAycGDR6O4poCNQaNCE5l6I4nqOgm1DowQlc5leVFaiyy1JKjXkzwrt9c2N9Y3B73MFmVR5oIQxUEtqRnmdHOnt7nbjMz03FR7amZmdqI1WTdGx0mglSaEMs/T3TQrc5ZKJKCDIIiCSGvFwCJeYcgspMDogBBqtZg9D2myhEESGEWoFCMiS6NhptIkVNqYuLe+svW9F9TO9t4Th7QtVl8/31lL64uTe07dMbmwz2mVX1rhi9fS109bBcFtt8488vZaK/CZz5Yuvvj5b6+cXtro9ZpHT9z1oR9IJmYLyUlYxOb9MjCGyxKVZwRGXwxyTXXVwDIv/CBzW4OwkTQPL8hT5175P39/G/yJ99zt1wbP//dv2ytbdx5YXFu+WLgMwJcGe3k5wNg29j360Z+qzYXf+Z3f2Ln85kJdxbVAEyuADGzn8jIDxpMHWydOqkLtLCwcOnJIFBS9vN/piI7jiWkdxtZxnqXOMmmDKOJvrIxk6J+IOOIVuEotpckgeeuct77IsunF+sT8zMrZc7MmigQzXzJaFEZQDpQHQdCaIiXk0LPkPcgK8GzC2ZMP3fzBj7SPzWx/7cq1P/xTLq978N7nBKKBAMSAYuAbOFQAUILXIFtZt7ZncmK+rbUq86w1OxEmEYtzA/EgcEMHD0IscOM341B54smWPooCQo1csnXgCjdIs91U6yDvOxVbTUHSnBibj3QoCgMQcb4oi0xHurnQbs3PDi5d9FCa0scU1iQZcEYIBjBPNzPXyYc6Rw+AHoigtOAlju7+wfcceceD8fS4U2Yq80VxYnbhyHf+8+8P+mstgBS8AWHIAdgiGnZNcIe4DYwyO9YYb7LnMs1JDyHa6j8RcYVzpQ8iMgGZKmCE2RgsraUQa/smb7337d/95ue2r62GYRw1xgLQEkTOFgLoxRd5GSqddncd+7GJKRVG7KBwzntfBhS3ZpJ9MzN33cRpyeK1dZ3exvVLV3a3tzx662X68E1JrXXhC9/YfOPyK7//5bv/waP3/8rfe/U//8/BxWuy2beNyYACgKDAAECFADn6yKWTbkyHQeeVa986c/nPs/9x4vbDD93/8P3vf58I7zty4PqVa86Xc/Pz293cptnOxlbSSBqtZhREriyWr51vjI3d+Y63Xbpy5XtPfHG3GISaQmsJA1HIYgJJQgwipZO4Rd5AFIpQkOgK7lAI3jsnHmyZu9JKnkNpbYEKQ6DIjJmgrk2gSAFXzubsgcWLVEb0IkP3Nmau/M6Fq2jSEXEaENF7x+hFHEmoCaqGSuGw/6YqNlGBY8q9m56fC7T2rszz0loLiFQ5EstwiytS6Z6qVh60GkbcALOIeC+V2RFWwfDMhOqvM+pxBAcNjc8rZ8zqM1RaWBj+I0OHTRi+28iRGvXdd96xudF1aSdqNJWpOZco1vVExSEFDa21QhRFXhhz9mWebm9tLA/6CAhG6SCq1Wv1RpREURyHHiDbLR36drs+1kiyfi4iUVJvtutRvRYmAYjDUmq1RJMBRfVGGIQmiI3SuqpLrJVSgCDOOvRKhwqJRMCzEA2NnkljEBplFBEIgy1s7iwzC4H3DMIkDETWs0GIauHE2MQbb1xfffk7zc3l248cU5LsXFwf9PJwKl6448jkgXnV5+zsCl1eHVy9NnBh/Y599UfumDgwKSXw+tb2J7+w8sylK6sDHyZ3P/b4gZO3iAYEAsFsNyu3ulaRCgJREk3GSoC7W9RsIAFkpeunxFCfG7OXy5Xf+dpSeTnZd0ut2brwb78y9twmOzWzsbna3wzDoFdwWiA36jo+/n0/98vbyxe++dv/3nVXF2pxq4FYlmVXwHnjpQ+q+d5HZn/mJ8begM1nX5TD+6LJmi+gt9mxWRbVGlFzDAhcWRRZD0AQVAXyVB0BVq6X1VUwcgEadQzsHROSIinTfHK6MXfw4NWzFyPrvEoQoErxqfxbFQChEYYCcs9lCb0B+PrUoQPvfuzQ42+b2rsvbinzUHT98+NF9zIOcg9FCKoEMEAImsCZYTElDywACkzX2rnZaYrH864N41oQBMIMqMRnRDSMixk2TjJqcABApEru0sp5Dx4UEjCjL1yRiXjU1OuUpt5PDDF5iExzcTqKlKmFDphYsnRgjKm364177xk887Syg7rtRhBNQYAgO2IVUFbmBWQMWCGUlb642krsOXF49uhhJ2V3c63f77MoMO2JowfnT9x+5umvOWAGVYIPQAGwESTwe0AfJOwG0eLbHqCklebOBFoHKM4DAqmKVwhkQBsFKCRVl+pFRJSAiLMuMurQ8ZvXr1w7d/bZcDuZCqMgilzhnVKV7aT1VgYDRaAI+yg6aQZhLY7CUCsTKVLowAMkwkJEXHCYTdTm5ssixyLt93eJvT60Z2xv+41PvbJ5/eKr/6M8+Q8+ePIf/tyFT3x6+ZVnaTJJ836RQe61oC6FdpgCUDHLiYm9u2W2ur2WdjtvPv3KypnLA7/THpsbn5+aP7z/3MtvzMzNbXbTjbXrWzu7zX4bEWu1KLdp1u8uXTjbun3y3d/3wetLVy6eP93nYrE50cKgYOijZzEBiUZIpG5JM1GQxMwDQhKW0hXsS2YoObVSWragXaCDxCRG1ZSKlDZVWo8Hrij9nqrv7TAgd6TzGu6bhs78SCDM4khpJPHOC3vCAIkEqQrEZkKtFFQ0naq1EWaQ+cVFE2BekGeGUZo7wls0/iGFCABAqPJIwMoJiKpWnYbmxyM26DA1hm/gQqM+CBkqyyKBYV6kCFf+nygwFB5X7KThJyMRAf3owyf7aSlZ4aFK8UVEVBQYRRbYigtjSiKDDIXnbFDs7LaLwcAwl86nzoVh2EySeiMBhLz07WajHYQkooGF0QOQMmEtTppR3IhJgRZSSOzBe+d8bsscRZz2lqUobJkXrvBKh7GKCLQKTGACVCTeF0UpADrUYRQIomVh623pyqLMiizLS8eu5BJZSCshFRgTKqUyyHx9+UInKlwrMFK63lrXlazHJyaO7pu6+bDPys5r52gzVVeXs86GPnSsfsf9UyduEgLK0s1vfOXCZz63cmFtAH72yB23Pfa4nh7z4gSst87208Fub2KibZTJipKFwgAjhVGoyJblbibpwGNqd1T53JnOtZcs1I69/9bslQubn37R7aw1VN7Ldwl4UDiERtzYf+RdD0/eetOrX/qT89/9Vg06+yfDFnoZFP0uKFBj8dRmZuffe2fjH3yo8fLO5qubF7Pi4GO3QAguLdPdDhJEjVpYi9j5ot933iHqoREaVpmhwxwKlhvoY4XKVA6gXC2aSFPWH3C7dfzUPS9/63mblSESI3jhAhwDIwSikRgzsRqKEvo5ZO3Fm+/56Z+aefBOE9eNaJ+57lbf1htEgePdELgAhcAeNIMnwAJIV4Q4oAyEwSHE47ccaO+dL4WC0KBo8SDAqM3Q4qnKCLhhNoIVLW94Zo2hIh0I14iQqlAi9giq3p6I6hO1dlNrcsqSwiiu6UCz46IsLXuwtrezG4yPL779rhe/dWr5+Sfb4GLotyFoQq2IxyHILu+uaLAIzKN/3wN7UAZMbXIqF5LMWym8t6FWUQiNWB+85fYLr76YpstNVCzkATWwBgqB5iFwjJNTLR/Rzmbamq6p0FShr8IeQJxYsYyERuuitAWANkQaCIlhCD6zcDxmbnvgvq3trc3ry/VkOjCBMYa9MKB3tlpKsvNl2u9vbr/++ssbK8txrMNmvdYeX9h/YHJmz9jkTGO86dFASCZM2rWEwCkvivNA67Jwswfnmu2Fs3/41c7LZ5/+jd+/9afev+ftj/bOvDG4umJv745Njk8fWKi9NC6WPKkcUDlOfE6gTBCquLnqs/7O4Mt/9uW9+xaTVuuWB982Pj/LWbk4N5sPdsowX11dr8VNSUy93lzv9XbXVrfWrs7sO/qBD37k45/obi5daZokNGGIYWikVFjmaSbZrvSIo0DFQZYiWoWBdyWjF1dYtOw8azEqMEESmJpWkdZauDKT51JcpQEXHN6r1Q0wbKIrXEYB8JAMhOB95UmOmkVKKUsQIgVDBTJXYaKIpFABSpUMk1sGrfbsWyCF3rMTzyIKRt1WdSSHZjUVdVPhcL0LI5OuoZk9DQt/dTMNsx4RYGjjCwgClQUW0ND4Em547VQjQLXCllFq8IhGxAI6jqMoDLGVeGAUUQoEQTywYOmcIlAhEhIRihfXiCcnmxqBEMG7snQ2d2jIhIEQMouzDq3jyqWV0Fl2VkwS6Fgzc1l6FkJRRV4yoPe5tb4nhWcurPOOFaIJKsWfAkQvvnRWHDhbZGkhgImEgFLY0lqxWTbI8mwwKMo8y4rcFYM8t8wmDOJ6vV6vo/dZp2jX999+6nj/u0udNy6U0WxjvlmGMLW4Z/z4HhkUKy+dCZfXTNf2d/owOd98+O7xu271HrRN86e+9cLv/8VgpYPQb7YWH3r/41NH9wpBOchJs+3mRTdLd7JGrSni896uCX1YIzKolIDz4koVkHdU9rd3X3itBzh/am/n5Q6fu4jb27WxstfdzVRZOmDTmjl46tQPfWx3d/WF//fjRWd5nrKxWhLkXA6yVLBhFuYOHdleW23fu2fiwPHi//5a98WlTlp233v32L4Z9i7rZ5yXyiTxWJuMspbLIgXBiu3P3kOF/lcgIVYOtKN6Wj0glQuJJkTFIkRYFu6Wx+5o/a/x7qXLohPM/dA4lDQr7cSXaB14gDKHcmL/0Qf+yd+bOHGMg6C/2+8WgDvp+pWtvNtX7ASkBCgBPJAAaKgsCMlX6CaUAFKCmT6wd/bBU7rVyHd7ZEKlNQCC8NAR5Ybr9yjPeuiZAkOHFIUAvkDwCgG1FiRRhGKCMIrqbaVCkcwoFdTCuBZFSeALdh2vTOAKW6bsam7y6NzJn/z+L1++KltbbeAa0P6wceT4O//q7J8UkCEwg68WhbY6IoAIJaf5ILU2g7DZDNAmYb2e1CMVTS6MTwbJZkpOqmAFMAAENgDZBrE+sBjoPu2bjuoTdbAlenCleGAoS+edOE8KXVkCVhkyaEArrYgqub945Q3IxN7WbXed+upXv3x9fdk0glqjASR54RUFAp4rl0SistBP/cWzly6ebgMGQHngdByEi82xhYX9t52cOXhsenbPRLtdj8eSCICQXR4YRUyFj+K9ixOPn7jkeltvXCz/1R/e/kPvPHTf/Re++2K20avPTs7N71+YmuGVtUbp+kBWJLOgCEAw1NQiba3tbfRf2z07vvjU5N5Dk9PzrtfZm012tlsbqxmLW1lb3hfPhmFkgnBj63rzWjNpjh2599hjl9/5qc/83lK2hgDTtTltjBPrQuULw5CxGXifs1cqUOz7QAhISKCUJhNqo4mCgALUWhgrv2gS8cgMIITAVdpgxb2q0NAqVxyJQDxUKNyo+65c19Gyt+yICEFX4SIEhIgVS419ZRkELOB8qWrxeHsCEIrSDrNVh/TNil9R9eUAWKlyRj72AH5Y5SvzOxl6QAANHU6Gf4wAXeAbY0DleF5tFW5wfqqKLyJYhQDIcMVQgUVaEQ1t7bgagqq9h5Aig4qgCp7yHghBFCkVVC6+LIJVm01KeUH2IsxofV7YyhbUFY69B0AuMLeusGVvd8dbATDWIosEITpmx56MAvQiIo4hTUlVO3KllBZG56znsihKY3QJjUGes/eDNC+zvrfeM7CwDvXs5IzSodJKmcgEhgGzNF2YbRycn7EXl7710hthvz87P4d1Hc42pw4cLnN3+Zk38/WVWpoXnTTTcXLz8fFTt7OBIIHiyurTn/rC8uXdopQB0J1vf8eB9zymJ5uOvUJG4TIrxLux8WbSbomFQMC+uVzMJAAGSfui5EKgEGCQjWz99Z2Z2w+5VsO9vqK3Bm3YGdielM6DUcns1LEH7v+nf/vcl792+g8/Eaa7AfhmrCjj0nmEWru9MD93fH171QKb3d72Z79YXttG6L/Smjzxtlv1VORKO9jYYg/J+FjSaAJQmaXsPA21hECkKrbksGPg0fhJlffzUGaIgEiqEp9agJ1Bt3Vw9sTjD778386U7AjYAKUABaJFb8Urb7lyIYX4+I99cPzOU0hFurmzsboMu7buDKwtmf5qDn0CsMAetAdQoKq6LwABoEPOxXuAuD594O/8eHT0wO7ugIE0AnseOvkpJcN2CEdNUFXz4cZfIgMSkQAIkzakNClCMqiFNBH3vSsQBVkJV7Mw9rq585xEMQCur64JehVFhx4+efff/9gz/+HPw/XlDSjboTuzdpod6KF3C2jAEpABS1AO2IFcevWstPdPhg00ONaohVE9MjE7H8XRWBisAXtADaSADWAAqMB54E3oFVe29vXc1OJUkJcb19dKxzoMUAdBqAb9LA4CDwDOMzsi9AUTOEACo6rBKfdWAMIo2HPr3puvH3vx1ZfGxnfiqBYYxcaU3gkjIuWljVut2qSePXige3Xt3lZMvcFqmfVK7u3uLp/evPLl5wkwqYWNPdMHjh6fObJvat+xicnWzKHFVqtRrzdVOyjju/NaxJ98cvuF9dP/88kTbzvSqtXy1W06dGRyYXbxwE1LKytUDgR9idrbLGqOm7Lhs56gb+nWTmGLvj33zOsL+5+9++3vHh+fGKR+ZnaKmZevLg9sz++dJk+kKIziaxfPx43GkeP33f3og6+9+uJLrz/DshGZJMIWEhpdC0ziuMHADJ59RQMATYhGaVJkiMiAAqKqhKMQehEB5yuwXYaj1pDBWbn4DO37Rw06DkMUhyi7VNwFZPFcGRggKaCqXiIqBCEAJIYqrRDBWVsbm5web4FI7pyqom6HaP9og1uJd4FGU4GMXt/QpHAE5eCNX9X/DSWdI1bn8O2h+gtGsoURQjQckUfcz+o2qyBdAM0o2pAThV4QqmwYhYQi4C0X1mWF7aWplAUpCkwQxYFCzIsy7WeKUAWajLYsRZblZeEzZ5lVEAKITXNblCYJ46g+KLNB3ttZ27LOmSAhDLUJkjAunUuLIowVBcDsfOlc4W1pQZTSBpG8996VSiNqiuOYUWmlvS+ZOYmSZCwKwzCKwiAOo9BUJHBBKh2vrq03a/VDh/YECMvnr2dXNw7cebh5YMYiBK1GuZFdfe7c9lZvKkG70mWtg1uOzr33vvq4sR5wUKz/5TfT167b3cG6Sxuzx46/6z3JwQPWIIO37MBZz9YkJmk1xWjr2ZaYvbk2kczC1DgExmc27wEWOuj53dMr+952ZGZf+9JXz3DhrF8XyCQrA52kjZlbf/7H9t//yGt/+rU3PvOJ5mDTaNIAbKF0GOixenMiSMbPX30j63dMJOW5NdMflKDXoYRbpvaduhUEehvdwW5XBSZqNFUcOMeDXt/5yvUeQYlnHsE8An9NTDJqfUYpywieHZEiIi1s87wMokd/+P2n//wv13vZhDbiMCLjqzZJSAAF0AK0xydqU/t0GPXTdJCnUvZDHbaCdp16SzurAs4BeVCAFXkfAdELK8ASyr44Bw6D5v2//FOT770/BV/ZPhWFD8NI0dBaHW9o3eEG+W3IjwYW0iTsjdKAaF1pkBCNqJiUR82IWNhMsVdKkyawLu0XvJtaLyYMvWck3RprKhFfFEkrevuPve/YLTc/+W//89bTr73e2xpDb7MyqPKDq5AOUCVwCeRRe8BeP6U3Tk/M7B3fs785loQRogAXxN7ngzQE5aHUAArAAEegDOAA/BZgNFa/+W8+3Lt26XO/8olLz311z/133f+TPzh18GjpvY5DZUJX2koU4Jk1gi89kFeIhCQkBN56Ie9rE8nt99y6urGyen25NdZoBa04CGzmDSkWVAhKYXtP450ffKR45ep9E7O7fDEoO9cBJ0wkoEpkx3Yw2F1/c33jzRWGEuu6uSc+ds/xO08+Mnf4tvbs/Gyr3Xz7/WO1yQv0pe4bZ179xuZsux7Gke/75vjE9OL+61E06G9aRELvixJBh3EzSPucdYGoEZpQON3pn376qamFhbF77xmfGOt0WgKYDcrNlbXNtc784mJYq7OznXTlwptvtNtTM7NHH3/HBy+tLO3uXF0q1+eDMMAm6SonKyRhBodESoSICJioOv2Iww4BfAXgAwsNVSOEWLXMPOqfq1PANwp/pf8aNUwjBj6gIgAR64WZUCmoHCBQKgc4VdmfDZMLBLHwxeKe+Xojsc53+wPnHOGN6l7JzYbczbeO4/CygQrxH4YfDHPeAUboTrV/GvU/I5fNSuM/mjWHNxeK8PC2qJbClZ/pMHoVAET02sa20VrQa1TakBARaV1hPorEkdcUxrHSlLIbiOsPPAlkuet102yQllwKiANh55137EEroyPDDGWWc+lUV1OQerD9tFcWRb2eoCbFXsD1i7S0nFubWefBsTjvxGVWPBMaRVppZYyuNxq1ehyEJgyDKAo1ERFqpaJQR0EYRIEOlFIKCcWDZ7Z5sbHdAfEH9s/WYkyXttdeebXdrk0eXIRGCINy89yVjZev5M7PTE5m17Y8Z2r6YOveW5v7p9IBhAF3n/7uhS89t3NtrdNfMvH4Le/80Nydd+haUHrHygKBWPZeTGxUYhCFvCBKr8NRLlESOEOFg7yfJ+zz6xvpxub8fTdd/ez3YGsH0m2ArgdCnJKZ/e/5D//YHE2+8Q9/d/mZp4JslxUqBezIQ4BR0lTtkmV59Zp3qQlFtKZ6HGbZwBduorn//ntbCzNSQn9z25Vp3GwlzTqhZIOidOmQ63UjYKESg4BIxQh664l6S8fOQw4EKEQi7dj1s3T2lj2nPvLB5//7J2OFCkERknMiTgQ0CIrPQFTYgBKdd846Qa63GrONPRN99c2v/Z6FAQF6JBYmIQJRIAwk4HNwOdgdkGjuwA/+y19auP/ugRu4LPeWdRiZwAx7FEFmRkBQeGNWB3yrVxu+clKkNQt6LyY0hA6VJm0EfMWlG0qgnXYF23wgTliBzXKdREmz3pxscZmXLIBkSc3fc/OH/vU/feJ/ffrcp7661Rt48BZ0DBwAAIACzIAjkJ74AtBbu3b23LO7/c7y/Yt3HR3fO5sEUbELL37+KdXvj4FyUGgQAhHQJTgHVMLk1N0PPv7//lz7+MFX/+ib/aW1Rz/64SM/+CEKGtaBMMetmC0Paz97UlXEFfiiBAExREgIpDRa60nx5IHZu++/74mvfnF7eSWJQh1EgTKWy8r1NRvkc4u12+85tfO2a6988YkDjTG1k2oR640BFRkVKmjqesQelWSul/Z7W2/0vvvGk2/80bNzCzN3fODtcyfvmTx8YOG+2+P62MWP/8XG0y91ru+0um7h2KGp20/OLMzNzO9d3rxaCoGUg7JMGMiETR10PVovsfKlMDtZvnj15aef3rt/3/jkTGt8Gino7uTdTrq53ImDRhDGGlyiO/lu79z5V1uTswdvO/KuB97zpa/92XaexslgzCWhVh6AgUkrLQQCCgMAlko+xaPHGdELEwCIr2AUGWoJK7b9sCMegeEjARUiDCspQJVIQZWghEmRgJTihvkqRIDIiB5ECxEQEYEAe4GAWMSBzB84aALM0qKwJYBHVjzEX6QiA1XHjW8sIAgrQcCN+2BIGJIblL0hKCWjBv9GKyR/HdofvSficIsAo+ut8swfxvAKC4D+3ndfNfU4NBIFoYkSMkFSi+NAB0SEwt4NPDhXgrXOuW5R5r3MFtYLOWvTfjbIs8JaFoukRBGRCoxWKfpCvANgYW9JFxRIEOtkbKKyHMjznIs+MwqQKAwiIl1tvFQcJlEQah0EyoShSZIoqcdRHARGI6IJSKOqMgIqn38PzFbIMiNkeVEW+SAdFIU9ecvx5ljSWx5c+qvvJP3+wsGGx9xuZKtvXFi7eKmOtG//ol6+uru84ffvbdx2YPahE2kXTA3U9pW1T30+f/Pq9dX1XShO3HvLXR98yExGpee8zNA4X1o7yIosE1CBBxOBV06E3SAt00E90cJc9Pqum+ax23FpbWEaNnbs1gbbHZaiDwD1mp+79T3/61+lu+nTP/nr28+/XCNPhErQOhQOvAprOtr2u7vdVERAoxinLccbA+MHDnI/NnP0gVOcqLybp7vbDBLWYh1q52GQDryvBlLBKqd6iPIzjDqAagzgIb2k6g0IgVHRMJBUAInEc8H+Hb/4saXTl1e/+90JhIgCBufRiWAGpQIOgQY7Wbq9m2+n5LkVjjVnFoKBee1f/Te3/EoDvAVksQaUA48gBOxEBmBTsBKMHf/QOx7/pY/Fs/NZPuAqCbIWKDSVTxYPJZHALAQ4TAqtqMzDZx8RhYe3mGYGa10YoFGqACSl2FeUYCAWceIsC7DzjsB7IaIKHRaTREGzxoQQx1W+dHTr+Pv+9390/UPf98Invrr01AuycjHrbURQCHgEiEEMOARIQQ0AAWx37cr5v9ouVpe3jx9tBvX+Sl67fHW6Vr862MgALZQAWABtAelgZt+HP/TIP/sZmg2f/fg3Ln72e49/+JH5w4eMnjOLjU6n6x0hBs4XhbXOlorQoEKtfHW/FZbYkBYkAYXaKM9Amg4eP7K8dOXs6efqE612ezKOAyy89QxaAfuyl29f33r97HNZ73Idp0QHsWcWtiK2KHsohEYbM27MdFRnmtyWdCPv99Lu9UvXt3/vT8YPfPvow/edeOxDU/tvKn/qg5vQX/viF69fvJJ9eWxsce/UbGt836H1N190NtMlOONLQNEhRLVYZ1T2IvbEDkzQLeD1118/9vpr971tJqklrrRzBxZ3Njrry6sba2t79s9rpURiqcHW2ubFc6ePHLvj3kfv3lg5/+QL31rZXY4mdCTjKOQRnHMBVTmzruI0ehFNxDC0wIFqTIUR3MI3mnmpUqKqNWql3/prjcWNNBJAJGFXoStQZQB4KyJUZQsLIkqVVFxtdwnBAlRPHQXm0L7DSpH1wlLNDaPhtWLqwBDgGU4cMlSCV8nsIwP9obEP8ujCkreo/ggiDDgk9QhUqjW80SeNfEIRh77n1exenSZEQWDPeml1B/wOSYqgUQcqCHVgwsiAqKhmFFnrAcQxVIJqLLM862WiVRhoUkRKGaW0jpEUA7rSFv2CGDRFoQ7AgEcxkdIhYUCAgAq1EKiAEkWomShMdBCCCUkZpXUQBlEcBlprTRgEOgwMaVUl4lRO/9U627EvSue9t955L8i+8FyUNgxCbaKj+/a3Jpq2Xz7/pefjqzv7F6c2r3YHy7tbg0vbF6/UjN83XZfuTv/qalyrB8dOLL77YYxRCZiaXPi9r2+8fu3yyoUOdBt7D973Ax9tnDwhkXY+U+AIfJnlZT/nvPBGe++JwbH1zhnJOM2iMABrs+2e9x4dqcRsX1mu6y67gcei672qT8PDpx7+7V8ZXD73tb/5G+X6+RooANSELNoKOAIml7ot70pUQKglBEo4SksOMOtlm+D3vu3u5i0HmKC7sVrkvcZEO4ojbUxhvSuzIWWGmYdgO99QCRKh90M3wCoxlyo+AQ3HRgQgqvx2VMEuy/LadPNHf/t//+Tf+9Vrz3wvLNMMXMQRQGGAJ8EYUD4rt598aeroYjQzpSHwa/Ds7/1Z9uS3HNgQtAAHIBY4BHLgBaQHZQbJoYffftuPfmjvIycx0WWRMwtYC8AIlQYT2CORhhvW8ALw19xMR43N8OyyQKXZdM4CASquzgFVsahQKWq897ZCwkykkihWkYna9Vq7YXPuXVlbub600u8iYdhMavWZianFhVPH5+44sPrqtTe+/OTmU6/tri4Nzl/LbKcErL6zBCzgaqAngES626+9Vgfyc/vh0toU9znLYmAAQqAcxIHJYOHxv/N3j/7AfUsvX/zW3/7dK999edynk5//+u5Ys/WBHzr6r38xjJPBgNOBzbpplqdxbHSgAq1JobdOvENEtF5EoQYgEC0E4oApUTfdddvK6rXNKyuNqK6VMUo7VzKLVsZ6fvP0+RdefjWGjk4poJoljRqthYxAhMl57amTC5BuhfVxhQsxrCOtDnA3LZdPX+m9ubz00sV7fuFnm0dv3vOzP6rLztLXX7nw1MtTJ184ePf9s3v2nG9O+s1lJex8XmCB0VgehhAEASvjJUBKrSSidlY3v/3lr99084lGu51mxZgJFg7uW7u6knV7PPBBUIuieNDPMYKlSxfHpydnjx6855GHzl07c23l8qpaNpNhSxoeQRR5VoxCIFQB4wgeGAG8DB0XGCtK5I0CXyVe3yiMIDwUVgl4fOtdh903jtKmkEgQrFgrTkhB5VJXGZLC0AlaWJjQEyBCAWXUTA7tPyjMaVZ4zwQjJBYRUIgJsRpFAICBgYc1WkYzTGV1XWFEAkNTnyFzCUcTwQg+Gp0FfuuCGLGbKvofjBa3VVALVxGX4EXfecfRrJu7MvOgcgeDrOin/fXtzSItgjgAdIyQJGEYm1qQRMbU6rWpqXZUeRCCsKAQklakSTzZwvp+WlgJgyAMQ9RK0JsAUYknzaAUQCCkHEVhqLUmRcoAaacJVKgNaaMN0XBzUV2SzFLV+YItO1+5OpbOOVc4z27oaQNaByoyjWZtrNEcb41hLle/fRbeXF5YaLju1mA1c7vbenowNl5rHJiJNvuda+ucmPj44fkP3ddYnNrZzBuJ6n37aXjt4ualpeViG+dnHvr+9809coqbQVGWggwgYqXMcpdlwk4rQ+LBisssF87nAy4yFHRFVux2mpNJ3Ms3v3N5jnvp+kqAspr6stU8+rGPHvzVH+h95swr//zXcX3VIJFGYnSCOaBoJNIizOgxIBKfsEMvRcp5luYee+BhYc+Bx99OY83BoL+7vaEUmigxtYBRssGA2SPcSEquVCbDtREAVN0CIvJoUgYgZkYWUEQITIKKRBCJQtJC6MWNHW7/7J/96y/9x0899fE/7Sxfi6Csg7TAxKCbUHOA5cuvL395av7xR6E/2Pz0d9QzzyN0Q1ADKAg0gw8APUgBLgdrJmc+8P/9mYOPvw3bTecLm+aFFZumgUGFBI4FEYhJa+/9jdVXpWaUkQoAq+iM6ougKiGPEcVxKQKoFBnyvvLcHe4+RMB7JuVBQKGJoyRsRukgO/PyxTNffXH3+Rfd1tauzcT0ogbCzJ76ngOHH7rv6CMPHnhg3+H7Dq1f2964uHX1W09vnj5/7blXsq2tQbFDMEBwFsADjQG6fHvzuZfN3l5oPbItwFmQHgwYwh7UFk89+H0//dMLJ2/66n/4zJnP/KntLQVACcQZpLzjVv/n56d+8m/Et8/wrl27en154+pNx26LYmU0KCQAL8KkiAjEFeIIlAFmdCQEigIhnpifuuX2O1946rvrq5vzB4w2oVIaxDu2ucsP3Xp8z9GbV8++0IHMECiMtU6QpPB9QhUIG0bvoSN+t0wbmlth0DSBajfHimwp3dzx2zsvvpz/j/9038987MD+UxN//2c0fPyNr3/j5W9/ffHW2/ce3jcx3l7dvExApU+JWRswcVAaggJJUwOUAJOhTYfnL61fuHT+RP32Zq220+st7ls4u9i89vqFJA727jlUryXdQafMeZf6ly+eT5LmgbsPP3jpHZ/59B910n68uxbXQ00hCnpkGG6VRFUiq9EjPgTCK4AF3+oUhlvfKjEa5S3uf/VAVQ8XDtt/JKwa8urUlGwrCppUIWAACkCqeJPhHreKqBXPdnJmcWF6ynmfW1dZ3zMNWZtIKMhQ+QKhMHOlzaw8gkBgZNg12mbgEMuvmA8jUlyFDY3G+xvEzmHaPHgerjoqyg8LwxAAAhEPAiIKvOjJpuZa3Zi2CkMGLKy1pUvTPO32+0VR2FKTipMgMCYwoVbk2YZaNRqJ0oEiqlK3EFEBioB3zpcFg9bKgCLQCCBVtqdDBTxUTCtQ2ihjCEdb9iqoVhjEi/PM4isrXM/imV0VGsCePbDzrvSudAIeFQaBNmEYhkEYxSYKY6PHGvUw0dtvbKyfvjLX0mOxWju7ubm03d9YbbRxcnwKil7/3JVyx8ORQ/UHTjaPLpYDibRR/Y3+E0+98cQLb6xe85Q89Nh7b3nv39AT076SX5CgZ3EWylK8jeIgSWoatLe26Pfz7ZWiv1WjGU+S9QqFEDcmBp/55lhfYt5NOzu9MthJDj72iX/SeODgxh987pVf+08u39FkSBOTDLRxDkBJIOC8ZRFhRxqN86EwWlExdVw+AMdm+uh7PjJz+90AKtveLrLtJEmieqKNcYW3RYaAKAgV0xOHD9GQDsyjBwaRCNgLESKAAiKFjgUAtAnIKCJNirwAKXK2RBCT1L/vV37u7T/2kZc+88Qzf/zZtVdfd1RkbNsavaOst5V/44X+TlJeeqM8c46gq4EdWA3oCCxDDjwAX+rWsY++98Ff/MH6gcXSsitdnrmyyJEhDBQpBb4yYwECZu8QFVTyX5AbR5iqeKG3jjMIMCISoSEqXAmjwX7UQOFQLgBCIgpRWAwZDbh5bf2zf/j7Z77wPXN9YHg3AqUAGuACKkld7E288NJzT1565Xuzt9x306l7J+bnp/fPHnvwkN3YXT+7evGbL5372jNrZ8+43asd6FfRmYugLARXry5N65qFMuWiD2EOyaF3P9w6duqxv/fDXJdP/Z1/9eqnP1MDHkOOgY4e2g/n12PQK3DlpU/85T3HfsYEAZpgYmZ/0og0iSL2LIpUrV1HYAVktMrTtChLQsOAxFy6kgRDY/YfP7qxvH7tyuVapzfWDjShAHq21hbBWPLRn/upL/2bcmX97K7rCuShZLHHpgbHlgBLLBE0EwkCWgGvyiCiZKI1F9SL9vLm0vW8e/2757609DsP/8IP3/TI20/84k/jTmdlc2v16sUDt5ycObD3+tnTHqxldMi1KMhdrOtxv9gRDxZExOeOFdTS9d7FV95Y3LMQh2PNVmNA6S3Hju9sbly7fm16bHJivJ3G3dWNzdzhirpWb43dfOy2+9/xyOXXrj1z5unNbmdc15vxBEqglbDCyn+HcWRyMOpyhvuiofkZ4ki7hxWxcngT8DCEd9RH32g9q555CKwo5dkzOyHQgFUy/Cjsliq1laC4Ec5uuVw8sLfVrDvnC1ve2F0N4fwhuRMrwIc9V4OvsFeVFUi14SGsCKuCMjTxGvIgYJjjXvX3WEnjpdIPwxBHqkYKGXb+1e7ZCxIQqupyA1JhpDWIN1rHsVGGALFeD4li8bV+t7az28vLIojD0BiNmkHAeSsQKKUAA4LAKFSgCAVUVcSJtIr1ULNZAWpAhERYkfuHIThehpwTEfbVDc7sEcWxs+K9IwIv3jtApRFI64CUrms1modAPJMCIkRCVEoppXUgCIYoqhuyduPMFZN1x1pN7qTZVgd0p5/uOEw7naViaVuvpGpqcvyBO6cfvtMBuNJGOlj73NPrf/bkmxfObsTRbQ89eM+PfDQ+st9pcOy8t4qZnXdZWQxy78qoNW6iyFsG9rvn1hprHecchCEw52URT42pzrZcvwq8sr26tpm6zuTUez79a+HRPUu/9amt//jxOO/loBRLWlpQijWHGgPrEoYCuRQkAYcQePYkwuXqoEihsNA4+uEP3PzRj+ipWsE86GyULmslEyYKRKGzzrlSPFf7KPAiSqqQZUGACvgbCsWp6py4kryQKKMb9ZoJIgZx7FBAnAdQznNe8iBPozAIQ2vmWg/8wgfv/qF3bLy89O0/+OTyN76Rp9k0qClTH19f7339K7rcAChzsB5cCs6C2mEpwJdxa+7uE/f+wo/PPXIPGsiss468ZSCMwhhEhL14YRD0DpDQmOrZBUICzXQD+xm6M77F0GAAAgHxSrFS3npvbRSo3ChXiKoOBgKgEiSjjNYEJKgMO3zh60//+X/59DQUhyBqQjYBdQs+ByaGFpfxatd2S+k988qLq1effnn6luN7j9+6cNPe2lRjYaax7/5Dp376nSsvXvnWZ758+qnnB+vd7bw/dag995F3uzPXrzz93cPBdGvviVpUW7z35KH3P6Dr7Vqr/o3//rlXPv0XNYAAhETl4PTYpNRxrb9WAKw8/2y6/hEaV1EzqplABypEAaIwCozSeb+3dv5i1u3PHthXb9Ugjr0wEQqzgoo46OtjtaO3HNve2txaXddEjXbbkDjmMk+jsWjmloXv/2d//6kvfP6pbz7RG/S9K7T2YxhqRqvAss/Re60CAc9YeGczNmzqYas5ufcWNdboLJ3vdjpL69/5nT+kfLD3wfcf/pEf7vyvj1+//Pqhh+7be++pK8+/sru5Wiq04nVg6vVktx/6gUn7VrT3nnYc73/o7gSV8i7r7GQ6XzhwVCzNzc7v2bvvzE7v0qVL45O3jtUnVze2ymyQGbWyenVianzxwKEP/PD71//z9etb59YGqzoIEhgDksrIwTMKAVbOPDhkvA93qG/x4gWgyoqrZI8yuimGJGj46y0FAozc+D2wAnDel94BIg5jyv0IfRmxOAFJI1e9NcKeg0fCOCjyzLMgIXgG4SEAD4Ig7GUkz/I4DPkVFlRICECALFzFB9ygPgx9H25AIzJc6SKKr3Z8Ve5KBSWN1MIVmoWIypDRWpFCIiJAUhpJO49JPVEKK1MdXzrvXJqVvZ5l4Ua9GYRahNi6yjMyicNaUguM8l6c9a5w3osXYM8FO89stDHGIAizFxStTaCDIFAGSSrbRkHnHQtU6zgnrIgquEo8sWMQIKW01ibAQGsBVNXkpKpvPo7WKJWqqUKwCQmdd3GShIHpX94eXL0+ZlzkrXSL7lZnKRtghOurg3jVq14+O7M4+dh9zQfu0hP1tO9I+8GFN9e+88obVy+d9/3jj3/o4R//qfjoTRgHIh58gcxE4h2DdUZcLTEorMnZNN++vsnbRW0n1720Pj0OFsNG24Mpz7/OK1d2VpYHQeIOPfShz/x931IX/7ffX/3EpzWkCBQCOa2NhGTICycMlWQlJJZKFIieSe062QDqQjAWTt/7Ax8+8qM/2Di2gCHZ9a2su1OrJ0FcD+IYEV2RKwTGyjMWWFV7omHbMRK2V4AmI2K1BGal41okCa1e2Vo7e25zeT3rbLFwo10bX1yY3rsvGB/PrS17eWiUClQchSpUk3fse9fRn3/y9yfP/+4noeTF9mzW28yzdYBSwFooMlDrAGxasHho7OSxRz7y2Oz9R7hWE2BXCIv3zgF6bTQBeMfgAMSyY/BMhhi8eEAkceio4mBUjQJQZTcoUHGuFdGNPg1NUHa994wUACmBygBVECugTURpB0xKiw4zlosvn09hHSEmwCkIHnjvr7hb71m69trVz30t6z09Bn2V9uoXrkfbwoOtiy+9+ByAGZtbfOyeB77vsfmpyWRx7KbZ9sFHjzz3tRe+8IdfWztz7lpoxtoturvWaqqDd5xYOHJzbawetWummQDEaxeWXvy9j9fBN4EE2CrOyby4EzzWHl/vL6fAuNspyrwdT7TGakA6MMpoiULD1i099cIX/9+Pr790Tomd3H/TzPHF/W+/Y9+pW0UlRg8RLmaPSrf3zO696aaLr77U2+oYE0bNMDCE3uXZbuEh2F9/6Cc+UptefPnl09fPv9rrdk1dk3dNMjEFQG5AAo5As5TeQ+mKTVgfsJU97fbh2b3hRLK12802/BP/8fN3qtbRU/fcoX/y8tefun76om5NzB68qbe5y3mJeRorwISCmthO6ZUUIBJpU49qdTy4b+HQwkTaHRT55sz8njCOas36/v1HVy8v99c716+vT0zOt5vju5mWkjtL62sTSxNTU4un9j368COf/uL6ermD/fWDjUhTaJDKarAVISBBBhFAEhjO7QCMjBXjBQR4KO6tQKGhtlZkWEYquRZXi4NqR8CMWjOwZcs0FAcLEjBARRUdfTovjKAIkZ2Q0QcPHlQIZemKIpchTC0VyYeHWjTkanPlAVUlJzOKUKEaOvsPq/tfh8OH+KcwI4AIs/jK26GKjcMbIw8CASqliUhrUlUTXpVQqcgUQkohgPYqGFgJEMT7Isv76WC3N8gHvhbHk9NtQ8bmPLApeFAozUZYTxJUqvBclr4sisJaW3Beem8dM2uNOrQIyJ6FnVE6SiJMANBUqkRgBAXD4HoQ8R4VVQsPrJhVSgJFyiggJEQ15KsPTQAqwitW9ZFwxAepoj84CUyjmXDPLb14LsjThLTt9/vZ4LyPkgMLg4lefyAJQJiM10/eG953f7hvMR949qI3Ozt/8vXlr3/3dJoHew8/+uM/PnHbMVdXFpidB3bIjAjAXmwRR8r1+7bvOKad09c6u2ULw+1XLw48SaMp4KOm2fzKafrWd7PVza3cho+eet8f/ON0pzj30f+Qf/PFFhBDBOgKRI+xAgrRhCAKxAuW2g/Yld7lwJnPHUCOQWN84r4H7zn6fe9rn7yd5pvcVK7IdzsbaTYYmxgz9ZgCKq1zzopnqCIqeMT7r1DEt1gEACMvQWAQpCQMly9vfOaPPrP6xHOtlWUq0eSOOGdy4Xhz/r5jx3/uhyeOHMuyIs0zFWgFFIXiUeJ6/Ojf/hu2uyp/9ZwrdgIEDZID9EGlENjGmN9//IF/9DcnDh9s7x13vd3dLNegAhMggQJljELxwF4Afe48iGMCKYU8ggJAhQJSRXtVlqbEzEqpSgvwFt8ZBTyAQgWoEIG9t5Yo1mEgpJlLGKJATEygmRlQgQCL9QrKCMpo2CoGdOx4+513jdHCoQ/f+8zf/Y3B5rNNyFLXNRvtOJqd+b5jb3z769e/89yrzzy79I3nHviJD97z6J1x0orH6u/7yEN7F2u/+1u/u3Zl48JffU/qU3N72lNHjswc3oOx9g416rKQF7/2XLm8OQWKwGdA/VrZ9u27ixl37TsZ4AAgMByGQRBQHGqtAhNqsn77zMbTf/qZl7/4uZ0La7m1CTi3bg3WZ453y53dzu7K2MxErVXXpL13iDqoBYuHD25vdvpb13e3OxROxFGC7It+TiIQS9RM7vuB+47cdfyNpw48/53v7KwtKbIouq3DiBICnxvnrfUgGhABOi7tblzL0+2pVmMiqicTjU0pOtg9/akvNnBy/623hthcW9toze0J9s7ha0me9jjtB6BFqzhSOUrEpYQKFCSIqtM307Y5PrGzvdlZ31y9ev7AzbdzFO+dW7i+b/613tLK8lKtMTm1eMBdv9QfdK0tNq8vbY5PN8fgrvc+eu7q1W+88s21dCeJ4zk1R6BBDWEehuFSYEhmwKomolTYOgz9z96CiKjqu6nil+BoJhguAIZ1p/pQ7731jKRVJcuqzEEBARTSsFqDZyGkgl3YaO7bv5eRM1uWrqSqSsmwYxdARcM7CBgCY7RSSIKEikhgSNMfcrix2kIMh4AKtarWuixcOYfA0CwRkZQmpUmpKj1VKUWEhBWr48ZKAJEEqmBJ0Gnptrb6jrNAEwGWtiydD0JTCwI7KFJb7GYD0qpZT+qNehwHheUyLbM8L10JiEQkQsBe6yDQZIwGBO+cA0+kjdFGBwoUwFB5x45Rqm5eESHp0FQXFOIQt1YVQ2kE3gJAdeBldGmP4LRKx4EjEiOSqjcSRbBxdaNzdWMSlcHcpWm/uz1dT5r33ZSn6fzWoDjbofH52v33tO+82QOwcDIOg4tb3S99/fT1N/ThQ4//0I8u3nEKYs0o7ErxHskOk96kRLScZ93L19aXdumhmzYuXFOT88Xy5WvnziX33k5x27L2zu1+9qv44pntLJz4ke978A9/dvu5ldM/92/SF68w2Dp4C95IsiMOONQgznEGkAJ0ADtsU2AEFBibmpncc8+xEx+8d/y24835WRC2SlOCiL4c9LPudhCoWqMehEQasfTi7A1sHKhKlbux/xw2O0hICr33IoCgmmP1IvW/+8u/tf7c0yegcbJePzg3Mz05GXSy80uX1zavrX7u6WeWtk795v82fegm1y8AnLjCIYMX5yAw4e3f98OvnN1defZl5QYppLsQcnvf9Hseaj3+8J4H72ws1tfOvPztT36l1V448dAtWmshT4R+6KPsAEEHkRCISBhqX5J48ezJGwFA9qgAgIQ9KlLDAZB4NLYPGX1VUAaCAQC2YnP2Na2ViQKXWRjKH6TCu1ChZ/ZQmka8cHDPNNQ0WAa/A8WbT//xPd9/GyDO3ta+9Td+8cmf/2WEzQy8hy24VhyNP/TIr75r48/++xvfemXl69/6wtlXzr//oQ/8wo83WjOJCffecuI9jz/yuX/3e70yiMdsp3N565b5+YN7Kvur0vkiLYrla4HlBJQCdsDjvXjqwF7tsk3obUPuIQ73L47PTogwoNY6LPv9N59++bP/92/vnj7bRIrZxQA1UARuZrx9y2MP5JJdO/3m5lLjwB0nmq2mMoFnIcLmRGPPkcNXvUu3N2hjlzTWGrG31jpgyYu0CMI4mlUn3nZnY37ymW9948Jzr+QuK1C1zHgiOqCw0CW7gfWitA7BFT7vDHbLgTTRUIIYB5HPB2udb//Of4Uf+9CRe+4OkmCrPzh0762bV65eeHZtkK1am0VJPUYzwCh1fQ6MYuTcZ90sTwvCQJu4OT538cz5xX2HTRjVxsaPHDm+sry8dW1lY2t11hwMk7G8KCiMV9dWA3r19vvb9cnm297xnourKxeXXtnY3W5MNVrQImFGAvZDD7S/ppMdyqSGlXaIhgAMhS8ypEjwEFikG/ziYXs9HBwBSm8teyAcoudVtl7VzaISRCEUJ4AASpVFObd3z+TEOHsunGMARUQy5JwSgQhQJdInUkJKKVU5AFbLUvHshIfENR66vFW8HYTh7nrEhkCkAEkprVCRIk0KVeXcA6OOebgWYakQsGFJxZEKTne2ut1OF6Bg613BpKkx3qo1oxJcv5/1MhdGtemJ1thYLdBkLecF9/MSvA+iIAwNCbKDsUaiNClFSAqFRETpkbp0OLWAY0aRINJKERENF5FIarjTBgR+S9kmFZYF1ZYSR2QtGskdBLH6RiKiQrIsWgcUhkU323z9Km/3wom67ZfloIQim5xo7b/9odZiNPjyC1euv2puu3ni3Se8hs0ra63FKHv53PX/9rk3XzvT0427PvL9D3/kfZioUoTLAoBJGIFpaNzELDbrDjpnzm+cXlm4dbbfLRoBdV47N3AydmQBmlobY19ZMq9dXeqU0z/1/nv+69/d+ORLL/zqb29fuFBC5EE8AEAIYBgShDAiHTSgz8bGkZmbPHBiYvbYfPvQ3Ni++WRmPJyomVBLAWW/ZM8QaCIUlmJ3pxj0ojiKk5qJQ9LovGX2IEJCokaEARjqEkeB1kPWWeVKG0RBUm/9ye/+yennXrlbjb3/pttvajca85N47ECwUKtdevXV//J57iytv3TthY9/5b3/x/H29FTR2y3z1DuHgEFQI6KxmfbksTt3nnxpkzsmHIebTt790x+r3X8SJxsqKTpPfPe1f/sv2zfddsc732vCJBcp0tJaS7YsdwbgfFgzYdwIYu2dYKiVNoVjQmRmEuaiQDJKB4Cj04lVWETVzOFbUwCSeABtxHkASyiErAhZK89Mw1PP7B2ZQEeaFZpmctdH3v38n39268ozJdAqePvmN+PPfHHhwZv7luITCzO3nhy8+loJXkORglt5+Usnf/U/3vLu37x7pf/yv/g/n/3Kc1f+8Dt/cHn18Z/8yK233AP15t1vf/jFz335zTPX5+cmym7n0rdfWDh6dObIQS9c+rwsOL9yddqWDRACG0AZyVj/ysU31SUC3wdfqvD9P/uRoAY2hyAI169tPv9XX/r8f/qvwcbaBJpYVQohAiQW78uCbDAxGdyyf+7y+bPb1xoK9tQnpgOjmVkpml2Yyvv9q2m6s9UBce3jR5So1Gd5UdiyrHmLgN7g5MHZB+vvz6B2/tlvC3d1LolqGBXXw1ZeN91ykHIRAIyJBMwFYC65H+Qw0AoEQdvutef/2x/DZvfo2+7XY3GWdm994M6t9bNbG9d7/e2pqXoAoaZEIBgIOPSuLGCrv4e1KxWC0VEjbhWvvPTybafuiZJo4eD+o0tHv7u6trx6dWp2sllvu6LfLXaLMrxweWlqz5X9B2uLt7Qfvv/e1c+9uVP0NvqbtXpSaVmESJBHbIeqJ5ARXRhHTWNVOkaNZPUmy5AlV/n9iAiQQEXKARRgFMuWwVcJANUiGcEPTRaxslwZuroBIAMuHjwYRcY5KcqCoEqOHEL31b5TjTB9urGNYxAhAGDx7GzhQIR5aAAENKRpIxEopQip6qBVtV6lURzkDdpDdfKH+48Re6i6OpCYWQQqlpPu7GyzcyEBaU0IgUk0aFcKRWZsrD67GNbCJIyMMgQV4YB00AhDpahSyDkxpBQNOVgVYR+QEMQzVBJMQhQQBbo6hepGb0833pIRm2/E14Phog+Hjb4M4Qwc/lxlZBAtLMzAIvWxSCNuXlnvnbsQlpkKJ9zWQG1tEBf1xtTUPbfH3Ft3L/qbbjr6s++jGmWb2fhUw/DOtU/86fJnn1wCmbz7/nt/4AdoosFaoLRKBIABPCEyOhARdmBtsd1zl66osju4vBV1euHq6c3z1/X4/Pgtt7TnWvbNza3f+cy1jTR+7Nb7fu/HV//gxSd/+Tf83j0P/MlvNO7YC1QH62zfF2nPO4qaUb0RkwZhZWqRCgKMAHXVtXth9p5d37rc+YFlgqhuiMDnRdntKHSN5oQKQqW1lCzshQSHbDGGkeYcbozDw9aBKtSEjK632lvXNr/5yS/VwO+ba8WTi8vXLsuZi/7Jl+qH9x/4qfvv+qe1lV/6zwZ2Os+cvvDC6dvfdrcJoiLroaAOAwyg6KdowOxdcKB2Qc8+cP/Jf/iLE0eOFTEJut5zp1/7l/+8NrVw5w9/SDfioizSwhWlg8Ggd/ECbKXei2oYHU5P3Dyrk4iEiJRClCr4wpY2T7WuKcOIoPSof6so/9VPZ6j0Z6UIEAG1EyhtKeJJEYKgqozjhuM8oDDb3JZI7LNB+/DC9//Or/yvn/wl3r6mIBhsb5757V+//kc3jd1+KDl4MmiqElBAETgCufrdNy78yTemj+0ZP7pw6nf+xb5/95+e/MO/WH72+SeuXM4/+P7bfuAjyezYj/6dH/9Pv/I7zZTDvYeWL7x58dnnWnMzKqoXWSE6clgLJJgE8hBnwH2wykvpdQnSgfBv/OY/PPz2+8h7rXTn+san/6//+82vftF0+5NQbwkIKy9AACQ0gPLK1bWdnqUZKHY7vLK+a6IgaZnamKmHSmv2zkRmZt9C1i2uddOt69uz81ljrGHZW+dFcFBkCslogyG39ozd+65HtzqXumcuDDTGgsq7uth2WNehTrOsn/f7AG00QIIU1KwgcBOwBpyC853V1z/5F2GeH3j3w8FYrA7O3vbo21789Jc3Nrqz+8NY1x2jZjcuUY9Ux/FW0XcgaT9jkjgJGdprly92dvc3a5P1dvPYyZMXX399vbO1fOXynoWbxicm0+UU7QAIr1w4X4sbc7P77rj3jpdf/u5LZ1/fTrtTUS82dQb0DKSQkKplIkA1KFaS0eFC9wZ+MKomIihDVLkam+EtzyxmGILSzE6cAJAICaCSEYNnyCUVFkFCBQrIA4RBdPTwUYU0KKxzNjBaIQ1ll1RZewizF88M7AWcL1kAuFIDIChRQEJKUKpuXhGRVhqJhkh5Be+QqqLAKnBntEe+gZoMyX8jBGWEIyF7BlXpY9g60ZEiCms1jaQMKaOUCYKg1ozjWtSsR0OHUwEWEPBaUU0pVKSqIG4WpVDTjUW6DIOUwVdsPQDSOCRUwWgcweHmBW68Qhwx0t/ax4/yCHFklkHDd6xu7iFGhB48S+HSelKPjBmspNdfuOQ3d+utek7sszLY2a21Ij3fiOtSPnlhu9s78sPvM2PNbH1QrW16F1d6Xz3X2dnxNx1+19//W+FMwxKwt4IekYkAhQAcEXBhrSvRcX9pOUZ/6IGbe2evJk8tTTRlZZBPv/PU/D2LktnyP37+2ndesQ/te+/Xfvnqp85+8R/97i0/9r6T//JDqjEr7NEzeCvAJBMoxABVrWNEYHHixXtxgCzsgb1H793Aems9i45rWhvxUvSzotsLtQprEYXGhEGRli4vofSilYwQn0pCwlJlgSl4a4cEpFQQhlqriy++2lu50oB8NhbYu5AK7X77zy3s8sr509dWH/+//smDv6o/95v/DtOdjdNX4dQJHYSEilC00iiQF7mpxxO3jtPNe3rb8e1/74embj4MRnHu/Xeff/7/+v8tLIwd+9EfDsfni4yzNEtLBisbz7/GG+uHbt+zsVpsn18lWYqmT7abBwT8ULYpjOhs1iuzjGpxdWYrnguOYq0Rh14pwqKUHj3gKKCstUhkFCmNziPSkF0BBAxC6JGUc9am2QDV4bvu/v7f+Gdf+uVfN4O1CeDQrsHS5vrS0wx/DsAxUATWAHgIVtPNL/3Hf09pdmh+/P3/8h/s/ZmPPThX+/YffzHdyV/6q686DO76oQ817jvxnp9+11d/75v75qabzcbq62d69903tqemAChRE7ff3v/Kq1AUHjAD6EM/gLEOiG7Mvevnf/DU3/wwBWTBdFY6/+mXf+vNb/9V27kplSQeNIATJMAAUIMqgNe6mxcvLue+Ubx4qbFt7RT7bq8YpGEQB7EJIm2dj+rx9IG5Xm9n6c2ta1cvH60dMxQj5p6d5AKBIlIgEsW07+DEow++44nVtL+7EaisDsKly72n1thYFJLofq+TAnjvM4aaip3PHXAAYID64LcG2/YrX8nbfPiu2+OJcO+JQytvHthdWvbgDBBmNhAKGJo6ArYFsEEv7Jm9MiqOwrCZXLl4/uab6srQ4oH9t9x771e/+ZXN9e1GuDG7f7qR1PJe1wRY7PSuvn6m3RpvL7Tuv/dtl66t9PPOSra5x0SBGCEWL25kEwsiKKoqF1WJHOpiRvoYgIqjWBWsqssGLzAUyt/wURDxzlvHSKiQpPJnY+RKz4U49HerZmwFDjiZnNi//wCDOPaokAi8VAabnpmd9WVZcFnavGTx1nPprBPvS6cRdRBEca2WhKHRWiulVIWXIKLGITOURg6io9z3Ebt1WIeHbIkhzjVaewzZ4MMPGlJCtUE9M9EmFYWKo1o9DEOjycRaaS2ISqGwMDB7cb4iVxACggfHLAyiQCMO7SSrgFbPglDhDYoqHR6CiCIFN6b1amSXG+Z3UPWsMrJIHSo2bgw18pbrr/AwCZaG5C70ZRkYPT7eIA/bZ5e7l5ZjgFq7Zgf9Xi8biwLTaITtCfvGypunV2cef2zyrsNsSxQ2TZ9dvrz9p19def3VbHHyzh/5/j133ebEAgMBAwqQkCLxDkAUgvceARx463z71ptr996x+V//59TVzcFcPZraM/fYO+Ns4P/kleuf+9bO2Nh7PvG/vfEX5//0H/2X+376vcf/8Ydsw4iI8QKIDAqBBKXiYjKgMHp27L0tnXgmALRlkRfinVKkyegoQMCgESmNYn3e3bVFWms3SBsdxQLgvAPxrBAFFYlDAuEh83OoMAciAq6ow1iZKRHw6pWl/mA3Ap+x/c6b37j76J3HfvKDbz715M65a+nll578rd96+6/+y0PvvFBeuhKXzqV5YBQCEYE2WhwIgxOIxptuuo1Ff+LgkWA8zreK3gvPnP+1f7wwHe778IfHDy+ygmKQ7nSykLTf3jXru4sn99UPLVi/svbcUwYE4D6tIgfihJ1WCICkys5qwVBTs0galJKhZe5QyTY8x0ORMCOQAAEqa9lZC8JKacRKQiVCQKRIgfNeYRXog1IWA+sLz4ff9860Wz7zL35b9y+FoHIousAFOAWsAZuABrwDO1Gfc7X66u7u5ndeXvnRX3v///n357//XTel4dJXv9ofmIt/9dWxscaRD99//D1vW/vqqxeuvD6xMJnvrHaWrozNzgU6YO+nbz10dX6CL23kIB3IPbSjqfkDx+965J/8WPvQnDKRsFpdufDv/va/OPfkt8YAWxTVmBQggkYhAxICIVINteSdS1/7AjXD2htXJm++yTRme6myu5mNSh1oClRci6ztN9vN+X37d9Y3Okub3ZmN5vRcoE2qyNrC5d55QQQmHdeSY3ef9Nvlk1/4XOlsYRyQtr4oBrshatQqDOO88BqUiE29KwAKUAISgRLgLuRbm6udz34VjD989z1TizM333dy6VVl2UeNehiEPqylLp2PjFd6k5QGVYt035YGIW4383J6a2V9e31j76EDTuD2e+69uLRy5bVXdrdXZ+anGq0xZ4ssz1ya7rqtKxfO3Hzn3bfdd/fF85effP6bnX5/rDZoU6QqWwQWEUZFlTpkiIJWLgvDxwfwRhwG3qDwAFQ9buUrQgDADKiQhL31JSMDqgqyECAmrspVFbZTMTqVMoSK2U4eXJiemSmt203TtLSOGWyVu+q9K4uiKMqizPI8HdiyKG1eFAUjGAxqYaPVrsemVktqcRTqsGK5V431sH+uet8hq6/ijVbBLsN0ALnRjQ/JMkMZG8Fbf191T+Kdd5713n2znnUUkAqNQiKFAMKOWcCWtjKVBGbvnGdSGlUFRhFprUhXUWVVf8YsDFxBS8Nbq5rEkIZb8sqBu9pdjF7x8F4YvuSK3i1csXlvTDYENPTrGBo5ATM47xnYikwlNWVMvt7rn7tAW9utyYaJot1rS3HCZrqFQS0fZNeWryUn9i0+ekKAqbDphQ03Zlc/9fXOp76aNuLGw6eOf+xhR9aD9aLM8DssUJEqpYr8RCseQ4ra7fpN+/DEPNWD3G5JmDRPHZi4Q+FffO/S/3jiylTtbZ/65TROzn/66Xf80Nv3/ditJWe9p5c6KxvReNCYnGhOjRNSmITeFs4LsjgWVxRSiRsCAlRg2RaOcotJhCEJgQiBoHjO0nywtU4sYVAjCnUY+5LZe2aPlWFahbkhVukhPPrJD2FP9JV0xZMoBHQEUNRAX/G0dOnN7tWX56Ynpk/ccjCOr587X7x+ducL3zr8gbelz7wqOaX9QdCKTWi4LJVGtkyC3kFjuj52/7FWXhhseVHb566d/z/+3TzaqXe9e+q+UxTUBt1+v5dDAfVW4NBSs1avJSoZk5e/k19aih451ViYIoPgkL1DwSiOfGezc30pmtuvgwi1YRYUAuFhIPbIug7prQJPiIq0iLJOnGetKoWI8WyRtVIGQEhjdRkoUKRDS1IUGUT1m3/gw2LGzv3Wfy2Wzw+gvwPlDuQECkBr4BiUAbVwYP7AL/3dzc2Nb372cxdfevMzv/mJDyY/dsePf6jMfPb62dq1wZt/+cL03mOzJ/Ye++j7r//mf9m9uFXbU9tdXhr0urVGM4yCvbfvOf+OkztfcRDgTHts/s6j+x++c+L4MWtQFGAQXX7j/G/+zD/ZeO35FuAEmoS9UUZ8dY4kQGVIKdDCikrufuObb0hx5L77Dz14W4EB5JB2i6CVRo0YAEygoiRmW7QnJ6cWjlzubKxeXmlMTMZxnGWFFVta60sBBWXupZQkqR2/49bVV164evGyQ7EhazBQ5gVQYYKxhXnNaZpZLorCpSrQSRQRUDMKXeEpH3Rtv8jyC6+cnd53eHJhcfHowZTyzeXd+fqsbrbStNfLsO7RJDF6DA1qjdo5W3pdqlaz3tvtrixfn5qfbjZDM1a7++47l6+dX+tumcvn9h843Gg1syJzZPohXLl0qT05OzFx4N4HHrp45fUr6xfXuxvN8ab2GgE9+Mo0hmCogBmCxzfgELyBJkNlr1CtjnHokF/tFiuMXDOwZ/ZcMnLVNwiAjLynhibNIEqRCGgiAU8KDt90c6PZSIu0X+R5UZZ5wSVbZ7l0tszLPM2ywlqbp3m/3xNxJtL15lij3mo3x5qNVr3RiOOwcjRHfIvzrgAZoLKcQFTDVO+hV/WNpXZlAlDZFA1NvyoJzejUCAOI4+orteJ00qyzKBQnDF7EVWIF771nBnDMQEqj0oq0IjKkAAGBiIazhYwyChAVqsq7dATv35i5RlDPSKRd/clDtUblXiHM1bRGihQPI8yGBNAbdxqAMAuCECkWZMI4jIN6wiVuXVzvX72e+LyRTEFaqoLrSaPO6e7A5jqemt8/cdMiAIIt+2+sltdXWnsnd554caXTSd79wNxPf38yMdFd62sjoaZRc8kgxFXIM4hwacucCi91Y6bncmfqM3Pb8Gw42T70kXdFS/nLn/3eKo3f/cc/Ed01f+U/PFf72mrtYZM88UTv8xdOn9/qHJhb/Oi9tbGa92WWls4GpMBZAcuexZU2CALSaAi9sBWXdTrZxu7Y9Lhq1iQ0EiRAyrP3g0HZ66ogIKWDOEEkbwtrLQtWIosK7JdhSwNDeUn1mI+eXy9M7ApwR2453FRJ4NPJVFQXlXB/ayc98wLsjZoTB2KzmOO+5ZX1gx96dHNllQuHSMoo2ylhHLi0RV44wbqO2odvary5XJsIJOxf+A//vuic5UfubrzvbdHkzPLrF20YRSZsNSmwvd3eRpFt8I7hb230n386mTp68/vflzTHLbL1wjasNUJV7iydeXWQcntyVgUxiCKoyGM44m3DWy0CkmdW1UOpIIgTD15IGwrCJM7TXAsSUhAFhEQaUTEpUmh0aJg49VaKHZ9M3PGxdx15x30Xv/3M9a8/0Tt3Ori2WqSDouj3gHZAMdSOP/rgnuM3TxT7gpn6C3/0pdXvXP7k//O1j+05fM/Pff/Ov/9zpKW1pd7LTzwb71msHdrfOnXs8rdfCursdzcGy2s4b8aSMIqiu3/48ZXbjy3s3duabDTmxuNGtHx1W1lqTo8vnX7913/8H3cuvV4DHIeoJgJgSg8xkAAYUKrKvEEBBOXBZ6VfqM/+xF1qX3twer1Q6C3k/bho1EyoiSCMjMu8i8PpfZPb63s6a9cHO7vtmZlao5FlJYk46wDRi4e8Z00RTY/deu+D2UY3z7e9c569EkVK19ozs4f3T51Y6GXZRq9TRB5BJ3ESho1W1CIUB+Cy7d6Vq4M831nfNioEwqTRypuuMNRemFlaXtVRbSfL2flwrG2976d9z96Dx6IwUTQxOTVY31q/fr3VHleGDh09dPjgTc8+8+zm+tZ4fSZp18fbbmtjrd8fQC26cOaN6GRrz/G9x286ubx9fScd9Bo77WBCe2KqMtBBBGmUtVt1cn8d/cFRCarugOGQXIFHVS4eEhEJi5PSikNFKIRQgfgAIiSgEJUQCQKLVkoIHKCpN0/ccSsQZUWZpoPebq/MiyIvijQtBpktCraWQQi1Qt1sTNZbSaPVaNSTOKnXkyiOw8AYqpajWEWAju4nBB7Sm3CY4sFAQ+0bDLn/ahhNAyNptLBYZme5tL4sy7IsrPPsK3MdzvNCjywWkVHYsfPCwgigUBGi0YJEWqkK4kEBdQOFHUJozCJV2gG8VfOrki6jCWuE4o9AqWpnjpXrdvWD4NEoICAoNBrd/tpAUH1cxfgXAfEkURIlZIh0sW3XX78u3UEtiXQzSlMbECVK7M4u66RxdH7y2B4yRCDl6mD3s080G43+udevPXuhuGP64E98YGzuoC1y73ycBICVDm+oEqxeiGcHLJJ7LnzYbvq8yxf7dcRr0wu3PHz/5Hz7/P/8o82ofuqPP9a8c27lj1/e+Vef3rNn+sj33bl++vK1M9318b3N+x/a+8jJ9kSiSEWhywd5nru4njjyvJtqFahAk1Jegy8BepJvbMrOsmkpHHhwiQrrChC9K3c7Ls/CqQkVRCZJEMWzd9ZVP5YbwUND74obE9ZofS4oACwszlpr/eHHTv7jX//f//Kf/ftr6+sJKWdC8dJyJa9COBk1a8Huxnmn3Nj+E3o8KNkrICVUDArvSu/YoZSOXdcrqDWP3RNG9e0XXqGvfb2+tzn1nndO7juxduVyya7VqGtfqt3N7NKGTbcDa4rnllbffGl3fOH4T3yoNr+nZMzK0nmJQmOs23j69PU3r0zdc199fI5M6FmEhVFUteOqRG0gCqByvyLAyt+diFARuxKEPXNcb2gKvAUsUHuNQC4t86JHVAI4E5fM6XZnrTPYsv2yNjkxPrd41/uO3vs3b/VOD64H629Eqy+mvS/9/tVrr06fuvX2X/pbE83xcGs3uvWu8Zz/autLF19//fd/7Q9/6b/8nff8zIef+n/+oMgGO5eX18+dn7tp/uhDD6y/eW6z09mzvk0u4yLTejLUsvf4nsWb94GgDjSA2t3tqUDPLs5ef+Pcv/qJ/8/uxTfqBG3RtSG9WTGgBTSgBTUienYVxEoIZcjTj+1v3raw9NKVUjKoN1SoS9/rZxEFuh01opiKgQ1CGJuszx9YPLu+dn1pqdFqJEEShHlZOPDMwiiYSpH3yp6x3Ug35o+WV190LmXh0MOYq0+gOnXyQ/HdsAn9ye0tF/p6u9kaa5KKsCByEjYamsru9a3VCxe3V5a7/d0gNFk/C8fqSX0cFM7u9M+feQ1IeSvo/G5ervf69VpgjCGthH29FlGrmXU76+trExPt2ljtvkceOHPh3MbWRnD9ym3TJxMVd0kHrh+qRmd768r5s0eO3vrgOx9448KLZ5fOraRbSdiIIREABziSBI9qPI4UUhUxZnQqhhR/GQpsAYbcRBFGJAKy4qwrUdGw5A0FR4KAhDD0ARXww+QiZaXce+TAwQN7y7JYXdu6dG2Jy6zMijTLykFeWkdEYRgmUZzEcWACbYL2RDOJwzAMwjAMA621opEHnKDQiEcDNwiuIzwfBIaaNgQEZAJB9Mzghb14J6W4Ii8HadrLBkW/zPPMltaVmfdii7JawOS20ALoHXtgquwnSBST977KBdSkKyUBIklVUyqmz5BiVcnvqmypkU5rxESCkXUjwI1XPVrAw1BjUW3oYMi+RRypMG4w/XnEYxrKIajqa0VEjFGGSBEFiBvXNnvX1hIW3YxyLyWg0aq/uu3yUg4tTt91VNU0AUDOcm2jVQ+Ks+fOvfH0WhQe+1t/Y/z4rdB3tlewKwEMAxMiA5OiihXADAioSOsgzNJBa26x3Lref+I7qqeDwwdm3/fu3QtrV5f0Pf/iF+p3zmz8wTe2/9mfNgNz4l37Yc1tP3d15+133/ITj+w/eiAOXZRotjDo5Wmva4uCiyweayTtmD3oyLAiEIHdwWC1Y3PVXjwSRzEzFlleDyNFZLMy6256tEGUqDAio511znsEZFSVYAoJABiEKu7ziGUFUImwq/UQgy+LAVCY1O78ifcfOXL49//57yy/+YIrdwMV3JrUx9C2B6lJL3eEYf42CCkcH9++cqEscxE/yLrjfgYQRYMC9s5ngBM3HZTe7tIn/mylVMc+8PjMfQ+5nMvdQVKv6bwPuxvZhZWdy7t7Tixefn3jSlbmjeOH/vYHxg4s2lLSjEWpKLBmsLn5yqtvfOfZiVP377vtdhNEjr33XitFVXLS8AmRIbNTkAgZKt4QGI2EigWUMklcX72+fOH5C92VTdWRrNMr+/2djY1sa0ncgNMsy3ZRl6UvHLDOPXgAX0lmohzDIFYYGAoUqjiaPnjgA3f7rF8YggRb9WDsHSd9mn3pv29feWX5k//2K3/z137glo+988X/8SW7nq2duTZ9YG7m5sOzN91+7nsvbVzbOdjrQXOQbW45E0WNyDFa57D06IVITczUNlfO/PpP/L2186/VQbVA1wENGARRoAiwugMUgBUgYQD0AOAEa6FrNV/4xutz4VhrvA06oCCAQHnxaZYqTZPjraQV2SKvqWh6z8zKlan165cm57Ym5/bHUZQVaVk6sAIiQMCK891Op7P12uWLcZEeDMF59ORy3+ntvPHVz/6KO7cwfftdJx46VUtMb7fX29jZ2dncuLrcW12z4tqtyWbSmJycqC/MMHqPMj01c/b8hTipTe9fzLv5Rndtc3O53Z5e3e52up20nEGDIaAOA2AgjXG9vb12bWPlcqsduowXDux54IGHv/SZv+z01ja318bqM4nZDoyyaVF43txci5utI4dvu//uR5bW1nf66XZzZ0HFw+GQAcEP3RUq47Zh6zwsKxUkysNiXuFE1V0ACMQCiOhBSrYOrEPUqCsUAEAYcRjSOBSiApJCAgEvgT56+y1RYpbXNl85e6a306kbw9aj9bEJWo1mlNSiJE6iKA5Do5UJw3o9MgEZbZRWlckV3aiCUKWZKRlxI2/ovapKaR07YRZxwtb7orR5WQ76RdHNs0E/L9Lc9wdpt9fZHnT7vf6ASBEhOEEW64ogilhEK6Odt2XphCu9KOlABYYASFNFOaKKkq+okjHwCPMf1vPKSX5IQn0L2H8LaKsQouGCengRVBc0V07F1WcbLfZEBFmkGnh4mMYJ4iq9BpBSpJRCHRjlS6ebCXvfX17N19bHxyIcS3byrNlsu6WVop/6ejJz26G41WQLwq7z3Lnw86+YRC+fuZxp2fuz79n79ndEFPQHmQBESVC57o3CeoCr+DQQBNKkkARIhdOzdn158MTp3db08R/+IT0Wvfbtq7f84nvrh+bW//03V//t56K8nLx53q7Q5a98ay2F6R84dvjoXo02MBq9313ZynZ3dYixpsHOrlJBY7pdcTRdbu324P/P1H9Ha5plZ53g3se89vPX+xtxw5vMSG8qM6uysnylqmQKJIGEAAHD6qZnmIE100PPglndDNOre+gGVkM3YhppADkoISFTJZWvyqqs9C68j7g3rjeff905Z+/54/1uFJF/ZebKjIgv3u+8++zneX7P4O4W9A5aM+OCvfRg38nCeH4EEgCdMcN2R2lPB76uxABM1pkit9aWb+nS68yAjIeHfxlcBMTyBguj05IB2ZkiSVm6ygvH/ovf/R/+8J//qzf/8E8Ptu4/sDlq6QDCFJRoB+QjCumz6Q5Nfyh8z5EmASilJ4QhEIHkQMZTE8nrl25/462J5x+ff+XngnqcbewL1GAy6vUG93YeXNtceuzpXqx2mquNF88dX1ypz0+aNC9ABHWlwaQ7m2vff/PKhx9NPfHSmU+/LHxlLTtnVSBF+dsazT/MQsAhuX3k72MqdQChhHRojdUgH7x3+7f+4T+ND5IaKihy0MrZDNh6oytyLoB8cLURKg6K0vXLRnJXDYGGVgIxyP7GtT/+u98Ss/XFVz65dOL02Mr8ypmV5S99/M8tTfy7f/IfL77x7g+/deS5V5945KeKH/zuazvrG93dvZkTx859+qXN6xud1WT33tappWXX6w5NW+CkkwGjA4e+F9Tr0WBr73/8xf/jwY3360JUQIREAiQA+CBU+QIo20OAy754YiQA44phj/IPth+fPb9w9rSyREKg7xWIBqyUlGXpYOgFoV9pxMN+Uq9X5hcW97bvPri/Vm1O+76nhZeoAoHAOGsL7XlhQ7Vmq6eW66vXb1oXBUhDx20wt7rrB927w3Tri4+e398c3tx6sLO2lu3upPt7nf12b+tgp9/zAhEp78j5Y0fOPKK0rk9P1MZqi72Jva39Zjg5f2Rub2ciK3pJahxYiWiMM9YhonMOgJWnUaqoMrF+705c8VbOXMiG7rlnnrp9+fLFW+/eWbvz6JnJueMrq3dWjaMwxl6ns7Vxf2pm4YVPffqdN9++vn2tc7A33qoqCAQhSiqVgNE4KkcJADikopRf6ZGvDA4XK6MBtNw3MLEtTG6Yy34s4JF4jIyMAgQgIiFLlMTEQhRoa7OLy8dP5rm5s7Zhs7RZiyPpIaMWMvS0H0We52vf87XSSmmttO9rVf6vypAaA7MjHFV3wIiFBSOOJzvnjCUhypIaTIq83R72U5cWQ0u5TWySDIYHe/39/Tzp9QbD1OYWhlTk3b39/jDPhrbIDAN7nvZ8vzrZajYmlCNX9g8XxoAlrTRLQKWkOrz5IMiH/ch8CMo7XPYIKrdBo6Ob4DA9PbqqH/rRS5sVjJyJD9dCQOxGyjWTK1+qRMA0inSwRAFEAgSBAAAlpfQEw8jdgQ6Ur2iQtNfWhEmCsVlrjGSJRQ5pToajY/PNx88KXzLbIs9Nv+d/cD2r5Skl/rMXHvkbf8EUlKQHtuDKeMUhMjkUgpilEoe/GgZCycxE0lmla5iwcnp9qxOOL9UeObq1uX/6y2fjenT/n/x2+w8+0HZYO+kru//Wd7/+UT6hXn7hMx8/VpEFSSGca2+3XZK2Jhq+77vcaBUWeU6uUEHARSGTfrqzA2nbn4xFtdHpWr2XAHaLmjeuPASRdvvJMG3OTnh+rPwA2FljyNqR4crRaH4YdQqNtiMIonxuyzgkEDOhkBIJGU1hcsuFP1H56f/n3372i1/88f/+m1e+9v0r5v5tM1yGucbe3pLsh9WxQX+fMir6A8/FChSQQGRFaJEsMFSqUsHad98UWhz/8ou1lWogioRSoIEY9rqr27tbaXz6eO2ZRx/cu7v85Zcbc7NaCWCrFCKnRXv/weUr733nR0lfPP6FV4997CUMJZVgu7L+hx1g+UUpv74j1xhgGZYEKREBpBBSlI8koScC6eX7W01rquAxoCXNEoGFdMwgNXgSCIA0gAUCYASSoAHAgCNgB5qAJHAEpNI8ub134/bvX4M/8yLv0b/12ae+/NONxxZ+7u/9+f/493/rg//w2vTyzPFPPPvM2va3vv3u2pV706eOjT+21Hpy8e533rl36/7CYythtdnd3G3MTwmNZetq4Itsd/sf/eX/y40P3woAY9aaLYAWICWwAokgrEAGACEICBzzyImCDKIY0njqP3r+0Sjyi73CuRStIaWlihhIaWlyKxB8DyHyKbNTC5PjG4s7d29PTW9OzM76kSdS4RAAWUqwJgtCHU5VTj55wWxtbXU30actNjnYPriwNfUrf+uvP/LM43/2H167/P03zO5+xLJGxmNuIlbi5t29vTWz398fXL90e+XsiXMvvVydqs4tH2Wx/uDO+skTx6ZnZzu9bHtr98TCii+UBwIJBr1hEHix0sSalPAj35Nya3Vjan6p3pjKOXj+mWdvPLi9ub6zMr0V6cmo0uoPh1limGzabW8+uH3h6U998vnPrP3RVj9p7wft8XBCsiLHJFmMzpWyFuDwHTDSxx5eBACZDznSYJkVCikEA1kqDBlQjCyQD1PE/DDANcofokApJSFawfMnj84szu3s7pmimBwb11JIUIGWYRB4nirnaRBCKun7SkgFQIVzLnfOkbU2MzYvrHUMBKWDQSBrrT2lAZkck+HM5EIAsUgK00m7m/e3drY6vf6+RktDxwUdbG902p10OCwI0zTNbBHFIgrj8cZiZTz2hVet1pozU42JuDY9MTVRVd12H4SUgmPfYx8FgpRKSDH6mEZyyU+U29LiX/542G0sSsH9P/sxUn3h0OY5ElnKTQ7xyKVaIlnJjSZuJOtGIsvodi+kUKgAEMExC1EyNJi5hLwzSC3kcH+QbrWjaiSroTtwAZHf7bqsryrYeuaRYGHBAlhiodGfauL2brc7EE8eiT7xXHVmevXtD7QvgloTlRbOkgMEhyiBESwxUCmeOstc5J7JZRhlB+thr+vNTkeLc9aJsWNjmvq3/9ev7v3x21OFUM0YUN+/t74uguDZo4/+l09Nz7UkkwPq7/SocLVmLKR0TF4cySjI+0N0ViFaci4bAg/Dpaqu1FUlgqmajuPhvfeMFEpotrazu0XOhlFDhxWhlbXWWUuuhKuWdyoqM9OjdSWOnvTSLy8EEpeKDThLQgCSVL52zhX5QITuyMdPLb/49zsfrf3ZP/2tK9/80dZgH7hzphUEgR52UQZhkReeFyiJwkmFhMQoZZ4Zb7pV7BzQB1emj001L6zUahE7LLqdoruVbK8n1q8+cnT+6ceUpqlgNooDqSVLIQrb39p7cPHqrR+9tX5vvXVs+ZN/8c+NrxxBiWX0XXgj0yqU9RkMRCQOfXw06urGcotbfmUkQk4uL3Jjs7ljs5XIc73EgVSgnCuE8hlZKemsZRAWGEEaKCcMZOAC0IFlYAeAwA6QQAhAD8AHFiAUFOtJ+9rv/jG0O+d+4ednz839+b/3s9//n//92//6q5H3l2Y++9zs1asHN64mO4/W5qorT5669u03ejv7e/fXZ45qKUe5JLYoEM3W8F/87f/h0ltvBKArEnwGxw7BIUgJqECRAEDJZBkcMFhkDSgYBaAEj1ktcNXd2tkdZLvX1gspw+Xq2JkFFXhsnV+VWok0zYE4rsZVh5Sb5aNHN1fv37t9qz7eCL0gjoJh3zpBSFJKttYEYYiLc5NnH9/94J0Hyfo+ZAi6Mrbwc7/0s6Go/jd/8R90V3fnm42VSnRifHy2Et54/54N1dTJY89ckNdvXr+7u9k9SN750Qc8VtXNeLzaqtTH1u9e3Ny625iaqOx0tB9aoqyfKamUVP20ONjrDYQI68JmRIPe5MRE0t25+eEHF579mBdWj58/+/i109977fWb925MLkwYA0Glyaaj0Rt0B3vr9wedvSc+88prVz68dP37W8MDP6hURU0iECPhaINcSr2l4njYwnVoOCyXODy6EsPhpdIRFzYldBKUhEOtAMAJwShQsAQSpcESQAhREHnV+OTZ83Ho7+ztIqLnBWycddzPi24vRwRPKikRBEotglD5XuiYhoOk1xmmqbUuz8HkuTHWuYKVEBLZC3S1UvW1Rle2O0nHUFhTGNPvdXoHO2v37vQP+rntoymKofWDarXRnF48ag2CjpTvVeu1sclqq9GqVavNSlSpelprEflhJIM4oKxQaZJrz0eJXqA838eyY0OMPorykypXQPif+X7KBrOHmZzDuYzo8LVanvUw8lcdLvaJqbwkEP3EyspwCEMtKdXlixtLuoUQh2qCfLg/YgQUSmZZPtaqI/PO3a3hMJsarxW5TTpmoqFM0sulk4tz9SdPs5DO2hHfb7a6Py760qPTK3PPPJ/s7xZpX4d1vxIwkGEgYk8gCKQStoplCww5YptaMMar1Ht3NoPeXu3UQuPpx8ePL7ve3t1/82c73/me72E2HBQbst/xkqmVqZ/9wtLnH2/OV8mk/a7d3e4NhzYQeGD2yBW64kfVShjXhFJsWNmcEZyn4oVJP4q80JeVKBaK1Lwn2vnmlpRo+ll7b0vEoV+pelEAANYZa601oDxRSvco8BCkAaP5/1DtOjQ5ADE4JmZUiMyWLAsUrjDtfpZ5uQ7i+MTyV/7l/23ng7W733rjwY/f1wutLC1MYWTFL/VxJVAwgy9ASoeggjAIK+nqZd3rtJ46W23MVPywfXf94MGd3uZVv9qaefKxseUT0g+RhD9eRTS212mv3bn7xttvfvfd7nZnauXk87/0q8eeOKNbldK4hCPuZ9npy0AADliAEqJs+ht9kQEZkYjIoZAopVTINk/zdGicmVieXnn62bvf+jaBRoEBS4cMJAxZASyEcqVzGdCUzzmU5kEpQAkgBUxABGyALLAHNgasgswAs43+6h/+4M671z/7D/7axKlzL/7Xf/7H/9O//9Y//fWP/+oXjn7+uQ///dfaq2uNmfPTR2fHWuPDgUkHeTLMvUoABEoigPTZvfFbf3r5B9/woKigp10BAAqkDyIG4YECkIoEoDSjvigWCCQEMSAJBIxUILaLi7/z7fadte563021Tv/Ms61j85BlrJXJbb1W93z/YKfD7MKaMmk0MTM+tbj44MqlnZ3dydmlOAyROU2GpnCAEoAKso2JxsLTj3sz081r17rMU4+cuPDJ5+aWx379H/1PO6v354OwjtTsFBWXxhaXWzO3BzY9ACR9eu5MzYs39rcv7m198OOPlKg+9eLH/ErQHKvdvXpp+ZHzzZmp7v2tfpZOzU9V6lUgQim3768WToxPmoW5GX+yadjYwnZv3bnVbBw79VjYrL784mdvX72/t7W9ubtVa8zuXd8OqtIPVWpcp9tdu3P58cc/8+ILr6xv3tntPojSgyCMFDIQEnKZYh0FIEcR4YejPzMIZFHudMrmXymkkMIRZ5QWbGBUd6KIEUS5lRBcVtIyCYEEgIpRQeHs0srRUydX0v5w/6C7e9Dlgmxq+4nLB4lxmc0M2TwIQikhqnhhTXlhbKzpdjrdnX7ST0GT8MUwz9Jelg9zCRadjRq1SlQBC8Ugs4WIa3FtfCyII+CMTWKG3Yl6/cSReSpsOuztbHabU5NHT52tN+va94Io9KMgCFUY+tLXSkhkFAgCFQsg5wBcEHmqXq8yoFZCKTlaGJdQ7XIzBsylNDwKIByaqbjkYow+TQIgZHCHX0txuMxnOpQiSxMTjW5dDwMJAFgmzgARUUlRAvAeBvoe3rwOZ1xgKHF5xOykkrafb93d9AMhwyhLMozYcs6DQe7p5sqKips2d8JzLHJMnMwgfXpBkho79Uig5M7GpvRUUK8KFOQInWXnSEk5Au2V7Q1EDtg5WxRW1eOxKE9yd2tr5vnnxj/3jImK7Dv3ev/h9amxKOtlG3fboCqD1tzZ/9evTD32eCgpN73ND6+0D7LCVTCsSi/ERNqUbH+YtymLnYpVtRWWZk6Qng4qoAUrhbnJuondKQaDfdmsCwVptzfsJlPLczqMhe9bMkVhTJELBYgPsTiH+vmhBQilQEf8UGlnlFg2jCO7coRxJAAVSqXzIi1MXnieFwXNx2Zb519dfPXx/sZaOuxQwWEUesJ4YH1pgZwCT5BESX6l5oxzu2usXTQ9VZmK8r32xpVL7e0NFzUWH326vnJSyYCl0oLTTmfr/r3b3/nx9R/96dZ+2jh99kt/6ZcWz17QjRpKIiQiFEQEhIdIc6TSgYcgmME9nCpwZG2mks+FiFJpqcPCOLLkityrN778K7/wz374hsmzSAYeealJEAkRfBSWCgHCApVKoYOy2gMYxE+sawAA7AFYAALsAxfAGrgKAhO+c+Xm1/6Lf/bYL35h/qVnz//i56/9+u/94H/7o1NfehQmxvvtg6TIa5PjUxfmr168nqQZAOugAgBsiLvpjR9fe+Pf/p6Fgyp4LYGZIw9QA1QBJXgWQKFwjAIkAhESgWVkh4DMGgCEVlJbw7ff+bDd27Igw31j9ruQoSnIJZkKUmOM1kqFcr/bmZ4c07GqVeMj8yvbd2+v3lltTc16ni6MFEr44DnnHDupvRTT5pn6xNmpc59/PKq3wno9bDZvXL3iIdaBKoHnO5e5Yn2/r+TM9NEnfVes7u3k26bD/aWxatLZr0no9nrvX/4wHB+78OijU0dObK21H9w7WJif6nFbCOFLX4LKIbOF6+22k4y15DjkqBLvH3Rdt28pHb7+RlAfm58+MnPk6PNPv/RHf/LbH1y88fTTzfpE7WB7F8BVxqLOXufm1csLy6efffHxy++f+M77D/YH7UZYb1BVgAB2jkCNGsRLTwmPBLLDUZYPZ1UkLvNKFti4IrOZYaeERJSjp4AACFCiAn7IlJMChUInGDx/+dTJRrO2sdW5d2/t/p0tCZoMFJmXpftF2jN55tgKAKWU8v24JrkSJP1OcTB0/ZQYdBw6xiRPiiwxSV5tVKdnWmNTE/UoFI6GB8kwhUrNn5iOxiYnQ7DdvR3b8qtjrTisDAb57uauw+2FM4tLp2dq1UYYBBIBhSTHSorRFAuILCRYyUIrBGDnSPmBLtPFQmJJRsXR5vtQNAFgIIFytMYZWYCodPKXvsxSHChDFzgaMx/eAJCZDgHbI4stAJceG3gYIgAWqlQcSqGylBDY0ejcgtEl4TCFQU5rz1My3W5ne924KiNJnXZWq1W8olsQimqr+dgpkEJKQqT733qDvv6Wak64+mz95dPV2YWsuzfs9hsTY2FYASmRLJPDMqNW/nQj8V0I55TlvFeIoCGE5HR4d7396K8e9ybC9lurN//V78HewdSCd+O9OwWo4Nzp1k99Zuz4ceQ0y4ru3kFuqTnR9ONGEEbodN73KYkhLdgBWvBJup5N0jQdZOB7QeS8SOddytb23EHP5MWO3Tv9yeeQXGd7SyiOK2NeFIMAZ5yzthRiRtjbMnQ3UmuA2aJEYBZlfJFHF18hy2ADkiNy7KwjdFJLFFJ72uSmSLPC5EEReaEcPznfWqj19npSeKGvtMklkdC+dVaSYpSSVRSH6cY+bu6moRg/tqi0v3PrQcKidvLC+LGjjeUpiVoW1uy1b12+8uGffvPtH1yyTlx45akXP/fFhXMnglowwj85IGYgds7gqNmunDIcsiyfEyERAIm4FEYPW1VL4aj8QkoyWZElaTLww+DEJx997pUX3/2Tb0QA6PJQCEI0zuRIDCSQgUX51T6kRo7mjTJEhDAK2MvR4MJDsAgOTKLMoAaiWN98+5/+zvr7F0995efP/Orfvfp7/+nSNy5r4TbTmwvPnPKq9RNPnbx/616RdjPK61FoCXQCvTsH7/67/3R1+5IE5QM4JwA8CVIDKhAkwRLAoQNutAIDKJB8ACMASSKbgGBz7+7BcDuBLAA/TAeV7W6QmjTXoAskdhaCUFgQG3t9X6hmEFJcmZufnFk+vnrxWmd7a3ZxoTBFkggiAK200KUq28+GXmBq403rpUSuyCAOw4nW2JpSIs/3ioHvqU5uVbtdy7oTk7MykLe3u4PbW/uUHAsru+j5GW5dXf8xfrse4NL8Y0cvnLt+6VaSuvrUdHdz1xlCBy4rnE0G7d1uYijNzKCYXlwymWzfa6fdTUdZHLeqn600ajMXnvnYxRsXb96+ubfTq4dB6MdSoTOFp3FwsL167/LZJ15+7plnLz+4sr213k7acTUMjc+i1L1EKaPSoRCJXP5xlxtuZABZLiOkJATnbOaygiyWpzuKUnIq5WIsWzaYRZkU0BKlyG1RX1o4ffYMOnf9xr07dx9srW0H2mNjgDQA2zQbDFKtZBD5An2ioN0tYFAYJ5WLw0pNK11YLgoSjkObzc5OLx9fOXpsYXxiXCFbY/NuPhxkUV1F45UoirMkc0XkeWFYDVNHwzzrZbu1qbBaj4CE55WNOzTC1LmRNwSgFHUlAJNlQpIASmIZHhvFBkr3i0BBI+RC+a5DZholiukQtDragAExAD2kZxy2t9PhsS1KWCqX2eDymRZSCvGwt/ZQMv7JDyRgcDxKnD1cFkF50SBiLIwZqzcFi517e5Dncd1jY1RBgQY+GABjcGS5uryEEgBo94N7a7/17fhP3vKOTHp/89WwOW8LTnsHyhNhrYpaIrCxlgWwM8gKRCn1CwHCIUuBnAyDpM+1MKS8bobbLY6OzkGa3v7d7+1dvrbYqndyaZemHdQX/9YXm88+FkaShVRBMHNiQch5zslZCwWbAmXVE6riO8IMGKFI885uEra0EEyGiThLTG/vgPayKlDmhvlQxpVxKPL2zpYfhJVmXYY+ERnjrDHl7sM5C2UrNiMCOeesdcAkBRIRgFBaCSkBGdzI9MaiBDk4ZueMQwNSaPY1AUotEcE5BwZQiqBRE17QO9DF0Aw63ck4VFXPDBJBQGzCWlUgyp0O31vXc2PNk7NZv9sxaXh8cWxuLoqqIgsgzbY+uPrOf/juhx98v8PmxCdfev4rr84cWfKqHrIlJucOy7DL15WQ6JiYyQEBIYIUIBQKYEsOiAUeujFwFC6HUe0xM5Ap8pLfYgqrQvez/+Dv3L/yYHD3+nQcBQYsySGyQ2dHObnRvF/iWgSwgIfRUBaAFhABHDACEqADZhAWXASuAq4DKsX0wVsXD7bs6c9/8eyXP7f29nv3f/TB7nB9emXx1CufnDlyZHyhke738yRhLZ0jhdzZ7Dz46HIG/Qj8EHUmJBIyYMAiA3TgnBLWFCFIAFZkBQonhAXHAIJYgCOmIjeObEgSQDdANUD0bz/YvnS7OnYSISCmPM8BmIGSorhzd/P08UVV8ZvTjTOnT23dXrt7687k7GwYRsMkyY1lRwxsmYWQWZGTABz0hJSZDCugw1BMzM35gZYAzhXdnNAT9/Kht7daAEdHjjaFP+zu9fe2Zip6SUehg/384ODy/ffk95s/N9tsNY4uT+3ttatxLax6W2tbQbU+NluhjELP6w1yQ9TeGSDsxFGgwfS7SULmzdferk6Mnb3w9PhS41Of/uK1jX91d3X9qTMnh/20HjeEkAKyXNp7Vz48furC6WdPnL18Zn9nuz3o1YK6LwIpZDmI8qgX+NBOz6Nl8miBCECMKJEBHVFuisLmIFCgFChRoAMgQBIoiLVgIUaedJRaCEkSC3bnz52bXZhbW9u5cnut20tyI/K8CGTBQACBX5ucmYoEU5ENcsPGYVwbL2iohKhVwlCgKzQX5AnhnFM6n5mdPro8dWR5tl6LnTFpliVIfiiEh0qANWmeDo3NCXCwtb21n26sb9ZrYnpirtFotcbGtecDEQgNODJMC8TyolySGw4N9ojAilw5vhMeOkUOo9I40qwOl2glzocOHbMPF0ClSx8AAMRo6Qw0ak0orT+iRFCWRzyWbg4e+UJLtC+WydXypsDicNUDAEyAAhCISu24rCwHrVQUBWBcZ2tLuFSgJiCP87DwsnRgPK9+ZkUGHjPRMOn+2dfVO+93O/uhaS22Fv1afWf1lsuGYRRrX0mBTGVpMwutDqsaSqnRKSldYjlPnCGVWpVyv+9mf/nnpK7n27sPvvu6mAjjJ5+jk7Ozi5XWuaWp00cDVBILcmBtQbl1hhiVUMpxQUQoSYeRH3jIZNLCJUb6bKmQGoNQK2bDUK2HmKbd7b3OIJk6fVorXfT6nf2dieWpIK6hlNbl1lgyDgCZCAgcEZFzYBmQnc2NVRKJyVjnLKOUKIQOtC8lSMFMIKQQQivPWANcZsny3EgV+L4KgUhayghljk7rYuDW3rrf23ow2N1r/vyy9nTeT1xSMKqoHgLmamvVHHSbX3xGBbW9bqLqY+MLc6oiNHu9Ye+Df/5r3/6Dbx0cpMdeeOrVv/aLC4+c0LGHRAiWrHVlgamlw9R66XtzTKMhvMT4AJAxhsiStQJRKCFQSa0YwVEpcjNZBkZn0aQ5WWNMzojBdPUv/y//7b/61b+9u7VejzxMOBSYERKUOg8dQtpL2vJoIzQKmgHLh6kKYA0oQUpwAEoCFuAMGmfytEg6V9/pt/ti+JWTr7wQC/XeH3Y/+O3Xx46cqU+Prayc2btypej1qMhljM4ROpsYNwTrYyWRgi0ricLBECwDxY0pbtX7a9s2LzS7OgSWTXkPFsyaQIITwIAs2cUoNesG+C1Z2d05GNzYWDw7MVGZZpR5YRwzGKrpYHdn596DzYWpmbAWzS3MLS8vXLn57s72xtzSiqdVptDlzhZWBcoRoxDOuSLJBUorwfHeeG1ycfn43OIjD65cMp506E1Wov00v9be7IM7Wq+HzbgxU9+zO6vZcOB416Y52BrU6sEUEK3efTA7M6OENEOqxXpjUNy8fCeHBa/eCKbnfbvpgRcqf3jQ9U2BhK1G03V6aZp++P77UoWPP/nUicfPPvPRJz+4/N6dnT1Pwd7u7tzClBTaC5rd7vDWlctPf+LF55772PWrt1Y3VjtZu16taRYMQowitD9RcnE0h5YUAkQoW9rRoCtckZvUgUFUGqXgcg9cegFQShBCMJGQCAxSSVQyZ1K15qPPXCCi1975YHNjx9O1equpsQikJamtq3heGGjNrq89dD1DzMLzlDAInqc0OkdOO3DMhURuzY7VmrVqJfYVmyId9vq93oBR5Xk26CSopSPT73cHnS4D9Xbbq9sdjZXZZx9pThwZm5gPowhQOiRnqZScoaw9EBKxvDcTAAglQCAXpFAKJmAigYIfUoaQuVwVMBwWCZbQIWYkR6M+ynJwd+W6iACRLI/aWw93Qfgw1nt4V+fRm6A0+lMJpycU4uELGmlkNDpMDo/eEocxVnBMGqSUuhikeftAK2Z2RBx6yg56A63MZPXY+WMggArTv7HW/urbeH9PzbSKY6eqK6eszZJhB42tTVcRFAM6lwtrHaIXBZ4SloGds7khY4SWRa+rkuFwtwdD0p7OWpMzsyvIefva9aVXzjTOH2mcO527MeF6UojujQc7Ls33u86SULpajevjYThW057PqPO0mw8LSk0RViRQ5tLC5JZNXK3E9YZF4v2B6Key23ZbG3bYRfBnji1btu3NVaJhWKnpKASBJrOmyGlUCQsgJIIF6xyxVEr5vuf5zKCY4qoCRDIuLXJylBsHEpy1XhigVEwMQiglC1uYohASpZI2S7QfSCWdMQKFIbaeDibr6Y++G1z50PvKJ5xmp1VhTaVWU8Jjkw1v3EpcWDtzehBqhHisHkNR7N3p7l28/4N//GvbF6+3Z71X//u/8dQrr3r1GjEBW0sky2v4Q75V+UwQMxEQUen0RRCSUTBbRybPs8xmmVBS+4H2QXoewgh/IhBAoCdllgyNsQoUEDpju+mgcXz6l/7Z/+N3/ut/tH73Tk2JiKQA8FEbtgYcASGAACEPdwI8SoLzqLfj8IaqAAhQgcdgC8A9gDYXAsgDdED7/TuXv/fHrdno6Cce5cK+9QevXf7Ojae//MzisWM7t+4Mdw+4yIGc9v3qZENqzamPqIhJgFWOE8gFwOz00nM/90V/efqNP3xt9Y03ciMQ8wC1ZRLA4AoGacv4DLjSVRGAilCFUm4n7Qdbe+NSchBZlmiYmJC4FkTDqt5dP6gH1cZYNR6vnj9z/N79d1Zv3ZqZW67VaoPhkEAIqcix9LQU0pKl3AofwRmbcY/2x6dnTj/xwu7qg+HARKEn0dPounlXdii+783wciOqDCrV+3kfyEnHdfDnVlaOHT2jIQw8am9tzxyd6W4dRBRFScxSgS2q0VhWbwwHOQ9MUAnAsrUOwErAihcJ20u63fXVW9OTY/MnHv3EJ57b29xKk/YwG1CejU/FfqTZeFmC165eO/30k48/8/TFdz/a2dlu93pjYa+q6qKcKqE8yAXIh4cSHN4EAJEZhSUuwGSUWTAoUAuFQh0eeYwAUqJAEKVCVDb3CmAJGZmlE2eWjx+9df3B/c3uIEXfYT2eiL3A9zrDwmQZSiyQSAI75fuR9qRk4axxQmFWGJlBnjvnLHIRR6o1HlYakRPU7XczY3vtbpFax7Lb7ifpoD/sDXpDVDjsZ0nmpPDqlaPLR44dPXVidmk6rMYCOTcWmbREjZKIEUiKksFP5RuQmEV5aghUIypD6bY5/Kv0SJXncHkZphJXDWWhJZTuCX6ozmLZxlBKvqPsqStTcmKUtUMafeg4Yv7gCE92iBFFYBB4uHgdseRcuRoQEkdRbmRgZ1y1GioUB7u9dJhXBUrDrsi5nxrpXBh6J45441V2XHSHV/7Dt8zdtcT1Zh9/zPup58K5Zr+3Z/O0UmsFUcwOnEfWuSTLvSgUAlgIwUgEgh0CFN2h9pTRygmb7Gyrqtc40qiOxXmne/e9+4tfenbhlcdtiqs//Gj3+s28fWA73d7ADPd7bHzy6+Hk7PLTp86+UqtEntACg6i/0c+7iRdkWiMpHuR5ZapRXZ7WTnZu7iTb3SjNyGWcpUUynDh1sjJRy/a7uzubMoijekNq6YisMc45RFGCxQUKBFRaC6n8SoQMeVJkg8SyC0NdadUAOCiMMa6305ZKV1v1MqfhDFpgIo0KZYqOHRWFNYLIkfU8T0tPOwSHWJkfF2fOd1+7jLt7QeuICABTF8SR1ly0h/2PrvWVf/zUihfWs93da2+/ufvR9ev/6c+Su/eG0GuOn/vZ//FvnfjMJ1BIQBQEZBlK+ikxgxNCIZa3vBKaWx7Co+4LIEeFc9Zmg8yRU54PDCYvBEjSLFRpcWJHTqJEIdJhYrMMkZksIiTDoVXJ2KmjX/7v/u5v/4P/fufWrUkVSiKfUQB4IBwwAZXWz0N+mOTD4P2IEQ+IABZQgTQAAyg6QANAlhUJ0Ha9jDKVqHv3btW/+h+r8S+efOWFrTvrnY2N/nZXBWEY+L3Ovun3TFjzGoGxbljkEhDYSiIPrAJDQLNHTr7wq7/gL6/MnjnmZic37lzvr+/7KCJAj5AZJQCDyxAZ0CEhkAAMQQFDh9IhJCZNrAKnFTvjMtZaSyl8T03Wx/eT7fbmThT6flCZWFlcPnbm+qXr+1u7k/OzQTToJ3vsSEtUiMROgigKQ4C+LyjL+86YCs8/cXTi8rGd9w4oNxDksZJkfS5obWO1320rdF2FPqi+Y09Bo95YPHkkmm7G9fpYc3z15jVw6dhkc7/fjuKIDEBWyGEvkCoM/HaakCJfiiIdZukwYjg46BjkGSch7d+9diWqt8Zmpx978pn33/lRN0mSTqc4qMmxpqdDJ7Ii6d2/e/3kuScvPP34R5cv3n1wY6e/7zXDAPyH8DcxyiWJ8uRCwDJ6ywIcOQsuN5l1BSN4qAXI0kUHjECu7K1Sh+xorQRLRATLBQf68U+8UCT5R5dXyQVxBTXGLChlR6bcvFtjiKVjpiwvLFlmyPP+fnfHWOcZiYV0uQPhHGXNhh7mzYOBoB2zvkOdPM2GCea5TUX3IEVU7W4OgK1Wc3p6Mg6rE3PTM1PVyYXx5ljFC2T5HKOSiKLsrxIAEmUJOQUGJhj1AWiBCIZBSa1HuNHDfTwcev8RwJGjsv+ACbj8bj5MdzEAoxDlFmK0Wi6zpzBCVYtDDAc+HORHAa/RFQEOE2T4cO1T6q80khpGSyd2pfjLCDJQCM73ArB0sLrHaa61LHqDgAvnEiO0nJpeePJpBmCTXfvmj9qvvyP6BzjT8p86sfjcI0Lleb+nQXphqP1AoDPOFKnNUwxiXbZlOmImtMzD9Tbmef14y5uZ0QmFl+/quDJ+cllqkXSLpadWwvn5wFUtu/1L11QQLn7qU16ok4N+bi0NyKEwfhQ2vHYnRT+I6lLVourShLt3PytMUSglvLAS1KdiCaZoJ1maWBokZhAJQ5MReHb+yRUFnGxuULcTRrWwNcYIztoiN+SclOjMaKsvPY1IQirf843jdDAYtIdeRfnMxCAlSiXTYU7Aga8ESmudYvaCwBPCWqu1cp7Os6HJMke2GGapEPVGU7EvEIVgXeHWk48c/K+/13/v5vji0cAPTdqVWoocbC/b2M6884tyzFt77843/udf37n+UfJgvwU7U8AM08//rb+6/OQnJCu2TOgAQaEQUhtBjqwjArIoBBA5ZnClsovlJRWBKDcmSU1WyMCvTTaART5I8jQhInIWhILDYCchAmE6TNgaRCGVdtZaa9k5YJw8d/QX/z9/73f/3/9q+80PYqCGYI/IB2UBLVgCoLKFu7yJHNoAxAh7guXiMwXjoEiBCowWTp5dObvcXb1y5eLVvSxJnbXp4NqtK7Vv/uDjf+Po47/wxevf+eHgYCOu+Fr6g2F3sL1Zn5o1aW5MikJ5oJkLDaiADNiq13j5V79cO39CN5oEhaxGU0vLdn2gKSMgVfI7ACSIBNmKko8KdZLE4gAKacmAaQF5BYADUxQkmYE9K7WSURAUzdpgf6N74LcmpKr6Jx+/cGf13u2rFyempxuVxmAwGPaGxORSo3yPmQSiNUYJRK3JUJa0o5nmU597ZePW6rC3ijaoo6cVArEk9gZpRWsVeVswTLnwmvH8qTMTJ1bqM9PzR5fQwN7mg52dvROnT03tNtL9xDC5oU0r0pPSCyXtEjIxO2PcoJ+z9IC9OPaIRZ6afre7tXHfut3HX3yhm3QvvdtPrFt9cO/0eKw4ikLI2Vx/+43pmYUTZ1bOnz+7sb/WSQf1ylB7vgJRWmDKU86NWtZLuUwwomO2ZAvKnDMALEu+DCqC0f6jdLIgIwiWAK7suBKSGNIinzt3fuXkyfcu3d3cy5SKIu2j9Bk9AZAbKAqTZSmxBXYmM0mS5lkfIDM2NSYP/MCXvoqUDFFISbrqK9HtJntbbS/0C7BOKl9hIISAUNeWxqamjsS1et1v1Px6I66FQa0Vh74SvsaRkYbxMCMDcNi4Vf4WeORrlxIBmawr0W2KiVGOEHJIDA/NF6MCe3bkDhekgMgSEcrpA0s/TzkyHTa0yNJ3i+IwhYQjH9Ch+3Z0+Tos4jm8eoz4RociJUG58BkZdEGIQ+FFOkPknO8pysze3V1RGCmdN0y1tkar1BdTj5+LJ6apMMl2e+uPv+F1tm1Yi46dmf7SF7zpybR/kPV7wgtVGEgtyBjBUGSZ5/tSAgCB8AQAKOxtdD760++qNHv8lz9XmV0ZO6n3Dzr1E9P5flcFrcp8c+/GvWK7t3rr/ayz3zr3yNSxKa9WD+tKSDZZKjWikFAQEAwzRmchKQpSfqhrc9ODnQND4PmyOl4NFeabg2S95zHZivQIfPIGojr34hP++KTtp/17dwWbamMyqFYJnTHWWouIjqhEVZcobukrz1NCCShMt5eF1bhaj5lMkQyZrHNu0E57nZ6WIm60sp32vfcvB63m5OK8inz0pMBASymVb62RJs+yotPZFwB+GPvaETs55/sXnkiub6VX1/XxWcFGKWf72fDmZrHvmpX6B7/xW1//R1/tDnYmgY6BbYCdhmr97HMrT35M68DYwiJB6TZgRzTKZJY5NXDlO55GXgIpJQqyxlrrXJGnKTmIYh9ROGb0lIZAKDny7wAiAQrJDqwTRWZzY8ubKTEKgewgz1Iv9seOHP3Vf/QPv/evf/eN3/k9cJ0AZAiMIDwQCOSASgOCGElQTAAFgC2/TGAtOAKw4Mnm/IUv//LUsyeiqDguvnh8df3GN7596/0b3e5ud5j++Ps/iCZaj7z4/PKFIzYfuKqSfpQe2CIfsCkcFI7B90NIyAMuDYYEKKstK8cL9pyx6UHbpQOvyKrgPLAKqDRgCBAEJJmdYxLkMTJizlYCOymEi0Ph+8pTUjEItmy5EKCQBXoiDitcn+jstqUIa/XK3PTSuWOnLr33fnt3szG9WA9jm+Y2L0iQYAAlyDkkLgpDzJHnZ4NsmLfnH1n82Bc/+/Xf/e3b/d5C6DekFszCyZwEKrub9vfyPG0Es09cmH3kycmVI83xFnrqYGd9bf1mng0m5mbmjiwM9vdvffChiUPRFLVGzZe+9kMSAsE4LtIijzwOA+n7oj8cVFxUV9Dd3kyMroytPPXCS7s7G1v37wxyt725OzVRjWIlHPZ7ezc+fPuZ5z957ty5S5eu3li71U0GNa+BzEDlio+IsYwjlYwDRnRAhm3hckuFYyeEkKgliNLlXsZpBKIUIEGwc1zWUihECZktcuU/95kXh7m7trrLSivjO1EYUziTWS5MMUyGPesy4lwL62ldj1E3gkatUYn8erUa+mHF80H6EtGBHDjhjE0Hne5+NzWk65XaWLUW+1XPj8JaVKlXm0Fc02GogkArKbHsZyFBAETkDP2EHVp6mUuTDhEwOQaJgg/5RSAEO1fkRpXcfnaHXE4GIjq03sPIdDmaiXBE2GMWKECOlFtmHiFFS5/5f1Z4zyNXz+HdoHT1C0DCkShc8qpLzguzc3QIjgAomzCB6PCqUaoU5JwWUimd7fWGm3t1JTjpJwcd0dSqWdWN+tipFfTQDov0zq25exupy8yTS/rVFxqzx4wzg4NOmqZhq6mVh1Ki4Twdak9JzzO5Vb4CYpTABsyQO+tr6d27KyeX+REfa9LGyvT43ve+f+yXfz6Kgtr8zNbVB9VWw0yNzT11HF023NkfdERU1V7NZwLKiqyTdFd7+7uZ9IOx6SieakqJSnhRrZZ0u3E9iKoVW7jeXgJklS8DEHGrSobj5mRlacbYfLC2kbd7HHnh5KTytC2MsZbIlbIkATEJKQQjSalQCGTIkn6lqRuNFgDa1PT3uslwoCLhrLBpPhgMW9LTGGy+f+vB7Y2x40dXnj/XWpyqNCIltdXoSaWVZujvHxzk/eHM9BLGKhuQmKnU/+qrnX/+G/Y//Wjml172FmKUVGTdzY+uVwSm37n9Bze/nUJ3HOJFwFkgCfV669HZV78ox+sZZaiksDzcOBj2+kGrUak3C8lsjZAKGNlZfOjOKJ8zImQoIba6EvlSsaW8m5AA1MqLwjKycqhFCSGENcwOrUV2RemAYAagsgCPrXUOc28s/sLf/qvHHnn8u//iX7Tv30BQHrAF44MiQAsORp6f8sYMVFbNACOQBpEBDyUOQkvb7/bvts+dXhk/cf74M0+d/unP3v3RpUtf/er65Xd3Hmxe+c7X4k57/pNPyFbNRY1ofJJ27nU6vb21BxMLNZc7ki4AJjDl0JED58C97R6N9zZv7Gmhsq3Ozr1NARkCGSAGTMFYIR1iyOgTGUafQAAJkB5wAMoBxZW4GlSiMEwITWGB2bFDm0uUIpaBqJFxO1sdz48jv3Li5Nl79y7fv/JRozVTrVWHg357mBBZiSIIPRbaUMEERW7QgVAyy4eqOHjsp57o7O698+0/3koP2kqHoGIRWYebqcljxPHxlUcfP/r02ag+Jj1pwG1vbFx8/83trfXhcFj54PKzz784sbK4ube5eufOuFlUCkToe16kZQCOpRPWFi7Q1aq2QLYoLIElOcgHRcI3L7313Oe+8PSTz9557/1OsrW50x2bKgwIA6qf9+/evrNy8vyxkyvHVxbvbt7tZYOCMwmBRARmx4AMQK60GhCjY3Zgc5cTGQKSJSQcxcgZBGWROyEqREHAWmK5SxKCCTglO3Ps2LFzixcvbu5tdZlDMKYoDOSJKVKwKXPhKVicrzcalVYjajSqcRhVAi/wlfaUFOAsoGVD7KzJCuwksshyqoZT03NBrRHWwyiWcSgirf0wUEpoLUCVZDtABwQMFqi0zxy6WgGFRAYpBLAjx46wbIYRpV/cGeeMNcZZa7I0zRUTMZX+UGbHjssuYmAAMTp0BTGUk//DeR+4POiRy7O8vAGU4L3RmqeEtJTDfxkFQMmihFWyGNlORyaWUWkAjTBBPGqVKSWC0kdaejEQJDOFsa8U7nQ6dtD1fUmZLdLEjTfZ98KTS0GrCY66m7urf/bm4MFe0jJLn3i+9vGPW89m7e7B3gF6gacDqT2TF2AKkxksSczSI8c2S0mAElKEeP6VZ4vbldVrt1v3DtIm18+eTfpdFhWJWjoMqvX+8N7cM0uNiZoh5xwE47GwwvPQDoyqVURVVGuVZqM1dmt/5+JqmvXYotBCafBj34+jsBELiZQ6csyeL1nGupqavlM0szQBkvdvb/TvrSU5eK2wNTuNyI6sLUzpdyRiYCZ2bEE4IEmFK6x1xmSVRqx8aQtrmAvj2psHoqIq1XpYj8MoBg9AEPXN7kdXNt5+/97r35s9c/r4sy/MPLIcNOqgU/Z1oOUYivaD/a2PLkVRUFQa8cJU5dT48Mxp/Sd/2n2tNvV/+Axo3rvXG9zv1f3w8s33Qhh6oGYiMZZYDwKNC/VPfa7y8ovQilXsbDv98Ne/tvndb/eSXT0x8ejf/Aszj14gZiYSgFIIdg643LJSqQ0xkZBSSt/3At/XhbGmsIgohUSQTM6CQ0AhJKBgpPIp9oQSJEt2ULmzLQmpEpW1DiCXNfXUrzz33C89fvfbG3/69//v7c1VYyAiAiAfMIfRy1WMLgSi7IB1IysyG8iHu3c3v3mVf6ivjtVPPnLuqZ/+meVHP3b0pfNj51Zuv/Hje7/573ev3r/4o3eGrjjx+U9XpxutpdnpwXEgmeckhYDCQZIxQGlTkUA+CLHfvfZn3/Q+at4dbHY2O6aTYH84AVqAI8AUOAPXI8qBKiBjQE1UBc3AGlwAYugKhHp0dKk2OS2Uj0UBzAzEIKwzBE6LQIfaiwOTHfT7B6IyEU1OLB1/7PL7b+xurE3PLVZrjW6nnwxSACGV0r4G5ZvcSSGMdRKsMy7p9KEmPvYzn5dZ++03frBvDxhkDFaCPz7WnDtxZu78+enji2E1YKWqzYq1dPX65Qc3buoii6XevnN/99jZysTE+LGjyW7ftyQKU/f8NK4hoQLjrLOO/SCs6Lg/6GsNWnpCAhtrsnTQvr1x+9bKE4+ePvX4Wx/+uN8ddPbbwdxknqfaC5Ne7/7dWysnHzmxcuLti++3uwfdJPECv8yOj5KRiIjgEMg5w2xNbtEQOSmFRFVOtTgKDI/0/4exL0fsaVmeXoKIMHziMy/k/eS1H1zZ2u3WWkXgsOpBbcyr6TiuRmNj1Xqt0mjGXqDjyJNl1MTaLMtNnnSTtDcYpElSOJBSKVX1vGarOl6Jw6gRRNVQa5CiXD8JIQ57iMulPHG5JMcR4peJ4DAZySAILRM6IYTUYC0XxuRFkaZZliWdXr/db+93DrJkmHQKNZrLH6YuAVCMqhQOf1aUYgQ/JWZ2h0Hf0W7p0PkJIEb9gzya4g7Z9DBaUJV3i9EGbnS9gJ8IASXouZQOZLmpYkIhGaD8JJgIEIrceNUYnOtubEPWBx2muckir9KomrgydeIMYFF0B1vffKv92of7gqP5063Pfry6ON7ZOSiKxILSQSDjCJUyeeKyoqQoQ9n/MMyzPNWVwJii29lfOnekX5lUbUh37qfDnZNnVnyWvYmWBDQ5izCYPrsct+rl2hh9LQGT7eH6ldW9yzdrE63pj51uzk5xrCtK9T+8vTXY5afO1x87nee51MDkhB8IrSErUHlobej7+SDNLTePLwk/zA+G9tod29lyWKjaTFxtMBtjnHN2hOBgZpACubSCFkWBSIZICqmUIFPY/lB5sj7RkhI77TYTSq1IEZkcgD0to70Dm/fy/v69a/d2Xrs8/8ITR156evb8kWDM16FQUrCg7vvX29dXsXGytrisJsdaLz+28W++Wv3xdfHTr5KPe+3dbtLu3LsGkMYgSZjYGAtsYLz6+BNTv/RScEKmZtD5YPVH//g317/1gwI6Pcj3oUjGWz998qwMBQok65gcOQdMZTR8tNZHQFQaiRGJUfkalWTnmNhZSzYjWwhfofKAUfsxMziSIKRFQQxYPqgCAQgBLRGgACmETYd39wd76dr1/S7rNbQkigX2iDkCFKAY6GEAFMEBKCtQkAYgABMTOEQtgjRznY2d93a+vXXn2qOf+8zZ516dXD7xzGdfWVpZvPVbf3zvu9++dvE9qFcfH6s3JiaLlWy3s2cIkkE63O1rLkHMoAEjwABUDsmDqxfzq0Uf0hACDxAgqMtGTYki9iaPTcpAt3vDjbXN7v4+g6gA5oAhCAKZAvTBetVWbX5G+Z61bhTPH/kumMhZW0gpte95lTAd9DToqOofO336xu2Pbl59f2y82Wq0Br3+MB0Ok0RrrVQgytpgKP9oWArJzmXDvvDkc3/hS40Txy/9+P12d685VpuanZg5sjB1ZCkcG2OBVkovjInk/Xv3716/le3uzE1UarFeu75x5c3XH/3cZ5sTY3eCPO9vzT159GAPk37HpCkl5HLng18JY61U0bWoFACSQQSSlvOie+mt743PzL/46mfuba5vrF/uHOyNjddDHWmEQS/ZuHdn4cjJY2ePzr41edDeG+aDVljTqPDQlwgIDoCIjKPCWSJLSEIIKaQcucAQSucjM6IqB1DBZTyEmVhIQUyG3czpU0tnTv7Jn7y7tp7PzEzMTlfnxvzJMb9Vj8NIV6uVMPSllI4ozUxeZOlgkAyH1pqkoNy4jBCEX602m5VarRrHcRj6vu8rpbAcfaG0KTNDyW9hwjLIyTAaSggAR6zm0RoGUCBhCTNlypI8SZIkSbv97u7e7vb27rDfLYxDT8a+H8XNsUVfgUA+rI0t8Rjlrl+IErhCRFBC2MpgIpWgGThsUitdnQQgRvFZGgnEZSx1ZAEdbXdHW6JyCzW6UPDDeV+ONkGiTKoe2o1QIjABM6IkQM9TcSXmwuys71pXUCEGB3ljYR4r9WBqsjo/RYXZv7HR+/4bnZ3tyukzk1/4zMTpcwUOti/ejmuNqFKRFU8p1oIy46iwzCCEBJTGuHyQoGItpUERTE1mnUHYmj1xvtm5Hyedidr8FA56fkX1dzYUjyPqxfNHhdIonAo8N0zz7b1IqMaTJ6fj1r3f/7ZJzXB2nht1un2/c/FeYbv9oQvmjkBNcuFQsTUswApPKgQ/8kzC7bZtnVqIJlo2KQbrW/vrazp0IP367KTUaB0UuRnlr3FUkgPEDAiOjCMvkBKVKQrRzfNsUPSH1al60Az9eCKuh0VuGcHztC8RPe2NjSupJZDKM4/VcPX+rd/bWn/tnZOfenblc0+3Ti75sQ+tcTz3yFY7SW5frV453hgbU8drcmJ8ePdBdulWUD2tht7enQ8S+8AHJcEKskTKwhifePLk//m/Grswv7F+7/6ffPuH/9/f7W/dboERQPuQp0CCtCcDA5lzBYLkspBbKsTyGSvLDVAoQYCmKKiwytOoGZjIOpfbosjCWCOBKXIhZKikIwkIhEh86P0DLr8WpUeIEYvC3H/vo9/73377/tW7ZpBNKy+3bhAYq7GacwcECvZB+gSKwAfAEkqBghE94bEDzRRZWweSUrVV1M+L9Subu6u/ef+Ndx759GfPf+LV6aNn47/Zqo+3Pvzed2688369WTn6/LPRZL1KmVKSUocFekUugBEEAVlACwTgUhhWgGegMXF0MZycGXhTC3OL9XpcOznlrXg2S7Yu73Ru3h1evff2xZuF7XYBLRCCAQAL0cKFE5OPLKPPhoy1ltlJocueQGZ21iEIIZUfxRmng+EwqET1ianjxx754I03HqzeWz75yPhYa5gM9ne200Rr5enYF1IwETE4ZiSnUBhjOulu4PsnXzh/9PFTpnB+pFXgKU9ITzKqYZ4J31O+19neeHDzYn9v6+jM9JjWva19j2V64+728etLj589+/i59ct3H9zenZw9FYbO5CZ3NjOF78swDDgvEjf0vKZGBQ6BFRMoxPbGg0tvv/7E8y8/99JLX/+j1YN2p763Pz27VPRzpcNup7PxYG12avr8iXO3bt5Lc5O7XCFKJmCUAhnYERnrCmstECDJsksUZWl2Z+SRZQYRgA7Tq0CWUEFJ5i6A88D/+GdeWnvQ3iP7pZ969OT05NR0XI0AhXXgisKmmdk+SNMsL0zR6SSZddbkAmQUx3FUqYVxFEW+r2Nf+Z6vtBCyhIuOglhcFpWNTtIyKgmHdcfsRrJuGWUnKaWSyILJGZPnaV4MBv2DzkH3oHt/+0HvIMkHQ6n9OKxOTS9NjI1XmrVqGEqtUQpFVKZuHbiHFVhlmfAoPVHuhqwrNREiJlf2MpZWKpCjvpiSlkQj/04ZFh7pATziO+PhdaqUAhiY8TCsxzRq3REjDaF8MxGAdAACpBDEzOC0QARIhmm+eRCG0jhbnRyfOHe6EFnr1BIwdDb6+29fMmsbU8uL03/xp8Y+9oKIRLGRZPsbtUYtqlUBQUvFiLbIrDVB4GvPdw5skRNTHISBp+2w0GGIUTPEcHjng/6bbzUeOQ9JUdhcVr3+4GByYZpBSAUMTIYGqzt3f//1xsb6/DNP4nOn8kg/yIrdf/HNyaUjUz/9mcSp2ssntn73jzrdtLW1V29MuyyvLrS075ExbMgPwXQHvfYwmGlWp2siz/prWztXLppkIESsavXKWBMFO2ONNUyEhw1qgCO9qpz1pJKyYOdgOBgMkixQdtC1gaf9iZrnBVmWFmmWZcVBt1utx5NPHmscXW5fajPkRdGvh61BnrfvXf/ot+/tXnn/xM9+8chLF/yxhlhYds8Wa/f/4877784++XjQ8uHpheIPX+//6bvVs4/Ig4G5ta7AGIAYWIOQEIux4xf+y78Wnmneff+jj/7l7175s68nbsMD6QAGYDow8GHy2CMnhS+d5ZLAwaNKInR0eP8GIVAgShbAxOlwiOyk9IVW6CjPC7/heWFgc8vZ0AjJVUABhKQUy3IzWT6/IzmBmZwElRwMvvHrf/q9t9+eZXXK9x5rND/YS1KAVOe1QknGHJnZMjJKB4SCkYVDYGBFjhRKQGUot2CkszUn6lLvySAd2OtvXNpd3d27v/XEz/7S7JGzp//Kz+vxyttf/Q9X3nqNarRw4mx1oiJIDjsHSdpxWucOJEgELwVb7nCnaosXTp+aePxsuDSRCT4w3lhjZm5lDnG4s3nz9tvvyV4x6HXrc82vnPiVP/3h19L1NQNGAyL49SPLx3/mxcryZIHkQCKUJlooSQfEXAZtUKDWnvXZgsmKohZWjp985O69e2tr98dnVyqVWrPZbHf3B/nAs1o5X3jITgqyDIAshBDAMrdJAZZcHvhB1IxBSyHBIFkCttZZ62s9bG9dee/1O2+9sTR39MzJ8/3OsOi7bKcYmLb/wTuLRxYXzj7W77p2N1WVgqU2TIM0YwcV35fg99OhMxwEngKUqASClpEQyGDuX7u+dOT40y89v3r/0ofvfXt1c7PVmojCOM2yJCkerN2enZ5ZOX6q3vxRd6+dmyzWfol8ZiZnybKxzhIQIkohUEhEeShIIpTIh5F1VDI4BmDhJIrDGZmGZI+de+zsI6dJ6kfPHW2GnueICsoHg3Y2GKZ5Px0OhyazyIw61NKLJibqlcDzPa8aay2VVkI+9EqWxjPi0sNzuCEBHNXiInJJIyinPnLIcuRPBiKwBGmaFXmRmayfDPf29jf3dtbuPkiTATJ7cW1+en7iRKveqFXiKK7ESgmWCh2hENYWqjC2bETDcq1PLEYoICjpRwwMTNY450apfWJCIVXJ3hIsSp/gQ+VWHGZ8YUQoY6JRBzyWniw8vN2XBUw/yRbzISyi/LvSDOQQ0JEzjFKillIKQTjoDqHXjyNZ9E1zaaFxZpFjGU9WKc32rlztvfv+IDcrr56d/dSTulbN03Tn9h3p1URQ1b6fDYdYj7AoXG5IKOmHSqgiSa0hpXVQCZiZnfM85VXCwUFy9ZvfosvX/JPH9969KqYnnB/4fsClzMJOoDSGP/je9cu/+Wdju+u3vn/5zH/7y61PPF9/cYIuvr/25pvr771df/WJo0ePHnt8+fWvXy7aXSUXCpuB8IQl6yjrDfN0kCepagVjR5paYbrV3b1+M9narITaOKhOLgRRzOyscUxWILhy212GZ1EKZsfWpbkRLKX2I08pkSUDC+RS0253mqHnV4MgjIBo9d6GFXjk6Mr0+cXFJ59Kbu1ytpXBMM8OqmGtSJmHw73XPzS3tjrvvnDmV75YWV6KJo+MzZ3aubWV9Q7CuXH12In+6zcOLt+b27Wb3319l/cb4OeQB2A9EH5z+bm/81+NP7a8/tpHb/3a/+/B5TcMHFSBSNImFG2HXdAvf/7zZ1/9lJWGjQUqKWcACgFBCix1AGAmcswshZBaE+ZFWqQ2Q5RCSD/WQSVGFADOZpYUjyLm4JQSAhwCkCMq/X/MQghAkFJknYPdvdua08irNBUuDipGzGyltwcAB9pMFyKkcrZxBmQKJEr7P4IPToFABg+EBz4D55ATuNy5AH1f6ywPhqsH13/v93urm0/9pb+xdPa5xc99pXB48Rtfv/f6RXD+zCPnW9MV9LXU5KRP4AsADzgDsmBjWT//ic+e+swL0eKEkSm2d6KBq022bJ5fffPNr/3Ob7n93VPHlqQ1N65un/2U+fxf/zvf+/q/c/e7fiWcPH/k7OdenjpzVITCsQXnynbvsuOJ4XAMc5aFRIU6UMg2y5JKVBmfmTxx5tzFt9/c31yNjx6JK7UwijudTpZlgc4CFYGWiOhMRsyOAAEE6jwzpCjNuoEBz/dACRBKaUXGovAE4N6DrcvvfJC1s47u7054flTLdX8YGGPyjes3199989SrX2keXVq/dneQDXr9dp71BoNe0h94NS/r9/c6fSNYepjaLAT2pYeStJASIe1u3br23lMvffmlT31qbev61u17G1ubS1NTGkRamN7OznDYn5qdnZtcOtjpFa5wyoATZSmAZefYOiAQIBGFkOLQAD/iho4+rFFHACIQEzKAQgGCgXPKw4nxT/65n52dnen1h1yY/V4ny4tev93v9ZLMIWvt6cCr1KtREMVR7IcV3w+0ECABEUuZ69AOVyKt2DHBCLFcAkrLVcwooyWAkaDc8zvrnHHOFFQkeTrMe/100Ouvr29s7x9s7e46W0S1eGp84syR042xiVqtGkQ6CnyphbFWSoHAhETkmBwDKeecQCHwoXsHLTlylogAQQqmsiK+9FMzEJGSo5WZKoHNQpa/5NJdhD+pgUdyVG54AGBEXAUmohJZVtpAD3PBh0XBfAh1cVTSOVCO/ntEztOsFlZYwGC7JyxUIn8Qy2CxHk3UoB6htMNhv//W2/bGVTU5G7/ybNRsGcr7uzud/QeV6QX0AofCMbFzRV4gkda+1L7LClcUKMgPI6W8PMvJOi8IfC9IB52tixvnnn++eezEW9944+m/OGutcGxG1iXnWFPey7Zv3t27e7codpy253t7UUPOXDjx7eLfnLRp1i7s/TOr3c1HWzO1+jUWVijyle9XInbkEusKa4lF5DUXJoIo6m8edO7cT9a3qZN5i/Ewi2bmJpWHZLnITTnHcIkqReBRN+Iowpr1B1L7UbPqKa/RqvSHiWXqDxKv39daKt8TUmklBsNkMOhXatH8lz7eudve+eEPwbkO90XSq/kVm2cWuLuz1//9b+YHB+f+8s/5K2f0ykneWu3dvj12dLz5yHLv5NHixzvpO6udq3cFYAUCAz0GC2Lu3C/8heqZxTuvvXv5135z9f67HmRa6ISKnst2wA7Bmz2y8tn/66/qVqUwCQArKQWXlRsPcyRE5MghEzESKiWlwDgkJMhzVxAw+GHMXIJayw0FSgHsSJT/w0MEfLnGRKYyZEjsbG4Ck/oqByECp7pFd96fWTT999MHriqVclOFVk5ZREJwSOTAMQliAtSAEpRj9EA5UAFAD1wBkFujietCDkn2u2b45ntJ/i+f/YXiyNmnVz79GZvzjde+cfXidTnWbE2c0LWaqtZY6gJUDhkAB0ACaKE1Pn9sudJssXSDTjdNbBREzriLf/r91373q5vdTR8gG7oTrUZe3Fj72veWlh759K/+5VvXLs2tLE0eXQxaMUsogJ11QCyEOKy6hZHljhEEMhFI0J4U6JthUZi8XglXjq/cvnH1wcb91nQr9uJWs9Uf9NI8870kCH1P+OypnG2RGgB2UNoPpSESUhORIxJGSB+QNAoRhJUsT7fu3ef9QdVTSXtvb+/W0smXqq3G+oODRDD2ivffeK954onp+ZVbH17vrd40bOygZ4vUQmGs7nR67V6nMuE7tMYlhU08rVChVqhZFJA9uHt1bnHl2PkTH3vpk7+z9mv31zcnJ1ux0IrZFun22oP5pbPL8yuXr1/OsrTwK57U5Mp4ETlAEuXYLx4iZ5CREcuEmCjrNRAAnCgPJwBBloW0zmbavfzZzx47e7LTG+zs7pthmmUJMQghlD82WY+jKIhDXykMfSmFlFqWIzE4KnNaxA6ABaJAUS45Rh56EKI8Vci5EoUCKBgsFYWh3DjjOHVpr9frJ8Nhb9je720/2Gsf9EyeAnrjk2PHj52dm2mNj9eq9WqkfKEFMzIDChICfV9Z58g4h8wO/MDzwFcjZDNzWfrLjkbuIXIAyGIEaBFCKKWAUUipVWkeHf2L0slcvstKWYAPPT3wsFJlZOtnZiYiKOs2Rozi8iArGZbl+6+UmBlghKkY/Qod5Xmh4oY17uDeDmYUTlZc6IeTU6pVFYHOOu3B1k5x/bZlrj5yoXnmMfbQdPL1W7dEGFeaNT8OKMslgsld2u1LT0ktuLA2S4BJ+V4Q+M5BVhiDHAUeSk62d9KhGj91shrVQmQldXW8vrffKwVrIpLAnOd+J9fO1BH8NN35tz+ePfuihyY9NvPGO984PTO5Gw2XAn33w4vxZE1HKITzKlGJsTfkrGOhddiKonqcdrvtzbWsu53t71ZCIaQHYSMarwtkS7YoCiyfnZLlNkrfAQAIpXTg25xtP80A/CjwIr+qRa8zVEKl3TSuxlEtFkrOzs3GvR5Asb2+P2Az+YlHbXutfofigT2AAeUUg8rBGDBZke388Hv3OvtTX/mrYqblhWPZ3p4xeTTVbF44Xrzb2f3hD8Mk90DLACjLJURHn/3cwovPbl+5+NFv/OvN+1clFDKgrSzbgWEBog9+vXbiL//D/2byxJFhmpA1aqQ1HfaLlmvC0eqeidE5Bwwq1Cr2I4X5EAvMpeexEK4AwdIaxwTSk1II1gyCUQmJI1GKmIhJcukfZqVkHMW1cEq7eyYQWaH2UdS0etH7VNp7a6u/gYE1gkJgEk4zMlDMQjASkALyQARABTAAWlAChAargAMAS9YHKwAzoLTfWX/3jR/st+3P/6WjH3vpyCc+BRnevvrh+qXrzaqeOVsPJ2phXA97ngYjgCxICdys1MYbs2aY9AeDzfXrhbNHzjyV7u9ffe37g+6BApaQD7v9xulzM+H6rXTr8je//7HPPPX8+VdBClCusNYSsTUSsZQ7ibksoUKBZT9EedAIEAispMKAizzjajQ+PbW0dOTS++/s7uxOz4SteiMZTuzv7+U2y62JZMzoGDxrLYMQqAgJNZF1jgyRttYoyRo8ITWiQiWKg6S/3a/GzYbG7Y3O9ur6WAUWpo7uRQ/ud5GFbO/1Lr71+pPNn5pfOLJ2/65Jc2czKhKBnOSDYpgmeT5dHXNZkXlYOAdIqLXSgfZ7ol/0B3vXr3xw5MTx51955d3X375++9q9rZ2zs8eUQsHQ7x4YymdmpmtxzWb9jIyU2gFC2T8uGYUsnYXwE+CrYwKQiAjgHhZEIlknBaBERCB0ST6cP3/huZc/3u0O19bX+/1B1ddxpVKLYq2VVioMA88rGTosRqSbMiVrXdldh0hEjASoCF15iS/1ZxLCEDBZcuzIpYbzpGBjcpOn1naHWZHYdm9/Y/XB6u11mxcMslarz87Mzs9PjrXGWlP1qKKVJ5BYeap8l1hrLDvJApwFgUoI0igRWIJQQhKq0nzpyAIe9rKUXnwQsmyJF0IICY4dlrWrqmT7AzMxElmiUuAdkZSJRgUwAMw0svGP5joGIDdKJYtRKwCXr1+gcmOJWKJCDyGgUpQuBkYgR8AUeIFr58ONTVFkUk+0ji2FU2OghSDsrXXb164ECr35o/M/9wm/Gpoi729v8XAQjbWiWlMrtd/rxpEAZ1AI6WvBYNI0z4yOtBeFjGCyNC9yFfhB7Nt0ePuH7yFBuk/5WjZ95IwULLxocqHJDOwckQPN6KPXiB273KPck3tDM7x+vXLEH/OPv0ffPNIvXhCVajC8u7YhpmYx4mgs9ioxO2eMKZKh9gWjp8MoTwoz6GXbDwYbD4CSeGwxBxlN1ILQY2BjnbO2PPRx1AE2MlMJKZAJlQeOrUeDTt+kJmpFKg6igo2hMunH5IQSYaMmA18FvL929+7FLdW13rmlIedui9TuZgYpAjdAD6AQhDAsOm9+BPu/MfHJl6ZWTrT7w7yf11q1weJEv0EHNz+CflcDbWYdAB6bOf7sp19uX7y99Qd/OLh1Xwo2hA/scA/sHhB4curUmb/y3/z9xScvZLYgFEoKcA5AIAgukYUACCxQuFIhAseERAAahadYMwilQtChp7RnLRSD1OQpSimEIizJ5DSCvpeVN1BWrjIjssmdEmGzvrB0Wt66vJt1bzRTk7o5hIn6/Emp3cH3BtmDXLlUkOdYOBQgmEGCDEAYcAUICX7Trw8LQWwt9B0YCwMP8hikAFcFsoA9gH6S9W7ceOvf/1uM5NFnP3XiMy9aKG5c+eDaO9eDcBlVpTE51tqMCBIDjgE0aPSqedcNu+3tjes79z4Mjy3qx/ytnfv91TUDhQeggfJeVqh63JwV6UG+2j64tL40O5Vbg0oq7YO1rFiWShvxYaWrgIc10DC6F1njpEAV+EVaJHka++HykZWbVz64detWozleqcRjzVbS2cvzJMuTrKj6PgohhK+cIQYnQAqpwVkCaXMrCJ0nssyGWiilfXKm349J5xBHgU/Uv7u2lZg/PH78ZDQ3F/S3bDbMk4PNt9/cXpifWjm1ubY27HeEYWUdMbl0sJnfmBh7phqEw+5BJpgaRI6FYOl5WgfIexKC9bU7d+7cevzp537mZ7/yj3/tn23ttsdr+1P1Rm/Q2+8czNqsNd2YX5i5c7vnyFhpEHwgFgJQyBI8wAxiZFsfGRXL3aPA0gvDDIRytJ0WQqQmi8cmXvnpn25OtA76ebNemZqoV7SnlNCIQqJEIZVAIHIOiIjZEishCECBACnK0nViS6OCXEHIDMI6sgVZ5ty4tMjyLLe56w6o3+5nw063PchNnlPurGMgtm5hYXlxcXJiolWrRUHohaHn+V4Y+AxkDRkwzhg3YnGiBAGMUkkgRgCJEmTpJWJnnWIiZ8vSDXTOCiFKduchOxRHjVwSFcqSFEREzrkyhsrAzjIxYRm5HJ33o3dIyRwVWO6cSvM6O+cQ0PM9oaQQQoAsK3mEQCmEVKp0AeGh23SEY0EEwAgjHfjtnYHb261VpEWM67WoWUcWplv0b2/k19ciyVyJq8enlYDhIO/e3RCo/GotjOM8zdkYFgEKoQKPkcCyyTOU0o9jKYU1LhlmqFS1UWNLva3u3ps3mrNjm5fWXM+b/8JT0N/EyWmpQhCiSDMCdmRl6FdnpzAObCpVnQD6H37/zebaI83ueAinEkA++XFx8OFWYhrVQNXqqD0ZeM45FMbzldDKWKnDEEzRub852Nxy6bAyNs66YlBWJipKCWI2hWFmARKQCVx5sSJglMjEDkgIVJ6nCYDAGEp7uU8gpRDCi1sVpXSeFFKrLMukJ+NaZf7IqbQXJZ2+rYUGIZHWkaymWZEcEEAD/AQKH0QBGTy4k73pT81+LptqmWFeq0XBkebeBND9jRpAFdQQDNXnT3zxczvd3q0//Pr9O5facNDhHCB4YDGDihdMPPPzn/7iX/2VsWNLZC0Qg7Nl1rTE9Y4cCCwYhaPyWlxWtDAZMmnhHIMg5flCh1ILyplSkxwMLGaVVl3IMt7jEIRAOboyjpSk0pxsEcFaEzX8uVNHll6fvJEe7A9TjLex79VVd/rIQiafvbX7Rm63UiiE4oBQgSUIGKgAMABDoB7oseOnlyee3Ny9YgYH/e6+aN9EcDXwPTAhWAPUBBwAZLbQd1ev/v7Xg9nFxbMnl195dmAOtj+8d997NxqfqoyNNaJaniRu1DATCOL11avXP3h//97NjGlxdg5AJp1+NuwzWAShAarD/OiGuw4UQiAdD9p7eeZSsAJyJT1kGlGOS/Mrje7iow1qWaWHCMRK+dJDKdGTgoGMNc3J8dnl4x+9+3Z3f7NRP95qttrtZtreS9NBqONQ1xxILTWQIceMhAKFp5hICFlYqyU6AR6x6XYLyrLd3aTX7qS9nAvh5UVib61fzUX/0XOfOHL0yLY0WVce9Ds333l37uSFlRPnVm9tBD6Hqgaml/TNePzU/PhyrD1r2lnPZf0Maso5AMEqjLSKsv5ARnTtg7dWjp998qWnz/3okUtvvbm5vd+qzCsdOuLdzQdj9fn5mdn1B/csOURWpdpYMuiZGVAeXpMAGUZB6xF1vnTXISI7klqCEoatAfjY5z937rnnlKQowDgMFJDkchkiRIliZ3LGuKI4DI0wa13e0LlUTxEckyWy1hBj7mzuOMtsNsjSNO33+t1kaE2W9dPuwXDQzbVS1qj6eDw73axWqpVqqJRu1KvVRsX3vRF6n5mJHJGSQggGB0VRMCAKIVEoWZ6zKAQ6AsbRLMjk2IKytlTDJTMhCmASsuzWFiAO66WYyZWbHiAAa21hCueo3BhZ64hI/MSZDnxYTyiFVNpDIaSUktk5EhrJglTC85TSSisJJXKP3KgEUsoSEsFEKATxqGPAAQGw1ootFru9wJIXBDLwwomWF/kmyV3Hyrtt+9Gdg/6mHQ9PoCKTD7d2815HVxtBdcxpZQ+6TE54nhd4pm0Q0NqCmf1Iay3Bcpblzrm4UREEhXODbtfrdU6cPdPNIdVWT471brwvtK/GfHKuKAqpFDoMArVwdC4aHy+2doudftLZvnNtozb9AF14YenUsWePVNVg/aOLO44aR6frc5M6VCjA5QVZa5G1UnG9Lizt3d4ZPNjyERwFldlJShilDhv1EvpWFEW5DBvtyvnQUVWS+KgMUKDyPCYAYR05m+fMWsa+V61qZEJHZJ2zvtQAOL44Ux2b6PeH+SDd2d27/2Hj/rffdbc73pomZ51NFXsSrALO8zRYW91//d3xv/KLvpYmMyKSohGRGQQwrEM4APn4k18ikK/9yR/evHMFYUjggV/FsdrxpZmTn33+6GPn58+dQgnGGmuRjfvJTMplnSWjLPEWxK7kiZf9dCgEWcfSsAyUirRAQYbcME8OBlmR+hUtfS1k6YwQQjiUUE7uZU6FDr3NjMIV1klv7tETp1eWDw7eTXJPcghgYVZXLjw5vbgwvDyzfeNrOdzt2kEC0ABFkA3BY6AA4sCLYWquPz9ePzd5YvJk9/7dzu52/WotWb0GvUQwC9AARgJqyD2wMsmGt25e/dE3xk7MjB2fO+Z/fHd4cO3Hb04srlQnx488/gzu3n1w/SMAqcNmdfHMzcu3b9y8swN7UkdNI3p5ke/3h2ByAAsQgWaQbmOTeyAhFvWaH3mMJCUiCoEshAACRCRHCDg6+QmpJHAQK8m+1lpJ6WlUwM4RF/kgoVj71eDYqVOXLn909eaN8dmZStiYGJs8GPa7nWEoe2EYeL4iKRHYgqOS1c2jjZKUzMCuGDD5SWe/GHbSYccPona7Y8McGcNISA+LpJMdrC3OT5PtdqthvupWV1f39lfHp48BBp4nVAj7Dw4Cb2xybmH+6HR3577UPloadvo4K/0wSNJE+mFcqxfp0Bm6f+PSzUsXZz/3iS9+4Yv3r18eDLobB/FUa1wor7NzsLh0dqIxFgVelqW2GHogUQARCxCMKET5Kjy0sx96cngEzzlk1ZSWBAFJYSZPHnv+1U+jFN1+39hUCwKUJEp8qhDAAgQTCUZUnpCCRjAJMI5yWzh2Za95ZorBIEsGKTjqJ9kgyXu9tN8dDvu9zsGecU5ppwADrWemx6ZnZ8Ko1mhF1ZqvlfZ95YiVUkoqOLTKlHKGNY5MeSgLR0iWtC+V1ExgiNhxOWmXZkxVGiuAlVISpRDAKGQpKaLAUQMpMBFZ59iSdY6JSqqPcdYUhohG8rRzgIhSKCmFQBw1dIOQUmslPU8IqZUGHnWElXQlIVgoeUgrQkRVOkdHtGhmkGpkQx1dBKSQIJVWGkS3LbOC/VA3mvWZMaGgt50mOz1sb6d3VrtczH3qnO9VTDbcXbtrnKvGcVivs7WDbk/7WmntEMvFJVnnaT+IQzbOGUtMfuBpXwshUOL22kYAVA0j1ZjwT501yiVX7mbvX5/4mZ+hKGImstbk1gu9yacXFj/29HtffZDb9KC/Mzu30pwY27+/dea589br3PrO719//6Y8eeLIq69MLs0JT9vCOmeZWHq+CkIAzPa7/Y1Nm/aLnb2JhWWlQuMNZeBVmjUAttY5a4UAdCO4Nh9ekUoLA4yqelBIqYNAeaSQEcE49jyPAQ25IPSklnWtBDCy05GvK2F9qu6A5tzyqWfPrL/08au//527f/JG+/Y94bAqg8hahMwg7XZ601sH+equevG4yQdUSNls5cwEhQY14Y2lTbx+8+4tk7rJ+aWl6ZkTJ+YfPd48tdxcmgwnAlBMzhSZNcY4Q0CEQiEdttmXDObSrollyRyXtaUoBHpCAqMQKMoMJtjcZL1ulmV+7MfVqvZ8RmGJJJcXAEEo2AECEiEDEPEI9eY4MzZYmFr6+Mtvf/TuIO9HFJ1fXp46vVBdrlZnK7Xlqf25sZ0f/cFm8dEO9HJwQwDrNSaWj88dXVk5syIXxg562epuG8PqzC+8uAyQPHi2e/PW+rfe2L5013SHHddGIA9QQJ+hUAc79oMr69c+qj357NTyyolf/Mq7t//3wf3VdCaYfPQc3uHd27dymxykyc7WPU42LOxVQQrriYP94a1bOzc/aAM6LYVlj30F6oc797JhngC3jk1WpqeFVkimNFADiLI+vpQ0AUCUNeSHTWpKKltYm2VsrHW5Y5ISnbHSDz3tTUzPLC2dvHzp/Y3NtWNHK2HoT7Ra68P7SZZG2VCqqlKKJApGR66wLFg4YAAhBSghDFJWDNJ0aAtDVjsMlKhkw0yHWkuN6Kw1d9eu16u6Fo9leeHpeH19/cff/dHn/sLJ6SOzpn9AaQoQTE6PTc8dM8OhzVMnsaKi7kG32x/O1ceN5TzL/XAsrvR3D7pd2/vwnR8++tyFc09eeOaJZ9740Q8POt04iFrKI1s4Z8ampqIwTgZD1gDgSAiHQiICipH2WuJmDlsef2JfBHbESo4a9gqXYRx/+pf//PjEeL876A37gY9KeZ7yPd/XQhCWxAVAFiSAHDsWBigrKCuKIs3SLDe5cUh+GCY27+51tjZ2q56fJOnubsc6EYZ+JQxaS0ta+5WqX4m9KAziauiFYRAEcS0AKLmVhSXHzjKgc+TISpRCAzM465wx7MghEo3kHktkjS2sKZyTAhBYotBaga8EamZWpTr7EB1EzOCImNgxA5Al68gZa6wjN4rqlgkBqZQs1zaAKFApqZUsWTTALKUQSgpRBvVluYeEUcUHowCBSKUojIf0fwAiIGYhQJavBjhETjBYBCKnUJDSyb3OcJgH85VouinY5in197uD1YPK9nZ3f1ceObb4+adlwEXfmE4ite/Xm57UybCTpmZito4IWTdBSwyMgNrTSvlJr8OMQgod+kJprWR3p73+7e+1KCeqVhtHuOCBY0PQ+/prZunI7Mc/rjzZ22+HtaqQwmtEL/yfvrKxu7P35psnT00uHn/s4lvX9Vj9Xr6TdXdtTVT//MtP/5UvLjy6kjvpiIvCFGmufOkImLlI0/5+r7/XLrptX4VBa6GAFJRVVc8PImYmR84aIlIoyw8FXWm0ZQJCBhQKR/8AlYd4qOUoDxDAJHkQawyULVyy3d26fdckQ3+yFTQm41YcN2ueJyvV4MQTp4+cOLL5pc+89ltf3fnRB91rW6g9Sv0hJRGI3vZOsLaB4KMo/KAFHERR2C1gH4p+oGhBTj370pml5fnFmTgKg6qPvnSKHDtbkqoMOQIgVEIgCiQQowt32elbTgd8qC8hEwIAStC+R8RgLToH5Jxx+TDNrAkbXtyqe15AIAiFMyw9qSVJFEh4mJpBRsQyFuysQAnWCu0d+9gzJ95+8Uff/yNtYaYZj7vAlzpYHJ+Yz/Oz891Hxm++88Mt0ysgT/3a9OOnly6cnZ5sNgFDkw9s/oN/84dvfefN1idPHX3y1NLE1JGPPdl87EL18oP+9Qfigw+3b9yJeM8laR8KYZXa2hjevicffTRjrEaVo0fO733zG8ZdaT5xsrO81NeysAcD6HQvr84rnhLKkEIe0tpHV3/j2urdjQoQGNEArKNn2K4O91LAZn1i5rkz4yeXSKPLSDCipbJ5iUZoLkQUh8lKKbVGxKKX9Lc30n7bWWOKPHcgJMSNuiMxOTWrdXhy+dSt2zfu3ro3M7molK6EcSVuWpslNvVcFHmBYLTIlsvRmQUqZCJyEjWDSIbFMB1K69LhkID9QA/6xodQkIHckRbt7sHVO1eXlud9z/eVJxF3bt1xSf7oExde/9rXkG1Yi+Nac1gU+911P+t7KoSgyiJe29hpTE4JVJ7GYVqQ8phsKIMbd65evXbtqWc/9qkvfuHmnRsPdh7EXW9yfNww7uzutBan643W3sE+gSsb4BlEmenDUe14OYPIUTuhACISEg7ddQwgCioKDZ/48k+dufAEM6IUzXo1CIUvtADFjI6ccdaVx6Z1aV5YcM4BMAyzYpCmg85gOBym/SEBxHFonDV5nvV6fi2uhoGaaEnUExP1Zi0up08/8pRSWPZxCVFO9wjCGGcdIyM5zoq8sNY5I4QKfJBSjDLDctT+iojOmsKyMTkzEFBR2MLkgkQch9ZprVlIqZTWzFwu9Us6AznnyDG5kZmTS4u2YIVMgEKgEkJJpYSUUpTIZ4FSSiFHAFUhRuoZCEYhEJkclO4iYEQJEhUBlKk8EKUqLcrlRgkHHV3bRzunkVDjHHmeFll6sL5lmGozE2GtnqdpQVAkA0/29y5dKygNK/V4chbIZXtDa3Q8PRY2mhIpG2SgtAoCYanX7lZCLaS0FkAKUxTWOiklI0tf+4HPzuxevrfzzrVmDjpohNVW+0ffc498hY+e2Vz91+I3/mDs+CPRfKvRoLywLk9RcHNx8mf/3l+/9PsLOh8cDGjiuSfmnjwRND2s65mTc7XpMR0pAMcp5f0hO2cLF080skEByGRzypK8s8NFIauhqga2m2WAca0mlefI5mlG1gocUVEBkYCAkah0LPNhwx0LIUcxPIGAqDylUJnMbK3vpb1usrd/cO1e9mALBoVXr8WPTHcGQ2uKxuKR4y9caM3MBn5l8YkzX3r0797/4Pr3/8mvb3/n3VaOOQkN1N3f0asPkv2urggQ0Ej7uc33wLUhF8cWjj7x0tEnT1dalVLeAhQliYqcQxYIggjIMBOXIUsgdgDALKRkBCqZmKUXuBSABDAhSyCEUhjgQQGCHVkiiMfrcT3S2mMQRUFKqSI3vh9IFB4KKscVIUHIctHLQAyAEmxR5Ia9VvCpv/LLiaDOBz/82o23F3a7C/ubzcULYkHKCWq8OPPo868+3az2OfdqtemZsWG7//rvf/fW179xXLpjYzMvTy1//ds//OB/+c57QWV+Uh//+JmFp784vTAxc2YFXn721geX1957Lbwqd7ZXM4BuZ9jc6Shnurf33/93vzN8+2Ke51PkwUFb1prHvvDKpT/6HVsMfMjZcgRZA4Iu4Pr97QHYGEQVZABuAiSj2GBroJBQP/WFlxc/8TSGKktTRJAggdlZQyCo7FBjkBKdI4FSKMnWdTb37l25POjutqrx+OxcGIaDTj/p97cO9ov+oFFraT+amp89unj8xrU39zdWx6eXlfKr1fpggC4zLrRIZYynBI6Vy2RkRia0jhXq4f5+2s9jTxSIjKSkVBAK4TEZLgygldprH7RDKSD0oqaqD2qOsr0Hd+ZOnY0jPczcxOLUMN3u3b13v719ruYVAecs6+MTqcv7vY6vK2AA/UAmkZSSh0Ot+N03f7xy7Oz80SPnH39y/Y8eDIbFMB9UqpNJmrXGx1vNyTt4hwEQSQivrBUUPALgAJYy8OiQGWFsiBBBaIHElp0R/MRLL3/6Z79UiSMyhadASKXKXRG53DpjsiwzqXWOOM+zwWAwSLMiSX2lBMphYnq9fjrIsnQYxxVF6HleEFSq05Ug8iqViqdkGHhBECgthAAh8f/P1H/H2ppe553gWusNX9rx5HBzqnCrbpFVJKuYKVIiJcqyrWBbljwtjQwbbnejDc+43R54Gu4BugdoDDA9A4yBbsAz7XZbDnKWZclKlEiqRLLIyvnWzeGce+I+O33hDWvNH9++lO8fF7j5nnP2fr/1Put5fo/SFKK4sCB5sOcQYowsAhxZayLUzrumaSJCalVrr4w++Bi0NmluEVUIPoYoFI1W1hghAlnQLGIM0kZ/g2jnPEB7q4shRBbhwAIszESglVFKa60el/URtssOBKVIKQKRdpZvBSPE1tsBzBJ58QEsQBOt2rO4dKEACCMIUYuCgDbThD+MH7fnAAjEwEIggKRIax2P5/5k1Ol285UlgLDz0d3BqXWuRuNv/RF/eBcyvf2la51B11d+vnektcl7HZ0bCS6UZV5YpbE8GKeGUAtw1FpxZHFeRECDMSbJM9TkZtWd194qdw/95qZsrLBt8JXv7E/C8k8/3/vU03vfff+d//lffOy//FmztZK5ys0rslYndOr5s6ee/ZVyNOES8tWBypQgYEptxXctogSb6qQaT2ySp92O0hohchBXN1782rnN8W5DNm9Yc4xIJusuIUII7INjFnqskLXwIVmgNYB+SA/B1k+lAVr3FBIqBBzdOnrnW69Nbt2J4xmP5x0hPoku52e/9oX09Pb1b7188xvvX/+Prw63+1ufuXr22UsbF84//5lPXHz27Lf/+b9+4+//6uytR102HBzfujO5P1p7atXHmmc1z44D+IzyU5+78vSLz+TDbpRACLG960GMdYA2gxk5+oi8MFsAEj8mNCKz0IJoAtQWQgi2DEFiAOL2tUKWLAVfKaWTgU2KQisUUswMBCwxhvb9q42xCCiRhduZhlVrABRmASGC6FFo/fzyX/ir//lrv/PEh9/45is3X7t59FH/4h+tPXf68vazaX8jtbbT4zTL8ryr0L395vf/yf/2T83dewNtlsP7mxd/8c989e9l7/5/f/f6y+/f29/73z/Y/sYfr1y52D/z9MaVl57+7HMXX7py8/VnRv/238/fvz2ZYe1xdJs/+s0fnPzet/dnewrMcNh75we/DWefeeZjn9g7unn4h9/pQWWBCiAGZyCpIQKgB60BupDMIJRcH0NTmMG1r750+ae+qFYHNYiQAg4xBomAjNKaaikSKhAQAWVAmjA9OHrnD3//8N79pfNnVk+dXj99Jivyk9H+wzu3j28e3J9Xa1tnl9fPFP3O9rkLb7z7rZs3Piz6K0madYoseue8i6H9NyJCFM0EgkwIxBIiAhJaQ+LdbHSSbSxFUpQltteBsdU2I8DGueiZKKS2e3I4splKjC06xcn8+Pa7b51/7pluP60muLRy5ujDu6P9W81Ybs+hWCdru3njvZvNXRz08v3ZkSJyRMba1Bkv+sGN6x+9+94LL1775Iufevnl35mfTKfj8dLypsSGiPqDgU6yup7leSRgUmpxfi1kf1nEfmVBfGt/tHCkAZQSTz/99Fd//meW1pZiYIFIyKEJZQghlLUL1awsq7IqXR0iAgqGk+PxfD6fnEx6eZoVmdVZoTp5r+vzTm/YSa3RVud5igTKQJomikhYYozzeRU5GqWNUojECGS0BObArqk5iraGkIJnkaYOjkW0NloZo7S2ylodom0lF6OVsTr6EBpHpImU1sooxYwuSIih5e4zi65qx8LIceGVA1FEinQ7QWptSBmjCAmVRlq4gEBIFBG3nh5mjsAcWEQrJSBxAd5rV9Rtrk4pVMrQY/obCoiQIgLV7osJiGghAvwwCdz+cQJA9J6TXFvUB8dTljgY9vNer5ycZIXtLWf3x4/i/k6SU3+4dvbHPmfSpBxNXNMUuSryRBE2s5JEdErNdO5jtJlBkeAaY00MUQuQ1kmSotEApFlPj+b7792sxqU+l9C2burp3u7s8OV/crx/85m/+1/F5B/u/6t/+cfZ7NP/5V+mPCFCZI4+KEugVd5ZA5EowkRIFEEgcPQhNGF2dDg/OdG2oH7SGRSoiSCy42Y+U8YvXT7V6+qHr7/J3kVAVjpb7iGhcIw+ApAixcLYJqq5HVhIFu0P7eNVkNTiQkCEAKSBa77xxs13fuf3i1E57BQd0+ulHdPLwNvZe/OkJ2cvPj97CPHmbfv2/d3X3/hemNBG7xM//bNXf+wnP/fzf3n7xU/9+//2f8F/91oDNRztlkfHGtbq2Wxy9OgE6rnJTj3/3It/6kfzQZ9ZmtIxCqCgioJaVHuDZEZuQ5fIwtRSYheef4lR2gszLtaKSikBRhYA1arXbQG8tolNLANoo1qrz2N3GbdxJyLQBlONpABYhIGIkFFibBMTLWiFCCL7KL6zmXzlP/vJz//cl6tqLnUEwnRQ6CTYxHjvj8ezMIpqq5g9On75137raP/OZoIYPYIf3/yNwva++ON/L279o7f+8J/vwMFs5+TDvVeX1evr9neWzj393F//+Usv/eTKysrBP/131+896Pl4/ZXvvvP7vz6a7WRANThbj+68tuce7X3s0+ee/dqPTo6q7O33HEwbkCOoPHgGzIEIQgKqhLoC8mCz5Y1P/cJPPfPlz2OvVyM554AUEXJkXgBiCEUgQkQGIKWUhFDN5jfefm3n7o3l1dW1U+fQ5pPprKodI+qik6TJbO4fPLiX9wdJkp+7cPrcuafufnj94lNzk+Rpaus89bPggF0M1ixmPSDRhEYosmKWCFEyStJkPDnqrmTGaofRJmmDVLHPc8WRonfsXarqVNm1orCoesbOpuOT+/eDK1dPnXlwe7duqqbxiVDEemce6HB8dbDlpzN3Us3XTnh9c7nff7S7T0iJzWze7E/mUeTtV39w9eoTZ5+48tSlj736g1fGs7H3s6JbaIi9Tje3xXQ+hRBZo1KKFyMU06JKdJFRQkQWVoCosO2ZKINLhsOv/Nyf2Tx30Te+app5XYa68S7MGlc1vprPJ8fHs/mMA09PKud4sJQnWdrJu/28b0kIlLU2zwdJYkxOaaZYYvAsyN4HYHAea+c4Sl03dVW3cWpkSdIs7xVWa00aVMzSXBORUYu9bAjKKFSklE60tYnRCgFR4mMptUVLECVpAhAFgLSKCCEyorSnuwB6F3RVNsroRLWWT1RKaaP0Y6a0+qEnf7GsgwUhY5HYVCzIHFnYubZET4BZa1LatJC4hciviEgtqillQXyAx4619rPf/kAWf4dwe3WHxzBWQkM2KJoej5TiYqkfkYBDZ7jhw8xPqmxW8Urav3QpW92I0VfT2Xw8T7uZtgZF5pOTvDAMNDk86K0uu+AheEIdY0BS3vtkkLVGYGUTZtp//97RO7eDQ90z2cA+uH+b1qB360H5djdZv/qJn/8b//EffG3nN7+585kXz336YyFGnSgBji6QViJeFAFR9LEuJwgQ5uP5oz3woZnV+fKqThKlLIJqv06h1PQfMQABAABJREFUqg14V02m1VhKBL0OUgIEUoUtugDsXOObigBaqsGiqqpF//EPmzmBsGUgAxG1yFiFGAXrWX337Rthb381z7sm1zo30VpKqcho6uPxflWpge7GGfspFk5vgNoZ3//9/+F//P5v/JvP/6VfeeJLn/+l/+G/++3yv1e/+wfTk0O/uyPyhGuOTh7eGw9U79PXLv0f/1x+YWP06ODue9enzTjNrGJvtAJjbaefpB2b56jQJhYDCEfkCEi8SAe22Ux5jI6SNsFFSgkwAbWlQ8jEJBFEKxRUDBSCAC0amwRQExEjcCRUKjEcWFBIPQbmibQR83aHhYSIFIEBfbCSdDLFGbNAYMYIjHVkDkJKW7K56d7Zuf3wo1u+mnCnc9fQ+kwsHI8/+N3OZ//8M8/9grr93aW7J4c6H5PMQ5w1x+Ht7773P4zO/+ifW/7YxeHP/WJ6932P1Y0P3hkfHGcAVzZ7mC/1bXFn962TB9nh3XsXP/vpz/3sT4821mfvf3T84O4YfA3Gg05AUsgYBNLlpctnO2cHF3/kU9vXnsr63YgaAK0iYWhvS0qiMEBsDXQRQMXQ2CSF6I8OHx6P9rJhvnl6PUupnM2m08OqdRUnpre5KeMpCx8dj7ZO94crS08+de3uR+8d7e0MhqtaG2uMVib6EILX2hAQEUhEidFg0IqcF/aBRPJe10IYHx0tD05FHTpZoZVqQl2kuUmSJjDV0uiwkvcHSV/IOVeniNXsaLK/f/65Zz98+62q3slS3zV0TPJ+PO5yAVhVtUz33uuc2XKzari0vtzw8axRWba3e5cMYuQbH717+/at5z75qR/94o9/8O6Hx8fz8WSyvnUOrB2urPR7y6OjvcDR4oL2KYgIhMCPN5EAiiRGQiQEZiECF4M36rNf+dILn/ukMTiZVaPx7ORkWs9m83lZVtV0XM6n0+nxsQ9xeX3YHxau9okxw2G3U3Rbk2gIvjUcGQUm1SLSVK5xLsRY+xoJkJSv68ASPCc2MdZam6CwsspYbZVOE0PQ7lliK7pEFIRUADgykDJEreU3inDgwCwQOTIq0KABofXjROCmEW6cSAQkpVEEFZFOU6u0tgRaq3ZG01prQgBU2K7MqX3sQDuhERI+7nJCQEUaCTBqRQu0pyJtNGkF7UklbV4YEajlsSyo1bJwAC6SAq1RTzgyt5sPjdjaShlaFlLQVhlCdzDuKuoOO7GcMSJZVR6N6HC/2j0SItw+lfUHkTlw0EWe9fuM1FRVjAK5qY5HOtGMEp2HIKS1SBQWUkIKAwdEo6wu70/e/8Yr0/sPLMz620VBXTo82Li65t7sjMbz6Q3TWT9PH/vMuZ94ZuvSaSmr6dGhC7Wx2hZZPfGKlGtqX0dSWmPwk/HJwaFGsN1u1i9UakgjACOBMDRN4BCVNpH0fPfR0e3dzWeeJIhAqLt9Y3VskxNIAsKhJfbBYtx77P9pDZAtZJuU4nZbrxfVKr6sZXLckyajjAQkYMSIVtJhQWD5yMTKuEfTqiplOk40XEx7F0Mx5/1H3/ro26/83/d/+cdf+it/5Wv/03/9zb8j5ve+4w+OFRPVMgphvrb23K/8ufz86Z033n/12z84PtpfOrWSJiYeH5b7+xR5dWOQdHouGxZnzqyeOp/1h8aY6D0QCSAqAhaIgkq1JuC2eRRbVgqq9k4I/HgxHKMLAhRBGwRkFtCAC5ULQVCAUCkxKjaevcMYEaV1xwMDBAEAbFPCSG2QIrQR+8fwQiJmABFWRiGIQsREBa/LhjJjSpI9k7xW0jq7vtzcevkfdJ78hcvdr/dofjFffp92jPOjqrzN87sP3h//w//XmedODb/0wvYXPjMblXff/VVbTi4Oli9e+NiKWXt3/72if3U5S3WDj167cebaM8Pi8yfnznde/2B1xiHNH97dWet2h8PtuNzNr11b/8TG0ibkmQJDpNCXZfABWuobtJsgUGC01Y6BI/HCIig+eMdRImVFN8tyxb7xLEaRMi4yqiTNehv9lWY2q8dTv+WTrNg+d2ll7cL44ACeCiikrTGJDcIxBC2WCUCo1SQjg021VaZxLgSfFkmxsXKwu9fJTqV5XneqXtGdTR5ioruQCOA01qv9tY9ffL6Jo+PpvYN6bIpUx+aDd19/6dylpJvt3761rNkksR/FNmato9eKdL85OnG5fbRbP3FK2Y2VM+vV7fnh8SwZrLrZIc9HkfCN73/n8tPPXHr+6XOXL33w2mv7+8enL81JS1KkvZVlvqerGDWAfhydf8ycAWBAAmEhQFLUrh+9NE7805/69I//ws8UiX20d3jn4c5oMnKOObqmrKv5fDatfBkspEvLw5WlftZNu91Omhub2DQ1yhgVY11Xs1nd+HJeCc1IEENgbYxWiYkCShSSSlCTRtKdTpGnmbFaKQIEFgYEYCatW0WoPXNbEIOIiIoxiucgnp0Pdd14zy25GRGMoqCiISStUdgz1y6wa1oSjwoEoAhJdztpC95UepH8UppUy/eExxAHRGoTuW03ASz4SdKSK0gUUXsqRWZYsDXa9xS1DQztd9SGgXGRFBZA1R5b7WDWuhjbqwBS2/JKSAASmCFyrhOIcHI8Hq4MzXB5Pp3azaG1iatVuH5Ynbgm15eff9pYW7n57GQaSXSagDaz8Une7TRl6er5YLgRa88+tOb59j1v08Q3jRB1+wV62X/99t1vvgZltbm2sf3FL9mi03n7sA/Z4fbmvVv3knf/vl7/0if+n38zOZ0a5NHuo8l4nMfQWVvi2dSVNSlTl3VT1kW/46v5bH+/Ep0vLyddC01NBNEH1ESaXHDADoFJWcr61A1qJWibAoonKvpLiIqjr+uaY9TKcIztgbhwR8kiDwAts3JRoClE+LjbkwGIG5eyF1biUZz3OA9Mpsg6HQHnmsOyPp5Uu4/cdC5AubJKm14vWSoHebmD8/nt/+U/UqOf/+u/8tm/85d/8/rD+c6YH46q/d3+j398aW24cu258ta9l//5r3F3+fM/+dWltc3pdBru3L5553dHH97xN9SZT5y/Pb716J338Olry9euddfWlDFgMAogilIUADQRC6AAgmLEwNyOGY9bpLmlqCyimywxRFTttAZAgqBIaVHMIRKQIhV9haGmBXoFFqXWuBB8hVm1M4hChYvOPI4/XKczSrtZEB/Bz2trslhD9CDITuTQ4AEFruan3/+1/r3XZiFeLVbHyp2Z0FqkN8QRxNsACEe7b+6eqY8un7l4HH28e7gB3S+8+NLyJ59867cefjDV/jNXP3v5PIZq5+03CkzSlSEt98587nM5LR+MHnSWkjS3U0rKvtVdmjZqfO+j+uAgtTrvDUPTVOUMXEDUmgSVtnm3Oxj0lgeg8zbZqlC3QmuR9fpLw6M7J/NpkxeFa5o8G3aHPUQtKArBWJX1umXlQj3tFPn6xvKFy09++Pp3qukk6XRIQCnwIUJgBNLKBmFQGpwLMUqDGi1p4xrOs2Rl7dS93d1xtb9sl3SWd1fWx8fvON8zUWLjxaQ2Ta8f3q3q473J/blRnZikKgnHJ8xw/sq1O699QFGqKjkG34P+5e7wjEmO2YYia2JFiJCwTdLh6a2HO4eslgSmhXLjan7jvbce3rxz9WNPfvozL73/9qsno+OqnkeMq6fW+stLoq3jRggFCSAuOPUL5AMwCLW3AmYAiCIzieuXT/3UL/98d9i9fefBzRs7uwf7la+sJmA2Wq1sLG2eSQgVBY1CSklWJFknS61SSmkNWpEg6iRRInHahBgtUWbTLM20tSxSVnNUqsgSpUgrpbSy2i7AaMJRJEZgDoq0VkpTO9UxIz+WPMF78c7XTaiapmUEaKPTxCilACWG0DjnEQ0zILoQfPDBeWFBQ1bbNh6mO3kWohABqcVMtAiNIS3aWx//JCzISQiP+UFt5/vCr8ncwkZIoVJtC5sQ/LAsss0HwGOs20KHW+yHH4fz2sOs5Y+KYGz7zAgBZNjL81xPateEJhsaKah5VPZWzjalv//W3bt/9JGrZyvbFzY+fQ2IhQXErWwPCZREzwBK4WxykuYdnaX1yRGQtN1ugEKaIkKM0ul1kiRx0/re91/mvQdLNtk+d4nUqfn33zh69WW3jHXOcm7jSOTccNh0o6+m47IZj8d12QxPJVmnmBwdVSfjNM9BWEH05TxORxyaYrjcW163MAeIgCwoOkmQFETB4JVGkyW21NjrZMUFbsaebVSULQ1BJLgmOoctxksWjHtYeJfb6xTR4pUN+JhjJcItewkCAxDqDlCGtuCIjZ8ZbRs35dhpSuWa8fGdvcmjHYpsjK0DSgma8qTTC0ezYR36GCe/+v17p5/e/Oonrv0Xv1K/vfP+739/7/57F3/is4NLZyn6N17+vfTc+hf/7F8YbK4pkywHU566nC099dFv/tbsOy8ffLS/de7M7l5JH9wJgzPYXUZtRVoXKKEQCggBssQo0DpYFLbtQe3uv/UXiIAiLcAgLBxFlNKAmgAECRhEKTJaG5VoldSR5Ie9FYgM7bo4RmCO7aemLVMC4SgRBEVBqw0wRhYSxUCEAYHBX3h2e/WJa7f37xnmhMMq0giy/Y4b1Uc8363J/sCHdTaf1P2HUF2H+hDqGRgACQBUq60wGB1d39kdP7210f3TXz882f3+6P3qic3nPvuj65vd977z7ZvvXx89ODCYHz662dNZYJ6WJ4nieTM+8CHrKNUf+HTZJmr37m4x7Fz98meKbs8SYema0nGMMbqkl62fPS3hTLq0oTsdFZFDiIERIe8Ohmun9+/duHf3/nZq+0vD2DR6QFmnp7VG5FDPXD3VmpqqKus6LbInn31y5/obe7sPts4/oayFeh5dqGNZ+TTThoEUIgEFkRADIwFBW76w0ltezlfGx+N8pWdSnfcHOr+wc/CoSDuJ1QbiER8e7T8cTU9W1pa//IUvU7HSQb7x5jff/tbLZy89tTJc3tupR0Hv+qgIbIpliF7B0vbm2YtnVZLqPCWtO8uD5ZWVR7crkD6ZoFBNR8dvfueVK09def7FT/7Gr60dHx+eHJxAlKLfW1tbFaVrNxcOqGzbE9R60VpQGUYERYhtA6ibuSbd6v+ZX/5LZ89t7ewc3Lh59/DRPqLpJN0iTRVI4yuMkCVZlqdGW4VkFJFGAPEhlnUNhKRcCyMk0cPekkHSRmultNGtpyHJlSBqpdrzHduTUtgzxxi8DzGyNZoUKa0YIUSIkZl9S+VEVEprpZQ2UBAQZUikUBQpRowxMqIiFWNso74oSKCUotRqkxptFAMoIK21UbqdKv+T03mh+bemQ1osz+Ax6a3tIBEOLG37e2uBFQZSqJVtr+OLaGKrUuCiB1gW6hE/zmSDLPQl+ZPovjwGDYloXMACCBUwhcPKHIzS1a7UVZg7V/lmNq93dk15Uhrfff5KsrYdWOpZxUJoja9D8MomSTObIiu0HVCGmREFFSEJMIK2kT1qrZNUEI927++8+ZoiZk0rZy80r735ne/+4eHh4ZlLZ9MXLlx69skzX/xcRFdNTuoqRO989N1BpzvooWee1diA6er1c5vz/cNyOmMiO+yb7lqmNzTfYG1jQNJktBZmX9XiPaTaFB1Ph7Od+9np7YiFn1XG2qzTRsB8CB4B5YexqVbUFviTb0KPFyhtg8/jlxMwKkgLU/S6E9JAKjITAegY5pPpPgr4cVOenByjlkQbYQaIjCpWNQcrLh3oogmNplg/2l9OTf7VF9+ZvXz7D75rzvc2zz+ZrRS3X/+u6aWffuEr+fJS3URq6kheCpU/sXUOvnpz5+7J/Zurn1678PT5ycNREyGI1kozKBahFmetCEVatAhpRImKFCABLTRCFACliBEB2tiEkJAQAygAhW3ZHwcBAUkSbU2GgiygFCmMSlCAAFgePxeVIC7KqGWBoOMFChhhEUQWIUCFIM1sunH+1C/98s/efeO1OL2pFfdVVyOiyFQ1qHCZPal4RtlOrO+GyRQ4A7MOXENEsJcuPyEgr/7aN7E57m5fnh6Wr7z53oN1++Uf+/TZC2fqg5PR3dnenQdjuJdDCjBlsAxiU9shPSuDANejmkZ1Y+tnX7jGub9x5+T5bn7x2aupMTxuqrKej8fj6SyEZrx/mBgZauolGnRGegHmUokZrq8P1049+OBt/JCTa1ezrDt+tO/7nKYJKRLnZrNxt5tZ3XF1lSZ2dWNpaW35zu2bW+cuaN2x2s6wcZ7r2hmrlSAARVKRmDmSZhZiiIE563XWtrbef/+9STNa6y71su7GymZz8hDLoIYaA99+cDct7Plrl37ml/7c5qkno+0fHO2/8dorb//b3/zlv/nkM5948ea/+Jeh8b3QRIC9yUFykpvMri31IVOY6rRfsONMq43t7dH93Rmk7Iq0iLPKvfXO9z/34Avnnzz/ieeu/eY3fvt471gDG8K19RVr7bwSiZEUsiATCMfWcQ7SWucAkAPxLDSYqa//xZ+7+PTV3d3jWw+OEOnclXNGGxIgIOAwn0+hvXn6mGSqV+TamBihrOvKz+vKgaJMo7XGaKOtBlyEcEMIIQRlNSrSghHboxRjICBEghBiiCF6RmYEhaRi4DJ4EXEhBg4xOo4t/xY0UpqmeWpqJ652ItIKAFGEQCvdbl0JARWR1sQgiFoDCUob/ZIoWqTtQyReUEEXuzlgWcisbaMQCMcfenMeA94AMbYIOGxlogXQjR5fCxZHeqv8L6yirXrdMj5l4dBuFUskwtavwQhEqFt0CQIg2jxBQyf7J7OPdqvZXGk7PZoPGVxV8407RpztDfqf/rhKMmbvq1AMekK6rudpt2s0zCbeaqvSLMxrQs3cELEiRAVI6Bvu9HLUFgI//P6Hh+/fThKpxvr+4f2H46N50e3+6a/1v/iJ/Nx2vtTXqS1P5lrpbk9XM+/TBIGbSSnccBRmtpmNUdhJnHtBrXWCgbna8+IENEehTBFhaFw1mofaK4Vx7F3t5/OSfMDAEQGTJEkMR66rGkRaM0trpm1zALDglwOwiBIQFGntoC1AvEUyBR8w79nlzaUHYCqENLcSYxMjUpxOxqUrS+OVBI0Qyxpck3W7CFg3VSjnXM64AFSJbC2ffeFimpjjvePB2YH+8pN2fcVVtTqcRYIz154fbqyhSHShnJ6wSpNhJ+oqPV2c//InHv7q3vEH+0/+4o9gamcMwTccrbRY9AV+pU19Iem2YELY1YEdCqJuv0KIQKhIRAiYA3NEVCyikGGxVyJELaREG0qsRojCXlqUswAykCJewAdZWjqCQLtdW5jNFhbACMJCJCCErKxMjg5skX/8x5/701/5kX/2bx88auZFobvB9mLIGTVo0kiQSdQ36logpmAsRAPsQfeXzz/7xS9/dG/n5M6tDRCqRvPXf1caOXPl3PlnL+XB3N3ZO7l+O4XxBiQpqBpUAsqC9Hsry2IflTMH0oMUkOeuuX73ztLape3u0KpeU7qJO9IBDRpdmJXuOqAgewmuPj40YJLhmjJWKYkuRgLbSc498bxv3O7Nd+MP3HB7w9jUffCBV+KDK2t39sLZ4eACQUB2WlO3390+8+QH129Wk0madbU2pKgO3vnAMQoHZEYFKG2niiBy4KZxQsZsXjo1OxkdP9xzOusPl/20s3unqwlsgrcPj0qlPvvCtc998YVufzipKufQgdp6/tN3P/r/Hdy6uX3lUr+/2hwcnwLTgLgTHg+azOj5bMzDwWBtJcmzKs6jcyvbg+FSMX70QBUZQZVBmB0+fP2Vly9cu/zJz37697/37dnJSRTH3HT7WbfXHY/2HEAmi3LH9j0i3JJBhVECxJmf+4T+1C/+hZc++/mDw/nDnUdV3VhrCDUICfjaO1dNTGq7eVebFAhCjLN5ZZJAyrQvNAdQaN3p5GmSKFIg0Dg/b5oQg9ZaKZKyCcwATEpprRExBGEWRUpIijTLMg3AvnGzedXO7tFz09RlNfdNcE0jKEWeFUWGLNEFIai9Cxw0KUAiRGXRagVIUVgRaNXimwkWuwVAESQNJFpQ2hhxq+hzWzfcSlELpzYQaYAWb8csQtIWUBLpFqm6iFSTImrTR7hwqiDg4wWeLHSL1oEV2/L5hfm73fe1OQNFIgAKkRTFEBb+FgCldPTh7d9+//YPrnefPLOZ5qpX1KUf3b+Pu8dN4tVw0H/qik5iaBggdFd6zaxRRFmWSPTRK5skuc6cO+HgVaaRFEtQKvFNlXWLvNuxRk1u7b37699KpnB+beO7998+Cgef/tv/h3zzfLe3TEojoas9VGEymWiiPM+SNA/ahRDFRSE2Ruuio4iqgyM3KaPzKqWIwGFEtQEDur10EcbQuNJxiArETyqePXLHo+GTF0CQnecQs6KDGmOMPnhCEgZmRlxwShaVRW1sdtF9u0AaElGr/reUKGBRuV29vJVur8Sy7gxXm3lVTsccGIhkOkMlvm7IBT0vbZpkwj5UzvmKGQ2bohfFb3/8yvL5bWGPsdm6vFlud+ZVeLhzt8ibJBtmS6uoU9JK6mp+8FDNQ/bEs4VND8KUzpw+WeqX9+9vvvrHna3znBhpYtvtJxJRQCloK2tJK9IKEbgux/s78+mIQ1Ba66xI88LmhUoytAYRUAtFWKQgCH7YHQNCEZCVUokliZGjEMGCMMogLDEyRODHMVZY9H+03HJmAREPgQTIGES0goRkU5mPx2mW/oW//Z/fuD9+7dV/9aCs1y0uhTgArcF4hx65kaCAOtCtgAwwQlzJll/8S39x9cWP7/3G7zwJ5sqVp1Z+/EeStf4zD2cPLPYoB46Hu7uH5WGE6IAKgvbdasFSzCrKxzbfpyl7Wo6QgtM2O/PCk5dW+t1+6sfjanbcyXt1rBEhG6RplhEBueBDU06OIDFpv4uSmET5GBXa7ubKZXpRGX105+7tN9/xwTfzSe1r28vXLj5V9JYjaYwQQx19UxTdc5euyh99+9H+YW9tO0lsi+2OgUNgo6UJHggZARRGlrYvK0RvFBZZ9sSzT1+vq9iMlVnq9ZYxLxxXrMJwqfOJq8+cf+JCjHKyP1JZCFTrNLvysedu/+Dpe29979K15y5cffLg4X0dsIl1VQfQKZNyZdPt9HvDVU3aKFU7N8jyzdMrN99/a9QIMBdJ92h88sZrr3/p4Z++8vwTa5tro4f75aRcWlq1me4NO3wnhuDBsBKURdpEWlc6CEeEMsTGwpf/wp/97E98dTxvbt8/8JGVUiwyn81JsPZVWdcSXJKks1lQSiulyGiFoEgDYONi2bisMEkvE8TaeeEmxtjm9LM0E4Gmdk3dNL6ByDoxibWkFAiAoCgmpEoqT0oIhWPjo2u80ZqIIgNHDFECc7slBoTA0TccmbWhTtYxWpFqC7UBACIzYZtsE6VaqUY4RG519RgRQdeNEyGQIBwXO7fFnuxPgBAt5mdhtV40xLc9ynpxlV6c4QgIjyseW1iUtCPWQrzGBYdPICK2jOf2yKIWaNVuGdojTQSNVowgUfI8UVrFo3r/tbf2D3YPVxL1KN86fbXTQUf4znu3qJdml0731zZRo5u4VvVydcOeQaIP4WRarW1k0dccXAQGhjTJGhcJBa1O8xwROTTvfuf1B7d2zxKCm/Z0uv7S1c1LH8+WBo00vkalMITZ8cGhd259ZWCSVEUJaZMqYBEJnCRWLMSGm7JJBp2km5TNvHEBgIMPRZITqYgUhGNk1KQzXU3YzWfNvE4Su7S+2kxm9bxyimzRIcKmCr7xEFtlvw1LK27n5lbAEuTFZxVggbGFx1QPFoHoXNB6eH5l88rZw7c+QMFu0qOqcdVcTBSBKMCYsALTyROtONRh3kRC7OYqHwBUWa+TP7tZrHUdc11WwCqx2Xvvv3n7+6898+KT60+sFiZn0gKqqeKd77+5/9prz/6Znzv10mcjCS91h1/53M1/9K9WP3jvVNHRGz2lhQHEt041RBRoKTVWIwI39WRv52T3IUA0NuUYnZ/4+dTYLO0Nk8FQJQYVYttJ0ibEmUWZdqaJLEJKJWkbEGvf3wItz59ZIiAsivek5aKoICwMkVvcoSzKKHxQCAo5QUhsGkNdTidm2P/r//1//Q//XvnaK9940MyOgVKAAUgCZEQ0UA4JAJRQMWgHnaXty5vPv/jg7s5s58bKUrF57Urx5V/ZXHLxg9en73/I9dxgTgxZkBK0N2lli2pedTBnbRNIg1b9fJh6p6LTMa6kK8999qXN5y6nncw18/LweFrHSXPMXorlJWNJa7QmIQsyF9c4GB0Tou2AqMRqCiGgqM7q0oVnPtlf2q5Ho3o6VxY6vSJfHiyvrmf9jgu+ns7JUmbzLC2WN1ZPn71w5/57V556RjxrZTQ6Dg1i1iKDRSRKJFGLRkUEQlRAwtxd6l/91Mfuvv6m5xPQNlvqFP3ls+dOZUVPZem0nEYcTKsm1daQ4hhS0/vsj3/9u//iH01HD85deer+nQfV7sNqdMwy7Sz1K+edr6y1nW7XJiY0VpGJKP315d7G6qOH+0tFz0kTjdk/2H3jtVe/+KVPXX3mYy/vfvPWgwdnnr6UZkV/MCBtWDxBA6IRUIECIUXkRRClbJpShc/+xNe+8vWf8ML7JydZTudWNonIV843bl7OTaBOlgtk0Jr3iBUp1QQkhFC72geOea8o0kSEZ2XZNLVvHAnZxGRpGl0IMXAUYCYBZbQ11ixc/O0uAJvGVfP5rKpdiAJgrCYkTWSMTazJs5Q5KiKjQSlCQGF2kQXAGGWsac332PaHRUZFLCIIra2ndUQTkSIkbPOVoqvKISBCFIkiqBSRUiIS4sIg20ow0JqGFncWIERSCh63iC1iSO378fHiWHiRvnwMWF1obYKsaIGdhscm0IWfaGEPb+MAHGHh3FaamKWZhvm9+64++M6Dca++82ObvQF+/OFbbylXy3K+/NyVvJ9EwMl4WvQyRB2bGgmRMMYIiKBNjK6Zzwg5ybog2EoL1hhUWmnjTiav/967s4ODlaEpp3vZZtG7eCYrMhAIVZxM5xS5KWdBwtLyIOl2SRlG9gJWG1K6mtfZoMsB6tnUVaW2CgxCrYkxAtg817aLoYrBmSwzqQnOcVtOzdE1PDjdE7LaEGtRpLJeFxGd9xwiITK0Eh0QMMGiWrOVf3DBMZHF7h4QHgN1RASEffB2rbP61OnR3dtB6gzy3GQcGuEYCRCtIUwyoynWZeVmJUXupRmlS1JOjqrZ9ksXl55/WndsdTTHqtm/fX8+nmpWq2mPggq1BK9UJA2mnPi733374AfvZhtPrHzsE5kZjOYn69tX3dlXq5u7fPrEdF10AVvQiIBRFgEZCDQxArmm2ns0P3yUZrboD8hmwuCaygfHGJ2bU6lRdchapYygEEZgEEXMTKg1KREAUmRSRYgSWhkTQJA5SuQF7AuFsJVlAQECQ4ghBojSJiERAIhDqCsHIWBBoPOuSKzdJF3v/NLf+7uDf/zs69/+g6OdjxqejoFTiAVQBlQDzGFag/TsMFk9ffVnvrB2YeP93/0wLat6Pr7+wbtXPvrGwccvc2643yureWHVsN/Nep162lXDtVFTN9YOdJok2RSFxG+bDqOzqkzODM++9OnTH38qLUzdNK6qT6px5ecIeri80l/pm8QSYPCOjBKlvAsymyla1LaithrIB0aG7tpAZ0n0ZywBAiuDxmoBrsuqCXXwzqjEhYaBu8P0/IWL1996ZV7N0zTVSKSVd01oqtRmAIJaUYDoxWaaEIBQK9KIQiIxJkWvt376xgcfMcOlZy9cfOZJbWhvbzQu66Ojk+76is3BOzCZIoBQz4frK8XF7e989w9f+vzXP/apFz96931/5xZzRlniI0wdseisP0wTrZikjIql2x8s9zb2Hhx0k7SpISWuy/qN17773LNXXvrCiy9/45t7t+8X3aKZSJoMSGvHMVLUQEpU25fAEkXzrKnn2Hz661/6sZ/9szZJxlPX7fW3u3miEJA4S+dlbbT2ecEg2qAmo7RuOwNCkMb7WPvGOaPJJlZrI5HrqmYfUp0YbbXRKMIcrdIqNVoRAChNSmmlSCkNiBy5ql1ZNbVzLgZpTWksJjNJqmyikVpjaJIoQsHIHFlQ2CxG5wV0BxeFMwtRhdoKBFQiEQQU0Q+N463xRrfNtgCiUKNGY3WrjCoiQmzL9QhV6wzFBakfaWHTe3xWiwhwK6dG5lbv50XU8z/ZKyMCyeLAp8f/EVl4gAQgtttlbMGQEjgSUZRIghgheukps9rN7lRHB7envV//t5uffKLcqdL1bglx6/lrmOjgYlIkZNXJ7tS7qKwFEA5eKU2ECgIGn2jVyVMA9p6NzQBR6QSCnNzYO/rGDzquZLJcNf2zm2euPp32eiFynHlfzTSi0aro943NJMICdKOtsdZa5b1XNkHFOEfvYtM0mlKtNTeQJKbfXxeOdTVnBpOlSmkm34ZgEaW7UhSdAhpuHDtRgSDtFsLc1D6ytEjC9gYFEFvNGpUWDrCwSUZEINVC9qANxgIDRhFmXzc2S1efPj3+8Hz9wZ5YSTBhzAF9UIxMShlLDFxLNFlSpFmaVjUJ70zH0kv7LzzRXVtpV9EuSDMrxx/dPf+TL21e3IjHRxIa31Q6UyK+HO3R9Z1ycrT35rvHt2/1LpzPVlePJo+6V7ZGf3yz2d/Xq3N7jkATR9HGkGqT4VoZRQD1fD4bHaHWttPDpABl2DuVJDpL2keZn08VoQSkLAVBVCQoyEIKYQHHBiSyJtFomGNbibLwEbQxd2CSFk2EAIvngsSIIXIEJYLERAARmDE4JoGKIwXoKRs1SDw2650/9dd/9sJLz19/9/X7771zsntf5iez6GpWJxwTyYuljfWzT5x5/sXP/NyPjA6bD19+I7t7X3fw/jsfPfoH/9sTP/uTG9unsNczNk27RX95wL1heHRwqv9EWe94N+kp09VpGZvpPDgN3SRV/d7qs88Pn7gcEzOrm7oqp+OpDzBcXik63byfKzIE4H0AiSA6MKFSEEI9HaM2NrU6M9Gz0gSISlNn0I3OCYPEiBQDAoi46H3jW75b45z30aT05NWn3vzjjaO9R5tbp/I0L6s6IETvMSRG68az0cbPA5BQhlmRaasUksRYOUGipe1Tq5OTg/3DJ1+41h8ORvNZ5Z1W2F9eqqdlWUxFK5Mkic1JWCf07Ge+/Mp/+PXxycML1y6hDklf1dPDBhDJxobL8cSXM2rU5GD04Rs39vfuEblGmnwpFaO6Sb88ChrkaPfhnbt3T5/aXk3745OTwKHo5LkpKBgXK02ADKqFWCAJhWk1Gzv3uZ/66k//0s8nRdfVTS9PE2O0wdaOz6kxFosiRaIYowshBI9MRKAIKSGGpE2AAgAqEiLnvVFolLbKaGVDjIo4zVJjtNaa1J94aSLHGKX2TVU186quaw+Ri7STGK2sJgIkMIpSS0pRZA6BWSIARRAfo7HK6taRwLFtbP/hoQytjxOQiSVGYSJaYMEXnV8IAjpJjESRiKSJtDJKgbRQH63ocR+xQEsDahe6rYIW5U9knYVNu62IWbS7LtonWkIh0WMvKADAogCQFxbQtiUZGKDdNi/MoQhEBIQGtLaaJTrw6x+7/JQc3//+t6Dn37t1s/e//upXv/Kl649ew+Xt4flLoKmZNDaz0XOo5zHGJNUcQ12WWZ4AU6hcdf8kDlKbHBtTJJ0+kjTOpRHLw/n3/vFvGT8+u95HXWuth+fODpfXhRRH1ERGkSZlqC0w4BhQxCub2CIAkUlsZ0AheJ0qsibJM18LkFhjkaLJDCZQjavWw09aAS9INRhjlidrl04lRbd6ND/Z2Qnecd7VSdouAH6YXAGIwsAL5ie2lTwcJYbIwSulYmClDRlCBJEYOWBrDxIgwuVLm9Wnnrk78sf7R8vYVaIVkTZgUBMARO9rVkipSjnEyvnxaLeyvPHJy8tPnQ/M053RdFTVTTRJd77r8jzJz1x89IPX3XxWjk6Somhm9cFbN3C034A/3L29f+vD4fmtTprJ1tnXXvtf0dHExZUMk9w03iHp9mWgiGyiAZUrp+XJCSOnWWazlBT6qiRE1MASidFHjhyr2cwyWE2oNcuiMqCFifAC/qO0TlFZjiKM1Gr8DEhEQgIQmUMIiyEjOHCRfZQYomfmYFIWpQKARBHRPijQCKU/iiOwWW+pT8r3e/ozX3vq41+53My+PptOZ+Wkck0IIHMXQjl6eHzm/KW1zc3R/PiVX//G0Ru3oNxfSfXpovjgxo1733h5+LN/qtfpzybVsL+9sr62srr18IObJ9WD1aA7ZCMqH5ua/aPpYTcfBgPJcCC9fDSZj6YnNhWjtU3MyuppmyeGNBLGCD46QZFI4EAYlNLIEHxdzceYZV1l8iSL8MMbEKNWEiKLRGYCiBJnvvF1ZRMDQklSxOB0NGubK6fPX7l97/rG6dMWMmOSqqqjizEETEgktnqDiAo+NJUPgcFzjIFQRZSTyR7qZvvqFmpiJwS60+uDjyR1PR81VZbY3JVlanNABOHl5aWtM2cf3r5+9vJT25dOiYE7N72O0cnMEE73Rx++9g5GenRn7/13ro8e3T//1OmVjX5iM21SZkHSFu3uzvEP3nh/bev0+rlT73/43nwyGxZrvUE/ybP6+CCw02SEQQOwxrJu9qv5Z77+tT/3l3+x00ubhrXSlQvT6URCsJliCYpsapUyNtEaxIS6KstpNSm9dwhEymRpUnTz1CpNmjQhgtFIqWnL0oMHALGpVQaFIHDkID4EH1z0LrRcHmZgyNOs3+sYbZCUQkCQKMAcQ/RV0yBilLZNi4g0IKapIdXmgCEKwcIxJwslhnnhMQIkFCRqo5QhRuc9IkRAEtSJMawFRbdyDi3sPASwCP0ygF6gG6S1UQgsAgKPh/zH4WoAbCspaFFFh0h/0vbdShbtakFEpDVoyIICtzjjBBG5bUwmaG8vC6QvQwk+LhXd09v59+HGzK317dHNIz59a165s597PustxYjMbFI7PTx0lbP91KZJFKldXF4eatFN0A/evENhalZ6p5/7eO+JJSCOEiXGwxv3737v3W6RXP25r13/1X/launnCVchhhgCM6AxukhSnVh2GH1dV6VNslDXIQS0hhUR2Fg1EcB0Mg6evPiGjcnQBAGejkcSxBgtlQdAjtLGuzlIsbSkso4E8OVMlJRlvXzqrFaqqZ33rs3Vtbcnbr20CAAYQ2Tg6INvPKJAiKFxMYIyJs2stu3vE1JkUDMAWbP9qSe8hzu//Z2de4c5qIwwVIFsEjyzKzE6iHHmfcPOS8D1teVPPH3qy893t9dm87JxTCjiuCiWMZrZg5P+k4nO8+m925jm4rbrvdnDb796fT46BJ+UPq/YPSzHRw933rm+d/Pg/BPD7NQpMxyIEHg2eVtWRDoxSMg+hNk0ukqnSdbroNbehyDezeuWJCSeXfAqsdoYacsEstxkOWL7y5EEFbUjGBqrFRFHh7iQQQGJI8QQAR2CKBEGQFKd1ATCgN47NhoRlLQBGgRkhUIsVptMEAzh4aMH5fy40+mk3RVKQ5F2O/3Bml6JKE6AGQxB5CAq0KQ6vvfw8GieDZXKuJrrSYzLpL74/NMH0il3Dlav9l2U8WyqEttZytPUjUYPFeWlUiopxKip99Osexx86pp0YKow2bu1pxUOV/q9/rBY6pBNiYyoNlQfFWmlxDNwREWkECIGAPDOlZMqLXLIOhIdC5BQjCzCMYQoEmNgBt/4ZuZ847Q1SiuFIsGF2uZZfvmpz7317k3vOMkoUWRQCQcPjKKVohgwSVSepePZyf2HD3bu39dBJXlx6akLw+X1peXTJwcH4Dkrej4CNyE1GUdPCTo3n06rLPeqriV6rSxHb01y7uq1H/z+b03rcTrorOPppq7Gx/vV0cHy0vK9O7eOpwdl2WCIqqLh0sr25um00LPUGW1c02gBYjg6nl6/9dGnx589ff7iOx+9Ph1NV5dO552+UWkpEkgpxkiIIE0zezgff+JHv/xX/89/zVqsZ9VsXu3tj+8/OvE1TkcjRhkMiyRRw+Eg72S9XifPUkQhlYhyTe3m83kMnCizsjbM8kCIIOi9BwkaW3+zAtTKgFRzQeHH26aWbxhD8N4jKkXKmoRYFBEqoXY8ZIksABAjhhCaEEKICshanSZaGQKAEGIERsEo3I7/SG0Ml5hj29lljNatteZx2w22ZnFUSWK1IiSghWVHWk8FLA4dkMUjAR63ti/IDo+RGkQL6R9AIaBWgD/M7jxW/qU98qGVpaOwPDactoBQag/7NpNBbRKs/U+0mMj2NwEi1q6cTx/e2L937ZPPHr/+9mqd/cIv/fT01Td1WixfewYMehdsqoWjn88QQSmFgKhRItvcTvdn1uitj1957zd/M9y661x4+uyKTbo2SaPE0d17eVVvnN1KmrP1nGotpQtN7bs+xthgDNC4CChNaDxg7WwvT9Mk+uAAuAk+YRAKTUAWVWjd6UDlwtyFGNCYqvJ1M+v0+xojs0i7XwmxpW/rTiJ1c/BoHg/HTd2Q0YP1NQFhCTH41ncF7S6FoE1dIAp7F3xTz5rIoo1CgRBjFFBBIWbapkpriYKoBMkqigxqOTv3lWekZ+/+7psn733kagdVDGHEQpEdAkOm7FKm11e7p5cHT5xe//iTK6e3K3ReYpqms+B95J62Js32b9zffPEkQxk9Gku/nk/m9x48eOfOvSZTm51Tly6cj/cfvv/+P5mejOYnx5/86WsrF86aXhZSLTFQkWijkIAUQdtmpCL7WhnIOkOru7WbRRdc2UQOBCAuullNhSFACSHUTmgOiNoaIcOPr7qtoZsR0SRi0xjaCap94SMicYwagtZiNLkoddU0HIzSCQmDD0FQa9Qk3sXAWiGQWIPCgYOPQkObv/nHr8TJQX7+2Y1T22neV/1OVhQq1SQUQvBkADFENkYtnb/cZ9PduFRT8uZv/c7DWw+mJ/unh9t/9hf/xnsPvlWejKQYlE1pM9tZXVWkGzefsK/y3vLm6umnzvL1e2vTuLt/CwtTT6rjyUMvvLa1yg59E7yP1rl58IlG0kaElSVCJSpGQq21iBAmiOxDiPWJn1hPGSiI7BmJpS0TWrSgoETwDZfzEAOR0oYQxNUVs+r1+2e3N3qD7t6jh72LV5JUEzEE5CBkBUlDFGOSgDKZjT/49vfztf6FK1d6y4OsV4TYIKrl5ZXbN26ePwdKG4ykUYFCT4F02tRQzl1CST0v+8PUkM3zHLfWh9trN298eOWZZ5bWhmF6KsTGc+j1tkY33rtz+/2Ntf6pM6c2Tq33hme2trdDHY/sCYBoxaR4qE1Vqvro6Oho1N/aMDYZHxzaZztbG+vLy6sHDz6InsCixBh8OKznVz/18f/Tf/PXdIa7OyePdh/d29vfuXcwnQeO1po0steJGiyvR4R53TgOWWWLNCmyfLCecoyt8CJBQmQgVTs/HZdlNfdNGaMnpdIs63b7SkHtHHMko6y2WqssybUmjoFjFIJEG5skhrTnWNY+xhg5AgApSoxFRSTaChhSGois1koBQ4SWEY0IxCLcKgqgtGolexUosvBj56A0Pjbet+GbLE+NTUhQcxQkQrWIdwkviOzt0f8no70At7EdkPZoBvoTiI8IL+Z8XMDbBIQjB2aOITLjD4luLSIUBNtl9J+UiD3OAuPCuC3MjCJaWWO01hJF6nDw4YEdj7/4l/988sSFve991DyY7Y+q/OyT3e0zDEBKiHD08GA+mWqyED2HGFw9nZTVtK5ndXX8KN9evfjVz7/9j/7d7MObo5sfrj73sWzYL6flbP+RSmJ5fHj3D3/zzKXz9+7Uo+t3P/jXv7f8+WfOXrtIEURCM6mPHxwe7Z30t8+eW1rWxiAizZrIEgWaqpbI1pI4BmVMroEJFCmlI2NwTTObqDx1tfOz0pi0nlTlbIZKYlMdHZz4YIvBIM72KC/STh9EnAsxsiJaxOdEAJGp5ad58a6ZzEKQrNNRRL5uUPTSWg8jzyfjoGPRH0RF3jEqCsIgwAB2Kbv85ecG2xt7f7S2870P+GhS+0bpRPVMsTZY2l7J1ld7p5eyzV53tZ8Nc45QlSHt9cBF5iDRm5U831QP37/d/+5r1Y0bD197cx34+F79/u1XN55cOrv59dXBUrK6Nb3xUEzonV6/9NILaW8gVvPKklpdSQc9sCZKVESgUSnNIm4+dfW8TWjGxtd1VY4nxpIxJpTz6XiaZtamKQCGEEITBGtlLMaI2ixaTxbSJiOyMkRWxxbnQEQKBQjJokZXN9V8DvV0PmsaF6BxEiISxehd5TDJVzaGqsiMBiRSmiIyBedDrOrZmaevfv3suW/80383unNQ5DQsNFbN0f692WjmRhNXleCwmU6ZqVjdXDm/2j93IV/pfuxrnwsYPviX/3E+ru7eHr04vPrpq52Xf/3fgOk416jMJL0B26wezxSqtcHW9gsvJMarRMn0ZHNzOyTe1RIUnb10anllRSkGpar53Lkq7eSqyFSIqJFQEyEZ0UBCIKy8IAdDALGq54eHqS1M3kGiGBmEGdobKBMie980jfcBtGhjiFRgbxPFoQrRDVeyi1sXH9x5/ZnLl2shQe0EtJM0sYQBVTTaVk19fHKSnzn3yeefzrsdo5RWqp7PUSVZWjDAwf7h6vJSFCBA1pQmqQt+MptW07KfJqEsXZblRYEg2urt0xfff+fNM+fO5pv9fLVIjhKtQTgkWS87vLG9dvXU2lavs5oY5NiM5xPXVKAEoxPxkNn11aWxVKODnbXeZicpjg4OGanop1qBAhKkEBvHPKv49OWn/+Z/97fY0vuvvf+Dd2/sHR+K1uDh9ObW8tp2z9rOUtYbFnluFCIICceqDrOmaUZjY3We6sQYnSphQe9npavqqmzmjauYRVOSF0mn280y22ZolVF5lhprjVbt4SfCPnjmyAJRIggEiY5DDLGd0zUpASAGrXWi2+IWEeAQmQhZOPjIjMYgEZIyANyCgNoQLqJyIhy5aULb4o6IibXKaGs1KSQBDYQsURwvsIuLDPCC8PBDTkNrqXsMb4jtAkG47VYAeWz1by/ksED8RmZmlsXHK4hIhIuOzXaPDI+5BgvQJQCz8KIfBFEREKrEsiAylntTmTf5sF9s2Gtf+/you9Y4j3men9qiNAOFiBBinB6f+KZGi8EZX09dqHqDji1Muhs//Mb17S9n/fPPbD5zZ3zjg5u/873Dg/rKl18c7c/xzn4eZbQ7P/MXn1i+dPrm/2Pf7Y7Cd9/SReH6/fT0am95/eij67vf/R40LPmSxIDKxgCYKC0SXAjeK0PQhuEIFQinhplRkSIDLpRNSQCurqtZZfsyn82no0mnSOeTqSkfZf2L9fF+eXK4/MzzWunAkYMHYURqsxqKKAi01WDBVdLUkb02idZagjgv3ZVuf9APjZ+fHI/2Dj1D2usCKueCIVqUrCGYjLY+sb3+xNKFH3/+5PYhZUYb0olNB6lNEsoSnRBrBIlNiK5hnSaAHNkD0tHB8dmt5SevPbH3yjtv/v1/fLj77qOD0H3v4NN/7W9fOHtxbmHrzHbW72NvUGxt5cO+7Qys7SitKbWSqAAQGvYuoEI0ShHF6H3l6tk01rXObGiCC9PY+Kzb0QihbCYnM4aAkFWjqXNeZ0nSAWkESquTMrMGwbTYQUQgIETUWlOiWZxARGUQkVAptBRHBzsHhw/vNkdj7hqTJJoj1246mTWuCa4OQtOj5bVzp/VwiEojGYws2pIiqWNZj7J+71N//mdC0eskEEb7N/7Nv//+73xTHR7Opo2DqJFD3WgR1RksXVhKz55e/cJnV85du/b5jxWWd3/tN6c7++/+7v/0mb/ypaWNtaOpIwSGqFLoDLrjg71e3lvfOLc8HDYnD0zjrJdkbS05tTatZyvddG17DaMABIni69hC4GNkJLGoCNAoBYK+8e3mzhiLiCwYKh/EzefHKYm2CbR0CABhIaQYo/MuNh44JjpJFCGKcAACkRhDk3bthScv3vng1eC9zRICHSkGHyiCR7DaJFqV8zoF9bGrT/T6AwHV5oBQgYDPijRJk/3j497KEIka9hqV0tS1SdRlXc0ndWGyWM5nWZGxRGPMyvq2evP1g/v3u4PlopN1Bh1j0moy39w460bTlaXTS4N11Ho6q9zJeDQaRYjOI3unFAiZtGvGVT0+Pj6zvZUldjqZVfN6eXMwXC6UVp49NE3JONw6+1/83b+mtfqDb37/+6+841i2ts+srCyvbyytrnSyxGoko01krkovDAiIzGXt501ZVpUPDYaoyVirRSkW8T6U5VyxLTqdfq+bZSZJtdaKCFmkW6BJDCC2CojzrgUeAKJ3wccA0A52bW0RaVKtOwhEUJFSSiEpQDG4mNKZIzMDI2mtlLUKQIIoiSHE2IS6ZWfFdjgSAebMamuTxBhBAuTWWahDiIDqPwE+42KR2wZkcAEAauEM0OYoF8skEJbWjxj/k2xvbPcC3BKEVGJVayVqNSMBxoVXsd0GtyWm8hgJh9i+fNp8IWHL9UQAiL6pZr2VfMDiS8g6Jh/Y3euPKMbO5a3OsBsVEFE5nTbzucktCgFR0cs7WIzHE/F1tp6deunq69+7ffUnBk/8xZ+D3d13/sk/e/TyG8Pz5978n//D8MZHcjDauHaq97WfufMH//qwmXYnI7pNo2/AB3duD156bunFT568d3/69kdr5y8nTRPKyicKFaWdzDcNNFFpVKkyJlGaYoxBJHAAAWQxiU47dnY892WpjD7ZO+yuDCliPauHq+tNNVV6zXipZjWQ6a4uIULwsazmEiKYx5BsWTSrQXChnHGIjMoYy8zORdPN024OCDoxSdGZzMr64CStYpYXaWKFWaWWSGmk4Bmi6E669uyZzWtnhRGY206H4KK0ES1sI7GQ5lZIoHGkgSWsDvL58eHm01c7F9/YefNORr09uD/MdE/Pl5650Pniiy44YGGrTWbSTkraGqtBG0AJwNwwIqfKkmovlSAxcojNdIYKTZoSpdG73GYco5uV5clUG9MZ9mLNdTlDTUoFEmaJsZm7ypg8SbRCVCgMzLJoxyRtjOc5i+h2dPNNdPPp+GS8NylPqL96bri9hIayRBmlU5C6icfHx3t3PjrYP3Y+9rdcvrzeH3a0tdpmGLGajvbv3p/3u1myTBrnoCPDe7/+rQ/e+6Nl0BVQAkoBZEAW0FaH9cF49MaDR+8ern/6kVpZHiwtvfCLP3n313/j4Xe//fb5rk9ywhiCz01/aW1t4/zF2e39pV7PLHUpy5r7ZTOZVVWFxA2RKnTesU1dcZQ00VmaF/3UZBbb1CcQ4eL0Z89Ka2oryoE0SKPZ5mkM3k8r0glZ22IxogiIRkThyCHG0GhFRZpaa4EJlFJIguCdh9ScvriZ9fr3Hu5snNoihc5HIWliSSYhIteUk9F+dJU2iMQcMLY1FYyIgYzudvK7j3bX19azPI2lD77MTa4SlaWmnrlpOU07qYWkrEptLaLk3d5gffvezRunr1xNO0VvsNwplg8m06Wl9fLUOO8u9wbL0/mUyUbkyEGYq3nFcV50i4jaaB3qODo48IDdwZJr6sDQ6/TWN7YD4fHseICqGD7xV//bv2XW+998+Xs3Hh6ce/LCqdObPTNUlOiUNPqyKoERAOez8mQ8bUIDAaIXhoCIkdn7MJ/XofLGQFrYLMs73c5Sb5CYTm/Q7XU7pDmylxAjBxYymkCAmZ33LriqbmKIqrVtAyCSJoVASIhEJrGJtkYrJNFKAUK7rl9sVxVKEAYhjQYtEKGmlgHd+BCCj8E755kjKaVA69RaaxVRohViW8AkHEP0LEG0IFEL35U2UwntI4tj25osC3WpZZEJt81ksKDSt6sFaAkzwkxIREq3jTIESmuUdj6L0B4qCxuQ8IIrBBxbo6gopRQqBia1AI62QQJAEI4KBDTkp3pnzz+5d38yfXDr6fVOr0gnEXpPnAergbmq5id7+74phW2MfrC2ZLOsnp6A9woY+0nxxNLmbMxUZxc2Zr7A4Ko7k3RjuejDya3DuAXnn7pq3e71//i786NpAXE4DdWbt/Tt/a4Z0PL545df65I+u7Juuh2pqlgklCfApHTC7BIimyTKGInROxeBQVGoPAIkqdLWEqpyNi26PTetypOJNJUl0hYFM9Qa6hiq2iRFlg8YwDWNr12L1uAYuG2tC5FjiE2NISBAmqTG2MiCmc56Ha2JkJFUp1+MTrL5vBYuo4/U72R5L80MIkUBiKFuogUbA2NLMmSOMeqFy9tbTUrpGAMix+gBiCTaIulLd4/k6HCve2370l/63PX7r2ytdM/P+dKV9Y3TZnVjyxZ9zK3KCaxFmwBBFODQeCcheAqSaEbdZhNDAAYSEXGNi8ElqWJEz14RY/S+LpuyBMWdbpeJ5s00EohvlLYxeqU0B9dUpW4yZSwq1K21jhEIlFKg06rx3Lo9CJgkSpl17PmPX60nTiQkvY7KqJxORMQi5pSGlQ4EVHZvvP+wHp1knZ5CVMq2EUhrzXQ2C4En9V4yGOab64Iez5/J31tKgIeUWF0gx0k4YQgROIHoGy7f3w1yM9t4MFX2ZLs49SOffPjy937w67+38vTVzsqqHVCa5UW31986NVhdGTfNEppyziejWZw7rJpqugcTn3e6oRaNnBWdTq/T7XSSPAEElsgihEJI0UsTnFKGCFhYnBeQ4EJkQWVUCLGaxBQ56yYmYecFQStCIGCILohjY9Is7yhlIosW8D7kaScGrxn6g2Jlfe3Bzv21U2smUXUTGGz0XmUFkw6u2X/wMMnzxCaI2ocKmKMSYkFCQC7yQsndcn7UybYSpWvvfGwSq9PcWK+aWVMWVbdXOO+rujZGi4bB1pkb7755PDo4tbSSFVnSH8aHUyI+vb2V5UlSZGU509rGsqmqed2UfjbP+1m3NwCdxRCEcTqt8jTrrvSP9w/rqhwU3Y3NrYrh5PiwOH/5v/o7f2Pr0tk33n6nBH3l6SudIteiCSXRQCQhxLpqWAQDTCfzpmlMZkxqrU2sVpFjQCYGF0AJFJnWibFWpUlirbHWaKOBQ5T2lA1BOMTgPbblu94HHwJEIFRGa0BsqT1GG40QhLVWibVqEd0SYY6tWzQEFyKiKKUZ2Pug2mpj1QJo2DV1WTXe1SJiSZvEJMZkSWqTxBIKIAOHEDm0Fh4mpYhEIylcRLJYACTGttCvbRgQ5tiGLdsRFIAFJDJQ+7RCRQQERmkEiSiIZLRqCT5tf1hbT8ESIZA8Ti0JcYs3ABHEFkMNgMTALTmOBBFAKRJoAUcMCvJOmvaW7Na58c07r33rvbUfebrTz5Mk626dI0KOoJAgOi3sx1NtUs0x+qocHUcv0YNw7G0Vnxg8UR6e6MlYWw+5Wh9jesIXrpy7c+Fd0np64/7k1q82t0Y1xAwyV/pEoTqaT155Z2378rIZrH366XNPPX8Sq0kTtDYC4JuaAQ0paxNjiJlDDKQouqi0DsRN8Ia0TrXOdDWaaAzaSj0+ro+nqWYNYrKlejJGwbmDfGPDmoQ5+sb54CAyLjK9giLgQmxKV87Zl0meJkokOhal0jxJTZooozBEYaZs0HORAUiIGs/KGGGYzOdK6fYr4J2LCn3tkywhApskpJTJwKSJkhibABIlRueDsrY1h3V7ab+TvPPObu+J+alPnP/E168d/871T338+STt7rz74aO33+c8F4PYz4rBarGxZnvD3qBnCmNMknYtsI6E7ADEh5Bo5uBrYSfNRClG1LFm1AQS6+k0OE7yLM2TGJvj0TyGoBUAgJ/V2ujMZh4BXRPK0tlEp0qDam/pCKwVGKPnEychalJRpHFVotxgKYtiyrQ6Odo73j9Uqc3z5Ohw+mA0SsQZMaajTz17enDcGR8cBVcLBmCJDIpEWZBJHULTlPOTvYNBXeW9/PJPfB763R7arQtXivUVqmnvD77xvd/6Pc8nAkgQDE9mt97N0qcwlzt/dP1wyE8+86TbPRp99NH86HjFwOrWKZvnqtuvSNnJUahPJrODeVXOauZGunOIERVSmqb94SDJ8ryTaWtEIMYIzEiilJEoc1cJLhAAIsCRAdi7EJhBFIYgoQ51BtwYWzjnNWlSCTccm9jMmxgkzVKri8UJgBYAmDD6oKIzido+e+HGvQ/L2bToFOOjiQCHUPbT7RA4cjWaVGdXV0kr730IDliINIuQUAwqyQdp2h/tHa30NxQpFELQMZKxeSdXzWhSTubVUjft9pq6QkhAmcFqL0n1zo2bZy5eTbMiLwqkGKrRqfObJlXG6qiEFNZNYBeV6O7prV4vU0oTpBVUAbCcT0KojbHG6mpeLw9XuquDRpCSlb/+t/4vT3zx+ds3rmOWne2fKdJMk9ZGJ5nJEAXQO+fy3NXeNbUwLC11005ujNVKIxAwEymtgRQhiFKIWredFO0aFgiBQFiYQRsi0BhC8EGEW2eQThJFpNtedU2LSJSAUQQEqACYY/CuiSEyx+C8b49gjgujvSDEyNQysNrLg3AIPtTeaJuYJE2tTWxLPRSOjCgIoXU7R2YGAEaS6KMGaG1JDC3ph1lEQowCbTXLwtZPC5oQERJpAWxpEAv8Q2zzzVqoHeraBwhjGzuAVhBqOUKt9hOAlbQfObVVJyIkrasURVi11iRAJIwhRuZEm2LQt5Tv39udz9QX/+xLNB25WHavPJMOB4IYxU+ORvVknvaKZjoWpZqyqk9ODg+neb/b1E4pwybB3FSjO/XuyOWds888u3Pw9rv/n/+9r+cXvvzJW3/47uEr75yM9yRJX/jUx8PNW/3BslX25vXbpyeNxo18/YWNi4PZ/t7xoyNavsaE7d7c1XUEQhKtsghCirQG5ggsNjH1zHOMSiubmKgxVGWS5vO9/fLkqLc6jKFUSe7dXDFHbDprK6TQ++BdE73TyoQYZGG3BuTAznHjSGtLSjiioFJGp9ZoZQi58bPRHGyytLzS7/Umx/PGR1CqqpvR8cmsbDrdLO8WGpEAFCmTJyrRFGM1nodamH027JBWNMzIezcea0ZtdGKtsVo4rmwts9DOvZ217e4LX/jq2/f8wxv3bnz06O73XnFhNptxIGcHaZaZwaDf6XcHq6v9reXBxtZwuTdcXk+XT/c6g25mIcl94wijeA+hUgpiBFFCrgnzCZDJ+1ZpHYNzQVA4NxhYWMgUWiEF70VAEoqucXXFQDpXilSLBVUKjMlCgOADs0gIfj7W1WjCjZhcTCLB7Vx/e3RwkHSWVs9cSGwx3hmX06Ph6cG5808iq/lJFevIkUgTMmttSBsMAcBVs4mbO3/ddYp89ezpz/9nP0XFAKCLxDrAypOnDg7C+6/8joFowaSgxjVPHuxtfu7nMI4//M7360lz9qltGZ8cHx2qTjI6fabfXVo/vZJv9GcPb48fPOxcOj2f1L72CWpNZEEpo/u9Tp7nymgi8NGLkxCjAdGJYeEQQJCUJkSACBwjIfoo3C7+nJcYILKWABhQMRhl0DAjx8bVpasrRCCDAjEykUIAbPunRWH0IcuyrdOnPJjDw9GZs0MlCKwAbGoyJ2Fa+gwTrVKFRG3VDmFk0UTBi0alTafbXd/fuV01syRNqLasFGqNLHkCrkhm9Wx2Mu4O+sZm3jUmhW4/27qw/ejedT87SbPBYNDl+Wjs8VJ6Ku8NQQtoCso714R5lfayXi9TCfi6Aa1Aqxj48PCIY0ACL7Esp6Rpab33yRc//uM/9bUXf+yLD48PY2q3BlmepAYNAAhFZAFmFGSllRaTk81Nb9BtA7Eco9IEkRHRKDJGA7SueHy8KEWOEqNrw3RApEBFRI0kBkiR0Vppja2ezgIRjdXUmnbbLItCCRxjjDHUTairpm6c9957rxShImssIbngWaR10LUATgEGFmuTbt4xxhpCQAIURIwQXAiNRxTFwp4D+xiEAUArFGHtgqcIIYbIUSKHEJgjiygkQmWStqtgUQxJC1fmIs3bMrUEQSsAUTGEEFl8DMyygBQACCilW/SDIoCFjXRRy0dECxpcW/y3yJYhi3AQiVEeJ7BIKdSAFsNsunX6XAzHUh5HypeevqyM5eiq6dyNZ8PNJfCh3nWdjVMc543n5fX1NM+yNBs/Gt96c7cu/fjmB+sYNq5cvPftN+v9+cEbH+lPPpsNrD86EH8Soe73V2e7Jx2bnvvkZ1779u8fgFsxcOv937j74f70vp6998HInLr6ics9ZInMjZcogX09bwg0WoWKFCmboq+8BLaZkhBAQGutrZofH9tOmI+Pjh/udpf7UoUqzjS4ajZRqS6GQwGJIfq6QQSOocVYtuRCjsE3VV2HrJcGQRBQmCBpY4xGmh6PDx8eO4bB6pIxNukWQ9RHR2OlaF7VwpzlaZqkaWZJk7E4H1ejm/eu/5vfzlMDbnxy76S3sd27fDrYqM+sn7/6hIqKa286PWMsMAfPSttio3uwc+AOzvHcFs9ce/C9N9x0+uUvfe7hwfFbb73z6GSyu3PgAS0cKECrKERHQEUKayvJcHPt7MbFK08/ufX8V4ZPXi7LKYaGffQxKGMgsmsaZRJSiQ/onPMcXM0g6BrnXAMatLLiXPAigsZQ9A6bhkUlNiMDAtA+ErXNgFXwQRg5hMMHD0Y335vOJ53h5pnzTy2vnzt9Znpy85FrSjmDK0+cSbr597/5KjkpJ1JOy5PJcb+/jKQIREL0TO1QEgOj4Ml4BDLNT23cef09FtNYv/LkM91ubp1PJnj+hU/dfOX3BJChURANcDiw8wcHYAo0a+9+eCy6t9RNClbuxoPJ5YfDleW19ZX1s6dvvfr2bDwFXVQVeyKA2lgLygy6nTzNCECEG+c4inMRgUAb0iSCQopUS8QL0bO08mrDEsRqHSkEJkGKEaNj74NKjETk4L1vmsr56G2SRIJAASMZYzgKMEVmRcjBxWCWVpZWBxsP79578plrxup6Pl8aFqSNFpnVM+z18sFQGRO8F45e2KiEIQqqGFGRWR4sHz28PT0+Tre2bWKDMqZN/6SqTwAjX0/H1WScryfEiCCpSS49cfXR7fv7D3Y2Li3b1OoOHe7d2p9duLix5oNnBACcl1VdS3clyVPjIQYUYLRKW8pL18QgCBQZfXQN11vn1v+b/9v/devi2XF54sVnuSlSrZhQOETHzIGjBJYoEVARZanF1rQYYwxsFGoCBgIUhhAW+Hxug/qkkKP4wM57iUGgbUVRijRTm2zFlkNorU5TE1naZRWKgAKO0UeWAC5471xwrnGOI0bPRmurrLKaFBprDFEbo8XW8kkEiqIIhxAiG6XbIG+UAC1Ih4AjcGwzR+xjVAq1sdZqEQg+6rJs2LOPtXCMQRRpa1SaGGu1VtoYhT+MHgEIYVvfu2h3WbDhFoZ/FMMQQuQYmGMEwPbRoQ0RENHjlG+bMiAiACRYWFARBYQiBJAQokQhxAARkfIiI6VApKlKSaAYDLPMuPvjIE2F+elTp5Um56Ibj6F2bJU41L1O3klmJ7u9fi/NcjRG6yQZ5vkwO/zw/uz2wcnJ9Ozq1uEfX59N9pOVlWxw6vjDD9Lxga+mGdLqubWV7RdOPnpjsnNrVk+Lnul0zN6d9/Y+enD7zUmfQD2xKYnRSlWurspSGcOCIULjo6W2R4FIGZ1RRJ9qBTEEF21iHZv9W3dWTp8C8eBinuX13AHUEubeVfnSqk1yYfbeuabkGEgUKgKOIBJj5Miu8WABtXI+iGNSZCiLtRvPq+rgYO/+YWRSgrkTztImemavAIgSmyTGKI1KIjrvD+9Ndz94eP9f/z6/9tHhg8PTl5afWl2WnSM/Vm/+8bfKBMe//KVTL36qm3b72gqim5VACkGW8twEmI7c3p77wbdf+ej4+MVrV7/wl36hFv7C4ejRRzs3X/3u7Q/eff/R8ayZD7tZJ+1sdLIPHx3feFiOdm6KPDj76vd/qqp/7PJfF0Ucua4rhkg6UhvkE6TWIWo0e1YAta+D874KumuVMaHm2FQmKTggaaMEo3McIph2uwBIkCQpCjRN5esAAlna5Y0L/Vxn3cFweXWwvRnyuLq7c3wwzYqeb0Sie+5LH4O5O7jx3sGtj0Ko1s9d0hYFI4MQAXAgANS26NDKMj68v5P0Olke775368arN+N3b7z0+cvZ8fThh8eT2wdZ0sFm3ObxLahKwt71b49O9jrLGzDPH+1MfWd+aWW5Ojqc3ttxZy51M7O6PtzVifNziCexqQE5xsZzGGZFpq0BdC5G7wGhcR5AGZWAAdLUagmAolmABZQEzxyCa5xNtEqMBFAcGXTwDAqB2yoJ5ACNCy54BgRSqEhAAKJEBYYEqb2c+xB8DHkv2Tp96gffeFMk6Qz7B7sjW2QAAEpVZbO+tlZ0lgRCVVccnVIaERQiawJU0TUmtfmgt/PoTr60nBc97ZVWiAwYSWzMe93x+Hh0dDBYHlqbA4Oxenl1fWVj/f333+1vXlJkjB2UTffgaHLqgmMnghAEYoxNo7Ksn+W9UE9Ui8EFnai0rEpNnCRWgWJXI8GpM2e80Hg2reoGOSJwU0aCKEEiB88hLlYqgooE2FUese2nJgJQSsHCOgMCGECQ24g5oNLIEDnGGEkASDGC1kqTQlCkVGKSNg+rlULVYpPaIkrxMUgUZnYhNiHWtWuq0junlS46RaewSWrb9GeUiCSCmCjV9tYLtVUtwMys0AepG1dWvo2IkgBqo5UmMgAMyEqTVkoZtTjPUYwmfXQ40QoNsdY6zVJrbJpZoxf97S3vBx97fgja3TgJt2r+nxBqRIQhgkRAMEaB1kikSZOi1vPTCkOkCACI/qTOJDK3kBbhNvrcAujExwCI2rR+FGBmP68hS0xnKJLPqtrVFWwu51sDQJgfj4/vHySEvpkFFmnseP9Ap8ZmKShhCT5WaZ4M+vqRjDsD1dlY+d1/88/mo90K5s0hlB+9H48PoeZUW8rS8z/2XP/pLxz9v19/++U/0n2TadPs71CW9lPjStbDzbM/88XN01sWaFI31DbXiooBAKgtISQQBkZCMuSCNxrdPCSdLMnSVHXA+7xrwlIXdCKY2sSGyngyvf5AaxVjiI2DENoHJDArxBADAYfYELDKLEL0vmnKGuLEdgZgbXRx786dW3/8dm9zrcDoKsfa1t4lhSLVUVoDWkJixnp/+ujm3hv/6vcefufd/jwscVA6efRgqo/m6UR/7OzH1fkn3/3w7Uf/+Jv7b++eeuFJk/Ty5S6EQIQqVYNuujZc7g5X3vrOh9998zuIwa4tzeEoXz69vbW09uyFs59/fv/mgyc/fHj9g3dvv/2D2/u7u8ezQYJPrRfHgI8CT05mb/zxDz7xp/c7ed95nM+ropu1LDZSZNJMKcOIrvGhaqrpuJ6VSqvh9gYi+OABPAuHeioFpYgIUZoo3mMGzIxESGSTVDCpykpAoms6eWdlZTW02kMIri5jnjSmMCuytD2IzmdZbqIaTaYHO3fq8XT12pX+cFUbE2MgrRiYOc6qKkntcG0tL/qGDJDqL3fPP3XxpCnHs+Z492HvwWjv5XfGszhMcg00B2pAISSejOMqg3B0tHvm8rXVddq/s3N4OFoZ9Ktbd0ZXTm9tnBt2BrHXK1nMUd1hrlwdMxrX0y7BvC6bk6CSNEKMMYBgkqVktLYKEaIE8ExasVLaIHL0rq4bJyCmSENAsahQUxBUipQhTRxBmDlE76PnCESklFKaARUs3oUCEiUaJC+emzLPO+cvnP32b/tHD24nNtUqJaWjd41zRCovOgZz74+9b5DA6FbWUALC4kWJUWZ5/dSdD0cn4+PeYDm3moWAo3dM0dgkt3peHowmS48G5y9DBK3IdLqbW+d+8EffnU2P0iwxEYdpb3VpzZdNJW5WzkMgFopMRScrimLuShJkFgSPCrppEps5KoyBnSubuhwurU5PxtPxtAklMwAIEzFEYPYuRI4cQAACCylsFQutSeHCuRkDaN2yzUAAFD/GGCywZxCYObAwIFFqrdb6h3koEiKNKBBjjN5LZMFIqFjEh8CBvTAIhMiuanwTAUxistRmSWKsNQjcDoHeu4bBCSu1IGEtTkbm0PbpKSJQLBx9e6EJWhtFgZRqq3zbqkiGyCIoyAw6MSrNksyQttYm1iilWlkGsa2ebPsnBWEh2QsiQFum2LZ1c+Q2kcbMsnCmtWE0wratEFhYWnHxsa904R/itu6VY8sCAqBFSSSC0VoZjY9LY7zjEMVoJAsCwfaL6TzffOqySbLQ1Ad3dyej6ZkzG/O5P9zZdXW2eSnpdHoiKjDWs3kM2F3qIbv5fHbq8trqSvfmb/yHbs/QPAEFs/2HqRcWYUwFCLupWakSLDkerkw64xBqq3W36JFqlFl78vyzX/lcPigmRwcsYvPckBaxretBk2FqtywQOPrSY4BoBZRlpSFJO2c3SLzN0qir4+PD1XNPIUsVmIzN+ktAFDwH7ySGlqUnzO2CSRg4eGuMNgkDel+X4wqFTNrRgM1osv/GR4/efK85PNYBiq0q5qkQemdJi1IopMCaZu4fvHl379tvjn7rW/ls2u8M6lmVAJTgPzgKFiC+/O9f/JWf61w4/94PXr79+2+OPrxVVs3qs5e73eLUudNEsP7kubS7Guf2xoevJSF+7DNXTQf3bt5fs52E8iChv5GtX3n66R9/zh9/5eTe3Vuvv/reD9786NXXpuUEKZzpdEtPBw/uTXaP1p5Zk/JEBSBmAtGamLTOLIFyjfNN7ZoqeG9ynWQ5i8eI1XwKgiZPuJoaSxgDRxND+85bhCEJ2SZKWRtDlACkMz/ekWoGhW3CPNQ1nUxqsUT51RcudHuD2eHUR57d3Tm4c7Mqp9sfv7x24ULezUWQQIgEWEjpIMqIsnkCqLKlXgh1iGm2PFg7e665Nw6zuYzCEto0A50NT7ia+1iiSXWeWJNg1HnRlG7//s6VT/21Z87c/v53/sN0MvZ3qxvf/n73JwbFRh87tqom99591caQWNVgo22YurKn0jb6B0p88InVOkGdaTASxbMTUjrRqoV6xrLx3gtw0e0YZepyarVWZKJSlGSIBIShdtzmSNEjiLLWmkSRaWFnyhgEYQiAWhhTY6vGCcvG1rDoJrc++OCFz342z3NECsEd7u03vi6SXuTGuwZEjNFaL0CRMUYfAikVo0/TbGllY/fOh91ud33tlCGMrBRacqKJJO/sHO7t3rvfKYarW2s2USBq89z50T//93c//HDjzCkpdJGozVPbSvvR6GgynSJbUaSWuvkg7WXmcERaMPgwb6pO2kFjdu4/PDoaAdl6XoeqFoDpdH48nQp73Qro/3+m/ivW0izL88OW2Xt/5tjrb7iMSFuZWZnZVdVdXW2me4bT0z0c0QAUyQFJiAJBPsgBoiAJ4IMeJUAAJUGAqAcBJEQHEByOhsNxHPZ090xXe1M+q7IqfURk2GuP/czee62lh+9ENe9jPMS595zv7L3M///7G2YTSUlkdxABkPdMRMTkhxSVgYYAu7jEYW6hYGqQJasKEQ0IEu8CByRgYtpZmVRzyugIMoIhAylAliwpA5hI3/cppWxm5FwRisBEdVkVBSHXo9IzA6ikBCAJBlQtk2kWy5YG9pWkrHnoQgCR1EwNmNg7AHUDEc4Qsiiq6IBlQBMVZARjJnY3Tg7QscfBlAtDazCQKnZK/x1gYjCjgpnRgL/f7YxlOOlM1QYiKRAzExLyjj6FgDAkrupPD/3B06YAuySyQQlEQOgQCBl3MQmDPn2gjEo2YzQGlFiVbnR0eOMbP8sBtheb9eVlPQsOcToan9VzV1I1qUNRiIoodJv28mJZVqGoi1e+/va0mOKzxc36kOD6lbdeNTc+f3a+1ZRDoSnlPn3697/78W/+ZP3R5wqUc38Syo3kdr3tzQw48NyaTmISwFBWauh9wUWFYKAy7JAGrw0os3OgIE3XbpZJvPkwu3Ob+tg1K0lqhuRSv94IZCx8qCsDzTk3TZNFOfBAVyXEnLOkmHJGNMkKGNCwnowCcUngmXWblk8XKaX1o6derVsu/ckNtzfKjcV22+5vxuPxbG9y/sXm0z/4nvvg07voN6BH5Ba+XKdlI7Ej6zRdPv1E/+4/euuX/63j2ZPlw+v1s+Yn//V///4fTfHO4b/yb/97B3ujyZ3bwbt/+k//yQ+/+/1f/bXf+MavffWzb72/XKVJl3pbpdSmPo2mU+8LLsuD128e3jn88jd+8fGPP/rRH/3pt/7s2w+uniXopEkXjx+9+ZX3+qaVXoqq9FWl2YwRnctiqqlrm9h3rvbMlUrum9Qvl31ORVUimZ/OfSgNTFVEEcAhIBqZCBK5ENhX/bY3BDZ2x0dkEvu4vry4/uLz7nrbjI6m04JaWzx5vLxcXj34uL1ehmp64+V3jt98pR6NlR2KEjnaaZ9zOS5z0zsKWDEX1vV5nfuyHI+Pj0411M+bvFjkrAfl8bnMiS4JMnJROJpLz0Yz9lTBlTVn3/qvv/br/+uT/T9+8sWj9gmu+k8e3b57eOflg9vHF5+u11fncz8HgbKqKwvrp2dY8d78SKIU42BBfRlCVVFgA83ZXHBFGZgod32MXex6BSlqX9flcrlkwhA4d5ZNvCYSY6NQle2qA1PHznvHjkNRlqFIIobo0BnBgJRHcmTApCn1B4ejV+698vFHn/688mxv5hElx/Vi0eR2rtKnlakURekDE7G9iF5jh2QGaGXpD06ON+vnH33nW/Su7h3eACJXBQg+dv384Lhv4mef/jj16aXmjZdefjUUNe9Nbh6OH3/w4bg+2m7bBjeAeTweX60WKhuz7OripA6hZAsIFlFj3/WbRm7dvrfdPP3un38r1FOHdey3kjpAFNG2bQvvBBFgwBPYgL0cvFDeB8ecZPi1SUV2OeaD22KAzu5UMwaAzochPd07z8wIQARmqCBmkFSBUc1i7IfBUcoKkNixqqWuzyIAEHxR1VXhvGMQg4HX75gMTROKSh+jaG5jJvLeBe/YEFLOKabU5xiTaCJk55wbfMbBeTUDdQNo2kzEUk7SJwVzwEPMvJgFx64sA7y4swaO/AD3Gf5cQ8ABLryb3ttwdKvCADUSUdUdaZoQmZmJhyjgv/hBG/g/Q0aH6K5tMbAhZoaY3M69gkg0kEp2oc0vdEhiGqMiFCaoAlKG8dHJ9PAoS2wvL3RzTWE/tWqT4sa9W1kFzLaLdT2fIEC7aWaHUxeKer8eHx80j5v1x48moK4Id268NHv1a//Nf/X/4Q6YSXqZhLK73EZdbtapAm+IQJycW5O0m+74zvFrP/tyYN+36srxdrvOsW2Mq1FdjysOPDCPHJEhmoiq5GTddtsut96K8cGcZdperz37g9dLPwop5r7tu75xfsKOVKVr29h2sIOvvlBImZpm1Rib1vmia1oDGRUFahKIANqsN7FPLvdV32zvd6lvy9SG/sSPRqvl4vz58/m06o9nV59drj74+OS6/9J49sH6SbO6nI1PtrokjQxE5J5p/PCDBzfg90e1qvq5Z1tsP1j+8MF3+1e+/M4v/+VfC+J+9Ju//Xf/o//w7S+/92//+/+rfvPs6afP7v7cl0ezihgBqhyTmpAnw5QVqHTlvemX7v7SjZ/5yltfffDNv/Pb33r/7z2C9eOnD8BhbhsAQ2CDwhxlSySYJMcUEaGuakMQVUuqMUuCoijYVxyK6d4RBa8maOjYkXdD9TFoi4MnH3yfoyk5x8VkapEyNdW4zfvbyWFMPEWTxRdfXG3XzeLKXN6/e3Pv5PZs/9hXhRqkviOmQKhZcxaQTABZcsx9XVdlNYoSY84seVL7+sZx/+D+2fNrbPtL3a7ykrUfeUTnsVTLMUdboDiqHDSPzz75rT/+j+688QY/ftJdNak/e/hH3xr9wvz29GSFH3eXq1XtNmr7s5tajdPmvH38dFJUYTKSJOw8uxB8QCNRLT2WVUHkJKV23aS+N6/TvUmgsFqtBawaTVSpS5u2XVvn/Lh2ZQihJoei5gGgLAi1qkrvQ9tsfeUMEYENTNTyELzjuO/j/OjgS2++9+3vvf/0yeNbd49TFADQ2PbbLsYuOEQAYkJzpubYiSmhMwU2NhGzPJ5M9/ZvfPT8h9/63d965d2vzg4ORpN5Vc8Q+Gqz3i7Tx+9//J3v/eGX3/u55pfifP/0+Xr55Ox6mkYMwGSX50/Wi8uiolFV92VMJqEsg/cJ07LZpr6L0mfrwXNy+Wq5Wi2/uPvmeynnmPqmbQEw+BDq2jseFpsESGjg0AGogkrOUTbLRhGKIgAL7BpKGubmtuNrgoo675zzTAyIxLCbh5tJ1oGMn8TElIByzjnFQcWNSMTYdz0g+eAr9sH7siqdczzkrYkCKSppFgE0BVEgcpKBEYiA0JhxWNBk2EHQyRBJmDl4KoNzjgl33DBABLWYBYEBDJGC9zxAE1Qgi8MhL49enDY74Bsa/DQ3bwABiYjJC6GoZMmaAYZ6nxiJnWck+mmolw26FUMDQYOs2cx2drABUEwESJ4dMBIy/RQ/sYMJ7bLhzUDBDHLSFCNq4rTte+6UTt942VcubVbXT88qcquziytp7r1ze+/0oO+73EZEr1m5dM75elwboBiV01FsaRWbx9I3Z88/Xv/p6GzZlm51fnaDRiPCMdkGuqbprazn9Xx9dZWIr00vI45G47t3b8xOa6EsGqWzzz/4TJnq0cjOlqPJeLo38p4BgNFEAQAcQRaIvY6O5o5DCCMTq0+q4I40adO1qU99fx1jrkLhmCTlGHvNCQcbuNkARXJAjNR1vcS+2zbLh/cF/Xo6KstifnKDORaEpFRRCNZtU78+v0gqxbbB0dRPa0XZtsXUK6wWlWTYbJbrRFA9hQvaNKN6jMKFAUsAdGfW/eDBD+8dHjv0jv0RydFl9dn64tv/8HdeP3374cHlf/3/+o+vt/R/+A/+vb1Xpj/6rR+EECeT0nsnllSVA2oPkhIw9VlR+yS5rsaTG+M3/4Wv3vzqG+/8/u2/+5//59vYVtOxeoMRJTRANceYUTVq6h0AhgAmue+lT6npc9R6MinrEIH2Tg69KxMIsTfTYlw552nQpA3TTeaiKLar5wbJBdAuZRFJ7WqzUpTXf/YNnN3efHH25KMfcHCHL92dHOzVo0kRajDVBEpq2RAtx2gGWTOYMRsYtdtutr9f1WVO45hT7rvpaF+x2Gyq66uUoe/78wjkQbxQ8IS+vIKU07bvMmsPzBzjgw9+/PThs5dObvT2NCpunj99+vn7wB7AYnCxa8ww0wGa0abJqdHDw2o25WqU0Tx7UAM1H7ioKud8Et1uNrHpydvsYBrIba5XSWB0MGIOcdvHdtNum/3Tk83Z2Wg0rUZjH4kKhkTeee+AHdnAHbEhh3sQErIqGggBdbnNarfuvuyg+uLTT7/05ddWV0sjyijWd5AzCSCjqSbJvmAAFs2ipgkEIuU+pYSBj05udH1+/PkH3//DPx9Pixu3b8z2T0I9FsRlv+5Fnj+8bC//cHu5Gu3f2VL+5Mnl26OTyXS/riaphevHT4uiGEw3Hm1vb66G0vWLTds1OalO9ve4xfZ6vWnas0V8yY2QQp9AxAi5LspQVgzgCRkhMJJnNJOck6aU0/Ora1MIVcWSBoUmIzCzdwMrAQANDQvnvCsISfHFIHvg8opklR3qGMARKWgWMTNmIvSIaJLZl2aQc0JywXtH7BGMICdN0ksaoMucFRgZTJHAF96DNwMA0dTHlNuub/vMiGVRB+fI2xAGQmQqWcGYiBzlLIOcP3i0HYEXFExyZkJw6OBFMMtw7P90zWGS809PYTVVlaxDIPSAoUAA73wIzjlHRAg0MJ8NUYdASVMR2xnMhih4QkcvAJDDJTyAowcCBezixway4y60YIgoNjSxvIrBDFOTc1bik1fvoeHycvX82cXedDJF/4Pf/5O7rx8XORtSIkAQUyHz1WjE6Jgx99JzCvNyWY0vj09jkPWafuY1krNcnOyxzeajKnaLV/+1v3z1wf3P/9n7XNSjfXd5fREdJHLFbP7JT+5fXf93X7r3ys13X18tVkd3bihiPRkXZekQJWdk5OBMVHL2wUuXlGx6MOGiVDXwTlQ5eAiMXiFD3EqMOeVMoTSkrBLbDkx3wmAQMHTkDFIyRSJXjCYne8Vktl4soRcAavtuc7lUyac3Tz9/+LFDLLx3SGm5RmJuIzSVG5dtu16PYHJ6cHB7odfx6mLVAmQIK9iumnyrrOuiXrQmEgHobHseY4sFhUyTcnKPtx/B6Oq7n//x3/1vrpaXzx58/ov/9r9572e+krfN/Y9+sllvct94NxEQzeIKH0pMvRmR5xCciykzaZaWijT+Uv1Lr/7N+Z3b7//hDylJWY0250sEsJgRwRmQGZLFoXuK2rcpt01OisFV44kr3exg5oLfLho/rnPO5H0xrplx18KKCDI5Lspy8bwBFXQUU2dikns1dV43z56HyFwX8+ObI5VQBPAECjGnJImNBMA5h8COcKCbAAIFVqdd17WbbV2Pt5tNAU5UiYwsk3A0QkCBxEAGFlUkNezKam9+fabX3TUDjJCnRhftNbTbbdP/zFe/+vDRfeGwOt8U0wPhUp2KGBL6bo1U4ja227PN9a36ptZo3nEWU1UScByCK1Q0baMmpYL292aMdHlxjUij+TiUo36VuvVyfXE5Pz2sy0nU9fXTR6P5jAKT1TEBa/IOiIvttgMxgEGbh+TcgOAFUQCD3KXY3LxzeHrj9Mnz5xbjaFK3WURx3UcgRu+yJBJzPiB4BDRByyqqmnvSlFPKGYu6vHP3pXE1Wlxdnj97+PmHHxb2mTAXsylzdbC/f/2k3myb7/7J96D+sZvyVrYWpNijozuHvuCcIxCAEhhW9Xg+22+abrlaxfUmZ4BQHu7fWl+mh589XpwtAldjP+oVCSClmLOUpTOVPkcjR6AZMW9SyllNmMizv3vnBsKunt9NpNGIkAkJTM3EFMyIADCrMcBQ7GvOWUWyiqoYoPM+OAeqSTKAkecQCganZkAWc2qaBkCZ6q4HNUhEYCxCbdKYYxJBY2ZfhtI5hOEcz1lSHkhgYqqSqyoUvnLO465Ez8msT0pMOSVAdezJoQqQoah4R+RRk4mp9HlQdjo1BUPDIRJ+h+TJklVy3i13h/pfc1ZTRceMFIJ35LwbQp2GW1CGHkLBQCybooGaOWaHhA6JmJwNpjdC+in9/6eDomH7++LY/ymPyEwVAYYsYyDMiAI8Otwbz0YppfNnz7jAajypw+irv/JXcpslRVFkoCEH2VQIsSgL5/jq7LLsJ7Oj/aM3X7199ZdvvXsw9sd73eMP/8G3v/6X/9KN21//9j/4+74Ps3t/yUv5xf/wexerCxfKPsXSh7/8v/t3bsa3/sH/43+7afXtojByVPjpyV4oyqIIvnAQxSQDW87SdlZNRoDYZQuFLwonSiig2YCIPRtxFlAxS0lE1MAVBajmPuXYmWR0TAhGDGAMlABS1mI8wyQulPt37lR7e9jlbrG6WlzX0xjmQQJ2sdPUjmaTuqigoO1m4wcFeEpQWPMAbRI1kN+b6NW2WywJPELawPZp17/Bo6OqDIbXuVEIi7TmZKXPlsqR9C8BL5btn/32NyP0k4Mbv/av/GtFKC/uP/nxR/f9qF5vWhfGhjaEKzI58yiSAZmZqqICxG7bt30D0UJRvPcbv3x679Xu6sKanrRnAE2RMGuGrDG325izJjUxyEAQxrMqjEYQitGsJKLtumEiyFkVqtGkCMVgZkcAQhq+clVdpz62/dZEVIGYnCvGk2mzXD+8/6h4tq4mc1cFSiCWJYIBaE6SMzMie2ISFdjFWBsYoA+Tg71mtX7+7NnhfG9/MlpcLz2B5b4qaHxYhdkkLjcMnCARYAYCwQJHv/iL/9J/+Z/938UV5HmTNYFuoVLIk0iPzzcZeLW8QhW3arzxVcpqUCTcbM4DHAjwNlc+xr3guKxAxTm2rOCYvROVrtk2654YZntzQP/86YWr/Hg+rbyP22Z9uV5dX5fjan5yaAKjvcMnH360d3ppvvT1TIc4BUBiHM8m2+2WGQWNmEJZOOcGWUdgKp3LfVeNpm995d3f+sf/6LMvHt577XXUFMUUkRAli2RThyDqCNBoVwSKquhQhzZRmYqq4Pn+3Bd+//hou9hsLpeLy+vVVTOZlXv13t0bd7744gmz8+Nx7cMD6KNlCmFvfnp0446jQK4m9N5BWY+LUCXRZtk0m47HdVlNitG4WyyWZxea83QSPESFSmOOTerajsDIWdemrAlETSRDRqTgnWP2wU+qitgbAJvtorZNTTMCDQQlMRUTjQKGA87SMvQp5ZRFhBgYOYRhkSAp7SSRzntmB2oa+77vtm2nJqXzqjn22vfJDE1YTJBwIO0Ez94PS1lKOTdt32wbiTGlLAKoEDyzeWUTyWqWd8CIF7IkEVVlEiYmZET0jgAxdRqziAqIBO/VzBHRrqY3A4CUs2RJElPfiykjAxAhMJL3TOidd8yENIRD4o4WgaaKAmaDLQIAAZ0jRHKMA+9/5wMYRvq4yyUYiv6BQ0e70x93vgk0UctqZsIIAIoOISGHkEM+uHuX2OW+7fp2drTnA7miqMmPJtjHViGYqCvrnEWFFIiQNOWu6yI516eD127/jW+87qoiYfX7/6f/83gyq06OvvJXfu6Df/gP1Vn45McP//QHIWPSNWYkkJP92WtPF+quXnnp1fFrp/u37zqEIhTOMTsOzvd97tYNiqBCm5ILJXnJglwWXHoByMnMgJhCIGYyICQjIpAeLIFhKEow1Rw158GmO7RBL8J9zCSHULPXfrMty2moqyhdmEy5byn4ySuzVzZvPvnut87vf+qrQnV7un/bU9ssN9oEGCVzlCLyVU7XRgjFdA6LpwBagyOQLXQPtpcH1cSV1Uk3Os8tAjWw1Q5GiIR4BKFGfNynWOZbr71y8/imJrk+e/zg4cNbr7y0XW7qaobOuj4pCAIBeUQyl5xa65UQclYRJYrSQUe+vnm6vLqQnAgcsx9ggppi7jpBQ3Lg0Xl2YeQrX1ZlRgAiZGqWayFkH6LmMJ0Xoxqc/+mjOMwviMgVlWbcrrvJLAAKUvBFIJ1a7mPXtm3LntBNgNywvVdFEARTSeYQ1diEABgQwch7R2TlqCpCuHq2OI+Lg72CiS4vLhnc/v589OrB6buvf/4H5wANgRHwoJU7rIvf/+3/YgnbN7/2jbMvHj99+vRoPj5wh+b05r033rhx4/d+5+P1ZtWuVge3Xa+dIgqKIPYxOjaux7RZuiyyjVL1rixQLRQMSNLLctttrp64cjyf7HOGi+WiHFWTvQkxpFVz/vTpqkn1uDh96SYkAsYQQr2///TTLzYZbt55uVltll88Ge2VN1+5O57M2mTNelXMaimruO2KvWlR+L5DsEzMfd/Vo/m773zp937nt3/y449eefNN7ylw0KQqOSURlYCOePA2DV5PAwBFNJScVUXativIIYsrkSG4yUF9MDvMN7ZN6pq4urhwk3EJdPt49o3f+JWffPjp50+f9anL0o9G46qaXDx9Ojq+MZ8dTkaTqizMpOviarmIkvf93vR43rF1cd3HZVGgY7SUzaTZpsvFpk9pb16fzGZtUQI4S2m39CRwZqimBDlmYACyjMQGGcCy5JR6iymLipll2407EE3FdNcrMPsXGH7vGJEJwJW7zF/kAbQp6FkzjevCee+JAUnEYkptihKViaoqjIrKUMlATdo+9THGrGhIwPVoPOgzB34EKHYxDnz+rIYA4BBf5ITzgCU2zZIREdGrYC+p7xMQes/esSVxYqKiKcYkKaUkkmNMOSdVqIqqqAuHHoYUeETgHe/fRGUIezEQleEMN0BCdoPnl4iYGHYJNbiDPg/EZ9NB8zlMuQebAeqLMMtdFM6gKxJQFDXAlI14eH2hEMY3TjXJ5mqjEXgcMgFQFBNzZTWatuskfVJVR8X1clWVAQkI1TMbg4h0mmbVxNfV2cNny+/9cBW784aev3I7zg+2D69+7z/+z6qYLaWTer+cH7x6ujd1+cN/8j88P/87VsI+nWwu19XdY/ZkCXO7hRiul93l5aKqSzQaTetyWqIpEAxYUxE1VcNdV2I7BUI2FNMUU4eMoahNIaesWYnoL1bgO+AexDZCtnIy7g3Spqe6BMPl+fPR3khR+l7ql05vfu0rV+dXnUiQ/uLJ8xuHdzBoTAZtBlDpNZaSE8X19mh6UO/tLa83AMxQGMAC2r5Vos3xaHoai4u4CY6u+oa9TMlZWZQVnXqfp+7Wq4d1WbRXi89+9OGDH336yu1bsmliu7XCr1cbydmU2PuyqrEiSQYq4kjBJCYwy4SCkLM0q67ZLMpx5Vyx6bZFwTGJqwrvWJGdK5kcqKEzsQwqgLlbd6LZ12NV9GFaTcbOB1M1B2hGCCoqIMhalgGQt+v1ZH6EnhSBnWcAn8e1HMVmbSDSdexLUzRUEABDRgJGQ1IFVQBCM3AcvGPS1DVL9uXJjcPF9ep8uVLTVlKzum67plLef/vV7qNP7599NIFCgAsqoazPnj5fbK9fPr3zs//63/zxH/7mh//dF7/8l778r/xf/4P/8F/697/1/KNwMi1GPq9St5Hm6ePbx0dZlqvNkkEI1qG6FWmCeUZbvf7xj9Oomtx5aXq8v7zq9m4cSxefPX4+mpXj0YzYb9t+PK5GeyPWvL3ePnv4ZNOsZoeHhzeO2LnF1XZ+um+Q56enYrb+9kfv/8HfsT4+/OThS1++GZbvxKMbDx88uf7J57N3X/25v/4Lk1mdF9fbbXu9XHlL9dEUzAPRnXunt45OP3v0qNtuR9P9uq7QWW8JcrIskkVNmYZ5rjiEaJkBkiqiEVITO19474CYVRURipEDdmE26pssZs220eLhIjXPr89undyeu48kb9rVsi6rhLS6WM+fPj+eHYxne+ikbZvNpllv+nIc9ven8+lscbW6XC567fcnlVGfUjTg7dpWi1W3bfhgMiorQAJBRexTFtMsuTdFU4m5j1GSkiPH3jEiOhgcv8xl8MyOCAxBxERFUrQMampqwReOWQHUpE9xAHc6YqKACgKmmlPquz6C6nhU++BhoPCaqAGxo2L39PZ9DwSxS13qswg5F4oSjFQsigIYEQjgEPSiCCklFZOUianiigbwO6CpDYBE5xkVYso55T4KM6NBl5ImIDS33Ww1a0xtlpyzMZFjrsvKsXOOmYZwykGLbyaqMLQLKjroowYmHRETEzpkJsbdiH/YB78Y8xgOUDkdyoJBH2qkmmGIOxxIcwoK8NPTP6vS0IKnxJBNJCa1EOrJRDWnvhmNitGoEoGkOfYd5NJXtVjsu67d9swAQinLyAMx1eNi0yVfOunMgLtoTz/64fLjM824DnJtzav/y//Zh//539aPf5hoFcKoLqr9aoyZnj5Zri8bS22fUsK6GFU6KHxin7Jk63rpJnsjRmaA6bxw5OIQsT143jKYAjomoqG/MVQQUYGkklIOo5HzXiTlFE2FvEOmHXgVEB2iOAXdbte+LMOoGvJ4fF2cvPEqmEXN26aryvrlv/rLm649+853oEub1eoqXAY/QhACVYXUCbrsXb3RvIiLYn6A1xcGTQEMAA6yQeyUFuvLMU1O0a9SvkZdb2Lno3AhSlcpgfHR3Rtg8fFHD7/5e3+82OZRPSEUj9pJSimnrmVgItacsrg+G5kNOClmNrWsmYly30vu1hfr228eGFg1G6Ho+LjcLYLIIYBllZxTTpJ6Q5VeTKGaTZidmffjyhflcMciDsFgu6ICCULp2PN2swR/zM7t0vWYOBRFMQYxiU3SrNr54A0NgRR0gNfaTng8GMuIkLPk2KyXl1f1/t7+weGcJ+cXTdNFZj+fzi+ePNuAr1tfnpwcnS+csw7oOvcI8XobzfJxtbf3tLgxf+mmDz665Wfu9luv/dnv/NNvL755gGEORY+w6LrZbN6tt5vUVdKWWRVbKie37n55efbs8R9/xC7ffuvy9Gfe27t1ktru6vJyPB3NDw8KH1TMV+V4Mk59vzg7O3923nRx73h2cHLsiZomlpOCiYZAp/39g/HX3vuT73x8/ulDub5cfyTf//jz8fzwxvGNH/+zb9U/eRjvP3/j7Z+Jaf3FRz9++sXD2UH59q//UnF0e3pyq96bvvHmW+//4w/Pz872ju+UoZzOJ+1qBTUTeJW+77quaeJkFCWbJYl5CAhh7zyI9CaaWBkGXmRWYxzsVli5w5N57vvjW7c+++xH9p0P/sYv/NorN4/Pm/VqcXFQnjKEVdN212syHVdlk1bt9WZzvRnt1/uz6enxAYDbXKxXq21Vl5NqsukoJfWBuhTb9bpZbZhOUWy7bWPSFFOKnWkeAhLFVCS3XTeUpkO6LgAycyh8URYKpSdgIx6ibUX6LCmmlJNnMqOEOmgmBwJTWXh2hWbpNMWcRFVVDCyEok/Wd1GzpNTHrOAGUKhIlmQgWRBAsiXJHAgUttuma2JKAkihds6xZ2eGfnCBq8u5BxVgUhEm9sRmaEPGogFmHEA/AFAG533BDDlLzklUXeyTqppC4cuqCsUgEgJAAbEsA4h4AJENcOZdbCQxDl4vggFJ5IZAGSB4YYbQHSl6t9oFheFwFwPU3bYABBQHvwLgEDA95BWpmIpqslwZE5DkxJo099mAObiiTH376NPPx0VFjihUqMYdqJoJIyIGl7tIoSyrctOsJFfVpChSXjQLUanGrpgE6GDx3fuLLKrt7ddeOz6tD37tvdu3D+7/5j/bfPyJXzfx6bPnFxfdg8e9JK7rmd9rfH3nl3+lPDjsRPqmTylV08J758ajGLU9X7BndphEsyoxOyJUUFEzHCJZTG144AVM1CRnzamoS2aWmCSngeSERoPwVkTMiJlDcNt1s226yf6+LwtgYnIKmtqeTSRvBaQ4GL/3L/71+wfzJ3/67dXDx08vz6fzDOpqosr7LHkESLk3T+1iPZ/MTuaH68VThQQABtqhRpdzCq02exDmUKBZtuR7aLxGgvPY+2pWTqfbZnv+5OF2fT4Z17dOb9XOF0B9gthEBHBFweyUIOeUkTywpxdNICOZUzBH1i2X5WSvGO0V0zFWoV+u0MxQNInEVlLOg70SFcGImMuqHtVceekBfSiqwgCNkIBoJ1wzMzUCASlDWTlaP38Wt6+UntuuByQkRed8PR1OeMmt5YSkgM7YANhooJTgQEeMmhx59iyScpS+y7paj6YzXxbj8Tj3S/MIIFkyAl9ePleLt9959/yLT63rHdg2bZN1HvL2+YM/+lv/l/OuqWbFBz/43o//N/+uAt2eVuniMnI5AscG5IrxZLaoV4XkgIWqa1Xmk7L0h/fq6Z89+fSi237//R881/7W5p2T20ez+Xg8O6jGI1RyIC7g+vmTZ0/PVoulL8KNO6fz/TE6bHupJ9Oi8GYmIjml1G244htvvnpy687m6efd9snTR5c/+uS7b9y+un00//TRs4+a5eX7H8cmf/j4s6jb46q2tN1/++39e/fKl19/7cvvjH/3nzx9+uiN9352PhnP69licbbZ9IHKghlNGtLlYsk+EGQRQYf1KJRFQItZk1kW3ekmkQdagcui5NTPKrt5qGc3nz784vLZxWJ99fLNm1cXH15dnp+8/pIF7pKuVguFDBa72F9eXnfXm5deOy3HBbNdLy+vnj3dbFb7R0fjUXG9vuw1B+fRYtd318uViPWdPLxaWZvBejMjE4AhcglFJPiCgFKKcdu2bROThuDLqgpVcm2riJCVkXxgdo4ADaEsqsq7Ic4FAMxYwIL3VVUQUp/6rmsNgBDZOXIBEbJo6nvpMxF4T+QYmMQICRySK0IWzTGXvvCexKzrM1ZUl4ZEigNLAg0MVBGtCL7wzkCQABA9AZmlnHLOgEwOsxiAIQ/5YgakRkQOHXDO6MbjOiVhBGIaMtwH7KgkEc2iqgi7r5jtynoehJ9IO6S4GeycdUPoGw+iHQM1AzHbeYltWHbCkD4sqmCD4AkMDGVnNxtEF2oqZoPJOqsYskUFyaQCAPXhmB31XQbgYjSqZ/McCSSza1OKqdukJqEZOmJnYoamse1hfxTKAg36phvvjSVj03Qfff++mxRVlFtfvenV1Gd/b/bqv/M32keXy0frqo2L9z+r0sIXMD+9oTFoRUffeEM9NcvNcnE9mtZVUbiAnFC7Lq67cDhTYDFVtMFXJ2oqMETfvgi9IRs2YykOC5xiNEHEKJJEyDlyzghNh7BMVDMmrsazfhuZHTEPUCYZWkgwIJrOZ2qSSfh4+pV/8Z+//cZbP/y933/26cdX6w21YrGYzGehcLbdFq4wzCAR2u6d6WtPezrvnzTWboNlVEHAnNkIQQygAFcDCqRxAiHtMO0dzY+OD5TSxfP7V8+fl86fHB6NS9Y+LjZrRKyrkSNnqpgo5URAjl1WZSQGJqDBD7JeXm/65vZrr46P9zAUse86Adk0STIDKAooKiCRZwYOg/ag8oHFjJhcFdTQTDl4HJwmgoZKRGKqBhy4LkZnD78f23cKz90AT0Vi9lig11otWq+WupyNKAE4JB4+H7KdG0VyRoQUu65dISZF2Vwvp7M9GtVEriqKmDpVqibjir2rR3l+3F6n5fnDq8VZNapIEkJOkK6aK2yg8dg6NqI+XRJ4pxahT9Kf0LREFwqnPYsrUznaJG0BbxCVHrfNg4er64Px2Bz8ePP86icfdr6698bp3t6hKwuHJEzdZn3x6cP19TLlPDveu/PSHV9WptJmM3TsnKiAYsxJVZvNOq/i+M7eDKfjr//q0x/+1nz/iybbTx49/Por9+4dlwnt7OrpedMllHsnx9Q33fny4+98MHnte/u37t5+7fjk9vGjx08k58lsOpnMzs8e99t1s1rVoSiCU/JuVB/cOJW+Xy2vq7qeTA6LIpgYpT5bYkVDJEZigmHmAICqVGA5c+5gdLA3WyT7+OLRy3unbGG7uEbmk+PDBz+hVdf10baxXzft04dnoZY6HITRbKu2WSw3F0u0cPvu3W5xvd1gVAACZN9HvVitI0JZBC/qPdVVTYwFMwEqkQEIIAOmmPs2OiByvpDkgws+oPeaFUxAFdgckCNzzM5zEYIjMkMwGAY6pFwERx5ysmwagnfeE6IppIE6pJJFbQhOceAcIaIF78ANm2YA9aMwQJ41S/CeKHhkJhSUpEaALwzIKDrY2gAQNWsC1qhJkorE1JmjMOQQ9L2iFYWnoBLVopgoIbmcMrNDsEGxNSg4s0iSjAZo5LwjYiaGXUowsnNIwPgCZG0IZAhktNPiKQCCaLaoOoB8ZMDfqQEh7Nxlw1IUaZcwPwzbcGALDf9ohCIChKqSVdCQwAHS6PAEGVVB0ak5goJZzXp2kHpp1p13IcdU1JWKMntXFm0TJROHwjuMbS/TskDT3Jw9275x63aXV+V4pjmLZeVoI6xf2x996dSnfPSrrwMYeENjU7WszLnvuu1irVFGxxVkM4iplc3lqhebBIcKJmpmw/szxLwxIQ+oQ4PdOy1gKhoze18UNQBkyWbIvkACRUQenko1QwFCX85u3CJm8n5wp6sqGLDzBopEDhywgooo3Py5Lx29dffzH37w+Ps/eP7jj6RtFtiOIWhK2EVY9cGU2uba4f7k9kYuC3VJKROAWAJaEvbgjZyqq1QUbAq6yBBYb9T1XuHs2dPrT+4/OVu+dOPliotmvTl/+tTmR3v7hxYjZCMAycqlK11AoIJ9GM194LRteonddn15dbZ3+/TktXueOMWYcxZVcY4L5wYl8kDWGLTDDpCIXAAERwDl0HeyIQyAcTMTUFAgGiyNauT89GDVde1yW54cEGQFRTJlZGItfNAZIAmASTRVNDHmn8rSDGQwsiMTpLi5WvTdUh3lRNv1RmLOImJmhmI6mpQh+IKD7fH1gwt6vmcmm5RyVIUUQR2woGWRglhQDQpAqog8cAmK6hPYiKjr1k7DtN5vNuuU2GIqseika5pGbAslFJvq6PbJu++9dnx8qBbbtm9W/dXFcvn4zI/K0XhvPC+PX7rhEZP5pOCDGxeBmVUkigxabwRShzQe3f/w473JVdutH10vbhzNaxTQdLlebDB3Ylng1ZOTv/SVL+f12TrGjy62H//5D977pV+oxrN3v/LVP/+D37u6uBiN6/FovyyPt+efX5+dbSwRMbny5qsvj4t60/XWY7LIgN77zvWKNhTOhIjAzI5piAmBEHyU1FnKPh+c7h+GPcvxwRcPnMrV1YUkPK73EWTTLbaL66LwF+dXz55cnNwrm74vqWwu24v7F+0mH7x0ezKu2+uzXsEEcxYir5rW68Vm21alrxCePXsaPDhm730xAHJLX5aF9w6rItej+bQWYBfQezIDTRJTBNEUs6oUZYGe0cBQCXXHQ0MCBkZz3htajiJgTA5pELOIqkjWnDWJ5SzjehQKAs2mOWk2RHCICl1MOQl6Q+YXxBzMkoykQCc5E2NZBEJWNGJnOW23W+2zIauRgSgaSE6xyymS99EwZ8kqRNB3HgzEkiTRbEzOqWrWBCYAlqMoqKoREZjznh2z9w4RCNAMhi5jUPmoGRmq2ZAUMDgJzMBEhlT7JKZZh+xjQDBEwhd5AC8i5Qff8GA6HhSniIiq5DwQCaipMJlITjGi6tCg1/OpAaScHRlDyqlB9ipWjuvL9Xm6jvP9/VBWABzFOJCvqn6x6rtUTeoQ3Haz4hsHaNY+XZWNvnnz8JNLLcoK2bSL7FhzRibFmAPpoGZAQLbtsjNGlESxKSb1tDogR+ePFgZ9o7ped1VdF2VhaiaKwESoqmqKhDiMfUQMbPC+gaGmDJadc847BNCUUY3YARkMEzTa3a4IBOhcSQYISGAKg7pqAHLrLs+NgCg4NDOycjR6+6/9/Ms/9+azzx9dnz/aXp71n19c//hh2GyKNs78CNt+tfqxVCNznHqJpIIQiZ74vHJhjGxAk4SSiIwTyaXrNllSiQnp2aOnn/zo4eV1eu1Lo8/OnmrTRO9feXU/oE8kgCaGReBiXA9cMBWI2146lGZzdb589vxJMatv3H0ZlZu+jV0Xu8ieQjVGRAJDMAKiQUNCAIMzaccK3CnH7MUdALozEe7sloAAQID1eJZyataLvZNjF1yOyUyJwADJeS7BU00gklhztN2eCgB3yiAAyyaiSQmrcS0pA9psf+aIsigTChH4EEJZjSfSbQEZvLoZH719d/bKPU25W22c+fe/+531apMpH8xHzXpbqhaqLsMUuHCjV/bnEfknz59cLlZb99Fs795kepASJ2u6dVxdb4t6r552TtPTZj0KfPfGrZOTQ03pwYP7nfQenGaYHB/evH1bAKcHIzBocxYTF7zzzsA0CwCbiig4JmSe7O9tSZ4t5bf+1n/SrB9n8qV3Nw/3j2/PrrfLJ5ebw+lo3ehBUXx+ufiNn3/zj7/9/bbJ8NmTT7/zozf+8i+/9bX3/vRP/vjpo8dvfvmt+f4e1WOjMBvPFoszBq25cp4s95PJXrvc9r0YYUw5ifDAk2dGZh8CM0tKaFAUZe679eXyycMn6+cXoSomk70Hzz+7fHTdjZWvu7Mv1pv1MsZFcO7xF58stu319SZyv24lm2kXLz59cP7wWbV/cHh6uslt17TjagaWsybvCNE268uLy4ube4fjsvLeX1+db7dNF1siHE1G0/Fsvj+v6wqMlMGRq6uq9FVwXgGFQYlzyiV579k5B2RmCgSaJSaRPidJhuC9Yxw4cbTDtKUURSAbgXnPo7pCwixmNuiITFSzSN8kDoLMEnNMCRI453hA4oACQBbNmlTVo2tjIsxZ1MwYSZN1KeeUDEFNiJ2kXgZmehJRAYMoQo4pZ0IAs5SSDwUzuj4JAJqk4fhg2EUQc+G9L5Bs0NIbIqohDdEuu7FNBmVAgV32cDZTo5yjpBxzir2pSFUWnpkInQ+Od/r/gQOBBDT49xEAB7Y5qu5KMN0BIfwOh5yFBFCBmMs6DArdyWQGJs3ZFY3rcjJC0cnedHW53DZNWVYueI09Mnsm8UVs2nJcF1XdNtdFVUHWR58/mU9pef2s2NsjXxpa1kTe7xLvs4oJE3FgySn20UhzFkt5Op2FMqhau8qPfnKx/+pBjL0y7h/PPNMQ2zMkKJgpqBI5JlJTAGPHOGgeU9TUmykzBu8l5+GEGmhJOzcEAO/W6UTOI6GJSpbdzpMAYZeqYwCMLGjeu10oG0rXdh3q8Vuv3/vlr+Volw8e/+QffvPxP/6dzkeIfei873PbpzvhpFZ9LItryA3gBtJ5bNYlAjU9VuT4dHK4Urluz3JFVJRnm9VnP/7wJ2fPYiBfV5eXZwHL+cG+N2BQUE45O4a6GrvSdSn1TUQm1dQvrq8ePnj46Hk42vvqu28Xhe+azbZpsmaw4VkY1FEDHsocIbIh0W6eOLyrjhDJbHiUcNAPw9AEw7DGBVA14ypwwZavz81eZ6SMA9fKwGwXiBMKAsTYaupzSjvg1y6fYhArm0FmR+Vk4p0vfOHKKks0RmIO7IA4qUnXxq5ZXq8c49FLN+3kdHW18K48uXWT92/aPzz5p//V376W5nRy9xZNHl49BEOBLICTavrq5M7vX33UO+ghdddX4sKNGy/HTIraxsXFZcHb/rDwprKN/Xg+DvvV5cW1JENf7k32ZnuzajwtqwJEnCv7fquASOBLX9al5dy2OfW9gjnvnXOiYjGRL8qK7r1+Ex6+4dqjhdY33nmzW69+65/9djUurlSrAuZ+cnZx2T67/pv/6l+ZPPrMfbS6+OjzP/97v3n753/u4PTkxq3Xn509f/PLX67H9fzw4MEHyRlNq5Gv6tnxlEk368XYzxjJT1gEu5hMgNkjIzvvfGDHIorErnBqtl6tHn78o08/e7y9bm/euFHgZBmLa9X1s4Wvny3PH9tW6nEZU/vpR58c3WJ2hUfPrprOj64vnz958FEPeOfucTUOV5dna0n7R+MABJaBS3LQbZvVcnPz4HAyG9/1Lx0fH8W+75t227Rt6tpeu2fXahdt06Jn54vpdHZwMK0rb0Z9tpiS5Vw6NxmV3jnH6AMjOCAiBvTE3hQsZ+2bJmYRVWLy3odQVDwU86YmkkUyZJEuZZEMOjzVSOw0KjvvXVWV1UDLQYCsVJShGg10ZkLTKKnvu77pupT6NjI5JmNHzllMggiMQsyByTmSrIDACH3WnLMvXHBhUC6xdwTkiBkBmRkJ1IwHNCwCIQ/j/b/oiXFnFBuigwfD4KBpUdMkkrLEaJJj7GNKomaeGFQA0RWBmJgBDRCMmIfYSRxO+V0yMqoBM4oZGAxEQR7ShRFFybKqqXN+UP4hUSjH2mz7uGGzqqqRsSiLMKr6GJmcL2vGzOwIlMAYQVNP7Ad5PSOsnm/J5OHZ89OfeZfLUlHNZAg9ALDhZkKC3PcqioDBexJTH8xUY1ov12efPj24tffq2y9vzp/3ORbBIZHmbKDMbohm2L1TCKY2eKAHXQSoaU46GFfLQlVEZKeZ3fkldiDun2I6dLgYwAB2YQo7aAYAICqYc4SDKz2l84vnl5cXwnh8eFMZ9w9Pjn/pa6+/+epvO/rx3/4nuOj2xAnQIaBEvF29smfXj7vFBngNzVNYL7tOC3wOaQ6zt156/dvrS140U46jsnr20WeXXzzuTI6mMwXRvvWTsTPrt60VmkTjtnemeTpO69RJVkRd29n1+dnnnzx79Pj4pTvv/ux7k/Fos1glFZGMbsglxUE1gIQUHBgIAuqLY57IkIYHz4a3lAcn/i6zbpht7J5VAEALdeVrt16cgzFR3uXq2M7fPmRxg1eECo0Yhxhkg91TPeRp4kAmcY79ZCQJ2r53wSFBliSpT12fskAfQSV3nTKdXz5ZXFx0m35vPMka06efrH78/knpbhU3OOnBnTev+6tnqwWDGtLp7PS3rx98tny+gXzoKzVdN5s7vvBlVXehT+t1PCPZrpe52663bdo7recHx69+7WvWNtkEAMuyCq5kKBXb9WqhJmrqHBFBQmEf2KEmTH0GhOCJHMc+a99WpT+4N7v57/5NbRKM909uzZbPN6tn59//7vvjuuiVxnWl0mrTPvrhB8e3T/erZ3EFernsry/379x97e03Pvj2HzbrVT2p59Pq+Ojw0dPHs/3J7ZdvYlnGrl8t171lAa3nIxFBkWGexuiQGZGyAogCUtfL+nr94EcfXT8711U7duWdmy9NDm6uNqvi6flnuFmtLy8vHmZFX8zW6y6E2LXb2djVo8m9115PSl88uL/ol/u3b+3dmORNWl41McLhaRU8AWSH6pzv2rhZNsPsNBSuCFNT1az9IDRT7ppu225SiiraxtSsm3azJQIkzGp5SOgruCpHZVEeHc3G45q5iJKJCMFxghRz16cutVkEHQTvGNEcA5KageasOVuOUWKf+5QUkBGJ2KEnj+R3US3eOzVRFTMj9r5yZKiEziDGnHMPAM65irEKhWpKORMCe+88m5ofZElD5Z5FDJAcBxvgRww0wD594VKX3aguVQ1UmQgIVY2GPg2AfkqFUPhplosNRoOh8R6AP6pZtO9T0iRKg51SMzK70pXeeSZiRFIFGCgjQ8n/ouce6tvhttnBV3EYJpFBEsnIBIQxa06kSsEhmKqgQ3RY1nWbe1BN264a18Y8HlWbdSNZUs7k3GDCYE39svVeQEkRAUhVrx9fpNWVtnn26g0iHJLqcQf8e6HBVNgtpNHQOIRCEZC46+PT+1+gSjn3m8snsVv7ekQIOUs2gSFx2UzNFMDvoOLAiAQoKmYqKYFK1za+YmbuU8y5J3JgQ57aQBzc7c+HwM4hBmjYDKOhKQLtEKyISIxgktuom+110xS1f++ddyfzfRO0wKHyPkB5Z/5X/+f/wvPf/9b14wcH6msYPI7pfurmdflG/QpQ4II+Xl1/a/15K/3Skjg9PPKvOH8lJY/2kIu0bjdnV/O6NOknPAJXi3FGp+QTe1+WYQqibd+3bbtdbtrtcnF5cXW+vLCU3vr5r37tr3xj72DPANtN27UpW8hiBDSE68Gu7d09ZmLGsjvzX7jzUQGY6AUrahesZGjDvHEHUEKlguvx4eL6SnKnuIsqpeGKRVBUHeC5GNDAIjIyoqAaiGYFEhzubQBCYtOBAqk5dqnftl2bYw8CvqjIsCyLcHwYJXrG8WjaRjn/+P4f/v/+ruu2e6mb9r2HYv+td77342+uUuzADNQFeLj64rI/X+iKsHAivqiVQte0bbPobM2em/V20VxKMmI8HJfa9Jvz5+uL57PZjAgBqO9ay6nrVwaaREDVO0IgTUkAVADZeXYWREQtJyAKjr2jZtNAYJqMoNfx3v62t+uHzSSOjoGMq6h02Wyhj07po48evval0+pwvOfcuAxxs3QO7ty6+/63fvfy/PHr775XkgSiGHE6PbjzymvPLs63fZuSbVeXs6MZF04QHCKzc/zCsm1GQORC7ruzpxc/+c4Pt88e13Uxm+wfHpzeOTncJNyfjA4O5m08OM95vb2Y1S9Xo5N60XnNVTYHcnzj9uGN21fPv7h+ds2TcHjndDQKl8vr5eWlD1SHgphFBVQYGUXXi0VKmQA1y66uZQjOuYJBYToNBjNCk5i3TVquN6ndpJyyGYs4DOBclrhabbSWBXO36YycqrKjYVjkmMd1MaJABORpkEL2SdqmETGQHDWJiAEgsHPO++CIgdEjF65wjpDBB3bs+gQShQhdAZ4tZotNv82ahwhCNWAqfXCIJiHnmEycc2CgWVGVaPguwRABlFCIkBjZ0EwGvzwa+IKdJ9LBY0kvcikBBtjpcOjvjqHhnN5VpoNcB1RNTCVLH2PbdqLiqxAKF/xuu+/ZMQ+JkEPk1wusCuJueI34wgoM9mLTYLvi1xABVB0HtmFslpHAFTxoxAHAV4UTq2zad03qe++YCw+FD9FJVJXkitJUqMDlugljTo+fcwj1aKQmZNKfX8RtU/kwOT70BWWT4RfdvT1ggABiYAiqCGiIVHoUAzQC9FT2mhZn7eLZ1fzGbBpKMRRNpuZ2SQZqCgCEhGYGBMQ00N0ki4qIWd9vR/MjJoqSbWecGCKgd37o4eY1UHiRnwwKSGhqyIq769pUs/Q5dhEUvA837x0U0xGJtU2XxAKPvMizbz384k9+9PC3fs99upxzIAUF9dX4Sex+kp/vr/jNo5tfevU9weIb8vbJF/PPm6df4Ha9WX14dl5mvR3GTfDtevN42RK7UeEnRvvOBWZCF/wUpfJYcMIcU7NtL56fP33y4Itnz549eSDbTXlYjcrZZz+0tL48vvv63o3D0XSvqmoPhJ4EJWVRAyUCU8iGgyBg2DYBou4qB1MgpsGipaLIYEpESPiiwN9BqZRDNdq78eCzj1LfYuFNbRjKvYic2MXQARgwAzsiMzEjG6AoCspAw0pMBXNSyZpjk1PXrtZIWFZVNatDKAghxgwkaFRUBSH318nBLITQXzxvoVPI5233kz/+x0XpnBh6o4yd9V3/PKaGwfbJjdRiF4vZNLdLWj8qSgizw+WTdpYSgvSCeQ0rkPr+07vvbWQyNkNmQ8Ztt3VIyKxq3jnmYfaIfdcDJPLsvEdGGlpMU0Lo2x7UuIQkjUq6fN5e/WST7i8n1B0WsFVJXV7E1qt7++WX3vr6L95//MHz8w3UvjycK0Cz6efzSVFVV+dnaFSFkpGqyXg2328XqdsKQNA+keTCOQfoDBjIUNk5NTUFInTeQy+XT68++ONvX96/v1eX+8WBG82OD/aXZ9dbbUPI+XQ26vz5thVVql0Vyq1CwWLtoj6dH790kpvrxf1PUrM8un3n1s2bznlNG2zWWozYI2bLKrHrXUkItlmv+07G41Ih7UQHqprF0FBNCB0jOyKG0SgUxR7YHNlUoNeco5JZ6vuspDn3sV2uN8RuSF2XcY7Ol2U5rqohUIAQEECZCrBsmPrYNzFDBqOq9GVVlUXBTKCQJTNxFYKqxZyS5ODMUNq+BzOMiMRimGLXt31OBqIcOJQBTQzAe3TelxrMVNQi5ryLhzFmYnZAgAMzHUlBQIwDmYKoooHbLXAHxaKBmQ6ZLaKDI0J1h26AXTYYIhGZoWkWtSySo6aUNRuR9+AcOWBgJBeYjAF3p9+QLqAIfzFdGRoAAwIb7pfdvIl2KNMXO3ADU8IeKYMyeUYiSaIm7BnMuCgCYm77ru0r5zz5sqrbtEUxRs5dGlV1l2gBvv/k0fH++PjLB5AsZtmsV9v1ev9gWu/PmTjGZLth1+7gHeYvqqZmpug8sQ9iggzAfPPVO7Hp243u3T4pJ4WJidiQEYrIL65HRI8MlhFeJCoDIcmwIxKRbL4YIaLEZAoUCF5krP109rabVyAC4Au7iSITe4dIZppirzkbWFEM0Y8loPSbvmv7TLR3fDQaFc+//8l3/t//7fPf/1Gx6XG7JcgZIvniQd39sH3yCJbH4GAND94/vzN55aA+PZ3uh7J8/fTgjx9888+e3b9t5cqn0dRj04sSY567Yjx1urmE7bxft2fcLi6KLLJYnj978vz+50+fP1mg2HQ8vnX75NWfefP45dcy0/qyffTh1Wc/+r0M0Y/90dHxG++8e3LzdphOCEBUUuo9s5mSkYAOOdTDwY/GA6prSDgSNWRAUxwAZkwD90mHQ50MfKinp+jH/XbteDr470wVCI0MhxW7wVCSueAG+ZqZocILCi0aYLacupRTl/uEkgFgNJsXZR3qELwDkZgikveOOXgkE0Nf0sHLJ1/7+nvf/HufbBhJHIObE1EX2TlHIKzRhMAYwsSNZkWNYjmr1yKEYlKVsds4Dkd7txbbH0/AA8Bj7TogZXA+AKBIJmRE88wDpZeGxFoBIADNZqCanA2yjsGsiWIWcwRVJt8umgzmXLlfT7B5+uBb33z4k29hRSKccySFW7eOf+kXf/XxoycffetDTTw+vvXSz781mdZ90wDifP/w4uq8S50vWNnXB5MHZ188Of9iNt0XpGax2TsY1XWJuwkoonOOnaGqgiuCJ7dZtJ9+8JNHn30yJpuNDkZhxJVv++bs8tKHUaisBxpNb9yUq5pCxXQ03W9GI9r0Fcr+wZ6YnJ09ffb4MdTVzZde25sdLRaL7fq6X1/NT0bo2ERi34sYjZCLcn19fX5xcev2IZEjp6jIQBkH105Mfez6raSo7B2H4Kp6XIZQIBpmEmcOCEZVSpr6PvQMoxF5NlURY4dEaGqLxSr23aBxH6JNfRWKIoQQgiuGaFxAYx5aypxzFjQE6FOSLEn7lPJW14BkBjlHUxkA3QhGaihCyB4cimpSY2Pk4VxWHfbJmpKAiRkksRDQETvmwtFQfe52roDEDNkcE4n9dLwz4Jf/gssJSDSkAJsO/E4DILNsJqZRUoyiqsBUVt6HoiwcvGjDB7To4LLYxYUBwAu8lprh7lTDwRlAg5IDYQgtlcE4gJhVU4xqCkApq/liF06mZirELIhclgAY25a6HKajAjGFCEjsPSfFLGXsPv7TT9tHj+Otk9N33wmlS22kB8+46zNzdTAXVCPgwcaOiGj0YvKOapINLBs7yzL8huQ4lMVoXu+Bk5yIfdduswIT+uC8IxAQkJ1WVtUQFJBhB9AwFcg5xWgi3pemmlV+eusMFondJTBMvAfQmeAu0ZWYg0fGlJLmSABF6V3wjGREKfaScp/BPO2fHo8qf//bD3/n//Z3qg/v70Xdn447y+tm+RjWj1Nza4r3Tl/5l49OixIf/sn3Hy6ef7y9voB5weWonMyPy4Ojoz/54PsrG9cV3Xp1P2m/3krXRE00m0y+/fmn33v8vEnJ+l4LMAel48m4vHVw8Itf+edu3nt9dHgyOZiMDpBHRVHVJh5ANEcW2nbx8tkXTz/8+PPvfm929+a9d96aTA6IuIuJCAQEyZGRmBEh8PCBINGg19+1jjRckZBzRgKDgX1lqmrO82S+H8pqvbrem46J2PTFVmvYKqgKDDcBGWQkNO9RFZRN8vD4I2pqN826YUKTFKrKh8CuYArOcU5t6jo1LUZjRuhil1Ju2jaluHe4J6+cFkW96lcFM4KBZAdECiQGyB4V2ZHBXjHbq+qr5TUgm+RZOUqHJw+f9OePn957/Q2QG5eLZqy27p9n533pUtvr3Ewt9dl7x8yyqxR0WJybmO1wZSzZUAUI0XEWFVVUUNMsLaCBI7G8zhs1WZw/66CHrky2rYDHYXQynXz3O392vbqQ3N/+2Xdf+eu/Pn/pqE1d7K9Hk/nBwdFHD+53TVfP6pt3T88vnj344OPCm72CTIUi7h8fudKrR2JGckzEzknu1WD43Dbr5eMvHmz6xlejjjgzmfTPF5tNs711WPeL7my1muzVN+qynDrmPB3vey5b52w0qUbHy8vN1dWlobtz++7x0W2C+WLxbHO1dfUoFIE8xdb65NouhUk/GU+adbO8XNy5d9q2/bbfohmzY3JAFIrShRCKOkmvvRiQikmWjNFQLUvsY5eViVQQCUeTMrAj50DMzIIjYEo5XW+2AKhi3jnnCJmrKpQheKYkknvp+1ZSNsLgnQENgV6JxKGYGcAwHBjKRYcIWdXMyEERmMAXVUAkUkKQDJqztKo4bAOHzRdSKAIaZNE+9W3MwUEIxIyENkgvDcFUCA3YXM4CQ42FCLJD8wy8nxdXwtCJG+7GzypqIhpzTjHHmFzhq+ARkInJEQGpge5svmaah9MchjmT/cVIAwg9IhEPwu0dOtpAhkwFMyPICB45C1g0iKqSkY3JTExSNjV0ho4MgAtPMaWuj1XJDl0Ig40fQmCyUq1+8tmD73538/Dul//GryQBAdssVil1oQY/GquqiYhkNEeDrpB2wkIkhF5ElEGwhCEpWXCIr2IGUoPUNahoQyQ5gKlksSRghIQoIAP5evjjU8o5JshR+j5lrUYTAEsxIzEhGQ56Ftu1SC+uSTJUU1AiRuYAhu22AdCiCoGICNQgJTXQnATJuboYzeu6pLMfPvjd/+d/9fw3/+Abe/uwaUMP2sYDmjTKDfQ3V/N//uW/sjc+fPDo/bOuqKDaQnsOVywYt89OfrgqDo/L2fTqcsOzI5zvKcc+x05dE+Xh/c+eXSwCU+3Hr75662s/+wsvv/3a6d2b1bQiDj44LllVNEofN0A5WzLr0DCZOV+E0eT23lsvvfbm1fn159//4I//7u+89M5br7/9TuHrXraOXQYAFQQGBXjRGYERZBwclYRgRDggEE0NFGg3bkMkYppMR8V4HNs1AXqiLqUh+wKH/4BMsiAO4NrdONIYgZQoEBEbpL5vt01RlJoSukDO+VDmnC23It4sxyTVqCKirmkG5IcPhUNeLZfi8e2vfv3TH/1oPxRXqxWNaKRwocmpnXetekeGo8pxwKnDBNSnjJvtTEWPbzxaLRcXz591n77+M19efPrpsy/OIlbz04Ojl+6WVY27IJCBmSVAgyoWAUAHsawpshGQ7ZBdSFEywABJZ++BKJtql9CpL53Ny/LeK9Ntu+02I66zdJbtyeOnF6vu8OTw5ltvvv5X/9Ls9gmUQdCBJka9cXz6Cbn16qouRpPJrK7mFY8pNR4KBLp556SeVsaqAOZ3Ua9oROwARAGEIPVN7DZg1ohebdoIZyWxIezP92aj+v7zZ1fr65uvvl7iyNV+s30+r0/LcX3Vlzn4rs/9pu0fLQ4ODw6P9yrvUoztsllfburxZFRNNWvKMXZJwfq2m5nTaNdnZ6l7tYvp0dNzzUKOggtVcN65EAp0GFzhA4FANmVHOGwrNHuFKIqGwXvvHKACimZTzUwEyKSAmMYlJq5dYE9ezRBQIGuWZJLFRIWZGR05yimrCZqiWY6SMTsXvGMugokDUABiY0JUUCIkJHZMQAhmkIehqGUTBMBMRKqDwnCAlxCQMDtkHGQjksxQkNETAYGqpTaCmdPBjKtmLwY1uwHqbvQ/CEwQDAxRJNuuy8gxiVr2gZxj5x0jD7Wq4W7qPVRmuFP5/DRdftdtKOrO9qWANCzehlHLEO2NgmZiKefSewKWpDmJwECJMEbyhZfUISA7UhN23qyM6y6mGJDZOc2aJRNY32NOdvHDZw9/8gkcxff/5McnN25DFllft5Dw5JCrUoZss5wZYTj7hz4JCBiRDFIW1cRmHJyagaJmUFB0iISaEci88aByNhi8YEMJD6oKu7xN0GzILDFKzDn2SOxDMbBCnHuh+Rz8OoaIMIzi0FSyqBozA7BIn3r1JZejilRNJEUQNWYkQF94doUviwLh+sOH93/nW+2f/uAA1uMlrVQvUk7QkhUHXM7E5PLqw9/9B5VNn9pVA9cOJAC00LZgG4CZGy8WZ/+Lf/Vf+B/+8A+7Eua3jy7Pr8azcTHy+zfD733zQeHdv/dv/M27b3/59NWXx/NS0RQEUAh1260LxSySmqyo2SRUBmoIGpMkajEWznlEP71x+O7RLx189smf/ebvt9vFO7/8i4Uv25i5cDt5wkDnVTQGZDLAnLMKiBo7c54H1/XwtpOaqhQlGdl4XFfldNOs1QTIEXLS7MDIBpzWkEShBqBgQyWFZgAMXglJ+5zavnDVZDrfLNdZo/eFEZhJu+19KHwg5z0z902XYgqVV3bMbOxIeX/k7vzKL9y89dJ7OP/7/+DvaMVXq02r+tJ08qzr2tTvj2bjomj7Hsb1X7l377tnT6/btG22ov7e3a8/vfpHq4ebxf7Fq6+99/3Fn7xy75VX3nvr5S/fnR8f56ycTQ1Syo4RBnEdDTgkADREUFGkQTAGZqqAQ5XAiMPgW1U1ZVbdnq2qo5Of+9f/9ce3X1s/fthuri4vzsopj6ejOvm3fu3r1XxSHx4uV9tx8NV8LCkSy9HpYVWFxcX1+OWpYtw/mbn33oSu2zvdB7TjW6ftdg1E6JgcAwAgDdRhNFBJapgEpnWVfFMZuT4aUi793nx66/RU2/X11Rfz/aPjoyPKhVl7vnpW3r5zuHe8iJu+61ZffNg1HcWt2qjrhizX5Xq1RsfzvRkh933u+l60I+mxn7Axo19cnPf9dlKVRNz2jXa5Bdo6MqSiqrx34zI474esMAYuiJAo+EDsggx4KksxikjMvYD1fWcOAzlCFE1mRr4MGtAEAYN3KtbFrSEAqCM3cNMkG5gFx6qUcgY0ICuCOQJRsiGOXQVRPSOAG0pxyGIgA7vZOQeG7N2upDYBRM0ZhrKb2DG5woOB5hdMB0IW7E28ASGgY8niBtEh2v9oLgM4ePOGM2jYFYiBmphJijmlnERVgD0PsaKenaGZaJIdSgXAhuSXQTcKAGpoBjpMbgZK2iD7EzNUwN2LD43QcA2omWgGEARQExkuWxEauBREBjDo6AcsHxfOtpq6zoQJIfeGmMrCmRr48XorsWHqm7P7F22rTWxSabHxxeG08KVAzNoTOdUBZDGc3mZ5sPKKxk6cpj6UlQNDUUldyp5I2WRowXeA5xf6cUPaWbWUcPB7D+UYmg1omb5vKQQXCgUDBcIhQA3MBH5aMcHuZjbJRpizgmYKXI+rUHtTGT4MM2R2SDYEOnEBRHL1xVNYt0HgsArPoHmo1ENYQGugjDJV8GAL6O/rWQVQAE0BZxAMXAvWgxYY+kAnCD/65h986e1bf/D504uNPv9ic3Drpfd+9V/76M//GydFn7azOb32tbeh5Ha76bpt7Lex6xiZvCvrgon7rgVQI+wNnWNCJOAYE4JKSmSQAYnDya277/7c9tu/+0fjav7q179GZKLg2dugeQIih0C7GePQnjJhFQI6jCmmlHbzSkCJaiUYWFGV1XS6ap63bSoqLwOBT5UAzfJApUUiAkUjAyIEomGRQ4AoJobmgkMmYChCBRRMUp9TzMJB0DkSiCn3beeCI6QsCZABwdCSaa5s/srJdz74fDXCq9V5zJRUMsEseIz5qAibGD/dPiNocNoniqu0/vj8uRSbCp/89Td+4ffe/8NP/+jj8a3Ll06O3vjKV/y+axaLUJd1vWckIIoEBigijoYfVhVmlKQ42CEABkfK0F8bwEC1Bx4wXp7RVsu2Dc+m+/sv/8ZXuvPXHn7yQZ1fufWl27PJLEzq8d5ku1nnwJqM0KxrzSy3nSvcaLb/5Onj4zu3ysIf3Tk6Otkbu8J7ShJFbLm6JqPAgZjtha4t56yoGjMgG3JvzhFNEPYBFI3I1a44ydW3H/wku8nr924fHOxvrhvpXO4iBt3b26vOvMTF8y/WXdzEUJDsG1Ef0+p627SpPjgsp/Nmu45o5sl15pyTvpVs3tfr9dXy+np+dLI/26t8yJIkaZbUCXa95tjHtkUm5wrHQ3KkIAzndkFoOIxgB0XZ8IAit9uutWiiOScEqieQnQ6fRUqiWXKO0SKaOvaELoRARMyIZkTgGI3Ue+dQTUSyCiiCSVZDYEZ8IfwekhpVFRBEJeOAWdBhoomDzBJ37B2mXXa7WlZTGMYhYCaazIbKgBCd7eJ+1eR/RGwexvOAhka08+5qzn1OOSYVIKayCqHwjhGMANTE8vDKqjSoIHdyz0FMaoMOUkFNlJnQ1GQQW5ANDTwM7JtBXyRmYiZg2m62iRP2ETV59JyyZhkQ8OwdvNCJS8ooWI4mcd1kUvLIAUWEQwFJy9moo7S2ZbnyzZPPmut09nQjgFRObnztXfbUblIW4eAgG6CBiClkE81iUSGLSNKN+mpcIDmmlNVYEQnMmDAlRUMB9MRoJJpBCdXccBe80KnnnMEsp5RzEkkxd66qXPBd7ETNocNhK46oYGgEsNs/K9gAvMk5l1Vdj8c+DIESCUAVbRdQJwMKHND0/P4X8fJ6Nh4X5PYqfwnyFFYLrBo2RSGAedKwI3DABsAAamIDh0xblSAyAcbGtnUKZ+vPzq7uvnfqrlb5+ebuG5V//qeyXpwGetBsfvuf/rO3fvmXx8enqWljjn3bBvZDoZe7TA7ByNLQSSctEDwBoWengkwIqlmzxR6DP7l5b+/o0z/7rd+//cZLYe+w60VICUmRhwQKQhDJg+vceQwuEFnfdn3TxL7LpuxdWdYhVAiEpN7RbDa7vHoIkiwm06EGVQdmlpFpWCYAgerQ6JKCEIIL3jRnUw4OxUyNedCIYGojmo3HxfBVNNU29kxc1ZWIMHJOklMqykAurJrLj370nfe/+a1Vv3TOUSIDzVH2KJSgrkkU81vhJJg9fHomKI0BpaZAWbTrta3vzQ8eXj1rL5f7ZVidffbuz/9Gs+6/9+ffuvfK6zdu39CEIgA0TH8JM9qg6hgsEwNzTVUUyGCApqoNMjwYNsNIbEY+qJFq2ggJzQOdupdfef349o0KQxJNObtx1YsC5fVq2W86RuirsjrYr/b3H/3oBx7yjZNjvlqMxzNn0rdN18frxcbIZ4PKewCgQRS3kyeSaUSgcV3uTab3nz1eIxMwS8FiRZ///OPvXayuT26cvPnO24jedCMJA5NBDuPRbFxcPDlfb5uWufY3eFQXVZElxdzWtXd83HfrvLgK9amrXdoSmYhtNKVi5DdNu7w4nx8el4VDqtDqXdgV7NA7JskQkkKOKqCmfcwqYGDMCN4xOmJCz96z9y4DeSbKOaOZWUAmdqRmWTKkjIaiOeeolokMzXzFzqFqtowK6B0jqymwKgCJZlWjQWKPwMMZqgLAqEBMZrZTFaqpoeasw2APd/5j55kBh4F2FlWxnPNQlovasCdWUXbofTBkRwA6JDbuJtQ2AIyHeQUZDkFfA8XCBIZRlCtc8I6GkBfTbJrFBFSyoQGwMzMeGgFEVRVREZOBmWTKgx5BDNERADs0UBBRBADMpiaSVGKKvXRq3u9Dm7MKinNqYGpDWyvDVUHDtMT5ikNFLVnXbgkdeNIYcw7sKIzCfDZuN+ZG18+/92fv/7f//fHRfK+aH5++9JVf/1X2DGKWBGjnTVZARMUoku2nK7V2s/FlNdqb4Kh0zmXdSUQFFNDEHBIaoSKoWMxKTDhQVHeBCANcnjRLTpKzSN/V+/tIDGYp5uAdOICYhogFCl5z9s7lXtGRKsRmW0zK8XTMnlUkdglIzdTQBDIoIaACOMXN5fWTjz92hmmVcjG2g9MaoAe3ZG28oOSCrSExdYZIgAy0Zb0gaa1pLXObxtB5yyfl6Wtvf+nq8ed93ywyJM1hb7xcPP3o489//l/+a2++fOe//P/+7R9998Hf+tv/7d/8t/5N77DJ4HwhaghKZiYxSlJ24BAzCJjEjEDDdBUUTFVBiajT7FSgdkdvvv3+D9//4rPPX/n6KbAOqzBGJB72QmKKTDS0mOQwR+m7XgiBuF1swBMhllWtmkXRM9bjSnOKm6aYBwI0yaoDFccYCc2QURCIFIBNbXjCMafYNjlFR2xqWfKwn5cskgVUILCoqEgSSX0a7U8UsO96JoopglkmlW7DdeX39ifTGnvs29RAVwMvlsvCV++99OrZdjUZOVN9ev3IIGfDBvjuq28D42ff/7M/OX98UJajcnwB/cPr9u139vx472heftng23/6p8rp5uFtRAaEnC2LDEG+xDsvOew6UQBQYkeEAAiWd0oPlWwYPBEDe/TEnPu2a548Op/e3h/NZjnnVY4GpGaDvTH1uU8xQl8WDnMqDg5u33j92fvfj30zGR3L2VWbGkt97Ls+azJpex1XhQ8BkcQETVQJDUSjxKRo1bS+9cq9i+eP14vLtjXX5yKHzebSTOr53pfefasOo+vlarNK2GSHoe/acn5jMppueHwdUQPVJzePTl+a7R867xFoOpvHpr8+u8QYRxWio6WqZ8eKXX9d751SA4uz85dez5CTWRZAIiAED+Y8gwJiCQYxWY9ZY4qDCpDRoRKQItDAPUgppjiMadWEeVCrDcloOWXVnAGIiQjBO3S+QgaVrCoxNoN5HY3B2FAAIFoUHWTMCAhMbqeABgM1sAwAqqymGY2Vk2QZYAU5OxpeXg3QRMmzJ6emoppjElViBCU1zJKzCJiKmGRFIzec9TRYcndITjB8AeUXy5JVVcR2uwj2bvhzzSxrLyqaFTCrEg7H58AThSxDzY+7pC9VRBg8ZghD7owyJQDCBLBb4VlWiDnn1Hep73K77du5H9td8sH3AtJzJUNzA5LVsqQuUsFo5NghO83qKycZBNQ0u7rabJeTg7kf+fnpLWbIm+6DH75/1f2nv/Crv/HuL777+Wf3rx5c7vmyno9mvupijm1USYgAmrNkUzVS8I6LArrtdnlZb8bjEIjRM+cU1cgG1xwYseEQ8zAMcIhVd7tzHEx2BiJqKpJSir2phWJEjCnnvuscGaj0bRdjX4zr0jlEUkMDyF1q1stQhOls7oMTsRQVmQZqsanh8O0WQQIT2F6cXz/69Oilu1fLVYwI9+7an4zJ4j4G6Bp11mvaWQLRCWFEFcgbsC2oSC5RnNnIH/z6/+R/+snV1Vl8dvDa0dHre9/+9EHv44dPLt/7xjsvf+Od0dd+ZjY6/i/+0//0D/7BN/cOx//cX/vrBbmN9oQI8AIIblkVnGP0ZIBigzdciQnADMTUFNGRAwVyerQ/r1guHn3x6s9+3RmACTLTTp1vAkO+NqoCgEkWzTkUwdVF7qPzLvbRJOcYnatMTA1CVW+aLvY9Owd5eJQTgO0EX0BmCIhDhpcisAuIlLfbftsYMTuMSTVlUADbZR9JEmYhxLbtmLGqC3aU+igimiIzmoJTa9rNatu/9LW3Xvu5r3XXm/s/+vj5/efN/Q/Xl+ck7uCll+fOPXz44NnDBxl0Mjuk2h9N937213/j6fnljz7+Xlo1qoBxvXG4tzcZ3TiQtuVJOD4++crPf/X73/1u8bbbPz6xbACYTYgUTXKSYaY6JNqiGRIxITOKmIGKyQBiQUQ1cUSlZwLVJM3yano62jveB+PUD7qPYYUlIGZZY5LY52aT2rTig6Pp4V45Ktt2XY+Om2bbyNIpqFlWkpTrqhrVtXNO1UwVhq8SGgIniaqRiuL4pZv3zt/85IffX64653qVGJ0fzWe333hjeri3brvrZ5dXZ+t5PXaq3epscvTS5OjgyeNxCF15dHTnlS+d3Loxm+73feNCkSCtnj9vVs34oKp9kcEkA7tCMsfVmu4447C6vDIxRpBhMTosgBRM6IUAkonYsYNgZFXEuJN1IjDC4Cg0Gfj+oIJEKAwsmEUkiymICqI6JGI3pLOgZRBCU5D4YvwDOUtWUskG5pheCNSIHRvIT00toJZ18CygvDCw5JwH0eBOYAuoOGxzJSUFVqSdXmdQAKtCFskpmSoQA0DXRVNzOcuLYPZhAbDL8sxD7Iuq5qSgIGQIjI4cG+hQcKpoloGswo6QiXRXceAwudCkiERANpxjaMY4eAjULJvEmHMSkQQweGNz38m2bbqu3cbG1UUWnezVbdMLAhpt+1zGnS+XByJMjhhKADCF3Kek2Tvj0hfIUYQKv1qtdLM92h/fefnmG3s32Jr7m6v1Qia3b60vHj88f/Af/h//99/4lV9+/c0vv/y1d8Y3bxZF0bWiORJYWbgEaKrqjNCPdNysVu16XU9mXHoJzg1mawQDIDQme0HOGIISOEsmt9vLDwgKySIpS8p9TJK1rktTy71YyrmFzWp5fXF+3Xf3Xnu5HE+QScQkSdNsm20zO9r33oto10cgIhAFHVhoQDgcT4NBr9umxfXl4c1Tcu5yc1bX89Gr7zz/5LukcuiKragCbgCXLIWyGXSQUCEBAtBU6KZhDXJ7dozdrGsuNo4Xy/xzd79xem1frD55893X3/ny23uMGNwbv/4r/0bX/K2/9Z98++/99sunN+9+6WeZSAg0iiEQA2qQwVLrHBEyoMIASmJCA1QcwBiEKBoojEub1y4tFyTkvBNVQAZGwyEzQwnZVNVU1ZDJgLJlTok9FKNydysMKyLLSMw+dG0HaOi9xi6L0IuHFHSnF/LEhllU0AVwzlLfNWtFQ9MUJUmWrvfOx9R572DQGQG64PrN1o+q2f7eerHquz7FvigKxywoXewNiZ0va++dnx3cvPfWm6urzZMfvP9P/tZ/Vp6clD/zeu42tL44KF55/c7x/Pbt8rAqR+O9l+88+fOGykmzuLp9MD5/0EzG/NZX3zgc1bJZRSbm4vToBrwDH3z/B+98JczmR0piSKLZsiIZMdkQL4uqArxL6EKxlLMYmfOO/kLhgb70EJOiUuVnx4dVPXJcqsmwcVQZzPFABqyUs7abPqqYySjgaDaLXWJ2qWu0j1wFU5IEklXR2JPzru+jqqKaYyqKsG3apuu6rimrIvhw+60vxaZ79OmjUVGwKY2K43u3j+6c9IBXF88ff/ZRD9XN4xtXm+d5u3Geqr2qOJz3lm9/6bWbr989OJ4XDH0Tkezy4qptm0jkyiK4IDGCovM153Xse1V25FeLRdtsd05XAzLIIilnS6ZmKmKG7JjQmWkyAJBhhYIAOWURVRmyPBRgCEYBHGa2w9zGuYI8grndBFgsi5ER80DDREmAjDokRaHpwHE0R05NkEgU1PIAHyQkJDTEHkWG0bgaDdxahqxqyMoQTUTjoGSAZJ0K4P+fqf96ti3L7jOxYeacy2x//PUm86atLF+oAsrAEqABjRrdJLtJiaEHKfQg6VkP/Q9ICjWjFepQKDqarW6G1FTTgECDBAhHAIVCFcpnVfrMm9fb4/Y526y15pxjDD2snZQi8iEj7o1742TuvdacY/x+39fTsXrYtqYsMaYcMxqA26TvRdT1NfqeU2abIZHl3GucN7CH/wBvZSZC0n5JC4xIjvsxKBIBIyDSJvBv/Y5NDExJNYmAbD6FYllTatsYU2y61bJdL1dtbFKOCOi8995NxuOLl/eLKuScZ9U4pxVaIgeYJLW5D8rknFLT9m4bLpyhWVLr1mqAxDgoi5yNfChD6jJO/aXXr9/6/Jfs9MmLL7rZz372C7/0s3/4X/xX77x5m8ruvWV3+P33Hr31/mu/9gv7L90qJ1WBrl12uWsRjQgYUAo0dV0Ki/N5uTUd19OCfAKK2oIKGAA574OxIWzAMyJC1jc1+kMBSsoiGSXn2KbUIXFRDQ0h5g56IndKOTbTuqx9cACWc4qaVZr10kDJu5y1i7kv66lu6Al9CBDNgIkIk0G0ols5XXd1GGCnSbrBhWvh43fXsqz9MGgwRdW8NlyhoFi/sPdInPNQuIa4DcODixd3Z/XTdz66385D2v7xd78vBBd3dq/furF/addytz5bC9XXf/GrP3/0wV/+2b9887f/9e7/YhYuXDbJ5gFUAfo1VlYxBFUP7FyPHGEmT05Rsplm9RQkRwADyGUo0A6ph01tJJpokhSBTNXEjBB60ioSmUY5O1uQQyAvMfmq8KWXnE0VyBmgOXKORS2pbDoVIICgpkyKvadBcv+f0VTieqGa2LkcDUxSiqlprBTvfJe6oldEmTL5nsdg2XLKXdO54JxzIhq7mFPigvf3JoAQ1+cucDGwg9ku8qsXHnz90qU9ujymGG7sfWWyPSlq7wrvAy5Xa4F1NRnyzrh7Cteu3ejO0tXPvXhw7Wo9rFKWQpUgs6eDiwfdOn7/29/54pe/Mt6akkhCIlRybNT/jKhiwGqqvWeqX7M58r0bRFUJkTx79gKwODotJtN6uBVCpWaQsomB0qazD/3rzCCBYwxD1dU6A4+2Z7FpCMsRFY9Xp5qyw4EYrlfrUIWqKAAAxKxHlxM6ZjBar2KzWEpMOBn6ob/8ykuj7QtFpphWVtHOlT0u/Pnx6tGH9w+P39u+8bPlbr26vxpQaQD1aLx9+WI9mlx58YXZ1swA5ufnWfL5etWulyTdoGZmJ8RmKoDkfAZJ7Tli6z2n83XTrENRcmol6SdLup76pFk2TEF0wOwcABgqWFIDAYNEZo7I1LrU788EDBg5eO5ZCpu0hyTsF6tofYwFRBBNEVQE+mghERiLIYoReKRsBJolA+QsKaU+1eVCyYpqaNlUsokCCnMwy5KyUYiKiD08SPrkjuYomk2hJ+8RYmeSYtIkKkDc45yBDPpmtqmpgknqu729tMcQetEjIfNG8rjpUpqBMnlE1Ayb0hRCf5nqUZemG7mviYhByl2UrJZTyuvlerlcrM7OJasvSw8FUbE1HhSVG4RQD0b1uCzrynnImpndyAdbtZIb5wEjg6AqAiMhpZQhZVLVTqthoZAge0bTJNB0YTqVKGVRnM6XmlUdb9+6rieTb/wnP28XJlr6DuTKdr2K8osvXnx0tDp88yffuf/kxpc/d/Nnvzi5uutDSYzadJISoACwD0VZ6Xq1Onv+tCqralwqEwW1LkpGIBDDANTvPMAUgdg5M0VFF5yqAgAjSRchNpajK4qiKk0kZUHPwMEX5f6lK+PptKhqRh/bBsFB150+e7Z14QCRe14C9Dug3lABPQ+a+/ei5AxckC9Fq9ymemKkmmS1d33nhfs33rr3QRaZ+NrnVOc2irRAkVAcsFoLEjLcgHILBruza9svvPz77/z+tx+9+8yP3KPjB4/ucVV+7a99bu/mS+X2RUPhbp1yE8bFZ/7mf/pQynt/8e23/vCP3vj13/C7M1it+gsqIjhizWKaIWFUY2ICSGIQJARH7BUyIYB3YJkdLNfza9tfJYNsfTq5X/Mz5AighgAUAFBbS5A8sxkLumbVSF4XVVU4b2ZdjEyFiIJiYIqpzTEzUyiCSUTRTQERwSxrpiygbCya29X6/JQBCh+Sdf3MFBjUErAL5GJsPQc0BYCyqgFhuViqmi+K0gcVaZsYmxQ8FkhsuF5HF8qBD2fHJ4cnd46PTr/0K9+YToegaJuZfUYyX5dmSZeYcp7NJuPp/gN4e96dz24clFcvjLa2gndAhoZGYKLs3OXrN0/PT3/w7W9//ue+PJ5sOVAg2tTa1NDIeUoRlIAMsokqEDEpoaGoEjkXCk+Uc1qeL8rpeHphz7lCRCVH7T/DG14joPTGKvIOxzuDyWRkHn09OLhw+c7bb1GgoixWT9ocbFBUBqnr8mx7RExJk4lYp2rAo94ponmVUMhagxrR42B3Otvd1647nh8PJsOqDuvz1fMHT0/uPzyz8a3dq2U1dmvl4dChZ6bZeLy9PRsNK+lWx4vWI6IjR0wKOVvpPfsCA1Mkz8COFUOWhWpTeFqm1XJxPKsvEqBjJHCEmFGJTA0cc78H7dni/SYUwRTBUImwd/GCQWDHDlFJkRCFLataLwYzE+yLK4RMqIYAmjcnjA0BAJmI2DA7LgjJRNucAUgNRClbVlVVBVCOaaNkR3AAG7MgqmwqupKjGuSsAog9eFTVVEBUs4lIRoCslkQQlMmZWY7Zh4IYXdNFU5AcFTRl2UwMkbgP8vSpRsRPUEF9ag6MmKz3TPYoOUDBrJq7zsCQKKWcU8455yQ5pzZ2Z6vF+flZjjnFVI9Gw2o4Hk52dmfDasDOOTJAc47LsgIyA7QNitn7KkjELiNSYFfiZlFi7BkJUhYvm7CrZGX2Mdvi7nE1c4PtfeW2KAtHyy5FX5WjnQlOB+7yrbWtc2w6IPVVczxfJHv95qcWZXkeF0/ffGv57Ojgiy9u37xZTUfsPTnrsphqKPyImE1Xzers8LkrAxelg2CiZgnJNEfFYAoqmRAQ1dSIyQenKhIFESXHdrWynES6qiqKsuwbPM57T47qQaiKMCg9uaYTNQ7sludn8eRs8NItdiEbIJEmMbU+4b0hrBOp9v5lcyCDYaB6u8nBTYd+WM9PjtLW3otf/vxZyvee3F96ng2qna7scrsWPSdqTJocR2YXodzF4cAsjEbfe+d7Pzl6m3a2v/T1z60ePHn85mE5ri+/cGV2eb/e31seH0ORnEbTNNgb/dKv/4Pvmb75wz8ZXbn8+i//ahgPu/W6h3M6AmHMItaPPtUCoap0nSazIgTvfU6SOkmCp4dzg8Fs//oGCMjcswHtEyhGDypBJiCTlC3nUIZqMDUbASA5p6aSc+zaQR00ax+DBCByyNkJqgIjK0CfLzLJZqYppVCVaBqbVey6ejgQ6RPABgZMJJJMlRyTOkDIUYoBE2IWabuGkKpBnVPsmq7rInsAx23Oq/MFENWD4fn85KO33rVqcv3G1XIwMAB0Tk2ariWG0rEBiIBkKwIPq+G03AH0T56dXL/xwoXti7PtKZrF1BkKCBmggYbKf+ozn0Wz73/n21/5xi9Vg4EliZJRiQGIkY2VMEkSkI110NCUUhRgIO+RuGtjs14q0Gg2Yy7MKMVWRJB041pmVKQ+dwFojM57n3sKJqGvRmdH55JTMSgJcXW+qLenorka+FCQStaUU8pN2zKT805VY9cxCHg3GA0MLZSVC4GBTo/X1d64LAbNfP3k4+fP79w7786H48nWzlblPXUwDoOSfRSpQJX8ejV/cvoMgS8eHHiqQijK4bg7XyeUoih8KKzIZYHkkTzJElPX+TLkLi6X55O9/U9Qr/aJq88cITsWBZWslnrcJRkriIih9qETMFDH6JyDTW1JzDRK7An4DtE79uyc534FkMREqd80fPLc30QtwUByTooEPmWJOXZdNOvJDyia1cQ7Dt4RIiI77lu7ZNbvZkFzjl2XJCEBB8+Oe0ujqKQsqBrRCPqbtxE7QlKzUBaExERu3XQqCpqgb3H1HLkNyQzQFPot2YZQD4ZoSJIUeiV61/Ucz9yJAkrqYo6Q82rdLBZN06xFgRHQO0Ucjba2ppOyrHe2J84FIgwFO+ecY9WkJt47U1SznCXHTqSLyy4Xw63BPrBXBTBKqjmrlcxEZVWmplOz4JymTGamfPro9Hv//JsvfePm7IUX2ZEPvqwLYvZVZcbsw1vf/8HlN25BNoydGkSIZ7ef+lhObl67fOWWgazbxZMfvHN8/+H2tWuzy5eH25PKl6tubWphUCBOM9jq7LSoy3prB33hgicCEbUknUYDAvsE+4CAjKran81z0m4Vm3VjiDnlwdYWAkrKKSdmQEaq6jAq2VEWyABcFNIuViePNXc+0AZftsGMIHMvUxPVHhYt/XycEAZjv3Npf372tJhsX7vRPn10eLg6mu5OXvraZ+R78vTJ0WG72kKsCk+dxZwyiAPe58EFV+WKTzu939xpzN/4+b/2c//oPyp3tr7/7/5o/uRRt+q6dpXbVdus2tyqJQpOYjS13Z3Z137lP/q9xeF3/+wvL197dffVF6GoVusVOTJyDgBdTwRh6McmJkjMwNIvnNpW1Fnb3n7/3Ssv3dq9cpUdKzEwiUYz6mMEPZ7PHDACEYrkLGgxBQJ2/bRIQXK3ahFQzRyQgVXjqS9KRALITMjBa9ZN5w5MTDWh9oV3VRAzQ8mmGNUUGQjJ0LFjRDZFJHaOQbH/Kq7aFQAUpe+/ELGLziExtzl2XTKA8WQUm/T86Kmryhc//ZIjJ8lETTU2XUQ074InttQ1i7N2tWYuhsNLflAD02LVTGbbk9mUmDUmNQBVdAYqpl4x+eBeev2No2dP3/rODz/7lZ91hWcz6U8JPV3cAIlMehQtqoKpusDomZhyim2zNsTRzlY5GAK42GXJRgwEPcCBnXcCoKJiYubQQE1yKyRUxG44qHxRLs/OysFw6kaHKinmelgEqkEtxRxz6tq26+JwOiTmruvadWfeBmXhSl/UZVGWneSmaajwoXDd2erwwcPjjz/GZl4oT6rBwc6UcpKuES4cgnSrtFr7wWi9Xj28ezeU1XhQjXxZ1MVwa7qer6xdgvNF8CkSsfVxbROLUQZlSAnb5aqnkoFp7pPpIgDQl0BFUtaYs4LgBqAJRpLVlACc9+w89xr5nloAmkUliuYMYOSdY3aezExEZNPh7SdA/VeBUEzBRDTGJJbMLMfzlEUFJeVehqJgIlZUnvyGnGlgWQXABEyyZBUwNpGUupSzgcCa2DlHfbrNiBiwj3hC1uQ8O8/eu/6x1FN3XNd2xMSGjhF5U7hRUSECMzJEMO7bw5sNuEhWU5asTduuF2vRnFW6dbdcrFar865rRcWMirKejqfbu7PRYFDVVRkCe++ZhCAw52hGKjnnlL13zpNlXeUuddrFHGPqurZNbduuLozSlVdeLKrhMuXcdZg3WykEcMGFwnFwzvfGV2dNs/7o0UfvvAlV+8ov/kIxI0vGjpK0rvaR/fMnz8uOr31xmCSumrRucwXOzhdHHz1ZZN7Obri9NTw4mO6MFmnVLpfP7z9KSbav7Q6Gw+Z8Cag+hHowXc5Pzp8fArpqe+a9UwgAOcZWTAmJ0aFB1uw996t1MwU0iV3XrDVGBDalsh6bWc6ZzRgMUatBxZ4zggpksUwMTVwcn6BlSyJZY9fFxQpQPLui9EXpgZygqqgJ9MF2EwkhXLq1/+Yf3W1OFy9+4UY+Wb3/0bNHzWoyq17+4ucnP3338KPbp3l+Dg7AJbCR88rhShgfcbsq83LCw5eu/vo/+ns3v/ylelwtni8HhQ/B8tmyLxlI6qSNmhM6ciFok4K125d2P/+zv/qb/+3/5Qc/+NavXbs8qAeda7vYcYnMvnScVRNkUYtiREgAKXYWTU0cMYHev/sxIL/y9S+PtrYymhEwGhCZqgF88rEmRENGX3hgJCBiQjOTHA0l55w6NUUkyBuz6Hiy44rSkESEmMGsT09u9i5ZNRugkAkqqljqOjD1zpsCESNCUkVE5D4SKs5zqELKmZ0LzMzEBJLNRJ3zxChZNJnm7L3LXTw6fRqbfP2Vl4pQppx7QSAqe+e898ExaVyfz+9++OFo77K2bTFNk62pETCzm46IfE6SYlTUHgSOTGykqgq5HJWf/sKXv/VHf3b3ww9efOVlcmSiQrr58YBg00KxrIJALjAVjMCpzSlmZVePBr4amHLOaqbOMxIxoGMKoSDuN3tEAXvrn2hGUY8AauzcYLx1cnSyPRlUwwEtluygqIusKeWYTboutusmA/jSJZG2la5V9qUv/WBQFEW5Ie8RllQ0Z+uj+w/PHnxU53ORtTo/2zuoB375ZH2sPGRoYzc/OYzdcjJ42RWlte+ePn8ynwzK4cQXxWgU1rPp8jADoMSYRTKboBIx0OYkAULdqtEsjChmKpokSc4GhohkpNa70DWwIwPPTkwkK4ESkan0JXTQlFMWMTECRO8IMTgCDo4BkyojGPTOCkQ1QhYyA7KMoipqOeu6FTWLqU1tm7IwukFZMrm+C1qVvYAeUS2nnACySA+lTSmKCApu0B8GyEDkPCEgkCAx+oIds6TUF7moR96LfiInQVNzRXC9eQaQsiiSMaD3znnXF7nUJEuXY45dXKc2rnPsYlxbFy3G5ujkVHLXrJJILquyHhbbs93xeDIeT6fT8WBYUn93IUIwydLm2Cw625jlIaUoUdBTcEwAXcblYt12KaWcLQtom9ezeqbROystQ+4aziJZ+scBO3TBOc8C2kctTSQ+fDA/evzxB7A+PAmTbWQsgheRRteD3er2Tw+HOa0Wx66qz5vzZWq2AVIossH6dKFnR+HyhemVq8VsUFFWka5t1/NTZhvMRkQUl63WjoMvRtPYrpeHzwihmk7ReSZHZaExoYKgpWwuEDEikqipWEp5fX6+ODyy1LFjx76sh47dulnllIChrssQMJuKUoo5tl3wtjo7W58tqGRpm9jE46cn54+PpJ2TyXAy3Lm6P9rdYeasQoaqeTMuB9zbnW5f3vnw7Tt7N/de/KXXbVI+vv3sqG1tSPzS9UFeHH+0XFC31tZTaZXmGJ+ldQj1zgvXXv25L33xr3x975Ur4DnmLqZFEfKIxDlC02axgHIYcyKzrEZm6DB1HVV86eVXXrj+8jt/8Zdf/OJnr916o0JvBRuAKWjOqBnMRID8wDPFLoKYYy7rQCk/enD3dH5y87Ov7V87sJ5cSAwm2F+0N5xwNISUBT0WpYdMkrq+s6eG2ivlNrtQNBNgD85Vgwn7Uj8JZmvuvcGmpjmLpKwGTD1y1NQ0dZEAgncmoihEjpmz5pxzH64RNec5d2m9XBaVA6K2S12bFIwcmUIWIaDggqkuzuY5wv7FS1U9MIOsYpB7BpbzhaEaWid256OPf/CTn/7sL18IaF1uQ0XsffSYDLJK27Wi2RWuvwQioLGQoqiQ0u7B/q03Xnv3B9/b2ZpsXb4CKH0fmJiybfTGZsLOUQjsHRjkFGOXAakY1q4oUdHAEMEHT6Q9r987x8SiWXPOZmYmBv2hTXs013pdD4eD8fj06fOtwY2yCD4wFEREqBg7Uevaplsu1/V4ROhijClGFAjelWXlCm+oiC6gc0Swisvnx/Onh9o20qy0W4Wty/uXrzLbYnG8aBYF57PTZ8+ePahHk6IuCx5VW3snZ+cnz+ezy2tfuqLiyVYRV66LeX3etKlxgMqNFYIBEBICZKCma0RURUFURTAbaA+6hJRyL+8gYjNFBFRREVAF6nVz1r/AIec+jkhkiMhAxN4jGEL8pEwLycQSkUMGQ8iyqZKYIii1ncSUgcwiSmYGV5dF4YNzrBaRgRj6c40ZqSH0pP42dl23XK0BrGRfuOB96P0WwRdlXXA/0OqxKQjsHRJYj5fpRztimqQn+7syeEZnAGqKqsnEETKjZ8pqy1WzataLVXt+cnJ+drRcd7HJKebmJJ6dt+t2kRSu3Lg8mUz397avXTmoR5V37DwzOQDrUtetc5Lef5JzyilpTFJV1XBUSxebZi0xGwAHIsOu0XXscm8VRgFHMWcXnOZOY0YFkCQp9iox6PlzrucOITs0wBwxr86L7vzsfnryzgfja18mQufZe47noCQur9YfPs3LnymGPrZrinlIWKOzsmgK5GmFsyJD4zpHgYAQQyBDhZxjZ8ZiKTeRQ81VNahG7WJ+/ux5SjLc3kLHbKwIqp1IZ8SeC0A2MUBMUZtV060WuVs65wDBhzIUpZnklFSzKypfFJtHHRkmK5Hierk+OzOUrLlZnCvMD+8+fPze7RyP8npBldt7cv3GS5/evrgPvmeOGiBBIEIKVfHCp195/y9/9OjByfWXrlx740UBfPbo+VJWWju7elmak+Xh06exa7XtFiogB6O9b/zKV77+d37t4hsv+cqlLKJ5fb5MbbSiGNQDPhGGwhWlEjrvUHtmYEZmrlzqtJ7MvvDlX/3gv/7Hb37vL6ezSzzZYuuiEaBSzg6U1Bw4RmuWKxMth6Gsg+X85N6dJw+eXHrtpb2rlwXJCL13fQ8aAFIWs76VjykLgrnAvWNKFbt1G7sutSkmLYtyMqv6NRagIik7HgyG3gXnXO9hAISe959FYuxUMhAj8v8PuQGmkkBLBJDcB3mRCbNEzNjLiVPskiQgIOYuppSSmvYH52SCBmVZrFepaVexa7d296txZQYSpZ+tgvZFT+2/mM1q/cGb7x2fnuWY3ZDMrCh8GZyoOLMeHe688873mRU1ATVCQuspYHbz1kt3br/945/84Bs7W34wRCMz3dgNTMGMyIciILMm7LqVKLBzRV2HoiLySMxMREyACtpzWZQ4qohkMIG+ZgYbyC8kyL21EG04Hh0+eAh4zTMGQsgqmnNKarJeNuv5uZKVwTPkHEVyDE659GXhCLC3xBqyqq1OT+TsuZ49XxwdF0mBcLY7u3T1Surk0d0HoLlUuXf3ztH8+PJoRJZdMZrsXD1ZQqNpcXI2Go7KqrKxzgfufLm0521wWBTeO4seeEBkGQXJLMYImvu+LRMha99/UjVDpR5HbwoqAoYogIokzOSQsm7y+YbAAMzcM/gIzIBEFY3MDExT1qz91akPEQACxdhtAA6Gpha8A7QM7J1nNgDIEkWNPbKS9kQxJedc5YMZxiazVwNkcsRWsi+Cd+xFs6A5ZueCI0JUUekD6b0wREFVFVAkgYgggvPOcnah8AQe0DY/fowppW7dtd1CvbZdvP/s+cnh2fGDx08+vLc4zWI0nIxGg3I8nV65dmk8m0z3t/YPZnt7M08UY845t13X5VVMse3addN0KeY2S8oqgOqr4XAwnsScz8/OsuTCex88aM5ZzWBQ1WVdFkXAgmLKy9WiLEok0RQZEFOWLJ8YFcFAkQxQVTIjM3khq53dCOHjo+O3fu/fX/jcrcGlETGDKgWygTruZNVqp4jUxAiCFXrzxKOy3q7Cjl+0R+2TNRcFFuzK4OvBcDguB6OiHnSrpcZOkFLUYoDlpHZ+f3VytDo6si6Xk6H5gIj97K1XagIAMeSU46pZL5btepVNy7qWrnVFFYqS2fWRcg6FKoqpkUNVaNfL0wUg+VCRd8ePH59MHh3cHJ48+Pjw3p3CLRns/MlidXjYHp1efOVTO1cvD8a1ambvzcSiqul0u37lZ15Zn3Xz45XzPLm8l53H6nTt5ingpe2vbs0XB6vu+Gy17E6Pzk6/8jd++Zf+4W/sX7kAAZuu6dF8OTWaY1WGMBms5ktXeFcEQkAyNQVQz8wUUJ0zKIb+5uuv2mznRz/64cG1m5deeaMYzgIjOFIxCY4VnHC7bgNTNR6S13Z+dv/2vePnJy985tXdq1fBlVyUgKhiABlEDAwN1NRxHx/KzjvclLZBiRAJkDiEQUDvvRmYJQcETKbCgYu6DmXJPqSUPgGjmRhYVtOkIAhi6CUnMkAyJBDTviQgSXv4IjsHZJIFDCUlUUQwX3nph0gCznkkAzFCKMpSRLNqjlIPBtWgRsAuyX9Q4hFRv1RARVVanKzvPpmbU4otq3lNg+Cqopa0KMkIM1Ig9j0yRWzDvlNT9NwHTgbD8ouf+8Yf/95v3/nwgxdefJ1KZ8yaOkQBQ3Lki2CE0rTNsgWHRVEVg0ERSqLQY697CTIymoKi9apO09w7QjaUQkAgImbLRoiiGYGHw9l6tU4CXFZZVJtVFZg8Q4RmvepW62o6ZERUS5oBNZSefb/zYjAAIo9OiZqsjx8+efrsTsgAVkxGs6vXb9aT6dGje3fufLyzW8t6cffuQyLCrLldcD0uB/W1F19L8+ftatWtl2U5qKpQlu7k6boF2ZqMtremLqCGXJgRZJNGVVWy9twHzbopOQiagKnz1i8MTHJKKasA9SVq61N3WVRyFFFTZUQkR0p9UFb7YHZ/Wc2ac1YFNQVG6QTAEMHEEFmt5yOjGauKKToODKlLXYqJmJ0QIampiLrCh8IhKaoVJThXOPVAQIQESICqSkbBuc1rC9Q0gxkCGkAyMUOznE1MUXLuq0pJMho4lawZEoppbtZt23VNmxcn7fz4tMtZtXl+fnzyrPUmF66+cHBhmrJevjY5OBjVo3oyG4Y6FINQB5dzPl+3TdN2KalaUlOBdRNTUgN23hUBvCuCC867mNLx4fHh6enWZBjYE5Fj32kaDN1kMh0OK+c4gbRtV5VYliwuCybF3qXVdFkANlKhCJi6yFyyc4YQHevB9NqFg4/uPnnrL/78U+/+yq0LXwSPqIqBqWZX+nVsBEFi6nxWUHIZGbWG4VbtQrWIq9XxMYZyuj+blduDoqgGpfMOlFyo1qfzbEAu6hptuOXrSS24PIflaplS40aDYlCFUID1+0iRTlSs7eJyucxxzYUfliPHPsXoqoK9V8CclclBooyGnhm9tIt2udCcJvu7HXM52YP7Tx5/8OH2jQsXX9pfzBcVDWezWlbr54+ePP7xj47mpzfP37j66VfqemDYf0HBVNjzcGfbj7rFslMw8aHe3yp3Z8XrzlIShTI49jW27ZOP7rz/5o/e+MWfG82meWNKld6H4IxNjFzhBsMOHkEBGBQgIiTFzMTsWNVS1xShLpm+/8G77358//XL1eL+w58s2hdef222f4CdsUMSzF3WlAZlwIJTc3529/mzO3dWHF7+2he2L10wpYyBiFTyZrn9CYkWDUMoJGdmT44MMWdFMANDRxVXBgxJ1KSLiVSQsisqETWCshwUZSXIm/YEqJqCiapoVtWMRujCprVsCMiSU1Jz5AVzTooivrRBUbakOWbrOWrOSc65SzGJ88G7IJIUcgg+i8aUclZfhLIcInDMOVAi9gBGCkTOkcsAjrCN+ez07Oz0qBpzt17m3Ip03kEZaL7qyDrQxG4DiTTT3tVKBuRJEQjYETvDSxcOrly6/v5b723N9kc7MyA0zYTKJSGz5Byb2C7XFPxgMCir0oUC2CGTIomimoEYkir0sDAUEe0lYtQj0gFUsiRVYQ5gG3ldXZZA0LQL9gGN58fLQaiG1Whlq261QjImJuwryKqgyEif2O5EQCB7XxByB/6Q7LBJFwdlGGxv3/j09rVXOk0P79zWNH/5hRc//PDO2dL2d8vcpigNa8ske3szv1UdPf3obHHiyir40pVeqE1N5/1kOB4AudhKk6VtU+Eieo65x4ypgKqZ5GSSAa0X/4Em1Ww5qYKJIVjOmiQ5T6SWJKecRA0BfH96Q2LPBJD744GhqeaUTXq+LIFCymJiPQ6oj3EqZAQCAjL17LEH4qsgAhOZqgAAGToAS20nOYFz7NixA5/R0LxjA1QRsIwIJgKEItIfkJHQQA2yGYqoWR/ZVFAjAlEzA++9e/jksbQyn5+vLK0X581y1axyt1DRsh7MSo+TMKsv8GBQbu+MJtPpeFKNxoWBEjo165omrtsUcb2Ki/USFIpQuFAE8illEMRg5LEsi+GgdC4gQJfj/GQFgFuz6agumZyJ+doPhnVRBAohSWxO1t16tYq53hpORmM3YGJIXQSIOa9TShtwkaqkqIhV7R1Tk22+Sm25H3YvK/x4eTT/zm/94cVXr0+uHyjlalj7shLnEkD7bD7bP+DOqcmWRwLlwo23pjidni0XYVjsXNqf7W4Ph9VwPPJFAYhdtwYyHpXd0bkBdIojQvbiKypkSiF0q0WaLyGnMKyRPHqvokDIgWSdkcx5B2aOmYhaRA7E3omI5GTZokQwqIpaJcW2zZKqSVkOSwPbuXLdNfHo0eP12frg5qW4lOb4vCzC7NpwsHf9/tvv3nv/+fLpXyB2ezdenuzuhYAIRo5VjDw6RFVhwqJgZvaBQBXUkAmIOXhCs4n7zpt/cvfd7+7tT6cX9pRRs6gKm6KhGhJX4kdRSYUdGpGJc5gtuMI5jq2Mt+rtqlx9+PA3//v/9gGc/epnvnzrV/76b/73/+Px+/df/7k3tva3ydfoXeqiKa6dnp8dHz14vpifXfrC65+99Xo1G+VoYgJOY+wYIG/EaNTzOvv6PPZ1a1PLOSXJOSsYMSEhM4uKtLlru5Rbw+GwHopozpkc+sCa1LTPlCLCBqGhSSRndhRqRqIkptDvOcVyBsdkmCWbiiaMlMzQ+2AgphBTNDURJYdlxRJT1uycJ3Ka2rOzdSicI6doSbPPPrPQprBnAFnAgakA5ZzWp8vYJUqxWaaYrRAr68IF0IV1qsCUJQMYEbH1A10wABXNoI4Ne6aeNMVocP7OfHW+KoalD8WmDtZAtpRjo6quKurhIBRljywlZgMytU3Am5kdAgAHJKbCezSzrukk5SypS12MkrMyUvAuODHNSUJRDOrxer2YDHc84nqxdlepYI6Lrj1rB6NhCB4UkVhTltwzi8hQs0FSy4piKbgqlENfbdXj3VCESy+9uv/CV301XJ0+XM9P9i5sP16s7zy+V+2+dnEy65rzZpnKXSa0ktLFK9dATp8+f675aVUOPeB0Om1hWY6KoipS4iKE7nl0GndGYFl62ptIjm0Hqqp9ZxDUFJJKypaSoqiqJ6aMomomMSVGFDURU7Oi8IgcszKBqBCwmOS8SWcTAgZHRgaAQBkysDJtqC3eO9sM/wAAyRRATTM7CiE49qo5qZEjUFNLmqQzS5R9UOc4Z/WOVVRyr3Ax095jDb0XDMx6YBYTGRgD/IcEa/CeGMSUgNXM/cHv/mlq2axbNk3bLT3adLI7mV64ePnqlcuXyxqTdatV61gAbGsnzLYnprZcrs/OzzkE6fvJDSyXXVLa251MhrVzISusl83Ak3ekCGYoUTB3znnv3M7+6GBvEIoASNqkNkcz8c454sXZcjE/syZl1GJYz8+WW9VwdHlkGcBawISpTW0Lhr2RhR2DITlWVY3qPSWr7z9fDiDkRh68+fYH3/3RFy7+FQ5UBDcej7wvWlqf3H9wcOmzVUenJCukAmy2PRhd2D8Pbndva3rhYjUY+MKXVeF8SGKqkRBzo87XRZm79WmYDpCT42QeCSFUFTPFbtXGnJbrYjBk8oCMKinpoB6wD6gDSCnGDrVP5DpHlFKXumwCURIjxSyx7eKiAUDnHSD5shrubZX+VXWFRvJc7l+58ByI2MlgvLt3MLp4MHrzrbe/890ff+t7n0qA7Mu6cEVQAwByiEyMnpDBQEHVJPc6NUaSFCVnYOwIotOPf/yjVz/96en+jMshWbTeBweoBVFYu2LYZQcEFMpQD+L5EpyAZzAoAArN6eHhb/+Tf/qtB3dmtPWFX/tr41uf//l/uHNy+70n9+89e/TMV5RizpqFkBS4qEeT/Rd+5mfG+xe5DDmBkmPHgAaCxCx9GRGJejdzlyTnnBOAuSKAac6CgL7wPfeSAIXZFNq2SU1XhLpKgghZEjlGRtDer9PTc3uUHEWFHKMJiSjTBo3Qt3l6xwKaWZ8SNYoxOe+JOGWRLEzU70QrH1IbAcGFYKoxdcvViokm4+lyMZdsAAzoJAO5HhqLaNI7kcgUNYtBRxCXTZdQlKxbu7IY7W49PnqqHMw5coxAImoiiGgIjMSGvAmmxG4ljz589P5P39q9eNGXjhwZauqSiDpEE6TSDcbjUBbsHCA779EV/fwUAHpjFyCqmi88EqFhu2hz03Xz+Wq17gBchcyYTV0IaAZAbCg5D0I5GI0W58ut7Yu+KKphHeqyi+16uVwtmuFk4Bz3XkKfJeZoZsxgSIbUc9+jRTYeFOFguH0GI3WpqoohJmm6xdkSUlq38mB+uLTJjYvXp9Py6dFZ6laBsSgsr06ILg5m0+7505M7Dyaz8f7F3b2d2RkhAnZNRi4ph+X5ajTcIYMcM5poTm3Xxm5tWU1ETZhQtMceJ7N+R2oxZ5FoBujQkVMDJiQLgMDGPUPBAFUyOFDt8Wm6CbmYkgEoZEjeoWMPYFnUEMwEaSPXBUCCDSgzBM/oiDALgSY1YwMCJkT25AIzEjASABsmBdWe2kxgoJo2ylNJROSdc8gGoGJ99dfACBkBQYH6dp+hu3P3bGu8tzWutsaXi6o8OBjPZtN6XI+3p7NJVZQcQdplE9dNl9vRdAyqy2U7P1uLQsXgi6J0LibNxtu1Hw4qx0TUGw6VfYi57drubLFaLZZMSC6A46IM21vjiffBM5UQWoutLpfrrktK6MoijIbg8Hy9TgLHZ41GrcpBMjLi1EXpcp+GF4C+vIeqmsWxK4NbtKtMYc+7CVu7Wt/7kx/c+toXpldmlnQymw53pvOu/eCtt1780l/lpk3QHENdEu8OJ4dtw+Nyur3LhSPPLhSClJMgo6uK3CYlMAh5nVTM1xUiI5Mrgq8kpegLD1SpKTCbQl63RMbBIzk1K8sCLFBl2KzjujNgImCm5bzJTeN9HXNMyXSp61XXnq6L0vUDO/YuSOm3+cIrtDg+SRGH21uJSMVcVXPh93amg7I4O3r8/oO79+883Ln6IiEBsaoY9PtxBIC+HKim0P8DCDkjguVkyJLt4rWXHj15dvT0yfjC3th5xP4ogz4MTbuqrApfa3JZkLgg9KZEbmDAYFCUuUL5ye/+s3/1B3/Ygn71M1dvXntB43z36uji9Z8B/bxK1i6qGORoCBxCUVZcV4CSTbto0Dv0oAehZ0Ql5GyGYC44MKDgfMHIJagwsQJLSqpKniUjO8dEWU0ANEm7Xg+HI0yKxKRqlp1zGMVgs0I1BVYCZDKTNonj2KXKBZGsKkiGYrHrQkATIUIffBbVnJnIGE3Bey9JmqYbjSpTMzAfCgVUtLZr27abzLZCCJJVVHyWUCCYgIJBNqYsargm9MzMhEVR+VDNV9ZEUaEYY+mLC3tXP3jnDvsihJLRpRT/A6m9Zzs7xyAGAqQ2Pz7/0Te/5ZK89OoLk70pFy52OSbx7IDIl6Ea12VV9l085xidg165rICExIQOAQgV1ovVyfOnR/fvLB7ePzqcLxerIofqyq0Xv/zqZBrMOQXOZpjUgjc153EwGt998NGNW34wGkwlIeA6tqv1GVMGFAQlJseEPrTLzlDRSM3A1PpZnMQIVBXF1nS2Wwyb42fts2eNu92Ewfn5s7PDo2eLRekHs3K0v3OpGLTpWc7dKhR5PCyf3Lnz5LBiN1Tis/XKMeOl3a1xLatlbNdnx6eDIaXcleRC4ToTQFIIKhLbLnYtGYCo9lBCUclZzTxzb1wRyVk1qRABkhF5QERS2ByoSQERsmHfmmYj6rfBRIRmWaJmIDJ2SAboAJh7MBMomaBumEIKhv1qRy2zkZH5ghBRRfu1wYYkBKBZRCCr5aQiAITOEWPPpDRGI9+XSnljgxERM0RwTP3ejJBERdWCI/f3//6vez8sKQ4mg7ouisIBMZB57xBzt+6aKJKVzQ9D0NZSSt06c9bxaFCWpUgGs0HJk9GIkbU/rOWMJs5RWrWnp+cnx8eLVRscD+uBC1DUfmtrNChLy7bO3TrHpo1p3clqvTxv58u1shsPAnAe7W4Fx+t1NHF+6MiFDBpzXJ0uAB0gIhF4n9sUgnpXeKTIDU/G9tJ1d/unY1mX9eD8zr33v/mDL/3tr8PQg6smF7ZWdf7onbdWT0+DdKqpZWwLt2i7QYzDQUDXc/HQCIixCCV5VoGMwp7NcmwbDN7XE+e9MZsJefLmRLJjj2zrNiGkQB5iTrkL41EoCskKPayVPLMYIJIz0rZtUpK6RCMEMGmbtGqIkMsSEFLXeCh8wY1Erku/DqntikG9c2UfiDVZTjk1asVwb//S48OH27vj2fbEh2BmKtr74TYZyl70CbTpdffSAQVVtYyFwQBKatPq+dP50wtE5MqS2HtfonnvgEPg0fAcyrMMGaBrmtbE1pld4AJdkT7802//d7/575+k5Y3Z7l//lW/UVX2+WnBZGDMH9uzNKmZGVXQk1qshwJBV0A3I+rWjKDEyE4gqoSZhH5C5LkrtLLexiU1uGwIr6oF3vhqV7DFiSjETIiTRlDRmO1/nWWd9rALUANHhJxV9BCRistyTIFBAU5tXyxWRV+jJi6KiyBZzx+SYWYH6K2eWnFtFhC6JqlRVGbscCgfsFDDFaCbr1aoqqtFkZjHlLIv1CsvKh6pv6BAFI0NFVTXIjl3w1Xg4G+/vPvvw49zFQAFSRh8mg7HP1DQRvRcRQ9Csm92eGjNKFlAws/N58+b3f3zeLl55/ZW9gz1XFFkEiMpBBUDOBV97Dr4nTDrPwP1oAkGVnDPpaxYsUZ99/PCtH37/3p135vefrI/mXcoCOJzUM1dcSFcHVDlG1d7UiZvPFbvRcDifrxxTWRY+0GLRrldd4ZyMB2bWA81AlZmCJ+k7xIQbbG5vCxEpysH2hZ3H29Mnp3c/uHt33tLuZOfw7Pjx0cmwGlRQhGo4m4w4rWPSuFxbknpQxZhuf/j+1VsvDwYD732TunWbtwejugznTw/X81a3dTCtDi7txCym2PtekEktq2YC3IhSchJRzYI9aA2MCBkdIDgjBetRv6AgptIJIBBy/9zVHmlLSsbQE4EMNKsBhADMDAT9vhvAhPQTPrKYaJbct1uISc1UVEmC40+8M6qmCpITWhYFYDQ1I6Osotmcd0Ye+vwWs2bdqFgExFSTminZxvKY0dihmBgIEWkW9+ILB+ZcXXm3YcmgZc2aU9vkHNs2r9tsyIH9oKxI6Oz8/Px0NdsuQ/CSU8qSUvTOBaV6UFZcmIGYczkD5MZyF3NZVnu7+4OyRkRywgVXdTGofMp2uuzOzpbH5+fNsmmPl2mpyyZ3sSkoD7cKX5TigLxokFD0A+EiNutuvaSeVEscimoxb6ta+9U6FzA6GLnLr81n31l9+M5r18s24Xf+6b/aOhhe+soXKYwq4kvjg4/ff1uPn03avA0l+Kr1tkpxXAcfvJGic0DUq6J7m52pITkHLp6ftuvlaLZb1JWKQkoi0rtw+tQwqKFhVQ4c4Pr8vFkcu/Oz2cEBh4HkzMxiyugIvCPSmJtVwwUaU1VXSiQxFiGHofdFLahRsjS5Ho/q8WBtSFX97OGjxdl8vL9XTQeADIDJ0uH58lnXXvz0Z1/63Ocn29v9Zqi3o/cAhV6oS9RXBABhw/lT0yyZTRGRy8la3fnjQ7l51i2HoAYhq6pXtGyE2RNES21eZ8nkQ0auq6IODpfzd//tb/2T/+fv//jRUST8hW98/tYXv7ZWl1VBMhFmSeocAG5s16bGCIhIiEgOzQCwP94wGigCo2MG7wdl8N6xO/ngwZP3Pn7w/tvt+en5yWERaO/KteHe1Suvf2r3xQthWBhLTkm6mNsux9ism0mKoAYBDQgZzBQAMwIaYh+rBAHQbAZMOeY2xtBFRMsxmfZHbUYkJOd9QUDsXOxa6HelYirmSt//UegCIJsJiKyaJTs/mMy8910UMHc6P1dy3vlRNcqUHFabmZWSgJgJeiombrty74odHz5D66qqUMhFXXMoVAWResNoztLXFZCtx2FJTst58/6P3354797+jf29G5eQnYr18lQjc577NHK2zOrIkxEp6AZkjL2TlZg5p/zRd3/wzd/+3ad37zXtuga8TGE8rC5sz9zFg6eT4fnT56NQ1OOBY8Bg/QZXQSKmelRJ0xooe25X+fH545K54LCkjkrH3gNol6JILka1iWUQJk/OskVSEMsKaK7eujzdv3n53fvvfjx/emh5Z3HWrJpVcpcvztJ5x6Uvi8H5arVqzpZrit1iMNgK3h0dPzt+frS/u3c8O1wcztdnje3t+6oyxfOzDqru4tWLXIWHT04zZSDz7CUliUlFBFDBAHoWMzrnfPBMpAAmmk0JgQl72TQCoplmFVVTQRbMxB7QQEGRCEyBgNEDqXfc8+E9U9/3zNYZIAKZgYqC2qbTiogGuY+omRqBgBmqAaiICPREfU2aRRiBHaGhZmBHBKYpJUBAYHWmGmN05BE5mWTJIuLIsWNU0JwiQjb1rs91kSuCQ+9BIWUFw5w1p9SlJnadgPX46KL0hLhed6ak2V042K2GLov6AusBMI3JYY6JiJxnz6RIGhVgnQfV8EpVVUUoSgYG0CZ1Z2fL46OzdRlCVVbO4WhUOJ/KFIczScjBBY+URYqcVWJek3eqHZbRVJpOk8S0jtoIe6fZGM2cdW2Mq7YsCmYbjx2Ni1UxBll+9JOPPveLv/7g42///v/9n/9K7Q9ufDGU1ejKgb71cXP0UT3KgxM3ACo15tQZgjNC9IyOHRdl6UJgIkMkU6eSFuuTe0+ydoPJwHsnahssuIohGAKhQ4SqBgZXFGWE48Xzo/V80Vw+2bv1ki/rlCOHoCzBezTrYsyqRVUpga9KctQurHQ19yNSQlLKJqISQhhujUMoiXlxcnh09+5wa1oNRio6Pzw7vPdgsDV98fVbu1cuU+G15xMi9y1w6D/aiNo3AvsxiG2kBUCmZuS4mm4f5mL7/smt8zPc3bE6IKAYipozIBcH+cylU0srIhWUohoE8OnJ8x/+5r/417/ze28/XyQoXru694Wv/epw74qyBbE+XUi91LR3dJlmAVTqma0I/VqWbLPM6vFx/RUavWNZtu//+Y9+9C9+6+Txo/NFa6Anp6cO8t7uh6OtyVu7f3zp0595/Rufn126lInOT87Xi7McOwMBUU0phIL6qEXSnmxJ1LOysppJjCoAmRgxp7heL1TMtEtdR4wESOR8cETMziERZZ9SJ5YBDJ1DNFVjYjXQnHPs1u2SSAfjWSiC2uZXc0zPHjwkybiPw/HQNDvDbAKbemQk8LPp4NJk6lJ399G907Onw+l1ZF/URVF4kQRGaIjokAQUeu8QEHUxnz87vfPmR3fufDjdri9e269GBXqnqP1v86VnZmNUNDaD/hGGaNArAfuQlTEHULj3w/f+7T/9ZydPHtdYvDDbGftyF2Q2G3/2iy9PXnv53XO8e/Tk/PicyWgwABNEZlLTCGDVYKAGmgXJNblL3TqlvFqtilEYTkauCIqwbppyULqy3KBSTZzzXl3SDGICsUurwg+m+/vV9t7D5vE6L/IKytbtjSaXw/DjvBjsD7iE5eL5sl0v2nC2bAcTm03rh891dXy6v31w4+r1N+c/PXx+fOPynhtUbjxonj0fZiQk1oBgwgLsiZwkUc2e+0SSgaGase/fqtQrdSSLgeUsxERMQGAqqAiEgZwhAwEiEPanbTPLaubAAQqDI1IGIDACBLOoknP/0kVQItuI2/o8tWTVrIapR6/lvkpuYD3zPGm/LwjO9QPAvntMZGZo0hu3hL0agPQvLqNkKYsQopI5BTZImlUlqQE4AgZMjkORojRdowo5ahdT7ux8tQCQalgTcxFYDWKK3jki2BuOyOPp+QKBqoELzguCKnSibbN26B0xEGiSlJMhlXWBjqLkrK1Ea1O7WDfNenl2boUPZVnUYbBdV2E69oHIE6I3AFYTkK7LMbUR20aarlsCrI/PF7Ope3r3/cXjF8eXdzNCWfjhsE5NNrWz5ZLRRkPe3wmPq+ES6ifx2Y3u6Ku//re/+5v/7r3/5neq//Wo3D8YDC7P9nfeeu+7RVpsgy/P26v71dlqTYrEHsm54ItBFaqyqEokNKW2W0tMZ4+ePr798YXXr4ymOwiUcuqVGgqGDgnYkQMyUIgpka6Wz561z0/Pnj1ZP3uWY7z4+qfA+ZhSKH01qFbLs7PzuSGS90zODQpCKg1ECxUh8kaKKXnqjWyCgL7i7YODYT04Pz06e3787PjjaFk6rerR9Tfe2Lu850ovitaj9tVUtQcZYl+WQwCkPgcDhgb9JAYRyPliMD1YVpduP3//S21UE0ZnRGholiPzWskMuW1WMftqNAqlZHn6zvu/93/8x3/0w3cWIqWjl/f3vvzVr1369OtN1/pRQUimlk0JAHTTHwKzHvMMZqgAaKiA1MNnlQkcu74yU1bBor33R9/6k3/yT9LJUhJUZX3zxZuVq7Q9PTs83Gb++O2Pfvze7Sc/+d7LX/3y7KXXqt3d5nwBmMSigfWNXwLgAGYCgJ7ZUlTTnk3agiTUnKKZYIJVd+ZDABBmli4ZOUfm+qcvmJoQAXtiQ3QOAXKW3sMrOXWr9XK1qkblcLRVlrVpb+jWsioGZfX8fP7www8lp518MBxPgvkQgppsfCtZ6tK9/uqtdz5+cHz49OTpo72LFwvvXfDFsMwxrpuWUIRQVYlBAQEhSzo/Onn/L3969/17xRgPLu1NtrZdUeon0QgG39spzKAHKIKgEYEBO2R2iKSKzjOSnz969rv//J8/fvJkCFRTdXG4c+PqtfuPb/9kLsdH8y+NYbB1YX/MyQg9ieWsyQE58j2LylVF6cOqXRGT94UFPHx6mKO9eG1nWNdMnGIGg7Is2BF0uj6dx8pNt7eC92ASU1IzgU7Nj7aqF1++eX76eLHqQLASuhBqWCys7aajHZG0PD3uls1xHJwu2wsQyulkWm/FeaNdvLS/dXxw6fYHHz588ODyC9fq6VjqY0OZH55mj9PxBJIUZeBQpJxj7DpN/P8n2mVygN4kZ0maxAD6hFSOgiSIn9jMiXrcDmHf5YN+n9+jDwVNsyioE8yETIA5ixohex/ADICMkA3MEAkNTUyANvJdA+xbCn2bF8FA+mI2UCBCZucMTGVTzAYTUVAVgZyiqJhugmz9yBeASLNGTQAiKbvAjiEncUym4poYcyPLtssxxyj9e6bwjkPRx7FbycGxEBAYWD5diiqnlNBjcx7prNdng1jXpiQbcZKpmvPknV9xzFHNpG272GXnkdFVYcAOkQwQ27RCLkKBQI6JDJURC8+mbAJFNeogzFerLmsEY+v89s6z24/e/fZPPvNrX/HjShFd8GenC89skoisrspLF4qjn/3MX773l83c/vxPvvO3/s7fu/T6B49+71vFpcmNX/hbl175+u2/fPP5yZPmZBWxEIXABZnPrUbluixDURVFiQ5FsyZNSTB2x0+evvfD7/thefHWy34wbGPbCA4KNDH2hApKAAxmkEWaRbc+O1wdH6LEvDw7ny8W8xMKbv/Tn4pdDHVZD4uTk649bHxZ++B75pSaGCMhuhDMUNWcY0EhQpWE6BCBSqrDlAf1cOugPT/v2gV5GG1tDScT9KGLAkQEWUFdn2ghon7a24//EXpNZzbrp+NMrmtiUdSDyejKlVcefPSTxTJermtkZHTWM0+0Wqu1xeyB8GdyVyBgc/T27//xv/3H/+ztx3ccDG7uDAYlf/pLb3z2r3zdB0zGZhYlM7FJxk+E1D3N03QjMEPoJUYAhqbg2BGigSkAMSLx8vD0R7/z+/nxsyv7B+j8MRVaTPz4ytifYFzn1N062Png8OjBj26fn529tm5f+MYvhrJq1ikZ9la7PvzAzvWIJCQCwJxEJWNGQhOvwrA4OkPvgmdBGRQDrDhiC0YxiUjHXr33zGRoRPQJR9qYTDWvU86x69quHAxnO/vOOxFDAkmqJr4IdT2YTKfN4fP7H36wXp7uHlwcb+0UVVUXpSEVDhATAVy6vPf1L33qe38pp89Ou2bNviDPFuhssVotVynnmLok5j1UtWPnsGnu/eD2k/fuWpv3buxvzSYhFNGEABmNiMk2y2IgBkQFymZOAYwdBTMBBGJyzpPgW3/6/WcfP9yiolItyN9+cnIGTRX8w3X84ffvtbf2vvD5g3I0ohjZMTAmMW/Uo/JURRFc5c+Xi2EohoOwWDh0dO3G7nR7opLapEquHtSFL4xAJD17+gRQ0YfRdITE3rmcYln6uiCG8MKNi83hK4/uPOaFVWRb5M/XazFXDScny8XRql0it8/Ol8+e2ws3R+Ot8UH76O0HR4+OtybTS7v79+/e/viDj8fDQTGuL1zdXa+7o6f3/GT74PLNJq5dCKEoNUc1NRMRtH7nY8aIiJw3JGbrhSfQT+/NwDSbIFIPyGdC5Q1NsCfRgAIioZCpKkhUICJlUFUDI1RWJiBA632JAKpKotr/1cy99siYWVVgoxFUlYyE/d4EzUzFeuNvNjHoh0mAqhnMcm/BQzQFI8SexAMAYtJzT3vrWC86JCJ3vli3XRbT4ENdOMcokgkrh5BMlov1crXuqziGSgySoF2J8+yCTzFaFkn95UeMzXsK7ARMkwBB4AKQyLiqPBsPy3oyrRDJUDbdFdyQ0mKytutcyIi8Ca5m8d4h4HmzpoXiAuuKnBMoy8fPH//J7/5k79aNK29cJmJXFuxYY+rdHcTuxZcOLJ+/+/GX4bf+NMHpT37vDz73d/9Xz/7ym4f/4k+r8WfcS9eLK/vH3/3oJMZoOCVYAFdhdHp6trrz+AIysycCbj2HUhEty/Lx8ztvf+BGW1/4la9v7+5GQ1EYlAUhMCD0gRnXyx+MRSAu5w8eHr1/b3x9++Dy/od3Hh0eHg62R+NLF6sLly2bL0oAbVNTlpVnJ1lMpDeWE6ICSM4mSQDQ9dBaFBM0RAdELlSFr4rhbAyQDQwdcD/hIehNWGSfTDZ7FgCA68NAoD1fAQ2AoSBOKpLFKO/W/KkXLj3+i+HR0Xow2a+HxWo1X52flNo1UpfiQ+pmMN4fFpwf/f5/+a//p//Xv7nXzS9w/bnXD+axG77w2Vf+5t90k9m6acBVJmrUO56xv6T2rkogIGIARUOBfs9FiEroRRQdIVBfs9esD996205OdoaT6XA7Iy3NkNJHd751sDu7dOXC+z9999JO/RSxoCYAAQAASURBVJXP3PzWjz6SR6en777ffebT9Wic2pXFTECWcq+OJCJj6vEGRj5FWZ+sRNrgqOag40HXrJaLlePgxOWcqtGgLEcGlkE0xZTSqm3ADBE1b2R7kvsRgRRFGYp6/9KFuh6gxx78imIZEiCUdTWaTXOCcb19evbk5HCxWn08Ozvf2TnIo2lVlUxQhAK8uunw9TdeG5SjxflSVm2LC0u58MXTRw/ufbAN63S2WKIvQ6B67Ms6pCenR+8/lGWcTYY7ky1OYXW2zCHWdTkYDhD6CRMDMWjfCZX+6qdqZkZMatD3+Jqjs8fvvFfn+PKly5cGVx8z3Fscf/Ts0dhBwWNanT9458EXXv/CaHt7tZzLuuvl8ilpWWEfmCSEemvSNMvRrC7rOvhib3d7Z3sHmZarBgyrybQsKyKIWc5P50+ePKmq4QUR7RSQLKonZqA2phhz7Yv9vd3F/aNlezydjkuI905PrKqCLx4/uXOccjUYyWL15M33Vy/dGl+/PLqU5h/cWX90ZzKrq73tyWz78ZvvvvfORzdfe/ny1o1H7f3nR48vjmahcsskCFgUBRISsUMHoGJISLZByucs/VSeAMGyIpF3jtByFurpA8AmKtafVkyyARgTYz8SMlVJn5y2NImo9jdRYcS+F5JFRAHAiBkJ3SenNOw9W5+4nPu3BThn1re3BLKJQswiKXcpbqSwgD54wt7piMERoIkKMbnABKROmQi5B1CjWEbA4J3m7J6fLJj9dDIe1gHM2hgNsOm6uOrWMS6X65yTY2dqKbUGEFuNXa7roiwLBSuRNsIINV/wdDwsfSFqmpUInHM+8LAaVINQOCakqKntcttFUwFwjnkD6DKMsY1d7GIrIg7ADJiR2GLSXFDVIK8yAA/A67OFOD15dHzhhf1iGMgRmlO04MNyHVdny+292cGNi5/6n//aj37rTwKcnPzp7+Tf+PUv/u/+wVv/xX91/5/+Py7+w3/4whuvPvjedxrkcx+F8p354vLBFNvq+YeHo2I4CyOoympQEvqc9fjp2Z033x3ujl//uZ8bz8ZZ1IirujZGTbmn/RH1WQBhTyYY07rrEpXDALT7xtUC4nf/7bdOP7pzevfjcrZXjobtstEMqRE3JiZn1N9DN1LS3OXURciJgutHaohKCgZs2RSl//gCAwIbqpGJYX+NzSk67q1EiIC9H42ADExFADKAEhj1bxVCc1Sww9Qxldcu1/XO9PR85XhQlqN23Zyf3LVmEf0tG1ezS/u//Mtff2m283/73/wX3/n2+w2017Yv/82fvfXB47t08/VP/d2/Xe5d0KjSh6ARwLR/6Fpf/BTrYVT91ssAcPPvPadeDYCQzZQIkDTGxpxefOX6xXSlg6ILk7OjRzOX7hw9aad+/+ClD96/c7xO+zcHL1y9/s4H76+PTuJiOdjahhTLcmigKSdJiRwhk/YY2xwdMrNr2mZxdFxWbjSqJ+XA7e57dxrXDahITgY2Gg+RPGKftAfNopolakpdTllVJCshFLUPVe19Qc4D9pm+ZACm0mOSAGG2t23o23W8tLt9fvTk6PmzR3cfnj49m+ztjbem0+lkMhlXtaudn+7tvxj2Hs+fzc/mMl8x+QLgzqMHMYItu+WqGYyHk3JnNqumg7J7dizzdYkwGQ2KUJ4tVvNj4bqcjCuNVo1qMBRQNSS2IqAPgQhEkwMvav2REhCyysnxSbs6q5F3JtNv/Mb//gdPf6J3vv1okJpn6WT+8EI93F2m++/eeeGN7cno4DwfS05JkievMQFzT5qsh4Pl+fJg90IdirIsmbYKN25y1zRdWRd1XQTnRS2tm6MHj6Fr9m/eKsuRoV+tY05Ql6FN+XwZz4/m66dHZ/cf0Xo9LcP2dPhksTyVuD270K7PP759ezqe7u+++O6HP3z6tH337QefPrhcj8fTna2f/uQno1n9UvA7uxfuVx/ffXx/sr+ze3B1Np4+fXTPAuWc23UjmnwJXRsBIThHznLCmAXMRIwICkYi7u+sZj1b0GxzbDLH5Jh7SW+fW7P+6s7qnDcRAOlv1j2KBxmxpyEi9YYQyZol9/OjvpueukRI0AujiHqlDDnuUUJqYKIJTCSnrCnmNkXpEjOHMgCgD94xIhMpKvfZP2ZjRAK1/r5ATM45AjIEzmbOZ8kI4GY7I+24WbV9KraJ7bpplmfLdtEIsIEw83g4KEJgXxOCSqxCubM7Dt6x40FZOWJkIDR1GzhX7jKYFIUPofBFYCRyYGZtXEfJ6zYhwmAwKKvgHBNgFIltB2gKVpQBCSHnPqRCbDuDsWv52YfHzz58uj5Lg8/jzapz9QTbJJ3QiLRrT5+f1cOgQusWnj05Hk22Z9vDK/Vo9Zmbt3+yPmkW3/ntf/U3/g9/5/Jv/u69H394/0+++dLf+093ZpcPnxzl3Anio6Nm/fYPtw/2tq5cis+fH3Oen3EAB/Nufvvw5PSwvrz9lf/4r472dpplZ0TOux70kBlMBZABCcGAABHJGRINL+zsH+ze/YvvNwu+fOvy67/0M+/+6MMn79+evPDqZGvKzmcVs9xHC8j5nDsDAOQcpVt3sVkTmycjQvK96A1MBIHE1BCZhAgAsLdsf2L4A0dootY/gQGg//yqfRL/7/dK0PMhFZQJyoFvzztQrQZp/8LWvSePTh4+KEevS4tP7590O4PxcLT96rXt66vVg6P/+v/0//ng8eMRlF946cbPf/VTP318XL76pS/8vV/2MFYRZBec79U0qgA9nJ4RcfN3O0OmTz7tCJuZj6kpOO+zqaqQIXkEMS/F3vb1S5cvPT89eut7P26szW1y7JfnJ+fiLt56cX58dHvZZQd1Pe24IOdM89n5cvf61aIaIkDKCZGYS2PalP9ByqKYbe/ndTo7PIlNHE/ScDgpL4TVYhnbaACpyy0vB4ORCx7Zb4i22vs/+l01mqkhqBlsoLmgm1Q74GbPsfmtdVmEiwfLxXrdrA8uXyuHo6OHT06PTp4ff1gMw+6Fvelke/9gOhtXBZVnaaeBrSeP3l+vzi+98ErhSmlbiNSu16u2TUlc6SboJGI6PsuLphoPh3UtIs9PF6dn62o4lLrKk9V0d8ZlvVo0bQJX8dZOqCfDwCUxZE0oZug2Eq1ssWlaadlwBL5e5rCaNw/u3ZxsV7ODo9ujp4d324eP3/rxD9/6yQdXXr5+86Vbg3qIYqCKhoicNTNyPRo8OzsEg1CXg0GVyzJlAzRyxWAwrqsBIndtd3R8dLQ6uXj94o0bF6gcncwX5+s4q0tRWTx7fvf+o49/erdoIc6XlSuvX9ktdP2wbTrndvYv3Xn03ur0/Mqtl3Zee3V3dXTvrXd++v13yv2d/WsXru7tP9zePbzzbP/ibFxfuHrjlffeeXc+X6/bFRXAdenLootxvVoxs2det23qogssiIZ9/NIQzTPRRs1BPcQdQJHBxAAV2BAw95J7s6zGRLixC1vKUXNmArSeUc592wuhB0aQ63P9KkzcX4WZWHMmAEDrcaSA1nsUU94Ah3LOKcUsWaIIGhupGZGri8oH33c41FTFCDcRZybugVcIBBsNTT8IytZfRlIEA2J23XoNkVbrdj4/W62WTdeJSoqJwU/HUzMMgWazSRWCgpUeDxBH47oIzhQNgBiYgNgALKbctnmxbJdNQwihKwpXIFFfrAeCQDwYVKPhwAc20JhT6rrY5rzp3akjZMKYIioiWVbxgYfT0DbVnTP/g7vPVzF+iZuLt65ZdIUzz0xo0qTl6XJQTMwYJTQn8tYf/+jirYvc4Y1/+LdPftd/8Md/XvzhN/N/9le/8h//tZ/8+L/pvvP2K393eeGFV9+6/U6dcNfZhdStnz0+OTk5efOt0faY2/MVZFxEr6mEsPXGK6984RXX5ZMHT8+O5xiK2fa0GtYEwAgK2A/hUA0JCQjY1XVAKKBpD16+ceed93N6duHyld3Hh6d3b6fl0gDYs0EmQ2JWVWPfbxQtQ1zFuG5NRQlzzIyE7Byz9kg/AQIwQmJERFXptXCg8B/O0bhJ94GJoiNTUTHXe8OYwQTMco4pRhBTVERHwQHyoBxOZtt3P3z323/0rZ8fDPJqwbY1HF8cXpgu7tz+zv/7T978vW89f3r06nTn1VcufeHz17/7wbOtz372hc++1JzGMDJBdbVDJjXtmbq9dsMACPpGGiLyZoSOm61wvxEgpl4W45gMQbJQEmlWcTFfpj3/4hd2Htx+dne56tIgFLKS9z9+97Nf+yv0/rvnz+fHd58OhpOtF68zwNHz55Pt0e6lSwQgXdu2sU+YKoAhpJQQERAHdQ0XLxHg8fPnKZ8x8nhr6jis16lrk8R1Dtn6F6mKZunF2LqBPgJiL138BCnRV4zVzBRtk20HJPYgqoBYFt65Uah4uVjNdnZH9aAcPH785PH9+w8OHz4eT3cOL23vH+yUw1mT+Kw7Xi3PtOsUyNWDutx/6eVXTg8PHz5+vJqvkMChj6ft+mwFJn5QDrZH5106Ppw380bmnRRD2w6jomzP2tS0Zrg+FdYaPZEPxOBQs0i/gmEF8mE4GQ62B0cn6cOjh9Mf/stnz5/xejTa3ts7uPXy3vXv/PvfPnp85/Aspq4bnn1IL1wJfrvVRkTa2DFhwNIQQlEuV2ddajFgNSoXZ23TrkfT2jENBgPvQtels/nZ4dPnk8H00oWLPgxOztfzs8VkNrG2OX748OPbb91/+PjsWC5Otgf16MrlC9vbxfFH501Mk9mUJ7Pm6KHjcy8rN2qvvnzx2fvvPH/+7Ju/+0evf/aFV158+es/+9rd++8v5kvaXt68fmG+OlpqPpvPJXc70+2yHrdtamI3mc4MZLVa5RwzOBDI2knOks0xOnQECJt0jYkaw4a/jQyWMfZEuKxmSkSMTmTDdTMwMiPoxV1ETApGG4kdWb8mRCJvKv2SykSSAZgoARE4QDMEAUtquetakWyUUk5tlyQjQOHLsvSOHTMVhYPeKa9moGBKPvRZJet3/wYIwIDIqGr9bUJRsyj0D+Us7vRovlqu22UrJk3TEvvJeBrIjUeT6XDYSRyMikFVpKxoeTwuUa1JqV21TdI2dpZUUYvS5xTbruvaHFMyA++LHDChuBA8h2pY1WXhmYkB1GKX2th2KdJmFAFMKCL9tpkFmKisilAHgSisZZEme9U7S43xdNmdVlXtEauBxxR1hTkaZHXsfPC5dTPevv0v/s17t3/nM7/xV/Ty1mf/0f8sP/jw+HH67u/89K//L//WwcEfffT0ycPvvvnKl974s3/7Oykn8PCFK5c/fPL8qFmdwOrs2fEYbABuWE9f/uyLn/47/8ne1187c/Gtb3/XX943hfb09PBRtb23NxpPyukUEFSEmRSAmdWMFb13zeqsOZ7XB7s3J1+4/d7t84+PoxJ6nR8fXzYAQAAiZsNeRQpqfdgXclZEb2SSOo0CktGUsGLHBoiWDQFIDZ2aIIBsQC4A2gsrlImBEBXZkSEwovfQP6wAFMCAra5rSqFZtV3XKRh5NPCumkwvX+q+U7/1nZ+89tqt4bR6+fNv8Fo++p0ffvNf/e7T9+4r5a+9snP94sFid/Jnh+cHn/nsG1//9MM7d8rtLS4KdA4QAM0xgRkTq/btRAJCEzYPAKTaayw3urlNLgg3sjNVASJFTirny/Xjxw+d50njDrYuF0SqVjCu193p+emdD9/f2r54Nl+cK7z42uWDl18oxyMONNqa1nWBZqu2SetOCQEcGpCQZcmIjklRykG1e/mCFbg4mp+uVlkNnYuaV22LoBXVAAACBgKKZphREHHDJgWx3rTVo521d+ap9fxHg372Brixyypk9K7iyjk+m8/R4YVrl4vZdLizN3/yfHF8+OCt+cnjJzycRjuPkAeNDKYBjCnQdDbe3b0xrMeYywU888C2bE/nT6Froa7L2Vi8Pzw5PDo9hJPEIdCMfDlt1gnBj6g8evS8WS6AZ34YDF01dlQgc590hJQUvN++efGLv/bLf75aPn789H/6iz/AytXDqbSzgcbm9F5DcgoYlXev37j4mVfKMCjId7JUy4yARCKCRI44aV6tV6EonXcmCXI3qLfa5It6oGhNu37+5PB8sTjY23Nh1HYkKe9vT7vF6uFPf3z7Jz9dteukZoYqTTGpXri6/+jJ/SeLE1Pdme5BdONivOR8/62fDv32+MULNy+Nfnp7vn42P/rgftrav/Xy1aKyp8+Pm+Vitj164cUr9w5PzlerQDrbGVVVeX7WZqByMBYhySoi2tttTBDReeKecatifUtRTFQTKAmBArChbQylxAyf9HIJiZn7OoExOGYARO4F6tD/WWoKCIZuY1VQMBAwZAp96oEFBMQMsmoW63I/6FEiVjFDdOiYXBnKogyuv0SjQr+DEBUVZEJRsUxI1NcLCFJvUgLsEaSShTyqgap6z2jm3vvpHTOqKzfZHm/tbFdV5YswLKtBXY5L33uOk6iYdm2cn6f5fLFoG6TAYJrRoXPMsYkxd4gWQjmsR6HsuT4c2NVViUSi2UBVre3yYrlaN02XOiIbDMeDUPoQBqUHhDJ4NGxy0pjbmCIYgDrAYHF3a/il7e17D+7HO0fV+GCeOYuPbUqSkmkxHRRVWRYBU/FoeZaeH06On3a//69OCOhnPvfF//z//L3/8Z8d3X/3zT9/4e//5//4//q//Y3DH735pV/8G5++vPPjO6eOQ+MGf+vnfumDZ3cHOzOL7dlifeFz1176jV/Emy/g9r6WaZThoMvDnUveudh2aRnPFyfPHj3eJyqnI/SmZpLFEB1Rs1qfHZ3On83z8Rkwl9tbNz/3+kc/unvy9LZuiSsQCJjJecYy9N0sBAPiHtsKgMysjaWYNMfcMWgSzUVVkWPlTwTVpp+k+tVg42xV7d1P/bkAkJEZLWqzaNrztlss0+JEC6ZxMdqeTKoiFNVwMlzH3EZFUOLyxks3r956+clPv/f+Ox/81X/wqzAa/9F/+c//+F/8u2p5fu3iaG97tirxvahSD176zM/cuHhhfnhMEIb1iIEIEVWRNq5Ehd59SgiAaozCyGiCSCr9SNRAgQiBzfr5ufW7bEPlMCwvfeqVp+9//ODRPUu5Gm2JnzVxEYqQE+ha1ovT4NxKm503Lkxfubl19aIfDgSUfAhFCQbsm3XToA8IgIpkvDxvGdNsa8xMMXXoebazC0inh0+Wy6VlAVEsy9n2rKpHLgRERGNkEOuvMaiqfbOiz1L0c17od3b9jKg/ePWgL+JPZgAImgk0eJ5Nh8sl59hNZiMMPBzW+fLu+eNnR8+eLJ4dHa0eNa7dG+CnDj7F5AF0MHSh6IIVeWtQddP2BI6PDyWfecC6HkGoj07Pjp8dNvPzkWHli8GOjyGuO3dxd7q8/+z07qHVLjR5fbpCDL6snGcURuhNViwpZUev/dxXRoOt7/7hH58+etIsmma9ePz0g7R89vzk9DguL3/q09WFK9dfffHC5d1qNCQyRMuq2hc4zAwsODbVtmvYOxFQE2YDouGwDj6kJOuz9fz0xCFXg5EvKyQ3LH13Pr/zo7c++smbbXdelOUQaMiAHJfa3Dk6Onzy7ORsPijcujvi5bBgiQLHzen5N/9g687W3v6VV1/ZyZgKZ8+fPhkhDy/NhmxHdw/nBW5Nqq6dolmoeWs8IXApKSFPplPN0sVWuo4LZgKCXn/bt/HZQKWngfdonZwBUVWcI2Zz6BSwhwURkGcy6Hnz2N8JpJ/8Sc9lMzPbiDwRiMTEqFfSCZgDVU05GegmnKYbcKeBppg5cAiuRDIo0NARE6FjBFBEUFVCdOx6XDky9WnR/pf6Wyj0EFYxZFATM+UMfZtchdDAubIc+PHOrBpOBs47H3i2VReeCaGTKD0UWzS26eh4voxxfrq4dvXilYsHdR3MwLK2TV4sG+ZhCN4H7z2HwEAIpgxGPpjkxTItzlZni3UbJcbODIaDImCoQjGsah98PXBAhGbRTBVWOS1WbRcTEVZVqOphGg4+/bWfvXF3ePKsKwauld6k2xC5ogrbO6NyVIynw6r0F4Y4uD754ONn99+fr8DSux+nM/7b/9nfOPnzb97+83/58S/9jZd//evf+cPfv333vYMXb+0+vOua7qdPP7x7eO/KcFbNDj7z1VcfnZ7+/u/82Zsn7xe3XolaV3V9/UtvvPDaa5YTBBeK4DgMpvWTu/eOnzwadDujrZmCqGSVZEi5iav5/PH7988Pj150ul/54XTy5U+/+Iff/7PVqbnSESgwgSoSGREyASMx9iEZJgQzSVG7NsU2rZVh5J0Xn805JlJCAwAiE0Po76s9lBaJ+q6ikREyouT18fz5/Qentw/Pnyye3nvuOL/8q69fvfl6Mrn/8f3Votu+tDMYDhUImX1RX3vxwtd+4Wf+zYMPnj86DKPxgw8e/8W/+3MvqwsXipqb+3OYTw+ufPVzL336U6Nx2ayXmqEaTxyFXtjYU60sawieCqd9CVk30H5yuMkFKRpi/39cQfo9QN9Y62/EYh1YmN7Y/9Lf+asf/tl3nj+468+W3WJuaGHolcAP2MhkgNNrV8ZXLlx68crk4r6gs5iLwVDAQAUcp64jBUnJow/MlvX48Blg2trah8Iv12uDXI2r9aJ+9vAhdDocj3e2dmbb22VdIaDlLJhQEbiXEhCg2CbqYZunfw8X2rA2sE/vbSKomyaeARj2qWyEIgQ3dbEJoVs7LGzdzte4tbdXSFrO59LFs7OzFjF1uW2TgZBlkwZAvRf2bt6eNfk4QB6HUe3Ldnm+aBfLo/kWyw4WngALa+J6a3trvTh7/uGjVZtmV0ZFKLVJXdt1MXJRgDdGZkA00JSaLiVfX/7cp6YXbjy4fef0/sP1+TzmRVyvqeRXL35h/8Ubo9n21sGkHheuCLGNGiWmnHM2CNrnEthZ1m7djEbjvm+uBjnrZFIz2rJZLs7PLMfRbFwNxqEaArjlan737Tc/eufN1WJdFezMjUO5junx6erSZE/RPzk8SWA+UAdud7Z7eO/x1I+WTV5au37wVBfDX/qbXxuW/v6Du2+/+cH9+88+/9XP716YntDz4+fzq6P9vWlYJKmqgfMuZUttItTRpIyxyTkrSLtsTRWdESKT847VOQAQFTN0wAxI6AyM+8xy1gyJC8+eFfqCjiFozpb7OoCC5KxqG5+Eoimw9yjIHnMWE3D98peQkJJlNYWsTdO2bRfbrGDOuaIsiNghOwQEJSJih9AXABQMRXMPtaVN+shMgTwbGABmzaoGAojG6BAtZwFUAFXFvrELBjmJ+9wXP1Ww3x4XVeUASFTBtOu61bqZL1bnZ6uz+Sqwn06mVw+uuBKVZFCW5BkViS0Tl2hFMUYCNGNHZeEds6BkURWLsSN2o/FwVFfbu2aiMeXCkSkgAHtG5wS0zZmQktiq6brUSUoxxzZ1KaV150edVrt87WfeeGZ6eu9D7pxjYwZQ1ZgzIjnvqiKx5no1DacDW76Znj+CVQd+Ce3iz3/rtQtLf/CpV1/e+93/7n/Y3enmYn/02799/eLFI5+2rNOUTs/b56fHywf3/s13qu0RBW6Z6xdefw2KyYM799794Uezg4u7O7u5aZmYkcjx9sH+4eNHhw/vD8dDDk7ATBU9E7vSjwjd4nA+f348GhbJ6Gx9JlWYTItJPWAmM8wxZYtIgEg9vdUso5kwSlJUQVAkU7H1am3BlYDOMISCuHc9b5I0arChvyIhoqmy92iQ1vHw9uOP/+wvHj95MvQ15QLFXf7C9YMbB6mx54fz0KV11MPb930ZPOFkOJ7sbG8f7N363PUL3712djL/4T/7g3e++faUrbi4e/rk4bHT0QvbN3/h1Rsv3RyEHNtzB8yFc0VtVJpAH+MHMVSIXduLNmADyTAk8C4QOyRHvg9Eg5psVm22KakCgqM+sYpCMn7h4NM7v/bgJx8+fv/D5cMMebnQFEKJu9Pq4GDvtZvFdLZz7UJdl1GkWbZcFRnUumRdT9oWlpxjQs/eVVUxfLrujh8degr1cOyda1aL2KaiCnsXD0hoOpuNpxNfBTNLuX+jG7PTXhgL2kdr+2sA9eOg/tgPn3jqyBmCmv5/ifqzYM2y684PW2vt4UzfeOe8N29OVZU1owoEARAkwalJqmeKLSvUflC0FfZDS7IcDoVfHY5QRIcf5AiF7fCDHQpZ3UGp5Xa3ups9sUmCIEhwAFAggCrUgBpyzrx5x+9+0xn23mstP5xb7dd6qMrKe+7Z+6z1//9+AklY0KCyknPU//+BCAMYMs54zBQVtwZd1ywvFo31ZrJxa+PVqjt9/v432/Wqi2m2XAnHRRtsmy5X8bJZdN1ThnUBmOc+KSzOzpp6mbE5GI55sY7sJenYOkI8u3+yWtSD64ON3R0AlASQCBV7+E5KHCMnEWbxGdkYE3K1O3lx8wvp9Zc4xmVTh241Xywm48mgKICTKzwjQkwxJu5aiQycYhustWoJyYBq064Qd3OXZ2WxWq6a9crv78amrdvVet0UeTEaD4vMe2NXy9XxJx89eP99vrzYyt2wzKfDMVL2/vlzBfvll+/ee/xYuC0r5CK78fabLt+ET43F9JXX3j45fn56cSJcr0+Oq5+6XfHO+R+8e+803vrCcu/WxvbW9uOHJ2hc5nUZOmOzBA5RmrRUTJJCw6lJHSQhJDCoyAokPedZYh/JI0RA6HWP1tieosgA2DdZIrCh/qfeQx36AY4CSmJREZaerG2dMYaMR1AQVvQWVAGkd76TyeBKUUrGWlegMBuHROJzjwqSIpCygAWHQNI35oF6uSMCpJ4OC2Cs5cTcs6JEDaIop9CvCMg5EgVWJWMISaKgIUSwuYHJZj6qchVOMdVdHQLHkEhxUFTeFAS26yKAQ+unG14A2rabH19eXC5jF4uyHE3KjfE0844lGiO5t2ghJuUeTy3cNK0AZc4bYwUgdwVi/1eWEnOIoW6CaAIhZbuYLS8u5oiS514NGmeG40F1VWjHrYNtXS+lWRNxaJdkdhPiYr5cLEVZikHFdTidr374/rOHsH4OatGpdatu9sHvfGNmfnDrL//tX/mNX/n2//gPLFp+9Nn1V9/e8KPFYt1o3PX+gEYNu7M4hwv34l/56iv/4V8v77wEuT14+dXlxeni6GRUDrKsSMKGlJCyQT6abKzXTexCWWaGkEUA1Ff51uEhdGxS6B48fXz6dKnl7Hwx2TZv/NrPDHZ2srLgVlxeGO4Dx6iiyEza07UkcQeoWelK55WxSyG0HSkqA2Tos9xYRIf9rKHHmBg0oL0MxqIlaeLxpw/f++b3wvLZK29/cTSaXp7Uey/ub708qir3+P7p9mSysX2Dcpcw1bXGrk4cqzKzmdu+uf8bf/s/+NZv//6//q1/OVitxiU9nq8fdO3rG9tbX7ixeW2PpEti0FpjvPNOjc/K3BM0i0W9mC/OV11ow3opYJGj8Z6y3ICQxTIfgDFoi6zKfeGLUeG8N9aRubrgiCKCKoKyqERNkZHMZn7z575w8KVX2vNlCHVKMbWNiPFVMb0+zcphXjpOsQuxxyWAQlINIbWrEGMCK5yCKlqfV8NRNajq9fn8+JjrOqHpVnVTr4yn8da0KkeFL40zPbklJjFoDToEMcqgSqbf6EFUJSAGJtb+hdAfXaREZBCEDUAC43yWEbfd5bOTpm1sVuS5o7wgp0BAmSnz0li6gfZEs8ez1fG8Fj5ybr1dZcYPgnaXpxdxdSHQLObzk+N77WUNcV1BKkwGztSYFnWABrbdIK0Zs0GnWZHcVjnoHp8tHj4z3t64e8ho606dN0VRZd47dBC1465u5suLRQzsMh2Nt4ejtitCOajMwHlbbdmB6tZ03UBiDLFt2FpBMkBqrJJBo7afpKckziChc3nVrlag6LPMZVlMYb1YM3ecYr1cC3ejQTUYDoyhlOLi7PzxZ/dWx6e5060sc0A+L8/XPNd0685N3du4/O47U2/FxsH+wc2X37yc6WC8Ec+elyP46S/+8uPHz+3s8tHTk8dhtbMzfvmNm89PG1Mid6kcZJPpiKBMTtBazHwxKteLumnqclyGmOquyb0HJ8BMSIxqAAkAQVXBoAWDRIiKQEqC2OOABICFEXtKBCQUQO57jnBV30EActYSIRGRVWXzeUIcQK31zCr9TEgFEDkFUEWL1vhMM04ppis2nEVjHIhCSolFkgIhSSIg+LzddeX8upKjcAIgZrHWKIj26+f+3gF6tYdQZZAoySBxl8ignUwGufdN08Ukl7P5ar0SoDLPt6aTvMi6JNXAZ4Zc4aRbz2ZNUWRC3pRlyUoTGVVFVQ0yb0i0D3K0odWootJD70uXG5OWoVu1LYdESNaZ0ITEKka4g7ppWYO3qEhWLYIOSmesMQbzKssHZVZQczkbDUb5Rn5xumPP6uX9D9zIBtXZ2WLn7k27MU1PTlanZ+uTeXux8OXIv/zaZ0/fddCAEg29z6qLNj1ZPfz4H/9P/8v/4u9+4Tf+7o/+L/9Nffl0dfrkjdd+4Vvf/59baKULmV+8XO58Yfzqcn25t//CdGsjcgcR8qrKi8Pi8qJe1dZaQBLBFJPJ3GAyHK8mwIKISEYxMauxlE2Lg1dfdKoPzHePf/zOk9P3Q0obgzs2y8g4cEYj+LzgCOgcg2WOnARFONk2xth1xlI+rIrSU7JN7LqmU0Zlli4mQGYkZmMJ+v6JMYDS094UVJm7pgmx3bm+bfJr127vD8fT4YtmsjnoQvfe77334JNHm+VgY7cstocuy9F55w1Vptwq8mHlXV68/uqzz+49++bvtl3gLixiO819NnBZMTSqwq4aDMmAMxaNi+gdKZ/NPvn97z16/GQ0zoyFospHG9tlNQCfU5V5B2iNIUvOqGKKq9W8nh+z9abc2h4OptlgaJ0xiJyktwCgipLtNVRo0Y9yWxjEKaIaaxRIBEJKkUiTAKsquMx7nyOaEDQmWi/bZr2k4VCVQ+zQZXaQbW5fsxSTpNVinkSb0HRd6zGvKrTeoFVBZgVWJovWOALbazZUtD9pUdEhJOW+2Ax9l5kVULX/EOY+Hw7ShPZ0tfjs8fHH92bnlw0kd226eef2dGdcDYdlmfncjaphaXxOxqSLi9nx2enHuFzu7IyHk2FqV7FeZlFh1ahJ3WoV560BHqKjPDcjn4Ht5oyMwGsxJtscKeHGqLKrsPz4iS7X196+lY+q56cNKExHVVWVhrL1uk3KiNCbtpeXNYTl2bP5cHM63drb3NkajgYeQYGkb2wgorME4rMCVVQTBw79hEMZSGLsrHMWvc2LZr0UhsK7fmXVxnXTdqnjpmkRMCsKQt+lhKGezWbnlwsFygFI0tb2Pk+vXSw+AcK7Lxy2T58OIRFC7crD27dH42rVruy45AyY0+Rgd/eVF84++Oi7f/Dd549P9e29L//sSyezjhOeXaxE7GBcMVCzbL13tsh9Plhc1O1qvXfjeu7yOjRoCBl6QY+1Vwx7ESW6+sLvryFqVBN30l/0NbEmjlc0JUOiQEDWGdO/ZXvMuCUyZKwBBVWD1OtQkPsuAYiqIhKh7cNDLIDC3jpNjMY4b68ODBUiY40DZL1KMxskUiBDJJBYhYUJkCPLlYY+qSpzBEBDpl9EJxEDxJxUtddmCGhK6d910MzZ8/nl8tSgU1GbZXlelmVB1raB2yaGGGxWedI6xXUTjk/O60atc+PRsCxLAWZNgMSECBBYQ9c1ISwXtct8UZQ+R7Qmt1lIkR1lmXHkk81X89XscqkMyOozGFeFy50Fn9nCeM2KPM9IVboYuthWo9Hu1jSE8FyBMpdC47W8OJ5tHRwWkwGGkFdV6QwECY/OfvRvfrhxa/N/+5/+7/7st377dPn0OWRkzemqXq2Xw/j09//x7/77/9n/6Wt/7dFffPO3v/3On/7U3/hfuXvbq+fPLTQR9HJ9fKugw9e/cDk7Xp+vnB80sQN0JvO+LNt1l7po80L6Hp8wOmMz364W1XREqD3kuAsRyfrCTV+824FlM2ifPA1d5zcmJhunvjWOSojcz4eFEydNjKqKYL2vE4lR6DhJCwIxrqVjiWBCxhRjsOVkCMYqAqiS7S8ZV0VCEVYVIplc2x5u7RifFbnFqrDr9Ojxo9/9J//0O99+f5fdpMbtawXl2nALVsrJYONg9/bPfnXvzS8cvvIKowkhNs7MjaHWZlBtlCXXsWuBGCfjUZFnaIx1Hl1elKVZr9/73g8//JPvmMHk4O7h/t1b5bAktMY4sAYREZNxFgwCEioQOk0aIoe27Z5fLNbPqs2d6f6uqap+GERExpJBY8mklEKISklEjTPGWFY1KEKaUqtMiY0FMETeZdY5Zukd7k5h2WEoriytIkzGjje3vIOmXrVty3UN4Iaj3GXW+wwUWBIoiWIvITPoAIxIjFGbxQI0ZVVpM99LfRVAGaiPBAFYY5EgKUdJzNrMF2ePnp9+8PHig099I4pWQR8/vXz2wf3x7nDn5sHO4f54d2dzc1IMSjS0fftg+2J9fHZU83oYQlWOQxdsG30jxLYLjn2+lvXAWM4LN930m9PTk4s6xElusJXEyZGtJqXN8vOjo9ls6bfy8tbkdL5eCm1vDKvJSKB9/HQmtrpx57D0XkWqSRwO27ier1YnJ4+fnD59sn/75u7+wWRjI88Kcs6oRhEgqMqKECSl1HVNU4eQesOdSLLgVclbW9jhbH6kKWJm89yWZXl+ejGfLVBpMV/HENRIFwOmhGRV1HFuiuFORaxp3km9bJ7NFuNRNfBVs3iWODQchtPt7b0D6wrwkYviOIKLWg6mkg0W7fIiLJPoeGtj8+Z+Pkrvf/RZUppMxtOiXKxCG3E0LMq8AoVlvQjSDQeDENrc+pSCzQyAF5X+vq7SuzZRFRhURIU1pRhSFBZIPTkLASXzxiAZ6/pCtTGIoEDY77/gCtvPvTgeBEWEOcrnNiIWNqgpifXGoAGrxjhOjAjWW2ds4qQsyhg4GSUwFplVhFEJrUFiZQRMzNqr30U49eVQNMaoKqJCP2pEFBW0RIog6qwHFADDklSEiOzlxfLkZN6Fdjr1w9GoLFw5Koa5Z4DQSRdi08TL8xMgu2qWbeiYg9dsMtmo8iz3XoBjjGsQNKYfgQWALsakujifx+7S2yyv8uEk29oc586QEiMJIwEPMu0Eu8vFvA2Fx8mk8plXMS6zAEACXZTYdJDSZDrcn+RHDyKuGwh1O1sNdwfGiDUQ6zqplKM81cmUbuPW7rW3bpV5eunt6zd+6uV3//Q77/3+nzWr548vj3PFaVf4009jev6X/6v/3P0fHn/8/T/e4dFXX/7rf3r6W4VwongRmg+eNz//9mv7r3zx5P6zzck2FZkETWnNwshQL9cVWux9pmCUAIts+fRiuN0ZZ4whBUIFImRArXDj7v7wxtYLXVqdLjDUlA+SMIgCi4okTsyCwj02sif3GJvR9rQLXVcv5rN5V68l1qlLJE6CZmU5vbnpYkZkrMuQSFhZVWMHqApEAM4bsKYcD0kE0Zk8jyKL9fIb/+ifffjPf2fXTF4ejW/k5Z7ZWqyef3x0dj+uW0Kf339w/9Hh1x7+jPH7h9cuUrpEsQqZt3fGk7XUXqidhyzP8sxb58FYJYtk6sXi3h99/90//cH09tZLP/2zo/3NvMqRRHrWmyoAuszplQAEQZWskifvnPNlkeWX6fnpxz+p5yf7r7+aDabCQgqooCKsyiw9Tr5fDUhKiTn0Eb62M8aKFVYx3oNC37MkhLwE2CjbtlBCjsFlJQqT0awqXLbjVqWr63wUQGOP8FZRZlZmMsZYA6YP8PShKtIEi/PLxeJ0Mp2Ot7ezslBASUqASVVEiSipggILrlZhfnJ59tHHD3/03ur4zIV6AmYTysPp/gvTyVlzdnbv7N69o8d7g+nNgxt3X75288ZoMByV02v7+w8fXtRB5hpTx0GNWNTQxOMlVJ4oB+NaSaMyK6fXzy5PL+fLIvcbWW7bWQPqpN3ePFwvZrOnJ+hh+9bOZZvWrKONrWK4dbFsF8+Pi4NrL792uyoGfU/E5vkoH4QwDsuN0hUnzx49e/h4vagPblzfvnaQ54JAltBYAgXmlLqua5uuaxWZyIIos8YUPIsxxltXX845dFTlzprBaHj+/PHqclaUlcRonSHvBYC58x53ru/5L5qzz96ndlav61XDs/Xp3sbN6zuDIt9b3nt8sVi4qhhvHwzHExV0We4Ho6ysquk4BvP8/sn3//zHKuFn37j7hZuHDrKujc2qGQyLrc2BEXsxr6NIXo0KW4Q2nl6cFEVWeZ8SqxWfOeUEoBYJ++wBXoG8QkqpR+azxBSFRVEtUP/5Z6xFAAEhidj/0qoqCEqfALji7okI9zkcJRGGHt/WZzYQRBIR9VhsNCgqQH2ViEMSBBJGTioAjKmHKgozRzYkYjiJ9rs1FbB4NTf+3BLU6+6ov0QpqDFkDIGq95kAgBCBoCHjjLBY58A6M9ncqbIipXh6tpjEiOMBWW26uFgsLuYXy/P1cp048Xg6KIrB/s7WweFuXhEgpshNUy+bVhRCm5ilqoqiHLDWzth2mcphPt4qmDE2AZNh1tOTi4tZLbFxDjc3Njb2N7YtMoe2azmyEjIbjHI2X5+enJ/VyzvX93a3i8X5en68jOsm1OvURbFxPC6xqZenF3ZSuDwz5JyrykEx3Jq8/8+//c/+3m+9+tVXb919dbSs/vSf/TeoYQLVr71y6wPSj3/we7d/8T+7/atffPTJdz/7s39w8Nrf/OJbvzj79HvcrCPENXSf/eBHf/k/+M8nB/HJ7GxcXFdjCGDdNT1hXS4vnfEms8aizbOizC4JmtVqMJkYiykJA1qnRrGOCQyUW9XQmOnOoL6cMyRVvhoVWJRGQCQKcxJQJBTTY4aHhroCJYb5QjoJbeLEcd0q+HIyJiAOgVnbtBJQ0aSqBAAKrsjLMle2YK0aEhTtLzHJPHj3o3s/fGcb9bWqPEwemvjB8sEKViktNsFYGtV1WH/vyUcfnIeL9W/8l//7qtrCiGk1vzsZ/fSN3Y8Xi7PlsfUUkySDmfFsDZENq/X97/3ow2/8WVG4l7/8xXJrw7iC6w7z/iEUIERj4GrvdfXoCzMQgCXFZLNy6m+px+XDR6f+J3svv+b9MIX+EWcA7bUY5qqcCTElTtwzWGIdBGKWZyZ3nCR0ayLjHJIhdFRulsM4jU3LKblMncUUExhF66wWmUGbPHAUTqISYmibRmMyRD7PXF6AJUbp29YC2u+nl+czn+VEjgoiYzUyWupRL4iYQjp/cnH+5Nns8ZMn734wn530v481aAZxl0yWjUbjYnu6cXZy9PjJ7OHD9y4+vPf0xTu33/rC6NpksDXdubZxuTwiMHW9EMmSmgZweXrUtWOjZF0Oqc3Kql0szi9PQtduVMMCAkMIaKscHcrF05O6abZ2R3ZYLWsdTAbjomoWzxadbt29c+fNm3nmUYEARcgiSwZkqKimLjNlVp4tLmZPnkvXZXlhtrcy66BXv4NySpFjCCFxAuxX4YqgHCLnyThbVlUbwmp5MZoO0dtqVOXloK3XvjAGFcn0FfS2bXxu3LAYv7Rpq1dOHtwPR0/PTpbja9du33lxPT9ar+aPnz9dQLi2c3379ktoXcstGnAuz4thaezJ/afvfOsbD44eXd/dfeXLr06mm/UsPHjyyFRud2+jGORNnVoNkHub5WRoeXFxcna0f3BorOlCqwiRVVMkREA1SKwKrKwSQmxjFFVhEknMAoiZd8Z7S32/iwlRrtgr0gPM9UrCDqAGESKziAooJERgJOjp7n2aQEiJUFPfK+8DEFcw6atJDoOoMCclRBAGAFVERUVNrAB9I9ESoSGDBCrJgLfWOAPQw3lMDyVgZQX4nGUhKUlKiVVEWDohRVtU/vr1jdx7Y4xEHo2GHNPlrGvq1Ww+n83n8+W8LKtbt3Zdlm1tjJzzeeE5hcVcOXHThS52bRvXXZtZMxoNIWnkYBAn06HdMFluAZC8CTE9ffq8nrfOu5fubivrfL2q101zGaqiTCIhdCEu25h85kl1cbm8mC0aRXTZvJEnzxazZ8t559toy1GZec+xTm0tzdDvVGJJTAVCprA6ksez7Mff/fST7/zhK6/dKndfCMO9OPvJi7v7dw52/+y9T6bncyzC9K27uDdeHx/vbdZ+/+eOXti/+Ml3Lt79cAVydLz4zjf/yVv/5f9mbzCcd43F3FhbDcvVZc3KwCjcWRFnCY3NM1cOy3a9LEZDNAYhGdMrmyIBiiIktU6jUV9mzTqo9O6A/nFRjgli7EJwMWR5Rg4skiImFCRrXZGVCb3hGMvKZYNpmfkUuFnOmnolsVNrfOYVWBlEZbCzaWialQNgBQMiBjWB6sXJxfFP7vmzeJBvTyQ7b3TG6yPqVk4w4V5uf+E//rtbr7/59J//gz/55rce/873Pvq5d+688Hqu5YVSCryx8caQ7h+fnqT5kox8zr6idd08/M6Pfvgv/yDPspe+8Fo5HuaFBcPee2MALIIhQgNkVLX3W1sFEGCR/iKlAGTVDP0mHGDTzh8/A+u3X7zrMGNFVRRl6ofrngCJWYBFGVQQkiznyZfZaJwrQLteppgUscioKEvnvCANxuN5jCmlPjF1hWxQ7JXoigBkAA2qWLA+8bJuNAVJobfnAZikKTQNh3Zzf7caD5fzRVe3Nm8cUpYZtJQ+jwSFpn72ybMnP3rQnT8/vn9/2SwDaO5dq7qM6Rz5NJzuzSWPZaW8d23/+sbOJ48+OzlePFp8dPH84vYXXyu3Dzc3DzfGx+3yuTYNGkK0Nejl/NKK2hS8VV+WXWrO58tuvSgzzCXFrmETYVwMJuPzo2fh+AI9bRwetIkmw3xIbv70fmPh2ttfOHzhZuEy/Bw4ACrKUXtENEE5qYAOtMoDa1ifcFwrj9V6JGBmAAKRlLh/gaCCMYgAqJhUmJMz3heFOnd+cbl1eB3B5lnms+pyOc+GBZJVq0hEyoOyrMqqCS0VWb63MWRpYjoYhTu3bq1WLSS+vDiql3VZVrdefXPz4Kb6PDKCkqeqGI6yBD/5i7+YLS4rYzcPd9jYJvBnjx7Pm/XtmweD8QiRUggxtn44cT7TEE+OT5q63ZxuKsfFcmEKS8YgXzEetAc7iABACDH2RS1lQvTOkCE0AKqKUZWEVQkMIgAJQBImML03VIAQhQwA93kQEBVIakzfCTEqoIoskPo2oSKzMrOAkkVOIso92V+UJQkZY4xBVCJLCgBoLVlj0KHpL/Z9n16V1VprrmKeiFdQaE7SVw6ihBhSSjFxT4YAUEueDFkEynPvvTGG2CB3KSU5Pbs4Pjq6XFyuVnVW5rfv7F7f2zbWZM4AkBpIMT09Ojl+cjqb13mVb2yO8kFZZkVV+sJ5l5GzznrHIRjV2XJ9/mhxuV4A2aJyxaAgb6wxVusw7+bnjZXF2Wy5qlfC2AlVg+H+9fF0ujmebqOBrCiapJ2DLgMuTbl/e3MaUzvjDpVcbJMIGEvAICJJOBvYL//a3fd/a7hard/54Pv+g08VzGv5zhcOdv/7P/zeBbg72yZ3aWN/v9nbvffBR9X3//Ubf/M/Opy+4ky4Voxm731Y14t73/te+Qev7n7pjdy5xAxkfF5VE7termMSMtCbWzFkLndFVbSzS47JGgeGDCFYQME+DZjqpEwxdRCTikqM0lMDeuUUABF6Z4xaCYHFIBAZZz3mg9zgpkuDJILMmctT0jC7PH/+rFmvvMVqMhxMRtVwCOqYU9Msw3xRA5HNfOl6uCgKs8bV6fzi8VrjeDQZxZh3qV1gvTJx5QS8dXnpBhvj17++v5nO3v3un5wdf+v/8d/9/H/6d/Zv337+6PgJ4786+WzbOFG3ProIbcic1wSM/OTR/T//199I6+bOW2+M9g8GO7uoYnNjnVFl7bWTvSRVObBQ0r4IhtaAKPURWFF0yQ7seGcnXMy6szkfNvlkwhwBkdAhqUFA63qgSD8UUobQRDJuMh17b5umtsY7xdB1dbsOdTccj8llSOCrolkvFZImMYSRQVVU09UcH1EFWZIh9L6wZrVetdol4z34zFASlrpeS7ve2J34wRRIm8WibVaY595ciUQUUbmbHR9/9oMfLx6epFU9axJjYTNt0XbQtmQF7PNYP7pY2xPZMXg4vX7rtbde293OP/vg9Oxsef/ss4s/m7z4qNo+vDbdPHr+7PTo4ca1V11VXs74QsKEG2YZ5JXz2LTtur1E7Qr1sW1iaPMqH29szpfL5dkior378mtJyzKfZNKePvyoHY5ufumLhy/eKoqsvw2mxMqskkS5LwOAoADYwhUyqCYDrQwIckRbOhVmFez9CkmFFQUMEpHB/uYbIaUA6ryzRVGtFst63VaDzBs7NLQE5RRBxaFXUSTIijylxIElNrGN5bi8/dbLOZmC8Px7P4ncDm05uLZ796Wbt159sxyPOwFJQVexgHBtZ/zk4QdL3dj/6df3tyfnD06ePjldjdePL853p5tbO1tkXBekXq+H5aCajKs8a5f14yePXF4MBlVbtyAiMfZxLg58xYQWAe4Ne2rJqAFUMoZAmIWt8QC9ZUeAFRDFAJLpGQ/S53sBlQUtKiMLA4gxhojUAtGVdRj7frhqShJZRESSoErUfunAMUXs6V0AhIAC1htE9D4zCMJiEXzmetwbK6swf253SqL93T6JxC5yTKJJRJJoCBEUQMB6632WZZkl8lkGItZbR2Rj4qZuQ9udr+rnj09PTy5tTtf392KKg2G1tzMuy4wIQwpNk7qY6uWaRUbT8d7BbjksPdnAYWujGIyHFmxkZk1d4tVyefHsYr5sdrdGh9e3wNnYYb2oPzx/XC/WAixB1qs1MKaQjMBkUPhBNR6Xm9c2d0bjNoRVCBCYuzZLYWNs4CRVpSvGG6vLGNsYWyyxRMhIgTkpC0cVhe3rk7/yH//mn/79s1l9v4NuKxv/9M2bf/zux5dpffDKF/f2KmjnZeZv3rr1rvzb99/9+Por72999Zd25eWNF15vX33lnX/xP81Xz56++8Pq2kZ1eA0FQxfJZj7ziLhe1TElBIhtjD5Y473NWpQUA2b94NiSIgNDr+NA5MDCkJoYA3vFXneFooXPc+8VnRqDCOt1WzcXReWHk5HJC59liITJszCCcBfqy4uj+5+FdrW1t1mOB1lVZOXQFKUljwrGm/llWs8vskGVlZ7IqkZhUWnb9al2y51htTMa8sV6hS1yrEAQeGXDsj79J//v/+qr9ulrr7223J2054+aH7/7ye9+48033vrk4/vPz06qo4cmG7Jk85m0y2SzTFw8e3z84b/5Y396eufOndsv7k+v7zAgKPrcEikn6M84FVZG4ciiCAbIgiKqQVIyCEBg+iyb2pHPNyYn57N63RbDaA0KoF5FLtAQgWhi5sjSRWFBlskwrwobY+csaYpd24b1alEvGGyb0sbmVoyISNZloRWyrKJAopERlZzRKCoABjSyihqDxlhtUw3sQ5eJIBJAQlAi0CQKyYJNIQJqaREQwZCyqkjbxfPnJ7OjZ07T2MOZgcIX69QmY0nMZjEZldM6zJtmHqA97YRPnsBw49brr96RNYQo8yZd1LMffgbXmsn1G6vBeL5cbdxI1WTw7Dmsoc3ZF3ZY2IlRbuqFTUCImjRxk2dZNdzuVny5uJTE+zfvohlWWYVt++zh+2FP7vz0l268eifPcgAC4SBJP1eOGEAAYmDm1IOOLNkqKyVzgGicteRSE4kBLQbm1EWJgGqJyFqviqgGQFhEJHiXl34wW5zX67rIC+dMNd4wq9ooFLkh75XRFdZnjplFJEaW1CTgrMyMKeqQsq3J5s7huBxuvnJ3urlji0IQmbmt58uLEwnCHM9njw2HL/7ilyf7106y6ZP3vtXcvj4upzvXdq3xHKWt2xSSKwc+z9nQqq0vZ6fX79zOfB4jk/cMV/OXPh6JCnjFyQdDhM5c6VQE0FpLlpRERBJr/+mLIAoo2kvjegQbglpD0B8S2svhAUSRiEVEMAFwfwkXEdUQOabAkYVjSoIESGqMKcs8zzNvrTWWkPpFlLWmZ80CSBKRmARRJIkAi6IisxBiYmWJiTV0IabQ/yWDiCEzGA7y3BNZ5xwZA8KSooraLkZgvpzPOw4Xx4v5Yo7iNocb125e25oURYHOMxBC4rqJZ/PFfLbsTQN7W5vDw6qo8szb9aLm6KrKaZImhcV6vrqcz2ddu+7ywo83h6umDXUrqu0qrOarlPjyYhU6Hk6rDLONrcnW9igr/aDy1th1U0vk1XpVN+lssW7bdsvoNtkqGy80yPx841YJrWNm4Sa0naxSFFosZswc24i+cgBmd7Rz+3Dw6fKiawZZ8cnRBSd8+dUvXfuFr2+++ioAuEF18+2vjK594/zeZ/c/fG9466Vsayf6cnL9y69P9bM/+eBiPjt99LTa287LfLWqQ4MuQ2vNYFh1TRvboEJdG50Fiy4nm5raZN4YZy0BXv24FEGYJTFzTG2MXQJBECTElBKJWgN12zZNjW1gkRDablUraDFi44ooEmOQFDildtlcHD3jZrm1M5lsTbzL0XogK8b08wqH1ZigWS66+WVZ5L6aSJHHNgBLlQ9HO5vDNRxU46fy4SLML10TNDmV7ehS9DNtf/e//n/KX/qro9HNl3F9qvP4w09b+vLrb/zqd7/5W7hcaNtWpjw6mS0fnl8+eb4K6w//4J2T7767XQ1uv/Ha9OCGMVnirih9n4m8+qhWUIUUmYUJrVzV7Qk09TNUax0aREIBw2jNcBDPlqv5arDRFWWpfV0QFJUMWE5JA2sXpAuo4omKgjQ1hBJjbFbLZr1YL+fNum7axBrzosrLYQydsUSWhOPnqIkrgAMioUEQAebUBnRJMXXCzvnMeVDpupZjk5XO5KOuaWOrp8+edrHemu5b54kMoOkpoKnrmvM6rMMQoqzrguusYSQbyWWF2ZpuUbWxvXbHEpq6C4CNpKefflSJbg1tee3WND9fnp4uu3j88Hwz0aicdDyz0Y2yapRV2sTCSKFtlnXL+rLpmkiaG98wVy4vq415zat1axWub+3u5mWZ6Xrx+LNPP40Devmtrx++9Kq3RUqiErUXmernY2UQUBXF1JtNWZQ1857RlOWwKApgiJ0QmRRCu25j07Gys77IczIgwiLJkDFEIQUiyPPy2ZN3Q93gphJQNR1O680yF49eTO4zPx6WDNrFIJwMqqJkmXeFV2N8YW599XXjjDeOBY13XRM8WYkhNKt1c+7yLb+1Rf6Q6jjsOtfpoBy48Y511cb2NC8rEU2x60IQT8bbohzEkJ49f65ir+3fQGNQ1RgrnYDKFb3R9CE6VdPnfBHRKAqooiMCAEBhUZQr0woAABLS1YpXVIAJMfNORBX1qjeuEEPsUb2JOUaOzClJ7NquY+yBcwAIYMmUVVbkWeasdc77/oLf/yf6/FAKIbJqSCkJp8TKKUbupaKFr4RTFztVVujbeAAquc+8c4AEANYY15OXFcmhsqSU0BpVtUfPzy8vlm2sCXS9akfl8GB/d2MyMtaySgxdYA5Ruy42bWLFajDYnG4UReGJQFPs4vxy3ixXZV6ul935yapuG4RUFG44rlzhQ5OOns7X54vT+Wp2uTDKxrr9w/2DO7cmw0k+8Mg6mPiqsk3qCKDuQhMii6Azzpl8UNQpLteXo8y64Ygdx27hyp3CF+tl6uouA43LJjYsKFnuXZ4r2MnesPqNL2M3/+AfBXv/k/PFDCA/vH74hd/4m6vNjfHdu2cnJ8WGe+G1Ozfe+sKP7j1+8NHH+3f/4nD/L7nNkq196Zd/beP2S/d/+I661NQrUxQ294oQu2Q8WWPyqjCAbReVIbY43JrG+Wm3XGqWQ2WBLGFP5VBW4cgcIncprrsoiVFFUn+rlaSxCzHJ5cWpzucmK8fTEaikGDVEtN45FAZD5K1vFguSbrpRbW5t2KI0PhO01AunWQDRZBlZS8bNz5/PTk82yLqqRFXvsps3bl/7q6/+8P/69x3tXru9d3/92w0vVXSIfluo9PlnHW9tbd6684avdkW+sfXh906eX/zk2//D2QZND7Z0vQyL5a2bg7PkP/uTH5vRxurxo8/+5Hu4XNz+2tvjl67bogRMmesf2Z7pJiKqLBxTVCBHV+wfZbKEmYkcEIwqZ+RN5iAJKBtXURKMiUSx7w/3CgFREEYVZZaUgAOCGu8sJRVpm9VqsejaBhHK0jFT6OLy4nwwmmS+yIusbRu9wvP3zIY+9IFwVaJHidLWTZVbXrH15XR3arJ8vWxUpBg442xXt13XHd+7d3k+37q1nw/G1hlBVGGFpMwSWgLJOFoIbbcuYF2CmZqiy5wSKjer2XFV2s2NjWfzuRXIgJKRpxePzlZZDf6lW7tWNVws667V+cysl76C9WpGNBxoKbJYNy1bMwvPQ9cEYCHDxhe5z9zoopE2dER+a7y5NfRDbS8enHz67NHSpRe+/gsHt17PXQWiCJAEVFUYekwwQW+kRdakAokTh8gxIaYy83lZGoOxbRJFA9Q1bWzbyB0aay0ab0CTCocYEJ1Dk1KbUnSOGC8idym02WCQD4rBtCqMBiNsstEkByehXndtEFZCzYoCraH+NWzEeGThlgXIBAEx4JW5a9fLNQugrqvh+OWvfOXxez9+9OnDjSYGgBuv3DHelK7MrGWJklLkRM5660o0y9XqweMH23u7W9OpsTZBcozG9gheQFIkQ+ZKwaI90wOlZ30jQI9ZdGicdWp6tqsqSNQEiqQIqkgAxsTIKoCIDMq9pDf1S4UQUwghJhFlScyo5I3PsqzK8yzzBnt23JWtKaWQes940piCsgiCSG/euqLAoKIwWDLWOgWIkkLsRDTLsjzLEFFYfOadsUgowirCwm2MaAwpclIyaABZkn10/0Gb7O7mcFCW17bccJDnZYZGo6RlvVpdXl4uV4tlV1WjajAcloPBwOe+BIYuyWIxn10uLy9WVWGA59WwMIUbDbOOcbZcPXlwfvxogeqFqVnWxWBw+4WD6cAUw2xjczqYlKNR6TwYQosoEv0SFvO5MGRImLth5b31A3KFL48eLJbAmUM1gqOsZmQzCmkNAfJIXUJBQU8KACpR0nrVUO4Of/Xn2w74T7/jTk5UzY2feV0PNq7dPYQcLp6cbfjBxsboq1/7+pNvfWd18ezxBz8e33xxNBkndF4Hu6+9Nr1zc30+Y4GL0/N8OnBlxiqxDS5z1hiTkU2IqBzabunzvYP46B51rdiCcyBDaggMipfQiETmADEQa0ICAlUB7/MUBRWdtZPhtA1YN8vYtJs7W12MoW45ifHeWGNMEUNkTsbZzJe+HEJegs0BkorGEMWIN5kiWedBMR8MlycnijTe3TVo1ODJ5fO/+OZfnM9+3Jx/50vXv7yR7zy+POokOmOaEDKT3RoOXv+Fn/mV//VvfvOPv/nu+fvXDsa+lvNHz57MwLy888bbP7f41p8+a2IMyeX4wz/4/eb8qD6/fOmLd0dv3q2mW2ITCShoEu71cZIARTWyCpDVqwCnqrEWvQFyBoFFhRKjOCTnXEfinMskkXSkAiy+zJmZgQmVJXIP4U0cU/IGOUnqJHKzXM7qi4uY0mhcFZVXqrrUdMu2nl/G8VZWeGuNXAlSQYEEFfv9PCCoinLTtQklCiS2125cz0p/cTZbX64mWyNjTWybrlmdPX88Pz8fbG7vXLteVUMk34OgDaHEziTIvB8PRnBxSkbbBALdoa1uF/mjWJ+eXrRkrVTXphVdu3bZrKjVgc8MyrxeHtet8OnO5AAnUIYB0MpptJofPf1sY//6YHPrLMzPUpeJNRIRxJEtzCBz3vt83WpoOaNiMpps5lmI6/dOPju6PI+Ad7/0Uy+98YXReKyqIUTVq+ZWT6ro55NXDFYRTRK7GEKDMSEZn+dZ5ghAkNFobLsQ2pCYgZwl8kZIQDQIu4QKmqkCmaRCZCcbX+QgnBQMKupwVHHbUQbD8ZhI6tVsvVrFENUQGkeIpKhJhZPzPjQds5AxPq8SpyzLU1it12chrnzmYrMSn20d3jTGfPLDv7h/9IM3Xv6ZzbtvuoFPdXKIIJR6oqC3g+GQFC6Oz0/PT17/2ivWQgg1A8fYQZIrdQdiX+HDK2z5VRpUQBUIoR8sAxH10A8RScxI5DOnoEQEIiLKIqLAiUGx57wJS4jMMbXtOqaoClnmvc+99YXLyfXYZuD+EU4xRY7CLMC9j16VhEKMIljkmbWGmQHRWWOtNWiNdcYaQOSUiMkYm2WmKHNnHQGoqDHEkjhyFLaGUDGJpJREoeuitUaTILB9482XjMkrb7Pch5Dqul3N18vYNquONREgicsIR2Ux2RzmeeEJ23VzenrRppR5HI/L2zf2TK6L+Xo56+br9XrRLlbNMkROiW15fW9nc2OMzo3G2cZ2mZckia01GtVZ1sBgjSBqSpZ0Y1i5vOAusarzxhA5B9XY0tb14+fnOutoUFRm2LYLMBlilroU14s06DOtSSIIaJ6VnEIbot8vXv9Pfv3mX/va7Oh0dbbYmJjpnRvJ0+xisbG9p7Elv/3y6y+/cnj9k4sHJ58dffDt3zsw7qWv/1yelSA02bieZ5PF4sJnFNsQkxTDikVCSGwYRQXVsgIvH737AZS4ubeHybc1i0s0cCSIqr0OUUG8BSaROhrDSCgExlr0uRISwmgyKvOiXFXLy2Z+sfSDIkaNqRkWmfMOrQVmb7ElzTNrDTGSohIrCKMjMipKJAYtOJ8V1UZXpbPHx5Hszt6esWbzYHu8vf3h7KwLx0XpLEzGxeasPlqkhqwtXtidbN3u7ty4tyPr0cVsvqi0eG177zlcDKieLOGnDm8e/5J/8tkDvZxdc/6jpg7LdnKwdf3tN8bb26gsbWJjCQASJRCBqKyggolFkcwVA846RIMW1Rog9JKUk2CCTrqiKI31mueu8PV8ISK2KNE45ZbQoaKCKCFYUnSJW0RmUssY265drZerJXAykKybmqwsi1Gsz1AEAXsCOhASWCBUTQIoir2pNXKKXQSArPAx6WR3czgdz05PF+fzcmAKS91lXS9Xi/lZvWy39q9tH94aTTfJuZ7FJaoCkmJISvlkM9/eq+eXMQJCdgrqMI0NlCZnXq8Se9B5HXc2N28MD46ePgtH80w69a4r5aReLFf1sNxyUBRuQ6FTkSp16ei4sJnmmbTRktjkS6FJUY6qSTJhVbehC4XzE2tMai7nl6fhfBEXwaS9l1+6+6UvT7Y3ATWEKJJUBRH7Wz9SP/XusUCqLJySdEEig2jmvLOV8zkSKEBMHGIXEguAdd5Za5xDQhZFxCRRkUSVkFRRfTmZ7pJzxlpmVUFIypEHwxEaWC+Wy4tliIGczYw3xgAoiiAiKvW6TW+8MUZTyrOKANfNat1dIMbCD2xkJEaH072b2/sXz+4v1ZZFlhvrwFPSq6C9LwohXwwGqeMPP/s4y6vdgz1HJrCAJKvC/TAHoX+rgmjC/7+Xog9bUm/4i8KRAZVZ4UrMS5aMJOlj+yyCakBIU4oxiYBetaWTKoKoN67MfJZlPncIhlkit3HdJRVl6FJIkSOwJmBAQ7bIrDHWoQEAZ431JrMeEVPsa/9MxhBZJAJUEUFCbw1hYQwS9YMqUQAOHFOsu4ZZrDGZzxQoxV4lzFGTNVYFbGZtVeXCvFi163b1/OTs8my2XNXW+2vb25tbu4Mqj0ny3NrMdCmeXS6aZVTWvd2JL8g5CqGZP18vZ83F8RzI+9xPpm6MNJxkgyK/trddVrlYyQ0aoCSSSFE5qmijoMhREggZRAWfZ+RoOBiC035+ElPEVXNSr37w+MnOLDtQObw5nH3w1I0P56uuzCC2NdCQAEMXQKQcDX2RoeLicu0NJA/TF7cmd6Zt2zoFIdcuVmVe+RyP7n8y3NzdeGH3+ld/4eOPPrnozuj9++vuG9VwnL3+uuTD9fFpOSqr8bRbL5vV0lYlkc2HReKEV4+DDSlQksXDz56cHL/45a9vXz/IBGIn0arEJq5X3NRxXhdVWfqhrC4xtRKvlKOoBARMJrFYAy73SCN0vl2vuzqS9XnpvcuQrLUObVIUg6pEDCqoCAlIUQVVAaxq4hTIOmvQV+VgY6NdLS6fnA2qifU5OvvC27c/Prh2/PHz+8vzEnQYzU5xUBiXuMO1ndFFOL9I3z5aHQ13i2vVWbvbuF8Y7f9hwy/deu1Pfvvbv/5X/5Osut09+XT28PGdzekjqPZ3bm7s7dHAhbRq10G9d2gMeQVMGkAFiKwxzpAtvDNG+54KklXAFJSIyFnMLJKSUUBXOeXCjwfdap2ipCgAAmr+3dAmAQlgQooMAYLHnBiACAyRc2tum2XTosuGKqjVeKssCmtIY+SY0Bj1fX8PVaFvEohw14bYsc9yooSZGQ2m9XK1OJvnGU6qvFnOl4u2XS2E0vbe/nR7YzCcosuSiqpGkZgiCMeYLlYLmg7KG7vN7LhZXLL3QHqcuFs+HwAWRAazro4Pu/aoWVyPN0ZUzvA8xjZBR3kWUTvu6vnJQMqiGA+GU6jys+ahi01UuzG+taIjaBNqrCibEpv2spNgYyxMUuwSr2uGZVfPoMYcbt558Y2f++WNmzfVYBs6jJEQBAXIOQO2pwSgXl3++8hgPxSTnnUExjhjbEwQJMWYQgyJhYy1jnzunbEMcjWPRhaV0AmiQVHn/HRzd3t75KzByNKlUIciyy3RerWq56uuDeQwL3JLRlVEQBQBFVEIoDdkCIMrMmNc7Jr1ctk2tXM5EpIz1jqBgAa3b96ESkc3dgz1i2iOKpi04yTWVEWJBKvLi0cnD195/eWU2hC7Lq0TJ1VFNb3I1hjqU/WcREH6zRySqkAEASDp5QB9JMgYUWAG5kSoKv2dB0EBhLRfpWgCYI7S5yx9bp2tAFlBOMRO6lXTNk2TQiI0xjhrnTHOEapTACJrSu985q21/bcpiACiMvf25hR7EHQUTsKSRBQNc7+GitppDFd/gBgDgUnMXSeFz6uReOO890WeGePAKMeUOrYhpriYdzFdXCwfPjmLdVvl2e7mtd2Drel4UOY5AsSQRLlbhyaGQZEfHuxaNBa1qZfPz+azi9WqCRzRUj6ZjDZ3R8ON0nkqSg/CZKy1FBJjYkREThBCigmEjDVoHIiS6fW2kFe5Ks1WbUqhZW0YV1188ODkvR9/enL++CRCeeuaKaaz2cXW1u3BzlSAMaoldlkmJuMuIDOJkqXRdNA1XWaoaZZKRN4oeU2IRG6QW4xlAd3iJN88vP31r/g//qPlx11dC3/y9ON/+zuwWNz6yi/mW5N104w3xs476xZBWTUxc1ZkmphZFZiAEDTfvvb4j7/T1fCzf+uvYmbD5aXBgcFufXmymp03R425uTfYm7jKLU5r6SUiCtaBBgEiUI0sqooEeZn5wjEDoTWOiEhVNLFERqYIGEWSqIHe8NlXjuUKR4PMMZDLyUA5HGzuXjt9frS8OLWOXDaodsd3f/0XL5fHJ4vjLRe3ktwwlSSpoy6fn60vLk6Pni1+748KtK9qblxq5ifjUbYbYHbyfHH52b/61//3n/0P/4/Xb7xw8sFPpIujlvn+8dEP77VZXuW8mq8Y7NCXPsvyapAVOThQFiQw3qKAoihDYuZOISZD0RQey4Evc3AmJpDIvSFnunttiRcSjDW5yTNJUUONhFHAUK9CU/CsrF1oEwcrxoDJfR5SalKaLcIghGo68sMyn2yScxwiR/bW41UqS1Wlt21zF7q6EdZiUBgyvshj081Pz5HSeFR2dbdarRNDNh3nVVlUo7LMyWdiSBj6EpACoEb0kGe05nV1fdyub7bzOiyWQySE4NYdSthweQI3I3Ou4Sx0s4t6YN2GqnM+qSxSaEOAHOuUUOlSc9W6LAZOK+KQVqdCtHP95dXzz9rl4jwswto4JGKugTtQACKgFkRBR3l++IVX7n7hi5s3bgJC2wQwZK3pG6mkgOiNJfO5TkSVRdmoOIvinHKIDIRqrbFgg7Shi/3N1lhEst5Z6wwgcNd1MfZsCIAUYzQWUeLI02S8MfBWWBPAfLE2ltBR17ahbjkGi1gWpXMZK2uU3qRwhdUm6i/QxhTOZMyxWS5Xl0sjxqEhhGwwZtYUxVoYb5STrVeGVQXOJeG+FmuQrMmSmEFZpk7fv/eTKsteuvUCgGHuCIiTACH0NURnCVAVQJjAIBoVIdAUIqOKXC0DmBOBMWSk5/2KEBpWYZGQ+qUAIgQVYYmi4rzJC2eIREE51E0twilJUgECFfQ2t6BZ5oxzhqyxtqd4ATAZS6pXlPSeMa4gyiKpRw4q2NCFtgkhppgCELGgCKQYBJQTd11nEKwjEsqKrKyqosDMe+8tICESWovGKidjrTFoH9x/HlUHAz8sB6+/eJPAWu/KMisKA6Cc2q7rFotWRMphsTEZOe8sYKjbeVvX63rZtcZnu+WwKPORy4s8K8aZyRCNGgOcsOsCEnRNvWo7EGnX7XQ68S5nAZ97RduTHxOmdZL1s+XiYvHg9NnF2dnFrG1W1dnlisMaHUy3b5Rotm7dugRtcZtIbv/a2w+++f1Yt6GucZJBkUGMxx8/HO5uDXc3ydmszBU0F+kCt20bw1pZU4yIpXFOmZ786Lv7Xy13bozf+NVf/sF6MU6dWS66ew/mEM+SLe8cwHgSum60UZUbO7m2mpKqIquzrgNxCorGWL322qtvzur3f+/3Fo+PdqaTrPAaOlOYrCjmJ7w8P1HD1XDHOBejaifQK1wQnXNEBgRBOAk6QjBiEL23SJY5MrNIooystdWgWlz6EFJMSQObPCNEICKNvUYOQVgCBrKZI0+D6RSQVqtFs6wFra/8a7/wZbb64e/8i4f37h+H+jzVlbWQjLJgIqhZL4UBBdUpHhN0lzBDY44qXcpHi8ejd/5+PW87gLqZD6BcXF4ef/MH2fHs5t3R7PLCEo4nu+PNndEus6TBqByMR8Y6FWEFrttwuexOz9fnq1W9GozyrZdfHI+n4Aq2ZJ3pVqu0XNdnz5t2Ra5czo5tnlUbQ1dm5BwiCiMqQJLCE1Z56iDWISwWKQlBBISqGjlWBFMVuclKPxoNhhtkbag7EdR+6gFKgAQogsoiMcZubVxmnXHOK+tyMe+aOh9YEWxqNdmgGJTZMHfeW+/JGgGIInK1QABUEOOL4dii9+16vmwmt/Yxqnt0FJcrbmtYswi0kXNAC8FBGoC4pMq8VkCbs1NjtHJFMRhZBd8mJV17Zu6iise8jJcX7eVqNNp8+a3jj3+UFutFVxMogSiQAfJgW9ACqsnW7uHdw+lrt8uNaQyx7poYgi0yKkrNnAWw2KNokDVwiiEF6aJo8sb5ojC5NZCFtlNCIORkurZru6AkWeZAlMhYR8ZAiJElGkOiYJQkZagqzJxintN4WK3rc3S2qxUMGGeScIwhpoCg5ajKsqLnKIte8UIAkMgIgEEyxlpHaDg0zXx+wSlY6wmdy0pWAm4ldmi8y23hnTUkrMwpxaDCtipAqPKFpfz4+bN7Tz574ZUXx5PBerVCJFKy6CKrAikgMnw+clcgFGXtZ7aAmlRREUH7VD5S0gSsSZKwkLEiPackpZQkgTEIAmTQO2N7i2iSkAJH1sQxiHW+LCu0AFcKF1UGshZICRGAXH8EiKhiYokcIzMJ9tWBKElQVXoHSrdcrZompJCy3DmXucx6a733VVGY3kOMxhAhGrX9sSEE0sZQd402wtLHKSDGZAdlEUGn42o8GmWZJ9Uo4BAFpO06VFXCqszI2qLMVSC0YV63p2dnP3lyaoBGo8ErLx1Mh2PrjDcU245IOKhARDKcuAsxtKEJgThleTEeZtb6LPMxaWCsu2ZeN5d1d1w3T8/b1Xl6+sGPHzy4v5rNCGA82tsYjd/+yvWDwwOXldUg3x2Mw2KWF96s67LqNnY2nr37k2ysk8K54SiqSUXx9OMne4YmGxOyIACRBUFM0na9ms8Wk+k0LJvUrk8fLB5880O/+eJkd+PFV28fffL63/q1X/uD//6/Pbn3gX33E142B/FndfeANjfJ3AhRhlVBhkS56yJjBGfBGBQQSSb3L335i7MffPf+Tz7ceOF26DprFcTn5XC0td0ena3Pz2azy8GgsGWBSP2lRw2Ac2QsSgJQQ4ikBJRSxxKcQ5aUUgJCA+JyX40m4/FqfnHWtm2Z5xIzzTIiQsUEAoqqvaQ2CZMjh7krhiMFwyoW0HsavLT9td1f3zu49p1/+QfzB/eXYbFumrBeJw4TUADsQCJgrQoAK+FoyWxu7QzHDDCF9fFnH0+mN0O13A4lBmhMOm0X+N4nzVMcbgzbi7rZ7eS10hRDtV7XLZArR5a5mT9fXB6dzz990D193q4u3XCQf/lNyD1Yh5auhqktz8+OH//g+4vHD6Wqtq/vr08+LQ9eGG1uD8ZDVxSECNYSOW9zUBRrk40xpHo9B8Bs7HxZDLLC2gKVfFaZrHTWqUaOHSsjISGl/mMZCAhFhENgCQactQRE7XrVLBZkk/cVJ8lHY1s6571zjiwhGb6KI6Wew2UIrXeqBMaW5MgWZMKSFngLfFbwolmcHC+fSsdYC+SWRCBXILQWnCVnwXHufenL3I7tSLKBwTR7/ugonFfRp+Z0sbwYuKHPByrns/P7+WRv7/Wvz+9/CIsLu65DaHNwlfVlMcQ8z6c7g8ODfCfrkNvFErFlVImSRQE0Oao6p5BENXQcYmzXTR1rDsu2XRfVeHNn27qKTGYocop1WxNCG2rEhAioSMYqGjLUj0TQWWRFRVFBon6Mz4hJtIU0X67Qos9z7w2CgiQRJsJyUFnjAYxqktSvJQwAEVGfrTRkkMhaTBzr9SKEtRIiZS6vjHHKYgGtsd5aYxANAVGIXWJlFosUI0aR6XTMof3o/Xfz5F957WVVbGIyRBJj6lmJIqSUNKrwlc8MAJAUWBUsEYAoIIHpde0qyqgcOcYkHJWCsIoIKEI/hXfGZc46S0Sq0KXUttEQFXlJqACUWATYgBHhpGKdJdtbJBUBXc9CT6IiimTRiEG+Op+AJQFRSinGyEFiTAbscOBGo6GgWrTWWgVw3hpjUFQlCUhgTmEdUui62Iv3AFEVnDPOekKb5wUR2v3DXUQn3DVtd3Yx4y6o6nQ8KMrcESqRMYZJmFNdt5HTYrU8eXqyqJvY8M0X7uxuTyaDQeY9GAUHJKIQu66VBFlVIGJVFXlupoRZZlKUromLRfP8YrmapbPZ4vJ4/tn85Ojs+clifvTg0kc/Lv3EV/u3Rrf2Nza2Dw72Ng5f2RhPKudytK6edetmMLxzMz04Ov3h6fjaztGn99cnTVYtGDAbDrOdzYqwOV/kpcuqASB6b9t14LZBQ+Wo3N6eNvPlx//q35pEj995r7H0i3/nf3F4bXh4fbd84eZXfuWvvXM+u5w9rz85Wukff/kv/cKzZ0e2DjuvviKD5LMsaeSu60KEYEzm0bjYMYIW0+z2L37tvT/6N0R/+drN6ydPntazVTktio3hxvU9PJoJdwqZG47QOEXA3oiIaqyBEBQVEMkYACG0zaoLsWGNZBAZW+i0QFMNxtsHHLhb18l3uavAEzqLIgQRuDdDS2QymAwa8iafZL5wnFJKzJ24sd3bGU73fmH31Vcf//hee/Lg+NHR2exps1jGdg7cAidJmmpNXZ3Qb97Y/5lf+Sv2ov7Df/PPx2no2ii5b6otaR9spnhzNNaQTuJ8cQzzk/raxgYsQvNwdirNYF6OcneZKDYa6tXq/vzZo2PbrTd3qv233zh8+42tuzdMVfoiD00bFNEAcwNK450bkuzp8fP3/vx9TW0+Gt64e/fGG69N9m8Pt6e+LG3hAUA7I+ook7HJfTFmSVllXOZN5lSNI1+UQzTWonZ17FIkaw0iav+7jILIgCqaNCqLNZbIp9St5rPQ1NW4MDYHq5as9c5ai5aIrAIaNZrYGDJWvXNdW4fVZdct2xiFGYCEsRo4bzYJqBu3aeBXmNLZ3BApmSqnYZ6xKK0RV+IIBs5ShYVPZX38dPXwfmzO1zN1g4PRfmoWq+USK5kO9vf18OHy6fzRj80L462XX2lmJ0Z52IVcoSxy76pio0rWdobXcQ5LIJs5k/XsMk3es+YgiqJIRGicNZl3JvNtuWBzvpjfe//j3Rvn12/ccG4krIKB27aB3nJOgIrUiz3BWSMCyhYVACURJo1okRBQQWNiTm192TVdXmXem5RUEhMBkfWekLCX4woLKBqwQAhokBAB++qscx4Q27puV3VK7HLnbZH5nBAAmL0jT4ICRElVmAE0pei9BzBto1vXrpHxD5589uT509sv3PbGLhZ1G1rgK2czS5/nAUTWxICkDAjg8kx7sKYGVCBjmK4gPSwaU2BBEeaYiAgBnXXOWgRFUmNAEUKMKXLst+XWEkGInblqGqCwBEmEGFljFG88OXKGnLOOQBUDK6DaXkWAntD2PRhViIE5JGgkz3yVFWbqrCUiAuV13cbQrtv1cr7qUgIlkxlUC4qS2DnnMpc7lxWFc54sVmXhrO99BURknbeIBqEAk1LiqOqtIWu7NoKidaiIXUptbEWQWZHs9VvXOXbTajCeTosyzzKnIkCGJaE1BqnMM1Z0pTfkyBJaXMzrk4vVerZ6djF/+GD50UfnZ8fLy9Oz5exZk+Ysq7KAOzdu7Y0PDm/eHI+ryW45nebj6aSqnGAXuha7KAjhPIjzxeH04r3P2h9/mL354vbdw9n55cnT5cFwNL1ZirqGJRw9Wx2dwy6CtRw5BA4i1mV5NqibdPSD+w/+8L0XX7p79vHJyerbr37ta4PNqWsXk53Jrf/ib10M5Mf/33+6OFnc+/RC6PdffPPtz37/uJ3N99963d/ctUPPYDjMw3qhtc2qUgQkmHJQ7N195cF77y/OL7Zu3Cmq6cWjByKcD+1oa8thmayxQD7zhgz0Nl9FVHDeUEsi3KcztJfFE8Uu9v8ECDlyxIRes8loygezo2fdfOmMNQ6MG4AlAxb7E0BBoddOMyiBAjky5AwZ9JYMzR8/ffdPf/DBD98f5rL10gtvf/03bZE0CrRdBDFWJWA7W558cK/Yu/HVX/1SM6ff/a//nsZgFnUj9UX+WUHjs3ahYHdT+ptfePuPzx8/PH026+Jn5+cvZzZvFh/8xUchdiPnh+r1tGsW9QrIm/KNr7/45q/93MbdV0bXtoigU2GBjiMSAUs+LHy2X21t7bz48gsc2uVq9uzo+OmTR/cfnp483rr7yu3X3tq9fauqCs0cC5CQNSbzRV5WIgKYyFogQPLOe+9yAYQY264GBWsMGkJCZFBVAyqMUVCFwFjjnYK267pbLF1ls2pIYEUADZJzQAREgqQiKbKqGABRPX769Mn9ex9/8v7jB887TcNJtXfj8KWXb01Hk3ySG7M5m8+Lwo4cjl5MRhS6WGUWvO/qOhyvhS9jvaxTvYzpsg3U1pegi5ytc3dv39me3nx678OFiiTexrQ1HIW4Pl+drJ+/Ox6/Nbl+kDQW3mQgBiHL8oghKtRNx0m89YVzLsv7o6GoqrIsnCVDSAaNsWSMIVv4rJqU063R9tbuztbphx+/d/L4ewf7B36j8HlVuCYh+6IAJQJQBE5qHSXVwvnceo4htrHWDhQQDCH1gWYkXa9XEFPmByAggRXR9Pn4q6ylcur59YQIiCgAhISAhMY5Zy2FrmvXdVcHn+fOe595Z1CEAcA6ZwxZR2BRERSQYzIG0diu5WFVDfPRs+PnP/rwx1t7O4e3by7rbpWaOoTQBlDoUc90dekWUSXUvpifYgdKV/ZoUY2JIfb7Or6qXwEhFIPCe2eQoM/7RwkhMffDbPj8ewK7NrClzFsiFMXMO/QegBXVKCGCMcZYSwYJ4XNBpOtLP4CEqIa072YHZlAquMisQyQyRgCsIWUJmhQ1ihjnhsPh2BljvPGOwDjELHPOGlEiItsPp4jQkCpwSkCqEmzbdM6jy80wK4ajQhIbQEnp2eOjzz55YK3Z2tvMyqyOYTgYVMNhVeWl9zG0gzy3mSfUmBqDRtUCEaMX1DZB08XYtG2z6Nq0bLtnz05/8L0frY6fPnr2vF5os9ayylGy3PD+ZHRwcPjCre1br72wv7dTTXYGQwcGkiRRTaE9n80unp4NsnxYVqh059aL7Wp2ZHDxww8GlRl/5e0FIR8vVp8+CyM/fPV61Awvs9mTYwaa7m9C5ggBE5VVrmCOv/fJe//wn53df3pnbytKd/Lk+Me/+7tf/Pd+La1qN7Z+uvn2f/SbZ+uleffe2bvvnj2Yb91t3O7GB9/+4fy8ka++ufXyoRnmNBiQQmgbDtE4Z4CM2swVWy/erpdrIp8PJwjm8ug0m/GkKqxzxnrn0BlrnL9S+AKxJEIgAuGr71AFYBVRcNYAoQqoKBLGriO1alK+MR2TWR0fpdWqs8kAmTIDZwktEaiygvQkZUgJ+pZt798FFJYHP3rwD/9v/69PHtzzpb3x8o2v/erpT/3SLw0mI+8maI3zBiLmBg5euPnux5/80R9844//0b+Yf/qTA7CZ6jnHkuml2zfl+ZPTePnh4nIyO9ub7j2vZ65LK4gfPDt63KwZ2jqEpjNzxDKCQCLY+eLf+JWf/tu/MLlxaPISWGIKzCElsd4DXIk5XJG5apC6ECNX2xvTg4OdF16ZPTt+/uwnP/nOnz/8+Cdf+sVfeen1L/lhYb3VSKAKljJnQVRReiMveU/GKKEkXi1n9WoFhP3SUxWuoo8gSGIQjCFrnSKF0HXtClCzQQVEAIhI1jpCS0SJIxkUIHLGcFrMzn78zjvHx8cO3cCMvvjG3nA89KNssDGuhnlmvSGvOYK1flBPd7cz7xRSbDmtz2fHJ6uTZ20zU0wr1y7q1aIJof/Qt1aseWH3xq3DN9brsEqXnNgZp3Ywj5cb4910ic3peV3eG771FtpMOKD31tkEitZD4AwL54vSZ1U1KMuiKLzJMnK29wRdWaoQ6eoCDMYQOcqL0WQy3L+++9lHH3/8o+92hu+8/sYgL3PnwJj+ACjzHPVqipC6wCGmEFKIACkl9jkkTSQkaLNs0NRz64011JtKDRlEITUMKbFAX8hQMGSUSAktGUNACv3ZJCoxhbqpwYr3mfdZluUIyikZY6mHrvfgfQQQdcYYQo6aZdV4c9zE1Q9/8q6z2Uuv3AYyF7NFE7ouJux3bAigQgAG1VqHSNC3eUWEBa7euaiqkVOf/kRQRMy8c+SMI2MNAl1N//toqHHeG7L0OdZEJXBS8d5YSwBojXXOGUJEIex9wIahPwJFWIVICE3/70FQRQFhVWGRBIRiUEyGyAbV9oDPNgRgsN4PhiUKoEFj0BiHiIaMiCKw9YRIKQkoApFqSswqkFhiCLGNImKNcwC4bmNqawABAE9mWdermLb2dwxqnmWKWmVuZ2uSZ5X31hJp4VOMzJgI2pAoMyK6atrZyWWrcTZbPju6bNfxcj6bHV+cPr2oV3FxPoNYcwjOu/Ew/+LPvPDmSy/50WhYGZO7bGiG42o8GhBaIkkxrufLtktF5g829g53971CCBxW7eXT5zocbr/81ne/+WP/zrtvv31re2f76en8ycNTf3lhcjO8PjEH27OT2ZNPH4vq1s1r1lk7rLIiX58vvvX3/9mzb78zHhcTJwdl8aQ+evTtH9zYnbz+5Tfj+cIcbG2Nyy/84tfu37mVDXD9x9+CWl/99beOdh/PHz5+9BctZmnnzk1TeRpX1touRFXNcoeIeZVXo7GvCgV2zuSDzaN7n7brZxsFldO9ya2XrwRBZHoBugAxXEEugUH7vI+kxIKESLZn7QIaRVSTUuAIyWdYbEyMse3FcazXHS4KNwakHgYOZBkZ+y8MYRUl8mAMMhgyHHB13MYnlzvk3GDA5/NH33/n2ubG7S9+xeYGUjIRua3rJszOz9fz8z//b//R0b1P9/PBS9PBZVqfJG/8aG9vB/b3f/JIGu0+OH36Yu5eGOzyssPETNR2be7w0I0mFpXjPLWd2p/9tZ/5tb/z13CQQeQ6rNsQgJN1aIvcqKgYNBCFYwxERJaMMotRC8PNoc+zfFSK0fe+/6Pf+f/8dvxNfvmnfybLfQIwxqgmIoMkAhZUgNCSVUBOqWvWbb0Wji6vLJnPzXja574BBBGQDJFhka6tYwi2yIn6wEyv6gTo8YpgNClalK59+Mknf/aHf2CMf/PttyYbW6oGjRABWIQMHJEmjbFrY0yassIbdN6YpLTq1per+v79J+f371Nc5zbP0JdFwd5K3XQpTqp8a2v77osvDjK/vJgveWlN2hxPJsPBbL5a1d2tye7D03v10YPldFwcHtbtup4vM2vLqszKwhhvDOZZOZ2Mx4PSZ944AwRAPVOGjCGiq+6XCKgkiSgIzip4qjYHL73xOgl++P4f1/MLuLYPZBKrRQQkSQoxzeezy4uztl2Dore5L6pi5J0xqKRMMSZAtDZXObeZY2UStOaKRoAoRIZUBQAReyGtoCFjAJVQSRQNoWjS1DWtqvrMOONKN/S27GLt3Oc/PkVCUlUjStjvGCDL8nI4ZOa/eP+HidPLr7xE3jd103ZtGwLiVYhfFTwBp8RJEdAYIsCQUhsajoGBEQnQIJBBctZaS8YAIBGCqLAEiU4VFNUba70hIFVAIkAAAoyKpGQJLJHpKczinenf6/3uDwnRiGUCIhUkp6qIluCqNoPMSUESS0oxhsQgqoiq/azMkVMEyySAFnuXKgCAdUbhKuuAwKSSQmLtOzA2CSskEWAAZpAYrHMoaOerpuu6dWyl62bPz2ZnC7S0f3jt9q0bG5OJs2QMKgioDkpvrRPQLqSm7UIIF/Pz47PT08XCDKoU+Xx+Pnt0dHYyW1/G02czjrVKCm3EaF0+2Sw28un1sirHG9nW3uDgcPv6y4eHNzen0xwYz+arxeXi2dNTAhNDVzeNUXN468be7iZ59ATLeX1yeX708Onxw+fbB9dvH96dvnV39emni/ef7P/8Zv7TX5yfnd//s+/zv33v4Ev75e7Ojf3rD/mEmLvlUozNi9x5Oz96+vz9j1pVt5QyMwfT6Z36RonDdBa/+htvPfrhDycvbhZlsX/9mh8V0/nFB598vDw5G1D51q/+6vzZyeXJ8fLRac5xcLhDg7HLnfUZWkQBIkM+Y0hseu0J58NyuLm9OHl8tl4cbO7bsmLpDAHZ3iUuAL2BBJAgcUJSEO5R3YDCQAhAvbjZGBVF4S6lsF6Taj4aG18089PY1Lhc+wLIZ5AZIouWegSPqghLp8GQFQtoVaQDjtdRfel2r+2YzcE6NYtHH69u7Of7hxK0TmF9eb64XMZVe/zn72DdvTLaPnCZXS/3MbscVs14E25c31rN7j078TGlJjz99F7us5ewOiZODmbKsU2dxIb8QFDUuMHAb/p3//zPs0lVTEbJusvlYjje2Do8sD1lHhhFEREtkTWg4EyWk4ldSjGQSrGzeTP7muabP/qjP/jWP/4ng2p4+ObbKpqEy0GmCqBJBBWBrCEEFklt061WoQtkiAwhKGLvTgZQVomirABgrKjGtkERFTVXz7oAEqJIr/kWVSBnHYo8fvjon/7P/2g0GP3SX/rlzelGiBo5fQ4VIiM9ZlRiUgFwmUODyiAAnPTyYvnJJ/c+/ehBvVpvZsRRxIROZY1pWOUb2datG6/s39xET8t1t+R5g40UvtrZxMlO5rKzR486m+0Mdk6Xs9Wnn1WTzXy6eTJ7ymdn/oKycU6+HJeT8WAwKKpeK5iE+7UHoCFCwv6TEkBVtMe3KSoyiCqpExzQ7p2bi7jsVksRUiRrnVWEJKuTo6PPHt5/770nDx/HGMqqHO3u33jltTtvvJBlNiQGSG1IuXVtO0sxDMoqRbbWGUQBVpEe6IEAqNIrSxQBURXF9qUED4gmqYamjU2gz9cVLjeKnbGU2IgIKBg0SOBQrbHCbFyWZYXNHCr8+IMPZhfLWy/eNN4tLldN07RdK4poRNBwb2OPLJw0KVoia0mN9LgqlisaqEHnnCPMy9z5EoCJNLEIJxAFRTQG0QAK9ecCoIIioSE01vbX6P5jS+VKComo/UHIoEGUFOmq6aQclbzRICFGScKKMQUBEWFQ5J6YnpEli8aCsCon1t4DBqSkqqrGGA4MRklNlERGQBRUuxg5KUsnLADKgsZaBgWBLiYCtO/94KPlukbSzFrmtJytKM8NmOl4uLFReW+RiKN0IUbRy8tmdnl5dnFx/5NPV3WKrMenZ48ePEWTx6aZnc3ayxULkqm6ptvaLvIsY8qu3bxWFpvT4ea13eHure1qZIFC5j0QnJ2tmlUYVblGOD85X80WXeCt7Y3NyUY1Hg3Hg3VK6/P58ekshJRIGgnG2WcnFzf35c4v/fxRe7Z89Jk8OKwOJsWdW5TZz/71N+TifPzStekXXj588WaShkVUg7PD1Db1g6dffWHjXuieL9pv/ODhG7/01zcun7z/wWPD1XR36wf/+H/Y+PJL1954fbIxziajIcuzd/7i2dHJvQ+fvPXiSwcvvzy+vn/+7MnzB/emYTm9c7eYTrOiEtKwXBMpCnftam/6gst81y7t0G/cOOS4vnzyqR9UpswhQmIxaIhMz+CSGFQTqBIp0dVbG1S0RxECCqoltJZUrbLmnmKS2HWiK5+Nqq3dtF6161mzXtsYMyixQiLbO4eEQYFFVIICgkRwOQ2n+cHhpj9/vJfSmGBeFevLo3h5kabTNkjXhXpeh0W492ffaX703p08K6JstxexA3CQb5Tr/eKz2cNr16Y7e7cuH3+QIK4kphY30K7JzIWnFo1xDKBd04BuZkUzGf/hb/+eINx986VXfu4tGBXFaLC9u1lmrmdrGTKq0t+5VVVVEImvxJbkMseIhRnceuk1uTz5/h/+0R/+q3/xN3f3Jjv7pIpIAExkABWNBUJhiV3brpexXqOIL0pjiXpjKzD0XE8BFRUEBQSklAImRhElI3rV7YB+Svf5pc0gNqvl7//u73br9Vd+6denkw0BFYhJoiGDrILgCImIjbEkpKq9oRVUlYBRu1AfnfHqYsfChFUC1JoSwObGYLC5tbl946UXX0OKj08foIAjjCGNh6PJxqga2zbbDuv6+erixnRzwrFtw+UnH+985ZeuH7y6LI7Xq4uLyxPCeRg3AGG9XpZlmReVL3NrjfXGGUOBDKIxZA2ZHoIN2g/hQZU5OWfQ2LwqNsZb56kDY4qiNGo0xPXR+cM//8HDj3+cum57WFk7bNrYXZzXi/PYbvlsqCxNWlmXaZTF/ML7zGa+Xi1tmcOV9EEVoadHo/a2N+nxC44QUQnRkCUiDm1sO+aIBp2zWZ73q9D+ZyKpV/iqN2iIVNiQzfPMZdl6vXz/o4+C6vXDPQQ9n82ZJQgHVk1BICGaHujQvwpByQMAohJ4MsOqUOC+ktBLHhUERTnW3jtgIQEg02viBD7/kAFVUBUlRFQV1v5JJLJGiXuTkYIiAimqWutUWFBbSSAoKXHklJi8QcCY+IquF4WMWmsR0Zr+j0OqLLHXUPcGclRAEWVgJFRgBNSoqGrIStL+GBKmlBIzgyoaNMYQEoAwIYGKsC0ySjH3lvavH46G1aC05EyRu7LKQtedzmYhpPWyO5u1i8X66PmTs/OTs4uTZ/efNE1y3qUkl+dLi7kBdc5OJluG8uVSR5t0eGMyGueTzb3rhzvG+o2t6WDgVbSRuq3jsq2X7UVXh1BHDQnBUpbfuLl5sDEaT0eJebVen53Nmthw0tPHq9PnZ9aGg/3twxdvhxDrbr57+zb/4k/X775z9GffWq3+KLt9d+OrXz/46hdPP/hJmrWznzzce+0lQVOv1s4SjvX0wdMf/fbvu5OTM148UXry0ZM7/94Lv/zv//XP/s9/7+lsmdqQCN/5t7/788UYh6PBoPS3br/6m3/j+L/7h+ef3Lu8f2f4xTcn4w0Z+cXy9PTZpZueu0Hhck+KoGCIEqt2TV5mAAKI1pvx1qbKHWqWzg3Q+BC6lJJAwr59AhAih3WLImSMIUPYK7Cgv5qpqqJgb1NBcnnGjIAcmsDdEjL1VZmNhr4oQltrChwFm4i5MdaCJSQUVmURZgXVENCUG6/sXvuFr5z9USMsuFqvFbLNaViujp+fsLGkzkb97AcfnPzo0xcL182b0/kFaJ5TTt6dpzApN3Nj3n//vZ07L4yfP2miNBBrWHdqVgy5eh9ZVFoQgnQNhq+/+cbi9guPH91bixzeuDEabWQH2zsH+4PRQJQCpxgCWEDEvoXaU8FUoSdJqDAROg+rVvzQ3rz7xuzo9OEnnzx4/73XRyPni7bpnO2LUCBALJxCCO061CtJyVCWuUJ79bIIEqr0Qk4QUAQ1AISYkkgMBIoEwAZsnwoEBAH0KQVnnKp/9ujpu+/98Oe+8pX9wxtAEAE6YQElogTqEBSpz5QrgPTGh14jxgyqPqac0zQzhSVJnJIK4MZwvLVzMLp++4UX7zjiT+5/ej5bvnDzenP2vE3d/mhvZ3MqNhvbcrG1e1lfLBNMJluzk+fd6fz5j9/Zf+unb966K5ggpdjVqalXy/p8eXGZ1eWwqQaDqiryKovWGoKrhDihITLOEiFZMkCg4AkJgAwRSp5T5owjMxwMUHA5nz98/7333/l+29SbB9defOulzUmxXK1PT+fC+vT0+QYyoM2LocvcoqnbLuTDDecz1pVqT/phEQQAY5Asff7w/zvkvZAhASWjnBLHGJpaUFyWZy4zxoESkLByClEBvckyg/3tW5PxxSAv/Pn52Q8+eA+c3b+x1yy72eVilbiuA6qAMpm+bcSUOUIibwGNIZMZ0xOBjDHeWiIv/cHfr3KTAgEZVGFFYBFRZBIEA6iKqgx9thhJYxJgNca0KRpDxP0j7Vn64qYqgCoYCADMKta5KKygSBYQV/MOQUxmrUFLljwbQjSEgEimfyP01CYRIWMQAIkIQEkFsFfPREmS1FjD3KqqigKDKFrnjDXweVkUVAwKEnRNct7ZF1+8FRo0FqY708GgLDLTNPH8/OL4+GI2Xz1+8uRiMVucr+YziG2cnT7uutgyM4vLXWhAGbyfFvmITKiyjDv9/xH1p8GWZVd+H7aGvc905/vmIV/OmZU1FwpjVQENoIFuNNDd6BZnqmnJCtGmadOSwwrZH6xwBBmK8AdbsiyHKcmmwt2kKJHdZM9oYkYDBaDmOSvn+eWbhzufYe+9lj+cBzqiIiMq8kvmzfvO2Xut///3I4iee2Ht7BOr88tJ2kjjRhbHXOWhzPP93aPj42ElVUDf6XUii8SUNOL+0kIjzQoXut0OgA6PhgFkND7efbxfzEqyyXMvPN2bez5GVxVyPBxWOTY7nM6V/aefv/fw3us/eaMXdzvJ8kZf5tbObTVksL1P03L38W7abxGziZIirzZ/du3OTz+g2b6HublOchDp26//8Weep+XLG4Vkv/d//+e6v1mN5YPX/vKpL3zFpplNo5Xz5888/3x+7c6jN2/Pn99o9uZaaXN+Zf3B+OODx1vTcdGaX2h12lEzA6IquBC8jdN60SjqbWZ6S32cnuUsVqAgJngPKlh/xXwo87ysUj2Z2EPdClQNEsQHgCDI7AlE1EZMbEgNkjJhEVSLCZKPsoZJ2CQdcfUlzQXnmNkYFGIl8CQsQQUDeBRpry5ufO3lySwfXb21NyoWz5wvofP4zu5SNt9dmg/j2eO3ruY/e3O5Kl2Z3x3k+xaAzZfmN46qalAdLHA49+Kzb/zsW9nFi1f+vb/94Ac/mGw/qoppqcGDRFJ5CAKBQSPIUtNqpO0r3/zN88eHH799dXWxqcbOLy515voioM570VAvQwCQar82gwoEVfEqQVQEOQS0bNS7OILTF8/fev/n1997b+3yE3OLy66sApONLRoCUfGuynM3K4ILSMxRzHHsylwZNQgTMUGF9S64rrgAE6Fq8AFQiVDJg7JqQGTvgzHAwGwNabh3547zevHSk0mWzMpCanCwsTVMuKbCi6DUPnEQESU0RDaAixJq97LFjd7+0eOqUBWOomyhP7d6+lR/eWlxbTVj/+DB9TuPN1fXzjc7dpaPIyeLvfluZ2FSQSumuXact+xoOGt2Oo12txo9qh7lo3a71bzcW1jK2i3LkS9LXzr1TlGB0TBba5UUAIgUEAlE6mRjCBZJPTgIBJjFsaIUVe45IKkFJJFqVEwG+5vvvb3/7nuJn7Q6rbl2b/fB3t6jvLXSb6WNB7t7RYam2WynnWbWEEx8eegdWBPZJA51U4oNCp5AWJUICbjuJSnUVixQQGICEA0QnHglNmRsFLONazKPBA3OA2KNTaiBy6oct5Msjbcfbb/9wbtxLzt/fmMymszysnTOB40joxIiskRsDTKzOXkDIRHXrmPvPDCKivclMgHWPGUUL8yECMFLJXVJgAAkeFEK9ZnKGsNa+4qILYZKQJHJeO+RPYpR9d57EO/FB9WyDOKdqhhj09QmjcR7RVQyppURWiQ6eTAoMDFKUPiFRTtIEEICIjIEqICuCMyEhlDBEmpQEG/IiA8CokGFAiCwNaBo1Aj6AEEUUGpvMDYaKZExi0vzSZzFEYuGw+H49u39e7cf3Lh6pwzeOTk6PJqMBvloXJbazNIkNnGcJHaBSJIG5tMAgllrLk3TpZVOlhrxVbOToQlxGrwfbz7eKSWgSDEp1JMWnJiktzjfn+utnplfmG+mWRrH7MrgSzfcP9gdH2aId+882j44unDx1OLCQtKMUKNGmoTKFeKD4uLqUqOZJQKzysXdqH3+4t0fvfPMlz67/uJnmnMNa2V+fV6L6WziGX2z3REIVeWpMpPtYaVFgPHzKxvPv/SFP7p5/fb1G7/7L7/9mS9++Y23b97/8RtzHfqlrz//8NW3H7Xnn/jqrwI7k9KZz312sLq+8+q7B9cf0jMG4ihJG+3VM244DeNqBgMwptdIRTSfTmdBRME553xFCGQgbsTz5zfG4ykiQ8BwcjVDFRWA4HxVBZvGGMQHEWTDaJE9BgFPQCrivTKBegUVVFBLCsag81XIp7MQIGpENo5NIyaxdTajmOQ2IraMJIQEiEEFFCZ53ojj7tkzF375C/cH+5fbzez0pXszd/bpc6eeuJIfT99/++OP/83Ps+GR0/DITaL5xayzdP1gu9xY07v3pBzNjrZWzv/m+nLvzsc/v/K3/8FTp/5W9sH7e9ubo81t2R/6fOxABMBCnAMfKvB8r+3M7b1b1773vYPlxhN/7etpu0XMXp2SWIvGWATA+iyFJ6trV9VFyOBFFIhMnEacT8ZpakzTeAv7BzuPH21ncVMwaBwDEQVF9S6UblYGF4AjZBsnKdXY56BBhQSDKgKBAEqtiEQkgxwBVaIiQIQoIkABxdfYCKljQ8493NnKuklnoe9ES3FIJOAAGFEMEhI4CCD1TkC9AASw1hRVUAfElhutkDRnDtlVC73V+bULa2dO9Za7jU4Uoz3Y3bm988hkC+sXzst0nI/yKObmXB+asc0Dqm91tTWyk9mhw1Z7YT5YOjzaGdzaiinSc06XFhudbtxspO2GqgcVUKBfsE6JkAkIsZYUOufrbhaQEgqSURTvXF66YlbLYsL4+BiC2fnovZ233gk7BxcubKxeeXI/l4PdA5f7e3eP5htG/Gw0OlrlM81WOzJpWepkMDnaH5w+cxYCqoCvHJNhJEWtW79IgFxjOBAFRJWZRQGVxXmt67RZbJCtTSNrFIMEDOIQ0EZsKBgICMgcZ62WTeH+nfs//+lbzbnm5ScuuNwXMx9ELDORRJFlMlgv+usAJCMzigghBgEVZUPKKk5EFYIqkjhPVCPqMGgNdEMgIKnZoXVt2ZBRNgxB6vUTCRNilQeOFAhVQcCLeEVFxshaDUDivIKquhI9QwkFGa7EB4HIcsQGgCQIKBAb0LoELPWmBBm03sQGEcIgwTATk3MBqVYbeEau937BC7OJOBatQDAEdeoBPRogZDJsrREFGxv1YuJGM47i6bTc3Dp8+Hjz4+u3bn94e+fOftZpWIrzWV6UEtSwCZUro5QDiMVKwAXRoNqf6126vNxotRdX+og4PjosCrd/eJTnYy/iHSTNxlK/dWp96cyp1XajnWVJYg0yxQ0jgCGE6bQaDSbj6biaTm7fuAcAvU5n4+xpw2Z+ea7TbqVZ43jncDws0k7UyBqsJEWYEcyKyrHOegutixu9xSzrWbI4LQrTybpnV83BsH1qIe028slMK19WhYt56emFwzd3p6W/8suf+IDyn928OXj/4xf/zl99US786T/9b6+8+Mzprz17cP+1B9/57tlPfjZd6MRZRoae+dVflRncufqwQtO/sCpAvc68dueT2GaNVtDAzGFa7Ny7631lbCwqxjIjoQoScRxR7oJIEK91sgQAQQ0SaDiJSCiphFBWQIDgUIUZQUWUJSiQqBIEj4isZG1EigTeuVBUBVggQ8DGMJss9VFSzvLSFRCAauM1AiIrkFRhNp0Jm+zi6eVvftWWYVya1eW19SfO5SN344033/iTv+TjQTu1M0XTmrvyyS+Xvf61n3xvenFp7ni/ceCzchZnpz7zuS/98Id/fPfWq598+RtXLnxl8eH28fZB9fgx3t8sy1KriQpMBkW2tFD0olu33nrrL18LYZicf6q7tmhjVPCM9TebVGuYFtUdLe/Eucp7AYWgpEGJiVSsaOWdzKTYm7ghwXjqy4n6qUkyHxw6AQEUCOJEAiJyREyGGEWcgmj9uFetp/uIjBQIlNCwVWKrYEUDArEy1Nd1FREIWgEoJVgURV4UKVsypgrOB09oEDC2dfpWQwgAgACkEoKoKqMJEtCHEHw1Uw5RM1td3Ti72Gn0Flc7y6eb7SZb8eiPjia7j3dRYe3UapSkxe5xPhq1s2az3eLIUAVGtJHFWbszPNgqrPQX5rtJkzg5HI6PHj2qfDWbjBqdfnOun7bSKI2iKLLGRoYJEUGofvqjKmBQIaKiKoOIBGSDxpAL4ErJR2U5dfm4kEBF7mbjh7vb98aHe/1e0mukg6OZZObs02ukPBq5vY+vTvLNuXOrjSQ2ceSkykt/dLg3GR5Mp4fWLFglXzlmpihGJIWAiACCQDVkHxjrBzECCIiAShAmi0DGsjEW67a2CgBHNhAqIYkEC0mSJSaiOx9d/8nPftafn3/pcy9U3h9MZoHQNmJUYBcExDAZYwQ0MnSSH0ICEud9fbwGUBSIjNUaIq3gUSwzoCIoBhIAsgx0EjqF+lTNNkjwodIg4gMQESqgkgHvAxIx04nvlVkFrKmpiAYaLCohKKrkZamV55TYEhK6KoA6ILBkVFVCXeWpjXlMgILqva/9DcYwKJwEEAQJAYXlF6t9lVpd7E9WahCCACExABsTmUgRWVQ8oKI5Hrvh+Pjuo/tX376xu7l5PDgaH02iZtRspYyWGQOGBsc24pgJsJoUrtWHc5fPoiJgtLSylMRYVX42OHAVeO+nufOVuXTh0vLifKvT68210zQCpjgBDCioblYWRT7zOJpO9/b2R4ORIoALkU3Pnz4PzIhsYxuqKuE0VIKZrJ5aorMmja06OToe7e7vTIpyNJ52+v31UwsLf/PX9q9dW2rYgABRnDUbcb/dXl0ApBDUlW42y9OstfaJC8X4c7OrB34k129PP/XlT757+92j60cf/MEf/+rXXunAwmyyAcn5i5/+ys//8e/e+tnPLv/GV6JGKwZodRqv/M5vvP+DV7c3dyC1nKCNU5tZG1nTzKgKoFz68vHNW4sXTidxAgFERUKwjADqvZRFBZJrCExQ10UAAImrynkf0IQQvFQVBKekRCB1ZuBkXwkiiEHq8S0CgCpbS8jEZRDxrqxmYr1FY8haYyw0M1+S+KAgSICgwTkfJISgBVaATqlz5bnZ/vHq/EJvcbEcHd1+7e0f/4s/xoO9pcy2UimoOXf67Norv/locDOZy5xtdxbbzWuVjIYA5dyZJ037jdtXr8/PL64/9cLKM2fmnziFxVM8nqp31WwyOTouxmMk2Mqnw/tXZWXu07/9+Y1LF7qdno0j79WVLkjAkw8DAYKqSgjOhRrbcLINR2E0DFA5lx+PB7v3r7799mww5hijFCqXNxoN54MPCgFUFEEJGY0xlpAM0YkK/mRrB6CgDOgBFFhQBNEQG2uAVIP+27ifiigEFUDSoKK1MdxVNc8OmQGBat12vZcEDEGMYQBQgXqU7FHAB+8r74IWVZG7uV539StfNYhRs4FJGqqSDVVejg8289FBo5G0WnMMjVle5LlbXl1IGw0UUlRQTKOs3WwfxVlRFLbRBonS0WS5zwDgD8bH+/uHzEmjafudtNOKuo2k0clajSRrpKlNkii2aK3xQarK+RBERIInZuTYIwevs+msmM6q3M3ysizKOIotxyZZmKTbs+nk8P7D3gItnF2djYtOe46ialJuVdnK/PxSmrYUwIuOx6NpMaiqweDoIDERMSCizz1bQ2wMMYgQIxISoNTpGaw3AopECAqkjGTRnAS3TujOVBcdBUIIwBglzXYc8+0bH//4xz9uNVpf/Mpn1Ovj7aNhUTDbGIhUAZCRQvBAQsDOi55MPkRqAbCcvOYJQBmoXokIJIy10xEVFNAwIbHWZiIVy5GqigZDxBgJhAAaVL0GCPULBhFARRGJjQEAYEVQL8FErIgoTCZAwARtDZcWEgYUqItuKIQiXkJAIAHBgEh1yV/ZWGu4rgeLD2yj4D2K1v01UEUGVKCYFBROEl9gjDE1StQiEYkqM5kI6/W0eevjGzffvba/vTnY262qCtl0uk0CdtWY0rTR4rmlhaXFRWvY+QrUee96i/0zZ07ZJKvycjya3HjvJhnbaHTXN9ZOXzgTG7u42ml3Y1VXv4lK56e5H41KX4ZZXg7Ho9lsFASCiAZoZFmr2ZprtwRMEmHWSUPhtvdH6+sLrU7T12+1KLAxRXDTcT7zVdRIUqYoixbm54ltEq8dD4fBYJSmjTQCqVwJEIPPC5fnRiRUzvTM/OLaeOnKsPU6F7Mb3/3WGf/NT7/yyre2/uL1P/3J6Patl77yuduDhl2KF5fOHt8dff//8d+vXXkmW1tLshjKSdLtX/zsJ269+UZelakxaYMiY8gaQOXIqFLlNHCz0VlRJkVCxSCihCDqRYqyio0lgsCRQh1nJiKqitI5p8zBVyHPtarQctSICDiInCDbVeHEXAtsiQh8XR0h4iiC0pfBlUWpBMQE3ouAV4UTT1VA4Dp7LCBsGAgTGyeEhmitv9jI0uPNnff/zfff/lff6k/GG4tZt/L7CO3FhfUXnzPNQXn74cW55V63l/U3yAc9OMRB2ewvL66cnd1+7fq3v3W8/fjs519ZOXuxHffIeymLMp823RwG9ZUbDWYNgIutZn+u32q3jY3LQsqqEldIkHpVClg/42rSIjJzLcv2zjMhiOSTcnvn4PV3Prj33ltuMBjH6Hylk6m6AvFkcyAAgAEUiMlaYqCa6uVEg9ZCSgbwBCgESACEQIzgAIAJiNlVpQZmRCQOEhBqu41XBOecArOlyWxa5VPqdo2xGgQQgvcADIj1oY+UAIFQa4m6ilf0aJXYtNKY67x2EjFHeeGQmUlH27vTncehGDeWLjQ660Egn0yAZWF5NcnaAf4tIIezuNHLFofuQGzV7s4f7GzZgHMmjuPF7eGD4cFRDoNh/BhbCIm1WTPr95uduaTT7S7N97tZ3EjiOApVVc4qr56IODIixgfMh2Of50RVJVUgVEuVd2m3u3zuAnjZeXxnMBnn+c70YTnfX5mMq93d++O4f+byxvLKcsLWee9VR+PBbDJau3IFo2w8GdvEGsshgPfeUk0gJUXlEwzpiZutXgqrChPVWRkmRlIVFRVS5Hr5qSABiUyz10wSc+uj29/+zo+6vdavf+PraRzf3H40qbwyx7GJmAyiQgAJQWq7S9AAWFvsndZCVqaTs5iAhOBCpRQhI6ogM4kiQp0qxnqhqgB1SU1EQw04rKvMgszEYOt/9xACkKoiAgVRUKlvuRwZUnVBAABIAcAyqxIiqzit23FUJ3RURRkZGVmpzhCJF6pTU6G2rAKxkSAKIt57hJOFgVFVQTCRZWMjBFCpd+1AeIJ1YAEFlCCGWRXNH/5/ftflZT6ZmRg67X4ASqJWhHgwOiQtO0u9drfVX2r3+r1WO01je3x8HJzL89n1Gzfv3X4snrI0ffmVZ1fWlheW5qOYtAyKuLs3yseTvCxDcOPRZObccDoTp6I8zYt+J07T1oVLp0Gx12oCM3hf5S7PC8AqafCli6uAgBpmeTkdjuM0CSKzvCzznA1HUZI1s3arLZULTHGzS/3+Bz/6/qe/8it2ZcmXYTYNO3e2qSy6/YYGMEKq1Owv9Bc25hbPWXd0+9bD+7N/Wp7eMK2F6e6j19/8+KVXTv/6f/BNGFf5mRU1aXF35yf/5Pe+9A/+ftxuTwfjZKHbmGudevry/qPHNoqMsWwt1c9jZRAFDZ3VxbnltTTOKieIxEYB1XsXgggzWCI1FOpp6El1xOWFr3yUoVesysLNSpPFHLMxVKdiTpiT9b8uCilJ8BokqICSsUhxzPWIu9LAYGICAvQCBK2smTQzk8TGMtR+UQmKCBoYGVyYDfLt9269852fvffD79u9/QYiFcXYNKuklTxxKl5p7x7dzSn/3C99cnHt+UJtNrc0mVXjo73myrlWe/XK2Rf44d13fvCTO7e3rnz15UtPPzPf7ts4FYFAFCGBKXqtDiIZZsMcRPKy9ME576SqxAeRejRBgPUpRpkYRECDSrDGMlI5muzcefCj7//04wd3hruDhNRSMtwtHrx/rd/pJe0+2gYgG8PiHaKgYTIMqsGHmt7lQ1BVJWENSKwkQIrMgL6uXJCNrDWuAAlOAkfGSu0GBBLxyAShstauzS+9M/lod293ZW09jaLcORVWpQDCCkGDKgYQVFRg0QASauaBkqpREFCBKI4ojsrCG8tIdrK/M3y8NToeJl0b95ejrFsNt8fjw7TF2VzLxiaId1JZJGWMUps0W5N8VFYuXTmFW4/2ho8nxbQnBWBAX2oIQcn7UDCKjJr3dsethm8m7aXlldMrS+uLzX6PDCGojaw1FhBdWQ4muZRVv9koKirzgbhCggtCyhAa0D+/kbbT46P9w53Dze2d3d09i67dmzt7/sr5Z57pzc8VTgAMgpZl2e4uXnjmE6GYjR49jGMGUEAgxOA8WrZMCBBOigi1vQWCCgCe0NcQDNdp6Jpti6AC4gkpBEWKO3PdONNr73/wb/7sR81e9o1v/mp/ceHWrdvHRwNKbcq/4GyKEgIggwozi4R6DeaDkpAqWBMBStDgRUVDDXkKwQchIqCAiEzGqKgTkeBrP2Q4aQyfSMAAEAHZRjExItYTW2NZa7aF1m0HDhpEUUSdqgh48YiEiHCywQ0nb0MUUEBFQGUCDUqCAaF+BNR6L/DBgSrWeUEJwQWvXoQt1+pHVsO2ftORIQQgYKqHokiIzAwsIB7EVx4sgpKpfOHzionSrKnA/fnu5Uuf2N+6u/V479z5jd58L0jliuArCDmOZuXgaLK3e3h0cDwYTebnep99+dlT59biOJWy3Nq6Nz4ezq2u2LHd2j0cDQaVL48Ph8V0ZtiaOLv87KVTa6sagiE1ETeaiau8qqcQQNEwLa90Ky9BtSicVDKZ5qPJxFVBjmfOBVDXbDXm5vrMUdZIsyyJwYC1eekRm8fHo+2P3z0df5KbPVHwxkwOK4i42Wy05htJK4IyNM8sNz/x3OTN16nS3XtbvW7rlZc+fauchMHsaOse7W6a+EkyvRf+1m/+5L/6vcffef3BC5/ZePnF4uAAxcfWzK0sgKoriqzdMBYVidmCYg1t7na6WbPhxIsE1OCqolKHKmRNHLM1EaqrSlGtJPga8yPijIhR9KiA4KQEoeAMUf1YBIWTqocPPuZIQhCoGeRGwYuaKIocKuSzypUzX4mmjVar1W4njdRSXFO33KysgoagATwENTHnk9nw8cP7H9198Ma7jz/6uDja11BETElvMSys4Fxz4fL5gGXF4exzT545f7GZLB5h1TizcvjBtcHjh63zF7m9BjL77Fcu04++/fbOw4/+Ip/sPbz0xPMbpy+0+73g03KWdztNXznFQGSC6rTIGQ2ouMoH709+gurVJACiMp4AexCQOFLk8nh6+ydv/+z1Hz882qQxcOkwjhe6nVZ/frw7mI2nTS8KngBTywQGKRAg+BBUg6ubNR5QQQQQghCSnIheKSgIsEpQgvrIxL4sK0fMhpi8CKKEuqlGEMfRhScuzb/77ltvvHnm0qXF3iKY0gUvlQuh8iGo+npWpFC3DgDUA4Y6uhEAQNTEEceJD8ocMchgf2/32tWD+7fIRFl3qdVtx7F3Ze6Lo26n02j1bBwFVRuY6pk5UZwloMXweODXoN1Zenz37vhg75goMsKih65QYyKObRL3qCmz6f7j7ardOBpONYIojaaTKWdxt9/t9jplEY4PjkbHM49yaq2fxun08agazQLkUZTGWcYUiuCiTtRL1xq9bqM5t7+/U84m8/3m2qkz65efbi70wFjWQGQnk6El2ji3zpI3Op1wGIMlRq58sEwI5H1gEEX8xcwfT9w8tZUGAJHqPCgoSI22BQ8qhCDBE3C738qa8UdvvPsn3/pup9//7X/n106fP721ffxgf5B7bApDCMGDEFg2QU+oyPKLopZKnbRAIvbBi5eAXk6GLFy/fkJwAmLZECsFqFP59S9ItYJLARABDKIikGVEDeJOVgQEeIL0UkA0hALKteyqLqKgRmy0tjFrrQ4OdduEI6uC4l1wQVWglt+d1FDg5AAIGlRD8IZRQpAgAkDEwUGaRKAqAvUqhTSEEE74oIgiwkhBJEhQUSQyJlIRUW/SJLZxK0laadZA9Iywt3+vKPInnzrX7XSq3E2rfDye7e0co0jpcgDXX1g+f/qsybJOr20Ts3d4dDQc5EeTMKuc6sFoGrw/Gk3LotQq5IVbW+ovL690enMbF061u00LYKyKg8Hx0WRaNhpJFEUcISYQN22KdHScVy44oaSRVSrdvgmVA+Asi01qGdFw1O1khqOIGASqqrRpPH/p2Xdf+1lnca1/vhUlZnGhhxXubW2bc6a32LfWKOPypXl8eu3d96aKUw9y9tzZX/ql/9nwvaucDgdl+Rf/3T89+9nzrTPz55fmPmQcbj768A9/eOrFZ0CClA6tMcZ0l3vDrYHzEiUpWxYBYhZRAOktddM0RawhfaJBbGoscvAuzjhOWDWUzOoFRJDJcAxKKCWjGiAbGYpNfcGs6bpAYOrkHEkgBlIVr3U9WAkCTKdlziGOG3kxq0aHJm3bzAKgn4Xb128/vnlr58bb+5sP9w/3yqlnm3A7WeiufPI3frm5uvjR9//igx99OH64B6ULlVtITNTvV4trjacuP33ulHaS0XTQ6PbXzmx0umkWO+/ay09fvP3hh7vXPlj78ssLp87e2bxXXGk8+9Injv/yncHe/s6fvjb76N7sC58+88QL/ZXVKE1ms8ICpo2mBg0+AFnnXVGUGgSBjCEyXE9Ma4wPExlmIgxgVDFUcHj30Rt/+N2t/dsdMO202egvdpL0ypNPRnPtYx0mlgIyIhdVxaqWiRCDBAmiTkJQDR5QamMT1GUkAERiJIXgUZlIfFCViCMfJeIqVzlmZyBCAQQM6jWogGfitY3zX/7ir777xs+//0ff/qVf+3J3fpmCGGM9oINKpTZ3V6oBkSwSAALXxga0AaM04dgoUhQxlGG0s79749qjW9ehLJYvLKetBZumhJV3gyLM+r2VZqvJRCIBDWoQAEBrOItt1iqqaQWT9lxvrts9HOxVRY4Oi9hWrciqYWGTC8iklVJY7fK5043+yvxCfzwZFjt5e3VxeeO0NfHIH+7v7+TDYv3sWrOVFJPZaHyQu7w732l1ewic5zNmDN5rFKX9tkNoLfWztJFk8cLicqvXUUNV6avCV1VeFEXMZn11cTSe5uNx0m4bq4qmlRgnIYptcN6HQKygqCcJrFqlcpIBACDxCoR191pB+YSFoohRZ76btfDqO+9++/vf7883futv/Mb5c2ePj4ZXb96eOphb6LAEEOPVA6EDZAGBAKriNECtPBMNoKokWqlzrhJQ1RNLCgKSMRKCjSwIISF4OUnTQ02uIIa6TQ5sCBSQWaGWS0s9eFGplS/190wFkZEVNQSBmsQKgADq1asnAEBgg3UdUp1W3sEvsrGILEoiQYLnE424qqCTgAzBg0IQqtNHJrKxIQsYRDWAgAY54WwbFRVUspEKiPdQL0C4prsSE5h2oxNHWWJazk1sFMVZcny073OfNTrHw3FZ5EU5jZJkWB5VsyIEbTSz7lzgNGKO9rYGtKc724/Hs2L11NLi2kpUudHg4GB/kDZaly490YqTKrjFxZ6JmJB8OZsOfJLYBKwrBMH0OpEaQMuq6Au/MxmRYlBwpQtB+gvtVpY6X3X7XWaj3gcgG9k0SYJo8OXEyXRYTGYztFnUPzNKr//oW9/+9d+ZS3rzpt2ywsXe8aP377SamVlaDgi2YVY77Y9CEOvPp/1PrzzdhnakGpybbzQ2B9v/4h/9o6VWZvxu2sW728eT1989eLB96srydHCcdTpoOYri9mLncOc43x105ppRZEVZALyr0tiYGlMF3pJGcZQ2orIsTMAQRWSIY1tN2FeigKJq4kji2AX9BRwlSqOGB4+qIaiikCdmjWN0QKoea2QK1kNELETEub37W2mrE0e6/2Br/nyvGsG9B/c++uk7r/75D493Ng3MLEgCnAAmYA/AP0i3ornuhS88f+f2ozsf3VgATJKovzT/xMbyXhX22tlnnr2wfmppFCp/FFr9fqfTjpPYcIhb3L1wyVO8ffMm5PnSUnTfD7ceYC9trLzwdOfavcn1G/fevbO9s3Pj4q0XfuUrl556ptfqVT6fjmbIao2BAFXl2BiuF4S1oEVE5MR/hEiCJKpeQunCdHR8sPeot9SLB0tzbtbKYdX6VGc3Hr7z+o1RxfavrSx0n/5cnZSqysqmkQQIQYJz4ILUTS5S0Ho1hyyAQFRfqH2gGnVPGCplJBtnZVWJr6rK1c+D+t4cQDRoVRZso+c+8WK3079z7eM/+Zd/tL60dOW5K0vri0xxRElQ59RpME68QlARrjl/SKhorMFAUkKUUAjVcGvvwUfXHl6/Xk5Gpy/Md3pdk/YcWud9NRs5DNl8N0oyMCTeI0E4sYhapJTieDqcjKf7zYVll3UK0jhGEWSK5jhqJ22QCVUieV4BdE+vLFw+01+9HEQefLT7+Pho6dLZtNEog68qB6HIEk0Q/dgfHwyPD4+73e7yygrHNp9VOhVDRqrSoyhTe7HfbrVjazhOkzTzCETGuWI6HpI1zdSCt/s7+74Ko7zod9rGWrJWBAg4eDVsnFepRVuKQECAgHBSaSUEUaz19KKgilRj+BWJ2vPtKLZXX//g+z/4WW+++/Wvf+XC2bOjcX79+u2Zm7U7CYXSC2gtPvZab+cBREMAJH+SflfvgyoyeoHgfCW1ycEYArLGMqC1cWQNGzbIQTQEAQLiesJTN38DAKCCGgQUUpYgCEhokAh+4Q8AEA0KRCclaAQkUgQUrH9XagcNU4AgQVARBJDIBS/BERKDVL6+WKDzHhEFhCwBgAQl+gVQLzLWxPXmSQWAKGIjGgCBmAgwiBdVskoUB68MhCwBg6p6L2TZDA6PmY6TZqPXbgd1+/vDsshdWU3Gs+DCdDIjkka7kbVbS6c2IkNJahXC4+3t6dC70jWypCrGUdbM8+ra29d95ecWW2urZ3r9+ZXVxWbCvbl2ox0joPoQJFSlK2bFdDwNwXsFNsYDBAesWBaO0RiDaTNp9aKqqnzlTGSTxMbGWGPBchJFJrKVD25ajQbDnb2joNpotyMTqYkuvPjSze/+yXuv/vyzv/prahM7b+bOrY/fPsj3jmKKqdmcPtBrf/HGo73DCQg3/dt3f4JHHz7c3Yxksu4rrspynN/d2928vdVO1EEscHTj/Q8ufPaZ0cMHSFHcaQJg1IznV/qP728Ph3mrhXFkqklxfDBZPbXA1gRRRbUmmo2L2WzQ7jd8cGytghgbGRP5vFIBRbUxA9vcVS2v4nVWejZkIVJUkCBeSoXEsOeIIvbOV5McIlZGw8YRFOCFrJsUu1sHy6dWRiHraGP0aPLt//K/eXTvegXuDMRPRJ0rvX4SZYtNMxwOv7W1+1Ge3/nJjVtvf+CPJ23O+kut9TOnT19+Zbh36+ZbH7/0uU/0T60m8+2ymHUZm61W0kjIGAcSZY3m0pm00dvZ3pqNhs12xybFUIZsF6TZ7D575UxrAd58/dq92e3d9/bubu98/rMvfP6V/tKpRjdyVelD5arSxFFkIgmBsJ6SiNS7wDo5I4oqLvjZZDYaHh/t7rXn537j7/+vb/zgB9Mf/sQebhdluDcuPtifXiNTVHru568tfebznZUlKgSDBwEJwfsqlBVIzfM5KRohAgYBJHEilhGJsM6FUwAABSdA1iZpo5iKirjKEytbQ4yo5MSHUIHBrJs8+Zmnzz57cbI73rt55+DW/ZBPsma3maatdieJIxt7L955hyEgKIhCALJIioYpjozP3fHDR7c/uHbr2s18MlhaabZ6TRNngCliJkU5m40RyribmShSUkRUBGUEUUWKyaSUzGg6me33zqyfevHC7PBusb0XRSxOGjHEYWojMw2uQgLAbqezvLKUtngwqYalj5hiDOOjA1Et81naaLFC5crDg72dnQNOW6fOrJgoLl2ow82I6BiNpbiRJFGTrQFjKIkq9RatgMxm07KcLc0tg9N3bl29f/8miesvz7ef+7zNuoqiLpAARKgB2HCogbd18RiZAGutJmh9HA+gWEMf6ooymbg510Z2773+xmuvvTV/qvOFL/3SxYsXqrK8e+P60WAPwIdpORHiKBJ1HgiCCBApsIbcOUZSBAII9XYViKyJ0BjLhIYNEnDtICMi5vrVTzVEBUgi5toEXxdClFhCAAIJoSbMIoMKEjMTicpJo6VO9YgSauUcIiLKCQC+jvsggoB3PkgQEQAQF4AJEJj5RNtW00tOZkQIABDAMgNTxKwIIsqG6/QBqtSbCgQwzIYI6sCVV++FBDjyZElUEpMIiFeNDESWDGpwzufHUyLLbJlJEYMKpymZAM5FsQVrAU1eTAsNhM0KXbvZMBSPj93O42201DHt0UERUXNtuXPuydV2rxUnttmMkshESRQcqDoQ1YBOaDgtjo+mxSxPmpFX9iKtZtqOUgCTNRKTUKNhreUkidPYcBylMVvCMncSlBXLPJ8ULp9Mysp359pV5caT0WQ6m59b6nUXL3/5a6/96T9bPXdm9elnMVB/dfng8dL2jUcG2w0T54dH+Wx6MNjdr8bimofvfzzQ6e5R3keAouQC57unTn/j0tE//xe7D7cXAFx+PHh4C0WjNJlOJ0knDaIhhChLF04t728d5GVgo8XMz0oXdztkCAICQT4zB3vD7lKjrMLkeNRaWsrzPIosM1dlKSEwWUJoNOOyLIIrvQtlGZIUmREQVMSHMihi1KicC6WWs1k1ySUUEBNHaJLUKXMat9YWdkcPx0WzMd9o9JOr3/6hf3j/k2lzVuhiIzu71L28cqWQ8UeP7xy4AjN6sb0yncWjw8P1pr3yueevV5Wfv/zucHL/3Rvdxfj8lfXMGPW+0WwAUpQmbCIAVQG22pvvL/UXHhzcGh3tnllZasxFSTtJ+taOk73t6Sjq/83f/Bvvv/nWjz668/jeox/tD+9cvfn8L3/1/NMXllaWWklLQBlNCMGVFZGEEGrYLjhQBlUE5iBQldVoPDw4OIyr8IXPfHYYFhc/Se9//y9zkElZTVCCsQtxVFb++OFWOT1kWgxQpz2CVC5IJergxP1Sh0xRAbDe/BoEr7VauI6CIHBNcAAFG8U+eFcWIOJBDIplUxPUvIqCRobSVtJdmrOXms996pPbt3Z+8Bd/dvPdb/Xnkmc+8+LK2fNJq5sZdFr6Ine+FC/MSCayGCFTNZnt3t++8957D27cmZXj2FIrbdqop0kbYkNx6sfH5WSUqYniJse2gqAn1k9ERCCJGTOmsXPFbOpdMb+6HD79+Xs/fWs62mN2w/FoyoQWgJKQxf3V/sKZC2mjU1RlWeRRxOjjx7ceDwdFnEVJbBpZB0JwrppMJyY1K8uLUZL5IJVz3lVsSLy3SZRmWRRnxEQmZmtFlYnZUJ4Xk8kYDCZp5BSb2dLB0dvFZDdr20ZCJjIuBGKo6xdoMASFmk+HiFpXa09oe3hC58AagcVIKsJkWr1GQPngp++/8dbby6eXX/n8ZzfOnp2U/ubV6/e3HgdXkSVIjYoTIRfU+4rq2wSQq18mhJZYNWhAYDLWWLaRiQjRGAO/uIUA1h0RUUBfN7lBABUZUTRIOPkiYb1bOLFr1Ul/wzVLUFUUGBEhBJGggOJCUISTSGsd50QMChBCCHXCOzjn6o1tPROq1w5EpibfiSoJckQIVL8bkJmJger/VdFQL1KilAgI67V7jdoDJLYqnomMMcBQU4GMMahqYo6YzeXLl48ORkIhjdrNdoMoDMfjaGW5N79sjXWVSxNrrQnBFPnBbDKuXGXIHBxMhwfTOEoX11YXVpcXFxaSJO72eq1O1l1IrIFiMkNWIBhPxlXlvC+CA1eJEw1OZzOJTdxKG6WD0WymIdjY9roNY9katgYEJcsaqlrk1XiSQwiWTZxYD0YMBHBgmAB8VbrKkVLWbjYSJgrdaGnhiae+9z9966/+R+eyhb6BcPq5i3cGw0lwMTmXjxj9hXMXFya5s2kIPBzJmYUVnXjuNj/7pV/e2d8+Ghx95uv/y7d+73+MZrs+rlo9W46PlM10VnZFGTmvKtUqa2Xdxf7ocJqlGhAXN5ajJEInkld5Obn90we3Nrd/7W98IeTleDDMVpeMjSrv1ZIvvZQlJAkzttvdYnQEwRNo3LQCgqIE9X/BxDFaI4U72twebO8U47ETF3WTZjcTUbWN3vrZrN9ZXLug4ufn5q2UzWZ0ZuPU+aP8Tj7cmF9/PJz89PjN4KY+c0dhynn48oWLy3Or3//Jvcn0kOfbabvzxsevH4+qtXbr7/4n/+7a2tKd6x82NzY6a2utZgMtE6rPq3KWpxknSWt5fvXjh1cnh8M4jeeXzgrm7bllsG53f/faaJeOHn3l3OnlSflHH9889tXeWw/+zf3fXX5i/VO/9PLl559fXl+L01TVV4YAVETEe1cRhCIIEkMURVXpEQARYhsuUTx7/Y3NQ9pz+1ePjyYgmYlXVrsbofzkTHagMEi+LARF1YsEFNQ64F2f+b3URzSQoCKArNaCaO3SVVFkZqY6C0oMqkBMUZIQQnBlqLurQdgyaQ1Vl9pd6MTtbO/PDseDO3cPNx/s7G493JXt8cHSmY31tbW1lfXm3HyaGgJLBow1BmMNMj4YbN+6ffX9q4/uPxBXpEAdMq1WW20iEZrMmsgU6maDgqLFLO0aY4MgMNUhWRJUCmAjTBtlgKQqqmLSWuquP3nOxtmjj67me9vu6GhSFMEH08paK0v9Jzf659YxMVpqnEbnLl0sJnlwLi9D5aet1cWIoqDOoc+yRqPbTuLIOSmKopjMFNAFsdamaRpxgkwIjMYECBYYCV1RlpPpNK96y52gCgQXz19sJL+9f7R7emO92ZoDYXBORWtsj2LdReI6AIqE9aD/pO6kSkB1l7V+LxDbrN3wbvbWzz54590PN86sfv6Xv7y+vlo49/H16w+3H5LRTm+udDqdjkkUtZAAKlo6jawxERq2Bk0cpSBAGpy6Wk3AhiOOAJAYgspJYFjViYr3qkpMdT2RmMRDCA6JEFCCENcQ8vo0T16EkZAUGQlABCGIFxACAaH6zUJcH06CegT0zishAvrgGVEB2BiuvTAKvnJCqAimvlwQEBvDbCNb98skhPqTBGAQRUIGE1DrP1g9OiMT1W8RcAKkzAyIUONya26JKLOJoghFzdqp1Shut7rJ0uIKIZbVNK+K/lx3aWW13UpVvECoKhlV1eCoeXBwIIDj4RRtWFpY7/bb7W6jvdDq95r9+V4cxT4ECAG8iIhFqpz3ZTkYT9QLxcYHhUAI0fLCXJqwYSwBIgNZO8myyCTISkEAPHjv82Imzo+nw9mo4kR7vXbXtER0dDyaTkbTyXg6nsZpsn5qrd3pWMMaxLlATE9+4kuvPvqj7/3Zn/767/xNzmwmSbyycufxfvv8Rjrf7Dx1tn2uU4VOa+XJo5GenXz87G999fD6/eF4cOkTnxhcv/nhn3/nr/zDL6wXyc/+2X95D2QyOj6+dz85da4Mmheu34miwEVRiWLaTCUgMc7NtwU7o4OxzI6lCoeHgwf3Hl841fODwfa9e5sf3WgvLmQLi+U4R0SvOB3nttmJ07TZ682qQpiMwSxQAIEABOC9A0DOYs9GDg5nN28cPnqUVwWlphsvep65opyMXGSo03h6aWXd+2m7kXSz7LkXn9g+Guc/fqNfRqefuPzo5rvT3fG4nByXPmWXadQsZvN28HS/9dbezoPbj5qn8LLE48h8/q9/Y/7Jy4OHh6+//taG+k9vnGrEsVecjieHR6PJtFyYS3zWS85fmv38e3uPHhhrzl9++sGt9zLbXNxYlWk22Xp4/eFdu0fnC/dykt4t4Oyli39ydPvGGx/cev/aysX1L3/zV64886m5/nyaxWBBvfdMhlCcM6BMhIjGoUbWAjejrPro3gd/+f5umgzmYWm1s72FJmbTaLmSaO/IQtHNDBkUEZV6oIgnZBlgAmSryCQSxAMGrQlFoEGVCQkIMChQnTliJg6+qu/vcRw7APBOMAQVVGbDxIZNAkr724NHm0cfvnf30Ycf02Qrw6qF4hXKvfG1/ffff/eNU73m+fNPnL78bHtlrTvfVtTJ8Wh4f+vux3fuXLu5f3ygKO0kXbBxN2liuxksClPWaHiUyvvBLHR7fRMnGhl1oYYPE4BhdIKcxTZtg02dqIga9HE/ibMzc6vzx/e29h7dHx3tqwm22W6tzPeW55iII2rHWdVIs4avelJ6F5yLI06sQSFCSCMTxTaO4rIqXJnPpnnwEoJESdzIUo4MIxEbhgjVAHpVqVyhuR8cj5qtLI3jypVEpjWXXmxfOu3Oo0EgEB9UQL3gSS3iJI0e6jSz1Fr0Wu2iCFQnKWsNLsfUbEXjfPrmj968du32U5+68qVXXppbWZ6Mi48/unpn+4GCRGjyvHQlirch5H5amdREiYlSC4JsmJGYyBowCIAUSaKgxBggCHgFdCKMLOFkDa2KSohyAqqmWg1fj/6lXthSXUqoUS61PR5UvFNFYCLBk/qghHr4DwAEHkRCrcJREPGhlqCpFwUVAKxp3aCiNSuegg9ouWYG2NgSABNbY4BJkL33aCl4IABDjIAMgAjeBxABEgsWkQWCovpCySoSgxcgYCRAJEOMBtEwg2l1Wu3eQn++2Wm1jbXBVwCh02+lWWIME0EgFdLWpExTxiwajWbNVrubtFqNLGskWTuJGlHEFKexYZMIaSBf+rLk0jmyrERJFFcsWZokaWI4siZKYxsZnuV57GWuG9mYykqqfFYWWlWhTtSK09mstAkgQajkYOfgYHffRrFoKCczQlo/vdLr9BrtVl3cUPRV8NZQb6H1qW9+6d0/+5MPfvCDZ7/8y5SY05c29g/L3dtbZzZWVr/44vH1e3NRGrXTw3uPFy+e7V7pn33pTKCgpVk+P3/zL//0z7/9ve4Xzg5/99BNog//9L3zp565cupUZO1gbxQxR4l1lXNVRcRxShRqg4gOj3N1od1Mmp10bXHOaujOtx7fNvffvrl46dSFLy4ksXVFmSRRMZ2I98ZCo5X6ba4EE0uJMQFQVFXIi1QwjcT6siyO98rRQTkr2gudheVlyAwm3oAfT/LB7XudXi9ZOm0YojR2s+r+Bx9e/+EPF6B47jOf/4sf/uFBpAflzMZsSrSNlvHVe7ffr/hSb2lhYzg6KqfHj7cC8ic+++ILTyy+9l///g9ffSN+Yr47KPb39+P1tSiKdg7yh+NB4QurPTCs6ylHXGzvpHG6sr6xu3U7lLNsCRbaqscHuwfHaaju35ue1WrNNBZh9Onlue/cn+2NDid74/2PH597+nvraxsbTz1/4aknFk4tmDiKEhXNPCAxitfg8snRTCjZurH78XffTR4+nIFpv7K08eT5h0cfCrhDzHf8NHVuAzBbnm83eyheggcRL0rEAAwITIwQpCpC5X1VKqCNYwt1BvEXLSREBWVGNqgSLKgEjyBEGsWGLTmpAIkYVcHGUXDy4Pbjq2+9/ejjG4Pd0awqjbpCpa0KAHklhmxRyJ3J8f6j1x9df/TcF75IT12aHO1vXXuw++H13e3jEeQxSdaw8zZabfSg2TVRi9LYZiZtNJWNukrGRWt1OUliw0kQEPWqWmdJhVxM2mxZG1E5mfiqDGWl1iTtKG60W3ON1nprNh77UILBKEuSViOvKs2rOE5iwxArGduIGoQArtIQRJCZoihCS9674KWqHNczGTZJlhhjiYyJorpLHdyMLCNx8H6a5xSZZisxaDSEoB6sCaQmtaq1ayCoADEBiQoFUAknBvYQAtaK4aAEhjQg1+JFUKQkiyjBrUePvved14az6iu//vnnn3uu2+0eHx+9+9YHD3Y2XXBRkghCUVbIKN4zcNYyREhgkMgmkSVLtXa1rm6BEBEhBVUS9iD1Ix0YIEAQT/U7Fg0yIimDkVA//rXOKbNl+MUGO4RAaEW1lkIqCCB5DaogArWCQkR8PQgKoCJevGoAZlUhNBq0Jl4o1MhYqvEhxMYyQxwRkojWvhr6hc/BWltCaYACgHBAMEGUEJlJUFUwaDDAErygniC2ImUmCQqqlk2aNoyxCkJAcRJLCObshfU4zVrNNIos1aIGFVf5o+PBaDxKozhK4yiKVdR4bWDcWWjO9xvNJCETWUsSpPRhNpuVuY+MjRjTiJGNsRbrmw1zmqRRmqIqEcSxZaLgpahKIDUmGDau8MPhNJ+VCqpB46j2enOr3TIRldP84HiwubU5GU477Wx1Y31xqd/rdLNmhkqiJz/LQWu/isSJ2VjfgF//9Y++/6ptNZ767Kfiln3imdXxwXHSazQ+fSl0s+ObH9//2T9/6/2P1n75a+fKT5RKKjSZTUY7t+x6f//Nt8JCeunf+9rg91+Fo+rR+/fO/WaYW+gPdo/Hg3GjkxhDpECMs8IbVec9InQXuzFZdsXmR3d237n/8t/5cjTfjTtNd3T86u/9WXNtZfX0aduIzKQy6FEDAvV7LWtpOp2YZhpZayLrg3plRYDAs+HEDw91VhZJw87T/LnTnblV52fTYqfR6/QhOt7cmYxG2SoZjkSRGmnSb0WM+9uHNzpbJc9m47yftOf7SxtZtnW021tq39ncevPurbX59suf+dT28fH1yWQ49qGQ7dcf3Pnhm8Wj7e7ywuXzp8H7opgkScRIDWaqNBZPOLP9NIkkPz5StWmn1ej189l45nF7e/jwxqPbm/vpfCdp8d4oP+X18N6j+UunXoo7N63dneTlKL/3xo3r5TVu/+XFy2tXPvv83MULZ86e3Th72jQbsyJ3LgwOjl1eJklSDoq7D48icMuQX9hfeO6VM9s//ujDYv9oK6eSArhmZ3H18uWkkaiv0yI1dx4gANQ0h7J0k0k5GQcRk8VsyPmKDKGiopwQqPH/P3xGS/UwAAisGgpBgiBSTa1ENsRmsLV/+9XXdXScgU0Qc8UUccUkxleT5kK7vfywGu2NjqScHO7PHly/s7t/eHTvzvjxfuG8ABnQDEK7oA6HRpOkQbadeRM3mu04bvkA1f4kLl0jS9K0mVgrEBSQDQIZURBEAoljm5kq9zOpCqSAqioekJImL56dD1XXl5UguKAKWnofq4KqAcQ4cpVYwwQQAgdBAWETEbELwTlXVGWNKDbGJI0mR5YQ4yip8+MQAjFDzecJngnTTsJsaqxJLeJhMCr1YrMOsysoaVAvNfwQRAMpIqDWMKUasaZQdxttxHEW+7L86J27P//p291e/2/91X/n8vlltXz/weN3333vYDTwgo1G00aEomSgCj5JGQENMxMpEgITWA2ChgBJQ/AqrvJEbC0ECahojBUVgHpor6pBgDT4mhIFAeoyGBpkINVQh5VFTjpejCQoFikEAgLnRTloff5GdJVTQlQIosEFUFVRHxwyMdRVCIySiIIiQTjplqkK2NhqQEA0hokIFACUDROSBGE04gWRKGJRQQ8A4NUTmOBUsAaRUwCU4BSQCMkYQ5FhQ0QRkYmMNdaQJUMgNRU8Miunlow1oXRBghcMpEVRDQajrb3HEdtO1owKSSK1TAmlly8utVsJBRHCKmBwFSIk9SXFGBX1IeSFKgAbTmysEAJBCBoZzaJYUcFXIYAIlmVeeccYTfJ8OinGo6LRSrIkIabIsARnwA+OD4+GAzJhNis1lFmj0e50u73+3MJyM4m0VkJUYTbNi9I575AIAzE5inFl/Zx+kd772c8aSWfjuSdbC418NDg42Fs6c6r/VJquLdHSRvepe6Mo29raFkWKzeGj3Zt/8NPb374atjent298+u/9by/9wxev/Y9/APv7e+/cvPxbv5KAHh0OZ5MiskyWTBylqclHMxdCksbNTsZB8sNJkkaDre27/+rP0vnfXn1maeVc791X7/3od7/z9f/Fb3bOLWW2kxliCL7yhLA0393dfBw0NrFhYwS9iAPjiSuYjcvhcXE8rCBqrqeY2PH40BW5TbuF7UHjDqQJIlgLsYmJIAA2ugtZZ2GyfdRorX3jV1Z/9JMf3xuMH+xuuiydT5N22uJmcnVy9GC3Gst7X3z6hW+c/61H+uC9H7/+8XdvuP3R6pkrK8trDWsvP/Xk4eiocEWrYearbIa+yQjAQJGJMzmeaFVFLcw681AWQBS3zkTSXA/cdYG8tBtdM4VX3WH3qp5fSGXkN1qt1mJSTsvrj3by4ejOm7N7V+8dSOhe6H7pc88889xzM4jiztz0aJiQvbTx5N7a6ZuL60ut+bmdzXw4/fDnt5OY00rMoFQIZ1pLz7z86YtPPSVpViqoEvMJ7xNQg3hflG40LoZTryHNjIrks7GQJknKBlVAIAhKQAkaFMHEFh0VxWw8nnqQVhRhyhwzBEViReIoYoxdUVWjvAWwjHYWd6mx3oBxdLgzgSpLmk+sX4nzo068Oz3YFpbDh3t7H1wvYRQBWUAEg2BYUZkrsGMyzU6SNtKiQY1WO45jPxpPdh6FWGi5Y7qZJwSySloWpYqbSTXJSz+ehtlkoTc/GA8kVE4BqoLFcMQ1x8ikNkkiF3wkSgIUWQBFxABChDZG1CCqQcSFgEqAUHlXeVcUM++coqCxaSNJ4gQMg4h6pxIkSM1TBgQVj8GnDWusUSVQQGQQUQ2q4p3zWhuuFQAFAgYQqZH4UtefACniqMZlaPCApIRxlBD73cOdd3/85u27uy984ZNffuVTvfneeDS5e/vm1Vv3Zrkz1rQaKTGRnkyxE/VSr/SZVSVUykarqiJQj6yiUIdjiGp+gwYFxSpUiEDGoJIDT0SgcnL4VceKQifjwQABQJlI/y0uCkShnsVLnQxFgyDgQqjHiQC12VUUoHYOCQgbImMkqDE2YoOAyKhYP8OQGDgiJAwIJjJw4k8ABAZkD4IGfQhexKuPbKJApGAME5sQgnipCT9xmp2010AjE1kbI5s4SawhgIAAJmLDlpnrTYCXYFxRVaVI8M47V3olU+TlaDDJ4vZSv9vptiImRDaGgoqATkazUHgXlCKbJiZLY+dclCbE7FwgVQQMEiIQFW8BozQhUSRwZQkKHrWqgvNBBREMCjNCu2nbcSNpRjayQKRBi3I2mow3H+3/9NUPW5leee7y+Qvn+925pJm1sowAiqISVC9ePZT5NC99ZDmKY5taMqQSAGl17Ux4zr33/tVornXq4saslx08fowGu8vzcbdz6nMvLjx5ZXw8KItZ8MEDGW3Fvtcx8TQxjen0we//v5/7P/+fFv53/5f7f/Dfbf7gx9ny6unPPdllGg2mrgykAOhNbMlQqCpE8FVIGaidJn6gu/f+9Y+v37h363P/yW9f+I3Pff87HyWvvvrohdW489l0cZWVFcG5ajoZx7G1cRzIUpoQoFEx6IACUPAa/HDsJ+O5hYYq79y+Myt9ZMz86dPJjPJjFyCNm2kUswYV1Yi4t3amvbE2uX2z38dyWKwsPSdws4RKJxVZeby9mRfy+aef+eja1bvbJY7f+5uLX7TTZG9/05HsFrNT0+FLX/3c8aOxfx7mFhYmkzzNqOc0MlEWGy8RdnrTtDWahsrlwE1uJcUuaVne2twao40SPG38SCpO46fX+3dujGdwZKb9F5tmVLnJwXQtiY8teo4ya6oAj/LD8sPJn9/Y+l7jBxOXt9b6mTFPX7rUXeqvPvvU6S8Po+nNrYe39vfDbOcBn5//6guX/uWP/40Buvw7//PnvvofcXTjMLhgwdoIgkNkhBNPl5/OqukULXc6TTJUli6fDH0I3GxR1ELCOtYjokE1QEDPOvOTxwdbD28V03Ey1++vLzd7HWMjAkEyjOBCFRkToUWdXT612r/4iaP2lenuzQ/e/M6Ww9PWE85nOjLD/WYxbRjwwcRAFWQAVQqhBMUkbmSN0siRcghRq9FXE6dRGieNANXx/s7j/SG0+xS3Z2XQ3B8dDAZHe1pNizzfH42OR7mdVQut5qXza2CTfDCtQpXF1lrDbBTUOw9Baum0KyuDBIjMLKga6o4t1mfxOoICoMF756tZORPvAISI4ySNkpQZFMEHER9UgdlGcawM6p04b2xkrAX5BcoPQgCvNboz1DUuQQAAAtAA9RxEAVGUmGsYk4JqjYhgg7HlYTG7/faNd9+5mTbsN7/5tSc/ccVStbW5efX67Z3dvUpDkmWNJCK2LgQEIGtUlCNLoCgntTITQZB63s4cVKk2UAJoHZmpRamqomwYvAh6RKhcqMeGhowCeBFDbBAJ+cRcpuR9UKlJ7ogkIOD1BLEAihI8qjAa0JoHpQJS89nqGBdbBkCDZEytBBfvRYJwbKLUECIAA0q9MEZCJYCgSmAUDFkFURAPQYJXVkNo0pSRRYI1sWdho8YwYR0EBQBMopiNNWzI2BC8McZaAkZAkOCDr4vDYKbTPM9d6UsCqgqPRDaJV5YXe/121ogJMQQXKh/Eo4TJZDo4KgjTfj+L2YhKMctVMYqIVASC96KihBRFhhkITz6Lalblpa9CCEppGjXTrL4QMqCyVqUXdkTqQoWBxPvKFdWsarSb3/j657udbrPTSNrNLIvZMKp450f5pJzlVQi+lG6/1WxkiGiMPUk7VUFBiOz6hXM2ST9492rcavaX1kaD4cHOkYmaHDMZSjpJo7+iToo898Lc5QufvNIoD/Y+SD5+933dvV38Z//5J/7G11efvbj3wY0733m1Kv3pV57ItONnUwmiAXwVkLjZbKRRFIMNg/Hmz9/Z+flrS73sZyD/6oev3nwwfu7Xvvyrv/nFR++9jR9cv0+lvXilk7SeePZSlGY6mVljVbEKogqWGGxUVhUBRLENaNOs3UyipMHbd7cBlGwUJdHx/g7sbw3coLtwOun0UEhVFDGgNlZ6Z37jpWL3xqP3f3I8cmZu/fNf+2Y2pqM7r169e3uW5wmmX/sH/+lf2dz8L/7Rf/bBZOvmH/znC2kDULWgJaD9/av2w62NK8v3f/rq2guXTXcJKE1S4qjqRdGoxBb2tUyH5WR2sJmcvTAYHt66ee/w7vbszuhoOOu4uHTBUJqOoZvil7Kl7802t8rJVzrzuxh+ejCqTLoUpYvLraPJLHe6IR1ruFHR9LgqgIY3xhPAH1x9bXbc/Cv/6f/xqV957sf/8u3ZyvzawpW/9utfON58eLhoL9y6d237+qMf/8nORjTlqFha7c6vGhdYiYAwlBoK8YU4R3HS6LU4YfEhQhKXqKvcbGqTzEQxBkEQQkYFcaEoi/Iw37+9fXzn0XjnoYBsn1o58/STnTMXsn4WRQaBxeUNCxc7nf1BfsTw/NkvWd78yUdvHvvjMrWj0dFfXv1dGU8tVB2cO3N6HRr9fDx2kxlNhpqPZo1G7/nnQpg92H00GPokipM0dVWIxRDybDoOo3GQIKHav3t/vLd1fDQ8PBiVk1kIUxVfFDrNAzqUbvuJxTOdKDJI3qnNGilHZagkiA9OBZCEETkyRV76WUWxsVFkDEb1uQ9UAwYJKoyqZVEWeS7oERQQbZpYa2potgQRCUJqOIpNCkC+8kGAOEIyWusuSfFEMBNURGoYQf2uAQtQl2lrZy4SkQAjeGRW5xDBJDEZqcpi897mW+9+nE/l2WcuP/fp5+YW2q6aXb9x68HO42mpLkDWaMQmIUZVjSwLKqMgk+hJkUpD3Y0KXoXIOBeCCBKAapwaIDTGKAiqAgoQKkhVeUAw1hCCBGVClWCMBdY61C+izKhKQUIIHpE1OF/V2FgERUNGQbz3xGhtJCHU4y5kQqY6ZiqiGkLdJUOiE01kULbGJEhAUK9DVBRAQRgZmRgJjKpXhYCg1lg2kQgE4wnrpAMQQB2tss04jg2qSghkDBEwGyI0ZGq7ahJbE7GCeh98CEGCuBBqJkRQrHyI2DababKUttoZmQgVCCUoVK4S9V7UF8G5qp02uqe7hiNjWJAAvIgUVVUWlfM6C77dTBtZyoBFWZVVWQUHQcmgNaYqdFqJgTBTIYG0kZjUhCrMinIymokGBJ0VZVU6FnDeM+HC8uL8XDuOEkUGhbIKWLkqhOl4PCtLChCncaObNBspsgERJQwqGsRLMGwUFSwtn9vgbvPNd6+9/MrLK6dP37t563B/e+30mSIv8sEIDZkkkaAmjbDFvZcux2vdYWPu1Nz6+9/754N3X93/aHP1ybWzX32ZqurGd34mk8mZl5/jOFINXjx4772PTQSCBw9373zvrcd/+D2G4+e+8au/fXHjT/7o21vbo4N/9t3/8H/1K596/it/+s//ydFrb2+30kvnXmz9x99cfv7FZqvrYNDM0vuPtluNtL/WoKkvbVoWgmy6bTYucuXgcOtBf2V+MV0vZtX44Gh363B7bytb7c6tLDdabSKQujygqiynP/lidTi4/63v+uu3Uhpe+96fI5MbDUZ5DkD9/ny3u3bM2F1aO9p9MCsHW+XkicunqeStyYEB+f7v//mnvvYF37aV6sYXlo1ljhKoIpNEtnKUj3g6CZBvfXD11HNn8uHBw++8tmmTVnONxtWBm747RSHuIGVb+1988spg0vjZ4wf/4ni7WcRXOr04an28v3O4XzWiuBxNv/nCZ2bH+zwYN+LozZ3jqeRD8IdQvPvaz395/xDN/Npzn7nw7//duaVLcwve/+x9Pj74e//w//Ddf/xPHn74wU/+X7+3/MpzS9/4TVUfAKppIT6wevUT8SUb02g0yNhyWoRiVhUFUwpsQlFIUYKNsN5CEiIiKcxmM1+M0n57brxoB4PN/e29D28dHx1tlMXKk093kHyAapqncXJ+fRmG49Hu+Nq9OzcP3n+8vaMtc9o0aTC2UqZgstbSmc9/4cwnPzmtZHY0NkOB4dRs3flgd9tBlDUb2WjspFw9tRrH6c7Rcac1P53oYDw+evyoGAxmRT4bTryU3peqbMjEqYmZUEoDZaWUF6PHjz+OWwn15xgUkEQElbw4lboDKwGA2Zo4dZBXpZcQgjVgDVDt2sQQRHwIPriyUlTDFIK3ccQnhl5VAfEBAA0bGxlE8S5I8HWDK4iXWuVGGE6i/CcaBKnRO4CoNXlQkZT0xGkSGZIA6jwDAKMP/nBv96NrNx/c210/tfaZl546d2YBLD/aevz4waP9o7ETStKkkXFkuXIheK0zk4brByaCqp6UDMSQUQcIZMnaOAIEAKmHLUgUfH0VQA2ABApgmE8GEcaSJan5K6h12F9FgoSgYEys9eIjuPpy4X2wzFFkBUJQDSAqDCEAQG0aIiCsUXgICBqlCYAG71SEjSHD9ROfjAEFY2xw4sWDKhIJgmEmIlEhBieBASwxc0QobNi5svbHMmGcxFA/6w0SIVJMbOqQmwZFQOcrZAJS51xQrSofgtf65kYMiGZuvttut70rmShOoyiJQLQqq9IH511elkDcbjazXsaIKqKqzBYUQMUH8KLBS+GErZnrtqyNUKEoq+F4MhiNZrNZWZSpjVutVpbF3SyNY2aOfHBFXmkBgJrPqsl0EkJAwFlRgZNGkjayhNk0m20FU3jnQlnMqtx7dYGNtUSdRidJ4jSOTcwIJ0xxOAlmMVtGhLolwYQL8y1wKx+/9/4nPvn8/Pzc5v0tY6K1sxt5ztPR4PjwOEmyjA0YlAjjc8tP/NYvbb2zkjTtvR++NR0eHG0353d7q7/0udk7H9z/yVUbmflLp+L5tgUKwWeRkbIKJe3ePXz43o2bH3+w0JPZzuTUyuUvfTM6++RTr//hn//of/hXz33+dwru3d1853Hip4HP/jk/HcLalRc4TlaXFo4OJ/vbB71OQpwgWUGbj2ZhUh5s7+eTwzQxHGeRTQJbmWyP8mHc6qydvnz6/PksaXjvFIARFLSAwFnjia/9anf19NY77+19fHvw4FE1GJTFTBspNftrn3spWkgmWy5pdVZ3m000rU7y8le/euHchX/6f/2v39m6c23/9rV/vd9aWPjc/NfXP28UY4diU+KU4ajYu3MtqvKp9w+v7p596MYfPcjv3C8rnTV2n5izB+MVNnnJssv5e+BOb+787c//Fuj37hxv7fAslOUvb2z8fH9nezY6j9njkL+yfOqFZ8/8s//+97E390Kvv7GQPN4//Muj8WaQxw/2W2uRmCRqtpcv9PeufvDxG2+s9xfmzm5c+dwX37553SyfuvyZr/cX1rzHPJ+5vMoHYygmxrokMa1Wg5mKvHST2eRofzwaVC5vzc91aFGcIyRipEAKdQwcUKTXb5Vpp27h9QUnmw/3Hu6O4zeLKN44dz4xzf2tvdHNB1xW/fnW7nT42rU/uT84GLtyrpl1seVpigIxEMfZNImm8/HiytJcFsGxuoPSvWt++vtvHd87PH3ldLrUWWx1z1+5cOvuzvbhMFpc2dra2rx2/eDDq1AVXTDlcQ5QtYxnpibh2e7GzONe8cCRpN1GaePNg7E7mraP8mavUXWXklYctLRRisQ1flaDePWEHLEttZQi1OyYAEFFyDBK7cT0ZNDUXSEbMzGzQWKR+rlKxjAZVgQffKhRsghSP0EBkFFqOa7zQYNK/XN4sl+HmrUMNfsNVCSKrFTuREQfQj4d3nv48Or1a8Hzp7/06fOrG41uZ+bL2x/e3D8+rMoSTZTESRJHBikEZyxBXdMF9V5UQNUriipQrfzRgIjixWMwbIkBCWv8g4bgQ6hdugpSJ0Q1SBzbOI4VCVStcg1eU613sydi6CDBS6gZXIbIVz6JrUEiQiRSUaD6LwtsCJEheFNrexFCECaqZ08AtZ9AoeaDAKECANT7FUscvGNmw8zWIIMFQACrSsYAkSAxE6igTQ1R5RyTFTQS1JAaGyOhACpiqKnRCqBBDYDqrMhdEWoIomEAy6AYxVFsI5OmNjKKGIFCnhdHO0fOh7IoTBT1eu04SkLQyEY2iVQgn+WzacnWWyIQKcUTG2YjANYwAEpZjSs3Hk3FuyzO5trtqsrLWR5Zrec3RVE5XwUQ8VqUZX2DDF4AEAy1O63Y2m6zZeOovnmNZ6WrcsFQFcImbTWa7U5KxAzKkSEgIq31CEEE6s4hExEQkgIEUQkOCbsLnaOqvH/nwfmnLs5y9+jOfVFZP3cqsguKh0c7u8AUp2mcpL5y3UvzjfXm+qcvrr3yS5Pb+42zq0tXFlevnEk2ugc3Hx5v7bpIT809aa1xITAqM/m8Otrev3n7xl3YORxA9PGfJQ+XeheeL/q49itP//T/+67c+ZPLS/P7u+0BTCf54F//wf+kiZk/fT6JU4zTuV7r8e7+1s6g3+2IR2CCmP3EBfKT2cRG0Xg6mk2LWYGH5cxmuHxm9cwT59Is80Gc92iUjWXiGh8WUl765NO9p584P6hGe0ez432ocmMlWz7VXz+lDWp0oyRqNNmdDuZUf+novd3XPhqsrV4YltX9w8fDaR4aXJ4QpHU8yY3K6M745qvv3f3pm0vNrBSyFUwmOT98aAqhuFpnGUn17Oc/1ezo5u727WuPptPtt2eDC++98Sur629g2BS/Ozz+wd27C3MNAzwZHwnIa9/5c748d7aRPDo+aiZt1/RrUbqmU8Fkfbl3/guX2vHo1p98rz8Y63h4/4Pb7z34ee8nP7z85We/8b//e73TTy5unE4b5mDrOOQ5BU9A4p2qh9giqitKN5qOjwbj8aSYzkIoh/sHNomybk8lIHKd5gYBkaqcTcbjw6TRiXu99NSZha2jQXWXsJo92n3U/KCVNLJk8XjzaOf2rWRnu9uMG53e/eP7x3kexQ1LEcuAQskgAtXe9Gj06FZ3ey2L7OhBfnxzML3x8fHejSkh7R6Vl06vP3F2eeNcVRS37+8GlKoqDh/dO3p8X6rJQrv10tkztx7tjqrp4WA2zP1UaDQYxgKdrLUUtfvNhRvj0Y2d7V6rncBw99b9xtxGq9O2kXrnmEiJNYhHCkHriCETex/K0gUnSgEVQCkysQKCZS+CqoQExloTnXwsqoBc+0MAMNSAPVQAZGAABRBEIyJYr3frHa+qqhJR/fSvhz8AKqJIYJjVeyIEDVU+3T08unHt4/2D4fq5U88/+Ux3cUHRHQ8Pbt5+MJ5NgbTZ6WAtjwEVCsSgISjSyRtHHAbyAQyjICAJoQEBFQVU5xwgoAoYEg2Vr4EjqITWsuG4trZgzAZYwwmWChEAQQS8iDWMyD4EFBIUEEWo7bscZ5Eq1DMiVbAMgeDk3akgomTpZIbmT95/KqogSEx8wndHQhT0zkdRFDREbADAmIjJAEEIgdnUegALRNaUZQXoAJCQGBmQmLEovYKLrIkiq8h64roMIQgECapYr5QRQeoeDJI1kTUcGWNto5FCAHO4td/qd5uNBAEYE4nVVy4nAkuJTQVVfDE4GgYkgyaE8mh/6L1HwjjlOEvTOLPGIIGrwnSWu8pVKhIgspi2bGJts9MIvsyns8Px2PugAYrCee8hgGWDZGNjm1knii0aZIsGyUYEEHJXBR8mgxljyFpZ2rZZlllr0siemNxQAIIEOPnqaUBAQq4lmoCgQYMGgySKbKm33J883D8+ODp15VKoykc370sV1s6udzt9YpwMJxpAUhEvJKjBZUvZxmc37BdPV166Cz1OfXOjg50Lo+2d44Od3tFxs9MgNopoUzYkzRYY5yJIlODhvUE53D7+y1vtb/30S3//7175pc+E128tEH5urpVUzTcf7+a5fvdf/PTFr//17MyGbcSt+U58dDybFd1eS7wLoWKGrN9gWIpjGh09rKbFYDCdVpDY7Mzl02uXL2T9btCg3iNArVBCACJS5ysANGKaUbPdbKymKKsCyobq3o0CNc6tnPrll29MD1Dw3c3RR3f/dInnv/Rbv/bJtdboj7+fYPrUl1+6/OzTAJhPZsbp8PHgnT/6ztVv/WBwuH95o3O4Pzu4e/fo9bdtnC49uX5qLvnpd6+NIJ5vue4z6+1nn55bvfnmt759bzD5/dGtp/1cW+l8c+4TL37i7Tdfyw8nnz3d+/Bw2s/indnggw/dc+vzn78898fvvJdtN7qee8ylicqDfSOydunS/v2dd9+/ev7Zp57+D/+D1/7xv+pfnt94+euNVldJFFlEDWvhSgNurtuWtvF+KgizMveT8dHmznQ0zXqd/rmzxFHlKw1uNhpxsx1lzaAhhOC9Lyf54f6jo1u3gLPFuU/qzI3FzF843cz3d6ezsHl42LxetA/uvX314N71LiBAK8nimGyAgoCbaYOKocPAihXAuMyLu3dmb3dG+6PHO7PrP3l9OtwZwnDl0sV2cqG5tL546oLa6OZHdx88PFxaaB3cund8uFMcjZtx66nljeFwsj09Lozl7tLqShNszweYz3Sh33wiw9fe+/DG/ri7Pv/CSy+7h3fz4+PNax92M9ueX2EDIZAgMzNWFQaVoEBgY4OqxazwvjSZZVT1KBiYrKJl8lVRMpskTukX+hMkJDD1EUqCr9VNgIgo/9bhoMGpiASoZ9qqqrU8F1C1Vpw6EEYWE9XSTCVV8W44HW7evfP+jTtZp/HK5z916vTZNE6H0/H2zubm1lYeIGunUZSQ1rcNBA0hSF3GUh+UFU9aBoG4Jm8rgFFUAlREJmKCsrarOkQSRqoRsGSiyEbMVPOo2dTqOQhBEFBICBlUjDGAtWqR6jETISkoMYkAEXD9c+SlxkIwnlChVevbAApA0Np1RPUuHBV98Gwoiqyo1p2BKLJEYDhiYjbGMKqiCz5LMgAIInEcqwSvwITB+RPpaCVkOIQQRQaQXfDgAjlXiwK1LlMQGbY2ipDABxfFadZsMSIyI0FNiYCATGSiOJEQ8qJiJMMoIHlwM19YtAfDo9l4Uub58KgYzHICFQ02NlI5NNHcQhfIAJRlUZTOO1eVrqoKV1RVgNBtpTbCsigQWYNUVTmZlVXlErbWGEaGCBtxpEDGmkaWZK0EEMrKudIP8pJAXdAsS3u9toltlkRIFBlCgFqfLiCMSIiGuTZkMv2i4Sl4cu4ASGzEhEAMRHEU6aJs3r0dx0+vPXkZmLbvb4Lza2fPtls9QlPOpr6QoBBxVFZlXlUhL4KNQMhVlVd0VRV14padg5Rms6mqNOf64kNeuWYjXXv+/FOvvBQNAstgrZ3eLndm03Ll9Eqnd7HsDLdmNz9+68ZSbJ/oNh/Z7NEkd0fDB7fvzK+vsTH9xe7BTuv46HDSTBqtVHIn6iNr0uU5jhG5Guw+NjH1s8bymXOrlzY4TgKRVzQ1tOsEGKbivIoyCoECeITarYuCIBiwLr4Hn/TST/y1X+uuroyvbR9+dP3sZPnpz1y+/I3P7WztPzO/cubJ88tPX0g6nSL3NoqL4eDDP/3h7e++enx4d6G5+Jkvv/LWz2/c+PDx4ODbf/1/87Uv/p2/8tb/7b8Z+n2bdKdb1/xakl3aOP/Fl7OFzl9+7wfXdzZvH9zrh6g/6f2VU0/71Sdu33pDd6oeuAvPvHB3d/vW/Yd/vrkdheHi6no8DaF0CHYhzWIqwmRU5ZOLn3p6dHQYz8/Np/GZf/wfGyKLBB6LwikDWzYGvZ+pBmNiE1kyzbLKy8l4tn9YzopmJ2l0skaaUto3bjQ9OprmeVwVJmuEKrjKafBMnNi0KPhoc+8o/nGCQqa5cPEzC9XR8O4H48ls+/q9/sKsN98b77Z94YsoLopRAnxqbm6c83ynuRbJ7cEABUbgZ1K0D93hO2/ZG/d0PF0Y7jkYN8GsneufvfR0u9tpL7THg4mKW+yb0e5mHiYNQ42YU44mxezB1qMHo71TZ5/89W98fZjjaz+/9/jOR9vlwZl+49DwxJV9K6fPnH7uzAW/2PrwzbfuvfnebDi48olPLWysR6l1yF6VicFXAiDiaziM5RgMIaNqqBsFyMTArirREEeWDcXGOHGoiET1iN15BZEAgjWlH5SIINSxG6kTP6ggqgJSmx205lUqqChRqAmaAqISXOkP93ev3727s7d37sKFJ598enmh4wG2Do8ebW4ejYYmSpo2NpEhRR9E1BMRgBomQOSaGCc1TA4jJCQlInFexIuv5/0kXpDJRkZAmQkV4tigAhAR1ql6UAE0yIwgGkTr+CcCIgvVKnatJUVCVFv56okXK0PwARRFhI1hxiCCxLVlE1UV6jMWBIHIGO+8YTSGARFI6u0LMaEhBTDGRNayNahoo4gQi6JMYsu2JtHydDYDRVdVXnxVFC64Ii+YyFhLzM1mIygaNghEBGmWEkHlvYmsNREhRVGECNaytTaOE4uUV5UvvQAmadTvdgWD4cio8mRUFEUxmcwUgC3GWSyMeVUNp9PZ4eFkWG3v7Q0P8+Uz65cun44iE8XGWAtAs7yqqqIopr6sAoCNoiSJ68/x+OjIVSGOE0RMIjvfaysSB0zTLEsiIlRxKlCFQEgE4kGAyFiDSAa1YYyNLLFlNsjASKr+hCGOdRgXgOpRHAOKUURiBRBSDAAAxFSjW+FkRwpRO4UjvPX+h+efvbJ68RwDb917WOTTjYuXsmbGFvPRjE/iunHltNB85852r9uK04SUnPfWaJpmvGj9rHAuFKMJGRaXh7KyneRzv/Orc3YhHO0v9jl7vPups3Mv/71//1G5/u3/6j8/2j9siNuf5PNt/nv/7jcmx4cfXd/RYSi9QFVxBKsbi9PhYLh/zEQ2jsI0OO+BudntEFKj2w8AcdZo9NrGWkVFQPRBIGgQUTQghukkfi0BHEoIgJXU3BUkw7+QY6gGdTahK1/6pL4sny6/bqJgrAKU6+trG6+8xImxNg6KSRqPj4cf/sWfP/rRj3Z3HgGEs5301IUXjuKLf/bf/j8vDKcUICsG71/bLKDj5jozUR2OJnv30pWN/qVLLy903//uD/L37k2Gg+PZzv/w5r9uhYxAWX0A8/jh5oVPPiEHE5/Sje3Zhc+fa2zttQf5vdGw011sZjYfD6VyWSvL2msqSoAEFkQdolcfQJnQAMc2qUpXTsdjGKdJI0tiAi/TyoVo/sIaEfqZK6qSpUIKwkxonAshBC/Ou1JBCWFuaQUuKLjbo/3DYMuqnDy6tnnq7OLTFz/94MHt2fSIw+z0819aWDl9fONWS2i6eZ/z8anF9bvTY93Z7izON5LO8WQyAi2RjLVhXB0d3MsgEEz74BpJ50xnbv30uhAgYq/XeeLCaTt2j6si3znKApL1FOJ72/ujmU+ap1768svrT5zd+ejWtNwsZ48Iitv7h3MUL0RpE+146+Dj936ydO7c8rNPHw2Obr97e/9o+omXPr1x4VzWaiGHypXelWVZhLKsvFelOGs2G1mQ4GoQsncxqwQSCSZhaw0yeVVEFAQVEQR1J9QyJFCsu6UnA3Kpbwpak421xvmDKiCrBgIFUmSEUIP/NbhqNh0+3nx8785dtcmnXvrc2dVTaSObFfnm9ubDg718VrIxSZbVa1gRZYusRhFAIADWEyAV1aBAdaI0kELlAtUP8BAMW1CIjVUgY60xVAO9iWv4tOLJbhoEFEFAMZyc28HUf/2gChoE9N+e5gmgnvMT1WFW5BrThnIy4CFRCarOB2YG7wkZEVIbMZMlIsaa5OOd96qgYsjYyBpjjLXGGLYGEZgjDd5aBqwBuQSKcZzk0xmAlrMSFMQJKRpjs7TRaLTSJDnZGTBnWcaRdVUVt5I4iUMIhBSnMRMhgw/iXVUpRMY2eo0kjlXEO6+CxgtIpZNpOZuNgoiqNEwWoUX4/zH1Z7+2Zdl5Jzaa2ay1dnO6e87tb/RdRnZMZpJJkRIliioVJFuqKlmWUeUHwzAgoSzD8JP9X/jFgA3bgIGyC7Igu8SqkqiiSJEUu2SS2WdkZEZmNDdu3Pb0Z3drrTnnGMMPc5+UAwjgPtx7zm5n843v+308DsPJyerkxcXi5Hxxerlz4+Dey7cPbx1M2k40r8axX4+LVW9mOQO50AXnY3QcHCERLJcLAm5CrAuTIxea4BCdIxfQM0mmzTAa2ZgLkXfO+Yaj9/UaWTt6TMBACLnmy4NnJl9UmLFyXADBTAkJCQxUwIrWq+k2FFo7QIgqVYmmuwfPP3n28L2PXnrntf2X7qF3H7/3/tX66q3Pvxu7KU50GNKw2vgYm2mroMhqSdbLjW02092d3BcO2jbtCJhSWq8HFyhGx+wACvp094vzqxPdJPfgC3+zvYc/eu/3f+//9Lt88ezu4QHLpLi8nByezO7/7f/47zZ/+gcjAbu4XmxsnX2MBzcPj58/W1+uJ7ttDG7YyCjFPDb7Xbc3KTVwSZRAoRTAagVDMDNRMQAxwKouqkFm9NUpYQbMVjunARUMqFaue3BtcOTBShrHYsbslKnf9E3j2hjPH7/44R/80Qf/3W/r49M9WBnQPuXTn3z7p0/HKczEypMnn12e+2Ht3Y29k2Th081r99UP48lnD1fnK437b/zSX8uvvkubTTo//eSDxxzD+rF1O271vLWTVf/DsxvUvX7n8JX5xeNvvH8qNA1CEXc//9b0zh0Zs0m2kpEYgYokqmcsqCREqiP/QJGhLeNVkR7HLEsjMofx6M6ha0DRBulXiz52a+zi7GYnkkAFRBCg1hCrKhm/+ovv7t3c+eDbP/j0yYcnJyfj2WI5nvzmVw7bOy//6PvPpuu2K+nW65+76m5cPTpePn+xgXO+2uyDH86W3109e+nugxFPL9fLBwcHnfXD+TJCPoe8AizQze/dOrpzT5nW61Xwbm++v7c/u3E0Xz8KxJG1DJmGflWcm9966eu/+tfuvPbuX/7J+9/+zp+V88d3Ox2LB4YW8bKsT9Tk2fD08Ucvn53cfPWt25/7KxZ/8vHDb33325uc1m+9+xpSk1Pu09BvVpvVlRVzoYttUHHEgbIBSEk5j6MZtU0MsXXEtc1eRRSAAKRANfTUVwmtnqKwmjzVFAx0W2WxFTlUoUo21yMABcKiJY/9ydnZZ58+PD2/vHX/3ttvvL1/4wYZXFxdPX765PnZ8Qgamtiis1yt7kYOUYGdMwMiUBEAx8hiavVgQ2C1NgPQNx6RHKurQTUiJqquRwJQU0QqoI7MjEwMCK2ggilhURVVIpB607Hruwv4OqM1rWozSQ0fIBIX5trOpFr5/oImFtghoyGyIwIiRDAlAvKsNaqO2DWNd7VwgJ3jENxWt2VyxGAO1AQKohNRKBadnzWdiuRpIfTskcmMybvgfSACVXMOATH6mE1ciK7xIgIAhpCyIJtTrC0zjqiN3Wy24z0DCKiSQzeOaXUxvDg+NZHb92/s789VYb1cL58vnj47/cG3vv/RJ48CN3/jb/36L/3KF27fv0GOZdSrVRmyZrXJtPNMZjuOoGl8aBwjNTGklFofFGE6aZvowDCXnMacNBkiIZFj71zORRTa6Ou0xykSoicGUCUDqZ2ZYCYVraRqhgWoHgJQTcGEiKv9TLVklZxzypJT0qLEjj01bdN6D2ClFPGl3YunT06Y6OiVB/Nbt1+fxA+/98O//KM/f/uL7+4eHDYtS5Gh75tpbKYNOdBRxmEEoaEfGBkFjEozaYwAx1JSKYjTWWcqZTYqrFc/e+/xex8+efLPN0d681e+8Hf+yT/a+ezZp7/7nZ+9/73dV145nU3/8L/9/3z32//y7/y9/2h+bz+Pax/o/GzdWjPdm2/SRjZjXoGPITSckhYVK8SMRqZaGL2JgSqjAxADIkADBdnyDepsynHIpWzrDxFryZFV68eWvM5AVIqCjDKOY792gWJDLKUfBCEPi83P/uRb/+7/+s92X5zeLjID3bvx4Oad+9/8/T/57tPxi69+7fVDOP/OBz++uBwXl0d9ppI2tvrZv1u/6/7uqlv98P0fd/PdELr5wc79t+9+7p2v/UJp+s/WT7/7ydHcldPTYdE/fv9naTGc//CnXWyg5AK4ande/R/9xuf+F/8Qpr5fLyM5KAqMgLqd2VZ6PABdH0hdjPO9yXDJ3rVONQ3r5cVmdrg/25+ogaQCyENenT375M47b8wP9zardRqTSdaUkImEIKuKztx09tqbJw+fnJ2e+fs3+KBfHZ+9/+Mf7d/a61VwtWg6uv3SnW7eZOSr78MzKJLlS+/84k+/963VuHrl7j1ZFUb37t//Tz977xuP//w9AkPs0DVyY37rF39pdnTzxenp8/OLm9HPd6yZT3jeEhWMuN7Yoh98aG/e/9zX/9avHd07+t6f/t573/vR1clTSpujWedREvr9JpwneLJaUE6W+OFffG948uTota+9/fWv3X773sknHz3/2UO5XN+8fxtiTGM2geA6JeymzdYHWVJJQog56yjJcfQhOK3zRpZcRBSNRfK1BwYNgBCMqqNHoZ6Qr3HDZgzVUSQFkWqtm6kKmKimNCyWi+dPHh2fnDaTnS//wi/efvBgNpnIWM4uzh89fXhxdVHQQoiRHSoCAIECA6OpmWUwtAJmoqOOqFjACBGxOtyh6v3eR4cEgFwjuWBiVhdrAEWDUgQACpht6Z9OTECtFupu6zuVcilVeScgwgxGTKwmpgYgxOhdUFBCKiIgSuwMlAyAMaADJCZSMCJSKWLqiNAxI6tZ9NGxm0662LQIhmguON5ypEFUrGQiLCJg4BtuYlO3WFMjJkI2BWJmtip6YF0MAYwMEQwExQSkH0tOpRqQ1psBAGL0TWzaOG1iRCBTQ0Zmb5KHfnTr1Xq57mc73e58guzGEdjBclh/+y+++5OPPx5WQ+uao/uv3Xvl3uHRPqPlomalcXT3YMbeq0LKMo5CUGKoVNPKnYg0ZwUD08VmwAq1KkVNEIHYUUYppW0b55mYAnMtacIiGYqoAJJjJrj2FRCYQi1sQDAB2vqOQU3FBESkSAnEQOQZfUOOmMkKWMnjol+XflQRYxda1x5MzhYX9gIP9m5Odna+8Cu//PF7P3r/+987ODh68Pbr3XzC3g1jRhMij01wqn3Kwyaxo27SFSmOwTv2jkuhYTMuF9BMOpjN+PXXymfPln/0F+Xy0U6/d3F89i///Ac3HJdPX7Ro0fYfHPyGXv0/P/mTD74P3/rV//3X6h3FBUrDEDDMd/d6WmgapIgyhsaDcSlFSwEmAzRUNmDnwBDJAygBSHXDgZLVulEoIojGTGbAAFrbQmtHiqEhYwjGrGIly9gnA/YckQIheOfV9OLs7Lv/3W8vH3/81f0jurw81553b7R/5X/Vvvd/c8tPDl6+86uvf+6Pf/e/+ssnP8mJpzDegHQF6dnZwL//u7Nfeef+gzuAePX86vjqTFfnRw3fe/PNvbf2X/7y6+1kis4ur4azH370we9+a3P6sJQ8banbv/Wlf/A3p2+9pa0fxnFMuV7zSQVw29WB1fmttUREC0jXNfs3D9PqikzKehzT6GftWMaLxdV0Os0GWSwE3w/9fG+3aaZaVGVEIlEDBOIQJ9N1f/XixZN8uYJRvvy1L+3Ndy+fy+mTZy/e/+ZPfvLBYLBz4/bkaN/NShfa6XJis5nb7L/2la+8+YWvPX/8011ortarzbC4tTP73M03p758+v7jjTU3bh5OZ/vd6y/f/eLbC0kfffaEu6aZTMysAHazPSJ3lYZl2mDDd197651f+ysHb9786b///b/49jdyYLsRcFUohHUeUW3MFFx3c4Lr9bL1hUpePX2kZ6u3vvjg3l//jZO7rz559Ik72lkCT2IMPmI/TBomx2SohKpWUkqpsHPI1HC87jMHM5QikopZrcAtAIbI2+9dvR3Us4SBkYJuGWlI2wwAVCgEmZqKyjiOq836xfGL58dPDeD+S6/evv/yjYN9H+Nm018en372/GSxWiFSG3wMjtlJEQQosN1AoBIV0Axhe6FHJCBiDCE4VyeaVBucERAAzYrUUYRhsYJqYKxoICQqADWsBsBgWnTbS4+IoMXETKXUYgIyqGlqRFQzcuiQEDmXjEjA5smJiWZBZ6GNZmD1gSIyIjvH3iNgjCG2vmmiGXjniRwBImJRYSbaNqKZSNFiqqWi0xBZCqoKgKkKIYIRoKKnlEaTWp+GhGhoRcQUAICRVHVMyQDAMMSIxh4joDAxIfrAsQsxOKWc0lbqQkDXRu8OQxe9GWTRcUiL5eL5s+fBuTffeGu+e3B0cHBw++joxrRASis5X/Xr9cpjmM4mXUtN8KHFSWTvgqiMY+77sd9sFNBFRkc52ziUfr0aU2H0k7bZmU+9Y/LA6BQheibHiEZQXx80FTVQkXrVR0ZRIyQBAQBfCzq0aClWLwGIlakXQgjO+RgQQYvmlK2k1OeU09j3Z8cXssnzw/l0PptMZ9ZISunppw/3jo529nbe+OKXDu/cfv7Rpz/59g9vPri1c3ijiY4ARdEMw868LBdgaGpjyiDoDaKjouYcd9Om7zc5j8G7vQd34Nd+lcYmf+eD3WFx+ujR5HD34+XwFNPxchw+/nbnnu1zu0bozxZpOTTdZD1cAjn2lofEjtr5TIeQUiJAAEUmB85Atz41AIVaMYrXfG8kUDCg629C9S9Xi0OtlSZiBa0fJWNm74UMVCDnMmRVa9oQg3OMjNB4RgAcN5TG/TDPaqhwAeOc/PPm/t2v/K2Xz/702fOLP4bPPt4snqeEYBFkDv0O8Cngp89P7j299cZ/9FdglI/6j05Pzn76w48sjc+ePb/92isvvRObycRNZvP5NM67w196GxwEj1aEObgIJUPRpJKAgMmBmmRFMiQiMqu+OlMBAjSx4oUn+7PJxd7i5FkaBna8f3AAakPKy9XgmYe+H9K4d/uO800eytgnQAI1ScW8gSC6oOg++O73Lp4/vXFwtEvduBomks/jJfoc2/n+zVde/dq783t3B7Ps3fzBwVu/+VfunC1/+W/8kiR354038/LF+z96r1iezuPPvvPv3auTN7/+lVFWrzx4Y3p0s92fkpYPP32qIbz2xutH+0djzsdXLx6/ePL42cnloswP7j149+1XPvduczj/iz/8V9//8290t1966+2/fvH8s9Of/ugk5U3wYz/cKDoDfml2M3S7pxfHwjaKW2/Wn37z2+/cfe3ojTtuhr5xPrAV01Sc94xGCKqgVrJI0ew8Oed99ITMwSGS1EGSqGhSkRpfgkrB00q63BrZkbb2OgNDcMSsUkFpoAiKZqp5HNer1enJiyfPn/Yl3T66eev+y7cOb1Z9f7levzh5evL46aIfmF1su+AY1SRnyVJAc0m55JSzqZFzwYUYY/CB0cG144OJ2FXnJiCYkZjV0gdTMGRUQ00J1ASIiM0YDYoKEYoJFBMtuRQico6lHp21ZpWZiEBk26hCwNeOfkQMPjjvzAzEnA/gFRBVDYCIXfCRmUNwzvsYmckhWmUxMLvgXe2KUxVVQSYQGKVsw0rF2LnQRCKWCqgqpZSkWBmAYCDkOA05l1Ky5mzO+SZ677yoBe+AXZXZ2bk2Nl03DdE7CuRAVcxMEQR1XQYnbLV/Tw1U3cHurhgZlLHI8nxVzLpp98qbL7361suzyWR3b3cWQy/aD5vFYn1xOfSrZATTCY99UVknIh8JES24bRGy8ZizOUMhJPOBQ5zu73bXfdzOOWIkJBUzyQJqmuW6mpnM1NS8d8gAgFIElICpiBAhExvQUIaUs5kyYvDOe2eIBIZAdS6FBIhqKKtxNFUfQuObbjInMyLyjl0IxJRTOr9YnZ+fLq8Wd1++c3jn9mRn58Xx80cffeo/evTgjXvdfNfFaMDs4g7sjP0opZS+GBgiUqQQKSchxul0mlI2lRHy3ut3PE+ezw6GH7zPI7966/Pllrt98/5n73/39OLk5E+exaOJc+Q86ZjI1S9hQWaKTnJ2RNC0yFwkq+g2TGOEhGD180CVI25YwSNQP21V7zcFuk6mWG082nJx0QgLADEDcfDEzmcKwRVAAskMyoAeQId++dln5dmTr379F09wevnRz15APgUI6Rk9/Gf7upxO9hanT775zd9/kZ4ZcmvsQSfAHugFwAWk2xl2pAv7Lt272Xo/DPLo45PF2abPOt3typC5nbumbaZt27SiAgglF0McRYzAlMg7pwoCAopoYAZadzKovnKsbd3EJeWmCfMbB2ePP9lcnbS7O961hhRdyv1wdnqhmuN8un/zpgOUYRg2fexmIYYiwuwRiB3GtjX2Mg5JVy8ernOO/cnjdbqku/uf/8KXb7z+7uzG3E18FnVqne++8Ktf9uL3DubPTzftg1ee/LufnI3PXr9x/8bu7Fu/81uHX/vS5//m1yf7R/u7eya2XqdHDx8NDLfu3G/me9yF5ZPLb/3e9z/71rdptb7z6htf/dt/73Bv+uThox/96R88+uz9g5du/8/+y396cPTuB3/+3refu4+efHBVdGrQkvdZDpv9m/NbM/rhs9PHA6QE+vFfvvfq3/6lmw/etcYROQRKIAmMHIKaFBUVUTUAHxvnnPeR2ZGhbfsSRMAsixRFMnaEiGoAuiXs1EYaBKqVhtfJJzMRqGAdMzUtRfq+Pz19/vDxZ4uzs539g7feeefu0a3p7n4MISe5WGxeHL84vniShnUTvG89bx2SpqWMKa9T34+bzWbISUOMXdfGGH3FIIRAxIy1Nav2tVQ3DmgxoBoNs1KEkA2RkUvJ2TKjVppCZQWaAJIYmIgwsgl4Zq7KaG2ScAysRdQ7MqzaGDEiIjnnmNgAihUTUTUXnfOha7o2tl3XeMfOO4BtNXzRgmCoxo40q6oFH8yMWOqEE+siDKYmuA1+oWjZrPuSSinDWFJKgxRBxKZtObBmLcVAERsHDn0b97tmMu8aHxFBFUyNHAbv2TliRqVSypDGPKZRZRyzVpcgogGbmBPJBhwCl6I+IAoE53xofeObNnrvkhUFZXI+Tg4Op3wEAAKKzgMbJCmrccwbCZHJEBBj2+7OGkRSFVHznrzzSFjLuGtCkAlUELSwJ0NTEVVyjuroHdFQoaSshgCghkUNwZrOI1HlLHlyzhEaMqMjrjmOopJLGVZjKdk554Nvmk7yGGITPOu2g80csYCWIuj87v6OFBnWm08fPbx56+Z0d//2g1d3do6eP/z02cfPMLy4fedW6CahncQQgnObvteSU1KRNGxAxTWtX6w23pMPDZMb+5EZu/uzG/b2MZSL7L5//qyJ7frs4o1XXnrzjS88/PRHH3786QhAj0/784u8WQNBE0Kp+xaTiiAaNc4LYuWZ16kOaO2iMFFgAkPcVsDBNm0IsC39QWKkWpS4bYZDMjAgBHJEzGq0GRbHj3K26cHUT+ZE2IQu9cPVo4c/+jd/8PGPf4o75Vd/43+y0z74o9PHH15CD7FfnR3/2b99BXAzAKXN5fppgjQHtwv+FjT3msNN0wyL5czHWwf3mxBaFyI3bQx/+z/+dUny0aNPlpeLhx98tn9rnB0czfcOXAgiCclqb3ZdbQBMTRHYc0iSEammPFWLKYHDOsOoCABTTWNWye3B3t3X3/7ZOq/7KwSH6EdNQz8o2PTo6PDWrd3DIyS/uDgzRVV1gAxYOZeEECft61/+8tGd23m9Gi7P16crv3vw0o037rz71o2X7mDXKmpSKymbCiBigLZtlHEcrj778GePT04amH3+V35dbLBPj89XhajdP9pFsP5ycbJcwqx5ae/WdDKZhIaRN2dXV+9/oP35F37jF9/9jb96sDN78s0ffP/f/PcPz5fTe0df+c2//cYXXjt5cmLpxfrsp3l5Pld59aBpNGxI3OzgjDLuHMyWz1+sVhEmuzsz7sdmN6YPR8urOJmImZlIUc3ZFAkVCb0LznnyzEgIrp4uVaxus9VtiUiIVZgu11W9gI5p6xtgs0rGAwAAqURlzHncbNbnZ+ePnz0+Ozv1TffKW5975d7t3b0bk+kEGIahPz8/fvr84uz0bJShaSM6lgTIoFCylCR5LLkUaFw3PZh5F2PTxG0NFpqBihgoEAGY5LoBANVMgwKgFVOrzGkCSTCa1v5EBdNSkLD2AYBDICN13BDUYWRlpzGD2vXRGIgI2DkC5zwHckSgIEVMUdEMXQy+m3RtjLPZtInRVyYdGgCOw6iiCuDYOSIzkJJFrWRJTESMiKKCtQMNgZQQSNX6fgQwkZLLmJJYLmLG6IAMDBEwutY3PhXx6NuuY8/siRwTYuxCDAGRSik557Hvh6sxC5ihiqopIJFjNIQsCgYOxlSmbevGnFKBCcVuEny7I1lFVFLRVMC7YmMpJeU8JgOKBFhAUhpKSorW+shNYI7WlqYJmOUqlc4hMzlHUixsK5IB63tXudfVLIDGxMhYitTTqilQlRmJRFUMCVnVVBQRvOOSMnhwXG21rq56otL3qZJwillKaRg2CIDURYzNpGHrtFqJDUSlpDKKmgkYMGOcTBCwmTaLxXKxvErjuLd3uHdjZz773OXZzeNnx4+enHh9fvPOzcn+vouNj149K6YyDoSYshrppI3juMkbMc8hBlFDz3tv3Wj3vxZu3Xjyje+dfO+9zWdntutf+fzBnbu/tvPuP1x9/GfPX3y2Pj7TnEGlQsDLWIgRgMwMwbAa/Am32UxAU0UkI1Kz6s+oLUYGFYxCiNvyvQo5qRXcdfOoQz3PhKBwufr0t/67y5/9CA9f2n/rlcnd184u1tgPaHr54+/++Pe/+cnzqxD4C7+U9u7thDuH9PyR5ny+ulxdrrKO0UfqvIuTNApAHIBSe7T/i3/n6M37N5ePhsXx7ttvT6g7eX75ySfPh9XZ3VuHn//1X7mzevvj9z9YHl9mIp5MJ7rbrzcxNhSqNmpgaBUuWW/0BkysoHCNcKzKKDJWfgOYmYCYGgoVPHj5gZvtHj/8tD8/T/0g4HZv3+mm09n+3s7BDvu46VXUiwEhaimAAojsgpbiI924u793Y1JSASkIjgNy69k5Bc2SskpJolIQjNk5gjIOqR/XT5+l48fT3fgLv/pr97761fOrx28Th4N938TNerBiF6s1Bbx3+KCLkVF07GHhbk2al+9Om/3X3n73i3vuwdNv/8Uf/rf/4rPzy/nB21/4zb/z9b/xm/1CH37w6Q//4I+G5WrH+JX9yYNZfLxM0/nBlYybxVLKInbzu0jm9++8fGdGbbpKzXx29vy5pIBEaio5ISIHZvbMjoiJELBWLhazKh6SAROaEaoqANbWa8OqhyMTYv12KQAqIaDp9koAkLOs15uzi9OnL569OD4N3t1/+bXbt27uHxzszrrQdAKwXiyePD99/PzT5dUVMbdtdM6xYxTIlbFpAITtpJtMkIg9E1cGBWNtYDEE2bKjzcCKiAmYCTIiQBEFQEUDECDn1IooEosBAooIADJ71QJqxIaKSMAQEFGtoAHVdBSzVbK0Qw7O+yAVbpohg6JHcgxI0fvYxP293UnXWu0VdWgmAlYLwQSUkNLQM5MSl1JKEUQUsSIWgmeHoYlMLFKQMDQNGORcKW1FBJhc1wXPrh6jXWBEUtMQPAIDgvPBew+IYiXlNJynzZBijAAGCM6xKgxZ1quBkZB4OmlnO7M2Nlksp2ymCsYcVcWdX64UCBlNa4WyARPGYIaenfeESFiNYWKS+vWyXyzXJQ1EoezKDFxoHAWMbYw7vFOgJtOGlGvtsqiighk6xwTF1BSNiZC5mj1FJJmQoSIUEyA1QFURAhQhJB88O8o5ISGgFikGhgQ5Fykl5ZwlySBF6sFFfBe6ru3a1nlPhJ6wCEptDixqpqJFVFDBN955QiCgsO/2TMrp0+dludk9Omx3pof3bk72blwdX12ePr+8ulquNy7E2d5e7NrgybnGVKSIqYlojI2WmsJOzNyPYygc55OXfuWd3Zu7n+3vnv3wR5ePHn/wwYcF3J3X3vzKP/rffee/+r9oP6Z1rwgqmdgRoxZARCSqEXszA0RiFtnWltQlnbl2VFTcloleV59XlVxJwRi2tMBqaGNkq+ja9frTP/r9h3/yh+Xqwj5+dvbxwzJy941MAAB/WklEQVT5y/Oz9eqz5ybj4/Pjw8PZS7uTYXQnP350+Etfvfv5Lz/8+CflxbqIkY6qFLh77ejB6NoPnjyaLEYl39yaTd64M7n3lYnbXZeT58jfefrop+99dHl2dXDbf/Twyezmwze++jcm84P3//Kbq00+PT/nZrrvGJgYCqNDrjxKIEQCAgYGEssITGhSR3qgoCYAZoyoVFMhBpIl2WhBu53JK59/N/ebMhR0hI6dcxwIAXMasox9WbnGIYOUBAaSxFxhQkUrlswDMiN5Zga0YiVZVrOSa6WrmioDi4Ehqq4Hkbjn7/zau292X3vt9dexc+Sn79z7KnHIpaS+X26GlMpsZ28WWxRdXV6eP31eCr3y2uGrX3jn2fvvD0+Pf/b+7373J98rLjx49Z07X/1Pfv0f/CN16dvf/Paf/va/GZ9+xrHcnDR7TTxdr3KY4jRerl9cnVxyw3cPD24cvBpv3tu5uYOz6dWzF6vVmmMLQGBoWZ1zPjimwI63cB6Aqjls3VQKoIAGYIhgSKBaKk8UcesCokrVQgUCMMBt+FZTTpv1+vLs/PHzFyenL8zw1p3D+7fu7x/sT6ddbBvvqchwenb+s59++tGnj0vup7Pp3qyLIRI5LaUUyVkFFOvp1KNzjpBqkH/LM5KshjUAQAQEDkGr1xRQtw5BreUrYADBEyG3EyakoiAqBERIotnMiioDAykXJKzt0CpWHwCqZlBApLYLplZKmcRmSKOZDWJpI63vHrx0d3c2A4LgiRjHIZU8bjbJZHvQUlDNCohEyEilqiAhIAChEUnuR2wCBCPCJMaASBhciLER1ZKzl7JebRi4axuu/e8OPEVAUbCSUkFDxCJlzBnQ0ljyWDabBKAiwkzT6SSEMG1nu/ODep2JjW+7wEguqScWUFXxjZeB3cH+PocQHVSOEgAYk/eO0RGB927WBdXS59wPOQ/Ijqc7ref62Nh7T3w9y06CaIwEjN5x/cipKgLU8xpxjclpXahMQQVSFiBQgGEouWTRLaocHRKwI1bHpFzGzNHlkrNkIgIKMo5934sWIgzku9iqQTNnDhya4IlK0SEPI1gdgYKimRUtiEBI5ImILImxMoIPHjDcuHPv9OlJenF8hNDOJpNJaF4+2r05Pz9+try6uri47Nfr/cN933axieAcYDEtAgiITM4xqYKqOuZccobcxDj//J0H+93knfvzx8cXP/4Rni1n88E1Nx785m/2E5FSzHE13BEhMqmB6XayXX3YdQ9AABUFM0St6/6WJoVmQPUWhVobTBVBVYEc1dlIldBrzVB/trj84MOzJ2fjYrH70t6tN1+Zv/PS9INPN2V5+uMXV3DMvfzd1+8+X5Snn/7w9iv3zi6ucq974PbVRnQ8mb798i+8MvE/+OzHv/bSPcnx0+OfXS4++R++8X+f/Nm/2M80P7r76W365Ox0Pt/563/tNxzT8uL0ww+eFvrjV3/htdsvv/rJR4+GZb/qN13fErDWKkFf3W1YPd6IJrCd6hGQkYKxCgAIVvwAoDGZAQIgUhHRZFnUeeemDXZVBKnEsFKylJLSpi/9JnZtaPzQD2YF0EwwGyBi2dJEDABkFCQUEwK0mp2rcologWKl5KxWspmg1wdfecPTbEBN6dIigQMgzEU2y2UexZS9gPW5v1j+7L0fHH/0gQM3PL2PkZOf/fk3v7lcrw9uvfzO137ZXHPny1+i1dX7P/jRH/7Wf70+/mjOPIsw7eD4akGzabh1x0KIk9V8bzrZPTi6tzOZteH2IUXsBTQlIgvOM1VpNG65wkiIoCD1GnnNTVEFRCMAqsknMxUoBoz1AFIdxkg17WVa7WUoKiXl1XpzcXH6/MWL49PjgnjjaPfOzdsHB4d7s1nbTNmDqFxeLh4/ffz+R588f3oawN24udt2HaErRQmLoQEhN+zAUY3wEmLtKdkaesSshowJUJEMhZXERBBqkRgyoRk4j6DOwKoRlOs3RItZ/eyjqtZJAwCYIShkMMgZCQUMEBgJAZwjE0OkIuYcDWO6WlyBQ09uf/8oxG5nOt/f2wnMKY9pTOOY1TR67zkUFVNjTypZHRGz95xTUTDPDMyeuW245LypLysQAnJ9Y4qNmgiRHMcQvQY29t4bKBoho6L1qUeGUgQJrehaNpIEDJrQtm17sNs4x+TQ6uDeTMzEikP2kZ3zMXgykKJFqtELkADNQoOubQLFxnMNXCgDsHeOqWRd9eO6HxwzomUtWlRAvAfvG+ccGrBjcsiGYgqK/ZC0FEAOwTe1f0GqKwlUBYly7eAgVBETKyKSDbDOMk2KlCK1GzSn0QUfXBCVNBRV8BT7MY/DyB5j61XyOIxSCiHFGNqmiTF4F5C0crINVc3SOOQiCMjsyLEjCj4QUZVcmNlUAc077xybIQjeunfUL1fPPn0829nZPTpwbXQtH9y/38z3Y3O+Ojt78ezEu7B3Y6/tOmIErlVoAKSAUGEpAhpiLJUPLhgPJ7enb8xfunn3c68OV6rqF/3YHd3fe3lagYK0RZoAEKICVPNwnd/qzyUgwm2rRgYzq44wAMTrqwJzTV7WqSkSqlYkCQCxohEom4mV3dffvrvIq+V49Mtfvve1rzY3dg7f/dpq/8HT/rea0+nHqI/Wmysp33/4+OLfy/nx2Xp9eQTjfnujefWd7t4Xm4n/zh/96/MXn77+hS8cfOF/PF3+5Du/88/e/8lnCT66BWHy8Xvx6Ojel9589WtfvPvqW62jtHn5wx9+/MGPH6uX2Y1Xbty7t3hxkvpcimgRMwPIpbhq8HDMWGfs9dtqogAI5NCEoOInq6SlquzIwJCRjOtKlceULIFBvUFVYUAMHfvlcpPFArDjSdGVFEHngJAQKsq+TuXqTgxqJlBAtsUeW/epbZsxRDQNKZemCey5qIyWQUuMDRNmy8mKecdILNzEpqw23/3zb3znO38m48Uk+5OrF2BxM/QqcPO1d179lV/b1bR4frL57P3PvvPfP/nhT92TT244O5h0zc48+4nfa5ujvTjvXBeZlLsmxsl01qgqOFIiQJSUCR0zI6KCEjJVZmc1AYMUNbHaj6JmapXQI6VmxxUAmQCoes9A0TtW1eo8QIdiMKZxtVydn54+P35xcXGpCvuHN44OD/cODubzWde0jQ8IOqzXJyenH3z68JOHj8Zk8/nOwe48do1zztCg5FRGE1AGrIonIQKmoiBESOyIlUWKilpR5z2hWjEhcOSQ0XmvhlIPQ9V+ykbkkLHmlIsUq+7AGvgVs+2kAMlVlV9M67bDzrkmhmqUJOJhTOMmHT99sdysPPsbN2+/9OabN28fFhHJBSynnOobB2hd7IqVOnkLwRXNikCemYkIyXF0Hrh+XISYnOfJbFJ9elkKElWbUL/p05DZkXPeMZeiYgJoaZMVcEjDOJauicvNhgDaSYNGIhY4ZJOunbB3znPbRe8d1oF8yqvVer0azcz7EL03wNUmm1qMjhw7pmHT5yQuNMEQ1UCLIbhRiqUBARVUso05L8o49qOABN84x8F5j6hmjGgAVjSZqmkxCCFg8GaAiCKl5gaBAJHUAMzKkAARmEVEipWSVY0AHbuStEgqpdQ5cIiBvSu5DJvezNquLSopJSL0zoERk026Ngbvva+mWlWsbodScrXCEBFUsFXtXWAkIiJyzFT7ihGNyOrdTasLAmITN6s1NqHPPS8vJzR3PjD5+aybtq7fmx4fH1+dnG0+eTqZxp2DXddFAXBICdATECEjSskbKTFGZE+gaOiij7PGvUro2zSIix3009FSRiw5ESFAjdhs7S712lRFHti2127v6mhsJkhsReveUMuc1bSW24FWjwxtpf/qWSZmzyAl7rY3vvJWvHV48mwxu7G3HkYcdXN6/K3vfOton/76f/F3dx4tjr/7/k9+/OjTcdiUhzSmVjYjtNO3vvjuP/wv2v077/3Z73zw4hOGTXJd6e7tHtx65z+7Pfyb//enP/neFdga+htnx7ceN9MX9+Gll5u9m346vfsuPfzg/UefPL+R7fCllw2xpJKSYockVsBMRU0Rmbw1zATbrewaYV8MgBhNqz6mdXBcRGopEyJaMeNt23it5AZTNjRFH6frxWK1WYWWic2gIHKIXdZcnbKECN5pESRWUas97EhqKForxNWAtArnpaAIgzXekakMpUCfTXxwBmzmoFbNkBhiM5uh9z9776NvfPtPzy5eeCzrrOMlUdtRE9569ytvfP3rB/f3n33nu88/+KOLj3006rzO37jbNV0zP/CHRwvwgNhMAjoNk+g9+yb4EEoRAF/bvsyAmcAqOEGJ0PFWIVMAVFCo7ehgBqg1xysGhLDV+gmrvV+t3hhNRUr9dIlaHstmM5yfn7x48fzy8hzJ7R8e3tg/2DnYm80mbdd5z4TQD/3i4vLTpyefPnp0ubiIvrl7a6/ppogAxAAVI0EKHpyKCKgJqGVBIGYEVt52Y1mRomZEaAa5qJE6dGCkqtmkpvvRFMDyOKInRkN1njw60oxZs5qaoamWrIRopsS1f5GJkZwj5uBc9FXO8KoVAUTrcT3kftq0L7/+xkv378/m8yZ4ZD+kEaWQQRcDERkxKKASIrguEnspvBkMTUSyKhvgmBJ6QjFDWK+vyDA2DRIjUt+Pm/VmMokuhnFMi4vlmJOPLsaGCA2giVGzpiTjOOZiBIxExEZMDC6GUE2rJeecNpNJR2RpRDCtz6+bdFpkHIblcrkolgUAXFX0WscGWFS9J7feDMyRmMzKOJRcSh7HPCYk9CE456AAAnr0Hp0n75ikSAEJ3sO1kUBEjbeW9C0SlrbaBRhkVWAWMyVcrwckZ2jBOSPKOpZRg8/Mngjr7Cd4XyQP63FMyRF1XeujZ+Cua8BMrNQsnPPOMSGRARQwRRCVnLOWXIoAYGxC08TIXAPrjrZQJ6u5Ut1C+xAolaGkBGa+bUEzB++aJq2Wp09PNuvldDaPk0mIjQu8c2Pe7c4vbxydP3p+fvJiMzxpui60PoQYm0aYlRB0C4Qax5S5No5aAWJiaiOx+UhqYyoy9iKmFWSIhGxkporbMz8hKm2Pa4DXaNkqNtZKc3d9NrV6nAEgQlITsC3Xkc2KqEjKKsYMMmxMtWmbyYNbGpvLz14sz87g48eXP3t49vR859Z0nB6+8htf583Vv/3uX967+fLf+8//D0++96fP//yPjfnw63/ttS+/uVyWRz9+7wTyDODJh9/+yeOfulu7X/37//Tv/tP/7V/+y9/6yYc/PXv6k09zpufH3fs/aV553d2+HcP09n03bz5/+tMPT3/yqCXaOXp55VcmitwyqKTRUC2TMTDCqOIRqRqXiE0KbjEtgECAQExGCttqQdpqG2RaC5QQETE4BwSkyORzBs16sLuTRcihgFDwLkQVgq0cUr/+ZqKVa1YThnVvEMSaga2rDiEpghDqdvsxA228Y2KRDGpmICVpsaHkzqF6UrKjozvT4L0LDsPNl9688crL053QHcxmO1EMcmzSwd1ZDG0b26bzPHVd59uWAu8BqGbv2Mx868AxMRtXbwAQYe3g2+Zet5gzh2D1JFwXTcCipT4FMP35iLeaEc0QixgxmgihAqORFSslSx6H5Wp1fn7+9MWL5dUi+nh089bRjZvzvf3pdBK6WA2lQxoXl5dPn794/NmzF2fnpDybzfZ2511olExETVSlFMlFwYDRG6ipKTMH33gC2F761NQUBRAckWPP5MzIGDw7Ak5ZVcWkuhuUgAQAimoqzJgIAQQJvfOq26FHYECDlDMABxddJF/P2t4Hx4xUZ2iqtFgthrFv2+7Nt9+9fXQ3tMGjeoSieehTlpTHnpFVcTLp1CClVMRiDGXM5GDs+02/Mctnp0sRizE0TfCetwOXYlkyXS1yMe/aVFJJw+aK1sMQg6fAMmRCpAab0FTrd9s1uzvRAHKB4P2m70VGRkagruvaNtbFgDGGyg1NeUyjY5rMOhc8IYtCk3S0vDNvgwuiEKL30SOhDTmP2Q1jyimPMiBYLQvVLEWKI1bAEHxom9gEQKjt8lS9hkxcDYZGiMKOFRBMAcF5YuRaJwcERKRFJKuIlC2ARUwNHDbRIZiOAxogWQwNbY+4aAZNg5NJFz2PuTjCybSjuqOWOhxgBctaLFvRWt/AVgQqCCo4Im6byIyI6BzVYDhXMy/zdnxoJknXm826X43rzTikJoRu2rkQ5nu7OU4uTs9Pn50cvzjdObxxdHQUmxYNQwi3bh/s788uTg8/e/hwtbhIZ+N00sx2pm07jTGI4xoyhaLmyZgAUUzBVIpTNXJUq4Y41hhiDbEgVAhUzX/B1mmhleVQR291UbJ6m0UDACRToboI1jkwEDqk+ghse+BDgs3p+XB2ZlQWp1eNn8wmQfLm+PF7x6theviqcLn3xVeWx09+9//8//qr/+R/rWl6b/fm1/+Tf/D5r339lVfv/KALfbm4/+V3ls/PP/y9P7x6cnpj56YOZ9/96Ke9FP2B7uzv/up/9k/+5j/+xw9+8OHv/Nf/x8effP+9i374wfDg135lZ3euhdRcs7fbvPnKOJ6efPrwpeluaGYl6zCWNqCpVRuYliwiWNOdhNWIzeRUlbd1IwBmhFy96Go1mbTVtpERkbZTESQgrMQghXR4NL0625SMk3YKTsn7RjBfLQ3RORERwgpsqV1MgkRUuzYBCA2IKlCl0giBENERo4IByNZ8i4AAIiKqabVZD4W8Xy4uZrPp27/0+fuvvLw4X6EBt+3BnYNuf2a5H9K4GRary2W3v//2r/91lYxQ26SCATKj6Khj74JnT4hInogdETEhVFQOmEoF1qPWYCDalohlBVTMXN2TyrakVrluAQJ14GpMYEJqVfE3s1LKkMvQb65Wi+dPnzw7Pt6s1t10597dO3eObu0dHMwms9B0zoOaDTktl4vjs4unL56fnZ4Nq37eNl036ZrgnC9qOV0XaUmulj9i9sCI5IgCUe2zt0rqQxYVRPCVkOwcIIpUnVOzSM5jllK9PWDs2AGa81RMh2GI5AiRHYmgC1yxZJoNFWLTEXGILjYueGbngImIpaiUvF5vLi7OTMvB0eHhwe2DoztdnBDbkFb9ZlBEIFbBlFyRAqZJDBCHfhjGQt7FtlMQHQdNlvLg2IdIZnh5uSglxxCISIopCCFJUZWBmXYPdlI/gmXnwmQ+cbsEzNPpvJu0kouKtG1D5Lppy+SQbL3psea6ACZtR86lnBGZDBx7QhthKFKcd4isxcaUS5ZqoiYD3zhnxMRmNmySFAV07vj0vN+MktbIjOibMJnMmolriTBG770jMgAHBI65Gvlr/s62baBQZ3IGZqaeHG45FSXnXNFQUgqTq3r20I/ogIAzGCsyYfAOQJiQmRwSMBaVMuTq0i1ZmjY0baNFsliWrKYMXJKM/diP2USZXIghBmbnQhsMkQCDD1QZgYTMVFs2PTlyRIgAaKBFkTyyI2bWpHksiOiG5JtoktFRjE3P3TiuTh49G5arw9u35jt76/XKOd9OuqM7h/O9+dmz8xePnlxcnKyWx123mM6nTdPGpvGBgXhLl0JiIjHd9D0CAZEUcw5VicwUwH5O9USAOhGoQREwrW7PugMgAlrdKEy1LkeAqNv4L9gWjGdVeavqEZsR8nRnv2zK1fpso+X44SOvGQkWp+Xo3bfvvP55W2dYLR/+aPf8Gfzuf/PN26/e/81//L956Rfemd2lnQcvTx78Tz9773vf/Ff/+vn7P5VHn06Ppn/zf/6f77b7/+r/+//42ScfXUL+17/1W2538kv/4H/55q++M5n8l9/7l//DD779neVVOn5+TOC4iUkKTYB558EXvvjJd352+uR4dmTIQfpNFmbvDOs5FUy0kFAk8mxgdYKHRKqyjXyrSgXU1OTA9YcP2JC42hOrgkRV4AeYzCcMsLzq+5xu371nmDJAtxNSKXkzVLScihChGWopVRMqoEQOJIOiYe2dqGUeZKjVXVutNNVhaVK0YkaLxi6kzTj2Mp0147gKk8ne/f39+0dI5KLLqUfajKVPMpJDp8ptQ812Slm3e0tFpBCwDxNyznmmirXkbc+5SC22r0t/5f5JHY+okagWGcmILIlaTYegbYdjdZdjJiA1NCmKDCopj3kzjKvV4vji7PTk5Oz83EBn892X3n3t7o2j+d5uN+1CiI6dgvSbcblZXlxdHR+fvDi72KwHIpzv7bbOM5MZ5iKiZUiDVH9pUTEL3sXgPTvyrqrlVruEHTGRD77GjksV/rY1k6pWtOiYyphSKqnkVEbxYTZpWuer1AeODWrtOSMi1kpLI3DkumnDhkwuxuBDHXOTMQFQAUtJifzNWy/dOjqcTDrnnRkO/WrMm2HoV4vFSrJ3MbAvGcM8SMrHJxfD0I99Rof9ch2buNn0uhnm8xuTWXtwdOQaXl0tRcRK3hStN9a9+U5oIrNzLrLzO/tTMM1j9t41XYNgTdvs7M7Zey2lophEVM1K0VRK0XqoE0IqmnTVL9ZrAQcADsGqJVGVGUMfRJWA05jBcOw3AlpAiT0j5zwsF+ucZbXZuIuzSwCYtTE2DWDY3Z03TfRuWz+2NQtAPaHiNlqKqKZVKjUFQ3TkQC2LqJilauJRExtSUjMr4kJV6qmbtdW3ZWopZUT0gV0Too8MaGo5p5TFBx+cCyEwkJIyYFa7Wq1ExBGzAxEtRQksNGHSxabpmJ1zRMx9EUfMgITqYqwDmS3XEKTkcl0mKjlpypLGEQyaaTfdmRITINVdesyja2j/zlHO+1enp1dnCwHKRaazXdGUiux0k7Zr7z24e3Bj9+T5+WePPrlcnC9X69m0m+/M2rb1TcuBiyg7LqJE7B0lEcxZDKygqDmiynRCBb2+tQLAFutQ1/3/4OHb/rGuftvoL1TNo8ZF6vphVKMyAMSIwala6+a+6+Z6ux/GYTXAkENk30ZyYTKbRO405Vtvf+kr/+nf19xPdnemB0E8ec/BkW9vXTy+/8G3/sXlk4/2YHjbNYfF6au/9pW/eryf/9WLZ08vyvr5t//t6pd/uTu4++6v/MIXvvjLz3766PKH7yW/PDs+Pbz3EnmfkxFO3N5L+282px/8OPjzeLAHlooQtx7BVTeZFtFSEgFSYMdQ8XYIRhVBuXWJ1DwwgDGi8nYtJrs2LyIRwrYX1MxT7UNPq7Onw+bg4OadxWLNTPt7B0s4zSlt6Y0CqobMBmW70kMVnSrn/Vokrr/GDJAULFz7tdW079dDP4bGe6bpvPWpeCbQnEuSkok9IEWOJRUZpSSRrPVMAiZmpiDbd9pEUMmDc4G5LvxUZ/2oBpVbWaTOJFTUAE3UtoEQFYJclNAZiKkRArHV6nMkUhC1AoxFkhYz0KI6jOOw7C8Wl8enpyenL/phbJru5s37R0eH+/vz6Xw2iTG0LTs2tc2mX23WV4vN2cWL86vLq/NlFvEhxOAcOq2OS1EFUwVVcg45OAhKxME7R4xI5NhUUyrM6L0j5lpEv8VMAxUpRUuWPI6jFhGVYiBJfSAfprMmcp15k4lo1uJjIFMB8oTEFGIIIRBgcD4Ex0ht06mZSCliKoKGhIDgptP5zs50Pt9tQ0dkWUrOqWjaLDcvTs8efvjhIo3TyazrmvnOQVnFRx8/On7x3DOoaB5GK2nTLw1o3IxHtx88uHP36bOL2IXJLOzs7EXeQUIPPrbdzmziqvzkHSB003Yya2vUCRDNLITIjnPRnMVMSypioiqbzWaz3vSbMaVxtVoOo3QOQXV9fnVViuQ8n06mO/PYTZC49ONqudrZmU93d9i5wIFCY+hW6yHLGgX7frNcLi4vlsurNf7z/+a3g4uRqYmxaVt2yFyLN9FMpGxZH6LAjipMLqtIkdqBUNsJog+mmooUlSwl50wAzvntXxMIE2pCZHIOsea2VYpjB0ZK4r1j4mpXQAR2W9ivKiJgERlzUdVUCoI6ds47NS2pMEIIPkSHSJVrpgoU3KybkSkzOl/VUVSwIio5pZSy5JKhThHJfGiZkSsivCJPGMnU+pQ9GjqXhlL6nMacpR/TBskfHO4713h07MN83rFDU9tcDcfHp2fPni2Wl55L17TdtJvMJi4GYkZ05BgJZYthApV6gAVToWt/XhU06oOoDu26stVdweo5X7VOCGxbv6FYPXoAiMbs6lRgS/Ct4Tokk4KA5kgRSlZiqtMvE1M1NHSenffsHBgQk6GmrFnEEzLC4vHVn/3z3374x3/oy7mtrsrZIr50/7W/8St3731J0vL52Rm7nfDm/dsvHU73prOdG+1sTmlcPPrw0YuLy9OznRuzPol3QULISHK+KIvTdj6P05lrm2a6v93OVDCXkhMy+Og4ROc8EqoUk62cRdfmWEKtcKNqXamFJHUCUBvStjYY5KZrqMDP3vv+px++/8rrr99/9dUh41DEN40VXS0XkkfBaklSNTCRWqNnYJDFTFVrC22dtJjqtcbpvPdOS8k5y5BKlhBq3gq4bYicQT0sqfOeiAR4W55qoqLV9KpIZgrb9nRQBQUhQEYFpEr81q1GJga1jKtUMfB6Q9xiUk0FTBUJCUQVtPrBABFrVLaypOqxupSSUu7Xq4vlxfnl5cmL09ViRRh29mc3do/2bhzs7uxP563z6L1jh6o6jHmxWC8W68uLq5PL8+XyKqXsfNN1TdM19WouOamUosCOY/COXWwZGRDAsQvOMSEAZNAyCgJRLbI3Ey3IpKoli6EVtVKL2EsuYwFiZueDa31wvvFMnlBNREouwo7YBwQI3rVdG2L0bWDg4EP0BGBIaGKqKNmGVHz0zEwcGVzThdlO6xyZQCnJiEAhldz3+fxy+eLJ8+Nnx4tl37Td0d0jaqeLy8vNUFpP43q5uLwaF+s0DLt7R/Odwxg8MFhO916+cePmQTvtYvDTyWTSdN207doqU7CqrsdxvVyBd0iUSyLEccze+TTK5eX56mqRxlRSKZLUFFgRsGk6yXm57tOoV+dnOg7nj06kaNNN3/jS67dfujvZ221iN6x6FJvOpj6Goua9n087YhnT0G+GnMSQJOeTF+eXiwX+8Te/lUcl0CZGMCACZkLUGpUHAMcMBiWboalYLkUrpBtgy1IHJEeqtVvBSikpDSoF0Hn0Ljpm9p7YOQIQM92Stcl7FjFkaLxncqLiHGL99SIpSRrSMAojU3QEAKToqEbvVKBI0VQAEQSHkqQUQm6b0HWTLkQiJDZGUjIASyppyEM/pHGT89YrEmOYzSddOyHgXMQ0i6GhMhEqZlPrh4LmmB0xEQ/r8fGT54uzk2bS3jg8DCGse2uCP7gxj01kxFJsuVydX16dHD8ZLhcOYTZpuvmsaZoQWnRclxCVLVNLFOrSTwaKqqKwhZgDVuTrFvhA9ctNiFYHBHU/2Boia1wMEQ1rMpzAzGpGpu4xtSbAVIkJqn3+2hFlCla9Wo4QjIjrTwPCUkolA6hmwmAjrp7o8cef/vQvvg1Xx7MjuffVXzx6911mVhcBVKUIiGEBSGjg5y0Wd3k8nD16sVw+GZcXPliM3nIQcwqF2OJ03s3n3c6+1gM2GgkYiEgO0TsfQwg1A1G9LnYNuLYqa1QiEDOSGiKoMTExETm1XB0/7LjtOlv07333+w9/+sHhncM3v/QuuZCUU8rek0opYxJLtd9KtFLEUEoBhJo2KiWpylbeNNMtixeACAysFFXzziE7R1jtueQdBrac05hMhJrIAFmVAbcQHRNGFsuGhKBWU3w1GauCTAzbObcWUUWoFkdALXVMXSlflYpGampaab9GuN1gqheuEg4klSKqItnKmNNmHFbLxeXl4uLs9Gq5EJHYdPsHe7uzg/nufDbpYjfpQggxEImoDeOwuFqcn69Pry4vLy6Wq1WW4p2LTWDvEMgTs3cBHaoly2CEwG3jQyByzIyAyuwJUKrmU08syGaQch61pHGoducsoioqUswAwHvnkIjRkUMiIl8ZAWhSTKwYe9e1XYiOnesaF5vgnPfBBx+ZXNKsWrukoBQto3ofYxO8c76JjH47QTfRIptNohhn84mPbiyyvFivroblqr+8GHd2pjfudNPdWdfF9Vr65XK92BBgHochaRNmezv7beRNP04anu5E56xtY2h928TQNMSoAClLP4zDul+n9fLy6vx81S+HZb+eTL0ZBR9K1n69LuOQx5T6hA5TGmcHszv3jvZuHLWxiTEg8PnZ1eNPn0mSGwe78+ksTN10Npns7DoOZEZISKSimzI2wTfOIWTJuU85jxK6rnW8uFyt+g3+yZ99UwRCdARbyqapIiqxc3UKw2QGY8oqlnIZUwIE3a47hmCqlnOpZFYgQAITsSJI2MTQtI33DhRytj4PZhZDDLUTgVFV2RMTajFEdCEgmSioljRW4y16H7zzhlBAkKzWLxOSmoKoGaRRxlSQrHHeMbHj6uNmx2iQVcc8jsOYxqxFsmRUC02YziZtCNtIlRg5Xw3oigYIKjaOfR6yb33XtM4TCJQsF1frvF4XFROdz6ZivBl6MGxjmM6nPnrfhFxkteivzq/Onj7tl1eMZb6zs7szb5oWfSAm0TqnAFEkQAVFFSAoKhXgiWZI1dsJals4BCHWgwwiq8kW0FhPqgBgSMRmVnMbAFoPxYhYlWuw7YCZttXUhIBEZFU/wjo+ta3OhwiINXcmmjXXTnCCwmgE5Kr2TqyElkuuuyyzN1BQICg5D2pIPqB5FBRJw2rcLC6H5UVaLcd+TKKhaeNOG9smhIlruLZ9Q513oDGz8943gZAMlKi6fCr5FggIwGrODRmvaalG6GCLd1EzcFVtBnf16Sff+MYfvv+9H9995ZWv/rWvz3Z3gJxkG8eCYECKW9yNAREq1gARQsVEgqhUTgxsofJkpiZaTefkEYGJfO0KR6heA1IrpgJkRVRNCC2PRVXUyERrYkOpNpmjEUC1rxp6rrM9QEQTExFQUsimus2cbme/dSNH1W3zCSkwmYgwg0gxJAVR0ZJLHtOYct9vlpvNYnl1vrhYL9YlW2z8fDabzXdns9lkNo0xOh9DYB/YIYKVIfXL5fLsYnl6fPr87Hx11RtC7Jo2Nt20Dc4BoOQybEZCcIGb0LjomB0Teeat68MUeavSCSiYigoYqmAu1UcuKsWzMyMVJSJiUjRy7F3g7cYPhmpKjAQKdRFhF7qua2Lw0TfRe481PYJIRghIQy5mFoInYkJ23jl23jERxhgMfM4ZAZsmWpHNkMAFH6KPDplKScM6lWLeN23XzufcTWLjXZ9ts9xsNmsZBRGBnG8a79xu03Y78zagSS5WENBIx3HcbPJ6GJeb4XK5uTi7GIfNuFqF2OQhrdabi7PL3Ruzl195JTZNzTd6x03j8pBXi160dNNudjDpdiYxNm0THGHKcnF+pUJtcG0IxuS8Y3ZUh5xbFR7UpLbII5pBQcUYg2uaylE5fXGB3/jGXwKhcwyAtq35rJmJaihhz6hm/ZiKaM5l7DPgtkfJe8/MKZVxTG0bvHchembnmJjIrIgUyzXISkAMCMG74Jrgq8KtiiZguUjlY5d6w5D/4GnwDmNsGBnBhM1Rvd0DEYlYLmqgmg0QmB0bFFPVkksGMHahZC0lDWlAM0ZkdtefA/DBBcdjGleLDTHH2LBz3jkAS1pAjAjIcZFSkgTvPdOY8lBke+xOhYB2pjsF8HK5WK0XKjCfTea7M3YuOC8C/Wa8PDl/9vRxWS3ZbDqJ051ZaFpkz57relxlHCYsReqdqg4BHFbyTz2OcwWiUTWwIkGNlm/rLAytjj2xdhYRIDkz0yqGbNGhtl3WEclqffzPwb9miPVMeT2CxBpQM9Oi194jqj8UuD5mNFBThAoPAEJEYrT6FAwBxYycw2vMbskgqsOqHzabMgwAhZ03FCnKzseGmWnb+VcP0gBYCybqYGOrjlehC9Gulf563gYAqi/OFiludRNlZmbN5Tu/89t/8o3fWa3s81/8yud/8UvTvV0XgkjFPGQwrPCp639dcdq4BZFeu21V9edTFzNT2SauESuLu/aHQLUpk4HW7h7TUlNqIlL7A6tnyQzIBApgpc9XDMo1tqyKgYqiuZhJKbA1S4GBYd24DYkYQetDAgQrYiq18E1E1TBLHseh74fNen21XF4uzq+Wq5ST83FnZ3d3vjOddpMmuthE5130zOydJ4cqpe/Ten11erU6OTk7O7tYLZamGlzTzSbtpAuhggzqDqw100eeg/MheO9cdERERU2K1rS/CmSRfhxTSqZla4I2wwqtZwreoRFo7TWvn3N2zpGAiGptaidqWx+cn0waH1xwTds20XtwhGIKIlJKkpyTGiZRZG4nsWmaGjh27GreMLpAzouYC76J0Qd2Zpshr3MSpdr2FT1NYjBmIo9qCEIMDhGRkgGbELkQoo8NOfYOiYmJGVHV+pQ2q7LaLI6fH5+dLzbDWIomyTEGMlUpu7v7oMqO18vNdKe9fefmbNY58kQEUMzEEHQsYyqeCANx8IRkamwoorno1h8C6DyyZ1f9LlgLEhkRORCTq5gjAQvMwbuSZb3Oi/V6tVrjH//pnzM7ZgCkkiqJBghRbAvuqQTPfhzHVByxZXGeAYiuKTQqBZlCqCgEACRXT5NVFCiKRTk4Fzw7FxzD9XIBZONYsqiAMrn6l+uXA5FMwbHruuCD51oJU0sXVKVoFlWFYcxSxHNoGu+YxKxoGseccmqaYIaqBlIMLTKHxjvnANkASx7zWJg4NL5OTbF6j9VyTobgHSPVaZyN45CTiOg45tA2MQY1LCmdnyxQ8607R027k20cxjz2GyKKoemmbWwa55yK9Mt0eXx68vTZZnkBmtq2m8ymoW3rC0JEqorb9Ot2zKuVbGK6JbJbhZ8BWO0DusZC17cBQVFJlAzVqrmOiGGrFyGAGuLWUmnXTU91qdwelSuUF39uJapGmu1cVW07L0FgM6so7OqBMTBTqeDpWk8A9RxhoNuCDKbqZSUyQyGAgiaqOWsSJBArpoCOnKvD7utntQ0pVV0DoKZzAbbuqO20m+B6kFfjc1olGrBt4gNrIsX1i+WPvvEnz54+unX3zVv3XprudYA4pkFV1SpZZvvcdQscAlCrtEFVrdPmGhmD7fsAplvtzK6VqS15GwEUVSofTMRAayC15oxNYQvZpKrOA5qBQjGuNz7Y4i2q4G9WgZ2mqlizXIrE9XWtVeZEW2OUGW5zoCWXNI5DkdQPV+vF1dXF+eXlajVoMd+6+XS2szufz3a76bxro3OsKiFGVEVPiFSkjCktLq9Ozs+PT0/PFqtxkZj8dBJnXcs+BO/ZeULlBkFVcsVgYWBA78CgSpiOKytUnXfBuwJQxtKnVErOKdcsLpMjQhe9dx4IPTsA6GJAYpEsNaNWoSiIgNsi9bZtQgihiSFQiDWcZEUljbDpNzn3aOQAEQm9n067dtKwc1t7ACIhE3Nwnn1smshMIoWIapkteleSFkUpgqYABZBVIQ1pcbXc9EMRmu9Nu27igs27WTuZcgyGkoe+iI2boR/GYbMZhnFYwsXlYrm6YIq7e7vT2Ex2p7NZA2SzaYPgpCp1aN0sGqiWgkoGNm56IGkmTRPier3eLDZ9zuTIGZWsqR/XV6sxy97e7nRn17eha51asWIhBAQKbfAhAJICjrkMm7QZxvVYRI3I8npzcn41rsb3P/4E//hPv1k38KrgV18xEQpSySUlySU33gcfcHtPRUYsZmhYoOScVQQdOWRjIACVUoW8EHzXNW0IzO46lGhoJqpIqGJFwFRETNAQKEsuOQFiHZMzknfRB0cEgJpyFhUkAlMpJrmMYypZ2fF8Ng9MRSVJMjNEdIwheMdOUaFYSklyNgDHTCG4+h8xM9RYYM655JLESsqbTT8OqbLGiLnaq+ofiljbtk0bwXDMshpXTx99Np4v7t+9PT+8GbuGkMb64UYMPnrnfPDRRyllfbW+vLg4O3kxrJclDZFwsjNt2knTRnQOkYkItvryVs9QUQLLqowMYDVPCLXrzswIqlhjVFdeJau5TtrGJBGJGOsSWdfWmjfe5kG3BnpAsi1q37arICBYJcht7wOACkg1h6VghLUgsI4mFYjABKyS53AbMdKaUqj30bp2VtkGqTYoqW3xsHj9LQdVub6CVKNnFYRAf55ggp/fY6zeCbZd5fUmVBMksE3MbcMYhFCGvF4sSYBcw56LpizF0qgICLY1X9Upqv7/TRq2lsrtsbvCOrZZQtUtjanmSeqTrX8D0bZ5skrxNkKDupADMrFqBUdWvHEl1KhppTyBYWVXGAiqVQA/GKqZokk1LlSIjGHt76qXORGTUiSVPAxDP/ab1fJ8tb48v1isVqUkF+KN/f3d+f58dzrpmraJjh1SCJ6RzHkG5ZJLPw6rob9aL88vr05Pzy8vL6AoOx847sxmbdd4z6KiCowuNCQgJWsqApKTKBTxIRKxmAqI5lIEGH078aA6ZJGUzQAJna8WHgpMzC5E//MGYCYOwdUPazaFgpUYAQTBYduE2LRtDOyDc448qpUkkvpxGDFnKVKIoPHOMXoiF8Jk0rELZiBozMTsiAiIYoyTydR71iKlZBX1wU27VpCGvq43Mg6jFEiCoJrzsDhfrMbUdJO2m/rA7DAEV/uQU8kpDWMuwzDmXLRkVph2OxPXbCTv7UwObu9P2uijY8KiAKRl0LEMmjDjmNabzZgcM4Xm6vjs4vyKLdM0hhC6aZeG1Pej5AwKKaUyCBEf7O9NuqabTHdu7HeNFy1bOwAwEgwpX16trxbp4ur88vL58cWLs6v1sOEyrlZXV4uLlWujg4h//Gd/roIiIhXDYDgOqR+TEjhHTdMgYRqKDy5ENgUQAdOSS0ple8cHmrSxqJaUgYkBashu0kV0QIjB+0oVNQMpBmpAWHIZpGgyQkJGJFRVRWUm51wMPjDV8H3OeTMMi/Ors4vL1OcQfK0BiF07bZtuOonBi4iBVZExRBdjUxe3IrJZbxbL9WrRe3CzWde0wccQm9DGwAibYRxyBtOcqrEVF+u1Sqm4TclasnRt8NETAjGDAofaqQz9WM7OLx9/9OHpo09DO3/11Tdu3NltZpPppGN2RUQVichMkZ1DHsYhp7xcXK2vrobFZb+5ANXYxHZSE2SxMnuRuWq7df1R3U50q1G9rkK1qnVb2AGVAlQvDdtiNXJcxQJEAKZtuA4Z6hFZ6ykTtku+bs9sRFBHqds1dqus1PX45zYtxK3FtDZQVFfq9pdVcEX9f2tqqmoRVEEH9XpvAEMigG1VDVQuVr1VgNQaEoAtDMNqKXklQyPSNgpRWSOA29YkVPz5L0T8OWT8OiJbleGsJReRIqIlG2z7krZr+vY+Xfc2rWOuypGtyzrWeYsBWB2C/PwihNvzdx3Og6lUiQdR9eevBZOveUlTRatoPrMi10EG285yRbezKLM666pzCDMlIi1mKvXNUVBTzaOkcejTsBk2i9VquVovF8thHAFoMm3nk9l0NpvP5920Cz6Q94DGDJaFHbMHM5Fs4yYvNv3VYnW6Oj87P1+s+lK0C3F3dz5rOvauUhlKSeOwQeSmDYA2przJJamwVgKKaiFCCo1HNARzHJgdQCkpZdN6fg91PhuCYzIrKuKd2yZcDEq1aWoRBUUgxCa0e/Pd2WTmGiJSZorBO+YiUEDzmJebzTgOUrhrmtls5oJnMiQJTCFGKZqL9WkApq6ZNLHxwQGyc75rYtPE4DiXNAzZO5q2MSsslr2kbIBpzBerYXE+bNYbQvUTt3twIzSNKpmmIWezQiCSy2K9Pju9WG8ys4sudJO4O5u/9ODeKw9ugaf1Zpk2SdQI5PT0Ynm1UlNRzSop6fLq7LOPPlPyL7/1WtdNHn/y2ZNHTyRtXBfYuZt3bs725pW2lMbkvZ928535fGc6cayeggXOmlbrYTUMm9Xm+Pgk5XJxefX8yZOHnz1eXl1s1oux3/imMw1chI2JWzeZN90Mf+8P/0TVRIQBQLGYjUNGQvQIqjkV9iRZUx7bJiJTKXm1GrRIcN7HOOm66Juui1qUHYXAgOQ8BefRmYmMRcZStvNIQAJjYhXcjH1OBYnaGEKIzGhbd4sVK6iKiJ6dKG364XyxevbkyeXpMTo/a6dNDN2k2zvY77oWDQzMe/Z+O1kmBEMsuaw2/TgWUzW1aTdp24YBFaRUURaNkPrcr676YRjUkBVcCEQ6mXUxRtNCzF3jOfDicp2lEAA7zllMjJhEpB/KuErnx2dPT49XV5eRy+H9ozt379w4vDmdTH30CDiMuRTFSjaBklNC0b4f1qvFcnm5uLwoQ++Yuti03axtuxBbV9W8rckFREzh56uEmaojkuvTLyIY0fVajmgV6Uj1bI0AwAiIlaB4rdJA7STaLtbb/AFUZW4LCahGyuslGonqmfzaCVN3jooVBNiaZWArDiEQ1+Xt2sEEdWi81WsMUKVOH+odpP7GSvgxUwCt8YgaL99mcK3aD+r5nPRan8LtzwetI4EK99leZVQM0cQAwaSWb+datXbteNDrkbFiVbqsAgkEcas7b9Mwdr2+V9m6jt7rXaomYgBMpRbYqCpWP5UZ1zsKcQ2vmmbY9luKqoHolm+HXLMg1fFT5w31KqMIKvrz11GlmtlLyuM4DMOmX65Xq369Xm9KUe9DjO3uzs5sNum6rmujjxERiioBKhi5GotAAd3kcb3ZrFfj4mp5cblcrpdFEwjEpvHBtzF2beOcI0M1y1lzTv1mzCU3E2aEJDaUIqqO2BE5dM6Hxvsq/kXniUmKpFyKZuewrVq79whoqqKSJRdVFcslS9Gci6iOaRxyMlAf4mw6O9o/2NnZqbFU572SShIk9D7krMv1etNvSpGDnflsNo8+oKtQCQ3RBR9SkdV604+Dj+3ubGc2m00nbX0LHBIH14ZAHlfrsW5FRTWnEoHYhSx81feL81Xq+9lObOcdY5BSTlerYbO6XCyH9SrnXEwcc2y6abtjzMGhd+gC7bST3f2dNjYienm5uLxYXFxdPP7k+fGT0wKlm7XmNKW8OF9cnqwfvP7S62+9eXZ88t63f3h5cTU/iLv7s6Zr9w52Xn7t5bt372YpDmFv/3DazZ1zIrq8WqyXfT9sTpfnT4+fLzcbGfoXD59fXCzXY1pcrLSk2BgxkHcvv/bKfLIHPcyne3cPX5Jut9c1/va//YOq8HrnvPNm1o9Vo0umBRVTyqBmiMgQm0COTcyznzaNi21wjusdSAAdMtXGdyRHBkUFctYh53HMWsTAIm9bNkVNDWPwbdNSZU+jgJUiMKZ+tVwtl8s05OAnsZ0Iak6jQ/QhRI6Arm1jaGPOQ065bSM78j74OjUyG8Y09MM6DVIgsJ9OptNJB2iqJaVxSFmKppT6zYCMaFByyVlSX2Y73e7e3PugZoTqgwfNmzGlnICQgJlI1fJYihQrooCoJmKL9TCOw+r87OT0aZI8m3aHN48e3L93cHjYdl10Xg1y0TSO1fiZx6Sim3EcN8OwWvTL1bBejsOagJrYNF3bdhPfxO00EVHMgLbHfFStRa9bXw8Z8lbGQaCqIdO2IhIBjJgqZgW2g9P657ri1wCUXQ+T6oJvhkDObX9ArQbcjhmViRCqfgum20O/AQGhmtW5CSAAwfX2AlWwRqWtYX0LcCAA2xZellrPbQBSsTzX3nYkAjQzrdAFqnRg4goH2u4f1w/QcMvLAKCK1Ibru9P1Slqk1ACLSY1gXAePUEwAFA3rEbsWi17HHY2QRWsV6Fb8R6PtwASxGlpqTBnoWriq+yMSVqWrvlbXuhpcV19rMSRUQgC0Wi9xPeqqeRHRYoaqNceSU0o5j2MahnFcD8thM+YhEXHTtW3bTrt507Vt07WTLkQH5AxEVfv1Oot678HEiMa+H1JeDcPFsLlaLFarTdnkfjUQ0XTWNJNu2rShoZKLSnUiGAqL6ljqXNDACnry7F1gBYKi7NgH14YmOt5qh2AqNpYyZiGCJjrvPCKIWs6ScsoiYxpz0ZJkHHNRBVFTKKZt10y6tu2aSdcF9lZd6apIhp7QoJQSQuOIxRgN9vYm09mEEdW0lCKijBy9Q4AsUiXDdjJp2zaGEJoQYxOY67CKmJ2jMcnVqmfmnMUA2hBCCGK83izHdW6Ci5NoYovNsNosRXIWXV6uLs9f9OshZWm6yauvvfrKK/cms6lh6Td5vVn0m42WPK761ZAIvWPvXNDkFpeL9Xp5ubnq+yvngyO/M9u5c/f+1O98/73vfvrh4/mN7v6rd3f3dzZpQNKbd27Mu3nTdMGhDyGlcnWxOD27evrs+fHzF8v15fH584uLS8l2cLAfsNndudG0MxU2s/3dKeKYMvhWd3f2m6ad7eztdLOm686uFvhvfuf3ibmC2GrqZEw5jcNm0y8ve2LsunY2mRiDmHmmGFzXhOg9MRlsj5ilFK1GEoR6ezMkKTIWQzTnGIGsqKESWCkjqimQi2E6nUTnillJeRz6klOpZF7R5Wa9XK7SIJPp7NbR/nxn2oTA3pEgkmcmFct59MERk6mJGoAmsaEf1Cw4F6Lzvt4JyBRTTkMahnFcXG1Kn0RKzrkfBiT03vvgo/fT+ZTZ5ZKZEIlzkZKTmIboYxMche31XqxcLxx5HIsUZBa1cSjjMK77xfGLx4vzc0fu5p3D27fuHt68tbe70/iIzEXKmEZTAJUkAkVLTqXPm369WS779XK9WuZhjQBt07TzeidooEbJrusTq/QjWpNDUo8zNViEVfdHJORiWo/026Sb1VrOemiuNJ0tAAa3jLCasjUgJKLt4fbaarCdENSS2C2+dDsiAMTt9Gj70LYNBbVliLkSO4GQtYABAFW5phovqcJqgAxEgLSWBhnCNu+s9vMNy2ALzqv8u+t1vZ7QZSvl14BUfbBEImJmZKimUhuoTEWKqhBgpUYDWKnN4GAgqLo16gPIVkczVM3XV7DrsTFezze2O51u/Urby48CMADifxg1b127hKSqpkVVkJyZbHF0qlDzNAIZihZVLVo0i5Q85pLHceiHIeehiCKaZwpt413o2raJbWhiEwM778gBETEkESkpjzmNxYiGXIY0rjbDar3ZDJvLy8tNGrQYI8Xo29iwd0C4xeYCZC2aS5asooS+1hm2beMdq6Yi6pwLwcfrEHA1L6nUlJnmnMaUh1wIoW1icA7AspSU8pCKiCKhqCKyiKgAe26d90zT2aRpWma/nbWr9lk2q361XpeUyRVyzIShiY0P8/3d2zf2J9NZvfWWUvJYiohDYB/BILaRvWek2AZ2rpgioWPXxobZiUgWqZ+hfjWSo5K0CHadb0IUsD5txmXOlszy8cnl2YvL/aMb+wezg/0dj34cNyUlLHkAbGLcObyxtzen6FIPz5+9ePr4yZPjz558+iRyeOON19987dXbd+6WZI8ev/jRDz/4yYc/2gyX893dGNppnO5Md9bLzcXFSdvEnb353uFe07ZZ5eBw9+7927uz3XFMy8vl+fnFi5OzJ0+enJ+enZ1eHB8fp5LGsR/zuLczP7z7YGd24/Of//zrL9/r+zTmsry8SDmtrk4eHz/U4vb392/evnvr1j4oPn3+HP/dv/9T2N6PpcZkmQgRPXsrAGBE5INvJg0yg2YEc56ZaPvlECsm45DMTNV8cKbmHDsXrjlQxQAC+dAEJmdWCEwlmaEPHIJTolIk5bxZrZaXi6vLpapN59OdnXnTTcxMrETPIUTna4bQVXtdzsUHqs7rcSg1xbbpx2Ez5izecdeGbtbGhgEpJ+n7IaU0bIb1eih92ayG6XzigmNAx4gOEUFKLoAiJcZITGnU4ENoHTk3mTbRexOVIqoVAONULafcj6OKAmC/GTj6Ks6O/fj86aNPfvLTfnU5v7H/xquv37t3/+DgaDZv2Lta/lbzLkXEUkkpSZZhGMZ+GDer9WYxrJZDv9Ai0ft2sjOf78S2ZRcJEWrhKTFe91nXOzUYELKZ8c9HAFt8UJ0Nb81A1VDJiKq6tf8TAVQmPgLU7r2t8bKucjVVLCp1WcUtbZhUBK1mn+oKCFsvE6FVzr4pVTIbIZHbzj9p21tgCoBcke0AoCpMYGhkJNtpLKkKGmhtgto+YKhiFKhdo0q0PoDKQENW2/pj1RCR2EQk55JLDUXXAOMWPFDn7lqqBm2KWnLdZdUEAa/rZguA4bUzt1qsDK5bGxRrFZcjMjIDqRvpdZuuSCn1KaORXc8YAKyIAaqB5ZxFVKTkVEQkyVhGFSk5m4HVGAGSOeYQfQjBR+/YEXvH3nmPiMhMDDUjoipqsl71pdgwDOshD4Ms+vV6s1kN63EooODIvHexa5rg1LBSULKUUpTYiDENxcw8e0LyHJCoa32MAdDymLIVRkSi2iBCZEW0FElFxiGbghYAYOd40kTnUUpJOfV5zENWpBib6J0PntkzeV/do4ShCc5RnZX04zBmabyLoVUkSyWXYlbqBEsQtZj3PJ9NOJAWlaJgGH0AtKZpibharpkYmQxEiq6HEQ1m0y7GEHwUsGFIompgPngQHFNBAUIXIpdcrtaLZb/aXFzlVcqFR8SmibduTg8Pp7u7e5PJZDKbtF1gtmEcnj46e/7k8uJyeXE5pFGdl3EcAvPrb7xy5/6dnZ2uCf7qcvHN7/z4m3/27adPHrNTJsxjVgEo6ENgz11sQIE9zndmt+4cvP6516bzSSl0cXHx8JPPjl88Oz49vbpcmooJqhpz4xw3sWEmNd+2u/devnHn1o3Y+DHn4+PTx48+O3ny/OpiU0Y7vH3jldcf7M+7i/OLQQr+69/5PUL2jolITbTUa58ScxdbJixZgw+xdT4wmBaVpKBFi8hmzCRWsjr23bRx3ollU21iJOYkkvucxqGo7Eyns9mkiY2ZgQghSpFswgBKlKWksWw2m37Tp5RB1UU/n00m06nzJCWXVIbR+jE5wlTxEgZZchNDSbmI+Bink4ljTsOQUzERMPOBmVhEESsSUtV0GAYiYvZlI7EN00kzaVoKlEvOY85SADB4DsFXx41jRjZEylJAzTEDoW2ncpCzpJRUFY0YKYkkK2romAwxp7S6Wjx78vTps8/6y1XTuls377382ku37t7anU1d49AwSUljkTGpmVYpKmeRkvpUSk7DZrNZ9Zv1sFyWPLBBnM67btrOpt61gIzEZEhMqlXOIaY6CtUa98VtXKDK+LS12ELNmZlp9fCoYSUNGRNu2yjFtusd1FgZEnH1+Fe3ejW+VKM8iG5NOVuXpNn2MIymW/X82rdPhECM1a9VT8Zb8CnUB8CqBevA1bCON7YiP5io1DG1iW6dyHV/q6uwCCCoasVCbAH4CNub6bXTQVWkFNh68Q1UmUBM6jpeKfLbewEAIRbZYqPrz6pmwp9fAqrgVl9vBCZmAwGUOt4EtSIGhqYFRUR/7jWqkYlSTAVyTmMah1y0lDLmLFpvRs47YheD9465bbzz7J33jfM+bBMRtelJRUTELGtOJac+j0PKqWSBlG21WS3WtT4vmVgzaRh50rU++jraUNNxSKVkAkJWAFYRIO6iZ3azrq3p6hpPk6JDGkspRqSqKlY016Ne0qIGntmxa2MMLsQYo49oliWPQxnSKFbYUdNGxx4NvEMOwbHzIThyCFB36uV6c3G5WI+pic3+wc7B/u6knXqEXMRAAAHIUrE85mGTl4vlet2baDdp9/Z3DvZ3gMG3DQN7dj54LQZMajYMKafkmJoudt3EN1GN1uv1MCbvHQdm8nnMaSFSZLW6urq8WqyXZdD93fn0YOojrRbl+ZPjzXA1Owzz3Xlw3sfJXrMf2hYj5uX65PnxZl2ayc7bn3/n5q19VFn3g5QxOJfGzZhTyunZi8vHDx8NffYe+5L7zfry9HIYczdtp/MdGcryYtlv1qbWte3OjR3ystmUk+Nn5+eLogJm7H3bTolD203+f029169t3Zbd1XsfaYa19trhhC/f7Eo2GBAivCHxxH8JEsgSyELCCAlQYYP9gOXCFW+Vq+pW3e/e+4WT9t4rzDlH6IGHsc6F93O08xijt97ar+3nwzzFmkstxfspjqOLrUdyiuhWcsSwv9nf3Bz2N3fRx+AtL8ubd+8YGf+Hf/rPTI0cDmmIyTvnzIBFyPmUUs9rIUAMjs0EQFjWrba1lsboYB6mIcVhnA3tspwNbYix80h7bCk4BJVca3DUPwQYsoCy5FaksSH4QNHFHm4KzrtAhAZqAgggZePjZbmc11p0GgdV9B4Vgbmx6JbLOMTD4WYcR0cEzM676FAEmHm5rE8fnsTUxzEM4To5prCfpugdEJBHMmDVVmurHFKMMano9WTqrSPGraroNbrpOrDCE6gxa61VTEXUY2gmtYmaATkAbbWB6botl9O2Hs+P7x4f373Z1nZ7d/PTf/ijH3z1xcPDQ5oGB3Cd9bkJi7TW7wJUE+FSC7cqtda85eVyvpzysplVVErDYdzdpJ7Xh4CO0HmEj57S/j52H431dn2ldwnluhWAbhK9midNBbEbWpCF+8mpor0UEK/73KuLtKfHrg5Iu2ruCED0Uc1A6MThroEbAYK7oka6K5jAkMDMmTdTwGt2wEzQOseewEhNCamzk6RXMXVrEFxN81eswNWz2dl6/TNVQxVm7PgNsy4HiQqYAKKIuavKpd1i2SUwbtKdh13QUutf/rVfhcgZCBqBAaAzNATp4eFuPDUVg9YnRUQTRRVQbsDShFW0VyG1Wvsil6G2kgnBUycnkIvOxxRdGlJCdHEIjrx3CI68cwLCIk1abS3nnHMpW85rZQEDbdecnlODy9rWy1prLrU5pGH0MfhxGhDBIQFAaUVRAIB7GXyR1mycxmkcYoxIlkKI0en1h2utaa61cgNTAGdKJqbKnWLivUtDSjEMY0rd6xMIzLFwLTVnVgBylAbnnANRYXEOQ/QueQ+ucW8D5JKbgRk5F1xwIURPiCrmCAhJQEUYiUJMjhCUEECLFmZm8Z6GKYSYppsRzY8pTfPYwzFgWGo7n56kybgfx5uDGFy2vF22WgozhxBa5Zrzelq2ywkBXQwWKkdXPtTj+8vjd09vn9ea1XmIEczy04czQHy4f/3y05effPny0xe3cYg/+9lPPv/pZ/f3h0D4+Pbp7bv3a1mW89EU0jTs5hkp5CWPboqDb2Cttbfff/j6778+ns9F+Pi8PD8+g7GnII23XFha3tp2OgPFcR5evzhgDLmYmB/m6f5w7+kiZt57AKcgTfLpdNrOW2UhH2729y9evX7x4n4ax6b87vuv37z5Ptdys9vh//TP/jdASqnPYK6nbADRx+CcA5EurjbRxpyrXK1uqt77OHVatKnqshUiG+ddIE+OYk8DEzhANViW7f3zc70sIYR53in6mutSNjEZUxynNPoYhzCOIYTY5W0WYbW+K+6+P1KHzgCwV4hfPdkAiOC8d3QVIlQN0Bz5vG5vPzxzztPNbp7nmAIFIpWmoq1bLKwU3pbtcr5UttefvNjtpm7F48bXtR0BG9TKIEodrWYgijF6IhIVbtKEhUUNWVoTA4VeFS/MCmLMTRSqKOv5eH56Or97926rT/M8fP7l5z/40Y8fHu73+zn60J2OLKKsjSsoKDc2NmY10dq4tbLlmvNluZR13XJp29KkOXPjOIVhCsMc4xBiuH5DyKGj684Au9gC0JH55LqvvucGTfRaTul6mAAYerKs+1GURbzz2BV5NQMjCoBqKt2OKSpddHGE1h/jClcUXV8sIHVywzXSBWoOfxtXM4Ve20e9CAYRsA8hYlcjqP1/+whTcoQfA2hq0KmFV0pP/6XpEwCqqoCiinTYg/XKNNBOwfN0LRq4nvLYuXImBgTA3XBlZiZ9jvy4mOipge7BUgDRpj2JgWgCYqaNuSmrFGYTNunmkr7mFQZ0gNL9ui5AoC6STj4EckAOXfBo5Lp64dCADKBUabmtNS/belovS85SBRQ8Oh8SOjKzilyYt63ltZhA8BQj2TVUZtDXMiIAKMJNGxB6InLeUa/kDN4FAvSJnCM1ExNuTU1bY2YRMecoOOcgBJ9I1Xl0RDFSSM4RgSNCMkICVIYqyq2ho+RIEaOjFCN56kC9PlkAoAkUliZiIoYOgQA6/8eF4PvkDcwKio4QXP8WDSkaohlJw+VcPnz4sF62+9c3L+8Pu/0OjVIc/RiusCvAdc2Pj++3Nac5MNj5uB6XxYELMe7G2YC++dUvv/76mzfffSBAQbi9e+V93rhuTU00ZF9kmB/ufvbv/fjuZjAustX9w+3Lu5ub3XC43e1mv2R++eJhvxtB2uOH5+fndbo9zEMc54iIjZsxhOidc2jUWC8lr2s+Pm2//ubXv/j5L86X/LzU5fLca+SdggtRrLs9zPspBkECAGGWy7KN+3G/m8bwsa9UjbWCtMrCbGRURYIfbg43w5DAdNm2p8cPx+d3IcX7hzv8w3/+r/Tjo62JOHJpiPspsSoX1msfCTkfTaSJqKgKpBCGISFS48K1AgE5PwzBu4BILiCBSUe4trYs5cPp+euvf93OyzQfbu9f3Ox3RF7MximNKZiZijhnQ0wukHOusCzrsi3bmtmhf7i7vbs7jMOAZIqKBEjo+gP143uPxWrlnOvp+XRZLsupDMMw7ObdHG4fbmKIpirM27oel+X0fNmWXBs7l1IICLS7mW9v96qsYN67ELxdQz7Xtyx9zH0S4FUBRyuVtzU3ZmFW1VIrq4gaGjILgrrotrXUxjFFMpRW1/OylnK+nI7Hx3x6VpBhSl988fnnX3zxcPti3u3GYeyHW/cz9C2BmnRpSFi4NtFWttZK1VLytta8lS2XkrmJmZHHGGIco48hhORjIufIOeciApELVxUGekSg93F8dJHSRyNQt+hQzyNfI1jUnSoiQETgFKST7QBcZx50kyRqfyZ0uYSum2UDM3RARNewLSD0wRy0S0mK0o0I2J32HVrZtXy7WuHN7GPpVx9OTYR76quXoJmofqzS6U5+u05DQGYm0A31nf6MpurJI1n/0cPVr2z20cnTh4zuN/oYM1BQE7Erj1P6z4hZVJhFpWoVbpXbliuAWJP+ngrOOxd88NE7RBdjRO+8Jx+IHAU/OOrDr1zZbqos2i15W+OS27rlkkttjVWc9z4kQBJQbloK15K3VlgaiyFicNEH5/rtxnztW0UMga7TkoJzDh1GH3p+vHt6UbSJgZmQiiozX4Ew6KILQ4xDSN5733vHTJibqAbf/7dJb+hSbq1y01bVGVFICVya/DSGaRydI1aurKaCDq92EtNmprWf7p4QvENPndtDzmPwMURvqpe1LPliYkY0pLBxW08VIfgU7sZ5Ooy3hynGVDctjZ+fTsfj45IvkWIKM6Lb6vr+3bd/8m/+bHe7e7j/bD7s/+Ef/P6Ll3tWfPv1r959eALRZhHB5wYkvLtBN+zGYbgZdim53SeHmxfzeBOnEMZhGMbY8QvKJsw5y3penAEwQvK7mxeUoDEv5/NyWUtZl2UThTgkBHjz9Pzrv//66XRcjtvpdHp6t929/uzVZ1+dj+9zXqUKCTrviKbgPSA5ojSkWpYqORCu5bJtmwE4NSJDIjNzHpVZDWIanPc+hnFIRKFlVlFVbcy1bT74eRrwf/8//qX3wZHrCy1RIzNHoAiOsCPAyDsEqq20yqISKEzj5JC4mfc4jtEnb9Krj1VMnSMxLTWvaz4ez4/vPzwfl5Di3f3dzXyz2+/mIXkCdDSNgwNj0S4X18atMvdT1kBETYGcG4KPHfbvsRPTFeFq6GZBwEChGazL5fy8HE/PbStx2n/55Wty3jsap0SOWqnn7bKc1vOWJfN2aTH6+/vbw/3tNI3gyLSpNOr9MtFfYcJm7iMzQczMwDsHiMyipix8OW/rutUmrdWSc23MpqDgvUshNuFl3QhdcAEMwdq2rbk1EQHTnPO2XC6n43k5EsrNfv/ik9efffrF3e3dzW6/mwbnHYKJqalJ6xdqYWaTxswowq1xqawqtZVSaq7SSi5ZOTfJjauBEjlyIYTk/eApuRCJfK/Fw97S0NV5o44zuC6JCa0/xfrwD9C1/KuTU3reicEMnCG6jyCjbujUHoolIkDqq1ITBSBEhyaGCgZG3VqPhNgxyP3lju5jN2AHYl8DAQZ6Nf98FH+uId6+mu4XhH3MHHT6KRKCWGMlIAAUlT7V9ZWB6fVINxO43rmAzkMHdNtVZlcVUSXCHo82ZW5NVJW1NWGuLLW12kpPlLfcsrKAQwQa52kYUopjjDHFXnIRB+eQnPdE3iOicx3ghKKmKkW41ppzKZm3LOu2la2spUoTI40p+tGrSS0lb61uZcm5Zm6iXMRQY+rDtwvkHfWGCwNTU0JEH8I0Bu/Iu57XDL1LxkyaXBtXFERYTEHNWHgIbnezi87HmMaUvPN9BCq19seRmrJobbVya1yrMjcVUFUI6G9v9vOYiEKgXvmEPSrMIixcubXWt17MqgAwzrvDtB+GIXiXvI/BB/LgHLd6WcuaS25VENZl+e7Nu9OH07ls97v59mYe5vknP/rxD3/4+RjSZvD8+Pzmu+OHDx++/ru//6u//YvKm7Pw+edf/s5P/9HhbnaE+7ub/e3+7a8fH9+/vXu4D75e8rJdzp9/9vnrT3/q4lj58uLmwE0l57vXD9M4DM4IJd5MYDLO0ZOnEBVBFExMDMpWHt+fRPSwP7iQLMb1ok+P3/763Xfff/urx7fv87ota67NwpBC8Esux8enWqo2G1OM0/3h8FAZW9mANPppN04UXN9smYoqI1LJFdBQpXBRNFULDoJ3alClIVJbt+jCfDOR90a0m3cxJinc22LA+SHZMA5mjP/8//rXaRgJrHHlKioKpqVlJAgxgGGvyesJGBei925K0XmnrIQUk+9/4K1yzpmFmblxO13yuiy1tuB8CnGeb+7uD+PU23Cu0C3nOz3IzExaK1s5Levlclkzp3G42e928xS8Y9FScl63XAohmCMwa2Lr+XJ8OtZWxmH0LqYQhzntx2TOheAfXtyFQJdTXrfMIgDI3Ept0iQEF32c0ugG12Ve74OKNc7Ogw/JuxC8D57I9To97SEku3Y/eWdQG7NJa7rVfFlLZd7WXHPRXtCnfeTXmrMgOOc8OVCVBqW1IuwInINW65ZXEWWunLfl+Hxaz3VbfIovHl796Cc//PT1p4fb+3EYUnTkiABF5beLYmAVZhUWM+2+ImZTLnkDa8y11cJc+dqh1JjFmgG6ngZCT+iInPcu+BAJ+xXrqfvA0F1tOuSuFntVwI/5LAAkMBZQw77R7QFZuIrwRL81ETn46JvEXkKo0kF1YPbbPPM1d2yEiGKKZmoCV5A9aJdtumAFoCL9JL7iG/rH7O24rlOBDB2qKCIo95hAr/jVnvnq0a/rohfxGhYwReyfjxI4EwM1ImBlNTUyFeFaKue8rrVkEayVO33Q0EAhuBhT8kMYY/SD9zGmOPgYXfAu9KIA5zwFM1AgB2KooCLaVGprtbZSt0ut21Zyaf09JE3VWifeiVktudS6blupBY1IAYhAMbjovI+exjnG5ImcuxqQOkiD1NARDUOI3bPjCI0UtJRSuYlJbpVFESk4300NaAjOdnMaxkjgiJxDU1NuWhqzCiC2yirKgN2xpL2r3rsxDsMuzeMQogdTR94TMouwNtbcuLYiLORIuG2loQKSH+bxcNjv532MERG8c945Biy5Xo7nb969+f7b3zydj+fzFqbp/vY20LBdtJ3W/+Af/+Qf/P6XqwAYs8nbx6dvfvXm7/7267ys22Vd1wVEHcX7h09+9nu/84MfvsIIjfl4Pv3ZH/38+em8nremm4HtwvSDrz796sc/+8nv/OwP/sPffXW7jz7s5v32fIrTAG375IefKurx3XPlgobiQ1XYlq1c1rzVy3nbcptubrfzsm58KeX7d8e3737z7Xff5LzWXGIgBGprY7MYfa5tmmcHCpB++tMf/fQf/HQ8TFqxSX7/7un47jwNobKetktwCNpQmzKo4BCHcRpyqePN6NM4jqOLBI58CMzclpK882MyU0R3f/ciOFdyDmHc7Xc394e7/TSOgcnwX/yrfw3gAM06SyTLti4s7OM1/O3JT/M4huBinMbYFRcRMWMiR56EOde6LaXUpmC1NZYm4hzSlIb9Yd5PY0gpBd9faUTdggKGoIrcSt/clZK3tdTavHfTNA7TOHhvgKXw4+n07u2H4/NxTN5FB6aVtZa2nS8xusP+Zt7dpOiB4MXDzXy7CzGoQGu8Ldu6FgSHZN6FXueNKtybxQhFrW5tzZUZwgDTLjlyntw8TWmMzv3/IW3Q+S3d2c1mbFKrMkIpvJWyrlm5GRo3rrXVJrxxWYsC++i6TbwVI0/dsKLCopVba8xEhGYt5y1vp9NpWbbz6YiupDE+vHz9+Vc/fHH34u7msB+noafgHRL2xg+4liW11lozYdEmrfRwV+UGqirMVZmZa+PMtbXWapUmmkWbaIPe6gaOkMgHct75QETexe4jQnRXJheh9b0x0NUi33O618iAdvEEDN31ZgdE14cHAgIi0D7ud80GAbvrBj7GuXoVHQGIgiBiz1GpKoihI5OPqWAWQHBEeo2TXVlTPQCnH802omqsACgiAKao+lscW4/SdSO/iV0lI7zSiczgymATMW6Fway1VtsmIrVVYyPvACCGGH30wQfvYxhCDD4OIXoXOxnSu24oQ4eAqiBixtZqY9HCZeNaaltKXddty2vJeSutVJZaa2udktyHIGVFAyFTM2XxzqeUYtf5QpyG6B16h84hAn3MjJuPrvMvwcDRNUzRf2tKq7mWLefW5EowRTcO4zzPYxqJiEzAwF9zu8YGIsrCYNaqIqFzvkmrW1Wz4D0hpuRTDC74FIIL5F1orShYt5C1JqyCCk2tcWuFybl5iKIGpj6mcRh2u3kaJ3IR0HLldauPp8v75+end2+en94vy+X53fut5t3+8J/+J//R7/6D391NB63u7dt3kLgw/PJXf/XdN98+vT/lpUkp6PzDi4dXn94Hn44fTk/P6+W8rstFpCIpeRen+dWLV4Sh1nZ7c/vZJw/3h/Hu4ebzr17vdrsphv1+fnl/H+J4yXx+PIddUrNlW5+2p1rQpf1y4cf37/PleH4+Mtc43rayffP1N7VJAzotK2srhYNHMwjOPKGJ+uBD8s9P53m/m6YEEj776tXN4fDwcE/ebZf17Xffv3376D15clXb55+/ur/dDSlGR0RhGvtsGUHhlHmcJkh+GOO8G6YUovc9ZyJmMaUUo/M4OO/HOIwRkRCB0V1OBf/XP/wXwsCtqFoprAI+OIeG5DqTJw1pGIeOzwY0abUWqbV0n14pTUQUtOZm5NIwBPIhxWmahyEO3ocU6Gr7AFHuXg5VUG6NddtKLmVbiwFOu2E3T0NKvmsuaGbGImstyyU/Pp7qxvd3O+8JEZ3vZh/Nl408He4OIdJWctnyMA4uRBE7n9bz0wnITfO4m0dyjoV77+K2FGUBRFBEJPQUAvngjWutPO/mcRjiEFOKzvdKsa429znsmjxqoqwgZstl3XIxEcFedHE9XvJS6pKVxBFiJDDUCiH5sm1PHy6E1xpNQnLet9qEm5gqWt1K5VK28/H0eHz/YVmK8/4wDS9fv/z04cu7h5d3h5v5Zh7nMSXvgyczMGVhYVVprTEY67WE1VREWVRVWYxVVLk1MTZhUW5ctTda1SatqTTmawUrIV13rc47igjgHAIhUQeM45X0Rs7AejTETK7NXB1x3K+K/ko160Xt16iw0ZXnBoZIRib6UX8yUBHyCNibT7oO/5FS8REKAfhxF9sFpY+5XVXpWqc2uXaXKrAJAbCxdraPgbD0e+sadu0JMTZ0H5FyCug6frBvIvqS28CRJ+8ghDQ47ygQqfMhAUGMHlD7W5uiBxDsfcZGYCqt5dZylm3jZVvXnLe8LCVvW865llql9RQwdXi+Cz5G74JzgXpQwaGR9/3PFz1FF5wPHR7tfW/BaIRQahMWIkpDjEMkABRozKIiJpWbiFblvBZtKmrDkOZpH3scMg1EQATeA5mWympmAlutDNwlNk8egFB7EgIcIjmIwXdMaY8FGICoikhrVVRba2Cg5FKgaZxSmsCAuRlbCG4ewzQkDI6QwHlW3LJelvXpw+Nvvvv2F7/4zfdvPmjjf/h7P/vRl1/FBDG6Tz97CYCXZXt6OpbWLufnP//Lv398c0ZSABrjdHv34rNX92l6fbm8PZ4fzSz4SD5qq9jaOA3zfh7Gmy+++NIQ97fjENP+Zj8Hdzm9/+Inn2/rZV3X4GIIlOIOfXDzlJ/LVvjx7btfv3m/tLVUacVOp6XkYmhmElNA88fj83I8+SmRm1pT7xGMQoiH/a3KsRbe74Y0pbv7OzJTB3m9iOruJnlyb379rjStRQuX+9sXv/O7P7o77BDt1av7zz99OU5ROpjBEQUagj8/n8dhvru9xSm12k5Pp6cPx+2yCoKLRN41a6200trt3f0nn7ze7UZtvGy1Sj2dV/xv/7v/vrGl4IicIU7TOKbR+U5wC72jGQx+G9bdloW778ps3XJrPIwpxhRjGIZhnKfkXBzTPMSPDh3UqwsEpNVcWy5SStnWRVjAehDG7m53h9ubMSUDrMJ1K6XmUrKqOPIhJXQu+RA8AUAMPsRefAVly02ECEShce25qtakZC6ZVfHufk5jBDNRE24iGmNw1Et6qbs41K5itXcYQxLRGGiaR0eISD5QV6JVWMSubEpyzmE1vZy29++f12UlIpdc9LFnpVQ0l1pzFWPnaNpPKUUzZZbj0/nt20cyY9EYYwxJr4eydFOmtMLcaqusWoqcT5f1uCynx/PppMV8mO/vdocXh9vXLw4P9/eHu8M0zsMwjtF77x0ICJqKdqMLi4i1KmomTVr3w/cdHxtCE+4xK+7dh62oSJMqrYqIiShXFTEDEf5oAO1tBEDQtZ5r2wAQqKrrjWM9Ydz3ynYFlRKZKRCigvaKsl5gCQhATu1a4dFFeiM1Rbo68K+badWuNilcKZ6dsc/CrCbcWNVaX5OotcYAwKoOCcy8d6K9+b37W68WWBX13nWwBBjGFAkIHaEZBYopxRjxugxG6j6Z4B16dNS364AEigbWV+adkW8mLMAqtdWt1LJueV2XdVuWtq11WdetZJYmpgbmHTjvY4gpjSkNMSbfF/VXu602ES7FEZJ3PdPiiYLv7K2rlAqOuIiheTDnHQZnoCzaas1bybk2bYDYakMkQozej2kg71JKMSQfvOkVZNRkuzb3BV9za01ZhNECofNhiCGENIQIiNx4nKP2aFhl6K49ttrER8+Vty2LtpBiSh59jD7upzlNwURJxDsAVVQbpzTvZxeHrdrjpf7q12/+7M//8jff/PL8fMy5eAyffvL5f/af/8f/6Pd/bz5Myly1fHj3+Kd//pf/7ue/fDwdTWTwO2uw2+2m/f2nn356/3DYzxBi8g4IuLQ6zLvD4W4cB9dxdaYxxWn0TSxFD2o5twBQtcYhnC/btjVVRKLtYjQ4Pzw8v/1wOR7fvfnuV795o4GkqQPvyISrEk375F2orZW8sahPkws3DhVkkZa5yM3Dfh7TsBvD9fyBh/tbg9ZK+/bbb7ctv3r96sXhdit1P4wY/f3Diy8+ezXOY97qti4hYEy0f3l7f3837MYhptYYCeMwcLXn5+W7b97+5u//7je/+s13f/ebqjrdTpTcWk9+HNdsr1589ur+Ew9yXo7LstWm+XLC//q/+Sfej2N0IcRxN8/zcP19IgQAYa1cuNZSeFvztqzPz8f9fBNjBEADjCnubuZpGOMQ0xB99P7ju7HzcJWFRWprudaWa+bWNlkvy7osN/s5hEhEh7ub/b6TZq0UXuta1pq3jASRCMnN07w/3JDHlDpVwMCAmWurrVQ1WC7bltv+ZowxcBe7FYn8MIzOU5PWGcqE6HufmGFlYZPrLUU+pJCGELxHU1Rzrr/6EXu3wVUm5utiEK4Dc2VZlnx6vmylNFNoamx8PakQwAqzgo3DOI2D96RgOZdaWs7ZmubS0hiTD2aaSy61SCcBMGfOhsRkqthqs8YovJ6X8/t8OtX1fMyXc24beBsmf/fi/vX9Z68++eTlw4vbu2ncpWkIMXp/DQKb9scYM7eqrKxyjdjr1bqj1pupTVkMFExEWTuUoM8J/fmorGLC0hXDvhwXkX6KGfbt8PWs7Mc1ImpVAVPrvUPWhzBTEWPng4p2SDsAOUMDVDUWARRWuQLDWdhMq4hqLWLaiWjXDaSidIZ5N9N0bqsndOR9CEYYgwcxhyhmIXgEIu/RemFi30KbiBE5QMKeDu5LCIQQr958MAVy3jsUdCkQYc9+VZYeTFMx4Saqjbk0zXW75LKu6zlflnPelk1rlWYA0YEnj85RGuMwhpCi972Bj4hcDxori4Jx61u1ymImFHtYySEBjBNNw0DBXUui9ep/7R3CpsCqRVoRtl65lcUQ0hgiBB9idEDBOef6FprQ94ImNAR0opyl5I2NDYmEDcCNo4/J+77RQucQffDeO3LWmmYuJbMwh9C/mtBUpTVEGKfofOQqohjTeLufx8FJK0QQgvOOTPR4Xs1RFcy1/NXf/fqv/93Xx8dzDG5KaR72h8N+d7t3zlIc13r55pvvPnx4lork/DTf7nb7w+HlYb4bk3/9yWGcA1gdgovRgMATjePkUnIxpTQAKucKzoCVG0PN65qzWdtqLVzWLQxu41wFtqJgwRC3S1PE56dtOV+MFK1VzYZkirtpmod5vVzWtfqUAEmNTHMTUXCOJuQT2Kattdr2d/dpGoYhqfDz4+OW1/v7u0Dow1ArD2EXhuFnP/0qDenLL++nmznEcHp/+rtf/PKP/p8/+fD0Hh3ub+Yf/OwHDy8+/cHPfrTfH86nS2vMrR3Pl29/+c133/5aS61r2845Ly3OIzj+8PaNenz15eeHw2fE+Pz+3Xr8gFHJj8Er/uEf/p+qLjic5sFHTwDonALX0nJtZa25lLJWM1SxLedS2uH2JkU3pKnv6ochdTuB99ifVqDGbLmV2rjVdtnW5bTkvCKAI+d9QgUjOex2KQ7TPBJCk6bcVm1SFRG9D1NKGBypGJhzPobU800CBqKFOV+WZVmc9yZwOufDzTzvJq7VBwqdeYvOkStNhjF47z6Ky0ZGW641F3AuDd6H4EL/M7jiG6/gge4EvMZWwV13lL/FrGE/IVtrubR1q2utObd1XbfaovPTmHwICgZkjhyKAfTqV0BEkW6AEOeJzJCg1LLmtTRD54C5aFWzZv2lzt6hV8tbqattzcpWy7q27bJelpIv23opNQtCSHF/c3P3+uHly/tXDw+H6TDP0xhiiiH2vYFdrfLdwCPcVHvjufVqF1UBgytTodfRKCiIdeFLjUVMOk6nues1r6zdqSnQ29I7sVMF1FRUpO+FkRBU1SHIlfTWeUGgptIX21W4ShPlVoFQUb27tnq54AmAyIMBgQ+hyyGeCNEheXTO9+qxzhc3tF5piQgG2pqaaBO5WpIMxLS7+9W0o/sBHKJx06u0hOYckQ+OumsHAQiBDElV+3Skhs2YpdZWapa81XVbt21btrxuy1K3WqoaOnQpjuMQo08hpuAiOSKy0E9zcr1joVNIe7KqtaaiqiSiIcC8GxH8NA3zMHvnDESwXw3SuJSWhUVFgS06xyCdAyNm4FyKcUwJOqCPjIA8kXPXuhwkp8pNAEBMwTkMPjnnGExMo/dOPRD43jbt8EraNIvBBxc6RyeXtTRB9EOK05ScD+uy1tqGKcXovUPmvjzCw81+t5tBNQbvo69iS87DfmoL/9Ef/+nf/Luvz+vzzc28m/dfffGj1w/34uLju8fj4+mX3/zieHk+nYpIHeP04uUnv/87P3t4uN/tkgt+3o/zzegUg4NSqhRWQ4fggtvvdgDYBJyPKhWsqWkTa0W2zNvl6XS5lAaXnE9Pj5dNQFpeiwvBxwkgNmMx82jbpaaY9g/JjbScL8ICiof9jpDKVpdLVvAiAICNM4KQJ1W2UpyzGL1CnOY9gLucj2XLYHr/8LCbk6rd3z/88Mdfvnr9yTjFLoEDQS357dPT22/f/vmf/sWf//HPuUmc0jxPLsZxnl++vk/zcHy8bFuRWrZty5dFWfa3hxcvH+5vX+eVl7y2uh0fPww346tXL+bbh3Iu77/5PnMddr42NmH8sz/7eWnqCKwn6gC48jlv62mptdUs5P04JmlamJMLYRx20xBimKcUh+g9+nAtoVTR1rgya5NcW9lqUz6flstpef/uicDuHm5vDrs0jtMwzPthvxsdEnNb13y6LOfTpbHEmMZhmHfTNA0+eCIz4VJ6jR4iwOm8blvOpdZSpzlN0+SQDveHECiv2Tva7ccQQ6c+AiIYIPUi9Z4yl16eGoJLY0RPIGBqoqxmLGbQ/7G1aujAAINzPlIMrjtGEIzZVK2XzHSRtLZ2WfNyrtuWg/fTLo29aciuFncH5K9gBgMEuQ7bCMatViBAR6piBGgkXEqpWy2FRQ3UNBgwS2naGIx8aVXKZlq4MVfmvOZa87Kty3Y5nk7LeV2XkDCk+HD/8HD/4sXdq4e7u8PuME/TMKTgyTskd33qdr+tMoOaSLf9i3TnPlzdNtZx1HAdG0SkN+J2wd1Qe8kLdams27r7JSoA9hF53xPC19rIXn6utQk3NkM0InTekQ/Rex+D9zF0TfnKtEBAIOuR7E6k6C29BArX2pS+AeiF4ixVhEGZhVnUxETZVFjUuvYoYuBMlIIjAO9DN8V6xJQSBRe9J0/oEQG0r09YSpXWylratuXaZNvyxnld15xLrarMzCDN0KkPlIY0jVMaxhjGXnTZxRo0J5yJpJXaTVKtMSCqQIweAIJD731KY4h+niMCKKBHNLFWed0uuW5b3cqmKhAHP4+jka3r1tacpXJRIxymMaWxPz3AzDtUlbVsBmCKIg2M+jedxQiwttJM5jQFH3zw0zzM40SEzpFzhNC/s9JF6OA8gsu5goKPLg1pDEMIhATblk1sGAMh1pwJdBgnQBpTvL2dcYgppqp6OZe/+Mu//eM//Zubu/3nn71+evP493/z3TQPP/29H9wc9g7JQL95++bnf/3zX/3qexJ///DwxVc//OmPf/LFZ58+HCafvPfoUNC0qUrlHv8QltZsGseUoqpdsd6CYiiyLpfn5bxtzJdzWZb24fyulvrh/dGRR0ps4fPPHyq33TTsd3eAFIeUZi+tnd4fCQg9Hk+nS1li8h2W44laro/vTxjJuyhqJVcVRWzcinAFxHna7eaDQnrz6+/DEL748rOHh/vPPnm9vxlBdZjCfj/FMbrojsfLtm7v3n9YzpfHp6fvvnl7fHwKw/Di5SdpGFotHx6fvv/2rSkLSiBQQFB1zs3T7vbuNg2jcAMfXjwc9of7vJZ12RqzShEw7+I87kIMmLDmCmT4b//kL1BdkQJkXGVdt+W41saE4IL3FMlja4IC+/vdPMTdzeSJyKEPvhOcVbWJtNoqSytVWBuLdQYXQi66nJfz6TyNft7N036epjFGP6TYrX7Lms/P50supjaMKYSQgh/HMfTaeARWLbk1zqa0HC/nZVWDmOLNYdzv5h5AI0etMCG6SETU65gEzKTrNircU6/eeT9Nyafg7Jqdk8aVS10LIyB4FhNWMgTnxjGKWgiUUvDeAQJ4NANp0kqrtTbmstaeRzIk773zjhCjQ0Bs3Fi0v3yCd57oqphA5+0TEbaSm4hPPg0JzK4wCGbmlrcioIakasZSSgV0iE4RtnU1a4ZSK6NBLaUx12am2GopZavblsu2LmvdtpaLqgTvd/Nut7893N3e7Pb7m91uHkKvLXMuOCJE12nSXb3prGRQ7gtkvdr89eoa175e4FqZm2jFHhBn/kji7LNgX54oIIgoiIgKGnCfDFTRKKYQ4jANMaYxxhiCJ0fdgSrcL7jSS1y0B61UpDUwa9xMjUXUhLVn8UxEFI2rsIjKx7yugQJEHw3MezLDEBwieXRDSu6aNfUxeueuTFzrSCQxMWkspXEpZcu55LKVui1L4bZc1lZba0UMWMTExRCHcZh2+xBDGLwLnqDXspF1OB/IR8IECDdTqaUagA+OwKUYhxSHlLqR33si8kBooK3V0pqUhlbXtTwfL+fzCZE9JQh4c3MTQwousHDeVjAykBC9TxExOCIRXnMGNIfIrJd1K621woRAiOR8msfofcnFzLx3cUjTbozBd+aSI9cj4irSzV0pDvt5MrVS2zSOwzAEF011Wzc18TEG57zHXu01D4MPXlTncYr7EYien+tf//LrP/o3f/lXv/jby/F4ONz94Eef3x5ukEYtvK2X59P3j6fj+XktrRHQbrz//T/4gy+++OLu1d1+NwzOE7IpkNch+pJr3rj3zzlEn6JzYRonZmlSS2vbtq1LWy+Xp8vT8fR0Op5a4fNpyYVdcOO8GyjuxsPhxacvXn9+OMCHp6dxjPM0xZDSGFxQbvz22+e3375ZtkyEbkCMgzRDZjAutQnXcTcH86XB8bRczmcHgohhGBCslCKCMcZXn7x4/cnLMQ2Own4exDTnbdsWM2VhQyXnno+nx8dz3aqPPvpRhTEmBwFJtbWl5vWSz+cLtzoMfrefAFFUxiHtd9Pd7T0QA4LHyIbbypfLRUUvp5MbiNAdDjdDGpzDz3/y+9+/+Xv8l//3H5et1bK21lSwlqIK8y4FH0ytZg4p7A7jbhrDSI5ip4igB1VoRTauZavbupW8iXJwkbybdsM0TMF7FWkMptokW+WeDzeCcRiJqLHUXJZtLVszxBgDAYYhTmNCMGUDBVEQtq1u3oMhtVqHFMcxhSF4R8xK4Iig1WaAznl0pKatsIGwSC2Nq5BHD27ajzFGn3xwrgvRrXJprbG0Wrd1CyGAgoq64D3GNA273cjMBkYODUDZwAOzcm2q5h211lSMCBAoxBBSQMSOgQAzUUHCLpj+tuHWVIm8Dx6RVLS1wmwhBuVmxs5BY+iwtt7UpQCmaNK4Sd8/qEhrrXJV6HlUqbnWxgIooqZWa3UOVaXkZiLWpJVStlK3upXGtdbSkMwPNIzjOB528/5wM83TNA9zCjF47wIRQC9kvzpsoD+su8DTnZNqZFI6LEBYq4q0Wpi55CLSVKV3fHXHjnfXAIgj7733MaU4+hAooPcBwQBRrbRWtnW5LOfz6bJdLst6yTlLU/x4p1ynOgWuCoC1sBmgM27SXaph6MeWd56Cu3ZQAVEgH0LvK4/ed0Ckox5NAAW41oyxamvSOG8151zztpbSSq1145xzj2MDYYcXBXIxpRjHEGMM3jnvg0cfDE3MhLtotokIOQ9mqIAOnXNq5gC8c2gw7QZPzvkriMXM+pIJwKpqabW2sq5rqY0MHKoK5txqq1vNT6dTGvxhmgkDORiGYR7Hw3jIvOaSnafW9JJZTEyllpJiTMkTuNatAJ3rMPhxnlIcHKCZEBI4EMCSmzL74FIMKQUEULVW2rwbD4e9KZpKTCGGCIitqTZOPoXkK0u/ZcEkTtM4RPOeCErlvOTz+fiv/+gX//ZP/vTDh/fmYT+N94e9GJyXsq5VqnFlJN7t0qevPv3yq6/ubu8P+9txN8bgPZFZFZY4BBYhwxBdWYR83N2M4xRj8obADDnXmtv5cno6HR8fP7z5/s35fD6ty4fvL87h7WG6uZm/+OKr119++cknL+Zx9NSptSi8qYKjQM4RAXnCsn04P3/4sDw9H4XRuXRZaxGtW3PYpLXj8Xx4uTvsDlx521ounLelldKqDlMYx72B5PXy8Orh4eF+v79plZ+fTwCsTnPOp+M5LwUdhBDnedrd7Fq1balDHNK4Q0BzxgXy8pi3TC5gcGwyDH4cojJzUxEreZ3nuXFLye1u9tM41Axv3nzwEdIQufBvfv39OEdrVqp+9ePPPv3kp+/P3+E/+af/izWMMYRIzAKGITjXXUYhhOiHsUcYHTjqQrGh5bUu3PIlV1ZuPa6ekWDejTfzfLjfTWkgQgIspeacSy3cWt7qlrfd/uB9aKWxiQmgMyRMMSFit0xvW26tGVwbcoXVeby5nZ0LYQweUJRbbefTubF5H2qt0hijH9Lggxc2EQawaUxELnjnY39QkRq0KqVDyk1UlDwpgLEaAojUrXgf0jjGEIhcN3rrx2UmiwBCq1UqI8E0TSF4FWWWXnvdYei1NUCI0SOhJyRHyqqmrsM4HTpPitijDKfT0QCV+btffRPNhv3w+Zefpzj2gCr0Cfa3hkeE3z6qS6mVW6tNDZSbgrIAM5tpaw3JXVlEBiDGVa86O4uwtNr7OLbSKtdm2hQVnIUwjWmapnm/m4aUYhpjCOScRw9oRNfqKutpL7XO+zMFMhRrgGCqrGwi/fOE3pFy9X0iEhEhAjpCAQBBFm6tlJJbybnkktdSS962wpqXDaS1xuS9C24IwUePAOQ8EqUQgI2AGgs5SimomQvkvLtGm7tpB7F1uycRARA66WAkwj4rKEsTEWlFamcXbGurhWstlaU1BWkKgIDkyCE67xyFcUoxRee9d86F6IJ3YGRqLEUMnFPlxq2ccm3c5Jzm3RCTI9fn6jSm4BwBeudEOtrECzdVE2ZwVmpp1rbLpakulwpIqjZN45giGWMa82bH44flcmmtpdF78GgURp+i9z71pE4pFUBa42aoimOKN7uZuZooORdj3O9nBODWQnTYu3m9R/I9vtGNAt5RHAMCXHf1Zj70UCOAWRojOWpiLCoiQ0otNxZN4+hiIIchOIOEDprw8Xj8zTfHv/6rv/j+7eOH749ctsP99Lu/99PP71/9+c//9O9++Za5pWm43d2+eHH/+Ref3r243U3TPE3eubzUxioI4zQOkYIPPkRyThkAYYxpmJJIzTUDQlPLhS/nfHk+//IXX79/97yVsq1PLpBzQ1n19atXX/3o1d2ru7vb3TjP05hCcr0yjUtTZgTXu1BBaxE+Pj+fLufMtTblSqXI8Wkzwt0+SM3PT9s003Qzr6e1tl4+h6jWmGutZjjNw83dXlsRUWtaq4poMw7RhxSGIRHReloMKI1jGobD/cNuePm8/WY9lZL5+cMxDH4YE4JGRzEMu2knzlRLbSU4InK16jiNXGVZl+PTpeXVBfPB727n25u7WriqWy4nVXFmIcXxcAvivvjRS/wn/+P/TOKDi8M0GgGo+kAxhN1+Cj4iqrEikJoB0VZrKa1yzSs3aarc2X8RA6IBYRicQ/WehhhCIEOrhT88Ph4fjyU3Inf/8uUw7JZ1Zd5MFdG5QI5wSAnJoV1DVdJqxwM47xz5YRrS4J13ylZLraWWNRtCHEYxLbkSmY8+eB+8V9Xo/Tin2N/jgI2llFK2WhvXpo1ZWUOkEPw4pj4DI1LJOaVoqoAkTVvjJZdWbd6PiBgiiahDVBYTG8cYUgieQNX5QATU9f1rxTgSocr1EOoFWkgo0s1Bumzrd9+/++a7X9elvHi4f/vmeHp+AoRPX3/2j/+D35+mqUpTZkLwV5ChGoJzjjx1Wy0zS2uV2XqZOqgoShNVabUqUmURUTDTTpOzjyOIKYgBCICoKppIE25tXctpu2xrbZsYSBP2EMYUY/Bx2MUhxMF1bT6EDoAH7zx0o1T36V9poVdGG3asm4mpsYGKtpqlcecFiBQuYsyVRURAK5JzhOicmpAhqAZylAJ57zyhsxA8OZQrIAhRjcCziCO60rsRe2OX6nViqSJkul5OrTbpsKBu1RHbcmuNW2NWaaUjSKRHyAgi9ieDw955EoKLKfrg4zA4F9Ghdkx1E1VDgu7dQjElR54IWblCMyRwwQffl77kfMAOSBQhh6ImqqVU0b4LqbUWVimtsQnULXgvqt6PPqUUJkDjujaBjVm4ENq0S86cNDWHRN57EINuo+LSqLV124wwpSkN42G+GZKj4JibqqhY9I4Icyno0FQJCcF9tEdj8CGNA3mvJq3Unr8J0RsqszjnXAiVeVmWxlzzVta2rDmXNkzT5XJajmfmijhWhsvxYoRPTytrScFb0ZvD/h/9zr//Yvfwy29+sdVL3I8vbm5v78f59mbyCT2lebLGYiIK7Vyejku8Pezu97t5JANHDhERXBhG0FbWpXKprV6k5q08PR3f/Or9m++fj+/e+xiD84d9vNm9vH3x6vb21bwLn35xP0werSBCrgVNm1RhO6/bVkpQR8Ebs0c453zJxQVq0oxVmqJRrTzsZq5lXVfyNI9DrWW9nNdLFjbvAzlPgEBOWUAljjGzno8XdNBKv+tdSu7u4QYDJj85H5VVUc1dOxVVtrU0H4ItSzkvYZzFZJhSSuN+OpAzcKZ2dcwNaTrcHh7fvavg87bKer5c3mvbvN+N8xzS7OKU5mk3TfeHsZV2ylprA7z4yXsIyfWDNgUHmIYYfAg+eE/otGErW3v79ris5ZJLKTlGYNP9ND483N7O+3HcOXCq2u1Dl+2yVV5zQRUgpwbow3yYbm9tHvcuTMsmLK03eBioNKYQDQCVTckAam27/eAMPDkfHDczFVQHis7BOA/TnMKLWxdI1HKpHUZOhs5771HUeaJw7aSVJlZqY+Zx8hMNBqRoXJlrMYAQPJHr7+XkYnQh1wwoTcR5mncDN0XvTDtvBrtLCtBc9IRX1L6P3lTpioh3jkBFe2uVmPY6RBFjgVZ4re3NN+//6m/+5s//9I+fP3x42PvXD6//i//qvzR7+d27x5ev7871vEh2PgTnkLVoMzUCYBbn3bQbqVtfiMiTp2AqqqBGoD2w6TtDjXzHfBohqjVEEm6oQETkwJFHMEKHCN55IDKlpnw5r6dtW9f1vD4fH5/fP7/ftpybmEhvC3DOpSEOMY5DCjEN8xR9GMchhOgQ+xrV0xWc87GvBQSo5w9abWbCVj1ZmJAgTD5dSwchDnEAF5CIWFBAmyA5IwLoY4S5AKKlqXJuTc0Btyprq6PvHb0AqNJarXUrWRXWyiVnafl4zJfWam3E7AnMyABVrDsv4xjTENOUAtA4TkQxpsHAAVin9gzJXyMNhhScKjezKs1UyETYRKw1zms1gzCGmykhpv1tNIUhpOCD1OIJt1wAIW8Lga3SGmsVbqXm0rN47D3WpnGMALg73A+e1ADAE6IYoCiha7lEb+est/t5l+7KspmJCIaQvEMS0UiOefBac76Pk6EKi7GWZYEWjIycQXCObC3VIZXGj8+X0vKyNE+mrI5omsbbw37e74yovzFUrankXPOWG5t3HgDWXM6nBVHXy9JbJLiZiyC55TU3NjMnAGXL6P222d3LQzX93Z/e74bd51/eWbbPPj1I/OTFw904BGhWM4cxtMrPl9PhkLac0bmY9O5l+tWbD+PtXquid2RGho3FJF+Wp29+9c2379+h8zAMT+8+rNvy+P1jiE4csxLuvITh5Y9eOXG3L+LNGMbgo4uE2qyNY6y1hF4PB7ht6/fP52XZPNoQBgyJoo9TRHRinAjrWuZh3o0ze28MZjCHW+TMZH4/5ZyVNfjETVJMa74MQyKK0WtMAqbTTWhKgH4YPII5o5IbOfEhMIsIzgcfgDNSgrSsJajdv7xLN/Ob33z/4e351Wcv15X282SK5J0jt5sTYTi9f/f+2+Ow393ePchwO+9unh+ffMK65FzW6TDcpZv5Zi/Qqsk5L9vz8f4m/r+D1YBkBYTKPQAAAABJRU5ErkJggg==", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_output = 2\n", + "\n", + "iter_seed = 88888\n", + "guidance_scale = 7.5\n", + "num_inference_steps = 50\n", + "negative_prompt = \"over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate\"\n", + "\n", + "\n", + "for i in range(num_output):\n", + " output = model.generate(\n", + " samples,\n", + " seed=iter_seed + i,\n", + " guidance_scale=guidance_scale,\n", + " num_inference_steps=num_inference_steps,\n", + " neg_prompt=negative_prompt,\n", + " height=512,\n", + " width=512,\n", + " )\n", + "\n", + " display(output[0])\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/LAVIS-main/projects/blip2/README.md b/LAVIS-main/projects/blip2/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f2e77486cf243a211743a61fd219a1e3c3074c3d --- /dev/null +++ b/LAVIS-main/projects/blip2/README.md @@ -0,0 +1,168 @@ +## BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models +This is the official implementation of BLIP-2 [paper](https://arxiv.org/abs/2301.12597), a generic and efficient pre-training strategy that easily harvests development of pretrained vision models and large language models (LLMs) for vision-language pretraining. BLIP-2 beats Flamingo on zero-shot VQAv2 (**65.0** vs **56.3**), establishing new state-of-the-art on zero-shot captioning (on NoCaps **121.6** CIDEr score vs previous best **113.2**). Equipped with powerful LLMs (e.g. OPT, FlanT5), BLIP-2 also unlocks the new **zero-shot instructed vision-to-language generation** capabilities for various interesting applications! + + + +### Install: +``` +pip install salesforce-lavis +``` +or install from source following LAVIS instruction. + +### Demo: +Try out our [Notebook Demo](https://github.com/salesforce/LAVIS/blob/main/examples/blip2_instructed_generation.ipynb) on instructed vision-to-language generation: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/examples/blip2_instructed_generation.ipynb) + + +### BLIP-2 Model Zoo +```python +# ================================================== +# Architectures Types +# ================================================== +# blip2_opt pretrain_opt2.7b, caption_coco_opt2.7b, pretrain_opt6.7b, caption_coco_opt6.7b +# blip2_t5 pretrain_flant5xl, caption_coco_flant5xl, pretrain_flant5xxl +# blip2 pretrain, coco +``` +- Use ```pretrained_{LLM}``` model types for zero-shot image-to-text generation with prompts. +- Use ```caption_coco_{LLM}``` model types to generate coco-style captions. +- Use ```blip2``` model architecture for image-text feature extraction and retrieval. + +### Image-to-text Generation Example +Let’s see how to use BLIP-2 models to perform zero-shot instructed image-to-text generation. We first load a sample image from local. +```python +import torch +from PIL import Image +# setup device to use +device = torch.device("cuda") if torch.cuda.is_available() else "cpu" +# load sample image +raw_image = Image.open("../../docs/_static/merlion.png").convert("RGB") +display(raw_image.resize((596, 437))) +``` + +Then we load a pre-trained BLIP-2 model with its preprocessors (transforms). +```python +import torch +from lavis.models import load_model_and_preprocess +# loads BLIP-2 pre-trained model +model, vis_processors, _ = load_model_and_preprocess(name="blip2_t5", model_type="pretrain_flant5xxl", is_eval=True, device=device) +# prepare the image +image = vis_processors["eval"](raw_image).unsqueeze(0).to(device) +``` + +Given the image and a text prompt, ask the model to generate the response. +```python +model.generate({"image": image, "prompt": "Question: which city is this? Answer:"}) +# 'singapore' +``` + +Ask the model to explain its answer. +```python +model.generate({ + "image": image, + "prompt": "Question: which city is this? Answer: singapore. Question: why?"}) +# 'it has a statue of a merlion' +``` + + + + +Ask a follow-up question. +```python +# prepare context prompt +context = [ + ("which city is this?", "singapore"), + ("why?", "it has a statue of a merlion"), +] +question = "where is the name merlion coming from?" +template = "Question: {} Answer: {}." +prompt = " ".join([template.format(context[i][0], context[i][1]) for i in range(len(context))]) + " Question: " + question + " Answer:" +print(prompt) +# generate model's response +model.generate({"image": image,"prompt": prompt}) +# 'merlion is a portmanteau of mermaid and lion' +``` + +### Feature Extraction Example +BLIP-2 supports the Unified Feature Extraction Interface of LAVIS. Checkout this [notebook](https://github.com/salesforce/LAVIS/blob/3446bac20c5646d35ae383ebe6d13cec4f8b00cb/examples/blip2_feature_extraction.ipynb) for an example. + +### Image-Text Matching Example +BLIP-2 can compute the image-text matching score using the same interface as BLIP. Checkout this [notebook](https://github.com/salesforce/LAVIS/blob/3446bac20c5646d35ae383ebe6d13cec4f8b00cb/examples/blip2_image_text_matching.ipynb) for an example. + +### Benchmark Evaluation +Follow [Dataset Download](https://opensource.salesforce.com/LAVIS//latest/getting_started.html#auto-downloading-and-loading-datasets) to prepare common vision-language datasets. + +Run [these scripts](https://github.com/salesforce/LAVIS/tree/main/run_scripts/blip2/eval) for evaluating pretrained and finetuned models. + +### Training +Stage-1 Pre-training (from scratch): +```bash run_scripts/blip2/train/pretrain_stage1.sh``` + +Stage-2 Pre-training: +```bash run_scripts/blip2/train/pretrain_stage2.sh``` + +Finetune for image captioning: +```bash run_scripts/blip2/train/train_caption_coco.sh``` + +The [config files](https://github.com/salesforce/LAVIS/tree/main/lavis/projects/blip2/train) can be modified for customized training. + +### Citing BLIP-2 +
+@inproceedings{li2023blip2,
+      title={{BLIP-2:} Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models}, 
+      author={Junnan Li and Dongxu Li and Silvio Savarese and Steven Hoi},
+      year={2023},
+      booktitle={ICML},
+}
+ +### 🤗 Hugging Face integration + +BLIP-2 is integrated into the Hugging Face 🤗 [Transformers](https://github.com/huggingface/transformers) library, and allows to leverage int8 quanitization thanks to [bitsandbytes](https://github.com/TimDettmers/bitsandbytes). This roughly halves the amount of memory required to load the model, without performance degradation. + +Documentation can be found [here](https://huggingface.co/docs/transformers/main/model_doc/blip-2). + +Usage in half precision (float16) is as follows: + +``` +from PIL import Image +import requests +from transformers import Blip2Processor, Blip2ForConditionalGeneration +import torch + +device = "cuda" if torch.cuda.is_available() else "cpu" + +processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") +model = Blip2ForConditionalGeneration.from_pretrained( + "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16 +) +model.to(device) +url = "http://images.cocodataset.org/val2017/000000039769.jpg" +image = Image.open(requests.get(url, stream=True).raw) + +inputs = processor(images=image, return_tensors="pt").to(device, torch.float16) + +generated_ids = model.generate(**inputs) +generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() +print(generated_text) +``` + +To leverage the int8 algorithm, you can run the model as follows: + +``` +import torch +import requests +from PIL import Image +from transformers import Blip2Processor, Blip2ForConditionalGeneration + +processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") +model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map="auto") + +img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' +raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') + +question = "how many dogs are in the picture?" +inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16) + +out = model.generate(**inputs) +print(processor.decode(out[0], skip_special_tokens=True)) +``` + +All models can be found on the [hub](https://huggingface.co/models?other=blip-2). diff --git a/LAVIS-main/projects/img2llm-vqa/README.md b/LAVIS-main/projects/img2llm-vqa/README.md new file mode 100644 index 0000000000000000000000000000000000000000..203346719ae228332434eccf9250c8b6a02ac9c6 --- /dev/null +++ b/LAVIS-main/projects/img2llm-vqa/README.md @@ -0,0 +1,116 @@ +## From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models + +This is the official code for Img2LLM-VQA paper. We integrate the implementation into LAVIS. + +Large language models (LLMs) have demonstrated excellent zero-shot generalization to new tasks. However, effective utilization of LLMs for zero-shot visual question-answering (VQA) remains challenging, primarily due to the modality disconnection and task disconnection between LLM and VQA task. We propose **Img2LLM**, a plug-and-play module that provides the prompts that can bridge the these disconnections, so that LLMs can perform VQA tasks without end-to-end training. + + + +The following images illustrate the technical procedures of answer extraction, question generation and caption prompts in Img2LLM. See paper for details. + + + + + +### Demo +We include an interactive demo [Colab notebook](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/projects/img2llm-vqa/img2llm_vqa.ipynb) +to show Img2LLM-VQA inference workflow: +1. Image-question matching: compute the relevancy score of the image patches wrt the question, and remove the generated noisy captions with low relevancy score. +2. Image captioning: generate question-guided captions based on the relevancy score. +3. Question Generation: generate questions based on the synthetic answers and captions. +4. Large Language Model: Pre-trained lagre language models, e.g. OPT/GPT-3 + +### Zero-Shot Evaluation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelEnd-to-End Training?VQAv2 valVQAv2 testOK-VQA testAOK-VQA valAOK-VQA test
Frozen-7Bbase ✓ 29.5-5.9--
Flamingo-9Bbase ✓-51.844.7--
Flamingo-80Bbase ✓-56.350.6--
Img2LLM-VQA-OPT13B x57.157.3 39.933.333.0
Img2LLM-VQA-OPT30B x59.560.4 41.8 36.936.0
Img2LLM-VQA-OPT66B x59.960.3 43.238.738.2
Img2LLM-VQA-OPT175B x60.661.945.642.940.7
+ +To reproduce these evaluation results of Img2LLM-VQA with different LLMs, you can follow this [folder](https://github.com/CR-Gjx/Img2LLM) : + + +### Citation +If you find this code to be useful for your research, please consider citing. +```bibtex +@misc{guo2023from, + title={From Images to Textual Prompts: Zero-shot {VQA} with Frozen Large Language Models}, + author={Jiaxian Guo and Junnan Li and Dongxu Li and Anthony Tiong and Boyang Li and Dacheng Tao and Steven HOI}, + year={2023}, + url={https://openreview.net/forum?id=Ck1UtnVukP8} +} +``` diff --git a/LAVIS-main/projects/img2llm-vqa/img2llm_vqa.ipynb b/LAVIS-main/projects/img2llm-vqa/img2llm_vqa.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7b0ad9dc0c9b2f0e07bbc623a6cc46a5105c73ed --- /dev/null +++ b/LAVIS-main/projects/img2llm-vqa/img2llm_vqa.ipynb @@ -0,0 +1,784 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "032a8c68", + "metadata": { + "colab_type": "text", + "id": "view-in-github", + "pycharm": { + "name": "#%% raw\n" + } + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "62aa48dc", + "metadata": { + "id": "_qxQ4_bEhkrd" + }, + "source": [ + "## Img2Prompt-VQA: Inference Demo" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7bcd92a8", + "metadata": { + "id": "dq8t0LJThhuJ" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: omegaconf in /opt/conda/lib/python3.8/site-packages (2.1.2)\n", + "Requirement already satisfied: antlr4-python3-runtime==4.8 in /opt/conda/lib/python3.8/site-packages (from omegaconf) (4.8)\n", + "Requirement already satisfied: PyYAML>=5.1.0 in /opt/conda/lib/python3.8/site-packages (from omegaconf) (5.4.1)\n", + "\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\n", + "/export/home/workspace/tmp/LAVIS\n", + "Processing /export/home/workspace/tmp/LAVIS\n", + "\u001b[33m DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n", + " pip 21.3 will remove support for this functionality. 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You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\n", + "/export/home/workspace/tmp/LAVIS/projects/img2prompt-vqa\n" + ] + } + ], + "source": [ + "# install requirements\n", + "import sys\n", + "if 'google.colab' in sys.modules:\n", + " print('Running in Colab.')\n", + " !git clone https://github.com/salesforce/LAVIS\n", + " %cd LAVIS\n", + " !pip install .\n", + " !pip3 install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz\n", + "else:\n", + " !pip install omegaconf\n", + " %cd ../..\n", + " !pip install .\n", + " !pip3 install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz\n", + "\n", + "%cd projects/img2prompt-vqa" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3d11ce1e", + "metadata": { + "id": "838168ff" + }, + "outputs": [], + "source": [ + "import torch\n", + "import requests\n", + "from PIL import Image\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "\n", + "from lavis.common.gradcam import getAttMap\n", + "from lavis.models import load_model_and_preprocess" + ] + }, + { + "cell_type": "markdown", + "id": "c02a0e61", + "metadata": {}, + "source": [ + "### Load LLM to use" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7428ac2d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Large Language Model (LLM)...\n" + ] + } + ], + "source": [ + "from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM\n", + "\n", + "def load_model(model_selection):\n", + " model = AutoModelForCausalLM.from_pretrained(model_selection)\n", + " tokenizer = AutoTokenizer.from_pretrained(model_selection, use_fast=False)\n", + " return model,tokenizer\n", + "\n", + "# Choose LLM to use\n", + "# weights for OPT-6.7B/OPT-13B/OPT-30B/OPT-66B will download automatically\n", + "print(\"Loading Large Language Model (LLM)...\")\n", + "llm_model, tokenizer = load_model('facebook/opt-6.7b') # ~13G (FP16)\n", + "# llm_model, tokenizer = load_model('facebook/opt-13b') # ~26G (FP16)\n", + "# llm_model, tokenizer = load_model('facebook/opt-30b') # ~60G (FP16)\n", + "# llm_model, tokenizer = load_model('facebook/opt-66b') # ~132G (FP16)\n", + "\n", + "# you need to manually download weights, in order to use OPT-175B\n", + "# https://github.com/facebookresearch/metaseq/tree/main/projects/OPT\n", + "# llm_model, tokenizer = load_model('facebook/opt-175b')" + ] + }, + { + "cell_type": "markdown", + "id": "2a63221f", + "metadata": { + "id": "2ffeec4e" + }, + "source": [ + "### Load an example image and question" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ae21675a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 335 + }, + "id": "2da65ed1", + "outputId": "31b7806a-95eb-418f-fed9-e55ee9cc51fd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What item s are spinning which can be used to control electric?\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# img_url = 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/projects/pnp-vqa/demo.png'\n", + "# raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')\n", + "\n", + "raw_image = Image.open(\"./demo.png\").convert(\"RGB\")\n", + "question = \"What item s are spinning which can be used to control electric?\"\n", + "print(question)\n", + "display(raw_image.resize((400, 300)))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4078b2c3", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5ba89c01", + "outputId": "04e3ab39-475b-4c8c-b335-45e0b739cc6e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "device(type='cuda')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# setup device to use\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "device" + ] + }, + { + "cell_type": "markdown", + "id": "780851fd", + "metadata": { + "id": "076f55e8" + }, + "source": [ + "### Load Img2Prompt-VQA model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b693e281", + "metadata": { + "id": "01be941e", + "scrolled": false + }, + "outputs": [], + "source": [ + "model, vis_processors, txt_processors = load_model_and_preprocess(name=\"img2prompt_vqa\", model_type=\"base\", is_eval=True, device=device)" + ] + }, + { + "cell_type": "markdown", + "id": "62525c37", + "metadata": { + "id": "f0e27d11" + }, + "source": [ + "### Preprocess image and text inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "05bac633", + "metadata": { + "id": "1c47f415" + }, + "outputs": [], + "source": [ + "image = vis_processors[\"eval\"](raw_image).unsqueeze(0).to(device)\n", + "question = txt_processors[\"eval\"](question)\n", + "\n", + "samples = {\"image\": image, \"text_input\": [question]}" + ] + }, + { + "cell_type": "markdown", + "id": "ba3b9c7a", + "metadata": { + "id": "ba7a0e77" + }, + "source": [ + "### Img2Prompt-VQA utilizes 4 submodels to perform VQA:\n", + "#### 1. Image-Question Matching \n", + "Compute the relevancy score of image patches with respect to the question using GradCAM" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "afb03d8d", + "metadata": { + "id": "6e1615fc" + }, + "outputs": [], + "source": [ + "samples = model.forward_itm(samples=samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0eda0e84", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 262 + }, + "id": "46c50f9c", + "outputId": "1588f183-c786-4de0-abc8-ecbfffe2f9a6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: what item s are spinning which can be used to control electric?\n" + ] + }, + { + "data": { + "image/png": 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kWc+f00TN0ziliFySDRteUoAWIIUgRVgJYsFYQipIXHCSHVhpmgw4jb6xCE4dAZA6DYwp+czcwJxsmNFwCynfLSkYTSabzcGMZBJIkhdN6isCgjcDCUjedFG88TkfTKITNo1TMsfzzEfQygGaJAmJ+SRJi5+zIjGxIlxLE6Ujrf+BpUtSkCR2K/keE2j5CGZ/WTnLMzOodLLsL4kAFJA33XxcSAop5J0XQhzMxF5dtDOcEkPX4LIfI6CQehMWaMpiTIBUmEDLS4LDc1MQSeNhAlNzGpyuHoyTEFVxwRGrEhiWRKaVwWqYMpII+fhEPjK3kDsDLlHczcUV78FoUNbigxlnEmBFxhY0iM25YJEbJGaeUxs+oZviBcJFBIMhsBOjbmBKJg3ExrNcY4URkiQB4wCC+X6yEA/sFBMUCoBG57Q3QwDAxxwvH2/Bx1vwkeBZjWTZhO8l9utNMks1g2lYg5rXYgAnzfeYKFfyz4RhCbIZTLXIsJMpWcT7jyClVgPK2yGoY9I8pISflBHufM5rsj3IBjiZLspYH37aJG9+495J/jLNgKfqs1mUfI/B+k/KaINVbQRwEliLaLDuYqBAjcebEKlO1zUmBg58UDsDixri8knO0z0IiVBck7jMLBNxSkA0aJTE7OMVJLh3gr5PX/LRof6z2RP8dfKgkoRHujhEeaIKS3FlTZ57yTlERDMi+FRCMpr3gS15F31NLkRG+s7g+oLeBbOuF+hNyPztLfQluDJmAJegZQwTZ3oeIz5eEG8iIIHpguazVtBe0R60j/4bH7SE9ymUq5+YGs0sI0WE/Eayn4rEfKuNzA8fv+dlcE4mY14HliA6AEJyCWSfT/qK/CS4WDT7DNSnObHkTC0hIIKLzCqL/Kam9AP7Swt9I04f0kWG5w7TmJhlArNwD0bC30I6rwk+wmhEhmTHFBDQnIudI5Q+ZIEPvkTNCyFbBhuzYeLUbHwk3G96X4hR2uiIN5GlFQG4xAbTUkzQ5lYExA8pJckc8GGhitPAmlwEpl4hAVWviNPsEDfRhWDTvaKZNSXwS2shpC1IXkYJaHPwhzge8ImxCOwruZuC2Nj43WCWio8BDx9mxCc3ixnAPqtiTZxZgASaG77OvObJ/tZhTpJ2k2uYI3EO8sQlGU7RZRnm7acdzwxQKXnrE7cVF2UaqUG7K8k8UVQ96ghJej5GQnxgEuoDSLle6HvB9Ya+NziNJQmGAEwF9KXQV4KvwNWCq6KTswxaMoBEWFR4ECchEhS1nXSgbdIqAXTVBX9ByvmRrJU1g53GCUuUNQUNgkkVhuWaw14lRxKG9+Kk+JQNHbVNljyN0cg4mhJNKISULD+Mc1zsUSsndhpRIcJCfBajZBNIszrfkI7gj0tGXWYnG+pygOlknKRnSqU4CfCS5MaoDz7MhaTojubwc7q+95EV+TTOSXgzZ2MIDES2kfKmsqJPY7cBUib5nogsTQOyWUVsSDfxotkiUCWa92S0kJgDkwDKJICKWeKmJzvxrQfjfc5oT+k3A7HfXPDhnn0OnGh+bp8WrgxglZ8bMBp9UElB6GABSLRAYvg2yK8J1oLYeA9+yH/a4A7xesk9ka6ZjqQGBkUl6RnMhncl5a9HQPNo9GsNyocN4PobBSifNEDUlCQtjR/sZ8krKQKT4iI44SN7ctEsiP6mkEIQHOHeFfS90GvwWwQXVKiZ6itDXwuuFvpa8SPB1+ArhcJhCg/ZgSjBPOwMdAaNwCQxAW9Qkhtir5qpcCpPkQ0TKXHZxGiyuEn0heWEljTwgRlKSrn2IZKoToM/I5kuG+fJTCbn+MTzmeTMTsIf2FvIydGsSdGwYPI8EAxAzVW2EaMyAMSx0iFDRga5HTDqk0dcxF6EkHaRMu6Dee4T08xjpQNbSgtQ02IczJHsw8uLNNaapceLYERauMIngElSoHUz6BrGb8MNkCKAQ71Ywu0N8FZiTtcGe+p1yM/aNOt8UHISZUc0+R41y4yo0ovkRNLMnNJUxXn3CWzT2MU1ltiTJ8y3Vx91T5p/zefO/jOCkIgJIKsmSeag6LIMKzHdOM2BbMhBejecO4j5Ru5aUpDpJmM4PxRZZ9UFaMzpYgiK/Izj2RlUOjL3hiTByemWbnLwowTKGxIxGTSLgrqQiOmcxfeh9sq5xJxiZkExgFMAJsGNwI0NfgTUPbbusUUfc0t0mGw1uMri2gLfFqgxMRQ+LJCsdTQyIqfZb5EnOz83eXHAIDzXFk80zzLXNQlEAlMwPl7HSxSyKEz5vMJmZf2m1smylOqiolmX6hGT9g3pXQnogmZIYLNZLK35wST7c4TgrEZTZIn8f415XgHATUwcDwOgG6IWzMH0DJJ9dimZPPtTZAOcdeBvWYN/whRQswFUm+ZgUt9DsDM63CU7yTNQpb9DniPZCNhqku+IouICMBkXQEmiD1MiczJ9YlmEcfFR0WkwayWfNSxUn8BXgurwRBcBQbbSuCXZSSKRbaq4xDzh3oLiG8DDR1BVTGQ6MW0nslQJWZ9kN0wSvTT6G8or+Zyvp7VsHJIivBtgFH/30YVgkI0lMyjIcF3Ny+SnHc/uJJdgFmlE5TRiQwhxWEWaJUk3onQy+J76MLDqBO1jGoEbzLrscyoEl8BpRAQnwY8VGXXYusPaDksfolZ5EAQvBpESEQ1Fqr4M9xATALPj1GsWYO832MMA/hsoZaKAJU25SZmiX8EETXhtMnxAPB8Xf1gUQVLDBJoNrakp1DO8kEFBxciO8SFcn25AIhMKw57GPQqqJzqCI/tgMC0Vn8gbyWwYHLIpyRMCUw4BC6+KuLAEk0m9yXQGkAipGylCp3HsSOCS6dmmaXF9yCWyseDfigicwEc02OkJACNb2gSsTTIRTiiDSaxpHq7LrYTks8iaYvQ3+pgCOKXAiw8BFx9A6ToB08wG0zgm/9+wYNO/BwWj0W2QfTR5FSfn+qAVE/Ak32YSV43AZZKxL1Ez4uPwJbQbzhvNiHiXEW4i+fDKRmrBpqsnzrWkvKj4p0Rakr9KonGhoMRE38i8f9bx7GkGpAkN0B4WaWg+ESZX82cFzf9OWlM1JmLGWjrnQ2GocxbvQiGmk5A+4FMbjNS6o4Z+JLiR4EeKjDuKuqWwbQAnXAao7CikQHEhX6cwOGtRY4fQcxKOdNObwx7ZRPa4JKCRWIHuJYZwI71GconIximGhWIj/dZBKDRR3TRiSbtLwqQBpAI+RPMgLpwgKsNSFoKg+qgGJfqpvAjGCKgNeiVFeOI1TFI4UWMn5pSfIjq+JWXjb5gpceJT+C2CkF7rDOA3WUxkU9dkK4FCGrQ4LZs+koSSOckvgbgBNvKoSL6mOLfJX5gWekql2FibmdGnlS3KkCDqQXoff2p2gpuYhPnJSF069/CESYI0uwU0gtAASEGJ+A0zdNMPNUyF5ICKiYAU0jWIQZAwZoHVBmXi1WC8D9fzIZqX8WUj7P8T8i8QkivDnNn4fINz3OfvBdAaxDUtguR3g1j+kiJ7BH+ZJkLxM45nN/GMYZCZCEpx8FNyH/FmSWHoKGihbQpoLPr1PkTsnDP43uBiTowzQm+FvojmXTW8fBV8Tow6bNViTYehjy83OCIlTEwe8OtgPwhhEqaf9WJj8PPHB+obpCp+MKqP5BvKvqjEpsxwI3mxZK0VxzMuoGGmN++Z3PDMOJMysWLUKUZHVRAxOEmLJEiUpG4PeDDJfR5i5ym6J8n8iOpmE0OU6OQkCLj3gbr7RJM0pojENAFvQscAZ23O3t4EqfxcG6CwyQAkhvczIZcw1htWzuCjMybnOG36mkhMHzZV9wDMpIUcZDWV0hB9hqme8JqJl8ApZrUHBhUA3EZ52CRqQK7/Dvcs14DIb2S5q5WNYgXJimpTfNUnZZhSFZL1Eny7WSZJLpe0/mzsl6ZxfSYAkczwNIGz2QCZ2Koo9XO6plg2ZHSz2Z7EiQ2KISRopxKyZPJmhTHM0E89nq1YWGLvHSFrhEHBR20drx7kKyxejWZBeuZAS1NtneB9KF/JTcRivVQq6HRFzHWqwktLjyl8zKYN3XgkPmjKTlWNf4kaxPtQNyW9iTQ9UXXNQrgppEZ9VsTEKFSamxA1ikKg6TuChMrfsEBcJBRmGLu8ls2mBtU8q4N59wl2ES8aOjJKcIBv+FLCc0t2UicuZWKCZ6BQkEwZxeKIHR0wiPocxQrYGLXc5m1IYhwxs0lgI3kkj4kSGZMlNrALJS3OaC5n2RzIXF+XzFR/DUs+EWzUXKokUdDDGHhSIa9avTbGg5mapTiOVwKj+MzJEb75e2RQKbE3gFNM9nV6rTNByGzZUHoJIOLa2GRDPrOmIbvdp/u2YR1kc1Q2102IxqkfniONXcYKIVpxw3OnyKiowfvQy0ckCLemSF9ilbLJrJMLgGy2BVNNh4WcgjrxNB5ysXJekwSzPLAvH5NO49hco+E/eTw7g8raIC7Ga4xg0IhJ64UIrUTtnuRB2OwI4H0Epo3J8yY6yNOkWc1FnGo1tC3JYUpBM0yF6wfYKvBa0PvrfXpM62OfHsH2YHs/0PboRzAqgdKmkKkMWibISpg8H4HLiOKiQG4WgqUIVppcNRqfx8RnkcFMSQ7kwTMZmejGokkA1Q3+o+AzCnNhNZSbQKiJ02h2CcG3hobFYDUwXW88RgzexFFMzCslbUkycclCFoA5TnpM40iZ42kenQmKxZUmKJiNerssSrG5nbggLMl9EBrEJaYTzeLkX/IxyTA6oEnEK5p0kp3iA/DnQ3/y92QSZ3FyPoLRUHFwjTltsCebASrdb4br69eQwfzRCAxhziWYplYjSCXml7S+Ju2DElhwpEEbzFOiSadJbyAerEh00mtWtCb7CGUAVDOkj4gk8hHuM+VFJ+DL9xT/bSS0n0kdQERShD89bwCEzJFksLQy9upGQOCnHM8MUJsOrUwtk37bEIAhpEwEJ83glFlNfKVFHzKyB+2iRqJQy6BJwgXxziDWBgBTIVVJwwZ78hbnQvSOpsSsBdsopgHbxJqp3sfkOiicYlP0RZPVkCaA3C4mPxchjJwa1UmkNd5oSIxEcoZ5iuzFFlZoSayej4mDMX+LJMAEzWciJcm5XA7oI9j1ST/n2Q40us8zNLAbH8otVMG4gLQ2fltjcbTI4CjN2jQtuGxr6ICgqjn7P66V+HwbrXSLmLNWRjBOpkBiq71kuQ/lmoOjPC1+xCDJRbARYND875j/E8c4PgJEH1hiG9HLHq+zwRSTYKV7in6l4ASXzLazvynJR5SVcP/CUKIsGwo8XF3yWA/yoEYzgwqv9H5SZpKmNK+qNAUx7WxwL+SHiLIjOqzBDWTOiaISmI4R/5NjEev3MClFIDEozaZZ/s7G9yRGINPv+XkZwCqlLVyjxn9JGO/ZAOqaBkp9xgMeXnfEDwssLWbi9HlPTNJkAKkcLTABZSMgDQ43IsqFhaq9jYInqLWIHfpDZ2YWW2GkPj22JffoKdrYzzrVUUUtGdphSNQ6YXGGRMzYbE7IGkhTJDItFiFEfeI9OIQOYtvfwRkeqHUQRl/GSvrCI4VDiugTko1hTN1CexufKTA/teSsaxeFPfk2jMTkU69Yl0yCBHgBhMO9C9aa2Bsr9KpSM/iiUq5K1nAagUrDdzXNe2QxHom+FUGT/7AErcjdAnKrCy8hPE9Y3MbrT2rSbDIF+28wDSKj0MDAUIltdNM9Rlad0XYobpdE9YlzEllZVvKQ/Y8mdSDwmy/9SYCKDHrjzNnV4BkAeDMqmgA9Re42f09+qAAGcZGrZkaT3QvE75Ld/lFsYlqBbvxNw28+KaZMzpJ1kNhfUk5DnCFF4BK7kiijQweDgRHpxjoBBkaVg2eyMU55en7m8ddK1Exm49B6NAHUgIopLzH97mM43GkoFI1pI1EJaAapQQkEQNLYKlVS+1ghJjoWuC74OBLiJ3aFN+HzcTHnOqk2ZZKHVhgmJtwF7eixGkDKEKrNQ/Ft6PZojF4fSBP9W16Q6PSXzWeXUBGuBMDwVjfCMpEhWtDSI1WPLfsIhI7NDNvQc8qizoVoZ2dRa5AOvDGxwb7mXuamk9w10jqJP8PZVAYzzyRW5IKUpI0tctW7N2D8J0AjstgEAJhomAWBzt0qk1O8ILQ0iVn+vtggYj4KcXSOa08W9hCpSlExzfKWOHQGnz4gr4/1dt4R/C/DSg0mxsZCCo+96TslRd5zKYoOD0kOy24yuo1QbTJBUx1cHilN1thGdEuH+c9jYIKjXlTzehpSI1O2d8bPOH6fcCxn9pyWTwLnpEhS+93w3FGNxq8GZaQpCphM/Pi5wSIPdMREaiUxlKgRlNJwb7K54S4lg1Zep5ru4S9BJ/4aHTX7HBZPESPJf0sgNZRxkHfRUAh5SBj6+F2nSo/iYs2WJ4KeJ9h6cdJNnwY70G/fB1osUVsnzirxu8YDqfauj+DUbwBSF0LFQ48hf82sM6IYqxjrI0ApIj4FR6I8RGdf9HuIL+KOM8nG9rkxmZc4+Qmk0vgJqFVs4bDWYaQPALVB1b0YVCzeCsZbXGHRwuKLAgqLKQQ6QnV+G8xLU0jImo9lGCn3yKQsgqQZSPcjBDOK2JjNYWxMpYgUKgl0NvqiqZRN+JieMKQVRACOr9QULqN8P4Bq8mEMpkgw1ExkMsR5UdF87yZpdrehuGLhMRKEaXA0R+Uig8mbbiMswgQQDPWAKZKWWcVwxEcdcijTjj3JNE8LHgltjhlAJiTQBtD0Qt5KK/VqyqCYtF1a0hl0P/G3YeHldZg+r2nc8is+RZRbTW1Ekq9Ih8RN1fCAAVRiaGQTfBIwDRhEyu8KIjOAUVJ88om7JIvDzwapZwaobmN8RCT3fs+9xmOJg1dI+TghOVMDUEXAcrKx/Q+xNi+9x4am1YFu4xTfSaw89wyZysOk5AmPIWBxiSUFpmSdhj49G5Rd4iQHR6zGDyux4pO8j1t2lA8+JyPhvo24KDImZ82ntHnxkalEFN5s/B80b4hnh+2M+uDuT7KkBIDC4I3DGBtC96XHd4FFahc3FrBgCkW7FMlSbGxhQi9YF80op6hXCh/gxqc+XBEkkhPaRlMWkZxqET3SaQVHAiSDKYKgYuK8SA6lZ8piyCvnum9E81yjw7wPkbGwaqwkH19KiSDvyCPRs64xDUJsEiIGH18SFQ2PgAWffXbRjExKMvSOibV54VrBt20IjomozEwEqaiahDAX4qN/NiqlMPQaBDb52yTIRvbZaAyiRPPtWr2aDmOUln/Qz0NEL8RagmJMLV6CORbvlU2lSwSWmMUo0dkfgeZaBFQSjCQTLr6XfUxRTqJMX7PhiLKQ7lOSyRlZ1E9EWofjmU08l8y6hNzx39nPgQ9v+aBlfezx5BWcT7by4LwLSX/pnBK/Fy21lHnuCL17LFijQ4Zt3jMsuiGT/2ETqGKiXQ4hphoXv3H/UQMlraDG463PkUKN5l4AkyguCqqOobA3aloF8RGkkmkR7Vl1GlqsOK6xjzw96qN974ZESQFVH3KOxOLVYUyBF4+zDmdjL29T4I3B2ODM97HLYugQOmjR1PY7vZQQCMClpFYB6TGAF4fYEFpMSn3Y1cVEf2LsSqBhMQ6adSNcl0ZHw/dFQd0GGMXxCWMXLxbnJ4MUG4s0znVBjFhKNGkkmtOOwFYjGCSTR9OuBfFcqZW0xIhaLpchDb0MjkYvucogW3KbASPSIo0gpQEoRWI/g/g8yX+ZuFEYk+gz8hqdh3GRG9kwG/WngPdwDxJXwpD6EH2oCUghN7MLYOgCO8o+o4FjZSAiAecnmNKGvglmo2Qnel6LibmmIdp0ipMU1F99PDODSikDROdgumDOt9EcCQ3mjmcDpAJyOk8YNklIEpIL02o1COpS/V7I0VEnObdFTBLGtOzCjeQ2sRuCn9hXutSQ0DiMmydo26Dth97kKd0hlPeEEHzQNINmJtJfFyHAGx/yt2JiJF5D8z0BtSY24mNjj7TYlI6BDocBdjmiFZR66uccfiJhnySpHL0t8VZxhcVbm52pcX+TsDFDLNvIobzMSmQQtBzdSuoxpmamsJGy0UTPoFoMkcnoEM6V+ekxojKQBIwaHkhiCH/I1pZh191PvIbi1Xjb0SQy8ZzqNGdeSyq0dHFOIUfMpIi5X8ZHxRHBwoYaUJPMw6zwBvDEDnKlG/cS8v+CXCVTM8+mgI8Z3OlzqdOApJY+hJSPPoGphiZ+Yob7zmBGXHcbgJ6BPU1TpAASHdOp/W9yU8hmY/doFSSoSNOe3k8sLbu4ZOASCZCH72ywqAxK0f0xqJfMQ/Ly+SuOZ+8HFalZWDSa5iH3ir4ulJILJ9HBxh4mdQONDZjQzxeXqLxsJH8S2Y0MoeNrwsTGGCSBYcPZGu8x+w02tHJqqOWjgyOAk+ZnNCbcc0pRSbOU/ahER6MovSjZotMAOMHEHbo3OBdYIT1o7H/lbXhek/NC4iBmTebjk4bESkFxeJACm8MyPhBEtcFUSczRxRtP+UExo05jwah4ABOifpH5KBKLPYf4kAfSdl/em4HopCUpBCofI4QocdODOD+enEQocccW4/Sa+YsSW5XE+QNSWkNgdQkYIgv34bmSbyqXESXncHwOtQ5TOIz1IeorKV1E8N7huwJvi9DuMmzFE5WFZEdzSmUYIkASrAKTcD8ATmLcIQkyjKmP+QNhrIOcp977XpQiTmEwTdnIiNcUOCVdWhLGxiij+I0xiwxqMw0s7BoTLYLo2AtAGsAqrZ/s1laiuR56l2gGLTI4pUWVeqBl1gSZdAQ2rtkMzJQpk8LhMz/reOY0g35D++Y6u2uMKfmf5LrGdYEFpO3Kh4iFzxRRjIlsR3Ky3rXsfSJIueF5QyQoqv6sahJwbgBo+l0GcEqWyJDPsfETzfZ7Mt8U2UjQ3QQojYmhZEbhNL5IJpEMHQdSe5mesClEb6EoworNW3BILAvIVY5sUJB4N0X8ZAQfK6j1eGOGfd2GLL0sGC49gw4mUujEGdrgmAhwxvjYaTQCLQIxK9/HSgAfeF3ccST6pSAyRwmbd8axCtniMfE17hUnXXzsPg0a0U8nkJRhmss47gjR5ArgkXrY544RadzD7YYkVKtQeLA9InG2Qlk/3gb/nrMObwq8FKFdcbx22mIcHwpbbfQh5CiUD4sw4H5i9kGujCjOe0xhwQc5MfHZglUqOPHBD2hi2ZFPABVqSFMcAyQHDkR1I/NdI0htJGZGmR/yjgZVMjCfDcnKTEgiGGqUwDAI2WiInwukchOU4k8TVk5mR5JqRQnsX0DUB2X3l+UXxOOvkagZf/F+WKBRo+SNNj0bQATEDgao4GJIyUeTL+RTpafXQNGjBlDIPzO+p/WZ6c8AH2z8afO4Ro8T1SZOYPqCJN9CssOHswSqPKB8ymlSElNIWjswElXySYc9AIksMrzybgteQv8r7xBTRHYUabim/Jrk4Nt81gCLYanFHBdRXGR+KXnUJsC4xiwS4Aw+QRvHNoGBifAoeTxgyP6X7NILQQ6imRtdfBEspE/DooOJl8YzlZHEDqcZ0f2Gf5Kw6NLYhShqDLZIumZ4Xicbpmb83YnEDPZUGhXixTkQEZ3aBoO3wautRkOPrZjGaqLZhcaopU85RqFVbxFZFi6wcBO7siUgyKJACIaErHM7UMFkeaQSkDhWqUNrcDFEJSKxN2m8H8l+1HhfaR1uKnXjUeOvmWLgBwd8UsACOVabmFFck5s4pOm8UW4koptK5veJJoe5+wR50s0VKfmM/Kzjr9FRcxiEHGpODl9P3C8s/O78INDDzyH6k+hz/ne8aUk/Nx4wXztSQt0ctc0/5yM5rgOU5Loo3RiQ7EQLz+S9YKwZmoBF4ZHkZY5YlGQyPbtPfdNd9M1sFF5pXExJOHKqQroXZ4J/zRnUGnw0B4yaGBmNKjMFIz7xStzCp0XjwvXFkaOn4sKz5TwehdzDSf1G/ECi+SBDj+vEBiKA5VIlyKHy1Osp7H+3EcF0ceyV+Hsc9lyIq0OqRx9KTHKCXDx8UgWZIQ9ApCnfysQi85jB7gvJ+VdaeKTsMUWPkQ4rXQSpaOYRlQyWIrYH0JHiqFAtiBtBRxAYgCkpWK9hEfk4blYMFjeUkOSIVow5mph0RZT9aA0YDZHs3KvJRxkzSW6SKeZJPzSvxSExM+376KLv1MdokYqPQJd8SsGBnqNwmSVFuSBEcxN7SlcXNLdqScot4EyKvqdInmQgy2stDUVG7mGt/qzj2RnUhqM5U+q4AHx0CKftojQuWJ8nNkUwIuWP2mCTrbCxIDb3zkpYEtozSJ7wZD7AhiAxMIGkHTa1ysB+4nV8SqUSBvlwYDYayiXhIWocEjiF53K9oL4I/iQfTYxYDJqzqz9RyjBQ3CQUgZwHVmQT9GBSQCF+JhRBx9QDDZ/1Ghy92lukC+xFOp/b0ZqNaGZqhTwIR/ShaerHnaYi88w8dhnehWtAoSZmtKfP+MDxUMIfJJ8lRjUjU4hZuyb26CI6jz3RbMnfIqeUeJEBmCyhGDl1u6gFV4OrFV8rZtRhqw5jNsGpxw7JYGjc+in5O9UK1IY+JnWFHYFAnebIScidG3A45OCZPKLE0qu8owqJefjoUotMGoN4k/1JXgAnQx2cak6nubaqo94STRb8ZonWwOCSmGUGFRVewHrZPF3Smhn0EgcI+VFJ2RNzx2J+3zXXSjhTPAUiJq+VJFSZh8QBucaofsrxzD4oyUwpsqYETo684YFzoceTdxb1gkvOsHjjPp0rImyI/F6PkOVOf4l+bqB/0gBkoDPRDTUwMqKmTxRWIafFpt14h1BpYHchTyS0pQj+r/CQEveuG6KWKUoVn9ML6sPzeg1MwkVwCjVpGuvSCFo9l35oBK2Yl5KnMy3KYGzl3YNJppvFJVCiwGmB60p8UyAbW2/bjriBpOZGayYGLXLkZ4N8q5CTMzWyFd2YEx/nK7p9SF0/U3va607dKLhueBKQ2JqE6DMh+05SQCWLWiylcnEReMjV/y4CkytilnplAkDV4GvBjxQdeUzdYSI4GVpixh0mhw8DCIfFaAkdrxwqfejPVBj6okILQcuYke9iNDQm5aat69EQQMILfYyfGhOZ+OaDJZCSKKciwScmkrADhGuKMdecZPFTci/7SKiSe4E4pyl1IKcPSMqFEoYaOxnWU7q39D2GBFgk9G5Pn0/JtSmIMbgxNMtJVkbpOgnEEvtSHbJI/5LjmdMMnEu5HJEt5aiUhHa9fWw+F7eK8mxsrhkHIKN11gLRfk0LJg10HJi8iCTpc72G9on650URByWxJDWxjDNfe8ivHSKuwZxSZwLya6hT8+oiqTPZJMgJij6UoThHYIoi0RcSTQ2bKvrBlZoXkq/B16A1ULug4YvovE0O3EHXpDuN0aTAmHoiOPmSvitx6xJZW+yaCFAEYOoU6Ty2DzvgBlDwg1AzCH2aF2Gg57oxdz4KaXhPrxW1ahTSQd4kzy/4HGVLaR9E53uOemuKmiWGKhvzlYqs45iWsVdYZXAjjaAk+Fph1GPqjqLsKaJJV0hqZqhIrGdI2jL4s1Ie17BSk//GWw/WbmTFhxwza0PdZqHBB5tiElZjni8Gq4L1kTGJZmaTV5MR8HG3XSPYmGaRfHuhzk9iw8Fhrgb1ldYOQ2yHIRct+CiVtIMwOlgQaQmmmG22VNL5U3Z+BJ4MRvmVUggYlHeWnw3ASCZktnQyXdi47s9GqWdkUBqdhMRe4qDO4PoATn1fBIDS2DrFmKztcqfAPAIMaVCeXJgp3odttGMpibqUJawYkx4uL1lyZb1ASvJMmxGqxuLVGPpXk5hA5CmadnWV2KI5IHu6rqoJ6Q+5wVty1EsEp1RXaOICMvQW1JrImkK7EV8RN3aIZshIoXJI3VOULrQrlh4rfTTgXMhZiXZYetpk+rnInHpf4drQSsasLbJWzDoURJtYEJ3Le3rNoemBPUXGLzGPLc6Lj2p1aJ6WFmYEqzifOTEmjrUIQyvaXLybQuLx2j74wTKTI4FY0lmRGcexzuZjbv0s+Dq2fx6DmwiMHWbUU1UNhWkp01hKj81xxs1FEBRniMqaDPpeLX00nUOwRzZiC6GnlVgTq8qJtk5gjZ4YwUbDjit9ZORWMN4MbEYie8FkQEaCea1isvvkk4fxmXDF8YoaXpMsp2RMrm2km8CENLZK7KoZvrO5a+UnQSb/UzbASTY+qxp9bbJhEQ23lghFGkOBwS8lmj/2lx3PXizsoqPUKa6XYNJ1lr4Le9j1IrFRmaGPxaKuDE7LrIXSoPmhJi4toqzpvYRyFB36NIXF5PNEJ7NhcLhDCjN7krtgACcf6FiYlMg5095hxstwrfhn6wOIebehFUiOYjI7DLsbm7CASg3bYcUe6r5OjElh5JDSUZYdtuix0mHF5V7qJhb6BIo+OHA1mp3hWSNI+SKUujQFprEBmGK3Btto3GJL8w63QQkwOMohj0HOeE+vGNrWIppssXWKTy1BUlLrIKmkTRw2mwDSR8ab53ATpGIoWzfmMf4/5bsFUABfmLAPYm3ox0I/FdwMzLSnGq+oizWVaahkRSkdlo7cxFCiTGBwagMQYVHK4L+TIryfwEltNJnD+EraXDUt2IQElph8HIE47qITNiU1UeFFK0PIO3JDcGCTnjUNfGKwsSGd18HgzwZilG/J74bfTBD/IfmYwNZCFlP0L5JSD8La81GJp/cSP9MINMntklhsiBPFBILk8JaBDw2gOayV5CZg4/yRS2WaMXzwpx/Pnkkes5+DWWdwnYngZOkIrXpdaehqEzc4MLhxoOFaKRRxgtUH72IvmFYwjYRGco2naATbhg0RrUvN5AxWg+2dXJE50zqocJQYqTYp1JzYW2A3oZpehob6Sgao0DlRML0PJXhR+ELSsWTNoWyC30bfoyr4P8KOMxI3dfDIuMfWHVUReqcX0lFIS/Ai9VhxGyHvNFFJu1u8mMCWKIJPDDKDc72BzgZzLrMmckG0xK2QEkOVOH4DVwkgQGRKPjElK7jofCYqF1eCL82wG29KtiEMRlAycQ47QdqYR+aTWEag0hBNtJndx8TYHK8bdHdIsDT4Suhr6KdCvy3odk89WzIuF0zMnLEsGMuSStYUBDYazmXotaCXgk4rGqnptI4BG3Bq6SkDIGlB720AphhsoA3tfXIyqQ6mrU/9jxnGL+ztGNZISFiPeUkxr09i7SNeIkPeMIt1IyLGkN7hdaiNDOt9UJWJoSaGjSgugUiUU1ETLZUBsJPpljwdxiQfYTx/dIpLJgGDG8xEBpDyviQzJ4nAaDIrlAS+ROaVzb2EJuHvqRvCTzue2Umes6JdSDDs+5LeGToRusLQVUI/MXRTQ78Ffksx04ayaihNQ2F6JMZWvRqcVnSuom8r+nWJLC3dQihWgl0rthWKNuzY6r3H+JjMGScesrEHyLDTb4zyuCJoYJfafRQmmw0ZoOJeZ7YD05twLacY5yMVHpx8ig5h7SLublwb+pHgJ4Z+Csx67LilLlfUdkUtDTUrSmkopKOkDSZdLo0OT+EVkAJH0OKdlPRa0lEBVcrrTTZH8Jc5GTaUTDuNbDT2FxeFShOYD9E0JJonZiPimNqjVCb3f3c16Eih6pHSIdZhzQAoXkMbGN9YpDHYFbmy36QkytiRgtg5QpLQR1pgkJznpBCjnmFr+76CfmrodgWz1zCZXLJlz9i252zJOVvmkqnMqWVFEdmTw9JpxVrHrHXCUqaIzkANjhKP0FPSaUXvSlxX4roigFJk8SENIqVCpMWmIQM/FfSaEH1MDJGUopHfS2AVyZemkhcToTiafbph/hAWeHIYJYaU3BeJI4XRl7w2w/eCr9JLiqZprpFO/pVkQGs0UUmcgcF8c0jMnDdDhgcDb0uBCzTNo2QmNYCoIPkv6W51o5bxEz9/yvGMJt4GOLkivmzYIsoa+trQTYR2W+j3FLPbMB1dMi3mTOSKkSyopMFGdeSxtFrTaM26nrKaTljvzmjWE9qrEXJlKBaKWwWzpWhNbM+bUgMGaCKm5HtJUR7JW6P7yoTQcwVaSuxgGWmqJzuSfQumFXw2jwTjfe45HoQz9L72RWBLfS24qaGfAVsd5WTFaEOzT+SKsVlSy4qaNRUBpGzuWhByWByGphMoxnRahXGhptExjR+xlkkYfyxGityfKoGPiRo6mXEmMRaGReCT0zVKoY/lFD6xwJLBLB0JfgxMHHbUUpQNpWmj4zk1yAmmU68FXV3Rj2vadoQrq+A01wD0NvZ6irOUTTsAE8siQtV/kLG0StQEU7kfC25LMPsts+kZu+Yp+/aYffOYffOUPTlhSy4Z6RKroUF3IxUrplzpNhd+D6s9HkMnNajiKXBa0vU1/bqCtcW2EvqIdRp71EfTxYcnzRthRrvIF2Ql59OGf95nxYCT2AwxKhHnCfvKDjsPoxvlY4ltJCUiyXRKQJSUjI+LfzCMr6USSACyzW2lJEXSIt9KzfySKafErd+j3CAJBnW4Vr7GJ31SmgvSU3sWEr7KYMZvWHzXUeWnvJeOZzbxQoFlSCfoe0uvcQeWSujG0O4I/YFS7l2xXZ+yY56ya07ZMadsyQVjWVLSYFAcloYRS52y8NvMdYd5scu83GEx2Wa1s0U7H+PmBrsQ3NJjGxO6YfYaEgp1eDqVQL29jZGzKuxA7MaCGxM2+Rx5KHrERge0N/jO0sx7xlpjVopfK74hNLvrJTt5iY35XSHBzzQW+qnBbXuKrSWTes7MXDIz52ybc7bkgpkE7R4AekVFQ6ltBmkQnLF0lFz1AmablY5Z6ZQVUxZ+ytLMKDSwraAdLc4UmMKFHU3YVEZ6TSPlDIW06GHYTSSZcqUEM2okuAn4Cci0oxw11MWKOgGsrCMLDOCqCo6CVqsApDJhaaeszYyeUWBPveK7YGanvl7hVnSj57UOaQ2DlYvaOIdjg+44JuNLds1Tbtj73LJ3uW3ucVMfsrs6Y3TVYNYuFOEWgptY1rMRZ9UehWlRL7Ras2IWcEQtna/o1zVmUWBWPpilXWROydGYmI2JAxkjlm5zF+lYRhSMNhOZlAbzsGNI8+gE14HtTWj7k0xvLwF0ovsiLdjEdvOedPHdzej8Jj8hv5cT9rIMZLcjhLwv3YiGp2uQ20aiSNz/chOkhNh/Z8NxLtlUz90REhLl9IpwEZMSVJMhm2zUv0kTz/u4uWbM/3EG+sgmuqmh34Ny/4q9+jGH5jE37H1umAccyjF7esK0W1C2LcYrDmiKgqaaMS92OWefU3/EqT/kzBxyYQ+Y13sst7Zp5yPcpcFeKcUKbOuxnc97gxFNBG9DrpGrJG/w2U9BZz3FuKEqWqxpsdHn4zE4X+CsR+0u3aLALwx+7bENmNaE1rRRUJ0RNJ673xLY7hhN58yKC3bMGdvylF055tCesicn7MgpW/2ccbukalps44ITOZU4WNDS4CvLFR6Kba7sjCu2mOsOl+wy1zVz7Um9PkJKg8WVBW1l0dagrQxbLRkfsskNoT4yZzUzROQiMF0D8SnIrKMarRgVCyZmwcRcMZYrJrJgJEsqaSjpAlhi6ChZ65ilzljINoW2mJFngeD6UfAvtop2Qt72IxZtpw4RKmBT+UyczgSgvlT8BMrpki17xoF9zC17l5fMe7zQfsz28QXFoxZz5pBlcHjpSPA7lvKoo7rVoTOllRFXsk0hHQKhSWJXIEuDXXjsCmhDj/pU45ZBP+V3FSHFIOVF5Zy2QkO75hhhccTn7A3aGaQRbCOYNRRrxTXBVxjaTRu8KNYPJVg+mb7RbxPEO4J7Bhol6+bMjBIYSHbIJ7YTVRepPCWZjYkJJV9gUiHR2g4bI+Qs8/C3jUq74fKbiuan3E/2V+aE3WjBQAasn3b8NQAqOGhxJkaxYmP8kdDPBLOzZrs65dA85k7xMS+Y93lOP+Lg4oTR0xX2tEcWDumg9z06Mpjdin7vIau9CefTXZ4Ut3js7/DU3+LE3OTMHnJZ7bIa7+BGJX4u2KVQNAbT+SFiIpEVxKQ9NzW4GbDVUI+XjMySUWYBXXwkQycV3jqqCazLEV09pl8U6JLggO6JxreEzpCRach2w3hyxZY9Y9ecsGeOOZSH7Pt73JGn7K9PmVwuKc465NJhrjyy9tDqwGoKQWtBJwYpe4rDnu2dS1ZbIy6qXcZ2QeXXWB9aAuMJjnOxOFvgRgW+rTAtoYumC+ZnNqGi1HmI4CUx8zqmPYzDs+jMUU5WjKtgnk7lkplcsmUuwu9mzoQrRrImhEOCid4wYqEzrnSHC1mF+wR8bVnNCty6xDagTWwwiJJ6bKNk9pERlDDOKmFXH18JOukZlQu2zTkH5pg75i4vtB+x+/E5xXsN9l6Ledoji8hkZgZ/VOCvPK537LxomE/3GOkKS7A3vVq0LTArwS49dq1IZOY5yVzI/kpfbOSB2RAQ8WOPmbSUVUtp2pwqktzdXi29j2ZkU9MvCtzCYBdKsVL82kOcN8FgdWMLVt0YEvVDPpIkP1QCr03WAj/RPgUf/6Yx4TiPchh2TeUuSSZl+BuQUROJgEUwD5OvKYX9NmEiAitoTnlQTYwwUfoAg7na4Gccz+yDQiVm30bqa4NT1Y0E3fZMRldsm6Dp7piPeNm/x+GjJ5QfrbF3W+zjHjlzcaF2FLMKdhvKw4L61orZnUsObz/laOcRD4vneehfYOqf46nc4HzWcFXt0lcTfGnwS6VoYkvgeD9axLSGCYERbLWMJgvGZs5EFozNFbWsIgvQ7JdwOEprsExZ256mnNCXFWYlmE5CkzlD8F9NQGYN49GcLXvOnnnCgXnMTXOf2/Ye+8sPuHmxonjUYB/3mKcd5tQhcx+0fKe5Yp0qgJNuGdzIUdwU7EFBcdhRHzaMtlZUkfGFItKYs0MRIlBVQTuyMSQOaUt0JWr7jSQUjb4zX8ZI40TwU4+ZNYzrBRM7Z6Kn7Jo52+aMHTmNrDA4oyd+wahvgz/HeVxZ0tgxc7PDmR5Q0YQxVUtnKvrxiG5S4JcybBmWVHNaBUGkN/9BXmI2mJ5m1DEyC2bmnH055pZ/wM7DC4p31hRvNdgPWsxxD4uwIPyOxT/naJoeW46YHbRMJ/PoWggg4HyBri3FWkMwZh0y7m2v1ywONeBiq2IfV4uW4MeeYmvNqArMchSjiKUEoAIJgQ4q1sWYVT1lNZ7RjEf4ukYvyOym0NDGWvywe42V5F8idn/xeYjk2rhlGpKZVvINDb+H84TOnwB+qNSI5EaEITnT6AYgDhwnRbI3UW4ohB98VUSfWMai9FkSS0zf/8u4Uzj+GsXC0VFOCrVHH0YNMu4Z2Su2zDkH8oSbPGD/9ITq/VUQpPcazN0OnjjclYfOYSY9ZsvAzQJ/p8C+WFG83FG/0rB1Z85sNGcsKyrWFPTY2jHfc7R2hloDRUhJkLAfYdC6NbgRMO2pRivGZsHUzLM/aCwLKgmlDx5LoxWNeEqpsDbkJMnI05gJrqjxTdxNxiiUip201KNwzi05Z1dOODSPuWkfcMfdY3J8RvlIKe61mAcd5nGPeeqQc4csFW0DpRELjASdGnTHUs485lwwN0v8wiNrZe/mObLvYza8ofchbN5LSSclzgQW5TqD7yVE9TSE6MUQ29UyFNZGZeImAls95WTJuLhiZi+YyTkT94ibds6eecq+PGWPE7bbC8bLNcWyD4rFBa3ZW49sV2xtX1FXa4zx9FrQmBFrnbAuZnSjMVramJoQgDP5xTLLS8qOZIgAMcGXEoqiYyQrZnLJrjll9+oc+6DFfthi320w73SsH/es157SCuNdi657iqrG3FS47BkdBvCQ6FXxPrRJNq1iWw2R4k7j1lJDuY634Rs+RnPVBNPOjFtG5YKZXDAz4TWRK8ayimak0lPQ6IilmXHpdnjqxxRbN1iV2/TUQ3pLHxODxee6N8mgEFmPbABUWorRlDLIRr6yhHrVRMOIZnROJUi+P91wqutg7olmVjRE4yIBSCkBOgBTbrkEG/V6wZ+WdjaWaGrmeth4n3+Zczwdf412K1HIIFdbqw22uClbalkxkQBSe/0p1XGDud9hP2iwb7WsP2h5cuE4a3saVWrbsV0Y9j+0zG6VlI96zGmPzB27S0f1ckexFVITNEE8wuW2oWOCEwNWQssOjSZYDVortu4oTQjxh4jaPAgSV9SyxiSAomahjspMQwKqCCoGKqEVpbdV2IdPFFP3lNWKWlaMZMHUXIZnNScc6jE7Z5fwqEc+6rB3e+SjFvvIwbGjOXcsV57WBcprjTCqhMnEUOwZyj1B1wIN8XkAA9vFFd3uU1qpaWREIyPa+OqkwpUV61GB76tcm6VC3DGEIfu7jObpODDLerxkYufMzDk75oxJ/5Cj8oQb9oRDecxBd8L0Yklx3mEuXDDN1xqcJIVgRkJ30TO9qXCodFXJ0kxZ6DZXskMla1Z1j6+KkFOVnMp+EPwkqBpXSNKuOd+oUCrbU9JQ6xV1e4I9vUKOW+yjHnOv5+pex6OrnoUqtQiHjeOgrjC3lf7SIStP6TqM1eggN/je5mx72ypFlwIvKYolQ3lJ9uNEBl0rVdUwNosw9/aEXfOUbTljJnNqWQMepyUrJly4LXQ9pRq/wIUWyFi42jZ0TRmc6K2Ea38CgBANLVo2kjwH42hwRksu4kummWbTbMh9igW/yfzKn9/8HvlcGh3t2eEdgwQaWZWqj2CYqBj5zq4DaLJSQ0mPsgGM+W43ahU/cTw7g2LIRNb0XwSqwjgK6SmlYSQr6rbBzB3m1GGeOPyjnkdnPR93PU+cY6ke64StznDUWp5feG4vPEWjMTMXJgXceuUBzaim1VEMv5d0RYVOLX0/RpBcbKk2ODEpPcb2MTGyo5KWkawZyYoxgZJbcXi1FNIztZ5ClrSmovM13tQhW7ssgrD2FmM8tugoTUctTT7fVBbBX9PNKc475NyjjxvMY8U+crh7LVcnnpPGc+U9jYYpKQTGrWFnZdhbGSatxUuPlgatBTPp0alQziyzyYKras5EthnLkpWsWcmaSsZ00mKrkr4q0N6EFiuQ2VQwTSX6nEBmAzhtmXN2zCl75gkjf5/nynOO5BEHyxNGJw32aYc565ELhyxCpAsFrQSZGtgx9LJiXBu29i+Zxmhl8PO1GNvTx63BhpYhbPg7dJCrwfMx2BMWjDgK6ahNx8w4qk7QuaM/adCTlvNlz4n3LNUzQqiw7C0Vs9Kwy0dKslTBx6RMjQCVzDrrFOsV4xPCx6wdH2IT4lMEVKDsqWwAqCmnzPr7HFVPObQnbHPGyK8w6nFScKkjdDWl3nqJE63B2ZDbNqlpphVuBTb5txiGQySadpL61If3VIZiIEm1L5GlZPiSZF0N4JRLS3JmZQy4JKe1DABk8jxsgJfG5M4wNJjUcz6VK6UoJ0oM/w09EjVBqm6cI93t4DP7acczApQSgqmWbJcmPqXDZyTeuDqP6xym6/Hrnm7puOodl95xrp65D/6ASwmLVlHKx3CzFuw0+GX8jmW6s+Dg9lNOdZeqmzA2M9asaIsRfV2jXZFbdQyqYLi30Eoj7DlXEECrlB5Lj4sgtTsuWWkbM5E7rIYsb6sOb2PuugkazcaWHYX0lITM8IqGsuuDo7UBv1RkqcjC0y+UywhOc03PCoUKXjwFMFoLk6VHF4pfGFiG2jpZK9IoZd9T1S2ltNeKYG3cqspYD4UPiajFRr/u+ArsCRj3lKOGkV0yMVfMzCXb5oypO2a/OOFQTjhYnTB+usYcd5inPebMYS4crDzSEpzttYS97ArBTRS3aKl3Wyobx5AeE1MjkgmROh8k38cmw5cN+cl/jEW2RJMn7FQSnL6FWEpTAB2919D7OzrgU8U+sSyHUuhN8Nn1hORXbYuQfZ/KgXyooUtJpEHSQ2Z0zllCwYApPKW0jGXJtl1wIHO23AO22vvst2tGLdAHRSnSMz64yRPG+Jg4utIZSzujHU2gDIXIeTAyOGqYV/EYO/ia0niGj+YsqI31maJ7EisUyCiR0gDS+IPfyBYfsswTIl0zwSSMR8DDwXeUTT2fmuLJsBFq9j9JfrQBsDYgw/8NMqhwvZh8lbJiY8atcyZoB61oGdFXFTItkFkBWwXlrGd2bhl5RwUUIvSRTbQoS69c9Z7DK4+dB6eyuXSYqx5ZnFOWc7Zqz5VrKXyHNS7kM8XiOUM0nz25F5ViUYqQmEdBrwVOws8ghKEXt2s7Fu2KvtwFm/cVDpPtJQ9sOjaz1wMEmrypA1Yoa4M3HaYQbGkoxVEK1NHZ4oESqEWoRSit4qxH6hophUY7vBesLUPWcvRBxTTHjekeNNdGAm/IO0p/scTQuGKrntK2VNJQy5qRLJnIgomec1gv2XYX1Jct5sJde8k8RiBdOJ+ICdnhLoCF946ubWltR+d7fIxIBUewDKZGFtcEOhs+iWtCnBwWkrs4OCxOCrQUqAXGgplatuuCFVA6z9gYdmqD3bH4bRuU3NSytlNWfkKjIzpfQ2Myewq7LXsKHcyg1FNdNcm6IoRaTmM8VnpKWkYs2TJXHBVLjq46/PGK5rKjlgJTC/vbFW1zSTuecOW3uUjOdNMhpQNrkfwfuTuztR5rwhZRJvWlScARIeLapp6RkWqMyqVgRHKAqyjWJCjaZFaxY0FUBN6Frc0SWEneCUIH8y2aiRqRJsxUzMfbSPIcMuP9MLv575GvOY/2G/t+feJ4ZoAyRunFY8Qg6hBvw35zreLainYcNMTc73BZ77J9NKe41cHzDnvqubWE9mlgDTPviQqZLWPYMYaxETTulJukt2t76sIyqgzG+U1DIKm6IXtXCM3aesF3wRRspabREY2OWUuD9WG7KEvPctWw6oVGpshon9MLx6VvKLcsnhLvgzngnYG4ZU/qW53KUFod0TBhXdaMpyv8LLCKas/SXQTH+K4zlFew6JQ2Nl2qjDAuDONKGe0azI0Cf2jgqKS8VdLtW9a1R0fKuoqOZx1nU7fTMgAuRdh9OCaVojFK6IOZkHA1bJvl6Ls1rS5p5ZJOLvDFBZOyYWRbqnWHaUPIXWKCYXYRiIR+IkXIBWMUXhp/9tUENTO8m+LcGKclztlh669IBDY7BOQ5HCR9k4xH/2yoR+yoWDGhnxaU2xZ/YJGblt25pTiFVeOoS2HnZoHeKfC3CvxRQbNbc8EOC91mrVO6foRpYrcHRzDtNIyPsZFBbZh1JtYwZr8PxI0IkkujpdY1ReMptMSowS3XuNZg65Ji3TMarShpKOgocIGtDY8YLI7I0sKWUwrGI9ZhbLg3lY3Kic18ogzxg+slpakn57chOssTQMSC7E1LQ0XQvg/lLbGn1dBRNVwvpbIlcQiX1mu/h0mTnIYwACbZya4QmJNz2Opnw9CzAZQEM6cw0BsoYq9x1wtFA/2qZDWbcml2OTVHHHOHnRsX2FeCc9V3nkIcL39ccnRWcHrV0RJ8R7XAbl2wu19gny/oDgxuprgpFLs1fTWiY0RPFfogUeC8hc6EuqnY29pEgfIiOFPSFmNWpsNoH4AJ6KVk0Vc0q0vKcgs722I+dyA7lNs7FO2EJxcOSrBShxB+HwDKeUM38lg6Gp2w8jOWss2l7nBh96h3rrB7yviFGrWKKSxmq6Ta7yhOO3bWQB/UkynA14rMLHJQ4HYFd1TArQp/s8DcKalvllzujTl1W8zZ5Up3WOgWK53S6IROR/S+xjVV6GrQkjciSLkgyT0QGt8ZyrKmkvAqKBGFZdOH3CiRWBAcQEhHAl1gDtRxAVSCzgx+t8DvW/xewdVYmJs9rnSHtc5odEzva2iLjY1TGXZvVgakIvokNApy4qY+fEd9Qac1a51wxTbr2Zj6xhr3XAULjwW2Tx2ztQsBkjsV/pUK90pF/1zF+dYup3rE3O+y0hluXWFX5IoE6wKDsjaATqAdIQHKxFY8aT9XiZ0GUpuWzLQjLQjAC2ItXsO9AbE7QSwLj7+rl1zDF/eIDR01jAb2ZD2mABGfaxvZbKUSGVOypBJh8aTykoAoSgSma+Z2ZETK8G9VitKwPH/AztHzIVJPauQYn4ufTNRMEUONYmKjle4hBJuICnOThXmHekWKmk+c7trxTAAlQGEUY31uHu98mORirRRzQzvbYm4POPGhwryu1/CyZ6o9dQ3VYYn5cMXuE2XraYsuFYsNW3ePgX2Lv1PCcxX+BYO7XdHsjbgwe1z5HVa6lRem68KitI3GKv4wAKGiPMymMyPWRjGlIMbQ+5KL1lL4gunkiFYM3pec+o7SH7DUKZ2dMppNuToXVlc9U1OGTQWMwXeGTgUZK9Y6Su0otaH0a4xf0lVzXnzV0BdzZKqwbfGHFnNe4M8sRRdMIwF6o8jYwiykGXQzxR0U2Js1clTAUcVya4sze8Slv8mFP+RKD7nSfRa6y9Jvs3ZTutUYWZSYVWxUl9qdaFhZXiUIaidoV+LLAm9KvIzATnF+itptztdXbI8n1NMOs62oi1notUcaH7evAmqDnxp01+IOC5rDmhOzzYk/4lz3mfsdVjql9WN0XYT2L/1GdX/yh5BMgoE9JVYgiQT0gnMFrdYsdcaF7nE22mdyZxlyylB0ZjAXHrfu0JHB3SjRl0f0r9fM72zzyDzHE3eLc7/Pot9CFyXFOtRbBnCKjMh6iugu8GhoK9NH8y71Ue8V7YKjO9SRjoNfyUzoRnPM1IXW10XIuHJjoRmVLGXC2o+DiUkd/WA2FKhvtJ+xIlirWOujKamBQdkhO3zoXhnQ5Rqox1cuEIaQguDBuQ6MQUwoXlEFNZ7dWnhhu2ZnMuL84oR+54DLbs1xPwo1lxLbTxvBdR2mLCFXJwzpCMn9k3f8Jpp6BDA16e9xg4eiKHI31r8RgEIg7J4TnJK42NirB12DXinrs5p5eRALYoPN1YxKXvzsx+zfOIUX17gXhfoM+qcFZWtwnWKsCcI1E3TXoLdrukNhfmPExfQWx/ocp/6IS91lqds0boIuRxQrKNdgm2COhB1dwXeKcaGvtHMTllsWN6pY+IKRmTIqHa260CtICk67FSN/QOMntM0Iv6wZrw2zXunXPaUNEQ8tBd+XtG6KzEJ9kRGP61rW2mImFVYesfPSPXYOG/RmcDLL3NFfWNTZ4MdBkUIwY4GxCYxkpPgdix5OWE6nnBX7nOkBF/6QC3/Iud/lzO+x0B1WfptVv0W7GGOuCsxqSDYM4xDl14RiVZcErChwZU1nxrSsWfkppZtRFXsUuubUC2bm2TZXlLVgZnbIfvdh0abcrX67ZLE15qw65MFqnxN/m1O9wYXusfTbtKspdmmwjYtlSSkCrBsOVDbMPh1C+sk0cYrvLa2OWPgtzv0+x/YW44MF+6qUI4M5KpArT7+2mFlJtyPIC2Pmd7a5O3qZe+5lnrrbXLgDmuUMOxeKlaPoQ5KkRSmKoHCMGerH1GtkD6FVse2VogW3trRbweRe6BaXfo9zmVOPG3b2z6lGHW7lcUXJelZyWe9x7ve59DssdYuVTmjdCNYm1P/1ms3MsJV6KP40cbssW7CxKWjwBymSx9JvpEEE6pISLQdzzAtM64JlG5hMqoozEjYKMYXl3r0P+fD+Y3ZffJ21tzhCP/zsYxKoR2XYXTyBimz8KiBqsukYQDL6GeJEJ5+ZqYoUcyDnvv37ApR3Du9biqIOzjjvoY973XWKXilqhdZOuTi8CbWlo2Ztt5jbQ24ePeBw/wn17cfM1oo7WYKWuFVHYW3Y121s8FuWbqfitBpxn32W+hrH/g4n/jaX/oiF36FbziiuLMXCB/bWBi1NsqW70J7Y9tB3gmtGrKcVZjKhK1uWdHlnYtWCs/Uls+IIXYf6rGotua/3yNmkrNBScR30fUnrZ7Bl6D00KFJNuOt3WJhtpnbM0dGcvYM54/WKat3Rz5cgJany0xShlKOrLF1dcikFq2JKNzrkSnc587uc+z2udJcr3Wbhd7j02zRuSrOe4K9K6qWBhQ8h85ZY7BqEL5WMeOdj24+g7Zwd0RiPLUOUEr9EezCV5V5T0I8nrGZnTCdzRm1L0fVIH7fQLoS+tLRVzZXd4pR9TvQGD/oZ53qHS7nJ3B+wbLbhsqRceIom1J0VLlTup95CyQxJC6AwihWNG4YSdx8WfFvS+jFLs8WFP+BYlljr6G+U7M7Oqe802LVnvbYUW2PmI8Nq7waPzB3u+5d54F/ixN1i0ewhZxXlladYe4o+gGZhFGNiC5m4ZbpgwHq8C33ICh/2MbSNYpeGrpmwtFtcmlBCU/kWjLCejBiPFvj1Fc6OufAjFv4WJ3qLU3/E3Afw7tZj7AqKFoo++MEsUFjFFp6yDOkHtghRe2MzAlwLJ4TQPUOnBSEjRuhQEAEgNcWzDquGiTVUJdS24OfvbHP/7nt86y++R98rt27eZGwtZrzNqlnTd47xuEaKChFl3Yf8p+BbCtdKOVyazEEEyU75KI/xH8baEG1Fgwn4N2XilYWlqg2oUmWHnQ7bVzfAXBExdH6L84OKdjxhqTuc2yOO5Xn27AnV9B6Hh2vMwQn7I8H0HRaPGkNvStZmzEJmPG2mPGp3cP5FzvwBF3rEpd9jvdzBnFeUc0+xgqKBogvaNkcHDDhHEKoeXCO4lcXXFleP6EvNPNh6oX3kKHcqRq1StCGcLrH6PPFVFcW3YFvBdErfF3T9Fm6rgNEE9VusmHGlexT9hNOyZdsumM6uGG+t0NkZ2+MSEyvCVQQnJQs/YqEjLtcFjZti+gMWbHHht5n7bRY6Y+VntG5K19T4RYlcWewyFJ+G5n5hBxdc8GckCuJF44YWQm6xKwYnE1ZbQQI639JSIUy50oquX3Jh9xmbBePRmnIcSkRKUUQKVlqz1AlLHwqaL3SPh27EQm6z5IhFu4s/H1NeQrnwlI2njKaUxVCIRuGMUSQPY+MorbJCwEJhhSuB3guuNXSuZmVnXOgB1veoGtZmws7sjMlsTkXPYrHCTnY4aScs5A4n7ibH7g4n/hbn7RH9+ZTyEqollB2UXiklmFOBQfms7MOWX56iAN+HBoZFD74Ft4R2XrOsdrg0LUVsPdNSM5cdapZ0OsfLhKvesja3OecGp/4mF/6AVbcNlxXVUikapexj7MGALdK9EF5F6CqQy1SuBRYCezIJ5qMDTAktbBSJ+UaWEY59adhxS5rlkum0pjIVTeP582+/w9nTE3a3dyiNwbQrZttbWL9gb6Q8ePiUxbxAi4peDGtvObx5K9xHNDeNmMx8g/mYosphQL0PWGHLIpfJDSTsb9DEG1UF3gt9CnXgoRXEW6QHu4YCpXVC145Y7tyg2dpiXh9yYm8z00t8e4v90uO7E47GBUXZxZ02QpfDTmvWfsx5X3DaTbDV7cAguh3aqxnmvKC8VMoFlGul6MKOGyZVA2iIEIUNHcD1oYLcrYKJ5goJOSrGUKIUKPPHPbvOU7vQF0qdUHgfNyA1Q5tfA64LgNj3StcK/XrCcrumnY5ZldssdB/XjbksPTNWTGXF2LQsujP2RyNEfeiS7UNv8UZLGh0x75WGMVW5F5qs+SlLN2bVTfDrGpaWagXVWmHtcY0J2dA9lPH5NW4rlTRXGgfvA3vpo9O39wW9m7Hasqy1p7FjxDVM7BaNu+BSWiYx4bKizz2cerX0VKx0TKNTVjrj3G/zoBFUb9B3u9jLiumFR64U1gkMiAClWDNEoCpgVobasAZBsIiNmlkF8Sa0KWlr1sWMuYRND3qpuWKHiV5Rs6Y0jst+wdjvccmURb/PpR5w4o64bPbozqaUp0J16anWnqr3lKqUVilLhy1CvlHw84Qe8wVBxgsfOnB65/GNUC4Vf2HoqhmX2x5KQ68VS91iInNK1jTdJSJTfFExdztccchc97nq9ukvppQXUCxDBnvhlJKBPdlCI3sSjA3pAaFGjoFtSFA0uT1NDo0l9pQEIICFw/Dux4+4fHyXtx9f8Nqrr7FTGbrpLg/f/5iLB3eZHjyPKUveO11yfO9DRluHjGdT6lHF4e1DJuUIW5SIdzkFIRWj551dUgJmatWiUItDjdJSkm33yKCvNen79wcoQWws+UhPLz2VgbYNnS5tF4hJ4YS2hX5Z0F/OWM6mrKb7zEcNq2abs0JYt2ecuglW29C4LI5pKrK87JSFjqi6A/r1mHJRMpuDzBWzhKpRRl1sCxzzVQI5V0Q9nTM4L7hekRL6RmMPJJN32qgkdKgenfZMRh7rydtvl65ntbriyZNTdnf32Nndpeuh8ya0PPbQdULfCW1T0C22Wc4mNJNtlmtLW5fMTMNIGkocZ80pR5MZTjU25icU/WqBUnLR9/TUTLsd2rbGtRW6ttilYdxA2fi4WwuYzuA7gmnmBesNpXiKTGrjDrwaIjF9H1qMWIVSofOho6Lvx8ztHm4yxdY9a3aYmn3W2jDXltJ0mCiQnZfYfjh2otQRaz/iym9xumipr/bZagqmS6VaeGyjdB2hcVt0X5UYpkVPr6El844BwdCSWq2EzUv76HQxPjyvWxc01RQRHxodypgr3WZkGiwdI+BJv2DUH7BiwsptsXZbLNfbuLOK6kyo5p5q5ak7pfRQGqEowq7D1oZdgc2QII1LdWkaxtlrrJlbK1wprS1o2OF8q6AtRyzYpmaJpWXVnWPsjO3JFo8WlrXssW630Msp5ZlQL5R6DWUvVISUk7KEsoSqkAhQJt+TRHkVQIwl5SICQ8+vBFUa/Gcp6xwviHq2Kku5NeVIxuwe7HN6/JQ7t0bc63refvNd/sF/8nNs336Z8+OPePd3f8Cv/eZ/wnh3C8Qym+1QjypWTUdVlqhz2LIIe0cY4fTkMWcn5+zt77J/cIQh7NS8bTpmpqeRmvOup+sdxlbBvxETqMVYftbxTABlBMZVACDvlc4J9IrvekoLugCrJbYv6NRT9oau9fQLob8UXF2xtMo79xxN1VNPdjh9fofd3Rmhw76E/jMY1BuW85aurUAnmBUhM3sdaHHRwqhXZh5qPGWslO8k9rVRpfOKI7QNOH/ze8yl4sbrn49bhKdgrcEK1GswaxNDyp5+veDPvvcjfvTuR3Sd8vytPf7Rb/wqo7KicJ7eW7z31C70Z+9bxTWCrgpcXXC53GPpJ/QjpSh7bOF5vNqhnewCxA6SYeNNdRbXG5aXa9aXHU0xZruYUrRgW4c0UPSKdYJvA4Da2FfJeqFWx7ptKOtRMJ9MxFgN7Sw8hJ12+vDMKgHAvELfKY0v8NMy5FXVHW3ZUtgOkT7sA0dSHgJYVC3Ol3RdiTYFuq4wjy5Cwmkv+EboGgmBil7DJigaSnvqQhkbm8vxnBqsCF3f01mLN8EXiQkauYhtUm1r6Zcj2rFAVePtlJ6WlXagYYv3B82cyegmnRvRNiNYVBRzw2iu1EulXAW5qXwwp8rSU1TRpCok5huFzaG8hp9GTDRFJG6wKdBK6JwgYLylb7aYb41YjXawZo2RjkW3hS1mwA5Plz30W9hFSXUJ1SKYmVUPlcIogtOocoxrpSiDIsUKtkgMhbCDSk4RMKReUanHk0YndNqNWgHxjqpQVm0D2tJiKHXFnllS7IzR1Zx773wf9R1uPuff/fF/iRpPQUXhOo52t3h8dkWzXOC6hqt1x/b2FOkqZtOKCuXJ02P+4Pe/yYcPzykt/Bf/+X/GzYM9KnVMLBg7ZjWf8+2/+B5vffSYqiz4+c9/huOzYy4WjoP9vb8ZgFJVVm2L6x0Gw/GD9zg5m/P87ZcQM+XPvvN9xtMjPvPG52gvGubrlpXrWPmOrb0tPnhwj6u+5Wy9xJVCMZ0xezpmPbni0clTjm4dsTOb8fDRI5678Rzt3HFxecLBjVE0Hx2FE0zjoG1RDIu+YdksUN9w8/YNuvUKYy3WWApbImpDrshoxPZom3Y+pywrRmVF1604OT0B9Vwen3CGZ1SPmM0mXF6e8eTsnK7tKcqCzjk+/Pg+r736Kqhg1HN5ds7xyQkvvfEKZV1iGqFfKGrh9P3HjF97Gb81orFgSmhOO1brbRAT0uBi6Fod+HWPncPJhye8fXzK7dkuu5MxO9Mddqe7dG3P3Y/vsjWZcbi7i+sdpS0oC8GdP+DBOz/gtb/7mxQmB+rR2LrDuwAQDqHvwpb0dB46g18phYNmDouLHjuraKdTtPRIoTjpMTYshrAdl+Abx9XpFRNbM9KCohWqJ46qUsYSTE7pJcyZJxZGdTjX48uKhdiw/6UPAOWbOcfv/pDdn/+79EJ+hUwJQTpgBaIW109Y1SPasmdhXEw+DNGjp+en7PZHIfVkJdilUi49VQtV66nj1ltWwBYOW/YUhacohLJQigKKCIzeaegU22vOA7JdwGpxoVe+ufKYXihaoV/U9KOatpqihef8wjJbb9G1U9zjNXVfUK9h1MC4VcbOU8XwZbe+YFrAuK4CIBmLMQYklO9IvKcQ4QuF7CaCZvD3RJ9TBCkfW8p4D4rl6r3v0Rc10reMC8/YnWH9TXbGcDU/43B7l6OtbWaVoV+vKAp445Xn0eUp87uGYrTF40ePKUcjkJLKWP7szbcwWF64vc/v/un3+fjxItxz7fmjP/lTvvqZO6zmS0ajEfPVinvHT3j34TFN42ga5e6jJyHCLIZ7j47/ZgDq9OyU/+r//U8pC0vfKY8fnoCMmU6+j8iE+byjd+/z1rtv0rsSj6FZ9izWyv7NXdquZ9UrjfcUo4LxvufyqsWs55z3BavJBVUBx0+ueLz1ESMEpKbYu2A9X3D+5JLd6YyJGD6++wBxDmt7lqsLxmPDZ19/iY/v3mexahmNp/zyL36Ro8Mjnh5/wIMffZ9Hlz1+cpvpdMpLz+9x7/4T7j48wdqwG8YPfwxiC7bGnvHYc7XoKKoeg+XkdMXbH/Yc3NhitVqxWDp+/Oa7HJ9c8cFHj/mVX/sGFJbe9/TiefqjY9YPGr7yS1/CeccHdz/k3Q/uc3T7Bq+/8jLTUc3FxTlX8xV1UXD3w7ucP11w63CLx28+5mH7kJH1VAhf+/kvMq0s//b3v4Uo7O2EbVVee/kW02nFyF/y52/9iHeWa158/oC6KtnbP+TG0R2MFGgPH3zwEW3nMXZM07d8/PgJ9WzCVd9xdGOHh/NLLtdLZFSxd7QDpbJqPZfLFZPJmKPDbdpViwXOTq9oli3tVc9nX3qOnbKCpmdte8RYLk/P2Nve4Wh3j7694tHpE+4/vM/dB0/4R//wP2B3e3fImekd667luFNmKefG2FjzCKoG6T1iArulA1YFaoJruo85SoUXzP2CalswTU/Vw/LpJcZBVU4Ym4JSBWtBrKOslKoMrNsUiikMdaGodxS2xomGdihEl4n2qPNUvsQ3Hevlms4D05pyWaAFFJOavlB6r5Rzy/Zezfa6YPxUqHpP5QylC73uvYPWeYxx/MFffIudvRk//7nbrNornrt9g8l0jDHBT6qu5cnZMScnF4ynY24cHDKqR8wXc7COH7/5Fq+8/Bmev30nBIi9p+9bVDQw6zuvwnqJPpnzw+/8EcfHH/HZO8/x2S98nqvVki995nmMKcF5vvbVzyCqVKZgtWr4vT/5Lr03lJOaWT3Gec97777DR/dOuX98yXhs6J2jb5XtnRInnjfffpcf/Og9litHPbJ48YzqAuc8V1exBMYbjLoQabTrn4k5oqo/84+fPMZ7N/ULv/U/BzE8PG5wruRgH5qVZT4PtvJkalgtCoqyxpqS1ULoektVFkBJ74NZUY4rJltwuwbz5BHH1YzV9i54oXeKeh9sZ1Gs9myNlMVcqF1H60F9R+t61k3PeBIKZmd2zXq5pB9t03XCwdGI2d4WI12yfXXG9z46RWYv0HvP7q5hcWVpWofrhKICVJmMCry0GFF6F0ofui4m0ZlQ+lJVStcVeFfSrA1He/tUWzUqHXt7u7jO8ej8gmXXc/P2DepJzfHTY87OVxhbcOvmNoJlPm+wzZz55Rxb1mHro87FGldPs+xoV0ptLTs7ltV6Tds6bNWzt7vN/OqU6VR4Y0/5wfd/jNx8DVDqqqDrDS+/cEBZFCwbw4P7Z9iiQL2hdwXLRui9oZoUbG9VrCWU4BSTirK2NM6hYrFFEeqzrKVvPKX1uC6kLeA7+lW43xIQFwp5u8ZhTclnXt7m6ckj5useEWW1VP7er/w8l/Ml2nkWq55eS16/NeJ3//BPef0XfpHV6T0mr36WF994mZX2FNOa4/MTLtctt+8c4pzj5PIimPHecGNvh6uTM7ZHYx58/JDVcs1q3rJVVDy5f0KzaLi9u8Ot/W1eeekWB4e7lJWjrjwiLV56urZhNCr4wQ++zbsfPOb5Ozf50he+hHM9db3Fn3/7Bzw9OWexcCgz1mvPooG1g2pSU81KGq+88fnXWLWXHJ+eYcTyW7/8NQ5mE966d0bhhbGtuDy95Pb+Lr5d89HHH2Btyw/e/iFlZTCm5fziismk5NVXbtL7ltIYTs5XnJ6cUowNXj2zcc3R0QH37t/HOQdYbt445ObBDr0r+MWvfon1csHD4yc8Oj7lF169jWtWzPo1f/x7/zWXV0v+yX/8v+DJo3fpzBZPn56wWrcs5gsObhzx5OkZW1tbnJ1dUu7cwu7d4Ohwn61xiXc9Te+prOXH772P0Z7vvnfGpCypx46vvXaHd957j7PWYqfjuL1a2DXo5LhnvfCMZyWrtQvbc7keZyznf/x//nNV/fonMeeZGJRzyuXSBg+8sbi+52JusdYz3jJ0a0/fQu86xCrqW9RYqrqk61r6VihMgRXLxCrbfsTDB5fsr1cspWR1tUaM4pxSlNFM6VzoIrktjK3neRbcffIxflLRFIfUdcdkUnC1bLlzMEGLU973I1ovLBaO1WqJbzt+7kZYuJddi/GwWgQfjTFC5xyrKyhq6L3S9y3OhRB9WYGxQteF5/K+Y9UaXNfRuzWHu1PqWcfZ5RLWPX/n9Vs8OV/w5qNznBOu2geUuyN2taWSnkdnDefNkqYRVC3F+pL9y4fcPtjn7tYBK/Wslh0qSoln/0gpxVCOBbU9TdeErb9EmWxNuVqeBjPINIxqR9t72i7UpX10/z7eWa4WwmxS4fueoihYLRzrhcVrwbgS1lcrFiuhVyhrj04LnAenXXCsW4PTsJ1TWRmMg2bVYnrDnjE8vz/h/uWai6s5R1NlXXmu1g33j894etLQtErvHFVd8+N33qNdeExf4TrBWUvVt1Sm4+7bf4qfX/H8ziFvNiserDrG04LGdzinnDw8wTllsVyxWjiKasKHeo/XtpVHp6c8aEq6dY/xhkXXh2z6dc/dew95/OAex08/5O/9+te4eHLKqOg4P3/CD9+5B9bypc+9xF/88D2ennt+9P4p/+rfvcn+3ow7h1u889EJ+Ir5lTKuV3SNwVPTdSakJtRrytryuHzIcn3J+UVDt1Z2fs7TXpzxB//t77O3NcEWFcvVmheOpty9d4/5aoX3LabomU7g9GKFd57j0xWPHl3w+q2WJ80O6hymhEmpXFx6Ts4aPn5wl9nEslh4VB1d94THT084Pe/4gz/6IZNxSTmyqLtkx13x/NEe090JR8+9zHPjGXfu3ID+IQ/PhMIIo6qkGxUcP3nEvUeX2PM1F6dP+MKXblKbgrosMEUNlOxOa9rlOTNxfPa11/jWD7/FC1PPo/kTnp6VnN59jwcf3WXnuRfRySHV9iFSFCzXocSncdGR3wKm4IWXnuf8j3865jwjQAmnxx29ElLyi4L10tE7Q114bCUsFp62E4ztY71cT1F5TOeDoDuDc7Bz85DXtuFPnq6Y7oxZn3a0/TJfq18EYFBVrHjOO0Gk5XU/p330Ju2dF9HxNt73XM17Wtdz+vQEe3XMutrBe8/KK74P5QL7+3vcamvO7jeoscwvQy1c510MkRr6zrPuerom+G5EQg5MoKGC6wVbWNQ7RiNLr8rVasFV24J3PL8/4odvv8n5Egw9vbOoazg7WTMrLEdjODqsuX/WQO/AtLx6a0a5NkzaJQf1AfcuGlzbYGrBSM+6AV+3TAvDV1+fce/+mnlvme32XC46JiNhe2vE6595lfcuOpo1SCH0zrFcWNT0WGPw2rG8AmMdXWMBizE9hnEo9Vh4LDZsiWQV23pG44p22VFWBX3r6DtHVwtjBNeHf3/2c7dDSYwWlKXymef22epPeHpxyn0zYVQo63VPLcqdyZobI8fdhaWtK2xxH/oliytPUS+5Wqx56eZnubW9x+NOMMs5i8Yxqi2/8qXXqO2If/Ot95k4w9i37JsVO4sTajfh8YMznD1gZDssikjPqu+pS4eRjrJwfPTggn/zB7/PuIIvvHzIw7sPeHhySbf2nJxdUI8E8YrrPV6Ftlnx9vsNprCs2g7XwdV6jZgy5G85i3M99Ba/Lnm4fMx0x+DnDTt2zBuH+/zwrfeYP14zaTxP5ktsCSM5o64WbI1DfzSVHhGhW4d8oimeUSH8/KsXvPn9ltGo5qKD08ttLs8cXR/YbbvqqEcFaB+U77xleam0vefyskfUs70FlUyYVTXr5ZJ6tsvR/i1aVyCjF/jc57eoxARLwjU8PTnnOz++z2njKMspBztbFCNDXZeA0jSXqCto24Ybe1Oeu33E1FrGdcnXn3+RVVHz3Jd+nsvqDkaU9YMPaO69hUxv0rox5Wifph9TFAUOx3Ra8sZnXuD7PwNzngmgCvEczQzSXbK7u8/jM1h5oSgL6l6ZmI6+rrikxzY9z+8btqczOu+4WoWciKu1Y37lef3QMjGeunvI3myPz49nNEoIP3cOY4RVu2ZUjzhbeOadozDKA9sg1lOXU27uwLIVZqVh3vVgK156/TUevOe4vVPRqacuSparBpor/OVDSnbpfUlZWQ4nFYe7W6xXLcumY1paprOKh0+WPJm37Ewrur6j64QCaI0iapmODeMxrBaWp8sl49KyPx6xUzXs7As725bdqaMqgi+lWXuePn6KW7T88pe/hNAwqwucGt64NeU7y20wHS/tdvTrDj/1HF8uGVnY3ap5cnqBK3awzZLDUcXzu4C/4P3LFaILxG6xePqI2u5RWUtphKboefHGNi/f3uLB2RlXK8vkRsXuqODj4yXr3vHScy/y8f0HHOxPcCPLwcSyXK15svLIWHnucEy7hntPz9nd3eaXPvcC5/MVTy7mPD5v2ZuN2K/OoFD+0Yv7PLrb8Nxuzxfu3OL01PKDu0/Ye36bR+eOhydzvvaZIy5OTnnhhV3OLx/iCgd+n7ff/ZBqXDKRMWVZcffxY849VL6lwHKjLuHkKWY847kKHh2fc3u75s6spm1Lzk+e0Cx7tiahk6X6DimVtlkhpsdLhzjwrqfqWvZmFbbvOZjW3NqxrKqaqhZePJzxg6tLZjcsq6sOVsKkMlzMO4yUVEYR24X8Pw0N37wvMFLR956uVXonHO0VlO2c99/6IU8enrBbe2YjjxdYuh5lydao5XLpGNee3juWV4avfO6Aq7MFe7rgwargnW+eY81TmuMl3zi8yb9sLPQlBiiNZ3taM6qV6ciwXHdMsIy2DaqOxVXPvLEsF57JZMwvfvkN/uK7f05V1dw8OuDW0Q6nT+5D23P7pRexxjDb2sJ5z2/8PVi1Pet1y8npGe/eP+Wj42Na19MvLtC24bmXX2Ln9h1efO6Q116Y8LWvvErbrinLEXfPLnn+6BXKUcG0/lUury55/4ff57vf+nOOdl7j/t2H2KNXufXaFzjY3uEffP0V/unPwJxn8kHt7Ozp//5/87/j/OyM3nken1zw5vt3KWd7/KNf/xVuHu3i2xVPLpfcOTri6uKcV1+6w+npJf/8d/6A1157HTCse2V3/5DtyZhv/uG/ZTzbYXE558tf/TK9d+zvHnJ2/Jhv//ADysk2u+MRs90Z6lbcuLXLww8+5mIx5/NvvMSd2wc065YHj49puhXL+YLLlfLG689RisEW8ODRU/Z3d5iNKt6//5QHx6f86t/9RbRt6JoVdx88pC5Kjp+ccHxySuc8n/3CZ/mP/uGvcvb0nHI04jvffZO2g8XVU8aTiqNbL+HXPU1f8PDRAx4/eszWbIvtvT1u3Dzk9sENHj855Z337vLuRw/o1j3jyZg7N3bou4ZRUbN/84AXXrjFYtVx8uQJ3nfUkxHTQjifn/GFz3+B9brhX/6bb/Jf/Of/BN+2/PYf/D5HB7t88TOv89HDJ4ynBQ8v3keXSl2OONo7olm3vPvuPa5WDS+/9BwvPv8ChRHOLxes256m8/heuHnziPffu8tzt1/ilRef4+79B/z2v/4mr7/8EiLK1XLNN77+Nf75b/8eL736As/dPOTmjT3WyyX/7J//K0Q949mIw71tPv+513jn/Q85efqUqhIm9Yj7T55w83CPb3zlF3jzzXdZrh1vvfcx//Af/AZPnlyws3OTutzh7GLJ+/ce8HR+xd3HT3j+xVvUkylOLNNJyWg8Zn9/j2a1ZququHf3AeKF9XLJnYMdHj18gvchMvSFL7zOCzdHnJw/ZTQeMRkLCy0QdUyqirsf/JivfvULnJ09xXrH7s6Yq8WCpnF88Y1XOb9ahe00jOHR+x/iENZdyfZ0wqpzvPT8c1ycz3EUTCcz2i7UF350/4yj/UPefv89vvS5V0GUvZ0xy2VLaeDPf/Bjirri5s1DXrmzzb279/iz73yX3/jVr1Fvb3F8fMGLL+yxt1Vzcv89Hp4sqNcd44Mt7n78IYrFz27z6su3+PDeKb/wudtcnJ5QVwVnpyc0vXLz9g3U9Tw5PmZ3f5/zFdy7dx/Vnn/0m7/IR/ef8ODBKet1w/2Hj5jPF8wvLnj15RfBdZRFz2RS8PDRFZO65ld+5Rsc3Njj4uoKs/M8P/iL73L/4494dO8Bz/3c3+Hg5g1+8ytvcPvwgEfnj/jBR2e8/fSUy6fHnNy7R7NuaVcLjHfcONrl8vyc6c4O07LGdx2XVwtW4xd5btfyrf/+//FTfVDPBFC7u7v6y3/371KNp9RFzSt3bjGbbvGjt9/h7Pyc0XTCV7/wJXZ2Jjw5ueC9Dx7yF9/7Dv/RP/nHvHDnDv/sv/sdHh0/5stf/gUePDlhagsePD5m/2CP8WjEarFivlgxm4y5vLzCFGPunTnUeG5veb72xTdAPH2vHB7s8jv/+vfY2d1l3Sz5ys99DvqWH7/7AaPRhBfu3OLtDx7Q9y2vvfw8p6dnYA2729uMRhP2ZlMen52xu73Nf/vbv8/XvvwFTs9PKTH06rla9vxP/kf/mHa94LXXXuH/+n/7rxhPprz99lt88bMvUlQl3/j6L/PmWx/heuXb3/0eu4cHbI3GvPLSS5ydXfHjd9/nw48f8spLL/LGS89x++YuZV3xX/7T3+UbX/kSjWtAoKon3L/3iPc+eI9bNw75X/5P/2N2tme8+fb7PHx8zJ98+/v83Bde4o3XX+bHP36Lk7Mzbhzt8MLzz7G1VfPu3fe4uGh598N3ePWl5/jCy1/kd37/j3juxk3+w9/6+7z/3sf86N0PeOH2bW7duUlpCv717/8hj5+cs2oLinLCL3zxs3zxjZd59PSUP/6LH3Ly5JTXX7nDuB5x9/5jPvv5z7I7qWi6Fa+//Dz/8vf+HV/58hf4rd/4Nf6f/80/5/GTJ3TdmocPH9L1a77xtZ+jrie89c4HPDi+4uhghzs3jiiKEudhVG9xuHODf/utd9iZ7dD2yv3jJ5Sjmt/6h7/Gydkl3/7Rh1RVSe9a9rZ3ODs9Y2c6oi4qfNPw0vN3OH78iL5bcTE/52q15PlbW9x7fJ+t2vA//k//Pn/4h9+EUvj1X/4G3/nBW3zvh28xndSsmoZ/+A9+hfViwcnTpzSd4+U7t3jnww95/vk7tOuWxarl61/5Cj/60dvMpmO2ZjN++Pb7zFeOz7z0Mr/0ta/wz/673+HmzRucnl2ybns++PAxn3n9FbamBSenJ1TTEcvFkq51rNdzXrxzyHq95PXXX+Wf/Yvf4fnnb+C7Fucdv/5LX+aP/uJ7PHh8zKsv32Z7axeA7/3gRxzsbvH5N17mc1/4LK32PLz/kN//d99mazbjF7/6c8xmU5aLFW+9+x5f+tLnmI0mGGN4enqC2LC12u2bd/izP/0T3nn/Y8rRhKoQHn38MQdHB9x+8RU+fPctmtUCZyrOHt3n7/39X+Hrv/wVpJiy8DX//X/zX9P0cPz4DKOOujZ88bOv8/ILd/jobMF7Fw7feWZ1yflywdV8ha0KDIadrTGdD/Wc/4PPvYYve1yz5jt//gEPHn7I4slb//5O8rbt+M5f/Ihv/PLXKa3nd//wm+zt73N2/JTewYMn53z3u29jjedzb7xG6wyj7W3+xb/6tzx3e5/5+pSvfu2LfPmLb3D1h0/Z29uimsDzt25z7/4jdm7vU5+dsbe3x9Xyks999haf8cp4NOLq6oKz+Tk3bxxQC3z48Yfs7G7RdQ3j8YT3P/6YL37+M9y4dZvnbh3SNCv29sbs7j5HKZ633nsfxFBXNZ//zMtcLU6ZzsasVnMmE8OHH33Eb/7mr/P7v/dNLlZLZtsT/k//l/87vu+wopycXbJ/eINbN27w5gdP2Kody4s5944XVPWImzcOOTs/58G9j1k3a548PadtWyZjyy984TafefkW33vzHvVozNd//g1Wyye8eOuIruv53T/6JqPJmNlkxI2DkLT6L37nt/ndP/gTeiy+OaddPmB3p+S9D+/zwYOHFD/q2N/b4us//0bI8O+E+x894enTU+69/4S9gyOKUcEf/+mf8Bfff4urdc/3f/weX3zjeTrnefu9j5nt7FOWQln0/OGf/hnvvP9jVmtl2XhefmGPUel5+vQh2zsT3nn3Bxzt7/LGS3ewpufOzR1K43n77Te59/ABD45PqAvDcrXk61/7HOeXZ5xfPuTgYJerxZJJpXzm9eepyzHf/cE7/NkPv4Nowa/92t/H94bv/uAD1Ck3drcoupYffOfHrBcrRrt7+K5DqyW/+JkXmYzHvPfBXR6dnfD+B+/hmjm//qtf4fWXX2LvYMbx00c8evoxs+0Rv/Ov/4Bbt/Zpm45/8a/+DfsHO7z+2kvcf/iEWzcO+dH3vs9Xf+4LfOuDjxmPx/zyL3yJ77/5NnvbO3z/3vuMxxV/8q1vMxrP6A289/Fdvv2Dd/iNX/sGn//iy/ybb/4+5/NT7rx4xLpbMF/M+ZW/81n+9b/9U07PHnPr6JCD/V3+03/8W3zvzbfouzGXp8csu+B3qkrD5XzOzRt7HD96wr2TEz5+8CB0rpaSshTuP3zEyy+9wK/88lf47nff5Jt/9j22JiPuPTxmPBpx58Y+n3vjVU7OL3jw+AlnZ2f89m//Lq+++jJVPWI2mWDLgi9+4XnWHr79Z3/CrTvP89EHb1EWlvPzU06ePuLexx/yv/pf/894fP8BP3jiWC4vY7VDzfxiga8rVldzJruHGN9w57U3uHHjNmY65eT8nMv7HzOVis9/+ecpZiVP3n2fD976LqOXP8dnv/g6VB2P3rvH6VnDD36w5PHxx/zqN77O137pi9jvG9588tZPxZxn7GbgObh5h8IYzi6WnM971t0lLzx3G+96Tq4WNN2ar/zc5/ns669wejHnf/gf/gqL5QVPj5/i9QU+uveAJ6fHvPzKc4zHMw7dDjdvHvLG6y/zzT/5Fp06npw8YX9/i7q2zGZTZoXlR08e8NVf+jrz/w9r//UkWX6maWLP7+hzXIaH1hEZEal1ZWXJLI0qiMaggRbonh70zI5ccrhco63RjOQljUZyr2jkGndonN3p7Z3pGUx3V6OhUSiggCqgdFZqFZmRobVrcbT48cLLeLV9AWv8ARHh5h7+nk+83/t023iux9LSFE9cPs3m2iZ3lx8xNXWEg4Pa518IF0lGseQwPDLA9tYWL770DIauk8QhpWIBz/exbQuZZlw8d5qJiVFUJeHy5TMYaoYXxjRbPu1Ok93tA86dOcHY+BhrG7sMDNgMFAtkqaSzXidotgkjl+GhEfZqba7fWyHw29i6zuWnLrH8eJ3d/Q0a3ZTDRo+g18WxdW4/eEChUMLQFc6dOc6du8vcefCANA2IwhjH0ZGZYO7UPCcXxuj2GhzW9xgsGBxfOsbpk0sIIWm3m5w9PUuzWeX2/RUoq6xu7jExkTJ5YoFXrzzD93/6Hkno0nV99qpdMkX5fI4CuZxkc7fBRthBKhqakcf1e7huj/3DGnESo4iMenWf7a1VLj9xluWVFX758XUEKedOHeWNF89y/sxJfvHe++ztV0GRjAwVUBQN1+tx5ZlzjA6XiOIMP/KIk4iJsSEazQM+ub6GZtiYpoXf6/HLdz+hUe9gORateguJQs3rcGpuFF2JCbwu9U6T0G9ydH6UJGrz1rt3+OIbz2HZCjlbMjJcwDAElqlx89YdXn/tRfK2xrvvf0yhWCBJI7zI58HGJqZtcWRugv/pr79LznFod1zKAzk+u/EAZMSf/OHv4gUhb/30XdxOl48++YRarUYYx5TKJr/+8CNa7R6D5RLLq4+JIpcXn77A+Pgw7354leu3P+OzO8tEccaxIxPcvHmXOyubIBOGtSKplEzPzRDFEUcX5+j1PEYnRlEUhVLB5fTxo6w83mJkZISD2gFLS4vki2XW1zZYfrTC7v4e6+vb5AtlRkeHSROXoeFhHNskkxn1Wpex0VFWNvfI5RymJsbZ3d7A9zx0Vf8cdQWlQpG62aTgpGz7PigKrb0d2lJndKqCU6jgByGGBoWRGS4+eR5h2IwMVhgtaPzV3/wlQ2NFsjRlaSjH3sAg+zs7HOysY9gadmGMKBMkgUEUZaRpSm3rMS+8+DQPPv7x/6Lm/EYtnm6Y8uSZJ3lwfxlV1ynmbHKFMgPlAXZ291A0geNo5AwLXdcYHx1gcWEB3TRYW92m2WohhGCgMkClMsDkxASDAwOMDlZYXlnh+u07dDsup06dwMk59No+lqVTrx+iCYXHm1sEQcBXv/QaUZzy8dXPCMIUTYVTJ44iMihXyuzvH1CrHTA2Pkan3aXVdTl58jiPHj5ifHySoXKBttthbmqCRr3JveUVco7N5uYuV154FrfdpdvrIFHYPWxybHGeC2dP0+20+eWvPiBKEsbGx9ANA9s0efR4g7HRUWq1ffI5h/2DJsPDY5TLBUqlIu1Gk54XsDg3y271gMGBMu1Oj6vXrrE0f4wfvf0zzp4/R85SGS4XOXn6JFkSce/+Ct1ul1xe4uRy2OgcdkOef/pJ3v3gEzQd6rU212/dxtRV5ufmmRkbQagqbpSytr5OHCc4ls3j9S1GKgNUGx1Uy6bXdTEtm7GRETZXH9LzYwaHhkjiDM9PcH0PVTfJsgShgKVCGCdEgY+uaTi2htA0ori/PDFNha9++Qt0Oh1++cv3eObyk9y8/5hi0SGKE+amxykViui6za27D2nUPc6dPUMQwNrGHlIqWLkiI4Mj6LpBMV/k1s17GLZNu90l8Ht89csvkRGxvrHN+sYmpaLB4cEutmOQLziMjVfY3dmh0Wwis5TBwSJjk2O43YBiTqXWaPD48RpC1alUhqlWq8RSIZ+zsA0V308wNPjKG69wsFXn0+X7aHrEGy+9gmI1+enby6ipil0AmRmcPn6SarOJbVrs7u6xtV/rx1PrCqNDRbI0I1/K94knQrK/12Jvfw/bNtENjddfepZiPs9H1z5ja2sPTTdYnJ9lf3+XdscHMqYmRtFUhfWNHY4tzZMmMYqmsba9B0nG/PwUc7OTOIU8U6PDfHbjIY8eP2Lx+AKNRh1FSo7MLnDp8ik6fsryyjrT4xNoadCnC8UxElA1jYHBEg+WV+n6Cj/86//E8y+8wNnjx7l5+z7PfOl3uP5og7X7D1F6NZ549csEwiDn9L1142WdX773Kzw34uUXnuTMzDTf/s73+MVPvkcaR+iFIV7/w29RMgwQ8M5PvsOFsyf5wkuvcOHCOa48e/nv3+IhVNqtDpZpkxoGTq5IteHS7rSIkpTpqQmKhQIz44NEUciR+XnW1jfwPZdcoUicJExNTlAsFcjncjx58TT7ewc0mlW2NzbxXZ8kTfB9n1Nnz9Kq7iPShJwl8IOAeCXm0qXzlEolfvSTt0hTGBrMMVgeYHh4kCNzc3x09SphEjE7O8PNW/eZn54giyOElGxv7YAQzE+fo9Wu82//3X/E8zxefOEp2s0eCyePoWg6Y2OjHB9YZHt7H912sCyD5ZUV7j54yLXrt5mbn2PuiEm73WFyZIjx4Txx1CYOPNpxk8WFJR4sb2Bbk9xZX6cbdfmDr32NzbVVdrfXENEU91Y2WV8/oNPyOH/uBKWCzdHpMU4sTdLtxdR6Hc6fPkqr2cJ1fRbnZ7FyNjt7u3z48UfEUUwYJLz4zEXOHFtkZrzCwUGt77COIur1GrNPnKYXhAQRPHXhOJqi8t5HtziyMIfveeSdHM9fOs3tW7OkSYZj53DjiGqrzepujbHRCRqNGsvLD5ifnqTZ6pGKjOefOMFTT5xhfXufjz69yfDYMGmScv/+fU4fW+SPvv4lTMvA7XSYmppgZXOL8dEhji0uUSmXmRwZZuHIIoqi8Z/f/DEnl8aZm1sk8APiGK7euI81M8XZM9NMDg8QBh7CcHDyOl6QMD0ziO0kbG/uMjo6wMRomcUjMxhOnovnz2OqGrNzY3RadVIUhG6iyhgjZ7C9vU+xMMDBwQG3b1xja+8QqagUDIXzp0+zdPIMt27f58orzzG2KPjswS0ayXXyZkxx2Gdp+AhZscWIfgrdELS7KQoBF88dxdIF7U6Hc2dOUSw4XL1+myz2eebSGQI/4WDvYwSSLInoBT67e7vsKwK32+GpJ07Safc4sTTJ3MwwH35yk3K+wGuvvchP3v4Fl544je+FVAZK5HI258+fpV5rsbq+zu07D/nCKy/wF99+k6X5eU4fXWDxyBF+fVCl1W7zhX/2Gqub9+hGGquPDynlcxyfm8YqGrg9F4GKFAqddpcgcNlfq5LFCXlFsr9fx/NCdNNheGiQhVdepOxY3Lz/CFEeR7Y9Ts6MYgqfjes3MTSb6NxZvK7La8dPkXpd4sClVC7wlQt5uqUiqaozcOxPUIXO/IlF4iT6OyXnN8uD0jWOnznJzuYOqiY4deoYH370GXlT59jxY5w6scDygxWKA3k21zbwfJ/9/QMuPnGeIAgZKBeYmZlhcXEBRVF47/2PuX//IYauMDw8wt7+Ab4fkKQpoyND7O/uUC7lGBsZYbda42tf+xLf/8FPqNeajI6N8ujhKjMTQ4yPj2LqCt//wQ9wcg6mYfL+B1cZHx/DD0PGxkZZ29ggX8wzPDjIn/35X1JrNhkfG+f80TmGhgZYWdtj4dhROs06nlBQFMlwpQyKwq27D1AVhSAKOHvmOJ7vg4BK0aTZbaLqNh23BpYgTDLa7Q7tdp3Dmk4g25hOxr0Hd+jUXWw7z/d++lMOqk1Onz7J4swUGxubFB2VgmNw585DkiShXMwxVhohi0zWN7fxIp8nLpzBMg2Wjhzhze/9hBeevcj02CgjAyVGBnREErO6XeNHP/slURwzNjTIi688R6PZotpus7N9yPBgiaDdYqBY4JXnn2ZoqMzk6DChH7JbrbK5U2VrZxtF+rSqm2Qy4V//02+SN0ze/+wGM1NjnDl5lDt3lrn38BELM+O89uIzaKbFRx9fJ8lS4iTh0vEl5gYrRFnGF994kSwDy7awjRyOrhF4PTQzz9e/8jJRLOn5IX/xn39BrdFCUQ0EHnnH4dSsTmnE4t1PHuDkCyzMzzAxNMxI0SDyWizNTzM9YhMnHsvbj/EzyYVTSxiKS8kBVRV4oc/a4zUyYTA8VKF+WCNfqvDKq1+hMlTBtBSyoMfIxDwPD+po8wlrbpvEFJw8M4w09zE0jdn5mJy+QUOOcxi12d+tYSkThKrFymobuzhNSJfltiDrhLj5GRSRcbOqEEQqcnSJocHFfmyuEKwHKrZlEZdtdrICWmmMR54F5CkuXGJscowb+xFubpamNYxWVAhsEy/N8GSOmlTwiwqDIxnvLx+iTD+JP1RBWgZuqrK+38HS4cHyA1puG6k4LC4sIdMIv+eiOyokCW3PI0bQ63Zp1dvcun6VJE0ZKJVJdJWB0VGmxsoMlAps71W5/eARh7tbfOXEDIf1Jvfu7qNqKv/8f/MvyeX63ihDqmjT0zz3+19BUxU0Ehx/j1SkCKGQzekEUYowHXbbf7dA/UYtnukU5Oyx09TqHcYmB+k0XYp5hz/+xpeQWcLK6mPGx0Yx9Ry5nM3a6io5x2ZtbZ0gjAmjhBeuPMft27fJJNy7u8z46CBPXTrP8OgYtUaHjz7+jNW1Nf70W79Pr90lkxn7e3tcunQJkYb0/JDTp85x/fp1qvU6U1MjXD5/mv/+//M/c1BvYDkmo6NjFJwcUlHo9Vx63Q4jEyOUi4Ps7u8zOjyIrelU6wfMz03y/R//iuNL85w/dYy//u6PiTNYPDKLCiw/XqNQLDAyOIBlGwR+Qr3dYW6k3N8+Fkvsbe3QaO0wND6N14XRoQr11gEP1h+g6Annjz/F9OQkP/7xL3BDjyjpcen8BS6duUClXGZ9c5cHDx6Rt0xkEpGkkvnFI9x/+JiPPrtBZbDE73zxDWYmRglDnx+//Styasb4+CjHT56ilDPZ2tzh01t32NvZJ0wyDttdnr30BD23y+bODrMTk6yubtL1Pf6b//KfMT5Youu7dLsBhwd1wjjk9NmTdPyA67cfsLvfwDQNfN/j5IklNFVhYnIcr9nmsFbF0HXCICYh4cLZMxiqJG/bXL95l/16i6PzE1y/fYunL5/DcsogDAYHyhzUmnTbLl4QUBgYpLa3T7Xapt7sUCpWKA3kyZKUIPBB0QjdLplQeOLcBZrdNqqi0nGbHO7VSGXK7Ow4m6urHNQb/N7Xf4d2p8lIpUyr1aTg2Gxu7fIf/vr7qKpganyUU8eW2G90GK2U+fWn1zh78hhhEDEzOc7i0SUUTVIq5EnihAdrG4RxTBDWyGiwfbhOrZOQKAsMT7Zp1+cZKo/gKnl84dDxAobKDkjoSQ1NN1DJCBMVRVXJsqjP7xP00U4SDENBVTTiNCEI+ymvQvSRRqrWT1SI4rRvlFQUTF3HMjXiqH8SrKYhc3ZIY38DszhIEvcDcfZu/Qo/zohjjxdffBmpadQPa2RByOtffJXGnQe8u/KIZn2f02dPMj03S5JmbK5s86Mfv4UiJF/40lcRisGRxSMcP3aCb//kE7ZWH6LGdY7k81iDRTRdo9lo4IcBA5OT9HoZr125RNft0PZcJALLySE7PmGaoIiMYqVCp9EkTgJ0RUd1ivyf/nf/q79/i5cJnZFnv8m4buAfPMau1Dg5O4ymKHx27R69XgeRphTsAmGpwNj4KKNjY9RqDRTV5/iJWZI0otls02i0OHPmJKdOH2NxYRZV0fDDiELe4InzpzjYP2BtbYN2p8sX33iNG9dvcvvefS5fusS1W3/BxMgIpmXT7XjcuveY0ZFBau06WR9vQL1Zp+cHPP3UkwSez+j4GM1mi4mxEU4fmycKI9bWN3n7F59QKZd48tQxvv2dHzO/uEShkGNsrIKKZGpmipytEabw6PEmGZKxgQoD5TxTo0O0uhHSD7CFyWB5iKFChhemjI5U8PxZnr58id29De4sr3DYbKGrgj/8+u+SN03iOCEVUK3X2N3ZZWlxntmleYSh8+Z33mJ+eoLXrjxFrdFi+d5tTA129vY4OjuCjCOmp6ZoVA/Z3/EIvZAnzpzEfvYJCraD2w24d/8xsZtyZHKSVqdHGLq8/OxzLE0P0uu1+zEzpsb05BgJsLGxxcKJY5w5e4rz5xRWN7cIg5ClxSmyJMNx8rzz01/weHOTZy6fZ3KoyOr2IVu7mwyWB/js1n2eungOefMOn978GMsu4vspK6v32D6o8sZLL3H3zgOuP3jEicUloo0dHj1ewUTj2WefpjJYpN2sEnsxVsEEVUHkK5w4dYqu22Pr9kNeuXwGURlETg+zsbPLyvo6qip45slzNKt7TE2MUinmqOQ0kjjidqfJS1cuMTs+QtG2yVBZnJvi2tVPaR7W+GWtiuM4zE4OcbizTrvVRGiC4coIegqWmaOx32Zs9ARtWUDTfPbrNeYnF5k6NYckIVUyMsuk13Qx3U0US2e7HVMuTZEzYu52dIRpkesesC8KxKqOJUL2mx6KY5JJFUuVTOdNNncPyHQTwzJJOz30qMVSpcDKowOEauANH8E385iaZNhWUQk5MVJgu+EhQh/f9wjimLnJQSzdptPtoHghS+ePIY4cZfHYEeIkQbXy/LMrzxJHEVngMVKy2TxsUj8M+V//i3/KaCWHpuq0a11OnDvDz28+pt2u4dBmfHaC82fP4QY99qtVvE6b7fUDvvL73+Th8mNWqy53Nx9Sj2yEyDi3kPH2X/+Ids9DIPmjP/h93v7Rd9na3GBgoMKrv/enf6fm/EYCZReKFEoluisfUfK32FjfYjsZY3sF/K7P3u4B1doAJ04c4/7qKuVykbd+8g6FQo6zZ84QJTEHh10mJ8cwDZ1CwaLXbXPn5l32dg9QDYPLl89z/+4yH370GSdPn+DC2fMc7O5jOTb/4MtvIASMjw5SssDQJbtdQZDGtPyQJNNwTJ1HjzdQVcFzz1xmemqcRq2FzFIWFuZoNJo8Wtnm4coq61vbXDx/ntmpcYz8AKfPnKbjety8dZvjx75GEProKPhxwOFBkySTnFyYgyTA81NqNZcf//yXdFyPr37pVbIoY21zmzhL+fIrV3jp6edZ21rnV+/fZm1ti17P44tffIFHK5usb27j2A6XLp1loJDjy2+8TC5nkRGzvdfgd7/4AooMqDdcOt0URRP81Xd/QKfR5eTSFM8/+yx3bt3l5deeoxNFhF6IqUGr3mR3bZtKZYD93U3qXRcnn+fc6aO8/MKzzI2V2dndw8Ok3uoSRwlRFBGlMaPjY7S6XX71wSdYlkWWhLx4+TKGZpOQ8Oab3+fpp55geKTM9PgkUdBiYXGBYqnMYf0A16vSrG3TdtucPnWRoNtBBaq1Nt2ux52795BJQur7DA2P8O1v/yU932NpfoFyQUFJfKTMyByT8fFJiraNZWrUWk3+/X/4S77w1EnspEOSxNzbrZMZOV579VkmJ8c4ONjHFIIg9PCTAFuXNDsuz1w6jUhTDg8OmRof5oMbD/jbH73FQDHH4rEjnDx+lGq1xvFjR9HSgM0goB0EWE6BgXKFf/fv/iMH9QavP/skW5s7DA0OUs4VODzoEHirnD25iECysXqX9e0tnr38JF4UE3Z2WN1ZQSqCV1+8wv3lm6DAG8dsDg52iTouw0nMl596DtIIw7IxLYv/6//9L8iyjPJgha7r0mp3aE1N0ny0Ti6f4+lJC80w0bOE4LCJZdk8fL9NHMWs7e0wWCxTKTmcWFpk77DKgevRax6wfvMaTddj9cEtypVBBgo20tIwMp+pkVGi9iFGt82zi4NomkkS+pi2TTs6oLm5wsbtaxhuG993SV2f1tYme/cf0CkWSKIERWiILGKorJLqBl4n5NTpKaSMKRoGly9exA8DdF2hUrI5MjfN8aMLDA2PkMsbvx2BStOMZsvDdxaI9DGy+RO0bB23XcUoJGiTo8ROymYgyFXG2Nrfo972afk+ye3beIGP0Fwq+QprG/scPTGFVBI+/Ogau3sHjIyNce/BMoEfMj83w5HpaQ72D3i8ucGJYyf47M49craNo4NjqUzOzJGzda7fvEu91SSVGcePLxEFMfeXHxElkvfe+5g0TXB7LufPn+WTqzepNupkScY//kf/kDgJ+fUHH6EIyaWLFygXLIYHcxhCsl9rc+POXZbmZ1l++IilhTkGigUO9mNqtSrrXsDM3BzTMxP4cch7H3/KhdNnKJQcBBmP11b5+XsfMz89S7FQQtc0DM1iqDzE1PgkSRQwN1YhlTpZJgnjhF7XY2J0lCgIuXZrlZHBApODOTLF4F6asLB4hKWlee7euY9MfK7duIFUTXRV5c6Dh3S7PjNzs2SqSisIOHvqBIpmsLq2w3BlmP3dGp8ur7FwbImR4RE+/Ogqx08sMFO2OWz0+MmbH1Cr1/jdr7xB7fCAe4/WmZoYpdfzePLiOUaKBdyBEULX5Vcf3eD5558lMmMOD/do+02sYpFjZ05iKDqfrD6m0fQplQdwowgvSRnOOVw4e4qDvR0unD3JmdOnKOYsFOmyc1hjeGwazbLZWn9Er+PzxS+8yPe/+wPGx0aJooiWHyLTGNUwefqZJ1AJadUPqNWrhJ7LysYuV65cRs9SKnqMoUq8NGVhbp5Wq8XVT69y4cknePXF53ByRe7evMOpZy6xt7PN7vYGleFxysOjlCpD/Me/+huq1Srf+J0vkImEgV6F8YlxVtfXMB2b0cEB2o0Wm5v9g+NvfvMbrCyv0AsCWt0uX3r1JT788DpClSg5Qdfr8auPP2KyMsjBTp1//o//EJGEeF6GYarcv/0B09PDkKYszk9z9d4jZudOc+/eA9p+QMt3efM7b2KZJo7jMDI4RCFvY1s6L12+yInwKAoxhqZBFpPL25w/d5o4Cum0XQaLRUYH8kwMOzxcWeV+rY6i6iTpTQYKZaYnJ4g6TXKFPA/Xt/jks5uoQiLe/TUfXLuBZqiMj4yhxS6NzTVOTI6Shi62ZZGUHP7sz77N9OwMC5M9zgzZ3HjrZwhFwR8dYX19nbxjMTYyipl0GSnZjA0N0qo3GUmtv1NzfqMZVG5kRi5+9b8iiwWqkhGl4OQcZCaJwgCpmp+z7TMEfdy3FAKShCTwyIIGZa1H9bBJEElGxgs4WkzcazGQM7nx2T1KIwXGpovYloaSJOS0Ye4vrzJQLCBlTBhlzM5OoqgKcRhSyhfZ3N6j3e0yNDiCqavcvH2fCxfOkfvc/NhotllZ3cI0dRbm5xgol9ndPaBcKRGEMe+88x4LC3O88carxEELTTGpVAbodbps7x0iVB2/1+HkkSnq1QZhkjIyPMTVG3d45solNjd3qNbbnFya5/TxRcIk5eCwyujgMMsrq1y/dQeBwtGlBWzbYLQyQL3RwS6Y+GGAZViMj4xiWwZ7B3WqtRZJmqIbAgKPWqvB5Ows0xNTSDLcdpudvQPurazQaTQxhEKuNETOUrn0xBmSNOOwWqNWa2AaBj0vwgsSxoeHaLk+fixx8gXq1TonTs5TO2jxeHsPRVFoNhrMTE3iBz5LczO0Wh1q1RoXzp3Etg3iMGVypIx7uM+PP73FmVPHqdcamKbC3QdrzM5MMjExgqXA3TvLlEcqLB0/xsdXb7Kyssbc5DjPPXmRYrlEq9ul2WzS7XTxei5GqYgkY31zm063x+mjR/nqS8/Q6/S4cX+NP//Lv2WgmGNhfpp/+adfx/V8pFBodnr8j3/xJkmS8F//8z9mZXWL+w+WeerJi7z/yQ16XsCXXnmefLnA49UtHj5e58XnnmOgYGEZGmEcUW+00XUd3dTpein3793nvfc/5iuvf4HnnzvHZ7cfMjc3C3HM2toaU+PjFCwVz/O4euM2r7zyKpXBEr/66Cob2wf8kz/+GoZp8P0fvEUgEg4PmliqzifXb3F0dprf/fKrPHnuGDfuPub4iWNc/exDVvd2KNklZsfH+enbb/P4oMMXXnmR7/3w7T4mShGMj40wNlphcX4aFY1MKuRsGzJJp9fl8doG3W4H14+xDI3nLj9BLudQa9Q4sTSPoyWQJbR7Idt7NVrtHopp0267zM3NcvzEAg/uP+T/+x++jaoYnDpxnN/7+j/g3//Ff8ILI8ZGhzB1jYmhYcbGhrhx9x6dbo/Deotmt8czTz9Jr1GlWq3T7AV84+tfZnn5MT/44U8pFfOMj47wzLNPMjUzxe1bdzlz4ijFvMN/+b/93//9Z1BCUSgNDdNutggDj9LgIJHbT9IrDBT6JwyGQZxmBK5PKlXazSqm7WCVhtEGhunGCaVRm6LSp4z4MoOyz2a7xuDTZ0lQOMgyNKmQ1z0eHVQpjp2kOGLRaxwwaFpUQ59ux0W3FWROJ1YjhAmDk0XCboquK2QyYnuniet1adR7jI2N8NwzT3H7zl1+8tY75PMFzp45xejIMKdOnsALfLY2t4lCj2qtQRRFXHziApquMT8xQrm4xNvvfUwUBjz31EVa7Q5Lp0/yydXbHBzWKRXzjI2PkcQJXrtFr91mc3WTVqfHuRNLDJb77fHBYRVDU9C1BE2zmMrn+OE7H7G4sECr3mJ8YgIpU8pFm4nhARRVo1CtoTkObb/XD/1LU9SczatPX6CQhXiZxt3dBp1Gg1sPHnPt1gPGx8fpNuucPb2In0KlXGRufpZ626XjhYyPjfM39x9hrdrcuf+QKJZ88/e+xs3bd6hW61i2RbcVo6Hz+isv0vV8BstF2h2XeysbFIs2uUKJ7//kF7x05Vm6HRehqBw5Mk+3VacyWOD55y9j2g6tXkDo+sxMT3Hl2aco5S08zydO+iDJcrFEnEQ8ePQQ3TCQacTkyDivP3+ZgmkgDQjiHt/8xhtMTExw8sg0lUqOMEl496Ob/ODHP6dYzPOlL17BJWZle5eDjs+9xxu0PJ83XnkB1dERms7AcJnz+VM06vvUqylrG7vEacrS0iJvvHqF9371EblSAWTCa69dYW13i8J9h3anTrvpMDM2yvkTi2w8WqGRxHRaHQYGhhgoO3x87Sbf+e73OXLkCBDzi5+/z3vvf8CZC6dYmp7kweoac1OTvPzcU5w5Nsf+QQ2357L26CGq0Dg6fYTRgRKNVo9MV3n6yeOgC77wxZfJ5w10keHkHNqdHuVSmaJT4LBapVzOUTuo8ld/+SZeIlk8Msvxo/N88ZUroCj0uj2cosXG9i6zw0VGKkUebR0yN1XBOjLG6m6XmSNzrK5v8O3/2/dIwohji8f5V//yv6CYz9FsHDI7NUaQJARJgu5Y2HnJ6sNrvPXzj+l0XFDAtmzee/dXVKsNsrQ/2P/+936Ioip89csvMT83ydbOPptbWyTAkdkpTN2i2/P+bs35jY6FJxbkc//q/0IQhARBQJRmWKaNqWvEYYimqrhuQColsdchQUUIgW5YGLreRzgLnTjNEKqBoYFMM6qHByRRiKab6IpASSNSVcfM5cniGEVRSeIIQzeJPI9MJFh2Hplk9BoHiCRlfhQMAlw3xBQRcRTRDAR2ZRR6DYZLKW6zwfvvfsriiWkWj08xPTnOrU9WELpKLmdy5tQxbl2/i65b7O7X0QyVqdEx5ifHMC2HJMsYGiywt1/j6o27bO/tk0QRz1y+gGWoPH/5DG/94hOef+IM71+7ix9G/P7X3qDgaDx4uM71m/e4dfchX/rSq1w6dwJby/jFLz/hnV9/xuBIhSefuIChCYbKQwyOlDjcPcQLu9x6sEKQgKII5o/MUKvV+cpTZ2hXqwjdZHR6kf/n//DnvPLSi9TqLfLFPMdnJzncXafrxdj5AUyrQLXV5Ts/eIsTx47x5LkL/OjtdymUbBzTod3sUj1sots2s1NjxFHCxdNHWVyY4sHjTX7xwTX+xbf+gK7b5Pa9+3x2/Q6OZeI4JqePLbJwZBqJZGd3n57b4+LxOSI0kjQjjiWf3LjF7OQ4E4MlMiFIyAijFJlFdLodPr15l5mZaSqFAhvbO8g0Y2ZqkrDTZXZ+EitnMjE8xvDwIKqisHWwyyef3eXbf/MjBgcG+pVjlvDw0SqGafLCU2dRFQiDFJGpKEWHqZkpmrUGhiL5i3//HeqdNpmE55+6xDe/8TvUq/v84Kfv0fY8DqsNGo0GSwuzTIyNoSmCL155gmajy/KDNbZ2tmm1XHKFPH/6rT9AERF3H23RcT10VWI7RbwwZGfvgCgNODIzx/2bt7h88SxFQ9DotAhjhUdrW7x45TKTo2PESUSm6kSJZL9WJUNSdV00RRDHHpqEYqVCGibMjo5TMC3+2//Xv+Err71EwbJJUbj+4BFnjy/yaOUxX3j2IqEAoats7FU5qLUYLubZ2Njk/c/u8rtffJGj85OEqYGXxNy694DDvSovPfccL7z8Er96/5fcvXePg4NDJicnqQwOc+vOfabGK6ReD+KItz++TaPZRVEVXnj2SZ57/hwffniDLMuoDBT6pmEvxMnnadSb1A4bCFVlcWGGIzMT1Bot7j1Y44MPPvgtGDWRtF0fmaXopoWj6SiKwNQUpGngBkE/YVDX8U2172cRgiRNcGwLQ+3nyOgSTEUlCHwSVWF8ZhKyFNcLiAMfy6kwNFBE0RTcXkCcpWhC6Yf2j5QglQRBSMFxGJ2dIEsltmUikZSEwLIMcqbF4Oc/26w1SYyURKwze6lCZaxCPYrZutmh2XNYPHGUXNFgq1nFGFCw7IzpfImN1TZb+7tkImRqepR2s8N/fPMzoijBNHRypsNLL13k2JFpHF3h1+9fZWykwnd/+j4r62t88xtf4cOPPqXX7bGxvUWhXOHppy7x6OFj7t1fplFvUK3XuXjhIjdu3Gb38Gf863/6LaYmBum26zze3OL++ha7u3uMjw5z5dlLxIHH2ZkpWvUG9VBhZmqBjf0qLz3/Ivfv3GVra5v/+l//K7qdFoZdYdBSmJgaZr/a5f7Dx8xOz3L2+Cl8P2TpyBFqjQ6qmmdjc5P5+VnOH1+ETJJISaEwRM/LWN/qcPniU/zwrfdRFIFlKbz8wissP3yIIqCQG8Syy9xfXuFn737EM89eQmoOhqoxVCrRbrb54otXcLsd0ixDpglCqNi6pNHzeeeDjxksVTizeIz64QGLEzM4moJqKES2Q7FUYHNjH1PL4fkBilD50ds/Z+ewzu+8/gqzs9O8+f0fIRB84dWnGB8fpL53wM5GnVNHj3DgBZi2gxqGBAd7RLrJ4GCJRGQUHIfJsQH+9gc/ZHl9k+3dQ9IkQ1VVBstFFqcmmBsdZmJ8jIdre2i5AqsHVSbGp3jh+Wnm56cRav9OdW5umE6vx+TMPK2Wz50bN7h1d5nnLp5kqZwnf3yB3Z099nM6qtB54uxRIkNnc3sP4fv00gyzVMaycoyMjJHIhHurj6lWG9RrDbIspZQr8K//yT8i8HxarS4qKgPFMrNToyi6yeDMNFIkFEeH8GVCyVYRuqDXi3jm0hOYhkGt3WVkdITpuaOURkqsbx1w9/4KK4/XWVnZ4Ctf+SqaKqkeHFAsDPDhx3fY3G2gKDA+PooQgm4vQNcNyuUyQZjw+msvsTBd5vGjFQ6rLRaXlhgeHabVaZBXVFKZMTs7zeT40P8/FXRlY4fr1+5ybOnI36k4v5FAqYrCYCHfD/vXVbJUEiUhpuUQhjGObREHIZqqYJUHSJOkLy6pgKyP9VFVDVUINFVjwLaJk5gwDEnTmIJjoJYKmIbZz2HyYtI0w9A1sizD0BS0LO4TKtKMOMvIkhhN7VMAgzgiCmPsJGKv1iQNUyrlAvlygUyCPnue4sASetGBLCOLYo45NpoQ7HVahO4UinEaK3E5MjfIev0zlo6OsHLnFrcfXsc0NHqpCpmKZVjkhyxKowYbnT32Nta5fWeH40uzdMMeuYLDn/+nN0EI2u0OlmWRJI/o9XpIKRkaGkAIBduyuXbzJk7O4Z/9yR9SLtr8+O13uXn/AU8/+zTnTy/xypUnuX7zHtODA2gMcuj6/NmPfsrk2Bi1rsfZU8d4/9cf8+DBA8bGR3jnV+8zv7DEnTvL/N6XX8GyTe4s7/DqSy/gdgL++ns/4/TRRYqOQ1Yy2D/ocvLYWV595hKaqrGzV8P3fazcIHsHNcaGJhkZGWZt/ZDDap1y2aZSUcnnh2jUajQ6Id6jff782z8iSWO0T+4waOcxTY2xVGNjfZuxwQq6biD5/PNOQnRdxxAa46URFuZnmZ8Y4sjoENsHBxRslUz255xCSkwl5ZOPruInMDpW5LmnL/Hh9dtcu32Hn/3yPRYWFviDr78KMuGDDz5jeXmN116+wvDEBE6SkWUZY2WTh2FG3tCYGh9hr3qIUyrwcGuH3d192i0PU1MZnRjjyMQoFcukWMjRjnySwyp3H66yt3tIvd3liXOnOXLyCO2uR7vZ4OHjVXb3GuimzYlWwg9/8GPypSJBGNCqt9m097mzvkG11eX82TM8efEEhi4YrNjs7fZYqbW493iT116+wuL0JPdXHhNHHovTkzQbXSqVYfa2dnjqmePEbpONnSpd1+P1N15h8fgR1nZrlIdsrt+5Qeh6DA1VsC2LYmUW142ZHB8mjAM+uXaTn//yQ771x99kbnaCeqPN+uoWYRTTarWRMuPGjWvkTQVV1/nsk0/Z2d1Gfs622t/dZ376dV569WXuLj+m1GgxNjeNouv87IM7PHi8w9zUOMePH8fQBZ7fJoo/x2bEMQqSnCYppA0+Xd0HJNVm67cjUGmWIUkxHAuZStzA7yN64gRdFXhBv0c1FIEhBDLLyNKUOE77ovP5piryPQzbpmDZCNnHJKmaSio0UtlHQJu2Tc/vr52DwENXNTTbJEnByOnkVJVUpkRxRppKUj0lZztkaQ/Pj9B0nZxtk2QJKSrlYg6ZpjiWSppmaJqJrqk4OROBoKJXSEtFVAWkUNiNEsaffJ0OgsrlU1huD8vSmZIGYRIh0gjXa3Oz2XePd8UM9pmMncynnd6i3mihGhUCt0ei2CiaRq/VREqB7dicvXwMU4Xt7TpHFqZ54tRpHLtLu9UgjnzC1GNndw0Zwpt/+2NeefFZfvruR+iWwWGtjtfzWV1fR1cFrUade48f8/U3niOnKzRihb9884dcvnSOt9/5FYpqIjWbUnGQR4/WqRSKiFQQhnD12iPWt2uMj09w/oxkoOTQDMBzBa1GjG2W6blt/uf//BZr65tYtsHF82fodBS6Pcnw8BTzU0fodlv86R/8AYf1GqahsVf3mJoYpd30MFQLP4hx3S6eW6PX20fRbKzSIKeOnOTSqfP4ocetu4+4v7LCE2dPUc4ZhF5Co9tl/6DK4cEBZy5ewDJ1NA0+uHaTt9+9iqYLRoYG+dKXXiYLA9766a+4ff8xzzx9iZwObn2Dg27A5PAotY5gZn6Wew9XeenllwiRHNRbzE/PMzE6jt/zGBwsktM1Dnb2OWh2SVWdpYVZcrbNw5UtnnnyAkvz00yNlwnCkGs37lJrdeh12/i+R1yrcfPGTV549ilGx0YAg1t37/CDj66yur6BbeX4x9/8KroqiTLBYLlMqTjC1Zt3mJoYxTBU/vObf8vi8UVMJaHdanP9+m3CMGKoXOa1l65Qsgx0O8f33nqPZrvL5OQwMmlT3W0wPlwgrZTQdIPJ8QmkaiNMhd7BAT3XY3f7gCfOn8H3u/z05++yuDjD2QtH2dnZ4dHKMtroINdu3MZQM3YOG7RaHcqlMs1WkzTNUITg1MmTfHr9Dp/euE0QRMwaJguLR8jn8oRRRpKkOHmHvf09kiRD01UMVSBEhiFiNEXy8e1ddvfrPH/lKSzH4tOPP/5f1JzfaAaVH5uVT3zr/4hu5iCTxGmC5/sMDVTIOw5hGJKkKWEcY5rG52DLCFVRsAwDTfSBg0EY9UlrQkWmEVKoCJGiqRqK0uekIT4nasi+qCVpH3dtmwaWofWNh3GMpqlouk7yOflDBeK0z3NLZYqqGiiqhqZpnw+nVUxVp+sFhFGIquhIJUNBoeBYfZyPVIiSkDAKyTIFgSRN+rGsqqqRIej5AWmWoYgMt9PDMHWcQh5dEWSZRBMSy7JoNVsE3S4yaBG1Dum4HRw7x8hAiZ2Hn5ArGehZxOhkgSRqcbjtYpcqzBxf5NG9TRq1gPyAw+ywgSlVdja2MRydbsfHyZsYjg2JxnOXL/LZtdvce7jK0OAAYZTy8pVncLs+rY6LqeV4tL7DkSMLmIrN3Yer7O93cf2MIIKx4VGevnyR3YMaO/s1/vQPv4yWJXxyZ5n3PrhGz/UZqlS4cP40ugoH+wc8d+k0s6MVAr+LnTNx/Q66GpLLW/xP/+ktysUCp08eYXFxBq/nsvLgMxxL4JSLhKnGwtwsuqaja4IbN+7w4PEar734DBOjA+wfNEBT+cUv3iNnO0zPTiCzBInAjyJ+8u6vyZcGkJFkdGKY+s4esxMj1Ftt8sUSr125gOpv8nB5jXos+eRBnX/4ta+AEDS8Hu1Wi0azybG5BfZ3D0nSiKfPLRIhiPyEn//qQwpOgS+//hw5w6BQHkAoKgVd0nV93DTFj1LefvfXDA8O8NyFk3x49RbFUo447rK2XefU6Sc4fvwo/+bf/hmWlWN8YpDRos3UaAGpO9TdiOnBIdZXNtlvNsg5Nh/fvM3eQYM//ZOvMTo4SK/j8em123z06XUMy+b3vvQKXqfD8qMV6o0Wx08epzBY5vrte4yOjRIFEVeevczc1ARhkhBGHls7+1hWnmtXb3D99n3iKEAIldmpIb7whSuITGFtc5dGq8lAJY9MFExTo970WTp+jFypyF9++03cXo+jS0u8/PJz/NWbP+TWnQcIoTA8VOQff+uPGB4ZQiA43D9Eyohqo0PkdbBNgaGqfRxW0Obu8iY3H+7yhddehiwhjkO+890f//1nUIpQKBSLfUptliDThOHBQaIoouO5ZEmKbZkEcUSSxHhBjC4UhK6QZBk91yXKEnTTImda/crKNNF1A8/3CIMI07KQElyvhyoEZs5CVTUsS8f1I9quR5gYFIol7CTpC4LMaIRd4hhyhkEYRv0nLQZBGGA7OWxLJ4xiNFSCMCTOEhRN0HNdSo5FQkK93UZRVUgzojghIyOLwbQ0cpZNJiWpBEvXCNMYM1VAkWS5HGgqURghhUB3HDRNxyk4WLaFYc3gBzFxkpJ3LHRVxfMjhs+/RJZGyKBLEEZ4fkxlzqRUKpKoBvMTKkdkRrXeorF1nbixwUFoo2Nh5ocZmRqg3ahzfKHCex99yPUbjwmCgIPDBhcvn8JXdml7CbV6yPbeOopqsrG2zemTp5iamMN0AvK5CoaZJ4pC3vn4Go12j8uXznH1zjI71Ro37z3Gti2effISMk349NZd4jDg9ReeZn+/xdZOlbGRQeRBgyR2OajXOag1ubO8w59+82tMz8xSKua5dWedVuAwPTdNqNgkccRaI4TMIwp9NuptnnryKaanxlnf2KVULjE5NkKWSPzARVUUdDPHt7/zAxrtDnNzi+zt7tHreqR+zEDJYaAyxPjkFJppEIce1+7tojtjjA2V+dOTQ5iOxb0Hy2SyT8xJwpTnLp3jww+vMj06QBD7VCqDLO9v8OXXXyFvm/Q8n2K+SLno0Gm0qPsSP4kplipUm9tksk9tvvloi2o7ZKPaplTM8ezzL7KwcITl+6vcX17rV+nly1x48gg7+zV+/ckyF86cwT3cpVyxiEKDME1YnJzhH7zwFOMTE+RzOcwo5Y2nL/Lq00+CpeMlCX/zt98HASPTU+QHB/nOD94iny/wzJNPsrGxymG9QbXZRMlSoihmoDKI7VicOHUMP47oeW2yTNDu9oj9LtduLlNv+xxbnELVbYy8Sd7KEcQtJsbGOawfcmxhjkuXLiB0hdXVDTY2N1A+/+41my2uXv2M117/AuVSgfJAif39XaqHVY4tLaAo0Gu30TTBytYhN5a3URWV0bFhKgNFkijmO9/9LcSt5EZn5Ok/+T9gaiZCZH2bvARQ0dQ+M7qQL9CsHRAmffFJ07QPCBAqiSKxNB0hBaVymW6ng24ZhF5AmCaYhtmvSpA4OYc07ZeLuqmjCkEYhvRB2QpxHOM4NnGS9eNcLQsvijEUlSCKEAqkSUaSxeQcGyEFumbgBz6uH2E7JqW8gwLESb897AURfhD0N49KH/MeBwlSCPJ5G1X0/66qKLR7XVIpsU2bOEtRFYkqJVEGAwMlLN3EMnWyLAMBuc+zebo9n0z0s66zJAWRYRt5YjLCMMI0VTwvIo4SUARJlJKmMWOjI/hhQKcbEIc+mm5AEuG2O6hZTOK2cJsHtNtd1KBBXhOYjoLb9llf38PQLWaPDKEpJpqZp9rIODJ3lEq5yIefrLC2vYuiaxw7c4T5pVH2djusre4SRQmLC/PUq3VC30NXYXRomMXZOUaLeXQlo3ZwSLVW572PruP7IVL2UemvXLnM68+f586DR7z5o59x+tgsz15coBqpOHb/9e/ubhHJjKfPnmRhepjHj9cwTI1Tx/pZV0LpE63vP97g29/5IZquc/7kCRxD5YNPb/DSlaeQUnD6wjE0zeDBg2XW1jZ47YWnKOaLZEnMo9U1FqYnkWmEECpCwPLqBmuHNU7NzfDjn/+SVCicPTbPhVPH0W2TqNuhHkQopsZYZYjIC7Btp393Fqd0eiFHj87zl3/7fe7cXyMIfWSaYVoaJxdnOHfyCLptU/dTfvbOr2nVGpw4tsRT549SyTu0G1XGKhW8KKAVw+RgmW4k2D3ocOP2HY4tzXD62AJqKrELDkMTo6iayn6tTrXaoNHs8OHH1wjCkGOLR3jllRewdcF/92/+jMtPP0mtWsU0bT746FPOnj3DyRNHGR0uUyrk+dsf/hDdzPP2T3+Jpmu0mh2effoJXnj+GUZHR1B1kwcPHrK7t8fpY0uQJBiWxfZhnfc/vsqdu48IAx9V0ZgcH+bLX34N3dD42c/f443XX2V3b4+8bdJut8kXSjxeW2N7c4fDwyq5vMPx08cZHx7m7NmTtNoue3t7/Lv/4c9+C1s8CRoCmSaEYUCv56KZNoqi4CUxTi5Ps9kiVyghfI/A75EmgiiM+rlCAhLDJAgDOr0OuqaTdLsYhoai6kRxgiAjjGPq9SqFfA40kyhJ0JEEbrvPnFcMpKLguj0UBLqZw/c7pG4TLwXdymHqGhJJHIX99gyVfFEFTUXTFUgTup0u7a6Lbpl0XA8hdExTpdfzaIcx+WKBOAqJ44gg8tFUHdf1kWToqooQOmnskmYJuZxFkmSkQuAFIVEUEgT9t9eLQuI0xTYt4igkSSWB52LlchTyDpoS4fsRURAiVIGhW+TzFkGcoBsqppKj3ev1b5kyMCyTTICqmoxMjKILjV4UYfsLlBFEnouaJnSaLaICTE4oZFFAoDooSh4vFDCkc/vARakpZMNnGB8/jyDEVVzub7tk6CSFAXTDYDuQSMNGqBlDY0WQGc1siyQwUFOdXhDRQ0Vx8gwPDjM1MYzb8ymUKvzwvZt8du02cWqyd+Bx72GNnUabimPgdVu0em1SkTE9Msnj9X2KeYczx0bZ2msgVIEqY+6tbPKLjz4DqXJi6QjVRhMh4fjx4zxx9gS5oUFiv8fyox1yRoknLjxJsxViKQFpEDBaLBEGPraZI408UAT1ZpfllXVa9Q4rW/uohsphs4WmaJi6ytjYMCfnJwlieLS2jWYYTJWHqPX2+Oijq5w5c4zt7S3W1nbp9nxUVVJ0LL766mk81yeUCm1f8vN3P6FcGuSNF1+imLOQcUQp56AkefScTSQFliHINJNPP7tDrd4hjBNu3FuhUW9y5dnL5AYrGI5Np92j1ujwcG2b73/vJwih8Prrr/DGq1dot1v4QnD82CJrK2vMzM3z1s/f4fHKBrv7VXb29vjTf/RNPrp6i3fe+Zip6Wk6nR5BEFIZqPD6G28wPFiienjAyuo6C/NzFByd/fohuUKJux9/xq/e/ww/TIijiOnJKZ556iIvvPAUd+7c58dv/ZJ6s81//E9/RS7n8PIrL6BoOh9/8hnNZouj89MUijkWji5Rb1T59fsf8XD5EcdPHqU8+NtCnwNdt4tlWHi+T0qKiCIUs0+aiHwf3XRotVpEYYBpmUgJUdRFkCGTBNu00DUDwzCpVw9AgG3nAYFdyKMq/etux8lhmjZSgue5tIIQXdNQhQKpRAZd9FweyyngdTwUFRQ7T06oJGlMFAUoqka+PEAcBmRJSq26h6boaEJFmjrdTpsszQiCCISkWCjS6URIKTFMg063Q+C5aKpOEqdIfISmkXMssjglShMMIVBUlTBMQJXEbgBJgm4YNKO+4GiqgqIauF6AKjJAoOsmMktpt9oIRcEwLSzHRKZgWBq7BwfITGDZFnEcY6gaGSrNRpVCPo+iavi+h6Ko5GwHhCQKU8IoQdNMDN3GKNsoKchMRUUlChQUxaKs2DTbbfTcAHYxT5wlZIZKKvvYdjVvQZYwtCSQQsWwdLIwxG3UaCkGpgFuEiHilLTTIty/R63aQeRMjj9xhpExm+Vb67x/+wFxGIJjMVUZRhKzVe/QbnscVjskiY8iJbmCzc/e+ZDyQIkXn36Cza0Gds6h2+ty7fYdoiSk4JSZnx7m4fo2hwc1cvk8/+SPv45imPTqPfarTZptj63tA8ZHBhgoOqz2PJrtDp7XY2R0EEPrcW/lMZ1Oh52DOs9eeYpPPvqEKE6JgoDUyhifGOfMkXEcS/B4u8p337nKLz+8yvTEBK+9fIX3PviEnb1DMplw8cQsR5eWkPIxUmaMDeU47Ak6nYT5ikniR9hC5fiRWa5cvsj9ew+o9nzUmVHCZpuo0aBYHESqNh9du02t2eKzm7eIwgghQFVOMTo3i+e53H7/Ks1mmx+99XOGRiZ4+eWXmZ6eYHJilJXH69QOq4yPjjAxNsbcfD8Jo1FrMTE+RqVcYGd9g//3f/9v2ds7IApj8o7DH/3+N3iwvEwYRvz0rZ/x+7/3D+h0e9y585DpqWkyIUBV6fo+bS8gjBOkTLny7FP83tffQCYRv3zv13xybZlavY6CQDN1Ll++yMzMDPu1Q4b3alx+4hI9t8Fc6QidZhcZxVw4f4q9vX1WHj6iUvktCZRAYpkOpqGSJQb54gCaotLtdlEUlQyBZQq6UkcXKoZtYgiVYqmMqWsEYR8XbSQJqq4zN1eg63b6iXumBQICr0cShaBofcJwklAcKGE7eYhCgqCH5RTxA4mimES+j6IoxEmIloJl53EMg0xmpDLD0TV2610URUGXILT+4J5EkkQRtg6qaYGq4LstNFUHmdCp7qPZZSzLQSgGhgEyTVGFhio1ojj4PBZDwzZN4tCn226jKiaqphKGAUmaoAkd0zYJo5hmvdlP4cznCNweim6QJgnIBEPVMCyLXs9Hs0ySMCGTMVmS4HV7SLJ+tYpCq9UCIMsUsiSmFh+gSIFTKmOZJnHQw7BzIPrbUVIdDY0ISRIlaFqMYVrECFI1wyo4BDJD6ApBGpLqEqmqCF0DmSENlczSMXNjmIpClkpCNyYNA1SjQCwsnEGfNAo51AYIOxFNaRI6FSqTJUQGXquKqia0Gg1m5iZoVtsIpUyWRIxPDbD2YI3dgx4tt0WsaNTrLd555yZCkywdmWZ7t0ovihgdqbC12+D8whKrGwc8eLTD49VNtvcOQEheufI0SaRwf3WXntulXK7gxZA1e4gk4ta9hwRxwpOXn6DTqeO6HmGUMDc1zjdee4YRR2F19TG3H2/zt29/TCozel5Ax/NQNAPTNImilI29BlPjYyzOTfHckxdxcgZvfu8n3Li9zle+9DLj4xM8vPeAS2dPMz46jB/4NFyf7779Dp3OZeanyn0C8lCe7771K+4uP2JoqEwQRJQKOUrFPMeOLtA8bNDttBgbGKLXDkhjyeBAmbm5cRqNNhIo5y2mZ2axbZuiyLj74AG1WpXnnr7I8MggN27cpet6uH5MkiS8+OLzfOuP/4BWs8mj1RVEqrJzcMj3f/QTzp0+xqmTiwgBW9t7RElKt+uyfO8BSeDx6isv8torV7hz7wH3769z9+EjfD/EMPpnW4Hn8b3v/Qw3DOj2PG7eusW169eYm5/hD7/5u+iqxsHhIVJC1w1QFEG73f7tCJSUknzeodloEng+SRSRif7TH0ODOKRWPUAxHITU6bZd4sTFylUgDfv/3AIyYZD6LpmMIQFFN2k326DpmJpKFCc4+TyqgFRR+sDIOABFQ7NKxBkIpV/VWKZFKj3cbkCq6ojMJTBMDM0gChOCsEMcg23p+FGAyGJUKZAyJQp8olCgSQuRJEReB0UzURRJ3ikhVJ1iMU+SSHQFmq6HqklSGZPEcd/dXh7A7fWwLBM7N4BQNXw/6s+kdA1NVfBcnyhOsE0dwzQwFEF+cAjX98AwkXGC67XRVB1VASVOMT//PamqoBgOpm6QpjGe1yOJMyqDFXRTI8sgTeL+DM4LaTebaGjISJJKAdLAd7uoeh5NyyFUHS8KSTQNJ59DaipxmtANelilPJmW4YsITVNIiUikJG+pKKpGGqf4aUYaxShlFccsY2s2YmmKJIhJgxhDaMQyJVdeYtw2IYn6gMleQNDpkJvqYhd0ukqNqYkxmvsH7LdahGrC3NwQrXCAWM9x++Ydai3B4FiB9WoP21Zwsx6mLTl1dp6Hj2q89c5N0iSlVHIYGynj+RHb25vs7DdJZMbYcIHI30Oz4MG+Rz6Xp1wcoFLJE3ouwrY4rHeYm5ngxPw4D9Z3WTtocOvuA7b2agwMDmGaGml2wNzsHL/81YdEYcTFs6c4sThDEkTsbGxzsLnL/MICg4ODXLh0mdGRClkKtUaXmZkpFo4fRVEUpqdmCP0YL4m483CXhaPHuPFwg16ccOrccfZ39vnGV7/M+dPHCDyXdrvF/Xv3Ob00SyYhyTKOnzjB2voWe3sHpFLyj/7RH5LFAappIgwdRaaoQuHJi2dRZEqaJSwcmaHdcYmTkPm5Jf7FP/1TVJHxs59fxw8Ttnf2yOdzbG1t8vTlCzi2y9Wr11nd2GFjc5dep4uqafzT/+JbnDt3gje/+yOaLZ/9nT3q1SZSqIRBhipSfK9LrOl8+vEd0iSi3ekwNDBAuVSmXmtTLueQCMrlAn63w737a/R8/7cjUIoQBL6LY1ug6JimgWHoaKLPBhOWiWI4JKFPnMaYloWGjVQEAQaqKshrgkgB31cwdJuSaZBgYms6O7vbVIaH0dHxew1SM0caJ8g0wy4OkDNNAt8jSSR520ERCkmaIlWLXNFACAXTUBFCJc5SFAWQgnyuQBSFWKbVb60UgUwCZLECQgH6IWG5wWEyMnrdkGY3pjKYJwgivE4PoetoqoXbc0njADuXI5UqWZKi6gZhmOF5PmQRumWiGApqDEGUkMv1RS5OEtI0oFmvoZsmEoVKqYiVs8myhMiPyRQd3TDQVIEfGggEChl+t4UiFGwzhzTB0EyEFCgi68/Cwow4CMib+f68yYshk5iWRqE0CGjEsYJl2hTyRcIsxU8jUlUDQ+3DJ9UMxTDQchqRIlENgWUqSAGhSEkNgabpGIqFSCVBHOFnXXK6g7B1SklIMazSTUy6CNoiJZUCo2TjDJSQvQI6OqmhMjia0PYCzIE5jERSDCJEGvDI9dE9DWbLDBfqxLGPH3ToegEyV2Hv8T6e56GoBUaWFpkeL2KKLm6nxaPtbXp6DiWBpXGXVA3xzIRiwSaXt3C7IbGMyZIINZdQr3doNj3KAwXW9ptIM2V6Ik+uonBh9CgrGzUOtmoMVYYp5HIMFAqU8wU2Nnf5+NM7DBVy5B2T/WqbW4/3MByDRivCPTrLw+VH/XGHG/LWzz7A0HTuLi8zPTfL2tYhxxfnqTXarG5sEfR6DOTzvPbSK+xubvLppze4/+gxWzt7fPMbX6a2t8XagcuHN++jawabW7tcef4Zrn52nbu3lzl98hjtWpUwChgdn2B4qMLKo2X29nbRVYOJiRGevHCSrh9QLhZJ0pRqo8beQZWc4/CtP/kjhBA8fvyYbjei50t++rMPCMIIEEgpePrSJZ5/5kk++fQ6j1e2iJKM/f0qEhUpBUJAFAUgVJzKNNrgFHr3kCtPTfHcs+cJwgCZBty6ucHI2DCl8TG+8eVXyJvvce/xNpsbnb+/QEmAFDTLoGAIDEXQ83y8OCGfs0l9DykFSQY5y0TVNLJUUsjnGDUM4jSl63qEYYxhOZimjqEqyChCFUl/5SgUVKuCIgIc0yHsNdB1lTTph7upmo6QffNnruCgZv0r7ySJGS5XSNMULwyI45BEU1A1FVUIut2EMIrIOwNYpka1HlMq2ei6iq72t5KdroumCXTDRqYSTddACHLFErau4oYxhmVjlQoULZt6p4eh6YR+0B++KxlJKsgbOjJK6CUJCQKhJFiGQ5pClsQU8gOgaDi2QyYzZAZ5p0BmSIIo6FsvFA2laBIEAflcGWkkqKZBEmc4jkm300PTVKTQiZOQVChI4RCFMSkWqq5QzhdouhlanJGGHkksUBWDbuCjGibC1kiylJxpY5YsQpGQWCqZnuE4OqqlolgqCRmKBkKVSJmSpAmmYSBiDYGCYZvkJQx1qkyPzvLZp/fo+iGVhXMoYUTBzKOgofs6Wdgn8ApTRbM1RCqJ/AjVsklDFcXQUFQDhRJ2eQgzihBIZCoRtsXYjEBJExLfJZUqrm0SSkno+MxXQtI0I2gcsNprENY7KIlPsZwnIyKO2gQtyZ37W2gmJImkNDiKXRymR4ytmzQThUx1EI7EdjQWl8ZZmB8g6mbEqeRnv/6UjY1DZmdnmV04QRwHJDaEvYgkhDAOuPfD92i1OuRzDu/88iOSJGV+YZbnn77I4GABU1PZ2t3h3spjZiaGuXDsBLmCw3/4q7+h2e6hqgoHhzUc28QxHP7qZ1fZ2D5E1RWSSKJrOp4X4HshjlPA8z1a3YgoTtCbHa59dpcPPvwYkBTyNiKT5IaGWFvf58yZQar1OjITnD57FkVRKRVz1OoNKqUyuq5hmDqXLz9Brd5kd3ePUrGIaWhcv3WXn7/7azY2dkll/+EvEZ/7A1XiDE6fPMUzzz3PgBlTtpcwlIRAUdg/7B/h319epVqt8id/9Hs8dfEUg5UKZwyFzY31v79AqYqCbllIKdFVjTQNKeRzGJqO7/t4CJx8iYIKURASp5ClKe1anY5hkcsX+z4iTUVXwe11yAyDNJWEqUK+NIypKsg4RGh5LN0gifLEaQppTC+K0HSLLImx7ByeGxD6PrpuoZkG1XobPwop5C0s0yYIQ6IkxXIcRoZsEBmqquH7PUo5i0yoGLZDr90jzWIyBLqiU7A0/DjF7faQQqFYKiNRKOTyDJYNQrcLqoFMU3pBgq7qqJpGwbRBKGiqgtQyhgy1PwcSKlIqJBkUBxyyJMT1Q1RFRUkFvV6CqiloikqWaHTaASoSXVEwDAtF6uSLFVrdNkIqyFhFxyaNJaah9o+0gwhFtdFtA1N3UFQdUzMoiZQkUxCmglB0FF1FRDF+HBP5GWbeIgxdcqUKUtOIUp8ozXBMi1RTCGVEqifohkTREhQlQ4gMy4oxVRVFAaF6pG4PP2zwyU6N1bSGrybYcghnfJZU0XBCl8Fkk4Nql2TwOEmskIgMJelj7hEC3dBRUw1dVfG9CDtvE0cJhqbi+R6JFEgkYSqJhWRouAKaRrPRIVF19IKNKSSYNlE8jRbGpGEHNZejkLcJ04QSPiJL8P0uIsvQRYoXtgmCHuVQo73VJQpstGaK125iGgHZZgfT0Ekiwc5uD4mBZpmkdoeDag3fl1x58RymrbO/26D26xqZ0LjwxBMc7O/w4MEKQvS9gJsbe9SbDW7cusfC/BSO4XBv+QHW5+v+ZrvH8PAQSIVSYYDvv/0rUDX+5B9+E9u26HZbfPf7P6d62GRsdBwQrG5sc//BCof7h6RpQqvZIU4ypMxQ1IwHm02O6AUuPnEByzL56OPrVAYH2NrcpDI4wiefXmdwaJChyiAbm3v9JVeUMDRUYWRkGFXVqAyU2NndZWF+jnJxgI3NLfYP6sRxgqIIIj9mdHiIr7/+DEHssrGxTzg+zvjUNJ98cp13fvEugR8hRIIiFExdI0pTJufnCFZW/k7N+Q3TDOblF/6r/5Y4S/HcgDiKSCX9I92cg5ACL47JJNimRSYhjvvbt17PJZfPQ5bhuh4iCTBMA6HbZFk/N8rWdaq1Oo5pglAJ0xQygWWayDTFi2NsM0eW9v+p87ZDz+1hGSZ+HFO0bcI4JE0T/DAhyySFgt2PfjB0FFWh63bRdA1dM4nSBF1R+gP1JMb3eoyUB2i5KUmWYWoKiiZQNQeZJiRJRpIl2KYJKcSAoWrILMUwDWSS4EcxMuujv/0owI2ivjWD/uv1o/6Rc88LEH0TGVn2+QPA0MgSQdEx6XRDkiToPwwMB93Q0VWVNAZNVwmjCMuwCJO+YdYPYiQC0zDQdZ0syYjjFE0zURUdRVFIpEKYxKApZIpCSAKWQaSBYqoESgyOjrAVsCRqXkPLCzBi4qSLLiIUGWEYGoaSYRg6aZqg6waaTBjo1UjSmO3DFjKW6PlRwtGTZIlCsX1AeNikU/fIRIni1Am0TMPr+PQaPWQUI1KQCZBKVEVHVwSGpqGqGroCtmGAUAkCjzRJkUIhCkJ0VSEK+5+lY5p93l8Kbs9FRjGKomCpGoqugYxQhMQPPdIsIOdYCAFhGGAaJnbORBOQhCH1vQ2IPNLAI/U61A4OCVyPNIM0DXEsDUNXOH78JMdPLLC2uczOxiZWTqM8XqScL7KxvE6r3WVkrITv+sR0sDKNJMoYGCyzsbFHo14j59i0ez4910fKvodvbHSMI0emKRVzmKZOHMc0mx5CNYjjENOwmJ4ZoZjPs7t3wO07d9nc3OHsmeMYps7q2iZJFDE9M0Gn2yGOQlzXp1gqUMjnGRoaII4C/CDizJmz7O7tsbOzh+eFGKZJIZ+nNFCm3mjQbfaI4pgwiDh2bInA97h7/xFhGGJbBqVykdGREUYG81iOw8z0NLNzU/zwRz/lV7/+CCRkWYSUGZWBIv/8H/8RQlXIFXOsPt7gf/yzf//b8EFJ9nZ30HMFTMNgwCnR8Ty6nR6tOGKkWMAQan8j1usgVRUpwbIdVNnlYGebXKlClkkGK0NoSl9FkyTBMjSiIEA3dax8f2ZjIgmTDIGk3W6hmzY5U6PaCpAZuN0AkWbIgkDXdSQKrbaHaVl9lI5M6HZdbMcmjBJUVZCmGa7rYtkJjm0jZIqhauzX6giZ0fECCsUSQRBjGyaaAj3Xx40SlEygIAnSiHwu1z96FoI4TWk3euiWRZIqJHGMzEL8IMH1QpLYw9AdvHaP4eFBXDfE7/bI5fKouokm+h6woBegqhq7O22EENhOHpnGRF5CFpm4aYhUDNIsxjIsslQBqdPtxcQJKArIfrFJhkIYpqhKTCpjUgR2zulHnWQZcZqgmBqZ7L+/gdtBLVgkKGRIMgR+2MNxVIoGmEqMrkocXUGRPkoWk9NVzLyGpqXIVCGK8vT2blM2UkzLICsGMODiRdBr7iCzOk7ZQCLoGB4i0cjlA4oyouMKVN3GVHREClEQ9a0cMsKwdCQglZggDhCGiuu1yaTE0FUy3SIWglRRcQMXS9OJ45RUSOyciUgyGq06tpVDSIFMQzRFoqk6ArO/RTUcco6FYdrYmoKwBQOVcerNDmmaUSk4TAcZbtB/eIVeQFxdYf36e9xfPqDa1lhbXsHMD+C3q1Q2UwYGQRElSqUB2k2PWOkyNjHN/mqNpZOL1A82abS6WLkCiiYYHLGZKxaIEo92K+TI0VE0ReP9j68TxxmGYaOpOooQGIYOQmH/8JAwiFE02Fjf74NSXZ9ut4Nt6jiDZR4/3qBYLDA0NMDw0CCB72MZCkmc8OjROqkUrDzawvN9dF0HFEZHhuhJ8IMA28rhuSHTM9Osr2+SSQVVs8jnihh6xOLCAoal0m61CWKNsBuws3vAQbXG/n6N+HN7AjIDJIqi8lff+SGeH3D+winGx0f+Tsn5DeNWQBMpmoTQD2jVDhgqlykMV7BNkyjLIIiwVUHZKRFl0O66RF6PfKmEYTv9bZMf4Hs9FCFwvYCo20Q3bFLDRqLSqtYoFYpYxRIiiAnCgPGpKXRUAt9HxgmFQoFKoYCuqbQ9F9uwUKRkpFwmzWI6XQ/HtikW82RSIgTYlkUusJBC4HkeapbS9X2yDAqlCnEQ4nkhQvVxTIuu71HJ5QmjmILlEKUJmmaQpQLPjzF0lTiRRHFGpmgYuoGtq2RaQpiklIoFbDMikVC2beJUkGYJbruNbpbRtQJZJkmkIE4EptH3iEV+jyhJyMjR7bYpOA5hmJGmKpnIyDk2qlCxLZsklQglIk4CLCffF2LdBiEoDeQRAhRVxY8i0izDtHWSICBKI1IvJDXUvgVB0Qm7XWQsMPQCEg3D0VBlmyzM0LUUR0vRMo+iBbqIsLUMU5EoaHRdnaS5gSE7lJwML80j8ilE25CoqEMOYXcTWwmJysOEhscICdneFomu0iqMk5AgTBMRZQQSVEOgCoNEpHhdlzSOyedsHNsEYZDGCU0/RE0luqmhiwyhKfSiDM1UCBNJu9HEFBqOU0Qx+pWlomn4vS4IiRt4aKYGcUTgp2hKgGZa/aosyYiiDJ2U1UYLRTfJOzkgw7QMqBxn6ukpEBLfcxk8M4MQOla3hox6hKrJ2HCZ/b1t6vWU4eFjHGyFmIVBpJhk57BJLxmm1wXHEeRVnf2DiG47Ik4SDvY2UVUNRXMYnRjF1HVa9WZ/dBEmjE+PkHdiNMVg58ClOFQhCSKqzQ4TR/LMjg/y+HaD8kCO8+cnKeY0VE0lSfsIuP39DlubBjv7DRzbZnZ2jo31LTRDY2JihMFKAcPoY9hLeYUkUbDtAppqYOg2M7PzlAfyZElCnERomkq1WiOKfELfI0PS63nIz+dVoICQ1OpNqrVGfzC/skYU/d1bvN+oxStPHpGv/Kv/M57boRdrKKqCrqqYtoEiQEUlSzMOqwdoQiFnm+i5In7gkzMdpFDQRIIfJNiOjaEp+FFImqrYlkHg+yD66QhBJlEQGKqGaRioqkar2aPVbCAUDU1XyOVL9NyALOyRKSZogpxpE0cRcZqiGzoy9snSGMMyKRXzJGlGKiWhH5AJgWnZZGkEWUoul6fnBsSpRCMjl89jaTppmqEbBlGQEKcZlm72aayKgh9l/cF0lqJ8rveWaSGimG4UYZo2aZTS8UMs3STOQFcEUZSBTMnnikgJ9WqVfKGAbeWJsxgvCLGs/nsUZ5KO6/bnSpaJoggcyyBJUzo9D6HSX/0b/fD5JMsQQsHQVHTTIEkTwqhffSiq1p+3KWBYOommYOQdsDQSLUFYEl+6+FqAWcjQ8yk5U5LTA0pWhq2F5NUAR4/RM5+cKQg8hV6tTdJZA6EhVBU/jtCL44wuHKOowP5Ok/WdOgWnjDr5BLFaQhEZWw/v0mn0SNVpsoEjEAqyKEakCqZmEocBZCmRH+D32kRuBxn5pKqBohqUBkcolyu0W1101cRQdLxWFxEL9FRiqzqW6LeSqhTYqkDJJKTQarYp2DqKppJJ+t6cVoss7d+QopvYpoHtOLhJRimfp9N1UVUV0zIQikIUx5Cm9A+0MhSZoWkGgr4dI4xTHEMlTRMsQ5DIjK7nQeyTpCGG3Z/pyjQkSfpG2zAIkYEHQiHxWiRxgqObiKhFwYlpdVq4bZfFxSPk8x3q+3sc1nwabQ/bthEiRHcUbE2n0+4xPGSgqQopYDkmqGCZBpHb4WC/S2nARmgmoSd4vLwBUiGXd7jw1BSG6RH5Co16wtaax+lzp5kYL9Pp+ARhgmUoFHIpMk1Y22rQ82MMS1KpWNT3PR48eEyaxEAGZChCIumj1odHK8zPlnnwYJtmq/3bCKwTtDo9xioD6FFGmGSkJJQcBwG4QUCYptjFMiQJQQaaBFPXqfXa5J08oesSSQXdcoj9hFRKet0WnqcjJFSrh8xOzTJcdIjjmFarReC6lIpFDF1jYGiULElw3c+PkzVBz8tQDUHRNEkEJGmMYZoMFstkWY4oCbF0jShNkEmGpUgUx8AxbPwopRdlhJ5LlmQEQYii9ctcXdNIo4Rmr4duWJiaiev5hGpGnCQYmk6351MpDaAKi1qtiWNbNFsdVBRMw8SNUhShkDPLBH6IUBUURcexMkQq6XUDFNMiXxogE4JeEvd5abaF0FXCz53cmkxRVEgVSSQTfD8EVQELAr+LUSyDquB1XXTbwgt8QqFDlKJrCtIQqKbdj77QSiRIYukThR6JnxLHkkjLIBSQ1xC5PGo+oliEATvBEgmW7JJTuuSFi5X6lKwMS03wUcmJLVLzkAwVqViEikocbFPpSkLXo6iPc+TYUYSexyfFT3ok0qQ0dRSVuwzkY3bthF5PgJ8i/S4918f3fOIoJEsi4sDv30cKFyEFqjRodkOcAQ1RVJAKCEVi6Q56qqKEKV6zRypTApmgSQW/G2BpOhoCVeunViiZhqLpyEyi6ia6EIRZhoIgVFRUJJrI+lFCOZs4CNk72MN0HFA1VJkSRSFut4OmGVi2TRAkOIZGEES0ZYJjFwilikwSDKFhFPszqTRTEJrAMByULCZVAuxChSROKJWLxFFCHCTYuk7X93F7PqWpHIVU0k4Tul6TjtwldDxMpYdTyqEpGkHYo+020XMZtbbfb9tjH0PXsPMWbreN142wrTzNZohiQhKn5EsTGIZOEATcuukiMkESR6RYgMN+TVLtuGRxTKtxyPjYKKgxBwc14sAlSRMULaNa69BteGSZQBFqP2xSCCQpCgrj02M8c+USK/fv0e3Ff7fi/CYVVGVqUX7lv/l/IHQBsUSmKdVmCy9KMLX+NknKFDXLiFBA9mcZ9cMDdMvBcfLEQYhp2whVxdI0ZJIQJzFSKJi6SRxFhKHP0PAEoe/R7blIRSH0+0+YSqlEp1EnSyNypSFkJgilwNBUDE0BSf8MxO3Q83wUVcXO50H2N2WGrhPFMZ7nE3QajI1U0FSLRAi0NCQROopukqUJShyRpRlekFAcGkaT0PEjer2QwIvIUCEV5B2TnJkjUfTP7/UihNBQs5RM7Q9ohaaQZKJfyYQhCIEqJPlcHsU0sE2DjIyO5+N7PVTTxnJshCJISRBqfyOYSkkmIJX9OZKmK4RRjFQVHNvqW0EUBWRGmmUITe1XpGHUX1gY/S+Qrqvk8jYoAjNn0ksCtLyJL0M6aQd72KA8pJLTPYqaj0UTR3Sx0wZ5pUtRDxiwIrTMw2vGhPV1ZNKfnaXCIkxNvMxBUS1mFhZppmW6sU2s5IlwSNU8XiCp92B/ewXDsGmqJTpuSuCmJGFK6IYkQQxJSpooCGFgWwWiTEPTdFTDQLdMBgcLDJcsFN2i2QqJ3BQZSgqqSdyNSL0ULYXMjwjqTSzNglSQxgkqAtvQMDSVIIyJ04QszVAUQS6XI4lTMkVg6DpSEYRxTD5nsb29g2Hn0VSBl8ToqkaWRKSJRNU0NK2/ndYEZGGEkLK/BVcVms0muiKwCw5JmmAbGprIiOKMMAopDVSQ9LsTL4jJ2TZRFNHregjNJI1C5OcdimbotGqH5PM59M/jgoSSEiY+Moo+5x8mJBJ0kaEZgmKpSBC4xK6P6/mkKNi2Tey5JEmKTAWpBJFpSJGiKBpRqmBYObIkRSYRMvQJvDZpmiHSBCkydENFyBhVTT5fqMR0ey3IYhKZ9InCSYBmqFTyan8r6Su4Xpf9R7+FyN8oCnm4stofxLWbJH4bPVdB03Samk4+n8dS+2WyqSkIRUPoOkOjo/R6PRSRMjBYxtA0wjAiCkNkEqEbBmECyASdDC+O6bYadDtNcvkSimZiaSZJkhIlGbmBYZK43wYJRUXTVNJU4kU+fhBgWBZF2yCfL4BQaLWa7G88wrZz5CuDCFOnmHMYHRzEMk28doduu40gRbdU4m4Tr9UERceLU4SiU23241VVq0wQ+mjCJJcvYJm5z1MTVPYPW+RyfV+KIlSGBsvEcYybJGhCI2c7mDkLLTCwLIdUpghFoCgKNd8FIclUBZHPoRgabhoT+D0M00BRTVRd7QudzJCfZ1ypukrFKRMnWX87R9pvk9OUNEmQGcgkQ5oZjplH1x10TSHLMtqJh6lbZEpCLBKEolMoOmiGgV1M0JQeukiIgg45PcDRA0pWzIAeYyctDL9J0YjI6SmB2Qa1g8xSMsUiVAx6aYTUCti00CyVnK1Q6yXIxCXya3i9kHa1RxikJFmIF3SJYhUZZYhEQyYqumb16TeBjqZC3pAUciGjxS6DhZicmaFIhY6fo9YZpeAMo5cKtFs+bsvHtHQ0MsJWgK6pFIaHibpdSGOEKtH1/kPg/8fan4TsuqZ7ntDvbp/ubb9utbuLiHPixMk8JzOp1DSzEEqhipopFkKBipNCJ4kjFRGcCSJS4EQcOHAgFOJABAcOCikbyLIo02zrdBFxInbsvVf3dW/ztHfr4H53nDPIUxCc2PDF/tZeO771rbXf5/9e1//6N1OIZAnBZYKbqK3FeUeSAoVk9hNC6SIRcdBdX7FeNQyzow4KayrGKaOzQGmDFhIrFEZmghGkxTHNDi0VV/stKoPQmnE583R8ou7WbLYbwDL0C91qhXeRdVMjAdW0yIv9K1Waum0wWjP3PeurO4xRVFYy9Cea2pJlRRQBqQ3tdlv8nFbT1hYtCiURVcPmykJOTGPP/u4Nfp7p+4lG1ShZQLvSFSEJYtK4ZcZPE/OYwF4hlSWR0UoQhWC1WaPFiFWCkCLt0iOVIOcAMhLRICXJwownTjOdruGn/+hfiTm/mZJcabLSGJmo2hZRd1zv99SVKdNKihiRub65xiL57uMDVhRvm18c/bCwcYnaWAwBHwTz8cg89qyuXrGcFqyQnA8HyIZxkcgKGiJtZQkCxsUzTkvJcFpm+nFh31hO/SOyXoEw+Hnhm4cHVlfXGFshleGz3/0DSAmXPHXdopTCzQuLD5i65aZuOJ1O6KqiqVds1xs+fjpwtWuJISHGgWqzxdZ7xiWQQqbSFSBZXGIOHlO1xauVRCHmbUWIEaUUsrbMRESCIDNPS48kl2A+JVBGYOuWLAUxJ87jUIhfW2EqixMJYRVBRqRRZC2IWiBrzVl4glpAa0xjEVLgYyZ4gQsehUZkVbQxyhOlIfpIvWrIUhI1VLYmJMd5mulag4kDcn6mqj1bG1hpTyUWhD+iOLMyMyvjaRno1p6UJoZ+YHSRKBcWUSNzYIyRp/czevOWUW4YzoLDrJlSwzxLwpxJsUHlBhk0Yimkva0VaxtZ15Grrudu61g3EW0Si8s89opvD5anwTI4i08aF4+k0GPUmi9vb/n8dos/T3x4OBCmxKrqkCmS0WirSSGggeQckoyUGllZMIoQA3t1Wa8lrE1FHHvsZoUzmt57KpOQOpN7xx89H2C9wdqaFANu8cjkOJ2OuHGk6TZUdZHeqBjwOaFCJM6OWmTsJZm2rhu0hrnvOfQj0c242aHbjrqtqdsOYUy5koZIFJJjP9NaQ8bS1iuUlnTZ0LtAFgE3Z652G8ZpYRgWhuGMkoa20UhpSTFQ6xY/e2LU+CBpmhaEYmUMLiVqJXFLKpFI7Y6TXaO1xlhNCkWd76NnvduQhb/weB6zbslKE0hYraiswIcelQW6tVzrmmn5LVldcs7stxsMEZZPrHbXNOsGfWHpgxD4aUZFUG3L7ctblJBMs2e/v2UaRowyrNs107zQtIL9dsN5LJctFxSibWh2FZNbWLUNbo7055kcF3btCpcyVVWjtMA2K16sJFIpstLUXRm5+2ni5vaW+3cfcONE3a0wUqONIM8Cg4QQWeaIahQ5LihlSEHQu4FNpTkfzjTrFbu2xRjNhw+SiEGmhJUK09RoWXE+j/gQ0VKiksNYwcaWF+LoHZObCW5CzhW6WZGiZzrdowmM84Tudph2gzKKmsy0DEhtiURcCGQBLjl8Cvgw0GzXCJ2xnQUrmVTAC0/MM4vrsZRJ1oiIybCWghQTKknitGBdz1oqrlrLWvSoELFIjPCc5oVpmWly4NULzar1KPfE8buvifFIs020dw21mdnIiT0Dq3RilWeUcRxE4Dl7FpFYbVrW0tAnzZgswzLho0b5LXnYEENDSppaGXQtsBW8WXuaeqFrEkYJXIq4RfI4SL55UjyNNeNiiVmW5kUhLlagzLUOXDdwVynWClp65HFEjJnfrw05JuIwEiaYXGb2EhEzInlkFuWDWI49WrIVga/i11Snhaw1QUAU4D5G/t/f3PNzF3mxfcnL15+jpeD31jsWDGV7rxDSFD9mZ6k6qAAVE9pHlE/l85RQ1QalV8gsiMOMwKG0KHE+mzVCdAzjTJ8VxxBZjhNLlRif3zMpTbvd02wqrNIM48icMyI7jHBca/jsZs8YFqxQaKNJQtKoLUJrrIEYM31Q2GaFEIqu6Xhxc8f5PDINjqChqzp0BlsnQhblOKYNbhmJCNpmha0sISyEsCCEQhuQpiZFjcLTGE1SULcWZoCE0Jpzf2I8Pvx2AKoymv2qZmUV+/WKx9PI0A9smpp1Z2maGiGuyQh8iFTRU+dEkJnJLeRlQRjDoT8wnSZiioSY2N+8QBuLtQpbGbJNxFhxHhb6cUZFjxUJpQ3jaWB0GWVKY0w/Luw2HddXV8Tg0NogOk30npv9jk/PJ3ICpWq0SGThCSET+wGqivOnE/39d4i2ZR57Nlc3NLsvqJorpsXhk8JPidV2R/IRKQ0r2+CWyP2ne5TUKFMxh/LiHs8nctMhJSQpkKuGSq6oKoOxkueDR7UNwS/ELFDW4nMkLo5z/0SWmawszbqlqWpc8AiZCcnTNgYrZla1wajyIuyMo608dZVolccSsPIjWoERGStBUi5UqYuM55njKDHdZ2z2LxBCc/zmPX/yR/+S2U/IKmCbxDzs+MN/8+/z/E9/xfn4xLYaafuRvYYff15zzQHff6RSMyonGtsR6sRqa0ldyzkpsr5lDFucuOYcas5ZMOea263Ay4kgy1VrjIrTZDj2FfcnzZ+9bzmNitEZQjKkLJFCInVGSejqzN0usd+A1YpOKnaVRRlLyJroNGkWMAvUnFEjqDljZpAuoZaEDAGVQGa49AVdigEojgKlaep9cTWIVGKoJagMf/et5u+016j1jiRBAIjvfZEgRSgFtimhYobkUTmjUkbmjEqpfOR8AcWEIJaI62LsKXqtLElCEbPBZ0XOFTEJQs7kXB7dlA7lAhkgbjMhClLIxBgJOYEYySaS80xUsaz+LIQoEdkiUsaTUdkSAoR5KBlsPrKEgEcR/Yl+nsr0ZzvmkNC1xdYtIUa0EZDLMcYIS1ZwHp7JU8neMkZR1RprNd4HVl3Hw/Mz26qibhtubn+Xv0pL/hsBVPCe03lhtV9hfI8l8OHDB4KVTNPM9vY1VW1RUvHNz/8M5RfG/kB79ZbbL74Ea3HTgrEVKI/erDC6ojYNk5DUlUXLik+HE3LxGKHZb1qMFPjoOYwL227NkhMiw+F5LFaHw8S8PBKnmXrV0m62DMcjjZLsr25IQqEBa2qyK+8CmxdfkEhcXyvUVz8ovrYMMUVyjDw8H/FC0hlFXTcM40xAQjKYrJFS0bQbEJIhBjb7DYg1Aai7hkgEKRDLQgBcDuSoaNcWefUGP51x739FkoG7ly9JOTP0RxKyTEspIYYzewNf1YYXVw3txtB0BlMrogoIa9BNhaoySiWUCRiVkTIiskdmyvdgKogLz7/4OV9/9y2Tl3D/jlfdH/L6Rz/m3T/5I7w6Y1RGmIyQienwyOM/+o/oj5/QpqyhKMnaRmozIztN072kM5GNkVghWN3ekJVkyuBPE9W6JvuMSAs+W6a5JU4NXq65P7WcXMf9UfCpjwwelqxJWaAQVDqxbhKrRrDbCLZryWqVkSpTm0xdgSSCyAjVkISCLIuI3GXyAmpJ6CWhloyeBcqBcaAD6ChQMSMziCRQZEQOSFHAClky8ZOsyVKShSRLQScVW3lFVgJEmXCzyCAyZWNPxTgfS9KrTAkZMxLK5ykjc0Lly89nkPl7WIKL47VkMcHl+lUM2wUK//KvVcpvJSXNVWWJyAGNQOZYvhcRESojiCACiIgQEfRISpnkJckr5KLJZwWzJiVFDhKVNSmceZhrrHRoK/HZMyMZfWZA0qeKZf+Sd88D9+eFgUwMibzaoS0s88A8HplOgVNcUNJzVFBVBkHGkhn+c4o7f2MOqm4sn54PVAQa6fhsJUh1x/XLV1htUMZyXhJvfvi7XO/2nMcBd3ouyun1lmZXc54DselIIbF4B0kRUyIFSbawX6+wmy39NGO1RGnN4TSxWe/YtDXzPPHweCSmyK6p2O82xLRlPvfM84h/fmC3u8MIibSW4AJSNcQApwmWkMgqlP63OCHmiW6/x5hicPYuUktDpSzncWSeInXToo2iMS39aSShiRmskVSqxuXE+XhAVIan+Yysa7wPqNpgrCHFsr5pLanVTFIavb0rBO04YoxkZQR+eGDTVdzsDLfa83ffNtTb4huMGmKTCJ0h1gLZJXQjMY0qlxOj0CKikkPlckUhOcLiCEc4Pn+kUiNSaEIKPP/JP8a8+2PGaUZX5YEQMqNUJpH5+n6gq1fs1hua25rdtWTzVsFqJsueijNGDERmXApQl5qo59PC0dU8vRvxqmW1+5yZFbFaoWRFVXfcXa3YYXiZBS4GQg5kVQDDCIFJHpVieQAvCuQsI0Lm8sApEEKWwg2VgWK6TgFQgA6oGNHxsk6ZgMoJTUSLhJYJJRMyxfKRM6SEkKUiLQMZWSbPCFkqkhDkpIqDX0iyECQBWSTIHpFTGVVJiHwBqnwRWGUo+djFcJ1S0UyVpqTLz4u/BFGigFFGlIIR8iV5A7gAWvn6BdpyzhdwL4ptmQIyZUQsrwGRirshx0BMgRQjORYTdjkLcwF4SUiakC9/jwkfEqeQiaeETxIXIKRylc5ZoD9ZPhOClz7zPCrenTMf7Vfot59jrMG2a3x2LFNPGh8QMtFLTX8+c3O7pVP2twNQicy6bXBGssREbS1SbXl+ekaeRr76/DV1XbNJiZC2nPuBtm2pANEk2rZDJME0HWg2K9btirAsfPvuiZw8q6uWUz9TVzWPfU/fz9im4npd2lbP0wk3zygJzbqlri2NBGLieDgTsyBLSwoTcepJWbCcFP0c0f49bdOx3r3CLgthLOfb1hhE3ZGEYpk9OXrG0wm0olt3NKbFL57Dpye6bo1qLFkZ+n5mCpHdaoVUmXwRl1ZGU9eW9aZFVBoXinmknyJCG7RUbKvAi3Vi+8MKJQKNzWxX0BmNNXdoDZWJoFqSyGQVCMaStCBXkJpMsg5aT9KOJB2aqbwomdF5QeJKRF34yLe/eGD8lSf0itYaXEy4mJmT4KePASkrlC2+SLu6ot1fsbvbsbrdsN0pNq1j0y504oAQR2YOKNSF/TF4ZqSMAHghETvFMh+JKG6/+DEzTQkTzOVBEkhcTrjv14pcEaZIHoeyBqWMjAKR5MUeIchCk5UlpVxc9IkyxUhJzpcHNwlEjIjokKlMKUpwsbUEtMqoHNDZo3JAZo8klJUsCZCJEimai1UKiulHZJKQ5QEWkpQlxLLbSZHIooDOZay5TD1QFjZA5gJ0JHLO5dchIzLFj5lTCQZMxZcZE+RU8vhjjKQUiTEXHi1Gok+EGAk+EZZE8onkYvm9h0j2gewD0UXcEnBLxLnM4hNzyOWAkjPhAr5RSBCaKAwuCZaoCBGWDCFrPAKPJCaB/37C4+IhTZDikRwDxOKP3P3wb/Lmi7eI2pLzjFeqkPLXe8zba5SAkCIhBbRS9GH57QCUXxx/8s//OW7u6T1s1x05Z5rVjm7T8PTwyHHsQVpyilxf3bDfruhDgGXm04dHEJHgEvI4MOSP9PNS1rHNLU+HnqvrazrT4KaZm+tbnPeEmCGBEprDYUQbxaa1VEYhlOJ4PBHzpe/OLXhREbEobRmHEasrlqFniTNh/IYgG+x6y7Jk+tOJ7dUegWB0CSs1S25odEtKBu8Sh+cTU3/k8aFntV1oNmuatib5hSUFgit8SruySCtJMvLx8Ts22y2qW3M6DyzLQte1/HDn+Xu/Y9m0Chk8xKK+T35BBEFyDh9nYp5IFJ4qSElSBm8EzgS89YTKI1cL1noqtWDFQi1mrJgJTFgWkkw8T5k4ziSpsNoglEFSYfSeunpB092wvn5Btb3Fti3KKJRMKOWxOmB1QNiEJ7LQIHN5YAt7Yllo0TgUkQwEqfDCYF98Tpctz+mKoGr6ZWaONYus8XnDEgQuOMKkiPNCOgdWWmHlUianFMpAkcqDn74HAlVWraRkWb3UX4ABgIgZGQLSB2Sq0NmiskcKjZDFKCxkQqhSNSaERGR1IepSgZSsLwvXX9YIJhAFYHPORDz5ktqaciblQMyQYtEHplT4nhQSKSRiyGSfSD6QXCgSkBARKUGK5WskSFkQY5lMsoAAZWrxAh8lLkp8lHif8V4SosGHRAgV0+wJPpNDJoZSlOl9IDpHjoGUMyEFUkrkFMkpQoyU8SmQ8+WalgsfJy7rpZSCKCWzVFzd3TKfT8h5RBfUJeWMjBmqFvn2D1Avf8y3TzPVMLDMJ5QVJZGWyHW1AxlxwXNzvWUYBrSqfjsApY3m8x//HiFEvvn2PRUTwg9IKfj0+IwF7h+f8fNI8gs/RbHdbEgYqnYL0ePOn0A2WKGYoySMI1opxsOJME48fPyEXd9BhuuVIC8ThyTxRLQxyKoluJl333wgSpC6RqtEnnvs9pYoa7SpyNJCViiVcC7i6YhZI7MhLYGoPafpGZ8ET+d3aF0X+0Ly5LgwngfapkHbhpubO4bVDucDddMQhcDlhK1rgsyErFC1JkmLri3T1EPVcAoRO57waSaJzFb3/OvXf0z96ZEcF1Ioa4dKEZNAXF6oZc1KCCUIQpKlIhpFtAZfaRwGZzQpghIei8PiMHmmEjOV9FjpUQJGWaNW12xXt6jmCtndkLsdudrgUYRsSEqROJPi6QIICWREilz6D30ub/oKkqxIbPDCstBgWVAlPBgQBCQBw6I0c1DM4hofHKMwBF2z5BovV6UEMq+oiGghkFWPCQnlbAGZGMjJIbKDHMjSkEQki0AWmSQ1WULWkPNAzqlAVBaIrBEeVJyQ/oh0M8LPiBDIzhPDAt4Rg0fECDGQQyhAkQQplm0sk0EkQkrElAkJfMgsvlDaxTEjShZXlsQsyUKTUiG4fVKkLIlR4JIkRE2KFS4IYpb4IFlcwPvI7DIhgo9FsFlwKzGHhI+JHGIRkOZ8WdViAeIUEDmicia5gIwJkRI6RXIKiJjQuQBhzhkDpJwuIHRZLfP3a6QgXTKeEkWflykTrMoKmRLh+RkZy/pYsCwDgub1D9n8/t/HZU0iUKtAd7Vlni1GR3KaERqWydFVEtfPnKSgXrVI9Z+DOb8JQMUQeD72dE3D69e3mAxPD/cEVSHCgmkrvvzd38GdJ2qbabQBWzOHzDQv6CTh+oqP988kP9O0G/TNGxafECGzOIcWEiUNlTLcPw3UtWG93bCLjo/PJyIKLSXCKCpdcXN1g4uetNqTUUSfGOYF8oKxNdPk2Ky2GN0wz46ma4tQLGXIHeumJuWEkpKcIlq2JWPdFIX6OA5Y74kp41IiziNLjHTbFcP5DFpiVh2Ld/jQo0WHaC2VgGZVl6yjvEGnxI/qP2Gbf8XlZY2SERmLi0vIwl/kCwcqY1kQkkxEygOSkkInUCEiF4UzBlEbhN2grMJYqCpNXVdYI9ECPpMCIQxZKkJWuGxYssClARFLJLCjI7MmLgl8IpUdg0VlMKBUxhpJrQWhUgQRWJgxeUbnCZEdOQVCLL2BMUGSmoDGZc2SKlwW+KgI3pLHATF49HRGTWfkfEbPAyIEiAHiQgpLecBSIJHLtJRzWelkLJOTKmGFQgiyKOtgvmxMIkZyTKQIImQI5cciJUSEJUD0JX8rBU1KhhgyMQp8BJ8lUZgSURPBRckSJUuQuGRYomC5rMo+QiBBlvgkit8zRWIsfssQQ9EKeY+4/Nl+v8+JFH79Y3HhogrlVqDB5Iy5DHMpZ0S68E2X1VOITM6pLKTy8nWJ5JzKVJgKAGUhLzxVJiFJuQALv/7f8u9JSlHG99Pj96CfRUZlyG5ClMGp8Hampfvqb7P50d/CaAXzRKUVUSpkDKjki2MBhTSSpoLHxwO7/RZbaSplSt7bbwOgtLZs2o5P736BqmqktKx2V0hpYLtGkGlWK+TVFdEH+qdnPn73kXGe2e72qKrCu/jrREArHTme2ayvqCvD86cj9WZHTlApwaiKHSEOJw4poa1lv+p49+mRRIXrF+7jPbv9js1qw6k/s0wDfvGst3ukNIim8BbWVsQs6OcFFwKrrsXUhkRJm4wIrDIc5jPGWJrK8Hw4k1KkWTXEkLje7Kgri8+J0Ts60xLzgq0hBAm2Q1aKYTyhq5p1tSWGgJaZrZX86Nqj5BWkjColMMgkUdkiRAPVFwh/RsYeoQ1ZW5IyRKmIdkPurhEqk87/jFR1sHtLfPklYqsQegI1kxiI9MR8QISPyHgmJ0dMkpAtc14R1Z7etQxhzcyace6YhoY4V0ivcEtmmSIuRYR21KsT6+1IWy+s2sCqgbrdImMi9d8RpxPBzzgXcKE8sGPsUGKkMomUJclDmgVqiKgpIGdf3vF92WPyBTguDDEiFzI6pmLkTaImyZakDUEqkjREBAFYoiCk0vjsA4QoiEGTk8ANnugSfvb4JeEXhx8XwuJJPhF8IqWAnxaiKxxkDB4pE0qWaSPldJkTislVq/LkCHnhsmWmbEMCpSRCgKXIFcrqWH5PUeUy9X3Pm1/ekL7nnnISiAxRFD5K5stlL+eLBCFdvlw5YmTxPYWeETldhL+X5VSKInUwApUEMZU/l3wBOCnKr12IMDC58HlOWZSpkPNMyrEcBr7/mmREEpffjkJuXnDzt/8N2ldfQM4s44BsGsZlwCoJKTKniBwdEEFmpGq4vdnhQ2QODnLCLe63BFBGs9utkfIrVk3NeZx5Op5ZrxoOpwNrDcwjs6lYty1BZV69fUWMjmFMTIun01BtO6ytqJUhh0h/OiArw9u3r1lv91Si4uPhSNs0+KRwMVClwPHwzC8eT2iVaboOs96QUubjxwfatWNdN2jbMYwDT48ndrsd0xyZl4VlHBF1Q9cWX9On85mrlSIgsN2O5Gf6aaDd7HGL4/njA5BpNxvGkMlZ8HQ4IKui13r47mcEBLFbY/yCWTfUqxVCC9ZNha00SpVyzrv6zN//SnG9+tucln+Tsc8kF5FR4meHjplxGNmkPf3Tt5zOJwSZaQkIoTjPE1/9oOFv/O03KLPCTzXBO9IpMfFEnAS6mtB4TB4wTOSlBwfeNYjcMHtBPyvGWGNbxafnyMd+5Lx4hvPAfDCEISLmRHYzG/vE5zcHXr9c2N5kcDVn1fGeikiFNJ4UI8u0xfsNOc5oNdPUoHR5F5a8QEYLk8C6QB0lJhShnj/P5LnkXMVgCHP5Pvy8QAgIqXERhqn4xbxP5DwT8oxPniTKRS+nQEoLUhXwELlMFRLQKUEISCKVVoRlAVeubSImSEU9rnNChgVFmVIy+QIWlwlDlJM+8i9ACC2QqqxCiYsM4CIcTbnwZORikC24KxDy+2NeWQ9lFkXaIkBLSCGV+NyS01iOfjGD/IvJKVLWM3n5/nMqI6u8/Hy+rF6ywHyZykTJDC+f/8X3Ii7TVVEtCHpREXaf0WxWhO9+hlrGctG8XCsF5ftSzQ77+d9h9fZHrLcbuvUKLRNsWoyV+LTgw4JSkc53IAKzW2hrw+Bnuq7DuVJisWlM8ab+NgAq5xLML0g8nE8soyNHOI0jCcPoPC9fv0Asjul0pKrXYDV1rkjCsd2uOd4/s4w9fT+xffkZat2xzPcM/YzLz4xzBAxWFzX32Qke3v0Sqyidds0KtOZwGonOIYm0bctw7Dl8esLYhqaqsVWLHx0pCeq65m7XslmvmXLxBY3Oler15JnjQldbpDUgAu2qYb1ucDEiyISY8W4CpclzXyqv9i/IfsbUNfvrLRiNzx6QxZBb1eQw88O15/PmwLd/FvjP+pHH8zdMo0csgbgkbjc1r7ZrGl1xnoBpYq0rrq/2zPPC1989sq32DB81f/KfPDLFBx7nzDFpRuOY65G4DVS3jvVmpDNnWtPTaEddZXSc0QaEtVgN0U8MLuGpsFZRxwZla15cVag2k449NZG31w2brkEai+9rns9r+iyZc8CrgKwTQi5IXRNyxAVFGA3zfWQeE35KiMVR+QnOE7lfyOOCXDxiduRpRMwzInpUjiV4sERVFiL+MiHkTEkpEOUdXonSLF1WPVG8jApkEuXfQ6BkeehKFZpDkpCuBCAG4vfjCqRc+JiUyBdpATn9WpEkEHzPwQvK1xS5ePOUlBhT7EMFNMqqSRbl8srlwkgR7MZUuJ1MAaTvxZgpl+bklL//dYrUIudMzN8T9fnX2fUyFhDKKZFivpz9ykVQistklTNaSnwIkIvxN13UBAhRgInS2vx9zEwGbHaop18Sn0Dnwil+r8cSKZO14eoHf8DV7/+XkJsrWBxz8IjjAz4llIJu1XJ4fmSeztSNQgtBrBRNpZmHU4lY+vTAphJMs+PTuTR3/1YAKoSESzAvASsTp+FU/G7ScuqP7K73SCW43qz56B2n/szt7S2Hw4n7Tw/ILEBWrFdbPttcE4QkzCP7uysmL+lPQ4kkMTCfzyTAysQffH7HZy93fP7mjsfjmW+eF5IwEB05Bw5DoK0Vq6rl8TQjE1irefXDV0ze8fbFDXUtsVXFnDLTNLGEyLiUDrL3z0fevrjl8fnM8/lA127ouo77Twdmt/D+6URlGqq24bkfWVJA5LJbL9HxeOzp9h27qx2RVJTBwfF3NvBlWoi/TKz7mev9hj/8ye8gJwdDAOe5erFj//ZLkvf4cUTE13z89ExdaeJw5rZ9RbaaV6927F9s8QK+u7/n6/ff8HKdqN9smK2E6w6xXZFlgwodghkvHMlmXE5o3XA6zzycPKcp8OkwMw1nhk8OTgk9R7Tz1MHTZsfpjx2aiFKCpAReS5KVUCmiMTipCLIiIjlPHh8hJ8kyefwUiZNDLAkdEjYHquCxKVKlQBUDigV1eefXxCIszPHXGiSRC0CVK3yxtSAFWVw+UulEF7LYVL7nooSAqqkvt/qIj77ogoS8ZNwvhMsqlGQiU+rnkyhAIP4SUMCFD6T8swJWAiMlRqoiYdCJGAXxe7uMKRozhCTLkuYhhMCJMm1FvhcflCsdueSepVRSKkTOF6V44ZWyKGQ5MRWQuPw9fa9hChfuUpSJ6PsrowvxsjpeQP5CfhduK1/I8L/gob7/S+Z4kURQ/pylBlMjV1fc/PAPuf7qJyzjGelHsjaAwKWAMpL1qsL7ibvbLW19xdyfOM4DV79+s9elAEXCYZ45nI4oYyD8llY8owRxHgkh8fR4z3a9wsqMYWE6nhj6ic9ev+LnX/+U5+OJaDT3v/hj+o/fkU2Lbu+wmysev/05+80eu71GC0Wae7brDtGcCMGznDMvtle8vb2G6GlshwuOj0/vSMnxxfUKaxOrasuFKSXnzGk4s9GOVbu+VD0Feu/x4Zk8VSxxKT4+JaikQlU1bVPze/sV3nvublrubloaWzMMPbe/cwtC8fR0IIRASJmfv4841ZRERas5LB4nYFgGDs8PJATX+w27RvLqqzfUXcdp/y2Hr9+xe/ECBHz4+C39w4musrhwpmpb3OnA+HTP8vjEH/+zP+J4PDH1I4MLOKv57Ks3XL98zcFHvv70yJ9/eiRWmuq6or3RrF4I7I1gaRJOQ8iZFEMJzhelVivmcoEKQZNmhRgk4uyQvccNjjBFQoT+QuZKIZECpFYIXQzKyipEFcEotPW0VtPlUAjSnCjH08wiHDHPRYDpMsF5ogtEH+mnQAxFz5OiKKtNLlNSjiXmipzLjy9zh7xMSqVYuhDChTZRSCnKuke6FDCMaFUYYi0kdbPCLycqGYvnLZYJTZFQIiNF+rXdRYh8+bW51JaBUmXKEaJMZ5UGTVGCGyULiF5M31JmMBIhJVFklpBJQqCEJCbwl4cfCngoRNFHCUH4HjJymaJK4H/+9TqaY/pLk2ZGXKYpcr7IHcSvRZ+XAbF8jriQ4/lCtv8FUH4/owkESAWmIlfrog2sOjA1m9s33H7xQ4Q2HMaJaRiJ9/ecpx6RAjKeqbXk6fY1VVvz8m7Lp4+fGCLY9Ypzf2Tpz0yHB/osqW3F9mbH51+9BRLdquFf/J9+CwAVQiBNz2jdkLXhl1//knUNcTlyc3dLEhXHx58x3v8ZdVXTKcV3n37G/vYV211LJSMf7v+UoGYUJ+zywL695nq34eFwz8dffUM/RIxuGJ+/4ac/n0lpwYuEkJnWlmTA6/UNLjt+9/PfoVUNicixP7NuttxdvyxV5TFha8vLzRafEsYYhLFIo0gsrLdbZrcgVBHkTfNM2zXc7a6Ylplj/8x4HqltzX7X0bQNMSU+/8FrXEwsMTE4xx+9+8B63XGcFJHE4TzwN77Y8+r6iof79/z0//Ef8t3X7/CfHukqiw2Z0/sn0uRoBdxsGr7drojzTHw4EJeBUz8RfWnl0BG8Nnx7eOKj/WN6FEMSCBTOWPq+5tNDhXrfoHeSuNKEWiGajK4lUiekKlNIDJKwCGKfYYiIYUH0C6r3qMmjZo9yAR0zxFC4FDJK6XIF0oIkFdloopJl5ZVFj2RsUc53VQMpM54zMqhyEEgRncpqpEWi0ZamCijrUCJiJHStICVH9J6Uypk9pEyMZQURQpClQElFTJlM0S4JIQuQyGIgTuny7v89f4TGooGK6DxzEPhgipzgIoZEpOKu1xEtMlolrM7URlCZkqCgyVQKlBJYk9BaFmG3KMQ3UqJU0eKhBchISgolMyiN85EkJZW8rHlZFFmBK0B7kaP+mowuK91lcgoZGS6raEikVMCp7G2X/8dlciLlv/T7LxMo/MU/+36aQlK2EFOR7aqAUrUC05CUghSR2rK7usV2a56PR4IPpWDETRibudm9IEVHTtdU64baKpZ5whNx7YpKlst/359JKXP1xQ94LSPdbsuUMs4FGgmfHo5/Jeb8RoF19fY6v/g7/+Wi+1ANSkpqLXHzI3Xd4uYFqRRzUWll4AAAc8FJREFUqjHNjhgiPkbapiOHCWLGJ1UuJz6UvTsLrGlIKbMsE6SMwFxIuSI5ICeM0SgNFo2uFEorUphBeVxYSsmAbfjs5VtSynTNmjcv3vL69nO+e/jAar1i8ZHVZlsIUGOwViOVZlwGzv2ItZbPXr3mPJzxISKUYF4mfCiVVCGWF5O2BpciWUuur28xbcX9uSemQNvVbNddcdrHzH/8H/wH/Ms//hlh8iQXyS6Q54B0gSpG1iLTpcA6O9o4EnxgTq7YFERiTKCEJKFZhOYJzSA0XluirQmVYa5rUlOROovoDLSG3AiwEgwXkgMIGZaAmSY2s2c9RvRYOCGxBLQLCB9oc6aJ5ToE5Y1caF24EyGQxhSeRUpCFkRxuVgJBUbhk+C57/ERlpAJMV64jss69r228rLWKQVVrclxRohCvuf0PTKVd3etLrkFQpNSIGV1IZvl5Tm9PKSXBzwmSUy6nMhJrLqWtmuILnE4HiFFjI5s1pJaeZpGYHDUVqDkZVVCQhb4lJBSlpC7XDRPUjX4IFG2LtMSEiEVMWSEqRC62Ld+DeI5o60ipnjpW8zE6MkxUBmJdxM5BhQJceHhUultI4dIDhfeKRQZSE652GQugPT9KFT4pO+V62UVTkIQpSYpSzI1VGuwNeiSG4U0hdCnrIspJV68/pJ2vULliG43tFebYpMRiWWZqBUgItKWOq3KCrq6oh96tLFs1g3PT4+M5yNVu0K3lv224d13n4g+UW1q5nHB1jVCCv5f/+v/7l8/sC7GgJ8PpOoFGYlfPKlShNhwPkP2oURMNBUqW2I80AjJfP7E2B8QMdB2t1TtLbQ1bVWxBEXb7skBhjmQkaQIRgKxBNJZMs6VHneRMyF4hDIo1dG0glYL+qlnngV/9rN7Fu8R5ht+9fCBl9tvaZqOX377C57HM1IZXr14RV23vLp9xcuXr0kh8jg/4oPHO88yOk5Dj60N0zwScwIFN1e3TNPEx8dHJjcTpaAfel6/eUOrJEkrclgY+owSEgmsf/B7fPgn3+EXRR5G8AlcRjhK7Xso2hybFZWs6YRji6BJ5SqXUiRLxZg8Ec1V1uzQpcg0OoZgmcPM4CyLq4mTJdYWKk02CnSZPEggIoiQ2PvI3bRgL8AkF0/2AR0iJga6XKJagoAlJgyC7Ep4GwLEolBaFl5IfE/+CjQZLQU5ZkbvWVIuk0NMpBQJAALmHImUB3+RAudh9gLbtKSk8KEAWhlMynoSckbJYoS+2/my9iEQUqJk4XRA4LLk6aR5OJY0y5ACxMxwOuGmgcpEXm4Cn70QrOtEQuCy4dRHTmd46sEHiCEidcQI0CpjrUSkiFGCqiqK964rsSM+5Ms6LMlaEVH4WBqzx4FirI0Sh2LxGVM1+HTJV8uaGAPBC0gSJTIim+LrCwmRJQqBThFFkR4UlUO6GIWLnadcByUJSdSWJCuiqUmmA61JyoCwZFkYtRLDe5nbki9XQSGotzfsbu4wzQovCpBe3V0TkyMkT21g1bb0y8gwzMyHA11lOfUHnozFRU82NV98+RbTWmq9Y91ZVIb79+8heJrVulicksBKLuv4v/qv3wig6m7LH/yD/xqH+weGqedud80SilI7VDWP3/6MtimNud5nhljx8ssfs8yRFHp2u5tCnJ3mIoaTHTd3V8QlktuGbANWGVwoNKIRFm2K/aPOJc4kRo/VmhAcUmUqpVncjK41r3ctImmUSGXd6Dpa07FMjkZvWVQZe99/+wFPKsWFV3s+f/Ml337zC+q2Q3rHr779mrbZsN1sOc09tqrY7beMpxOH/kgKgc2qo25aTGWJ8wJKYesi+kxzYPYBUuLt27dsa8v90wHpBpSf0T4gY0CEVCwIoYzOA4kjnndCIoXBSsVLmbhhpsmOtQoooQlZk0lMIbInEnJkjgGSwztNXAwHLXFK4ZUkofBZFD9XEDyExBChCYrKR1qXMD5gvSenSJ8TU85EBA4u3Ia8eErLSV1odSGY8yWXqajNJYX/CqmQueqyduTL27sgIWVGCagtVCRCDPgYUdOMJyFCSXOYUCRRl8cxZyqT+P3PPW+uYxEUJkFMmTkoDr3h/bPiudcsPpDS9wVaEaUi6yZzsxXcbhNdDT4plliXrjc3oEXidpuxCip9AUMFSmSszjS2TH5agNYRISUpGSYvmL1gcppxViyhYl7yJZEi4kuoBa0UdGRELVB6Ipb5jAhEDej0l1TbGUIgmTJFiVzW/XypYCvZBKqEDaoKaSqyqkimIkkLoqRWIiTi8rlSCoS6rH5FGJljQmRPThlpKnTTobstc5Ao71ltG5QRxDAz9gce778lxcTmZk+VF2pjSFWNlyBWa2rhyHOgNo7hcI9ZrUgpYE3Dp4cnliipV3tWq4rgZq62G87Twqoyvx2A0lIiV69Z1zfIx3usXJhOH3CxxrlE1mueHh44Vpm7V1/w1Zcvud2uOD99QJuXLKyo1y/59PH/y/z8NW1d8f6TQVS3XN19SWtqJp+otMEoQ8wKYwwhJYKPDM+faFZbXJTU3RXnw4Fv3v+UtDyUq4fWKLVi3e2w1tBOM/vVzP76BVLBm9VLYiz/sZtVw7IEYvJ88/XXzIMDD48xMxxHTs8D9/cf6Jeer774ksVIqu0VbdVAnsvEUUF2AecjPkV6KVl1HT6AVoplWbjbX/HjL7/g/O6RH8uIEhC1QjmHJULyCBGYZCBkj7eCxU+MOTEkyXdR8k5qKqm5EVDlzFZmZPBkkfC+gEFKFhkDRmvM7LnWCqFKDAoSfMr4DEOIRJ+R35tNY+KYEilGTMqYnEilPfPXQsGcS2tJpBDtXkiS12RRPFoCgZYajcBSJheTE4qIJWFURJNKXj3x4rjPiABBZPoscDlzDjAJgU/lIRI5YPEYqS50gOQXHxS/uq/QpkbkwLJIJqfxQRGzQ6SFfMlX0hXsN3C1gm0HVsUS1w5srGfVxdKkslAkDQmkTCXL63L3zzkxe8lxhnMP/SKYXAHsjELkMjUqHUFpvOsRQhUCXOgS5nhpKBKiGKWzTuyub3CpxGgbIYk5IMhoqxHLiM8RJS3JgjQGoS0haRYMSRtQFWiNEhIur2kpNUqWpIUS6ZDIOZB8gLCUSZaSbc9lkqq7K1ZXdzTdugQoKkW32dKuVqTpSP/uA4MGtdmy3l4hhCMEj1zVGCO5bixET0iWaczsX72mrhRaKs6Lx1aaUz/y4mbPeRjwySNUxWmcefr6OxDQNn81QP1GHNTq9m3+O/+N/z4+t2Asuup4frinqiy7mzuapsJIhQ+O+PyImwPvHh5Zlp66bqhz4P27bzgfDggk1foFWbSFqIwJoi+qdCrwDlyPMhVBWuJ8JvkRqTRWaNrNmnb3GSM1SMUynwnLTBwPkAIpKUKckTGw2b9i9+VPuF01JDKVUSxuIUpFV2nIkeHUg9bU7ZbJLXhXGlOPj++Z+meq3S2rbodB0F1fUbWm9KstM/3pxMPxRO8DhsA4nouuJmRWXc1y7KnNlq+2a64ajRQLejghhEETeVlZhjTghWdRmeeHd1hbkYJjnBemlBnGkX5yGNMRKVEVqV9obctmvWEaHSIWI6hqW1wqpKvImUobCIE8z8gYC5AIgXQO6WdkiEW9TEImRxSZ6GesVMXz55ZyKUJeVq+yNuZLiioIfC6qbp8FsyhVWQXmSgpG5kJzFLbqQthGCs1zsW7kfJENZBpTEf2CkZaUEh6FF5KYwsXIWq5upZpMsFYSEx32oi2aKD5GcZEDZAFJSJJKKCNprMTWEikSRoLSES0jQtUIWXE4npiXzDBHpukyvQhB1dQoBAbYrXfkcUTlzNXbz3j74x/z3Z/8nD/4B3+f6BIfHx9ZG8vx3Qfm4Vwy+pua58dnvvov/D0el8A5ZqZhQKXEcO65vd3y0//sn7HE769vAmksSivqpsJFRZYKaRU5xnJiFKK0tYrLkSBEkpuI80COHmVapN2AsmQh0MD2+gUvvvgB1y9fFY41J5ZlQfiF0/NHEprNfo/ICw8fv6Fer1C1ZrPd0c8zdWuYTwcCoGxZFxstS979MjIGOPcnqqoiTWdihkYKNvsrti+uOZ9OHKdIdAveB/75/+F/+NfnoBKSoHZYqTifzyynnip45uMT3374ms3+lqu7N6iu5eQFddtx98pwfPczWjxjSKyvP2P18icoYVBSYQVsrt5QGYGbPSkrVLVhUymmaSEgUKpmmGe2tUUKiTXw/sMntquatlsTpcHHQGMrTqcT4zTTNhtSSszjeIk3bbha1/hl4fF0ZpiLBx8qVpuObfuCbz8+Mk+Zrt0gVCZrQXvdUW89MQnOzrPdbXgcFP3DmfVmQz84Fi9xvsYogRKe7WZDFpLXV1s00H4psUqyU5m7fcd12yLixNSPrFvNw+M72ryitoL1yhLj5/zTf/FHfPdpYN2tqHTDm7dFFHh/mnl8PiGcQq1fc5gjH4cZoyzbbsW2tgilOTw/s91sSnuylIxzwGpNjoFD35PigpIKkRw6BpRI6FoRpzO73QZ/eMbe7Ni2He9+/nOkrrAJpsdHpGkIKROWpcSFxExdW1gCISXWlJRFk8pkKWQR6SSlSkxIjrgUCCIXH30qHFOkkNARWFyJ41jSRTB4IcwL3VQ4C0V52GphUTmRhCVXpuiBfCzes1QgMeZIyKFE3uqWPki+evUDrq6uefXqjm61ohzcBNpqPn26x5iKtm14fnwgpUBbt/zBH/zNIt4NgbtXr5iOZ54PJ+4+e4VUGu8Su+2WxSeMrhjH6WKM1ry7/8huu2ccJ6bZ8YtvvuM//ef/kqf7B37wgx+W12ld0169RIYS6xKXMzFMaKkRYSb5TCChkiVdjN2uP5D8gtQV0pT/3iEERHSl6EFCjsUzub99S3f9hhcvb/A+4pexbIOpJOZiFI1+TVtZtAYjW/owk9xIo8rX2W0a+mFEKYOPnjz3CGPJ2tIvHuciZIWSlofHZ4T3qKpGbNd0ynIcPDfXL1kFR9+PzI8f/0rM+c04qKrib/zkJ7hl5HAaWNWaafGQEguCTdtxHkcUgd/96rPLg9/y+PhlCUUzDUPfk4W+TJmGStdMc+Td119zOg3IZg/9B8aq4sXdC06HgXf377nd7xHG8PHhmW7dsntxS60th3PP1VVNIytOxxNtmrh+cc16f8PiI8lnno89T08H7mMxtB6OA01TYwXFTuMTQ1i43W1YnMdKg20L55CripQFxpaL4mkceHG9JYs9Lka2q5oUM8/37/E+IJtr1o2mbTr2jULlQKtKVnsKjsPDCV+N1FrgnGOYex6OE3Msrb1312s+Pj6w3V/xt6863r5+wW7VIFzg7CP3Hz+huCILyftnx7efTmhzhw5nlGxYrzqOj0cIVZFUzBNCCvbdmvvnA8vUl+lKKjodmXxmu22Y5gGFJ1Ua70cmFp5P96TDJ8xGo5RhtV6x/vwVL1++YJo8z09PLCGw7tY0dcOf//kvOZ9K5bY1FT4F3DTSNraYQ23NPI2chgPaB/arjtPTMyoFurYhnwfGxydi9MBMFEUdrREscHn3l6X7MItff+Q0XS58paFZSYVJglYkStI4LEISVx1Jabptx/l44P27P2eajzw9f4dUgpevXnM6nTifz3zx5Q/4e3/4N9jvtwUEloVpGYnSs99fUdni8dy9uWH36gofIs+PT3z9q685Ho9Ms2O12tOfe/7wD/81lFY8PLzn+XDPdndFDJ7dynBVQ6oVr2/X1FVVPG39lsPzkf74yOQnXt1e8+7xkZvdHj2P9OdnlmMghvlyqQtkVy7AQuhi5C0nBpLUCFEEZEJamjyyXVvODyMpG473H2h2t0ShkEpRW8WqMQTveewnrFVM44JbJpZ55O7Fnpwdda3xLrOxFdmXK2XTKq6qludjxkqBFBZRl1KGVVdjjEH6gd16z6f7D7goqKxh9/rtX4k5v9GK1928zX/v3/2fYJuKb7/9loc/+k9ItuPuJ/9FXr96SegP2M2GmBVKa67WHeMYeL7/FqTh7u4VQkrWdQ0JnI+M48LH54lljnRNzePjE+Nh5Hw6UAnH1PckVbG6el1acrNE1w2VksjsabXCKNhf7xC2xbnINM+IBFZAt13TNhusNbgl4ULgdB44PDzTbNbstxucv3ShKYHWprjMRUapwiX0/cTxfMDWHV1bs1l1+Jx4/+me4/GI1RrTtnRdw8oahmnmPI7cXe9olcTPE60K7K3mzdWa58cTT8+POJHQKvHw/IhKjm5V8eblmn/xi2/4bL/mb/3+l+z3LW1lOJwGPnz3DYNL7DvD9XbD1796x+M5cRgWNkZAcHgsRiu2qw19UDw8L1gcbWOYZsfj4YAxFSjB0+EZY0vygfczh/MZU9coIusadOwZpol+CLSrNe16Q1012DSBUDgXiMEBgloJfAqs247KBIJzl9gYiXMRLRbmUAhs7weaWjKPC8ehnJqvdw3fvH/m3bf32BiYgipq8eRZ1ZbJLbiYEa6INjOZLDTLRWJQNFsSmQSOkkigUCUlQhaCP0tQovBHSkmMTNStoWpWNN2KZQkAvH7zimkZubq6A4qC/PHxAz44gvfUdcX++oa6Muw2O6QQBFGqp47HA6v1jvfvPpJiorINxlqGYeJ86IlJ8uazz2majsPxyM9+9ufcf3pgtepw40xlFefzc9EcScjJY63EhwVJIFzWsXwJh5Mil6qvVAj27yvGszCgTBkGREmsFFKBLOviZndLZSvWd18SZM3rH/6I7GaeHx+omgr8uYhHU8InTzYrhNUoP5MUdF1DmI+czgPXr1/ifECkzDiOJATNqqOqLF2l6NqKcVqIUnK7X+NDZJkm0GUjctHzH/4v/9v/yhXvNwKo7auv8r/1D/9XvLq9heD5xde/xCUIumbXtoU30gZTGdqqIsSAcwuPh4Gu0nTt+tepgB/e3yOWmapZo7st3mVOw4g1DcenI0/Pn7ha7XDjicN5omo2pLjQbnZs91dYK3DDUILqcqarLE3bkPzAMs2ECGPUhOlMt3uBqWrmsed2d0VT1RgreXx4ZgqFZN53FqMrlPDksNCsNlhjCQnO/UJMAWNXSCVorGFwHi0kswssbuY0DIxL4mq3xvtA3/eM99+x9E+0d18SEfzOvuJms+Hn7z9idGTbKtZrxb7V2Fqy3zT86NWeP/7ld0whsbKR5+PAD99eMbtSMplzRGjLrz4caWXial0DglW74ttPBz4dHVpClYZiYsVSN6XQcep7rq7X9INnWiaGJRZv20V1bYxGEumnhBED1w34JHBB0DQWmx2TL6FulXAXkaDg1A/IVPRiu82G89BDijydHTc7zewi+y7QtHuGcUJIGObESh849OWqF3LJYhqXCe8ljXVUSuHCTCUTSWjOC7gpMnqFsYoQZbFfeWibjlYvRJ/46X2kU4JtG1mCxGfJec5YJdk0gmVJCGNxIVHLyPE8ImVGag1CF2W5lKiqxbmAiSdEisxJMSdNVytkDuQ4o5QGqWjbFZmEc4EsLNO4oKUkoJBZ4xIsDpbFU9mGdr0jxMzpcCDFhG27Uo9uFMfTAzl6yPFysEjEGC7E/K916AAl4TOEooESZWoqn2uErpBSF8BKEYEgobBVTd1taLoOSVG4v/jsC86P99RNha0sot3SrtfEDC4E6DbYtkOLhNHQdA3jMGIqQ0gRXVVUWl08rZIYMz4kPn685/nhicYasjaoqmK/a3F9T4yCOSnqxvAf/fv/nb8+QO1e/yD/4b/zP8Z0DTFmTs9PxNnTbDbs15baGD59uufp6Uyz3aBFySbKStA2Laf370gJPjx8orv+nF3XMQ0Dvj+xX1UsXvJ89mRbE4Lkar/l9PhIf3zii9d3ZAEf7p8ZxxMyuAIim2uq1Y7ZzUg3MB6fWJaASTOiXqFNx8tXb6jWO+ZxZBgn2rpmPJ2ZxyekahESTg8fSP7IMh4JbqKpSipo3ay4efUlm5c/IMsOP4244wNjf+L67e8ibcvx8Iy2FW7xNHng/Tc/pV1dc/P2d0i2wR2fcOcnpsXzdm8Jw4lztaauBJ9+9WcEo+i2e25ud6AkKYysLVS24jxM3G1q7nYdX98/4ZaZP/jsBY/BEDB8e5rY1SvqWqNS5pf3DxwOI8s0s5zv6a5fI01LxcD4+B1zslTbFwipEASSO3PXaM7jxHp3VaaZ6xtyTNg4UilFozKH80TNwOdXLb2PCCpizpc0ihPPc+R2t0aECaMcIkWmxWNNQOuagGbykfefPjCGBqEMTZXpx5Jn/vlVxVoPuCDZ2xNzSDhhWVlZXhdLzXlytLWFLLnqEspeY2Si93AYNToeEbrmw7Pn7S6So6RfBIfzhNKSSklO5zM3K8HiIuNwZokZHySrKlOpkoqp84hAIk1b3uSyIYdnfARNCajTBJYAPldYK2h0oLEG1I4lK069o9MLMSXO3tDPkcpaqngubTvtNc/nAR81wlRYrS55aB7iwhJFmYCWI7ZZEzKFFM8eq0sRRUCQo2dZArq7xkdIoXhR0yVrSoiKJaaLxKDYZfheQ6YUIoFqmpIV31akmGg3G0wlUapCNTXaGOrNljCe2b18QQwTgbI1lEKOyBJmPn3354Rp4suf/CF+ONI/H+mubxHrO+bjE/t1jWx2uBDxQrJqLOdxZu4X/uP/zb/32wCoH+a/+9/6n9G2LS5ETueeLIuIbBpmmrZiu1kTcr7UHBdyzPUTQiv6aeb0dE9aImkZiMtIFvbX0RbN5hbTranrlvPpxHg8cv70LcKuMO0KpQ05eLYrg08wD0/MwwFhWmJYmJ7fo3VVVMtuRtUdu/0dQles2i2z95yOJ+bTB6IPkCPGGrAbcszEMCOyRG+uCcsIQqLIJN+jqg5l2+LyToLsI6i6KIrHI6ZekVIJMTNaF3mEj0jT0FaKql1jasvN7XXJIK8SH5+fGdzMq5tyFEhx4vl4pB96Xm4buqbm5B1mc0VtKz70jnHokcpgjGGZHU0asFXFsCTc6YlBNvisMKZimgcqCf35TBOO1Ntr1PYlQmogcbz/lvH+A75eg6rRQrLuaubxjGzWbNd76tri7j+wSIv1E7cvX1HbAhLaDcSYef94usStCCKlVfexP3G13fKiswglefd45tPzmSlLUvRIIlkm4nQkZcWqSkSlaKqKXdvRqAllLCsdydLyqyNYlbnqKjrjidOZLDRNV8ou1jbz4annF0+BWsObfYtEMLtEkpr748DdumJyxeIijcEIkGkixqIsskZgkkMQ6ceR+yFirGXVWLLcFGdB8Ag/o9OMVeUCJVQN3ws1hWQRaz4ejixuRgVBrlsq2yFSRIZz6eULGZcNYjkRRaZtVjRNR2OL2DKbFeMy0h/vicrQtnu8HxjHZxptqGVAhJEwD1TtFWEZcOMBKQxJGrIfIRbzgEiiyBKEIOoNeT6Sk4d6C2aNbDYQZ+L4iLUdqm1RKhOXhRBntLZUuxu6RuKnR6K2RNWhlaJWoajRw8Q490zOMw5n0jKh7Iru9nfQ6x1GRoyGdnvF7fWWpmoIuVwwz8PA/+d/+w//+gDVXb/Ov/uv/zu4+ch33/w5srtjf/M5tU7EakX/8WtWtUHWHbPZ0XVNifx1EbdMCHfkfFqYhyOdge72LU1WRNswO6iNxoji1D6eTsSQaawhEZhcSSnEDVT60rAhQKuEURWYDh9CEdAhqJRC2RVzzFztr6jrlpwykwvEmEjRl2bWDLZbMy2OlDw5BXa7HdF73v3qz3HziHML169/yOeff44m8e7TCTcH2s0WlTIvbm9YW8Wnjx9JQlPZuoTg5rJi9u/+mOeHe6S16M0NtuuIWoMQzIf3oOD2dodOnuTOqMrwo89ec7fWhAT/5E//nPc//xf84d/8W7z5/HO0ENxsK375MKOk5IvrBrLk//kn35Kw7EymbTuO48inU0a5I+nxl0y2Qa9v0EJA8EhdEU3DhGLb1YiUOR+fOfYjr16/YT48ch4HrG1589kXqBBhPOCWhe3Vnv7pmWV4pl7f0HvND3aax3GhsjVkwYttw8M008/FWLsy0KrAUz8wR8i62GKUzEjXM8eEm3uc2ZEyvNqoklsUE4fzCdNtEVh89HyxiVgpCaIiYDmNM03Tct04pBu4PyXmZcHbPTEk3h0C03hAN3tWlcRIgY4TpEgfJcwHpKkwEjyKd099qUAXibu7FwSpmPqBrjZ0NlI3G9w08DgFlpRhHqi6NXW3ZsoVp/OZNE9kKdnVloaJYDZoBC6WGF8fHUpVLMuE1pq2WZHnRx4fHxDdDbUMeDfSrbYIYwhuYImSqtpjdJESZARz0sgwIlMkuhNCNYRocKpC+hllO6pmxXJ4xxwl9fqanDXn8ydWTcUUMnEZyGFCNTc0my0Sh7AWKS3n4wMpLSgp0LZCtKWzsWkazs8fWXcbYgr0z++otq+4efOK8eNHxnHAo5DZM48nchJsNzWVLMGGgys0wfPjM+d/8X/96wPU5u7z/Lf+m/8jXl6t0EBWBisz21XHx+PA82lAuInd3QvGZWKePG1dIXTFer1G5sy8TBitWdcd9+eFcZzIWbL4IrQMPtBoW5IuY+kVq3Ck4OmnkRQD07gwzX2Jmg0TYj7jomM4H0lWsb37Md3mBde7O2RdkZOiaSqUkIzzQs6Su5srKinp5wliIiBIItHYimUaOY4jWhUVdIiwWnV0Tc04zXRtx6ZbkRKcRs/pPDCfe0KWzC7RGEXd1LSrLVN/4k9/+jNW65p1XaOsoa4NRkKKMybNhJTR1rC/XoOEbz4d2W8abncVH56eeXg88/H+E0oZmrywa2pevLxis9uw3245TImHfuKbDw+4eaHrOl7uGjpjiSRur1bcqMT//f/3L3E5E6ee+3ffUG/vePn5lzTrW4JfuP/ua4w0xJTY3r0mnJ755ptfYusV3eqWh9MZU69p1ltWxvB8ODH2J7S2vNqtUNERZWmNsTqxNQI3O7xpmBJIP5KmM7cvXrHZWD48z7x79w1+PuDcdEn+TKyvXmDWV7jTEwHJdSv49HQkSMurV2/pfcL6ibubG7Yry+OnJ96/+xohFUY3nEPCrm8RWaDrlhwlx/GMFoLsPbdXK/Iy8nQ4MJ6faDZ3LCFibUNdSaZxZJkHUDVhPiFICF1RtTvu7m6o0sg4nOhpEAIW59lXAiMDrdYICbbqiulZKIbZI6Um1xtOp2f607EkXJ4+gm2Q9ZZ2c0PKERlmUk7oZoc1ErJDJxiff0UKA/XVD1lSqScLw2MpHGhumMcTQoDvn9hcf45dXeFPH3He026uSElhpETUa5bZM5w/4ZxDseD7IzFMKFUhmhZtV7ip0BY5S2zTEpYT0U+oeoMIZ4SyXH35E+qmYxonUBk3HGi6DUpKfChq/2a1huVIPD9SK8kxQBQV09hjuivkMvP8+Aue//H/5a8PUM3+Vf7i3/6HXF1fU9c1XVM66gQZHwIZOJxOl7AsDzGxTCds1WKqhnkcYT5R1TuIE7tVx9gv1FcvaHZXnIeFytTIHAnzTD9OgKQ/n4h+RmlBs9oze8E8DCzTERUnjFFkIXDDEZkW2rbhdn+LrNbododut4QQ2LaaYXIsUdHUpehRSYnRYDV8+O4bxiiQVYu2htuVZtU0+JRZFs9q1bHMgdWqLUYFNzFOC1MogWSNsaQM59EhjWVZPK4/sThPCgsiBZSfMKJnd/OaerNiPB54eb3ifLznm6//jJdf/B7CrPj09ESae576gc31NUYp3NLz8M2fQQi8fnHFerfl9vYtL9684SEYZhf5+O07xrGnqRTCRR4OR4QWRCE4Pt4TXU9cerRp0M2W7dUNVkQeD2VdyxK0bWnqhrv9hrrdlcp3oai6LcwD290153nh8NwThcYKwenjLxHacL3fcf/4RL9k2tqwzmeyavDCsm0zIRueF0+jEqdhpm00IgeSyAyzp7aGttJUTcUSBWN/RsSRtJxoty8w9ZYxFKvQpjYcP/yClze3NKstp/PIycPgInP/jNQrdrdXVMrw8HTg+PCepluzbhv6c880zQgZeXFzy7AsCBT1qiF6hxWR4zDjppFKCra3rxBC0c8TlXsuvTk+MYxPxLnH2BrpF1S9xrZrmqoGdyarDh9m5qSomi2Shf7xQzm6NFvWmyuiEChZIaRjGnqOn75GSk29e4Gt17ilp23XDENfAqqyJ7iIm0d26zVumdDVBtlsIQv2V9eo4PAhchwWgs/sXrwip8zcPxNzpn9+xNqKIqEYiQnq9TVVVVGtO+ZpIPgJEUMR8ApJlJKlP0DySFtTtxuilBil6R9+ybptqK7fULct/tPPkUpx9+Zz5tHTzzPrdYNZ7zicA9v1BrdMPH73S4Zx4lf/t3//rw9Q9fY2f/Vv/LtlXVOa1WpD01rOUyBEQWUr2qriPA0IFMPpmRgX1ps9QhnqquPu9RtWXcv5eELJIv1XSjGeTsR5IsmKMJ7wfmF//ZrnKeKCY9WtsXiszKy2W2YXinJalRD7iGRxCylLjBJoAX6eyBl8KFcSkcFoxeF4KCpo7yALdL1Ca0h+RmTPODtCSuViYWriJSPI1Cu61QqRE4/39/jzU6mJCh5lDFrAcDohtSW5cg62uzeIVHKKlmXE6ArdtDSVZjl9oq4bopBl+poP5dqhKlyKdF2HdwtxOmC1RJiKnAJGwewjy3Bgs9nz8vaW21ev+PR0xMcFkRMua6qqYplH+o/fUmvJSXasVx1Vs6GfPZXVXF/tOTw9EUKiqSwhBIbBEdzA89MzRLjartnefo6pGj589y3eObLzhGXBrq9Z3bzi+PBEzom1zvTjyOADOYyIuae9eU3V7ViGD2hfGpo3647KRKxW+HHgEDK6sghl6boGWzcsbmbbKFbZcRhnvn04I5stK+FJuiJ4T0iwai0ZAzmgTMvUP9NYw3ZzhdaSPDwwzzM+V5z7E3p9jY+x1JrnTJSG0+HAMo/YylBpjXcji/fY1TXLcAIyQgRevfkB+lK4aVc1p6cjeXmmMZkQiuK9H564vb7lyzef0VQtfhz45v4T/bRwPJ8w9QqhmhLdmyey6Yi5IaaZqpLEpGgbS3Az8/N3EGds3ZCTIOsVaItbSvrB9aplWQKnvMbWHQSPlpIUHAlBt7tFSYOfT2QXOPVHnM+EsKC6stVEd6ZpWgSJbFuqZk1YZqSUzKdvmKaJZnuNSLB4h9aKylZUbcM4nZnOZ7qrO3Zdzen0TAyO7faO9WrFOUNMguX5I3F4ZljKlbPd7rFVzWrTkucz//T/+L/46wPU+u7z/Lv/1r/HvIzge2IMVMZwd3OHi4FaG2zXstKJ7x6OnA5HlmVivb/jqZ/RSnO92xJDZKUTst2AczhZIUI50U45E8aRaR4RwpCyppETWSpOj98Ss6ZqVuRlQYiIavc0jcFFAVITo8S5pegs3JnbXYuLHj8MuCRRuqLa3hKcJ7gSsaLrFeH8HXVXc/v290vEbwYXPI0uGTftakuSiig1MkGInrpuMNKQs8e7wDL0yOgxyhCCxyjJyxcvMdaSvSNGh48SU7UoZoQyTM4jRGZ2C8dhICyO8zwTvKdrDToHrAysKsV63fE0BhySKCzb1qC0YfblPB2mE2o6Mp4OrK9fcH17Q2NrFjeQcuAwZ54/fcLljFm/RAnPenPFuR/wvngK+9MJ5xzWdnTrLXnuOZ96Nts7DseBlAJm6ZmCQFUNb1++BtPy/ptfkmJiJRaUrXGrOxoZ2ZrEkjPvns5UTblWTfPIi33NpmsgLZw9nJ2nVqVjzvXPLFHhomMZemoj6axGNCsW53A+Udm6+M9IDNPMdnvDpi2FFuvVmpQVXSUZF8en+wPnqVwkK60IKXF++kBanhG6QpgNm6uXLCEXsa1SDNOEJmKaNc1mx7ptsSqjJUxLAAIpRKbZkUikOGNVRRCJRmqkErTNis26wQqIy7mkN7jAHGHqB9aNRipI9Rqja8Z5JMWEqRSKmkjgfDzjfIlhKTVnzSWgESoNm7rBVHWJ8fUeN/elc08apmkB1SBVhReZZRjp54ngA3W3YXt9hTYKkQNVmknzkSgNdVPjUyLL0p5NigQ/4oaiWJ9TpF3dcjoemYZnQigpqMo26BwJ3pGrNburO/rzY/EMqoYYJmqrUbJimSdE1TA9fo0/HXj4x//nvz5AVbuX+dW/9l+htprKdri5L7yE6TDdHr3aU9uaECJt0+CWkfV6x/bqCucjMUXmOfDx3Qc2q5ppWtjUJVf5PM2kpLBVhWl29McnRE705zMrK2h0YgmOYY64ZSF7hzWKyWUyASEM1uiSbSMUpt0xn48lr3A544Yncs6Xeh5NtdpSX72ksQ3kyDSdsN0GXTU05U6LWrXsr16wMoJus0FLzZKKSvd8PpGlxYcyBQxzYpxLuH9wHhEiUgikVhilENGV6NrK4paBECHFcilMfsSYjhBHmHog8Xw6UDUNut1ibMty+kSah3IC7hqiqqg1yBSZQmC1vWazbtFSEGPgxdqS575cWGXNmA3btqK1RVGcTIeP4dfFDrOPeJeYlwkrNDlljFRIqTC2I4eAD5mHhwfmpydOS0SohiWBNRVxOpdphcRpdggkTVtjrEaFhW69QlmDItEPZ2SeaEy56OW6vUgWAlIkquRYbzpsZUslUcoEBHMUjB6e7z8xD8/48YiSCVJAolAi02222HrFtmnYNg2srjlRM4eERNBaWHykJSD8GS1kcRcMjmQaYgpsuobNakOjBbtKUDUtITpGt3BaEqMXTLGARIwJN/ZoGbAiMfdHAjOqvkZcLEHFx5fxImGkZW1VsfKKwHq1puk2oC1Pg+NwHpiWhRwWlvGECyW8TkpVQvYu6RDGaBoNtakwVUtIokztsQBLZRvWdcU8T/il1KYFKVmWBSs9ybQkaahMiTvRWjJNPVOIxW+YM9ZaspAYI5AZfPZF5JsjSknOkyv5VwiUUYxjT394oF2tST4WO5zzKCWpjaJbb9luiwXNxcwSAio6hLb8p/+7f7UX7zfjoG4+y3/jv/4/4PzwHikVTbvBI/GL4/r6lm63YZg9IkeqnFgitJs1WsI89JzPPf4ijFRhwqdE1V1TGcWqqxn7gWF0SJF4+O47utu35OnI/Xd/ikRQb25ZmUw/TDS3X+Hc99KASIqlnNFHgV8mJBlZdbh5IaelSAR0RYoe4U6sbj+jqmxRD/sTkUjV3ZG1KLzLeGAYR6paMz5+h65XbPe3CGuZzkfQFdvtNZ2VPJ/OrNotSRuGqcTC7DpLZyS2amj9xHHxxJhJWfN8PFC3HTp7DsPI2B/obI3d7lmmCd01yLRQi0zVtjyPM2sJsjWsuhYZR3qXOE0zRmRyVvSP7/DLTLfako2l7Toen09ordB1R7duqbsdXV2jFDjv0EZhsqCSlqAUIkuWeSamTAyCmBWVLIeFqR9gcQRRI5WlMhXnOVHZikYLtqsVRgqyO6ONYdXViDTTu4TSkcYqEJrHeebT4wPKVqzXbUlCyJ4gdTEWB8+qlmijsVJRm4wm4BK0wpH9RHYTkmJz0bah1hIhAlYrEC1oicrxInwU5OxJ2SJVCSgMMRPS9xVSipgjQhpiLgmeQkqWmMjRUSnBMvbEecDIhJEKZzZQ15AkKYoSsoek0YIkPOQSAZSExoeLel1EzovCK4NWGZM9TTojk8PYGluvWLAMwVyCEculi5xxWTK6QAqpGKWTB2HIWZawRgzTvHA8nDFSEYDz84HsJ169eAtpIUtFXa/wlKC8mItzolIJYy0+BMb+wJIVxpbD1tgPvLrZcLNvWbJAKY3KCRc8MUu0FkiKrEYqRcyC8zBzmoZiT1pm+tGzrjRGRWKIjC4jdWa325OyYJhGwvnMP/rf/0//+mZhgaA/9hynRLu74s2XP+Jm1XL89IEFzbJM7JuKdrWlqSvO/cLx9EhKAd3ukdNMrUsudn94IKfATavR1QvOH9+xWrd0a82UDK/+wT/g3btHjsMZs3mDrdcooTj2J4JPxIePoDS77Yb1+obV9R0+J7SypGXBTT2ruuXh+ZH+fGSeB9brHXe3d7Rtw8u3n/H0+EhwC7prC9glzzw8sSyZQ2gQy8B2e8Wus7jzPSrPnJ56otZsdYTTO86qRpkavVqjE9zdrADN+fmRj+/eUa93RB+JQvPm9Ru265bNcUMIieBm+gV0lTHrFU3T8OUPf8TN/hqZF87DCecjehio6ga1DAxuZN/tscZx03Uk2+KD583bL1kpx0omfvrpzOr6mq++VMwxsIRczudZ0LQNbWVJMeGCw6iKrpb0SyBNHjkNuHnh0UmsrWl2N7RdzbrdQhRkNFrX9Kczbu757PqmOAaCZ5oXfIysa8mn4wmRF5p1S0PFoT+B1hiRuNns2baK52lh6p8RSrG5ucPGmWgUX//yF8wR1jcvyK7HLzNZKKzSbDYdq2bPRpWkhqdk2CuLSDDPga5SHI4eqTTJOdZGsu06RC7lCePo2TUSnUZIDpkMbhoQRbGIyyt6F6gttLamkaCqhtlukMaUVSlpRCp5TkL7EsETI0Ikoi+t0+gGlROmEigZUSHTbQQJT46OGAJK5JJMkDxhPpET1BmIASkES5QkVZPwrIBARFUNpMAUBYswtMZipMbrxJvVFcEFXNbkXYeSukyOekuFQGtAKZYkmMNCrQWNzlSUai6/2pOVQWrBfDogjWfbzMjo8UiUrnFIVquSO3WePe+fzszDIxrJarNCKYUPgnHxOB9ZkiQ66Lo1swpIXQDquXcIKWhtS3X9V1ef/2Yyg5df5f/qf+9/Tu9KIH/bdpDLReX++YQbB3ZXW7KbMW3D1dUd3737yPncs73a8mLbsJICUTUMPlNbS1gWXIwc7z8R3UJIimqzBaH45hff4FXF9W6DGAf6ccDomqAV+Mx33/wCKQUvP/8hwXsOD+8RQtGEA8s0orob9p/9Lo0MRK15/PCO09N7VjLRWs3ZOfa7G67fvOXm8x9SV5axP4HMfHw4kQGpJHI+8dM//SNs0zB8/AXT8QPOjex3t6jtWzZ3b1mt1vzoRz/hNAceHp/LCpcElQJrDP1pxFQWISTrqmaz33K1rkpsfYwMPvPxw0ckid2q5m5nGM4nHk89x3FECEVtLB6J7SrWXYeIA24piZvSSKqm5uHDB959fOR21/HFTceH+yd+9JPfI4XEhw/f8e4wMS+B9WbP5uaa0+Mj5/e/pG3XvP38hyy6wVK4vCWBCon/f3tvEqrbsq5pPRExRox6/OWsV7Grc87N4qooCikmKjZEVBSs0EaKoiAqYm1aIYiZDREzbSgoCIoooi1BtKFgV1TEvJl57z3n7LP3qmf1l6MuYkTYGCuzIQom96C3sd7mYi6YizVn/BHf977v0xQVVdkRL7dIL2OZ5fRty+5xz+3lgu3mAhwkyuD7HvuqY7CWJJDUXYWOQ9qmoh+aOVWQpiShh+cGglCTJh77sqcbOopqZOzOTGaiOp1o+pYkXWE/O7e3y5TFak2kHKGySM/D+TFtW6GcY+xbHk4VcZJRNi2hJ3l1vaI3kkRb6OeojRGKcfKYUJ/7n5i9PN0MOd0kEbHq8a3hUNQslmviKKRoBywOqUMCNzP2nBQYFL7y8VyDmocOGJkAE840+H6AUQF932EdNIMj8P9C86rDFw6kYxw/o86sQQmNZB7iK2Y6cW8cTgZYA4FSeJ7PyNxWMTk9L0+MwkyWYfxMlQHkNFCWJ8qqYRIzvMHXActYopUjiDRJFKB8Sajmfv3BibnHSzpCT+P7HoMZ6IaJZjRoqZDSY3KG9vMzMvA109DTdzXdOM1P1M8bRaG8OTuIwNc+vlaEoSbSIf/dn/xjv44s3jfub/on/22kUmglKcuONNGEQUTdNHTVmbZpKQ9PPJ1b7l6+IphajBfx9scfUH1NttpyfbEk8gOsr6kGi5Oai82Si80K7QxPn95Tlx29U4go53g4EKY5xkriIESJz104n8GFnYN33/+K/rRjEvbzCj1lfX1N4Hnsnx857ndI15Ksrri8e8UmD+naGqk8lJ4HimnkUZ3uEdPAvjFEq2suLy9h7Om7jt1uT98UtF1H23T0xfNndpkA15EGKSrdEinBX/2X/WFMkDOMlt3zI7Ef4OmQh8cdr27W7J/3VFYipE8mOpZywF9eczifeNrtSNOcze0djA0TjnSZI8zEuevp2o7ytGPyNOMwwtgz1AfOTQ06Yiz32OIRqXz06pqLu1ekm1uCNGad5/THJ2zf8PFQ44RPXxX89CffsNxeYft5jqEseDoi8BI+PBS0zUBfVhSnisNuTzVMRItLFnkG3Qk3GqLtHXcvb5FOUlUncs/i6YDsYkGohnnw7As8BY3p6bqe9TLlahmjmBicY38safuWOPDouolRSjw1b2aV55GKgTgM5pqWceRpf+Kp6Hk4F/RVha89Au0TpxlSh2RxhJSO0+FEV5ckWuL6BrTk5uYVxkk+fnhHki1J8wXGjMRxinQjVTfNw18hkEJisLTdyDBaklDPzvssYJlE1H1H7HsU5xI/0BybgbKqsVNP040EOiDPMvxAsUzCmaw7WTyl8KVjHXv40wRiZLAKpcTnHKGjGgz+NM1DcuvheQKcB07iJss4TVjnYayi6w3SzVtepM8qixi7jvN5nhGGUcTgoGl6UI5Ae0hPMGFJAh+kxVcevmfn7nUmPuNCgRku6nCfM5zeXJEzOUbj8DxJVVeYfkAo5pA4EtTsmVRynlEKIRFiZu7VXcfkHP/zn/5Hfw1GzZuv3d/yT/9pfB3w6dMTv/yd3yKyE36WIaeJomnp6oqhORGnaxZ5Qlkc2N7eUt7/iIg2ZMsrskjhJpBhyPXdy899QFCfzoynZ8r9AZGu6FXKzc0VmdacmhY3DCyzlMlKpPIJvNmLYqxDKEnbG3wleX54pG47sJahPhNEmouXr7HW0PcdnhQcnx+Y3MRis6YrTlTPbzBDR1HV+FGGn2+4uH3FxXbD7v0vOJ8PXL36KU56LLIYqRTP94+UxR4l1IxvVoo4XeHHGdqMdF2PGUcG55EsliRxzOnjB1Z+hwfcnyf06oq2OBBrQbRY8fjmDU+nHeubr3l1d0nqQ4MkTGJcc8aLUnSUcHuxREjHoSwZOkPd1iRJQG/FjAXC4Usf6Qva+swwzHUy52om9+oo5PJiw9AZqsMjSbpksBI3CS7XS56eKo67A+t8xagXNMea0SqyJGWxzBhGQ1G19HXJxEQaxYTZAjmNmKbm1I54piTVEpkuEUowlB+x7ZEgWVGYkbFvWVxdY8aBx09vMGbi9uufcrlZcXx6mNsLlCRbZsRhhJIzB6KuG/riCekptOfjBT5909IPhmaccG5A+yHJckWW50ShnudcVuB7Ek8KunGaDYXjyL5oqLoeKSSJNEwuoDKGujNMk6PvO6au47x7ize2pJsXqGTJ5XaNr+f5y/lcIAUEoSYIJN7Y008OpxR2GEBHXF9e4ilHEijOZTmjzKSHdOB7EqYeg89oBrSvaYeRcTS4af6+A6WwpqcsKqSnieMMJXy0p7EWRqeY7EyL6UdB4AUILKeqom1aksBjvYxJIx8pHZOdGGbmKEo5UDANHaOb6AZD5Id0k0HiI5SlHXqUkKzTkMnOt7PJTjgE2veQEpRQBMFs9hVSYMyEE3PA3jkYTE/82TJh7PR5A2r5j/+Zv/3X44P6jb/5H6THZ7HIkQ60s3hxTNt2lD1I5bO+3CCcN19Zu4GpOWIcbFYL0vUSN07s7z+RZDnr7Za267BO0vUDWgh8K3j3+IH9oeUP/MGfMfQTVTujqVOd4kUBWZzSj47n/ZHDscRNEC4y8jCiOO4oq5owjEE5Vus1XVPx/PCeU3Hm8uqaF69fMtiJuqnp6pLieEaJiWSRkeYrgiREC4/7H39JuX+L9jxefvMbJFd3KOXxuNsThRF5HMLYUhUn6nYkzlckWYabBOemRzrYPTxQt+NcpRrFHH/15yi7Hr24w/VHTFOjsw3V/p6mKvDTBUm+YrHdcPvyJbEnGPsGe3hLZQTruxfc3t0gERR1QTM5oiAg1B7bLGGRheAcZdtzf9jTlA0Cx8OxZLtekEQhUijCwOP86Ymm7fGyBf2kOB9L9vfPZFFCGmWcOsHx1FE+P+GmiaEt8YOQi9c/JdtuqU4zM9B5wVwj3JTofMViuYahw9p56O07x373kdaO5KslXhggpx4/mPuLfCmJwwClFXYwnJueNIkwzqKlo2sqnnbPBA5susCTE7QnpIDF1S2BL2ZEOj5pOt+IlefP/eKm41x19ELR1y2BtPRCIdsGFSVE+ZahqTBCzrMk5iG91AGRDmiaisPTI0ppqr4j9BTZYokQguP+mbE6ECUpi+uXXK6XvHv4xOnhE0iFiDKyIEAlIWKcPyC15xi6nlj2RMmSKYxJ4hA7Wpxw1HVN5EtUGOFgdrj7agYiG0tvJjwvQPsBzswvCWkVvXEMVoBxaD/ASoU18/MriXx8JTCDwTKRp/PCKIyCmZIjHMKN9ONIluX0ZmQYDaMZOVQ12+UCgcBME+s8QQjFw7FASMk4TUS+TxJq0jjG/QVyslRMppu3pGWFwzEYgxIS5c24N195RIHPf/HH/67f+wHlpWt399f+3XhewOnpPcL3iOIVnvRZXtyQ31yxXq3YLpeM48R+vyNONU07UhUFgVaMRYGYavpx4nhqSPKMIE65WK7ohc/QjDztjhz3O/wwZZoUSMv19QukCtDaY6hnD1GAR77MScKQumqR0qMZerbrFZ6Ye3zef3g792ZPPX21p3fz9fLm9SuEnHBDTdNOdONItkyJ45iLVcrx6SO+jjBmZF+P83vb9/A8D+sEp6oljHwwE6ZvcJPFeSFB4JOEIUXVksQBFon/uSbj8ccf+fRckC2WxGnO6CTRcOD4eE/nfMpyfh7cXF4worAq4CffvcbzFU3TsXt+JAkUm3XOzc0VQSB5eNpzOB7ZlS1NUzCcd3hRTJ6ljM4SRwnL1ZqriwVREPDDxwemtiNPonnlOw5UjZnDzF7Aueyoy44gSBl7Rzt4mEHSlmd0mNDbiUFIoiQmjWOG8kQxWs51jRgalBnYbra0x2eK/SPJYkt73nN985LLu0vqtiJJQkYBXd+jdcDUHnl887u8+fCecZi4e/0TtrevWGQp5XFH0xryy0tCz9GMA57WVE2DryTT6S19UxFffc1yGROqGVM/9CPHrmPjzwfc+2MFXjzf7l3H9eUd6fXXOCXn4Lh1WOlT9RNt12Kdo6w6VnkCzsOMI33X4Mcx29WSLNIIRtQ0EUQ+Rd3xfDwTRQFBlCJ9j6IbiAKP+lxyLk9s8oT7+weGpibJU/w4J0nm52rV9LTdAIyI3tA7i5CaLE1ZpjG+rzFmoK5aFklEGoZMVtLWDb7y6HpD3U1EUU7fD/PzTyj6cUJJj1h7ROGcUTWm47ls6Z0l8L3ZF9ZU2GbPZCzbZUpvBqqyIkgX5Nsr2rYlCBR5HCAQxHE0F+RJyeQgDgICrfB8wTiY+etDje/7aM/HYimbbm5w6EeQDid9Yi2Jg4D/8l/9e38NPqj1rbv5Q38dkzGYeM365U/YbLZMxlL3HcpMrFYh682WsTc8/Pb/wig0tz/7Q7P3JAzZrnPMCIOzNFXNw/OBsTwwVQdefvsHuHv9NW3V8vbDR9aLlCRdU+6OnB4+kl7cEUQL+mHicrVltJam7rFW8eZXP9IPI1G24vb2ktCfr5BBGDKZEd918+rXl3jSkqUhcRLTFSeKccQJxePjjmNxYJVEHJ8fKI47otU1X337LThB3TTEnqBsWqax48V3P0N7s//mcC5IwoSh74imiiwOaMT8HCubnucP7zndP+DCuT/cixNCJRBTj5etMQaiSCPFjBUareXw/MjUndgsUsLApxgdd0ufizjEJin4MaMVVH1Lbzys64m14lBWmHGkLY+kmwvKZsC0LZ4aSbIlWIFpKp6fnxF9g/AD0uuvuFpv6UdHW41k2RJPRQjnczx0WOnhCcPzbof1A2QQEiUhwvewpufDDz/MlgGlaYsD4/kJ0zUoB1jIsgylAK3J0oBofUGUJNxc33D/6T2H+w/s93vSJMZ6CVG+ZLXIUa7n44d3aDXCUNEQsb2+xdc+XfFElCQMMiBfL7B9CZ5HlsSM41z01nU9owScR7Hfc9w/0Nc1CEG6uuVylaLFhNARfduwChQ6W+OlGVLIeds5jDPm3NcssgW74oiWIJ3l4dRQnQ8gJXmo6IcRz/cYuxqlBEiH1iEORZjmOKmo25ogiOmGbr4hBZrJTHR9S9sZIi3xTUWQLlksV0zGcDhWmH6kPDyjhML4MVKGjE05t4EgUU4wqpgsX7LM0tn3FUX000TfNQz9wDB95ulJQVkcyZcZ2vNxYsLakSjU6ECjvRnHnkY+SRRRNAOn8wmmAT+M584oJwiERXqSMApnavFkaIeBj5/uub65JolDnBk4VRVOSqI4wVeKY1FirCH0AvI85r/6V34NB1R+9ZX7a//Yv8729gqJo+sN1fmA5/nsDyee93um3T1Xd1coHTN1DXEwE0wr43Pz8iVJktC1JY9PJfUwUj994Hzcc/PNz7hKAyY8ns4drmloLAgvxJYlrZHkWvLyq58R6IS37x/mmEMQES/WaD9gu0yJgpB+HOn6lm5oiT0PlOO0e8CNNUGW4dmWZJnTOcfkJGkcUnUN+90R7ETdjXjC4CuPejBEcUzkSVbbDePQEeQrTrsn7r//LYbygE4XXN1+S5oGnIuG592e1XbD5fVLPE/RVTVjW7IONO8ejnz3058gdMbT4YATmijQ4CZG43j/8Z76dMBMhnL3aYY3LFb46QI7WTws+SJjKD6AjsALGJszYegz9Q3jNDHVjxhrefHdH+bi9bdU5xO+8jk/fMDIABHnxHFA01rqskCOA+NoiZQDFzAScXH9GlN1PHzcY4KM1eYClED6AVZCEHgoX9I0LefDDqQi0BrphZi2pdo/48t5npKEAWm2Igg0TXOiLU6EcYSfLqjLgrE+MbYNbVEiTU9++Yr04oaQkcdPb2EamNoT56YkvfqGVz/7Gb43MpwPxKFDBTEyiQiVQXoBoejpiz2NCJBac25n0GfTVSgk6816foY40FLRns8UvUE6h5YSP5qjNqYuaA97Bj/h7uqCzWZF21TsnncIN7LYXCF97/PctWHwFdebHF8p6q6lbDtUEHGRx1R9S+hrlouU1ozUpzPV8YmHT59Y3n3Fcr1hMBNJECHsgLCWenBUfY9CscxjpslijAMkddX8xS2e9mNGJ2iKAWMNVvgo6RF6AVr/hY3ezCdM4hA/nF8CPhNeoGi7jmEcKNuGOPDojQXpZoOxs1R1DVi8IERJhScnpGP2PHoe1blCa0F12nM+HcBZvDDF+jGx6PCnikmFGAGBjrnMBG/ff+Lqq5+SLJdI5/hv/8Q//Hs/oC5e/cT9A//6f8jzueJwPLF7944kTVhtN6ggQErFfveM7Xs8z0P6EekyJ/AVIGn7nqkqaPoOLQRZ6LM7HLFhwmKxous6uqLh+XhCuZGb61uy5SXTBKMxqNGh0yX1saLeP3DYF2SX35KvL9nvjoRhyNXVBW3TUJcn+tOOxSJjVIp4lREnIcfzmXL/jOnO6CQD7aFcTxilWAFF0xMqwTJL+PjjL3h8/z1dUxIEMZOY+fVaGs67Z0bTz0/N7ALthygcw9jRVk9IocjzDcnFHfgLnBmJkgVhtkQHGmctZjDoIGaszoRhCF5IPRiq0xnf9xA6YhgNzo7YcSRZ5ESRpj48s79/iwojFtd3pKFCKYkxA33X09Rnhq7GDRWbl1+xXGS4zzACOxi6umS7XWODkMPTkamuKUfHZAyMksvbrxlHSVEMaC/BCW/+1HUWrRVWa5zv4aaJ1TKhbHucg1BrYuUThAFdVRNqH2UhkPMtYexbhJ0Yx5ayavCDkMEJQgHDYJBSkEYBVT9iJ2i6ltcvXzCOhqfdjqbrEUrgTMPp46/o25KWnjjS3N5eEW4uubvOQTh+57f+PIeixIwjOoqJs5zlYkm+zHESzOjwPEUo4f7De47HmigMQAqMFURBhJCO3ePTbIDNFyRhQKIlobQ0BFzcXOJjEVJyrjus1px2j0xDTbS4wHqKJNIkejahTl2H50Oc5kx2ou468kj/RWz7YX9C+RolHGZ0sz8s9lHOUdQ9SoX4QTD3pCoPZw2elMQ6Joli+tFxPje8e/OWepAsL69w44AxE2mSsFplLOOQ0U48n0s8JTHD3IRqxhGtZmq0833aoceZER1ozNDjS6ispDnPcZfqtCcOfOKrlwgEo51QzqHkXC08jD1Cz4H5tmlBgukb+rIiCQQ2TMg3F3RtjTCO3/7P//ivoQ9q+8L9Vf/Av4i0Ey+//ZY80Fjn8IMQT0r6cUA6iZBzMWnX1LTjRB6HTMNI07VcX1/y/HTkuWi4uFiwzRaEWlHXFUppuqFn/1wyMaL9jN/5xY8MdYvCkUUpm5uvOJ06TkVD8XREC0m4vCBKMz69fct2uyJcrAh9n+p0II4kx6dP7N78FvnNS/L1FcvLC54ePzF0NRfXK+Io4s37D6ggBgRxGiGs4LDb0XQ1CInpB0xb4RhJ8i1WRygLQ3uiefjz0DUYz0PpFKlzZJzjqTnTRNewXF+yffEaP0wwZmQw8zq3LM4oIfCDhOUiJdAewzDgnMQ6x/nwjOkGFssVBAFOWHQQYoYGYR1hHJOnAR4jh4f3SNvTegFBkmGacnZTLzd42kc6QXvcMboZALnZrLm6usa0wwzcNAIdxOwenzk+Fjw+HhHJHXmS4zyJTlKEkgymxYl5Dd30IyqO0VKwjDTa1+xOJWKY2yym8oizDvyAw9MDsq+J1teE+ZJQBwg7MY09rdM4JYl9hfAE5eEEzYHl5R35coFSijDQSOeo2gLtWZATVV0wjM2cWPAEfiAY2wbtWYI8x4wDXdsjBWzXGRjD7lzy6u6ON5+OvP3V91THPelqy0+//QpPeVStoS5rsAbPm+kpT7sCF4Tkic96ueSHN+9xU8+rV7ckcUjb92RJBIwM1uGkm1f5fYMDRufww2juF5/E3IOuBdLOtJ16GDgUFbotcA5ksmQVBwSBmjem/UTbS3ylCHyFksH8dUKQhyF913JqHcdjTVF1LNZrkijCc5bd4cwwjug4ZJXmnI57zs+fmFREmC+5u16zSHyUcHSToxkGyrJkGSv6pkIFMWEUzhASIaj3j3Qj4Ol5a6k96q5D2Pk5HC9SXlwsCXTAZCcOZUU/GobJsAg8yr7HThNxFHMqTgS+z//07/5jv46w8Ev3R//RP0m536OSmNe3F2At7398S3d6JlmvQAWEiwUXlxeIaWI0hqEf0IHPt69fUhcF+1MJUlHVLeenZ8rTM4dP90xSEV19gxdEdKeSJNJ0VmDKHjsZvGjBaDQ6yYn8ECbom46hKqhOZ6zwWV5do6QkyXOa4kjft5hpYhxqlquc5XbN0Jz59PZ7jFKESYYdGiY3G8qCJGB7dcerVy8QGJpmoDydaLsB4UdIHdB1PafdE9XjG9ryGef5xNkV8eqaIPDo+w7nZp+I8sM5k4dFKYW0AypMWa4W+GL2sRg7bzzO5xPSWJznkS4WSDsHqJM8o28bxsmBEigp6MozI46rZYwfBox1SZwnCAnKk5zLgjgK2K5XTHbAth3W8+nsNM9PupKpGvj+WKDDBUpIPM/HFA2fPjzNvil/yWp9wSpdYREUw8Dj/kBVl7hpYHNxwfrqEiEFdd1+ZgEatK8Rk2McJoaun8nDdqSrSrqqpDyfyKI50e4tVojJ4jyN1nLulo8CqtHQtD2vLjcIwdyw2k3E2kN5kqEueDztyPOIKPGIo5nN1o0dvnJcXG5wWN68+0TTNfjaAzNR7R7pnOLq5g6tPPwgpGgGAk/BZD6HuRWjgbE3nMsWO40MxhEnEaZrKR/eUxtLnGVstmvSxCMSA2Nfss1igjRDBRFZHGCtQyqH+ezob9sOpxS7Uz1vP4Vks0hI44CiqLDDRD05nJIcHh85niumvmeRbcmvLokCje95REFE4PkcTgVjU3N42tGNgtootNasghjfF4ggYBAeYpxwAtIoYLRQ7D4hTcfrb75Bac3Q95we71lsV3hhxmAMzf4THx/2tBa++/qOm5tLznVHO87eKyEdbT8inWByjuuLjDDwEAgm09PUJaFv8cOYfrScTkeSfEkc+AzC43x4BOeRLHP+hz/5D/3eD6j1i2/dH/mH/g3iIMJJifZ9ivOJ8v4ji/UaAo2bJiRghzPn52f8q29w0uP1zQWrRcrQj5R1ze55zzLPiOO5E7zvR6yTtGYiDkLGbmR3KGh7Q19OLBNNGqWce0nXGbROGeqWsR0ZJkc7Tlxs1zgkTdWw3CwRUnI4HKn3D4yeYrXJibTCSkffNUShJlvEBNpDOcfjx3e4vmN9e8s4DeggJkkSksjnfKowXYWfZJRVg1Dz2nfsGjozzWX7UhJNLa2ZEN5scQijEOVABQFqGqlPe6Jsg/J9Ak99rkQZZ+pv07CIQwSK42m+gufLBQhBUzd4QQTMoMlz21OcT2RJhGPCDzXXl2s8JXh8fqYqCxIf8u2SzTKjHByBElgEF4vZ4XxuWobOzPzBCTw8kiAEfLA+TTvx8HhCiJAozijbnkGI2UfUdSAczg9YZhEXqyXa8zgXJVXVsUgCmKAoW+q2QVnDNIyfNzsa4XksFwmhp6mGnu/vD8T2TLJc8eLmlmM98MOnR6TvEyhvzlZOBoaeyYr5AzNVJIEl8GDyBB8fP6FCzd1lxt3FEissZVnzqw/39F3LN6+uMBaatmeRZjMdZhwwThJ4AUmg6MozTWdngrKBlhBjDEmoSUKf52PF+XzEC0IC5QgCxeRAtkc+3j/gBTEIh59n/PSnXxNKwamuMW2H9CSe1phpxNcaT4CUgsE4TFd/bugQuDCZLTxS0hlLOdi5f0wImCYCLIdjxcXlJcJBrDXNaChqxzj26GlisV6yXeaUVUfZjkzMgA87jKAl+/sn9p8+IKYOh2CzWeGHCZNSeEx0k+Nqu0QEIeeyo+tahqHH2c/516lC9geazuCFMZMXzyOdPEJ6HjqICbVHXe5pD4+c9o8YJ8i3L0hWK5ZpTGssfpSw3Sz57//Nv//X4CS/fOn+hj/2L3P7zbfEoeLw8Mh+/8wULQiCmMEM9MczVljiJGG5WrDMUgBsV1NWFY+Hjkhb3FCyvvqa3s4Ur0Xk8eH73+H+UCEWd0RBRO4rjseCy+sX5FFIO0IoI+w44kbL88kwWMkqn4vYy7pDS7h/94auLnnx099ARzFp4GNsT2cMUeTj+/D8/ER3fGDqT1TlmSjQDHrBapXj+4p2dCRpTpzEYAb2Hz9h3YAOAxweUZSy2G5ZJJqhHxn6Bj9Kcc5ijUEGPk/PB6RUjP0wY4mkQCGIkogo1CRxTNcNfHo6kCUxjCPC10j5OcBrBoQXUJTVjPVxE0J4LMOebpKM4wwRiKOQq2WIDSJOdUNRntg/PND0A4fHd7x6eUUTZCxWG15tE/bnijzLcSiiKMQMBk96DMZweDxxbuHrizXTYLk/jTSdpGsHqm7i9uaKrmupmxYdeqipB0+RRRHOSoyU2LYjyXN8qeY5lJjjFFXb4aRg/3xAaZ/VIsPX89xtEtB3DULHjKanbSqqqmF9fTOTop3AdQPvf/gBrUNubm+5WadM7QnjOTZLjTU1u8M9t5crVBxjUPzw7gMOh+dFbFcJWRLQNS0fHnfoKCXzPcIwIU8j6qqlPZ2RnkdvHMoPscLy4blgnSWffUITbV1hpccwWS4WMReX863gXJecio7D7glPjATZiskZ1usNWkycjid6NwEe0lNcrTOqfiDwQowdKc8nhLUEgSZfrOjaFoPkUDQopUnCcL6dDyOhDumaDj+K5/750wmrNNW5IFKKapKkWQzDiB9EuCBhk6U45z73pkFXHCmKgm5S1KdnNDBIzdXdLWES4ysom5Yo0hhncNaxzBLSUHE+7Kjqmkb6MDl85ciyjDDWKKU4nE70fcfQdYzj3K4ZphF5GhNnMaGvCMJoHkFon//sn/87f+8H1PbVT9zf8S/8KabJ8P7tB374/gdUtOD1d9+wTROaoSOO5oaA1SLl7YcHmv0TgRswCLLtDS9f31HVFbvdiWGSpLHP2zefSAJHHC/4+uuvOB7O3N/f01cNMsiI0yW2ramrgUXo0Rcd+8rhVAQqxvM1YRiRr9b0/YAvHG1v5sHk0M5BYd+gogSL5bB/omsb+ubEWB1BzN1U2fr6s8lyxJcQL1csF3Owd7c/Ypojng65vbvDE4LWjCjnaA/PeGnO9fUW5QamsUcFEcW55HK7Igo8BvO5KH8YMEKQxyHGzPDSphuomn5eOXtzJCDwNcPQIvGQnkdR1eyf9tRVQY/i9uaS60WA9DWeViTaIYXB4JBScShqvn//kcPuGdufGZoz2fVrtJgIk5QkkMRJzmKzwY6GsTcEyudc9ZTNwG2S01uJ8lKOxcju2LFaLIjDkEmpuXPI9gxdRxoEjKg5HFocZ4JIGGHqkpCBsjNMdiJb5DgZYtoObQdaNMQxVzeXc01L3dAMI50ZWC6WpIuMJPTp2o7zsUQ6EM7NEZNAo91IHDha25NmPqkPH4/PeGODkZJzXbFapCxXOd0wksUBv/z5LynbissX3xGIuUxtmSU8nWr6tkc5yc3FBeemYRoHsBM6iBhHA9NAtlzNG6+yZWxK+vbMcrPCMy33xxIvTokiwWJ1wc3Nhu9/fMt2tSRZJjx8fODTux/INnes8gQF5DEceo/3796hjEV7Hq1VrC+3NHVDpBXSDxiMZRwsYaAROLreUu+f8SbDtz/5DhkmKCn4eH/kXJR0A6zykFBMM37Ki2FoqAdLN8IwTdA0FMdH4nzFKP359mcMYehzcbFilQe0g2GcDL4OiIIZcjAJQTuMBL6PkxPOTKSJRvqasWvIknC+GQ49TjiaYaRpDV3b4CmPc1WinEVFCZtFjNaK//5P/t9HXf6S2wyEAE8FLFZb/sa//gVelGCt4fC0m5v2gojQt4xdTxpFcHFDHHu8ur5mk2Yc65qH3ZHFaoWxAu0sr++2nIuKi6sLnB2pTwdUV5HHEUGeMUlNN1luXl2R5Ut8p7ioBwKliYOY590RpSPyOOXxVHJ8eqbtGoLAI9YaOwpEmKHFyNuf/xmC9YbrXBAtt5j0J+AHrLcb8jQiDny6vgchiKNwDpIOIzfLGD/+jiQIENJhxolT1ZDEMcl3X3EuC/7cn/2zVKcd4eqa1cWWTPt0Fp53BU+7A5fbNUIo+qFHGMMiS7CMxNqhHZyLE6fnFj/JGMoSL83ROsT3NYeypRlnvPcyy5mM4eFgkH6LVZJVouebhpzYPx9o+hYpFevVhn4IWH37E3QS40mFnXoYSzx6fvfP/jkur2/59sXNXA63SlksfAKdEE0ACt+bP0jiOGd3LDEjtO2IM5YkjlDefAs5nAui5YZy/8jH3/ot2vNHPJ0SXr5ms73CjAO5avAjjcAnQlL2JYcnCPIlY1PgDQ1pusIPJZGWNJ/x6p6ynD59Ik6XoFNOfUH58R198YlejCS+pbY9i8tLvvrJdxg7sdiEHJ7vKeqW+8cnYq04Hs74nmVsf844Tlxd3tJXBf3keH13h0Ry//5HqhY64/B0SL6MMcbRNgPNVCK1xgnBpBOORU3xeKZ5eocNNAvpM+ITJD1FXeNLBc6hhY/SPn684jd+8g2Ylnff/5LuqSRJL9mutpRNyzj2mOKZdioZ+p7aT1hlCzwHMsiQnkZLn1Bb3DjDKN6//0S62tINA8/v37F/uEd5AU2SEocxi+0li3VCkKSEqU9dFOCnmMWS7OIa5Sw68OiqM8vVJXEUUh2eeDwURKtLgjgi1IKhN7SjJc9ypGqYxhFpDcZZ3r7bIezAdrsFKanrntP5TBwFSO0x9gNusjTlJ8zhBIsNOlkyGcWhHf6fz5y/lBvU5eufur/7X/7TDMaRxTGer/h0/0wUheTZTPWVCp6fD/zqt/8c/TByc73GyojtzQ2Rr3g+HNl9/EC43HB1fY20hsPDjuP5wO2rb4iyGDcJTNdjneD48IAdBsqiZhgUBBGr5RbPKfrBMYkIz/Po6wbph2AnhAWpPHQA2WaDMSPjOOLGjtPzB6IsQccpzo1sNwuck3NdbRbOQ+uxQ3mafpzom5LdocTXmsurLXmaczqfEc6gfR/hSQSCx6c9oxmoi4ogTYjimCz0ybOIrh2w1qIDhXWCMPSxBpySFIc9RVGRpynnc0Xb9rxcr4mzEHSIDmL+j99+gzGGV1crQqW4L1vWeUxxLOcqjnSmAZ+KE8PYEy0yfDkTWpZZivEkSeBzvzvz9sdfUBx3jE2JMB1Opywvb1ldvsC3E72cZ2bt7oivJDpbE6oYKwM2F9e8+/EdQ3EmDDP8OCbwPRarK8rTgaeHjzSDw/k+OozAE4RJihl66sOB9rzDGsfi4iV+FIETODOS5Bm4nv3ukc1X35JeXCGkYBoHqrZjaFo8IdFS4ylBcTxx2p2wTYPWE0mestjmrC9ykjxgd/+euqmp2orx/EC6WJBtb0izmLZqaI87ulEig5j1aonVEdXzM6GEq5srpB8hfXC9QWtFUTT0BqI4Ic9TsD3n/Y7t9SWLJKYxI1VVsT8ceHG7JQznTabvC5axzzgMDJOY5zPSI40T0tDjfleQ+IKnx0fquuT+WNPWHUmSsLq44fj+F7z58Q13r37GV999zWa14Vg2VM1AEPjg5kxf/fyM0BHffPsaayxN23A6t6wWGUkcE4aayTqqdj5QxlEyOAFTj/b1DNTFwjQxmgmEYxgHjHN048R+t2O7SklCN8NM1JyNzT9HqmAi8D2YevI4ZJSavm85HI6UTcd5f+b1V7fc3myYJsd+f8Z6AZfbBcs4YJos/8E/8Tf/GnxQr3/i/pE/8Z/MLYJjzYREOMVoJg5ljTMTi0XK2/f3HJ6eWW0WCGfxw/TzJsfQWEdxPNPUHatFwuV2RTlMeAKMsaRpRB6lPO2OdG3D8eMnLl++Josy4mDGGfVG8rQr5m1itiVJUmxZ0LrPAeLiyMP+xCQ9NuuYF3e3xMncKKkcdM7w9PTM/eMzfhSwWecs0oTJwWaR0XQdRXFGIan6Hl9INHMmSTiBC0JWqww3mNk3IxRdecQNA+e257TbEaqBr7/9CUrP3pXFIsP3PKq6Y7KGcZwouo6n+x2DmUjDgGUaY5uSd+8+0dcVyeaGdHtN7HtkeUzuMf+wo4iiiCwJMU1B088r3E7MJN6urua5gR9x3D1y3j+SZCkiTBFAc3iirAomZ1FegDMjwjl8P0T6CZ5O6JsG5WnGYWKsa6QKscPAUD4R6ICRhNFYkjjh7vVXBElK3bTcbFOiIMIqn9HB036HHUfMaHDDhPMjpqbidO5QOv5M8hlphw4r51C0J0esmPDSDcoLCNXMIRwMhAI26yVhkIA1jNOI9CyeZ1CuoRk62uLIcrvC4WiHjijWc6Ha2OMmi7XQjSNeGJGHId2xoBgMnvCQzuJpj3p/T9uMZJsb6qYjSAKSLJ/7ys3AWJ25efmSfJGQJgEPD/e8+/5XhGnKy1fX5Osl7WDwlZ3RZkoRhDOLzxOCsqkwk+BYd5THgqku8aOUyY9o2p6qGTg+PzIeHol8wfbyBS9evsaGMVUzskhCqmbk09OBpm4xXcv13RUB7jPWylGdTiwWG0Yci0VGrH3aqkZIjzCKQSjieHbMH4oKz/fnqhdjeN6fEKbl4no7O+KxPD8/MfQ1A4IojjFjRz8ZpDN4ArSvmIaOIM9IkpAgCJjGAYNiucrJQo+hqUnzbO50k5L9oSAMFP/1v/ZrGJJfvv6p+9v/+T/FIo0pyxLjLJss5dzOMABfzQat0SqmceRwPOBPA9tlQr69ZpwmjlVH7MPhw1uCfMFquaQ6HqkHg4dkcf2S7398T181rLYXDEPP9XZLGsXsH/dzj1SU8v79E+NgyJcrtBCcDiVxmqODea0/Tpam70ijkCj0OR12tE3JNHb0g0EnCybXs10vGB2IcWCYDJvNEjv2vPn+HUGsidKcwQmy0GMVeqyWOWVvcAK6tqMZJ3a7E30z0LcnuvJAUxb40Ypv/4o/TJZEdHXFUnucDk+cugapIxb5mlRCaxXVKEkDSxZq7u+fsF6M1Clt19PVLUPd4JSkqU44Y1jcfkPkK4QdMWPHzdUKfM0wdJyrAi0nstWK1TJmu1pz//RMNzQIqTidGs775znDlcUkgU9vFUL45EnK5mKJ6QcO+zNVNaIkSC/Gfq6uLU8VTV3g+zHZ9ortZoVwhmWaMU4ToS8QmLmt0ffwJJT1SN1/9hQ5SVs2WBUjhIZJYSWcyzPjOGCVRoeaZJGwSGOknDd4wsyf9tZYPCFmmETfIaSlHTuWeUDsWyY58njaU9zfo31It1dc3V2xzlLO5wMfH/e8+9UvOT+9Z7l+wc1PfsrlKieJkhkPPkE3Ce4/PqECwTLL5yUHiqZpEL6H7Gvy1ZK7m1ucHWiHFmcNSRRilWQyA1VTUx0f6ZyHkj7180eGfmRaXjJNAlcfcENLePGSuxcvaMsznnOsLy54fnriYV+y3VyQJylKeQyjnZ+AQ48ZJszkcMOADAKchWFy+FpjzIhnHZOUVOczoh9pRzEf1ucdXd0QRgmryxvixRLGgb5t0MmKxSKjaXvCKMbzHE3b0g8jSAh8yJOAINLUbctylSCko2kbilNBEId8fXcxvxQ8RRr7vH86sD+X2EmwzlOsM3RDT7M/8uK7r1ktMn714zsOVc//+u//478GsvDdN+6v+Xv/ubk/3Fjy9ZqXt5d0TcX9454ffvlLvDAiS1LOxz2n+7eIvsRFK+6++w2+fv2CU93SViPF6Zlu6Mk9yeXVBS++/Q4/8HFW8ebtJ6znsYh83OhmoonpeHwsEGFKeW7I4oBFmqHTLafHHaMZqZyekUVji6c1vVX4YYjBMbQdkT9RdeYvNjsa4cjzDF9a3DAQ+JpPuwPl4UyyWuIFAUpJ+qKkK/eYccIqjYpifF+BGVht5kqPumtRUiKdpe57VnmOUB7f//x3Mc5xdX3N2LdUVcWIT5ImpIEPTvDw9pe0g6OtTjgrSNOU9cUNMkoJgxBPKOzkKOuauiwQzoId6JHoMEHgWKyXjG1Dtgip6oo4y2iKkvK0x49CvCAikIYo9vHdyHp7SVUW6OUGOU7keUocJxx3BafHB2qhybOcMIg5FRVVVWONwzqFF/iEcUxTdXRNi6d8bq83LLMI7UkiX2HdSFf3lF3LKstxY0M3CXw/pWwNTSfwpGYyDqU0rRnxfD3XAEtBN4w0fYP2NRLBuWhAzE//y80Kz1meHh6YxpauP1NWR6QcibOUF69fsVyHJIHH7uP3/PjjD+wfHzkd9lil+I3f/Cv56tufMPYd+3OHcobRKZq+49sXt3REFFVLW/eEEoTWLNKIEUtR1QxlyTL2efH6JVmiZ0KK1gzDwGKRIJ3l0/5A3/RoLTgcd5RFTby6JksC6rpmu1oShAFpnDK6uV/JGkdxrrBOkMcBUkoOp4o0zVBKY0dL2TaMbcdT0REkKcXxxDqPEb5mNJY48LhYpHzYHbH9SObBw3EGhowOkjQm1BFj2/LwsKc5n4l8D5mtCeT8sxcvUnb7PVhBlPgkkSb2HNkixQt9/EBQ1Q0fDyVfXS1INVgliYKAyRlwc0/5w7GiPBzo2pbldk2cLxiGAU/YuXcdb6Z7K8F/9a/9fb+OJ95P3d/0j/2btMPAIltgHOTJjLr++Hhg7DoYOxrjqJ/uud89o4KUMF+yTGKuliHLqxeErsfKACfdzKXzQ7rR8PjwSHV4phkmpM5oDjM4of4MVEgXa/rznp4AkAinWSwzwjhH+zMh5enNG4wZCUKffH2BcYKnx0+4oSPQmsXmAj+JsGYi0Za+bzkfS4ZxJExzJs8j8iRXN5dcXayRTlLUNcPY4UvJu1/8nPa4J15v0NmS5XqFNZbIn7NpSZqRRoqqbUGpz25fh5wskwDhSZp6pGk7do8fePn6a7p+YpigbVumcWK7WdF0I0NvcHYiDmcn7/l8ojzVPH76SJxlrC4vyfOcfhrm1kQFnpQM0/yLNY0DaE0S+wRCcO4qtJIsFksmZ7n/eE/ZDzgz8fXLO3CO9WZNHEXUdUvftayShG6StJ3BfN4yWRTOOoS1KE+BgM1iQd103O+eac4nBqWpqpqxGzAOsjAgTxOa0XK32sxm3M5x2DUoFHe3l/iBpiwKfvXLn/Pp8YlBRaRJysVyvq0pIcniGGst1alg/3RPvtDkq4Ri94GufkSma/w4wEiP8ryjqdu5vdGfQ906iEiWCwIpSAKfu6s1u6cHjo1gfziwzmKurl+wXMQIK+hGw+QE+6pBOIkvHMaNxL4iiCKe9s80neHqIud2u8Raw7lueDgeOR0Lnt/9ipvNiuTymqs8JNKw7xTC98nilDyJEVLxix/ecb8vcZ0hX2RESUoaJjztDnz88Q1/6A/8Bq9e3rE/HKjKikmG2GlkspI49AkDTW9G+tHQnI+YYeJkBNXhjE4zvnt9SxoFdEZgpnng3Q8jiyzj07t7jJT4Q8/mYkm2XFDVNf3QUtYd1elAGIcEWYqvJ+riRF3tCUzN7VdfcXV9ycPuTNsbnBJMxY5VniHiDKkEl9sNvvYIw4i2Gzjsd0hrOHZwriqSOOR/+nf+kd/7AbW5+8b90b//n8NLZlLsYEbU5HB9hcEjjRJ2Tx9IwwA/zvjFm0d0FBCKCeMcr776mjTPYOhojf1sPHQUhz1dUdEPI4dPD+h8Sbpa0xQlOoxRyuO7774m8jT7Y0lZzk2YxinWWTKb75oeT8w1taOxtPsnivKEilO8ICUOfeq2ZpomhIOxryh2Tyy3Gy5ffIWvPKSvEDjSwGOcDEkYYKxldzjR1POMpG17PDnX+GZZRpaFFKczH978gGAOWI7VAWUtxtMEqy3rNCbIUoqq5dXdliiMUb6kKBrqusG2NcJMZHGIjVO26y2hDqk6w/PhSFFUc31r07C82NJUBUoKxqEhyzIWm808P5CWwJ8H8UGgMNPEoSjoqoLHpz2xtMS3rxj7js0qx/cDnk8lOvDYZhF1PcdnlBC8+fREd9ojVcDdNiXJt0xCs1pkCCSH44ljWVOXFbfXt6TBnLWs+p4kCsnSGTtftR1aSIT00NJDa01nBOdDRdsbLi+uENPcZ9SMluOppu1brIM4XRBEGmkmzGR4eNqzWaTkaYQQjrqpYBpYZJp2rPn+Vz/HTR2by2uSzYr1MsdJizEDtq8Rvs/V9oLLZca7+wfOZc3T/kRftvgB0A4srm9oekO3eyTLlyyyJZOc1+p54CMDzXNRYY1lsc6YpoFhv2N/2HOuCqQXYLWPFI51HLJNJZfLmD684NR2RJ4mTzRd2+LMwHn/SFGUKKUZ/S16scGTgmTYM8iUzvk0A1yslsi6oTw9U4yKrjqTrq7IspiLzZI0iZic5bA/MDbtjCgPIozjc0rCcTqVn6k+Pk74aK0JPUXTGbabNcLNptI8DjCT4Xm/pyhLpv6MHyfoWHN8fGD35rfA9ugowY+i+WediXhzR765YBKONNKkvsQPU6r6RJrEvLha87zb8/iw47DfcXp4xx/56/4oF199w3/0T/1tvwYn+d237i/7e/4lHj5+pOlqsu2WNFuAlPRNyVgXjIMl3654cXVBUTaMfUezf+TcD/h9R9V3eJsXREHIixdXaDtRnU9IT/Pi5S39JNCBJvJDqqokCIJ5dvF5O9EUHZOdCHTM5CzCGEYnUTohS0J2hxP9MBJHAVZ4iGn2dSRhhBCC0Ro8HGHkIxFUbU9vDc4JqrLEWvu588mShAFZGiD7Hi/JcQiUA+cMvtYci4IP93vOVYvnSYRwxElO2zS4oUGHHhOSQIdcX2/pmxqpNKOde6mHduBqs0LhUKFGomjbgaYu2W42SClpmwFrHcZJIu1RtAN5pAl9n3cfP3H/tEcoj0lCpjVh6EPg4ZsW07dMno8nFTIK2a5WbJYR1jqU5+Opibqs2O/PPB/O/PLnv4MUPn68RoUhWEuaJSyTEF8olpstTkjaruPT24/4UUwQhaRxyMO7dxAEBEnMy+0ah2W/P1CUJaHvEUcRV+sL8kVOUQ60/YTWMVooPBWwP7aMVs1A1s8ZM2csKgzxPUnb9Lx/8z1XVxdIX6MkvLjbguswY82P737k6sUNQ1sRRYokiRi6jqo8sb9/w7vvf+Tlb/4VXNy+pKlbrOk5Hgs+/u7/Rppv+Mv/yN/IMo/5P/73P8Nqe8nV5YYwCnFjz4gCJ2babxQwDoa6HyiqM2/f3dO2FToMyZYbXt1t5gYBO6GUR3k8In0PqwKEEgS+j2dadm8+0PcNaZqR39wSaI0fJGyzjL7teT6cqauW73/7z7NJE/Lrr3g+t5TPD4RxQrjassgTlkmErySeM1jJjJ2yEx8+fcJ0HXGWcXHzkjiQHA41x3PDubP0vSE0PUiPxeXNZ16gY5HGOGswfcvQNlRNx6k643uC7TIhXKaoQLFcJMQaqmHgcKqYnMWJua00S2Oe90eMc7Snz7g36ePZjoeH94xOkq+25OGcpki2l/w3/9Y/+OsIC9+53/x7/jjHuqE9nXEeRElKlGSMVUFdnOdtUOijhp6qb6mLE7atwcHi7mvi1ZY4zSmPBeVpj2damAx+HCGtJV5uef0H/iCTGfj5n/ld7NCRJiF1VWIJWF+/YGha7m5vkFKy+/iW46liEB6bzYpN6JFc3tI2PUVZMFjL1WbNy9srTqcTET2LzSXOTKgwZmLg0/0j1k1cXdwwToam6Tg8fEIvVggz0DcNMgzZ5jEXF9dI4SibBs+TGOM4lDUWi69CfGEwFs6HPc73iKMUlMTi8JnACbpxJIoCtouYyAs4nk8452g/M/WaZgQpaKoKP0qpqoZl6JHlaxozIqzl7iKln0ApH2Hnedrz4xPa98iWS1zxyM2LW+Iso58Mx3JgGDsmO5IGmmNZYifH0/uP+GHExc0t0ptnTG03ECoPKyaSKCWPQpIkRLn536GAx2PJsRkwfU+eZkRhwKEosNZyqmrKc0HTzXzAtjoQ5Ruu7+6Itc84Whgc1g/ZP+25Xm1IkhQPTdkYfvzxHetlzubikjCK+HT/RN91SNOTpiFhrPHiCIfD9BXCNpTW4ZzlWFfzE8kTHPZ7TucjOtTo0CeLE0YnmKYJM01s04TduaQeepbZgrbtaeqGsW5Qw47Lu29IVxt2j3vyZcLd3TX1eU+eJvhuYvfhR9I0wkRL8AN0qKl7y+58YhxGsixkaBr6tmW5XuImKDrD4XlPWx1wQ02e5nz7m38VizjGjAOjcTw8HzC7BzqZc9gfiVcXbC+u0DpAe45pkpR1g5tAS8PkJh7v96SrnIs0pDmVnMYBz/eI8yXKWVLt07YjVgas1ws8z6Pt58K50AvoipJj1ZMvUnyt8KTDThNBHHKqas7ViTwJQFpwhtH0XG1ymnHAWcOpaOnaIxeZ5lyUjM7H83xs3/P1H/wZbVFxfnrgdDyzvHvNq69e03Y1x92ep+cj//t/+i/9OmZQP3N/6z/7pzhUDQhQQFG1FFWFFhPb9ZYoCvC1T9sOFFXNMpm/4ThJcVJhJ4OcRkLPp7GSoqqIfI80y9CeIAw1Q2f4xZsPjO2A6RuU8thev5i7lK3j48cngjimqXq6piLJF+R5zmaREQaazhiEdQgmfG/mnT28f8PD/SObl19xc31JHGqmyXDe7znXLX1xYJwm2qbC8wOkn/Di9R3gOJUNZppIkpCFP69YOydgsig3AzrzNKRqW37n+7fYocYXhnS5YbW9IQrUXH2sI47Pe8TYMdRnhmEkWy7oJkWxf0bGC5pxIskXfPPiBmsMWRJzOBTs90dGK/B8hRWCNE7I4wDHRNOPPD4+U5cVm8wnCD3a8xGSnDDJePXqDuyEmwbqpmUaLXEWE0U+gfYJgpC262maBqV8Pn18wg81qyzncp2RKEfVjZwOR/Zv33F6/IF2AO/yG7Lbl3ife6X3D4+EUYSXL5GeZJkkCDcxNB1JIJDKR/gJ3eCIJDgk97/6kWoIicOY/OKSQIf4zN3W0tc0Xcd+d6RqOpqyoB167rZLLl5eca4rhOnwPcH6asVykYKwFHXJZAy9ndD+XBVjTcfbX71BJwlfv3qN9AVSSYRQjE3D+XxmMiPaD0kXq7mlQ3u0Xc8iiVBScDw8Mww9q9WWRazZPz5Tnp4QUcpieYkXen/RLtEXNT98+ERTNUxjB2NFNzjGIGezXrNdLTASRFGyvrnk4f7Aoezp6oq+qLFSs1isidOc5TJHCoWYJoQfgOdI/YBxsox9jxIO6Qe4yWDsQOB5WARxEhGEEVnkkYYxj7sjk5no+5FD0bBabfCkoq5ahmFinBzaEwxdg5jm+hj8gMwXeGoiXGQ8fXjLYrMizKJ53jz2mKFndB5vfvgFTXng1etvWC63RHk2A0baefDvpMI0LV44t2wuFxkKR1Gc+R//1K+hzeDmm99w//Cf+E9mmrAUWGsZx3k+oNWcht+fj4RhwO3FBVg4tS0PhwLlFHkakISaZZrge/Og9VjVPJ8rNouEvq6ZRgvKpzd2Nlsq8D1FcS45vXvD/dMz6cUty/WGIArxZchikdK2A5Ef0LYNZdvwfCrmwXcY8mK74uPHD5yrmourK4qyoq468uWCdveRYRi4vLvj6uaSi2xJO400zYASjt5Z3r15j5o6LlcxMlxSNi3C13x4844wTrlYRAzjwNPznlNZo+XIebfDKs03337D5TIlzzJeffvVZ8KyoxsmhBB4UjHZuTc68uZfSD8IaNuW4/098XJNmiXs758ZHERRzLuP9wztMF+zsxhjLJ5UtENP27YssxTrRrYXG4TvkyQBddtjzIASkGUpyzTk+LmWOAk04zgw9B37/YG77Yaqs3x8uGc0gpvtFuXNeKF4MvROMhjDseqh7wjyBCEChtGgAw0CPu6PeEJRtQOrLKE9ziV8Ol9jJ0Hx+MjUdfTFgfX2hjBZ4cUrnvcl3eGEsw6tfZwMuXhxy8V2Q1GXGDMQOoMfwnqVzg2QnqA2PYdTwf75E5qRYL3l61e3KClphgFpRuqxYxKaZRyyWWVo7fHj+3twEHg+QkmeTzUPT088fPxEX5znqhFPUh0eeHr4gNAx2WJFFEZM/QnVnrj47je5/eYbVusMpSTL5ZKmn+iGkfO5oB864iAiCEKsUBTneRYKlkB5hEFEWZxJFyvqduRct0Q6ojudmQZHqBWTlawuL2jaDh1ECCFnC8A0/59aHKssQXgKMY1kqcZYxzBAGGo8qTDGMVmB9nyafsI4eH4+0hmL6jracS6iq5oaiyNfrlhcpHPecuwYfM16leJ5gsPTA+3piUEJDseay23GJBXWOYLuzOLlNyR5RtF0ABg7gZBEnkfbVtx/+MjQtlSHPUYGPP6P/97v/YASQjwDb/9f/4Uv+qIv+qL/d3rtnLv4v/7hX9IB9UVf9EVf9P+l5P/f38AXfdEXfdH/k74cUF/0RV/0+1ZfDqgv+qIv+n2rLwfUF33RF/2+1ZcD6ou+6It+3+rLAfVFX/RFv2/15YD6oi/6ot+3+nJAfdEXfdHvW305oL7oi77o963+T9wLqbrVFY7gAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Gradcam visualisation\n", + "dst_w = 720\n", + "w, h = raw_image.size\n", + "scaling_factor = dst_w / w\n", + "\n", + "resized_img = raw_image.resize((int(w * scaling_factor), int(h * scaling_factor)))\n", + "norm_img = np.float32(resized_img) / 255\n", + "gradcam = samples['gradcams'].reshape(24,24)\n", + "\n", + "avg_gradcam = getAttMap(norm_img, gradcam, blur=True)\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n", + "ax.imshow(avg_gradcam)\n", + "ax.set_yticks([])\n", + "ax.set_xticks([])\n", + "print('Question: {}'.format(question))" + ] + }, + { + "cell_type": "markdown", + "id": "32f2b67b", + "metadata": { + "id": "c2d7cf41" + }, + "source": [ + "#### 2. Image Captioning\n", + "Generate question-guided captions based on the relevancy score" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "dc925ece", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "49ac307b", + "outputId": "93535276-fdd0-4620-a833-cc7da0ff4776" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Examples of question-guided captions: \n" + ] + }, + { + "data": { + "text/plain": [ + "['a turbine turbine wind farm with a boat',\n", + " 'a orange and yellow speed boat moving around waves with wind farm in the background',\n", + " 'a speed boat with wind turbines in the background',\n", + " 'a man driving a motor wave boat behind wind turbine turbines',\n", + " 'a high speed boat speeding on a road']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "samples = model.forward_cap(samples=samples, num_captions=50, num_patches=20)\n", + "print('Examples of question-guided captions: ')\n", + "samples['captions'][0][:5]" + ] + }, + { + "cell_type": "markdown", + "id": "4018c40a", + "metadata": { + "id": "b708efb6" + }, + "source": [ + "#### 3. Question Generation\n", + "Generate synthetic questions using the captions" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5d1b50ed", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "f065b898", + "outputId": "152ef7d0-9071-4397-f68f-00534a274784" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample Question: ['what is in the background of the boat?', 'what is behind a boat in the background?', 'a turbine turbine wind farm with what?', 'what is in the background of the boat?', 'what is the boat moving around waves with wind turbines in the background?'] \n", + "Sample Answer: ['wind.', 'turbines.', 'boat.', 'wind turbines.', 'speed.']\n" + ] + } + ], + "source": [ + "samples = model.forward_qa_generation(samples)\n", + "print('Sample Question: {} \\nSample Answer: {}'.format(samples['questions'][:5], samples['answers'][:5]))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9758f236", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['what is in the background of the boat?',\n", + " 'what is behind a boat in the background?',\n", + " 'a turbine turbine wind farm with what?',\n", + " 'what is in the background of the boat?',\n", + " 'what is the boat moving around waves with wind turbines in the background?',\n", + " 'where is a speed boat with wind turbines in the background?',\n", + " 'what is in the background?',\n", + " 'in what part of the picture are wind turbines visible?',\n", + " 'where are the wind turbines?',\n", + " 'what type of boat is on a windy wind farm?',\n", + " 'who are on a speedboat as wind turbine farms reflect in the background?',\n", + " 'what is in the background of the boat?',\n", + " 'what type of wind farm is in the background?',\n", + " 'what is moving around waves with wind turbines in the background?',\n", + " 'what type of boat is being pulled beneath wind turbines?',\n", + " 'where are the wind turbines in the background?',\n", + " 'a turbine turbine wind farm with what?',\n", + " 'what is the wind turbine in the background?',\n", + " 'how is a high speed boat moving around waves?',\n", + " 'what type of boat is going across the water with wind turbines behind it?',\n", + " 'what type of boat is being driven by wind turbines?',\n", + " 'how many people are riding in an electric boat?',\n", + " 'what color is the speed boat in the background?',\n", + " 'how is a jet ski boat being driven?',\n", + " 'how is the speed boat moving by wind turbines on the ground?',\n", + " 'what type of boat is moving around waves with wind turbines in the background?',\n", + " 'what is the name of the boat that is moving across the water with wind turbines behind it?',\n", + " 'what is in the background of the boat?',\n", + " 'what type of boat is in the background?',\n", + " 'what color is the boat in the background?',\n", + " 'is there a boat in the background of a wind turbine?']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "samples['questions']" + ] + }, + { + "cell_type": "markdown", + "id": "d2bb868a", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### 4. Prompt Construction\n", + "Prepare the prompts for LLM" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "62b4db44", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "Img2Prompt = model.prompts_construction(samples)" + ] + }, + { + "cell_type": "markdown", + "id": "7df71c75", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### 4. Load LLM and Predict Answers\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6d1dfecf", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# In this notebook, we only use CPU for LLM inference\n", + "# To run inference on GPU, see https://github.com/CR-Gjx/Img2Prompt for reference\n", + "device = \"cpu\"\n", + "\n", + "def postprocess_Answer(text):\n", + " for i, ans in enumerate(text):\n", + " for j, w in enumerate(ans):\n", + " if w == '.' or w == '\\n':\n", + " ans = ans[:j].lower()\n", + " break\n", + " return ans\n", + "\n", + "Img2Prompt_input = tokenizer(Img2Prompt, padding='longest', truncation=True, return_tensors=\"pt\").to(device)\n", + "\n", + "assert (len(Img2Prompt_input.input_ids[0])+20) <=2048\n", + "\n", + "outputs_list = []\n", + "outputs = llm_model.generate(input_ids=Img2Prompt_input.input_ids,\n", + " attention_mask=Img2Prompt_input.attention_mask,\n", + " max_length=20+len(Img2Prompt_input.input_ids[0]),\n", + " return_dict_in_generate=True,\n", + " output_scores=True\n", + " )\n", + "outputs_list.append(outputs)\n" + ] + }, + { + "cell_type": "markdown", + "id": "9470640e", + "metadata": {}, + "source": [ + "#### 5. Decoding to answers" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c7363d02", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'question': 'what item s are spinning which can be used to control electric?', 'answer': 'wind turbines'}\n" + ] + } + ], + "source": [ + "outputs_list\n", + "\n", + "pred_answer = tokenizer.batch_decode(outputs.sequences[:, len(Img2Prompt_input.input_ids[0]):])\n", + "pred_answer = postprocess_Answer(pred_answer)\n", + "\n", + "print({\"question\": question, \"answer\": pred_answer})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ddd1a15", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "include_colab_link": true, + "provenance": [] + }, + "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": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/LAVIS-main/projects/img2llm-vqa/img2llm_vqa.py b/LAVIS-main/projects/img2llm-vqa/img2llm_vqa.py new file mode 100644 index 0000000000000000000000000000000000000000..5f1a394fb70a873c6546071871f4765220b59750 --- /dev/null +++ b/LAVIS-main/projects/img2llm-vqa/img2llm_vqa.py @@ -0,0 +1,187 @@ +#!/usr/bin/env python +# coding: utf-8 + +# Open In Colab + +# ## Img2Prompt-VQA: Inference Demo + +# In[2]: + + +# install requirements +import sys +# if 'google.colab' in sys.modules: + # print('Running in Colab.') + # get_ipython().system('git clone https://github.com/salesforce/LAVIS') + # get_ipython().run_line_magic('cd', 'LAVIS') + # get_ipython().system('pip install .') + # get_ipython().system('pip3 install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz') +#else: + # get_ipython().system('pip install omegaconf') + # get_ipython().run_line_magic('cd', '../..') + # get_ipython().system('pip install .') + # 'pip3 install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz') + + +# In[3]: + + +import torch +import requests +from PIL import Image +from matplotlib import pyplot as plt +import numpy as np + +from lavis.common.gradcam import getAttMap +from lavis.models import load_model_and_preprocess + + +# ### Load an example image and question + +# In[ ]: + + +#img_url = 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/projects/pnp-vqa/demo.png' +#raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') +raw_image = Image.open("./demo.png").convert("RGB") +#display(raw_image.resize((400, 300))) +question = "What item s are spinning which can be used to control electric?" +print(question) + + +# In[ ]: + + +# setup device to use +device = 'cpu' +#device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +device + + +# ### Load Img2Prompt-VQA model + +# In[ ]: + + +model, vis_processors, txt_processors = load_model_and_preprocess(name="img2prompt_vqa", model_type="base", is_eval=True, device=device) + + +# ### Preprocess image and text inputs + +# In[ ]: + + +image = vis_processors["eval"](raw_image).unsqueeze(0).to(device) +question = txt_processors["eval"](question) + +samples = {"image": image, "text_input": [question]} + + +# In[ ]: + + + + + +# ### Img2Prompt-VQA utilizes 4 submodels to perform VQA: +# #### 1. Image-Question Matching +# Compute the relevancy score of image patches with respect to the question using GradCAM + +# In[ ]: + + +samples = model.forward_itm(samples=samples) + + +# In[ ]: + + +# Gradcam visualisation +dst_w = 720 +w, h = raw_image.size +scaling_factor = dst_w / w + +resized_img = raw_image.resize((int(w * scaling_factor), int(h * scaling_factor))) +norm_img = np.float32(resized_img) / 255 +gradcam = samples['gradcams'].reshape(24,24) + +avg_gradcam = getAttMap(norm_img, gradcam, blur=True) + +fig, ax = plt.subplots(1, 1, figsize=(5, 5)) +ax.imshow(avg_gradcam) +ax.set_yticks([]) +ax.set_xticks([]) +print('Question: {}'.format(question)) + + +# #### 2. Image Captioning +# Generate question-guided captions based on the relevancy score + +# In[ ]: + + +samples = model.forward_cap(samples=samples, num_captions=50, num_patches=20) +print('Examples of question-guided captions: ') +print(samples['captions'][0][:5]) + + +# #### 3. Question Generation +# Generate synthetic questions using the captions + +# In[ ]: + + +samples = model.forward_qa_generation(samples) +print('Sample Question: {} \nSample Answer: {}'.format(samples['questions'][:5], samples['answers'][:5])) + + +# #### 4. Prompt Construction +# Prepare the prompts for LLM + +# ### Generate answer by calling `predict_answers()` directly +# + +# In[ ]: +Img2Prompt = model.prompts_construction(samples) + + +from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM +def load_model(model_selection): + model = AutoModelForCausalLM.from_pretrained(model_selection) + tokenizer = AutoTokenizer.from_pretrained(model_selection, use_fast=False) + return model,tokenizer +def postprocess_Answer(text): + for i, ans in enumerate(text): + for j, w in enumerate(ans): + if w == '.' or w == '\n': + ans = ans[:j].lower() + break + return ans + +model,tokenizer = load_model('facebook/opt-6.7b') + +Img2Prompt_input = tokenizer(Img2Prompt, padding='longest', truncation=True, return_tensors="pt").to( + device) + +assert (len(Img2Prompt_input.input_ids[0])+20) <=2048 +# print(len(question_input.attention_mask[0])) + +outputs_list = [] +outputs = model.generate(input_ids=Img2Prompt_input.input_ids, + attention_mask=Img2Prompt_input.attention_mask, + max_length=20+len(Img2Prompt_input.input_ids[0]), + return_dict_in_generate=True, + output_scores = True + ) +outputs_list.append(outputs) + + + + +pred_answer = tokenizer.batch_decode(outputs.sequences[:, len(Img2Prompt_input.input_ids[0]):]) +pred_answer = postprocess_Answer(pred_answer) +print({"question": question, "answer": pred_answer}) + +#pred_answers, caption, gradcam = model.predict_answers(samples, num_captions=50, num_patches=20) +#print('Question: {} \nPredicted answer: {}'.format(question, pred_answers[0])) + diff --git a/LAVIS-main/projects/img2prompt-vqa/README.md b/LAVIS-main/projects/img2prompt-vqa/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0ef861e132d0e6ef429164e8978dba9b0ce4a859 --- /dev/null +++ b/LAVIS-main/projects/img2prompt-vqa/README.md @@ -0,0 +1,16 @@ +## From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models + +This is the official code for Img2LLM-VQA paper. + +We have renamed **Img2Prompt-VQA** to **Img2LLM-VQA**. See the [new project page](https://github.com/salesforce/LAVIS/tree/main/projects/img2llm-vqa) for details + +### Citation +If you find this code to be useful for your research, please consider citing. +```bibtex +@misc{guo2023from, + title={From Images to Textual Prompts: Zero-shot {VQA} with Frozen Large Language Models}, + author={Jiaxian Guo and Junnan Li and Dongxu Li and Anthony Tiong and Boyang Li and Dacheng Tao and Steven HOI}, + year={2023}, + url={https://openreview.net/forum?id=Ck1UtnVukP8} +} +``` diff --git a/LAVIS-main/projects/instructblip/README.md b/LAVIS-main/projects/instructblip/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d7de18de165919cfbd5c17c63646e8011fa25af7 --- /dev/null +++ b/LAVIS-main/projects/instructblip/README.md @@ -0,0 +1,109 @@ +## InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning +This is the official implementation of InstructBLIP [paper](http://arxiv.org/abs/2305.06500). +InstructBLIP proposes a new vision-language instruction-tuning framework using BLIP-2 models, achieving state-of-the-art zero-shot generalization performance on a wide range of vision-language tasks. + + + +### Install from source: +``` +git clone https://github.com/salesforce/LAVIS.git +cd LAVIS +pip install -e . +``` +We will soon support installing InstructBLIP with PyPI + + +### InstructBLIP Model Zoo +```python +# ================================================== +# Architectures Types +# ================================================== +# blip2_vicuna_instruct vicuna7b, vicuna13b +# blip2_t5_instruct flant5xl, flant5xxl +``` + +### Prepare Vicuna Weights +InstructBLIP uses frozen Vicuna 7B and 13B models. Please first follow the [instructions](https://github.com/lm-sys/FastChat) to prepare Vicuna v1.1 weights. +Then modify the ```llm_model``` in the [Model Config](https://github.com/salesforce/LAVIS/blob/main/lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml) to the folder that contains Vicuna weights. + + +### Instruction-following Image-to-text Generation +We first load a sample image from local. +```python +import torch +from PIL import Image +# setup device to use +device = torch.device("cuda") if torch.cuda.is_available() else "cpu" +# load sample image +raw_image = Image.open("../../docs/_static/Confusing-Pictures.jpg").convert("RGB") +display(raw_image.resize((596, 437))) +``` + + +Then we load a InstructBLIP model with its preprocessors (transforms). +```python +from lavis.models import load_model_and_preprocess +# loads InstructBLIP model +model, vis_processors, _ = load_model_and_preprocess(name="blip2_vicuna_instruct", model_type="vicuna7b", is_eval=True, device=device) +# prepare the image +image = vis_processors["eval"](raw_image).unsqueeze(0).to(device) +``` + +Given the image and an instruction prompt, ask the model to generate the response. +```python +model.generate({"image": image, "prompt": "What is unusual about this image?"}) +``` +``` +Output: "The unusual aspect of this image is that a man is ironing clothes on the back of a yellow SUV, which is parked in the middle of a busy city street. This is an unconventional approach to ironing clothes, as it requires the man to balance himself and his ironing equipment on top of the vehicle while navigating through traffic. Additionally, the presence of taxis and other vehicles in the scene further emphasizes the unusual nature of this situation. In general, ironing clothes is typically done in a more traditional setting, such as a laundry room or a designated ironing area at home or in a commercial laundry facility." +``` + +Generate a short description. +```python +model.generate({"image": image, "prompt": "Write a short description for the image."}) +``` +``` +Output: "a man in a yellow shirt is standing on top of a car" +``` + +Generate a long description. +```python +model.generate({"image": image, "prompt": "Write a detailed description."}) +``` +``` +Output: "A man in a yellow shirt is standing on the back of a yellow SUV parked on a busy city street. He is holding an ironing board and appears to be ironing clothes while standing on the vehicle's tailgate. There are several other cars and trucks visible in the background, adding to the bustling atmosphere of the scene. The man's presence on the back of the SUV creates a unique and creative way for him to do his laundry while commuting to work or running errands in the city." +``` + +Use nucleus sampling instead of beam search. +```python +model.generate({"image": image, "prompt":"Describe the image in details."}, use_nucleus_sampling=True, top_p=0.9, temperature=1) +``` +``` +Output: "In the city street, a man in a yellow shirt is ironing clothes on top of his car, which is parked on the side of the road. He is surrounded by other vehicles, including a taxi cab and a truck, adding to the bustling atmosphere of the urban environment. The man's task requires him to balance precariously on the back of his car as he works on his laundry, highlighting the creativity and resourcefulness of New Yorkers in their daily lives." +``` + +### Demo +Run Gradio Demo locally with ```python run_demo.py``` + +### Instruction-Tuning +In order to train the model with instruction data, prepare the dataset where each sample contains three items: "image", "text_input", and "text_output". Follow LAVIS documentation for running model training. + +### Qualitative Comparison with Concurrent Multimodal Models (e.g., GPT-4) + +The response from InstructBLIP is more comprehensive than GPT-4, more visually-grounded than LLaVA, and more logical than MiniGPT-4. The responses of GPT-4 and LLaVA +are obtained from their respective papers, while the official demo is used for MiniGPT-4. + + + +### BibTeX +``` +@misc{instructblip, + title={InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning}, + author={Wenliang Dai and Junnan Li and Dongxu Li and Anthony Meng Huat Tiong and Junqi Zhao and Weisheng Wang and Boyang Li and Pascale Fung and Steven Hoi}, + year={2023}, + eprint={2305.06500}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` +### Usage and License: +The model is intended and licensed for research use only. InstructBLIP w/ Vicuna models are restricted to uses that follow the license agreement of LLaMA and Vicuna. The models have been trained on the [LLaVA](https://llava-vl.github.io/) dataset which is CC BY NC 4.0 (allowing only non-commercial use). diff --git a/LAVIS-main/projects/instructblip/run_demo.py b/LAVIS-main/projects/instructblip/run_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..ca59b3ddc2b15c557cc5be98683f9322807c39b3 --- /dev/null +++ b/LAVIS-main/projects/instructblip/run_demo.py @@ -0,0 +1,119 @@ +import gradio as gr +from lavis.models import load_model_and_preprocess +import torch +import argparse + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description="Demo") + parser.add_argument("--model-name", default="blip2_vicuna_instruct") + parser.add_argument("--model-type", default="vicuna7b") + args = parser.parse_args() + + image_input = gr.Image(type="pil") + + min_len = gr.Slider( + minimum=1, + maximum=50, + value=1, + step=1, + interactive=True, + label="Min Length", + ) + + max_len = gr.Slider( + minimum=10, + maximum=500, + value=250, + step=5, + interactive=True, + label="Max Length", + ) + + sampling = gr.Radio( + choices=["Beam search", "Nucleus sampling"], + value="Beam search", + label="Text Decoding Method", + interactive=True, + ) + + top_p = gr.Slider( + minimum=0.5, + maximum=1.0, + value=0.9, + step=0.1, + interactive=True, + label="Top p", + ) + + beam_size = gr.Slider( + minimum=1, + maximum=10, + value=5, + step=1, + interactive=True, + label="Beam Size", + ) + + len_penalty = gr.Slider( + minimum=-1, + maximum=2, + value=1, + step=0.2, + interactive=True, + label="Length Penalty", + ) + + repetition_penalty = gr.Slider( + minimum=-1, + maximum=3, + value=1, + step=0.2, + interactive=True, + label="Repetition Penalty", + ) + + + prompt_textbox = gr.Textbox(label="Prompt:", placeholder="prompt", lines=2) + + device = torch.device("cuda") if torch.cuda.is_available() else "cpu" + + print('Loading model...') + + model, vis_processors, _ = load_model_and_preprocess( + name=args.model_name, + model_type=args.model_type, + is_eval=True, + device=device, + ) + + print('Loading model done!') + + def inference(image, prompt, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, decoding_method, modeltype): + use_nucleus_sampling = decoding_method == "Nucleus sampling" + print(image, prompt, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, use_nucleus_sampling) + image = vis_processors["eval"](image).unsqueeze(0).to(device) + + samples = { + "image": image, + "prompt": prompt, + } + + output = model.generate( + samples, + length_penalty=float(len_penalty), + repetition_penalty=float(repetition_penalty), + num_beams=beam_size, + max_length=max_len, + min_length=min_len, + top_p=top_p, + use_nucleus_sampling=use_nucleus_sampling, + ) + + return output[0] + + gr.Interface( + fn=inference, + inputs=[image_input, prompt_textbox, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, sampling], + outputs="text", + allow_flagging="never", + ).launch() diff --git a/LAVIS-main/projects/pnp-vqa/README.md b/LAVIS-main/projects/pnp-vqa/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3de39462754cd58009675a784674cdd7570d38f2 --- /dev/null +++ b/LAVIS-main/projects/pnp-vqa/README.md @@ -0,0 +1,116 @@ +## Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training + + + +This is the code for PNP-VQA paper. We integrate the implementation into LAVIS. + +### Demo +We include an interactive demo [Colab notebook](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/projects/pnp-vqa/pnp_vqa.ipynb) +to show PNP-VQA inference workflow: +1. Image-question matching: compute the relevancy score of the image patches wrt the question. +2. Image captioning: generate question-guided captions based on the relevancy score. +3. Question answering: answer the question by using the captions. + +### Evaluation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelVQAv2 valVQAv2 testOK-VQA testGQA test-dev
PaperLAVISPaperLAVISPaperLAVISPaperLAVIS
PNP-VQAbase 54.354.255.255.323.023.334.634.9
PNP-VQAlarge 57.557.558.858.927.127.138.438.4
PNP-VQA3B 62.162.163.563.534.134.042.342.3
+ +To reproduce these evaluation results of PNP-VQA with different sizes, following steps below: + +#### Navigate to the root directory +```bash +cd LAVIS +``` + +#### VQAv2 Val +```bash +bash run_scripts/pnp-vqa/eval/eval_vqav2.sh ## 54.2 +bash run_scripts/pnp-vqa/eval/eval_vqav2_large.sh ## 57.5 +bash run_scripts/pnp-vqa/eval/eval_vqav2_3b.sh ## 62.1 +``` + +#### VQAv2 Test +```bash +bash run_scripts/pnp-vqa/eval/eval_vqav2_test.sh ## 55.3 +bash run_scripts/pnp-vqa/eval/eval_vqav2_test_large.sh ## 58.9 +bash run_scripts/pnp-vqa/eval/eval_vqav2_test_3b.sh ## 63.5 +``` + +#### OK-VQA Test +```bash +bash run_scripts/pnp-vqa/eval/eval_okvqa.sh ## 23.3 +bash run_scripts/pnp-vqa/eval/eval_okvqa_large.sh ## 27.1 +bash run_scripts/pnp-vqa/eval/eval_okvqa_3b.sh ## 34.0 +``` + +#### GQA Test-dev +```bash +bash run_scripts/pnp-vqa/eval/eval_gqa.sh ## 34.9 +bash run_scripts/pnp-vqa/eval/eval_gqa_large.sh ## 38.4 +bash run_scripts/pnp-vqa/eval/eval_gqa_3b.sh ## 42.3 +``` + +### Citation +If you find this code to be useful for your research, please consider citing. +```bibtex +@article{tiong2022plug, + title={Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training}, + author={Tiong, Anthony Meng Huat and Li, Junnan and Li, Boyang and Savarese, Silvio and Hoi, Steven CH}, + journal={arXiv preprint arXiv:2210.08773}, + year={2022} +} +``` diff --git a/LAVIS-main/projects/pnp-vqa/pnp_vqa.ipynb b/LAVIS-main/projects/pnp-vqa/pnp_vqa.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0650f83b6f988b7e8f74d8bb1ae7565291a68c38 --- /dev/null +++ b/LAVIS-main/projects/pnp-vqa/pnp_vqa.ipynb @@ -0,0 +1,415 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0746610a", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "_qxQ4_bEhkrd", + "metadata": { + "id": "_qxQ4_bEhkrd" + }, + "source": [ + "## PNP-VQA: Inference Demo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dq8t0LJThhuJ", + "metadata": { + "id": "dq8t0LJThhuJ" + }, + "outputs": [], + "source": [ + "# install requirements\n", + "import sys\n", + "if 'google.colab' in sys.modules:\n", + " print('Running in Colab.')\n", + " !git clone https://github.com/salesforce/LAVIS\n", + " %cd LAVIS\n", + " !pip install .\n", + " !pip install transformers==4.25" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "838168ff", + "metadata": { + "id": "838168ff" + }, + "outputs": [], + "source": [ + "import torch\n", + "import requests\n", + "from PIL import Image\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "\n", + "from lavis.common.gradcam import getAttMap\n", + "from lavis.models import load_model_and_preprocess" + ] + }, + { + "cell_type": "markdown", + "id": "2ffeec4e", + "metadata": { + "id": "2ffeec4e" + }, + "source": [ + "### Load an example image and question" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2da65ed1", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 335 + }, + "id": "2da65ed1", + "outputId": "31b7806a-95eb-418f-fed9-e55ee9cc51fd" + }, + "outputs": [ + { + "data": { + "image/png": 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de0PsRZWO1YM8mqU51ENuRUvoY06BmqqZiLVZcR67UGfOQ/dhNWOTkrSLYFpl0DZW5OzSqvUmyDG9d0AvVztvOW1YRt5xIKPMsHFWM680pagsQVB2KmMc26EMhDTvtdQdszJmeu9dXbtWngVqdqyKnAGHiBi0Aw6xkhIPanMg0sXccLi6+vz9q5c///lPP/n0+2+8+f033/zgk4/nGAd1g4qxhJ0sqWsrMKaWNj053xqPv1JPLh+YjYUYSxozR88ST4bY0hMUHYCIqlDmLiJhEuagloWIbsIsrKoJLW/KaolD6rQ1AZVa9WqR46w2T7dVPNVk5qSqNzNrRRdB9dIIQ8EEC3MUEak5w1wstZq5FVWayFqxl2zZDVlqJWqlnVmSaTC4T0XWcE9MVW9CUEYsRJSJCCVDCKMXsgStCAFoAO9OUIF8/NGPQHPpOStidheFTgJGYXhpRxG+UyTY+vHBi89+dP3RPA8d+6f70xM3bV2Axfuf+/O/+kd//K3rmydFADCKwbKKKgkrY2/H5q5SFTkY53HL1CaLmw1yvbz623/373346Yf/+D/7z+a+i6xZVaNKgyaaYupq3qXlnDv3pzF07mZTYzkejnBLeA0UjCPmtt9b7z+Sn9zcXmcRBu1u0s0gbkuDjLSIc9RP+TpBQQRCmHFQVEtVUpTFijwXCwAFRTERSMTIP/idP/qFX/4rh89d/HvtN7769T/7J9/+5tuvf//JJ588+vh96KrLEUKGkiVmZKBKtAQptgiUQkKMiwi16GijWDonkrXmvNa1zrbSsVrIwTV8WhepnCxmMrqgiZpdnaLRmrd+2qaI5thAQk10jTTTRQtiE4XcBlkFCBQCtrVkagpZlCCESgo0D0QTeVpAIMneqlSQLFGhiPzv/uP/PRbdKxRu9BLxtjz7zGeee+m5lz7z3NGOLqwut+Ms0GeeuZqTDKDZYiZFUGiWthlNdBlzg7lpE7Jkc5VxM2byoHZ5vDhXEmViEeWmDRoEF4/cUSLWZ2xqZXCdwCIN69huZOXYk2mltKYiFJirbfsJMh12WZejt1Enx0/lpO7NiG2OFF4ej7cpYDaNo9lWMuDeJPdwlUYotGbOOXTxG4FUXbhLzdMcq3UGWFqGvUYbvL69fu+T97//xlvvvPnO4yenKK4CDZkxNsnG6pVA7FY6/Wv+8OfXF27feVo5fPisKKs7NlFAjza1Np9VWLi7MCjRPDRQM4Rh8rHub+bjW50OqPnZUmPvXFK1cofkpPXoozgjB283BCu5zYxUqQaBGaVLl6TOChNFTtIAF6TIUEJbd/EJLoJFkGI7FVWjp07TISrJHrvWnVSGqDQEU+jAXhwZnSC1XAVMSCo9iUAppUhQVERUMgEVU2RAoXffogtIlMFMmoqJAVouI0XDXnzm+a//+a++/YPvPf340+ub0+Pra3ezZkJlYUZBShUgTRzUyoSy7uCot6tlOVq1zjH49Hae5t6lL3D1HJhBI8RI1UxxwnXmpImm6tbKTJq5LabN/abm9XbKCAAqixm7H5S25TYtfbn/2Z/50svPPP+nv/db1zcfu6r3bssBNAi06CJShSJGnJVbpjKTU6yRbAUsonRPQGOviCBAgRCi4iKqDi2dieODZ5Zja8ov/czPf/kXf+7q3oOnHz9+43vf+973v/XRRx9y6v17Vy+/8tIbr7+hMithFQoJsTIpL2ZKqlK1tEELlajUVDGTgHJggWhTsFmzIwkUzdsilrmJxJLM5eLZV3/2YO173//jbXvCyIwhUmbNZC0VipuUy8yKfdt/qqSKuntJgxQzoaIaFSZqmZTsTRA4F1gkYIpQEKKwJgK/lRRKVl/ZDLV2efj8w+de/ewzDx64WUYuvmblpSyTU8a4zDZL0wwHlVGxBRZhO3CwRF2lN50o17Zfz+p+7OvNzdb6GlFubWbA0HsTIIJiS2TCRAUydtPY5naw47qut7HNUV0xt3HV74UWHXOcrDjBvdN7dzRRbtCW7L3tvKMeQtFNvThTWcIoXcRdarsdWeYHq1HzfBJV90NVwqyO62bMUZfWonJCzI9RhHPm8KU3dGm80OPXHnzx85///Pt/4em733/rO99//dG7P+JQ1eXQ2DLKRqIaCMiPtqev3HvBH7R6vMMmejWWUsI0okT2ziblU3ZDNLFSoYLJoMgqIYOxBwsphlKhuqdICNlQJmCDtBPRiQvt3kZRdDZfevocMSrGSJIpFJPeVRxSzSeichekQIRLTUklpDLl7FEtQzsURVEXVBpToVEQCgGqiqpmY4l2FKfpiDItGgwilAqtSjM2QYiQMJAoiiihKAWikKTg7i4VMMlEirgVYShRtea235xPqcvxuW179PS0q669NTPJygCbKRChifgpYK0KQFTVRNdDP2RV1Q4fuc+5iZkqnGptjmSivPoKDYyAMwkGQRIeYmbSrZpOm91Gp2G/g8JYFeG2zfNCN4JE8unj99+V2yen7WmTdrCuVFMXlR2iNJZQMur2sOhiPYuM8DmRYYf16vkXTh+/z50FklJFgBAQFLh5Y6VVuYg4t+uPtmu69d/90b/6nd/6N+vFxc99/Re/9ku/8Bd/7Vdvb85vf/c7X/7iz3/4/k+++53viJdZQLUmEixQI4wIQchUpVG1lBR4GyRTO0UxmQnVGDZNFqJJybKIr1o+nUsTLaiIre7NcY6MAdD00NtSUWKamsqsmvs+7yg7A1RM7MgsFlRKEAK4ONOzArZlGcWq0tWUmKikiKiKEMO1ehvLIc0s2v3ltc+9evXcw/bsPU64r4tLBpt3W5a1a1fKxFJWI7eam00r9F0PdkDDFgFRJC8ptu/WLlJpiXsXl+c5mlhlAEnK+TzXw5oOyFAoSpww1cVhpMBv5m5KabLNUMpWoWpGHtqyjR1uq7qLzqxNIiWPdUcHc59zjr1pyyptZoIRcaFmSWgTFcxRMbG0frlkVEg2VZJG+KxGLCJnCqVR4JZBMV+JKRJAa8fFKO32/LkHz7785+/9wte/8uN33vnB6++98857nz7+eFOqGrOnlCy4Rr5++8mfee45vd0ySppqpZYk2DSXzpqWKVxQ6dO8FGSoKcVoqakJm2jCWYWihgOOsQegBhfVwVmOVgJwaYtAmaIt2ZtMr2gQidojNgYMCj2zmWlTdLNWtU1ShYU0FqhTOCIyQ12bOjV1gZVq6hFGkoYwUgUaSlW6YBWhe8pMsEqUNJWCWPvpmWQFE0kVEeLOmQZTSiEFRAFQFQAlSakoYkqVqSXGvvL9/Wa5WB9df1rIZb1/p9BVooBFVWFhYUBkJunuShHD8bAeGnVkoN2ceXva4eJLQ3mkVxTMwBTTTCtxL0smTCglZKZq9+oeWtr7SXh7u1WEAU3EzaLkH/5v/9G3//Ab73zn7W7rJtenj37y9IMfCUBpOygoy23p3nTJ0IRXAv04hMzqhhQzqnH5zKtfeOazz79x/TRO12U6K6uocBVJlMNEFEqZ0KatEZGSyknvWlXb6fyN3/m3f/zbv3vxcH3xpVf+xq/9zSeP3vvn/+3/gy5FQJKQEpCJAFiEiOHOOJZUgaSClQBKl2Cx9lKw0wIzY2ppRtyeNjmINe2yaS+V0/XpytrV8eLJpynCJq21BarWS6AVaS5jFgiBQKREvB+lX7jkmFkcktPuLsIkJcUG0DJVFRBUEVCIQRsMAvpD66EGj/vPPnj+lc9/9sWX4ZkuCtWyaErDUqquDiB5XmxG3p9+QWHkycCDF9M4GysEoI9iNiarUwV1vZ3Xi2MTJyhkb81NVJDcQTRpmiYm2WVWLrZKegrJ7IuL28yIpDaxhMJwvMixsZjS0JoXrsrDNHNbtJ0qW7/IElR5b9171Na4w30T6Sa6GJuQFGQRgVOVZqK19cp6ZpxriuqaLK/MlFn3jhcjZKRsSBfLmVm5tt71qvXl+LUvvfZzX/3lR0/ffuPt73zjT24+vXl6um2+wIe5/CRufsbu379/MecenDNLCwSiD6ulolSUpWG+AV5oJWloxhHWeKTgNJ+itCNVzpF0MaKsXMqCQ9ZYTc2XgShTLe8dpOw53DqHocwaWmfOPmdORu2zYZq1TFXrsF31zDKbLhrQMqoWJCp1kxKF71LWvImCQBOAyQwpV0VS1AA2mloGk0qlGD3FXKtESBdSCRcpalEFSBaEqqK0hJYQlSBVhIAQWiSraHoeH73zg6Uxa/pySIsCRJuarxTJmtBLOZ7WlMyFaoWY87AuffExd3fEnuO0g9nv9QXcT7lHHsSE2i0VRWsKwwwoygQASDGRVsbQ8FX0HLHfDIWZtrR+qnjhhWd/5c/9hRx8/dtvNgkoDB2WwCAqOWNSE3Oq9VikHe0wWzvVgpiGsTIStTUJW86hD3u7vLj/6Pp6MpMFERVnYZWuojurFKqarFk64W4ER3IxNk2BwpX77fzet7/7vW9/E4RJV3GhSd55W00QSZSa3ukcAEUAFJkgNRdQKCO12ERrcvSaBpbk2UqqUJYzLZntGmy3N+205O3jDxlpgu4ilsFNBA2tybJvtxmpEIiKm/e1L5drO8JyoOeYcTqDk7iVPqWqVIoJCpCZoFBAVS1oVbqJp6euy8PPvPzKZ1+7vLzvS5/zrKc8rMtglFkEnM70ILrJKTcyh0lTX9tKVsxUFYciZhfRzlDALc77Um2qqUeSnLG6ZWHkTNcCVrRF7Unt2nozi4oJFZiCzZeI2E8ntMOoXNz223NbWrlZam6YMg9dhPCmFVPMNI3n/crU3HVtYwsrFfHq7emTs68IZvOV1pLD4S7drdD6djo/WJdUGxCsXTA7Swt7qZlnjW3f7/hXOSe6d3O9d5xZzSjSDv3YBu+99NLzDx/+yle++v5PfvTdb73+k3ceffr0Ue63t73eP+8X9y/r8dkHaEqlp6QuJzZodgkTlk1ISDZ6g9EhQYHhZt+HCZIsEYuk5c6Otsve4cHhU4yabsVY5+ixzKpp0N6WGa21W+KMA6pByrtXdu57cpscNcNDmpFNAXGzFBbMwlIKdCUz95ASSGFOCNy9mgJSCdFYxQKEkmFIpbp0VpI6pEnJkFIpVe6Z0yxhokimVqQU1IrSRLQEdccFR975JrSqAAKYrPzo7TfJUKiNRJbZokKqCwtVE2YTzRrVl0BVvfDZl1/43Avvvvldg+xZ57idFX3tyxAWHrz43N/+9b/5//lv/7scFVQBk6E086Sckqqkql2sy6ILR8Cwg7fnW9LUnGJZ8lf+6l/7tb/+1/7Vb/6Lf/Uv/nXXlhyahlJYmXpLrSoIKDq2xPl6Vzssqda1LeGcMEKuCpIzgXHeZfgixqziRBGGtHJZWnkJnZ41qBnQOe9exCgpVUgSRJEFSHRVwgBWhbSSlJHSlQpNAKJAGYDSKUSDO4JCg0MKqjFoREdl6UyEVc9Cto0DKib00mLUGML945+889EHb1UkoCauuUT3cF0CLRCVMwsARBY7lLq3dfUlVLTWFtJZvGq3EIyuMS8Y+9gip5t4+dRRcOHaxUtyls8S18O9lz73+c+9/OK9y3WfueXp8tA14CIzuJiPqv18e9EvwQw1LTRp0tpJ6rawoB+Ky4S734p4q2BmKcJXM5ix8dBWKyFKHE+f3B7benE4TomWSMJ8lcDIClQyZ7FLc7VtqJuYUWa5uR+XEJYkCg8u7s1xSwEct+M297o6HsUgB+/aXeTxftK+YGI+uW6X63I8AtHQQKk5xUWaFQTFubPLKmwFoihNq2REtWTvNlmqBmLkdNf1mYstsqq6GbyPqkR18eaisFBbHj74wrOXL33xcx8/uv7wnZ+88Z3XX//BT95/+vTFzxz6Q+eT0lCwUqh0GrGUEBmUzKWrJkokcrpy7T6Y7jiGdYuzIbwbk8NSpEwSZVgtpUq1NgADS0kmk9aqKSEIUZmKtYiWozGGUaGKfs7zqFkpLBlbUqFN804/EhUUUVIi0KSoWmUJSIyRe1MTMThNpFyNWmKkZFSRJY2qAlUpqdnUKgnVEhSoKEGpqYsUxahKhiIFICUJEDBCCd55ynhnUhJnMYtSJXPLWbSuqu7eTXViD1D7ilCR5suy3jseH2znm7iJsaebiLYaoSZzG2+//U5UpEggKYICSBEQXahNYm22aEfAxFNi27aYqWrUIvDCCy/9wtd/8f/4n//n158+OnZPl1kU6U0VwkoIhaQ3tcXGHjmtiqfzU0CldbFefYU3z1DqaqnYJbfLi8WEd9BMpWnr5k3nouhCKp6w7G78JAqipovfsdMqU8JdJCZoAVBTzQsFKGEaQEUgSZhoFQhzsSLKCRSSVRpRrDQIpCgFMS3VspQSoaUZCjK1WG7CjJwkBCICt57oOUubYNW513Z7Lg5VXCxH+rHZsvqq9AFJkeZoFBEEukmuprRF++W27zWHgXSnLvfX53G+nvPmlhjm/trP/NwLL75wuaRENL3UxaQNs8WxPOhtz7pYOlUps0Gai0dj0FKEdWmLRB5aE6s5hzVDGSn7GOtFc2TOneTaDjJVV78ZJ1nd3WIMaJ4ystTacSaT1Ze+WCeLyY1xce/BjHPm+V5fmi/niDI0VRuUquPF1agRCEAu18MiOHOE2dxvL9vxYl1HcUiyq6kjQ3whVcpaExqzCqRFLcvSy1Aya1+6VQ4l1uNlnveZQWGMfVGD5Jyjy3qprUlNpo2s3uBVlSyGlHl37xjbYeUrr937zEvPfumXvvTzb//o3W+99ckP33/pnuctDS5j9iJrhrWoXiUglbAUy5wY5mXep+eOuM2dB+jAhdQpQdZBOTertqDVbbg4FtYxqoDNsiQX853cIqBSrmreA6ImrUdoJ5tBNjFt0eOJFXfqzJnztJ1p0sQK5g4ohKaqUXchDDFJIFA7rBHu0xyaJlqAaUHUlUSxSZnPdJMUUJSqjSUVRDVBMyAUorOoUFMRlEiBMKgAw5Qk1KCAkCymqDqFJCkECzGZRmFkwM2tA0J4NCr18fnmfm5t9etbbvsJpX3xFNah66zx6c3v/JvfOvRFJFSnQCiaNQoi2lRa162DyoAqyX3uY9tFaFaQMlsfffLh//m//McJNFeI7FpJaAK5K6PgZyldbOldiGO7CNXzfipOSHKeZSYCoeMmdrVs6K33LW/10M81RbD07n7Q1snU49mEGNZ0JVEpnINO2QWTZ4qKFaM0yGxMwSI/9UCIokemECqEGuqnjlUIAE01MqYIaAK3pCEhSgJA3YWYqk3ktCmAZmPDXjvSjnCFJnIygdawGiyMStbkXFiswJ2Aa2aiF735ZcOybYQYfUsZxaZ1oQwuNXOKWtFFr9TOLcVbvfjq5xvv/fD1P9l5oohK+vMPLy9ak2hgWss5E5pJYUavcm3btpsBZJeeycwCVc1x3mqelmZ7DqMiUxfdRCbReusx3EqFQqj6VoGN1g8X3ffbc2sWM/12O17ee7qd0+GtIdnKUrlxRkF5qpgMuqEYTWBmOePgy8zY7wz+YlfrFfbpZqvIucKOxwroNpqZLO3EIsuALKG4qkdMBXvH3Hn0JUWihgTbcQnM4jS6zMxieROphtZISCfh4lJSpmls8DHnumhQCqm9gVEcbBQYi80vuegXf+5LP/PzX/n4xx89/uPvxaPXZZ/TXAMCyylW1kynnmlOiEufOVPujn0jytY16sm5Hw5T1W83LY9VXSh3w59ITSAIE6TLHJBNmZHG6m6iEqULaZw7KkQPqtCa3qleNptlA6CcNcDIOSViYh8Zomba3dvaNSqCqOkMdeuchEqp7gASQqrKRDaIe8Osg1gpkijtVJgbMlr99HZnUcRCJFwUWkhQBFnCcgdTQcpdmkeAJKQA3pHDQKAIE7hQhVqTNepGz2K2tGZUwIS+yFL94r3TD/dxvdqFW28NHsZW0+l9RZWPgJmhz9KSCS0RNcGyWDdjscDJuN42oYioqal6lYiQCEBFZCCiFASlysvYUgoNrTcta6VqzQxjbISIipa4qgtnDWKO4jhN3T59J1/P7ZSF1pZ7V/eSVmWtSUqUkaRbUzKHu65DwhHIylEiRIZLs5IgigGBSBOKAnZHW5OEECJQMFWqWJ6h5CgWzGlNMlAETdSABpslBv7UqCJWIp3KUiE0lWDdjb5yVBytFWTMEkJ0GsMgRFVmXt8+xRiHw3Z1+Yy5VxG4bYvVriYXjbmL0B0jF+1Xomypirxo4XpYBEcf0RQuOf3QPPNsdjRbJ25bt8XX2k9Ng1iy0FuzriR6+qnKohZvc05tDQKAJl4Q6e60B93O27b0BTTmnuKP923D6dCOkiYoFYf4uXh1vKxmT5FDqh0MUasuFmIubLWkcr/p7iObinHOtbdzFswmM6X2KEitqszMZMAMvtKrvDhcZDJicqG4YkiyBDmyV4PUGNoJ2Pk8bfGQOvQexFaEuI4sjO6+CekNrJoBc6RoyVbVXazQTZt51LmVUkqQCJOy0aSaYaPt6LPZouZsLz7zzPHrb77xwbj50Kep+tRyI8Z0tqZ9hiCxpKg7c0ZNCWnlK9uDdrzDIqZ0rGkinsAUaZBNGEpJdZQSHBIN1sWE4lnDqlIryszctQugQlQzVWFXWXOVqoHUrmSKOHU/o6IQxczBSPqdHK2pprJUiUoKI6pgKpFJyowO0HVUmUBQ4spQlaZaRDVvHCgScN5d8maAVGQjRARQEYoYRSVChAIvAEAJ1LQCSgjKaYSVZjFWt6NZEucYnCMzqnlbLkeMAcq6ns/XJtaXo1oxPaqrp2EOGaJsIa6N4QHChBATLov1VSLYdK3M0zgX2WCUI6igzLk9eHjxpZ/9+jf/8E8n95TUEoGKZkpZdvHS5gZ1lALCGvuGGn01QDG0CbzVq6+99uEHnzy53TIplY8/eSQ1IbTUfRu1rHePSJBJZQMsW63OJQqzJk2WbvQtmVJtTmF5N501oqTKVJmyUwEWUlAKmSg1VZAQ8SoFkyIG5QBTNAIEVUgqQlFVRmEZgLu3bvRysjgqSlJhnY7GEMmp2VWNPmG6hmnKHhVJkbHfju18+2gRl0X75cpcLrQjx26horpJ1z5FCErXmjILIrquuHdx2G+WrCLDK6Y4Zt0062bi0j1tTx0d2vV2xrE7qEmcmQL07sUUUUm6wArapVBiWpxjMOecLJFm5hCsrVG4xfmyHxUU0O8fd9bI4GJr6io1KpP56ObTB/euiKmFGkNZdvTCBJ2es0bBUluHuWBdGHMiUkXboaforNKsxWI3jFKBXqhveU51sd7qzhswmh58sb3OrcHERuC0b1fPHBSY5rPqcFgxp6geVCaKw04jmtGOPs/ZNInqiWKag3SYALZFaUmnWikjiaQGDlYtFmEP9/v3PvOXv/z2e0+P1yKJFss5Imq6iVRfidTYnXR0o1A3CkobrIeqVJPN0ydEAHVRdlY0oYmX+pl1VTwzQ9kYhSblUVkSdxBA1SpIDlERSbGTVAHSUGx2YXaWbaocdRl294csJDIixthmcYqCzbysoFQzlUaWVhbocMJFQoi5B01uXMxcK02REAkxlzA1Aj8lTdCqm0r51kGGoPRulq0ZQCudQBo8BQudhQRKh4jfkSam2myujqUvZdBdzqdt5DaHtIyMeP31b2KcZ+TajjAdGoCnmcq8pCE9aqsmolo2JWaDTsHhcrnUZUQBFdJOsW9jKqSJQxcWRp7+yq//5V//9//GH/+7P/qj3/+GaWaSIsqCqFEnS9SO0pSEi3WNfYtxEhUVm0Hzfj1u/uE//I9efOVzH7z/wduv/+gPf//f9ss1rm9FD65RlefTNvPW9bLZcTFdup49B23xdacUpwksbVTiQp3WeE9Hjbpt1WQ+jjaFcmfSbWJ7VIqoiIP8afaRwiQkYA3iIrvM/W5oZ1EQ0JIgEKZxF7OQNhmBaMZSPWEWKfSu3XROE6nu7F7sM9Ry4KQ2Kcf0pYqSezEjcebkrO00Xc+732pfT53dO6Ht4vIcwX0scHqeXcAckZnZVDlPRPjFlZ9zNOuQ6n1V05xlvqJRPCv30+324HhkhKIJQVY0792DMke0gpzzYl22MSZSlOhd3eV26NqbqTSj1Onm9oZh3VXEyp02Is00yBKtkQcVu7w875svjYWUutSDnrRJnyCjLwddGjyxbbs0iFpbumYVmFaqgsjSGlmulycG9jE56tiVxj2HSCkWb4w5AaErOFhtbV52fXt7XNf1zuSX0Rc/x5yZSF9ai7EhEtMUcO2lnAwmD7W6+sZRQklYZ9ScDBV36NoqSkcpBSI6NV/7hZ97/Ec/Ov/pD0HYBpvVAOYMt7JMDFUp2AytFhRaF8yctZsdqId9jkCuRW2yl+7qyOquVsLA3qQIKTVAa1yjyvWIRgYMMeaugGuxWBaianVQQlTKoWxwINA9vUpEB1c4vZ2bVVRl7hyZe9YuqsIuogYVVShFFKSo8C6tDEVyj3O3llaEtKYUdsCpU3Q2QU0XmSYl7UxvVVBJVClL2aFmKzkVyHCWGJE6wt3UqrKympmawtewIzAuL1qO/VQiJiZySM6PPt5zNFurao5b11KY6SBRoiXm7WHWzjtfvqmI3b9/+drnX/74nR9Jzu5SEXPfCKHpNDXdX/niK3/3P/yH4+n+f/0v/+m7P3ivt5aEibIMUoWQSpO21Np3o23wGKjbfdtFlmWNGSJC7L/8K7/88PlXPn5y+uLXvnS8eu549MO9q3Xld//krXdf/2OITlYbkXl9ljGkrUsbHoflMNugiRg058FQJtepyEKcW9NDb53gvPTiNvZp5zvNFGIppBVKkHeODU1UlpZSmMUqIWAkQAVERFiila7MSlMpbcxS0ARVZPCOeaQbelOryXRIl2yOs3L3U59c9nsbVlmrdjGmQYQ5OCIzK/bYMK6Vem79bLKPW7O2trUtohO7ILJOy3o+HM5zTyYFfhKu3mXxIeqZpncRds05Lhb4um4xNcpcUjy3fVnblpHX0S4Og1WCrjrHPI3tuCziJmoI9nvHCGx7tMteHMfjpUIqpojcTYjdem2DTem2FC+bjjTqMmb0LoYVoY8f3fT7B3Obzt1oGbFHmvniYw7OuDocTHDaw40OQDQh5zgdrFf324hFpFQWX84Ze8wGOXMTWzoWZsil7xHt3mWNEV6sZCDJ4QjlZB1MMjZfIEZFFqtSAU+IiewyAUxmd/UURQuvFM6Yqh0wpVuBEUvX1GJrn/3Vr3z7jR8cJuLgTUV3iDVoCxZMvBgshVBowgI7cOyHkXiCzK7NwJo9hCVlbiwkjbMLN+QKbSUBMZduOVT2QEnuKgY6VIHdQqNNOqRcq1OTjinzACIzsFoHkkcdQSsczMsyMrsuNcaYY7LmHASJRs126GpwFYpm0k2UVjOjMFkwCKeWJnQyXFuRVeIOy8qgCJJKFUAEQYa6FpQaYIGmXVlWUZCkeFWYeDnSpKkLvVgm2Mc2cmpfezOzjnCPsnZESNaYcx81VOhNKMfd1RoDM9VUoJVmWoFml/evXrxZn+xjFOpmezJiFzX0NoXHZf31X//bv/tvfvv3fuf/tzp6Xwq8E+G8NOxOl1hX74spCLoVsJ3HHrleXDAhYgQprKo//L3fvrr30rzdHty7+NLPfvnimfvPPf/whWc/+3///jdNbKJWbYOyS7Hmfp6FuDmd5nLS7svlhalIgSIKnIVc0SFGvZ27Qlpr1lvaim3klDkLOQURFBQdQmqJiFoqhaxiFVpjJatK5c7I6weiFacogZ2zoVyZqHkXCgTMXNUi2RlpPu2nsdE5RbToGroLz9ocKk2PvAt1j2ZZimKMrCqUjCHg6XxW1c1brYeHOC5r205Pl7hy7DNPIhQVN11WtkhMIQqUViaoMrHa01SbeQThbVSuS59KSWzXt6t3VWwaXA6326jVs5ksttDGPj6VcfBFtW1zqomJkO5NDdyZUYKCFiQCKLC4H7f5VA6+ZwkOBy1p6lemnVWbLS5mMSvlp9jb1IdyVjFzsWbu47xn5LIeRDdtUVmrWTefM/raIKZSPUjvSZOii3IW0lRTTRghoi52V0kHbTbZvIWgHw6n81nV2NlkiT28mydYQoHTK1SWXpFK66JkRkZ4S8isXNTJ0tR91Atf/tkPv/yD0x+/s87aBduxN3g7Sy8P0GYMJAwabIYJdHXn3A1mreewoAoWmsH7rKAMiKR2E6h4Faln6shcAwfXSaA0XIbcYebaLTvUaRTbiyJFnwDTy3KhVDQNmlqjhs5s5tGYM43ksmrbpSIiMWfKdRbm7Wz9squblTWDCYMFNFuDxdjdyAxBH6pT4EJDiQgEDlDKCfWm1aJKzASKYLG0mpV7w94qMTXtSIWIlmt3MXGGYL9j0W5vTlAzd4icI0Xi1/79f+/m4/Prb71xc/2o6TqAbbIPhZzmPta+VOtQV9BMmFNUY0qmX1w+c7Ndn07XT04nEbPm6pqZ87T9s3/yT3Ly0A9lUbGLOBNFitYBiBQ9XLHzxImkSxtbnU/R+9JUTtsmqoUi+K1vfpPiMfsXvvT5v/rX/kq3/tb3v/fow2f++3/+/9LqKjDqkKBKV0lMQryYqNPpqZ98Xp+xLLGseugufNYvSFYF4GJtMDpgKKDXSu/tmCZz5uk0G8uYIYJ0QBVZhRIzE5CcEBU1VhIFmSlIkbKGEkuoKjVnVQFQmLrpUiWiIqGr2G2luQtTEajDVMdqD67uu/ZafQ/iPGSMp3qNCkfVlBlSEUUkUpEoxBwc+6fyFLfiT/vp0fvnp08hBCDu3kwHobosrhRkmllJV/NeNaJgB9Ud+2lm7qWmV0sTXj37QIErXU6FhF2uV1VDWs2xmS7mbnNoj0y6yj7GcjjUzNSiRHPYyGh9aR1jP0eG6Q32kcHgascFvbhTZT0cqLJXuoBbujkN3X2fe/OWM1cxm9iwyfF4vLw8n8+zpsErqUZCKmmQbU6qVtYOKlTuovxMg7qa5K7GQYh2rTkybHGFHG0RZrLGnBHVnLOAmmtrW0WM2ZcWMReoqYzaQyCB1SwmEmROQMTV1EYGROec0vvn/+LXvv3Wj+vJgEBAS7QoVcyUSk3llOzWglmGrovFaD2ugoSdlJ0XRYRqY4gSCdWVoMnIJiJm1JFQNoVYMTSS0lMpkkCbPZmoeVAv0Uh26EmFowhlH3sBaEvqkW0x7ihAtaQEXgVGgzWIjgicY8bcJfaNQFNlU12su6NUVaQUdFRCTaW7YsR046izpCxyhGtJqgsZxd58nXWqrNaWwiIiqiWmJjsbOw+aNbSa9saGfRePaGF6cb6eOe140QKYI3/p67/4a3/rbz7zwoN/+z9/4y/+2q9870//+F//5v/UFaoQlGE3cd2ZtaeJqlBFrVnX6bVh+kWLj+b1zVOKeuutW3eRQqrRFvGzyGTQmIQDmsqJGox11ePFsuVU9glE7nPfBSqw04nFplJKaEorP1c0MM6PP/rxO7//B9+8uvfg5vrj2yfnDikpJXZGo/qsMBLmogRU1Wi9cHM6xXZrtyqKi/6gAcuq1iBiN7Z0atbGynVd5lQkuq/tYg2rc8a+lUO431BOEIBolKlFroCAdzoHtKbAglb0n/bNVibyrlpBBYuvqj4raKnTNNhc7oKrzbrdv3f/ueceXCzcGk/7tY5bPR2OS+ty9Ha73czcdjq0mw0HomZXWSa3nAWeQcvY8rxtZ4HelUx0is8gFF1KkyWEQk276Lg9AUrXbWZPrECt7fb2ZJuxNWF0dUBUj83I2lwsC+u6YssSrNqFWBQz56GJ5O5opG97NJNmDcRPeyxzG8o8mqtrriYr51ksKAYgYhzbWiLFDRDvfc6JUhCHw2pmgZkjFkyg1tXOO910jN1STD2Z02FIJdWsVKn0Ga5rIJ3MzAJZXJf1epvXT5+8+NzDAmdOYUnW0g/baSyHRhSzzBtZy8x1XWZOpyDqrobAWSI2ZtxVx51nmgqkHt1eV9SM/eb8dH32+eOX7l989fmnf/yezFr26kR0SUzVOSTNPEAF0LIgteDe5b3Tp08nc1VblKIM4q5OOgRE+miVTSCpY4pQXZsUyUEpHdbOhSWhLgm2ChNSk6VD65zzgalVilClbVQJLiXNmMYbQxRIttY0mQ0FHMZUU65tSkPP3kBWjsE5uec+NdTRTXWUmcmCWkhStHg3Mfy0ZH3PqLtuzVSyqdbq0uWYIVvBQDaZmjrC1NXMu3OOzlVhyJE2p1Kl71uMEU0NpMEKTsq3v/PGz/IXn3/2ZbFlbqFBExcntdbUpNKUnDW2cIk7o2ZvqqctR1+PT5+cIuAXl12bC5tYGEVUxc0gNVybYRkhKgohXDHJZUk5N6i0Y87YzufI2cwpUcpma2H08AxePffwz33t69/4vX/76IMf/8v33mdfP/34wya1+HFiWpGKotxZZ1X6pEywoHdTxiw2ZhkTyMgYH3eqn+RwXKofdl+0HY5tyYoREYJuYdJRq2cd7aQXXWeWdqprYsgsoZexNFUSAyilKxsMDkOVViVqaiXrLi8jttadkOgItXOWzaHMWpos6/Hi/uHexbZdv/7DN2sMbhDvc5Fr6v3Gg8qBF6Y+PLPoTZipcRSDONdyhpVo9l3PJ0Sy9I57G3O49YOotJnFOPqSLIDIOphdnzcpj4wJTFXboh8P7eJq30YZw9BFuusuw7IUOLBF2plSUl2479uhLbNSRZ2oZDZM8/O+HV1XkxLenm9KsfbjOM/1uDLEvLYZMXde7329Z9JxPUPP1i3LNBu0ud2VdaKZl9NdJ7MiBFSVpatQG6HCBGLHsjTcwQ2zOQdM6AQNaafTk5l16OvcR3AcD+3xx49LnC43tXtBfAxg3M4GswWpwgIrC+raoRW5bWOGIWa4LcthnefTXdtmUirThZn7Tz5476UXXjg/vXGXV//cz//Jmx/6dUaUFihMltDsDl1WVU+Wluozrzz3l37jL7/1xo++8603Pnrvx0Cph5QuITe2CdHyOJQJiLo7JxM17a6LBLWaSVsww53K2QVJSdVQnHLOCGcbqRYhBwzQyiX3zjTaLIWaMqq8pFrPauYbjr6WyQ6QomreNKPUVhxGzDnHyMw87aLp7mwZ1tQ9tDR1haOouoiWKJIToqcsqXlod4a5ZndQgUhFCt30LiaxebriovRuB8a0BVLceZo3VEpbRFuJNOGbf/Tdb/3pu3/w23/yZ3/l52/eePpb/+I3L4+XI6wylbERYaIARDR3zwVaJTW3wvbJpz9818Btm77cWw+XXbyrsCqZ5WUsh4at0TqDlKkZTbRi2HI42tJmoOnmk/sW86wq3ntmNJGVNaOPij/zq7/ya7/2N1966cU//MYflSL7XthUFMrETinRLDRoC0kiq8xSFFlWWolUErB2l5kw7WbMikKdbm/yete27MvNqdnlcpxqqfAmwdJlX2YnFiizTe2rpKuX9zAWRxZ3QxHTqow5gZQOhlZSOsFi3rXoSVO0RkFUiDQV2/0u59bQ+vLMA3L/0TvfG7cnFCmArR6V+1kET1s+1Tgse5dlcTkjYKHSZtkuOSxbuSVW99kxzfoQL0rVPvedu+tUSpgQx/7kyelquT8rUGnN7NBouIxGQtQ8rR/WmVxLC9WAaRmR23561vpc9GY7L3K5eN/yHJnuLSIhCrGRqZ6muMwqU5NKlaLgsJBp6pe0JAHO81YOWZbFFKQYbnlq8PPNaIfDnfUmY1/aisLpdtfeF1SVADiPYb2fB5mOqtWbg11GYtLVij3rNMfFoSFuqX2CaDYz5u2NKnztKuHO1JwgxQpVY9s4i4gBbHgS11Wl7iKyenctrWFqsix+caHm0rjCuE1tRlETs2lbzF/6ytey5Mn14/XYlpfl4tVnx7c+QPdtGwe1ViYlMyNxbnbnjLFF+jby4Weef/Hlz/35v/jLb73+5nf/9Fs/evfdT/Jphq2z+5IDQBaamhgKXYomJJJlBjV0mdKMKpzGRLQTmL1ANRAqFaPOIjmqS198bwadCClqHaZSD7sWsXuUpmRIiCWwc5roAsKKUkVZ5ZJe3qZlneftqJ2z5jilq7VVm3nrhejeTBrHCANgkSjrhhozBMMto8TNVvNEjaTBxJUS0GnWWERVU4eMYI4xmbWs3tScBpaK2OImePz003/9L36TmFfHA6VgsKoCh7K0WpYzRbPLvLi8txOnLea+vfvGd8gSXc0vmthiEEx2gcJoLqx0som20ECDhubkMw9fePGF5z/68Y9tqIlu43y+fQLSvLPUfKnYI+vq4cP/+D/534zZ/pv/+r/5+//gHwRjhoSFakHXcVezJ4wiNEUpUNI9o1PvetREpMSiiqSwHCVSGaJ0GN1FKyVijhjI4Teqiy5Lv1pUfuoBbXWhdS1GRcvSxUwrpbXNbOXiwOzdDUi+cHy4a3v/o7dnRZlpTrIc2tAUXSF3CtRdr0QJBQTt2NdW8sF77415UpE/84t/6Zn7Lw3Mn3z0k7e+/4e9L9uWtBrx+PLw3GLLcYRjDbP0gkoMdu3aIOpQVxXWgHFM8+M9qc1TK/dZi+1j2tqHIs6hIFwEtYq6iVoPtTmziJizd5fKgoxMc3+AVUNo7IemhdBa1S2wjbmaq3uo7LHlObpDC+jrLimBg/lhaZTKrUJFoSAVbNJKgDZXVQji4ULUJY/7rHLdx3DlvMPSh2XPXKTUrHtXGiBsmOCYxRqQXLur2RiJSu1tOSyRpbfbk/1mmyMi1mM/3luUHimwi9K4U4Er3D0tsZgPiixyGtMakWWFythvntzEbo7MpGrouqzrcV0u12WVdogWXaR5aEo/ZMaYdfzM/bmdL565+MxXXv7hTx7Zp7O7QZCbVmW2KMsCSlAssuYYAhirrf1rv/S1r3zptceffPDdN3/8p2/84JMf/HCcP128bFn2yACh5pmNWiInTTeZqEKTu9UGTSdS4GLOimKt5QGYBi0gJXVdKVTZRdMBC8tWIsN3s+LsSQ/LqdnUDlyKYcSo2dR09ZUXW5xMq5VZv5q15ABjRO37GLWnNIc1+nJQx9KosImDWsF3DbJXTioLWqXjbkeLd61mqiVyMFm9n/dtKp3RJMa8SUqzruhOV4BAisImLCp1MRW6MwPiplpThDtBiLqMouWyLuviBzFTm5tq3TxOpEhUjdx9q11qSyv21UTbgsAypzSoGlNNmov7My+9cu/hvQ8+/NjZpSJvNmaamaqKCFligFyFri+/9to//T/9s7/6q7/6/PP3Ymwq0u+2x4iKr5lJzIJJphitUNNIEjqlBL0gRMGLRbkLP8quuiAFJIt+t6xKG5XKgXE75u12tuOyirsuJQ2HjuPeNvFHRqd0XWtmhzdZXSohw+pwPD548dUPH31aJlB3VCIBdayNXdgjg5p30dBgeMGFx96P1q8/erTHULTPvfLqX/v1v1zGm6fx0Qe3/4s/97987/TBp09un7z/Ts1x4hlsvUDhXrNWmF4e1+eWe0dv0vW49H59uqnxuM2d14Kagxe+dKi4Npxvro9X9xz7ZdNMnBhY1GFTiKzVj2JYcxfJLXcnB0PM+inn0owWc2+msg00S2RJj7lX4OALi00aTVIRqimgqyaQyNja8QDHVpC9Zux9WWblhSxnHafcVywRI4ZOKUlom7fzdLUeIaoozHHVfcYs6AzCZB/bnYBRYAiseaRXUt2NReZ2GvteD33p5vN8uisCQtBNIOWQManFRVMUBeiE3KWrqFcOCHTRqiSleDFzQRXnqMjT6Wacbjbi/REov5De76/Lwa21dnl5dTzck566n7WfGRevPscr8ye7mGRSAFNChNl0NrSYMtauj29v5s31s5/97D5u01Rbe+mVLzz78iu/9Be//s4PfvT9b3znh++8dX197cAQ0BcBOCEgGisqyQRdxBSS4a60C1S2NqdOCXAaPO9lmyJpjJSIM1VqmiUG0G00iVm6l3TQtSLPiguyUZgJtGbmoOzMXFGglsqQnm1fG4cfxU5WY9sxZ+25K9ITvfHQzeQuq8coKhPu7EQNsDJYLLMsk2Iu9tyz92zDeZ/akDOTsUcJjnSjrCCpUU1RegCjahWWZJYphJRIdS7ay6AzK0oG/EqXRS9NZOkQadxzSKfk4d5ylxVSwipjnhlxVpPA0u9TWmIs0jaLu3WH2/lG28PWF27j9jyu97NA3EwVZj+NbCOnyfj00aN/8I/+XrL+n//1f+WVzfVcJiSQpKr0QlJFy1BaTBpRTIlirOklvRBZkySkpThKIECLBACNUEH6nVFEPVpmZaFOpxQVnDeTp3XPww4hcINqjyoMcaKZ4s7oLjoLN9enJjRDmFcOEgpX7SlSGgIjWYyKJERlimoan479dt/UFunrB0+e/s+/+zt//+/9xre/8TuvvXbfWnupPzw8+uDxh++wUPMsy1UehcCxXR0uD+vlg2p9yC3Gbrtsjz8h4+imbLbIGDvDfWzjal0GtgdX98cMOSzn7VxlKDsujIBVa11L4nS+nrR2cWjWxr71Q0dkNGbtm/eC10Rfl8g5WJbni4se5MfjrNSmvR3l6fl0XI5WWFsf2J+eTrK2Lrkb2XQtqvbG1oxPb59UjHW5mKZHv3ri00yPkZzp/dK7F2ku55jn69NqfWo1hxas9apaTXeg21Gy7YypcWVep/Ont7djjvuX969ziEvzdRvXLUtdVLQLa85FFaIiMvZ075QSRldMaQRcXBMkAqQWqtzK3R2HB+vFGQlhjNh31szT9UfXn87TyP20+eLH9Xh5ufi9qxeee+7zn//8+SvvvffRd5BSRbbRSzv7JKv7LCBp5W2z09Mb/zyrjllVbUypLnZV7Yufe/XLL732/pMP3n7r7Te++877P/rgZt+HH5vvVrt4FXWpdkaV5zK17lKPoKktMAD0PGDJCBS1yYi5sxjeTKyiKcrGDcrTZgFKLbDgrVImRFuViDRoKyFrilCMJkOyU01yVZuuar4k3Syhdh414xobbp7avsLsyaFb89VMAGJBeulssvWmmjNjnkuGSaddPPNwf3qdj0scRdyepLAcWxM3+mxiWzkKXjlRQp0iougihpK7jLSohKOZ8Axg8aW1JQ/WrA/dd4yn59Ok6XJx5xuSORVdVBb1fY5C3Ow866PWFzUd3ZHt8q7ueeSytHsP2o8//ngbp6JZb+5tcVfFLI0tR9/j8aN/+o//6ee+8Pk//dM/QUw33XKUCmCJlCqTSEuEFJZCdE1hn5zUNGiWROWd6chdIVBpOat0NDHQ7iJqRSZRASjVaRBJp00IUANiN4/HY91ab60vyuFeZhdibSJx0Kw1GBQ9lFzJ8aM6XuNTFgRiws3TXRQzUgtdsy+gZ4ZTVj9nxXgc3Fuz3psKfvCDd/6H//5//OY3v/3qF1/96i/+vH780Q+/+QcAVC4SNUV788PxeLx4eGgXcb754MffvX36VBKiirnLop9Q4evS+qI+Wnia3+yR1o6LLZb7vOXlsU51z8Q3Ka2MfaKXTJjuOz1CimoeYVowkdWwZ6x9nRm3nAKuqtq0Sppai7HVoEDox8MFM9wdWabrglZSM3fCmbMUIbRMq7p3dSk5rTSjQF2OEnsFeusCjVDdx3bZuq8rrBVxEXWa22h6pc1m1c22qOWMRLlCqm4/ebw9uV4OSz+0WRuazhyJrUmBdYpRzdQA1jRosg+BLIMipiolahI6garhAmhVhReq5mQRTiCZNGi5i+NQbCH9uG17v8Im85P33jvZ9p6t3i7epD1zvLTccH81kXGzuXiNgTs7larsXjkY2bI++fgJYRhzdd+QU2kmRvektfby8eUXP/vZX/7FX3jvh+9+980ffP+NH10/fTrA5U6WkHChwqCiq3JGyyz0xJRKTGOFiNXadg4FHwhO3aeqltcck9wSdys2KXkqUtGqdauU0601E3TBrGraelWHljVWzmbpvpY1OZ6QYdObtVoop4V7krXtlVMS++1ZlWqre/clzbmNuXbp1RJGweI51UUY2+xtlbZUZuRt5am3Y29XxTtsIqyoSngVWma5liKVdLGpdQSZMjNl1EFQDTiY2mwmu9FqztubOU+tLd5Uqu4q50x2RCIdJosnSmLUtu2UZCyrLWYypGaX6137xdVpvD7mtH6xHJfVTNySZdloYDzVrPPp8Z9887dpiy09KjNhggSAcBGDI70AE6UqgaVUdBm4U5RIQUlJwdGZOmy4iogpbQk7mQTEIT0AMkGZMDGWlXJUGg0KEwq1bmu//bRrT2Vbo7VlijWVLtDZXOMp53N9uWftfNJkQY19oSqyxHv7acIBEE4pKKqGGysn5rYe7h1LvXVU/f63v1PL+r233n3zzbc8Y3E7tvWcQ73RyrXnOSfPt+ePHn/0ozmnoxds4qQS2LSsUHOeMfuxHx94W9G5wA4c1wdfOGcMHo9tMRs7jo7d4uZ69M57F1e7hpunlDNzu/HDIVVOWYpWm/SlEzXPZ2+tmo19djFdW9twOp2bXkIyJQZYaElaF/WOsnHaHh6X7XTTl2WyzggLLKbNzFxPMTACFGmeEVCJMS561/NYzLoYKcOhoVehkqmtGbVEZqSaaMnjp+eacXxwZU5hISQzW9etQqN80UqtWWp1510SKEQa0auoIoKqNNo5lTWgEwQzr8sQdJUynnKvGQsUpUmd2Eeexn5GcCBK4um7ny4er379YZE3p5ub066S9vCQ53NbREYHQGaN1Eq1YsPsYazt0acQs4POfR8VkpJNe2uX/XCeyZz3rY1nzR5cfubLP/vLHz9543uvf+v179z8+HqElIovMZNOiyzA2AwV8dNjmVJwWJlT4EBPhAYRCgltKF4KWcwUCy9Dyu6UCE1TFROWQdI8ocIIMaUrFG6MAjig01aR6FCtkvXC57Iiq3uNW8vGwBznud8O2bHJsa9Lu8ziBGEKSAcXhJvsOa+u7g+DRJ7389KWw3qJ1BIV3GW0W4MRUaLSUjm1moojPWVfBelWsq9UI8QarF/5sroM0evT7e312d2XpaneVWPplEL65WxnjXAB5mKLq8xgIXVkyv4pd1gb+5ObmxOC+17N+nKx6rqqaCmE0VWRuLBnlFM4YxwjDGkNqpKQCvWWWErB9afLzogiQqNAhRolKtzgCiZLtTABBx1Ig0rWVEJgJS7q3WK/K9W666wWglAhZ1CREA1TU6gCRTmdbqA3rXwql/Xg/bDYwXvZMr2DLABNj818tyQDqYWS7hMFRIIS6EA3hAQ4t9Ptg8PB1/a01b32TJt+5mUmL6+w9Pnxp59k7Rf3H/hiFlSTm08/ePrkUVaIqAMvvPIaDsbG65v5yQ/fUfz/afqTmN229L4Pe7q1dvO+79edvm5ft+6tWw2LxSpKpkJGgkVLgSQLsgMrSBzISRTYMJA4gwwCI4g9MTJM4EECB4Yd2IPEsRU5HliBJFsWJXYlUaLIIll16/bn3tOf83Vvs/deaz1NBh8z38OFjfU86////QI81CvgXgQdWsVgDoZgZnFvyTQFl+CpmpusxoS6xLxERCFGQmx2PHZLUgPSsJH7mK1Vd3Qecg3fzYcuACPtWztLuR+6xUqEpz5xYA90Ne8bBdkqeRpSbqqrNOpN9prYACpyRICWIIAKuUPiBh5lMV53GiAk4RYSimzqx926zdPsdXGXTEgUEDoddhdTGnseeo8WZgxcACujECH2AQhGiQBLlYFQbjx7DggUJpJmrcjuYeYFMRyW1jSCSwsMIuUu4VImoxBnLwHQmrtiNdeOMGeeG1THIQHOrs/P3/nu+2nVPX387DwqjAySgMLIEBgDCQPMLYqiBdAgsvzBF1+tfjtjt6+Lcazv3HohtjpZvfG1N3Lf11iiVAoerV+l4fS1/va94+/88DvnXzz/o5989NVXj64PL4BZIEOgk4OjCBkAmCM6mBCgt4UzYsfLEgC+IfSA2ndhdQwv5FrCjQxbZrNaXQIiZRj4hmPMTbFpRkaiBk3cATKzoluAICMII6a0eGBwn/kGyAuCAsa69LnOxXwxm/cFCIxlNRClRNIzUdhew4JozOOG8stl65T78TRxihThEeDVoENOQApk4JkSKFMwAgUTY4oQB2bJ4BbIwzDmvOoBlEo02G63TtFxf4MAuDkCwBaUvvb2m4fYvTpcufG0m5iIxTOTG4KrALuB1f0nv/uPtR3AaRiHzNDMKjoDJxCLSpiQBvM9C3W8JqMoBlYTukMbvQGxphSKZEbCSs6AzaEADO6MsaABiTtASINAqDk8+xAMSIGkC4SYCLiiLmEoGODqFjfviOxC6AaBDEBhxskNKCLMIyQCwc3UokwHL1uu67tqvOpeHnYGxNIDZlRPDJA4KVYXbQAAgiwAEY0berMRB0NtrV3wftWjEo6QOBPlDoO7Hq/PvzzsD4IJFfp+6MLn5TBfvxIn4rVafe973/zlf/5XDdOL7fQ7/+THR5e7eXdO6OaV1GU51BX2SFWDyrIMfebaou+bUM94sMQiRAsqt1CdwGnJxFb9OnU3zraB1Js6eCJxM5JA5lMcoRXRGzuPpQTA/Xa7Tf2AgKG+wTwzSKLUSiT2CkEEmdiruDGhBQFIKyUPvfcQhPNck9HxerO4EksgNCiOChQZ+WAtes4QtQQGcuD+8jqWqd+sI2MDhQCGFPjHEgFXswA16ADNK6BhIBNpeA8DGzi0JdQYEMDdKJwdiATJd7vtZrUm9QJQApuDNWvuWEPbngiACAAc3DEuHz29+Olndn29Hvj8kyu3w7e++/Zbx9JKO2SRPusSWKq5Y0AgQIIWHIBUAzrqLlv6vSeA+Sz3Hhp/eHGcoRznp5sn+fUTeW3s75xgz10lal4Iex57Gm9/Z/ONb7355MmrTz757OPPHr58djW3SoynliePgtCj9ZEUeYYZkg6JmjsmR4uMnTGohAYeinmgJFCsjsYO1gb3QNeSigBFEoCbQCYPmBVCAlytEEhQj0TuCkmZGjBJ04zhafCNYe9QAbTv+iFJB0YWvljVBa1Mu+op82qdE/fSSxhVzJFrVW0xHh1hSm6MYSknNacUoOBqgQRObL1oQEwoFRJxiAeTk7BoVBk4uowQSjFjO1xNNMOQV8I5E1OAQ6jqMSTa5O6Nk6uPLkdIlWDBQgwQ6AGNEDjCGBQH8ajNonJKtRaCSF1412fM7FQlG4opcOo4QIAipyIaAKEuFTr3FqxIRk6gCm4J+8BoCEglBUcQgDuCiyNENPEOApvUGxQMEaTKCINRNS3Ef5wCBMdAciLUSIQIN9gv4BteIlCAA2JAA09GiGAMERVauXy8vXwMBGAoAvkm1F7RoXMBpMYqZr0BglnChdCD1ST3o6xY5+vDcqjeVl2eiSIPDcGW6fLJFlC5G0c5hjwukapOU92rS5KEDMT5qiyX0+V7b3/wRx99dnrUTQJI7haEQsqSscuSFzMj77ue2dzZIrKF1yWJBggoJgpTMdQeo0t0VUMwSUQsyjclTzfS6CEhiSeBZtPei9wcQ3bEEFgfbdS0cWjmrutR6/Zgp0NyMOijNK0FiCB3navW1pAw587UATEIB+yxo4pevK1EHM3cBEhNMWzeTrlfd6uOaLJGF9eXGG29XhGhgqab+HgAuwkyNAlrCo7JIlokVDRzzhUzZTC4qchAoDh5AzdEx6Mj6YZEnB68cdIqktp0KNfXy7SYC7aIUpaI8EDiMC0S+PnPPnn62efZ9WxDb75+urvYZ4cHd47n8A82r73sls9fPIc5omarlWAONEUKxAQ8Y83EC9qrO9xx4G5/MmX1SJblQu3y1eHhiwLOqwy3j4d37g4P1sPpWsIbo2Nmkrdff/Pt17/+/R9effzlFx999PGLr57utguVuMPrPczBLg6ZRldJGh5GRAbqECkQcJkCm2Rio7AeyIK9hmgml8b7giWg4+YpkXo0A6HWoHkooQCiZcSIFGFSFAk0BNFYexV2JO4a5oEtkUUFRtkwNQlpQq0U4GpgV4cltKSUVsy7/knYYXc1dkPOfQLAZOwOhI3Y3TSTM4I55sQtgtXdEnAJNEgBmpNwoIx9P+ZgdwwCPsxw2C99HpSdElAn1cyNMsmDk6Mr6H7/x5/2vty6s3721YW5cmCfRnXH0JtAAiRWhzzQcd7sjOCA82HxaZu63oc7Y+6ErHl0RAN7bUmYOtYKthLEPCyI2EEGjFahuctAXsxLc3dCIQCMqtBFlwMLWBAQCiOFNyAET+Is3gIscGloTuBIBJiQAJMGAJFHCjAgJABkgUAJC2x2o4sM13BgFrdATyzxx4rTIIAsDBlyRBCpJ1PFbAkoESQHINEIoU6gAaelLatONulku7uqdd62OQAItgaWQrp+zJs+IHJQwylRqVhrmYVzcMe9DcRfffrVj/13zn5l9ek//dF2uSrzwZMBJzQECPwb/9XfTZycwCMSBAgJcZmWFDzkvuWpo4Slm8oCQwJCD825c8dSak4WASnlWnXZL+t+w+iNHLvOp/by6nJ1+0gYbdEkqZfEhI4xt3J1fXnvzj1JUp1DlYlmtZQJyoIpUZADOjhVlT6rWkdiDNkghBsFhVuEOPpSgIGFY5lCxDCTKoBvL/fm1g2SKVl4FgmyP+4yknjE3CKL7+d9rXoi3EQRhSIjg7tzSqYNSaOGBLlH9bpe94+/+nxzuvnut77++KvPT+7c7VCWadYa6PL4+dNtqQjSqmkzEk5C5dXl049+FjoNGxltee+dd49v319My+Hqcr8s15c9b6YLOTwvfC3lckmmzVWjengJC7Sc5TK3s1/55vFZX1rrrjme7/HgubikJH3fGRIlV3Wyba90//T46/fWb93FtYRaWSxRb4w1mpb5xbPHf/jRp5/+9MvD5bzzBShSbTXIGVKDuUVBZaBk1jNpbLcoZiyhFIUAI8CMvIWgA7WZVdXJiIANMTGNmJUaMPaYg8gEySCTVAk1SCiNFcE6E3JwRCMG9xzgvqhFgs6bGru7OkY4l6U2m6pqjQNjDgvBdHZ0JzJRBylu4Cc0A8+Bbt4zhTYByw5LWLiLokpq7oKKBiJDXvVIjj2Smyz+4uWLQFaWBLjpeuxprxWDTrp1J/LV9fb0ztm/8+/8b99/773/8P/yH/+N/+L/wSxH43G0KNY4gMIdw9GHdU4sszJb3l1tp3pFAJnG1IkMbJIGwD6wpQ6ym2oU8M6IBQ+KogJ5Mec/3szM0zQpuCF4RAZSCAF08woe7EJBQU3hpjyOzogLYEPKBdQBEISAKBxQkgtENKiYPIw8DAMohBEFTAFRxGBS5AiRsBt2e3W3KACEQl3uJTKDhEeJxkzMORAiapQiJMidZ/DWCHPVpZOxJ3HVCGvWArGFN44+j4KCUZNpDYukA1OtfnkwBOyGNQ8xdFlm9Kqc+n09aCzGbgaCnZclYMH//G//bS6RIiVOEK45u9uNkidCjE2sYU0WWmGSJA2BpBtzDq2EQqkD98VqbiEAgTYtE+VulL5MVTrKWeYAqcSZ/IbUhlbmmTAjeT+ORRXcrFnXJa11Phxu33twqErkpGZ0o6gEdjilbtHmQs0WN+Chq6qi3uXkWMPDQS73121/6KnbrAYLDWAMzA4uMHmhgIEzERQDa9OyzNtpepCHuSPk5M3cCsCSB+pyD8phNQLV/OjW6VePHl08ufzlP/uns7UXj76489bdxHT16hUEcgh1/YdfPLq6vAxwUMvIbV9ePnnckd67e/K9X/z+O9/81vnz69c/+OZmPXz4D35r+vITavTJ44e7A51/qrRTu3Q6WPNQKogNqzbSuQcdUN44e/2Dr7UBkSWp0RS7Q7HLpdvWXoNK5pS6jvucl+pVp7KR/puv3f7uO/m4r5MTCyEttVRstZb5MD19+vyf/fgnXz2/XHaXAVoY0oLq4aKgIo6B6G3rAmYpzMFbIDaBUCddMBxjdCOnFtC0GTAQWx8pBJxo8C6FIBmHQaKJLSt1abhKwQYYkTi3cMMIaAkDgwI4WebACkVjSS248RJQO2WOqV7Oi7uyzb7mLH32jZykhIyA1CIfDM1bhz5UIHUlbAnCljAL6MxNEoBBtxpBaIU8ohTTy/OXPs06dlP4CfYDyCJ1JpXUr/v1xXb39je+8W/92/+bq2cH9ek73/7Gz37yk3////R/tqXp7DorEDt5UBl6Ph76qOEVAbr9Mp3vL5Cgz5mH3As3DVBChyV57vAYVgaD5hgQVJdGlXQEzQmqytKArIWqRpiqBpvaRKjsyWEIaykWBa6RJYDQWpgHMEnc8PeR6CYIHMrMHXQQ4aBIUREDHcH5pjZsgcjEYjSTUG1I7syCLTSsQSHkYVhjsAMiRM4QhNPsXRrdouGMrEkTWxeCjU2MEnpjVjVs1hMnxACYXCtpz71ZIFpOcOhymK4cAODq6lV4SO6Ojk8TkwQriGNXmmqYZBn6vD9/VXaXKaPYsqCiOvAqFYCmZeg78AiP0DYKKziI90w8w/78Mh+fMIBrxeRAEWZNG2cEClAyi45Hb27o0GHRWrE5oitkkNQNS1mMIOcOInUCVg+cekQcs5SpCchKhsP+GrvB1RgwCUOzoCRMdWnBoGGBoAlcWwqqqlmYAYBld5htuzvq8yCdqRq7IeZAYlJTBHJCQ21LNcrChB5mtdIYzrUWosbJB/B1H0dnA9iwPywKGCjvv/+tu/ffeP7l41/4wfevX766ePVid71/7cHdV7UJyfHpUSNsy2zT9b17J08fP9u+vCLHdtjnlH74J3/p9Z/73sPHzw5XW3j06P33v3HvnTf+6U9+f3fRps36g3/u53796Y+4hEsDAVBiEdPW7I+5awjY9i9L2ZBnCKAEnkWGTLd7qjrvFn9puj0MM3TX3OfNejxdlXn+nc+f/OTJ+M03bn/3te7seDHrh4xBOUmHcuuD43e/fv+ry+efffLi808+e/Tq4kYHDnwgRHYo5j2twFRZKpRgNA+IcCagXtQkKMACnSMRMJrOSVVNPHHgAjpZrIEQQFWJGIgUG5hkzQmsShVJ4sQNEt2U2TmYzYFC1pyR3bOQQiAy29HmREaMRsYN21LLtGgYCI2DdCBkXe6dSQKIUUGqSEpYzUIjOSRKIf1r33gt9mU7X0NYeFvavC/L2HXNYeSBAJobAWanqL6P+eRs9b/4n/1Pj47uXj75WBW/evTy7//Xv162kMY1wMHECIACLQQklRoZuRea2rTYgZG6foQclDuIyEmup4KOGgsY7QOZ003oZkiDRL9PDVI9ahFI1nWNicx6dPBY2qHDBOoWCm4AHJEC4IbXnjAFVo8kkTUM4AZJi4xeyUmoQKhbghADwuYsiATsGu4pcbPswZIdQTAsLCAC3b0iYko5UTKHBsoMJhGRsOsRAyKACVBQ2MwgUheCLDUW8EaYnbBgjRwAxC2laBmsMntKBcOCWb15yRmyDKUuVlV30ffJO8RICWEck1PCPh+2l/P+JRF14y1Z1U4dUu6iagJLHXYILSysRTjokEAUzKFRlrFbDf3GQaq1SZc1tATizsnoUA7cS5j1kso8ASKJLKBonnYLMl9uF6R0vt1tTo+PTjaJAiM6wgWAQKqhZR4MaEw1ApkAiQzbUkfiuVbcrAuouiUHQRFwB0KmnFIpSgzT5bYc5tVm4AS1GpEIcphr1FnYwghYHWYADDKE5qZuEVC5Qjgj9FkG1mnvk6ReQbQw5dv377//zW+llE93W3BUx/Hs9te/9b1/9o9/c9PzejVsr6cI//Djn14++vytB3fHjpZVXl7UjfTp7Oh73/8+jMNBA9FO1vLJj//Zi8ePV+vxqpNHh+ff+sGvfPCn/vnf+Tsf2vYyxD01QnGoJARZzFUCyLFZpkABdeQyN3ALJkxsmfHuqrtTeenr+TQ/m9LUum3fE6/6zVCl/P6jRz/7cvUL7xx/7y3lyCIQw67uxk1/a7zbrW69/dq3lx/84hdfPfv9n3z0+PGXhysmCOalclCgICNjC2dkDLSARIYQQGJumVFRqrJ0mHDpkaaAHB2jHtgU6QAmQKQ8RDIBM3MO88iMneZqwUxI4ejJA8kmihmgBzTq0NuA3EMaPQOBYQlGIhxPwHSstcxay7TMu53NbUxZ+pH7NUqv2SI8o2cLR5JuzNVbA6VudXpksF0OWlh3pldXlzx2BZBQMlLDpmq9iFQIMB5zU/h3/91//1f/wp/7t/7X/+Ppcv+//9/9H7768snd1amX+YqaknKIAB3J0FlyDAsQ5nl3Oc3bIW9WkCPJYSo/+FO/+Mv//T/9sx//5OPf+/STxx8zQQsveqXamvT9YRw6XJ91xRoQJYcWjsxE0nGgesINUkSGAnWvB3FwJbKWXAk5QoCyYEA0ROwSN1UEQkQkBhcBwYCELmEM1iK5kpPcKFQ5XCAcsDbLkFNOxZq5BkRP/Ur6SljRcwRj9kIBiBlDgwgIOPQmdcfEAJoAESgAmbEBExITsVfqch+qgCCYKFKJOqCJNbWoyolWyh5eluXgNgFgz2tZYt8OjWyupTXL0nXjcaQjgfXQhSAAo4eSq87tADLOpeUMZi04ZclpMulwXicSb8s05q7j0VGXqmtMGKGUWmUKh6SafZn2nNbT3I7G7tade7t57oh251d3h2Nz211f3D6+05pqCudanDmIagMWjcgs5bCEJADOJFX8sOxx0jSkMtUECYPZ3UN3Wo5Wqwzx7PJlXert4xODauYJ0Uw5aEN5gWCIorqiJOYqGJhbkBAhTQgYtWVB6VJO3ubmiWQcA2RXlrv3v/bt73wnANx1tR6/+72fm7SS8xtvvPn86WdfPPyj9Sp3sjKsHts33zzuUd3o1t0Tqnb9dNfdu3X61uswrC8vL792//7jh588ff7sb/3Xf8sb37pz+zvffP/nvv/9kzt33nzvrc8/u8h9Ku6wB8EbrG0EY+/oikszEDi0ncuGxbOCG2BAuENzS2wd5ddP4PVjv671SWmXbZp3wnR7HI8hXf7uo6dfvrrzS9+q948E2v0HZ00xqnAyZx/z7Q9unXzju2+ev3j+8YcPP/zxT6cLY/dgNAiPApICkrQQcY1qnoTYsYBHF3mRZqgNkCGgFwNjj05JWTXAWNaRaoCZhgsCVWkO2DkJaIqYCBrFsYIhqBsFW0ZA7HTloUrsmLPj6NBhW5gMgXOfMjGM/TrSfJht0jDfH2Lb5mHNXZf7rlNnoBSjk8/cLKFhKbXdXh9NL88P4LtpboQ9i0DyQGOPgECqkXJiCHUhy5YH+e2/9/e3T7/86qtPL853d+/f7bRcvdoBilCoxzgmGbq6WHasGV7My+VSpetSl9Fhr3Lv9dt/7a//z3/600e/9C/8Cz/3wx9ePHv5//6bf/Py+qkmZI3UyqwVavS+0SiHTjZ51TkEtQC0yDUBCXfBCAEN1zkniG2rXDXbotrC9Oa/r80MmZTCoqE1QgciiKQVMIw4IQSCgToSQ0rOoMBpNFoU1JwLkbiaLh5GmCRtQJKSAgEHkVNzVkECNzBypuirGaJShBpFFBZFCyUj8F74ABhAwBDeArFIeF2OTSDBAspIHaaJ3LB1aWgteZ2sedv6AQ/ggGEOQdyth7O+6zCxg0uViKX0LJCkUEhjToCut9ari2mLEKC2Xh9b0DQX2qzq4itO2BZotIAHcgy0X3YkueMULTScuTsex4Xo1mbl+2mLPotId7RZrZgkWYml1FqsVJtB1rnBnHnFAcRpKsugCmHNHFLXd32zskkdsViDeihG2g15mbcWuj46tkO5ePFSet8cdwDmkS3UkgoI1KhtKqADEAtUNghr4RTERjotbWqgkSOSL6aquZtUi/nocnx0597t8faD129CDYFQ1BBCIAD14vrFsr0MK3U/X22fP/ricxzQV7wYIYt7jMcnly9KMcDV+uTkOBa9enn+9OFjm+rds5NhdbTenL317Q9uv/6mE73zC9/5yd/6x6fjejkUZCRM5A4SwBHgTqA+ffDem1+9+uLp+b4ERBpUjcPFAiOZ45IQGTERHXd4PM5zjX1Lzw/L7tUJnhylFTzz53/3D9IP37r3/TeaOxEDGQMn6cwUwkNyvnP3/t27v/jzP//FR5/95JPPHn35bJ6X3qgDWDw0KxGGD+ySAlQCw6yFEN3clMxuZjrnyFYDsCFSBHlGt0Xjph0QlmBmck+D4U0lCA0bxYIRScTwBk0VwA3CSCKguBP2CJDC2DOB47gu2noByTTGuljVsuhU6uGq7NOU0iYP2PWQU4QDsYs5NN0v+TQxcMw8Hzytj5AwudyYfxGyQ5iDEyFimytSv2ZE9n/6h7/PLMPxuK91KXtKzg1HZGTokizoLBKIC9l2vzCtJHOlwziuksNf/it/ealw92tvHPSgkgrg1VQ4jVZnRhQG5EgB8+Gg7jpPuGrgGN0wcK5YDJGxK8INQnhItRlph71xBU/JzEut0AIiQgHdzIEk7EaX6wwEoMThxDOkoAByJsdQcEfAJBmCQz1jQgJr6u4AKeV1ymioSMHE3JgqCYKBJQYJDCRVk4ziIBbNwIUN5xWPDIgIXqZOsjcD1sYJrQNtRK3olIC5agusQp5FSxFIeTiicVgzLW0+tMjUsbkGkHQ5OjQvticRiaVC9dRna86ZyLHMB+RQb6fdOjtWC1iMcw+A2nSTN6AFhd3+mAhdljnltMyzWeWUgQSByn7Oq2x1DgkMyJRS4kItQJSQqGcPwhiFp9L6UUyLK+11l1dj/HFMMXrmME0RzLxzG8bN6RGh2dXh8tWLp994921sfv7yOvd5XCdTNQ8kEOQQOizTJiXTIODqjh4R6uGA6Fqqtg6EV8Ph6mKBzoLa3LCVMpfdXH7hB3/ym+9/YI7INwp1cAPwNBuM4Eu7ePns0+nVs1RrRQtOhL1A34NCSkYZyKY6nb5Bt99+K3c49jSMfcWe6Zuf/ewzNZvK/LXX11//9vup6yD8ne+8k07Gdg2Ssi5Fw4kIAJ1CrUXgVExB/vL/5F/5+OOv/tlv/7OHT5513WpIEgEpGCH3EWQObuwWKH1HfrzS+wNe1u35YvPuNLpbwY9/9vnZm2fpeKVRc854KKQlYaIQw8y8cdHxDO/9ymsf/PD7Dx998eHPPnv80eeXTy46ZOzQMXJzB1QLAAkEFySIuOESWBghc9Wm1OUUQZ6iARMKI6oTY8Gm1gDTDOyQCYCsDc6eEUFImYkqGZgHkbEjtsEdBGaTIOyD+5pUBMIHxERNM1p0BClWA2+aNN9Ny+4wLdPhcIDUrcZNn3M/3JgTS/UckHV5+moE8eAGkRmZGYDcnDAAnALYgU0JJk3o0g/9kDBa82I1hzB0gLWB9+sVs0jVAPcgOxQ1y6nnQOJ1Wdqf+bN/Zt7W3/y1X3/z3XdPbt978LX7f+M//c9CCwslJyQHJmaZ3JhCHDJ3S9XWaqoIos4Kw9B1aEQewRScKAH2wQV1MUiSrMteD4TYd+hel9LcHJw7ZEED4EpYsYZXIQKPRBDogWGACGJh2EAiBzCAmRVAJh44DSCaA28cX+bOgUzGbAHIAk0rZDAzdkiSEWFxIWMJdvHFNYVwEBPdRFfDIgAMvPKsjuwQ3BdKQEL9LE65AYlpagF9wpQDu3BttpgCCBKm1Dd3CQPucgUXEkB3ayMPVUwjCMgoaEgzGEO4tixUyrVwx5z3y74jIXN0BCYB2M3zSDmnLguxRdElOfgNhBXc3TOBLjqXSVI/5I4GXKbrq8uLB6s39nYQ6dbjarfb7fbteHM0dNJqc2uBah1F4O7lSzLr12O3Wr/25jvb62m/3UvK6+NBPSSvtChBRLgdbGCO2iRyEWjm0tzNHBAcdJm7JK+ePb6atid3Ni3Q1Nu8RMRqffwn/9Sf+eYH35LEutRwc0/eHJDRQby9fPrw/PzTp48+Xg2k6ejF9fliyzBmYm1CIISA7rC5c++D77z95rvvfvbop//gv/u1d45vrTfd197/+rsfvLvU3fHRrfd+6U/ce/ddcEK11Wa4+/UHz/7x5zmn6Bp4YOUwdAByCvMBhz/40Sd/9q/+i29/s//617/x5WePfud3f+/J46865houoIkA+lzM1GsASySlyDn399Z+e1N29vLgqwmw6fbFxYPbp3NxD662JCbqUoAyogYCQCIx4OOj0/ffS6+99mD/Sz/46vPHH//4wxePHk9zUfClwyWCNdONI90RhUBBgMIUwgICBJ0EDbvMixYCFBnFEF1A1IlMGzqA84ItRLMLF4EAFQUiNcaA5IzMDRdxUAxjZoPEZGKCLoYSHUGnbMWgIFNKjLHmYdUfLWXa6bKoXVztWOaViFAG0YutPXp5FYrHq+P5xntKrObVlJAFiQRaKx6YOMxhsQNoSy7KHBrIVuHm7cII+NDTVG0AXCXU4np9GMacE1MxxJ67zfd+4Yfbw9X6eL0/bGU1LPM1pyBMoG3oxxKlArBzbkSoIN4oHJInd7CDOxW1aal4tVpvhmGgxJ4gAlJCM0oBDgzQJTJGFCRdKEFeaoOb2pRra14o/f+LatNgKYEU8yAABCK3WAQCAjBxVYsw4SHnPjEwMBmERnA4WEvVMbl37m2GibFnJoBgxwoRqeNmjnbwRbl37BNgkBIqlopExFKaB3IIVqTsHm7grK4d5VVkU6+obV7ApXl4NEeDjiUIjYIMonC49Hk41Jn7XFUBlcm1QsaM7Jd1Okoyt2Ub5cHZMRdwAxAHJAM6Ohmbqu6rlSDpSCgwTUtxZlPoU97kvkLLwNUCTcWRbi7Ao7gqIDYj5P7O3TuuOnTDxeVuHPtNn5XHUZJCXG13TFG89Yon66MqwH3OiSF4Puh+29KwwhxLKwi+xCI8CIlqAS3NqIm4LaGIxRKDJ5gP8/b8fJPyhz/75MlnX569du/u6b1OGX1KTPfffvfBW+/eunNfHXWp4d4s4AatCT7vt9PVs5/++B+V8uLO3ZO7d16/2h0uP5q2L84JUk2NsHezepjeefvdb//gv2dTe/ns/Ozk7J2/8BdPuv7l+aOr3dUH77z16U/+4OT2yXfee1+0Xp6/uN5t33n366+/98bz333IHijgxd2JgskgIoh43acv/uCTf/Bf/b13fu4tovjhD7//jQ/e++rRFw8/++yzj78wWFYnq8QCNUc4QlxvSxQ3jwqec6bb4+E2xMTlCi/q9qQeVnI0G/EYxm6JKVwATB1zRgUMQPehy12+fXoaX7t97xc++ObzZ08//fCTjz778vmry740DJsliBwtAJ2RPRkDsmcGMsxMSmAR1UcLi84AUW78qhDhHUAty8JKLAjozYw0dR7aFUwMQcgGCLIwBqRwAoXKTkN0ToigYqaQnTGCSZLkaOZsypZQx44AkpilBtN0mKbJ6rLbl6cPPw9vD47vYRbKSwZAR6CEyOjRea7RsOdis5I6BqXwWpGFPBIBcD8rMKiQHOehNlR1wAjSy91lZOrXAyzWdSsiKKr/4X/wf/vuz3/3/W98/fb92wzwo9/6rS8+/zTR4J6bmhKicDKOMAqZ2BsjqREBdC4hCpQ8lqVM51NmGccVb0buMBgIpSczzNp4JUNEBWTMfTAlngGiQwgPmEkc2AMQCwoQWABx5xEdE7p7eALMPV/bbNYAKUnX5yykCVIxcxZm75DARF0TmtcEsHbi2lq6QS8hYDMxr53gYFRnwBRkgRkqZ5eW3IBIMAsXRSVKZIyO4ZQoQT6YAkMvKTetGA5O4YnAAYxDo4KpQBuQpafk1EIbd+yAVXEz5iwdRg0OoiygyaLtlmhhwjlldMVwDTAklq7MhdGJ8rgmotqsRPQHqoyJHbktY6Ym6Gao1DOSdA2hFu3GFN0wpqNi2mo9Wa3KNHe5W42p7heFOLp7ZqWsCPuIII7kmbu62OOnX2bz7miFCGyKDiCBidwKmpN7YuHAcNcuJi0EcX29e/X86csvn54/ew5hAQi555TRoM51HOT9b72/ufMgr0+04Xa/jL3kYCQUNB7l6uWLD3/3t9nmuru69/rrt792R0G79dCtjq1cLHXyIZPa2erO2A3f+/k/dXJyhke+P7zaX756/b2fi67bdESvzq/2j/78n/vVo6M7rBXqzF5g2WI9vPHegz9cEZQgJWgEhkiE6BaCgGix6ob/8j/5m//SX//zd+4ef1GMx6Oj9em777311lt3vnz41Yvtq3HNvVM27oej86Nycbnb1qKQoTrBwoKeZby/crSHn3507/4bQ3+ciZnXbTHNolgQsjYjjgQOyBYBrhwU5sN6/eY3vvnm17/x/curz3/62Ue//5Mnz56YGoaCsAc4uxECItRAkEbBFD2yWTAIM7BBkHrHYjlAAcE7rgjcgBwUSxNpbAQQYQiUNDd2hzYCWIBRl6ItWc3BDziKaAZiE3MmCWJ3J0F2SJiACNC6WCVXSMBDOjoFuyqHpqW1ZDzPC0myCAxOFhmDURCpJ8oABq2SOGc3tFI9ibqoa2fYC5KghSRmxczo0WPHdHm1PZT59snXikvD5tF6h4xU2vRbv/Ebv/Pb//T2nbvz4Xo37fphhFZJZNYIJDEjziWDRYteUKVDBlAEMyIZpKfoctpur0qtYIIKNNAcXU5pDGKu0N0kl5IFdZKmXjwoW4Cito7z0kXJ0F0rJyBzs9bII1GP3ktrAZFyAjEtcziQ5BAxMAZqQdqRpoAgwkwVAtHdGIF8IEuMbdUWZydkVKwgOQyWORst5AYLQS6IOY2VhLBZTGggsHJHFiM/TNOlus9BDpFRUsucUgFKgskSaENcgN1xjdB5mJpI9RLWEg5akwlBAgOs2jA7MVSvrdUxdYCuDsYsIl7qEKJTMyXu++PbrBjaAlEHTDbvxk1XgwfmBeqVTomTNRxSRmYDIBJK0ZFcXFysVmOAaFMEWeekpQySGrTm26W29epMMLLkpTQCGvLIRc9fvUwEm+O1zrVX8a6bsTEJtSAlQ0L3KrWBZZOf/M4fPPz0p9Zq0Tll6jkHFUEU6be1TnUCtu5odXW4vvvau8cnt/f7CqDuxXjVIPcCiPz4yye7i6f3bq2u9sv73/9mHvK0HJIQOmJIt+ourl5NixzHyde//8HP/+IvK0rRFvP08Gc/fvDaLeqSeYDJeHRnGAaGGmqJS91d9JJeu39/On8pqwobr2aaIyICDCPEABgVFc2lDbLAr/+X/+AHf/7nz86upkNUAYC2Wh1bcCvGm4QMRGCit++fmiAV8wJNTZulBoTh5NSlrvrFoy88r49Obp2enHa5MwH3nHpCNWBogOGByCAYzYQ5AAA5OlnL8c+ffu/bP3j/8eMvP/zZF5/89OHFq61TRGek1QKEAEM7MQviwJZ6dkyNDKMk9ZSHip23JbQxYsZMTNWQNuCEjQlBEFEx2BOkVhbIBMAC1gfBnBqCkweINXbiIgzKg4EPUaGK4RB83QfjSV+LpX42Gy3XaOluTs1r85ibqi/TDDNiGjiLIIqjBAEBAJtXJE4kodSjH0J36pSHUA1FYBrXYyesrXEmJd3vp8N2fzIeCRNWSwyVW1XuyaO2setA8qtXTzvBPPYKsKZEEJFRA9i9xcScJbi0GgoUxJHEs2FgksBWbbag1CfiwIEikVulpvtALLM00chdGiOJOwp44rEKNoklV8sIlqGFZLdQa3acj4az47Nbdz/68KOb4IdxqvPBWiWm3Kd+ncEhHBXcbjAPbh7Y4EYf0gtSpoYRoqGpU9OouOJIlGplhwQIDIkdAa1RmskDpz64eCrADkEB0zRr3Xm0QMAIQOE0NkQFMFMwDyUEaBxg7A59IEQs4KKmkKC49bzKsUAX2NSc3UO6vk4FjMjB2ObSqHlxtqbz1e5oXHVJZjPVhTFZ+NglsCZrKTARc7MQoeOz48M0D8PK3Rs4MBuEYQTh6uS4LAuZemYo7cn5qySdjGNrFpxMPNxFxMIAvC5FUvfi8YtIOIxpadYNazYIMnCv2nrqwsPIgpqC6rL8+n/zG48+edgf5U1PuSNlUKgJhcy9VowA10PM6+5Id/Hbv/NP/sJf+oub4w4RllaiaV5lK/XpF5+/ePLlOIR2enL7LPWpmaKkcGhl6aVtRjEbU/Fo88urZ+e752dnd2qdPv3w97FZ2R5+6+//f3dz/cGf+JMjDRSwm3ZJ6HC9qCCiRyvNl/2h8pjKDiN3GAg+kzmEIxh7I2KKOEp9/Wz55Nc+/+W/+kuVL7eX+6vrg9ujjjivOqokA5qXUqPuFsnD2WpNxvtWa9GyO6A5A6rVSUJSVp1eXZTz6+fHR6dnt+6A9Z5q5BSq5AiEhh5ZEiVxMXdwjoAudxVq2qzf+973XnvvG7/0z10//NnDD3/604ePvoImGDcGZUyMs8rMDokzEEe0wOAIc0ljx3IyjMO634zDwLRZjcEEgEFo4WPOjHbQJdNmyLIAMoBoPYCbkRBhg0BfrGDQIkwW/WKThKu5WdOyYXQLPEwIvq9lcJqWohrKWNZuw6K18VKt2bZe7/YwypgSp548IQAEIYGQJ0pUvZYkwjS2JkkW7LrT9esPTi8fPklEGGoRF9sdS85Ho5bWU64ZlghhXhSJx4wEVoYkezAHouCKDopsDsAElgPVAUUs0CC0gwzMzKvMwTCZTV6jH4mAu4hESNnL0qpiOiIArWWpWtJEyYix64Z8QFsPmj2FJUpgKRAwufkMKTt1m3v3V05niS5JPbi4T9ocMacj4l4jJck6NaYiIq6O5pzEAQKTN5NEi1E2RMAD+Vl/zLMU3y9RKqMhqQcHbKjnKBSGaBFtCVHrkZGwEthcd3gz6EQEBHFAtgrJDNFbBCiIm6IBCUWGqU4dqd242SkNqj7Pl7dWq2hLLRVoJGbdq1CHIh2wcdhy6JIAeWsld7mxrwduBhmOS5lYpKFQ05Q3cyvN5xVzvT6Mm9XRsCqlSWDRxboMIMgk6Jm5G0YkWrxJx6f3bzXnXW3YHKAbjo5EkrsyhrjbXB4+eTZQGterHmLe1gYWyZlUQAWQEzYwNzOqPdHPfvrxq6++PDnuilU2QvBlUSAJYEFpYYrcEAyxTIdyvf1ke/3J5+9+4+vvLXMb8krr8urzzx99/tH1/lXXhwcc0SYBBwRQhHtzrL5ALOF2NB6RXgXaH/7oH1w8evjBN79Nkn/0m/8wc1ye09HZmPLZyy8evf3WNxS0lsPzzx99/vTJ6clRt+4pJZsLzQNBphZ/vDDjBRAJbpDsDIhBVSJG3mx/7+p38u9/93/0i994+/2Pf/b5pw9/xpabxa2791srrRoisbElD5s5ZJ3l8fVVwzr2g1cToIUQIUYEJnSCevXq0fVuPLm9PlnnnBNwj+wQjUDBLNARgAmJSBcChETNAAwH3nR38tnZybd/+K3PH371yYeff/7J51fbbUTKQQBs7OLaoR/fWh0fH9+6f+ferdsnxyfDus/rMUsnwH13408O5HD0CLDmHXelNQYi8AbGQZZRzTJ33DAF1ViELTntMQyFzNVrF1jRSTBaCxfEygHuuLNWvPUNatXFGmJM88G0RfGXh4NN0+HiWoG2rZEGAVwvh0OrViGWoNYPgezeeXWoQbji4XiVd+xgFVzaATDy8YYB3TsoYMzUFfZAHIzQxJAqEFsSMDC5wSY6IujgmF0czZAXd0oyJMIUBkbAN8W/3fU+MEmXk0XmrlQ47dMP/+KvHB3d/tHf+vXLyxbJs+DUltRQiy9lkshJWnJIQQVJkFeMRUBRMMA4prqsxiM77rvdFAaHeQKzvh/51rqWINM+IHs4cdPgIGJUCAlJSoHFpalSjmjACehrJ5t9faUaPWKixZgqSbQCipiysIm3AJxEOIYELbDOZQdRAwARARAJMmNYdaQwTonC3VpLxFpdfU+pGdUKJTyLunWwGiRaTIfWCClSN9ViUz0aV4AYCOYgIrePTxCzsnHOACh919DCW0pDjGjmhK6etDmAHUknnHDkuemQCKx2kChJJZCUI7y5JQdvWmpJ6wGQMZwMckqS2eelEYETBorA5XTYv7xKQz5ar0NbXdQTVC4sHJEcWwKeSwWikenh519htMPF876DaoUDqhaHSDyyy6KLO6Y0Jpa2mERoKblLdWkf//Sj97/xfsD84uXTpw8/v3z2BfYtAM2Eu9X+MI2rKPMSnWCfFm3NLQ/HDXYXV9es+Npbd2+57a4u/8lv/UZ/dApuCel62/JZfvP22ZPHn58/e26kn3/xs3vHJ5fXV48fPnzzrTcp5c36mBelwBygTLU1RUgYGUAhagSDEwZF7+gneTz/3Zf/7eHv/tV/43+4Ou37F5LT+s1vfPBLv/KnGePHv/uPHn7xERbFHIjhgbbECItxnJ6NTiMZXk2HCGJhwkD2zGxB+93z7eFytT66f+u2E0cEuXWGGhws4SGElDoPCyBOzMjBwchlmcdufP+bq2+8986rV+cffvrFJx99cvX4ZQf2tePjB6+/9q2vv3Pn3q3VZuWcs7tTBCV3qwZNY6ktMZTSgpQHKlUF8z4KOFqYBTCje0AhCGraBJJ6E0gN0wJmqGAWQhTZIAiMgIyTZacYyI0g1oh9aBeo4UQcCMAswGRe/UacgwFxUIvouohmy6yzLh5Nl+WwV/WC82Fe5l24eXh/NEYt5Xw/b3d+cX7SjcloaoaJ2CEFlGEjYUhIweBZs2FMCRmdCJEiUCyBY1MnVnIjaAgEniiFVZRE0rHBdNi3wn3mm84T+bgeh7/+b/6rv/Wbv/H4ixf/6r/5V3/z7/7j3/zRPyEpm5NxuV6YhMhrq3YwLhKW0iiecGLzHIJp1dDM3NyGgdPKeFnKXNrMhF0/OgUxUThCY2GXTsPIASGatcTY2M1JmncUFaAE9MhfPX8SrNYRuKBw4tpJmgTaAgt2nJdUgbBLunCoE7XiWioCACIAAEJHMFAX1FmQsyu37JAoFqjj2brLw/7iZXJuwBVAPGx7fXHr7Jb06/l630ny8PVqXApyl0BVG9A41OlCUDCCUXjoWaM2NfecU/XmDghk4c6euoE5HTEVD0KjxdBBjgatgZQpIDMRUPEoAl3qDQ1qTTRY42m7f3T18PV331yvEqE6Rsd5f7Xdvjpfbzacqfi8AfFBlnbgINJc3YJwcU/cf/bRh19+9MmzTz8eBQk9kgGk6gCeHJ0NR2Lk5BGLtajKm+yuvDphx1SvPvrDH69XedPB5fOH4IUT55T7xPVQCsL1tl5f2r17d8iwHlpVBcMm2N87ibKfdvO82Hz5cswDiLx8/LhHOKTUrU+unkydPmnL8vDZJ09fXvb98Nq9d+7142FYP3z4chiPlo3RDrlZ9j78ABbkbIuhUdys3Gsg9jUwC1vEqa53n+rf+Y//u1/4V777wx/+iTsP3nn9zW9WoVB7+5sfULbPPvzQ570MbAhQ660Oo+H+4ZPu1u2j+7eHza3aYplKWwwDgyUznmKukfbXV1/sr2+d3l+vT5gRVIGEFRiTAVdUQAYAKC0DGoWKrfoULbATBop7p7/82t2f//nvPX74pJN45/4bMHSM2EvyJJAwzWXf2mFu19urAj6XlhwEwcxAxK/NQzuWuLFaAYR5WAjjXAtyygA7bYrumIDHZoWoUVDRcEJJTOYC4lGxSxZEAClsSLw4FQSUngiNLFOUVqnvIBiVFlcQysHgVQlDpINxs/a2zM79rTyejCdMcOfBCRruL7f7afrhD75Pkee631+88qKXu/2svt1etsvD+av9szqVcs4ztIUruGEEsjUnIgpC4D4Rh+6xJGI059ARIqUcENVRiAlp0nk3lQE4RRhyHnL12Jze1T5dXLTvvP+984vtN3749njr7tuvPfitf/T3Prr8GRNG9IFG4KptIoJDoUDoozmnGHoA4h69F6T1cDRfTLVeBrQ+bzgNCNFnjIYKscUA9EAJJ5a2IfHqCgYJB80HckNak4haCE4UCpGARshacVtn6aTrUJuTN0kyN4Aw0tLAl7rEzbUqAiBAhNOAIcgdWCDMFkthtDl/70/84r/2r//r/8X//f/1e69+C1zJYUAXkSzM5EElNKUEvEn5ctpKl7FBDjns9xAEGijkyKHRDNAjOxMgB9xkrQjIG4Yt11e745M7k3Og5ZR1XtDCCNAjEZVlQeIkxMQl3GuVQDN7+eLF8dmtzenJ2b07NfaL1R4EhB4+e3Z4eXX76ChSahSGgRAUNHIGhKUVMKekTLi9ePH7v/0Pfbcc9xncnLFBaaEjbzBCAZXaZAfQVGQILgl9XhiAy3KwsutYk+BnH/3B/bMTVnv16vKNN95Y5gJqnUi4t2W5btPx7bPM9uL5xfnl/u23vw6kSfh4szbOT756tBnksOzvPHhtN10e59SP4+LtxeXFtD+ft3Z12GPErdNbzx9+odHIoyy2XS4fP/ry67ffEWcxxEBFVGLn5FFC3ANYUas7eAS4Q6U4wn77yfSP/p8//nP/y1+VTg7LTN1Rm3d9373z+vu6K58+/LDVGVs7llWps7BcPnl8pHbr1no47iJJbWk61Kv9smgdsU8iCJC4L6Vdvnx5dbVdn602p7eQMim4NnIzCmHuHYiTmgMCOgiLCwSGWsOQ6WLxZg++dnfseFivctcPaXx+eXV+cW6t0XauGMgiklOSu6f59nqdmTCHGrkHY2AYyaBNbwCmbgtJZk5uiA0SQsFSpxYVAQyzA/HuMLUwJNRZOcihtIilmrZote6uXJEO3kqjjO5egL0pUurVWw7CroMAABAGwOi6niMUXXIOUohQrUj+7MmLk80ZShqOVo2wAQ0DD4lrs+EN6hJkgFhcIxYMrd7mw3w4XF3v9tvp8urV7npfJpuvy+VUDjqjs/tQUhDWzvpsllE0o3UdOySH6zoHgzA61a5PAkCpPn/6yX/yH/ynJ2cn3Sa/evqy34zvvffaz/7gpz/+/T9cdyu3hSNxwuo6ptSqAQBCLHP1CWdCBt6MQnPluhyt0qu6aIuhO9psTkjw4M0chJOBN4JkzhosbBDZAzBCLYE4QeXIEUMEBjT3HrlRSiwULlVWyNJaR7x4GFEzN8AggcRt2YcbAAQEQEgiZFHoEMFqYRCknoyGMf2lv/av/Yt/6V8+XGyvLubqBrTcKBdkpNwyz6Z5MVnn2tr24pV2lLsumpHDydFm8pL7wZwOy+7o+KQ1bUvjYZxrHVKCCAneXe/How0gbtYrFlSrLKhWug4YQ6hboERpY8pzWTxkmqbUD2GeAiLiME904H48Rg+IZtVa4MtnT6Zlvn37bhZwDzYtBJWQa00YS1hj7xOJ4e/96Edf/ORTX/bD0DtoJ9kjyjxhRbOyOR33Oi91JrRjSaaupNhT6nqFpoqIGBA583Eez46GsPVPP/o8rV7dPl3/3u/9Ud/jd37u57quO7s7Gvj59dX6eHV0dgoO2ows7V/thh5Xp93VKyXC+1nefOP1/fm1tqW2htVfPd/uLqfNneN3331dvEovFfPV81cafv+tN+pumK+aA6mYBhEwziXAgAECyIgN1BwwKAiALBZmOZPh8Mj+zv/1b/+F/9VfufOr7057DcnIsN7w/ddeO79+cvXyaXYs0wGpXR2uslTSak1z2DgOI8bx0dhfLZeXu1YKEaU+JcCBcSLTaPuXL7bb66PT20fd0Zg7D8VmkQQQHMCJl7YMKbuTlrKdDqXuS22EabUa1+tMQjrZ+fnFtj2Gge+enaSua3Psp1YPE1g0b6a2PRyEM858WOZm7rb0XZrrTgiRwcDQwWwZV4M1SykhwGbMIckxxj6vV4mCjo7cE2j42I19YJBbtCSdtnA3bQYUAcW0JQstelDdtmV7vdVi5DEflrnVqSzLEmp65RiAZpVEFrUkuafrLJiO8iefPe5S7sbOPXHHJ8f9ZhwFAEoVEahKiRBs5GjMqe/7k+O7bwt5CJMQt9J8qtfzfDXNly9ftqU8ung1XV7rdr/q+ucvX+0PS3Sp63pctKnmPnsoiLD04gwGJykv0+Hzff3q4d/cdGM6Pvr61+791j/8tc3JMSzap9xXCoSZWauTsWBSwa5GY6jou9qWq6U/zIGHqsvL3QWLHK2PM6dSjMEEExFGFsBgxxQUaOEYgcFZoMst9ikaGpIXrSmlhoCMUKGoBTIgApRwLOGVvDErhyg6ctHaVAnCARAgEIgSYYdGgS4EFl6NTOM7H3z73ffefPHiC5xRy95hRm4tEjjjf/O3f+2Abg49psXq0GdYanG1xEKku3kcVkvUMW+qttqmrh8QBWtATuAAbn2fneh6vzs5OWbGpZYyL6vMoA4I0qWlaepHQqZFEbhJcMJpWhwCzDYhIbzVwkjIJBlbnde0efH05bRMq80wdn0LZUM23fuCQ2ZVAK/gIkmvd5/8/h98+dHPyn6WHiFL8aUHieLaCnpvDTGpSgNmAx+xH7zX7lDYaDj9zi/83Jj7UCUB83YyjHdv3xl72u9fRfiq763RbrqQ3AGkiHJd9PjsZLMekVNAXB12h8vy2U8+vPtgLUGPvry8c/v0/v2T6ao+/PCT41WOwOvrtr2YL3bXJ3fPju8cbdYZrKJjLUsDuXXv7nrM7Yntfjb7hO7e9ru2TK6t1VbCBcxbq0gMmICIEhEKDZxQSNToKpe/+O/9te/98g8unz2zsqvz5Ucf/tFh96Lsrtt8oJB5t0/SQGdZb+69/YOTr93tNn2SRJjAYYl4/PTq1as9SxrHRNKpg2lYqYvVSinz+uz49tHxioMLQoMQIgI7HKaOO1Q/7HfLUpRqN/RZ+oFlvr7cGg5Df3p6ko42r15ePH1x3ua9Le5mDOacHIjwxv8tqIEsToFoGOhIGiUhhnFzZrabVpkbatMcEIzBwuhhRTCbuZEZKCNJ9BqQMjGHeww5EZEg5DycbHoS5dStxzWgW6tjzjl3UNQYUs4iEuH7adnupxZ2OExX11vTChbCmXqx0naH5Wq/EErdL3MsFWvP3HvqU2eM1HWJ+HTsUQy7IVCyoHtt7sTi7rkFZApunYBZNCUGhnbdFPalffnw8bzdX1ycP//qyfNp75am3TaNyDCwI1mHiJVr7Rp7xgLVAfW6z7E4iFI0c6XEsZCtcGwWHuZoDYwYYMy6qJYaBcwmCAParDercdUlsFa9BwBHDAosRikCEggJqRuAQRZQY7UiUVwBEltgRPMSHEDMnkExMHuqGNUMkDo3BSgisnht+4PWBSFuRkIWYk4YPGAnKYHrDDZpJM6ghyC7++DN27eOPvrDD9UnBzBPACSz1ySUDSEhUQRo6jNrUnJPsDk+K6Vq8+fXl92m2/QrYqnom3XHaorAKLBoN3TeD6puGA4OSOaSKHOi5q3Led5POPSdAkGICKiv+nWxBRnLtux3dcZY5w1J64cMNDx++sr27eTOKXUmVc2rMyFBqFOohzs6Bb746Mvf/tv/7WaVTFsasyS38CwdlEbonAghmc+GBoBMijgTR9+lWa2LbCFUE4sX3ztnyZ0AowuEbk66lBgDtQ7UcWtqzX/y8bP17eOVLlpY8lCX2ZcFbXn9a/eXdgAzq1vE091UkJduk19eHA4HK00lwenXNiebcbfsSuCtvj87vdPIWrE6H0qKTtYSC4PP5hiIlgAqEJpjRKFEgIBBoeAAhhwRrF1NliSf7Yb/z7/9n/G/1+4+6D/+oz9QWy5ePJv215tNx+PY9vMm91FsmiOtx4FcrCZPCThJsBAh3H/9dLU+evX8wrYLrDhYOhFr1vEwhy9tfvns6TKtz27fYUgWgWKZDBivry6bWYT2fR/YhdWybBdn4DycHCnjTz/+6tWrC0NYr9Lx0cCbFRkI+PXcymy6FLfpJn2jEQThzcmlWouEyaJjPsSNrTogjJEwoBGBBgAjASYLt557ROqI0K3LVIwSC3rD8FobUl99vtgt5xddtKWxgpcwg9TlLFoaE/WpB6IgW3dpzGkYhtzno8369PRoNSQmQB44CflMNFxcTvWwK/vl2fWr63KohwZzLGF1qbCfStMn5JyQUk8hiURYJBMPOQ95g4TVIHmKHCTkDmDKYwCuBvnurVs9emuhYfM0LXvbX58/OX/58uXu/PxwfX0x7+fqihOHagIgsEFWgTobuThSJdbE4RBWZkdU4psyrOQM5kSMOQNj3c/hMQ4sq3FJqXrNCdQVECSITCjyzI0SWSMQKwjmhYQaGHGSyACh0SQiARoTUN+jE9TJGxNykIcQCrgJh1lt5WB1RgIIvFnk5ZyBTVyZSANYLbjmRKHhJGL64qtHLx4ZiQMgGhOAUZHCec0BQqCAbs2UGQ0IObelLTgJywmsKBekvH91tb51nHKe57mXHB6GYRlsOnCfG8JSlhV1kvhQJmHd+Co3MS/IYMvM/Uk1Jdb5MA2Bq7ErLHaUbgNcT5MbdZujFvPzJ89baWd3N4iKMzijmWI/LGVS1P1hIcM12aMvv/hHv/YbiXG7LFlivVrvy1YjyDpJx4GTcLu62qaRu3Fs6r7UnFKP7Fq8SQGK1iK7gQsMbjaEAwamEALNSTGwRIXFBOpSP/3o49feeu3k6Ah6YqKw8vFnnx2v1gTYwq4O5cHQnx6vL89f7qch9+FI59u9FWeGSLxabcahuz2MGoukhJ1JJ8dnx4f9TqfQV3On7KpipsyM7CaYPDlA6RA8UzAgMRsBIib38MaQIZxGvo1Hf/P/+B/9lX/jVznp/uJ8nQm7Pgtb8+1ut3v+vE/p7htvr07uNNXA6tgxCLI78YBMPQy3Ug/Hj7+82O0mTBIpMfWEtiHsBEqLen31fNof3bm3Oj6JWpfdspRrMEqSA6WV5hAsSbjX8N1uf/38PMjPxuH+23c10VTm3aG9fPRwmXdLa1MzUcbSmKEFFqw1QXKWRrXaFA0QkgcD1nBkAfQE0WUhoUUrGiEks9ZlsEq7qXEStdpJ1yWsFAKRkZVwihh57EiZiIiRhDD6zF2fxG2pNXOuyEXbihECr3aHa0K8vnZX1XBMDDgklL6XTkahrhs9YiA7PjsZTrok7MCr3Bs1sxYz7qayhWm+2tZmF1cHbbgs8/5Q7RwMoIVh4r6TNfddTqmXlDAC+jQwYQ1lx0ZYkTbjyZDs5Ozoza+/VRwrRdttd7vD9WHXrtvly+unr16+ur7eXR2yMjRnA5HBuYZYh6gI6N5pAHP0KVzDPDxJiOCiANCPw1GPLDU00KZqA6ccOSVvVAQIsVsIUh8Emio59mamKTrGRGJVEbBHE6GDMwCCGohghgbqTFHBiyJFFaRZfakBgIGIgIIOnQIzQoJUAkKDgzKmCs2osjKGELggqpoCBjVARwIBoGUphAlVmVi6zEBqNr+6yEdrd8wBAX56enQotdsMZhXm2o/rRpwlrDk4RN/XkNKWE+kQabF2tjm+POy1lZXkUtvQJ6RctrOHIYmDQhq2+1mGlQVOra02R+Ci03T58pnN5ezereRhGoEMjh2kadtSN5bAuZVc2pePvvj4pz/pgIxg1kUg13LocTD1Ms9KnjIwU7/qYUQvpnUCCOE+jObmLZATo0bMC6+TAyBydRCstU2tl8uX5/04ZBkswswM/MFbr637lTVRkAZe2/bo7PaTR0+H1FvzdbcpEQCCqC8vtpAxE/ddilb6PtMqD6s+j30p5ep8uj7fn9xbPXj79QNNZamXjw93trfYOZxQHY2RAG9snk4MHYJxoDk4cAImDQaD5A4BwImwB3vQTv/Of/Rr3/wLr3M/gbb18UmDRgbr1Srdu5OFx02fMiwwb/hIMgyZgKFyIEZyM6+rUY8f5OXpLoo195pBEieUkXOfYOpomcrFq+c9AoXvdE7ivXAtdVEXSR3zweL84qpM29VIp8cSedhe7J89ubw+XFeC1bA6HdfDye0AmlV319M812aGNbousXCZFiQkcfJUzd1BPSIA0J2gmZ5uNplpuWwdSgNb3xpOutT23sliHnVhdmhl8kSzR3WazRvYAXbESJYImzGIE5uHByAZGjNP5oqYkPohJaIkXMzHfrXpgaXvWDqGzfHR2dmZY764OCxRVEufztHDgKBhFjGxVZ8zdST98Xh6/81NXg8KYbNi0WVutdZyKLvD4dnher/Yshy21wXTzdyAljgEKKcjzGMvMnZNm4hQwt77gWnMHsTr1ekDJicQpjrVaVn209yutl99+fz82csXry5fXr9sy6FPDMCKIdkHFspSZlPngAwCc5tD8tH6qMu5smZDbHnJzoTHIEGtEHmUzBsXb1gkeLR1UodODY9a27suxahLR4JhsChpDVhRJ0RIHmbSSMwyhYU3ylObPQwgECACuug5oWAkzw1RA9yNAUMxMDVvAqAUfuMJoYwQgQ0AIVCyYk/9bErCvYxg5bDsFejk6Lj0adqX1Th66ARaB+7pJJap7XYCmcaumaDJbrujVFebERsmxZJcsmDxVCKOZL8cci+cWYuNq0GXpc4TJbguU6fUGViHB6teTTi/fPIkgd462yz1MEjvN5t1RwIDwFYVakvT/OwPf/rZhx+iuATVYoEg7BG633vH2K+4zU0waRj1ETunyoTAXWbvgoyJQ61psQxmqMFJQgOKKQo62q7Wk9VxU/cE7lCqeupI+qfPp9Pbp0erk1KKoJ6NPeMAHN9891tfffb5Fx99cnGxTV2cnt6u0FvZ3XpwT6rN0zQej6f375aydF2/0TUpsetyfR19V1tJwdqckYIRgcXBHQmTRzNwp+ggoQIDEhHX6ECSoIVpCQQ0qSIxxCBzfvzbT46+S9jHrfUpKIl0dEK3Xr/bdQkJx80RjcOwGnPKgUEEEu5m4W6mGq1Pced4dXG+7MoEUaFJkoQSQ5JVAunTrDDttgKc+tzC67Lv0rAehiB49ux8e5iGcX1y60xj+ezhJ9NSbq1P7t669c0PPsA+JwIwtRpavelit+Ll1fbli1fkhTshkdpKqc3MsnMENYYKLSfogawFyFDnOmzGe6fHWmM3TZtuMy/1ejkM67wex6uXl4nzyOlqv4ubrUtwYgmyGxKcCwBpoLRoxAQErlHbAYUEOQz1oBC+uC2OL9rlkCiIwTExqzdmP+42Ea1b9ygpC/YZ82oYcd2lNfS4PywOBwMQZ3RH1L7P4N51+ezkBBJtbh89eOPud/pMEq3p9mpail5cby/3+8v9davTvD9cW7pq5uFEiSWPxyuBkH54480HfRdwWEIy9ZFZhuOeurTZZCr+9NnlbntRrezL7tmTJy++ev7q+fbV/nDYTT6paglrjGmDMoMtENwlka45cFij1BgHhw7sfPd0P0/NrAOgfnaJ9ckZoKSMY46WvCgAgIlGytNyTSwBhpzYwz1ULcIZ0NEYkZHYvewPS12QiBDAkQhX0o1Mi7MGlaQQbtAqI4I0dYGbDwOZzTCUAoIJI8IBRcuC3I1DatogZJqKoqecZ4ZA6lar67bkCNe2OtpUtV76zXHX3PZlzllYs6WcVynMqVnLHO4MQCTj2LemENSEwL2Bzc02fcfaMcFhugqgtJKMcjb201y/+OzjRHJ8chxRe6dpXowxBA2A3EVw1rJZyf5q++X5V9CxhCAEIa7TIDFHkJPV8KSJE1lY33XhTQWKNhQkINVSQcEEWmvkLlEBaiC4EiVg25btuuR7J0dcYyotomsFMZ1KN96+fe+Db93q1/0mydX1rhuOpt3lZ5/Gn/3z/4Ou72/dOn388PO5wK07D2atfYrV8elSd/l03PDZ6fFmPDqqWiPq6igPa9nvtlVLORQLv3P8WrxygsAgCHe0JmRGyYXBAAMgOOXQCAMCJmQHQOMOE4A0UI+oviTq/PJo9+XFve/fc8HMkhm77ggYUuJxHLujTb/aMOUgMDBrroCA4U19qdP14TAtd249CIflYmm1GFTM/RRI0CUXdE4imHsEpgoUMq6P+358fn79/OWL1Ke7t84uzw+fPv6MR3j7wZuv3b875s4sWpBDpWiUKDAxeSeDQhuOj+7euq3Tcn5xUSWGoZ+v95qqNhgRK7k6ojWuNUsXQKoeRcd+mMWOxj67tojEKcz7JJvNMPa9kKyOVudX1+5YWuMgcMyCpcVMkSNFsAkCOQBZoKQB/MbdEAmgg3BEI/cEScK9NaBZDSjmUlrTFNiu9goQjil7SFAYIVh0fc7DyP3YCY1nq+H2apUaeIKCdvlE2cLRiYFV83qQlIeuO16Nb55+7U1moOjU9vN8fSiH3TLtDrtp2W4Ph8vLfW2G3ecPn44dDSn3q7WQlqW6E0jcPlnfv30rr49gykyySvb+d779wXs/5wp7W66up+vn1+cvX+6fPZ8ud9vdfNjvrOGtO/fRIREiuYIzA0Tsrq63yzaYyKiXEZOAhF/bEtXSNXMKkp4586p5HgioW3K4K0tLTgRRAE0IAFldTWRycmv7MmEEABBxBHLKtSNy9kgNKZqLt+5GXmuhoQiQOCXG5u4Y6MiIFAIQDiLd8QoWr2Xq+y7MJyDkRGYzzIP78bghGgxiCaaofYZ6qONwDMGdQyYG11t313WaEsgBvbHfQha3QugiEpSteZAzhNlCjq1ks3kpt2/dnufJKarp9eV+f37ZI6VhtV3qWqKD7AzNqoAFgAGGL3kl54+/+qN/8qNpewEqjmlMzBjarDo7h/Nc9zAZyZGmI+qOztrLab6+Sl3SxQroIVUEjOAsksgtIGEQICG7IxJplYtX092T24fSmg+djMcnZ29//f2jW7dTNyLSXMsaaHNyyzht1pumpblmALWQLNR1z84v9oft22++0XW8Ptvcf3C/zap17jcpR7LokRAl78K0hEe4WaaRdaKKXqEYmCuhmTVRAJRgIq0RSMhE2IIaORESgKM5B0sQUhBUMHSqz/t6QavbY7W5zwJd5i4Nw9CP43B2SsjsZKhWSgQyccMWWPfTFRKf3F5pubx1NIicvrrYX0wV5loVX9VYDRuhPErf3HZtv8bu1rjpR/700aPD3u9tHuwPVw+ffIYsf+qHv3B8sgKvjjJbODExpeBOOsDox85VaxhiREjfDfkMj+/etQTRlgyuEoCcUMKDAqE2M6uqTsSIAycekjm+PH/RXN0SqmtUM9ejU+KYpsMK8tGqW+ZZW4OqGqLzsuKo0DmHAhUHkQQKDUDNJPfNtCGgCzTNHCsEATCvKQE0FGImbywQxU1QUmYKKyAVUAsyKgYtu/2ed7Vjbp6B6SSP69Q7u6wYUndiqd+s25iGnHs/cFL2OcfLsJK7UQXWKY39kIbhta+NR91rTlSWcj1t9zNfX2/3lxfXdbqeyv5cew5PoqAr4e2+XG0fUteve9ysuzWu9sW3lwUtYoize8end4d35D3Y+wrL5curp6+2T1493e+Xl+ev6nTw2Xrmgn49L1Yt86ipQcdEq9wPlUI1OiEz27VoDdZweZJXhEmpdDnfCNlEQOfSYsZEjB24YKol3Cl0V8OIKRw8PLphJbmvjirUSZiqIgOiCGEQOqJQowg1UQ53AHQqNw2eAEA0aV6AQFJ/qGaLDn3al5I3/ZqZlKd5UnXpMggWq730mbrDNCnHuF55ddr06JFR9tPBKfroZi2CUdWXZoq2GYaORBWY+ZQhyrKd5rxajzkRDEbt6urq6snVuF6Nm4zh6GJmjYTBO3YPUogWrWP82W/8zh/83u9GlPUgCMxNsJUEEYtXNGjQkjklxwJAvXe7Vxe7y0W6zjNoDeIajBKSHYCtI1qaz9N2ddRVBPQQEK0uPSfo+zt3vnHrARJX6O6/8QaCWxiAbTpBAC8GVldHw7e+8+15KRFx78Hr/eZYMoPrisZXzy82x3dff+NtElsP4mXNQ0LMrgXrEhWON6ePLl++enF5QsP57vy+bYJISSvXCpoMEDBQlMCpBSMpJerMG7MzIIIS4k2+yAEg3IHDsXMc83j56HD3/burow0G9SfHR0dHnIiSJOnd4Wp3db19cefuWSmwv346iixaUx7G3O92U9flYbVeHfWnt25f7ueLy8PlQYf+KHedJApVm5eeZT2u5zZ9+OHT1dExS/m9j396tBo++N63bp0ce9ViCwU6QpYeMBYoXTfeu3V70gIMErHfT9t50aZupkyLq7XInKtb9QCD7WFa5y7USaRC5LwhDJC4PkyCISkNR2ebnAgoNYPEDcAACFwQIti8EHkEgbpZgLVW66K2tMbhoe1QlqvdZK66VAqaSpncS/VYlKsJEIbP1AbpBby5pqAuJ9JoSfZGNw8gEIQyhDEQIkQvbEbuQea5S8VmXYoLxqwgsFW0Zwm5B8H/H0v/0XPLmp5pYo95TUQs99nt9z42T+ZJV0ySVSx0q6pVUEMOkAZqQA3NpL8gTfUjJEAD9V+QIECQIAOhfLFIJqtIJk1m8mTmsdvvzy0X7n0fo8HmNMYLgVj3c9/XpWQQ82mMJyku2naxBmzCwJIHcd29QUX3FONikSj4YrG8ODk9+fxZETv0Q3Wpg4robjpsb/dztcTt9nZ3JxM7p5C4iRQCiq+pW7YN5YZDmFNVTIvHZz96cv65Pymu82T9cNy/3L5+effq6o7ql3Bxcn17dMGUubbZQjASTBao4Uox+U5kHo+7eZw9Yqy+5K5JOWUj1lLJGg3GlhEsoGfHUua5ijlEiBoy5wxtSCGxp1nEa23AjciBFRwtCakZukYgMDIpDK6ICATFhCGgeIgpiR4NGuXAHR7HA3OInBkZmEacqktmJmDi1ntZ566aDFamfs4cRIWRA2FCd4wbTFWFVy0qhHJUtR0I7ufNekN1RCMCanKjhFfDESZBqMPbm4XTSbsQECGprhpIsYZiAi7EwU1U/+6vfvH3P//z1LUaUlFBiCll1BrJIIKohRiLBvUYQDKxj5OKLGMzkYmWFJCBolHklqCIC2Ay1XE3wmUwBCKYbRa3+4+ffPTZ52ePHnXt+rsvv61TRWRRUYUYoqjWKvv9cbVYhYbAeLFYmtmx72/f3LJh7Jrq0/1H9z787HsKysQxZ05kHhFhFucYNUzL9fK/+q9/8u7d4eVvv7GvpMxJay0G78WWII4UMRi7EiKkYEiiRuyJKDoIsjoECggurszRFAIAmyej4dqnHV4+e+jm6806dY26Dv1+2A2b9UrGAUQO29t5kOs3d8+ePO13vXrfPHiwbFe9TuwCUlLqfvijD19eH4cvnje5CWg4zaYQI+dFd7Xf94d+eXLx6s3z2eqPf/zpernhjGWeAcAY0RM5toHvPbjgNonY2A8G1E9TfziWo5Y6l3mq7hZI+xmJYpvNkYEcqQoJqNRqVYggeqkqwFAnjfMcok1VEKYU2ETd1QxNDNxCZHMHroa1ViflaA7sHpCQBIhSppQ3uTtbnVkEdo3ICmEErGWOIHU/zCIjaD8NpWoVnecyzJM4aGEx7YDY0C1FiJMKRAY0twpqKTSqkADaEKvOhjZXBUSey0jZQNq5t5HnAKZzCTTkWObXrgEDBaZV0yxXbdvmbtnmHMIQAscQSogE8JYDp5zOcl4vTmKTH6UHxMgZaA5//6vfTVrfXN3OYxnHYZIZkfb9ge+ueZEWXbeIgRsInhBzw8uAErIs4upyc/bDHyWpXkiA6bdfvPjyN1+/fffy9nA8CMXIoUEwViWjIUYFwxSa6lVUx91Q99QsT4SoSU2kfMRSxDvAlXZg5dj3JphpSRyEGTvC3ETks9hU02nY9z5nJe6jFCJmYIvWsQLHWXFwYpTorgAQOaiAOQYcNECsVo2RQlxQOw2T9EPbrtyNDHJM4EbMCLg9HiM3MXcokIlczQHFgAwWbTfNMqNgmxTc0Bbn6zzLq5urh2dnTR33474WODs5XzTdUCsQF6s3L143jOneWkWahodaXJSIQAoZEUAAYoT++uY3v/ib9XJBgGM/V2BPYjTnyFo9dS3XAFjblHKMUSAiuBpyFONYR04EhEUqAguNiGAaXajJQXV0LEQQiA793Oblj370+w+efIghOcRh8gcPH7mbqe/3/fnZCQcCDF3XXL19k4/N+fk5k3NIMbRNyudn67MH6+HYX94/D7m45hhXav7i1fPLxxdY5zKJVj4eys/+6F/8o3/8XwLkMo7/8v/wf339H36dGMELoxkpECmiE6ITA5NhNSBmUEV3QBQkBzIFZEMLQBERHV28qHpbu6//9pvNs8Xp+crEx7FM01CPO5A6DkeuHp2Hmz4gPVicv3t1hZGWy+X+sO9WC24iOIRAT7//gxrPvvqL/5hxFd2JAEJE8NQ213c7c4o5v/j1b+89urx89oANwE2KFdUm50QRQxsIGflmfyg7q8DsKGKHfjwexmhWfTYvdSpFdLjbq4gnVsZxe5yrmAsBFFEhnqfSUEJEI4qU3EHcgKFt0iJF89KmxMBuiAjinBM51RAiWZyjEyJXYAjqngmqMxgGQAGbqwTw4DaWsRDPNkSzJkchQIcOu9wBCeL76iTB7nAYpc5zSRjmcoDqyZvjPKeYAdpjrAjWqoKr1ImRmHGJsQpICIMF8qkNNgtHDQwwm1DILefDoZ8nVdV+ZNwCMapbTNymBfPydNk1izZGXizSsmvGJMFlVEGdmpaaZV7E5dNPP2TQH//ge0J4s716d31VCh4PZXu4nQ+77fZWjRZxkWNYtCnHkNsm5hwzE5sX4OBM3E/TR589/ux7T2Wa7g7jyzeHq6vnL96+OV4dB0O0jFSRSTmFpKG6Q6xKd9OoMgdgCpFW3brpSMP2sO+PN2UqkAOsVi0uMyhHp0jZGYsSYdsshFOcqfF6LH0FcRcORF4NJkWjYEyMkpHdQc0rYA0TzOwcHDIFm2NH0DVU1CAQMp5onqQ40jwMbdM8fPZ4d3OIgSBQEQExijTPIzhR4GkaSgR35dFy24zF3f3exSkFVALGxeJkYSJlnAIHGK1//jbPtnpwoZNVmWo1dM8pCgJxAFMjnb0ELS//9rdLbQzU3SO1cxmRpqZZehEzn+sUQ1TwKhXNcnYQ4NBWUnGdiRMjc9BRElLAAoQZWhFsCA+HYykWOKpCF07+2X/133/69OPbXR9DWayWMae2awCcmc/PTt8Dy5lgvV4wwd3d7m67f/Xy+bJrP/r40w8//eCrX70jMHNxcFBiS6whJF0sA3t1g+E4vrvtP//ZP/3Jz/5LgAgAqW13d7sknAAEwzjNHIlNCRGZTFTFCGJDQUURAcAVAZAY2U2NgIDUHB3dVSIghFCh3MjVV28R6slJTpjLfk9S9vuDRj6+vR7H6fLefRvL8W4Qmx999Bgjhxg9EGKKeblcXo4l/ut/+e/K7Oscm+gScZgxhbw9DoHDzd3t4W778WcfxpOuaGlCcjUEzKkNFF2r2DgUEUBzTCGCSKnVAQnxpGEGMM5ASebqhuH8TK0qGRCOx6mfJ5nLYbc/9GNFTBaDWC3TXGWqUA0KuJEvujwwpsg9oRqYOzJdbYd75ydVZ3AMSu9urrpFo6N5dWFPOdh7KjJh1zYYYkopc0i5McLccEzZMMSYu4iCxswuMvcTOmCErsnVqlq1qvOURdHVa6n7cQSGFeZYTeYyoKYU56m4QxCfZ52zByUXDkDMUY0MHZEBuZqMZDVoCKgCDDQjAAasthu3yuPNwG5IjjFA18Zm0Z51p816/ejspLq1kF5d3X337gpcW26bwDnjs8cPl4sNAMdQD2O92x5ub/d3t8fdfr/d7gIFgL2ypDYvOTdNl1cRwVbtEtzAagppsVo9uHeO/KQAHnZle7198+7N8+++2t2N+21vEyUnSABAhNFjgDKO887q/j1eu8xD0DEhhbZrl60DLCiFECRM5ASEOEsKsQAZMzQxtYHND/s7I/NIYo0YEYo7KHNIXEvv7OgWiBegJWJGC7PMRwDR+eziXj8M87G/PFmSmRjqrBScBbuWJz8YsCqctu3dcQvuRgHCMqU2QxCF3C4AjAAgBQ9Y7w5xvSq1BhaXue8PIbRvvn4B4/jo6ZNpKPXNFqjWMiMEXDW2pLRYCyq4QJHddPPy+iuw6kDmPhtisBwizBK9Isy11uIrMRXIMmubAvkkegxNRudWMKo7cZMaIiYDpQGYUl46KIrksDw/vf/q+Zf/4//5/+CDTz692+9mF6bkqI+fPUZyMydABwVHQgR3U+0WXUrZEderz969eVVlalYdM7tYoDQNNk98cdIFVEZ59vDhDEQRx209Wfu9i/ObF6/e3Fyh4mZxUt7u1yFNw9HVAkd1V3cDIzLPwgmhuBUJxEwopuIeKLAJMMzBxeZIOVRyQ3dSdUTjga+/vrUOqzSbdVeOR1QZi7CWJjFCCyHl1QKMyeJy0WBKqT05P7/Mq8t2c06c/8O//LOmwnK1TBFihKHvHWAcZwZ+c/XKtXz6+feKVKwzNzzC3MUuzjDtBmsbCaYupZogBfdQqjapWbYxMkmNROYmJm5aGy4A2UE9BsLAaWr9PFBA8iJV5mMdtTpX2e1vD9M09nUYpttjXwFN6jjOR4pGbkiAiPieF1uHuVeAs9Xy9Kxbtq2uYZxr1UkK2VjKNPe1XAWUqoaIjkiupgTM5oFDagKjS42LRdNsWiFdNO3FeplDbnK7yl0MIYROYkgiNg0H18PQp7GUeT6WshEz172BIJJoXHYJ6jgXyCmINhQnTzOUDkNG7KUmCxTIUCGQK5tmU4xcmFSDGKgp1opz8cMw8N3+ql5BiN+m1lk3jy9IbNmmpmuaRrMTB3/z7tYZgWObm5Nld3LWPnjwYfIWwY7jbne3v7k+7Mbp1c2tFAM4AoecQwpOKS1WG0Bs2iQkIQaY62rTPdysfviDD03/cBiG7d3u+dffvH3+/N3Vdb+f98MkDbeLDp3FTOatSnEgxOBYXTCqNA0yREdzSlxr61wXzYTms4PxnKB6ByKLzdqIxlJ1rhm51K0jLtvlVCawEAHRKv5///3P37ukZKxTf2hWq2aVQLxWY6KGAEJGdtCorqOUVc5F5tvDoV2u5Hjs97vz9WK56MaJJvRNk3PIwzgu2mDOWn22Gh3jYnGUedhfn66X+9vj/u3d3I/3Tk59GA43E4G6j+5eiiho5dqenWNOi/WK2f/yFz//7V//1SrmWosRIbFbaUJEoFJmNA8xlBkAC1Ls3ZtAS1ZN1SmUglgcDbHxTFQKTVNtkqeGOUcO7JDWl0//yT/95+/evvqf/S/+pwrYtm0tsFg1ipZCu931l+dngIoA6OgEBk5AYKCmBhYDqxgy/+Wf/vGf/av/57s3b55+8NEPvv9xu1nGJubEzMiJHTMBEySMea418VLEX79+s/37F8OfPE87Gce5P8ocxGYR1TZgDWWOSo5Y0AaHQhGZwSbQxEwO7q6ATIjYoDtgrWxgYIzSid+b82dNWi9WXVgkUiByNFdGaqk7uXz8yc/+8WJ9cvvdb+Z5582y2Vw+ePopxzwX/Ff/5o+Pb+8WXZtzyMvu+m5v1QTEFL/97ndPHp9+8OTpb55fceBYnZg3eWV38/FwExYdrtYFpbI7cschp0QGRsgAxdTAMwdHL1IDGDINRASOrhFcahKHwABQo3NALjo5Ijs4h6Je59FE5jIfh2G33R/uDlfjsZ9VKgh5RKBi7nVSESZG6Jgr4zRNXe5STgTapBCIpnGoOqPg3W7qixt7QFXjGTQGQKmuwUtDbBJ1dpFpjuhkCWMk8oZit25i7k4yd6scl6vzdhWhUpdiSMToILVMJjhN81yGYZxwrDLbTgTmMohDQDdjCqZ4nIsjMpQJqwupMNYaMxdUAXW1KMiQR1VkcS0o0TC3wGh6oL7NkG2tnrktuQurxWKTFieXmxRS4ADRiGauyNY0y+X56Wq9CuvV0jHupsmH6fbq9u3t8bgfZegHmywtKUKbU5cDNi1mXjfpYbe6vd6aCzWIRAEig+y2/e27u1ev3z5/c/XmxfO3u5tZJhArpZjN7BVood4S6dkqR04VMG6WXcBojQLuQ21mZ2tmKIyoKAWVJp2KIDlMM7f5/sV96+vzb38nZk6D6hxEBRGBwjAfNotNbhsnqFbMNYHHdDJWCYGHMmEAMwX3Ny9fQQgXZ5e0iffOLol0GobYtNX9qEXcGIFD0qG4Q1Wpbg02sQvLsDpe3Q1v7nxf7q3Oyu1Rb7bcRLNqtcxZmocpL/LqdNN03bffvXrz5rta/Ldf/D0iazVHFHYGadldCnDCGAhBKjqMiS3GVrXmllLIt4cdM6IBe8s5apjFKjNyQDG0uUTTLnXKdbu9fvXq5eHY/4c//k8//OEPbnjrBdotQcqbzdlq3QCqAzm6mjEQuDsZEBAR+fsqmM/ztO/vTh+cf/C9jy8fPo6MiphWJyEmdAghAnAImVIuZpQgQgCbP/z0ye1Mv/qzF2pGiERiOFeuxDaao3sw8IAckCKJuDgAkxsZ/QNOyFSjsJEpi0JPIZAHh0KONoYwJexsLjNBaBdLrMqhDQEvzi9Xm4sQMq/X648/IzIMsWmWWHk4DP/5L/7m+t3VZnGSyM9OVtd3t2Smri56c3X1g08++PFPv7ffHVpiIyPS1RTmq+M4jXjW2jo7KCKm/N6MZwUkEkcHBEB05uCACMaBAgUGBIHKgVhBx0iemY0NkUmQAF3BMUxuIGgVHXLTQLsO0eLyYg2jfSz1+uZufzhux6OWOlGphmHCNiRkQsNxGExxe9w7ooOfXWzcCwEsFuuH61XT3HHbjjL7XBpob/a743QcxcQVcDJRB1SoHCmgE6CCKXjVstvNYtsdmruNxZuGI7lHDyE3y7Ou61aLvF4t2pw355cPU0tq7tCXGUc9jLux1nEqWnyeKkV28Tz7LiLEFEwRsSijeoLC4AiAwO5GHCavlGNVKFYja0dUqx10amBm3+3H9vp2uwgdvviua5aLLuXFYnOyWuWc2cu0v3t15UpgxDmeLpYPTlYPHj94+HHuurAE++2rl6+u++Ndb8fite6PwyRlWq/6pu8P42rZ/ex73//y9XciBmZx1T1ad88+ffpPFOZj/+rdm1dv3rz87tVXX317u70psiDPTYqURFJCo6mv+7ofcjgJZqnVGCWL6KxgWC0iVQNCiiGClBiSCJozxJKi1TILiqEHnGbmIARn6/WCQl/FFFNqMAhWmedxMA2cU4oZ0HOsWtOiuX/xIBgQRTcYZy0V2GWVF6MbBYyJd2XOIRoCsjKjWNW+zsNh993rtqfz2M6HI07ScHOUqdDcnfj5/TUlm2UkwOPxGHGc+pui8d7lvZtX11AUYyg6N4EcCQy0WmjIdTKgGIAxBkwN6aE/jti0YUOkRQdHBSeafSqV0AJRCBGITeQwFm3jPB2ub7er9elf/cXffvvNtw8/enyyXF2cLmaIYmGxTOM4oGWMWNTazJGjqrojAJgBAjCzmQQKP/jxH7TLtTjXeU7M3ebSDMjB1GupSmmZFo15cnKxGIKUY2pbMUgeDbwmdwdicDCMTNN7v68lAFMKwA6EZoSoIk7ATITv5+biQZjRVc0IklkFm2g41ngitWhDq0XbnT86XW9Ovvrmt6MfT1pAKCiam6VqOd5ev7v7ts5yNdTnr94uVk3A6fzs/t3uVlRFVd3ubt/94HsffPa9D2eZy2yBIgCUfn+4vcLU4v2TfNZonbkaxYBG6u6mGBgIDFFVgUlNibgJrOrCBAqRsJgiQQixiS2gzwGtzhjQiWJIjMHNZ7EZFYpn7EAFEDUTMDraumvFdD/0t3e3d9vdbhqOPiZmJQgU15AQgLvF4djvir+8OiCIlmp+qI9hHsdHi3U/79/eXAWKHzx7ild+eDMpiKGDqc2urorOgatNyghE7hhBkUDBjQMCT6YCEEWtym5XDDQwBGTCgEAhUlh1yy4/XJ8sluvl8vRk0bQ5BhEp5Wh63Pfjvu9KKbPVoXdimBVrSbzwYBrAEUImEcHYgoNZ9VnUUYjMFsyqNrSwbDkVt2lWJx2n/uq6d76j6G2KDedmkdcnq9WiWbZNQt9td9ubg4FxokXXLRdxuV5974PL1SeNjOPtcXcz7be3++Pd9G5/W4GP81x//oshSKa0XMUOid8z3IiaTffs7OMnP/wEqo7b49u3d198/eLFty+url4rw4iUYnNGvBuOMIr58YjHqTcNoV2saoRIIQliSGKFWVLKWgoauFcJgIlJ0R3JGf8f/+9/28S2txIa5FKtBkRuFm1hC0CKgxQliW0Iom6BYiByY6LdOCzjoo6zgKWulXGkmCEgoLQx9odJkZbdAqYJAhnS7t3V7vWr1GtrEcSLIYIdx23cTBfPTmfrVeYyzkT5OM+qSuavb64Vm6mXN19+zSLiMro4YabEhmCWkpHLUGv0zBQQQ8xwW3qVuOGl6qA0KTpUbzBXcEOPQQgdmBxCoCwRBGl1dvmjn/z05YtXdzd3Br5YdA8fn3/w8fcfPvrg2dNH56fnWmC/39/Nk4OlEJgjQoiMOTAxAMrxsB/u7oDx7N4DM97d7VMKjoCI7s5MYD6ZpoAJiDE7s4GrlPH53Z/8d/+v1ZVP03yEg6l4KW5qTCBKjs7kKsGSVnZyBAUHdSVGQ3MzByQOThXAfAIKqVJFJm+lPPLFJ01ItdN8draYa/n9P/pnRdWp3r/3UX88CobRrZ8P0343HfU4QO9d262Z5dmDi2Eod0OvjlJke3f15NGDn/7kc5c6FXvz9sbQ3r683b7eNjHl084osFomhoglcaLsxAZmicGRGdWUGMkAVGMgJaiG5IRShRwIQ3CvyEyOROaMIG4IAOrBQ2VQIh1KIieA4mrR1YQEM6dZRVWL1r4/9uPw7vpuv+1nkSJaxvKevTSWaazTZCDzTGqTEKCS2/mqO7vcUMbW0tQfnr+46mdwsFFqDAHc3VzdlNygVgLHkBwXjhQJAWZHQlYWY+sA0BScHTFQdHAHF3UQBEOxktEjRUyhArZNd7JcL5aLi/Plet1Rxy3HJsT9YT9LP+6tHL2U4yDjcSoABApVUNQZTUhdYZ5NXAgsEI0qLfGGc+/aT1JIWVOt0XAOWYgIKnswB1wGyszYLC5PThbLtOja0/WaSeqo1TQQxJCbdXuyXq9Pu5YjVDgMh3dXN4d+HIbpahhII7iepsBEtF5wm9HqMqYUyAMUKQsOhjDP5XAc3lzdPv/69d2rd9tXd4OWufQLtwlNWAmgWrTITeImNCVEYO+gV6FRkDSu16fKcv3i62F/qDqCagjYaaV2sep9b7UsgCMhaBGGKtRGiE3SgUSqKRimAkqu8zwKUkmuYLv9/v56iYmGelg1m+peQFdd1091MKGIAVF2w/b56zyVDmJVcXBwGdGaTSqLw7u7b1icQhgnMZOifnZ5impPu+7Yl6/efokixHDerXb9MCE1hG4yW5kUCaFOalapmQmphTW4OcK474m9snEDmMI8SzVnsErQZABjgI6ZXfumbet47PtD07ZtPPbbw9Vufxxv3766Ot386vs//OHlgwcpxMPd/s3bN8CYm2bVbVLsUstdCsQ+zGOXmicPz3djfzxuL84fsK/ubndmWqe5aVtsExHn3LlMUgQ5A0whdaZpuTrBAIKWnaW6KQ4TaE7uBgldAQ0Rmve8UURwdwInQgU3cwRQ+IcnVZw5GilYYAeYBSamwpoc2gaTH67f/eoXf/n9z38PM795/s3tu+cvX7xa3r+X150aiS9L6lpeLADOLy+GUnf7XSUuAuV4vH9++v0ffCYmFElniZG/+/bdu+vtYr3I60Wp0/bl27aEJqR0vg4bVqwhMzq4OFJwtfeEKmZmDsVnck4UVS0GzqJG+P7MR+TV5kz5H6rN1aqJRjIXrB5iAJWKQbhRqwwQGBGAYkKsxBjD6clicX56Noyyvb67u3k7ElfH3kplX6aGJ3dDZo3sPaMgv5r13VevVpkvVmeP7l9sb3U89gHVwc3MCZwAjBAVTWFWJo9OTgSECMToqEVAKtgsMSATFAAXACAWqBAYEblwiimQQ2AHJ4Nhd9hfb2cR0cjBmzYsF93ZarVcr0/vdevV8tGjMyQBh36axuEwHvvdYRxHr/1UgSg5B3HLQdEDtVozACKio4kjOYCQckrR2cxL4FCR3WuZaag67OubqzsiWzTxpGnOlsuTRdecrb0NAD7s7q62u/RcI/F6dbpZ5icPHy7Xm0p2d3u7u94dD/1xv707HKZhIOeQeRlT0yRoqO1y45CAUjwJ69X56cUPPn9WJz1cHZ+/fPP29au3z1/Ot4fGDVDnMspMd8PEENJqlXMoxhVCiU1GCJEzRg4twUhuQhaWKr3bVAJyXC9idixzFWUV4nm2EgpabEOdwebatBEzzNUZFwQurieLlZsCeggZZx3G0mSS41jIu2U3VIkx1v748qsvGxEmGEsNiDOOIvPd7d3JxUpM9i+3Sw+LeyeBQz/Nr9/uYxcf3b9cqB+GFyFTmWW9OqWiEZNxdSgatEgN3taqRULGAFa54XGwbG3VUblFUHeYpQbUkJertqvj/j24zt1rrWK2zg0KzPN0vNpvNqc9h7BqaIAUF4tI1u//+i9+vj451VlM5Hb3lggQggqvmhVFnkGljEXlyaOnD++fXtw/MwIUvX/vqa/reJg4Y0BjU1dzAOQojPvhNnA9u4yRPTQx587hVqIVlcLizgqsgAzOrgnYgBzE2QARDRyIAdRViYJhBiq1JiJ+3woO7iJgADPIXuX2GGOYvNxcH0vpv/rtL4Ojaw3JY7e4uLw8oo37UqW1kFK7yT5fnC1cyu1+y5F1lDJMTaQf/eB7pA4M7zf3v/vdtzfX+/X52XKRTWasVsdSduM4Y77dNxfL5cWZAUAEtuASPGAkFVWwEDgYFHyvfAuK6kRk7gDsjlQ5YJzRsmNyHhAiEUVwY7YwzAU5ghuBMecGGnBHrS1BjQzOVaCYdbFdLGOzSKdnCx2Gq7vt7TClAQ/zTNGBGyhI2KOHfp4DODrJzC+OtxN4IWfW4LVhGk3UUc0VlZDFYxNyMkdwBwLz2Q2oRvCo7u9JrO5EgAzVFMzJDVydEqIiMEWuUhDZrQSGEJgBmFQUVHW/v7u7eqElTk2j7hdtvjw/Wa02y7OTs9Xiwf37z57mIjrM+2k+lnEe9+NY61TmWhnmhDLWyOYY1hzcwgziQkTKDNQkRVapiJObeCH2YsYShgnKXO62fUTMDTVNs1x061VsFxvKjed4teu3x6PLu4YXqW3vny6fPjpP+SEYidHV9d2Lly93x/E4Tle7G0hdl+KiieuUmiDGHjphoZaa9Khd3j/7fvnhXOqLt7c3X3/31Zuvhndv6mFqZCkkMvd2UFeecsRNDUTRctudN7Hd2cFcOCD+//7lnwiqcgDEAO7VmSkQjccDmBalZtF162U/Hss0p5wBNXIki8d+SImWobGAM2owblM71wG8JIoIaer3FsM8z4ev38zbaRWjq5aijflxuPYVLM9gu9s9315lkcZpSvHs/j0GWy4W0HTDcDePg1KYt/yb//yrVUujbM1DhDDDVAXGQZgpsJlKUDU3btsUG6i+H7fIrSOYa44WiESjQow5IFSbJUIBJIiboJpbkIDN6b2nH3zv3eHWzALg5uz0pGuWOUBIAITmiK5eVWA/HEodx/08TuMkwj6rxf3uuNqkH37+2dNHj2fBB4+effTxh7Xi8TCYVVBFIovZKZArWK2qlHObUpr8j//P//fh11dYfSz9LMVHcCUPFZDYmQzMigEgkouYmwbIFky9gKZEHswFQQwALJC4zlaRMaBPXYFnJT2gk9Vp8JFExnGK3AUKy/VydXpvwjpRWFx+OJd2nngTu3aFm/Plu3fX5qRFRGSajj/7vR+tN4syWnFLOf/t33zx8tW71ea0XXa1jqSqs1y9eOsjNpUbZzVfnK0WTy5tFZwBgTkEIBAU5hA9VjJzRDOPhOrR3AkqOTkFIaYws7kbqhl4AqEIpuBO1V2tNClLNcCAqoHJ0Qi8ijqxsItU8ABARWadxyBaxfpZDof9cb8/juPhcNgfZmKcRK72W0OUqmQYOU5awT07kIK7iRd1MIgFDElFPEBN0UshRKSAChIjk6q5mzLVCFy0myjNMWUdI82JgIQFjIFCjEmKRERHUQCEKG4YDQGtmnOoOkchcVIjFK/TNFFuKHC01SovIZ6cnZ/dOz05Wy7algDczGEax+l4mMdxGqZJzOcqoM7CE7nTTCiz+iwYpBarVRwUEFw9A6CROKNWWyAn0sgcOKaGR5GY+GyxOVtt8iovm2Wg7MQ6j+JTaJuT9cmDy3snpx1HO4522G2Hu+s3N/Obm8OscwOeOVEbqdGzZo0YODMHfP9pWsHQZ5F6fXd8++Lu5dcvv/32y/3xTqs2gmKi2COH5eV5bs+uX3x12L5D5y6HcGRENS/FjZQ4RzYEwnCxOD3MI0cE4Lt3t5wgNswhzf0RvY674ziXZx8/1VoEkzm6+bzbNd1inI3bOJWp6Zpa69WXL2hXVhRhQnJILlK0qLYLXj/urvrD9a6edmE4ls//6Mff+/H3QcrXf//FX/7855//9IcxtCnF1/tXFgXaJpY0HmWSCugphbjkWgoyJjQDK6bA5oFSos5jIYvNcj4MOiUAdwOOrlAxE2YG6mqt5JOl4MyPPvxgZvIlfXj+pMu5jYmbHJA5IIXIEBFMrcokZmF1PosfQXyYy83d1ue7/b6mvGja9O723ZOPnlYZv/jibxab9v6zTxvCaRyxSClFrIjXyNYlKpWPfXUj5pRWiyO8JQd3RkoQDBXQTU0BSLwCuhKhGCNSigrVzN7P26spqJozIKaADgLKgXiG6iY2zmzAgUXLst2IDsvTFpxTSKFZDEU1th998uN8+eQXf/vFqmlC68vz7s3Va7NQJzXCMvY//fFnXRsZPCYk6v7+N9++ebvtlier1aJacVUxLS614VJmqxU1soXtzd3e9fyTx5wBQ0WE91EegRKwGkBAQHCoyGgawADN3R3BnAsxiFRiR3dRDILuKACIFAObCmNUd05sJkrO/1DyAHBgYDNDN6qqZBY0EK44LNLG16ur49207l5d3XhFN9R52pUJGM28iGBEU62OTC5ayDgIm7mCoQXAIiCoSPBeGAkJjDFoiCyTBak4eB5xMSyXcL7Uli+//Wre9R6cDd1MTSyG5CJO6ASs2oQ4leg8UBQ145A4pDBXJxO2FEI0M53FdXushxpe3w36m2+ArF2kk5N1u8z3L89Pl6uLB+epy1D341CGfuqP03CUbMOkhMKNY0GagBoOBjJiVXQiNldmLFUYGQEUEMFnkf1RGnU81Hfvhm2+Ck2Mi7BYrpfr9XLd5hwN5WZ39/rNFQcOGS/ONqcn6w8/+eDDj+Jk3u/3N6+vrq/3745DHWmPd8y06NqA0OWwamOkKCF1KT281z68vPjJTz7pd7/3+uXrX/z1L99+99aOk5XjBPPu+W/dKFZHcCUsHgMrNtAcdrtmsVL3qjMwFS1zyBjb3HEpBdHXufMICk7totQJ1/m0Ow1NOI5HIGpSA1X7CqJi0QabZ5HDcTy8fgd3IxARVigy+SRphsDEuDhvKs3PPr33y+++rbH7b/7X/+3J2WKeDy2sNj/4MYBfHQ9WSWofl2ShyASirhxJlYGkolYgjJRsAiSkzEBmpZ96V3B3FfQKKhJLNexyi+CKDu7RgCSEkOLal+fnzx59cO/evXaRT9eb1KTjcXRzz5GMc0Bxi0HRwBAlsVUsGmxulHC1PkntmU9b9dd322tX9Qm+/NUX9x88OB5uf/4nf/YHnD/66JP9Le7LQen9OL2WaZYZInMDKQkmDmdnmxt0RgwGs6mgAAfG8N72gERI6GaGAEQAHi2qzbENqOBIZNERPNrkIqpuhR3ovXxHSyopGlWzytlbMBuJta/DfiyKaXV68fZ6u31xbGKLAOcXy/3tLRQX0xmhjscPn9zLLTv4PBRs0++++eabb1+l3Gw2S8Y61VFKBedpLIgBsSC66BQ8dcr1pp/iu+XTU12EqjUFAANwJzYoChhSgOrCiOBIwEwoLhy4Ini1DAHQKyowm6ojOWIEIA5FTUQJeXYJiOiubpTYwEE8UDQr5LWJdNSkQIhICEaCZPfbe3pqy/WqP8zjMEzzrmxnRDRHB3auCkTOjqJg5iESIHkE8skh1hmtQkjBc/DZKliMUgJlq83cvA2nU84QOBgfmULDBX30GpVZCAAVEAHBsYIHcUnO6Kg4B4VYU2Ka2NHnAE5AwYkpgPsYakvE1SraiAUQkOA4lf3rQyH6uy9fLDGfte355abbpNOze/fOHz58EMh9nvrt1O+O/XwcZdLDYYbggxeSeZVCFROjatZyhFGzkSKZg5pVNzaQgsixAkRR3cv1sdrLt80yLRfNGeeuXS/WJzk1Cnp7fXv75u63gdrUnawWp6erz3/wWQrpbnd8+/bm9m5/vb0ab28CpynyHWEMDXXtIhMlDZbbkMIJLU/Wzz754O5m+/VXL375N7968frKB3qPb3TvEVRlDkhe5rlZNGERzKspU+AG+ND3eLR1OnXG1GUnBMR57hEaV1qklQvOgtUxmYPUeeibvCgqMQWEylju3r7F3WwcFF1s9gCuMJdKjDfTddZzhtVpt/69n3zvJ//ojx7cW2kZYmYXg85/8KNPVje3v/rFb8B9E5d9d3bc3ipXUWJFzly1qNSTxUpccEAIkhetKY79QTyHnJjMxXObuiDmMWEiB2c3QnIMubn/8ZP7j+4/ffx4sz5pFjEyZWosmCD2w8SBkjIYICNGJkfQELNy1lRjCOwAw1TaFMFXjx/56fl69/r6OA2vb+76Ijnwu9dfX93e/jf/q//2YnWeH957e3XrgzSJHXMt83tgfD8eQ4irh+fVNRpGQHaA6MojewgGqjNiMMRgYIQKkohFyQIKV0R0RYjOWB3mCjozetUWGFUNBEiKchOiuh2PYxvVoU7zVGcbBxpm1LgqTd8tHjSMq9O0vd3p7BGyifh8eHy5eHi+mMfjVEKXVm9fv/vy65chtatVwzz142CqIC6zoXgQndzmgIiWRZwwKtnNUVYNNetiEhDdUYmRNTNVM1QKShyCoIsWN+eAYsUEgYICqasTuklAAiZESKqDVI8RkUyVyEFjcFXw6pJCqiylzoHA3d19wWEyFTMiJno/Fg4TwuUZXVzSUMZ79zdvrm++e3N92A3TVMWSkxDBXENDnQVFKsRRlYtNAbEDhsSAhOQAajUCD1J90tq0ujpnIgsKKjhOM6Y+LwPv3KwgRqiOViE4sbgBEZlDLVCtVkBRWmBkrwJcDNggMpt7diRmgBqZOHAlcVFwdAOi4MgCKFqHg0Xj407/7m++daSL9eLsZHVx7+Th/c3ZsweLdlkO9TjOd9u7QWS73/k8AfhsPJiDldSqm1XEWaojmuPMCqFj5wxFXA2xVAH1eTsed8chENN1aHLomtDli667bNYhpqLzm6v+1Zu3beTlsr04O/vkw2effE7u082rN3fXx+v98Wq/7w/X9Qa65Sql3LWCMXnkGTWGcPHg8uzR2Q9//wd/9+uvfvWf/+bFV1+VOiAmdAaAID5hgoRZptItc4XSKrLpAGF/vGkShXUrIRyKZOBusb56e71oV4xhPvRzocVyyaDsEM/WjJHMTQqobb99TtdD8EaYQYohqplaTQrh1EzLmzdX69OHb1/v2fDyZAn12CSKzf1Jdofbnjg+vLivn85f//ab/ji6oSG5BSTElAcZZjX1Sg0sKJJlIyYgR8IYwNmROVMITT9ckUITEyMRYsopLtLi/unFoyfdcv344f2L83Ovpq7ojJMoKCJzjJkpsKL4+7PLrKbGoLbI+WzdadVhrEh4uBoDwmrZrU9WZ4vF65urV29uv/rq+YP7mxB9Olz91b//1z/+R3/47LOfXlz689++HXZlfd4FarU6mfbHfrlZxTOmLtQakHpS+gf+l1MVY88qAEwEDiyIaOr6Pq9md3AGA1QntVBmsh4qzJCo5WyDGzoFBVDmyDDXYpRi9lm3d9tJ2GEZustmuQqm0dimaZgLUtKCUOFi2Ty9f17HvYFA0824+O3X1xWW6y7FVMdhkipSRRWqiQOAQyCaTJUwVw2RXB1Aj693q/WCO66CETkYEIGzIZMoAEQQq9WkCgQlVXICDigeEiNT0ZqQ3LXMyjEJKTlTNVUBBBIrVtUNTAo4qJZaAN5HhTVgCGCBDMDGYQwxgZJzHW1mV0CKwXgZHzZnF/dP59v+dn/Y9ftJBZy1epnm3odxEJnnNuRIoZTpfakF2OtsLFl9IlDBiZZyduk5COUEsxsGVxvHMedTo6NoqOJQhRkgNZjCDJI4VXKsBYXGIJYxakETc5stIJqCImGbOJqpkXu04AtidSIMmEhE2FmdQOeZ5zczZETiWSS8vtndHcffvXxHQYnl0fnlvbMH9+9d3Ls4b5dLAxyHbX+33fVlN0zDMAQMg8yh1mYRA+PUT6i1tzQOJbATgJkkhWBsTAVqDx7Nw8Hl2E/kryl2mZbdqlt05ycny66rga+nafvt86+/fKE5rE4W56frR987fYzxMPbjbv/izfXubjzuhn1fryPGlHLKy5yYOLB3bfzHP/nh73/w2fPvvv7jP//Tr758DWohcIicA5OLO8GxH4zMgevxuNxcpssL0Ln283K1LszMhIBd0zUhcg4hLIbDQcYmrLvD4ZinNncFmWzSu5c3clVXFItbVKsqdZqZgwEKgMjwR3/weTU4HMehH3fH/X7Yn2xWzqjmgWPiHDHNtjtZ0uX56njcdidLSHZ32IGHWlwhNBR3pVz1/VlKgQhirEg6ms5Jm7rqAK1alRgyKUrBibBb5weffPDgg3uXFxel6aZZdtPclcrEMpapjsBuBdsQuoiYSd1TjApoYF0MhjzWUCeYkVPIq6WnmKn67W4bOMUUlufnH3Y5Fw9jv1oliFb2+s2vvxh3/Xa7/eQHP3368MEreNEfrhZxcfv89pe/+PPHT+99/RfT06cfts9O97/chlCUqhfEQs6gpArgiAHd0ZxM3DAEUqPoFQ3cQiLDqMYOOus0soSMXgoiOJgbDUNpZm2aDt8bgWNmp9z5NOOzj3/cnVw20KDOgDxvCUJQNxOLyR99+AAcLHVMuFze+/O//u0w+SLHRQjDbgegUNVkVBfA6BpEawhuZZ5EBoyuxAoNsI1luNvHZlVMkZGJVcGgGjEhG6G6AXloUrHqBBG5miGD2IgKGdDNjF3RiswjKaoHA1dTkUA4Gow6N0DiOMDE6AgoDCLVrBCqgZKbuE5zdQuOpnWqpSLGzWbpqEScM6wu1ptNd5jW+2kU86kKKIDKDKZjmcdZQIehUa2mDh6mWF1mdyqBKkBuq6F3+TSvwsHeCbwn428fnT3cXeHN3ZDb/v6H+cHJanszfvuyB2r2UzXENbfozlXd6QC+RGRFEKEUVIwgzMnIBJDG6GhISE4+uwcCptg62/vPzyCH6mXUFqmieQ6zj21gsyCav/7u+pvvbgJYRl+eny3P1h89e3B2vjp52LrT2B9NZbftd7f7qUxV5+VyMUzAAB0AaSBSp0nIiwAAE2TzMpuZgpgoADBeEdzdbVfbu1evn8fQdacnm9Xi3nrVxSZ7vn3bX13tmKztugcPLzYPLzb3T2V33PfDy9vbu5vDcTds/bhvY9s0667pOCAirPmDH390/r2Hf/vXX/71f/rF7noftJRSymKxNDQVIY4QKC2ylzkyWdvUWa/fvDs7PZcipfoiNsWVgHalDyF2KUYKs1nbYClS/Li/uT5cb5eKykGhUDWs0jhNMo9u1CbKvh3682XniWHRjtb++z/5448ePfn9f/TDcbgys6zdSLN4AcbV6cmn8WTf9/3xgC+pFq1DHfuKiJvlyqWIeLUw13nwgrMYvu+Jc6TkU+lCU82L+71n9z774Wef/PDz04dnUuvN9U10RPD5sAc1cIgdU5SE0IBz4oKSMQSEQIREAKYmiZaAYFBrKZFS20R+sAwLHIaiZT70Qj5fXq7nup5cAZCa4My//e7L63FbQvjxJx/tbl7nFG5ur//df/i3n33yyWK5wKF899XvHn7y0fDFVfJmEmcsEEhRFSuiBUyg9F534OaKBU0LmQd2dwYFZEBEZ3Qf6piZB0inkpO68KiDjLf7ZpFXqzWwuVlF7pZn3YP7cX2ZYgdTIa6TSgwn6Iw2E0yXl5uqh6nCza7/yWc/+Zu//s2720Nq89km7N680gBARtXMsVZ4/w5ABq2FSQrUiSmaIqG7skm5vYUF87J1VDMAQI2Gjq4AiK6VkIoKp2zqxGRawVUcAgQvwMyzTA4IAFUNTIuaARSXGKKqohQBF46kLGAIYEWJSc2KmqGTSmByCoRIBB1Eifnd1W3LwdARiAgjB3dZtG3TWlEZp7FOY1WhecbFou2yum02G8PiYFCYrM4+jn6cfDvRuB8GT5sZltNxlMJICMSO9ThcPbx/CXZYPuKLB/DgtPve5x/Lv/nbb1/cDqE1DFGdHR1cDAJRISQyAJCijEAoVRNQl11CBDdgjRyhYplBEnJyApf3XZMuRCdt0NlsdkvoqDUTKpgjaNUR5hrj4eoGr25+/esvFl1zcXLy4N79y/sXZxebp2erTz76cH+cDmPfH+7y7jhOg4UszoKTFS6u2oU6q09o1ZgVSNwINaGBmChaF62LNM315vWb3W3zhilTWq8W52enq2WOIR2H6cvffusqoYkPN6enJ5t2s95v9nd3hzc3u8NQ+sNt37ZdbJqmwQSrJoYm/94f/fSDjx/+3V/9MrhZExuZZ0VfLpdSNThaym4pRRy1bA/HLiZES0iGbuotswFkTk0X2f1wOCwWm7mWaHR4e6XvbjeWkkOdi5OKVQOTRMbl4nKxuVyns1Z83L175zM2IT5cLIrZL/7qr+Zh/slPfwhYi+6r+mzVQtctu0QlRJR5bFPOqA+fPnv58tXb6xsQbWNG015txqwqnD1ADZ6niZxDcCplouXypz/64Weff/rw8RPDsBtGMs9zbdk1EEfKSAqAiEuO1r5fWBAjdpjfR/dA5gDMkcQNKkYXJK2FKuRAJ6sFEx33BRhjbMZhvrx/73dff9ffzY5e1cahvnj9Zt/L+Ed/cO/+PZJjUv7+5x+dPXpyevbg3r1HL19+Q27cBbkuDM6MAmjuiMzI4OSE6ICEBARejdyWoMH719M6NCmZIaTASRBKmQx6CxsIXGgOGVIJgS2AN4FQAoTZnRen0F6EdlFtIhRXc4oUuNRi9XC6DpGmaayvX90+/vjzr15fffPy5aJbbdb5uHsnPgmwFGFxcUZrQM29IFRwDxTIxc3qPHJozJEK0GjQj96QgkDMDujFCJGQCgECMSNLRQ1kpMUiUBGpquLqCuRe68yOYGiIwZwQJhVkEjVHaAirFgOIBgXMkRBRZWZCAlZgcBuPx77K6cmqluqKzPHi/MxVtFYOTIbV1YMHIlQLjF1cadPtpV+OcSzzUdRnB3UnFNeGQdm28m6Eu4Yxq66owblS2SkPxMrImVEjzHJHcdrch325xV3DHPpC3//Z/c29zX/65UsSZCUJ6K7uypqKoaFESi5EgRxEPBQhJYqGAiyowZQigRm5h2IaTCMaGpqyuYInTBlJfK4oBlVIKrtXAJtngAjUYgMpVMPbu912f/zr33wRop013ZPLR2eXD1YX68ePzklk7Pubm8PtfrgbFbBJKL2pYFKvFRwxWQHk6uximIEpsnItguo1UZBSa+BhKtfH6fX17arhbtGl1fp0tVo1TSV/dXt3db3DNp607fnJar3a3N1Od7v9tr+7Gu7akje5GUoT2pQyX16s/vm/+Cch50WX8nZ7W9HkOCxDaiCWthnGqY5FprJqO458t79Zd6sm5mkcw2oJxvNswV1CmGzKelw3zZuvfzddXzUlI+Y51zKJzDUGArfe56cfPb14kPb9m7mUY69TL5uUY4ebzXIq9tPuhy++fYNmjz+8nwixumvNOcWmmWGYpnJzdVvEY9Ps63T2+N7JxcV3z1/KLFxq8H6SDJPVKG2GAK7g7BPHxeb+2We//6P16T1vu9k8RotDUdGSfN01HaUQ0mBHBwvqgIYYMDcIntlUCxulyNVNDJw4AACyVQ8hGfgkJVtg5EXjrLa3O5PStWnZdk/uyb/+0z/rEogUtcTU/N1f/s2bb7/+2R/+wR/+4QfGx9Po/fXrCKlZrE+fflL76ieuOwHBilWhqBgxIwdVZUyIpoouCEYzWVhFjRD2SXpLDSO5WEUkdCxVTHBmUKoFDqTT9uZK1rEqPbp3WcqMcUFp0+RF6+gy1rkgd7lbTTrVcULrAzWqcHs4nt57eLK892//zZ/FZrleNMNxN/RjIFcZSUAxOCqpuqtaNVN312IBAxomRDSoVREAZp/6m7A0xpaIAKOINkwIqGIIVkVcpA6VKBGjoJopuSEWB5NqYujECi4IAYENECk41VkUvaATEAmAozqoGxMGADYFLxhacW+7iIaVBZFG06xarRgAMarMFEnBiUKVwsHMzYGBw4m13sS2pEX1eRrGYSjKCiH59Hx4OeDNcpFWvNRJ2jgcyg54iISKDbh3KRjxtgz7+YqcIeI4691uO0zjerW+//By8Us1xRnZrUYQBBwQs8eAIVh1MnEm5iBCNZZYFYs6MUKGhJUqUDB2AAc38AAQTSZ0TZhdo4jSDM5qbkiI0EQKGEYTcBtlGGfkzM6AborAgHfH7f5uP/3yC+q687PV5dnZR8/und8/uf/4fq3UH/f94W439vvRZg6DIzN4BbUwqYIpC6hUItxPtckRTTlxD9PlZr1YLt7dHd4cix0mfLddNIu24dOT7tFqE3M3zqUMJQXKTbPatIt1OjnQm/12u5/sYMNi6mTVFW+7GCKFaDj0vaEvlwtyGIfZiBCpY7S4iKkhjn0ZL85O6zTuxpLbPJI5OLZJjSzApttIv795+dV4e0XI2ITaHwjIDCqqVAnEEHFznq5vrryWq9e7dn12tll3iY+7uzuwucix+ubheh7H3esDrxYthYSRNQuqAoLZ5cVFcxwOU9+s2yZnL/UpPzkeptoPyU8Wh/4YylAtdAufnIJVgo++/+zjTz9vT5YUuKrM84gVtNZF26RlSDHmykSSlTOTkFbk4IGNMCohVA9I6FoJOFgQI6X35SFIjo6mkYpIZE4cNYSMYcSJMre5e8wPnn3z6LvvvgvOx76UOAtg265+99uvY9Tf+/79B08v+ymMtfYm5TjlGZePL26/+5pxr3FmhyhWVTSgIzHohEKaAuioswFMU8G88MZhQkONVJ2ZlIoEQx182JtTnnCDSBwMW27LcOj7xfEw4eZh2y66gD4dwAp4bLo1uFoZt/t3Z+fNQfXu9i7m/OGTT//tv/9Lw7RYJIehzn1mdzNSUidTQC9S1YyAQMQcsMyjATGGapzAQXUKFrCrleBYCLBXiIyMNMEY4mxAjqGXnjGkwKKDKSIYsidGABBArLG8P4qZOIAiOQWR2a0GDAHdQBCTCY5eADxSAHARFJWWgpmKskoARHRoEo0oYoqIAkaGDKyGRWpmAYLBuIUWrDqZkxNQToka5C6HHGv143h8ffN2gNt1B+wk4CGHTMuYm3fHK1frKKWwXC8X/XDL1R3QkRchGtuhzGBlJjuMEYLD+z5yKcERwMGmmbVJnWN0RrAada6TOSIpgnrwmSmIaw1BEV1jyxowsYGbAPj7NbgDuVumMCecxBolQiR2QAqITKgu2YDrNAIGiBHJhkquIzWQwOB4e3W8fv3uq6+/Oc1NbuLmweUHjx89fvbxA1E3ux2Ou93t9fYgYSaDTmJxD4zgXKsRDwnJoJBhA/zg8ekHl0+ur7ffvb16vb2rLtvDtt9rf4vXMXe525yuLy/PE7e7cSyyX3TrxeXyg9PIN/27t4e+n4+zXqyWrimkFO7m/WK5hJrGqV6sT8pxcoRai0sVmK1abNrlcjkNBcIyd6xuxABAmIMBDdMuWdi/uB7f7jpeMrPWSuZaqqunFBW0qOOihSDNunnxvP/Fr7/7o5+ddsucGE/Xm8N4pBxj0TKPuT3ZnNybfCrYEyWVEZk5lvVZyot7h/1wmCZLwdXbLndtXi6X+/3QT4fl2foBPanTeCx1vN2b1Q9/8MnTTz9enZ5FRAJYErkVymHVLEwlVEMRCREEEFIkylgFVV3Vi1cLygjBuMxohohForM5FpGYs5kBGKGRgwtJJEoJKSPMQGEU4OXq4dMHd7vd7dVtdV2t1u1icXa2IaIX377ZcFicLh7dP7n9zXfmcnN9O10dfvbxD+/+8rckjgJOVAjcQsAIBOLGnkxdZIrgGGwmsTBDq3Y04taBCDxRu0hpy+XpTy9/76OP//5XX+ys5NWacugWOYelKDUnj8LivM2dTKPKaNy1y2XFQGrbd1c5DCZhdwBwfry+94u/+Lu5TKHJhvPt9rYJkRzVyV1nrQruPjOSmlUp4MjI0zQFSmZYFGIAiJ67hUc282EaJXjEGNSci7EUdQQij+yI5IbmAYqVbIDOABEctIo7Ro7glYLX9yYVK0gGBGYGiODRTJkd7b0itaCjm7n5BCUg5sCeI2iKaO4QgchdSBsAQVFQ8Jx4QQokQyR0VaTCasgIQBVQwIBw1Sypwe389qq8Cbx3IeRcpHLKaJwxPr38aDsdQmwebD7IWaf5QMwUokkYK2SNoLwFqVqvvjtq7QgwtfPZQ+66GBI7pO9e3fSHqfM2KwUEYx4Dmuh7GwMAsAgAKwEwBoRqauY9WjZko+AWETKKuxswgqUYiQ2qIKG4B6RkgBRiRiWMtXTEI0A/C3kwBmY3qAFx1SyzUNHam22/en739fPJYfP4wcPLi9PT9ScffvQ9iLt+1/e3h+1u7n2CMqrGmDZdk4OJzjp5a/jk/NHZ6ULE//6blyaOUIKpu9bKWvWun17vr19fvTtrNmf37y03CxsPRwup6R5crBrMtzeHvh+e3+1XpT1bSchEaJZDrlJRsEuNBU7MoNJlPsyTzmNs4+2wMwsbWgfGcjyuF6uqBlVWxLdv3tTdsMBlFCh1BCsEpqBAXspsbsL05NHjArpcnTTt8eHj+/0w9d1iYD8ey3icmxAihgYbp/ry5tsmLAJpWrZpxbFrqjBPLeyPxLjARa/KgXG2dtL1OrTrQepaDRAEdDWN9dB0l6dn9z960pydhJAYNYq2MXDMlCDFUKsn8BjT6KYKkUytgIM6AXskeG9gBpJZ3WJwgWBUiyIjMs/FACgwiY3sIpw8NCZOBK6uo7RtZo6rk+WDR/cPfZ+68PTZw7aNi0VQhzrH37149+JPvvvf/C//h2uaXzz/Kse8eXJxN992T5bHL0fy5GbQMHgQFEt18srX0HmatCAxgBBy6ppa0nw7GAIaARpy4Ib+iz/4o5/87HupHC4e/NG/+tOf55N1s+wiB7E0h2UMp4kzlh7F0DJiijGZ+tvXr23umwaH3ZaYl5uLqzd3t9sSmtgtw2F3TUAyKzgWtQqmpm5KMai7iqAhc5jGIqIUHcjDgmNMyogJBWZRMAWo4ERNTg6gjiYWGcitUTZzQwUmNy9gAWM1l1rdqnsNvDAzMIgY1Rzd3s8PHczN3NgdnIwxgKtiES0UGCOLUb+fr262d9u9H8d+u78eBgxpzTmv8+J8s7lsLu6dhKAgRQTBJDGZzeaukdzBDJyZGXjWgGEux+3d882a5hnKXELwlAIjT1F0GoOEj588nVW3N7cpuXtEi8sc3xMXJDQNQqlzEVifLxexlXHKqwMvq+ckAG1ofvKTTdnHF1/3cqgMOKp6aIgAtIJWVAwhKqIqmMFoLoyxAjolj+isXgN79Rk5FMSoGdQULAIlZ2WfVT0TiVe1Ct4ggUAxA8/VFUoBMA0SIrtpQYeI6rZpmgWHsT98+c1Xv/vma4x21p48PXv48P6980ePHj37gCd4d/tquz+Mx2pqow4ccxMiz/L8xeub6/31ru+3/YaTI/dWBizssHBMhAfT/X57c5gWx8NqkZ8+erSidT0eF5vl43vnl+3i+Zu37w5121ezKSTFw/W+25wkinM/hJRGmU9y66xa5sV6vd1vdRrXi5ziYi6WmpzSCsVNSnGToT/e7TuP667t93sTAVCnYqlaw7WUKvNytXQe7m6md/Ob8+Vq/fln5/c+PmtW9z54Mkk57ra3b9+o2mF3exxe29yT50qsECjEwIjezDRyDGBA4MLuEIyw63LumrPH6XRztuwWMu7/3b/9/2T29SfPVuvN+vzMANCBIKVcQ7AQAQkVPORAYIj2PgoxN0dHZK8RAYkdGcnMSHK3ZIogc5VyLMIGq3VnCA6uqgGSoao5SXCtQpUyRgqzzNlouWwvL0/m+vD1y6thf9eljUFIKYNDFf3eswe//vnPnz16tAKYJuNcDtOBTnNqFj6PDsUDuKtAHdupBI17awpgCCPMZBq9TRgr1zlLhUqAVrQG/fizT//wD38cY66IP/7ZJ7xe/cc//4vD3UDLILnl9iznHB1IsYoZ4mrRQpF+uy3D9elJLKpaJUUqMhZFp9g1edpvvRSDqIAiomYiQkRsVAzqrKgOCCI69FOIObVNByk4mBMguQFSBWIpwhElzpPtgzeMDAag6AAzaIgoYuj4nopoagDvZ+BGGmqpiEDv50vA+P7E6OguAV3BjFOpDmSERcVy2Fy/vf3qm2+/+93bt29vh2lmmLPOSjYRF6fGCFBHqhjo7PLe0wf3nz08efzofpMWRWsgJUoCwAZBEQDAvLrXUL9+9+W2Xi9SouVp7Q9lHBHRAZmSkpVq+8PBGQ7zHqcqekDQGNwJS+ZJq9sQiWzSsODF+endzbv9eLApUpJFG0Jbmni8/+jDDx5+9Ju/fPHizU5jpjoHAI6G6Iyk78W6ioCupgECOaL4xMqRqgMXTwApYDBCqMnBkicCFAnqwlhdo2vmrBA11qoeA7JJBWADBp4NSKjSTESNsondTUWbplnk9uhV3MUPt9tf3+y/+PI3KYWT9ery8YNH906/9+FHpjypbLfbw2FPQMe6vd1u30AvXk7Osowyqs0Q0IjV3rdTJnCLNLvp8Tj0h+2+f/jw8qOLe2UYxzJulvnR48v2etjdHOepD023jMvNvh+amExlVoIc51KY6CgeCDWmuXpMaRimEFIVZWICSSHoNL96/qaDeLY67a9u5zqqqaqITdR57LDbdIgpEA5XL7hNJYjR+bK5/6Of/kHThN3+cDgMH3786Qcffwzvs0OZ//5v/uLXf/0XFyerxB4sYa1TP81D1cpkTQQ4a9Nifd61y7OTTWhyWJ3kGKvZ29/83WZxssf53r377ckaau0cQ0IOGqIgm3rO1Dp4YFafAGswJE7Vm2roVpkVENTVHRCJAwUGQMCEVmpchlgDEjK7gYIqqjknshyFzC2qujsCuFlAbJsmN3zv3sl2uz3udgh6wnmzaEkqSFGp/Vxfvrs6O7s/HaYc2biJjxbTlzMdK6o6KrgjgqDXgLQOw858ZuWqYDpMD9f3c1cffXhyf3O5e3ftlfu6//S/93v59GSxWJ4//KkG/vF/cfnR5z/6P/6f/rv98d3jzz9MaRkp1DIEqMbeLjcFVfuyfff29IRN50AYlxshrhLEuFnEod+69uTu4IPNiOYubmYVBHECdDcSQUQHDiGmkDiQiSsEBHBVN0KKqkIc5rHkJjpJgSFpiBBjQAcHEHHlkNAczM0tIjlyYRdUEDRkInQyVTeXxApu5OToplp9NoeqFsnMw/Pnr7/4qz97+fW3w3ykRIaUOyaiWSO7RCTjaIJRaI0kXA63b/769btf/HkNqfnxj3/wT/7ZP2kICIaqGCmS0/tiOQG8e/fy5Zvfcna3sFisIMZhGA7Ddle2Ma0heJva2g9N5ib7od+6T6lJcy0xLjpygKJadB4SLvbHeSzPXStzSwxNRAp1kAkH6v2rs8Xh3iebd7tdPVR3NGIjp4DqCBxEiRwI3QMY2UHmCk4EWQbiJOrqUMCcKCsERFMXt8CkYIwIal3MUp1gTjnwoG5QmSNACGCI7gEcG1Z6b6YTxICTqlEVs1KNAxgWDBiRS9VX19Or3c2vv8D1Yr0+OXv6wdPLs5MHj+7Pc+k3m5d318d+4N6E6bjkzWLxKIQX372syLVKwIBICbyZhB0psgyH5y/HYXt4+PjJ5mxzszusliePL89a95c7DVe7PTdN4BCX2RR8KB0kQrCAtaCO06ZtkEKkzCgNgyFKra4A1bav3yZOF+v1cH07+azkUgtYFagujmONcR1zDqj9oWJsXry5ffHy8D/6Fz9scjCXJqfz81NzBXAzJQpIfH237YulYU4L4TI5tcvVxflFGykiQNN1q7PT0C4JAKyIlL4/vLl6N9Zh2u+53Tw4i08/ujfWur/ZHY/T/e4hBwRAghhCTOgIblYrJ3VKxuIVCCOSoQsIujEymALB/F5740GkiAMZQTInAjXiiMhOkgEnMCF18BSoTuLI4Bw8cPVgdNp0D1fnL6b5UCbZXnPVs/WyxcCBweHtze7ps++3Z4ubfmALW53nE4IbJQ3RcOZKTOySQ9Asei8G9MvuHBHWj04unp2cPzxfnyyJIcUn076+fXdYnpxWcssmVgmd1M/OVv+7//3/9v/2f/lXkhcZSaees3kliksz9mm8u7oKC8DsjsgQgYGw0Zm7FKD0Q+mRkIShyhQKGrAJOBkQqrMVU3NHggDOyJVJHRwwoKGbEKQRjAHUXE0JgcRlmClFy6ESEhmDEaAqVS4J0QxDasnNvZBqQhRXJHRkUOw4jD4ABlcnx1kd1BWC45RzeP1898d/9pdfvngRnRmMm5aCzj5nEBQ2AAROhjOpojE5uJhFT5oCRkkV9E//4j8da/8/+a//mRgQ2eSG4Mykjk561b8pVFTVuLSWYjhZLFbOxxrL0B9NCJ3Wy+BWu5wcVyA8eiEnCim7u/kRorC4EUZXVUVRrFRrjovdWDNj5kZGeTm8jvPu7Em7LvnNa9z3ijoHa9DM6hwwg88MEsPSnAO5U6g6m9dkroDGWRAFixBkQ6o4gIfogMhuLeXgpDRl5sgK2U3AIxkFEzGjCNwS5mBCXsmQKExlGqcbFw2t1nFt2SNF5IZ8hhK5VXURPAzj1fDN7159uw7p/PT88sG9BxcXn977Aalp6a/eba+2t2I6Tbv12bofdToWGj2mYgaK4BEDqhgOMwSVw/Td/f70w6eXMh3nEE/vne4RQ25XkYN5vdrfnixblXrz6u7JkyeMuOgaJ/mH3PO93DUCQQAjRn/z5rUj3Ls4s2EANIxIiCRWYQoLpGUoh9If9jmtctecX+Y+lH/0o5+OIz773keKhgYxcEqdv591AO73Q0rh0x/89NHTj+djf7e92U/zg9OTk5MH6/Wm7ToDMRdxr9OAOoNDLfXu5tW4v03d+mbs22Xz9OkHzgjmTdcuFgu1igBMjBQwgOME5qrRPYPD+0YoIiJYYCQHAUR8/5+EA0YwUKR+FneKQIyMYAHBpBJEcXAEM7OAGqi4Y2RArKX2+yPMGg0BabVeh7sbtLJu0rPH54HZPZW+KmLXhS9fvHz1evvo4SMVmhyAwSKZiYsyoEfeXFyePqbLy9OnH37QrDab1Wa42xkXccpplVLsyRDj4d2LoY6b3LYNr5ermHPgqjhtt68UTmO6qAkxVFDPlCaRvGrHaSw3W5Xj6emCXKqBMXahnSbIgaPMu3kLxK6kVcAkGlcwEWPwCibVkntQdWZlkSoRSIDAUB2DkaAhKjmIKJCjCWPUAhwBBRXJkxbRyDE4M2BxFTMnJnDF6qAERMQQUcXITM2LGYBrrYAsXtELkXHMu5vdX/3pX33xxcujxK5tyWdyUAEwipxYIJpTIHFT8kAOrhQALVFAB4ukyupgpyfdb371yx9/78mjZ2cv3rxMeXF59oA8AMD19mo3XU8+MickRSdwqMI5n7RtiREOs0wmB62RfWn5Xvfwtn8Xy4BxnXK8214Ta/RMmiY1dIOIXtERUrs0g0XTNk0DHmSaGWzWiUNpztY/fvRo3NHXX3xjBZSzwsgwFJAZCE0dZjUPFgJYCM4+aUzo1gmqQ3BVAAvBwWcxCJbMC8loCETkWj0KspElZnp/X1SMSDEAApIjtQhWkydy1yJ3xd2RgepUdBEm8EpANufaStYjV1RqmQXqq6vrt7f7F/kFUlifL+7duzy7f/H4g4txlO/evdltDxxsyS1Ex7I/2HiMQEytBiViYhErOpY387Hffvr0g3bdRtXLi1WodXJ/T49ogdLsw+rsZD/0TUpAHmIYj30tFlpcLRupVR0Q4dXrVw7l/OLCpqlMc3WDSNl0hKmQsOOrr79b5NWTZ/fOzjcckhg2oZSjsneL1QLMwQER4H1/GbDM+su/+/VHH3682tzbnD80gcd1zJlCCNO+f/Xt7xZt7hoOXRjRImadi4iYVis9Bdtfb3dvb558/MQ4iYgVBVMLnlJkJiRFUnMQQ7QWAAKBqZmXyMlMDRwDoWJEFFMkcneMihmmoQe2kBIBQnVgRzI0ALNMaAwGDOgRySCIuCecwNSrBhvZwWtoQte0dS4XXdgseTgOKSRgWV2u28jzVMucj8e7YdZDnQJwc8L+zqCa6KSZf/8P/mjzKC3W2Dan4xH+9Od/8uSD9XJ12XVnimEEbxYbqGWcp2GYfvWrv7l/cbZof4CbkKhqKUXCn/znX6qlk7RkmrmNdZBmvZnLPB22x+H23oPOoSLmiGlm2qmbw0ngadw7u4OAAYKpiRbEgMERRQORUFUHRhed56ooiIUgNwEZwQbtgSkK5UglVskEwQFglkIWA2YUSAkQ1CyaB9OZUnATI+vLXqV310V7kigQWOx4fP8zA/IiTDjDZAgNBqv8H//jn37xq1/P+7FrVyFKQQkuETWSo0YmRkAmiBnUeZA5AwaM71tdjhIMk1kFQIkAkCH+1S/+sseHk+7W4WwoaRlPx2G8unu9n26dlZjaJqUuzlIsRgDgYCE2oPvAONQhQ7sEonlWhYZbjzzWytxAQIITDEKH66mMOWembIYzBZUZVY7HXZu6ZUteDduMwAoz5bsHH52v1w+++23f7109SakOKRJ1QadSzZ1NHHQ0HZl8njLx0S06ZNTCZOaEBADuhIbV0QgDRUeZwD1KG0NyYK/gDowYpGlTec+E5kpkxZwxJAxLgjo7qzWA4zwfuxCcZ1XQAiJGTJU1WSA3w0lmKDWiHvfXX3/xTbPIpxfp2ZPPPr7/cFifbPc7UB+3c95ZGnin1qvOiBGc0DwRGaD7/jB9883L+0/vX5xtkmvIgZy9liGkzMCrk5MQeHd7q1JPT8+qCzu1uZnHScCC837qj2WYoZxenIo6iqOho4WF/vS/+nw2EdX+6u7p27Pl5SUHjW2YqhFS44ErfPTJD1OMYAII/t47426mtcBPf/rTGOLtdqfuOSd2b1JX5n5/+/r6xe/egG7OlovVEkNGRB0nsaKAZq04X129fvLocZO7vkwMAHNpODBApATgkR3Q3DNBY+DIM4EQBocgKm6CiYvK+4F0CK7qBp5yqGJaCyESh7HMHbg5ugEiIhkTTFqMYnDFOhsBMgMQAyYAUcsUqhbMRIFPupOH9y7bRWyoe/fqXXdydvrwQX9zd72/W55vnj9/dXfY5yaePXiM2Q63W6Va2+Mf/ot/+skPH1L3fh08/8Uv/tIc/vGzf14moWh12g29/P9p+rOm25bsPA8bTWbObq319fvb7dmnr0I1KBBVaEWCAAiJIkXCDNthm8FwhH3ln6RL+8I3ph0hXUghibQpAgQJgABUqO6cqjrtPmf3X7u6OWdmjsYXm/oJc86IzJhjvO/z3Fy/BihMulp2F9vtv/+Tf1urbGu1vD7uhj0ub3fxYBEHB8KUa06pN3fcb8r26uhez4li6mwygKAGM/iqifO02Ws2B2YksurihMIopRoAEdQqAdUIJjUDN/Pdbr643ZSd6DRPWFF2jqG1RSBLy7g6OmtOlsvlMDQ9CgB7ZA8GoADBqlcEI2di3o+3H3/+wwJbpxqxOzo4Pz94ZAzbaX94cEbKCBVUA7J5+unPn/zVD390e32Z2qCLJmtpFAi7CAls8jcSZVQMwSEoKRITx1ANARARopopQ3QLXmewjOQphVcXz9rnm7ceH7uvr6+ex5P2yxefFlhHZqMmYujj0Mclaw5g6oqCtWSoHiAkZHDdlmtlAMYUBoy21r0ygDAGnMvOGyIMKnMMHJuwL1sGxIJAZLPMjiFQ8EKUmhSrbF7cXIUQH//KsWxC3heF1evrgtg5wKDuBedcd2Vy9aihUs22qxyx0AheIWBswIyKUWCBaOYhmLlhFHBNjKwFlNRCBFYm62hEJAYDIwiT1MQQyAG9BzOhNgYFRRIQQYuc3bC6oWVLwDypEApW4FC0CkoGQExlP4/T+tmXN0enp+enZ3fvnPUpYS+70/WrzZ7Wu2acx5pdvefQIlQOGZ2Bbna78YuR9K3z87thzuXk6MBjGWtev1wfnByvt+tF3+7X+zxl7BkEd+ttbBpvOMSkstaST4+OA7EDpoBAk/j++DAFyn2TSvHDB6ePH92dxyo6FqsqZuRujF06f/QQQJEZkadpfP78+dtvvz2NpQgu2xgQ+hi5SbENKEVL2a+vrl5+MeXrcdxPu3jn3n2Pgxm3oSBDt7xftH366U9jH5uDDgmDltub2yElBgtEoECE5NERCJNUcFJCQEd3V69KHDioGxFFSEXVCdGw4ZaEVSbCkq3WEiJFtqwQwSMhIOoIZugJAU0FCoCGENgD133LaZ81WRRlRuSYcqlNe7hYHYYwwmqMq351fmc4vCNh8enHv3SXRw/PH929q5qeb19TC0Xr937/V7/xmx9wTwgNoo7z5mTZx+5w2m3neUQyMNFSA9VcrOhewYZlevDo4Uc/+dFuve9OllfLk9CkoT3GqKpZJhdPERPuZr1eLxapbUPAFGOay2gO7qkjL9NOy2yBwdQEVQgwOAq4tUgiksHBHBz307ibp6vr64urq92crTpVqASRiKAWVvMdzpKuyL94DSEeHbbnj+88fu+D8/MFR3Inq6iai+2X3SBupqVbxsSUNStsa72drq6e3Xw+tA0J7beHXXewWB22vpBr+Yt/9x9/+umnqY+pjVSlUUJkRQ2k7OIk4AmjUhAgQiAgkMAEyKhgAvqGMNZmlepoUJlmRwRntDStDZ3qNDPJerPe1bXZhjEkJzZK1A7Lo8R5t72ZyggAi7hYdf1y0b94eQGxUQINgaEOq2a3vomAzBMwZ7x1zl3LBbmABQ6AwGU2iwLJopnOsVmIQ28FkIOqOMxzASnc08O37hGG9YxH9w831/D8VWZKiVKI6tKFWcZyq6EGVTMbPbXKDCFLjYwtuJoIIQGRVnQFd7DgyIJoGJomVHQFBfWCIRIYKkoIPBABI1IAzHsExsDiRK4hcqpkMc5WuMQFRAFVYmN0NlMHDQ4EOJHk4IliiE17c3t7eXn70S8+e3Dv/MP33l0dnzTLxfFyeXOx/XyztWpYckZ1ZnMzFIpYZX761ZeCEE4Xh7c2RxOXerDqI2O/WES34/PDqih1WiwSNu1m3HPD15vtbi5Hq+OgbuaEKbVx4hsMdHp8BxUxcpeCmo3VOFBiJ6HtPmNsG2g3o17dblaHi+1296Mf/+zXvved29u1qKthNwQkNIG+66hDBGcONVdGNvX9bgQpqQ3V3DUcHZ3N8yur+3r94vZmZ1M9OX8YsLWqatS2HKGEJiTqGBNFQQ8Aiq6BvHit6uwJ1CEwc2MKDIrgSmQBEczAnLTWOlsGMAZil6BBwEkdyMUNXQNG9kCYqhQwd6+IKDZ1QzRIatF0AvfsGtv2+YuLf/vnf/5P//EfHLdLietmcfTl50+/+PRJ2e3u3zlbNIcB7fbFc+wO+6PFZXN9cHoekqlswHtS7EJXSdVVxuvduveIVhHUDRWyzFInMUCs83xwtLq4fB47Oj17a4QmxmFJIKXuZU7ehr7NY5nGG+51cdAKWNvyVKc5cHlDqUFTqhhIFQwM3Z3d3M3ATR3gDbmY3V+9fP3Fsxfb/d5AMigAJScjB1cDZCCqQEDsTXI0crf9dDV/fn3zyUdfn947+ea3v/PhN95r2KFC6+RjtYAWIBm/fe+dz1/O2WwnG+RxCEE0DActxnHycmd5/PGf//KH//4X8zgtmyBSVTIyzoEIMDAFEXX1EMgAGBOhuwMjAAVnoeBJsSK4o4MRREXGGigUJzQyJYguYwan6nXVpXlaaxkNRRHEPWvdQR5KJbLYAFDsh1Xf98tVd3l1XfUKZuF+0S1O+3nt+1FtDlZEG0pUq3cNQy0SKKUueYBZU3t8Me53bklq4jBLnaV6BQqzSKAmNbFRr9Ocn756xSk5Ut/A/Udn6xvME0/oGCIF6lrCvMTalTyjmbqRByq5aRKqMJqBZgYGDEpzAQjBogfIC4hkXismIGicyKu5GmiDgHJgTIjiMHttYoghbMTdoHU2xBwChcDcNnNtKiryFVZCieatNQXBqCLGZEBuG7WlUROaPM+A+uXXT79+/fq9R3e/9e47J93y8G5/dHLw4tmLl14n1xY0ASJ65yaMm4j+/GWYy0gNtKFBj5V5P5UhNeQy7qcU24FaBOUOuPj66uVUS7/oYnQ3qGCsszEVFugaSyn1/ZsFtoJScMEC6oJweHiYBcZb/fAb33rw6D44TONUcl4uF9///venPDFbG8M0ZxNsUnTVFMLm+oJcmcxBct4ljuo8zzOF3evn25QsEKjlUnK/WnIkR3PJmscmQYMsAMBITAwMxKLqAESZ0FARARDIAcjeVK2iqhkTITlMjF4lgzMAB4qoTlCZCCAFZkd3YhVQhUABFAkQHMHRALNB2y80mzM4u0lNSE3g1MRlwIvPX/3tq1ejyfjjX2zW14/uPrh/eprnm0hRq9er9UZ3y0ePFvfab7/3oFtsXbN7lFqlZnM7OztV8Nc3F8vFMjgXKRJQSy2VRb1WHQ6Oa4bzu+ehOZhtaGjRkLe8SAABAABJREFUtSvNea4VOTRNPxad8k5hOjlbzDIGol3ddN3peCsq9XBozPLeFIldtWhhBwAUAP1fIeys6IIXV9effPL5JFLFAkMkrOgAER0jCUA10+AIbkKwAw9ejbgaRBIUePH17uunT588+eZv/Ob3j4+PXFXdkMgdasG+P2mbXsvcJVfMhk5s6PXk6OGrF+N/8y//5MXnLxIOHkF9ihACMrh59AwCFghUAAACsEeGYCwcxCE5BZTI0YjU0BDFCLyEYKDAHqqRijMBBj++cxhSe3l5s1ptuoNlvpi5SbmOELBr4rTbTnS9WoWD44XbQYwtJFoslp9/9mXOY+rSOK+Hvm2jlTHHwDVzSpzrpEXAaBYr4otFB0Wz58q0XA5lmlWtOgC6iW1mgcaXjCswQDWWWnW8nQRgcdiKKc5dA+ohFHNAZ3AEW4Uma/Q2kNfYKChWVGRTc1fq0B2MPQQEjUhkyIhmRUoFjm6eooB1xC3QhKoArTu6BeRgocWG0Nz08M18wBEBAyKZu5lHk+AM1ImzOoOh5AL1zZaq8RhSQJTRJqQWI1cRARMvv/zqs9369bv3Hp2f3WtievzWA7q+vLzZ+FzZRa0hDMRFTJQw7KWcDIeuQk2DTNvNTm5u7929M84zE6qoJzOEduim/b4fQt8FsoqRJ8kMaS65gvQHi4PTlTCQAwIBOprk/aYfejNQUxWfFI/v3OVgrnDnzuk/uPv7ruL/6w1+e3WlELpucDcCAvW+babd7by/yTaqzUZcRFgyMwIGpKY6FPHimFL0ZJNklb3H6gTKDEAeAaEQdILFwR24vmn/AzApEFUF9CyCRskBiRBc0cj+05hKAyiZE8Y3bfoQEoKZCjADEqZY1NmryqS1mgFwRIgUosxjtdlRmybaLKTaBPvme28nRBC8eHFx9/49HmTZB6Tpzr3jEOO8LdzVenFz+/rV6ZCun/5S5Orjj+N3f+c3usNYZsmzWpX9dt+fH2/3wjITuBLVLLfr9XB4YM3i/O1f//rnf3X/rXs3+7DdN6uuZ887mAC9p94xeJ5k3i4HKnmrmlPsm7a/2eQy87JpTw+W19ezO1QTMXVVUycKbwaOqNncEWB9c/3xz36WzQyROaCD1UwxMKpyDUHRVNCcyM3gTWGmkvgb/G6JBDEwUfnpjz/66snVb//e3/nONz8wM5fMzGaBiJsQN/sSQ2Q2RUDMQcKzj178zV88vbqe+xjdqwJzePNN32hkyRlREcFSatQjkICRqVQCjZAEAM1c3ADAiIHMOnZHE1ADJEgWq8Fsps+fb6jnZsEh1SLrUUaMaigJ42G/GFJHPtUJuB26bhUSQqJV20UX1DGXEUJ8ebVporex9woxtmLzLCNhU8TnYgxc9qWU6c2NsOyHHHCzi2UKNk+pS21qC+p6v55z6AJ2DZHbvmQnut7NQ2ByY4kLtz2KWiA3RwSkFChRqMbRMQJyghibPagVDaYD0JtI6RzcJbcxmoQQmpgaABN3DqA1m45EAQzVdIcaDHsY2Li4UfCIGMwUQ+9MQBVh9jKj1BCix4WRkokWJ3dzVGso5gAKOZBHUPfJIhQyyRoKYOTL9eZm/OX5ev3hN77JHN49eHvF118/e0WsoD5HzeiLEivMYTfPy4W2HLTWjHBwfiS73dZ1eXQioAigmne3Gy/AqWuYrBIqcBPYHNwdKpDdu3eWGNQkIDKauUS0oWvB0FXFJSsent7vhx4BAN8EryoxP3v6ercvd+/cUbMQOe83aWggY0WvZd7tNlevX253t1VqCkpogZRBGmq0Ijjm/RyxGyj0TFnUVbvA1V2RYoiG7maKuXpwQ0ZUt4CtavboCE7OGFPTJikSCYuMwEZG7kHJCCuCERFBFHVHd0A2Q6JSKlKYtYQUqYp7Fs1GWAWIQhPjbtyYZnFj54A4y7wcwsFBrGMd2vj+vfPR8jxlDH5w2vWLLiATwDj4/OxqsZubvo+Q28Xy1avt1798+vg7j0rNKiZTTZhOlndWj86//uKXeXsVKIrCwWKoQOd333r5/NU0TU7DZva2b2JSmXeoJXUr50bnXLc3MVYHNdGAIcYhj6EW46Cnq2E/7Ta7vTruS1FwdkZ3FTBQAANTQ5jy9NOf/6RaFmIjZAwuFBhMKbATqoC7QsQk6qQeDMlQAMEVkICC2EwGAG0fadq9/lf//f/06utnv//7fy9E0poBgABW3dHLV0+oV44QzFI9+OJnl0+/utllGGJPIgDoAObmEdzJjcmwR2YEoAjgzK6O6kTMHIDIgawCuiMhEqOZRNeoWNXFPINiaF0dqDrybm3zvpzeWTCYOYHV5OjuCWgYhpOT46QKYggUA6UE5rVMO3cJiaFpkPvEHoL33ZFM2WGac6mualVcU2QrstlsMLATLpuOLBqqErtrzShVaiSMlD2LV2q7lqKK2Owite5gr1tqdy0uMQRABHU3Ku4IkhDRnQyJkqA2MaQQilrtuIcQUhB3N1wwuTcoTg2HltuGlOqcM1TzjopgzJhq0EKBAlMwd8dKEZjIzTOjVRgAq4sDtjGWqojJxQMSoitEYlqAs4gRbVEdPCgEAAQgEKpaFEoIB4rKoWJ4ebPZ/e3fvv3u47PV4YOHp6ELn3z5JLkF0hAbnwChhOF4FRDGMvObhKVC6gYzG9ebEjK3PeTi04zOcdkjuZupo2VBoAqTWuEYEsWgSEgs1bw6KjJWJyuGCO6qFpZHR0jw6SdfPnx0HlMkJFUdx31K3TTu275LMQFp3q+vrq5Ozk4p4W6cdrvddrsxD1rdVRFJRANWilGcxD3Fru1aNwNzBiSxNjACEZB5cEPHQJQcR7A5MXFArI24uXvfttA1bwKNIBlRndChOChSDG8QKZbV1cicDIjAnBwB0MxSCKamWtQkazWkWmWxGjTPWiYCN/VSzNSr63LRqMxt23CiAHy6WHiiew/vD4u0WHQ3t1vzsjrrl+tFcnz/Ww8OlsPTm5uY6fJmfXJxaOwciEM04JfPXl2/2O9uLhYHYV2LkiKYWbx++hTqlNrF5RaJli0G0KIuQz9Q6PZz9bwtsg29b+axDXHZLUTCzR4cadVZgf3r630RdaTqMJfSUUQDc3VydwdHFX365ZP9NEIK7uYAZhawMcAQJqaoHhGYIwoCqQZVR5CWVJVsDhrRKlNVCOgWKTNaovC3f/3X+/X6n/7xH4UASA4alulOExaTrV2pjwdf/+L2s4+uY2wIZ/EcoXVzD9UJkgVjVlUgR1SE4BiIlEnBAiQCgAQIJZODUHIjetPMAmNQrATGAcg9V3Wzhiki5MhcpilQx2DPnr/I08biouP+vUdv3zk7Uaip6bWWJsBiiYFb0f1u3FbwbnliPPRd3yEnxsPF4nL+bKzZ3cS0qkPjEcNcamhCRTDGyaSpSuaRUaNFi6heZvYSW26ilmLdyxk9Q68HYabQDWnoQFhsqjbUAKQ1UhBTxkKcRBEjMIkwQqKK0GMcrZRokWTBSQSQMToQUsXcLrxfiMloTSZla0If8FiP8q59vc55Vgc2M2VzVDQiSFXVibeuwsKCLogUVZwZKQp7UYxVAQECM6hEoqQeQ1Rk1RlwVpKm65lQFSIwVQ6RpOrPP/qsPnh4ev/w9N7SwuPPvv5KRA4x3XAeqwad5iliaJrq2kHjgEQ2NOH19Ua9BgXNmZm4TaPlyNhwaFJQFS3FzUS8b5Y4Q60aogVmyeocpE5ShYDyvOc5rq/mb3/vLM/5Zz/76enZaUotOD579nTRD6bw/PmzD3/lGyHgWOpuXA9tYCmCfHxykLepzBLSAgDmajTnrh8K58Ba56iWYzNUL4aplJwMAjZkAJiqYcKkWiGi08ReAUAxmgIAJQqOSNjtp6wwkRsLoyNkI2zFjRwBCAzBQ2RHQyNidycqIsSMyA5Imk1KNQshZkGMyI2tr7ejChi5qCkWA500tq1EFsvNMg3YHZ2enT48btoGoLm62puLVG/6/s6dh+r58P1HueTz09PPX26++PTLt96/33bLuSoySVCQorkOXRRwIDeqVtxF2K/bFK4rK3YHsU+oosDaIHUoKUnd7dcUy1T0DUJJ2Tdz1pAa8oB+dTvOWdxsmksVDchFFBBIhYAyGqNLKZcXNxxbBQBD5hDJwA35jSLAAnI2UAIFjc7OplgJNaIrEqFGADVWIHIHV4oiNHXL5rMvP/3X/7b9/T/8negFRWLgk9MHT67GjsKrF/XTT56l2DrUQIIWFUWDGhlgUjMUaDhIEGVxB5cUIkR0FG8bnBXdrAnMaoLqZKoW0MzcUIwbMZw8BxMA9cCVwAGisbt2XSNuJ/eOb23eWm26dHKwSOwWgkoBLofnqxBU57zZ57/60d9OlVNYWi4730hDscDN7tVUb2LieTZRSW1rEUuuHHhmZfcAiC6ZJuYYRatCF7tls8yjzu5VDbWTLe08uDUR2iUvICUDcE+AhkTJ0NBhnloza8SBAFEZgTFGzmJMnghbbqq7CjA5MGvkUoRs2yx9sWoCQwmlwZE8uIYeOfFtc3Q4nB+/vJzyTZHqFUoBE0tJrAlcIRsFRKCIpgiuJTohMUjU1HkcyMkFIldiIiDUhJSdZkWjgIiMlhw4JgAIRKbGIRri58+fVZvD/Tv3jofkb3/6+dPdblKWCCE0DW82tys86FIrm7101LSxVD+7cwcQbzb7Uqdh6CIFmDWGEFrWPMk8O5hVK+LHfWySBUJyyLUQBpdCygwqU/3oZ7+4d/7Bydn9xfIAtf7xP/uvwBzASxWITRu7v/jTP33/2x/0fdSa2S0hDJHATYqjM3pEo65N6BQaq7rvaXASMZ+KhsCRMfFgmsu4TrGrFik6g0eK7gYRKs2mChAAQ/UACBxiExO61XlS2zNbExvkqFaLASGlFAzc3N2MCdwUXdnRHUGRjRTJmdFqNCnu1QGcDWDo+jxO0342p5pzFVeHWrOUGbwZs5bdbne1uXP/zjRfFs27Pa6G49uXVxiwGzoT2Rc5ODjujx4nzdfXV48/fMwtWIcVBREYEZWQ8Ktnnwfkxx9+cLDstrv1BDLa3giyHEuG5RAH9lpnc+mGhVuICtN+zwwzTmrQhZRiEk1SmhgdSfdmuWRGEnABAwBQExAAD2YExGRMOJZ5nneEDIgAZG7ABizEHjAysSG4qpqxsKBBw+QQC7PjzFDBUBSdCNndxSxZiM7Fpe3ok89+2q/Cr/3q91vUoOn46Jz6+PQXn3/1N79koRkMHMkiubkJuwcO7uzBgQgAAZiBO0gaEZCVGQnUIwVFVzQgjWjiwcCERIndzMSrMVmt7EBUBd9Y3lHc+8WiCSvgcHDYf+/o+Ormqosddxi7tGoWIWG1XQyUp/r82bM//av/cLsp984/WLahD+WqbvZ7jQHmvBWypgyo3IZFiG2tBSwb64CxVmAThDhlH292cgtlh9e6cb/koIboFhnbVriJvje84qnEmTE1XRdDE2KHMJeq6o6G6GkSekPKYsOITkqFUNmr18YDhoW5VNcUF1k37ruTe6E/JKmVpRnSodsIKIZsOu9ZxMtxCo/P2/2KdzdQpNtlmzM5B4Q5KswzcIyG1SNiwUNkxBoEWd/kJQAd1YE4DKpAcVZnxT72O8OIVt32oEHU0TuAngl9iszZ8dXlbQjd4/Ph7kGvDx/87KuvZc5d8LBYnKX8aoFmVqBNGNiqT3mqblW0zLVvO0QihIYYBG2qotI0rdfJZA5NB1S4p8zQGjq5uq5vbxdDgw4M/J3vfvezry4fnnjdj69eXR6fHPUHPRH+9H/5W14cPLv96ujg+J133wGQPO5uri7A5jxmrPjqdvPk66++++0PDdPLF6+OTw4hJdYmzzIYKwaZMYaGvAEKWDWGUGECFtTYRwZzIgJEjo2pa3EHBiBkADdHEJGsU2QjAK/moEW9RK6qjQKAq5m5xhDYA3pARFdwMTAgYsVIXN7Av72CmAduyXB/fQvVyFlUJq1gVHMGL12PUGsgXq4Gn+frnIe+bZdthXJ4tLi+HW9vx3E73txcP/7wbQj06sXFwbI7Ozt0Gz1GGeeG26pqCuAOZBhirbVzcjWoSJTa5enuqutCmyCRiKsRRwbEgOP2YiqvcaHRY4+tic2OpgRuwSe3MomqVwPb2ySRqnpyJwNmfjMpCsQOEJgDk6sTkKoYgRjGRMiE+AbaiQkISGVWdxQwBwKO4NCwA7npBGSgjggeXejNpBeClwHxycc/dbXf+s3fHnW7zs/HefvRD39aCwIk8GBo4oUcI2FwojdDLOMcwAIipKBgxEYWMDlR44qu5O4UAAkIGgD26q5mAgpq7i7F0dwZg4gZYaJgJhwUDatCEzgUGNdjg3TQrOqI2sa2HQjZJO+ud7XApz//ZF6XZZNKuSne9U3XUhVx8y2wuGEAD8xCsdbag9fAI1qUWBV11vKqvt6NLt5kDtQGDEpYqBIFEAqOBGI1J3QptM57Gx2dDpqyGg54RSGye3A0FU2aWlOHSmwNzgGRLbqiIzFFRrUowsJQh1TO32sWq7SZpiwoIbaROzib8y07BloYWlts3N12R2ePzu/YWXjyxcvEYEOaxecKAZCK5lIMzBxaaLuiOUHh8EYFGQkQQIFnswhOpsDBQBLBENkUyCkpiPoMmj2QAjBhM0FoquhXXz9rHU9OD8/u9Pfmk8tnDrALdb5OKQgE8QDsMpcUm9VwMK4vyzgOXcfRQwITpRhKVXAwbkbxOsoyLhYnJ+88Pt2PL6pVBRETEPziySfvffA2aTuLXbx+cRSW85Mv/5s//bPv/v3fWOiKpqOMw8OHj7L5y+34zV/7ljnJnDc3t2bq6GJWt1vzitFeXL9UZGo6T6wSJDOAiQsgGjUz1D668BSVODa11ghM7qoKIYBZQJBcCCEmElMgAmVgLDZXrdRy3w9oUKdc5hooUVVwYDQDVxUmJCR4o8SjoFQnrxiJonMwMBJENXAxJ2ooyTSTQ9+0ecql5CxVi+m8ffjw5OxsYfuMfUur4eb5VaEO3I4WodQ5tj23pLNt1uOw7B++ex8gpDjUWcqoQ3NimMMiTXMVM+YkNt59dM+8cU67fZnMJUPXnu7GYaO2WHXFa621cAhNC5a86mZe+wJn08O0DBD24BVY3VuvzJSLSrHiPMGYaR/bnsy1aIKECEok4IhOhn23PD46u7h4rVpyqeqhaQ7ICRX/k60PKbiaCsZK+gZuDECWGgarXsWZikoENECjNxplRENhndQOqH/y6S+6RffWuyeb7e3f/MVPtlviNrJI416dM3AAZnAHrwjcEoYUHdhQI2t4A78nZQK3BhHAnUgo1FICKZhTNQVwQBTBSOYI4oQkmp0UnKFSw0Eg81IkzVrGmnE7TcNwNHRHIbSrxYq6UMdpnCaV+sWXn7++epn6FQOZepEpBUIjVwePpE0yY2fAUEVAzVCrsYIQ8HRT1pfSbbWFSByETAHJgSBSAWIP6AjJrCK2DNWB0VMABSlTfr1fP/dbWPRD3532tEyBHKw1zy4hYMWo6mQawYHAEdm0wwhqqb3lYc3dASKctqtWplx3k8Y+nUJszbYATdENcRXZwb7ZB2774dE37rx+ebPdSOR2kbpSRmws7QJSUrfJbWYXQCdQADaCCiEFAUyqFpw9JAHFQgS9ABtXAES0gBGpqtUZJtTAfszeus6EP3/5xaP01qN49uHjc8vjxY0ErDjW+fXtxaNH7/SJJ5jW6+t0uJzy2HRN25NaZn+zKeEYKbNVYShaDZs+6v7q1cVu0TObiRoguOq3v/XN2adaSxFnS+9843uK9NY33r9eX/UH93a7cRQ6vHdfp/2vfPcbw6InxNv9/r/77/6H/+x3fl21XF9fXr561ix6jPD8xSsEXhyfFFFXT9EMYJcNpDah4YgI4JVMzUQIiJRTQkPMJl1qXBQwOhowUkTCUKRaLVYxctd3AyAbqWMmABQrUDCSuxMhYyADcHQ3NdVatTrFBBFSYlSdZkGBnKsBdX1fJ9vs9kRmWudpMiMzVBOAcnQwaCnctEAwztOmFBE5iCuoenJwtKtycLbEKof9annYrw5PwdPhydG0u/CpOhlzDAhtSnWstYBYIErIUYI7enDYFF1Tnr1t4wqxIKmKhthyCKgwbm9D63vNdapOCSIqJoWU0Kd8M6mDFkDKZs/nlxjHZbMkiFqU6BAMIKAhkJkBRAoPHj18+fp5rrMZOkDRwqZdamLbGhNTQFGpxVDMkBRVSkByEEfRyGJEkFwMHVFBQNDRXa0YhTBNGhfN5198NMPp9np7+WyTmgWIomGA4ICG7qiAGICJggMAeuCISkZuDEGNKM5MKUEb+/0uI2lsWdVNq5hWUFADFXUBN1fzCggAWN3EEcxFYLZmf3x8vxEuGTd1NJHT/gTJUyd9ArQ65hsn+OiTX/zis0+4XaJENzDCnexZvSd2lCLKFtSLVjXiooJkM5bKDvPi+kLGFzVAW0IJRm6IyOBUCIN5iyECkEMFESAFUycIRm7kEEMUdWIQld3N7e1lTmHVnR72sS3j9uQM3/3g7Y8/fbWmEiF0xi2n6El1Bp5P7y1iP0/VwGwu8yw511q1OmCdK4UegwgVKDgVwRh202bRp1hZLC9XQAybUa+eXu73ZcO1r6mJrom6g16A22xeK0GcBBgZRBNAi1QoihMwRvBZzSLHyKYqrg1wQ9Y7MEFW2M+ULVOCidiZnr162cX44PTO2aO7m1rDPOftbvvg9BQ1r3cSGSLW25vXIVFqopgDkrlSiNlJJRct4M5ibdNj5Gl7cSZndb0RhcViKVCcaDeBYu+Yjpad3+b+IN1cXj78ztt36TuaA4d8lHoNevv8mtz7ofvq6y//+j/+9f/xX/yLm4vXn//8R5cvv6p1P+epanKQmBiVq1NsY0qoOItKvp36o4GQYgHROcVg9iYqWtwjQWSIXqvFgpiCDwbFfKSirZFyG9qWQlRwd8sl17xL0AEgIAlRGxlFQRQYxQoCVAAxDNwEbBuaW/BtnsA076QiQeNAOu3XbiPHdLm+Ut2pqgqQ1rvHRzz7fh7dZbFcEPHi+KBuczJqQtxM26zpvF2JFR/K6vQEIeUspRaM1q0Wutuyk5grcTek2bZScLy5XR0foHgljdw+vXmVVulokZaRCKXajEAtdWpWZTfKlUoZIp/c6RF8rnFWxFDbBqzQWOeW01R3r3ZPrutl7DzsraxLL8ehCQUCoEY0ADLnYsX76eRx+/p5CdI7OXU2HBwuYDDGEIyCZ3OIjpqCc4hSSnXRVd8Bhte302bvkVLBSkCsGgiFDYsmYzAXmw0FqXn1+dXF641bQ7myAVBUUiQMZEqkyAEa9sSIzqzkysnQO5YeuUBskYnz3t1jACtlXwMouDtnFq1kiobFrKqCKQi4BVQMxdQ8cCF7eO/8/uHD7zx873a//dEvftgOoaouHJZttulmLWAN7rfzJ1+9MBgCLZwVAgCjKZaKibwLVCPamDV7RQ0GjlZhMrUAcftiqpfU8lG2PTAlRK5kGIU0ABRGIiZ3IzAopCgKFFqASgaoTEApcoXMBZVbR8oy1Rd1DrpawO//0X957+TOJ794WnQfjuJup+O4OD1ITZdXd6U7qVY9zYEUifxme2OG4ho8MswJadk3M6hZY9my1IguWWDgJrlD4yBtDzqF6+2O52hktzVrlkbk3vndviF2wtnnxoqpG9daEFwEwRAIHUJgNY6mVMFrdEVIFZlBe0ALR4qT7G9tn9JhFzg5bra7g9Xhqh/unhyFlnGmhEZQPEDb9NHHqda5aQ4KerKATshopoHEvQRVR+8b4uTI+ujRw2UbAFnNMFjJvNl7aO/euffwcHUQONLtev0f/916vfvzj/729Pytzc0u3b+fju4gNn/57//s6PToN377t7/46gsmqfm25KtpvuxbUBqyKpo3TUQyAehBu0iCxVWoOhNBFEHMWAJEIGq5KZpLBGFhjwM3bgjuwhUMCNE1ijoigSMauBmA76a9VGVPphQJQwxEACqmsyNSMwQP01xH1RQaAk1426fFOI067+soJQslatp0c3lbxk3XBZHJataq6hJIzs8PzxbD+ubq5OhElfb7fRPDglNdAipVUUA7HhrZTXORkNKQBizSehDxSUA9Qug3V1fctJWqFi8Z2FOyWvclLdoi6l1zcOc+89C1DbGLWM2hXyzVLQJsttdM4l6jQVKeOe0LAXLDRa2Km0N8PW5erj9dl1cKDNXGilLwIC4qu1N+syolNGTcwmYjX731zr2r67LfSUc8UBvEKeweHg+P3jpyDS9f37y8yFtGUlsmOT1ffuP9tx49uBMTX1yu/+f/+cc//+QCU+sKLIV9Dog5yu0U3VrhmQvj7sDW1WdzSrMaYwW2AERmBuoY0dGdhRgiQwRlIABSM4ZCbFobBKqYdSYgUgVV9WpeXYzf/KOaismbmB05ESohBoDCVR26Ltx7/OhgebfrjpcHZ6Ll5dVXrONZe3TElGGajPu4mEXfPjv7+ukrBq+tM2KPHEOjFAqCscwFrKKjKBayNnIES7sRty/cp7knN2BHCoQI5BwIMYIyxIAhvsEfEgbw5DF4EohoicAtjDOrW081ABNDagMaiVMpmK9z+B//7U/++I/+sD9Y9DLFpFvcQCr7rvKhCZqVFLjbu8l23y37kFCkJE8BCJEh8iwzRESaER3R3OZEBASp7ShQM9judv/g4aId6Itf3l7uPFDTxqZPfYsUmKpb6ELnjAKcGCuXghE4FzXJDlrNAc0BQqAGAR2YoroblAA2MEvoKgrMTgQcbbpdj8tFf8p3zxZheXLYHJ2YCKi0sdnfXGhdc/TddtMOR0Bqilne7MEzuUCVpomLNiBLYchN2oYajcbbzeVu26/uP3z87aPTcw9AIiL1q+tL3F4+++rFi4+e3ZRxHujRe9+YaVgcLD74xoe73fin/+p/bJpwenzyi49/BDbdv3uYdyjuF9fX0+1E1pkTRUdSYHR2MaJiw6KvZhjSXGVJPLMOi2W+LQQMLkyKPpsqUKymRDNDIGMBcVA2yqMo4BvXe98tUGSeJg6uhAQICEDMHJx4nqQCcGIm61gOWqomry4uuJoCQOJh2W1ubvJm38WGkW63V9Mkc43o3rEcH7SqlQaKLfXNEhOYlLlWoP7LJ88Wm+7x47OyXTftELsmNl1okrqUWsU1hVakcNN99NmL1bC68/Z5ppz66DPdTrkLrRl9+M3vf/nkskMlakJoqk4la9d2IXUmkvfXXcOA3V6qE6iHkls3TFzR8lhUlUTKi5tnt7pPvAxmWsooWzWuYerSwsXI2N/U0PzqenzStjFvt5Sx9RANklmYr3/nt97/ze/ejy26DFXPXlxsP3nyqmxu33908isfvpUYdvOWjN6/d/DOP/+j//a//zd//dMv2759dO/s/XfvPXh0Fxn+w1988v/7d78stR0tF9kNxg1GMI1IxupEbBgc0aM5AoIGA8oJGZ0dOWgJaAAkoCFYQHQll2oM6qo1A5ZiZh7YZivZzUEdwGuwyQrD3EAwD4Fh1PLowQnH8dPPfnpydHSnPfq1d7/5MflwZxmJxUVRgiVy77vm7vFBnqfXm7Fre0E1NGI3VKKohYKl7GRK6GzUBI5UwvblrY4tSXXPiMDQIBCCMQZGNsLswE4ETk10V5bYhkaBRtAYgJ0ICdENGVgCM5kCqKBXD0QcwT7+28+efXLBy4pHxnOwSqcnqW3LPk9ttyoWOFCIVGVe7yulGluI0HIJaBFFjWVfRyOHBGaBgSEE01JqCEZI2oYEOB8ft0fff/yjL1/mG7u7PG4XC4pYa769vlkcHPehTczgEDFZrIFjCgoKVbxMs4qpU4yRRBGiohsZmpN5jUXcGg2DgqtkpRT55curYTEMXQzVsVitUg/aNk83ZbxpBzJmn4kNBGcXV0FgY3Z1HRIvFk1ELVb6JiGKAV1dTtvX5fOL+bf+3oM7d++rzSKoQG3Tx9B+8fmndYq8PEg1hWTz9vrkzrCd1//hz/51GxoUYOL96eHd+3eGw26Soi0N2B6DlPm61C01DXATmshtjMDBwRnMI0MK3lABoWqMqe84tVgrM8WEABkJzCk6ExACBlJARQez6hgBAioCGIABSojoaAgzIRuau4NCLWpggYFREKqhbfb4/PXLCoEsYsI0NPNuX253fQhNG7b77bjfK2gl6LxxkbaNVaWPfc52eX3RtM1c66bkabuDxKvjE+C+bRIFELXjs5NhNRhCtsmxumpirO4/+NXvz8rp/HSe1nT7Kot0i7A4HAT46Yvdk9dTGw8WfQLweZxT8mZJWoWM5jxxqFKlOg/L5TbjbYGEGrGUkkVoO+8ubr/ycN1yRqVscwahKl08mmQdatfQkqAx21ucN+NndwbvYNkdc/cWfvnka3cjuf3u+/f/wffuQ93dXk0EXYjywf3mm2/db+Ah+DjubvIegLFIURm7dvV/+t/+4z/6vUsiX60Gc9nkDB7/xR//ts3rP/urC+ShACpDIhgwkCOwuKs7mBECkRkEgWTEgZEBmlBDAFBQNhRmJ5q8qqKCgVRTcTMERSBHKK5ShYwczKnmWhUI2YoiIXueTu91548WQ+tZt3/5kz//J7//jwKF9z/41t63V+PNpszOvjoYkBqPuW2XJydgcS+SC5kRCiq6oxg6smgIqJZIGLC1jJdP1jgyIgI1BozOkbFog1QShgihICHUAAzuBgCERmkE12Q9AGglN3dqHcCrs4oDECAaa2BvTIPyiKt4Pe7R963S6uC47fqO4oA+O242O8DgHgJED1Tq7CWEJkIM3DhlJwcBz6LRG/fJHc3jer3tUsPJwW2c9lDBlSLFGO3X3j/fr1V3NmMRZVR7eHKqogoKb95yCqyMhAGIOTojEW9zcTEwdcZsFZkRPACikFhdcGBPQFJCqApmmKd8u5/bpg03r6/6ZZc82l7KZs0pIQfmGFuqLmSKJgwOiOY6TuPqaBk5I2jJU3QIlaz64vD827/2B7/XtWpeZGYCRpQK835z8eJit8mmcpMnQGSGSJV436elyqKFBJSda3/gN+tXhofmBuhTqhwQGcp+OlwuKYSOImQJDRxyLJGm/MbM4zufi3KaHOPcL4ayvmWDnAESEytTBWvcWN3cq6IFYMIwuzl6RCJHkVl1RxQRuSEEgFzNgQNE8KRRg+ekaujbKY/77BTNIkYduuWcx3K77Zw5pTnn/V6sEgNE9KlknWXOYlrRWD19/cWz07OTftmVeUxt8/bb91YHy2naKVrAJpEedEPiuNntQPZeMkGo6DNgPFocL87P3vu1m8sXX3z8JzC9PD5dSPCbV1tb37Y8pBCZLU97AFstjqtTIK31MtOtZOlCs1wdQkyb2z0wdGx52u1Jv7p5fbt+lm1t+Cb5rwo+s/UcAmGh69tclu3JAs8RdcxPGiw/eOvd89VRk0L41eZm887V5ZrIv/uNR3HebHZrpCQwR42yE2qKIqjtwREhuTCGCDpTyegXd09SzXPdvCoVTbhgJtn883/yhy++/pdfPH8tkZplihTZGnUyakAwI9RobtK4RQdTqIATEbIHixWhkEXBrlJxhJCpKqqoCCCAKpI7QJRJDR17Za2+Mawo1nrvpB46sN1wYm9/sDg5SomaLqTry/njTz8dvvOdGFoet6XsiQBTCxGGLubaLxfLqorM4rDZbGZVRXdRp6pQgYBiUKhoQKo3L7LuIwAQ6RveEWgihy5IAcrVwCUlK6IIHCjlagwxOJVQAgmpOwdzHJ3dZkZ3DEAFMBCwaY00kifCJkOOrQCTbX3Em7c+ODk76se8aaXJMI/jFKBJiH3sAUw1oxlwFiwcF7nU4LTgwWZJ2ALNUIuVcPPilpe3iLHAZHUcs4rp0XA8pKP+MJToMisJxZjcDJ0LA6MFEzNhbtw9EHoFFYgeQ4CoSoqjZ+pQsqIgIQhYhIQKbu7sjFjZs85k9Or68ni5Cq9fvPhm/66STJvb6tr1yygIrk5lrh7J2ISJFHQab4JUxx6d3e1ssTAArFZcusMQuq7OmQNzcPQkU86zQq2mWmJf3SlJzXmI8fTusQDurjbTxUj9sjugO49OV4f9bldNR7K+WyVLpWQf96MVl6KRHERDaANijWhqBK5ekRKqGZHVSmoxRlgOdb9HqApGxEhkCGqV0ECjkSk6OaYQXSySF6zFSnAgckOfPUShQF2GMBv2/bJrRp2nutdxv6/gCgvVTASr49Pd1c3u6rKN4B3nOm3Wt+OsQEHLhA67qaSEc3VSbVObgPqDBSQ8u3NM7il0m9f724v1g3fu39xsdb49PehS4jJvRXY1ZzdPLGZQq3qQRbNomuPFYg/cZKXQJETsu+BwgKFJCcwnrfMwLDl0ZplpuppfiBeQYOoa9PZ2ZqUlWSmbwvL02fOnz79MS8BILBicxIywkqdEqHUHAZvORKTEAWzc3j75+9/91jdPTmTci3Lg8dFR/+7do4Ao+13ZzwSBjKlBh0rakIowgicFMwItxYwdoVJhDp6rVjN3Bxin6Wa/OT5cnj66/3/7v/6zH//0FxrSnXvHi+VqM05/8u9++PmTXeHW3QC9OgRObObFpFEyimoYtoZESgahOLiX4rn6xApopGwGiDWgGSO5FkLNKoUoOiVsZsYGufj08P3TD791zBy3NyMN0IU49Pmjj/7im/ce3793h7vjl6+fZtgvF2dJScfqMnPfHPKJ3ey4icOinfcTYxpL2Y1jiIcd4u3+0mSWmpfNnd/4wXf/7Z/8aR6dEBoIrgzICBhcAqGykZMLN+QK6oQYumQMMQSgypk8qhbAGoEBOmJXJILgLmyUAhgiKlQo5GbYWFHwvK/yy49+efyDXzk5PrXZx1pGmFHFHLqzO8e42lxe7n2rGamNDg1kIiOgtVKpbjYJClVUxFx3VRUjE0B29MAwT5sG2tgumhYiRaTurUfvXL28ff3qcioThYYAEgcTFqBKGQeSHGuBxlOXxN3DyJu8g5zMUBoDRqHg7hU8aWqYUxBCACgg42xTuHf+AKFO8w0F58AAVYHUvVghEPMa2NEJRJdNOr1z2DIJFgDXNwAEgOjwi5/85Ed/+8k/+eP/TTWDiqVWEQtMgkGHfkI6OD7sVwfTuGsaCZZefHH5+vIS+v6tDx+enA3HZ8M8bVZ9IpTAITUhQpwi9QfrKe2yZqsphShgDYCIJwPNoo6c2GoduoWp7PImakohhsVyP60LcC3ep2wKhmpWGh7IHRhGqhTMCUQdQAmcgay4E1NE5DApQr+4c/4wMj5/9rN8e+O5uinFThxq4oPFsN7crG/XTGCJRWU3z+pv/F8gztt5qgRa9OXr7cEQN+PtmMfTs8PzB/fU6t37d6f1uEEz4Iur3evX6xTo7fffKQhadrXsqwA6KLBVRyHz0PXnAK7uaLFvF0X2jt3p2cP1DFPeR+SaZ+KQ2g6sNonWm2k/S2ibto3zVMdJcw0pRsaJJW52l5fXnwQZoSRjVFJ0iGCI2KTW5hzQo2uYkBUbypvN1+eL9tce3YHNTdmjNuKMVh3zrgnI6sCsWquaS0jkGEWdEaM7uat5sWBqCGiOrE7k5EjVoLqGhM0OG8Bxd3n33uH9t39XxWtxqQb3Fvfv/f5//V//tzc7NTXCApAKtkANYQYb3ZKBW0VIgoRu7c6B0atMDmrVXUxbxMBBKfobcJ9ANAdRtEU4IJVR9jvNw2l655vneaqvvnr+zntvrfpBdJNpDhE+//Kju+dnqR2Oj86ePN1JK8NJM1AXCFdLvZxvIXq7bLxbpbZpUre+WV9eXl6v51c3m9tRWFOs6cMPfvXDd95++skvP/rlJxTYRQGTG5vFyksCxWCiDgDAgdBYwQGQnFSBWTggcKREbo2YRVbT4OjUC7sJmE6KML8hNmuSmYJhTYYRNtvy4x99+ff/6DfSytCYR24qCgkkblartlSb0CCcn36w28p2vIhNVFfXUWVWnIvhLLmB1DUhIEUPjKFYwRgZeJ7WInPbdU0j4Hr56rPD1eHJ4YP1WMxht5kvr9aiGYIRh1qcENruDaA3IEFi0k1eKxl6NG2Is1fDCjEIRI/WAvfmZElGl7mE/rhbry+medeGtutb9aIeFdBMe5bDVdc10RURGdlIJao6QQixlCoAFbGa9svF6eJ8mkYTjByqioJ7YGLouqYdmu6gG7rl5atXB4v4/MsvdK6PHr0Fdw6OHx1HKFCrK0Zm0WIopRAObdf2J0dH+4Z3mwpFLFpMCFZeP311cnJaRBSBLQHHueY2xVL2ZW7bxaF7SP2RePWapwmAUNE5EFR7Q62CwE0lVagAFFrVWR09RIoNE0OzvHf+4PDg4NXTrz7+yV8vBiJVB/LA2a2L4SC129frebcJ5G3Xq+rN7bVKFlAAcaJqWt0AAAyfvbh91XhsoMx1t5Vdkdubm5PhIEYA9jzPmMOjhw9eP3tWxMFpLnPNM3goUmvlOWsW64ekYNO4ju3i0ft/52c//NdOrGUI8WC/e9UREVTIcnBw7IjC5tP0+sVzcWv6tmqpjEUDAye23bidbX52+8k0b90lb7e6FeaokVZDS6ZBpc5zF+NJ163a/iA+4BqnYj/4zrd7m9b7nelK1cgZQuUQ0BnADSVAYNBSzWIQtYiISEToolrUXRAZydA4IKs7AiIygDcRTo4XIYBb3W/W3La1kpuwcd2PZyeHH7xz/9/97Uvnhh0apezlDQHILagBWXZwcwFgBHcAJQEoRAjgCOACyALAgtHdjcxBkI3IMVaRGd2B8bu/9g4E+/rLbZX2vXe/++Lrz6yNECAO/c2+3Gw3J83B+fEDLs3R4cECDYo37Zkt7M7x3TmPex3HvO+7rmn7qAYAJ0d+bzUcPeevvrrcXEyYDVHeOr//xadfuRMyqLt6dnLAZKYxBGcS88BgLBW8QG05dBwAiBEBqDqYg0UipGjBzAxrJFJTJ6XoZChkStJACoI79TJpCu3mcv4P/+bPv/973wx96FsWU8BKlgEmT4p4cPfw0b3Tx59vP7NasYHkDtoEq5t5Z4WBQi3KgF3DZiYVagsMlohd6yTzvL5ksoODc8CT1zcbTquT4weHB8uLi/FyszWUYaVnp6dffHoF7oGRUihCRUskH1bt7HmeMAtbcKiQiabGUtCGQ0+IBK5BKI2zBN2v81z6/jSEKFrRIwMCgJsenx4cHwwIigJuVH029NkLoquKgbgwcETlo2H5zvsflLwd9+Nf/tnf/L3f//tpWFLAFvHh+cnrrxf9ctm03f14/uLJZ+3Qbcf59uWrxweLKU9DH4WsH4bN1a5fDJPUy9dXZ3dPD3voOTTHp1mv825XXQIlJB6G1X4/c4pSp0YXsVmOY+HIbYz7q/VAbWwHwA4BkQkwKjhTMpsnBXAdPJrz1hXcU+jcIIYOHDDxmxZWG2h88fzZxz9c7y6bA3SAKhEcBbA/6DDb9ur1flZliE3Mmjfb9VxFC0iFqp5Vx9miJ/SqJsBhM5eyHqNbrWUs28i8VlmuDus0bte3DLbP+eRwODs99lryOKKqqSjILLCfdRav/vpHP/zXauH49P7B0fm9t3/zqy9+hjHlWpbtQU8wztu2WzbtUL0S1Oevn0utzCFvZw6k2JrQorEyb7Pbi/Hp1e4LomGkGaGsIJlCsbpjXaaeSyaZfv1bH7z3+P6S2oRtcBu636h52m4vqrmBkjsYmoMooFNEd3cGds0cG4CQxYjdXIMhARKwqMIbDgp6BSGraIYObgHBDQSJEUITOi2m5lUxWrVSkzQPj45BPucugCAgBYwVxoQQNJCjuQqIKwMAeMGiFipFAwV0Y4wkDmqIpGymAESo2FESKzXvIwip33s43D1d5DXmben7ZQr91cXN8nxYdmdNWt5Zna9ijJMm6t45f7fahPkmyWwqgk0BiOnoENplfwhEWuxud7ZqltfzfHpwdu/u2+/duwKlo9MzLbLbr++fJMm02U0QoiA4uOikKuQxNC0EZ+QI1GCYA9eIO/GgyEQRAznsOU6aGwNCcmbRGQqSBkRwMnZuLGyDZLY0caIo4KbqSNevx49++tV3vvcBg0IUVtf9HJdDlXp49vbR4YPt+np38wx1LnMGLCfDg0UL42r7anP7fH39xjY81pkIiHv0MCsAQXRMTApInOZx8maidrGTzf7FPM4n5sw0L3t+570jcO1anSd3jUhg5OoILi37ncOwd1qvJVsJyQE5svVUOgH0yE1fyTi41zHIXJrYBAwBGM2kOIGKl27VH52fYy7EFhCgmhdwopwaKm7mwA0Gm9ZjgkAtOpMLN137D//RHwCKwjYavrr+evbbe4/OSgEMotOt2wbC8HqnaAIu2yfP+0fHZ82CSBJByRki9H13+fzKljviYMyNF04U0GUc57rHEilAFyOUMt9cH5+eAC1342yLNIRm/epicS6pGwImoBgGVHXEYOLUhuoCTp1TIDeRJnJFCczmQBTQZN5O19MLlCwmizZmCuOMkbuujU2Efdnvb661bt152XdznfebvVcIHmopDu6IWSsRUxZzc6jiDhCCtWClXy3PV31Q2U67OuWb62tGDIDssDpeTCUPASNKljlPGobGXK36fjfWMnbhxmu42V48f9LeffsbbbxTioXALaOiutOwWIkCEdXrbZ0naAMAr0IzVVZLbWxULrNkQXr94nkgps6M3mg5KTRDZB/zlkIDMr//1vkHj+7I5naigDGg+X6qgMEMEQPCVKpT04gGYnV0AAIOmeZCrhbQIFKNnhKAeUZU8BrRARVMwQMTIaqKALIB11rZG6hODSIYgUSwAiLATFDq5u/81jsvp1cXrzZlDLezXGx2zAsTBxREVVcxdqqIMzgDkoOgGjvCm2OMWNEYFVWJGiAKnupcvvHeO7/1g2998tFf/eTjLw8P0u1FPlic/95/9naMCJHP3n885Wuk7uTo8d2zYdE34ElVyrh78uyjd89Sg1mUam2fXV9qODo9e7ToOqYWBw4IHdSVqqkjxr/z7hGy3erV7sq6v/vdXh+z5P/4w5//7OfPpxyy+RxmIlbFiKkJpKiIBBoCNK7GjDXwFCr33UFatLDLuzVlrbOYaGAeHQpjg8xuauCAbaEQW4yRfM9q7ga1DnF18Vl9fjS/82hhqqbNPOnllR4Ob58tHiH6Z8+f5DISsRMtFgt2IYeW22lfTSU0qbqXWlKICSgCNZGDUhMikoDTIp2jx/1ui6oYMSJcXVwieyKIgLvry6aPR0ftjXlVMC9AQA6NAaCHxF1P865odoaaECO1kTAmx6hVMMToTGoxVKK2C+ROAKaKZKxG4KmJiTkOnZi4FEZP1GStvVMFw9DmUks1qe7BQ/Kua5C5a5LJ+OzpV9N2TQg3txfjbmzbYbd7LfMeioN3Fxevj3oausXRnYVKLfs53T+3WqDh5WrYTfvA4fDgUKw8+eLLwGkVS7NYzNnNM0txbPM+Z92enNy9uLgd9y+7w7s51/1uGpYH3DRX11dtNw7tYex7h5wCVkuODbEyhireACdyJiRiClGtMoJLHfO4396iCRGoJZ4otOnw7n2Amjfr3fX1pqwdrONETNNuXN/ukYKIj+NeTbPqnM3c3YpAdSQBJACQAmDA2DWLq+ubRcKj1YGp3bg+eufdw8PDecrvvPt+1x7m6SpyV1wdQYSsFPIa2DoKqeWseT9PJckvPv1F0sM7d+4ExjbY5fXVol1gSBAM8n6br6GNUWrLAUMss8WAhLvdbh8auLr4Uuf1m+fvul5EzRliYix902TQgfV7jx9RXudx67HF2nKXIpObEAREJDU2UzSIiDoDBsUGgQGcwd0KUoyMbsDuGlCKGYFpBSF3s+AuQhzN0d8wqtlBdeAI7mIKJuCZFKwCGIDuz48O/y//h/+iZM9Zx1L+/D9+/D/8m5+sbVDDxtEAKhuYJjdTcWRUB3BgBAtVyRwMwBESRAclgzqNXRf/d//kH54exrPDGg/7l9v9z3/+7B/80a+enhwOXbPXPS1jGxJrjmG+vKi2s7Pjk65tpnz75KtfvDt8yMcxIAX0KPNmfp2nfPf0zrA6wNSsp92iozY75jJlycNc2T3iKjaHD97G/ALn7T/9w9/+7rfWf/bXnzx/vr2YKwiaGez3oUkWQmCCHlljMiEASezdsFodnhwdIfHFy5fbm2vpMo9zlR0zhQAgiBYpApgG9eBqoM7WIIoxgbsKGvzix1+en/1uQ/1qlda7q3nC+w/fWsbw7NVn4+ZFDB24Lbs2EBz3C3D9/PNPb6Y8xH4IlB2ui7mhhAw1IuzRQzM8CLExnR1UaZ71tq7hYLkSjMRJdW6bQAHH0bIURExRXBEBxFuXYu5u0WsMiYYTmK9mEW8isJgzuxgDYGqYghnus4TYDIjuMDu5VK8wVXI16YybLiUKWaaaixMCIgVmgZB8nGZwZyNm50ZTF0x1NfSl6M16XaY9jvn5q+eh6/vu4OnXT169uDw/Wr58/uryq+slh/v3+tPzO9aQdl3OeZunYA4hjPNkRVM/nB6cepEXl+uyvuz6IwDomhRi7FIcJ0/ztB/H9W2OTff1q6/v9zE2Q9TmZnvbd6nvlmiwu76C7bbvuOlaJ2UkF6PgQ9ssugU3MSC7YVHdjBd5LFBhc3NNJs6UAbldrlbHh6dHN5vx4vlT3dwyZE4GToRhs93d3qyJUt4VVc8qzlrMDcnNDNwiWDUgUilvfo5E8m6/wVKXR2cIiGrLvpeaPWA6WBCnOW8+/viX6POjh6vY4ixZTcyl7yMxEXfAgEOaxMtMi1XPCZuEWktA4tiXWlOjN+MtxNgCRA4OfFmrxdiw7LfXYGGat89ffwRcAdsQHamktsUSNSuZJDCt00Eb76yOtpcvdJqrxRihVgfQwEQgTcOTuRg6cHIkRzRlNFR04yCioIECuSFNYooY0Ijc2JuxKAZvAUyNkF3BTUwE3sBMQSFQdUG3OqtLqCWnAE3oUazUUgRFS2rKH/3Br3/yybO//vgK0zI7KCCABmKvRKRIKkzqQIBE1HrMTEYWCTrDsYyhb7//u7/xO7/7/dPzw+df/hTK9Pju+ecvfjbmmkvuY5sgOKdFCHNAxKB5qiU8v95WyI8f3wvcELc36+35nUeMuru9gawP7zw4uHN3nLZSb9Os+vJFc3iwiE1jvlbfldvbUimGZXNIkY2WRFDGm0f3l//53/21v/zhp8PuCIpePH3+R3/4u8su/Mmf/sex4nrcpgCBW6YoRI7kJpvtLhi89fjd+eHjy+sr3273uyudipapspoVNMpu9c1m3CCaYowRRSSc3T97/Pbjj774fNzWd7/7wdXV04DN/fMHi66fps2zZ58r5xV3J6vTse5ijAfHh8J8p+y/3v1kJllC2zddwZRLZfYUKXJYdWcJehbKsr2GrTrVHTaxC9qOroSFDKbsSJKISrWCEzWcDNUCOUSDWLoZmVxjIG/58Hh19XKbvRhl4kQKrQaJrsEJALyGwFCzB8ZcNoDBHItrmTYPwgJlDvFot1+3fY8AFSerULFWnpVr3k9WuOlT03d909S82e1fg9jt5es851z2i5NWJGzn7c3+9vD4zpdfPnnx5MsDXhwcLO68d7/rUiYyD3OVeV9LKdiEhjlwE4f+4vXT+cZ+5cPvXXz5o8Oh2dmchkasOjEnDKCrlrabm9Q2TRN+8uNPf+3X/w44IPejhLyfQ0PDohUV2837zcZBGJmAOKHEuOuXRBgAQAE87nWay96ruUW0Ljah6bhtFqjTk19+ud+L5Dk6OSObg9LlejPmWYzzOIF6ahsiHLMKGHsMguahOqAZGJsEMALFKtM0b8o6v/ri1cNHxyfnB3fun4P5tNnfufPgi88/vnPaD33O0zzt2JjVvJRiAm8AMZaFRE0EyhCb/vB4hSAhNBfX+6FfZaGITPM2uIixxhLbMO550tQEkrrdTbtFd/LVyy80lNiYY6WEgbsAjl5dfYhxkWLXwLcevpPc2ChiJ9kVzNicwRCIWylaVasBKVSvRCwYwYGhvnHjmptCFXd0czNCYkRAVRUmVq8IQESiKibmZmqOHDk4or8ZjYmDuqnlKbergbhD50adq8xV5ynbai6mFWckAm0YwWAEiM7RSAIw/if7q3fGlQkRUbGInhyv/v5v/r1v/+p37t8/zfvbr7/+ZL15vd1tPnv+2uvUeJ5uLuLDRytuMLWrvtls2zzJojt868FbiyFd3bxgmLrlwYfvfbhKQoBYpOF4fHTaH5+1bgMxS2lAHt8/wQAVlDx0Nd5UgchdF5uotZaK2h6uYhvK9WbV4/e/9+7x64svvvzy/ncfvf324aOD/lt3/uDptv4//1//Zp6vse8RVyg0TdvrmwsLDSmL8A9+53ffflxev/h6ro9L1a+/fmJXL1ZlCmIbzFvArFrcEjAAtqEzq8d3zkOM/+D3/pDbrmuOjk7cV0fHx6cE/vTi2TiNbZdOlslhs83b1N9db3dT8Jtp48E2CrbLxsUDupKV0K2a08WCgPJ0sQwhJdzs0vXIHSwh4xBX8za/uJrNrKd+6OOtKFanRSrB3WtiQ6e2TchQPHRAAMWZQ1rU2cfdFbITgSlpYjEhh47DklOYaXZkBHYKc528MjGB2WeffPz8i1+en513fX9851HqD6qCVi1iN5sxMjiFwzuHw2IpCkwxT7fZJh3XnidR8w5h0mn9artbHw2tjLkbmnfffqvJ+t433+8fHF+/fBkYQ+DQriA0i3ZZbHK3kKJO8+2L6xdfPr28eXr//ACYItC0u+Um7XIx434R3EKCOO5vWw7B+enXTx7cvyceEGITWjXdbicib7kJHB0BwLWWMpWiOOZNC0AgiYJqmA0Ejdk59TG1qcEs4/XN1fb2ulIhgYDuITnjfi91N89uOUvNFRg58pTnKRekQMaWjVzcKyK4C5ghqEgGq4eLxenpsj3tPv/p5xwCOrx6dfEbv/XbgPTg4cPN1avd5uJoNeyQpi0pzY6qilZJa1ksh5KnWnn23sPxyeFRCn7QH6xvrsCYKEZ2VAmAEaD4GDhV7Ld1ZvRkIqWkfjHVm/30OgSMnDAQUGw4RBnvHC7fv/f4cGhb1iFErrrfrc0EwVzcUK1hd0PkalU9EwurgEUOjTubsQBSIDPNXquDGyBwkGTASOSIYlM1dSQEnOccIGBgA6yipqhGgNL2ScUAwc3mUqvalKfBGqdUiINXA1N3hCSVb7cThkbVowGwsYeqDmhgzMaCxkRsbkyKiDXfPzv//m//4Fvf/nDZRXT95c9+vLm9Ui+Hx48wTt84ePCbv3N2ujw6PD5MGNkjMFBzQEePFLQJhhyR6CSsyn7LZN98/O4iQoCsu6vG/W4kqZedcGPcMhi6JMaqO4FXJd+Oc1jea/tI4Jtpp/Os7M3iPHZte3683+wXcfte6pouTvu8396s4zh0dG8YEMd/9F/97tHJ8vnXm+1Wf/jRzxtgt0TT/PqLT3922LeL1mruFssw8NF4spdZNhlbSALDrE3FXQZEY3EHw2Cfff7L1WL4yz//c8Dmf/9//ufdMhyc3GeO87x98vWTZb+4f7DY5v3Wx7iK293Vq4udDzrOm8OTrlfYXay3O3GmwMQ40zYn3LFIw/0sqWmXx/H8xeubMoQs0wMrS04XZhYxoNdZoVtcf/0qDrh8dAp5F1CIG0SkxmNVRs+1AIYgfmfV3szJipW5Ymgy155aUbAUmTCUUVmFOLhHElPI5B4jt23XI037KyujKt//YHG9v+goLdrluN40qWkOD5sudk1wcYTAjCjmYiFotCKz/s1f/eTuyaIbogAhlHPueV4+/cVnz1+/OKKy2U/CGsMMxuXV1dG987aFkzvH8zzHDHmfmybdOz105oyCql0M7h5CuLne3d7MTUpDDGAmFZd9s7td75aLfjgQ2W81LVN72Cw0ahbJWRRaCMZMbVgEoiymAhhTNSDyLrJYCoGJqM77cV8mHUsFQwN1dggE6nCz2Y37OVRY5xHMA5F43W/VLYCzWXF3R8OGplzViZVqVQcDrkjzydEhGBeo3/j2W4w6rBZnD1dxuTg/O1qvr3PZaZXtroyjoigmNaxq6NA8ffL0wX3ebtbD0WOnVYypjw0Dgtj2dt0vV24GlpE09l2wJrocL04++eoqOzQN47w2ycjNfv2kMeWmQUcG7dJhorjg5gfvf7Bw0mlGKKPnhOCERBwAK4qBlOLIDggIygRASOBViiMlUkJn78DYDNATqqthDEncE0WkJApTxVLR3dyLmweOKMnMSi6lYnUnrI0iOaC7ilSFKj7lMaRDZEf38p9OyhpAb8ebk0O/2tLNjozNUfWNqAkAlSo4GjCAOYkTY/zd3/3B3/3PfjAsO7NiPuk0HvTx4f1vYN92cdHFHqJ5IJcKBm6u7iFFYCFhwNDAZGCIXgnrVKkZvXqRFNhQLXGigESSDEHJzQTD8+vtruSx0vVc2tXJ6aKNyPM8/fDHH58slr/ynW8dxtVf/tWfNMvFg3feW3QLPKhv3z1L3jSl/uiTv9xf33z+8bO33jn6znceP7hzN/5m/y//3/8TyHq5HBRhCINU/+Sv/yK2/XCw4tj1R0e+mXg9uSm0lJg77jfbklPgMnLx++/d//Xf+7tvvfvocJFeffXy//F///9cPnv1K9/7ZqRECLeXF8vF4u7RYc3T1jMOAKrVkTvwIF27mt2iCRz6lNcxMIMyYNXN9eb2rDtg7V9djn3s134rwDFjZPTCXddF2JYiGiIEqDIOy3adx66MMaB58kCoc9IYkVXFOCqBV20TrxaH15t9JpmCkWmTE8VGmFKKYcHJQjbIpuIsURx1YihDE4bIV5ub9Th/9aNfPnj5+bu/+r2daGR+8M4HVUuXBkSkgNSQm4vOMcZ+cXC789u5vHz+siHgGJjTtBvnMR8th7nYzSSraruLi31VbtqyW19d7WZwY3zrrftfffasTKJTvrq4PLt/N6MNfYvkTdOAea0FiY+HA3lVbm/Xi4dnKuzQv/fht/7mx3/z9MXFB/earumA1ef9Zt5DgGbRNl3KGACsRcSAjeHQdkbEjApCyD7NaDrJDFLQUWQPplTbiGjA6DCX+eL6trqICe0tEnIMebZRaF8NLfMboKAJI7iQi7moQ3CoioKgXYjFTPIMUhBseTgMA/RdPDtY5P2V5qmWsUx7VI0cRXMR3Za8bFvRfH7v/Ob2areewlEi7NqhMdbA4eXVq9gugYea5+hzGtwMY2ofHdy9vJn388RdD5j3NTslAJ110lCRXZGcliF1iPmsWw0yXdxcNdC2gTCiIrKDAGFwl725uByQRhMPmMiTaY5AgKbq5kBABJBdEKxBULOsqhRF0bOQ1klkV3OZDZyQuZiajIEmIK+Ws/hYveGgNWQ0AJTsalbAQuSGg5uCAaiTVpdMHE9X8b/8g+8f/c0Xv/zo1YvtzRzYoQveqpN4RYSIHqnNVQ4OF3/8j//hN771IEutdWwiNQHa2PZHLaQ0cWSm6lN0wO3O5j2ZAUpqEii7SsnVcgl90zYNgAaQtALEfcMebQaXoQ3VMVEFjBU8q2yq/+TLr26mfbccYtNbaJbLJcs8NN3VfvP89ub8wb3D07tg8M6H73318vnzze2949Mvv/zyZz/76OLi6hvvffCbv/5bn/34f/n1HxxztJ/97MfX6/VyceBB/ot/9g9v9tsvf/7pr7773q9+97ux51Lts0+e/av/71988fnP+4Njncdo3hkKMoRGkWe4bgIeL4cPv/m4bxor87jRZbd6+/E7atp3AyHVfNOk8N7b77R9ulnf8Hqtsgk8aOoNY8WdICZUwkQrR/bb8TbUwNWV2L2tNYQmcB2+eDrlIXSHJ2bmENelnCwW07gRTXzQaDUiaRYxV5ZNictB0AnUCYqpM2JgAnb11JpqiWct6E7NCLWJ3f+fpf9c2ja9zju/tdaZrnTHJ745dO5GAyACQRBikkRRYWYUyx57XPZHV/mDN8Ib4C3wlMslqWS5amx5LMvSiKTEIJAEkdFAd6PD22984v3c6UpnWssfMPtxHL9/VGQtWkrEpJOEcfBWaUGKkFFlY1RRLdaby5hVYAqMrq4vXp6d3nkwOznd7jeFO51Pj+LAoIgQhJM1SisJGCVmYEy9v7m4CJxdM913LSnbdddN03Ch69uH1FRtt81A7c162K5n05OCZH9x/rTzY45Iikfcb9ant0/jGOzxISrprjaTakKKyKBAPr53a/35L5PkdvRYYbGYu7pJ43hxcXN8OlPOlqpW1jGlYdgrU2bsSEkEDckakSxpgAigQkjA0bHizJ5HlMQg0XttCCkL0xDC7qbt/K8SYoMmNGAM6V3XjaP4rETllD1z5gwAPGbf9i0zHs6X6+4atSYClQVBJEpZTm4254WDctZsu/706OTmcpWFx2HVbzZZtNYm9czojk/v/viP/uPRpLp152iIHrWanj4AKmtrrbGlRUg5jDypKxFhGLSDspp27VhUFrF6/vJck5vXtt1sKdmMLNxtxxvWokgZVFopgDjGnaqmkIOGzHkchAqxQkSoQLRwJkVJYsYYUnaoUGkAQCEShATKIHjHmVAzkUr5V8S/D0zJx5r0kCFpijgJ6qB8dFjYut3tcrca4hZzoJhCwBGkZ4hRaZXKzMLCmVJmj1A2NSOLxBQSMAhzjpIATKbXHz54cPdx+3vhBx9/9Md//cHzKwFWwIkyECinVRrT/du3/v4/+Dunt6uuXdXV3DlFyA2C9TeVzdmbQrmknQBRlG51TnkorDGOOCGKQwG02lkSiomDYUgkWGKBaEUxJAZBESDZdOnV1fXZdusReqBt29VVw0r7OM5mpSV0oEOOF5cXzroHDx4xGkCeNtPjabunfNNd11P8m7/3G2cvLv7qez+qp7NvfP3r7foi+TZd3cxObh82iz/4u3ev9v1HH376zt/+m++//XpVuyRelL51e3GybP78u9/7/Pn5gJKRx9gzSBu7DFCJOEjTubu4vjq89UABUoh+CL/1O78elY7j0Ggj7Y3uN88ubryFQhtjSHOR2VQHx0kIxucIKyMMUKRQH8wbWzu/HtAK6wBJWV0BalMwlLGwJfmQkkcioYw4oMbNPiUMk8YEAdEGrBljwGCM1rkLyhkWRDLAZCknygqcJHQGm6kb2qRyQewSgaCQ1r1mnaI3pIzowJ40+NQVqhz37byZjcMAqkwpktIpcLe6OTy60zSTdr+rm4WtqnHsOEZn1Hf/5M8///yzf/LP/mHbDp9+8kmGeHAyy3lxuV5r404Oj/bdrsseUS8PZ9HHPsHNzXqiYX64uPfwwfrsFUPSWmXkmHNRTk4Wh9cvzusDN1s0tjGZ4NX1dVnVmMRZI0Zef/MBxvz82cXdo/siu6NDu9/wuI9Xm3G+cEIjSEAts7LMIxijckwMOUsPnNIInoAYQIAhDREISFBYRLPWVCQc+tRud/3YhrHnLEBIChBBBYFXl9fjMAybPsTu6O6pM/rppy93N0OjOUMXMdX1ZLZYXJydJV1U8xmVIIVD0qV2907uGd5zGC83eycvRdzx7WNLOmVQzh4cHk0WD+Z339lfb4/K7x/Obc6RYiblwC2tberasgKiYr3euaJGzChecFgcHrd9LopJPS9/8rNPdn6YNIVD2naMjKB4HLc+986SFqWMKBs0IgWWMRa2obgZ0gDaaFQkmBEYUAitND7FPiYB57STxKCJjCIRyILsgFEAE0JOUQCSOC9uJIduKs3CFROt63q2kKpUhQ3jWM3HtN/a6ENYh80KeQ+xlbj3Ettx5CwKlPyqiqi01pYB+r5HVJBk9CHmYKzlEHMKhDB36ne+9e4vnz/3NESv1itPTKJwFYbDWyd/73/5Dw+bOvqVcEDfKiwEbBu3B84bJQpA5UzJj3HsUw8SiklVkAOdrNYEiBmNQhJJRgkiRyZmjDEJJcAkgxCoSK1Pf/nzpx9endlJ1TRTAjhq5g/v3p/Np2Ni40pClwTGEGaTyRtoZqRrQUacWBdFUuojIRbSTN1bb95bHk1eXKyeXL986/4DCnJ0T0FlOScVwYbw1bfevHv3Qc4pcvKew9gJ63v3br398PCo5j6HlL1ft+t29FVxvd3n6N9/8Ki5e/D//rO//vVv/44hCuKLeaUw7Nre73z0wDJ0jM1iHvaXvh+UzlabFCBSXEwOD5s75xebFCljeXpyyyrAm6eDb4Ul5QE1sNiA0JyUDxuXhknfhmgoi0y1I88xcSZCSYYcoxqZA+QcY5GcKMrMMKSmaEIGZlAOjJIcpTDGMh+Vc9/nFKHQGjChGMMaOWsFAgrGMABmi7Dt28H3FhIgGla7fcfIdWMWs6Mscv3qfLlsjHPrzWo2Pa7LYuhSjOHxa689fv0tBLvZb1MeUhqsLTiRTCplCl3aycGyG7bXl6/uH92WXOzHrnRVOaHDW0vn7PLWYfR97zGGyJxXN891kulk5ow1ZMLgtVZ13QwhgCCjsgZK69q+C703HNvzpzVmqmHT9uOAF4nns3JelApo1w4QQXskAEIGlXzySpcpgRbWxAGjRsWAgICaRh9T731sQ5YcxEcBhRACB/RjPww+boeXT54zsiLMEDB6UDButo6dhKR1AuHQdZ98+FNNGhlfffZsMS/jrMF7Ex6GSeWEc267pjHnqys/xmZpp8dHzpVlWW37WBdHs5PXnn/xF1/7+jeuVh8PMQFY0ktdzKw2mRIaGPodS3amxpxY0mw6zUJRQs64v9w9v9hqV0ymVbu6ziQiLDm03Zog5hTJRKsKzKgh10oenh5wipJD4p6zGZBEoyIlSogg5aRQfPZsXMrBKJURmDgRM0nOo9Y6kTBpJNoF6MWxbhQVQjplzVlbrdurK3KYo4gktCoxWlXVi4k9ugMpFlevzMUXA7dCMQJyhpS9z1FYOOsYMkgilQgISaxSmhhiskBjCiFHZxvmOFvAwfJgvZpev1y/ut4eHZz8r/4X/2za2JDaW4sJJ/brVzmqq3XadvvTw+L0YDaZVKpwLJb1gIFo3I+tT+CZvXNOo9LMHeoswEpFlpyZAQrtqtkhmwIB6rJRCiXm31q+9drN+XZoHVmCtFxO5oulsipjqakUzl5GSc2STBSpNWjNKSfQoEqNEueHS+vHsQ+LujBtPDxsLq6vwi/Hr739TYUEOgolDnj39LSqq8RJK8mSL549b4q6rKvE3TsPDw5/7VHvB8hhHOLHr14+7fr5qnt0Mvnqu29fXN/8v/5d+Nf/8l/+zu/+jZPbd6wa0Im2CFXuhthnbkOsjSxUqaDPRroYnCmbAi2JH40rb1XzkpXjYX9zsyFkV+jkx5wiorCipJRSel5X4GcvXl5A1EiRjFmNctH2RpfZj3FwyWJGtlYlZEQWZDIIKWsAwcwEwpkQlEGLFJjGDFobzRk1I6JVqCRJAo2gWt8CcWHM5mZdEhaOkOjs8kyzbSqbVUQgU1Zu0tgKt/sLf82kNsXrtZ0vJ7PDGLuTpkoZFY/3H95p7Li9uu6GFCgpNFmr1Xo1+LEqZ4tlrA8alWU+fzPuYx9vqnkjrO48fPzFZ5+yH1LKY9eybzM4DTrv0+c/+HRxOiuq8nJ1g5UrDiaCnIZsm3K1bo2ph3a7KIVZhQyYBqVMNHy22nY7d/f4VDeFWJIwIAuSQgA2JnHUGhQQMBpVxTjmFFOWbT9IJpUEkKOAcVYHPn/5oluvUg+S4tCPNkmtdKaU8mitER+YQ2VUkkiWgZMDi2Aw5xjD2GOFhrd+t4uqXqqJW/vt1IRSwJRqUGnv28vLK6VtoxWpopzdjmLPnn2YZFzcfvPs5qWCLdupqW6TqgtbZPCl1uNmM6nnApBTZImVm/vAgpCBnz69BnBV5VBS14UkijkCyBgHSqMyrKCoI9xZLk7m09vz0vLo16NFQ6xSpj4HBGUUEghBZMzMAMLCzF7YgRCDAoCcJESGGJWSBEIpui5XSdt6ukDEtmtT39XOQGZrVJ8yIUiC1EUfczI5BsmSDerQi20WtVS6MJNisrk46/fnPoMl4sgxJhZAIAUKScpCWcEgEHMSTdrZL16etx2X04M4wptvvvHb37736WfP7t17PJ8UkHYHTVmXWsmk628U9wcTRbZ8cbNHMy2X015rpY2RiUoHehI4Dzh0Yxiuhk5yMMBaaQ+C2Yf94HMqpwuezayd6cKQcig6o1fOzSqYnDzyo+y3bYztpDSOGRMxBgRBYZCIpI4W9W7fY/SYWWlk1ps4tpIo5EoX3vB+HE1RmBRv377j+/755bPF5PComsRR9MxWrhE/Vhj7/ZolPTyZAYvnjbOyOJ37GMtJlXq8XHetd2VpSrN557UHlQFohwKW29b83//Nf7pzevRb33nfaJzOZiRp6vjASMFkVJJqqri5xvbZfqcNjnHltAG0RlU5xaFfUY6I2cfBaRN6L2KFs6jMkIRVu+OxXWttYsyGu5mG8+uQ0VqlfJJ+73GCZHWhNRN6jjTEujAEPIbWGRPhV8odKgcZIBKjgnldrsPOkSMgxSIwsna6zYE4WWOC9xhHKnPVzJyhBDrtxpRGTEzWHBzeaeaHfth7ZYFGi8PZxYf9eDKrlq6ZiiXjiaXrh/XNehO1souqwJzj2I8SUbpubKbze7fulkYTKu/l6Ghy8WlP+9Chf/5yvNi2zKy0KW2FWkFOyaaLF5fg+8uzp/W8qpeHbEDvsEc0GpDxixfnVVGnQnU8iCqfPHlSJD0lRodg633rv/j8oljMTammBlhr0hRCMNoIQ+KQIWNWMfmu60GS0ioLEUBRGs5JchqHbnd+ffX5c0IJfQKdjTaoJJkgmDQDciAzyUksqtpSUG3WVU4MKSFrDIIaqLAaAdvx5mefD6e368OymKscwsBZl4Wt5yGm67OzG4aDY1eY0ayfjju2xVTrurLLXc7aHIiaWEdowYJLbQK0qbSQs/C4PJgSacJUgjvb7m92+7pwy8ZdnZ8FkMCMwIDZ+1EZZZVaqOIbj944mbicx7RrmQURjCpdjkMEtronmGDUYpkKYC0yIuscUjImsIKsnWTICJA55ywRqRpCuc+1MqVKud137MhUzhVOMo5tS0Q+CiM0k4NSlyXKSNEZkjasr16Eoa0LcK6UVPReV4ujYlK9fPEs9htW0u77qqw5szJZm9hgTaQBBQgExFn7yRdPTFOIgsraL7/1Tj1ZlrWblhNXJaWNs4617rYtlUvhxgQ/kQTTsqkXMQBZgzkbsoLF4JoMydhUKhLvOezHsad6XhDhbktqH/tOVOWqOiSf93s/DOthLHVyVcXT0gxopZpOStElJPJh4DBGBmsKo23pSk0qjh3iIGwzZ4kalUlohrG37TVk48WCsYE1Wi0hN7aK4/jR2Qfd8OjRvYfQ7VFlwK6/Ph82vZvUs8MaQxpTGGLOXADkYXez38WLdauLBcWdBpgXpkLbjvnoVv3gS1/7+Uefl6V7dXnz/uuP7t++a1QOF6+MAiUDQ0qcBXXyAqh8Cku3cAoSpNX6pdTjryC7fd8ZZ0IOq/1NZae1npQYVNaFAVs4I5UfmTEXmk3pUk61boBZK0GFxJETD8wGpI+deJkcHImNCQEyABWotZNfGRZkPTSO+yoXCR1kLUgIrBFJdIGg0CGr0W9Lm6Pvri+HWdOAD5YEORKpsqrHYbi4/uVkOjFVZS2G7ebyxQu43cZ6Pabkc1LaHB5Ww25buqaAlEBAWTRQCpWmbqrS7wIpnTJEiMrZ6259tdlaTtAYDiHHqNA2RRGJBu1Wz17R7kJrI9TU05qMrK9XhitrDpblZHm6+PSLFyFmV6nr9WYap2UV5wfl0s4S29TUyKzBYIKb1WX0vqpNaZ1zhWsc+2FWVLH3u671Ic9mc0VUuCrlyD6w4HoYQkg+p+B9Hru5xXZoBSLEeoDRKJRfYfqAKfcUN0IGnSsZCZ33mH0SigEjG2WsIjKxC5rQZd9ePrlZQbecGoOLw3l1YMIIV7Hb6RhHfvriJ+/wm9OqFKtv5Dp5yRI0zZFmQNA4xzETmTGEZj5nIUm9sVJUVUKQkP0+31z0qPRiovLYxzFCAsrIIqAj6VGEEc3dw9uzWq03r4qMDCQiSmPWTKU1EkaJFEEQ0RgAsqQAiUVSSlED5MJgziolhRgssB/QDnnRsx2TnRY1Qd+Nu7peVLMFq1KhMtkjR5soJOlDO4Q+ZHGTaQTeba+6cVdorY1CTIoUodWFmlTTew/e+OAnfzl2lzh6n4FYgRqnE0s4VyIOoQe2RsWQrjcbwChoposFIwj7+7eONWFOHjUalfub4ZMvvnjt9YfW1FqNAO3i9EAVZQhj0UszX7bDSDhg5hxZ62LX9ut2S7mXaKrJZHRULgxMm6WQYU4ifhyNxNI5NZ/o7Fl2kkBD0uwh7TwR6IV2lejKsIDRIEI++JtNll1I+wi1nh0p1IlVfXBrcWOWWpWl7TOMwqvVJrBRJFXZGG2efvH5n332yY+r5p1Hd7789uuKewW91VzWGGUoSZeq8BIDUxg5DH4+X9zm6qIzrz9+1N9dak3GOT05/uZv3fPif+/bX7rz+v3D09OjeeEK6fchDPH85uz68lWWLiMEQnaF1LpQalkvlSiPqevHqrKAsNt7hVVT0s36urtp60WFRqncTAAsBOVi0tgXtiFrgLqcDfHJrN63LQuDppgpc6KCvZAxZddv4q5XMzsqUUBas1UZhBQ54Ew6MStj6sISMGqlFDCiArSax45I7fabPu6IBhNDowozpkk181b6wYioMVrs5OZy6zTWpsQArpwtDu/eXK7/wx//m/fevlvaJhPqPE9pEFNUpDQHJjMCaKM5RKBcFhqzROGECYX32221rKJPnmVeOe5S38YQOyyQACRDYehguUxkSbtHb73x8x/9+Z2Tky5HNSnq0mbvC1caWxgFKmsMcuv4yIIWXXiEJkObBua4PGxCNLuxa3f9TchZUV3o6u7t3e5qMq0WE1cU9OHPP3VV3XkvopiziBJlY2ZNikptJpS22UQMkCJGpW0KooCAyJoaLSDp0Y9KRaeUKAGFQ4YYEDTVWtX19Nnq2eGyQGTnRyc63LT7MV29XMep21/vJnU5PT115UTxAD8e7p4cTucHmzHMZ4eubq53sZ43s8aSZJSUmW2pWKWUsoRueThjJFR4cXWDMN91eTqpb58cfvbJ54RO8gCZRXC7W6XckUYRWzqwedj6xLlGQoV95MBis2SDkIKQoZCywtGwRvBKsWItoBML5sGS/lUcUQi8muz4ZBunKSaHKFGDm1itt5tWslKTuSkLU5gUSYDI2No5QtysVteXFyr64DsFGgEFIycQipmTWHt11XZhaxcnQLnvdgwR0mAoFmUpqDKPAiTIzHlSVb/xtS//l5/89HrIE1sZoIJUaSxBYtDKaOH++vqiUPji2ctJ0xwvl8XMVmU95qSc8r5tUqMlvfzs5yAxaOetUaY4vzqbTnRNS5cSuTKYstJlSRog++hJLEkSBAbtiUiXWelM5HMCHjD0kq7EV1iUKcTtesMpa4WNzQDEsQBlEbgwlkmdHBYH1UKBB1QVyZiG+fIwQCFJYAyVq6rGnJ89ffbFkxcXXzy8Mzmpqm7oWQOikWjX6wFNnYQA1ZPPXzw8PZjNGgaPFZ0eNnxwG/KGU9CaNjeXp/cO3nrj4cF8oatKIFxdrbs+KTehRVpfvUKaNtPZvCxd7fZ+xTEiQQEIIhpdbm3iMTM3U72P6zhmG4tG3XUy4dxFhSYBql0JWrvJ9bD3hif18ktffe38fHV+cQGACiZX+9GPuwlR7IQANZdtyMtASRXJZAKKETNkkWyscopUJvEWcyTSWpPGlJkziBbkYdgZCQtD3lMSUcZt9gPvIjTT9XZYTGeT0thCvbG4qzhTGkCsqZaPv/T+6+Ds8nB19RnHZBXth72zaEQYCa3LAYqi3uVhjK02hnVImBMzIkDyE6U6KmWCCiDu4n69DuNIZR0j8jigktnJrKzLduAudn/yF991qM8/XVVzSwe8H7uTk2WWPSM11lolOUQR1BbLQhuU1IZxv4tDOzlVemmOVLXvrR9zyqhF/HAznenjk2ZZzzhB8c7j693+oy9ukByQLozJgCyQUiyU1ZOJKnyKiV0yQEqBspa9IOQMkRmEtVYUASli5uAqRMZ9yKSMBnrx7HPlyOfeEFp0mWPkiGiZMN60Ywgk0L28yubq0eN756tuvdktFrvjo9uHj082A2s3skpkbfLekBIQV5gsGfJYuALAAuD6ajWOctXdJIoP7t3OGbohJwEBRhFIolBLSqStctWQQWMp8aaHqBisEkUAzIhIxBYkxAw6qyyaRbQGEiKWnHJOgMljJZGigl7qLR6OuhbJGIk45KGPWCAZDnB9eek2GzQObAVolwcnpqA8xsvzq3ZzSTCyMpkJhGwlCAmgEBFOQ7vrORsRxSNN6tOsq3236dJaKYPiMoJoEtHoOUfv+/27d2/dOZ3/6NOz2WQ+taBw5BQJyRoDiGdnmz5EZgohbbfnq83m8aOHKnobI0myheK0qa2+8/qDm659fvaq3+yNLbPSN8F3fGP85UFzx6NGhsxsnDXKhDQARhYpss4Mvc+ttKGqbUmGFmSPcm459NKnoRugHRbzmVSubTvtZgcnt6yeJLSAGjmXwFJaz31WHqUs1SylqHNGzmScpDypivr+w6v1xqH62S9f/smzF19+8/6bb98ax7Fv49Xl/vjxspw3NUBT14BiKEwX2W/4/OzMTsajCXAGR+FbX3k8OZ5Q5SKOu/3Vy9WlbwMZF5Jv41rNcD45NKbwOVSFxN2oslaAxikU19TT7abVZblcOJadyuxDvbxzcnTyOLQdNSxk+5DOXr58+sEHV2vPyhbTaanM7HBx/9H9h3dPGl0+fbEp66KHYUweSBklGk1HXEpPrJAox2gURcpOgQFioSSjMfU0lZ5z5IRGUKmMokVyDrEykOJQOpBBddv984vrxszevfvW7XsPry7OQWtQOgtoQrZQN8uHj75iXEkIv/7t375evfP9v/jPV+fPl1CxslXlAjOxYkm/+MnH08V0OnOxA445ce5DcEbnmNI+DX0/WS7HmKKPo7AqVPRx9HR9c/XGO4+qRckJQhhijtPara6G7W7/jdffVk5FysvlDERfX+8xpSFnVxqloTAuR0RngbmA0mK6ePlycjhRyinrlKJx31XTUit21sSYvIglN62rzOHrX37rcrVfb4NVBlg88JDyxnsHdnJQjzBSJlExZU9iCIAlAYgOgFrIBkzpV53IwGytMykl6VFsYVSGaNk0pkoeIqess+RMSmkHR7YJIt4PgiIEk+UpcMtGZRx/+dmHom5Pi6VROiMzErOyrhZWRCmMu8l0AVhkyeubbaEX++2r5dzNF9WPv/czREwpIgCnTJILba2xgkSKnp1fvrWsJ4W63u6EKsFfUW0sREwgJmbPkmAQQqWMoJAQ6RxE2HjGnNlpF9it83ykJoJijspgTNoq1JTz2APLkCRyokS5a2OU7c1muliGfojjqFEQnGRgIecU0iCigijJTKgBORPHLCQ0Jk22mVunNXDX+WE1eKFiApxVQtCF9zGGnXX6K4/fTLkJggRKKbLkEMN2f601HB0dpZwF0qKxGD34K8lBmJ2ySludXGbcBLrcxtn0YNiciY7WOA+y2W3hxWfz+sAVM1Yp8Hh59qquFnVdKQTkMNpIYsqCki/b7VbvpKwnqplzMSE3kZicy9UBck7CuZgvlZtzWYbMzEFSZMwcw37YI8pm115v16jZWlOW88bZ1fUlI0a/M4iPD07ee/P9bn+THr+xLHz2fUpqEDUiZI2qaEzicnZU3zlgG5s89p0/291MCmWk8IKL5bKPTmHe7levzr646S6zo1pNIZbzWcW5J8t7v6JRZ0BBVEYi8CdXn9yZnxqqUGOWYdJMkAZgQDbz8vBk9sgpss4ZtQBth8jdVl6efxCKIudxdTbmRPTk2Q++99eL+fLtt7701ltvOW1OfLG9Wg3gydpCpB1D8NlAz1QBhtIKCelkADKgEKNoPzILJDAQADUoFNKBUAoAhUbcdr+CEdII84NTp+jZi4+LqtbKqFRFnciBIHSBKt2QcoQkkoNfj/3N7/+9f3R9/smLJ58tl80QM7ZdzgJYffTR+btv1GnwwBwgOJCcZOh9GnpOgMRagQ44cEZtmnJmMq63HR3dPn3wAGM7qScxyXi9nc7LrvfmcKLm5Wxx1HU3u85zZjLQhaiVNgSltSAyjp0kFYcejg4f3fu2911/c7FfXccxYWG/ePbs3bde2+97vQZVuLXrrcExxn3bCmtjm8lk0XV7zeystcoM+5wx+Jj37U5rITsZW9EiWgkDVG6mAhNQGltJA5MmUwNQP7KI7cMA8UaTJjYgiowilUDQ2RIB+j4BpsxpRjO7oGBC7tdcz6uyKRfzLgGSnU1KY7W1yu9brVUmo5wDTF23ZVBsyDb61YtLIHd+3RYED+4df/rZkyHuFRqlICYhQFYoUUiEjMiwr2xl7SyCVzEEnSCpAciQxBwBBQlRJZ9R0BRaZ0iCgGBAxcxeREYhjzq7x/XyfhXH9eomBc+EymKAUYLS1uiiqEG6Xet9ZkiGdEhpv7oona3qkr200ScQbYGs9qnINkWBAOwImFXGnCUTIZIgszZmMT0NxTrH8fyin1QwaQolSJwQue9zHnmyXHpGQdDaoAafxnizshBtXQBHCV1OO9znKuOvdsPZFgJZU2aMI6rrdsja1FruLw7WffAaCLCaVeN2uLg+P7nbZI0qmeDH1fOfHx4eLk+PTKlFk4wIqGxROtC7dntxdu3opmjqyXLJqsyWkECCb7d7U1lBI4k1IKIkTjGFkPqYglGliAZIWrFRlNM+1dNNXrddmDnnJpNyWqy21zX4Ervke9QW3bLrtjSdLg9uFaoOyd9996tJJZBR2tW05JBHxZUjw0pfby7b6NN6WLW7Mbfl0mij75zcub66ySorBw4pRlSaNCsiVATWKW9kFzaU/OrqgpwoE4UVB8teGruooBhj6FkMGC8YIv70kxcdKEg5ZRbQDGiNKQmHkH/8g59+/tmLN7703ttv3Dt5eLLrwnafAo8EkiLFgSEyGzUkLomUhixiACxSQMUWeBSbMZMVhgJJK8y9jAXQrGrGOJxdnStw9w/uZInR+6ubi6PDQ+93LBUkmwmb+eLB3QfOKpGMAMa6xbTI/apsmuPbt/ft9dX65s7ieLY8LSZH/8e3v7G7vrm6fvn88w/73b6Noe07h6SRyZVuUuz7TkK2pTm0B/t22G12od0f3Tv5yQ9/8vqD26WrXV1VSYw1y9vlVx4/iHb2+O1vPP/kJ6/6s1eXl009EZVzDgCGOWURYJBIR6fHRw/fOTx6CCAC/tUnH5w/+0Ri+q1f/1aX82bXt+2+WtD19np5OI+ZN0MuXJXYxgwJFKPkHIxW09IOadP7fc4xo5qDikgCApCtMfODaXu9UQowmCxF9iolAAVa68l0Np4PgNnVlQhJjl3bQSZRBTKZHAuFjIgAhUvToiJR2/Vw7s8evvYQPSRwTXloqRRDYx60AGapCqdBYhyH2M4WR8rpXbu72WwHb7t+OD0+Dp1cn28MFSFmziwxZOIkachd4liyu3908M0HDy37EQNY5hxz1FklRmAUTWy0xCQQCMAkg5QMCAthwsB5jKI8Hhze+RvF4phQhbGzuqDV9X6/QU6qdgd372lTglbKGT8Gv++6m3W/3aBkgTgmb0Bl5fpIilSpVBIcSMuYAKWwKku0xWx+dAxp5KHP4yCxkxwiJ6LGVrMU2t57gGHWFIAAogsLVE1X4z6CKrUF7nM/jv1YV42FatystN/lKiMGzZiVDUI5JR88afJoyZqn16t9wkbXFXNTTIjGF31HqlTG5Zm53F0V/XI+mXHIk4M7pw/fTGncrC/wejepmmZ6AqBJ6aipnNegaHe5atstQ7KuIuOUc8bU06OJB4asYchsshhBhU4XddHA1CVRs+PxPj/gGFIah9CPul8uJgh972Wu54pEt1ulk6Ywgt56ODio3zh9LQev2TuaUtHEPCgkCEr0QKafO7WsKkO6Byh0ce/ttxO2HHKXx0+ff5Ri59sNc0o+FUwqJ7IaNOQQJCCKJqXKJAXUu6G1hVuezBKvc86sFFu9uV598tefPN9v+hQxCQLGYeTkq7oa+p5EEkSrUDOREKVMCnfD5Xf/sv/k6Re/89tfPT6aUxmj135R7bohtSEADiKRlNMUORA4jTaLIJN2KqNYScgMmMFoDYAZ2Gc/BtalkbIYAicRRahY5sZA8CIOMYEmgupwcVLYEkAEEJHHth26fjN0CXzmVM3Ke/WdAgo3WQTGYlLeX75+5/WHX37//R//53/z/PMvmGkcPc4a56wPuRv6xlql1bD3kaBYTh7cv9tKOLQ6kzq/vO59KKdzMkgrdnXVbtqUZKLh8e2Dw9Ksb1qbQmMsQgjZg1hFGoCsnRVmst+skP2YU9nM33j3a5dXFxdnZ9t9n1juvP7Ilc5H8XEwpuTaANgoJrMHhCSZOYsCXUKRoLOyPJh3MQ9tZ7VKCEIYUnp59vxgNuWcOx806cIWfY4iqe9jzlLVjY9t50eNVJcGIgApnyl2kfyYtLBCI+QKrTKOu8hITy/P9jF8+6vfXB6dmGJekFNaDUMWEIuojEHk3W5V184ItJv9en1TFpObXV8YVRr98vm5QpVzZiQQsESBszDmnJZV896j1x8fzWHY7LoucnCG4hg8CzpwoJAgCSgkIjGUOQ8+WmVAM2WdAyWljPezYvFeULN2tR/7/TDshyFnYSTttJ4tjkLKTEECxTYSaGMnd1877DfXZ8+ejVkYOY4eCl1PZpizKQyHgT0DaSytuBIhuWrhvRgiVy90sZQchMccN9AOmgcyxbbvcugXUysgKedp7bp2lGB1nVPYc0iEMJ824MqUAkqMKt7s2jHERrtSS2Gt98Nqtx5G1Rw9uD1bXO47KsoCpSENMULyFtE1szEOkUFbs+lWzmCRyQcpmlq7qpouE/Dlq+dDGA9PbiMUIkqZaX18UByeKA4KpB/7EPp+2Lt6VEWNdiaCIpRTRGSmTEIMRCaLEpUVh1SQiZhsXdPgB7K2DFLpm/OXjxbNFEKOu87qH3785LW3fuPseVeX16+98bjrVqyTMQuXdIg7xITKKYI7R3Nti6Stt/mtL73TjmPqNhLyxfrai7DVXewHH40zQCRiBDVFUhQYgJXTmiw7Ydr7UZVFYkreCMj1mf/8lxcXzy5zF9naNkcQoZwsEYvSgRRCRjEZEiTAImQAUkkyRCLwz188/Xf/YfMP/tbfPD5YDEprlpImUOTtZtNBTCqPwAlNI0oCoCabc8IUtZgECIBKBQadxwgDZxXbFIPGk1szJcaWIoMvNcRgx5bRJpQoBJPZdD5b0v8MtkDX+jTooroznZd9+5z9NsXRVdP12husAlMIyWjIGa8vLhgBtamr+mA2zYVa7zvfeWd0ohTGULuaJGiEAVIQOTycowiE0FRlBq0UDAp+8dFTZefrF5/b9Wq/Ok9CjWlCHyF4ZVVZVpBN3/akhAwWjbLVTEapcpuz9O1weGsaklmtP2ZQez+kHFAMCqTouY/aGm0wA0MWAVRIIjBGIKoZqjGkkDKIFIRAmIkyZ8OT2ClmVqpAwnaMQxyM5cI5yDlBIqWSD4q0lzSpp0OXYoqMiUnQoEaNWrVtZkrsoQ8jZdxfd92QDl1pFVkLOYzEZkhDNZmCon2/74fW2okuaLVeW1Nst2Pw462TI+/bzKOgACAIZ8gMWYkwiFX67sHR7Znrr16yjwIqZhZE7VzoI6csTKRtZo1EWoUsiUEEegEtghxRsOxwMrn1vnfTFy8+zn0e2TMGZmMrV88cJQnRx5T6821lXQIWKTxEciTBoyt9l3JCR2g5Qr9jq8vFXIJrL69TTncOT0tbxTDmGGO/TSrtE1gqrdKmqMqjpXbb/fOf7W/WV5ur1x/dUljGGKuyHIbuvG2jPrRtaxCyLevlgdY2ACYANZ1//tnLZ8++UNZN6+bRwbworKtmJ81y13qYlh+dnfekGqDCKCTxmYcQNCnnoFocbi63Y+w33TrHsVJ2WjR9R9a4i7PL6aw+fe/9se83Y6xrV5Zz1I0goEQkDpx0Ma1Zk3CCxJCTH1lpIAtJUURlMefR527cC+i6KSxC5OQVJslibdn7GzRyCLlp8jzvUaVE/GonAe88ePD6P//v/2+vvfX4wduvqaL2ELT0CsiSTroHsKKtLgtvp/sQtC2fXj/7yY+/f3dx+Pbj1w4X1er6pnLFlMqTW8UqrXacCUgJalDKVl7GkJNVhFhuu35MuawVUfat+sFff/by2Q0Ep3XBBQ6UGDPEVKLOAKNnymwJiSEBRRaARIoMqCiitUZEK2Gzufkf/u0f/92/81t37t2RwStENnRgDxoe+jDEICTGCAiyJhJkyklnT0SJACEjWG0EbBCjMUvyIcc8Tut5ElEJy2pqy+zbzehXFpaccj2dTOZzIULJybfAcXp8iyV121djuLZWV6rIxizuHQuZ+aTJnBSa5y9++eSDPzF+7Nhjadu2i12ezJsiURjHi/X1cragDJcvXl5fX7//1XfnB1XlzNVqfXNxeevoCCBMiiIU1m/GV/tX3/mt71z+9K9jt1vFUBySIxX9gGK6fhe6vDpbLU4O3/nWcfQxwmDFGpyRy0YpRerN2a8tDm9fPn+y253vri6HuHeVW1/v5pMlIiIqFEO/yv1gEksyZFfUpIoIO1VSGjELk2gfYpaUVZSoJUNGLYKINlMgDsycIwFaZQhF5zGzAWM4RQQRUUxVQYAi4jmhNwEy64SF0oE8ozo8EOOMJg99HkdmZ4rKlgVjXt1cxXGk+QEpEpQosh3GaVU5MpftpagoGTEThuRzSJycAImRZK2u+qFrd/vCOGJRSABQWYM5rtPYxliTKcBA7hUaq6qcR0lDIJ1BK3QZa2he2+qy3a9HL1lGrULyrG2xPDhIkoqiJFTNpD450r4bRyXGTLp+/+LlM98PWhEBgFCUVHImhSH69dnLerLQlavJra8uVpyaSalQgSRlnSm1AxV3OwIWcVeX17v9Zr6oGihm0xIy5ByVLofMuzEYtdeQsy6qeZPR+qxjDj7jvkNd3X/n/ZOs8qeffvZqN2pdmKJURVHU9qLtB+vmi+M5Yg1SKekDq2JCKmqdLdKsKbkPPqa9HwcZOt+ZsDOg/NBt91f67q25nWTdDCmS7HUKmZA5GzJaOdEJUTOS6IySFQlC5jhoZRmZMhlqAJnzbj+ejwEQyArUTmmDjuMiYwxwBFyozMg9m1UsOfFbbzz+7g9/qsr0yeef7f79H/3jf/YP8+46DNehl2Z2NKLNEMGVPYSgZ10ea2N3+8/dxF7G4F985q1HHaaqeev2a1Vtz7rm568+LYgkg3IF55jRF4qKxCQqDpGYnXZPPrn87h9/FPpYVBYVciJSrszoMqVRLLueVAy+VCpxSIgpA2ZJmJDQgCNWRAGVpdGVpPZh9z/94ff+23/yX82nVUgDJFaFplAYbbBWHIUA+pQjQVI6jKwTR2ExmJHBkA4yZBNEKVcERJ1Z9QJ2CFqiTsFUpWJkhO3Q35kfTeoFYkZJyBkhKFeP7fXYrnPaFgqAGMkq4n7Yiy1d2ShULOz31xTi2eWN50hj/uL5+XR6cPve5Nn5Fwe2Rl+eX7TX+70J/mR5PIZ0WFUh8HQ+LyploHaq6OJ+6MbQjU6Kz3764wPQCWxd1BFUPa0g1PvtDe42l+e7MfqFHC7vvJ5D6MYAzogtJPdKVPbAgPPFkoWbffPao3eeP/v86cvPqaBeUolagXKkBoOaJYMYzhqRlTo9PeLNPgJ6DRTFAGEfMgdlaQs5+2iZkJy2hdaOJeZfuWQxMyEiCEESe7MLIqQtEoCGROiELUFAk3SOrHTStlBqfvvu4uBOEowKd6tuUrrMw2I2BU2b9Wa3X02appg1m901B+5CFBTXUDtuI+eYM0ZJIZOQZSVACbOC4NBeXTx/5947u8tNP3KlE/MgwARGacSYPbDyvdIVaVEY0DbBB4ljZldo4xXlyd3Z6f2zs6u+GxLh4miqmX2XwVrfjwS896330Ro9mU6zaCHX9tf7cTsOHSmVRTEQgWjQI5AAEobkPafr45OTyWySUk5pjGGInAQgDVxCNo1tps1w+Wy9eqnn09/4zt/+/IO/6LYXSt/2YQcYBcv54eIW43bVsxV9cJScBeaYupTy7qYbwzibGJvzk/WmUk3tyt04FvZggkWXNp2iajY7OKjL/aZGTJn7iLaZtXG13W3rWlAba1xmzJB84DYmk9vS2mZSb7fbl+t1qkSZrEuzGm8Ma+HU7jY5yXJxW9XK2FqhCiG4qiiKktGKTTm2AuQBIQcFIcdto0iER4ZxEEqqqDK2m9uQAFkkEykwRqScUj1fWlLGuuVSf/P/8//74y9e/vS3f/M3D+bNbrW9evXytnJoEAUQpqKZUyqo4qF/9+7j1+7e++TyYjtsZs00d1s/hF+8+PT0YHE1tGosErFgrhvT+WzRoVbjiLnrxnaY2Hp3kRWe/O/+u+/84me/+PEP/otVtNcw+qQzkDCh9kiSpUDNkpkVCholqJhzmZPxxBOkESTrVNoGva8q8mP3n/7oT//xP/pdBToCl6oqnUTNPifWLJBcdJyz4KiU1tYF35oomYqOWCOGACBgRj8yRGeoFjQMoHWK4d7Ba3qnRj1MzPTg4LR0JbAC4sw+hMh8kfweIRSFISwi+6Qk+1BgqVDFMbrSnJ0///ijX4Tdvo1ibUEx3bt9S1DtLvanx/e2lxsl5cM7d/7X/+wffvaX333yV98ft3L9RXv64ORsdTYtleIc87hbtc6pqyE1jlRqJ4eTbEIa3M0+H7/2pkXgZ0/2V6uixPnhoavoi08/fvzmu9azMzZKhmRLasgJ2igETEofHZHAZLn84sWTuiq0opTHMbItCSFj9EDkMybwmFhDRPHsB7GFzzLGZLRWHkgEKQNqDSgq+9AlL0aLKp2P0TMXVFhjU9ciCIEQcWFtThmyCJnMVFQl8zBEb5XRhKjMN7/26xM0hNp3UUCHTFXVlKZIPl2dv0ARpQkln71YNfNb22Fb1jbC4D3HBCwKIAILMxMSIjECE7M1nzw9e/3O8aMHDz/86GdS2AK1EkZCFlRK5RQjDyOwArZIzEEAAidGj+LIHB6/9s2LV19oGJ0TFXMlhkxZn2ilyUtIDDPlFEgIyRbFky9e7HedoSqSKCqBRAkigbCkzJETACoC5Hx4MJtMXMxDFgemRGUqbS0YxIh+76/O9rHr2vXrrz1+//13PvzB93/x458cHDaYIxAxmW4cycaj0wZIB24YjKTRIt8M3acvXqBPD44WmtPVzeVqkIOjk/14mYZkXEyL6sXm0h7OmsNGum7KpAV3kRPKrlsHjFo5IpsJSaMjFcaUkyByFtMlJgi21OuxzynWbjxuHpTTu5giYUKrJPoQNjCUTIYT79st7GViJ9XEECrUBVqhyMCgQLQoCCHmVFCYULQZ4zaQjyQApJN2ERVMFg6cjSCFNVVVzrI2k7e+9GuP3jhi6EQ1ZT3dtZ/oq8uTe8dMWQhMUfpIyCAJPBmotLbq0J3MqslmnXQFo+lf7F6AqEld9OO494PNFZsMwCSk1GKXcDI9PD491K5Sqqp1OW677/7Fn7JTY/RjToRJS3QaFGcdOCs1ss3ZGsUArXIGmFRmkhRJAZUqj8p4oIw5lbZ6eXb9gx//8utfe9ey0mAYszOGBKKGIQIQQUyGlHWONWbDfhxZEEXrEQm1cNpLytpNqrKwumQfCtCA+Oz5iwx+Pp0nXZ6e3jq8dYpkhCNnn33AtNNGWBsG1CJGV0rrRKooj//1v/53xye3fvtv/XZppz/+3s8e3D8oytIqLqxZntT7fujXu/5yf+/NL7/5a79x68GjEDsDoryfVfNuO25XNxTw/MWWQNZhCx5Tht0gfNAd3H305Iffg9pN799/9+7bH/zsk//m7/++Bnv97JkTmC2P3eTg9PQ2ajs7bko3yxA5iGKFyBGCGNC2rpsKBD7+2fevL6/feufBrJnsB70ZIUMwSievWMHAIwnlmNfbzttitx/QR2vLiJFVbo4nkgVjq7WByGxDSAARLNk0JGTRRKh0tkbG0SRghMrpFAZjLRnyiQSiMBtdxKyVVsjpzffePTo4KoBA5fXq2hVVDOPs6FSDXl3dtJvNZDotXX328qxqDjZDBCOTUnX9KIElBRQWICkgDhGBS6Cc8yB9pGSa+j//l//yv/9v/+nJsjm72WZdFACkMgha5YBVZB5TNuCYsnCXgAbWIphILJUf/vjHDB4EiTBHf30VF4vbIAVp0E67ycxgVRocxvCLX3y8bzsiVIpBKUDNOSmMiRkBFQABCDMGLoh2ry7CesskpJ0uZs18VjijWYykfbu6uXxamuJbv/6bb7z19off/5MPfvBnHNummQQZUy6catgz5xRgqKdN6l0WlMDP1s8++vzJ4e3br71xH9uVY1VW0+CHp23btef1pLTDi7Aey6kFjP5mNRVVWGKRNuagQVQGQSTHhALCwCipcEoQOCcCkgg6kZYiI5TN9O7BvdJOB0RQSQjLcoYcIaWktEAO/bae1YLao48DTMArvtFKAyitKibNpMkqY1BJgG4Nw16RABGAGjJu+jw9PDBubpm6zVqSdL0fJUxmzfGdxWq1ffCQ2i4Zqu/ef82H4Psxy5iFEybSBJwSZo/y6vzlTXsliW42K219hhxjMEI+jB5FNNW2Gfd71rqwk6k7VHo+vz9t7LTj4ISoTy9efPGHf/4fB5USkEJllESfhYwnVoCYgIRRZ+2SZDZURQZGLjUqRq8kYTaoPIMIK0HOAyj7ow8/fP3tB/OyzDSiUOSoJ5ZDpmwYklKASNZaq6zP2FEe21ixaCOGhLSgKiZ6MimrkjkXgXSGbRx22+uqLq9HXy5uHxydkC0yMDIoVCKQsSg1Xq7OlLUHh0ccMwkV9Uyb6e3bt//iz/5w2D6BEL/99bfHvhMlmhKxoEIQcbUr0L37/q8dndwahkHFMGE0hBcvX3FdLtNyt7p68eKq34+2KCbLuSeTdBgj/MV/+suJCneWywDurYf3Hr/+Ztjvuv34N/63/4cXH/109eSXgWW32d96+GVRCUU0aNAAOQORwyZlb8sCCJTS+76rmmK5nCGDoJAyOUbOmlQZUms0KMQcWYwOaCZYs6gwZtRAJZlpMdy0ACgqk0IRcA7IqOTZ1tXdu3c///CT0PcYMXEGpaxWSquUKTEwpZRRawTJyKmyzhkTjX34+lu/am6tt1tkwCTL+aIpGx+751985srSFkU3hv26W5we9furSQVx2FPSKSdCMSCcURCYkFFYYoLIWbFkTnAwPxTO86bcbLbj2GpXYmRNypJNiMIxAUrOSTSyAAAZGUbPqlJKTRRvB40KKWVHZvDjzeXLJBhzVNbpsikmyxwl+HB6cnu5jGP06/UNcwBgyCFTJEQQUIBKIIdYaVQ8agUcfGJpk+fc1+vNvftH1qrL1fMnH3wgOf3Tf/LP3vvmN84++sUvfvSXNzdXZVEY7UJiraTEX21NFbHJmSJLyN2r81WX/Xtf+jIgP3v+2b2DqaiqKm112W32N+VsMjlYTouysUoX1mcu+nFaTQPGMUaPRM4SRYw+pnEYWIiNK9IQndailAxICrHQKhkjBQaeVNOL3U3bv1wslk3ZGG1Qa2srMugAhn7tcvS9N6bWjoOiAXXDovPI3GLYK2VQW2II4DKqnJQfIymxRhOSGA2Q+7Fv6gkqbapi03ViyBGa0R80+LMXN5mT5ARISpMDdf7i1WRagrFMmkQyB+UEUYzCFD05o42KIbKglTrmEEyvmIiJM2os9vswmS9Opm+jWDaII3eb9vOXF7/87PO//OFf9X5fOCXELimbVSYzJs8FMSlBqAgEfhWpFMQSRAGKIIESQjYoyHpIioQLocxkDHX7/Qc//+x3fv3ryKJER8k5gwSMMSsCQAYUpVAbYZS6KZkxRdYkRsBpoww5FXKhUpYY2ZMxoesUh+wxo53YAk0JTKiiDyNmCRw1CQuFgL7bn5yeAovn8OFPf3TxYrfbD6+9thB/E0JOJplSEAoE0KiGMa9eria6nNy+y8Xk//mv/vXv/cO/L9eXzz78uTIyq4w9nn/j7/wBSrq46j74yQcf/uAnV9e7cnoAwVdHi8Oj2elciYazp798/Pje08vry1fPIrfdD9eldYnYGfjkJz84vPfG4dFSBBBEENHonBNk1qQ4szD7NH722ceTpnKFzQwVuu31mMGQgnHsc8o2qYJ4vd4Omzb0IyadfDfkbGdOib56cukg2tIye+O0Bc0ESmTX9RDV1bMzlQU1REiooK4nMXillRLnJY95sLYjmSQhJhKVW5/uvvOVopmJSpHT+mZTlo4oL45qNuPTL34Z0t7hMnB4/vzs7uH9VbtTlEuyGm2X+wicRBGicPzVJENYfuWyBNCch3vHB7//1bcg7lIK87q4Cptx7GtdEGQmtMYIQO+7LiZlCkdgs7KIAa1VE0PGzKf1ocmkxsg5S9q3vY+k3K1bty2r8/OzL64+FVGWlIRxcXx4fXkpWXIYFCEhASdk1goNoTA0ZVXWFsClmIZBujGg0oupO5jweP3Zy9Xuw48/1KJ+92//wXu//h3u21/+6Acvn36cBNCRtQY9UCEsgSEa5wBw3+bdyC+unw6j3H/4Xoyy6demLBKZAbzR9tZh1VE7vXNaNLYpS2Ndzglz60hVChLCkGOmRjIqtBpiFg5DVBWHABZIIWkip4Q5k3ClxA87MKYNbRIfYPjow88M6runp/duHWcEVC4Lie8lBqOTcV7QxJwyumCmkapKBpMHwizSA0vVxR5Y8kgajXGFJczKKWWNafsu727MZFqUhRnG/Xo9mxidZUYGkoRhT5VjHge/unx1/fFHn3zjm+/XhweQAQCspgQSh6E7WztTYoU+91nZkozFsYs9B1WUhVJpHHuOlBiHEFNgGf3Z6urjp599/NMPr15djZyzpkI3isekACUZQVGlZPKcUs4WNSBqjQJJGU2irdIRUkIQAWKpRg4KEgMBsxCJqCil85//8sNvfenLdVX2MZIBgBBIQFNKg4aslTIKNXBIKUVJqhhy0iKltsGnlnMuUt6JJDU2ZWlJzyaTQAMgjgoVSd+1xfJAsmxW19NJnVP0vsNF+eDeY+UccGcY+xB+/qMfXn5x1Xbt4zdORtDnlxtDcnpUHS7KGCCmvq7do/fu4VB+9Td/7/t/+l8O57NJPdu8fG4raJYnXT++9Z3fuPXWWyHwnfeaX//9v7te3Xzvu3/yn//DH75//933f+2BD/vViyeFLb/929+CMLx68cuMmHwfL57sbKkLAR5LY0J/A3AIkkEAQIAABZgDKIMFoWC3vRr311VZ7cc4+OH6kk114CADsNKqQquZc4qsdDbIxDEBVupIFz5GP4QKC22xnJaKD9vNhbNcuKrv2pJMCH5AtqUb/SgiunRdGAhpG3rMZLW1tiEbwxigU7GkIHE6Ob336DWnDBhYt3tlQAPNJzOtKfj+xbOXhWt8imkTx5j1fDJeb60iRCNZKCdNFHIWBiUggCIAqEAUcESUk8J9673XKgn7XZ9zbZ1Utd/3oZNQkEYGFkACpRFizoHZEqugkrZojZk5NaGqdEWVgKLnqXKLo1tFMbPV7Mmzj3ftjTK6MTYy5ASrq7Pt1ZXSKhvRkJGJsyhNCnBWTZBQCCezma1KVKS0c67YXF+31+eTohtuLj/7+NmLi8vqYHnv7jtf+87vu8Ojz//wT/7yB380xlGrXDjFIsIAyInzxFlQ+epm+2qbXq5Xm83wxutfIYFdu9vsN8dHczC1QgrMB7fu65PjAUYzreuyNgJDu29YH9U1mTCOfhNB1QYQgoxEKgonlVnEZXbWAZImDS73PpW2oEwhBVIyQMg5kdZVVckwTk3uVq+C0PxoCVCw0SxUl5ajF5aK3OB7LxGURdUonwxGpckikWHKY1YAdppTdtpGCYiZINtC90OHzkWlJ6WdipsUGR0XUd89aBwYxTrwEFK+czQ7vfMbRpuUfWmajMrLDlkqW2x3N95S6NL5xSsFdj6f7LvzSbM4PLl/enB8vbrkIAIKMMXBg/izy8t/8c//H5u2Vc5aD41VHhMpM3BkomC6AnTSIopcVJjRolaKQEEGUKgUiwIQzslgYnFJReaMIshKDKKwjEmCiLrarL54+uL999+m3GZRhTa5FN97RGJBg2pMqUujH9OQWJQutWgwFcWosvccrndDbU0x4cW0STnYiTs4PR527boftteXl08+r8rSFXZ5cMhhn8edK0plnZddAccp1lfPPvji2ZPLV+eg4fSoOl7Ou+BfvtqdnpzkkFbXq7pxzpgQQJQc3Lu3ODzFqjh99GB7vWru3jv++jffeO319fX1q7NXd4W00UTIOczmzR/81//1d37vd1/+7EcvPv9p0ajJwWnfd0+ePV1MFkVZ7H0vAerSRS1YmL4bCm20LQQQCIAh59y146SplUZEAFEovLu57nbXx4vp2I3rVZvHqihURMYQrUBEHDCLIFa1kTBHNZpxDJlRVFYKI5aoy8kIcny0XLdrTTJ0PQEJKY5Rk0OLWoyihmVIORTKImdmwJCKqWXRiEksIBHa+sE771a2yJIQ1G61L4uCCRbHy8Ty8S+fjFFUgQCwurh+9M7b/ZAVolNKK2q7XWLhjCAYclaEzEmBZMlZqeixkPj6ncnC+G614og5QYpQuWqMufM+j6iVKAKFypC1iJ0PCAoNILFAYcopGSJkyihiCuvKqhISZyzH7nQ5T02tUe/2B+cXN5OqMEqEyFYVEk1njaX6i0+f7LYvb91+MDs+3I09IVyenaVNW9VNVcvYDwcH83zz/JOf/vTq6rIfpa4X0/L07pvvP3r77f3V0z/84//xenfRGINMjZsqUCMHYpw4pSldXVxdbGFIRiMVpswZfRyPltPbt2amKIyrNYBVaiex70PtGgKDyipGG3YzU9TaxNi1cVTFhJRhTGQUZAYjKBkTKa1TRmuNczZxsNYUpYP/uXalCCBxAk3Kyels2pSm3Y9nq02bYTI/IlSlcYRMIixInCZWdWPb+5hMXTT12PUm9ECWDWthAzorpFIBklaGOaoMSIQOOfZaQBQH5bs+OCHfbu8cNlbZHEFCLkHVDnVTcqY+hCyDEmMSZ7JF4R7eevjv//OfNHeWFy92OcHNpFemJDW129C1T8uiOTl6o/dxUuFBeUClu//o4bRqHtw9fPPdt1/88vmz589X600CFhWBNLIgaSuYM2itWHHkFASMUMEkiIAAIRjOJBKFI2MGYhk0qJRRQUalvJDL2kb1/Z/+5I23HyutFSLmjAKKVJIoSbWRB9tpUKhsTkELWCDtakVxyjqDvzk8aGD0E7NIfUKlrQWrlbdKDamguLn54vrcLQ5PnTN+GDeby9nhkclNu9mBXI678dlnT9rd+u79I2V1JYqzSE5vv/5gaLumbsbkzy9vtNLOLTO6t772mBazv/VP/7HRqlvvY+zf+q2/ud321fzUJoNonHXMGQkAJKdUNvWb3/qWrtUvf/jXId4g8XCdj47uzGaH4+WLopqrgrAARGOPlkjzfogcslIoBAIgOQMRIAALQObUv3z+GXJGrYexu3v35MNfXBQxIEEm4JglZUk5/Oqxhoq0QgAdVNY0qtybXOqIPSPL+eoJMWQR7Zy2VihoHdkosawY290I1WAtKQEOkoCVUsI6E5DSojzEvDi5dXDndgSsTXF9sVLJcVYHh3Pr7Ppm9/Szs2JSJ6bQx7KaN+XibL1VEpfTRQijzyOAMDNgAoqRMiIYSUIyjJ5ZNIm2GQVj3yGgkpRTQoFSuxQgJQbkXw0NHKgREmFMzJELJIN6oouGtCJiSqA4ZlFijHVWa6tIH927p4nCOB76dPx4+Nmf/k9lHk0xcaZwczutCmfM2+89avfLqi6UsccHMwL+0hsPlWhbzw4P5z/5yz/93p/98ZNPP1ttz7Ux1jbzo9P5yaNH775r69n3/od/98Ff/aA8OBTxZAStC6AssAJtjGvbdT/qdh+mR8vDY0fUKN00B3U1nwLZMEYfhqATUg6QqllhogeOqe84imFelgWmMEQUt7CuAtQiqIg1siQQ0TF7JkBEzFlxmkwa3w8QxxiSsto6h5pTTJxzbfCwrnofB+OgrF6+vFAX26qs7t8+nrpy121FUV1UKo81RU3hpvPjbJptsV9tvAUJ2SmtgEmJNowIJTmVNRrOKmtEgBzTLoIoI1TOg+/7XScESjClqHKWvk3Zkw0oyurkc+TkdEbJEI0+efj4W7+ev/ezn1g1Wd5evveV96rmaNVe9fvLD37+Q+vq114f6+ni5rK79fiuqabtxdViYr/ya+9OC/Pw2+/Gb76+3q+37XB9fX6z3n/69JxJ/wqfTRyNxSELZlACIpQyak1IbQAOWdlsp1xEFRKPCNwpZMqZk8KSYy5scXV1/ery4t6dQ46ZiFQGHYASjZR8jpYNAXAGIgUEow9aRIExCGhE2dCWkykR5tQhYIzWD6PJUNd10dRC/ebmhSJYHh1uNpchBOKiNhMwfrO5abfrzXZbWCoNiDUx0o++9+NmUt86OZzUTW3K3WrY7YaT0yXqfPbyrBCtYn724vlPfvazGvXDo+lrX31/Mpsz8Je++c3E8od/9Ee/+Zt/oyis9x6VcYAs+tH73wYqf/GD/+gMsYfnz88QUmVLKHQ2QAYUo0P78uzya78x8aHVrrCktZK5JZYAqJFIILf91dXVs8m0OTpYpjQkHh+/ce/yeigzjopGhCzCSDkHAtTaxuSJtHKG/QCYGaEbfCmqQvRdYg0JwSRImjmxkEqJ8p51Jo7RiFNgw0goWlvx4H1KYpwwG2OK6fTk7gNTlpp0Yul3nStnRGm+mCTvf/nxpymCRNGO1l372pvv77qRIDvNBAJIjJwjBp9EQKGmjMwhC8ecICdlNKryZ59cHn7l/uzW7auXX/SJM6qEzATKSGAeUgraKACWAGq0BocQ+mzBTY6O7hJpo4yAEojnT58dPHpzfngwnyz6vrt+dQWqLEtbGffs449/8dd/pMK+mc59aK/aSwN8TVYITVXNDm8/eb6JXUdjVFq/9pUvueUhde366S+un3wY2vW2W+nSSjaL5a1bj987uPfw+OTk4tmz/++/+BeFKpAzqyxaiaKUuSKl2RCqara87vThgfGgrSmsds6RlRD8SKY02hHCLm1732UNkIGM04Y4BA6+ckpxSpI6JilmtigIIPuxVAVksAWV5WTXbzGDElEoCiGLWG05RQVoCK0zWQZFoDKelDWm3IbYW3Hzshj76/Ob6cPpZj84a1+t92MOd49vHU8mWo3k3MxxZF8UpZQTIPTdbsS0OD4FUZATgRpElBPmrMEIJ41sMCEqQBd01bX9buBsmSixJGSfxh1ISOgVYibKVMdIMcQspp4vxKS3v/ze6e27P/3kZ1wAc17dXFaziaSxLmcc5IMff++9r34DqXBlZcGen12gM10UijEXVFXV6a3qFiK8/kCh/pf/6t9/dnblbCGm9KgcphIxIjJ7RsyoOCQIkIVAKY+8V15IIGvGzAoBdWbKCLGQIKOO9MXTZ3fvH6lESUAQnTHrHKLKJYoPg49pUizEs8RoELUrlkN3VRZFAlBk+tBZgaI4YDdEwsRaqbKwOKaeCt4Pg9ntCaHf7Tilbn3x4Qe/OFpMq1mxH3PX9moyWa822ciYeXZ89CvF83DiFMrB4qSum+urK6Xc80+f/+DPv3vr4f2H9+9v15u6qd5+fC8hlKYe+5ZREKSq3Dj2ZeWsc5oo5BjHWNT17Oj25OiN04P5z7/3VxraplGERBpBUyZwhS2UPT5d9Lvzg+VhDj5xqfI4rleaFugaQRaRzc3NMLbzRcOx04jaFKBrsw85SWLWWrGHLCAsmXPOKUkUDMq6nhMkrjxpMaqILF4Km9kX5ARhP7Y5sysnIbICg8zWoiZDYkFL5ICQIzKhUZCjZCWmmM8mJ0eA4Ei9vLmMJZMNd04OrJPd+ub586dKWwXsu91y2iir2/3OYq5L533ctb0IgiCDSAaDCAmEwSeJMSEDKOFeLq7k361/8rvffnDy+uNPP/lo9JEVC7PTBea4GyVkQCOKRDApMiApxcS2njTLIJx8Mtq9vHjed+0tq4uqTmOUEGLYD721pvmr733X5vHBvZPN+ZCRVeGQlNXKoNWmmR/fYqdvTZpuv9aZD5bH5fygDzEmr8Ow77abdpNSLMmS0bce3VscHyyXR8vl6f/1//x/erW6PDye5pBQ22q50KCJPXGtssShLWfu8GB+cRPL6ayaFXGIAD1548dhclC6eu6p0sNu2LaUYdLMqCwYbc57I+NUEXLqYg5EGTxGMOiUgpgip6hsmZMs6gNLar9dxRh64HI6AWUy/Or9LpiztTiScMjOOe8Z2apsbFGXx5Url8d37gDpqOjOG+8BGYeaEVjGLBGhJ8gWjbaF5G4+s4FhDIOtFlhORDdICpQC8RkTDttx2GjMgCpFFX24Xq0Z1PLgULvMjA6QYgrj3sVqiIHdJKgUBTb9Hlxdcy4UrXc3ed83RTlgH8c2pWHPez+Ex8ePG1189uzHZ7/84v0v/y1SBnz40Y9+/ovPLj78/GWtjZtN7h8fv/3o3nRZgZaytPcevPXioiVtMFChlebgAUaMNYAhFKDgAwGiCIEEyqNiDGKiYUucgqCSbDSDhmw0kcb1Zp2BUSSjhCwxBkjegPa5C/GyqBeFxThGVgyidApE6Nq+12XJQMbBsL+22hKWzD4DMlDKpZ02l6++OF4Aj+56s9nvri5XF58NHHrZXxEru+1j8IGqNmUotCtq9/zq5duPHkP2zOwFt+vNy8tLQvD9s6OT8vajw7Orl7fvPN637Wy5ZFdKYhIGSFlEmL72a990hRNhhbrt2rJ2elIR0MXFbj3Ab3/tt7qIH/7iu6lPB/XcgFIowNkmbJxtDqb7yy9QUj2ZaZ2QxMwbVIipBa04wA+/992xH3RZZM/KFje78Yvzj+bLu6CSI8jMyhG3fQiRo+eUlTKZRmbpQwQYNVpCJAIUrS0rVUxr1/c5Rys2h+CzD6htKswQ2QqUgNYo4aiYsjLMFALqjGLN4vghGFcUjrOM+33ZVLZS9YHNAB999JlkAYpa1b2Py+PTzdhTjtZkpe1msxmGQIYyZxRhyJEFCYNAiFGRIq2jD23XM+kVy7/97l//N99559Gdux98/HOf2agCM1Vk2cLW++xzJg9ghbUTxVHmi1tZVZYSD+P6/MX1+TOjZ+3QFs44rYfNSsUg4y4W4AozL4qb845IE4gkAGciiSnM/Oh2FyF7b42qZ3OToOsGLFrWRuXs87jx3aodMmDK6c1336kPlvVkcv7px39x9eIXf/zv58tZFBaJAmwKnYgjMyIGJTp467ma1E0y5ByxLYqaDGjlJiKCwZbZDwOBb8oq55gwqZDQRx7388KVKktkIEfKhUwEmSxzzm0ctNGahCTkzmMzdVUzxqFUYj1lzpGFlbHEKWcalYPSVJSbSk3cHAoyBaAmUqd3KUhWgholZa+AEjMqZGFAZFTeg210DD1QJFBasvNMaetpX0yRjAasicoMQwrUb0ZTigKdwROF24czAdssboFYBQzIk8XE72O7G0aJIaJXMgbcdnvW/d3bD4yr8FD7JrVFfHn+NPuhLLSX0YHcaWaWRZ08+MnnryjrwhabzU3X31gDTOomZb64XJ1f/uznnz+4f+fBo9NFnVKvmno5wKjUaAByTkrihGkSTY/ZcCIVIqXMhoBRPGQwYlRmExLpnBQwR8eFBhslCMH1ttuHZGLSYFLfcxYGZIgpDSBhYmvmBMgZUBmn55PF6kU3rMfq1FhTaq25Cet+1xSNZezC4GU9Zvve7a/3szUh5DiuV1cvnn9ezY7HmF5dXdiNqcomp2Cd3q832hZ3Ht2LIWLI+83aaTW0PAzD2eWFaHXr9GQjPA75L370/b93erub3Pz273w75TzuNylKiNEUVkSs1WVZiWQkWq/Xl198NFPRNovFo/ff+/I77335PZH4a9/53ftvvvXRRz9+9vFHCxumNSLmcWxLV3/48+/ff+eNW8e3Q9szj6osNKm0ur6+/GJ5eP8nP/7Z6uXTu/eOc2ZSMgxdURb7bmOqRe1qGcWS8iSqqHgYY47AQVIWYzfrLYZkK60KQ4EJRRuXYlbWAJLvBwIiBTkkh9qPnggm1nEWCV4VymiEhCwyLSa7dtRKTY5uHdy9XZbTSk+ePHtiSCuOR5P5xJYvn569fHWltV7OJkDZVTOkRuKQfTdbLIZh6IcByMTIOYsCsSgZOCEGH5kzZxaiGBOwsGRSZRbd9u1pPZ9NJmc3axFgQEfZaSlCbkNMlK0DlkTKaOOShBRGi3HbP7+63LIAOHt8+3buxz5mJYkkO2trq2ql2n0+fftrz558cVjnDpPNkVThh3hx9mJ2fFdpwxBRrC6LiMP56lLr8vTwaHOxf/Xy1c36pirK4zu3l6e3nDG93y5Oqh/+2/9xURsQjsmjkCucIhMDWGXQkDImZ46RbZFRiCOTRlIqJ5ECCDUL39xcA4lG1xS6G3eD9yK5FCoDzx04TC1IKmoAEmZiyDEBCiIIZEVWcgaltt2mHwatqS5LGSOnBEEqW0xrZ0yhsUGgJAMgJ9YgRIyEKbOXDCmlUv9qh4o5egQIIiEFFnx2dnZ8eCcJ7Nt2PrW5C8pg9J2yWojGm+esUVUT5xqlwI8t1g0brpQTGAkhREBdKl0msBlaVGInZT2/5fuQU98T+VGGfe66vO/2afTalgBkDN07ut3ttpebC6V09KNlnFSQuqEirbNtpgswuqrt4/sL31+9WrWNVKAK0amF/qNXL19e3JRW+UTsjJUMwkbQ+xwhO2EgPcTAMhBAJJcFC1RGQRDPlJQ2DEQohlABKZDMiRSBQMipa8ep0n0KWSDnLASiouBIGqyyIYACAgFi0L2W2iz6q3OBAY5tZDRqhprGcUArHHMAUNB/8tPvNbOJnU6uL3cvn766ded0jNhMC701XzzdGNr8+jdeK+2MUu8hffriuXHm9GDK46hBP391uR+GyXQWOJrCilLauIur1T//7/8v73zp1/7p/+a/c85dPH95vdo1kwUhLpYHH/zipyeHx5Nmst7d/Nl3//wf/c43Pvrj/0Tzoy8d33bljJBEQAAPlrd/6zt3r975enf59PyTHw/DoChftavF7ak17vrVyjkDJsIYGXBzs3r68ZPV5fr5k180FRJyYGaMItlp+PpXvnR2EzgDapcxghCyYlMMvNcEcQh98NWsxM4yjG075j5kjmU5EWOq0qw265DFWGuU7hB3koiK2pbzSRnHHpBdqQpbXl+sgPRuvQUhqczd19/UlStM2bXD6ENZmKZSy6bOXXjy2dPEVDdVXZWX69XxvVshjzkOyAxRwj6QKEHRIJRY2AMAMkXvUwyaNCF0fkw5QfQORCI/vnfrzbv3usvnU2t5vji/2TKqrJggFRa7mIcQMmqFlAFsXfu4+uzJqzhCgNba4xLKsmyUNoicnQTFOJmIKbJPRVm/8fWvgZj9+l8dVHMFpFA51EpS6jebV+Py1t2qnGHCGElP5pO6btd7zrLf7v7Gb/3uuv33lXLvfekrjXUzK3178ctffry/ekENEIKGSRi9NUYpnaOQsVnA5qAqB2Xdd7nfDbo2fbz2KU6Xy6lU236MqAD00cHRyIg0jgn9GJTCUcJx4QotPofB1GIn2fc5JxTOklMORKBIARqtdb+9GWLow6BNoZSudEmokdOkmThnCZWCSeGqcbwOqQWIifPYj0YrppgjW0WZ3NVqtdpuhJXRiiUFTpLJFc10tkjDbj6vSxXQ2BCGRDmGkciJzrEbTNvlskoAtjwio3y3mk0MA4uIMmrw3rc36Gokm4a95ixaQGeldEOq1KAoH9DMX26vry7vzw4RIhIURXX/9MFuu08x5iTCYDSpUoWWj47uHh0dRY6b7aod1ndeX0YdXz3dKz3NygCh1lpF68eBDDlSntlq5AhitOcYmFsYOhMIIvlKJaMAQBAZmShgzgqFtSGlgFNOGUVICyIp1Cr2u5ummSamIWZEARKWHpW3ygCCIIIohYAAmmMoUOENcw43spodlHU5Mcr2cNPlrcmsOWu0ikoTSg6UGIScD8RZnCrffO3hpNkWxhjrlMsGoDHl5uUABd66f+C3m6GNi8mEFNb1ZL256ftxsVhuN0HlBDr++Iff9yn+/h/8raHfP378cDY7iDkh4ounn/7kL/+029z85u985x/8wW8ax1/6+39A5UmIUNbIAIjgx8Fog6QPD08WUzctcHXTba9fbvaXxirebai8NuVhDiBdX8/m5dGdb/1X7wrCm7/21ScffveTX3xQF3VhG2/TpClVUgZyEgalE+fEUaMiJI0aJRGITkEZFYT9EOKQIUkEaMgIqRSzcQ2YLDFLFm0sIhubxQ5dHAtrrNLa6PXoQamiKBxWPqXp7dsnt+9m0cL84vw5KgTA2fxAmeL85dn6ZmMKU0yKbhyKqtLWZA6+bytrN9t+6FJWQCKYmDllBRnV/5+n//r5Pk3S+7CqutM3/NKTnze/nXt6ws7s7GzgktxdcpekAimDgmjI8oEsGAZ85D/DgOEDwyc2BNiAAMoQRAKk4opR4mpXG2Z3cugw3W/3m5/4S990h6ryQa99dv8BNy5U1VX1ufLIXNgiOrQ5sxYQKWjEC86a6uvvn8SxK2PyIAtHvGgvb9dJrScka0LjZNqP/RiqORLkNA4xmWaxODnfrxEN2ao2MZd1d/TOqus3F7uto2ZmvJOkmV8++fjy2bP53PdZ5k29XC2oKKrjBCn1t08/O7l7L8xX9bw19cyQO1ycdOvrzLy+vjmcz5v58u6d089//r3X69tXL54aa8KiznlLrgC62gVf21jGxnqEUoXQLoK17aaTnrVums0wvtq8XB6fHDWLIg4n9oZMW0XgULvdsE+FgcgSzgouLaqKqAVTk3U8sqoyaNN4m9BDyLEEwFRiyp0AiMEEsksRIoKAiIIyiez2G9dAM28XZv7506efPv9s23UGw3KxxEo9yqJqS9t2cWuCHiyayoXt2BmxaZLjxXHtwuXltimxqbxGQf1LI5vVSvRKoTo6JO+MQW1Od+vbL548LW88qpy1ClMSVkdAjpytPLpqGrux76FMhU0qpZCupzypP3/8pvGeNQKAqLJICJUzvkjmFA+a47kLYyoE/q133rUoeeiePfsiISDCYmW3nb3ZDMKVg1lLM0SESooBAZCsYAGULYBhYOFcEMQgqSFRFcGiyqEAERS0CoDGIarhbMlGZmONMJPCsln2u/1eim+WALaQaCkCE0uytkFn8lAyAIAaBCvjuIvsTYubWOy04ckckW2D9SuNhsuNl+yMNU25vn2mdFbP6e6js/52yFlEx77fukLKaTMMxsnR4Yy8LA9nReB6s427rWYA66LkSouK5nHCgLM6UIYhp+VB8/Sjj/7L18+zpLM7j/7O3/t7/TScnp/+9u/8xsc/+u6j82/ODw/UJ8si44bVLO59U1UIAAGd8WQMEnFJal2i+s6bb/70px8SRLMDd7B65/E7zeqOCioCIlRAqkzkrjfXKenDR29++vETpMY1VTWv+k0UZrAWiwYhKZIKs7InL8BqoIFMyYqIAnhnxJM1howYY7KyqiEgMCaOg0dYGhHjVYsaMsGLCIM477WVYdzXtXN19fCdr1XVfL8ZY0g5l0U9884ul6vI8aNffAwG67YKld/uu+XJgSimMefEEXTIEdkLEIlQKmKRGTKz5GIsOGPzqHEsZI0l44AWpj5d4KrG8bpjLgCspTQ+LJrlVTdmhwBiiOa1n7qYxkRVQRgNo/R69savvvH2r3z80z93jU25gIGkuQxj3naLZUV53MaOZVx/9Ivd7bY9PN7u+9qs3n7vq63HnGzuO+UJjU9UtffOTKgW9QKVXr549uMff+/yxacnh20F5XQ1+6N/8ftabjkyGIfWLI5X/dXIyI6SmAqdKJbCPJtXZw8OuJu2VwOHNszrOPC2273xzhvF4dVw6U1dh+AIUXPab/KEibOzdaXBFT4AnHkcVV5vR39y6rxknRRIGZUJyDuGVagXtY+5d1jf7AeTxDh0kVPaIeDyYOWhdFebYegC8hjnSzdv6znborXWzofK+KUxzLOmUQVgqSzcXc5IXSnSd4OlcLBcSMlo7ItXu66vF96nKVpnbVsLLRJbwrDl0IQFVX6IKSmF5XFfxHL57PMn89XJ2YM3xNddHKftTbe5uLl8bVCOl+3Pf/LJ/bffy1rIheWdB9Y5lDSlQdWAMUYzEg6cRItL+exeW3niHFZHJ7M7d1MaZBivXr2wzgZrUnDtjKLAeisWvcNWfAJbLJqcYiJrDChnCxKKTAUMAoOgeoBsFNgnASF1gWxWYQtgEUSAGdQUwsIabGsdhhCGuN3b/aJWpRal8uT3KU+QDVoFNpYYVVVVxdpgtHaJsolkrlKe++uuC3FcHszRNFQXYpfjOOzXNzfd089evvHVuyJ083o/m7fVvFrWh2lUkeyd6bvY96laVM2hf/Lpxe311YPHx0UKsMYpd5Ri5DRNlW1TKa72s+BS7GcVWMoVtc+fPPlH//A/U0x3z+/86nd+7d7dO1LG/dDV7GLc3X7y0el7f0UBDJmSE5Ix3l+8eP7Rz3/yW7/7e1M3zQ/vkavefPfd/fWnvC+L47vV/AQUlOTL1W/VL1OMc10ffvDLf/P7P/qLT1/9+FdOjuu66nYibJwrjC4XkC9tIOUQfJYsObKUbCUoGS3BVOLJBqfCkDmWHqxVtJIojtmwZtkTmpKdC2FKnMdExIKRjFNkMJSGafHgzumDh1IkVLzt+tZ4Knx659g68+Lzq826axeLpm62272tK1vNSGi/2YtGNIiEhpMwJmRHajFoBs0ZRayzAtJP+wLqhBzbUFoqultHLa2r29QPoknJcEmLWchZrvtbCmTgy7vCKvFUmLyxhoh5/PFf/PGv/73/Q1UtrXTVvTdc05YYxykulwtCHce9qcRwB9O2NShoXLOsF6cQVocPVsjVbH5YHR+b0BQp3frq45/9/OmnT599+skXX3xCJrcnp6ZxTTd+8sMfrnevmyY4Cot5G4IpOeYiVfBuYRkcWBRma835nbNxiPvLLizv4mzhiCTkh2++MT9eXG1fEkawBk1VOFllD74ItqEW5yFPXoaV9ZqnSeV1Jw8fzVkzMhOrKnEqirIKs2OnMyfF2MPZ3Xsr3E6TsdTtdrfbfTZwc/N6jueLQItmseNx2FzYqjRV7WxDU6xrr5RCtYgdz6tQEpOiJ6tjscBBHeT6eH5YhVlmuHfvjd3Vq0zglgsKpvbWoO1JCxqDtmlnFn3aszPWNvPqzgPubsTAyYN7lXdl2kx5T5a67vbF1Res5o17bx62zS/9xoPV3Tub9e3l69eXr1+TMd74cL5wrjLGtkaffP797fa2rdz58dnZ0VEediWE2ewUyLGk5xcv911nKihojPM+SOs4wpCS1fli3s6mDjAXB6QIqaDJZHI0ooTsSb2hgsqJAItRUkW2wISFyCh6RGELnjkDGA/eioptVvN5G3QvecqTc3XDzFYEptIuZst2hc7bOmGXQL1xZNVVh60r5ARzk9z0mtO5ok+3dutnlbE1SvE1Qt6fncyndvH65RrJHSxXXHKcSnD1ONwO+3673YfK3314Ym0LyqtZ/eTJhbskr6ai6v75yVRwu96kVBzhatFsU7JVuB7HBFzX4GQ6Om567tvGvb784l//m/WvfuO9+aoGy5Mxh8uj03c/cMcHPFzcXm0PH76nGg1g8O3J6Z3p+qaQzubH3fri7a988+mP4qv+2enjR2SdCgMrIIIqABKiKNSrI8LDT3/++yqYOdxcdMYEDhZDFTsmAGOtdbYw+4zofGav6FhNgUzGOmeFioioClBqKjdkmYZCIp48syh5P6shEmZShSIZwVJlNZDx1rJFX7/3zW+uDmbrV7fGmqnf1diE2i4OZmM/PPvFM9/OrK+lSJxSc7TyPgz7aZgmpKKABtCIsAUxCkhYcskTESCRDf72dlMAlFiVraBnl0vcbOR//uOX3/rG4uD+g7S/3G/XWljZzZomSbcbLsEYA4112bCfpmgbVDIo0cLtH/7+/+vNR+fDtpurJfKswIxEbpBJHPo07V++zlrIuTkgG5sdXg9T2/EbD8/r04dTjJ/87McfffdPX3z68a7vtyWLtavTU018dnbnxbOPtt31rl/PqpUFCkY074eUClA1c6GxGGBma5FILFXTVvWsjMPs9NAdnI8seb8PTVUvZibg8eHy5vY2iwiKCogAC5KbFZFxnCqPNYK3quI3u0uFXPt2HDfMKsyiOI0TEIQajQxGEECsOou2nR8m5EC0H7PkNGu8tXFivd3uXq23ob596+G7i3r+5p3HfTo/nB9C0SgJ4Ho/TIIgoDHGfb+nesZijleHZ4uDwhHVTEl904zDOudhXhFiUbVTkgJuNl9ZqrkoICozWGPreeayy9GEWZFoYyJkUV6ilsNjnJ9V7REa087qsWhoVw8fV6+vXn/66ef37jwOoRLRVKab66svnr4kW9e+evPsHiZWG5K4EGbKEFP+6Bc/n3Qf1E9rvdqvBauqNst233X9+vqp5jODtRRmKQUyczLksslZGcCykFFEZnSKIJD9hB5ZLIKnLCYjShVm2yjkaytYORCSk2UzFwwgSLnANoTWurZ1zezuPeNh6pSLNk1V3anGgRHIUgSFYkJFQXOqFj3uX8T2fj3l0u2mpvLetImHerasSI5WZG+r7/3wY7iPpycrQrq+eBmCuXh5ub69On5w98XlrZ83y3l1e33x7rtvHZ0ebi7XV88uTk5WKQ1SinGOHGXJ1sQ2hBe7fraYB2OTY2AKrspSXNWwxqurZxCOTmaP5weP1jebYdwtbjc//P6/fuudbxzCu4ROmFfHi8OTX/7hf/tPl6fz9hu/ut/1R6fLdjGfret53SoQqABqASVEQhBEVOS4//S737188uHXv/X20clB2o8cYXF2nK6G3WYdvCHJzrrssinUTaWbUmJKYmJMNc0AC+GoKMqAit12UnGBaqakqgiuZOmGiUwAIGMJGHNCV0hIXfCO6tXd+288ereP0yS5DBqcQ0irkyPj3bNnT3MZDw4PSuJu3zfVrKmXqrLvtgwFkDWDU8xaABEA1VBiVgUCaJv25nbDImjAKdkkdlKdpkk01u7DZ7tPXz/99rcO3z8/PTpdja8+n7qoyIumZq52wwgKAtZaNlnzxK6yZHRR0ZHXx4+OP/ssRUns9XJzbUCyFjaOCYHLyekDhfLs+XMggTRcPnv26M1HV9tdc3nLr67/xX/7T19+8aHKBKbKprH1/OjgqIv96ens4tVrw+Xy5cum9kbVaYTYp8SuoVDN2rbyHrzz3rfADQ+7w7aGXMJyrubIhFl3e5kkVc0KEaGfNBUnHsmiKANkFdBsp1Q1Fanibr9wKJhZeZu6ZJyR6F1Q9Kxs0DvXcpkMGWcAoFhCwAQApBbBFrQchwrdwtth7G66eKMpk9teXc9nB/58NquXq8Mz7xrKFLyPx6c3u9vdsE1FDegX19vapz7q+an3UPb9bkgFYk7b3eli3jgCFUa3HVSb1dnxfSNGCg9TR8F56xlyQXbzxqoFRiyBiyhPmocq5zvVfIfKpKMScJdy0QzBWQB7786jN994p7Aq8OXNq1cvX57dfXyCZmXjSeNKSjEb52aGoUh89sVHm/1lMalMuYygORQE54xrPPdJSr65fEEmGG8RsxhKaaybVJiB1YuNSIzFKErJ1aqKk3Kajs5XstkHla3Kr/3Wtx6fvPFf/KP/HgIZtUHA175p6olHNUqOCnDur49PyLTW1joNE4t3hoRL09ZtO+u2g60M9ZD86bzbdewsZGk7H19E+yjESktfIqFvVrtpO1k+ROq72+XcbfYb4+jBg7MTtxDWt77y5qvXYXV2Pqa03uxWs/roYNWv91cXN85Wt7upyL7QoDnXbUOVXa5WV9fXn370i5K5aebzZdOVaCYIIpvdNgqcn59t17mdc1qM0/YV5THULcwO66PTR++/q4gp9dY6g4HzePHZR5//9PbkvTdnq4Pnn/wobq8QDShqKSmmJx9+/O43vmoA4jhY79Bjmfbb509q540uXr8ePv3o58cHy8eN7deDI1tyMiRkrRUHKbOoArIAGJeErWYCSpMRVWtLLkjaqGAIGNUgqDFWXVsgt/PZNIxcooIYS9YjOqh8zdY++spXhLHbDIvF8vMXzwOSb3B+uIwTP336sl40RDCkmJjndVvXs3V33U97wQLKqIYVDJAF4sIsjEKV8dba9WY3jZPxDoWsGiuMpeQSi2rmnAr3U/2Hf/HJqzsXv/sr3zk5OH/WfZLLoGpDWJlsx6EgeWOgApMipahVWy9mCxZ+8dnns2plnZcxpn4qXZzSZByCQabiUG9eXigY37bvPr770ZPrn3z3B9/+5V/++JOffvajP319/UralstRFGaE08UKNCza0HeX1xdPZjrN1frIiB3YpA5dVVeVqUJb11Xl0WBQsAnEom2dhTjaZR0RSFPar2fLFVOgXPqbzb4buKndoVOVFFPOkzFGrUiarOMDAwsUA+5q3w1YaT1PogQlpl5YCPlodWZ1uWxr4ig8GgWFIoCItUEz7veNM4t6xszX652gQyyxpG2Or64u53a5nC/ASMoxGMsxbcf+4nY7DFtWpbphhqt+kCxF4npYv7y6Knmsme8fL2aNmXImCJlm9dmhbRbr9dX2dmMt5ZKrZnZ0dALCCEYZDQOIFaOlkpTV5JCnGGXUoeO+lOYAAZVFBMWbwm51cLzdJVFEo9bOzu6/OU1lurk6rNBgYu9SATYhcl7vdz/46Q+7aagX1XI5H03c9KN3RjkVTCMpiARkoDywkqE0ysHM/if/+7//P/7LP/mjf/Pns1lAsBR4XrW/9JVvnr5zZOvKCXnfjlfdn/3JX/y1995/9717yuZXv/P1H/3w55ZmM5o1JgSaMXPbtiPcEJCWrt8X705taDhDKQadWqQMnMsoBHa9uw6z2fm9o+HDl3Nye54AfLWFzYveP5rVBilpmcCt5qVMO5Kmmd89t1XTEMnFs+frzdWbb73btvrWo7ei1Upqp3z97PX2erOcz+dHi0Lo6hOJ/OSz6+NmcdDOZ3Xb1NX8YH790jazSh1ncrWh27JX0qqpHIYK3GJxkKe87/eouPALiANvn50eHv2r3/83737z10/OTvt998M//Z+++cFX5q0/PL7XttXtzdBfXDz56KNHv/zr7KrU7dX6693+ZLc7nM27m3WzmNW2TUzPebgYNj/7b/6HO3fuFh2OTmY361dGWwRTlAUBAbLqlBIXNmAq52OZNAQDjEUtWgXgggxijQMAFvWGjcHKO8IwliTCZLiULIq+JrLFWuONCXfvNierV5ubZTN//fIlobDiwclda83LZ8/zyM3sYJz6KRfXVmFRR0n7/aCqQECABg0aATWcgZAAwEgJth6madt1zhlHHkRBWVDYQnSQdBpKNpUBrXKaffT089/42vsrhbaqUzfmNAnaUNU57ffj3pAh9QQMBQ3Ngnc2wM3VOuV0/4PHB3Udh6E9OUjeCDqQUsH0yRdPwMGjx28JmNM3337/23/jX/6zf/G//OGfLmd0003ZHybWYdqnjC64/W5dHbs7y8X/5z//Z7/2S1/9/Mc/n1sFLqYq1hsX6qrxobK1r523NoDFgGoi38wWFYBFTKhYk929vvRIUM1L1tevnt1evkoJ5idn986PpimVoqi2RHb4l+eb84C1TUOMG0C3OtDIUzcu2xoLaorWk5fc+soRARtRNKhGjBoziiQeyJbTs0ZzEqFTs8g5pS3vx/7e2VnlmvVwPVu0CFpKXG9fX189Ww8DG5rPapG8K5qmggZDcJu0e7l+WaEeEryxPHLOD1yKawwuSzU3Pry8+GIap8xxLMXaMAwj2XXw5Jy1hJqnGCOSMwnJAxCqbTM4wrr0cbffOTTBkoDrdwWN33ajc6jgSpFpKOvd/nb77Nz1q5P7KTKzZba7qVgKf/5nP/zejz4nH3/1N752fueUz9yPfv4/ba92rXXkIdB8cqCsUDgTj2l8cHb+H/+H/6CemV/6tW/u+vHs+GRez+1cZquj06N54Z4JGutzzuH+4e/9r/4tMEWBrbW//de/aYmf/PgaBBvnZlZs69rGozQx92i55FiiDqVIlsqiM2CMSywZIEmxdrlQLnRUzU6XctV7YwpDoKrd5c2LsdybkUeYJhjEtX4yGU1oWnvx+tWiMtbA6uT46fXLo8Xx47tnYxmeXl1vu/1pqN9+8030YIKyxbHP/aZ/69HDOA2+Bkl7k11tdHF+MO7yrD2xqLeXr8D4guRtXYVw/61HjrjxRjvupi6aSGhBYDafv/f1t/frF1dPPz05Ofqf//W/+hf/9T/63/6D/5AoDtsYDEy77sXnr77z7z7w9RwLgIM33nk7+Grohm6cFufnUfT584uLzfbX/9qvvfrs6eefPz86Pb64upryIjiDNkBClSJaACCEZoBJJYNiCOoxu1KRqreS2YlYTDKVZNkBIHMBkyYRxEXJYhwum2bHBYFUWKIQyJjjg7v3geqmspOU3ZCbugqNOThYlZK/eP65bdwUJY9SBJdt42o/7Po8TUSAiKTGATIZUbJGjRrVUle2qNzcbIyhQFQVzWCTJgadiCbHfUzFj1RnX9lpPd2dLWZtiNcXtVqtljdlL2lfkVCQGFNXCI0PwWiepCQQLbkcHh9s18P6F99/9OCtd371rz39yZ99/vHPD++9/fCtr1x++jO/OHr06M3Xn/zswTsfMCJ7+Pf+N//B//P/8n/++HYXCxv1637IMIlwFZr53N+/d/wP/+//j3fefnT16jMPvUVjKo++drUNtW8bZ4icR2vEkrEoJXNt8Gwxmy9n1bwhu6B6IdWezJKdMwk32w3OQg2+sK4vN6JCrhbDzGKStDM9DDI3wgW2Y9mDMQycRiGo66ox/ma/P3h06CFT3pOppACIURB0ZKQx9sC6YeaOsisWkimUso5Tt1q2948OBX0W6Kf4xYvPQjPb7W9j2u6GHdXNzC9YuaAip1KiANm6ilh8ZZcgd1xglGJcaRahWnHGUXi9vkrckbfB1ogKqmnkGJPxjQO0tmje1RDTCIJeJyNEWTxam8lBY+yQoopqBlCFgAwyxRcvXzz5/Nmzz68g4r4Mzm7/T//B3yqlCNJYJKIpgK9fX/3kx5/W1cF7HyzunR46lMr7v/17v/6T7314cvT4Fz//+frlJTYuS4Ek4OAbX3/nP/oH//57b33dsK1nn88Ws26/r9WpnfYJ+2kEjJZUock8CoHxaHxg5pJTwfydv/Ktu4+7J794rakMOd8/W4pLhp0DjCAlazWUw8VByb0iY06FDQWrPCGiXTYNFHhxedO+fby52c6pUslZiyu+uZSYk33syJFEHWM/nzfZzwRLtTzc76490NH5yWxWYomfv35CYHLEz19cvvG3f/VwttheX++HbjlrTh+92y/Wn/WfKjhf1yhpmqaUIjE69BfPr6a4qxrTOFz4VR/L+ft3zh6fbS6uq6q5ef3aWdMcrqahw8KvL19su3h0fOfg+JzT+h/8R//2Z09fvvGbvwlkuovn3Yvnzz5+8u67jz796fe+ePHyG7/0K2D8+fFhhjyMw/J4xVIA9cMPf3waXNWEo9O7N5sp5qwYrm7Gk2NonEuaCK1gsajOjLOZ26UIhkwBZz1HMmpTjoVZQXxjeYq5y5yLO0DnXcnqAAySjNNQoG2Xw6TjOGHlIISD07OD81MERTLr63XwNTMfrI6CwhcvXvXD0DRLEEaVxvjQtgrab9cAioigAABZGIW8IgKqEQOk5K8vbjx6p+QLFGBWBuFI40ZuLqcXPe8Uh5mrWl6MuD49ObOkfU6K4IxbNsuSc592QrqYzeN6kFzIgnFEElXIgCkcm9ls3A6vP/rzs/t3jhr7otsMH/1oyzKrdL1/9W/+8Xf/7f/4//jwKx9QPfvoZz/5w//2v17ULXh3s9mOfTpeLF9f9M6gdfnXv/3L/9X/+z9raz2fV88/umnnDYmaYGxdhZp8UGPJ29qYyjtHKHUIq5P54eyNhSdLMCk04SjMV7M8Pnv6OkzFgpzdOR5N0axp0u31pQ++XQZFLLms2ubefF5RD6BR9KrrJ7XIuGiO33jjKzrsjupmly+rUFsEA93UR2+MkgjGYRydXZGvRCOaA0Pa1AQ5m5R8qAXYVJspD1OU69v19QZtu0l5RyXXLgBQ4ggkBITWryq32Xc0xuWsdaCHtmXfuNUx2kpJR+CYhm3XWURHwRhAtETKhblInqYdcQ9aY55jCjoF0pJ6IWQyphAWB2xYbGM8JkrDFPOYhv3Fy2evnnwxjbt25r713uPNmn/yi6vf/c1vrmaeS0rktwKdJWL/gz/6PlX+4Xn79a+8XQUcOUWIs4P6N//GN0vxj948+vyL53XTMnKO8fzBvQcP78e0/sGP/lWgg6PzO1UlEo0QoPDCmYgJSNG7DJBEAGwzr0DQAbFiUU0lLlb25GTWXed6MfNziHBrrLIFTYkB9nFX80ytzUpJdFYFQJKsJlu7vV237UE0Gu7W4e48PhuCx1HZKB3kkG6mLW7NvZAcSgn9RqplRd669rCxtF9vbi6vT08P2/ni1cXF2MHLy63zWgumnlXC8em9/vUn2u7vnp2VYfvpFy/6OJLKNGYpBIVz7EsGoiqszj1NnMXYcHb+eExQzeddt/NtvZgth6ln7ddXu49+8tw5vPri5ZjGo3tnv/prf/WX3nzrsz//04df++VPf/AXX3z4U3cw++bv/j27XJiqdaECZ4kokLUulCF9+uHndx8drwLmuvnh//Lhn//4J2++ddcbQZJFszqaLcaxOLSiGQEIAJBcZf3CVbQoQ0nrbc6x63vBkqG0s4Wqq6u5ckbMFDQpqhgBKCljLFMpWSAXKyIeJBO+8f77VQikqKJpzApYN9ViMcspffH8mW/qkjPkzDk2h4dNO5Ouw5QBERVEgRAAVEUdgCJm0GXbXlzdFC4GCC0BYiwFCJjjRXx+sf+8571aRRShAJAVvBRX14uuaobxEjU6qA6aZe74Ng5kYT6b7fddLhmMt1bFAJECR2sCuf5m/exH/+qfL2sfjCVnH947evHq6YvnT3/pd/6tr//234pT/93/7r/7o9//x0dn9wraGEuo28eP3n7y2ecx5sWi/lu/87f+5T/5r65eXf7df+dv/OxPf3+2oEJggwmV9T5UFXmfDRrrwDtELTPn3nx42kIs+/V+vU8M/vztg/kBAO33w+3mkqY1GCME4skyeQEJtQ2VqIgqqh6vjluHDIJIPOQpDmo94uxkfmyjxkkP5ssPOXbdem6Mr8mSeOcFNU5ZxANykSwqTEmy7bkYC+QDiEHgWsU56y2/++bCvrjal0S+wcQpZ4eQkEuh2gRUcu0s5sy7vUU4aEPWbJuZYCOJC5bNpk+pc8aqkhhjLaKxrAyELmjxFEumorux22o6qCFYVEmGUJiNGMOIjKxW0JriEotX6aV74zx87fEHy2pW1b46OPhv/vs/eP908SvvPEo4IpqJjdgabfjsyedPLz47vnf467/5bWONrbyLA3ByFstYMu7Rp7feOiPjqbFNWwOixK2zwVeHivjZ05+44BQ4Sykp1RQckq/rmDPY4h0hEIgpqgUKEltRrzClHZbbYNqj45ngbXDEZPNU0LWmZJZp6PfBLhERQjWmyMKIgQHtGEd0/s7ds27oZ++ejONrWccKDWZBzASuv4kRRr7nsHZQeNhsbV1haKitVtVye/P64yefG0IRQpg5yF99915d8na/my3vrBZH/csPu5tX69evEfju2fGTZy+UtTbBG7doZ4qmbDYx7e4fv/fFF6/IVGhnzt/x1IPDeqV5SoI2eE8yJxxXB/OLy3WzXNw/X+RR/vQP/nB5cCp77jfrxqiCf/Or3zEn929vb98+P2EuAFrG3A19O5+l1BscuJTb/XpuYX7YvvO1h6uDRRz6m/Xl+aP7ahWASRlUCI2yerGYeEZNP1z367XpMQ1FsxQQ1ziHhtkIA6KGxgnpOGZSZwDQWmYGW6QkTeocGslvPH7jwfk9zuJd9frqlgxkKIvjlfX21cvnMRcywVrDKVLl3Lx2oJvtjjkpOQQhUUBFAjRoCVlk1rbXN7fTNCKRGowkrKAEDNzH3Yv905F79AYYHVZEbaTiW//Dz58czppvv/9+s5zx2E8Tp81NXUKrup26osZaTBmHqM2iVosAxaABjj7YbrhM+bTjWUw7Vvj4B38OyCd3H3/jN//qx3/0r7/3P/yTzc3Vg6985YvtcHW1rurw/jtvv3rx+vmLF/OD1be/880Pf/CDn/3ge3//3/93nvz0e80M2GVL3nkNzntHTbDGkkVHFoxhy3B+dNxaefnpx5wKWT/Z+dv33yDnxu3ts88+yWkXfPCmFbBZ0Fk35VgUJRcXBBhIYOyvdDk3iBhNjqiJnHHM1mmQfpIYl6uT2WzerV/7xWmAijTLFNG5tl54M+vUjDIJj0m2KQEBRS6hqkPbBE+2bjE7onFhAXF1sd4Og7K3fU791AOToqlDjVLCKEfLpVg/c0FKcdWCi+3jyBYyicHsHJRpKEquCgANi6oaKSyZ2ZPzQTnbepY0vSjRplIz0X6qLAWvJIziPDpnkKskamKSuXNkrKKCTon54x8/f/b807/3O79jJJaII/qxWnTR8y5+/0//uD4I3/zV9+/eP+qjqINqRDdBLtnXnvxC2EKyrkJBAGZyRlCFJXE/SjSmiLBrjRPi2osqJlYmooDAzhIieWOC4JQyGkppKgVmi/mDt6Hf+qJxlKHxNaqdO1MkMO4KaJym+eKMTCgsU4pIAEBIxp4eHBUDuR8cEJw3M3hw+aefNZ2pACLETGlmqrSWSRXuYA6g1kPHbmJcOdecLE+dbXwax2E3Ik5vPDo+Pjzr7cHxG49Tf/3FJ3+mhguF/e2NNz4s5x988N5uu+1udmkcj86WflmVNLlgv3jycR4zsPb5+dXLZ+enlTPgqPJVXRJnE7ylu/dPgzNFh3vnq2CdX4T1dj1tbteXV+uLX2y3sN4PX/32tzRtpFtvX78QS4A4WxyMN/vUX5m4PaTNxZOnWPocpyaUt+6cThOnTKuj4/nhMm6YiVhFtRCCCFoMvlKvypBLHIR3jSonrk3NICwgGq0XERTjBTC0DRbBmLOU7JEsUiTLhNb4pnnw1rtInlRSwVyyCoSZmR+0WfLTz5+SkmYoyqrg5m3T1uN+t069GMQvl/uRFMVYRENqwCH2+13/JU+WQAmMoRKLMhSCLHHSTm0gZsMcSCtn2cB8sbr/1a9996Off/T5q6+9eedgjov6zr3H9+nFJ1lfFAi7brKEjA7YVTAzol+u3JIRawGGUsqYrKa8IYJXz7uYk7XL//m/+E83N9fHy5M7773388vtF8+u33zj8be/9fVut7l8linHr7zzjXGI//r3/9Hv/jt/s7/5SOWZ8zOHoFZ8sD5454ksGXLeOSRjAEBlNTsofd93YL3HRMuzR7PZmSDud683t8/r5YKwUkK05JzzqJmSLZpj7CTWfkFM47hXaLxojnGSkXwjozBnKBlcNMLCdhUWJFtL3hICYylgLDk0wkMBo0Zy7uMwCsA4FQN2GrMmnoKbLVrnagIkLovK+8NZDDxkdxsDEWUGVZ+ytAiNM1amyhMwdAIJJzIdgEVrNcmYxj5PFMV6ryVyROO9KgGrISIwBVitOm+lFBycCA2KZO3NfjuNnbOiCszgELgUowEz3D0/D01bVPqhu7y5+cM//uO//jvfni3NmMrIJleVYOOC/MVf/Nm6v/gbf+u3Ht47dgi1MYq5eNNFra0HthkyCxvjiiEjxpONJQoAIiNnQamME5FCXCQJGEGmCosYFlRga/2YpAw7Y3xCcMYMQ6xtLUIKPJvZq+vON2yxmnIWHWsXsp1PcV9568NsSrmyylm4kDXGgrVo7M3N1fHigABHBFlC895J+vCaEhX2wokAHBrdlCknd6dOcykuO056kzRB1TZuda+jC8hZBOom1POlbe5cPP9ihrt60WSwP/nhk1//9jeuXrwIzsUUrQ/GNkO38c73qW9mbRe5ZLi6uDhanq6q8L0/+P2/83f/ul0eKBgUMBasrRkNkc4O519bfo0Upj3/6Ge/ePudO8BxiOVyTDe3Y6jCvuva9SVN3dXzjtr26OAEtD5/cO/5hz/YPPlpXbnbbcc5TdaMcXr+/BVhVS3DgwfneUxpSkQrJmUGlAwWnbOYuSaYYIZy5A5W66evAToR8MbkYtPUK0bnQykuq4AFETaGpbBlKiQ52oAihLMHp83xaVcgGNdvu6JYGTg/PfYOr55f9TGSD8A5Z/HWV83CgM3dKCxIllIBBGsAFJz1gkDOD+O4HwayUBI4g4gESgSExpBC8hmUyapOqW3qD77y3urwcBong4jG3PvKBzcvP/+TJ5+d1u7cX33w+Gt3T877/jWILSaspwzI1sRhvPHNSbBBjTKyBap8GPrOrFoOPseSgQVtFucFlvcfc1V9/PTixcXVb/3GX229vnr17OzxGy+f/Yvl0eLs7t1/+H/7v77/zW/dvT//7Mc/bA4WygaleNvYyofKeb/wFr1lRSE1oIgYnbP7m1tNmWyrbnl2/haiFZ40pzvnd7oxB1dVjdvHCdhbxMZYtZlTKhmyVWtx1gYDigxjLjFHYyy6VlK6vb4+ODniHPf95snzp/fOFuqg5DLzYbKehccpO6moao2SJccxK6KzVgRSiblLta723TRbzMOiAVustXM3r0LvcqyjMTd2HSUW6EqvJdZcBW8UqYv6bLOZndytfVKP05j6/S6zGgLvQTGCGs4ml2hN5cSSASU1CBYsgc/k1W2sdcpVipOdB1+13bDr+93N5eXuZn1xuR8nOqjM3//bv13PAyqC+C8+/eK3/upvHB3Ycdx30twkOFjVVqRbj7948sVf+b3ffPD41DsABUuak3rvXFNZlqTOMjlHUIrjUCCps1a9qBRhJlHUCRXgS4I/KYEAiRTE7EPgqHkcCgszYCWGTNoOOo3Rk2/C3Bzuxlgt1GItysLRGIuGfPGNNh5rnjIIWkYUEjKRlEStUCDwkdVUvh/7RbDm8RK96b//spXKGitcBCWg5SHJi4nONB6jZTYRh/2u0tqsgl0cN76Km22MkZA21y/unB3s+13JKLl655239rthtpwno9OUoPD8oJ5gdb2/9aE9PjgLfXr26mXV2KOTum0O33j7UWjrlLhtF8RsLbNqYgSB2YxSyZwLIb/3tTcdgRYbZqur7hKCTVBeXrwyB83m4rIb+tXRkeNczVegsDw4f/GLH+52N0OU58+uxpiHOCjIwcH8/OSoUfOD7/3oeHVvfrQiRQVUUCBkFYVsw5ynYeKy6dbOWRiMKKYpTTkbcIZM4iHlHmkGQo5UOGshJVuDL3VAFH9QP37/685WKRZjYdftEb2Z2XbeAuOzl9dIlSkESrkMNHfOVzGV/TiSIioGGzJyAnbWKqA3hhTGoSdDokzeIpA1SFxMEeetMqaYVIXi/qvvf+Ur77xNDscpImCcxjJIqHxTt73fPB15mrZ3ps2d5ZuHfqEATTZDlnGMAqUbX9epCmauCgjowBaqSnUU7rxdY+Z+3O6nhCWKsX62z/zhp7+Ynz74m7/3y+nzz3h1yCSVM69urn/t937vX/3T/3I+q//a3/zNpx//96u7d2fheNdtfOx8Bu8rHyCYGNB6tUoEkhH46GhVeXrVbUTLvi+ru0fzk9NCSaahMf6t+2/fbofmaNV112Pfg5G2NdUyAIXL6/71hqGaEhSHNcYCpLtx3K/Hg/M7D+++20+x9MkwWPIROqVyeXF9s2zssg6FXaiglKmo+ABIGmPcd5qFoYSAzIWUCN0iHPilu76+aIF824Cp0Q422CZHb/f3sredvLrZN8RntamtTJJvB+qSPbj7FjlXeBw310OKqupcNatrkIIEuXCSAY0reSKsBP2Xp8QWHecChL5aWIPCJFhNcSfUtGFG4fD+/a8D8x/+mz+/uN6A2a/Hblnm3jqyaTFzB3PLY9zH8NGzm7c++EBHigZ/8OMfffu3f+3enaXxaB2JRm99HFNBUUO2alATploTIEhB9i4oG8gsKs45EGQF1tEYhmIVdczRGGsECYihqEMVZ8CyhyJjTmPqI6pJKUXWeeNdpZaxZIwpG/qShp5BRCQPuVvYVGHgBMagocJkSsk26rCc1cZQcH5lq5vbTTVvqkdhieH2e88Co81amDOXmTMwUf8qQiQ4rMUg56S3yfRiV3U9W4bTA463m/20WjTcr1vTyK43wAVyTmKaSjmJjIrkwB8dzmwNKm7c9s4qx/2d89OppHunZ8f3zgH5u3/2/QePPviV73xn2l3leBunEkILaNpKUh64oPXV/mbngzt/BJnSsB2LyPd/+MN9nAKjsnz04mZ7Mxw8fYXeko1aA2E187auXSnTNz54c7fduKppnB7U1dffef+zp9dBirEkDFgUCouwQunWW8jRMFGk2G+VYyrE6kXVkTFU0AVb+9xDKRk8CoqC8Q1ZCoY9ql3duX927wFymRnb7XpLVpAPzw9djVcvb/f9aMjQBIposPLzRRWqy9evplJI1aKxSEyo1gICoWmc29zcBgZEK4oFANk50WBIQZk1YyQqp/Xy6x989dEbj6bUx5LAWQRsQQMaJDvxrs/mpsuXw/rdtzIHRvQL4+3MSMrrAn2cQGfxS4wIgiUjUIz3UtmCKhkiQ6dm209X24vN9e7gzqOvf+uvQ/8q337U3nnQnpxXcXf97MnR0fL286dXn3747/8n/7tu88U0qqlnT1/vfOmDY7IuEAXUYHExc42viZxBdlYOD452m/Vmu5+idrk/Xx5a10iJMo1FICwPzpdHnEfTtqvKSREPyZCCq+oHJ0f3FqayOSbLpR/77f7q2cVlALs0ofWubhp74BxSUj45Pnrn0YPd9fXZ+SOO665ET1lEyFSZOHbXY+z7bk3GWkSOyaBBIgN0e/nMNFZFLMyMoogwGGMoBOdNZewAtq9yKePkFKfM65x7rQ/uHpNpL2+3N1dPEcaqCqFuJQtncLZKhQWsIEMuRpUoOfRFQMWIoirCl9h5JULjHVdutS9dnFJT1wVK3YTHj+/v+pFL3A4Z0CgYW9lHbz2cODG4P/3p0zv3vjprVv2Ub7cvlof5gw/u9NstTBSzKRjRFPaWrLOTSuFq7o0JxZmUOtAMQGlKXArDBBpikdBUxgQAKqDgUFmct8TApAnAkJvP5g7cmIZplGmSWduSVc12GMt6v61qRFQuxRqjf4lyFSaQIc+QHIZEhMQOAQsKoHK2VWUBME659HsoWtkqTel22h6czBffvD9+/0VwFUIuLMiawNepdhfThEUPUbyUlCtRuE5UFqEN7I52fezGG+j7cTNIHm0Dh2eHTeXH/Q0Xul1384O5sWleN9XcrtfZBJvz9Pbbj0PbjAUfvv/N6sgrb37nd35TxY/jRkzywRjTgHEJVVIyplY1xosJaizOonv0ZtP1Y7/vj8QYpBxHA+b6+uZ2Gr/64KELYFt0lTdSWyhfeedxkomltPYwshAJWp4ftPL8orK2gAChGODEhlAIwbIl50y1u4ky9gpF0ORsVYgCZPnS/TCBiEuWjChkAJA0V95kP07w3oM3EUBTQeN3m4GMbedmNasN6/XVNRlFIHSIiqF2dd1qnGI/KKIRY4EExCMxoUgGhPV+QEJjPJeiCkpoDFgwSUd3RMuV9bPwqHon5XsIVZo2gkxGDKIFsuxI9fLi1ceffLodJ+vqIv71zV7eA/KNpqF2dOfkCAl5HTfjtDxQgegQvuSrEibpL6ZrT2a+H9PnL59f325OT8/+xt/5u0tTfvb9f3F6flqq0+bs/OTe/dvnTy8+/ezh6eGf/LN//s1f/uXDQ/uDHz1vqnucPciu4ph1gno2I515//DB+eFBVabJQqUohDHHbIx/72u/lBLc7od7D+4CQlAzdqOtWloeOGHpYgi1K6mUXMZIpi3YGNPMauMdsPoYdchKYf7ee2cOxEDlwYiq815KIgDI8vDO/e+/fHW9vj2eE4CdYjHCWbaTi30/7Ls9OKvM3nhBKgrGKcs0jWMe5ODwSER2t1ez+aIUJa8UsKCj0PhFOW2W6Ro3t8PIFfnj04PDftq/fv5kOw6FB6sRSLIaQ4IGjTpjLAAShKIZDWaJ3QTgKwAFdEgWnNGSRVGwVLUThtouG89Sct933dCfPzzA8JUvPv/0djd0Y25rVLXYNFOEn/zsk+JWd9+8M/IYZQTX3Xn7bNyvmXNWw+S8b9UYQ+jIt+THYcr7OF8eZyhjMbupG/KUYibQohOXbLzLmQPVFptCEUBm9awIC6F1KNkEqZah0VK8n0MfC5tQzZw1QsUa2Q0p950JITiXcgEgcgRGyz6mxKkykyqBAZBMZNAqezLGpoyEtvKep5GZV2e+28RV1eYsctbiV8+3P79u1ARyUYotbBiMQb7oSjZwbHnh192worpf76s91YervasikaCNnlPepy325fLxncPahQH07skdT8ZUvppZLp2LHFUm5bae2bamJL5Zro5Obq5GkDGYzNOtarRgsYhorLwBNCmhgEbWk4fvn959MHRjyYV5zCWyMcqMIgjw203Y7bel24z9fr277Tf9IrTLeRX3vfEz5ly1aHIqruymviS4e+9EDUThgkqKX2ICiZylidhmoNT3lXUFLEI2mNHYlDIZLwVAiiiCCopp7byUxOomRuXi756eP37IEzR+3u0nV7U5xYODg6CuX/dXF2skRIRM7BFcW1eB9q9vUQoRogBnNkEdkBUxPsSpp1KMWFXMUhRAcyIQa6yb68NfOqjmjGWKicbsrfGpFMhcUqlNNata8low/vyTV1fdlaGG41CAv/fzn7734N4HDx/23SFLWoA9f1SlMv7w5z/adEOpHKAiuoA2Qwpi824/GvrpF0+WB8u//bf/3VrGlz/7g2F3dXzvtDhvVP/kD//w1csvfuVXvr44dP/yX//R8ji9+fWHn/zijxH2aowVmTUUXANoSENCOTw+OJq77ukLzaQOvPOKuVk+mB8dAWUHtBqyrdqCZI2iiHMhUO2NFttJ3NuSAJTqRrHORSANlaw1A6KrEdRC295BNKKRioExWUMWCijHzFPi2dHZr/z6b8b9NsVCtgGwU78pJb/ev8o5VYsZIykig8uCy9NjhHR9+RKdm4c6tPM+dzzuY+qKSO1nddWa1ivqbHGy79bsJ5yxY+vC/Or29c3mdsxsMA95UCxeLQA4azLnktQAOzRqVBBGKEiqkjEWZ4DBOF+rWpPRBmA0Ixm0KkperUnocyWS+hxX5wers691t5ttLM6HwkOE2+//+KNPPtn83r/3GxBoGrucro7vLrOFENkVwFkDCEoudXu0mF1CDL5GVtsNe+s48T6AABR0iGq0WANGi4AiSwqVA/BpmhCxFBYUZIcK6JQNoZ9td5vbaW/IliSYAagglMqHbj/EPKFrnHcsuUgC5W7cs4VR80IzRaVitWUgQFFBsgFtZfHVq6u28fOmXW/WaIxH8nW93nTuvqtlmT/amwShBIWRgAGojQd8NW1yKsfetIudis3qdBhuBObLZhFg5lsfpqbe513XrT9/fjWvmtXBojnw05iEe8lht59uLgYX8qJdBGMRsXL2J9/7g2dPTu8/PF8enzBMFi2PTQQuOhpjUiomU8zZ+qqCJqxW1eq4Whb4MuuKFAFRUYSNtTH2ppn1ZIpIPW5LjJu+FPZjyihgQ/3JZx+/+cY90smQZZP9jLohGfBYmJTVFABkYRFn0Uxj54NF50mgAog4iKIUGGMhtsBJDZADwMihSsx5iDB6QXnv3beqesFDKaSbbmcRqxZX87lRfvHyBaohNKpiGSHU7eygpHETN4UiKCWkYC06AeTFYj7lFFO2SlSKEhrLCHx67DxVpUzHby6qRVHNdR3ESCLrbEURSnJG+rapLdFiuSBHWkzODpwRmQrzq5L+03/y333j/jvvPmwXc5IxB0PHh6d/5dd++y++9/3b26vl8ogAyRBYBY3F4C6N7z08P2ir+MXPgLoF3o5zWHc3r57+IrOVHB48epyLfPeP/2hemeb++W33GdmhrinzHtBVs9ZZk+LYT7YhPV/Nh/V17PcIZE2Vclsdns1Pj50VMCqSHBJVSMbIyP3+tqJVC2J8MLNZnl5rmlgpsXjPnkpVCqBmxGAcWCpAWToCbFytmnJ/XQVS6NiQsQ25JirZxZGvF8P65YRobNNjPw5c3PF8bhOM6jFYJ5FLTBevvvDW1PWsCaFu54zcdfvaGxYR5Cnvskxem7qpQVxTL2HJatBP8Oz1y64rwdWMUz92hQtZl9WBwphGyh4oI0BNwTQNMAojGrRgi3CSAg5YouRkjZ2JnTs/sUwG1SYubKwxNoQA5OyoeZi0WR2I2NtYMKV9F5/fbNuHp3uYTNcHyq4lLoWVpgSq4CUzZ4aSczYixNlWJnO2zgDx2EU2zldWKWZVAGuJcmREJVLFMEwJQAgh8zBxLKImTuRb5NEIALi+vw4zDwWojIkxT2XkjGiNq0BGIgHjCkTDWQsxZ5TSNJ7AlKKgWvoJrSdyQmBvX78+WS3mc+/qcNNt2/nS+yoN49DvKyjtzKU7Zkph9oSXUnFQTQVZMyOS1nsjMeuZoXlJFEd1VeFys3ZT5eeLUjf1okKpS5LuetNv+s0+mcsrFaqIfGWvxxFN++7p3dJPaKnyxgLGFNeXv+i7G/rgK6f373bTqB5Qk5YppmjIJqPQ1AqusjNEAyCqAgCA8KVeAQIZqwLONSencx54d3t7fXk78RBWi73IbDaDwt47H+zQq69rgBgcdF2W7MkaRKtKCogWOUMBo/1AVrF2qoCChMZQLsXmTMqllDirG6qhlLFkHsdOMWRWgfHg7OSNR29OKZJzecrCKBZWZ0eFCqf0/OISXUUAIsCOTOvRmmk7KLCSgjKpkEGLJjg3xXi726IxWDRiBHGp5DffP3n7a8c3t1eENVUWa100B5yzGS1kw+y6MWc2VTM3jgzZSLgM9dHZff70yn6ZuFOgiO1U/uTDjz/8TL7y7vG9VVvrOGzX7zb33nvzzY85ppTUuKyKqgAgeV9TfT5rnn72w3e++hvT2EyZb4cnfcwHJ4t1LvdPHvaXw6c/+2jaQO0PwFTn52eN0anvgPLVze3u9Yv9NvdDDJX95q9/uyK8Xt/uu3z66P1meWSruWk8mMKlC56KqHHOhEqRQYbCyYHYca8yZZ2c9Uk5JvauMTBgIlKrgB4Mj9HVC9eg6ogFFZ0zgjhacGPqM5PqTuwKZ/NikBlyMxMpOefNNJoqkPFZCqFrmrmoKqdisnWGkFIpdV0lnlgzEaUohPglAApU8hDzmBYHM2O1aWvnMXfTcVKxetP1OgxGIDhvyEumxKIGEAZAa9CoK7BlZSJDLlhL6owDNCVTTslVxqJLORMaJlRWD6AFODMAudCgDFwc+BmnmJR7LS3OUHF1cHcH01/8xR/MwvKdtx49eHycVA0Z5uycy8zCqpxVOCMDAZcEggxsyFITXBwdeQXNlpHsgtxkSyFlUvhLMgaDQikKgkQypYSDZGu7/XVrKnRCzobaWSYWmHqSbFRsYaMCKbHAYK0CuKlkZUMEhAIAxnohFDQoYAwSij04OhimwXpAp5NmGvqS8zCMB8u5MKslnnG+wxrc9U8uDdfkQDRmUnR2Vmwd404lJpZVPYFhnBqvnGV9MVTzuZvbuj68d3e1n6/315vd7dX62aur28177751dnZYt20BvtrctH4W6qZqm37qEg/tzE359vt/8f23t/zoncdRJ02qJVj283YVS9qXtcVgsWmaGYACACICAACoqv7lEwABAEJVRS5njx4PQ5/SlNK0yWPfdaXo6vD0448+u3//6M7dxeb2stuWqp0DQBIhVVWQnCVLAtWUxBN7p3FAQgKS7KDnyvhSySBD9EIJ8hRr35RiuJgy5VzLw6+9G5r5MKZq3ty8WiNrvQirVWOQnn12PWV0FlAUUdXQfNYagHFIqsYaFCkGkQwgUhEZ+96qQSRAFjJlMmhqt/CbbmtaaZq68vOUpzglRNQCmGjcTbXDeRuMA0N4sDqYFJ+9fPrg4YO7H7749OkvvA8glQpnGF1Tttr+8ElZn67fvVMTyc3mydnxG3dOz29vN2PM3vt6FmbNbD+U59fPL0tdH9ztb7d9Xl9sX0DQt95+a3MRP/35R6vx9H/853/yb/+v/6NHv/VYBM/unB3W3Yff/ZfvPjivHMvDst51my5td+uTk/rds6Pu8ovd7b49fOvgwVu2CVwMYkbOTtWDzVDQVUDeIRpNCwuBu7LJYFGM5DxYW82sN6CInEhUJhUoUXlUMt7VwRPF2Nu55UmTTCJClCvwUjjlV8OwNot76Be28Vbi7csXZSyurQLZMU++CYK03Ww9iKqYomQJRZBgmoaC2RDFpJUXAoJsBEzVzpwz25vrtp1Vy6XxQRs8OijduHm2u0ZgAjKKoBkQUBSMAxCjaMir+ZKzZoBsFikyJonOBy5s0GAUS5wAQbJ33grkiS0ZEUaHRZhsVRF55Kx9lFG57Dlr6+4+ut/cvhp9VjD2y6hgh8ggzDZU4H2KYxEl7wooIgCoIwQVyRmaGidTJiaw1holCMYaaxNzhoJWhjKAgHcBMEjkLKm2YFIBxdFJOGpBNDM78h6JY2bvGE3OoGgQ3MipG2/bKlShKZiB0GINWgk4NcwIhI7QIjgpYNGZHDHy5KM5OjyGMa53GzerMdipi8gUqpBbiDPjrdt8fN10YlkMQtRkDHmkxWS7q6SxMnOearOXoVJnkfJ+O43W1bNm1SyXdyq/WjSr+WxRtdfgQj+NvibgsWAzGW1sRa7icbDWC6EJ1gV6+eonL1///ODknrC++947s2pBaJGn4XaLhIlzjfClYP3/1Qq+RN+D4pfHo8pqZLWsgcNsdpLLJDx0w76um+3lvr/uvEcg2XXjeh9taAEYSMihqkoRFmRUFCUfEIGcy0mMpWGMJbKrOASsfYAo/TBqThYtoLHkF8vFUKexgbPH75Soc9903cAxB2sOlpXFAtk8f30ZqhoksxIotr6dV83NzdWYRJnQgEdnDSGBIPTdGIwn4MIFAImNEWMbQiyr5ZJtKMCpjCCFQIf9OO1HV8g1xtdSIKtx6Lyb1bsXN5rmqc9vnL/54WcfqvFOBqNiwBqsFM3O0g9uxnWO7x81UF8fnjxeHqx88IBYVbXxMJX04Lg+vbv66NPL2lS3++vLq89tLfcfPTxaNO0eVl9/6+Lm8rge/+U//s/V4bxezGrzm99591e+sko3X2zSVLf1YWjv3Ju7R15Khu3lbnPdLO+cPPgKWMeFGYszwimbYBCRkpKxREGBiqoPiCYLRMxAYo0KiAXmwqOCASzKzBGzmFQsceWKEUoKFCMxIdcHrm543BrJwAIsVG4UCoRTdC7G/vrlq1mz9GiUR/JoGh+nhEXFoRgCJrSuqRxPOZWEXkVRRXNmI2oFFUvKxCKlTEPvyZFpajE5lduTA3+7cxe3U8GiKKpk0FnnjEUWQoXaWyCIHJWgaVsVKZyZR+FiTM0CpLawFIlfRh9oEVALYL78t6iQohKRMwZsBoU8KZgEKrP5orZ0dX3lZ4vlcm6VDNIUk7feOoeWVMESi4pVUs4WMiqIAVZjEzvjUpogkDUWACIXUSUiw5ZhTBIxVpWN5ITY2DJTl9BJn0pVz5uw8EVHKfv9sM0TsBhjU4wxS+bCeTCWaxsgE6JQERCo3MksnH8Jr0NEyeS8ByVUsDJNddMWpraaM0PMgEg2OBYZpmidbev54eFic3uRz6hxp/qzjdyYWsXDCJSLVSt+xka3MA3Sn6LM/TDlGas3lkvKN1fj1rqT+Xy5ak/n7cGiPjjcb15yHvoMJSU7rwIU7gezbLwaQ1XSYtkZguByyv3Lz3fOLF+0jfDFarY8PGowMXhmyYBfqpQiEqiCflltgaqqApERVh7GkiNYO6QCucQxDt0w91Wu8u22Wy6WLPL6cstKgvnLYTsbEEI2JjMXUCdlSsxirAPbNimqYlksA0BBysaahpbMmCQomC7DrPLkzQT5O7/xN1bNMUX23m9vrw3Z0ITV8ZK0XFxe7eNUNaFSHKOAaUO97OPUjT2SWCZD5EBAhEH3Yw/qDCCAFiySwSWoSKuZvPHgCCnmhDkDkHLWvmzyVByS9QpWyKElH6OWLMPYp5ychGHojEcQ4DI5LBZIGMQQeQsGU9JnG913m8+e5K6j99+4Y9WV0l28+IShRIaK6I03P/jmB2/95Ke/+Pynf5ZyWp0cL+Tsrs101qRz8yvffPje23e+++NPt91oCR/eOf0r3zjpLp/sbi7JEpdGyg6AF7VYbCnnachnb96X4FEKZDtrqQg7Yx0JcHFFx/3o5swWwFBiLfvRlCQqfdHVfIklliIIWUM2zihQKX52/BhmB5AoTnsmdfVMjS+EVX1iHEqxr37xA18BNi2DwX5nhqGg2w7DOO6a9kCcjTm17azEbAXJ+UnzkHS1ODg6P+u2F7nfO2M1F9Ayt1VJSQmAyCHxFCeMRKAs035sCC2Fen4Wx+2DO9oP8SaqIFa+0kKx5BrR+ConFogoVKERQo6TQe+tq4yb0kBGGUWgDFOxxhhDnWgwwVsjuYhk66xkJhAgSqpRRZ3TXLDAlDJLrBp/9vhcnZ80O0GJwEpFkUU4s7D4xnfDxArWUSpR2SqBIEDfWTJFM5mgwMQwSh7GQcE6V3tjGmoTqTADegUFKypuGvb7283oY3fZL6vKOE+hWh4utDCXAgj9dNv1W+Jo0RmcGfQg2atrcLbyZ7Vtk0y5aAG27AgBKBOCRbRWqLZtSmOXslo6uX8exzR008HBLBez6aNry1Z6jvHxm2+Rr/d//iwM6LNT0YionDygFqVIMyz7yK5xYnRAVuDaEufcXW5kzKuDQ1fPD8/C4cHJdv0il6tkjOhguDbAqR8JbNICCt44hRyqahpH7qbXN1cp9fPjw24/73o7a4KXAGSssQr6ZW2F/7/OUBGFMI/x+mpze/XapbGIGffj7eWaeTIWxn6vuS6AdWtKmnZ7aasaTdlETsKVImSx4FSRsghQEVQQBMWMHo11GhqjlYXI2ltrfV8Ga70aEWMJKAHeav7mb/21u2++3cfpuJ5thylmDEZny4rIENMXz66td5xwEioy1rO5Dy7HbeqLQArBktWSi7Nhu9+UwsEETgVNUXBV1sZAVA4LB0EsWUdFxJRR992AlZ211TSOE5CrTVs3vM9Xl6988FUVMDQ9T4AwX8zmth4gC1C2VpEQSC2oYZMpk95Uze7ZePvPf/DZWx8/PDqYNegsCwHZNoH5+JOP33zfvvP+GeF7yO7x2/dnQbdXL7pNlszeuaOze3/nt79+M02o+f7sdLq6GLdr40yWHAphMR6LZhQar/t1tbzTHp8bNWWfzTygIco9I4sqCHBK/bQ9Oo+gjnw1Pzzpn7+6ff6KsWjdVsYE75tZqwQFSlZwVFCgz7CqDnIo1TxI7IzhMo0Wg1QSEyoRs72+ugLa1u1hvToAEW9dCK0JHTNj1mWofaiKNxbt+vZyyilgANTN/joNgxHOKJVByJIkszIWRGtt8EZVNIOFlEdJCr0uDk98HSyknKbHd46np686SVPZSQYVk5ittCiu9DFUwWONarjiCaYaldmA8UA1FjYqyKxIhTNyUZtKHsEYAVTJSGoQSQGKekP9NHhyhcZYJlKJXTk5P46YBWTCiSXGCGlUWSsYNNbWVVV53/qgRUTtoBgygtFIoEDO1jpmdRAlcxYDzqPlkgEhOCi5FETQzBqT6jQU7Ettq6wKDFTV1cwb7wsoWefVbba73CfJwsbZApVTg6isqUhj5z5UQGCA0lgYyQafyCJTC8Zm9l031QHQlsp67+vb15dVaFCUAPMwoppAYRkWt9urm/1Nez5rfu189+PncBFnxUDRWLhCYxEM6clQuSRccTlgrbQUKWDJGpTY39yWIc9XK7cMENzp/XfjeLxfX3XbKxCCXMZuyqoMaq1nRjBZrFSz5vMfPMECzdv3m+DambPBATVAQdDmohXgX5ZZhAKAgOO+e/365TBsBPwihCevn5S+eKtgUgKmlAtzSpkIF/Mqq60Ko+A09N3N9vD0yLVYMpesoEgEKCBFSYE4p/2AEgsqeqeayGFxwN2Upwihqj1OgomBWv+tv/rrdx+8EZM6smjsZrcmFO/tyemhCl9frzfTFtAa1VIEQghNRQTDvlMBNYaMVRTj664bhCEYBzkXVaMaBFtxSMA0nd5ZuIo4lpJpv+9ySkhgEQZWdTY460K968ru1fbmZt3OKhPaKduboTOowCmXLD6IAosoZmMRiVRFiohkb6Q5n+0v+g+/2F+v86OzxcmJr1s1kEkJIb/44hdn54/ef/urBJr6F69fr7moiFjrNOWLF89sHfx8FaxZv/pU+s4aIwUAbJTJYEgCQ5yUM0Pz3hu/rBqQhDFXlRGyRtkYBYPMJaPuuv0iTcY6gFCtHtTLw+poNq1vyKMLtbMOnZ/GCOg055hgiNrWLknUqjGE1vg07faxd857sqwwgmkfv+GGo5svPnn96nOzuWmPTqrGd6k43wJiVXlwEEWC9+N2n4bek4baE+Vpc4tCbFBFY0xGpSgzfEmEiZoFyUCRKeesPoTEaYOj+roV03TTS+dS7fcvrjdJsmHIpcpUvNsvqPG26rINfnBYY/Fka8YE3oNxhUeLxhKNUhDUkk2ZteQQWlAlNApAhIqiIgqQchQtim4YeywEGlMemA99qJgLxam1Vi3mRgtqz2niqdsP2bmRrA9N3TSmFGBIuagBRVUxXAqqccYhFK9qEPpxLGzJO2exyERovK27bZf3uaGKvAUF7+pUWLtSty5r6oehu16nmC0YRyFp2fPQciHeKylI5ataRCAXV7kBQQBA1bAAQD+OlmZgrBFhA8aqrwosbLW+3TSLhQe3Wlb7ri+Zh+04q2YBa1Z5Hm/vfePx7mfP80X0hQySpBSVOWBg2yihwPW0pQMIizBgRNaKyLmGBXaXN24gXDkzP/b+7tHZ0XJ5r9t+TiYOY8xqFGzw4CyXNHGsvKt+6Ze/OpvPskjMVKEBCgoGkLjIl+N1UUVEFUTh25vrYVjPTD48OMLZcRxvdz+61mQ5WLRUWc+pIBgWMA5EIXP2jhCtgXD/5KhZtRM7BQuUlQWJiEFYS85jt5lVbuxHdWaMA26Sb5emaaY0OHIpZbRYsqCb/eqv/5Wze/emxI5s41zOMaWRMK9Wx4DMDM+e34BgIS1QrMNQhWbWcJriNFkybCAldY6mHFOKlbNYoAgrATHM1bZg96pihCFdX183LtzcjsPYE3HdtAzITK2pPGu/KVevXw/7vTrjXRs3cTcNwzjVVqbcb0e0HACVLKODAqKxgGbN4tDoEIfWhbNVd3E97fJud3una+6c29NVRcDGcpr4iyc/LkSB5iuPSBmoZjMKZwKLALHHHG/Bz4MSEadScpHEjCYZSFLMKMmCe/+D72TyuZTglJyzYY7GkrlVLZmRBWFx8vj0QIMXVaSggaKiv3uXywh5ZIyas+aESft+2k88RevnR4ezA6Fscy+cQJOgNfUiNCesFoqAGhsqHfYCYBf1zc3VF1fPh9zeffz+/PDAOI+VYVTjHaAO/caCGmfQgAyD1VxsANBSmBTIO0LTNG2aeqPAuQgplESGLBJwtqJ5Gmyo23ZxfHx29fyJc34/dOQCZEVkTmVbMkOam6ZwEFBy4HLSAuhC4tHWnJjZuKIuWEeIKmhtECkpsTGUNaO1KEYVsw5ssgJphrHfSC6GKKnM7y6gThmF0HMJrKowJcqJGFVrQLGhxJSUR9F9SgvfEJCysBS1BtAiEYpaIKM4WB5Gdr4FUMmsJXtLKiDseVRSq2Qz81jGIfVlhxVVldmn1BcpqmCMFxGPliXzxOghGyyGiJ2nOiVuG5OSFCVCMIDOmARiV43FMWLh3bA5PFqhhS5nrPyyOal9GNfbfcmr40PvKs0HQ781VuJm54TN3Lzx7bc3P3uVntxaRXQWCnOCohHImuJarWMcZEp0qFpRnHJxyZnaGaOJ+Zpvbq7aw/nsqKrbA9+0Ircx7aY0CfegjTVufXMbh2a5WFXBVI4c10M0VEy3G2lpLGfIAlIQAfHLGXnc3l4ZKAetL8IAsDxYbmXSYlWTKmjG1Wo+iqaUphKd9Rkwiy6DSwJI1nszDRsb7ltwQgkQMoBIAVBjrXXGn86HyxTEcaGsWEYd0hoJfeUx6sDCLnznd3/r8OGD6+62qeZgCZ29uL1kowvvlgdzMnC76a42W2t8BgBSQpi3M2HZ7m+VxNUeVVVGyMJDDlRZMKK5QMYsNYbWeVD+0kWe9lOzrG62N+OUm7YxBPNFa9jeXO1cYxiVwex2kYd0fHxmtemnNGlU2gOWtnG8M3vurUltoFC5YiSa6Cx7Y5KKQRqniMHgLMhQxmKvLksc8njoj+ZmtbDFKqDNhTKFENqASswGUUTQuAKacRNg4awBjgVo0jKWKSU/PziIww5UiuJieYjODWlnm8a0tXcV2GCMgzBjBktkQpWwYnRaohoxtrHicm5s8Oa4UMp8/XraT1LZZ5+/6EXuPXhzsaRqPrfWAjLxJAKMgdysrS2jVWXgTCB9t12/fJJycqGez+lmeskAxhkGXsyqQlL7qpnNr64uQNl7k1A5Dw6Q2Sr95RwCrRUAFGQE38xMLGUcMiSx5K2rTJ1lwsKQAIuy5nl70NW7e+fh9Hr/6nZjbLBIjfUsOQIHw4GwwLBNG4uiMje2rTwZMYTkyVQ2kDkw5MVZFkH0yLlwIWOYc5HJFJVSCnIpcbi9BUBF1w2juNJQzeiQKHMygTrO4jFP7MQaJEaRKEGNApbCxJBNtK5WBVZOqdSNSzl7450xQmJEqrYGdk5Kydk6S1SYyRjT1D77pFos2RnPSyGxnIeJszoMpLBPwoCH7QHkUsQbH0RV2HEqbTgEdsJsrBvGQVjVG3GGETnpYtZYiNKGStBXbPquR0NoLQXb5ck401qTU0xTqoNzZiYg7Xzlcf7sBx998vKlH7DaaTFARmMUC4YQPQakECxgpnIDcdJyCK6xYmScxspXFtFntaQy7DelnzUHYTbT6mwWjnzcr28vnj67rGo4Pz3KMW83+3Y2m/Zb65pq1WYo6AM4AgRDxlov+mVHqFe3N94qGQDjQJDKMHQX89VZmJ9018+tZ7U4ScbK6AA5Rk2FEUWIGLx1WJkx91zQ+mRJixAQFgXnDSIYWzW12eyuYDbjXlOegKqJo+oU6lqtLWiV4K/+9u8ePHj0+edP53WYL4MqadY0Zg+0aBbWOQZ9ffmKkdFYLICqPnhXzXKKw7hHQoEMWqzRMo41ugICrMSIgI3aQxOIpUcoJKjAUbbrnUc4XM1Fsa5mBmG72Q5jT9XMN3WtpnAOYebbttv1TB4zVWYrMHUDEQJPJVNJo3iT0RM1zjVKBhWQ9Msw0mKs91ZYZbQ1YaWjuU3b1USHywNTO2SN0z7F/vh4XoNYQUaOMmY2aqBwymXvSIXMyOlys3704FfvPfzK7uLSQAqtP1gcMKkNrpkvyBklEIiqKPUMC0COORG5VMqICEy+VKgODTCayh881LG/+OxF2guh0+ro7v07VVBnh8rnMq69LQpMVEkxRtTkFMtYWDzVaeivnz9nRaxbkLA4Ov/q+ePdFNE5a8GgzpsmjenlixdpGurgORWJWUnEVqDeJQYHSkYRGIpVjT17X+cxImqiQkSIkFJUi7ZqUp6kFAavhKvj47pzv/but/7Zn/zBxBGxJcpOZEhpUzKNu9A4JAy+UpgcMRaPQtYFAeCSfKnBNGMZC+SArRJbMta4cerGYU9IjmicuovLV6Xr6qb11ZIsGXCyYVH0gTBz29bdxEklGIdsAKxCwgokFkKUUgCgaEkS0ZBkUUAUMsayqhCOUSzaXkc2k5BhAQG1qhRC5Zv1/pI5kkHViAWxeAO2QGIwhDWavB6v91OpqyaQtX4OKeSocztv5zUbn6ZEjpBQQRAhgLXGZVZrfVO1dqKUGY7vPd6s1zY0U5mslsDU+OB8LVkuXl+sFnMkkxMvDxb5avPyz3569clzoxxHieAKROMlZVFSKGZWLUjIu8aRmQh8tjlyXKZmVaE33VSCza3zhiAz2+zTZuh3e5q75XxZ+ZN7j86OVuubyydc/KtXL06PDqtQ/+Cnvzg4Ovyl828JSWMrZZeFlAgMkKqAIllCc3P73KE8uPugFIzstttbVx0/fuP+k2mdYiRKyKSYrCWPMA5x24+LlQuH86YKYy7ZaBftJEVVHRoxRTKC8UiSUxz7CTI5JXBqXMi5FITF6qBplkPkQfN3/tpvnN+99/mT53Hfv3XvDhRpg+nWXcjGBlwcLgmqsR+urzYoyCwebEGGxmcy49gZtRaVxALIuJ8qCkCsMopYUXBAK1sFsRmUDbIiWhqZTZJwNOty74K3Og6bbtuPzeLAzzwgXr1+FbmrDg7Xsc+smGNrtZqdbOOTP/w3z7d9JA8gljX1WUigsZlaVkRUIlCyLrGmnOp5sLHPpRusw1mYdNUN+XW3PT9ol7YxUDa7qefhdLlwBKqTZpcmGguF2cSmqrAyoPuudF2+uLpZHU2hdm3tq9oKqg3zejGzwaABo0i5jzga771rAJBzxLEAsDpvrMucwQhgTKyagLS0d09+9r9898T5o4d3LUUno0WLRqxuQRgBuHCJUtLkUY0BIziB22/G48OziTgVIFZBTMKFIyG2zaytXN6N+/22MJrgwUDpBwBBAAciWhCZRRw6YESDjAIl9tMEqr6qgQBUi4h3NKY8TH3wNWcAY7xHZEDlw/nig0dv/MXnP8kOikDUHIzJBWw1Pz16CAg321fOFEwkJRZbMSBjVuenwtazx0xFFL7MmGBFk4sQeQbpx+2Ye9c2i8WBWEUFB66kPOUIw86WUClcDtfWhVDXygZJE7MoAJkiETUj/n9Z+tOmW5PsPA9bU2Y+w977Hc5YVd1d3Y2pCZASAZCEKTvMkBxh+VebEQoHZVukCBMAATTYc1fVqTO8w977GTJzDf5w9CMyI9fK+76usapidyD2oIyDgJpubhKWgxODh+/kaDkxIEGEdioCSF0vhmsGuDw+WFXhY+2CxJmEiRUvT+fv135tgr97/OWX92+yD4klONq+j9McjAaWJTNFYkDOOZJEV8QICQaZxrk1rXWLCA8cBln3y4xFeizbMp/u59MpTSnl4oR5909//YvysN/QcGmrC2B0aJ26jEicWD22y9mQA+v98aYksc3A0vZtdo0AAQAASURBVL5jnNVuOd0WBV18VwxwSRqZU0pjX+rT5YMcjuXmdHP80XhzY3r1dFiX89N1v72dTje5tz6mo/ddwYiyI6kZEIIBoRPqi7vTKALNQ4OBxPuH3/63Icvd7Xh9bGttLr0wQ0Czcl1q69e7ux9Vh+vDdTkv0zC9uLn5uGDLYtHJggqCshmqWiB25N7c1pbTaNxr22vj6I5j+Tf/7t++fP2DX//6+2/fffjRT37ozOKRpTw9fSCh4TCkiRH7+/ef9hZAmAAJUWS4mY9a+35eGFkkUi7vH84ZGZANQ53UNAXf4TAqN/OG3lA7VDmQzHh8PUrJ/LTHsi20hRRAmcZchHuDerGxzNnzdmlVI0Dffnk3Mvz8v27/7e/ej0doZMWydm4pGvlQCEbqSdkZw6UrEXSEplhSJge2Tbecpjd+0K2++9D2QMsgRLxuurfHsUyFEfZWd6sAutVPF3l5elMou7x589WPfv7zXzyd//3/+O/+BylkFJ6K3NwTBmjV/epAe1UeRkK2klUAMfXLdylRDZaCFgbYTLVbgwaCSea7P/xXf3l3vNfL4rqPx2EcxdECmgLndPClhV4ldQJq2q/XbcPT4fY1Lsvzh+934H/69ve/+f23BDLPp5/97E8zISOdlx0BMMFQku6rI6ibIKt3EmjR1adEIeEUbE6ALvPp/vUfLOdflM6YxE27dUKgIAe1vpGk6MhQUjno03dfv7j9+e/HC1Dz6O1zFmqY5h+N8vLS3l3bpQgfhRCI1TMRplTNH7ZLCy6JCJhI9r6Hm20VhB0U3QE8cc5DSVwM1GuFQEPsjgmATc29Y1+1vfCDWUcPs0YYbLkFAZJZJKQCHK1hIhKkQFRh4qVXHpKxK0XvEKYpjVfdCANRAIdtedRdt2XtS2UsmJLr7kgrOdpz7ZeznZsM4IeKVvfGQbWb7t6CBY/ZoPU+JAF0c+uuqQw5mDW6IHOWAVMSTABlmjFYhZv2WpWI1l4nsJu7475ddbPj6f75P/2T/+48R95FNqfWmyREoEA2UK7AHpnhYnv1Rou/Gt+ypNlxrAy1Pm1n88j3M1C+1soYIJZQocFRDi3Fdr6u53W53+f745xfvnr7Zuvb9vG718CSsW0K3MvoVNUZe0SrPWZHYoBQc0aXUhQCyL23eYr1enWSXGIYieR4rY8ZqCAnBHL/o6+/epFzM/zlN+/2rb19K2nvHEXK3OqWkBJoiHTviIhJxLLZM/VG4aXroePyYenHw//5//rv5jcvfv/L3z++/3Sax9PtQcEP492npx2kGCyvX7/KybX299+/AyAgZw4Ay2MehnR+fKraSh4kycf3HxEhYbKuGuY9ktM9TUMUrWoRlhCsA0rYdjgWQluuV3RmpGrVvCh4GsGjR2S09IP7H5rC44f3mOjudh5m6ZX+419/ULbbFyNL+fDN6gCcYBiQp94J3SkxpEQc6KiSUmuwQ5JBQivsK/HKA7nMpzdz4GXZoGvloWyenjVSq7kNBlobvXz19fFwTMNpml/N4x1Eu3v5o//9P/37//d/+Y//t//L/1R3zVkQWtu3urxnbMRlSMdJJFSdiBBDY992iELzYJgQyaz2dt5bzzj2hDzOX8/F9s0JCVJhRexIrpFSPlE+Srs2P1sKN7LmPB6LnC7Xp/N33z7t669/97u//uUvnup+yNPdi7f/jBW91g3CLIDHeTrN5ftP3xsYkSAKymcoaaB1RMHgcEBh4WyOAt7qlloahrz27qGSM1GGCqFbN010X9ItZEJ5lzL+6U9++r/90z+ixUjSsFaqzx//oV3SEk+dkOJUvQvTRIpWVa0X6tSxoukolJnAQsMaE3NKHVZbKlhob5lZkcBQULx39qi9Y9KQpASdQ3e7GBFxIgoI5KAwBGyqHkTorVVhRkpIYsrhkWeGbett54GTwsCJg93CkDq4aoqG6xmevt8saq1uKa6fPujy3GlfQAfIIw11ZCXlfkG4sdq6xHXd6qYwHPDy9MV8/9m8IYJm6qERrVl2Ksx4RJWn9TrP89NyySlfL9e7l6d9uxqJsUyntLRz3ZYiOXNp2w6InUgBDsexPW2hpGqYedcWTgEEpFIoNkeIrV4fP3w6Hu7nMe99BYPc5v1dvTxsx7eH4/HOTVvbvPd5BHK1lI7DaAbn61NbH+o0H1/fjlN+8ZM/XOob36/sqa/NvKeExNFaXa7XFy/uzByRbm7un59XQ2IQQFv7xRPjdLi5v3v38F1bliAhHFtPEFsaDKXK+OJTrUH89usvCIUlkwsHbtWRxFUluKO79cRsxNZDMnkJ792wa9b7L7/85//6fzzc3f7uN78+f7qWTC9eHnJi4hQpHq/PEXFzupnKjVB7Ol+WdWESRETIwMRl3ve4Pl8DAZkfPn2yZsf52NsWEYIiDe5kOkZuPTzYQIMAhAhwWc5g99sS1j2XQYm9Z7CcWNX73qxtmkrW3lsPKUVyOk7H2laSE5349svjzV1+flo0WmQ2cgFACDCXRERiISY5Cbe2WuqrqwQnAqO69083x1e34z2XKOOdHDn2FmBIpuq1RtgKbfjTn/3FD9/+BHofEh3SYd+vIWu6of/u//SX1fo5GCWDK67vsV8ENwBziK5xeWrj8YZ8M1USmW7uW90IBWmAwNa26LU+noGq3GjiAderRIUSEIau4dIdgwrD2Dd0KCyH8M00BWdMR5bh+v7hum3LstwfDv/zv/m3V/OndVXioaRw27YVEFMaMvKn7z8YmBB5hIUzigZ40ETuDICCELlwBE4e529+Tug9fPn4oUxjKgUJtWlbjcSUcQ28u31Tynj79gefHr79kuDud+X9+WkTAkQ2oNRXXSQVBLEOq9XEkZKZ7ZHIg+fxXsDANdyXfdUGLMpEWlfMHQKbBgHrvhnskSTc3apagyyNcrQoxI0BDKv2NBBCCKUIcwBmSiz7bojeuqd0YMnqBIHOGrYLsa5KEdeo4D5F4jTEDqEIQzhtH5Zvfnf5xjYLV8TUA2ssbugsEGDqAEFpQQr19LTghLCDfSK4TTdfjkdnAoScxQ2sBzgagqIjdIlSG8kwj8u+stB4HMuhnC+Xl3ev+q4INEraYi/zCN2X9bxv7w93A76e9dtzjrgZDm3vEWTeMRxdwI3EQ23C1Lvr3tWWfUpJJ0FJY+bNb7QsdV/Wc7yw6X7Aad5A9209jnTiY9uMhuHEqFtf22XvazkMN7dfDOPMp4OC614fvntMd7Gd1475zQ8RgAgVAIZh+vih195EMPoiEUmptZjuX9zf//j3v/rV6TCPOXWmdoZ62VCtroo5h+ntPD6f1/O6HaebIIYAIERJ7qRQA0MIUvDArESRSyfez5e7L3/0L//t/zCd3n7zu98/fzx/9tjd3t8xcI55v/bwIPGb+4MjmMo33z4aEDFwJAFGhmEY2lpB+5zydl7ath+HY1i0cASH5jcw3nwmBTZvFj6Akq3eK+2/fff7+Xf4g6/frMv5xMSC03z/9NAzibbYN+jNJQ3Pl9VqZ4g3b+6C6rLqMNibV2OLXaOD+0Tc3HoyE/AWnJiZCdA8jHkab4mo7c+tBpMiJ0AqVIrL+eljymmYXwzDNB1H39dlPYfsxINB+oOv/+wnP/pD6p5yHsc0YD1kBEdVHspJMdW1pZK6dz2vcwKAFCQOHNoz2abA3b1aur3P46HWnQE+F0oQCP1YbP321/80vX3z9svXkE2AGLlVA/5swh5ApHYnkj5k7LU/XbWBpgMqGPIXP/xjaaq2yd0pPL25ec3HEzho31sLIMbM1tan5ycNpVLUNbE1AzNIJTEUM9+0TwIFBAwdNNyEciTpptM4Medwa+uKhkIcgWjULpcdfHx5W2Seysn3HZk7sdPOQUi5k3V0VgfsQdYgs/XeepIhpYkhLeenzgl1QvCuntMBkQCRaABDgx6cwY1B9203RQvkYHNED6+KMmtQt+b71ntMclPKQAQKHkZCiO4h6BbJhAwJcNMaxEmwmZZ0enxaWu0qFUVW66ax1U7ubb18ePj0629+sbYWRE6tBFHLwWXFlQkWax0AHaGPIpkhrtgVtC5XzncMkvPIgQCUJbXaQiECSiodC3YfjkMVlqzw/buP82FaAfLxVOb758fHm3EWpr7peDg8PHxans5Wm6Pd/uBu+Prl9nE99tRlHeYUta+emNzcFV0CyRBZQhxjX/R5f1a+fTWztKoZCwYaEvJhf6xPny7yepJXh0iTdv1wfizzlCUmpwETUWrm9dker5/GQ55vcj4MY57W8aYcb7a9nR+f2raYVg/ugJkJeWqb5omDA0TatqLjvpy/+PonP//P/5m3yjdjtL1Fe//9x+r1rr+cCgkg15hT9ljbdkEZGcECg3sApUgso9meAttml2sd5mNv+uUf/Oyf/fmfD4fTr3/1zePThTg31dPNHeSRSHLGpw9nySTCZZKAdrn29x8eMRgckIQch7lMCa/rcwLqVetyncejA3jsROSdb214kQ9Rt+TUqgKjEy8eFWGPfsnnv/vVf7u5OQD6uvXMqVlX1zSkpilbMA8fzk/b1hj3uzfl5qiP1/q8nv/+H//+cn33+V+eSUtGXVm3ThlLZg3oYSVIBM17jQ2TgwKC7ODN20BDavX6+MAHAh4UZUhTQnQaXx5erv3Tsj6+ffn1H339M4oYEhQJwIqxZd7R+3E4FoLnVaVHQL/6pZRymO9rXTu3Dr0gguq0XwyIYmi7KluI9GUbhnsVJyx8nKeU5trG03HX5q5jRiOU8dYicJg8DU1r4AQ0BjjAsO2t9SSfMQBte3h4DLAvvvoRiOAwt0itWmttLGk8zFjKsl3qukKGAgzERrTDlQIohlBEDiKegTAIpuk4H6/PH3evRZJw6a513wM2DkqETh2iU6SpjEC6LQ/lICpMCGOZvnzz5vdPDxmLQSCE69o8mJAwRziiMpCag3Xb9qweYZEyoYGBRxNy6ycaELD37j0AzDk8MCQNBoCAhcoezG4G3UZv1pfnd9isYF4vPB+PJubVvEfgVU0dBw/YdSeV2pIgmS/QM/ncPFQcdWs9OmgKf96vD9flfH53XZptXSCRTi3B2vo5/GBddguRHaNqZLLDcDfiWCr1MI3WsETc3vLtHU0jlVb3xFmIt7312lJhdQRIBp1GIN8ks3zx9Q9DlQOjITWbHLhvl6ud7l9J8tBWo+VxLE4Py3V6e5x/9uNP/+XbAx1OoyE1WFcjreDRZXdNjGg9B0aekOq+LpfHRNPNoUzNOibiNKRw1Mgo+7tdF00vJrhNlsrDvmNtN4fpSMGEBaIHmC77pUaTcWkyDS9fvmhBPI8vxnnb9m9+9buhnHDIWtI8jfX6CbywJIXoFAF6+fRrTscf/8lP/+Hv//qGZ4i4XNfzci1jceCUM6n23iKir+vhcNOIpmncl9UDEEEahDFACqY0lFLGy1Zvvvzhn/73/yrn6Te/++7x+ZMkMjekuHlxQziWfNC+1b0NOb+8O8zTyML/+E+/tc8FU3CiMCQqZd3WWnfB2Pta5sGCIdxcMfgY40kGjBA+mu5EAgVXaN0MBJZ4KDf87tP3/8//7d//ydc/+/rLL42C171uezqcWAmVz8t2Pj8TtjdfzC/enlQ9WH/5q7//9P73YVliRbQtORxQtVND3NDRdfMAkhIi7mDdPJMXikDcAJ1NYSeZMmZyyDEcpxd5ODJItfPW91SOPxhfff3yy5IRIsLDKNRbQAVDKONZokvVLCyIANEYqFwjNoImmMsgCuvj2bbK+eZwup3ScL2ep5wu61LzIx0PLZwYaB5f/OFPfV0hLta3BkmmCXNiZvXkmpAEZfQg2a/P779tmzdi0z4B2b5vlyWQJ7ltgjGOFJ6gizAYZszPT49qNZARNWIDIHAH4OCEsbKnHkGUs4xJhpQzADgC5YxJ6rb1uod0BkQjlASIBmEQe2u5WEq8Xffx9n6Yjtb3H796/Te/+MUCwjKwV6PrAIOHOGJmQlDBLIxEvdVl36KkkXcUKNNwZPZoC8ZokCIZJU6e3JTCgwhRQCuhRTRG7do51bqvl2snAEQQTLr25/fPr9/ec6ItLpshemZAh9YSkXnqmBM5AFnSis/9HBCUx9C97Q/fvv/+3fncrEFYR/ZC1aKBOxgyR/fdV6GGweQUwY5wrs/BUbgMghw500mtozeBGt4C4HaeEKRDc/SIAixETTyyp6goWwtj+Pj8eDgcB1f31chkGCB7871d2v2L+9Z7KkegIuF3d+XV269/AWn5T7++5xeQrvOEH2rTCBEEJ2VIwLBaQpFhVI11XbSRvR7GxBKeHDOkzkiAqfO22fZubY/78PZmPB621p6X2kTnEQfkIQ0NA6KbwfNlwdryvuOcx3Gah+F5tU/reUrnIY33hxMNoZC6qwCQ5EEO23opzBj28u2b26evnj69HzAISmIrFGgQxAiGCTL6i2GKwN1CSASkmYKHR0SiAISuz+fL01q/+uOf/exf/ivq9Nvf/P7h8TklDlR3Os6HNIyEBuhP582DEtHNUCSi7f3dN++J0CEokAjkyFhwebg6+V5bygIgdXdvmlnGzl9gmoMtOBTMAxMtuK7UTUJxq/R0GIeHj7Su9enh009++KMsw/W8mnPfAL22Bk/npUe/vYGXXxyJ+brbf/2nX356OisIJYxNBFmE5NaPE7ZLi4p9YBmBB84lJQqmzuQABlm6o6MziFZYrWPUzLQudVvrfHq5XnqR24E1an11fy8z7ukaAKiWMDjR6sWE8zh0wt2K9a3ZKohAsLE2vSABIYOxkZTjq/PvvzmNN354iQmpVZRC3Lbt6XCaOTq3hsyAzmxupqGt+nBAsE0NGp0iJ5apdQff2uU99O18vj5cH29evpYTE+N8nNQVcxH368dngy6Mw2HWGg9P31XbFRJREcLwbl7JxUmMCK1QAJcULOZw/+Ju2Za6XhCDkNdax3FOeW79GVzDzP8PSWUXRNfu7CbodR8MnYsiH/Lh9Xj89XJlhuwJ09yJmzEBMboAESbCCG+Zxw6OUdQ6IC675iERDRqKtiEhYAiLSXiL8IyQCDujh3cmAEiIVOtS+EhOkkzVnP3D86etXX/6468gsAZiQHJXIXZGo7bsMCAnTODr9bLxMkrqNiztfD6/X+pmPDGM6Iu6A1pAB63EMiT03oHsCoHiBJo8I4JW6FOX6UjOjlF9dWxFRgd1V1OdjzOlUffNIQBTRAFoAYGcPEJar17t7nRKY9brdav1MByJZqhLNBtLJoRJhnk6bIjjYV7W8+Xy3d1f/JD3/fx3v5cikcoAt7HX3daEHOgGnXP2Hg44zfNibd0v+AHy6fYwFFDrYTnlZIGYiSJ7Wa9p/29bO/bx9YgTbN2Wfh4Tnwaabw8hBGYZvXffg9CYl+04RRmLZdEOS6/L+p5HGMBcy/E0lDxYd8ISQOMwVYs/+9N//v/9D/8B3IARsYLy04cnntKJA5OPo2hbCXL+XMCHwmAWaqHOoIYQPBze/PEf/flP/+SfXS7rr//+H60rCXXzCHTil/evCDEngGSX1rKkcRqGUsDht7/5fUQQEwAQkxCWkrV23zprBKAiqqsDoAUaFi6CbBoUYNRD/Kz7xXdjN7Zr/2S8gKWBEyi4dUO3HluD4+0r7L4uT89Vl23PDIebk2MCh7/9x7959/DOABzIsWEqzWKceCw0iGzndVdtWfZ9BQqMXkiEUiAGUetQ2cIYHBn9Elcg65Zv5dX5+QLwXaaSy7FI/vJH95BqG1UyQUBf9622kgYgpENC7uHIwihD35fPvEVDzCIFWAIZRB02z3LzBeYZBlJ3Gcaq1oXKfLAAJ/d6zSFIRryjq6cbylN1xNYhFx6nyCWUou+hV05purn3bx/mebq5uaGczLs196Bn30vY9fH744uXlCcL3vXcbAuAYZ5MwVs4Zk4JnCDo5vBiTHOvD107qUHE5eGjg/W62udpDLyLTONN77mbspCBJRlGIVstkXhUq8qSVD0NmUg0OMtx5G7gDkZBaDRiRmRmYjDmog4iN1kSMZtp7ev1unvdi0F5cdNRySs2Jk2KmYmJGIhcm0BAkGHKwsheewUaKISic4BTVG2GdF6vnz5+mg5HBjfoDkEkyGRm1mpVm0pp0XZUs3V3UH1+PH/48Pi0rO3azLWhgwWpOUGAd9AAHwEyRgBCAxl9OA4ypCHajUj0bhCduZC7QRCNxKdWeShDBKjrtu97rfMEBGFGKWXOvK5nySfuxtY61I4IxJhCz9//frq/d0OV5KB2mlf0gySMzXkM4vf78+u/+snO/vhffveqzPdT3srDZTn0rQW0LmihygjoSek0l23k/en5mw9Lf/V6HEYmUe8KkTKNyGB+ounQ8uVJF618iuFlgZEt9Lw+r7rcHe/GSYBb3/Zkgg55or7V3mw4lXEmQGym664GvG/t8WkdBykFMiOQL/s5Qo639199/dPnD9/n9WksPwFdJANZcIALmcI83VBQPV9dbuhwsssORpmkamcqx7vX/+Kf/9HLn/7kb//z3/7yF790A2VxcOYUBsxwmGfIOOXDw9KVYkKc59kQ976/e3zoBIBKxB4gQgWnum6wGtSaiffP9IDAAumAcosjKhqHausYG9q7dsFDYnCzXm3BrOaIyeu6K0Iu4/m8j3lKMlZdLxd93jUjv7wrMsJl237/q1/8/Ff/YEBuK5omyjHHdaljHlJOI6TT8dASkMC22cenpdpq3okTOEBIRkxZVuscIWqdaW04Yibju8OrL9++YsTm26tX84uXp20/X/cdPRGiglEmzzRIRo+97QotS4oozIxg4CjE5OSABBCtg2ODkJx6W/fn93v1hLDqBjJgmhwESKKwehvIKWq1SKev4vQiorKaSXEXrG62MW1GDjxDjTevvthLkWmIZhDIeRCeJPR3//Q3337z6z+7+atXr169+/RNq63gRO7kvfaWaAocQwIcXh7eDDI9Xr7d2yU5ISKgBUNtlcw9AjGEels+teWcygETqVZ27JcNAlCyBbBCMvRAa5YTJgTKdJxzeUZMp0iE3kmEgbMkZHLylFJQQmOMCHJkWFonMgom3Xt/Tkk4DRGhtnuoUCbMBEYYDO5AwMncgSIYADIEgaA1gvCRsys64LL06RDUET1lAgHYwYE7Sauq68WR24rV+nJusVyXj88fPi69N7do48BeO1v6oz/6s08PH5/O7zvsq23m/mqYb6bXtWvqepKUS/bUG9AeIDSEgYQDCAYwAYkzQwCp91ZrIpYCICtaZhQM51DRtU/TuE8ISHOLeT6K+9ZrztyBCDQMu1EpEgNve+faMmdmeeo1/eztjPLdX//yRZapHCXwES+9WZiIQJgCp4bADhPy8Hpal/79u+/m0/3t7f14COnQa2CJseReDYigFK5rfV8vH56m12n84qSDNNRP54dcZT4Nh+OMLSDDKMgO6ODrpSHxcS4cuaWO4cJdYXnqwTYyZLZBUoKUKP/oB19+H0wan+bvW/OO0fcdb27GQbB4ysWWmsHR9opTjeS2cRqH8f7m9sWLFy9ymv6X/+X/9eF374bCkDACJRAcLfz+5gaEEyZHuj5cZ6F5kGEQyvj820/bsiNzJqCAYJrmkcz3dWsYyEIQpBEeqcMJ+Ugy1kCMJkhhmOJ5vVYJLkGYNl2gIGBE7MFoZMG4XqJXn27HurSn5/N1rUeZ8kzjvSDq03b533/xd+t24SQGmAAZxdWYkQGKSeSkrhZMxqdhGL+6++3736s+ZnQBwSgOzcKyAJgXTMLmgtoa4Ho384TEQjeH480B1+unvZ977QMPBqAeSTLVHlYXD8TIgmqdgIDEgoWJImMXo9qhC2RxlsyA1raOTyvnhIJpvuGUW1szOYJblITm1MGKB7a6+XYlpi4kRO3hffhuHJmZXNTDo5U5E4u6IcRS1WWQVJYPnz5+8+3NzRscDsE7bDtNQ8rp+vET1zSWG0HZdcOuQFy3h6fL70MABI2MYQjyti/cXZkAURzIRyKHaHVdjRVRM53AAMiQsLU1IJTR3bjuPMzuwr0PXISEMAVRJGAuAsDMREIkEZDzkGnYl5VSXPat7o4xMgalUIPPZjlCRMgZhQFbXxfrwTyNM1jPTOrhgIBMTuo1goBHCXEx4w4KDaj1OlLtFmDJzDtIaAcwcFkqYOmP54f3D7+x4HX3relnYZ6x/+s//8vtuuZ8c3f34vff59+9w7WedV1DEzIQ2mko05BYoLN5a9mPCkzmwR4Aam7dIALBGRyjcZBYl3FIlM3EQiH34HBy2WtjKWbABE5cIH+8fro53ZNRXeti23TIfrm2fhCEvm7V/TBhSYOBW+/3f/VTeTE8/od/uN/HiSINtk647nvbu4ISOAQSIEYQ4DyPc4IW/buH3974fDwcRbK4ezMnnVKBcPZ8wFzbYf3NZXuuetun1wPNUtX06dK3/XY8jgUcmVJBtdzQmC0ZDSRCSj0wkqeUhqvvi/W6xRJahD5dP92+uBmGzAlpBO27r7Fiek4lA+WOSn277FvdgQlSTuUmHY/TeINAOaX3H77/5t13fWnHU26mZs6OguAQKdHtzQ0TCMvS1cCPRHdTGZKse3v/+NTdhVMCNu+YqAzD3jal2qyJsLoJQnE80XygJOEGXUAwwhN92p6usJdDcUwN9dE/ePkUZqCYmdYQ3/j8cb25Paxbe3x87Ms2IOWRhxMlMe1hi/3ln/3rv/7r//h4+ejExMWI17qnhCBg3Dt0YFLrHdKAkcf06vXN+3dX8whWhBpkATAwimQUGGXgLe8fdkJc+sMLng7jy/lGAvddl7YvzKkxRufiIkCMpsBUcimczKCSAZohA2jvao0BSJxl9Eg4mCKIMDjScBwOk4Sruq17r6tbK4eDCJmZGjBO6VSUBwXV5+Xp08f7N69K8uXp0/b0/Om796+/+OHtFy8jVY2mEGhpXbbzspUjQse2PvduA+X5dFN7yxje9tr2kgaQYby9rZfn2q4zs7tedGUmAEIgBmq6EDlBsKCCsmQ0BCAU1m7EbmZEGQnNVcLMAYUUwBnCa9RW4gYo78g5I7B3XAQkYYJAiNE5GxohJKGmttsuIhrt6fyo5vwZWRlu0THI3dhHDjd2YIdEOU1d97avgZS4EDO5BgzoLinUe9hOId0aREWvcwEKVndFDCDrCrFFcyCutbYAVlWNZoMBYbYyCDlk5x//6GcvDvc+3SEPXe2Hb76Eau8+2Olw0uaGe/U1CzsDM0xcuhm4stUWvZKgD4WLm1MSjWgRSWjfmwfmYUaeP9Pxbqe76NwbCudoyau2ooRCj+erRj8V6OoZ4LmrtvTyizegRgZDynIa+rbuW+M05OMc4PJq/un/49/84n/929P751tIhdJhHFZcSs9L7S4BqoYhgAYWHCBQCl7O1839QOlGRiyjm6tVQhgpDD0L3vCry7pc7Hze1nQznF6f0igbaN0upS/DMN7evhhyFkJXlWe3BWIuNDCiA3cEmylpJ8LcrK/Nu/Wlfcweu4oM93GpGfBytch2O44DJwy0YZKJUW5CRzbkBM2ibuu+rxGWypgVl/VqBOAoEG7uFPPpRkoBtFHKu4+PlIkKjcchCK7Xdl5ayQUxQMGAS5kDBluvumwT595agJPZDQ6nKAJIgCmxqgPSt+fnj7hbzoIBGJflUv2ScQvlqJGEiaR1cwBVv1yeL5etUMZDSaeMsl/7UlcYy3Q63A3p4PU9s93e3j88LuRqHCyZUri2oFIwt27NVNZ6I6ONLx+fPrAYYk2FifKRMwJesVqEb9ech9p2SgVyBlJTcCaAMqRXnIduQDkAnBlHTM65M4qwtuqKXRUpuSkLA4OkBEhIIQmRyl6v8VmDaHuBsUGg6ZSCEFZboSeAQjiqVWZGmQxFGAv5p6cPj7a//vGP57svyeX5+RmpkV1H0g1iN2hWK2gDT2Zu+3i6+aP//l/l2xdpPrhejDtCIyTksiwLAIApE3XAkMQgrB6A5ADuhAnRwZozIBEhELO5e0CwCJugo6e1nxmjA6mDcGIgtlB0EHBhYG61CVCRvJEHuDsnQsIaDklGiOZewwUBmejp0+Ny3kEwcCUmCgHdyIrBEIAR2uoGxYRmwTRNQ2+h+6a9yTgDCkaEdQIMC+0qRA6MUtxxXX0aRo3NQgEcIkWYC3RoQIpNMXxIQGCBbI7uYmgSWMohILlispjCzdrLm/F3j4bAZkI4DkCAvEPT1gNCZOrSergHq0aDKEQBoG7dTACQeG+t9sZtT7MCoRCNLNu+pVQkgpF5PMwl0Horcy4GbttVbULMU6mSunVGM43LdZ0zPX58PI7z+Xx+8eJFMurIn4r+0f/8Fw//9Te//v/98tbSgYfTJLhrEV/W607REypAuHEYBwwAOI874WW/9NiL1ZKOag0YUsmOGMEQPXsa66mAblo/PX0qL6fTq/F0d3SF88O6nt+9uD/pYZjGmxzRdqh6oVpIIsC4CHFBQiuYcwJ33FRbWwE4z3/4Z3+h20qtX1oP4ZIpE2DENApwqS5+3sJqQ9emXdXBDZGAJJVSzE0BgwCcraLNxxlIhjGb+77VMnM5FGf2iMeHJ+0mWRzVa3hAmabebbm2XhUoXAMpxpDRSCCYGdzdoxG+v56fqMcpd+2E0lq91mfjBWpAY3dlhkR43VcUenp+vFwX9JRvp8PLU2JrW73Ua0ljSrTvNeU5ySCoj8+Xa2soLogWBt2KCEqR5sAQkoOckF/df9E33fZHYeLmxAqQPJiYbetkgGlblvDObtPu0NdFBQQiedrWljlhDh6kpFJAIAiaXR+XcGUcKGWwAFLDoJQMOwYE6Nqr0GTgFI6MW1vLFYsQ9aqkhEAebb1SARozwmf3NQMxoLdec+EMaX9+nk7z4eX925JA17rvQ7jWWGoz4ZynV4eXGgCAA87zi9QQDWgYTjzcebukzHuzMpRuKzmYAbNEBAElZo8gQQ4gl+6dmKlwCouuHm5hBgBAroGAFhUJhJAtfdbQj4lEmALV+ma7FC5Tubt5Ke8+sAdTEqHPsxtIrXYGB6uaZQDkWrWtZ0lohlWbp6xNj5IzYgtTMPTEGKrh0B226hjBWcitt7oCZWANNgciSomTQkWmwATC1WJpmsdEvVN0J2oO3jui7tEX6jlS3a8F+qbR0dUDsA15Oh2HzyYOMg9TQJynuxPdPm1XRSB0CUkdkIiMEFxtdybiQhojOKJHRCq5tn2QkYG6hZm5qQhygq4A6BZq3hW65Hlmtb4tOE9hOqUhkHirY8p5ygTVepUmmJhOuVSlPeYyp2m8HYdtvZ5rd0gA/K5b+Zc/nV7O3//Hv1se+ys8HIv08ETgMC5aF63mgAAWZhDgO/HAhb2ntTbtzwMQIdc1uAyGMQ5UKEEj2Lj0sqNWrc9r29/X+y9fZjl00MdlW7YlyWW+O058GCBq3XqHgiXWCFpwJuIMDo6EOSkYGrhDt6B8DLZhAk6FI1y1mrm3vva1XqWDCyBhEghXYENHZozPWaEIVTNrHihjHsognMZhftxWHkoRvz0ckKnt++PjA6cUjGAAEfNUsvh2WbxVArce0LAIzyEJCMPUQhC6xEPdP9pqJ2rcmAQ8tvZoZcllsL5r9EDrrUfv2v3x/VPOsa+Xw3yTilPfda+9b1noeCx1vW5bHE634+VuXZ8LH76ay6fH91qt8j4KeY/etzlikFKZLUIw5WG4f/Xqt799IgB2ZYqtWcCIgKmJGTXrUoa7aRoGBYIWHEgIO6NxgqAVlakNarqCuVFvgUqlTCCZiKxt+7ZbeM4U6BQokLu2NZ5Z0ufERdf98fJ4W0BckQMCE5Tez2vbxuE+81A7RDJAUvfheLzLPwbH1vW67OM8jTenekFvvtT1vF6vy2bj7WgyxGgAIoLgvW+RONFU0vjm9Vfr+rC0Z4im7epmZBQhShphGNQDKUkNEIcwM3T3MI0CyE7m7uhClPKg2rs2w4aeTZFk8DAAdUckBAWmSGbW697rppt5Z0jsgXGFTupJERU64A6gZrthbLUDIZIUPga78XX3y4i34IjeAjCiA/wfESjve+8EhA1CxsnMbb9yChIy5s+iFsGhqhIgOxL483Z+Mb5yxYhu2AM3jwgQazu59UgPe90BjYDBMIG1/ZAPBcgVKdgFO5obUMBxPlyWMwgEBgYSEQcBoKdw1FUlRyUOZypQvLujm3c04wBh6q0xUpZiCuEMkAETkaGHhBu0CIXzZWWWdd0T4IGkqlEionxI7jVYMifBmRjhdnrRAbBdobsTZU6Iqdq2XCvfHb7+v//V81//+vlX67QtmfE0TOBwk+fVarfaQqtba9Z0dzYCYvDmsNvmyISICLhdkQmCSEZKTJFRfXTyGC/qOvhjfZRjnm7HeUwy0rbt3757kPz8aj5wHkWwfd4y1AUaQ9q4jJ24mwkwRLOOE2fbuhIggy1b8161m2P0DYC6KyY2U4HEKMGD6sbYxDNmRnAwIgQdeFU73BxSnnLB3X153sbEU5KC1ME+Xp67WpEBwqsFEJxuXnQkvX4ffaMQaD4HT5YJxcPdEQB3jmuvn3yBGwYyNlq3CmjX/izDErGramLGgq0zcIEWj+v3R58k5XFI0yQgPY8cW2aiXrsZrMt+SPP96WWm4Qevvp6wfJ9v3z8/ntv7XbtRk0BIaSgZBXvDiJCUEjszIwBBATNg5ESJGGgLrZNMX719Gdwa7EmIaiQ4SpkI12YEBknIDRhzbbWqB6Y8nCDTMGHbmpqyyM3N4fMgRAHQHRElJxHxblVt0z2H9z2CipoxAwoCpL77+vsPr758a0jbtpbpiJgbF2AGqwCm+76oltNBxlMNYON5wkD6dL389jfviPI4T8xDnodm/cWXXyGhh7kAIOuu0SroJjSYIwAgICgSIgS17oFoAWwa4M0DgasgujGCBAxQaldCPp3u9+3a247j8KM//mff/fzXfXl207UzifSw6I4KYgBhCKm7PsezdGAUcGYWCK5YicC9bxbRIHN2M4AtJ24QJGSwVT0GJo09EBDFuK0aycEphbP31pRkGIXJ6g6WKBUgEkEIzpQjPFh7D2+6LdcSgiCAKjAFW/cOBr1DeEOQhkQYYTkakA+nl68d3XUzF8lEFBDe9n1ONiVZohbMLBOM4noho2t0REQRVdDPkaEEmdPejWHX3ApRdL3qpoWDOQIDMKdJvSI3VJK27ClJLrk1n4dT1YVQzIIpHs/Px5uZCT49f/rxj/7gfN373jjlfb2qQ5kYx+l+OkXX3hcC6rtDbB3w5r/7g6e7D/X9efnufbn2mYYxhFk6ZkikZpSx4b5pbapEFF2bswA3a81bULD6tnG3PdimVIZ04jIXTCVUMS5bc12XdavnlG6n6f40JGheL2uN1VOyxHlhzomoqjTo645EjHmJyByF0u6bJSguUHsARsDAaORIxdy5e1ctlEJNMUIInNEYAnq04PDumcSEhzKWcUb0cRzq3gkQw4+nI1EkpeV5KyEpsIJKJBRWpvW6bHvrbtx9RJ5ECjA4ukN3d6Trvj/Y2WfuaB62txpACqRCCGuKxSVMEiXH1sG9tlXbGuU0pvlwM00zBBiihEPrupuqqalhh2KZ8iEx7buV4f5lPtBTudZ3HnUYjyTeWUsaMs91q6Vtw0DHMdWlBUoHyOQ5Uy6lCPeIVzfjUPowURmTqeUMuC8BoGiQCG3qTqCo1TwICg+lBKCFtwYS2JE6YDcXEYZwAEQfswQzWEjmMAUna111iGBJEsgYhZgQtff6/uPT7d1d102DxyHtUTn6AKxYNPbt/Mxbm29PIBMOI+ab+cZz9/lFCwRJlPPQMQBwGg+McL0+hoe2ZlXBKMHEyjtSSApEjjA3IEKIcOUgJwPvmcVUicRRFbBAal3TmHvry/nsrp/P93ff/iasZaI9jDJXdE6gGQF4OJ6OXbb151uYMoFHY2AJ8iWCAgAItaN1TCChTgCIGtYJApw64M4rSQmEFu4kRX3Hay9JQFo/qxMZdN2lwFAObmqtA7MHoDfCwcPNuiFAd+zC7CTqEFBZuocIlYzX3Wu1Wqlkc+sATdeD0MvbF5mTgSG4Nvdwd5UU969e2g6/fPe7XnCrDXxF2hDMA1kpQpHz58/QVPfIyjgnEiA0CjHsDSKJBSWUQn4YMkVoVwCW481M5h/eX9NwvD49Benh7hU0nyiGQ5JUVt9h5vP6yIa389SaHk/Hddm6mVH06gQ9FelBzZZSpnIYMlD+0cvli8PxT9+0X3y3/eJjWStFzJILcHcDB4458Xy4PzDaupyv11b37s3UeW9VAwK0WQtQ7n0YjkNmzqwIo6UDzrVua6+L9rNty7kPh5RvqYwQEJuC97qFQeaEORWcSVKjddvATVMCtqOQie6DFWYLghAPHyV3w2tdKJAlWVDBMNBuykTorIgdoINRSWZoUmgQwmEckjBdmiOSCJdUyH1d9+fLenO62y/X3T2nLMOoDLFt0DwZZ+cT5qyfdcqGaJW0hj351QdyMgetoS0ipdTakwybyn7ZK0tyxmSoe+ven/tq31keTscxg/Rqqq0vy86Mdbu0zhpqtdvqdXXioSvu3brT1m3iGfPt2nfboQ6ehdgIaMA87GYROIxD31YmD3bBEQAbBA+cbtI0ACPf3t5rNMaqLYAGxiG5IGSm3HuHwABPU2YGAlNrhoQtuiuM5FZ2QvEGAepB6EMid5j5cP/yjS7r0/vfYK9KFghhnLEQ5BAKc2h8fVhhx+P9je7r5h5TMYoiwTJOSdbL+Zvf/rfjfLp/86XmmdLY0FOC48uTYhChm3l0kVHK0azTOGQyp9J3gspm3gCdOdwQyD3SkB3QwdzcwwKQicAbCgETEqj2QDEC29eUy+lwXLat9j3UL48fOBITeKDrbpgHTplL1b2ubV2fDc4aTjSYmyK0EAHgMOEkyFt1b+iZWm+ZGQQdFIHZh1AzXpkAgB3Uoje/b7izdYTu4UbBsEH3HQe3dR6Suyq02jsHZTAOQnIE6ARI5oBdHbMFOlI4uiR03Vpnq8ElqTbuNjH/+PWb2+EAgcS0YkMYTSmoUXL09OMvf/x82b6tizvEuh9PUgmtEZugBykx55yRSZuuJG4WrSXA12vdSf0wHgRFzYHCofXoiqFB8nx9Is0vXr8GiO28OeciZVmvLJAItTdr7VCm6/Xx9vaOcqHAS11goDxM+7Zsl/P9zVRdyzB4d6eyq9WiMmTZrmWcTv/6n9c/Wf3D49Ovv798uty2GDAPIshEcyYJrXqp9aEt120FDqcwJjBX9aow5TQO8zhmBkXsZUyk0Vs5OtxRerb65B7SbevLxnuu03HOhzkTqTYPXM3FencUGgKJGFR3h2wawkzAkMHAMTEqmwMbJcwhIIC7Kg3sWyVEt+BAAhLInZN7CAWQk+TCacAxOrVWiWieDsIJrL9/fOjum+5r24lEhWUY2Dpse1KkLjPkpCDE5hDAyrBAf+qbFZYhuTULWLeGzFvfVD8OvGzXFZygFAqMjuumLfau9f36UdKv7v7Fnz2vsJmEns/Xx5zG5XFFy06CKK11VT6Mt6rSdG1dQSFME44vDqfH88N3v73cv8x0V1NOCFS9JyTOg4wHAoAwZIFwhPBcNLzGcBznrvtnCMjAo+WBaTylcVdvbSOE6TCoe8XGgdY0kDglMyXQaRjEqVbjCPMYB3YNawphKrzXPVRb3fTxYSojIAIn5BqwROjl8lRkGvLtct7V43A3rn1HOM3Dscembpnl+elal2UifH736yGPe++rOdHQwzVi2/vLl2+Ob++kZKCcJe3a217rUi0ECAmjizt4NkiIPJUOSMxgnQgsHIwwSCEgwFU5gN2MunmgVbTteT+jDGBG3kmwITSkRAmdKQgDtDVrDcPUt9a7M1nvGSRxq94dT4wOaPtu67oz5aoBgIjs6IaMweRBRhKZTYhSROl96+khwMcYwcI8UNBDGRAdzHr3KGnIkrq3jubaKXAYh121swOrOndHqI2BgcBCmYIEum7PUcsuNzFexeYkb29foaNDIBI5RQQ6JB6YowOl6fDy1Zvf/fYXnBhjzBWMugdZFtmDDKzbrlgmnoYvgE2tnS+fbqZ7wzKkdMijAFugcMqZLaoHtu7iHdfrotHm40icmFJf9xo9Am5lgL4NeVx7BckoLJCE5KlfXWI/P58ON7pe6/W6WRuPTIHX84MgH+YBC8ZYEGXZaxW7/YMXb97e2NP66effPH+8lMt2wmFottX2sK3X2jc2v8nBHh18dUi67c9rbwlPh+l+5jlIJCL23qKBJ0c2xxPIwXGvttd22dWL17rtZ5U55ZGHMgELh5z7Q9g+wEDuuZRqIUTFAJ82zTkG9vDMDOreiTApupgWdHVFiITUkDoioCdABnJUtD7kU/N8KmlIdN0f2FTykMdUvWPE4+NV93Zue2YeQnoIc7Lz2ZaWPBWEAkxggeAYDr16XW2zQjGkJXp3r2aILJS6rZ2Wy/q8NphSzpHd6seHa2s2Cr74wUkOQyb5//zN/3o6jX/5L//Ffrmui37a3h8kJSllvOkt1vXTWGZm2Tczc2gNG3aU+TgeJlm36z/+7af7e37z4/3L1zblCRjMLIhoylo1ESuEuAokhMAsivD6B18iMjjP+TiUOVG+LvvZNosY0iAFDyXX2oAM3ZACGblwry0nGQphDzbR6uM8pOLLdVuuzaPLSNfzB93XDq1Wn6YRKM7XpemyrOvDp3fa97evfvT1j19y4u8/fPfxIq9ff8WhDGBQFHdzvXv75Xy6Ox0OEOvy6QNqOz+c1TMKMkvvvpXzeDzQAN537avHujw+7uezajU3JjQET+AC4aCukYfEuddKAYZobikx9cKBaCFgRNwponZEa+FMhVCO9y/q9bHXq4WKjBAEJJlBhEg8oKmtj8/va/MYsrqWlFMUwzVcI6L7/nhxNy1eASeO4tE0wkUQycHDe0B2cO17o6GhFD6DcnNtGEEQHlWJAVA3p1g1dUyCwlSKSMQarW9798xgtlybMCG7OBJyMw9EAHRJm63c+dp3HdjR3x5fTocbEjY1UGfIjNzDEmGYhcqOcb2ujMgQBYrp3jgCPWkTLk4AQIBoTVe5pjQmGYY8MQNQOt7fOWdJAzcoyGSoKuBaUkgZ58RDo2YBIOwYkK3WFaPsQBEooEOh7hzBl4cnxmQKWON2GLtH97i27fDiJDIgr6/e3ELPgO0Sl+GY25MHyjiNkCgOCTnNfzl9/N03y6++Xy9m52dm2tWDgFOmjAXBmu2+77t3wbjPq/uzbvf5lSBHj6BEqQl/JvpDg8rGM5bRhjG8qu5ddYC+1Y3jOm44SZk4p5HB0XpT62tTIJaxI44M0sEdiNwCIIqTB0ZgmDZGDxdJGB4jxVpj1VZNxRAckACNURIJCXOrzpJOQx6zKtvjh7VuCmqYaBqOuHk6DgTw/nxBdXZIwNkgEMEUDIKisvlIgNDRFtPWK5NlISn9vH/X+dGg9bbt7Bmn6jvJ+sXd8XQYgMLFt1Xv7sthkm193xWGnJfLViGCtK5137uhg2RiDtdt2U039z7O42Ec8wBQ9tuXJqM8X6vZt29fvqRSMNgtMAE5cG+BQJkDAJTALd+exsPLJGMumQx6xXO7IoAI51EoUYt2aT1M1QwDO2gihto5PM9jpIyhtq5ImEpKYAwc0MdxJgZtH0O1AsTd/VaGIdPN7Z0BvwL8UfzJ9Xz2tTfbl9qe1mc7w3XpP/jx19s8BBGhR+ssw+n1i5z8w/unx/VqGjZIrVfKt0nK8TTygMyG3mu7mm+CuC/Pbd+UIQlbNwAzB6AIUAIDNcOVhN3dVN0NjIcymSTrHcB7cBjOx1MXPmRuzw9tV5i8zAfbq4ChekRAMpZEeezhvde2tncPzx4lgiJ8g1U7cCTwBVhad1MSPgLaACNIal7BjECJhnBUs61uruoea6wKUetiShmUB6ytISGJNEc3QOaSk+tmujKDpSMnYMhbrWzVmFaDEbXDXvBQkkc0Zu3d6776aNq7GlejZO12zClSiSTCF9fmMYBJmGsAIzg+r5fvz2exw8GS56pldVOBGRHRIRxiIDNFJwBnbTQdhjR6ESDL6kFtx9oBb8c5gKwhA4KrJBDI2GoXGGUsT8t5CMHF0u1Qw8Kbu5hjKGgLB7s8Pr/58gsVvKzXPE+e0rq2Gal2K9MEvqFAD8o2cGSSHQWZ8npdpmnaMq5bffX2Dd6e2qf96Ze/g6dtDCYuW2yBhO4MVMA9edEM5ovH3z59s0S8Pd3dlsNAJcyJnR3cIMvgbtFdLB8Lnsi25lX7xlYlsHlsrV7dEk95GHNOyUGBIq59WwEUAUmYeSBG9pQSYU9qDCFprGjhmo0LhkQkD/XorrVrogkpQ04pMSaqtXo1GDFlEXMEvD4+9nWjBCPl4gRCPSHtC65rtjh5yh4R0RnCEZie2tIlmkTD2NT2fRNEJkJmA7vYuUYl9MyYM1z3Bff+o7dvNl0ezk/jMFEKczf0DfS7p0fqninnWT5viJe2PC77wLT2y+OHS3QVcqLAxNMgeaAG9t3T9y9+lDAcKSLqh6fvTvcvCg9oiIzTMEDKe9eofYwNsjEP9y9eTtMEkdSlbzVCnaiMA6RQVPNFTdVgzCMZVm0hARhmmkVESkKsdVdth+nUe+2ElUmmAYG2dmb2HggxwgFcsjIDkqM36IJlur/3oYESWczH29bMiblMykXtGm1jtSyDef3226fL03X3pJ9fQNZf3b/Z6zLcno63x3k6SJnIBzPRvgF8zkLl1togBSzMGoNDqLtBWEMIoxBiBGZClK1Vt5oIAwiZFZDydHs6tf2sBinx+eG9Q0s8USC4BFC3nofDJGPUdul2wb61iojulnMGRg8ABTdA5w/vz9dLJ55KTjnbcJBxPkD35OhmQKwOl+enNBcy3PuG4Asse0cBoQUDCQkJMUkKB8qDdq2FMsfBmFKBnixLQoFN3fagtEU4YNiqRoHGJNqVjDkSoWpfo+MPvvjqy1dvLepiAS4cIhAWPqbU990kwC5aNTF64q6KsClGAitgQIMGBHRvO1FCnDKBAFkzhSCU3ZsZDM7GSkSSkyMqOAKKJCGEsZTm/vTx8urtcFtyPT+9vL3ZuuZMLmWr+zRPHG05X1+9vO/dP52f5+M0UJkg5PaQsR3T6JEuyzVBnPdHmuaB522tZcoAeL2sRAgEUyopeUST21FeIb25uf7TN/3bT7iZoETiDsaMYyS/qgyHvVtbLufz098/nx/fvPny5Zu3p1clRJwkSW+KCghICEpoEINHCRp4Gl3XVvcWmwANGVLfZG1D3qxNU0mZjjkbxq6171cGrlWIgVJnAUQIiqLSQB0Nuruim2vTEphSMuTwQXmMiNu5JE7bckbiIng4Db3uuuv+dE5EkTjn0s1pKCKyfnooTQ9WJChC8bPyLWLxfROHhM7RQ7spgBFlYQZmyNx8XtpzYQMP73R9vELoq3t8+nhhGg/DUJj27QrEy25jmTTW8/ZchAsLcXven+seMszm2nb9zEAwgXIY83CIlEy5biVnSqzbzoADjXLeL6TX23SLic06lcxDiadd91VgN4oXNydG22rvKyTi4zwZk6L30IimvRMJAQBHFgZgg0BVcTpORyHWtnuv8zTkIVnD2rugDAV8b125a7TO2TOIB0O3Fq2bmgcAexnydJigY55PN3jat50Qi2jbHnXftvNzCW5YO0TVzpQwT6bdtX31wz9+/YOvrusTAjGJY5hv4brvZ201Z+nMPRipEMuYJNZn6IgkiMGERuDuDgi9uyC4QYSYk7oIhCTndL68f37+QKEi5G6SuQc1r4LCIpASSUrTQRlCCabh3bePj5cHykdgQRBrAQQOhpAf3p/ff/dMAc2qBjp6EvlnP/1pnnNzI4jWa7dOELY7WGLACE2IrXqNJjkTIIIDBQQQBVYA9a35UsDGo9blLt3v2jGqkVqvGRxwUOcau0MWZJa56aU3zzpkzckHJPvBV3843785X9+DQHbCagWSSQAwkm99kRyYzakvcUZEqmhQRs7oWC06BgclCCEwqsgDk5An9izGdX1GHnIqETwAJmR1hyzq3t2FGbfWWCQX+/j48Tge1g2cg7L0ptfLcrw/DKXQEcOgdT/evQpUrzsrhbfnfQOAp+fzcHOaxoTOhwLPW08sZSglYe/tdJpqa7bv83zaBwhI6Apq08vbWsjfTP2XH66/eiomfBhQCBJkHLd9C8Sbu5s84PX56qDvtg/PeH159+oWT7cqIyUPrwgsxJ2aKZIguweQ4h3PobH22GpX3toUe2+ZBaxdkpn3MaU8l1wGJlG11rtbB84sHAQeBhCNajIGY+aUczFbGjgAGJOLTEOZmaVz3xuTHMrgBJ7l8nj27pwycIBQjciZo/l+2QaTiUoHt8BwM/dVdUnuozhqUzNXAktMCMicJA8y+u3x7vnjN4u1etn9vfYOwy1/3HbFHCQGrdYACGIYMc35SMMx7Zfl/BG47rQCpWkorZ6bkjg5AhCz5JSG6uxKl+fLgIJmVocIx6T7roOMb++++OL+Kyq+Xh6u/aIIMA+Xxx6tzQnAh7q5G07jPJSUE9W2gWsayB1AxJUwkYeGGsHnMpkEOAubaq+NUIZUtHU0GTKn5GB9N7pubt3IQ+EZq6+Qat2i7bZj+CCDHtwQgJzdXc0SJ7d6PT+JlGaVhuQh6tJ7Z8EOqIAuiUo+3NwyMRNor6G4X3Rd340l7eveqpLvg4iZ5jxo65A6ccOckIoAqoaFYhIKIEEmUCeDHqgNyYNQo0vIkDNg1mwY5mjRKQ1IlYwACQTn6TjACA336n31vpjhpE5Ibq0BAGN2w+tDrRefhtGsg4EqB/LzuT/3/avpnrX13npsKFgADZQ4AmBpBaqiufGOZgwjOARFixYcZjFFSY28wvXp+iTnJ3kuKVXdlZzJjz4iq0qs7VnYhUqiZd/r87IRD9opdr+5KW9vbw+Ru5V1vzgZBw05E7M2g1TCaq3xcF6XZsRYPZhYwR1iEO7eMVQjgTGBpJKZMkWGkNN8o60HADkJZjeUcegJ9n0Pc3QPB1nr9bLU483N4ZSf1oosQ54yIbK3arXvt3i8Pp+3dR/mo2sf8iEROgINvKhtaqc8kvbr+ihBKQ0o9OI496ZWK9E4QnIyF06dtmUN7ZL5erncHm+qGdY6jbn8xR9Nr168+/kv9uv11XxERL8ZFCsBpB4oAxUiRAj7uD99PO9fffXDK+i8022Mkw7gYa5ZRu7mumJOTlQJSGMMPmpeKa6ugaal92ySSCT3bvv+HGDDOJZhPAwjM3ZBRAh3UXCiMc0JwRMbYWLMKoecW/XH1UMsZ56S9KedO8okUMZqCt0ePz13AyppyAyATjFIulxXutoYpVsYKiE01CvaJrGXMGpu3syDkMKFJZeZUyp5HAf44auvf/v+10/rZguiApAMgadczr22tnw6wyg5jwcCja51Wyae7uT++HJ8vn7guqp3FwRL3kKDznUb5/HuMBSh7v27bz70df/hmy8vy/Uffv6bw81pHMoPXn71sx/8bBhfmnphaGW5bt89PH08w5WmEpAxjSgFCG9uTkHotgdlAB0KDYchPF/2q3mE99Y7AwKEG9beA10vTxmlt56ZuToh8pSB1H1rvZ6rdfdwDmvOPmPJKWMgpIFm9siUhcmZgxAjADIAI0HW8O5KggqgAWgdBQwSBR6Vo4Z2crWHj58UFxby2J8fP7Sl0eElGNnWgbFbTYX29SkAuzEgmgUBkQiP7G1v0EGVWNwi3DCUEriTR07C6IDoHm5IWzcgLEMyCKQDC4gQpQzgELsAsW3L84csYIAOAR6CCQKo4vXS6xkKjcZR0YOEgdS3UuLdtx+3ff36y9fDOE2Y1LbSudq1xd4jQZoCU6sKiSUIYAMkhKRA3WBr654gO3AzAu+9/d4+ZivgouQI26cqjSkVKTlhfL6UnzyoE0gz8khQ53FQbw9PHyCgUN577ZGbXw55AiOHkBBzf3x4vDw9T6eSPZcAVVMPTJLVg7IiGQCBsIt4EsrAOeW07rsEElGjoGAhIQR0995BGJlk055Lkky76lhSYGuhydzMQtL96/u6qSvO800ZZd/UrJGHNUsjEAuSEMphKNvH39PxlnlQrZI6WK9tLd7WGpoDOY1prHU5nE7r3oBzmmbb63R7vHz6NDLf/dFXL/7wq29+9RtYr+1y9S0Ox1OTvV0W8xSSGSjcBUkNfvHtb7/+wz+wl6f+2F8929jzQAUIHXzHTADZuwNhA2LraMnySdPIrmB7rd01ptST45idrLW2bbt1E8Ey52GUF+PAgxhLa8BCFOG+bbbbdWndLmtNt68Q57HMprDVSjRIIknEFtfr/vD0TCi91VmOXlu+GYORLvWm54zUFYjAQHfQPUElQEKwUGv0+YmYiNIkJY3T7VAGxO02398eXyjue2BfW6hi54myp9wjao+1Nskava179X5OLyYh19bPD0tvvTVcYrPtnCyjUjef9BhTPs6wXs/7dX/56pZLAOW3X/BP3/7opz/9SR6G4tTCkejp6UNdnw7j9MXdm8NeDme8tgpVJXSYUtXFKZhUdSWIcTgBo5qHbtmUovTAzhGBzkg5BUVThQAEasrbvo0FKSqGEdTee+2IpYxjwuBuZtYb4jgfwJwAEbCxYXgLSCDwWZ8NaIFApOhEABYKSgiMZIgAWDgjoRTZrXXbjGyW3Pct9t1tX/oqEVvfswV4Bc80jb1Xjz3YP/eidmVXYyHGcFIHJIaAbu5mLMACKZrf3kzLtnb1bhpUtakZATMCC43ImUOZmQd0CRWUaSqXY7JgDg1CYgNw7dbdyC0swrNnCxQChsQD9mX99aezgPzxH/8gWwAkJyzGHSGAB6pWvHG+xyPAttrZcRgVGxBhOqZA34RKT9aVGcqI2i0qVHPOac5zJLOgsjZ0q2MKxoBwaOLqVi1T/Mkf/6jF3rQlGIQpSwpkDNyuazjLNHUmqHHII5Cuux1csxyyJAUHJXfsyBKUEBJTRiLnRHkqc858XboTYGETwIoFDOs1OQKLMTYMIaIXx7u17oNIRl7bfns/goUnIQfqDQicIEV4bdF7KtM8TVZT7YYYSbihEUCep77toDiOo689KU0lqVsxmqRYoYfHj/PxYAajzKtenr5/OB0POaUrw/vrE9J4OB1e/+ynUZf6/PzN3/6ioCQSVy9ayb2ieQQlipGOp+Pw8i7dvdSX7duHS/7Y5nWfo6RGCSdyd+YeLimsdQJixkwsDtHohmXvfQ29uvbsMCEkyCWHhEZfHvvzp+fvbFF2ZhFgQkdCInI3Qm+uQ75JNA2cBzBovG+eDyVNkpwg4PJ0JUAwy1kYSQO8JNirXLYMGO6O5IwXg8bcBYzRTGtvSaiwVA8sx1zyPN+M41iK9N0Ll7c3P3hc3kOBWo2xf+z6Y7eROcY0TOXp0/P1ejWwfY3d6/bxe1hs2Z8XvaKyASsgVU0IDm6my+Py7uOn+ebEELfH41pr9vL1F3/6r//sNYOEdYeu2J+fP/zD3/3Dti5/+Rd/XuYDam1re3Mod2S1XU0rWFPrLuJICUmjISE5bpc1GiiwMzBJ8QhJXYAoam0JmJOHByICixO0vUJQGDDnqQyICOrNom1GKDRA733MhQFVu4PlwmjYrCFASgnCwdHdASJ2Z4iUPsctgkMQMDyCwdEzU7VOzbz2S98SDhHWbAPMIFDdAhqCcSdJ3NFUQxzFAj8zZ6Mjs2O2CAsRGRPyF198sWzr5fFDuNXlgu4cUNXKxEIJ0cyBmYL2juh1P+Qjcg5Qd+sW337zqJpS4ohAgKq+dAvClKh7VwcAIYNBUD1a0+Aohe9ubgpG2N6iKzCBsOfi1j+zhEH2hoDsadRIipKEMJSCQooGFiVjXt1TecGCUBcNIGEIzSkbAgS0Lmq9IYfNvj+7o/P4Zz/7g7f3d+ZljVWtW+ehZFdlHnMW6L7vWxmSZ7s/Hu+ej0/7soWFt+IsnMwp0B29aQAAW2eeJilFhuN8G8Bdu0okQHZwCjxwx1BwIgELiZDjMC/nxcI9GaKEwsOnD6/vX217C2/Pz9e3X7x9Pp/dfToeOcWnTx+aHg5lOj8/y2m67Nfb0+3aVjUvjEVMXC9mG9vx1REue8Zk5BOJT8fWtqB6ONweb8Z+WQGDRKb5SGg39680OmJAHvIpXv3pH377y2986zevfmDXBzjvEGoJjWCayssffX188WoYpiZII0w/PX767v2nT+v9R79rA8WIiE7V0XMpYdR1ZwCyQEPHPGCKtU1Uqvba6gbWisLIMg55FMSTga6+iPdam6OFuRAzZSZKxYrcBpZjGQuX636hTJQQiCNAt7pdFgqQhGNOyaFNA4vAugwNIAgIFWnx2gfxhE7m4dY6AXpgdc/jCHkcxjROecgkLIodFb58+eqfvpGUKU/qFdduv/z+45//7Cf68B1g55EeH1Zs8vxY+7lmKAkYmDAOHoAagehNwsAIFc28taa6BhD0697qOpXTP//hncRoYBh+fvjub375d99/+N6a/9Vf/tXdzX3v2vv287/5ry/v3/z4Z3+S3tAwCkBMJfcQV0+MPaBtm/rStSfKQMiJxpTz5+WNuLYVM0awuYX5OCUk6V2JuHWCGNHDO0W33vbqRpLH4wFwQWcPzTk7YjZMjh5ICAGm2iPC3NFZSBIzUmysCYHBEMJVmnrhxJH6rhAQfVs3ne9fYF0HLq1nHjiJdkNgVm1aw23kcc6CBbMQqm4GO0RnL8KDu3r0cT4I5u+//a5FCzQADwOEiIDhkFtdkAoJBUa0EHD0qBY5Uw5o6AEde/3w+MSUwzAVbF33rYdFIAAaAmXOroQULdQ8iDMFZfH5hpy2wJAoim2DPXMQood06wywgwZ58iKRUi4WO3ozlKu7LbU0RaRdjOeSZMhcMjFnkyDvxkCqPqDvFHt3wh5FdLU/+Or+j378Q3PEgBSh2B27Wko8gKJTDMfS1j26BsDxMH7x4mZ730DmTbfwGACYCgaRWk5DBitCiWhIk3CZymSmRDLLUDAntyAWGfa2OkCEEgGgiyJ+vJyHw8wU3fvt/WGWqS/qQL3C3f3rx2Upc9n2mhkcKR/GrbeBh3Eeni/bFy9ejDlVjw+XB09cbg6L6XgqWM2vvSjuGIwAGkMZYuNg/v79h+mQx/ngIj366eYuYQPwcZBWm4dwmdO9vJLUt3bz4qY8PiyX7cvb08p2XZY5+P7lmzRPaFbGbA6e+Pb1fXt5v3+1/frdY/70dKhwi2Xw0T1X3gEZiIS49QbRAHViYacSTjBUj21py97b1X0KKpEy5DQxp8AwtEBHxzA1x733fnNrY7aSrUPdayqcByCA8Ngv6/ZwTUhlSAWTe5QyuBFebUC+UFOMK2JL4Kkbe3M1cxIgEEMAQSpSxuF0OKbUSy7uglI33w83L3/85R/++vv3mWT3/z9Lf9Zsy5JdZ2JjNu4esdbazTn33A6ZyEyAIAGQIIslsUSTVCaTmUy/W82DrMyKRViRIIg2E5nIvO1p9t6riXD32ehh50O8x4NbhPv0Mb7PB8o//eq7n799d1pP718+bFc7v0dcMruWWYJjFwtPgsL3SRnJ1XgLu3mUSJ4UEpsOhOO6zdsN+PjX63/5xb/888v28f2Pv/z1r/5WamttOb19+OqrnwixSF4uP7y502Wtb+7fRY1yH3JfI0GTcpvILCJhPseQWmpbmhRhEJxYo+rcr0IojOHTkgqxUFp2C0eSW1ZtniMzREqtQIwohGp3y+E1GNX7yMKc7A5mDUcmRXgpUlUjAAg4DW6ZBULm7kEpSNrHtM1gWZpMF9R6m2nn57u6wBWeMVyEUkrEpER4xG1va0syozA1p5jOhQhpHIns024zdpt7cjgXJh7hx2WxaRJZqWRImpEmSCyPD5//0U+//MkP3/5tH+nKnOV6PoOTJKHimZZAcBF0Dmf1YWEGMYvoNoKIwQk6ntrxQN0u3dXSIy9QpEmmlgDSdut3vDjFnC7WL+Ps6Tn9NiVD+TYuKbtly+yts5KWQzns5Yh6Wmthm2lzD8BFOaL4GXQSkZ+9e6yZW7AyNCQxQUFUIlGUnbLnXhrPzYBZSvvy8589b/60XVcOgie7SYLWGJg8uRahen84Fq1N61LLjx+fMlJZCxYKWVpTz2qwgEWgEEF0zvnFuy9voxfmVnjuY5jZbW+HtbSyzegR83z+4rN3t32WUo+Pd7e+7W53D/dZJktJZDCW+1OaucmhtfRZtJ0/PedasJSDJwum38A0u11eLp+9/ZnPMvv2cv54OK7L6d4dkf5qoXVky6rHIx+Pk+XdZ19/8TZn5YdV7bpXI16Xjt6EgqTPEkRUmpvpm/vlbik/yfHx+sP78/K0LXGsojKLgAOhrRLCfUZGOKWQuerkR9cT2Ri8D58tqGLX7DQjIvQ1LuwMG8R0OGSp92W590J9avBSFhEuVdPo/OG6+EJCJbVAewaa6rnjFjPGEJuUkznrK9TbpmeClImZG5WlSF0WOhSGL+UAIHKSaKW6T//p1//qH7/9z2ie5OtNthv/L3/9d//X//AXc1+ef/1yu2iCToMREYMTsJwgY0iSjbBwBNw8wpMtOyUoqsI8UBvX+p/+83/667//q8MJqHu5W9kP2/nyb/7sj2s9+bjGvPj55SdfvqXj8enD9+tDezx8Fm7TfHa7q/VwXAO0bz1s3j0ciXO6RUYlUkrfxxwOEoYvFLFKCQkfYLjZNBQ5ZpgIJxMlCgtHOc+t1MPSFgb6bvO1LOe7ecKlUKv6OtB3ShJmozkiNFDBSApuZh7DAx7ZRaVVDSOzVlrNMS7X2aim3pJW9yyaRlTaIUZGEIXBMqVkUmSAWfVOq8z+QpbEfNm2VpfIV7EJq7ITgYgZzlrWKtH6vLj1Aedx++Gbv09U1kJ1lm6RtuNm/VMnSl99QlIXUs8It5wRadDQJe91LfQA1uE+M5e1eB9uI5I7JZgRSpEzRvIRbDZHXseYdokojrk7Z7nddA71fe7pjAZIxAx4YBfixOCGuu53x3Y8FNYsy7EQdnkhXvyWf/DZZz//+R+TRJgxV3eDJzwpWQsMo1KIJ1ijqG1myFrrv/jFL95//Pjh6RMxIqZvyS7cypa3yIVxd9AHEVUVwJFOxEkyGWHjUE/hGHYrdTWjDCWuao6irDzOH67r11+zR/T9+PhQW7l+fFqOX5IlIseYlZen99fHN1Ucn16e62M7HY/vnz7cPRw385uhGch4eF9W6cMOh5UqAfx0eX/3diGpxFE4P//si0pLt/1QBHdvgZsU2j1yhEU+rgeF7X0EpbGwMDKX2iRyn12Pi71sNbFIpeiL1qRlZvbY0cRSlqLUaFcqX5wuL7f3P7y0T+c3We581Vc2HmnyimAnUE62HLOnsnAtlmuwWY9eD8w7X2/pNxpVKDlY0s38dC+ttaUl89M2yqHOsFVPKuXy/pN96s1aImpK2fN2ECe+O9vsduPYxXeBlRCGBxkAEYEUSlLUw/F4PJVWQqhKdXNImWEUgEnh8eXdFz9582f/9OEvuaJNqkKfrtvf/Pfv8mOefzQtR4vYMpFWnRLGlShkjmFMgFru7NDJk/z1jl0SmBbCY0atEWRPn94bN1Ze94jL9ac/+flPv/wDCwfj1//0D7/9+189vPvsj/7089O705s3zW2E5Bjj7nB3f1oi5u22X68X4eX3+CObw6yza0aTVss69v10vCucm+3D+hy3btN4NamKGyM8QVlJSoYLR42kve8DW1jPOB4OxYxj55zMALmTxyvsjQumRCSIZkqYGixhNOe0wWVlrZDp3vvzbV3uy1Hff/ubtQnVkh7gQCOj5NBM1qI5Z5LZPqSl1uaTmFswOMvd3efP9l3AOWnMmQQh4SAhalVmD4awyiTfxwungBsRBLlgfvjnv7x/9/WpHLZ9u1zidsmgNsXDbboyVSkcboosPOvhaIxWuAVKLNBlqBvNgG1uns09PGSDYgZYokr2eX0Zn7YrRvYRnkpX9Asw0zIkQ8CVZJ8jiFQJFD1R3SsKzYie10teyuAcK1zvRvm6zirU4s0fvOk539QHzpvNyCxJwcLCGnDmBNcmZY7ZpOyIGVFgrdK7z94dlzefzh8vzzeJLYoDs4WuUU9LJYZWLU232zUTKSVVjSBSS6vdk3Q1c9YCR8ZQT+/z9ng6qmfsezith+Pufnna3h4fum/T7baNPnB3X7SK9dFEP3/8nEHsXht9/PR0d7g/VJK7EsgQ3qGZ2Qr5iKYClfNutR0aOAqBNo9bK7Mn1nJYSBx8GbsULWvbxq5NsLDvYFWK5EhVmdNpzlZbefvwfN0osy7a3VhKMZF2QI6iFYiLDzo1kiqPj8ef/MH26eOn7z89f7wtm97v8kYOL/tNiiZMufL0gAWnw0WZQjSWcDpMuqdTZ5o8ORCcYv5c4vnhISmFGSPSk4TB4KI25u3TVTwKixcG85Zu64FMsLtTvJB5VRYC3HwOgKSsVNhyYeha47RgWYoU5wFySk4oSyJjDkgcKOa//eO/uNx+ex3fPBdbtH22fPH+Hz+hy6muHhRRZg4KIUqDWLpQB9wtIkXIKRLESGvKSWnubBRJ15eb5ywFm1E5+4n5hW+fn774s7/4U9UQ8uu5K9//q//4//zJT/7wsDbQWB8RNW57BFpZ66fnT5mGjISzTq0JFJglwtPMogqSyXn05K2Pft1izJihhyOvKzkaE4UlYIGdZwNpkJCmsxPSMbethy+VllJZpRvBWQKqEl49jCVF1zC26dEp05lDKpVj6e4rl2JJ9rzbFrGI1IeHR/GFkqadEVmkzBgRiZjEEdVBLQk94Q6qS6axv2zzBXQ/lRaXjNxgEHlIKUn7hHEFS/qkaUYmynCyCJEsTdOjqqqwe9626/cffvf++UNbT3Xeduwas6ZGrMqMddEUzayQ8DHFsURRSJLN7p4z2QCPsDHHHOHkYrQPndms+otcbnOcDU51MrxEJmkaMigARyqh2GDoEEq4DSbiYKctw8AnOhrHeJno++FUf/L5fdO4vlxo17vjKjrNoS4e5ghwFlXHvCF0kdknKAA3C9YQlYeHtdAxp539hjpnZKQkzVKcwlkYxLe5e0rRRb1mzlKKgJAdponiQZKu5Bp98oGfni/rYbXM6/WMHeuyHtuyjX326+n0Jp0i5uX89Pbhi4gMsFtWLU/btZ0ObqQsr5raUlll3W/7WnP0nbNeLs+fvX345sePYzvX0xphLeM8hm/93d19hlsiie9P95iTiUYai6SIomrgBFm0Xhwicr+sKmWyng4kJGE951iKPO+uaxXO2S0KBdHD6Y4T09PY6U3l5W39Ev08/+nT+cPldiexDDuSzIwgOqzHzbqbE8AZDnZEIm0kVKoUn1mLpM9yz3GQ46IHT72MQ0hJdbiy2PPenwdPKIKcAtxXQSv+3J8v12vsdL8wBRGFvwpCGASIlFap4nQ6ZGHRSszKVNMAdUmkJwUIgCa20/L41cMfPW/2h18X+ePDL//uefcslaG5m08PAhJ+U2RnmhzqJZJiGI2OkVyUkmdghIoIscMdKZCxhTtR4rYbL2ir/um//pMv3n5OMRjQpf7p//g/saxh+9h6aROh83K2rnVp3juCPMTnQAoJpk1xj/2WOYlJuVZoxMz08+159H2/jYVawSJ0sHiVLGVEoGhSergFDnpgWgW8LIcjaeXqtCUgXEp4bfq68C0dnISKKLdtgoKCICnKpbAKTfKCfGUVZHeiotoYUtoBaDXyev1+DAShp6noAoluO0+hILQiFUkwLypChxv6dr2RM2Emo3HAEcmDLRBzvykva2vuYMi2dY7UUgF2EJeaQF3V3G3uoz99eP/tviNocS8lsQIWYzJnQFkIllWklgyxINs74EKF0NZyN3waT5KtUQ43kHJv2/Pld98+f3jqcxCFrJqR7gQgLTwYNh0EBkk6wyOnQjV5i6SUA4SzG2LY5MIzNG+I/UpvHipXm5YHfpl9qU00MziSGmsDz0Qqg7gP80gpHJlzTPKsi8Cud2uZX725et+v5yiSOrWI0CosDKYkYk64gprUZIgg0MOGcs6QiLKQULLel/by/Gn0PB6Otzmo8rjcVhBAhqzLCs8v3n52ub0wBwJj7Kfj3fXWa1pNtj3uHx95jPPTdTJfMN49PqxEY8a+jfWg2co+7fHujjL4oLaPy/dP8u5xGo1pxj7B+/l8vx5UCOFNyProbpQOUuJy9lGO9/u+3fbrKqckrW2VxDa7qBatj0e6erd0WYoQJJk8LMMRpDikXtjqV5/lVy50t3249R+2/O7y7up18KJKlo3rEGPG676YkgdFkLMpA8RJNPd1jsMqQhVcExbeqsJcVi7Jt/e3vAWCSYkpzby3Rdzm9fkqt7hr3HjvY0bsHMrEXIyg7bDUppVM404XYRkUhEo0EjxsC+4EEkESIpiJf/LlH93NpLb/8KsPn37zscpBKLo70hXGmdNtJiuxJNhbYFMRRgyHBXmESkE4UgRsGZGRExwa4CLswR9f+r/7/A9/8Yd/NhII9r4XrfscGL0U1loNl29//PZ+XZJ9Pd31tN1mWlQuSJ8WL89PtRYwwDws9fcup77vuw/DdEGdpEFUJA/LGpRH1gmkiobVJDAyqdWlaFGp7imioOaZ4SHJSqxaRswY6Wlz2Nhdi66VRciye0oIk3LxKExuw6KfX25rOamQx5C1lKm5bVLdKwURtgTlJEomdlZRIN134iDO3lX5wPUY0o9U3Z8jTYwIZKIpwTTEHbB9bCIrgpApwunm4AQoUdZlYsBtjj597vvstzhnJi9SdEMvtQjXqrVWSmm8LN1HwcqTId59B3VO9jEBQwwgKqSAbi/49jfPHz5cn6ZzFHFKcJ8zmDpNBjOxmb3O2Nwi0oomESbCOYMhMITVTJ9gNzLSSkkcJjfTUhdhctsUVEgWKhZuoBHc9AgzRGRmdLiHkTsli5p57FF0oZCFbz/98rPvf4jz1kUXH5SVWRbiKqLRt0gjyqSA03pXpMIGEERSGPlKfdWX3KbG48NnESEAddw9vCX3OUa7O20plfS67xDVyv022Af326kV84DE+brfbL8/Nb47LitkHqdHn2NZ7qSKqgSRMbixzb5fumU9nt5a+sN9mx43y37eHk9L+gXlYG7KIloPZcKmhrK0c7/1/QWUKNijr7LGdJ+jCJFID2NgUfGK2+YtxTITSGZhTbN0JfDIWVXe0LK/LZfDsX397vl8jR83fd7Wsx1NVEWCJIun8YjZCNyRMwliaJ02of5wUCmNK00a02sRt3k4nPrYn354Jg8pMjCp0Gwxl9LGuG1P45C8yG2/mU+LoCqVREWXdanteChNNKQhY9nmJpKJpVNq0fTdrDc0pTDCzKQcb+7vxsd2Pven344Fp14NnjLawcaI7swIqcZRYtetGUKNk9Mr0kSSHEHpSh7BDspK5AiPmEKUnAtqWPz5v/63i1SKcd3MBm6xufCb+7dJk9W32xPzdp2V1n45v0QkpW/zts84HUsppz6vt30QMTMPN1Tse4xxu922jCJOq4hTLYfKKy3rEmk8fCkyM5g5QCaUIbv73idZT0+zLWDIljZJeW49ncijX3fP1IWXu8pKMPPw1sismEGW1nOf+96fz/N6ZSulNIIAAmnCbVx7muc+67JyFkRazeBQYcQEl6QMJvOsy0JpHBZFzIOg4TOZGKTB4OFBRMfwkT6jk3ApWkhSiB2T0oQOS1sy47o/PV8uv/72/fPGNrlxVkkRr62uuhpxJIZQq+sdHYh2EOXiblZKi6gz3dxG7MN3zexXev/PTy/v9z6WfWgEIZMo1EGiTu6M9IiREaLqKg6uPjX3aFWtaHKSb+Q0SiSJhko4BQ82kZyk3/54yahleYhcJsY2vLWiWYtlCC67EQshJDImhk+T4QBLCeLbGPeokTV4O7VV3v78d9990908MT24EhW1SHdirSQtgCpehX0CRI7iIUQBJLRqES10r0WlyOXpU9FyXOrot330nLUtRyCFc5x9u/TTwx3VZYqPOe7qiZN4mdftKlk37qVbuhuDTuv51omddT1oni+fqPOqh5OWVN7wpCSMUmsKMd/p4VR4kBc6yINvQzTcy/Ba7kvQVG1j37mU48MxScDKhRzJLEQTcApSLgw6ng515tj2FJFaxxyDqBZGXaRVdfjIutZj5Id5OX1xfPz6ze3psr+/jKfdr/2wmWxjpaatehprwxgFmpLwyFOTU22lLsa2zSxQyklYjuvlu+sModKdSUMIHGsrqdFHcDGd/XKOzKJVOBctC5GspdUWVY2ssTC3AfKypI/Ce2EGEydXOkZSUmTugkqhonl3vH/+8ePzd7tQNM89AtQRNHmdPIlRzKdR5+VKVoIlEemJxuRSUqh2HxNuJSJRSTgYlKBkYpn93//7v/jpT3/WfRPQp6en3/zzr96+efzFz/5F0UG+1Xkj5ax3Mzg6rt9eiYzZruOS++V2aF//7BfEq81+fv6UHsux7i8Wc4e9iDy2w5GXLHp8c/cWhYw2kGfv/TZLkQEj1gzx7mPbtIhWTqQGMYOlaELaQ0YMxe02xhiAlsrH49oWkLisRzdnziI0LeZlh2Jst+vlY2ZKUmch8rXUhUuYn3Ex8EqLpwb1CINFckJXdQRzZBaHMSnpqbTz9sRpTCW5cHiqmQ12YaWRAE0lkmyWe3gUOkZshEmg9MrrgZa2zz63/PHT9Vf/eNkHHarev/my3a8PrTVdmBYS9NFHjptvl/HiFAmzGJFKo1pkgohTuYT6+2+ef/1XZz/f2lovaTNd3CSJuSZoYkBRMyuX2wyAwz0GN9GWNJQnMRvvYcxsTHBaWYwCqUmgUIZb5Mun81/+1d/9X/7D/xBgFtoyjyqDh5ABCEhGJ6gZRsRE2h5EQiVLKYRyve31CHCz3E9F3h7vfnd56vXytryNDqZpEchEMkheR+9ei133zGpMIbtOpCyurBSEgG9z0fp4/6iFwOmi7e6RWCX3ftlSH9vdYd+2OTc9nEamR459l9oc+fH6spzWu6XBldIohgba6W7bPyZ2k3Uux/CbYuKyn+4PrmQWC8v1djm0B6ltXra1VZtm+0XB8zaW9T4KTQQoy6JcHouUggizfbrU4oX2jJjzVBY91WGxLvLpfCMRWoqysIcQT58pBOGZTimeid2LLKdTyfQpfHp335c2f4Z+OfftRh87vd/WndhYgk5aAHKEFxqLqCoTATQzudVUaSo88/z0stVMyhVRQ/eKuXLGvG1jokzbqUQtwuBWWimlMTUirdVLlULNhkXPTDNvJAqNyL7NYCJikkCmlATIKLa8jPn8zT992qZSSvoWZAMBpZnhES0jBRp0jHShCEWmI1gLJatxcBCEjZQInFvMmVFFGmUNK+vdH/3Zv44gJH79/a/+29/8b19//cX9u7fP13982sbj6bhIZS63QVaVldAN4I4g0lIe991/97v30ojmdj3vRMu+3YY5ixYaKns93JfWpB5GGDxAuZ9HWBdoOEYEkccMCTq1IlW7T5WkCHNkgIUmYl43H5GZpXIulVS4iS6ikshopd62aya1Y3NzN7tce5lsBEsXkIqyNMlGccE+RNsOV2GHE0h1ybCxm7YmkcyMgFjueY64OE+HVJXQIqzTbwmPEpFFCMjgnEhTTqT0cS0tGRLg0EpVI4ZdX7bzp1/+6u9vfXv8/POf/eQPiB9RuWYmYFmz1tb25r1EufRP19sWMZiLpxjC6Zo5ySQH/fYf3//6N9vtNlspPouhG1khKikJhApRg0OJw52IkoKRhYt5Zmal0WUJZ4iIFo5Uot1iComiJXOU7tcIeMr//td/9/Xnj3/yh3+U7ha5s5AETAIMRpKDfBsbi/LAMjQKe6SNWWXNRebYtDSV5OJ3d3d1PxuFmy1tEZLbvs0MqSrCIkpFPB1kSiRcE4ByEpORru00bvs4b4u0dblDCSAQhsTDoT1f+P6zh/AIjpvfiBJP3lD1cOw0A32er49lvV03asuh6NOnJ4IftbRGI/S6jaB+uj9NW/vL7X5ZZ5i05m69h7BKYx957ftdW828tAZRmHTxVpuAL5eL1NbIzcMJkvnY1vP5vC7VWUEHGeFMu83FcWxtMJBBkXBKVimH6+WlPRypFCEQqGMfMaWJTetipRcmvvTL3bvT2aR+9c5ftpfLNl+2fOlLd7YgN8OU+8+wamm6j+wcreiW83Ro4X4bt3mnoObDbef9oLZUucWwbaBrlbosK6pwoaLZpBZdSSA6HcoEIvcIuBYuqjaCiEQYr95szX3c0oTYoB8tP7z/9PLjD7dQ5j3FpVWZABBirmBSxnRCRhixKwMAi7oBnk5muakIwNMnGBopiRoohiDO0pbDid1++5u/+f/+5f/vX/3FT9/94WHL9600o/zofn7xx9Nnxy++1JzMUOK59wSVpbEh0z09BpNLStQF0LpkIy/w6YkRYbcbiA5r2/uZWRxFUpKJAOEiUpOQZiIhxZXd+xQSlWpG+z7ShkVyq6VyUgARblJUlVS430afM0GtLkRMhNi7DyPVV71jgSzS3LL79vL0HhYB9Li1wwE0CIIZAU/invVUSjKciIPFhkgSSSSZTSpr0apEllvACgmSbWYmkZYwJ4CEps0kEV3a/WOofPr48fzx+x9/eC/L+uf/9udvP/8Dn8OHJaUQORMg4QMZZipY0Z9evntCdlkWSFuPq2iMqe9/vPzmH98/fRjIwuxpPCmMkFIGJ1SQzgKypJBkGAJKSty4KvPFPcEqlDEqhSPolfAVmcJgBPtEh7VaJM3rIE356//yd1+9+2pZhJmnzdqUtcSMpFmbTIeUBe4EiwrANCWTPYO1cCDNM2WE1crv7u+/f7lVqZFjc+5hZv7u8HBgkUzhJJsuzEwBJw9iJuQC0Y/Xl6Ll9ObkhPN+8du+lrLUw/npOsWWQ7nuthLbdTsK3t099tsYt1Gyrcf6/sMPPONuOV52mxSGYbA3p+PS+HZ7LryYpm8vOjpDAij3x48vT0tZI2SpUmQ9Xy7Hw6EVfP/DN2/ffbUNp4phjogmypEFUllKimXc5iwQmilFq5KTjuE3G6XWw+HgvoHKfru1ViLoet3a6a6th2WpThHh276XolXa7eUSPVcpIAottRzeboB7o2Zc5yH5tNx98bAMQ59P5zNGDz7Qsq4ulTTQc2GvVEhlocsYs9FuxpATa2d4KZbpxFLp8XCwUoZIeFaVtjQI9WGjMgsXkfBpIpFcWCJ8jsEsAiKKdCcSoiCoByVf6mFsF/zzr88TEAxGhjtNIii7wwyESaREAXNGJhi/X4FlWXwPSiOIiAzETkIJGdkShZg5k+bL5en/8//+f83Yf/3DP/30j+++/sm7YRsAy8ncIq33cdeOwj7n3kpb1tVoiEcmWOHxavddSbnVVF2IARL1U7/12rgux8B4eX7erkAJgE/Lo1SR2qq8gvHKNA8X4Rjj5u6cCpE5Y79OQS6HSgykk5tQpvLoHaFVDrPvtntpSxB1C07jiO16HTlmjoDXPPmk3JiUPt0+3a6fmngGKXvaACHdEmAmEqpEyOQRziBVoUoUESAmTyPfKKlpwagzI2KwFCbxlJkRmYrUIuDG0g7Ht7TU27ier88fz88fzvvD2y+pHr3PTJveKZVZuocKr6ozdLfrb775hx+/+7VY05L79cN0Fypv1rvvf8Rvfv0jvEaKzV29aDqXGInokCImwYScs7Ao027DyAUskIyYBFaKJAupkZoDCyWUsixF+wxysGIWiBCNkMQRKRPvv/n093/zy7/4d38yKTYzJzArcirzsdGYtGXtvk+KqV5B6FDWVGcCSaRZxsJFEePtw2niRKbTrMMu43rQE5IoImG1njzCiS3DJRmsAeUgTAUmBLf99nC6n/t+7r29Xcrx+FDXp6f3rR0EMVxOb9eXj7fnfVtOh+Wow+YY3ZnL/dqWk0wbbvve7z57c72cPT1EBzak1vY4ZtSa+3798EKSKMCnuS/LoXvuHjLdRdc3b7md4nJ7sx6S977ny9M1FUWZI1xZSE+Vh9vOI5PYZ5Gy976Hl6bMRCQ28/5wF5QxhnNoQVqfMlSFnZaloujo/Xh/nHNnByBX61KzNu59VznWUoiKCvVxTuXycH/44mTXm03DTbgWMLlSCLvEIktb28vc9P7uNBKR7TbyrkalEWZzlvtTLFJCS2TRqJyqGqTK64YuMSslFzUPtskOTjJzKkKi6YNZRJRC1sU2+VSOV2a6vLeP3744Uo0zfdAr+1/ZSchdBWNahiSKRRfaaWnkhTxyQB0RQUsSVL05exhRBOeAqwgi1eKHX/0aS6Lk/bs7M1IgvEeWEU8sJMzff/xm/fptoayBcHcK8umpr3bF5A72tKzahGutqgKzmDuC/LZd61KexzVvu0io1CaHw+lOVBjwsW9+U9Eg6t3JWUh6+uX2DLS7u3vhEGblxDQbNhLhkU77zZ794nM2pojYI5mI0n3adr0GjMlqOsXLeUPMG6u++NNaLXhSlkIlsnQKoqxCiFRaWNgQazLcpu+eAhXRmpkkSMpAiB4RJbxEdItkEtH03OOV+4eakGW5K61e9vP18mH263W/ubMmS7pFn7FFcBMqnLd5e//p+6dP19v55dZ/3HJv5YGDHMRaqlTqh3/8x5fffndlX5smLNkI6sxOoeLKbFVChMZwT54IymCRyiWuA5JomIgkYjiniGkqD7hEFqaS7pxMJQn7PJVEgDQz6zAyTvzyv/7NH/3BZ6cvP5sAGYrOokEhOh+QbrxtGU4EJwtQSkwrCaQU1RSiqUkUYqxx/3jynWeQRa9VwDSUUCKZy1rm5cZFhOGEGVAIc4aantqhNrllyFqXvUs7TOenDz/erfXuuEaQzdhg2GZg9g1a1+1yvW3X+8d6XBcnCiIfc9teHt+8hfB697hfLsdWabrWw/mylcqL8Bdv3l5e5ezph1JnWltPn7cjxmTS9nC3DTue7l5eLgvn5enp/fl6/9np/u0jUiiJ3NGImcgAlQgfZovS6XiMHJIiomP2rfcIXg7LQ1tYsPtt9M78wJCiAWS+Kk+kBoNBTesa1JbF1uXWwTCStIwqJQldko2Op9MY83nup0JulgwCKmtTcmNihdDhyJVYi9wU06en86qNFCyBWmgTpYDGHqkEFaAhJgchwgUJtFf0LmsAlJHhROLxNOaLjScc90zXzr/5m2/6xQWIQGaK4EYBjxmJpMTwdCJnViKuCBUjipmcYKZdmQBGejAwR2FwiJgHo/epzKk+EZj02Zfti3dHm59qpYBZ2m3uvPHb4+M3H3/88tYPD6eRplYPdLphRnpgBFlCw5gpjIx9tjwR+Pn2kddmHjGuAVWWYTFGdtjdaqerk1zHnMFVWunufUykayJMetphWWtdkbDwglQhs8ElYSyRYeGSFxthwwB2iWQSnubFghJM4UIzqiix3m7RJWUp7EnshRUuKao8k6kTJRAkNOYsal0JWTyQKiDOSBFxn6LV5m3oWaQUFocgAWK3uZKKcDL34NbueT1svtvtPC4vn55efvzh6e709bosw/O8XwdRzBg//ub8/OH906frdplOC4sU49ICMZ3Ct5AAnb7/9vnDD7cIIHcGhUuRZZLvDsBdi4oyCY9cXDyzZ3aKBBVD1cpJmFMBAyuRIqxO9aX0QmWm9lAGSidh4qMDY6TnDAOImQn1+br/1X//5f/85eeBGkE5XIQt7HbpUgmRmhQBUs09u4doy4TntJmNdMeglOIC0iP7lAgzMZAoRHRtN+uHstgEkksGpdZMCmvCTMUTWkT26wjw03kb4XfLUrVufQrTdr4ty/1y93DdLnnbiyw95of33wdIWcqgWtvL2F9oY2EjcjfzaEsrrQawXfsSelpKEYr0EVHXNYFt3zxxpw9l2AhSLfcUY5uHstxsPl/PV8HIfVHC3iFErGlJzEgOd8qUmESaSRnGESk0x7DIda3qPnekGYqGs8rKIYwC5ojBoGNbzefed1YdY65SxCKMnMCghcmFzr1LBHkc1jWZxhhCNbGBUoJaW3pOULLkjA4MEbAKkddarnNcx5SkItLABIxMZnbySXkQstG5CPdhPqMtAbZMIZsghxjASKYhnPvs0344v/xGKuc2ZVA86/e/+aSxum+UloQAiVsiQyRTSJJjciYQKTRpCCJRkoRBHAdJTukuoQNgAczSnVwoKyHTwXJOv787/Q//5md3B0vb5hAnDH+Ni+j5Ot+8++K8P92/qaOrwCSNuRT2GSaJDCQFKGN6CrZ+69fKcmQaPk2kbreXsEinogfmcn3Z5stvwHuylPZQ6kHSAXPvi65V1rv7tRQa3YVRtXDa7JNCmIRhc/a2LFnVOJLFZhRmJmEgAU8mlldAOoQzHaRRDBgzWHEkxsTICEFozXSwMhIA4CylBPWt73V9c6x323ZlgvkgCsBV+Hr5SKyH49v79XjeNyptWU+2XRzdqd69/ZqUe972cbs+X69P2/ff/FDXx3K6u43t5fpytfn+0/M3v/u+P51fK5SZBJGJ6Y7IHULNiDi9y7ffP3361N00ghAykKIKCAUWAIKpGL6koVpKJtKZE5RAZI5kDY7izDs7h7WAkiRNH061oVVgfXP6xdvP//mffrxGEAXBIj0iSGEGi0Brf/vtd//i/ad3b0ipkKgQmY8zro0k0zRcQFuHBVHhEWNkFIiRMwjMOYnkqGpp3ujgoiYKkbYcwrO4PJSF3SFEyMwgTxViiSBHqg7bx96TpNbldDrOaS3t8Xj6+OHHSpxy26UfT8v5fN573j++he+b98PDZy/nvbwMNOJG0Qe8+1jroRFhOR6281bWEzE8HKSZBF72EUR6OL5BJsOcsguGhkvitlWVbb883h9rlb7Ucd6zSJDQNNJyOW+JbMdKxOzdvDvxpBSS11oqBSLAWrOaja0EqqgQaCmbmYGEGIn04EBljQSBqUpW6Q5nDsQ+rml0V6syDd+2sGVZPT3NVYgZDBJWjjDsU4QoOEEU8rql4SL7INCJVBNBMSRdVVg1QMlonJLdRys0IDMiQSqVYqTwnpRC4lZcq+rmN14Lpvax53Uca/v2tx8v0xzFmTPD03vYBDi5BLHKyOn5Si3NpAA7jHVpyiwUzMJeGDGIOFLZdocBTiiEkgQoZQThz//jVw9flLBZ9BBZAiQla5VCenc8fvv97+zH+MXPfupzcx+Fapq7QsoSYeruMAIzVgpAyOcrz4g4IjAAiiDRA/EaQbdtu/gI7sCUPJ/W+4fPvmQuRbgeV6nQCg8T1aWuDLI5NG24TbNhsxya1DLNEZ4xnYXCM51e9aaC5KAAg2eQcHWvkhbZa10VOn0ndpCkp6ZEFgQXxuw9WG+dFVSScN1S1lrUx+AI1szYYCq8bOOc9ImOn8lxudzG1WJdT0WX47osVSj38el5PH+8nj99eL7dvfmJnO7OfZ/eL/3222+++ea79/yqO0sDOUOmz86a1Cj3xnOAwvz8Yd7O7JOCXkXUTMqTCE4E4QCRTHeiAUJymltKsEjvnCRSZZIVCVPyQKYspPCYYrnkQVSdeS52mX3dQNFzdDMeU52CIEEQjbAZ6Zv/7tfffPb2S2eeEdMHK/U+3V0olCmSQ6zn5BT1jEyONb2nsFAVJwkkaHDT0tj81NaQAm0IXlXLa50dsEBwmianSHBNYjaNjMggwEc/Lve75rlf6UZLO+Ycl22LAQ86PHyG7rxWG52vEPd2rB69vd7KlUXqA6e8HuRHWkJYVRr2rc+RrVWRaCqR6HNPTKRDluv1/O7+joKD6zZjacdFK8Y88Lq+PQwFWG7Xp9PDm7I2j2FmQmW73bjScrpjb68XbRkxZ1foNA5VgMMiOJTC4COCpSCgrObw4KS6tCpikV5VCUmZ3YdTFuaFpBTpvo05kqLWMraxeChTaLikzSQWSQlHbnGgRqyu8jLmdFFXlkKcRFA3CWclDyBio3BFDOPCWsowg5AkFanJYe4sTIwbIuEgp4xDW6bF1eK857ffnxE0eRjIQ4Ii+PUBLAFLDhI3D6Rm1khhThZlSnp9DSBI0vIV5BLSyKcCEb57iGj0+NN/+/jmM7qMp5JChQFhUWWqshb4ZX/58PTpj/7gX/ZB03bR4tLmQKKKKOVkmo6JhICQYk6qWavaLoB5TEV1ZmNJJvIAAyiUmQi3Mfs1uC/rkWKdTpk0hmmppDLdM3KO3W0fYxLx8VSkrJG5MJmHsMyZmSksUpQSw7ZUEJWWVIRnklAWJgTDhyMSwSzCWrUU1xkTifTO6QIgC6GCBOTb5VmXSpmFdJ+dIIxBmMf2EJB9jPu2fPXZ57unlKIFEbNf9v363fPz88vzp4+3XR++Sj7s/gTmT8+3v/qvf7ttw1J8JiygnO4lwz2nIjnFwPAe/HIesVF6IAlCQCbncE9lKpIZycLJHl3Dk0swEwurUjoRg4r5Ds6wpRQGdeKsRGQymCyMfSBalJJ9O394dvPx8TJBkrRbpFCTquGRwE6lLt/+09Mf/8lYDkwCVTKKquX14DjJXZiNigOQBDwzbEdxR4YACJmEbE5CmhJz5SbaTNpwCIuIjpiRRFwyXZ0YRagwBzCUUJajtqVlJBLb5epESzmW5bALHVt5+vDx+fmpPb57PLSPL+dybOvdYrfZ7paZtH96OTw+mPD68MZeLjmNC4NLIXXraSGMfus1vN4tNkfv5h51KVVVhJeHt+nIMGm6WzCVbc4VIswmwUifA4tYBCkIzE5pudwfM2E7ZVBRFsB9Hls9325ST7b1FZVLmZJOFr+v74VwkCQltLZ0SkTYrMfjuL2QZ5Ga5HU5gmHDSuG6VEQldgIIqUQWlgqqFBsVE241XhmeNFNQmjgQ57l4ZdYMn5JceJnB5p1pCHE6hQcDBESqyCRPETNoxgKPSCIaRILFkwKaHFknEv0a54tbcOScEcmVHBJW3S3hRUZ4gBTrxHDK1yI6FZrq7JkpQuHFkiT7yOoZGslKpuBB3onHHD//07d/8R9+Xtgk24xg0shUpmSKvPQxdhtE5eMPH8dPe8XBg7wAVAG3meSixKy1UsCv5vA4si4CBHlgJnm6M0uSJ43wLuRkBOB1S3CB6HNXXZZWrPs4G9fGyeCEWN/7GF3IEKjtsJYmSp6OTCXdPCyjaVViOLuZaElWpirpOYfqmmkRr4f0ycTEEiDRBVCSxO/LwSAiIEk2N0oXLsTiL9enhRepB1ApXIk8Z6FYEsGOBGXl4qwkvm3b/ul6vj5dfrzupfOXs93CLeeFpMDyH//un16ettoqIqYJciAlrXpYTCUjpZ2jboZ9G30II0VdVUcikSLI1xxIYGRIreReIzO4ezixQsSJSJQzcm85axZVsRnGLAUeIZmHgUnEjAQVzIMqptMEgiXQaqWM6UbdUpwED+3QWD+8XJ+ePn6hh23uojWballtZhKlyMxEUE0dGc4QYiFQgkkNYdlDXvfNM8wz1MQpI80IqqIRGR6gwqnEPIOYUMjSDJR6Qt28M4uu9cOHH33izeN9I6pk3ad7nB7vTusJnp52PN2dt6cJkGO7zXoQbq22xcxZSU56u9ziEg9vTtftYnNbuGpbNYTEJSf3aFRpVQ/L4aVRd2MuxBKFGuvlsnMta5Hdh/ssi8aIg7Q0zDILv47MPT0PWllqN9/7eeXDbU4qa1uWZFpFW2Sk326dOA53jXJu++7KTAtFILxQHduWRACXevL9VgpqPUQGg4xiDqusrZTz2MTCzEOgoMKqDhCXVE5MQtbiwyukWDLpmV47L2Q8FGEzjUoBCEwMJFNEcCCwwITplgGAGc0khLwgzMtMYBxL2+IO3ma+qI45ynYziz0og0LAlBlJgzgwBgAmyyFJAaCgMH5vjkYKiycyGZzpXYmcyrRkMwUyCME57d0X7X/+v/3ibj3YzJguZJsN98zeA4E0JncKm/t1yPP55d3bNZI0G9tgyVwIEpU4iuxuZAZH5CCVkdPkGtQ1iwaYk4jGsNdkBjicXovwHjS2ru+/39+c7jPT02k9EJTSt3mlJIISt6VJuJ0/bXqoZSnkxqkarVRRgk3zMIsIn5hDaIAjJIHJqOCEOEJYWibCPfK1vBiEzAhkciDDXAdRIS4AJGVJ5emW04k2bES91QWRmc4itJShzIjry9Pt6f3E/v7pU/d1+H0fM9yHm9ajgH77D//98sP3tYiZj1HkmiZhO8KAmpOzWmfwoOhXnwYCApSoHHzUwtlBNEIjuCUV1JEZ6ZmRxKtLkZgTmIxCQRYp4OAWHmMgIY2TimfJiCwOSPXqnWLC13VdgkMmu73mIUwLN2JC8ZRjaaeVn8N/+PbXbz/7eWOBzeucWqJM35tILhycgUBQpqQRN07IFK6VwsOOJJ6FBErWMtlTiJIwlYK0WiYhRCJzZqSBCqiQ0lJftqvO6bUst/Oe29649ZzrcvDRr/suTc/7XMsybmMpstuwsZ9KgVAioorWqu/eZOSxyHZ9Psrh07Q95tK33K7rob3/8X1Z7w4Pd97kue/OtKwnM5/DCR5ErbZh7ohxdTBK5Z42XJVViTOsqfo2wKzJHDnDpYqbzeTeN1Fph7tb76jl5r0KKafNcBAJ5wQFYTglPR7vLDOZpC6GvNzOlLmeTpHQUpRaxHw5Px2OByJOidF7kZJMlOzJM1mkFkhxKCPEmN15CSKCF4Eyiki+al0ZQwLMJYlnBlFq4QgiAli0DnZ4JIlzKpf0RLgxg0TADrDA0zKHEp3K2mO1ja33GL2EJDxIYxBH5aRBnckVocEetc8UZVYpqiolctc0IU6haSbBISVzRsIiE2nWOYsNaov+n/7vf/zmixPlMsck6ongBAUhySlMyBLkdFqX6/n6zz/+9u1nf8ZMmQZi4XBExJzhkRgZkdR4xeA5Z2L0MSNWhgizskaCOd1j5mRYQImJASVKptvl0p+fkyhrOd7vo/eFmJmUazudjksh8mHjGna9XWlwE4FtRVst5BmO4R5hsd9uaQliJ0oIvx6phAJJVChAYBJqr99QFJXptpNPZJTCgYODE5RGE06kUnV4H2FNiKR1cKK3IuvjSbjGnjnHdnne7Pz9p8s2mXV9ue45hlNASlG1fn36+J1wNUjmmMOJYOB9zz6Cdjg7GCp0iTGNKBXEyIhMD9AEF+020kkCwqRFPXLOBIjBDK5uVemWngAwQa9hWCWuIYTkwAyOSclQinid7O6IfttdKxe717p5DvEAqjKP4NdlhblqTedx/TTmT+ryWEkWnx7TyOecPpxQKxeQqObIAXImxcwYGczMJYU9ZEwiy1KVSVQLQ0GkQmbEUlzUPJiyEITZ0n2fQqK3fntc3qzanm8vCSnl92UlKUWL9iFalsv1ZlFEyuX29Obzz37743dvltNnp9PT5RzjJqW2harg6rPWei+tdDscD6TyxcPb4Lpvu9YV5YiYfe6lLPf3x/H8CSnh87Jfkmiav/vi7e18RpLIYgZV4j3CrcNrwULqNjWJGMNiG5OV6kLpWVScKOEiAsuVNTxDKCQOx7uS7snKynPO6VOgTZe7BlMQ0jqppEhErOtJXu9UVJbHewEy/Vjr1l1aidl7JrloaRaTkMrKicxUqUkwIsv0IBCJNGA4pUgmyDxIIpgxU5K4Ckt6IDlBUCEmBgmnRAJMHju0umd6VuaFlpsht0kdFCwxCosTgTQkM0ERQoEOGBZWlUpFBKlJA8LMkkACQoOMCOlwn8ScDgbSM8L/6I8+/9kvfvby8h55hZFKMkNJPT3ZiTPhAS5Ep5X3vl+3bTorYN6ViwW5ZQZnuM2eDs2SVIOBMB+EVGYWUk12A3MqkSfcM5gQwhl9H60tj6fj++fnMS4qjMm3fou7mW3NDK1LOd2BFRG9Dyp1aeQeSWXMzWd3e4V1WoygSR4WPohgAIlyFo8kilfPzYwg5UUVMYbtmguHcxpJhod4IzTnDDYBBZBEUqVA+nYJXjNuiFrL8Xh/r8vJe0a/7fun6/X5H/75++vUuzdfn2+7+TzUdZsUyQR++fSpW1A9ZJ9pLmFOkuBkNriNrIFZ6BkWTEQ1yMIgmVXY0j3hDkMRYiUbLIjpE0heZGWLXUiUWyRPn5SF2WeoljRhrkwW2AAPLfvEIuNQvDCl00YC5ffbTV+DwIaSqsqcbD5Z9xXlWNeEJ0BFObvbeWqzMGGJQmKJYQQdMUWJWRhlEv++y+8SZJBg0jle4QscHlQOmSUJVVRfa0yZCPHgzDzw60ROxt5thnrN29jKoXz2+Zvb0421nrdzEZ7bnhcwl3Z/avVh7P2y74e3j0Ltsd4fRPq23a8rM7+M/Ztvf/z8eHp79/mHcSXlpR6Hb55ox9NwW7JIsM8gB4DNt8Py2tEnCC+Hdr5td48PBpRSjlxi78FtTl9IWClJMp2ckClOmLlIMeF9bHozEVlLGSMghZAWFiSpwux3a43rLZJB7guPNBZSIUqoch9gTs7pZpnqxiqVOOfoRnGJ67Gt4hDwUstLvABE7BLBUZTYe/oaBBOaDnKwp00yrkGJAovkniMpnTjDidPNqhRNwAMilplBSA8CAio8MzwR4ZYhUOIA5owbxU48wndmMg4YEUyJJsWkiMgMTxA7i5SlKokCxEjOADQ48/VrIxwcZqFgRiZ2LWGOSWaKL/7wOLczZzjvDhk9S12JlFQ8dnNEGqXc+tiv27RZXi8IgsIMFQl32zNyhpF0zbWEQmijIRzkUsi1aCaPEZ7JLOIhnhJ8fdmez082tvuHA8kp+f7tg/zD3/9XqGehpv6URPdUa9u2Sz7RmAeYM4kROyY8VLCux5g+Y3pkePS9CxSICCcODy9EbqosS11iTKGSAhQxG1IpbNay+BjM1D2g0uembCKaEomhqFxqZHLKoZ1Adc4XJToe3mpdI9LHRnF7OT/9/a++GVROD1+4SQywKBfRUEua3Z4+PO1xq4/H8UPOKdOCPNiZfBwb0yJsNKY5U0nyGSFCQZQhEAkkJ0MmibmDc2Nn4krFIjyNXqfvGbfEYPY0ScGE+whSyChLUAy2kNQ1k41Za4A67TAhn1zFZsuE0U0yiifCByxx3DZ/Kv5wtwI5A92uzB64sywRIqyVxZTDkKARLkGqVaCKKtnDTJmQybVUEY9gRCk1SZOKeV8WNg+kKzEC4UAEKFlp6/swM3OltSi3aYNTam2RiPDL2GJ671YK66d8WI8097aKKs8xDsdjvz7dH+9ZZFoe1sNX/GUr5cenH63Rvl2Z6+16rcvRbW7z6m4LvzXrgBDktC5jPsegejiazcbteH83hWeYUktDEY2cnH4h06UdXJUI5pOyap3dmb00Slp6d1VX4kqFRG9+C/JayvDY574q0i1pWVs7z02rrBCn7L07u3n7/bgWqUV1kfA+3MKMFXPsUSsBJT1tSgxQMFcGQcmDzJgDXDjIFEogluJBnsn5mv8mZJRI4zBNDqssCAuSAmzDLEhFhCk8mYg8kmCSnEbBCrvt74M+beO6u0fBbbhH7bZlEickJnMyE6WnkM0sRV9HOMwaQfR6WAN5jAJmSk9PC3Xl3SgTMJ+Y7tfB//Lfv/nFnz2wGIidfJqhqKObbYKplFVUoiIJC7/cbkiM8+X9j9/dH76gaEZWJH1YgBgsLMniLOyhYR2jMivIZ5Kya4Jy+mA4R3z47vtf/+qbth4+++KehH/48Klvv3q8O2wz+r6lQPjlw9Oz6OHLw2PVuu/7PvZGUlR6ePKyEKDWx3gdH1MS87K0Gu5pRNQjXMHi6bQTVgoC1z003e5buVHf977WVYv4np4wIs8gyZUGkhjF5w67EUnRYwYt9S6sL+tX7Xivh0NybLfn7fz+dv3wj7/6ofvbu7dv9t1zjkXFAcr1s7ePn54+xbS+j31635/TyjAagkyyEdpE1+SMfgNN0kRxzhQ2xCs8XZSQFqMyKGMiQUXcmBMMV0QkG9VUM98zEkqEaaCEMDkH8pomRUqI9/SabIlrd+LF5RhiBcYZxpcxdDiE4ZmNSkmSjM75PjqwPEgbY3++XkPaSQ7qbcfeUpDsYBFhqkFO5jSmlALiJIYkqFVuJAtUZ/RM01KSycJLoVp534eqUhhFaCIpKFtYZiCBVGg9nmybSbHvXVE8rTKdjne3bS8H3N/fPf/uh4/fvH/z7u2yFKLQhZSolmNIPt9ehFVDa9Hnp6ep8eW7r/ZPW5rfPdxdrqNVLbWe6EDAw/H08nJpSzFs7sayeDoJs5PMcHhZyx6u4KCa1q3fTu/uNnOeqKxDiFNAMpoD5tc4rvetwnxCZVw3kHabEinFC6VZ8LLipGbBlCXroiXmHuZVPVEPx9ZjK/Uu+963Sz0dPELL79Hdb0vhVtLCdusvLxwWnHsmCG+4Rs7IGOGr1NCakzMlXAhAlkhDk3RbJktmdSMlkOxEUls4Mk1KycwF3MNSFOnOSekVSZy1UNCgcrttH7vdetoMvcyyG4XRBJozmaSEugenlcKUlDpFhkwWQYgSJZC+awQcEEYypSZvOwUyePI+0yX/D//Tl3/xH74uvFkMIiHStVFG6ZMkKQOmWEw1I2trWt5//ylCrtv+63/+7Vef5eHu4eAZYpFsiiKJKVECiDQnGOmYKUItnSjZETEHRgSX373/9Jd/+Ust867Rj88zr6ooeb2cP0qXkMKc1E0Q9OOPHz5787Y0ZZLdnVI1K7OF8GbBY1YlBJNohJWiSQ7rmDdYAPyaMONydK5Ok4EIE61uwclVdVFlpCmPfRcmIqJUD1OVtFRfzK++b0KoS3Eq5XBsx2M9nCLsdnuZ/fzDj99///5j55OW4/m8gzMoWr1rubT7dx592uy+vVy2nKfrfk7vWRiTIEF3zBXiSSZkSR6ZaQoCVU5LC6fpxhxO2CBOEcRusQrFZFOdMZmkZAJ9I4pUhAmDGdFYIAVJQnMYZ+uVzMJICgjJHlmCO9EEVyPwCITq4nMYA8lKzDBRP/fxfC7rHZc5xSommUyGZwa7RmRiQpkQhZUKUaYbJY3JM8marLrcE/MEJIMoMgjKwbOoikvJIAZRzWmc04EAPDlMMUGF9frdxy/fvYmsezeP/Tb2FInrtUnLkc/ldnjztso4PNy9WgxuMUWkkcjwNZWT+9i6yHlc7pZHG9b7fnm5vPvp56XysFmOC5l3615JDnWOmypDtJC+XC53j48hGOY8s4Eu/YpDY7e6tKJEk5rUGecOI1kRFtILk2gzxJxeqrDAkaUuPuextuEWCBEcaKWpAnzcz6fljot2MwLVyNB6Hb2kK4mF5TQRedV1JDkKxwyZbJGp5JLlzcO81u3lolrcjDKVaYQ1F3GPxKtJrlCQ0it5IZAVLKIzp4MkUaX1gEdG2qLKSeTBlKw8MgRJAg8TZoqgqh6jrFRT+jCaA86YI+lGYiWDAkNCE2q0a03OpEgWUQVRMhO/ChmkQYItDDlpynD2bkIUOS3cSOk//j/+5N/++c/jFftunrQIa4qPOQ5UKVd2yuIF2Odt0r5dnjB3hp7FaPtwusrhbvFc3TupvSbPlNu0Ru4Ks9haknCNQCpnWAmfBsBfLpd/+PXf/vg8jy3ouB/LeoxD0cOILK0gvQaIyNTI7Pz8w69/J1/85GtFQhSMYNHSmJklrL8oOILAS8LQby36Ni7DXgo1zgJn5XI4fU7t8Pzpl0uTEXtmTk9zA2Eg2pJLqz7Homo2nEIAmkOYmANaDSjHthzX9e5RU4Ni7Jftej2/fHr/4w/ffnueUmjxPj9BF2Jh5jEGS2Wp55dPCr65TbZgq9xMDaOzZ86cFHNyKTUn4DOZkzKC4RQIEWUiT8wMSvJIZ0fR8DBSD7h1ZiC8p2fAggVqAKUUCqYMi0AYC4EXo1L4quaRcGsBkrFTTuNDgoU6IwA2IyF+VdAqbR6WWalcPlzeG33xjtey3OnbQGSaOqfkREZ49A1tiBzEj5FGGtN3EJE2Es10MWLCRKhUyDKIlfX3/SeGQDMFQgyKjGQAJkwAkZMeSMV1hEuhosfJJZ1VZfSNpGpSORzaeh/hnDq2cbveSIDH++enp4Po4Xjo+7x/d/f1H95lj37dHh/uDuuaBGKOjJfLfl9LLZVA5vN8Pusup/tH0lJGJU+JTDeseol9PaxwVy2BGJxI2OYalE5E0bjsYSQ5t524pacbdncSomErv5KHKQMeLMIUlkxaj1AlyswglmHTN9/njsNB5JDoXGXM3s/nWpZS225dhXIbpNXCBHQsdcYuweKZoOFWRBYucx9t0dd/MTEToiQvosOzWIJyiwlhClXKHSOyMoWgZ/cR0rRM37Nogi0ImR7E4FaWSRFhPkdsU6MGaLt1zHOVuTHC3ZFTIROUxMkMsEphZglCIwFiSiInJcoAmB1hrjnDTkE59GWWq82/+A9f/PGff7HZM/sidBJ2T22lIrsKTYiZT9ufX572Wx/9muI+h0KcAmzT+bqdr9cnW3JdSCTCEymTaQwTDNctxNhFIwmU4cHk7MTet+39++/u7+Lhrb6873G+cauPVSQQEH8Fj3qGhqqA08y+++F31/38+HiqteLusRSAlbNIllruLU1UzLoniuSieTh+PsabftvS3aaL1KcPvzV2Yb/ukjQGbT342B6KHutyCkHVQ2uPVQRzfNw/+OjMjYkj7bAe2+GOZa1NmS1jzj5mn7NffvvNP/zVX/3y03k8fvnmsy/fzsBRlUQiwsIGBtn0OYId3gkm0iT75jyNGc5MZDRnTCB/HyoTM6tgDE/xqDrgWipmssPdufy+D52THJY5wz0ThaoGjVdDGSWbVqEthmV6BDOTwHhWZw02ZoRtMVAohMy9pzjJMF+CA+FwINjRiQqUfR64dJ/np9tXX3z+cPpchLvDwanu5BSC13RupGRnLqIEDOozUihVZPWRLOnhAc9ycGZmZvfDstjswUGwREQS8WtAYjLrDKfCcw5dlpqkc2y6ioGv+22VpRbKEoc3h7F37qMW7RFXm+WwlIxS5GW7rg9HiHy6TZbFrkk52lKgettnURWOfbfD8bi26tebR/C2R/ibN2/73D+9XN68PSzLYVyvSpHh+zZyOTSpiy6DiHJEqZky+01lkZFrqddt383uH5YZWZosrT0/vWxp60Fqa46cPqpKmKe2brtgErWmrTDDx77v7bh2zUX0s8NjjzRKUpj50hozW8CnVxYw5NR6TMk4aNPAoZaLRKQlixf2xE5knrLbcSlmY1J4CiNQ1EYPD6pwOAECTItSJM09aaIFmFjc3RlEyYmItOEqmtO58prd5u2yX7YJIx12DUd2rbSKvvQAkJncGROODJFKSEkov9auWIk5B4BBEBRwokSGyQDdfO83WuPf/fm7f/N//IJiiohjipPQ4db3H77/9vLpx62Pm2HSVLFUMNaFVENWvQ9EaldqzO1qtD1/OIhRe0xQn6NptUjikWyJyKQBDk4FqZF7DnLr+/m85awr3//sJ3f//fzN9TpPJx9LPxQwL5puyCw1xdN6gpIL+7x9+vTydC5FD8vTu3dvH9/evTmdYItBuIqqZEqYjwyhWCSW9Xh3/xjhwm3ueTfPZr1vE9ZS+qEJCzdpWmoqjFIihEqy1Hp8c6q3bStUWjmQsDYtku4Wltfz7XK51cofP336X/7z//63v/yn620Oph/HruvjsR19CKtGBLMmtbA57GZ887k10TvhG2jnGwt46mATZc2k9OHwIGQoM4NQy0jkzESOHOASDM7kkYWYhXoYERNYECllshDICaCsxC1UKQMzhSOgBqLsS2AWjUKcwdyLhBLNPMRikKd9CDcOIZplKTa3AUrStCTSnuklTnf68z/+CZfjtGQl86u5K2VxV8LOQaJERAhCCV85WVgiJMwhGDaGJUuyCBia2UQREQmW1lkzAafqcix1ynBLLc3TKV3d8uXpRypF6TgzHt/cz5GGFOa8jcp0O583uj1+9m67YZ4vn7/7fMOcFJXVh8cMrUtZ15y7CIXU4fN2ObeVBNr7jTX3fj0U7dtWToceyVJPJw0LSlvv6q1fS9ET1rEHELIUmTOlzM2EJrPtyNNdM5FY9OhQT5Hj7bLv2NelLnWxPmvRzjGHrdMjyKLrUjkkA2KjlEpEUltyKUI+x5gutVWOOSFSfExpFUKsYaMPmwetMVOr7GHXpFjFbqwZ1Vk3k6a32uYMcxJSJTdAI4vIqrLDM4Z6ee0tZQKsjqEFjLobO0Momcg4iYzCpo1DKZUKFVy2D7ftu5FP3fdpIxDdKeHpQFShGjmVhBydQqoC/IoVJ0okK2O+zpMISeGhOTuyRjCZWb92tT/9P3/+h3/y5buv7oYZASxpldqk3/3yl//wm2+1KseYYfSKlm6VC1MshMYapS7u03m+LWWmLMcTgmFlv15TVVWRMwIBA6hGlSSIIAOEjEQgA/sFY4ullMjW1u3NF4ff/e7l/LE/H66iYGaZDF2cChMv1Yq0Vpfpt24v++D9Ni7X/t2P34vYX/zp//gvf/GvZo5gKDWXQZEzMK67b/3Ng3JWlqU1ac3N7hRfI+YYDipJFpiF2CKmOjIlmeBJPJOUj/cPC2diUgDX28fsHQjWw/uPv/vhx+++++79f/lvv/7+MiZVoeYaz7f+zW9/9+f/6tBjFyuECoCZn/bN0jJ9zulSQrzWPHgO4FaDg2hmBYUFpyAVARGyyBCYIZmhzJThmcEKrRoBH+wkCRdNyWlE5N6ThQmUSKKBLTyyEIKbohrTxFQ4RDhAM9kL8xwRzEScKTWFTZIJlNl7voo0/FURii5G1f/0X/7hm8fj3s+qd0ETwR5lggSEHMqZPDNO0yZoJDUmcSeGEBdnGrYzijJR+FICxrVqnzOlRBZ2JgpQJnmQQwUOmyGChlAPG9HVfL/WXAk+FznUopsNljy1NzrPxtFHL55rEbvdutoSzkEz6WE9tkLnDz8sxyMbtm2bcwrk+eP+cDgc7nX4ZT0oDwTJNkMpOTM4Rt/ujm2fdt53mfr5w32juF7O/XZWVDc+qgrh04g+tkMr4YOg5n2aQRZKIswASqyqZeZsUY5yF+zkJNvkJlcicY95u44tI6WWwqIeQRTC7m6p+irGCh4zIRHsKOWYxdOWdrjeLpPnndYYtKKMtFqKRLYkUzaV25xlHAqtbLfMIMXKcXYfwVQlJRISpJGBgOriQaTu7OmTQWFRCitZqVSYxAg6J5/P+w+7XywJHDmVqeyxDdrQhm8mxNWKkYMDICGq/Ht5J1EmsSh2n0AgzeFWHGPQ1M+/Orz5+cO7z+sXf3AIZ4m60Onl/OPTeUME+vxvf/crRV2XisZhG7X6xVc/HfNGFJmaIdmc6/FI8uPH62Q0YAneo3SgsBRVZWRkCgQ5fTdToVVZEU4UM2YG96v1q4MoyKgAkccjH47C4XYZ27osZA6mwExetXz2xeOxlCIox88cftvt+cPL+6ez3G4v4/3/+lf/6fuPH7/44gFM6dm03a/HylksQzJtbuPjmL0oc2TqUvUmstz2MHuvQtxWJvGY1tPhPZjIi9al3uWMy+3mNkqoxwuVmU4vz5ePNv7bX//3f/rVN+dPtvlipQXdkKQoAfnh+x/uHtuXX321xZaxVg0bF/g5fIZ5lFV4HGQzubLit4IJvHFNlhw5bDCSWeCYMySR1JnFWJJYMpeYTt6IuVL3ndDCabokAbwQhwZqid2Skhw7au4Gm1RZFlLR3HmkNXOH5I6JiFeSSSJNiCMfnKZPjtrWZaYlOELcCBKNEdT/xU8//xc//4piG7j32DUnRCLYiQZnIRdM6MoTJZMYye4WBWsr67IenrYdAeapugKAmUKLoidlJqXza3VTjDWUMwIzIU3DJrIo7pZGJ8ScffJkjhRNYDj2l9uOdrqQ+/P58XTPpc6Kp8uLBPu16+E0pRwKV4WznW8fD7VJqSmtSH188+789NG7taVufdS2HNfjdF8EMeY+QrmSLDJvj20VLJieABdOgQP72I7rmqxtFFUwKNJnZFWpVZG1iKZ1UBDw/+fpz5qkSbLzTPA9i6qambtHfFtm1goUSBBCNoXspnTzYm6m+ctbZGTmoqWnmzs2AixUFYCqrMz8lohwdzNVPUtfRM38BRNRM7Xznvd5KpNljt4JxQp5ya52meNRZZT44Zqn1tatZvocIzKWugI0YRDMkXMepVTP/uqPCrcbZhGRKnZ3iSjbos57v41hU/MFmeKVeFn05Zh3O07LQqzdJpxKXUWW6URBKkIRr+q6wWLhkSxEkjSOW4ourSUymEE6KFSAyMqnkqcRdwYJmmECgzOh3MUHHQaqAAVnwlUXcgmiEErJnJaHRCmOmWkRQq40Hcd0K+vpj/7ka6n36b7JW7/ib371ly+37/vAy5ejZlNdtkXb8lpSJW11re8RC8k9Mo3CqRwWRNDyxo6pRY5+nNaakmWlzBnUzAczR2aAjJEZ4qOQJmLGyB7H9T59sgAhlMIpK+v703q4XUdf7qaBdmKm2E76/v1bVZo2XhHXICyVlx8/PH51Lnd6fv7mtx+///Wvf/3L36Rw2zTfXZav3r9//9UHdvz13/9qU+0wznhTy7vLibRwiBCZheWL86pta1Cpy72P8Fw3HTbO23nWz0rrfXZWvT6P3/3Dr7779Lth4z78N19evvt09yMLaxbPtMIZTJ6mkCj0y3/8+3T96kc/Mszp7OaMSUFA0yxbmze/1kU85GHihATAoXs/MpMDHoNIepgksTDDORXztfQISJgnRiQXojrTiSk8mpIWbinpKenJFKk60YgietIcoASMiehVlkYenJQBApickKiEogSU6dKnRVWKqDHBcJJhednOb959GAFNPTgLUaHWY1cqmqFQxuYRfCiAADNVp0hWZCWux97DPDNBQlxN6kSeC3lwTi6FVMFpMa2ySixpwXNoOmoM90zRvY+HtsH9mHfwwsSeuN3ujrIfg/sLi0eOVvA07uvlTRvF3R8/fJMkS3oV7rDT2/ds4fc9Bm2nlSRBB+m4dhIrTdtxe3ZplFRq7btLOaXyJOciYnJ7ui1blhPX03rcJ8leVrn3O1TDs22LikSEsoy5N6mvsIQZAqLkmL07UGt53p/dMo2W9RTd7sf1kNhqA2hKhnJPv2Cb/eAiIuQe95zra3FU2ZiGobaC5LgdGXI+PY5xPwJQxmkVmxlI4QkgX7cl4PfdGKgMQYK5qC7L9TZ4RmVmCoYRQPASIdBXkjozuUcLck8u1VMsDue5iD7UD/am3z4+AdMdxJkRDFVtgNFUItvTNbVBKDphJQAw99DgFCYOoyOQCU6Lw6OHjMBf/vUnX+R/+BcfRh7f33718dOn/Tgu5y2TVUwLUSnU6ruvf9b3ly/z98u6gtLzoLiZhVNl2UoRYiutHiNuNk1sCVfVDE3uww9ijMEsDFBkgi2TeoYCGHG/7j2Q8HjNSKlUXljGNe6BxV5eNF7KV2UUX1d+//6slX24BNeyFJHQnGTuc1mAsLfyZlsf352+/4fvftfH+vbdjzVuP3z6fMz7h7c/Ni//51//+tmPNHtb6bJWkCD1YTVKT2kumB7NmWpDLXZzKrSda2HCAPS0378Q4unT+Pbbl2O3WTE4Qa5JogXwNKNkRJk6RZSTTJBJv/m73x73+Omf/DhSABLAoguzaFk3PQZJqxR+Sq3BL/cx91lSMlVMjKwnUDnMMkJw0qoACNxTEC7/fzhcjoxXAi4po9XCCQypMdIHU0lhhDGRJU0gg1RKhgHuJMxCIDJDIkQyHRkDhCqUPi2ngxFKsJzsOYlWKWvbiNSjCILI3ZfSF2QQUgBWBmladR5S2TKJw4gSMdyP2+HCVqRo9chKEoik8EQkJYdRKpKYKBGWITwA1sLEcxoQulH1+64Fp/ODVtn3+fvvPmrT9+/fbo1rleJ+W2NPEPTLpy9KuJzOUdhsUImnOcX1rJfj+tSkbpeHW/qn60st0sobrVGInp8+LWddSwsjgmgTQDAzRg9mLdt2acdxIwcnlEBCka5NROWAvvQ+jn5ZtqLUtnWMcexoS5VFCZlkZmikI8xrKnE6xu1e2gaUrSqYbi9j9rGVtpTF9plk9/szl3ZaLm+g16N3yfW1bg4iZA1Obv3e60mDtRhzspokCCPWyxlkTq6qDHY7bEpZT2Q5ZxJTWTa6h8WMLEnkFMJZgRDM9EQqSVnOlOSeKjUymSChlAnCdCzbu/X46uPT3xcWIVEqIbLo3oI32mZeByUxGhFzkhNTTA6TZBFJzdxBRimUaSgj/TbumLM6/ff/8Pfk45/+8w/38aWsWbbz48O77/7xuW3M9UhuWblubzyY6rMFcfUlxLu4WacbT+87Mr3VZQxLiVr1lfPhvruDsoDSrYNKYfEMCi+kyOV+HDbdw5iRkDmD8KrbOAXHDR+JfKnl3uk+Dl7WS6kZkWECMME92YiTi6hQsxi0Irkvip+tH370zfvff967ewVntDnx8YerGep61p0Hzc9u1xfrM3cfK2nenbfreubaiox5n/dOgVyZivI19x1dX39wwmEczmXUBdS1kIZ6VpEMuxcNTxgHiJXVKJzCM3vrv/34j8OOX/zxnylhJiUocYCsVF7W7W5XbSok/ToCZkxCupAMIJzzFTcILgoE5QiuzWBwZ2+WyX+g4qToTmmsm7P3dBAhZR/YuMDd2DPDk0jFLThfr2gyI8HuCI1UAEyDPN0QElp8jloTpRkiiI4MkLYEV+GVL6ettdbj2GglqohcMic6sZISyIU5JVkQDMoqScaHzdn3p7UuxAl3Iq+tgCLDlralB4SzkCMR4maXRZNg8D5sqyc4wolK6P35+uar9zdYP+ythvt4eHhYlpqjH/cDsep508dNRfrTeL7dt8uiPtvMwKCBOadkht1G7XtYQn74/ImJtvJwqXI1Iy0RSSnH6JftMQGeE3GUU7Ub9pxeeqXWoFWqZGiMjLJjJqwFqbK7ZrrNCdB+v/r0bXvHEiD38GldmSMpLEUU0Crl0/7UliwJnjEzlVuGF0gfPThEW2u1z8FKCFqWdeREUKMFNP0Y/baXy6UuJ4kAbAAncD23lbbbtUuOpsLMRECtI8aY+7ZTrWW6jTkvW91f8Hl0dmpaIrtSCjNYXhvBt2HqpLWQJCH+gCSLV+5JFlVEfXv5ybTncXRNZm5RR2mz8EiYhZCaAJ58CyW6NWek8mv2GAN9FBQjmtF5zLWPHDZyJPHs5a/+6+8uF33z1brPYJaEZ/RaeAI5p6w28nnguNtYdvvV3/7m/jxKxP32dPVMTw52eNEswdHydJaHP70EnKJPT+EJq4BMn3CnBETMsk+Eg10zS+REKtICnomgUpnPBc/zuBJXKfee1WQaRySBmGj6HBmtSJiFz0yIehBYFrCkoMr8SSvznr3fHUjBMEgtP6/nt0/43HsIlsyn2/QxfA9iPXpHfc0eKXkjhgYFRQ8assYiiHC8dq52iVBmanBKAmdkr1aqyISIJufKjYLvPFKZC0vYheQ4bn/5N//hj372R28fPszRfJpUMKtoWZdFxS0IRafm3fNcETZ6DQwwi7trZIIz5yoy4k7JJ9ROu3NYEEVKpgjVZUV9mLKPcS1oE9S5gp21A2QekcmZSsTyKkSvnCVkUs5g3UcqB7MY1XtOD6vcFAJ2DXIOM9csUOHKdNLzZdUq0y0kFV0oUJIyZmaANbiSCEAoQQxBZLJLGpdEzM5FmMBcuMgMFi4M7jZZiyTlhFJT0fAg4fCQ0pAOs4rISM0F2fvuBhHJupaMTAVmar2sx8ttayeJOsjWh+XHXLVysJXKJVehHNMQGdW30/n29DxzrmsJn0fcxFh57aPrssKrpBHbfu8NUonmtGhLQRQPVJNltWkdPnw8qDaWZEyzChpHTy5zWwVY1vNR+hGz9iwUy1LN9CBvi1QUD3dKDNuIx54Hz42LlMpFaVC/jt7vsq4xoZwt65zMkTNDtIWFY5CDpW7nYh5UeQBNaoR3DK5Slsb3YcNLay6p8G099WMG9ePYJ5EWSZsX5q/fnO+f9+4By8oZkERh3jMmvK2qJdxzmItn1uKZ07RyJtwBUeJTOT22n36KX+/zRXpmmi4lZdUCGj2YKSnyD5ZxEWgmeRwSlZSIEJ2pcNbDLIfQLEzTcwwwrutf/F+f/tm/ao9fn9f2MKMb3YOHe4mQNdrL95+/++3np0+3/bi/HEOCKQKaDqlEFUhQCIIirzpebP+jScJzrmLE/JzEwx0EYZFMmzGDIl7RTELOI4y5VMV0mwTQXBf9+v2jf//c3ZlomNv9xS8tWdmW5DR6ncctugzHnIeRNUYHRIwmkrVJNS4uY9nvnlypKdJ0raft3eV+m3HjjDdv8CN4v/en78anz/ARz/O6taVqKXx++9WHj59/228/4A+zHU4zLgcnwHne2tRxmMVcT1kOBgVJYZRIgTClZeMFSoIeVIzJeXw6+ne//POfv//ZL776J0t7c/idyJZynsOE2qmfjrxr4WWgB0eyZLh0Z1dwybJGeeE5BTzCBZGzkQ8ZRppREhksIitJeiRBMg4GKWNSLAqakxDMQpOIOMkCMHHyLE6AdOKkYJB5DBeihBmphCeRVyX1mawp4iqV6CIoNXZMz6jmWpVYcxhjUVUKThodTOon5oyZJAgUkqayR8wiWghOVDQkKbDq6kdmZhYLL2sq41ApLHKHhWdZWDKUclqAScdxP0gUfL/u1+nndyuQbh4jwHJpLV9uSiWbZtHF+nBrb05uzsAg2XgjBxn233+ulXXCXZbaRBiyeohyHnE/bL55+zhmbOuDHR0NFZEgMbzKUmYMgjZetBHIiy5B6PNIp1pPWTV8SGmTOCvFMWdkJmh64yKQ+8u9tCZVXslBVZsjC9eFm80uQubBRbbtnIwkMds5i4CcONJUyGOOOVe5hKd51HbyTGAHIKVej0OFYFgcHbPDV1nCblyO83l7/kwRu8csm0jqsMylFWq77elZhDJ0TFe8YsYHAE84A0KZOWM2KemcM5MkQgROrKfzNze6PX36RD7SRPUhVJzvm+RhcOTgCbE6uZQizEBOzyyDJJIQNn2gh3U9cg6eDG2pScwfn2b/T/2f/Yvy0z+2j0/P1y6ZxU33bs8/PH/+4TvrGQRPAzMlkQEuqZjIWxpDlkGTPUESrdRHUElMTw+3lCM5CNnnKqxMvs+uCRWwks2g1wa6KvEaOSIGKb09/UTR7tfnfc89xvWgt7deDLHidZstozLKuiwWSbnPwaND2FmFX7lmZRWKTGMJj+QEI0OElB4/vB252JzLDu33y/vz20f6ZvePP3y536+zu2dc3m7Dbnu/lVrk6PBOdWGGFfLCa10opDkTpalR8mJqcK3hCHiSiSVlDEkLzn3I3ZIVtERNfPvpV1+efvjxN//sR+9/VKdy8WPZ7ECt1vhlKhshWSYs0mYyghZlKmSHR1oHFWZD3CSLg3VJHyopWCjJqSc7OVeTsJ7OMKpE6h7OmqyvMSCnOXlGqcycFElGiikCRu2moFBSVk/xI2xJGQCRVCoLt1xyp/50vX/76en9T9+cuTTPQmVGJJUER4A0UBJsqozMNpmhQQwZlAkBCxNRq2vRJYwosors497aibmQTaE+CZRG4RC8sj9I5YhJVSJCm56Spai0fbbWVLbnl08MFtJ+76PmHndqyxYkI3DSLx+f3hQ5PV4OO3Y7qB9bPVlmhS5tme7AH5LGhTF8hvvj+WGaH3OOeePNualXPr48s55a2/rx0vudWn3YGpkx1D1h3SmFBUU1EeiGsTt6z0uTtS1Isj4MoqXR8dK4aDLPzJgkUmrrs2cgMpLIMEkVJEySiRkJLgkoheVEdolStcDJ/CDm4AghUIb5HF1LbbXBQ4jltOWcdh18akAN8rJI2fR2h2T2+35pi00rbXu4nMeXHsbdY2la2cI5iYgDkm7mCYCFxR2HZxY+YAIi65McaSGndfvpcvv0+em3xa0sa5RIhQuyRwgdGJqmuhoTFREWHxMjk8SM1Cj3LumSYzAMJeeAJHKQ2Ocv/jd//fnx7Xq/HjZEUo/P9y8/9NnTUzU04aL1dY0+wIyKY7q4ENEffuoo05iykSjVpDnJMyWDnEmCIpMihUiSPfaGRmgsXvB6iWeSwhPknSiV+LI0zQuyZ8/jvt97GEAxX/Mhy3UMaydBLqoJpsgE7umRzoniqMRoCr5sbuN+fUKSFuWTLm3dY9nH3Ipto7z0+3Iuj2d6c9H7bbWZv/nHT9/9/rfCLJIxpwYnCXkhpJJp1fTDg9vYKjcpjhxDDEJFFj2CzZJmOlmziTEPZYsksixF6gYPdO/973/134TGNx++YU+pjVyKUy3PsLvyMiItA68zzxCKmQoTrZRpngkQIVzRODkwiDwieYLGZDGDTjjI9RVRNqZregoSyAATOIAohJiDhJ2KCSkr3C0AilMtJZIiMnNQWRYRhjMvVBA+KNbTcqaT3bodPes5BAfciJxTPJGvOYtKSoUaKJAbkAgLd2KpOhJhWVZOqQAXTc8RIE9PEJW8m4mLlQSg0OapLGPvAeKAJmtpl7rokHjz9Qk+77cBLjZ7BRciJL58+fLmw1ey0N32sMyTvuTA7Y5MLriOYzKd2nkrZ8s45v503HUub7b1hy+/79PfvPugrfj14KDLthBFxLzf7PRwCZPnsZMFa9O2hWTvvfCaWuO4kyRYSWQeByKrtoWLogsD5Mf9aLU6hvmc7IOmuF3aSTJfnp5USwqY6j47FUrlIhyWlOlB5kaCWtTjSJutCAH96BEwn8wKkjmHIFm41NKPIyM3LarsW+VrrkbWZ1lbzFDx7UF3l8wY5vfBRQTH3La2jW1/mRNB2BUhlYIQFGCiUtjSzZKzcE2MGZNFyIUgnXtVRlglfHX5Op5v19sT1V3KM8iylORB6WyQKKqlSmVPN0OwR4/oCR5H0Dw4XRyJGEI5Q91oihN7+qcf9r/+8+9+9LM3Y7/6YfunUe4lMCYGQQQRKUgkiEoBMYu2JEsfEQwYURVrC2ojQNKQYPfyyttK4qJi3f/bX/23n/3Rh4fH9xRStHHLcUyfCkoVCpGwNuaRuDMygiySSeeImdynpZtnpqGhIU8jXKRQgmJXEc/qdpCzhxA1yilERde1XRZdXp6e08AUNjOTN22SSGo1I91ZZFnWDDKZP/3mw9/+6rvrSKOQiEtrdSkUMGCjylx5rZktCuy4va7ErlulIjyYSkT1iaBBATPLGqzVh2SAlihJYwpEuHj87te/Kizv3rwXMaaA0Olh+9Rvf0ANMcVkRgIx/TU780YM8xlGRVcpTcqIWaiKaGiGW/YsxJM6KMOAdGFyzkkgigBmmLKAMpACEc6kDEBf6X1cGMnokk15E6HUSUxrbWvVZ98pIpW4tlbWspykpWbmCGMGa0YK0sOrVGVkQkOUxAgOGQQPn84qizA3RAQHV2jDHMLheN1yMGJgokVbiu7sTjqPrCnFyWaAHCO0qFqM2+enuCyPlwdcjSh1VefwkafL9vn6dHr/7lS2q+ce8fZ0LszHy20pS+9zfr49XN71DAO9HH1blnVdvhwv4CCmtm3sTgwO/3DZ5pz7CCI1tyT36N2i33tb20lb3g6mprwm1Li3x4vZMQPexypyrtqDCLwS3553tFhLqySe0d1FqEnzMa7367Kt2+mx3yYAUMGcrLAwc/jM6d2JtBUtetyPmNciy957UnqmllIbmOjlZW9tYSJwllqGWWFWJWRKKXVhuw0LY6Za2Ls3KpflfO/XLvkyZmE8VNqovTtfPvYfwszMllqZAMBBMdKhAHE6u2WQkTJyEzL2qS5B1G3cP92O3/b988KeD+e4fRbczMac5tE1QiFA48RaK4VfZx8J6x3RAQ5LZk/v0Rr5lJhDabg1ngwWhpv8/a/uoBpKz889XRKTQIWbJEdNKiGpFPSKLgpCh5u7cIVHxhQW3+2Xf/nLh/OqSxt7J6NKIZpMKcn/6f/6+7/926eU8795v3rvqMXDWZtkRuzEJpxSlEYxv4eNjF5IXjxeRmrwamJiUCxtEwpihUrmJGhhJclwEWEkwmVSiHjJ5pA5Y2tv2vs389in3e92e6Vvimyms3EZ9/3IZKwPjz+zflT5/OHx9je/+XanhVI3wVePPuR4cRerTOV5vMwj22kpZcXhJCqyhVitFMnOi3DZ2fIYAuKSQ+VgkkieV1dMEhfKjHnbj//+y//xX5KoNHhIHXIp68v1iycrEUdIMkjCoiQ03Tt7aronp0XRLjHDFaVoq9UGWx7NBjhNnWcvJu4UEHBEkBsjJCdBiRGaocIc7Ek5iVgzfVaSM1fMG9FCQiRL0ZVd9xFXojbifFoe5LS0i5zlsM/VzqqU0CShnIwQVmVhMkME8qDBWQQLoniq8ut8zRdlJ05ZfaCBC/OwUGFPHhMczMygQHhAgvlOvgLhpoqCKOR69GMJPdeLmd9udyaw1e3teczr58+fqMj29s2np+f+dHv/9k0yUTAnXm735bL6Hub9dH54ef58qcvod16X7XJ5fLjExFra88uLooT5x5cvSDagVazrEiOu172287bU7bz1p8/R75MeY9kU6VFmB7mJ0NjNljZrszk6O2tUPgdzUHy5Pi/r1pYlIofZVpfd9vBgtFVcRUiXw23kYN9AqNVnp207JRJBo48IOV/eCqYdd1nr2HuYC9u7h8tuE4QxZqHc2uLuk1kT1Tkph0bGjB7c1iGzRj4sLXLgmHv67ubXl6ynZaGHx8unJ8S0ax+arE0RSW5ORxJRiluWQhODqUyf9/2jz33l9tvP39+PLyIvc7/PCVkv5+Xtw/Lyd/uvjJAUPQhJQjEEKYCgzBF+swidFGFOYeKZiQAmN3DYMJZOmZZISQrr+Q+/+dSWkkngTOKVhEulJKGk8JIlSRwdaYU50UoEJTswI2+O0vWv/o9v56H/8//zf2DK4GEpGkzU/uv/929//d++l9Tf/PKHf/LHH9a1Hq5MG4CgI6lbitFKEnUhu43DDCHDbl8+38dAUEJIqtZaJSlJJtC8CoQouCrHNY/iWg+/ztxXPVEIYSvCXB0BKSWpVF15rH1M68PneP15oVox95z88PChnPV4fN/Wr+vy67/73be//fjUi5I2lxjZX557Pw4nDqe3aZevHpSvJha5q7SAYM2CNHOWUStxlBlJjEoCGofkKm8lQmimzq7yw97/++9//8c/+kkSg3Aq5WRyVZvEFmo5qyzETmawIGJ3J1EEWHR0SN1ZOJEEPvGbbMeTX3k6G4blJFLSyJkURElJiSRICSV4AGBxRAIJSYoZvTGRkXPLylLpcChVyoJMRCxky3Kq21LPrTwo8vlxecyiM4NeX0Cc6VFUoTMZ6WoUTAgEhQmMiEpmZCRhUnjEIkmglrQQTRgzI1gJtfB060FFluk5Y5dC3cIpkYAKAfr48GZ/uo7nazvVpdRJHhk1SNaFlO4+nz59mmO0IjXz9vLS1rael+PYR5BWWarO2+3p86fy4T2HxH4P2JfvPz48vulux4TQFM3dmYWRGciZwbVIxBgTHpdDZj+m8MrG1O+3o5RSl4sZ+tHfPDwicJjPxDGmrIk5dVDZ9PJwHkfsLwNSlMuI6cytVL96grjVYd79YPCX5+etLYuitaUfO6uk8bacmKWnD+tpds62kAaxoDBJrUnGcFyv4/zQmDngbtGPpxShQvASExA/L9tt3JjstK5wGnMPouH2ef/UnB4eLm/fvv3h+4+W4RbdrakiJMUDAcoUIokWaTb2cfv05buI200wfEQcc96m3V5uHS/Xt+8+vP36g/zVr4pxgIkREc49tO22L0pmg8bAAIcoaGSM7MNNCEx4TbfdkDHTg8HEGYVmQI5swmAnaS6ZBOZSzCWFSAzcdGXydFSnonmE35BAjbTJEG1//Rf/uC7lz/7NN0nOrGn8F//hN3/+X7510iZ+/bb/zV///b/6t39KmSqjcyG4O0U6vYrMk4poivzwcnx66T7Jgy+PZy3hogwQ2TQd96QlS5ExZ/Dr8UTERBjw2tlcEuyJVhtHUnJTRGZrq0gNKTGq3XIKDp8ljCNgndpS6eHtm/O//ddf/9mf3f/i7/7ur/7833+353pZ1lfeeqEUAvnDw1KKBpU/XIMcgtKtj9gNgwmtIkESNIely2QdhbtAZ22IIzvreq5x6/06eiWtlTmhW9X7PW47tdKiIhMeHACRe2ZSplUSdgqLTEBJmMP95bgdlABZhCMO8iIlhCwQQULMHCyEIAoAlMEuAaF0UjESeGbTBSyDU8uGKgSLpAYmiwhaV25nX84kD7bP5/ePj++++gZqIsV7RKSQBisTCb+qQctMd+vEFJQkhvQkJhJWAalyLZSSsynlq0RqaR6QDJAXAXGSh0+8kYs7rn6zcGU1eLjr6ndfsNuBIZb8cvQ3l0veQ9oyaBY+bVg67V3w2+uNp1Xldl4p5369p6E+XlDwo5/82MeUWpWySPU5fvj+ezm18+ndqho51+1sw1otQrg9X9fz6VyXHjM1vV+lMdFZNo2YW+GOeb0+t8ZtW8NDiWFZWTRzWl9kEeVKocJeYMlC7GSYvZpVEWu5H77PQ6m9PZ9f5v10akVUgWEOEgL3OS7bZYxuyqpc9cTdMb1HcD0lUeSwGapU2sUBS2qiT8fnQqVqE8IYY78eSsF5qu1C/ZpicmnJ8XS7hgiy7X3MH354+3j+0VePH7986SMFzaaromDpPkjEch5zVuMquiyF2e79M0igxJ7dbJ/dfJ/7MZCyPdQ3zX5/FaQmc2KPiUFh81ZwzD7DAYl0TjLrk4wyKAcliMBAJkcyBCDm8MolRFm1MBXKQ8R5bCkCCEfyK5+mRgQLEBrplp5IiawpmWHse46V+C/+469R7V/+z/9k/3z87X/8u9/88lt2MeJhmWi//uXTn/7p/vhQI7UCRyazzjg4DiaqVQLUv9y//filD2YrJEbpgU5QdgivxGr77fgy81RHThKLoGMMZGSyECJSl6bEIKJkEUgyYJnFI5QpOQdXWii9F4YVY5q328cEtJ1FdRDadvo3/+Kfy/Hltn8us3FCFm0zJ1kXJqZS6x7XGcdWLo2lj91jWMylbo3YMXvY0ftSG1vdLRyzH9c09kHGGVIkPY/dutW6ZCgY5byOL5+FtZEPMrMsRYXUJlsMUgLAKHCoxuDEDCGekcPdEcLBSEpUgGKAlEBKrAnSapE+M0jAGuJMpl5msIuBQ8CRJqoETVYYr4WCHJEQCvGlFOcYPPv19qMPX/+zP/l5FSZN4ZIl0t3CiXWigYDswpxE6RBh4kQGG6c2zyQPAOu6DVa1VJY9MqiKE0WKqA1TIYISMWkWYvOZkStrTpsch0C/64eSqpaZlio/On89fGRbhtucdr6cXp4/vlw/f/XVj4rw6XGLjJfnO+C2j8fL40xkKQhGkNSo9XR72vf7sT0sFHH/8jG2tSytpwuIMvu0WhdxgDXMp1Fbz5zOXIvPIKZaMmYtLZDTZmUc5lrUxgykgApLzP7U7/V0cjCZGaZznmo7e50zpoSRr6VgeKatA3pZ3KOQjlqOMfow0WLpy6KN2dxcyBhyauLhZt5xhCNejUU8JkXmGMdWN5UG0oi+UPBa3LzjEF7CNSOJ8XY7Y+I+ZniKTrh++W5sC21tnT7D2cItrXIRKeHOAYow+sNicK1n6ouFAQmtWs8l8tpHV+/Xz4+F//iPfvSfP/81pqULkVjOGMFcLHnYa+RCPWwGgtnAGU6vYQPC0jKZM5LIX/VilEtphYUrH8hMqjQXFU4J9leuSCQLU74OxDWdkhgVUIv/n5YxJiGc/9tffHs5ffjV3/zq4z9+IS7ISmRGY0Cvn/0v//r7/+nfbgxtdC+aNivHJDcmdiJ3j4kI4SkxcH6UN28v3S36nZzeXL5hAvFYt5Ond08G0mW6CVGCMlAI8CSNkqoQfm0e4hVz8TqKFo95ECULM4Wop/u8z2cr7aalWMaYePn4/elctb1Fz2dy0wF3IamVGFYBWh94smbhEA/rNtetrcKELNJ4LCInQ5qH9yHpSZjiJoW1coR7uJfwSibwWYpsy8KqFNLCTMIswpJQPIMbMZxQOCCNIgMZr77wgDtbaMKcXCM5KRSATWZIEkUmUohDnMmhTO5iIBMWcRRkEkVwiLiCC9mJGhH2nAnKSlqG1hLI+/32s5///Bf/5KdlebUosbpCKNnMR0Y4d6IiVNOrAFUbKDwjPAudpkumEbGoqnLk1FIISEsoUaJJS4+qjZQoY7duGaYw9sFG7kuiggtY9dy8Wzu3+z4a637/EpQfoy9SPXv3YRQMNGLdTuxZ2/ZyvFgwFZlxaMi3v/5HIXvz+L7o6en5k0c8vns7zZJNlrjv8+3yUBFZyQj7tIrSREJoHnZaz2jKY5Ig0mL4wqu2pd+7eZLyx0+f6rpdVHO4Cdba7t1EWOpmCXgsqpkOSKb2gBN7hGF28yYSoMRy88kpBAqQVpWkMDCxh9m9i7Ah2J2ZU3gRBtR7QJwJZNSoHDFqWYQ4GT3utVDVZZ305cuzy8zuJqpCSKf0D5fTbdjT88tuRYW1oHfi5Fozwv3IpNbDmYIzClISzhFzcpbT+uHpenf7QuqWHFmVtpPMqLun3a/X2+6Oqo5pZEhzF+RwcllGEgIJc4SDkATSQHpIZlqOpBQyDjhRMLfGq6i4FUTJCocKkrkLqavBgo2ZxaggPSYkOEGEKYgSivBIWMJjCJX021P87/+v/0SUiaSM5DGIfUhkcKG/+u/fXj7kn/3JT6el6qXkEgmzG0ARqWjsLUxsKNz+p3/xi1b188uVDl5Pp5mDUCKoZz8tlz5sjCsDTN7tCIOKpEcp5OQLSgl2AMQUQAaQETZHZM6CmBFIrlGmu9EwtaM/y+2431+gm9sBOSgiOTbywRlFOV1jLBIYe1Zt9SJHLOXx7S/e/fIf/yrs42QxMjWvuRZdrmM+z5dgYlIoaE5enawUOJrlzNvLy7sPjww2phr8fqm3c/numBFFKN1NFUSeaUo1E6ozIzKqzAizFHd1U0uQUAaFgYIzMjiTwarKwDAPhACFnAIIYlZUeA4mrnxKD5EAWatxIlfxOx+WfXVuVUm0lazsP//ZL37+i59VSu8mrIVFhCwDTI0qLIIzA2DKBDNALNkA0so5UUUziIRFNdMoQMpprKkuHPC0sXARINyd6bC51mo2MlMzQxDCYtCRynvRJvfb3VLm2Iti7DukbO8fsoeaXGTz1g/k6xvOj+OyLBN0F7nFIS9fHlVau2hZ5+xLq6JSUxLqxcb9IC69GzKltbC5qCyrIu3j9z9oFWi53W42HMqiWZKE4uV+ZcBmJuk8SVvZfGfhh3Ud4yCbJJWIjrE3XsIILMFMOScxGIVJUjMYhWaS0wAzhLsNSmEUcs6YwAwGg+bR21KHYKYxlv16M4r1tGmooYQJYbYljDls8OuXHOwQ87lppcDzfucaWSkpkykpi9JDO9GT7ddDFVLkmNN5AIWKUEY6w1JBzkB4hL++YErOr9+8//i8Wx7KydXNwnVhkVO1zy/P/d6Fl7ROkQaekEgBKc+hcTjbNHhCmcOItLPMiKIGDhkanpEkSckaomUyOQRcMHNDEwqri/M2Qh2QPNQlIZbGlIJU5kiEYwBQ4YiT1iP8NgapeGYECSchleRVachMBdTd55P++f/5W10uf/yTtyRgQqElsPscE/xyffndt98qaJTx8z96/6Mf/XjfX4hVVMuiScicFBuyBaxWVTrv16dj0MP5rapf7wOvWQFXUpFAMF65BBpTzB1isCAwQD5r+vCpICKaOi00EGAHH2jKcy6lunTIwVeYLsVsKQKZ1N6+qafn54/Xez89fnMq7963x9+/fOqjRDUtjakdbtd+QxrVek8XTJUAqSUBIUii/PTp01ov37x7zLkzydvTyh/o2xvPQVQCIO/TkwopZ4kSe7oHcQjpkrkf6lGQqWQUk0BJNNUSjKgSUC1tpnvYdNcMoE6KIhIk04KEXidbIeZKrIVEh2IX28MbyaWyVF+2hWJ//+GPv/mTf1rz3ri4JzAhlKzIAEBUwPvweyODJbgqbRbkoslcWGfMUkKCg8grHK66ApJIKfrKfK4KIgjY2Sc8EwRLLqMPcAiR+SDRIFIpqW3x4eQma82krW1PfXz6+PFyPk/t0/bHb95+eXqJ25evT2+k6Mf9mSNFSyPU2m63677fL6fmyBnC+9RT6d0EUkQdJmSn08kJwYIUH/709CV91PbQrYOk6jqtc1FW7RG9T/XkmK2eH05v5t7nvS/r6X575gIpOOaQhEIjMdJWkcgwH8I6fYqUqkr2ygwrKrKPnWu4hTCZ7axEJfd9V1W3jkA4dzjC36Sgyexz3O97AlXylqeHdYSJEYGJ2d0qM9y5cmrlpBLDEYcnJ6+kSR4MVn7z9qSc/X7f3ZlRtMwZQgSSVMIwRM5MixoxmZzQPYJ1+fD+5y/X7/fjWRmkfpVUKVVwpL/7Jo8eH/vIpGJ1eu0OV2ImBXEGZTCzhzNUnADx8AQLoNMDGRyvNQ0PdpIGZs7IGEjNGubMRDSRRCFEZJGOFCJiBlf0LJLS5JhDicSyJTwx3SFsmWSootOTEF5QSiCSTD3w/FT/9//Pb5Z/Jz/9prgoUylZO3o/rr//+H2fLlyV5/v3l9Om6zyLbAQuykAyVypy776coL4H07KtAzaOeRwzUoX1lVlxJERKJDPD4eZTiSLAke73SBjsldE5Q9xWDycWEqa2+6tnQ79WnG252/mGy+2w7F9Id5ZsyfU4juNlZEg/Xub9+tgerljvR7BvQnocfh8HK69awzKIZ0CiIOCSDrIghUT4r7/99cgPP3l8SDO3RKX370/j6T4dyZxsxEAShwlzgpIITEQxJJxTU4oTGTgpAsggTgcoGYnRB5EFcVJYgBLE5bUYOysX0EI5pZNwUBpAERw6kY30xNkusl7aQc/vytc//dEvkidHCadMB3timnciVlJBG9OZPSxAnFzBNUMzZC3VpwkJRYKUqWg0SlmFABoZQYyASBWGaOYchWmYl0pOxg4B0h1OKmW4QVgzupl+ePiaEU92u99H5fJ+bYcdff+y8HaqUpgf1oWJfXYlmuNYR6rM9XxmLQ8ftufb9fPthZLLuj22i5kd92cSef/V+6PfCDmHoag7kAnPy+l8uzGjHn1EGMZ8+3gOwN05/MPl4enli6eXuvbdiLO+uUhKMXeygzIJRStFDkRWnm4sPG1I02VrMUOTKTNsiPJrgHQce0SUCmYQsVNO8peXlzeXi0gNT47kWsJzItrWaLhFvBz3D+3Sj/2w46yrFQ2CaHXzsGGErDr2m1ZKYRtDjS2gpRC8YE6a60NdFv30/WdMS8hairvtPqsoqk4LZyEXzpJ5jwzhCq6aBYuPYwSOma+XBt73FyZuS3z4cR7++PTbZ457o6bEgT8UqBXsmkYZr7tJqBbuOZNSGIBoYpJzpqYQWBICQ+bUNJUaKYSStiJ6MoMoEghjDi6aMQaJiWYkeVKFW1oSqIqIZ7Kg1DkPRFLgdY3Qk5IEBQxPcP/c/8v/8TeXf1e2R2pUCjfmfd/n09OLiITlkuWHX//+49eXn/z0x8dtMh3Jj1zAjsh5HBbzpBlE1DnqSn3SMFGukAA7JRPBhQn0unoJghBrRhOJWWdasFAGMjQF1BwTOMAGXsNnJlc5cWyZazs95vJxIurbn16/fXoZz/enF1DHIsXVeT59+UfKsbWT0QA3eAHP0s6l+Kt7PgjmTFwk4jZnGJvl9FSStPmbf/z1x4+nn3+4ZA7retby1ck/XXMQYuEZGSF7poQDUbXYnEhShiSJEXmyexCBKqhN3iNTIsHJGQgHB0kmk0cqoXNYJjFHuHMKUdgUEZ+mAmF9qIVLvbytyxqd+uX08Itv/myhksdOSzOfJGDOxHSntayEyWla64gJAmtBUUumUEamGVlclnVap9JcamYJSymccxbnJHYCpTOTwZkZzu4uqsm9w1+vvZQ8X+MFYrXe+822t5WA0e2IuT6sMky3bc79GPe1vbkfk8tCBMuwvi+q51qt28u1u9rp4U09XeK4Fafs4zrsYTtd1haVkE6BoOjmY1qGX2qrtYbb5fw2mC/rRuQ2wxgqhZjM7fBRto1xIV2WwpZHtzCkioDBQpV03g9Evm44CgsSlZg8bi9H1WVwJNuBHZPXbeVg69i28/04qpDQyqLSkvs4ZjZBUT1zceZh1t3W2ogzpr9b32smkFo355XB3Q9lVS5c0J+vi5a2XbyP8ISsR9+dEjEDliKcoUlQff/N2+frft979HtJWUu5x6yIKjwiXnXdwDL3a1Vmfs3jl3W5vNzukIeF2OyIkL4bAcu2tPMLlRr7ymJE7m4MzWRnEQkSzmDvh3kqoBLIAGgaEsQgYgaEkMmC5PBQIXKRyCJEgSANgr7SXTgLa4CKVBThJPYRM1VpklghAwWRznCidEcSCxVHDUHCiTOEMN16hD2U+vx7+y//+df/y//jFyOf0xSht5cZSSSuqZLLy0f/3/7ff/kv/7X963/+Z5XdufQZMm+cCy3VJrQ2xKCY5pNJRFGQJU1cRFlAllZJMjLDATJzG+FBlJUDcPPIHEi3wB1ETBYwocUh5laagLxocbuzu8C14vyj8/x8q1Lr8pCykh0WUZLMk+P0WItbKm2uc2bc8pqM2gqNDoeny8i0zOHCxEDidc9Jvnu5We9ffa3hYz+kLLlG+gAnd7e0XEkzc8I9p3MSiTCJU0ROQgqcINNrskNeCzweDkJ9pR0whXB6WE5HVOIaERkgDuW0JM6z6lLL+ZEvK5W1cCMKe7Ncfv71n2ir3eaijUhKZRZEGhFUl6RGZBGThWvWopRCM17dtUOKBrIU1UrHSCJ2luDQyoM4vCs3R0CyBi3JFi4kCUpnkUaUPXsimhROzHAlqpG61XNT2scVmsT0ZmmrxJPdcdBhdj5VTwJJH7GPY+4v37x/t1GqlMN9Dlov55m2sDLVdV2ySsiMOc7r+e57fzlq0WGDSz36ve93sYXLllq4gIL77c7FMnPhS+5ubj0nFUZUJIcdyrRKIZYjbfLgnNld6puRzKLMvN+fXXltm2ohiKSyQSt7VZvc73Ndm8/IgbqW1FiaRid3aqXRerKs+3FoVc5yu91VZatLHwOGjHKf4SVn70S1C8FGqnMWSvIEV/VhUgpTIZ+tEZOMPsIjPV+FiQISZgbOD49lifHyMbpNn61UzGn+SkxloADCpcCnz0x2CJZtve1KSk1LoJVmp9O7sNl9f3P+/A/t/ttf35+/fIT6Q2lLaUDlAiKxpNvL3g+bSR5WAjX5OLySmqVNeyUjCZH6VFYQmBSv2ZMkGBHJHKoqliA00oiY+WqjYDKiACNF2CMKCQNTk5IFyiwU4elEIGRmSpJlClOkjBFK/KtfPl1Ov/mnf/ZHu42np6d737lqxIQQBAfTDP73//Evjqfr//rv/tdLO133K+WLB4+wex4iJ3bnOfD6rECeMyiduYE50xHp6a95WzgnMsUyg4goI80DCA0fzgeyJSpQCtgoAm4woqMyk3ujFtMGZiny8O7CKYVbhk6L0JFTpT0oRaVLThuHM52IbgPZLbslF8mjw6amVFUPmA3KDix7hnOWlOdheHIty6EeJ3bAw9m4iMBZWPdxsIoQOF0YicwMJinExHqbadlVEGCYwEmVCKTBmdNBFk6Mkmis5FERzGKBZJZGm+rKrMh3Us4XFSVP5uXxx29/gmxH35kqlcZMFg5mhIBY9JRRiAOZQlxUmDwkYkYaijKrePCyrPu4pWoSSxFyWxfOQUbwKp5R0pPrDAsGxGdMKAkhB8kk5aI9Mx0IrSIeWlrlLFlgcbjZhSsdtpbNa439oKDr7souGadVVd/a5xsvciczt8eHx7v1yihMtJwnUCFkee2DJGNyK3W6Fa0a/lD16oULO8L6KMntXKal3acudY4enZT5VLdhTpQEOuy+NCGi5+fnvR/bu/VBSyWe92dpTFxsQnmZ7BPF+l50rrKICBkyaSuPvL4gqNaV5wGbRHk/riLLHMahq54mkmstKZS8tfLUbxWVgoR4Mk6L7n1XbgUUPL3NSM8c01yrShWAk9iOuXAFDxWUZUHk59s1lST9lc2nqQWhhdZ3H/b9ltd9jGcvC5GsAR6YLJOCmAut6XakVWaL28q2nCht9nFspZKWnJi5PlZ6+88vf/oLfPf7rz9fv5OyhnkhPLx58/x8n6N8LF+eXq5LiSjLdPc+V5Pz+RLJTx9vtx/2iFBiArFkVDWCwolrh60JCYfSgFfRkoI/HHC2V4M4MaPCo6hTpqQgpFGxnEwcpB6YPGZz0iyWrWaEzPDu0aVUUI755//+y/N31D4ItbzN+1B3TY00yhBWWR6X+v3vfvf3v/z7f/Uv/sdtsUlG080IMGA3GR1IW0jdxkGlJgpIDJERHozkzIywjJmAa3jvAYQHgd2Dg5KCQiEekco1kEIIjpGTI8IZ8Wqh5MxMVI4UgIPm2EEKC6bVYojGoIhWpdbqfMBarr2bVL1IO52W65cv83nUYN7WcK2amYzb3t3Tw4OvV/r6fTmL7aleqPFMYi2VCkf6uSiIZoQUoY5Jr2RBc6EcWZOMuScITBwO5Qx4TLRkojk5k0thIBITlMatSlWQeCt5LmsBPWwP775+0y4FzEUXEkIRjxQqVYgynCS5MpgJpDRhTNBk5mpZRk68st9HkFaHN1rW2iLnESlUmIRsSnKd5HMM4kQyUySFSqiA2aalsNYk+H04LJmIgDApeI2nRbvZvY96LpRxKsv1ehepDG7qQL9ZL6elaWuhgPKiRHTsndQ/vHnf+ySZ5hzQfe7UPG6WR5etYVtorx8/fvrw4VGI3Ki2sp1yLa06jW7Mvh8vw+zx4d0+RiQffojZ43oKT64FxEU3AL2TAetpzYBbKMTzoGiRnTgLMYRlWmnrdd6EMMI1Q9IzURWMdIQhr8com5YQYcICA14smjKn3HsvZSEQkWpyIbJwXVRh26IUmT6ZAwQkm/mx35u0wqUNEMe+6ACNmTozJOS0rGk2e4nMyKCZnKsknAJct23V+vSCuQ/KBNNscoycRCoiCYqoUu0I3rfk1fUQo6WtSjZz2OI5PO4qjMvDup3PX9223/zu+x9ePlfEm4dFVwTsfGJkW1cxKj/cbt5KRayLXK8zkCyafxjywPUVDsJNGHQrKU6aTTu7OmcyTSjE4ZgGkmDpIOHkhBCEGAEmMrCwECZr+uSGppRIN/h002QNXoX9VSMEmY7//nef9DN+8ienhRkhBB5EKFnJZoYH/fjrrwz29999e7q0RQprImaTh+iThF7FeZwgUNHFA94PlJW1iEEcQRocHJlh9jofnekhnrDciSVTzNXdEs5sQck6kX3OSTkrBlMJAwlTmqZlhNksy8Zae5iKskcOE+U57pPuaKdOqW5nfe/RiHpF40Lnr94ekr/99FTZf/zTn7WFxvX+5uXlOuZhdOxx7DFGaM1KxuQw2m9Bnk2ZAk6tuxNPJikuxEGcaeIjSwirMcRCSYKNU0ZAjMrMgxLMyaCYbkifJZWoIopn5WVZ32zruem7d9v58p61kgBAK8pw+IAkk4C5k8EXFaGMSEEwA8HoAslh+yQgI4S0MCwhsmRSLXyMkKRSWYSSvBIro8NK2SIIYa0wIzSkFgmb02CSmTNyquB1VZAUFu4UxkV10fPC9+OuSURSyjLTGb7IOl1aOy/Ct0+fqD2AW/hR1sU34egU2Fpjw4EZPB5XVanX/anDT4XT+uG0LE2ZhKWszcbeah3TEiqFhHD00ZbFzDnomP18XlgiQCx1ZLrtxZJJGPXxcZlzvtxe4Lisj4wqXDLJu0XGdql3HyrCWiIhrDGPvt+kbgEZlDPmzHk/+inLm/PpOMZIhjIhidj6sDTWUKdKqwolRdEKkAdbOL+eB5vpQYLIeHhzMZpmQYVmP/aYqapoZVmS837c023VdYzj3g8VbaojKJO08MiRC96WN/7l9tL7y5zirIxIAHLMuWr1PESX8+nHOrfn+/cLRlus6n2fBkZIEy7z+jInifDlsv6Cv3l7Wff79fn4QvXcNg3nyKZCgvbjd5eb7Uf/cpv7HaCGZWWbyUaeGUkO1EwSDMYCMQqBVKOafPg0agISIpbyyt4GMpBUNCLh0dpKILU8OI2d0gRMUcyzpq7Kx7RIVyIQH+EdyWlcmEL2l/zy3fHN14sEOJNEICE+JlLq+Y//5E+XbQUIxnM6MXtO36+1LdPU5ph+l5SiSwSHQ5VBtKJ4OClnTh+u0N5tJidrcjpGAIlwHjOIBEls7hEuJED4OCgjEUbZSoVQhIG7j0HEpeiYkyDEJFrM9+C8HyMRPm7pkFoBbd4uqtM/Va6Ae+D0wD+p5f7y5bj+rtT3uiwbSzG7H8dXb+oMut7v5oN835RRg4iPMXVZ3r/7qu9y3F/6bcw9SYkqGzKUmOO1lURhguBQcXIg6LXXgAQlSEAUSYRtLdLQdErjZdlKrefHh3dvlvVCE7FSlMrJjshCC7c2wkCBAFeOxOtmm3J1SgVhUkQ+3z8qn1feiJSCmUgiePByqpkBylKLscSrKrHojnAlRyBp04p5nCFwMj6OuA/SJUu6Rxi9RoRFkik4nDiI9ePn7988vj/VlhbHfV/XbV01Ka/7bikNi7/c+/Nc3gfEjT1BlJjTX3SYTRu++7FdxoeH932Qcb3PW3HftuW43bfLahkZUE4Qex/J2hO1cCSZT0ncjtva1oe1GXwfEylL3cp0mmE5ggliPGxbt4fLRWaQaJpNv1Y9t9PZ5oHhKzGOSZTJrsnXPkm0SAu3poycg13Weg9bMrSd5uFkWArHnB5uMcUnU1lbi5jdJxOpM/nxOi6eZgqitrgPUNocBtMoh09lOqX4zEnmwpTZWGZh0RZpJCsHaHrAVBp5CiucEKO8X7dD8TzGbpyzAMMjuZgH0Ro5e8RS3mrZMu7DfhDrLVcBLVqJjbUcN7/14xqjcbw7n8dp+3z9kreoTehU71HvMf12NMGmOMyfX7oNblm4yUjrjioiZkZ+JDpRpcUirKGGEyQ4mMPYpzO9IqEBQRChkLglMbPUAAgQNqo5U7JnIxEhSkmYIWYpHDkpA9lcq72OnlwMSmX84PeWITnzD00G00TF+Xw+n96s63ZZNhVRXWbs0Xm/9lMtDPOZESvBUwiZrwcq02lOaWoUEZZp3TJZYuarxoHk4JkcNVKSdgIIDOLpjsyifCnn03r67sungSMMRGsCMAY7sbw+h3BDZoBCMzwDOYdXqnaHWZd27rYzyUYrkkxqIFNHO8nD6ccW7lGcm4kSpkgW1m2VUst9eNum9ynzptS/evPw7s3XmmV5tyzy/v78/P3vX3748oSY1zGmiFamCCZhxQj3dBcPzlCjV9B8cCTHiEUgJ+Yym+Jh5Vxoa3VtvLWDOTKYBFZLkiQKF1IpROpCiCwQRAIWqQMk8IAPyjppXOfz+L4u8XBawlM001OY1iJrbTPHmK6tNjQ4g7NQue+7EXORNHSzysXgralRAkLETuqja1IrnEjLnBlJEg4QKYHSXUs7fGwPzWd4j2TMtNnvNo7aKk4NTQuRar0NpxiP29nDCZwt9V74KJ9wtRmacm7nEy+8pyDqUpBhfcxx7/1OyHV7c7t1ZjGCrk2kbqcWielzuGlrPl0UBkipyu129L2PS9nceOQQwm73Uuv9mNf96bx5a+vTy12Gn9uKQjnnfkw0lVKqqKb1vteWWre983FMw8IiUjI8C1WLSXUZ+/Q+6tau+/MC+BxRkCjnWo45qOignBZbE4ESpc0s9ZLuC0sgIViSxzhe7kdRWbe6tmW/TynyWFcfg2XmEWNOrhIRgdTSLA9t8ub95b4f80vkmE0khCk5Y3hKZ6kZLBq8irz3e8Z45iWphrurnDadfg/lOg495rzo6Y9+8kdj9Jfrl/NxvHv/xjJ++HTdb/fbuC9LnVdOuKIhhHMmDQjlMEVaUXYk9Yl1Tq5IUTjliTSD95R0GpkBUwSTmBlP4sIgRGSmz1Rk8EjNBt4ninCBp3lGWQhZprWSQ6dTCjVGCM+e2Uccn7180KShBg2pW501vv7mq3dv3hWtQlaUZhgRbWU7r+Wbb7759vtvgSnCIEGKiAKvFIIc6eIcPo0sOV/5zMSO7GNMhKT39J7BCuGEA5wxbUw3Dnz1+O68Pow9fnv7XeRdShQuROFRCWKBWoogp/dMyszCHE7pOCiIiezq7F5PefiFKzA11Iw9YbQ7sra3S1n72EUsmboXR+GIWhMSYSpLuZRzZH386i0rEy9bXR91uX150uX3lw/l+fPL/u1nJybiVE43BLNRcmQhAELgQGXqCLAIsjWmS0alxno+aSwguEqtq9ZtE4WUmsI+JwlYhINAmqJcFJnEU9wNYSGZljSUIeB+fAm7JtWsX7NwRFAKSFIiKY9j2syiTJlaBOQ2ZwCtrA52uKgS8RRjTJBEp+3cXo6OOUsRgEMA9tc6PsI5U6nPUSdzI22pbNbHmNv6UMGXx9Pzfr3bWB4u0jZ4WO8RtzfLBUT96My4LNud5XR62OOuJdfU9dyA9BGka7/va+TS6g/X23lpmy4GLcWP660tTU+bd0tlz6hcOHK/Xs2n5ay6blKV6Lpfl5Tzm9P9/rQUES3HMIEuso40B47Rw7E+ngYFpl7W5cq3Y96OGVgs3H2Ow3LZlqZU22KOTrsWCg8jCdh1vxVZqjSCgMpUK7VdX/p6lpdMs7mVLQrd/K5pcKMDoJpJSQDIEbe9VyJSKnWdffA0bQlKhGIk3ElLrvA+jnkISyEOn1wx4Smkm1Z926732/1WpicjMon0HjkZNZh0Y5aybsTHbj8c8WlSBkdlvrx9O3P6qc2XT30e1N2i141a1Zhxvc/zV++Pd2+/+7T+/re/X5ZclMxiDFuF3JBIJU6CkgzkQbMkFVtnosAlM6GcTBT6WtRTZgY8iNmJJZOTAs6aA0lOJcBAarEoJcFJpVYRJoMk0+zgnMwpjefQTEJUWmaH3KOeNZyVQBzn9c2f/fzPzutKXGa+DJ8RWRQZy33Of/z0XR97LXIcnYlaubRsGZRMme5CSMMrOjM5wjhJmcIkLc3CbFrmiJ3AS2omyIMTBs+BT9erWbw5Xb7055fx3McdurVgywmCMO80OWWfY4FhTpF2eXjTXz7OIxaNblOsFwD69m6xlVLA06a5oeakoPBG9K6du3KfQwVuzmLDrmZhQ+qyPXx4XPgB3Eg1qIJzUlzevdkxc+H3bz68/+ab//Q3v7m+mIAS7BGcUNOhlpwcKODC8ExInM5ca5ULdKEtVEudlMQmJbft3dpqhDDBI6uGaEmIsxl1kIogoihYhRXCrNOnBVMtPxzfvczfVUjs46ovl/MbhjoHq9LaDvjLGIVLFAWxzbmJRpowEzEnTqyg1MoKGZljFFcX14oj1obEzACnI0FKEU0pwtWZjuvLUmqES2xhe13XQQ6Vbo7krSyCUlAN477vl/N6gu4Wy3oKGGesq+zH563wfTJzquIYHQyO+PzpM20P520TdgueRgmvIEmuhpenH8B4s73ze9xfvpwuJwgxF2KeCKN623ctui3F7VjXJfo4blfeVpakaUuhqoWCa8XMkZoCdTghfExt7ehHuj1sj07ZfahkkJlO8RBHrTrZ+4ymr5aOnPfRlkYilHk5NQLc6WF5nDGE48O62nVGlT3QWiMhHoPIBPWybeP2tPLakS7CwXwgbKqkm2WQc5ZWhItMG8d9xrHoRvYK9AuJqCT0eFo2vj8fX445mZWppoYjQ6YFhQhVSDnrSfP0fP9+zg4apWUqR+bldOq902EZFAliJkZriqBiUh7fv3z7yXUaR0RaUyRqH4IwJRtO5IR0SZnmfk9tGeqch2fB9GodwaU6XIhhFBmu+QqVSE/KXGQiJPV05HCkImuSshjx3Z05dUkzJqqF0pHOMSUFitDj7ss5H7fNtoU5JY4/+dlPvnnznl4RNFhEKChiGEvsFrSLSkk/hJx0SY8Mp1pc2SyF+dWRV+N1GBUOTLckZyQs03SMNCjIMgYinYBgNyXGy3G33Ne6XEq79nEfsw9aSRfpoNWtHsYZyJgek1mf9rldmp6rBLmB5HIdzxWd5VZ4dXNpoQ/LvCtFcAyb13tY55pE++2IMSfFYZYTJdppOb29fBBIra2WB0Q1m5nOUlDq41dfT6EFev7wFdr6n//jf50vMwXBCSGOZMplU0IokQq9LRhIbLIsy8pEOdZSZgQV1lJO7x/bIlUpko1QmErUkucEcwxur10BY9IEByfo4CwSGojjfrXbqLpJJrvd79d1PUtpFiD2RhlmRLxt5wkZFK0pF7XDCOjhq6jYlFaDxcNFih99aUrpMgcLmDRJZoLd1A1zOGe0ptu793HsFkeQDLNL3SZowLWwJMJM23J9eSnED2ujt0tkeTpmcs65D8vTsq11eVwXmPn9Tqfl5f6y+86s2/my0HkflvNaVirM+/2ekaXUZTuH2aIyyfroQrKsTYqMHuTQVo55u2eu61Zos3EbYyciVvUiNVKcTqLT5/7y1OqWjOOwlVYy2v1OMd+d3kBL8KScDO2zRwQJGCFOivW4v9QVExajb+spCSE89hSPdCNRleKjK0NIdvMA2zFBaK1WDpAFTyoU2eYM5amtckbs+7qUUsrRRxgDjqA+UAorNss7aZRT6btfbVBEqRrmRdiQI0Yt8vBwSfH7fvPeAWPRKK0yxW0EH1DLCdGHtw8lR+/7Rxt7xp4yoVFrW9aH8DHmPPoBiVYyrDfKy0n3P37/17+6W5dFmhOMs5VgPw4klTLCOZOTOJSountiOiQmEUUpSKYMj/AkLlLcwzBImyGTwJCULsQewk0CDgsmRuJ1TUFgQCRzJqt7dT4Ygz2ATCLV3m/F122tU8ZpPf34m/cMIhAhCiNihocRJ41FRbMijRENhCARsrBFCiJZimRyRhBmCoUFMhkODzciRYQg4EZME+4MimnEAqaAB6Zj3MadDPP44XN8fJnKP5SM01KCn6SqXM6n9VRqGT7NexD157k8nK25uy3bw+fn2Meu+kSSLGef2Cqfl+X51gMKldvxHKmctfc+bUDIPD9sX33z8KEsxUgiZGmnyk2ppNXr2BHCLIX03emhaI2YP/n6/Pyzh9/97rM0TiJxyZnQhmrTB5hFuAp5JBUuxS20oMwQA8Xwy7Ju2pJgmQKsvJhZqYXIiwjitT/uygIfyXw4kkoYqsykOW9PCzzKJRh+HBk2xxQ9iYjUUnXZjzshCEFAhTRmBAxctKyWSdMWpIRmMlXr1oQk4aMPhGQokoLJkQ4UoFSOpCBlYt5OPkcRdQ4ryzhs9uP8WHMfOg7ZamnF3O5HRIA0SawtLeN03XdSnj6V1ULbZTvCickmWquHvcraqedRtYw5qBAlpEpydVArVAr7oOPo7dTunLlWMfbIU33UWsPsen8phWqpN3i4V8iIeOVfoyiUhgDmp1Ml2+0glzwtJ3KeFlA+9p5pycxMYVZJNj1d77uWRUUYgZUp4ccooBI8hw8/UiRoViZyuyOl1IzUU73nfr895fBSdLvUDs6QsgilRdDNJzeBe0SvIrkYvbZXhdzmcb/KEkRgb8t2iX7b95vbWNqrPygKq3sO4Xop5VyOvt+frm7TM7gVX2VGBSSVle4iCy+rsKTtNp76cQ2dyZ2ElrWeWmvLxT0ZGONufvVx/PE/+dHp3cPf/fIf/uGHL8GUFG5+HzIYAlpSkT55WMLTqGTh0FwyI6jGUKaAjMJUOAgGno0kky3AwslwCoQ6W8I5U5UyMpOJKeFJEkGRNtjBkEAQCUQSgvCctuf9lg8rI/Dh7TfbtiQmi7S1hA9kg+2ZwS0vTRXpiT2YUiJ5UHKVSVFYkB7Tk9MKH2QZVgD3iKRMYRaSMWcnmoieZAF2ePoU1Iz44fvn2/NxfbpiRuS4Z1hHCUX0f2CnUkE163Hanh7fbd+8f3Tda8nx8rL3Y3mzkjiZn9pyvfkc97rcIqK20zH2VcpJT8/7fu/3o+bIjjE8YISNTz//0Yd3lw9r3Ua/S4RgZa11qQTKWTaR2/Fi3cjtJE2XNXNYl5/88Y86BaRXLf065zE9ukssjQN0+IzUEbQ5SbFJ0lESpVRNGmstmsOMPKSwUTiIe06RAR4qBWCnfG3yMLJwOazOdIqb8X7QEYpIoknCZHG3OBiRyUo6h48xllqFYchMKiJjOIIliQjMpJypsDlbrDGnVEqSGYNTQTbSmVKXOmeBJpAgRLjqCK/a97ksMmUeSZJlKe3p5draup7Ot2tvsq6ykfLUY/Z8004pfo9eC7LvEDmUPF47/bRo26SARAwLM2vs6cfdCLm0ZRw3N79cFiCPY/Qp2PNxqT4iNYRIKAMBz5gjw4q20trwXU1ihtZ6O277/L9Z+pMmWZosPRM7o6qZuUfEvfcbcviyRlQBYKHAbrQIKcJecMUlV/y13FO4ZbOFJNAYasxKVGXmN9wb4e6mqmfiIvIvmJm4q57zvs9zXtr2HtVmZI26z8GCKA3YRzoFAckyq8LeJAgDgZjfHudFhdrKUWl9xmhtfyRmGnFwS0reWnehWYhEYWgOvEYXRsVuIKqyHV9+fNu3y46QHJjAqAiJ7gaBW5s2Y56F0LJ5Rr9uybDGvbetjKpo2LK1tm1DwHE+hOty2SvJK2cDsUmAssmTfnh8efM18zypC4tSyJnDiwAIQIuA+mWXF3/86P5GcuZaZ8RGXYC3TVQwfX+s44v9EPb4eOz8p3/89PWHc+Q//e3vXmu8nROSwn1LTlEmYFuKZChWrSoJljNGENdqzkiUjIGJnYQqAFQa4BJKSVZsS+YiyQQBJ0BEFKItU1Fb8YxbsFlBoheUFFekR2KixfbTa1y+wib7y/5VQyYMkkwPYKnZqU7J1QlYDWL5MoeWIpiCLp24hjsDMyeCYwEX2Kp8z7oXsVpG2XIfXiNgRZkkQXIQ51rD4r//0+ff/OaHKKoCQqrK4UleMTGinwz2MIIohdvb+Jff3b/ff/f0os/fvlyf+zlu98/rq/1TLj9arV7jzdfj7jwzYtNr2V21XS77KGPD4iUEGPihP//s2z/9uLfWLySHlHIV6lYMqLycGFiQJc4Vb50R8OrETbZEbteXT9/62+u/ECJ1AUyKZGQhrSKsudxxJO7IVJLEvTdWYgIt3NzgIdAJNHBOcUJBJILCKigTR+OcyIrEAQieFEBixdMTcB80ISZWEnkyOJ6FJrJtXYd5QrH0BAXkd6/YMk/toQTuHakxRSYRm3lAFVYh3ws2wERBxkqaFiSS5ZDViItTSFutoSpnZAPKiEzAo4lBhtzHJGbu25fXLxd5QsrHym55wt3JedLTvnPDmR6Fh/a387bG+XJcIa2nQqMf43HF/nL55n6/0UycALHW9hZ2FvaX/vSw15HTAwS6CusGXjOH21tI42I+1yPmYGy7HkxwKwurfb/c5zByBrg7YHUhnQ3AmSoCcusC4dl6hoteOYyhYOtrec+OVJGFrKedgq317bSTMlmFRfOcWlobmk8EAcKQ8OUC0Ao9/atPLwWKVJoxE9KrOGoXjZZFLKTcMLCooBISmwhs23kbXIWqWxdtz2sOabzz03kfn7+cIpI7vh/CAIsAQvD504uPR57+05orVmfalRF0VtR0BXGCja61afCn2/0HWPfisaC4Vk1HaaGJ2C6Xr2F9+fz9Dw757aXzdf9w2f7u73695uNxUwxyqsKikkW5MZGXUxkRMLcBRJ4wBu8F2pCbcwdwWaq1ZxrISHs3WasrOgE3sOkQyIARByNgRouCLACMdJxaWNA0pGgNT+I2ZrjPb7/++eV6qcIqSAt59y3mIyvKzhx9MaRPWxIJ5g/kThQjSqm3TCgf2qiYTxcHSAtgYsGyzDTAKs+wAkNiMKgo8zpH/fTTD5+/vzPSBJOSiJWFMsVmpWNguhM6O81MsNV6wU+Pdd7987/c+4vy9cJtzuf14XrdrKuDyTaSM9d9vH0EvioMt8KnTVGIN9xBzYh++fEXT/seBGHYWbAjQfXeMt9xUgnFIUnX59dHBM1OFpzm5/XY7W1d9+a+l1skDtDMtbMyX9ZcFyhzmMSMU1OfWRGLEgR7U1WMqp55EIGSAKBXEhAbIKJjUkZ5GQeQRhUTVoJXv48z7LaYC2BHbZQOFYQzckQ974K+ajw6YWG7Q5OMHRnMqIELJMpOCFpG2EoYwGV5jEZbhXOWE0BiC9qRHNJWBRZgBAZWis0bQTwdz7/73Wfd95ddf7JZjpyZMLpQ73JPh43Ot5+ASLsKLK2kdsHOM4ozt6YklLG4IEJvr2vb9G6PVu26X3DYT/nGwBHR2z5urwKk+x4Ay07ZDwDfkUQ6RJ3nLCZlFERuMqEetwdUXJ97QHilMD71XrDaob6mp23bprrf0t1XR4ixtG3h6QbIgsy2YiftWWVruXEXZBznbbseURUJQcDY3RZWlg+p7mOIbM6EAJURBiwqDBUQZYGVsRjR3SITEwkqKAlEUgvlvpzYgIsKpQjG5HzPWMVa1iEzcE1rrXVtIRXG6zF8GSg1bUmayuglBr09Bxnzdj/H3e8kDlDEQp0l1IadNEmayK7XzeaXsLeAN+CHbAqoyxYX0fDH7f6IiZJRJ2jtO337zcc18u/+6w8MTCkzLUFK6I4WDFXZQFumiyUVZbC7aA9wFySBJFCGRFMnqCNpZp7MTbHMIYgjjDMECaMEa5o5RHq5uwpqKiIIRBWHFAZe6fKrb//ol7/6uehOtAFgAKoos9NGyxl4C1Z7iFuN+QhQEWUclML0fo/wBgweIsQBK9lxQ6pKz4ICglhR6YUAXVIccPoIn4/z9cfvv9yDCFgTszTCxGiuNTMQyQDLApgcUh2au3INiBl1QBsPtPaqV7Kbt0/sR3QRZnlEBRVDflm3s9quPWOqQQEEQFG27bg8PQEzQiAmcxQGV3EBI4uKk6PmNKesrrIs3bE3yUx0bNE67dq2B4IS7fhY2UkptaA3MLiIU8OKCdBXWfcFGKKti2AVwOZgkEsrwU1KOYgIESFo+kCkHjmyvjDIYqrEte4rLGV6woEMhcYys6Dp0Y4mTQEIwDJ678ShZZsSMs7TW2tcpAHv0ypIQCIscJ9dmCLB1q54S9faWcBgwPs+R9SsEAAqRZvGCBvr6TiUsAiQ4AJMBUl58wGXJwTamp5fzkj8tD3l/TFWbFoq/RZjb1dB1nArunSJlgn4zo7eLpwrNsUf3n7YP37yZcxN+DIrGDrWXGu1psIQa00LlZYRBcid26X9MF8n0XXbjr45po0Vlc/btdxv52iXA4CJAcD8LGI6AFkxeDPPKieEMNucytJbvfnjzNikB4LPqcR+f6QH7X342lt/aXuuiirZ1MvueW9Ny5IwRdtcmauQOQuNCgrG42RCZlIVNx8DPc4d59PTtXwQSrx3fyMRaLllwbFdFBa636lw0yRMyOpUAEc7HvM+zJYN2UJEvBKFFUhUBES2DQfP22f3TE0kLMHtspvdw8MNWuubfGS5rjxO+9FnpJ+BGcn3++N+vyOlIhBtDwcGeXq5fL3yxx/O++8HpFC2qNGgSGQyRlYLUlSvFKwixWJMAKYFFQSkBKjJuVU0qspWyYlpBUEVFVke7kANQDCSASpQlryb34lKigBbZAQXA83HfNquW+9IDEC99wB3DARgJoEO1apgIxqwFqRnehIiQWFZeAyUOLExalSgUFZycuTMjLXCAtNrWgQKFlbhiPXw6ffb4/XuN0BosYoWnAjmIOnBsArCq5CBrDAQCSq9DJILaKwEsYoqYLiX3db86fPLz58+fWjZQ3qlDULxYCVZdMdsBbIgzRVg+/DhhQoZe4IByirAxljk1Agwy1mygBl5zoVMFd1T4za2996DC4EwNqIYZbPnnq2ogGFrG/D2YQfIGPO8PyIhRJE1S+ayc9tb8hkJBYxLGzQiQ0AvfFQmnwHki09ypaKkCavKHIq0ZYmFEQFwPWo5wB5wyNapgcjrGh60oRKWYB7cMiOAGrdlVoi+01V5t5JAz8RC4XeSn2dFZ2Rb+Y6/wXBVKBTsaKaiMp0RZI758vKikK/rDCxY3rf+dvsiTe9zrNu8Pm3by0fBBgsd+vHSGG3ZK5pDRvStqM45xprX45qJiP2Qth7jsj2lw0G7ZFz2TQHPsgxv25UdiqI3HvOcY2jvgrhf2mPc3x6vT8eHdjRMgRUYkMs5EbYe4cClh8x1B4Rwdk8GQ0ciMiszCyzW3EWBBRPco9KyXEVQaK7ZCAW4AkXbqCKEdC+gmWiVG0/tbFEFiChYmV6smp6MrEiG5eWIrNohcnqM4SpdlSFtmh28S9bMzEiLQGE8Wi8Gc8hwW7G8d2WPqmoI8NTPeTJ11ic/H2/jtvu+H0eWR0SgJ3NEPW37h9bHOX943Dw90JgLRTfROdZ0h0olKXo6tss8v2C9Iq4FFlrUmbyyqLwJcEnbd/r4nH/0R9/8S/z+9bdWCR8+7V9987N/+PV/l9KGJVAeRaSYVJmAkJGACK0Ts6JTMJZmjYob5jVAQ6KYIIZgFVCERJBVeiRLQVElNeFJ5jU60lkQSgQy/PGrn3/79HwAICsDYFZoowBIUswCwKqkLIVKYIXdIX2GCwRlsXfuaYaQpCG7RBhmxZoADsWRGeHEwAzouLyg0tcZdgbwuOvruThzVSY6GVxdPvPDw9MKqAu+Z0eBixEyyT0AQxQRihJqoblngPiEV381lA8fn7JIUpgFGHykexFl8LAohuO7b35xvV4IguAMTEcUUmFA2JdjY0KtygiAIiEJKdaMlYPZzzRn5A3D2W78pFeht0QpiyYNECmTmDcGLSKqfT/WDM9REsjWGXN5xMZcWT4JE89yF4Fdn4YhoXr6GesWg2hQPDCg77pyYtZW4nQmdyrteDTRy3ZpvO2bIuM419YvxEclEhEARThItyJkSkbBmJnYlBy88l0/NHxVJjNIAVDYtEDSLgIKmAAWXMEs7XI8vgwQPtda7l7BTW42b8N31pZAFh+Op8kwRtLtRgzZEGddrl2RMx3KbZ7FsbBa29KyIRxbmaFo+/z5i+zX/XgKy3k6N5Gm53l/vP2215bEn8/PxHxcnjxt2ThHSJOjP395nHq0roBYCTHn2GQLzEmRs7TTWkbYgkSpG6/GOL1g4tb1UX735e8ldQEEWbkcCZnP8/Gy7YqMBe/po0SYViuccbudt2JXuWQ4FAOocMPKoipI6VDTBPF9KdaEKsEf5hzX47BRqpyKjIiPoCqgqkxhGbWYZGWoolsR4lPfAREse28RMapAe1HdxtgZL8/H49X8h89Pm1TTVUiUF+ZcE5SOi9b+8WHr9nqbN9empNX3ZlAWNOYigta67l8BHOmvOV61revL5fPbWMYM2DCtgFF+/nJpDM9X+vHr8fT88a//p7/+l9/+/h/+8Tcc4JkAgcCZdCr0QgCHIiwRFGW59EbcKPDh76fI6UjvUQ0B5CVuifnOgLKJRrhYtocngyuRgFI5qAXKFrB/6P/uf/q37di0X1iw0SaYBYFAUIzg9B7qXvMsCCRPLTgJM6IeNmZqd++luyBwjTmAMiMKEoASqDIxZ8JkMI7E5LtbEN7n+PG39/FjcWgUlXuIYeBMDsQMYocgiJwVkdJmklQiJhUwteDymEKkwY0oCl+XdaDf/MbO8fj6q4soQ+EweuRsyBvvNQahfPfLn3/78lTMOcFyBDAmoOMTdy4ooQjL9+MPrC67kG4Ci/Kz38yrQJBkb6LEsOinL79hiYseCwYAKBKyWZ0FgtVRlAg60ttZwyaUbKn4Pq4QQ8FgB7XiDAZej5W9Jaflmva2Hud4Vcq9I/InogaUovnV9i1TP2rf6dDt4H7hPKBqjHtBsiioRpaKBNEZkbJ15Ivy8CUZjAXhgboKgAAEbQaGg1lt3PZjb7HGMMfEE3IpAYJOAxnzB5ESpL3TMGDEre+v8fjYOnkWBu9tlEHhS1cz6Ae/rrNO051hJARgQ0DPMtWLwO42W5c1bsNduG/bkQkIo8KD4PfzfOJr1yusc++9iGPNQmJuThnzHLE0dKwYo7Za3NcOzZSwky6PiUEp0iL9uBwE7VwVWApkvpz3pyeNfKw5VA/tzWzdHuu5HcSo2BpueJASzRmAXBWqikRXlfm4u89934tGZKQzEsRcXaMq58OqwiWYIUkgOcK31osgUaQI0bmJkWM6WPTeRlWmC1AKQiz3EGprWWppYw/+vMalb+kBAOM8WZi0Xzmqgpg/PF/g4ff51n2S7htLlIFqQ7hXpVZvepGn9fZ4mzFOK86u2rQnqZnDcihj2S9dtb/A+ZDaPeTt7Y1gWIVXzQFPH67fPV9/8fUv8d8QSg+3T9x+8fLyu5/uBRl/KCP7lsBKWOQEwi60mIlEnqFVKwO4pUZkgBEjlSJCcIPoYQ+AkyKTs6oAcO+4Enpgkd4hdHHTnLB+9avvvvn2+XJcC1GYCZCYA5ORlUB4m/7IqMSWsAIBlTGUmatSA5fH43HbimaD55ePyjsmEWFUuU13YNSM293TPAowayU+xrj9/tc/PN5snZ0yI5ELOVpklSOwEGEpgyFXFMEMQ8KoIAwkdnBLKGBKyjQiWRUFuSZW6g/jtNv49pfSu7hzATrACXc0+vrl46E7YUOgWRAAGEgMUOlnSkeCSqDHY+1bUxFmQE9K0t6j5es8LVEadsVW61fffPXx+fk3P/zNfDw2vda6tVwo6lV3i2zsICDWhDa8rAGoDZkJ6X7ec3pDoOjrLOquXA8oT/7s93G6Qcw58twfYFFUGI2Pl86/+vjHH16+zWpJ0MwAeVYXgsrwQNVW0mYhERBDeghrEVeGZ7XeOqJUFvfb8EpUFY8AzMaUM5CKwAiwqWKGY6ke6IUIDV3S9w2AbCx/3B8DiQ1PIa7HikoQflv3bLhv24rzct3cV668Hs9u9TpOagc3Gefnx/S9cTKutJiUiwOZAETifvtpU79++jhtZdVY50VaUE3O02ZlXqiPnJb+/PKpY47zPGRrnGNFuN2HYcPeDwFIH+d89FaQMXxx79t+gYSYMAU3SoiFCQdsGRnT/TFUiJEpkqGGvXXV+xouQsxb8KoqcyV+6rtFFeHnL/P56bJf+piThTKMqq57MzMHFUJAXFlt22JFxgRs4e6KgROQs2RWWmGmwHxcGJP1beKa96edeu+GvsLSvW89q2YaITbWmIuLmJtXtowqgK57fwob81wO1vYdEBFSpLfMgJTOpCiT5ni8zjfPDDchFlXMFQGnTU2VZKYOL5fCK63/fnv8A9DMSg/+ze+/f758+HB8cL+be0BePh2/+t/9xd/9P/+/RwnQKiAU5QyozCIpqVjGswMuc2PZRPZtc1wPXxjpPpavTRncwh3ooWAIAYVSKjAmQiuoAJAGKAicMPql/+mvftmImFhYFJFJHAoYkKgp2sQqrUqmDtUSz9aAUty8ZWbx5j0gF72tFT/+8GPTC7O2ptIokYmy1jnTV4kVVxhSzPv91//wu/OLkalbBicgeIpZVNrGwZFmUcWVmAmeRgII0BWrwLMwiwMDy3BpkWUmADLmmTateX2JGj5/9hVhkyYOayW3Yf7zr7/DUh+sjQXBA6Os0GkjlwrAdzgieo15IjUSAtGCqKLL8zXu+HZ/kGLUgggi/Nm3f6K7/sNv/mPeTmcpQsK+FTrVwBW8GNNSlNu3H7aiQkiYCTC16+Xaz0c9HgMECFXEAAB1BElEQVTmurByA9MfSIg6uZEIqdp0NZMf8Tywfffhz66Xb5cBSCGTJ2ZhoVVhApqVbJcOmimFiay3MRoSUUIlsnihEr+btKsY0QLRwgsCVban/R0JC/hOnChkhkpECk9SlIMxxggs0f7y1YdO/TZ+XODBreF2pjM2IVozcdtPgLfbIOQieH3cXbhJCep9CJOQto2ZbQD5ktpZ+769no/t0wFpjzkbs3vIrgPWY9z2yqfr8/k4s0ISGIQmQFacbvjGXdu2Q8fvf/ryqVAbv9U6MU5KOMfTh6c2CQDuj8/YO2/HtcS+fIatE7ceNOBE5kbctD1eXwnweDqmWFDNcyGCNqFCLDx9sDQl3oR+Oh/E/TwXQFoUlGnAxh1ISLXWXGOdY/TLwTvdy8qhox6yuZ1VKQ0TUra+EmKlZiMVt3qSg2kDYQOUkFxZBZsxmD/O5Q1b75PzYffL5TlX2nJtPRQo1QuihUfdx2Dhg+EqIAQjYrznvoSvV6Wd7X6Os86xWKkLSz+EOGtqYmVyOz59+O5j//lPX17+5ae/OceNsNz4x9/d6IM+f2RzOEIh41c///radQ5T2ZYDAKsApPUSKT6LRobU2kAfPJGhM5MLwlixRswiHiUeKhnkBpBQNID3kqK3k/FIIYDAVcKkMtP/w3/461/87GsRLC5C2ESJxaGQsFFVgki5o4BWRngyKCA1KQYDzMCJojaAUAl2C7/bI9wrs1ij1S7VEE4DwEJYLPHj79/+7u9/f7uNgu5BKfR+/zSKyGQAi8gCLvaowgzMomDEjCwQKnFKzJIokUiKQMYAynfWKmDZMAjg16Dw+ekblovBgnAo4IM2rcmUrFfuz3a/RZgyKCMLR6wmLRO00Yqcvkg0IEAQCyFBlY8myBhRBszKHo/ny/5v/+SvfvMP/+nHL8bUdDscB5FjDkWLoARhEWF0lZmzWX2knbUzaWh9iRm5H0iRUPjgyuvlY8yxbGY59k3CRyBq+/j1N6HcQyIzzLGIgIuRiO63e7GCcjFW5qUxeBKQozJCw2qYgAQ+pW/nTDfbt5YV6CGA79UQEHajKmhd17wRSkKRdHPAKJEqUsUu2Ng93ezjfnkrc6dcqZ0w0dKFNZZXLYDcgMuq81NrWRD3z28XeQbNmZTBG22oba63Rz5m3T3i+fIBQ9aX2yRv+yEqYbb3C1isxyRiA+KE990QE173p9f7DzsoQVDB5WmvTd7qgVTX60EDaWJFURPLaLCxtMSAW2jv2fu6n5S0HcdKT8ZzLvN1fX4ZlX3b4jFf+iUjdJVQGxkqGhXuIIU787VtZS4AEw1687uP5ZRQSNuuHsBuWIWeW3ARIEIyFQk+VjxmP5qF78DRKW3/vDzZriwNZUUmJU1X1KXIQFkp2kNhrLMx4wjYAqUSeHF5psZ7aon6rp3k8XjcpteA1og6yXuDwZMQN2mXq8yWr/fztPn2sKYqqlVazWvGWlUlwvLp63+9XX/5+8+//f7Hfwp/hRq/+/2PrB/bZZ8QWfHtp/3/8j//j//3/8f/O5IBqwreV8qLwCEQsANLoEItmEG0EyU6UAEpAYVHBWIlZbITMDuhIoJHYVMgzMxEUgZEj9Ten562JABSRGEhImEsZiSghpjARRGADBI+iCCRInjZpCQiZQYrq0yMVpBIBAFEPRPHevMRVsWVrBx+h6rXL+Mf/utv728IoA5kxAiQoRmBWIKlyO8lbghrmIEYxAgdoZigiiJgBw6s5EwoR5RCykIoQgiERB6ZYEy1f/kycBud8YCm1b762TfSKFEANkxRrjq0HsSA6/UxxZQveiBysXBLqCCbDkrL587AovAeJ1cWYNkwTQkzC3o7vvmTf3v+5h/fPv+AgC5b+JcOkEGGGlkVAxkq22kjueiCyOfH5y7Wsvx3P30/hZiJsRFwA3nu9ba1R8KqQXLQ0ANFUwjfS/BQgAiYWMC6IoCJm1Jjq2RAIrI1FVVRkQVxFTgLanGWIdKmyogaqcBQVYlA7Q/X48T06trLZVUaGkgpNTF0TMKZxPJ6f6P7+dXLB6ge00ocQ1RaU4iCaQHJH1+e8nEu9LY/t4zXx01ZEiYQN6w1cmKAz2O/QJSnwXsTIskiAaUCSTmhdpa3xxugeaaiSN+zYgWwcEVCikIj5mGBDI/H7XrdkPLx9oYqSBKCReinndM3y6NVbFsCg1VBva77hZFb4dYjc+/iBIEs04QktKXFPGdQbccFEdFWJH553PdrKzsbNMgqTA7eaFu+gN3TzFukb9cdkB/32wasW3fOUTMxXZyccxKCHwKTCJlXZXZcUG4TEBGAGEIKEh4529EkMMap0pmlP12Mq3LmOwciU60OlZm+HufluDxdnpb7eY77dElRjicFAI8shyCm1uUjXsXa/RFrzPDYpEcj3kErRyyzzGJp2y9+9vzNVz/73fe/+e33f3P/fL/d7Jv9QJJKKJj7tTaSFUaAQEVQLN1MDGFXEgSPGEAEZDefYAEz4q2geTAZUCJDVTkxU8dKowjGzOzNSwgav+ccRBCLzjiHcBM5KJmKobigILMIHUCyChArI51QkZJkMRch2pQFhLC1nIjjVAkvBAfydz4tK3NMACRu4PPHtx/efojf/ea2HqXIr8CYpFGUDtisUrhjIVAaF0A1KoxYACt5ATNkYmQVAmZgUBFjmSuQOJRIEha4WjoBMEs5zlGQt7fKol98e/2jP/uOrhuVYbEZJVlRACVxlaNVmS+5RtKRVSKXcoZQd/M0aUpIVMhM6SVNEDgdl7mvpH6ELIXrL7/7V1/a9bx/6R3nKr/fhpQjblgJD09kPzoTsWWt1raZp+r2/PJ8O1+/PJbkvOIz8ceI2HifLHcGNtIFhY+PP2vXlyt5OGYhRhYABBRTpQeLsPYAcoS+tT9oyoqQ0yohs23CSF7ktSyXUpeCXCe5A5Yn0NazGNgLwwO5CCERCdAgJwfKj7/7vO3H5eniHrtu3skCkKAd2/RXqrQBXpAZXHjddqDuO7yOz99kSFRnaZe+1vSYjMygRnH63FCA1/2cl01jlGdIO7zS13lG7Kp+ntfrZVXsrStKx3auMWtS0m09juPSGng6i2rmGo9k5jRk2OWKhYZpvjqzbDDevjziyM3fF8EdOzIcm4bg6216rQJsvflpyghN3Rca1wxWh+p3s/Hl8wfZX/Zn5ADEcA4yq9IMEBLhINS2oRdFRKY1RmGbAQhWi5jDiEqkojEG45jQSFNhYmqxm7OjMmZEMiPi7Xzd2yWzHFwOToAcbr6ShBcKwVqWsW5QUr1vXbYGAAXBlfpynGPN+4w06thJS95DLlnMILZrky3LZTxOm8NvlVtHgitLYI3hFUKSW/v4y59fnj99+/bDj59/908/fn795tNzFjH286wsaEwBXsqRSAXNIaDew5dULJqQNtKzSuKWOQLYHLgM/8DvlEJzs6RalJ9iq+YDKPIClCQ1Iban/u/+4i+v+2XZYH5iTnpn6jdakVG+Khj/cICIKCaCjCzSYtmaNmxh9hjhG5Y0NK9EZKtclRHI1RAQoczjNub4nP/82/s5J1U67lUbpSs6IQYM0SjPKmcBLhqZyQhZC7PCurRIiAIWkqohlAiRRkVKDOWIuRFYZBAAZVUhYKEVii+Bk19/utMfs4BA0m3Z1mQvyqJCKsSE3tvl2HI/WEGhqBwBi7UQDAdQMm5SCFCwyQWyZxgjXC/7jz/9xNCFusAsavsv/uT18dPvfvzn6/Yy4HquzwiPkkTAVYT+puyJbNCas9J+OyrYAeXL26MfdVUf7Q2pa9F171TyOV7XWK3xdtnv83HFrXcdbkBoBQREiTOcuCM3KGLARmArLUUIGSAxhNGyKKQJhUVAbJ3qzCpAYVJkIItMz2psOS9A3MgjMFODSXdbQ56fLtw7ID1uo+8baXt9PNqewATCc8FTa0j2+vr2cn2ZkRgnAR66P9a9oIXK2xgxhzAYYYpr40vTsZwV9+NoGaz7l7d7dOdNoQgJSbC367SoSPAKCoO04dQQMG08guDxkEiivhHQU9991b5dgaCcuCLnGucM5svTnnEUb0Fyn48oFNV2PYIoo9DSxn0/dozFUJWJSFRBrQH2TdFyUi0h7KpxLljFXaOKA+B1nsq8hQWIXmI4NM3aEAN8KUnWsjVo0z+UhQlH3cvn1rsVnr648rKjLw9LVskKZOImnz9/6UIimbXmHISgXXnTx+P0tXDCtgmgq4iDFJLHmVldFYmQXAKetv5qxtDPOR4wuOPBT4wt3JSEMYlqVT6/7GHb4+1cvio8EUj0OLYIdos0r2qX9vXLL3/2s49//LvPvx75pSNi+bRIRS6GzD9kxKlAAgoCcCEJ9+WzlSnJGbBAOQ5Pz1qMhaAJvKQ2IS4axIgBCAmc1BiTj/3L+HJ9vv7ln//Jd3/8sxJIxAQmJpQEDADM9OlWTC1CIEVXY2iNIhsuV9B+eVorCfOBfMs7AXEVMWAKMcVYXWA6JUqCFeTtp9fXH5YNjKCCjAypHUGCsiojAyi8zUSsBQjBzBlFSAghyoJiGDPAK/etIMAIgxkEhhlLAQY7VbBLVDBkGBOr5irFQs7XW/7H//abf/9Xf8kIESuTw9WQGrYqKC7d5HppSI4l7760IkYyBM8sEbY1VJoAautnZGBVg/D1/Hzc7m+EjZqojCh5un7iDX/6/T9vVznuItkwy1lrJhdWskcsuLM8XQ5dMR7rTop7bOvN7JCjFmeFbgooWzU4HO3rp59/e/1FZynWc7m7FRGQ7O3IZUJNuS1kKjmQOOu2hurRdMNKpmBApZ0TyVwzzQW4zfhcVNrYMwKSWwPOjBDdAMBXQtL7H5ZnATQBwR9++qEfL00aZG6dKvi0e9ejydF25EoR2b765vVtMETTsoiDZN/744zznMsWI2zt8qHJ5zk+yEdZtnJYdNEmdp5jeA6GnoX317e+XdvGWY6pnXf2WGlv68yktdbxYStVn4RE5xwXxr7Jm42+Xw0oENaaPu/7flyOy5jrdh/KLKqFSK2v24NVPBylV8LedsbMXMTABZF1jvsuelujIMmYRJQkmK2qGD29A51zHods16sXAk2RlgvKs5qx7FVTKwl770KCnpjFlBTpvW9lwdPB+UFQiZxStUYOd+mtJ+T0lYLctDDcvbya9Ji+cj3cWPSy7URFzOBE0BIDiQQprUihiwKw+TyODRIA1SrOeV/jy3U/CAmhVRkgqF4qomu1D2COXx6wgmuaKHr43oWSJoR5rciU/Re//DczHnb/bI/P66y8T7xsqi0sAQEVHS0CoYoaAVJEw2W9aiIOJEVTgwPbnLGqOSRgJhezbLzR8JPdiLjw+pz/8//53/+3v//1Icc3nz60rfGuY62MYpaAys4GC6mUKJFEuTEgDOKWlMtWlwNNy2LjOqe9g0CUmUyXBQJDJYMjRheYhYUoxIL85ZHhQCmOPICazYYHZ1/myQDAiANLCthpMCQWAmkDRCzIdCuEbK0osycQ0IL3XZYmlkm6CyWnn5nQSFzIK6TCHB/AW/Qv/3L/dfv1X/zlH2FF2nTEiiJARHC7Q7ZGYpmFwU1HlkjPqkBxWolT9ICAKlTChjgdoZIQqHBrm0dAta7XrKyEoz/rB/zH3/w9RXXSGTMCCMKznHC6bbStwmGPnVGV7k1aGcH+ds9n3fZrC9zSw8p4O355OX7+8mcfX75GWGETiHvfzB0JK8sTQBR1I+DKko1RwAoaS0QmImFDFCokHgvdvXY5ADIhk4oQHAABIasSG+hMD0DxsBW4C0utZcoktrJvmwiqSOMymwVr68JFdp5sDgXt6O7xeLt/ev4AUE65zjf3rem+Fx+y63bM4T/evujT9XWcmtY2ilzIXBOo2sddJmYEfby8FLZ12sZMTJaBY0b5vgvX9uW2xo+fv356CcQ5Y7/sKg2KQ2i4Px/CHAXBIIWRWa33jEDKt7fPINhENpENOaF8TWmajE2vHo8oEG0WDpGsqgUJjiDhVACI4oqkJNgRiSwWOG86Xmeseb02oCIhm7OJesTWNEhDMnwVgAEgAXtFhQul5Pk4j+MJgecI6g37iijLKKgEZBVD5jQC2ZtWOASo4Mt1TwNVnvMRFkxcbuFz26k8z3M45Lb31joxEvOcjgRc9HT96pyvb/cvzFvT1B2aahhExsIhugm1DxiWPqfNdZbXLO9NoLipTI+EshEH7NnBjqd/+z9dPr/if/7b/yzEXXpgQhphQ2KuRLZKr1RAWWn+Xgoo22DTkuj2PsNRYkdPrCMSUMtTDx3pv/rz7/7qr/78r//9n3/+8SczEL0E3K9PXRtVhfYtMBGSGzMCABAFYQoxJk3PZ37O0MVcmGvd1piJAIxYINQSoRAIgnowhKbXWFGJIY8fswxW2ajiagraEbRSwIBnskeRolRwIAVWhK/Ed3gwYBg5cd+zGgSUmiIJSyQ4EWpWhUcKKzNNcwRgSHciYIQEmIECJYy/+fX32tqf/MmvxnKSgoJpjg2RYNkZeTQRBc4CEnaPTRpzJsS+9QSZqzIRoRoDOMVyYqEmTaVsQhhBIwgAV/yg16N+ln/7j39zrjs0oAIsTsxVVoCeufIeeGhcOSZWDhqcUZETA+aQNEa+NkVkTN63LbIUkQlnOLgSchVYxqok3A1EEESRpO5zofYsbAUgptyAnDGLYyxIoEPKY0IWK7pZU80AzKoZyNq05RxrTIMiQCHclObjJk/7B8Px+fUzamjvom3Yw2ZS08veEuH19fbytE8bsrNRXvQyxmdb47RqGk1wI/B5L5Tl5G9fkBvvfUauuSwhh1+2Z6DJJQDYG0VpFdq5TBZ0ejo2G/eo3Ft/+vC8iL0Mii3HdlxFca5o7Qk80JwrBeF1GPLSbUtgArachZYZmV35UJUIezvv1J4eyxiFlQDQVkrfVDHTpcBtnDmRd8ZUxCoYNpGBirVvCNPPyYSPL7MXHdfDAMbr5EuPqjCfkl2QPebpqL0qkMAe51Ilabtqw7KcLBBWHTs1JMBMtwxCyMKwgAAWRaGaSBBCUEJv5+vywbx3ZGTfUArSPVrfiOq9ptcAC8Ahkao3He5t35l4jPl6ezSL3lujvgsuunjSu16BiXRjuZcxrJh3c6jYSLuqcqYnClVylnzz8z/9v/7fvvsf/vvf/6//r//lb/7L32DVpW3pRNKEafItsREiGlQpwsSYmHthXxATi5UYDIECpXwqeENeXQNKmfbLlRDB7fnT1YAoWhVLp0bYCEsSIQEjkZUJsZLTMDE2AWks6BiwTHOcyy1IEVE0BSMwCLdw93z3twJTShN2O1+/nD99H25VCYwIAVA5sD2ooIwUq4hWBrBFMYYmneUhPZi4GKsMq2mBIctxuq+0DtCEAAWpeeYl0nElWVdVzAnWDNELmZCYJgfnfUsw+i//8Dtsxy9++UuHDQHQkkSywpCmkCEkVGNWEgkAH4qFkF0Va59+ZyJgAEziOueq9GN7RqBdyc45w1tn0uZusPzl+eVf//m//i9/+5/f7AurMHRIu1QSqBhwLlxartzjx/X9bZ5NtISeoBS7YGZvRoSVR9t775XlSU74bj5CJpSKTEpg7lKy/P502aECAYkVipUQeFEuZKwU8yePJbocYq4ioiY81jzP2aQVVlMqqgBvTMnJjQNSuGcm6y485kk3Vghwlv2RJrpvXb6MMx/5tL/0j/3Vl0OCUAjcMip8V3GVQCzUMVfEQ7b+dL2SuYEti10ulsvX2pFmjP3YY1XkMqjKWQUp+ViPl+2amHTZbK5xfiGlrV/M7e31frlcWOCnLz80vV7a1SI8UoCrgBKkiIsNwZcR4dYuzFwZUDnRo1JYxjoTYsR83g4ifMz72+ebbnR02bStugwf0sjdQHHrmxbdH18e63bZnaDCc+frZTsaoyAt8GjwyPNo1+FennMOKRDoIFvmKMj+clUUjhKYaV5EQuhjBQQCJRAyEmlYGsxcpqwsZBElAdTUcb0jPLgTC1MB4FiTqbsQZsz7oz9dOBkimICYHOBmXlg9ibRvTMQ+5325K+bLhqIHICYlMIRXEbV9141oNvG832+eAWEF0C88YSJstMotqeDP/vjPfvUnf/Trf/nNf/pf/z//8h//1jOP40BixbZgVReXVIenwI2FgWGEVQQyCCmhADOgIPIsZHE5J+NO8umrD8zi4ZJMCNQKkCRRkgoiw1mYSRLAIYUCSCsQeWu5W5rhOu1xn7PpvmNbtszfe3OCM7AShN0BgRyKEIs8C7///ZdhZ3JqYnOt1IGe5JLvLmUyp7PeCemeUQCc2ISVAz2SQFqKYg7IE7FYxVCdhDhQQqSlASwuslTAREw1rHKEwsS5KiEm47nO/XrxpP/t7/65b9effQXJhbo5EQaxw+MR+7U7AlRiGVB5RlhSE0t0z3DoBMZ1cEufUBjlHt64YaVuGy43X9WkhME9EJ8/fvXXf/U//M2v/+v3n386awqL2o4JHdAQZtaF3wrWnG+OhRws0LI2rgcRimIC0v7zr/7oIhsGRkF4BpRQgACxhi+hLYWCsZsw8VzBTtIkKQcBYWsETyyJdvrMSsGs4IgU7eD1B6e9aEQAoaAURKLTISyEAVWZLNlIzgZN9jxPKiQoMMPevn+7PT1de0LUicqxvLF86Mc55ulfjiYN9qa4ltnjbZOOrTlzpgOg8NEA1m0Qa+uwEWHV2+fPnWVjyVtl1TLfXy4vijbPH9CejmcQqSyHijmDcHvaN2x5m/DI49stamGjOS0EWfDSr3mGFVcVQcJE2vuKkN4z/O2nz/uxy1MHd0bRQl8LCXAZDjPaXs91tOzHfvQnrxDUysgIyHyuXL7u9/VyPO2yMbo1PKvmmInY+GDGAtu7hHshd+FwutloG2IyIELgipDLMR8TI6AJqTSQQFwBjzEu163QlSiYE8IhHACypGCtVYSUiQDhExG5oBVWgEPMmNfj4AkDp7xL6c0FCZg7yVgzq6KovxyViIb0WOd5yutJT8/ZZGNB90SoapHSmA51lYv7jDU96y0mAlMVswhjpoeXZP35z//oL7/785/+x9/97X/93/7+H39z3k8GFpDZSiJlIYRM5GQhOiX2LBg4lyB5NGgFdPJJsh6s7xyV4/ocEIAVbtI0wzDACYOAkVsBpGRikhGGZPPFqDujmKy1vNI6AZASgkhZkqz0ygFxIGERFigTw/JcSAxVK+br+WYQWcsRkqEoLXKPbEOC2uqIsAipaglxIqwgdUb0wEEqZdwWIfIum3kYmTAl46IKDKCCGYx7QE0xq9ycQFpaCWeVN0wqTgNllTNEKoP/5r/9bZNfPj1fncZmz5oUkWbZPVOwmJAAq7IgkEdmGLgF9j1hEkKs4CLpIqK5IjlEQMSVsGd/zFNAQJsjnOtkgT/51XcA+duffqh61/w5lJIJhkUjxNbnvtNA9meCl6ddSS79Zduf9v5y7U8bbhkVgA5VhJRMREQSWchSTauRhR9bRxSbrtoQkRkBQpCb9rmCMAnPg7WhPqyelFwgAbRpulcVJnLJsoiGSsxUFOQFEFzmlSXn7RSFCKj0WY8k8HCBRrNWWFSoaJ0ndh1wVpBm7bTZIpCKHn1/ykQzF1DPaNt+cPfzduLQvp3mn9e4AIPl/nxUFOsWlelvr69vqr0JOdHbee/cQKiWsYNDbK0Xiez7ZTvefGAt5QN1ezxO88fe+vP20df7O4xdZcUcYBvsSvh8vQrC7fFQVFvZUFh5+Ppyjuvl+ObpZRXe1/2+zlYdM6hTED3e3j7IZblI7b3v6z76pT0QaCuIVSRetCMUB2p5raxEatNz7zu6jbHSrYlQQRXeLVn4nCdFNmkeSVIYyVhCQAIaRLpbehZiEknPZZ4FWO/z+PWYVnBsHZhIBM65Neoi4cFAQDTnBDPetgLK6QXFVb2xA1DrTUhAYiJt8boe9sjLsfUuDJRYxCkEhKjatATvml5fYiZWjimMUYgpvaoButn09fSzr//Dd/+nf/e6/ub/97d//w//9GU8YrwxAmuGEgMEPpKyHHrKAbiQhVJJTsvaiSqes53j/qf/9rufffXhPNc7Y3KWBWRDes8NF2HUu0Qeo9bGatY2vjpzxCgYnkUQTLAVeqxIwERgIIeMGAjFZBaZSKgbMqQzPD0eP057JGKCkMhC8PBI92iNRYvMcwpJTkykZCBgSscqD5YGSVkwWAFMCzl6oaQaEipzZSx0alILBHCj0tRus4gfCGc5NtwMAtKqOpCsJanpj0fpP/7db//VX8iVVCkTKjfKtWBqphjHe7c5IWaF7t3cUBAVkToJQauIs+5VWXvrZQYBIIiEQrIDnrcHEwGUkEK4nfXV8bWwfP/2091yAAx/fKSW/iV8R7zmbSrM56eXf/fHf9a2jfXyUV+aUtJmACcAKgOQmxEiUigzEc9pIIrMEIUO1Np0RGJnJCFKfxIFEEFKhgpFI9pgZqE70GJkEHVIZKpViFgQIDQ9WBUSkFOKJciJTgwhX1iyXy7mqwplgTI7lg/n5/1FD7/dz/U2yuj6zMQNe1hUUVhdto6suVOc076M7bjQeDzuP7Li/qyJSSHSt16QuX6KaE0iV2XQFcWwAyQGCCp2m24WjTiqatWcN9xNpNWajcxbJZy9Hesx59uddnSNMSdACRJ16ZprTankZAHyeV/jYbT3fnnHGHWCb19eCMLH/T2xLSgFGVKENcbcjj2Ex5102xsz77rKzNqo0XakqBhT917ckmPZg7X7HO7RNu09YeFtLiCPgrFso30/nmfaskVMM5OjWLgXpU2EsgICXQGR1vtOkIaIu1blO0/werlyAXMl0sBoxEoS4bOsU6PETODeEwDcQESCKgZU5gBEAqFhjszUe9PkucY57hM20qdrT1hCGiYEWQnVd9xYrXGsAFjD3jN7xAhFKI3AI2OdDkf/y//jv/vTv/5Xb19ef/+b3/7D3//dD7//Xog0ceEehLGDW5Flp/fPzkmTCbvzHvPY8U//9BulUD6K0NMQQQkaFVamrUgswWQjpgpfswO0rhhhCSt9JHBUcBUUICIytITFhsloEEIJJSTLi1A8ZmFk4P1+hg8ErGq+CggAoHGH6Dd1hlnJ4JpZBR4wIYsQhMEd0QURC11pIi1AKehChKIV1UtbMdViMCT2OpIm4KMIvBIZBFtBnUjqRAiO+BDQLMYA6N//NPUfv//z777Rp0mdGTpirjG29kwFjVplMjLh8mUMguRFSHqU58vLbk7r7WZR0JJ3mbZaSFcJQNK2XyHNmuPw2LYn8Fbrx0u36/Xy6x9+/On733HVHVJwo7jc5tqe5c/+8i8+fvjVp/ZJtifyBJGUgKxGVEFR4BmVlQiExY3zHW4swkCUKMybtNfbnQkEUaIaCERGnyIMwo+HIbKo2OO+oWJrFLHSkoCQuFFGFiYXySIoQGSAJMIFkAhsKf255938PJ0hiS990327rbNO16RzPCJGNtiuBwSC2yzgp4ZeO6l51pwxA4mu1+OeRjRpT9KNS+y067EDRy6DTS2dlecwIRKsxsiMSBS+XvPRsIG5cXjh+Thf+tGQ7RzoMWykoqpvH7br02WtVYgGq124Any5QxPKyxY1U1KBpLaNGYZXrsfpZ2/84XgGoEw/lTLXwR2QzWdjLWS3CM6sasy3MYBRtK/lkKMRajT3ZC1POs8FbKwFGIpUVMtnCIBnbxsLRuVFtLJsTUkE4lCyczigjZOJump5ISNxgwLEetxvmd7bhk3RTEXGXCqCmcAw3SpJUAkRKV8+Xs5h6aatoxB7drWTshUb8qqQpuwclqy89U4hjxyEvF8uy8LW+vzTZ2mqOyOoVAFyMmXiEzTYujGikKyMGQMDipgUCndkI54ZDkYq3/zi25999em7P/vVf/qbv/unv/2nGLNiqOnm6eGI24IRhuiOtbT1rz5cP3zcXn754dvvfoFACVFIyCCQG3N4OCQKF+G7MxN9YmLbelfNmhVj+KN8OTIBdaE/VH0ysXATGGEgDRJgoYBGOWIClTnOWLd7+nquHBEuIJWJIAI9aFaZAQAwJQCFZQV2jqIIliBGyj+MHjydkQ21mHfOsnJVR8W0Dq1wouRaUVFYGdgcEhuryJwLKMqTWbwqES2LkMkjM3787eePL896kW1JzgMaj/Lm6+XyVCjmE4UZOEYqtsoOKPfzse/7w+t+S9mfIKwQGGnnDTAzFwojSUJVQGvNIe/jztrkwm1ulfUXv/xjJPgv//Q7pHqWhqmb1Id/892n41mscetQwAJLnFGlLEBdwM0LiogAoaATb+u8MzE3QSLOeN4PXyeE9ctWQgVkUSyiFBQUIeACkGXQIgsLUYUBqgyAiKAKCAgA3BsRViRGkrBLFSwMYZUvcfJYKm3/5sM5FiDcxttZdn060s67Den90+VDVFkVI16OayknPsyjkByibRIZa55JdFHt0Zb7St2un2zdsRwFt6yr7Pdh3QAlRhgUoWgtQ8TwBYLH0UnkdM+Kc947yHG9LI8tWkYSFFoh1HHd5/Lb+XbZDiHanvaKOleEp2Le8o4ojba17LhcIJNPXHHOtAJ5Rm0hXx5zYlArFJhzpnIxnOOevC79ckArZUMyrE1RVdG5t77YxmMmIBbGqkI7jt46IlA9PCNKCYslZZUXlJXzLm8Pw8e5N8mo1rfbHJS1ITLzqigATFQmyKgMCGXQ8Kgic2dEWBlpKkdhLndAtD9cmtJO476FJVb5ekAX6FvUkCgEJ0hiHnNgYJcGcnhGa04KY0qOvK+7NlWqnRswFpQhGiSz7MKLFzSA5bUqLIEwiAKhc6tajjjDRfH66fJ/+PAf/vd/9ddvt8/j9afzcfv8w28V+W2dyLOzUEHb28tXH1rvIZMh961rbxGJBQQkhPmOyk2AYgSUzo0RXTkP4av5+Xp+b3NIExApgEBjwHyPv4UnQszKBGUgal5UgVm4PDwpgaxyFXgQBSOevQAC78ReLJAilO+eQZ4MU7AtoCDEEHZLLEOHyMwKEEpBpNQsKIgqtodE69IWoeqJUXDbkXCqExRwFkwzQtRCaxC1dmRxNKkFwQHqZMn/9M//Xa8/+/bp4+u0feO2cdAqCmA18vBJCSQSBkClSB3D7X4HNMCM8ofxdXNFgWKBjCqClTN8bdsRxsp4HGwPG+gs6I6E/Kuvfv76+XzcP3NmdOTeRbe3B/xif5LswFWYmFgVnhxcXgaAWQiAVNmbjrlWZO+7lEQYIyPVjCmbBisFEpYKKEYrgbVDRCt6D0yb4bZhhLFoxfK1qCkgABQBFRSWFaUg5Vi1kLBwSxWVJkIXPWRT0bxSnJwLFatyYed9e8l5wvm4tGMk970By0+30d5X7ZBCLQa5LYhAaqI7Mr+9/kt2szKpsnN61vXy5AXncK7cerv0J5sOkMRoy9Shd960hYd4NMZ52XOsec6lJLtwYFpNiznPCn/ZjhmeY0pryAsJ+MwECsWI1QR4rktmrlOFk5Bk02AoXLGAIEuIoksR0N0nd7kcB1bZnAMnNADGsIVU0vasR8WQFAgaNTv3XfvtMWdFsKoKFr0+7iJ8dIWaPgykcSsEzggKIG5EimEN6VnJ0INr+QhcG6l6ApPRxkXnnITcSH08Xs/7frm21pkpKguSGd3N56IsSEQgn0uTSDZWBgJI6MRpZsncLoCZaecaCiHShYCLfhwLoX36+OTrnLfbiVGa1FSFq6NAJnECbIKM2XvzGWOsEcMqCrYVhUSiHcuoIo0y/HLwsX+FP/vweLz+/LsXI8+1uByBKqMwAQNpLVt9P7CpZzECZ0JhJYYywiIhQiwqLDNjjU7aR523easEbN08BQhJmAU8AdAtqCq5Z0YnFdAwgkpIZwl5dyK6VZ2O02oGL0pAX0zcqNesd09iFBdBwqLiKigqRCCOMjCV1QTdJQFDoJwpg6BQNwQyEwnEakJAcIbvRc3ZCkIWZVI0W0JUAoZFXpVpIPgutnEEJ1pZ5w9v+87Hn157n1f52HCL5TddGxYK1AkFWATMFBE2FxNcnvfe5O63xX+oN1ZkFwUyhGaJ+S4A8XKogOQFKtfOc9hPhZkBTa5/+vXP/jmjCXWlS3se57nWzCMAnbKAuxS4BwovC+ZUZksHYSbZCO+PGSS1X6pEUS67FuTK1ToTIiIBJlAJ78Qe8EgHRiapdd5QATTBI5KKGJtyZjFy1QZwxzFZmHalZAqgetfUl4Ucut3f5pe0J+870pfb53Zcm+6QE8Og8jwX9i1Blrt386oH1nnen66HcJMgIWTaoMps3dZSaEBCgD7PTfqxXW7nSM/oiIJlUZBrGGauZCgW5t7wfp7nYz5fLizaUiDi6OpmE+p8rF2lCn68PYixcxtpWdW1mxk18rAFlE4Mqdpq0La3khgZELRtstwRmIlOcNf1mLd0l3opIGKdjxlZl+uV+laFvqYqbCTzfKxEEGTqHl5UXVkAThvbthOAFSNIWOp2JMZM67JNzwBjrisJAgq3qIo5kMAgjYpVfQ4ojPRkBKZFxdTzfm69e1ZBXvqeCFVAlVheopkpBCJyH492XKhoLHe3y76nVQkbZVVgUcjGgFhGAK21jjwr38ZDtT01PXiPyJmekv3DPh3uD8s5nhq3XQkRUFcAIRE6YhXDdnRJWr4gddQCBJurNwFAhOJGgblWaci1PfWuM0eNM95+9LRVnjFJClM22Y/9QAIkwMTCKoGAhApNKKziQkJiHp6qlwA45w2p+r4DFFiAAxZGoBNkhaiYJ5Ts/dDC9EQolg6YWAnFkFDJUMTIqowBJvLISYbd0QsXtZmVBIiOmRkansCU7sAOUOR/2LfbnMXVpAFSYVoBMFFHotmwAHNCdRFJnL6AEpWbZQtAAoOTGaIAUDwhohBJRGdFgF0EMuT7f7Knp89//HG76o4qHhljGkYTTkZ/mHALDEt63gVJPIAisqohZW8r1iYNGSbAO2oqlmEUS0LD9IwRhSTciDdLJ2lZ0drl46dfcF9NmD3j1YnZwqMDIkXSoUrgINhA5ipCVOTlUEIP8IXQ2iaIjoFY0nXOoapNdEuLUuQMLEMMoMBzhIvugFburIy1QUZneyRmAQEtBGGmSMG2IQFAeQZkiQW2ikQ0OSNAKKvG+eAZhB70qOpKes6HEh0lAttc3q7bmHeRvLaeyWAWHkRgEG3bClo9lpdH2jsQ57yfT5+eoG1CVBUAru8fK6BPR5vb81eJShiR5/0c+769PR4qrR9Xyfzp/pYFx3EoBgVUxVy+v1yQmTAZEYFs+rqv1nW6+Xm/NClqYPG9TeycEcTlK3PFStPemoin9S5lLCUEfLdBRCKc7oEAJ216+GnaGGnT1ifUck8f+y6bM0SdFo/H+OqyC2HEygKINJ/GC7cP0ndUB3LPgsqEsqqE3FhX5DAnM65SlS4dihfU3cYeuVEb5yRmELKKgiLEnLNt7ABZhUBCtPGejp4ZmawSVEGeQh6QaUrNC9jn0aiqHAEQm8iTSiSExXHZYuWXcVMpIlYledp9rrJ5u42NNxbsfWOR9MpIlgZZSCDKEMQIVTgsyjIgpCoqqXUsgEpwQuQdejs+UH85x+M+bh4DyVsTFOIEJmG6EFqBB1dAsOVagALESAVp7ojJvsbKdGUKQK8UJkFMRIzIyqpyc0/cmJWUooArqgqdG5ARRlaN1trun755Go/fjTOh0icnzrwUBAUmMuOqwYCEbaJ3BQxHykVOwi1LzVvpTntBsG7I27YzVOgsiBEMkSEB3YUgPXFKZC1IaagFJRIkmCVpWYGIBySmxS2cNmwNBYMIfa7f/PY33/zqZdjtuH7giXOMSkJtIcEbt/CzjNtm/uaZT58+Hc+Xc50WFL40TQWzFYJiImV0pciYZlhJgEVRcLfwBA6sEQawQqxtmKSA2zg9IxQFDfjSlxs2muitk5sBMZNmZAFR4a77sAcid6CjanJIbxHTpze9Yugt46KK5Y2A6lG1hxcQIsKaju+7wHUeIlq4IWW9m6rFst6f81ZWNgFlRMwyJiRgYpVNZfFqTKnwxtiwA2uaOUe2HRPl0kJBonKchxxVC8pTVPt2+3JGVOsKLATUtt39QRVUNRd8erpGxOvnL0/Pz/Z4MDhY4KrHebexPl63GvdVWUSk8vVXL63pGlO5IYB5ArJk6rJHvh3H1zl8b0wQ85xb26VpZjDrSvTJF2b90B7njQGrs/vi6Q3KIVaVqBLABG/IHBv4JhqR04x8jG3fGxJUBWE1cl9rzcxWGaq9IMe44TKFjgQX2UYsr0rIoBweBJ0BnmSjzkUPy+jynFm25tY3QKIqVVm3GVFP/VJowQGMsKISk3kDsTRsXNgoa4WPdOktvUg0CYatOVf2rkAsnTyEaev9sWYaoryPWLMiD2LFdCB3RNFKB4bxeLTLxbEckSJzmrIICyJCls2gUkpirLc86+2uQ5vSsTfZ9nDnKE2x9MBUYWGhirmsMhgjzYvaxgQESdBAZ/K9kqi358unl6/mfEsfMS0xGUaDXH6qNEkhzwVQEdxZNkSGzIi0QryfnxkFgaiqVoqIAlBVQTVmS8zCTAAvyvRY8IfEEiQkFxOIkiSuEkenrh2IHDpHUVExnjWdEFgCs2W1EBri6otXQEk2TVakSnfCFci4cRfqIkRNlKFPnPMEBiVHLjLRmUYplEAEADZIbmhYcNSeWVKGWVhVgCqcggzUK2sZIju9fnk8f/99fv1x8jkIeUDmHBSQbTtXrcyk6JWJAsVrxFvcJDoz1QZhQMU0GTIFEMCBM7SyrJYz7v4eO+BNqJjD5rT5oIyd04EQ6ZGvqfGyPX/9/AGLRSirEEAROuoZAeAoFAldVRFmhHKHy/FAaIBP/RLnfZOnhJ4FwolgGxyNt6p7+lzLdlYCXCfKtvetxQDwRAmsdXkPh1om8AgoGlylwATZWw8oX7Zte4WKvS1IHHESiNImYVk+VwJXF9lUDGtMx8fb0/FMhIEgBEltoE1dx3YFxLdxkrBVPWw1wkOPMF8FWqGBj5/uk9aGmxYDZLjt15d7eAdrun9efjRSEfRygPN2a9yZaRMEwRnZ8GWdyyMuRz/Li6kApyMCP+xBjMydkQQEu6REGdqam17H6RBIjcNX5+YZowZ422oDLmgHk13khYioKpYlARIVeN+kIAvEwgmqVlIRMQf4qORNrsyPab06BiRHP1QADHMOf6fBVrHwYVbhZhmEGT6BibiQ5GFGFgVyvt2AeTu24u4Bm7YIO8cJjaB1kAj39CJhTJ4+uPVImvPsx7aGQ4AyYkJAMbpBeHlGEaGILDMWOeck1cjFgEKcj0nKCrQeNso/XHcVKJQAmHlH4bYdMeB+v59+lz23/rQ3SLtBupIah6frhtJaJHtFs3mm+VIvb2ra9p49YgSog5yRrM9HfxlHeAzJG/FsAQCWBFi5MQcJCTuYZDKAE2kVoldVIQQSEnIBJFiWJxYzgC2U8MHSLGosZ2TIhGIVCAAvBEjm5r5mLEMm6SSv5K7Vnd3TqraVLpgtk2JBJhRi5gV7gggkAGBDJJpoTTKwAbgXiEUhMXfoWbYQYVCedSLmVekpGmWMzCA0BHR9pIOkVqDkmqKyEINrJ8uyaH0vquJ8Hfe/+/2vv/vuSaVfLjsUu9NPNdQKcnMGhCyYW9+hChPnOEuT+14FQvvKzARq0ahBvLMhqRYYWOaJXA87I1VJCbZHzKxkakHR+PplnPfwCzRquydiRb0j9YCsCjEJqrOucMRSbec5CmFrOwZ5Ge9aYVZAhSoVoEot6I7gCQbAttKhCUusmcgFnOEoBEb3oEImLqpsUAG4CKkYKwKzYDISQ6PWKtJiyrbR7XRL2JChILIe88Goez8S6hyjXVrhDK5bZvMipkDwwjVcwCxuRdJVMoq87LQFxbx12R1i2xozvr3dr09HmZ/mJ9jL9WCQ6UnQheTDdcPwtx9fN9VNdY7H3KlduiY20BS2wrf7Z5W2IlCpN1mRW5MdyOK0CgNHryr3uXDrPQPbNUQUlVOMzfNEQUx1h60jowNs48sp89w+fnyLOebYksbt3i/78dyR04MgBSqA8frhaa7JWy/W4cEOGyoKc9cWKwAA0N0LkYFUAMoJJFNQDAU4yXK1nTzidX7h1gBAtIVbskFNd5O2Q9IwNxsAuZHc394K8GgbByB5MUOpTwSauUky4bmcdBU9ztfLfqR7TAcsqjKBBcYNsFJZ32u8gGV+bv0IwMxsmzLKhKLWEjNxRtU+e5RVqy4NLODLmOCroxzUtQsqhldGZCAVIREQCG7vk93FOfM+E8i2jTdpFpnoWBjJDFCtIxwBE2k09YwRGcQK+V5OTq9AqvQkpqwIAKxGoKAN8pHlkJ1IVoyCCtyU33+u6f18/Ac+sbeoZbkACAiB4DF+LFxMpdATedRKDgJ0i17SVhVUMaFSB92QFGESBAAQELC8x+YLNWYuVEIpo96phDFKESowQnM9eX3Ufe0zFmy2l5dDIRRgLVxtl7W8dBUKFpoPVUGWBLFcqqqI3//+h3/+7fcr5yf4GdcWjsjLeCK+qRzvNeNWD3DEJkjl5WX3TcDSJNBRnRFQpCDf05LaM2adgSFYAhCCGO65xia8sBwAMX766cHYiygSSBkQAhyKqjARWSSxIBNElAmqyr21rtyiSqh6o4eNe8axH5WmCZki+oSUQDfM/c1UjyMwh//QL32nCshTIVh4MkJBFhCWlMOS+v/X9GZLljTZdd4e3T3iZGZV/UM3utHdAEiQhC5kJpHv/woaTDKJNBkJCkCP/1BVmedEhPuedJHQE8TlDlu+1vdVpItoLeDqKyyZCjGrkEHcryR6efo4rwkI3EZH4iqosGWI6hc87c8zZiZ5umJAk8d5PNGoxLL18vw01zyvS5q+vLwQUWclr16Vcx0R25P6/U0LBXl73oeVYsRwEj3jja226k/j6d081G99Qto5F6Q8vRyvVxJvbTunkbJNM0y0ZY+z3563plDErI3a4/Mbsw5mx0wGizPXbPx0HJM0IyxduNjPOZ6frtNt3gWr7JE2N2EGpI3Gk6KyeVWmUkbU6daw77cXrsTziIrBrYUQ88OPhItQocTDXt/etnZzZpZC9PftYBQlBlClgQAdvmb5h/F8Hl+08cunl7UmC2a6F2Xl1huagacSJ2EWRCQwlKWtObY9YUiDVVHl0JSxRrU0O86DCYMRCQkgKwmKiZJwnevxOLjx8/NTFn59vdPoAgAIwgBWlb6jOnRTCi+ARKbMlG2jrK/28Nd8lq335KaDJT0p3NIWQkqLjL6js6yAIF4z55UhVJIizCAtsbISI2pkiKJc6xKlWBOTutIyS/djTRCmgiZYTAVQlWue0g5mL5dKSLQs++nLTz/++LrJpw+3T7edpLXRFYGdLB0qhRGQMjn//PPv//vv/3MAX6hO/cTN81KAWpkGwAIkVMszDesmgoqLFlQ2AEnKJA/I4EKyKpMmSALT4jiH9q3h5CzyNE9d6BMa8BUdqfgJSCAX2pWwJVciVBNyqPEoibqqnJGjHBjKa28a4V//8Pn7bTvzK0mINoShmVGH2zW2jwjlmY1GlYI0ZOFyxpqICArUBBMBRLgEEKbPSTwcVkEUUkRE2eFHaCa6JXQdr1/fahYPvngZZSAosWYBcxRmGBUSkiNYhqpMu7gx6xaAosQkClgpLBsGY1qCoXDHoiosOMuLqrFc9zfKamBZmVREpRALF+uWNqEqyZkJ1qwIJiTicmRPBSpBBwcAkVCp6sBcYsveqoRxDF3LQduc/rJ98gwobwDH+RWYfRUhFfEALIK0A4lJ5ZrX6P+avzbmsJxv1+22ecS8ruQmXVslYKUiIADX1jcqBqfjnB5Z4ER00w6eD7d5XW5XkyESxpa1Pj7t53khc0b+5acf2vO+Irfn7l59bBeuKQXQLUp1Q2lC/Pb5FRmIVImysrVtXj62vffnOY+TYdBgq4y87c/RILzsKrerGhN3BcHCdZzHPJ+G7GP4qkfmXBcP6Hy7zrPoAoRP330DqXXZvC7tLAPmTARZ11LFKizMfbAXAPq+3455+HX1PrDK7LqWicp0p3Dz0LGRoLuBkGdWZOuIbOeRAztDrlySLNoA6Zxna52ImJgQ57GkgIULaqWt8DZurel1+LKjtU6quSwqY7mAQiHTLbOCZhGPtqWb53WlD9UmvQXZjHW8Sufb04vIIGVd1CrPiMHd0rlJyyqGpuAzysvKwKoxYyPRIuq4MIgiCrhDWmvZONPffB2XVVInBO0MkFSSUYBFHHOuAjAvj3P6/S9ffv7hxz9i5Tn/BartSqj5cnse/cPt9umlb8q85uNc95+//PAvf/on5zUBTSgEhLIRI/AizBaJVY60Cgqo82TXCFAmAhVhZ3OgOXESZPoGRutz0hIoxCrYEgU5VNe8iiLQY12jbycZdx9NW+oRmAvo0iugoYA/AmO1ECEotlW6k6c3xJhOjOfP5/yVcCvEAxA3fQFY4XBej6bPY+vEVO5WJ6K+X7SEYgBP8whNxQ4zHTBFsEMDzKPPyPAHh1mUH3aERMRi2s5r/uWHz6N9QkKJ8Wl8oyieRu+/pwiMTBjuBiWYRYBRtQBAxKvc7JvnGwFdaJ2ahghpEaLgpiHeAPpP9y+tNYQT4BpDIN9zFsSVWBdyLkvlXhRmtjGIDiqnAkRIIsEOUHMtx1RlebWiIUXIvc30TComR+BtO8LPCLIj+ioKX6UFmLi3baXPuWiX5PSKZvI85SGoJde6DBb2HlmoI6kRpMIc287I8/Ojtn4WRmtVUdMH63ldc82XNtJgZURM7f1Jh3g8bWNmytYWVcQUwAbyyKV7j8oq6NTOnx994+UXjT1P6HyBgsEo88BDGm77DUtZkOKKhKCwmD7bY92fN8oCzAqAEnBPQUSI1jSxhkp6BCa/n2SliFgOACRMDFw2ujCIL4eZHnYNwYioiXNG5VLR59HdwaK4FQFy1vIEQNJWlVglUJvuCFGULKjbBmYZ3pnXeWShbLsKQqRndXWpRtMH9fOw2NgBuauwkEWzLIRGbFUgCAy4kpi2voU5ugvi3loCOTIQg0mmIesZbrUwACkPPwS5SRsRmRYzVPZZWK0n+0+ff+jtWVsbozUETcQFbxZIIsxF6byYvLneQ6ajWV0UqsyuO8EsS2mYGNFWrpmO9LGPb7ydkg/KR8Thmh22KklIUdpGPx/GmFBpSx6vhnHDuFCw9DHT0/X+QzSZgH+kEOW17Ou8LgvC3oEIIKURaRC7AR8OpUJltyMp6Cy4U2asX6AItwO5EXKiYzxqJS0kwYBm0jkMCixbBOWTCACLlbO0HpOqAtfy7UmrGk+LRV5MuzwvMDuXr9RgEG+EZQ4BRJV1ZPICCiaEejvjx6/Hx45DWsCJIZsUsQLHOY/n52cFBkoWioqO7ImA3IFLMS97vF2X18vLLczDCrwa+sV5rLOW4RkRy2NZAfFosP/p9/+cB9+6FtStjQ/7By3lIg6atCwTSbLgzLxVPvWRXhLQx2DWyBIR3W+P17u8z+YJkQiZivDECs0eRNxB2xnXhZO0MzAXMokphtkAWUnpmcqsPcOSOpJUeiIESwNMNyUtWJQl+xiAtCYsD6baN74fj3rLTZp7SRKhcwAqJ6I5CJR2MsNkeisnK0UYIoYmIEzQCpagRVQlUmVkeGaxodzfHltQcAtCEcrHcptTYBCjsJ/XELLzkqeXWGF+UR9BFJC2rEtz4tf72bgDaiG3fttRE1kgz/lGPaXQzPtQwLzWUhQHAhUFjFyrKmIBJA19hLXCIc1Po4zWe7BekTxtgTdRISqq+/1BhKpNCbE1Rk4DBiBEJL7W7FKMcZ0zS+f1+PDUCFB0v9ZhaVvf/FyMJIhMPamImQXmKkQk0YwCRwRcNc84N9ouC3MHqMYcUdraPObx9oqA3+zPXgHVIIJGTzNhtkThWgaIFe5OhUBVySrmJisQqnepWoSULBFmbm4nSlNqBSu8ijIoJLIoYaBZmBkWY/p5BSTiwNTMAlUdDL4uO9b9Ku378xjbkOf+tNyuPKtggASXI3TZd8lVy1esBclhDbhiIykqYkbYPAMynUzpiXDzemo+pz9mXpJOgJCZmYwuimfwzMeyo2qnpA7rDXzBxUiFqeoI9yPzKHRcshMGASAQaqdAr0YgvFVZKpRhYUcwQkMEwCemAEjE5szAyIhAAnVEYcMNl8/lvlVnIxDi8oejHE89EQdLJUPKxeHlO0xlYdhxsUBClVcKIQmgqBdFYA+VKOKCShXMwIouFCfk6+P15epe0D8Wucl4dmis8JjnMa+NB7eYluSbODFqMTGz2UVMUMfj4cK4N1kAKImZYFmTzb94rAU9EHoR1/bD5+OnBygyVGjgtt0MhKEYC4sV+0IvQAxg5EK0yIokZOXhAQD44ePTafcTl0pnkvSZ7MjaWbXgHTOOTKr6uO67SIdiFQqwTC/YcJPCJddypIJBhDjSCQGdrCMkrHBERgJkQ2CUOq4S4d42buhBXtqfTGJl9C4izFQVWcVVEjSfWh/EM1dEoNTT1pna4+1xpfW+AwEV1uMwq9vzR+eMmFCUyjOjveyD5PV4ANOmzaj6dqtCd1PV4/UOSh8/fphZ5/2+Pd2SMRBItamsOXtrV54p9aQUs1Q6IClkKjlqRLkZKNxruSE5bDd+OAtwZr1voBBxHamCCuIUBTnvxxC+mqDKDXRaEcIV65NuX+73M33rWy4XqIyryFlugBZoYcwCQdeMqwIUpCnZ9K7jsgmK7KnEfX+5lqfgcbzmUhFonVgErPxYFjF0T8KAvCJQyyPY1ksTIFxVULBx2wmiaE7DzqKKHBl5pbdteIRdC+AWVdHo8Zi7tL23yLzujy7QRObygNLWM/zyILNtv/l7uylJua01SQALiSAsJRG527myhJ46EDlRehKhZyESN9IiT1zX9fN1NGlju21dbrzNY3FiQBX04ugKLfkSweinnRkBSHcrRBIoIurcC4EAgwqLC4Ro/4AvHo/M5TZjXVQsVQkukkWEEg6vJSa8toQFLTiHGFEhGrRY61b8EejODRthWJXnZVdq8oeP/CWH1rQok1NyOQekVKGVK1AZQSJolErTG6BHZWQSTcaZQVcKwAnVB046+IQcdoIhdquivHrOQGrEzFA4ybEmhKciBpRXWBU6LOCSQszhrAiLywGy8BL86f76G/tAXXnJEEGAwa1t7efr9Y+ff/70tH3Uj4Tdza4Vt20gwbQJjCCq0GDyWi6qCRBA84zzhMc1ozJbrIpWrS36+rDPx+OkMHzX1BH3vfWOmYAQYM5cgZKQFgxAXT0qEtvYgVpk9dYAYK6Lu4rSvGIINEZEHEVQUBZ8Thnbta5Ik06AtCxuva1z1mM5AwoB8LunoqDAvVmp8kxSSmawVjYdSVg6Aso951PCM8KbXXc7G44IgLEVwfX1/nFwzcmMs8ojWZCLrscFhVvfuAPMFbGgQgQi3NZqzFU4nm6lWCjoUOGfPjw91sUIV80k76qvj0cvXpjIzERSMF72dhuP5Y/Xu2oziPLVtJnFBmPNNc+DmRyAEZlr1jUBNlYRNjPw+dTENZpud6tEN79sOZMuCkYUAEZ+afyuIToVmPtGiFDhoeyGyczu82K/0nnfNqmwoGIU4aqInLGqjAsaNca6LifeaWtrGXLjojVL36EgCGhlsRwNkHgIO9v1mNe1P3/DhGUG4VdEqKDCN0/PtbJE3vXr4QWIviojlQWSEoGhEMszygGQLTJz0U0g01YINuUqBAA4Xt/6ENlonSsLeXQEpMitN/LKjKzCBGZsAFi1AoDUVgozWTCikhiUKK90AVJn0wCULAhPBgRlTqpal11XxOOkbbShGyNLDTU/E40wEzgrNLcmGGQmAbwiokI5hSXS30+oZHXZ5koEQh2QphyQNo8j3NaaVhPy07dP9NV/tvxqJ1MGZZ3kA971jJtgMDlrKe0ZYTPSOCKVJfb0+tLWS6vNQabBBacQbNWy2Gq1RKJ0WMnoRbcFGEXAWXSvKDiIkbMAJFCtgnBmTHwUAKzgmamZHXEtLhCHIIRYlohZSQGU6ZksBFlQhOlZVijppZAh5iUZ9fW4vrx+/fjSYUbg+dzy5fnTtHjan758/vHxuIbut+etjYqVjzUpo3UmUYCSsTN4UZ7X0Yr9WGGxVgbErHVlzOu0VbJkVbufr3mdz/uTYzXu4MDlmWBYKIgAygq2ApJYi8DCk0RUE7wI9u1fR0vCiEkITsYd99FpUc2E5bbtfTHY48umuLdWISvJ3bs8S66VdZk3GQEFxF4mjJhoZUnohQk5I9voFUXMa05p+56Jb3kaeGvs9hh9R44I32/t8TjiOF4+PicGcnZp6WVe7elGBJSxVrWhXrMr97GvyUDAzKnsVI+f37a2bftm67y+fM1KuY1aNt0fRmPbVNus4KxED6Yvx2nX2loTade8pLHZZGZELMw2dIztyzXdVi6QthfFZXdU+enrTx+fd+wC7uEJa96kZcA2Bs4QprnstNAmRsHJl133c337zTdJNK9zL6p5vuX69vbct55enFxYtLwhhUcBA5asEMIZ2cY4z4f7NdqtQmElx+pOwuKcTrnMGutxTNk60LvcqxNj01vlONfVm2gnP4oEEyMCGrbyMPJUziuqcm8bVkKjqIrK09eN1dMy83g79+cncO/IuDLrREMiooqm4+24a+vF5IQ8lAuKOLMQRxdwDCCgfEe4weXBhEwaKNtoGLkJlnvUAsUqi1iR4Q+TJ2YRRE6qRDBbHImVo7UiXqsex/mAJbp1bb3jB+9XxmIKzACPXJ0ZShB5kPg6rdzsYiBMkKajFIECHXuvxCSiSOUnft49oy6v+Xjij8ofbvmLv3z9z0WXlTfylQ6CK4BJemuklglxOuXC1CDoTUnyMgMb0p9ltQ5z5RsYsi91mYmheEKGR4PK6SydYlxktQvZooc1ZXIPqhM9KrFwmDlAMy4EIUebSHR4PdaURcKiXEVyQYAgJwJqhROAUK9ATPz1736nQ3/4yx8lJGEhxO1iaVwhHEENo/I8HwGhbRPiT1t/+P3Ljz/6WuN53J53KAo/j5kN9603pO2Soygw6nw8Xl8PbWx1vfmXuU5MLoeoQuV5PkYXzPZh71NQqm2k7zCXlUWEAyEsCwu4WmsrLKC4KSJi2mhtU/Jjjne9TThBbyqotKoQAVHcSxgQgIK4aQGumA6AiVCpG5WDFSGXgnvFyuVEO0tiWFUURyCQWCQnLFjZSir1ylCC2z7iMbWpEDLWGe5IqNCfb7htquTrGm1wgc8FEOlp00XUCWUfLLDiinRlBU+rANYPY7RnnVQQers9AcCC8DDMfBmfkOD17XVswymRk4tb0rZ3UszHHKqEtPXRSE+7DruAlZwJMWsAv/v9uHgnzW+++84ZviZ3eD6OVxQowoENoL7iOh5TkQT4/np/edmQ8M0vEcT0axp4ZedVtS66ysDzqe0+V7hRU+ZCIc9gohMBOYra4TnjeNlenPEqR0ewFK6qhcqsUA7HcmQBkQC3M4MuQLq1F1+m4jOsEYsIMAHBwOaGolvxgoiMSKgIV4LwFZVMSoJESimA18uH52CwuTqQIUMxtwI0Vg0A1i7Us+A657MIIdzXvFsgtZYYyhXeqoApoxKwgFAoAVYdgpyJARmKkShJFFBY0klIKldmkaplCL0XSKiK3ItYhQsgj+t1TlJr0FS1nlgfljP1/UuRUWDi3ode1Qq5otxjzpBAFAShghBCriIAykDqAd6fn/S2l5XH9xj3nfm/Hv/PMX8W1g2GVWACJWYTj0o/KVNgtCYhFLTK1gbbJrsNgNPEo40khARIdERowO5J9S46wqx88JVcZHktQ5QKhKugszMJULpxJIQvxQAfHrRWiAQL4MUFFr2QC3HBokbhRSzaBkJxVrLroOdvni5/VNYCQbEWvhFDyulICITG3EkVK3Od0Id0Gfw0H+v1y9sxj9Zg2wZXrixbk8pe2oeLgjphNiek5kWLYPUyJRWRL3ChVayrGr5Er41LYBTu2j88f4fMVNwFLc5CzqoCb8oKGCZFvOszp1Lhsw5Y7gEizcPAuRM1UaTALdNrHicUFtA8VhPqoxcCcBAWIcX0ldVlQOHDz5swQYZDISTicitmwhaVyAHLKyuprnJhiN5G+nE9jsG3xJ4l6cszVTYAy8YeWNMB4e06202Rq64LsswCuZ1+dcDr62WNtjYsUroIIqS0PsJWhc3r+nDbDZIKnz99bCznQiojznnNtm9ZeD4uRfbrKgYmGNzXitQy9/NYiiq0UbWe9vn14VBxa2O0GflO9a1CRuptq/DWMR0XEC4zc0Eat6f5OBnFCmNeH8bW2qCCp62FWSOA7KNTQrhHYizzXOtj28A8yoOZgjll+op0RRHmc12MA5qgRHrm1hwr18WzzP1c8enpRglUFBnUFKAmHlTI0EL8uGYCbty0cs5JLLFWG0qEE0UUr2v2prjJ/e1o0Gv6ZSe2EQhbuXiZubeNpKH7aQ++dTQmyInOgFrUC8p8NS4tTkoLZCJLhmLS++vrdd7H1gJLYBfZEN8ZXAAMXnae5407QQsiHUiUCZAQAISZkIVKVZKYEVgVUrjKtAknzfP86by65sexc3tGQQkAYseguqLCqwCEMFISkCjEqio90oUIsAgJEZMRoDbEmVUyqrwrF+Wuv9mfPv309fOf//T7L+efcx6MzIOWm0MQct+FGM0SUCpvFHsgghRRjEmIIie/qrrTFkm1DqoHwZULSTjFVxSu1iOgCrCQSkqeOZnJAJeLV4RMhnCzXFzwkhKB0TCre5Ake6ZSCMAFBSRuxgzIsKYzSzj8t//391deBCkoGoTQg5i43ubXw3ZtheWJmNrLy6aR0tY2mgZej+P+px/r++9/9aG9bFsdfp7TwB9PH24fP738/OMrHScOno+fwKr3saKoSCEj4qBJzhIsfZNWgvTxw8d96wVBtBoxFGQiCmAqAJt5QUhrAAFFbdt1a9e5iDmhoEhbY0aD2VjBgBJ7SlMKWIBF1D0qC0i61yQgbB1tOZlXEqIXYxJEoaKbtxRECJ/FnMuUe2JWLi0SxuZrVhwpWJ6FkOl9lIqiAwMBFKaZOzByb+EmHuUZldJaYkoWJoI0piLhrDivy48p+gzPN5uhrIPLwp0pkoL07bFuY1xrVgPwML9IBJePraFKZqISIBAXzDm7yGANzMsXJmBw584cucyrkc4Lq8cR/tRbHAclQBELr8sYc6iuyypp74Mwkan1HVDNIFdyy8w65snMbqsTP+97RPXb8+QBSRGYy7hhIotKL7yvCY2ot3U/tWsvQEhHjIwSBuasAiGhCASsUsIhREHImL6IKCuIRXikuUaqAwQTVpSZeSVkRhj02+28Hpy17QOCCDoTTYtAPxmlUElFiCCTSaqpIVatKhSea/XWe/oRXo7CtbEuAILqJAqYVX3spI0VOR0LKcMg06Mhi0hO2Pcn8EKkJgKZkae7KWslNtIkve5H5X1/+si9R1KVIyQRKTBQmZDH+vz6RrxoNFDmxqPJVrdQmRYxgTkAAotEmAXSkwAR08yLuFgCSSAZmQMzC0lS0k1G+/CNvjy//OI3v/77z29/Od6+/vT559fjJ6mTwLQDo1tcAY2LpWTftkl1+D3nOfY9BQtlPLpxgb+WGpRUhrBMKAzTgqpY55UJAYJNGWrDUbDGKCWimRlyQGAmQQMMw1oJYQCGV5VAvhPmswpREYiQU+DCCWIOwMX4dcWmBUkUngBNS2MQslFMqicBBCiuZBXGyogE4rHtx2MJtHmPz/Dz9su/SiFtA+o6z8dyuz8WZGjPnTehX1B7ua8fBztBXIAX2WCwCGRAKRCFGB1VqKqgST9XxiRCb0QGaPneFuDWbpGQYKP3rPTi4oaVCMszujZAtAIE8gXTnTdcZSH1UZ6g8qqcXgUyC7owkHgCSe+IEQ6ZyrLCpoWOnmBNFREuw3KIrCgQErkwpx/gx/b0sZK1YF6riDJLMMBjsN629iV8FZRbg4hrPd2e3q4j3tW2CVSIbeQ6B2goDeJIQpW7PYgZi0BQJS1jzQts9d7NVzGTQvks8yxWKYfZdbxUO+a5kAqSz1hudGs4xvr5Me+vt++fn7bnx+dXVmbVukKyh9txf+2p3dMIodTXBA9mfv36VpTSacjo0q/TZ4NEx+IMAk8ojVxF6+k2smzOB1gx98G97AoGGoIsPt3DCUpbmwyj9f2bbT1OcGeSVaXF4UjIloHEyHNlIGJ6KAEk2DmpUwUBF1t2uRlNi+lJqYIcDCkkUeXhRF1IGKBOK0ERWvMCLGKUoI46w1A4w5DQEkZrNWdVKiuTTq4LsjIsJArRLkoY2rMiIBKRhZS0vBJZdMzrTEgBAXR4tyKTKmGgJQQWVlVd1WVQiQcAYoF35fCsZaiCWJ7umZ0gC7q0Ay08B1Kl18pl7OvYmm7cd9FBo1AhHw6nBUR4FggJgiSishfCyixIAIpKTNCOjlBYLArYYk5kAcjvP/1WPv3mb35rb+fXt5///Hp8fT0/f338aM7AjQpVUTSvXJXQxogziolvz/tVF03n5lWZ1NAlwcO8oDABoIIwsXHPQsqOZ23DvZ9FTIwlTowUkpkJNRkDsTx7ECBbVYUx5sJKBEpXZUMO8FIDm9Van9gnLhZXSzEikNwC0B+VUz0kgIY3BtbeECHSqxKkUx/kp+a87l/+DPDNd99uiJ0qtZ3LSERZzAPRpFUjufF+vN2FYIM+VSnpAfeTQ8s32J7Hxw/PL4UCSHN5pSIi4SQHJAxAIWm0c3bMEIaX/fnt/ho8kEVqCRYRSSa3tiAq44qqmzaE9la68cXOXszMEbWKgC43EOJ3pRckozK4MEtUtBHupFKFwtSw0wWtxIWWLwG7JF33bZAAgkIV5+vrXbdNtT3zU5qv5ebexp4AuRwIv54HM0cFeeb0VIwJKPx2nk6lwiioeREDq1YSYIQHCQpArXOR+2Rq3BBjufbtWJ4JEmRvVwLt2y1Z/Dg09XydSEadKuE2tqE9KVFAlAngqtSNRvbvx4fW2KgQ8TgvjHjZb2b+tO+8UVA+rvNaCSB9a+kXZVQRFrNu2vR+PWL59rTP+wOW3Qb5Zcfxtn/7YWXMdTWQEniER1UvpcdyFkDOKndPAoIyszmNiUEYIIt4uVUsbhLlFiGOTTRVlp1rvYkKM6X7QE0URlgRDoWjoeiRU0aPA2KxkLIkSxLL9ZgUgEUeWencAFEum00ZgVeWz5lQUcWrRtNXW4mBvmpdt6en01cRULAfJ1TovqdjQwVKycoCIJprqjRBIYWsCPc0YwoEcpsoLTCxnKSInwC0MqRTALaShnys6ZzMRaoFFZ5RCFSZcB5zxfno0qQ33BGLdWBWAi5zz6qkDGQQoWJIgIrIqgIAsytJqQCzFhkQMGFVELawRIBPL99/6h8t/PDr6/H5xx///POXnzzOt/P0GdilVZthmSvAQm/0QZQTD8ClnLyYXzNSR1ymUEpRTGsmholgin1tFWm5QEEz0THYMYoKcnkZJBFxpKWHjCAUJp9LVA8srEVEmKrYEmVUQIIpGgC2aCUdFNKCi6VWnffz50/YgSCJECqoIlOZgdwBShuL5XGkwzrzxz99/fTxdmvctPHepq23hxHo1pvZWR4M0vgJMxqP78Z2t88nRNDaaXvSl1988500vWIxU2AkFXJAcTFVFWSWFEo6TKb88OElw6yAGTBNKAmiIXSkuayUIlwiUAGuVOgu1MBIGMKUeTJt2iPqWhPVizED0tdgqsSqwiJINgvVttwUiRhPX8z0nCKy1oft6We48DRqhFxN20tr0JQB5/LrnF026OKQFKmqReQJBKjSduJpbwtTpV9pEUYqdk01f9qfBfK8/H58+fbDrUivazErgbQ2EvE8z+u6NpEZUYMpiQwz8+ww09BpR1l1JMdKbdMXxN3Xr3wjCdm4so7j3vZnRgDaA+BMe+rbOi7h1pRE+wV4a7UYr7Ug/IrVBh3zzc7HN0/P1CUxE5MItY/znHCaeTbVagKc6MOP1UZ3xbnimWh5NUQtDAQ7ruXJTYaICp1XJJDq3onPNZmJlM2siS4P1tb4vdTmkUGAkTMsEVEUK/1xuRRTp6KQ4IqsVpkZViQefiKW22Is6t0qFEg8SjnZOZkanxFkRUSNKyGIQVlm5Wj6uE6z2J6enSgDGwsCoTREM4CCFAUvz39NjkBVBeA6l3QBKKxQESu65iRWhHeuQZy+RvuUERlGKVBIiW5hmSDcs1JoXkuQvQB8IjECgZRn2PVmsFQwS5S30VTJr2sGQQIkZFYiVnpIwyxKK2WZTppYFQtcCBuQEBtSqYK7Xy7VlPrWdtGXbz/8zuz+dr394Yc/zC9fC3DbGr59eeDXiQtrMthovABBqg72IqANPBtiYUFRMJYgGmAkbDjLxHWLziB2OdGthb+WGbCCIHguFwFsaB7VWlUKaYGGYWuA4IzSiN2DlNhrUhkhByBOiIYogpqVwXA/H/cvb218X3kRi0cUYoFEcIr3Wwsjqp3CMWHN9cc/H8r8NMbYN2S+HmfWeV5ilwESUh3H9Yc//0t/uj3pdz/8+PNab/v24fuX3/z6r365j5d5BrWKTCIqSwEKFCOoDAYgxRRMTBWSp+3LY4EIRrRCAQIcwIokWhhVvnIM6QQPmzAGcQGEETEUZ4CycRUilBQ6UnAKN02Mx5pBJQ3DEJmnGyoFFFIVhfliICkhTHgaN/e4jpldd6LCwhVfHtNmtMaZkViHHR/GDsWc1YRXcrjPOmXv1xWP4z4EhLCkGm/OcUKAVaMm23Od13R//vDRKqgVhFGGgvG+l1AvVPa1HIVUuJQWQ9rJhO2mGChDhTDcQMlorYdR28yMkWhOiLXy0V+ePfh8rDqmI+Wm4BVED44EgYvCatPWGx++VNsCpDwFuYoec7bedpECfHtYf745BCrdcz7RllZlZ987JMTjxIan0jXv2lHbAKALkkubSqAfviBo6D7zXNfKwCQUVyiRAYiRkRmhqkU1L6CEQi8m1sHIRIsC1TAVH9di5qexgeYZcRzH3kdALQgVylhF4MFlC4dekZg4SAjTwy0zQTNXQFFynKttG49bpCn2cozCcEVloYi6Kke+L8WAauV1vAHh1m5SPD0iSYQ1kJmSJFkrfbmx9krrhCZSVVpFjIbRWU5IIZFKGswklOnBUDrPO3dNYjIosUCAQve1linR3scZVojuVZgJblDgEEWbYAQKK1GQ08J0LPVKiJJU7gC0PAuIoCSMIC29tfF9f/ru5ddZNe3CmX/46Z//2x/+y7wej7fjGbfebygEYstTwHcnAdIhj7AVubBUWgconH4mOUeRAZ5oIZEEKxNQhADKLHLwaGwL418FK1cdjERYyVAaYQqVUAnBwMHEAC1wJSUvY0fqjQBRIOWnw89/+ue/A/jt999pTQMSGY4pMKAkyok5WTEd6aSMcHg919fXYBbhJiiV9ZefPo/t+W/+7d/+8Z9++K///F+PeOzXh3n+cF7+/be/+Dd/9z/89le/zCwPwA6QIbB7YpCnuwqvdCRsPJAbQ+OIb15e0qI8q1K142VUUF2C3sfqKIlETLydR0yEvTeBEG4ZwPbuaEKEDnSScE8tPFAqSh7uyViRgMENISPBY3nrOqmGQLCsQMnLv+zSva55V5INx+e3L9zaAG6curcCF4TjcTzdtqEtpmfVdRwqo2MNkBmZlFvrUvF2PG7MmK5ZhRKQFhbL2OPb7z6+HufKYsbGuDImAK6Mx3x+vnVnZgGiea2M4rJxAwZ+3H3Z6sA+ZF73pnzWKKabMEW83Zcp0ZpyG4zW+f8vlMlOwkooQIeyTeMZAFTIVDBas4pEhsIkjPDWqMBRqAN9/+HTdLumISMpkzARNVRhPiP4ZfOAAvCgFr3t47ITQRDI7Syy3tXuM9G9vAjlXaigVFTn+Wi9WUCWCNAxF7dBhVyVUR6ejFUAzDPDLVb6GLQooiwiNhEWfmBWHkXvKXiCjMfB37K2+HraeepQHZ5mEZmFCMVpbip022XaW1HDQioEzImmEZooWYzIJIXemoCn0C2K2hj2/ljE4rbIHLkhEhdYLiYSoI0g3UQksRCLiVDUsjYoX04Cnk6ZQthQ7MqNW+duDJWQFWGuZAAwsU5ORCYVKBUWIfRAYVRArwIsValAKwYWEq4qo0qgzLIIAmRRK2cglc1sCbIRZFE5ZILiE27029+2v/qrX375+vnr17fXH7+O0e7r8fPPbz5z5EKcoeERlEQW7/voiUWIBQnsGJqOS+LSUARwR6xknkGKVImphJkNAsxJOlB55ailiclbFUokoXagk9BLABFwRhXJO3+hMBYgzXIRPd1/er1/8+3esOXEEIIB7BUeFl4rPGw0QALoXKQQGhXcCYruXx+vx/zFr/7m6+vxz7//8/Li/QWguR2fPn779//wD7/8/leVkFYZSe8lhqqKXPW+a3ysQOR9yBCma62Pzx9I+3EemMCEnkFczCkCRGmQC7getqO4+VonMhSGV9ii4JIOdrlQL5zXmoFMhWRehYQ4oKLKoLxizWqkCERFuRCAI8IzilEWOFBtXh+wBbFXZdYWWGFXzv3luWlzN1IAzvM4Kquq1rLWt9akDkeh0eRF9/vx9vLhhZc/zvtt7OVhuVCo7Zym94wQtGMCUCGJ0Ou0m45vPj4f5/2YtDDaaGGx5vX8sXfY64qWUMDiFCd82nds6MabDKoqBm0SYTXGJcSR7BlmixEHd0IpmHYltzFu6AsqEyKuwMFD1RKIMTO3pnYtlL5sBVkBb9u+1smQpDrvbyQqXcipQYsMxGiNhW5UuMILSRD9vDPV43ogDBFdYa1TAfSm4Bnmj2tZXNrpHTfukbtIVsxp0ne3xSoJACIzghlJdJQ6WFFykCJuvVmWFhRILRBS1nLIMSpyQmKjQaQeKdywVWYJ8OEXYjFheiCRz5CMMbQQgwEJgSidVCiTHdLDCiKoCLhgJYLnkpIOvDqm0LrfdxkdxTk6C69JiI61IjCiClg7AYElSbvinGvdxh5FgixS5texipkZ6PE4EZM3jASjSiiCGFBezklAwoRbV0SZ1ySic11JiAxVjJmchYKC9I76qyiEPCsQ0S0rCJG4qrKktWlZhVWFqbf+oX+7/+J7gr/ly94e1+vPP7z+EX76+vNPBmZoINRBBRiqruCDU0ma9LnulMTBipBgANn6DoBmGeaDW2IchUidCzprEApb5hVJ4gUjnASSqvSgWFkI4BR1eZIAqgCGeRFpojgyMbB+tlkXfN/HEwq0Bl0aYN+GUeDytcT9RAhGGa2lk9csjMd1fz2Pf/dv/oFJ/s//8t9ejyjuVPSYS1v/9//273773bdQFFaAjO8SIkwIorqGLMKxQkUIWYpkJqDi7cPT4/6ISMjq0j2yCFGJEASpEDHBLHrrMU+BZO0VICSMdLi5lGtOP8hRqAtTxEpq4Ksq+P3etV4pyqspe1BVD0zw9CqC8jTprW2mJTgpWbfgEtWbthONgPKqCMRSLczJgV4V27bf9ucKfD2PjuM6HtxqBs+J7Niwba2Fgwkm98yVWt7k8AURLOlpP7+eL08fOstonOC3Mb74hZz7jQ6fiKa3/cvnh0DbPwwIu5+2jx1KHuf53skuX0CBjB965/b003n//PXrp+cPU1pDjPs9X8bJMlfZ60UfBdERMjNI1Cu2Lo/XtyZKQPf7Q6BBFgtZmhBejzcR4SIVCoRYKzpzROYdCRoKHDFQLe8MgtwCKjssT+qtt95THXzalKbvr00WBQUI+nh9PO2NETxsI7nmddufMoiTYF5E7A62zj42Oxe47y9jlnlAEhNTZeD08wpuLIOYkDzYy6BU9nKDCC6CRMIqqMpc53X5OVrnapBZrTTFsyynxTHaSxBYUdoU6lSEjAYzyRuR++VQDbQHI7yrtWD5XAVImzBHVSEIEQIyiDapjGVT2pZZthyZnsat4h09uRixpBKwEKKCtoEIi0FIzFc5SuTr25e2bcUSZntr9+Mu262aGAJh30hnzAx4XzUiYCZgJYs6BHHtgDWTiVamuRElAjs4CTkUOyEolAMWICQW89On54/f7fkf/nre79effv7zH/74+y8/fX57e2SpE+RGCJWRWNXp+SoTpY2EXC6IC4AJymLLakJJcCYFs8jJUFBgBeVsOBATMwl4QZtd2U8AoIQdYfbmSVxMFQCFpQswCBW6YD99fvn6+Pj0MRkG0Dld5Kn1XTgcl7kzZ0ICaRSqEiNe/ljr+u1vfvPUt//lf/9ff7o/AAnBctqtP/3D3/zDb7/77YZPE5JVEcBsWmJAs0AWTQbynkWDiJkSyYK+/f7FbF5zKXEXBA8GUu2jiwBhYQbaeTw93wxqnbU9N+BehJ5QGEJcYcqskC16Mi8GhqGRmHgKOicGoBUAbKOtOFYB17BwziQGBkgGqauunROCEIAoMaSxgemHfX65T8rA4IQqRCBfsd2kzJL1uF+F7hoYFudMHqNyHlc+b5DpDNR6R6GA+/HFRdv2lBVpriL9tiO2l2045E9fP3//8mn0Fpx2nVvf6Lafs4oiOW26pCfHVefj89enbU/JRcbzYsSx7ctXE28C2BigXvbbZVOzKNzNq3Qb+nj98qQNCAMBtAziev0MxbZyxtWaAjJSFZug+nlCAks/1qGDZW+SUKIwHTKA28ryWG7L1mPsQwWEO2ardbTW0tLCkIhSwagws5IYvxnjuJY5VDKKEASSHn7sAMJInc/zaNIqS7iutcKyE87jklt3NKwKcwxDuwC5VBYGWlEyQSPGKmdESy0sghLGSg/L3oT3vZFSAiSiCEDMeUiTzj0r3BNFGIIiocqWL/SqJE5MASxEDgTPCEpJ7vsHC9cqYTkzVJvHKi9MAUwiAoJjHq0PAiAANKsiJU6AgiQWyAFlygSMFpWVQIhZgpzhopozQoq43c8pmWy+bFVrWZhQmzCxLq4EBItZScDmmUhA0YqTEIGYKhugJxQti+QoLsANCAGIUTMDSIiwohIASD58et6/e/73f//v1uP605efv/745Z9++OH3P/yJ5wm2mDvSbptFmQTSv84DA8BbA87NiRlgRz3KoigABPmsFOyNMUE9fEtqTBJVxFkAkNBLedRiqixwx0RI2sY+RmvbPVC8UeTPX177t4PNVHVF1jkB/bIZbiL0/ribWcjLc85rvdy+abL/b//Hf/n9n386GYeqhH18/vY//O7f/u23f71vT0dFIFIGQjKCBwgW8CoRr1tVoWJ0deY57buP3zLQ435QEQMrwoRQFVUqLIT3LVACVoIf11kMpJCVGaWkyxEplCqTiJ4PzMgzkBXeAfSEhAwRuUygINAZcQA4IHTAd3wWFJqHJJWxi0gjMbve3l63MXzvYXMfbRHcbd1ka0TnXMoMTgS56owMAuAG0Fj1tvIhks9j+3x8XdN+/fFX57vBDJIA/byAcCQTCZNG5NfjosZFK8AXEZNej7cs7/0Gaf526r6z4k7t60+fL0QyZ3QhutKHNM5R7r58FazH2RC76Lwfybw/76h0fPl660+N9CgfN2Uc5wyA9Lkq/Gm/QfJ9ert9ooL19jpu2omhkFVpH2+X3c/7R7pB6+/0ouuavY9IQcY2sDW+OtmyiPMDExYm6eFLkWHBCkfB0SQLIBVjEecmHCyhQo6x4kzXtpsFShEVMgFRa0rQ8jFvT9uKyy01jcNDMZm5ClqJLyIS6nYuROoqnpbr6tyJtqMeXajMlOWi1bQJYHh6mALFjAUxlGXoZZQZ4KUtMooACl0JFmJ5BRKhgh+mkapRiVVZJSCeXpqBxsSF9ThOAhrb8IURgSoKiUiFnhDEEeUi5NMG95ABhbEsAQSBIgrZ0p+2zdZcgEWNW62VWOzua+UF1phxTdKwrIQuoqXM79Yf6O5lbskFhVmBjAlKRAM0MbVQywJrZoSt4mQJiIQiokz4V4hTGDqklQPw6PLL3/3iV7/79j/Ef7jO84fPf/zy5x//8fc//vHnez2uleuMjso5KsE7UXO2tU7OtngwPCNdpVBQAAjUWYCgspo+AYUjNcSl2LHonZfOpA11EVVH8I/P2/bxJTGX0Ir4Zn+utb68fobEX3/89kNhSl5xZFoiFvSqIM5MQ2A3P9eJvpXz//J//19/+Pm1FHEAQu37x9/++m/+6pvfbLdPsyIAiIAq0CstmZDTkfmd9MIoIRJA7vZp30cb9/tbJuzp0uDNqvWNhJgViQISoSIeo+O0ysrW1FACqiG+D5eFyJcTEAsG5rvDHikscChhCoQzV4kAQGVkMhF6LBXmBMeamUpDDJPsZHl5u9ayt9YGNz3n2gCLdVk8YYPIECQFZspMrIyKJlguc2XbPiRFgRPD8izi24dhZYLNqMwzqtPkr1++Hh/Gh6ddK895IWEXnpc/ac+53HCl696CqYrxqd2Ph5gunngbL/rhy89vQzpTjtYfXw+EGvsWa5JHJC6u1voVa5NtXverHJiet3Zc15U+gt4eX6jtnAmVffTM9GNt0CrpmCexlDSvuh6mjTnBbH18ftGiZWAY6R6IZqsJERIkB9Deh8k85v2L35P4NnT4lldR7woOlNHYl2Mitn6WQ2ICS0lZkghxaVYB+jLxKIDXa47BBAge1Ux7Iw6oRoiUoKQJGSyZD0iggm30jAS0zAkQ5qtEocDQEApXqggmQRYxk7LZuhwEmIQsAZEJUBgjrARXJCWPolZ4YT1qDaL087qv7fmF9xaGGliIwGSZTUgK1mFhwENhvlUyqHg6Qq7rTr1VosgGGStCVBY4ZhCSZ5K08BLUgHQMQEViTOJSm4ZAiCCNA2SuK4SZG1gK62nGFVQsBJbReRfVrUlWwVqJCYRZBOmNiCTck0efWQUJbO6VTlGUySwYORsTE4P2IFBIQUIw9QnOkfE0bvSrv/2b3/39f/yf7fPd/vTjj3/88+9/+KefX7/al/NfRDJTDAmaDEQTtMhbYmUPmIDQYVM3oeaSWBhlLvygJoU3SUiy/DASE2LxJeTbN4M/8GVWi5yWAsK+F4CmfPn6Rhbtl9+NrSmjTWvIhQalnpy5CMBmVLaH23/+f/7LY63etQi595fW/+Y3f/2L5+9++fH7h/ujbLBSISIUO5fGBCCtPCnm1m7A3ZIK6WlrL1s/3h6RIUyCHRMY6taGCCDwsmjCq+wK7I3W47H33lUNCRIxKsKaQFby1uwMztjIzgp2agGBIogHO6PApQxMvS+4U5wN0EADkBFFaJkDpGB7wnS3exvkjpyCV3aWJNykgS1QtYiCECmzEBIoaSpD8Xw4kELAmvPpw+06HpH5/Pxhrnm899eRe+v7/nFyfHz+GKOIw5ctyo/7EwNu+55Qq44rcWxdwK/jjbte6OPW4lHMoK0hzW8+MsQeBWlBkY/HnQHcLc273LTLSufGdj0IALaOrObFo40H0/Jdb3fw4HiW5lVpSUTI1Jh+Pr7e+haeUhARecXY5Hm7YYb72cbt7e0rJLT92a/FERmWQZ6MWQWe6RUgKGuaHyeybEJcYOVnus9r19vjPFuTdZpnfPz0ITiqMiEhk4Dux+O2bX1saBErqmA0uR+zbU+A5jnPM0ASMAWqi67qhAjxXqjMSMoAlQHAWdlRk9Gh1nKp7FQRUEXcdXERg0//+nbsey8WEEXPTj0hZngXqQplzsyoKELuo70Xki9D5CONkDoxvuu1/PI0kupSDcSsLFKVGYMJM5NBuARZbV4V0Z42zYhlV3kkZSLYIkEiPK+jCguQCpkpchVUIkPlyzZmWNX7wK2yLB0ZmARXZMa17CJVTtyQiJsVeghUALwTXSWBWYAxzIxRLJJIPKDAJHh5GRZmYnhnTeIUHNQmJcJkFA2KaYU+mv7dX//u3//2b+d/ivvj+unzn3788sM//uO/fP3p9bpHrlXKoiMwG2uCJqWFX4hPjJXkIG4xam7DEzR584wOrTHcvYgbPdHS6ziOp/bt83ZDbZ+++0TF2DZYZzVYNc9roR46WlQRJLF7BGRr0CNfSwKj/bd/+u8PuMPYr2Ptz3Lr+Ne//uXT2P762++sDLlGAaVRsXsmQaARV4A6IPWNUIlwlAP2TcfXt1cMgRImXIVhoLeNWiIAIHbmylrFJE/ndW/aG/WstLiANZAQlTnNoxJQeKWXeYQjtezkAJdPEYwk6c+FQUEEQlTpK7R8ETGAGQMjoBB9ydwFGCOexy0Owyyh7QCQMMw6rzMJFQGwEBAQSJsjLV+JpjTseqjE+fpahUQ1j9nHwIE2PZfRdSZ7MmbUU2gBnU329nxdsXUR6dPm3RbtQ4ofP/6krekQocZIl86qOq9pdgwGbc+B2CmDZBe9Xr+02xONDVCrikXb03asL1AAVixUmbnc/dQx1jpEBJDOMEr0y/f9hsJzXttQbTTtvF/n9y9PWbjmK6Am0rlOzBytVRQWOOD01btk1VzXs47znJG5tcYBhCybWp4MF5a8vX7ZX/Y+WmYgFngyMRNlmsFEQkvfdSPHpi0IcK6OvCq4yfTKJIBkqkjrT3xlhYIEwaoGEJkTal4HViG1W+9KetriTlQZlu5GylDiGcsdUdc0FMZc2AgDYxo0SGKKYuA1l1BZGoGwIWO6x/1tttHH/oTpBBhZztQY2dN8Rbg2zZtCqUCeM4KQocAL6D2RTybMsIJ3nQHW9KAioQayfDVpzOqxzGxdJtx6V8DFKgjgZlCxjR6wwn353PoQgRWXR3FArurKq64AjFieNAtGo2TN5C4NCSCEszCyCxZUkZIwcTqmcgpIgS6gKwzRsaIi08NFIkoJ3q3QQpJEQNwYYTHM9Ob7E+y33/zN7/72P/6P/wmm//T5y3//4U9//v1f4PV4++mHY56L21IajXNSMLaMaSm3ZyQLCiYJ5AWspZiLAH/z3ffPv+ip89PHX/7i0y9GVgoR6/F6xDrXev16fcGEv3z9+gsVTRcVA6eM9EaVBaucy9c//eM/Xq9nH+WWY4MPO/7608u34/mvvv2dtu2MoMwNxCsDVhUTccRkAQ+BdituaYgVY5QKrSMzN8LamAlhQo5t326D+Ax3po5Iax4IgoRh1rQR1cqJhRVQlMUBbVRGejERvTN8VwKkwUTupNErmehtnX2/nfWKQJQ7YYPpQmV2NSICzgQZjjmoABiYGlLm29tj9F5Wd1wNqBdlAg4hqGOeRQFBM2FrlCLnZY2IBQKwMi29cbfL265IcR5ftD/1MU6br69ftk/fuQdtnBllQI3msa7DiKQJRaDeXnRwKczjOq7zm09PZZq2emclfbtsf94yKhJkdITymBTJm9halYJ7jzaSoqpIyvzSCixKVlapaajIQ4/juq7ZqG+NFKTz4MKEAKV1hmoTaJngiKenQjz3ThSXnb0LCoRPXySspIKmCUVbo4q8VpUOFLiWCW7bzoWJdcXa2xj5Dt1jd4sEEr9sCrSduxDFadAoGFkKya/IYgR4ZFImQyFXk5SCSAKahixB2G83jozlfh3jdhuNvM67m+JoXFkzYTArA0QFhGeUdMrLT5tNu0Kdx0lNr8scgQmxyJBba7XmQIi86gFmAQKs/VhXFxHkxDJM7Y0QYwaQXuUqpFIZdT0qVYYQ+IX4bkoUFhaVmPOLHfved26WnpREAijgi4lu+54ZpDQj4j1Di4pcCCkIRIKRkfbeXGLiSrvbQUBIjFmZ5MqZHh5oVYxdRZogRu9sYYDcmTPwXWfTUaL8bMUJL4goDZMt3GaaLSeq8MRmK3vvCbTywgxXYuWOlTEKLYEyYFP5xV+9fPub79v/1B6P18fjy49//vM///HHf/nhM1412YMW9os0GLfyrUEIZBQhc0A46v/4P/z9v/k3v9pGm4hZHSxaetAixNvz7gsbIr7C45yT1uv5+QVvSL0ICwZCAXgi/+HnH37/Lz/MZYF4Wd9v7en29On56eP2zcvtOxa5+6lIjABQCEBEUOAxEbCSmXck4MLM4N5mPF3z9VmfgxTgolpV1W/7UxMuJ0fFBiBzOpUw0WmHiCL2OQ9kakKI5ZkFtY4JxCCAUbJyCRSoRPCCUsV399qawO31NCRo5dc8ASkTBBEA1nVBQevt/wMQmaTTO4ViUwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is the black objects on the salad called?\n" + ] + } + ], + "source": [ + "img_url = 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/projects/pnp-vqa/demo.png' \n", + "raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') \n", + "# raw_image = Image.open(\"./demo.png\").convert(\"RGB\")\n", + "display(raw_image.resize((400, 300)))\n", + "question = \"What is the black objects on the salad called?\"\n", + "print(question)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5ba89c01", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5ba89c01", + "outputId": "04e3ab39-475b-4c8c-b335-45e0b739cc6e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "device(type='cuda')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# setup device to use\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "device" + ] + }, + { + "cell_type": "markdown", + "id": "076f55e8", + "metadata": { + "id": "076f55e8" + }, + "source": [ + "### Load PNP-VQA model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "01be941e", + "metadata": { + "id": "01be941e", + "scrolled": false + }, + "outputs": [], + "source": [ + "model, vis_processors, txt_processors = load_model_and_preprocess(name=\"pnp_vqa\", model_type=\"base\", is_eval=True, device=device)" + ] + }, + { + "cell_type": "markdown", + "id": "f0e27d11", + "metadata": { + "id": "f0e27d11" + }, + "source": [ + "### Preprocess image and text inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1c47f415", + "metadata": { + "id": "1c47f415" + }, + "outputs": [], + "source": [ + "image = vis_processors[\"eval\"](raw_image).unsqueeze(0).to(device)\n", + "question = txt_processors[\"eval\"](question)\n", + "\n", + "samples = {\"image\": image, \"text_input\": [question]}" + ] + }, + { + "cell_type": "markdown", + "id": "ba7a0e77", + "metadata": { + "id": "ba7a0e77" + }, + "source": [ + "### PNP-VQA utilizes 3 submodels to perform VQA:\n", + "#### 1. Image-Question Matching \n", + "Compute the relevancy score of image patches with respect to the question using GradCAM" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6e1615fc", + "metadata": { + "id": "6e1615fc" + }, + "outputs": [], + "source": [ + "samples = model.forward_itm(samples=samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "46c50f9c", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 262 + }, + "id": "46c50f9c", + "outputId": "1588f183-c786-4de0-abc8-ecbfffe2f9a6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: what is the black objects on the salad called?\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Gradcam visualisation\n", + "dst_w = 720\n", + "w, h = raw_image.size\n", + "scaling_factor = dst_w / w\n", + "\n", + "resized_img = raw_image.resize((int(w * scaling_factor), int(h * scaling_factor)))\n", + "norm_img = np.float32(resized_img) / 255\n", + "gradcam = samples['gradcams'].reshape(24,24)\n", + "\n", + "avg_gradcam = getAttMap(norm_img, gradcam, blur=True)\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n", + "ax.imshow(avg_gradcam)\n", + "ax.set_yticks([])\n", + "ax.set_xticks([])\n", + "print('Question: {}'.format(question))" + ] + }, + { + "cell_type": "markdown", + "id": "c2d7cf41", + "metadata": { + "id": "c2d7cf41" + }, + "source": [ + "#### 2. Image Captioning\n", + "Generate question-guided captions based on the relevancy score" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "49ac307b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "49ac307b", + "outputId": "93535276-fdd0-4620-a833-cc7da0ff4776" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Examples of question-guided captions: \n" + ] + }, + { + "data": { + "text/plain": [ + "['a salad in a lunch box with olives, olives and black olives',\n", + " 'a salad and olive salad with black olives on the computer desk',\n", + " 'a salad with black olives and a plate of olives and a keyboard cl',\n", + " 'salad salad black salad bowl salad salad salad salad, black salad salad and black salad',\n", + " 'a salad and salad platter with black olives, olives and olives']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "samples = model.forward_cap(samples=samples, num_captions=50, num_patches=20)\n", + "print('Examples of question-guided captions: ')\n", + "samples['captions'][0][:5]" + ] + }, + { + "cell_type": "markdown", + "id": "b708efb6", + "metadata": { + "id": "b708efb6" + }, + "source": [ + "#### 3. Question Answering\n", + "Answer the question by using the captions" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f065b898", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "f065b898", + "outputId": "152ef7d0-9071-4397-f68f-00534a274784" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: what is the black objects on the salad called? \n", + "Predicted answer: olives\n" + ] + } + ], + "source": [ + "pred_answers = model.forward_qa(samples, num_captions=50)\n", + "print('Question: {} \\nPredicted answer: {}'.format(question, pred_answers[0]))" + ] + }, + { + "cell_type": "markdown", + "id": "47fb5774", + "metadata": { + "id": "47fb5774" + }, + "source": [ + "### Generate answer by calling `predict_answers()` directly\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8d9a4de5", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8d9a4de5", + "outputId": "c057146e-009a-4272-8bfb-36f92e610518" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: what is the black objects on the salad called? \n", + "Predicted answer: olives\n" + ] + } + ], + "source": [ + "pred_answers, caption, gradcam = model.predict_answers(samples, num_captions=50, num_patches=20)\n", + "print('Question: {} \\nPredicted answer: {}'.format(question, pred_answers[0]))" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "include_colab_link": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.8.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/LAVIS-main/projects/xinstructblip/README.md b/LAVIS-main/projects/xinstructblip/README.md new file mode 100644 index 0000000000000000000000000000000000000000..98e0deb91de52e96d8a89a16afcb12e031998aa3 --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/README.md @@ -0,0 +1,259 @@ +# X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoning + +[Artemis Panagopoulou](https://artemisp.github.io), [Le Xue*](https://www.linkedin.com/in/le-tycho-xue-5abbb9157/), [Ning Yu*](https://ningyu1991.github.io/), [Junnan Li](https://sites.google.com/site/junnanlics), [Dongxu Li](https://sites.google.com/view/dongxu-li/home), [Shafiq Joty](https://scholar.google.com/citations?user=hR249csAAAAJ&hl=en&oi=ao), [Ran Xu](https://scholar.google.com/citations?user=sgBB2sUAAAAJ&hl=en), [Silvio Savarese](https://scholar.google.com/citations?user=ImpbxLsAAAAJ&hl=en), [Caiming Xiong](https://scholar.google.com/citations?user=vaSdahkAAAAJ&hl=en), and [Juan Carlos Niebles](https://scholar.google.com/citations?user=hqNhUCYAAAAJ&hl=en) + +[![arXiv](https://img.shields.io/badge/arXiv-1234.56789-b31b1b.svg)](https://arxiv.org/pdf/2311.18799.pdf) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/projects/xinstructblip/demo/run_demo.ipynb) +*equal mentorship contribution. + +## Overview + +X-InstructBLIP a simple yet effective multimodal framework built on top of a frozen LLM, capable of seamlessly integrating and managing an ad-hoc number of modalities. Despite each modality projection being trained individually, X-InstructBLIP demonstrates joint reasoning abilities on par with models specifically trained on combined-modality datasets, such as video-audio. +![Architecture Overview](assets/architecture.png) + +--- +## Installation + +### LAVIS Repository +``` +git clone https://github.com/salesforce/LAVIS.git +cd LAVIS-XInstructBLIP +pip install -e . +``` + +### Evaluation Dependencies +For the evaluation code, make sure that `java` is installed in the system by running +``` +apt-get update +apt-get install default-jre +``` + +### KNN Cuda +Install [`KNN_Cuda`](https://github.com/unlimblue/KNN_CUDA) which is required for the 3D encoder. +``` +pip install --upgrade https://github.com/unlimblue/KNN_CUDA/releases/download/0.2/KNN_CUDA-0.2-py3-none-any.whl +``` +Make sure that `ninja` is installed: +``` +wget -P /usr/bin https://github.com/unlimblue/KNN_CUDA/raw/master/ninja +``` + +### Install NLTK-CMUDICT +``` +>>> import nltk +>>> nltk.download('cmudict') +``` + +## Pretrained Models +### Language Model Weights +First download the Vicuna v1.1 weights following the instructions [here](https://github.com/lm-sys/FastChat). Update the parameter `llm_model` in `configs/models/blip2/blip2_xinstruct_vicuna7b.yaml` and `configs/models/blip2/blip2_xinstruct_vicuna13b.yaml` and in the demo configs under `projects/xinstructblip/demo/configs` to the path of the downloaded model folder. + +### X-InstructBLIP Weights +Weights of the model are released [here (7b)](https://github.com/salesforce/LAVIS/blob/main/lavis/configs/models/blip2/blip2_xinstruct_vicuna7b.yaml) and [here (13b)](https://github.com/salesforce/LAVIS/blob/main/lavis/configs/models/blip2/blip2_xinstruct_vicuna13b.yaml) . When loading the model using the LAVIS codebase they should be automatically downloaded. +``` +from lavis.models import load_model +model = load_model("blip2_vicuna_xinstruct", "vicuna7b") +``` + + +### BEATs pretrained model +Download the pretrained weights of the BEATs encoder [here](https://valle.blob.core.windows.net/share/BEATs/BEATs_iter3_plus_AS2M.pt?sv=2020-08-04&st=2023-03-01T07%3A51%3A05Z&se=2033-03-02T07%3A51%3A00Z&sr=c&sp=rl&sig=QJXmSJG9DbMKf48UDIU1MfzIro8HQOf3sqlNXiflY1I%3D) and update the key +``` +audio_encoder_kwargs : {"checkpoint_path": "/path/to/BEATs_iter3_plus_AS2M.pt"} +``` +in the X-InstructBLIP (7b) and (13b) configs found in `configs/models/blip2/blip2_xinstruct_vicuna7b.yaml` and `configs/models/blip2/blip2_xinstruct_vicuna13b.yaml` respectively. + + +## Config + +X-InstructBLIP can be modified in various ways based on the config. Here is a documentation of each input field. + +### General Settings +- **arch**: Architecture of the model, `blip2_vicuna_xinstruct`. +- **model_type**: Specific model variant, `vicuna7b`. +- **load_pretrained**: Boolean to load a pre-trained model (`True`/`False`). + +### Pretrained Model Paths +- **pretrained**: Path to the pretrained model. +- **load_finetuned**: Load a finetuned model version (`True`/`False`). +- **finetuned**: Path to the finetuned model. + +### Model Components +- **image_model**, **pc_model**, **video_model**, **audio_model**: Models for image, point cloud, video, and audio encoders. + +### Pretrained Component Paths +- **pretrained_image_qformer**, **pretrained_pc_qformer**, **pretrained_video_qformer**: Paths to pretrained models for image, point cloud, and video qformers. + +### Load Attention and Projection Settings +- Parameters for loading attention mechanisms and projections in image, PC, video, and audio components. +- **projection_only_{modality}**: use projection instead of q-former for specified modality +- **load_attention_{modality}_qformer**: load pretrained q-former cross-attention to text for particular modality +- **load_ln_type_{modality}**: key specification of encoder layer norm to load from state_dict. Will be of the form `{load_ln_type}_ln`. +- **load_qformer_type_{modality}**: key specification of q-former to load from state_dict. Will be of the form `{load_qformer_type}_qformer`. +- **load_projection_{modality}**: boolean whether to load pretrained LLM projection for modality. + +### Encoder Settings +- **image_encoder_kwargs**, **pc_encoder_kwargs**, **video_encoder_kwargs**, **audio_encoder_kwargs**: Encoder arguments for various modalities. + +### Precision and Freeze Settings +- **image_precision**, **pc_precision**, **video_precision**, **audio_precision**: Precision (e.g., "fp16") for different modalities. +- **freeze_image**, **freeze_pc**, **freeze_video**, **freeze_audio**: Freeze respective model components. + +### Query and Text Settings +- **num_query_token**: Number of query tokens. +- **llm_model**: Path to the language model. +- **prompt**: Query prompt format. +- **max_txt_len**, **max_output_txt_len**: Max lengths for input and output text. +- **apply_lemmatizer**: Use lemmatizer on text. + +### Text Input +- **qformer_text_input**, **llm_text_input**: Whether to use text input for q-former and language model. + +### Modality and Cues +- **modalities**: List of used modalities, e.g., ["audio"]. +- **use_cues**: Use cues in the model. + +### Shared Q-Former Settings +- Shared qformer related parameters, including paths, load settings, and dimensions. + +### Additional Settings +- **prefix**, **postfix**: Texts for model input. +- **predict_with_gen**: Model prediction includes generation. +- **proj_dim**: Projection hidden dimension, defaults to 1. +- **special_qformer_input_prompt**: different q-former input prompt than the llm model. + + + +## Demo +Gradio based demo is available: +``` +cd projects/xinstructblip/demo +python run_demo.py --model_type vicuna7b +``` +The model configs can be adapted, and are found in `projects/xinstructblip/demo/configs` + +## Result Replication +A collection of configs for training and evaluation of all the numbers reported in the paper are available at: +`lavis/projects/xinstructblip/train` and `lavis/projects/xinstructblip/eval`. For internal reproducibility the data paths are absolute paths in the servers. Replace those with the corresponding data links and storage paths for your data as downloaded in section Data. + +Files can be run as follows: +``` +python -m torch.distributed.run --nproc_per_node=8 train.py --cfg-path path/to/train/or/eval/config +``` + +for example to train the 3D Q-Former one can run +``` +python -m torch.distributed.run --nproc_per_node=8 train.py --cfg-path lavis/projects/xinstruct_blip/train/vicuna7b/pc_training.yaml +``` +It is recommended to use 40GB GPU RAM. If this is not available, results can be partially replicated by loading the Vicuna LLM model in 8bit instead of 16bit. More details [here](https://huggingface.co/docs/transformers/main_classes/quantization). + + +## Data +X-InstructBLIP is trained and evaluated in a collection of public and generated datasets as shown in the figure below. +image + + +The data configuration files in `LAVIS-xgen_mm/configs/datasets` will automatically download the data annotation files when loading the datasets. The (image, audio, video, 3D) data can be downloaded from the following links. Once the datasets are downloaded, update the `storage` field in corresponding config file with the path in which the data is located. +### Image +#### Train +* [Conceptual Captions 12m](https://github.com/google-research-datasets/conceptual-12m) +* [SBU Captions](https://huggingface.co/datasets/sbu_captions) +* [Visual Genome](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html) +* [COCO](https://cocodataset.org/) +* CapFilt combination of CC3M, CC12M, and SBU +* [LLaVa150k](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K) +* VGQA: see Visual Genome +* [VQAv2](https://visualqa.org/) +* [OKVQA](https://okvqa.allenai.org/) +* [AOKVQA](https://allenai.org/project/a-okvqa/home) +* [OCRVQA](https://ocr-vqa.github.io/) + +#### Eval +* [GQA](https://cs.stanford.edu/people/dorarad/gqa/about.html) +* [VizWiz](https://vizwiz.org/tasks-and-datasets/vqa/) +* [Flickr30k](https://github.com/BryanPlummer/flickr30k_entities) +* [NoCaps](https://nocaps.org/) + +### Audio +#### Train +* [AudioSet](http://research.google.com/audioset/) +* [AudioCaps](https://audiocaps.github.io/) +* [WavCaps](https://github.com/XinhaoMei/WavCaps) + +#### Eval +* [Clotho](https://zenodo.org/records/3490684) +* [ClothoAQA](https://zenodo.org/records/6473207) +* [ESC50](https://github.com/karolpiczak/ESC-50) +* AudioCaps for DisCRn + +### Video +#### Train +* [WebVid2m](https://github.com/m-bain/webvid) +* [MSRVTTT](https://cove.thecvf.com/datasets/839) +#### Eval +* [MSVD](http://www.cs.utexas.edu/users/ml/clamp/videoDescription/) +* [MusicAVQA](https://gewu-lab.github.io/MUSIC-AVQA/) +* [VATEX](https://eric-xw.github.io/vatex-website/about.html) + + +### 3D + +* [Cap3D](https://huggingface.co/datasets/tiange/Cap3D) + + +## Data Augmentation + +We release the scripts to generate data both for the training data augmentation and generating the DisCRn dataset. + +### QA Data Augmentation + +The files `projects/xinstructblip/data_aug/3d_qa_data_generation.py` and `projects/xinstructblip/data_aug/audio_qa_data_generation.py` are used to generate the 3D and Audio QA data from Cap3D and Audiocaps respectively. + +#### 3DQA +Download the captions for the objaverse data [here](https://huggingface.co/datasets/tiange/Cap3D/blob/main/Cap3D_automated_Objaverse_no3Dword.csv). Then the script takes 4 arguments: +* `shard`: 1 through 4, for speed the data is processed in 4 batches and can be spread across machines or run consecutively in the same mahine +* `mode`: `color_removal` removes color concepts form captions since the encoder does not encode color, `qa_gen` generates question answer pairs, and `rtc` performs the roundtrip consistency check, `all` runs all of the process in this order. +* `split`: `train` or `val`. Assumes a separation of the data which can be found in these [`train`](https://storage.cloud.google.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_train.csv) and [`dev`](https://storage.cloud.google.com/sfr-xinstructblip-data-research/data/objaverse/cap3d_cap_final_val.csv) files. If the split is not added to the csv then it can be set arbitrarily and all the data in the file will be processed. +* `original_data_file`: the path to the captions for objaverse downloaded above. + +#### AudioQA +Download the Audiocaps captions from [here](https://github.com/cdjkim/audiocaps/tree/master/dataset). The script takes similar arguments as above: +* `shard`: 1 through 4, for speed the data is processed in 4 batches and can be spread across machines or run consecutively in the same mahine +* `mode`: `qa_gen` generates question answer pairs, and `rtc` performs the roundtrip consistency check, `all` runs all of the process in this order. +* `split`: `train`, `val`, `test`. +* `original_data_file`: the path to the captions for Audiocaps downloaded above for the relevant split. + +### DisCRn +The dataset is found here: [Audio-Video](https://storage.cloud.google.com/sfr-xinstructblip-data-research/data/discrn/audiocaps.json) and [Image-3D](https://storage.cloud.google.com/sfr-xinstructblip-data-research/data/discrn/objaverse.json). +The files `projects/xinstructblip/discrn/data_generation/objaverse_img_3d.py` are `projects/xinstructblip/discrn/data_generation/audiocaps_video_audio.py` generate the image-3d and audio-video cross-modal reasoning pairs for the DisCRn task. +#### Image-3D +The arguments are as above, with the same 3D caption data +* `shard` +* `original_data_file` +* `split` +* `mode`: in this case it can take the values `property` to generate properties, `get_pairs` to identify new pairs,`instruction_gen` to generate instruction answer pairs and `rtc` to perform round trip consistency. `all` will run them all in order. +* `rnd`: adds identifier in output files in the case of multiple generations. + + +#### Audio Video +The arguments are as above, with the same audio caption data. Note that you should update `VIDEO_PATH` in the script with the equivalent downloaded videos from Audiocaps. An easy way to do so is using the `youtube-id` associated with each of them and a package such as [`youtube-dl`](`https://github.com/ytdl-org/youtube-dl`) +* `shard` +* `original_data_file` +* `split` +* `mode`: in this case it can take the values `property` to generate properties, `get_pairs` to identify new pairs,`instruction_gen` to generate instruction answer pairs and `rtc` to perform round trip consistency. `all` will run them all in order. +* `rnd`: adds identifier in output files in the case of multiple generations. + + +## Cite + +``` +@misc{panagopoulou2023xinstructblip, + title={X-InstructBLIP: A Framework for aligning X-Modal instruction-aware + representations to LLMs and Emergent Cross-modal Reasoning}, + author={Artemis Panagopoulou and Le Xue and Ning Yu and Junnan Li and Dongxu Li and + Shafiq Joty and Ran Xu and Silvio Savarese and Caiming Xiong and Juan Carlos Niebles}, + year={2023}, + eprint={2311.18799}, + archivePrefix={arXiv}, + primaryClass={cs.CV} + } +``` diff --git a/LAVIS-main/projects/xinstructblip/data_aug/3d_qa_data_generation.py b/LAVIS-main/projects/xinstructblip/data_aug/3d_qa_data_generation.py new file mode 100644 index 0000000000000000000000000000000000000000..0d933aa71433736aaca8595cd0c5fe4e7084d4d1 --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/data_aug/3d_qa_data_generation.py @@ -0,0 +1,105 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +from tqdm import tqdm +import argparse +import pandas as pd +import torch +import os +from transformers import T5Tokenizer, T5ForConditionalGeneration +from fuzzywuzzy import fuzz + +parser = argparse.ArgumentParser(description="") +parser.add_argument("--shard", type=int, help="The shard number to process.") +parser.add_argument("--mode", type=str, help=['color_removal', 'qa_gen', 'rtc']) +parser.add_argument("--split", type=str, help=['train', 'val']) +parser.add_argument("--original_data_file", type=str, help=['Download csv file from https://huggingface.co/datasets/tiange/Cap3D/blob/main/Cap3D_automated_Objaverse_no3Dword.csv']) + +args = parser.parse_args() +shard = args.shard +mode = args.mode +split = args.split +original_data_file = args.original_datafile +# original_data_file = f'/export/einstein-vision/3d_vision/objaverse_captions/objaverse_blip_captions_no3d_{split}.csv' +output_dir = "./3d_qa_data" +os.makedirs(output_dir, exist_ok=True) + +## Load Model +tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl") +model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl", device_map="auto", torch_dtype=torch.float16) + +def get_output(input_text, input_len=128, output_len=128): + input_ids = torch.cat([tokenizer(inp, padding='max_length', max_length=input_len, return_tensors="pt").input_ids.to("cuda") for inp in input_text]) + outputs = model.generate(input_ids, max_length=output_len) + outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) + return outputs + + +if mode == 'color_removal' or mode == 'all': + df = pd.read_csv(original_data_file, names=["sample_id", "caption"]) + print(f"Total captions: {len(df)}") + start_index = shard * (len(df) // 4) + num_rows_to_extract = len(df) // 4 + df = df.iloc[start_index:start_index + num_rows_to_extract] + ## remove color. + no_color_captions = [] + captions = df["caption"].tolist() + num_examples = len(captions) + bs = 64 + for i in tqdm(range(0,num_examples, bs)): + input_text = [f"Rewrite the sentence {c} by removing mentions of color." for c in captions[i:i+bs]] + no_color_captions.extend(get_output(input_text, input_len=128, output_len=128)) + + df['caption_no_color'] = no_color_captions + df.to_csv(os.path.join(output_dir,f'Cap3D_automated_Objaverse_no_color_shard_{shard}_{split}.csv')) + +if mode == 'qa_gen' or mode == 'all': + df = pd.read_csv(os.path.join(output_dir,f'/Cap3D_automated_Objaverse_no_color_shard_{shard}_{split}.csv')).dropna() + df = df[df['caption_no_color'].apply(lambda x: len(str(x).split(' ')) > 10)] + print(f"Total number of data: {len(df)}") + captions = df['caption_no_color'].tolist() + num_examples = len(captions) + bs = 32 + questions = [] + answers = [] + extractive = [] + for i in tqdm(range(0,num_examples, bs)): + try: + input_text = [f"Generate a potential answer word from the following text: {c} " for c in captions[i:i+bs]] + answers.extend(get_output(input_text, input_len=180, output_len=128)) + input_text = [f"Generate a question for the answer using the context. Context: {c} Answer: {q} Question:" for c,q in zip(captions[i:i+bs], answers[i:i+bs])] + questions.extend(get_output(input_text, input_len=180, output_len=30)) + extractive.extend([fuzz.partial_ratio(a,c)>90 for c,a in zip(captions[i:i+bs], answers[i:i+bs])]) + except: + from pdb import set_trace; set_trace() + + df['question'] = questions + df['answer'] = answers + df['extractive'] = extractive + print(f'Number extractive: {len([e for e in extractive if e])}') + df.to_csv(os.path.join(output_dir,f'/Cap3D_automated_Objaverse_no_color_qa_shard_{shard}_{split}.csv')) + +if mode == 'rtc' or mode == 'all': + df = pd.read_csv(os.path.join(output_dir, f'Cap3D_automated_Objaverse_no_color_qa_shard_{shard}_{split}.csv')).dropna() + print(f"Total number of data: {len(df)}") + captions = df['caption_no_color'].tolist() + num_examples = len(captions) + bs = 32 + questions = df['question'].tolist() + answers =df['answer'].tolist() + correct = [] + for i in tqdm(range(0,num_examples, bs)): + try: + input_text = [f"Answer the question given the context. Context: {c} Question: {q} Answer:" for c,q in zip(captions[i:i+bs], questions[i:i+bs])] + outputs = get_output(input_text, input_len=256, output_len=20) + correct.extend([fuzz.partial_ratio(a,c)>90 for c,a in zip(outputs, answers[i:i+bs])]) + except: + from pdb import set_trace; set_trace() + + df['correct'] = correct + print(f'Number correct: {len([e for e in correct if e])}') + df.to_csv(os.path.join(output_dir, f'/Cap3D_automated_Objaverse_no_color_qa_correct_shard_{shard}_{split}.csv')) + + df[df['extractive'] == True][df['correct'] == True].to_csv(os.path.join(output_dir, f'/CAP3DQA_final_shard_{shard}_{split}.csv')) \ No newline at end of file diff --git a/LAVIS-main/projects/xinstructblip/data_aug/audio_qa_data_generation.py b/LAVIS-main/projects/xinstructblip/data_aug/audio_qa_data_generation.py new file mode 100644 index 0000000000000000000000000000000000000000..1783105ec66efd53892296112ef99a3f3151b51e --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/data_aug/audio_qa_data_generation.py @@ -0,0 +1,86 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + + +from fuzzywuzzy import fuzz +import torch +from transformers import T5Tokenizer, T5ForConditionalGeneration +from tqdm import tqdm +import argparse +import pandas as pd +import torch +import os +from transformers import T5Tokenizer, T5ForConditionalGeneration + +parser = argparse.ArgumentParser(description="") +parser.add_argument("--mode", type=str, help=['qa_gen', 'rtc']) +parser.add_argument("--split", type=str, help=['val', 'train']) +parser.add_argument("--original_data_file", type=str) + +args = parser.parse_args() +split = args.split +mode = args.mode + +original_data_file = #f'/export/home/audio_datasets/audiocaps/video/AUDIOCAPS_32000Hz/{split}.csv' +output_dir = "./audio_qa_data" +os.makedirs(output_dir, exist_ok=True) + +## Load Model +tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl") +model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl", device_map="auto", torch_dtype=torch.float16) + +def get_output(input_text, input_len=128, output_len=128): + input_ids = torch.cat([tokenizer(inp, padding='max_length', max_length=input_len, return_tensors="pt").input_ids.to("cuda") for inp in input_text]) + outputs = model.generate(input_ids, max_length=output_len) + outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) + return outputs + +if mode == 'qa_gen': + df = pd.read_csv(original_data_file) + print(f"Total number of data: {len(df)}") + captions = df['caption'].tolist() + num_examples = len(captions) + bs = 32 + questions = [] + answers = [] + extractive = [] + for i in tqdm(range(0,num_examples, bs)): + try: + input_text = [f"Generate a potential answer word from the following text: {c} " for c in captions[i:i+bs]] + answers.extend(get_output(input_text, input_len=128, output_len=128)) + input_text = [f"Generate a question for the answer using the context. Context: {c} Answer: {q} Question:" for c,q in zip(captions[i:i+bs], answers[i:i+bs])] + questions.extend(get_output(input_text, input_len=128, output_len=128)) + extractive.extend([fuzz.partial_ratio(a,c)>90 for c,a in zip(captions[i:i+bs], answers[i:i+bs])]) + except: + from pdb import set_trace; set_trace() + + df['question'] = questions + df['answer'] = answers + df['extractive'] = extractive + print(f'Number extractive: {len([e for e in extractive if e])}') + df.to_csv(os.path.join(output_dir, f'/audio_qa_{split}.csv')) + +if mode == 'rtc': + df = pd.read_csv(os.path.join(output_dir, f'/audio_qa_{split}.csv')) + print(f"Total number of data: {len(df)}") + captions = df['caption'].tolist() + questions = df['question'].tolist() + answers = df['answer'].tolist() + num_examples = len(captions) + bs = 16 + correct = [] + for i in tqdm(range(0,num_examples, bs)): + try: + input_text = [f"Answer the question given the context. Context: {c} Question: {q} Answer:" for c,q in zip(captions[i:i+bs], questions[i:i+bs])] + outputs = get_output(input_text, input_len=256, output_len=20) + correct.extend([fuzz.partial_ratio(a,c)>90 for c,a in zip(outputs, answers[i:i+bs])]) + except: + from pdb import set_trace; set_trace() + + df['correct'] = correct + print(f'Number extractive: {len([e for e in correct if e])}') + df.to_csv(os.path.join(output_dir, f'/audio_qa_correct_{split}.csv')) + + df[df['correct'] == True][df['extractive'] == True].to_csv(os.path.join(output_dir,f'audio_qa_final_{split}.csv')) \ No newline at end of file diff --git a/LAVIS-main/projects/xinstructblip/demo/configs/vicuna13b.yaml b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna13b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3fe9004f43a0fabb7a35a9f17d34e0ad33f00794 --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna13b.yaml @@ -0,0 +1,77 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna13b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-13b" + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image", "video", "audio", "pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + add_space: False + remove_start: False \ No newline at end of file diff --git a/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b.yaml b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a5275f849b78fd35c81ff718d24e0f03ff75a765 --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b.yaml @@ -0,0 +1,78 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image"Ã¥ + load_ln_type_video: "video" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna7b" + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image", "video", "audio", "pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + special_qformer_input_prompt: "a short description" + prefix: "" + postfix: "" + add_space: False + remove_start: True diff --git a/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_blip_init.yaml b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_blip_init.yaml new file mode 100644 index 0000000000000000000000000000000000000000..618baedbdee73e35951902aa0a7aa78dfc31ffdc --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_blip_init.yaml @@ -0,0 +1,74 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_attention_image_qformer: False + load_attention_pc_qformer: False + load_attention_video_qformer: False + load_attention_audio_qformer: False + load_ln_type_image: "" + load_ln_type_video: "" + load_ln_type_audio: "" + load_qformer_type_image: "" + load_qformer_type_pc: "" + load_qformer_type_video: "" + load_qformer_type_audio: "" + load_projection_image: False + load_projection_pc: False + load_projection_video: False + load_projection_audio: False + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna13b" + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image", "video", "audio", "pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" diff --git a/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_no_init.yaml b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_no_init.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cb0dc4d788ce7a22783092b809eb3406f487a23b --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_no_init.yaml @@ -0,0 +1,74 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_no_init.pth + load_finetuned: False + finetuned: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_no_init.pth + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer_no_init.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer_no_init.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_no_init.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna7b" + prompt: "" + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image", "video", "audio", "pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" diff --git a/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_nocue.yaml b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_nocue.yaml new file mode 100644 index 0000000000000000000000000000000000000000..19daf722cfa06a32b5abf79ed4d34f051f795c1b --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_nocue.yaml @@ -0,0 +1,75 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/pc_qformer_last.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b_nocue/audio_qformer.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna7b" + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image", "video", "audio", "pc"] + use_cues: False + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" diff --git a/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_projection.yaml b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_projection.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aacb9b87a727ded34b548feed7437ea7be44a0f9 --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_projection.yaml @@ -0,0 +1,83 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/pc_qformer_linear.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: False + pretrained_pc_qformer: False + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: False + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: False + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: False + load_projection_video: True + load_projection_audio: False + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna7b" + prompt: "describe the 3d model" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: False + llm_text_input: True + modalities : ["audio","pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" + projection_path_audio: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/audio_qformer_linear.pth + projection_path_pc: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/pc_qformer_linear.pth + projection_path_image: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/linear_projection_7b/image_qformer_linear.pth + projection_only_audio: True + projection_only_pc: True + projection_only_image: True + diff --git a/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_rand.yaml b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_rand.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6b4a29ee0fd3526816f7f22f89f0cc9dfe951edd --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_rand.yaml @@ -0,0 +1,75 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna13b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: null + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: null + pretrained_pc_qformer: null + pretrained_video_qformer: null + pretrained_audio_qformer: null + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-7b" + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["image", "video", "audio", "pc"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "" + postfix: "" diff --git a/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_v2.yaml b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_v2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fca6f97c351a991d03e8d875298a9f39fb3761f0 --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/demo/configs/vicuna7b_v2.yaml @@ -0,0 +1,78 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + + +model: + arch: blip2_vicuna_xinstruct + model_type: vicuna7b + load_pretrained: True + pretrained: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + load_finetuned: False + finetuned: "" + stage1_url_or_filename: https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth + image_model: "eva_clip_g" + pc_model: "ulip2_pointbert" + video_model: "eva_clip_g" + audio_model: "beats" + pretrained_image_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/image_qformer.pth + pretrained_pc_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/pc_qformer_improved.pth + pretrained_video_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/video_qformer.pth + pretrained_audio_qformer: https://storage.googleapis.com/sfr-xinstructblip-data-research/model/xinstructblip_checkpoints/vicuna7b/audio_qformer_improved.pth + load_attention_image_qformer: True + load_attention_pc_qformer: True + load_attention_video_qformer: True + load_attention_audio_qformer: True + load_ln_type_image: "image" + load_ln_type_video: "video" + load_ln_type_pc: "pc" + load_ln_type_audio: "audio" + load_qformer_type_image: "image" + load_qformer_type_pc: "pc" + load_qformer_type_video: "video" + load_qformer_type_audio: "audio" + load_projection_image: True + load_projection_pc: True + load_projection_video: True + load_projection_audio: True + load_projection_type_image: "image" + load_projection_type_pc: "pc" + load_projection_type_video: "video" + load_projection_type_audio: "audio" + image_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + pc_encoder_kwargs : {} + video_encoder_kwargs : {"image_size": 224, "drop_path_rate": 0, "use_grad_checkpoint": False} + audio_encoder_kwargs : {} + image_precision: "fp16" + pc_precision: "fp16" + video_precision: "fp16" + audio_precision: "fp16" + freeze_image: True + freeze_pc: True + freeze_video: True + freeze_audio: True + num_query_token: 32 + llm_model: "/path/to/vicuna-7b" + prompt: "" + max_txt_len: 128 + max_output_txt_len: 256 + apply_lemmatizer: False + num_few_shot_examples: 0 + few_shot_prob: 0 + qformer_text_input: True + llm_text_input: True + modalities : ["audio", "video", "pc", "image"] + use_cues: True + shared_qformer: False + pretrained_shared_qformer: Null + load_attention_shared_qformer: False + load_qformer_type_shared: "" + load_projection_shared: False + load_projection_type_shaped: "" + load_ln_type_shared: "" + shared_qformer_num_features: 512 + prefix: "USER: " + postfix: "\nASSISTANT:" + predict_with_gen: False + clean_tokenization: True diff --git a/LAVIS-main/projects/xinstructblip/demo/demo.ipynb b/LAVIS-main/projects/xinstructblip/demo/demo.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..bbd55848a40758f85d3d255eb513efeab12e5776 --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/demo/demo.ipynb @@ -0,0 +1,393 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Copyright statement" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + " # Copyright (c) 2023, salesforce.com, inc.\n", + " # All rights reserved.\n", + " # SPDX-License-Identifier: BSD-3-Clause\n", + " # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# X-InstructBLIP Demo\n", + "\n", + "Before proceeding **download the Vicuna v1.1 model weights** following the instructions [here](https://github.com/lm-sys/FastChat). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "LLM_MODEL_PATH = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## SETUP THE ENVIRONMENT\n", + "!git clone https://github.com/artemisp/LAVIS-XInstructBLIP.git\n", + "!cd LAVIS-XInstructBLIP && python -m pip install -e .\n", + "!python -m pip install --upgrade https://github.com/unlimblue/KNN_CUDA/releases/download/0.2/KNN_CUDA-0.2-py3-none-any.whl\n", + "!wget -P /usr/bin https://github.com/unlimblue/KNN_CUDA/raw/master/ninja" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gradio as gr\n", + "import torch\n", + "import argparse\n", + "import numpy as np\n", + "from omegaconf import OmegaConf\n", + "from lavis.common.registry import registry\n", + "import random\n", + "\n", + "import trimesh\n", + "import pyvista as pv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prelim\n", + "Set up seeds for reproducibility and identify device type. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def setup_seeds(seed=42):\n", + " seed = seed\n", + "\n", + " random.seed(seed)\n", + " np.random.seed(seed)\n", + " torch.manual_seed(seed)\n", + "\n", + "device = torch.device(\"cuda\") if torch.cuda.is_available() else \"cpu\"\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3D file to point cloud" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Raw Files\n", + "\n", + "#https://github.com/mikedh/trimesh/issues/507\n", + "def as_mesh(scene_or_mesh):\n", + " \"\"\"\n", + " Convert a possible scene to a mesh.\n", + "\n", + " If conversion occurs, the returned mesh has only vertex and face data.\n", + " \"\"\"\n", + " if isinstance(scene_or_mesh, trimesh.Scene):\n", + " if len(scene_or_mesh.geometry) == 0:\n", + " mesh = None # empty scene\n", + " else:\n", + " # we lose texture information here\n", + " mesh = trimesh.util.concatenate(\n", + " tuple(trimesh.Trimesh(vertices=g.vertices, faces=g.faces)\n", + " for g in scene_or_mesh.geometry.values()))\n", + " else:\n", + " assert(isinstance(scene_or_mesh, trimesh.Trimesh))\n", + " mesh = scene_or_mesh\n", + " return mesh\n", + "\n", + "def convert_mesh_to_numpy(mesh_file, npoints=8192):\n", + " print(\"Loading point cloud.\")\n", + " # Load the mesh using trimesh\n", + " mesh = trimesh.load_mesh(mesh_file, force='mesh')\n", + " mesh = as_mesh(mesh)\n", + "\n", + " # Subsample or upsample the mesh to have exactly npoints points\n", + " vertices = mesh.vertices\n", + " num_points = len(vertices)\n", + " if num_points < npoints:\n", + " # Upsample the mesh by repeating vertices\n", + " repetitions = int(np.ceil(npoints / num_points))\n", + " vertices = np.repeat(vertices, repetitions, axis=0)[:npoints]\n", + " elif num_points > npoints:\n", + " # Subsample the mesh to the desired number of points\n", + " # indices = trimesh.sample.sample_surface(mesh, npoints)[0]\n", + " vertices = mesh.vertices#[indices]\n", + " print(\"Point cloud loaded..\")\n", + " \n", + " return vertices\n", + "\n", + "\n", + "\n", + "def load_mesh(mesh_file_name):\n", + " return mesh_file_name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Preprocessors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Load Preprocessors\n", + "from lavis.processors.ulip_processors import ULIPPCProcessor\n", + "from lavis.processors.clip_processors import ClipImageEvalProcessor\n", + "from lavis.processors.audio_processors import BeatsAudioProcessor\n", + "from lavis.processors.alpro_processors import AlproVideoEvalProcessor\n", + "\n", + "pc_pocessor = ULIPPCProcessor()\n", + "image_pocessor = ClipImageEvalProcessor()\n", + "audio_processor = BeatsAudioProcessor(model_name='iter3', sampling_rate=16000, n_frames=2, is_eval=False, frame_length=512)\n", + "video_processor = AlproVideoEvalProcessor(n_frms=4, image_size=224)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Model from LAVIS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from lavis.models.blip2_models.blip2_vicuna_xinstruct import Blip2VicunaXInstruct\n", + "model = \"vicuna7b_v2\"\n", + "cfg_path = {\n", + " \"vicuna13b\": './configs/vicuna13b.yaml',\n", + " \"vicuna7b\": './configs/vicuna7b.yaml',\n", + " \"no_init\": './configs/vicuna7b_no_init.yaml',\n", + " \"projection\": './configs/vicuna7b_projection.yaml'\n", + " \"vicuna7b_v2\": './configs/vicuna7b_v2.yaml'\n", + " }\n", + " \n", + "config = OmegaConf.load(cfg_path[args.model])\n", + "config.get(\"model\", None).llm_model = LLM_MODEL_PATH\n", + "print(cfg_path[args.model])\n", + "print('Loading model...')\n", + "model_cls = registry.get_model_class(config.get(\"model\", None).arch)\n", + "model = model_cls.from_config(config.get(\"model\", None))\n", + "model.to(device)\n", + "print('Loading model done!')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inference Function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def inference(image, point_cloud, audio, video, prompt, qformer_prompt, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, decoding_method):\n", + " if qformer_prompt == \"\" or qformer_prompt == None:\n", + " qformer_prompt = prompt\n", + " use_nucleus_sampling = decoding_method == \"Nucleus sampling\"\n", + " print(image, point_cloud, audio, video, prompt, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, use_nucleus_sampling)\n", + " if image is not None:\n", + " image = image_pocessor(image).unsqueeze(0).to(device)\n", + " if point_cloud is not None:\n", + " point_cloud = convert_mesh_to_numpy(point_cloud)\n", + " point_cloud = pc_pocessor(point_cloud).unsqueeze(0).to(device)\n", + " if audio is not None:\n", + " audio = audio_processor(audio).unsqueeze(0).to(device)\n", + " if video is not None:\n", + " video = video_processor(video).unsqueeze(0).to(device)\n", + " \n", + " samples = {\"prompt\": prompt}\n", + " if image is not None:\n", + " samples[\"image\"] = image\n", + " if point_cloud is not None:\n", + " samples[\"pc\"] = point_cloud\n", + " if audio is not None:\n", + " samples[\"audio\"] = audio\n", + " if video is not None:\n", + " samples[\"video\"] = video\n", + "\n", + " output = model.generate(\n", + " samples,\n", + " length_penalty=float(len_penalty),\n", + " repetition_penalty=float(repetition_penalty),\n", + " num_beams=beam_size,\n", + " max_length=max_len,\n", + " min_length=min_len,\n", + " top_p=top_p,\n", + " use_nucleus_sampling=use_nucleus_sampling,\n", + " special_qformer_input_prompt=qformer_prompt\n", + " )\n", + "\n", + " return output[0]\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Demo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "\n", + "setup_seeds()\n", + "\n", + "image_input = gr.Image(type=\"pil\")\n", + "\n", + "pc_input = gr.Model3D()\n", + "\n", + "audio_input = gr.Audio(sources=[\"upload\"], type=\"filepath\")\n", + "\n", + "video_input = gr.Video()\n", + "\n", + "min_len = gr.Slider(\n", + " minimum=1,\n", + " maximum=50,\n", + " value=1,\n", + " step=1,\n", + " interactive=True,\n", + " label=\"Min Length\",\n", + ")\n", + "\n", + "max_len = gr.Slider(\n", + " minimum=10,\n", + " maximum=500,\n", + " value=250,\n", + " step=5,\n", + " interactive=True,\n", + " label=\"Max Length\",\n", + ")\n", + "\n", + "sampling = gr.Radio(\n", + " choices=[\"Beam search\", \"Nucleus sampling\"],\n", + " value=\"Beam search\",\n", + " label=\"Text Decoding Method\",\n", + " interactive=True,\n", + ")\n", + "\n", + "top_p = gr.Slider(\n", + " minimum=0.5,\n", + " maximum=1.0,\n", + " value=0.9,\n", + " step=0.1,\n", + " interactive=True,\n", + " label=\"Top p\",\n", + ")\n", + "\n", + "beam_size = gr.Slider(\n", + " minimum=1,\n", + " maximum=10,\n", + " value=5,\n", + " step=1,\n", + " interactive=True,\n", + " label=\"Beam Size\",\n", + ")\n", + "\n", + "len_penalty = gr.Slider(\n", + " minimum=-1,\n", + " maximum=2,\n", + " value=1,\n", + " step=0.2,\n", + " interactive=True,\n", + " label=\"Length Penalty\",\n", + ")\n", + "\n", + "repetition_penalty = gr.Slider(\n", + " minimum=0.,\n", + " maximum=3,\n", + " value=1.5,\n", + " step=0.2,\n", + " interactive=True,\n", + " label=\"Repetition Penalty\",\n", + ")\n", + "\n", + "\n", + "prompt_textbox = gr.Textbox(label=\"Prompt:\", placeholder=\"prompt\", lines=2)\n", + "qformer_prompt_textbox = gr.Textbox(label=\"Qformer Prompt:\", placeholder=\"prompt\", lines=2)\n", + "\n", + "iface = gr.Interface(\n", + " fn=inference,\n", + " inputs=[image_input, pc_input, audio_input, video_input, prompt_textbox, qformer_prompt_textbox, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, sampling],\n", + " outputs=\"text\",\n", + " allow_flagging=\"never\",\n", + " examples=examples\n", + ")\n", + "\n", + "iface.launch(share=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/LAVIS-main/projects/xinstructblip/demo/run_demo.py b/LAVIS-main/projects/xinstructblip/demo/run_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..f7cfe20a045d0e4d84c5c3999b51e25265c5a998 --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/demo/run_demo.py @@ -0,0 +1,255 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import gradio as gr +from lavis.models.blip2_models.blip2_vicuna_xinstruct import Blip2VicunaXInstruct +import torch +import argparse +import numpy as np +from lavis.processors.ulip_processors import ULIPPCProcessor +from lavis.processors.clip_processors import ClipImageEvalProcessor +from lavis.processors.audio_processors import BeatsAudioProcessor +from lavis.processors.alpro_processors import AlproVideoEvalProcessor +from omegaconf import OmegaConf +from lavis.common.registry import registry +import random + +import trimesh +import pyvista as pv + +#https://github.com/mikedh/trimesh/issues/507 +def as_mesh(scene_or_mesh): + """ + Convert a possible scene to a mesh. + + If conversion occurs, the returned mesh has only vertex and face data. + """ + if isinstance(scene_or_mesh, trimesh.Scene): + if len(scene_or_mesh.geometry) == 0: + mesh = None # empty scene + else: + # we lose texture information here + mesh = trimesh.util.concatenate( + tuple(trimesh.Trimesh(vertices=g.vertices, faces=g.faces) + for g in scene_or_mesh.geometry.values())) + else: + assert(isinstance(scene_or_mesh, trimesh.Trimesh)) + mesh = scene_or_mesh + return mesh + +def convert_mesh_to_numpy(mesh_file, npoints=8192): + print("Loading point cloud.") + # Load the mesh using trimesh + mesh = trimesh.load_mesh(mesh_file, force='mesh') + mesh = as_mesh(mesh) + + # Subsample or upsample the mesh to have exactly npoints points + vertices = mesh.vertices + num_points = len(vertices) + if num_points < npoints: + # Upsample the mesh by repeating vertices + repetitions = int(np.ceil(npoints / num_points)) + vertices = np.repeat(vertices, repetitions, axis=0)[:npoints] + elif num_points > npoints: + # Subsample the mesh to the desired number of points + # indices = trimesh.sample.sample_surface(mesh, npoints)[0] + vertices = mesh.vertices#[indices] + print("Point cloud loaded..") + + return vertices + + + +def load_mesh(mesh_file_name): + return mesh_file_name + +def setup_seeds(seed=42): + seed = seed + + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + +if __name__ == '__main__': + setup_seeds() + + parser = argparse.ArgumentParser(description="Demo") + parser.add_argument("--model", default="vicuna7b_v2") + args = parser.parse_args() + device = torch.device("cuda") if torch.cuda.is_available() else "cpu" + + + pc_pocessor = ULIPPCProcessor() + image_pocessor = ClipImageEvalProcessor() + audio_processor = BeatsAudioProcessor(model_name='iter3', sampling_rate=16000, n_frames=2, is_eval=False, frame_length=512) + video_processor = AlproVideoEvalProcessor(n_frms=4, image_size=224) + + cfg_path = { + "vicuna13b": './configs/vicuna13b.yaml', + "vicuna7b": './configs/vicuna7b.yaml', + "no_init": './configs/vicuna7b_no_init.yaml', + "projection": './configs/vicuna7b_projection.yaml', + "vicuna7b_v2": './configs/vicuna7b_v2.yaml' + } + config = OmegaConf.load(cfg_path[args.model]) + print(cfg_path[args.model]) + print('Loading model...') + model_cls = registry.get_model_class(config.get("model", None).arch) + model = model_cls.from_config(config.get("model", None)) + # if args.model == 'flant5xxl': + # model = Blip2T5MMInstruct.from_config(config.get("model", None)) + # else: + # model = Blip2VicunaMMInstruct.from_config(config.get("model", None)) + model.to(device) + print('Loading model done!') + + + def inference(image, point_cloud, audio, video, prompt, qformer_prompt, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, decoding_method): + if qformer_prompt == "" or qformer_prompt == None: + qformer_prompt = prompt + use_nucleus_sampling = decoding_method == "Nucleus sampling" + print(image, point_cloud, audio, video, prompt, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, use_nucleus_sampling) + if image is not None: + image = image_pocessor(image).unsqueeze(0).to(device) + if point_cloud is not None: + point_cloud = convert_mesh_to_numpy(point_cloud) + point_cloud = pc_pocessor(point_cloud).unsqueeze(0).to(device) + if audio is not None: + audio = audio_processor(audio).unsqueeze(0).to(device) + if video is not None: + video = video_processor(video).unsqueeze(0).to(device) + + samples = {"prompt": prompt} + if image is not None: + samples["image"] = image + if point_cloud is not None: + samples["pc"] = point_cloud + if audio is not None: + samples["audio"] = audio + if video is not None: + samples["video"] = video + + output = model.generate( + samples, + length_penalty=float(len_penalty), + repetition_penalty=float(repetition_penalty), + num_beams=beam_size, + max_length=max_len, + min_length=min_len, + top_p=top_p, + use_nucleus_sampling=use_nucleus_sampling, + special_qformer_input_prompt=qformer_prompt + ) + + return output[0] + + # examples = [ + # [None, None, './examples/audio/110714_wren.wav', None, "What do you hear?", 1, 50, 3, 1., 2.2, 0.9, "Beam Search"], + + # [None, './examples/point_cloud/banana.glb', None, None, "Describe the 3D model.", 1, 250, 5, -1, 1.4, 0.9, "Beam Search"], + # [None, None, './examples/audio/Group_of_Dogs_Barking.wav', None, "What do you hear?", 6, 250, 5, -1., 1, 0.9, "Beam Search"], + + # ] + + + + # iface.launch(share=True) + # Create a Blocks interface + with gr.Blocks() as demo: + # Create a row for input components + with gr.Row(): + + image_input = gr.Image(type="pil") + + pc_input = gr.Model3D() + + audio_input = gr.Audio(sources=["upload"], type="filepath") + + video_input = gr.Video() + with gr.Row(): + prompt_textbox = gr.Textbox(label="Prompt:", placeholder="prompt", lines=2) + qformer_prompt_textbox = gr.Textbox(label="Qformer Prompt:", placeholder="prompt", lines=2) + + # Output text below the inputs + output_text = gr.Textbox(label="Output") + + # When the button is clicked, the inference function will run + submit_button = gr.Button("Submit") + + + with gr.Row(): + min_len = gr.Slider( + minimum=1, + maximum=50, + value=1, + step=1, + interactive=True, + label="Min Length", + ) + + max_len = gr.Slider( + minimum=10, + maximum=500, + value=250, + step=5, + interactive=True, + label="Max Length", + ) + with gr.Row(): + sampling = gr.Radio( + choices=["Beam search", "Nucleus sampling"], + value="Beam search", + label="Text Decoding Method", + interactive=True, + ) + + top_p = gr.Slider( + minimum=0.5, + maximum=1.0, + value=0.9, + step=0.1, + interactive=True, + label="Top p", + ) + + beam_size = gr.Slider( + minimum=1, + maximum=10, + value=5, + step=1, + interactive=True, + label="Beam Size", + ) + with gr.Row(): + len_penalty = gr.Slider( + minimum=-1, + maximum=2, + value=1, + step=0.2, + interactive=True, + label="Length Penalty", + ) + + repetition_penalty = gr.Slider( + minimum=0., + maximum=3, + value=1.5, + step=0.2, + interactive=True, + label="Repetition Penalty", + ) + submit_button.click( + fn=inference, + inputs=[image_input, pc_input, audio_input, video_input, prompt_textbox, qformer_prompt_textbox, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, sampling], + outputs=output_text + ) + + # gr.Examples(examples, inputs=[image_input, pc_input, audio_input, video_input, prompt_textbox, qformer_prompt_textbox, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, sampling], outputs=output_text) + + + demo.launch(share=True) \ No newline at end of file diff --git a/LAVIS-main/projects/xinstructblip/discrn/caption_baseline/predict_audio.py b/LAVIS-main/projects/xinstructblip/discrn/caption_baseline/predict_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..4562ac48f602d90c6aeec13fcbd99de35c3bfe90 --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/discrn/caption_baseline/predict_audio.py @@ -0,0 +1,66 @@ +#!/usr/bin/env python3 +# coding: utf-8 +# @Author : Xinhao Mei @CVSSP, University of Surrey +# @E-mail : x.mei@surrey.ac.uk + + +# location: WavCaps/captioning +# clone from +import librosa +import torch +import torch.nn.functional as F +from models.bart_captioning import BartCaptionModel +from lavis.datasets.builders import load_dataset +from tqdm import tqdm +import pickle + + +## comment out balancind code in dataset before running. also comment out loading data. +ds = load_dataset('audio_video_discrn')['val'] + +checkpoint_path = "/export/home/WavCaps/Cnn14_Clotho_Spider_31.pt" +audio_path = "" +cp = torch.load(checkpoint_path) +config = cp["config"] +model = BartCaptionModel(config) +model.load_state_dict(cp["model"]) +device = torch.device(config["device"]) +model.to(device) + + +entity2pred= {} +for ann in tqdm(ds): + new_ann = ann + for i,audio_path in enumerate([ds.get_audio_path(ann, 0), ds.get_audio_path(ann, 1)]): + if ann['sample_ids'][i] in entity2pred: + continue + try: + waveform, sr = librosa.load(audio_path, sr=32000, mono=True) + waveform = torch.tensor(waveform) + + if config["audio_encoder_args"]["model_arch"] == "transformer": + max_length = 32000 * 10 + if len(waveform) > max_length: + waveform = waveform[:max_length] + else: + waveform = F.pad(waveform, [0, max_length - len(waveform)], "constant", 0.0) + + else: + max_length = 32000 * 30 + if len(waveform) > max_length: + waveform = waveform[:max_length] + + waveform = waveform.unsqueeze(0) + + model.eval() + with torch.no_grad(): + waveform = waveform.to(device) + caption = model.generate(samples=waveform, num_beams=3) + entity2pred[ann['sample_ids'][i]] = caption + except: + print(ann['paths']) + # print(caption) + + +pickle.dump(entity2pred, open("./entity2pred/entity2pred_audio.p", 'wb')) + diff --git a/LAVIS-main/projects/xinstructblip/discrn/caption_baseline/predict_image.py b/LAVIS-main/projects/xinstructblip/discrn/caption_baseline/predict_image.py new file mode 100644 index 0000000000000000000000000000000000000000..93d8cba67725ce879a23661e7b64099575c26bf6 --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/discrn/caption_baseline/predict_image.py @@ -0,0 +1,253 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + + +import librosa +import torch +import torch.nn.functional as F +from models.bart_captioning import BartCaptionModel +from lavis.datasets.builders import load_dataset +from tqdm import tqdm + +from lavis.models import load_model_and_preprocess +from lavis.processors.alpro_processors import AlproVideoEvalProcessor + +import argparse +import json +from PIL import Image +import os +import random +import torch +import torch.backends.cudnn as cudnn +import numpy as np + + +def check_positive(value): + ivalue = int(value) + if ivalue <= 0: + raise argparse.ArgumentTypeError("%s is an invalid positive int value" % value) + return ivalue + +def check_float_range(min_val, max_val): + def helper(x): + x = float(x) + if x < min_val or x > max_val: + raise argparse.ArgumentTypeError("Value must be between {} and {}.".format(min_val, max_val)) + return x + return helper + +def setup_seeds(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + cudnn.benchmark = False + cudnn.deterministic = True + +# Create the top-level parser +parser = argparse.ArgumentParser( + description='A demo script for using instructBLIP models for tasks such as image captioning or Visual Question Answering (VQA).' +) + +# Model Name +parser.add_argument( + '--model-name', + default='blip2_vicuna_instruct', + choices=['blip2_vicuna_instruct', 'blip2_t5_instruct'], + help='The name of the instructBLIP model to use.' +) + +# Model Type +parser.add_argument( + '--model-type', + default='vicuna7b', + choices=['vicuna7b', 'vicuna13b', 'flant5xl', 'flant5xxl'], + help='The type of the model to use.' +) + +# Task +parser.add_argument( + '--task', + default='caption', + choices=['caption', 'vqa'], + help='The type of task to run: captioning or VQA.' +) + +# Number of tasks +parser.add_argument( + '--num', + type=int, + default=1, + help='The number of outputs to generate.' +) + +# GPU ID +parser.add_argument( + '--gpu-id', + type=int, + default=0, + help='The ID of the GPU to use.' +) + +# Image path or URL +parser.add_argument( + '--image_path_or_url', + type=str, + default='', + help='The path or URL to the image file.' +) + +# Prompt +parser.add_argument( + '--prompt', + type=str, + default='', + help='The text prompt to use in tasks. Enclose it in quotation ("")' +) + +# Seed +parser.add_argument( + '--seed', + type=int, + default=42, + help='The seed for the random number generator.' +) + +# Minimum Length +parser.add_argument( + '--min_len', + type=check_positive, + default=1, + help='The minimum length for the generated text.' +) + +# Maximum Length +parser.add_argument( + '--max_len', + type=check_positive, + default=250, + help='The maximum length for the generated text.' +) + +# Beam Size +parser.add_argument( + '--beam_size', + type=check_positive, + default=5, + help='The beam size for the Beam Search.' +) + +# Length Penalty +parser.add_argument( + '--len_penalty', + type=float, + default=-1, + help='The length penalty for Beam Search.' +) + +# Repetition Penalty +parser.add_argument( + '--rep_penalty', + type=float, + default=1, + help='The penalty for word repetitions in the generated text.' +) + +# Top P +parser.add_argument( + '--top_p', + type=check_float_range(0.0, 1.0), + default=0.9, + help='The cumulative probability threshold for Nucleus Sampling.' +) + +# Decoding Method +parser.add_argument( + '--decoding_method', + default='Nucleus sampling', + choices=['Nucleus sampling', 'Beam search'], + help='The method to use for decoding the generated text.' +) + +# Temperature +parser.add_argument( + '--temperature', + type=check_float_range(0.1, 5.0), + default=1.0, + help='The temperature to use in the generation process. Higher values increase randomness, lower values make the output more deterministic.' +) + +args = parser.parse_args() + +if torch.cuda.is_available(): + torch.cuda.set_device(args.gpu_id) + device='cuda' + print(f"Script will run on cuda device {args.gpu_id}") +else: + print("CUDA is not available. The script will run on CPU.") + device='cpu' + + +print(f"Setting up seeds [seed={args.seed}]") +setup_seeds(args.seed) + +print(f'Loading model {args.model_name} with LLM {args.model_type}...') +model, vis_processors, _ = load_model_and_preprocess( + name=args.model_name, + model_type=args.model_type, + is_eval=True, + device=device, +) + + +vis_processors = ds['val'].vis_processor +print('Loading model done!') + +def inference(image, prompt, min_len=1, max_len=250, beam_size=5, len_penalty=-1, repetition_penalty=1, top_p=.9, decoding_method='Beam Search',num_captions=1, temperature=1., video=False): + use_nucleus_sampling = decoding_method == "Nucleus sampling" + print(image, prompt, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, use_nucleus_sampling) + if not video: + image = vis_processors["eval"](image).unsqueeze(0).to(device) + else: + image = vis_processors(image).to(device).unsqueeze(0).half() + # image = torch.cat(processed_frames, dim=0).mean(dim=0, keepdim=True).to(device) + + samples = { + "image": image, + "prompt": prompt, + } + + output = model.generate( + samples, + repetition_penalty=float(repetition_penalty), + num_beams=beam_size, + max_length=max_len, + min_length=min_len, + top_p=top_p, + use_nucleus_sampling=use_nucleus_sampling, + num_captions=num_captions, + temperature=temperature, + ) + return output[0] + + + +## comment out balancind code in dataset before running. also comment out loading data. +ds = load_dataset('image_pc_discrn')['val'] +entity2pred= {} +for ann in tqdm(ds): + new_ann = ann + for i,video_path in enumerate([ds.get_image_path(ann, 0), ds[.get_image_path(ann, 1)]]): + if ann['sample_ids'][i] in entity2pred: + continue + with torch.no_grad(): + caption = inference(video_path, "describe the image", video=False) + entity2pred[ann['sample_ids'][i]] = caption + except: + print(ann['paths']) + # print(caption) + + +pickle.dump(entity2pred, open("./entity2pred/entity2pred_image.p", 'wb')) \ No newline at end of file diff --git a/LAVIS-main/projects/xinstructblip/discrn/caption_baseline/predict_pc.py b/LAVIS-main/projects/xinstructblip/discrn/caption_baseline/predict_pc.py new file mode 100644 index 0000000000000000000000000000000000000000..aaf281adf337481f6d73bf49137d2d1a73801352 --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/discrn/caption_baseline/predict_pc.py @@ -0,0 +1,250 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + + +import torch +import torch.nn.functional as F +from lavis.datasets.builders import load_dataset +from tqdm import tqdm + +from lavis.models import load_model_and_preprocess +import argparse +import json +from PIL import Image +import os +import random +import torch +import torch.backends.cudnn as cudnn +import numpy as np +import torchvision + + +def check_positive(value): + ivalue = int(value) + if ivalue <= 0: + raise argparse.ArgumentTypeError("%s is an invalid positive int value" % value) + return ivalue + +def check_float_range(min_val, max_val): + def helper(x): + x = float(x) + if x < min_val or x > max_val: + raise argparse.ArgumentTypeError("Value must be between {} and {}.".format(min_val, max_val)) + return x + return helper + +def setup_seeds(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + cudnn.benchmark = False + cudnn.deterministic = True + +# Create the top-level parser +parser = argparse.ArgumentParser( + description='A demo script for using instructBLIP models for tasks such as image captioning or Visual Question Answering (VQA).' +) + +# Model Name +parser.add_argument( + '--model-name', + default='blip2_vicuna_instruct', + choices=['blip2_vicuna_instruct', 'blip2_t5_instruct'], + help='The name of the instructBLIP model to use.' +) + +# Model Type +parser.add_argument( + '--model-type', + default='vicuna7b', + choices=['vicuna7b', 'vicuna13b', 'flant5xl', 'flant5xxl'], + help='The type of the model to use.' +) + +# Task +parser.add_argument( + '--task', + default='caption', + choices=['caption', 'vqa'], + help='The type of task to run: captioning or VQA.' +) + +# Number of tasks +parser.add_argument( + '--num', + type=int, + default=1, + help='The number of outputs to generate.' +) + +# GPU ID +parser.add_argument( + '--gpu-id', + type=int, + default=0, + help='The ID of the GPU to use.' +) + +# Image path or URL +parser.add_argument( + '--image_path_or_url', + type=str, + default='', + help='The path or URL to the image file.' +) + +# Prompt +parser.add_argument( + '--prompt', + type=str, + default='', + help='The text prompt to use in tasks. Enclose it in quotation ("")' +) + +# Seed +parser.add_argument( + '--seed', + type=int, + default=42, + help='The seed for the random number generator.' +) + +# Minimum Length +parser.add_argument( + '--min_len', + type=check_positive, + default=1, + help='The minimum length for the generated text.' +) + +# Maximum Length +parser.add_argument( + '--max_len', + type=check_positive, + default=250, + help='The maximum length for the generated text.' +) + +# Beam Size +parser.add_argument( + '--beam_size', + type=check_positive, + default=5, + help='The beam size for the Beam Search.' +) + +# Length Penalty +parser.add_argument( + '--len_penalty', + type=float, + default=-1, + help='The length penalty for Beam Search.' +) + +# Repetition Penalty +parser.add_argument( + '--rep_penalty', + type=float, + default=1, + help='The penalty for word repetitions in the generated text.' +) + +# Top P +parser.add_argument( + '--top_p', + type=check_float_range(0.0, 1.0), + default=0.9, + help='The cumulative probability threshold for Nucleus Sampling.' +) + +# Decoding Method +parser.add_argument( + '--decoding_method', + default='Nucleus sampling', + choices=['Nucleus sampling', 'Beam search'], + help='The method to use for decoding the generated text.' +) + +# Temperature +parser.add_argument( + '--temperature', + type=check_float_range(0.1, 5.0), + default=1.0, + help='The temperature to use in the generation process. Higher values increase randomness, lower values make the output more deterministic.' +) + +args = parser.parse_args() + +if torch.cuda.is_available(): + torch.cuda.set_device(args.gpu_id) + device='cuda' + print(f"Script will run on cuda device {args.gpu_id}") +else: + print("CUDA is not available. The script will run on CPU.") + device='cpu' + + +print(f"Setting up seeds [seed={args.seed}]") +setup_seeds(args.seed) + +print(f'Loading model {args.model_name} with LLM {args.model_type}...') +model, vis_processors, _ = load_model_and_preprocess( + name=args.model_name, + model_type=args.model_type, + is_eval=True, + device=device, +) + +ds = load_dataset('image_pc_discrn') +vis_processors = ds['val'].vis_processor +print('Loading model done!') + +def inference(image, prompt, min_len=1, max_len=250, beam_size=5, len_penalty=-1, repetition_penalty=1, top_p=.9, decoding_method='Beam Search',num_captions=1, temperature=1., video=False): + use_nucleus_sampling = decoding_method == "Nucleus sampling" + print(image, prompt, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, use_nucleus_sampling) + if not video: + image = torchvision.transforms.functional.rgb_to_grayscale(Image.open(image), 3) + image = vis_processors(image).unsqueeze(0).to(device) + else: + image = vis_processors(image).to(device).unsqueeze(0).half() + # image = torch.cat(processed_frames, dim=0).mean(dim=0, keepdim=True).to(device) + + samples = { + "image": image, + "prompt": prompt, + } + + output = model.generate( + samples, + repetition_penalty=float(repetition_penalty), + num_beams=beam_size, + max_length=max_len, + min_length=min_len, + top_p=top_p, + use_nucleus_sampling=use_nucleus_sampling, + num_captions=num_captions, + temperature=temperature, + ) + return output[0] + + +import pickle +## comment out balancind code in dataset before running. also comment out loading data. +# ds = load_dataset('image_pc_discrn')['val'] +pc_path = "/export/einstein-vision/3d_vision/render_pc_discrn" +entity2pred= {} +for ann in tqdm(os.listdir(pc_path)): + if '8192' in ann: + with torch.no_grad(): + sample_id = ann.split('_')[0] + caption = inference(os.path.join(pc_path,ann), "describe the image", video=False) + entity2pred[sample_id] = caption + # except: + # print(ann) + # print(caption) + + +pickle.dump(entity2pred, open("./entity2pred/entity2pred_pc.p", 'wb')) \ No newline at end of file diff --git a/LAVIS-main/projects/xinstructblip/discrn/caption_baseline/predict_video.py b/LAVIS-main/projects/xinstructblip/discrn/caption_baseline/predict_video.py new file mode 100644 index 0000000000000000000000000000000000000000..6f3c62c7b0928f411cf5d7fef603fbb5d21fd7e9 --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/discrn/caption_baseline/predict_video.py @@ -0,0 +1,262 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + + + +import torch +import torch.nn.functional as F +# from models.bart_captioning import BartCaptionModel +from lavis.datasets.builders import load_dataset +from tqdm import tqdm + +from lavis.models import load_model_and_preprocess +from lavis.processors.alpro_processors import AlproVideoEvalProcessor + +import argparse +import json +from PIL import Image +import os +import random +import torch +import torch.backends.cudnn as cudnn +import numpy as np + + +def check_positive(value): + ivalue = int(value) + if ivalue <= 0: + raise argparse.ArgumentTypeError("%s is an invalid positive int value" % value) + return ivalue + +def check_float_range(min_val, max_val): + def helper(x): + x = float(x) + if x < min_val or x > max_val: + raise argparse.ArgumentTypeError("Value must be between {} and {}.".format(min_val, max_val)) + return x + return helper + +def setup_seeds(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + cudnn.benchmark = False + cudnn.deterministic = True + +# Create the top-level parser +parser = argparse.ArgumentParser( + description='A demo script for using instructBLIP models for tasks such as image captioning or Visual Question Answering (VQA).' +) + +# Model Name +parser.add_argument( + '--model-name', + default='blip2_vicuna_instruct', + choices=['blip2_vicuna_instruct', 'blip2_t5_instruct'], + help='The name of the instructBLIP model to use.' +) + +# Model Type +parser.add_argument( + '--model-type', + default='vicuna7b', + choices=['vicuna7b', 'vicuna13b', 'flant5xl', 'flant5xxl'], + help='The type of the model to use.' +) + +# Task +parser.add_argument( + '--task', + default='caption', + choices=['caption', 'vqa'], + help='The type of task to run: captioning or VQA.' +) + +# Number of tasks +parser.add_argument( + '--num', + type=int, + default=1, + help='The number of outputs to generate.' +) + +# GPU ID +parser.add_argument( + '--gpu-id', + type=int, + default=0, + help='The ID of the GPU to use.' +) + +# Image path or URL +parser.add_argument( + '--image_path_or_url', + type=str, + default='', + help='The path or URL to the image file.' +) + +# Prompt +parser.add_argument( + '--prompt', + type=str, + default='', + help='The text prompt to use in tasks. Enclose it in quotation ("")' +) + +# Seed +parser.add_argument( + '--seed', + type=int, + default=42, + help='The seed for the random number generator.' +) + +# Minimum Length +parser.add_argument( + '--min_len', + type=check_positive, + default=1, + help='The minimum length for the generated text.' +) + +# Maximum Length +parser.add_argument( + '--max_len', + type=check_positive, + default=250, + help='The maximum length for the generated text.' +) + +# Beam Size +parser.add_argument( + '--beam_size', + type=check_positive, + default=5, + help='The beam size for the Beam Search.' +) + +# Length Penalty +parser.add_argument( + '--len_penalty', + type=float, + default=-1, + help='The length penalty for Beam Search.' +) + +# Repetition Penalty +parser.add_argument( + '--rep_penalty', + type=float, + default=1, + help='The penalty for word repetitions in the generated text.' +) + +# Top P +parser.add_argument( + '--top_p', + type=check_float_range(0.0, 1.0), + default=0.9, + help='The cumulative probability threshold for Nucleus Sampling.' +) + +# Decoding Method +parser.add_argument( + '--decoding_method', + default='Nucleus sampling', + choices=['Nucleus sampling', 'Beam search'], + help='The method to use for decoding the generated text.' +) + +# Temperature +parser.add_argument( + '--temperature', + type=check_float_range(0.1, 5.0), + default=1.0, + help='The temperature to use in the generation process. Higher values increase randomness, lower values make the output more deterministic.' +) + +args = parser.parse_args() + +if torch.cuda.is_available(): + torch.cuda.set_device(args.gpu_id) + device='cuda' + print(f"Script will run on cuda device {args.gpu_id}") +else: + print("CUDA is not available. The script will run on CPU.") + device='cpu' + + +print(f"Setting up seeds [seed={args.seed}]") +setup_seeds(args.seed) + +print(f'Loading model {args.model_name} with LLM {args.model_type}...') +model, vis_processors, _ = load_model_and_preprocess( + name=args.model_name, + model_type=args.model_type, + is_eval=True, + device=device, +) + +ds = load_dataset('audio_video_discrn') + +vis_processors = ds['val'].video_processor +print('Loading model done!') + +def inference(image, prompt, min_len=1, max_len=250, beam_size=5, len_penalty=-1, repetition_penalty=1, top_p=.9, decoding_method='Beam Search',num_captions=1, temperature=1., video=False): + use_nucleus_sampling = decoding_method == "Nucleus sampling" + print(image, prompt, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, use_nucleus_sampling) + if not video: + image = vis_processors["eval"](image).unsqueeze(0).to(device) + else: + image = vis_processors(image).to(device).unsqueeze(0).half() + # image = torch.cat(processed_frames, dim=0).mean(dim=0, keepdim=True).to(device) + + samples = { + "image": image, + "prompt": prompt, + } + + output = model.generate( + samples, + repetition_penalty=float(repetition_penalty), + num_beams=beam_size, + max_length=max_len, + min_length=min_len, + top_p=top_p, + use_nucleus_sampling=use_nucleus_sampling, + num_captions=num_captions, + temperature=temperature, + ) + return output[0] + + + +## comment out balancind code in dataset before running. also comment out loading data. +ds = load_dataset('audio_video_discrn')['val'] +import pickle +entity2pred= {} +for ann in tqdm(ds): + try: + if ann == None: + continue + new_ann = ann + for i,video_path in enumerate([ds.get_video_path(ann, 0), ds.get_video_path(ann, 1)]): + if ann['sample_ids'][i] in entity2pred: + continue + try: + with torch.no_grad(): + caption = inference(video_path, "describe the video", video=True) + entity2pred[ann['sample_ids'][i]] = caption + except: + print(ann["paths"]) + # print(caption) + except: + print(ann) + continue + + +pickle.dump(entity2pred, open("./entity2pred/entity2pred_video_2.p", 'wb')) \ No newline at end of file diff --git a/LAVIS-main/projects/xinstructblip/discrn/caption_baseline/render_images.py b/LAVIS-main/projects/xinstructblip/discrn/caption_baseline/render_images.py new file mode 100644 index 0000000000000000000000000000000000000000..d31a2d52241f3a555020569b199bd2e101431d9c --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/discrn/caption_baseline/render_images.py @@ -0,0 +1,59 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + + +from tqdm import tqdm +import pickle +import open3d as o3d +import numpy as np +from PIL import Image +from lavis.datasets.builders import load_dataset + +ds = load_dataset('image_pc_discrn')['val'] +seen = set() + + +pcd = o3d.geometry.PointCloud() +vis = o3d.visualization.Visualizer() +vis.create_window(width=800, height=600, visible=False) # Set to off-screen mode +print(len(ds)) +for i,ann in tqdm(enumerate(ds)): + # Convert the numpy array to Open3D PointCloud + for j,sample_id in enumerate(ann['sample_ids']): + if sample_id not in seen: + seen.add(sample_id) + else: + continue + + # path = ds.get_pc_path(ann, j) + # points = ds.pc_processor(path) + # from pdb import set_trace; set_trace() + + # pcd.points = o3d.utility.Vector3dVector(points[:, 0:3]) + + # # Create a visualization window and set up the view + + # vis.add_geometry(pcd) + # vis.run() + + # Set viewpoint parameters if desired (this is optional) + # For example, set the camera to view from a specific position: + # vis.get_view_control().set_lookat([x, y, z]) + # vis.get_view_control().set_front([x, y, z]) + # vis.get_view_control().set_up([x, y, z]) + # vis.get_view_control().set_zoom(zoom_factor) + + # # Capture the image to a numpy array + # img_array = np.asarray(vis.capture_screen_float_buffer(do_render=False)) + # # Convert the numpy array to a PIL Image + # img_pil = Image.fromarray((img_array*255.).astype(np.uint8)) + # # Close the visualization + # vis.destroy_window() + + # # Display the image (if desired) + # # img_pil.show() + # # from pdb import set_trace; set_trace() + # img_pil.save(f'images_dirscn_rgb/{sample_id}.jpeg') +print(seen) \ No newline at end of file diff --git a/LAVIS-main/projects/xinstructblip/discrn/data_generation/audiocaps_video_audio.py b/LAVIS-main/projects/xinstructblip/discrn/data_generation/audiocaps_video_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..80b7de5df9ad738dbe057c49147085a2dfb5bfe1 --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/discrn/data_generation/audiocaps_video_audio.py @@ -0,0 +1,162 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import random +from sentence_transformers import SentenceTransformer +from sklearn.metrics.pairwise import cosine_similarity +import numpy as np +from tqdm import tqdm +import pandas as pd +import argparse +from fuzzywuzzy import fuzz +import pickle +import os +import torch +from transformers import T5Tokenizer, T5ForConditionalGeneration + + +parser = argparse.ArgumentParser(description="") +parser.add_argument("--shard", type=int, help="The shard number to process.") +parser.add_argument("--mode", type=str, help=['property', 'get_pairs', 'rtc']) +parser.add_argument("--rnd", type=int, help=["Round of generation"]) +parser.add_argument("--split", type=str, help=["train, val"]) +parser.add_argument("--original_data_file", type=str) + +VIDEO_PATH = '' # f'/export/home/audio_datasets/audiocaps/video/AUDIOCAPS_32000Hz/audio/{split}' + +args = parser.parse_args() +shard = args.shard +mode = args.mode +rnd = args.rnd +split = args.split +original_data_file = args.original_data_file + +output_dir = './audio_video_data' +video_list = os.listdir(VIDEO_PATH) +df = pd.read_csv(original_data_file) +# df = pd.read_csv(f'/export/home/audio_datasets/audiocaps/video/AUDIOCAPS_32000Hz/{split}.csv') +df['file_name'] = df['youtube_id'] + "_" + df['start_time'].astype(str) + ".mp4" +df = df[df['file_name'].isin(video_list)] + +if mode == 'get_pairs' or mode == 'all': + sim_model = SentenceTransformer('all-MiniLM-L6-v2') + + def get_pair_by_sim(c1, clist): + current_embedding = sim_model.encode([c1]) # Shape: [1, D] + sentences_embeddings = sim_model.encode(clist) # Shape: [100, D] + similarities = cosine_similarity(current_embedding, sentences_embeddings) # Shape: [1, 100] + sorted_indices = np.argsort(similarities[0]) + top_indices = sorted_indices[-10:] + sample_idx = np.random.choice(top_indices, size=1, replace=False)[0] + return sample_idx + +if mode != 'get_pairs': + tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl", padding='max_length', max_length=512, return_tensors="pt", truncation=True) + model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl", device_map="auto", torch_dtype=torch.float16) + + def get_output(input_text, input_len=128, output_len=128): + input_ids = torch.cat([tokenizer(inp, padding='max_length', max_length=input_len, return_tensors="pt").input_ids.to("cuda") for inp in input_text]) + outputs = model.generate(input_ids, max_length=output_len) + outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) + return outputs + +if mode == 'property' or mode == 'all': + start_index = shard * (len(df) // 4) + num_rows_to_extract = len(df) // 4 + df = df.iloc[start_index:start_index + num_rows_to_extract] + print(f"Subset size {len(df)}") + captions = df['caption'] + captions = [c.lower() for c in captions] + properties = [] + bs = 16 + for i in tqdm(range(0,len(captions), bs)): + input_text = [f"What are three properties to describe an object with description: {c}\n Properties list:" for c in captions[i:i+bs]] + properties.extend(get_output(input_text, input_len=250, output_len=256)) + + df['property'] = properties + df.to_csv(os.path.join(f'audiocaps_property_shard_{shard}_{rnd}_{split}.csv')) + +if mode == 'get_pairs' or mode == "all": + total_pairs = 100000 if split == 'train' else 10000 + pairs = set() + df = pd.read_csv(os.path.join(output_dir, f'audiocaps_property_shard_{shard}_{i}_{split}.csv')).dropna() + captions = df['caption'] + indices = set(range(len(df))) + exit_flag = False + while total_pairs > len(pairs) and not exit_flag: + for i, row in tqdm(df.iterrows()): + if len(pairs) == total_pairs: + break + curr_caption = row['caption'] + candidates = indices.difference(set([i] + [p[0] for p in pairs if p[1] == i]+ [p[1] for p in pairs if p[0] == i])) + if len(candidates)>100: + sample_idx = random.sample(candidates, 500) + else: + exit_flag=True + break + captions_sample = [captions[i] for i in sample_idx] + pair_idx = get_pair_by_sim(curr_caption, captions_sample) + pairs.add(tuple((i, pair_idx))) + + pickle.dump(pairs, open(os.path.join(output_dir, f'/audiocaps_pairs_disc_dataset_shard_{shard}_{rnd}_{split}.p', 'wb'))) + + +if mode == "instruction_gen" or mode == "all": + import pickle + pairs = pickle.load(open(os.path.join(output_dir, f'audiocaps_pairs_disc_dataset_shard_{shard}_{rnd}_{split}.p', 'rb'))) + + df = pd.concat([pd.read_csv(os.path.join(output_dir, f'audiocaps_property_shard_{s}_{rnd}_{split}.csv')) for s in [0,1,2,3]]) + examples = [] + prefix = "Given entity A with caption 'A cat meowing and humans speaking on the background.' and corresponding properties: agile, independent and domesticated, \ + and entity B with caption 'Loud barking and traffic' with properties aggressive, loud, and high energy you can generate a set of instruction answer \ + pairs to compare and contrast the entities as follows:\n \ + Examples:\ + Question: Which entity is louder A or B? Answer: Entity B. Explanation: The dogs barking can be heard through traffic making them louder than the cat meowing.\n\ + Question: Which entity shows more social behavior, A or B? Answer: Entity A. Explanation: The cat is surrounded by people whereas the dogs seem to be agitated by traffic.\n\ + Question: Which entity is in higher danger, A or B? Answer: Entity B. Explanation: The dogs seem to be surrounded by traffic which could injure them.\n\ + Generate three such Question, Answer, Explanation tripplets for Entity A with caption {} and properties {} and entity B with caption {} and properties {}\n\ + Examples:\n" + bs = 16 + start_index = shard * (len(pairs) // 4) + num_rows_to_extract = len(pairs) // 4 + pairs = list(pairs) + process_cap = lambda x: x.lower() + + # from pdb import set_trace; set_trace() + pairs = pairs[start_index:start_index + num_rows_to_extract] + for i in tqdm(range(0,len(pairs),bs)): + try: + input_text = [prefix.format(process_cap(df.iloc[a]['caption']), df.iloc[a]['property'], process_cap(df.iloc[b]['caption']), df.iloc[b]['property']) for a, b in pairs[i:i+bs]] + outputs = get_output(input_text, input_len=512, output_len=512) + curr_examples = [{"entity_A":df.iloc[p[0]]['audiocap_id'], "entity_B":df.iloc[p[1]]['audiocap_id'], "caption_A":process_cap(df.iloc[p[0]]['caption']), "caption_B":process_cap(df.iloc[p[1]]['caption']), "property_A":df.iloc[p[0]]['property'], "property_B":df.iloc[p[1]]['property'],"output":outputs[j]} for j,p in enumerate(pairs[i:i+bs])] + examples.extend(curr_examples) + except: + continue + if i%1000 == 0: + pickle.dump(examples, open(os.path.join(output_dir, f"disc_examples_{shard}_{rnd}_{split}.p", 'wb'))) + pickle.dump(examples, open(os.path.join(output_dir, f"disc_examples_{shard}_{rnd}_{split}.p", 'wb'))) + +if mode == "rtc" or mode == "all": + import pickle + examples = pickle.load(open(os.path.join(output_dir, f"/disc_examples_{shard}_{rnd}_{split}.p", 'rb'))) + prompt = "Given entity A with caption '{}' and properties {} and entity B with caption '{}' and properties {}. Answer the question: {}. Answer:" + rtc_examples = [] + bs = 16 + from pdb import set_trace; set_trace() + for i in tqdm(range(0,len(examples),bs)): + try: + questions = [e["output"].split("Question:")[1].split("Answer:")[0].strip() for e in examples[i:i+bs]] + answers = [e["output"].split("Answer:")[1].split("Explanation:")[0].strip() for e in examples[i:i+bs]] + curr_examples = examples[i:i+bs] + input_text = [prompt.format(e['caption_A'], e['property_A'],e['caption_B'], e['property_B'], q) for e,q in zip(curr_examples, questions)] + outputs = get_output(input_text, input_len=512, output_len=30) + rtc_examples.extend([curr_e for j,curr_e in enumerate(curr_examples) if fuzz.partial_ratio(outputs[j].lower(),answers[j].lower())>90]) + except: + continue + if i%1000 == 0: + pickle.dump(rtc_examples, open(os.path.join(output_dir, f"/disc_examples_rtc_{shard}_{rnd}_{split}.p", 'wb'))) + pickle.dump(rtc_examples, open(os.path.join(output_dir, f"/disc_examples_rtc_{shard}_{rnd}_{split}.p", 'wb'))) \ No newline at end of file diff --git a/LAVIS-main/projects/xinstructblip/discrn/data_generation/objaverse_img_3d.py b/LAVIS-main/projects/xinstructblip/discrn/data_generation/objaverse_img_3d.py new file mode 100644 index 0000000000000000000000000000000000000000..ad9aa7854a3e7bf3572cd012c66ba3b2870bb7e4 --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/discrn/data_generation/objaverse_img_3d.py @@ -0,0 +1,156 @@ + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + + + +import random +from sentence_transformers import SentenceTransformer +from sklearn.metrics.pairwise import cosine_similarity +import numpy as np +from tqdm import tqdm +import pandas as pd +import argparse +from fuzzywuzzy import fuzz +import pickle +import torch +from transformers import T5Tokenizer, T5ForConditionalGeneration + +parser = argparse.ArgumentParser(description="") +parser.add_argument("--shard", type=int, help="The shard number to process.") +parser.add_argument("--mode", type=str, help=['property', 'get_pairs', 'instruction_gen', 'rtc']) +parser.add_argument("--rnd", type=int, help=["Round of generation"]) +parser.add_argument("--split", type=str, help=["train, val"]) +parser.add_argument("--original_data_file", type=str) + +args = parser.parse_args() +shard = args.shard +mode = args.mode +rnd = args.rnd +split = args.split +original_data_file = args.original_data_file + + +# captions_dir = '/export/einstein-vision/3d_vision/objaverse_captions/' +output_dir = './3d_img_data' + +if mode == 'get_pairs' or mode == 'all': + sim_model = SentenceTransformer('all-MiniLM-L6-v2') + + def get_pair_by_sim(c1, clist): + current_embedding = sim_model.encode([c1]) # Shape: [1, D] + sentences_embeddings = sim_model.encode(clist) # Shape: [100, D] + similarities = cosine_similarity(current_embedding, sentences_embeddings) # Shape: [1, 100] + sorted_indices = np.argsort(similarities[0]) + top_indices = sorted_indices[-10:] + sample_idx = np.random.choice(top_indices, size=1, replace=False)[0] + return sample_idx + +if mode != 'get_pairs': + tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl", padding='max_length', max_length=512, return_tensors="pt", truncation=True) + model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl", device_map="auto", torch_dtype=torch.float16) + + def get_output(input_text, input_len=128, output_len=128): + input_ids = torch.cat([tokenizer(inp, padding='max_length', max_length=input_len, return_tensors="pt").input_ids.to("cuda") for inp in input_text]) + outputs = model.generate(input_ids, max_length=output_len) + outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) + return outputs + +if mode == 'property' or mode == 'all': + df = pd.read_csv(original_data_file) + start_index = shard * (len(df) // 4) + num_rows_to_extract = len(df) // 4 + df = df.iloc[start_index:start_index + num_rows_to_extract] + print(f"Subset size {len(df)}") + # captions = df['caption_no_color'] + captions = df['caption'] + captions = [c.lower().replace('a 3d model of' ,'').replace('a 3d rendering of', '').replace('3d model', '').replace('3d rendering', '') for c in captions] + properties = [] + bs = 8 + for i in tqdm(range(0,len(captions), bs)): + input_text = [f"What are three properties to describe an object with description: {c} Properties list:" for c in captions[i:i+bs]] + properties.extend(get_output(input_text, input_len=250, output_len=256)) + + df['property'] = properties + df.to_csv(os.path.join(output_dir, f'/objaverse_property_shard_{shard}_{rnd}_{split}.csv')) + +if mode == 'get_pairs' or mode == "all": + total_pairs = 300000 if split =="train" else 100000 + pairs = set() + df = pd.read_csv(os.path.join(output_dir, f'/objaverse_property_shard_{shard}_{rnd}_{split}.csv')).dropna() + captions = df['caption'] + indices = set(range(len(df))) + exit_flag = False + while total_pairs > len(pairs) and not exit_flag: + for i, row in tqdm(df.iterrows()): + if len(pairs) == total_pairs: + break + curr_caption = row['caption'] + candidates = indices.difference(set([i] + [p[0] for p in pairs if p[1] == i]+ [p[1] for p in pairs if p[0] == i])) + if len(candidates)>100: + sample_idx = random.sample(candidates, 100) + else: + exit_flag=True + break + captions_sample = [captions[i] for i in sample_idx] + pair_idx = get_pair_by_sim(curr_caption, captions_sample) + pairs.add(tuple((i, pair_idx))) + + pickle.dump(pairs, open(os.path.join(os.path.join(output_dir, f'/objaverse_pairs_disc_dataset_{shard}_{rnd}_{split}.p', 'wb')))) + + +if mode == "instruction_gen" or mode == "all": + pairs = {(i, j) for i in range(1251) for j in range(i, 1251)} + df = pd.concat([pd.read_csv(os.path.join(output_dir, f"/objaverse_property_shard_{s}_{i}_{split}.csv")) for s in [0,1,2,3]]) + examples = [] + prefix = "Given entity A with caption 'An intricate point cloud representation of a cat, where each dot maps the contours of feline elegance.' and corresponding properties: agile, independent and longevity, \ + and entity B with caption 'A moment frozen in time, the playful energy of a bounding dog captured in intricate detail.' with properties social, loyal, and high energy you can generate a set of instruction answer \ + pairs to compare and contrast the entities as follows:\n \ + Examples:\ + Question: Which entity is more agile A or B? Answer: Entity A. Explanation: While both are agile in their own ways, cats are typically considered more agile due to their flexible bodies and ability to navigate narrow spaces.\n\ + Question: Which entity shows more social behavior, A or B? Answer: Entity B. Explanation: Dogs are typically more social, often seeking companionship and engagement with others, while cats are more independent.\n\ + Question: Which entity is more likely to exhibit solitary behavior, A or B? Answer: Entity A. Explanation: Cats are more likely to exhibit solitary behavior as they are known for their independent nature.\n\ + Generate three such Question, Answer, Explanation tripplets for Entity A with caption {} and properties {} and entity B with caption {} and properties {}\n\ + Examples:\n" + bs = 8 + start_index = shard * (len(pairs) // 4) + num_rows_to_extract = len(pairs) // 4 + pairs = list(pairs) + process_cap = lambda x: x.lower().replace('a 3d model of' ,'').replace('a 3d rendering of', '').replace('3d model', '').replace('3d rendering', '') + pairs = pairs[start_index:start_index + num_rows_to_extract] + examples_no_3d = [] + for i in tqdm(range(0,len(pairs),bs)): + try: + input_text = [prefix.format(process_cap(df.iloc[a]['caption']), df.iloc[a]['property'], process_cap(df.iloc[b]['caption']), df.iloc[b]['property']) for a, b in pairs[i:i+bs]] + outputs = get_output(input_text, input_len=512, output_len=512) + curr_examples = [{"entity_A":df.iloc[p[0]]['sample_id'], "entity_B":df.iloc[p[1]]['sample_id'], "caption_A":process_cap(df.iloc[p[0]]['caption']), "caption_B":process_cap(df.iloc[p[1]]['caption']), "property_A":df.iloc[p[0]]['property'], "property_B":df.iloc[p[1]]['property'],"output":outputs[j]} for j,p in enumerate(pairs[i:i+bs])] + examples.extend(curr_examples) + examples_no_3d.extend([e for e in curr_examples if '3d' not in e['output'].lower() and 'entity' in e['output'].lower().split('answer:')[-1].split('explanation')[0]]) + except: + continue + if i%1000 == 0: + pickle.dump(examples, open(os.path.join(output_dir, f"/disc_examples_{shard}_{rnd}_{split}.p", 'wb'))) + pickle.dump(examples_no_3d, open(os.path.join(output_dir, f"disc_examples_no_3d_{shard}_{rnd}_{split}.p", 'wb'))) + pickle.dump(examples, open(os.path.join(output_dir, f"/disc_examples_{shard}_{rnd}_{split}.p", 'wb'))) + pickle.dump(examples_no_3d, open(os.path.join(output_dir, f"disc_examples_no_3d_{shard}_{rnd}_{split}.p", 'wb'))) + + +if mode == "rtc" or mode == "all": + examples = pickle.load(open(os.path.join(output_dir, f"disc_examples_no_3d_{shard}_{rnd}_{split}.p", 'rb'))) + prompt = "Given entity A with caption '{}' and properties {} and entity B with caption '{}' and properties. Answer the question: {}. Answer:" + rtc_examples = [] + bs = 8 + for i in tqdm(range(0,len(examples),bs)): + try: + questions = [e["output"].split("Question:")[1].split("Answer:")[0].strip() for e in examples[i:i+bs]] + answers = [e["output"].split("Answer:")[1].split("Explanation:")[0].strip() for e in examples[i:i+bs]] + curr_examples = examples[i:i+bs] + input_text = [prompt.format(e['caption_A'], e['property_A'],e['caption_B'], e['property_B'], q) for e,q in zip(curr_examples, questions)] + outputs = get_output(input_text, input_len=512, output_len=30) + rtc_examples.extend([curr_e for j,curr_e in enumerate(curr_examples) if fuzz.partial_ratio(outputs[j].lower(),answers[j].lower())>90]) + except: + continue + if i%1000 == 0: + pickle.dump(rtc_examples, open(os.path.join(output_dir, f"disc_examples_rtc_{shard}_{rnd}_{split}.p", 'wb'))) + pickle.dump(rtc_examples, open(os.path.join(output_dir, f"disc_examples_rtc_{shard}_{rnd}_{split}.p", 'wb'))) \ No newline at end of file diff --git a/LAVIS-main/projects/xinstructblip/modelnet_baseline/render_images.py b/LAVIS-main/projects/xinstructblip/modelnet_baseline/render_images.py new file mode 100644 index 0000000000000000000000000000000000000000..910fcdb548b067794c8e822267e897a1939c2851 --- /dev/null +++ b/LAVIS-main/projects/xinstructblip/modelnet_baseline/render_images.py @@ -0,0 +1,41 @@ + + # Copyright (c) 2023, salesforce.com, inc. + # All rights reserved. + # SPDX-License-Identifier: BSD-3-Clause + # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause + +from tqdm import tqdm +import pickle +import open3d as o3d +import numpy as np +from PIL import Image + +data, labels = pickle.load(open('modelnet40_test_1024pts.dat', 'rb')) +for i,points in tqdm(enumerate(data)): + # Convert the numpy array to Open3D PointCloud + pcd = o3d.geometry.PointCloud() + pcd.points = o3d.utility.Vector3dVector(points[:, 0:3]) + + # Create a visualization window and set up the view + vis = o3d.visualization.Visualizer() + vis.create_window(width=800, height=600, visible=False) # Set to off-screen mode + vis.add_geometry(pcd) + + # Set viewpoint parameters if desired (this is optional) + # For example, set the camera to view from a specific position: + # vis.get_view_control().set_lookat([x, y, z]) + # vis.get_view_control().set_front([x, y, z]) + # vis.get_view_control().set_up([x, y, z]) + # vis.get_view_control().set_zoom(zoom_factor) + + # Capture the image to a numpy array + img_array = np.asarray(vis.capture_screen_float_buffer(do_render=True)) + # Convert the numpy array to a PIL Image + img_pil = Image.fromarray((img_array*255.).astype(np.uint8)) + # Close the visualization + vis.destroy_window() + + # Display the image (if desired) + # img_pil.show() + # from pdb import set_trace; set_trace() + img_pil.save(f'images_test40_rgb/{i}.jpeg') diff --git a/LAVIS-main/run_scripts/albef/eval/eval_albef_nlvr.sh b/LAVIS-main/run_scripts/albef/eval/eval_albef_nlvr.sh new file mode 100644 index 0000000000000000000000000000000000000000..f68cff8aaa0873349d257211a2b31eea2a858f4c --- /dev/null +++ b/LAVIS-main/run_scripts/albef/eval/eval_albef_nlvr.sh @@ -0,0 +1 @@ +python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/albef/eval/nlvr_eval.yaml diff --git a/LAVIS-main/run_scripts/albef/eval/eval_albef_ve.sh b/LAVIS-main/run_scripts/albef/eval/eval_albef_ve.sh new file mode 100644 index 0000000000000000000000000000000000000000..c5fd5e421b89b51676958e4d5f651014d00e7359 --- /dev/null +++ b/LAVIS-main/run_scripts/albef/eval/eval_albef_ve.sh @@ -0,0 +1 @@ +python -m torch.distributed.run --nproc_per_node=16 evaluate.py --cfg-path lavis/projects/albef/eval/snli_ve_eval.yaml diff --git a/LAVIS-main/run_scripts/albef/eval/eval_coco_retrieval.sh b/LAVIS-main/run_scripts/albef/eval/eval_coco_retrieval.sh new file mode 100644 index 0000000000000000000000000000000000000000..2830d414fd7695fa04d96427453393d4ae7577de --- /dev/null +++ b/LAVIS-main/run_scripts/albef/eval/eval_coco_retrieval.sh @@ -0,0 +1,2 @@ +# python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/albef/eval/coco_retrieval_eval.yaml +python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/albef/eval/ret_coco_eval.yaml diff --git a/LAVIS-main/run_scripts/albef/eval/eval_flickr30k_retrieval.sh b/LAVIS-main/run_scripts/albef/eval/eval_flickr30k_retrieval.sh new file mode 100644 index 0000000000000000000000000000000000000000..962e027d972c904e8eee1b25250c3e897093152b --- /dev/null +++ b/LAVIS-main/run_scripts/albef/eval/eval_flickr30k_retrieval.sh @@ -0,0 +1 @@ +python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/albef/eval/ret_flickr30k_eval.yaml diff --git a/LAVIS-main/run_scripts/run_browser.sh b/LAVIS-main/run_scripts/run_browser.sh new file mode 100644 index 0000000000000000000000000000000000000000..ba3c8ba9c3767f37d5315711fe6bb330c973d61e --- /dev/null +++ b/LAVIS-main/run_scripts/run_browser.sh @@ -0,0 +1 @@ +streamlit run app/dataset_browser.py --server.fileWatcherType none diff --git a/LAVIS-main/run_scripts/run_demo.sh b/LAVIS-main/run_scripts/run_demo.sh new file mode 100644 index 0000000000000000000000000000000000000000..b6d87ed56180be7acfc60ae43820f77ba7d3cedd --- /dev/null +++ b/LAVIS-main/run_scripts/run_demo.sh @@ -0,0 +1 @@ +streamlit run app/main.py --server.fileWatcherType none