




import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


SCREAMING_SNAKE_CASE__       :      Any  =   {
    'iou_prediction_head.layers.0': 'iou_prediction_head.proj_in',
    'iou_prediction_head.layers.1': 'iou_prediction_head.layers.0',
    'iou_prediction_head.layers.2': 'iou_prediction_head.proj_out',
    'mask_decoder.output_upscaling.0': 'mask_decoder.upscale_conv1',
    'mask_decoder.output_upscaling.1': 'mask_decoder.upscale_layer_norm',
    'mask_decoder.output_upscaling.3': 'mask_decoder.upscale_conv2',
    'mask_downscaling.0': 'mask_embed.conv1',
    'mask_downscaling.1': 'mask_embed.layer_norm1',
    'mask_downscaling.3': 'mask_embed.conv2',
    'mask_downscaling.4': 'mask_embed.layer_norm2',
    'mask_downscaling.6': 'mask_embed.conv3',
    'point_embeddings': 'point_embed',
    'pe_layer.positional_encoding_gaussian_matrix': 'shared_embedding.positional_embedding',
    'image_encoder': 'vision_encoder',
    'neck.0': 'neck.conv1',
    'neck.1': 'neck.layer_norm1',
    'neck.2': 'neck.conv2',
    'neck.3': 'neck.layer_norm2',
    'patch_embed.proj': 'patch_embed.projection',
    '.norm': '.layer_norm',
    'blocks': 'layers',
}

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Tuple:
       lowerCamelCase   :      Optional[int]			 =					{}
       state_dict.pop("pixel_mean" ,_SCREAMING_SNAKE_CASE    )
       state_dict.pop("pixel_std" ,_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Tuple			 =					r".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"

       for key, value in state_dict.items():
              for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
                     if key_to_modify in key:
                            lowerCamelCase   :      Dict			 =					key.replace(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

              if re.match(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ):
                     lowerCamelCase   :      str			 =					int(re.match(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ).group(2    )    )
                     if layer_nb == 0:
                            lowerCamelCase   :      int			 =					key.replace("layers.0" ,"proj_in"    )
                     elif layer_nb == 1:
                            lowerCamelCase   :      int			 =					key.replace("layers.1" ,"layers.0"    )
                     elif layer_nb == 2:
                            lowerCamelCase   :      Tuple			 =					key.replace("layers.2" ,"proj_out"    )

              lowerCamelCase   :      List[Any]			 =					value

       lowerCamelCase   :      Tuple			 =					model_state_dict[
           "prompt_encoder.shared_embedding.positional_embedding"
       ]

       return model_state_dict

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE="ybelkada/segment-anything"    )	-> List[str]:
       lowerCamelCase   :      Union[str, Any]			 =					hf_hub_download(_SCREAMING_SNAKE_CASE ,f'''checkpoints/{model_name}.pth'''    )

       if "sam_vit_b" in model_name:
              lowerCamelCase   :      Optional[int]			 =					SamConfig()
       elif "sam_vit_l" in model_name:
              lowerCamelCase   :      Optional[Any]			 =					SamVisionConfig(
                  hidden_size=1024 ,num_hidden_layers=24 ,num_attention_heads=16 ,global_attn_indexes=[5, 11, 17, 23] ,)

              lowerCamelCase   :      int			 =					SamConfig(
                  vision_config=_SCREAMING_SNAKE_CASE ,)
       elif "sam_vit_h" in model_name:
              lowerCamelCase   :      Optional[int]			 =					SamVisionConfig(
                  hidden_size=1280 ,num_hidden_layers=32 ,num_attention_heads=16 ,global_attn_indexes=[7, 15, 23, 31] ,)

              lowerCamelCase   :      int			 =					SamConfig(
                  vision_config=_SCREAMING_SNAKE_CASE ,)

       lowerCamelCase   :      Optional[Any]			 =					torch.load(_SCREAMING_SNAKE_CASE ,map_location="cpu"    )
       lowerCamelCase   :      List[Any]			 =					replace_keys(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Optional[int]			 =					SamImageProcessor()

       lowerCamelCase   :      List[str]			 =					SamProcessor(image_processor=_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[Any]			 =					SamModel(_SCREAMING_SNAKE_CASE    )

       hf_model.load_state_dict(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					hf_model.to("cuda"    )

       lowerCamelCase   :      Dict			 =					"https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
       lowerCamelCase   :      Tuple			 =					Image.open(requests.get(_SCREAMING_SNAKE_CASE ,stream=_SCREAMING_SNAKE_CASE    ).raw    ).convert("RGB"    )

       lowerCamelCase   :      Dict			 =					[[[400, 650]]]
       lowerCamelCase   :      str			 =					[[1]]

       lowerCamelCase   :      Dict			 =					processor(images=np.array(_SCREAMING_SNAKE_CASE    ) ,return_tensors="pt"    ).to("cuda"    )

       with torch.no_grad():
              lowerCamelCase   :      str			 =					hf_model(**_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Any			 =					output.iou_scores.squeeze()

       if model_name == "sam_vit_h_4b8939":
              assert scores[-1].item() == 0.579890251159668

              lowerCamelCase   :      str			 =					processor(
                  images=np.array(_SCREAMING_SNAKE_CASE    ) ,input_points=_SCREAMING_SNAKE_CASE ,input_labels=_SCREAMING_SNAKE_CASE ,return_tensors="pt"    ).to("cuda"    )

              with torch.no_grad():
                     lowerCamelCase   :      Any			 =					hf_model(**_SCREAMING_SNAKE_CASE    )
              lowerCamelCase   :      List[str]			 =					output.iou_scores.squeeze()

              assert scores[-1].item() == 0.9712603092193604

              lowerCamelCase   :      Any			 =					((75, 275, 1725, 850),)

              lowerCamelCase   :      Any			 =					processor(images=np.array(_SCREAMING_SNAKE_CASE    ) ,input_boxes=_SCREAMING_SNAKE_CASE ,return_tensors="pt"    ).to("cuda"    )

              with torch.no_grad():
                     lowerCamelCase   :      Union[str, Any]			 =					hf_model(**_SCREAMING_SNAKE_CASE    )
              lowerCamelCase   :      int			 =					output.iou_scores.squeeze()

              assert scores[-1].item() == 0.8686015605926514

              # Test with 2 points and 1 image.
              lowerCamelCase   :      Optional[Any]			 =					[[[400, 650], [800, 650]]]
              lowerCamelCase   :      Any			 =					[[1, 1]]

              lowerCamelCase   :      int			 =					processor(
                  images=np.array(_SCREAMING_SNAKE_CASE    ) ,input_points=_SCREAMING_SNAKE_CASE ,input_labels=_SCREAMING_SNAKE_CASE ,return_tensors="pt"    ).to("cuda"    )

              with torch.no_grad():
                     lowerCamelCase   :      Optional[Any]			 =					hf_model(**_SCREAMING_SNAKE_CASE    )
              lowerCamelCase   :      int			 =					output.iou_scores.squeeze()

              assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      str  =   argparse.ArgumentParser()
       SCREAMING_SNAKE_CASE__       :      int  =   ['sam_vit_b_01ec64', 'sam_vit_h_4b8939', 'sam_vit_l_0b3195']
       parser.add_argument(
           '--model_name',
           default='sam_vit_h_4b8939',
           choices=choices,
           type=str,
           help='Path to hf config.json of model to convert',
       )
       parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
       parser.add_argument(
           '--push_to_hub',
           action='store_true',
           help='Whether to push the model and processor to the hub after converting',
       )
       parser.add_argument(
           '--model_hub_id',
           default='ybelkada/segment-anything',
           choices=choices,
           type=str,
           help='Path to hf config.json of model to convert',
       )

       SCREAMING_SNAKE_CASE__       :      Any  =   parser.parse_args()

       convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)












def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> list:
       lowerCamelCase   :      Dict			 =					len(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					[]
       for i in range(len(_SCREAMING_SNAKE_CASE    ) - pat_len + 1    ):
              lowerCamelCase   :      Dict			 =					True
              for j in range(_SCREAMING_SNAKE_CASE    ):
                     if s[i + j] != pattern[j]:
                            lowerCamelCase   :      Optional[int]			 =					False
                            break
              if match_found:
                     position.append(_SCREAMING_SNAKE_CASE    )
       return position


if __name__ == "__main__":
       assert naive_pattern_search('ABCDEFG', 'DE') == [3]
       print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))






