



def 		a__      (		_UpperCamelCase      :			list[list[int]]			,_UpperCamelCase      :			int			,_UpperCamelCase      :			int			,_UpperCamelCase      :			set				):
   __lowerCamelCase				,__lowerCamelCase    				=     len(_UpperCamelCase				), len(grid[0]				)
   if (
       min(_UpperCamelCase			,_UpperCamelCase				) < 0
       or row == row_length
       or col == col_length
       or (row, col) in visit
       or grid[row][col] == 1
   ):
      return 0
   if row == row_length - 1 and col == col_length - 1:
      return 1

   visit.add((row, col)				)

   __lowerCamelCase    				=     0
   count += depth_first_search(_UpperCamelCase			,row + 1			,_UpperCamelCase			,_UpperCamelCase				)
   count += depth_first_search(_UpperCamelCase			,row - 1			,_UpperCamelCase			,_UpperCamelCase				)
   count += depth_first_search(_UpperCamelCase			,_UpperCamelCase			,col + 1			,_UpperCamelCase				)
   count += depth_first_search(_UpperCamelCase			,_UpperCamelCase			,col - 1			,_UpperCamelCase				)

   visit.remove((row, col)				)
   return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()











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,
)


a_								=       {
    """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__      (		_UpperCamelCase      :			Optional[Any]				):
   __lowerCamelCase    				=     {}
   state_dict.pop('''pixel_mean'''			,_UpperCamelCase				)
   state_dict.pop('''pixel_std'''			,_UpperCamelCase				)

   __lowerCamelCase    				=     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    				=     key.replace(_UpperCamelCase			,_UpperCamelCase				)

      if re.match(_UpperCamelCase			,_UpperCamelCase				):
         __lowerCamelCase    				=     int(re.match(_UpperCamelCase			,_UpperCamelCase				).group(2				)				)
         if layer_nb == 0:
            __lowerCamelCase    				=     key.replace('''layers.0'''			,'''proj_in'''				)
         elif layer_nb == 1:
            __lowerCamelCase    				=     key.replace('''layers.1'''			,'''layers.0'''				)
         elif layer_nb == 2:
            __lowerCamelCase    				=     key.replace('''layers.2'''			,'''proj_out'''				)

      __lowerCamelCase    				=     value

   __lowerCamelCase    				=     model_state_dict[
       '''prompt_encoder.shared_embedding.positional_embedding'''
   ]

   return model_state_dict





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Any			,_UpperCamelCase      :			Optional[int]="ybelkada/segment-anything"				):
   __lowerCamelCase    				=     hf_hub_download(_UpperCamelCase			,F"""checkpoints/{model_name}.pth"""				)

   if "sam_vit_b" in model_name:
      __lowerCamelCase    				=     SamConfig()
   elif "sam_vit_l" in model_name:
      __lowerCamelCase    				=     SamVisionConfig(
          hidden_size=10_24			,num_hidden_layers=24			,num_attention_heads=16			,global_attn_indexes=[5, 11, 17, 23]			,)

      __lowerCamelCase    				=     SamConfig(
          vision_config=_UpperCamelCase			,)
   elif "sam_vit_h" in model_name:
      __lowerCamelCase    				=     SamVisionConfig(
          hidden_size=12_80			,num_hidden_layers=32			,num_attention_heads=16			,global_attn_indexes=[7, 15, 23, 31]			,)

      __lowerCamelCase    				=     SamConfig(
          vision_config=_UpperCamelCase			,)

   __lowerCamelCase    				=     torch.load(_UpperCamelCase			,map_location='''cpu'''				)
   __lowerCamelCase    				=     replace_keys(_UpperCamelCase				)

   __lowerCamelCase    				=     SamImageProcessor()

   __lowerCamelCase    				=     SamProcessor(image_processor=_UpperCamelCase				)
   __lowerCamelCase    				=     SamModel(_UpperCamelCase				)

   hf_model.load_state_dict(_UpperCamelCase				)
   __lowerCamelCase    				=     hf_model.to('''cuda'''				)

   __lowerCamelCase    				=     '''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
   __lowerCamelCase    				=     Image.open(requests.get(_UpperCamelCase			,stream=_UpperCamelCase				).raw				).convert('''RGB'''				)

   __lowerCamelCase    				=     [[[4_00, 6_50]]]
   __lowerCamelCase    				=     [[1]]

   __lowerCamelCase    				=     processor(images=np.array(_UpperCamelCase				)			,return_tensors='''pt'''				).to('''cuda'''				)

   with torch.no_grad():
      __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
   __lowerCamelCase    				=     output.iou_scores.squeeze()

   if model_name == "sam_vit_h_4b8939":
      assert scores[-1].item() == 0.579_890_251_159_668

      __lowerCamelCase    				=     processor(
          images=np.array(_UpperCamelCase				)			,input_points=_UpperCamelCase			,input_labels=_UpperCamelCase			,return_tensors='''pt'''				).to('''cuda'''				)

      with torch.no_grad():
         __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
      __lowerCamelCase    				=     output.iou_scores.squeeze()

      assert scores[-1].item() == 0.9_712_603_092_193_604

      __lowerCamelCase    				=     ((75, 2_75, 17_25, 8_50),)

      __lowerCamelCase    				=     processor(images=np.array(_UpperCamelCase				)			,input_boxes=_UpperCamelCase			,return_tensors='''pt'''				).to('''cuda'''				)

      with torch.no_grad():
         __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
      __lowerCamelCase    				=     output.iou_scores.squeeze()

      assert scores[-1].item() == 0.8_686_015_605_926_514

      # Test with 2 points and 1 image.
      __lowerCamelCase    				=     [[[4_00, 6_50], [8_00, 6_50]]]
      __lowerCamelCase    				=     [[1, 1]]

      __lowerCamelCase    				=     processor(
          images=np.array(_UpperCamelCase				)			,input_points=_UpperCamelCase			,input_labels=_UpperCamelCase			,return_tensors='''pt'''				).to('''cuda'''				)

      with torch.no_grad():
         __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
      __lowerCamelCase    				=     output.iou_scores.squeeze()

      assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
  a_								=       argparse.ArgumentParser()
  a_								=       ["""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""",
  )

  a_								=       parser.parse_args()

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






