





'''simple docstring'''






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_		:  List[str]		 =						{
    "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        UpperCamelCase__		(						__magic_name__  :   Tuple      )				->			Optional[Any]:
 '''simple docstring'''






 snake_case__     :					List[str]    =  {}
 state_dict.pop("""pixel_mean"""		,       __magic_name__      )
 state_dict.pop("""pixel_std"""		,       __magic_name__      )

 snake_case__     :					Optional[Any]    =  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:
    snake_case__     :					Tuple    =  key.replace(__magic_name__		,       __magic_name__      )

  if re.match(__magic_name__		,       __magic_name__      ):
   snake_case__     :					List[Any]    =  int(re.match(__magic_name__		,       __magic_name__      ).group(2      )      )
   if layer_nb == 0:
    snake_case__     :					str    =  key.replace("""layers.0"""		,       """proj_in"""      )
   elif layer_nb == 1:
    snake_case__     :					Union[str, Any]    =  key.replace("""layers.1"""		,       """layers.0"""      )
   elif layer_nb == 2:
    snake_case__     :					List[Any]    =  key.replace("""layers.2"""		,       """proj_out"""      )

  snake_case__     :					Any    =  value

 snake_case__     :					Any    =  model_state_dict[
     """prompt_encoder.shared_embedding.positional_embedding"""
 ]

 return model_state_dict



def        UpperCamelCase__		(						__magic_name__  :   int		,       __magic_name__  :   Optional[int]		,       __magic_name__  :   Tuple		,       __magic_name__  :   Optional[int]="ybelkada/segment-anything"      )				->			Optional[Any]:
 '''simple docstring'''






 snake_case__     :					str    =  hf_hub_download(__magic_name__		,       f"checkpoints/{model_name}.pth"      )

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

  snake_case__     :					Union[str, Any]    =  SamConfig(
      vision_config=__magic_name__		,       )
 elif "sam_vit_h" in model_name:
  snake_case__     :					Union[str, Any]    =  SamVisionConfig(
      hidden_size=12_80		,       num_hidden_layers=32		,       num_attention_heads=16		,       global_attn_indexes=[7, 15, 23, 31]		,       )

  snake_case__     :					int    =  SamConfig(
      vision_config=__magic_name__		,       )

 snake_case__     :					List[Any]    =  torch.load(__magic_name__		,       map_location="""cpu"""      )
 snake_case__     :					List[str]    =  replace_keys(__magic_name__      )

 snake_case__     :					Union[str, Any]    =  SamImageProcessor()

 snake_case__     :					str    =  SamProcessor(image_processor=__magic_name__      )
 snake_case__     :					int    =  SamModel(__magic_name__      )

 hf_model.load_state_dict(__magic_name__      )
 snake_case__     :					List[str]    =  hf_model.to("""cuda"""      )

 snake_case__     :					Tuple    =  """https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"""
 snake_case__     :					Optional[int]    =  Image.open(requests.get(__magic_name__		,       stream=__magic_name__      ).raw      ).convert("""RGB"""      )

 snake_case__     :					List[str]    =  [[[4_00, 6_50]]]
 snake_case__     :					int    =  [[1]]

 snake_case__     :					Optional[Any]    =  processor(images=np.array(__magic_name__      )		,       return_tensors="""pt"""      ).to("""cuda"""      )

 with torch.no_grad():
  snake_case__     :					Tuple    =  hf_model(**__magic_name__      )
 snake_case__     :					Optional[Any]    =  output.iou_scores.squeeze()

 if model_name == "sam_vit_h_4b8939":
  assert scores[-1].item() == 0.579_8902_5115_9668

  snake_case__     :					Tuple    =  processor(
      images=np.array(__magic_name__      )		,       input_points=__magic_name__		,       input_labels=__magic_name__		,       return_tensors="""pt"""      ).to("""cuda"""      )

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

  assert scores[-1].item() == 0.9712_6030_9219_3604

  snake_case__     :					Any    =  ((75, 2_75, 17_25, 8_50),)

  snake_case__     :					List[Any]    =  processor(images=np.array(__magic_name__      )		,       input_boxes=__magic_name__		,       return_tensors="""pt"""      ).to("""cuda"""      )

  with torch.no_grad():
   snake_case__     :					List[Any]    =  hf_model(**__magic_name__      )
  snake_case__     :					Union[str, Any]    =  output.iou_scores.squeeze()

  assert scores[-1].item() == 0.8686_0156_0592_6514

  # Test with 2 points and 1 image.
  snake_case__     :					List[Any]    =  [[[4_00, 6_50], [8_00, 6_50]]]
  snake_case__     :					Dict    =  [[1, 1]]

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

  with torch.no_grad():
   snake_case__     :					Optional[Any]    =  hf_model(**__magic_name__      )
  snake_case__     :					List[str]    =  output.iou_scores.squeeze()

  assert scores[-1].item() == 0.9936_0477_9243_4692


if __name__ == "__main__":
    A_		:  str		 =						argparse.ArgumentParser()
    A_		:  List[Any]		 =						["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_		:  str		 =						parser.parse_args()

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










'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						assert (
						    isinstance(a    ,	a							) and number_of_steps > 0
						), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
						if number_of_steps == 1:
												return 1
						__snake_case ,       __snake_case						 =   1, 1
						for _ in range(number_of_steps - 1							):
												__snake_case ,       __snake_case						 =   current + previous, current
						return current


if __name__ == "__main__":
	import doctest

	doctest.testmod()

