




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,
)


UpperCAmelCase     :     int							=	{
    """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					:       Optional[Any]						):





  """simple docstring"""



  a__     : List[Any]  ={}
  state_dict.pop("pixel_mean"	,	_lowerCAmelCase						)
  state_dict.pop("pixel_std"	,	_lowerCAmelCase						)

  a__     : 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:
        a__     : List[str]  =key.replace(_lowerCAmelCase	,	_lowerCAmelCase						)

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

    a__     : Union[str, Any]  =value

  a__     : Union[str, Any]  =model_state_dict[
      "prompt_encoder.shared_embedding.positional_embedding"
  ]

  return model_state_dict
def 					_A	(		SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       Optional[Any]="ybelkada/segment-anything"						):





  """simple docstring"""



  a__     : Tuple  =hf_hub_download(_lowerCAmelCase	,	f'''checkpoints/{model_name}.pth'''						)

  if "sam_vit_b" in model_name:
    a__     : Dict  =SamConfig()
  elif "sam_vit_l" in model_name:
    a__     : Any  =SamVisionConfig(
        hidden_size=1_024	,	num_hidden_layers=24	,	num_attention_heads=16	,	global_attn_indexes=[5, 11, 17, 23]	,	)

    a__     : Dict  =SamConfig(
        vision_config=_lowerCAmelCase	,	)
  elif "sam_vit_h" in model_name:
    a__     : Any  =SamVisionConfig(
        hidden_size=1_280	,	num_hidden_layers=32	,	num_attention_heads=16	,	global_attn_indexes=[7, 15, 23, 31]	,	)

    a__     : Dict  =SamConfig(
        vision_config=_lowerCAmelCase	,	)

  a__     : str  =torch.load(_lowerCAmelCase	,	map_location="cpu"						)
  a__     : str  =replace_keys(_lowerCAmelCase						)

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

  a__     : str  =SamProcessor(image_processor=_lowerCAmelCase						)
  a__     : Dict  =SamModel(_lowerCAmelCase						)

  hf_model.load_state_dict(_lowerCAmelCase						)
  a__     : Union[str, Any]  =hf_model.to("cuda"						)

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

  a__     : Optional[Any]  =[[[400, 650]]]
  a__     : Tuple  =[[1]]

  a__     : str  =processor(images=np.array(_lowerCAmelCase						)	,	return_tensors="pt"						).to("cuda"						)

  with torch.no_grad():
    a__     : Optional[int]  =hf_model(**_lowerCAmelCase						)
  a__     : str  =output.iou_scores.squeeze()

  if model_name == "sam_vit_h_4b8939":
    assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

    a__     : Optional[Any]  =processor(
        images=np.array(_lowerCAmelCase						)	,	input_points=_lowerCAmelCase	,	input_labels=_lowerCAmelCase	,	return_tensors="pt"						).to("cuda"						)

    with torch.no_grad():
      a__     : List[Any]  =hf_model(**_lowerCAmelCase						)
    a__     : Any  =output.iou_scores.squeeze()

    assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

    a__     : Dict  =((75, 275, 1_725, 850),)

    a__     : Union[str, Any]  =processor(images=np.array(_lowerCAmelCase						)	,	input_boxes=_lowerCAmelCase	,	return_tensors="pt"						).to("cuda"						)

    with torch.no_grad():
      a__     : Optional[int]  =hf_model(**_lowerCAmelCase						)
    a__     : str  =output.iou_scores.squeeze()

    assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

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

    a__     : Optional[int]  =processor(
        images=np.array(_lowerCAmelCase						)	,	input_points=_lowerCAmelCase	,	input_labels=_lowerCAmelCase	,	return_tensors="pt"						).to("cuda"						)

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

    assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


if __name__ == "__main__":
     UpperCAmelCase     :     List[Any]							=	argparse.ArgumentParser()
     UpperCAmelCase     :     Optional[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""",
     )

     UpperCAmelCase     :     List[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					:       int = 1	,	SCREAMING_SNAKE_CASE					:       int = 1_000						):





  """simple docstring"""



  a__     : Any  =1
  a__     : Any  =0

  for divide_by_number in range(SCREAMING_SNAKE_CASE	,	digit + 1						):
    a__     : list[int]  =[]
    a__     : int  =numerator
    for _ in range(1	,	digit + 1						):
      if now_divide in has_been_divided:
        if longest_list_length < len(SCREAMING_SNAKE_CASE						):
          a__     : Optional[Any]  =len(SCREAMING_SNAKE_CASE						)
          a__     : List[str]  =divide_by_number
      else:
        has_been_divided.append(SCREAMING_SNAKE_CASE						)
        a__     : List[Any]  =now_divide * 10 % divide_by_number

  return the_digit


# Tests
if __name__ == "__main__":
     import doctest

     doctest.testmod()






