





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,
)


lowercase      :       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 A_	(		A__				)  ->		int:
       a__						:	str =				{}
       state_dict.pop('pixel_mean'		,      A__				)
       state_dict.pop('pixel_std'		,      A__				)

       a__						:	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:
                            a__						:	str =				key.replace(A__		,      A__				)

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

              a__						:	Optional[Any] =				value

       a__						:	List[Any] =				model_state_dict[
           'prompt_encoder.shared_embedding.positional_embedding'
       ]

       return model_state_dict



def A_	(		A__		,      A__		,      A__		,      A__="ybelkada/segment-anything"				)  ->		str:
       a__						:	int =				hf_hub_download(A__		,      F'checkpoints/{model_name}.pth'				)

       if "sam_vit_b" in model_name:
              a__						:	Tuple =				SamConfig()
       elif "sam_vit_l" in model_name:
              a__						:	List[str] =				SamVisionConfig(
                  hidden_size=1024		,      num_hidden_layers=24		,      num_attention_heads=16		,      global_attn_indexes=[5, 11, 17, 23]		,      )

              a__						:	List[str] =				SamConfig(
                  vision_config=A__		,      )
       elif "sam_vit_h" in model_name:
              a__						:	Tuple =				SamVisionConfig(
                  hidden_size=1280		,      num_hidden_layers=32		,      num_attention_heads=16		,      global_attn_indexes=[7, 15, 23, 31]		,      )

              a__						:	Optional[int] =				SamConfig(
                  vision_config=A__		,      )

       a__						:	Tuple =				torch.load(A__		,      map_location='cpu'				)
       a__						:	Optional[int] =				replace_keys(A__				)

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

       a__						:	Optional[Any] =				SamProcessor(image_processor=A__				)
       a__						:	List[Any] =				SamModel(A__				)

       hf_model.load_state_dict(A__				)
       a__						:	Optional[Any] =				hf_model.to('cuda'				)

       a__						:	List[Any] =				'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'
       a__						:	str =				Image.open(requests.get(A__		,      stream=A__				).raw				).convert('RGB'				)

       a__						:	int =				[[[400, 650]]]
       a__						:	List[str] =				[[1]]

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

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

       if model_name == "sam_vit_h_4b8939":
              assert scores[-1].item() == 0.5_79_89_02_51_15_96_68

              a__						:	Any =				processor(
                  images=np.array(A__				)		,      input_points=A__		,      input_labels=A__		,      return_tensors='pt'				).to('cuda'				)

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

              assert scores[-1].item() == 0.97_12_60_30_92_19_36_04

              a__						:	str =				((75, 275, 1725, 850),)

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

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

              assert scores[-1].item() == 0.86_86_01_56_05_92_65_14

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

              a__						:	Any =				processor(
                  images=np.array(A__				)		,      input_points=A__		,      input_labels=A__		,      return_tensors='pt'				).to('cuda'				)

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

              assert scores[-1].item() == 0.99_36_04_77_92_43_46_92


if __name__ == "__main__":
    lowercase      :       Dict									=  argparse.ArgumentParser()
    lowercase      :       List[str]									=  ["""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""",
    )

    lowercase      :       List[Any]									=  parser.parse_args()

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






from __future__ import annotations

from collections.abc import Callable

UpperCAmelCase__   	=							list[list[float | int]]
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Matrix:







 """simple docstring"""


 _lowercase							      =len(__snake_case						)
 _lowercase							      =[[0 for _ in range(size + 1						)] for _ in range(__snake_case						)]
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42

 for row in range(__snake_case						):
  for col in range(__snake_case						):
   _lowercase							      =matrix[row][col]

  _lowercase							      =vector[row][0]

 _lowercase							      =0
 _lowercase							      =0
 while row < size and col < size:
  # pivoting
  _lowercase							      =max((abs(augmented[rowa][col]						), rowa) for rowa in range(__snake_case     ,						__snake_case						)						)[
      1
  ]
  if augmented[pivot_row][col] == 0:
   col += 1
   continue
  else:
   _lowercase      ,    _lowercase							      =augmented[pivot_row], augmented[row]

  for rowa in range(row + 1     ,						__snake_case						):
   _lowercase							      =augmented[rowa][col] / augmented[row][col]
   _lowercase							      =0
   for cola in range(col + 1     ,						size + 1						):
    augmented[rowa][cola] -= augmented[row][cola] * ratio

  row += 1
  col += 1

 # back substitution
 for col in range(1     ,						__snake_case						):
  for row in range(__snake_case						):
   _lowercase							      =augmented[row][col] / augmented[col][col]
   for cola in range(__snake_case     ,						size + 1						):
    augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
 return [
     [round(augmented[row][size] / augmented[row][row]     ,						10						)] for row in range(__snake_case						)
 ]
def 				UpperCAmelCase_					(							__snake_case						)     ->			Callable[[int], int]:







 """simple docstring"""


 _lowercase							      =len(__snake_case						)
 _lowercase							      =[[0 for _ in range(__snake_case						)] for _ in range(__snake_case						)]
 _lowercase							      =[[0] for _ in range(__snake_case						)]
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42

 for x_val, y_val in enumerate(__snake_case						):
  for col in range(__snake_case						):
   _lowercase							      =(x_val + 1) ** (size - col - 1)
  _lowercase							      =y_val

 _lowercase							      =solve(__snake_case     ,						__snake_case						)

 def interpolated_func(__snake_case						) -> int:
  return sum(
      round(coeffs[x_val][0]						) * (var ** (size - x_val - 1))
      for x_val in range(__snake_case						)						)

 return interpolated_func
def 				UpperCAmelCase_					(							__snake_case						)     ->			int:







 """simple docstring"""


 return (
     1
     - variable
     + variable**2
     - variable**3
     + variable**4
     - variable**5
     + variable**6
     - variable**7
     + variable**8
     - variable**9
     + variable**10
 )
def 				UpperCAmelCase_					(							__snake_case = question_function     ,						__snake_case = 10						)     ->			int:







 """simple docstring"""


 _lowercase							      =[func(__snake_case						) for x_val in range(1     ,						order + 1						)]

 _lowercase							      =[
     interpolate(data_points[:max_coeff]						) for max_coeff in range(1     ,						order + 1						)
 ]

 _lowercase							      =0
 _lowercase							      =42
 _lowercase							      =42

 for poly in polynomials:
  _lowercase							      =1
  while func(__snake_case						) == poly(__snake_case						):
   x_val += 1

  ret += poly(__snake_case						)

 return ret


if __name__ == "__main__":
  print(f'''{solution() = }''')

