





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)



'''simple docstring'''




import operator as op




def 							lowerCamelCase	(      UpperCAmelCase__       :					Optional[Any] )			->      int:
							lowercase_					:			Optional[Any]										=       []
							lowercase_					:			str										=       lambda UpperCAmelCase__      ,      UpperCAmelCase__ : int(x / y )  # noqa: E731 integer division operation
							lowercase_					:			Optional[Any]										=       {
							    """^""": op.pow,
							    """*""": op.mul,
							    """/""": div,
							    """+""": op.add,
							    """-""": op.sub,
							}  # operators & their respective operation

							# print table header
							print("""Symbol""".center(8 )      ,      """Action""".center(12 )      ,      """Stack"""      ,      sep=""" | """ )
							print("""-""" * (30 + len(UpperCAmelCase__ )) )

							for x in post_fix:
														if x.isdigit():  # if x in digit
																					stack.append(UpperCAmelCase__ )  # append x to stack
																					# output in tabular format
																					print(x.rjust(8 )      ,      ("""push(""" + x + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )
														else:
																					lowercase_					:			str										=       stack.pop()  # pop stack
																					# output in tabular format
																					print("""""".rjust(8 )      ,      ("""pop(""" + b + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )

																					lowercase_					:			Optional[int]										=       stack.pop()  # pop stack
																					# output in tabular format
																					print("""""".rjust(8 )      ,      ("""pop(""" + a + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )

																					stack.append(
																					    str(opr[x](int(UpperCAmelCase__ )      ,      int(UpperCAmelCase__ ) ) ) )  # evaluate the 2 values popped from stack & push result to stack
																					# output in tabular format
																					print(
																					    x.rjust(8 )      ,      ("""push(""" + a + x + b + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """      ,      )

							return int(stack[0] )


if __name__ == "__main__":
						_lowercase						: Tuple						=						input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
						print("\n\tResult = ", solve(Postfix))





