

"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




def 			_lowercase       (     __lowerCAmelCase				)							->					Optional[int]:
      if "cls_token" in name:
            SCREAMING_SNAKE_CASE__ :   Any   		=						name.replace("""cls_token"""	,       """vit.embeddings.cls_token"""				)
      if "mask_token" in name:
            SCREAMING_SNAKE_CASE__ :   Tuple   		=						name.replace("""mask_token"""	,       """decoder.mask_token"""				)
      if "decoder_pos_embed" in name:
            SCREAMING_SNAKE_CASE__ :   List[Any]   		=						name.replace("""decoder_pos_embed"""	,       """decoder.decoder_pos_embed"""				)
      if "pos_embed" in name and "decoder" not in name:
            SCREAMING_SNAKE_CASE__ :   List[Any]   		=						name.replace("""pos_embed"""	,       """vit.embeddings.position_embeddings"""				)
      if "patch_embed.proj" in name:
            SCREAMING_SNAKE_CASE__ :   Tuple   		=						name.replace("""patch_embed.proj"""	,       """vit.embeddings.patch_embeddings.projection"""				)
      if "patch_embed.norm" in name:
            SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						name.replace("""patch_embed.norm"""	,       """vit.embeddings.norm"""				)
      if "decoder_blocks" in name:
            SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						name.replace("""decoder_blocks"""	,       """decoder.decoder_layers"""				)
      if "blocks" in name:
            SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						name.replace("""blocks"""	,       """vit.encoder.layer"""				)
      if "attn.proj" in name:
            SCREAMING_SNAKE_CASE__ :   str   		=						name.replace("""attn.proj"""	,       """attention.output.dense"""				)
      if "attn" in name:
            SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						name.replace("""attn"""	,       """attention.self"""				)
      if "norm1" in name:
            SCREAMING_SNAKE_CASE__ :   int   		=						name.replace("""norm1"""	,       """layernorm_before"""				)
      if "norm2" in name:
            SCREAMING_SNAKE_CASE__ :   List[str]   		=						name.replace("""norm2"""	,       """layernorm_after"""				)
      if "mlp.fc1" in name:
            SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						name.replace("""mlp.fc1"""	,       """intermediate.dense"""				)
      if "mlp.fc2" in name:
            SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						name.replace("""mlp.fc2"""	,       """output.dense"""				)
      if "decoder_embed" in name:
            SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						name.replace("""decoder_embed"""	,       """decoder.decoder_embed"""				)
      if "decoder_norm" in name:
            SCREAMING_SNAKE_CASE__ :   Tuple   		=						name.replace("""decoder_norm"""	,       """decoder.decoder_norm"""				)
      if "decoder_pred" in name:
            SCREAMING_SNAKE_CASE__ :   List[str]   		=						name.replace("""decoder_pred"""	,       """decoder.decoder_pred"""				)
      if "norm.weight" in name and "decoder" not in name:
            SCREAMING_SNAKE_CASE__ :   int   		=						name.replace("""norm.weight"""	,       """vit.layernorm.weight"""				)
      if "norm.bias" in name and "decoder" not in name:
            SCREAMING_SNAKE_CASE__ :   List[Any]   		=						name.replace("""norm.bias"""	,       """vit.layernorm.bias"""				)

      return name




def 			_lowercase       (     __lowerCAmelCase	,       __lowerCAmelCase				)							->					List[str]:
      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						orig_state_dict.pop(snake_case__				)

            if "qkv" in key:
                  SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						key.split("""."""				)
                  SCREAMING_SNAKE_CASE__ :   List[Any]   		=						int(key_split[1]				)
                  if "decoder_blocks" in key:
                        SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						config.decoder_hidden_size
                        SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						"""decoder.decoder_layers."""
                        if "weight" in key:
                              SCREAMING_SNAKE_CASE__ :   Dict   		=						val[:dim, :]
                              SCREAMING_SNAKE_CASE__ :   str   		=						val[dim : dim * 2, :]
                              SCREAMING_SNAKE_CASE__ :   Any   		=						val[-dim:, :]
                        elif "bias" in key:
                              SCREAMING_SNAKE_CASE__ :   Any   		=						val[:dim]
                              SCREAMING_SNAKE_CASE__ :   str   		=						val[dim : dim * 2]
                              SCREAMING_SNAKE_CASE__ :   List[str]   		=						val[-dim:]
                  else:
                        SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						config.hidden_size
                        SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						"""vit.encoder.layer."""
                        if "weight" in key:
                              SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						val[:dim, :]
                              SCREAMING_SNAKE_CASE__ :   Any   		=						val[dim : dim * 2, :]
                              SCREAMING_SNAKE_CASE__ :   str   		=						val[-dim:, :]
                        elif "bias" in key:
                              SCREAMING_SNAKE_CASE__ :   List[str]   		=						val[:dim]
                              SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						val[dim : dim * 2]
                              SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						val[-dim:]

            else:
                  SCREAMING_SNAKE_CASE__ :   Any   		=						val

      return orig_state_dict




def 			_lowercase       (     __lowerCAmelCase	,       __lowerCAmelCase				)							->					Union[str, Any]:
      SCREAMING_SNAKE_CASE__ :   List[str]   		=						ViTMAEConfig()
      if "large" in checkpoint_url:
            SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						1024
            SCREAMING_SNAKE_CASE__ :   Any   		=						4096
            SCREAMING_SNAKE_CASE__ :   Dict   		=						24
            SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						16
      elif "huge" in checkpoint_url:
            SCREAMING_SNAKE_CASE__ :   List[Any]   		=						14
            SCREAMING_SNAKE_CASE__ :   List[str]   		=						1280
            SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						5120
            SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						32
            SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						16

      SCREAMING_SNAKE_CASE__ :   Tuple   		=						ViTMAEForPreTraining(snake_case__				)

      SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						torch.hub.load_state_dict_from_url(snake_case__	,       map_location="""cpu"""				)["""model"""]

      SCREAMING_SNAKE_CASE__ :   List[str]   		=						ViTMAEImageProcessor(size=config.image_size				)

      SCREAMING_SNAKE_CASE__ :   List[Any]   		=						convert_state_dict(snake_case__	,       snake_case__				)

      model.load_state_dict(snake_case__				)
      model.eval()

      SCREAMING_SNAKE_CASE__ :   List[Any]   		=						"""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

      SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						Image.open(requests.get(snake_case__	,       stream=snake_case__				).raw				)
      SCREAMING_SNAKE_CASE__ :   str   		=						ViTMAEImageProcessor(size=config.image_size				)
      SCREAMING_SNAKE_CASE__ :   Dict   		=						image_processor(images=snake_case__	,       return_tensors="""pt"""				)

      # forward pass
      torch.manual_seed(2				)
      SCREAMING_SNAKE_CASE__ :   Dict   		=						model(**snake_case__				)
      SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						outputs.logits

      if "large" in checkpoint_url:
            SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						torch.tensor(
                [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]				)
      elif "huge" in checkpoint_url:
            SCREAMING_SNAKE_CASE__ :   Any   		=						torch.tensor(
                [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]				)
      else:
            SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						torch.tensor(
                [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]				)

      # verify logits
      assert torch.allclose(logits[0, :3, :3]	,       snake_case__	,       atol=1E-4				)

      print(F'''Saving model to {pytorch_dump_folder_path}'''				)
      model.save_pretrained(snake_case__				)

      print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
      image_processor.save_pretrained(snake_case__				)


if __name__ == "__main__":
  a       :Dict     =			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint_url",
      default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
      type=str,
      help="URL of the checkpoint you\'d like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )

  a       :Union[str, Any]     =			parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






from __future__ import annotations




def 							a		(				snake_case__:   list[list[int]]   ):


				'''simple docstring'''

				# preprocessing the first row
				for i in range(1		,  len(matrix[0]   )   ):
								matrix[0][i] += matrix[0][i - 1]

				# preprocessing the first column
				for i in range(1		,  len(snake_case__   )   ):
								matrix[i][0] += matrix[i - 1][0]

				# updating the path cost for current position
				for i in range(1		,  len(snake_case__   )   ):
								for j in range(1		,  len(matrix[0]   )   ):
												matrix[i][j] += min(matrix[i - 1][j]		,  matrix[i][j - 1]   )

				return matrix[-1][-1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()




