
'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def 		A_							(						_lowerCAmelCase :     Tuple				):



      """simple docstring"""




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

      return name







def 		A_							(						_lowerCAmelCase :     int					,						_lowerCAmelCase :     Optional[int]				):



      """simple docstring"""




      for key in orig_state_dict.copy().keys():
            _lowerCamelCase					: Tuple		    = orig_state_dict.pop(SCREAMING_SNAKE_CASE_				)

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

            else:
                  _lowerCamelCase					: Union[str, Any]		    = val

      return orig_state_dict







def 		A_							(						_lowerCAmelCase :     int					,						_lowerCAmelCase :     List[str]				):



      """simple docstring"""




      _lowerCamelCase					: List[str]		    = ViTMAEConfig()
      if "large" in checkpoint_url:
            _lowerCamelCase					: Tuple		    = 1024
            _lowerCamelCase					: List[Any]		    = 4096
            _lowerCamelCase					: int		    = 24
            _lowerCamelCase					: Any		    = 16
      elif "huge" in checkpoint_url:
            _lowerCamelCase					: Optional[Any]		    = 14
            _lowerCamelCase					: int		    = 1280
            _lowerCamelCase					: Tuple		    = 5120
            _lowerCamelCase					: Optional[Any]		    = 32
            _lowerCamelCase					: Optional[int]		    = 16

      _lowerCamelCase					: int		    = ViTMAEForPreTraining(SCREAMING_SNAKE_CASE_				)

      _lowerCamelCase					: List[str]		    = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_					,						map_location="cpu"				)["model"]

      _lowerCamelCase					: Any		    = ViTMAEImageProcessor(size=config.image_size				)

      _lowerCamelCase					: Optional[Any]		    = convert_state_dict(SCREAMING_SNAKE_CASE_					,						SCREAMING_SNAKE_CASE_				)

      model.load_state_dict(SCREAMING_SNAKE_CASE_				)
      model.eval()

      _lowerCamelCase					: Dict		    = "https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"

      _lowerCamelCase					: List[str]		    = Image.open(requests.get(SCREAMING_SNAKE_CASE_					,						stream=SCREAMING_SNAKE_CASE_				).raw				)
      _lowerCamelCase					: Tuple		    = ViTMAEImageProcessor(size=config.image_size				)
      _lowerCamelCase					: Any		    = image_processor(images=SCREAMING_SNAKE_CASE_					,						return_tensors="pt"				)

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

      if "large" in checkpoint_url:
            _lowerCamelCase					: Any		    = torch.tensor(
                [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]]				)
      elif "huge" in checkpoint_url:
            _lowerCamelCase					: str		    = torch.tensor(
                [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]]				)
      else:
            _lowerCamelCase					: int		    = torch.tensor(
                [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]]				)

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

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

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


if __name__ == "__main__":
 UpperCAmelCase_							: List[str] =							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.'
 )

 UpperCAmelCase_							: List[Any] =							parser.parse_args()
 convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	list[int]		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     bool:
						_lowercase					       =					len(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					[[False] * (required_sum + 1) for _ in range(arr_len + 1       )]

						# for each arr value, a sum of zero(0) can be formed by not taking any element
						# hence True/1
						for i in range(arr_len + 1       ):
												_lowercase					       =					True

						# sum is not zero and set is empty then false
						for i in range(1		,   required_sum + 1       ):
												_lowercase					       =					False

						for i in range(1		,   arr_len + 1       ):
												for j in range(1		,   required_sum + 1       ):
																		if arr[i - 1] > j:
																								_lowercase					       =					subset[i - 1][j]
																		if arr[i - 1] <= j:
																								_lowercase					       =					subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

						return subset[arr_len][required_sum]


if __name__ == "__main__":
		import doctest

		doctest.testmod()