

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





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

      return name





def      A__					(			__A  :						Any     ,				__A  :						Optional[int]			)					->Dict:
      for key in orig_state_dict.copy().keys():
            __A    =orig_state_dict.pop(snake_case__			)

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

            else:
                  __A    =val

      return orig_state_dict





def      A__					(			__A  :						Dict     ,				__A  :						Any			)					->Dict:
      __A    =ViTMAEConfig()
      if "large" in checkpoint_url:
            __A    =10_24
            __A    =40_96
            __A    =24
            __A    =16
      elif "huge" in checkpoint_url:
            __A    =14
            __A    =12_80
            __A    =51_20
            __A    =32
            __A    =16

      __A    =ViTMAEForPreTraining(snake_case__			)

      __A    =torch.hub.load_state_dict_from_url(snake_case__     ,				map_location='''cpu'''			)["""model"""]

      __A    =ViTMAEImageProcessor(size=config.image_size			)

      __A    =convert_state_dict(snake_case__     ,				snake_case__			)

      model.load_state_dict(snake_case__			)
      model.eval()

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

      __A    =Image.open(requests.get(snake_case__     ,				stream=snake_case__			).raw			)
      __A    =ViTMAEImageProcessor(size=config.image_size			)
      __A    =image_processor(images=snake_case__     ,				return_tensors='''pt'''			)

      # forward pass
      torch.manual_seed(2			)
      __A    =model(**snake_case__			)
      __A    =outputs.logits

      if "large" in checkpoint_url:
            __A    =torch.tensor(
                [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]			)
      elif "huge" in checkpoint_url:
            __A    =torch.tensor(
                [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]			)
      else:
            __A    =torch.tensor(
                [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]			)

      # 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__":
   _lowerCamelCase      :					Any					    =      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.'''
   )

   _lowerCamelCase      :					int					    =      parser.parse_args()
   convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










"""simple docstring"""
from typing import Dict

from .base import GenericTensor, Pipeline



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def  lowerCamelCase(self  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_				):
		if tokenize_kwargs is None:
			A_		:    Dict						     = {}

		if truncation is not None:
			if "truncation" in tokenize_kwargs:
				raise ValueError(
				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""				)
			A_		:    Optional[Any]						     = truncation

		A_		:    Optional[int]						     = tokenize_kwargs

		A_		:    Optional[Any]						     = {}
		if return_tensors is not None:
			A_		:    Optional[Any]						     = return_tensors

		return preprocess_params, {}, postprocess_params





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				):
		A_		:    List[Any]						     = self.framework
		A_		:    Optional[int]						     = self.tokenizer(lowerCAmelCase_  ,  return_tensors=lowerCAmelCase_  ,  **lowerCAmelCase_				)
		return model_inputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.model(**lowerCAmelCase_				)
		return model_outputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=False				):
		# [0] is the first available tensor, logits or last_hidden_state.
		if return_tensors:
			return model_outputs[0]
		if self.framework == "pt":
			return model_outputs[0].tolist()
		elif self.framework == "tf":
			return model_outputs[0].numpy().tolist()





	def __call__(self  ,  *lowerCAmelCase_  ,  **lowerCAmelCase_				):
		return super().__call__(*lowerCAmelCase_  ,  **lowerCAmelCase_				)

