






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def 						_a    (      a :int )							->			List[str]:
 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 :Union[str, Any]	,							a :Dict )							->			str:
 for key in orig_state_dict.copy().keys():
  a		  =      orig_state_dict.pop(a )

  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 :Any	,							a :int )							->			List[str]:
 a		  =      ViTMAEConfig()
 if "large" in checkpoint_url:
  a		  =      1_024
  a		  =      4_096
  a		  =      24
  a		  =      16
 elif "huge" in checkpoint_url:
  a		  =      14
  a		  =      1_280
  a		  =      5_120
  a		  =      32
  a		  =      16

 a		  =      ViTMAEForPreTraining(a )

 a		  =      torch.hub.load_state_dict_from_url(a	,							map_location='''cpu''' )['''model''']

 a		  =      ViTMAEImageProcessor(size=config.image_size )

 a		  =      convert_state_dict(a	,							a )

 model.load_state_dict(a )
 model.eval()

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

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

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

 if "large" in checkpoint_url:
  a		  =      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:
  a		  =      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:
  a		  =      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]	,							a	,							atol=1e-4 )

 print(F"""Saving model to {pytorch_dump_folder_path}""" )
 model.save_pretrained(a )

 print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
 image_processor.save_pretrained(a )


if __name__ == "__main__":
     UpperCAmelCase__				   =		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__				   =		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 _lowerCAmelCase				(      snake_case_	):







   def  lowerCamelCase		(	self						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	**UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       if tokenize_kwargs is None:
           snake_case    :      Optional[Any]      					=    {}

       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)"						)
           snake_case    :      List[str]      					=    truncation

       snake_case    :      Union[str, Any]      					=    tokenize_kwargs

       snake_case    :      List[Any]      					=    {}
       if return_tensors is not None:
           snake_case    :      Tuple      					=    return_tensors

       return preprocess_params, {}, postprocess_params







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	**UpperCamelCase__						)	->		Dict[str, GenericTensor]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.framework
       snake_case    :      str      					=    self.tokenizer(UpperCamelCase__						,	return_tensors=UpperCamelCase__						,	**UpperCamelCase__						)
       return model_inputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      int      					=    self.model(**UpperCamelCase__						)
       return model_outputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__=False						)	->		Union[str, Any]:

       '''simple docstring'''




       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						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       return super().__call__(*UpperCamelCase__						,	**UpperCamelCase__						)





