





'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






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

     return name






def        _lowerCAmelCase			(  __snake_case  : Optional[Any] ,							__snake_case  : str			)     ->     Optional[int]:
     for key in orig_state_dict.copy().keys():
          __A						: Tuple      =       orig_state_dict.pop(__snake_case			)

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

          else:
               __A						: Dict      =       val

     return orig_state_dict






def        _lowerCAmelCase			(  __snake_case  : Any ,							__snake_case  : Optional[int]			)     ->     Optional[Any]:
     __A						: List[str]      =       ViTMAEConfig()
     if "large" in checkpoint_url:
          __A						: List[str]      =       10_24
          __A						: Union[str, Any]      =       40_96
          __A						: Tuple      =       24
          __A						: List[str]      =       16
     elif "huge" in checkpoint_url:
          __A						: List[str]      =       14
          __A						: List[str]      =       12_80
          __A						: Optional[Any]      =       51_20
          __A						: Union[str, Any]      =       32
          __A						: Tuple      =       16

     __A						: Union[str, Any]      =       ViTMAEForPreTraining(__snake_case			)

     __A						: Union[str, Any]      =       torch.hub.load_state_dict_from_url(__snake_case ,							map_location='cpu'			)['model']

     __A						: List[Any]      =       ViTMAEImageProcessor(size=config.image_size			)

     __A						: Union[str, Any]      =       convert_state_dict(__snake_case ,							__snake_case			)

     model.load_state_dict(__snake_case			)
     model.eval()

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

     __A						: Tuple      =       Image.open(requests.get(__snake_case ,							stream=__snake_case			).raw			)
     __A						: str      =       ViTMAEImageProcessor(size=config.image_size			)
     __A						: Optional[int]      =       image_processor(images=__snake_case ,							return_tensors='pt'			)

     # forward pass
     torch.manual_seed(2			)
     __A						: int      =       model(**__snake_case			)
     __A						: str      =       outputs.logits

     if "large" in checkpoint_url:
          __A						: str      =       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						: Optional[int]      =       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						: Union[str, 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__":
  lowercase__    :      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.'''
  )

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






'''simple docstring'''




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       ['''image_processor''', '''tokenizer''']
     lowerCAmelCase					=       '''BlipImageProcessor'''
     lowerCAmelCase					=       ('''BertTokenizer''', '''BertTokenizerFast''')


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: int      =       False
          super().__init__(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: Optional[int]      =       self.image_processor


     def __call__(						self						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = True						,	_UpperCAmelCase = False						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = 0						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = True						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          if images is None and text is None:
               raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None:
               __A						: int      =       self.tokenizer
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
               return text_encoding

          # add pixel_values
          __A						: List[Any]      =       self.image_processor(_UpperCAmelCase						,	return_tensors=_UpperCAmelCase)

          if text is not None:
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
          else:
               __A						: int      =       None

          if text_encoding is not None:
               encoding_image_processor.update(_UpperCAmelCase)

          return encoding_image_processor


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.batch_decode(*_UpperCAmelCase						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.decode(*_UpperCAmelCase						,	**_UpperCAmelCase)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       self.tokenizer.model_input_names
          __A						: Tuple      =       self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))