





'''simple docstring'''






import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


A__							:  Union[str, Any]    =logging.get_logger(__name__)

A__							:  List[Any]    ={'''vocab_file''': '''vocab.txt'''}

A__							:  List[Any]    ={
    '''vocab_file''': {
        '''facebook/esm2_t6_8M_UR50D''': '''https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt''',
        '''facebook/esm2_t12_35M_UR50D''': '''https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt''',
    },
}

A__							:  Dict    ={
    '''facebook/esm2_t6_8M_UR50D''': 10_24,
    '''facebook/esm2_t12_35M_UR50D''': 10_24,
}
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      with open(lowerCAmelCase				,      """r"""    ) as f:
            _lowerCAmelCase					=				f.read().splitlines()
            return [l.strip() for l in lines]





class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     List[Any]			=  VOCAB_FILES_NAMES
      _lowercase:     List[Any]			=  PRETRAINED_VOCAB_FILES_MAP
      _lowercase:     str			=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase:     Union[str, Any]			=  ['''input_ids''', '''attention_mask''']




      def __init__(     self     :					List[str]     ,     __snake_case     :					List[str]     ,     __snake_case     :					Tuple="<unk>"     ,     __snake_case     :					Optional[Any]="<cls>"     ,     __snake_case     :					int="<pad>"     ,     __snake_case     :					List[str]="<mask>"     ,     __snake_case     :					Tuple="<eos>"     ,     **__snake_case     :					Tuple     ,     )							-> Any:
            super().__init__(**__snake_case    )
            _lowerCAmelCase					=				load_vocab_file(__snake_case    )
            _lowerCAmelCase					=				dict(enumerate(self.all_tokens    )    )
            _lowerCAmelCase					=				{tok: ind for ind, tok in enumerate(self.all_tokens    )}
            _lowerCAmelCase					=				unk_token
            _lowerCAmelCase					=				cls_token
            _lowerCAmelCase					=				pad_token
            _lowerCAmelCase					=				mask_token
            _lowerCAmelCase					=				eos_token
            _lowerCAmelCase					=				self.all_tokens
            self._create_trie(self.unique_no_split_tokens    )




      def 							lowercase__       (     self     :					List[Any]     ,     __snake_case     :					int    )							-> str:
            return self._id_to_token.get(__snake_case     ,     self.unk_token    )




      def 							lowercase__       (     self     :					str     ,     __snake_case     :					str    )							-> int:
            return self._token_to_id.get(__snake_case     ,     self._token_to_id.get(self.unk_token    )    )




      def 							lowercase__       (     self     :					Tuple     ,     __snake_case     :					Tuple     ,     **__snake_case     :					Optional[Any]    )							-> Tuple:
            return text.split()




      def 							lowercase__       (     self     :					Optional[int]     ,     __snake_case     :					List[Any]=False    )							-> List[str]:
            return len(self._id_to_token    )




      def 							lowercase__       (     self     :					Optional[int]    )							-> Union[str, Any]:
            return {token: i for i, token in enumerate(self.all_tokens    )}




      def 							lowercase__       (     self     :					Optional[int]     ,     __snake_case     :					str    )							-> int:
            return self._token_to_id.get(__snake_case     ,     self._token_to_id.get(self.unk_token    )    )




      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					int    )							-> str:
            return self._id_to_token.get(__snake_case     ,     self.unk_token    )




      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					List[int]     ,     __snake_case     :					Optional[List[int]] = None    )							-> List[int]:
            _lowerCAmelCase					=				[self.cls_token_id]
            _lowerCAmelCase					=				[self.eos_token_id]  # No sep token in ESM vocabulary
            if token_ids_a is None:
                  if self.eos_token_id is None:
                        return cls + token_ids_a
                  else:
                        return cls + token_ids_a + sep
            elif self.eos_token_id is None:
                  raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!"""    )
            return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token




      def 							lowercase__       (     self     :					Union[str, Any]     ,     __snake_case     :					List     ,     __snake_case     :					Optional[List] = None     ,     __snake_case     :					bool = False    )							-> List[int]:
            if already_has_special_tokens:
                  if token_ids_a is not None:
                        raise ValueError(
                            """You should not supply a second sequence if the provided sequence of """
                            """ids is already formatted with special tokens for the model."""    )

                  return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
            _lowerCAmelCase					=				[1] + ([0] * len(__snake_case    )) + [1]
            if token_ids_a is not None:
                  mask += [0] * len(__snake_case    ) + [1]
            return mask




      def 							lowercase__       (     self     :					List[Any]     ,     __snake_case     :					List[Any]     ,     __snake_case     :					Tuple    )							-> Union[str, Any]:
            _lowerCAmelCase					=				os.path.join(__snake_case     ,     (filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""    )
            with open(__snake_case     ,     """w"""    ) as f:
                  f.write("""\n""".join(self.all_tokens    )    )
            return (vocab_file,)




      @property
      def 							lowercase__       (     self     :					Optional[int]    )							-> int:
            return self.get_vocab_size(with_added_tokens=__snake_case    )




      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					Union[List[str], List[AddedToken]]     ,     __snake_case     :					bool = False    )							-> int:
            return super()._add_tokens(__snake_case     ,     special_tokens=__snake_case    )













'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


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

      return name
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      for key in orig_state_dict.copy().keys():
            _lowerCAmelCase					=				orig_state_dict.pop(lowerCAmelCase    )

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

            else:
                  _lowerCAmelCase					=				val

      return orig_state_dict
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				ViTMAEConfig()
      if "large" in checkpoint_url:
            _lowerCAmelCase					=				10_24
            _lowerCAmelCase					=				40_96
            _lowerCAmelCase					=				24
            _lowerCAmelCase					=				16
      elif "huge" in checkpoint_url:
            _lowerCAmelCase					=				14
            _lowerCAmelCase					=				12_80
            _lowerCAmelCase					=				51_20
            _lowerCAmelCase					=				32
            _lowerCAmelCase					=				16

      _lowerCAmelCase					=				ViTMAEForPreTraining(lowerCAmelCase    )

      _lowerCAmelCase					=				torch.hub.load_state_dict_from_url(lowerCAmelCase				,      map_location="""cpu"""    )["""model"""]

      _lowerCAmelCase					=				ViTMAEImageProcessor(size=config.image_size    )

      _lowerCAmelCase					=				convert_state_dict(lowerCAmelCase				,      lowerCAmelCase    )

      model.load_state_dict(lowerCAmelCase    )
      model.eval()

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

      _lowerCAmelCase					=				Image.open(requests.get(lowerCAmelCase				,      stream=lowerCAmelCase    ).raw    )
      _lowerCAmelCase					=				ViTMAEImageProcessor(size=config.image_size    )
      _lowerCAmelCase					=				image_processor(images=lowerCAmelCase				,      return_tensors="""pt"""    )

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

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

      print(f"Saving model to {pytorch_dump_folder_path}"    )
      model.save_pretrained(lowerCAmelCase    )

      print(f"Saving image processor to {pytorch_dump_folder_path}"    )
      image_processor.save_pretrained(lowerCAmelCase    )


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






