





"""simple docstring"""




import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       str										=  BertConfig.from_json_file(_UpperCAmelCase					)
  print(f"""Building PyTorch model from configuration: {config}"""					)
  A_  :       int										=  BertForPreTraining(_UpperCAmelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_bert(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)

  # Save pytorch-model
  print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
  torch.save(model.state_dict() ,					_UpperCAmelCase					)


if __name__ == "__main__":
   lowerCamelCase_     :						Tuple						=					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--bert_config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained BERT model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   lowerCamelCase_     :						Optional[int]						=					parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






"""simple docstring"""




import copy
import random

from transformers import CLIPTokenizer

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        super().__init__(*snake_case_					,      **snake_case_  )
        A_  :       Tuple										=  {}

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        A_  :       str										=  super().add_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
        if num_added_tokens == 0:
          raise ValueError(
              F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
              ' `placeholder_token` that is not already in the tokenizer.'  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      snake_case_=1					,      **snake_case_  ):

        """simple docstring"""

        A_  :       Tuple										=  []
        if num_vec_per_token == 1:
          self.try_adding_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
          output.append(snake_case_  )
        else:
          A_  :       Tuple										=  []
          for i in range(snake_case_  ):
            A_  :       List[str]										=  placeholder_token + F"""_{i}"""
            self.try_adding_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
            output.append(snake_case_  )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
        for token in self.token_map:
          if token in placeholder_token:
            raise ValueError(
                F"""The tokenizer already has placeholder token {token} that can get confused with"""
                F""" {placeholder_token}keep placeholder tokens independent"""  )
        A_  :       Any										=  output

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=False					,      snake_case_=1.0  ):

        """simple docstring"""

        if isinstance(snake_case_					,      snake_case_  ):
          A_  :       Optional[Any]										=  []
          for i in range(len(snake_case_  )  ):
            output.append(self.replace_placeholder_tokens_in_text(text[i]					,      vector_shuffle=snake_case_  )  )
          return output
        for placeholder_token in self.token_map:
          if placeholder_token in text:
            A_  :       List[Any]										=  self.token_map[placeholder_token]
            A_  :       Optional[int]										=  tokens[: 1 + int(len(snake_case_  ) * prop_tokens_to_load  )]
            if vector_shuffle:
              A_  :       Optional[Any]										=  copy.copy(snake_case_  )
              random.shuffle(snake_case_  )
            A_  :       List[str]										=  text.replace(snake_case_					,      ' '.join(snake_case_  )  )
        return text

      def __call__(     self					,      snake_case_					,      *snake_case_					,      snake_case_=False					,      snake_case_=1.0					,      **snake_case_  ):

        """simple docstring"""

        return super().__call__(
            self.replace_placeholder_tokens_in_text(
                snake_case_					,      vector_shuffle=snake_case_					,      prop_tokens_to_load=snake_case_  )					,      *snake_case_					,      **snake_case_					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      snake_case_=False					,      snake_case_=1.0					,      **snake_case_  ):

        """simple docstring"""

        return super().encode(
            self.replace_placeholder_tokens_in_text(
                snake_case_					,      vector_shuffle=snake_case_					,      prop_tokens_to_load=snake_case_  )					,      *snake_case_					,      **snake_case_					,      )