


'''simple docstring'''


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


_UpperCamelCase 							=  logging.get_logger(__name__)

_UpperCamelCase 							=  {"""vocab_file""": """sentencepiece.bpe.model"""}

_UpperCamelCase 							=  {
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

_UpperCamelCase 							=  {
    """camembert-base""": 512,
}

_UpperCamelCase 							=  """▁"""







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				VOCAB_FILES_NAMES
     lowerCamelCase__       						=				PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase__       						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase__       						=				['input_ids', 'attention_mask']


     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase="<s>"			,     lowerCamelCase="</s>"			,     lowerCamelCase="</s>"			,     lowerCamelCase="<s>"			,     lowerCamelCase="<unk>"			,     lowerCamelCase="<pad>"			,     lowerCamelCase="<mask>"			,     lowerCamelCase=["<s>NOTUSED", "</s>NOTUSED"]			,     lowerCamelCase = None			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       List[Any]      =							AddedToken(lowerCamelCase			,     lstrip=lowerCamelCase			,     rstrip=lowerCamelCase		) if isinstance(lowerCamelCase			,     lowerCamelCase		) else mask_token

          __A	:       Union[str, Any]      =							{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=lowerCamelCase			,     eos_token=lowerCamelCase			,     unk_token=lowerCamelCase			,     sep_token=lowerCamelCase			,     cls_token=lowerCamelCase			,     pad_token=lowerCamelCase			,     mask_token=lowerCamelCase			,     additional_special_tokens=lowerCamelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **lowerCamelCase			,     )
          __A	:       List[Any]      =							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
          self.sp_model.Load(str(lowerCamelCase		)		)
          __A	:       List[str]      =							vocab_file
          # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
          # sentencepiece vocabulary (this is the case for <s> and </s>
          __A	:       Optional[Any]      =							{"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
          __A	:       List[str]      =							len(self.fairseq_tokens_to_ids		)
          __A	:       Optional[Any]      =							len(self.sp_model		) + len(self.fairseq_tokens_to_ids		)
          __A	:       str      =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None		):


          '''simple docstring'''





          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __A	:       Union[str, Any]      =							[self.cls_token_id]
          __A	:       Union[str, Any]      =							[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     lowerCamelCase = False		):


          '''simple docstring'''





          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCamelCase			,     token_ids_a=lowerCamelCase			,     already_has_special_tokens=lowerCamelCase		)

          if token_ids_a is None:
               return [1] + ([0] * len(lowerCamelCase		)) + [1]
          return [1] + ([0] * len(lowerCamelCase		)) + [1, 1] + ([0] * len(lowerCamelCase		)) + [1]


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None		):


          '''simple docstring'''





          __A	:       List[str]      =							[self.sep_token_id]
          __A	:       Tuple      =							[self.cls_token_id]

          if token_ids_a is None:
               return len(cls + token_ids_a + sep		) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return len(self.fairseq_tokens_to_ids		) + len(self.sp_model		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Tuple      =							{self.convert_ids_to_tokens(lowerCamelCase		): i for i in range(self.vocab_size		)}
          vocab.update(self.added_tokens_encoder		)
          return vocab


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          return self.sp_model.encode(lowerCamelCase			,     out_type=lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
          elif self.sp_model.PieceToId(lowerCamelCase		) == 0:
               # Convert sentence piece unk token to fairseq unk token index
               return self.unk_token_id
          return self.fairseq_offset + self.sp_model.PieceToId(lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(index - self.fairseq_offset		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							[]
          __A	:       List[str]      =							""
          __A	:       Any      =							False
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    if not prev_is_special:
                         out_string += " "
                    out_string += self.sp_model.decode(lowerCamelCase		) + token
                    __A	:       str      =							True
                    __A	:       Any      =							[]
               else:
                    current_sub_tokens.append(lowerCamelCase		)
                    __A	:       Dict      =							False
          out_string += self.sp_model.decode(lowerCamelCase		)
          return out_string.strip()


     def __getstate__(   self		):


          '''simple docstring'''





          __A	:       str      =							self.__dict__.copy()
          __A	:       Dict      =							None
          return state


     def __setstate__(   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       int      =							d

          # for backward compatibility
          if not hasattr(self			,     "sp_model_kwargs"		):
               __A	:       Union[str, Any]      =							{}

          __A	:       List[Any]      =							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
          self.sp_model.Load(self.vocab_file		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None		):


          '''simple docstring'''





          if not os.path.isdir(lowerCamelCase		):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
               return
          __A	:       List[str]      =							os.path.join(
              lowerCamelCase			,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

          if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase		) and os.path.isfile(self.vocab_file		):
               copyfile(self.vocab_file			,     lowerCamelCase		)
          elif not os.path.isfile(self.vocab_file		):
               with open(lowerCamelCase			,     "wb"		) as fi:
                    __A	:       str      =							self.sp_model.serialized_model_proto()
                    fi.write(lowerCamelCase		)

          return (out_vocab_file,)





'''simple docstring'''


import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[str]      =							BigBirdConfig.from_json_file(SCREAMING_SNAKE_CASE							)
     print(f"Building PyTorch model from configuration: {config}"							)

     if is_trivia_qa:
          __A	:       List[str]      =							BigBirdForQuestionAnswering(SCREAMING_SNAKE_CASE							)
     else:
          __A	:       List[Any]      =							BigBirdForPreTraining(SCREAMING_SNAKE_CASE							)

     # Load weights from tf checkpoint
     load_tf_weights_in_big_bird(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					is_trivia_qa=SCREAMING_SNAKE_CASE							)

     # Save pytorch-model
     print(f"Save PyTorch model to {pytorch_dump_path}"							)
     model.save_pretrained(SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
      _UpperCamelCase 							=  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(
          """--big_bird_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."""
      )
      parser.add_argument(
          """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
      )
      _UpperCamelCase 							=  parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
      )

