






'''simple docstring'''

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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__						:int          =			logging.get_logger(__name__)

UpperCAmelCase__						:Dict          =			{"""vocab_file""": """spiece.model"""}

UpperCAmelCase__						:List[str]          =			{
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

UpperCAmelCase__						:Tuple          =			{"""bert_for_seq_generation""": 512}







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     Union[str, Any]   		=			VOCAB_FILES_NAMES
       snake_case__    :     str   		=			PRETRAINED_VOCAB_FILES_MAP
       snake_case__    :     Dict   		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case__    :     List[int]   		=			[]
       snake_case__    :     List[Any]   		=			['input_ids', 'attention_mask']




       def __init__(					self      : List[Any]	,    A__      : List[Any]	,    A__      : List[Any]="<s>"	,    A__      : Optional[int]="</s>"	,    A__      : Any="<unk>"	,    A__      : str="<pad>"	,    A__      : Any="<::::>"	,    A__      : Optional[Dict[str, Any]] = None	,    **A__      : Any	,    ):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				{} if sp_model_kwargs is None else sp_model_kwargs

            # Add extra_ids to the special token list
            super().__init__(
                bos_token=A__	,    eos_token=A__	,    unk_token=A__	,    pad_token=A__	,    sep_token=A__	,    sp_model_kwargs=self.sp_model_kwargs	,    **A__	,    )

            __lowerCamelCase						:       Optional[Any]				   =				vocab_file

            __lowerCamelCase						:       Dict				   =				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(A__					)




       @property
       def 					a_					(					self      : int					):


            """simple docstring"""







            return self.sp_model.get_piece_size()




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				{self.convert_ids_to_tokens(A__					): i for i in range(self.vocab_size					)}
            vocab.update(self.added_tokens_encoder					)
            return vocab




       def __getstate__(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.__dict__.copy()
            __lowerCamelCase						:       int				   =				None
            return state




       def __setstate__(					self      : List[Any]	,    A__      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				d

            # for backward compatibility
            if not hasattr(self	,    """sp_model_kwargs"""					):
                 __lowerCamelCase						:       int				   =				{}

            __lowerCamelCase						:       str				   =				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(self.vocab_file					)




       def 					a_					(					self      : str	,    A__      : str					):


            """simple docstring"""







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




       def 					a_					(					self      : List[str]	,    A__      : str					):


            """simple docstring"""







            return self.sp_model.piece_to_id(A__					)




       def 					a_					(					self      : Any	,    A__      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				self.sp_model.IdToPiece(A__					)
            return token




       def 					a_					(					self      : Union[str, Any]	,    A__      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				[]
            __lowerCamelCase						:       Dict				   =				""""""
            for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                      out_string += self.sp_model.decode(A__					) + token
                      __lowerCamelCase						:       Optional[int]				   =				[]
                 else:
                      current_sub_tokens.append(A__					)
            out_string += self.sp_model.decode(A__					)
            return out_string.strip()




       def 					a_					(					self      : int	,    A__      : str	,    A__      : Optional[str] = None					):


            """simple docstring"""







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

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

            return (out_vocab_file,)











'''simple docstring'''

from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def     __lowercase  ()   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				HfArgumentParser(_lowercase     )
     __lowerCamelCase						:       List[str]				   =				parser.parse_args_into_dataclasses()[0]
     __lowerCamelCase						:       Dict				   =				TensorFlowBenchmark(args=_lowercase     )
     try:
          __lowerCamelCase						:       List[str]				   =				parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          __lowerCamelCase						:       Tuple				   =				"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
          __lowerCamelCase						:       List[Any]				   =				""" """.join(str(_lowercase     ).split(""" """     )[:-1]     )
          __lowerCamelCase						:       Tuple				   =				""""""
          __lowerCamelCase						:       List[str]				   =				eval(str(_lowercase     ).split(""" """     )[-1]     )
          __lowerCamelCase						:       Tuple				   =				[]
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in TensorFlowBenchmark.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]     )
               else:
                    wrong_args.append(_lowercase     )
          if len(_lowercase     ) > 0:
               __lowerCamelCase						:       Tuple				   =				full_error_msg + begin_error_msg + str(_lowercase     )
          raise ValueError(_lowercase     )
     benchmark.run()


if __name__ == "__main__":
   main()



