


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


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{'vocab_file': 'spm_char.model'}

SCREAMING_SNAKE_CASE							    =							{
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

SCREAMING_SNAKE_CASE							    =							{
    'microsoft/speecht5_asr': 1_0_2_4,
    'microsoft/speecht5_tts': 1_0_2_4,
    'microsoft/speecht5_vc': 1_0_2_4,
}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      VOCAB_FILES_NAMES
     _lowerCamelCase   =      PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase   =      ["""input_ids""", """attention_mask"""]


     def __init__(	self  ,       __A  ,       __A="<s>"  ,       __A="</s>"  ,       __A="<unk>"  ,       __A="<pad>"  ,       __A = None  ,       **__A  ,       ):
        __a					=  {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__A  ,       eos_token=__A  ,       unk_token=__A  ,       pad_token=__A  ,       sp_model_kwargs=self.sp_model_kwargs  ,       **__A  ,       )

        __a					=  vocab_file

        __a					=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(__A    )


     @property
     def 	snake_case_  (	self    ):
        return self.sp_model.get_piece_size()


     def 	snake_case_  (	self    ):
        __a					=  {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    ):
        __a					=  self.__dict__.copy()
        __a					=  None
        return state


     def __setstate__(	self  ,       __A    ):
        __a					=  d

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

        __a					=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(self.vocab_file    )


     def 	snake_case_  (	self  ,       __A    ):
        return self.sp_model.encode(__A  ,       out_type=__A    )


     def 	snake_case_  (	self  ,       __A    ):
        return self.sp_model.piece_to_id(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  self.sp_model.IdToPiece(__A    )
        return token


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  []
        __a					=  """"""
        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
              __a					=  []
           else:
              current_sub_tokens.append(__A    )
        out_string += self.sp_model.decode(__A    )
        return out_string.strip()


     def 	snake_case_  (	self  ,       __A  ,       __A=None    ):
        if token_ids_a is None:
           return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]


     def 	snake_case_  (	self  ,       __A  ,       __A = None  ,       __A = False    ):
        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__A  ,       token_ids_a=__A  ,       already_has_special_tokens=__A    )

        __a					=  [1]
        if token_ids_a is None:
           return ([0] * len(__A    )) + suffix_ones
        return ([0] * len(__A    )) + ([0] * len(__A    )) + suffix_ones


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        if not os.path.isdir(__A    ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
           return
        __a					=  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:
              __a					=  self.sp_model.serialized_model_proto()
              fi.write(__A    )

        return (out_vocab_file,)







from collections.abc import Callable




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  a
   __a					=  b
   if function(lowerCAmelCase__   ) == 0:  # one of the a or b is a root for the function
      return a
   elif function(lowerCAmelCase__   ) == 0:
      return b
   elif (
       function(lowerCAmelCase__   ) * function(lowerCAmelCase__   ) > 0
   ):  # if none of these are root and they are both positive or negative,
      # then this algorithm can't find the root
      raise ValueError("""could not find root in given interval."""   )
   else:
      __a					=  start + (end - start) / 2.0
      while abs(start - mid   ) > 10**-7:  # until precisely equals to 10^-7
         if function(lowerCAmelCase__   ) == 0:
            return mid
         elif function(lowerCAmelCase__   ) * function(lowerCAmelCase__   ) < 0:
            __a					=  mid
         else:
            __a					=  mid
         __a					=  start + (end - start) / 2.0
      return mid




def  a   (lowerCAmelCase__   ):
   return x**3 - 2 * x - 5


if __name__ == "__main__":
  print(bisection(f, 1, 1_0_0_0))

  import doctest

  doctest.testmod()



