






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


A        =			logging.get_logger(__name__)

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

A        =			{
    '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',
    }
}

A        =			{
    'microsoft/speecht5_asr': 1024,
    'microsoft/speecht5_tts': 1024,
    'microsoft/speecht5_vc': 1024,
}


class 							__a							(  __A						):





  '''simple docstring'''



  UpperCAmelCase__      :				str				    =						VOCAB_FILES_NAMES
  UpperCAmelCase__      :				List[str]				    =						PRETRAINED_VOCAB_FILES_MAP
  UpperCAmelCase__      :				Optional[int]				    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  UpperCAmelCase__      :				Dict				    =						["""input_ids""", """attention_mask"""]


  def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__="<s>" ,    UpperCamelCase__="</s>" ,    UpperCamelCase__="<unk>" ,    UpperCamelCase__="<pad>" ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
      SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {} if sp_model_kwargs is None else sp_model_kwargs

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

      SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   vocab_file

      SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
      self.sp_model.Load(UpperCamelCase__       )


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


  def   __snake_case			(						self       ):
      SCREAMING_SNAKE_CASE_   :	List[str]													=   {self.convert_ids_to_tokens(UpperCamelCase__       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab


  def __getstate__(						self       ):
      SCREAMING_SNAKE_CASE_   :	str													=   self.__dict__.copy()
      SCREAMING_SNAKE_CASE_   :	List[str]													=   None
      return state


  def __setstate__(						self ,    UpperCamelCase__       ):
      SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   d

      # for backward compatibility
      if not hasattr(self ,    'sp_model_kwargs'       ):
          SCREAMING_SNAKE_CASE_   :	Any													=   {}

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


  def   __snake_case			(						self ,    UpperCamelCase__       ):
      return self.sp_model.encode(UpperCamelCase__ ,    out_type=UpperCamelCase__       )


  def   __snake_case			(						self ,    UpperCamelCase__       ):
      return self.sp_model.piece_to_id(UpperCamelCase__       )


  def   __snake_case			(						self ,    UpperCamelCase__       ):
      SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.sp_model.IdToPiece(UpperCamelCase__       )
      return token


  def   __snake_case			(						self ,    UpperCamelCase__       ):
      SCREAMING_SNAKE_CASE_   :	int													=   []
      SCREAMING_SNAKE_CASE_   :	int													=   ''
      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(UpperCamelCase__       ) + token
              SCREAMING_SNAKE_CASE_   :	Any													=   []
          else:
              current_sub_tokens.append(UpperCamelCase__       )
      out_string += self.sp_model.decode(UpperCamelCase__       )
      return out_string.strip()


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

      SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [1]
      if token_ids_a is None:
          return ([0] * len(UpperCamelCase__       )) + suffix_ones
      return ([0] * len(UpperCamelCase__       )) + ([0] * len(UpperCamelCase__       )) + suffix_ones






  def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
      if not os.path.isdir(UpperCamelCase__       ):
          logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
          return
      SCREAMING_SNAKE_CASE_   :	Optional[int]													=   os.path.join(
          UpperCamelCase__ ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

      if os.path.abspath(self.vocab_file       ) != os.path.abspath(UpperCamelCase__       ) and os.path.isfile(self.vocab_file       ):
          copyfile(self.vocab_file ,    UpperCamelCase__       )
      elif not os.path.isfile(self.vocab_file       ):
          with open(UpperCamelCase__ ,    'wb'       ) as fi:
              SCREAMING_SNAKE_CASE_   :	Tuple													=   self.sp_model.serialized_model_proto()
              fi.write(UpperCamelCase__       )

      return (out_vocab_file,)







from timeit import timeit



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				if number < 0:
								raise ValueError('the value of input must not be negative'      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   0
				while number:
								number &= number - 1
								result += 1
				return result



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				if number < 0:
								raise ValueError('the value of input must not be negative'      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   0
				while number:
								if number % 2 == 1:
												result += 1
								number >>= 1
				return result



def 					_lowerCamelCase(			):





				'''simple docstring'''







				def do_benchmark(lowerCAmelCase__				:		int      ) -> None:
								SCREAMING_SNAKE_CASE_   :	int													=   'import __main__ as z'
								print(F'''Benchmark when {number = }:'''      )
								print(F'''{get_set_bits_count_using_modulo_operator(lowerCAmelCase__      ) = }'''      )
								SCREAMING_SNAKE_CASE_   :	int													=   timeit('z.get_set_bits_count_using_modulo_operator(25)'    ,    setup=lowerCAmelCase__      )
								print(F'''timeit() runs in {timing} seconds'''      )
								print(F'''{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase__      ) = }'''      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   timeit(
								    'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'    ,    setup=lowerCAmelCase__    ,    )
								print(F'''timeit() runs in {timing} seconds'''      )

				for number in (25, 37, 58, 0):
								do_benchmark(lowerCAmelCase__      )
								print()


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()