




'''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_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {"vocab_file": "spm_char.model"}

UpperCAmelCase_ 		=      {
    "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",
    }
}

UpperCAmelCase_ 		=      {
    "microsoft/speecht5_asr": 10_24,
    "microsoft/speecht5_tts": 10_24,
    "microsoft/speecht5_vc": 10_24,
}





class     __lowercase						(  lowerCAmelCase__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      ["input_ids", "attention_mask"]







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

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

     __a			=					vocab_file

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







   @property
   def 		UpperCamelCase__			(	self      )				->    Any:
     return self.sp_model.get_piece_size()







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					{self.convert_ids_to_tokens(_lowerCamelCase      ): i for i in range(self.vocab_size      )}
     vocab.update(self.added_tokens_encoder      )
     return vocab







   def __getstate__(	self      )				->    int:
     __a			=					self.__dict__.copy()
     __a			=					None
     return state







   def __setstate__(	self      ,							UpperCamelCase      )				->    Optional[Any]:
     __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 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[str]:
     return self.sp_model.encode(_lowerCamelCase      ,							out_type=_lowerCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Dict:
     return self.sp_model.piece_to_id(_lowerCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Any:
     __a			=					self.sp_model.IdToPiece(_lowerCamelCase      )
     return token







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    int:
     __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(_lowerCamelCase      ) + token
         __a			=					[]
       else:
         current_sub_tokens.append(_lowerCamelCase      )
     out_string += self.sp_model.decode(_lowerCamelCase      )
     return out_string.strip()







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    List[int]:





     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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = False      )				->    List[int]:





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

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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Tuple[str]:





     if not os.path.isdir(_lowerCamelCase      ):
       logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
       return
     __a			=					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			=					self.sp_model.serialized_model_proto()
         fi.write(_lowerCamelCase      )

     return (out_vocab_file,)









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							int			):
  __a			=					0
  while num > 0:
    digit_sum += num % 10
    num //= 10
  return digit_sum


def 						SCREAMING_SNAKE_CASE       (      a_	:							int = 100			):
  __a			=					1
  __a			=					2

  for i in range(2				,					max_n + 1			):
    __a			=					pre_numerator
    __a			=					2 * i // 3 if i % 3 == 0 else 1
    __a			=					cur_numerator
    __a			=					e_cont * pre_numerator + temp

  return sum_digits(a_			)


if __name__ == "__main__":
  print(f"""{solution() = }""")



