
'''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


A__   :		str 		=logging.get_logger(__name__)

A__   :		Any 		={'vocab_file': 'spm_char.model'}

A__   :		Union[str, Any] 		={
    '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__   :		Dict 		={
    'microsoft/speecht5_asr': 1_024,
    'microsoft/speecht5_tts': 1_024,
    'microsoft/speecht5_vc': 1_024,
}







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       =VOCAB_FILES_NAMES
 lowerCamelCase				       =PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase				       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase				       =['''input_ids''', '''attention_mask''']
 def __init__(   self			:    List[str]				,   lowerCamelCase			:    int				,   lowerCamelCase			:    str="<s>"				,   lowerCamelCase			:    Dict="</s>"				,   lowerCamelCase			:    Optional[Any]="<unk>"				,   lowerCamelCase			:    Dict="<pad>"				,   lowerCamelCase			:    Optional[Dict[str, Any]] = None				,   **lowerCamelCase			:    Optional[int]				,   ):



   """simple docstring"""





   __A		:     Any												=		{} 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		:     Optional[int]												=		vocab_file

   __A		:     List[Any]												=		spm.SentencePieceProcessor(**self.sp_model_kwargs       )
   self.sp_model.Load(lowerCamelCase       )
 @property
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   return self.sp_model.get_piece_size()
 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		{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			:    Tuple       ):



   """simple docstring"""





   __A		:     Any												=		self.__dict__.copy()
   __A		:     Any												=		None
   return state
 def __setstate__(   self			:    str				,   lowerCamelCase			:    Any       ):



   """simple docstring"""





   __A		:     Dict												=		d

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

   __A		:     Any												=		spm.SentencePieceProcessor(**self.sp_model_kwargs       )
   self.sp_model.Load(self.vocab_file       )
 def 		lowercase_(   self			:    int				,   lowerCamelCase			:    str       ):



   """simple docstring"""





   return self.sp_model.encode(lowerCamelCase				,   out_type=lowerCamelCase       )
 def 		lowercase_(   self			:    Optional[int]				,   lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   return self.sp_model.piece_to_id(lowerCamelCase       )
 def 		lowercase_(   self			:    List[Any]				,   lowerCamelCase			:    List[Any]       ):



   """simple docstring"""





   __A		:     str												=		self.sp_model.IdToPiece(lowerCamelCase       )
   return token
 def 		lowercase_(   self			:    Dict				,   lowerCamelCase			:    Dict       ):



   """simple docstring"""





   __A		:     Tuple												=		[]
   __A		:     str												=		""""""
   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		:     Union[str, Any]												=		[]
     else:
       current_sub_tokens.append(lowerCamelCase       )
   out_string += self.sp_model.decode(lowerCamelCase       )
   return out_string.strip()
 def 		lowercase_(   self			:    Union[str, Any]				,   lowerCamelCase			:    List[str]				,   lowerCamelCase			:    str=None       ):



   """simple docstring"""





   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 		lowercase_(   self			:    Optional[Any]				,   lowerCamelCase			:    List[int]				,   lowerCamelCase			:    Optional[List[int]] = None				,   lowerCamelCase			:    bool = 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       )

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






 def 		lowercase_(   self			:    Tuple				,   lowerCamelCase			:    str				,   lowerCamelCase			:    Optional[str] = None       ):



   """simple docstring"""





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

   return (out_vocab_file,)





'''simple docstring'''
from __future__ import annotations







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[int] )				->       int:





  """simple docstring"""

  if not nums:
    return 0
  __A		:     List[Any]												=		nums[0]
  __A		:     Union[str, Any]												=		0
  for num in nums[1:]:
    __A							,					__A		:     Union[str, Any]												=		(
        max_excluding + num,
        max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ),
    )
  return max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     import doctest

     doctest.testmod()



