

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''': 1024,
    '''microsoft/speecht5_tts''': 1024,
    '''microsoft/speecht5_vc''': 1024,
}

class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							VOCAB_FILES_NAMES
       __SCREAMING_SNAKE_CASE				    =							PRETRAINED_VOCAB_FILES_MAP
       __SCREAMING_SNAKE_CASE				    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __SCREAMING_SNAKE_CASE				    =							["input_ids", "attention_mask"]


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase="<s>"      , _UpperCamelCase="</s>"      , _UpperCamelCase="<unk>"      , _UpperCamelCase="<pad>"      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							None:






              """simple docstring"""


              __snake_case       	=							{} 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      , )

              __snake_case       	=							vocab_file

              __snake_case       	=							spm.SentencePieceProcessor(**self.sp_model_kwargs   )
              self.sp_model.Load(_UpperCamelCase   )


       @property
       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              return self.sp_model.get_piece_size()


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							{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   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							self.__dict__.copy()
              __snake_case       	=							None
              return state


       def __setstate__(		self      , _UpperCamelCase   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							d

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

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


       def 			a		(		self      , _UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


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


       def 			a		(		self      , _UpperCamelCase   )      ->							Any:






              """simple docstring"""


              return self.sp_model.piece_to_id(_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							self.sp_model.IdToPiece(_UpperCamelCase   )
              return token


       def 			a		(		self      , _UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							[]
              __snake_case       	=							""""""
              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
                            __snake_case       	=							[]
                     else:
                            current_sub_tokens.append(_UpperCamelCase   )
              out_string += self.sp_model.decode(_UpperCamelCase   )
              return out_string.strip()


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase=None   )      ->							List[int]:






              """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 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = False   )      ->							List[int]:






              """simple docstring"""


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

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






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							Tuple[str]:






              """simple docstring"""


              if not os.path.isdir(_UpperCamelCase   ):
                     logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
                     return
              __snake_case       	=							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:
                            __snake_case       	=							self.sp_model.serialized_model_proto()
                            fi.write(_UpperCamelCase   )

              return (out_vocab_file,)




import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


UpperCamelCase__		 =     logging.get_logger(__name__)

class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       def __init__(		self      , *_UpperCamelCase      , **_UpperCamelCase   )      ->							None:






              """simple docstring"""


              warnings.warn(
                  """The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
                  """ use YolosImageProcessor instead."""      , _UpperCamelCase      , )
              super().__init__(*_UpperCamelCase      , **_UpperCamelCase   )

