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


__magic_name__								=logging.get_logger(__name__)

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

__magic_name__								={
    '''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''',
    }
}

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



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	str						=VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE_	:	int						=PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE_	:	List[Any]						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE_	:	str						=["input_ids", "attention_mask"]






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





       '''simple docstring'''





       UpperCamelCase__										=      {} if sp_model_kwargs is None else sp_model_kwargs

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

       UpperCamelCase__										=      vocab_file

       UpperCamelCase__										=      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.Load(SCREAMING_SNAKE_CASE_	)






  @property
  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       return self.sp_model.get_piece_size()






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	): i for i in range(self.vocab_size	)}
       vocab.update(self.added_tokens_encoder	)
       return vocab






  def __getstate__(self	)		->      int:





       '''simple docstring'''





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






  def __setstate__(self   ,				SCREAMING_SNAKE_CASE_	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      d

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

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_	)
       return token






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=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   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = False	)		->      List[int]:





       '''simple docstring'''





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

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      Tuple[str]:





       '''simple docstring'''





       if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
            logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
            return
       UpperCamelCase__										=      os.path.join(
           SCREAMING_SNAKE_CASE_   ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

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

       return (out_vocab_file,)




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__								=logging.get_logger(__name__)

__magic_name__								={
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						="vit_mae"






  def __init__(self   ,				SCREAMING_SNAKE_CASE_=768   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=3072   ,				SCREAMING_SNAKE_CASE_="gelu"   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=1E-12   ,				SCREAMING_SNAKE_CASE_=224   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=512   ,				SCREAMING_SNAKE_CASE_=8   ,				SCREAMING_SNAKE_CASE_=2048   ,				SCREAMING_SNAKE_CASE_=0.75   ,				SCREAMING_SNAKE_CASE_=False   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[Any]:





       '''simple docstring'''





       super().__init__(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      hidden_size
       UpperCamelCase__										=      num_hidden_layers
       UpperCamelCase__										=      num_attention_heads
       UpperCamelCase__										=      intermediate_size
       UpperCamelCase__										=      hidden_act
       UpperCamelCase__										=      hidden_dropout_prob
       UpperCamelCase__										=      attention_probs_dropout_prob
       UpperCamelCase__										=      initializer_range
       UpperCamelCase__										=      layer_norm_eps
       UpperCamelCase__										=      image_size
       UpperCamelCase__										=      patch_size
       UpperCamelCase__										=      num_channels
       UpperCamelCase__										=      qkv_bias
       UpperCamelCase__										=      decoder_num_attention_heads
       UpperCamelCase__										=      decoder_hidden_size
       UpperCamelCase__										=      decoder_num_hidden_layers
       UpperCamelCase__										=      decoder_intermediate_size
       UpperCamelCase__										=      mask_ratio
       UpperCamelCase__										=      norm_pix_loss



