






"""simple docstring"""





class      a						(					_UpperCAmelCase		):
    pass






class      a						(					_UpperCAmelCase		):
    pass






class      a						:






    def __init__(       self     :							Optional[Any]					)     ->		List[str]:
      lowerCamelCase_         =						[
          [],
          [],
          [],
      ]






    def    UpperCamelCase       (       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		None:
      try:
        if len(self.queues[priority]					) >= 100:
          raise OverflowError('Maximum queue size is 100'					)
        self.queues[priority].append(SCREAMING_SNAKE_CASE_					)
      except IndexError:
        raise ValueError('Valid priorities are 0, 1, and 2'					)






    def    UpperCamelCase       (       self     :							Tuple					)     ->		int:
      for queue in self.queues:
        if queue:
          return queue.pop(0					)
      raise UnderFlowError('All queues are empty'					)






    def __str__(       self     :							Dict					)     ->		str:
      return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues					)					)






class      a						:






    def __init__(       self     :							int					)     ->		Dict:
      lowerCamelCase_         =						[]






    def    UpperCamelCase       (       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							str					)     ->		None:
      if len(self.queue					) == 100:
        raise OverFlowError('Maximum queue size is 100'					)
      self.queue.append(SCREAMING_SNAKE_CASE_					)






    def    UpperCamelCase       (       self     :							List[str]					)     ->		int:
      if not self.queue:
        raise UnderFlowError('The queue is empty'					)
      else:
        lowerCamelCase_         =						min(self.queue					)
        self.queue.remove(SCREAMING_SNAKE_CASE_					)
        return data






    def __str__(       self     :							List[str]					)     ->		str:
      return str(self.queue					)







def      lowerCamelCase__       (							)      ->				List[Any]:
  lowerCamelCase_         =						FixedPriorityQueue()
  fpq.enqueue(0		,				10 )
  fpq.enqueue(1		,				70 )
  fpq.enqueue(0		,				100 )
  fpq.enqueue(2		,				1 )
  fpq.enqueue(2		,				5 )
  fpq.enqueue(1		,				7 )
  fpq.enqueue(2		,				4 )
  fpq.enqueue(1		,				64 )
  fpq.enqueue(0		,				128 )
  print(UpperCAmelCase_ )
  print(fpq.dequeue() )
  print(fpq.dequeue() )
  print(fpq.dequeue() )
  print(fpq.dequeue() )
  print(fpq.dequeue() )
  print(UpperCAmelCase_ )
  print(fpq.dequeue() )
  print(fpq.dequeue() )
  print(fpq.dequeue() )
  print(fpq.dequeue() )
  print(fpq.dequeue() )







def      lowerCamelCase__       (							)      ->				Dict:
  lowerCamelCase_         =						ElementPriorityQueue()
  epq.enqueue(10 )
  epq.enqueue(70 )
  epq.enqueue(100 )
  epq.enqueue(1 )
  epq.enqueue(5 )
  epq.enqueue(7 )
  epq.enqueue(4 )
  epq.enqueue(64 )
  epq.enqueue(128 )
  print(UpperCAmelCase_ )
  print(epq.dequeue() )
  print(epq.dequeue() )
  print(epq.dequeue() )
  print(epq.dequeue() )
  print(epq.dequeue() )
  print(UpperCAmelCase_ )
  print(epq.dequeue() )
  print(epq.dequeue() )
  print(epq.dequeue() )
  print(epq.dequeue() )
  print(epq.dequeue() )


if __name__ == "__main__":
  fixed_priority_queue()
  element_priority_queue()








'''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__		:					Tuple												=  logging.get_logger(__name__)

A__		:					List[str]												=  {"""vocab_file""": """spm_char.model"""}

A__		:					str												=  {
    """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__		:					List[Any]												=  {
    """microsoft/speecht5_asr""": 1024,
    """microsoft/speecht5_tts""": 1024,
    """microsoft/speecht5_vc""": 1024,
}





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Optional[Any]							     =			VOCAB_FILES_NAMES
    lowerCamelCase  :					Any							     =			PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  :					Union[str, Any]							     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  :					int							     =			['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:
         __lowerCamelCase   :	Any								=		{} 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_			,     )

         __lowerCamelCase   :	List[Any]								=		vocab_file

         __lowerCamelCase   :	str								=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(SCREAMING_SNAKE_CASE_	)







    @property
    def       lowercase_				(							self	)				->     List[str]:
         return self.sp_model.get_piece_size()







    def       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	str								=		{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	)				->     Optional[Any]:
         __lowerCamelCase   :	Dict								=		self.__dict__.copy()
         __lowerCamelCase   :	int								=		None
         return state







    def __setstate__(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         __lowerCamelCase   :	Union[str, Any]								=		d

         # for backward compatibility
         if not hasattr(self			,     'sp_model_kwargs'	):
              __lowerCamelCase   :	List[str]								=		{}

         __lowerCamelCase   :	Dict								=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(self.vocab_file	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         return self.sp_model.encode(SCREAMING_SNAKE_CASE_			,     out_type=SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     str:
         return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     Dict:
         __lowerCamelCase   :	Any								=		self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_	)
         return token







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[Any]:
         __lowerCamelCase   :	Union[str, Any]								=		[]
         __lowerCamelCase   :	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(SCREAMING_SNAKE_CASE_	) + token
                   __lowerCamelCase   :	int								=		[]
              else:
                   current_sub_tokens.append(SCREAMING_SNAKE_CASE_	)
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	)
         return out_string.strip()







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

         __lowerCamelCase   :	str								=		[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       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None	)				->     Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
              return
         __lowerCamelCase   :	Tuple								=		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:
                   __lowerCamelCase   :	str								=		self.sp_model.serialized_model_proto()
                   fi.write(SCREAMING_SNAKE_CASE_	)

         return (out_vocab_file,)






