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


__lowerCamelCase					:	Optional[Any]       =  logging.get_logger(__name__)

__lowerCamelCase					:	Tuple       =  {'''vocab_file''': '''spm_char.model'''}

__lowerCamelCase					:	Dict       =  {
    '''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''',
    }
}

__lowerCamelCase					:	List[str]       =  {
    '''microsoft/speecht5_asr''': 10_24,
    '''microsoft/speecht5_tts''': 10_24,
    '''microsoft/speecht5_vc''': 10_24,
}






class 		a__						(					A__					):
					A	       =							VOCAB_FILES_NAMES
					A	       =							PRETRAINED_VOCAB_FILES_MAP
					A	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A	       =							['input_ids', 'attention_mask']

					def __init__(    self      :       int,_A      :       Tuple,_A      :       str="<s>",_A      :       int="</s>",_A      :       int="<unk>",_A      :       Tuple="<pad>",_A      :       Optional[Dict[str, Any]] = None,**_A      :       List[Any],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {} if sp_model_kwargs is None else sp_model_kwargs

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

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    vocab_file

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(_A    )

					@property
					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							return self.sp_model.get_piece_size()

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    {self.convert_ids_to_tokens(_A    ): i for i in range(self.vocab_size    )}
							vocab.update(self.added_tokens_encoder    )
							return vocab

					def __getstate__(    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.__dict__.copy()
							SCREAMING_SNAKE_CASE_  :		str				      =    None
							return state

					def __setstate__(    self      :       Dict,_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    d

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

							SCREAMING_SNAKE_CASE_  :		int				      =    spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(self.vocab_file    )

					def 				__UpperCamelCase   (    self      :       List[str],_A      :       str    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       str,_A      :       int    ):







							"""simple docstring"""






							return self.sp_model.piece_to_id(_A    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    self.sp_model.IdToPiece(_A    )
							return token

					def 				__UpperCamelCase   (    self      :       Any,_A      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    []
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    ""
							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(_A    ) + token
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    []
									else:
											current_sub_tokens.append(_A    )
							out_string += self.sp_model.decode(_A    )
							return out_string.strip()

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[Any],_A      :       Any=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 				__UpperCamelCase   (    self      :       str,_A      :       List[int],_A      :       Optional[List[int]] = None,_A      :       bool = False    ):







							"""simple docstring"""






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

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [1]
							if token_ids_a is None:
									return ([0] * len(_A    )) + suffix_ones
							return ([0] * len(_A    )) + ([0] * len(_A    )) + suffix_ones






					def 				__UpperCamelCase   (    self      :       str,_A      :       str,_A      :       Optional[str] = None    ):







							"""simple docstring"""






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

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

							return (out_vocab_file,)

def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Tuple				      =    int(lowerCAmelCase	)
		if decimal in (0, 1):  # Exit cases for the recursion
				return str(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    divmod(lowerCAmelCase   , 2	)
		return binary_recursive(lowerCAmelCase	) + str(lowerCAmelCase	)




def       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		str				      =    str(lowerCAmelCase	).strip()
		if not number:
				raise ValueError("No input value was provided"	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    "-" if number.startswith("-"	) else ""
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    number.lstrip("-"	)
		if not number.isnumeric():
				raise ValueError("Input value is not an integer"	)
		return f'{negative}0b{binary_recursive(int(lowerCAmelCase	)	)}'


if __name__ == "__main__":
		from doctest import testmod

		testmod()
