



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_							:      Optional[int]      =  logging.get_logger(__name__)

UpperCAmelCase_							:      List[str]      =  {'vocab_file': 'spm_char.model'}

UpperCAmelCase_							:      Tuple      =  {
    '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_							:      List[Any]      =  {
    'microsoft/speecht5_asr': 1_024,
    'microsoft/speecht5_tts': 1_024,
    'microsoft/speecht5_vc': 1_024,
}






class      UpperCamelCase    (						lowerCamelCase__     ):
	lowerCAmelCase	:   Optional[int]				=	VOCAB_FILES_NAMES
	lowerCAmelCase	:   Dict				=	PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase	:   int				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase	:   List[str]				=	["""input_ids""", """attention_mask"""]



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__="<s>"			,	UpperCAmelCase__="</s>"			,	UpperCAmelCase__="<unk>"			,	UpperCAmelCase__="<pad>"			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							A__							=       {} if sp_model_kwargs is None else sp_model_kwargs

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

							A__							=       vocab_file

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



	@property
	def 			__A							(       self		):
							return self.sp_model.get_piece_size()



	def 			__A							(       self		):
							A__							=       {self.convert_ids_to_tokens(__snake_case		): i for i in range(self.vocab_size		)}
							vocab.update(self.added_tokens_encoder		)
							return vocab



	def __getstate__(       self		):
							A__							=       self.__dict__.copy()
							A__							=       None
							return state



	def __setstate__(       self			,	UpperCAmelCase__		):
							A__							=       d

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

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



	def 			__A							(       self			,	UpperCAmelCase__		):
							return self.sp_model.encode(__snake_case			,	out_type=__snake_case		)



	def 			__A							(       self			,	UpperCAmelCase__		):
							return self.sp_model.piece_to_id(__snake_case		)



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       self.sp_model.IdToPiece(__snake_case		)
							return token



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       []
							A__							=       ''
							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(__snake_case		) + token
																			A__							=       []
													else:
																			current_sub_tokens.append(__snake_case		)
							out_string += self.sp_model.decode(__snake_case		)
							return out_string.strip()



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=None		):
							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		):
							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=__snake_case			,	token_ids_a=__snake_case			,	already_has_special_tokens=__snake_case		)

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



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							if not os.path.isdir(__snake_case		):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
													return
							A__							=       os.path.join(
							    __snake_case			,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

							return (out_vocab_file,)










def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       str:





						"""simple docstring"""





						A__							=       len(_A    )
						A__							=       len(_A    )
						A__							=       (
						    first_str_length if first_str_length > second_str_length else second_str_length
						)
						A__							=       []
						for char_count in range(_A    ):
												if char_count < first_str_length:
																		output_list.append(first_str[char_count]    )
												if char_count < second_str_length:
																		output_list.append(second_str[char_count]    )
						return "".join(_A    )


if __name__ == "__main__":
						print(alternative_string_arrange("AB", "XYZ"), end=" ")





