




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								=       logging.get_logger(__name__)

__A								=       {"vocab_file": "spm_char.model"}

__A								=       {
    "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								=       {
    "microsoft/speecht5_asr": 10_24,
    "microsoft/speecht5_tts": 10_24,
    "microsoft/speecht5_vc": 10_24,
}







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			VOCAB_FILES_NAMES
			snake_case_  						=			PRETRAINED_VOCAB_FILES_MAP
			snake_case_  						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_  						=			['''input_ids''', '''attention_mask''']


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



							'''simple docstring'''
							__lowerCamelCase									=       {} if sp_model_kwargs is None else sp_model_kwargs

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

							__lowerCamelCase									=       vocab_file

							__lowerCamelCase									=       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
							self.sp_model.Load(lowerCamelCase__				)


			@property
			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							return self.sp_model.get_piece_size()


			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       {self.convert_ids_to_tokens(lowerCamelCase__				): i for i in range(self.vocab_size				)}
							vocab.update(self.added_tokens_encoder				)
							return vocab


			def __getstate__( self				)       -> List[str]:



							'''simple docstring'''
							__lowerCamelCase									=       self.__dict__.copy()
							__lowerCamelCase									=       None
							return state


			def __setstate__( self     ,		lowerCamelCase__				)       -> List[str]:



							'''simple docstring'''
							__lowerCamelCase									=       d

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

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


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> List[str]:



							'''simple docstring'''
							return self.sp_model.encode(lowerCamelCase__     ,		out_type=lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Any:



							'''simple docstring'''
							return self.sp_model.piece_to_id(lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Any:



							'''simple docstring'''
							__lowerCamelCase									=       self.sp_model.IdToPiece(lowerCamelCase__				)
							return token


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       []
							__lowerCamelCase									=       ''
							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(lowerCamelCase__				) + token
															__lowerCamelCase									=       []
											else:
															current_sub_tokens.append(lowerCamelCase__				)
							out_string += self.sp_model.decode(lowerCamelCase__				)
							return out_string.strip()


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



							'''simple docstring'''
							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowerCamelCase__     ,		token_ids_a=lowerCamelCase__     ,		already_has_special_tokens=lowerCamelCase__				)

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






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> Tuple[str]:



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

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

							return (out_vocab_file,)








def      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: int )	->			list:





				"""simple docstring"""


				__lowerCamelCase									=       word.split()

				def justify(UpperCamelCase__							: list					,	UpperCamelCase__							: int					,	UpperCamelCase__							: int ) -> str:
								__lowerCamelCase									=       max_width - width
								__lowerCamelCase									=       len(UpperCamelCase__ )
								if len(UpperCamelCase__ ) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												__lowerCamelCase									=       words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												__lowerCamelCase									=       spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												__lowerCamelCase									=       (
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(UpperCamelCase__ ):
																num_spaces_between_words_list[i] += 1
												__lowerCamelCase									=       []
												for i in range(UpperCamelCase__ ):
																# add the word
																aligned_words_list.append(line[i] )
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * ' ' )
												# just add the last word to the sentence
												aligned_words_list.append(line[-1] )
												# join the aligned words list to form a justified line
												return "".join(UpperCamelCase__ )

				__lowerCamelCase									=       []
				__lowerCamelCase									=       []
				__lowerCamelCase									=       0
				for word in words:
								if width + len(UpperCamelCase__ ) + len(UpperCamelCase__ ) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(UpperCamelCase__ )
												width += len(UpperCamelCase__ )
								else:
												# justify the line and add it to result
												answer.append(justify(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__ ) )
												# reset new line and new width
												__lowerCamelCase			,		__lowerCamelCase									=       [word], len(UpperCamelCase__ )
				__lowerCamelCase									=       max_width - width - len(UpperCamelCase__ )
				answer.append(' '.join(UpperCamelCase__ ) + (remaining_spaces + 1) * ' ' )
				return answer


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

					testmod()


