
"""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


_lowercase							   =				logging.get_logger(__name__)

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

_lowercase							   =				{
    '''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''',
    }
}

_lowercase							   =				{
    '''microsoft/speecht5_asr''': 10_24,
    '''microsoft/speecht5_tts''': 10_24,
    '''microsoft/speecht5_vc''': 10_24,
}







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Optional[Any]      =    VOCAB_FILES_NAMES
						_lowerCamelCase:    List[Any]      =    PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:    str      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:    Tuple      =    ['''input_ids''', '''attention_mask''']


						def __init__( self     :      List[str]  ,A_     :      int  ,A_     :      List[str]="<s>"  ,A_     :      Optional[Any]="</s>"  ,A_     :      Optional[Any]="<unk>"  ,A_     :      str="<pad>"  ,A_     :      Optional[Dict[str, Any]] = None  ,**A_     :      List[str]  ,)     ->   None:
							A						   =   {} 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_  ,)

							A						   =   vocab_file

							A						   =   spm.SentencePieceProcessor(**self.sp_model_kwargs      )
							self.sp_model.Load(A_      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   str:
							return self.sp_model.get_piece_size()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Optional[Any]:
							A						   =   {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     :      str      )     ->   Any:
							A						   =   self.__dict__.copy()
							A						   =   None
							return state


						def __setstate__( self     :      Optional[int]  ,A_     :      str      )     ->   Tuple:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      str      )     ->   List[str]:
							return self.sp_model.encode(A_  ,out_type=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Union[str, Any]      )     ->   Union[str, Any]:
							return self.sp_model.piece_to_id(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      Dict      )     ->   List[Any]:
							A						   =   self.sp_model.IdToPiece(A_      )
							return token


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple  ,A_     :      Optional[Any]      )     ->   List[str]:
							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(A_      ) + token
									A						   =   []
								else:
									current_sub_tokens.append(A_      )
							out_string += self.sp_model.decode(A_      )
							return out_string.strip()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Dict  ,A_     :      Optional[int]=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 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      List[int]  ,A_     :      Optional[List[int]] = None  ,A_     :      bool = False      )     ->   List[int]:
							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=A_  ,token_ids_a=A_  ,already_has_special_tokens=A_      )

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




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      str  ,A_     :      Optional[str] = None      )     ->   Tuple[str]:
							if not os.path.isdir(A_      ):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
								return
							A						   =   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:
									A						   =   self.sp_model.serialized_model_proto()
									fi.write(A_      )

							return (out_vocab_file,)

"""simple docstring"""


from argparse import ArgumentParser

from . import BaseTransformersCLICommand






def       _snake_case				(		snake_case__   :     Optional[int]     ):
	return DownloadCommand(args.model	,      args.cache_dir	,      args.force	,      args.trust_remote_code     )







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						@staticmethod
						def 						_SCREAMING_SNAKE_CASE      ( A_     :      ArgumentParser      )     ->   Any:
							A						   =   parser.add_parser('download'      )
							download_parser.add_argument(
							    '--cache-dir'  ,type=A_  ,default=A_  ,help='Path to location to store the models'      )
							download_parser.add_argument(
							    '--force'  ,action='store_true'  ,help='Force the model to be download even if already in cache-dir'      )
							download_parser.add_argument(
							    '--trust-remote-code'  ,action='store_true'  ,help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'  ,)
							download_parser.add_argument('model'  ,type=A_  ,help='Name of the model to download'      )
							download_parser.set_defaults(func=A_      )


						def __init__( self     :      Dict  ,A_     :      str  ,A_     :      str  ,A_     :      bool  ,A_     :      bool      )     ->   Union[str, Any]:
							A						   =   model
							A						   =   cache
							A						   =   force
							A						   =   trust_remote_code




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[int]:
							from ..models.auto import AutoModel, AutoTokenizer

							AutoModel.from_pretrained(
							    self._model  ,cache_dir=self._cache  ,force_download=self._force  ,trust_remote_code=self._trust_remote_code      )
							AutoTokenizer.from_pretrained(
							    self._model  ,cache_dir=self._cache  ,force_download=self._force  ,trust_remote_code=self._trust_remote_code      )