


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

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

A__:    int					    =       {
    '''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[str]					    =       {
    '''microsoft/speecht5_asr''': 1024,
    '''microsoft/speecht5_tts''': 1024,
    '''microsoft/speecht5_vc''': 1024,
}







class A__       (						A__      ):
      __UpperCamelCase       :  Optional[Any]	       =							VOCAB_FILES_NAMES
      __UpperCamelCase       :  List[str]	       =							PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase       :  str	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase       :  Tuple	       =							["input_ids", "attention_mask"]






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




             '''simple docstring'''
             _a    : List[str]     					={} if sp_model_kwargs is None else sp_model_kwargs

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

             _a    : Any     					=vocab_file

             _a    : Optional[Any]     					=spm.SentencePieceProcessor(**self.sp_model_kwargs	)
             self.sp_model.Load(UpperCamelCase_	)






      @property
      def __UpperCAmelCase       ( self			:str	)       ->					Optional[Any]:




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






      def __UpperCAmelCase       ( self			:Tuple	)       ->					List[Any]:




             '''simple docstring'''
             _a    : Optional[Any]     					={self.convert_ids_to_tokens(UpperCamelCase_	): i for i in range(self.vocab_size	)}
             vocab.update(self.added_tokens_encoder	)
             return vocab






      def __getstate__( self			:Optional[Any]	)       ->					List[Any]:




             '''simple docstring'''
             _a    : Tuple     					=self.__dict__.copy()
             _a    : Union[str, Any]     					=None
             return state






      def __setstate__( self			:Optional[int]     ,						SCREAMING_SNAKE_CASE			:Dict	)       ->					Dict:




             '''simple docstring'''
             _a    : str     					=d

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

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






      def __UpperCAmelCase       ( self			:Any     ,						SCREAMING_SNAKE_CASE			:str	)       ->					Optional[Any]:




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






      def __UpperCAmelCase       ( self			:Any     ,						SCREAMING_SNAKE_CASE			:Optional[Any]	)       ->					Optional[Any]:




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






      def __UpperCAmelCase       ( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:List[str]	)       ->					Optional[Any]:




             '''simple docstring'''
             _a    : Dict     					=self.sp_model.IdToPiece(UpperCamelCase_	)
             return token






      def __UpperCAmelCase       ( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:List[str]	)       ->					List[Any]:




             '''simple docstring'''
             _a    : Any     					=[]
             _a    : 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(UpperCamelCase_	) + token
                           _a    : Optional[Any]     					=[]
                    else:
                           current_sub_tokens.append(UpperCamelCase_	)
             out_string += self.sp_model.decode(UpperCamelCase_	)
             return out_string.strip()






      def __UpperCAmelCase       ( self			:Tuple     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:List[Any]=None	)       ->					Optional[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 __UpperCAmelCase       ( self			:Any     ,						SCREAMING_SNAKE_CASE			:Optional[int]     ,						SCREAMING_SNAKE_CASE			:Union[str, Any] = None     ,						SCREAMING_SNAKE_CASE			:List[str] = False	)       ->					Any:




             '''simple docstring'''


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

             _a    : Optional[Any]     					=[1]
             if token_ids_a is None:
                    return ([0] * len(UpperCamelCase_	)) + suffix_ones
             return ([0] * len(UpperCamelCase_	)) + ([0] * len(UpperCamelCase_	)) + suffix_ones






      def __UpperCAmelCase       ( self			:List[Any]     ,						SCREAMING_SNAKE_CASE			:int     ,						SCREAMING_SNAKE_CASE			:Dict = None	)       ->					Union[str, Any]:




             '''simple docstring'''


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

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

             return (out_vocab_file,)






'''simple docstring'''
import re

from filelock import FileLock


try:
		import nltk

		__snake_case							      =     True
except (ImportError, ModuleNotFoundError):
		__snake_case							      =     False

if NLTK_AVAILABLE:
		with FileLock('''.lock''') as lock:
				nltk.download('''punkt''', quiet=True)


def 			a      (			__a   )  ->	str:



					'''simple docstring'''






					re.sub('''<n>''' ,					'''''' ,					__a   )  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(__a   )   )