





'''simple docstring'''






import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


snake_case_     =	logging.get_logger(__name__)

snake_case_     =	{"""vocab_file""": """spiece.model"""}

snake_case_     =	{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}


class   _lowercase						(							a				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase="<s>"    ,   _UpperCAmelCase="</s>"    ,   _UpperCAmelCase="<unk>"    ,   _UpperCAmelCase="<sep>"    ,   _UpperCAmelCase="<pad>"    ,   _UpperCAmelCase="<cls>"    ,   _UpperCAmelCase="<mask>"    ,   _UpperCAmelCase=["<eop>", "<eod>"]    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       A       :       List[str]							=						AddedToken(_UpperCAmelCase    ,   lstrip=_UpperCAmelCase    ,   rstrip=_UpperCAmelCase					) if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					) else mask_token

       A       :       Tuple							=						{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           do_lower_case=_UpperCAmelCase    ,   remove_space=_UpperCAmelCase    ,   keep_accents=_UpperCAmelCase    ,   bos_token=_UpperCAmelCase    ,   eos_token=_UpperCAmelCase    ,   unk_token=_UpperCAmelCase    ,   sep_token=_UpperCAmelCase    ,   pad_token=_UpperCAmelCase    ,   cls_token=_UpperCAmelCase    ,   mask_token=_UpperCAmelCase    ,   additional_special_tokens=_UpperCAmelCase    ,   sp_model_kwargs=self.sp_model_kwargs    ,   **_UpperCAmelCase    ,   )

       A       :       List[str]							=						3

       A       :       int							=						do_lower_case
       A       :       Optional[int]							=						remove_space
       A       :       int							=						keep_accents
       A       :       Union[str, Any]							=						vocab_file

       A       :       Any							=						spm.SentencePieceProcessor(**self.sp_model_kwargs					)
       self.sp_model.Load(_UpperCAmelCase					)

       try:
           import jieba
       except ModuleNotFoundError as error:
           raise error.__class__(
               '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
               '''See https://pypi.org/project/jieba/ for installation.'''					)
       A       :       Any							=						jieba
       A       :       int							=						str.maketrans(''' \n'''    ,   '''\u2582\u2583'''					)


   @property
   # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
   def 					snake_case				( self					):
       return len(self.sp_model					)


   def 					snake_case				( self					):
       A       :       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					):
       A       :       str							=						self.__dict__.copy()
       A       :       Optional[int]							=						None
       return state


   def __setstate__( self    ,   _UpperCAmelCase					):
       A       :       str							=						d

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

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


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       if self.remove_space:
           A       :       List[str]							=						''' '''.join(inputs.strip().split()					)
       else:
           A       :       List[str]							=						inputs
       A       :       int							=						outputs.replace('''``'''    ,   '''"'''					).replace('''\'\''''    ,   '''"'''					)

       if not self.keep_accents:
           A       :       Optional[int]							=						unicodedata.normalize('''NFKD'''    ,   _UpperCAmelCase					)
           A       :       Dict							=						''''''.join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase					)]					)
       if self.do_lower_case:
           A       :       List[Any]							=						outputs.lower()

       return outputs


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       Dict							=						self.preprocess_text(_UpperCAmelCase					)
       A       :       Optional[Any]							=						self.sp_model.encode(_UpperCAmelCase    ,   out_type=_UpperCAmelCase					)
       A       :       Dict							=						[]
       for piece in pieces:
           if len(_UpperCAmelCase					) > 1 and piece[-1] == str(''','''					) and piece[-2].isdigit():
               A       :       Any							=						self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase    ,   ''''''					)					)
               if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                   if len(cur_pieces[0]					) == 1:
                       A       :       Union[str, Any]							=						cur_pieces[1:]
                   else:
                       A       :       Union[str, Any]							=						cur_pieces[0][1:]
               cur_pieces.append(piece[-1]					)
               new_pieces.extend(_UpperCAmelCase					)
           else:
               new_pieces.append(_UpperCAmelCase					)

       return new_pieces


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       return self.sp_model.PieceToId(_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       return self.sp_model.IdToPiece(_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       List[str]							=						''''''.join(_UpperCAmelCase					).replace(_UpperCAmelCase    ,   ''' '''					).strip()
       return out_string


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       A       :       Dict							=						[self.sep_token_id]
       A       :       str							=						[self.cls_token_id]
       if token_ids_a is None:
           return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = False					):

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

       if token_ids_a is not None:
           return ([0] * len(_UpperCAmelCase					)) + [1] + ([0] * len(_UpperCAmelCase					)) + [1, 1]
       return ([0] * len(_UpperCAmelCase					)) + [1, 1]


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       A       :       Tuple							=						[self.sep_token_id]
       A       :       int							=						[2]

       if token_ids_a is None:
           return len(token_ids_a + sep					) * [0] + cls_segment_id
       return len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1] + cls_segment_id


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       if not os.path.isdir(_UpperCAmelCase					):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
           return
       A       :       str							=						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,)


   def 					snake_case				( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       A       :       List[str]							=						super()._decode(*_UpperCAmelCase    ,   **_UpperCAmelCase					)
       A       :       int							=						text.replace(''' '''    ,   ''''''					).replace('''\u2582'''    ,   ''' '''					).replace('''\u2583'''    ,   '''\n'''					)
       return text







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


snake_case_     =	{
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	["""SpeechT5Tokenizer"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SpeechT5ForSpeechToText""",
         """SpeechT5ForSpeechToSpeech""",
         """SpeechT5ForTextToSpeech""",
         """SpeechT5Model""",
         """SpeechT5PreTrainedModel""",
         """SpeechT5HifiGan""",
     ]

if TYPE_CHECKING:
     from .configuration_speechta import (
         SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
         SpeechTaConfig,
         SpeechTaHifiGanConfig,
     )
     from .feature_extraction_speechta import SpeechTaFeatureExtractor
     from .processing_speechta import SpeechTaProcessor

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_speechta import SpeechTaTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_speechta import (
              SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
              SpeechTaForSpeechToSpeech,
              SpeechTaForSpeechToText,
              SpeechTaForTextToSpeech,
              SpeechTaHifiGan,
              SpeechTaModel,
              SpeechTaPreTrainedModel,
          )

else:
     import sys

     snake_case_     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
