"""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			   = logging.get_logger(__name__)


__A			   = "▁"

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

__A			   = {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

__A			   = {
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:List[Any]						    = VOCAB_FILES_NAMES
    _UpperCAmelCase	:int						    = PRETRAINED_VOCAB_FILES_MAP
    _UpperCAmelCase	:str						    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _UpperCAmelCase	:Any						    = ["input_ids", "attention_mask"]






    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase="</s>"    ,			_UpperCAmelCase="<unk>"    ,			_UpperCAmelCase=[]    ,			_UpperCAmelCase = None    ,			**_UpperCAmelCase    ,			):
       lowercase__:   int					=      {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           eos_token=_UpperCAmelCase    ,			unk_token=_UpperCAmelCase    ,			additional_special_tokens=_UpperCAmelCase    ,			sp_model_kwargs=self.sp_model_kwargs    ,			**_UpperCAmelCase    ,			)

       lowercase__:   int					=      vocab_file
       lowercase__:   Union[str, Any]					=      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
       self.sp_model.Load(_UpperCAmelCase							)






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






    def _snake_case			(   self							):
       lowercase__:   Optional[int]					=      {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							):
       lowercase__:   Tuple					=      self.__dict__.copy()
       lowercase__:   Optional[int]					=      None
       return state






    def __setstate__(   self    ,			_UpperCAmelCase							):
       lowercase__:   Optional[Any]					=      d

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

       lowercase__:   Tuple					=      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
       self.sp_model.Load(self.vocab_file							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       return self.sp_model.encode(_UpperCAmelCase    ,			out_type=_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       return self.sp_model.piece_to_id(_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       if index < self.sp_model.get_piece_size():
          lowercase__:   Any					=      self.sp_model.IdToPiece(_UpperCAmelCase							)
       return token






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   str					=      []
       lowercase__:   List[str]					=      ''''''
       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
             lowercase__:   str					=      []
          else:
             current_sub_tokens.append(_UpperCAmelCase							)
       out_string += self.sp_model.decode(_UpperCAmelCase							)
       return out_string.strip()






    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
       lowercase__:   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:
             lowercase__:   Optional[Any]					=      self.sp_model.serialized_model_proto()
             fi.write(_UpperCAmelCase							)

       return (out_vocab_file,)

"""simple docstring"""






from typing import TYPE_CHECKING

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


__A			   = {
    "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:
       __A			   = ["SpeechT5Tokenizer"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "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

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