"""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": "sentencepiece.bpe.model"}

__A			   = {
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

__A			   = {
    "facebook/xglm-564M": 2_0_4_8,
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

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






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

       # Compatibility with the original tokenizer
       lowercase__:   Optional[int]					=      7
       lowercase__:   Dict					=      [F"""<madeupword{i}>""" for i in range(self.num_madeup_words							)]

       lowercase__:   Tuple					=      kwargs.get('''additional_special_tokens'''    ,			[]							)
       kwargs["additional_special_tokens"] += [
           word for word in madeup_words if word not in kwargs["additional_special_tokens"]
       ]

       super().__init__(
           bos_token=_UpperCAmelCase    ,			eos_token=_UpperCAmelCase    ,			unk_token=_UpperCAmelCase    ,			sep_token=_UpperCAmelCase    ,			cls_token=_UpperCAmelCase    ,			pad_token=_UpperCAmelCase    ,			sp_model_kwargs=self.sp_model_kwargs    ,			**_UpperCAmelCase    ,			)

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

       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
       # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
       # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       lowercase__:   List[str]					=      1

       # Mimic fairseq token-to-id alignment for the first 4 token
       lowercase__:   Union[str, Any]					=      {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

       lowercase__:   Optional[Any]					=      len(self.sp_model							)
       lowercase__:   List[Any]					=      {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words							)}
       self.fairseq_tokens_to_ids.update(_UpperCAmelCase							)

       lowercase__:   Tuple					=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}






    def __getstate__(   self							):
       lowercase__:   List[Any]					=      self.__dict__.copy()
       lowercase__:   int					=      None
       lowercase__:   Optional[int]					=      self.sp_model.serialized_model_proto()
       return state






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

       # for backward compatibility
       if not hasattr(self    ,			'''sp_model_kwargs'''							):
          lowercase__:   Optional[int]					=      {}

       lowercase__:   Dict					=      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):

       if token_ids_a is None:
          return [self.sep_token_id] + token_ids_a
       lowercase__:   Union[str, Any]					=      [self.sep_token_id]
       return sep + token_ids_a + sep + sep + token_ids_a






    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 None:
          return [1] + ([0] * len(_UpperCAmelCase							))
       return [1] + ([0] * len(_UpperCAmelCase							)) + [1, 1] + ([0] * len(_UpperCAmelCase							))






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   int					=      [self.sep_token_id]

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






    @property
    def _snake_case			(   self							):
       return len(self.sp_model							) + self.fairseq_offset + self.num_madeup_words






    def _snake_case			(   self							):
       lowercase__:   Tuple					=      {self.convert_ids_to_tokens(_UpperCAmelCase							): i for i in range(self.vocab_size							)}
       vocab.update(self.added_tokens_encoder							)
       return vocab






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






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       if token in self.fairseq_tokens_to_ids:
          return self.fairseq_tokens_to_ids[token]
       lowercase__:   Dict					=      self.sp_model.PieceToId(_UpperCAmelCase							)

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       if index in self.fairseq_ids_to_tokens:
          return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Optional[int]					=      ''''''.join(_UpperCAmelCase							).replace(_UpperCAmelCase    ,			''' '''							).strip()
       return out_string






    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__:   Dict					=      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__:   List[str]					=      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__)
