



"""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 logging


UpperCAmelCase						:		str			= logging.get_logger(__name__)
UpperCAmelCase						:		Optional[Any]			= {"vocab_file": "spiece.model"}

UpperCAmelCase						:		str			= {
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

UpperCAmelCase						:		Optional[Any]			= {
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

UpperCAmelCase						:		Union[str, Any]			= "▁"


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   VOCAB_FILES_NAMES
    lowercase__      =   PRETRAINED_VOCAB_FILES_MAP
    lowercase__      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

    def __init__(				self			:				Any ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				Tuple=True ,  lowerCAmelCase_			:				List[str]=True ,  lowerCAmelCase_			:				Union[str, Any]=False ,  lowerCAmelCase_			:				str="[CLS]" ,  lowerCAmelCase_			:				int="[SEP]" ,  lowerCAmelCase_			:				Dict="<unk>" ,  lowerCAmelCase_			:				Dict="[SEP]" ,  lowerCAmelCase_			:				Any="<pad>" ,  lowerCAmelCase_			:				Optional[int]="[CLS]" ,  lowerCAmelCase_			:				Any="[MASK]" ,  lowerCAmelCase_			:				Optional[Dict[str, Any]] = None ,  **lowerCAmelCase_			:				str ,  ):







      """simple docstring"""







      lowercase_								= (
          AddedToken(lowerCAmelCase_ ,  lstrip=lowerCAmelCase_ ,  rstrip=lowerCAmelCase_ ,  normalized=lowerCAmelCase_)
          if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_)
          else mask_token
      )

      lowercase_								= {} if sp_model_kwargs is None else sp_model_kwargs

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

      lowercase_								= do_lower_case
      lowercase_								= remove_space
      lowercase_								= keep_accents
      lowercase_								= vocab_file

      lowercase_								= spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(lowerCAmelCase_)

    @property
    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







      return len(self.sp_model)

    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      lowercase_								= {self.convert_ids_to_tokens(lowerCAmelCase_): i for i in range(self.vocab_size)}
      vocab.update(self.added_tokens_encoder)
      return vocab

    def __getstate__(				self			:				Tuple):







      """simple docstring"""







      lowercase_								= self.__dict__.copy()
      lowercase_								= None
      return state

    def __setstate__(				self			:				Optional[Any] ,  lowerCAmelCase_			:				int):







      """simple docstring"""







      lowercase_								= d

      # for backward compatibility
      if not hasattr(self ,  """sp_model_kwargs"""):
        lowercase_								= {}

      lowercase_								= spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(self.vocab_file)

    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				Optional[int]):







      """simple docstring"""







      if self.remove_space:
        lowercase_								= """ """.join(inputs.strip().split())
      else:
        lowercase_								= inputs
      lowercase_								= outputs.replace("""``""" ,  """\"""").replace("""''""" ,  """\"""")

      if not self.keep_accents:
        lowercase_								= unicodedata.normalize("""NFKD""" ,  lowerCAmelCase_)
        lowercase_								= """""".join([c for c in outputs if not unicodedata.combining(lowerCAmelCase_)])
      if self.do_lower_case:
        lowercase_								= outputs.lower()

      return outputs

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				str):







      """simple docstring"""







      lowercase_								= self.preprocess_text(lowerCAmelCase_)
      lowercase_								= self.sp_model.encode(lowerCAmelCase_ ,  out_type=lowerCAmelCase_)
      lowercase_								= []
      for piece in pieces:
        if len(lowerCAmelCase_) > 1 and piece[-1] == str(""",""") and piece[-2].isdigit():
          lowercase_								= self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCAmelCase_ ,  """"""))
          if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
            if len(cur_pieces[0]) == 1:
              lowercase_								= cur_pieces[1:]
            else:
              lowercase_								= cur_pieces[0][1:]
          cur_pieces.append(piece[-1])
          new_pieces.extend(lowerCAmelCase_)
        else:
          new_pieces.append(lowerCAmelCase_)

      return new_pieces

    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				int):







      """simple docstring"""







      return self.sp_model.PieceToId(lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				Dict):







      """simple docstring"""







      return self.sp_model.IdToPiece(lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				int):







      """simple docstring"""







      lowercase_								= []
      lowercase_								= """"""
      lowercase_								= False
      for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
          if not prev_is_special:
            out_string += " "
          out_string += self.sp_model.decode(lowerCAmelCase_) + token
          lowercase_								= True
          lowercase_								= []
        else:
          current_sub_tokens.append(lowerCAmelCase_)
          lowercase_								= False
      out_string += self.sp_model.decode(lowerCAmelCase_)
      return out_string.strip()

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				List[int] ,  lowerCAmelCase_			:				Optional[List[int]] = None):







      """simple docstring"""







      lowercase_								= [self.sep_token_id]
      lowercase_								= [self.cls_token_id]
      if token_ids_a is None:
        return cls + token_ids_a + sep
      return cls + token_ids_a + sep + token_ids_a + sep

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				List[int] ,  lowerCAmelCase_			:				Optional[List[int]] = None ,  lowerCAmelCase_			:				bool = False):







      """simple docstring"""







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

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

    def 					_UpperCAmelCase	(				self			:				List[str] ,  lowerCAmelCase_			:				List[int] ,  lowerCAmelCase_			:				Optional[List[int]] = None):







      """simple docstring"""







      lowercase_								= [self.sep_token_id]
      lowercase_								= [self.cls_token_id]

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




    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Optional[str] = None):







      """simple docstring"""







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

      if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase_) and os.path.isfile(self.vocab_file):
        copyfile(self.vocab_file ,  lowerCAmelCase_)
      elif not os.path.isfile(self.vocab_file):
        with open(lowerCAmelCase_ ,  """wb""") as fi:
          lowercase_								= self.sp_model.serialized_model_proto()
          fi.write(lowerCAmelCase_)

      return (out_vocab_file,)






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase						:		Optional[Any]			= {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		str			= [
           "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "WavLMForAudioFrameClassification",
           "WavLMForCTC",
           "WavLMForSequenceClassification",
           "WavLMForXVector",
           "WavLMModel",
           "WavLMPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavlm import (
                  WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavLMForAudioFrameClassification,
                  WavLMForCTC,
                  WavLMForSequenceClassification,
                  WavLMForXVector,
                  WavLMModel,
                  WavLMPreTrainedModel,
              )

else:
       import sys

       UpperCAmelCase						:		Optional[Any]			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

