




import os
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


a_   		=     logging.get_logger(__name__)

a_   		=     {"vocab_file": "sentencepiece.bpe.model"}

a_   		=     {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

a_   		=     {
    "camembert-base": 512,
}

a_   		=     "▁"

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Any    =					VOCAB_FILES_NAMES
      lowerCAmelCase__			: Dict    =					PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__			: Dict    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__			: Optional[Any]    =					["input_ids", "attention_mask"]







      def __init__(     self					: Any ,       snake_case					: str ,       snake_case					: str="<s>" ,       snake_case					: Tuple="</s>" ,       snake_case					: Union[str, Any]="</s>" ,       snake_case					: Dict="<s>" ,       snake_case					: int="<unk>" ,       snake_case					: Dict="<pad>" ,       snake_case					: int="<mask>" ,       snake_case					: Dict=["<s>NOTUSED", "</s>NOTUSED"] ,       snake_case					: Optional[Dict[str, Any]] = None ,       **snake_case					: Optional[Any] ,       ):
          # Mask token behave like a normal word, i.e. include the space before it
          __UpperCamelCase  		=						AddedToken(snake_case ,       lstrip=snake_case ,       rstrip=snake_case				) if isinstance(snake_case ,       snake_case				) else mask_token

          __UpperCamelCase  		=						{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=snake_case ,       eos_token=snake_case ,       unk_token=snake_case ,       sep_token=snake_case ,       cls_token=snake_case ,       pad_token=snake_case ,       mask_token=snake_case ,       additional_special_tokens=snake_case ,       sp_model_kwargs=self.sp_model_kwargs ,       **snake_case ,       )
          __UpperCamelCase  		=						spm.SentencePieceProcessor(**self.sp_model_kwargs				)
          self.sp_model.Load(str(snake_case				)				)
          __UpperCamelCase  		=						vocab_file
          # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
          # sentencepiece vocabulary (this is the case for <s> and </s>
          __UpperCamelCase  		=						{'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
          __UpperCamelCase  		=						len(self.fairseq_tokens_to_ids				)
          __UpperCamelCase  		=						len(self.sp_model				) + len(self.fairseq_tokens_to_ids				)
          __UpperCamelCase  		=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: List[int] ,       snake_case					: Optional[List[int]] = None				):

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







      def       snake_case	(     self					: List[str] ,       snake_case					: List[int] ,       snake_case					: Optional[List[int]] = None ,       snake_case					: bool = False				):
          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=snake_case ,       token_ids_a=snake_case ,       already_has_special_tokens=snake_case				)

          if token_ids_a is None:
              return [1] + ([0] * len(snake_case				)) + [1]
          return [1] + ([0] * len(snake_case				)) + [1, 1] + ([0] * len(snake_case				)) + [1]







      def       snake_case	(     self					: Optional[int] ,       snake_case					: List[int] ,       snake_case					: Optional[List[int]] = None				):
          __UpperCamelCase  		=						[self.sep_token_id]
          __UpperCamelCase  		=						[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 + sep + token_ids_a + sep				) * [0]







      @property
      def       snake_case	(     self					: Optional[Any]				):
          return len(self.fairseq_tokens_to_ids				) + len(self.sp_model				)







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						{self.convert_ids_to_tokens(snake_case				): i for i in range(self.vocab_size				)}
          vocab.update(self.added_tokens_encoder				)
          return vocab







      def       snake_case	(     self					: Optional[Any] ,       snake_case					: str				):
          return self.sp_model.encode(snake_case ,       out_type=snake_case				)







      def       snake_case	(     self					: Dict ,       snake_case					: Tuple				):
          if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
          elif self.sp_model.PieceToId(snake_case				) == 0:
              # Convert sentence piece unk token to fairseq unk token index
              return self.unk_token_id
          return self.fairseq_offset + self.sp_model.PieceToId(snake_case				)







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: Dict				):
          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					: Optional[Any] ,       snake_case					: int				):
          __UpperCamelCase  		=						[]
          __UpperCamelCase  		=						''''''
          __UpperCamelCase  		=						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(snake_case				) + token
                  __UpperCamelCase  		=						True
                  __UpperCamelCase  		=						[]
              else:
                  current_sub_tokens.append(snake_case				)
                  __UpperCamelCase  		=						False
          out_string += self.sp_model.decode(snake_case				)
          return out_string.strip()







      def __getstate__(     self					: Optional[Any]				):
          __UpperCamelCase  		=						self.__dict__.copy()
          __UpperCamelCase  		=						None
          return state







      def __setstate__(     self					: int ,       snake_case					: Optional[int]				):
          __UpperCamelCase  		=						d

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

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







      def       snake_case	(     self					: str ,       snake_case					: str ,       snake_case					: Optional[str] = None				):
          if not os.path.isdir(snake_case				):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
              return
          __UpperCamelCase  		=						os.path.join(
              snake_case ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

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

          return (out_vocab_file,)












from ....configuration_utils import PretrainedConfig
from ....utils import logging


a_   		=     logging.get_logger(__name__)

a_   		=     {
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: int    =					"mctct"







      def __init__(     self					: List[Any] ,       snake_case					: Optional[Any]=8065 ,       snake_case					: Optional[int]=1536 ,       snake_case					: Any=36 ,       snake_case					: List[str]=6144 ,       snake_case					: Dict=4 ,       snake_case					: str=384 ,       snake_case					: List[str]=920 ,       snake_case					: Dict=1E-5 ,       snake_case					: Union[str, Any]=0.3 ,       snake_case					: Optional[Any]="relu" ,       snake_case					: str=0.02 ,       snake_case					: Optional[int]=0.3 ,       snake_case					: int=0.3 ,       snake_case					: Any=1 ,       snake_case					: int=0 ,       snake_case					: Union[str, Any]=2 ,       snake_case					: List[Any]=1 ,       snake_case					: Dict=0.3 ,       snake_case					: int=1 ,       snake_case					: Optional[int]=(7,) ,       snake_case					: List[Any]=(3,) ,       snake_case					: Optional[int]=80 ,       snake_case					: List[str]=1 ,       snake_case					: int=None ,       snake_case					: List[str]="sum" ,       snake_case					: Tuple=False ,       **snake_case					: List[str] ,       ):
          super().__init__(**snake_case ,       pad_token_id=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case				)
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						attention_head_dim
          __UpperCamelCase  		=						max_position_embeddings
          __UpperCamelCase  		=						layer_norm_eps
          __UpperCamelCase  		=						layerdrop
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						pad_token_id
          __UpperCamelCase  		=						bos_token_id
          __UpperCamelCase  		=						eos_token_id
          __UpperCamelCase  		=						conv_glu_dim
          __UpperCamelCase  		=						conv_dropout
          __UpperCamelCase  		=						num_conv_layers
          __UpperCamelCase  		=						input_feat_per_channel
          __UpperCamelCase  		=						input_channels
          __UpperCamelCase  		=						conv_channels
          __UpperCamelCase  		=						ctc_loss_reduction
          __UpperCamelCase  		=						ctc_zero_infinity

          # prevents config testing fail with exporting to json
          __UpperCamelCase  		=						list(snake_case				)
          __UpperCamelCase  		=						list(snake_case				)

          if len(self.conv_kernel				) != self.num_conv_layers:
              raise ValueError(
                  '''Configuration for convolutional module is incorrect. '''
                  '''It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '''
                  F"but is `len(config.conv_kernel) = {len(self.conv_kernel				)}`, "
                  F"`config.num_conv_layers = {self.num_conv_layers}`."				)






