
"""simple docstring"""




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


_A			:       Any   		=       logging.get_logger(__name__)

_A			:       Any   		=       {
    """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 		a__						(      a_						):
   __lowerCAmelCase            =				"""mctct"""
   def __init__(			self	,    _a=8_065	,    _a=1_536	,    _a=36	,    _a=6_144	,    _a=4	,    _a=384	,    _a=920	,    _a=1E-5	,    _a=0.3	,    _a="relu"	,    _a=0.0_2	,    _a=0.3	,    _a=0.3	,    _a=1	,    _a=0	,    _a=2	,    _a=1	,    _a=0.3	,    _a=1	,    _a=(7,)	,    _a=(3,)	,    _a=80	,    _a=1	,    _a=None	,    _a="sum"	,    _a=False	,    **_a	,    ):
          super().__init__(**_a	,    pad_token_id=_a	,    bos_token_id=_a	,    eos_token_id=_a				)
          lowercase    :				Optional[Any]       		=							vocab_size
          lowercase    :				Optional[Any]       		=							hidden_size
          lowercase    :				Dict       		=							num_hidden_layers
          lowercase    :				Any       		=							intermediate_size
          lowercase    :				List[str]       		=							num_attention_heads
          lowercase    :				int       		=							attention_head_dim
          lowercase    :				Dict       		=							max_position_embeddings
          lowercase    :				Optional[int]       		=							layer_norm_eps
          lowercase    :				Optional[int]       		=							layerdrop
          lowercase    :				Optional[int]       		=							hidden_act
          lowercase    :				str       		=							initializer_range
          lowercase    :				Optional[Any]       		=							hidden_dropout_prob
          lowercase    :				Tuple       		=							attention_probs_dropout_prob
          lowercase    :				Optional[int]       		=							pad_token_id
          lowercase    :				Optional[int]       		=							bos_token_id
          lowercase    :				Dict       		=							eos_token_id
          lowercase    :				Union[str, Any]       		=							conv_glu_dim
          lowercase    :				Dict       		=							conv_dropout
          lowercase    :				Optional[Any]       		=							num_conv_layers
          lowercase    :				int       		=							input_feat_per_channel
          lowercase    :				Optional[Any]       		=							input_channels
          lowercase    :				Tuple       		=							conv_channels
          lowercase    :				Any       		=							ctc_loss_reduction
          lowercase    :				Union[str, Any]       		=							ctc_zero_infinity

          # prevents config testing fail with exporting to json
          lowercase    :				str       		=							list(_a				)
          lowercase    :				Tuple       		=							list(_a				)

          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}`."""				)




"""simple docstring"""




import os
import re
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			:       List[str]   		=       logging.get_logger(__name__)

_A			:       List[str]   		=       {"""vocab_file""": """spiece.model"""}

_A			:       List[Any]   		=       {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

_A			:       str   		=       {
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}

class 		a__						(      a_						):
   __lowerCAmelCase            =				VOCAB_FILES_NAMES
   __lowerCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase            =				["""input_ids""", """attention_mask"""]
   __lowerCAmelCase            =				[]
   def __init__(			self	,    _a	,    _a="<unk>"	,    _a="<s>"	,    _a="</s>"	,    _a="<pad>"	,    _a="[SEP]"	,    _a="[MASK]"	,    _a="[CLS]"	,    _a = None	,    **_a	,    ):
          lowercase    :				Dict       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else bos_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else eos_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else unk_token
          lowercase    :				List[Any]       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else pad_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else cls_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else sep_token

          # Mask token behave like a normal word, i.e. include the space before it
          lowercase    :				List[Any]       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else mask_token

          lowercase    :				Optional[int]       		=							{} if sp_model_kwargs is None else sp_model_kwargs

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

          lowercase    :				Optional[Any]       		=							vocab_file

          lowercase    :				List[str]       		=							spm.SentencePieceProcessor(**self.sp_model_kwargs				)
          self.sp_model.Load(_a				)
   @property
   def 			__magic_name__			(			self				):
          return self.sp_model.get_piece_size()
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							{self.convert_ids_to_tokens(_a				): i for i in range(self.vocab_size				)}
          vocab.update(self.added_tokens_encoder				)
          return vocab
   def __getstate__(			self				):
          lowercase    :				Union[str, Any]       		=							self.__dict__.copy()
          lowercase    :				Dict       		=							None
          return state
   def __setstate__(			self	,    _a				):
          lowercase    :				str       		=							d

          # for backward compatibility
          if not hasattr(self	,    "sp_model_kwargs"				):
                 lowercase    :				List[str]       		=							{}

          lowercase    :				Any       		=							spm.SentencePieceProcessor(**self.sp_model_kwargs				)
          self.sp_model.Load(self.vocab_file				)
   def 			__magic_name__			(			self	,    _a				):
          return self.sp_model.encode(_a	,    out_type=_a				)
   def 			__magic_name__			(			self	,    _a				):
          return self.sp_model.piece_to_id(_a				)
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				Union[str, Any]       		=							self.sp_model.IdToPiece(_a				)
          return token
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				List[Any]       		=							[]
          lowercase    :				List[Any]       		=							""
          lowercase    :				List[Any]       		=							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(_a				) + token
                        lowercase    :				int       		=							True
                        lowercase    :				Union[str, Any]       		=							[]
                 else:
                        current_sub_tokens.append(_a				)
                        lowercase    :				int       		=							False
          out_string += self.sp_model.decode(_a				)
          return out_string.strip()
   def 			__magic_name__			(			self	,    _a	,    _a = False	,    _a = None	,    _a = True	,    **_a	,    ):
          lowercase    :				int       		=							kwargs.pop("use_source_tokenizer"	,    _a				)

          lowercase    :				Union[str, Any]       		=							self.convert_ids_to_tokens(_a	,    skip_special_tokens=_a				)

          # To avoid mixing byte-level and unicode for byte-level BPT
          # we need to build string separately for added tokens and byte-level tokens
          # cf. https://github.com/huggingface/transformers/issues/1133
          lowercase    :				Any       		=							[]
          lowercase    :				Dict       		=							[]
          for token in filtered_tokens:
                 if skip_special_tokens and token in self.all_special_ids:
                        continue
                 if token in self.added_tokens_encoder:
                        if current_sub_text:
                               sub_texts.append(self.convert_tokens_to_string(_a				)				)
                               lowercase    :				int       		=							[]
                        sub_texts.append(_a				)
                 else:
                        current_sub_text.append(_a				)
          if current_sub_text:
                 sub_texts.append(self.convert_tokens_to_string(_a				)				)

          # Mimic the behavior of the Rust tokenizer:
          # No space before [MASK] and [SEP]
          if spaces_between_special_tokens:
                 lowercase    :				Dict       		=							re.sub(R" (\[(MASK|SEP)\])"	,    R"\1"	,    " ".join(_a				)				)
          else:
                 lowercase    :				Union[str, Any]       		=							"".join(_a				)

          lowercase    :				int       		=							(
              clean_up_tokenization_spaces
              if clean_up_tokenization_spaces is not None
              else self.clean_up_tokenization_spaces
          )
          if clean_up_tokenization_spaces:
                 lowercase    :				Union[str, Any]       		=							self.clean_up_tokenization(_a				)
                 return clean_text
          else:
                 return text
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          if not os.path.isdir(_a				):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
                 return
          lowercase    :				List[Any]       		=							os.path.join(
              _a	,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

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

          return (out_vocab_file,)
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          lowercase    :				List[str]       		=							[self.cls_token_id]
          lowercase    :				Tuple       		=							[self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep
   def 			__magic_name__			(			self	,    _a	,    _a = None	,    _a = False				):
          if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=_a	,    token_ids_a=_a	,    already_has_special_tokens=_a				)

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

   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Tuple       		=							[self.sep_token_id]
          lowercase    :				Tuple       		=							[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]


