

'''simple docstring'''



import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase						:  Optional[int]							       =				logging.get_logger(__name__)

_UpperCamelCase						:  Optional[Any]							       =				{"vocab_file": "sentencepiece.model"}

_UpperCamelCase						:  int							       =				{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

_UpperCamelCase						:  Tuple							       =				{
    "google/rembert": 256,
}



class 				_snake_case					(   __A			):
  SCREAMING_SNAKE_CASE			:       Dict				=     VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE			:       List[str]				=     PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE			:       Any				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

  def __init__(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=False      , _SCREAMING_SNAKE_CASE=True      , _SCREAMING_SNAKE_CASE=True      , _SCREAMING_SNAKE_CASE="[CLS]"      , _SCREAMING_SNAKE_CASE="[SEP]"      , _SCREAMING_SNAKE_CASE="[UNK]"      , _SCREAMING_SNAKE_CASE="[SEP]"      , _SCREAMING_SNAKE_CASE="[PAD]"      , _SCREAMING_SNAKE_CASE="[CLS]"      , _SCREAMING_SNAKE_CASE="[MASK]"      , **_SCREAMING_SNAKE_CASE      , ):





      '''simple docstring'''

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

      lowerCAmelCase 						=							do_lower_case
      lowerCAmelCase 						=							remove_space
      lowerCAmelCase 						=							keep_accents
      lowerCAmelCase 						=							vocab_file

      lowerCAmelCase 						=							spm.SentencePieceProcessor()
      self.sp_model.Load(snake_case_	)

  @property
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return len(self.sp_model	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def __getstate__(				self	):





      '''simple docstring'''

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

  def __setstate__(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							d
      lowerCAmelCase 						=							spm.SentencePieceProcessor()
      self.sp_model.Load(self.vocab_file	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=False	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.sp_model.EncodeAsPieces(snake_case_	)
      return pieces

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      return self.sp_model.PieceToId(snake_case_	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      return self.sp_model.IdToPiece(snake_case_	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.sp_model.decode_pieces(snake_case_	)
      return out_string

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE = None	):





      '''simple docstring'''

      lowerCAmelCase 						=							[self.sep_token_id]
      lowerCAmelCase 						=							[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   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE = None      , _SCREAMING_SNAKE_CASE = False	):





      '''simple docstring'''

      if already_has_special_tokens:
          if token_ids_a is not None:
              raise ValueError(
                  'You should not supply a second sequence if the provided sequence of '
                  'ids is already formatted with special tokens for the model.'	)
          return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE = None	):





      '''simple docstring'''

      lowerCAmelCase 						=							[self.sep_token_id]
      lowerCAmelCase 						=							[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   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE = None	):





      '''simple docstring'''

      if not os.path.isdir(snake_case_	):
          logger.error('Vocabulary path ({}) should be a directory'.format(snake_case_	)	)
          return
      lowerCAmelCase 						=							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_	):
          copyfile(self.vocab_file      , snake_case_	)

      return (out_vocab_file,)






"""simple docstring"""



import functools
import operator

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


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'sew'





			def __init__(			self   ,      snake_case_=32   ,      snake_case_=768   ,      snake_case_=12   ,      snake_case_=12   ,      snake_case_=3_072   ,      snake_case_=2   ,      snake_case_="gelu"   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.0   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.02   ,      snake_case_=1E-5   ,      snake_case_="group"   ,      snake_case_="gelu"   ,      snake_case_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,      snake_case_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,      snake_case_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,      snake_case_=False   ,      snake_case_=128   ,      snake_case_=16   ,      snake_case_=True   ,      snake_case_=0.05   ,      snake_case_=10   ,      snake_case_=2   ,      snake_case_=0.0   ,      snake_case_=10   ,      snake_case_=0   ,      snake_case_="mean"   ,      snake_case_=False   ,      snake_case_=False   ,      snake_case_=256   ,      snake_case_=0   ,      snake_case_=1   ,      snake_case_=2   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_   ,      pad_token_id=snake_case_   ,      bos_token_id=snake_case_   ,      eos_token_id=snake_case_							)
										A__       :     Dict								=			hidden_size
										A__       :     Dict								=			feat_extract_norm
										A__       :     str								=			feat_extract_activation
										A__       :     Optional[Any]								=			list(snake_case_							)
										A__       :     str								=			list(snake_case_							)
										A__       :     Any								=			list(snake_case_							)
										A__       :     Any								=			conv_bias
										A__       :     Any								=			num_conv_pos_embeddings
										A__       :     Any								=			num_conv_pos_embedding_groups
										A__       :     str								=			len(self.conv_dim							)
										A__       :     Tuple								=			num_hidden_layers
										A__       :     int								=			intermediate_size
										A__       :     Union[str, Any]								=			squeeze_factor
										A__       :     Union[str, Any]								=			hidden_act
										A__       :     List[str]								=			num_attention_heads
										A__       :     List[str]								=			hidden_dropout
										A__       :     Dict								=			attention_dropout
										A__       :     Tuple								=			activation_dropout
										A__       :     Optional[int]								=			feat_proj_dropout
										A__       :     Optional[Any]								=			final_dropout
										A__       :     int								=			layerdrop
										A__       :     List[Any]								=			layer_norm_eps
										A__       :     int								=			initializer_range
										A__       :     Dict								=			vocab_size

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

										# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
										A__       :     Optional[Any]								=			apply_spec_augment
										A__       :     int								=			mask_time_prob
										A__       :     Tuple								=			mask_time_length
										A__       :     Optional[Any]								=			mask_time_min_masks
										A__       :     Any								=			mask_feature_prob
										A__       :     List[Any]								=			mask_feature_length
										A__       :     Any								=			mask_feature_min_masks

										# ctc loss
										A__       :     str								=			ctc_loss_reduction
										A__       :     List[Any]								=			ctc_zero_infinity

										# sequence classification
										A__       :     Union[str, Any]								=			use_weighted_layer_sum
										A__       :     str								=			classifier_proj_size





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return functools.reduce(operator.mul   ,      self.conv_stride   ,      1							)





