

'''simple docstring'''






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

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
      from .tokenization_deberta_va import DebertaVaTokenizer
else:
      snake_case_					  =	None

snake_case_					  =	logging.get_logger(__name__)

snake_case_					  =	{'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

snake_case_					  =	{
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

snake_case_					  =	{
    'microsoft/deberta-v2-xlarge': 5_1_2,
    'microsoft/deberta-v2-xxlarge': 5_1_2,
    'microsoft/deberta-v2-xlarge-mnli': 5_1_2,
    'microsoft/deberta-v2-xxlarge-mnli': 5_1_2,
}

snake_case_					  =	{
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase				):
    _A         =							VOCAB_FILES_NAMES
    _A         =							PRETRAINED_VOCAB_FILES_MAP
    _A         =							PRETRAINED_INIT_CONFIGURATION
    _A         =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A         =							DebertaVaTokenizer




    def __init__( self    ,					lowercase__=None    ,					lowercase__=None    ,					lowercase__=False    ,					lowercase__=False    ,					lowercase__="[CLS]"    ,					lowercase__="[SEP]"    ,					lowercase__="[UNK]"    ,					lowercase__="[SEP]"    ,					lowercase__="[PAD]"    ,					lowercase__="[CLS]"    ,					lowercase__="[MASK]"    ,					**lowercase__    ,					):






          """simple docstring"""






          super().__init__(
              lowercase__    ,					tokenizer_file=lowercase__    ,					do_lower_case=lowercase__    ,					bos_token=lowercase__    ,					eos_token=lowercase__    ,					unk_token=lowercase__    ,					sep_token=lowercase__    ,					pad_token=lowercase__    ,					cls_token=lowercase__    ,					mask_token=lowercase__    ,					split_by_punct=lowercase__    ,					**lowercase__    ,					)

          SCREAMING_SNAKE_CASE_				:      List[str]					=	do_lower_case
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	split_by_punct
          SCREAMING_SNAKE_CASE_				:      int					=	vocab_file
          SCREAMING_SNAKE_CASE_				:      List[str]					=	False if not self.vocab_file else True




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__=None      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__=None    ,					lowercase__=False      ):






          """simple docstring"""






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

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




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__=None      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Dict					=	[self.sep_token_id]
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	[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   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__ = None      ):






          """simple docstring"""






          if not self.can_save_slow_tokenizer:
                raise ValueError(
                    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                    "tokenizer."      )

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

          if os.path.abspath(self.vocab_file      ) != os.path.abspath(lowercase__      ):
                copyfile(self.vocab_file    ,					lowercase__      )

          return (out_vocab_file,)




'''simple docstring'''






import functools
import operator

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


snake_case_					  =	logging.get_logger(__name__)

snake_case_					  =	{
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase				):
    _A         =							"hubert"




    def __init__( self    ,					lowercase__=32    ,					lowercase__=768    ,					lowercase__=12    ,					lowercase__=12    ,					lowercase__=3072    ,					lowercase__="gelu"    ,					lowercase__=0.1    ,					lowercase__=0.1    ,					lowercase__=0.1    ,					lowercase__=True    ,					lowercase__=0.0    ,					lowercase__=0.1    ,					lowercase__=0.1    ,					lowercase__=0.02    ,					lowercase__=1e-5    ,					lowercase__="group"    ,					lowercase__="gelu"    ,					lowercase__=(512, 512, 512, 512, 512, 512, 512)    ,					lowercase__=(5, 2, 2, 2, 2, 2, 2)    ,					lowercase__=(10, 3, 3, 3, 3, 2, 2)    ,					lowercase__=False    ,					lowercase__=128    ,					lowercase__=16    ,					lowercase__=False    ,					lowercase__=True    ,					lowercase__=0.05    ,					lowercase__=10    ,					lowercase__=2    ,					lowercase__=0.0    ,					lowercase__=10    ,					lowercase__=0    ,					lowercase__="sum"    ,					lowercase__=False    ,					lowercase__=False    ,					lowercase__=256    ,					lowercase__=0    ,					lowercase__=1    ,					lowercase__=2    ,					**lowercase__    ,					):






          """simple docstring"""






          super().__init__(**lowercase__    ,					pad_token_id=lowercase__    ,					bos_token_id=lowercase__    ,					eos_token_id=lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	hidden_size
          SCREAMING_SNAKE_CASE_				:      List[str]					=	feat_extract_norm
          SCREAMING_SNAKE_CASE_				:      Dict					=	feat_extract_activation
          SCREAMING_SNAKE_CASE_				:      str					=	list(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      int					=	list(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	list(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      List[str]					=	conv_bias
          SCREAMING_SNAKE_CASE_				:      str					=	num_conv_pos_embeddings
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	num_conv_pos_embedding_groups
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	len(self.conv_dim      )
          SCREAMING_SNAKE_CASE_				:      Any					=	num_hidden_layers
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	intermediate_size
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	hidden_act
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	num_attention_heads
          SCREAMING_SNAKE_CASE_				:      Any					=	hidden_dropout
          SCREAMING_SNAKE_CASE_				:      List[str]					=	attention_dropout
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	activation_dropout
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	feat_proj_layer_norm
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	feat_proj_dropout
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	final_dropout
          SCREAMING_SNAKE_CASE_				:      Dict					=	layerdrop
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	layer_norm_eps
          SCREAMING_SNAKE_CASE_				:      Tuple					=	initializer_range
          SCREAMING_SNAKE_CASE_				:      List[str]					=	vocab_size
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	do_stable_layer_norm
          SCREAMING_SNAKE_CASE_				:      List[str]					=	use_weighted_layer_sum
          SCREAMING_SNAKE_CASE_				:      Any					=	classifier_proj_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)`, but is `len(config.conv_dim) ="
                    F" {len(self.conv_dim      )}`, `len(config.conv_stride) = {len(self.conv_stride      )}`,"
                    F" `len(config.conv_kernel) = {len(self.conv_kernel      )}`."      )

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

          # ctc loss
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	ctc_loss_reduction
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	ctc_zero_infinity







    @property
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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

