"""simple docstring"""




from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class 		__A				(					SCREAMING_SNAKE_CASE_						):
     _UpperCamelCase				:     int						   =						DistilBertTokenizer
     _UpperCamelCase				:     int						   =						DistilBertTokenizerFast
     _UpperCamelCase				:     Optional[Any]						   =						True






     @slow
     def        __A    (						self    ):
       _lowerCAmelCase		:			Dict         =	DistilBertTokenizer.from_pretrained("""distilbert-base-uncased"""    )

       _lowerCAmelCase		:			Union[str, Any]         =	tokenizer.encode("""sequence builders"""							,	add_special_tokens=_lowerCAmelCase    )
       _lowerCAmelCase		:			str         =	tokenizer.encode("""multi-sequence build"""							,	add_special_tokens=_lowerCAmelCase    )

       _lowerCAmelCase		:			Any         =	tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase    )
       _lowerCAmelCase		:			int         =	tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase							,	_lowerCAmelCase    )

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
           tokenizer.sep_token_id
       ]








import functools
import operator

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


SCREAMING_SNAKE_CASE						:List[str]			=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:Dict			=  {
    '''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'wav2vec2'



  def __init__(  self							:							Any     ,					_lowerCAmelCase							:							Optional[Any]=3_2     ,					_lowerCAmelCase							:							List[Any]=7_6_8     ,					_lowerCAmelCase							:							Dict=1_2     ,					_lowerCAmelCase							:							Tuple=1_2     ,					_lowerCAmelCase							:							Optional[int]=3_0_7_2     ,					_lowerCAmelCase							:							Any="gelu"     ,					_lowerCAmelCase							:							List[str]=0.1     ,					_lowerCAmelCase							:							Dict=0.1     ,					_lowerCAmelCase							:							Union[str, Any]=0.1     ,					_lowerCAmelCase							:							Dict=0.0     ,					_lowerCAmelCase							:							Optional[int]=0.0     ,					_lowerCAmelCase							:							List[Any]=0.1     ,					_lowerCAmelCase							:							List[Any]=0.1     ,					_lowerCAmelCase							:							Dict=0.02     ,					_lowerCAmelCase							:							Any=1e-5     ,					_lowerCAmelCase							:							Any="group"     ,					_lowerCAmelCase							:							Union[str, Any]="gelu"     ,					_lowerCAmelCase							:							str=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)     ,					_lowerCAmelCase							:							Optional[Any]=(5, 2, 2, 2, 2, 2, 2)     ,					_lowerCAmelCase							:							Optional[Any]=(1_0, 3, 3, 3, 3, 2, 2)     ,					_lowerCAmelCase							:							int=False     ,					_lowerCAmelCase							:							int=1_2_8     ,					_lowerCAmelCase							:							Union[str, Any]=1_6     ,					_lowerCAmelCase							:							Union[str, Any]=False     ,					_lowerCAmelCase							:							List[Any]=True     ,					_lowerCAmelCase							:							Dict=0.05     ,					_lowerCAmelCase							:							List[str]=1_0     ,					_lowerCAmelCase							:							Tuple=2     ,					_lowerCAmelCase							:							str=0.0     ,					_lowerCAmelCase							:							Union[str, Any]=1_0     ,					_lowerCAmelCase							:							int=0     ,					_lowerCAmelCase							:							Optional[int]=3_2_0     ,					_lowerCAmelCase							:							Optional[Any]=2     ,					_lowerCAmelCase							:							str=0.1     ,					_lowerCAmelCase							:							Optional[Any]=1_0_0     ,					_lowerCAmelCase							:							Tuple=2_5_6     ,					_lowerCAmelCase							:							Union[str, Any]=2_5_6     ,					_lowerCAmelCase							:							Union[str, Any]=0.1     ,					_lowerCAmelCase							:							Optional[Any]="sum"     ,					_lowerCAmelCase							:							List[str]=False     ,					_lowerCAmelCase							:							List[Any]=False     ,					_lowerCAmelCase							:							List[str]=2_5_6     ,					_lowerCAmelCase							:							List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)     ,					_lowerCAmelCase							:							List[str]=(5, 3, 3, 1, 1)     ,					_lowerCAmelCase							:							Union[str, Any]=(1, 2, 3, 1, 1)     ,					_lowerCAmelCase							:							Optional[Any]=5_1_2     ,					_lowerCAmelCase							:							Any=0     ,					_lowerCAmelCase							:							Optional[int]=1     ,					_lowerCAmelCase							:							List[str]=2     ,					_lowerCAmelCase							:							Tuple=False     ,					_lowerCAmelCase							:							Any=3     ,					_lowerCAmelCase							:							List[Any]=2     ,					_lowerCAmelCase							:							Optional[Any]=3     ,					_lowerCAmelCase							:							List[str]=None     ,					_lowerCAmelCase							:							str=None     ,					**_lowerCAmelCase							:							Tuple     ,					) ->			Optional[int]:




     """simple docstring"""
     super().__init__(**_lowerCAmelCase     ,					pad_token_id=_lowerCAmelCase     ,					bos_token_id=_lowerCAmelCase     ,					eos_token_id=_lowerCAmelCase	)
     snake_case_            =       hidden_size
     snake_case_            =       feat_extract_norm
     snake_case_            =       feat_extract_activation
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       conv_bias
     snake_case_            =       num_conv_pos_embeddings
     snake_case_            =       num_conv_pos_embedding_groups
     snake_case_            =       len(self.conv_dim	)
     snake_case_            =       num_hidden_layers
     snake_case_            =       intermediate_size
     snake_case_            =       hidden_act
     snake_case_            =       num_attention_heads
     snake_case_            =       hidden_dropout
     snake_case_            =       attention_dropout
     snake_case_            =       activation_dropout
     snake_case_            =       feat_proj_dropout
     snake_case_            =       final_dropout
     snake_case_            =       layerdrop
     snake_case_            =       layer_norm_eps
     snake_case_            =       initializer_range
     snake_case_            =       vocab_size
     snake_case_            =       do_stable_layer_norm
     snake_case_            =       use_weighted_layer_sum

     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
     snake_case_            =       apply_spec_augment
     snake_case_            =       mask_time_prob
     snake_case_            =       mask_time_length
     snake_case_            =       mask_time_min_masks
     snake_case_            =       mask_feature_prob
     snake_case_            =       mask_feature_length
     snake_case_            =       mask_feature_min_masks

     # parameters for pretraining with codevector quantized representations
     snake_case_            =       num_codevectors_per_group
     snake_case_            =       num_codevector_groups
     snake_case_            =       contrastive_logits_temperature
     snake_case_            =       feat_quantizer_dropout
     snake_case_            =       num_negatives
     snake_case_            =       codevector_dim
     snake_case_            =       proj_codevector_dim
     snake_case_            =       diversity_loss_weight

     # ctc loss
     snake_case_            =       ctc_loss_reduction
     snake_case_            =       ctc_zero_infinity

     # adapter
     snake_case_            =       add_adapter
     snake_case_            =       adapter_kernel_size
     snake_case_            =       adapter_stride
     snake_case_            =       num_adapter_layers
     snake_case_            =       output_hidden_size or hidden_size
     snake_case_            =       adapter_attn_dim

     # SequenceClassification-specific parameter. Feel free to ignore for other classes.
     snake_case_            =       classifier_proj_size

     # XVector-specific parameters. Feel free to ignore for other classes.
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       xvector_output_dim



  @property
  def        lowerCAmelCase__	(  self							:							str	) ->			int:




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




