
'''simple docstring'''





from __future__ import annotations







def snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					Optional[int]				       =							0.00
  UpperCAmelCase__      :					Tuple				       =							0
  for resistor in resistors:
    if resistor <= 0:
      UpperCAmelCase__      :					List[Any]				       =							F"""Resistor at index {index} has a negative or zero value!"""
      raise ValueError(_lowercase		)
    first_sum += 1 / float(_lowercase		)
    index += 1
  return 1 / first_sum







def snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					Tuple				       =							0.00
  UpperCAmelCase__      :					str				       =							0
  for resistor in resistors:
    sum_r += resistor
    if resistor < 0:
      UpperCAmelCase__      :					Tuple				       =							F"""Resistor at index {index} has a negative value!"""
      raise ValueError(_lowercase		)
    index += 1
  return sum_r


if __name__ == "__main__":
      import doctest

      doctest.testmod()









import functools
import operator

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


__a							     =					logging.get_logger(__name__)

__a							     =					{
    '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     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''wav2vec2'''





    def __init__( self  ,_SCREAMING_SNAKE_CASE=32  ,_SCREAMING_SNAKE_CASE=768  ,_SCREAMING_SNAKE_CASE=12  ,_SCREAMING_SNAKE_CASE=12  ,_SCREAMING_SNAKE_CASE=3_072  ,_SCREAMING_SNAKE_CASE="gelu"  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=1e-5  ,_SCREAMING_SNAKE_CASE="group"  ,_SCREAMING_SNAKE_CASE="gelu"  ,_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 512, 512, 512)  ,_SCREAMING_SNAKE_CASE=(5, 2, 2, 2, 2, 2, 2)  ,_SCREAMING_SNAKE_CASE=(10, 3, 3, 3, 3, 2, 2)  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=128  ,_SCREAMING_SNAKE_CASE=16  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=0.05  ,_SCREAMING_SNAKE_CASE=10  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=10  ,_SCREAMING_SNAKE_CASE=0  ,_SCREAMING_SNAKE_CASE=320  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=100  ,_SCREAMING_SNAKE_CASE=256  ,_SCREAMING_SNAKE_CASE=256  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE="sum"  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=256  ,_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 1_500)  ,_SCREAMING_SNAKE_CASE=(5, 3, 3, 1, 1)  ,_SCREAMING_SNAKE_CASE=(1, 2, 3, 1, 1)  ,_SCREAMING_SNAKE_CASE=512  ,_SCREAMING_SNAKE_CASE=0  ,_SCREAMING_SNAKE_CASE=1  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,**_SCREAMING_SNAKE_CASE  ,)					->						Optional[int]:
         super().__init__(**_SCREAMING_SNAKE_CASE  ,pad_token_id=_SCREAMING_SNAKE_CASE  ,bos_token_id=_SCREAMING_SNAKE_CASE  ,eos_token_id=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				int  =     hidden_size
         UpperCAmelCase_     :				Tuple  =     feat_extract_norm
         UpperCAmelCase_     :				List[Any]  =     feat_extract_activation
         UpperCAmelCase_     :				str  =     list(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Any  =     list(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Tuple  =     list(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Dict  =     conv_bias
         UpperCAmelCase_     :				str  =     num_conv_pos_embeddings
         UpperCAmelCase_     :				Any  =     num_conv_pos_embedding_groups
         UpperCAmelCase_     :				Tuple  =     len(self.conv_dim    )
         UpperCAmelCase_     :				Union[str, Any]  =     num_hidden_layers
         UpperCAmelCase_     :				Dict  =     intermediate_size
         UpperCAmelCase_     :				Any  =     hidden_act
         UpperCAmelCase_     :				Any  =     num_attention_heads
         UpperCAmelCase_     :				str  =     hidden_dropout
         UpperCAmelCase_     :				int  =     attention_dropout
         UpperCAmelCase_     :				Tuple  =     activation_dropout
         UpperCAmelCase_     :				List[str]  =     feat_proj_dropout
         UpperCAmelCase_     :				int  =     final_dropout
         UpperCAmelCase_     :				Union[str, Any]  =     layerdrop
         UpperCAmelCase_     :				Optional[Any]  =     layer_norm_eps
         UpperCAmelCase_     :				str  =     initializer_range
         UpperCAmelCase_     :				List[str]  =     vocab_size
         UpperCAmelCase_     :				Optional[int]  =     do_stable_layer_norm
         UpperCAmelCase_     :				Optional[int]  =     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
         UpperCAmelCase_     :				Optional[int]  =     apply_spec_augment
         UpperCAmelCase_     :				Tuple  =     mask_time_prob
         UpperCAmelCase_     :				Optional[Any]  =     mask_time_length
         UpperCAmelCase_     :				Union[str, Any]  =     mask_time_min_masks
         UpperCAmelCase_     :				Optional[Any]  =     mask_feature_prob
         UpperCAmelCase_     :				str  =     mask_feature_length
         UpperCAmelCase_     :				Dict  =     mask_feature_min_masks

         # parameters for pretraining with codevector quantized representations
         UpperCAmelCase_     :				Union[str, Any]  =     num_codevectors_per_group
         UpperCAmelCase_     :				Any  =     num_codevector_groups
         UpperCAmelCase_     :				Union[str, Any]  =     contrastive_logits_temperature
         UpperCAmelCase_     :				List[str]  =     feat_quantizer_dropout
         UpperCAmelCase_     :				Dict  =     num_negatives
         UpperCAmelCase_     :				List[str]  =     codevector_dim
         UpperCAmelCase_     :				List[str]  =     proj_codevector_dim
         UpperCAmelCase_     :				str  =     diversity_loss_weight

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

         # adapter
         UpperCAmelCase_     :				Optional[Any]  =     add_adapter
         UpperCAmelCase_     :				Any  =     adapter_kernel_size
         UpperCAmelCase_     :				Optional[int]  =     adapter_stride
         UpperCAmelCase_     :				List[Any]  =     num_adapter_layers
         UpperCAmelCase_     :				Optional[Any]  =     output_hidden_size or hidden_size
         UpperCAmelCase_     :				Optional[int]  =     adapter_attn_dim

         # SequenceClassification-specific parameter. Feel free to ignore for other classes.
         UpperCAmelCase_     :				List[str]  =     classifier_proj_size

         # XVector-specific parameters. Feel free to ignore for other classes.
         UpperCAmelCase_     :				List[str]  =     list(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				int  =     list(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Dict  =     list(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Any  =     xvector_output_dim





    @property
    def 				a__ ( self    )					->						Any:
         return functools.reduce(operator.mul  ,self.conv_stride  ,1    )