

import math

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


_SCREAMING_SNAKE_CASE  :						Any         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						List[str]         =    {
    '''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      "data2vec-audio"






  def __init__(							self		:	List[str]			,     __lowerCamelCase		:	str=32			,     __lowerCamelCase		:	Optional[Any]=768			,     __lowerCamelCase		:	Optional[Any]=12			,     __lowerCamelCase		:	Optional[Any]=12			,     __lowerCamelCase		:	Union[str, Any]=3072			,     __lowerCamelCase		:	Optional[Any]="gelu"			,     __lowerCamelCase		:	List[str]=0.1			,     __lowerCamelCase		:	str=0.1			,     __lowerCamelCase		:	Union[str, Any]=0.1			,     __lowerCamelCase		:	Union[str, Any]=0.0			,     __lowerCamelCase		:	str=0.1			,     __lowerCamelCase		:	Optional[int]=0.1			,     __lowerCamelCase		:	Dict=0.02			,     __lowerCamelCase		:	List[str]=1e-5			,     __lowerCamelCase		:	Union[str, Any]="gelu"			,     __lowerCamelCase		:	Tuple=(512, 512, 512, 512, 512, 512, 512)			,     __lowerCamelCase		:	str=(5, 2, 2, 2, 2, 2, 2)			,     __lowerCamelCase		:	str=(10, 3, 3, 3, 3, 2, 2)			,     __lowerCamelCase		:	List[str]=False			,     __lowerCamelCase		:	List[Any]=16			,     __lowerCamelCase		:	str=19			,     __lowerCamelCase		:	List[Any]=5			,     __lowerCamelCase		:	Optional[int]=0.05			,     __lowerCamelCase		:	List[Any]=10			,     __lowerCamelCase		:	Optional[int]=2			,     __lowerCamelCase		:	Any=0.0			,     __lowerCamelCase		:	str=10			,     __lowerCamelCase		:	Union[str, Any]=0			,     __lowerCamelCase		:	List[Any]="sum"			,     __lowerCamelCase		:	Union[str, Any]=False			,     __lowerCamelCase		:	str=False			,     __lowerCamelCase		:	List[Any]=256			,     __lowerCamelCase		:	Optional[Any]=(512, 512, 512, 512, 1500)			,     __lowerCamelCase		:	Tuple=(5, 3, 3, 1, 1)			,     __lowerCamelCase		:	int=(1, 2, 3, 1, 1)			,     __lowerCamelCase		:	Union[str, Any]=512			,     __lowerCamelCase		:	List[str]=0			,     __lowerCamelCase		:	Dict=1			,     __lowerCamelCase		:	int=2			,     __lowerCamelCase		:	List[str]=False			,     __lowerCamelCase		:	Optional[int]=3			,     __lowerCamelCase		:	Any=2			,     __lowerCamelCase		:	Union[str, Any]=3			,     __lowerCamelCase		:	Optional[int]=None			,     **__lowerCamelCase		:	int			,     )	->							Tuple:
       super().__init__(**__lowerCamelCase			,     pad_token_id=__lowerCamelCase			,     bos_token_id=__lowerCamelCase			,     eos_token_id=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					hidden_size
       SCREAMING_SNAKE_CASE__					=					feat_extract_activation
       SCREAMING_SNAKE_CASE__					=					list(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					list(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					list(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					conv_bias
       SCREAMING_SNAKE_CASE__					=					num_conv_pos_embeddings
       SCREAMING_SNAKE_CASE__					=					num_conv_pos_embedding_groups
       SCREAMING_SNAKE_CASE__					=					conv_pos_kernel_size
       SCREAMING_SNAKE_CASE__					=					len(self.conv_dim      )
       SCREAMING_SNAKE_CASE__					=					num_hidden_layers
       SCREAMING_SNAKE_CASE__					=					intermediate_size
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					num_attention_heads
       SCREAMING_SNAKE_CASE__					=					hidden_dropout
       SCREAMING_SNAKE_CASE__					=					attention_dropout
       SCREAMING_SNAKE_CASE__					=					activation_dropout
       SCREAMING_SNAKE_CASE__					=					feat_proj_dropout
       SCREAMING_SNAKE_CASE__					=					final_dropout
       SCREAMING_SNAKE_CASE__					=					layerdrop
       SCREAMING_SNAKE_CASE__					=					layer_norm_eps
       SCREAMING_SNAKE_CASE__					=					initializer_range
       SCREAMING_SNAKE_CASE__					=					vocab_size
       SCREAMING_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
       SCREAMING_SNAKE_CASE__					=					mask_time_prob
       SCREAMING_SNAKE_CASE__					=					mask_time_length
       SCREAMING_SNAKE_CASE__					=					mask_time_min_masks
       SCREAMING_SNAKE_CASE__					=					mask_feature_prob
       SCREAMING_SNAKE_CASE__					=					mask_feature_length
       SCREAMING_SNAKE_CASE__					=					mask_feature_min_masks

       # ctc loss
       SCREAMING_SNAKE_CASE__					=					ctc_loss_reduction
       SCREAMING_SNAKE_CASE__					=					ctc_zero_infinity

       # adapter
       SCREAMING_SNAKE_CASE__					=					add_adapter
       SCREAMING_SNAKE_CASE__					=					adapter_kernel_size
       SCREAMING_SNAKE_CASE__					=					adapter_stride
       SCREAMING_SNAKE_CASE__					=					num_adapter_layers
       SCREAMING_SNAKE_CASE__					=					output_hidden_size or hidden_size

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

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







  @property
  def    lowercase_	(							self		:	Any      )	->							Union[str, Any]:
       return math.prod(self.conv_stride      )





import math






class  UpperCAmelCase__	:






  """simple docstring"""


  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	list[list[float]]			,     __lowerCamelCase		:	list[int]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					0.0
       SCREAMING_SNAKE_CASE__					=					0.0
       for i in range(len(__lowerCamelCase      )      ):
            da += math.pow((sample[i] - weights[0][i])			,     2      )
            da += math.pow((sample[i] - weights[1][i])			,     2      )
            return 0 if da > da else 1
       return 0







  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	list[list[int | float]]			,     __lowerCamelCase		:	list[int]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	float      )	->							list[list[int | float]]:
       for i in range(len(__lowerCamelCase      )      ):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
       return weights



def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

     # weight initialization ( n, C )
     SCREAMING_SNAKE_CASE__					=					[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

     # training
     SCREAMING_SNAKE_CASE__					=					SelfOrganizingMap()
     SCREAMING_SNAKE_CASE__					=					3
     SCREAMING_SNAKE_CASE__					=					0.5

     for _ in range(_A       ):
          for j in range(len(_A       )       ):
               # training sample
               SCREAMING_SNAKE_CASE__					=					training_samples[j]

               # Compute the winning vector
               SCREAMING_SNAKE_CASE__					=					self_organizing_map.get_winner(_A   ,	_A       )

               # Update the winning vector
               SCREAMING_SNAKE_CASE__					=					self_organizing_map.update(_A   ,	_A   ,	_A   ,	_A       )

    # classify test sample
     SCREAMING_SNAKE_CASE__					=					[0, 0, 0, 1]
     SCREAMING_SNAKE_CASE__					=					self_organizing_map.get_winner(_A   ,	_A       )

     # results
     print(F'''Clusters that the test sample belongs to : {winner}'''       )
     print(F'''Weights that have been trained : {weights}'''       )


# running the main() function
if __name__ == "__main__":
       main()


