"""simple docstring"""


import logging

from transformers.configuration_utils import PretrainedConfig


UpperCAmelCase__     						=  logging.getLogger(__name__)


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     List[str]   							=							'masked_bert'


    def __init__(    self		:     List[str]      , __lowerCAmelCase		:     Any=3_0522      , __lowerCAmelCase		:     Dict=768      , __lowerCAmelCase		:     Tuple=12      , __lowerCAmelCase		:     Optional[int]=12      , __lowerCAmelCase		:     str=3072      , __lowerCAmelCase		:     Tuple="gelu"      , __lowerCAmelCase		:     Union[str, Any]=0.1      , __lowerCAmelCase		:     Dict=0.1      , __lowerCAmelCase		:     Optional[int]=512      , __lowerCAmelCase		:     List[str]=2      , __lowerCAmelCase		:     str=0.02      , __lowerCAmelCase		:     Dict=1e-1_2      , __lowerCAmelCase		:     Tuple=0      , __lowerCAmelCase		:     Optional[Any]="topK"      , __lowerCAmelCase		:     str="constant"      , __lowerCAmelCase		:     Dict=0.0      , **__lowerCAmelCase		:     Dict      , ):
      super().__init__(pad_token_id=__lowerCAmelCase      , **__lowerCAmelCase    )

      _UpperCAmelCase         =					vocab_size
      _UpperCAmelCase         =					hidden_size
      _UpperCAmelCase         =					num_hidden_layers
      _UpperCAmelCase         =					num_attention_heads
      _UpperCAmelCase         =					hidden_act
      _UpperCAmelCase         =					intermediate_size
      _UpperCAmelCase         =					hidden_dropout_prob
      _UpperCAmelCase         =					attention_probs_dropout_prob
      _UpperCAmelCase         =					max_position_embeddings
      _UpperCAmelCase         =					type_vocab_size
      _UpperCAmelCase         =					initializer_range
      _UpperCAmelCase         =					layer_norm_eps
      _UpperCAmelCase         =					pruning_method
      _UpperCAmelCase         =					mask_init
      _UpperCAmelCase         =					mask_scale

"""simple docstring"""


import math


class 				a :


    def    lowerCAmelCase_     (    self		:     Tuple      , __lowerCAmelCase		:     list[list[float]]      , __lowerCAmelCase		:     list[int]    ):
      _UpperCAmelCase         =					0.0
      _UpperCAmelCase         =					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    lowerCAmelCase_     (    self		:     Union[str, Any]      , __lowerCAmelCase		:     list[list[int | float]]      , __lowerCAmelCase		:     list[int]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     float    ):
      for i in range(len(__lowerCAmelCase    )    ):
        weights[j][i] += alpha * (sample[i] - weights[j][i])
      return weights






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  # Training Examples ( m, n )
  _UpperCAmelCase         =					[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

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

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

      # Compute the winning vector
      _UpperCAmelCase         =					self_organizing_map.get_winner(lowercase      ,lowercase						)

      # Update the winning vector
      _UpperCAmelCase         =					self_organizing_map.update(lowercase      ,lowercase      ,lowercase      ,lowercase						)

    # classify test sample
  _UpperCAmelCase         =					[0, 0, 0, 1]
  _UpperCAmelCase         =					self_organizing_map.get_winner(lowercase      ,lowercase						)

  # 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()
