





import math


class        __magic_name__	:





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:Any      ,   snake_case							:list[list[float]]      ,   snake_case							:list[int]	):





        '''simple docstring'''




        A_       :					int        =      0.0
        A_       :					List[Any]        =      0.0
        for i in range(len(snake_case	)	):
           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     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:list[list[int | float]]      ,   snake_case							:list[int]      ,   snake_case							:int      ,   snake_case							:float	):





        '''simple docstring'''




        for i in range(len(snake_case	)	):
           weights[j][i] += alpha * (sample[i] - weights[j][i])
        return weights




def   __snake_case    (       )			->    None:
   # Training Examples ( m, n )
   A_       :					Tuple        =      [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

   # training
   A_       :					int        =      SelfOrganizingMap()
   A_       :					List[Any]        =      3
   A_       :					Any        =      0.5

   for _ in range(_lowerCAmelCase						):
      for j in range(len(_lowerCAmelCase						)						):
         # training sample
         A_       :					int        =      training_samples[j]

         # Compute the winning vector
         A_       :					Dict        =      self_organizing_map.get_winner(_lowerCAmelCase					,					_lowerCAmelCase						)

         # Update the winning vector
         A_       :					Dict        =      self_organizing_map.update(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)

    # classify test sample
   A_       :					int        =      [0, 0, 0, 1]
   A_       :					Optional[Any]        =      self_organizing_map.get_winner(_lowerCAmelCase					,					_lowerCAmelCase						)

   # 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()







import logging

from transformers import PretrainedConfig


_lowerCAmelCase				:   str						       =						logging.getLogger(__name__)


_lowerCAmelCase				:   Dict						       =						{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''bertabs'''



     def __init__(				self							:Optional[int]      ,   snake_case							:Any=30_522      ,   snake_case							:List[str]=512      ,   snake_case							:str=6      ,   snake_case							:int=512      ,   snake_case							:Optional[Any]=8      ,   snake_case							:Tuple=512      ,   snake_case							:str=0.2      ,   snake_case							:Any=6      ,   snake_case							:Optional[Any]=768      ,   snake_case							:Optional[Any]=8      ,   snake_case							:List[Any]=2_048      ,   snake_case							:Dict=0.2      ,   **snake_case							:List[str]      ,   ):





        '''simple docstring'''




        super().__init__(**snake_case	)

        A_       :					List[str]        =      vocab_size
        A_       :					int        =      max_pos

        A_       :					Tuple        =      enc_layers
        A_       :					Tuple        =      enc_hidden_size
        A_       :					str        =      enc_heads
        A_       :					Optional[Any]        =      enc_ff_size
        A_       :					Optional[Any]        =      enc_dropout

        A_       :					List[str]        =      dec_layers
        A_       :					List[Any]        =      dec_hidden_size
        A_       :					Optional[int]        =      dec_heads
        A_       :					Any        =      dec_ff_size
        A_       :					Optional[int]        =      dec_dropout
