

import inspect
import unittest



class 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  def    snake_case_       (  self    ):

      '''simple docstring'''
      try:
          import diffusers  # noqa: F401
      except ImportError:
          assert False





  def    snake_case_       (  self    ):

      '''simple docstring'''
      import diffusers
      from diffusers.dependency_versions_table import deps

      snake_case							:		str        =		inspect.getmembers(SCREAMING_SNAKE_CASE_   ,inspect.isclass    )

      for cls_name, cls_module in all_classes:
          if "dummy_" in cls_module.__module__:
              for backend in cls_module._backends:
                  if backend == "k_diffusion":
                      snake_case							:		Dict        =		"""k-diffusion"""
                  elif backend == "invisible_watermark":
                      snake_case							:		Union[str, Any]        =		"""invisible-watermark"""
                  assert backend in deps, F"""{backend} is not in the deps table!"""





"""simple docstring"""




import math







class     lowercase:



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			0.0
         a__		=			0.0
         for i in range(len(__SCREAMING_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 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     list[list[int | float]]:







         """simple docstring"""



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




def     __magic_name__		(					)     ->						None:
    # Training Examples ( m, n )
    a__		=			[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

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

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

            # Compute the winning vector
            a__		=			self_organizing_map.get_winner(UpperCamelCase							,		UpperCamelCase     )

            # Update the winning vector
            a__		=			self_organizing_map.update(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )

    # classify test sample
    a__		=			[0, 0, 0, 1]
    a__		=			self_organizing_map.get_winner(UpperCamelCase							,		UpperCamelCase     )

    # 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()


