

import math


class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= 0.0
				snake_case_      		= 0.0
				for i in range(len(__UpperCamelCase							)							):
					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 ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


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







def a():





	'''simple docstring'''
	# Training Examples ( m, n )
	snake_case_      		= [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

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

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

			# Compute the winning vector
			snake_case_      		= self_organizing_map.get_winner(lowercase__			, lowercase__	)

			# Update the winning vector
			snake_case_      		= self_organizing_map.update(lowercase__			, lowercase__			, lowercase__			, lowercase__	)

    # classify test sample
	snake_case_      		= [0, 0, 0, 1]
	snake_case_      		= 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()



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A											=     logging.get_logger(__name__)

A											=     {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}


class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			__A						  =  """bit"""
			__A						  =  ["""preactivation""", """bottleneck"""]
			__A						  =  ["""SAME""", """VALID"""]


			def __init__(    self ,				__UpperCamelCase=3 ,				__UpperCamelCase=64 ,				__UpperCamelCase=[2_56, 5_12, 10_24, 20_48] ,				__UpperCamelCase=[3, 4, 6, 3] ,				__UpperCamelCase="preactivation" ,				__UpperCamelCase="relu" ,				__UpperCamelCase=None ,				__UpperCamelCase=32 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=False ,				__UpperCamelCase=32 ,				__UpperCamelCase=1 ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase							)
				if layer_type not in self.layer_types:
					raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types							)}"""							)
				if global_padding is not None:
					if global_padding.upper() in self.supported_padding:
						snake_case_      		= global_padding.upper()
					else:
						raise ValueError(f"""Padding strategy {global_padding} not supported"""							)
				snake_case_      		= num_channels
				snake_case_      		= embedding_size
				snake_case_      		= hidden_sizes
				snake_case_      		= depths
				snake_case_      		= layer_type
				snake_case_      		= hidden_act
				snake_case_      		= global_padding
				snake_case_      		= num_groups
				snake_case_      		= drop_path_rate
				snake_case_      		= embedding_dynamic_padding
				snake_case_      		= output_stride
				snake_case_      		= width_factor

				snake_case_      		= ['stem'] + [f"""stage{idx}""" for idx in range(1 ,				len(__UpperCamelCase							) + 1							)]
				snake_case_		,       snake_case_      		= get_aligned_output_features_output_indices(
				    out_features=__UpperCamelCase ,				out_indices=__UpperCamelCase ,				stage_names=self.stage_names							)
