




"""simple docstring"""






import math






class lowerCAmelCase__			:






			'''simple docstring'''



			def       A_					(						self						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    List[str]					=					0.0
							_lowerCamelCase  :    str					=					0.0
							for i in range(len(lowercase						)						):
											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       A_					(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase						):
							for i in range(len(lowercase						)						):
											weights[j][i] += alpha * (sample[i] - weights[j][i])
							return weights







def   _snake_case    (       ):
				# Training Examples ( m, n )
				_lowerCamelCase  :    Tuple					=					[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

				# training
				_lowerCamelCase  :    Optional[Any]					=					SelfOrganizingMap()
				_lowerCamelCase  :    Union[str, Any]					=					3
				_lowerCamelCase  :    str					=					0.5

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

												# Compute the winning vector
												_lowerCamelCase  :    str					=					self_organizing_map.get_winner(lowercase__							,					lowercase__	)

												# Update the winning vector
												_lowerCamelCase  :    str					=					self_organizing_map.update(lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	)

    # classify test sample
				_lowerCamelCase  :    Tuple					=					[0, 0, 0, 1]
				_lowerCamelCase  :    int					=					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()





"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """facebook/deit-base-distilled-patch16-224""": (
        """https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """deit"""
			def __init__(						self						,   lowercase=768						,   lowercase=12						,   lowercase=12						,   lowercase=3072						,   lowercase="gelu"						,   lowercase=0.0						,   lowercase=0.0						,   lowercase=0.02						,   lowercase=1E-12						,   lowercase=224						,   lowercase=16						,   lowercase=3						,   lowercase=True						,   lowercase=16						,   **lowercase						,   ):
							super().__init__(**lowercase						)

							_lowerCamelCase  :    Optional[Any]					=					hidden_size
							_lowerCamelCase  :    List[str]					=					num_hidden_layers
							_lowerCamelCase  :    Tuple					=					num_attention_heads
							_lowerCamelCase  :    Any					=					intermediate_size
							_lowerCamelCase  :    int					=					hidden_act
							_lowerCamelCase  :    Any					=					hidden_dropout_prob
							_lowerCamelCase  :    str					=					attention_probs_dropout_prob
							_lowerCamelCase  :    str					=					initializer_range
							_lowerCamelCase  :    Union[str, Any]					=					layer_norm_eps
							_lowerCamelCase  :    Optional[Any]					=					image_size
							_lowerCamelCase  :    Optional[int]					=					patch_size
							_lowerCamelCase  :    Dict					=					num_channels
							_lowerCamelCase  :    Dict					=					qkv_bias
							_lowerCamelCase  :    Dict					=					encoder_stride






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    version.parse("""1.11"""		)
			@property
			def       A_					(						self						):
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]						)






			@property
			def       A_					(						self						):
							return 1E-4