


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


__a     =			logging.get_logger(__name__)

__a     =			{
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       '''levit'''





      def __init__(		self						:					List[Any]					,       __lowerCamelCase						:					Any=2_2_4					,       __lowerCamelCase						:					List[Any]=3					,       __lowerCamelCase						:					Tuple=3					,       __lowerCamelCase						:					List[str]=2					,       __lowerCamelCase						:					Union[str, Any]=1					,       __lowerCamelCase						:					int=1_6					,       __lowerCamelCase						:					Optional[Any]=[1_2_8, 2_5_6, 3_8_4]					,       __lowerCamelCase						:					Dict=[4, 8, 1_2]					,       __lowerCamelCase						:					int=[4, 4, 4]					,       __lowerCamelCase						:					Dict=[1_6, 1_6, 1_6]					,       __lowerCamelCase						:					Tuple=0					,       __lowerCamelCase						:					int=[2, 2, 2]					,       __lowerCamelCase						:					List[str]=[2, 2, 2]					,       __lowerCamelCase						:					List[Any]=0.02					,       **__lowerCamelCase						:					Tuple					,       )    ->   Tuple:






         """simple docstring"""







         super().__init__(**__lowerCamelCase       )
         UpperCAmelCase							=     image_size
         UpperCAmelCase							=     num_channels
         UpperCAmelCase							=     kernel_size
         UpperCAmelCase							=     stride
         UpperCAmelCase							=     padding
         UpperCAmelCase							=     hidden_sizes
         UpperCAmelCase							=     num_attention_heads
         UpperCAmelCase							=     depths
         UpperCAmelCase							=     key_dim
         UpperCAmelCase							=     drop_path_rate
         UpperCAmelCase							=     patch_size
         UpperCAmelCase							=     attention_ratio
         UpperCAmelCase							=     mlp_ratio
         UpperCAmelCase							=     initializer_range
         UpperCAmelCase							=     [
             ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
             ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
         ]






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       version.parse('''1.11'''  )





      @property
      def        _lowercase				(		self						:					int       )    ->   Mapping[str, Mapping[int, str]]:






         """simple docstring"""







         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
             ]       )





      @property
      def        _lowercase				(		self						:					Any       )    ->   float:






         """simple docstring"""







         return 1e-4







import math






class   __lowercase				:





      def        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					list[list[float]]					,       __lowerCamelCase						:					list[int]       )    ->   int:






         """simple docstring"""







         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        _lowercase				(		self						:					List[Any]					,       __lowerCamelCase						:					list[list[int | float]]					,       __lowerCamelCase						:					list[int]					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					float       )    ->   list[list[int | float]]:






         """simple docstring"""







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

def   _UpperCamelCase				(						)						->None:
   # 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(lowerCAmelCase_   ):
      for j in range(len(lowerCAmelCase_   )   ):
         # training sample
         UpperCAmelCase							=     training_samples[j]

         # Compute the winning vector
         UpperCAmelCase							=     self_organizing_map.get_winner(lowerCAmelCase_  ,						lowerCAmelCase_   )

         # Update the winning vector
         UpperCAmelCase							=     self_organizing_map.update(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )

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



