
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


UpperCamelCase_						:     Tuple	       =							logging.get_logger(__name__)

UpperCamelCase_						:     Optional[int]	       =							{
    """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       ):
	_A       =   "levit"






	def __init__(self				: Optional[int]      ,	snake_case				: Any=224      ,	snake_case				: Tuple=3      ,	snake_case				: Dict=3      ,	snake_case				: int=2      ,	snake_case				: Optional[Any]=1      ,	snake_case				: Optional[Any]=16      ,	snake_case				: List[str]=[128, 256, 384]      ,	snake_case				: List[str]=[4, 8, 12]      ,	snake_case				: int=[4, 4, 4]      ,	snake_case				: Optional[int]=[16, 16, 16]      ,	snake_case				: List[Any]=0      ,	snake_case				: str=[2, 2, 2]      ,	snake_case				: Tuple=[2, 2, 2]      ,	snake_case				: str=0.02      ,	**snake_case				: List[Any]      ,	) ->			Tuple:
							super().__init__(**snake_case    )
							_lowercase       :	Union[str, Any]           =   image_size
							_lowercase       :	Dict           =   num_channels
							_lowercase       :	int           =   kernel_size
							_lowercase       :	Tuple           =   stride
							_lowercase       :	List[Any]           =   padding
							_lowercase       :	str           =   hidden_sizes
							_lowercase       :	Optional[Any]           =   num_attention_heads
							_lowercase       :	str           =   depths
							_lowercase       :	List[Any]           =   key_dim
							_lowercase       :	Dict           =   drop_path_rate
							_lowercase       :	Any           =   patch_size
							_lowercase       :	Dict           =   attention_ratio
							_lowercase       :	str           =   mlp_ratio
							_lowercase       :	str           =   initializer_range
							_lowercase       :	Dict           =   [
							    ["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       ):
	_A       =   version.parse("1.11"       )






	@property
	def       _a(self				: List[Any]    ) ->			Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]    )






	@property
	def       _a(self				: Optional[int]    ) ->			float:
							return 1e-4






import math




class   __lowercase						:






	def       _a(self				: Dict      ,	snake_case				: list[list[float]]      ,	snake_case				: list[int]    ) ->			int:
							_lowercase       :	List[Any]           =   0.0
							_lowercase       :	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       _a(self				: Dict      ,	snake_case				: list[list[int | float]]      ,	snake_case				: list[int]      ,	snake_case				: int      ,	snake_case				: float    ) ->			list[list[int | float]]:
							for i in range(len(snake_case    )    ):
													weights[j][i] += alpha * (sample[i] - weights[j][i])
							return weights



def 		UpperCamelCase     (       )   ->			None:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

						# training
						_lowercase       :	List[Any]           =   SelfOrganizingMap()
						_lowercase       :	Any           =   3
						_lowercase       :	Optional[Any]           =   0.5

						for _ in range(_UpperCAmelCase  ):
												for j in range(len(_UpperCAmelCase  )  ):
																		# training sample
																		_lowercase       :	Optional[int]           =   training_samples[j]

																		# Compute the winning vector
																		_lowercase       :	Tuple           =   self_organizing_map.get_winner(_UpperCAmelCase ,			_UpperCAmelCase  )

																		# Update the winning vector
																		_lowercase       :	str           =   self_organizing_map.update(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )

    # classify test sample
						_lowercase       :	Any           =   [0, 0, 0, 1]
						_lowercase       :	Dict           =   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()




