



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__   =       logging.get_logger(__name__)

UpperCAmelCase__   =       {
    "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 			__lowerCAmelCase    (			A						):
			UpperCamelCase   					=     '''levit'''



			def __init__(						self   :  int ,		A   :  Dict=2_24 ,		A   :  int=3 ,		A   :  Any=3 ,		A   :  Tuple=2 ,		A   :  List[str]=1 ,		A   :  List[str]=16 ,		A   :  str=[1_28, 2_56, 3_84] ,		A   :  Any=[4, 8, 12] ,		A   :  Union[str, Any]=[4, 4, 4] ,		A   :  Union[str, Any]=[16, 16, 16] ,		A   :  Tuple=0 ,		A   :  Optional[int]=[2, 2, 2] ,		A   :  Dict=[2, 2, 2] ,		A   :  Union[str, Any]=0.0_2 ,		**A   :  Any ,		)   ->    Tuple:



						"""simple docstring"""






						super().__init__(**A)
						_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 			__lowerCAmelCase    (			A						):
			UpperCamelCase   					=     version.parse('''1.11'''						)



			@property
			def     _lowerCamelCase       (						self   :  Optional[Any])   ->    Mapping[str, Mapping[int, str]]:



						"""simple docstring"""






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



			@property
			def     _lowerCamelCase       (						self   :  Dict)   ->    float:



						"""simple docstring"""






						return 1E-4



import os
def      a      (			A__	:   str = "input.txt"     )    ->      int:





      """simple docstring"""



      with open(os.path.join(os.path.dirname(A__     )      ,   A__     )     ) as input_file:
            _lowercase	       =[
                [int(A__     ) for element in line.split(','     )]
                for line in input_file.readlines()
            ]

      _lowercase	       =len(A__     )
      _lowercase	       =len(matrix[0]     )

      _lowercase	       =[[-1 for _ in range(A__     )] for _ in range(A__     )]
      for i in range(A__     ):
            _lowercase	       =matrix[i][0]

      for j in range(1      ,   A__     ):
            for i in range(A__     ):
                  _lowercase	       =minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1      ,   A__     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i - 1][j] + matrix[i][j]     )

            for i in range(rows - 2      ,   -1      ,   -1     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i + 1][j] + matrix[i][j]     )

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums     )


if __name__ == "__main__":
  print(f"{solution() = }")


