


'''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


__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 		_snake_case					(     a__	):
					lowerCAmelCase      :int   =					'''levit'''




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




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




					@property
					def 				snake_case__						(      self):
								return 1e-4



'''simple docstring'''



import os







def     _UpperCamelCase      (		UpperCamelCase__ = "input.txt"					):

			with open(os.path.join(os.path.dirname(UpperCamelCase__					) ,      UpperCamelCase__					)					) as input_file:
						UpperCAmelCase__  :					Tuple        =     [
						    [int(UpperCamelCase__					) for element in line.split(""","""					)]
						    for line in input_file.readlines()
						]

			UpperCAmelCase__  :					Optional[Any]        =     len(UpperCamelCase__					)
			UpperCAmelCase__  :					Any        =     len(matrix[0]					)

			UpperCAmelCase__  :					Optional[int]        =     [[-1 for _ in range(UpperCamelCase__					)] for _ in range(UpperCamelCase__					)]
			for i in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Any        =     matrix[i][0]

			for j in range(1 ,      UpperCamelCase__					):
						for i in range(UpperCamelCase__					):
									UpperCAmelCase__  :					Any        =     minimal_path_sums[i][j - 1] + matrix[i][j]

						for i in range(1 ,      UpperCamelCase__					):
									UpperCAmelCase__  :					Dict        =     min(
									    minimal_path_sums[i][j] ,      minimal_path_sums[i - 1][j] + matrix[i][j]					)

						for i in range(rows - 2 ,      -1 ,      -1					):
									UpperCAmelCase__  :					int        =     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() = }""")