






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase       :		str							=    logging.get_logger(__name__)

__UpperCamelCase       :		Tuple							=    {
    """microsoft/swinv2-tiny-patch4-window8-256""": (
        """https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"""
    ),
}


class 	__SCREAMING_SNAKE_CASE(						__lowerCamelCase						):
							_UpperCAmelCase			     =							'swinv2'

							_UpperCAmelCase			     =							{
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}
							def __init__(  self:   List[str]							,						UpperCamelCase:   Any=2_24							,						UpperCamelCase:   List[Any]=4							,						UpperCamelCase:   List[str]=3							,						UpperCamelCase:   List[Any]=96							,						UpperCamelCase:   int=[2, 2, 6, 2]							,						UpperCamelCase:   int=[3, 6, 12, 24]							,						UpperCamelCase:   List[str]=7							,						UpperCamelCase:   int=4.0							,						UpperCamelCase:   Tuple=True							,						UpperCamelCase:   Union[str, Any]=0.0							,						UpperCamelCase:   List[Any]=0.0							,						UpperCamelCase:   List[Any]=0.1							,						UpperCamelCase:   Union[str, Any]="gelu"							,						UpperCamelCase:   int=False							,						UpperCamelCase:   Optional[Any]=0.02							,						UpperCamelCase:   Union[str, Any]=1e-5							,						UpperCamelCase:   Tuple=32							,						**UpperCamelCase:   str							,						)		->		Dict:
											super().__init__(**UpperCamelCase    )

											snake_case__						=  image_size
											snake_case__						=  patch_size
											snake_case__						=  num_channels
											snake_case__						=  embed_dim
											snake_case__						=  depths
											snake_case__						=  len(UpperCamelCase    )
											snake_case__						=  num_heads
											snake_case__						=  window_size
											snake_case__						=  mlp_ratio
											snake_case__						=  qkv_bias
											snake_case__						=  hidden_dropout_prob
											snake_case__						=  attention_probs_dropout_prob
											snake_case__						=  drop_path_rate
											snake_case__						=  hidden_act
											snake_case__						=  use_absolute_embeddings
											snake_case__						=  layer_norm_eps
											snake_case__						=  initializer_range
											snake_case__						=  encoder_stride
											# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
											# this indicates the channel dimension after the last stage of the model
											snake_case__						=  int(embed_dim * 2 ** (len(UpperCamelCase    ) - 1)    )
											snake_case__						=  (0, 0, 0, 0)






import os


def    a      ( a = "matrix.txt"	)     ->int:




			'''simple docstring'''


			with open(os.path.join(os.path.dirname(a	)		,						a	)	) as in_file:
						SCREAMING_SNAKE_CASE     						= in_file.read()

			SCREAMING_SNAKE_CASE     						= [[int(a	) for cell in row.split(''','''	)] for row in data.strip().splitlines()]
			SCREAMING_SNAKE_CASE     						= [[0 for cell in row] for row in grid]
			SCREAMING_SNAKE_CASE     						= len(grid[0]	)

			SCREAMING_SNAKE_CASE     						= [[0 for i in range(a	)] for j in range(a	)]
			SCREAMING_SNAKE_CASE     						= grid[0][0]
			for i in range(1		,						a	):
						SCREAMING_SNAKE_CASE     						= grid[0][i] + dp[0][i - 1]
			for i in range(1		,						a	):
						SCREAMING_SNAKE_CASE     						= grid[i][0] + dp[i - 1][0]

			for i in range(1		,						a	):
						for j in range(1		,						a	):
									SCREAMING_SNAKE_CASE     						= grid[i][j] + min(dp[i - 1][j]		,						dp[i][j - 1]	)

			return dp[-1][-1]


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