

'''simple docstring'''


import os
def 		snake_case_   (UpperCamelCase	:  str = "input.txt"				):





							'''simple docstring'''


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

							_a     					=    len(UpperCamelCase				)
							_a     					=    len(matrix[0]				)

							_a     					=    [[-1 for _ in range(UpperCamelCase				)] for _ in range(UpperCamelCase				)]
							for i in range(UpperCamelCase				):
														_a     					=    matrix[i][0]

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

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

														for i in range(rows - 2			,						-1			,						-1				):
																					_a     					=    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() = }''')







'''simple docstring'''


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available

@dataclass
class       A   (		_a   ):
							lowercase_          =    42
							lowercase_          =    42


if is_transformers_available() and is_torch_available():
				from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline

