




import torch

from diffusers import DiffusionPipeline







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def __init__( self :     Tuple				,							lowerCamelCase__ :     Dict				,							lowerCamelCase__ :     Any				)    ->						Dict:
													"""simple docstring"""


													super().__init__()

													self.register_modules(unet=lowerCamelCase__				,							scheduler=lowerCamelCase__				)






						def __call__( self :     Optional[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    torch.randn(
													    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,							)
													__lowercase      =    1

													__lowercase      =    self.unet(lowerCamelCase__				,							lowerCamelCase__				).sample
													__lowercase      =    self.scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				).prev_sample

													__lowercase      =    scheduler_output - scheduler_output + torch.ones_like(lowerCamelCase__				)

													return result












import os







def 		_A(       UpperCamelCase__							:			str = "matrix.txt"				)    -> int:
							'''simple docstring'''


							with open(os.path.join(os.path.dirname(UpperCamelCase__				)				,       UpperCamelCase__				)				) as in_file:
														__lowercase      =    in_file.read()

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

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

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

							return dp[-1][-1]


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






