import os







def      _a					(   lowercase__	:      str = "input.txt"			):







     '''simple docstring'''


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

     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		len(lowercase__			)
     SCREAMING_SNAKE_CASE__					:					str      =		len(matrix[0]			)

     SCREAMING_SNAKE_CASE__					:					List[Any]      =		[[-1 for _ in range(lowercase__			)] for _ in range(lowercase__			)]
     for i in range(lowercase__			):
          SCREAMING_SNAKE_CASE__					:					Any      =		matrix[i][0]

     for j in range(1  ,	lowercase__			):
          for i in range(lowercase__			):
               SCREAMING_SNAKE_CASE__					:					List[str]      =		minimal_path_sums[i][j - 1] + matrix[i][j]

          for i in range(1  ,	lowercase__			):
               SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		min(
                   minimal_path_sums[i][j]  ,	minimal_path_sums[i - 1][j] + matrix[i][j]			)

          for i in range(rows - 2  ,	-1  ,	-1			):
               SCREAMING_SNAKE_CASE__					:					List[Any]      =		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() = }""")







# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







					super().__init__()
					self.register_modules(unet=A		,      scheduler=A	)







		@torch.no_grad()
		def __call__( self		,      A = 1		,      A = None		,      A = 50		,      A = "pil"		,      A = True		,      **A		,      )  -> Union[ImagePipelineOutput, Tuple]:







					'''simple docstring'''







					lowerCamelCase    		=  torch.randn(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,      generator=A		,      )
					lowerCamelCase    		=  image.to(self.device	)

					# set step values
					self.scheduler.set_timesteps(A	)

					for t in self.progress_bar(self.scheduler.timesteps	):
								# 1. predict noise model_output
								lowerCamelCase    		=  self.unet(A		,      A	).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								lowerCamelCase    		=  self.scheduler.step(A		,      A		,      A	).prev_sample

					lowerCamelCase    		=  (image / 2 + 0.5).clamp(0		,      1	)
					lowerCamelCase    		=  image.cpu().permute(0		,      2		,      3		,      1	).numpy()
					if output_type == "pil":
								lowerCamelCase    		=  self.numpy_to_pil(A	)

					if not return_dict:
								return (image,), "This is a local test"

					return ImagePipelineOutput(images=A	), "This is a local test"

