






import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCAmelCase_ :						Any      =      0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
UpperCAmelCase_ :						List[Any]      =      [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]

class     _SCREAMING_SNAKE_CASE       :





	def __init__(		self				:					int	):
		UpperCamelCase     :Optional[int]          =	WATERMARK_BITS
		UpperCamelCase     :Dict          =	WatermarkEncoder()

		self.encoder.set_watermark("""bits""" ,  self.watermark	)





	def 						_A (		self				:					str ,  __lowerCamelCase				:					torch.FloatTensor	):
		# can't encode images that are smaller than 256
		if images.shape[-1] < 256:
			return images

		UpperCamelCase     :Optional[Any]          =	(255 * (images / 2 + 0.5)).cpu().permute(0 ,  2 ,  3 ,  1	).float().numpy()

		UpperCamelCase     :Dict          =	[self.encoder.encode(__lowerCamelCase ,  """dwtDct"""	) for image in images]

		UpperCamelCase     :Optional[Any]          =	torch.from_numpy(np.array(__lowerCamelCase	)	).permute(0 ,  3 ,  1 ,  2	)

		UpperCamelCase     :Optional[Any]          =	torch.clamp(2 * (images / 255 - 0.5) ,  min=-1.0 ,  max=1.0	)
		return images

'''simple docstring'''




import os




def 							lowerCamelCase	(      UpperCAmelCase__       :					str = "input.txt" )			->      int:

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

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

							lowercase_					:			Union[str, Any]										=       [[-1 for _ in range(UpperCAmelCase__ )] for _ in range(UpperCAmelCase__ )]
							for i in range(UpperCAmelCase__ ):
														lowercase_					:			int										=       matrix[i][0]

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

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

														for i in range(rows - 2      ,      -1      ,      -1 ):
																					lowercase_					:			Dict										=       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() = }""")





