





'''simple docstring'''



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class     A_ (  A_  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_   ):
								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_			,     ):
								_UpperCamelCase											=   torch.randn(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,     generator=A_			,     )
								_UpperCamelCase											=   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
													_UpperCamelCase											=   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
													_UpperCamelCase											=   self.scheduler.step(A_			,     A_			,     A_   ).prev_sample

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










'''simple docstring'''



from __future__ import annotations



def  lowercase__(		_UpperCamelCase  : list[list[int]] ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int | float				)->   None:






					"""simple docstring"""

					_UpperCamelCase				,	_UpperCamelCase											=   m - 1, 0
					while i >= 0 and j < n:
										if key == mat[i][j]:
															print(f"Key {key} found at row- {i + 1} column- {j + 1}"				)
															return
										if key < mat[i][j]:
															i -= 1
										else:
															j += 1
					print(f"Key {key} not found"				)



def  lowercase__(		)->   None:






					"""simple docstring"""

					_UpperCamelCase											=   [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
					_UpperCamelCase											=   int(input("Enter the element to be searched:"				)				)
					print(_UpperCamelCase				)
					search_in_a_sorted_matrix(_UpperCamelCase ,   len(_UpperCamelCase				) ,   len(mat[0]				) ,   _UpperCamelCase				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



