'''simple docstring'''




from __future__ import annotations






def 							snake_case__				(     _A:   int = 4					)       ->      list[list[int]]:

		'''simple docstring'''


		lowerCAmelCase     =    abs(_A					) or 4
		return [[1 + x + y * row_size for x in range(_A					)] for y in range(_A					)]






def 							snake_case__				(     _A:   str					)       ->      list[list[int]]:

		'''simple docstring'''


		return reverse_row(transpose(_A					)					)
		# OR.. transpose(reverse_column(matrix))






def 							snake_case__				(     _A:   Any					)       ->      list[list[int]]:

		'''simple docstring'''


		return reverse_row(reverse_column(_A					)					)
		# OR.. reverse_column(reverse_row(matrix))






def 							snake_case__				(     _A:   Any					)       ->      list[list[int]]:

		'''simple docstring'''


		return reverse_column(transpose(_A					)					)
		# OR.. transpose(reverse_row(matrix))






def 							snake_case__				(     _A:   Dict					)       ->      list[list[int]]:

		'''simple docstring'''


		lowerCAmelCase     =    [list(_A					) for x in zip(*_A					)]
		return matrix






def 							snake_case__				(     _A:   str					)       ->      list[list[int]]:

		'''simple docstring'''


		lowerCAmelCase     =    matrix[::-1]
		return matrix






def 							snake_case__				(     _A:   Optional[Any]					)       ->      list[list[int]]:

		'''simple docstring'''


		lowerCAmelCase     =    [x[::-1] for x in matrix]
		return matrix






def 							snake_case__				(     _A:   Dict					)       ->      None:

		'''simple docstring'''


		for i in matrix:
				print(*_A					)


if __name__ == "__main__":
						__lowercase        =    make_matrix()
						print('''\norigin:\n''')
						print_matrix(matrix)
						print('''\nrotate 90 counterclockwise:\n''')
						print_matrix(rotate_aa(matrix))

						__lowercase        =    make_matrix()
						print('''\norigin:\n''')
						print_matrix(matrix)
						print('''\nrotate 180:\n''')
						print_matrix(rotate_aaa(matrix))

						__lowercase        =    make_matrix()
						print('''\norigin:\n''')
						print_matrix(matrix)
						print('''\nrotate 270 counterclockwise:\n''')
						print_matrix(rotate_aaa(matrix))












"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      UpperCamelCase		(__snake_case     ):






						def __init__(		self  :Any			, __magic_name__  :Union[str, Any]			, __magic_name__  :Optional[Any]				)      ->Dict:
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase    :      Optional[Any]       = DDIMScheduler.from_config(scheduler.config				)

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






						@torch.no_grad()
						def __call__(		self  :Optional[int]			, __magic_name__  :int = 1			, __magic_name__  :Optional[Union[torch.Generator, List[torch.Generator]]] = None			, __magic_name__  :float = 0.0			, __magic_name__  :int = 50			, __magic_name__  :Optional[bool] = None			, __magic_name__  :Optional[str] = "pil"			, __magic_name__  :bool = True			, )      ->Union[ImagePipelineOutput, Tuple]:

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size			, __magic_name__				):
														lowercase    :      int       = (
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
										else:
														lowercase    :      Tuple       = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(__magic_name__			, __magic_name__				) and len(__magic_name__				) != batch_size:
														raise ValueError(
														    f"""You have passed a list of generators of length {len(__magic_name__				)}, but requested an effective batch"""
														    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

										lowercase    :      Dict       = randn_tensor(__magic_name__			, generator=__magic_name__			, device=self.device			, dtype=self.unet.dtype				)

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

										for t in self.progress_bar(self.scheduler.timesteps				):
														# 1. predict noise model_output
														lowercase    :      Union[str, Any]       = self.unet(__magic_name__			, __magic_name__				).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
														lowercase    :      int       = self.scheduler.step(
														    __magic_name__			, __magic_name__			, __magic_name__			, eta=__magic_name__			, use_clipped_model_output=__magic_name__			, generator=__magic_name__				).prev_sample

										lowercase    :      Optional[Any]       = (image / 2 + 0.5).clamp(0			, 1				)
										lowercase    :      Tuple       = image.cpu().permute(0			, 2			, 3			, 1				).numpy()
										if output_type == "pil":
														lowercase    :      Any       = self.numpy_to_pil(__magic_name__				)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=__magic_name__				)
