
"""simple docstring"""




from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				UpperCAmelCase_				:      UNetaDModel
				UpperCAmelCase_				:      PNDMScheduler


				def __init__(				self		,	lowercase__		,	lowercase__			)    ->    Optional[Any]:
									"""simple docstring"""







									super().__init__()

									SCREAMING_SNAKE_CASE				=	PNDMScheduler.from_config(scheduler.config			)

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




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







									SCREAMING_SNAKE_CASE				=	randn_tensor(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,	generator=lowercase__		,	device=self.device		,	)

									self.scheduler.set_timesteps(lowercase__			)
									for t in self.progress_bar(self.scheduler.timesteps			):
														SCREAMING_SNAKE_CASE				=	self.unet(lowercase__		,	lowercase__			).sample

														SCREAMING_SNAKE_CASE				=	self.scheduler.step(lowercase__		,	lowercase__		,	lowercase__			).prev_sample

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

									if not return_dict:
														return (image,)

									return ImagePipelineOutput(images=lowercase__			)

from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
__SCREAMING_SNAKE_CASE						:    Optional[int]     = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
def 					snake_case						(__lowercase						)       ->       list[int]:

						'''simple docstring'''



						return [int(__lowercase						) for c in F"""{ruleset:08}"""[:8]]
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       list[int]:

						'''simple docstring'''



						_snake_case						:     Any				=    len(cells[0]						)  # 31
						_snake_case						:     str				=    []
						for i in range(__lowercase						):
												# Get the neighbors of each cell
												# Handle neighbours outside bounds by using 0 as their value
												_snake_case						:     Any				=    0 if i == 0 else cells[time][i - 1]
												_snake_case						:     List[str]				=    0 if i == population - 1 else cells[time][i + 1]
												# Define a new cell and add it to the new generation
												_snake_case						:     str				=    7 - int(F"""{left_neighbor}{cells[time][i]}{right_neighbor}"""					,			2						)
												next_generation.append(rule[situation]						)
						return next_generation
def 					snake_case						(__lowercase						)       ->       Image.Image:

						'''simple docstring'''



						_snake_case						:     str				=    Image.new("RGB"					,			(len(cells[0]						), len(__lowercase						))						)
						_snake_case						:     int				=    img.load()
						# Generates image
						for w in range(img.width						):
												for h in range(img.height						):
																		_snake_case						:     List[Any]				=    255 - int(255 * cells[h][w]						)
																		_snake_case						:     Union[str, Any]				=    (color, color, color)
						return img


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Any     = bin(int(input('Rule:\n').strip()))[2:]
				__SCREAMING_SNAKE_CASE						:    Tuple     = format_ruleset(int(rule_num))
				for time in range(1_6):
								CELLS.append(new_generation(CELLS, rule, time))
				__SCREAMING_SNAKE_CASE						:    List[Any]     = generate_image(CELLS)
				# Uncomment to save the image
				# img.save(f"rule_{rule_num}.png")
				img.show()