



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class       __lowerCAmelCase				(  lowerCamelCase__ ):
				__lowerCamelCase				=       (EulerAncestralDiscreteScheduler,)
				__lowerCamelCase				=       10




				def 							snake_case     (							self		,       **_snake_case	):



									"""simple docstring"""





									_lowerCAmelCase      			=					{
									    """num_train_timesteps""": 1100,
									    """beta_start""": 0.0001,
									    """beta_end""": 0.02,
									    """beta_schedule""": """linear""",
									}

									config.update(**_snake_case	)
									return config




				def 							snake_case     (							self	):



									"""simple docstring"""





									for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=_snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]		,       [0.0002, 0.002, 0.02]	):
														self.check_over_configs(beta_start=_snake_case		,       beta_end=_snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									for schedule in ["linear", "scaled_linear"]:
														self.check_over_configs(beta_schedule=_snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=_snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.scheduler_classes[0]
									_lowerCAmelCase      			=					self.get_scheduler_config()
									_lowerCAmelCase      			=					scheduler_class(**_snake_case	)

									scheduler.set_timesteps(self.num_inference_steps	)

									_lowerCAmelCase      			=					torch.manual_seed(0	)

									_lowerCAmelCase      			=					self.dummy_model()
									_lowerCAmelCase      			=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									_lowerCAmelCase      			=					sample.to(_snake_case	)

									for i, t in enumerate(scheduler.timesteps	):
														_lowerCAmelCase      			=					scheduler.scale_model_input(_snake_case		,       _snake_case	)

														_lowerCAmelCase      			=					model(_snake_case		,       _snake_case	)

														_lowerCAmelCase      			=					scheduler.step(_snake_case		,       _snake_case		,       _snake_case		,       generator=_snake_case	)
														_lowerCAmelCase      			=					output.prev_sample

									_lowerCAmelCase      			=					torch.sum(torch.abs(_snake_case	)	)
									_lowerCAmelCase      			=					torch.mean(torch.abs(_snake_case	)	)

									assert abs(result_sum.item() - 152.3192	) < 1e-2
									assert abs(result_mean.item() - 0.1983	) < 1e-3




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.scheduler_classes[0]
									_lowerCAmelCase      			=					self.get_scheduler_config(prediction_type="""v_prediction"""	)
									_lowerCAmelCase      			=					scheduler_class(**_snake_case	)

									scheduler.set_timesteps(self.num_inference_steps	)

									_lowerCAmelCase      			=					torch.manual_seed(0	)

									_lowerCAmelCase      			=					self.dummy_model()
									_lowerCAmelCase      			=					self.dummy_sample_deter * scheduler.init_noise_sigma
									_lowerCAmelCase      			=					sample.to(_snake_case	)

									for i, t in enumerate(scheduler.timesteps	):
														_lowerCAmelCase      			=					scheduler.scale_model_input(_snake_case		,       _snake_case	)

														_lowerCAmelCase      			=					model(_snake_case		,       _snake_case	)

														_lowerCAmelCase      			=					scheduler.step(_snake_case		,       _snake_case		,       _snake_case		,       generator=_snake_case	)
														_lowerCAmelCase      			=					output.prev_sample

									_lowerCAmelCase      			=					torch.sum(torch.abs(_snake_case	)	)
									_lowerCAmelCase      			=					torch.mean(torch.abs(_snake_case	)	)

									assert abs(result_sum.item() - 108.4439	) < 1e-2
									assert abs(result_mean.item() - 0.1412	) < 1e-3




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.scheduler_classes[0]
									_lowerCAmelCase      			=					self.get_scheduler_config()
									_lowerCAmelCase      			=					scheduler_class(**_snake_case	)

									scheduler.set_timesteps(self.num_inference_steps		,       device=_snake_case	)
									_lowerCAmelCase      			=					torch.manual_seed(0	)

									_lowerCAmelCase      			=					self.dummy_model()
									_lowerCAmelCase      			=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									_lowerCAmelCase      			=					sample.to(_snake_case	)

									for t in scheduler.timesteps:
														_lowerCAmelCase      			=					scheduler.scale_model_input(_snake_case		,       _snake_case	)

														_lowerCAmelCase      			=					model(_snake_case		,       _snake_case	)

														_lowerCAmelCase      			=					scheduler.step(_snake_case		,       _snake_case		,       _snake_case		,       generator=_snake_case	)
														_lowerCAmelCase      			=					output.prev_sample

									_lowerCAmelCase      			=					torch.sum(torch.abs(_snake_case	)	)
									_lowerCAmelCase      			=					torch.mean(torch.abs(_snake_case	)	)

									assert abs(result_sum.item() - 152.3192	) < 1e-2
									assert abs(result_mean.item() - 0.1983	) < 1e-3








from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
A__			   = [[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 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					return [int(snake_case    ) for c in F'{ruleset:08}'[:8]]


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

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


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					Image.new("""RGB"""			,		(len(cells[0]    ), len(snake_case    ))    )
					_lowerCAmelCase      			=					img.load()
					# Generates image
					for w in range(img.width    ):
										for h in range(img.height    ):
															_lowerCAmelCase      			=					2_55 - int(2_55 * cells[h][w]    )
															_lowerCAmelCase      			=					(color, color, color)
					return img


if __name__ == "__main__":
	A__			   = bin(int(input("""Rule:\n""").strip()))[2:]
	A__			   = format_ruleset(int(rule_num))
	for time in range(16):
		CELLS.append(new_generation(CELLS, rule, time))
	A__			   = generate_image(CELLS)
	# Uncomment to save the image
	# img.save(f"rule_{rule_num}.png")
	img.show()



