

'''simple docstring'''






from __future__ import annotations

from PIL import Image

# Define glider example
lowerCAmelCase__											=	[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
lowerCAmelCase__											=	[[0, 1, 0], [0, 1, 0], [0, 1, 0]]


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = []
 for i in range(len(A__      )      ):
  __lowercase							  = []
  for j in range(len(cells[i]      )      ):
   # Get the number of live neighbours
   __lowercase							  = 0
   if i > 0 and j > 0:
    neighbour_count += cells[i - 1][j - 1]
   if i > 0:
    neighbour_count += cells[i - 1][j]
   if i > 0 and j < len(cells[i]      ) - 1:
    neighbour_count += cells[i - 1][j + 1]
   if j > 0:
    neighbour_count += cells[i][j - 1]
   if j < len(cells[i]      ) - 1:
    neighbour_count += cells[i][j + 1]
   if i < len(A__      ) - 1 and j > 0:
    neighbour_count += cells[i + 1][j - 1]
   if i < len(A__      ) - 1:
    neighbour_count += cells[i + 1][j]
   if i < len(A__      ) - 1 and j < len(cells[i]      ) - 1:
    neighbour_count += cells[i + 1][j + 1]

   # Rules of the game of life (excerpt from Wikipedia):
   # 1. Any live cell with two or three live neighbours survives.
   # 2. Any dead cell with three live neighbours becomes a live cell.
   # 3. All other live cells die in the next generation.
   #    Similarly, all other dead cells stay dead.
   __lowercase							  = cells[i][j] == 1
   if (
       (alive and 2 <= neighbour_count <= 3)
       or not alive
       and neighbour_count == 3
   ):
    next_generation_row.append(1      )
   else:
    next_generation_row.append(0      )

  next_generation.append(A__      )
 return next_generation


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = []
 for _ in range(A__      ):
  # Create output image
  __lowercase							  = Image.new('''RGB'''     ,				(len(cells[0]      ), len(A__      ))      )
  __lowercase							  = img.load()

  # Save cells to image
  for x in range(len(A__      )      ):
   for y in range(len(cells[0]      )      ):
    __lowercase							  = 255 - cells[y][x] * 255
    __lowercase							  = (colour, colour, colour)

        # Save image
  images.append(A__      )
  __lowercase							  = new_generation(A__      )
 return images


if __name__ == "__main__":
 lowerCAmelCase__											=	generate_images(GLIDER, 16)
 images[0].save('''out.gif''', save_all=True, append_images=images[1:])








from typing import List, Optional, Tuple, Union

import torch

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

class 	A_       (a_			):
	UpperCAmelCase__					=			42
	UpperCAmelCase__					=			42
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				super().__init__()
				self.register_modules(unet=_A							,					scheduler=_A		)







	@torch.no_grad()
	def __call__(    self							,					_A = 1							,					_A = 5_0							,					_A = None							,					_A = "pil"							,					_A = True							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				self.unet.config.sample_size
				UpperCAmelCase   		=				(batch_size, 3, img_size, img_size)

				UpperCAmelCase   		=				self.unet

				# sample x_0 ~ N(0, sigma_0^2 * I)
				UpperCAmelCase   		=				randn_tensor(_A							,					generator=_A							,					device=self.device		) * self.scheduler.init_noise_sigma

				self.scheduler.set_timesteps(_A		)

				for t in self.progress_bar(self.scheduler.timesteps		):
							# here sigma_t == t_i from the paper
							UpperCAmelCase   		=				self.scheduler.schedule[t]
							UpperCAmelCase   		=				self.scheduler.schedule[t - 1] if t > 0 else 0

							# 1. Select temporarily increased noise level sigma_hat
							# 2. Add new noise to move from sample_i to sample_hat
							UpperCAmelCase		,			UpperCAmelCase   		=				self.scheduler.add_noise_to_input(_A							,					_A							,					generator=_A		)

							# 3. Predict the noise residual given the noise magnitude `sigma_hat`
							# The model inputs and output are adjusted by following eq. (213) in [1].
							UpperCAmelCase   		=				(sigma_hat / 2) * model((sample_hat + 1) / 2							,					sigma_hat / 2		).sample

							# 4. Evaluate dx/dt at sigma_hat
							# 5. Take Euler step from sigma to sigma_prev
							UpperCAmelCase   		=				self.scheduler.step(_A							,					_A							,					_A							,					_A		)

							if sigma_prev != 0:
										# 6. Apply 2nd order correction
										# The model inputs and output are adjusted by following eq. (213) in [1].
										UpperCAmelCase   		=				(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,					sigma_prev / 2		).sample
										UpperCAmelCase   		=				self.scheduler.step_correct(
										    _A							,					_A							,					_A							,					_A							,					step_output.prev_sample							,					step_output['''derivative''']							,					)
							UpperCAmelCase   		=				step_output.prev_sample

				UpperCAmelCase   		=				(sample / 2 + 0.5).clamp(0							,					1		)
				UpperCAmelCase   		=				sample.cpu().permute(0							,					2							,					3							,					1		).numpy()
				if output_type == "pil":
							UpperCAmelCase   		=				self.numpy_to_pil(_A		)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_A		)



