




'''simple docstring'''


from __future__ import annotations

from PIL import Image

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


def 						SCREAMING_SNAKE_CASE       (      a_	:							list[list[int]]			):
  __a			=					[]
  for i in range(len(a_			)			):
    __a			=					[]
    for j in range(len(cells[i]			)			):
      # Get the number of live neighbours
      __a			=					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.
      __a			=					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 						SCREAMING_SNAKE_CASE       (      a_	:							list[list[int]]				,					a_	:							int			):
  __a			=					[]
  for _ in range(a_			):
    # Create output image
    __a			=					Image.new('RGB'				,					(len(cells[0]			), len(a_			))			)
    __a			=					img.load()

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

        # Save image
    images.append(a_			)
    __a			=					new_generation(a_			)
  return images


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






'''simple docstring'''


import torch

from diffusers import StableDiffusionPipeline


UpperCAmelCase_ 		=      "path-to-your-trained-model"
UpperCAmelCase_ 		=      StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

UpperCAmelCase_ 		=      "A photo of sks dog in a bucket"
UpperCAmelCase_ 		=      pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")



