





'''simple docstring'''



import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


__magic_name__   :						Optional[Any]    =			False


class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



       '''simple docstring'''






       pass







@nightly
@require_torch_gpu
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



       '''simple docstring'''






       def 	UpperCamelCase(					self	):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()

       def 	UpperCamelCase(					self	):
         _snake_case     		=				VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"	)
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(lowercase_	)
         pipe.set_progress_bar_config(disable=lowercase_	)

         _snake_case     		=				"A painting of a squirrel eating a burger "
         _snake_case     		=				torch.manual_seed(0	)
         _snake_case     		=				pipe(
             prompt=lowercase_						,   generator=lowercase_						,   guidance_scale=7.5						,   num_inference_steps=2						,   output_type="numpy"	).images

         with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(lowercase_	)
           _snake_case     		=				VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase_	)
         pipe.to(lowercase_	)
         pipe.set_progress_bar_config(disable=lowercase_	)

         _snake_case     		=				generator.manual_seed(0	)
         _snake_case     		=				pipe(
             prompt=lowercase_						,   generator=lowercase_						,   guidance_scale=7.5						,   num_inference_steps=2						,   output_type="numpy"	).images

         assert np.abs(image - new_image	).sum() < 1e-5, "Models don't have the same forward pass"






       def 	UpperCamelCase(					self	):
         _snake_case     		=				VersatileDiffusionTextToImagePipeline.from_pretrained(
             "shi-labs/versatile-diffusion"						,   torch_dtype=torch.floataa	)
         pipe.to(lowercase_	)
         pipe.set_progress_bar_config(disable=lowercase_	)

         _snake_case     		=				"A painting of a squirrel eating a burger "
         _snake_case     		=				torch.manual_seed(0	)
         _snake_case     		=				pipe(
             prompt=lowercase_						,   generator=lowercase_						,   guidance_scale=7.5						,   num_inference_steps=50						,   output_type="numpy"	).images

         _snake_case     		=				image[0, 253:256, 253:256, -1]

         assert image.shape == (1, 512, 512, 3)
         _snake_case     		=				np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2









'''simple docstring'''



from __future__ import annotations

from PIL import Image

# Define glider example
__magic_name__   :						str    =			[
    [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
__magic_name__   :						List[Any]    =			[[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

    # Save cells to image
    for x in range(len(SCREAMING_SNAKE_CASE__       )       ):
      for y in range(len(cells[0]       )       ):
        _snake_case     		=				2_55 - cells[y][x] * 2_55
        _snake_case     		=				(colour, colour, colour)

        # Save image
    images.append(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				new_generation(SCREAMING_SNAKE_CASE__       )
  return images


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


