





from __future__ import annotations

from PIL import Image

# Define glider example
_lowerCAmelCase				:   Optional[int]						       =						[
    [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				:   Optional[Any]						       =						[[0, 1, 0], [0, 1, 0], [0, 1, 0]]

def   __snake_case    (       _lowerCAmelCase			:			list[list[int]]						)			->    list[list[int]]:
   A_       :					int        =      []
   for i in range(len(_lowerCAmelCase						)						):
      A_       :					str        =      []
      for j in range(len(cells[i]						)						):
         # Get the number of live neighbours
         A_       :					Optional[Any]        =      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(_lowerCAmelCase						) - 1 and j > 0:
            neighbour_count += cells[i + 1][j - 1]
         if i < len(_lowerCAmelCase						) - 1:
            neighbour_count += cells[i + 1][j]
         if i < len(_lowerCAmelCase						) - 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_       :					List[Any]        =      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(_lowerCAmelCase						)
   return next_generation

def   __snake_case    (       _lowerCAmelCase			:			list[list[int]]					,					_lowerCAmelCase			:			int						)			->    list[Image.Image]:
   A_       :					int        =      []
   for _ in range(_lowerCAmelCase						):
      # Create output image
      A_       :					List[Any]        =      Image.new("RGB"					,					(len(cells[0]						), len(_lowerCAmelCase						))						)
      A_       :					Union[str, Any]        =      img.load()

      # Save cells to image
      for x in range(len(_lowerCAmelCase						)						):
         for y in range(len(cells[0]						)						):
            A_       :					List[Any]        =      255 - cells[y][x] * 255
            A_       :					int        =      (colour, colour, colour)

        # Save image
      images.append(_lowerCAmelCase						)
      A_       :					Tuple        =      new_generation(_lowerCAmelCase						)
   return images


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







import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					Optional[Any]        =      UNetaDModel(
            block_out_channels=(32, 64)      ,   layers_per_block=2      ,   sample_size=32      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")      ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")      ,   )
        return model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					Any        =      VQModel(
            block_out_channels=[32, 64]      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,   latent_channels=3      ,   )
        return model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					int        =      CLIPTextConfig(
            bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-05      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=1_000      ,   )
        return CLIPTextModel(snake_case	)





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					Dict        =      self.dummy_uncond_unet
        A_       :					Any        =      DDIMScheduler()
        A_       :					List[str]        =      self.dummy_vq_model

        A_       :					Union[str, Any]        =      LDMPipeline(unet=snake_case      ,   vqvae=snake_case      ,   scheduler=snake_case	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

        A_       :					Any        =      torch.manual_seed(0	)
        A_       :					Optional[Any]        =      ldm(generator=snake_case      ,   num_inference_steps=2      ,   output_type="numpy"	).images

        A_       :					Dict        =      torch.manual_seed(0	)
        A_       :					Any        =      ldm(generator=snake_case      ,   num_inference_steps=2      ,   output_type="numpy"      ,   return_dict=snake_case	)[0]

        A_       :					int        =      image[0, -3:, -3:, -1]
        A_       :					Any        =      image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        A_       :					str        =      np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]	)
        A_       :					List[str]        =      1e-2 if torch_device != "mps" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < tolerance




@slow
@require_torch
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        A_       :					int        =      LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

        A_       :					int        =      torch.manual_seed(0	)
        A_       :					List[str]        =      ldm(generator=snake_case      ,   num_inference_steps=5      ,   output_type="numpy"	).images

        A_       :					Tuple        =      image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        A_       :					Any        =      np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]	)
        A_       :					List[Any]        =      1e-2 if torch_device != "mps" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
