"""simple docstring"""






import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel



def   a_						(					lowerCamelCase					,			lowerCamelCase=False       ):
				UpperCAmelCase__             =							OmegaConf.load(A__       )
				if display:
								print(yaml.dump(OmegaConf.to_container(A__       )       )       )
				return config



def   a_						(					lowerCamelCase					,			lowerCamelCase=None					,			lowerCamelCase=None       ):



				if conf_path is None:
								UpperCAmelCase__             =							'./model_checkpoints/vqgan_only.yaml'
				UpperCAmelCase__             =							load_config(A__					,			display=A__       )
				UpperCAmelCase__             =							VQModel(**config.model.params       )
				if ckpt_path is None:
								UpperCAmelCase__             =							'./model_checkpoints/vqgan_only.pt'
				UpperCAmelCase__             =							torch.load(A__					,			map_location=A__       )
				if ".ckpt" in ckpt_path:
								UpperCAmelCase__             =							sd['state_dict']
				model.load_state_dict(A__					,			strict=A__       )
				model.to(A__       )
				del sd
				return model



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__             =							model.encode(A__       )
				print(f'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''       )
				UpperCAmelCase__             =							model.decode(A__       )
				return xrec



def   a_						(					lowerCamelCase					,			lowerCamelCase=False       ):
				UpperCAmelCase__      ,	UpperCAmelCase__             =							string.rsplit('.'					,			1       )
				if reload:
								UpperCAmelCase__             =							importlib.import_module(A__       )
								importlib.reload(A__       )
				return getattr(importlib.import_module(A__					,			package=A__       )					,			cls       )



def   a_						(					lowerCamelCase       ):



				if "target" not in config:
								raise KeyError('Expected key `target` to instantiate.'       )
				return get_obj_from_str(config['target']       )(**config.get('params'					,			{}       )       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase=True					,			lowerCamelCase=True       ):
				UpperCAmelCase__             =							instantiate_from_config(A__       )
				if sd is not None:
								model.load_state_dict(A__       )
				if gpu:
								model.cuda()
				if eval_mode:
								model.eval()
				return {"model": model}







def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):



				if ckpt:
								UpperCAmelCase__             =							torch.load(A__					,			map_location='cpu'       )
								UpperCAmelCase__             =							pl_sd['global_step']
								print(f'''loaded model from global step {global_step}.'''       )
				else:
								UpperCAmelCase__             =							{'state_dict': None}
								UpperCAmelCase__             =							None
				UpperCAmelCase__             =							load_model_from_config(config.model					,			pl_sd['state_dict']					,			gpu=A__					,			eval_mode=A__       )['model']
				return model, global_step









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 					lowerCamelCase__       (						A__       :					list[list[int]]	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for i in range(len(A__	)	):
              __lowerCamelCase   =						[]
              for j in range(len(cells[i]	)	):
                     # Get the number of live neighbours
                     __lowerCamelCase   =						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.
                     __lowerCamelCase   =						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 					lowerCamelCase__       (						A__       :					list[list[int]]  ,			A__       :					int	):





       '''simple docstring'''



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

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

        # Save image
              images.append(A__	)
              __lowerCamelCase   =						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:])

