


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 							_UpperCAmelCase				(UpperCamelCase__     :  list[list[int]]						):
       _A      :       Tuple				    = []
       for i in range(len(UpperCamelCase__						)						):
              _A      :       Tuple				    = []
              for j in range(len(cells[i]						)						):
                     # Get the number of live neighbours
                     _A      :       Tuple				    = 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(UpperCamelCase__						) - 1 and j > 0:
                            neighbour_count += cells[i + 1][j - 1]
                     if i < len(UpperCamelCase__						) - 1:
                            neighbour_count += cells[i + 1][j]
                     if i < len(UpperCamelCase__						) - 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(UpperCamelCase__						)
       return next_generation
def 							_UpperCAmelCase				(UpperCamelCase__     :  list[list[int]]				,							UpperCamelCase__     :  int						):
       _A      :       Union[str, Any]				    = []
       for _ in range(UpperCamelCase__						):
              # Create output image
              _A      :       Optional[int]				    = Image.new("RGB"				,							(len(cells[0]						), len(UpperCamelCase__						))						)
              _A      :       Optional[Any]				    = img.load()

              # Save cells to image
              for x in range(len(UpperCamelCase__						)						):
                     for y in range(len(cells[0]						)						):
                            _A      :       Dict				    = 255 - cells[y][x] * 255
                            _A      :       List[str]				    = (colour, colour, colour)

        # Save image
              images.append(UpperCamelCase__						)
              _A      :       List[Any]				    = new_generation(UpperCamelCase__						)
       return images


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


"""simple docstring"""




import importlib

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


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=False  ) ->				Tuple:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       if display:
              print(yaml.dump(OmegaConf.to_container(__snake_case  )  )  )
       return config


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=None     , __snake_case				:       Union[str, Any]=None  ) ->				Tuple:
       if conf_path is None:
              lowercase    :				List[Any]       		=							"./model_checkpoints/vqgan_only.yaml"
       lowercase    :				Tuple       		=							load_config(__snake_case     , display=__snake_case  )
       lowercase    :				List[Any]       		=							VQModel(**config.model.params  )
       if ckpt_path is None:
              lowercase    :				List[str]       		=							"./model_checkpoints/vqgan_only.pt"
       lowercase    :				Optional[int]       		=							torch.load(__snake_case     , map_location=__snake_case  )
       if ".ckpt" in ckpt_path:
              lowercase    :				str       		=							sd["state_dict"]
       model.load_state_dict(__snake_case     , strict=__snake_case  )
       model.to(__snake_case  )
       del sd
       return model


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       Union[str, Any]  ) ->				int:
       lowercase		,    lowercase		,    lowercase    :				List[Any]       		=							model.encode(__snake_case  )
       print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""  )
       lowercase    :				str       		=							model.decode(__snake_case  )
       return xrec


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[int]=False  ) ->				int:
       lowercase		,    lowercase    :				Union[str, Any]       		=							string.rsplit("."     , 1  )
       if reload:
              lowercase    :				Any       		=							importlib.import_module(__snake_case  )
              importlib.reload(__snake_case  )
       return getattr(importlib.import_module(__snake_case     , package=__snake_case  )     , cls  )


def   __magic_name__	(      __snake_case				:       str  ) ->				List[str]:
       if "target" not in config:
              raise KeyError("Expected key `target` to instantiate."  )
       return get_obj_from_str(config["target"]  )(**config.get("params"     , {}  )  )


def   __magic_name__	(      __snake_case				:       Any     , __snake_case				:       int     , __snake_case				:       List[Any]=True     , __snake_case				:       Dict=True  ) ->				str:
       lowercase    :				Optional[int]       		=							instantiate_from_config(__snake_case  )
       if sd is not None:
              model.load_state_dict(__snake_case  )
       if gpu:
              model.cuda()
       if eval_mode:
              model.eval()
       return {"model": model}


def   __magic_name__	(      __snake_case				:       Optional[int]     , __snake_case				:       Any     , __snake_case				:       Optional[int]     , __snake_case				:       List[str]  ) ->				Any:
       # load the specified checkpoint
       if ckpt:
              lowercase    :				Dict       		=							torch.load(__snake_case     , map_location="cpu"  )
              lowercase    :				List[Any]       		=							pl_sd["global_step"]
              print(f"""loaded model from global step {global_step}."""  )
       else:
              lowercase    :				int       		=							{"state_dict": None}
              lowercase    :				Optional[Any]       		=							None
       lowercase    :				List[Any]       		=							load_model_from_config(config.model     , pl_sd["state_dict"]     , gpu=__snake_case     , eval_mode=__snake_case  )["model"]
       return model, global_step


