




import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json




def      __magic_name__							(					A						:   Union[str, Any]  ):






			'''simple docstring'''







			a       =     filter(lambda A  : p.requires_grad,    model.parameters()  )
			a       =     sum([np.prod(p.size()  ) for p in model_parameters]  )
			return params


__lowerCAmelCase						:							List[str]			     =						logging.getLogger(__name__)




class        snake_case__				(pl.Callback  ):
				"""simple docstring"""



				def 				__UpperCAmelCase				( self							:	Union[str, Any]   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Any				)   ->	Optional[Any]:
							a       =     {f"""lr_group_{i}""": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups				)}
							pl_module.logger.log_metrics(__lowerCamelCase				)





				@rank_zero_only
				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	pl.Trainer   , __lowerCamelCase							:	pl.LightningModule   , __lowerCamelCase							:	str   , __lowerCamelCase							:	Tuple=True				)   ->	Dict:
							logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""				)
							a       =     trainer.callback_metrics
							trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}				)
							# Log results
							a       =     Path(pl_module.hparams.output_dir				)
							if type_path == "test":
										a       =     od / '''test_results.txt'''
										a       =     od / '''test_generations.txt'''
							else:
										# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
										# If people want this it will be easy enough to add back.
										a       =     od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
										a       =     od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
										results_file.parent.mkdir(exist_ok=__lowerCamelCase				)
										generations_file.parent.mkdir(exist_ok=__lowerCamelCase				)
							with open(__lowerCamelCase   , "a+"				) as writer:
										for key in sorted(__lowerCamelCase				):
													if key in ["log", "progress_bar", "preds"]:
																continue
													a       =     metrics[key]
													if isinstance(__lowerCamelCase   , torch.Tensor				):
																a       =     val.item()
													a       =     f"""{key}: {val:.6f}\n"""
													writer.write(__lowerCamelCase				)

							if not save_generations:
										return

							if "preds" in metrics:
										a       =     '''\n'''.join(metrics["preds"]				)
										generations_file.open("w+"				).write(__lowerCamelCase				)





				@rank_zero_only
				def 				__UpperCAmelCase				( self							:	Union[str, Any]   , __lowerCamelCase							:	Dict   , __lowerCamelCase							:	str				)   ->	int:







							try:
										a       =     pl_module.model.model.num_parameters()
							except AttributeError:
										a       =     pl_module.model.num_parameters()

							a       =     count_trainable_parameters(__lowerCamelCase				)
							# mp stands for million parameters
							trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6}				)





				@rank_zero_only
				def 				__UpperCAmelCase				( self							:	Dict   , __lowerCamelCase							:	pl.Trainer   , __lowerCamelCase							:	pl.LightningModule				)   ->	Tuple:
							save_json(pl_module.metrics   , pl_module.metrics_save_path				)
							return self._write_logs(__lowerCamelCase   , __lowerCamelCase   , "test"				)





				@rank_zero_only
				def 				__UpperCAmelCase				( self							:	Optional[int]   , __lowerCamelCase							:	pl.Trainer   , __lowerCamelCase							:	Tuple				)   ->	Union[str, Any]:
							save_json(pl_module.metrics   , pl_module.metrics_save_path				)
							# Uncommenting this will save val generations
							# return self._write_logs(trainer, pl_module, "valid")




def      __magic_name__							(					A						:   Optional[Any],    A						:   Union[str, Any],    A						:   int=1,    A						:   List[Any]=False  ):






			'''simple docstring'''







			if metric == "rouge2":
						a       =     '''{val_avg_rouge2:.4f}-{step_count}'''
			elif metric == "bleu":
						a       =     '''{val_avg_bleu:.4f}-{step_count}'''
			elif metric == "loss":
						a       =     '''{val_avg_loss:.4f}-{step_count}'''
			else:
						raise NotImplementedError(
						    F"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
						    " this function."  )

			a       =     ModelCheckpoint(
			    dirpath=A,    filename=A,    monitor=F"""val_{metric}""",    mode="min" if "loss" in metric else "max",    save_top_k=A,    )
			return checkpoint_callback






def      __magic_name__							(					A						:   List[str],    A						:   Optional[int]  ):






			'''simple docstring'''







			return EarlyStopping(
			    monitor=F"""val_{metric}""",    mode="min" if "loss" in metric else "max",    patience=A,    verbose=A,    )














"""simple docstring"""





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
UpperCamelCase_						:					int      				=			[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]



def      __lowercase       (     a			:					int			)       ->       list[int]:
      return [int(a			) for c in f'''{ruleset:08}'''[:8]]



def      __lowercase       (     a			:					list[list[int]]							,		a			:					list[int]							,		a			:					int			)       ->       list[int]:
      __snake_case   :     Optional[Any]	      =len(cells[0]			)  # 31
      __snake_case   :     List[str]	      =[]
      for i in range(a			):
            # Get the neighbors of each cell
            # Handle neighbours outside bounds by using 0 as their value
            __snake_case   :     Optional[Any]	      =0 if i == 0 else cells[time][i - 1]
            __snake_case   :     Dict	      =0 if i == population - 1 else cells[time][i + 1]
            # Define a new cell and add it to the new generation
            __snake_case   :     Optional[int]	      =7 - int(f'''{left_neighbor}{cells[time][i]}{right_neighbor}'''							,		2			)
            next_generation.append(rule[situation]			)
      return next_generation



def      __lowercase       (     a			:					list[list[int]]			)       ->       Image.Image:
      __snake_case   :     int	      =Image.new('''RGB'''							,		(len(cells[0]			), len(a			))			)
      __snake_case   :     List[Any]	      =img.load()
      # Generates image
      for w in range(img.width			):
            for h in range(img.height			):
                  __snake_case   :     int	      =255 - int(255 * cells[h][w]			)
                  __snake_case   :     List[str]	      =(color, color, color)
      return img


if __name__ == "__main__":
    UpperCamelCase_						:					Optional[Any]      				=			bin(int(input("""Rule:\n""").strip()))[2:]
    UpperCamelCase_						:					List[str]      				=			format_ruleset(int(rule_num))
    for time in range(16):
        CELLS.append(new_generation(CELLS, rule, time))
    UpperCamelCase_						:					List[Any]      				=			generate_image(CELLS)
    # Uncomment to save the image
    # img.save(f"rule_{rule_num}.png")
    img.show()


