code: 
  
  
  
  
  
  
  """simple docstring"""
  
  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 	__lowerCamelCase			(						__UpperCamelCase )		->					str:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							List[Any]             =    filter(lambda __UpperCamelCase : p.requires_grad   , model.parameters() )
  		lowerCAmelCase_     :							Union[str, Any]             =    sum([np.prod(p.size() ) for p in model_parameters] )
  		return params
  
  
  lowercase__      =				logging.getLogger(__name__)
  
  
  class 	__lowerCamelCase       (				pl.Callback    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str		,    a_							:			Tuple		,    a_							:			Tuple			):
  				lowerCAmelCase_     :							List[str]             =    {f'''lr_group_{i}''': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
  				pl_module.logger.log_metrics(a_			)
  
  
  
  
  
  		@rank_zero_only
  		def      lowerCamelCase (     self							:			Union[str, Any]		,    a_							:			pl.Trainer		,    a_							:			pl.LightningModule		,    a_							:			str		,    a_							:			List[Any]=True			):
  				logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''			)
  				lowerCAmelCase_     :							int             =    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
  				lowerCAmelCase_     :							List[str]             =    Path(pl_module.hparams.output_dir			)
  				if type_path == "test":
  						lowerCAmelCase_     :							Tuple             =    od / "test_results.txt"
  						lowerCAmelCase_     :							List[Any]             =    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.
  						lowerCAmelCase_     :							int             =    od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
  						lowerCAmelCase_     :							int             =    od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
  						results_file.parent.mkdir(exist_ok=a_			)
  						generations_file.parent.mkdir(exist_ok=a_			)
  				with open(a_		,    "a+"			) as writer:
  						for key in sorted(a_			):
  								if key in ["log", "progress_bar", "preds"]:
  										continue
  								lowerCAmelCase_     :							Any             =    metrics[key]
  								if isinstance(a_		,    torch.Tensor			):
  										lowerCAmelCase_     :							Optional[int]             =    val.item()
  								lowerCAmelCase_     :							Dict             =    f'''{key}: {val:.6f}\n'''
  								writer.write(a_			)
  
  				if not save_generations:
  						return
  
  				if "preds" in metrics:
  						lowerCAmelCase_     :							Optional[int]             =    "\n".join(metrics["preds"]			)
  						generations_file.open("w+"			).write(a_			)
  
  
  
  
  
  		@rank_zero_only
  		def      lowerCamelCase (     self							:			int		,    a_							:			List[str]		,    a_							:			int			):
  				try:
  						lowerCAmelCase_     :							List[Any]             =    pl_module.model.model.num_parameters()
  				except AttributeError:
  						lowerCAmelCase_     :							List[str]             =    pl_module.model.num_parameters()
  
  				lowerCAmelCase_     :							Dict             =    count_trainable_parameters(a_			)
  				# 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      lowerCamelCase (     self							:			List[str]		,    a_							:			pl.Trainer		,    a_							:			pl.LightningModule			):
  				save_json(pl_module.metrics		,    pl_module.metrics_save_path			)
  				return self._write_logs(a_		,    a_		,    "test"			)
  
  
  
  
  
  		@rank_zero_only
  		def      lowerCamelCase (     self							:			str		,    a_							:			pl.Trainer		,    a_							:			List[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 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase=1   , __UpperCamelCase=False )		->					Optional[int]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		if metric == "rouge2":
  				lowerCAmelCase_     :							Optional[int]             =    "{val_avg_rouge2:.4f}-{step_count}"
  		elif metric == "bleu":
  				lowerCAmelCase_     :							List[Any]             =    "{val_avg_bleu:.4f}-{step_count}"
  		elif metric == "loss":
  				lowerCAmelCase_     :							Tuple             =    "{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." )
  
  		lowerCAmelCase_     :							int             =    ModelCheckpoint(
  		    dirpath=__UpperCamelCase   , filename=__UpperCamelCase   , monitor=f'''val_{metric}'''   , mode="min" if "loss" in metric else "max"   , save_top_k=__UpperCamelCase   , )
  		return checkpoint_callback
  
  
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase )		->					Union[str, Any]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		return EarlyStopping(
  		    monitor=f'''val_{metric}'''   , mode="min" if "loss" in metric else "max"   , patience=__UpperCamelCase   , verbose=__UpperCamelCase   , )
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  from __future__ import annotations
  
  from PIL import Image
  
  # Define the first generation of cells
  # fmt: off
  lowercase__      =				[[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 	__lowerCamelCase			(						__UpperCamelCase )		->					list[int]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		return [int(__UpperCamelCase ) for c in f'''{ruleset:08}'''[:8]]
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )		->					list[int]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							int             =    len(cells[0] )  # 31
  		lowerCAmelCase_     :							List[Any]             =    []
  		for i in range(__UpperCamelCase ):
  				# Get the neighbors of each cell
  				# Handle neighbours outside bounds by using 0 as their value
  				lowerCAmelCase_     :							List[str]             =    0 if i == 0 else cells[time][i - 1]
  				lowerCAmelCase_     :							Optional[int]             =    0 if i == population - 1 else cells[time][i + 1]
  				# Define a new cell and add it to the new generation
  				lowerCAmelCase_     :							List[Any]             =    7 - int(f'''{left_neighbor}{cells[time][i]}{right_neighbor}'''   , 2 )
  				next_generation.append(rule[situation] )
  		return next_generation
  
  def 	__lowerCamelCase			(						__UpperCamelCase )		->					Image.Image:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							List[str]             =    Image.new("RGB"   , (len(cells[0] ), len(__UpperCamelCase )) )
  		lowerCAmelCase_     :							List[str]             =    img.load()
  		# Generates image
  		for w in range(img.width ):
  				for h in range(img.height ):
  						lowerCAmelCase_     :							Union[str, Any]             =    255 - int(255 * cells[h][w] )
  						lowerCAmelCase_     :							List[str]             =    (color, color, color)
  		return img
  
  
  if __name__ == "__main__":
  					lowercase__      =				bin(int(input("""Rule:\n""").strip()))[2:]
  					lowercase__      =				format_ruleset(int(rule_num))
  					for time in range(16):
  										CELLS.append(new_generation(CELLS, rule, time))
  					lowercase__      =				generate_image(CELLS)
  					# Uncomment to save the image
  					# img.save(f"rule_{rule_num}.png")
  					img.show()
  