code: 
  
  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        __SCREAMING_SNAKE_CASE    (      a__						:      List[Any]					)				->							Union[str, Any]:
  	__A   : List[str]								= filter(lambda a__					: p.requires_grad ,model.parameters()					)
  	__A   : Dict								= sum([np.prod(p.size()					) for p in model_parameters]					)
  	return params
  
  
  UpperCAmelCase_       :				List[str]		   =      logging.getLogger(__name__)
  
  
  
  
  
  
  class     lowerCamelCase_			(  pl.Callback  ):
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    Any				,			__A	:    Tuple       ):
  		__A   : int								= {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	:    List[Any]				,			__A	:    pl.Trainer				,			__A	:    pl.LightningModule				,			__A	:    str				,			__A	:    Dict=True       ):
  		logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""       )
  		__A   : List[str]								= 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   : int								= Path(pl_module.hparams.output_dir       )
  		if type_path == "test":
  			__A   : List[Any]								= od / """test_results.txt"""
  			__A   : 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.
  			__A   : Optional[Any]								= od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
  			__A   : Tuple								= 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
  				__A   : Optional[int]								= metrics[key]
  				if isinstance(__A				,			torch.Tensor       ):
  					__A   : List[Any]								= val.item()
  				__A   : Optional[Any]								= F"""{key}: {val:.6f}\n"""
  				writer.write(__A       )
  
  		if not save_generations:
  			return
  
  		if "preds" in metrics:
  			__A   : int								= """\n""".join(metrics["""preds"""]       )
  			generations_file.open("""w+"""       ).write(__A       )
  
  
  
  
  	@rank_zero_only
  	def 					lowerCAmelCase_					(    self	:    str				,			__A	:    List[str]				,			__A	:    Optional[int]       ):
  		try:
  			__A   : List[Any]								= pl_module.model.model.num_parameters()
  		except AttributeError:
  			__A   : List[Any]								= pl_module.model.num_parameters()
  
  		__A   : str								= 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	:    Optional[Any]				,			__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	:    List[str]				,			__A	:    pl.Trainer				,			__A	:    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        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[int] ,a__						:      Union[str, Any] ,a__						:      Dict=1 ,a__						:      Tuple=False					)				->							Any:
  	if metric == "rouge2":
  		__A   : List[Any]								= """{val_avg_rouge2:.4f}-{step_count}"""
  	elif metric == "bleu":
  		__A   : Union[str, Any]								= """{val_avg_bleu:.4f}-{step_count}"""
  	elif metric == "loss":
  		__A   : str								= """{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   : Union[str, Any]								= 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        __SCREAMING_SNAKE_CASE    (      a__						:      Union[str, Any] ,a__						:      List[str]					)				->							Any:
  	return EarlyStopping(
  	    monitor=f"""val_{metric}""" ,mode="""min""" if """loss""" in metric else """max""" ,patience=a__ ,verbose=a__ ,)
  
  
  
  
  
  
style_context: 
  
  import math
  
  
  
  
  
  
  class     lowerCamelCase_			:
  
  
  
  
  	def __init__(    self	:    Dict				,			__A	:    int				,			__A	:    int				,			__A	:    int				,			__A	:    Any       ):
  		__A   : Union[str, Any]								= x
  		__A   : Union[str, Any]								= y
  		__A   : Optional[int]								= step_size
  		__A   : Dict								= function_to_optimize
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
  		return self.function(self.x				,			self.y       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    int       ):
  		__A   : Dict								= self.step_size
  		return [
  		    SearchProblem(__A				,			__A				,			__A				,			self.function       )
  		    for x, y in (
  		        (self.x - step_size, self.y - step_size),
  		        (self.x - step_size, self.y),
  		        (self.x - step_size, self.y + step_size),
  		        (self.x, self.y - step_size),
  		        (self.x, self.y + step_size),
  		        (self.x + step_size, self.y - step_size),
  		        (self.x + step_size, self.y),
  		        (self.x + step_size, self.y + step_size),
  		    )
  		]
  
  
  
  
  	def __hash__(    self	:    str       ):
  		return hash(str(self       )       )
  
  
  
  
  	def __eq__(    self	:    Union[str, Any]				,			__A	:    Optional[int]       ):
  		if isinstance(__A				,			__A       ):
  			return hash(str(self       )       ) == hash(str(__A       )       )
  		return False
  
  
  
  
  	def __str__(    self	:    List[Any]       ):
  		return F"""x: {self.x} y: {self.y}"""
  
  
  
  
  
  
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[Any] ,a__						:      bool = True ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      bool = False ,a__						:      int = 10000 ,)				->							SearchProblem:
  	__A   : int								= search_prob
  	__A   : List[str]								= []  # list to store the current score at each iteration
  	__A   : Optional[Any]								= 0
  	__A   : Any								= False
  	__A   : Dict								= set()
  	while not solution_found and iterations < max_iter:
  		visited.add(a__					)
  		iterations += 1
  		__A   : Union[str, Any]								= current_state.score()
  		scores.append(a__					)
  		__A   : List[Any]								= current_state.get_neighbors()
  		__A   : Tuple								= -math.inf
  		__A   : str								= math.inf
  		__A   : Optional[int]								= None  # to hold the next best neighbor
  		for neighbor in neighbors:
  			if neighbor in visited:
  				continue  # do not want to visit the same state again
  			if (
  			    neighbor.x > max_x
  			    or neighbor.x < min_x
  			    or neighbor.y > max_y
  			    or neighbor.y < min_y
  			):
  				continue  # neighbor outside our bounds
  			__A   : int								= neighbor.score() - current_score
  			if find_max:  # finding max
  				# going to direction with greatest ascent
  				if change > max_change and change > 0:
  					__A   : str								= change
  					__A   : Optional[int]								= neighbor
  			else:  # finding min
  				# to direction with greatest descent
  				if change < min_change and change < 0:
  					__A   : List[Any]								= change
  					__A   : List[str]								= neighbor
  		if next_state is not None:
  			# we found at least one neighbor which improved the current state
  			__A   : Tuple								= next_state
  		else:
  			# since we have no neighbor that improves the solution we stop the search
  			__A   : str								= True
  
  	if visualization:
  		from matplotlib import pyplot as plt
  
  		plt.plot(range(a__					) ,a__					)
  		plt.xlabel("""Iterations"""					)
  		plt.ylabel("""Function values"""					)
  		plt.show()
  
  	return current_state
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  			def        __SCREAMING_SNAKE_CASE    (      a__						:      str ,a__						:      Any					)				->							Dict:
  				return (x**2) + (y**2)
  
  			# starting the problem with initial coordinates (3, 4)
  			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  			UpperCAmelCase_       :				Any		   =      hill_climbing(prob, find_max=False)
  			print(
  			    '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
  			    f"""{local_min.score()}"""
  			)
  
  			# starting the problem with initial coordinates (12, 47)
  			UpperCAmelCase_       :				List[Any]		   =      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  			UpperCAmelCase_       :				Any		   =      hill_climbing(
  			    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
  			)
  			print(
  			    '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
  			    f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
  			)
  			def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      List[str]					)				->							Any:
  				return (3 * x**2) - (6 * y)
  
  			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  			UpperCAmelCase_       :				Dict		   =      hill_climbing(prob, find_max=True)
  
  
  
  
  
  
  
  			print(
  			    '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
  			    f"""{local_min.score()}"""
  			)
  
  
  
  
  
  