

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__ ,)








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()}"""
			)





