

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_rag import save_json






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[Any]:
	__lowerCamelCase    :    Dict 	=			filter(lambda lowerCamelCase__      : p.requires_grad     ,			model.parameters()      )
	__lowerCamelCase    :    List[str] 	=			sum([np.prod(p.size()      ) for p in model_parameters]      )
	return params


a		=logging.getLogger(__name__)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[Any]:
	if metric == "rouge2":
		__lowerCamelCase    :    Any 	=			'{val_avg_rouge2:.4f}-{step_count}'
	elif metric == "bleu":
		__lowerCamelCase    :    Union[str, Any] 	=			'{val_avg_bleu:.4f}-{step_count}'
	elif metric == "em":
		__lowerCamelCase    :    Optional[Any] 	=			'{val_avg_em:.4f}-{step_count}'
	else:
		raise NotImplementedError(
		    F"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"
		    ' function.'      )

	__lowerCamelCase    :    List[str] 	=			ModelCheckpoint(
	    dirpath=lowerCamelCase__     ,			filename=lowerCamelCase__     ,			monitor=F"val_{metric}"     ,			mode='max'     ,			save_top_k=3     ,			every_n_epochs=1     ,			)
	return checkpoint_callback






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Dict:
	return EarlyStopping(
	    monitor=F"val_{metric}"     ,			mode='min' if 'loss' in metric else 'max'     ,			patience=lowerCamelCase__     ,			verbose=lowerCamelCase__     ,			)






class 	A_		(	pl.Callback    ):
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      List[Any]):
								__lowerCamelCase    :    Union[str, Any] 	=			{F"lr_group_{i}": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
								pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE__)
							@rank_zero_only
							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      pl.Trainer       ,SCREAMING_SNAKE_CASE__		:      pl.LightningModule       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=True):
								logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****")
								__lowerCamelCase    :    Any 	=			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    :    int 	=			Path(pl_module.hparams.output_dir)
								if type_path == "test":
									__lowerCamelCase    :    int 	=			od / 'test_results.txt'
									__lowerCamelCase    :    Optional[int] 	=			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    :    List[str] 	=			od / F"{type_path}_results/{trainer.global_step:05d}.txt"
									__lowerCamelCase    :    Tuple 	=			od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
									results_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__)
									generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__)
								with open(SCREAMING_SNAKE_CASE__       ,'a+') as writer:
									for key in sorted(SCREAMING_SNAKE_CASE__):
										if key in ["log", "progress_bar", "preds"]:
											continue
										__lowerCamelCase    :    Dict 	=			metrics[key]
										if isinstance(SCREAMING_SNAKE_CASE__       ,torch.Tensor):
											__lowerCamelCase    :    List[Any] 	=			val.item()
										__lowerCamelCase    :    Tuple 	=			F"{key}: {val:.6f}\n"
										writer.write(SCREAMING_SNAKE_CASE__)

								if not save_generations:
									return

								if "preds" in metrics:
									__lowerCamelCase    :    Dict 	=			'\n'.join(metrics['preds'])
									generations_file.open('w+').write(SCREAMING_SNAKE_CASE__)
							@rank_zero_only
							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Dict):
								try:
									__lowerCamelCase    :    Any 	=			pl_module.model.model.num_parameters()
								except AttributeError:
									__lowerCamelCase    :    Tuple 	=			pl_module.model.num_parameters()

								__lowerCamelCase    :    Optional[int] 	=			count_trainable_parameters(SCREAMING_SNAKE_CASE__)
								# 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		:      str       ,SCREAMING_SNAKE_CASE__		:      pl.Trainer       ,SCREAMING_SNAKE_CASE__		:      pl.LightningModule):
								save_json(pl_module.metrics       ,pl_module.metrics_save_path)
								return self._write_logs(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,'test')


							@rank_zero_only
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      pl.Trainer       ,SCREAMING_SNAKE_CASE__		:      int):
								save_json(pl_module.metrics       ,pl_module.metrics_save_path)
								# Uncommenting this will save val generations
								# return self._write_logs(trainer, pl_module, "valid")







from __future__ import annotations






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			)  ->					None:
	__lowerCamelCase    :    int 	=			len(lowerCamelCase__      )

	# If row is equal to the size of the board it means there are a queen in each row in
	# the current board (possible_board)
	if row == n:
		# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
		# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
		boards.append(['. ' * i + 'Q ' + '. ' * (n - 1 - i) for i in possible_board]      )
		return

	# We iterate each column in the row to find all possible results in each row
	for col in range(lowerCamelCase__      ):
		# We apply that we learned previously. First we check that in the current board
		# (possible_board) there are not other same value because if there is it means
		# that there are a collision in vertical. Then we apply the two formulas we
		# learned before:
		#
		# 45º: y - x = b or 45: row - col = b
		# 135º: y + x = b or row + col = b.
		#
		# And we verify if the results of this two formulas not exist in their variables
		# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
		#
		# If any or these are True it means there is a collision so we continue to the
		# next value in the for loop.
		if (
		    col in possible_board
		    or row - col in diagonal_right_collisions
		    or row + col in diagonal_left_collisions
		):
			continue

		# If it is False we call dfs function again and we update the inputs
		depth_first_search(
		    [*possible_board, col]     ,			[*diagonal_right_collisions, row - col]     ,			[*diagonal_left_collisions, row + col]     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					None:
	__lowerCamelCase    :    list[list[str]] 	=			[]
	depth_first_search([]     ,			[]     ,			[]     ,			lowerCamelCase__     ,			lowerCamelCase__      )

	# Print all the boards
	for board in boards:
		for column in board:
			print(lowerCamelCase__      )
		print(''      )

	print(len(lowerCamelCase__      )     ,			'solutions were found.'      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					n_queens_solution(4)




