

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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Dict      )			->      Any:




				'''simple docstring'''





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


lowerCAmelCase__	   = logging.getLogger(__name__)



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]	,       SCREAMING_SNAKE_CASE_:							str      )			->      str:




				'''simple docstring'''





				if metric == "rouge2":
								A__        =			"{val_avg_rouge2:.4f}-{step_count}"
				elif metric == "bleu":
								A__        =			"{val_avg_bleu:.4f}-{step_count}"
				elif metric == "em":
								A__        =			"{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."      )

				A__        =			ModelCheckpoint(
				    dirpath=SCREAMING_SNAKE_CASE_	,       filename=SCREAMING_SNAKE_CASE_	,       monitor=F'val_{metric}'	,       mode="max"	,       save_top_k=3	,       every_n_epochs=1	,       )
				return checkpoint_callback



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Tuple	,       SCREAMING_SNAKE_CASE_:							int      )			->      Any:




				'''simple docstring'''





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





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







			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						List[str]:
							'''simple docstring'''






							A__        =			{F'lr_group_{i}': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups       )}
							pl_module.logger.log_metrics(lowercase       )


			@rank_zero_only
			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase=True       )  ->						None:
							'''simple docstring'''






							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=lowercase       )
											generations_file.parent.mkdir(exist_ok=lowercase       )
							with open(lowercase      ,      "a+"       ) as writer:
											for key in sorted(lowercase       ):
															if key in ["log", "progress_bar", "preds"]:
																			continue
															A__        =			metrics[key]
															if isinstance(lowercase      ,      torch.Tensor       ):
																			A__        =			val.item()
															A__        =			F'{key}: {val:.6f}\n'
															writer.write(lowercase       )

							if not save_generations:
											return

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


			@rank_zero_only
			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						Optional[int]:
							'''simple docstring'''






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

							A__        =			count_trainable_parameters(lowercase       )
							# 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      ,      lowercase      ,      lowercase       )  ->						Optional[int]:
							'''simple docstring'''






							save_json(pl_module.metrics      ,      pl_module.metrics_save_path       )
							return self._write_logs(lowercase      ,      lowercase      ,      "test"       )


			@rank_zero_only
			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						Any:
							'''simple docstring'''






							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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[list[str]]	,       SCREAMING_SNAKE_CASE_:							int	,       )			->      None:




				'''simple docstring'''





				A__        =			len(SCREAMING_SNAKE_CASE_      )

				# 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(SCREAMING_SNAKE_CASE_      ):
								# 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]	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       )



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      None:




				'''simple docstring'''





				A__        =			[]
				depth_first_search([]	,       []	,       []	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

				# Print all the boards
				for board in boards:
								for column in board:
												print(SCREAMING_SNAKE_CASE_      )
								print(""      )

				print(len(SCREAMING_SNAKE_CASE_      )	,       "solutions were found."      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	n_queens_solution(4)

