
from heapq import heappop, heappush

import numpy as np




def 							a		(				snake_case__:   np.ndarray		,  snake_case__:   tuple[int, int]		,  snake_case__:   tuple[int, int]		,  snake_case__:   bool		,  ):


				'''simple docstring'''
				lowercase_    ,      lowercase_	    =   grid.shape
				lowercase_	    =   [-1, 1, 0, 0]
				lowercase_	    =   [0, 0, -1, 1]
				if allow_diagonal:
								dx += [-1, -1, 1, 1]
								dy += [-1, 1, -1, 1]

				lowercase_    ,      lowercase_	    =   [(0, source)], set()
				lowercase_	    =   np.full((rows, cols)		,  np.inf   )
				lowercase_	    =   0
				lowercase_	    =   np.empty((rows, cols)		,  dtype=snake_case__   )
				lowercase_	    =   None

				while queue:
								((lowercase_)    ,      (lowercase_))	    =   heappop(snake_case__   )
								if (x, y) in visited:
												continue
								visited.add((x, y)   )

								if (x, y) == destination:
												lowercase_	    =   []
												while (x, y) != source:
																path.append((x, y)   )
																lowercase_    ,      lowercase_	    =   predecessors[x, y]
												path.append(snake_case__   )  # add the source manually
												path.reverse()
												return matrix[destination], path

								for i in range(len(snake_case__   )   ):
												lowercase_    ,      lowercase_	    =   x + dx[i], y + dy[i]
												if 0 <= nx < rows and 0 <= ny < cols:
																lowercase_	    =   grid[nx][ny]
																if next_node == 1 and matrix[nx, ny] > dist + 1:
																				heappush(snake_case__		,  (dist + 1, (nx, ny))   )
																				lowercase_	    =   dist + 1
																				lowercase_	    =   (x, y)

				return np.inf, []


if __name__ == "__main__":
						import doctest

						doctest.testmod()






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 							a		(				snake_case__:   Any   ):


				'''simple docstring'''
				lowercase_	    =   filter(lambda snake_case__   : p.requires_grad		,  model.parameters()   )
				lowercase_	    =   sum([np.prod(p.size()   ) for p in model_parameters]   )
				return params


__a         =   logging.getLogger(__name__)




def 							a		(				snake_case__:   int		,  snake_case__:   Any   ):


				'''simple docstring'''
				if metric == "rouge2":
								lowercase_	    =   '''{val_avg_rouge2:.4f}-{step_count}'''
				elif metric == "bleu":
								lowercase_	    =   '''{val_avg_bleu:.4f}-{step_count}'''
				elif metric == "em":
								lowercase_	    =   '''{val_avg_em:.4f}-{step_count}'''
				elif metric == "loss":
								lowercase_	    =   '''{val_avg_loss:.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.'''   )

				lowercase_	    =   ModelCheckpoint(
				    dirpath=snake_case__		,  filename=snake_case__		,  monitor=F'''val_{metric}'''		,  mode='''max'''		,  save_top_k=1		,  every_n_epochs=1		,  )

				return checkpoint_callback




def 							a		(				snake_case__:   str		,  snake_case__:   List[str]   ):


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


class 					lowercase__(							pl.Callback				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Optional[Any]:
					lowercase_	    =   {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 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     pl.Trainer			,				SCREAMING_SNAKE_CASE_    :     pl.LightningModule			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Tuple=True			)			->	None:
					logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''			)
					lowercase_	    =   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
					lowercase_	    =   Path(pl_module.hparams.output_dir			)
					if type_path == "test":
									lowercase_	    =   od / '''test_results.txt'''
									lowercase_	    =   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.
									lowercase_	    =   od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
									lowercase_	    =   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
													lowercase_	    =   metrics[key]
													if isinstance(SCREAMING_SNAKE_CASE_			,				torch.Tensor			):
																	lowercase_	    =   val.item()
													lowercase_	    =   f'''{key}: {val:.6f}\n'''
													writer.write(SCREAMING_SNAKE_CASE_			)

					if not save_generations:
									return

					if "preds" in metrics:
									lowercase_	    =   '''\n'''.join(metrics['''preds''']			)
									generations_file.open('''w+'''			).write(SCREAMING_SNAKE_CASE_			)
	@rank_zero_only
	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Union[str, Any]:
					try:
									lowercase_	    =   pl_module.model.model.num_parameters()
					except AttributeError:
									lowercase_	    =   pl_module.model.num_parameters()

					lowercase_	    =   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 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     pl.Trainer			,				SCREAMING_SNAKE_CASE_    :     pl.LightningModule			)			->	Any:
					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 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     pl.Trainer			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			)			->	Dict:
					save_json(pl_module.metrics			,				pl_module.metrics_save_path			)
					# Uncommenting this will save val generations
					# return self._write_logs(trainer, pl_module, "valid")




