




'''simple docstring'''






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       __lowerCAmelCase (   snake_case__						):
			__UpperCamelCase				:      Dict									=       filter(lambda snake_case__						: p.requires_grad ,				model.parameters()						)
			__UpperCamelCase				:      Union[str, Any]									=       sum([np.prod(p.size()						) for p in model_parameters]						)
			return params


_lowerCAmelCase				     = logging.getLogger(__name__)



class  A		(			pl.Callback		):




				'''simple docstring'''





				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	Tuple:
							__UpperCamelCase				:      List[str]									=       {f"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
							pl_module.logger.log_metrics(_UpperCAmelCase      )
				@rank_zero_only
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase=True      )     ->	None:
							logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"      )
							__UpperCamelCase				:      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
							__UpperCamelCase				:      Tuple									=       Path(pl_module.hparams.output_dir      )
							if type_path == "test":
										__UpperCamelCase				:      int									=       od / "test_results.txt"
										__UpperCamelCase				:      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.
										__UpperCamelCase				:      Union[str, Any]									=       od / f"{type_path}_results/{trainer.global_step:05d}.txt"
										__UpperCamelCase				:      Union[str, Any]									=       od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
										results_file.parent.mkdir(exist_ok=_UpperCAmelCase      )
										generations_file.parent.mkdir(exist_ok=_UpperCAmelCase      )
							with open(_UpperCAmelCase			,  "a+"      ) as writer:
										for key in sorted(_UpperCAmelCase      ):
													if key in ["log", "progress_bar", "preds"]:
																continue
													__UpperCamelCase				:      Optional[Any]									=       metrics[key]
													if isinstance(_UpperCAmelCase			,  torch.Tensor      ):
																__UpperCamelCase				:      Optional[Any]									=       val.item()
													__UpperCamelCase				:      str									=       f"{key}: {val:.6f}\n"
													writer.write(_UpperCAmelCase      )

							if not save_generations:
										return

							if "preds" in metrics:
										__UpperCamelCase				:      List[Any]									=       "\n".join(metrics["preds"]      )
										generations_file.open("w+"      ).write(_UpperCAmelCase      )
				@rank_zero_only
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	List[str]:
							try:
										__UpperCamelCase				:      str									=       pl_module.model.model.num_parameters()
							except AttributeError:
										__UpperCamelCase				:      str									=       pl_module.model.num_parameters()

							__UpperCamelCase				:      Optional[Any]									=       count_trainable_parameters(_UpperCAmelCase      )
							# 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 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	Optional[Any]:
							save_json(pl_module.metrics			,  pl_module.metrics_save_path      )
							return self._write_logs(_UpperCAmelCase			,  _UpperCAmelCase			,  "test"      )





				@rank_zero_only
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	List[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       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__=1 ,				snake_case__=False						):
			if metric == "rouge2":
						__UpperCamelCase				:      List[Any]									=       "{val_avg_rouge2:.4f}-{step_count}"
			elif metric == "bleu":
						__UpperCamelCase				:      List[Any]									=       "{val_avg_bleu:.4f}-{step_count}"
			elif metric == "loss":
						__UpperCamelCase				:      Union[str, Any]									=       "{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."						)

			__UpperCamelCase				:      List[str]									=       ModelCheckpoint(
			    dirpath=snake_case__ ,				filename=snake_case__ ,				monitor=F"val_{metric}" ,				mode="min" if "loss" in metric else "max" ,				save_top_k=snake_case__ ,				)
			return checkpoint_callback




def       __lowerCAmelCase (   snake_case__ ,				snake_case__						):
			return EarlyStopping(
			    monitor=F"val_{metric}" ,				mode="min" if "loss" in metric else "max" ,				patience=snake_case__ ,				verbose=snake_case__ ,				)









'''simple docstring'''






from doctest import testmod
from math import sqrt

def       __lowerCAmelCase (   snake_case__						):
			__UpperCamelCase				:      list[int]									=       []
			if num < 1:
						return facs
			facs.append(1						)
			if num == 1:
						return facs
			facs.append(snake_case__						)
			for i in range(2 ,				int(sqrt(snake_case__						)						) + 1						):
						if num % i == 0:  # If i is a factor of num
									facs.append(snake_case__						)
									__UpperCamelCase				:      List[str]									=       num // i  # num//i is the other factor of num
									if d != i:  # If d and i are distinct
												facs.append(snake_case__						)  # we have found another factor
			facs.sort()
			return facs


if __name__ == "__main__":
				testmod(name='''factors_of_a_number''', verbose=True)



