



"""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_rag import save_json






def      snake_case__   (		__lowerCamelCase				:    str	):
			"""simple docstring"""
			lowerCamelCase__		:		Dict			=filter(lambda __lowerCamelCase	: p.requires_grad							,		model.parameters()	)
			lowerCamelCase__		:		Tuple			=sum([np.prod(p.size()	) for p in model_parameters]	)
			return params


_lowercase : Union[str, Any]      						=						logging.getLogger(__name__)






def      snake_case__   (		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    Dict	):
			"""simple docstring"""
			if metric == "rouge2":
						lowerCamelCase__		:		Dict			='''{val_avg_rouge2:.4f}-{step_count}'''
			elif metric == "bleu":
						lowerCamelCase__		:		int			='''{val_avg_bleu:.4f}-{step_count}'''
			elif metric == "em":
						lowerCamelCase__		:		Any			='''{val_avg_em:.4f}-{step_count}'''
			elif metric == "loss":
						lowerCamelCase__		:		Tuple			='''{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.'''	)

			lowerCamelCase__		:		Any			=ModelCheckpoint(
			    dirpath=__lowerCAmelCase							,		filename=__lowerCAmelCase							,		monitor=f'''val_{metric}'''							,		mode='''max'''							,		save_top_k=1							,		every_n_epochs=1							,		)

			return checkpoint_callback






def      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			return EarlyStopping(
			    monitor=f'''val_{metric}'''							,		mode='''min''' if '''loss''' in metric else '''max'''							,		patience=__lowerCAmelCase							,		verbose=__lowerCAmelCase							,		)

class        __SCREAMING_SNAKE_CASE	(      pl.Callback   ):


					'''simple docstring'''





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

								if not save_generations:
											return

								if "preds" in metrics:
											lowerCamelCase__		:		Tuple			='''\n'''.join(metrics['''preds''']     )
											generations_file.open('''w+'''     ).write(SCREAMING_SNAKE_CASE__     )



					@rank_zero_only
					def 		snake_case       (       self							:  str,  lowerCamelCase							:  int,  lowerCamelCase							:  List[Any]     )->					Any:
								try:
											lowerCamelCase__		:		int			=pl_module.model.model.num_parameters()
								except AttributeError:
											lowerCamelCase__		:		Tuple			=pl_module.model.num_parameters()

								lowerCamelCase__		:		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 		snake_case       (       self							:  Union[str, Any],  lowerCamelCase							:  pl.Trainer,  lowerCamelCase							:  pl.LightningModule     )->					List[str]:
								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 		snake_case       (       self							:  Optional[int],  lowerCamelCase							:  pl.Trainer,  lowerCamelCase							:  List[str]     )->					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")






from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				__lowerCamelCase    					=	prime_factors(__lowerCAmelCase							)
				if is_square_free(__lowerCAmelCase							):
								return -1 if len(__lowerCAmelCase							) % 2 else 1
				return 0


if __name__ == "__main__":
					import doctest

					doctest.testmod()



