





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__  (      __a				):
	snake_case_						:     Union[str, Any]     =		filter(lambda __a				: p.requires_grad				,	model.parameters()				)
	snake_case_						:     List[str]     =		sum([np.prod(p.size()				) for p in model_parameters]				)
	return params


_SCREAMING_SNAKE_CASE 		=						logging.getLogger(__name__)


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):

	if metric == "rouge2":
		snake_case_						:     Union[str, Any]     =		'{val_avg_rouge2:.4f}-{step_count}'
	elif metric == "bleu":
		snake_case_						:     Optional[Any]     =		'{val_avg_bleu:.4f}-{step_count}'
	elif metric == "em":
		snake_case_						:     List[str]     =		'{val_avg_em:.4f}-{step_count}'
	elif metric == "loss":
		snake_case_						:     str     =		'{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.'				)

	snake_case_						:     int     =		ModelCheckpoint(
	    dirpath=__a				,	filename=__a				,	monitor=f"""val_{metric}"""				,	mode='max'				,	save_top_k=1				,	every_n_epochs=1				,	)

	return checkpoint_callback


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	return EarlyStopping(
	    monitor=f"""val_{metric}"""				,	mode='min' if 'loss' in metric else 'max'				,	patience=__a				,	verbose=__a				,	)


class SCREAMING_SNAKE_CASE_  (   pl.Callback   ):






						def UpperCAmelCase_			( self : str						,				_A : Optional[Any]						,				_A : List[str]		)							->      Optional[int]:







							"""simple docstring"""


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






						@rank_zero_only
						def UpperCAmelCase_			( self : List[str]						,				_A : Tuple						,				_A : List[str]						,				_A : Optional[int]						,				_A : Optional[int]=True		)							->      None:







							"""simple docstring"""


							logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""		)
							snake_case_						:     Optional[int]     =		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
							snake_case_						:     str     =		Path(pl_module.hparams.output_dir		)
							if type_path == "test":
								snake_case_						:     str     =		od / 'test_results.txt'
								snake_case_						:     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.
								snake_case_						:     Any     =		od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
								snake_case_						:     Any     =		od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
								results_file.parent.mkdir(exist_ok=A_		)
								generations_file.parent.mkdir(exist_ok=A_		)
							with open(A_						,				'a+'		) as writer:
								for key in sorted(A_		):
									if key in ["log", "progress_bar", "preds"]:
										continue
									snake_case_						:     Dict     =		metrics[key]
									if isinstance(A_						,				torch.Tensor		):
										snake_case_						:     List[Any]     =		val.item()
									snake_case_						:     List[Any]     =		F"""{key}: {val:.6f}\n"""
									writer.write(A_		)

							if not save_generations:
								return

							if "preds" in metrics:
								snake_case_						:     List[str]     =		'\n'.join(metrics['preds']		)
								generations_file.open('w+'		).write(A_		)






						@rank_zero_only
						def UpperCAmelCase_			( self : List[str]						,				_A : List[str]						,				_A : Optional[Any]		)							->      int:







							"""simple docstring"""


							try:
								snake_case_						:     Tuple     =		pl_module.model.model.num_parameters()
							except AttributeError:
								snake_case_						:     int     =		pl_module.model.num_parameters()

							snake_case_						:     Dict     =		count_trainable_parameters(A_		)
							# 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 : Any						,				_A : List[str]						,				_A : str		)							->      Tuple:







							"""simple docstring"""


							save_json(pl_module.metrics						,				pl_module.metrics_save_path		)
							return self._write_logs(A_						,				A_						,				'test'		)






						@rank_zero_only
						def UpperCAmelCase_			( self : Any						,				_A : Optional[Any]						,				_A : Dict		)							->      Union[str, 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")








def 							SCREAMING_SNAKE_CASE__  (      __a = 60_08_51_47_51_43				):

	try:
		snake_case_						:     Optional[Any]     =		int(__a				)
	except (TypeError, ValueError):
		raise TypeError('Parameter n must be int or castable to int.'				)
	if n <= 0:
		raise ValueError('Parameter n must be greater than or equal to one.'				)
	snake_case_						:     Any     =		2
	snake_case_						:     Any     =		0
	if n == 2:
		return 2
	while n > 2:
		while n % i != 0:
			i += 1
		snake_case_						:     Tuple     =		i
		while n % i == 0:
			snake_case_						:     List[str]     =		n // i
		i += 1
	return int(__a				)


if __name__ == "__main__":
						print(F'''{solution() = }''')

