





"""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 					a__    (    _SCREAMING_SNAKE_CASE			):




		"""simple docstring"""


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


lowerCAmelCase__   	=	logging.getLogger(__name__)


def 					a__    (    _SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE			):




		"""simple docstring"""


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

		UpperCamelCase      =		ModelCheckpoint(
		    dirpath=SCREAMING_SNAKE_CASE_     ,		filename=SCREAMING_SNAKE_CASE_     ,		monitor=F"val_{metric}"     ,		mode="max"     ,		save_top_k=1     ,		every_n_epochs=1     ,		)

		return checkpoint_callback


def 					a__    (    _SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE			):




		"""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 				_lowerCamelCase					(				pl.Callback       ):


						def 			snake_case_      (self      ,							__a      ,							__a						)       ->      int:
								UpperCamelCase      =		{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 			snake_case_      (self      ,							__a      ,							__a      ,							__a      ,							__a=True						)       ->      List[str]:
								logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"						)
								UpperCamelCase      =		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      =		Path(pl_module.hparams.output_dir						)
								if type_path == "test":
										UpperCamelCase      =		od / "test_results.txt"
										UpperCamelCase      =		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      =		od / F"{type_path}_results/{trainer.global_step:05d}.txt"
										UpperCamelCase      =		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
												UpperCamelCase      =		metrics[key]
												if isinstance(_A      ,							torch.Tensor						):
														UpperCamelCase      =		val.item()
												UpperCamelCase      =		F"{key}: {val:.6f}\n"
												writer.write(_A						)

								if not save_generations:
										return

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


						@rank_zero_only
						def 			snake_case_      (self      ,							__a      ,							__a						)       ->      List[Any]:



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

								UpperCamelCase      =		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 			snake_case_      (self      ,							__a      ,							__a						)       ->      int:
								save_json(pl_module.metrics      ,							pl_module.metrics_save_path						)
								return self._write_logs(_A      ,							_A      ,							"test"						)




						@rank_zero_only
						def 			snake_case_      (self      ,							__a      ,							__a						)       ->      Optional[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 	_a      (						SCREAMING_SNAKE_CASE_							:				int = 1_00_00_00     ):
					__lowerCAmelCase				   =       [i - 1 for i in range(limit + 1     )]

					for i in range(2      ,      limit + 1     ):
										if phi[i] == i - 1:
															for j in range(2 * i      ,      limit + 1      ,      SCREAMING_SNAKE_CASE_     ):
																				phi[j] -= phi[j] // i

					return sum(phi[2 : limit + 1]     )


if __name__ == "__main__":
					print(solution())




