



"""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  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: Optional[int]     =   filter(lambda __UpperCAmelCase      : p.requires_grad   ,				model.parameters()      )
		_lowercase				: Union[str, Any]     =   sum([np.prod(p.size()      ) for p in model_parameters]      )
		return params


UpperCAmelCase:  List[str]			     =				logging.getLogger(__name__)





class   UpperCamelCase	(     pl.Callback       ):




	"""simple docstring"""






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

			if not save_generations:
					return

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






	@rank_zero_only
	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_   ):
			try:
					_lowercase				: Optional[Any]     =   pl_module.model.model.num_parameters()
			except AttributeError:
					_lowercase				: str     =   pl_module.model.num_parameters()

			_lowercase				: List[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    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_   ):
			save_json(pl_module.metrics				,pl_module.metrics_save_path   )
			return self._write_logs(UpperCAmelCase_				,UpperCAmelCase_				,"""test"""   )






	@rank_zero_only
	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_   ):
			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 (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase=1   ,				__UpperCAmelCase=False      ):
		if metric == "rouge2":
				_lowercase				: List[Any]     =   """{val_avg_rouge2:.4f}-{step_count}"""
		elif metric == "bleu":
				_lowercase				: Tuple     =   """{val_avg_bleu:.4f}-{step_count}"""
		elif metric == "loss":
				_lowercase				: 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."""      )

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


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





"""simple docstring"""




def __snake_case (      UpperCamelCase = 10**6    )       ->    int:


		"""simple docstring"""





		if n <= 0:
				raise ValueError('''Please enter an integer greater than 0'''    )

		a__   		=     list(range(n + 1    )    )
		for number in range(2						,					n + 1    ):
				if phi[number] == number:
						phi[number] -= 1
						for multiple in range(number * 2						,					n + 1						,					UpperCamelCase    ):
								a__   		=     (phi[multiple] // number) * (number - 1)

		a__   		=     1
		for number in range(1						,					n + 1    ):
				if (answer / phi[answer]) < (number / phi[number]):
						a__   		=     number

		return answer


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




