
"""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						(   a_ :							Optional[Any]     )->							Dict:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= filter(lambda a_     : p.requires_grad		,		model.parameters()     )
		SCREAMING_SNAKE_CASE       :     Any				= sum([np.prod(p.size()     ) for p in model_parameters]     )
		return params


lowerCamelCase__ :       Dict						     =			logging.getLogger(__name__)





def __A						(   a_ :							Optional[Any]		,		a_ :							Optional[int]     )->							str:




		'''simple docstring'''






		if metric == "rouge2":
				SCREAMING_SNAKE_CASE       :     Dict				= '{val_avg_rouge2:.4f}-{step_count}'
		elif metric == "bleu":
				SCREAMING_SNAKE_CASE       :     int				= '{val_avg_bleu:.4f}-{step_count}'
		elif metric == "em":
				SCREAMING_SNAKE_CASE       :     List[str]				= '{val_avg_em:.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.'''     )

		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= ModelCheckpoint(
		    dirpath=_lowerCamelCase		,		filename=_lowerCamelCase		,		monitor=F"val_{metric}"		,		mode='''max'''		,		save_top_k=3		,		every_n_epochs=1		,		)
		return checkpoint_callback





def __A						(   a_ :							str		,		a_ :							List[Any]     )->							Any:




		'''simple docstring'''






		return EarlyStopping(
		    monitor=F"val_{metric}"		,		mode='''min''' if '''loss''' in metric else '''max'''		,		patience=_lowerCamelCase		,		verbose=_lowerCamelCase		,		)




class 				lowercase__(				pl.Callback   ):
	'''simple docstring'''

	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :Union[str, Any]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= {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   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :Dict		, lowerCamelCase_    :str		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Tuple=True					)							->			Union[str, Any]:







			'''simple docstring'''




			logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"					)
			SCREAMING_SNAKE_CASE       :     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
			SCREAMING_SNAKE_CASE       :     List[str]				= Path(pl_module.hparams.output_dir					)
			if type_path == "test":
					SCREAMING_SNAKE_CASE       :     str				= od / 'test_results.txt'
					SCREAMING_SNAKE_CASE       :     Dict				= 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.
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= od / f"{type_path}_results/{trainer.global_step:05d}.txt"
					SCREAMING_SNAKE_CASE       :     Dict				= 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
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= metrics[key]
							if isinstance(_A		, torch.Tensor					):
									SCREAMING_SNAKE_CASE       :     int				= val.item()
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= f"{key}: {val:.6f}\n"
							writer.write(_A					)

			if not save_generations:
					return

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





	@rank_zero_only
	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Union[str, Any]					)							->			Optional[int]:







			'''simple docstring'''




			try:
					SCREAMING_SNAKE_CASE       :     Any				= pl_module.model.model.num_parameters()
			except AttributeError:
					SCREAMING_SNAKE_CASE       :     Any				= pl_module.model.num_parameters()

			SCREAMING_SNAKE_CASE       :     Optional[int]				= 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   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Optional[int]					)							->			Tuple:







			'''simple docstring'''




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







	@rank_zero_only
	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Dict		, lowerCamelCase_    :Optional[int]					)							->			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")







"""simple docstring"""






from __future__ import annotations

from fractions import Fraction





def __A						(   a_ :							int		,		a_ :							int     )->							bool:




		'''simple docstring'''






		return (
		    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
		)





def __A						(   a_ :							int     )->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
		SCREAMING_SNAKE_CASE       :     List[str]				= 11
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= int('''1''' + '''0''' * digit_len     )
		for num in range(a_		,		a_     ):
				while den <= 99:
						if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
								if is_digit_cancelling(a_		,		a_     ):
										solutions.append(F"{num}/{den}"     )
						den += 1
				num += 1
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10
		return solutions





def __A						(   a_ :							int = 2     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 1.0
		for fraction in fraction_list(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= Fraction(a_     )
				result *= frac.denominator / frac.numerator
		return int(a_     )


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





