


'''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   __lowerCAmelCase					(							lowerCamelCase   :						int					):






	'''simple docstring'''




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


lowerCAmelCase	:		Dict				=   logging.getLogger(__name__)



def   __lowerCAmelCase					(							lowerCamelCase   :						Any				,		lowerCamelCase   :						str					):






	'''simple docstring'''




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

	__lowerCAmelCase												=  ModelCheckpoint(
	    dirpath=lowerCamelCase				,		filename=lowerCamelCase				,		monitor=f'''val_{metric}'''				,		mode="max"				,		save_top_k=3				,		every_n_epochs=1				,		)
	return checkpoint_callback



def   __lowerCAmelCase					(							lowerCamelCase   :						List[Any]				,		lowerCamelCase   :						int					):






	'''simple docstring'''




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






class 					UpperCAmelCase__	(		pl.Callback ):



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  {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        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase=True     )     ->			None:
						logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''     )
						__lowerCAmelCase												=  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												=  Path(pl_module.hparams.output_dir     )
						if type_path == "test":
							__lowerCAmelCase												=  od / "test_results.txt"
							__lowerCAmelCase												=  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												=  od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
							__lowerCAmelCase												=  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
								__lowerCAmelCase												=  metrics[key]
								if isinstance(UpperCamelCase			,				torch.Tensor     ):
									__lowerCAmelCase												=  val.item()
								__lowerCAmelCase												=  F'''{key}: {val:.6f}\n'''
								writer.write(UpperCamelCase     )

						if not save_generations:
							return

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



					@rank_zero_only
					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			Optional[int]:
						try:
							__lowerCAmelCase												=  pl_module.model.model.num_parameters()
						except AttributeError:
							__lowerCAmelCase												=  pl_module.model.num_parameters()

						__lowerCAmelCase												=  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        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			int:
						save_json(pl_module.metrics			,				pl_module.metrics_save_path     )
						return self._write_logs(UpperCamelCase			,				UpperCamelCase			,				"test"     )



					@rank_zero_only
					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			List[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")




'''simple docstring'''



import re



def   __lowerCAmelCase					(							lowerCamelCase   :						str					):






	'''simple docstring'''




	__lowerCAmelCase												=  re.compile(
	    r"^(?:0|94|\+94|0{2}94)" r"7(0|1|2|4|5|6|7|8)" r"(-| |)" r"\d{7}$"					)

	return bool(re.search(lowerCamelCase				,		lowerCamelCase					)					)


if __name__ == "__main__":
	lowerCAmelCase	:		Optional[Any]				=   '''0094702343221'''

	print(is_sri_lankan_phone_number(phone))