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


lowercase__											=logging.getLogger(__name__)






def      UpperCamelCase_				(      A__  ,      A__						):
	if metric == "rouge2":
		a_      				=						"""{val_avg_rouge2:.4f}-{step_count}"""
	elif metric == "bleu":
		a_      				=						"""{val_avg_bleu:.4f}-{step_count}"""
	elif metric == "em":
		a_      				=						"""{val_avg_em:.4f}-{step_count}"""
	elif metric == "loss":
		a_      				=						"""{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."""						)

	a_      				=						ModelCheckpoint(
	    dirpath=A__  ,      filename=A__  ,      monitor=F'''val_{metric}'''  ,      mode="""max"""  ,      save_top_k=1  ,      every_n_epochs=1  ,      )

	return checkpoint_callback






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







class   a_ (      pl.Callback					):




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						{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} *****'''	)
				a_      				=						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
				a_      				=						Path(pl_module.hparams.output_dir	)
				if type_path == "test":
					a_      				=						od / """test_results.txt"""
					a_      				=						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.
					a_      				=						od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
					a_      				=						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
						a_      				=						metrics[key]
						if isinstance(UpperCAmelCase					,   torch.Tensor	):
							a_      				=						val.item()
						a_      				=						f'''{key}: {val:.6f}\n'''
						writer.write(UpperCAmelCase	)

				if not save_generations:
					return

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




			@rank_zero_only
			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase	):
				try:
					a_      				=						pl_module.model.model.num_parameters()
				except AttributeError:
					a_      				=						pl_module.model.num_parameters()

				a_      				=						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")


'''simple docstring'''
import re






def      UpperCamelCase_				(      A__						):
	a_      				=						re.compile(r"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$"""						)
	if match := re.search(A__  ,      A__						):
		return match.string == phone
	return False


if __name__ == "__main__":
		print(indian_phone_validator('+918827897895'))
