



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     :				Tuple		):

				"""simple docstring"""
				a__				      =		filter(lambda __a		: p.requires_grad ,		model.parameters()		)
				a__				      =		sum([np.prod(p.size()		) for p in model_parameters]		)
				return params


UpperCAmelCase							 =       logging.getLogger(__name__)

def 			A_      (	__a     :				Union[str, Any] ,		__a     :				str		):

				"""simple docstring"""
				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}"""
				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=UpperCAmelCase__ ,		filename=UpperCAmelCase__ ,		monitor=F'''val_{metric}''' ,		mode="""max""" ,		save_top_k=3 ,		every_n_epochs=1 ,		)
				return checkpoint_callback

def 			A_      (	__a     :				Any ,		__a     :				Optional[int]		):

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




class  __snake_case      (   pl.Callback):







							'''simple docstring'''






							def _a      (							self  ,  a_  ,  a_		):
											a__				      =		{F'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
											pl_module.logger.log_metrics(lowercase_		)


							@rank_zero_only
							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_=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=lowercase_		)
															generations_file.parent.mkdir(exist_ok=lowercase_		)
											with open(lowercase_  ,  """a+"""		) as writer:
															for key in sorted(lowercase_		):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			a__				      =		metrics[key]
																			if isinstance(lowercase_  ,  torch.Tensor		):
																							a__				      =		val.item()
																			a__				      =		F'''{key}: {val:.6f}\n'''
																			writer.write(lowercase_		)

											if not save_generations:
															return

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


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

											a__				      =		count_trainable_parameters(lowercase_		)
											# 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 _a      (							self  ,  a_  ,  a_		):
											save_json(pl_module.metrics  ,  pl_module.metrics_save_path		)
											return self._write_logs(lowercase_  ,  lowercase_  ,  """test"""		)







							@rank_zero_only
							def _a      (							self  ,  a_  ,  a_		):
											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_      (	__a     :				int ,		__a     :				int		):

				"""simple docstring"""
				return abs(__a		) if a == 0 else greatest_common_divisor(b % a ,		__a		)

def 			A_      (	__a     :				int ,		__a     :				int		):

				"""simple docstring"""
				while y:  # --> when y=0 then loop will terminate and return x as final GCD.
								a__      ,			a__				      =		y, x % y
				return abs(__a		)

def 			A_      (	):

				"""simple docstring"""
				try:
								a__				      =		input("""Enter two integers separated by comma (,): """		).split(""","""		)
								a__				      =		int(nums[0]		)
								a__				      =		int(nums[1]		)
								print(
								    F'''greatest_common_divisor({num_a}, {num_a}) = '''
								    F'''{greatest_common_divisor(__a ,		__a		)}'''		)
								print(F'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(__a ,		__a		)}'''		)
				except (IndexError, UnboundLocalError, ValueError):
								print("""Wrong input"""		)


if __name__ == "__main__":
	main()

