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


_lowercase    =		logging.getLogger(__name__)


def A				(__lowerCamelCase				:List[str]			,     __lowerCamelCase				:Union[str, Any]		):
						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=UpperCAmelCase__			,     filename=UpperCAmelCase__			,     monitor=f'val_{metric}'			,     mode="""max"""			,     save_top_k=3			,     every_n_epochs=1			,     )
						return checkpoint_callback


def A				(__lowerCamelCase				:Tuple			,     __lowerCamelCase				:Union[str, Any]		):
						return EarlyStopping(
						    monitor=f'val_{metric}'			,     mode="""min""" if """loss""" in metric else """max"""			,     patience=UpperCAmelCase__			,     verbose=UpperCAmelCase__			,     )





class   UpperCAmelCase_					(		pl.Callback			):






						'''simple docstring'''






						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			{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  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase=True				):

												"""simple docstring"""



												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=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
																								_lowerCAmelCase  						=			metrics[key]
																								if isinstance(lowercase_   ,       torch.Tensor				):
																														_lowerCAmelCase  						=			val.item()
																								_lowerCAmelCase  						=			F'{key}: {val:.6f}\n'
																								writer.write(lowercase_				)

												if not save_generations:
																		return

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




						@rank_zero_only
						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												try:
																		_lowerCAmelCase  						=			pl_module.model.model.num_parameters()
												except AttributeError:
																		_lowerCAmelCase  						=			pl_module.model.num_parameters()

												_lowerCAmelCase  						=			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  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												save_json(pl_module.metrics   ,       pl_module.metrics_save_path				)
												return self._write_logs(lowercase_   ,       lowercase_   ,       """test"""				)




						@rank_zero_only
						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""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 typing import Any




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						pass






class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			data
												_lowerCAmelCase  						=			None




						def __iter__( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self
												_lowerCAmelCase  						=			[]
												while node:
																		if node in visited:
																								raise ContainsLoopError
																		visited.append(_lowercase				)
																		yield node.data
																		_lowerCAmelCase  						=			node.next_node




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												try:
																		list(self				)
																		return False
												except ContainsLoopError:
																		return True


if __name__ == "__main__":
						_lowercase    =		Node(1)
						_lowercase    =		Node(2)
						_lowercase    =		Node(3)
						_lowercase    =		Node(4)
						print(root_node.has_loop)  # False
						_lowercase    =		root_node.next_node
						print(root_node.has_loop)  # True

						_lowercase    =		Node(5)
						_lowercase    =		Node(6)
						_lowercase    =		Node(5)
						_lowercase    =		Node(6)
						print(root_node.has_loop)  # False

						_lowercase    =		Node(1)
						print(root_node.has_loop)  # False
