



"""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	:						Dict       ):

		'''simple docstring'''


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


lowercase  							=					logging.getLogger(__name__)




def 	UpperCAmelCase     (		A	:						str     ,				A	:						int       ):

		'''simple docstring'''


		if metric == "rouge2":
				_UpperCAmelCase							 =		'{val_avg_rouge2:.4f}-{step_count}'
		elif metric == "bleu":
				_UpperCAmelCase							 =		'{val_avg_bleu:.4f}-{step_count}'
		elif metric == "em":
				_UpperCAmelCase							 =		'{val_avg_em:.4f}-{step_count}'
		elif metric == "loss":
				_UpperCAmelCase							 =		'{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.'       )

		_UpperCAmelCase							 =		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	:						Union[str, Any]     ,				A	:						Optional[int]       ):

		'''simple docstring'''


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






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







							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case						)			->							Optional[int]:
									_UpperCAmelCase							 =		{f'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
									pl_module.logger.log_metrics(snake_case						)






							@rank_zero_only
							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case=True						)			->							None:
									logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'						)
									_UpperCAmelCase							 =		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
									_UpperCAmelCase							 =		Path(pl_module.hparams.output_dir						)
									if type_path == "test":
											_UpperCAmelCase							 =		od / 'test_results.txt'
											_UpperCAmelCase							 =		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.
											_UpperCAmelCase							 =		od / f'{type_path}_results/{trainer.global_step:05d}.txt'
											_UpperCAmelCase							 =		od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
											results_file.parent.mkdir(exist_ok=snake_case						)
											generations_file.parent.mkdir(exist_ok=snake_case						)
									with open(snake_case	,       'a+'						) as writer:
											for key in sorted(snake_case						):
													if key in ["log", "progress_bar", "preds"]:
															continue
													_UpperCAmelCase							 =		metrics[key]
													if isinstance(snake_case	,       torch.Tensor						):
															_UpperCAmelCase							 =		val.item()
													_UpperCAmelCase							 =		f'{key}: {val:.6f}\n'
													writer.write(snake_case						)

									if not save_generations:
											return

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






							@rank_zero_only
							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case						)			->							List[Any]:
									try:
											_UpperCAmelCase							 =		pl_module.model.model.num_parameters()
									except AttributeError:
											_UpperCAmelCase							 =		pl_module.model.num_parameters()

									_UpperCAmelCase							 =		count_trainable_parameters(snake_case						)
									# 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	,       snake_case	,       snake_case						)			->							int:
									save_json(pl_module.metrics	,       pl_module.metrics_save_path						)
									return self._write_logs(snake_case	,       snake_case	,       'test'						)






							@rank_zero_only
							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case						)			->							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"""





from __future__ import annotations

from cmath import sqrt




def 	UpperCAmelCase     (		A	:						int     ,				A	:						int     ,				A	:						int       ):

		'''simple docstring'''


		if a == 0:
				raise ValueError('Coefficient \'a\' must not be zero.'       )
		_UpperCAmelCase							 =		b * b - 4 * a * c

		_UpperCAmelCase							 =		(-b + sqrt(A       )) / (2 * a)
		_UpperCAmelCase							 =		(-b - sqrt(A       )) / (2 * a)

		return (
		    root_a.real if not root_a.imag else root_a,
		    root_a.real if not root_a.imag else root_a,
		)




def 	UpperCAmelCase     (		):

		'''simple docstring'''


		_UpperCAmelCase ,					_UpperCAmelCase							 =		quadratic_roots(a=5     ,				b=6     ,				c=1       )
		print(f'The solutions are: {solutiona} and {solutiona}'       )


if __name__ == "__main__":
					main()


