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


_snake_case				:   int     =  logging.getLogger(__name__)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[int]	,			_SCREAMING_SNAKE_CASE		:   Tuple  ):
						if metric == "rouge2":
												_SCREAMING_SNAKE_CASE				= "{val_avg_rouge2:.4f}-{step_count}"
						elif metric == "bleu":
												_SCREAMING_SNAKE_CASE				= "{val_avg_bleu:.4f}-{step_count}"
						elif metric == "em":
												_SCREAMING_SNAKE_CASE				= "{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."  )

						_SCREAMING_SNAKE_CASE				= ModelCheckpoint(
						    dirpath=__snake_case	,			filename=__snake_case	,			monitor=F'val_{metric}'	,			mode="max"	,			save_top_k=3	,			every_n_epochs=1	,			)
						return checkpoint_callback



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Tuple	,			_SCREAMING_SNAKE_CASE		:   str  ):
						return EarlyStopping(
						    monitor=F'val_{metric}'	,			mode="min" if "loss" in metric else "max"	,			patience=__snake_case	,			verbose=__snake_case	,			)




class 			lowerCAmelCase (				pl.Callback			):




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= {F'lr_group_{i}': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
								pl_module.logger.log_metrics(_A      )




		@rank_zero_only
		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=True      ):
								logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'      )
								_SCREAMING_SNAKE_CASE				= 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
								_SCREAMING_SNAKE_CASE				= Path(pl_module.hparams.output_dir      )
								if type_path == "test":
														_SCREAMING_SNAKE_CASE				= od / "test_results.txt"
														_SCREAMING_SNAKE_CASE				= 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.
														_SCREAMING_SNAKE_CASE				= od / F'{type_path}_results/{trainer.global_step:05d}.txt'
														_SCREAMING_SNAKE_CASE				= od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
														results_file.parent.mkdir(exist_ok=_A      )
														generations_file.parent.mkdir(exist_ok=_A      )
								with open(_A					,						"a+"      ) as writer:
														for key in sorted(_A      ):
																				if key in ["log", "progress_bar", "preds"]:
																										continue
																				_SCREAMING_SNAKE_CASE				= metrics[key]
																				if isinstance(_A					,						torch.Tensor      ):
																										_SCREAMING_SNAKE_CASE				= val.item()
																				_SCREAMING_SNAKE_CASE				= F'{key}: {val:.6f}\n'
																				writer.write(_A      )

								if not save_generations:
														return

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




		@rank_zero_only
		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
								try:
														_SCREAMING_SNAKE_CASE				= pl_module.model.model.num_parameters()
								except AttributeError:
														_SCREAMING_SNAKE_CASE				= pl_module.model.num_parameters()

								_SCREAMING_SNAKE_CASE				= count_trainable_parameters(_A      )
								# 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					,						UpperCamelCase					,						UpperCamelCase      ):
								save_json(pl_module.metrics					,						pl_module.metrics_save_path      )
								return self._write_logs(_A					,						_A					,						"test"      )




		@rank_zero_only
		def     lowercase       (				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'''







def 			_a		(	_SCREAMING_SNAKE_CASE		:   list[int]	,			_SCREAMING_SNAKE_CASE		:   list[int]  ):

						# Check if the input is valid
						if not len(_SCREAMING_SNAKE_CASE  ) == len(_SCREAMING_SNAKE_CASE  ) == 3:
												raise ValueError("Please enter a valid equation."  )
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("Both a & b of two equations can't be zero."  )

						# Extract the coefficients
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= equationa
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= equationa

						# Calculate the determinants of the matrices
						_SCREAMING_SNAKE_CASE				= aa * ba - aa * ba
						_SCREAMING_SNAKE_CASE				= ca * ba - ca * ba
						_SCREAMING_SNAKE_CASE				= aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("Infinite solutions. (Consistent system)"  )
												else:
																		raise ValueError("No solution. (Inconsistent system)"  )
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_SCREAMING_SNAKE_CASE				= determinant_x / determinant
																		_SCREAMING_SNAKE_CASE				= determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)