






"""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 			_snake_case    (					lowerCamelCase__     :   Dict  )    ->     Any:
							lowerCamelCase_	:							int									=filter(lambda lowerCamelCase__  : p.requires_grad    ,						model.parameters()  )
							lowerCamelCase_	:							int									=sum([np.prod(p.size()  ) for p in model_parameters]  )
							return params


A__	:    Union[str, Any]			 = logging.getLogger(__name__)

def 			_snake_case    (					lowerCamelCase__     :   Tuple    ,						lowerCamelCase__     :   Union[str, Any]  )    ->     List[Any]:
							if metric == "rouge2":
														lowerCamelCase_	:							Optional[Any]									="{val_avg_rouge2:.4f}-{step_count}"
							elif metric == "bleu":
														lowerCamelCase_	:							int									="{val_avg_bleu:.4f}-{step_count}"
							elif metric == "em":
														lowerCamelCase_	:							List[str]									="{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_	:							Optional[Any]									=ModelCheckpoint(
							    dirpath=lowerCamelCase__    ,						filename=lowerCamelCase__    ,						monitor=F"""val_{metric}"""    ,						mode="max"    ,						save_top_k=3    ,						every_n_epochs=1    ,						)
							return checkpoint_callback

def 			_snake_case    (					lowerCamelCase__     :   Optional[int]    ,						lowerCamelCase__     :   Dict  )    ->     List[Any]:
							return EarlyStopping(
							    monitor=F"""val_{metric}"""    ,						mode="min" if "loss" in metric else "max"    ,						patience=lowerCamelCase__    ,						verbose=lowerCamelCase__    ,						)







class        lowercase__      (							pl.Callback       ):


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  Union[str, Any] ):
										lowerCamelCase_	:							Optional[int]									={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      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  pl.Trainer       ,	snake_case__					:  pl.LightningModule       ,	snake_case__					:  str       ,	snake_case__					:  List[str]=True ):
										logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
										lowerCamelCase_	:							Optional[int]									=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_	:							Dict									=Path(pl_module.hparams.output_dir )
										if type_path == "test":
																	lowerCamelCase_	:							Tuple									=od / "test_results.txt"
																	lowerCamelCase_	:							Tuple									=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_	:							Dict									=od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
																	lowerCamelCase_	:							List[str]									=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
																								lowerCamelCase_	:							Any									=metrics[key]
																								if isinstance(snake_case__       ,	torch.Tensor ):
																															lowerCamelCase_	:							Tuple									=val.item()
																								lowerCamelCase_	:							List[Any]									=F"""{key}: {val:.6f}\n"""
																								writer.write(snake_case__ )

										if not save_generations:
																	return

										if "preds" in metrics:
																	lowerCamelCase_	:							List[Any]									="\n".join(metrics["preds"] )
																	generations_file.open("w+" ).write(snake_case__ )


			@rank_zero_only
			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  List[Any]       ,	snake_case__					:  Optional[int] ):
										try:
																	lowerCamelCase_	:							Optional[Any]									=pl_module.model.model.num_parameters()
										except AttributeError:
																	lowerCamelCase_	:							Union[str, Any]									=pl_module.model.num_parameters()

										lowerCamelCase_	:							List[str]									=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      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  pl.Trainer       ,	snake_case__					:  pl.LightningModule ):
										save_json(pl_module.metrics       ,	pl_module.metrics_save_path )
										return self._write_logs(snake_case__       ,	snake_case__       ,	"test" )


			@rank_zero_only
			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  pl.Trainer       ,	snake_case__					:  List[str] ):
										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 collections import namedtuple

def 			_snake_case    (					lowerCamelCase__     :   float    ,						lowerCamelCase__     :   float    ,						lowerCamelCase__     :   float  )    ->     tuple:
							lowerCamelCase_	:							Optional[Any]									=namedtuple("result"    ,						"name value"  )
							if (voltage, current, power).count(0  ) != 1:
														raise ValueError("Only one argument must be 0"  )
							elif power < 0:
														raise ValueError(
														    "Power cannot be negative in any electrical/electronics system"  )
							elif voltage == 0:
														return result("voltage"    ,						power / current  )
							elif current == 0:
														return result("current"    ,						power / voltage  )
							elif power == 0:
														return result("power"    ,						float(round(abs(voltage * current  )    ,						2  )  )  )
							else:
														raise ValueError("Exactly one argument must be 0"  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



