





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 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]					)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				str           =   filter(lambda UpperCamelCase__					: p.requires_grad       ,			model.parameters()					)
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   sum([np.prod(p.size()					) for p in model_parameters]					)
						return params


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






def 		lowerCAmelCase  (	UpperCamelCase__   :   List[str]       ,			UpperCamelCase__   :   Dict					)      ->    int:

						"""simple docstring"""





						if metric == "rouge2":
												__SCREAMING_SNAKE_CASE:				str           =   '''{val_avg_rouge2:.4f}-{step_count}'''
						elif metric == "bleu":
												__SCREAMING_SNAKE_CASE:				Dict           =   '''{val_avg_bleu:.4f}-{step_count}'''
						elif metric == "em":
												__SCREAMING_SNAKE_CASE:				Dict           =   '''{val_avg_em:.4f}-{step_count}'''
						elif metric == "loss":
												__SCREAMING_SNAKE_CASE:				List[Any]           =   '''{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.'''					)

						__SCREAMING_SNAKE_CASE:				Dict           =   ModelCheckpoint(
						    dirpath=UpperCamelCase__       ,			filename=UpperCamelCase__       ,			monitor=F"""val_{metric}"""       ,			mode='''max'''       ,			save_top_k=1       ,			every_n_epochs=1       ,			)

						return checkpoint_callback






def 		lowerCAmelCase  (	UpperCamelCase__   :   List[Any]       ,			UpperCamelCase__   :   Any					)      ->    Any:

						"""simple docstring"""





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





class 				a			(      pl.Callback			):






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				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(_lowerCAmelCase					)






				@rank_zero_only
				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase=True					):

										"""simple docstring"""




										logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   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:				Union[str, Any]           =   Path(pl_module.hparams.output_dir					)
										if type_path == "test":
																__SCREAMING_SNAKE_CASE:				Dict           =   od / '''test_results.txt'''
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   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:				Optional[int]           =   od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
																__SCREAMING_SNAKE_CASE:				List[Any]           =   od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
																results_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
																generations_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
										with open(_lowerCAmelCase			, '''a+'''					) as writer:
																for key in sorted(_lowerCAmelCase					):
																						if key in ["log", "progress_bar", "preds"]:
																												continue
																						__SCREAMING_SNAKE_CASE:				Tuple           =   metrics[key]
																						if isinstance(_lowerCAmelCase			, torch.Tensor					):
																												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   val.item()
																						__SCREAMING_SNAKE_CASE:				Tuple           =   f"""{key}: {val:.6f}\n"""
																						writer.write(_lowerCAmelCase					)

										if not save_generations:
																return

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






				@rank_zero_only
				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										try:
																__SCREAMING_SNAKE_CASE:				Tuple           =   pl_module.model.model.num_parameters()
										except AttributeError:
																__SCREAMING_SNAKE_CASE:				Dict           =   pl_module.model.num_parameters()

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   count_trainable_parameters(_lowerCAmelCase					)
										# 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 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										save_json(pl_module.metrics			, pl_module.metrics_save_path					)
										return self._write_logs(_lowerCAmelCase			, _lowerCAmelCase			, '''test'''					)






				@rank_zero_only
				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""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")










def 		lowerCAmelCase  (	UpperCamelCase__   :   int       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float(moles / volume					) * nfactor					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float((moles * 0.08_21 * temperature) / (volume)					)					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float((moles * 0.08_21 * temperature) / (pressure)					)					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float((pressure * volume) / (0.08_21 * moles)					)					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()



