


"""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     lowercase			(SCREAMING_SNAKE_CASE_		:				Any  )   ->						Optional[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


__UpperCamelCase						=      logging.getLogger(__name__)


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						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=SCREAMING_SNAKE_CASE_ ,			filename=SCREAMING_SNAKE_CASE_ ,			monitor=F'val_{metric}' ,			mode='max' ,			save_top_k=3 ,			every_n_epochs=1 ,			)
						return checkpoint_callback


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						Union[str, Any]:
						return EarlyStopping(
						    monitor=F'val_{metric}' ,			mode='min' if 'loss' in metric else 'max' ,			patience=SCREAMING_SNAKE_CASE_ ,			verbose=SCREAMING_SNAKE_CASE_ ,			)


class 					lowerCAmelCase	(					pl.Callback    ):




		'''simple docstring'''







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Tuple:
								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(lowerCAmelCase__				)







		@rank_zero_only
		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__=True				)     ->							None:
								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=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													=					metrics[key]
																				if isinstance(lowerCAmelCase__						,       torch.Tensor				):
																										SCREAMING_SNAKE_CASE													=					val.item()
																				SCREAMING_SNAKE_CASE													=					F'{key}: {val:.6f}\n'
																				writer.write(lowerCAmelCase__				)

								if not save_generations:
														return

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







		@rank_zero_only
		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							str:
								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(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 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							int:
								save_json(pl_module.metrics						,       pl_module.metrics_save_path				)
								return self._write_logs(lowerCAmelCase__						,       lowerCAmelCase__						,       'test'				)







		@rank_zero_only
		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Tuple:
								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


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str  )   ->						list[int]:
						return [ord(SCREAMING_SNAKE_CASE_  ) - 96 for elem in plain]


def     lowercase			(SCREAMING_SNAKE_CASE_		:				list[int]  )   ->						str:
						return "".join(chr(elem + 96  ) for elem in encoded  )


def     lowercase			()   ->						None:
						SCREAMING_SNAKE_CASE													=					encode(input('-> '  ).strip().lower()  )
						print('Encoded: ' ,			SCREAMING_SNAKE_CASE_  )
						print('Decoded:' ,			decode(SCREAMING_SNAKE_CASE_  )  )


if __name__ == "__main__":
	main()


