


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     ):


		"""simple docstring"""




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


lowerCAmelCase       =							logging.getLogger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




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

		lowercase__            =      ModelCheckpoint(
		    dirpath=SCREAMING_SNAKE_CASE						,				filename=SCREAMING_SNAKE_CASE						,				monitor=f'val_{metric}'						,				mode='''max'''						,				save_top_k=1						,				every_n_epochs=1						,				)

		return checkpoint_callback
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




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


class        _a							(			pl.Callback			):

							def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	Union[str, Any]							)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      {f'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
									pl_module.logger.log_metrics(UpperCamelCase_							)

							@rank_zero_only
							def       lowerCamelCase_ (							self:	Tuple    ,						UpperCamelCase_:	pl.Trainer    ,						UpperCamelCase_:	pl.LightningModule    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	Optional[int]=True							)			->   None:





									"""simple docstring"""







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

									if not save_generations:
											return

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

							@rank_zero_only
							def       lowerCamelCase_ (							self:	Any    ,						UpperCamelCase_:	Tuple    ,						UpperCamelCase_:	Tuple							)			->   Optional[int]:





									"""simple docstring"""







									try:
											lowercase__            =      pl_module.model.model.num_parameters()
									except AttributeError:
											lowercase__            =      pl_module.model.num_parameters()

									lowercase__            =      count_trainable_parameters(UpperCamelCase_							)
									# 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:	str    ,						UpperCamelCase_:	pl.Trainer    ,						UpperCamelCase_:	pl.LightningModule							)			->   Tuple:





									"""simple docstring"""







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


							@rank_zero_only
							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	pl.Trainer    ,						UpperCamelCase_:	List[str]							)			->   Union[str, Any]:





									"""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")








"""simple docstring"""






def      _snake_case			(				lowercase__		):
				if not isinstance(lowercase__							,    lowercase__		):
								raise ValueError('Input series is not valid, valid series - [2, 4, 6]'		)
				if len(lowercase__		) == 0:
								raise ValueError('Input list must be a non empty list'		)
				if len(lowercase__		) == 1:
								return True
				_lowerCamelCase     :			List[Any]    =    series[1] - series[0]
				for index in range(len(lowercase__		) - 1		):
								if series[index + 1] - series[index] != common_diff:
												return False
				return True







def      _snake_case			(				lowercase__		):
				if not isinstance(lowercase__							,    lowercase__		):
								raise ValueError('Input series is not valid, valid series - [2, 4, 6]'		)
				if len(lowercase__		) == 0:
								raise ValueError('Input list must be a non empty list'		)
				_lowerCamelCase     :			Optional[int]    =    0
				for val in series:
								answer += val
				return answer / len(lowercase__		)


if __name__ == "__main__":
			import doctest

			doctest.testmod()