



'''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 	__lowerCamelCase	( lowerCAmelCase_    )     -> Union[str, Any]:
		_a	: Any								=				filter(lambda lowerCAmelCase_    : p.requires_grad		,     model.parameters()    )
		_a	: Any								=				sum([np.prod(p.size()    ) for p in model_parameters]    )
		return params


__lowerCAmelCase		     =  logging.getLogger(__name__)



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_    )     -> str:
		if metric == "rouge2":
				_a	: str								=				'{val_avg_rouge2:.4f}-{step_count}'
		elif metric == "bleu":
				_a	: List[str]								=				'{val_avg_bleu:.4f}-{step_count}'
		elif metric == "em":
				_a	: Optional[Any]								=				'{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.'    )

		_a	: Optional[int]								=				ModelCheckpoint(
		    dirpath=_lowercase		,     filename=_lowercase		,     monitor=f"""val_{metric}"""		,     mode='max'		,     save_top_k=3		,     every_n_epochs=1		,     )
		return checkpoint_callback



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_    )     -> Union[str, Any]:
		return EarlyStopping(
		    monitor=f"""val_{metric}"""		,     mode='min' if 'loss' in metric else 'max'		,     patience=_lowercase		,     verbose=_lowercase		,     )

class       __magic_name__		(       pl.Callback  ):







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

									if not save_generations:
											return

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







							@rank_zero_only
							def 				__lowercase     (				self   :			str      ,_UpperCAmelCase   :			List[str]      ,_UpperCAmelCase   :			Any						):
									try:
											_a	: List[Any]								=				pl_module.model.model.num_parameters()
									except AttributeError:
											_a	: List[str]								=				pl_module.model.num_parameters()

									_a	: str								=				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 				__lowercase     (				self   :			List[str]      ,_UpperCAmelCase   :			pl.Trainer      ,_UpperCAmelCase   :			pl.LightningModule						):
									save_json(pl_module.metrics      ,pl_module.metrics_save_path						)
									return self._write_logs(lowerCamelCase_      ,lowerCamelCase_      ,'test'						)







							@rank_zero_only
							def 				__lowercase     (				self   :			Optional[int]      ,_UpperCAmelCase   :			pl.Trainer      ,_UpperCAmelCase   :			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")











def       SCREAMING_SNAKE_CASE__  (       _lowercase     : str			)   ->							str:




		'''simple docstring'''
		if not all(char in '01' for char in bin_string			):
				raise ValueError('Non-binary value was passed to the function'			)
		if not bin_string:
				raise ValueError('Empty string was passed to the function'			)
		lowercase__					:      Tuple      =		''
		while len(_lowercase			) % 3 != 0:
				lowercase__					:      Optional[int]      =		'0' + bin_string
		lowercase__					:      List[Any]      =		[
		    bin_string[index : index + 3]
		    for index in range(len(_lowercase			)			)
		    if index % 3 == 0
		]
		for bin_group in bin_string_in_3_list:
				lowercase__					:      int      =		0
				for index, val in enumerate(_lowercase			):
						oct_val += int(2 ** (2 - index) * int(_lowercase			)			)
				oct_string += str(_lowercase			)
		return oct_string


if __name__ == "__main__":
			from doctest import testmod

			testmod()


