


'''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    :     List[str]		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					filter(lambda lowerCAmelCase		: p.requires_grad		,		model.parameters()		)
	__magic_name__	:   Union[str, Any]					     =					sum([np.prod(p.size()		) for p in model_parameters]		)
	return params


lowerCAmelCase				:Optional[int]        =      logging.getLogger(__name__)





def lowerCamelCase      (      lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Dict		):





	"""simple docstring"""







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

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





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     Optional[int]		):





	"""simple docstring"""







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





class _lowerCamelCase	(						pl.Callback							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						Dict  ,					_A		:						Any      )  ->			Dict:
			__magic_name__	:   Any					     =					{F'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
			pl_module.logger.log_metrics(_A      )







		@rank_zero_only
		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						pl.Trainer  ,					_A		:						pl.LightningModule  ,					_A		:						str  ,					_A		:						Optional[Any]=True      )  ->			None:
			logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'      )
			__magic_name__	:   List[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
			__magic_name__	:   Any					     =					Path(pl_module.hparams.output_dir      )
			if type_path == "test":
				__magic_name__	:   Dict					     =					od / 'test_results.txt'
				__magic_name__	:   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.
				__magic_name__	:   str					     =					od / F'{type_path}_results/{trainer.global_step:05d}.txt'
				__magic_name__	:   Tuple					     =					od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
				results_file.parent.mkdir(exist_ok=_A      )
				generations_file.parent.mkdir(exist_ok=_A      )
			with open(_A  ,					'a+'      ) as writer:
				for key in sorted(_A      ):
					if key in ["log", "progress_bar", "preds"]:
						continue
					__magic_name__	:   str					     =					metrics[key]
					if isinstance(_A  ,					torch.Tensor      ):
						__magic_name__	:   Tuple					     =					val.item()
					__magic_name__	:   List[str]					     =					F'{key}: {val:.6f}\n'
					writer.write(_A      )

			if not save_generations:
				return

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







		@rank_zero_only
		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						Union[str, Any]  ,					_A		:						Optional[int]      )  ->			Optional[Any]:
			try:
				__magic_name__	:   List[str]					     =					pl_module.model.model.num_parameters()
			except AttributeError:
				__magic_name__	:   Tuple					     =					pl_module.model.num_parameters()

			__magic_name__	:   str					     =					count_trainable_parameters(_A      )
			# 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		:						List[Any]  ,					_A		:						pl.Trainer  ,					_A		:						pl.LightningModule      )  ->			Optional[Any]:
			save_json(pl_module.metrics  ,					pl_module.metrics_save_path      )
			return self._write_logs(_A  ,					_A  ,					'test'      )







		@rank_zero_only
		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						pl.Trainer  ,					_A		:						Any      )  ->			Union[str, Any]:
			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 lowerCamelCase      (      lowerCAmelCase    :     str		):





	"""simple docstring"""







	return "".join(chr(ord(lowerCAmelCase		) - 32		) if 'a' <= char <= 'z' else char for char in word		)


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

							testmod()