


"""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 import save_json
def     UpperCAmelCase    (					_lowercase   :      int  )	->   Optional[int]:


	"""simple docstring"""




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


lowercase_							 =       logging.getLogger(__name__)







class       __a	(   pl.Callback       ):







				def     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase      ):

					'''simple docstring'''






					lowerCAmelCase_			       = {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			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase=True      ):

					'''simple docstring'''






					logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""      )
					lowerCAmelCase_			       = 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
					lowerCAmelCase_			       = Path(pl_module.hparams.output_dir      )
					if type_path == "test":
						lowerCAmelCase_			       = od / '''test_results.txt'''
						lowerCAmelCase_			       = 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.
						lowerCAmelCase_			       = od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
						lowerCAmelCase_			       = 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
							lowerCAmelCase_			       = metrics[key]
							if isinstance(UpperCAmelCase			,  torch.Tensor      ):
								lowerCAmelCase_			       = val.item()
							lowerCAmelCase_			       = F"""{key}: {val:.6f}\n"""
							writer.write(UpperCAmelCase      )

					if not save_generations:
						return

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







				@rank_zero_only
				def     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase      ):

					'''simple docstring'''






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

					lowerCAmelCase_			       = 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			,  UpperCAmelCase			,  UpperCAmelCase      ):

					'''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			,  UpperCAmelCase			,  UpperCAmelCase      ):

					'''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     UpperCAmelCase    (					_lowercase   :      List[Any] ,      _lowercase   :      str ,      _lowercase   :      int=1 ,      _lowercase   :      Tuple=False  )	->   Dict:


	"""simple docstring"""




	if metric == "rouge2":
		lowerCAmelCase_			       = '''{val_avg_rouge2:.4f}-{step_count}'''
	elif metric == "bleu":
		lowerCAmelCase_			       = '''{val_avg_bleu:.4f}-{step_count}'''
	elif metric == "loss":
		lowerCAmelCase_			       = '''{val_avg_loss:.4f}-{step_count}'''
	else:
		raise NotImplementedError(
		    F"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
		    ''' this function.'''  )

	lowerCAmelCase_			       = ModelCheckpoint(
	    dirpath=_lowercase ,      filename=_lowercase ,      monitor=F"""val_{metric}""" ,      mode='''min''' if '''loss''' in metric else '''max''' ,      save_top_k=_lowercase ,      )
	return checkpoint_callback





def     UpperCAmelCase    (					_lowercase   :      Optional[int] ,      _lowercase   :      Optional[Any]  )	->   Union[str, Any]:


	"""simple docstring"""




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



"""simple docstring"""



lowercase_							 =       [0.485, 0.456, 0.406]
lowercase_							 =       [0.229, 0.224, 0.225]
lowercase_							 =       [0.5, 0.5, 0.5]
lowercase_							 =       [0.5, 0.5, 0.5]
lowercase_							 =       [0.4814_5466, 0.457_8275, 0.4082_1073]
lowercase_							 =       [0.2686_2954, 0.2613_0258, 0.2757_7711]