



'''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 	__lowerCAmelCase       (				lowerCamelCase :    List[Any]       ):






	'''simple docstring'''




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


lowerCAmelCase						:		List[Any]						      = logging.getLogger(__name__)






class        UpperCAmelCase__						(		pl.Callback			):



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      Union[str, Any]:
						__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 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase=True  )  ->      None:
						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 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      Optional[Any]:
						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 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      Optional[Any]:
						save_json(pl_module.metrics      ,					pl_module.metrics_save_path  )
						return self._write_logs(UpperCamelCase      ,					UpperCamelCase      ,					"test"  )



					@rank_zero_only
					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      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")



def 	__lowerCAmelCase       (				lowerCamelCase :    int					,		lowerCamelCase :    List[Any]					,		lowerCamelCase :    Union[str, Any]=1					,		lowerCamelCase :    Union[str, Any]=False       ):






	'''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=lowerCamelCase					,		filename=lowerCamelCase					,		monitor=f'''val_{metric}'''					,		mode="min" if "loss" in metric else "max"					,		save_top_k=lowerCamelCase					,		)
	return checkpoint_callback




def 	__lowerCAmelCase       (				lowerCamelCase :    Optional[Any]					,		lowerCamelCase :    Optional[int]       ):






	'''simple docstring'''




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




'''simple docstring'''



from sklearn.metrics import accuracy_score

import datasets


lowerCAmelCase						:		Optional[Any]						      = '''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


lowerCAmelCase						:		str						      = '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


lowerCAmelCase						:		int						      = '''
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,							_KWARGS_DESCRIPTION			)
class        UpperCAmelCase__						(		datasets.Metric			):



					def 							UpperCAmelCase_			(     self  )  ->      Optional[Any]:
						return datasets.MetricInfo(
						    description=_DESCRIPTION      ,					citation=_CITATION      ,					inputs_description=_KWARGS_DESCRIPTION      ,					features=datasets.Features(
						        {
						            "predictions": datasets.Sequence(datasets.Value("int32"  )  ),
						            "references": datasets.Sequence(datasets.Value("int32"  )  ),
						        }
						        if self.config_name == "multilabel"
						        else {
						            "predictions": datasets.Value("int32"  ),
						            "references": datasets.Value("int32"  ),
						        }  )      ,					reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"]      ,					)



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase=True      ,					UpperCamelCase=None  )  ->      List[str]:
						return {
						    "accuracy": float(
						        accuracy_score(UpperCamelCase      ,					UpperCamelCase      ,					normalize=UpperCamelCase      ,					sample_weight=UpperCamelCase  )  )
						}