



'''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						(      _lowercase	)   ->    str:
							UpperCAmelCase  :	Dict							  =	filter(lambda _lowercase	: p.requires_grad			,       model.parameters()	)
							UpperCAmelCase  :	Optional[Any]							  =	sum([np.prod(p.size()	) for p in model_parameters]	)
							return params


a     :    Optional[Any]      						=					logging.getLogger(__name__)



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Union[str, Any]:





							if metric == "rouge2":
														UpperCAmelCase  :	Dict							  =	"""{val_avg_rouge2:.4f}-{step_count}"""
							elif metric == "bleu":
														UpperCAmelCase  :	str							  =	"""{val_avg_bleu:.4f}-{step_count}"""
							elif metric == "em":
														UpperCAmelCase  :	List[str]							  =	"""{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."""	)

							UpperCAmelCase  :	Dict							  =	ModelCheckpoint(
							    dirpath=__snake_case			,       filename=__snake_case			,       monitor=F'''val_{metric}'''			,       mode="""max"""			,       save_top_k=3			,       every_n_epochs=1			,       )
							return checkpoint_callback



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


class     UpperCamelCase_				(							pl.Callback    ):





	def 				_lowercase(						self	,	A	,	A					)    ->    int:
								UpperCAmelCase  :	Tuple							  =	{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 				_lowercase(						self	,	A	,	A	,	A	,	A=True					)    ->    None:
								logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''					)
								UpperCAmelCase  :	Union[str, 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
								UpperCAmelCase  :	Dict							  =	Path(pl_module.hparams.output_dir					)
								if type_path == "test":
															UpperCAmelCase  :	int							  =	od / """test_results.txt"""
															UpperCAmelCase  :	Tuple							  =	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.
															UpperCAmelCase  :	List[Any]							  =	od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
															UpperCAmelCase  :	str							  =	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
																						UpperCAmelCase  :	Dict							  =	metrics[key]
																						if isinstance(__a	,	torch.Tensor					):
																													UpperCAmelCase  :	str							  =	val.item()
																						UpperCAmelCase  :	Dict							  =	f'''{key}: {val:.6f}\n'''
																						writer.write(__a					)

								if not save_generations:
															return

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





	@rank_zero_only
	def 				_lowercase(						self	,	A	,	A					)    ->    List[str]:
								try:
															UpperCAmelCase  :	List[Any]							  =	pl_module.model.model.num_parameters()
								except AttributeError:
															UpperCAmelCase  :	Tuple							  =	pl_module.model.num_parameters()

								UpperCAmelCase  :	Optional[Any]							  =	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 				_lowercase(						self	,	A	,	A					)    ->    int:
								save_json(pl_module.metrics	,	pl_module.metrics_save_path					)
								return self._write_logs(__a	,	__a	,	"""test"""					)






	@rank_zero_only
	def 				_lowercase(						self	,	A	,	A					)    ->    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'''




from scipy.stats import pearsonr

import datasets


a     :    str      						=					"""
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
"""


a     :    Dict      						=					"""
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results['pearsonr'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        ['p-value', 'pearsonr']
        >>> print(round(results['pearsonr'], 2))
        -0.74
        >>> print(round(results['p-value'], 2))
        0.15
"""


a     :    Optional[int]      						=					"""
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""

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





	def 				_lowercase(						self					)    ->    Optional[int]:
								return datasets.MetricInfo(
								    description=_DESCRIPTION	,	citation=_CITATION	,	inputs_description=_KWARGS_DESCRIPTION	,	features=datasets.Features(
								        {
								            """predictions""": datasets.Value("""float"""					),
								            """references""": datasets.Value("""float"""					),
								        }					)	,	reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"""]	,	)






	def 				_lowercase(						self	,	A	,	A	,	A=False					)    ->    int:
								if return_pvalue:
															UpperCAmelCase  :	int							  =	pearsonr(A	,	A					)
															return {"pearsonr": results[0], "p-value": results[1]}
								else:
															return {"pearsonr": float(pearsonr(A	,	A					)[0]					)}

