"""simple docstring"""






import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
       from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm


def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]	)				->					Dict:
      '''simple docstring'''


      if is_torch_version("""<"""      ,	"""2.0.0"""	) or not hasattr(lowerCAmelCase__      ,	"""_dynamo"""	):
            return False
      return isinstance(lowerCAmelCase__      ,	torch._dynamo.eval_frame.OptimizedModule	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :bool = True	)				->					Optional[Any]:
      '''simple docstring'''


      lowercase														=			(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

      lowercase														=			is_compiled_module(lowerCAmelCase__	)
      if is_compiled:
            lowercase														=			model
            lowercase														=			model._orig_mod

      if is_deepspeed_available():
            options += (DeepSpeedEngine,)

      while isinstance(lowerCAmelCase__      ,	lowerCAmelCase__	):
            lowercase														=			model.module

      if not keep_fpaa_wrapper:
            lowercase														=			getattr(lowerCAmelCase__      ,	"""forward"""	)
            lowercase														=			model.__dict__.pop("""_original_forward"""      ,	lowerCAmelCase__	)
            if original_forward is not None:
                  while hasattr(lowerCAmelCase__      ,	"""__wrapped__"""	):
                        lowercase														=			forward.__wrapped__
                        if forward == original_forward:
                              break
                  lowercase														=			forward
            if getattr(lowerCAmelCase__      ,	"""_converted_to_transformer_engine"""      ,	lowerCAmelCase__	):
                  convert_model(lowerCAmelCase__      ,	to_transformer_engine=lowerCAmelCase__	)

      if is_compiled:
            lowercase														=			model
            lowercase														=			compiled_model

      return model


def     UpperCAmelCase__ (       )				->					str:
      '''simple docstring'''


      PartialState().wait_for_everyone()


def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :Union[str, Any]	)				->					List[Any]:
      '''simple docstring'''


      if PartialState().distributed_type == DistributedType.TPU:
            xm.save(lowerCAmelCase__      ,	lowerCAmelCase__	)
      elif PartialState().local_process_index == 0:
            torch.save(lowerCAmelCase__      ,	lowerCAmelCase__	)


@contextmanager
def     UpperCAmelCase__ (       **lowerCAmelCase__  :str	)				->					int:
      '''simple docstring'''


      for key, value in kwargs.items():
            lowercase														=			str(lowerCAmelCase__	)

      yield

      for key in kwargs:
            if key.upper() in os.environ:
                  del os.environ[key.upper()]


def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]	)				->					str:
      '''simple docstring'''


      if not hasattr(lowerCAmelCase__      ,	"""__qualname__"""	) and not hasattr(lowerCAmelCase__      ,	"""__name__"""	):
            lowercase														=			getattr(lowerCAmelCase__      ,	"""__class__"""      ,	lowerCAmelCase__	)
      if hasattr(lowerCAmelCase__      ,	"""__qualname__"""	):
            return obj.__qualname__
      if hasattr(lowerCAmelCase__      ,	"""__name__"""	):
            return obj.__name__
      return str(lowerCAmelCase__	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :int	)				->					Union[str, Any]:
      '''simple docstring'''


      for key, value in source.items():
            if isinstance(lowerCAmelCase__      ,	lowerCAmelCase__	):
                  lowercase														=			destination.setdefault(lowerCAmelCase__      ,	{}	)
                  merge_dicts(lowerCAmelCase__      ,	lowerCAmelCase__	)
            else:
                  lowercase														=			value

      return destination






def     UpperCAmelCase__ (       lowerCAmelCase__  :int = None	)				->					bool:
      '''simple docstring'''


      if port is None:
            lowercase														=			2_9_5_0_0
      with socket.socket(socket.AF_INET      ,	socket.SOCK_STREAM	) as s:
            return s.connect_ex(("""localhost""", port)	) == 0




"""simple docstring"""






from scipy.stats import pearsonr

import datasets


__lowerCAmelCase					: Any        ="""
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.
"""


__lowerCAmelCase					: Optional[int]        ="""
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
"""


__lowerCAmelCase					: 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 				_A		( datasets.Metric			):

      def        A__ (		self		):







            """simple docstring"""



            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        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase=False		):







            """simple docstring"""



            if return_pvalue:
                  lowercase														=			pearsonr(__lowerCAmelCase	, __lowerCAmelCase		)
                  return {"pearsonr": results[0], "p-value": results[1]}
            else:
                  return {"pearsonr": float(pearsonr(__lowerCAmelCase	, __lowerCAmelCase		)[0]		)}



