

from scipy.stats import pearsonr

import datasets


snake_case_   :   int        =       """
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.
"""


snake_case_   :   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
"""


snake_case_   :   Any        =       """
@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       snake_case__  ( datasets.Metric     ):







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''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 			__lowerCAmelCase				(			self			: str			,  lowercase			: str			,  lowercase			: str			,  lowercase			: Dict=False  ):





								'''simple docstring'''



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





"""simple docstring"""






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
snake_case_   :   Union[str, Any]        =       abspath(join(dirname(__file__), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)



def   lowercase_	(    _lowercase       :		List[str]	):




					'''simple docstring'''






					config.addinivalue_line(
					    "markers"							,      "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"	)
					config.addinivalue_line(
					    "markers"							,      "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested"	)
					config.addinivalue_line("markers"							,      "is_pipeline_test: mark test to run only when pipelines are tested"	)
					config.addinivalue_line("markers"							,      "is_staging_test: mark test to run only in the staging environment"	)
					config.addinivalue_line("markers"							,      "accelerate_tests: mark test that require accelerate"	)
					config.addinivalue_line("markers"							,      "tool_tests: mark the tool tests that are run on their specific schedule"	)



def   lowercase_	(    _lowercase       :		List[str]	):




					'''simple docstring'''






					from transformers.testing_utils import pytest_addoption_shared

					pytest_addoption_shared(_lowercase	)



def   lowercase_	(    _lowercase       :		Tuple	):




					'''simple docstring'''






					from transformers.testing_utils import pytest_terminal_summary_main

					UpperCAmelCase			:   Optional[int]      =				terminalreporter.config.getoption("--make-reports"	)
					if make_reports:
										pytest_terminal_summary_main(_lowercase							,      id=_lowercase	)



def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		Dict	):




					'''simple docstring'''






					if exitstatus == 5:
										UpperCAmelCase			:   List[str]      =				0


# Doctest custom flag to ignore output.
snake_case_   :   Union[str, Any]        =       doctest.register_optionflag("""IGNORE_RESULT""")

snake_case_   :   Optional[int]        =       doctest.OutputChecker







class       snake_case__  ( lowerCAmelCase_     ):







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: Tuple			,  lowercase			: Optional[Any]			,  lowercase			: int  ):





								'''simple docstring'''



								if IGNORE_RESULT & optionflags:
													return True
								return OutputChecker.check_output(self			,  lowercase			,  lowercase			,  lowercase  )

snake_case_   :   List[str]        =       CustomOutputChecker
snake_case_   :   Optional[Any]        =       HfDoctestModule




snake_case_   :   List[str]        =       HfDocTestParser


