
"""simple docstring"""
def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  word.split()

						def justify(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						) -> str:
												__lowerCAmelCase     =  max_width - width
												__lowerCAmelCase     =  len(_lowerCAmelCase						)
												if len(_lowerCAmelCase						) == 1:
																		# if there is only word in line
																		# just insert overall_spaces_count for the remainder of line
																		return line[0] + " " * overall_spaces_count
												else:
																		__lowerCAmelCase     =  words_count - 1
																		# num_spaces_between_words_list[i] : tells you to insert
																		# num_spaces_between_words_list[i] spaces
																		# after word on line[i]
																		__lowerCAmelCase     =  spaces_to_insert_between_words * [
																		    overall_spaces_count // spaces_to_insert_between_words
																		]
																		__lowerCAmelCase     =  (
																		    overall_spaces_count % spaces_to_insert_between_words
																		)
																		# distribute spaces via round robin to the left words
																		for i in range(_lowerCAmelCase						):
																								num_spaces_between_words_list[i] += 1
																		__lowerCAmelCase     =  []
																		for i in range(_lowerCAmelCase						):
																								# add the word
																								aligned_words_list.append(line[i]						)
																								# add the spaces to insert
																								aligned_words_list.append(num_spaces_between_words_list[i] * """ """						)
																		# just add the last word to the sentence
																		aligned_words_list.append(line[-1]						)
																		# join the aligned words list to form a justified line
																		return "".join(_lowerCAmelCase						)

						__lowerCAmelCase     =  []
						__lowerCAmelCase     =  []
						__lowerCAmelCase     =  0
						for word in words:
												if width + len(_lowerCAmelCase						) + len(_lowerCAmelCase						) <= max_width:
																		# keep adding words until we can fill out max_width
																		# width = sum of length of all words (without overall_spaces_count)
																		# len(word) = length of current word
																		# len(line) = number of overall_spaces_count to insert between words
																		line.append(_lowerCAmelCase						)
																		width += len(_lowerCAmelCase						)
												else:
																		# justify the line and add it to result
																		answer.append(justify(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)						)
																		# reset new line and new width
																		__lowerCAmelCase   ,     __lowerCAmelCase     =  [word], len(_lowerCAmelCase						)
						__lowerCAmelCase     =  max_width - width - len(_lowerCAmelCase						)
						answer.append(""" """.join(_lowerCAmelCase						) + (remaining_spaces + 1) * """ """						)
						return answer


if __name__ == "__main__":
			from doctest import testmod

			testmod()


"""simple docstring"""
from scipy.stats import pearsonr

import datasets


SCREAMING_SNAKE_CASE_					     =	'''
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.
'''


SCREAMING_SNAKE_CASE_					     =	'''
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
'''


SCREAMING_SNAKE_CASE_					     =	'''
@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       lowerCAmelCase_		(				datasets.Metric ):



							'''simple docstring'''





							def    A__						( self				)       ->  Union[str, Any]:
													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    ,				snake_case_    ,				snake_case_    ,				snake_case_=False				)       ->  Dict:
													if return_pvalue:
																			__lowerCAmelCase     =  pearsonr(snake_case_    ,				snake_case_				)
																			return {"pearsonr": results[0], "p-value": results[1]}
													else:
																			return {"pearsonr": float(pearsonr(snake_case_    ,				snake_case_				)[0]				)}
