




"""simple docstring"""







__A   :  Tuple					=      'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'

def 						snake_case__       (					_lowerCamelCase				)			->bytes:






		"""simple docstring"""


		if not isinstance(_lowerCamelCase,  _lowerCamelCase				):
				__lowercase				:		Optional[Any]												=				F'a bytes-like object is required, not \'{data.__class__.__name__}\''
				raise TypeError(_lowerCamelCase				)

		__lowercase				:		int												=				"".join(bin(_lowerCamelCase				)[2:].zfill(8				) for byte in data				)

		__lowercase				:		Dict												=				len(_lowerCamelCase				) % 6 != 0

		if padding_needed:
				# The padding that will be added later
				__lowercase				:		Optional[Any]												=				b"=" * ((6 - len(_lowerCamelCase				) % 6) // 2)

				# Append binary_stream with arbitrary binary digits (0's by default) to make its
				# length a multiple of 6.
				binary_stream += "0" * (6 - len(_lowerCamelCase				) % 6)
		else:
				__lowercase				:		List[Any]												=				b""

		# Encode every 6 binary digits to their corresponding Base64 character
		return (
		    "".join(
		        B64_CHARSET[int(binary_stream[index : index + 6],  2				)]
		        for index in range(0,  len(_lowerCamelCase				),  6				)				).encode()
		    + padding
		)

def 						snake_case__       (					_lowerCamelCase				)			->bytes:






		"""simple docstring"""


		if not isinstance(_lowerCamelCase,  _lowerCamelCase				) and not isinstance(_lowerCamelCase,  _lowerCamelCase				):
				__lowercase				:		Optional[int]												=				(
				    "argument should be a bytes-like object or ASCII string, "
				    F'not \'{encoded_data.__class__.__name__}\''
				)
				raise TypeError(_lowerCamelCase				)

		# In case encoded_data is a bytes-like object, make sure it contains only
		# ASCII characters so we convert it to a string object
		if isinstance(_lowerCamelCase,  _lowerCamelCase				):
				try:
						__lowercase				:		Union[str, Any]												=				encoded_data.decode("utf-8"				)
				except UnicodeDecodeError:
						raise ValueError("base64 encoded data should only contain ASCII characters"				)

		__lowercase				:		Union[str, Any]												=				encoded_data.count("="				)

		# Check if the encoded string contains non base64 characters
		if padding:
				assert all(
				    char in B64_CHARSET for char in encoded_data[:-padding]				), "Invalid base64 character(s) found."
		else:
				assert all(
				    char in B64_CHARSET for char in encoded_data				), "Invalid base64 character(s) found."

		# Check the padding
		assert len(_lowerCamelCase				) % 4 == 0 and padding < 3, "Incorrect padding"

		if padding:
				# Remove padding if there is one
				__lowercase				:		str												=				encoded_data[:-padding]

				__lowercase				:		Optional[Any]												=				"".join(
				    bin(B64_CHARSET.index(_lowerCamelCase				)				)[2:].zfill(6				) for char in encoded_data				)[: -padding * 2]
		else:
				__lowercase				:		int												=				"".join(
				    bin(B64_CHARSET.index(_lowerCamelCase				)				)[2:].zfill(6				) for char in encoded_data				)

		__lowercase				:		Optional[int]												=				[
		    int(binary_stream[index : index + 8],  2				)
		    for index in range(0,  len(_lowerCamelCase				),  8				)
		]

		return bytes(_lowerCamelCase				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()






"""simple docstring"""







from scipy.stats import spearmanr

import datasets


__A   :  Optional[int]					=      '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'

__A   :  List[Any]					=      '\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {\'spearmanr\': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results[\'spearmanr\'])\n        -0.7\n        >>> print(round(results[\'spearmanr_pvalue\'], 2))\n        0.19\n'

__A   :  Optional[int]					=      R'\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n            Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n            Kern, Robert and Larson, Eric and Carey, C J and\n            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n            Harris, Charles R. and Archibald, Anne M. and\n            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n'




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


				"""simple docstring"""





				def       snake_case    (     self					:	List[str]						):
						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.spearmanr.html"]    ,   )



				def       snake_case    (     self					:	Optional[int]    ,   lowercase__					:	str    ,   lowercase__					:	Any    ,   lowercase__					:	int=False						):
						__lowercase				:		Optional[int]												=				spearmanr(lowercase__    ,   lowercase__						)
						if return_pvalue:
								return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
						else:
								return {"spearmanr": results[0]}
