



'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a     :    Optional[int]      						=					logging.get_logger(__name__)

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				['pixel_values']





	def __init__(						self	,	A = True	,	A = None	,	A = PILImageResampling.BILINEAR	,	A = True	,	A = None	,	A = True	,	A = 1 / 255	,	A = True	,	A = None	,	A = None	,	**A	,	)    ->    None:
								super().__init__(**A					)
								UpperCAmelCase  :	Any							  =	size if size is not None else {"""shortest_edge""": 256}
								UpperCAmelCase  :	Dict							  =	get_size_dict(A	,	default_to_square=A					)
								UpperCAmelCase  :	List[str]							  =	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								UpperCAmelCase  :	List[str]							  =	get_size_dict(A					)
								UpperCAmelCase  :	Optional[int]							  =	do_resize
								UpperCAmelCase  :	Any							  =	size
								UpperCAmelCase  :	int							  =	resample
								UpperCAmelCase  :	str							  =	do_center_crop
								UpperCAmelCase  :	List[Any]							  =	crop_size
								UpperCAmelCase  :	str							  =	do_rescale
								UpperCAmelCase  :	Tuple							  =	rescale_factor
								UpperCAmelCase  :	Tuple							  =	do_normalize
								UpperCAmelCase  :	int							  =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								UpperCAmelCase  :	Optional[int]							  =	image_std if image_std is not None else IMAGENET_STANDARD_STD





	def 				_lowercase(						self	,	A	,	A	,	A = PILImageResampling.BICUBIC	,	A = None	,	**A	,	)    ->    np.ndarray:
								UpperCAmelCase  :	List[str]							  =	get_size_dict(A	,	default_to_square=A					)
								if "shortest_edge" not in size:
															raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''					)
								UpperCAmelCase  :	Optional[int]							  =	get_resize_output_image_size(A	,	size=size["""shortest_edge"""]	,	default_to_square=A					)
								return resize(A	,	size=A	,	resample=A	,	data_format=A	,	**A					)





	def 				_lowercase(						self	,	A	,	A	,	A = None	,	**A	,	)    ->    np.ndarray:
								UpperCAmelCase  :	Optional[Any]							  =	get_size_dict(A					)
								return center_crop(A	,	size=(size["""height"""], size["""width"""])	,	data_format=A	,	**A					)





	def 				_lowercase(						self	,	A	,	A	,	A = None	,	**A					)    ->    np.ndarray:
								return rescale(A	,	scale=A	,	data_format=A	,	**A					)





	def 				_lowercase(						self	,	A	,	A	,	A	,	A = None	,	**A	,	)    ->    np.ndarray:
								return normalize(A	,	mean=A	,	std=A	,	data_format=A	,	**A					)






	def 				_lowercase(						self	,	A	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = ChannelDimension.FIRST	,	**A	,	)    ->    Any:
								UpperCAmelCase  :	Tuple							  =	do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase  :	Optional[int]							  =	size if size is not None else self.size
								UpperCAmelCase  :	Optional[Any]							  =	get_size_dict(A	,	default_to_square=A					)
								UpperCAmelCase  :	str							  =	resample if resample is not None else self.resample
								UpperCAmelCase  :	Union[str, Any]							  =	do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase  :	Dict							  =	crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase  :	Any							  =	get_size_dict(A					)
								UpperCAmelCase  :	Dict							  =	do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase  :	Tuple							  =	rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase  :	Any							  =	do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase  :	Dict							  =	image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase  :	Dict							  =	image_std if image_std is not None else self.image_std

								UpperCAmelCase  :	Tuple							  =	make_list_of_images(A					)

								if not valid_images(A					):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

								if do_resize and size is None:
															raise ValueError("""Size must be specified if do_resize is True."""					)

								if do_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

								if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

								if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

								# All transformations expect numpy arrays.
								UpperCAmelCase  :	Tuple							  =	[to_numpy_array(A					) for image in images]

								if do_resize:
															UpperCAmelCase  :	Optional[Any]							  =	[self.resize(image=A	,	size=A	,	resample=A					) for image in images]

								if do_center_crop:
															UpperCAmelCase  :	int							  =	[self.center_crop(image=A	,	size=A					) for image in images]

								if do_rescale:
															UpperCAmelCase  :	Optional[Any]							  =	[self.rescale(image=A	,	scale=A					) for image in images]

								if do_normalize:
															UpperCAmelCase  :	str							  =	[self.normalize(image=A	,	mean=A	,	std=A					) for image in images]

								UpperCAmelCase  :	int							  =	[to_channel_dimension_format(A	,	A					) for image in images]

								UpperCAmelCase  :	int							  =	{"""pixel_values""": images}
								return BatchFeature(data=A	,	tensor_type=A					)






'''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]					)}

