





from scipy.stats import pearsonr

import datasets


lowerCAmelCase							:       Optional[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.
"""


lowerCAmelCase							:       Optional[Any]					 =	"""
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							:       Tuple					 =	"""
@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 			snake_case_    (  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 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase=False					):

										"""simple docstring"""




										if return_pvalue:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   pearsonr(_lowerCAmelCase			, _lowerCAmelCase					)
																return {"pearsonr": results[0], "p-value": results[1]}
										else:
																return {"pearsonr": float(pearsonr(_lowerCAmelCase			, _lowerCAmelCase					)[0]					)}










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, is_vision_available, logging


if is_vision_available():
							import PIL


lowerCAmelCase							:       str					 =	logging.get_logger(__name__)





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Any		      =     ['''pixel_values''']






				def __init__(  self			, _lowerCAmelCase = True			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = PILImageResampling.BILINEAR			, _lowerCAmelCase = True			, _lowerCAmelCase = 1 / 255			, _lowerCAmelCase = True			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   size if size is not None else {'''shortest_edge''': 384}
										__SCREAMING_SNAKE_CASE:				List[str]           =   get_size_dict(_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   do_resize
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   size
										# Default value set here for backwards compatibility where the value in config is None
										__SCREAMING_SNAKE_CASE:				List[str]           =   crop_pct if crop_pct is not None else 224 / 256
										__SCREAMING_SNAKE_CASE:				List[Any]           =   resample
										__SCREAMING_SNAKE_CASE:				Any           =   do_rescale
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   rescale_factor
										__SCREAMING_SNAKE_CASE:				Any           =   do_normalize
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   image_std if image_std is not None else IMAGENET_STANDARD_STD






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = PILImageResampling.BICUBIC			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   get_size_dict(_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)
										if "shortest_edge" not in size:
																raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""					)
										__SCREAMING_SNAKE_CASE:				str           =   size['''shortest_edge''']

										if shortest_edge < 384:
																# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																__SCREAMING_SNAKE_CASE:				Dict           =   int(shortest_edge / crop_pct					)
																__SCREAMING_SNAKE_CASE:				Any           =   get_resize_output_image_size(_lowerCAmelCase			, size=_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   resize(image=_lowerCAmelCase			, size=_lowerCAmelCase			, resample=_lowerCAmelCase			, data_format=_lowerCAmelCase			, **_lowerCAmelCase					)
																# then crop to (shortest_edge, shortest_edge)
																return center_crop(image=_lowerCAmelCase			, size=(shortest_edge, shortest_edge)			, data_format=_lowerCAmelCase			, **_lowerCAmelCase					)
										else:
																# warping (no cropping) when evaluated at 384 or larger
																return resize(
																    _lowerCAmelCase			, size=(shortest_edge, shortest_edge)			, resample=_lowerCAmelCase			, data_format=_lowerCAmelCase			, **_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										return rescale(_lowerCAmelCase			, scale=_lowerCAmelCase			, data_format=_lowerCAmelCase			, **_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										return normalize(_lowerCAmelCase			, mean=_lowerCAmelCase			, std=_lowerCAmelCase			, data_format=_lowerCAmelCase			, **_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = ChannelDimension.FIRST			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   do_resize if do_resize is not None else self.do_resize
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   crop_pct if crop_pct is not None else self.crop_pct
										__SCREAMING_SNAKE_CASE:				Tuple           =   resample if resample is not None else self.resample
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   do_rescale if do_rescale is not None else self.do_rescale
										__SCREAMING_SNAKE_CASE:				Any           =   rescale_factor if rescale_factor is not None else self.rescale_factor
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   do_normalize if do_normalize is not None else self.do_normalize
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   image_mean if image_mean is not None else self.image_mean
										__SCREAMING_SNAKE_CASE:				str           =   image_std if image_std is not None else self.image_std

										__SCREAMING_SNAKE_CASE:				List[Any]           =   size if size is not None else self.size
										__SCREAMING_SNAKE_CASE:				Dict           =   get_size_dict(_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				List[str]           =   make_list_of_images(_lowerCAmelCase					)

										if not valid_images(_lowerCAmelCase					):
																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 or resample is None:
																raise ValueError('''Size and resample must be specified if do_resize is True.'''					)

										if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
																raise ValueError('''crop_pct must be specified if size < 384.'''					)

										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.
										__SCREAMING_SNAKE_CASE:				Tuple           =   [to_numpy_array(_lowerCAmelCase					) for image in images]

										if do_resize:
																__SCREAMING_SNAKE_CASE:				str           =   [self.resize(image=_lowerCAmelCase			, size=_lowerCAmelCase			, crop_pct=_lowerCAmelCase			, resample=_lowerCAmelCase					) for image in images]

										if do_rescale:
																__SCREAMING_SNAKE_CASE:				Any           =   [self.rescale(image=_lowerCAmelCase			, scale=_lowerCAmelCase					) for image in images]

										if do_normalize:
																__SCREAMING_SNAKE_CASE:				int           =   [self.normalize(image=_lowerCAmelCase			, mean=_lowerCAmelCase			, std=_lowerCAmelCase					) for image in images]

										__SCREAMING_SNAKE_CASE:				str           =   [to_channel_dimension_format(_lowerCAmelCase			, _lowerCAmelCase					) for image in images]

										__SCREAMING_SNAKE_CASE:				List[str]           =   {'''pixel_values''': images}
										return BatchFeature(data=_lowerCAmelCase			, tensor_type=_lowerCAmelCase					)



