
"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import LevitImageProcessor
class       lowerCAmelCase_		(				unittest.TestCase ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_    ,				snake_case_=7    ,				snake_case_=3    ,				snake_case_=18    ,				snake_case_=30    ,				snake_case_=400    ,				snake_case_=True    ,				snake_case_=None    ,				snake_case_=True    ,				snake_case_=None    ,				snake_case_=True    ,				snake_case_=[0.5, 0.5, 0.5]    ,				snake_case_=[0.5, 0.5, 0.5]    ,				)       ->  int:
													__lowerCAmelCase     =  size if size is not None else {"""shortest_edge""": 18}
													__lowerCAmelCase     =  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
													__lowerCAmelCase     =  parent
													__lowerCAmelCase     =  batch_size
													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  image_size
													__lowerCAmelCase     =  min_resolution
													__lowerCAmelCase     =  max_resolution
													__lowerCAmelCase     =  do_resize
													__lowerCAmelCase     =  size
													__lowerCAmelCase     =  do_center_crop
													__lowerCAmelCase     =  crop_size
													__lowerCAmelCase     =  do_normalize
													__lowerCAmelCase     =  image_mean
													__lowerCAmelCase     =  image_std




							def    A__						( self				)       ->  int:
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "do_center_crop": self.do_center_crop,
													    "size": self.size,
													    "crop_size": self.crop_size,
													}
@require_torch
@require_vision
class       lowerCAmelCase_		(				A__				,				unittest.TestCase ):



							'''simple docstring'''





							_snake_case						      =		LevitImageProcessor if is_vision_available() else None




							def    A__						( self				)       ->  Any:
													__lowerCAmelCase     =  LevitImageProcessingTester(self				)




							@property
							def    A__						( self				)       ->  Union[str, Any]:
													return self.image_processor_tester.prepare_image_processor_dict()




							def    A__						( self				)       ->  List[Any]:
													__lowerCAmelCase     =  self.image_processing_class(**self.image_processor_dict				)
													self.assertTrue(hasattr(snake_case_    ,				"""image_mean"""				)				)
													self.assertTrue(hasattr(snake_case_    ,				"""image_std"""				)				)
													self.assertTrue(hasattr(snake_case_    ,				"""do_normalize"""				)				)
													self.assertTrue(hasattr(snake_case_    ,				"""do_resize"""				)				)
													self.assertTrue(hasattr(snake_case_    ,				"""do_center_crop"""				)				)
													self.assertTrue(hasattr(snake_case_    ,				"""size"""				)				)




							def    A__						( self				)       ->  Dict:
													__lowerCAmelCase     =  self.image_processing_class.from_dict(self.image_processor_dict				)
													self.assertEqual(image_processor.size    ,				{"""shortest_edge""": 18}				)
													self.assertEqual(image_processor.crop_size    ,				{"""height""": 18, """width""": 18}				)

													__lowerCAmelCase     =  self.image_processing_class.from_dict(self.image_processor_dict    ,				size=42    ,				crop_size=84				)
													self.assertEqual(image_processor.size    ,				{"""shortest_edge""": 42}				)
													self.assertEqual(image_processor.crop_size    ,				{"""height""": 84, """width""": 84}				)




							def    A__						( self				)       ->  List[str]:
													pass




							def    A__						( self				)       ->  Optional[int]:
													# Initialize image_processing
													__lowerCAmelCase     =  self.image_processing_class(**self.image_processor_dict				)
													# create random PIL images
													__lowerCAmelCase     =  prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=snake_case_				)
													for image in image_inputs:
																			self.assertIsInstance(snake_case_    ,				Image.Image				)

													# Test not batched input
													__lowerCAmelCase     =  image_processing(image_inputs[0]    ,				return_tensors="""pt"""				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,				(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )    ,				)

													# Test batched
													__lowerCAmelCase     =  image_processing(snake_case_    ,				return_tensors="""pt"""				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,				(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )    ,				)




							def    A__						( self				)       ->  Union[str, Any]:
													# Initialize image_processing
													__lowerCAmelCase     =  self.image_processing_class(**self.image_processor_dict				)
													# create random numpy tensors
													__lowerCAmelCase     =  prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=snake_case_    ,				numpify=snake_case_				)
													for image in image_inputs:
																			self.assertIsInstance(snake_case_    ,				np.ndarray				)

													# Test not batched input
													__lowerCAmelCase     =  image_processing(image_inputs[0]    ,				return_tensors="""pt"""				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,				(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )    ,				)

													# Test batched
													__lowerCAmelCase     =  image_processing(snake_case_    ,				return_tensors="""pt"""				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,				(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )    ,				)




							def    A__						( self				)       ->  Any:
													# Initialize image_processing
													__lowerCAmelCase     =  self.image_processing_class(**self.image_processor_dict				)
													# create random PyTorch tensors
													__lowerCAmelCase     =  prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=snake_case_    ,				torchify=snake_case_				)
													for image in image_inputs:
																			self.assertIsInstance(snake_case_    ,				torch.Tensor				)

													# Test not batched input
													__lowerCAmelCase     =  image_processing(image_inputs[0]    ,				return_tensors="""pt"""				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,				(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )    ,				)

													# Test batched
													__lowerCAmelCase     =  image_processing(snake_case_    ,				return_tensors="""pt"""				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,				(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )    ,				)


"""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]				)}
