


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

import datasets


lowerCAmelCase						=					"""
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						=					"""
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						=					"""
@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	):






       def   UpperCamelCase						( 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.pearsonr.html']			,     )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=False   )->      Any:
            if return_pvalue:
                 _A				=							pearsonr(_UpperCamelCase			,     _UpperCamelCase   )
                 return {"pearsonr": results[0], "p-value": results[1]}
            else:
                 return {"pearsonr": float(pearsonr(_UpperCamelCase			,     _UpperCamelCase   )[0]   )}









'''simple docstring'''
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DeformableDetrImageProcessor






class        lowerCAmelCase_  (     unittest.TestCase	):






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase=7			,     _UpperCamelCase=3			,     _UpperCamelCase=30			,     _UpperCamelCase=400			,     _UpperCamelCase=True			,     _UpperCamelCase=None			,     _UpperCamelCase=True			,     _UpperCamelCase=[0.5, 0.5, 0.5]			,     _UpperCamelCase=[0.5, 0.5, 0.5]			,     _UpperCamelCase=True			,     _UpperCamelCase=1 / 255			,     _UpperCamelCase=True			,     )->      Tuple:
            # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
            _A				=							size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
            _A				=							parent
            _A				=							batch_size
            _A				=							num_channels
            _A				=							min_resolution
            _A				=							max_resolution
            _A				=							do_resize
            _A				=							size
            _A				=							do_normalize
            _A				=							image_mean
            _A				=							image_std
            _A				=							do_rescale
            _A				=							rescale_factor
            _A				=							do_pad






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_rescale": self.do_rescale,
                "rescale_factor": self.rescale_factor,
                "do_pad": self.do_pad,
            }






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase=False   )->      List[str]:
            if not batched:
                 _A				=							image_inputs[0]
                 if isinstance(_UpperCamelCase			,     Image.Image   ):
                      _A		,				_A				=							image.size
                 else:
                      _A		,				_A				=							image.shape[1], image.shape[2]
                 if w < h:
                      _A				=							int(self.size['shortest_edge'] * h / w   )
                      _A				=							self.size['shortest_edge']
                 elif w > h:
                      _A				=							self.size['shortest_edge']
                      _A				=							int(self.size['shortest_edge'] * w / h   )
                 else:
                      _A				=							self.size['shortest_edge']
                      _A				=							self.size['shortest_edge']

            else:
                 _A				=							[]
                 for image in image_inputs:
                      _A		,				_A				=							self.get_expected_values([image]   )
                      expected_values.append((expected_height, expected_width)   )
                 _A				=							max(_UpperCamelCase			,     key=lambda _UpperCamelCase   : item[0]   )[0]
                 _A				=							max(_UpperCamelCase			,     key=lambda _UpperCamelCase   : item[1]   )[1]

            return expected_height, expected_width






@require_torch
@require_vision
class        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=DeformableDetrImageProcessor if is_vision_available() else None






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							DeformableDetrImageProcessingTester(self   )






       @property
       def   UpperCamelCase						( self   )->      Optional[Any]:
            return self.image_processor_tester.prepare_image_processor_dict()






       def   UpperCamelCase						( self   )->      List[str]:
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'image_mean'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'image_std'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_normalize'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_resize'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_rescale'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_pad'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'size'   )   )






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.image_processing_class.from_dict(self.image_processor_dict   )
            self.assertEqual(image_processor.size			,     {'shortest_edge': 18, 'longest_edge': 1333}   )
            self.assertEqual(image_processor.do_pad			,     _UpperCamelCase   )

            _A				=							self.image_processing_class.from_dict(
                self.image_processor_dict			,     size=42			,     max_size=84			,     pad_and_return_pixel_mask=_UpperCamelCase   )
            self.assertEqual(image_processor.size			,     {'shortest_edge': 42, 'longest_edge': 84}   )
            self.assertEqual(image_processor.do_pad			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Optional[Any]:
            pass






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

            # Test not batched input
            _A				=							image_processing(image_inputs[0]			,     return_tensors='pt'   ).pixel_values

            _A		,				_A				=							self.image_processor_tester.get_expected_values(_UpperCamelCase   )

            self.assertEqual(
                encoded_images.shape			,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,     )

            # Test batched
            _A		,				_A				=							self.image_processor_tester.get_expected_values(_UpperCamelCase			,     batched=_UpperCamelCase   )

            _A				=							image_processing(_UpperCamelCase			,     return_tensors='pt'   ).pixel_values
            self.assertEqual(
                encoded_images.shape			,     (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )			,     )






       def   UpperCamelCase						( self   )->      List[str]:
            # Initialize image_processing
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random numpy tensors
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase			,     numpify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     np.ndarray   )

            # Test not batched input
            _A				=							image_processing(image_inputs[0]			,     return_tensors='pt'   ).pixel_values

            _A		,				_A				=							self.image_processor_tester.get_expected_values(_UpperCamelCase   )

            self.assertEqual(
                encoded_images.shape			,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,     )

            # Test batched
            _A				=							image_processing(_UpperCamelCase			,     return_tensors='pt'   ).pixel_values

            _A		,				_A				=							self.image_processor_tester.get_expected_values(_UpperCamelCase			,     batched=_UpperCamelCase   )

            self.assertEqual(
                encoded_images.shape			,     (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )			,     )






       def   UpperCamelCase						( self   )->      Dict:
            # Initialize image_processing
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PyTorch tensors
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase			,     torchify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     torch.Tensor   )

            # Test not batched input
            _A				=							image_processing(image_inputs[0]			,     return_tensors='pt'   ).pixel_values

            _A		,				_A				=							self.image_processor_tester.get_expected_values(_UpperCamelCase   )

            self.assertEqual(
                encoded_images.shape			,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,     )

            # Test batched
            _A				=							image_processing(_UpperCamelCase			,     return_tensors='pt'   ).pixel_values

            _A		,				_A				=							self.image_processor_tester.get_expected_values(_UpperCamelCase			,     batched=_UpperCamelCase   )

            self.assertEqual(
                encoded_images.shape			,     (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )			,     )






       @slow
       def   UpperCamelCase						( self   )->      List[Any]:
            # prepare image and target
            _A				=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
            with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'			,     'r'   ) as f:
                 _A				=							json.loads(f.read()   )

            _A				=							{'image_id': 3_9769, 'annotations': target}

            # encode them
            _A				=							DeformableDetrImageProcessor()
            _A				=							image_processing(images=_UpperCamelCase			,     annotations=_UpperCamelCase			,     return_tensors='pt'   )

            # verify pixel values
            _A				=							torch.Size([1, 3, 800, 1066]   )
            self.assertEqual(encoding['pixel_values'].shape			,     _UpperCamelCase   )

            _A				=							torch.tensor([0.2796, 0.3138, 0.3481]   )
            self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			,     _UpperCamelCase			,     atol=1e-4   )   )

            # verify area
            _A				=							torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,     _UpperCamelCase   )   )
            # verify boxes
            _A				=							torch.Size([6, 4]   )
            self.assertEqual(encoding['labels'][0]['boxes'].shape			,     _UpperCamelCase   )
            _A				=							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,     _UpperCamelCase			,     atol=1e-3   )   )
            # verify image_id
            _A				=							torch.tensor([3_9769]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,     _UpperCamelCase   )   )
            # verify is_crowd
            _A				=							torch.tensor([0, 0, 0, 0, 0, 0]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,     _UpperCamelCase   )   )
            # verify class_labels
            _A				=							torch.tensor([75, 75, 63, 65, 17, 17]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,     _UpperCamelCase   )   )
            # verify orig_size
            _A				=							torch.tensor([480, 640]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,     _UpperCamelCase   )   )
            # verify size
            _A				=							torch.tensor([800, 1066]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,     _UpperCamelCase   )   )






       @slow
       def   UpperCamelCase						( self   )->      Any:
            # prepare image, target and masks_path
            _A				=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
            with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'			,     'r'   ) as f:
                 _A				=							json.loads(f.read()   )

            _A				=							{'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

            _A				=							pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'   )

            # encode them
            _A				=							DeformableDetrImageProcessor(format='coco_panoptic'   )
            _A				=							image_processing(images=_UpperCamelCase			,     annotations=_UpperCamelCase			,     masks_path=_UpperCamelCase			,     return_tensors='pt'   )

            # verify pixel values
            _A				=							torch.Size([1, 3, 800, 1066]   )
            self.assertEqual(encoding['pixel_values'].shape			,     _UpperCamelCase   )

            _A				=							torch.tensor([0.2796, 0.3138, 0.3481]   )
            self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			,     _UpperCamelCase			,     atol=1e-4   )   )

            # verify area
            _A				=							torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,     _UpperCamelCase   )   )
            # verify boxes
            _A				=							torch.Size([6, 4]   )
            self.assertEqual(encoding['labels'][0]['boxes'].shape			,     _UpperCamelCase   )
            _A				=							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,     _UpperCamelCase			,     atol=1e-3   )   )
            # verify image_id
            _A				=							torch.tensor([3_9769]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,     _UpperCamelCase   )   )
            # verify is_crowd
            _A				=							torch.tensor([0, 0, 0, 0, 0, 0]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,     _UpperCamelCase   )   )
            # verify class_labels
            _A				=							torch.tensor([17, 17, 63, 75, 75, 93]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,     _UpperCamelCase   )   )
            # verify masks
            _A				=							82_2873
            self.assertEqual(encoding['labels'][0]['masks'].sum().item()			,     _UpperCamelCase   )
            # verify orig_size
            _A				=							torch.tensor([480, 640]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,     _UpperCamelCase   )   )
            # verify size
            _A				=							torch.tensor([800, 1066]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,     _UpperCamelCase   )   )





