


"""simple docstring"""







from scipy.stats import spearmanr

import datasets


__lowercase    =				"""
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""

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

    Example 2:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results['spearmanr'])
        -0.7
        >>> print(round(results['spearmanr_pvalue'], 2))
        0.19
"""

__lowercase    =				R"""\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@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, {\.I}lhan 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, Ant{\^o}nio 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  ):







    """simple docstring"""




    def      __snake_case							( self    :   Tuple):
      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    :   Dict    ,			__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   Tuple    ,			__UpperCAmelCase    :   Any=False):
      a						:		int											=      spearmanr(__UpperCAmelCase    ,			__UpperCAmelCase)
      if return_pvalue:
        return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
      else:
        return {"spearmanr": results[0]}










"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_pytesseract_available():
 from PIL import Image

 from transformers import LayoutLMvaImageProcessor






class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    def __init__( self    :   Optional[int]    ,			__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   Tuple=7    ,			__UpperCAmelCase    :   List[str]=3    ,			__UpperCAmelCase    :   int=18    ,			__UpperCAmelCase    :   int=30    ,			__UpperCAmelCase    :   Optional[int]=400    ,			__UpperCAmelCase    :   int=True    ,			__UpperCAmelCase    :   Optional[Any]=None    ,			__UpperCAmelCase    :   Union[str, Any]=True    ,			):
      a						:		Optional[int]											=      size if size is not None else {"height": 18, "width": 18}
      a						:		Any											=      parent
      a						:		int											=      batch_size
      a						:		str											=      num_channels
      a						:		Dict											=      image_size
      a						:		Dict											=      min_resolution
      a						:		Optional[int]											=      max_resolution
      a						:		Optional[int]											=      do_resize
      a						:		Any											=      size
      a						:		Dict											=      apply_ocr






    def      __snake_case							( self    :   Optional[int]):
      return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class 						_A  (  _a   ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase	:							List[str]     =	LayoutLMvaImageProcessor if is_pytesseract_available() else None






    def      __snake_case							( self    :   List[Any]):
      a						:		Optional[int]											=      LayoutLMvaImageProcessingTester(self)






    @property
    def      __snake_case							( self    :   Optional[int]):
      return self.image_processor_tester.prepare_image_processor_dict()






    def      __snake_case							( self    :   List[Any]):
      a						:		Union[str, Any]											=      self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__UpperCAmelCase    ,			"do_resize"))
      self.assertTrue(hasattr(__UpperCAmelCase    ,			"size"))
      self.assertTrue(hasattr(__UpperCAmelCase    ,			"apply_ocr"))






    def      __snake_case							( self    :   str):
      a						:		Dict											=      self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size    ,			{"height": 18, "width": 18})

      a						:		List[str]											=      self.image_processing_class.from_dict(self.image_processor_dict    ,			size=42)
      self.assertEqual(image_processor.size    ,			{"height": 42, "width": 42})






    def      __snake_case							( self    :   Union[str, Any]):
      pass






    def      __snake_case							( self    :   List[str]):
      # Initialize image_processing
      a						:		Any											=      self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      a						:		List[Any]											=      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						:		str											=      image_processing(image_inputs[0]    ,			return_tensors="pt")
      self.assertEqual(
          encoding.pixel_values.shape    ,			(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,			)

      self.assertIsInstance(encoding.words    ,			__UpperCAmelCase)
      self.assertIsInstance(encoding.boxes    ,			__UpperCAmelCase)

      # Test batched
      a						:		Dict											=      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,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,			)






    def      __snake_case							( self    :   Union[str, Any]):
      # Initialize image_processing
      a						:		List[str]											=      self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      a						:		Optional[Any]											=      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						:		Dict											=      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.size["height"],
              self.image_processor_tester.size["width"],
          )    ,			)

      # Test batched
      a						:		List[str]											=      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,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,			)






    def      __snake_case							( self    :   List[str]):
      # Initialize image_processing
      a						:		str											=      self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      a						:		Tuple											=      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						:		Optional[int]											=      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.size["height"],
              self.image_processor_tester.size["width"],
          )    ,			)

      # Test batched
      a						:		List[str]											=      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,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,			)






    def      __snake_case							( self    :   List[str]):
      # with apply_OCR = True
      a						:		List[Any]											=      LayoutLMvaImageProcessor()

      from datasets import load_dataset

      a						:		List[str]											=      load_dataset("hf-internal-testing/fixtures_docvqa"    ,			split="test")

      a						:		int											=      Image.open(ds[0]["file"]).convert("RGB")

      a						:		Dict											=      image_processing(__UpperCAmelCase    ,			return_tensors="pt")

      self.assertEqual(encoding.pixel_values.shape    ,			(1, 3, 224, 224))
      self.assertEqual(len(encoding.words)    ,			len(encoding.boxes))

      # fmt: off
      # the words and boxes were obtained with Tesseract 4.1.1
      a						:		Tuple											=      [["11:14", "to", "11:39", "a.m", "11:39", "to", "11:44", "a.m.", "11:44", "a.m.", "to", "12:25", "p.m.", "12:25", "to", "12:58", "p.m.", "12:58", "to", "4:00", "p.m.", "2:00", "to", "5:00", "p.m.", "Coffee", "Break", "Coffee", "will", "be", "served", "for", "men", "and", "women", "in", "the", "lobby", "adjacent", "to", "exhibit", "area.", "Please", "move", "into", "exhibit", "area.", "(Exhibits", "Open)", "TRRF", "GENERAL", "SESSION", "(PART", "|)", "Presiding:", "Lee", "A.", "Waller", "TRRF", "Vice", "President", "“Introductory", "Remarks”", "Lee", "A.", "Waller,", "TRRF", "Vice", "Presi-", "dent", "Individual", "Interviews", "with", "TRRF", "Public", "Board", "Members", "and", "Sci-", "entific", "Advisory", "Council", "Mem-", "bers", "Conducted", "by", "TRRF", "Treasurer", "Philip", "G.", "Kuehn", "to", "get", "answers", "which", "the", "public", "refrigerated", "warehousing", "industry", "is", "looking", "for.", "Plus", "questions", "from", "the", "floor.", "Dr.", "Emil", "M.", "Mrak,", "University", "of", "Cal-", "ifornia,", "Chairman,", "TRRF", "Board;", "Sam", "R.", "Cecil,", "University", "of", "Georgia", "College", "of", "Agriculture;", "Dr.", "Stanley", "Charm,", "Tufts", "University", "School", "of", "Medicine;", "Dr.", "Robert", "H.", "Cotton,", "ITT", "Continental", "Baking", "Company;", "Dr.", "Owen", "Fennema,", "University", "of", "Wis-", "consin;", "Dr.", "Robert", "E.", "Hardenburg,", "USDA.", "Questions", "and", "Answers", "Exhibits", "Open", "Capt.", "Jack", "Stoney", "Room", "TRRF", "Scientific", "Advisory", "Council", "Meeting", "Ballroom", "Foyer"]]  # noqa: E231
      a						:		str											=      [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
      # fmt: on

      self.assertListEqual(encoding.words    ,			__UpperCAmelCase)
      self.assertListEqual(encoding.boxes    ,			__UpperCAmelCase)

      # with apply_OCR = False
      a						:		Optional[int]											=      LayoutLMvaImageProcessor(apply_ocr=__UpperCAmelCase)

      a						:		Dict											=      image_processing(__UpperCAmelCase    ,			return_tensors="pt")

      self.assertEqual(encoding.pixel_values.shape    ,			(1, 3, 224, 224))






