






'''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 				__SCREAMING_SNAKE_CASE							(				unittest.TestCase  ):




      def __init__(	self			, lowerCamelCase			, lowerCamelCase=7			, lowerCamelCase=3			, lowerCamelCase=18			, lowerCamelCase=30			, lowerCamelCase=400			, lowerCamelCase=True			, lowerCamelCase=None			, lowerCamelCase=True			, )				->Dict:



         '''simple docstring'''
         __a					  =     size if size is not None else {'height': 18, 'width': 18}
         __a					  =     parent
         __a					  =     batch_size
         __a					  =     num_channels
         __a					  =     image_size
         __a					  =     min_resolution
         __a					  =     max_resolution
         __a					  =     do_resize
         __a					  =     size
         __a					  =     apply_ocr






      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}







@require_torch
@require_pytesseract
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase   ,   unittest.TestCase  ):
      __a	       =LayoutLMvaImageProcessor if is_pytesseract_available() else None




      def 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         __a					  =     LayoutLMvaImageProcessingTester(self   )




      @property
      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         return self.image_processor_tester.prepare_image_processor_dict()




      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         __a					  =     self.image_processing_class(**self.image_processor_dict   )
         self.assertTrue(hasattr(lowerCamelCase			, 'do_resize'   )   )
         self.assertTrue(hasattr(lowerCamelCase			, 'size'   )   )
         self.assertTrue(hasattr(lowerCamelCase			, 'apply_ocr'   )   )




      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         __a					  =     self.image_processing_class.from_dict(self.image_processor_dict   )
         self.assertEqual(image_processor.size			, {'height': 18, 'width': 18}   )

         __a					  =     self.image_processing_class.from_dict(self.image_processor_dict			, size=42   )
         self.assertEqual(image_processor.size			, {'height': 42, 'width': 42}   )




      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         pass




      def 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''simple docstring'''
         # 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=lowerCamelCase   )
         for image in image_inputs:
            self.assertIsInstance(lowerCamelCase			, Image.Image   )

         # Test not batched input
         __a					  =     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			, lowerCamelCase   )
         self.assertIsInstance(encoding.boxes			, lowerCamelCase   )

         # Test batched
         __a					  =     image_processing(lowerCamelCase			, 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 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         # 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=lowerCamelCase			, numpify=lowerCamelCase   )
         for image in image_inputs:
            self.assertIsInstance(lowerCamelCase			, np.ndarray   )

         # Test not batched input
         __a					  =     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					  =     image_processing(lowerCamelCase			, 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 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         # 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=lowerCamelCase			, torchify=lowerCamelCase   )
         for image in image_inputs:
            self.assertIsInstance(lowerCamelCase			, torch.Tensor   )

         # Test not batched input
         __a					  =     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					  =     image_processing(lowerCamelCase			, 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 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         # with apply_OCR = True
         __a					  =     LayoutLMvaImageProcessor()

         from datasets import load_dataset

         __a					  =     load_dataset('hf-internal-testing/fixtures_docvqa'			, split='test'   )

         __a					  =     Image.open(ds[0]['file']   ).convert('RGB'   )

         __a					  =     image_processing(lowerCamelCase			, 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					  =     [['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					  =     [[[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			, lowerCamelCase   )
         self.assertListEqual(encoding.boxes			, lowerCamelCase   )

         # with apply_OCR = False
         __a					  =     LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase   )

         __a					  =     image_processing(lowerCamelCase			, return_tensors='pt'   )

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







'''simple docstring'''



from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


__UpperCamelCase   :							Optional[int]         =   """\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

__UpperCamelCase   :							Tuple         =   """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

__UpperCamelCase   :							Optional[int]         =   """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						str     )	->      List[Any]:

   """simple docstring"""
   return float((preds == labels).mean()     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						List[str]     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     simple_accuracy(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   __a					  =     float(fa_score(y_true=SCREAMING_SNAKE_CASE__,       y_pred=SCREAMING_SNAKE_CASE__     )     )
   return {
       "accuracy": acc,
       "f1": fa,
   }



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						Optional[Any]     )	->      int:

   """simple docstring"""
   __a					  =     float(pearsonr(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )[0]     )
   __a					  =     float(spearmanr(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )[0]     )
   return {
       "pearson": pearson_corr,
       "spearmanr": spearman_corr,
   }



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,   _KWARGS_DESCRIPTION  )
class 				__SCREAMING_SNAKE_CASE							(				datasets.Metric  ):




      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         if self.config_name not in [
             "sst2",
             "mnli",
             "mnli_mismatched",
             "mnli_matched",
             "cola",
             "stsb",
             "mrpc",
             "qqp",
             "qnli",
             "rte",
             "wnli",
             "hans",
         ]:
            raise KeyError(
                'You should supply a configuration name selected in '
                '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'   )
         return datasets.MetricInfo(
             description=_DESCRIPTION			, citation=_CITATION			, inputs_description=_KWARGS_DESCRIPTION			, features=datasets.Features(
                 {
                     'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'   ),
                     'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'   ),
                 }   )			, codebase_urls=[]			, reference_urls=[]			, format='numpy'			, )






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase   )				->Tuple:



         '''simple docstring'''
         if self.config_name == "cola":
            return {"matthews_correlation": matthews_corrcoef(lowerCamelCase			, lowerCamelCase   )}
         elif self.config_name == "stsb":
            return pearson_and_spearman(lowerCamelCase			, lowerCamelCase   )
         elif self.config_name in ["mrpc", "qqp"]:
            return acc_and_fa(lowerCamelCase			, lowerCamelCase   )
         elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
            return {"accuracy": simple_accuracy(lowerCamelCase			, lowerCamelCase   )}
         else:
            raise KeyError(
                'You should supply a configuration name selected in '
                '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'   )