






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  _lowercase   (   unittest.TestCase						):
      def __init__(     self				,			a				,			a=7				,			a=3				,			a=3_0				,			a=4_0_0				,			a=True				,			a=None				,			a=True				,			a=[0.5, 0.5, 0.5]				,			a=[0.5, 0.5, 0.5]				,			a=True				,			a=1 / 2_5_5				,			a=True				,			):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       snake_case__  :    Tuple							       =size if size is not None else {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}
       snake_case__  :    str							       =parent
       snake_case__  :    List[Any]							       =batch_size
       snake_case__  :    Union[str, Any]							       =num_channels
       snake_case__  :    Dict							       =min_resolution
       snake_case__  :    Optional[Any]							       =max_resolution
       snake_case__  :    Tuple							       =do_resize
       snake_case__  :    str							       =size
       snake_case__  :    List[str]							       =do_normalize
       snake_case__  :    Optional[int]							       =image_mean
       snake_case__  :    Any							       =image_std
       snake_case__  :    Union[str, Any]							       =do_rescale
       snake_case__  :    Optional[Any]							       =rescale_factor
       snake_case__  :    Dict							       =do_pad
      def 						lowercase__					(     self   ):
       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 						lowercase__					(     self				,			a				,			a=False   ):
       if not batched:
        snake_case__  :    Tuple							       =image_inputs[0]
        if isinstance(a				,			Image.Image   ):
         snake_case__       ,   snake_case__  :    Dict							       =image.size
        else:
         snake_case__       ,   snake_case__  :    Tuple							       =image.shape[1], image.shape[2]
        if w < h:
         snake_case__  :    Optional[Any]							       =int(self.size["""shortest_edge"""] * h / w   )
         snake_case__  :    List[str]							       =self.size["""shortest_edge"""]
        elif w > h:
         snake_case__  :    Optional[Any]							       =self.size["""shortest_edge"""]
         snake_case__  :    Optional[Any]							       =int(self.size["""shortest_edge"""] * w / h   )
        else:
         snake_case__  :    Optional[int]							       =self.size["""shortest_edge"""]
         snake_case__  :    str							       =self.size["""shortest_edge"""]

       else:
        snake_case__  :    int							       =[]
        for image in image_inputs:
         snake_case__       ,   snake_case__  :    Optional[int]							       =self.get_expected_values([image]   )
         expected_values.append((expected_height, expected_width)   )
        snake_case__  :    Optional[int]							       =max(a				,			key=lambda a   : item[0]   )[0]
        snake_case__  :    Any							       =max(a				,			key=lambda a   : item[1]   )[1]

       return expected_height, expected_width







@require_torch
@require_vision
class  _lowercase   (   _A						,				unittest.TestCase						):
      _a							:						List[Any]										=							DeformableDetrImageProcessor if is_vision_available() else None
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =DeformableDetrImageProcessingTester(self   )
      @property
      def 						lowercase__					(     self   ):
       return self.image_processor_tester.prepare_image_processor_dict()
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(a				,			"""image_mean"""   )   )
       self.assertTrue(hasattr(a				,			"""image_std"""   )   )
       self.assertTrue(hasattr(a				,			"""do_normalize"""   )   )
       self.assertTrue(hasattr(a				,			"""do_resize"""   )   )
       self.assertTrue(hasattr(a				,			"""do_rescale"""   )   )
       self.assertTrue(hasattr(a				,			"""do_pad"""   )   )
       self.assertTrue(hasattr(a				,			"""size"""   )   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =self.image_processing_class.from_dict(self.image_processor_dict   )
       self.assertEqual(image_processor.size				,			{"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}   )
       self.assertEqual(image_processor.do_pad				,			a   )

       snake_case__  :    Optional[Any]							       =self.image_processing_class.from_dict(
           self.image_processor_dict				,			size=4_2				,			max_size=8_4				,			pad_and_return_pixel_mask=a   )
       self.assertEqual(image_processor.size				,			{"""shortest_edge""": 4_2, """longest_edge""": 8_4}   )
       self.assertEqual(image_processor.do_pad				,			a   )
      def 						lowercase__					(     self   ):
       pass
      def 						lowercase__					(     self   ):
       # Initialize image_processing
       snake_case__  :    Tuple							       =self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       snake_case__  :    Optional[Any]							       =prepare_image_inputs(self.image_processor_tester				,			equal_resolution=a   )
       for image in image_inputs:
        self.assertIsInstance(a				,			Image.Image   )

       # Test not batched input
       snake_case__  :    Union[str, Any]							       =image_processing(image_inputs[0]				,			return_tensors="""pt"""   ).pixel_values

       snake_case__       ,   snake_case__  :    Tuple							       =self.image_processor_tester.get_expected_values(a   )

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

       # Test batched
       snake_case__       ,   snake_case__  :    List[str]							       =self.image_processor_tester.get_expected_values(a				,			batched=a   )

       snake_case__  :    Union[str, Any]							       =image_processing(a				,			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 						lowercase__					(     self   ):
       # Initialize image_processing
       snake_case__  :    Tuple							       =self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       snake_case__  :    Tuple							       =prepare_image_inputs(self.image_processor_tester				,			equal_resolution=a				,			numpify=a   )
       for image in image_inputs:
        self.assertIsInstance(a				,			np.ndarray   )

       # Test not batched input
       snake_case__  :    Union[str, Any]							       =image_processing(image_inputs[0]				,			return_tensors="""pt"""   ).pixel_values

       snake_case__       ,   snake_case__  :    List[str]							       =self.image_processor_tester.get_expected_values(a   )

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

       # Test batched
       snake_case__  :    int							       =image_processing(a				,			return_tensors="""pt"""   ).pixel_values

       snake_case__       ,   snake_case__  :    Any							       =self.image_processor_tester.get_expected_values(a				,			batched=a   )

       self.assertEqual(
           encoded_images.shape				,			(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )				,			)
      def 						lowercase__					(     self   ):
       # Initialize image_processing
       snake_case__  :    Union[str, Any]							       =self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       snake_case__  :    Optional[int]							       =prepare_image_inputs(self.image_processor_tester				,			equal_resolution=a				,			torchify=a   )
       for image in image_inputs:
        self.assertIsInstance(a				,			torch.Tensor   )

       # Test not batched input
       snake_case__  :    str							       =image_processing(image_inputs[0]				,			return_tensors="""pt"""   ).pixel_values

       snake_case__       ,   snake_case__  :    str							       =self.image_processor_tester.get_expected_values(a   )

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

       # Test batched
       snake_case__  :    Optional[Any]							       =image_processing(a				,			return_tensors="""pt"""   ).pixel_values

       snake_case__       ,   snake_case__  :    str							       =self.image_processor_tester.get_expected_values(a				,			batched=a   )

       self.assertEqual(
           encoded_images.shape				,			(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )				,			)
      @slow
      def 						lowercase__					(     self   ):
       # prepare image and target
       snake_case__  :    Tuple							       =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
       with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""				,			"""r"""   ) as f:
        snake_case__  :    List[str]							       =json.loads(f.read()   )

       snake_case__  :    Union[str, Any]							       ={"""image_id""": 3_9_7_6_9, """annotations""": target}

       # encode them
       snake_case__  :    str							       =DeformableDetrImageProcessor()
       snake_case__  :    Dict							       =image_processing(images=a				,			annotations=a				,			return_tensors="""pt"""   )

       # verify pixel values
       snake_case__  :    Any							       =torch.Size([1, 3, 8_0_0, 1_0_6_6]   )
       self.assertEqual(encoding["""pixel_values"""].shape				,			a   )

       snake_case__  :    Optional[Any]							       =torch.tensor([0.2796, 0.3138, 0.3481]   )
       self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]				,			a				,			atol=1e-4   )   )

       # verify area
       snake_case__  :    str							       =torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]   )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]				,			a   )   )
       # verify boxes
       snake_case__  :    Dict							       =torch.Size([6, 4]   )
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,			a   )
       snake_case__  :    Optional[Any]							       =torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]				,			a				,			atol=1e-3   )   )
       # verify image_id
       snake_case__  :    List[str]							       =torch.tensor([3_9_7_6_9]   )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,			a   )   )
       # verify is_crowd
       snake_case__  :    Optional[int]							       =torch.tensor([0, 0, 0, 0, 0, 0]   )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,			a   )   )
       # verify class_labels
       snake_case__  :    Optional[int]							       =torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]   )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,			a   )   )
       # verify orig_size
       snake_case__  :    Dict							       =torch.tensor([4_8_0, 6_4_0]   )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,			a   )   )
       # verify size
       snake_case__  :    Any							       =torch.tensor([8_0_0, 1_0_6_6]   )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,			a   )   )



      @slow
      def 						lowercase__					(     self   ):
       # prepare image, target and masks_path
       snake_case__  :    Optional[int]							       =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
       with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""				,			"""r"""   ) as f:
        snake_case__  :    Tuple							       =json.loads(f.read()   )

       snake_case__  :    List[str]							       ={"""file_name""": """000000039769.png""", """image_id""": 3_9_7_6_9, """segments_info""": target}

       snake_case__  :    List[Any]							       =pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""   )

       # encode them
       snake_case__  :    List[Any]							       =DeformableDetrImageProcessor(format="""coco_panoptic"""   )
       snake_case__  :    Dict							       =image_processing(images=a				,			annotations=a				,			masks_path=a				,			return_tensors="""pt"""   )

       # verify pixel values
       snake_case__  :    List[str]							       =torch.Size([1, 3, 8_0_0, 1_0_6_6]   )
       self.assertEqual(encoding["""pixel_values"""].shape				,			a   )

       snake_case__  :    List[str]							       =torch.tensor([0.2796, 0.3138, 0.3481]   )
       self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]				,			a				,			atol=1e-4   )   )

       # verify area
       snake_case__  :    List[Any]							       =torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]   )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]				,			a   )   )
       # verify boxes
       snake_case__  :    Any							       =torch.Size([6, 4]   )
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,			a   )
       snake_case__  :    Optional[Any]							       =torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]				,			a				,			atol=1e-3   )   )
       # verify image_id
       snake_case__  :    List[str]							       =torch.tensor([3_9_7_6_9]   )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,			a   )   )
       # verify is_crowd
       snake_case__  :    Any							       =torch.tensor([0, 0, 0, 0, 0, 0]   )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,			a   )   )
       # verify class_labels
       snake_case__  :    Any							       =torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]   )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,			a   )   )
       # verify masks
       snake_case__  :    Any							       =8_2_2_8_7_3
       self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()				,			a   )
       # verify orig_size
       snake_case__  :    int							       =torch.tensor([4_8_0, 6_4_0]   )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,			a   )   )
       # verify size
       snake_case__  :    int							       =torch.tensor([8_0_0, 1_0_6_6]   )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,			a   )   )













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

import datasets


__lowerCamelCase	:		Any										=   """\
@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}
}
"""

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

__lowerCamelCase	:		str										=   """
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 			A__							(      _a  :					int		,  _a  :					str	):





 '''simple docstring'''







 return float((preds == labels).mean()	)







def 			A__							(      _a  :					Optional[Any]		,  _a  :					Any	):





 '''simple docstring'''







 snake_case__  :    str							       =simple_accuracy(_a		,  _a	)
 snake_case__  :    str							       =float(fa_score(y_true=_a		,  y_pred=_a	)	)
 return {
     "accuracy": acc,
     "f1": fa,
 }







def 			A__							(      _a  :					int		,  _a  :					Optional[int]	):





 '''simple docstring'''







 snake_case__  :    str							       =float(pearsonr(_a		,  _a	)[0]	)
 snake_case__  :    Optional[int]							       =float(spearmanr(_a		,  _a	)[0]	)
 return {
     "pearson": pearson_corr,
     "spearmanr": spearman_corr,
 }







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,				_KWARGS_DESCRIPTION						)
class  _lowercase   (   datasets.Metric						):
      def 						lowercase__					(     self   ):
       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 						lowercase__					(     self				,			a				,			a   ):
       if self.config_name == "cola":
        return {"matthews_correlation": matthews_corrcoef(a				,			a   )}
       elif self.config_name == "stsb":
        return pearson_and_spearman(a				,			a   )
       elif self.config_name in ["mrpc", "qqp"]:
        return acc_and_fa(a				,			a   )
       elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
        return {"accuracy": simple_accuracy(a				,			a   )}
       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\"]"""   )





