


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 DetrImageProcessor







class 						_snake_case    ( unittest.TestCase				):




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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_pad": self.do_pad,
       }




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

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

       return expected_height, expected_width







@require_torch
@require_vision
class 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			DetrImageProcessor if is_vision_available() else None




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			DetrImageProcessingTester(self       )




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return self.image_processor_tester.prepare_image_processor_dict()




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:str									=			self.image_processing_class(**self.image_processor_dict       )
       self.assertTrue(hasattr(_lowerCamelCase   ,    '''image_mean'''       )       )
       self.assertTrue(hasattr(_lowerCamelCase   ,    '''image_std'''       )       )
       self.assertTrue(hasattr(_lowerCamelCase   ,    '''do_normalize'''       )       )
       self.assertTrue(hasattr(_lowerCamelCase   ,    '''do_rescale'''       )       )
       self.assertTrue(hasattr(_lowerCamelCase   ,    '''rescale_factor'''       )       )
       self.assertTrue(hasattr(_lowerCamelCase   ,    '''do_resize'''       )       )
       self.assertTrue(hasattr(_lowerCamelCase   ,    '''size'''       )       )
       self.assertTrue(hasattr(_lowerCamelCase   ,    '''do_pad'''       )       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			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   ,    _lowerCamelCase       )

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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # Initialize image_processing
       a		:List[str]									=			self.image_processing_class(**self.image_processor_dict       )
       # create random PIL images
       a		:int									=			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		:Any									=			image_processing(image_inputs[0]   ,    return_tensors='''pt'''       ).pixel_values

       a				,							a		:int									=			self.image_processor_tester.get_expected_values(_lowerCamelCase       )

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

       # Test batched
       a				,							a		:Dict									=			self.image_processor_tester.get_expected_values(_lowerCamelCase   ,    batched=_lowerCamelCase       )

       a		:Optional[Any]									=			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,
               expected_height,
               expected_width,
           )   ,    )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # Initialize image_processing
       a		:Tuple									=			self.image_processing_class(**self.image_processor_dict       )
       # create random numpy tensors
       a		:int									=			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		:Optional[Any]									=			image_processing(image_inputs[0]   ,    return_tensors='''pt'''       ).pixel_values

       a				,							a		:List[Any]									=			self.image_processor_tester.get_expected_values(_lowerCamelCase       )

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

       # Test batched
       a		:Union[str, Any]									=			image_processing(_lowerCamelCase   ,    return_tensors='''pt'''       ).pixel_values

       a				,							a		:Optional[int]									=			self.image_processor_tester.get_expected_values(_lowerCamelCase   ,    batched=_lowerCamelCase       )

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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # Initialize image_processing
       a		:Any									=			self.image_processing_class(**self.image_processor_dict       )
       # create random PyTorch tensors
       a		:int									=			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		:int									=			image_processing(image_inputs[0]   ,    return_tensors='''pt'''       ).pixel_values

       a				,							a		:Any									=			self.image_processor_tester.get_expected_values(_lowerCamelCase       )

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

       # Test batched
       a		:Tuple									=			image_processing(_lowerCamelCase   ,    return_tensors='''pt'''       ).pixel_values

       a				,							a		:int									=			self.image_processor_tester.get_expected_values(_lowerCamelCase   ,    batched=_lowerCamelCase       )

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




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # prepare image and target
       a		:int									=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''   ,    '''r'''       ) as f:
             a		:Dict									=			json.loads(f.read()       )

       a		:Tuple									=			{'''image_id''': 3_9769, '''annotations''': target}

       # encode them
       a		:List[Any]									=			DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''       )
       a		:Optional[int]									=			image_processing(images=_lowerCamelCase   ,    annotations=_lowerCamelCase   ,    return_tensors='''pt'''       )

       # verify pixel values
       a		:Union[str, Any]									=			torch.Size([1, 3, 800, 1066]       )
       self.assertEqual(encoding['''pixel_values'''].shape   ,    _lowerCamelCase       )

       a		:Union[str, Any]									=			torch.tensor([0.2796, 0.3138, 0.3481]       )
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,    _lowerCamelCase   ,    atol=1e-4       )       )

       # verify area
       a		:List[Any]									=			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''']   ,    _lowerCamelCase       )       )
       # verify boxes
       a		:List[Any]									=			torch.Size([6, 4]       )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,    _lowerCamelCase       )
       a		:Dict									=			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,    _lowerCamelCase   ,    atol=1e-3       )       )
       # verify image_id
       a		:List[str]									=			torch.tensor([3_9769]       )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,    _lowerCamelCase       )       )
       # verify is_crowd
       a		:Tuple									=			torch.tensor([0, 0, 0, 0, 0, 0]       )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,    _lowerCamelCase       )       )
       # verify class_labels
       a		:Dict									=			torch.tensor([75, 75, 63, 65, 17, 17]       )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,    _lowerCamelCase       )       )
       # verify orig_size
       a		:List[Any]									=			torch.tensor([480, 640]       )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,    _lowerCamelCase       )       )
       # verify size
       a		:str									=			torch.tensor([800, 1066]       )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,    _lowerCamelCase       )       )




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # prepare image, target and masks_path
       a		:int									=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
       with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''   ,    '''r'''       ) as f:
             a		:Union[str, Any]									=			json.loads(f.read()       )

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

       a		:Optional[Any]									=			pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''       )

       # encode them
       a		:Any									=			DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''       )
       a		:List[Any]									=			image_processing(images=_lowerCamelCase   ,    annotations=_lowerCamelCase   ,    masks_path=_lowerCamelCase   ,    return_tensors='''pt'''       )

       # verify pixel values
       a		:Dict									=			torch.Size([1, 3, 800, 1066]       )
       self.assertEqual(encoding['''pixel_values'''].shape   ,    _lowerCamelCase       )

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

       # verify area
       a		:List[str]									=			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''']   ,    _lowerCamelCase       )       )
       # verify boxes
       a		:Tuple									=			torch.Size([6, 4]       )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,    _lowerCamelCase       )
       a		:Dict									=			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,    _lowerCamelCase   ,    atol=1e-3       )       )
       # verify image_id
       a		:Optional[Any]									=			torch.tensor([3_9769]       )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,    _lowerCamelCase       )       )
       # verify is_crowd
       a		:Tuple									=			torch.tensor([0, 0, 0, 0, 0, 0]       )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,    _lowerCamelCase       )       )
       # verify class_labels
       a		:List[Any]									=			torch.tensor([17, 17, 63, 75, 75, 93]       )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,    _lowerCamelCase       )       )
       # verify masks
       a		:Any									=			82_2873
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,    _lowerCamelCase       )
       # verify orig_size
       a		:Optional[Any]									=			torch.tensor([480, 640]       )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,    _lowerCamelCase       )       )
       # verify size
       a		:Dict									=			torch.tensor([800, 1066]       )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,    _lowerCamelCase       )       )









import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
     from scipy.stats import pearsonr, spearmanr
     from sklearn.metrics import fa_score, matthews_corrcoef


snake_case							:       List[str]										=	(
    '''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
    '''library. You can have a look at this example script for pointers: '''
    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						Optional[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      return (preds == labels).mean()
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      a		:Dict									=			simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)
      a		:Any									=			fa_score(y_true=UpperCAmelCase_					,    y_pred=UpperCAmelCase_		)
      return {
          "acc": acc,
          "f1": fa,
          "acc_and_f1": (acc + fa) / 2,
      }
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						List[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      a		:str									=			pearsonr(UpperCAmelCase_					,    UpperCAmelCase_		)[0]
      a		:Union[str, Any]									=			spearmanr(UpperCAmelCase_					,    UpperCAmelCase_		)[0]
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
          "corr": (pearson_corr + spearman_corr) / 2,
      }
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						Dict		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      assert len(UpperCAmelCase_		) == len(UpperCAmelCase_		), F'''Predictions and labels have mismatched lengths {len(UpperCAmelCase_		)} and {len(UpperCAmelCase_		)}'''
      if task_name == "cola":
            return {"mcc": matthews_corrcoef(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "sst-2":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "mrpc":
            return acc_and_fa(UpperCAmelCase_					,    UpperCAmelCase_		)
      elif task_name == "sts-b":
            return pearson_and_spearman(UpperCAmelCase_					,    UpperCAmelCase_		)
      elif task_name == "qqp":
            return acc_and_fa(UpperCAmelCase_					,    UpperCAmelCase_		)
      elif task_name == "mnli":
            return {"mnli/acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "mnli-mm":
            return {"mnli-mm/acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "qnli":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "rte":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "wnli":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "hans":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      else:
            raise KeyError(UpperCAmelCase_		)






def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						List[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      if len(UpperCAmelCase_		) != len(UpperCAmelCase_		):
            raise ValueError(F'''Predictions and labels have mismatched lengths {len(UpperCAmelCase_		)} and {len(UpperCAmelCase_		)}'''		)
      if task_name == "xnli":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      else:
            raise KeyError(UpperCAmelCase_		)





