


'''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 ConditionalDetrImageProcessor





class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=7			,     SCREAMING_SNAKE_CASE_=3			,     SCREAMING_SNAKE_CASE_=30			,     SCREAMING_SNAKE_CASE_=4_00			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]			,     SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=1 / 2_55			,     SCREAMING_SNAKE_CASE_=True			,     )				->     Optional[Any]:
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         __lowerCamelCase   :	Optional[int]								=		size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
         __lowerCamelCase   :	List[Any]								=		parent
         __lowerCamelCase   :	str								=		batch_size
         __lowerCamelCase   :	Optional[Any]								=		num_channels
         __lowerCamelCase   :	List[Any]								=		min_resolution
         __lowerCamelCase   :	List[str]								=		max_resolution
         __lowerCamelCase   :	Tuple								=		do_resize
         __lowerCamelCase   :	Dict								=		size
         __lowerCamelCase   :	Optional[Any]								=		do_normalize
         __lowerCamelCase   :	Optional[int]								=		image_mean
         __lowerCamelCase   :	Union[str, Any]								=		image_std
         __lowerCamelCase   :	str								=		do_rescale
         __lowerCamelCase   :	List[Any]								=		rescale_factor
         __lowerCamelCase   :	List[Any]								=		do_pad







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

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

         return expected_height, expected_width







@require_torch
@require_vision
class    UpperCAmelCase_  (_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    lowerCamelCase  :					Any							     =			ConditionalDetrImageProcessor if is_vision_available() else None







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	List[Any]								=		ConditionalDetrImageProcessingTester(self	)







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







    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	List[Any]								=		self.image_processing_class(**self.image_processor_dict	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,     'image_mean'	)	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,     'image_std'	)	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,     'do_normalize'	)	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,     'do_resize'	)	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,     'size'	)	)







    def       lowercase_				(							self	)				->     Dict:
         __lowerCamelCase   :	Optional[Any]								=		self.image_processing_class.from_dict(self.image_processor_dict	)
         self.assertEqual(image_processor.size			,     {'shortest_edge': 18, 'longest_edge': 13_33}	)
         self.assertEqual(image_processor.do_pad			,     SCREAMING_SNAKE_CASE_	)

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







    def       lowercase_				(							self	)				->     Tuple:
         pass







    def       lowercase_				(							self	)				->     Union[str, Any]:
         # Initialize image_processing
         __lowerCamelCase   :	List[Any]								=		self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         __lowerCamelCase   :	Optional[Any]								=		prepare_image_inputs(self.image_processor_tester			,     equal_resolution=SCREAMING_SNAKE_CASE_	)
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_			,     Image.Image	)

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

         __lowerCamelCase				,       __lowerCamelCase   :	int								=		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_	)

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

         # Test batched
         __lowerCamelCase				,       __lowerCamelCase   :	Optional[Any]								=		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			,     batched=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Any								=		image_processing(SCREAMING_SNAKE_CASE_			,     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	)				->     Dict:
         # Initialize image_processing
         __lowerCamelCase   :	Dict								=		self.image_processing_class(**self.image_processor_dict	)
         # create random numpy tensors
         __lowerCamelCase   :	Any								=		prepare_image_inputs(self.image_processor_tester			,     equal_resolution=SCREAMING_SNAKE_CASE_			,     numpify=SCREAMING_SNAKE_CASE_	)
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_			,     np.ndarray	)

         # Test not batched input
         __lowerCamelCase   :	Optional[int]								=		image_processing(image_inputs[0]			,     return_tensors='pt'	).pixel_values

         __lowerCamelCase				,       __lowerCamelCase   :	Optional[int]								=		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_	)

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

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

         __lowerCamelCase				,       __lowerCamelCase   :	Union[str, Any]								=		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			,     batched=SCREAMING_SNAKE_CASE_	)

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







    def       lowercase_				(							self	)				->     Tuple:
         # Initialize image_processing
         __lowerCamelCase   :	Any								=		self.image_processing_class(**self.image_processor_dict	)
         # create random PyTorch tensors
         __lowerCamelCase   :	Union[str, Any]								=		prepare_image_inputs(self.image_processor_tester			,     equal_resolution=SCREAMING_SNAKE_CASE_			,     torchify=SCREAMING_SNAKE_CASE_	)
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_			,     torch.Tensor	)

         # Test not batched input
         __lowerCamelCase   :	Optional[int]								=		image_processing(image_inputs[0]			,     return_tensors='pt'	).pixel_values

         __lowerCamelCase				,       __lowerCamelCase   :	Union[str, Any]								=		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_	)

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

         # Test batched
         __lowerCamelCase   :	str								=		image_processing(SCREAMING_SNAKE_CASE_			,     return_tensors='pt'	).pixel_values

         __lowerCamelCase				,       __lowerCamelCase   :	Dict								=		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			,     batched=SCREAMING_SNAKE_CASE_	)

         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	)				->     int:
         # prepare image and target
         __lowerCamelCase   :	Optional[int]								=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
         with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'			,     'r'	) as f:
              __lowerCamelCase   :	Optional[Any]								=		json.loads(f.read()	)

         __lowerCamelCase   :	List[str]								=		{'image_id': 3_97_69, 'annotations': target}

         # encode them
         __lowerCamelCase   :	str								=		ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'	)
         __lowerCamelCase   :	List[str]								=		image_processing(images=SCREAMING_SNAKE_CASE_			,     annotations=SCREAMING_SNAKE_CASE_			,     return_tensors='pt'	)

         # verify pixel values
         __lowerCamelCase   :	Tuple								=		torch.Size([1, 3, 8_00, 10_66]	)
         self.assertEqual(encoding['pixel_values'].shape			,     SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[Any]								=		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]	)
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			,     SCREAMING_SNAKE_CASE_			,     atol=1E-4	)	)

         # verify area
         __lowerCamelCase   :	List[Any]								=		torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,     SCREAMING_SNAKE_CASE_	)	)
         # verify boxes
         __lowerCamelCase   :	int								=		torch.Size([6, 4]	)
         self.assertEqual(encoding['labels'][0]['boxes'].shape			,     SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,     SCREAMING_SNAKE_CASE_			,     atol=1E-3	)	)
         # verify image_id
         __lowerCamelCase   :	Dict								=		torch.tensor([3_97_69]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,     SCREAMING_SNAKE_CASE_	)	)
         # verify is_crowd
         __lowerCamelCase   :	str								=		torch.tensor([0, 0, 0, 0, 0, 0]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,     SCREAMING_SNAKE_CASE_	)	)
         # verify class_labels
         __lowerCamelCase   :	List[str]								=		torch.tensor([75, 75, 63, 65, 17, 17]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,     SCREAMING_SNAKE_CASE_	)	)
         # verify orig_size
         __lowerCamelCase   :	List[str]								=		torch.tensor([4_80, 6_40]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,     SCREAMING_SNAKE_CASE_	)	)
         # verify size
         __lowerCamelCase   :	Optional[int]								=		torch.tensor([8_00, 10_66]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,     SCREAMING_SNAKE_CASE_	)	)







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

         __lowerCamelCase   :	List[str]								=		{'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

         __lowerCamelCase   :	Union[str, Any]								=		pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'	)

         # encode them
         __lowerCamelCase   :	List[Any]								=		ConditionalDetrImageProcessor(format='coco_panoptic'	)
         __lowerCamelCase   :	Dict								=		image_processing(images=SCREAMING_SNAKE_CASE_			,     annotations=SCREAMING_SNAKE_CASE_			,     masks_path=SCREAMING_SNAKE_CASE_			,     return_tensors='pt'	)

         # verify pixel values
         __lowerCamelCase   :	Union[str, Any]								=		torch.Size([1, 3, 8_00, 10_66]	)
         self.assertEqual(encoding['pixel_values'].shape			,     SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[Any]								=		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]	)
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			,     SCREAMING_SNAKE_CASE_			,     atol=1E-4	)	)

         # verify area
         __lowerCamelCase   :	Optional[Any]								=		torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,     SCREAMING_SNAKE_CASE_	)	)
         # verify boxes
         __lowerCamelCase   :	List[str]								=		torch.Size([6, 4]	)
         self.assertEqual(encoding['labels'][0]['boxes'].shape			,     SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,     SCREAMING_SNAKE_CASE_			,     atol=1E-3	)	)
         # verify image_id
         __lowerCamelCase   :	Tuple								=		torch.tensor([3_97_69]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,     SCREAMING_SNAKE_CASE_	)	)
         # verify is_crowd
         __lowerCamelCase   :	str								=		torch.tensor([0, 0, 0, 0, 0, 0]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,     SCREAMING_SNAKE_CASE_	)	)
         # verify class_labels
         __lowerCamelCase   :	Any								=		torch.tensor([17, 17, 63, 75, 75, 93]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,     SCREAMING_SNAKE_CASE_	)	)
         # verify masks
         __lowerCamelCase   :	Dict								=		82_28_73
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()			,     SCREAMING_SNAKE_CASE_	)
         # verify orig_size
         __lowerCamelCase   :	List[Any]								=		torch.tensor([4_80, 6_40]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,     SCREAMING_SNAKE_CASE_	)	)
         # verify size
         __lowerCamelCase   :	Dict								=		torch.tensor([8_00, 10_66]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,     SCREAMING_SNAKE_CASE_	)	)










'''simple docstring'''



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class    UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     int:
         if dst_width < 0 or dst_height < 0:
              raise ValueError('Destination width/height should be > 0'	)

         __lowerCamelCase   :	Dict								=		img
         __lowerCamelCase   :	Any								=		img.shape[1]
         __lowerCamelCase   :	Optional[int]								=		img.shape[0]
         __lowerCamelCase   :	Dict								=		dst_width
         __lowerCamelCase   :	str								=		dst_height

         __lowerCamelCase   :	Dict								=		self.src_w / self.dst_w
         __lowerCamelCase   :	List[Any]								=		self.src_h / self.dst_h

         __lowerCamelCase   :	Optional[int]								=		(
             np.ones((self.dst_h, self.dst_w, 3)			,     np.uinta	) * 2_55
         )







    def       lowercase_				(							self	)				->     List[Any]:
         for i in range(self.dst_h	):
              for j in range(self.dst_w	):
                   __lowerCamelCase   :	Union[str, Any]								=		self.img[self.get_y(SCREAMING_SNAKE_CASE_	)][self.get_x(SCREAMING_SNAKE_CASE_	)]







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         return int(self.ratio_x * x	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         return int(self.ratio_y * y	)


if __name__ == "__main__":
      A__						,					A__		:					Optional[Any]												=  800, 600
      A__		:					List[str]												=  imread("""image_data/lena.jpg""", 1)
      A__		:					List[Any]												=  NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
      )
      waitKey(0)
      destroyAllWindows()






