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 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase=7 , __lowercase=3 , __lowercase=30 , __lowercase=400 , __lowercase=True , __lowercase=None , __lowercase=True , __lowercase=[0.5, 0.5, 0.5] , __lowercase=[0.5, 0.5, 0.5] , __lowercase=True , __lowercase=1 / 255 , __lowercase=True , )       ->	List[str]:
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        __UpperCamelCase     :Any			  =		size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
        __UpperCamelCase     :List[Any]			  =		parent
        __UpperCamelCase     :Tuple			  =		batch_size
        __UpperCamelCase     :int			  =		num_channels
        __UpperCamelCase     :Union[str, Any]			  =		min_resolution
        __UpperCamelCase     :Any			  =		max_resolution
        __UpperCamelCase     :Tuple			  =		do_resize
        __UpperCamelCase     :int			  =		size
        __UpperCamelCase     :int			  =		do_normalize
        __UpperCamelCase     :Dict			  =		image_mean
        __UpperCamelCase     :List[Any]			  =		image_std
        __UpperCamelCase     :Any			  =		do_rescale
        __UpperCamelCase     :List[Any]			  =		rescale_factor
        __UpperCamelCase     :List[Any]			  =		do_pad







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

        else:
            __UpperCamelCase     :str			  =		[]
            for image in image_inputs:
                __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		self.get_expected_values([image])
                expected_values.append((expected_height, expected_width))
            __UpperCamelCase     :Tuple			  =		max(__lowercase , key=lambda __lowercase: item[0])[0]
            __UpperCamelCase     :List[Any]			  =		max(__lowercase , key=lambda __lowercase: item[1])[1]

        return expected_height, expected_width


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






    '''simple docstring'''






    a__	:						Tuple						=      ConditionalDetrImageProcessor if is_vision_available() else None







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :Optional[Any]			  =		ConditionalDetrImageProcessingTester(self)







    @property
    def 			UpperCamelCase__   (	self)       ->	str:
        return self.image_processor_tester.prepare_image_processor_dict()







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase     :Any			  =		self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(__lowercase , '''image_mean'''))
        self.assertTrue(hasattr(__lowercase , '''image_std'''))
        self.assertTrue(hasattr(__lowercase , '''do_normalize'''))
        self.assertTrue(hasattr(__lowercase , '''do_resize'''))
        self.assertTrue(hasattr(__lowercase , '''size'''))







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        __UpperCamelCase     :int			  =		self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1_333})
        self.assertEqual(image_processor.do_pad , __lowercase)

        __UpperCamelCase     :str			  =		self.image_processing_class.from_dict(
            self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=__lowercase)
        self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84})
        self.assertEqual(image_processor.do_pad , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	str:
        pass







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

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

        __UpperCamelCase  ,       __UpperCamelCase     :str			  =		self.image_processor_tester.get_expected_values(__lowercase)

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

        # Test batched
        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		self.image_processor_tester.get_expected_values(__lowercase , batched=__lowercase)

        __UpperCamelCase     :List[str]			  =		image_processing(__lowercase , 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 			UpperCamelCase__   (	self)       ->	List[Any]:
        # Initialize image_processing
        __UpperCamelCase     :str			  =		self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        __UpperCamelCase     :List[Any]			  =		prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , numpify=__lowercase)
        for image in image_inputs:
            self.assertIsInstance(__lowercase , np.ndarray)

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

        __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		self.image_processor_tester.get_expected_values(__lowercase)

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

        # Test batched
        __UpperCamelCase     :Dict			  =		image_processing(__lowercase , return_tensors='''pt''').pixel_values

        __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.image_processor_tester.get_expected_values(__lowercase , batched=__lowercase)

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







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

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

        __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		self.image_processor_tester.get_expected_values(__lowercase)

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

        # Test batched
        __UpperCamelCase     :Tuple			  =		image_processing(__lowercase , return_tensors='''pt''').pixel_values

        __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		self.image_processor_tester.get_expected_values(__lowercase , batched=__lowercase)

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







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

        __UpperCamelCase     :int			  =		{'''image_id''': 39_769, '''annotations''': target}

        # encode them
        __UpperCamelCase     :int			  =		ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50''')
        __UpperCamelCase     :Optional[int]			  =		image_processing(images=__lowercase , annotations=__lowercase , return_tensors='''pt''')

        # verify pixel values
        __UpperCamelCase     :Union[str, Any]			  =		torch.Size([1, 3, 800, 1_066])
        self.assertEqual(encoding['''pixel_values'''].shape , __lowercase)

        __UpperCamelCase     :Optional[Any]			  =		torch.tensor([0.27_96, 0.31_38, 0.34_81])
        self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowercase , atol=1E-4))

        # verify area
        __UpperCamelCase     :str			  =		torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowercase))
        # verify boxes
        __UpperCamelCase     :str			  =		torch.Size([6, 4])
        self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowercase)
        __UpperCamelCase     :Dict			  =		torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowercase , atol=1E-3))
        # verify image_id
        __UpperCamelCase     :List[Any]			  =		torch.tensor([39_769])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowercase))
        # verify is_crowd
        __UpperCamelCase     :Optional[int]			  =		torch.tensor([0, 0, 0, 0, 0, 0])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowercase))
        # verify class_labels
        __UpperCamelCase     :Tuple			  =		torch.tensor([75, 75, 63, 65, 17, 17])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowercase))
        # verify orig_size
        __UpperCamelCase     :Union[str, Any]			  =		torch.tensor([480, 640])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowercase))
        # verify size
        __UpperCamelCase     :Dict			  =		torch.tensor([800, 1_066])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowercase))







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

        __UpperCamelCase     :Dict			  =		{'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

        __UpperCamelCase     :Tuple			  =		pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')

        # encode them
        __UpperCamelCase     :Tuple			  =		ConditionalDetrImageProcessor(format='''coco_panoptic''')
        __UpperCamelCase     :Union[str, Any]			  =		image_processing(images=__lowercase , annotations=__lowercase , masks_path=__lowercase , return_tensors='''pt''')

        # verify pixel values
        __UpperCamelCase     :int			  =		torch.Size([1, 3, 800, 1_066])
        self.assertEqual(encoding['''pixel_values'''].shape , __lowercase)

        __UpperCamelCase     :Dict			  =		torch.tensor([0.27_96, 0.31_38, 0.34_81])
        self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowercase , atol=1E-4))

        # verify area
        __UpperCamelCase     :Optional[Any]			  =		torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowercase))
        # verify boxes
        __UpperCamelCase     :Any			  =		torch.Size([6, 4])
        self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowercase)
        __UpperCamelCase     :Dict			  =		torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowercase , atol=1E-3))
        # verify image_id
        __UpperCamelCase     :List[Any]			  =		torch.tensor([39_769])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowercase))
        # verify is_crowd
        __UpperCamelCase     :Optional[Any]			  =		torch.tensor([0, 0, 0, 0, 0, 0])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowercase))
        # verify class_labels
        __UpperCamelCase     :List[Any]			  =		torch.tensor([17, 17, 63, 75, 75, 93])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowercase))
        # verify masks
        __UpperCamelCase     :Dict			  =		822_873
        self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , __lowercase)
        # verify orig_size
        __UpperCamelCase     :str			  =		torch.tensor([480, 640])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowercase))
        # verify size
        __UpperCamelCase     :Any			  =		torch.tensor([800, 1_066])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowercase))










def     a(      A       :    list       )		->       list:
					"""simple docstring"""





					if any(not isinstance(A ,     A       ) or x < 0 for x in sequence       ):
										raise TypeError("Sequence must be list of non-negative integers"       )
					for _ in range(len(A       )       ):
										for i, (rod_upper, rod_lower) in enumerate(zip(A ,     sequence[1:]       )       ):
															if rod_upper > rod_lower:
																				sequence[i] -= rod_upper - rod_lower
																				sequence[i + 1] += rod_upper - rod_lower
					return sequence


if __name__ == "__main__":
				assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
				assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
