"""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 DeformableDetrImageProcessor


class   UpperCAmelCase_			(					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	,    ) ->      Union[str, Any]:
             # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
             lowercase__    :	str     					=			size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
             lowercase__    :	str     					=			parent
             lowercase__    :	Optional[Any]     					=			batch_size
             lowercase__    :	str     					=			num_channels
             lowercase__    :	Optional[Any]     					=			min_resolution
             lowercase__    :	int     					=			max_resolution
             lowercase__    :	Any     					=			do_resize
             lowercase__    :	Dict     					=			size
             lowercase__    :	Union[str, Any]     					=			do_normalize
             lowercase__    :	Optional[Any]     					=			image_mean
             lowercase__    :	List[Any]     					=			image_std
             lowercase__    :	int     					=			do_rescale
             lowercase__    :	str     					=			rescale_factor
             lowercase__    :	List[Any]     					=			do_pad


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

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

             return expected_height, expected_width





@require_torch
@require_vision
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Any										=						DeformableDetrImageProcessor if is_vision_available() else None


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Union[str, Any]     					=			DeformableDetrImageProcessingTester(self							)


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


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	str     					=			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 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	Optional[int]     					=			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							)

             lowercase__    :	Optional[int]     					=			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 	_UpperCAmelCase (     self							) ->      Any:
             pass


      def 	_UpperCAmelCase (     self							) ->      str:
             # Initialize image_processing
             lowercase__    :	Tuple     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PIL images
             lowercase__    :	Optional[int]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    Image.Image							)

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

             lowercase__					,   lowercase__    :	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
             lowercase__					,   lowercase__    :	Dict     					=			self.image_processor_tester.get_expected_values(a	,    batched=a							)

             lowercase__    :	Optional[int]     					=			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 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             # Initialize image_processing
             lowercase__    :	Optional[Any]     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             lowercase__    :	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
             lowercase__    :	Optional[int]     					=			image_processing(image_inputs[0]	,    return_tensors='pt'							).pixel_values

             lowercase__					,   lowercase__    :	Dict     					=			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
             lowercase__    :	Tuple     					=			image_processing(a	,    return_tensors='pt'							).pixel_values

             lowercase__					,   lowercase__    :	List[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 	_UpperCAmelCase (     self							) ->      List[Any]:
             # Initialize image_processing
             lowercase__    :	str     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             lowercase__    :	Union[str, Any]     					=			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
             lowercase__    :	Union[str, Any]     					=			image_processing(image_inputs[0]	,    return_tensors='pt'							).pixel_values

             lowercase__					,   lowercase__    :	List[Any]     					=			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
             lowercase__    :	List[str]     					=			image_processing(a	,    return_tensors='pt'							).pixel_values

             lowercase__					,   lowercase__    :	Optional[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,
                 )	,    )


      @slow
      def 	_UpperCAmelCase (     self							) ->      Any:
             # prepare image and target
             lowercase__    :	Tuple     					=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
             with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'	,    'r'							) as f:
                    lowercase__    :	Dict     					=			json.loads(f.read()							)

             lowercase__    :	Tuple     					=			{'image_id': 3_9_7_6_9, 'annotations': target}

             # encode them
             lowercase__    :	Optional[int]     					=			DeformableDetrImageProcessor()
             lowercase__    :	Tuple     					=			image_processing(images=a	,    annotations=a	,    return_tensors='pt'							)

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

             lowercase__    :	Dict     					=			torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]	,    a	,    atol=1e-4							)							)

             # verify area
             lowercase__    :	Any     					=			torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['area']	,    a							)							)
             # verify boxes
             lowercase__    :	List[str]     					=			torch.Size([6, 4]							)
             self.assertEqual(encoding['labels'][0]['boxes'].shape	,    a							)
             lowercase__    :	Optional[int]     					=			torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]	,    a	,    atol=1e-3							)							)
             # verify image_id
             lowercase__    :	Any     					=			torch.tensor([3_9_7_6_9]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']	,    a							)							)
             # verify is_crowd
             lowercase__    :	List[Any]     					=			torch.tensor([0, 0, 0, 0, 0, 0]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']	,    a							)							)
             # verify class_labels
             lowercase__    :	str     					=			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
             lowercase__    :	Tuple     					=			torch.tensor([4_8_0, 6_4_0]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']	,    a							)							)
             # verify size
             lowercase__    :	Union[str, Any]     					=			torch.tensor([8_0_0, 1_0_6_6]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['size']	,    a							)							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             # prepare image, target and masks_path
             lowercase__    :	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:
                    lowercase__    :	Optional[int]     					=			json.loads(f.read()							)

             lowercase__    :	int     					=			{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

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

             # encode them
             lowercase__    :	int     					=			DeformableDetrImageProcessor(format='coco_panoptic'							)
             lowercase__    :	Tuple     					=			image_processing(images=a	,    annotations=a	,    masks_path=a	,    return_tensors='pt'							)

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

             lowercase__    :	Union[str, Any]     					=			torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]	,    a	,    atol=1e-4							)							)

             # verify area
             lowercase__    :	Dict     					=			torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['area']	,    a							)							)
             # verify boxes
             lowercase__    :	List[Any]     					=			torch.Size([6, 4]							)
             self.assertEqual(encoding['labels'][0]['boxes'].shape	,    a							)
             lowercase__    :	Optional[Any]     					=			torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]	,    a	,    atol=1e-3							)							)
             # verify image_id
             lowercase__    :	Dict     					=			torch.tensor([3_9_7_6_9]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']	,    a							)							)
             # verify is_crowd
             lowercase__    :	Union[str, Any]     					=			torch.tensor([0, 0, 0, 0, 0, 0]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']	,    a							)							)
             # verify class_labels
             lowercase__    :	int     					=			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
             lowercase__    :	List[Any]     					=			8_2_2_8_7_3
             self.assertEqual(encoding['labels'][0]['masks'].sum().item()	,    a							)
             # verify orig_size
             lowercase__    :	Optional[Any]     					=			torch.tensor([4_8_0, 6_4_0]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']	,    a							)							)
             # verify size
             lowercase__    :	List[str]     					=			torch.tensor([8_0_0, 1_0_6_6]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['size']	,    a							)							)


"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 MobileViTImageProcessor


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=7	,    a=3	,    a=1_8	,    a=3_0	,    a=4_0_0	,    a=True	,    a=None	,    a=True	,    a=None	,    a=True	,    ) ->      List[str]:
             lowercase__    :	Tuple     					=			size if size is not None else {'shortest_edge': 2_0}
             lowercase__    :	Union[str, Any]     					=			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
             lowercase__    :	Optional[int]     					=			parent
             lowercase__    :	Optional[int]     					=			batch_size
             lowercase__    :	str     					=			num_channels
             lowercase__    :	Any     					=			image_size
             lowercase__    :	Optional[Any]     					=			min_resolution
             lowercase__    :	int     					=			max_resolution
             lowercase__    :	List[Any]     					=			do_resize
             lowercase__    :	List[str]     					=			size
             lowercase__    :	str     					=			do_center_crop
             lowercase__    :	List[Any]     					=			crop_size
             lowercase__    :	Union[str, Any]     					=			do_flip_channel_order


      def 	_UpperCAmelCase (     self							) ->      int:
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }





@require_torch
@require_vision
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[Any]										=						MobileViTImageProcessor if is_vision_available() else None


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Tuple     					=			MobileViTImageProcessingTester(self							)


      @property
      def 	_UpperCAmelCase (     self							) ->      int:
             return self.image_processor_tester.prepare_image_processor_dict()


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	List[str]     					=			self.image_processing_class(**self.image_processor_dict							)
             self.assertTrue(hasattr(a	,    'do_resize'							)							)
             self.assertTrue(hasattr(a	,    'size'							)							)
             self.assertTrue(hasattr(a	,    'do_center_crop'							)							)
             self.assertTrue(hasattr(a	,    'center_crop'							)							)
             self.assertTrue(hasattr(a	,    'do_flip_channel_order'							)							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Optional[int]     					=			self.image_processing_class.from_dict(self.image_processor_dict							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 2_0}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 1_8, 'width': 1_8}							)

             lowercase__    :	str     					=			self.image_processing_class.from_dict(self.image_processor_dict	,    size=4_2	,    crop_size=8_4							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 4_2}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 8_4, 'width': 8_4}							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


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

             # Test not batched input
             lowercase__    :	Union[str, Any]     					=			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.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

             # Test batched
             lowercase__    :	List[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,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             # Initialize image_processing
             lowercase__    :	Dict     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             lowercase__    :	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
             lowercase__    :	List[Any]     					=			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.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

             # Test batched
             lowercase__    :	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,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )


      def 	_UpperCAmelCase (     self							) ->      Dict:
             # Initialize image_processing
             lowercase__    :	str     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             lowercase__    :	Optional[Any]     					=			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
             lowercase__    :	Union[str, Any]     					=			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.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

             # Test batched
             lowercase__    :	Tuple     					=			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,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

