




"""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 DetaImageProcessor

class    UpperCAmelCase_  (       unittest.TestCase						):
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_=7							,		UpperCamelCase_=3							,		UpperCamelCase_=30							,		UpperCamelCase_=4_00							,		UpperCamelCase_=True							,		UpperCamelCase_=None							,		UpperCamelCase_=True							,		UpperCamelCase_=[0.5, 0.5, 0.5]							,		UpperCamelCase_=[0.5, 0.5, 0.5]							,		UpperCamelCase_=True							,		UpperCamelCase_=1 / 2_55							,		UpperCamelCase_=True							,		)		->    Tuple:
					# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
					__lowercase       :   Optional[int]        =    size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
					__lowercase       :   int        =    parent
					__lowercase       :   Optional[int]        =    batch_size
					__lowercase       :   int        =    num_channels
					__lowercase       :   Optional[Any]        =    min_resolution
					__lowercase       :   Union[str, Any]        =    max_resolution
					__lowercase       :   str        =    do_resize
					__lowercase       :   Dict        =    size
					__lowercase       :   List[str]        =    do_normalize
					__lowercase       :   Optional[int]        =    image_mean
					__lowercase       :   List[Any]        =    image_std
					__lowercase       :   Tuple        =    do_rescale
					__lowercase       :   Tuple        =    rescale_factor
					__lowercase       :   str        =    do_pad
		def      _lowerCamelCase				(		self    )		->    List[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      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_=False    )		->    Any:
					if not batched:
								__lowercase       :   List[Any]        =    image_inputs[0]
								if isinstance(UpperCamelCase__							,		Image.Image    ):
											__lowercase       :   Tuple        =    image.size
								else:
											__lowercase       :   List[Any]        =    image.shape[1], image.shape[2]
								if w < h:
											__lowercase       :   Optional[int]        =    int(self.size['''shortest_edge'''] * h / w    )
											__lowercase       :   int        =    self.size["""shortest_edge"""]
								elif w > h:
											__lowercase       :   Any        =    self.size["""shortest_edge"""]
											__lowercase       :   Dict        =    int(self.size['''shortest_edge'''] * w / h    )
								else:
											__lowercase       :   Union[str, Any]        =    self.size["""shortest_edge"""]
											__lowercase       :   Union[str, Any]        =    self.size["""shortest_edge"""]

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

					return expected_height, expected_width


@require_torch
@require_vision
class    UpperCAmelCase_  (       _lowercase					, unittest.TestCase						):
		UpperCamelCase						 =DetaImageProcessor if is_vision_available() else None
		def      _lowerCamelCase				(		self    )		->    int:
					__lowercase       :   Optional[Any]        =    DetaImageProcessingTester(self    )
		@property
		def      _lowerCamelCase				(		self    )		->    Union[str, Any]:
					return self.image_processor_tester.prepare_image_processor_dict()
		def      _lowerCamelCase				(		self    )		->    List[Any]:
					__lowercase       :   str        =    self.image_processing_class(**self.image_processor_dict    )
					self.assertTrue(hasattr(UpperCamelCase__							,		'''image_mean'''    )    )
					self.assertTrue(hasattr(UpperCamelCase__							,		'''image_std'''    )    )
					self.assertTrue(hasattr(UpperCamelCase__							,		'''do_normalize'''    )    )
					self.assertTrue(hasattr(UpperCamelCase__							,		'''do_resize'''    )    )
					self.assertTrue(hasattr(UpperCamelCase__							,		'''do_rescale'''    )    )
					self.assertTrue(hasattr(UpperCamelCase__							,		'''do_pad'''    )    )
					self.assertTrue(hasattr(UpperCamelCase__							,		'''size'''    )    )
		def      _lowerCamelCase				(		self    )		->    Union[str, Any]:
					__lowercase       :   str        =    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							,		UpperCamelCase__    )
		def      _lowerCamelCase				(		self    )		->    Dict:
					pass
		def      _lowerCamelCase				(		self    )		->    Optional[Any]:
					# Initialize image_processing
					__lowercase       :   Optional[int]        =    self.image_processing_class(**self.image_processor_dict    )
					# create random PIL images
					__lowercase       :   List[str]        =    prepare_image_inputs(self.image_processor_tester							,		equal_resolution=UpperCamelCase__    )
					for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__							,		Image.Image    )

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

					__lowercase       :   List[str]        =    self.image_processor_tester.get_expected_values(UpperCamelCase__    )

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

					# Test batched
					__lowercase       :   int        =    self.image_processor_tester.get_expected_values(UpperCamelCase__							,		batched=UpperCamelCase__    )

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

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

					__lowercase       :   int        =    self.image_processor_tester.get_expected_values(UpperCamelCase__    )

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

					# Test batched
					__lowercase       :   str        =    image_processing(UpperCamelCase__							,		return_tensors='''pt'''    ).pixel_values

					__lowercase       :   Optional[Any]        =    self.image_processor_tester.get_expected_values(UpperCamelCase__							,		batched=UpperCamelCase__    )

					self.assertEqual(
					    encoded_images.shape							,		(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )							,		)
		def      _lowerCamelCase				(		self    )		->    Tuple:
					# 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=UpperCamelCase__							,		torchify=UpperCamelCase__    )
					for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__							,		torch.Tensor    )

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

					__lowercase       :   List[str]        =    self.image_processor_tester.get_expected_values(UpperCamelCase__    )

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

					# Test batched
					__lowercase       :   List[Any]        =    image_processing(UpperCamelCase__							,		return_tensors='''pt'''    ).pixel_values

					__lowercase       :   int        =    self.image_processor_tester.get_expected_values(UpperCamelCase__							,		batched=UpperCamelCase__    )

					self.assertEqual(
					    encoded_images.shape							,		(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )							,		)
		@slow
		def      _lowerCamelCase				(		self    )		->    int:
					# prepare image and target
					__lowercase       :   str        =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
					with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''							,		'''r'''    ) as f:
								__lowercase       :   Tuple        =    json.loads(f.read()    )

					__lowercase       :   Union[str, Any]        =    {"""image_id""": 3_97_69, """annotations""": target}

					# encode them
					__lowercase       :   List[Any]        =    DetaImageProcessor()
					__lowercase       :   Union[str, Any]        =    image_processing(images=UpperCamelCase__							,		annotations=UpperCamelCase__							,		return_tensors='''pt'''    )

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

					__lowercase       :   str        =    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]							,		UpperCamelCase__							,		atol=1E-4    )    )

					# verify area
					__lowercase       :   Optional[int]        =    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''']							,		UpperCamelCase__    )    )
					# verify boxes
					__lowercase       :   Optional[Any]        =    torch.Size([6, 4]    )
					self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,		UpperCamelCase__    )
					__lowercase       :   List[Any]        =    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]							,		UpperCamelCase__							,		atol=1E-3    )    )
					# verify image_id
					__lowercase       :   str        =    torch.tensor([3_97_69]    )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,		UpperCamelCase__    )    )
					# verify is_crowd
					__lowercase       :   Tuple        =    torch.tensor([0, 0, 0, 0, 0, 0]    )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,		UpperCamelCase__    )    )
					# verify class_labels
					__lowercase       :   str        =    torch.tensor([75, 75, 63, 65, 17, 17]    )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,		UpperCamelCase__    )    )
					# verify orig_size
					__lowercase       :   Optional[Any]        =    torch.tensor([4_80, 6_40]    )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,		UpperCamelCase__    )    )
					# verify size
					__lowercase       :   List[str]        =    torch.tensor([8_00, 10_66]    )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,		UpperCamelCase__    )    )
		@slow
		def      _lowerCamelCase				(		self    )		->    Dict:
					# prepare image, target and masks_path
					__lowercase       :   Dict        =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
					with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''							,		'''r'''    ) as f:
								__lowercase       :   List[Any]        =    json.loads(f.read()    )

					__lowercase       :   Dict        =    {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

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

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

					# verify pixel values
					__lowercase       :   List[str]        =    torch.Size([1, 3, 8_00, 10_66]    )
					self.assertEqual(encoding['''pixel_values'''].shape							,		UpperCamelCase__    )

					__lowercase       :   int        =    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]							,		UpperCamelCase__							,		atol=1E-4    )    )

					# verify area
					__lowercase       :   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''']							,		UpperCamelCase__    )    )
					# verify boxes
					__lowercase       :   Optional[Any]        =    torch.Size([6, 4]    )
					self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,		UpperCamelCase__    )
					__lowercase       :   Optional[int]        =    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]							,		UpperCamelCase__							,		atol=1E-3    )    )
					# verify image_id
					__lowercase       :   Tuple        =    torch.tensor([3_97_69]    )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,		UpperCamelCase__    )    )
					# verify is_crowd
					__lowercase       :   List[Any]        =    torch.tensor([0, 0, 0, 0, 0, 0]    )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,		UpperCamelCase__    )    )
					# verify class_labels
					__lowercase       :   Tuple        =    torch.tensor([17, 17, 63, 75, 75, 93]    )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,		UpperCamelCase__    )    )
					# verify masks
					__lowercase       :   List[str]        =    82_28_73
					self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()							,		UpperCamelCase__    )
					# verify orig_size
					__lowercase       :   Any        =    torch.tensor([4_80, 6_40]    )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,		UpperCamelCase__    )    )
					# verify size
					__lowercase       :   Union[str, Any]        =    torch.tensor([8_00, 10_66]    )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,		UpperCamelCase__    )    )











'''simple docstring'''







class _lowercase	:




							def __init__(							self:				Tuple			,							UpperCamelCase__:				list[int]     ):
														lowerCamelCase__					:	Union[str, Any]         =       len(UpperCamelCase__     )
														lowerCamelCase__					:	Union[str, Any]         =       [0] * len_array

														if len_array > 0:
																					lowerCamelCase__					:	Union[str, Any]         =       array[0]

														for i in range(1			,							UpperCamelCase__     ):
																					lowerCamelCase__					:	Tuple         =       self.prefix_sum[i - 1] + array[i]




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int     ):
														if start == 0:
																					return self.prefix_sum[end]

														return self.prefix_sum[end] - self.prefix_sum[start - 1]




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	Dict         =       {0}
														for sum_item in self.prefix_sum:
																					if sum_item - target_sum in sums:
																												return True

																					sums.add(UpperCamelCase__     )

														return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()

