



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 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=7 ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=30 ,					__UpperCAmelCase=400 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=None ,					__UpperCAmelCase=True ,					__UpperCAmelCase=[0.5, 0.5, 0.5] ,					__UpperCAmelCase=[0.5, 0.5, 0.5] ,					__UpperCAmelCase=True ,					__UpperCAmelCase=1 / 255 ,					__UpperCAmelCase=True ,					):







											"""simple docstring"""



											a__	:							int      						= size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
											a__	:							Optional[int]      						= parent
											a__	:							Union[str, Any]      						= batch_size
											a__	:							str      						= num_channels
											a__	:							Union[str, Any]      						= min_resolution
											a__	:							int      						= max_resolution
											a__	:							str      						= do_resize
											a__	:							Tuple      						= size
											a__	:							str      						= do_normalize
											a__	:							Optional[int]      						= image_mean
											a__	:							int      						= image_std
											a__	:							Union[str, Any]      						= do_rescale
											a__	:							Optional[Any]      						= rescale_factor
											a__	:							Optional[int]      						= do_pad



							def 		_A	(							self				):







											"""simple docstring"""



											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 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=False				):







											"""simple docstring"""



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

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

											return expected_height, expected_width





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





							'''simple docstring'''

							A				:Union[str, Any] =       DetaImageProcessor if is_vision_available() else None



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= DetaImageProcessingTester(self				)



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self.image_processor_tester.prepare_image_processor_dict()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= 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_ ,					"do_rescale"				)				)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,					"do_pad"				)				)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,					"size"				)				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= 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 ,					SCREAMING_SNAKE_CASE_				)



							def 		_A	(							self				):







											"""simple docstring"""



											pass



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											a__	:							Dict      						= 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
											a__	:							Dict      						= image_processing(image_inputs[0] ,					return_tensors="pt"				).pixel_values

											a__	:							List[str]      						= 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
											a__	:							Optional[int]      						= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ ,					batched=SCREAMING_SNAKE_CASE_				)

											a__	:							str      						= 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 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.image_processing_class(**self.image_processor_dict				)
											# create random numpy tensors
											a__	:							Union[str, 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
											a__	:							List[Any]      						= image_processing(image_inputs[0] ,					return_tensors="pt"				).pixel_values

											a__	:							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
											a__	:							Tuple      						= image_processing(SCREAMING_SNAKE_CASE_ ,					return_tensors="pt"				).pixel_values

											a__	:							List[str]      						= 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 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PyTorch tensors
											a__	:							Optional[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
											a__	:							Optional[Any]      						= image_processing(image_inputs[0] ,					return_tensors="pt"				).pixel_values

											a__	:							Optional[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
											a__	:							Optional[Any]      						= image_processing(SCREAMING_SNAKE_CASE_ ,					return_tensors="pt"				).pixel_values

											a__	:							str      						= 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 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
											with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" ,					"r"				) as f:
															a__	:							List[Any]      						= json.loads(f.read()				)

											a__	:							List[str]      						= {'image_id': 3_9769, 'annotations': target}

											# encode them
											a__	:							Union[str, Any]      						= DetaImageProcessor()
											a__	:							int      						= image_processing(images=SCREAMING_SNAKE_CASE_ ,					annotations=SCREAMING_SNAKE_CASE_ ,					return_tensors="pt"				)

											# verify pixel values
											a__	:							Optional[int]      						= torch.Size([1, 3, 800, 1066]				)
											self.assertEqual(encoding["pixel_values"].shape ,					SCREAMING_SNAKE_CASE_				)

											a__	:							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] ,					SCREAMING_SNAKE_CASE_ ,					atol=1E-4				)				)

											# verify area
											a__	:							Optional[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"] ,					SCREAMING_SNAKE_CASE_				)				)
											# verify boxes
											a__	:							Optional[Any]      						= torch.Size([6, 4]				)
											self.assertEqual(encoding["labels"][0]["boxes"].shape ,					SCREAMING_SNAKE_CASE_				)
											a__	:							int      						= 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
											a__	:							Union[str, Any]      						= torch.tensor([3_9769]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,					SCREAMING_SNAKE_CASE_				)				)
											# verify is_crowd
											a__	:							List[Any]      						= torch.tensor([0, 0, 0, 0, 0, 0]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,					SCREAMING_SNAKE_CASE_				)				)
											# verify class_labels
											a__	:							Optional[int]      						= torch.tensor([75, 75, 63, 65, 17, 17]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,					SCREAMING_SNAKE_CASE_				)				)
											# verify orig_size
											a__	:							List[Any]      						= torch.tensor([480, 640]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,					SCREAMING_SNAKE_CASE_				)				)
											# verify size
											a__	:							Optional[Any]      						= torch.tensor([800, 1066]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,					SCREAMING_SNAKE_CASE_				)				)




							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
											with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" ,					"r"				) as f:
															a__	:							Any      						= json.loads(f.read()				)

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

											a__	:							List[Any]      						= pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"				)

											# encode them
											a__	:							Union[str, Any]      						= DetaImageProcessor(format="coco_panoptic"				)
											a__	:							Optional[Any]      						= image_processing(images=SCREAMING_SNAKE_CASE_ ,					annotations=SCREAMING_SNAKE_CASE_ ,					masks_path=SCREAMING_SNAKE_CASE_ ,					return_tensors="pt"				)

											# verify pixel values
											a__	:							List[str]      						= torch.Size([1, 3, 800, 1066]				)
											self.assertEqual(encoding["pixel_values"].shape ,					SCREAMING_SNAKE_CASE_				)

											a__	:							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] ,					SCREAMING_SNAKE_CASE_ ,					atol=1E-4				)				)

											# verify area
											a__	:							Optional[Any]      						= 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"] ,					SCREAMING_SNAKE_CASE_				)				)
											# verify boxes
											a__	:							int      						= torch.Size([6, 4]				)
											self.assertEqual(encoding["labels"][0]["boxes"].shape ,					SCREAMING_SNAKE_CASE_				)
											a__	:							Optional[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
											a__	:							Optional[int]      						= torch.tensor([3_9769]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,					SCREAMING_SNAKE_CASE_				)				)
											# verify is_crowd
											a__	:							Union[str, Any]      						= torch.tensor([0, 0, 0, 0, 0, 0]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,					SCREAMING_SNAKE_CASE_				)				)
											# verify class_labels
											a__	:							Optional[Any]      						= torch.tensor([17, 17, 63, 75, 75, 93]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,					SCREAMING_SNAKE_CASE_				)				)
											# verify masks
											a__	:							Any      						= 82_2873
											self.assertEqual(encoding["labels"][0]["masks"].sum().item() ,					SCREAMING_SNAKE_CASE_				)
											# verify orig_size
											a__	:							Tuple      						= torch.tensor([480, 640]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,					SCREAMING_SNAKE_CASE_				)				)
											# verify size
											a__	:							Tuple      						= torch.tensor([800, 1066]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,					SCREAMING_SNAKE_CASE_				)				)






'''simple docstring'''



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 1_00				)	->						int:
     __lowerCamelCase  :      Union[str, Any]														=						n * (n + 1) * (2 * n + 1) / 6
     __lowerCamelCase  :      Union[str, Any]														=						(n * (n + 1) / 2) ** 2
     return int(square_of_sum - sum_of_squares				)


if __name__ == "__main__":
      print(f'''{solution() = }''')






