
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 DetrImageProcessor





class       _lowerCAmelCase   (      unittest.TestCase			):




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=7							,							_UpperCamelCase=3							,							_UpperCamelCase=30							,							_UpperCamelCase=400							,							_UpperCamelCase=True							,							_UpperCamelCase=None							,							_UpperCamelCase=True							,							_UpperCamelCase=1 / 255							,							_UpperCamelCase=True							,							_UpperCamelCase=[0.5, 0.5, 0.5]							,							_UpperCamelCase=[0.5, 0.5, 0.5]							,							_UpperCamelCase=True							,							)							-> int:
							lowerCAmelCase_         =      size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      batch_size
							lowerCAmelCase_         =      num_channels
							lowerCAmelCase_         =      min_resolution
							lowerCAmelCase_         =      max_resolution
							lowerCAmelCase_         =      do_resize
							lowerCAmelCase_         =      size
							lowerCAmelCase_         =      do_rescale
							lowerCAmelCase_         =      rescale_factor
							lowerCAmelCase_         =      do_normalize
							lowerCAmelCase_         =      image_mean
							lowerCAmelCase_         =      image_std
							lowerCAmelCase_         =      do_pad




				def 	__a		(   self	)							-> Any:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_pad": self.do_pad,
							}




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase=False	)							-> List[Any]:
							if not batched:
										lowerCAmelCase_         =      image_inputs[0]
										if isinstance(UpperCAmelCase__							,							Image.Image	):
													lowerCAmelCase_         =      image.size
										else:
													lowerCAmelCase_         =      image.shape[1], image.shape[2]
										if w < h:
													lowerCAmelCase_         =      int(self.size["shortest_edge"] * h / w	)
													lowerCAmelCase_         =      self.size['''shortest_edge''']
										elif w > h:
													lowerCAmelCase_         =      self.size['''shortest_edge''']
													lowerCAmelCase_         =      int(self.size["shortest_edge"] * w / h	)
										else:
													lowerCAmelCase_         =      self.size['''shortest_edge''']
													lowerCAmelCase_         =      self.size['''shortest_edge''']

							else:
										lowerCAmelCase_         =      []
										for image in image_inputs:
													lowerCAmelCase_         =      self.get_expected_values([image]	)
													expected_values.append((expected_height, expected_width)	)
										lowerCAmelCase_         =      max(UpperCAmelCase__							,							key=lambda _UpperCamelCase	: item[0]	)[0]
										lowerCAmelCase_         =      max(UpperCAmelCase__							,							key=lambda _UpperCamelCase	: item[1]	)[1]

							return expected_height, expected_width





@require_torch
@require_vision
class       _lowerCAmelCase   (      lowercase__   , unittest.TestCase			):
				_lowercase     						=DetrImageProcessor if is_vision_available() else None




				def 	__a		(   self	)							-> Tuple:
							lowerCAmelCase_         =      DetrImageProcessingTester(self	)




				@property
				def 	__a		(   self	)							-> Tuple:
							return self.image_processor_tester.prepare_image_processor_dict()




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      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_rescale"	)	)
							self.assertTrue(hasattr(UpperCAmelCase__							,							"rescale_factor"	)	)
							self.assertTrue(hasattr(UpperCAmelCase__							,							"do_resize"	)	)
							self.assertTrue(hasattr(UpperCAmelCase__							,							"size"	)	)
							self.assertTrue(hasattr(UpperCAmelCase__							,							"do_pad"	)	)




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      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							,							UpperCAmelCase__	)

							lowerCAmelCase_         =      self.image_processing_class.from_dict(
							    self.image_processor_dict							,							size=42							,							max_size=84							,							pad_and_return_pixel_mask=UpperCAmelCase__	)
							self.assertEqual(image_processor.size							,							{"shortest_edge": 42, "longest_edge": 84}	)
							self.assertEqual(image_processor.do_pad							,							UpperCAmelCase__	)




				def 	__a		(   self	)							-> Optional[Any]:
							pass




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							lowerCAmelCase_         =      prepare_image_inputs(self.image_processor_tester							,							equal_resolution=UpperCAmelCase__	)
							for image in image_inputs:
										self.assertIsInstance(UpperCAmelCase__							,							Image.Image	)

							# Test not batched input
							lowerCAmelCase_         =      image_processing(image_inputs[0]							,							return_tensors="pt"	).pixel_values

							lowerCAmelCase_         =      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
							lowerCAmelCase_         =      self.image_processor_tester.get_expected_values(UpperCAmelCase__							,							batched=UpperCAmelCase__	)

							lowerCAmelCase_         =      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 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random numpy tensors
							lowerCAmelCase_         =      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
							lowerCAmelCase_         =      image_processing(image_inputs[0]							,							return_tensors="pt"	).pixel_values

							lowerCAmelCase_         =      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
							lowerCAmelCase_         =      image_processing(UpperCAmelCase__							,							return_tensors="pt"	).pixel_values

							lowerCAmelCase_         =      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 	__a		(   self	)							-> Tuple:
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							lowerCAmelCase_         =      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
							lowerCAmelCase_         =      image_processing(image_inputs[0]							,							return_tensors="pt"	).pixel_values

							lowerCAmelCase_         =      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
							lowerCAmelCase_         =      image_processing(UpperCAmelCase__							,							return_tensors="pt"	).pixel_values

							lowerCAmelCase_         =      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 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
							with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"							,							"r"	) as f:
										lowerCAmelCase_         =      json.loads(f.read()	)

							lowerCAmelCase_         =      {'''image_id''': 39_769, '''annotations''': target}

							# encode them
							lowerCAmelCase_         =      DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"	)
							lowerCAmelCase_         =      image_processing(images=UpperCAmelCase__							,							annotations=UpperCAmelCase__							,							return_tensors="pt"	)

							# verify pixel values
							lowerCAmelCase_         =      torch.Size([1, 3, 800, 1_066]	)
							self.assertEqual(encoding["pixel_values"].shape							,							UpperCAmelCase__	)

							lowerCAmelCase_         =      torch.tensor([0.2796, 0.3138, 0.3481]	)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,							UpperCAmelCase__							,							atol=1e-4	)	)

							# verify area
							lowerCAmelCase_         =      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"]							,							UpperCAmelCase__	)	)
							# verify boxes
							lowerCAmelCase_         =      torch.Size([6, 4]	)
							self.assertEqual(encoding["labels"][0]["boxes"].shape							,							UpperCAmelCase__	)
							lowerCAmelCase_         =      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,							UpperCAmelCase__							,							atol=1e-3	)	)
							# verify image_id
							lowerCAmelCase_         =      torch.tensor([39_769]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,							UpperCAmelCase__	)	)
							# verify is_crowd
							lowerCAmelCase_         =      torch.tensor([0, 0, 0, 0, 0, 0]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,							UpperCAmelCase__	)	)
							# verify class_labels
							lowerCAmelCase_         =      torch.tensor([75, 75, 63, 65, 17, 17]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,							UpperCAmelCase__	)	)
							# verify orig_size
							lowerCAmelCase_         =      torch.tensor([480, 640]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,							UpperCAmelCase__	)	)
							# verify size
							lowerCAmelCase_         =      torch.tensor([800, 1_066]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,							UpperCAmelCase__	)	)




				@slow
				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
							with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"							,							"r"	) as f:
										lowerCAmelCase_         =      json.loads(f.read()	)

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

							lowerCAmelCase_         =      pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"	)

							# encode them
							lowerCAmelCase_         =      DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"	)
							lowerCAmelCase_         =      image_processing(images=UpperCAmelCase__							,							annotations=UpperCAmelCase__							,							masks_path=UpperCAmelCase__							,							return_tensors="pt"	)

							# verify pixel values
							lowerCAmelCase_         =      torch.Size([1, 3, 800, 1_066]	)
							self.assertEqual(encoding["pixel_values"].shape							,							UpperCAmelCase__	)

							lowerCAmelCase_         =      torch.tensor([0.2796, 0.3138, 0.3481]	)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,							UpperCAmelCase__							,							atol=1e-4	)	)

							# verify area
							lowerCAmelCase_         =      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"]							,							UpperCAmelCase__	)	)
							# verify boxes
							lowerCAmelCase_         =      torch.Size([6, 4]	)
							self.assertEqual(encoding["labels"][0]["boxes"].shape							,							UpperCAmelCase__	)
							lowerCAmelCase_         =      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,							UpperCAmelCase__							,							atol=1e-3	)	)
							# verify image_id
							lowerCAmelCase_         =      torch.tensor([39_769]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,							UpperCAmelCase__	)	)
							# verify is_crowd
							lowerCAmelCase_         =      torch.tensor([0, 0, 0, 0, 0, 0]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,							UpperCAmelCase__	)	)
							# verify class_labels
							lowerCAmelCase_         =      torch.tensor([17, 17, 63, 75, 75, 93]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,							UpperCAmelCase__	)	)
							# verify masks
							lowerCAmelCase_         =      822_873
							self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,							UpperCAmelCase__	)
							# verify orig_size
							lowerCAmelCase_         =      torch.tensor([480, 640]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,							UpperCAmelCase__	)	)
							# verify size
							lowerCAmelCase_         =      torch.tensor([800, 1_066]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,							UpperCAmelCase__	)	)






'''simple docstring'''





def 				_lowerCAmelCase			(   __magic_name__						:     int = 600851475143						)				->  int:

						try:
												lowercase :							Any    		=int(__magic_name__						)
						except (TypeError, ValueError):
												raise TypeError('''Parameter n must be int or castable to int.'''						)
						if n <= 0:
												raise ValueError('''Parameter n must be greater than or equal to one.'''						)
						lowercase :							Optional[Any]    		=2
						lowercase :							Dict    		=0
						if n == 2:
												return 2
						while n > 2:
												while n % i != 0:
																		i += 1
												lowercase :							Union[str, Any]    		=i
												while n % i == 0:
																		lowercase :							Optional[int]    		=n // i
												i += 1
						return int(__magic_name__						)


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





