





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				:     Union[str, Any]							,	a				:     List[Any]							,	a				:     Any=7							,	a				:     Optional[int]=3							,	a				:     List[Any]=30							,	a				:     Tuple=4_00							,	a				:     str=True							,	a				:     Any=None							,	a				:     List[Any]=True							,	a				:     int=[0.5, 0.5, 0.5]							,	a				:     str=[0.5, 0.5, 0.5]							,	a				:     Union[str, Any]=True							,	a				:     Optional[int]=1 / 2_55							,	a				:     List[Any]=True							,	)	->Dict:
													# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
													SCREAMING_SNAKE_CASE__     :				Dict   =					size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
													SCREAMING_SNAKE_CASE__     :				Any   =					parent
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					batch_size
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					num_channels
													SCREAMING_SNAKE_CASE__     :				Dict   =					min_resolution
													SCREAMING_SNAKE_CASE__     :				int   =					max_resolution
													SCREAMING_SNAKE_CASE__     :				int   =					do_resize
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					size
													SCREAMING_SNAKE_CASE__     :				str   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					image_mean
													SCREAMING_SNAKE_CASE__     :				str   =					image_std
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				Dict   =					rescale_factor
													SCREAMING_SNAKE_CASE__     :				Any   =					do_pad

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

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

													return expected_height, expected_width







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






							"""simple docstring"""

							snake_case_							 =						DetaImageProcessor if is_vision_available() else None

							def      A_  (      self				:     Any    )	->int:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					DetaImageProcessingTester(self    )

							@property
							def      A_  (      self				:     Dict    )	->Dict:
													return self.image_processor_tester.prepare_image_processor_dict()

							def      A_  (      self				:     Union[str, Any]    )	->str:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					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      A_  (      self				:     Optional[Any]    )	->List[str]:
													SCREAMING_SNAKE_CASE__     :				Any   =					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							,	A_    )

							def      A_  (      self				:     List[Any]    )	->List[Any]:
													pass

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

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

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				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
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Tuple   =					self.image_processor_tester.get_expected_values(A_							,	batched=A_    )

													SCREAMING_SNAKE_CASE__     :				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,
													        expected_height,
													        expected_width,
													    )							,	)

							def      A_  (      self				:     List[Any]    )	->Any:
													# Initialize image_processing
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													SCREAMING_SNAKE_CASE__     :				List[str]   =					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
													SCREAMING_SNAKE_CASE__     :				Dict   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Tuple   =					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
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					image_processing(A_							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Tuple   =					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      A_  (      self				:     List[str]    )	->Any:
													# Initialize image_processing
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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
													SCREAMING_SNAKE_CASE__     :				int   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				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
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_processing(A_							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				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,
													    )							,	)

							@slow
							def      A_  (      self				:     List[Any]    )	->int:
													# prepare image and target
													SCREAMING_SNAKE_CASE__     :				str   =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
													with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"							,	"r"    ) as f:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					json.loads(f.read()    )

													SCREAMING_SNAKE_CASE__     :				Tuple   =					{"image_id": 3_97_69, "annotations": target}

													# encode them
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					DetaImageProcessor()
													SCREAMING_SNAKE_CASE__     :				Dict   =					image_processing(images=A_							,	annotations=A_							,	return_tensors="pt"    )

													# verify pixel values
													SCREAMING_SNAKE_CASE__     :				Tuple   =					torch.Size([1, 3, 8_00, 10_66]    )
													self.assertEqual(encoding["pixel_values"].shape							,	A_    )

													SCREAMING_SNAKE_CASE__     :				Any   =					torch.tensor([0.2796, 0.3138, 0.3481]    )
													self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	A_							,	atol=1E-4    )    )

													# verify area
													SCREAMING_SNAKE_CASE__     :				List[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"]							,	A_    )    )
													# verify boxes
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					torch.Size([6, 4]    )
													self.assertEqual(encoding["labels"][0]["boxes"].shape							,	A_    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	A_							,	atol=1E-3    )    )
													# verify image_id
													SCREAMING_SNAKE_CASE__     :				int   =					torch.tensor([3_97_69]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	A_    )    )
													# verify is_crowd
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					torch.tensor([0, 0, 0, 0, 0, 0]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	A_    )    )
													# verify class_labels
													SCREAMING_SNAKE_CASE__     :				Any   =					torch.tensor([75, 75, 63, 65, 17, 17]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	A_    )    )
													# verify orig_size
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					torch.tensor([4_80, 6_40]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	A_    )    )
													# verify size
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					torch.tensor([8_00, 10_66]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	A_    )    )






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

													SCREAMING_SNAKE_CASE__     :				Any   =					{"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

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

													# encode them
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					DetaImageProcessor(format="coco_panoptic"    )
													SCREAMING_SNAKE_CASE__     :				str   =					image_processing(images=A_							,	annotations=A_							,	masks_path=A_							,	return_tensors="pt"    )

													# verify pixel values
													SCREAMING_SNAKE_CASE__     :				Dict   =					torch.Size([1, 3, 8_00, 10_66]    )
													self.assertEqual(encoding["pixel_values"].shape							,	A_    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					torch.tensor([0.2796, 0.3138, 0.3481]    )
													self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	A_							,	atol=1E-4    )    )

													# verify area
													SCREAMING_SNAKE_CASE__     :				List[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"]							,	A_    )    )
													# verify boxes
													SCREAMING_SNAKE_CASE__     :				str   =					torch.Size([6, 4]    )
													self.assertEqual(encoding["labels"][0]["boxes"].shape							,	A_    )
													SCREAMING_SNAKE_CASE__     :				str   =					torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	A_							,	atol=1E-3    )    )
													# verify image_id
													SCREAMING_SNAKE_CASE__     :				List[str]   =					torch.tensor([3_97_69]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	A_    )    )
													# verify is_crowd
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					torch.tensor([0, 0, 0, 0, 0, 0]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	A_    )    )
													# verify class_labels
													SCREAMING_SNAKE_CASE__     :				Tuple   =					torch.tensor([17, 17, 63, 75, 75, 93]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	A_    )    )
													# verify masks
													SCREAMING_SNAKE_CASE__     :				int   =					82_28_73
													self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,	A_    )
													# verify orig_size
													SCREAMING_SNAKE_CASE__     :				int   =					torch.tensor([4_80, 6_40]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	A_    )    )
													# verify size
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					torch.tensor([8_00, 10_66]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	A_    )    )










def       UpperCAmelCase							(     _lowerCamelCase      :  int = 4_000_000     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Any   =					[0, 1]
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					0
						while fib[i] <= n:
												fib.append(fib[i] + fib[i + 1]     )
												if fib[i + 2] > n:
																		break
												i += 1
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					0
						for j in range(len(_lowerCamelCase     ) - 1     ):
												if fib[j] % 2 == 0:
																		total += fib[j]

						return total


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