

from math import ceil, sqrt



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int = 1_0_0_0_0_0_0      )			->      int:
				A__        =			0

				for outer_width in range(3	,       (limit // 4) + 2      ):
								if outer_width**2 > limit:
												A__        =			max(ceil(sqrt(outer_width**2 - limit      )      )	,       1      )
								else:
												A__        =			1
								if (outer_width - hole_width_lower_bound) % 2:
												hole_width_lower_bound += 1

								answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

				return answer


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




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      ,      lowercase      ,      lowercase=7      ,      lowercase=3      ,      lowercase=30      ,      lowercase=400      ,      lowercase=True      ,      lowercase=None      ,      lowercase=True      ,      lowercase=[0.5, 0.5, 0.5]      ,      lowercase=[0.5, 0.5, 0.5]      ,      lowercase=True      ,      lowercase=1 / 255      ,      lowercase=True      ,      )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
							A__        =			parent
							A__        =			batch_size
							A__        =			num_channels
							A__        =			min_resolution
							A__        =			max_resolution
							A__        =			do_resize
							A__        =			size
							A__        =			do_normalize
							A__        =			image_mean
							A__        =			image_std
							A__        =			do_rescale
							A__        =			rescale_factor
							A__        =			do_pad


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''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      UpperCamelCase		(    self      ,      lowercase      ,      lowercase=False       )  ->						int:
							'''simple docstring'''






							if not batched:
											A__        =			image_inputs[0]
											if isinstance(lowercase      ,      Image.Image       ):
															A__    ,				A__        =			image.size
											else:
															A__    ,				A__        =			image.shape[1], image.shape[2]
											if w < h:
															A__        =			int(self.size["shortest_edge"] * h / w       )
															A__        =			self.size["shortest_edge"]
											elif w > h:
															A__        =			self.size["shortest_edge"]
															A__        =			int(self.size["shortest_edge"] * w / h       )
											else:
															A__        =			self.size["shortest_edge"]
															A__        =			self.size["shortest_edge"]

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

							return expected_height, expected_width

@require_torch
@require_vision
class 					a__						(					snake_case							,	unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       DetaImageProcessor if is_vision_available() else None


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			DetaImageProcessingTester(self       )


			@property
			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(lowercase      ,      "image_mean"       )       )
							self.assertTrue(hasattr(lowercase      ,      "image_std"       )       )
							self.assertTrue(hasattr(lowercase      ,      "do_normalize"       )       )
							self.assertTrue(hasattr(lowercase      ,      "do_resize"       )       )
							self.assertTrue(hasattr(lowercase      ,      "do_rescale"       )       )
							self.assertTrue(hasattr(lowercase      ,      "do_pad"       )       )
							self.assertTrue(hasattr(lowercase      ,      "size"       )       )


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			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      ,      lowercase       )


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      Image.Image       )

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

							A__    ,				A__        =			self.image_processor_tester.get_expected_values(lowercase       )

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

							# Test batched
							A__    ,				A__        =			self.image_processor_tester.get_expected_values(lowercase      ,      batched=lowercase       )

							A__        =			image_processing(lowercase      ,      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       )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase      ,      numpify=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      np.ndarray       )

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

							A__    ,				A__        =			self.image_processor_tester.get_expected_values(lowercase       )

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

							# Test batched
							A__        =			image_processing(lowercase      ,      return_tensors="pt"       ).pixel_values

							A__    ,				A__        =			self.image_processor_tester.get_expected_values(lowercase      ,      batched=lowercase       )

							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]:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase      ,      torchify=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      torch.Tensor       )

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

							A__    ,				A__        =			self.image_processor_tester.get_expected_values(lowercase       )

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

							# Test batched
							A__        =			image_processing(lowercase      ,      return_tensors="pt"       ).pixel_values

							A__    ,				A__        =			self.image_processor_tester.get_expected_values(lowercase      ,      batched=lowercase       )

							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       )  ->						Tuple:
							'''simple docstring'''






							A__        =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
							with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"      ,      "r"       ) as f:
											A__        =			json.loads(f.read()       )

							A__        =			{"image_id": 39769, "annotations": target}

							# encode them
							A__        =			DetaImageProcessor()
							A__        =			image_processing(images=lowercase      ,      annotations=lowercase      ,      return_tensors="pt"       )

							# verify pixel values
							A__        =			torch.Size([1, 3, 800, 1066]       )
							self.assertEqual(encoding["pixel_values"].shape      ,      lowercase       )

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

							# verify area
							A__        =			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"]      ,      lowercase       )       )
							# verify boxes
							A__        =			torch.Size([6, 4]       )
							self.assertEqual(encoding["labels"][0]["boxes"].shape      ,      lowercase       )
							A__        =			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,      lowercase      ,      atol=1e-3       )       )
							# verify image_id
							A__        =			torch.tensor([39769]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,      lowercase       )       )
							# verify is_crowd
							A__        =			torch.tensor([0, 0, 0, 0, 0, 0]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,      lowercase       )       )
							# verify class_labels
							A__        =			torch.tensor([75, 75, 63, 65, 17, 17]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,      lowercase       )       )
							# verify orig_size
							A__        =			torch.tensor([480, 640]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,      lowercase       )       )
							# verify size
							A__        =			torch.tensor([800, 1066]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,      lowercase       )       )


			@slow
			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
							with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"      ,      "r"       ) as f:
											A__        =			json.loads(f.read()       )

							A__        =			{"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

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

							# encode them
							A__        =			DetaImageProcessor(format="coco_panoptic"       )
							A__        =			image_processing(images=lowercase      ,      annotations=lowercase      ,      masks_path=lowercase      ,      return_tensors="pt"       )

							# verify pixel values
							A__        =			torch.Size([1, 3, 800, 1066]       )
							self.assertEqual(encoding["pixel_values"].shape      ,      lowercase       )

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

							# verify area
							A__        =			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"]      ,      lowercase       )       )
							# verify boxes
							A__        =			torch.Size([6, 4]       )
							self.assertEqual(encoding["labels"][0]["boxes"].shape      ,      lowercase       )
							A__        =			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,      lowercase      ,      atol=1e-3       )       )
							# verify image_id
							A__        =			torch.tensor([39769]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,      lowercase       )       )
							# verify is_crowd
							A__        =			torch.tensor([0, 0, 0, 0, 0, 0]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,      lowercase       )       )
							# verify class_labels
							A__        =			torch.tensor([17, 17, 63, 75, 75, 93]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,      lowercase       )       )
							# verify masks
							A__        =			822873
							self.assertEqual(encoding["labels"][0]["masks"].sum().item()      ,      lowercase       )
							# verify orig_size
							A__        =			torch.tensor([480, 640]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,      lowercase       )       )
							# verify size
							A__        =			torch.tensor([800, 1066]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,      lowercase       )       )

