






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 DeformableDetrImageProcessor


class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):

							'''simple docstring'''





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

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

											return expected_height, expected_width


@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE(	__SCREAMING_SNAKE_CASE    ,  unittest.TestCase	):

							'''simple docstring'''





							_UpperCAmelCase     =       DeformableDetrImageProcessor if is_vision_available() else None
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Optional[Any]:
											snake_case__									=     DeformableDetrImageProcessingTester(self						)
							@property
							def lowerCAmelCase_ (     self:      int						)		->		int:
											return self.image_processor_tester.prepare_image_processor_dict()
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Any:
											snake_case__									=     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 lowerCAmelCase_ (     self:      Tuple						)		->		Union[str, Any]:
											snake_case__									=     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						)

											snake_case__									=     self.image_processing_class.from_dict(
											    self.image_processor_dict       ,	size=42       ,	max_size=84       ,	pad_and_return_pixel_mask=_a						)
											self.assertEqual(image_processor.size       ,	{'shortest_edge': 42, 'longest_edge': 84}						)
											self.assertEqual(image_processor.do_pad       ,	_a						)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Any:
											pass
							def lowerCAmelCase_ (     self:      str						)		->		List[Any]:
											# Initialize image_processing
											snake_case__									=     self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											snake_case__									=     prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=_a						)
											for image in image_inputs:
															self.assertIsInstance(_a       ,	Image.Image						)

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

											snake_case__							,						snake_case__									=     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
											snake_case__							,						snake_case__									=     self.image_processor_tester.get_expected_values(_a       ,	batched=_a						)

											snake_case__									=     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 lowerCAmelCase_ (     self:      Union[str, Any]						)		->		str:
											# Initialize image_processing
											snake_case__									=     self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											snake_case__									=     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
											snake_case__									=     image_processing(image_inputs[0]       ,	return_tensors='pt'						).pixel_values

											snake_case__							,						snake_case__									=     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
											snake_case__									=     image_processing(_a       ,	return_tensors='pt'						).pixel_values

											snake_case__							,						snake_case__									=     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 lowerCAmelCase_ (     self:      List[Any]						)		->		Optional[int]:
											# Initialize image_processing
											snake_case__									=     self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											snake_case__									=     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
											snake_case__									=     image_processing(image_inputs[0]       ,	return_tensors='pt'						).pixel_values

											snake_case__							,						snake_case__									=     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
											snake_case__									=     image_processing(_a       ,	return_tensors='pt'						).pixel_values

											snake_case__							,						snake_case__									=     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 lowerCAmelCase_ (     self:      Optional[Any]						)		->		Union[str, Any]:
											# prepare image and target
											snake_case__									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
											with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'       ,	'r'						) as f:
															snake_case__									=     json.loads(f.read()						)

											snake_case__									=     {'image_id': 3_97_69, 'annotations': target}

											# encode them
											snake_case__									=     DeformableDetrImageProcessor()
											snake_case__									=     image_processing(images=_a       ,	annotations=_a       ,	return_tensors='pt'						)

											# verify pixel values
											snake_case__									=     torch.Size([1, 3, 8_00, 10_66]						)
											self.assertEqual(encoding['pixel_values'].shape       ,	_a						)

											snake_case__									=     torch.tensor([0.2_796, 0.3_138, 0.3_481]						)
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,	_a       ,	atol=1e-4						)						)

											# verify area
											snake_case__									=     torch.tensor([5_887.9_600, 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
											snake_case__									=     torch.Size([6, 4]						)
											self.assertEqual(encoding['labels'][0]['boxes'].shape       ,	_a						)
											snake_case__									=     torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,	_a       ,	atol=1e-3						)						)
											# verify image_id
											snake_case__									=     torch.tensor([3_97_69]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,	_a						)						)
											# verify is_crowd
											snake_case__									=     torch.tensor([0, 0, 0, 0, 0, 0]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,	_a						)						)
											# verify class_labels
											snake_case__									=     torch.tensor([75, 75, 63, 65, 17, 17]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,	_a						)						)
											# verify orig_size
											snake_case__									=     torch.tensor([4_80, 6_40]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,	_a						)						)
											# verify size
											snake_case__									=     torch.tensor([8_00, 10_66]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,	_a						)						)


							@slow
							def lowerCAmelCase_ (     self:      int						)		->		Any:
											# prepare image, target and masks_path
											snake_case__									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
											with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'       ,	'r'						) as f:
															snake_case__									=     json.loads(f.read()						)

											snake_case__									=     {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

											snake_case__									=     pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'						)

											# encode them
											snake_case__									=     DeformableDetrImageProcessor(format='coco_panoptic'						)
											snake_case__									=     image_processing(images=_a       ,	annotations=_a       ,	masks_path=_a       ,	return_tensors='pt'						)

											# verify pixel values
											snake_case__									=     torch.Size([1, 3, 8_00, 10_66]						)
											self.assertEqual(encoding['pixel_values'].shape       ,	_a						)

											snake_case__									=     torch.tensor([0.2_796, 0.3_138, 0.3_481]						)
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,	_a       ,	atol=1e-4						)						)

											# verify area
											snake_case__									=     torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 5_879.6_562, 7_634.1_147]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,	_a						)						)
											# verify boxes
											snake_case__									=     torch.Size([6, 4]						)
											self.assertEqual(encoding['labels'][0]['boxes'].shape       ,	_a						)
											snake_case__									=     torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,	_a       ,	atol=1e-3						)						)
											# verify image_id
											snake_case__									=     torch.tensor([3_97_69]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,	_a						)						)
											# verify is_crowd
											snake_case__									=     torch.tensor([0, 0, 0, 0, 0, 0]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,	_a						)						)
											# verify class_labels
											snake_case__									=     torch.tensor([17, 17, 63, 75, 75, 93]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,	_a						)						)
											# verify masks
											snake_case__									=     82_28_73
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,	_a						)
											# verify orig_size
											snake_case__									=     torch.tensor([4_80, 6_40]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,	_a						)						)
											# verify size
											snake_case__									=     torch.tensor([8_00, 10_66]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,	_a						)						)












def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'  )

				snake_case__									=     str(bin(_A  )  )
				binary_number += "0" * shift_amount
				return binary_number
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'  )

				snake_case__									=     str(bin(_A  )  )[2:]
				if shift_amount >= len(_A  ):
								return "0b0"
				snake_case__									=     binary_number[: len(_A  ) - shift_amount]
				return "0b" + shifted_binary_number
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number >= 0:  # Get binary representation of positive number
								snake_case__									=     '0' + str(bin(_A  )  ).strip('-'  )[2:]
				else:  # Get binary (2's complement) representation of negative number
								snake_case__									=     len(bin(_A  )[3:]  )  # Find 2's complement of number
								snake_case__									=     bin(abs(_A  ) - (1 << binary_number_length)  )[3:]
								snake_case__									=     (
								    '1' + '0' * (binary_number_length - len(_A  )) + binary_number
								)

				if shift_amount >= len(_A  ):
								return "0b" + binary_number[0] * len(_A  )
				return (
				    "0b"
				    + binary_number[0] * shift_amount
				    + binary_number[: len(_A  ) - shift_amount]
				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




