



"""simple docstring"""






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 ConditionalDetrImageProcessor

class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					def __init__( self							:						Any,						lowerCamelCase							:						Dict,						lowerCamelCase							:						str=7,						lowerCamelCase							:						Optional[int]=3,						lowerCamelCase							:						int=30,						lowerCamelCase							:						Tuple=400,						lowerCamelCase							:						Any=True,						lowerCamelCase							:						Tuple=None,						lowerCamelCase							:						List[Any]=True,						lowerCamelCase							:						int=[0.5, 0.5, 0.5],						lowerCamelCase							:						Any=[0.5, 0.5, 0.5],						lowerCamelCase							:						int=True,						lowerCamelCase							:						Dict=1 / 255,						lowerCamelCase							:						Union[str, Any]=True,						)->      Any:
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								lowerCamelCase__	:	List[Any]				=size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
								lowerCamelCase__	:	Dict				=parent
								lowerCamelCase__	:	str				=batch_size
								lowerCamelCase__	:	Optional[Any]				=num_channels
								lowerCamelCase__	:	Optional[int]				=min_resolution
								lowerCamelCase__	:	Dict				=max_resolution
								lowerCamelCase__	:	Dict				=do_resize
								lowerCamelCase__	:	List[str]				=size
								lowerCamelCase__	:	str				=do_normalize
								lowerCamelCase__	:	Dict				=image_mean
								lowerCamelCase__	:	List[Any]				=image_std
								lowerCamelCase__	:	List[Any]				=do_rescale
								lowerCamelCase__	:	Optional[Any]				=rescale_factor
								lowerCamelCase__	:	Dict				=do_pad



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

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

								return expected_height, expected_width





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


					'''simple docstring'''





					_a         =							ConditionalDetrImageProcessor if is_vision_available() else None



					def 	snake_case ( self							:						Optional[int]  )->      Tuple:
								lowerCamelCase__	:	Any				=ConditionalDetrImageProcessingTester(self  )



					@property
					def 	snake_case ( self							:						Union[str, Any]  )->      Tuple:
								return self.image_processor_tester.prepare_image_processor_dict()



					def 	snake_case ( self							:						Tuple  )->      Tuple:
								lowerCamelCase__	:	str				=self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(lowerCamelCase,						'''image_mean'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,						'''image_std'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,						'''do_normalize'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,						'''do_resize'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,						'''size'''  )  )



					def 	snake_case ( self							:						Tuple  )->      Union[str, Any]:
								lowerCamelCase__	:	str				=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,						lowerCamelCase  )

								lowerCamelCase__	:	Dict				=self.image_processing_class.from_dict(
								    self.image_processor_dict,						size=42,						max_size=84,						pad_and_return_pixel_mask=lowerCamelCase  )
								self.assertEqual(image_processor.size,						{'''shortest_edge''': 42, '''longest_edge''': 84}  )
								self.assertEqual(image_processor.do_pad,						lowerCamelCase  )



					def 	snake_case ( self							:						Dict  )->      List[str]:
								pass



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

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

								lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=self.image_processor_tester.get_expected_values(lowerCamelCase  )

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

								# Test batched
								lowerCamelCase__			,     lowerCamelCase__	:	int				=self.image_processor_tester.get_expected_values(lowerCamelCase,						batched=lowerCamelCase  )

								lowerCamelCase__	:	Optional[Any]				=image_processing(lowerCamelCase,						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 	snake_case ( self							:						Any  )->      str:
								# Initialize image_processing
								lowerCamelCase__	:	List[str]				=self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								lowerCamelCase__	:	List[str]				=prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase,						numpify=lowerCamelCase  )
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,						np.ndarray  )

								# Test not batched input
								lowerCamelCase__	:	List[Any]				=image_processing(image_inputs[0],						return_tensors='''pt'''  ).pixel_values

								lowerCamelCase__			,     lowerCamelCase__	:	Dict				=self.image_processor_tester.get_expected_values(lowerCamelCase  )

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

								# Test batched
								lowerCamelCase__	:	Optional[int]				=image_processing(lowerCamelCase,						return_tensors='''pt'''  ).pixel_values

								lowerCamelCase__			,     lowerCamelCase__	:	Any				=self.image_processor_tester.get_expected_values(lowerCamelCase,						batched=lowerCamelCase  )

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



					def 	snake_case ( self							:						Tuple  )->      Tuple:
								# Initialize image_processing
								lowerCamelCase__	:	Optional[int]				=self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								lowerCamelCase__	:	Dict				=prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase,						torchify=lowerCamelCase  )
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,						torch.Tensor  )

								# Test not batched input
								lowerCamelCase__	:	List[Any]				=image_processing(image_inputs[0],						return_tensors='''pt'''  ).pixel_values

								lowerCamelCase__			,     lowerCamelCase__	:	int				=self.image_processor_tester.get_expected_values(lowerCamelCase  )

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

								# Test batched
								lowerCamelCase__	:	int				=image_processing(lowerCamelCase,						return_tensors='''pt'''  ).pixel_values

								lowerCamelCase__			,     lowerCamelCase__	:	List[Any]				=self.image_processor_tester.get_expected_values(lowerCamelCase,						batched=lowerCamelCase  )

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



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

								lowerCamelCase__	:	int				={'''image_id''': 3_9769, '''annotations''': target}

								# encode them
								lowerCamelCase__	:	Optional[int]				=ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''  )
								lowerCamelCase__	:	List[str]				=image_processing(images=lowerCamelCase,						annotations=lowerCamelCase,						return_tensors='''pt'''  )

								# verify pixel values
								lowerCamelCase__	:	str				=torch.Size([1, 3, 800, 1066]  )
								self.assertEqual(encoding['''pixel_values'''].shape,						lowerCamelCase  )

								lowerCamelCase__	:	Union[str, Any]				=torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
								self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3],						lowerCamelCase,						atol=1E-4  )  )

								# verify area
								lowerCamelCase__	:	Tuple				=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'''],						lowerCamelCase  )  )
								# verify boxes
								lowerCamelCase__	:	str				=torch.Size([6, 4]  )
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,						lowerCamelCase  )
								lowerCamelCase__	:	List[str]				=torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0],						lowerCamelCase,						atol=1E-3  )  )
								# verify image_id
								lowerCamelCase__	:	str				=torch.tensor([3_9769]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],						lowerCamelCase  )  )
								# verify is_crowd
								lowerCamelCase__	:	Dict				=torch.tensor([0, 0, 0, 0, 0, 0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],						lowerCamelCase  )  )
								# verify class_labels
								lowerCamelCase__	:	Dict				=torch.tensor([75, 75, 63, 65, 17, 17]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],						lowerCamelCase  )  )
								# verify orig_size
								lowerCamelCase__	:	List[Any]				=torch.tensor([480, 640]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],						lowerCamelCase  )  )
								# verify size
								lowerCamelCase__	:	str				=torch.tensor([800, 1066]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],						lowerCamelCase  )  )




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

								lowerCamelCase__	:	Optional[int]				={'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

								lowerCamelCase__	:	int				=pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''  )

								# encode them
								lowerCamelCase__	:	List[Any]				=ConditionalDetrImageProcessor(format='''coco_panoptic'''  )
								lowerCamelCase__	:	str				=image_processing(images=lowerCamelCase,						annotations=lowerCamelCase,						masks_path=lowerCamelCase,						return_tensors='''pt'''  )

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

								lowerCamelCase__	:	Tuple				=torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
								self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3],						lowerCamelCase,						atol=1E-4  )  )

								# verify area
								lowerCamelCase__	:	Any				=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'''],						lowerCamelCase  )  )
								# verify boxes
								lowerCamelCase__	:	Tuple				=torch.Size([6, 4]  )
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,						lowerCamelCase  )
								lowerCamelCase__	:	Tuple				=torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0],						lowerCamelCase,						atol=1E-3  )  )
								# verify image_id
								lowerCamelCase__	:	Optional[int]				=torch.tensor([3_9769]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],						lowerCamelCase  )  )
								# verify is_crowd
								lowerCamelCase__	:	Optional[int]				=torch.tensor([0, 0, 0, 0, 0, 0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],						lowerCamelCase  )  )
								# verify class_labels
								lowerCamelCase__	:	List[Any]				=torch.tensor([17, 17, 63, 75, 75, 93]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],						lowerCamelCase  )  )
								# verify masks
								lowerCamelCase__	:	Optional[int]				=82_2873
								self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item(),						lowerCamelCase  )
								# verify orig_size
								lowerCamelCase__	:	List[str]				=torch.tensor([480, 640]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],						lowerCamelCase  )  )
								# verify size
								lowerCamelCase__	:	Optional[int]				=torch.tensor([800, 1066]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],						lowerCamelCase  )  )









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					list[int]			):
			"""simple docstring"""

			# 1. Validate that path exists between current and next vertices
			if graph[path[curr_ind - 1]][next_ver] == 0:
						return False

			# 2. Validate that next vertex is not already in path
			return not any(vertex == next_ver for vertex in path			)






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					list[int]							,	__lowerCamelCase       :					int			):
			"""simple docstring"""

			# Base Case
			if curr_ind == len(__lowerCamelCase			):
						# return whether path exists between current and starting vertices
						return graph[path[curr_ind - 1]][path[0]] == 1

			# Recursive Step
			for next_ver in range(0							,	len(__lowerCamelCase			)			):
						if valid_connection(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			):
									# Insert current vertex  into path as next transition
									lowerCamelCase__	:	Tuple				=next_ver
									# Validate created path
									if util_hamilton_cycle(__lowerCamelCase							,	__lowerCamelCase							,	curr_ind + 1			):
												return True
									# Backtrack
									lowerCamelCase__	:	int				=-1
			return False






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					int = 0			):
			"""simple docstring"""
			lowerCamelCase__	:	Tuple				=[-1] * (len(__lowerCamelCase			) + 1)
			# initialize start and end of path with starting index
			lowerCamelCase__	:	Union[str, Any]				=start_index
			# evaluate and if we find answer return path either return empty array
			return path if util_hamilton_cycle(__lowerCamelCase							,	__lowerCamelCase							,	1			) else []




