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					):

					def __init__(    self      :       Any,_A      :       int,_A      :       Tuple=7,_A      :       Tuple=3,_A      :       int=30,_A      :       str=400,_A      :       Any=True,_A      :       Optional[Any]=None,_A      :       Optional[Any]=True,_A      :       Dict=[0.5, 0.5, 0.5],_A      :       Dict=[0.5, 0.5, 0.5],_A      :       str=True,_A      :       Optional[int]=1 / 255,_A      :       Dict=True,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
							SCREAMING_SNAKE_CASE_  :		int				      =    parent
							SCREAMING_SNAKE_CASE_  :		Dict				      =    batch_size
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    num_channels
							SCREAMING_SNAKE_CASE_  :		Dict				      =    min_resolution
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    max_resolution
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    do_resize
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    size
							SCREAMING_SNAKE_CASE_  :		Dict				      =    do_normalize
							SCREAMING_SNAKE_CASE_  :		Any				      =    image_mean
							SCREAMING_SNAKE_CASE_  :		Any				      =    image_std
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    do_rescale
							SCREAMING_SNAKE_CASE_  :		str				      =    rescale_factor
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    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      :       Union[str, Any],_A      :       List[str],_A      :       Tuple=False    ):







							"""simple docstring"""






							if not batched:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    image_inputs[0]
									if isinstance(_A,Image.Image    ):
											SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    image.size
									else:
											SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		int				      =    image.shape[1], image.shape[2]
									if w < h:
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    int(self.size["shortest_edge"] * h / w    )
											SCREAMING_SNAKE_CASE_  :		int				      =    self.size["shortest_edge"]
									elif w > h:
											SCREAMING_SNAKE_CASE_  :		int				      =    self.size["shortest_edge"]
											SCREAMING_SNAKE_CASE_  :		int				      =    int(self.size["shortest_edge"] * w / h    )
									else:
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.size["shortest_edge"]
											SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.size["shortest_edge"]

							else:
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    []
									for image in image_inputs:
											SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.get_expected_values([image]    )
											expected_values.append((expected_height, expected_width)    )
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    max(_A,key=lambda _A    : item[0]    )[0]
									SCREAMING_SNAKE_CASE_  :		Dict				      =    max(_A,key=lambda _A    : item[1]    )[1]

							return expected_height, expected_width







@require_torch
@require_vision
class 		a__						(					A__    ,		unittest.TestCase					):
					A	       =							DetaImageProcessor if is_vision_available() else None

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    DetaImageProcessingTester(self    )

					@property
					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							return self.image_processor_tester.prepare_image_processor_dict()

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[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 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    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,_A    )

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							pass

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.image_processing_class(**self.image_processor_dict    )
							# create random PIL images
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    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[Any]				      =    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_      ,    SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.image_processor_tester.get_expected_values(_A,batched=_A    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    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 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							SCREAMING_SNAKE_CASE_  :		Any				      =    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_  :		Any				      =    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_  :		Optional[Any]				      =    image_processing(_A,return_tensors="pt"    ).pixel_values

							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    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 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    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_  :		Tuple				      =    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_  :		int				      =    image_processing(_A,return_tensors="pt"    ).pixel_values

							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		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 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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_  :		Any				      =    json.loads(f.read()    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    {"image_id": 3_9769, "annotations": target}

							# encode them
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    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, 800, 1066]    )
							self.assertEqual(encoding["pixel_values"].shape,_A    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    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_  :		int				      =    torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]    )
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"],_A    )    )
							# verify boxes
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    torch.Size([6, 4]    )
							self.assertEqual(encoding["labels"][0]["boxes"].shape,_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    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_  :		Union[str, Any]				      =    torch.tensor([3_9769]    )
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"],_A    )    )
							# verify is_crowd
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    torch.tensor([0, 0, 0, 0, 0, 0]    )
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"],_A    )    )
							# verify class_labels
							SCREAMING_SNAKE_CASE_  :		List[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_  :		Optional[Any]				      =    torch.tensor([480, 640]    )
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"],_A    )    )
							# verify size
							SCREAMING_SNAKE_CASE_  :		str				      =    torch.tensor([800, 1066]    )
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"],_A    )    )






					@slow
					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    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_  :		Optional[Any]				      =    {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"    )

							# encode them
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    DetaImageProcessor(format="coco_panoptic"    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    image_processing(images=_A,annotations=_A,masks_path=_A,return_tensors="pt"    )

							# verify pixel values
							SCREAMING_SNAKE_CASE_  :		Any				      =    torch.Size([1, 3, 800, 1066]    )
							self.assertEqual(encoding["pixel_values"].shape,_A    )

							SCREAMING_SNAKE_CASE_  :		Union[str, 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([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]    )
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"],_A    )    )
							# verify boxes
							SCREAMING_SNAKE_CASE_  :		Dict				      =    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_  :		Dict				      =    torch.tensor([3_9769]    )
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"],_A    )    )
							# verify is_crowd
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    torch.tensor([0, 0, 0, 0, 0, 0]    )
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"],_A    )    )
							# verify class_labels
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    torch.tensor([17, 17, 63, 75, 75, 93]    )
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"],_A    )    )
							# verify masks
							SCREAMING_SNAKE_CASE_  :		str				      =    82_2873
							self.assertEqual(encoding["labels"][0]["masks"].sum().item(),_A    )
							# verify orig_size
							SCREAMING_SNAKE_CASE_  :		str				      =    torch.tensor([480, 640]    )
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"],_A    )    )
							# verify size
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    torch.tensor([800, 1066]    )
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"],_A    )    )

from collections import defaultdict




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Any				      =    1
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    True
		for v in tree[start]:
				if v not in visited:
						ret += dfs(lowerCAmelCase	)
		if ret % 2 == 0:
				cuts.append(lowerCAmelCase	)
		return ret




def       _snake_case	(   ):
		"""simple docstring"""







		dfs(1	)


if __name__ == "__main__":
		__lowerCamelCase   ,						__lowerCamelCase					:	Union[str, Any]       =  10, 9
		__lowerCamelCase					:	Optional[int]       =  defaultdict(list)
		__lowerCamelCase					:	dict[int, bool]       =  {}
		__lowerCamelCase					:	list[int]       =  []
		__lowerCamelCase					:	Optional[Any]       =  0
		__lowerCamelCase					:	Any       =  [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
		for u, v in edges:
				tree[u].append(v)
				tree[v].append(u)
		even_tree()
		print(len(cuts) - 1)
