

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 DetrImageProcessor



class    _a						(unittest.TestCase    ):


					'''simple docstring'''






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






					def   __A      (			self      ):
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_pad": self.do_pad,
						}






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

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

						return expected_height, expected_width



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


					'''simple docstring'''






					UpperCAmelCase__:       List[str]  							=	DetrImageProcessor if is_vision_available() else None






					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		DetrImageProcessingTester(self      )






					@property
					def   __A      (			self      ):
						return self.image_processor_tester.prepare_image_processor_dict()






					def   __A      (			self      ):
						A__							:   List[str]					       =		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_rescale"""      )      )
						self.assertTrue(hasattr(A__   ,	"""rescale_factor"""      )      )
						self.assertTrue(hasattr(A__   ,	"""do_resize"""      )      )
						self.assertTrue(hasattr(A__   ,	"""size"""      )      )
						self.assertTrue(hasattr(A__   ,	"""do_pad"""      )      )






					def   __A      (			self      ):
						A__							:   List[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__      )

						A__							:   List[str]					       =		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   __A      (			self      ):
						pass






					def   __A      (			self      ):
						# Initialize image_processing
						A__							:   int					       =		self.image_processing_class(**self.image_processor_dict      )
						# create random PIL images
						A__							:   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
						A__							:   Dict					       =		image_processing(image_inputs[0]   ,	return_tensors="""pt"""      ).pixel_values

						A__       ,       A__							:   List[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
						A__       ,       A__							:   Union[str, Any]					       =		self.image_processor_tester.get_expected_values(A__   ,	batched=A__      )

						A__							:   int					       =		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      ):
						# Initialize image_processing
						A__							:   Union[str, Any]					       =		self.image_processing_class(**self.image_processor_dict      )
						# create random numpy tensors
						A__							:   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
						A__							:   str					       =		image_processing(image_inputs[0]   ,	return_tensors="""pt"""      ).pixel_values

						A__       ,       A__							:   Union[str, 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
						A__							:   Optional[Any]					       =		image_processing(A__   ,	return_tensors="""pt"""      ).pixel_values

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

						A__       ,       A__							:   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
						A__							:   int					       =		image_processing(A__   ,	return_tensors="""pt"""      ).pixel_values

						A__       ,       A__							:   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,
						    )   ,	)






					@slow
					def   __A      (			self      ):
						# prepare image and target
						A__							:   Dict					       =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
						with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""   ,	"""r"""      ) as f:
							A__							:   Optional[Any]					       =		json.loads(f.read()      )

						A__							:   Dict					       =		{"""image_id""": 3_9769, """annotations""": target}

						# encode them
						A__							:   List[str]					       =		DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""      )
						A__							:   Dict					       =		image_processing(images=A__   ,	annotations=A__   ,	return_tensors="""pt"""      )

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

						A__							:   Any					       =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]      )
						self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   ,	A__   ,	atol=1e-4      )      )

						# verify area
						A__							:   Dict					       =		torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]      )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]   ,	A__      )      )
						# verify boxes
						A__							:   List[Any]					       =		torch.Size([6, 4]      )
						self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape   ,	A__      )
						A__							:   str					       =		torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]      )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   ,	A__   ,	atol=1e-3      )      )
						# verify image_id
						A__							:   Any					       =		torch.tensor([3_9769]      )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]   ,	A__      )      )
						# verify is_crowd
						A__							:   Union[str, Any]					       =		torch.tensor([0, 0, 0, 0, 0, 0]      )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]   ,	A__      )      )
						# verify class_labels
						A__							:   Any					       =		torch.tensor([75, 75, 63, 65, 17, 17]      )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]   ,	A__      )      )
						# verify orig_size
						A__							:   str					       =		torch.tensor([480, 640]      )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   ,	A__      )      )
						# verify size
						A__							:   Union[str, Any]					       =		torch.tensor([800, 1066]      )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   ,	A__      )      )






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

						A__							:   Dict					       =		{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

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

						# encode them
						A__							:   Union[str, Any]					       =		DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""      )
						A__							:   Dict					       =		image_processing(images=A__   ,	annotations=A__   ,	masks_path=A__   ,	return_tensors="""pt"""      )

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

						A__							:   Optional[int]					       =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]      )
						self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   ,	A__   ,	atol=1e-4      )      )

						# verify area
						A__							:   Any					       =		torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]      )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]   ,	A__      )      )
						# verify boxes
						A__							:   Optional[Any]					       =		torch.Size([6, 4]      )
						self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape   ,	A__      )
						A__							:   Tuple					       =		torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]      )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   ,	A__   ,	atol=1e-3      )      )
						# verify image_id
						A__							:   List[str]					       =		torch.tensor([3_9769]      )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]   ,	A__      )      )
						# verify is_crowd
						A__							:   Union[str, Any]					       =		torch.tensor([0, 0, 0, 0, 0, 0]      )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]   ,	A__      )      )
						# verify class_labels
						A__							:   int					       =		torch.tensor([17, 17, 63, 75, 75, 93]      )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]   ,	A__      )      )
						# verify masks
						A__							:   Union[str, Any]					       =		82_2873
						self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()   ,	A__      )
						# verify orig_size
						A__							:   Dict					       =		torch.tensor([480, 640]      )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   ,	A__      )      )
						# verify size
						A__							:   Optional[int]					       =		torch.tensor([800, 1066]      )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   ,	A__      )      )






from __future__ import annotations

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			list[int]   ,				lowercase_:			int )					->				tuple[float, list[float]]:
	A__							:   Tuple					       =		list(range(len(lowercase_ ) ) )
	A__							:   Union[str, Any]					       =		[v / w for v, w in zip(lowercase_   ,				lowercase_ )]
	index.sort(key=lambda lowercase_ : ratio[i]   ,				reverse=lowercase_ )

	A__							:   float					       =		0
	A__							:   list[float]					       =		[0] * len(lowercase_ )
	for i in index:
		if weight[i] <= capacity:
			A__							:   Optional[int]					       =		1
			max_value += value[i]
			capacity -= weight[i]
		else:
			A__							:   Union[str, Any]					       =		capacity / weight[i]
			max_value += value[i] * capacity / weight[i]
			break

	return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()



