"""simple docstring"""

import unittest

from knapsack import greedy_knapsack as kp







class 	UpperCAmelCase_			(		unittest.TestCase):






							def  _UpperCamelCase     (						self					: Optional[Any]    )			->			List[Any]:
												_UpperCamelCase 		=      [10, 20, 30, 40, 50, 60]
												_UpperCamelCase 		=      [2, 4, 6, 8, 10, 12]
												_UpperCamelCase 		=      100
												self.assertEqual(kp.calc_profit(__A	,		__A	,		__A    )	,		210    )






							def  _UpperCamelCase     (						self					: str    )			->			List[str]:
												self.assertRaisesRegex(__A	,		'''max_weight must greater than zero.'''    )






							def  _UpperCamelCase     (						self					: List[str]    )			->			List[Any]:
												self.assertRaisesRegex(__A	,		'''Weight can not be negative.'''    )






							def  _UpperCamelCase     (						self					: Optional[Any]    )			->			Dict:
												self.assertRaisesRegex(__A	,		'''Profit can not be negative.'''    )






							def  _UpperCamelCase     (						self					: Union[str, Any]    )			->			Tuple:
												self.assertRaisesRegex(__A	,		'''max_weight must greater than zero.'''    )






							def  _UpperCamelCase     (						self					: Dict    )			->			Union[str, Any]:
												self.assertRaisesRegex(
												    __A	,		'''The length of profit and weight must be same.'''    )


if __name__ == "__main__":
		unittest.main()



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      lowerCamelCase_      (	unittest.TestCase	):




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




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

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

		return expected_height, expected_width






@require_torch
@require_vision
class      lowerCamelCase_      (	_lowercase						,      unittest.TestCase	):
	_lowercase		:   Tuple     					=				DetaImageProcessor if is_vision_available() else None




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Tuple		    =			DetaImageProcessingTester(self							)




	@property
	def        lowerCAmelCase_       (     self						:     List[str]							):
		return self.image_processor_tester.prepare_image_processor_dict()




	def        lowerCAmelCase_       (     self						:     List[str]							):
		__A :			Tuple		    =			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						:     Any							):
		__A :			Dict		    =			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        lowerCAmelCase_       (     self						:     Optional[Any]							):
		pass




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		# Initialize image_processing
		__A :			Tuple		    =			self.image_processing_class(**self.image_processor_dict							)
		# create random PIL images
		__A :			List[Any]		    =			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 :			str		    =			image_processing(image_inputs[0]					,      return_tensors="""pt"""							).pixel_values

		__A    ,				__A :			int		    =			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 :			List[Any]		    =			self.image_processor_tester.get_expected_values(__A					,      batched=__A							)

		__A :			List[str]		    =			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						:     Optional[int]							):
		# Initialize image_processing
		__A :			Tuple		    =			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 :			Optional[int]		    =			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 :			Optional[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,
		    )					,      )




	def        lowerCAmelCase_       (     self						:     Dict							):
		# Initialize image_processing
		__A :			int		    =			self.image_processing_class(**self.image_processor_dict							)
		# create random PyTorch tensors
		__A :			Dict		    =			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 :			Tuple		    =			image_processing(image_inputs[0]					,      return_tensors="""pt"""							).pixel_values

		__A    ,				__A :			Optional[int]		    =			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 :			Tuple		    =			image_processing(__A					,      return_tensors="""pt"""							).pixel_values

		__A    ,				__A :			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        lowerCAmelCase_       (     self						:     Tuple							):
		# prepare image and target
		__A :			Tuple		    =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
		with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""					,      """r"""							) as f:
			__A :			Any		    =			json.loads(f.read()							)

		__A :			int		    =			{"""image_id""": 3_9769, """annotations""": target}

		# encode them
		__A :			List[str]		    =			DetaImageProcessor()
		__A :			List[str]		    =			image_processing(images=__A					,      annotations=__A					,      return_tensors="""pt"""							)

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

		__A :			Union[str, 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 :			Tuple		    =			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 :			Tuple		    =			torch.Size([6, 4]							)
		self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,      __A							)
		__A :			List[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 :			List[str]		    =			torch.tensor([3_9769]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,      __A							)							)
		# verify is_crowd
		__A :			Dict		    =			torch.tensor([0, 0, 0, 0, 0, 0]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,      __A							)							)
		# verify class_labels
		__A :			List[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 :			Any		    =			torch.tensor([800, 1066]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,      __A							)							)




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

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

		__A :			Any		    =			pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

		# encode them
		__A :			Any		    =			DetaImageProcessor(format="""coco_panoptic"""							)
		__A :			int		    =			image_processing(images=__A					,      annotations=__A					,      masks_path=__A					,      return_tensors="""pt"""							)

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

		__A :			Optional[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 :			Union[str, 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 :			Union[str, Any]		    =			torch.Size([6, 4]							)
		self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,      __A							)
		__A :			Union[str, Any]		    =			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 :			Any		    =			torch.tensor([3_9769]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,      __A							)							)
		# verify is_crowd
		__A :			Any		    =			torch.tensor([0, 0, 0, 0, 0, 0]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,      __A							)							)
		# verify class_labels
		__A :			Optional[Any]		    =			torch.tensor([17, 17, 63, 75, 75, 93]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]					,      __A							)							)
		# verify masks
		__A :			List[str]		    =			82_2873
		self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()					,      __A							)
		# verify orig_size
		__A :			Tuple		    =			torch.tensor([480, 640]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,      __A							)							)
		# verify size
		__A :			Any		    =			torch.tensor([800, 1066]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,      __A							)							)





