'''simple docstring'''

import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 BridgeTowerImageProcessor
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A = True		,						__A = None		,						__A = 32		,						__A = True		,						__A = 1 / 255		,						__A = True		,						__A = True		,						__A = [0.48_145_466, 0.4_578_275, 0.40_821_073]		,						__A = [0.26_862_954, 0.26_130_258, 0.27_577_711]		,						__A = True		,						__A=7		,						__A=30		,						__A=400		,						__A=3		,						) ->     Optional[int]:
								_lowerCAmelCase										=parent
								_lowerCAmelCase										=do_resize
								_lowerCAmelCase										=size if size is not None else {"shortest_edge": 288}
								_lowerCAmelCase										=size_divisor
								_lowerCAmelCase										=do_rescale
								_lowerCAmelCase										=rescale_factor
								_lowerCAmelCase										=do_normalize
								_lowerCAmelCase										=do_center_crop
								_lowerCAmelCase										=image_mean
								_lowerCAmelCase										=image_std
								_lowerCAmelCase										=do_pad
								_lowerCAmelCase										=batch_size
								_lowerCAmelCase										=num_channels
								_lowerCAmelCase										=min_resolution
								_lowerCAmelCase										=max_resolution






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "size_divisor": self.size_divisor,
								}






		def 		UpperCamelCase__       (   self		,						__A		,						__A=False				) ->     Union[str, Any]:
								if not batched:
														_lowerCAmelCase										=self.size["shortest_edge"]
														_lowerCAmelCase										=image_inputs[0]
														if isinstance(__lowerCamelCase		,						Image.Image				):
																				_lowerCAmelCase										=image.size
														else:
																				_lowerCAmelCase										=image.shape[1], image.shape[2]
														_lowerCAmelCase										=size / min(__lowerCamelCase		,						__lowerCamelCase				)
														if h < w:
																				_lowerCAmelCase										=size, scale * w
														else:
																				_lowerCAmelCase										=scale * h, size

														_lowerCAmelCase										=int((1333 / 800) * size				)
														if max(__lowerCamelCase		,						__lowerCamelCase				) > max_size:
																				_lowerCAmelCase										=max_size / max(__lowerCamelCase		,						__lowerCamelCase				)
																				_lowerCAmelCase										=newh * scale
																				_lowerCAmelCase										=neww * scale

														_lowerCAmelCase										=int(newh + 0.5				), int(neww + 0.5				)
														_lowerCAmelCase										=(
														    newh // self.size_divisor * self.size_divisor,
														    neww // self.size_divisor * self.size_divisor,
														)

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

								return expected_height, expected_width

@require_torch
@require_vision
class   SCREAMING_SNAKE_CASE			(						_A    ,			unittest.TestCase):







		"""simple docstring"""
		lowercase   :      str       =							BridgeTowerImageProcessor if is_vision_available() else None






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=BridgeTowerImageProcessingTester(self				)






		@property
		def 		UpperCamelCase__       (   self				) ->     List[str]:
								return self.image_processor_tester.prepare_image_processor_dict()






		def 		UpperCamelCase__       (   self				) ->     str:
								_lowerCAmelCase										=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'				)				)
								self.assertTrue(hasattr(__lowerCamelCase		,						'size_divisor'				)				)






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								pass






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								# Initialize image processor
								_lowerCAmelCase										=self.image_processing_class(**self.image_processor_dict				)
								# create random PIL images
								_lowerCAmelCase										=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										=image_processing(image_inputs[0]		,						return_tensors='pt'				).pixel_values

								_lowerCAmelCase										=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										=image_processing(__lowerCamelCase		,						return_tensors='pt'				).pixel_values

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

								_lowerCAmelCase										=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										=image_processing(__lowerCamelCase		,						return_tensors='pt'				).pixel_values

								_lowerCAmelCase										=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 		UpperCamelCase__       (   self				) ->     str:
								# Initialize image processor
								_lowerCAmelCase										=self.image_processing_class(**self.image_processor_dict				)
								# create random PyTorch tensors
								_lowerCAmelCase										=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										=image_processing(image_inputs[0]		,						return_tensors='pt'				).pixel_values

								_lowerCAmelCase										=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										=image_processing(__lowerCamelCase		,						return_tensors='pt'				).pixel_values

								_lowerCAmelCase										=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,
								    )		,						)






'''simple docstring'''

import unittest

from knapsack import knapsack as k
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								_lowerCAmelCase										=0
								_lowerCAmelCase										=[0]
								_lowerCAmelCase										=[0]
								_lowerCAmelCase										=len(__A				)
								self.assertEqual(k.knapsack(__A		,						__A		,						__A		,						__A				)		,						0				)

								_lowerCAmelCase										=[60]
								_lowerCAmelCase										=[10]
								_lowerCAmelCase										=len(__A				)
								self.assertEqual(k.knapsack(__A		,						__A		,						__A		,						__A				)		,						0				)






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=3
								_lowerCAmelCase										=[1, 2, 3]
								_lowerCAmelCase										=[3, 2, 1]
								_lowerCAmelCase										=len(__A				)
								self.assertEqual(k.knapsack(__A		,						__A		,						__A		,						__A				)		,						5				)






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_lowerCAmelCase										=50
								_lowerCAmelCase										=[60, 100, 120]
								_lowerCAmelCase										=[10, 20, 30]
								_lowerCAmelCase										=len(__A				)
								self.assertEqual(k.knapsack(__A		,						__A		,						__A		,						__A				)		,						220				)


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





