

import unittest

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


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import TvltImageProcessor




def        __UpperCamelCase     (      _A					:							Union[str, Any]      ,  _A					:							Optional[Any]=10      ,  _A					:							List[str]=10      ,  _A					:							str=False      ,  _A					:							Dict=False		)				->Optional[int]:



							"""simple docstring"""
							lowerCamelCase_								=[]
							for i in range(image_processor_tester.num_frames		):
														video.append(np.random.randint(255      ,  size=(image_processor_tester.num_channels, width, height)      ,  dtype=np.uinta		)		)

							if not numpify and not torchify:
														# PIL expects the channel dimension as last dimension
														lowerCamelCase_								=[Image.fromarray(np.moveaxis(_A      ,  0      ,  -1		)		) for frame in video]

							if torchify:
														lowerCamelCase_								=[torch.from_numpy(_A		) for frame in video]

							return video




def        __UpperCamelCase     (      _A					:							Union[str, Any]      ,  _A					:							List[Any]=False      ,  _A					:							str=False      ,  _A					:							Dict=False		)				->int:



							"""simple docstring"""
							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

							lowerCamelCase_								=[]
							for i in range(image_processor_tester.batch_size		):
														if equal_resolution:
																					lowerCamelCase_								=lowerCamelCase_								=image_processor_tester.max_resolution
														else:
																					lowerCamelCase_	,   lowerCamelCase_								=np.random.choice(
																					    np.arange(image_processor_tester.min_resolution      ,  image_processor_tester.max_resolution		)      ,  2		)
																					lowerCamelCase_								=prepare_video(
																					    image_processor_tester=_A      ,  width=_A      ,  height=_A      ,  numpify=_A      ,  torchify=_A      ,  )
														video_inputs.append(_A		)

							return video_inputs


class 			_SCREAMING_SNAKE_CASE      (  unittest.TestCase):







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE=7     ,						_SCREAMING_SNAKE_CASE=3     ,						_SCREAMING_SNAKE_CASE=4     ,						_SCREAMING_SNAKE_CASE=18     ,						_SCREAMING_SNAKE_CASE=30     ,						_SCREAMING_SNAKE_CASE=400     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]     ,						_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=None     ,						)->							List[Any]:
													lowerCamelCase_								=size if size is not None else {"""shortest_edge""": 18}
													lowerCamelCase_								=crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

													lowerCamelCase_								=parent
													lowerCamelCase_								=batch_size
													lowerCamelCase_								=num_channels
													lowerCamelCase_								=num_frames
													lowerCamelCase_								=image_size
													lowerCamelCase_								=min_resolution
													lowerCamelCase_								=max_resolution
													lowerCamelCase_								=do_resize
													lowerCamelCase_								=size
													lowerCamelCase_								=do_normalize
													lowerCamelCase_								=image_mean
													lowerCamelCase_								=image_std
													lowerCamelCase_								=do_center_crop
													lowerCamelCase_								=crop_size







						def        _snake_case				(			self							)->							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,
													    "do_center_crop": self.do_center_crop,
													    "crop_size": self.crop_size,
													}


@require_torch
@require_vision
class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__ ,    unittest.TestCase):
						_UpperCamelCase:Dict	=		TvltImageProcessor if is_vision_available() else None







						def        _snake_case				(			self							)->							Tuple:
													lowerCamelCase_								=TvltImageProcessorTester(self							)







						@property
						def        _snake_case				(			self							)->							str:
													return self.image_processor_tester.prepare_image_processor_dict()







						def        _snake_case				(			self							)->							Optional[Any]:
													lowerCamelCase_								=self.image_processing_class(**self.image_processor_dict							)
													self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,						"""image_mean"""							)							)
													self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,						"""image_std"""							)							)
													self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,						"""do_normalize"""							)							)
													self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,						"""do_resize"""							)							)
													self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,						"""do_center_crop"""							)							)
													self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,						"""size"""							)							)







						def        _snake_case				(			self							)->							int:
													# Initialize image_processor
													lowerCamelCase_								=self.image_processing_class(**self.image_processor_dict							)
													# create random PIL videos
													lowerCamelCase_								=prepare_video_inputs(self.image_processor_tester     ,						equal_resolution=_SCREAMING_SNAKE_CASE							)
													for video in video_inputs:
																				self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)
																				self.assertIsInstance(video[0]     ,						Image.Image							)

													# Test not batched input
													lowerCamelCase_								=image_processor(video_inputs[0]     ,						return_tensors="""pt"""							).pixel_values
													self.assertEqual(
													    encoded_videos.shape     ,						(
													        1,
													        self.image_processor_tester.num_frames,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )     ,						)

													# Test batched
													lowerCamelCase_								=image_processor(_SCREAMING_SNAKE_CASE     ,						return_tensors="""pt"""							).pixel_values
													self.assertEqual(
													    encoded_videos.shape     ,						(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_frames,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )     ,						)







						def        _snake_case				(			self							)->							Optional[Any]:
													# Initialize image_processor
													lowerCamelCase_								=self.image_processing_class(**self.image_processor_dict							)
													# create random numpy tensors
													lowerCamelCase_								=prepare_video_inputs(self.image_processor_tester     ,						equal_resolution=_SCREAMING_SNAKE_CASE     ,						numpify=_SCREAMING_SNAKE_CASE							)
													for video in video_inputs:
																				self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)
																				self.assertIsInstance(video[0]     ,						np.ndarray							)

													# Test not batched input
													lowerCamelCase_								=image_processor(video_inputs[0]     ,						return_tensors="""pt"""							).pixel_values
													self.assertEqual(
													    encoded_videos.shape     ,						(
													        1,
													        self.image_processor_tester.num_frames,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )     ,						)

													# Test batched
													lowerCamelCase_								=image_processor(_SCREAMING_SNAKE_CASE     ,						return_tensors="""pt"""							).pixel_values
													self.assertEqual(
													    encoded_videos.shape     ,						(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_frames,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )     ,						)







						def        _snake_case				(			self							)->							Tuple:
													# Initialize image_processor
													lowerCamelCase_								=self.image_processing_class(**self.image_processor_dict							)
													# create random PyTorch tensors
													lowerCamelCase_								=prepare_video_inputs(self.image_processor_tester     ,						equal_resolution=_SCREAMING_SNAKE_CASE     ,						torchify=_SCREAMING_SNAKE_CASE							)
													for video in video_inputs:
																				self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)
																				self.assertIsInstance(video[0]     ,						torch.Tensor							)

													# Test not batched input
													lowerCamelCase_								=image_processor(video_inputs[0]     ,						return_tensors="""pt"""							).pixel_values
													self.assertEqual(
													    encoded_videos.shape     ,						(
													        1,
													        self.image_processor_tester.num_frames,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )     ,						)

													# Test batched
													lowerCamelCase_								=image_processor(_SCREAMING_SNAKE_CASE     ,						return_tensors="""pt"""							).pixel_values
													self.assertEqual(
													    encoded_videos.shape     ,						(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_frames,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )     ,						)



from __future__ import annotations




def        __UpperCamelCase     (      _A					:							int      ,  _A					:							list[int]      ,  _A					:							list[int]      ,  _A					:							int		)				->int:



							"""simple docstring"""

							# Base Case
							if counter == 0 or capacity == 0:
														return 0

							# If weight of the nth item is more than Knapsack of capacity,
							#   then this item cannot be included in the optimal solution,
							# else return the maximum of two cases:
							#   (1) nth item included
							#   (2) not included
							if weights[counter - 1] > capacity:
														return knapsack(_A      ,  _A      ,  _A      ,  counter - 1		)
							else:
														lowerCamelCase_								=capacity - weights[counter - 1]
														lowerCamelCase_								=values[counter - 1] + knapsack(
														    _A      ,  _A      ,  _A      ,  counter - 1		)
														lowerCamelCase_								=knapsack(_A      ,  _A      ,  _A      ,  counter - 1		)
														return max(_A      ,  _A		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
