"""simple docstring"""







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   a__      (		UpperCAmelCase__       :			List[Any]		,			UpperCAmelCase__       :			Any=1_0		,			UpperCAmelCase__       :			int=1_0		,			UpperCAmelCase__       :			Dict=False		,			UpperCAmelCase__       :			Any=False			):
						UpperCAmelCase__						=		[]
						for i in range(image_processor_tester.num_frames			):
												video.append(np.random.randint(2_5_5		,			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
												UpperCAmelCase__						=		[Image.fromarray(np.moveaxis(UpperCAmelCase__		,			0		,			-1			)			) for frame in video]

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

						return video


def   a__      (		UpperCAmelCase__       :			List[str]		,			UpperCAmelCase__       :			Dict=False		,			UpperCAmelCase__       :			List[Any]=False		,			UpperCAmelCase__       :			List[Any]=False			):
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

						return video_inputs


class 			_lowercase		(   unittest.TestCase  ):

				def __init__(     self       :    Optional[int]       ,    _lowercase       :    Optional[int]       ,    _lowercase       :    List[Any]=7       ,    _lowercase       :    List[Any]=3       ,    _lowercase       :    List[str]=4       ,    _lowercase       :    int=1_8       ,    _lowercase       :    Optional[Any]=3_0       ,    _lowercase       :    List[str]=4_0_0       ,    _lowercase       :    Dict=True       ,    _lowercase       :    Optional[Any]=None       ,    _lowercase       :    Tuple=True       ,    _lowercase       :    Optional[int]=[0.5, 0.5, 0.5]       ,    _lowercase       :    Optional[int]=[0.5, 0.5, 0.5]       ,    _lowercase       :    Optional[int]=True       ,    _lowercase       :    Optional[int]=None       ,    ):


										'''simple docstring'''
										UpperCAmelCase__						=		size if size is not None else {"""shortest_edge""": 1_8}
										UpperCAmelCase__						=		crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}

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

				def        A (     self       :    List[str]     ):


										'''simple docstring'''
										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 			_lowercase		(   __lowerCamelCase      ,      unittest.TestCase  ):
				lowerCamelCase__						=       TvltImageProcessor if is_vision_available() else None

				def        A (     self       :    List[Any]     ):


										'''simple docstring'''
										UpperCAmelCase__						=		TvltImageProcessorTester(self     )

				@property
				def        A (     self       :    List[Any]     ):


										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()

				def        A (     self       :    List[Any]     ):


										'''simple docstring'''
										UpperCAmelCase__						=		self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(_lowercase       ,    """image_mean"""     )     )
										self.assertTrue(hasattr(_lowercase       ,    """image_std"""     )     )
										self.assertTrue(hasattr(_lowercase       ,    """do_normalize"""     )     )
										self.assertTrue(hasattr(_lowercase       ,    """do_resize"""     )     )
										self.assertTrue(hasattr(_lowercase       ,    """do_center_crop"""     )     )
										self.assertTrue(hasattr(_lowercase       ,    """size"""     )     )

				def        A (     self       :    Any     ):


										'''simple docstring'''
										UpperCAmelCase__						=		self.image_processing_class(**self.image_processor_dict     )
										# create random PIL videos
										UpperCAmelCase__						=		prepare_video_inputs(self.image_processor_tester       ,    equal_resolution=_lowercase     )
										for video in video_inputs:
																self.assertIsInstance(_lowercase       ,    _lowercase     )
																self.assertIsInstance(video[0]       ,    Image.Image     )

										# Test not batched input
										UpperCAmelCase__						=		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
										UpperCAmelCase__						=		image_processor(_lowercase       ,    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        A (     self       :    Tuple     ):


										'''simple docstring'''
										UpperCAmelCase__						=		self.image_processing_class(**self.image_processor_dict     )
										# create random numpy tensors
										UpperCAmelCase__						=		prepare_video_inputs(self.image_processor_tester       ,    equal_resolution=_lowercase       ,    numpify=_lowercase     )
										for video in video_inputs:
																self.assertIsInstance(_lowercase       ,    _lowercase     )
																self.assertIsInstance(video[0]       ,    np.ndarray     )

										# Test not batched input
										UpperCAmelCase__						=		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
										UpperCAmelCase__						=		image_processor(_lowercase       ,    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        A (     self       :    Union[str, Any]     ):


										'''simple docstring'''
										UpperCAmelCase__						=		self.image_processing_class(**self.image_processor_dict     )
										# create random PyTorch tensors
										UpperCAmelCase__						=		prepare_video_inputs(self.image_processor_tester       ,    equal_resolution=_lowercase       ,    torchify=_lowercase     )
										for video in video_inputs:
																self.assertIsInstance(_lowercase       ,    _lowercase     )
																self.assertIsInstance(video[0]       ,    torch.Tensor     )

										# Test not batched input
										UpperCAmelCase__						=		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
										UpperCAmelCase__						=		image_processor(_lowercase       ,    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"""],
										    )       ,    )


"""simple docstring"""







def   a__      (		UpperCAmelCase__       :			list[int]			)->	int:
						UpperCAmelCase__						=		len(UpperCAmelCase__			)
						if n in (0, 1):
												return 0
						return sum(query * (n - i - 1) for i, query in enumerate(sorted(UpperCAmelCase__			)			)			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

