




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	(			__lowerCamelCase							,    __lowerCamelCase=10							,    __lowerCamelCase=10							,    __lowerCamelCase=False							,    __lowerCamelCase=False					)   -> Any:
	a		       =							[]
	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
		a		       =							[Image.fromarray(np.moveaxis(__lowerCamelCase							,    0							,    -1					)					) for frame in video]

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

	return video





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase=False							,    __lowerCamelCase=False							,    __lowerCamelCase=False					)   -> Dict:
	assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

	return video_inputs

class 	__lowerCAmelCase			(			unittest.TestCase				):

							'''simple docstring'''





							def __init__(		self		:str	,   __magic_name__		:Optional[Any]	,   __magic_name__		:Dict=7	,   __magic_name__		:Tuple=3	,   __magic_name__		:Optional[Any]=4	,   __magic_name__		:Tuple=18	,   __magic_name__		:List[Any]=30	,   __magic_name__		:Union[str, Any]=400	,   __magic_name__		:Tuple=True	,   __magic_name__		:str=None	,   __magic_name__		:Union[str, Any]=True	,   __magic_name__		:int=[0.5, 0.5, 0.5]	,   __magic_name__		:Dict=[0.5, 0.5, 0.5]	,   __magic_name__		:List[Any]=True	,   __magic_name__		:Any=None	,   ):







								'''simple docstring'''







								a		       =							size if size is not None else {"""shortest_edge""": 18}
								a		       =							crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

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





							def    lowerCamelCase__							(		self		:Any       ):







								'''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 	__lowerCAmelCase			(			lowerCAmelCase__   , unittest.TestCase				):

							'''simple docstring'''





							UpperCamelCase__        =						TvltImageProcessor if is_vision_available() else None
							def    lowerCamelCase__							(		self		:List[Any]       ):







								'''simple docstring'''







								a		       =							TvltImageProcessorTester(self       )
							@property
							def    lowerCamelCase__							(		self		:Tuple       ):







								'''simple docstring'''







								return self.image_processor_tester.prepare_image_processor_dict()
							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								a		       =							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_	,   """do_center_crop"""       )       )
								self.assertTrue(hasattr(lowerCAmelCase_	,   """size"""       )       )
							def    lowerCamelCase__							(		self		:Dict       ):







								'''simple docstring'''







								a		       =							self.image_processing_class(**self.image_processor_dict       )
								# create random PIL videos
								a		       =							prepare_video_inputs(self.image_processor_tester	,   equal_resolution=lowerCAmelCase_       )
								for video in video_inputs:
									self.assertIsInstance(lowerCAmelCase_	,   lowerCAmelCase_       )
									self.assertIsInstance(video[0]	,   Image.Image       )

								# Test not batched input
								a		       =							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
								a		       =							image_processor(lowerCAmelCase_	,   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    lowerCamelCase__							(		self		:Tuple       ):







								'''simple docstring'''







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

								# Test not batched input
								a		       =							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
								a		       =							image_processor(lowerCAmelCase_	,   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    lowerCamelCase__							(		self		:Dict       ):







								'''simple docstring'''







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

								# Test not batched input
								a		       =							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
								a		       =							image_processor(lowerCAmelCase_	,   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"""],
								    )	,   )







import math





def 	__A	(			__lowerCamelCase					)   -> bool:

	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5							,    int(math.sqrt(__lowerCamelCase					) + 1					)							,    6					):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True





def 	__A	(			__lowerCamelCase = 6008_5147_5143					)   -> int:

	try:
		a		       =							int(__lowerCamelCase					)
	except (TypeError, ValueError):
		raise TypeError("""Parameter n must be int or castable to int."""					)
	if n <= 0:
		raise ValueError("""Parameter n must be greater than or equal to one."""					)
	a		       =							0
	if is_prime(__lowerCamelCase					):
		return n
	while n % 2 == 0:
		n //= 2
	if is_prime(__lowerCamelCase					):
		return n
	for i in range(3							,    int(math.sqrt(__lowerCamelCase					)					) + 1							,    2					):
		if n % i == 0:
			if is_prime(n // i					):
				a		       =							n // i
				break
			elif is_prime(__lowerCamelCase					):
				a		       =							i
	return max_number


if __name__ == "__main__":
		print(F'{solution() = }')

