



'''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, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import MobileViTImageProcessor

class     UpperCamelCase_				(							unittest.TestCase    ):





	def __init__(						self	,	A	,	A=7	,	A=3	,	A=18	,	A=30	,	A=400	,	A=True	,	A=None	,	A=True	,	A=None	,	A=True	,	)    ->    Tuple:
								UpperCAmelCase  :	Union[str, Any]							  =	size if size is not None else {"""shortest_edge""": 20}
								UpperCAmelCase  :	Tuple							  =	crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
								UpperCAmelCase  :	Dict							  =	parent
								UpperCAmelCase  :	List[str]							  =	batch_size
								UpperCAmelCase  :	Optional[int]							  =	num_channels
								UpperCAmelCase  :	Optional[int]							  =	image_size
								UpperCAmelCase  :	Any							  =	min_resolution
								UpperCAmelCase  :	Optional[int]							  =	max_resolution
								UpperCAmelCase  :	str							  =	do_resize
								UpperCAmelCase  :	str							  =	size
								UpperCAmelCase  :	int							  =	do_center_crop
								UpperCAmelCase  :	int							  =	crop_size
								UpperCAmelCase  :	List[str]							  =	do_flip_channel_order






	def 				_lowercase(						self					)    ->    Union[str, Any]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_flip_channel_order": self.do_flip_channel_order,
								}




@require_torch
@require_vision
class     UpperCamelCase_				(							__magic_name__   ,  unittest.TestCase    ):
	lowercase    =				MobileViTImageProcessor if is_vision_available() else None





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Union[str, Any]							  =	MobileViTImageProcessingTester(self					)





	@property
	def 				_lowercase(						self					)    ->    Any:
								return self.image_processor_tester.prepare_image_processor_dict()





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Union[str, Any]							  =	self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(A	,	"""do_resize"""					)					)
								self.assertTrue(hasattr(A	,	"""size"""					)					)
								self.assertTrue(hasattr(A	,	"""do_center_crop"""					)					)
								self.assertTrue(hasattr(A	,	"""center_crop"""					)					)
								self.assertTrue(hasattr(A	,	"""do_flip_channel_order"""					)					)





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Optional[Any]							  =	self.image_processing_class.from_dict(self.image_processor_dict					)
								self.assertEqual(image_processor.size	,	{"""shortest_edge""": 20}					)
								self.assertEqual(image_processor.crop_size	,	{"""height""": 18, """width""": 18}					)

								UpperCAmelCase  :	Union[str, Any]							  =	self.image_processing_class.from_dict(self.image_processor_dict	,	size=42	,	crop_size=84					)
								self.assertEqual(image_processor.size	,	{"""shortest_edge""": 42}					)
								self.assertEqual(image_processor.crop_size	,	{"""height""": 84, """width""": 84}					)





	def 				_lowercase(						self					)    ->    Optional[Any]:
								pass





	def 				_lowercase(						self					)    ->    str:
								# Initialize image_processing
								UpperCAmelCase  :	List[str]							  =	self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								UpperCAmelCase  :	str							  =	prepare_image_inputs(self.image_processor_tester	,	equal_resolution=A					)
								for image in image_inputs:
															self.assertIsInstance(A	,	Image.Image					)

								# Test not batched input
								UpperCAmelCase  :	Any							  =	image_processing(image_inputs[0]	,	return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape	,	(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )	,	)

								# Test batched
								UpperCAmelCase  :	int							  =	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,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )	,	)





	def 				_lowercase(						self					)    ->    int:
								# Initialize image_processing
								UpperCAmelCase  :	Any							  =	self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								UpperCAmelCase  :	Dict							  =	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
								UpperCAmelCase  :	int							  =	image_processing(image_inputs[0]	,	return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape	,	(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )	,	)

								# Test batched
								UpperCAmelCase  :	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,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )	,	)






	def 				_lowercase(						self					)    ->    List[Any]:
								# Initialize image_processing
								UpperCAmelCase  :	List[str]							  =	self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								UpperCAmelCase  :	int							  =	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
								UpperCAmelCase  :	str							  =	image_processing(image_inputs[0]	,	return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape	,	(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )	,	)

								# Test batched
								UpperCAmelCase  :	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,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )	,	)






'''simple docstring'''




import math



def    __lowerCamelCase						(      _lowercase	)   ->    bool:
							assert isinstance(_lowercase			,       _lowercase	) and (
							    number >= 0
							), "'number' must been an int and positive"

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

							UpperCAmelCase  :	str							  =	range(3			,       int(math.sqrt(_lowercase	) + 1	)			,       2	)
							return not any(not number % i for i in odd_numbers	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase=1			,       **_lowercase	)   ->    Union[str, Any]:
							UpperCAmelCase  :	Optional[int]							  =	factor * value
							UpperCAmelCase  :	List[Any]							  =	value

							while not is_prime(_lowercase	):
														value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

							if value == first_value_val:
														return next_prime(value + 1			,       **_lowercase	)
							return value

