
'''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 MobileNetVaImageProcessor






class       A   (		unittest.TestCase			):





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


				"""simple docstring"""

				lowerCAmelCase__									=							size if size is not None else {"shortest_edge": 20}
				lowerCAmelCase__									=							crop_size if crop_size is not None else {"height": 18, "width": 18}
				lowerCAmelCase__									=							parent
				lowerCAmelCase__									=							batch_size
				lowerCAmelCase__									=							num_channels
				lowerCAmelCase__									=							image_size
				lowerCAmelCase__									=							min_resolution
				lowerCAmelCase__									=							max_resolution
				lowerCAmelCase__									=							do_resize
				lowerCAmelCase__									=							size
				lowerCAmelCase__									=							do_center_crop
				lowerCAmelCase__									=							crop_size







	def 							__SCREAMING_SNAKE_CASE     (    self	:				int							):


				"""simple docstring"""

				return {
				    "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       A   (		lowerCAmelCase__     ,					unittest.TestCase			):
	snake_case__					:Optional[int]         =   MobileNetVaImageProcessor if is_vision_available() else None





	def 							__SCREAMING_SNAKE_CASE     (    self	:				Dict							):


				"""simple docstring"""

				lowerCAmelCase__									=							MobileNetVaImageProcessingTester(self							)





	@property
	def 							__SCREAMING_SNAKE_CASE     (    self	:				Optional[Any]							):


				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()





	def 							__SCREAMING_SNAKE_CASE     (    self	:				Any							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.image_processing_class(**self.image_processor_dict							)
				self.assertTrue(hasattr(_lowerCamelCase					,							"do_resize"							)							)
				self.assertTrue(hasattr(_lowerCamelCase					,							"size"							)							)
				self.assertTrue(hasattr(_lowerCamelCase					,							"do_center_crop"							)							)
				self.assertTrue(hasattr(_lowerCamelCase					,							"center_crop"							)							)





	def 							__SCREAMING_SNAKE_CASE     (    self	:				List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							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}							)

				lowerCAmelCase__									=							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 							__SCREAMING_SNAKE_CASE     (    self	:				Tuple							):


				"""simple docstring"""

				pass





	def 							__SCREAMING_SNAKE_CASE     (    self	:				int							):


				"""simple docstring"""

				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
				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
				lowerCAmelCase__									=							image_processing(_lowerCamelCase					,							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 							__SCREAMING_SNAKE_CASE     (    self	:				Dict							):


				"""simple docstring"""

				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
				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
				lowerCAmelCase__									=							image_processing(_lowerCamelCase					,							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 							__SCREAMING_SNAKE_CASE     (    self	:				Union[str, Any]							):


				"""simple docstring"""

				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
				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
				lowerCAmelCase__									=							image_processing(_lowerCamelCase					,							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  ( lowerCamelCase_	:     int ):

			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       _lowerCAmelCase  ( lowerCamelCase_	:     int = 6_0_0_8_5_1_4_7_5_1_4_3 ):

			try:
						__lowercase   =							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.''' )
			__lowercase   =							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 ):
												__lowercase   =							n // i
												break
									elif is_prime(lowerCamelCase_ ):
												__lowercase   =							i
			return max_number


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





