






"""simple docstring"""






def  __lowerCamelCase					(					lowerCAmelCase__ = 6008_5147_5143				):

				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__							 =       2
				A__							 =       0
				if n == 2:
								return 2
				while n > 2:
								while n % i != 0:
												i += 1
								A__							 =       i
								while n % i == 0:
												A__							 =       n // i
								i += 1
				return int(lowerCAmelCase__				)


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










"""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 PoolFormerImageProcessor





class       snake_case_       (    unittest.TestCase       ):



	"""simple docstring"""

	def __init__(      self  ,					__a  ,					__a=7  ,					__a=3  ,					__a=30  ,					__a=400  ,					__a=True  ,					__a=None  ,					__a=0.9  ,					__a=None  ,					__a=True  ,					__a=[0.5, 0.5, 0.5]  ,					__a=[0.5, 0.5, 0.5]  ,					):


					"""simple docstring"""

					A__							 =       size if size is not None else {'shortest_edge': 30}
					A__							 =       crop_size if crop_size is not None else {'height': 30, 'width': 30}
					A__							 =       parent
					A__							 =       batch_size
					A__							 =       num_channels
					A__							 =       min_resolution
					A__							 =       max_resolution
					A__							 =       do_resize_and_center_crop
					A__							 =       size
					A__							 =       crop_pct
					A__							 =       crop_size
					A__							 =       do_normalize
					A__							 =       image_mean
					A__							 =       image_std






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return {
					    "size": self.size,
					    "do_resize_and_center_crop": self.do_resize_and_center_crop,
					    "crop_pct": self.crop_pct,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					}





@require_torch
@require_vision
class       snake_case_       (    _lowerCamelCase    ,							unittest.TestCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							List[Any]   			=					PoolFormerImageProcessor if is_vision_available() else None


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       PoolFormerImageProcessingTester(self						)


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return self.image_processor_tester.prepare_image_processor_dict()


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.image_processing_class(**self.image_processor_dict						)
					self.assertTrue(hasattr(__a  ,					'do_resize_and_center_crop'						)						)
					self.assertTrue(hasattr(__a  ,					'size'						)						)
					self.assertTrue(hasattr(__a  ,					'crop_pct'						)						)
					self.assertTrue(hasattr(__a  ,					'do_normalize'						)						)
					self.assertTrue(hasattr(__a  ,					'image_mean'						)						)
					self.assertTrue(hasattr(__a  ,					'image_std'						)						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.image_processing_class.from_dict(self.image_processor_dict						)
					self.assertEqual(image_processor.size  ,					{'shortest_edge': 30}						)
					self.assertEqual(image_processor.crop_size  ,					{'height': 30, 'width': 30}						)

					A__							 =       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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					pass


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.image_processing_class(**self.image_processor_dict						)
					# create random PIL images
					A__							 =       prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=__a						)
					for image in image_inputs:
									self.assertIsInstance(__a  ,					Image.Image						)

					# Test not batched input
					A__							 =       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
					A__							 =       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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.image_processing_class(**self.image_processor_dict						)
					# create random numpy tensors
					A__							 =       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
					A__							 =       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
					A__							 =       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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.image_processing_class(**self.image_processor_dict						)
					# create random PyTorch tensors
					A__							 =       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
					A__							 =       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
					A__							 =       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'],
					    )  ,					)


