"""simple docstring"""





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor
class  _snake_case     (					unittest.TestCase		):






      def __init__(       self      :			Union[str, Any]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int=7							,					UpperCAmelCase      :			Dict=3							,					UpperCAmelCase      :			int=18							,					UpperCAmelCase      :			Tuple=30							,					UpperCAmelCase      :			str=400							,					UpperCAmelCase      :			Dict=True							,					UpperCAmelCase      :			int=None							,					UpperCAmelCase      :			Optional[int]=True							,					UpperCAmelCase      :			List[Any]=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			List[Any]=[0.5, 0.5, 0.5]							,					):
             __lowerCamelCase					:		Optional[Any]		   =     size if size is not None else {"height": 18, "width": 18}
             __lowerCamelCase					:		Any		   =     parent
             __lowerCamelCase					:		str		   =     batch_size
             __lowerCamelCase					:		Dict		   =     num_channels
             __lowerCamelCase					:		Any		   =     image_size
             __lowerCamelCase					:		List[str]		   =     min_resolution
             __lowerCamelCase					:		int		   =     max_resolution
             __lowerCamelCase					:		Optional[int]		   =     do_resize
             __lowerCamelCase					:		int		   =     size
             __lowerCamelCase					:		Any		   =     do_normalize
             __lowerCamelCase					:		List[Any]		   =     image_mean
             __lowerCamelCase					:		Any		   =     image_std







      def       lowerCamelCase__     (       self      :			Tuple    ):
             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "size": self.size,
             }

@require_torch
@require_vision
class  _snake_case     (					a__				, unittest.TestCase		):
      snake_case__			  =			DPTImageProcessor if is_vision_available() else None






      def       lowerCamelCase__     (       self      :			Optional[int]    ):
             __lowerCamelCase					:		Optional[Any]		   =     DPTImageProcessingTester(self    )






      @property
      def       lowerCamelCase__     (       self      :			Optional[int]    ):
             return self.image_processor_tester.prepare_image_processor_dict()






      def       lowerCamelCase__     (       self      :			Optional[Any]    ):
             __lowerCamelCase					:		int		   =     self.image_processing_class(**self.image_processor_dict    )
             self.assertTrue(hasattr(UpperCAmelCase							,					"image_mean"    )    )
             self.assertTrue(hasattr(UpperCAmelCase							,					"image_std"    )    )
             self.assertTrue(hasattr(UpperCAmelCase							,					"do_normalize"    )    )
             self.assertTrue(hasattr(UpperCAmelCase							,					"do_resize"    )    )
             self.assertTrue(hasattr(UpperCAmelCase							,					"size"    )    )






      def       lowerCamelCase__     (       self      :			str    ):
             __lowerCamelCase					:		Any		   =     self.image_processing_class.from_dict(self.image_processor_dict    )
             self.assertEqual(image_processor.size							,					{"height": 18, "width": 18}    )

             __lowerCamelCase					:		int		   =     self.image_processing_class.from_dict(self.image_processor_dict							,					size=42    )
             self.assertEqual(image_processor.size							,					{"height": 42, "width": 42}    )






      def       lowerCamelCase__     (       self      :			str    ):
             # Initialize image_processing
             __lowerCamelCase					:		Tuple		   =     self.image_processing_class(**self.image_processor_dict    )
             # create random PIL images
             __lowerCamelCase					:		Any		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase    )
             for image in image_inputs:
                    self.assertIsInstance(UpperCAmelCase							,					Image.Image    )

             # Test not batched input
             __lowerCamelCase					:		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.size["height"],
                     self.image_processor_tester.size["width"],
                 )							,					)

             # Test batched
             __lowerCamelCase					:		Tuple		   =     image_processing(UpperCAmelCase							,					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.size["height"],
                     self.image_processor_tester.size["width"],
                 )							,					)






      def       lowerCamelCase__     (       self      :			List[Any]    ):
             # Initialize image_processing
             __lowerCamelCase					:		int		   =     self.image_processing_class(**self.image_processor_dict    )
             # create random numpy tensors
             __lowerCamelCase					:		Union[str, Any]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					numpify=UpperCAmelCase    )
             for image in image_inputs:
                    self.assertIsInstance(UpperCAmelCase							,					np.ndarray    )

             # Test not batched input
             __lowerCamelCase					:		List[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.size["height"],
                     self.image_processor_tester.size["width"],
                 )							,					)

             # Test batched
             __lowerCamelCase					:		Any		   =     image_processing(UpperCAmelCase							,					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.size["height"],
                     self.image_processor_tester.size["width"],
                 )							,					)







      def       lowerCamelCase__     (       self      :			int    ):
             # Initialize image_processing
             __lowerCamelCase					:		Union[str, Any]		   =     self.image_processing_class(**self.image_processor_dict    )
             # create random PyTorch tensors
             __lowerCamelCase					:		Union[str, Any]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					torchify=UpperCAmelCase    )
             for image in image_inputs:
                    self.assertIsInstance(UpperCAmelCase							,					torch.Tensor    )

             # Test not batched input
             __lowerCamelCase					:		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.size["height"],
                     self.image_processor_tester.size["width"],
                 )							,					)

             # Test batched
             __lowerCamelCase					:		Any		   =     image_processing(UpperCAmelCase							,					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.size["height"],
                     self.image_processor_tester.size["width"],
                 )							,					)
"""simple docstring"""





def    lowercase_   (   _lowerCamelCase:	int = 600851475143      ) ->      int:



							'''simple docstring'''

							try:
														__lowerCamelCase					:		Optional[Any]		   =     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."      )
							__lowerCamelCase					:		Union[str, Any]		   =     2
							__lowerCamelCase					:		int		   =     0
							if n == 2:
														return 2
							while n > 2:
														while n % i != 0:
																					i += 1
														__lowerCamelCase					:		Dict		   =     i
														while n % i == 0:
																					__lowerCamelCase					:		Union[str, Any]		   =     n // i
														i += 1
							return int(_lowerCamelCase      )


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