"""simple docstring"""


import math







def        __a (		A   ):






     '''simple docstring'''



     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(A   ) + 1   )     ,					6   ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True







def        __a (		A = 60_08_51_47_51_43   ):






     '''simple docstring'''



     try:
          lowercase__								=				int(A   )
     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(A   ):
          return n
     while n % 2 == 0:
          n //= 2
     if is_prime(A   ):
          return n
     for i in range(3     ,					int(math.sqrt(A   )   ) + 1     ,					2   ):
          if n % i == 0:
               if is_prime(n // i   ):
                    lowercase__								=				n // i
                    break
               elif is_prime(A   ):
                    lowercase__								=				i
     return max_number


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 OwlViTImageProcessor






class  a__     ( unittest.TestCase						):


   def __init__(  self,					_UpperCAmelCase,					_UpperCAmelCase=7,					_UpperCAmelCase=3,					_UpperCAmelCase=18,					_UpperCAmelCase=30,					_UpperCAmelCase=400,					_UpperCAmelCase=True,					_UpperCAmelCase=None,					_UpperCAmelCase=True,					_UpperCAmelCase=None,					_UpperCAmelCase=True,					_UpperCAmelCase=[0.48_145_466, 0.4_578_275, 0.40_821_073],					_UpperCAmelCase=[0.26_862_954, 0.26_130_258, 0.27_577_711],					_UpperCAmelCase=True,					):
        '''simple docstring'''



        lowercase__								=				parent
        lowercase__								=				batch_size
        lowercase__								=				num_channels
        lowercase__								=				image_size
        lowercase__								=				min_resolution
        lowercase__								=				max_resolution
        lowercase__								=				do_resize
        lowercase__								=				size if size is not None else {"height": 18, "width": 18}
        lowercase__								=				do_center_crop
        lowercase__								=				crop_size if crop_size is not None else {"height": 18, "width": 18}
        lowercase__								=				do_normalize
        lowercase__								=				image_mean
        lowercase__								=				image_std
        lowercase__								=				do_convert_rgb


   def 						snake_case__						(  self	):
        '''simple docstring'''



        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
        }






@require_torch
@require_vision
class  a__     ( _a       ,       unittest.TestCase						):
   snake_case_				=   OwlViTImageProcessor if is_vision_available() else None


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				OwlViTImageProcessingTester(self	)


   @property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        return self.image_processor_tester.prepare_image_processor_dict()


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_resize"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"size"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_center_crop"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"center_crop"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_normalize"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"image_mean"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"image_std"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_convert_rgb"	)	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size,					{"height": 18, "width": 18}	)
        self.assertEqual(image_processor.crop_size,					{"height": 18, "width": 18}	)

        lowercase__								=				self.image_processing_class.from_dict(self.image_processor_dict,					size=42,					crop_size=84	)
        self.assertEqual(image_processor.size,					{"height": 42, "width": 42}	)
        self.assertEqual(image_processor.crop_size,					{"height": 84, "width": 84}	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        lowercase__								=				prepare_image_inputs(self.image_processor_tester,					equal_resolution=_UpperCAmelCase	)

        for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase,					Image.Image	)

        # Test not batched input
        lowercase__								=				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
        lowercase__								=				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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ),					)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        lowercase__								=				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
        lowercase__								=				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
        lowercase__								=				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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ),					)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        lowercase__								=				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
        lowercase__								=				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
        lowercase__								=				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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ),					)




