


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		1
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		2
						while i * i <= n:
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		0
												while n % i == 0:
																		n //= i
																		multiplicity += 1
												n_divisors *= multiplicity + 1
												i += 1
						if n > 1:
												n_divisors *= 2
						return n_divisors


def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		1
						__SCREAMING_SNAKE_CASE						:     List[str]					=		1

						while True:
												i += 1
												t_num += i

												if count_divisors(lowercase__      ) > 500:
																		break

						return t_num


if __name__ == "__main__":
						print(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 ViTImageProcessor


class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     List[str],       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     Union[str, Any]=13,       _lowerCamelCase :     Any=3,       _lowerCamelCase :     Optional[int]=2_24,       _lowerCamelCase :     str=30,       _lowerCamelCase :     Dict=4_00,       _lowerCamelCase :     Union[str, Any]=True,       _lowerCamelCase :     Any=None,       _lowerCamelCase :     Optional[Any]=True,       _lowerCamelCase :     Any=[0.5, 0.5, 0.5],       _lowerCamelCase :     List[str]=[0.5, 0.5, 0.5],       ):

     '''simple docstring'''
     __A									= size if size is not None else {'''height''': 18, '''width''': 18}
     __A									= parent
     __A									= batch_size
     __A									= num_channels
     __A									= image_size
     __A									= min_resolution
     __A									= max_resolution
     __A									= do_resize
     __A									= size
     __A									= do_normalize
     __A									= image_mean
     __A									= image_std






   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     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		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	str						 = ViTImageProcessor if is_vision_available() else None


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= EfficientFormerImageProcessorTester(self  )


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     return self.image_proc_tester.prepare_image_processor_dict()


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_mean'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_std'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_normalize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_resize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''size'''  )  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     pass


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       Image.Image  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random numpy tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       numpify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       np.ndarray  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )






   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       torchify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       torch.Tensor  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )




