


import math




def      a_				(   SCREAMING_SNAKE_CASE__ :				Union[str, Any]					):
       '''simple docstring'''





       _lowerCamelCase	:		Optional[Any]          =0
       if n % 2 == 0:
              count += 1
              while n % 2 == 0:
                     _lowerCamelCase	:		str          =int(n / 2					)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

       _lowerCamelCase	:		Optional[int]          =3

       while i <= int(math.sqrt(SCREAMING_SNAKE_CASE__					)					):
              if n % i == 0:
                     count += 1
                     while n % i == 0:
                            _lowerCamelCase	:		str          =int(n / i					)
              _lowerCamelCase	:		int          =i + 2

       # this condition checks the prime
       # number n is greater than 2

       if n > 2:
              count += 1
       return count


if __name__ == "__main__":
 lowerCamelCase						 =	51_24_21_83
 print(F"""The number of distinct prime factors is/are {exact_prime_factor_count(n)}""")
 print(F"""The value of log(log(n)) is {math.log(math.log(n)):.4f}""")






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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import CLIPImageProcessor




class A  (       unittest.TestCase					):






      def __init__( self						:					Union[str, Any]      ,       lowercase_						:					Any      ,       lowercase_						:					Optional[Any]=7      ,       lowercase_						:					Union[str, Any]=3      ,       lowercase_						:					Any=18      ,       lowercase_						:					Optional[int]=30      ,       lowercase_						:					int=400      ,       lowercase_						:					List[Any]=True      ,       lowercase_						:					List[str]=None      ,       lowercase_						:					Optional[Any]=True      ,       lowercase_						:					Tuple=None      ,       lowercase_						:					str=True      ,       lowercase_						:					Any=[0.48145466, 0.4578275, 0.40821073]      ,       lowercase_						:					Optional[int]=[0.26862954, 0.26130258, 0.27577711]      ,       lowercase_						:					Dict=True      ,       ) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		List[str]          =size if size is not None else {'shortest_edge': 20}
             _lowerCamelCase	:		Optional[Any]          =crop_size if crop_size is not None else {'height': 18, 'width': 18}
             _lowerCamelCase	:		Any          =parent
             _lowerCamelCase	:		List[Any]          =batch_size
             _lowerCamelCase	:		Union[str, Any]          =num_channels
             _lowerCamelCase	:		str          =image_size
             _lowerCamelCase	:		str          =min_resolution
             _lowerCamelCase	:		Optional[int]          =max_resolution
             _lowerCamelCase	:		str          =do_resize
             _lowerCamelCase	:		Tuple          =size
             _lowerCamelCase	:		Any          =do_center_crop
             _lowerCamelCase	:		Any          =crop_size
             _lowerCamelCase	:		Optional[Any]          =do_normalize
             _lowerCamelCase	:		List[Any]          =image_mean
             _lowerCamelCase	:		int          =image_std
             _lowerCamelCase	:		Any          =do_convert_rgb






      def 	lowerCamelCase	( self						:					int			) ->						Tuple:
             """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,
             }






      def 	lowerCamelCase	( self						:					List[str]      ,       lowercase_						:					Any=False      ,       lowercase_						:					str=False      ,       lowercase_						:					Union[str, Any]=False			) ->						Tuple:
             """simple docstring"""




             assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

             if equal_resolution:
                    _lowerCamelCase	:		int          =[]
                    for i in range(self.batch_size			):
                           image_inputs.append(
                               np.random.randint(
                                   255      ,       size=(self.num_channels, self.max_resolution, self.max_resolution)      ,       dtype=np.uinta			)			)
             else:
                    _lowerCamelCase	:		Dict          =[]
                    for i in range(self.batch_size			):
                           _lowerCamelCase     ,					_lowerCamelCase	:		str          =np.random.choice(np.arange(self.min_resolution      ,       self.max_resolution			)      ,       2			)
                           image_inputs.append(np.random.randint(255      ,       size=(self.num_channels, width, height)      ,       dtype=np.uinta			)			)

             if not numpify and not torchify:
                    # PIL expects the channel dimension as last dimension
                    _lowerCamelCase	:		Optional[int]          =[Image.fromarray(np.moveaxis(lowercase_      ,       0      ,       -1			)			) for x in image_inputs]

             if torchify:
                    _lowerCamelCase	:		Tuple          =[torch.from_numpy(lowercase_			) for x in image_inputs]

             return image_inputs




@require_torch
@require_vision
class A  (       UpperCamelCase_   ,							unittest.TestCase					):
      UpperCamelCase__							:  Tuple				=CLIPImageProcessor if is_vision_available() else None






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						Dict:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =CLIPImageProcessingTester(self			)






      @property
      def 	lowerCamelCase	( self						:					Tuple			) ->						Dict:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def 	lowerCamelCase	( self						:					int			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =self.image_processing_class(**self.image_processor_dict			)
             self.assertTrue(hasattr(lowercase_      ,       'do_resize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'size'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_center_crop'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'center_crop'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_normalize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'image_mean'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'image_std'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_convert_rgb'			)			)






      def 	lowerCamelCase	( self						:					Any			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		int          =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	:		Optional[Any]          =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 	lowerCamelCase	( self						:					Tuple			) ->						Optional[Any]:
             """simple docstring"""




             pass






      def 	lowerCamelCase	( self						:					List[Any]			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		str          =self.image_processing_class(**self.image_processor_dict			)
             # create random PIL images
             _lowerCamelCase	:		Any          =self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_			)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_      ,       Image.Image			)

             # Test not batched input
             _lowerCamelCase	:		Optional[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.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )      ,       )

             # Test batched
             _lowerCamelCase	:		Optional[Any]          =image_processing(lowercase_      ,       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 	lowerCamelCase	( self						:					List[Any]			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =self.image_processing_class(**self.image_processor_dict			)
             # create random numpy tensors
             _lowerCamelCase	:		Tuple          =self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_      ,       numpify=lowercase_			)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_      ,       np.ndarray			)

             # Test not batched input
             _lowerCamelCase	:		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.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )      ,       )

             # Test batched
             _lowerCamelCase	:		str          =image_processing(lowercase_      ,       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 	lowerCamelCase	( self						:					List[str]			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		int          =self.image_processing_class(**self.image_processor_dict			)
             # create random PyTorch tensors
             _lowerCamelCase	:		Dict          =self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_      ,       torchify=lowercase_			)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_      ,       torch.Tensor			)

             # Test not batched input
             _lowerCamelCase	:		List[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.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )      ,       )

             # Test batched
             _lowerCamelCase	:		str          =image_processing(lowercase_      ,       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'],
                 )      ,       )




@require_torch
@require_vision
class A  (       UpperCamelCase_   ,							unittest.TestCase					):
      UpperCamelCase__							:  List[str]				=CLIPImageProcessor if is_vision_available() else None






      def 	lowerCamelCase	( self						:					Union[str, Any]			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		int          =CLIPImageProcessingTester(self      ,       num_channels=4			)
             _lowerCamelCase	:		Any          =3






      @property
      def 	lowerCamelCase	( self						:					Union[str, Any]			) ->						Tuple:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =self.image_processing_class(**self.image_processor_dict			)
             self.assertTrue(hasattr(lowercase_      ,       'do_resize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'size'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_center_crop'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'center_crop'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_normalize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'image_mean'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'image_std'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_convert_rgb'			)			)






      def 	lowerCamelCase	( self						:					Optional[int]			) ->						Optional[int]:
             """simple docstring"""




             pass






      def 	lowerCamelCase	( self						:					Any			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =self.image_processing_class(**self.image_processor_dict			)
             # create random PIL images
             _lowerCamelCase	:		Optional[Any]          =self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_			)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_      ,       Image.Image			)

             # Test not batched input
             _lowerCamelCase	:		Any          =image_processing(image_inputs[0]      ,       return_tensors='pt'			).pixel_values
             self.assertEqual(
                 encoded_images.shape      ,       (
                     1,
                     self.expected_encoded_image_num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )      ,       )

             # Test batched
             _lowerCamelCase	:		Optional[Any]          =image_processing(lowercase_      ,       return_tensors='pt'			).pixel_values
             self.assertEqual(
                 encoded_images.shape      ,       (
                     self.image_processor_tester.batch_size,
                     self.expected_encoded_image_num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )      ,       )


