code: 
  
  
  import math
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__			)		->			Union[str, Any]:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    0
  		if n % 2 == 0:
  				count += 1
  				while n % 2 == 0:
  						__lowercase=    int(n / 2			)
      # the n input value must be odd so that
      # we can skip one element (ie i += 2)
  
  		__lowercase=    3
  
  		while i <= int(math.sqrt(lowercase__			)			):
  				if n % i == 0:
  						count += 1
  						while n % i == 0:
  								__lowercase=    int(n / i			)
  				__lowercase=    i + 2
  
  		# this condition checks the prime
  		# number n is greater than 2
  
  		if n > 2:
  				count += 1
  		return count
  
  
  if __name__ == "__main__":
  	lowerCAmelCase					 =	5_1_2_4_2_1_8_3
  	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}')
  
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  @dataclass
  # Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  
  
  if is_transformers_available() and is_torch_available():
  	from .modeling_roberta_series import RobertaSeriesModelWithTransformation
  	from .pipeline_alt_diffusion import AltDiffusionPipeline
  	from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
  
  
  
  
  
  
  