


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}')










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






