




'''simple docstring'''



from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


lowerCamelCase_	 =		logging.get_logger(__name__)  # pylint: disable=invalid-name







class 	lowercase_					(				A     ):


   """simple docstring"""

   lowerCamelCase_       						=		['''safety_checker''', '''feature_extractor''']



   def __init__(					self      :			Optional[int]						,   __lowerCamelCase      :			AutoencoderKL						,   __lowerCamelCase      :			CLIPTextModel						,   __lowerCamelCase      :			CLIPTokenizer						,   __lowerCamelCase      :			UNetaDConditionModel						,   __lowerCamelCase      :			Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]						,   __lowerCamelCase      :			StableDiffusionSafetyChecker						,   __lowerCamelCase      :			CLIPImageProcessor						,   __lowerCamelCase      :			bool = True						,   ):

      """simple docstring"""
      super().__init__()
      if hasattr(scheduler.config						,   "steps_offset"					) and scheduler.config.steps_offset != 1:
         _SCREAMING_SNAKE_CASE =   (
             F"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
             F""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
             "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
             " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
             " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
             " file"
         )
         deprecate("steps_offset!=1"						,   "1.0.0"						,   __lowerCamelCase						,   standard_warn=__lowerCamelCase					)
         _SCREAMING_SNAKE_CASE =   dict(scheduler.config					)
         _SCREAMING_SNAKE_CASE =   1
         _SCREAMING_SNAKE_CASE =   FrozenDict(__lowerCamelCase					)

      self.register_modules(
          vae=__lowerCamelCase						,   text_encoder=__lowerCamelCase						,   tokenizer=__lowerCamelCase						,   unet=__lowerCamelCase						,   scheduler=__lowerCamelCase						,   safety_checker=__lowerCamelCase						,   feature_extractor=__lowerCamelCase						,   )
      self.register_to_config(requires_safety_checker=__lowerCamelCase					)



   @property
   def 							lowerCAmelCase_    (					self      :			str					):

      """simple docstring"""
      return {k: getattr(self						,   __lowerCamelCase					) for k in self.config.keys() if not k.startswith("_"					)}



   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			Optional[Union[str, int]] = "auto"					):

      """simple docstring"""
      if slice_size == "auto":
         # half the attention head size is usually a good trade-off between
         # speed and memory
         _SCREAMING_SNAKE_CASE =   self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      self.enable_attention_slicing(__lowerCamelCase					)



   @torch.no_grad()
   def 							lowerCAmelCase_    (					self      :			Optional[int]						,   __lowerCamelCase      :			Union[str, List[str]]						,   __lowerCamelCase      :			Union[torch.FloatTensor, PIL.Image.Image]						,   __lowerCamelCase      :			Union[torch.FloatTensor, PIL.Image.Image]						,   __lowerCamelCase      :			float = 0.8						,   __lowerCamelCase      :			Optional[int] = 5_0						,   __lowerCamelCase      :			Optional[float] = 7.5						,   __lowerCamelCase      :			Optional[Union[str, List[str]]] = None						,   __lowerCamelCase      :			Optional[int] = 1						,   __lowerCamelCase      :			Optional[float] = 0.0						,   __lowerCamelCase      :			Optional[torch.Generator] = None						,   __lowerCamelCase      :			Optional[str] = "pil"						,   __lowerCamelCase      :			bool = True						,   __lowerCamelCase      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,   __lowerCamelCase      :			int = 1						,   ):

      """simple docstring"""
      # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
      return StableDiffusionInpaintPipelineLegacy(**self.components					)(
          prompt=__lowerCamelCase						,   image=__lowerCamelCase						,   mask_image=__lowerCamelCase						,   strength=__lowerCamelCase						,   num_inference_steps=__lowerCamelCase						,   guidance_scale=__lowerCamelCase						,   negative_prompt=__lowerCamelCase						,   num_images_per_prompt=__lowerCamelCase						,   eta=__lowerCamelCase						,   generator=__lowerCamelCase						,   output_type=__lowerCamelCase						,   return_dict=__lowerCamelCase						,   callback=__lowerCamelCase						,   )



   @torch.no_grad()
   def 							lowerCAmelCase_    (					self      :			Tuple						,   __lowerCamelCase      :			Union[str, List[str]]						,   __lowerCamelCase      :			Union[torch.FloatTensor, PIL.Image.Image]						,   __lowerCamelCase      :			float = 0.8						,   __lowerCamelCase      :			Optional[int] = 5_0						,   __lowerCamelCase      :			Optional[float] = 7.5						,   __lowerCamelCase      :			Optional[Union[str, List[str]]] = None						,   __lowerCamelCase      :			Optional[int] = 1						,   __lowerCamelCase      :			Optional[float] = 0.0						,   __lowerCamelCase      :			Optional[torch.Generator] = None						,   __lowerCamelCase      :			Optional[str] = "pil"						,   __lowerCamelCase      :			bool = True						,   __lowerCamelCase      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,   __lowerCamelCase      :			int = 1						,   **__lowerCamelCase      :			List[str]						,   ):

      """simple docstring"""
      # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
      return StableDiffusionImgaImgPipeline(**self.components					)(
          prompt=__lowerCamelCase						,   image=__lowerCamelCase						,   strength=__lowerCamelCase						,   num_inference_steps=__lowerCamelCase						,   guidance_scale=__lowerCamelCase						,   negative_prompt=__lowerCamelCase						,   num_images_per_prompt=__lowerCamelCase						,   eta=__lowerCamelCase						,   generator=__lowerCamelCase						,   output_type=__lowerCamelCase						,   return_dict=__lowerCamelCase						,   callback=__lowerCamelCase						,   callback_steps=__lowerCamelCase						,   )



   @torch.no_grad()
   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			Union[str, List[str]]						,   __lowerCamelCase      :			int = 5_1_2						,   __lowerCamelCase      :			int = 5_1_2						,   __lowerCamelCase      :			int = 5_0						,   __lowerCamelCase      :			float = 7.5						,   __lowerCamelCase      :			Optional[Union[str, List[str]]] = None						,   __lowerCamelCase      :			Optional[int] = 1						,   __lowerCamelCase      :			float = 0.0						,   __lowerCamelCase      :			Optional[torch.Generator] = None						,   __lowerCamelCase      :			Optional[torch.FloatTensor] = None						,   __lowerCamelCase      :			Optional[str] = "pil"						,   __lowerCamelCase      :			bool = True						,   __lowerCamelCase      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,   __lowerCamelCase      :			int = 1						,   ):

      """simple docstring"""
      # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
      return StableDiffusionPipeline(**self.components					)(
          prompt=__lowerCamelCase						,   height=__lowerCamelCase						,   width=__lowerCamelCase						,   num_inference_steps=__lowerCamelCase						,   guidance_scale=__lowerCamelCase						,   negative_prompt=__lowerCamelCase						,   num_images_per_prompt=__lowerCamelCase						,   eta=__lowerCamelCase						,   generator=__lowerCamelCase						,   latents=__lowerCamelCase						,   output_type=__lowerCamelCase						,   return_dict=__lowerCamelCase						,   callback=__lowerCamelCase						,   callback_steps=__lowerCamelCase						,   )







'''simple docstring'''



import math
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple						)				->				List[Any]:
   _SCREAMING_SNAKE_CASE =   0
   if n % 2 == 0:
      count += 1
      while n % 2 == 0:
         _SCREAMING_SNAKE_CASE =   int(n / 2						)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

   _SCREAMING_SNAKE_CASE =   3

   while i <= int(math.sqrt(__A						)						):
      if n % i == 0:
         count += 1
         while n % i == 0:
            _SCREAMING_SNAKE_CASE =   int(n / i						)
      _SCREAMING_SNAKE_CASE =   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}''')

