





'''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


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

class 					snake_case					(   __lowerCamelCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Optional[int]      		=["safety_checker", "feature_extractor"]






 def __init__(	self	:							int							,     __A	:							AutoencoderKL							,     __A	:							CLIPTextModel							,     __A	:							CLIPTokenizer							,     __A	:							UNetaDConditionModel							,     __A	:							Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]							,     __A	:							StableDiffusionSafetyChecker							,     __A	:							CLIPImageProcessor							,     __A	:							bool = True							,     ):
        super().__init__()
        if hasattr(scheduler.config							,     'steps_offset'     ) and scheduler.config.steps_offset != 1:
               __UpperCamelCase	=  (
                   f'''The configuration file of this scheduler: {scheduler} is outdated.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'							,     __A							,     standard_warn=__A     )
               __UpperCamelCase	=  dict(scheduler.config     )
               __UpperCamelCase	=  1
               __UpperCamelCase	=  FrozenDict(__A     )

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






 @property
 def 	_lowerCamelCase (	self	:							List[str]     ):
        return {k: getattr(self							,     __A     ) for k in self.config.keys() if not k.startswith('_'     )}






 def 	_lowerCamelCase (	self	:							Union[str, Any]							,     __A	:							Optional[Union[str, int]] = "auto"     ):
        if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               __UpperCamelCase	=  self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(__A     )






 def 	_lowerCamelCase (	self	:							Any     ):
        self.enable_attention_slicing(__A     )






 @torch.no_grad()
 def 	_lowerCamelCase (	self	:							Any							,     __A	:							Union[str, List[str]]							,     __A	:							Union[torch.FloatTensor, PIL.Image.Image]							,     __A	:							Union[torch.FloatTensor, PIL.Image.Image]							,     __A	:							float = 0.8							,     __A	:							Optional[int] = 5_0							,     __A	:							Optional[float] = 7.5							,     __A	:							Optional[Union[str, List[str]]] = None							,     __A	:							Optional[int] = 1							,     __A	:							Optional[float] = 0.0							,     __A	:							Optional[torch.Generator] = None							,     __A	:							Optional[str] = "pil"							,     __A	:							bool = True							,     __A	:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,     __A	:							int = 1							,     ):
        # 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=__A							,     image=__A							,     mask_image=__A							,     strength=__A							,     num_inference_steps=__A							,     guidance_scale=__A							,     negative_prompt=__A							,     num_images_per_prompt=__A							,     eta=__A							,     generator=__A							,     output_type=__A							,     return_dict=__A							,     callback=__A							,     )






 @torch.no_grad()
 def 	_lowerCamelCase (	self	:							Optional[int]							,     __A	:							Union[str, List[str]]							,     __A	:							Union[torch.FloatTensor, PIL.Image.Image]							,     __A	:							float = 0.8							,     __A	:							Optional[int] = 5_0							,     __A	:							Optional[float] = 7.5							,     __A	:							Optional[Union[str, List[str]]] = None							,     __A	:							Optional[int] = 1							,     __A	:							Optional[float] = 0.0							,     __A	:							Optional[torch.Generator] = None							,     __A	:							Optional[str] = "pil"							,     __A	:							bool = True							,     __A	:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,     __A	:							int = 1							,     **__A	:							List[Any]							,     ):
        # 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=__A							,     image=__A							,     strength=__A							,     num_inference_steps=__A							,     guidance_scale=__A							,     negative_prompt=__A							,     num_images_per_prompt=__A							,     eta=__A							,     generator=__A							,     output_type=__A							,     return_dict=__A							,     callback=__A							,     callback_steps=__A							,     )






 @torch.no_grad()
 def 	_lowerCamelCase (	self	:							Tuple							,     __A	:							Union[str, List[str]]							,     __A	:							int = 5_1_2							,     __A	:							int = 5_1_2							,     __A	:							int = 5_0							,     __A	:							float = 7.5							,     __A	:							Optional[Union[str, List[str]]] = None							,     __A	:							Optional[int] = 1							,     __A	:							float = 0.0							,     __A	:							Optional[torch.Generator] = None							,     __A	:							Optional[torch.FloatTensor] = None							,     __A	:							Optional[str] = "pil"							,     __A	:							bool = True							,     __A	:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,     __A	:							int = 1							,     ):
        # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
        return StableDiffusionPipeline(**self.components     )(
            prompt=__A							,     height=__A							,     width=__A							,     num_inference_steps=__A							,     guidance_scale=__A							,     negative_prompt=__A							,     num_images_per_prompt=__A							,     eta=__A							,     generator=__A							,     latents=__A							,     output_type=__A							,     return_dict=__A							,     callback=__A							,     callback_steps=__A							,     )







'''simple docstring'''
def     lowercase__       (       __lowercase						:  int = 20					)			->				int:



       """simple docstring"""






       try:
              __UpperCamelCase	=  int(__lowercase					)
       except (TypeError, ValueError):
              raise TypeError('Parameter n must be int or castable to int.'					)
       if n <= 0:
              raise ValueError('Parameter n must be greater than or equal to one.'					)
       __UpperCamelCase	=  0
       while 1:
              i += n * (n - 1)
              __UpperCamelCase	=  0
              for j in range(2    ,  __lowercase					):
                     if i % j != 0:
                            __UpperCamelCase	=  1
                            break
              if nfound == 0:
                     if i == 0:
                            __UpperCamelCase	=  1
                     return i
       return None


if __name__ == "__main__":
    print(f'{solution() = }')
