



'''simple docstring'''






def 					snake_case_      (  lowerCAmelCase_	)->	tuple[int, int]:





       '''simple docstring'''


       if len(lowerCAmelCase_	) == 1:
              return a[0], a[0]
       else:
              # split A into half.
              _UpperCAmelCase						:		Union[str, Any]      =				a[: len(lowerCAmelCase_	) // 2]
              _UpperCAmelCase						:		str      =				a[len(lowerCAmelCase_	) // 2 :]

              # 2 sub problems, 1/2 of original size.
              _UpperCAmelCase  ,_UpperCAmelCase						:		str      =				max_difference(lowerCAmelCase_	)
              _UpperCAmelCase  ,_UpperCAmelCase						:		List[str]      =				max_difference(lowerCAmelCase_	)

              # get min of first and max of second
              # linear time
              _UpperCAmelCase						:		Dict      =				min(lowerCAmelCase_	)
              _UpperCAmelCase						:		List[Any]      =				max(lowerCAmelCase_	)

              # 3 cases, either (small1, big1),
              # (min_first, max_second), (small2, big2)
              # constant comparisons
              if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
                     return smalla, biga
              elif biga - smalla > max_second - min_first:
                     return smalla, biga
              else:
                     return min_first, max_second


if __name__ == "__main__":
   import doctest

   doctest.testmod()








'''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_							:				List[str]						=			logging.get_logger(__name__)  # pylint: disable=invalid-name



class 							lowercase (						_lowerCamelCase       ):


 """simple docstring"""


 UpperCAmelCase					=						["""safety_checker""", """feature_extractor"""]







 def __init__(							self						,a_						,a_						,a_						,a_						,a_						,a_						,a_						,a_ = True						,)	->     int:
        super().__init__()
        if hasattr(scheduler.config						,"""steps_offset"""							) and scheduler.config.steps_offset != 1:
               _UpperCAmelCase						:		Tuple      =				(
                   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						:		Any      =				dict(scheduler.config							)
               _UpperCAmelCase						:		str      =				1
               _UpperCAmelCase						:		Any      =				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 _snake_case				(							self							)	->     Dict[str, Any]:
        return {k: getattr(self						,a_							) for k in self.config.keys() if not k.startswith("""_"""							)}







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







 def _snake_case				(							self							)	->     Tuple:
        self.enable_attention_slicing(a_							)







 @torch.no_grad()
 def _snake_case				(							self						,a_						,a_						,a_						,a_ = 0.8						,a_ = 50						,a_ = 7.5						,a_ = None						,a_ = 1						,a_ = 0.0						,a_ = None						,a_ = "pil"						,a_ = True						,a_ = None						,a_ = 1						,)	->     List[Any]:
        # 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 _snake_case				(							self						,a_						,a_						,a_ = 0.8						,a_ = 50						,a_ = 7.5						,a_ = None						,a_ = 1						,a_ = 0.0						,a_ = None						,a_ = "pil"						,a_ = True						,a_ = None						,a_ = 1						,**a_						,)	->     Tuple:
        # 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 _snake_case				(							self						,a_						,a_ = 512						,a_ = 512						,a_ = 50						,a_ = 7.5						,a_ = None						,a_ = 1						,a_ = 0.0						,a_ = None						,a_ = None						,a_ = "pil"						,a_ = True						,a_ = None						,a_ = 1						,)	->     List[str]:
        # 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_						,)



