

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


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






class 	__magic_name__				(       snake_case							):
     UpperCamelCase_  :int								=			["""safety_checker""", """feature_extractor"""]






     def __init__(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase = True		,	)->    Tuple:
         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                 F"The configuration file of this scheduler: {scheduler} is outdated.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"		,	_lowercase		,	standard_warn=_lowercase  )
             UpperCamelCase_      =	dict(scheduler.config  )
             UpperCamelCase_      =	1
             UpperCamelCase_      =	FrozenDict(_lowercase  )

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






     @property
     def   UpperCAmelCase_							(							self  )->    Dict[str, Any]:
         return {k: getattr(self		,	_lowercase  ) for k in self.config.keys() if not k.startswith("_"  )}






     def   UpperCAmelCase_							(							self		,	_lowercase = "auto"  )->    Any:
         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(_lowercase  )






     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         self.enable_attention_slicing(_lowercase  )






     @torch.no_grad()
     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase = 0.8		,	_lowercase = 50		,	_lowercase = 7.5		,	_lowercase = None		,	_lowercase = 1		,	_lowercase = 0.0		,	_lowercase = None		,	_lowercase = "pil"		,	_lowercase = True		,	_lowercase = None		,	_lowercase = 1		,	)->    Optional[int]:
         # 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=_lowercase		,	image=_lowercase		,	mask_image=_lowercase		,	strength=_lowercase		,	num_inference_steps=_lowercase		,	guidance_scale=_lowercase		,	negative_prompt=_lowercase		,	num_images_per_prompt=_lowercase		,	eta=_lowercase		,	generator=_lowercase		,	output_type=_lowercase		,	return_dict=_lowercase		,	callback=_lowercase		,	)






     @torch.no_grad()
     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase = 0.8		,	_lowercase = 50		,	_lowercase = 7.5		,	_lowercase = None		,	_lowercase = 1		,	_lowercase = 0.0		,	_lowercase = None		,	_lowercase = "pil"		,	_lowercase = True		,	_lowercase = None		,	_lowercase = 1		,	**_lowercase		,	)->    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=_lowercase		,	image=_lowercase		,	strength=_lowercase		,	num_inference_steps=_lowercase		,	guidance_scale=_lowercase		,	negative_prompt=_lowercase		,	num_images_per_prompt=_lowercase		,	eta=_lowercase		,	generator=_lowercase		,	output_type=_lowercase		,	return_dict=_lowercase		,	callback=_lowercase		,	callback_steps=_lowercase		,	)






     @torch.no_grad()
     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = 512		,	_lowercase = 512		,	_lowercase = 50		,	_lowercase = 7.5		,	_lowercase = None		,	_lowercase = 1		,	_lowercase = 0.0		,	_lowercase = None		,	_lowercase = None		,	_lowercase = "pil"		,	_lowercase = True		,	_lowercase = None		,	_lowercase = 1		,	)->    int:
         # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
         return StableDiffusionPipeline(**self.components  )(
             prompt=_lowercase		,	height=_lowercase		,	width=_lowercase		,	num_inference_steps=_lowercase		,	guidance_scale=_lowercase		,	negative_prompt=_lowercase		,	num_images_per_prompt=_lowercase		,	eta=_lowercase		,	generator=_lowercase		,	latents=_lowercase		,	output_type=_lowercase		,	return_dict=_lowercase		,	callback=_lowercase		,	callback_steps=_lowercase		,	)



def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	str:



    """simple docstring"""






    if power < 0:
        return -1
    base %= mod
    UpperCamelCase_      =	1

    while power > 0:
        if power & 1:
            UpperCamelCase_      =	(result * base) % mod
        UpperCamelCase_      =	power >> 1
        UpperCamelCase_      =	(base * base) % mod

    return result




def  lowerCAmelCase(			)->	Tuple:



    """simple docstring"""






    print(modular_exponential(3 ,	2_0_0 ,	1_3 ) )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 main()
