'''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


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



class   UpperCamelCase					(       lowerCamelCase_       ):







    """simple docstring"""





    A     :    Union[str, Any]				=			["""safety_checker""", """feature_extractor"""]





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


         """simple docstring"""







         super().__init__()
         if hasattr(scheduler.config							,							'steps_offset') and scheduler.config.steps_offset != 1:
              a					:							Optional[Any]							= (
                  f"""The configuration file of this scheduler: {scheduler} is outdated.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_)
              a					:							Tuple							= dict(scheduler.config)
              a					:							Tuple							= 1
              a					:							Dict							= 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    SCREAMING_SNAKE_CASE_     (					self     :						int):


         """simple docstring"""







         return {k: getattr(self							,							lowerCamelCase_) for k in self.config.keys() if not k.startswith('_')}





    def    SCREAMING_SNAKE_CASE_     (					self     :						int							,							UpperCAmelCase_     :						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
              a					:							List[str]							= self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(lowerCamelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         self.enable_attention_slicing(lowerCamelCase_)





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


         """simple docstring"""







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


         """simple docstring"""







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


         """simple docstring"""







         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_							,							)





def 		snake_case_   (				lowercase__     :    str     ):






				'''simple docstring'''






				_lowerCAmelCase  						=53_81
				for x in s:
								_lowerCAmelCase  						=((hash_value << 5) + hash_value) + ord(lowercase__     )
				return hash_value & 0Xffff_ffff

