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						:    Optional[Any]     = logging.get_logger(__name__)  # pylint: disable=invalid-name



class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		['safety_checker', 'feature_extractor']



							def __init__(						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_ = True      ,				):
													super().__init__()
													if hasattr(scheduler.config      ,				"steps_offset"				) and scheduler.config.steps_offset != 1:
																			_snake_case						:     Union[str, 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																			    f"""The configuration file of this scheduler: {scheduler} is outdated.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"      ,				lowercase_      ,				standard_warn=lowercase_				)
																			_snake_case						:     List[str]				=    dict(scheduler.config				)
																			_snake_case						:     Any				=    1
																			_snake_case						:     Dict				=    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				):
													return {k: getattr(self      ,				lowercase_				) for k in self.config.keys() if not k.startswith("_"				)}



							def        UpperCamelCase       (						self      ,				lowercase_ = "auto"				):
													if slice_size == "auto":
																			# half the attention head size is usually a good trade-off between
																			# speed and memory
																			_snake_case						:     str				=    self.unet.config.attention_head_dim // 2
													self.unet.set_attention_slice(lowercase_				)



							def        UpperCamelCase       (						self				):
													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      ,				):
													# 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_      ,				):
													# 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      ,				):
													# 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 					snake_case						(__lowercase						)       ->       int:

						'''simple docstring'''



						if not isinstance(__lowercase					,			__lowercase						) or number < 0:
												raise ValueError("Input must be a non-negative integer"						)

						_snake_case						:     List[Any]				=    number & ~(number - 1)
						_snake_case						:     Union[str, Any]				=    0
						while intermediate:
												intermediate >>= 1
												index += 1
						return index - 1


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)