





'''simple docstring'''







from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


lowerCAmelCase_					:    Optional[int]      	=			logging.get_logger(__name__)  # pylint: disable=invalid-name
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				def __init__(  self     :					Dict					,				__a     :					CLIPSegForImageSegmentation					,				__a     :					CLIPSegProcessor					,				__a     :					AutoencoderKL					,				__a     :					CLIPTextModel					,				__a     :					CLIPTokenizer					,				__a     :					UNetaDConditionModel					,				__a     :					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,				__a     :					StableDiffusionSafetyChecker					,				__a     :					CLIPImageProcessor					,				):
								super().__init__()

								if hasattr(scheduler.config					,				"steps_offset"					) and scheduler.config.steps_offset != 1:
												_a								= (
												    f'The configuration file of this scheduler: {scheduler} is outdated.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					)
												_a								= dict(scheduler.config					)
												_a								= 1
												_a								= FrozenDict(__a					)

								if hasattr(scheduler.config					,				"skip_prk_steps"					) and scheduler.config.skip_prk_steps is False:
												_a								= (
												    f'The configuration file of this scheduler: {scheduler} has not set the configuration'
												    " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
												    " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead 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("skip_prk_steps not set"					,				"1.0.0"					,				__a					,				standard_warn=__a					)
												_a								= dict(scheduler.config					)
												_a								= True
												_a								= FrozenDict(__a					)

								if safety_checker is None:
												logger.warning(
												    f'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
												    " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
												    " results in services or applications open to the public. Both the diffusers team and Hugging Face"
												    " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
												    " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
												    " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."					)

								self.register_modules(
								    segmentation_model=__a					,				segmentation_processor=__a					,				vae=__a					,				text_encoder=__a					,				tokenizer=__a					,				unet=__a					,				scheduler=__a					,				safety_checker=__a					,				feature_extractor=__a					,				)




				def   UpperCamelCase__   (  self     :					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
												_a								= self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(__a					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								self.enable_attention_slicing(__a					)




				def   UpperCamelCase__   (  self     :					Dict					):
								if is_accelerate_available():
												from accelerate import cpu_offload
								else:
												raise ImportError("Please install accelerate via `pip install accelerate`"					)

								_a								= torch.device("cuda"					)

								for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
												if cpu_offloaded_model is not None:
																cpu_offload(__a					,				__a					)




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def   UpperCamelCase__   (  self     :					Optional[int]					):
								if self.device != torch.device("meta"					) or not hasattr(self.unet					,				"_hf_hook"					):
												return self.device
								for module in self.unet.modules():
												if (
												    hasattr(__a					,				"_hf_hook"					)
												    and hasattr(module._hf_hook					,				"execution_device"					)
												    and module._hf_hook.execution_device is not None
												):
																return torch.device(module._hf_hook.execution_device					)
								return self.device




				@torch.no_grad()
				def __call__(  self     :					Optional[Any]					,				__a     :					Union[str, List[str]]					,				__a     :					Union[torch.FloatTensor, PIL.Image.Image]					,				__a     :					str					,				__a     :					int = 5_12					,				__a     :					int = 5_12					,				__a     :					int = 50					,				__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					,				**__a     :					Tuple					,				):
								_a								= self.segmentation_processor(
								    text=[text]					,				images=[image]					,				padding="max_length"					,				return_tensors="pt"					).to(self.device					)
								_a								= self.segmentation_model(**__a					)
								_a								= torch.sigmoid(outputs.logits					).cpu().detach().unsqueeze(-1					).numpy()
								_a								= self.numpy_to_pil(__a					)[0].resize(image.size					)

								# Run inpainting pipeline with the generated mask
								_a								= StableDiffusionInpaintPipeline(
								    vae=self.vae					,				text_encoder=self.text_encoder					,				tokenizer=self.tokenizer					,				unet=self.unet					,				scheduler=self.scheduler					,				safety_checker=self.safety_checker					,				feature_extractor=self.feature_extractor					,				)
								return inpainting_pipeline(
								    prompt=__a					,				image=__a					,				mask_image=__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  _lowerCamelCase				( lowercase						:      bytes						)	->	str:
				return "".join([hex(lowercase						)[2:].zfill(2						).upper() for byte in list(lowercase						)]						)






def  _lowerCamelCase				( lowercase						:      str						)	->	bytes:
				# Check data validity, following RFC3548
				# https://www.ietf.org/rfc/rfc3548.txt
				if (len(lowercase						) % 2) != 0:
								raise ValueError(
								    "Base16 encoded data is invalid:\nData does not have an even number of hex digits."						)
				# Check the character set - the standard base16 alphabet
				# is uppercase according to RFC3548 section 6
				if not set(lowercase						) <= set("0123456789ABCDEF"						):
								raise ValueError(
								    "Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters."						)
				# For every two hexadecimal digits (= a byte), turn it into an integer.
				# Then, string the result together into bytes, and return it.
				return bytes(int(data[i] + data[i + 1]    ,							16						) for i in range(0    ,							len(lowercase						)    ,							2						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
