






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


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


class 							__a							(  UpperCamelCase_						):





		'''simple docstring'''



		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    ):
						super().__init__()

						if hasattr(scheduler.config ,    'steps_offset'       ) and scheduler.config.steps_offset != 1:
										SCREAMING_SNAKE_CASE_   :	Dict													=   (
										    F'''The configuration file of this scheduler: {scheduler} is outdated.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       )
										SCREAMING_SNAKE_CASE_   :	str													=   dict(scheduler.config       )
										SCREAMING_SNAKE_CASE_   :	Any													=   1
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   FrozenDict(_a       )

						if hasattr(scheduler.config ,    'skip_prk_steps'       ) and scheduler.config.skip_prk_steps is False:
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   (
										    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       )
										SCREAMING_SNAKE_CASE_   :	Dict													=   dict(scheduler.config       )
										SCREAMING_SNAKE_CASE_   :	int													=   True
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   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   __snake_case			(						self ,    UpperCamelCase__ = "auto"       ):


						if slice_size == "auto":
										# half the attention head size is usually a good trade-off between
										# speed and memory
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(_a       )


		def   __snake_case			(						self       ):
						self.enable_attention_slicing(_a       )


		def   __snake_case			(						self       ):


						if is_accelerate_available():
										from accelerate import cpu_offload
						else:
										raise ImportError('Please install accelerate via `pip install accelerate`'       )

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   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   __snake_case			(						self       ):


						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 ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = 512 ,    UpperCamelCase__ = 512 ,    UpperCamelCase__ = 50 ,    UpperCamelCase__ = 7.5 ,    UpperCamelCase__ = None ,    UpperCamelCase__ = 1 ,    UpperCamelCase__ = 0.0 ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = "pil" ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = 1 ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	int													=   self.segmentation_processor(
						    text=[text] ,    images=[image] ,    padding='max_length' ,    return_tensors='pt'       ).to(self.device       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.segmentation_model(**_a       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   torch.sigmoid(outputs.logits       ).cpu().detach().unsqueeze(-1       ).numpy()
						SCREAMING_SNAKE_CASE_   :	Any													=   self.numpy_to_pil(_a       )[0].resize(image.size       )

						# Run inpainting pipeline with the generated mask
						SCREAMING_SNAKE_CASE_   :	Dict													=   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 ,    )










import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union


@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[Union[str, Path]]				    =						None
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				Optional[Dict]				    =						None
		UpperCAmelCase__      :				Optional[str]				    =						None
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						True
		UpperCAmelCase__      :				Optional[int]				    =						None
		UpperCAmelCase__      :				int				    =						1
		UpperCAmelCase__      :				Optional[Union[str, bool]]				    =						None
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				Optional[Dict]				    =						None
		UpperCAmelCase__      :				Optional[str]				    =						None


		def   __snake_case			(						self       ):
						return self.__class__(**{k: copy.deepcopy(UpperCamelCase__       ) for k, v in self.__dict__.items()}       )