

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

class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	Tuple							,			A__      :	CLIPSegForImageSegmentation							,			A__      :	CLIPSegProcessor							,			A__      :	AutoencoderKL							,			A__      :	CLIPTextModel							,			A__      :	CLIPTokenizer							,			A__      :	UNetaDConditionModel							,			A__      :	Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]							,			A__      :	StableDiffusionSafetyChecker							,			A__      :	CLIPImageProcessor							,			)					-> Union[str, Any]:
						super().__init__()

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										    f'The configuration file of this scheduler: {scheduler} is outdated.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

						if hasattr(scheduler.config							,			"skip_prk_steps"				) and scheduler.config.skip_prk_steps is False:
										lowercase         = (
										    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__				)
										lowercase         = dict(scheduler.config				)
										lowercase         = True
										lowercase         = 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      :	Union[str, Any]							,			A__      :	Optional[Union[str, int]] = "auto"				)					-> List[str]:
						if slice_size == "auto":
										# half the attention head size is usually a good trade-off between
										# speed and memory
										lowercase         = self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(A__				)




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Optional[int]:
						self.enable_attention_slicing(A__				)




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Tuple:
						if is_accelerate_available():
										from accelerate import cpu_offload
						else:
										raise ImportError("Please install accelerate via `pip install accelerate`"				)

						lowercase         = 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[Any]				)					-> List[str]:
						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_1_2							,			A__      :	int = 5_1_2							,			A__      :	int = 5_0							,			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__      :	Any							,			)					-> int:
						lowercase         = self.segmentation_processor(
						    text=[text]							,			images=[image]							,			padding="max_length"							,			return_tensors="pt"				).to(self.device				)
						lowercase         = self.segmentation_model(**A__				)
						lowercase         = torch.sigmoid(outputs.logits				).cpu().detach().unsqueeze(-1				).numpy()
						lowercase         = self.numpy_to_pil(A__				)[0].resize(image.size				)

						# Run inpainting pipeline with the generated mask
						lowercase         = 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'''

import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


__lowerCamelCase : Union[str, Any]											=			logging.get_logger(__name__)

class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	Tuple							,			*A__      :	Union[str, Any]							,			**A__      :	Optional[Any]				)					-> None:
						warnings.warn(
						    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
						    " use CLIPImageProcessor instead."							,			A__							,			)
						super().__init__(*A__							,			**A__				)




