






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


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

class 				lowerCAmelCase_	(		__snake_case			):


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

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																		    F"""The configuration file of this scheduler: {scheduler} is outdated.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

											if hasattr(scheduler.config ,						'skip_prk_steps'      ) and scheduler.config.skip_prk_steps is False:
																		_lowercase							:   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' ,						_lowerCAmelCase ,						standard_warn=_lowerCAmelCase      )
																		_lowercase							:   str 							=   dict(scheduler.config      )
																		_lowercase							:   int 							=   True
																		_lowercase							:   Union[str, Any] 							=   FrozenDict(_lowerCAmelCase      )

											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=_lowerCAmelCase ,						segmentation_processor=_lowerCAmelCase ,						vae=_lowerCAmelCase ,						text_encoder=_lowerCAmelCase ,						tokenizer=_lowerCAmelCase ,						unet=_lowerCAmelCase ,						scheduler=_lowerCAmelCase ,						safety_checker=_lowerCAmelCase ,						feature_extractor=_lowerCAmelCase ,						)


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


				def 						__a					(					self      ):
											self.enable_attention_slicing(_lowerCAmelCase      )


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

											_lowercase							:   Tuple 							=   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(_lowerCAmelCase ,						_lowerCAmelCase      )


				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 						__a					(					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(_lowerCAmelCase ,						'_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 ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase = 5_1_2 ,						_lowerCAmelCase = 5_1_2 ,						_lowerCAmelCase = 5_0 ,						_lowerCAmelCase = 7.5 ,						_lowerCAmelCase = None ,						_lowerCAmelCase = 1 ,						_lowerCAmelCase = 0.0 ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = "pil" ,						_lowerCAmelCase = True ,						_lowerCAmelCase = None ,						_lowerCAmelCase = 1 ,						**_lowerCAmelCase ,						):
											_lowercase							:   str 							=   self.segmentation_processor(
											    text=[text] ,						images=[image] ,						padding='max_length' ,						return_tensors='pt'      ).to(self.device      )
											_lowercase							:   Any 							=   self.segmentation_model(**_lowerCAmelCase      )
											_lowercase							:   str 							=   torch.sigmoid(outputs.logits      ).cpu().detach().unsqueeze(-1      ).numpy()
											_lowercase							:   Optional[int] 							=   self.numpy_to_pil(_lowerCAmelCase      )[0].resize(image.size      )

											# Run inpainting pipeline with the generated mask
											_lowercase							:   str 							=   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=_lowerCAmelCase ,						image=_lowerCAmelCase ,						mask_image=_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 ,						)







"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


__UpperCamelCase    : List[str]				 =						logging.get_logger(__name__)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def __init__(			self     :							Any  ,*lowercase_     :							Dict  ,**lowercase_     :							List[str]						):
						warnings.warn(
						    '''The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
						    ''' Please use ImageGPTImageProcessor instead.'''  ,lowercase_  ,)
						super().__init__(*lowercase_  ,**lowercase_						)




