





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    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __init__(self				:   Any       , UpperCAmelCase_				:   CLIPSegForImageSegmentation       , UpperCAmelCase_				:   CLIPSegProcessor       , UpperCAmelCase_				:   AutoencoderKL       , UpperCAmelCase_				:   CLIPTextModel       , UpperCAmelCase_				:   CLIPTokenizer       , UpperCAmelCase_				:   UNetaDConditionModel       , UpperCAmelCase_				:   Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       , UpperCAmelCase_				:   StableDiffusionSafetyChecker       , UpperCAmelCase_				:   CLIPImageProcessor       , )       ->List[Any]:



   '''simple docstring'''



   super().__init__()

   if hasattr(scheduler.config       , "steps_offset") and scheduler.config.steps_offset != 1:
     lowerCamelCase__:      List[str]			  =(
         F"""The configuration file of this scheduler: {scheduler} is outdated.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"       , UpperCAmelCase_       , standard_warn=UpperCAmelCase_)
     lowerCamelCase__:      Tuple			  =dict(scheduler.config)
     lowerCamelCase__:      List[str]			  =1
     lowerCamelCase__:      Optional[int]			  =FrozenDict(UpperCAmelCase_)

   if hasattr(scheduler.config       , "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
     lowerCamelCase__:      Tuple			  =(
         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"       , UpperCAmelCase_       , standard_warn=UpperCAmelCase_)
     lowerCamelCase__:      Optional[int]			  =dict(scheduler.config)
     lowerCamelCase__:      str			  =True
     lowerCamelCase__:      Optional[Any]			  =FrozenDict(UpperCAmelCase_)

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   Optional[Union[str, int]] = "auto")       ->List[Any]:



   '''simple docstring'''



   if slice_size == "auto":
     # half the attention head size is usually a good trade-off between
     # speed and memory
     lowerCamelCase__:      Dict			  =self.unet.config.attention_head_dim // 2
   self.unet.set_attention_slice(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Tuple:



   '''simple docstring'''



   self.enable_attention_slicing(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->List[Any]:



   '''simple docstring'''



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

   lowerCamelCase__:      Dict			  =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(UpperCAmelCase_       , UpperCAmelCase_)

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->str:



   '''simple docstring'''



   if self.device != torch.device("meta") or not hasattr(self.unet       , "_hf_hook"):
     return self.device
   for module in self.unet.modules():
     if (
         hasattr(UpperCAmelCase_       , "_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				:   int       , UpperCAmelCase_				:   Union[str, List[str]]       , UpperCAmelCase_				:   Union[torch.FloatTensor, PIL.Image.Image]       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   int = 512       , UpperCAmelCase_				:   int = 512       , UpperCAmelCase_				:   int = 50       , UpperCAmelCase_				:   float = 7.5       , UpperCAmelCase_				:   Optional[Union[str, List[str]]] = None       , UpperCAmelCase_				:   Optional[int] = 1       , UpperCAmelCase_				:   float = 0.0       , UpperCAmelCase_				:   Optional[torch.Generator] = None       , UpperCAmelCase_				:   Optional[torch.FloatTensor] = None       , UpperCAmelCase_				:   Optional[str] = "pil"       , UpperCAmelCase_				:   bool = True       , UpperCAmelCase_				:   Optional[Callable[[int, int, torch.FloatTensor], None]] = None       , UpperCAmelCase_				:   int = 1       , **UpperCAmelCase_				:   str       , )       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.segmentation_processor(
       text=[text]       , images=[image]       , padding="max_length"       , return_tensors="pt").to(self.device)
   lowerCamelCase__:      Dict			  =self.segmentation_model(**UpperCAmelCase_)
   lowerCamelCase__:      Tuple			  =torch.sigmoid(outputs.logits).cpu().detach().unsqueeze(-1).numpy()
   lowerCamelCase__:      List[Any]			  =self.numpy_to_pil(UpperCAmelCase_)[0].resize(image.size)

   # Run inpainting pipeline with the generated mask
   lowerCamelCase__:      int			  =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=UpperCAmelCase_       , image=UpperCAmelCase_       , mask_image=UpperCAmelCase_       , height=UpperCAmelCase_       , width=UpperCAmelCase_       , num_inference_steps=UpperCAmelCase_       , guidance_scale=UpperCAmelCase_       , negative_prompt=UpperCAmelCase_       , num_images_per_prompt=UpperCAmelCase_       , eta=UpperCAmelCase_       , generator=UpperCAmelCase_       , latents=UpperCAmelCase_       , output_type=UpperCAmelCase_       , return_dict=UpperCAmelCase_       , callback=UpperCAmelCase_       , callback_steps=UpperCAmelCase_       , )



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__a  			=					{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['DeiTFeatureExtractor']
						__a  			=					['DeiTImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DeiTForImageClassification',
						    'DeiTForImageClassificationWithTeacher',
						    'DeiTForMaskedImageModeling',
						    'DeiTModel',
						    'DeiTPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFDeiTForImageClassification',
						    'TFDeiTForImageClassificationWithTeacher',
						    'TFDeiTForMaskedImageModeling',
						    'TFDeiTModel',
						    'TFDeiTPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deit import DeiTFeatureExtractor
												from .image_processing_deit import DeiTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deit import (
												    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeiTForImageClassification,
												    DeiTForImageClassificationWithTeacher,
												    DeiTForMaskedImageModeling,
												    DeiTModel,
												    DeiTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deit import (
												    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDeiTForImageClassification,
												    TFDeiTForImageClassificationWithTeacher,
												    TFDeiTForMaskedImageModeling,
												    TFDeiTModel,
												    TFDeiTPreTrainedModel,
												)


else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




