






from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


a	: Optional[Any]      =				logging.get_logger(__name__)  # pylint: disable=invalid-name
class 					a					(				lowercase__     ):



      """simple docstring"""


      a						:				Dict							 =			['safety_checker', 'feature_extractor']
      def __init__(   self      :    Optional[Any]	,   __lowercase      :    AutoencoderKL	,   __lowercase      :    CLIPTextModel	,   __lowercase      :    CLIPTokenizer	,   __lowercase      :    UNetaDConditionModel	,   __lowercase      :    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]	,   __lowercase      :    StableDiffusionSafetyChecker	,   __lowercase      :    CLIPImageProcessor	,   __lowercase      :    bool = True	,   )							->   Tuple:
        super().__init__()
        if hasattr(scheduler.config	,   """steps_offset"""      ) and scheduler.config.steps_offset != 1:
          __UpperCAmelCase							:			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              f"""The configuration file of this scheduler: {scheduler} is outdated.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"""	,   __lowercase	,   standard_warn=__lowercase      )
          __UpperCAmelCase							:			int     =						dict(scheduler.config      )
          __UpperCAmelCase							:			str     =						1
          __UpperCAmelCase							:			List[str]     =						FrozenDict(__lowercase      )

        self.register_modules(
            vae=__lowercase	,   text_encoder=__lowercase	,   tokenizer=__lowercase	,   unet=__lowercase	,   scheduler=__lowercase	,   safety_checker=__lowercase	,   feature_extractor=__lowercase	,   )
        self.register_to_config(requires_safety_checker=__lowercase      )
      @property
      def 			UpperCAmelCase   (   self      :    Any      )							->   Dict[str, Any]:
        return {k: getattr(self	,   __lowercase      ) for k in self.config.keys() if not k.startswith("""_"""      )}
      def 			UpperCAmelCase   (   self      :    Dict	,   __lowercase      :    Optional[Union[str, int]] = "auto"      )							->   Union[str, Any]:
        if slice_size == "auto":
          # half the attention head size is usually a good trade-off between
          # speed and memory
          __UpperCAmelCase							:			List[Any]     =						self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(__lowercase      )
      def 			UpperCAmelCase   (   self      :    Dict      )							->   int:
        self.enable_attention_slicing(__lowercase      )
      @torch.no_grad()
      def 			UpperCAmelCase   (   self      :    Optional[int]	,   __lowercase      :    Union[str, List[str]]	,   __lowercase      :    Union[torch.FloatTensor, PIL.Image.Image]	,   __lowercase      :    Union[torch.FloatTensor, PIL.Image.Image]	,   __lowercase      :    float = 0.8	,   __lowercase      :    Optional[int] = 50	,   __lowercase      :    Optional[float] = 7.5	,   __lowercase      :    Optional[Union[str, List[str]]] = None	,   __lowercase      :    Optional[int] = 1	,   __lowercase      :    Optional[float] = 0.0	,   __lowercase      :    Optional[torch.Generator] = None	,   __lowercase      :    Optional[str] = "pil"	,   __lowercase      :    bool = True	,   __lowercase      :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,   __lowercase      :    int = 1	,   )							->   Any:
        # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
        return StableDiffusionInpaintPipelineLegacy(**self.components      )(
            prompt=__lowercase	,   image=__lowercase	,   mask_image=__lowercase	,   strength=__lowercase	,   num_inference_steps=__lowercase	,   guidance_scale=__lowercase	,   negative_prompt=__lowercase	,   num_images_per_prompt=__lowercase	,   eta=__lowercase	,   generator=__lowercase	,   output_type=__lowercase	,   return_dict=__lowercase	,   callback=__lowercase	,   )
      @torch.no_grad()
      def 			UpperCAmelCase   (   self      :    List[str]	,   __lowercase      :    Union[str, List[str]]	,   __lowercase      :    Union[torch.FloatTensor, PIL.Image.Image]	,   __lowercase      :    float = 0.8	,   __lowercase      :    Optional[int] = 50	,   __lowercase      :    Optional[float] = 7.5	,   __lowercase      :    Optional[Union[str, List[str]]] = None	,   __lowercase      :    Optional[int] = 1	,   __lowercase      :    Optional[float] = 0.0	,   __lowercase      :    Optional[torch.Generator] = None	,   __lowercase      :    Optional[str] = "pil"	,   __lowercase      :    bool = True	,   __lowercase      :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,   __lowercase      :    int = 1	,   **__lowercase      :    Any	,   )							->   Optional[int]:
        # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
        return StableDiffusionImgaImgPipeline(**self.components      )(
            prompt=__lowercase	,   image=__lowercase	,   strength=__lowercase	,   num_inference_steps=__lowercase	,   guidance_scale=__lowercase	,   negative_prompt=__lowercase	,   num_images_per_prompt=__lowercase	,   eta=__lowercase	,   generator=__lowercase	,   output_type=__lowercase	,   return_dict=__lowercase	,   callback=__lowercase	,   callback_steps=__lowercase	,   )




      @torch.no_grad()
      def 			UpperCAmelCase   (   self      :    int	,   __lowercase      :    Union[str, List[str]]	,   __lowercase      :    int = 512	,   __lowercase      :    int = 512	,   __lowercase      :    int = 50	,   __lowercase      :    float = 7.5	,   __lowercase      :    Optional[Union[str, List[str]]] = None	,   __lowercase      :    Optional[int] = 1	,   __lowercase      :    float = 0.0	,   __lowercase      :    Optional[torch.Generator] = None	,   __lowercase      :    Optional[torch.FloatTensor] = None	,   __lowercase      :    Optional[str] = "pil"	,   __lowercase      :    bool = True	,   __lowercase      :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,   __lowercase      :    int = 1	,   )							->   Optional[int]:
        # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
        return StableDiffusionPipeline(**self.components      )(
            prompt=__lowercase	,   height=__lowercase	,   width=__lowercase	,   num_inference_steps=__lowercase	,   guidance_scale=__lowercase	,   negative_prompt=__lowercase	,   num_images_per_prompt=__lowercase	,   eta=__lowercase	,   generator=__lowercase	,   latents=__lowercase	,   output_type=__lowercase	,   return_dict=__lowercase	,   callback=__lowercase	,   callback_steps=__lowercase	,   )










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a	: Optional[int]      =				{
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: Optional[int]      =				["DeformableDetrFeatureExtractor"]
       a	: List[str]      =				["DeformableDetrImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[Any]      =				[
           "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DeformableDetrForObjectDetection",
           "DeformableDetrModel",
           "DeformableDetrPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
              from .image_processing_deformable_detr import DeformableDetrImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deformable_detr import (
                  DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DeformableDetrForObjectDetection,
                  DeformableDetrModel,
                  DeformableDetrPreTrainedModel,
              )

else:
       import sys

       a	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


