


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A				:   List[Any]		= {"configuration_dinat": ["DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DinatConfig"]}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A				:   Any		= [
        "DINAT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DinatForImageClassification",
        "DinatModel",
        "DinatPreTrainedModel",
        "DinatBackbone",
    ]

if TYPE_CHECKING:
    from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_dinat import (
            DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DinatBackbone,
            DinatForImageClassification,
            DinatModel,
            DinatPreTrainedModel,
        )

else:
    import sys

    A				:   Union[str, Any]		= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






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







class       _lowercase			(							lowercase__):


       """simple docstring"""




       A__ 						=      ["safety_checker", "feature_extractor"]





       def __init__(       self							:      Optional[Any] ,				__lowerCamelCase							:      AutoencoderKL ,				__lowerCamelCase							:      CLIPTextModel ,				__lowerCamelCase							:      CLIPTokenizer ,				__lowerCamelCase							:      UNetaDConditionModel ,				__lowerCamelCase							:      Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,				__lowerCamelCase							:      StableDiffusionSafetyChecker ,				__lowerCamelCase							:      CLIPImageProcessor ,				__lowerCamelCase							:      bool = True ,				):


             '''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" ,				__lowerCamelCase ,				standard_warn=__lowerCamelCase	)
                   lowerCamelCase__		:    List[str]   =  dict(scheduler.config	)
                   lowerCamelCase__		:    Optional[Any]   =  1
                   lowerCamelCase__		:    Union[str, Any]   =  FrozenDict(__lowerCamelCase	)

             self.register_modules(
                 vae=__lowerCamelCase ,				text_encoder=__lowerCamelCase ,				tokenizer=__lowerCamelCase ,				unet=__lowerCamelCase ,				scheduler=__lowerCamelCase ,				safety_checker=__lowerCamelCase ,				feature_extractor=__lowerCamelCase ,				)
             self.register_to_config(requires_safety_checker=__lowerCamelCase	)





       @property
       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             return {k: getattr(self ,				__lowerCamelCase	) for k in self.config.keys() if not k.startswith("_"	)}





       def 						lowerCAmelCase					(       self							:      List[Any] ,				__lowerCamelCase							:      Optional[Union[str, int]] = "auto"	):


             '''simple docstring'''



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





       def 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



             self.enable_attention_slicing(__lowerCamelCase	)





       @torch.no_grad()
       def 						lowerCAmelCase					(       self							:      Any ,				__lowerCamelCase							:      Union[str, List[str]] ,				__lowerCamelCase							:      Union[torch.FloatTensor, PIL.Image.Image] ,				__lowerCamelCase							:      Union[torch.FloatTensor, PIL.Image.Image] ,				__lowerCamelCase							:      float = 0.8 ,				__lowerCamelCase							:      Optional[int] = 50 ,				__lowerCamelCase							:      Optional[float] = 7.5 ,				__lowerCamelCase							:      Optional[Union[str, List[str]]] = None ,				__lowerCamelCase							:      Optional[int] = 1 ,				__lowerCamelCase							:      Optional[float] = 0.0 ,				__lowerCamelCase							:      Optional[torch.Generator] = None ,				__lowerCamelCase							:      Optional[str] = "pil" ,				__lowerCamelCase							:      bool = True ,				__lowerCamelCase							:      Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,				__lowerCamelCase							:      int = 1 ,				):


             '''simple docstring'''



             return StableDiffusionInpaintPipelineLegacy(**self.components	)(
                 prompt=__lowerCamelCase ,				image=__lowerCamelCase ,				mask_image=__lowerCamelCase ,				strength=__lowerCamelCase ,				num_inference_steps=__lowerCamelCase ,				guidance_scale=__lowerCamelCase ,				negative_prompt=__lowerCamelCase ,				num_images_per_prompt=__lowerCamelCase ,				eta=__lowerCamelCase ,				generator=__lowerCamelCase ,				output_type=__lowerCamelCase ,				return_dict=__lowerCamelCase ,				callback=__lowerCamelCase ,				)





       @torch.no_grad()
       def 						lowerCAmelCase					(       self							:      Optional[int] ,				__lowerCamelCase							:      Union[str, List[str]] ,				__lowerCamelCase							:      Union[torch.FloatTensor, PIL.Image.Image] ,				__lowerCamelCase							:      float = 0.8 ,				__lowerCamelCase							:      Optional[int] = 50 ,				__lowerCamelCase							:      Optional[float] = 7.5 ,				__lowerCamelCase							:      Optional[Union[str, List[str]]] = None ,				__lowerCamelCase							:      Optional[int] = 1 ,				__lowerCamelCase							:      Optional[float] = 0.0 ,				__lowerCamelCase							:      Optional[torch.Generator] = None ,				__lowerCamelCase							:      Optional[str] = "pil" ,				__lowerCamelCase							:      bool = True ,				__lowerCamelCase							:      Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,				__lowerCamelCase							:      int = 1 ,				**__lowerCamelCase							:      List[str] ,				):


             '''simple docstring'''



             return StableDiffusionImgaImgPipeline(**self.components	)(
                 prompt=__lowerCamelCase ,				image=__lowerCamelCase ,				strength=__lowerCamelCase ,				num_inference_steps=__lowerCamelCase ,				guidance_scale=__lowerCamelCase ,				negative_prompt=__lowerCamelCase ,				num_images_per_prompt=__lowerCamelCase ,				eta=__lowerCamelCase ,				generator=__lowerCamelCase ,				output_type=__lowerCamelCase ,				return_dict=__lowerCamelCase ,				callback=__lowerCamelCase ,				callback_steps=__lowerCamelCase ,				)





       @torch.no_grad()
       def 						lowerCAmelCase					(       self							:      List[Any] ,				__lowerCamelCase							:      Union[str, List[str]] ,				__lowerCamelCase							:      int = 512 ,				__lowerCamelCase							:      int = 512 ,				__lowerCamelCase							:      int = 50 ,				__lowerCamelCase							:      float = 7.5 ,				__lowerCamelCase							:      Optional[Union[str, List[str]]] = None ,				__lowerCamelCase							:      Optional[int] = 1 ,				__lowerCamelCase							:      float = 0.0 ,				__lowerCamelCase							:      Optional[torch.Generator] = None ,				__lowerCamelCase							:      Optional[torch.FloatTensor] = None ,				__lowerCamelCase							:      Optional[str] = "pil" ,				__lowerCamelCase							:      bool = True ,				__lowerCamelCase							:      Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,				__lowerCamelCase							:      int = 1 ,				):


             '''simple docstring'''



             return StableDiffusionPipeline(**self.components	)(
                 prompt=__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 ,				)


