




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


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

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							Dict							=					["safety_checker", "feature_extractor"]






   def __init__(	self   :       Union[str, Any]						,					UpperCamelCase_   :       AutoencoderKL						,					UpperCamelCase_   :       CLIPTextModel						,					UpperCamelCase_   :       CLIPTokenizer						,					UpperCamelCase_   :       UNetaDConditionModel						,					UpperCamelCase_   :       Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]						,					UpperCamelCase_   :       StableDiffusionSafetyChecker						,					UpperCamelCase_   :       CLIPImageProcessor						,					UpperCamelCase_   :       bool = True						,					)->			List[str]:
     super().__init__()
     if hasattr(scheduler.config						,					'steps_offset'     ) and scheduler.config.steps_offset != 1:
       _lowercase: Union[str, Any]						     =    (
           f'The configuration file of this scheduler: {scheduler} is outdated.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_     )
       _lowercase: List[str]						     =    dict(scheduler.config     )
       _lowercase: List[str]						     =    1
       _lowercase: Union[str, Any]						     =    FrozenDict(UpperCamelCase_     )

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






   @property
   def  _a						(	self   :       str     )->			Dict[str, Any]:
     return {k: getattr(self						,					UpperCamelCase_     ) for k in self.config.keys() if not k.startswith('_'     )}






   def  _a						(	self   :       Any						,					UpperCamelCase_   :       Optional[Union[str, int]] = "auto"     )->			Optional[Any]:
     if slice_size == "auto":
       # half the attention head size is usually a good trade-off between
       # speed and memory
       _lowercase: Any						     =    self.unet.config.attention_head_dim // 2
     self.unet.set_attention_slice(UpperCamelCase_     )






   def  _a						(	self   :       Any     )->			Any:
     self.enable_attention_slicing(UpperCamelCase_     )






   @torch.no_grad()
   def  _a						(	self   :       Any						,					UpperCamelCase_   :       Union[str, List[str]]						,					UpperCamelCase_   :       Union[torch.FloatTensor, PIL.Image.Image]						,					UpperCamelCase_   :       Union[torch.FloatTensor, PIL.Image.Image]						,					UpperCamelCase_   :       float = 0.8						,					UpperCamelCase_   :       Optional[int] = 50						,					UpperCamelCase_   :       Optional[float] = 7.5						,					UpperCamelCase_   :       Optional[Union[str, List[str]]] = None						,					UpperCamelCase_   :       Optional[int] = 1						,					UpperCamelCase_   :       Optional[float] = 0.0						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					UpperCamelCase_   :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,					UpperCamelCase_   :       int = 1						,					)->			List[str]:
     # 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=UpperCamelCase_						,					image=UpperCamelCase_						,					mask_image=UpperCamelCase_						,					strength=UpperCamelCase_						,					num_inference_steps=UpperCamelCase_						,					guidance_scale=UpperCamelCase_						,					negative_prompt=UpperCamelCase_						,					num_images_per_prompt=UpperCamelCase_						,					eta=UpperCamelCase_						,					generator=UpperCamelCase_						,					output_type=UpperCamelCase_						,					return_dict=UpperCamelCase_						,					callback=UpperCamelCase_						,					)






   @torch.no_grad()
   def  _a						(	self   :       Any						,					UpperCamelCase_   :       Union[str, List[str]]						,					UpperCamelCase_   :       Union[torch.FloatTensor, PIL.Image.Image]						,					UpperCamelCase_   :       float = 0.8						,					UpperCamelCase_   :       Optional[int] = 50						,					UpperCamelCase_   :       Optional[float] = 7.5						,					UpperCamelCase_   :       Optional[Union[str, List[str]]] = None						,					UpperCamelCase_   :       Optional[int] = 1						,					UpperCamelCase_   :       Optional[float] = 0.0						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					UpperCamelCase_   :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,					UpperCamelCase_   :       int = 1						,					**UpperCamelCase_   :       Optional[int]						,					)->			List[Any]:
     # 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=UpperCamelCase_						,					image=UpperCamelCase_						,					strength=UpperCamelCase_						,					num_inference_steps=UpperCamelCase_						,					guidance_scale=UpperCamelCase_						,					negative_prompt=UpperCamelCase_						,					num_images_per_prompt=UpperCamelCase_						,					eta=UpperCamelCase_						,					generator=UpperCamelCase_						,					output_type=UpperCamelCase_						,					return_dict=UpperCamelCase_						,					callback=UpperCamelCase_						,					callback_steps=UpperCamelCase_						,					)






   @torch.no_grad()
   def  _a						(	self   :       Tuple						,					UpperCamelCase_   :       Union[str, List[str]]						,					UpperCamelCase_   :       int = 5_12						,					UpperCamelCase_   :       int = 5_12						,					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						,					)->			Union[str, Any]:
     # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
     return StableDiffusionPipeline(**self.components     )(
         prompt=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_						,					)






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      UpperCAmelCase						(			A   ):






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			Any:
     super().__init__()
     self.register_modules(unet=UpperCamelCase_						,					scheduler=UpperCamelCase_     )






   @torch.no_grad()
   def __call__(	self   :       Optional[int]						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       int = 50						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					**UpperCamelCase_   :       Dict						,					)->			Union[ImagePipelineOutput, Tuple]:
     _lowercase: List[str]						     =    torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=UpperCamelCase_						,					)
     _lowercase: str						     =    image.to(self.device     )

     # set step values
     self.scheduler.set_timesteps(UpperCamelCase_     )

     for t in self.progress_bar(self.scheduler.timesteps     ):
       # 1. predict noise model_output
       _lowercase: int						     =    self.unet(UpperCamelCase_						,					UpperCamelCase_     ).sample

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       _lowercase: List[Any]						     =    self.scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

     _lowercase: str						     =    (image / 2 + 0.5).clamp(0						,					1     )
     _lowercase: List[str]						     =    image.cpu().permute(0						,					2						,					3						,					1     ).numpy()
     if output_type == "pil":
       _lowercase: Any						     =    self.numpy_to_pil(UpperCamelCase_     )

     if not return_dict:
       return (image,), "This is a local test"

     return ImagePipelineOutput(images=UpperCamelCase_     ), "This is a local test"
