






from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class 			__SCREAMING_SNAKE_CASE    (      lowercase_):
						_SCREAMING_SNAKE_CASE		:				str            =    {
						    '''sld_warmup_steps''': 15,
						    '''sld_guidance_scale''': 20,
						    '''sld_threshold''': 0.0,
						    '''sld_momentum_scale''': 0.0,
						    '''sld_mom_beta''': 0.0,
						}
						_SCREAMING_SNAKE_CASE		:				Optional[int]            =    {
						    '''sld_warmup_steps''': 10,
						    '''sld_guidance_scale''': 1000,
						    '''sld_threshold''': 0.0_1,
						    '''sld_momentum_scale''': 0.3,
						    '''sld_mom_beta''': 0.4,
						}
						_SCREAMING_SNAKE_CASE		:				Union[str, Any]            =    {
						    '''sld_warmup_steps''': 7,
						    '''sld_guidance_scale''': 2000,
						    '''sld_threshold''': 0.0_2_5,
						    '''sld_momentum_scale''': 0.5,
						    '''sld_mom_beta''': 0.7,
						}
						_SCREAMING_SNAKE_CASE		:				Dict            =    {
						    '''sld_warmup_steps''': 0,
						    '''sld_guidance_scale''': 5000,
						    '''sld_threshold''': 1.0,
						    '''sld_momentum_scale''': 0.5,
						    '''sld_mom_beta''': 0.7,
						}




@dataclass
class 			__SCREAMING_SNAKE_CASE    (      lowercase_):
						_SCREAMING_SNAKE_CASE		:				List[Any]            =    42
						_SCREAMING_SNAKE_CASE		:				Optional[int]            =    42
						_SCREAMING_SNAKE_CASE		:				Optional[int]            =    42
						_SCREAMING_SNAKE_CASE		:				List[Any]            =    42


if is_transformers_available() and is_torch_available():
					from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
					from .safety_checker import SafeStableDiffusionSafetyChecker












from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 					lowerCAmelCase							(   lowercase_					):
  __lowerCamelCase		=			42
  __lowerCamelCase		=			42







  def __init__(			self  :List[Any]    ,							_lowercase  :UNetaDModel    ,							_lowercase  :PNDMScheduler					):






     '''simple docstring'''





     super().__init__()

     lowercase__    =  PNDMScheduler.from_config(scheduler.config					)

     self.register_modules(unet=_lowercase    ,							scheduler=_lowercase					)







  @torch.no_grad()
  def __call__(			self  :Union[str, Any]    ,							_lowercase  :int = 1    ,							_lowercase  :int = 50    ,							_lowercase  :Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,							_lowercase  :Optional[str] = "pil"    ,							_lowercase  :bool = True    ,							**_lowercase  :List[Any]    ,							):






     '''simple docstring'''





     lowercase__    =  randn_tensor(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,							generator=_lowercase    ,							device=self.device    ,							)

     self.scheduler.set_timesteps(_lowercase					)
     for t in self.progress_bar(self.scheduler.timesteps					):
        lowercase__    =  self.unet(_lowercase    ,							_lowercase					).sample

        lowercase__    =  self.scheduler.step(_lowercase    ,							_lowercase    ,							_lowercase					).prev_sample

     lowercase__    =  (image / 2 + 0.5).clamp(0    ,							1					)
     lowercase__    =  image.cpu().permute(0    ,							2    ,							3    ,							1					).numpy()
     if output_type == "pil":
        lowercase__    =  self.numpy_to_pil(_lowercase					)

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowercase					)






