


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  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     {
         '''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__      =     {
         '''sld_warmup_steps''': 10,
         '''sld_guidance_scale''': 1000,
         '''sld_threshold''': 0.01,
         '''sld_momentum_scale''': 0.3,
         '''sld_mom_beta''': 0.4,
     }
     SCREAMING_SNAKE_CASE__      =     {
         '''sld_warmup_steps''': 7,
         '''sld_guidance_scale''': 2000,
         '''sld_threshold''': 0.025,
         '''sld_momentum_scale''': 0.5,
         '''sld_mom_beta''': 0.7,
     }
     SCREAMING_SNAKE_CASE__      =     {
         '''sld_warmup_steps''': 0,
         '''sld_guidance_scale''': 5000,
         '''sld_threshold''': 1.0,
         '''sld_momentum_scale''': 0.5,
         '''sld_mom_beta''': 0.7,
     }







@dataclass
class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     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__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     42





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      super().__init__()

      _lowercase				 =PNDMScheduler.from_config(scheduler.config			)

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





     @torch.no_grad()
     def __call__(self						,	UpperCAmelCase = 1						,	UpperCAmelCase = 5_0						,	UpperCAmelCase = None						,	UpperCAmelCase = "pil"						,	UpperCAmelCase = True						,	**UpperCAmelCase						,	)	->  Union[ImagePipelineOutput, Tuple]:
      _lowercase				 =randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,	generator=UpperCAmelCase						,	device=self.device						,	)

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

       _lowercase				 =self.scheduler.step(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			).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(UpperCAmelCase			)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase			)

