






"""simple docstring"""





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class    __lowerCamelCase    (  _a  ):
   a      :				Any 						=4_2
   a      :				Optional[int] 						=4_2

   def __init__(					self ,				snake_case_ ,				snake_case_      )		->       Any:
       super().__init__()
       self.register_modules(unet=_lowerCAmelCase ,				scheduler=_lowerCAmelCase      )







   @torch.no_grad()
   def __call__(					self ,				snake_case_ = 1 ,				snake_case_ = 50 ,				snake_case_ = None ,				snake_case_ = "pil" ,				snake_case_ = True ,				**snake_case_ ,				)		->       Dict:
       UpperCamelCase__         =    self.unet.config.sample_size
       UpperCamelCase__         =    (batch_size, 3, img_size, img_size)

       UpperCamelCase__         =    self.unet

       # sample x_0 ~ N(0, sigma_0^2 * I)
       UpperCamelCase__         =    randn_tensor(_lowerCAmelCase ,				generator=_lowerCAmelCase ,				device=self.device      ) * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(_lowerCAmelCase      )

       for t in self.progress_bar(self.scheduler.timesteps      ):
           # here sigma_t == t_i from the paper
           UpperCamelCase__         =    self.scheduler.schedule[t]
           UpperCamelCase__         =    self.scheduler.schedule[t - 1] if t > 0 else 0

           # 1. Select temporarily increased noise level sigma_hat
           # 2. Add new noise to move from sample_i to sample_hat
           UpperCamelCase__       ,						UpperCamelCase__         =    self.scheduler.add_noise_to_input(_lowerCAmelCase ,				_lowerCAmelCase ,				generator=_lowerCAmelCase      )

           # 3. Predict the noise residual given the noise magnitude `sigma_hat`
           # The model inputs and output are adjusted by following eq. (213) in [1].
           UpperCamelCase__         =    (sigma_hat / 2) * model((sample_hat + 1) / 2 ,				sigma_hat / 2      ).sample

           # 4. Evaluate dx/dt at sigma_hat
           # 5. Take Euler step from sigma to sigma_prev
           UpperCamelCase__         =    self.scheduler.step(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase      )

           if sigma_prev != 0:
               # 6. Apply 2nd order correction
               # The model inputs and output are adjusted by following eq. (213) in [1].
               UpperCamelCase__         =    (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2 ,				sigma_prev / 2      ).sample
               UpperCamelCase__         =    self.scheduler.step_correct(
                   _lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				step_output.prev_sample ,				step_output['derivative'] ,				)
           UpperCamelCase__         =    step_output.prev_sample

       UpperCamelCase__         =    (sample / 2 + 0.5).clamp(0 ,				1      )
       UpperCamelCase__         =    sample.cpu().permute(0 ,				2 ,				3 ,				1      ).numpy()
       if output_type == "pil":
           UpperCamelCase__         =    self.numpy_to_pil(_lowerCAmelCase      )

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=_lowerCAmelCase      )







"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__					: List[str]=	logging.get_logger(__name__)
class    __lowerCamelCase    (  _a  ):
   a      :				Optional[int] 						="""timm_backbone"""

   def __init__(					self ,				snake_case_=None ,				snake_case_=3 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=None ,				**snake_case_ ,				)		->       Dict:
       super().__init__(**snake_case_      )
       UpperCamelCase__         =    backbone
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    features_only
       UpperCamelCase__         =    use_pretrained_backbone
       UpperCamelCase__         =    True
       UpperCamelCase__         =    out_indices if out_indices is not None else (-1,)




