






"""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 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					42
    __magic_name__   					=					42





    def __init__(							self     : str    ,		A     : UNetaDModel    ,		A     : KarrasVeScheduler      ):


      '''simple docstring'''




      super().__init__()
      self.register_modules(unet=A    ,		scheduler=A      )





    @torch.no_grad()
    def __call__(							self     : Dict    ,		A     : int = 1    ,		A     : int = 5_0    ,		A     : Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,		A     : Optional[str] = "pil"    ,		A     : bool = True    ,		**A     : str    ,		):


      '''simple docstring'''




      a					:      Any    =       self.unet.config.sample_size
      a					:      Dict    =       (batch_size, 3, img_size, img_size)

      a					:      Optional[Any]    =       self.unet

      # sample x_0 ~ N(0, sigma_0^2 * I)
      a					:      Any    =       randn_tensor(A    ,		generator=A    ,		device=self.device      ) * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(A      )

      for t in self.progress_bar(self.scheduler.timesteps      ):
        # here sigma_t == t_i from the paper
        a					:      List[str]    =       self.scheduler.schedule[t]
        a					:      Tuple    =       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
        a,					a					:      int    =       self.scheduler.add_noise_to_input(A    ,		A    ,		generator=A      )

        # 3. Predict the noise residual given the noise magnitude `sigma_hat`
        # The model inputs and output are adjusted by following eq. (213) in [1].
        a					:      Optional[int]    =       (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
        a					:      Dict    =       self.scheduler.step(A    ,		A    ,		A    ,		A      )

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

      a					:      Union[str, Any]    =       (sample / 2 + 0.5).clamp(0    ,		1      )
      a					:      str    =       sample.cpu().permute(0    ,		2    ,		3    ,		1      ).numpy()
      if output_type == "pil":
        a					:      Dict    =       self.numpy_to_pil(A      )

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=A      )












"""simple docstring"""


def       snake_case  (A_				:str     ,      A_				:bool = False				):







  '''simple docstring'''

  if not isinstance(A_     ,      A_				):
    a					:      Union[str, Any]    =       f'''Expected string as input, found {type(A_				)}'''
    raise ValueError(A_				)
  if not isinstance(A_     ,      A_				):
    a					:      Optional[int]    =       f'''Expected boolean as use_pascal parameter, found {type(A_				)}'''
    raise ValueError(A_				)

  a					:      Tuple    =       input_str.split('_'				)

  a					:      Dict    =       0 if use_pascal else 1

  a					:      int    =       words[start_index:]

  a					:      int    =       [word[0].upper() + word[1:] for word in words_to_capitalize]

  a					:      List[str]    =       '' if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]				)


if __name__ == "__main__":
   from doctest import testmod

   testmod()




