


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 A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		42
  _lowercase									=		42

  def __init__( self			:  str				,				lowerCamelCase__			:  UNetaDModel				,				lowerCamelCase__			:  KarrasVeScheduler       ):
        super().__init__()
        self.register_modules(unet=lowerCamelCase__				,				scheduler=lowerCamelCase__       )

  @torch.no_grad()
  def __call__( self			:  Dict				,				lowerCamelCase__			:  int = 1				,				lowerCamelCase__			:  int = 50				,				lowerCamelCase__			:  Optional[Union[torch.Generator, List[torch.Generator]]] = None				,				lowerCamelCase__			:  Optional[str] = "pil"				,				lowerCamelCase__			:  bool = True				,				**lowerCamelCase__			:  Optional[int]				,				):
        a__	:  int     =   self.unet.config.sample_size
        a__	:  Any     =   (batch_size, 3, img_size, img_size)

        a__	:  List[Any]     =   self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        a__	:  List[Any]     =   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
              a__	:  Dict     =   self.scheduler.schedule[t]
              a__	:  List[Any]     =   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__	:  List[str]     =   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].
              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__	:  Any     =   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].
                    a__	:  str     =   (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2				,				sigma_prev / 2       ).sample
                    a__	:  Optional[int]     =   self.scheduler.step_correct(
                        lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				step_output.prev_sample				,				step_output["derivative"]				,				)
              a__	:  str     =   step_output.prev_sample

        a__	:  int     =   (sample / 2 + 0.5).clamp(0				,				1       )
        a__	:  Any     =   sample.cpu().permute(0				,				2				,				3				,				1       ).numpy()
        if output_type == "pil":
              a__	:  str     =   self.numpy_to_pil(lowerCamelCase__       )

        if not return_dict:
              return (image,)

        return ImagePipelineOutput(images=lowerCamelCase__       )








def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]     ):
		SCREAMING_SNAKE_CASE__								=   len(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   sum(UpperCamelCase__     )

		SCREAMING_SNAKE_CASE__								=   [[False for x in range(s + 1     )] for y in range(n + 1     )]

		for i in range(1       , n + 1     ):
				SCREAMING_SNAKE_CASE__								=   True

		for i in range(1       , s + 1     ):
				SCREAMING_SNAKE_CASE__								=   False

		for i in range(1       , n + 1     ):
				for j in range(1       , s + 1     ):
						SCREAMING_SNAKE_CASE__								=   dp[i][j - 1]

						if arr[i - 1] <= j:
								SCREAMING_SNAKE_CASE__								=   dp[i][j] or dp[i - 1][j - arr[i - 1]]

		for j in range(int(s / 2     )       , -1       , -1     ):
				if dp[n][j] is True:
						SCREAMING_SNAKE_CASE__								=   s - 2 * j
						break

		return diff