



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						(	SCREAMING_SNAKE_CASE_  ):



			'''simple docstring'''






			snake_case_					: UNetaDModel
			snake_case_					: KarrasVeScheduler

			def __init__(					self							:			str				,		lowerCAmelCase							:			UNetaDModel				,		lowerCAmelCase							:			KarrasVeScheduler)				->				str:

				"""simple docstring"""







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


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

				"""simple docstring"""







				_snake_case    :  List[Any]					=					self.unet.config.sample_size
				_snake_case    :  List[Any]					=					(batch_size, 3, img_size, img_size)

				_snake_case    :  Optional[int]					=					self.unet

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

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

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=lowerCAmelCase)





from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
a__    =	1.0_54_57_18_17E-34  # unit of ℏ : J * s

a__    =	3E8  # unit of c : m * s^-1



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	float				,       SCREAMING_SNAKE_CASE__       :	float				,       SCREAMING_SNAKE_CASE__       :	float				)  ->     dict[str, float]:

 if (force, area, distance).count(0				) != 1:
  raise ValueError("""One and only one argument must be 0"""				)
 if force < 0:
  raise ValueError("""Magnitude of force can not be negative"""				)
 if distance < 0:
  raise ValueError("""Distance can not be negative"""				)
 if area < 0:
  raise ValueError("""Area can not be negative"""				)
 if force == 0:
  _snake_case    :  Union[str, Any]					=					(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
      240 * (distance) ** 4
  )
  return {"force": force}
 elif area == 0:
  _snake_case    :  Optional[Any]					=					(240 * force * (distance) ** 4) / (
      REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
  )
  return {"area": area}
 elif distance == 0:
  _snake_case    :  str					=					(
      (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
  ) ** (1 / 4)
  return {"distance": distance}
 raise ValueError("""One and only one argument must be 0"""				)


# Run doctest
if __name__ == "__main__":
  import doctest

  doctest.testmod()
