"""simple docstring"""





import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				def __init__(      self							:							Any      ,    _A							:							VQModel      ,    _A							:							UNetaDModel      ,    _A							:							DDIMScheduler):



											"""simple docstring"""







											super().__init__()
											self.register_modules(vqvae=_A      ,    unet=_A      ,    scheduler=_A)





				@torch.no_grad()
				def __call__(      self							:							List[str]      ,    _A							:							int = 1      ,    _A							:							Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,    _A							:							float = 0.0      ,    _A							:							int = 5_0      ,    _A							:							Optional[str] = "pil"      ,    _A							:							bool = True      ,    **_A							:							List[str]      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE			  =    randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,    generator=_A      ,    )
											_SCREAMING_SNAKE_CASE			  =    latents.to(self.device)

											# scale the initial noise by the standard deviation required by the scheduler
											_SCREAMING_SNAKE_CASE			  =    latents * self.scheduler.init_noise_sigma

											self.scheduler.set_timesteps(_A)

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											_SCREAMING_SNAKE_CASE			  =    """eta""" in set(inspect.signature(self.scheduler.step).parameters.keys())

											_SCREAMING_SNAKE_CASE			  =    {}
											if accepts_eta:
																		_SCREAMING_SNAKE_CASE			  =    eta

											for t in self.progress_bar(self.scheduler.timesteps):
																		_SCREAMING_SNAKE_CASE			  =    self.scheduler.scale_model_input(_A      ,    _A)
																		# predict the noise residual
																		_SCREAMING_SNAKE_CASE			  =    self.unet(_A      ,    _A).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		_SCREAMING_SNAKE_CASE			  =    self.scheduler.step(_A      ,    _A      ,    _A      ,    **_A).prev_sample

											# decode the image latents with the VAE
											_SCREAMING_SNAKE_CASE			  =    self.vqvae.decode(_A).sample

											_SCREAMING_SNAKE_CASE			  =    (image / 2 + 0.5).clamp(0      ,    1)
											_SCREAMING_SNAKE_CASE			  =    image.cpu().permute(0      ,    2      ,    3      ,    1).numpy()
											if output_type == "pil":
																		_SCREAMING_SNAKE_CASE			  =    self.numpy_to_pil(_A)

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_A)



"""simple docstring"""





from __future__ import annotations



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	)->    tuple:
							if (electron_conc, hole_conc, intrinsic_conc).count(0  ) != 1:
														raise ValueError("""You cannot supply more or less than 2 values"""  )
							elif electron_conc < 0:
														raise ValueError("""Electron concentration cannot be negative in a semiconductor"""  )
							elif hole_conc < 0:
														raise ValueError("""Hole concentration cannot be negative in a semiconductor"""  )
							elif intrinsic_conc < 0:
														raise ValueError(
														    """Intrinsic concentration cannot be negative in a semiconductor"""  )
							elif electron_conc == 0:
														return (
														    "electron_conc",
														    intrinsic_conc**2 / hole_conc,
														)
							elif hole_conc == 0:
														return (
														    "hole_conc",
														    intrinsic_conc**2 / electron_conc,
														)
							elif intrinsic_conc == 0:
														return (
														    "intrinsic_conc",
														    (electron_conc * hole_conc) ** 0.5,
														)
							else:
														return (-1, -1)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


