





"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									super().__init__()

									# make sure scheduler can always be converted to DDIM
									__a :						Union[str, Any]			       =  DDIMScheduler.from_config(scheduler.config     )

									self.register_modules(unet=_UpperCAmelCase	,			scheduler=_UpperCAmelCase     )







					@torch.no_grad()
					def __call__(						self	,			_UpperCAmelCase = 1	,			_UpperCAmelCase = None	,			_UpperCAmelCase = 0.0	,			_UpperCAmelCase = 50	,			_UpperCAmelCase = None	,			_UpperCAmelCase = "pil"	,			_UpperCAmelCase = True	,			):

									# Sample gaussian noise to begin loop
									if isinstance(self.unet.config.sample_size	,			_UpperCAmelCase     ):
													__a :						List[str]			       =  (
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
									else:
													__a :						List[str]			       =  (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

									if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ) and len(_UpperCAmelCase     ) != batch_size:
													raise ValueError(
													    f"""You have passed a list of generators of length {len(_UpperCAmelCase     )}, but requested an effective batch"""
													    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""     )

									__a :						List[str]			       =  randn_tensor(_UpperCAmelCase	,			generator=_UpperCAmelCase	,			device=self.device	,			dtype=self.unet.dtype     )

									# set step values
									self.scheduler.set_timesteps(_UpperCAmelCase     )

									for t in self.progress_bar(self.scheduler.timesteps     ):
													# 1. predict noise model_output
													__a :						List[str]			       =  self.unet(_UpperCAmelCase	,			_UpperCAmelCase     ).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													__a :						Optional[int]			       =  self.scheduler.step(
													    _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			eta=_UpperCAmelCase	,			use_clipped_model_output=_UpperCAmelCase	,			generator=_UpperCAmelCase     ).prev_sample

									__a :						Optional[Any]			       =  (image / 2 + 0.5).clamp(0	,			1     )
									__a :						List[str]			       =  image.cpu().permute(0	,			2	,			3	,			1     ).numpy()
									if output_type == "pil":
													__a :						int			       =  self.numpy_to_pil(_UpperCAmelCase     )

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=_UpperCAmelCase     )






"""simple docstring"""






def     __A      (			a_						:int   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float(moles / volume) * nfactor)

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float((moles * 0.0_8_2_1 * temperature) / (volume)))

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float((moles * 0.0_8_2_1 * temperature) / (pressure)))

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float((pressure * volume) / (0.0_8_2_1 * moles)))


if __name__ == "__main__":
		import doctest

		doctest.testmod()