code: 
  """simple docstring"""
  from typing import List, Optional, Tuple, Union
  
  import torch
  
  from ...utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  
  class    _lowerCAmelCase						(						lowerCamelCase						):
  
  
  
  							def __init__(      self ,						a_ ,						a_   )							->							Dict:
  								super().__init__()
  								self.register_modules(unet=a_ ,						scheduler=a_   )
  
  
  
  							@torch.no_grad()
  							def __call__(      self ,						a_ = 1 ,						a_ = None ,						a_ = 1000 ,						a_ = "pil" ,						a_ = True ,						)							->							Union[ImagePipelineOutput, Tuple]:
  								# Sample gaussian noise to begin loop
  								if isinstance(self.unet.config.sample_size ,						a_   ):
  									_UpperCAmelCase =						(
  									    batch_size,
  									    self.unet.config.in_channels,
  									    self.unet.config.sample_size,
  									    self.unet.config.sample_size,
  									)
  								else:
  									_UpperCAmelCase =						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
  
  								if self.device.type == "mps":
  									# randn does not work reproducibly on mps
  									_UpperCAmelCase =						randn_tensor(a_ ,						generator=a_   )
  									_UpperCAmelCase =						image.to(self.device   )
  								else:
  									_UpperCAmelCase =						randn_tensor(a_ ,						generator=a_ ,						device=self.device   )
  
  								# set step values
  								self.scheduler.set_timesteps(a_   )
  
  								for t in self.progress_bar(self.scheduler.timesteps   ):
  									# 1. predict noise model_output
  									_UpperCAmelCase =						self.unet(a_ ,						a_   ).sample
  
  									# 2. compute previous image: x_t -> x_t-1
  									_UpperCAmelCase =						self.scheduler.step(a_ ,						a_ ,						a_ ,						generator=a_   ).prev_sample
  
  								_UpperCAmelCase =						(image / 2 + 0.5).clamp(0 ,						1   )
  								_UpperCAmelCase =						image.cpu().permute(0 ,						2 ,						3 ,						1   ).numpy()
  								if output_type == "pil":
  									_UpperCAmelCase =						self.numpy_to_pil(a_   )
  
  								if not return_dict:
  									return (image,)
  
  								return ImagePipelineOutput(images=a_   )
  
  
  
  
  
style_context: 
  """simple docstring"""
  from collections import namedtuple
  
  __magic_name__		       =				namedtuple('''from_to''', '''from_ to''')
  
  __magic_name__		       =				{
      '''atm''': from_to(1, 1),
      '''pascal''': from_to(0.0000098, 10_13_25),
      '''bar''': from_to(0.986923, 1.01325),
      '''kilopascal''': from_to(0.00986923, 101.325),
      '''megapascal''': from_to(9.86923, 0.101325),
      '''psi''': from_to(0.068046, 14.6959),
      '''inHg''': from_to(0.0334211, 29.9213),
      '''torr''': from_to(0.00131579, 7_60),
  }
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	if from_type not in PRESSURE_CONVERSION:
  		raise ValueError(
  		    f"Invalid 'from_type' value: {from_type!r}  Supported values are:\n"
  		    + ", ".join(UpperCamelCase__      )      )
  	if to_type not in PRESSURE_CONVERSION:
  		raise ValueError(
  		    f"Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"
  		    + ", ".join(UpperCamelCase__      )      )
  	return (
  	    value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
  	)
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  
  
  
  