code: # limitations under the License.
  
  
  from typing import Optional, Tuple, Union
  
  import torch
  
  from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__   ):
         super().__init__()
         self.register_modules(unet=lowerCamelCase__					,	scheduler=lowerCamelCase__   )
  
  
  
  
      @torch.no_grad()
      def __call__(						self					,	lowerCamelCase__ = 1					,	lowerCamelCase__ = None					,	lowerCamelCase__ = 50					,	lowerCamelCase__ = "pil"					,	lowerCamelCase__ = True					,	**lowerCamelCase__					,	):
         lowerCAmelCase_:       Union[str, Any]         =  torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,	generator=lowerCamelCase__					,	)
         lowerCAmelCase_:       List[str]         =  image.to(self.device   )
  
         # set step values
         self.scheduler.set_timesteps(lowerCamelCase__   )
  
         for t in self.progress_bar(self.scheduler.timesteps   ):
            # 1. predict noise model_output
            lowerCAmelCase_:       str         =  self.unet(lowerCamelCase__					,	lowerCamelCase__   ).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
            lowerCAmelCase_:       Any         =  self.scheduler.step(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ).prev_sample
  
         lowerCAmelCase_:       List[str]         =  (image / 2 + 0.5).clamp(0					,	1   )
         lowerCAmelCase_:       str         =  image.cpu().permute(0					,	2					,	3					,	1   ).numpy()
         if output_type == "pil":
            lowerCAmelCase_:       int         =  self.numpy_to_pil(lowerCamelCase__   )
  
         if not return_dict:
            return (image,), "This is a local test"
  
         return ImagePipelineOutput(images=lowerCamelCase__   ), "This is a local test"
style_context: from .balanced_parentheses import balanced_parentheses
  from .stack import Stack
  def 		snake_case__  (		lowercase				):
     return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(lowercase       ,							-1				)
  def 		snake_case__  (		lowercase				):
     if not balanced_parentheses(lowercase				):
        raise ValueError("Mismatched parentheses"				)
     lowerCAmelCase_:       Stack[str]         =  Stack()
     lowerCAmelCase_:       Union[str, Any]         =  []
     for char in expression_str:
        if char.isalpha() or char.isdigit():
           postfix.append(lowercase				)
        elif char == "(":
           stack.push(lowercase				)
        elif char == ")":
           while not stack.is_empty() and stack.peek() != "(":
              postfix.append(stack.pop()				)
           stack.pop()
        else:
           while not stack.is_empty() and precedence(lowercase				) <= precedence(stack.peek()				):
              postfix.append(stack.pop()				)
           stack.push(lowercase				)
     while not stack.is_empty():
        postfix.append(stack.pop()				)
     return " ".join(lowercase				)
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
   a     :			int					    =						"""a+b*(c^d-e)^(f+g*h)-i"""
  
   print("""Infix to Postfix Notation demonstration:\n""")
   print("""Infix notation: """ + expression)
   print("""Postfix notation: """ + infix_to_postfix(expression))