# 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"
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))