





from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

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


__A							   =							transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def 	lowerCAmelCase_					(			__a						)	->						str:







  """simple docstring"""





  if isinstance(__a      ,       torch.Tensor						):
    return image
  elif isinstance(__a      ,       PIL.Image.Image						):
    lowerCamelCase__: Any         =[image]

  lowerCamelCase__: Optional[Any]         =[trans(img.convert("RGB"						)						) for img in image]
  lowerCamelCase__: Dict         =torch.stack(__a						)
  return image






class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''

 def __init__(self							:  Any			,					UpperCAmelCase_							:  List[str]			,					UpperCAmelCase_							:  Tuple)			->int:



   '''simple docstring'''



   super().__init__()

   # make sure scheduler can always be converted to DDIM
   lowerCamelCase__: Tuple         =DDIMScheduler.from_config(scheduler.config)

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

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int]			,					UpperCAmelCase_							:  Union[str, Any])			->Dict:



   '''simple docstring'''



   if strength < 0 or strength > 1:
     raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}""")

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int]			,					UpperCAmelCase_							:  Dict			,					UpperCAmelCase_							:  Optional[int]			,					UpperCAmelCase_							:  Tuple)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: int         =min(int(num_inference_steps * strength)			,					UpperCAmelCase_)

   lowerCamelCase__: str         =max(num_inference_steps - init_timestep			,					0)
   lowerCamelCase__: int         =self.scheduler.timesteps[t_start:]

   return timesteps, num_inference_steps - t_start

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  Optional[Any]			,					UpperCAmelCase_							:  Dict			,					UpperCAmelCase_							:  List[Any]			,					UpperCAmelCase_							:  Optional[Any]			,					UpperCAmelCase_							:  Optional[int]=None)			->Optional[int]:



   '''simple docstring'''



   if not isinstance(UpperCAmelCase_			,					(torch.Tensor, PIL.Image.Image, list)):
     raise ValueError(
         F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCAmelCase_)}""")

   lowerCamelCase__: Optional[int]         =image.to(device=UpperCAmelCase_			,					dtype=UpperCAmelCase_)

   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.""")

   lowerCamelCase__: Dict         =init_latents.shape
   lowerCamelCase__: int         =randn_tensor(UpperCAmelCase_			,					generator=UpperCAmelCase_			,					device=UpperCAmelCase_			,					dtype=UpperCAmelCase_)

   # get latents
   print("add noise to latents at timestep"			,					UpperCAmelCase_)
   lowerCamelCase__: Union[str, Any]         =self.scheduler.add_noise(UpperCAmelCase_			,					UpperCAmelCase_			,					UpperCAmelCase_)
   lowerCamelCase__: int         =init_latents

   return latents







 @torch.no_grad()
 def __call__(self							:  Tuple			,					UpperCAmelCase_							:  Union[torch.FloatTensor, PIL.Image.Image] = None			,					UpperCAmelCase_							:  float = 0.8			,					UpperCAmelCase_							:  int = 1			,					UpperCAmelCase_							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None			,					UpperCAmelCase_							:  float = 0.0			,					UpperCAmelCase_							:  int = 50			,					UpperCAmelCase_							:  Optional[bool] = None			,					UpperCAmelCase_							:  Optional[str] = "pil"			,					UpperCAmelCase_							:  bool = True			,					)			->Union[ImagePipelineOutput, Tuple]:



   '''simple docstring'''



   self.check_inputs(UpperCAmelCase_)

   # 2. Preprocess image
   lowerCamelCase__: Dict         =preprocess(UpperCAmelCase_)

   # 3. set timesteps
   self.scheduler.set_timesteps(UpperCAmelCase_			,					device=self.device)
   lowerCamelCase__    ,   lowerCamelCase__: str         =self.get_timesteps(UpperCAmelCase_			,					UpperCAmelCase_			,					self.device)
   lowerCamelCase__: Optional[int]         =timesteps[:1].repeat(UpperCAmelCase_)

   # 4. Prepare latent variables
   lowerCamelCase__: int         =self.prepare_latents(UpperCAmelCase_			,					UpperCAmelCase_			,					UpperCAmelCase_			,					self.unet.dtype			,					self.device			,					UpperCAmelCase_)
   lowerCamelCase__: Tuple         =latents

   # 5. Denoising loop
   for t in self.progress_bar(UpperCAmelCase_):
     # 1. predict noise model_output
     lowerCamelCase__: Dict         =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
     lowerCamelCase__: Optional[int]         =self.scheduler.step(
         UpperCAmelCase_			,					UpperCAmelCase_			,					UpperCAmelCase_			,					eta=UpperCAmelCase_			,					use_clipped_model_output=UpperCAmelCase_			,					generator=UpperCAmelCase_			,					).prev_sample

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

   if not return_dict:
     return (image, latent_timestep.item())

   return ImagePipelineOutput(images=UpperCAmelCase_)







'''simple docstring'''



def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any]						)-> Dict:




     '''simple docstring'''





     __snake_case            =	[]
     __snake_case            =	[]
     __snake_case            =	{
         '''^''': 3,
         '''*''': 2,
         '''/''': 2,
         '''%''': 2,
         '''+''': 1,
         '''-''': 1,
     }  # Priority of each operator
     __snake_case            =	len(_lowerCamelCase						) if (len(_lowerCamelCase						) > 7) else 7

     # Print table header for output
     print(
         '''Symbol'''.center(8						) ,						'''Stack'''.center(_lowerCamelCase						) ,						'''Postfix'''.center(_lowerCamelCase						) ,						sep=''' | ''' ,						)
     print('''-''' * (print_width * 3 + 7)						)

     for x in infix:
          if x.isalpha() or x.isdigit():
               post_fix.append(_lowerCamelCase						)  # if x is Alphabet / Digit, add it to Postfix
          elif x == "(":
               stack.append(_lowerCamelCase						)  # if x is "(" push to Stack
          elif x == ")":  # if x is ")" pop stack until "(" is encountered
               while stack[-1] != "(":
                    post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
               stack.pop()
          else:
               if len(_lowerCamelCase						) == 0:
                    stack.append(_lowerCamelCase						)  # If stack is empty, push x to stack
               else:  # while priority of x is not > priority of element in the stack
                    while len(_lowerCamelCase						) > 0 and priority[x] <= priority[stack[-1]]:
                         post_fix.append(stack.pop()						)  # pop stack & add to Postfix
                    stack.append(_lowerCamelCase						)  # push x to stack

          print(
              x.center(8						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						sep=''' | ''' ,						)  # Output in tabular format

     while len(_lowerCamelCase						) > 0:  # while stack is not empty
          post_fix.append(stack.pop()						)  # pop stack & add to Postfix
          print(
              ''' '''.center(8						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						sep=''' | ''' ,						)  # Output in tabular format

     return "".join(_lowerCamelCase						)  # return Postfix as str







def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any]						)-> str:




     '''simple docstring'''





     __snake_case            =	list(infix[::-1]						)  # reverse the infix equation

     for i in range(len(_lowerCamelCase						)						):
          if infix[i] == "(":
               __snake_case            =	''')'''  # change "(" to ")"
          elif infix[i] == ")":
               __snake_case            =	'''('''  # change ")" to "("

     return (infix_2_postfix(''''''.join(_lowerCamelCase						)						))[
         ::-1
     ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
     UpperCAmelCase_							:	Dict	     =		input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
     UpperCAmelCase_							:	Optional[Any]	     =		''''''.join(Infix.split())  # Remove spaces from the input
     print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')






