import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class __snake_case       (		lowerCamelCase_	):




   def __init__(					self    :       Optional[int] ,			_lowercase    :       VQModel ,			_lowercase    :       UNetaDModel ,			_lowercase    :       DDIMScheduler   ):



     """simple docstring"""






     super().__init__()
     self.register_modules(vqvae=_lowercase ,			unet=_lowercase ,			scheduler=_lowercase   )




   @torch.no_grad()
   def __call__(					self    :       List[Any] ,			_lowercase    :       int = 1 ,			_lowercase    :       Optional[Union[torch.Generator, List[torch.Generator]]] = None ,			_lowercase    :       float = 0.0 ,			_lowercase    :       int = 50 ,			_lowercase    :       Optional[str] = "pil" ,			_lowercase    :       bool = True ,			**_lowercase    :       Optional[Any] ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   randn_tensor(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,			generator=_lowercase ,			)
     SCREAMING_SNAKE_CASE__    							=   latents.to(self.device   )

     # scale the initial noise by the standard deviation required by the scheduler
     SCREAMING_SNAKE_CASE__    							=   latents * self.scheduler.init_noise_sigma

     self.scheduler.set_timesteps(_lowercase   )

     # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
     SCREAMING_SNAKE_CASE__    							=   """eta""" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )

     SCREAMING_SNAKE_CASE__    							=   {}
     if accepts_eta:
       SCREAMING_SNAKE_CASE__    							=   eta

     for t in self.progress_bar(self.scheduler.timesteps   ):
       SCREAMING_SNAKE_CASE__    							=   self.scheduler.scale_model_input(_lowercase ,			_lowercase   )
       # predict the noise residual
       SCREAMING_SNAKE_CASE__    							=   self.unet(_lowercase ,			_lowercase   ).sample
       # compute the previous noisy sample x_t -> x_t-1
       SCREAMING_SNAKE_CASE__    							=   self.scheduler.step(_lowercase ,			_lowercase ,			_lowercase ,			**_lowercase   ).prev_sample

     # decode the image latents with the VAE
     SCREAMING_SNAKE_CASE__    							=   self.vqvae.decode(_lowercase   ).sample

     SCREAMING_SNAKE_CASE__    							=   (image / 2 + 0.5).clamp(0 ,			1   )
     SCREAMING_SNAKE_CASE__    							=   image.cpu().permute(0 ,			2 ,			3 ,			1   ).numpy()
     if output_type == "pil":
       SCREAMING_SNAKE_CASE__    							=   self.numpy_to_pil(_lowercase   )

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=_lowercase   )

import os

__lowerCamelCase    :				Union[str, Any]							     =	{'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   0

  SCREAMING_SNAKE_CASE__    							=   0
  while index < len(__UpperCamelCase			) - 1:
    SCREAMING_SNAKE_CASE__    							=   SYMBOLS[numerals[index]]
    SCREAMING_SNAKE_CASE__    							=   SYMBOLS[numerals[index + 1]]
    if current_value < next_value:
      total_value -= current_value
    else:
      total_value += current_value
    index += 1
  total_value += SYMBOLS[numerals[index]]

  return total_value



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   """"""

  SCREAMING_SNAKE_CASE__    							=   num // 10_00
  numerals += m_count * "M"
  num %= 10_00

  SCREAMING_SNAKE_CASE__    							=   num // 1_00
  if c_count == 9:
    numerals += "CM"
    c_count -= 9
  elif c_count == 4:
    numerals += "CD"
    c_count -= 4
  if c_count >= 5:
    numerals += "D"
    c_count -= 5
  numerals += c_count * "C"
  num %= 1_00

  SCREAMING_SNAKE_CASE__    							=   num // 10
  if x_count == 9:
    numerals += "XC"
    x_count -= 9
  elif x_count == 4:
    numerals += "XL"
    x_count -= 4
  if x_count >= 5:
    numerals += "L"
    x_count -= 5
  numerals += x_count * "X"
  num %= 10

  if num == 9:
    numerals += "IX"
    num -= 9
  elif num == 4:
    numerals += "IV"
    num -= 4
  if num >= 5:
    numerals += "V"
    num -= 5
  numerals += num * "I"

  return numerals



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str = "/p089_roman.txt"			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   0

  with open(os.path.dirname(__UpperCamelCase			) + roman_numerals_filename			) as filea:
    SCREAMING_SNAKE_CASE__    							=   filea.readlines()

  for line in lines:
    SCREAMING_SNAKE_CASE__    							=   line.strip()
    SCREAMING_SNAKE_CASE__    							=   parse_roman_numerals(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   generate_roman_numerals(__UpperCamelCase			)
    savings += len(__UpperCamelCase			) - len(__UpperCamelCase			)

  return savings


if __name__ == "__main__":
   print(F"""{solution() = }""")
