


'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

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

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	UNetaDModel
     lowerCamelCase				:	KarrasVeScheduler





     def __init__( self       :      str    ,      UpperCAmelCase__       :      UNetaDModel    ,      UpperCAmelCase__       :      KarrasVeScheduler    )				->  int:
        super().__init__()
        self.register_modules(unet=UpperCAmelCase__    ,      scheduler=UpperCAmelCase__    )





     @torch.no_grad()
     def __call__( self       :      Dict    ,      UpperCAmelCase__       :      int = 1    ,      UpperCAmelCase__       :      int = 5_0    ,      UpperCAmelCase__       :      Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,      UpperCAmelCase__       :      Optional[str] = "pil"    ,      UpperCAmelCase__       :      bool = True    ,      **UpperCAmelCase__       :      Dict    ,      )				->  Union[Tuple, ImagePipelineOutput]:
        lowerCAmelCase     =			self.unet.config.sample_size
        lowerCAmelCase     =			(batch_size, 3, img_size, img_size)

        lowerCAmelCase     =			self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        lowerCAmelCase     =			randn_tensor(UpperCAmelCase__    ,      generator=UpperCAmelCase__    ,      device=self.device    ) * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(UpperCAmelCase__    )

        for t in self.progress_bar(self.scheduler.timesteps    ):
           # here sigma_t == t_i from the paper
           lowerCAmelCase     =			self.scheduler.schedule[t]
           lowerCAmelCase     =			self.scheduler.schedule[t - 1] if t > 0 else 0

           # 1. Select temporarily increased noise level sigma_hat
           # 2. Add new noise to move from sample_i to sample_hat
           lowerCAmelCase     ,  lowerCAmelCase     =			self.scheduler.add_noise_to_input(UpperCAmelCase__    ,      UpperCAmelCase__    ,      generator=UpperCAmelCase__    )

           # 3. Predict the noise residual given the noise magnitude `sigma_hat`
           # The model inputs and output are adjusted by following eq. (213) in [1].
           lowerCAmelCase     =			(sigma_hat / 2) * model((sample_hat + 1) / 2    ,      sigma_hat / 2    ).sample

           # 4. Evaluate dx/dt at sigma_hat
           # 5. Take Euler step from sigma to sigma_prev
           lowerCAmelCase     =			self.scheduler.step(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )

           if sigma_prev != 0:
              # 6. Apply 2nd order correction
              # The model inputs and output are adjusted by following eq. (213) in [1].
              lowerCAmelCase     =			(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2    ,      sigma_prev / 2    ).sample
              lowerCAmelCase     =			self.scheduler.step_correct(
                  UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      step_output.prev_sample    ,      step_output['derivative']    ,      )
           lowerCAmelCase     =			step_output.prev_sample

        lowerCAmelCase     =			(sample / 2 + 0.5).clamp(0    ,      1    )
        lowerCAmelCase     =			sample.cpu().permute(0    ,      2    ,      3    ,      1    ).numpy()
        if output_type == "pil":
           lowerCAmelCase     =			self.numpy_to_pil(UpperCAmelCase__    )

        if not return_dict:
           return (image,)

        return ImagePipelineOutput(images=UpperCAmelCase__    )









'''simple docstring'''




import os

__snake_case    						={"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}





def 	a_					( lowerCamelCase	:     str				):
   lowerCAmelCase     =			0

   lowerCAmelCase     =			0
   while index < len(lowerCamelCase				) - 1:
      lowerCAmelCase     =			SYMBOLS[numerals[index]]
      lowerCAmelCase     =			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 	a_					( lowerCamelCase	:     int				):
   lowerCAmelCase     =			''

   lowerCAmelCase     =			num // 1000
   numerals += m_count * "M"
   num %= 1000

   lowerCAmelCase     =			num // 100
   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 %= 100

   lowerCAmelCase     =			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 	a_					( lowerCamelCase	:     str = "/p089_roman.txt"				):
   lowerCAmelCase     =			0

   with open(os.path.dirname(lowerCamelCase				) + roman_numerals_filename				) as filea:
      lowerCAmelCase     =			filea.readlines()

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

   return savings


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





