

'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

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






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:UNetaDModel
 __SCREAMING_SNAKE_CASE					:ScoreSdeVeScheduler



 def __init__(   self		:   List[Any]			,				a__		:   UNetaDModel			,				a__		:   ScoreSdeVeScheduler      ):
      super().__init__()
      self.register_modules(unet=a__			,				scheduler=a__      )



 @torch.no_grad()
 def __call__(   self		:   Tuple			,				a__		:   int = 1			,				a__		:   int = 2000			,				a__		:   Optional[Union[torch.Generator, List[torch.Generator]]] = None			,				a__		:   Optional[str] = "pil"			,				a__		:   bool = True			,				**a__		:   int			,				):
      __magic_name__						       =    self.unet.config.sample_size
      __magic_name__						       =    (batch_size, 3, img_size, img_size)

      __magic_name__						       =    self.unet

      __magic_name__						       =    randn_tensor(a__			,				generator=a__      ) * self.scheduler.init_noise_sigma
      __magic_name__						       =    sample.to(self.device      )

      self.scheduler.set_timesteps(a__      )
      self.scheduler.set_sigmas(a__      )

      for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
           __magic_name__						       =    self.scheduler.sigmas[i] * torch.ones(shape[0]			,				device=self.device      )

           # correction step
           for _ in range(self.scheduler.config.correct_steps      ):
                __magic_name__						       =    self.unet(a__			,				a__      ).sample
                __magic_name__						       =    self.scheduler.step_correct(a__			,				a__			,				generator=a__      ).prev_sample

           # prediction step
           __magic_name__						       =    model(a__			,				a__      ).sample
           __magic_name__						       =    self.scheduler.step_pred(a__			,				a__			,				a__			,				generator=a__      )

           __magic_name__       ,  __magic_name__						       =    output.prev_sample, output.prev_sample_mean

      __magic_name__						       =    sample_mean.clamp(0			,				1      )
      __magic_name__						       =    sample.cpu().permute(0			,				2			,				3			,				1      ).numpy()
      if output_type == "pil":
           __magic_name__						       =    self.numpy_to_pil(a__      )

      if not return_dict:
           return (sample,)

      return ImagePipelineOutput(images=a__      )









'''simple docstring'''




from random import randint, random




def 						UpperCamelCase	(			a		,						a		,						a		,						a = False		,						a = False		,						a = 5		,						)				->     list:






     '''simple docstring'''
     __magic_name__						       =    [[-1] * number_of_cells]  # Create a highway without any car
     __magic_name__						       =    0
     __magic_name__						       =    max(a		,						0			)
     while i < number_of_cells:
          __magic_name__						       =    (
              randint(0		,						a			) if random_speed else initial_speed
          )  # Place the cars
          i += (
              randint(1		,						max_speed * 2			) if random_frequency else frequency
          )  # Arbitrary number, may need tuning
     return highway




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    0
     __magic_name__						       =    highway_now[car_index + 1 :]
     for cell in range(len(a			)			):  # May need a better name for this
          if cells[cell] != -1:  # If the cell is not empty then
               return distance  # we have the distance we wanted
          distance += 1
     # Here if the car is near the end of the highway
     return distance + get_distance(a		,						-1			)




def 						UpperCamelCase	(			a		,						a		,						a			)				->     list:






     '''simple docstring'''
     __magic_name__						       =    len(a			)
     # Beforce calculations, the highway is empty
     __magic_name__						       =    [-1] * number_of_cells

     for car_index in range(a			):
          if highway_now[car_index] != -1:
               # Add 1 to the current speed of the car and cap the speed
               __magic_name__						       =    min(highway_now[car_index] + 1		,						a			)
               # Number of empty cell before the next car
               __magic_name__						       =    get_distance(a		,						a			) - 1
               # We can't have the car causing an accident
               __magic_name__						       =    min(next_highway[car_index]		,						a			)
               if random() < probability:
                    # Randomly, a driver will slow down
                    __magic_name__						       =    max(next_highway[car_index] - 1		,						0			)
     return next_highway




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     list:






     '''simple docstring'''
     __magic_name__						       =    len(highway[0]			)

     for i in range(a			):
          __magic_name__						       =    update(highway[i]		,						a		,						a			)
          __magic_name__						       =    [-1] * number_of_cells

          for car_index in range(a			):
               __magic_name__						       =    next_speeds_calculated[car_index]
               if speed != -1:
                    # Change the position based on the speed (with % to create the loop)
                    __magic_name__						       =    (car_index + speed) % number_of_cells
                    # Commit the change of position
                    __magic_name__						       =    speed
          highway.append(a			)

     return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()






