'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

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






class   _SCREAMING_SNAKE_CASE   (   _SCREAMING_SNAKE_CASE     ):



 def __init__(   self		:   str			,				a__		:   int			,				a__		:   Dict      ):
      super().__init__()

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

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



 @torch.no_grad()
 def __call__(   self		:   Tuple			,				a__		:   int = 1			,				a__		:   Dict = None			,				a__		:   int = 0.0			,				a__		:   List[str] = 50			,				a__		:   Tuple = None			,				a__		:   Any = "pil"			,				a__		:   List[str] = True			,				):

      # Sample gaussian noise to begin loop
      if isinstance(self.unet.config.sample_size			,				A_      ):
           __magic_name__						       =    (
               batch_size,
               self.unet.config.in_channels,
               self.unet.config.sample_size,
               self.unet.config.sample_size,
           )
      else:
           __magic_name__						       =    (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

      if isinstance(A_			,				A_      ) and len(A_      ) != batch_size:
           raise ValueError(
               F'''You have passed a list of generators of length {len(A_      )}, but requested an effective batch'''
               F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

      __magic_name__						       =    randn_tensor(A_			,				generator=A_			,				device=self.device			,				dtype=self.unet.dtype      )

      # set step values
      self.scheduler.set_timesteps(A_      )

      for t in self.progress_bar(self.scheduler.timesteps      ):
           # 1. predict noise model_output
           __magic_name__						       =    self.unet(A_			,				A_      ).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
           __magic_name__						       =    self.scheduler.step(
               A_			,				A_			,				A_			,				eta=A_			,				use_clipped_model_output=A_			,				generator=A_      ).prev_sample

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

      if not return_dict:
           return (image,)

      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()






