
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						42
							lowerCAmelCase_ 						=						42
							lowerCAmelCase_ 						=						None


class       _lowerCAmelCase   (   __snake_case					,							__snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						2

							@register_to_config
							def __init__(self  ,  UpperCAmelCase = 0.02  ,  UpperCAmelCase = 100  ,  UpperCAmelCase = 1.007  ,  UpperCAmelCase = 80  ,  UpperCAmelCase = 0.05  ,  UpperCAmelCase = 50  ,  )					->       Any:
														# standard deviation of the initial noise distribution
														_snake_case       =  sigma_max

														# setable values
														_snake_case       =  None
														_snake_case       =  None
														_snake_case       =  None  # sigma(t_i)

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       torch.FloatTensor:
														return sample

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       Tuple:
														_snake_case       =  num_inference_steps
														_snake_case       =  np.arange(0  ,  self.num_inference_steps  )[::-1].copy()
														_snake_case       =  torch.from_numpy(UpperCAmelCase  ).to(UpperCAmelCase  )
														_snake_case       =  [
														    (
														        self.config.sigma_max**2
														        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
														    )
														    for i in self.timesteps
														]
														_snake_case       =  torch.tensor(UpperCAmelCase  ,  dtype=torch.floataa  ,  device=UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       Tuple[torch.FloatTensor, float]:
														if self.config.s_min <= sigma <= self.config.s_max:
																					_snake_case       =  min(self.config.s_churn / self.num_inference_steps  ,  2**0.5 - 1  )
														else:
																					_snake_case       =  0

														# sample eps ~ N(0, S_noise^2 * I)
														_snake_case       =  self.config.s_noise * randn_tensor(sample.shape  ,  generator=UpperCAmelCase  ).to(sample.device  )
														_snake_case       =  sigma + gamma * sigma
														_snake_case       =  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

														return sample_hat, sigma_hat

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = True  ,  )					->       Union[KarrasVeOutput, Tuple]:
														_snake_case       =  sample_hat + sigma_hat * model_output
														_snake_case       =  (sample_hat - pred_original_sample) / sigma_hat
														_snake_case       =  sample_hat + (sigma_prev - sigma_hat) * derivative

														if not return_dict:
																					return (sample_prev, derivative)

														return KarrasVeOutput(
														    prev_sample=UpperCAmelCase  ,  derivative=UpperCAmelCase  ,  pred_original_sample=UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = True  ,  )					->       Union[KarrasVeOutput, Tuple]:
														_snake_case       =  sample_prev + sigma_prev * model_output
														_snake_case       =  (sample_prev - pred_original_sample) / sigma_prev
														_snake_case       =  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

														if not return_dict:
																					return (sample_prev, derivative)

														return KarrasVeOutput(
														    prev_sample=UpperCAmelCase  ,  derivative=UpperCAmelCase  ,  pred_original_sample=UpperCAmelCase  )





							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       str:
														raise NotImplementedError()

'''simple docstring'''
from random import randint, random






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE = False							, _SCREAMING_SNAKE_CASE = False							, _SCREAMING_SNAKE_CASE = 5							, ):
							_snake_case       =  [[-1] * number_of_cells]  # Create a highway without any car
							_snake_case       =  0
							_snake_case       =  max(_SCREAMING_SNAKE_CASE							, 0				)
							while i < number_of_cells:
														_snake_case       =  (
														    randint(0							, _SCREAMING_SNAKE_CASE				) 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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  0
							_snake_case       =  highway_now[car_index + 1 :]
							for cell in range(len(_SCREAMING_SNAKE_CASE				)				):  # 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(_SCREAMING_SNAKE_CASE							, -1				)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							# Beforce calculations, the highway is empty
							_snake_case       =  [-1] * number_of_cells

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






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(highway[0]				)

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

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

							return highway


if __name__ == "__main__":
				import doctest

				doctest.testmod()