






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class 		SCREAMING_SNAKE_CASE__	(  UpperCamelCase__					,      UpperCamelCase__     ):

  '''simple docstring'''







  _lowerCamelCase    =  1


  @register_to_config
  def __init__(      self		:			Any       ,				lowerCamelCase		:			Tuple=2000       ,				lowerCamelCase		:			Optional[Any]=0.1       ,				lowerCamelCase		:			Any=20       ,				lowerCamelCase		:			Optional[int]=1E-3							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Union[str, torch.device] = None							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    torch.linspace(1       ,				self.config.sampling_eps       ,				__snake_case       ,				device=__snake_case							)


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			List[str]=None							)						->	Union[str, Any]:


         """simple docstring"""

         if self.timesteps is None:
                raise ValueError(
                    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""							)

         # TODO(Patrick) better comments + non-PyTorch
         # postprocess model score
         _UpperCAmelCase							=    (
             -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
         )
         _UpperCAmelCase							=    torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
         _UpperCAmelCase							=    std.flatten()
         while len(std.shape							) < len(score.shape							):
                _UpperCAmelCase							=    std.unsqueeze(-1							)
         _UpperCAmelCase							=    -score / std

         # compute
         _UpperCAmelCase							=    -1.0 / len(self.timesteps							)

         _UpperCAmelCase							=    self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
         _UpperCAmelCase							=    beta_t.flatten()
         while len(beta_t.shape							) < len(x.shape							):
                _UpperCAmelCase							=    beta_t.unsqueeze(-1							)
         _UpperCAmelCase							=    -0.5 * beta_t * x

         _UpperCAmelCase							=    torch.sqrt(__snake_case							)
         _UpperCAmelCase							=    drift - diffusion**2 * score
         _UpperCAmelCase							=    x + drift * dt

         # add noise
         _UpperCAmelCase							=    randn_tensor(x.shape       ,				layout=x.layout       ,				generator=__snake_case       ,				device=x.device       ,				dtype=x.dtype							)
         _UpperCAmelCase							=    x_mean + diffusion * math.sqrt(-dt							) * noise

         return x, x_mean





  def __len__(      self		:			List[Any]							)						->	Union[str, Any]:


         """simple docstring"""
         return self.config.num_train_timesteps





UpperCAmelCase_		:					Tuple          =  0  # The first color of the flag.
UpperCAmelCase_		:					Any          =  1  # The second color of the flag.
UpperCAmelCase_		:					str          =  2  # The third color of the flag.
UpperCAmelCase_		:					Tuple          =  (red, white, blue)




def 		lowerCAmelCase_ (						lowerCamelCase ):
				if not sequence:
								return []
				if len(lowerCamelCase ) == 1:
								return list(lowerCamelCase )
				__magic_name__						:						int										=0
				__magic_name__						:						str										=len(lowerCamelCase ) - 1
				__magic_name__						:						Optional[Any]										=0
				while mid <= high:
								if sequence[mid] == colors[0]:
												__magic_name__					,			__magic_name__						:						Tuple										=sequence[mid], sequence[low]
												low += 1
												mid += 1
								elif sequence[mid] == colors[1]:
												mid += 1
								elif sequence[mid] == colors[2]:
												__magic_name__					,			__magic_name__						:						Optional[Any]										=sequence[high], sequence[mid]
												high -= 1
								else:
												__magic_name__						:						Optional[int]										=F"The elements inside the sequence must contains only {colors} values"
												raise ValueError(lowerCamelCase )
				return sequence


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase_		:					List[Any]          =  input("Enter numbers separated by commas:\n").strip()
							UpperCAmelCase_		:					Optional[int]          =  [int(item.strip()) for item in user_input.split(",")]
							print(F"""{dutch_national_flag_sort(unsorted)}""")




