





"""simple docstring"""



# 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 						__snake_case   (						SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	1

 @register_to_config
 def __init__(       self   ,		__lowerCamelCase=2000   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=20   ,		__lowerCamelCase=1e-3    ):



     '''simple docstring'''




     __A       :		Tuple							=	None
     __A       :		Union[str, Any]							=	None
     __A       :		Any							=	None

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase = None    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	torch.linspace(1   ,		self.config.sampling_eps   ,		__lowerCamelCase   ,		device=__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase=None    ):



     '''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
     __A       :		List[Any]							=	(
         -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
     )
     __A       :		Tuple							=	torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff    )    )
     __A       :		List[Any]							=	std.flatten()
     while len(std.shape    ) < len(score.shape    ):
         __A       :		Optional[int]							=	std.unsqueeze(-1    )
     __A       :		Optional[int]							=	-score / std

     # compute
     __A       :		Optional[Any]							=	-1.0 / len(self.timesteps    )

     __A       :		Optional[int]							=	self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
     __A       :		str							=	beta_t.flatten()
     while len(beta_t.shape    ) < len(x.shape    ):
         __A       :		Optional[Any]							=	beta_t.unsqueeze(-1    )
     __A       :		List[Any]							=	-0.5 * beta_t * x

     __A       :		List[Any]							=	torch.sqrt(__lowerCamelCase    )
     __A       :		List[Any]							=	drift - diffusion**2 * score
     __A       :		Union[str, Any]							=	x + drift * dt

     # add noise
     __A       :		Tuple							=	randn_tensor(x.shape   ,		layout=x.layout   ,		generator=__lowerCamelCase   ,		device=x.device   ,		dtype=x.dtype    )
     __A       :		List[Any]							=	x_mean + diffusion * math.sqrt(-dt    ) * noise

     return x, x_mean







 def __len__(       self    ):



     '''simple docstring'''




     return self.config.num_train_timesteps











"""simple docstring"""



def 						__lowercase	(  snake_case_    :    list		)  ->list:



    '''simple docstring'''







    for i in range(len(snake_case_		) - 1							,0							,-1		):
        __A       :		Union[str, Any]							=	False

        for j in range(snake_case_							,0							,-1		):
            if unsorted[j] < unsorted[j - 1]:
                __A					,			__A       :		Union[str, Any]							=	unsorted[j - 1], unsorted[j]
                __A       :		Optional[int]							=	True

        for j in range(snake_case_		):
            if unsorted[j] > unsorted[j + 1]:
                __A					,			__A       :		Optional[Any]							=	unsorted[j + 1], unsorted[j]
                __A       :		Union[str, Any]							=	True

        if not swapped:
            break
    return unsorted


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       a_					=     input("""Enter numbers separated by a comma:\n""").strip()
       a_					=     [int(item) for item in user_input.split(""",""")]
       print(f'''{cocktail_shaker_sort(unsorted) = }''')




