





'''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 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = 42
    UpperCamelCase     = 42
    UpperCamelCase     = None




class 					a_     (				snake_case_      ,    snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = 2


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

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


    def 			snake_case_(		self ,		A ,		A = None       )		->   torch.FloatTensor:
        return sample


    def 			snake_case_(		self ,		A ,		A = None       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			num_inference_steps
        _SCREAMING_SNAKE_CASE									=			np.arange(0 ,		self.num_inference_steps       )[::-1].copy()
        _SCREAMING_SNAKE_CASE									=			torch.from_numpy(A       ).to(A       )
        _SCREAMING_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
        ]
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A ,		dtype=torch.floataa ,		device=A       )


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

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

        return sample_hat, sigma_hat


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

        if not return_dict:
            return (sample_prev, derivative)

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


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A = True ,		)		->   Union[KarrasVeOutput, Tuple]:
        _SCREAMING_SNAKE_CASE									=			sample_prev + sigma_prev * model_output
        _SCREAMING_SNAKE_CASE									=			(sample_prev - pred_original_sample) / sigma_prev
        _SCREAMING_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=A ,		derivative=A ,		pred_original_sample=A       )





    def 			snake_case_(		self ,		A ,		A ,		A       )		->   List[Any]:
        raise NotImplementedError()





"""simple docstring"""




def   __magic_name__	(      __snake_case				:       list  ) ->				list:

       if len(__snake_case  ) < 2:
              return collection

       def circle_sort_util(__snake_case				:       list     , __snake_case				:       int     , __snake_case				:       int  ) -> bool:
              lowercase    :				List[Any]       		=							False

              if low == high:
                     return swapped

              lowercase    :				Union[str, Any]       		=							low
              lowercase    :				str       		=							high

              while left < right:
                     if collection[left] > collection[right]:
                            lowercase		,    lowercase    :				Optional[Any]       		=							(
                                collection[right],
                                collection[left],
                            )
                            lowercase    :				Tuple       		=							True

                     left += 1
                     right -= 1

              if left == right and collection[left] > collection[right + 1]:
                     lowercase		,    lowercase    :				str       		=							(
                         collection[right + 1],
                         collection[left],
                     )

                     lowercase    :				Union[str, Any]       		=							True

              lowercase    :				Any       		=							low + int((high - low) / 2  )
              lowercase    :				Tuple       		=							circle_sort_util(__snake_case     , __snake_case     , __snake_case  )
              lowercase    :				List[Any]       		=							circle_sort_util(__snake_case     , mid + 1     , __snake_case  )

              return swapped or left_swap or right_swap

       lowercase    :				int       		=							True

       while is_not_sorted is True:
              lowercase    :				int       		=							circle_sort_util(__snake_case     , 0     , len(__snake_case  ) - 1  )

       return collection


if __name__ == "__main__":
   _A			:       str   		=       input("""Enter numbers separated by a comma:\n""").strip()
   _A			:       Dict   		=       [int(item) for item in user_input.split(""",""")]
   print(circle_sort(unsorted))


