





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   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     42
 lowercase_						=     42
 lowercase_						=     None



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     2

 @register_to_config
 def __init__(self							:  Optional[int]			,					UpperCAmelCase_							:  float = 0.02			,					UpperCAmelCase_							:  float = 100			,					UpperCAmelCase_							:  float = 1.007			,					UpperCAmelCase_							:  float = 80			,					UpperCAmelCase_							:  float = 0.05			,					UpperCAmelCase_							:  float = 50			,					)			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Dict         =sigma_max

   # setable values
   lowerCamelCase__: int         =None
   lowerCamelCase__: np.IntTensor         =None
   lowerCamelCase__: torch.FloatTensor         =None  # sigma(t_i)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  Optional[int] = None)			->torch.FloatTensor:



   '''simple docstring'''



   return sample

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  Union[str, torch.device] = None)			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =num_inference_steps
   lowerCamelCase__: List[Any]         =np.arange(0			,					self.num_inference_steps)[::-1].copy()
   lowerCamelCase__: Union[str, Any]         =torch.from_numpy(UpperCAmelCase_).to(UpperCAmelCase_)
   lowerCamelCase__: Union[str, Any]         =[
       (
           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
   ]
   lowerCamelCase__: int         =torch.tensor(UpperCAmelCase_			,					dtype=torch.floataa			,					device=UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str]			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  float			,					UpperCAmelCase_							:  Optional[torch.Generator] = None)			->Tuple[torch.FloatTensor, float]:



   '''simple docstring'''



   if self.config.s_min <= sigma <= self.config.s_max:
     lowerCamelCase__: List[Any]         =min(self.config.s_churn / self.num_inference_steps			,					2**0.5 - 1)
   else:
     lowerCamelCase__: Any         =0

   # sample eps ~ N(0, S_noise^2 * I)
   lowerCamelCase__: Any         =self.config.s_noise * randn_tensor(sample.shape			,					generator=UpperCAmelCase_).to(sample.device)
   lowerCamelCase__: Optional[Any]         =sigma + gamma * sigma
   lowerCamelCase__: str         =sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

   return sample_hat, sigma_hat

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any]			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  float			,					UpperCAmelCase_							:  float			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  bool = True			,					)			->Union[KarrasVeOutput, Tuple]:



   '''simple docstring'''



   lowerCamelCase__: Tuple         =sample_hat + sigma_hat * model_output
   lowerCamelCase__: Optional[Any]         =(sample_hat - pred_original_sample) / sigma_hat
   lowerCamelCase__: Optional[int]         =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 				SCREAMING_SNAKE_CASE_   (self							:  List[str]			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  float			,					UpperCAmelCase_							:  float			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  bool = True			,					)			->Union[KarrasVeOutput, Tuple]:



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =sample_prev + sigma_prev * model_output
   lowerCamelCase__: List[Any]         =(sample_prev - pred_original_sample) / sigma_prev
   lowerCamelCase__: Any         =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 				SCREAMING_SNAKE_CASE_   (self							:  Dict			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  List[str]			,					UpperCAmelCase_							:  Optional[int])			->Any:



   '''simple docstring'''



   raise NotImplementedError()





from __future__ import annotations
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[int]:
   # Checks if the entire collection has been sorted
   if len(lowerCAmelCase_ ) <= 1 or n <= 1:
      return

   insert_next(lowerCAmelCase_	,    n - 1 )
   rec_insertion_sort(lowerCAmelCase_	,    n - 1 )
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Dict:
   # Checks order between adjacent elements
   if index >= len(lowerCAmelCase_ ) or collection[index - 1] <= collection[index]:
      return

   # Swaps adjacent elements since they are not in ascending order
   UpperCAmelCase					,	UpperCAmelCase      					=  (
       collection[index],
       collection[index - 1],
   )

   insert_next(lowerCAmelCase_	,    index + 1 )


if __name__ == "__main__":
 __a	 =      input("""Enter integers separated by spaces: """)
 __a	 =      [int(num) for num in numbers.split()]
 rec_insertion_sort(number_list, len(number_list))
 print(number_list)



