

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   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  None







class   __lowercase      (_UpperCAmelCase		,   _UpperCAmelCase ):
 _UpperCamelCase			  =  2
 @register_to_config
 def __init__(							self      ,							A_ = 0.02      ,							A_ = 100      ,							A_ = 1.007      ,							A_ = 80      ,							A_ = 0.05      ,							A_ = 50      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					sigma_max

       # setable values
       __lowerCAmelCase					:						int					=					None
       __lowerCAmelCase					:						np.IntTensor					=					None
       __lowerCAmelCase					:						torch.FloatTensor					=					None  # sigma(t_i)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->torch.FloatTensor:



       '''simple docstring'''





       return sample
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					num_inference_steps
       __lowerCAmelCase					:						Dict					=					np.arange(0      ,							self.num_inference_steps					)[::-1].copy()
       __lowerCAmelCase					:						Optional[Any]					=					torch.from_numpy(A_					).to(A_					)
       __lowerCAmelCase					:						Tuple					=					[
           (
               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					:						Optional[int]					=					torch.tensor(A_      ,							dtype=torch.floataa      ,							device=A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = None					)					->Tuple[torch.FloatTensor, float]:



       '''simple docstring'''





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

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

       return sample_hat, sigma_hat
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_ = True      ,							)					->Union[KarrasVeOutput, Tuple]:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					sample_hat + sigma_hat * model_output
       __lowerCAmelCase					:						int					=					(sample_hat - pred_original_sample) / sigma_hat
       __lowerCAmelCase					:						Tuple					=					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 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_ = True      ,							)					->Union[KarrasVeOutput, Tuple]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					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=A_      ,							derivative=A_      ,							pred_original_sample=A_					)





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_					)					->Any:



       '''simple docstring'''





       raise NotImplementedError()





def 		_lowercase (     lowercase__   ,	lowercase__						):
      return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2







def 		_lowercase (     lowercase__   ,	lowercase__=0						):
      return sorted(lowercase__   ,	key=lambda lowercase__						: x[column]						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__=float('''inf'''						)						):

      for i in range(points_counts - 1						):
            for j in range(i + 1   ,	lowercase__						):
                  __lowerCAmelCase					:						List[str]					=					euclidean_distance_sqr(points[i]   ,	points[j]						)
                  if current_dis < min_dis:
                        __lowerCAmelCase					:						Tuple					=					current_dis
      return min_dis







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__=float('''inf'''						)						):

      for i in range(min(6   ,	points_counts - 1						)   ,	lowercase__						):
            for j in range(max(0   ,	i - 6						)   ,	lowercase__						):
                  __lowerCAmelCase					:						Union[str, Any]					=					euclidean_distance_sqr(points[i]   ,	points[j]						)
                  if current_dis < min_dis:
                        __lowerCAmelCase					:						int					=					current_dis
      return min_dis







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__						):

      # base case
      if points_counts <= 3:
            return dis_between_closest_pair(lowercase__   ,	lowercase__						)

      # recursion
      __lowerCAmelCase					:						Optional[Any]					=					points_counts // 2
      __lowerCAmelCase					:						Optional[Any]					=					closest_pair_of_points_sqr(
          lowercase__   ,	points_sorted_on_y[:mid]   ,	lowercase__						)
      __lowerCAmelCase					:						str					=					closest_pair_of_points_sqr(
          lowercase__   ,	points_sorted_on_y[mid:]   ,	points_counts - mid						)
      __lowerCAmelCase					:						Optional[int]					=					min(lowercase__   ,	lowercase__						)
      __lowerCAmelCase					:						Tuple					=					[]
      for point in points_sorted_on_x:
            if abs(point[0] - points_sorted_on_x[mid][0]						) < closest_pair_dis:
                  cross_strip.append(lowercase__						)

      __lowerCAmelCase					:						List[Any]					=					dis_between_closest_in_strip(
          lowercase__   ,	len(lowercase__						)   ,	lowercase__						)
      return min(lowercase__   ,	lowercase__						)







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Union[str, Any]					=					column_based_sort(lowercase__   ,	column=0						)
      __lowerCAmelCase					:						Any					=					column_based_sort(lowercase__   ,	column=1						)
      return (
          closest_pair_of_points_sqr(
              lowercase__   ,	lowercase__   ,	lowercase__						)
      ) ** 0.5


if __name__ == "__main__":
 _UpperCamelCase 			=		[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
 print("Distance:", closest_pair_of_points(points, len(points)))


