




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__	):
     _a											=		42
     _a											=		42
     _a											=		None







class 		lowercase							(     UpperCamelCase__,UpperCamelCase__	):
     _a											=		2

     @register_to_config
     def __init__(						self	,  _a = 0.02	,  _a = 100	,  _a = 1.007	,  _a = 80	,  _a = 0.05	,  _a = 50	,  )		->							str:
           # standard deviation of the initial noise distribution
           _A							:  Tuple    =		sigma_max

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

     def       a__			(						self	,  _a	,  _a = None       )		->							torch.FloatTensor:
           return sample

     def       a__			(						self	,  _a	,  _a = None       )		->							int:
           _A							:  List[Any]    =		num_inference_steps
           _A							:  int    =		np.arange(0	,  self.num_inference_steps       )[::-1].copy()
           _A							:  Tuple    =		torch.from_numpy(_a       ).to(_a       )
           _A							:  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
           ]
           _A							:  List[str]    =		torch.tensor(_a	,  dtype=torch.floataa	,  device=_a       )

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

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

           return sample_hat, sigma_hat

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a = True	,  )		->							Union[KarrasVeOutput, Tuple]:
           _A							:  Any    =		sample_hat + sigma_hat * model_output
           _A							:  Optional[Any]    =		(sample_hat - pred_original_sample) / sigma_hat
           _A							:  Optional[int]    =		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       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a = True	,  )		->							Union[KarrasVeOutput, Tuple]:
           _A							:  Union[str, Any]    =		sample_prev + sigma_prev * model_output
           _A							:  List[str]    =		(sample_prev - pred_original_sample) / sigma_prev
           _A							:  Union[str, 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       a__			(						self	,  _a	,  _a	,  _a       )		->							Dict:
           raise NotImplementedError()







import operator







def        lowerCAmelCase_			(  snake_case_,snake_case_ = False,snake_case_ = None  ):
      _A							:  str    =		operator.lt if reverse else operator.gt
      _A							:  Optional[Any]    =		solution or []

      if not arr:
            return solution

      _A							:  Dict    =		[arr.pop(0  )]
      for i, item in enumerate(snake_case_  ):
            if _operator(snake_case_,sublist[-1]  ):
                  sublist.append(snake_case_  )
                  arr.pop(snake_case_  )

    #  merging sublist into solution list
      if not solution:
            solution.extend(snake_case_  )
      else:
            while sublist:
                  _A							:  Union[str, Any]    =		sublist.pop(0  )
                  for i, xx in enumerate(snake_case_  ):
                        if not _operator(snake_case_,snake_case_  ):
                              solution.insert(snake_case_,snake_case_  )
                              break
                  else:
                        solution.append(snake_case_  )

      strand_sort(snake_case_,snake_case_,snake_case_  )
      return solution


if __name__ == "__main__":
  assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
  assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]

