




'''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       (						_a       ):
							lowercase_							     =							42
							lowercase_							     =							42
							lowercase_							     =							None



class      A       (						_a	,_a       ):
							lowercase_							     =							2







							@register_to_config
							def __init__(    self			:    Dict				,     lowerCAmelCase_			:    float = 0.0_2				,     lowerCAmelCase_			:    float = 1_00				,     lowerCAmelCase_			:    float = 1.0_0_7				,     lowerCAmelCase_			:    float = 80				,     lowerCAmelCase_			:    float = 0.0_5				,     lowerCAmelCase_			:    float = 50				,     )     ->				Tuple:


														"""simple docstring"""


														_a      	=							sigma_max

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







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


														"""simple docstring"""


														return sample







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


														"""simple docstring"""


														_a      	=							num_inference_steps
														_a      	=							np.arange(0				,     self.num_inference_steps   )[::-1].copy()
														_a      	=							torch.from_numpy(lowerCAmelCase_   ).to(lowerCAmelCase_   )
														_a      	=							[
														    (
														        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      	=							torch.tensor(lowerCAmelCase_				,     dtype=torch.floataa				,     device=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict				,     lowerCAmelCase_			:    torch.FloatTensor				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    Optional[torch.Generator] = None   )     ->				Tuple[torch.FloatTensor, float]:


														"""simple docstring"""


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

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

														return sample_hat, sigma_hat







							def   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    torch.FloatTensor				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    torch.FloatTensor				,     lowerCAmelCase_			:    bool = True				,     )     ->				Union[KarrasVeOutput, Tuple]:


														"""simple docstring"""


														_a      	=							sample_hat + sigma_hat * model_output
														_a      	=							(sample_hat - pred_original_sample) / sigma_hat
														_a      	=							sample_hat + (sigma_prev - sigma_hat) * derivative

														if not return_dict:
																					return (sample_prev, derivative)

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







							def   __lowerCAmelCase     (    self			:    Dict				,     lowerCAmelCase_			:    torch.FloatTensor				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    torch.FloatTensor				,     lowerCAmelCase_			:    torch.FloatTensor				,     lowerCAmelCase_			:    torch.FloatTensor				,     lowerCAmelCase_			:    bool = True				,     )     ->				Union[KarrasVeOutput, Tuple]:


														"""simple docstring"""


														_a      	=							sample_prev + sigma_prev * model_output
														_a      	=							(sample_prev - pred_original_sample) / sigma_prev
														_a      	=							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=lowerCAmelCase_				,     derivative=lowerCAmelCase_				,     pred_original_sample=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Tuple				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    Optional[Any]   )     ->				List[str]:


														"""simple docstring"""


														raise NotImplementedError()






from __future__ import annotations

from math import pow, sqrt

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if (resistance, reactance, impedance).count(0			) != 1:
            raise ValueError("One and only one argument must be 0"			)
      if resistance == 0:
            return {"resistance": sqrt(pow(UpperCAmelCase__ , 2			) - pow(UpperCAmelCase__ , 2			)			)}
      elif reactance == 0:
            return {"reactance": sqrt(pow(UpperCAmelCase__ , 2			) - pow(UpperCAmelCase__ , 2			)			)}
      elif impedance == 0:
            return {"impedance": sqrt(pow(UpperCAmelCase__ , 2			) + pow(UpperCAmelCase__ , 2			)			)}
      else:
            raise ValueError("Exactly one argument must be 0"			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()