






'''simple docstring'''

from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=0			):
       UpperCamelCase_					: List[str]            =				1.0 if scale is None else scale
       UpperCamelCase_					: Optional[Any]            =				0.0 if loc is None else loc

       super().__init__(__lowerCAmelCase      ,  [AffineTransform(loc=self.loc      ,  scale=self.scale      ,  event_dim=__lowerCAmelCase			)]			)
 @property
 def 	_UpperCAmelCase							(				self			):
       return self.base_dist.mean * self.scale + self.loc
 @property
 def 	_UpperCAmelCase							(				self			):
       return self.base_dist.variance * self.scale**2





 @property
 def 	_UpperCAmelCase							(				self			):
       return self.variance.sqrt()






class      A     (   nn.Module    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  **__lowerCAmelCase			):
       super().__init__(**__lowerCAmelCase			)
       UpperCamelCase_					: str            =				args_dim
       UpperCamelCase_					: Tuple            =				nn.ModuleList([nn.Linear(__lowerCAmelCase      ,  __lowerCAmelCase			) for dim in args_dim.values()]			)
       UpperCamelCase_					: Tuple            =				domain_map





 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Optional[int]            =				[proj(__lowerCAmelCase			) for proj in self.proj]

       return self.domain_map(*__lowerCAmelCase			)






class      A     (   nn.Module    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase			):
       super().__init__()
       UpperCamelCase_					: Union[str, Any]            =				function





 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  *__lowerCAmelCase			):
       return self.function(__lowerCAmelCase      ,  *__lowerCAmelCase			)






class      A     :






 """simple docstring"""





 __a      :    type
 __a      :    int
 __a      :    Dict[str, int]
 def __init__(				self      ,  __lowerCAmelCase = 1			):
       UpperCamelCase_					: str            =				dim
       UpperCamelCase_					: Optional[Any]            =				{k: dim * self.args_dim[k] for k in self.args_dim}
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       if self.dim == 1:
             return self.distribution_class(*__lowerCAmelCase			)
       else:
             return Independent(self.distribution_class(*__lowerCAmelCase			)      ,  1			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  ):
       UpperCamelCase_					: Any            =				self._base_distribution(__lowerCAmelCase			)
       if loc is None and scale is None:
             return distr
       else:
             return AffineTransformed(__lowerCAmelCase      ,  loc=__lowerCAmelCase      ,  scale=__lowerCAmelCase      ,  event_dim=self.event_dim			)
 @property
 def 	_UpperCAmelCase							(				self			):
       return () if self.dim == 1 else (self.dim,)
 @property
 def 	_UpperCAmelCase							(				self			):
       return len(self.event_shape			)
 @property
 def 	_UpperCAmelCase							(				self			):
       return 0.0
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       return ParameterProjection(
           in_features=__lowerCAmelCase      ,  args_dim=self.args_dim      ,  domain_map=LambdaLayer(self.domain_map			)      ,  )
 def 	_UpperCAmelCase							(				self      ,  *__lowerCAmelCase			):
       raise NotImplementedError()





 @staticmethod
 def 	_UpperCAmelCase							(				__lowerCAmelCase			):
       return (x + torch.sqrt(torch.square(__lowerCAmelCase			) + 4.0			)) / 2.0






class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Dict[str, int]			  =    {"df": 1, "loc": 1, "scale": 1}
 __a      :    type			  =    StudentT
 @classmethod
 def 	_UpperCAmelCase							(				cls      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Union[str, Any]            =				cls.squareplus(__lowerCAmelCase			).clamp_min(torch.finfo(scale.dtype			).eps			)
       UpperCamelCase_					: Optional[Any]            =				2.0 + cls.squareplus(__lowerCAmelCase			)
       return df.squeeze(-1			), loc.squeeze(-1			), scale.squeeze(-1			)






class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Dict[str, int]			  =    {"loc": 1, "scale": 1}
 __a      :    type			  =    Normal
 @classmethod
 def 	_UpperCAmelCase							(				cls      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: int            =				cls.squareplus(__lowerCAmelCase			).clamp_min(torch.finfo(scale.dtype			).eps			)
       return loc.squeeze(-1			), scale.squeeze(-1			)






class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Dict[str, int]			  =    {"total_count": 1, "logits": 1}
 __a      :    type			  =    NegativeBinomial
 @classmethod
 def 	_UpperCAmelCase							(				cls      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Optional[Any]            =				cls.squareplus(__lowerCAmelCase			)
       return total_count.squeeze(-1			), logits.squeeze(-1			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_     ,      UpperCamelCase_					: Optional[Any]            =				distr_args
       if self.dim == 1:
             return self.distribution_class(total_count=__lowerCAmelCase      ,  logits=__lowerCAmelCase			)
       else:
             return Independent(self.distribution_class(total_count=__lowerCAmelCase      ,  logits=__lowerCAmelCase			)      ,  1			)





 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None			):
       UpperCamelCase_     ,      UpperCamelCase_					: str            =				distr_args

       if scale is not None:
             # See scaling property of Gamma.
             logits += scale.log()

       return self._base_distribution((total_count, logits)			)














'''simple docstring'''

from __future__ import annotations

from collections import namedtuple



def 				snake_case       (  a_				:			float     , a_				:			float     , a_				:			float						)							->					tuple:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				namedtuple("""result"""     , """name value"""						)
      if (voltage, current, power).count(0						) != 1:
            raise ValueError("""Only one argument must be 0"""						)
      elif power < 0:
            raise ValueError(
                """Power cannot be negative in any electrical/electronics system"""						)
      elif voltage == 0:
            return result("""voltage"""     , power / current						)
      elif current == 0:
            return result("""current"""     , power / voltage						)
      elif power == 0:
            return result("""power"""     , float(round(abs(voltage * current						)     , 2						)						)						)
      else:
            raise ValueError("""Exactly one argument must be 0"""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






