

'''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 						lowerCAmelCase__   (      A__					):



 """simple docstring"""



 def __init__(		self					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       Distribution					,							__SCREAMING_SNAKE_CASE					:       str=None					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=None					,							__SCREAMING_SNAKE_CASE					:       Any=0			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      1.0 if scale is None else scale
      __SCREAMING_SNAKE_CASE     =      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					:       List[Any]			)  -> Any:


      """simple docstring"""




      return self.base_dist.mean * self.scale + self.loc
 @property
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> List[Any]:


      """simple docstring"""




      return self.base_dist.variance * self.scale**2


 @property
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Optional[int]:


      """simple docstring"""




      return self.variance.sqrt()







class 						lowerCAmelCase__   (      nn.Module					):



 """simple docstring"""



 def __init__(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Dict[str, int]					,							__SCREAMING_SNAKE_CASE					:       Callable[..., Tuple[torch.Tensor]]					,							**__SCREAMING_SNAKE_CASE					:       Tuple			)  -> None:


      """simple docstring"""




      super().__init__(**lowerCAmelCase__			)
      __SCREAMING_SNAKE_CASE     =      args_dim
      __SCREAMING_SNAKE_CASE     =      nn.ModuleList([nn.Linear(lowerCAmelCase__					,							lowerCAmelCase__			) for dim in args_dim.values()]			)
      __SCREAMING_SNAKE_CASE     =      domain_map


 def       UpperCAmelCase__		(		self					:       int					,							__SCREAMING_SNAKE_CASE					:       torch.Tensor			)  -> Tuple[torch.Tensor]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      [proj(lowerCAmelCase__			) for proj in self.proj]

      return self.domain_map(*lowerCAmelCase__			)







class 						lowerCAmelCase__   (      nn.Module					):



 """simple docstring"""



 def __init__(		self					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       Dict			)  -> Dict:


      """simple docstring"""




      super().__init__()
      __SCREAMING_SNAKE_CASE     =      function


 def       UpperCAmelCase__		(		self					:       int					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							*__SCREAMING_SNAKE_CASE					:       Optional[int]			)  -> Optional[Any]:


      """simple docstring"""




      return self.function(lowerCAmelCase__					,							*lowerCAmelCase__			)







class 						lowerCAmelCase__   :



 """simple docstring"""



 lowerCAmelCase__  					=      42
 lowerCAmelCase__  					=      42
 lowerCAmelCase__  					=      42
 def __init__(		self					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       int = 1			)  -> None:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      dim
      __SCREAMING_SNAKE_CASE     =      {k: dim * self.args_dim[k] for k in self.args_dim}
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       int			)  -> Dict:


      """simple docstring"""




      if self.dim == 1:
           return self.distribution_class(*lowerCAmelCase__			)
      else:
           return Independent(self.distribution_class(*lowerCAmelCase__			)					,							1			)
 def       UpperCAmelCase__		(		self					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Optional[torch.Tensor] = None					,							__SCREAMING_SNAKE_CASE					:       Optional[torch.Tensor] = None					,							)  -> Distribution:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      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					:       int			)  -> Tuple:


      """simple docstring"""




      return () if self.dim == 1 else (self.dim,)
 @property
 def       UpperCAmelCase__		(		self					:       int			)  -> int:


      """simple docstring"""




      return len(self.event_shape			)
 @property
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> float:


      """simple docstring"""




      return 0.0
 def       UpperCAmelCase__		(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int			)  -> nn.Module:


      """simple docstring"""




      return ParameterProjection(
          in_features=lowerCAmelCase__					,							args_dim=self.args_dim					,							domain_map=LambdaLayer(self.domain_map			)					,							)
 def       UpperCAmelCase__		(		self					:       int					,							*__SCREAMING_SNAKE_CASE					:       torch.Tensor			)  -> List[str]:


      """simple docstring"""




      raise NotImplementedError()


 @staticmethod
 def       UpperCAmelCase__		(		__SCREAMING_SNAKE_CASE					:       torch.Tensor			)  -> torch.Tensor:


      """simple docstring"""




      return (x + torch.sqrt(torch.square(lowerCAmelCase__			) + 4.0			)) / 2.0







class 						lowerCAmelCase__   (      A__					):



 """simple docstring"""



 lowerCAmelCase__  					=      {"df": 1, "loc": 1, "scale": 1}
 lowerCAmelCase__  					=      StudentT
 @classmethod
 def       UpperCAmelCase__		(		cls					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       torch.Tensor					,							__SCREAMING_SNAKE_CASE					:       torch.Tensor					,							__SCREAMING_SNAKE_CASE					:       torch.Tensor			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      cls.squareplus(lowerCAmelCase__			).clamp_min(torch.finfo(scale.dtype			).eps			)
      __SCREAMING_SNAKE_CASE     =      2.0 + cls.squareplus(lowerCAmelCase__			)
      return df.squeeze(-1			), loc.squeeze(-1			), scale.squeeze(-1			)







class 						lowerCAmelCase__   (      A__					):



 """simple docstring"""



 lowerCAmelCase__  					=      {"loc": 1, "scale": 1}
 lowerCAmelCase__  					=      Normal
 @classmethod
 def       UpperCAmelCase__		(		cls					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       torch.Tensor					,							__SCREAMING_SNAKE_CASE					:       torch.Tensor			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      cls.squareplus(lowerCAmelCase__			).clamp_min(torch.finfo(scale.dtype			).eps			)
      return loc.squeeze(-1			), scale.squeeze(-1			)







class 						lowerCAmelCase__   (      A__					):



 """simple docstring"""



 lowerCAmelCase__  					=      {"total_count": 1, "logits": 1}
 lowerCAmelCase__  					=      NegativeBinomial
 @classmethod
 def       UpperCAmelCase__		(		cls					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       torch.Tensor					,							__SCREAMING_SNAKE_CASE					:       torch.Tensor			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      cls.squareplus(lowerCAmelCase__			)
      return total_count.squeeze(-1			), logits.squeeze(-1			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Optional[int]			)  -> Distribution:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      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					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Optional[torch.Tensor] = None					,							__SCREAMING_SNAKE_CASE					:       Optional[torch.Tensor] = None			)  -> Distribution:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      distr_args

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

      return self._base_distribution((total_count, logits)			)










from __future__ import annotations


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		0.00
						__SCREAMING_SNAKE_CASE						:     List[str]					=		0
						for resistor in resistors:
												if resistor <= 0:
																		__SCREAMING_SNAKE_CASE						:     Any					=		F'''Resistor at index {index} has a negative or zero value!'''
																		raise ValueError(lowercase__      )
												first_sum += 1 / float(lowercase__      )
												index += 1
						return 1 / first_sum


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		0.00
						__SCREAMING_SNAKE_CASE						:     int					=		0
						for resistor in resistors:
												sum_r += resistor
												if resistor < 0:
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		F'''Resistor at index {index} has a negative value!'''
																		raise ValueError(lowercase__      )
												index += 1
						return sum_r


if __name__ == "__main__":
						import doctest

						doctest.testmod()
