




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(		UpperCamelCase     ):



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_	  =					1.0 if scale is None else scale
       lowerCamelCase_	  =					0.0 if loc is None else loc

       super().__init__(A_		,							[AffineTransform(loc=self.loc		,							scale=self.scale		,							event_dim=A_						)]						)

 @property
 def        a__				(       self      :						List[str]						)     ->			List[Any]:

       """simple docstring"""






       return self.base_dist.mean * self.scale + self.loc

 @property
 def        a__				(       self      :						Any						)     ->			Optional[int]:

       """simple docstring"""






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





 @property
 def        a__				(       self      :						str						)     ->			int:

       """simple docstring"""






       return self.variance.sqrt()







class 						A(		nn.Module     ):



 '''simple docstring'''


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

       """simple docstring"""






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





 def        a__				(       self      :						Any		,							A_      :						torch.Tensor						)     ->			Tuple[torch.Tensor]:

       """simple docstring"""






       lowerCamelCase_	  =					[proj(A_						) for proj in self.proj]

       return self.domain_map(*A_						)







class 						A(		nn.Module     ):



 '''simple docstring'''


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

       """simple docstring"""






       super().__init__()
       lowerCamelCase_	  =					function





 def        a__				(       self      :						Dict		,							A_      :						Dict		,							*A_      :						str						)     ->			Any:

       """simple docstring"""






       return self.function(A_		,							*A_						)







class 						A:



 '''simple docstring'''


 UpperCamelCase		     =		42
 UpperCamelCase		     =		42
 UpperCamelCase		     =		42

 def __init__(       self      :						List[Any]		,							A_      :						int = 1						)     ->			None:

       """simple docstring"""






       lowerCamelCase_	  =					dim
       lowerCamelCase_	  =					{k: dim * self.args_dim[k] for k in self.args_dim}

 def        a__				(       self      :						Any		,							A_      :						Tuple						)     ->			Union[str, Any]:

       """simple docstring"""






       if self.dim == 1:
             return self.distribution_class(*A_						)
       else:
             return Independent(self.distribution_class(*A_						)		,							1						)

 def        a__				(       self      :						List[Any]		,							A_      :						Tuple		,							A_      :						Optional[torch.Tensor] = None		,							A_      :						Optional[torch.Tensor] = None		,							)     ->			Distribution:

       """simple docstring"""






       lowerCamelCase_	  =					self._base_distribution(A_						)
       if loc is None and scale is None:
             return distr
       else:
             return AffineTransformed(A_		,							loc=A_		,							scale=A_		,							event_dim=self.event_dim						)

 @property
 def        a__				(       self      :						List[str]						)     ->			Tuple:

       """simple docstring"""






       return () if self.dim == 1 else (self.dim,)

 @property
 def        a__				(       self      :						Optional[Any]						)     ->			int:

       """simple docstring"""






       return len(self.event_shape						)

 @property
 def        a__				(       self      :						Tuple						)     ->			float:

       """simple docstring"""






       return 0.0

 def        a__				(       self      :						int		,							A_      :						int						)     ->			nn.Module:

       """simple docstring"""






       return ParameterProjection(
           in_features=A_		,							args_dim=self.args_dim		,							domain_map=LambdaLayer(self.domain_map						)		,							)

 def        a__				(       self      :						Any		,							*A_      :						torch.Tensor						)     ->			Optional[Any]:

       """simple docstring"""






       raise NotImplementedError()





 @staticmethod
 def        a__				(       A_      :						torch.Tensor						)     ->			torch.Tensor:

       """simple docstring"""






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







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		{'''df''': 1, '''loc''': 1, '''scale''': 1}
 UpperCamelCase		     =		StudentT

 @classmethod
 def        a__				(       cls      :						Dict		,							A_      :						torch.Tensor		,							A_      :						torch.Tensor		,							A_      :						torch.Tensor						)     ->			Optional[Any]:

       """simple docstring"""






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







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		{'''loc''': 1, '''scale''': 1}
 UpperCamelCase		     =		Normal

 @classmethod
 def        a__				(       cls      :						Any		,							A_      :						torch.Tensor		,							A_      :						torch.Tensor						)     ->			str:

       """simple docstring"""






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







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		{'''total_count''': 1, '''logits''': 1}
 UpperCamelCase		     =		NegativeBinomial

 @classmethod
 def        a__				(       cls      :						Optional[int]		,							A_      :						torch.Tensor		,							A_      :						torch.Tensor						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					cls.squareplus(A_						)
       return total_count.squeeze(-1						), logits.squeeze(-1						)

 def        a__				(       self      :						List[Any]		,							A_      :						Union[str, Any]						)     ->			Distribution:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					distr_args
       if self.dim == 1:
             return self.distribution_class(total_count=A_		,							logits=A_						)
       else:
             return Independent(self.distribution_class(total_count=A_		,							logits=A_						)		,							1						)





 def        a__				(       self      :						Optional[Any]		,							A_      :						Optional[int]		,							A_      :						Optional[torch.Tensor] = None		,							A_      :						Optional[torch.Tensor] = None						)     ->			Distribution:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					distr_args

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

       return self._base_distribution((total_count, logits)						)









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=UpperCamelCase     )
class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		field(default='''language-modeling'''							,   metadata={'''include_in_asdict_even_if_is_default''': True}     )

 UpperCamelCase		     =		Features({'''text''': Value('''string'''     )}     )
 UpperCamelCase		     =		Features({}     )
 UpperCamelCase		     =		"text"

 @property
 def        a__				(       self      :						List[Any]						)     ->			Dict[str, str]:

       """simple docstring"""






       return {self.text_column: "text"}



