


'''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 						__UpperCamelCase	(						lowercase__							):





							def __init__(  self		:List[str]  ,_UpperCamelCase		:Distribution  ,_UpperCamelCase		:Optional[int]=None  ,_UpperCamelCase		:str=None  ,_UpperCamelCase		:Optional[int]=0			):
									snake_case_		:							int			       =						1.0 if scale is None else scale
									snake_case_		:							List[Any]			       =						0.0 if loc is None else loc

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





							@property
							def 		a__   (  self		:Union[str, Any]			):
									return self.base_dist.mean * self.scale + self.loc





							@property
							def 		a__   (  self		:Union[str, Any]			):
									return self.base_dist.variance * self.scale**2





							@property
							def 		a__   (  self		:List[str]			):
									return self.variance.sqrt()





class 						__UpperCamelCase	(						nn.Module							):





							def __init__(  self		:List[Any]  ,_UpperCamelCase		:int  ,_UpperCamelCase		:Dict[str, int]  ,_UpperCamelCase		:Callable[..., Tuple[torch.Tensor]]  ,**_UpperCamelCase		:List[str]			):
									super().__init__(**_UpperCamelCase			)
									snake_case_		:							Tuple			       =						args_dim
									snake_case_		:							Tuple			       =						nn.ModuleList([nn.Linear(_UpperCamelCase  ,_UpperCamelCase			) for dim in args_dim.values()]			)
									snake_case_		:							Tuple			       =						domain_map





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:torch.Tensor			):
									snake_case_		:							Tuple			       =						[proj(_UpperCamelCase			) for proj in self.proj]

									return self.domain_map(*_UpperCamelCase			)





class 						__UpperCamelCase	(						nn.Module							):





							def __init__(  self		:Any  ,_UpperCamelCase		:List[str]			):
									super().__init__()
									snake_case_		:							Dict			       =						function





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Union[str, Any]  ,*_UpperCamelCase		:Dict			):
									return self.function(_UpperCamelCase  ,*_UpperCamelCase			)





class 						__UpperCamelCase	:
							lowercase			:		type
							lowercase			:		int
							lowercase			:		Dict[str, int]





							def __init__(  self		:str  ,_UpperCamelCase		:int = 1			):
									snake_case_		:							str			       =						dim
									snake_case_		:							Any			       =						{k: dim * self.args_dim[k] for k in self.args_dim}





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:Optional[Any]			):
									if self.dim == 1:
											return self.distribution_class(*_UpperCamelCase			)
									else:
											return Independent(self.distribution_class(*_UpperCamelCase			)  ,1			)





							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Optional[torch.Tensor] = None  ,_UpperCamelCase		:Optional[torch.Tensor] = None  ,):
									snake_case_		:							Any			       =						self._base_distribution(_UpperCamelCase			)
									if loc is None and scale is None:
											return distr
									else:
											return AffineTransformed(_UpperCamelCase  ,loc=_UpperCamelCase  ,scale=_UpperCamelCase  ,event_dim=self.event_dim			)





							@property
							def 		a__   (  self		:Optional[int]			):
									return () if self.dim == 1 else (self.dim,)





							@property
							def 		a__   (  self		:Dict			):
									return len(self.event_shape			)





							@property
							def 		a__   (  self		:int			):
									return 0.0





							def 		a__   (  self		:List[str]  ,_UpperCamelCase		:int			):
									return ParameterProjection(
									    in_features=_UpperCamelCase  ,args_dim=self.args_dim  ,domain_map=LambdaLayer(self.domain_map			)  ,)





							def 		a__   (  self		:Tuple  ,*_UpperCamelCase		:torch.Tensor			):
									raise NotImplementedError()





							@staticmethod
							def 		a__   (  _UpperCamelCase		:torch.Tensor			):
									return (x + torch.sqrt(torch.square(_UpperCamelCase			) + 4.0			)) / 2.0





class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Dict[str, int]   		=  {"df": 1, "loc": 1, "scale": 1}
							lowercase			:		type   		=  StudentT





							@classmethod
							def 		a__   (  cls		:Optional[Any]  ,_UpperCamelCase		:torch.Tensor  ,_UpperCamelCase		:torch.Tensor  ,_UpperCamelCase		:torch.Tensor			):
									snake_case_		:							List[Any]			       =						cls.squareplus(_UpperCamelCase			).clamp_min(torch.finfo(scale.dtype			).eps			)
									snake_case_		:							List[str]			       =						2.0 + cls.squareplus(_UpperCamelCase			)
									return df.squeeze(-1			), loc.squeeze(-1			), scale.squeeze(-1			)





class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Dict[str, int]   		=  {"loc": 1, "scale": 1}
							lowercase			:		type   		=  Normal





							@classmethod
							def 		a__   (  cls		:Union[str, Any]  ,_UpperCamelCase		:torch.Tensor  ,_UpperCamelCase		:torch.Tensor			):
									snake_case_		:							Tuple			       =						cls.squareplus(_UpperCamelCase			).clamp_min(torch.finfo(scale.dtype			).eps			)
									return loc.squeeze(-1			), scale.squeeze(-1			)





class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Dict[str, int]   		=  {"total_count": 1, "logits": 1}
							lowercase			:		type   		=  NegativeBinomial





							@classmethod
							def 		a__   (  cls		:List[str]  ,_UpperCamelCase		:torch.Tensor  ,_UpperCamelCase		:torch.Tensor			):
									snake_case_		:							str			       =						cls.squareplus(_UpperCamelCase			)
									return total_count.squeeze(-1			), logits.squeeze(-1			)





							def 		a__   (  self		:Dict  ,_UpperCamelCase		:Union[str, Any]			):
									snake_case_		:							List[str]			       =						distr_args
									if self.dim == 1:
											return self.distribution_class(total_count=_UpperCamelCase  ,logits=_UpperCamelCase			)
									else:
											return Independent(self.distribution_class(total_count=_UpperCamelCase  ,logits=_UpperCamelCase			)  ,1			)





							def 		a__   (  self		:List[Any]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:Optional[torch.Tensor] = None  ,_UpperCamelCase		:Optional[torch.Tensor] = None			):
									snake_case_		:							Any			       =						distr_args

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

									return self._base_distribution((total_count, logits)			)



'''simple docstring'''






import random
from typing import Any





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		for _ in range(len(lowerCamelCase_ ) ):
				snake_case_		:							Union[str, Any]			       =						random.randint(0				,      len(lowerCamelCase_ ) - 1 )
				snake_case_		:							Any			       =						random.randint(0				,      len(lowerCamelCase_ ) - 1 )
				snake_case_       ,      snake_case_		:							List[str]			       =						data[b], data[a]
		return data


if __name__ == "__main__":
							__A	:	Optional[int]     =     [0, 1, 2, 3, 4, 5, 6, 7]
							__A	:	Optional[int]     =     ['python', 'says', 'hello', '!']
							print('Fisher-Yates Shuffle:')
							print('List', integers, strings)
							print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))