


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        _lowercase      (			A__       ):






				'''simple docstring'''



				def __init__(       self			:List[str]			,			lowerCAmelCase__			:Distribution			,			lowerCAmelCase__			:str=None			,			lowerCAmelCase__			:Optional[Any]=None			,			lowerCAmelCase__			:Any=0	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		1.0 if scale is None else scale
										__SCREAMING_SNAKE_CASE						:     List[str]					=		0.0 if loc is None else loc

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


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


				@property
				def 		__magic_name__(       self			:List[str]	)		->			List[Any]:
										return self.base_dist.variance * self.scale**2



				@property
				def 		__magic_name__(       self			:Union[str, Any]	)		->			Optional[int]:
										return self.variance.sqrt()




class        _lowercase      (			nn.Module       ):






				'''simple docstring'''



				def __init__(       self			:Optional[Any]			,			lowerCAmelCase__			:int			,			lowerCAmelCase__			:Dict[str, int]			,			lowerCAmelCase__			:Callable[..., Tuple[torch.Tensor]]			,			**lowerCAmelCase__			:Tuple	)		->			None:
										super().__init__(**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		args_dim
										__SCREAMING_SNAKE_CASE						:     Tuple					=		nn.ModuleList([nn.Linear(lowerCAmelCase__			,			lowerCAmelCase__	) for dim in args_dim.values()]	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		domain_map



				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:torch.Tensor	)		->			Tuple[torch.Tensor]:
										__SCREAMING_SNAKE_CASE						:     str					=		[proj(lowerCAmelCase__	) for proj in self.proj]

										return self.domain_map(*lowerCAmelCase__	)




class        _lowercase      (			nn.Module       ):






				'''simple docstring'''



				def __init__(       self			:List[str]			,			lowerCAmelCase__			:Dict	)		->			Dict:
										super().__init__()
										__SCREAMING_SNAKE_CASE						:     int					=		function



				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:Optional[int]			,			*lowerCAmelCase__			:Optional[int]	)		->			Optional[Any]:
										return self.function(lowerCAmelCase__			,			*lowerCAmelCase__	)




class        _lowercase      :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			type
				SCREAMING_SNAKE_CASE__				:			int
				SCREAMING_SNAKE_CASE__				:			Dict[str, int]


				def __init__(       self			:List[str]			,			lowerCAmelCase__			:int = 1	)		->			None:
										__SCREAMING_SNAKE_CASE						:     str					=		dim
										__SCREAMING_SNAKE_CASE						:     str					=		{k: dim * self.args_dim[k] for k in self.args_dim}


				def 		__magic_name__(       self			:Dict			,			lowerCAmelCase__			:int	)		->			Dict:
										if self.dim == 1:
																return self.distribution_class(*lowerCAmelCase__	)
										else:
																return Independent(self.distribution_class(*lowerCAmelCase__	)			,			1	)


				def 		__magic_name__(       self			:Tuple			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:Optional[torch.Tensor] = None			,			lowerCAmelCase__			:Optional[torch.Tensor] = None			,			)		->			Distribution:
										__SCREAMING_SNAKE_CASE						:     List[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 		__magic_name__(       self			:int	)		->			Tuple:
										return () if self.dim == 1 else (self.dim,)


				@property
				def 		__magic_name__(       self			:int	)		->			int:
										return len(self.event_shape	)


				@property
				def 		__magic_name__(       self			:Union[str, Any]	)		->			float:
										return 0.0


				def 		__magic_name__(       self			:List[Any]			,			lowerCAmelCase__			:int	)		->			nn.Module:
										return ParameterProjection(
										    in_features=lowerCAmelCase__			,			args_dim=self.args_dim			,			domain_map=LambdaLayer(self.domain_map	)			,			)


				def 		__magic_name__(       self			:int			,			*lowerCAmelCase__			:torch.Tensor	)		->			List[str]:
										raise NotImplementedError()



				@staticmethod
				def 		__magic_name__(       lowerCAmelCase__			:torch.Tensor	)		->			torch.Tensor:
										return (x + torch.sqrt(torch.square(lowerCAmelCase__	) + 4.0	)) / 2.0




class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Dict[str, int]						=		{"df": 1, "loc": 1, "scale": 1}
				SCREAMING_SNAKE_CASE__				:			type						=		StudentT


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




class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Dict[str, int]						=		{"loc": 1, "scale": 1}
				SCREAMING_SNAKE_CASE__				:			type						=		Normal


				@classmethod
				def 		__magic_name__(       cls			:Union[str, Any]			,			lowerCAmelCase__			:torch.Tensor			,			lowerCAmelCase__			:torch.Tensor	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Dict					=		cls.squareplus(lowerCAmelCase__	).clamp_min(torch.finfo(scale.dtype	).eps	)
										return loc.squeeze(-1	), scale.squeeze(-1	)




class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Dict[str, int]						=		{"total_count": 1, "logits": 1}
				SCREAMING_SNAKE_CASE__				:			type						=		NegativeBinomial


				@classmethod
				def 		__magic_name__(       cls			:List[str]			,			lowerCAmelCase__			:torch.Tensor			,			lowerCAmelCase__			:torch.Tensor	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		cls.squareplus(lowerCAmelCase__	)
										return total_count.squeeze(-1	), logits.squeeze(-1	)


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:Optional[int]	)		->			Distribution:
										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		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 		__magic_name__(       self			:Optional[int]			,			lowerCAmelCase__			:Optional[int]			,			lowerCAmelCase__			:Optional[torch.Tensor] = None			,			lowerCAmelCase__			:Optional[torch.Tensor] = None	)		->			Distribution:
										__SCREAMING_SNAKE_CASE			,			__SCREAMING_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)	)






def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       list     ):


      '''simple docstring'''



      for i in range(len(lowercase     ) - 1	,							0	,							-1     ):
            lowerCamelCase_		     =    False

            for j in range(lowercase	,							0	,							-1     ):
                  if unsorted[j] < unsorted[j - 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j - 1], unsorted[j]
                        lowerCamelCase_		     =    True

            for j in range(lowercase     ):
                  if unsorted[j] > unsorted[j + 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j + 1], unsorted[j]
                        lowerCamelCase_		     =    True

            if not swapped:
                  break
      return unsorted


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 lowerCamelCase				:		Any     =   input("Enter numbers separated by a comma:\n").strip()
 lowerCamelCase				:		Dict     =   [int(item) for item in user_input.split(",")]
 print(F"""{cocktail_shaker_sort(unsorted) = }""")



