


"""simple docstring"""







import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 					a__			(				SCREAMING_SNAKE_CASE    :    int = 8				):


  '''simple docstring'''







  lowerCAmelCase     :     List[str]								=							ascii_letters + digits + punctuation
  return "".join(secrets.choice(SCREAMING_SNAKE_CASE				) for _ in range(SCREAMING_SNAKE_CASE				)				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  i -= len(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Any								=							i // 3
  lowerCAmelCase     :     List[Any]								=							i % 3
  # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
  #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
  lowerCAmelCase     :     Union[str, Any]								=							(
      chars_incl
      + random(SCREAMING_SNAKE_CASE				,      quotient + remainder				)
      + random(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
      + random(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
  )
  lowerCAmelCase     :     List[Any]								=							list(SCREAMING_SNAKE_CASE				)
  shuffle(SCREAMING_SNAKE_CASE				)
  return "".join(SCREAMING_SNAKE_CASE				)

  # random is a generalised function for letters, characters and numbers
def 					a__			(				SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  return "".join(secrets.choice(SCREAMING_SNAKE_CASE				) for _ in range(SCREAMING_SNAKE_CASE				)				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    Optional[Any]				,      SCREAMING_SNAKE_CASE    :    Tuple				):


  '''simple docstring'''







  pass  # Put your code here...
def 					a__			(				SCREAMING_SNAKE_CASE    :    Optional[int]				,      SCREAMING_SNAKE_CASE    :    Optional[int]				):


  '''simple docstring'''







  pass  # Put your code here...
def 					a__			(				SCREAMING_SNAKE_CASE    :    Dict				,      SCREAMING_SNAKE_CASE    :    Any				):


  '''simple docstring'''







  pass  # Put your code here...
def 					a__			(				SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    int = 8				):


  '''simple docstring'''







  if len(SCREAMING_SNAKE_CASE				) < min_length:
    # Your Password must be at least 8 characters long
    return False

  lowerCAmelCase     :     Optional[Any]								=							any(char in ascii_uppercase for char in password				)
  lowerCAmelCase     :     List[str]								=							any(char in ascii_lowercase for char in password				)
  lowerCAmelCase     :     Optional[int]								=							any(char in digits for char in password				)
  lowerCAmelCase     :     Union[str, Any]								=							any(char in punctuation for char in password				)

  return upper and lower and num and spec_char
  # Passwords should contain UPPERCASE, lowerase
  # numbers, and special characters
def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     int								=							int(input("Please indicate the max length of your password: "				).strip()				)
  lowerCAmelCase     :     int								=							input(
      "Please indicate the characters that must be in your password: "				).strip()
  print("Password generated:"				,      password_generator(SCREAMING_SNAKE_CASE				)				)
  print(
      "Alternative Password generated:"				,      alternative_password_generator(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)				,      )
  print("[If you are thinking of using this passsword, You better save it.]"				)


if __name__ == "__main__":
     main()






'''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      ( UpperCamelCase__	):



    def __init__(self    :    int   ,			__a    :    Distribution   ,			__a    :    Dict=None   ,			__a    :    int=None   ,			__a    :    Any=0 ):
         UpperCAmelCase_           =   1.0 if scale is None else scale
         UpperCAmelCase_           =   0.0 if loc is None else loc

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



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



    @property
    def 					_lowercase						(self    :    List[Any] ):
         return self.base_dist.variance * self.scale**2






    @property
    def 					_lowercase						(self    :    List[Any] ):
         return self.variance.sqrt()




class        __A      ( nn.Module	):



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






    def 					_lowercase						(self    :    List[str]   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   [proj(__a ) for proj in self.proj]

         return self.domain_map(*__a )




class        __A      ( nn.Module	):



    def __init__(self    :    Union[str, Any]   ,			__a    :    List[str] ):
         super().__init__()
         UpperCAmelCase_           =   function






    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    List[str]   ,			*__a    :    Optional[int] ):
         return self.function(__a   ,			*__a )




class        __A      :
    a__       :						type
    a__       :						int
    a__       :						Dict[str, int]



    def __init__(self    :    List[Any]   ,			__a    :    int = 1 ):
         UpperCAmelCase_           =   dim
         UpperCAmelCase_           =   {k: dim * self.args_dim[k] for k in self.args_dim}



    def 					_lowercase						(self    :    Any   ,			__a    :    Any ):
         if self.dim == 1:
              return self.distribution_class(*__a )
         else:
              return Independent(self.distribution_class(*__a )   ,			1 )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    Union[str, Any]   ,			__a    :    Optional[torch.Tensor] = None   ,			__a    :    Optional[torch.Tensor] = None   ,			):
         UpperCAmelCase_           =   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 					_lowercase						(self    :    Any ):
         return () if self.dim == 1 else (self.dim,)



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



    @property
    def 					_lowercase						(self    :    Tuple ):
         return 0.0



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



    def 					_lowercase						(self    :    Optional[int]   ,			*__a    :    torch.Tensor ):
         raise NotImplementedError()






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




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"df": 1, "loc": 1, "scale": 1}
    a__       :						type          =  StudentT



    @classmethod
    def 					_lowercase						(cls    :    Union[str, Any]   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a ).clamp_min(torch.finfo(scale.dtype ).eps )
         UpperCAmelCase_           =   2.0 + cls.squareplus(__a )
         return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"loc": 1, "scale": 1}
    a__       :						type          =  Normal



    @classmethod
    def 					_lowercase						(cls    :    Tuple   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a ).clamp_min(torch.finfo(scale.dtype ).eps )
         return loc.squeeze(-1 ), scale.squeeze(-1 )




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"total_count": 1, "logits": 1}
    a__       :						type          =  NegativeBinomial



    @classmethod
    def 					_lowercase						(cls    :    Optional[Any]   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a )
         return total_count.squeeze(-1 ), logits.squeeze(-1 )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    str ):
         UpperCAmelCase_    , UpperCAmelCase_           =   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 					_lowercase						(self    :    Optional[Any]   ,			__a    :    int   ,			__a    :    Optional[torch.Tensor] = None   ,			__a    :    Optional[torch.Tensor] = None ):
         UpperCAmelCase_    , UpperCAmelCase_           =   distr_args

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

         return self._base_distribution((total_count, logits) )




