





import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation



def    __lowercase   (      _SCREAMING_SNAKE_CASE = 8	) ->   str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						ascii_letters + digits + punctuation
      return "".join(secrets.choice(lowercase_	) for _ in range(lowercase_	)	)



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   str:


      '''simple docstring'''


      i -= len(lowercase_	)
      SCREAMING_SNAKE_CASE		=						i // 3
      SCREAMING_SNAKE_CASE		=						i % 3
      # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
      #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
      SCREAMING_SNAKE_CASE		=						(
          chars_incl
          + random(lowercase_ ,						quotient + remainder	)
          + random(lowercase_ ,						lowercase_	)
          + random(lowercase_ ,						lowercase_	)
      )
      SCREAMING_SNAKE_CASE		=						list(lowercase_	)
      shuffle(lowercase_	)
      return "".join(lowercase_	)

      # random is a generalised function for letters, characters and numbers



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   str:


      '''simple docstring'''


      return "".join(secrets.choice(lowercase_	) for _ in range(lowercase_	)	)



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[Any]:


      '''simple docstring'''


      pass  # Put your code here...



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Optional[int]:


      '''simple docstring'''


      pass  # Put your code here...



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      pass  # Put your code here...



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE = 8	) ->   bool:


      '''simple docstring'''


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

      SCREAMING_SNAKE_CASE		=						any(char in ascii_uppercase for char in password	)
      SCREAMING_SNAKE_CASE		=						any(char in ascii_lowercase for char in password	)
      SCREAMING_SNAKE_CASE		=						any(char in digits for char in password	)
      SCREAMING_SNAKE_CASE		=						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    __lowercase   (      ) ->   Tuple:


      '''simple docstring'''


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


if __name__ == "__main__":
      main()





import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class     _a						(__magic_name__		,   __magic_name__		,   __magic_name__				):


					'''simple docstring'''






					@register_to_config
					def __init__( self    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__ = False    ,       ):
						super().__init__()

						A__							:    List[str]        =		nn.Embedding(A__    ,       A__			)

						A__							:    Any        =		nn.Embedding(A__    ,       A__			)
						A__							:    Optional[int]        =		False

						A__							:    Optional[Any]        =		nn.Dropout(p=A__			)

						A__							:    int        =		TaConfig(
						    vocab_size=A__    ,       d_model=A__    ,       num_heads=A__    ,       d_kv=A__    ,       d_ff=A__    ,       dropout_rate=A__    ,       feed_forward_proj=A__    ,       is_decoder=A__    ,       is_encoder_decoder=A__    ,       )

						A__							:    Dict        =		nn.ModuleList()
						for lyr_num in range(A__			):
							A__							:    str        =		TaBlock(A__			)
							self.encoders.append(A__			)

						A__							:    Tuple        =		TaLayerNorm(A__			)
						A__							:    str        =		nn.Dropout(p=A__			)






					def   __A			( self    ,       A__    ,       A__			):
						A__							:    List[str]        =		self.token_embedder(A__			)

						A__							:    Optional[Any]        =		encoder_input_tokens.shape[1]
						A__							:    Optional[int]        =		torch.arange(A__    ,       device=encoder_input_tokens.device			)
						x += self.position_encoding(A__			)

						A__							:    Optional[Any]        =		self.dropout_pre(A__			)

						# inverted the attention mask
						A__							:    Tuple        =		encoder_input_tokens.size()
						A__							:    Union[str, Any]        =		self.get_extended_attention_mask(A__    ,       A__			)

						for lyr in self.encoders:
							A__							:    List[str]        =		lyr(A__    ,       A__			)[0]
						A__							:    List[Any]        =		self.layer_norm(A__			)

						return self.dropout_post(A__			), encoder_inputs_mask



