


'''simple docstring'''




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





def 	a_					( lowerCamelCase	:     int = 8				):
   lowerCAmelCase     =			ascii_letters + digits + punctuation
   return "".join(secrets.choice(lowerCamelCase				) for _ in range(lowerCamelCase				)				)





def 	a_					( lowerCamelCase	:     str	, lowerCamelCase	:     int				):
   # Password Generator = full boot with random_number, random_letters, and
   # random_character FUNCTIONS
   # Put your code here...
   i -= len(lowerCamelCase				)
   lowerCAmelCase     =			i // 3
   lowerCAmelCase     =			i % 3
   # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
   #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
   lowerCAmelCase     =			(
       chars_incl
       + random(lowerCamelCase	, quotient + remainder				)
       + random(lowerCamelCase	, lowerCamelCase				)
       + random(lowerCamelCase	, lowerCamelCase				)
   )
   lowerCAmelCase     =			list(lowerCamelCase				)
   shuffle(lowerCamelCase				)
   return "".join(lowerCamelCase				)

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





def 	a_					( lowerCamelCase	:     str	, lowerCamelCase	:     int				):
   return "".join(secrets.choice(lowerCamelCase				) for _ in range(lowerCamelCase				)				)





def 	a_					( lowerCamelCase	:     str	, lowerCamelCase	:     Tuple				):
   pass  # Put your code here...





def 	a_					( lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     Tuple				):
   pass  # Put your code here...





def 	a_					( lowerCamelCase	:     int	, lowerCamelCase	:     Dict				):
   pass  # Put your code here...





def 	a_					( lowerCamelCase	:     str	, lowerCamelCase	:     int = 8				):

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

   lowerCAmelCase     =			any(char in ascii_uppercase for char in password				)
   lowerCAmelCase     =			any(char in ascii_lowercase for char in password				)
   lowerCAmelCase     =			any(char in digits for char in password				)
   lowerCAmelCase     =			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_					( ):
   lowerCAmelCase     =			int(input('Please indicate the max length of your password: '				).strip()				)
   lowerCAmelCase     =			input(
       'Please indicate the characters that must be in your password: '				).strip()
   print('Password generated:'	, password_generator(lowerCamelCase				)				)
   print(
       'Alternative Password generated:'	, alternative_password_generator(lowerCamelCase	, lowerCamelCase				)	, )
   print('[If you are thinking of using this passsword, You better save it.]'				)


if __name__ == "__main__":
       main()









'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__snake_case    						=logging.getLogger(__name__)

@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	bool		=     field(default=__lowercase       ,	metadata={'''help''': '''Whether to use SortishSampler or not.'''}		)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}		)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[Union[str, Path, GenerationConfig]]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }       ,	)





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[str]:
        lowerCAmelCase     =			super().to_dict()
        for k, v in d.items():
           if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
              lowerCAmelCase     =			v.to_dict()
        return d





