




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

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase = 8)      ->					str:
     a     =				ascii_letters + digits + punctuation
     return "".join(secrets.choice(__UpperCamelCase) for _ in range(__UpperCamelCase))

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

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

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					str:
     return "".join(secrets.choice(__UpperCamelCase) for _ in range(__UpperCamelCase))

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					Optional[Any]:
     pass  # Put your code here...

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					Union[str, Any]:
     pass  # Put your code here...

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					Tuple:
     pass  # Put your code here...

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase = 8)      ->					bool:

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

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


if __name__ == "__main__":
   main()







import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class       a__	( pl.LightningModule				):







     def __init__(     self  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



          super().__init__()
          a     =				model
          a     =				2
          a     =				nn.Linear(self.model.config.hidden_size  ,		self.num_labels			)







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          pass







def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
     # load longformer model from model identifier
     a     =				LongformerModel.from_pretrained(__UpperCamelCase)
     a     =				LightningModel(__UpperCamelCase)

     a     =				torch.load(__UpperCamelCase		, map_location=torch.device("cpu"))
     lightning_model.load_state_dict(ckpt["state_dict"])

     # init longformer question answering model
     a     =				LongformerForQuestionAnswering.from_pretrained(__UpperCamelCase)

     # transfer weights
     longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict())
     longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict())
     longformer_for_qa.eval()

     # save model
     longformer_for_qa.save_pretrained(__UpperCamelCase)

     print(f'''Conversion successful. Model saved under {pytorch_dump_folder_path}''')


if __name__ == "__main__":
   lowercase__				:      Optional[Any]	   = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--longformer_model",
       default=None,
       type=str,
       required=True,
       help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
   )
   parser.add_argument(
       "--longformer_question_answering_ckpt_path",
       default=None,
       type=str,
       required=True,
       help="Path the official PyTorch Lightning Checkpoint.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   lowercase__				:      Union[str, Any]	   = parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )

