


import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      int  ,   __A			:      List[str]=10_24  ,   __A			:      Tuple=10_24  ,   __A			:      int=False  ,   **__A			:      Dict    )     ->		Tuple:

       """simple docstring"""

       a_ :		Any							=  AutoTokenizer.from_pretrained(__A    )
       a_ :		Union[str, Any]							=  SeqaSeqDataset(__A  ,   __A  ,   __A  ,   __A  ,   type_path='train'  ,   **__A    )
       a_ :		int							=  tok.pad_token_id

       def get_lens(__A			:      Optional[int]    ):
              a_ :		int							=  tqdm(
                  DataLoader(__A  ,   batch_size=5_12  ,   num_workers=8  ,   shuffle=__A  ,   collate_fn=ds.collate_fn    )  ,   desc=str(ds.len_file    )  ,   )
              a_ :		Union[str, Any]							=  []
              for batch in dl:
                     a_ :		Optional[int]							=  batch['input_ids'].ne(__A    ).sum(1    ).tolist()
                     a_ :		Dict							=  batch['labels'].ne(__A    ).sum(1    ).tolist()
                     if consider_target:
                            for src, tgt in zip(__A  ,   __A    ):
                                   max_lens.append(max(__A  ,   __A    )    )
                     else:
                            max_lens.extend(__A    )
              return max_lens

       a_ :		List[str]							=  get_lens(__A    )
       a_ :		Dict							=  SeqaSeqDataset(__A  ,   __A  ,   __A  ,   __A  ,   type_path='val'  ,   **__A    )
       a_ :		int							=  get_lens(__A    )
       pickle_save(__A  ,   train_ds.len_file    )
       pickle_save(__A  ,   val_ds.len_file    )


if __name__ == "__main__":
   fire.Fire(save_len_file)






from string import ascii_uppercase

UpperCAmelCase_				:			Dict   =	{char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_				:			Optional[int]   =	dict(enumerate(ascii_uppercase))







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Tuple							=  len(__A    )
       a_ :		int							=  0
       while True:
              if x == i:
                     a_ :		Tuple							=  0
              if len(__A    ) == len(__A    ):
                     break
              key += key[i]
              i += 1
       return key







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Optional[int]							=  ''
       a_ :		Any							=  0
       for letter in message:
              if letter == " ":
                     cipher_text += " "
              else:
                     a_ :		Optional[Any]							=  (dicta[letter] - dicta[key_new[i]]) % 26
                     i += 1
                     cipher_text += dicta[x]
       return cipher_text







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Any							=  ''
       a_ :		Optional[Any]							=  0
       for letter in cipher_text:
              if letter == " ":
                     or_txt += " "
              else:
                     a_ :		Union[str, Any]							=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
                     i += 1
                     or_txt += dicta[x]
       return or_txt







def 	SCREAMING_SNAKE_CASE_						(      )     ->		None:

       """simple docstring"""

       a_ :		Tuple							=  'THE GERMAN ATTACK'
       a_ :		Dict							=  'SECRET'
       a_ :		Optional[Any]							=  generate_key(__A  ,   __A    )
       a_ :		Union[str, Any]							=  cipher_text(__A  ,   __A    )
       print(F"""Encrypted Text = {s}"""    )
       print(F"""Original Text = {original_text(__A  ,   __A    )}"""    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()


