




import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

snake_case_ : Optional[Any]    		=       3


def    A						(__A   :    int       )   ->      int:







     """simple docstring"""


     print('''Generating primitive root of p'''       )
     while True:
          UpperCAmelCase_          =							random.randrange(3   ,				__A       )
          if pow(__A   ,				2   ,				__A       ) == 1:
               continue
          if pow(__A   ,				__A   ,				__A       ) == 1:
               continue
          return g


def    A						(__A   :    int       )   ->      tuple[tuple[int, int, int, int], tuple[int, int]]:







     """simple docstring"""


     print('''Generating prime p...'''       )
     UpperCAmelCase_          =							rabin_miller.generate_large_prime(__A       )  # select large prime number.
     UpperCAmelCase_          =							primitive_root(__A       )  # one primitive root on modulo p.
     UpperCAmelCase_          =							random.randrange(3   ,				__A       )  # private_key -> have to be greater than 2 for safety.
     UpperCAmelCase_          =							cryptomath.find_mod_inverse(pow(__A   ,				__A   ,				__A       )   ,				__A       )

     UpperCAmelCase_          =							(key_size, e_a, e_a, p)
     UpperCAmelCase_          =							(key_size, d)

     return public_key, private_key


def    A						(__A   :    str   ,				__A   :    int       )   ->      None:







     """simple docstring"""


     if os.path.exists(F"""{name}_pubkey.txt"""       ) or os.path.exists(F"""{name}_privkey.txt"""       ):
          print('''\nWARNING:'''       )
          print(
              F"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
              '''Use a different name or delete these files and re-run this program.'''       )
          sys.exit()

     UpperCAmelCase_     ,       UpperCAmelCase_          =							generate_key(__A       )
     print(F"""\nWriting public key to file {name}_pubkey.txt..."""       )
     with open(F"""{name}_pubkey.txt"""   ,				'''w'''       ) as fo:
          fo.write(F"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""       )

     print(F"""Writing private key to file {name}_privkey.txt..."""       )
     with open(F"""{name}_privkey.txt"""   ,				'''w'''       ) as fo:
          fo.write(F"""{private_key[0]},{private_key[1]}"""       )


def    A						()   ->      None:







     """simple docstring"""


     print('''Making key files...'''       )
     make_key_files('''elgamal'''   ,				2048       )
     print('''Key files generation successful'''       )


if __name__ == "__main__":
    main()








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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def     __lowerCAmelCase			(			a__						,    a__						,    a__=1024						,    a__=1024						,    a__=False						,    **a__  )       ->  Optional[Any]:
				__a										=    AutoTokenizer.from_pretrained(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''train'''						,    **a__  )
				__a										=    tok.pad_token_id

				def get_lens(a__  ):
								__a										=    tqdm(
								    DataLoader(a__						,    batch_size=512						,    num_workers=8						,    shuffle=a__						,    collate_fn=ds.collate_fn  )						,    desc=str(ds.len_file  )						,    )
								__a										=    []
								for batch in dl:
												__a										=    batch['''input_ids'''].ne(a__  ).sum(1  ).tolist()
												__a										=    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										=    get_lens(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''val'''						,    **a__  )
				__a										=    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)