





'''simple docstring'''

import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

UpperCAmelCase					:		List[str]		=							3




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



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




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



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

      _snake_case		:	Optional[Any]					     =     (key_size, e_a, e_a, p)
      _snake_case		:	Optional[int]					     =     (key_size, d)

      return public_key, private_key




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """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()

      _snake_case		:	Tuple					     =     generate_key(lowerCAmelCase_	)
      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    (							):





      """simple docstring"""



      print('''Making key files...'''	)
      make_key_files('''elgamal'''			,      2_048	)
      print('''Key files generation successful'''	)


if __name__ == "__main__":
   main()









'''simple docstring'''

import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
   import optax
   from flax.training.common_utils import onehot

   from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
   from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class lowerCamelCase						(unittest.TestCase					):







  @slow
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	Tuple					     =     FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''		)
        _snake_case		:	Any					     =     AutoTokenizer.from_pretrained('''google/mt5-small'''		)

        _snake_case		:	List[str]					     =     tokenizer('''Hello there'''     ,     return_tensors='''np'''		).input_ids
        _snake_case		:	Dict					     =     tokenizer('''Hi I am'''     ,     return_tensors='''np'''		).input_ids

        _snake_case		:	Any					     =     shift_tokens_right(lowercase__     ,     model.config.pad_token_id     ,     model.config.decoder_start_token_id		)

        _snake_case		:	Any					     =     model(lowercase__     ,     decoder_input_ids=lowercase__		).logits
        _snake_case		:	Tuple					     =     optax.softmax_cross_entropy(lowercase__     ,     onehot(lowercase__     ,     logits.shape[-1]		)		).mean()

        _snake_case		:	Tuple					     =     -(labels.shape[-1] * loss.item())

        _snake_case		:	Union[str, Any]					     =     -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)


