
'''simple docstring'''







import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812







def 				SCREAMING_SNAKE_CASE__	(   )       ->	None:
	print('Making key files...'	)
	make_key_files('rsa'			,				1_024	)
	print('Key files generation successful.'	)







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	tuple[tuple[int, int], tuple[int, int]]:
	print('Generating prime p...'	)
	_snake_case   						=		rabinMiller.generate_large_prime(__A	)
	print('Generating prime q...'	)
	_snake_case   						=		rabinMiller.generate_large_prime(__A	)
	_snake_case   						=		p * q

	print('Generating e that is relatively prime to (p - 1) * (q - 1)...'	)
	while True:
		_snake_case   						=		random.randrange(2 ** (key_size - 1)			,				2 ** (key_size)	)
		if cryptoMath.gcd(__A			,				(p - 1) * (q - 1)	) == 1:
			break

	print('Calculating d that is mod inverse of e...'	)
	_snake_case   						=		cryptoMath.find_mod_inverse(__A			,				(p - 1) * (q - 1)	)

	_snake_case   						=		(n, e)
	_snake_case   						=		(n, d)
	return (public_key, private_key)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	None:






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

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


if __name__ == "__main__":
					main()








import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
       snake_case__					:							Optional[int]													=     argparse.ArgumentParser(
           description=(
               'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
               ' Distillation'
           )
       )
       parser.add_argument('--model_type', default='bert', choices=['bert'])
       parser.add_argument('--model_name', default='bert-base-uncased', type=str)
       parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
       parser.add_argument('--vocab_transform', action='store_true')
       snake_case__					:							Optional[int]													=     parser.parse_args()

       if args.model_type == "bert":
              snake_case__					:							Dict													=     BertForMaskedLM.from_pretrained(args.model_name)
              snake_case__					:							Union[str, Any]													=     'bert'
       else:
              raise ValueError('args.model_type should be "bert".')

       snake_case__					:							Optional[int]													=     model.state_dict()
       snake_case__					:							List[Any]													=     {}

       for w in ["word_embeddings", "position_embeddings"]:
              snake_case__					:							Tuple													=     state_dict[f'{prefix}.embeddings.{w}.weight']
       for w in ["weight", "bias"]:
              snake_case__					:							Optional[Any]													=     state_dict[f'{prefix}.embeddings.LayerNorm.{w}']

       snake_case__					:							int													=     0
       for teacher_idx in [0, 2, 4, 7, 9, 11]:
              for w in ["weight", "bias"]:
                     snake_case__					:							Union[str, Any]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'
                     ]
                     snake_case__					:							Dict													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'
                     ]
                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'
                     ]

                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'
                     ]
                     snake_case__					:							Optional[int]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'
                     ]

                     snake_case__					:							Optional[Any]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
                     ]
                     snake_case__					:							List[str]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
                     ]
                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'
                     ]
              std_idx += 1

       snake_case__					:							Optional[int]													=     state_dict['cls.predictions.decoder.weight']
       snake_case__					:							str													=     state_dict['cls.predictions.bias']
       if args.vocab_transform:
              for w in ["weight", "bias"]:
                     snake_case__					:							int													=     state_dict[f'cls.predictions.transform.dense.{w}']
                     snake_case__					:							Optional[int]													=     state_dict[f'cls.predictions.transform.LayerNorm.{w}']

       print(f'N layers selected for distillation: {std_idx}')
       print(f'Number of params transferred for distillation: {len(compressed_sd.keys())}')

       print(f'Save transferred checkpoint to {args.dump_checkpoint}.')
       torch.save(compressed_sd, args.dump_checkpoint)





