






'''simple docstring'''







import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
__lowerCAmelCase									=   logging.getLogger(__name__)


__lowerCAmelCase									=   'Hello world! cécé herlolip'


__lowerCAmelCase									=   namedtuple(
    'BertAbsConfig',
    [
        'temp_dir',
        'large',
        'use_bert_emb',
        'finetune_bert',
        'encoder',
        'share_emb',
        'max_pos',
        'enc_layers',
        'enc_hidden_size',
        'enc_heads',
        'enc_ff_size',
        'enc_dropout',
        'dec_layers',
        'dec_hidden_size',
        'dec_heads',
        'dec_ff_size',
        'dec_dropout',
    ],
)





def 		_UpperCAmelCase (				__A				: Tuple    ,		__A				: List[Any]   ):
				a_		:	Optional[int]									=   BertAbsConfig(
				    temp_dir='''.'''    ,		finetune_bert=__A    ,		large=__A    ,		share_emb=__A    ,		use_bert_emb=__A    ,		encoder='''bert'''    ,		max_pos=5_12    ,		enc_layers=6    ,		enc_hidden_size=5_12    ,		enc_heads=8    ,		enc_ff_size=5_12    ,		enc_dropout=0.2    ,		dec_layers=6    ,		dec_hidden_size=7_68    ,		dec_heads=8    ,		dec_ff_size=20_48    ,		dec_dropout=0.2    ,		)
				a_		:	Optional[Any]									=   torch.load(__A    ,		lambda __A    ,		__A   : storage   )
				a_		:	str									=   AbsSummarizer(__A    ,		torch.device('''cpu'''   )    ,		__A   )
				original.eval()

				a_		:	str									=   BertAbsSummarizer(__A    ,		torch.device('''cpu'''   )   )
				new_model.eval()

				# -------------------
				# Convert the weights
				# -------------------

				logging.info('''convert the model'''   )
				new_model.bert.load_state_dict(original.bert.state_dict()   )
				new_model.decoder.load_state_dict(original.decoder.state_dict()   )
				new_model.generator.load_state_dict(original.generator.state_dict()   )

				# ----------------------------------
				# Make sure the outpus are identical
				# ----------------------------------

				logging.info('''Make sure that the models\' outputs are identical'''   )
				a_		:	Optional[Any]									=   BertTokenizer.from_pretrained('''bert-base-uncased'''   )

				# prepare the model inputs
				a_		:	List[Any]									=   tokenizer.encode('''This is sample éàalj\'-.'''   )
				encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(__A   ))   )
				a_		:	List[Any]									=   torch.tensor(__A   ).unsqueeze(0   )
				a_		:	Union[str, Any]									=   tokenizer.encode('''This is sample 3 éàalj\'-.'''   )
				decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(__A   ))   )
				a_		:	List[str]									=   torch.tensor(__A   ).unsqueeze(0   )

				# failsafe to make sure the weights reset does not affect the
				# loaded weights.
				assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight   )   ) == 0

				# forward pass
				a_		:	Dict									=   encoder_input_ids
				a_		:	List[str]									=   decoder_input_ids
				a_		:	Any									=   None
				a_		:	Union[str, Any]									=   None
				a_		:	Any									=   None
				a_		:	List[str]									=   None
				a_		:	int									=   None

				# The original model does not apply the geneator layer immediatly but rather in
				# the beam search (where it combines softmax + linear layer). Since we already
				# apply the softmax in our generation process we only apply the linear layer here.
				# We make sure that the outputs of the full stack are identical
				a_		:	int									=   original(__A    ,		__A    ,		__A    ,		__A    ,		__A    ,		__A    ,		__A   )[0]
				a_		:	List[Any]									=   original.generator(__A   )

				a_		:	List[Any]									=   new_model(
				    __A    ,		__A    ,		__A    ,		__A    ,		__A   )[0]
				a_		:	Optional[int]									=   new_model.generator(__A   )

				a_		:	Optional[int]									=   torch.max(torch.abs(output_converted_model - output_original_model   )   ).item()
				print('''Maximum absolute difference beween weights: {:.2f}'''.format(__A   )   )
				a_		:	int									=   torch.max(torch.abs(output_converted_generator - output_original_generator   )   ).item()
				print('''Maximum absolute difference beween weights: {:.2f}'''.format(__A   )   )

				a_		:	Any									=   torch.allclose(__A    ,		__A    ,		atol=1E-3   )
				if are_identical:
								logging.info('''all weights are equal up to 1e-3'''   )
				else:
								raise ValueError('''the weights are different. The new model is likely different from the original one.'''   )

				# The model has been saved with torch.save(model) and this is bound to the exact
				# directory structure. We save the state_dict instead.
				logging.info('''saving the model\'s state dictionary'''   )
				torch.save(
				    new_model.state_dict()    ,		'''./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin'''   )


if __name__ == "__main__":
		__lowerCAmelCase									=   argparse.ArgumentParser()
		parser.add_argument(
		    '--bertabs_checkpoint_path',
		    default=None,
		    type=str,
		    required=True,
		    help='Path the official PyTorch dump.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    type=str,
		    required=True,
		    help='Path to the output PyTorch model.',
		)
		__lowerCAmelCase									=   parser.parse_args()

		convert_bertabs_checkpoints(
		    args.bertabs_checkpoint_path,
		    args.pytorch_dump_folder_path,
		)












'''simple docstring'''







from binascii import hexlify
from hashlib import shaaaa
from os import urandom

# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526

__lowerCAmelCase									=   {
    # 1536-bit
    5: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 2048-bit
    14: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AACAA68FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 3072-bit
    15: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
            + 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
            + 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
            + 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
            + '43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 4096-bit
    16: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
            + 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
            + 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
            + 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
            + '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
            + '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
            + '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
            + '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
            + '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
            + '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199'
            + 'FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 6144-bit
    17: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08'
            + '8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B'
            + '302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9'
            + 'A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6'
            + '49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8'
            + 'FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C'
            + '180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718'
            + '3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D'
            + '04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D'
            + 'B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226'
            + '1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC'
            + 'E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26'
            + '99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB'
            + '04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2'
            + '233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127'
            + 'D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
            + '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406'
            + 'AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918'
            + 'DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151'
            + '2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03'
            + 'F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F'
            + 'BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
            + 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B'
            + 'B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632'
            + '387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E'
            + '6DCC4024FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 8192-bit
    18: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
            + 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
            + 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
            + 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
            + '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
            + '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
            + '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
            + '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
            + '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
            + '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
            + '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD'
            + 'F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831'
            + '179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B'
            + 'DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF'
            + '5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6'
            + 'D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3'
            + '23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
            + 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328'
            + '06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C'
            + 'DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE'
            + '12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4'
            + '38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300'
            + '741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568'
            + '3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9'
            + '22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B'
            + '4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A'
            + '062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36'
            + '4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1'
            + 'B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92'
            + '4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47'
            + '9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71'
            + '60C980DD98EDD3DFFFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
}





class 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      int = 14       )      ->      None:
							if group not in primes:
											raise ValueError('''Unsupported Group'''       )
							a_		:	Union[str, Any]									=   primes[group]['''prime''']
							a_		:	List[str]									=   primes[group]['''generator''']

							a_		:	str									=   int(hexlify(urandom(32       )       )	,    base=16       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      str:
							return hex(self.__private_key       )[2:]






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      str:
							a_		:	Dict									=   pow(self.generator	,    self.__private_key	,    self.prime       )
							return hex(__SCREAMING_SNAKE_CASE       )[2:]






			def       SCREAMING_SNAKE_CASE   (			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      bool:
							# check if the other public key is valid based on NIST SP800-56
							return (
							    2 <= key <= self.prime - 2
							    and pow(__SCREAMING_SNAKE_CASE	,    (self.prime - 1) // 2	,    self.prime       ) == 1
							)






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      str:
							a_		:	str									=   int(__SCREAMING_SNAKE_CASE	,    base=16       )
							if not self.is_valid_public_key(__SCREAMING_SNAKE_CASE       ):
											raise ValueError('''Invalid public key'''       )
							a_		:	Optional[int]									=   pow(__SCREAMING_SNAKE_CASE	,    self.__private_key	,    self.prime       )
							return shaaaa(str(__SCREAMING_SNAKE_CASE       ).encode()       ).hexdigest()






			@staticmethod
			def       SCREAMING_SNAKE_CASE   (			__SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      bool:
							# check if the other public key is valid based on NIST SP800-56
							return (
							    2 <= remote_public_key_str <= prime - 2
							    and pow(__SCREAMING_SNAKE_CASE	,    (prime - 1) // 2	,    __SCREAMING_SNAKE_CASE       ) == 1
							)







			@staticmethod
			def       SCREAMING_SNAKE_CASE   (			__SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      int = 14       )      ->      str:
							a_		:	Tuple									=   int(__SCREAMING_SNAKE_CASE	,    base=16       )
							a_		:	Any									=   int(__SCREAMING_SNAKE_CASE	,    base=16       )
							a_		:	List[str]									=   primes[group]['''prime''']
							if not DiffieHellman.is_valid_public_key_static(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
											raise ValueError('''Invalid public key'''       )
							a_		:	Optional[int]									=   pow(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							return shaaaa(str(__SCREAMING_SNAKE_CASE       ).encode()       ).hexdigest()


if __name__ == "__main__":
		import doctest

		doctest.testmod()




