






'''simple docstring'''







import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def 		_UpperCAmelCase (				__A				: int    ,		__A				: List[str]    ,		__A				: Tuple    ,		__A				: Any    ,		__A				: Optional[Any]   ):
				# Load configuration defined in the metadata file
				with open(__A   ) as metadata_file:
								a_		:	Any									=   json.load(__A   )
				a_		:	Optional[int]									=   LukeConfig(use_entity_aware_attention=__A    ,		**metadata['''model_config''']   )

				# Load in the weights from the checkpoint_path
				a_		:	List[str]									=   torch.load(__A    ,		map_location='''cpu'''   )

				# Load the entity vocab file
				a_		:	List[Any]									=   load_entity_vocab(__A   )

				a_		:	int									=   RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']   )

				# Add special tokens to the token vocabulary for downstream tasks
				a_		:	str									=   AddedToken('''<ent>'''    ,		lstrip=__A    ,		rstrip=__A   )
				a_		:	List[str]									=   AddedToken('''<ent2>'''    ,		lstrip=__A    ,		rstrip=__A   )
				tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}   )
				config.vocab_size += 2

				print(f'Saving tokenizer to {pytorch_dump_folder_path}'   )
				tokenizer.save_pretrained(__A   )
				with open(os.path.join(__A    ,		LukeTokenizer.vocab_files_names['''entity_vocab_file''']   )    ,		'''w'''   ) as f:
								json.dump(__A    ,		__A   )

				a_		:	Optional[Any]									=   LukeTokenizer.from_pretrained(__A   )

				# Initialize the embeddings of the special tokens
				a_		:	int									=   state_dict['''embeddings.word_embeddings.weight''']
				a_		:	Dict									=   word_emb[tokenizer.convert_tokens_to_ids(['''@''']   )[0]].unsqueeze(0   )
				a_		:	Tuple									=   word_emb[tokenizer.convert_tokens_to_ids(['''#''']   )[0]].unsqueeze(0   )
				a_		:	List[str]									=   torch.cat([word_emb, ent_emb, enta_emb]   )

				# Initialize the query layers of the entity-aware self-attention mechanism
				for layer_index in range(config.num_hidden_layers   ):
								for matrix_name in ["query.weight", "query.bias"]:
												a_		:	List[str]									=   f'encoder.layer.{layer_index}.attention.self.'
												a_		:	Optional[Any]									=   state_dict[prefix + matrix_name]
												a_		:	List[Any]									=   state_dict[prefix + matrix_name]
												a_		:	List[str]									=   state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				a_		:	Tuple									=   state_dict['''entity_embeddings.entity_embeddings.weight''']
				a_		:	List[str]									=   entity_emb[entity_vocab['''[MASK]''']]

				a_		:	Optional[Any]									=   LukeModel(config=__A   ).eval()

				a_					,      a_		:	Tuple									=   model.load_state_dict(__A    ,		strict=__A   )
				if not (len(__A   ) == 1 and missing_keys[0] == "embeddings.position_ids"):
								raise ValueError(f'Missing keys {", ".join(__A   )}. Expected only missing embeddings.position_ids'   )
				if not (all(key.startswith('''entity_predictions'''   ) or key.startswith('''lm_head'''   ) for key in unexpected_keys   )):
								raise ValueError(
								    '''Unexpected keys'''
								    f' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"   ) or key.startswith("lm_head"   ))]   )}'   )

				# Check outputs
				a_		:	Optional[int]									=   LukeTokenizer.from_pretrained(__A    ,		task='''entity_classification'''   )

				a_		:	Any									=   (
				    '''Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'''
				    ''' new world number one avoid a humiliating second- round exit at Wimbledon .'''
				)
				a_		:	List[str]									=   (39, 42)
				a_		:	Dict									=   tokenizer(__A    ,		entity_spans=[span]    ,		add_prefix_space=__A    ,		return_tensors='''pt'''   )

				a_		:	Dict									=   model(**__A   )

				# Verify word hidden states
				if model_size == "large":
								a_		:	int									=   torch.Size((1, 42, 10_24)   )
								a_		:	Dict									=   torch.tensor(
								    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]   )
				else:  # base
								a_		:	List[Any]									=   torch.Size((1, 42, 7_68)   )
								a_		:	Optional[Any]									=   torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]   )

				if not (outputs.last_hidden_state.shape == expected_shape):
								raise ValueError(
								    f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'   )
				if not torch.allclose(outputs.last_hidden_state[0, :3, :3]    ,		__A    ,		atol=1E-4   ):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								a_		:	str									=   torch.Size((1, 1, 10_24)   )
								a_		:	Tuple									=   torch.tensor([[0.0466, -0.0106, -0.0179]]   )
				else:  # base
								a_		:	str									=   torch.Size((1, 1, 7_68)   )
								a_		:	Union[str, Any]									=   torch.tensor([[0.1457, 0.1044, 0.0174]]   )

				if not (outputs.entity_last_hidden_state.shape != expected_shape):
								raise ValueError(
								    f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
								    f' {expected_shape}'   )
				if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]    ,		__A    ,		atol=1E-4   ):
								raise ValueError

				# Finally, save our PyTorch model and tokenizer
				print('''Saving PyTorch model to {}'''.format(__A   )   )
				model.save_pretrained(__A   )





def 		_UpperCAmelCase (				__A				: int   ):
				a_		:	int									=   {}
				with open(__A    ,		'''r'''    ,		encoding='''utf-8'''   ) as f:
								for index, line in enumerate(__A   ):
												a_					,      a_		:	Optional[Any]									=   line.rstrip().split('''\t'''   )
												a_		:	Optional[Any]									=   index

				return entity_vocab


if __name__ == "__main__":
		__lowerCAmelCase									=   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
		parser.add_argument(
		    '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
		)
		parser.add_argument(
		    '--entity_vocab_path',
		    default=None,
		    type=str,
		    help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
		)
		parser.add_argument(
		    '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
		)
		__lowerCAmelCase									=   parser.parse_args()
		convert_luke_checkpoint(
		    args.checkpoint_path,
		    args.metadata_path,
		    args.entity_vocab_path,
		    args.pytorch_dump_folder_path,
		    args.model_size,
		)












'''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()




