




"""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  :			int        =       {
    # 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        lowercase      :







				def __init__(							self   :					int      ,     _lowerCamelCase   :					int = 14       ):



					"""simple docstring"""

					if group not in primes:
						raise ValueError('''Unsupported Group'''       )
					A_  :				Union[str, Any]	   =		primes[group]['''prime''']
					A_  :				Dict	   =		primes[group]['''generator''']

					A_  :				Dict	   =		int(hexlify(urandom(32       )       )      ,     base=16       )







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					return hex(self.__private_key       )[2:]







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		pow(self.generator      ,     self.__private_key      ,     self.prime       )
					return hex(_lowerCamelCase       )[2:]







				def 					a_  (							self   :					Union[str, Any]      ,     _lowerCamelCase   :					int       ):



					"""simple docstring"""

					return (
					    2 <= key <= self.prime - 2
					    and pow(_lowerCamelCase      ,     (self.prime - 1) // 2      ,     self.prime       ) == 1
					)







				def 					a_  (							self   :					Optional[int]      ,     _lowerCamelCase   :					str       ):



					"""simple docstring"""

					A_  :				List[str]	   =		int(_lowerCamelCase      ,     base=16       )
					if not self.is_valid_public_key(_lowerCamelCase       ):
						raise ValueError('''Invalid public key'''       )
					A_  :				Optional[int]	   =		pow(_lowerCamelCase      ,     self.__private_key      ,     self.prime       )
					return shaaaa(str(_lowerCamelCase       ).encode()       ).hexdigest()







				@staticmethod
				def 					a_  (							_lowerCamelCase   :					int      ,     _lowerCamelCase   :					int       ):



					"""simple docstring"""

					return (
					    2 <= remote_public_key_str <= prime - 2
					    and pow(_lowerCamelCase      ,     (prime - 1) // 2      ,     _lowerCamelCase       ) == 1
					)







				@staticmethod
				def 					a_  (							_lowerCamelCase   :					str      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					int = 14       ):



					"""simple docstring"""

					A_  :				List[str]	   =		int(_lowerCamelCase      ,     base=16       )
					A_  :				str	   =		int(_lowerCamelCase      ,     base=16       )
					A_  :				Tuple	   =		primes[group]['''prime''']
					if not DiffieHellman.is_valid_public_key_static(_lowerCamelCase      ,     _lowerCamelCase       ):
						raise ValueError('''Invalid public key'''       )
					A_  :				Optional[int]	   =		pow(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase       )
					return shaaaa(str(_lowerCamelCase       ).encode()       ).hexdigest()


if __name__ == "__main__":
		import doctest

		doctest.testmod()











"""simple docstring"""

import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase  :			Optional[Any]        =       logging.get_logger(__name__)

_lowerCamelCase  :			Any        =       [
    ('bert.bert', 'visual_bert'),
    ('bert.cls', 'cls'),
    ('bert.classifier', 'cls'),
    ('token_type_embeddings_visual', 'visual_token_type_embeddings'),
    ('position_embeddings_visual', 'visual_position_embeddings'),
    ('projection', 'visual_projection'),
]

_lowerCamelCase  :			int        =       [
    'nlvr2_coco_pre_trained.th',
    'nlvr2_fine_tuned.th',
    'nlvr2_pre_trained.th',
    'vcr_coco_pre_train.th',
    'vcr_fine_tune.th',
    'vcr_pre_train.th',
    'vqa_coco_pre_trained.th',
    'vqa_fine_tuned.th',
    'vqa_pre_trained.th',
]

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Union[str, Any]	   =		torch.load(_UpperCAmelCase  ,      map_location='''cpu'''	)
	return sd

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=rename_keys_prefix	):




	"""simple docstring"""






	A_  :				Any	   =		OrderedDict()
	A_  :				Union[str, Any]	   =		torch.arange(config.max_position_embeddings	).expand((1, -1)	)
	# detector_d = OrderedDict()
	for key in d:
		if "detector" in key:
			# detector_d[key.replace('detector.','')] = d[key]
			continue
		A_  :				List[Any]	   =		key
		for name_pair in rename_keys_prefix:
			A_  :				Any	   =		new_key.replace(name_pair[0]  ,      name_pair[1]	)
		A_  :				Dict	   =		d[key]
		if key == "bert.cls.predictions.decoder.weight":
			# Old bert code didn't have `decoder.bias`, but was added separately
			A_  :				Any	   =		new_d['''cls.predictions.bias''']
	return new_d

@torch.no_grad()
def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	assert (
	    checkpoint_path.split('''/'''	)[-1] in ACCEPTABLE_CHECKPOINTS
	), f"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""

	# Get Config
	if "pre" in checkpoint_path:
		A_  :				List[Any]	   =		'''pretraining'''
		if "vcr" in checkpoint_path:
			A_  :				Optional[Any]	   =		{'''visual_embedding_dim''': 512}
		elif "vqa_advanced" in checkpoint_path:
			A_  :				Tuple	   =		{'''visual_embedding_dim''': 2048}
		elif "vqa" in checkpoint_path:
			A_  :				str	   =		{'''visual_embedding_dim''': 2048}
		elif "nlvr" in checkpoint_path:
			A_  :				List[Any]	   =		{'''visual_embedding_dim''': 1024}
		else:
			raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`."""	)
	else:
		if "vcr" in checkpoint_path:
			A_  :				Any	   =		{'''visual_embedding_dim''': 512}
			A_  :				int	   =		'''multichoice'''
		elif "vqa_advanced" in checkpoint_path:
			A_  :				Any	   =		{'''visual_embedding_dim''': 2048}
			A_  :				Any	   =		'''vqa_advanced'''
		elif "vqa" in checkpoint_path:
			A_  :				int	   =		{'''visual_embedding_dim''': 2048, '''num_labels''': 3129}
			A_  :				Any	   =		'''vqa'''
		elif "nlvr" in checkpoint_path:
			A_  :				List[Any]	   =		{
			    '''visual_embedding_dim''': 1024,
			    '''num_labels''': 2,
			}
			A_  :				List[Any]	   =		'''nlvr'''

	A_  :				Dict	   =		VisualBertConfig(**_UpperCAmelCase	)

	# Load State Dict
	A_  :				List[str]	   =		load_state_dict(_UpperCAmelCase	)

	A_  :				List[str]	   =		get_new_dict(_UpperCAmelCase  ,      _UpperCAmelCase	)

	if model_type == "pretraining":
		A_  :				List[Any]	   =		VisualBertForPreTraining(_UpperCAmelCase	)
	elif model_type == "vqa":
		A_  :				Optional[Any]	   =		VisualBertForQuestionAnswering(_UpperCAmelCase	)
	elif model_type == "nlvr":
		A_  :				Any	   =		VisualBertForVisualReasoning(_UpperCAmelCase	)
	elif model_type == "multichoice":
		A_  :				Optional[int]	   =		VisualBertForMultipleChoice(_UpperCAmelCase	)

	model.load_state_dict(_UpperCAmelCase	)
	# Save Checkpoints
	Path(_UpperCAmelCase	).mkdir(exist_ok=_UpperCAmelCase	)
	model.save_pretrained(_UpperCAmelCase	)


if __name__ == "__main__":
		_lowerCamelCase  :			Dict        =       argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
		parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
		_lowerCamelCase  :			Optional[int]        =       parser.parse_args()
		convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)





