



import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
UpperCAmelCase__       =	[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 							A     (					_UpperCAmelCase    :       Union[str, Any]							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Union[str, Any]							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       str   )    ->			str:







			'''simple docstring'''
			for attribute in key.split('.'   ):
						_UpperCAmelCase			=       getattr(_UpperCAmelCase							,       _UpperCAmelCase   )

			if weight_type is not None:
						_UpperCAmelCase			=       getattr(_UpperCAmelCase							,       _UpperCAmelCase   ).shape
			else:
						_UpperCAmelCase			=       hf_pointer.shape

			assert hf_shape == value.shape, (
			    F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
			    F" {value.shape} for {full_name}"
			)

			if weight_type == "weight":
						_UpperCAmelCase			=       value
			elif weight_type == "weight_g":
						_UpperCAmelCase			=       value
			elif weight_type == "weight_v":
						_UpperCAmelCase			=       value
			elif weight_type == "bias":
						_UpperCAmelCase			=       value
			else:
						_UpperCAmelCase			=       value

			logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."   )





def 							A     (					_UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Optional[Any]   )    ->			Any:







			'''simple docstring'''
			_UpperCAmelCase			=       []
			_UpperCAmelCase			=       fairseq_model.state_dict()

			_UpperCAmelCase			=       hf_model.feature_extractor
			_UpperCAmelCase			=       hf_model.adapter

			for name, value in fairseq_dict.items():
						_UpperCAmelCase			=       False
						if "conv_layers" in name:
									load_conv_layer(
									    _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       hf_model.config.feat_extract_norm == 'group'							,       )
									_UpperCAmelCase			=       True
						elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']   ):
									load_adapter(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase   )
									_UpperCAmelCase			=       True
						else:
									for key, mapped_key in MAPPING.items():
												if key in name or key.split('w2v_model.'   )[-1] == name.split('.'   )[0]:
															_UpperCAmelCase			=       True
															if "*" in mapped_key:
																		_UpperCAmelCase			=       name.split(_UpperCAmelCase   )[0].split('.'   )[-2]
																		_UpperCAmelCase			=       mapped_key.replace('*'							,       _UpperCAmelCase   )
															if "weight_g" in name:
																		_UpperCAmelCase			=       'weight_g'
															elif "weight_v" in name:
																		_UpperCAmelCase			=       'weight_v'
															elif "bias" in name:
																		_UpperCAmelCase			=       'bias'
															elif "weight" in name:
																		_UpperCAmelCase			=       'weight'
															else:
																		_UpperCAmelCase			=       None
															set_recursively(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase   )
												continue
						if not is_used:
									unused_weights.append(_UpperCAmelCase   )

			logger.warning(F"Unused weights: {unused_weights}"   )





def 							A     (					_UpperCAmelCase    :       str							,       _UpperCAmelCase    :       str							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Optional[Any]							,       _UpperCAmelCase    :       Optional[Any]   )    ->			Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       full_name.split('conv_layers.'   )[-1]
			_UpperCAmelCase			=       name.split('.'   )
			_UpperCAmelCase			=       int(items[0]   )
			_UpperCAmelCase			=       int(items[1]   )

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    F"{full_name} has size {value.shape}, but"
									    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
									)
									_UpperCAmelCase			=       value
									logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."   )
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    F"{full_name} has size {value.shape}, but"
									    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
									)
									_UpperCAmelCase			=       value
									logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."   )
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
									    " found."
									)
									_UpperCAmelCase			=       value
									logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    F"{full_name} has size {value.shape}, but"
									    F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
									)
									_UpperCAmelCase			=       value
									logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
			else:
						unused_weights.append(_UpperCAmelCase   )





def 							A     (					_UpperCAmelCase    :       Dict							,       _UpperCAmelCase    :       Union[str, Any]							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Optional[Any]   )    ->			List[str]:







			'''simple docstring'''
			_UpperCAmelCase			=       full_name.split('adaptor.'   )[-1]
			_UpperCAmelCase			=       name.split('.'   )

			if items[1].isdigit():
						_UpperCAmelCase			=       int(items[1]   )
			else:
						_UpperCAmelCase			=       None

			if "adaptor" not in full_name:
						if "proj_ln" in full_name:
									# has to be layer norm
									if "bias" in name:
												assert (
												    value.shape == adapter.proj_layer_norm.bias.data.shape
												), F"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."
												_UpperCAmelCase			=       value
												logger.info(F"Adapter proj layer norm bias was initialized from {full_name}."   )
									if "weight" in name:
												assert (
												    value.shape == adapter.proj_layer_norm.weight.data.shape
												), F"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."
												_UpperCAmelCase			=       value
						else:
									# has to be projection layer
									if "bias" in name:
												assert (
												    value.shape == adapter.proj.bias.data.shape
												), F"{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."
												_UpperCAmelCase			=       value
												logger.info(F"Adapter proj layer bias was initialized from {full_name}."   )
									if "weight" in name:
												assert (
												    value.shape == adapter.proj.weight.data.shape
												), F"{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."
												_UpperCAmelCase			=       value
												logger.info(F"Adapter proj layer weight was initialized from {full_name}."   )
			elif isinstance(_UpperCAmelCase							,       _UpperCAmelCase   ):
						if "bias" in name:
									assert (
									    value.shape == adapter.layers[layer_id].conv.bias.data.shape
									), F"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."
									_UpperCAmelCase			=       value
									logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}."   )
						elif "weight" in name:
									assert (
									    value.shape == adapter.layers[layer_id].conv.weight.data.shape
									), F"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."
									_UpperCAmelCase			=       value
									logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}."   )
			else:
						unused_weights.append(_UpperCAmelCase   )





def 							A     (					_UpperCAmelCase    :       List[Any]   )    ->			Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	,					_UpperCAmelCase			=       emb.weight.shape
			_UpperCAmelCase			=       nn.Linear(_UpperCAmelCase							,       _UpperCAmelCase							,       bias=_UpperCAmelCase   )
			_UpperCAmelCase			=       emb.weight.data
			return lin_layer





@torch.no_grad()
def 							A     (					_UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Union[str, Any]							,       _UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       Optional[Any]							,       _UpperCAmelCase    :       Dict							,       _UpperCAmelCase    :       Dict							,       )    ->			Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase			=       WavaVecaConfig.from_pretrained(
			    _UpperCAmelCase							,       add_adapter=_UpperCAmelCase							,       adapter_stride=_UpperCAmelCase							,       adapter_kernel_size=_UpperCAmelCase							,       use_auth_token=_UpperCAmelCase							,       output_hidden_size=_UpperCAmelCase							,       )
			_UpperCAmelCase			=       MBartConfig.from_pretrained(_UpperCAmelCase   )

			# load model
			_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]							,       arg_overrides={
			        'config_yaml': config_yaml_path,
			        'data': '/'.join(dict_path.split('/'   )[:-1]   ),
			        'w2v_path': checkpoint_path,
			        'load_pretrained_decoder_from': None,
			    }							,       )
			_UpperCAmelCase			=       model[0].eval()

			# load feature extractor
			_UpperCAmelCase			=       WavaVecaFeatureExtractor.from_pretrained(_UpperCAmelCase							,       use_auth_token=_UpperCAmelCase   )

			# set weights for wav2vec2 encoder
			_UpperCAmelCase			=       WavaVecaModel(_UpperCAmelCase   )

			recursively_load_weights_wavaveca(model.encoder							,       _UpperCAmelCase   )

			# load decoder weights
			_UpperCAmelCase			=       MBartForCausalLM(_UpperCAmelCase   )
			_UpperCAmelCase	,					_UpperCAmelCase			=       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()							,       strict=_UpperCAmelCase   )
			logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}"   )
			logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"   )

			_UpperCAmelCase			=       SpeechEncoderDecoderModel(encoder=_UpperCAmelCase							,       decoder=_UpperCAmelCase   )
			_UpperCAmelCase			=       False

			_UpperCAmelCase			=       MBartaaTokenizer(_UpperCAmelCase   )
			tokenizer.save_pretrained(_UpperCAmelCase   )

			_UpperCAmelCase			=       hf_wavavec.config.to_dict()
			_UpperCAmelCase			=       tokenizer.pad_token_id
			_UpperCAmelCase			=       tokenizer.bos_token_id
			_UpperCAmelCase			=       tokenizer.eos_token_id
			_UpperCAmelCase			=       'mbart50'
			_UpperCAmelCase			=       'wav2vec2'

			_UpperCAmelCase			=       tokenizer.eos_token_id
			_UpperCAmelCase			=       250_004
			_UpperCAmelCase			=       tokenizer.eos_token_id

			_UpperCAmelCase			=       SpeechEncoderDecoderConfig.from_dict(_UpperCAmelCase   )

			hf_wavavec.save_pretrained(_UpperCAmelCase   )
			feature_extractor.save_pretrained(_UpperCAmelCase   )


if __name__ == "__main__":
							UpperCAmelCase__       =	argparse.ArgumentParser()
							parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
							parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
							parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
							parser.add_argument("--config_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
							parser.add_argument(
							    "--encoder_config_path",
							    default="facebook/wav2vec2-xls-r-1b",
							    type=str,
							    help="Path to hf encoder wav2vec2 checkpoint config",
							)
							parser.add_argument(
							    "--decoder_config_path",
							    default="facebook/mbart-large-50-one-to-many-mmt",
							    type=str,
							    help="Path to hf decoder checkpoint config",
							)
							parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
							parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
							parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
							parser.add_argument("--encoder_output_dim", default=1024, type=int, help="encoder output dim")
							parser.add_argument("--start_token_id", default=25_0004, type=int, help="`decoder_start_token_id` of model config")

							UpperCAmelCase__       =	parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.dict_path,
							    args.config_yaml_path,
							    encoder_config_path=args.encoder_config_path,
							    decoder_config_path=args.decoder_config_path,
							    add_adapter=args.add_adapter,
							    adapter_kernel_size=args.adapter_kernel_size,
							    adapter_stride=args.adapter_stride,
							    decoder_start_token_id=args.start_token_id,
							    encoder_output_dim=args.encoder_output_dim,
							)





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

UpperCAmelCase__       =	{
    # 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       __lowerCAmelCase   :



			def __init__(						self       :  List[str]			,		A       :  int = 14)       ->   None:



						"""simple docstring"""






						if group not in primes:
									raise ValueError('Unsupported Group')
						_UpperCAmelCase			=       primes[group]['prime']
						_UpperCAmelCase			=       primes[group]['generator']

						_UpperCAmelCase			=       int(hexlify(urandom(32))			,		base=16)



			def    _lowerCamelCase	(						self       :  int)       ->   str:



						"""simple docstring"""






						return hex(self.__private_key)[2:]



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       pow(self.generator			,		self.__private_key			,		self.prime)
						return hex(A)[2:]



			def    _lowerCamelCase	(						self       :  Tuple			,		A       :  int)       ->   bool:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  str)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       int(A			,		base=16)
						if not self.is_valid_public_key(A):
									raise ValueError('Invalid public key')
						_UpperCAmelCase			=       pow(A			,		self.__private_key			,		self.prime)
						return shaaaa(str(A).encode()).hexdigest()



			@staticmethod
			def    _lowerCamelCase	(						A       :  int			,		A       :  int)       ->   bool:



						"""simple docstring"""






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



			@staticmethod
			def    _lowerCamelCase	(						A       :  str			,		A       :  str			,		A       :  int = 14)       ->   str:



						"""simple docstring"""






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


if __name__ == "__main__":
							import doctest

							doctest.testmod()
