"""simple docstring"""




import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


__lowerCAmelCase    :							Optional[Any]													=						logging.getLogger(__name__)







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		if os.path.exists(_lowerCAmelCase     ):
				if os.path.exists(os.path.join(_lowerCAmelCase			,  """config.json"""     )     ) and os.path.isfile(
				    os.path.join(_lowerCAmelCase			,  """config.json"""     )     ):
						os.remove(os.path.join(_lowerCAmelCase			,  """config.json"""     )     )
				if os.path.exists(os.path.join(_lowerCAmelCase			,  """pytorch_model.bin"""     )     ) and os.path.isfile(
				    os.path.join(_lowerCAmelCase			,  """pytorch_model.bin"""     )     ):
						os.remove(os.path.join(_lowerCAmelCase			,  """pytorch_model.bin"""     )     )
		else:
				os.makedirs(_lowerCAmelCase     )
		model.save_pretrained(_lowerCAmelCase     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__=False     ):





		"""simple docstring"""




		lowerCAmelCase__        =   2
		if unlogit:
				lowerCAmelCase__        =   torch.pow(_lowerCAmelCase			,  _lowerCAmelCase     )
		lowerCAmelCase__        =   p * torch.log(_lowerCAmelCase     )
		lowerCAmelCase__        =   0
		return -plogp.sum(dim=-1     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		logger.info("""lv, h >\t""" + """\t""".join(f"""{x + 1}""" for x in range(len(_lowerCAmelCase     )     )     )     )
		for row in range(len(_lowerCAmelCase     )     ):
				if tensor.dtype != torch.long:
						logger.info(f"""layer {row + 1}:\t""" + """\t""".join(f"""{x:.5f}""" for x in tensor[row].cpu().data     )     )
				else:
						logger.info(f"""layer {row + 1}:\t""" + """\t""".join(f"""{x:d}""" for x in tensor[row].cpu().data     )     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=True			,  lowerCamelCase__=True			,  lowerCamelCase__=None			,  lowerCamelCase__=False     ):





		"""simple docstring"""




		lowerCAmelCase__        =   model.config.num_hidden_layers, model.config.num_attention_heads
		lowerCAmelCase__        =   torch.zeros(_lowerCAmelCase			,  _lowerCAmelCase     ).to(args.device     )
		lowerCAmelCase__        =   torch.zeros(_lowerCAmelCase			,  _lowerCAmelCase     ).to(args.device     )

		if head_mask is None:
				lowerCAmelCase__        =   torch.ones(_lowerCAmelCase			,  _lowerCAmelCase     ).to(args.device     )

		head_mask.requires_grad_(requires_grad=_lowerCAmelCase     )
		# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
		if actually_pruned:
				lowerCAmelCase__        =   None

		lowerCAmelCase__        =   0.0
		lowerCAmelCase__        =   0.0
		for step, inputs in enumerate(tqdm(_lowerCAmelCase			,  desc="""Iteration"""			,  disable=args.local_rank not in [-1, 0]     )     ):
				lowerCAmelCase__        =   tuple(t.to(args.device     ) for t in inputs     )
				(lowerCAmelCase__		)        =   inputs

				# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
				lowerCAmelCase__        =   model(_lowerCAmelCase			,  labels=_lowerCAmelCase			,  head_mask=_lowerCAmelCase     )
				#  (loss), lm_logits, presents, (all hidden_states), (attentions)
				lowerCAmelCase__        =   (
				    outputs[0],
				    outputs[1],
				    outputs[-1],
				)  # Loss and logits are the first, attention the last
				loss.backward()  # Backpropagate to populate the gradients in the head mask
				total_loss += loss.detach().cpu().numpy()
				if compute_entropy:
						for layer, attn in enumerate(_lowerCAmelCase     ):
								lowerCAmelCase__        =   entropy(attn.detach()			,  _lowerCAmelCase     )
								attn_entropy[layer] += masked_entropy.sum(-1     ).sum(0     ).sum(0     ).detach()

				if compute_importance:
						head_importance += head_mask.grad.abs().detach()
				tot_tokens += torch.ones_like(_lowerCAmelCase     ).float().detach().sum().data

		# Normalize
		attn_entropy /= tot_tokens
		head_importance /= tot_tokens
		# Layerwise importance normalization
		if not args.dont_normalize_importance_by_layer:
				lowerCAmelCase__        =   2
				lowerCAmelCase__        =   torch.pow(torch.pow(_lowerCAmelCase			,  _lowerCAmelCase     ).sum(-1     )			,  1 / exponent     )
				head_importance /= norm_by_layer.unsqueeze(-1     ) + 1e-20

		if not args.dont_normalize_global_importance:
				lowerCAmelCase__        =   (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

		# Print matrices
		if compute_entropy:
				logger.info("""Attention entropies"""     )
				print_ad_tensor(_lowerCAmelCase     )
		if compute_importance:
				logger.info("""Head importance scores"""     )
				print_ad_tensor(_lowerCAmelCase     )
		logger.info("""Head ranked by importance scores"""     )
		lowerCAmelCase__        =   torch.zeros(head_importance.numel()			,  dtype=torch.long			,  device=args.device     )
		lowerCAmelCase__        =   torch.arange(
		    head_importance.numel()			,  device=args.device     )
		lowerCAmelCase__        =   head_ranks.view_as(_lowerCAmelCase     )
		print_ad_tensor(_lowerCAmelCase     )
		return attn_entropy, head_importance, total_loss







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   compute_heads_importance(_lowerCAmelCase			,  _lowerCAmelCase			,  _lowerCAmelCase			,  compute_entropy=_lowerCAmelCase     )
		lowerCAmelCase__        =   1 / loss  # instead of downsteam score use the LM loss
		logger.info("""Pruning: original score: %f, threshold: %f"""			,  _lowerCAmelCase			,  original_score * args.masking_threshold     )

		lowerCAmelCase__        =   torch.ones_like(_lowerCAmelCase     )
		lowerCAmelCase__        =   max(1			,  int(new_head_mask.numel() * args.masking_amount     )     )

		lowerCAmelCase__        =   original_score
		while current_score >= original_score * args.masking_threshold:
				lowerCAmelCase__        =   new_head_mask.clone().detach()  # save current head mask
				# heads from least important to most - keep only not-masked heads
				lowerCAmelCase__        =   float("""Inf"""     )
				lowerCAmelCase__        =   head_importance.view(-1     ).sort()[1]

				if len(_lowerCAmelCase     ) <= num_to_mask:
						print("""BREAK BY num_to_mask"""     )
						break

				# mask heads
				lowerCAmelCase__        =   current_heads_to_mask[:num_to_mask]
				logger.info("""Heads to mask: %s"""			,  str(current_heads_to_mask.tolist()     )     )
				lowerCAmelCase__        =   new_head_mask.view(-1     )
				lowerCAmelCase__        =   0.0
				lowerCAmelCase__        =   new_head_mask.view_as(_lowerCAmelCase     )
				lowerCAmelCase__        =   new_head_mask.clone().detach()
				print_ad_tensor(_lowerCAmelCase     )

				# Compute metric and head importance again
				lowerCAmelCase__        =   compute_heads_importance(
				    _lowerCAmelCase			,  _lowerCAmelCase			,  _lowerCAmelCase			,  compute_entropy=_lowerCAmelCase			,  head_mask=_lowerCAmelCase     )
				lowerCAmelCase__        =   1 / loss
				logger.info(
				    """Masking: current score: %f, remaining heads %d (%.1f percents)"""			,  _lowerCAmelCase			,  new_head_mask.sum()			,  new_head_mask.sum() / new_head_mask.numel() * 100			,  )

		logger.info("""Final head mask"""     )
		print_ad_tensor(_lowerCAmelCase     )
		np.save(os.path.join(args.output_dir			,  """head_mask.npy"""     )			,  head_mask.detach().cpu().numpy()     )

		return head_mask







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   datetime.now()
		lowerCAmelCase__        =   compute_heads_importance(
		    _lowerCAmelCase			,  _lowerCAmelCase			,  _lowerCAmelCase			,  compute_entropy=_lowerCAmelCase			,  compute_importance=_lowerCAmelCase			,  head_mask=_lowerCAmelCase     )
		lowerCAmelCase__        =   1 / loss
		lowerCAmelCase__        =   datetime.now() - before_time

		lowerCAmelCase__        =   sum(p.numel() for p in model.parameters()     )
		lowerCAmelCase__        =   {
		    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(_lowerCAmelCase     )     )
		}

		for k, v in heads_to_prune.items():
				if isinstance(_lowerCAmelCase			,  _lowerCAmelCase     ):
						lowerCAmelCase__        =   [
						    v,
						]

		assert sum(len(_lowerCAmelCase     ) for h in heads_to_prune.values()     ) == (1 - head_mask.long()).sum().item()
		model.prune_heads(_lowerCAmelCase     )
		lowerCAmelCase__        =   sum(p.numel() for p in model.parameters()     )

		lowerCAmelCase__        =   datetime.now()
		lowerCAmelCase__        =   compute_heads_importance(
		    _lowerCAmelCase			,  _lowerCAmelCase			,  _lowerCAmelCase			,  compute_entropy=_lowerCAmelCase			,  compute_importance=_lowerCAmelCase			,  head_mask=_lowerCAmelCase			,  actually_pruned=_lowerCAmelCase			,  )

		lowerCAmelCase__        =   1 / loss
		lowerCAmelCase__        =   datetime.now() - before_time

		logger.info(
		    """Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"""			,  _lowerCAmelCase			,  _lowerCAmelCase			,  pruned_num_params / original_num_params * 100			,  )
		logger.info("""Pruning: score with masking: %f score with pruning: %f"""			,  _lowerCAmelCase			,  _lowerCAmelCase     )
		logger.info("""Pruning: speed ratio (original timing / new timing): %f percents"""			,  original_time / new_time * 100     )
		save_model(_lowerCAmelCase			,  args.output_dir     )







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--data_dir"""			,  default=_lowerCAmelCase			,  type=_lowerCAmelCase			,  required=_lowerCAmelCase			,  help="""The input data dir. Should contain the .tsv files (or other data files) for the task."""			,  )
		parser.add_argument(
		    """--model_name_or_path"""			,  default=_lowerCAmelCase			,  type=_lowerCAmelCase			,  required=_lowerCAmelCase			,  help="""Path to pretrained model or model identifier from huggingface.co/models"""			,  )
		parser.add_argument(
		    """--output_dir"""			,  default=_lowerCAmelCase			,  type=_lowerCAmelCase			,  required=_lowerCAmelCase			,  help="""The output directory where the model predictions and checkpoints will be written."""			,  )

		# Other parameters
		parser.add_argument(
		    """--config_name"""			,  default=""""""			,  type=_lowerCAmelCase			,  help="""Pretrained config name or path if not the same as model_name_or_path"""			,  )
		parser.add_argument(
		    """--tokenizer_name"""			,  default=""""""			,  type=_lowerCAmelCase			,  help="""Pretrained tokenizer name or path if not the same as model_name_or_path"""			,  )
		parser.add_argument(
		    """--cache_dir"""			,  default=_lowerCAmelCase			,  type=_lowerCAmelCase			,  help="""Where do you want to store the pre-trained models downloaded from s3"""			,  )
		parser.add_argument(
		    """--data_subset"""			,  type=_lowerCAmelCase			,  default=-1			,  help="""If > 0: limit the data to a subset of data_subset instances."""     )
		parser.add_argument(
		    """--overwrite_output_dir"""			,  action="""store_true"""			,  help="""Whether to overwrite data in output directory"""     )
		parser.add_argument(
		    """--overwrite_cache"""			,  action="""store_true"""			,  help="""Overwrite the cached training and evaluation sets"""     )

		parser.add_argument(
		    """--dont_normalize_importance_by_layer"""			,  action="""store_true"""			,  help="""Don't normalize importance score by layers"""     )
		parser.add_argument(
		    """--dont_normalize_global_importance"""			,  action="""store_true"""			,  help="""Don't normalize all importance scores between 0 and 1"""			,  )

		parser.add_argument(
		    """--try_masking"""			,  action="""store_true"""			,  help="""Whether to try to mask head until a threshold of accuracy."""     )
		parser.add_argument(
		    """--masking_threshold"""			,  default=0.9			,  type=_lowerCAmelCase			,  help="""masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."""			,  )
		parser.add_argument(
		    """--masking_amount"""			,  default=0.1			,  type=_lowerCAmelCase			,  help="""Amount to heads to masking at each masking step."""     )
		parser.add_argument("""--metric_name"""			,  default="""acc"""			,  type=_lowerCAmelCase			,  help="""Metric to use for head masking."""     )

		parser.add_argument(
		    """--max_seq_length"""			,  default=128			,  type=_lowerCAmelCase			,  help=(
		        """The maximum total input sequence length after WordPiece tokenization. \n"""
		        """Sequences longer than this will be truncated, sequences shorter padded."""
		    )			,  )
		parser.add_argument("""--batch_size"""			,  default=1			,  type=_lowerCAmelCase			,  help="""Batch size."""     )

		parser.add_argument("""--seed"""			,  type=_lowerCAmelCase			,  default=42     )
		parser.add_argument("""--local_rank"""			,  type=_lowerCAmelCase			,  default=-1			,  help="""local_rank for distributed training on gpus"""     )
		parser.add_argument("""--no_cuda"""			,  action="""store_true"""			,  help="""Whether not to use CUDA when available"""     )
		parser.add_argument("""--server_ip"""			,  type=_lowerCAmelCase			,  default=""""""			,  help="""Can be used for distant debugging."""     )
		parser.add_argument("""--server_port"""			,  type=_lowerCAmelCase			,  default=""""""			,  help="""Can be used for distant debugging."""     )
		lowerCAmelCase__        =   parser.parse_args()

		if args.server_ip and args.server_port:
				# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
				import ptvsd

				print("""Waiting for debugger attach"""     )
				ptvsd.enable_attach(address=(args.server_ip, args.server_port)			,  redirect_output=_lowerCAmelCase     )
				ptvsd.wait_for_attach()

		# Setup devices and distributed training
		if args.local_rank == -1 or args.no_cuda:
				lowerCAmelCase__        =   torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""     )
				lowerCAmelCase__        =   0 if args.no_cuda else torch.cuda.device_count()
		else:
				torch.cuda.set_device(args.local_rank     )
				lowerCAmelCase__        =   torch.device("""cuda"""			,  args.local_rank     )
				lowerCAmelCase__        =   1
				torch.distributed.init_process_group(backend="""nccl"""     )  # Initializes the distributed backend

		# Setup logging
		logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN     )
		logger.info("""device: {} n_gpu: {}, distributed: {}""".format(args.device			,  args.n_gpu			,  bool(args.local_rank != -1     )     )     )

		lowerCAmelCase__        =   GPTaLMHeadModel.from_pretrained(args.model_name_or_path     )

		# Distributed and parallel training
		model.to(args.device     )
		if args.local_rank != -1:
				lowerCAmelCase__        =   nn.parallel.DistributedDataParallel(
				    _lowerCAmelCase			,  device_ids=[args.local_rank]			,  output_device=args.local_rank			,  find_unused_parameters=_lowerCAmelCase     )
		elif args.n_gpu > 1:
				lowerCAmelCase__        =   nn.DataParallel(_lowerCAmelCase     )

		# Print/save training arguments
		os.makedirs(args.output_dir			,  exist_ok=_lowerCAmelCase     )
		torch.save(_lowerCAmelCase			,  os.path.join(args.output_dir			,  """run_args.bin"""     )     )
		logger.info("""Training/evaluation parameters %s"""			,  _lowerCAmelCase     )

		# Prepare dataset
		lowerCAmelCase__        =   np.concatenate(
		    [
		        np.loadtxt(args.data_dir			,  dtype=np.intaa     ),
		    ]     )
		lowerCAmelCase__        =   (torch.from_numpy(_lowerCAmelCase     ),)
		lowerCAmelCase__        =   TensorDataset(*_lowerCAmelCase     )
		lowerCAmelCase__        =   RandomSampler(_lowerCAmelCase     )
		lowerCAmelCase__        =   DataLoader(_lowerCAmelCase			,  sampler=_lowerCAmelCase			,  batch_size=args.batch_size     )

		# Compute head entropy and importance score
		compute_heads_importance(_lowerCAmelCase			,  _lowerCAmelCase			,  _lowerCAmelCase     )

		# Try head masking (set heads to zero until the score goes under a threshole)
		# and head pruning (remove masked heads and see the effect on the network)
		if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
				lowerCAmelCase__        =   mask_heads(_lowerCAmelCase			,  _lowerCAmelCase			,  _lowerCAmelCase     )
				prune_heads(_lowerCAmelCase			,  _lowerCAmelCase			,  _lowerCAmelCase			,  _lowerCAmelCase     )


if __name__ == "__main__":
			main()





"""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    :							Any													=						{
    # 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       a_      :




					def __init__(	self						:     List[str]  ,						snake_case__						:     int = 14     ):
							if group not in primes:
									raise ValueError("""Unsupported Group"""     )
							lowerCAmelCase__        =   primes[group]["""prime"""]
							lowerCAmelCase__        =   primes[group]["""generator"""]

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							return hex(self.__private_key     )[2:]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   pow(self.generator  ,						self.__private_key  ,						self.prime     )
							return hex(snake_case__     )[2:]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     int     ):
							# check if the other public key is valid based on NIST SP800-56
							return (
							    2 <= key <= self.prime - 2
							    and pow(snake_case__  ,						(self.prime - 1) // 2  ,						self.prime     ) == 1
							)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     str     ):
							lowerCAmelCase__        =   int(snake_case__  ,						base=16     )
							if not self.is_valid_public_key(snake_case__     ):
									raise ValueError("""Invalid public key"""     )
							lowerCAmelCase__        =   pow(snake_case__  ,						self.__private_key  ,						self.prime     )
							return shaaaa(str(snake_case__     ).encode()     ).hexdigest()




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




					@staticmethod
					def 						_SCREAMING_SNAKE_CASE						(	snake_case__						:     str  ,						snake_case__						:     str  ,						snake_case__						:     int = 14     ):
							lowerCAmelCase__        =   int(snake_case__  ,						base=16     )
							lowerCAmelCase__        =   int(snake_case__  ,						base=16     )
							lowerCAmelCase__        =   primes[group]["""prime"""]
							if not DiffieHellman.is_valid_public_key_static(snake_case__  ,						snake_case__     ):
									raise ValueError("""Invalid public key"""     )
							lowerCAmelCase__        =   pow(snake_case__  ,						snake_case__  ,						snake_case__     )
							return shaaaa(str(snake_case__     ).encode()     ).hexdigest()


if __name__ == "__main__":
			import doctest

			doctest.testmod()




