import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :	int							     =		logging.get_logger(__name__)



def   __A		(			_A							,				_A=False					):



 """simple docstring"""


 __a       =  OrderedDict()
 for key, value in state_dict.items():
  if encoder_only and not key.startswith("head"					):
   __a       =  "segformer.encoder." + key
  if key.startswith("backbone"					):
   __a       =  key.replace("backbone"							,				"segformer.encoder"					)
  if "patch_embed" in key:
   # replace for example patch_embed1 by patch_embeddings.0
   __a       =  key[key.find("patch_embed"					) + len("patch_embed"					)]
   __a       =  key.replace(f"""patch_embed{idx}"""							,				f"""patch_embeddings.{int(_A					)-1}"""					)
  if "norm" in key:
   __a       =  key.replace("norm"							,				"layer_norm"					)
  if "segformer.encoder.layer_norm" in key:
   # replace for example layer_norm1 by layer_norm.0
   __a       =  key[key.find("segformer.encoder.layer_norm"					) + len("segformer.encoder.layer_norm"					)]
   __a       =  key.replace(f"""layer_norm{idx}"""							,				f"""layer_norm.{int(_A					)-1}"""					)
  if "layer_norm1" in key:
   __a       =  key.replace("layer_norm1"							,				"layer_norm_1"					)
  if "layer_norm2" in key:
   __a       =  key.replace("layer_norm2"							,				"layer_norm_2"					)
  if "block" in key:
   # replace for example block1 by block.0
   __a       =  key[key.find("block"					) + len("block"					)]
   __a       =  key.replace(f"""block{idx}"""							,				f"""block.{int(_A					)-1}"""					)
  if "attn.q" in key:
   __a       =  key.replace("attn.q"							,				"attention.self.query"					)
  if "attn.proj" in key:
   __a       =  key.replace("attn.proj"							,				"attention.output.dense"					)
  if "attn" in key:
   __a       =  key.replace("attn"							,				"attention.self"					)
  if "fc1" in key:
   __a       =  key.replace("fc1"							,				"dense1"					)
  if "fc2" in key:
   __a       =  key.replace("fc2"							,				"dense2"					)
  if "linear_pred" in key:
   __a       =  key.replace("linear_pred"							,				"classifier"					)
  if "linear_fuse" in key:
   __a       =  key.replace("linear_fuse.conv"							,				"linear_fuse"					)
   __a       =  key.replace("linear_fuse.bn"							,				"batch_norm"					)
  if "linear_c" in key:
   # replace for example linear_c4 by linear_c.3
   __a       =  key[key.find("linear_c"					) + len("linear_c"					)]
   __a       =  key.replace(f"""linear_c{idx}"""							,				f"""linear_c.{int(_A					)-1}"""					)
  if key.startswith("head"					):
   __a       =  key.replace("head"							,				"classifier"					)
  __a       =  value

 return new_state_dict



def   __A		(			_A							,				_A					):



 """simple docstring"""


 for i in range(config.num_encoder_blocks					):
  for j in range(config.depths[i]					):
   # read in weights + bias of keys and values (which is a single matrix in the original implementation)
   __a       =  state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""					)
   __a       =  state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias"""					)
   # next, add keys and values (in that order) to the state dict
   __a       =  kv_weight[
       : config.hidden_sizes[i], :
   ]
   __a       =  kv_bias[: config.hidden_sizes[i]]
   __a       =  kv_weight[
       config.hidden_sizes[i] :, :
   ]
   __a       =  kv_bias[
       config.hidden_sizes[i] :
   ]



def   __A		(			):



 """simple docstring"""


 __a       =  "http://images.cocodataset.org/val2017/000000039769.jpg"
 __a       =  Image.open(requests.get(_A							,				stream=_A					).raw					)

 return image



@torch.no_grad()
def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  SegformerConfig()
 __a       =  False

 # set attributes based on model_name
 __a       =  "huggingface/label-files"
 if "segformer" in model_name:
  __a       =  model_name[len("segformer."					) : len("segformer."					) + 2]
  if "ade" in model_name:
   __a       =  150
   __a       =  "ade20k-id2label.json"
   __a       =  (1, 150, 128, 128)
  elif "city" in model_name:
   __a       =  19
   __a       =  "cityscapes-id2label.json"
   __a       =  (1, 19, 128, 128)
  else:
   raise ValueError(f"""Model {model_name} not supported"""					)
 elif "mit" in model_name:
  __a       =  True
  __a       =  model_name[4:6]
  __a       =  1000
  __a       =  "imagenet-1k-id2label.json"
  __a       =  (1, 1000)
 else:
  raise ValueError(f"""Model {model_name} not supported"""					)

 # set config attributes
 __a       =  json.load(open(hf_hub_download(_A							,				_A							,				repo_type="dataset"					)							,				"r"					)					)
 __a       =  {int(_A					): v for k, v in idalabel.items()}
 __a       =  idalabel
 __a       =  {v: k for k, v in idalabel.items()}
 if size == "b0":
  pass
 elif size == "b1":
  __a       =  [64, 128, 320, 512]
  __a       =  256
 elif size == "b2":
  __a       =  [64, 128, 320, 512]
  __a       =  768
  __a       =  [3, 4, 6, 3]
 elif size == "b3":
  __a       =  [64, 128, 320, 512]
  __a       =  768
  __a       =  [3, 4, 18, 3]
 elif size == "b4":
  __a       =  [64, 128, 320, 512]
  __a       =  768
  __a       =  [3, 8, 27, 3]
 elif size == "b5":
  __a       =  [64, 128, 320, 512]
  __a       =  768
  __a       =  [3, 6, 40, 3]
 else:
  raise ValueError(f"""Size {size} not supported"""					)

 # load image processor (only resize + normalize)
 __a       =  SegformerImageProcessor(
     image_scale=(512, 512)							,				keep_ratio=_A							,				align=_A							,				do_random_crop=_A					)

 # prepare image
 __a       =  prepare_img()
 __a       =  image_processor(images=_A							,				return_tensors="pt"					).pixel_values

 logger.info(f"""Converting model {model_name}..."""					)

 # load original state dict
 if encoder_only:
  __a       =  torch.load(_A							,				map_location=torch.device("cpu"					)					)
 else:
  __a       =  torch.load(_A							,				map_location=torch.device("cpu"					)					)["state_dict"]

 # rename keys
 __a       =  rename_keys(_A							,				encoder_only=_A					)
 if not encoder_only:
  del state_dict["decode_head.conv_seg.weight"]
  del state_dict["decode_head.conv_seg.bias"]

 # key and value matrices need special treatment
 read_in_k_v(_A							,				_A					)

 # create HuggingFace model and load state dict
 if encoder_only:
  __a       =  False
  __a       =  SegformerForImageClassification(_A					)
 else:
  __a       =  SegformerForSemanticSegmentation(_A					)
 model.load_state_dict(_A					)
 model.eval()

 # forward pass
 __a       =  model(_A					)
 __a       =  outputs.logits

 # set expected_slice based on model name
 # ADE20k checkpoints
 if model_name == "segformer.b0.512x512.ade.160k":
  __a       =  torch.tensor(
      [
          [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
          [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
          [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
      ]					)
 elif model_name == "segformer.b1.512x512.ade.160k":
  __a       =  torch.tensor(
      [
          [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
          [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
          [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
      ]					)
 elif model_name == "segformer.b2.512x512.ade.160k":
  __a       =  torch.tensor(
      [
          [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
          [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
          [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
      ]					)
 elif model_name == "segformer.b3.512x512.ade.160k":
  __a       =  torch.tensor(
      [
          [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
          [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
          [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
      ]					)
 elif model_name == "segformer.b4.512x512.ade.160k":
  __a       =  torch.tensor(
      [
          [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
          [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
          [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
      ]					)
 elif model_name == "segformer.b5.640x640.ade.160k":
  __a       =  torch.tensor(
      [
          [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
          [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
          [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
      ]					)
 # Cityscapes checkpoints
 elif model_name == "segformer.b0.1024x1024.city.160k":
  __a       =  torch.tensor(
      [
          [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
          [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
          [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
      ]					)
 elif model_name == "segformer.b0.512x1024.city.160k":
  __a       =  torch.tensor(
      [
          [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
          [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
          [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
      ]					)
 elif model_name == "segformer.b0.640x1280.city.160k":
  __a       =  torch.tensor(
      [
          [
              [-1.1372E01, -1.2787E01, -1.3477E01],
              [-1.2536E01, -1.4194E01, -1.4409E01],
              [-1.3217E01, -1.4888E01, -1.5327E01],
          ],
          [
              [-1.4791E01, -1.7122E01, -1.8277E01],
              [-1.7163E01, -1.9192E01, -1.9533E01],
              [-1.7897E01, -1.9991E01, -2.0315E01],
          ],
          [
              [7.6723E-01, 4.1921E-01, -7.7878E-02],
              [4.7772E-01, 9.5557E-03, -2.8082E-01],
              [3.6032E-01, -2.4826E-01, -5.1168E-01],
          ],
      ]					)
 elif model_name == "segformer.b0.768x768.city.160k":
  __a       =  torch.tensor(
      [
          [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
          [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
          [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
      ]					)
 elif model_name == "segformer.b1.1024x1024.city.160k":
  __a       =  torch.tensor(
      [
          [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
          [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
          [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
      ]					)
 elif model_name == "segformer.b2.1024x1024.city.160k":
  __a       =  torch.tensor(
      [
          [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
          [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
          [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
      ]					)
 elif model_name == "segformer.b3.1024x1024.city.160k":
  __a       =  torch.tensor(
      [
          [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
          [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
          [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
      ]					)
 elif model_name == "segformer.b4.1024x1024.city.160k":
  __a       =  torch.tensor(
      [
          [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
          [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
          [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
      ]					)
 elif model_name == "segformer.b5.1024x1024.city.160k":
  __a       =  torch.tensor(
      [
          [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
          [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
          [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
      ]					)
 else:
  __a       =  logits.argmax(-1					).item()
  print("Predicted class:"							,				model.config.idalabel[predicted_class_idx]					)

 # verify logits
 if not encoder_only:
  assert logits.shape == expected_shape
  assert torch.allclose(logits[0, :3, :3, :3]							,				_A							,				atol=1E-2					)

 # finally, save model and image processor
 logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""					)
 Path(_A					).mkdir(exist_ok=_A					)
 model.save_pretrained(_A					)
 image_processor.save_pretrained(_A					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	Tuple							     =		argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""",
      default="""segformer.b0.512x512.ade.160k""",
      type=str,
      help="""Name of the model you'd like to convert.""",
  )
  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  SCREAMING_SNAKE_CASE :	List[str]							     =		parser.parse_args()
  convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



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

SCREAMING_SNAKE_CASE :	Tuple							     =		{
    # 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[Any]     ,							__SCREAMING_SNAKE_CASE :      int = 14       ):
   if group not in primes:
    raise ValueError("Unsupported Group"       )
   __a       =  primes[group]["prime"]
   __a       =  primes[group]["generator"]

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

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   return hex(self.__private_key       )[2:]

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  pow(self.generator     ,							self.__private_key     ,							self.prime       )
   return hex(__SCREAMING_SNAKE_CASE       )[2:]

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      int       ):
   # 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 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  int(__SCREAMING_SNAKE_CASE     ,							base=16       )
   if not self.is_valid_public_key(__SCREAMING_SNAKE_CASE       ):
    raise ValueError("Invalid public key"       )
   __a       =  pow(__SCREAMING_SNAKE_CASE     ,							self.__private_key     ,							self.prime       )
   return shaaaa(str(__SCREAMING_SNAKE_CASE       ).encode()       ).hexdigest()

  @staticmethod
  def 		_UpperCAmelCase   (		__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_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(__SCREAMING_SNAKE_CASE     ,							(prime - 1) // 2     ,							__SCREAMING_SNAKE_CASE       ) == 1
   )

  @staticmethod
  def 		_UpperCAmelCase   (		__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      int = 14       ):
   __a       =  int(__SCREAMING_SNAKE_CASE     ,							base=16       )
   __a       =  int(__SCREAMING_SNAKE_CASE     ,							base=16       )
   __a       =  primes[group]["prime"]
   if not DiffieHellman.is_valid_public_key_static(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
    raise ValueError("Invalid public key"       )
   __a       =  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()


