



'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    DPTConfig(embedding_type='''hybrid''')

     if "large" in checkpoint_url:
          _a           =    1_024
          _a           =    4_096
          _a           =    24
          _a           =    16
          _a           =    [5, 11, 17, 23]
          _a           =    [256, 512, 1_024, 1_024]
          _a           =    (1, 384, 384)

     if "nyu" or "midas" in checkpoint_url:
          _a           =    768
          _a           =    [1, 1, 1, 0.5]
          _a           =    [256, 512, 768, 768]
          _a           =    150
          _a           =    16
          _a           =    (1, 384, 384)
          _a           =    False
          _a           =    '''project'''

     if "ade" in checkpoint_url:
          _a           =    True
          _a           =    768
          _a           =    [1, 1, 1, 0.5]
          _a           =    150
          _a           =    16
          _a           =    '''huggingface/label-files'''
          _a           =    '''ade20k-id2label.json'''
          _a           =    json.load(open(cached_download(hf_hub_url(__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()}
          _a           =    [1, 150, 480, 480]

     return config, expected_shape




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
     for k in ignore_keys:
          state_dict.pop(__A    ,      __A)




def lowerCAmelCase    (__A):



     """simple docstring"""



     if (
         "pretrained.model" in name
         and "cls_token" not in name
         and "pos_embed" not in name
         and "patch_embed" not in name
     ):
          _a           =    name.replace('''pretrained.model'''    ,      '''dpt.encoder''')
     if "pretrained.model" in name:
          _a           =    name.replace('''pretrained.model'''    ,      '''dpt.embeddings''')
     if "patch_embed" in name:
          _a           =    name.replace('''patch_embed'''    ,      '''''')
     if "pos_embed" in name:
          _a           =    name.replace('''pos_embed'''    ,      '''position_embeddings''')
     if "attn.proj" in name:
          _a           =    name.replace('''attn.proj'''    ,      '''attention.output.dense''')
     if "proj" in name and "project" not in name:
          _a           =    name.replace('''proj'''    ,      '''projection''')
     if "blocks" in name:
          _a           =    name.replace('''blocks'''    ,      '''layer''')
     if "mlp.fc1" in name:
          _a           =    name.replace('''mlp.fc1'''    ,      '''intermediate.dense''')
     if "mlp.fc2" in name:
          _a           =    name.replace('''mlp.fc2'''    ,      '''output.dense''')
     if "norm1" in name and "backbone" not in name:
          _a           =    name.replace('''norm1'''    ,      '''layernorm_before''')
     if "norm2" in name and "backbone" not in name:
          _a           =    name.replace('''norm2'''    ,      '''layernorm_after''')
     if "scratch.output_conv" in name:
          _a           =    name.replace('''scratch.output_conv'''    ,      '''head''')
     if "scratch" in name:
          _a           =    name.replace('''scratch'''    ,      '''neck''')
     if "layer1_rn" in name:
          _a           =    name.replace('''layer1_rn'''    ,      '''convs.0''')
     if "layer2_rn" in name:
          _a           =    name.replace('''layer2_rn'''    ,      '''convs.1''')
     if "layer3_rn" in name:
          _a           =    name.replace('''layer3_rn'''    ,      '''convs.2''')
     if "layer4_rn" in name:
          _a           =    name.replace('''layer4_rn'''    ,      '''convs.3''')
     if "refinenet" in name:
          _a           =    int(name[len('''neck.refinenet''') : len('''neck.refinenet''') + 1])
          # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
          _a           =    name.replace(F'''refinenet{layer_idx}'''    ,      F'''fusion_stage.layers.{abs(layer_idx-4)}''')
     if "out_conv" in name:
          _a           =    name.replace('''out_conv'''    ,      '''projection''')
     if "resConfUnit1" in name:
          _a           =    name.replace('''resConfUnit1'''    ,      '''residual_layer1''')
     if "resConfUnit2" in name:
          _a           =    name.replace('''resConfUnit2'''    ,      '''residual_layer2''')
     if "conv1" in name:
          _a           =    name.replace('''conv1'''    ,      '''convolution1''')
     if "conv2" in name:
          _a           =    name.replace('''conv2'''    ,      '''convolution2''')
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          _a           =    name.replace('''pretrained.act_postprocess1.0.project.0'''    ,      '''neck.reassemble_stage.readout_projects.0.0''')
     if "pretrained.act_postprocess2.0.project.0" in name:
          _a           =    name.replace('''pretrained.act_postprocess2.0.project.0'''    ,      '''neck.reassemble_stage.readout_projects.1.0''')
     if "pretrained.act_postprocess3.0.project.0" in name:
          _a           =    name.replace('''pretrained.act_postprocess3.0.project.0'''    ,      '''neck.reassemble_stage.readout_projects.2.0''')
     if "pretrained.act_postprocess4.0.project.0" in name:
          _a           =    name.replace('''pretrained.act_postprocess4.0.project.0'''    ,      '''neck.reassemble_stage.readout_projects.3.0''')

     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          _a           =    name.replace('''pretrained.act_postprocess1.3'''    ,      '''neck.reassemble_stage.layers.0.projection''')
     if "pretrained.act_postprocess1.4" in name:
          _a           =    name.replace('''pretrained.act_postprocess1.4'''    ,      '''neck.reassemble_stage.layers.0.resize''')
     if "pretrained.act_postprocess2.3" in name:
          _a           =    name.replace('''pretrained.act_postprocess2.3'''    ,      '''neck.reassemble_stage.layers.1.projection''')
     if "pretrained.act_postprocess2.4" in name:
          _a           =    name.replace('''pretrained.act_postprocess2.4'''    ,      '''neck.reassemble_stage.layers.1.resize''')
     if "pretrained.act_postprocess3.3" in name:
          _a           =    name.replace('''pretrained.act_postprocess3.3'''    ,      '''neck.reassemble_stage.layers.2.projection''')
     if "pretrained.act_postprocess4.3" in name:
          _a           =    name.replace('''pretrained.act_postprocess4.3'''    ,      '''neck.reassemble_stage.layers.3.projection''')
     if "pretrained.act_postprocess4.4" in name:
          _a           =    name.replace('''pretrained.act_postprocess4.4'''    ,      '''neck.reassemble_stage.layers.3.resize''')
     if "pretrained" in name:
          _a           =    name.replace('''pretrained'''    ,      '''dpt''')
     if "bn" in name:
          _a           =    name.replace('''bn'''    ,      '''batch_norm''')
     if "head" in name:
          _a           =    name.replace('''head'''    ,      '''head.head''')
     if "encoder.norm" in name:
          _a           =    name.replace('''encoder.norm'''    ,      '''layernorm''')
     if "auxlayer" in name:
          _a           =    name.replace('''auxlayer'''    ,      '''auxiliary_head.head''')
     if "backbone" in name:
          _a           =    name.replace('''backbone'''    ,      '''backbone.bit.encoder''')

     if ".." in name:
          _a           =    name.replace('''..'''    ,      '''.''')

     if "stem.conv" in name:
          _a           =    name.replace('''stem.conv'''    ,      '''bit.embedder.convolution''')
     if "blocks" in name:
          _a           =    name.replace('''blocks'''    ,      '''layers''')
     if "convolution" in name and "backbone" in name:
          _a           =    name.replace('''convolution'''    ,      '''conv''')
     if "layer" in name and "backbone" in name:
          _a           =    name.replace('''layer'''    ,      '''layers''')
     if "backbone.bit.encoder.bit" in name:
          _a           =    name.replace('''backbone.bit.encoder.bit'''    ,      '''backbone.bit''')
     if "embedder.conv" in name:
          _a           =    name.replace('''embedder.conv'''    ,      '''embedder.convolution''')
     if "backbone.bit.encoder.stem.norm" in name:
          _a           =    name.replace('''backbone.bit.encoder.stem.norm'''    ,      '''backbone.bit.embedder.norm''')
     return name




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     for i in range(config.num_hidden_layers):
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          _a           =    state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''')
          _a           =    state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias''')
          # next, add query, keys and values (in that order) to the state dict
          _a           =    in_proj_weight[: config.hidden_size, :]
          _a           =    in_proj_bias[: config.hidden_size]
          _a           =    in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _a           =    in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          _a           =    in_proj_weight[
              -config.hidden_size :, :
          ]
          _a           =    in_proj_bias[-config.hidden_size :]




def lowerCAmelCase    ():



     """simple docstring"""



     _a           =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _a           =    Image.open(requests.get(__A    ,      stream=__A).raw)
     return im




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



     """simple docstring"""



     _a    ,       _a           =    get_dpt_config(__A)
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     _a           =    torch.load(__A    ,      map_location='''cpu''')
     # remove certain keys
     remove_ignore_keys_(__A)
     # rename keys
     for key in state_dict.copy().keys():
          _a           =    state_dict.pop(__A)
          _a           =    val
     # read in qkv matrices
     read_in_q_k_v(__A    ,      __A)

     # load HuggingFace model
     _a           =    DPTForSemanticSegmentation(__A) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__A)
     model.load_state_dict(__A)
     model.eval()

     # Check outputs on an image
     _a           =    480 if '''ade''' in checkpoint_url else 384
     _a           =    DPTImageProcessor(size=__A)

     _a           =    prepare_img()
     _a           =    image_processor(__A    ,      return_tensors='''pt''')

     # forward pass
     _a           =    model(**__A).logits if '''ade''' in checkpoint_url else model(**__A).predicted_depth

     if show_prediction:
          _a           =    (
              torch.nn.functional.interpolate(
                  outputs.unsqueeze(1)    ,      size=(image.size[1], image.size[0])    ,      mode='''bicubic'''    ,      align_corners=__A    ,      )
              .squeeze()
              .cpu()
              .numpy()
          )

          Image.fromarray((prediction / prediction.max()) * 255).show()

     if pytorch_dump_folder_path is not None:
          Path(__A).mkdir(exist_ok=__A)
          print(F'''Saving model to {pytorch_dump_folder_path}''')
          model.save_pretrained(__A)
          print(F'''Saving image processor to {pytorch_dump_folder_path}''')
          image_processor.save_pretrained(__A)

     if push_to_hub:
          model.push_to_hub('''ybelkada/dpt-hybrid-midas''')
          image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''')


if __name__ == "__main__":
  lowercase_									=    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint_url",
      default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
      type=str,
      help="URL of the original DPT checkpoint you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default=None,
      type=str,
      required=False,
      help="Path to the output PyTorch model directory.",
  )
  parser.add_argument(
      "--push_to_hub",
      action="store_true",
  )
  parser.add_argument(
      "--model_name",
      default="dpt-large",
      type=str,
      help="Name of the model, in case you're pushing to the hub.",
  )
  parser.add_argument(
      "--show_prediction",
      action="store_true",
  )

  lowercase_									=    parser.parse_args()
  convert_dpt_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
  )








'''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

lowercase_									=    {
    # 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     :



 '''simple docstring'''







 def __init__(self					,  A = 14			)       ->	None:


      """simple docstring"""



      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    a__    (self			)       ->	str:


      """simple docstring"""



      return hex(self.__private_key			)[2:]



 def    a__    (self			)       ->	str:


      """simple docstring"""



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



 def    a__    (self					,  A			)       ->	bool:


      """simple docstring"""



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



 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



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



 @staticmethod
 def    a__    (A					,  A			)       ->	bool:


      """simple docstring"""



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



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


      """simple docstring"""



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


if __name__ == "__main__":
  import doctest

  doctest.testmod()



