





import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


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


lowercase_          =		"""https://openaipublic.azureedge.net/jukebox/models/"""
lowercase_          =		{
    """jukebox-1b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """1b_lyrics/prior_level_2.pth.tar""",
    ],
    """jukebox-5b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """5b_lyrics/prior_level_2.pth.tar""",
    ],
}




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Optional[Any]:





  if key.endswith('.model.1.bias'		) and len(key.split('.'		)		) > 10:
    lowercase__           =  key.replace('.model.1.bias'		,						'.conv1d_1.bias'		)
  elif key.endswith('.model.1.weight'		) and len(key.split('.'		)		) > 10:
    lowercase__           =  key.replace('.model.1.weight'		,						'.conv1d_1.weight'		)
  elif key.endswith('.model.3.bias'		) and len(key.split('.'		)		) > 10:
    lowercase__           =  key.replace('.model.3.bias'		,						'.conv1d_2.bias'		)
  elif key.endswith('.model.3.weight'		) and len(key.split('.'		)		) > 10:
    lowercase__           =  key.replace('.model.3.weight'		,						'.conv1d_2.weight'		)

  if "conditioner_blocks.0." in key:
    lowercase__           =  key.replace('conditioner_blocks.0'		,						'conditioner_blocks'		)

  if "prime_prior" in key:
    lowercase__           =  key.replace('prime_prior'		,						'encoder'		)

  if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
    lowercase__           =  key.replace('.emb.'		,						'.'		)

  if key.endswith('k'		):  # replace vqvae.X.k with vqvae.X.codebook
    return key.replace('.k'		,						'.codebook'		)
  if "y_emb." in key:
    return key.replace('y_emb.'		,						'metadata_embedding.'		)

  if "x_emb.emb." in key:
    lowercase__           =  key.replace('0.x_emb.emb'		,						'embed_tokens'		)

  if "prime_state_ln" in key:
    return key.replace('prime_state_ln'		,						'encoder.final_layer_norm'		)
  if ".ln" in key:
    return key.replace('.ln'		,						'.layer_norm'		)
  if "_ln" in key:
    return key.replace('_ln'		,						'_layer_norm'		)

  if "prime_state_proj" in key:
    return key.replace('prime_state_proj'		,						'encoder.proj_in'		)
  if "prime_x_out" in key:
    return key.replace('prime_x_out'		,						'encoder.lm_head'		)
  if "prior.x_out" in key:
    return key.replace('x_out'		,						'fc_proj_out'		)
  if "x_emb" in key:
    return key.replace('x_emb'		,						'embed_tokens'		)

  return key




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Tuple:
  lowercase__           =  {}
  import re

  lowercase__           =  re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'		)
  lowercase__           =  re.compile(
      R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'		)
  lowercase__           =  re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'		)

  lowercase__           =  re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'		)
  lowercase__           =  re.compile(
      R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'		)
  lowercase__           =  re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'		)

  lowercase__           =  re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'		)
  lowercase__           =  re.compile(
      R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'		)
  lowercase__           =  re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'		)

  for original_key, value in state_dict.items():
    # rename vqvae.encoder keys
    if re_encoder_block_conv_in.fullmatch(__snake_case		):
      lowercase__           =  re_encoder_block_conv_in.match(__snake_case		)
      lowercase__           =  regex_match.groups()
      lowercase__           =  int(groups[2]		) * 2 + int(groups[3]		)
      lowercase__           =  F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
      lowercase__           =  re_encoder_block_conv_in.sub(__snake_case		,						__snake_case		)

    elif re_encoder_block_resnet.fullmatch(__snake_case		):
      lowercase__           =  re_encoder_block_resnet.match(__snake_case		)
      lowercase__           =  regex_match.groups()
      lowercase__           =  int(groups[2]		) * 2 + int(groups[3]		)
      lowercase__           =  {'1': 1, '3': 2}[groups[-2]]
      lowercase__           =  F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
      lowercase__           =  F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
      lowercase__           =  prefix + resnet_block
      lowercase__           =  re_encoder_block_resnet.sub(__snake_case		,						__snake_case		)

    elif re_encoder_block_proj_out.fullmatch(__snake_case		):
      lowercase__           =  re_encoder_block_proj_out.match(__snake_case		)
      lowercase__           =  regex_match.groups()
      lowercase__           =  F"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
      lowercase__           =  re_encoder_block_proj_out.sub(__snake_case		,						__snake_case		)

    # rename vqvae.decoder keys
    elif re_decoder_block_conv_out.fullmatch(__snake_case		):
      lowercase__           =  re_decoder_block_conv_out.match(__snake_case		)
      lowercase__           =  regex_match.groups()
      lowercase__           =  int(groups[2]		) * 2 + int(groups[3]		) - 2
      lowercase__           =  F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
      lowercase__           =  re_decoder_block_conv_out.sub(__snake_case		,						__snake_case		)

    elif re_decoder_block_resnet.fullmatch(__snake_case		):
      lowercase__           =  re_decoder_block_resnet.match(__snake_case		)
      lowercase__           =  regex_match.groups()
      lowercase__           =  int(groups[2]		) * 2 + int(groups[3]		) - 2
      lowercase__           =  {'1': 1, '3': 2}[groups[-2]]
      lowercase__           =  F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
      lowercase__           =  F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
      lowercase__           =  prefix + resnet_block
      lowercase__           =  re_decoder_block_resnet.sub(__snake_case		,						__snake_case		)

    elif re_decoder_block_proj_in.fullmatch(__snake_case		):
      lowercase__           =  re_decoder_block_proj_in.match(__snake_case		)
      lowercase__           =  regex_match.groups()
      lowercase__           =  F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
      lowercase__           =  re_decoder_block_proj_in.sub(__snake_case		,						__snake_case		)

    # rename prior cond.model to upsampler.upsample_block and resnet
    elif re_prior_cond_conv_out.fullmatch(__snake_case		):
      lowercase__           =  re_prior_cond_conv_out.match(__snake_case		)
      lowercase__           =  regex_match.groups()
      lowercase__           =  int(groups[1]		) * 2 + int(groups[2]		) - 2
      lowercase__           =  F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
      lowercase__           =  re_prior_cond_conv_out.sub(__snake_case		,						__snake_case		)

    elif re_prior_cond_resnet.fullmatch(__snake_case		):
      lowercase__           =  re_prior_cond_resnet.match(__snake_case		)
      lowercase__           =  regex_match.groups()
      lowercase__           =  int(groups[1]		) * 2 + int(groups[2]		) - 2
      lowercase__           =  {'1': 1, '3': 2}[groups[-2]]
      lowercase__           =  F"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
      lowercase__           =  F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
      lowercase__           =  prefix + resnet_block
      lowercase__           =  re_prior_cond_resnet.sub(__snake_case		,						__snake_case		)

    elif re_prior_cond_proj_in.fullmatch(__snake_case		):
      lowercase__           =  re_prior_cond_proj_in.match(__snake_case		)
      lowercase__           =  regex_match.groups()
      lowercase__           =  F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
      lowercase__           =  re_prior_cond_proj_in.sub(__snake_case		,						__snake_case		)

    # keep original key
    else:
      lowercase__           =  original_key

    lowercase__           =  replace_key(__snake_case		)

    if F"""{key_prefix}.{key}""" not in model_state_dict or key is None:
      print(F"""failed converting {original_key} to {key}, does not match"""		)

    # handle missmatched shape
    elif value.shape != model_state_dict[F"""{key_prefix}.{key}"""].shape:
      lowercase__           =  model_state_dict[F"""{key_prefix}.{key}"""]
      print(F"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""		)
      lowercase__           =  original_key

    lowercase__           =  original_key
    lowercase__           =  value

  return new_dict




@torch.no_grad()
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE=None		,						_SCREAMING_SNAKE_CASE=None		)    ->       Optional[Any]:





  for file in MODEL_MAPPING[model_name]:
    if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split("/"		)[-1]}"""		):
      lowercase__           =  requests.get(F"""{PREFIX}{file}"""		,						allow_redirects=__snake_case		)
      os.makedirs(F"""{pytorch_dump_folder_path}/"""		,						exist_ok=__snake_case		)
      open(F"""{pytorch_dump_folder_path}/{file.split("/"		)[-1]}"""		,						'wb'		).write(r.content		)

  lowercase__           =  MODEL_MAPPING[model_name.split('/'		)[-1]]

  lowercase__           =  JukeboxConfig.from_pretrained(__snake_case		)
  lowercase__           =  JukeboxModel(__snake_case		)

  lowercase__           =  []
  lowercase__           =  {}
  for i, dict_name in enumerate(__snake_case		):
    lowercase__           =  torch.load(F"""{pytorch_dump_folder_path}/{dict_name.split("/"		)[-1]}"""		)['model']

    lowercase__           =  {}
    for k in old_dic.keys():
      if k.endswith('.b'		):
        lowercase__           =  old_dic[k]
      elif k.endswith('.w'		):
        lowercase__           =  old_dic[k]
      elif "level_2" not in dict_name and "cond.model." in k:
        lowercase__           =  old_dic[k]
      else:
        lowercase__           =  old_dic[k]

    lowercase__           =  'vqvae' if i == 0 else F"""priors.{3 - i}"""
    lowercase__           =  fix_jukebox_keys(__snake_case		,						model.state_dict()		,						__snake_case		,						__snake_case		)
    weight_dict.append(__snake_case		)

  lowercase__           =  weight_dict.pop(0		)
  model.vqvae.load_state_dict(__snake_case		)
  for i in range(len(__snake_case		)		):
    model.priors[i].load_state_dict(weight_dict[2 - i]		)

  Path(__snake_case		).mkdir(exist_ok=__snake_case		)
  with open(F"""{pytorch_dump_folder_path}/mapping.json"""		,						'w'		) as txtfile:
    json.dump(__snake_case		,						__snake_case		)

  print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""		)
  model.save_pretrained(__snake_case		)

  return weight_dict


if __name__ == "__main__":
 lowercase_          =		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""jukebox-5b-lyrics""",
     type=str,
     help="""Name of the model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default="""jukebox-5b-lyrics-converted""",
     type=str,
     help="""Path to the output PyTorch model directory.""",
 )
 lowercase_          =		parser.parse_args()
 convert_openai_checkpoint(args.model_name, 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

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     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    Optional[Any]      ,					a      :    int = 14	)->       None:





     """simple docstring"""







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

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       str:





     """simple docstring"""







     return hex(self.__private_key	)[2:]


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       str:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    int	)->       bool:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str      ,					a      :    str	)->       str:





     """simple docstring"""







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


   @staticmethod
   def 	SCREAMING_SNAKE_CASE_							(   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 	SCREAMING_SNAKE_CASE_							(   a      :    str      ,					a      :    str      ,					a      :    int = 14	)->       str:





     """simple docstring"""







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


if __name__ == "__main__":
      import doctest

      doctest.testmod()


