import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()



def   __A		(			_A							,				_A							,				_A							,				_A							,				_A							,				_A = None							,				):



 """simple docstring"""


 __a       =  {}

 if train_file is not None:
  __a       =  [train_file]
 if eval_file is not None:
  __a       =  [eval_file]
 if test_file is not None:
  __a       =  [test_file]

 __a       =  datasets.load_dataset("csv"							,				data_files=_A					)
 __a       =  list(ds[list(files.keys()					)[0]].features.keys()					)
 __a       =  features_name.pop(_A					)
 __a       =  list(set(ds[list(files.keys()					)[0]][label_name]					)					)
 __a       =  {label: i for i, label in enumerate(_A					)}
 __a       =  tokenizer.model_input_names
 __a       =  {}

 if len(_A					) == 1:
  for k in files.keys():
   __a       =  ds[k].map(
       lambda _A					: tokenizer.batch_encode_plus(
           example[features_name[0]]							,				truncation=_A							,				max_length=_A							,				padding="max_length"					)							,				batched=_A							,				)
 elif len(_A					) == 2:
  for k in files.keys():
   __a       =  ds[k].map(
       lambda _A					: tokenizer.batch_encode_plus(
           (example[features_name[0]], example[features_name[1]])							,				truncation=_A							,				max_length=_A							,				padding="max_length"							,				)							,				batched=_A							,				)

 def gen_train():
  for ex in transformed_ds[datasets.Split.TRAIN]:
   __a       =  {k: v for k, v in ex.items() if k in input_names}
   __a       =  labelaid[ex[label_name]]
   yield (d, label)

 def gen_val():
  for ex in transformed_ds[datasets.Split.VALIDATION]:
   __a       =  {k: v for k, v in ex.items() if k in input_names}
   __a       =  labelaid[ex[label_name]]
   yield (d, label)

 def gen_test():
  for ex in transformed_ds[datasets.Split.TEST]:
   __a       =  {k: v for k, v in ex.items() if k in input_names}
   __a       =  labelaid[ex[label_name]]
   yield (d, label)

 __a       =  (
     tf.data.Dataset.from_generator(
         _A							,				({k: tf.intaa for k in input_names}, tf.intaa)							,				({k: tf.TensorShape([None]					) for k in input_names}, tf.TensorShape([]					))							,				)
     if datasets.Split.TRAIN in transformed_ds
     else None
 )

 if train_ds is not None:
  __a       =  train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]					)					)					)

 __a       =  (
     tf.data.Dataset.from_generator(
         _A							,				({k: tf.intaa for k in input_names}, tf.intaa)							,				({k: tf.TensorShape([None]					) for k in input_names}, tf.TensorShape([]					))							,				)
     if datasets.Split.VALIDATION in transformed_ds
     else None
 )

 if val_ds is not None:
  __a       =  val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]					)					)					)

 __a       =  (
     tf.data.Dataset.from_generator(
         _A							,				({k: tf.intaa for k in input_names}, tf.intaa)							,				({k: tf.TensorShape([None]					) for k in input_names}, tf.TensorShape([]					))							,				)
     if datasets.Split.TEST in transformed_ds
     else None
 )

 if test_ds is not None:
  __a       =  test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]					)					)					)

 return train_ds, val_ds, test_ds, labelaid


SCREAMING_SNAKE_CASE							     =		logging.getLogger(__name__)
@dataclass
class    A_ :
  _SCREAMING_SNAKE_CASE    				=			field(metadata={"""help""": """Which column contains the label"""} )
  _SCREAMING_SNAKE_CASE    				=			field(default=a_			,       metadata={"""help""": """The path of the training file"""} )
  _SCREAMING_SNAKE_CASE    				=			field(default=a_			,       metadata={"""help""": """The path of the development file"""} )
  _SCREAMING_SNAKE_CASE    				=			field(default=a_			,       metadata={"""help""": """The path of the test file"""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=1_28			,       metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
@dataclass
class    A_ :
  _SCREAMING_SNAKE_CASE    				=			field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
  _SCREAMING_SNAKE_CASE    				=			field(default=a_			,       metadata={"""help""": """Set this flag to use fast tokenization."""} )
  # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
  # or just modify its tokenizer_config.json.
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,       )



def   __A		(			):



 """simple docstring"""


 __a       =  HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)					)
 __a       ,     __a       ,     __a       =  parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir					)
     and os.listdir(training_args.output_dir					)
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
      " --overwrite_output_dir to overcome."					)

 # Setup logging
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"							,				datefmt="%m/%d/%Y %H:%M:%S"							,				level=logging.INFO							,				)
 logger.info(
     f"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1					)}, """
     f"""16-bits training: {training_args.fpaa}"""					)
 logger.info(f"""Training/evaluation parameters {training_args}"""					)

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 __a       =  AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,				cache_dir=model_args.cache_dir							,				)

 __a       ,     __a       ,     __a       ,     __a       =  get_tfds(
     train_file=data_args.train_file							,				eval_file=data_args.dev_file							,				test_file=data_args.test_file							,				tokenizer=_A							,				label_column_id=data_args.label_column_id							,				max_seq_length=data_args.max_seq_length							,				)

 __a       =  AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path							,				num_labels=len(_A					)							,				labelaid=_A							,				idalabel={id: label for label, id in labelaid.items()}							,				finetuning_task="text-classification"							,				cache_dir=model_args.cache_dir							,				)

 with training_args.strategy.scope():
  __a       =  TFAutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path							,				from_pt=bool(".bin" in model_args.model_name_or_path					)							,				config=_A							,				cache_dir=model_args.cache_dir							,				)

 def compute_metrics(_A					) -> Dict:
  __a       =  np.argmax(p.predictions							,				axis=1					)

  return {"acc": (preds == p.label_ids).mean()}

 # Initialize our Trainer
 __a       =  TFTrainer(
     model=_A							,				args=_A							,				train_dataset=_A							,				eval_dataset=_A							,				compute_metrics=_A							,				)

 # Training
 if training_args.do_train:
  trainer.train()
  trainer.save_model()
  tokenizer.save_pretrained(training_args.output_dir					)

 # Evaluation
 __a       =  {}
 if training_args.do_eval:
  logger.info("*** Evaluate ***"					)
  __a       =  trainer.evaluate()
  __a       =  os.path.join(training_args.output_dir							,				"eval_results.txt"					)

  with open(_A							,				"w"					) as writer:
   logger.info("***** Eval results *****"					)

   for key, value in result.items():
    logger.info(f"""  {key} = {value}"""					)
    writer.write(f"""{key} = {value}\n"""					)

   results.update(_A					)

 return results


if __name__ == "__main__":
  main()



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()


