


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

_A			=						{
    # 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    UpperCAmelCase__	:



  """simple docstring"""






  def __init__(      self ,       A_ = 14   )  ->	None:
       if group not in primes:
            raise ValueError('Unsupported Group'   )
       __UpperCamelCase   							=primes[group]['prime']
       __UpperCamelCase   							=primes[group]['generator']

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






  def _a (      self   )  ->	str:
       return hex(self.__private_key   )[2:]






  def _a (      self   )  ->	str:
       __UpperCamelCase   							=pow(self.generator ,       self.__private_key ,       self.prime   )
       return hex(A_   )[2:]






  def _a (      self ,       A_   )  ->	bool:
       # check if the other public key is valid based on NIST SP800-56
       return (
           2 <= key <= self.prime - 2
           and pow(A_ ,       (self.prime - 1) // 2 ,       self.prime   ) == 1
       )






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






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






  @staticmethod
  def _a (      A_ ,       A_ ,       A_ = 14   )  ->	str:
       __UpperCamelCase   							=int(A_ ,       base=16   )
       __UpperCamelCase   							=int(A_ ,       base=16   )
       __UpperCamelCase   							=primes[group]['prime']
       if not DiffieHellman.is_valid_public_key_static(A_ ,       A_   ):
            raise ValueError('Invalid public key'   )
       __UpperCamelCase   							=pow(A_ ,       A_ ,       A_   )
       return shaaaa(str(A_   ).encode()   ).hexdigest()


if __name__ == "__main__":
   import doctest

   doctest.testmod()









import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Features    ):
     __UpperCamelCase   							=np.inf

     def set_batch_size(SCREAMING_SNAKE_CASE__      : FeatureType    ) -> None:
          nonlocal batch_size
          if isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
               __UpperCamelCase   							=min(SCREAMING_SNAKE_CASE__      ,					config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS    )
          elif isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
               __UpperCamelCase   							=min(SCREAMING_SNAKE_CASE__      ,					config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS    )
          elif isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ) and feature.dtype == "binary":
               __UpperCamelCase   							=min(SCREAMING_SNAKE_CASE__      ,					config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS    )

     _visit(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

     return None if batch_size is np.inf else batch_size






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = False ,       A_ = False ,       A_ = None ,       **A_ ,       )  ->	Dict:
         super().__init__(
             A_ ,       split=A_ ,       features=A_ ,       cache_dir=A_ ,       keep_in_memory=A_ ,       streaming=A_ ,       num_proc=A_ ,       **A_ ,       )
         __UpperCamelCase   							=path_or_paths if isinstance(A_ ,       A_   ) else {self.split: path_or_paths}
         __UpperCamelCase   							=_PACKAGED_DATASETS_MODULES['parquet'][1]
         __UpperCamelCase   							=Parquet(
             cache_dir=A_ ,       data_files=A_ ,       features=A_ ,       hash=A_ ,       **A_ ,       )






    def _a (      self   )  ->	List[Any]:
         # Build iterable dataset
         if self.streaming:
              __UpperCamelCase   							=self.builder.as_streaming_dataset(split=self.split   )
         # Build regular (map-style) dataset
         else:
              __UpperCamelCase   							=None
              __UpperCamelCase   							=None
              __UpperCamelCase   							=None
              __UpperCamelCase   							=None

              self.builder.download_and_prepare(
                  download_config=A_ ,       download_mode=A_ ,       verification_mode=A_ ,       base_path=A_ ,       num_proc=self.num_proc ,       )
              __UpperCamelCase   							=self.builder.as_dataset(
                  split=self.split ,       verification_mode=A_ ,       in_memory=self.keep_in_memory   )
         return dataset






class    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	List[Any]:
         __UpperCamelCase   							=dataset
         __UpperCamelCase   							=path_or_buf
         __UpperCamelCase   							=batch_size or get_writer_batch_size(dataset.features   )
         __UpperCamelCase   							=parquet_writer_kwargs






    def _a (      self   )  ->	int:
         __UpperCamelCase   							=self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

         if isinstance(self.path_or_buf ,       (str, bytes, os.PathLike)   ):
              with open(self.path_or_buf ,       'wb+'   ) as buffer:
                   __UpperCamelCase   							=self._write(file_obj=A_ ,       batch_size=A_ ,       **self.parquet_writer_kwargs   )
         else:
              __UpperCamelCase   							=self._write(file_obj=self.path_or_buf ,       batch_size=A_ ,       **self.parquet_writer_kwargs   )
         return written






    def _a (      self ,       A_ ,       A_ ,       **A_   )  ->	int:
         __UpperCamelCase   							=0
         __UpperCamelCase   							=parquet_writer_kwargs.pop('path_or_buf' ,       A_   )
         __UpperCamelCase   							=self.dataset.features.arrow_schema

         __UpperCamelCase   							=pq.ParquetWriter(A_ ,       schema=A_ ,       **A_   )

         for offset in logging.tqdm(
             range(0 ,       len(self.dataset   ) ,       A_   ) ,       unit='ba' ,       disable=not logging.is_progress_bar_enabled() ,       desc='Creating parquet from Arrow format' ,       ):
              __UpperCamelCase   							=query_table(
                  table=self.dataset._data ,       key=slice(A_ ,       offset + batch_size   ) ,       indices=self.dataset._indices if self.dataset._indices is not None else None ,       )
              writer.write_table(A_   )
              written += batch.nbytes
         writer.close()
         return written





