




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

lowerCamelCase__					:  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 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						str							,							_lowerCAmelCase    :						int = 14		):
       if group not in primes:
             raise ValueError('Unsupported Group'		)
       SCREAMING_SNAKE_CASE_      		=     primes[group]['prime']
       SCREAMING_SNAKE_CASE_      		=     primes[group]['generator']

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



 def  lowerCAmelCase_    (				self    :						List[Any]		):
       return hex(self.__private_key		)[2:]



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     pow(self.generator							,							self.__private_key							,							self.prime		)
       return hex(_lowerCAmelCase		)[2:]



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



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						str		):
       SCREAMING_SNAKE_CASE_      		=     int(_lowerCAmelCase							,							base=16		)
       if not self.is_valid_public_key(_lowerCAmelCase		):
             raise ValueError('Invalid public key'		)
       SCREAMING_SNAKE_CASE_      		=     pow(_lowerCAmelCase							,							self.__private_key							,							self.prime		)
       return shaaaa(str(_lowerCAmelCase		).encode()		).hexdigest()



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




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


if __name__ == "__main__":
   import doctest

   doctest.testmod()

import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
   raise RuntimeError("""At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training""")

# TF training parameters
__lowerCamelCase				=			False
__lowerCamelCase				=			False


def      UpperCamelCase   (       __lowerCamelCase	:					Namespace       ):
       return TrainCommand(__lowerCamelCase       )






class     UpperCAmelCase						(		A_					):



   @staticmethod
   def 							_SCREAMING_SNAKE_CASE  (snake_case__						:   ArgumentParser					)						->      int:


          '''simple docstring'''


          snake_case			:  Any     							= parser.add_parser("train"       ,		help="CLI tool to train a model on a task."					)

          train_parser.add_argument(
              "--train_data"       ,		type=snake_case__       ,		required=snake_case__       ,		help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."       ,		)
          train_parser.add_argument(
              "--column_label"       ,		type=snake_case__       ,		default=0       ,		help="Column of the dataset csv file with example labels."					)
          train_parser.add_argument(
              "--column_text"       ,		type=snake_case__       ,		default=1       ,		help="Column of the dataset csv file with example texts."					)
          train_parser.add_argument(
              "--column_id"       ,		type=snake_case__       ,		default=2       ,		help="Column of the dataset csv file with example ids."					)
          train_parser.add_argument(
              "--skip_first_row"       ,		action="store_true"       ,		help="Skip the first row of the csv file (headers)."					)

          train_parser.add_argument("--validation_data"       ,		type=snake_case__       ,		default=""       ,		help="path to validation dataset."					)
          train_parser.add_argument(
              "--validation_split"       ,		type=snake_case__       ,		default=0.1       ,		help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."       ,		)

          train_parser.add_argument("--output"       ,		type=snake_case__       ,		default="./"       ,		help="path to saved the trained model."					)

          train_parser.add_argument(
              "--task"       ,		type=snake_case__       ,		default="text_classification"       ,		help="Task to train the model on."					)
          train_parser.add_argument(
              "--model"       ,		type=snake_case__       ,		default="bert-base-uncased"       ,		help="Model's name or path to stored model."					)
          train_parser.add_argument("--train_batch_size"       ,		type=snake_case__       ,		default=32       ,		help="Batch size for training."					)
          train_parser.add_argument("--valid_batch_size"       ,		type=snake_case__       ,		default=64       ,		help="Batch size for validation."					)
          train_parser.add_argument("--learning_rate"       ,		type=snake_case__       ,		default=3e-5       ,		help="Learning rate."					)
          train_parser.add_argument("--adam_epsilon"       ,		type=snake_case__       ,		default=1e-08       ,		help="Epsilon for Adam optimizer."					)
          train_parser.set_defaults(func=snake_case__					)



   def __init__(self						:   List[Any]       ,		snake_case__						:   Namespace					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  Any     							= logging.get_logger("transformers-cli/training"					)

          snake_case			:  List[Any]     							= "tf" if is_tf_available() else "torch"

          os.makedirs(args.output       ,		exist_ok=snake_case__					)
          snake_case			:  Any     							= args.output

          snake_case			:  List[Any]     							= args.column_label
          snake_case			:  Tuple     							= args.column_text
          snake_case			:  str     							= args.column_id

          self.logger.info(f"""Loading {args.task} pipeline for {args.model}"""					)
          if args.task == "text_classification":
                 snake_case			:  Optional[int]     							= TextClassificationPipeline.from_pretrained(args.model					)
          elif args.task == "token_classification":
                 raise NotImplementedError
          elif args.task == "question_answering":
                 raise NotImplementedError

          self.logger.info(f"""Loading dataset from {args.train_data}"""					)
          snake_case			:  Tuple     							= Processor.create_from_csv(
              args.train_data       ,		column_label=args.column_label       ,		column_text=args.column_text       ,		column_id=args.column_id       ,		skip_first_row=args.skip_first_row       ,		)
          snake_case			:  Optional[Any]     							= None
          if args.validation_data:
                 self.logger.info(f"""Loading validation dataset from {args.validation_data}"""					)
                 snake_case			:  Dict     							= Processor.create_from_csv(
                     args.validation_data       ,		column_label=args.column_label       ,		column_text=args.column_text       ,		column_id=args.column_id       ,		skip_first_row=args.skip_first_row       ,		)

          snake_case			:  Union[str, Any]     							= args.validation_split
          snake_case			:  Optional[Any]     							= args.train_batch_size
          snake_case			:  List[str]     							= args.valid_batch_size
          snake_case			:  List[Any]     							= args.learning_rate
          snake_case			:  List[Any]     							= args.adam_epsilon



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      List[str]:


          '''simple docstring'''


          if self.framework == "tf":
                 return self.run_tf()
          return self.run_torch()



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Optional[Any]:


          '''simple docstring'''


          raise NotImplementedError



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      Tuple:


          '''simple docstring'''


          self.pipeline.fit(
              self.train_dataset       ,		validation_data=self.valid_dataset       ,		validation_split=self.validation_split       ,		learning_rate=self.learning_rate       ,		adam_epsilon=self.adam_epsilon       ,		train_batch_size=self.train_batch_size       ,		valid_batch_size=self.valid_batch_size       ,		)

          # Save trained pipeline
          self.pipeline.save_pretrained(self.output					)





