import random



def A__  (			lowercase:     List[Any]					)				->		bool:
       A						:     Dict         =num - 1
       A						:     List[Any]         =0

       while s % 2 == 0:
              A						:     Dict         =s // 2
              t += 1

       for _ in range(5					):
              A						:     List[str]         =random.randrange(2,		num - 1					)
              A						:     List[Any]         =pow(__UpperCamelCase,		__UpperCamelCase,		__UpperCamelCase					)
              if v != 1:
                     A						:     Tuple         =0
                     while v != (num - 1):
                            if i == t - 1:
                                   return False
                            else:
                                   A						:     List[Any]         =i + 1
                                   A						:     Optional[int]         =(v**2) % num
       return True



def A__  (			lowercase:     int					)				->		bool:
       if num < 2:
              return False

       A						:     List[str]         =[
           2,
           3,
           5,
           7,
           11,
           13,
           17,
           19,
           23,
           29,
           31,
           37,
           41,
           43,
           47,
           53,
           59,
           61,
           67,
           71,
           73,
           79,
           83,
           89,
           97,
           101,
           103,
           107,
           109,
           113,
           127,
           131,
           137,
           139,
           149,
           151,
           157,
           163,
           167,
           173,
           179,
           181,
           191,
           193,
           197,
           199,
           211,
           223,
           227,
           229,
           233,
           239,
           241,
           251,
           257,
           263,
           269,
           271,
           277,
           281,
           283,
           293,
           307,
           311,
           313,
           317,
           331,
           337,
           347,
           349,
           353,
           359,
           367,
           373,
           379,
           383,
           389,
           397,
           401,
           409,
           419,
           421,
           431,
           433,
           439,
           443,
           449,
           457,
           461,
           463,
           467,
           479,
           487,
           491,
           499,
           503,
           509,
           521,
           523,
           541,
           547,
           557,
           563,
           569,
           571,
           577,
           587,
           593,
           599,
           601,
           607,
           613,
           617,
           619,
           631,
           641,
           643,
           647,
           653,
           659,
           661,
           673,
           677,
           683,
           691,
           701,
           709,
           719,
           727,
           733,
           739,
           743,
           751,
           757,
           761,
           769,
           773,
           787,
           797,
           809,
           811,
           821,
           823,
           827,
           829,
           839,
           853,
           857,
           859,
           863,
           877,
           881,
           883,
           887,
           907,
           911,
           919,
           929,
           937,
           941,
           947,
           953,
           967,
           971,
           977,
           983,
           991,
           997,
       ]

       if num in low_primes:
              return True

       for prime in low_primes:
              if (num % prime) == 0:
                     return False

       return rabin_miller(__UpperCamelCase					)



def A__  (			lowercase:     Any = 1_024					)				->		int:
       while True:
              A						:     List[str]         =random.randrange(2 ** (keysize - 1),		2 ** (keysize)					)
              if is_prime_low_num(__UpperCamelCase					):
                     return num


if __name__ == "__main__":
    _lowercase							:      Optional[Any]      							=generate_large_prime()
    print(('''Prime number:''', num))
    print(('''is_prime_low_num:''', is_prime_low_num(num)))







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
_lowercase							:      str      							=False
_lowercase							:      Optional[Any]      							=False



def A__  (			lowercase:     Namespace					)				->		Optional[int]:
       return TrainCommand(lowercase					)



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  @staticmethod
  def 		SCREAMING_SNAKE_CASE_		(				SCREAMING_SNAKE_CASE__    :					ArgumentParser     )    ->				Dict:
         A						:     Optional[Any]         =parser.add_parser('train'      ,	help='CLI tool to train a model on a task.'     )

         train_parser.add_argument(
             '--train_data'      ,	type=SCREAMING_SNAKE_CASE__      ,	required=SCREAMING_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=SCREAMING_SNAKE_CASE__      ,	default=0      ,	help='Column of the dataset csv file with example labels.'     )
         train_parser.add_argument(
             '--column_text'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=1      ,	help='Column of the dataset csv file with example texts.'     )
         train_parser.add_argument(
             '--column_id'      ,	type=SCREAMING_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=SCREAMING_SNAKE_CASE__      ,	default=''      ,	help='path to validation dataset.'     )
         train_parser.add_argument(
             '--validation_split'      ,	type=SCREAMING_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=SCREAMING_SNAKE_CASE__      ,	default='./'      ,	help='path to saved the trained model.'     )

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







  def __init__(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Namespace     )    ->				List[Any]:
         A						:     Optional[int]         =logging.get_logger('transformers-cli/training'     )

         A						:     Dict         ='tf' if is_tf_available() else 'torch'

         os.makedirs(args.output      ,	exist_ok=SCREAMING_SNAKE_CASE__     )
         A						:     Optional[Any]         =args.output

         A						:     List[str]         =args.column_label
         A						:     int         =args.column_text
         A						:     Union[str, Any]         =args.column_id

         self.logger.info(f'Loading {args.task} pipeline for {args.model}'     )
         if args.task == "text_classification":
                A						:     Optional[Any]         =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}'     )
         A						:     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      ,	)
         A						:     Dict         =None
         if args.validation_data:
                self.logger.info(f'Loading validation dataset from {args.validation_data}'     )
                A						:     List[Any]         =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      ,	)

         A						:     Optional[Any]         =args.validation_split
         A						:     str         =args.train_batch_size
         A						:     Any         =args.valid_batch_size
         A						:     Dict         =args.learning_rate
         A						:     List[str]         =args.adam_epsilon







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Union[str, Any]:
         if self.framework == "tf":
                return self.run_tf()
         return self.run_torch()







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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				str:
         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     )






