






import random

from .binary_exp_mod import bin_exp_mod






def 							A     (       a_							,a_=1_000					)     ->						Optional[Any]:
					if n < 2:
										return False

					if n % 2 == 0:
										return n == 2

					# this means n is odd
					__UpperCamelCase      :					List[Any]       		=n - 1
					__UpperCamelCase      :					Dict       		=0
					while d % 2 == 0:
										d /= 2
										exp += 1

					# n - 1=d*(2**exp)
					__UpperCamelCase      :					Optional[Any]       		=0
					while count < prec:
										__UpperCamelCase      :					Dict       		=random.randint(2							,n - 1					)
										__UpperCamelCase      :					Optional[Any]       		=bin_exp_mod(a_							,a_							,a_					)
										if b != 1:
															__UpperCamelCase      :					List[str]       		=True
															for _ in range(a_					):
																				if b == n - 1:
																									__UpperCamelCase      :					Tuple       		=False
																									break
																				__UpperCamelCase      :					Dict       		=b * b
																				b %= n
															if flag:
																				return False
															count += 1
					return True


if __name__ == "__main__":
							A_       :str       =						abs(int(input('''Enter bound : ''').strip()))
							print('''Here\'s the list of primes:''')
							print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))








'''simple docstring'''

import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase_					= logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase_					= argparse.ArgumentParser(
          description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
      )
      parser.add_argument(
          """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
      )
      parser.add_argument(
          """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
      )
      parser.add_argument("""--vocab_size""", default=30_522, type=int)
      lowercase_					= parser.parse_args()

      logger.info(f"""Loading data from {args.data_file}""")
      with open(args.data_file, """rb""") as fp:
            lowercase_					= pickle.load(fp)

      logger.info("""Counting occurrences for MLM.""")
      lowercase_					= Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      lowercase_					= [0] * args.vocab_size
      for k, v in counter.items():
            lowercase_					= v

      logger.info(f"""Dump to {args.token_counts_dump}""")
      with open(args.token_counts_dump, """wb""") as handle:
            pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)



