


"""simple docstring"""





import random

from .binary_exp_mod import bin_exp_mod


def    _snake_case  (  _snake_case :	Optional[int]       ,      _snake_case :	Dict=1000	):





      if n < 2:
            return False

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

      # this means n is odd
      lowerCAmelCase    :		int				=      n - 1
      lowerCAmelCase    :		Tuple				=      0
      while d % 2 == 0:
            d /= 2
            exp += 1

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


if __name__ == "__main__":
 snake_case__						:   int			 = 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"""





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a														=		{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """AutoformerForPrediction""",
				    """AutoformerModel""",
				    """AutoformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_autoformer import (
				    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AutoformerConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_autoformer import (
								    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AutoformerForPrediction,
								    AutoformerModel,
								    AutoformerPreTrainedModel,
								)

else:
				import sys

				_a														=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






