


"""simple docstring"""


import random

from .binary_exp_mod import bin_exp_mod


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase=1000 ):

			'''simple docstring'''




			if n < 2:
						return False

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

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

			# n - 1=d*(2**exp)
			__lowerCAmelCase							       =     0
			while count < prec:
						__lowerCAmelCase							       =     random.randint(2       ,					n - 1 )
						__lowerCAmelCase							       =     bin_exp_mod(_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase )
						if b != 1:
									__lowerCAmelCase							       =     True
									for _ in range(_UpperCamelCase ):
												if b == n - 1:
															__lowerCAmelCase							       =     False
															break
												__lowerCAmelCase							       =     b * b
												b %= n
									if flag:
												return False
									count += 1
			return True


if __name__ == "__main__":
	A	:  List[Any]            =     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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A	:  Union[str, Any]            =     {
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:  Optional[int]            =     [
	    "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TimesformerModel",
	    "TimesformerForVideoClassification",
	    "TimesformerPreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_timesformer import (
		    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimesformerForVideoClassification,
		    TimesformerModel,
		    TimesformerPreTrainedModel,
		)

else:
	import sys

	A	:  List[str]            =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




