


'''simple docstring'''



import random

from .binary_exp_mod import bin_exp_mod


def 				lowerCamelCase__				(	A       :      List[Any]				,       A       :      Dict=10_00					):




						'''simple docstring'''






						if n < 2:
												return False

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

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

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


if __name__ == "__main__":
	_lowercase      :      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


_lowercase      :      List[str]            =					{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      Dict            =					[
	    """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimeSeriesTransformerForPrediction""",
	    """TimeSeriesTransformerModel""",
	    """TimeSeriesTransformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	_lowercase      :      Tuple            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





