






'''simple docstring'''





from __future__ import annotations

import math

def _UpperCAmelCase   (	a						:	int ):

							if num <= 0:
														snake_case__	  =       F'''{num}: Invalid input, please enter a positive integer.'''
														raise ValueError(a )

							snake_case__	  =       [True] * (num + 1)
							snake_case__	  =       []
							snake_case__	  =       2
							snake_case__	  =       int(math.sqrt(a ) )

							while start <= end:
														# If start is a prime
														if sieve[start] is True:
																					prime.append(a )

																					# Set multiples of start be False
																					for i in range(start * start  ,					num + 1  ,					a ):
																												if sieve[i] is True:
																																			snake_case__	  =       False

														start += 1

							for j in range(end + 1  ,					num + 1 ):
														if sieve[j] is True:
																					prime.append(a )

							return prime


if __name__ == "__main__":
	print(prime_sieve(int(input("""Enter a positive integer: """).strip())))








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__       =     {
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """LiltForQuestionAnswering""",
	    """LiltForSequenceClassification""",
	    """LiltForTokenClassification""",
	    """LiltModel""",
	    """LiltPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lilt import (
		    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LiltForQuestionAnswering,
		    LiltForSequenceClassification,
		    LiltForTokenClassification,
		    LiltModel,
		    LiltPreTrainedModel,
		)

else:
	import sys

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