





'''simple docstring'''







import math






def  _lowerCamelCase				( lowercase						:      List[Any]						)	->	bool:

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5    ,							int(math.sqrt(__UpperCAmelCase						) + 1						)    ,							6						):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True






def  _lowerCamelCase				( lowercase						:      Any = 1_0001						)	->	int:

				try:
								_a								= int(__UpperCAmelCase						)
				except (TypeError, ValueError):
								raise TypeError("Parameter nth must be int or castable to int."						) from None
				if nth <= 0:
								raise ValueError("Parameter nth must be greater than or equal to one."						)
				_a								= []
				_a								= 2
				while len(__UpperCAmelCase						) < nth:
								if is_prime(__UpperCAmelCase						):
												primes.append(__UpperCAmelCase						)
												num += 1
								else:
												num += 1
				return primes[len(__UpperCAmelCase						) - 1]


if __name__ == "__main__":
					print(f"""{solution() = }""")

"""simple docstring"""






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__)
