"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__A  =				{
    '''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
    '''tokenization_tapas''': ['''TapasTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TapasForMaskedLM''',
				    '''TapasForQuestionAnswering''',
				    '''TapasForSequenceClassification''',
				    '''TapasModel''',
				    '''TapasPreTrainedModel''',
				    '''load_tf_weights_in_tapas''',
				]
try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFTapasForMaskedLM''',
				    '''TFTapasForQuestionAnswering''',
				    '''TFTapasForSequenceClassification''',
				    '''TFTapasModel''',
				    '''TFTapasPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
				from .tokenization_tapas import TapasTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tapas import (
								    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TapasForMaskedLM,
								    TapasForQuestionAnswering,
								    TapasForSequenceClassification,
								    TapasModel,
								    TapasPreTrainedModel,
								    load_tf_weights_in_tapas,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_tapas import (
								    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTapasForMaskedLM,
								    TFTapasForQuestionAnswering,
								    TFTapasForSequenceClassification,
								    TFTapasModel,
								    TFTapasPreTrainedModel,
								)


else:
				import sys

				__A  =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
"""simple docstring"""





import math


def    lowercase_   (   _lowerCamelCase:	int      ) ->      bool:



							'''simple docstring'''

							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(_lowerCamelCase      ) + 1      )						,   6      ):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True


def    lowercase_   (   _lowerCamelCase:	int = 10001      ) ->      int:



							'''simple docstring'''

							try:
														__lowerCamelCase					:		Optional[int]		   =     int(_lowerCamelCase      )
							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."      )
							__lowerCamelCase					:		list[int]		   =     []
							__lowerCamelCase					:		Tuple		   =     2
							while len(_lowerCamelCase      ) < nth:
														if is_prime(_lowerCamelCase      ):
																					primes.append(_lowerCamelCase      )
																					num += 1
														else:
																					num += 1
							return primes[len(_lowerCamelCase      ) - 1]


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