
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__a  			=					{
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['PerceiverFeatureExtractor']
						__a  			=					['PerceiverImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'PerceiverForImageClassificationConvProcessing',
						    'PerceiverForImageClassificationFourier',
						    'PerceiverForImageClassificationLearned',
						    'PerceiverForMaskedLM',
						    'PerceiverForMultimodalAutoencoding',
						    'PerceiverForOpticalFlow',
						    'PerceiverForSequenceClassification',
						    'PerceiverLayer',
						    'PerceiverModel',
						    'PerceiverPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
						from .tokenization_perceiver import PerceiverTokenizer

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_perceiver import PerceiverFeatureExtractor
												from .image_processing_perceiver import PerceiverImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_perceiver import (
												    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PerceiverForImageClassificationConvProcessing,
												    PerceiverForImageClassificationFourier,
												    PerceiverForImageClassificationLearned,
												    PerceiverForMaskedLM,
												    PerceiverForMultimodalAutoencoding,
												    PerceiverForOpticalFlow,
												    PerceiverForSequenceClassification,
												    PerceiverLayer,
												    PerceiverModel,
												    PerceiverPreTrainedModel,
												)

else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






import itertools
import math




def 				a						(							snake_case__:					int					):


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




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					2
				while True:
								if is_prime(snake_case__					):
												yield num
								num += 1




def 				a						(							snake_case__:					int = 10_001					):


				'''simple docstring'''
				return next(itertools.islice(prime_generator()						,   nth - 1						,   snake_case__					)					)


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




