


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case							:	int					      =				{
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:	List[str]					      =				[
						    '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''Wav2Vec2ForAudioFrameClassification''',
						    '''Wav2Vec2ForCTC''',
						    '''Wav2Vec2ForMaskedLM''',
						    '''Wav2Vec2ForPreTraining''',
						    '''Wav2Vec2ForSequenceClassification''',
						    '''Wav2Vec2ForXVector''',
						    '''Wav2Vec2Model''',
						    '''Wav2Vec2PreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:	Any					      =				[
						    '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFWav2Vec2ForCTC''',
						    '''TFWav2Vec2Model''',
						    '''TFWav2Vec2PreTrainedModel''',
						    '''TFWav2Vec2ForSequenceClassification''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:	List[str]					      =				[
						    '''FlaxWav2Vec2ForCTC''',
						    '''FlaxWav2Vec2ForPreTraining''',
						    '''FlaxWav2Vec2Model''',
						    '''FlaxWav2Vec2PreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
						from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
						from .processing_wavaveca import WavaVecaProcessor
						from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavaveca import (
												    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavaVecaForAudioFrameClassification,
												    WavaVecaForCTC,
												    WavaVecaForMaskedLM,
												    WavaVecaForPreTraining,
												    WavaVecaForSequenceClassification,
												    WavaVecaForXVector,
												    WavaVecaModel,
												    WavaVecaPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_wavaveca import (
												    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFWavaVecaForCTC,
												    TFWavaVecaForSequenceClassification,
												    TFWavaVecaModel,
												    TFWavaVecaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_wavaveca import (
												    FlaxWavaVecaForCTC,
												    FlaxWavaVecaForPreTraining,
												    FlaxWavaVecaModel,
												    FlaxWavaVecaPreTrainedModel,
												)


else:
						import sys

						snake_case							:	str					      =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








def 							__lowercase       (			__lowerCAmelCase							:    int       ):

							if num <= 0:
														raise ValueError('Input must be a positive integer'       )

							a__					=					[True] * (num + 1)

							a__					=					2
							while p * p <= num:
														if primes[p]:
																					for i in range(p * p ,   num + 1 ,   __lowerCAmelCase       ):
																												a__					=					False
														p += 1

							return [prime for prime in range(2 ,   num + 1       ) if primes[prime]]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						snake_case							:	Optional[Any]					      =				int(input('''Enter a positive integer: ''').strip())
						print(prime_sieve_eratosthenes(user_num))




