
"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCamelCase__   					=		{
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase__   					=		[
							    """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """Data2VecAudioForAudioFrameClassification""",
							    """Data2VecAudioForCTC""",
							    """Data2VecAudioForSequenceClassification""",
							    """Data2VecAudioForXVector""",
							    """Data2VecAudioModel""",
							    """Data2VecAudioPreTrainedModel""",
							]
							lowerCamelCase__   					=		[
							    """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """Data2VecTextForCausalLM""",
							    """Data2VecTextForMaskedLM""",
							    """Data2VecTextForMultipleChoice""",
							    """Data2VecTextForQuestionAnswering""",
							    """Data2VecTextForSequenceClassification""",
							    """Data2VecTextForTokenClassification""",
							    """Data2VecTextModel""",
							    """Data2VecTextPreTrainedModel""",
							]
							lowerCamelCase__   					=		[
							    """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """Data2VecVisionForImageClassification""",
							    """Data2VecVisionForMaskedImageModeling""",
							    """Data2VecVisionForSemanticSegmentation""",
							    """Data2VecVisionModel""",
							    """Data2VecVisionPreTrainedModel""",
							]

if is_tf_available():
							lowerCamelCase__   					=		[
							    """TFData2VecVisionForImageClassification""",
							    """TFData2VecVisionForSemanticSegmentation""",
							    """TFData2VecVisionModel""",
							    """TFData2VecVisionPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
							from .configuration_dataavec_text import (
							    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DataaVecTextConfig,
							    DataaVecTextOnnxConfig,
							)
							from .configuration_dataavec_vision import (
							    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DataaVecVisionConfig,
							    DataaVecVisionOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_dataavec_audio import (
														    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DataaVecAudioForAudioFrameClassification,
														    DataaVecAudioForCTC,
														    DataaVecAudioForSequenceClassification,
														    DataaVecAudioForXVector,
														    DataaVecAudioModel,
														    DataaVecAudioPreTrainedModel,
														)
														from .modeling_dataavec_text import (
														    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DataaVecTextForCausalLM,
														    DataaVecTextForMaskedLM,
														    DataaVecTextForMultipleChoice,
														    DataaVecTextForQuestionAnswering,
														    DataaVecTextForSequenceClassification,
														    DataaVecTextForTokenClassification,
														    DataaVecTextModel,
														    DataaVecTextPreTrainedModel,
														)
														from .modeling_dataavec_vision import (
														    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DataaVecVisionForImageClassification,
														    DataaVecVisionForMaskedImageModeling,
														    DataaVecVisionForSemanticSegmentation,
														    DataaVecVisionModel,
														    DataaVecVisionPreTrainedModel,
														)
							if is_tf_available():
														from .modeling_tf_dataavec_vision import (
														    TFDataaVecVisionForImageClassification,
														    TFDataaVecVisionForSemanticSegmentation,
														    TFDataaVecVisionModel,
														    TFDataaVecVisionPreTrainedModel,
														)

else:
							import sys

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





def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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

	snake_case_		    =     [True] * (num + 1)

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

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowercase__				:     Tuple 		=						int(input("Enter a positive integer: ").strip())
		print(prime_sieve_eratosthenes(user_num))
