
"""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__)




import random

from .binary_exp_mod import bin_exp_mod



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase=1000      )   ->					str:


				"""simple docstring"""

				if n < 2:
								return False

				if n % 2 == 0:
								return n == 2

				# this means n is odd
				snake_case__	:      Tuple     		=	n - 1
				snake_case__	:      Tuple     		=	0
				while d % 2 == 0:
								d /= 2
								exp += 1

				# n - 1=d*(2**exp)
				snake_case__	:      List[str]     		=	0
				while count < prec:
								snake_case__	:      List[str]     		=	random.randint(2  ,  n - 1      )
								snake_case__	:      Optional[Any]     		=	bin_exp_mod(__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )
								if b != 1:
												snake_case__	:      List[Any]     		=	True
												for _ in range(__lowerCAmelCase      ):
																if b == n - 1:
																				snake_case__	:      List[str]     		=	False
																				break
																snake_case__	:      str     		=	b * b
																b %= n
												if flag:
																return False
												count += 1
				return True


if __name__ == "__main__":
				A__								=						abs(int(input('''Enter bound : ''').strip()))
				print('''Here\'s the list of primes:''')
				print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))

