
"""simple docstring"""




from typing import TYPE_CHECKING

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


A__   :					List[Any]							=   {
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__   :					Union[str, Any]							=   ['LayoutLMv2TokenizerFast']

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__   :					int							=   ['LayoutLMv2FeatureExtractor']
  A__   :					str							=   ['LayoutLMv2ImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__   :					Optional[int]							=   [
      'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
      'LayoutLMv2ForQuestionAnswering',
      'LayoutLMv2ForSequenceClassification',
      'LayoutLMv2ForTokenClassification',
      'LayoutLMv2Layer',
      'LayoutLMv2Model',
      'LayoutLMv2PreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
  from .processing_layoutlmva import LayoutLMvaProcessor
  from .tokenization_layoutlmva import LayoutLMvaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_layoutlmva import (
        LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMvaForQuestionAnswering,
        LayoutLMvaForSequenceClassification,
        LayoutLMvaForTokenClassification,
        LayoutLMvaLayer,
        LayoutLMvaModel,
        LayoutLMvaPreTrainedModel,
    )
else:
  import sys

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


"""simple docstring"""




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



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

  _lowercase:			Tuple       =     [True] * (num + 1)

  _lowercase:			List[str]       =     2
  while p * p <= num:
    if primes[p]:
      for i in range(p * p ,				num + 1 ,				_UpperCamelCase						):
        _lowercase:			List[str]       =     False
    p += 1

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()

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