'''simple docstring'''


import math


def    _lowerCamelCase       ( lowerCamelCase_   :				int  ):



     """simple docstring"""





     if n <= 0:
          raise ValueError('Only positive integers have prime factors'  )
     UpperCAmelCase_				:  str  = []
     while n % 2 == 0:
          pf.append(2  )
          UpperCAmelCase_				:  Tuple  = int(n / 2  )
     for i in range(3      ,	int(math.sqrt(lowerCamelCase_  )  ) + 1      ,	2  ):
          while n % i == 0:
               pf.append(lowerCamelCase_  )
               UpperCAmelCase_				:  Tuple  = int(n / i  )
     if n > 2:
          pf.append(lowerCamelCase_  )
     return pf


def    _lowerCamelCase       ( lowerCamelCase_   :				int  ):



     """simple docstring"""





     if n <= 0:
          raise ValueError('Only positive numbers are accepted'  )
     UpperCAmelCase_				:  List[Any]  = 1
     UpperCAmelCase_				:  List[str]  = 1
     while n % 2 == 0:
          temp += 1
          UpperCAmelCase_				:  Any  = int(n / 2  )
     div *= temp
     for i in range(3      ,	int(math.sqrt(lowerCamelCase_  )  ) + 1      ,	2  ):
          UpperCAmelCase_				:  Any  = 1
          while n % i == 0:
               temp += 1
               UpperCAmelCase_				:  List[Any]  = int(n / i  )
          div *= temp
     if n > 1:
          div *= 2
     return div


def    _lowerCamelCase       ( lowerCamelCase_   :				int  ):



     """simple docstring"""





     if n <= 0:
          raise ValueError('Only positive numbers are accepted'  )
     UpperCAmelCase_				:  Any  = 1
     UpperCAmelCase_				:  Union[str, Any]  = 1
     while n % 2 == 0:
          temp += 1
          UpperCAmelCase_				:  str  = int(n / 2  )
     if temp > 1:
          s *= (2**temp - 1) / (2 - 1)
     for i in range(3      ,	int(math.sqrt(lowerCamelCase_  )  ) + 1      ,	2  ):
          UpperCAmelCase_				:  Dict  = 1
          while n % i == 0:
               temp += 1
               UpperCAmelCase_				:  str  = int(n / i  )
          if temp > 1:
               s *= (i**temp - 1) / (i - 1)
     return int(lowerCamelCase_  )


def    _lowerCamelCase       ( lowerCamelCase_   :				int  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[str]  = n
     for x in set(prime_factors(lowerCamelCase_  )  ):
          s *= (x - 1) / x
     return int(lowerCamelCase_  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()







'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase       :     Tuple			=    {
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase       :     int			=    ["""MarkupLMTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase       :     Any			=    [
          """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MarkupLMForQuestionAnswering""",
          """MarkupLMForSequenceClassification""",
          """MarkupLMForTokenClassification""",
          """MarkupLMModel""",
          """MarkupLMPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
      from .feature_extraction_markuplm import MarkupLMFeatureExtractor
      from .processing_markuplm import MarkupLMProcessor
      from .tokenization_markuplm import MarkupLMTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_markuplm_fast import MarkupLMTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_markuplm import (
                MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarkupLMForQuestionAnswering,
                MarkupLMForSequenceClassification,
                MarkupLMForTokenClassification,
                MarkupLMModel,
                MarkupLMPreTrainedModel,
            )


else:
      import sys

      UpperCamelCase       :     Dict			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure)

