



from typing import TYPE_CHECKING

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


UpperCamelCase_									=   {
    '''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_									=   ['''MarkupLMTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_									=   [
          '''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_									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure)








import math
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						math.log(2,   10   ) * number
    _lowercase     			=						round((log_number - int(_lowercase   )),   15   )

    return difference
def 							UpperCAmelCase   ( _lowercase = 678910   ):


    '''simple docstring'''



    _lowercase     			=						90
    _lowercase     			=						0

    _lowercase     			=						math.log(1.23,   10   )
    _lowercase     			=						math.log(1.24,   10   )
    _lowercase     			=						0

    while position < number:
        _lowercase     			=						log_difference(_lowercase   )

        if difference >= upper_limit:
            power_iterator += 93

        elif difference < lower_limit:
            power_iterator += 196

        else:
            _lowercase     			=						power_iterator
            power_iterator += 196
            position += 1

    return previous_power


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"{solution() = }")
