



'''simple docstring'''





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)











'''simple docstring'''





import math
from timeit import timeit

def 	a_					(							_lowerCAmelCase	)				->				int:
  __lowerCamelCase	:	int   	=			0
  __lowerCamelCase	:	Optional[int]   	=			abs(_lowerCAmelCase	)
  while True:
    __lowerCamelCase	:	List[str]   	=			n // 10
    digits += 1
    if n == 0:
      break
  return digits

def 	a_					(							_lowerCAmelCase	)				->				int:
  return 1 if n == 0 else math.floor(math.log(abs(_lowerCAmelCase	)	,10	) + 1	)

def 	a_					(							_lowerCAmelCase	)				->				int:
  return len(str(abs(_lowerCAmelCase	)	)	)

def 	a_					(							)				->				None:
  from collections.abc import Callable

  def benchmark_a_function(_lowerCAmelCase	,_lowerCAmelCase	) -> None:
    __lowerCamelCase	:	Any   	=			F'{func.__name__}({value})'
    __lowerCamelCase	:	int   	=			timeit(F'__main__.{call}'	,setup='import __main__'	)
    print(F'{call}: {func(_lowerCAmelCase	)} -- {timing} seconds'	)

  for value in (262144, 1125899906842624, 1267650600228229401496703205376):
    for func in (num_digits, num_digits_fast, num_digits_faster):
      benchmark_a_function(_lowerCAmelCase	,_lowerCAmelCase	)
    print()


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()






