




'''simple docstring'''



from typing import TYPE_CHECKING

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


UpperCAmelCase_							:	List[str]	     =		{
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_							:	Optional[int]	     =		[
         'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'XLMForMultipleChoice',
         'XLMForQuestionAnswering',
         'XLMForQuestionAnsweringSimple',
         'XLMForSequenceClassification',
         'XLMForTokenClassification',
         'XLMModel',
         'XLMPreTrainedModel',
         'XLMWithLMHeadModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_							:	Dict	     =		[
         'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFXLMForMultipleChoice',
         'TFXLMForQuestionAnsweringSimple',
         'TFXLMForSequenceClassification',
         'TFXLMForTokenClassification',
         'TFXLMMainLayer',
         'TFXLMModel',
         'TFXLMPreTrainedModel',
         'TFXLMWithLMHeadModel',
     ]


if TYPE_CHECKING:
     from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
     from .tokenization_xlm import XLMTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlm import (
              XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMForMultipleChoice,
              XLMForQuestionAnswering,
              XLMForQuestionAnsweringSimple,
              XLMForSequenceClassification,
              XLMForTokenClassification,
              XLMModel,
              XLMPreTrainedModel,
              XLMWithLMHeadModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xlm import (
              TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXLMForMultipleChoice,
              TFXLMForQuestionAnsweringSimple,
              TFXLMForSequenceClassification,
              TFXLMForTokenClassification,
              TFXLMMainLayer,
              TFXLMModel,
              TFXLMPreTrainedModel,
              TFXLMWithLMHeadModel,
          )

else:
     import sys

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










def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						int:


						'''simple docstring'''






						assert column_title.isupper()
						__snake_case				=		0
						__snake_case				=		len(_lowerCAmelCase			) - 1
						__snake_case				=		0

						while index >= 0:
												__snake_case				=		(ord(column_title[index]			) - 64) * pow(26   ,						_lowerCAmelCase			)
												answer += value
												power += 1
												index -= 1

						return answer


if __name__ == "__main__":
	from doctest import testmod

	testmod()





