






from typing import TYPE_CHECKING

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


UpperCAmelCase_ :						List[Any]      =      {
    '''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_ :						List[Any]      =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



def    __UpperCAmelCase (	__a      :			str	)			->     int:

					"""simple docstring"""

					assert column_title.isupper()
					_a	:    Optional[Any]         =		0
					_a	:    List[Any]         =		len(__a	) - 1
					_a	:    List[str]         =		0

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

					return answer


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

			testmod()



