





'''simple docstring'''







from typing import TYPE_CHECKING

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


lowerCAmelCase_					:    Tuple      	=			{
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    List[str]      	=			[
					    '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:
					lowerCAmelCase_					:    Tuple      	=			[
					    '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

					lowerCAmelCase_					:    Optional[int]      	=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      int						)	->	bool:
				if num < 0:
								return False

				_a								= num
				_a								= 0
				while num > 0:
								_a								= rev_num * 10 + (num % 10)
								num //= 10

				return num_copy == rev_num


if __name__ == "__main__":
					import doctest

					doctest.testmod()
