


from typing import TYPE_CHECKING

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


snake_case				:			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:
						snake_case				:			List[Any]	     = [
						    '''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:
						snake_case				:			Any	     = [
						    '''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

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











def      A__       (    SCREAMING_SNAKE_CASE_   :      int						)  ->  bool:






   """simple docstring"""







   if not isinstance(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						):
      _UpperCAmelCase           =   F'''Input value of [number={number}] must be an integer'''
      raise TypeError(SCREAMING_SNAKE_CASE_						)
   if number < 0:
      return False
   _UpperCAmelCase           =   number * number
   while number > 0:
      if number % 10 != number_square % 10:
         return False
      number //= 10
      number_square //= 10
   return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()