



from typing import TYPE_CHECKING

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


a_    :  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:
     a_    :  Dict						  =		[
         '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:
     a_    :  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

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




from __future__ import annotations

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		2
      lowerCamelCase             =		[]
      while i * i <= n:
            if n % i:
                  i += 1
            else:
                  n //= i
                  factors.append(UpperCAmelCase__			)
      if n > 1:
            factors.append(UpperCAmelCase__			)
      return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()