
from typing import TYPE_CHECKING

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


lowerCamelCase_						: Optional[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:
							lowerCamelCase_						: 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:
							lowerCamelCase_						: List[str]         =  [
							    """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_						: Tuple         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	int:
				while second != 0:
								UpperCamelCase_:							Optional[Any]	=						first & second
								first ^= second
								UpperCamelCase_:							Any	=						c << 1
				return first


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							lowerCamelCase_						: List[Any]         =  int(input("""Enter the first number: """).strip())
							lowerCamelCase_						: Tuple         =  int(input("""Enter the second number: """).strip())
							print(F"""{add(first, second) = }""")






