





from typing import TYPE_CHECKING

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


_a     :    Dict         =						{
    '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     :    Union[str, 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:
			_a     :    List[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     :    Union[str, Any]         =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


from math import loga






def   lowerCamelCase__							(							__lowerCAmelCase    :    int							):




			"""simple docstring"""


			if a < 0:
						raise ValueError("Input value must be a positive integer"							)
			elif isinstance(__lowerCAmelCase       ,				__lowerCAmelCase							):
						raise TypeError("Input value must be a 'int' type"							)
			return 0 if (a == 0) else int(loga(a & -a							)							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



