
from typing import TYPE_CHECKING

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


__a  			=					{
    '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  			=					[
						    '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  			=					[
						    '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  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










def 		__A		(	__lowerCamelCase    )      ->       int:
	a										=							hex_num.strip()
	if not hex_num:
		raise ValueError("""No value was passed to the function"""    )

	a										=							hex_num[0] == """-"""
	if is_negative:
		a										=							hex_num[1:]

	try:
		a										=							int(__lowerCamelCase							,			16    )
	except ValueError:
		raise ValueError("""Invalid value was passed to the function"""    )

	a										=							""""""
	while int_num > 0:
		a										=							str(int_num % 2    ) + bin_str
		int_num >>= 1

	return int(("""-""" + bin_str) if is_negative else bin_str    )


if __name__ == "__main__":
		import doctest

		doctest.testmod()

