


from typing import TYPE_CHECKING

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


__UpperCamelCase    		=       {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       [
						    'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'AdaptiveEmbedding',
						    'TransfoXLForSequenceClassification',
						    'TransfoXLLMHeadModel',
						    'TransfoXLModel',
						    'TransfoXLPreTrainedModel',
						    'load_tf_weights_in_transfo_xl',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       [
						    'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFAdaptiveEmbedding',
						    'TFTransfoXLForSequenceClassification',
						    'TFTransfoXLLMHeadModel',
						    'TFTransfoXLMainLayer',
						    'TFTransfoXLModel',
						    'TFTransfoXLPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
						from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_transfo_xl import (
												    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    AdaptiveEmbedding,
												    TransfoXLForSequenceClassification,
												    TransfoXLLMHeadModel,
												    TransfoXLModel,
												    TransfoXLPreTrainedModel,
												    load_tf_weights_in_transfo_xl,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_transfo_xl import (
												    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFAdaptiveEmbedding,
												    TFTransfoXLForSequenceClassification,
												    TFTransfoXLLMHeadModel,
												    TFTransfoXLMainLayer,
												    TFTransfoXLModel,
												    TFTransfoXLPreTrainedModel,
												)

else:
						import sys

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






def        UpperCamelCase_(				_A   :str  )->		int:
			UpperCamelCase__					=       hex_num.strip()
			if not hex_num:
						raise ValueError("No value was passed to the function"  )

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

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

			UpperCamelCase__					=       ""
			while int_num > 0:
						UpperCamelCase__					=       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()


