

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_     =			{
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_     =			[
			    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """XLMRobertaXLForCausalLM""",
			    """XLMRobertaXLForMaskedLM""",
			    """XLMRobertaXLForMultipleChoice""",
			    """XLMRobertaXLForQuestionAnswering""",
			    """XLMRobertaXLForSequenceClassification""",
			    """XLMRobertaXLForTokenClassification""",
			    """XLMRobertaXLModel""",
			    """XLMRobertaXLPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta_xl import (
			    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaXLConfig,
			    XLMRobertaXLOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaXLForCausalLM,
						    XLMRobertaXLForMaskedLM,
						    XLMRobertaXLForMultipleChoice,
						    XLMRobertaXLForQuestionAnswering,
						    XLMRobertaXLForSequenceClassification,
						    XLMRobertaXLForTokenClassification,
						    XLMRobertaXLModel,
						    XLMRobertaXLPreTrainedModel,
						)

else:
			import sys

			a_     =			_LazyModule(__name__, globals()['__file__'], _import_structure)



from typing import Union

import fire
import torch
from tqdm import tqdm



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_ = "cpu"       ,      lowerCAmelCase_ = None       )->       None:
				lowerCAmelCase_       :					str			 = torch.load(lowerCAmelCase_       ,      map_location=lowerCAmelCase_       )
				for k, v in tqdm(state_dict.items()       ):
								if not isinstance(lowerCAmelCase_       ,      torch.Tensor       ):
												raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'''       )
								lowerCAmelCase_       :					int			 = v.half()
				if save_path is None:  # overwrite src_path
								lowerCAmelCase_       :					Tuple			 = src_path
				torch.save(lowerCAmelCase_       ,      lowerCAmelCase_       )


if __name__ == "__main__":
						fire.Fire(convert)