



from typing import Union

import fire
import torch
from tqdm import tqdm

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			str = "cpu"				,     _SCREAMING_SNAKE_CASE       :			Union[str, None] = None    ):
				"""simple docstring"""







				__a       =		torch.load(_SCREAMING_SNAKE_CASE				,     map_location=_SCREAMING_SNAKE_CASE    )
				for k, v in tqdm(state_dict.items()    ):
								if not isinstance(_SCREAMING_SNAKE_CASE				,     torch.Tensor    ):
												raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""    )
								__a       =		v.half()
				if save_path is None:  # overwrite src_path
								__a       =		src_path
				torch.save(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )


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





from typing import TYPE_CHECKING

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


lowerCamelCase__			= {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NezhaForNextSentencePrediction""",
						    """NezhaForMaskedLM""",
						    """NezhaForPreTraining""",
						    """NezhaForMultipleChoice""",
						    """NezhaForQuestionAnswering""",
						    """NezhaForSequenceClassification""",
						    """NezhaForTokenClassification""",
						    """NezhaModel""",
						    """NezhaPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nezha import (
												    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NezhaForMaskedLM,
												    NezhaForMultipleChoice,
												    NezhaForNextSentencePrediction,
												    NezhaForPreTraining,
												    NezhaForQuestionAnswering,
												    NezhaForSequenceClassification,
												    NezhaForTokenClassification,
												    NezhaModel,
												    NezhaPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
