




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a													=			{
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a													=			["DebertaTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a													=			[
				    "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "DebertaForMaskedLM",
				    "DebertaForQuestionAnswering",
				    "DebertaForSequenceClassification",
				    "DebertaForTokenClassification",
				    "DebertaModel",
				    "DebertaPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a													=			[
				    "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFDebertaForMaskedLM",
				    "TFDebertaForQuestionAnswering",
				    "TFDebertaForSequenceClassification",
				    "TFDebertaForTokenClassification",
				    "TFDebertaModel",
				    "TFDebertaPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
				from .tokenization_deberta import DebertaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_deberta_fast import DebertaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deberta import (
								    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DebertaForMaskedLM,
								    DebertaForQuestionAnswering,
								    DebertaForSequenceClassification,
								    DebertaForTokenClassification,
								    DebertaModel,
								    DebertaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_deberta import (
								    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDebertaForMaskedLM,
								    TFDebertaForQuestionAnswering,
								    TFDebertaForSequenceClassification,
								    TFDebertaForTokenClassification,
								    TFDebertaModel,
								    TFDebertaPreTrainedModel,
								)


else:
				import sys

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





from typing import Union

import fire
import torch
from tqdm import tqdm




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


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