

"""simple docstring"""




import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					ConvBertConfig.from_json_file(lowercase__      )
					A			=					ConvBertModel(lowercase__      )

					A			=					load_tf_weights_in_convbert(lowercase__			,							lowercase__			,							lowercase__      )
					model.save_pretrained(lowercase__      )

					A			=					TFConvBertModel.from_pretrained(lowercase__			,							from_pt=lowercase__      )
					tf_model.save_pretrained(lowercase__      )


if __name__ == "__main__":
	__A     :    Any    =		argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--convbert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ConvBERT model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	__A     :    int    =		parser.parse_args()
	convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)








from .prime_check import Test

Test()


