





import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   Optional[int]:







	'''simple docstring'''







	lowerCamelCase__              =	RemBertConfig.from_json_file(__snake_case							)
	print('''Building PyTorch model from configuration: {}'''.format(str(__snake_case							)							)							)
	lowerCamelCase__              =	RemBertModel(__snake_case							)

	# Load weights from tf checkpoint
	load_tf_weights_in_rembert(__snake_case ,__snake_case ,__snake_case							)

	# Save pytorch-model
	print('''Save PyTorch model to {}'''.format(__snake_case							)							)
	torch.save(model.state_dict() ,__snake_case							)


if __name__ == "__main__":
			_a     :   List[str]						=   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(
			    "--rembert_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained RemBERT 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     :   List[str]						=   parser.parse_args()
			convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)










_a						=   "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

_a						=   [{"type": "code", "content": INSTALL_CONTENT}]
_a						=   {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}



