






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def  a__ (__lowercase							:Optional[Any]					,  __lowercase							:Tuple					,  __lowercase							:Union[str, Any]   )		-> Optional[Any]:
				# Construct model
				if gpta_config_file == "":
								_A	:				List[Any]   =      GPTaConfig()
				else:
								_A	:				int   =      GPTaConfig.from_json_file(__lowercase   )
				_A	:				Tuple   =      GPTaModel(__lowercase   )

				# Load weights from numpy
				load_tf_weights_in_gpta(__lowercase					,  __lowercase					,  __lowercase   )

				# Save pytorch-model
				_A	:				Any   =      pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				_A	:				List[str]   =      pytorch_dump_folder_path + '''/''' + CONFIG_NAME
				print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""   )
				torch.save(model.state_dict()					,  __lowercase   )
				print(f"""Save configuration file to {pytorch_config_dump_path}"""   )
				with open(__lowercase					,  '''w'''					,  encoding='''utf-8'''   ) as f:
								f.write(config.to_json_string()   )


if __name__ == "__main__":
	_UpperCamelCase			:	Optional[Any]								=argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	parser.add_argument(
	    '--gpt2_config_file',
	    default='',
	    type=str,
	    help=(
	        'An optional config json file corresponding to the pre-trained OpenAI model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	_UpperCamelCase			:	List[str]								=parser.parse_args()
	convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)











import warnings


warnings.warn(
    'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
    '`from accelerate import find_executable_batch_size` to avoid this warning.',
    FutureWarning,
)



