



"""simple docstring"""






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      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Optional[Any]:
			# Construct model
			if gpta_config_file == "":
						__lowerCAmelCase :    List[Any]				=			GPTaConfig()
			else:
						__lowerCAmelCase :    int				=			GPTaConfig.from_json_file(__lowercase      )
			__lowerCAmelCase :    Tuple				=			GPTaModel(__lowercase      )

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

			# Save pytorch-model
			__lowerCAmelCase :    Any				=			pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
			__lowerCAmelCase :    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__":
	__snake_case				:  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.'
	    ),
	)
	__snake_case				:  List[str] =       parser.parse_args()
	convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)







from __future__ import annotations



def  a__ (__lowercase							:list					,  __lowercase							:int | None = None					,  __lowercase							:int | None = None   )		-> None:
				if start is None:
								_A	:				Optional[int]   =      0

				if end is None:
								_A	:				Union[str, Any]   =      len(__lowercase   ) - 1

				if start >= end:
								return

				_A	:				Union[str, Any]   =      (start + end) // 2

				slowsort(__lowercase					,  __lowercase					,  __lowercase   )
				slowsort(__lowercase					,  mid + 1					,  __lowercase   )

				if sequence[end] < sequence[mid]:
								_A  ,				_A	:				Optional[int]   =      sequence[mid], sequence[end]

				slowsort(__lowercase					,  __lowercase					,  end - 1   )


if __name__ == "__main__":
	from doctest import testmod

	testmod()



