



"""simple docstring"""






import math






def      snake_case__   (		__lowerCamelCase				:    int	):
			"""simple docstring"""
			lowerCamelCase__		:		str			=0
			lowerCamelCase__		:		List[Any]			=0
			while num > 0:
						lowerCamelCase__		:		int			=num % 8
						lowerCamelCase__		:		Dict			=octal + (remainder * math.floor(math.pow(10							,		__lowerCamelCase	)	))
						counter += 1
						lowerCamelCase__		:		Any			=math.floor(num / 8	)  # basically /= 8 without remainder if any
						# This formatting removes trailing '.0' from `octal`.
			return f'''0o{int(__lowerCamelCase	)}'''






def      snake_case__   (		):
			"""simple docstring"""
			print('''\n2 in octal is:'''	)
			print(decimal_to_octal(2	)	)  # = 2
			print('''\n8 in octal is:'''	)
			print(decimal_to_octal(8	)	)  # = 10
			print('''\n65 in octal is:'''	)
			print(decimal_to_octal(65	)	)  # = 101
			print('''\n216 in octal is:'''	)
			print(decimal_to_octal(216	)	)  # = 330
			print('''\n512 in octal is:'''	)
			print(decimal_to_octal(512	)	)  # = 1000
			print('''\n'''	)


if __name__ == "__main__":
		main()









"""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      snake_case__   (		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			# Construct model
			if gpta_config_file == "":
						lowerCamelCase__		:		Dict			=GPTaConfig()
			else:
						lowerCamelCase__		:		Tuple			=GPTaConfig.from_json_file(__lowerCamelCase	)
			lowerCamelCase__		:		Optional[Any]			=GPTaModel(__lowerCamelCase	)

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

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


if __name__ == "__main__":
		_lowercase : 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."
		    ),
		)
		_lowercase : Any      						=						parser.parse_args()
		convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




