

"""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 						A							(  snake_case__		,					snake_case__		,					snake_case__     ):




			'''simple docstring'''


			if gpta_config_file == "":
						SCREAMING_SNAKE_CASE__										=   GPTaConfig()
			else:
						SCREAMING_SNAKE_CASE__										=   GPTaConfig.from_json_file(snake_case__     )
			SCREAMING_SNAKE_CASE__										=   GPTaModel(snake_case__     )

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

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


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





"""simple docstring"""




def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   len(snake_case__     )
			SCREAMING_SNAKE_CASE__										=   len(snake_case__     )
			SCREAMING_SNAKE_CASE__										=   (
			    first_str_length if first_str_length > second_str_length else second_str_length
			)
			SCREAMING_SNAKE_CASE__										=   []
			for char_count in range(snake_case__     ):
						if char_count < first_str_length:
									output_list.append(first_str[char_count]     )
						if char_count < second_str_length:
									output_list.append(second_str[char_count]     )
			return "".join(snake_case__     )


if __name__ == "__main__":
						print(alternative_string_arrange("AB", "XYZ"), end=" ")


