





"""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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						# Construct model
						if gpta_config_file == "":
												UpperCamelCase				:		Union[str, Any]				   =			GPTaConfig()
						else:
												UpperCamelCase				:		Dict				   =			GPTaConfig.from_json_file(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Optional[Any]				   =			GPTaModel(SCREAMING_SNAKE_CASE     )

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

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


if __name__ == "__main__":
		__magic_name__       :					Tuple						=			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."""
		    ),
		)
		__magic_name__       :					int						=			parser.parse_args()
		convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)










def 		lowerCAmelCase  (	UpperCamelCase__   :   list       ,			UpperCamelCase__   :   list       ,			UpperCamelCase__   :   int					)      ->    list:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   len(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   [[0] * n for i in range(UpperCamelCase__					)]
						for i in range(UpperCamelCase__					):
												__SCREAMING_SNAKE_CASE:				str           =   y_points[i]

						for i in range(2       ,			UpperCamelCase__					):
												for j in range(UpperCamelCase__       ,			UpperCamelCase__					):
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   (
																		    (xa - x_points[j - i + 1]) * q[j][i - 1]
																		    - (xa - x_points[j]) * q[j - 1][i - 1]
																		) / (x_points[j] - x_points[j - i + 1])

						return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()



