





'''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 				_lowerCAmelCase  (			lowerCamelCase_     :					Tuple						,    lowerCamelCase_     :					str						,    lowerCamelCase_     :					Optional[Any]			):
			# Construct model
			if gpta_config_file == "":
						__lowercase			  =							GPTaConfig()
			else:
						__lowercase			  =							GPTaConfig.from_json_file(lowerCamelCase_			)
			__lowercase			  =							GPTaModel(lowerCamelCase_			)

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

			# Save pytorch-model
			__lowercase			  =							pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
			__lowercase			  =							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__":
							_SCREAMING_SNAKE_CASE   =      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.'''
							    ),
							)
							_SCREAMING_SNAKE_CASE   =      parser.parse_args()
							convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)












'''simple docstring'''



def 				_lowerCAmelCase  (			lowerCamelCase_     :					Union[str, Any]			):
			__lowercase			  =							1
			__lowercase			  =							2
			while i * i <= n:
						__lowercase			  =							0
						while n % i == 0:
									n //= i
									multiplicity += 1
						n_divisors *= multiplicity + 1
						i += 1
			if n > 1:
						n_divisors *= 2
			return n_divisors




def 				_lowerCAmelCase  (			):
			__lowercase			  =							1
			__lowercase			  =							1

			while True:
						i += 1
						t_num += i

						if count_divisors(lowerCamelCase_			) > 5_0_0:
									break

			return t_num


if __name__ == "__main__":
	print(solution())





