






"""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:Optional[int] ,						__lowerCamelCase:str ,						__lowerCamelCase:List[Any]   ):

						'''simple docstring'''






						if gpta_config_file == "":
												__magic_name__            =     GPTaConfig()
						else:
												__magic_name__            =     GPTaConfig.from_json_file(__lowerCamelCase   )
						__magic_name__            =     GPTaModel(__lowerCamelCase   )

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

						# Save pytorch-model
						__magic_name__            =     pytorch_dump_folder_path + "/" + WEIGHTS_NAME
						__magic_name__            =     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    			=       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    			=       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:int = 2_0_0_0_0_0_0   ):

						'''simple docstring'''






						__magic_name__            =     [0 for i in range(n + 1   )]
						__magic_name__            =     1
						__magic_name__            =     1

						for i in range(2 ,						int(n**0.5   ) + 1   ):
												if primality_list[i] == 0:
																		for j in range(i * i ,						n + 1 ,						__lowerCamelCase   ):
																								__magic_name__            =     1
						__magic_name__            =     0
						for i in range(__lowerCamelCase   ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


if __name__ == "__main__":
			print(f'''{solution() = }''')



