from functools import lru_cache





def        lowercase(     UpperCamelCase_    )						->     Tuple:







 '''simple docstring'''
 UpperCamelCase          =	2
 UpperCamelCase          =	set()
 while i * i <= n:
  if n % i:
   i += 1
  else:
   n //= i
   factors.add(__lowerCamelCase    )
 if n > 1:
  factors.add(__lowerCamelCase    )
 return factors





@lru_cache
def        lowercase(     UpperCamelCase_    )						->     int:







 '''simple docstring'''
 return len(unique_prime_factors(__lowerCamelCase    )    )





def        lowercase(     UpperCamelCase_    )						->     Optional[Any]:







 '''simple docstring'''
 return len(set(__lowerCamelCase    )    ) in (0, 1)





def        lowercase(     UpperCamelCase_    )						->     Dict:







 '''simple docstring'''
 UpperCamelCase          =	2

 while True:
  # Increment each value of a generated range
  UpperCamelCase          =	[base + i for i in range(__lowerCamelCase    )]

  # Run elements through out unique_prime_factors function
  # Append our target number to the end.
  UpperCamelCase          =	[upf_len(__lowerCamelCase    ) for x in group]
  checker.append(__lowerCamelCase    )

  # If all numbers in the list are equal, return the group variable.
  if equality(__lowerCamelCase    ):
   return group

  # Increment our base variable by 1
  base += 1





def        lowercase(     UpperCamelCase_ = 4    )						->     List[str]:







 '''simple docstring'''
 UpperCamelCase          =	run(__lowerCamelCase    )
 return results[0] if len(__lowerCamelCase    ) else None


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





"""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)




