'''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 (	__lowerCamelCase			:						int ,		__lowerCamelCase			:						Tuple ,		__lowerCamelCase			:						Tuple				)	->							Tuple:
      '''simple docstring'''


      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__":
       lowerCAmelCase_ : 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."
           ),
       )
       lowerCAmelCase_ : Optional[int]						  =      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 (	__lowerCamelCase			:						int = 600_851_475_143				)	->							int:
      '''simple docstring'''


      try:
            lowercase_     =      int(__lowerCamelCase				)
      except (TypeError, ValueError):
            raise TypeError("Parameter n must be int or castable to int."				)
      if n <= 0:
            raise ValueError("Parameter n must be greater than or equal to one."				)
      lowercase_     =      1
      lowercase_     =      2
      while i * i <= n:
            while n % i == 0:
                  lowercase_     =      i
                  n //= i
            i += 1
      if n > 1:
            lowercase_     =      n
      return int(__lowerCamelCase				)


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






