



"""simple docstring"""





import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				):
      # Construct model
      if openai_config_file == "":
            UpperCAmelCase_      			=							OpenAIGPTConfig()
      else:
            UpperCAmelCase_      			=							OpenAIGPTConfig.from_json_file(lowerCAmelCase__				)
      UpperCAmelCase_      			=							OpenAIGPTModel(lowerCAmelCase__				)

      # Load weights from numpy
      load_tf_weights_in_openai_gpt(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				)

      # Save pytorch-model
      UpperCAmelCase_      			=							pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      UpperCAmelCase_      			=							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       	=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--openai_checkpoint_folder_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(
      """--openai_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       	=						parser.parse_args()
  convert_openai_checkpoint_to_pytorch(
      args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
  )









"""simple docstring"""





from maths.prime_check import is_prime
def        a__     (				lowerCAmelCase__				):
      if not isinstance(lowerCAmelCase__     ,      lowerCAmelCase__				):
            UpperCAmelCase_      			=							f"""Input value of [number={number}] must be an integer"""
            raise TypeError(lowerCAmelCase__				)
      if is_prime(lowerCAmelCase__				) and is_prime(number + 2				):
            return number + 2
      else:
            return -1


if __name__ == "__main__":
  import doctest

  doctest.testmod()




