


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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				Tuple:
  # Construct model
  if gpta_config_file == "":
    snake_case__						= GPTaConfig()
  else:
    snake_case__						= GPTaConfig.from_json_file(__lowerCAmelCase				)
  snake_case__						= GPTaModel(__lowerCAmelCase				)

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

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





'''simple docstring'''






from typing import Union

import fire
import torch
from tqdm import tqdm







def 							UpperCamelCase_     (       A__   :  str				,       A__   :  str = "cpu"				,       A__   :  Union[str, None] = None   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  torch.load(A__				,       map_location=A__   )
  for k, v in tqdm(state_dict.items()   ):
    if not isinstance(A__				,       torch.Tensor   ):
      raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""   )
    lowerCAmelCase_     : List[str]     =  v.half()
  if save_path is None:  # overwrite src_path
    lowerCAmelCase_     : List[str]     =  src_path
  torch.save(A__				,       A__   )


if __name__ == "__main__":
  fire.Fire(convert)



