


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   _UpperCAmelCase       (     UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Union[str, Any]      ):
      """simple docstring"""




      if openai_config_file == "":
            __lowerCamelCase     :      str       						=						OpenAIGPTConfig()
      else:
            __lowerCamelCase     :      List[str]       						=						OpenAIGPTConfig.from_json_file(UpperCAmelCase      )
      __lowerCamelCase     :      List[Any]       						=						OpenAIGPTModel(UpperCAmelCase      )

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

      # Save pytorch-model
      __lowerCamelCase     :      Optional[int]       						=						pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
      __lowerCamelCase     :      Optional[Any]       						=						pytorch_dump_folder_path + """/""" + CONFIG_NAME
      print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""      )
      torch.save(model.state_dict()						,	UpperCAmelCase      )
      print(f"""Save configuration file to {pytorch_config_dump_path}"""      )
      with open(UpperCAmelCase						,	"""w"""						,	encoding="""utf-8"""      ) as f:
            f.write(config.to_json_string()      )


if __name__ == "__main__":
    __UpperCamelCase    :						List[str]     =							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.'
        ),
    )
    __UpperCamelCase    :						str     =							parser.parse_args()
    convert_openai_checkpoint_to_pytorch(
        args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
    )







from typing import Any





def   _UpperCAmelCase       (     UpperCAmelCase      :			list      ):
      """simple docstring"""




      if not input_list:
            return []
      __lowerCamelCase     :      int       						=						[input_list.count(UpperCAmelCase      ) for value in input_list]
      __lowerCamelCase     :      Tuple       						=						max(UpperCAmelCase      )  # Gets the maximum count in the input list.
      # Gets values of modes
      return sorted({input_list[i] for i, value in enumerate(UpperCAmelCase      ) if value == y}      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()



