

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_  (							a					,   a					,   a  ):

      """simple docstring"""



      if openai_config_file == "":
            SCREAMING_SNAKE_CASE_				:  Dict           =     OpenAIGPTConfig()
      else:
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     OpenAIGPTConfig.from_json_file(a  )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     OpenAIGPTModel(a  )

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

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


if __name__ == "__main__":
    lowerCAmelCase     :   Dict						     =  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     :   Tuple						     =  parser.parse_args()
    convert_openai_checkpoint_to_pytorch(
        args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
    )









lowerCAmelCase     :   Tuple						     =  {
    'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
    'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
    'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
    'V': '...-', 'W': '.--', 'X': '-..-', 'Y': '-.--', 'Z': '--..', '1': '.----',
    '2': '..---', '3': '...--', '4': '....-', '5': '.....', '6': '-....', '7': '--...',
    '8': '---..', '9': '----.', '0': '-----', '&': '.-...', '@': '.--.-.',
    ':': '---...', ',': '--..--', '.': '.-.-.-', '\'': '.----.', '"': '.-..-.',
    '?': '..--..', '/': '-..-.', '=': '-...-', '+': '.-.-.', '-': '-....-',
    '(': '-.--.', ')': '-.--.-', '!': '-.-.--', ' ': '/'
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowerCAmelCase     :   Optional[int]						     =  {value: key for key, value in MORSE_CODE_DICT.items()}

def    A_  (							a  ):

      """simple docstring"""



      return " ".join(MORSE_CODE_DICT[char] for char in message.upper()  )

def    A_  (							a  ):

      """simple docstring"""



      return "".join(REVERSE_DICT[char] for char in message.split()  )

def    A_  (							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Tuple           =     'Morse code here!'
      print(a  )
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     encrypt(a  )
      print(a  )
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     decrypt(a  )
      print(a  )


if __name__ == "__main__":
    main()






