

import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


lowerCAmelCase     :   Tuple						     =  ['small', 'medium', 'large']

lowerCAmelCase     :   str						     =  'lm_head.decoder.weight'
lowerCAmelCase     :   List[Any]						     =  'lm_head.weight'

def    A_  (							a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  str           =     torch.load(a  )
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     d.pop(a  )
      os.makedirs(a					,   exist_ok=a  )
      torch.save(a					,   os.path.join(a					,   a  )  )


if __name__ == "__main__":
    lowerCAmelCase     :   List[Any]						     =  argparse.ArgumentParser()
    parser.add_argument('--dialogpt_path', default='.', type=str)
    lowerCAmelCase     :   List[str]						     =  parser.parse_args()
    for MODEL in DIALOGPT_MODELS:
        lowerCAmelCase     :   Union[str, Any]						     =  os.path.join(args.dialogpt_path, F'{MODEL}_ft.pkl')
        lowerCAmelCase     :   List[Any]						     =  F'./DialoGPT-{MODEL}'
        convert_dialogpt_checkpoint(
            checkpoint_path,
            pytorch_dump_folder_path,
        )









import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  int           =     TaConfig.from_json_file(a  )
      print(f"Building PyTorch model from configuration: {config}"  )
      SCREAMING_SNAKE_CASE_				:  Tuple           =     TaForConditionalGeneration(a  )

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(a					,   a					,   a  )

      # Save pytorch-model
      print(f"Save PyTorch model to {pytorch_dump_path}"  )
      model.save_pretrained(a  )


if __name__ == "__main__":
    lowerCAmelCase     :   int						     =  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    lowerCAmelCase     :   List[str]						     =  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






