


import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


SCREAMING_SNAKE_CASE			: List[str]				   =					["small", "medium", "large"]

SCREAMING_SNAKE_CASE			: Any				   =					"lm_head.decoder.weight"
SCREAMING_SNAKE_CASE			: Optional[Any]				   =					"lm_head.weight"



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		int:
    _lowercase			:				Union[str, Any] 						=						torch.load(lowerCamelCase_					)
    _lowercase			:				List[str] 						=						d.pop(lowerCamelCase_					)
    os.makedirs(lowerCamelCase_				,					exist_ok=lowerCamelCase_					)
    torch.save(lowerCamelCase_				,					os.path.join(lowerCamelCase_				,					lowerCamelCase_					)					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Optional[int]				   =					argparse.ArgumentParser()
 parser.add_argument("--dialogpt_path", default=".", type=str)
 SCREAMING_SNAKE_CASE			: str				   =					parser.parse_args()
 for MODEL in DIALOGPT_MODELS:
  SCREAMING_SNAKE_CASE			: Dict				   =					os.path.join(args.dialogpt_path, F"{MODEL}_ft.pkl")
  SCREAMING_SNAKE_CASE			: Union[str, 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 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    # Initialise PyTorch model
    _lowercase			:				Optional[int] 						=						TaConfig.from_json_file(lowerCamelCase_					)
    print(F'''Building PyTorch model from configuration: {config}'''					)
    _lowercase			:				Union[str, Any] 						=						TaForConditionalGeneration(lowerCamelCase_					)

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

    # Save pytorch-model
    print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
    model.save_pretrained(lowerCamelCase_					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Optional[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."
 )
 SCREAMING_SNAKE_CASE			: Any				   =					parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


