


"""simple docstring"""






import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


__snake_case												=							get_logger(__name__)
def    A_ ( _lowerCAmelCase  :       str,  _lowerCAmelCase  :       str							):






     """simple docstring"""

     if text.startswith(_lowerCAmelCase							):
          return text[len(_lowerCAmelCase							) :]
     return text  # or whatever
def    A_ ( _lowerCAmelCase  :       Dict							):






     """simple docstring"""

     return {remove_prefix(_lowerCAmelCase,  '''model.'''							): v for k, v in sd.items()}
def    A_ ( _lowerCAmelCase  :       List[Dict[str, torch.Tensor]]							):






     """simple docstring"""

     _a     						=					{}
     for k in state_dicts[0].keys():
          _a     						=					[sd[k] for sd in state_dicts]
          _a     						=					sum(_lowerCAmelCase							) / len(_lowerCAmelCase							)
          assert isinstance(_lowerCAmelCase,  torch.Tensor							)
          _a     						=					new_t
     return new_sd
def    A_ ( _lowerCAmelCase  :       str,  _lowerCAmelCase  :       str,  _lowerCAmelCase  :       str							):






     """simple docstring"""

     _a     						=					AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase							)
     if os.path.isfile(_lowerCAmelCase							):
          _a     						=					[pl_ckpt_path]
     else:
          assert os.path.isdir(_lowerCAmelCase							)
          _a     						=					list(Path(_lowerCAmelCase							).glob('''*.ckpt'''							)							)
          assert ckpt_files, f'could not find any ckpt files inside the {pl_ckpt_path} directory'

     if len(_lowerCAmelCase							) > 1:
          logger.info(f'averaging the weights of {ckpt_files}'							)

     _a     						=					[sanitize(torch.load(_lowerCAmelCase,  map_location='''cpu'''							)['''state_dict''']							) for x in ckpt_files]
     _a     						=					average_state_dicts(_lowerCAmelCase							)

     _a						,   _a     						=					hf_model.load_state_dict(_lowerCAmelCase,  strict=_lowerCAmelCase							)
     assert not missing, f'missing keys: {missing}'
     hf_model.save_pretrained(_lowerCAmelCase							)
     try:
          _a     						=					AutoTokenizer.from_pretrained(_lowerCAmelCase							)
          tok.save_pretrained(_lowerCAmelCase							)
     except Exception:
          pass
          # dont copy tokenizer if cant


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



"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    A_ ( _lowerCAmelCase  :       Dict,  _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     _a     						=					RoFormerConfig.from_json_file(_lowerCAmelCase							)
     print(f'Building PyTorch model from configuration: {config}'							)
     _a     						=					RoFormerForMaskedLM(_lowerCAmelCase							)

     # Load weights from tf checkpoint
     load_tf_weights_in_roformer(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     torch.save(model.state_dict(),  _lowerCAmelCase,  _use_new_zipfile_serialization=_lowerCAmelCase							)


if __name__ == "__main__":
   __snake_case												=							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(
       '''--bert_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained BERT model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   __snake_case												=							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)