


"""simple docstring"""






from unittest.mock import patch

import datasets
from datasets import Dataset
def    A_ ( ):






     """simple docstring"""

     _a     						=					Dataset.from_dict({'''col_1''': [3, 2, 0, 1]}							)

     with patch('''tqdm.auto.tqdm'''							) as mock_tqdm:
          datasets.disable_progress_bar()
          dset.map(lambda _lowerCAmelCase							: {"col_2": x["col_1"] + 1}							)
          mock_tqdm.assert_not_called()

          mock_tqdm.reset_mock()

          datasets.enable_progress_bar()
          dset.map(lambda _lowerCAmelCase							: {"col_2": x["col_1"] + 1}							)
          mock_tqdm.assert_called()



"""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)