
import argparse

import torch

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


logging.set_verbosity_info()


def     UpperCamelCase      (					__lowerCamelCase  :				List[Any]       ,      __lowerCamelCase  :				Optional[int]       ,      __lowerCamelCase  :				int					):
							# Initialise PyTorch model
							snake_case :     Dict           =   RoFormerConfig.from_json_file(lowercase__					)
							print(f"""Building PyTorch model from configuration: {config}"""					)
							snake_case :     List[Any]           =   RoFormerForMaskedLM(lowercase__					)

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

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


if __name__ == "__main__":
			__lowerCamelCase							= 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."""
			)
			__lowerCamelCase							= parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







def     UpperCamelCase      (					__lowerCamelCase  :				list       ,      __lowerCamelCase  :				list       ,      __lowerCamelCase  :				list       ,      __lowerCamelCase  :				int       ,      __lowerCamelCase  :				int					):
       if target == 0:
              answer.append(path.copy()					)
       else:
              for index in range(__lowerCamelCase       ,      len(__lowerCamelCase					)					):
                     if target >= candidates[index]:
                            path.append(candidates[index]					)
                            backtrack(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      target - candidates[index]       ,      __lowerCamelCase					)
                            path.pop(len(__lowerCamelCase					) - 1					)


def     UpperCamelCase      (					__lowerCamelCase  :				list       ,      __lowerCamelCase  :				int					):
       snake_case :     Optional[Any]           =   []  # type: list[int]
       snake_case :     Tuple           =   []  # type: list[int]
       backtrack(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      0					)
       return answer


def     UpperCamelCase      (					):
       print(combination_sum([-8, 2.3, 0]       ,      1					)					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()





