
'''simple docstring'''





def 							snake_case				(		UpperCAmelCase = 9_9_8_0_0_1						)->      int:







      """simple docstring"""

      # fetches the next number
      for number in range(n - 1   ,	9_9_9_9   ,	-1						):
            __A													=      str(UpperCAmelCase						)

            # checks whether 'str_number' is a palindrome.
            if str_number == str_number[::-1]:
                  __A													=      9_9_9

                  # if 'number' is a product of two 3-digit numbers
                  # then number is the answer otherwise fetch next number.
                  while divisor != 9_9:
                        if (number % divisor == 0) and (len(str(number // divisor						)						) == 3.0):
                              return number
                        divisor -= 1
      raise ValueError('That number is larger than our acceptable range.'						)


if __name__ == "__main__":
 print(f'''{solution() = }''')








'''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 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      # Initialise PyTorch model
      __A													=      RoFormerConfig.from_json_file(UpperCAmelCase						)
      print(f'Building PyTorch model from configuration: {config}'						)
      __A													=      RoFormerForMaskedLM(UpperCAmelCase						)

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

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


if __name__ == "__main__":
 a__		: Tuple  							=							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."
 )
 a__		: Union[str, Any]  							=							parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






