




'''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_							(   __snake_case							:    int     , __snake_case							:    str     , __snake_case							:    Dict     )							->		int:



       """simple docstring"""
       # Initialise PyTorch model
       lowerCamelCase_							=RoFormerConfig.from_json_file(UpperCamelCase__     )
       print(F'''Building PyTorch model from configuration: {config}'''     )
       lowerCamelCase_							=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_   :	List[str]							   = 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_   :	Dict							   = parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)










'''simple docstring'''




from .hash_table import HashTable


class 						__UpperCamelCase (				lowerCamelCase__       ):






       def __init__(							self,	*lowerCAmelCase,	**lowerCAmelCase						):



              """simple docstring"""







              super().__init__(*lowerCAmelCase,	**lowerCAmelCase						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase=None						):



              """simple docstring"""







              lowerCamelCase_							=1
              lowerCamelCase_							=self.hash_function(key + i * i						)

              while self.values[new_key] is not None and self.values[new_key] != key:
                     i += 1
                     lowerCamelCase_							=(
                         self.hash_function(key + i * i						)
                         if not self.balanced_factor() >= self.lim_charge
                         else None
                     )

                     if new_key is None:
                            break

              return new_key




