




from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime


class  lowerCamelCase     (		_lowerCamelCase    ):






			'''simple docstring'''





			def __init__(			self      : str							,    *lowerCamelCase_      : Any							,    **lowerCamelCase_      : int	)    -> Any:
								super().__init__(*lowerCamelCase_							,    **lowerCamelCase_	)

			def 				UpperCAmelCase__							(			self      : Dict							,    lowerCamelCase_      : List[str]							,    lowerCamelCase_      : int	)    -> Dict:
								__magic_name__				:						Tuple     				=				(
								    next_prime(value % self.size_table	)
								    if not is_prime(value % self.size_table	)
								    else value % self.size_table
								)  # gt = bigger than
								return next_prime_gt - (data % next_prime_gt)

			def 				UpperCAmelCase__							(			self      : Optional[Any]							,    lowerCamelCase_      : Any							,    lowerCamelCase_      : List[Any]							,    lowerCamelCase_      : Dict	)    -> Optional[Any]:
								return (increment * self.__hash_function_a(lowerCamelCase_							,    lowerCamelCase_	)) % self.size_table


			def 				UpperCAmelCase__							(			self      : Union[str, Any]							,    lowerCamelCase_      : Optional[Any]							,    lowerCamelCase_      : str=None	)    -> List[str]:
								__magic_name__				:						Any     				=				1
								__magic_name__				:						List[str]     				=				self.hash_function(lowerCamelCase_	)

								while self.values[new_key] is not None and self.values[new_key] != key:
													__magic_name__				:						int     				=				(
													    self.__hash_double_function(lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_	)
													    if self.balanced_factor() >= self.lim_charge
													    else None
													)
													if new_key is None:
																		break
													else:
																		i += 1

								return new_key









import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       ConvBertConfig.from_json_file(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       ConvBertModel(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       load_tf_weights_in_convbert(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )
			model.save_pretrained(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       TFConvBertModel.from_pretrained(SCREAMING_SNAKE_CASE       ,						from_pt=SCREAMING_SNAKE_CASE )
			tf_model.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		__A    :				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(
		    """--convbert_config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        """The config json file corresponding to the pre-trained ConvBERT 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    :				List[Any]    						=	parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)






