





"""simple docstring"""




import math
from collections.abc import Callable





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : float         =   xa
						_UpperCamelCase      : float         =   xa
						while True:
												if x_n == x_na or function(_lowercase       ) == function(_lowercase       ):
																		raise ZeroDivisionError('''float division by zero, could not find root'''       )
												_UpperCamelCase      : float         =   x_na - (
												    function(_lowercase       ) / ((function(_lowercase       ) - function(_lowercase       )) / (x_na - x_n))
												)
												if abs(x_na - x_na       ) < 10**-5:
																		return x_na
												_UpperCamelCase      : Union[str, Any]         =   x_na
												_UpperCamelCase      : List[Any]         =   x_na





def 	a_    (							_lowercase       ):
						return math.pow(_lowercase	,						3       ) - (2 * x) - 5


if __name__ == "__main__":
						print(intersection(f, 3, 3.5))










"""simple docstring"""




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						# Initialise PyTorch model
						_UpperCamelCase      : List[Any]         =   MobileBertConfig.from_json_file(_lowercase       )
						print(F"""Building PyTorch model from configuration: {config}"""       )
						_UpperCamelCase      : List[str]         =   MobileBertForPreTraining(_lowercase       )
						# Load weights from tf checkpoint
						_UpperCamelCase      : Union[str, Any]         =   load_tf_weights_in_mobilebert(_lowercase	,						_lowercase	,						_lowercase       )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
						torch.save(model.state_dict()	,						_lowercase       )


if __name__ == "__main__":
						UpperCamelCase_									=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(
						    """--mobilebert_config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """The config json file corresponding to the pre-trained MobileBERT 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."""
						)
						UpperCamelCase_									=parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



