



"""simple docstring"""


from collections.abc import Callable

snake_case_								=			Callable[[float], float]  # type alias for a real -> real function

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      ):
						UpperCAmelCase			 =					float(lowercase_		)
						while True:
												try:
																		UpperCAmelCase			 =					prev_guess - function(lowercase_		) / derivative(lowercase_		)
												except ZeroDivisionError:
																		raise ZeroDivisionError('Could not find root'		) from None
												if abs(prev_guess - next_guess		) < 10**-5:
																		return next_guess
												UpperCAmelCase			 =					next_guess

def 					_lowerCAmelCase (  lowercase_		):
						return (x**3) - (2 * x) - 5

def 					_lowerCAmelCase (  lowercase_		):
						return 3 * (x**2) - 2


if __name__ == "__main__":
				print(newton(f, fa, 3))









import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]    ,   snake_case						:     Optional[int]			)							->				List[Any]:
   # Initialize PyTorch model
   __lowerCamelCase:					str	 =						CanineConfig()
   __lowerCamelCase:					Union[str, Any]	 =						CanineModel(snake_case			)
   model.eval()

   print(f'Building PyTorch model from configuration: {config}'			)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(snake_case    ,   snake_case    ,   snake_case			)

   # Save pytorch-model (weights and configuration)
   print(f'Save PyTorch model to {pytorch_dump_path}'			)
   model.save_pretrained(snake_case			)

   # Save tokenizer files
   __lowerCamelCase:					Optional[int]	 =						CanineTokenizer()
   print(f'Save tokenizer files to {pytorch_dump_path}'			)
   tokenizer.save_pretrained(snake_case			)


if __name__ == "__main__":
   _A		:					int          =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to a folder where the PyTorch model will be placed.''',
   )
   _A		:					int          =							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


