





import argparse

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


logging.set_verbosity_info()





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    List[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    CanineConfig()
			UpperCAmelCase       		=    CanineModel(UpperCamelCase__			)
			model.eval()

			print(F"""Building PyTorch model from configuration: {config}"""			)

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

			# Save pytorch-model (weights and configuration)
			print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
			model.save_pretrained(UpperCamelCase__			)

			# Save tokenizer files
			UpperCAmelCase       		=    CanineTokenizer()
			print(F"""Save tokenizer files to {pytorch_dump_path}"""			)
			tokenizer.save_pretrained(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. 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    :   str          =       parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)










def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    float:







			'''simple docstring'''

			if mass < 0:
						raise ValueError('''The mass of the body cannot be negative'''			)
			if radius <= 0:
						raise ValueError('''The radius is always a positive non zero integer'''			)
			return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)



