"""simple docstring"""



import argparse

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


logging.set_verbosity_info()

def    _A(		lowerCAmelCase ,     lowerCAmelCase       ):
							# Initialize PyTorch model
							A__					:    Dict														= CanineConfig()
							A__					:    str														= CanineModel(a_       )
							model.eval()

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

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

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

							# Save tokenizer files
							A__					:    Tuple														= CanineTokenizer()
							print(F'''Save tokenizer files to {pytorch_dump_path}'''       )
							tokenizer.save_pretrained(a_       )


if __name__ == "__main__":
				_A         =		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         =		parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






"""simple docstring"""



def    _A(		lowerCAmelCase       ):
							if not isinstance(lowerCAmelCase ,     lowerCAmelCase       ):
														A__					:    int														= F'''Input value of [number={number}] must be an integer'''
														raise TypeError(lowerCAmelCase       )
							if number < 0 or number % 2 == 1:
														return False
							A__					:    List[Any]														= int(number**0.5       )
							return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
				import doctest

				doctest.testmod()





