



import argparse

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


logging.set_verbosity_info()






def 			__lowercase     (				a__							,     a__     )       ->				int:
							# Initialize PyTorch model
							__SCREAMING_SNAKE_CASE								=      CanineConfig()
							__SCREAMING_SNAKE_CASE								=      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
							__SCREAMING_SNAKE_CASE								=      CanineTokenizer()
							print(f"""Save tokenizer files to {pytorch_dump_path}"""     )
							tokenizer.save_pretrained(snake_case__     )


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




def 			__lowercase     (				a__     )       ->				int:
							return int(a__     ) if x - int(a__     ) <= 0 else int(a__     ) + 1


if __name__ == "__main__":
			import doctest

			doctest.testmod()






