





'''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 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Tuple:
	# Initialize PyTorch model
	snake_case__  					=      CanineConfig()
	snake_case__  					=      CanineModel(lowercase__       )
	model.eval()

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

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

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

	# Save tokenizer files
	snake_case__  					=      CanineTokenizer()
	print(f'''Save tokenizer files to {pytorch_dump_path}'''       )
	tokenizer.save_pretrained(lowercase__       )


if __name__ == "__main__":
			a__				: Tuple									=   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__				: List[str]									=   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)










'''simple docstring'''




from typing import Callable




def 	__lowerCamelCase (  UpperCAmelCase_       )      ->str:

	def wrapper(UpperCAmelCase_       ):
		snake_case__  					=      function_with_docstring.__doc__
		return target_function

	return wrapper



