




'''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 (   snake_case__ ,				snake_case__						):
   __UpperCamelCase				:      int									=       CanineConfig()
   __UpperCamelCase				:      List[Any]									=       CanineModel(SCREAMING_SNAKE_CASE_						)
   model.eval()

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

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

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

   # Save tokenizer files
   __UpperCamelCase				:      Optional[Any]									=       CanineTokenizer()
   print(F"Save tokenizer files to {pytorch_dump_path}"						)
   tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_						)


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









'''simple docstring'''






def       __lowerCAmelCase (   snake_case__ = 4_000_000						):
			__UpperCamelCase				:      Tuple									=       1
			__UpperCamelCase				:      int									=       2
			__UpperCamelCase				:      str									=       0
			while j <= n:
						if j % 2 == 0:
									total += j
						__UpperCamelCase		, __UpperCamelCase				:      List[Any]									=       j, i + j

			return total


if __name__ == "__main__":
				print(f'{solution() = }')



