



"""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  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase      ):
		# Initialize PyTorch model
		_lowercase				: Union[str, Any]     =   CanineConfig()
		_lowercase				: Union[str, Any]     =   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
		_lowercase				: Optional[Any]     =   CanineTokenizer()
		print(F"""Save tokenizer files to {pytorch_dump_path}"""      )
		tokenizer.save_pretrained(__UpperCAmelCase      )


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








def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase							):
    __a					=							[1 / first_term]
    __a					=							1 / first_term
    for _ in range(number_of_terms - 1							):
        first_term += common_difference
        arithmetic_progression.append(_UpperCAmelCase							)
    __a					=							[1 / step for step in arithmetic_progression]
    return sum(_UpperCAmelCase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(sum_of_harmonic_progression(1 / 2, 2, 2))





