






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_  )   ->  Union[str, Any]:


 """simple docstring"""



 a		=			TaConfig.from_json_file(_UpperCamelCase  )
 print(f"""Building PyTorch model from configuration: {config}"""  )
 a		=			TaForConditionalGeneration(_UpperCamelCase  )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(_UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase  )

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""  )
 model.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
    UpperCamelCase__						:      List[str]								=					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--config_file""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
        ),
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    UpperCamelCase__						:      List[str]								=					parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)













def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  int:


	"""simple docstring"""



	return n * (2 * n - 1)





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  bool:


	"""simple docstring"""



	a		=			(1 + 2_4 * n) ** 0.5
	return ((1 + root) / 6) % 1 == 0





def   SCREAMING_SNAKE_CASE__						(   snake_case_ = 1_4_4  )   ->  int:


	"""simple docstring"""



	a		=			start
	a		=			hexagonal_num(snake_case_  )
	while not is_pentagonal(snake_case_  ):
		n += 1
		a		=			hexagonal_num(snake_case_  )
	return num


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





