


'''simple docstring'''







import os

# Precomputes a list of the 100 first triangular numbers
lowerCAmelCase     :  Union[str, Any]											=    [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				List[str]       =							os.path.dirname(os.path.realpath(_A       )       )
       _lowerCAmelCase     :				List[Any]       =							os.path.join(_A ,    'words.txt'       )

       _lowerCAmelCase     :				int       =							''
       with open(_A       ) as f:
              _lowerCAmelCase     :				Any       =							f.readline()

       _lowerCAmelCase     :				str       =							[word.strip('"'       ) for word in words.strip('\r\n'       ).split(','       )]
       _lowerCAmelCase     :				Dict       =							[
           word
           for word in [sum(ord(_A       ) - 6_4 for x in word       ) for word in words]
           if word in TRIANGULAR_NUMBERS
       ]
       return len(_A       )


if __name__ == "__main__":
       print(solution())





'''simple docstring'''







import argparse

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


logging.set_verbosity_info()






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							TaConfig.from_json_file(_A       )
       print(f'Building PyTorch model from configuration: {config}'       )
       _lowerCAmelCase     :				Union[str, Any]       =							TaForConditionalGeneration(_A       )

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

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


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

