"""simple docstring"""





import os





def 				a_     (      ):





       '''simple docstring'''

       with open(os.path.dirname(_lowerCAmelCase     ) + '/p022_names.txt'     ) as file:
              lowercase__			:		int       =	str(file.readlines()[0]     )
              lowercase__			:		Tuple       =	names.replace('\"'   , ''     ).split(','     )

       names.sort()

       lowercase__			:		Union[str, Any]       =	0
       lowercase__			:		List[str]       =	0

       for i, name in enumerate(_lowerCAmelCase     ):
              for letter in name:
                     name_score += ord(_lowerCAmelCase     ) - 64

              total_score += (i + 1) * name_score
              lowercase__			:		List[Any]       =	0
       return total_score


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






'''simple docstring'''





import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__a          =		logging.getLogger(__name__)







@dataclass
@add_start_docstrings(TrainingArguments.__doc__	)
class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		field(default=_a		,		metadata={"help": "Whether to use SortishSampler or not."}	)
    lowercase					=		field(
        default=_a		,		metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}	)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `max_length` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `num_beams` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
        }		,		)




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    int    						=      super().to_dict()
          for k, v in d.items():
                if isinstance(snake_case_ ,     snake_case_						):
                      snake_case__  :    Optional[int]    						=      v.to_dict()
          return d
