






"""simple docstring"""



import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm
 import torch_xla.debug.metrics as met




class 			lowerCAmelCase__			(    __magic_name__   ):
   def __init__(		self      :	str							,			*snake_case__      :	List[str]							,			snake_case__      :	Any=None							,			snake_case__      :	Union[str, Any]=None							,			**snake_case__      :	Any							):


        '''simple docstring'''
        super().__init__(*snake_case__							,			**snake_case__							)
        UpperCAmelCase__						:    int							=							eval_examples
        UpperCAmelCase__						:    Union[str, Any]							=							post_process_function
   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	Optional[Dataset] = None							,			snake_case__      :	Tuple=None							,			snake_case__      :	Optional[List[str]] = None							,			snake_case__      :	str = "eval"							,			**snake_case__      :	Union[str, Any]							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    int							=							gen_kwargs.copy()
        UpperCAmelCase__						:    List[str]							=							(
            gen_kwargs["max_length"] if gen_kwargs.get("max_length"							) is not None else self.args.generation_max_length
        )
        UpperCAmelCase__						:    Optional[Any]							=							(
            gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"							) is not None else self.args.generation_num_beams
        )
        UpperCAmelCase__						:    Optional[int]							=							gen_kwargs

        UpperCAmelCase__						:    List[Any]							=							self.eval_dataset if eval_dataset is None else eval_dataset
        UpperCAmelCase__						:    int							=							self.get_eval_dataloader(snake_case__							)
        UpperCAmelCase__						:    Dict							=							self.eval_examples if eval_examples is None else eval_examples

        # Temporarily disable metric computation, we will do it in the loop here.
        UpperCAmelCase__						:    Union[str, Any]							=							self.compute_metrics
        UpperCAmelCase__						:    Union[str, Any]							=							None
        UpperCAmelCase__						:    Any							=							time.time()
        UpperCAmelCase__						:    Union[str, Any]							=							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
        try:
             UpperCAmelCase__						:    List[Any]							=							eval_loop(
                 snake_case__							,			description="Evaluation"							,			prediction_loss_only=True if compute_metrics is None else None							,			ignore_keys=snake_case__							,			metric_key_prefix=snake_case__							,			)
        finally:
             UpperCAmelCase__						:    List[Any]							=							compute_metrics
        UpperCAmelCase__						:    Union[str, Any]							=							self.args.eval_batch_size * self.args.world_size
        if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
             start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
        output.metrics.update(
            speed_metrics(
                snake_case__							,			snake_case__							,			num_samples=output.num_samples							,			num_steps=math.ceil(output.num_samples / total_batch_size							)							,			)							)

        if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
             # Only the main node write the results by default
             UpperCAmelCase__						:    Dict							=							self.post_process_function(snake_case__							,			snake_case__							,			snake_case__							)
             UpperCAmelCase__						:    Union[str, Any]							=							self.compute_metrics(snake_case__							)

             # Prefix all keys with metric_key_prefix + '_'
             for key in list(metrics.keys()							):
                  if not key.startswith(f'{metric_key_prefix}_'							):
                       UpperCAmelCase__						:    int							=							metrics.pop(snake_case__							)

             metrics.update(output.metrics							)
        else:
             UpperCAmelCase__						:    Tuple							=							output.metrics

        if self.args.should_log:
             # Only the main node log the results by default
             self.log(snake_case__							)

        if self.args.tpu_metrics_debug or self.args.debug:
             # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
             xm.master_print(met.metrics_report()							)

        UpperCAmelCase__						:    int							=							self.callback_handler.on_evaluate(self.args							,			self.state							,			self.control							,			snake_case__							)
        return metrics




   def       __a							(		self      :	List[str]							,			snake_case__      :	Dict							,			snake_case__      :	Union[str, Any]							,			snake_case__      :	Union[str, Any]=None							,			snake_case__      :	str = "test"							,			**snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							gen_kwargs.copy()

        UpperCAmelCase__						:    int							=							self.get_test_dataloader(snake_case__							)

        # Temporarily disable metric computation, we will do it in the loop here.
        UpperCAmelCase__						:    int							=							self.compute_metrics
        UpperCAmelCase__						:    Tuple							=							None
        UpperCAmelCase__						:    Union[str, Any]							=							time.time()
        UpperCAmelCase__						:    List[Any]							=							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
        try:
             UpperCAmelCase__						:    str							=							eval_loop(
                 snake_case__							,			description="Prediction"							,			prediction_loss_only=True if compute_metrics is None else None							,			ignore_keys=snake_case__							,			metric_key_prefix=snake_case__							,			)
        finally:
             UpperCAmelCase__						:    List[Any]							=							compute_metrics

        UpperCAmelCase__						:    str							=							self.args.eval_batch_size * self.args.world_size
        if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
             start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
        output.metrics.update(
            speed_metrics(
                snake_case__							,			snake_case__							,			num_samples=output.num_samples							,			num_steps=math.ceil(output.num_samples / total_batch_size							)							,			)							)
        if self.post_process_function is None or self.compute_metrics is None:
             return output

        UpperCAmelCase__						:    Any							=							self.post_process_function(snake_case__							,			snake_case__							,			snake_case__							,			"predict"							)
        UpperCAmelCase__						:    int							=							self.compute_metrics(snake_case__							)

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()							):
             if not key.startswith(f'{metric_key_prefix}_'							):
                  UpperCAmelCase__						:    Optional[int]							=							metrics.pop(snake_case__							)
        metrics.update(output.metrics							)
        return PredictionOutput(predictions=predictions.predictions							,			label_ids=predictions.label_ids							,			metrics=snake_case__							)










"""simple docstring"""



import os


def 	SCREAMING_SNAKE_CASE__       (  )->		Optional[Any]:






     '''simple docstring'''



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

     names.sort()

     UpperCAmelCase__						:    Optional[int]							=							0
     UpperCAmelCase__						:    List[str]							=							0

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

          total_score += (i + 1) * name_score
          UpperCAmelCase__						:    Optional[int]							=							0
     return total_score


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


