






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 				_A			(     UpperCamelCase				):
    """simple docstring"""

    def __init__(				self      :     Optional[int] ,     *__SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=None ,     __SCREAMING_SNAKE_CASE      :     Tuple=None ,     **__SCREAMING_SNAKE_CASE      :     Tuple      )      ->							Any:
           super().__init__(*__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=eval_examples
           __UpperCAmelCase     				=post_process_function



    def _a							(				self      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Optional[Dataset] = None ,     __SCREAMING_SNAKE_CASE      :     List[str]=None ,     __SCREAMING_SNAKE_CASE      :     Optional[List[str]] = None ,     __SCREAMING_SNAKE_CASE      :     str = "eval" ,     **__SCREAMING_SNAKE_CASE      :     str ,     )      ->							Dict[str, float]:
           __UpperCAmelCase     				=gen_kwargs.copy()
           __UpperCAmelCase     				=(
               gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""      ) is not None else self.args.generation_max_length
           )
           __UpperCAmelCase     				=(
               gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""      ) is not None else self.args.generation_num_beams
           )
           __UpperCAmelCase     				=gen_kwargs

           __UpperCAmelCase     				=self.eval_dataset if eval_dataset is None else eval_dataset
           __UpperCAmelCase     				=self.get_eval_dataloader(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=self.eval_examples if eval_examples is None else eval_examples

           # Temporarily disable metric computation, we will do it in the loop here.
           __UpperCAmelCase     				=self.compute_metrics
           __UpperCAmelCase     				=None
           __UpperCAmelCase     				=time.time()
           __UpperCAmelCase     				=self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
                  __UpperCAmelCase     				=eval_loop(
                      __SCREAMING_SNAKE_CASE ,     description="""Evaluation""" ,     prediction_loss_only=True if compute_metrics is None else None ,     ignore_keys=__SCREAMING_SNAKE_CASE ,     metric_key_prefix=__SCREAMING_SNAKE_CASE ,     )
           finally:
                  __UpperCAmelCase     				=compute_metrics
           __UpperCAmelCase     				=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(
                   __SCREAMING_SNAKE_CASE ,     __SCREAMING_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     				=self.post_process_function(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )
                  __UpperCAmelCase     				=self.compute_metrics(__SCREAMING_SNAKE_CASE      )

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

                  metrics.update(output.metrics      )
           else:
                  __UpperCAmelCase     				=output.metrics

           if self.args.should_log:
                  # Only the main node log the results by default
                  self.log(__SCREAMING_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     				=self.callback_handler.on_evaluate(self.args ,     self.state ,     self.control ,     __SCREAMING_SNAKE_CASE      )
           return metrics



    def _a							(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     Optional[Any] ,     __SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     int=None ,     __SCREAMING_SNAKE_CASE      :     str = "test" ,     **__SCREAMING_SNAKE_CASE      :     List[Any]      )      ->							Dict:
           __UpperCAmelCase     				=gen_kwargs.copy()

           __UpperCAmelCase     				=self.get_test_dataloader(__SCREAMING_SNAKE_CASE      )

           # Temporarily disable metric computation, we will do it in the loop here.
           __UpperCAmelCase     				=self.compute_metrics
           __UpperCAmelCase     				=None
           __UpperCAmelCase     				=time.time()
           __UpperCAmelCase     				=self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
                  __UpperCAmelCase     				=eval_loop(
                      __SCREAMING_SNAKE_CASE ,     description="""Prediction""" ,     prediction_loss_only=True if compute_metrics is None else None ,     ignore_keys=__SCREAMING_SNAKE_CASE ,     metric_key_prefix=__SCREAMING_SNAKE_CASE ,     )
           finally:
                  __UpperCAmelCase     				=compute_metrics

           __UpperCAmelCase     				=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(
                   __SCREAMING_SNAKE_CASE ,     __SCREAMING_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     				=self.post_process_function(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     """predict"""      )
           __UpperCAmelCase     				=self.compute_metrics(__SCREAMING_SNAKE_CASE      )

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










"""simple docstring"""

def 		_snake_case	(   _snake_case				:   str				)       ->   str:






      '''simple docstring'''


      return " ".join(
          ''.join(word[::-1]				) if len(_snake_case				) > 4 else word for word in sentence.split()				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(reverse_long_words('''Hey wollef sroirraw'''))

