
import math
import time

from transformers import Trainer, 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        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  def __init__( self			: Any			,							*UpperCamelCase__			: Any			,							UpperCamelCase__			: Union[str, Any]=None			,							UpperCamelCase__			: List[Any]=None			,							**UpperCamelCase__			: Union[str, Any]    )			->	Any:







    """simple docstring"""






    super().__init__(*UpperCamelCase__			,							**UpperCamelCase__    )
    __magic_name__				     =       eval_examples
    __magic_name__				     =       post_process_function
  def 							_lowercase							( self			: Optional[int]			,							UpperCamelCase__			: Optional[int]=None			,							UpperCamelCase__			: str=None			,							UpperCamelCase__			: List[Any]=None			,							UpperCamelCase__			: str = "eval"    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       self.eval_dataset if eval_dataset is None else eval_dataset
    __magic_name__				     =       self.get_eval_dataloader(UpperCamelCase__    )
    __magic_name__				     =       self.eval_examples if eval_examples is None else eval_examples

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

      # Prefix all keys with metric_key_prefix + '_'
      for key in list(metrics.keys()    ):
        if not key.startswith(F'''{metric_key_prefix}_'''    ):
          __magic_name__				     =       metrics.pop(UpperCamelCase__    )
      metrics.update(output.metrics    )
    else:
      __magic_name__				     =       output.metrics

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

    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()    )

    __magic_name__				     =       self.callback_handler.on_evaluate(self.args			,							self.state			,							self.control			,							UpperCamelCase__    )
    return metrics





  def 							_lowercase							( self			: Dict			,							UpperCamelCase__			: Any			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: Optional[Any]=None			,							UpperCamelCase__			: str = "test"    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       self.get_test_dataloader(UpperCamelCase__    )

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

    __magic_name__				     =       self.post_process_function(UpperCamelCase__			,							UpperCamelCase__			,							output.predictions			,							"""predict"""    )
    __magic_name__				     =       self.compute_metrics(UpperCamelCase__    )

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



from __future__ import annotations

from collections.abc import Iterator




class        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: Dict			,							UpperCamelCase__			: int    )			->	None:







    """simple docstring"""






    __magic_name__				     =       value
    __magic_name__				     =       None
    __magic_name__				     =       None







class        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: Union[str, Any]			,							UpperCamelCase__			: Node    )			->	None:







    """simple docstring"""






    __magic_name__				     =       tree
  def 							_lowercase							( self			: Optional[Any]			,							UpperCamelCase__			: Node | None    )			->	int:







    """simple docstring"""






    if node is None:
      return 0
    return node.value + (
        self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
    )





  def __iter__( self			: int    )			->	Iterator[int]:







    """simple docstring"""






    yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

