

'''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 					A__      (      _snake_case			):



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





           '''simple docstring'''
           super().__init__(*UpperCamelCase__		,				**UpperCamelCase__       )
           A_						=      eval_examples
           A_						=      post_process_function



    def 					snake_case_			(					self		,				UpperCamelCase__ = None		,				UpperCamelCase__=None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = "eval"		,				**UpperCamelCase__		,				)						->      Dict[str, float]:





           '''simple docstring'''
           A_						=      gen_kwargs.copy()
           A_						=      (
               gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""       ) is not None else self.args.generation_max_length
           )
           A_						=      (
               gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""       ) is not None else self.args.generation_num_beams
           )
           A_						=      gen_kwargs

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

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

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

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







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=None		,				UpperCamelCase__ = "test"		,				**UpperCamelCase__       )						->      Dict:





           '''simple docstring'''
           A_						=      gen_kwargs.copy()

           A_						=      self.get_test_dataloader(UpperCamelCase__       )

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

           A_						=      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

           A_						=      self.post_process_function(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				"""predict"""       )
           A_						=      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}_'''       ):
                         A_						=      metrics.pop(UpperCamelCase__       )
           metrics.update(output.metrics       )
           return PredictionOutput(predictions=predictions.predictions		,				label_ids=predictions.label_ids		,				metrics=UpperCamelCase__       )



'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			float:
       _validate_point(UpperCAmelCase__				)
       _validate_point(UpperCAmelCase__				)
       if len(UpperCAmelCase__				) != len(UpperCAmelCase__				):
              raise ValueError("""Both points must be in the same n-dimensional space"""				)

       return float(sum(abs(a - b				) for a, b in zip(UpperCAmelCase__,       UpperCAmelCase__				)				)				)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			None:
       if point:
              if isinstance(UpperCAmelCase__,       UpperCAmelCase__				):
                     for item in point:
                            if not isinstance(UpperCAmelCase__,       (int, float)				):
                                   A_						=      (
                                       """Expected a list of numbers as input, found """
                                       F'''{type(UpperCAmelCase__				).__name__}'''
                                   )
                                   raise TypeError(UpperCAmelCase__				)
              else:
                     A_						=      F'''Expected a list of numbers as input, found {type(UpperCAmelCase__				).__name__}'''
                     raise TypeError(UpperCAmelCase__				)
       else:
              raise ValueError("""Missing an input"""				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			float:
       _validate_point(UpperCAmelCase__				)
       _validate_point(UpperCAmelCase__				)
       if len(UpperCAmelCase__				) != len(UpperCAmelCase__				):
              raise ValueError("""Both points must be in the same n-dimensional space"""				)

       return float(sum(abs(x - y				) for x, y in zip(UpperCAmelCase__,       UpperCAmelCase__				)				)				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
