'''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							(	lowerCamelCase					):




  '''simple docstring'''



  def __init__(      self			:       Optional[Any]  ,							*UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       Union[str, Any]=None  ,							UpperCAmelCase__			:       Any=None  ,							**UpperCAmelCase__			:       Tuple  )				->List[str]:
        super().__init__(*UpperCAmelCase__  ,							**UpperCAmelCase__  )
        UpperCAmelCase_            =  eval_examples
        UpperCAmelCase_            =  post_process_function






  def  lowerCAmelCase__ (      self			:       List[str]  ,							UpperCAmelCase__			:       Optional[Dataset] = None  ,							UpperCAmelCase__			:       Union[str, Any]=None  ,							UpperCAmelCase__			:       Optional[List[str]] = None  ,							UpperCAmelCase__			:       str = "eval"  ,							**UpperCAmelCase__			:       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(UpperCAmelCase__  )
        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(
                  UpperCAmelCase__  ,							description='''Evaluation'''  ,							prediction_loss_only=True if compute_metrics is None else None  ,							ignore_keys=UpperCAmelCase__  ,							metric_key_prefix=UpperCAmelCase__  ,							)
        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(
                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
              UpperCAmelCase_            =  self.post_process_function(UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )
              UpperCAmelCase_            =  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}_"""  ):
                          UpperCAmelCase_            =  metrics.pop(UpperCAmelCase__  )

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

        UpperCAmelCase_            =  self.callback_handler.on_evaluate(self.args  ,							self.state  ,							self.control  ,							UpperCAmelCase__  )
        return metrics






  def  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       Tuple=None  ,							UpperCAmelCase__			:       str = "test"  ,							**UpperCAmelCase__			:       List[str]  )				->Optional[int]:
        UpperCAmelCase_            =  gen_kwargs.copy()

        UpperCAmelCase_            =  self.get_test_dataloader(UpperCAmelCase__  )

        # 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(
                  UpperCAmelCase__  ,							description='''Prediction'''  ,							prediction_loss_only=True if compute_metrics is None else None  ,							ignore_keys=UpperCAmelCase__  ,							metric_key_prefix=UpperCAmelCase__  ,							)
        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(
                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

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






'''simple docstring'''
import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowercase__  :      Tuple        =					pytest.mark.integration



@pytest.mark.parametrize('''path'''							,    ['''paws''', '''csv''']					)
def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      inspect_dataset(_UpperCamelCase							,    _UpperCamelCase					)
      UpperCAmelCase_            =  path + '''.py'''
      assert script_name in os.listdir(_UpperCamelCase					)
      assert "__pycache__" not in os.listdir(_UpperCamelCase					)



@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning'''					)
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''					)
@pytest.mark.parametrize('''path'''							,    ['''accuracy''']					)
def  __lowerCamelCase       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      inspect_metric(_UpperCamelCase							,    _UpperCamelCase					)
      UpperCAmelCase_            =  path + '''.py'''
      assert script_name in os.listdir(_UpperCamelCase					)
      assert "__pycache__" not in os.listdir(_UpperCamelCase					)



@pytest.mark.parametrize(
    '''path, config_name, expected_splits'''							,    [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_config_info(_UpperCamelCase							,    config_name=_UpperCamelCase					)
      assert info.config_name == config_name
      assert list(info.splits.keys()					) == expected_splits



@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''							,    [
        ('''paws''', None, ValueError),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  Union[str, Any]					):


      '''simple docstring'''


      with pytest.raises(_UpperCamelCase					):
            get_dataset_config_info(_UpperCamelCase							,    config_name=_UpperCamelCase					)



@pytest.mark.parametrize(
    '''path, expected'''							,    [
        ('''squad''', '''plain_text'''),
        ('''acronym_identification''', '''default'''),
        ('''lhoestq/squad''', '''plain_text'''),
        ('''lhoestq/test''', '''default'''),
        ('''lhoestq/demo1''', '''lhoestq--demo1'''),
        ('''dalle-mini/wit''', '''dalle-mini--wit'''),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_config_names(_UpperCamelCase					)
      assert expected in config_names



@pytest.mark.parametrize(
    '''path, expected_configs, expected_splits_in_first_config'''							,    [
        ('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
        ('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
        ('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_infos(_UpperCamelCase					)
      assert list(infos.keys()					) == expected_configs
      UpperCAmelCase_            =  expected_configs[0]
      assert expected_config in infos
      UpperCAmelCase_            =  infos[expected_config]
      assert info.config_name == expected_config
      assert list(info.splits.keys()					) == expected_splits_in_first_config



@pytest.mark.parametrize(
    '''path, expected_config, expected_splits'''							,    [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_infos(_UpperCamelCase					)
      assert expected_config in infos
      UpperCAmelCase_            =  infos[expected_config]
      assert info.config_name == expected_config
      assert list(info.splits.keys()					) == expected_splits





@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''							,    [
        ('''paws''', None, ValueError),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  Optional[int]					):


      '''simple docstring'''


      with pytest.raises(_UpperCamelCase					):
            get_dataset_split_names(_UpperCamelCase							,    config_name=_UpperCamelCase					)





