

'''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__    (       A__  ):





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





       '''simple docstring'''






       super().__init__(*_a							,					**_a )
       _SCREAMING_SNAKE_CASE							    =eval_examples
       _SCREAMING_SNAKE_CASE							    =post_process_function





  def 						A  (		self      :	Union[str, Any]							,					_a      :	Optional[Dataset] = None							,					_a      :	List[str]=None							,					_a      :	Optional[List[str]] = None							,					_a      :	str = "eval"							,					**_a      :	Tuple							,					)	->    Dict[str, float]:





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE							    =self.eval_dataset if eval_dataset is None else eval_dataset
       _SCREAMING_SNAKE_CASE							    =self.get_eval_dataloader(_a )
       _SCREAMING_SNAKE_CASE							    =self.eval_examples if eval_examples is None else eval_examples

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

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

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

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

       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() )

       _SCREAMING_SNAKE_CASE							    =self.callback_handler.on_evaluate(self.args							,					self.state							,					self.control							,					_a )
       return metrics





  def 						A  (		self      :	Tuple							,					_a      :	Optional[int]							,					_a      :	List[Any]							,					_a      :	int=None							,					_a      :	str = "test"							,					**_a      :	Tuple )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =gen_kwargs.copy()

       _SCREAMING_SNAKE_CASE							    =self.get_test_dataloader(_a )

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

       _SCREAMING_SNAKE_CASE							    =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(
               _a							,					_a							,					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

       _SCREAMING_SNAKE_CASE							    =self.post_process_function(_a							,					_a							,					_a							,					'predict' )
       _SCREAMING_SNAKE_CASE							    =self.compute_metrics(_a )

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



'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def        _lowerCAmelCase					(						)      ->			Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =ArgumentParser(
         description=(
             'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
         )				)

     # Optional arguments for the launch helper
     parser.add_argument('--num_cores'							, type=_UpperCamelCase							, default=1							, help='Number of TPU cores to use (1 or 8).'				)

     # positional
     parser.add_argument(
         'training_script'							, type=_UpperCamelCase							, help=(
             'The full path to the single TPU training '
             'program/script to be launched in parallel, '
             'followed by all the arguments for the '
             'training script'
         )							, )

     # rest from the training program
     parser.add_argument('training_script_args'							, nargs=_UpperCamelCase				)

     return parser.parse_args()


def        _lowerCAmelCase					(						)      ->			Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =parse_args()

     # Import training_script as a module.
     _SCREAMING_SNAKE_CASE							    =Path(args.training_script				)
     sys.path.append(str(script_fpath.parent.resolve()				)				)
     _SCREAMING_SNAKE_CASE							    =script_fpath.stem
     _SCREAMING_SNAKE_CASE							    =importlib.import_module(_UpperCamelCase				)

     # Patch sys.argv
     _SCREAMING_SNAKE_CASE							    =[args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores				)]

     xmp.spawn(mod._mp_fn							, args=()							, nprocs=args.num_cores				)


if __name__ == "__main__":
     main()
