# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


_lowerCAmelCase        =       logging.get_logger(__name__)


if is_torch_available():
    import torch
    from torch import nn
    from torch.utils.data import Dataset

    from transformers import Trainer






    class     __UpperCAmelCase(    snake_case_				):






     """simple docstring"""




     def __init__(    self ,       __magic_name__ = 101      ):

            """simple docstring"""

            A_							:       List[Any]	      =				length
     def __len__(    self      ):

            """simple docstring"""

            return self.length
     def __getitem__(    self ,       __magic_name__      ):

            """simple docstring"""

            return i






    class     __UpperCAmelCase:






     """simple docstring"""




     def __call__(    self ,       __magic_name__      ):

            """simple docstring"""

            return {"input_ids": torch.tensor(_lowerCAmelCase      ), "labels": torch.tensor(_lowerCAmelCase      )}






    class     __UpperCAmelCase(    nn.Module				):






     """simple docstring"""




     def __init__(    self      ):

            """simple docstring"""

            super().__init__()
            # Add some (unused) params otherwise DDP will complain.
            A_							:       Optional[Any]	      =				nn.Linear(120 ,       80      )
     def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None      ):

            """simple docstring"""

            if labels is not None:
                   return torch.tensor(0.0 ,       device=input_ids.device      ), input_ids
            else:
                   return input_ids


def       a__      (					)		->	str:
       A_							:       Optional[Any]	      =				HfArgumentParser((TrainingArguments,)							)
       sys.argv += ["--output_dir", "./examples"]
       A_							:       Optional[Any]	      =				parser.parse_args_into_dataclasses()[0]

       logger.warning(
           f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
           f"""tpu_num_cores: {training_args.tpu_num_cores}"""  ,   )

       # Essentially, what we want to verify in the distributed case is
       # that we get all samples back, in the right order.
       # (this is crucial for prediction for instance)
       for dataset_length in [1_0_0_1, 2_5_6, 1_5]:
              A_							:       int	      =				DummyDataset(_SCREAMING_SNAKE_CASE							)

              def compute_metrics(a							) -> Dict:
                     A_							:       int	      =				list(range(len(_SCREAMING_SNAKE_CASE							)							)							)
                     A_							:       Tuple	      =				p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
                     return {"success": success}

              A_							:       int	      =				Trainer(
                  model=DummyModel()  ,   args=_SCREAMING_SNAKE_CASE  ,   data_collator=DummyDataCollator()  ,   eval_dataset=_SCREAMING_SNAKE_CASE  ,   compute_metrics=_SCREAMING_SNAKE_CASE  ,   )
              A_							:       str	      =				trainer.evaluate()
              logger.info(_SCREAMING_SNAKE_CASE							)
              if metrics["eval_success"] is not True:
                     logger.error(_SCREAMING_SNAKE_CASE							)
                     exit(1							)

              A_							:       Dict	      =				trainer.predict(_SCREAMING_SNAKE_CASE							)
              logger.info(p.metrics							)
              if p.metrics["test_success"] is not True:
                     logger.error(p.metrics							)
                     exit(1							)

              A_							:       Any	      =				2

              A_							:       Any	      =				trainer.evaluate()
              logger.info(_SCREAMING_SNAKE_CASE							)
              if metrics["eval_success"] is not True:
                     logger.error(_SCREAMING_SNAKE_CASE							)
                     exit(1							)

              A_							:       Union[str, Any]	      =				trainer.predict(_SCREAMING_SNAKE_CASE							)
              logger.info(p.metrics							)
              if p.metrics["test_success"] is not True:
                     logger.error(p.metrics							)
                     exit(1							)

              A_							:       str	      =				None

       logger.info('''🔥 All distributed tests successful'''							)


def       a__      (					a							)		->	str:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
    main()







def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						str					)->str:
      return " ".join(sorted(set(sentence.split()					)					)					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()