code: 
  
  
  
  
  
  '''simple docstring'''
  
  # 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
  
  
  lowercase_			       =				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       a_  (       snake_case_ ):
  
            '''simple docstring'''
  
  
            def __init__(	self				,	A = 101 )	->     int:
                _SCREAMING_SNAKE_CASE             =    length
  
  
            def __len__(	self )	->     Optional[int]:
                return self.length
  
  
  
  
  
            def __getitem__(	self				,	A )	->     int:
                return i
  
  
  
  
        class       a_  :
  
            '''simple docstring'''
  
  
            def __call__(	self				,	A )	->     str:
                return {"input_ids": torch.tensor(A ), "labels": torch.tensor(A )}
  
  
  
  
        class       a_  (       nn.Module ):
  
            '''simple docstring'''
  
  
            def __init__(	self )	->     int:
                super().__init__()
                # Add some (unused) params otherwise DDP will complain.
                _SCREAMING_SNAKE_CASE             =    nn.Linear(120				,	80 )
  
  
  
  
  
            def     snake_case_(	self				,	A				,	A=None )	->     int:
                if labels is not None:
                    return torch.tensor(0.0				,	device=input_ids.device ), input_ids
                else:
                    return input_ids
  
  
  
  
  
  def lowerCamelCase		(  )		->str:
      _SCREAMING_SNAKE_CASE             =    HfArgumentParser((TrainingArguments,)					)
      sys.argv += ["--output_dir", "./examples"]
      _SCREAMING_SNAKE_CASE             =    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 [1001, 256, 15]:
          _SCREAMING_SNAKE_CASE             =    DummyDataset(__lowerCamelCase					)
  
          def compute_metrics(__lowerCamelCase				:					EvalPrediction					) -> Dict:
              _SCREAMING_SNAKE_CASE             =    list(range(len(__lowerCamelCase					)					)					)
              _SCREAMING_SNAKE_CASE             =    p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
              return {"success": success}
  
          _SCREAMING_SNAKE_CASE             =    Trainer(
              model=DummyModel()      , args=__lowerCamelCase      , data_collator=DummyDataCollator()      , eval_dataset=__lowerCamelCase      , compute_metrics=__lowerCamelCase      , )
          _SCREAMING_SNAKE_CASE             =    trainer.evaluate()
          logger.info(__lowerCamelCase					)
          if metrics["eval_success"] is not True:
              logger.error(__lowerCamelCase					)
              exit(1					)
  
          _SCREAMING_SNAKE_CASE             =    trainer.predict(__lowerCamelCase					)
          logger.info(p.metrics					)
          if p.metrics["test_success"] is not True:
              logger.error(p.metrics					)
              exit(1					)
  
          _SCREAMING_SNAKE_CASE             =    2
  
          _SCREAMING_SNAKE_CASE             =    trainer.evaluate()
          logger.info(__lowerCamelCase					)
          if metrics["eval_success"] is not True:
              logger.error(__lowerCamelCase					)
              exit(1					)
  
          _SCREAMING_SNAKE_CASE             =    trainer.predict(__lowerCamelCase					)
          logger.info(p.metrics					)
          if p.metrics["test_success"] is not True:
              logger.error(p.metrics					)
              exit(1					)
  
          _SCREAMING_SNAKE_CASE             =    None
  
      logger.info("""🔥 All distributed tests successful"""					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Any					)		->Any:
      # For xla_spawn (TPUs)
      main()
  
  
  if __name__ == "__main__":
        main()
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import math
  from timeit import timeit
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
      _SCREAMING_SNAKE_CASE             =    0
      _SCREAMING_SNAKE_CASE             =    abs(__lowerCamelCase					)
      while True:
          _SCREAMING_SNAKE_CASE             =    n // 10
          digits += 1
          if n == 0:
              break
      return digits
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
      return 1 if n == 0 else math.floor(math.log(abs(__lowerCamelCase					)      , 10					) + 1					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
      return len(str(abs(__lowerCamelCase					)					)					)
  
  
  
  
  
  def lowerCamelCase		(  )		->None:
      from collections.abc import Callable
  
      def benchmark_a_function(__lowerCamelCase				:					Callable      , __lowerCamelCase				:					int					) -> None:
          _SCREAMING_SNAKE_CASE             =    F'{func.__name__}({value})'
          _SCREAMING_SNAKE_CASE             =    timeit(F'__main__.{call}'      , setup="""import __main__"""					)
          print(F'{call}: {func(__lowerCamelCase					)} -- {timing} seconds'					)
  
      for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
          for func in (num_digits, num_digits_fast, num_digits_faster):
              benchmark_a_function(__lowerCamelCase      , __lowerCamelCase					)
          print()
  
  
  if __name__ == "__main__":
        import doctest
  
        doctest.testmod()
        benchmark()
  
  
  
  