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
  
  
  _lowerCAmelCase       :      Optional[Any]							=							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_						(       _UpperCamelCase		):
  
  
  
         def __init__(  self  :			Union[str, Any]  ,			A  :			int = 1_0_1		):
              _UpperCAmelCase    :			Tuple              =					length
  
  
  
         def __len__(  self  :			Optional[int]		):
              return self.length
  
  
  
         def __getitem__(  self  :			Optional[int]  ,			A  :			Optional[int]		):
              return i
  
  
  
  
  
  
  
   class 			UpperCAmelCase_						:
  
  
  
         def __call__(  self  :			Dict  ,			A  :			List[str]		):
              return {"input_ids": torch.tensor(A		), "labels": torch.tensor(A		)}
  
  
  
  
  
  
  
   class 			UpperCAmelCase_						(       nn.Module		):
  
  
  
         def __init__(  self  :			Dict		):
              super().__init__()
              # Add some (unused) params otherwise DDP will complain.
              _UpperCAmelCase    :			Union[str, Any]              =					nn.Linear(1_2_0  ,			8_0		)
  
  
  
         def 		snake_case_      (  self  :			Union[str, Any]  ,			A  :			List[Any]  ,			A  :			Optional[int]=None		):
              if labels is not None:
                   return torch.tensor(0.0  ,			device=input_ids.device		), input_ids
              else:
                   return input_ids
  
  def 		__snake_case		(   )						->     Union[str, Any]:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Dict              =					HfArgumentParser((TrainingArguments,)       )
       sys.argv += ["--output_dir", "./examples"]
       _UpperCAmelCase    :			int              =					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_001, 256, 15]:
            _UpperCAmelCase    :			Any              =					DummyDataset(SCREAMING_SNAKE_CASE__       )
  
            def compute_metrics(SCREAMING_SNAKE_CASE__			:		EvalPrediction       ) -> Dict:
                 _UpperCAmelCase    :			Optional[int]              =					list(range(len(SCREAMING_SNAKE_CASE__       )       )       )
                 _UpperCAmelCase    :			Union[str, Any]              =					p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
                 return {"success": success}
  
            _UpperCAmelCase    :			Optional[Any]              =					Trainer(
                model=DummyModel()		,						args=SCREAMING_SNAKE_CASE__		,						data_collator=DummyDataCollator()		,						eval_dataset=SCREAMING_SNAKE_CASE__		,						compute_metrics=SCREAMING_SNAKE_CASE__		,						)
            _UpperCAmelCase    :			str              =					trainer.evaluate()
            logger.info(SCREAMING_SNAKE_CASE__       )
            if metrics["eval_success"] is not True:
                 logger.error(SCREAMING_SNAKE_CASE__       )
                 exit(1       )
  
            _UpperCAmelCase    :			Optional[int]              =					trainer.predict(SCREAMING_SNAKE_CASE__       )
            logger.info(p.metrics       )
            if p.metrics["test_success"] is not True:
                 logger.error(p.metrics       )
                 exit(1       )
  
            _UpperCAmelCase    :			Dict              =					2
  
            _UpperCAmelCase    :			str              =					trainer.evaluate()
            logger.info(SCREAMING_SNAKE_CASE__       )
            if metrics["eval_success"] is not True:
                 logger.error(SCREAMING_SNAKE_CASE__       )
                 exit(1       )
  
            _UpperCAmelCase    :			List[str]              =					trainer.predict(SCREAMING_SNAKE_CASE__       )
            logger.info(p.metrics       )
            if p.metrics["test_success"] is not True:
                 logger.error(p.metrics       )
                 exit(1       )
  
            _UpperCAmelCase    :			Any              =					None
  
       logger.info("🔥 All distributed tests successful"       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str       )						->     Any:
  
  
  
  
  
       '''simple docstring'''
  
       main()
  
  
  if __name__ == "__main__":
   main()
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import sys
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		list[int]       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       if not numbers:
            return 0
       if target == 0 and target in numbers:
            return 0
       if not isinstance(SCREAMING_SNAKE_CASE__		,						(list, tuple)       ) or not all(
           isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ) for number in numbers       ):
            raise ValueError("numbers must be an iterable of integers"       )
  
       _UpperCAmelCase    :			List[Any]              =					0
       _UpperCAmelCase    :			str              =					sys.maxsize
  
       while right < len(SCREAMING_SNAKE_CASE__       ):
            curr_sum += numbers[right]
            while curr_sum >= target and left <= right:
                 _UpperCAmelCase    :			Any              =					min(SCREAMING_SNAKE_CASE__		,						right - left + 1       )
                 curr_sum -= numbers[left]
                 left += 1
            right += 1
  
       return 0 if min_len == sys.maxsize else min_len
  