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
  
  
  __A  :					Optional[int]												=	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				(     _A		):
  
  
  			def __init__(self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	int = 1_0_1):
  								A    =      length
  
  
  			def __len__(self				:	Dict):
  								return self.length
  
  
  			def __getitem__(self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	List[str]):
  								return i
  
  
  
  
  
  	class     __UpperCamelCase				:
  
  
  			def __call__(self				:	str  ,  __SCREAMING_SNAKE_CASE				:	Dict):
  								return {"input_ids": torch.tensor(__SCREAMING_SNAKE_CASE), "labels": torch.tensor(__SCREAMING_SNAKE_CASE)}
  
  
  
  
  
  	class     __UpperCamelCase				(     nn.Module		):
  
  
  			def __init__(self				:	Union[str, Any]):
  								super().__init__()
  								# Add some (unused) params otherwise DDP will complain.
  								A    =      nn.Linear(1_2_0  ,  8_0)
  
  
  			def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple=None):
  								if labels is not None:
  													return torch.tensor(0.0  ,  device=input_ids.device), input_ids
  								else:
  													return input_ids
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					A    =      HfArgumentParser((TrainingArguments,)   )
  					sys.argv += ["--output_dir", "./examples"]
  					A    =      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]:
  										A    =      DummyDataset(lowercase__   )
  
  										def compute_metrics(lowercase__   ) -> Dict:
  															A    =      list(range(len(lowercase__   )   )   )
  															A    =      p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
  															return {"success": success}
  
  										A    =      Trainer(
  										    model=DummyModel()    ,							args=lowercase__    ,							data_collator=DummyDataCollator()    ,							eval_dataset=lowercase__    ,							compute_metrics=lowercase__    ,							)
  										A    =      trainer.evaluate()
  										logger.info(lowercase__   )
  										if metrics["eval_success"] is not True:
  															logger.error(lowercase__   )
  															exit(1   )
  
  										A    =      trainer.predict(lowercase__   )
  										logger.info(p.metrics   )
  										if p.metrics["test_success"] is not True:
  															logger.error(p.metrics   )
  															exit(1   )
  
  										A    =      2
  
  										A    =      trainer.evaluate()
  										logger.info(lowercase__   )
  										if metrics["eval_success"] is not True:
  															logger.error(lowercase__   )
  															exit(1   )
  
  										A    =      trainer.predict(lowercase__   )
  										logger.info(p.metrics   )
  										if p.metrics["test_success"] is not True:
  															logger.error(p.metrics   )
  															exit(1   )
  
  										A    =      None
  
  					logger.info("🔥 All distributed tests successful"   )
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					# For xla_spawn (TPUs)
  					main()
  
  
  if __name__ == "__main__":
  	main()
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      1
  					while b > 0:
  										if b & 1:
  															res *= a
  
  										a *= a
  										b >>= 1
  
  					return res
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      1
  					while b > 0:
  										if b & 1:
  															A    =      ((res % c) * (a % c)) % c
  
  										a *= a
  										b >>= 1
  
  					return res
  
  
  
  
  
  