




# 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


__SCREAMING_SNAKE_CASE     : Tuple      =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      (		_a		):
           """simple docstring"""

           def __init__(     self	: Dict						,	A__	: int = 1_0_1      )					->							str:
                 lowerCamelCase__   	=					length




           def __len__(     self	: str      )					->							Optional[Any]:
                 return self.length




           def __getitem__(     self	: Union[str, Any]						,	A__	: Union[str, Any]      )					->							int:
                 return i






      class       A      :
           """simple docstring"""

           def __call__(     self	: Tuple						,	A__	: List[str]      )					->							Dict:
                 return {"input_ids": torch.tensor(A__      ), "labels": torch.tensor(A__      )}






      class       A      (		nn.Module		):
           """simple docstring"""

           def __init__(     self	: Dict      )					->							Any:
                 super().__init__()
                 # Add some (unused) params otherwise DDP will complain.
                 lowerCamelCase__   	=					nn.Linear(1_2_0						,	8_0      )




           def 			__snake_case (     self	: Optional[int]						,	A__	: int						,	A__	: Dict=None      )					->							List[Any]:
                 if labels is not None:
                       return torch.tensor(0.0						,	device=input_ids.device      ), input_ids
                 else:
                       return input_ids

def       lowerCAmelCase_      (	)       ->      Dict:


      '''simple docstring'''




      lowerCamelCase__   	=					HfArgumentParser((TrainingArguments,)    )
      sys.argv += ["--output_dir", "./examples"]
      lowerCamelCase__   	=					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 [10_01, 2_56, 15]:
            lowerCamelCase__   	=					DummyDataset(lowerCamelCase__    )

            def compute_metrics(lowerCamelCase:							Union[str, Any]    ) -> Dict:
                  lowerCamelCase__   	=					list(range(len(lowerCamelCase__    )    )    )
                  lowerCamelCase__   	=					p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
                  return {"success": success}

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

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

            lowerCamelCase__   	=					2

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

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

            lowerCamelCase__   	=					None

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

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[Any]    )       ->      Any:


      '''simple docstring'''




      main()


if __name__ == "__main__":
      main()
import math
def 					__lowercase						(    lowerCamelCase__       )					->bool:

  '''simple docstring'''




  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5			,					int(math.sqrt(lowerCamelCase__       ) + 1       )			,					6       ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True
def 					__lowercase						(    lowerCamelCase__ = 2_00_00_00       )					->int:

  '''simple docstring'''




  return sum(num for num in range(3			,					lowerCamelCase__			,					2       ) if is_prime(lowerCamelCase__       )       ) + 2 if n > 2 else 0


if __name__ == "__main__":
     print(F"""{solution() = }""")

