





'''simple docstring'''

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()










'''simple docstring'''

def lowerCamelCase		(  __lowerCamelCase				:					int = 10**6					)		->int:

    if n <= 0:
        raise ValueError("""Please enter an integer greater than 0"""					)

    _SCREAMING_SNAKE_CASE             =    list(range(n + 1					)					)
    for number in range(2      , n + 1					):
        if phi[number] == number:
            phi[number] -= 1
            for multiple in range(number * 2      , n + 1      , __lowerCamelCase					):
                _SCREAMING_SNAKE_CASE             =    (phi[multiple] // number) * (number - 1)

    _SCREAMING_SNAKE_CASE             =    1
    for number in range(1      , n + 1					):
        if (answer / phi[answer]) < (number / phi[number]):
            _SCREAMING_SNAKE_CASE             =    number

    return answer


if __name__ == "__main__":
      print(solution())



