





"""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_     :      str								=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   (  lowerCAmelCase_		):



           def __init__(       self					,     a__ = 1_01   ):
                  _lowerCamelCase    	=					length



           def __len__(       self   ):
                  return self.length



           def __getitem__(       self					,     a__   ):
                  return i







      class 	__a   :



           def __call__(       self					,     a__   ):
                  return {"input_ids": torch.tensor(_SCREAMING_SNAKE_CASE   ), "labels": torch.tensor(_SCREAMING_SNAKE_CASE   )}







      class 	__a   (  nn.Module		):



           def __init__(       self   ):
                  super().__init__()
                  # Add some (unused) params otherwise DDP will complain.
                  _lowerCamelCase    	=					nn.Linear(1_20					,     80   )



           def 						snake_case_			(       self					,     a__					,     a__=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_   (       )->		Tuple:
       _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 [1_001, 256, 15]:
              _lowerCamelCase    	=					DummyDataset(UpperCamelCase_		)

              def compute_metrics(snake_case   :					str		) -> Dict:
                     _lowerCamelCase    	=					list(range(len(UpperCamelCase_		)		)		)
                     _lowerCamelCase    	=					p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
                     return {"success": success}

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

              _lowerCamelCase    	=					trainer.predict(UpperCamelCase_		)
              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(UpperCamelCase_		)
              if metrics["eval_success"] is not True:
                     logger.error(UpperCamelCase_		)
                     exit(1		)

              _lowerCamelCase    	=					trainer.predict(UpperCamelCase_		)
              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 SCREAMING_SNAKE_CASE_   (       snake_case   :					Tuple		)->		List[Any]:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
      main()













import math




def        lowerCAmelCase_       (							UpperCamelCase_     )   ->						bool:

       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(UpperCamelCase_     ) + 1     )    ,						6     ):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True




def        lowerCAmelCase_       (							UpperCamelCase_ = 600851475143     )   ->						int:

       try:
              UpperCamelCase_      				=     int(UpperCamelCase_     )
       except (TypeError, ValueError):
              raise TypeError("Parameter n must be int or castable to int."     )
       if n <= 0:
              raise ValueError("Parameter n must be greater than or equal to one."     )
       UpperCamelCase_      				=     0
       if is_prime(UpperCamelCase_     ):
              return n
       while n % 2 == 0:
              n //= 2
       if is_prime(UpperCamelCase_     ):
              return n
       for i in range(3    ,						int(math.sqrt(UpperCamelCase_     )     ) + 1    ,						2     ):
              if n % i == 0:
                     if is_prime(n // i     ):
                            UpperCamelCase_      				=     n // i
                            break
                     elif is_prime(UpperCamelCase_     ):
                            UpperCamelCase_      				=     i
       return max_number


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

