






'''simple docstring'''






from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


__A    	=   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_    (    UpperCamelCase_							):







									def __init__(self					,   __a = 1_0_1)			->      Dict:
												"""simple docstring"""






												__snake_case				:      Union[str, Any]     							= length







									def __len__(self)			->      List[str]:
												"""simple docstring"""






												return self.length







									def __getitem__(self					,   __a)			->      int:
												"""simple docstring"""






												return i




						class a_    :







									def __call__(self					,   __a)			->      List[str]:
												"""simple docstring"""






												return {"input_ids": torch.tensor(__a), "labels": torch.tensor(__a)}




						class a_    (    nn.Module							):







									def __init__(self)			->      Union[str, Any]:
												"""simple docstring"""






												super().__init__()
												# Add some (unused) params otherwise DDP will complain.
												__snake_case				:      List[str]     							= nn.Linear(1_2_0					,   8_0)







									def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a=None)			->      str:
												"""simple docstring"""






												if labels is not None:
															return torch.tensor(0.0					,   device=input_ids.device), input_ids
												else:
															return input_ids




class a_    (    UpperCamelCase_							):







			@require_torch_neuroncore
			def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
						"""simple docstring"""






						__snake_case				:      Optional[Any]     							= F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
						__snake_case				:      List[Any]     							= self.get_auto_remove_tmp_dir()
						__snake_case				:      Any     							= F"""--output_dir {output_dir}""".split()
						__snake_case				:      Union[str, Any]     							= ['torchrun'] + distributed_args + args
						execute_subprocess_async(__a					,   env=self.get_env())
						# successful return here == success - any errors would have caused an error in the sub-call




class a_    (    UpperCamelCase_							):







			@require_torch_multi_gpu
			def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
						"""simple docstring"""






						__snake_case				:      Dict     							= F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
						__snake_case				:      Dict     							= self.get_auto_remove_tmp_dir()
						__snake_case				:      Optional[Any]     							= F"""--output_dir {output_dir}""".split()
						__snake_case				:      str     							= ['torchrun'] + distributed_args + args
						execute_subprocess_async(__a					,   env=self.get_env())
						# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
						# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
						#
						# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

						__A    	=   HfArgumentParser((TrainingArguments,))
						__A    	=   parser.parse_args_into_dataclasses()[0]

						logger.warning(
						    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
						    f'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
						)

						# 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_0_1, 4_0, 7]:
												__A    	=   DummyDataset(dataset_length)




												def        _SCREAMING_SNAKE_CASE  (				A  :       EvalPrediction  )    -> Dict:







															"""simple docstring"""
															__snake_case				:      str     							= list(range(len(A  )  )  )
															__snake_case				:      Dict     							= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
															if not success and training_args.local_rank == 0:
																		logger.warning(
																		    'Predictions and/or labels do not match expected results:\n  - predictions: '
																		    F"""{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"""  )
															return {"success": success}

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

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

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





												__A    	=   None







'''simple docstring'''






__A    	=   {str(digit): digit**5 for digit in range(1_0)}




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> int:







   """simple docstring"""
   return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A  )  )




def        _SCREAMING_SNAKE_CASE  (				)    -> int:







   """simple docstring"""
   return sum(
       number
       for number in range(10_00  ,		1_00_00_00  )
       if number == digits_fifth_powers_sum(A  )  )


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