


'''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


__UpperCAmelCase       							=				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   ,       lowerCamelCase_ = 1_01  )			->				Union[str, Any]:
																lowerCAmelCase__			  =      length







									def __len__(						self  )			->				Any:
																return self.length







									def __getitem__(						self   ,       lowerCamelCase_  )			->				int:
																return i




		class 						a__							:





									'''simple docstring'''






									def __call__(						self   ,       lowerCamelCase_  )			->				Any:
																return {"input_ids": torch.tensor(lowerCamelCase_  ), "labels": torch.tensor(lowerCamelCase_  )}




		class 						a__							(				nn.Module    ):





									'''simple docstring'''






									def __init__(						self  )			->				List[str]:
																super().__init__()
																# Add some (unused) params otherwise DDP will complain.
																lowerCAmelCase__			  =      nn.Linear(1_20   ,       80  )







									def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=None  )			->				List[Any]:
																if labels is not None:
																							return torch.tensor(0.0   ,       device=input_ids.device  ), input_ids
																else:
																							return input_ids




class 						a__							(				a__    ):





							'''simple docstring'''






							@require_torch_neuroncore
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      F"""--output_dir {output_dir}""".split()
														lowerCAmelCase__			  =      ['''torchrun'''] + distributed_args + args
														execute_subprocess_async(lowerCamelCase_   ,       env=self.get_env()  )
														# successful return here == success - any errors would have caused an error in the sub-call




class 						a__							(				a__    ):





							'''simple docstring'''






							@require_torch_multi_gpu
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      F"""--output_dir {output_dir}""".split()
														lowerCAmelCase__			  =      ['''torchrun'''] + distributed_args + args
														execute_subprocess_async(lowerCamelCase_   ,       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

		__UpperCAmelCase       							=				HfArgumentParser((TrainingArguments,))
		__UpperCAmelCase       							=				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 [101, 40, 7]:
				__UpperCAmelCase       							=				DummyDataset(dataset_length)


				def    _snake_case  (	A			)		->       Dict:
											lowerCAmelCase__			  =      list(range(len(A			)			)			)
											lowerCAmelCase__			  =      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}

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

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

				__UpperCAmelCase       							=				2

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

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






				__UpperCAmelCase       							=				None






"""simple docstring"""



def 						__lowercase	(  snake_case_    :    int = 2000000		)  ->int:



    '''simple docstring'''







    __A       :		List[Any]							=	[0 for i in range(n + 1		)]
    __A       :		str							=	1
    __A       :		Dict							=	1

    for i in range(2							,int(n**0.5		) + 1		):
        if primality_list[i] == 0:
            for j in range(i * i							,n + 1							,snake_case_		):
                __A       :		List[Any]							=	1
    __A       :		Union[str, Any]							=	0
    for i in range(snake_case_		):
        if primality_list[i] == 0:
            sum_of_primes += i
    return sum_of_primes


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




