






'''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


__lowerCAmelCase									=   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 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






					def __init__(			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      int = 101       )      ->      Dict:
									a_		:	Union[str, Any]									=   length






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







					def __getitem__(			self   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      int:
									return i





		class 						SCREAMING_SNAKE_CASE       :






					def __call__(			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      int:
									return {"input_ids": torch.tensor(__SCREAMING_SNAKE_CASE       ), "labels": torch.tensor(__SCREAMING_SNAKE_CASE       )}





		class 						SCREAMING_SNAKE_CASE       (       nn.Module							):






					def __init__(			self   :      str       )      ->      Union[str, Any]:
									super().__init__()
									# Add some (unused) params otherwise DDP will complain.
									a_		:	Union[str, Any]									=   nn.Linear(120	,    80       )







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





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






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





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			@require_torch_multi_gpu
			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      List[Any]:
							a_		:	str									=   f'--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
							a_		:	Tuple									=   self.get_auto_remove_tmp_dir()
							a_		:	int									=   f'--output_dir {output_dir}'.split()
							a_		:	str									=   ['''torchrun'''] + distributed_args + args
							execute_subprocess_async(__SCREAMING_SNAKE_CASE	,    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

		__lowerCAmelCase									=   HfArgumentParser((TrainingArguments,))
		__lowerCAmelCase									=   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]:
				__lowerCAmelCase									=   DummyDataset(dataset_length)





				def 		_UpperCAmelCase (				__A				: EvalPrediction   ):
								a_		:	Union[str, Any]									=   list(range(len(__A   )   )   )
								a_		:	Union[str, Any]									=   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}

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

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

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




				__lowerCAmelCase									=   None












'''simple docstring'''







import math





def 		_UpperCAmelCase (				__A				: int   ):
				a_		:	str									=   []
				a_		:	Tuple									=   2
				a_		:	Optional[Any]									=   int(math.sqrt(__A   )   )  # Size of every segment
				a_		:	Optional[Any]									=   [True] * (end + 1)
				a_		:	Union[str, Any]									=   []

				while start <= end:
								if temp[start] is True:
												in_prime.append(__A   )
												for i in range(start * start    ,		end + 1    ,		__A   ):
																a_		:	List[Any]									=   False
								start += 1
				prime += in_prime

				a_		:	Any									=   end + 1
				a_		:	Optional[Any]									=   min(2 * end    ,		__A   )

				while low <= n:
								a_		:	Optional[Any]									=   [True] * (high - low + 1)
								for each in in_prime:
												a_		:	Any									=   math.floor(low / each   ) * each
												if t < low:
																t += each

												for j in range(__A    ,		high + 1    ,		__A   ):
																a_		:	Union[str, Any]									=   False

								for j in range(len(__A   )   ):
												if temp[j] is True:
																prime.append(j + low   )

								a_		:	Tuple									=   high + 1
								a_		:	str									=   min(high + end    ,		__A   )

				return prime




print(sieve(10**6))




