




"""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  lowercase(					__a			):







						'''simple docstring'''





						def __init__(    self:		Any,				a_:		int = 101		):




								'''simple docstring'''



								_snake_case							:		int								=  length


						def __len__(    self:		Any		):




								'''simple docstring'''



								return self.length


						def __getitem__(    self:		List[str],				a_:		List[Any]		):




								'''simple docstring'''



								return i







					class  lowercase:







						'''simple docstring'''





						def __call__(    self:		int,				a_:		Dict		):




								'''simple docstring'''



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







					class  lowercase(					nn.Module			):







						'''simple docstring'''





						def __init__(    self:		Optional[int]		):




								'''simple docstring'''



								super().__init__()
								# Add some (unused) params otherwise DDP will complain.
								_snake_case							:		str								=  nn.Linear(120,				80		)


						def 			UpperCamelCase_       (    self:		Optional[Any],				a_:		int,				a_:		List[Any]=None		):




								'''simple docstring'''



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






class  lowercase(					__a			):







	'''simple docstring'''





	@require_torch_neuroncore
	def 			UpperCamelCase_       (    self:		List[Any]		):




			'''simple docstring'''



			_snake_case							:		int								=  f"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
			_snake_case							:		List[str]								=  self.get_auto_remove_tmp_dir()
			_snake_case							:		Dict								=  f"--output_dir {output_dir}".split()
			_snake_case							:		Tuple								=  ["""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  lowercase(					__a			):







	'''simple docstring'''





	@require_torch_multi_gpu
	def 			UpperCamelCase_       (    self:		Dict		):




			'''simple docstring'''



			_snake_case							:		Union[str, Any]								=  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()
			_snake_case							:		str								=  self.get_auto_remove_tmp_dir()
			_snake_case							:		str								=  f"--output_dir {output_dir}".split()
			_snake_case							:		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


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_01, 40, 7]:
										A_      =    DummyDataset(dataset_length)


										def  UpperCAmelCase__	(snake_case__							: Any   ):
												"""simple docstring"""

												_snake_case							:		Tuple								=  list(range(len(snake_case__   )   )   )
												_snake_case							:		Optional[int]								=  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"""


def  UpperCAmelCase__	(snake_case__							: int = 1_00_00_00   ):
  """simple docstring"""

  _snake_case							:		Dict								=  [i - 1 for i in range(limit + 1   )]

  for i in range(2		,       limit + 1   ):
    if phi[i] == i - 1:
      for j in range(2 * i		,       limit + 1		,       snake_case__   ):
        phi[j] -= phi[j] // i

  return sum(phi[2 : limit + 1]   )


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


