





"""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		:				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  __UpperCamelCase       (		a__		):






									def __init__(     self   ,      lowerCAmelCase__ = 101   )			->				List[Any]:
										a   :	Union[str, Any]			=       length






									def __len__(     self   )			->				List[str]:
										return self.length






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


		class  __UpperCamelCase       :






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


		class  __UpperCamelCase       (		nn.Module		):






									def __init__(     self   )			->				int:
										super().__init__()
										# Add some (unused) params otherwise DDP will complain.
										a   :	Any			=       nn.Linear(120   ,      80   )






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


class  __UpperCamelCase       (		a__		):






							@require_torch_neuroncore
							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Any			=       f"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
								a   :	str			=       self.get_auto_remove_tmp_dir()
								a   :	Union[str, Any]			=       f"""--output_dir {output_dir}""".split()
								a   :	Optional[int]			=       ["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  __UpperCamelCase       (		a__		):






							@require_torch_multi_gpu
							def 					__a     (     self   )			->				List[str]:
								a   :	Any			=       f"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
								a   :	int			=       self.get_auto_remove_tmp_dir()
								a   :	int			=       f"""--output_dir {output_dir}""".split()
								a   :	Any			=       ["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

		a		:				List[str]  			=    HfArgumentParser((TrainingArguments,))
		a		:				Tuple  			=    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]:
				a		:				List[Any]  			=    DummyDataset(dataset_length)






				def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					EvalPrediction      )				->Dict:






					'''simple docstring'''






					a   :	List[str]			=       list(range(len(_lowercase      )      )      )
					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}

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

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

				a		:				Optional[Any]  			=    2

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

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







				a		:				List[Any]  			=    None









"""simple docstring"""




import baseaa






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->bytes:






	'''simple docstring'''






	return baseaa.aaaencode(string.encode("utf-8"      )      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					bytes      )				->str:






	'''simple docstring'''






	return baseaa.aaadecode(_lowercase      ).decode("utf-8"      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


