


"""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__				:							Dict						=     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_	( UpperCamelCase):

			"""simple docstring"""





			def __init__( self			:				List[Any]			,			UpperCAmelCase__			:				int = 1_0_1   )       ->	Dict:
								__SCREAMING_SNAKE_CASE						   =				length




			def __len__( self			:				Optional[Any]   )       ->	Tuple:
								return self.length




			def __getitem__( self			:				List[Any]			,			UpperCAmelCase__			:				Any   )       ->	int:
								return i




		class  UpperCamelCase_	:

			"""simple docstring"""





			def __call__( self			:				List[Any]			,			UpperCAmelCase__			:				str   )       ->	str:
								return {"input_ids": torch.tensor(UpperCAmelCase__   ), "labels": torch.tensor(UpperCAmelCase__   )}




		class  UpperCamelCase_	( nn.Module):

			"""simple docstring"""





			def __init__( self			:				List[Any]   )       ->	Optional[int]:
								super().__init__()
								# Add some (unused) params otherwise DDP will complain.
								__SCREAMING_SNAKE_CASE						   =				nn.Linear(1_2_0			,			8_0   )




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




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





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




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	@require_torch_multi_gpu
	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	Dict:
						__SCREAMING_SNAKE_CASE						   =				F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
						__SCREAMING_SNAKE_CASE						   =				self.get_auto_remove_tmp_dir()
						__SCREAMING_SNAKE_CASE						   =				F"""--output_dir {output_dir}""".split()
						__SCREAMING_SNAKE_CASE						   =				["torchrun"] + distributed_args + args
						execute_subprocess_async(UpperCAmelCase__			,			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__				:							Any						=     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 [1_0_1, 4_0, 7]:
				a__				:							int						=     DummyDataset(dataset_length)




				def    UpperCAmelCase__       (lowerCAmelCase_   ):




									'''simple docstring'''


									__SCREAMING_SNAKE_CASE						   =				list(range(len(lowerCAmelCase_   )   )   )
									__SCREAMING_SNAKE_CASE						   =				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__				:							str						=     Trainer(
				    model=DummyModel(),
				    args=training_args,
				    data_collator=DummyDataCollator(),
				    eval_dataset=dataset,
				    compute_metrics=compute_metrics,
				)
				a__				:							Dict						=     trainer.evaluate()
				logger.info(metrics)
				if metrics["eval_success"] is not True:
						logger.error(metrics)
						exit(1)

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

				a__				:							str						=     2

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

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



				a__				:							int						=     None




"""simple docstring"""







from __future__ import annotations




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None   ):




					'''simple docstring'''


					if start is None:
										__SCREAMING_SNAKE_CASE						   =				0

					if end is None:
										__SCREAMING_SNAKE_CASE						   =				len(lowerCAmelCase_   ) - 1

					if start >= end:
										return

					__SCREAMING_SNAKE_CASE						   =				(start + end) // 2

					slowsort(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
					slowsort(lowerCAmelCase_  ,						mid + 1  ,						lowerCAmelCase_   )

					if sequence[end] < sequence[mid]:
										__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				sequence[mid], sequence[end]

					slowsort(lowerCAmelCase_  ,						lowerCAmelCase_  ,						end - 1   )


if __name__ == "__main__":
		from doctest import testmod

		testmod()
