





"""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        _lowerCamelCase      (       _lowercase ):


												def __init__(self   ,							__a = 1_01		) ->   Any:
														UpperCamelCase   =	length


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




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





						class        _lowerCamelCase      :


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





						class        _lowerCamelCase      (       nn.Module ):


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




												def     snake_case_   (self   ,							__a   ,							__a=None		) ->   Any:
														if labels is not None:
																return torch.tensor(0.0   ,							device=input_ids.device		), input_ids
														else:
																return input_ids





class        _lowerCamelCase      (       _lowercase ):


						@require_torch_neuroncore
						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	F"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
								UpperCamelCase   =	self.get_auto_remove_tmp_dir()
								UpperCamelCase   =	F"--output_dir {output_dir}".split()
								UpperCamelCase   =	["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        _lowerCamelCase      (       _lowercase ):


						@require_torch_multi_gpu
						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	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()
								UpperCamelCase   =	self.get_auto_remove_tmp_dir()
								UpperCamelCase   =	F"--output_dir {output_dir}".split()
								UpperCamelCase   =	["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

						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       a__					(					_SCREAMING_SNAKE_CASE    ):




														"""simple docstring"""


														UpperCamelCase   =	list(range(len(_SCREAMING_SNAKE_CASE    )    )    )
														UpperCamelCase   =	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"""





lowerCAmelCase__     			=			'''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase__     			=			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase__     			=			{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}






