
"""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


_lowercase							      = 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_ (					__UpperCAmelCase			):



								'''simple docstring'''


								def __init__(       self   :					Optional[int]     ,A_   :					int = 101					)   ->      int:
									A        =      length


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




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







		class  lowerCAmelCase_ :



								'''simple docstring'''


								def __call__(       self   :					int     ,A_   :					Tuple					)   ->      List[str]:
									return {"input_ids": torch.tensor(lowerCamelCase__					), "labels": torch.tensor(lowerCamelCase__					)}







		class  lowerCAmelCase_ (					nn.Module			):



								'''simple docstring'''


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




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







class  lowerCAmelCase_ (					__UpperCAmelCase			):



						'''simple docstring'''


						@require_torch_neuroncore
						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      int:
							A        =      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        =      self.get_auto_remove_tmp_dir()
							A        =      F'--output_dir {output_dir}'.split()
							A        =      ['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  lowerCAmelCase_ (					__UpperCAmelCase			):



						'''simple docstring'''


						@require_torch_multi_gpu
						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Any:
							A        =      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        =      self.get_auto_remove_tmp_dir()
							A        =      F'--output_dir {output_dir}'.split()
							A        =      ['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

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






				def 					_snake_case (						snake_case__      :   int   ):
					A        =      list(range(len(snake_case__   )   )   )
					A        =      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}

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

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

				_lowercase							      = 2

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

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


				_lowercase							      = None
"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase__  :  Tuple						     =	{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  List[Any]						     =	[
						    'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ViTMAEForPreTraining',
						    'ViTMAELayer',
						    'ViTMAEModel',
						    'ViTMAEPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  int						     =	[
						    'TFViTMAEForPreTraining',
						    'TFViTMAEModel',
						    'TFViTMAEPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_mae import (
												    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMAEForPreTraining,
												    ViTMAELayer,
												    ViTMAEModel,
												    ViTMAEPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
						import sys

						lowerCAmelCase__  :  int						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



