





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


UpperCAmelCase_  							=				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__ (			UpperCamelCase_       ):

					'''simple docstring'''


					def __init__(					self,	__magic_name__ = 101							)				->   Dict:






									"""simple docstring"""
									UpperCamelCase__							:       Union[str, Any]      =   length




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






									"""simple docstring"""
									return self.length




					def __getitem__(					self,	__magic_name__							)				->   int:






									"""simple docstring"""
									return i






		class 					lowercase__ :

					'''simple docstring'''


					def __call__(					self,	__magic_name__							)				->   List[str]:






									"""simple docstring"""
									return {"input_ids": torch.tensor(__a							), "labels": torch.tensor(__a							)}






		class 					lowercase__ (			nn.Module       ):

					'''simple docstring'''


					def __init__(					self							)				->   Union[str, Any]:






									"""simple docstring"""
									super().__init__()
									# Add some (unused) params otherwise DDP will complain.
									UpperCamelCase__							:       List[str]      =   nn.Linear(120,	80							)




					def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__=None							)				->   str:






									"""simple docstring"""






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






class 					lowercase__ (			UpperCamelCase_       ):

			'''simple docstring'''


			@require_torch_neuroncore
			def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[Any]      =   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__							:       List[Any]      =   self.get_auto_remove_tmp_dir()
							UpperCamelCase__							:       Any      =   f"--output_dir {output_dir}".split()
							UpperCamelCase__							:       Union[str, 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






class 					lowercase__ (			UpperCamelCase_       ):

			'''simple docstring'''


			@require_torch_multi_gpu
			def    UpperCamelCase__						(					self							)				->   Tuple:






							"""simple docstring"""
							UpperCamelCase__							:       Dict      =   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__							:       Dict      =   self.get_auto_remove_tmp_dir()
							UpperCamelCase__							:       Optional[Any]      =   f"--output_dir {output_dir}".split()
							UpperCamelCase__							:       str      =   ['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

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




				def 	lowerCAmelCase_					(						__UpperCAmelCase:    EvalPrediction   )   ->			Dict:
								UpperCamelCase__							:       str      =   list(range(len(__UpperCAmelCase   )   )   )
								UpperCamelCase__							:       Dict      =   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}

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

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

				UpperCAmelCase_  							=				2

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

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





				UpperCAmelCase_  							=				None








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_  							=				{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  							=				[
      'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'InformerForPrediction',
      'InformerModel',
      'InformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_informer import (
        INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        InformerForPrediction,
        InformerModel,
        InformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_  							=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

