




'''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


UpperCAmelCase_							:	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      lowerCAmelCase (			__lowerCAmelCase):


            def __init__( self     ,	__SCREAMING_SNAKE_CASE = 101	)   ->	List[str]:



                 '''simple docstring'''




                 __snake_case            =	length


            def __len__( self	)   ->	Any:



                 '''simple docstring'''




                 return self.length




            def __getitem__( self     ,	__SCREAMING_SNAKE_CASE	)   ->	int:



                 '''simple docstring'''




                 return i





     class      lowerCAmelCase :


            def __call__( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



                 '''simple docstring'''




                 return {"input_ids": torch.tensor(__SCREAMING_SNAKE_CASE	), "labels": torch.tensor(__SCREAMING_SNAKE_CASE	)}





     class      lowerCAmelCase (			nn.Module):


            def __init__( self	)   ->	Tuple:



                 '''simple docstring'''




                 super().__init__()
                 # Add some (unused) params otherwise DDP will complain.
                 __snake_case            =	nn.Linear(120     ,	80	)




            def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None	)   ->	Optional[int]:



                 '''simple docstring'''




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





class      lowerCAmelCase (			__lowerCAmelCase):


       @require_torch_neuroncore
       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	F'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
            __snake_case            =	self.get_auto_remove_tmp_dir()
            __snake_case            =	F'''--output_dir {output_dir}'''.split()
            __snake_case            =	['''torchrun'''] + distributed_args + args
            execute_subprocess_async(__SCREAMING_SNAKE_CASE     ,	env=self.get_env()	)
            # successful return here == success - any errors would have caused an error in the sub-call





class      lowerCAmelCase (			__lowerCAmelCase):


       @require_torch_multi_gpu
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __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()
            __snake_case            =	self.get_auto_remove_tmp_dir()
            __snake_case            =	F'''--output_dir {output_dir}'''.split()
            __snake_case            =	['''torchrun'''] + distributed_args + args
            execute_subprocess_async(__SCREAMING_SNAKE_CASE     ,	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_							:	Union[str, Any]	     =		HfArgumentParser((TrainingArguments,))
     UpperCAmelCase_							:	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]:
          UpperCAmelCase_							:	int	     =		DummyDataset(dataset_length)







          def       _UpperCamelCase			(_lowerCamelCase				:  EvalPrediction						)-> Dict:




               '''simple docstring'''





               __snake_case            =	list(range(len(_lowerCamelCase						)						)						)
               __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}

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

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

          UpperCAmelCase_							:	Any	     =		2

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

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







          UpperCAmelCase_							:	str	     =		None







'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


__snake_case						  =		logging.get_logger(__name__)





class       _a    (		__a      ):


  """simple docstring"""

  def __init__(		self      :       int   ,				*lowercase_      :       Tuple   ,				**lowercase_      :       List[Any]      ):







    '''simple docstring'''


    warnings.warn(
        """The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
        """ Please use PoolFormerImageProcessor instead."""   ,				lowercase_   ,				)
    super().__init__(*lowercase_   ,				**lowercase_      )

