


import torch
from torch import autograd




class  A_ (			autograd.Function  ):







 """simple docstring"""





 @staticmethod
 def 	_UpperCamelCase     (      _a       :			Union[str, Any]							,     _a       :			torch.tensor							,     _a       :			float							,     _a       :			bool					)     ->      List[str]:
      UpperCAmelCase    :					Optional[int]			=      inputs.numel()
      UpperCAmelCase    :					Union[str, Any]			=      int(0.0_0_5 * nb_elems					) + 1
      if sigmoid:
           UpperCAmelCase    :					Dict			=      (torch.sigmoid(_lowercase					) > threshold).type(inputs.type()					)
      else:
           UpperCAmelCase    :					str			=      (inputs > threshold).type(inputs.type()					)
      if mask.sum() < nb_min:
           # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
           UpperCAmelCase    :					Optional[int]			=      inputs.flatten().kthvalue(max(nb_elems - nb_min							,     1					)					).values
           UpperCAmelCase    :					int			=      (inputs > k_threshold).type(inputs.type()					)
      return mask

 @staticmethod
 def 	_UpperCamelCase     (      _a       :			int							,     _a       :			Union[str, Any]					)     ->      List[Any]:
      return gradOutput, None, None




class  A_ (			autograd.Function  ):







 """simple docstring"""





 @staticmethod
 def 	_UpperCamelCase     (      _a       :			Dict							,     _a       :			torch.tensor							,     _a       :			float					)     ->      List[Any]:
      UpperCAmelCase    :					Optional[int]			=      inputs.clone()
      UpperCAmelCase	,			UpperCAmelCase    :					Dict			=      inputs.flatten().sort(descending=_lowercase					)
      UpperCAmelCase    :					Optional[Any]			=      int(threshold * inputs.numel()					)

      # flat_out and mask access the same memory.
      UpperCAmelCase    :					int			=      mask.flatten()
      UpperCAmelCase    :					Any			=      0
      UpperCAmelCase    :					Union[str, Any]			=      1
      return mask

 @staticmethod
 def 	_UpperCamelCase     (      _a       :			Optional[int]							,     _a       :			List[Any]					)     ->      List[str]:
      return gradOutput, None




class  A_ (			lowerCamelCase_  ):







 """simple docstring"""





 @staticmethod
 def 	_UpperCamelCase     (      _a       :			torch.tensor							,     _a       :			float					)     ->      Optional[int]:
      UpperCAmelCase    :					List[str]			=      inputs.clone()
      UpperCAmelCase	,			UpperCAmelCase    :					Dict			=      inputs.abs().flatten().sort(descending=_lowercase					)
      UpperCAmelCase    :					Tuple			=      int(threshold * inputs.numel()					)

      # flat_out and mask access the same memory.
      UpperCAmelCase    :					Any			=      mask.flatten()
      UpperCAmelCase    :					List[Any]			=      0
      UpperCAmelCase    :					Dict			=      1
      return mask




# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__lowerCamelCase     :					List[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 __snake_case							(		lowerCamelCase_						):




      def __init__(       self     :							Tuple			,      _lowercase     :							int = 1_01 ):



        """simple docstring"""






        SCREAMING_SNAKE_CASE__     				= length




      def __len__(       self     :							str ):



        """simple docstring"""






        return self.length




      def __getitem__(       self     :							Optional[Any]			,      _lowercase     :							Union[str, Any] ):



        """simple docstring"""






        return i







   class __snake_case							:




      def __call__(       self     :							Dict			,      _lowercase     :							Dict ):



        """simple docstring"""






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







   class __snake_case							(		nn.Module						):




      def __init__(       self     :							Tuple ):



        """simple docstring"""






        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        SCREAMING_SNAKE_CASE__     				= nn.Linear(1_20			,      80 )




      def       __a		(       self     :							Any			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Dict=None ):



        """simple docstring"""






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



def       __SCREAMING_SNAKE_CASE    (   )				->   Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= HfArgumentParser((TrainingArguments,)      )
  sys.argv += ["--output_dir", "./examples"]
  SCREAMING_SNAKE_CASE__     				= parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
      f"""tpu_num_cores: {training_args.tpu_num_cores}"""					,	)

  # 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 [10_01, 2_56, 15]:
    SCREAMING_SNAKE_CASE__     				= DummyDataset(__UpperCamelCase      )

    def compute_metrics(__UpperCamelCase       :			EvalPrediction      ) -> Dict:
      SCREAMING_SNAKE_CASE__     				= list(range(len(__UpperCamelCase      )      )      )
      SCREAMING_SNAKE_CASE__     				= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

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

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

    SCREAMING_SNAKE_CASE__     				= 2

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

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

    SCREAMING_SNAKE_CASE__     				= None

  logger.info("""🔥 All distributed tests successful"""      )



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, Any]      )				->   Dict:







  """simple docstring"""





  main()


if __name__ == "__main__":
   main()
