

'''simple docstring'''

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
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 			A_					(							_lowerCamelCase				):






      '''simple docstring'''



      def __init__(						self					:	List[Any]	,     lowercase_					:	int = 101			)		->      Tuple:
       UpperCAmelCase			:      Union[str, Any]		   =					length


      def __len__(						self					:	str			)		->      List[Any]:
       return self.length




      def __getitem__(						self					:	Optional[Any]	,     lowercase_					:	Any			)		->      int:
       return i




 class 			A_					:






      '''simple docstring'''



      def __call__(						self					:	Optional[int]	,     lowercase_					:	List[str]			)		->      Any:
       return {"input_ids": torch.tensor(__lowerCamelCase			), "labels": torch.tensor(__lowerCamelCase			)}




 class 			A_					(							nn.Module				):






      '''simple docstring'''



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




      def   UpperCAmelCase_			(						self					:	Union[str, Any]	,     lowercase_					:	Any	,     lowercase_					:	Dict=None			)		->      Union[str, Any]:




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




def  UpperCamelCase(	):
 UpperCAmelCase			:      Any		   =					HfArgumentParser((TrainingArguments,)      )
 sys.argv += ["--output_dir", "./examples"]
 UpperCAmelCase			:      List[Any]		   =					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]:
  UpperCAmelCase			:      Dict		   =					DummyDataset(lowercase__      )

  def compute_metrics(UpperCAmelCase_      ) -> Dict:
   UpperCAmelCase			:      Tuple		   =					list(range(len(lowercase__      )      )      )
   UpperCAmelCase			:      Tuple		   =					p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
   return {"success": success}

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

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

  UpperCAmelCase			:      int		   =					2

  UpperCAmelCase			:      Union[str, Any]		   =					trainer.evaluate()
  logger.info(lowercase__      )
  if metrics["eval_success"] is not True:
   logger.error(lowercase__      )
   exit(1      )

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

  UpperCAmelCase			:      Tuple		   =					None

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




def  UpperCamelCase(	UpperCAmelCase_      ):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
 main()












'''simple docstring'''





def  lowerCamelCase_  (     lowercase__):
 return int(lowercase__) if x - int(lowercase__) <= 0 else int(lowercase__) + 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()

