
from __future__ import annotations

from collections.abc import Iterator
from typing import Any






class UpperCAmelCase	:





   '''simple docstring'''


   def __init__(      self  :							str    ,    lowerCAmelCase_  :							Any  ):

      """simple docstring"""
      _A:						Any	    = data
      _A:						Node | None	    = None


   def __str__(      self  :							Union[str, Any]  ):

      """simple docstring"""
      return F"""{self.data}"""






class UpperCAmelCase	:





   '''simple docstring'''


   def __init__(      self  :							str  ):

      """simple docstring"""
      _A:						Node | None	    = None
      _A:						Node | None	    = None


   def __iter__(      self  :							Optional[int]  ):

      """simple docstring"""
      _A:						str	    = self.front
      while node:
         yield node.data
         _A:						Optional[Any]	    = node.next


   def __len__(      self  :							Tuple  ):

      """simple docstring"""
      return len(tuple(iter(self  )  )  )


   def __str__(      self  :							int  ):

      """simple docstring"""
      return " <- ".join(str(lowerCAmelCase_  ) for item in self  )


   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      return len(self  ) == 0


   def 				__magic_name__       (      self  :							List[str]    ,    lowerCAmelCase_  :							Any  ):

      """simple docstring"""
      _A:						Optional[Any]	    = Node(lowerCAmelCase_  )
      if self.is_empty():
         _A:						Dict	    = node
      else:
         assert isinstance(self.rear    ,    lowerCAmelCase_  )
         _A:						Optional[Any]	    = node
         _A:						Any	    = node


   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      if self.is_empty():
         raise IndexError('''dequeue from empty queue'''  )
      assert isinstance(self.front    ,    lowerCAmelCase_  )
      _A:						str	    = self.front
      _A:						Tuple	    = self.front.next
      if self.front is None:
         _A:						List[str]	    = None
      return node.data


   def 				__magic_name__       (      self  :							Union[str, Any]  ):

      """simple docstring"""
      _A:						Dict	    = None


if __name__ == "__main__":
   from doctest import testmod

   testmod()





# 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


UpperCAmelCase__	:  Union[str, Any]      =   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 UpperCAmelCase     (		SCREAMING_SNAKE_CASE__     ):





      '''simple docstring'''


      def __init__(      self  :							List[Any]    ,    lowerCAmelCase_  :							int = 1_0_1  ):

         """simple docstring"""
         _A:						str	    = length


      def __len__(      self  :							List[Any]  ):

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


      def __getitem__(      self  :							Any    ,    lowerCAmelCase_  :							Any  ):

         """simple docstring"""
         return i






   class UpperCAmelCase     :





      '''simple docstring'''


      def __call__(      self  :							Any    ,    lowerCAmelCase_  :							Union[str, Any]  ):

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






   class UpperCAmelCase     (		nn.Module     ):





      '''simple docstring'''


      def __init__(      self  :							str  ):

         """simple docstring"""
         super().__init__()
         # Add some (unused) params otherwise DDP will complain.
         _A:						Tuple	    = nn.Linear(1_2_0    ,    8_0  )


      def 				__magic_name__       (      self  :							List[Any]    ,    lowerCAmelCase_  :							List[str]    ,    lowerCAmelCase_  :							List[str]=None  ):

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






def 						lowerCamelCase__							(      )   ->     Optional[int]:
   _A:						Any	    = HfArgumentParser((TrainingArguments,)   )
   sys.argv += ["--output_dir", "./examples"]
   _A:						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]:
      _A:						Any	    = DummyDataset(a   )

      def compute_metrics(a   ) -> Dict:
         _A:						Dict	    = list(range(len(a   )   )   )
         _A:						List[str]	    = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
         return {"success": success}

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

      _A:						Optional[Any]	    = trainer.predict(a   )
      logger.info(p.metrics   )
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics   )
         exit(1   )

      _A:						str	    = 2

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

      _A:						Optional[Any]	    = trainer.predict(a   )
      logger.info(p.metrics   )
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics   )
         exit(1   )

      _A:						str	    = None

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






def 						lowerCamelCase__							(      a   )   ->     Optional[Any]:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
   main()



