





"""simple docstring"""


import sys
from typing import Dict

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


a__					:     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      lowercase_				(     __lowerCAmelCase							):

          def __init__(			self   ,					__SCREAMING_SNAKE_CASE = 101     ):






           '''simple docstring'''

           snake_case__    							=				length

          def __len__(			self     ):






           '''simple docstring'''

           return self.length

          def __getitem__(			self   ,					__SCREAMING_SNAKE_CASE     ):






           '''simple docstring'''

           return i



   class      lowercase_				:

          def __call__(			self   ,					__SCREAMING_SNAKE_CASE     ):






           '''simple docstring'''

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



   class      lowercase_				(     nn.Module							):

          def __init__(			self     ):






           '''simple docstring'''

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

          def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE=None     ):






           '''simple docstring'''

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





def 							a__			(  )    ->  List[Any]:

 '''simple docstring'''






 snake_case__    							=				HfArgumentParser((TrainingArguments,)	)
 sys.argv += ["--output_dir", "./examples"]
 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 [1_0_0_1, 2_5_6, 1_5]:
  snake_case__    							=				DummyDataset(_UpperCAmelCase	)

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

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

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

  snake_case__    							=				2

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

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

  snake_case__    							=				None

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





def 							a__			(  _UpperCAmelCase	)    ->  List[Any]:

 '''simple docstring'''






 main()


if __name__ == "__main__":
   main()






"""simple docstring"""


class      lowercase_				:

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				data
        snake_case__    							=				42
        snake_case__    							=				42





def 							a__			(  _UpperCAmelCase	)    ->  None:

 '''simple docstring'''






 snake_case__    							=				0
 snake_case__    							=				x





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  None:

 '''simple docstring'''






 snake_case__							,   snake_case__    							=				find_set(_UpperCAmelCase	), find_set(_UpperCAmelCase	)
 if x == y:
  return

 elif x.rank > y.rank:
  snake_case__    							=				x
 else:
  snake_case__    							=				y
  if x.rank == y.rank:
   y.rank += 1





def 							a__			(  _UpperCAmelCase	)    ->  Node:

 '''simple docstring'''






 if x != x.parent:
  snake_case__    							=				find_set(x.parent	)
 return x.parent





def 							a__			(  _UpperCAmelCase	)    ->  set:

 '''simple docstring'''






 snake_case__    							=				({0, 1, 2}, {3, 4, 5})
 for s in sets:
  if node.data in s:
   return s
 snake_case__    							=				f"{node.data} is not in {sets}"
 raise ValueError(_UpperCAmelCase	)





def 							a__			(  )    ->  None:

 '''simple docstring'''






 snake_case__    							=				[Node(_UpperCAmelCase	) for i in range(6	)]
 for v in vertex:
  make_set(_UpperCAmelCase	)

 union_set(vertex[0]	,     vertex[1]	)
 union_set(vertex[1]	,     vertex[2]	)
 union_set(vertex[3]	,     vertex[4]	)
 union_set(vertex[3]	,     vertex[5]	)

 for nodea in vertex:
  for nodea in vertex:
   if find_python_set(_UpperCAmelCase	).isdisjoint(find_python_set(_UpperCAmelCase	)	):
    assert find_set(_UpperCAmelCase	) != find_set(_UpperCAmelCase	)
   else:
    assert find_set(_UpperCAmelCase	) == find_set(_UpperCAmelCase	)


if __name__ == "__main__":
   test_disjoint_set()

