



'''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


SCREAMING_SNAKE_CASE__      					=     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__ (_lowercase   ):



        def __init__(				self     ,UpperCAmelCase_ = 101      )  ->							Tuple:
            lowercase__		       =						length



        def __len__(				self      )  ->							Any:
            return self.length







        def __getitem__(				self     ,UpperCAmelCase_      )  ->							int:
            return i





 class 				snake_case__ :



        def __call__(				self     ,UpperCAmelCase_      )  ->							Any:
            return {"input_ids": torch.tensor(A_      ), "labels": torch.tensor(A_      )}





 class 				snake_case__ (nn.Module   ):



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







        def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=None      )  ->							Optional[Any]:
            if labels is not None:
                return torch.tensor(0.0     ,device=input_ids.device      ), input_ids
            else:
                return input_ids


class 				snake_case__ (_lowercase   ):



       @require_torch_neuroncore
       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						F'''--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '''.split()
           lowercase__		       =						self.get_auto_remove_tmp_dir()
           lowercase__		       =						F'''--output_dir {output_dir}'''.split()
           lowercase__		       =						["torchrun"] + distributed_args + args
           execute_subprocess_async(A_     ,env=self.get_env()      )
           # successful return here == success - any errors would have caused an error in the sub-call





class 				snake_case__ (_lowercase   ):



       @require_torch_multi_gpu
       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						F'''--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '''.split()
           lowercase__		       =						self.get_auto_remove_tmp_dir()
           lowercase__		       =						F'''--output_dir {output_dir}'''.split()
           lowercase__		       =						["torchrun"] + distributed_args + args
           execute_subprocess_async(A_     ,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

 SCREAMING_SNAKE_CASE__      					=     HfArgumentParser((TrainingArguments,))
 SCREAMING_SNAKE_CASE__      					=     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 [101, 40, 7]:
  SCREAMING_SNAKE_CASE__      					=     DummyDataset(dataset_length)




  def        lowerCamelCase  (				_snake_case  :						EvalPrediction       ):





      '''simple docstring'''


      lowercase__		       =						list(range(len(snake_case__       )       )       )
      lowercase__		       =						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}

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

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

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



  SCREAMING_SNAKE_CASE__      					=     None









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						List[str]       ):  # noqa: E741





    '''simple docstring'''


    lowercase__		       =						len(_snake_case       )
    lowercase__		       =						0
    lowercase__		       =						[0] * n
    lowercase__		       =						[False] * n
    lowercase__		       =						[False] * n

    def dfs(_snake_case  :						Tuple     ,_snake_case  :						int     ,_snake_case  :						List[str]     ,_snake_case  :						str       ):
        if parent == root:
            out_edge_count += 1
        lowercase__		       =						True
        lowercase__		       =						at

        for to in l[at]:
            if to == parent:
                pass
            elif not visited[to]:
                lowercase__		       =						dfs(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case       )
                lowercase__		       =						min(low[at]     ,low[to]       )

                # AP found via bridge
                if at < low[to]:
                    lowercase__		       =						True
                # AP found via cycle
                if at == low[to]:
                    lowercase__		       =						True
            else:
                lowercase__		       =						min(low[at]     ,_snake_case       )
        return out_edge_count

    for i in range(_snake_case       ):
        if not visited[i]:
            lowercase__		       =						0
            lowercase__		       =						dfs(_snake_case     ,_snake_case     ,-1     ,_snake_case       )
            lowercase__		       =						out_edge_count > 1

    for x in range(len(_snake_case       )       ):
        if is_art[x] is True:
            print(_snake_case       )


# Adjacency list of graph
SCREAMING_SNAKE_CASE__      					=     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}



compute_ap(data)




