# 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


__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   (     lowerCamelCase__   ):
      def __init__(  self		,   snake_case__ = 101						)	->							Any:
            '''simple docstring'''





            UpperCAmelCase    : List[str]      =length
      def __len__(  self						)	->							Tuple:
            '''simple docstring'''





            return self.length






      def __getitem__(  self		,   snake_case__						)	->							int:
            '''simple docstring'''





            return i







     class 			__snake_case   :
      def __call__(  self		,   snake_case__						)	->							Tuple:
            '''simple docstring'''





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







     class 			__snake_case   (     nn.Module   ):
      def __init__(  self						)	->							List[str]:
            '''simple docstring'''





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






      def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__=None						)	->							Tuple:
            '''simple docstring'''





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





def   lowerCAmelCase_ (				)->  Tuple:






      '''simple docstring'''





      UpperCAmelCase    : List[str]      =HfArgumentParser((TrainingArguments,)						)
      sys.argv += ["--output_dir", "./examples"]
      UpperCAmelCase    : Union[str, 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    : int      =DummyDataset(__lowerCAmelCase						)

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

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

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

            UpperCAmelCase    : List[str]      =2

            UpperCAmelCase    : Optional[int]      =trainer.evaluate()
            logger.info(__lowerCAmelCase						)
            if metrics["eval_success"] is not True:
                  logger.error(__lowerCAmelCase						)
                  exit(1						)

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

            UpperCAmelCase    : int      =None

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





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  List[Any]:






      '''simple docstring'''





      main()


if __name__ == "__main__":
     main()

__snake_case			      =    [[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 			__snake_case   :
 def __init__(  self		,   snake_case__						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =name
       UpperCAmelCase    : Tuple      =[]
       UpperCAmelCase    : str      =[]
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							int:
       '''simple docstring'''





       self.inbound.append(snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Dict:
       '''simple docstring'''





       self.outbound.append(snake_case__						)






 def __repr__(  self						)	->							Union[str, Any]:
       '''simple docstring'''





       return f'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase=3		,					__lowerCAmelCase=0.85						)->  Any:






      '''simple docstring'''





      UpperCAmelCase    : List[Any]      ={}
      for node in nodes:
            UpperCAmelCase    : str      =1

      UpperCAmelCase    : str      ={}
      for node in nodes:
            UpperCAmelCase    : int      =len(node.outbound						)

      for i in range(__lowerCAmelCase						):
            print(f'''======= Iteration {i + 1} ======='''						)
            for _, node in enumerate(__lowerCAmelCase						):
                  UpperCAmelCase    : Optional[int]      =(1 - d) + d * sum(
                      ranks[ib] / outbounds[ib] for ib in node.inbound						)
            print(__lowerCAmelCase						)





def   lowerCAmelCase_ (				)->  Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase    : Union[str, Any]      =list(input('''Enter Names of the Nodes: '''						).split()						)

      UpperCAmelCase    : str      =[Node(__lowerCAmelCase						) for name in names]

      for ri, row in enumerate(__lowerCAmelCase						):
            for ci, col in enumerate(__lowerCAmelCase						):
                  if col == 1:
                        nodes[ci].add_inbound(names[ri]						)
                        nodes[ri].add_outbound(names[ci]						)

      print('''======= Nodes ======='''						)
      for node in nodes:
            print(__lowerCAmelCase						)

      page_rank(__lowerCAmelCase						)


if __name__ == "__main__":
     main()
