

'''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


_lowerCAmelCase         =						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   _SCREAMING_SNAKE_CASE   (   __a     ):



       def __init__(   self		:   Any			,				a__		:   int = 101      ):
            __magic_name__						       =    length



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



       def __getitem__(   self		:   List[str]			,				a__		:   List[Any]      ):
            return i






      class   _SCREAMING_SNAKE_CASE   :



       def __call__(   self		:   int			,				a__		:   Dict      ):
            return {"input_ids": torch.tensor(a__      ), "labels": torch.tensor(a__      )}






      class   _SCREAMING_SNAKE_CASE   (   nn.Module     ):



       def __init__(   self		:   Optional[int]      ):
            super().__init__()
            # Add some (unused) params otherwise DDP will complain.
            __magic_name__						       =    nn.Linear(120			,				80      )



       def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   int			,				a__		:   List[Any]=None      ):
            if labels is not None:
                 return torch.tensor(0.0			,				device=input_ids.device      ), input_ids
            else:
                 return input_ids






class   _SCREAMING_SNAKE_CASE   (   __a     ):



 @require_torch_neuroncore
 def 			snake_case__  (   self		:   List[Any]      ):
      __magic_name__						       =    F'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
      __magic_name__						       =    self.get_auto_remove_tmp_dir()
      __magic_name__						       =    F'''--output_dir {output_dir}'''.split()
      __magic_name__						       =    ['''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   _SCREAMING_SNAKE_CASE   (   __a     ):



 @require_torch_multi_gpu
 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    F'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
      __magic_name__						       =    self.get_auto_remove_tmp_dir()
      __magic_name__						       =    F'''--output_dir {output_dir}'''.split()
      __magic_name__						       =    ['''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

      _lowerCAmelCase         =						HfArgumentParser((TrainingArguments,))
      _lowerCAmelCase         =						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]:
            _lowerCAmelCase         =						DummyDataset(dataset_length)




            def 						UpperCamelCase	(			a			)				->     Dict:






                 '''simple docstring'''
                 __magic_name__						       =    list(range(len(a			)			)			)
                 __magic_name__						       =    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}

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

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

            _lowerCAmelCase         =						2

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

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


            _lowerCAmelCase         =						None









'''simple docstring'''




from __future__ import annotations

from fractions import Fraction




def 						UpperCamelCase	(			a		,						a			)				->     bool:






     '''simple docstring'''
     return (
         num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
     )




def 						UpperCamelCase	(			a			)				->     list[str]:






     '''simple docstring'''
     __magic_name__						       =    []
     __magic_name__						       =    11
     __magic_name__						       =    int('''1''' + '''0''' * digit_len			)
     for num in range(a		,						a			):
          while den <= 99:
               if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                    if is_digit_cancelling(a		,						a			):
                         solutions.append(F'''{num}/{den}'''			)
               den += 1
          num += 1
          __magic_name__						       =    10
     return solutions




def 						UpperCamelCase	(			a = 2			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    1.0
     for fraction in fraction_list(a			):
          __magic_name__						       =    Fraction(a			)
          result *= frac.denominator / frac.numerator
     return int(a			)


if __name__ == "__main__":
      print(solution())






