


'''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


a_					:    int          =    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  a     (     UpperCamelCase_       ):
        def __init__( self      ,				__magic_name__ = 1_01						)					->							Dict:
             _a			      =							length
        def __len__( self						)					->							List[str]:
             return self.length




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





 class  a     :
        def __call__( self      ,				__magic_name__						)					->							List[str]:
             return {"input_ids": torch.tensor(__a						), "labels": torch.tensor(__a						)}





 class  a     (     nn.Module       ):
        def __init__( self						)					->							Union[str, Any]:
             super().__init__()
             # Add some (unused) params otherwise DDP will complain.
             _a			      =							nn.Linear(1_20      ,				80						)




        def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__=None						)					->							str:






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




class  a     (     UpperCamelCase_       ):
       @require_torch_neuroncore
       def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
            _a			      =							f'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
            _a			      =							self.get_auto_remove_tmp_dir()
            _a			      =							f'--output_dir {output_dir}'.split()
            _a			      =							['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  a     (     UpperCamelCase_       ):
       @require_torch_multi_gpu
       def 							__UpperCAmelCase		( self						)					->							Tuple:
            _a			      =							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()
            _a			      =							self.get_auto_remove_tmp_dir()
            _a			      =							f'--output_dir {output_dir}'.split()
            _a			      =							['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

 a_					:    Optional[int]          =    HfArgumentParser((TrainingArguments,))
 a_					:    Dict          =    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 [1_0_1, 4_0, 7]:
  a_					:    Dict          =    DummyDataset(dataset_length)






  def 	_A				(lowerCAmelCase__  :EvalPrediction		)   ->  Dict:




       '''simple docstring'''



       _a			      =							list(range(len(lowerCAmelCase__		)		)		)
       _a			      =							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}

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

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

  a_					:    Optional[Any]          =    2

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

  a_					:    Union[str, Any]          =    trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)






  a_					:    List[Any]          =    None





'''simple docstring'''






class  a     :
 def __init__( self						)					->							List[Any]:
      _a			      =							''
      _a			      =							''
      _a			      =							[]
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      if m == -1:
           return n + 1
      elif n == -1:
           return m + 1
      elif self.dp[m][n] > -1:
           return self.dp[m][n]
      else:
           if self.worda[m] == self.worda[n]:
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				n - 1						)
           else:
                _a			      =							self.__min_dist_top_down_dp(__magic_name__      ,				n - 1						)
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				__magic_name__						)
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				n - 1						)
                _a			      =							1 + min(__magic_name__      ,				__magic_name__      ,				__magic_name__						)

           return self.dp[m][n]
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							worda
      _a			      =							worda
      _a			      =							[[-1 for _ in range(len(__magic_name__						)						)] for _ in range(len(__magic_name__						)						)]

      return self.__min_dist_top_down_dp(len(__magic_name__						) - 1      ,				len(__magic_name__						) - 1						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							worda
      _a			      =							worda
      _a			      =							len(__magic_name__						)
      _a			      =							len(__magic_name__						)
      _a			      =							[[0 for _ in range(n + 1						)] for _ in range(m + 1						)]

      for i in range(m + 1						):
           for j in range(n + 1						):
                if i == 0:  # first string is empty
                     _a			      =							j
                elif j == 0:  # second string is empty
                     _a			      =							i
                elif worda[i - 1] == worda[j - 1]:  # last characters are equal
                     _a			      =							self.dp[i - 1][j - 1]
                else:
                     _a			      =							self.dp[i][j - 1]
                     _a			      =							self.dp[i - 1][j]
                     _a			      =							self.dp[i - 1][j - 1]
                     _a			      =							1 + min(__magic_name__      ,				__magic_name__      ,				__magic_name__						)
      return self.dp[m][n]


if __name__ == "__main__":
 a_					:    Dict          =    EditDistance()

 print("****************** Testing Edit Distance DP Algorithm ******************")
 print()

 a_					:    Tuple          =    input("Enter the first string: ").strip()
 a_					:    Dict          =    input("Enter the second string: ").strip()

 print()
 print(f'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
 print(f'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
 print()
 print("*************** End of Testing Edit Distance DP Algorithm ***************")

