



'''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   :			Dict					  =    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 			UpperCamelCase_ (							__snake_case			):





								def __init__(  self		,		A = 101    )			->			int:
															UpperCAmelCase     :		Dict							     =		length





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






								def __getitem__(  self		,		A    )			->			Optional[Any]:
															return i




							class 			UpperCamelCase_ :





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




							class 			UpperCamelCase_ (							nn.Module			):





								def __init__(  self    )			->			Union[str, Any]:
															super().__init__()
															# Add some (unused) params otherwise DDP will complain.
															UpperCAmelCase     :		Optional[int]							     =		nn.Linear(120		,		80    )






								def  _lowercase(  self		,		A		,		A=None    )			->			Dict:




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

class 			UpperCamelCase_ (							__snake_case			):





	@require_torch_neuroncore
	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		List[Any]							     =		f'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
								UpperCAmelCase     :		Optional[int]							     =		self.get_auto_remove_tmp_dir()
								UpperCAmelCase     :		List[str]							     =		f'''--output_dir {output_dir}'''.split()
								UpperCAmelCase     :		Optional[int]							     =		["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 			UpperCamelCase_ (							__snake_case			):





	@require_torch_multi_gpu
	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		List[Any]							     =		f'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
								UpperCAmelCase     :		Optional[Any]							     =		self.get_auto_remove_tmp_dir()
								UpperCAmelCase     :		List[str]							     =		f'''--output_dir {output_dir}'''.split()
								UpperCAmelCase     :		Optional[Any]							     =		["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   :			List[str]					  =    HfArgumentParser((TrainingArguments,))
							a   :			List[str]					  =    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   :			str					  =    DummyDataset(dataset_length)



														def       __lowerCamelCase  (					_lowercase		)      ->			Dict:
																					UpperCAmelCase     :		Optional[Any]							     =		list(range(len(_lowerCAmelCase		)		)		)
																					UpperCAmelCase     :		List[Any]							     =		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   :			str					  =    Trainer(
														    model=DummyModel(),
														    args=training_args,
														    data_collator=DummyDataCollator(),
														    eval_dataset=dataset,
														    compute_metrics=compute_metrics,
														)
														a   :			Any					  =    trainer.evaluate()
														logger.info(metrics)
														if metrics["eval_success"] is not True:
																					logger.error(metrics)
																					exit(1)

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

														a   :			Optional[int]					  =    2

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

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


														a   :			Dict					  =    None








import functools



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 UpperCamelCase	:					Optional[int]       	=   len(_lowerCAmelCase     )
 UpperCamelCase	:					List[str]       	=   len(_lowerCAmelCase     )

 @functools.cache
 def min_distance(_lowerCAmelCase							,   _lowerCAmelCase     ) -> int:
  # if first word index is overflow - delete all from the second word
  if indexa >= len_worda:
   return len_worda - indexa
  # if second word index is overflow - delete all from the first word
  if indexa >= len_worda:
   return len_worda - indexa
  UpperCamelCase	:					Union[str, Any]       	=   int(worda[indexa] != worda[indexa]     )  # current letters not identical
  return min(
      1 + min_distance(indexa + 1							,   _lowerCAmelCase     )							,   1 + min_distance(_lowerCAmelCase							,   indexa + 1     )							,   diff + min_distance(indexa + 1							,   indexa + 1     )							,   )

 return min_distance(0							,   0     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
