




import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


UpperCAmelCase_    =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      (			a__	):





						'''simple docstring'''



						def __init__(			self							,			UpperCAmelCase_ = 1_01    ):
													lowerCAmelCase =		length



						def __len__(			self    ):
													return self.length






						def __getitem__(			self							,			UpperCAmelCase_    ):
													return i




		class 				__UpperCamelCase      :





						'''simple docstring'''



						def __call__(			self							,			UpperCAmelCase_    ):
													return {"input_ids": torch.tensor(__UpperCAmelCase    ), "labels": torch.tensor(__UpperCAmelCase    )}




		class 				__UpperCamelCase      (			nn.Module	):





						'''simple docstring'''



						def __init__(			self    ):
													super().__init__()
													# Add some (unused) params otherwise DDP will complain.
													lowerCAmelCase =		nn.Linear(1_20							,			80    )






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



def    UpperCAmelCase							(  ):
							lowerCAmelCase =		HfArgumentParser((TrainingArguments,)				)
							sys.argv += ["--output_dir", "./examples"]
							lowerCAmelCase =		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 [1001, 256, 15]:
														lowerCAmelCase =		DummyDataset(_lowerCAmelCase				)

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

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

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

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

														lowerCAmelCase =		None

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



def    UpperCAmelCase							(  _snake_case				):
							main()


if __name__ == "__main__":
		main()







"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       str,  _lowerCAmelCase  :       str							):






     """simple docstring"""

     if len(_lowerCAmelCase							) < len(_lowerCAmelCase							):
          return levenshtein_distance(_lowerCAmelCase,  _lowerCAmelCase							)

     if len(_lowerCAmelCase							) == 0:
          return len(_lowerCAmelCase							)

     _a     						=					list(range(len(_lowerCAmelCase							) + 1							)							)

     for i, ca in enumerate(_lowerCAmelCase							):
          _a     						=					[i + 1]

          for j, ca in enumerate(_lowerCAmelCase							):
               # Calculate insertions, deletions and substitutions
               _a     						=					previous_row[j + 1] + 1
               _a     						=					current_row[j] + 1
               _a     						=					previous_row[j] + (ca != ca)

               # Get the minimum to append to the current row
               current_row.append(min(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)							)

          # Store the previous row
          _a     						=					current_row

     # Returns the last element (distance)
     return previous_row[-1]


if __name__ == "__main__":
   __snake_case												=							input('''Enter the first word:\n''').strip()
   __snake_case												=							input('''Enter the second word:\n''').strip()

   __snake_case												=							levenshtein_distance(first_word, second_word)
   print(f'Levenshtein distance between {first_word} and {second_word} is {result}')