


def 	A (		A__		,		A__			):
     # The longer word should come first
     if len(A__			) < len(A__			):
          return levenshtein_distance(A__		,		A__			)

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

     UpperCAmelCase    :					List[str]			=      list(range(len(A__			) + 1			)			)

     for i, ca in enumerate(A__			):
          UpperCAmelCase    :					List[Any]			=      [i + 1]

          for j, ca in enumerate(A__			):
               # Calculate insertions, deletions and substitutions
               UpperCAmelCase    :					Any			=      previous_row[j + 1] + 1
               UpperCAmelCase    :					Union[str, Any]			=      current_row[j] + 1
               UpperCAmelCase    :					Any			=      previous_row[j] + (ca != ca)

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

          # Store the previous row
          UpperCAmelCase    :					List[str]			=      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}''')







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case				=			{
    "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			[
          "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "NystromformerForMaskedLM",
          "NystromformerForMultipleChoice",
          "NystromformerForQuestionAnswering",
          "NystromformerForSequenceClassification",
          "NystromformerForTokenClassification",
          "NystromformerLayer",
          "NystromformerModel",
          "NystromformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nystromformer import (
                NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                NystromformerForMaskedLM,
                NystromformerForMultipleChoice,
                NystromformerForQuestionAnswering,
                NystromformerForSequenceClassification,
                NystromformerForTokenClassification,
                NystromformerLayer,
                NystromformerModel,
                NystromformerPreTrainedModel,
            )


else:
      import sys

      _snake_case				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



