




'''simple docstring'''

def    snake_case__     (  a       ,      a	)			->       Tuple:





			'''simple docstring'''






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

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

			snake_case__          =						list(range(len(A__	) + 1	)	)

			for i, ca in enumerate(A__	):
						snake_case__          =						[i + 1]

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

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


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

		a__         =     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_torch_available, is_vision_available


_snake_case				=			{
    "configuration_oneformer": ["ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "OneFormerConfig"],
    "processing_oneformer": ["OneFormerProcessor"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			["OneFormerImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			[
          "ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "OneFormerForUniversalSegmentation",
          "OneFormerModel",
          "OneFormerPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
      from .processing_oneformer import OneFormerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_oneformer import OneFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_oneformer import (
                ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                OneFormerForUniversalSegmentation,
                OneFormerModel,
                OneFormerPreTrainedModel,
            )


else:
      import sys

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



