



'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase							    =						{
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						['ImageGPTFeatureExtractor']
   _UpperCamelCase							    =						['ImageGPTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						[
       'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ImageGPTForCausalImageModeling',
       'ImageGPTForImageClassification',
       'ImageGPTModel',
       'ImageGPTPreTrainedModel',
       'load_tf_weights_in_imagegpt',
   ]


if TYPE_CHECKING:
   from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
      from .image_processing_imagegpt import ImageGPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_imagegpt import (
          IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ImageGPTForCausalImageModeling,
          ImageGPTForImageClassification,
          ImageGPTModel,
          ImageGPTPreTrainedModel,
          load_tf_weights_in_imagegpt,
      )

else:
   import sys

   _UpperCamelCase							    =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











'''simple docstring'''





def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				int:
  # The longer word should come first
  if len(_lowerCAmelCase	) < len(_lowerCAmelCase	):
    return levenshtein_distance(_lowerCAmelCase	,_lowerCAmelCase	)

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

  __lowerCamelCase	:	int   	=			list(range(len(_lowerCAmelCase	) + 1	)	)

  for i, ca in enumerate(_lowerCAmelCase	):
    __lowerCamelCase	:	Union[str, Any]   	=			[i + 1]

    for j, ca in enumerate(_lowerCAmelCase	):
      # Calculate insertions, deletions and substitutions
      __lowerCamelCase	:	Any   	=			previous_row[j + 1] + 1
      __lowerCamelCase	:	int   	=			current_row[j] + 1
      __lowerCamelCase	:	Any   	=			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
    __lowerCamelCase	:	Optional[int]   	=			current_row

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


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

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






