



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase							    =						{
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						['BartTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						[
       'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BartForCausalLM',
       'BartForConditionalGeneration',
       'BartForQuestionAnswering',
       'BartForSequenceClassification',
       'BartModel',
       'BartPretrainedModel',
       'PretrainedBartModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						[
       'TFBartForConditionalGeneration',
       'TFBartForSequenceClassification',
       'TFBartModel',
       'TFBartPretrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						[
       'FlaxBartDecoderPreTrainedModel',
       'FlaxBartForCausalLM',
       'FlaxBartForConditionalGeneration',
       'FlaxBartForQuestionAnswering',
       'FlaxBartForSequenceClassification',
       'FlaxBartModel',
       'FlaxBartPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
   from .tokenization_bart import BartTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bart_fast import BartTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bart import (
          BART_PRETRAINED_MODEL_ARCHIVE_LIST,
          BartForCausalLM,
          BartForConditionalGeneration,
          BartForQuestionAnswering,
          BartForSequenceClassification,
          BartModel,
          BartPretrainedModel,
          PretrainedBartModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_bart import (
          TFBartForConditionalGeneration,
          TFBartForSequenceClassification,
          TFBartModel,
          TFBartPretrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_bart import (
          FlaxBartDecoderPreTrainedModel,
          FlaxBartForCausalLM,
          FlaxBartForConditionalGeneration,
          FlaxBartForQuestionAnswering,
          FlaxBartForSequenceClassification,
          FlaxBartModel,
          FlaxBartPreTrainedModel,
      )

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}''')






