



'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase							    =						{
    'configuration_gpt2': ['GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT2Config', 'GPT2OnnxConfig'],
    'tokenization_gpt2': ['GPT2Tokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						['GPT2TokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						[
       'GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'GPT2DoubleHeadsModel',
       'GPT2ForQuestionAnswering',
       'GPT2ForSequenceClassification',
       'GPT2ForTokenClassification',
       'GPT2LMHeadModel',
       'GPT2Model',
       'GPT2PreTrainedModel',
       'load_tf_weights_in_gpt2',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						[
       'TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFGPT2DoubleHeadsModel',
       'TFGPT2ForSequenceClassification',
       'TFGPT2LMHeadModel',
       'TFGPT2MainLayer',
       'TFGPT2Model',
       'TFGPT2PreTrainedModel',
   ]

try:
   if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						['TFGPT2Tokenizer']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						['FlaxGPT2LMHeadModel', 'FlaxGPT2Model', 'FlaxGPT2PreTrainedModel']

if TYPE_CHECKING:
   from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
   from .tokenization_gpta import GPTaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_gpta_fast import GPTaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gpta import (
          GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTaDoubleHeadsModel,
          GPTaForQuestionAnswering,
          GPTaForSequenceClassification,
          GPTaForTokenClassification,
          GPTaLMHeadModel,
          GPTaModel,
          GPTaPreTrainedModel,
          load_tf_weights_in_gpta,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_gpta import (
          TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFGPTaDoubleHeadsModel,
          TFGPTaForSequenceClassification,
          TFGPTaLMHeadModel,
          TFGPTaMainLayer,
          TFGPTaModel,
          TFGPTaPreTrainedModel,
      )

   try:
      if not is_keras_nlp_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_gpta_tf import TFGPTaTokenizer

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

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}''')






