def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str ):
       # The longer word should come first
       if len(lowercase_ ) < len(lowercase_ ):
              return levenshtein_distance(lowercase_						,  lowercase_ )

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

       _UpperCamelCase   :				List[str]       =     list(range(len(lowercase_ ) + 1 ) )

       for i, ca in enumerate(lowercase_ ):
              _UpperCamelCase   :				int       =     [i + 1]

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

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

              # Store the previous row
              _UpperCamelCase   :				Any       =     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}''')




import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


UpperCAmelCase							= get_logger(__name__)


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str ):
       if text.startswith(lowercase_ ):
              return text[len(lowercase_ ) :]
       return text  # or whatever


def SCREAMING_SNAKE_CASE (   lowercase_:		Union[str, Any] ):
       return {remove_prefix(lowercase_						,  'model.' ): v for k, v in sd.items()}


def SCREAMING_SNAKE_CASE (   lowercase_:		List[Dict[str, torch.Tensor]] ):
       _UpperCamelCase   :				Union[str, Any]       =     {}
       for k in state_dicts[0].keys():
              _UpperCamelCase   :				Tuple       =     [sd[k] for sd in state_dicts]
              _UpperCamelCase   :				str       =     sum(lowercase_ ) / len(lowercase_ )
              assert isinstance(lowercase_						,  torch.Tensor )
              _UpperCamelCase   :				Dict       =     new_t
       return new_sd


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str						,  lowercase_:		str ):
       _UpperCamelCase   :				Optional[Any]       =     AutoModelForSeqaSeqLM.from_pretrained(lowercase_ )
       if os.path.isfile(lowercase_ ):
              _UpperCamelCase   :				Any       =     [pl_ckpt_path]
       else:
              assert os.path.isdir(lowercase_ )
              _UpperCamelCase   :				str       =     list(Path(lowercase_ ).glob('*.ckpt' ) )
              assert ckpt_files, F"could not find any ckpt files inside the {pl_ckpt_path} directory"

       if len(lowercase_ ) > 1:
              logger.info(F"averaging the weights of {ckpt_files}" )

       _UpperCamelCase   :				Tuple       =     [sanitize(torch.load(lowercase_						,  map_location='cpu' )['state_dict'] ) for x in ckpt_files]
       _UpperCamelCase   :				Optional[Any]       =     average_state_dicts(lowercase_ )

       _UpperCamelCase  ,					_UpperCamelCase   :				Tuple       =     hf_model.load_state_dict(lowercase_						,  strict=lowercase_ )
       assert not missing, F"missing keys: {missing}"
       hf_model.save_pretrained(lowercase_ )
       try:
              _UpperCamelCase   :				str       =     AutoTokenizer.from_pretrained(lowercase_ )
              tok.save_pretrained(lowercase_ )
       except Exception:
              pass
              # dont copy tokenizer if cant


if __name__ == "__main__":
 fire.Fire(convert_pl_to_hf)



