






import os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE       							=  'block_records.npy'


SCREAMING_SNAKE_CASE       							=  logging.get_logger(__name__)

def    _lowerCamelCase		(  __A			:							str						,						__A			:							int       )					->    np.ndarray:
    import tensorflow.compat.va as tf

    _UpperCAmelCase :					Dict	     = tf.data.TFRecordDataset(__A						,						buffer_size=512 * 1_024 * 1_024       )
    _UpperCAmelCase :					List[Any]	     = blocks_dataset.batch(__A						,						drop_remainder=__A       )
    _UpperCAmelCase :					Union[str, Any]	     = next(blocks_dataset.take(1       ).as_numpy_iterator()       )

    return np_record
class 							A_			:






 '''simple docstring'''




 def __init__(		self							,     _A							,     _A							,     _A=2							,     _A=1000							,     _A=100							,     _A=100000							,     )       ->						Tuple:

     """simple docstring"""



     from scann.scann_ops.py.scann_ops_pybind import builder as Builder

     _UpperCAmelCase :					Any	     = Builder(db=_A							,     num_neighbors=_A							,     distance_measure='''dot_product''')
     _UpperCAmelCase :					str	     = builder.tree(
         num_leaves=_A							,     num_leaves_to_search=_A							,     training_sample_size=_A)
     _UpperCAmelCase :					int	     = builder.score_ah(dimensions_per_block=_A)

     _UpperCAmelCase :					Optional[Any]	     = builder.build()


 def        snake_case__ (		self							,     _A)       ->						List[Any]:

     """simple docstring"""



     _UpperCAmelCase :					str	     = self.searcher.search_batched(question_projection.detach().cpu())
     return retrieved_block_ids.astype('''int64''')



class 							A_			:






 '''simple docstring'''




 def __init__(		self							,     _A							,     _A)       ->						Tuple:

     """simple docstring"""



     super().__init__()
     _UpperCAmelCase :					Dict	     = block_records
     _UpperCAmelCase :					int	     = tokenizer


 def __call__(		self							,     _A							,     _A							,     _A							,     _A=None							,     _A="pt")       ->						Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase :					Optional[Any]	     = np.take(self.block_records							,     indices=_A							,     axis=0)

     _UpperCAmelCase :					Any	     = self.tokenizer.decode(question_input_ids[0]							,     skip_special_tokens=_A)

     _UpperCAmelCase :					Union[str, Any]	     = []
     _UpperCAmelCase :					Optional[Any]	     = []
     for retrieved_block in retrieved_blocks:
         text.append(_A)
         text_pair.append(retrieved_block.decode())

     _UpperCAmelCase :					Union[str, Any]	     = self.tokenizer(
         _A							,     _A							,     padding=_A							,     truncation=_A							,     return_special_tokens_mask=_A							,     max_length=_A)
     _UpperCAmelCase :					int	     = concat_inputs.convert_to_tensors(_A)

     if answer_ids is not None:
         return self.block_has_answer(_A							,     _A) + (concat_inputs_tensors,)
     else:
         return (None, None, None, concat_inputs_tensors)


 @classmethod
 def        snake_case__ (		cls							,     _A							,     *_A							,     **_A)       ->						str:

     """simple docstring"""



     if os.path.isdir(_A):
         _UpperCAmelCase :					List[Any]	     = os.path.join(_A							,     _REALM_BLOCK_RECORDS_FILENAME)
     else:
         _UpperCAmelCase :					Dict	     = hf_hub_download(
             repo_id=_A							,     filename=_REALM_BLOCK_RECORDS_FILENAME							,     **_A)
     _UpperCAmelCase :					Optional[int]	     = np.load(_A							,     allow_pickle=_A)

     _UpperCAmelCase :					Optional[int]	     = AutoTokenizer.from_pretrained(_A							,     *_A							,     **_A)

     return cls(_A							,     _A)


 def        snake_case__ (		self							,     _A)       ->						Tuple:

     """simple docstring"""



     np.save(os.path.join(_A							,     _REALM_BLOCK_RECORDS_FILENAME)							,     self.block_records)
     # save tokenizer
     self.tokenizer.save_pretrained(_A)


 def        snake_case__ (		self							,     _A							,     _A)       ->						Tuple:

     """simple docstring"""



     _UpperCAmelCase :					List[Any]	     = []
     _UpperCAmelCase :					Optional[int]	     = []
     _UpperCAmelCase :					Union[str, Any]	     = []
     _UpperCAmelCase :					Optional[int]	     = 0

     for input_id in concat_inputs.input_ids:
         _UpperCAmelCase :					List[Any]	     = input_id.tolist()
         # Check answers between two [SEP] tokens
         _UpperCAmelCase :					List[Any]	     = input_id_list.index(self.tokenizer.sep_token_id)
         _UpperCAmelCase :					Dict	     = first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id)

         start_pos.append([])
         end_pos.append([])
         for answer in answer_ids:
             for idx in range(first_sep_idx + 1							,     _A):
                 if answer[0] == input_id_list[idx]:
                     if input_id_list[idx : idx + len(_A)] == answer:
                         start_pos[-1].append(_A)
                         end_pos[-1].append(idx + len(_A) - 1)

         if len(start_pos[-1]) == 0:
             has_answers.append(_A)
         else:
             has_answers.append(_A)
             if len(start_pos[-1]) > max_answers:
                 _UpperCAmelCase :					Tuple	     = len(start_pos[-1])

             # Pad -1 to max_answers
     for start_pos_, end_pos_ in zip(_A							,     _A):
         if len(start_pos_) < max_answers:
             _UpperCAmelCase :					List[Any]	     = [-1] * (max_answers - len(start_pos_))
             start_pos_ += padded
             end_pos_ += padded
     return has_answers, start_pos, end_pos











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

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

  _snake_case      : Dict														=		list(range(len(lowerCAmelCase ) + 1 ) )

  for i, ca in enumerate(lowerCAmelCase ):
    _snake_case      : Optional[int]														=		[i + 1]

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

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


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

      lowerCAmelCase_        =			levenshtein_distance(first_word, second_word)
      print(F"""Levenshtein distance between {first_word} and {second_word} is {result}""")




