'''simple docstring'''





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


UpperCAmelCase							   =      """block_records.npy"""


UpperCAmelCase							   =      logging.get_logger(__name__)







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_       )	->	np.ndarray:
     import tensorflow.compat.va as tf

     a  :    Dict				=						tf.data.TFRecordDataset(lowercase_						,      buffer_size=5_1_2 * 1_0_2_4 * 1_0_2_4       )
     a  :    Union[str, Any]				=						blocks_dataset.batch(lowercase_						,      drop_remainder=lowercase_       )
     a  :    List[Any]				=						next(blocks_dataset.take(1       ).as_numpy_iterator()       )

     return np_record






class 							_SCREAMING_SNAKE_CASE:
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_=2							,     lowerCAmelCase_=10_00							,     lowerCAmelCase_=1_00							,     lowerCAmelCase_=10_00_00							,     )	->str:
        from scann.scann_ops.py.scann_ops_pybind import builder as Builder

        a  :    int				=						Builder(db=lowercase_							,     num_neighbors=lowercase_							,     distance_measure='''dot_product'''    )
        a  :    Optional[Any]				=						builder.tree(
            num_leaves=lowercase_							,     num_leaves_to_search=lowercase_							,     training_sample_size=lowercase_    )
        a  :    Any				=						builder.score_ah(dimensions_per_block=lowercase_    )

        a  :    List[Any]				=						builder.build()


   def _lowercase(    self							,     lowerCAmelCase_    )	->Dict:
        a  :    str				=						self.searcher.search_batched(question_projection.detach().cpu()    )
        return retrieved_block_ids.astype('''int64'''    )






class 							_SCREAMING_SNAKE_CASE:
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Tuple:
        super().__init__()
        a  :    Optional[int]				=						block_records
        a  :    List[str]				=						tokenizer
   def __call__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_=None							,     lowerCAmelCase_="pt"    )	->Tuple:
        a  :    Dict				=						np.take(self.block_records							,     indices=lowercase_							,     axis=0    )

        a  :    int				=						self.tokenizer.decode(question_input_ids[0]							,     skip_special_tokens=lowercase_    )

        a  :    Optional[int]				=						[]
        a  :    int				=						[]
        for retrieved_block in retrieved_blocks:
             text.append(lowercase_    )
             text_pair.append(retrieved_block.decode()    )

        a  :    Dict				=						self.tokenizer(
            lowercase_							,     lowercase_							,     padding=lowercase_							,     truncation=lowercase_							,     return_special_tokens_mask=lowercase_							,     max_length=lowercase_    )
        a  :    List[str]				=						concat_inputs.convert_to_tensors(lowercase_    )

        if answer_ids is not None:
             return self.block_has_answer(lowercase_							,     lowercase_    ) + (concat_inputs_tensors,)
        else:
             return (None, None, None, concat_inputs_tensors)
   @classmethod
   def _lowercase(    cls							,     lowerCAmelCase_							,     *lowerCAmelCase_							,     **lowerCAmelCase_    )	->str:

        if os.path.isdir(lowercase_    ):
             a  :    str				=						os.path.join(lowercase_							,     _REALM_BLOCK_RECORDS_FILENAME    )
        else:
             a  :    Tuple				=						hf_hub_download(
                 repo_id=lowercase_							,     filename=_REALM_BLOCK_RECORDS_FILENAME							,     **lowercase_    )
        a  :    Dict				=						np.load(lowercase_							,     allow_pickle=lowercase_    )

        a  :    Tuple				=						AutoTokenizer.from_pretrained(lowercase_							,     *lowercase_							,     **lowercase_    )

        return cls(lowercase_							,     lowercase_    )
   def _lowercase(    self							,     lowerCAmelCase_    )	->Optional[int]:
        np.save(os.path.join(lowercase_							,     _REALM_BLOCK_RECORDS_FILENAME    )							,     self.block_records    )
        # save tokenizer
        self.tokenizer.save_pretrained(lowercase_    )


   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Any:
        a  :    Optional[int]				=						[]
        a  :    List[Any]				=						[]
        a  :    Dict				=						[]
        a  :    Any				=						0

        for input_id in concat_inputs.input_ids:
             a  :    List[str]				=						input_id.tolist()
             # Check answers between two [SEP] tokens
             a  :    Optional[int]				=						input_id_list.index(self.tokenizer.sep_token_id    )
             a  :    str				=						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							,     lowercase_    ):
                       if answer[0] == input_id_list[idx]:
                            if input_id_list[idx : idx + len(lowercase_    )] == answer:
                                 start_pos[-1].append(lowercase_    )
                                 end_pos[-1].append(idx + len(lowercase_    ) - 1    )

             if len(start_pos[-1]    ) == 0:
                  has_answers.append(lowercase_    )
             else:
                  has_answers.append(lowercase_    )
                  if len(start_pos[-1]    ) > max_answers:
                       a  :    Any				=						len(start_pos[-1]    )

        # Pad -1 to max_answers
        for start_pos_, end_pos_ in zip(lowercase_							,     lowercase_    ):
             if len(start_pos_    ) < max_answers:
                  a  :    Optional[int]				=						[-1] * (max_answers - len(start_pos_    ))
                  start_pos_ += padded
                  end_pos_ += padded
        return has_answers, start_pos, end_pos
from __future__ import annotations

UpperCAmelCase							   =      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class 							_SCREAMING_SNAKE_CASE:
   def __init__(    self							,     lowerCAmelCase_    )	->None:
        a  :    int				=						-1
        a  :    int				=						-1
        a  :    list[int]				=						[-1] * length
        a  :    Node | None				=						None
        a  :    Node | None				=						None


   def __repr__(    self    )	->str:
        return F"""Node(min_value={self.minn} max_value={self.maxx})"""







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Node | None:
     a  :    Dict				=						Node(len(lowerCAmelCase_       )       )
     a			,							a  :    Tuple				=						min(lowerCAmelCase_       ), max(lowerCAmelCase_       )
     # Leaf node case where the node contains only one unique value
     if root.minn == root.maxx:
          return root
     a  :    Optional[Any]				=						(root.minn + root.maxx) // 2

     a  :    list[int]				=						[]
     a  :    list[int]				=						[]

     for index, num in enumerate(lowerCAmelCase_       ):
          if num <= pivot:
               left_arr.append(lowerCAmelCase_       )
          else:
               right_arr.append(lowerCAmelCase_       )
          a  :    int				=						len(lowerCAmelCase_       )
     a  :    List[str]				=						build_tree(lowerCAmelCase_       )
     a  :    List[str]				=						build_tree(lowerCAmelCase_       )
     return root







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if index < 0 or node is None:
          return 0
     # Leaf node cases
     if node.minn == node.maxx:
          return index + 1 if node.minn == num else 0
     a  :    List[Any]				=						(node.minn + node.maxx) // 2
     if num <= pivot:
          # go the left subtree and map index to the left subtree
          return rank_till_index(node.left						,      lowerCAmelCase_						,      node.map_left[index] - 1       )
     else:
          # go to the right subtree and map index to the right subtree
          return rank_till_index(node.right						,      lowerCAmelCase_						,      index - node.map_left[index]       )







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if start > end:
          return 0
     a  :    Optional[Any]				=						rank_till_index(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )
     a  :    Any				=						rank_till_index(lowerCAmelCase_						,      lowerCAmelCase_						,      start - 1       )
     return rank_till_end - rank_before_start







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if index > (end - start) or start > end or node is None:
          return -1
     # Leaf node case
     if node.minn == node.maxx:
          return node.minn
     # Number of elements in the left subtree in interval [start, end]
     a  :    Optional[int]				=						node.map_left[end] - (
         node.map_left[start - 1] if start else 0
     )
     if num_elements_in_left_tree > index:
          return quantile(
              node.left						,      lowerCAmelCase_						,      (node.map_left[start - 1] if start else 0)						,      node.map_left[end] - 1						,      )
     else:
          return quantile(
              node.right						,      index - num_elements_in_left_tree						,      start - (node.map_left[start - 1] if start else 0)						,      end - node.map_left[end]						,      )







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if (
         start > end
         or node is None
         or start_num > end_num
         or node.minn > end_num
         or node.maxx < start_num
     ):
          return 0
     if start_num <= node.minn and node.maxx <= end_num:
          return end - start + 1
     a  :    Tuple				=						range_counting(
         node.left						,      (node.map_left[start - 1] if start else 0)						,      node.map_left[end] - 1						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )
     a  :    int				=						range_counting(
         node.right						,      start - (node.map_left[start - 1] if start else 0)						,      end - node.map_left[end]						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )
     return left + right


if __name__ == "__main__":
  import doctest

  doctest.testmod()