





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


lowerCAmelCase_   =   "block_records.npy"


lowerCAmelCase_   =   logging.get_logger(__name__)




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

       _snake_case      :							Dict     				=						tf.data.TFRecordDataset(lowercase_      ,		buffer_size=512 * 1024 * 1024  )
       _snake_case      :							Union[str, Any]     				=						blocks_dataset.batch(lowercase_      ,		drop_remainder=lowercase_  )
       _snake_case      :							List[Any]     				=						next(blocks_dataset.take(1  ).as_numpy_iterator()  )

       return np_record

class 				A    :



      def __init__(					self     ,  lowercase_     ,  lowercase_     ,  lowercase_=2     ,  lowercase_=1000     ,  lowercase_=100     ,  lowercase_=10_0000     ,  )      ->   str:



             '''simple docstring'''

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

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

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



      def    __a (					self     ,  lowercase_					)      ->   Dict:



             '''simple docstring'''

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


class 				A    :



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



             '''simple docstring'''

             super().__init__()
             _snake_case      :							Optional[int]     				=						block_records
             _snake_case      :							List[str]     				=						tokenizer



      def __call__(					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_=None     ,  lowercase_="pt"					)      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							Dict     				=						np.take(self.block_records     ,  indices=lowercase_     ,  axis=0					)

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

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

             _snake_case      :							Dict     				=						self.tokenizer(
                 lowercase_     ,  lowercase_     ,  padding=lowercase_     ,  truncation=lowercase_     ,  return_special_tokens_mask=lowercase_     ,  max_length=lowercase_					)
             _snake_case      :							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    __a (					cls     ,  lowercase_     ,  *lowercase_     ,  **lowercase_					)      ->   str:



             '''simple docstring'''

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

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

             return cls(lowercase_     ,  lowercase_					)



      def    __a (					self     ,  lowercase_					)      ->   Optional[int]:



             '''simple docstring'''

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



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						[]
             _snake_case      :							List[Any]     				=						[]
             _snake_case      :							Dict     				=						[]
             _snake_case      :							Any     				=						0

             for input_id in concat_inputs.input_ids:
                    _snake_case      :							List[str]     				=						input_id.tolist()
                    # Check answers between two [SEP] tokens
                    _snake_case      :							Optional[int]     				=						input_id_list.index(self.tokenizer.sep_token_id					)
                    _snake_case      :							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:
                                  _snake_case      :							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:
                           _snake_case      :							Optional[int]     				=						[-1] * (max_answers - len(start_pos_					))
                           start_pos_ += padded
                           end_pos_ += padded
             return has_answers, start_pos, end_pos













def 						A_			(  lowercase_  )			->						list:
       if len(lowercase_  ) <= 1:
              return data
       else:
              return [
                  *quick_sort([e for e in data[1:] if e <= data[0]]  ),
                  data[0],
                  *quick_sort([e for e in data[1:] if e > data[0]]  ),
              ]


if __name__ == "__main__":
   import doctest

   doctest.testmod()






