

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__     :					str								=			"block_records.npy"


UpperCamelCase__     :					Dict								=			logging.get_logger(__name__)


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						str       ,      _SCREAMING_SNAKE_CASE    :						int				):





    """simple docstring"""


    import tensorflow.compat.va as tf

    SCREAMING_SNAKE_CASE_													=  tf.data.TFRecordDataset(_SCREAMING_SNAKE_CASE       ,      buffer_size=512 * 1_024 * 1_024				)
    SCREAMING_SNAKE_CASE_													=  blocks_dataset.batch(_SCREAMING_SNAKE_CASE       ,      drop_remainder=_SCREAMING_SNAKE_CASE				)
    SCREAMING_SNAKE_CASE_													=  next(blocks_dataset.take(1				).as_numpy_iterator()				)

    return np_record



class 					__snake_case     :

  def __init__(    self  ,						_A  ,						_A  ,						_A=2  ,						_A=1000  ,						_A=100  ,						_A=100000  ,						):
      from scann.scann_ops.py.scann_ops_pybind import builder as Builder

      SCREAMING_SNAKE_CASE_													=  Builder(db=_A  ,						num_neighbors=_A  ,						distance_measure='dot_product')
      SCREAMING_SNAKE_CASE_													=  builder.tree(
          num_leaves=_A  ,						num_leaves_to_search=_A  ,						training_sample_size=_A)
      SCREAMING_SNAKE_CASE_													=  builder.score_ah(dimensions_per_block=_A)

      SCREAMING_SNAKE_CASE_													=  builder.build()


  def   lowerCAmelCase__   (    self  ,						_A):
      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  self.searcher.search_batched(question_projection.detach().cpu())
      return retrieved_block_ids.astype('int64')






class 					__snake_case     :

  def __init__(    self  ,						_A  ,						_A):
      super().__init__()
      SCREAMING_SNAKE_CASE_													=  block_records
      SCREAMING_SNAKE_CASE_													=  tokenizer

  def __call__(    self  ,						_A  ,						_A  ,						_A  ,						_A=None  ,						_A="pt"):
      SCREAMING_SNAKE_CASE_													=  np.take(self.block_records  ,						indices=_A  ,						axis=0)

      SCREAMING_SNAKE_CASE_													=  self.tokenizer.decode(question_input_ids[0]  ,						skip_special_tokens=_A)

      SCREAMING_SNAKE_CASE_													=  []
      SCREAMING_SNAKE_CASE_													=  []
      for retrieved_block in retrieved_blocks:
          text.append(_A)
          text_pair.append(retrieved_block.decode())

      SCREAMING_SNAKE_CASE_													=  self.tokenizer(
          _A  ,						_A  ,						padding=_A  ,						truncation=_A  ,						return_special_tokens_mask=_A  ,						max_length=_A)
      SCREAMING_SNAKE_CASE_													=  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   lowerCAmelCase__   (    cls  ,						_A  ,						*_A  ,						**_A):
      if os.path.isdir(_A):
          SCREAMING_SNAKE_CASE_													=  os.path.join(_A  ,						_REALM_BLOCK_RECORDS_FILENAME)
      else:
          SCREAMING_SNAKE_CASE_													=  hf_hub_download(
              repo_id=_A  ,						filename=_REALM_BLOCK_RECORDS_FILENAME  ,						**_A)
      SCREAMING_SNAKE_CASE_													=  np.load(_A  ,						allow_pickle=_A)

      SCREAMING_SNAKE_CASE_													=  AutoTokenizer.from_pretrained(_A  ,						*_A  ,						**_A)

      return cls(_A  ,						_A)

  def   lowerCAmelCase__   (    self  ,						_A):
      # save block records
      np.save(os.path.join(_A  ,						_REALM_BLOCK_RECORDS_FILENAME)  ,						self.block_records)
      # save tokenizer
      self.tokenizer.save_pretrained(_A)


  def   lowerCAmelCase__   (    self  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  []
      SCREAMING_SNAKE_CASE_													=  []
      SCREAMING_SNAKE_CASE_													=  []
      SCREAMING_SNAKE_CASE_													=  0

      for input_id in concat_inputs.input_ids:
          SCREAMING_SNAKE_CASE_													=  input_id.tolist()
          # Check answers between two [SEP] tokens
          SCREAMING_SNAKE_CASE_													=  input_id_list.index(self.tokenizer.sep_token_id)
          SCREAMING_SNAKE_CASE_													=  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:
                  SCREAMING_SNAKE_CASE_													=  len(start_pos[-1])

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





def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						str				):





    """simple docstring"""


    return "".join(chr(ord(_SCREAMING_SNAKE_CASE				) + 32				) if 'A' <= char <= 'Z' else char for char in word				)


if __name__ == "__main__":
       from doctest import testmod

       testmod()


