code: 
  
  
  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
  
  
  A_    :					Optional[int]             ="""block_records.npy"""
  
  
  A_    :					str             =logging.get_logger(__name__)
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						import tensorflow.compat.va as tf
  
  						a_					=		tf.data.TFRecordDataset(UpperCAmelCase__       ,    buffer_size=512 * 1_024 * 1_024      )
  						a_					=		blocks_dataset.batch(UpperCAmelCase__       ,    drop_remainder=UpperCAmelCase__      )
  						a_					=		next(blocks_dataset.take(1      ).as_numpy_iterator()      )
  
  						return np_record
  
  
  
  
  
  class        lowercase_				:
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase=2			,    _UpperCAmelCase=1_000			,    _UpperCAmelCase=100			,    _UpperCAmelCase=100_000			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													from scann.scann_ops.py.scann_ops_pybind import builder as Builder
  
  													a_					=		Builder(db=_UpperCAmelCase			,    num_neighbors=_UpperCAmelCase			,    distance_measure="""dot_product"""		)
  													a_					=		builder.tree(
  													    num_leaves=_UpperCAmelCase			,    num_leaves_to_search=_UpperCAmelCase			,    training_sample_size=_UpperCAmelCase		)
  													a_					=		builder.score_ah(dimensions_per_block=_UpperCAmelCase		)
  
  													a_					=		builder.build()
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_					=		self.searcher.search_batched(question_projection.detach().cpu()		)
  													return retrieved_block_ids.astype("""int64"""		)
  
  
  
  
  
  class        lowercase_				:
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().__init__()
  													a_					=		block_records
  													a_					=		tokenizer
  
  
  							def __call__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase=None			,    _UpperCAmelCase="pt"		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		np.take(self.block_records			,    indices=_UpperCAmelCase			,    axis=0		)
  
  													a_					=		self.tokenizer.decode(question_input_ids[0]			,    skip_special_tokens=_UpperCAmelCase		)
  
  													a_					=		[]
  													a_					=		[]
  													for retrieved_block in retrieved_blocks:
  																			text.append(_UpperCAmelCase		)
  																			text_pair.append(retrieved_block.decode()		)
  
  													a_					=		self.tokenizer(
  													    _UpperCAmelCase			,    _UpperCAmelCase			,    padding=_UpperCAmelCase			,    truncation=_UpperCAmelCase			,    return_special_tokens_mask=_UpperCAmelCase			,    max_length=_UpperCAmelCase		)
  													a_					=		concat_inputs.convert_to_tensors(_UpperCAmelCase		)
  
  													if answer_ids is not None:
  																			return self.block_has_answer(_UpperCAmelCase			,    _UpperCAmelCase		) + (concat_inputs_tensors,)
  													else:
  																			return (None, None, None, concat_inputs_tensors)
  
  
  							@classmethod
  							def 						lowercase__     (   cls			,    _UpperCAmelCase			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if os.path.isdir(_UpperCAmelCase		):
  																			a_					=		os.path.join(_UpperCAmelCase			,    _REALM_BLOCK_RECORDS_FILENAME		)
  													else:
  																			a_					=		hf_hub_download(
  																			    repo_id=_UpperCAmelCase			,    filename=_REALM_BLOCK_RECORDS_FILENAME			,    **_UpperCAmelCase		)
  													a_					=		np.load(_UpperCAmelCase			,    allow_pickle=_UpperCAmelCase		)
  
  													a_					=		AutoTokenizer.from_pretrained(_UpperCAmelCase			,    *_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  													return cls(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													np.save(os.path.join(_UpperCAmelCase			,    _REALM_BLOCK_RECORDS_FILENAME		)			,    self.block_records		)
  													# save tokenizer
  													self.tokenizer.save_pretrained(_UpperCAmelCase		)
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		[]
  													a_					=		[]
  													a_					=		[]
  													a_					=		0
  
  													for input_id in concat_inputs.input_ids:
  																			a_					=		input_id.tolist()
  																			# Check answers between two [SEP] tokens
  																			a_					=		input_id_list.index(self.tokenizer.sep_token_id		)
  																			a_					=		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			,    _UpperCAmelCase		):
  																															if answer[0] == input_id_list[idx]:
  																																					if input_id_list[idx : idx + len(_UpperCAmelCase		)] == answer:
  																																											start_pos[-1].append(_UpperCAmelCase		)
  																																											end_pos[-1].append(idx + len(_UpperCAmelCase		) - 1		)
  
  																			if len(start_pos[-1]		) == 0:
  																									has_answers.append(_UpperCAmelCase		)
  																			else:
  																									has_answers.append(_UpperCAmelCase		)
  																									if len(start_pos[-1]		) > max_answers:
  																															a_					=		len(start_pos[-1]		)
  
          # Pad -1 to max_answers
  													for start_pos_, end_pos_ in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			if len(start_pos_		) < max_answers:
  																									a_					=		[-1] * (max_answers - len(start_pos_		))
  																									start_pos_ += padded
  																									end_pos_ += padded
  													return has_answers, start_pos, end_pos
style_context: 
  
  
  import os
  import unittest
  
  from transformers import MPNetTokenizerFast
  from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  
  @require_tokenizers
  class        lowercase_				( UpperCamelCase__			,unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		MPNetTokenizer
  							snake_case_        =		MPNetTokenizerFast
  							snake_case_        =		True
  							snake_case_        =		True
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().setUp()
  
  													a_					=		[
  													    """[UNK]""",
  													    """[CLS]""",
  													    """[SEP]""",
  													    """[PAD]""",
  													    """[MASK]""",
  													    """want""",
  													    """##want""",
  													    """##ed""",
  													    """wa""",
  													    """un""",
  													    """runn""",
  													    """##ing""",
  													    """,""",
  													    """low""",
  													    """lowest""",
  													]
  													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""vocab_file"""]		)
  													with open(self.vocab_file			,    """w"""			,    encoding="""utf-8"""		) as vocab_writer:
  																			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		"""UNwant\u00E9d,running"""
  													a_					=		"""unwanted, running"""
  													return input_text, output_text
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer_class(self.vocab_file		)
  
  													a_					=		tokenizer.tokenize("""UNwant\u00E9d,running"""		)
  													self.assertListEqual(_UpperCAmelCase			,    ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]		)
  													self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)			,    [9, 6, 7, 12, 10, 11]		)
  
  
  
  
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer_class.from_pretrained("""microsoft/mpnet-base"""		)
  
  													a_					=		tokenizer.encode("""sequence builders"""			,    add_special_tokens=_UpperCAmelCase		)
  													a_					=		tokenizer.encode("""multi-sequence build"""			,    add_special_tokens=_UpperCAmelCase		)
  
  													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase		)
  													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  													assert encoded_sentence == [0] + text + [2]
  													assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]