





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 			lowerCamelCase_		(					lowerCAmelCase:		str			,							lowerCAmelCase:		int )->						np.ndarray:
  import tensorflow.compat.va as tf

  _snake_case      : Dict														=		tf.data.TFRecordDataset(lowerCAmelCase			,							buffer_size=5_12 * 10_24 * 10_24 )
  _snake_case      : List[Any]														=		blocks_dataset.batch(lowerCAmelCase			,							drop_remainder=lowerCAmelCase )
  _snake_case      : Union[str, Any]														=		next(blocks_dataset.take(1 ).as_numpy_iterator() )

  return np_record



class 				_lowerCAmelCase			:


  '''simple docstring'''







  def __init__(					self					:    Tuple					, UpperCamelCase					:    Optional[Any]					, UpperCamelCase					:    List[Any]					, UpperCamelCase					:    Optional[Any]=2					, UpperCamelCase					:    Dict=10_00					, UpperCamelCase					:    Tuple=1_00					, UpperCamelCase					:    Dict=10_00_00					, ):


    '''simple docstring'''





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

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

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







  def UpperCamelCase_       (					self					:    Union[str, Any]					, UpperCamelCase					:    Optional[Any]							):


    '''simple docstring'''





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




class 				_lowerCAmelCase			:


  '''simple docstring'''







  def __init__(					self					:    List[Any]					, UpperCamelCase					:    List[Any]					, UpperCamelCase					:    Tuple							):


    '''simple docstring'''





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







  def __call__(					self					:    Any					, UpperCamelCase					:    List[str]					, UpperCamelCase					:    Any					, UpperCamelCase					:    int					, UpperCamelCase					:    Optional[int]=None					, UpperCamelCase					:    str="pt"							):


    '''simple docstring'''





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

    _snake_case      : Any														=		self.tokenizer.decode(question_input_ids[0]					, skip_special_tokens=UpperCamelCase							)

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

    _snake_case      : Union[str, Any]														=		self.tokenizer(
        UpperCamelCase					, UpperCamelCase					, padding=UpperCamelCase					, truncation=UpperCamelCase					, return_special_tokens_mask=UpperCamelCase					, max_length=UpperCamelCase							)
    _snake_case      : int														=		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 UpperCamelCase_       (					cls					:    List[str]					, UpperCamelCase					:    Optional[Union[str, os.PathLike]]					, *UpperCamelCase					:    str					, **UpperCamelCase					:    List[str]							):


    '''simple docstring'''





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

    _snake_case      : Optional[int]														=		AutoTokenizer.from_pretrained(UpperCamelCase					, *UpperCamelCase					, **UpperCamelCase							)

    return cls(UpperCamelCase					, UpperCamelCase							)







  def UpperCamelCase_       (					self					:    Tuple					, UpperCamelCase					:    Any							):


    '''simple docstring'''





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







  def UpperCamelCase_       (					self					:    Optional[int]					, UpperCamelCase					:    List[str]					, UpperCamelCase					:    Dict							):


    '''simple docstring'''





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

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

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










'''simple docstring'''







import unittest
from timeit import timeit






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       first_num if first_num >= second_num else second_num
		_snake_case	       =       max_num
		while (common_mult % first_num > 0) or (common_mult % second_num > 0):
				common_mult += max_num
		return common_mult






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return b if a == 0 else greatest_common_divisor(b % a						,							lowercase_  )






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return first_num // greatest_common_divisor(lowercase_						,							lowercase_  ) * second_num






def 		_lowercase		(	):



		'''simple docstring'''







		_snake_case	       =       (
		    "from __main__ import least_common_multiple_slow, least_common_multiple_fast"
		)
		print(
		    "least_common_multiple_slow():"						,							timeit("least_common_multiple_slow(1000, 999)"						,							setup=lowercase_  )						,							)
		print(
		    "least_common_multiple_fast():"						,							timeit("least_common_multiple_fast(1000, 999)"						,							setup=lowercase_  )						,							)





class      lowerCamelCase_							(       unittest.TestCase							):




					'''simple docstring'''






					lowercase__    = (
					    (1_0, 2_0),
					    (1_3, 1_5),
					    (4, 3_1),
					    (1_0, 4_2),
					    (4_3, 3_4),
					    (5, 1_2),
					    (1_2, 2_5),
					    (1_0, 2_5),
					    (6, 9),
					)
					lowercase__    = (2_0, 1_9_5, 1_2_4, 2_1_0, 1_4_6_2, 6_0, 3_0_0, 5_0, 1_8)






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for i, (first_num, second_num) in enumerate(self.test_inputs ):
									_snake_case	       =       least_common_multiple_slow(__UpperCamelCase,  __UpperCamelCase )
									_snake_case	       =       least_common_multiple_fast(__UpperCamelCase,  __UpperCamelCase )
									with self.subTest(i=__UpperCamelCase ):
											self.assertEqual(__UpperCamelCase,  self.expected_results[i] )
											self.assertEqual(__UpperCamelCase,  self.expected_results[i] )


if __name__ == "__main__":
						benchmark()
						unittest.main()





