






'''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


a_      = 'block_records.npy'


a_      = logging.get_logger(__name__)



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				np.ndarray:

						'''simple docstring'''


						import tensorflow.compat.va as tf

						a_				     =   tf.data.TFRecordDataset(lowercase__	,buffer_size=512 * 1024 * 1024				)
						a_				     =   blocks_dataset.batch(lowercase__	,drop_remainder=lowercase__				)
						a_				     =   next(blocks_dataset.take(1				).as_numpy_iterator()				)

						return np_record





class 		SCREAMING_SNAKE_CASE__   :



		def __init__(    self:    Optional[Any]				,   a:    List[str]				,   a:    str				,   a:    Optional[Any]=2				,   a:    Optional[int]=10_00				,   a:    Optional[int]=1_00				,   a:    str=10_00_00				,   )  ->Optional[int]:






								'''simple docstring'''


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

								a_				     =   Builder(db=a				,   num_neighbors=a				,   distance_measure="dot_product")
								a_				     =   builder.tree(
								    num_leaves=a				,   num_leaves_to_search=a				,   training_sample_size=a)
								a_				     =   builder.score_ah(dimensions_per_block=a)

								a_				     =   builder.build()




		def _lowerCAmelCase     (    self:    Dict				,   a:    str)  ->str:






								'''simple docstring'''


								a_     ,       a_				     =   self.searcher.search_batched(question_projection.detach().cpu())
								return retrieved_block_ids.astype("int64")





class 		SCREAMING_SNAKE_CASE__   :



		def __init__(    self:    List[Any]				,   a:    List[str]				,   a:    int)  ->int:






								'''simple docstring'''


								super().__init__()
								a_				     =   block_records
								a_				     =   tokenizer



		def __call__(    self:    Union[str, Any]				,   a:    Tuple				,   a:    Dict				,   a:    int				,   a:    Optional[int]=None				,   a:    Optional[Any]="pt")  ->Any:






								'''simple docstring'''


								a_				     =   np.take(self.block_records				,   indices=a				,   axis=0)

								a_				     =   self.tokenizer.decode(question_input_ids[0]				,   skip_special_tokens=a)

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

								a_				     =   self.tokenizer(
								    a				,   a				,   padding=a				,   truncation=a				,   return_special_tokens_mask=a				,   max_length=a)
								a_				     =   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:    Union[str, Any]				,   a:    Optional[Union[str, os.PathLike]]				,   *a:    List[Any]				,   **a:    Union[str, Any])  ->int:






								'''simple docstring'''


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

								a_				     =   AutoTokenizer.from_pretrained(a				,   *a				,   **a)

								return cls(a				,   a)



		def _lowerCAmelCase     (    self:    int				,   a:    Tuple)  ->Dict:






								'''simple docstring'''


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




		def _lowerCAmelCase     (    self:    str				,   a:    Dict				,   a:    str)  ->int:






								'''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				,   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:
																										a_				     =   len(start_pos[-1])

        # Pad -1 to max_answers
								for start_pos_, end_pos_ in zip(a				,   a):
														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











'''simple docstring'''





from __future__ import annotations

a_      = [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:    Optional[int]				,   a:    int)  ->None:






								'''simple docstring'''


								a_				     =   -1
								a_				     =   -1
								a_				     =   [-1] * length
								a_				     =   None
								a_				     =   None




		def __repr__(    self:    Union[str, Any])  ->str:






								'''simple docstring'''


								return f"""Node(min_value={self.minn} max_value={self.maxx})"""





def 			__UpperCAmelCase				(lowercase__				)		->				Node | None:

						'''simple docstring'''


						a_				     =   Node(len(lowercase__				)				)
						a_     ,       a_				     =   min(lowercase__				), max(lowercase__				)
						# Leaf node case where the node contains only one unique value
						if root.minn == root.maxx:
												return root
						a_				     =   (root.minn + root.maxx) // 2

						a_				     =   []
						a_				     =   []

						for index, num in enumerate(lowercase__				):
												if num <= pivot:
																		left_arr.append(lowercase__				)
												else:
																		right_arr.append(lowercase__				)
												a_				     =   len(lowercase__				)
						a_				     =   build_tree(lowercase__				)
						a_				     =   build_tree(lowercase__				)
						return root



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						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_				     =   (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	,lowercase__	,node.map_left[index] - 1				)
						else:
												# go to the right subtree and map index to the right subtree
												return rank_till_index(node.right	,lowercase__	,index - node.map_left[index]				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						if start > end:
												return 0
						a_				     =   rank_till_index(lowercase__	,lowercase__	,lowercase__				)
						a_				     =   rank_till_index(lowercase__	,lowercase__	,start - 1				)
						return rank_till_end - rank_before_start



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						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_				     =   node.map_left[end] - (
						    node.map_left[start - 1] if start else 0
						)
						if num_elements_in_left_tree > index:
												return quantile(
												    node.left	,lowercase__	,(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				(lowercase__	,lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						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_				     =   range_counting(
						    node.left	,(node.map_left[start - 1] if start else 0)	,node.map_left[end] - 1	,lowercase__	,lowercase__	,)
						a_				     =   range_counting(
						    node.right	,start - (node.map_left[start - 1] if start else 0)	,end - node.map_left[end]	,lowercase__	,lowercase__	,)
						return left + right


if __name__ == "__main__":
							import doctest

							doctest.testmod()



