"""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


_UpperCamelCase         =		"block_records.npy"


_UpperCamelCase         =		logging.get_logger(__name__)

def    _A(		lowerCAmelCase ,     lowerCAmelCase       ):
							import tensorflow.compat.va as tf

							A__					:    Union[str, Any]														= tf.data.TFRecordDataset(lowerCAmelCase ,     buffer_size=512 * 1024 * 1024       )
							A__					:    Optional[int]														= blocks_dataset.batch(lowerCAmelCase ,     drop_remainder=lowerCAmelCase       )
							A__					:    str														= next(blocks_dataset.take(1       ).as_numpy_iterator()       )

							return np_record
class 	__UpperCAmelCase						:







			'''simple docstring'''





			def __init__( self      ,			snake_case_      ,			snake_case_      ,			snake_case_=2      ,			snake_case_=1_000      ,			snake_case_=100      ,			snake_case_=100_000      ,			):







										'''simple docstring'''


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

										A__					:    Optional[Any]														= Builder(db=snake_case_      ,			num_neighbors=snake_case_      ,			distance_measure="""dot_product"""       )
										A__					:    str														= builder.tree(
										    num_leaves=snake_case_      ,			num_leaves_to_search=snake_case_      ,			training_sample_size=snake_case_       )
										A__					:    Union[str, Any]														= builder.score_ah(dimensions_per_block=snake_case_       )

										A__					:    Union[str, Any]														= builder.build()





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Optional[Any]														= self.searcher.search_batched(question_projection.detach().cpu()       )
										return retrieved_block_ids.astype("""int64"""       )





class 	__UpperCAmelCase						:







			'''simple docstring'''





			def __init__( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										super().__init__()
										A__					:    Tuple														= block_records
										A__					:    Union[str, Any]														= tokenizer





			def __call__( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_=None      ,			snake_case_="pt"       ):







										'''simple docstring'''


										A__					:    List[Any]														= np.take(self.block_records      ,			indices=snake_case_      ,			axis=0       )

										A__					:    Optional[int]														= self.tokenizer.decode(question_input_ids[0]      ,			skip_special_tokens=snake_case_       )

										A__					:    Union[str, Any]														= []
										A__					:    Any														= []
										for retrieved_block in retrieved_blocks:
																	text.append(snake_case_       )
																	text_pair.append(retrieved_block.decode()       )

										A__					:    int														= self.tokenizer(
										    snake_case_      ,			snake_case_      ,			padding=snake_case_      ,			truncation=snake_case_      ,			return_special_tokens_mask=snake_case_      ,			max_length=snake_case_       )
										A__					:    int														= concat_inputs.convert_to_tensors(snake_case_       )

										if answer_ids is not None:
																	return self.block_has_answer(snake_case_      ,			snake_case_       ) + (concat_inputs_tensors,)
										else:
																	return (None, None, None, concat_inputs_tensors)





			@classmethod
			def 		lowerCamelCase				( cls      ,			snake_case_      ,			*snake_case_      ,			**snake_case_       ):







										'''simple docstring'''


										if os.path.isdir(snake_case_       ):
																	A__					:    int														= os.path.join(snake_case_      ,			_REALM_BLOCK_RECORDS_FILENAME       )
										else:
																	A__					:    str														= hf_hub_download(
																	    repo_id=snake_case_      ,			filename=_REALM_BLOCK_RECORDS_FILENAME      ,			**snake_case_       )
										A__					:    Any														= np.load(snake_case_      ,			allow_pickle=snake_case_       )

										A__					:    List[Any]														= AutoTokenizer.from_pretrained(snake_case_      ,			*snake_case_      ,			**snake_case_       )

										return cls(snake_case_      ,			snake_case_       )





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    str														= []
										A__					:    Any														= []
										A__					:    Any														= []
										A__					:    Any														= 0

										for input_id in concat_inputs.input_ids:
																	A__					:    List[str]														= input_id.tolist()
																	# Check answers between two [SEP] tokens
																	A__					:    Optional[int]														= input_id_list.index(self.tokenizer.sep_token_id       )
																	A__					:    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      ,			snake_case_       ):
																															if answer[0] == input_id_list[idx]:
																																						if input_id_list[idx : idx + len(snake_case_       )] == answer:
																																													start_pos[-1].append(snake_case_       )
																																													end_pos[-1].append(idx + len(snake_case_       ) - 1       )

																	if len(start_pos[-1]       ) == 0:
																								has_answers.append(snake_case_       )
																	else:
																								has_answers.append(snake_case_       )
																								if len(start_pos[-1]       ) > max_answers:
																															A__					:    int														= len(start_pos[-1]       )

        # Pad -1 to max_answers
										for start_pos_, end_pos_ in zip(snake_case_      ,			snake_case_       ):
																	if len(start_pos_       ) < max_answers:
																								A__					:    int														= [-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

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_UpperCamelCase         =		TypeVar("T", bound=bool)
class 	__UpperCAmelCase						(Generic[T]    ):







			'''simple docstring'''





			def __init__( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    T														= value
										A__					:    RandomizedHeapNode[T] | None														= None
										A__					:    RandomizedHeapNode[T] | None														= None





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return self._value





			@staticmethod
			def 		lowerCamelCase				( snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										if not roota:
																	return roota

										if not roota:
																	return roota

										if roota.value > roota.value:
																	A__							,       A__					:    Union[str, Any]														= roota, roota

										if random.choice([True, False]       ):
																	A__							,       A__					:    Any														= roota.right, roota.left

										A__					:    Dict														= RandomizedHeapNode.merge(roota.left      ,			snake_case_       )

										return roota





class 	__UpperCAmelCase						(Generic[T]    ):







			'''simple docstring'''





			def __init__( self      ,			snake_case_ = ()       ):







										'''simple docstring'''


										A__					:    RandomizedHeapNode[T] | None														= None

										if data:
																	for item in data:
																								self.insert(snake_case_       )





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Dict														= RandomizedHeapNode.merge(self._root      ,			RandomizedHeapNode(snake_case_       )       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[str]														= self.top()

										if self._root is None:
																	return None

										A__					:    str														= RandomizedHeapNode.merge(self._root.left      ,			self._root.right       )

										return result





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										if not self._root:
																	raise IndexError("""Can't get top element for the empty heap."""       )
										return self._root.value





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[Any]														= None





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    int														= []
										while self:
																	result.append(self.pop()       )

										return result





			def __bool__( self       ):







										'''simple docstring'''


										return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





