

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:			str       							=	"block_records.npy"


_a:			Dict       							=	logging.get_logger(__name__)



def 					__lowerCAmelCase					(  A				,   A			):
	import tensorflow.compat.va as tf

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

	return np_record

class   __UpperCamelCase			:




	def __init__(						self		:  int						,		lowerCAmelCase		:  List[Any]						,		lowerCAmelCase		:  Dict						,		lowerCAmelCase		:  Tuple=2						,		lowerCAmelCase		:  Optional[Any]=1_000						,		lowerCAmelCase		:  Union[str, Any]=100						,		lowerCAmelCase		:  Any=100_000						,		):


		'''simple docstring'''







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

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

		UpperCAmelCase_				 =	builder.build()




	def 					__A    (						self		:  Union[str, Any]						,		lowerCAmelCase		:  Tuple							):


		'''simple docstring'''







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


class   __UpperCamelCase			:




	def __init__(						self		:  Optional[Any]						,		lowerCAmelCase		:  Any						,		lowerCAmelCase		:  str							):


		'''simple docstring'''







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




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


		'''simple docstring'''







		UpperCAmelCase_				 =	np.take(self.block_records						,		indices=_A						,		axis=0							)

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

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

		UpperCAmelCase_				 =	self.tokenizer(
		    _A						,		_A						,		padding=_A						,		truncation=_A						,		return_special_tokens_mask=_A						,		max_length=_A							)
		UpperCAmelCase_				 =	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 					__A    (						cls		:  Dict						,		lowerCAmelCase		:  Optional[int]						,		*lowerCAmelCase		:  Optional[Any]						,		**lowerCAmelCase		:  Union[str, Any]							):


		'''simple docstring'''







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

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

		return cls(_A						,		_A							)




	def 					__A    (						self		:  str						,		lowerCAmelCase		:  Union[str, Any]							):


		'''simple docstring'''







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




	def 					__A    (						self		:  Any						,		lowerCAmelCase		:  int						,		lowerCAmelCase		:  List[str]							):


		'''simple docstring'''







		UpperCAmelCase_				 =	[]
		UpperCAmelCase_				 =	[]
		UpperCAmelCase_				 =	[]
		UpperCAmelCase_				 =	0

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

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


from itertools import permutations


def     _UpperCAmelCase				(     ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  list(map(''.join       ,      permutations('0123456789'				)				)				)
    return result[999_999]


if __name__ == "__main__":
       print(solution())


