"""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


SCREAMING_SNAKE_CASE__       =	"block_records.npy"


SCREAMING_SNAKE_CASE__       =	logging.get_logger(__name__)





def  lowerCAmelCase__     (      _UpperCamelCase							:					str			,      _UpperCamelCase							:					int       )       ->					np.ndarray:





						"""simple docstring"""






						import tensorflow.compat.va as tf

						snake_case             =      tf.data.TFRecordDataset(_UpperCamelCase			,      buffer_size=5_1_2 * 1_0_2_4 * 1_0_2_4       )
						snake_case             =      blocks_dataset.batch(_UpperCamelCase			,      drop_remainder=_UpperCamelCase       )
						snake_case             =      next(blocks_dataset.take(1       ).as_numpy_iterator()       )

						return np_record



class 	lowerCAmelCase_     :



				"""simple docstring"""

				def __init__(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase=2		,     lowerCAmelCase=10_00		,     lowerCAmelCase=1_00		,     lowerCAmelCase=10_00_00		,     ):






										"""simple docstring"""

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

										snake_case             =      Builder(db=lowerCAmelCase		,     num_neighbors=lowerCAmelCase		,     distance_measure='dot_product'     )
										snake_case             =      builder.tree(
										    num_leaves=lowerCAmelCase		,     num_leaves_to_search=lowerCAmelCase		,     training_sample_size=lowerCAmelCase     )
										snake_case             =      builder.score_ah(dimensions_per_block=lowerCAmelCase     )

										snake_case             =      builder.build()

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

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





class 	lowerCAmelCase_     :



				"""simple docstring"""

				def __init__(					self		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

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

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






										"""simple docstring"""

										snake_case             =      np.take(self.block_records		,     indices=lowerCAmelCase		,     axis=0     )

										snake_case             =      self.tokenizer.decode(question_input_ids[0]		,     skip_special_tokens=lowerCAmelCase     )

										snake_case             =      []
										snake_case             =      []
										for retrieved_block in retrieved_blocks:
																text.append(lowerCAmelCase     )
																text_pair.append(retrieved_block.decode()     )

										snake_case             =      self.tokenizer(
										    lowerCAmelCase		,     lowerCAmelCase		,     padding=lowerCAmelCase		,     truncation=lowerCAmelCase		,     return_special_tokens_mask=lowerCAmelCase		,     max_length=lowerCAmelCase     )
										snake_case             =      concat_inputs.convert_to_tensors(lowerCAmelCase     )

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

				@classmethod
				def 		snake_case			(					cls		,     lowerCAmelCase		,     *lowerCAmelCase		,     **lowerCAmelCase     ):






										"""simple docstring"""

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

										snake_case             =      AutoTokenizer.from_pretrained(lowerCAmelCase		,     *lowerCAmelCase		,     **lowerCAmelCase     )

										return cls(lowerCAmelCase		,     lowerCAmelCase     )

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

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

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      []
										snake_case             =      []
										snake_case             =      []
										snake_case             =      0

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

																if len(start_pos[-1]     ) == 0:
																						has_answers.append(lowerCAmelCase     )
																else:
																						has_answers.append(lowerCAmelCase     )
																						if len(start_pos[-1]     ) > max_answers:
																												snake_case             =      len(start_pos[-1]     )

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



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE__       =	{"tokenization_bert_japanese": ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"]}


if TYPE_CHECKING:
					from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer

else:
					import sys

					SCREAMING_SNAKE_CASE__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


