





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


__lowercase							:Union[str, Any]							=	"block_records.npy"


__lowercase							:List[str]							=	logging.get_logger(__name__)







def    UpperCAmelCase				(  _lowerCamelCase   :	str      , _lowerCamelCase   :	int  ):





						'''simple docstring'''


						import tensorflow.compat.va as tf

						SCREAMING_SNAKE_CASE__  :					List[Any] =       tf.data.TFRecordDataset(_lowerCamelCase      , buffer_size=512 * 1_024 * 1_024  )
						SCREAMING_SNAKE_CASE__  :					List[str] =       blocks_dataset.batch(_lowerCamelCase      , drop_remainder=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       next(blocks_dataset.take(1  ).as_numpy_iterator()  )

						return np_record



class  _a  :






							"""simple docstring"""

							def __init__(   self					:  List[str]				,						a					:  List[str]				,						a					:  Union[str, Any]				,						a					:  Dict=2				,						a					:  Union[str, Any]=10_00				,						a					:  Optional[int]=1_00				,						a					:  List[Any]=10_00_00				,						)						->Dict:
													from scann.scann_ops.py.scann_ops_pybind import builder as Builder

													SCREAMING_SNAKE_CASE__  :					str =       Builder(db=a				,						num_neighbors=a				,						distance_measure="dot_product"     )
													SCREAMING_SNAKE_CASE__  :					Dict =       builder.tree(
													    num_leaves=a				,						num_leaves_to_search=a				,						training_sample_size=a     )
													SCREAMING_SNAKE_CASE__  :					str =       builder.score_ah(dimensions_per_block=a     )

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






							def   A_	(   self					:  Any				,						a					:  Union[str, Any]     )						->Optional[Any]:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.searcher.search_batched(question_projection.detach().cpu()     )
													return retrieved_block_ids.astype("int64"     )







class  _a  :






							"""simple docstring"""

							def __init__(   self					:  Optional[int]				,						a					:  Any				,						a					:  Any     )						->Optional[int]:
													super().__init__()
													SCREAMING_SNAKE_CASE__  :					str =       block_records
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       tokenizer

							def __call__(   self					:  List[Any]				,						a					:  Optional[Any]				,						a					:  int				,						a					:  Dict				,						a					:  Union[str, Any]=None				,						a					:  Optional[Any]="pt"     )						->int:
													SCREAMING_SNAKE_CASE__  :					str =       np.take(self.block_records				,						indices=a				,						axis=0     )

													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.tokenizer.decode(question_input_ids[0]				,						skip_special_tokens=a     )

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

													SCREAMING_SNAKE_CASE__  :					List[str] =       self.tokenizer(
													    a				,						a				,						padding=a				,						truncation=a				,						return_special_tokens_mask=a				,						max_length=a     )
													SCREAMING_SNAKE_CASE__  :					Any =       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					:  List[Any]				,						a					:  Optional[Union[str, os.PathLike]]				,						*a					:  Optional[int]				,						**a					:  Optional[int]     )						->Any:
													if os.path.isdir(a     ):
																			SCREAMING_SNAKE_CASE__  :					Any =       os.path.join(a				,						_REALM_BLOCK_RECORDS_FILENAME     )
													else:
																			SCREAMING_SNAKE_CASE__  :					List[str] =       hf_hub_download(
																			    repo_id=a				,						filename=_REALM_BLOCK_RECORDS_FILENAME				,						**a     )
													SCREAMING_SNAKE_CASE__  :					Any =       np.load(a				,						allow_pickle=a     )

													SCREAMING_SNAKE_CASE__  :					Any =       AutoTokenizer.from_pretrained(a				,						*a				,						**a     )

													return cls(a				,						a     )

							def   A_	(   self					:  Any				,						a					:  List[Any]     )						->Optional[Any]:
													# save block records
													np.save(os.path.join(a				,						_REALM_BLOCK_RECORDS_FILENAME     )				,						self.block_records     )
													# save tokenizer
													self.tokenizer.save_pretrained(a     )






							def   A_	(   self					:  Union[str, Any]				,						a					:  Any				,						a					:  Optional[Any]     )						->List[Any]:
													SCREAMING_SNAKE_CASE__  :					Any =       []
													SCREAMING_SNAKE_CASE__  :					int =       []
													SCREAMING_SNAKE_CASE__  :					Any =       []
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       0

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

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






from itertools import combinations_with_replacement







def    UpperCAmelCase				(  _lowerCamelCase   :	int = 100  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					list[int] =       [*list(range(1      , 21  )  ), 25]
						SCREAMING_SNAKE_CASE__  :					list[int] =       [2 * x for x in range(1      , 21  )] + [50]
						SCREAMING_SNAKE_CASE__  :					list[int] =       [3 * x for x in range(1      , 21  )]
						SCREAMING_SNAKE_CASE__  :					list[int] =       singles + doubles + triples + [0]

						SCREAMING_SNAKE_CASE__  :					int =       0
						SCREAMING_SNAKE_CASE__  :					int
						SCREAMING_SNAKE_CASE__  :					int
						SCREAMING_SNAKE_CASE__  :					int
						SCREAMING_SNAKE_CASE__  :					int

						for double in doubles:
												for throwa, throwa in combinations_with_replacement(_lowerCamelCase      , 2  ):
																		SCREAMING_SNAKE_CASE__  :					int =       double + throwa + throwa
																		if checkout_total < limit:
																								num_checkouts += 1

						return num_checkouts


if __name__ == "__main__":
				print(f"{solution() = }")