
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     a						(							snake_case__: str						,       snake_case__: int ):


				'''simple docstring'''
				import tensorflow.compat.va as tf

				lowercase_					  = tf.data.TFRecordDataset(snake_case__						,       buffer_size=512 * 1_024 * 1_024 )
				lowercase_					  = blocks_dataset.batch(snake_case__						,       drop_remainder=snake_case__ )
				lowercase_					  = next(blocks_dataset.take(1 ).as_numpy_iterator() )

				return np_record
class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      int	,							SCREAMING_SNAKE_CASE_  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Dict=2	,							SCREAMING_SNAKE_CASE_  :      Dict=1_0_0_0	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1_0_0	,							SCREAMING_SNAKE_CASE_  :      List[Any]=1_0_0_0_0_0	,							)	->					Any:
					from scann.scann_ops.py.scann_ops_pybind import builder as Builder

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

					lowercase_					  = builder.build()




	def 						_lowercase  (	self  :      Optional[Any]	,							SCREAMING_SNAKE_CASE_  :      Dict	)	->					List[str]:
					lowercase_  ,     lowercase_					  = self.searcher.search_batched(question_projection.detach().cpu()	)
					return retrieved_block_ids.astype('''int64'''	)

class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      str	,							SCREAMING_SNAKE_CASE_  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      Any	)	->					Optional[Any]:
					super().__init__()
					lowercase_					  = block_records
					lowercase_					  = tokenizer
	def __call__(	self  :      Any	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      str=None	,							SCREAMING_SNAKE_CASE_  :      List[Any]="pt"	)	->					Tuple:
					lowercase_					  = np.take(self.block_records	,							indices=SCREAMING_SNAKE_CASE_	,							axis=0	)

					lowercase_					  = self.tokenizer.decode(question_input_ids[0]	,							skip_special_tokens=SCREAMING_SNAKE_CASE_	)

					lowercase_					  = []
					lowercase_					  = []
					for retrieved_block in retrieved_blocks:
									text.append(SCREAMING_SNAKE_CASE_	)
									text_pair.append(retrieved_block.decode()	)

					lowercase_					  = self.tokenizer(
					    SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							padding=SCREAMING_SNAKE_CASE_	,							truncation=SCREAMING_SNAKE_CASE_	,							return_special_tokens_mask=SCREAMING_SNAKE_CASE_	,							max_length=SCREAMING_SNAKE_CASE_	)
					lowercase_					  = concat_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_	)

					if answer_ids is not None:
									return self.block_has_answer(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	) + (concat_inputs_tensors,)
					else:
									return (None, None, None, concat_inputs_tensors)
	@classmethod
	def 						_lowercase  (	cls  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Optional[Union[str, os.PathLike]]	,							*SCREAMING_SNAKE_CASE_  :      List[Any]	,							**SCREAMING_SNAKE_CASE_  :      Dict	)	->					List[str]:
					if os.path.isdir(SCREAMING_SNAKE_CASE_	):
									lowercase_					  = os.path.join(SCREAMING_SNAKE_CASE_	,							_REALM_BLOCK_RECORDS_FILENAME	)
					else:
									lowercase_					  = hf_hub_download(
									    repo_id=SCREAMING_SNAKE_CASE_	,							filename=_REALM_BLOCK_RECORDS_FILENAME	,							**SCREAMING_SNAKE_CASE_	)
					lowercase_					  = np.load(SCREAMING_SNAKE_CASE_	,							allow_pickle=SCREAMING_SNAKE_CASE_	)

					lowercase_					  = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_	,							*SCREAMING_SNAKE_CASE_	,							**SCREAMING_SNAKE_CASE_	)

					return cls(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      int	,							SCREAMING_SNAKE_CASE_  :      Any	)	->					Optional[Any]:
					# save block records
					np.save(os.path.join(SCREAMING_SNAKE_CASE_	,							_REALM_BLOCK_RECORDS_FILENAME	)	,							self.block_records	)
					# save tokenizer
					self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_	)




	def 						_lowercase  (	self  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]	,							SCREAMING_SNAKE_CASE_  :      List[Any]	)	->					Optional[Any]:
					lowercase_					  = []
					lowercase_					  = []
					lowercase_					  = []
					lowercase_					  = 0

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

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

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






from __future__ import annotations

__a        = 'Alexander Joslin'




def     a						(							snake_case__: int ):


				'''simple docstring'''

				if number <= 0:
								lowercase_					  = F'''n must be greater than 0. Got n = {number}'''
								raise ValueError(snake_case__ )

				lowercase_					  = [number + 1] * (number + 1)

				# starting position
				lowercase_					  = 0
				for i in range(1						,       snake_case__ ):
								lowercase_					  = min(table[i + 1]						,       table[i] + 1 )
								# check if out of bounds
								if i * 2 <= number:
												lowercase_					  = min(table[i * 2]						,       table[i] + 1 )
								# check if out of bounds
								if i * 3 <= number:
												lowercase_					  = min(table[i * 3]						,       table[i] + 1 )
				return table[number]


if __name__ == "__main__":
						import doctest

						doctest.testmod()




