'''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


A_        =   "block_records.npy"


A_        =   logging.get_logger(__name__)


def 					UpperCAmelCase__      (							_lowerCAmelCase						: str,			_lowerCAmelCase						: int)						->		np.ndarray:



						"""simple docstring"""

						import tensorflow.compat.va as tf

						snake_case   :					Tuple							=						tf.data.TFRecordDataset(_lowerCAmelCase,			buffer_size=512 * 1024 * 1024)
						snake_case   :					List[str]							=						blocks_dataset.batch(_lowerCAmelCase,			drop_remainder=_lowerCAmelCase)
						snake_case   :					Optional[Any]							=						next(blocks_dataset.take(1).as_numpy_iterator())

						return np_record




class 		SCREAMING_SNAKE_CASE_				:



			"""simple docstring"""






			def __init__(       self							, snake_case_							, snake_case_							, snake_case_=2							, snake_case_=1_000							, snake_case_=100							, snake_case_=100_000							, ):
									from scann.scann_ops.py.scann_ops_pybind import builder as Builder

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

									snake_case   :					List[str]							=						builder.build()






			def      lowerCamelCase   (       self							, snake_case_				):
									snake_case							,				snake_case   :					Union[str, Any]							=						self.searcher.search_batched(question_projection.detach().cpu()				)
									return retrieved_block_ids.astype("int64"				)







class 		SCREAMING_SNAKE_CASE_				:



			"""simple docstring"""






			def __init__(       self							, snake_case_							, snake_case_				):
									super().__init__()
									snake_case   :					str							=						block_records
									snake_case   :					Any							=						tokenizer



			def __call__(       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_=None							, snake_case_="pt"				):
									snake_case   :					Any							=						np.take(self.block_records							, indices=snake_case_							, axis=0				)

									snake_case   :					Any							=						self.tokenizer.decode(question_input_ids[0]							, skip_special_tokens=snake_case_				)

									snake_case   :					Dict							=						[]
									snake_case   :					Optional[Any]							=						[]
									for retrieved_block in retrieved_blocks:
															text.append(snake_case_				)
															text_pair.append(retrieved_block.decode()				)

									snake_case   :					int							=						self.tokenizer(
									    snake_case_							, snake_case_							, padding=snake_case_							, truncation=snake_case_							, return_special_tokens_mask=snake_case_							, max_length=snake_case_				)
									snake_case   :					List[str]							=						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_				):
									if os.path.isdir(snake_case_				):
															snake_case   :					Optional[int]							=						os.path.join(snake_case_							, _REALM_BLOCK_RECORDS_FILENAME				)
									else:
															snake_case   :					Optional[Any]							=						hf_hub_download(
															    repo_id=snake_case_							, filename=_REALM_BLOCK_RECORDS_FILENAME							, **snake_case_				)
									snake_case   :					List[Any]							=						np.load(snake_case_							, allow_pickle=snake_case_				)

									snake_case   :					Optional[Any]							=						AutoTokenizer.from_pretrained(snake_case_							, *snake_case_							, **snake_case_				)

									return cls(snake_case_							, snake_case_				)



			def      lowerCamelCase   (       self							, snake_case_				):
									# save block records
									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_				):
									snake_case   :					Union[str, Any]							=						[]
									snake_case   :					Optional[Any]							=						[]
									snake_case   :					str							=						[]
									snake_case   :					Any							=						0

									for input_id in concat_inputs.input_ids:
															snake_case   :					Optional[Any]							=						input_id.tolist()
															# Check answers between two [SEP] tokens
															snake_case   :					Optional[Any]							=						input_id_list.index(self.tokenizer.sep_token_id				)
															snake_case   :					Optional[int]							=						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:
																											snake_case   :					str							=						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:
																					snake_case   :					List[str]							=						[-1] * (max_answers - len(start_pos_				))
																					start_pos_ += padded
																					end_pos_ += padded
									return has_answers, start_pos, end_pos






'''simple docstring'''

from math import sqrt


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int)						->		list[int]:



						"""simple docstring"""

						snake_case   :					Dict							=						[1]
						for i in range(2,			int(sqrt(_lowerCAmelCase)) + 1,			1):
												if number % i == 0:
																		values.append(_lowerCAmelCase)
																		if int(number // i) != i:
																								values.append(int(number // i))
						return sorted(_lowerCAmelCase)


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int)						->		bool:



						"""simple docstring"""

						return sum(factors(_lowerCAmelCase)) > n


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int)						->		bool:



						"""simple docstring"""

						snake_case   :					Dict							=						factors(_lowerCAmelCase)
						snake_case   :					List[str]							=						len(_lowerCAmelCase)
						snake_case   :					Optional[Any]							=						[[0 for i in range(number + 1)] for j in range(r + 1)]
						for i in range(r + 1):
												snake_case   :					int							=						True

						for i in range(1,			number + 1):
												snake_case   :					List[Any]							=						False

						for i in range(1,			r + 1):
												for j in range(1,			number + 1):
																		if j < values[i - 1]:
																								snake_case   :					Union[str, Any]							=						subset[i - 1][j]
																		else:
																								snake_case   :					Tuple							=						subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

						return subset[r][number] != 0


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int)						->		bool:



						"""simple docstring"""

						return abundant(_lowerCAmelCase) and not semi_perfect(_lowerCAmelCase)


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)
						for number in (69, 70, 71):
												print(f"{number} is {'' if weird(number) else 'not '}weird.")





