


"""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


lowercase  		=			"""block_records.npy"""


lowercase  		=			logging.get_logger(__name__)



def 			A__		(					_UpperCAmelCase    :      str				,    _UpperCAmelCase    :      int	)							->	np.ndarray:

	'''simple docstring'''


	import tensorflow.compat.va as tf

	snake_case__  :						str		=      tf.data.TFRecordDataset(_UpperCAmelCase				,    buffer_size=5_12 * 10_24 * 10_24	)
	snake_case__  :						str		=      blocks_dataset.batch(_UpperCAmelCase				,    drop_remainder=_UpperCAmelCase	)
	snake_case__  :						List[Any]		=      next(blocks_dataset.take(1	).as_numpy_iterator()	)

	return np_record


class 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__=2	,					lowerCamelCase__=1_000	,					lowerCamelCase__=100	,					lowerCamelCase__=100_000	,					)   ->	str:







								'''simple docstring'''




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

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

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







							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




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


class 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								super().__init__()
								snake_case__  :						Tuple		=      block_records
								snake_case__  :						int		=      tokenizer




							def __call__(						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__=None	,					lowerCamelCase__="pt")   ->	Optional[Any]:







								'''simple docstring'''




								snake_case__  :						str		=      np.take(self.block_records	,					indices=lowerCamelCase__	,					axis=0)

								snake_case__  :						Dict		=      self.tokenizer.decode(question_input_ids[0]	,					skip_special_tokens=lowerCamelCase__)

								snake_case__  :						List[Any]		=      []
								snake_case__  :						Optional[int]		=      []
								for retrieved_block in retrieved_blocks:
									text.append(lowerCamelCase__)
									text_pair.append(retrieved_block.decode())

								snake_case__  :						str		=      self.tokenizer(
								    lowerCamelCase__	,					lowerCamelCase__	,					padding=lowerCamelCase__	,					truncation=lowerCamelCase__	,					return_special_tokens_mask=lowerCamelCase__	,					max_length=lowerCamelCase__)
								snake_case__  :						Tuple		=      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        UpperCAmelCase (						cls	,					lowerCamelCase__	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	Dict:







								'''simple docstring'''




								if os.path.isdir(lowerCamelCase__):
									snake_case__  :						Tuple		=      os.path.join(lowerCamelCase__	,					_REALM_BLOCK_RECORDS_FILENAME)
								else:
									snake_case__  :						Any		=      hf_hub_download(
									    repo_id=lowerCamelCase__	,					filename=_REALM_BLOCK_RECORDS_FILENAME	,					**lowerCamelCase__)
								snake_case__  :						int		=      np.load(lowerCamelCase__	,					allow_pickle=lowerCamelCase__)

								snake_case__  :						Union[str, Any]		=      AutoTokenizer.from_pretrained(lowerCamelCase__	,					*lowerCamelCase__	,					**lowerCamelCase__)

								return cls(lowerCamelCase__	,					lowerCamelCase__)




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Tuple:







								'''simple docstring'''




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







							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	int:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      []
								snake_case__  :						int		=      []
								snake_case__  :						int		=      []
								snake_case__  :						Any		=      0

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










from collections.abc import Callable

__magic_name__       =     Callable[[float], float]  # type alias for a real -> real function





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			):
					"""simple docstring"""






					lowercase						   =       float(_UpperCAmelCase				)
					while True:
										try:
															lowercase						   =       prev_guess - function(_UpperCAmelCase				) / derivative(_UpperCAmelCase				)
										except ZeroDivisionError:
															raise ZeroDivisionError('Could not find root'				) from None
										if abs(prev_guess - next_guess				) < 10**-5:
															return next_guess
										lowercase						   =       next_guess





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					return (x**3) - (2 * x) - 5





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					return 3 * (x**2) - 2


if __name__ == "__main__":
		print(newton(f, fa, 3))






