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


lowerCAmelCase__  	=      "block_records.npy"


lowerCAmelCase__  	=      logging.get_logger(__name__)

def 	__lowerCamelCase							(				__a    : str      ,							__a    : int		)      ->       np.ndarray:
	import tensorflow.compat.va as tf

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

	return np_record
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=2       ,  lowerCAmelCase_=1000       ,  lowerCAmelCase_=100       ,  lowerCAmelCase_=100000       ,  ):
		from scann.scann_ops.py.scann_ops_pybind import builder as Builder

		_lowercase   =Builder(db=lowerCAmelCase_       ,  num_neighbors=lowerCAmelCase_       ,  distance_measure="dot_product"       )
		_lowercase   =builder.tree(
		    num_leaves=lowerCAmelCase_       ,  num_leaves_to_search=lowerCAmelCase_       ,  training_sample_size=lowerCAmelCase_       )
		_lowercase   =builder.score_ah(dimensions_per_block=lowerCAmelCase_       )

		_lowercase   =builder.build()


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   ,  _lowercase   =self.searcher.search_batched(question_projection.detach().cpu()       )
		return retrieved_block_ids.astype("int64"       )

class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		super().__init__()
		_lowercase   =block_records
		_lowercase   =tokenizer


	def __call__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_="pt"       ):
		_lowercase   =np.take(self.block_records       ,  indices=lowerCAmelCase_       ,  axis=0       )

		_lowercase   =self.tokenizer.decode(question_input_ids[0]       ,  skip_special_tokens=lowerCAmelCase_       )

		_lowercase   =[]
		_lowercase   =[]
		for retrieved_block in retrieved_blocks:
			text.append(lowerCAmelCase_       )
			text_pair.append(retrieved_block.decode()       )

		_lowercase   =self.tokenizer(
		    lowerCAmelCase_       ,  lowerCAmelCase_       ,  padding=lowerCAmelCase_       ,  truncation=lowerCAmelCase_       ,  return_special_tokens_mask=lowerCAmelCase_       ,  max_length=lowerCAmelCase_       )
		_lowercase   =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 __lowerCAmelCase      (   cls       ,  lowerCAmelCase_       ,  *lowerCAmelCase_       ,  **lowerCAmelCase_       ):
		if os.path.isdir(lowerCAmelCase_       ):
			_lowercase   =os.path.join(lowerCAmelCase_       ,  _REALM_BLOCK_RECORDS_FILENAME       )
		else:
			_lowercase   =hf_hub_download(
			    repo_id=lowerCAmelCase_       ,  filename=_REALM_BLOCK_RECORDS_FILENAME       ,  **lowerCAmelCase_       )
		_lowercase   =np.load(lowerCAmelCase_       ,  allow_pickle=lowerCAmelCase_       )

		_lowercase   =AutoTokenizer.from_pretrained(lowerCAmelCase_       ,  *lowerCAmelCase_       ,  **lowerCAmelCase_       )

		return cls(lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		# save block records
		np.save(os.path.join(lowerCAmelCase_       ,  _REALM_BLOCK_RECORDS_FILENAME       )       ,  self.block_records       )
		# save tokenizer
		self.tokenizer.save_pretrained(lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_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       ,  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:
					_lowercase   =len(start_pos[-1]       )

        # Pad -1 to max_answers
		for start_pos_, end_pos_ in zip(lowerCAmelCase_       ,  lowerCAmelCase_       ):
			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




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						):




			"""simple docstring"""


			if not all((nth_term, start_term_a, common_ratio_r)							):
						return []
			UpperCAmelCase__		:list[float | int] 		=    []
			UpperCAmelCase__		:str 		=    1
			UpperCAmelCase__		:Any 		=    common_ratio_r
			for _ in range(int(SCREAMING_SNAKE_CASE							)							):
						if not series:
									series.append(SCREAMING_SNAKE_CASE							)
						else:
									power += 1
									series.append(float(start_term_a * multiple							)							)
									UpperCAmelCase__		:str 		=    pow(float(SCREAMING_SNAKE_CASE							)     ,						SCREAMING_SNAKE_CASE							)
			return series


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						__snake_case					:							Optional[int]           =							float(input('Enter the last number (n term) of the Geometric Series'))
						__snake_case					:							List[str]           =							float(input('Enter the starting term (a) of the Geometric Series'))
						__snake_case					:							Optional[int]           =							float(
						    input('Enter the common ratio between two terms (r) of the Geometric Series')
						)
						print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
						print(geometric_series(nth_term, start_term_a, common_ratio_r))





