





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 lowerCAmelCase__(__snake_case	,__snake_case							)    ->				np.ndarray:







	'''simple docstring'''







	import tensorflow.compat.va as tf

	lowerCamelCase__        =		tf.data.TFRecordDataset(__snake_case	,buffer_size=512 * 1024 * 1024							)
	lowerCamelCase__        =		blocks_dataset.batch(__snake_case	,drop_remainder=__snake_case							)
	lowerCamelCase__        =		next(blocks_dataset.take(1							).as_numpy_iterator()							)

	return np_record






class  __A   :

			'''simple docstring'''







			def __init__(   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase=2	,  __lowerCAmelCase=1_0_0_0	,  __lowerCAmelCase=1_0_0	,  __lowerCAmelCase=1_0_0_0_0_0	,  ):



				'''simple docstring'''







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

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

				lowerCamelCase__        =		builder.build()





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__    ,     lowerCamelCase__        =		self.searcher.search_batched(question_projection.detach().cpu()							)
				return retrieved_block_ids.astype('''int64'''							)






class  __A   :

			'''simple docstring'''







			def __init__(   self	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				super().__init__()
				lowerCamelCase__        =		block_records
				lowerCamelCase__        =		tokenizer





			def __call__(   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase=None	,  __lowerCAmelCase="pt"							):



				'''simple docstring'''







				lowerCamelCase__        =		np.take(self.block_records	,  indices=__lowerCAmelCase	,  axis=0							)

				lowerCamelCase__        =		self.tokenizer.decode(question_input_ids[0]	,  skip_special_tokens=__lowerCAmelCase							)

				lowerCamelCase__        =		[]
				lowerCamelCase__        =		[]
				for retrieved_block in retrieved_blocks:
					text.append(__lowerCAmelCase							)
					text_pair.append(retrieved_block.decode()							)

				lowerCamelCase__        =		self.tokenizer(
				    __lowerCAmelCase	,  __lowerCAmelCase	,  padding=__lowerCAmelCase	,  truncation=__lowerCAmelCase	,  return_special_tokens_mask=__lowerCAmelCase	,  max_length=__lowerCAmelCase							)
				lowerCamelCase__        =		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							):



				'''simple docstring'''







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

				lowerCamelCase__        =		AutoTokenizer.from_pretrained(__lowerCAmelCase	,  *__lowerCAmelCase	,  **__lowerCAmelCase							)

				return cls(__lowerCAmelCase	,  __lowerCAmelCase							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				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							):



				'''simple docstring'''







				lowerCamelCase__        =		[]
				lowerCamelCase__        =		[]
				lowerCamelCase__        =		[]
				lowerCamelCase__        =		0

				for input_id in concat_inputs.input_ids:
					lowerCamelCase__        =		input_id.tolist()
					# Check answers between two [SEP] tokens
					lowerCamelCase__        =		input_id_list.index(self.tokenizer.sep_token_id							)
					lowerCamelCase__        =		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:
							lowerCamelCase__        =		len(start_pos[-1]							)

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










import os





def lowerCAmelCase__()    ->				int:







	'''simple docstring'''







	lowerCamelCase__        =		os.path.dirname(os.path.realpath(__snake_case							)							)
	lowerCamelCase__        =		os.path.join(__snake_case	,'''triangle.txt'''							)

	with open(__snake_case							) as in_file:
		lowerCamelCase__        =		[[int(__snake_case							) for i in line.split()] for line in in_file]

	while len(__snake_case							) != 1:
		lowerCamelCase__        =		triangle.pop()
		lowerCamelCase__        =		triangle[-1]
		for j in range(len(__snake_case							) - 1							):
			curr_row[j] += max(last_row[j]	,last_row[j + 1]							)
	return triangle[0][0]


if __name__ == "__main__":
			print(solution())



