




"""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


UpperCamelCase__ :    Optional[int] 					=							"block_records.npy"


UpperCamelCase__ :    List[Any] 					=							logging.get_logger(__name__)




def       snake_case__	(       lowerCAmelCase   :		str				,					lowerCAmelCase   :		int		):
					import tensorflow.compat.va as tf

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

					return np_record
class     A__      :




	def __init__(   self  :	Dict      ,    lowercase  :	Tuple      ,    lowercase  :	Dict      ,    lowercase  :	str=2      ,    lowercase  :	Optional[int]=1_000      ,    lowercase  :	Optional[Any]=100      ,    lowercase  :	Optional[Any]=100_000      ,    )					-> int:
						from scann.scann_ops.py.scann_ops_pybind import builder as Builder

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

						__snake_case          =	builder.build()




	def 							a_		(   self  :	Union[str, Any]      ,    lowercase  :	Tuple	)					-> Union[str, Any]:
						__snake_case          =	self.searcher.search_batched(question_projection.detach().cpu()	)
						return retrieved_block_ids.astype("""int64"""	)

class     A__      :




	def __init__(   self  :	str      ,    lowercase  :	Optional[Any]      ,    lowercase  :	Optional[int]	)					-> int:
						super().__init__()
						__snake_case          =	block_records
						__snake_case          =	tokenizer




	def __call__(   self  :	Dict      ,    lowercase  :	int      ,    lowercase  :	Any      ,    lowercase  :	Tuple      ,    lowercase  :	int=None      ,    lowercase  :	int="pt"	)					-> int:
						__snake_case          =	np.take(self.block_records      ,    indices=lowercase      ,    axis=0	)

						__snake_case          =	self.tokenizer.decode(question_input_ids[0]      ,    skip_special_tokens=lowercase	)

						__snake_case          =	[]
						__snake_case          =	[]
						for retrieved_block in retrieved_blocks:
											text.append(lowercase	)
											text_pair.append(retrieved_block.decode()	)

						__snake_case          =	self.tokenizer(
						    lowercase      ,    lowercase      ,    padding=lowercase      ,    truncation=lowercase      ,    return_special_tokens_mask=lowercase      ,    max_length=lowercase	)
						__snake_case          =	concat_inputs.convert_to_tensors(lowercase	)

						if answer_ids is not None:
											return self.block_has_answer(lowercase      ,    lowercase	) + (concat_inputs_tensors,)
						else:
											return (None, None, None, concat_inputs_tensors)




	@classmethod
	def 							a_		(   cls  :	List[str]      ,    lowercase  :	List[Any]      ,    *lowercase  :	int      ,    **lowercase  :	Dict	)					-> Optional[Any]:







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

						__snake_case          =	AutoTokenizer.from_pretrained(lowercase      ,    *lowercase      ,    **lowercase	)

						return cls(lowercase      ,    lowercase	)




	def 							a_		(   self  :	Tuple      ,    lowercase  :	Dict	)					-> List[str]:
						np.save(os.path.join(lowercase      ,    _REALM_BLOCK_RECORDS_FILENAME	)      ,    self.block_records	)
						# save tokenizer
						self.tokenizer.save_pretrained(lowercase	)




	def 							a_		(   self  :	Any      ,    lowercase  :	Any      ,    lowercase  :	Tuple	)					-> List[Any]:
						__snake_case          =	[]
						__snake_case          =	[]
						__snake_case          =	[]
						__snake_case          =	0

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

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

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












"""simple docstring"""

import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCamelCase__ :    Optional[int] 					=							10_00_00_00_00_00
UpperCamelCase__ :    int 					=							1_00_00

UpperCamelCase__					,					UpperCamelCase__ :    List[str] 					=							os.path.split(__file__)
UpperCamelCase__ :    Any 					=							os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))




def       snake_case__	(       lowerCAmelCase   :		int				,					lowerCAmelCase   :		int		):
					__snake_case          =	pa.Table.from_pydict({"""col""": [0] * chunk_size}		)
					__snake_case          =	pa.concat_tables([table] * (num_examples // chunk_size)		)
					return datasets.Dataset(lowerCAmelCase				,					fingerprint="""table_100B"""		)
@dataclass
class     A__      :
	A_							:				int
	A_							:				int
	A_							:				int
	A_							:				int




	def 							a_		(   self  :	Optional[Any]	)					-> Any:
						__snake_case          =	np.random.default_rng(self.seed	)
						__snake_case          =	rng.integers(low=self.low      ,    high=self.high      ,    size=self.size	).tolist()




	def __iter__(   self  :	str	)					-> int:
						return iter(self._sampled_values	)




	def __len__(   self  :	Union[str, Any]	)					-> Any:
						return self.size




@get_duration
def       snake_case__	(       lowerCAmelCase   :		datasets.Dataset		):
					__snake_case          =	dataset[0]




@get_duration
def       snake_case__	(       lowerCAmelCase   :		datasets.Dataset		):
					__snake_case          =	dataset[-1]




@get_duration
def       snake_case__	(       lowerCAmelCase   :		datasets.Dataset		):
					__snake_case          =	dataset[range(len(lowerCAmelCase		) // 2				,					len(lowerCAmelCase		) // 2 + 1_024		)]




@get_duration
def       snake_case__	(       lowerCAmelCase   :		datasets.Dataset		):
					__snake_case          =	dataset[RandIter(0				,					len(lowerCAmelCase		)				,					1_024				,					seed=42		)]




def       snake_case__	(       ):
					__snake_case          =	{"""num examples""": SPEED_TEST_N_EXAMPLES}
					__snake_case          =	(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print("""generating dataset"""		)
					__snake_case          =	generate_100B_dataset(num_examples=lowerCAmelCase				,					chunk_size=lowerCAmelCase		)
					print("""Functions"""		)
					for func in functions:
										print(func.__name__		)
										__snake_case          =	func(lowerCAmelCase		)

					with open(lowerCAmelCase				,					"""wb"""		) as f:
										f.write(json.dumps(lowerCAmelCase		).encode("""utf-8"""		)		)


if __name__ == "__main__":  # useful to run the profiler
		benchmark_table_100B()


