'''simple docstring'''

import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Any,      _lowerCAmelCase    :			Dict=100,      _lowerCAmelCase    :			int=" ")    ->		Tuple:



						"""simple docstring"""

						snake_case :       Union[str, Any]				=    text.split(_lowerCAmelCase)
						return [character.join(text[i : i + n]).strip() for i in range(0,      len(_lowerCAmelCase),      _lowerCAmelCase)]


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Optional[Any])    ->		List[Any]:



						"""simple docstring"""

						snake_case				,					snake_case :       Union[str, Any]				=    [], []
						for title, text in zip(documents["title"],      documents["text"]):
												if text is not None:
																		for passage in split_text(_lowerCAmelCase):
																								titles.append(title if title is not None else "")
																								texts.append(_lowerCAmelCase)
						return {"title": titles, "text": texts}


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Tuple,      _lowerCAmelCase    :			Any,      _lowerCAmelCase    :			Optional[Any],      _lowerCAmelCase    :			str,      _lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			Optional[Any])    ->		Optional[int]:



						"""simple docstring"""

						snake_case :       Tuple				=    load_dataset(
						    "csv",      data_files=[csv_path],      split="train",      delimiter="\t",      column_names=["title", "text"])
						snake_case :       Optional[Any]				=    kb_dataset.map(
						    _lowerCAmelCase,      batched=_lowerCAmelCase,      num_proc=1)  # if you want you can load already splitted csv.
						snake_case :       List[Any]				=    [kb_dataset.shard(_lowerCAmelCase,      _lowerCAmelCase,      contiguous=_lowerCAmelCase) for i in range(_lowerCAmelCase)]
						snake_case :       List[str]				=    kb_list[process_num]

						snake_case :       Tuple				=    "data_" + str(_lowerCAmelCase)
						snake_case :       List[str]				=    os.path.join(_lowerCAmelCase,      _lowerCAmelCase)

						snake_case :       Any				=    DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base")
						snake_case :       Any				=    ctx_encoder.to(device=_lowerCAmelCase)

						def embed(
						    _lowerCAmelCase    :			dict,      _lowerCAmelCase    :			DPRContextEncoder,      _lowerCAmelCase    :			DPRContextEncoderTokenizerFast,      _lowerCAmelCase    :			List[str]) -> dict:
												snake_case :       int				=    ctx_tokenizer(
												    documents["title"],      documents["text"],      truncation=_lowerCAmelCase,      padding="longest",      return_tensors="pt")["input_ids"]
												snake_case :       int				=    ctx_encoder(input_ids.to(device=_lowerCAmelCase),      return_dict=_lowerCAmelCase).pooler_output
												return {"embeddings": embeddings.detach().cpu().numpy()}

						snake_case :       Dict				=    Features(
						    {"text": Value("string"), "title": Value("string"), "embeddings": Sequence(Value("float32"))})  # optional, save as float32 instead of float64 to save space

						snake_case :       Optional[Any]				=    data_shrad.map(
						    partial(_lowerCAmelCase,      ctx_encoder=_lowerCAmelCase,      ctx_tokenizer=_lowerCAmelCase,      device=_lowerCAmelCase),      batched=_lowerCAmelCase,      batch_size=16,      features=_lowerCAmelCase,      )
						dataset.save_to_disk(_lowerCAmelCase)



def        UpperCAmelCase__   (				_lowerCAmelCase    :			List[Any],      _lowerCAmelCase    :			Optional[int])    ->		Dict:



						"""simple docstring"""

						snake_case :       Optional[Any]				=    []

						for shard_address in glob(str(_lowerCAmelCase) + "/*/"):
												data_shard_list.append(load_from_disk(_lowerCAmelCase))

						snake_case :       List[str]				=    concatenate_datasets(_lowerCAmelCase)
						faiss.omp_set_num_threads(96)

						snake_case :       Union[str, Any]				=    faiss.IndexHNSWFlat(768,      128,      faiss.METRIC_INNER_PRODUCT)
						concat.add_faiss_index("embeddings",      custom_index=_lowerCAmelCase)
						concat.get_index("embeddings").save(
						    _lowerCAmelCase)  # since we load the index in to memory,we can directly update the index in the disk






'''simple docstring'''

from __future__ import annotations


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



						"""simple docstring"""

						if len(_lowerCAmelCase) == 0:
												raise ValueError("find_max() arg is an empty sequence")
						snake_case :       Any				=    nums[0]
						for x in nums:
												if x > max_num:
																		snake_case :       int				=    x
						return max_num


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)





