

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 	lowerCamelCase__	(   _a							,     _a=100							,     _a=" "):
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    text.split(_UpperCAmelCase)
	return [character.join(text[i : i + n]).strip() for i in range(0							,     len(_UpperCAmelCase)							,     _UpperCAmelCase)]

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

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

	SCREAMING_SNAKE_CASE				: Optional[int]			       =    "data_" + str(_UpperCAmelCase)
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    os.path.join(_UpperCAmelCase							,     _UpperCAmelCase)

	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base")
	SCREAMING_SNAKE_CASE				: int			       =    ctx_encoder.to(device=_UpperCAmelCase)

	def embed(
	    _a							,     _a							,     _a							,     _a) -> dict:
		SCREAMING_SNAKE_CASE				: Tuple			       =    ctx_tokenizer(
		    documents["title"]							,     documents["text"]							,     truncation=_UpperCAmelCase							,     padding="longest"							,     return_tensors="pt")["input_ids"]
		SCREAMING_SNAKE_CASE				: str			       =    ctx_encoder(input_ids.to(device=_UpperCAmelCase)							,     return_dict=_UpperCAmelCase).pooler_output
		return {"embeddings": embeddings.detach().cpu().numpy()}

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

	SCREAMING_SNAKE_CASE				: int			       =    data_shrad.map(
	    partial(_UpperCAmelCase							,     ctx_encoder=_UpperCAmelCase							,     ctx_tokenizer=_UpperCAmelCase							,     device=_UpperCAmelCase)							,     batched=_UpperCAmelCase							,     batch_size=16							,     features=_UpperCAmelCase							,     )
	dataset.save_to_disk(_UpperCAmelCase)


def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: int			       =    []

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

	SCREAMING_SNAKE_CASE				: Tuple			       =    concatenate_datasets(_UpperCAmelCase)
	faiss.omp_set_num_threads(96)

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


import re

def 	lowerCamelCase__	(   _a):
	def docstring_decorator(_a):
		SCREAMING_SNAKE_CASE				: List[Any]			       =    fn.__doc__
		SCREAMING_SNAKE_CASE				: List[str]			       =    func_doc.split("\n")
		SCREAMING_SNAKE_CASE				: Dict			       =    0
		while i < len(_a) and re.search(r"^\s*Examples?:\s*$"							,     lines[i]) is None:
			i += 1
		if i < len(_a):
			SCREAMING_SNAKE_CASE				: Tuple			       =    example_docstring
			SCREAMING_SNAKE_CASE				: List[Any]			       =    "\n".join(_a)
		else:
			raise ValueError(
			    f"The function {fn} should have an empty 'Examples:' in its docstring as placeholder, "
			    f"current docstring is:\n{func_doc}")
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    func_doc
		return fn

	return docstring_decorator