'''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 		_lowercase		(     lowercase_						,							lowercase_=100						,							lowercase_=" "  ):



		'''simple docstring'''







		_snake_case     	=	text.split(_a  )
		return [character.join(text[i : i + n]  ).strip() for i in range(0						,							len(_a  )						,							_a  )]






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







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

		_snake_case     	=	"data_" + str(_a  )
		_snake_case     	=	os.path.join(_a						,							_a  )

		_snake_case     	=	DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"  )
		_snake_case     	=	ctx_encoder.to(device=_a  )

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

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

		_snake_case     	=	data_shrad.map(
		    partial(_a						,							ctx_encoder=_a						,							ctx_tokenizer=_a						,							device=_a  )						,							batched=_a						,							batch_size=16						,							features=_a						,							)
		dataset.save_to_disk(_a  )






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	[]

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

		_snake_case     	=	concatenate_datasets(_a  )
		faiss.omp_set_num_threads(96  )

		_snake_case     	=	faiss.IndexHNSWFlat(768						,							128						,							faiss.METRIC_INNER_PRODUCT  )
		concat.add_faiss_index("embeddings"						,							custom_index=_a  )
		concat.get_index("embeddings"  ).save(
		    _a  )  # since we load the index in to memory,we can directly update the index in the disk











'''simple docstring'''







import math





class 					lowerCamelCase_							:




					'''simple docstring'''






					def __init__(   self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase			):
							_snake_case     	=	x
							_snake_case     	=	y
							_snake_case     	=	z






					def __repr__(   self			):
							return f'''Point({self.x}, {self.y}, {self.z})'''






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2  )  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





