




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			(UpperCAmelCase_ ,     UpperCAmelCase_=100 ,     UpperCAmelCase_=" "		):
    a__  			=							text.split(UpperCAmelCase_		)
    return [character.join(text[i : i + n]		).strip() for i in range(0 ,     len(UpperCAmelCase_		) ,     UpperCAmelCase_		)]
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    a__			,		a__  			=							[], []
    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			(UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_		):
    a__  			=							load_dataset(
        """csv""" ,     data_files=[csv_path] ,     split="""train""" ,     delimiter="""\t""" ,     column_names=["""title""", """text"""]		)
    a__  			=							kb_dataset.map(
        UpperCAmelCase_ ,     batched=UpperCAmelCase_ ,     num_proc=1		)  # if you want you can load already splitted csv.
    a__  			=							[kb_dataset.shard(UpperCAmelCase_ ,     UpperCAmelCase_ ,     contiguous=UpperCAmelCase_		) for i in range(UpperCAmelCase_		)]
    a__  			=							kb_list[process_num]

    a__  			=							"""data_""" + str(UpperCAmelCase_		)
    a__  			=							os.path.join(UpperCAmelCase_ ,     UpperCAmelCase_		)

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

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

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

    a__  			=							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			(UpperCAmelCase_ ,     UpperCAmelCase_		):
    a__  			=							[]

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

    a__  			=							concatenate_datasets(UpperCAmelCase_		)
    faiss.omp_set_num_threads(96		)

    a__  			=							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












def 							__lowerCAmelCase			(UpperCAmelCase_		):
    return n * (2 * n - 1)
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    a__  			=							(1 + 24 * n) ** 0.5
    return ((1 + root) / 6) % 1 == 0
def 							__lowerCAmelCase			(UpperCAmelCase_ = 144		):
    a__  			=							start
    a__  			=							hexagonal_num(UpperCAmelCase_		)
    while not is_pentagonal(UpperCAmelCase_		):
        n += 1
        a__  			=							hexagonal_num(UpperCAmelCase_		)
    return num


if __name__ == "__main__":
 print(F"{solution()} = ")






