"""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        __a (		A     ,					A=1_00     ,					A=" "   ):






     '''simple docstring'''



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







def        __a (		A   ):






     '''simple docstring'''



     lowercase__								=				[], []
     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        __a (		A     ,					A     ,					A     ,					A     ,					A     ,					A   ):






     '''simple docstring'''



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

     lowercase__								=				'''data_''' + str(A   )
     lowercase__								=				os.path.join(A     ,					A   )

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

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

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

     lowercase__								=				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        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				[]

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

     lowercase__								=				concatenate_datasets(A   )
     faiss.omp_set_num_threads(96   )

     lowercase__								=				faiss.IndexHNSWFlat(7_68     ,					1_28     ,					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"""
def       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     str:



       """simple docstring"""







       return " ".join([word[::-1] for word in input_str.split()]   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
