




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,      lowerCamelCase=100,      lowerCamelCase=" " ):
  lowercase     :Tuple   						=    text.split(lowerCamelCase )
  return [character.join(text[i : i + n] ).strip() for i in range(0,      len(lowerCamelCase ),      lowerCamelCase )]



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

  lowercase     :Optional[int]   						=    "data_" + str(lowerCamelCase )
  lowercase     :int   						=    os.path.join(lowerCamelCase,      lowerCamelCase )

  lowercase     :Dict   						=    DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base" )
  lowercase     :List[Any]   						=    ctx_encoder.to(device=lowerCamelCase )

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

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

  lowercase     :Optional[int]   						=    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,      lowerCamelCase ):
  lowercase     :Union[str, Any]   						=    []

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

  lowercase     :Optional[Any]   						=    concatenate_datasets(lowerCamelCase )
  faiss.omp_set_num_threads(96 )

  lowercase     :List[str]   						=    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









from unittest.mock import patch

import datasets
from datasets import Dataset



def    UpperCAmelCase__	(  ):
  lowercase     :Any   						=    Dataset.from_dict({"col_1": [3, 2, 0, 1]} )

  with patch("tqdm.auto.tqdm" ) as mock_tqdm:
    datasets.disable_progress_bar()
    dset.map(lambda lowerCamelCase : {"col_2": x["col_1"] + 1} )
    mock_tqdm.assert_not_called()

    mock_tqdm.reset_mock()

    datasets.enable_progress_bar()
    dset.map(lambda lowerCamelCase : {"col_2": x["col_1"] + 1} )
    mock_tqdm.assert_called()



