

"""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       UpperCAmelCase__    (      _A				:			List[str]     ,				_A				:			Dict=1_00     ,				_A				:			Union[str, Any]=" "							):





  '''simple docstring'''







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




def       UpperCAmelCase__    (      _A				:			str							):





  '''simple docstring'''







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




def       UpperCAmelCase__    (      _A				:			List[str]     ,				_A				:			List[Any]     ,				_A				:			int     ,				_A				:			List[Any]     ,				_A				:			int     ,				_A				:			Union[str, Any]							):





  '''simple docstring'''







  a__  =load_dataset(
      '''csv'''     ,				data_files=[csv_path]     ,				split='''train'''     ,				delimiter='''\t'''     ,				column_names=['''title''', '''text''']							)
  a__  =kb_dataset.map(
      _SCREAMING_SNAKE_CASE     ,				batched=_SCREAMING_SNAKE_CASE     ,				num_proc=1							)  # if you want you can load already splitted csv.
  a__  =[kb_dataset.shard(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				contiguous=_SCREAMING_SNAKE_CASE							) for i in range(_SCREAMING_SNAKE_CASE							)]
  a__  =kb_list[process_num]

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

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

  def embed(
      _A				:			List[Any]     ,				_A				:			Any     ,				_A				:			Dict     ,				_A				:			str							) -> dict:
    a__  =ctx_tokenizer(
        documents['''title''']     ,				documents['''text''']     ,				truncation=_SCREAMING_SNAKE_CASE     ,				padding='''longest'''     ,				return_tensors='''pt'''							)["input_ids"]
    a__  =ctx_encoder(input_ids.to(device=_SCREAMING_SNAKE_CASE							)     ,				return_dict=_SCREAMING_SNAKE_CASE							).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(_SCREAMING_SNAKE_CASE     ,				ctx_encoder=_SCREAMING_SNAKE_CASE     ,				ctx_tokenizer=_SCREAMING_SNAKE_CASE     ,				device=_SCREAMING_SNAKE_CASE							)     ,				batched=_SCREAMING_SNAKE_CASE     ,				batch_size=16     ,				features=_SCREAMING_SNAKE_CASE     ,				)
  dataset.save_to_disk(_SCREAMING_SNAKE_CASE							)




def       UpperCAmelCase__    (      _A				:			int     ,				_A				:			Any							):





  '''simple docstring'''







  a__  =[]

  for shard_address in glob(str(_SCREAMING_SNAKE_CASE							) + '''/*/'''							):
    data_shard_list.append(load_from_disk(_SCREAMING_SNAKE_CASE							)							)

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

  a__  =faiss.IndexHNSWFlat(7_68     ,				1_28     ,				faiss.METRIC_INNER_PRODUCT							)
  concat.add_faiss_index('''embeddings'''     ,				custom_index=_SCREAMING_SNAKE_CASE							)
  concat.get_index('''embeddings'''							).save(
      _SCREAMING_SNAKE_CASE							)  # since we load the index in to memory,we can directly update the index in the disk



import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

lowerCamelCase									= [(1,)]




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  a__  =bool(os.getenv('''RUN_SLOW'''     ,				0							)							)
  if run_slow:
    assert True
  else:
    assert False, "should have been skipped"




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  a__  =bool(os.getenv('''RUN_SLOW'''     ,				0							)							)
  if run_slow and torch_device == "cuda":
    assert True
  else:
    assert False, "should have been skipped"







@require_torch
class 						__magic_name__      (							unittest.TestCase							):

   '''simple docstring'''




   @slow
   @require_torch_gpu
   def 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






     """simple docstring"""






     check_slow_torch_cuda()



   @require_torch_gpu
   @slow
   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     check_slow_torch_cuda()



   @parameterized.expand(lowercase_	)
   @slow
   def 			_UpperCAmelCase			(    self,   lowercase_=None	)							->   Tuple:






     """simple docstring"""






     check_slow()





@slow
@require_torch_gpu
def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  check_slow_torch_cuda()




@require_torch_gpu
@slow
def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  check_slow_torch_cuda()




@slow
@pytest.mark.parametrize('''param'''     ,				[1]							)
def       UpperCAmelCase__    (      _A				:			str							):





  '''simple docstring'''







  check_slow()




@pytest.mark.parametrize('''param'''     ,				[1]							)
@slow
def       UpperCAmelCase__    (      _A				:			Tuple							):





  '''simple docstring'''







  check_slow()
