





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_						(						_UpperCAmelCase       :Any     ,	_UpperCAmelCase       :int=100     ,	_UpperCAmelCase       :Optional[int]=" "							)  ->							List[Any]:
     '''simple docstring'''


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

def     UpperCAmelCase_						(						_UpperCAmelCase       :Optional[int]							)  ->							Union[str, Any]:
     '''simple docstring'''


     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     UpperCAmelCase_						(						_UpperCAmelCase       :List[str]     ,	_UpperCAmelCase       :List[Any]     ,	_UpperCAmelCase       :str     ,	_UpperCAmelCase       :Dict     ,	_UpperCAmelCase       :int     ,	_UpperCAmelCase       :Optional[int]							)  ->							Any:
     '''simple docstring'''


     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       :dict     ,	_UpperCAmelCase       :DPRContextEncoder     ,	_UpperCAmelCase       :DPRContextEncoderTokenizerFast     ,	_UpperCAmelCase       :str							) -> 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     UpperCAmelCase_						(						_UpperCAmelCase       :Optional[Any]     ,	_UpperCAmelCase       :Tuple							)  ->							List[str]:
     '''simple docstring'''


     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










from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments

def     UpperCAmelCase_						(						)  ->							Optional[int]:
     '''simple docstring'''


     A_					      =					HfArgumentParser(_UpperCAmelCase							)
     try:
          A_					      =					parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          A_					      =					'''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
          A_					      =					''' '''.join(str(_UpperCAmelCase							).split(''' '''							)[:-1]							)
          A_					      =					''''''
          A_					      =					eval(str(_UpperCAmelCase							).split(''' '''							)[-1]							)
          A_					      =					[]
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]							)
               else:
                    wrong_args.append(_UpperCAmelCase							)
          if len(_UpperCAmelCase							) > 0:
               A_					      =					full_error_msg + begin_error_msg + str(_UpperCAmelCase							)
          raise ValueError(_UpperCAmelCase							)

     A_					      =					PyTorchBenchmark(args=_UpperCAmelCase							)
     benchmark.run()


if __name__ == "__main__":
      main()



