


"""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__			(							_UpperCamelCase						,      _UpperCamelCase=100						,      _UpperCamelCase=" "			):





						"""simple docstring"""

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






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__						,					UpperCAmelCase__												=     [], []
						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        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase			):





						"""simple docstring"""

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

						UpperCAmelCase__												=     '''data_''' + str(_UpperCamelCase			)
						UpperCAmelCase__												=     os.path.join(_UpperCamelCase						,      _UpperCamelCase			)

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

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

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

						UpperCAmelCase__												=     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        a__			(							_UpperCamelCase						,      _UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     []

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

						UpperCAmelCase__												=     concatenate_datasets(_UpperCamelCase			)
						faiss.omp_set_num_threads(96			)

						UpperCAmelCase__												=     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



"""simple docstring"""







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def        a__			(							):





						"""simple docstring"""

						UpperCAmelCase__												=     ArgumentParser(
						    description=(
						        '''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'''
						    )			)

						# Optional arguments for the launch helper
						parser.add_argument('''--num_cores'''						,      type=_UpperCamelCase						,      default=1						,      help='''Number of TPU cores to use (1 or 8).'''			)

						# positional
						parser.add_argument(
						    '''training_script'''						,      type=_UpperCamelCase						,      help=(
						        '''The full path to the single TPU training '''
						        '''program/script to be launched in parallel, '''
						        '''followed by all the arguments for the '''
						        '''training script'''
						    )						,      )

						# rest from the training program
						parser.add_argument('''training_script_args'''						,      nargs=_UpperCamelCase			)

						return parser.parse_args()






def        a__			(							):





						"""simple docstring"""

						UpperCAmelCase__												=     parse_args()

						# Import training_script as a module.
						UpperCAmelCase__												=     Path(args.training_script			)
						sys.path.append(str(script_fpath.parent.resolve()			)			)
						UpperCAmelCase__												=     script_fpath.stem
						UpperCAmelCase__												=     importlib.import_module(_UpperCamelCase			)

						# Patch sys.argv
						UpperCAmelCase__												=     [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores			)]

						xmp.spawn(mod._mp_fn						,      args=()						,      nprocs=args.num_cores			)


if __name__ == "__main__":
						main()