

'''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  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				List[Any]=100    ,				SCREAMING_SNAKE_CASE :				Dict=" "  ):
  UpperCAmelCase  							=		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  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[Any]  ):
  UpperCAmelCase							, UpperCAmelCase  							=		[], []
  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  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				Optional[int]    ,				SCREAMING_SNAKE_CASE :				Optional[int]    ,				SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				Any  ):
  UpperCAmelCase  							=		load_dataset(
      'csv'    ,				data_files=[csv_path]    ,				split='train'    ,				delimiter='\t'    ,				column_names=['title', 'text']  )
  UpperCAmelCase  							=		kb_dataset.map(
      SCREAMING_SNAKE_CASE    ,				batched=SCREAMING_SNAKE_CASE    ,				num_proc=1  )  # if you want you can load already splitted csv.
  UpperCAmelCase  							=		[kb_dataset.shard(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				contiguous=SCREAMING_SNAKE_CASE  ) for i in range(SCREAMING_SNAKE_CASE  )]
  UpperCAmelCase  							=		kb_list[process_num]

  UpperCAmelCase  							=		'data_' + str(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		os.path.join(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )

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

  def embed(
      SCREAMING_SNAKE_CASE :				dict    ,				SCREAMING_SNAKE_CASE :				DPRContextEncoder    ,				SCREAMING_SNAKE_CASE :				DPRContextEncoderTokenizerFast    ,				SCREAMING_SNAKE_CASE :				str  ) -> dict:
    UpperCAmelCase  							=		ctx_tokenizer(
        documents['title']    ,				documents['text']    ,				truncation=SCREAMING_SNAKE_CASE    ,				padding='longest'    ,				return_tensors='pt'  )['input_ids']
    UpperCAmelCase  							=		ctx_encoder(input_ids.to(device=SCREAMING_SNAKE_CASE  )    ,				return_dict=SCREAMING_SNAKE_CASE  ).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(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  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Dict    ,				SCREAMING_SNAKE_CASE :				Any  ):
  UpperCAmelCase  							=		[]

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

  UpperCAmelCase  							=		concatenate_datasets(SCREAMING_SNAKE_CASE  )
  faiss.omp_set_num_threads(96  )

  UpperCAmelCase  							=		faiss.IndexHNSWFlat(768    ,				128    ,				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





'''simple docstring'''




from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


_a			:	Optional[int]								=  TypeVar('DatasetType', Dataset, IterableDataset)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				DatasetType    ,				SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				int  ):
  if isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ):
    return _split_by_node_map_style_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )
  else:
    return _split_by_node_iterable_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )


