






"""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__    ,			a__=1_00    ,			a__=" "   ):





						'''simple docstring'''






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


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






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


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__    ,			a__    ,			a__    ,			a__   ):





						'''simple docstring'''






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

						lowerCAmelCase      :Dict				  = 'data_' + str(a__   )
						lowerCAmelCase      :int				  = os.path.join(a__    ,			a__   )

						lowerCAmelCase      :Dict				  = DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'   )
						lowerCAmelCase      :Union[str, Any]				  = ctx_encoder.to(device=a__   )

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

						lowerCAmelCase      :Union[str, Any]				  = Features(
						    {'text': Value('string'   ), 'title': Value('string'   ), 'embeddings': Sequence(Value('float32'   )   )}   )  # optional, save as float32 instead of float64 to save space

						lowerCAmelCase      :List[Any]				  = data_shrad.map(
						    partial(a__    ,			ctx_encoder=a__    ,			ctx_tokenizer=a__    ,			device=a__   )    ,			batched=a__    ,			batch_size=16    ,			features=a__    ,			)
						dataset.save_to_disk(a__   )







def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :str				  = []

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

						lowerCAmelCase      :Any				  = concatenate_datasets(a__   )
						faiss.omp_set_num_threads(96   )

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







"""simple docstring"""


import safs




class 	__UpperCamelCase    (    safs.SaFileSystem	):
					pass