






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	(			a_				,a_=100				,a_=" "   )	->		str:
					__UpperCamelCase :   int									=text.split(a_   )
					return [character.join(text[i : i + n]   ).strip() for i in range(0				,len(a_   )				,a_   )]






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

					__UpperCamelCase :   Optional[int]									='data_' + str(a_   )
					__UpperCamelCase :   str									=os.path.join(a_				,a_   )

					__UpperCamelCase :   Optional[Any]									=DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'   )
					__UpperCamelCase :   List[str]									=ctx_encoder.to(device=a_   )

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

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

					__UpperCamelCase :   str									=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 				A	(			a_				,a_   )	->		Optional[int]:
					__UpperCamelCase :   Optional[int]									=[]

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

					__UpperCamelCase :   List[Any]									=concatenate_datasets(a_   )
					faiss.omp_set_num_threads(96   )

					__UpperCamelCase :   Any									=faiss.IndexHNSWFlat(768				,128				,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









from typing import Callable






def 				A	(			a_   )	->		List[str]:

					def wrapper(a_   ):
										__UpperCamelCase :   Dict									=function_with_docstring.__doc__
										return target_function

					return wrapper

