


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							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   str=1_00     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]=" "							):
     __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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int							):
     __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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   Tuple     ,	SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   List[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__     :   Optional[int]							) -> 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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   Dict							):
     __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(7_68     ,	1_28     ,	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









import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A									=		50_0000

_A			,	_A									=		os.path.split(__file__)
_A									=		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.select(range(0     ,	len(SCREAMING_SNAKE_CASE__							)     ,	2							)							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.sort('numbers'							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.shuffle()

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.train_test_split(0.1							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset     ,	SCREAMING_SNAKE_CASE__     :   int=10							):
     for shard_id in range(SCREAMING_SNAKE_CASE__							):
          __UpperCamelCase       				=dataset.shard(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				={'num examples': SPEED_TEST_N_EXAMPLES}
     __UpperCamelCase       				=(select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print('generating dataset'							)
          __UpperCamelCase       				=datasets.Features({'text': datasets.Value('string'							), 'numbers': datasets.Value('float32'							)}							)
          __UpperCamelCase       				=generate_example_dataset(
              os.path.join(SCREAMING_SNAKE_CASE__     ,	'dataset.arrow'							)     ,	SCREAMING_SNAKE_CASE__     ,	num_examples=SCREAMING_SNAKE_CASE__							)
          print('Functions'							)
          for func in functions:
               print(func.__name__							)
               __UpperCamelCase       				=func(SCREAMING_SNAKE_CASE__							)

     with open(SCREAMING_SNAKE_CASE__     ,	'wb'							) as f:
          f.write(json.dumps(SCREAMING_SNAKE_CASE__							).encode('utf-8'							)							)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_indices_mapping()





