





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     lowercase__       (       __lowercase						:  Any    ,  __lowercase						:  int=100    ,  __lowercase						:  str=" "					)			->				Dict:



       """simple docstring"""






       __UpperCamelCase	=  text.split(__lowercase					)
       return [character.join(text[i : i + n]					).strip() for i in range(0    ,  len(__lowercase					)    ,  __lowercase					)]
def     lowercase__       (       __lowercase						:  str					)			->				List[Any]:



       """simple docstring"""






       __UpperCamelCase	,						__UpperCamelCase	=  [], []
       for title, text in zip(documents['title']    ,  documents['text']					):
              if text is not None:
                     for passage in split_text(__lowercase					):
                            titles.append(title if title is not None else ''					)
                            texts.append(__lowercase					)
       return {"title": titles, "text": texts}
def     lowercase__       (       __lowercase						:  Union[str, Any]    ,  __lowercase						:  int    ,  __lowercase						:  Tuple    ,  __lowercase						:  Dict    ,  __lowercase						:  List[Any]    ,  __lowercase						:  Any					)			->				Dict:



       """simple docstring"""






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

       __UpperCamelCase	=  'data_' + str(__lowercase					)
       __UpperCamelCase	=  os.path.join(__lowercase    ,  __lowercase					)

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

       def embed(
           __lowercase						:  dict    ,  __lowercase						:  DPRContextEncoder    ,  __lowercase						:  DPRContextEncoderTokenizerFast    ,  __lowercase						:  Any					) -> dict:
              __UpperCamelCase	=  ctx_tokenizer(
                  documents['title']    ,  documents['text']    ,  truncation=__lowercase    ,  padding='longest'    ,  return_tensors='pt'					)['input_ids']
              __UpperCamelCase	=  ctx_encoder(input_ids.to(device=__lowercase					)    ,  return_dict=__lowercase					).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(__lowercase    ,  ctx_encoder=__lowercase    ,  ctx_tokenizer=__lowercase    ,  device=__lowercase					)    ,  batched=__lowercase    ,  batch_size=16    ,  features=__lowercase    ,  )
       dataset.save_to_disk(__lowercase					)


def     lowercase__       (       __lowercase						:  str    ,  __lowercase						:  str					)			->				List[Any]:



       """simple docstring"""






       __UpperCamelCase	=  []

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

       __UpperCamelCase	=  concatenate_datasets(__lowercase					)
       faiss.omp_set_num_threads(96					)

       __UpperCamelCase	=  faiss.IndexHNSWFlat(768    ,  128    ,  faiss.METRIC_INNER_PRODUCT					)
       concat.add_faiss_index('embeddings'    ,  custom_index=__lowercase					)
       concat.get_index('embeddings'					).save(
           __lowercase					)  # since we load the index in to memory,we can directly update the index in the disk







'''simple docstring'''
def     lowercase__       (       __lowercase						:  str    ,  __lowercase						:  Tuple					)			->				str:



       """simple docstring"""






       sequence.append(__lowercase					)

       __UpperCamelCase	=  0
       while sequence[index] != target:
              index += 1

       sequence.pop()

       if index == len(__lowercase					):
              return None

       return index


if __name__ == "__main__":
    a__							:							Any			=input('''Enter numbers separated by comma:\n''').strip()
    a__							:							Any			=[int(item) for item in user_input.split(''',''')]

    a__							:							Tuple			=input('''Enter a single number to be found in the list:\n''')
    a__							:							List[Any]			=int(target_input)
    a__							:							List[Any]			=sentinel_linear_search(sequence, target)
    if result is not None:
        print(f'{target} found at positions: {result}')
    else:
        print('''Not found''')
