


'''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__			(						__lowerCamelCase       :							Any       ,    __lowerCamelCase       :							Optional[Any]=1_0_0       ,    __lowerCamelCase       :							Optional[int]=" "							):







   '''simple docstring'''




   _UpperCAmelCase   :   Union[str, Any]							    =text.split(__UpperCamelCase							)
   return [character.join(text[i : i + n]							).strip() for i in range(0       ,    len(__UpperCamelCase							)       ,    __UpperCamelCase							)]





def lowerCamelCase__			(						__lowerCamelCase       :							Any							):







   '''simple docstring'''




   _UpperCAmelCase    ,       _UpperCAmelCase   :   Tuple							    =[], []
   for title, text in zip(documents['title']       ,    documents['text']							):
      if text is not None:
         for passage in split_text(__UpperCamelCase							):
            titles.append(title if title is not None else ''							)
            texts.append(__UpperCamelCase							)
   return {"title": titles, "text": texts}





def lowerCamelCase__			(						__lowerCamelCase       :							Optional[int]       ,    __lowerCamelCase       :							List[Any]       ,    __lowerCamelCase       :							Dict       ,    __lowerCamelCase       :							Any       ,    __lowerCamelCase       :							Optional[int]       ,    __lowerCamelCase       :							str							):







   '''simple docstring'''




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

   _UpperCAmelCase   :   List[Any]							    ='data_' + str(__UpperCamelCase							)
   _UpperCAmelCase   :   Tuple							    =os.path.join(__UpperCamelCase       ,    __UpperCamelCase							)

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

   def embed(
       __lowerCamelCase       :							Any       ,    __lowerCamelCase       :							Any       ,    __lowerCamelCase       :							Dict       ,    __lowerCamelCase       :							Optional[Any]							) -> dict:
      _UpperCAmelCase   :   Dict							    =ctx_tokenizer(
          documents['title']       ,    documents['text']       ,    truncation=__UpperCamelCase       ,    padding='longest'       ,    return_tensors='pt'							)['input_ids']
      _UpperCAmelCase   :   Union[str, Any]							    =ctx_encoder(input_ids.to(device=__UpperCamelCase							)       ,    return_dict=__UpperCamelCase							).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   :   Any							    =data_shrad.map(
       partial(__UpperCamelCase       ,    ctx_encoder=__UpperCamelCase       ,    ctx_tokenizer=__UpperCamelCase       ,    device=__UpperCamelCase							)       ,    batched=__UpperCamelCase       ,    batch_size=1_6       ,    features=__UpperCamelCase       ,    )
   dataset.save_to_disk(__UpperCamelCase							)





def lowerCamelCase__			(						__lowerCamelCase       :							Tuple       ,    __lowerCamelCase       :							Any							):







   '''simple docstring'''




   _UpperCAmelCase   :   List[str]							    =[]

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

   _UpperCAmelCase   :   List[str]							    =concatenate_datasets(__UpperCamelCase							)
   faiss.omp_set_num_threads(9_6							)

   _UpperCAmelCase   :   Optional[int]							    =faiss.IndexHNSWFlat(7_6_8       ,    1_2_8       ,    faiss.METRIC_INNER_PRODUCT							)
   concat.add_faiss_index('embeddings'       ,    custom_index=__UpperCamelCase							)
   concat.get_index('embeddings'							).save(
       __UpperCamelCase							)  # since we load the index in to memory,we can directly update the index in the disk



"""simple docstring"""


def     _lowerCamelCase(__UpperCamelCase = 4000000       )				-> int:
	_lowerCAmelCase								=1
	_lowerCAmelCase								=2
	_lowerCAmelCase								=0
	while j <= n:
		if j % 2 == 0:
			total += j
		_lowerCAmelCase						,							_lowerCAmelCase								=j, i + j

	return total


if __name__ == "__main__":
			print(F"""{solution() = }""")



