




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




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		int   )						->							Union[str, Any]:
    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       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Optional[int] ,  __UpperCamelCase    :		List[str] ,  __UpperCamelCase    :		Dict ,  __UpperCamelCase    :		Dict ,  __UpperCamelCase    :		str ,  __UpperCamelCase    :		int   )						->							Optional[Any]:
    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(
        __UpperCamelCase    :		dict ,  __UpperCamelCase    :		DPRContextEncoder ,  __UpperCamelCase    :		DPRContextEncoderTokenizerFast ,  __UpperCamelCase    :		str   ) -> 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       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Tuple ,  __UpperCamelCase    :		int   )						->							Optional[Any]:
    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











import itertools




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Tuple   )						->							List[Any]:
    return (
        int(''''''.join(combination[0:2]   )   ) * int(''''''.join(combination[2:5]   )   )
        == int(''''''.join(combination[5:9]   )   )
    ) or (
        int(''''''.join(combination[0]   )   ) * int(''''''.join(combination[1:5]   )   )
        == int(''''''.join(combination[5:9]   )   )
    )




def       SCREAMING_SNAKE_CASE			( )						->							Optional[Any]:
    return sum(
        {
            int(''''''.join(pandigital[5:9]   )   )
            for pandigital in itertools.permutations('''123456789'''   )
            if is_combination_valid(__UpperCamelCase   )
        }   )


if __name__ == "__main__":
 print(solution())





