
'''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 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    Optional[int]=1_00					, _lowerCamelCase  :    Tuple=" "						)      ->  str:
  _lowerCAmelCase    :					List[Any]							=   text.split(_lowerCamelCase						)
  return [character.join(text[i : i + n]						).strip() for i in range(0					, len(_lowerCamelCase						)					, _lowerCamelCase						)]


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[str]						)      ->  Tuple:
  _lowerCAmelCase			,   _lowerCAmelCase    :					str							=   [], []
  for title, text in zip(documents["""title"""]					, documents["""text"""]						):
    if text is not None:
      for passage in split_text(_lowerCamelCase						):
        titles.append(title if title is not None else """"""						)
        texts.append(_lowerCamelCase						)
  return {"title": titles, "text": texts}


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Tuple					, _lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    Optional[int]					, _lowerCamelCase  :    Any					, _lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    Any						)      ->  List[str]:
  _lowerCAmelCase    :					int							=   load_dataset(
      """csv"""					, data_files=[csv_path]					, split="""train"""					, delimiter="""\t"""					, column_names=["""title""", """text"""]						)
  _lowerCAmelCase    :					Dict							=   kb_dataset.map(
      _lowerCamelCase					, batched=_lowerCamelCase					, num_proc=1						)  # if you want you can load already splitted csv.
  _lowerCAmelCase    :					Optional[int]							=   [kb_dataset.shard(_lowerCamelCase					, _lowerCamelCase					, contiguous=_lowerCamelCase						) for i in range(_lowerCamelCase						)]
  _lowerCAmelCase    :					Union[str, Any]							=   kb_list[process_num]

  _lowerCAmelCase    :					Dict							=   """data_""" + str(_lowerCamelCase						)
  _lowerCAmelCase    :					Union[str, Any]							=   os.path.join(_lowerCamelCase					, _lowerCamelCase						)

  _lowerCAmelCase    :					Any							=   DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""						)
  _lowerCAmelCase    :					int							=   ctx_encoder.to(device=_lowerCamelCase						)

  def embed(
      _lowerCamelCase  :    dict					, _lowerCamelCase  :    DPRContextEncoder					, _lowerCamelCase  :    DPRContextEncoderTokenizerFast					, _lowerCamelCase  :    Dict						) -> dict:
    _lowerCAmelCase    :					List[str]							=   ctx_tokenizer(
        documents["""title"""]					, documents["""text"""]					, truncation=_lowerCamelCase					, padding="""longest"""					, return_tensors="""pt"""						)["""input_ids"""]
    _lowerCAmelCase    :					List[str]							=   ctx_encoder(input_ids.to(device=_lowerCamelCase						)					, return_dict=_lowerCamelCase						).pooler_output
    return {"embeddings": embeddings.detach().cpu().numpy()}

  _lowerCAmelCase    :					Optional[Any]							=   Features(
      {"""text""": Value("""string"""						), """title""": Value("""string"""						), """embeddings""": Sequence(Value("""float32"""						)						)}						)  # optional, save as float32 instead of float64 to save space

  _lowerCAmelCase    :					List[str]							=   data_shrad.map(
      partial(_lowerCamelCase					, ctx_encoder=_lowerCamelCase					, ctx_tokenizer=_lowerCamelCase					, device=_lowerCamelCase						)					, batched=_lowerCamelCase					, batch_size=16					, features=_lowerCamelCase					, )
  dataset.save_to_disk(_lowerCamelCase						)



def 		_UpperCAmelCase			(					_lowerCamelCase  :    str					, _lowerCamelCase  :    Union[str, Any]						)      ->  Any:
  _lowerCAmelCase    :					Any							=   []

  for shard_address in glob(str(_lowerCamelCase						) + """/*/"""						):
    data_shard_list.append(load_from_disk(_lowerCamelCase						)						)

  _lowerCAmelCase    :					List[Any]							=   concatenate_datasets(_lowerCamelCase						)
  faiss.omp_set_num_threads(96						)

  _lowerCAmelCase    :					Tuple							=   faiss.IndexHNSWFlat(7_68					, 1_28					, faiss.METRIC_INNER_PRODUCT						)
  concat.add_faiss_index("""embeddings"""					, custom_index=_lowerCamelCase						)
  concat.get_index("""embeddings"""						).save(
      _lowerCamelCase						)  # since we load the index in to memory,we can directly update the index in the disk




'''simple docstring'''

def 		_UpperCAmelCase			(					_lowerCamelCase  :    int = 1_00						)      ->  int:
  _lowerCAmelCase    :					Dict							=   [[0 for _ in range(_lowerCamelCase						)] for _ in range(m + 1						)]
  for i in range(m + 1						):
    _lowerCAmelCase    :					List[str]							=   1

  for n in range(m + 1						):
    for k in range(1					, _lowerCamelCase						):
      memo[n][k] += memo[n][k - 1]
      if n > k:
        memo[n][k] += memo[n - k - 1][k]

  return memo[m][m - 1] - 1


if __name__ == "__main__":
     print(solution(int(input("""Enter a number: """).strip())))


