
'''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 							A_  (    __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	List[Any]=100      ,  __SCREAMING_SNAKE_CASE    :	List[Any]=" "			)					->							str:





  """simple docstring"""

  __A		:  Optional[Any]       				=		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 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]			)					->							Dict:





  """simple docstring"""

  __A				,       __A		:  Dict       				=		[], []
  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 							A_  (    __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	Any      ,  __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	Tuple      ,  __SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	int			)					->							List[str]:





  """simple docstring"""

  __A		:  Union[str, Any]       				=		load_dataset(
      """csv"""      ,  data_files=[csv_path]      ,  split="""train"""      ,  delimiter="""\t"""      ,  column_names=["""title""", """text"""]			)
  __A		:  str       				=		kb_dataset.map(
      __SCREAMING_SNAKE_CASE      ,  batched=__SCREAMING_SNAKE_CASE      ,  num_proc=1			)  # if you want you can load already splitted csv.
  __A		:  List[str]       				=		[kb_dataset.shard(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  contiguous=__SCREAMING_SNAKE_CASE			) for i in range(__SCREAMING_SNAKE_CASE			)]
  __A		:  Union[str, Any]       				=		kb_list[process_num]

  __A		:  Dict       				=		"""data_""" + str(__SCREAMING_SNAKE_CASE			)
  __A		:  List[Any]       				=		os.path.join(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)

  __A		:  Any       				=		DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""			)
  __A		:  Any       				=		ctx_encoder.to(device=__SCREAMING_SNAKE_CASE			)

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

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

  __A		:  Union[str, Any]       				=		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 							A_  (    __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	str			)					->							int:





  """simple docstring"""

  __A		:  Optional[int]       				=		[]

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

  __A		:  Any       				=		concatenate_datasets(__SCREAMING_SNAKE_CASE			)
  faiss.omp_set_num_threads(96			)

  __A		:  int       				=		faiss.IndexHNSWFlat(768      ,  128      ,  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





'''simple docstring'''
from __future__ import annotations







def 							A_  (    __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  )					->							list[float | int]:





  """simple docstring"""

  if not all((nth_term, start_term_a, common_ratio_r)			):
    return []
  __A		:  list[float | int]       				=		[]
  __A		:  Union[str, Any]       				=		1
  __A		:  List[str]       				=		common_ratio_r
  for _ in range(int(__SCREAMING_SNAKE_CASE			)			):
    if not series:
      series.append(__SCREAMING_SNAKE_CASE			)
    else:
      power += 1
      series.append(float(start_term_a * multiple			)			)
      __A		:  List[str]       				=		pow(float(__SCREAMING_SNAKE_CASE			)      ,  __SCREAMING_SNAKE_CASE			)
  return series


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A__ :		Optional[int]					=float(input('Enter the last number (n term) of the Geometric Series'))
     A__ :		List[Any]					=float(input('Enter the starting term (a) of the Geometric Series'))
     A__ :		List[Any]					=float(
         input('Enter the common ratio between two terms (r) of the Geometric Series')
     )
     print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
     print(geometric_series(nth_term, start_term_a, common_ratio_r))



