






"""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 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	Any=100		,				UpperCamelCase     :	List[Any]=" "    ):
		A__											=							text.split(lowercase_    )
		return [character.join(text[i : i + n]    ).strip() for i in range(0		,				len(lowercase_    )		,				lowercase_    )]






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

		A__											=							"""data_""" + str(lowercase_    )
		A__											=							os.path.join(lowercase_		,				lowercase_    )

		A__											=							DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""    )
		A__											=							ctx_encoder.to(device=lowercase_    )

		def embed(
		    UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	int		,				UpperCamelCase     :	Optional[Any]		,				UpperCamelCase     :	Optional[Any]    ) -> dict:
				A__											=							ctx_tokenizer(
				    documents["""title"""]		,				documents["""text"""]		,				truncation=lowercase_		,				padding="""longest"""		,				return_tensors="""pt"""    )["""input_ids"""]
				A__											=							ctx_encoder(input_ids.to(device=lowercase_    )		,				return_dict=lowercase_    ).pooler_output
				return {"embeddings": embeddings.detach().cpu().numpy()}

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

		A__											=							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     :	Dict		,				UpperCamelCase     :	Any    ):
		A__											=							[]

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

		A__											=							concatenate_datasets(lowercase_    )
		faiss.omp_set_num_threads(96    )

		A__											=							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 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Dict    ):
  A__											=							len(UpperCamelCase    )
  for _ in range(
      0		,				int(((no_of_elements - 1) / 2) + 1    )    ):  # we don't need to traverse to end of list as
    for j in range(0		,				no_of_elements - 1    ):
      if (
          lst[j + 1] < lst[j]
      ):  # applying bubble sort algorithm from left to right (or forwards)
        A__											=							lst[j + 1]
        A__											=							lst[j]
        A__											=							temp
      if (
          lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
      ):  # applying bubble sort algorithm from right to left (or backwards)
        A__											=							lst[no_of_elements - 1 - j]
        A__											=							lst[no_of_elements - 2 - j]
        A__											=							temp
  return lst


if __name__ == "__main__":
      print("enter the list to be sorted")
      lowerCamelCase__			=   [int(x) for x in input().split()]  # inputing elements of the list in one line
      lowerCamelCase__			=   double_sort(lst)
      print("the sorted list is")
      print(sorted_lst)




