






"""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(  snake_case_						,       snake_case_=100						,       snake_case_=" "		):
       """simple docstring"""





       lowercase__:    Tuple        =      text.split(snake_case_		)
       return [character.join(text[i : i + n]		).strip() for i in range(0						,       len(snake_case_		)						,       snake_case_		)]






def     A(  snake_case_		):
       """simple docstring"""





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






def     A(  snake_case_						,       snake_case_						,       snake_case_						,       snake_case_						,       snake_case_						,       snake_case_		):
       """simple docstring"""





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

       lowercase__:    Tuple        =      "data_" + str(snake_case_		)
       lowercase__:    Dict        =      os.path.join(snake_case_						,       snake_case_		)

       lowercase__:    Optional[Any]        =      DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"		)
       lowercase__:    Optional[int]        =      ctx_encoder.to(device=snake_case_		)

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

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

       lowercase__:    Any        =      data_shrad.map(
           partial(snake_case_						,       ctx_encoder=snake_case_						,       ctx_tokenizer=snake_case_						,       device=snake_case_		)						,       batched=snake_case_						,       batch_size=16						,       features=snake_case_						,       )
       dataset.save_to_disk(snake_case_		)







def     A(  snake_case_						,       snake_case_		):
       """simple docstring"""





       lowercase__:    Tuple        =      []

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

       lowercase__:    Dict        =      concatenate_datasets(snake_case_		)
       faiss.omp_set_num_threads(96		)

       lowercase__:    Dict        =      faiss.IndexHNSWFlat(768						,       128						,       faiss.METRIC_INNER_PRODUCT		)
       concat.add_faiss_index("embeddings"						,       custom_index=snake_case_		)
       concat.get_index("embeddings"		).save(
           snake_case_		)  # since we load the index in to memory,we can directly update the index in the disk














"""simple docstring"""





class 		_a       :





   '''simple docstring'''






   def __init__(   self			,				UpperCAmelCase_=None)	-> List[Any]:




          '''simple docstring'''




          lowercase__:    str        =      data
          lowercase__:    Any        =      None





   def __repr__(   self)	-> Optional[Any]:




          '''simple docstring'''




          lowercase__:    Any        =      ""
          lowercase__:    Dict        =      self
          while temp:
                 string_rep += F"""<{temp.data}> ---> """
                 lowercase__:    Tuple        =      temp.next
          string_rep += "<END>"
          return string_rep







def     A(  snake_case_		):
       """simple docstring"""





       if not elements_list:
              raise Exception("The Elements List is empty"		)

       # Set first element as Head
       lowercase__:    Any        =      Node(elements_list[0]		)
       lowercase__:    Optional[int]        =      head
       # Loop through elements from position 1
       for data in elements_list[1:]:
              lowercase__:    Dict        =      Node(snake_case_		)
              lowercase__:    Dict        =      current.next
       return head


UpperCamelCase			=      [1, 3, 5, 32, 44, 12, 43]
print(F"List: {list_data}")
print("""Creating Linked List from List.""")
UpperCamelCase			=      make_linked_list(list_data)
print("""Linked List:""")







print(linked_list)






