

'''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=1_00	,							snake_case=" "  )    ->			str:
			_lowercase    :   Union[str, Any]      =     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  )    ->			int:
			_lowercase  ,   _lowercase    :   Union[str, 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  )    ->			int:
			_lowercase    :   Optional[int]      =     load_dataset(
			    "csv"	,							data_files=[csv_path]	,							split="train"	,							delimiter="\t"	,							column_names=["title", "text"]  )
			_lowercase    :   Optional[int]      =     kb_dataset.map(
			    snake_case	,							batched=snake_case	,							num_proc=1  )  # if you want you can load already splitted csv.
			_lowercase    :   int      =     [kb_dataset.shard(snake_case	,							snake_case	,							contiguous=snake_case  ) for i in range(snake_case  )]
			_lowercase    :   Union[str, Any]      =     kb_list[process_num]

			_lowercase    :   Optional[int]      =     "data_" + str(snake_case  )
			_lowercase    :   Optional[Any]      =     os.path.join(snake_case	,							snake_case  )

			_lowercase    :   Dict      =     DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"  )
			_lowercase    :   Optional[Any]      =     ctx_encoder.to(device=snake_case  )

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

			_lowercase    :   List[Any]      =     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  )    ->			Optional[Any]:
			_lowercase    :   List[str]      =     []

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

			_lowercase    :   str      =     concatenate_datasets(snake_case  )
			faiss.omp_set_num_threads(96  )

			_lowercase    :   Optional[int]      =     faiss.IndexHNSWFlat(7_68	,							1_28	,							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__						:


   def __init__(				self    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   Tuple      =     data
      _lowercase    :   int
      _lowercase    :   Node







def       _A	(			snake_case  )    ->			None:
   _lowercase    :   Any      =     0
   _lowercase    :   str      =     x







def       _A	(			snake_case	,							snake_case  )    ->			None:
   _lowercase  ,   _lowercase    :   Any      =     find_set(snake_case  ), find_set(snake_case  )
   if x == y:
      return

   elif x.rank > y.rank:
      _lowercase    :   Dict      =     x
   else:
      _lowercase    :   Optional[int]      =     y
      if x.rank == y.rank:
         y.rank += 1







def       _A	(			snake_case  )    ->			Node:
   if x != x.parent:
      _lowercase    :   Optional[int]      =     find_set(x.parent  )
   return x.parent







def       _A	(			snake_case  )    ->			set:
   _lowercase    :   Any      =     ({0, 1, 2}, {3, 4, 5})
   for s in sets:
      if node.data in s:
         return s
   _lowercase    :   List[Any]      =     F'''{node.data} is not in {sets}'''
   raise ValueError(snake_case  )







def       _A	(			)    ->			None:
   _lowercase    :   Union[str, Any]      =     [Node(snake_case  ) for i in range(6  )]
   for v in vertex:
      make_set(snake_case  )

   union_set(vertex[0]	,							vertex[1]  )
   union_set(vertex[1]	,							vertex[2]  )
   union_set(vertex[3]	,							vertex[4]  )
   union_set(vertex[3]	,							vertex[5]  )

   for nodea in vertex:
      for nodea in vertex:
         if find_python_set(snake_case  ).isdisjoint(find_python_set(snake_case  )  ):
            assert find_set(snake_case  ) != find_set(snake_case  )
         else:
            assert find_set(snake_case  ) == find_set(snake_case  )


if __name__ == "__main__":
    test_disjoint_set()






