






def 						_UpperCamelCase (     UpperCamelCase_							:       int		)	-> int:

						"""simple docstring"""



						if not isinstance(UpperCamelCase_     ,							UpperCamelCase_		):
												raise TypeError('recursive_lucas_number accepts only integer arguments.'		)
						if n_th_number == 0:
												return 2
						if n_th_number == 1:
												return 1

						return recursive_lucas_number(n_th_number - 1		) + recursive_lucas_number(
						    n_th_number - 2		)







def 						_UpperCamelCase (     UpperCamelCase_							:       int		)	-> int:

						"""simple docstring"""



						if not isinstance(UpperCamelCase_     ,							UpperCamelCase_		):
												raise TypeError('dynamic_lucas_number accepts only integer arguments.'		)
						lowerCAmelCase__    ,							lowerCAmelCase__         =    2, 1
						for _ in range(UpperCamelCase_		):
												lowerCAmelCase__    ,							lowerCAmelCase__         =    b, a + b
						return a


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					__snake_case     :     List[Any]       =  int(input("""Enter the number of terms in lucas series:\n""").strip())
					print("""Using recursive function to calculate lucas series:""")
					print(""" """.join(str(recursive_lucas_number(i)) for i in range(n)))
					print("""\nUsing dynamic function to calculate lucas series:""")
					print(""" """.join(str(dynamic_lucas_number(i)) for i in range(n)))













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 (     UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Union[str, Any]=100     ,							UpperCamelCase_							:       Optional[int]=" "		)	-> int:

						"""simple docstring"""



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







def 						_UpperCamelCase (     UpperCamelCase_							:       List[str]		)	-> Any:

						"""simple docstring"""



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







def 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       int     ,							UpperCamelCase_							:       Optional[int]     ,							UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Tuple		)	-> Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    load_dataset(
						    'csv'     ,							data_files=[csv_path]     ,							split='train'     ,							delimiter='\t'     ,							column_names=['title', 'text']		)
						lowerCAmelCase__         =    kb_dataset.map(
						    UpperCamelCase_     ,							batched=UpperCamelCase_     ,							num_proc=1		)  # if you want you can load already splitted csv.
						lowerCAmelCase__         =    [kb_dataset.shard(UpperCamelCase_     ,							UpperCamelCase_     ,							contiguous=UpperCamelCase_		) for i in range(UpperCamelCase_		)]
						lowerCAmelCase__         =    kb_list[process_num]

						lowerCAmelCase__         =    'data_' + str(UpperCamelCase_		)
						lowerCAmelCase__         =    os.path.join(UpperCamelCase_     ,							UpperCamelCase_		)

						lowerCAmelCase__         =    DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'		)
						lowerCAmelCase__         =    ctx_encoder.to(device=UpperCamelCase_		)

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

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

						lowerCAmelCase__         =    data_shrad.map(
						    partial(UpperCamelCase_     ,							ctx_encoder=UpperCamelCase_     ,							ctx_tokenizer=UpperCamelCase_     ,							device=UpperCamelCase_		)     ,							batched=UpperCamelCase_     ,							batch_size=16     ,							features=UpperCamelCase_     ,							)
						dataset.save_to_disk(UpperCamelCase_		)







def 						_UpperCamelCase (     UpperCamelCase_							:       List[Any]     ,							UpperCamelCase_							:       Dict		)	-> Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []

						for shard_address in glob(str(UpperCamelCase_		) + '/*/'		):
												data_shard_list.append(load_from_disk(UpperCamelCase_		)		)

						lowerCAmelCase__         =    concatenate_datasets(UpperCamelCase_		)
						faiss.omp_set_num_threads(96		)

						lowerCAmelCase__         =    faiss.IndexHNSWFlat(768     ,							128     ,							faiss.METRIC_INNER_PRODUCT		)
						concat.add_faiss_index('embeddings'     ,							custom_index=UpperCamelCase_		)
						concat.get_index('embeddings'		).save(
						    UpperCamelCase_		)  # since we load the index in to memory,we can directly update the index in the disk





