
"""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 __snake_case (      UpperCamelCase						,					UpperCamelCase=100						,					UpperCamelCase=" "    )       ->    List[Any]:


		"""simple docstring"""





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

def __snake_case (      UpperCamelCase    )       ->    Optional[Any]:


		"""simple docstring"""





		a__		,	a__   		=     [], []
		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 __snake_case (      UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase    )       ->    Dict:


		"""simple docstring"""





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

		a__   		=     '''data_''' + str(UpperCamelCase    )
		a__   		=     os.path.join(UpperCamelCase						,					UpperCamelCase    )

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

		def embed(
		    UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase    ) -> dict:
				a__   		=     ctx_tokenizer(
				    documents['''title''']						,					documents['''text''']						,					truncation=UpperCamelCase						,					padding='''longest'''						,					return_tensors='''pt'''    )['''input_ids''']
				a__   		=     ctx_encoder(input_ids.to(device=UpperCamelCase    )						,					return_dict=UpperCamelCase    ).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(UpperCamelCase						,					ctx_encoder=UpperCamelCase						,					ctx_tokenizer=UpperCamelCase						,					device=UpperCamelCase    )						,					batched=UpperCamelCase						,					batch_size=16						,					features=UpperCamelCase						,					)
		dataset.save_to_disk(UpperCamelCase    )




def __snake_case (      UpperCamelCase						,					UpperCamelCase    )       ->    Tuple:


		"""simple docstring"""





		a__   		=     []

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

		a__   		=     concatenate_datasets(UpperCamelCase    )
		faiss.omp_set_num_threads(96    )

		a__   		=     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






"""simple docstring"""




__lowerCAmelCase							:  Any 				=		{
    '''0''': 1,
    '''1''': 1,
    '''2''': 2,
    '''3''': 6,
    '''4''': 24,
    '''5''': 120,
    '''6''': 720,
    '''7''': 5040,
    '''8''': 40320,
    '''9''': 362880,
}

__lowerCAmelCase							:  List[Any] 				=		{145: 145}

__lowerCAmelCase							:  Any 				=		{
    145: 0,
    169: 3,
    36301: 3,
    1454: 3,
    871: 2,
    45361: 2,
    872: 2,
}

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if n in CACHE_SUM_DIGIT_FACTORIALS:
				return CACHE_SUM_DIGIT_FACTORIALS[n]
		a__   		=     sum(DIGIT_FACTORIALS[let] for let in str(UpperCamelCase    )    )
		a__   		=     ret
		return ret

def __snake_case (      UpperCamelCase						,					UpperCamelCase = None    )       ->    int:


		"""simple docstring"""





		a__   		=     previous or set()
		if n in CHAIN_LENGTH_CACHE:
				return CHAIN_LENGTH_CACHE[n]
		a__   		=     sum_digit_factorials(UpperCamelCase    )
		if next_number in previous:
				a__   		=     0
				return 0
		else:
				previous.add(UpperCamelCase    )
				a__   		=     1 + chain_length(UpperCamelCase						,					UpperCamelCase    )
				a__   		=     ret
				return ret

def __snake_case (      UpperCamelCase = 60						,					UpperCamelCase = 1_000_000    )       ->    int:


		"""simple docstring"""





		return sum(1 for i in range(1						,					UpperCamelCase    ) if chain_length(UpperCamelCase    ) == num_terms    )


if __name__ == "__main__":
					print(f"{solution() = }")




