



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					(  lowercase__						:					Any				,					lowercase__						:					Dict=100				,					lowercase__						:					int=" "	):



			'''simple docstring'''





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




def    UpperCAmelCase					(  lowercase__						:					Optional[Any]	):



			'''simple docstring'''





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




def    UpperCAmelCase					(  lowercase__						:					Tuple				,					lowercase__						:					Any				,					lowercase__						:					Optional[Any]				,					lowercase__						:					str				,					lowercase__						:					Union[str, Any]				,					lowercase__						:					Optional[Any]	):



			'''simple docstring'''





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

			a__					=  "data_" + str(_lowerCAmelCase	)
			a__					=  os.path.join(_lowerCAmelCase				,					_lowerCAmelCase	)

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

			def embed(
			    lowercase__						:					dict				,					lowercase__						:					DPRContextEncoder				,					lowercase__						:					DPRContextEncoderTokenizerFast				,					lowercase__						:					List[str]	) -> dict:
						a__					=  ctx_tokenizer(
						    documents["""title"""]				,					documents["""text"""]				,					truncation=_lowerCAmelCase				,					padding="""longest"""				,					return_tensors="""pt"""	)["input_ids"]
						a__					=  ctx_encoder(input_ids.to(device=_lowerCAmelCase	)				,					return_dict=_lowerCAmelCase	).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(_lowerCAmelCase				,					ctx_encoder=_lowerCAmelCase				,					ctx_tokenizer=_lowerCAmelCase				,					device=_lowerCAmelCase	)				,					batched=_lowerCAmelCase				,					batch_size=16				,					features=_lowerCAmelCase				,					)
			dataset.save_to_disk(_lowerCAmelCase	)





def    UpperCAmelCase					(  lowercase__						:					List[Any]				,					lowercase__						:					Optional[int]	):



			'''simple docstring'''





			a__					=  []

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

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

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


'''simple docstring'''

from __future__ import annotations


def        UpperCAmelCase__   (				_lowerCAmelCase    :			list[int | float])    ->		int | float:



						"""simple docstring"""

						if len(_lowerCAmelCase) == 0:
												raise ValueError("find_max() arg is an empty sequence")
						snake_case :       Any				=    nums[0]
						for x in nums:
												if x > max_num:
																		snake_case :       int				=    x
						return max_num


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)





