




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__						,			UpperCamelCase__=1_00						,			UpperCamelCase__=" "   )     ->     Optional[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__   )     ->     Optional[int]:







				'''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__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Union[str, 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__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   ) -> 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__						,			UpperCamelCase__   )     ->     Optional[int]:







				'''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(7_68						,			1_28						,			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











from ..utils import DummyObject, requires_backends

class 		lowercase_       (	metaclass=lowerCAmelCase__				):
					__UpperCamelCase     	= ["torch", "transformers", "k_diffusion"]




					def __init__(   self:		Tuple,     *_lowercase:		Dict,     **_lowercase:		Optional[int]):


									'''simple docstring'''


									requires_backends(self,     ["""torch""", """transformers""", """k_diffusion"""])




					@classmethod
					def     _lowercase						(   cls:		Union[str, Any],     *_lowercase:		Union[str, Any],     **_lowercase:		Optional[int]):


									'''simple docstring'''


									requires_backends(cls,     ["""torch""", """transformers""", """k_diffusion"""])







					@classmethod
					def     _lowercase						(   cls:		List[str],     *_lowercase:		int,     **_lowercase:		List[Any]):


									'''simple docstring'''


									requires_backends(cls,     ["""torch""", """transformers""", """k_diffusion"""])





