





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  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Dict    ,      UpperCamelCase__:							List[Any]=100    ,      UpperCamelCase__:							Optional[Any]=" "			):
		SCREAMING_SNAKE_CASE__								=					text.split(UpperCamelCase__			)
		return [character.join(text[i : i + n]			).strip() for i in range(0    ,      len(UpperCamelCase__			)    ,      UpperCamelCase__			)]






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Optional[Any]			):
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					[], []
		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  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Optional[int]    ,      UpperCamelCase__:							int    ,      UpperCamelCase__:							Dict    ,      UpperCamelCase__:							Dict    ,      UpperCamelCase__:							List[Any]    ,      UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					load_dataset(
		    """csv"""    ,      data_files=[csv_path]    ,      split="""train"""    ,      delimiter="""\t"""    ,      column_names=["""title""", """text"""]			)
		SCREAMING_SNAKE_CASE__								=					kb_dataset.map(
		    UpperCamelCase__    ,      batched=UpperCamelCase__    ,      num_proc=1			)  # if you want you can load already splitted csv.
		SCREAMING_SNAKE_CASE__								=					[kb_dataset.shard(UpperCamelCase__    ,      UpperCamelCase__    ,      contiguous=UpperCamelCase__			) for i in range(UpperCamelCase__			)]
		SCREAMING_SNAKE_CASE__								=					kb_list[process_num]

		SCREAMING_SNAKE_CASE__								=					"""data_""" + str(UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__								=					os.path.join(UpperCamelCase__    ,      UpperCamelCase__			)

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

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

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

		SCREAMING_SNAKE_CASE__								=					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  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Optional[int]    ,      UpperCamelCase__:							Tuple			):
		SCREAMING_SNAKE_CASE__								=					[]

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

		SCREAMING_SNAKE_CASE__								=					concatenate_datasets(UpperCamelCase__			)
		faiss.omp_set_num_threads(96			)

		SCREAMING_SNAKE_CASE__								=					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"""




import torch
from torch import autograd




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float, lowerCAmelCase__		:    bool							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    inputs.numel()
										_UpperCamelCase			:					int     =    int(0.005 * nb_elems							) + 1
										if sigmoid:
																_UpperCamelCase			:					Dict     =    (torch.sigmoid(lowerCAmelCase__							) > threshold).type(inputs.type()							)
										else:
																_UpperCamelCase			:					Optional[int]     =    (inputs > threshold).type(inputs.type()							)
										if mask.sum() < nb_min:
																# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
																_UpperCamelCase			:					int     =    inputs.flatten().kthvalue(max(nb_elems - nb_min, 1							)							).values
																_UpperCamelCase			:					List[str]     =    (inputs > k_threshold).type(inputs.type()							)
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    Optional[int]							)	->      Union[str, Any]:




										'''simple docstring'''



										return gradOutput, None, None




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Union[str, Any]     =    inputs.flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    int(threshold * inputs.numel()							)

										# flat_out and mask access the same memory.
										_UpperCamelCase			:					Tuple     =    mask.flatten()
										_UpperCamelCase			:					int     =    0
										_UpperCamelCase			:					Tuple     =    1
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    Union[str, Any]							)	->      int:




										'''simple docstring'''



										return gradOutput, None




class  _a						(      _lowerCAmelCase				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Optional[int]     =    inputs.abs().flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					List[Any]     =    int(threshold * inputs.numel()							)

										# flat_out and mask access the same memory.
										_UpperCamelCase			:					List[str]     =    mask.flatten()
										_UpperCamelCase			:					List[Any]     =    0
										_UpperCamelCase			:					str     =    1
										return mask



