


"""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 						lowercase				(	_snake_case		: List[Any]	,  _snake_case		: List[Any]=100	,  _snake_case		: Dict=" "	)	->Dict:
	"""simple docstring"""





	__snake_case      :		List[str]         =							text.split(_snake_case	)
	return [character.join(text[i : i + n]	).strip() for i in range(0	,  len(_snake_case	)	,  _snake_case	)]






def 						lowercase				(	_snake_case		: List[str]	)	->Optional[Any]:
	"""simple docstring"""





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






def 						lowercase				(	_snake_case		: List[Any]	,  _snake_case		: Any	,  _snake_case		: Dict	,  _snake_case		: Optional[Any]	,  _snake_case		: Dict	,  _snake_case		: Dict	)	->Dict:
	"""simple docstring"""





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

	__snake_case      :		List[Any]         =							'''data_''' + str(_snake_case	)
	__snake_case      :		Tuple         =							os.path.join(_snake_case	,  _snake_case	)

	__snake_case      :		Any         =							DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''	)
	__snake_case      :		List[Any]         =							ctx_encoder.to(device=_snake_case	)

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

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

	__snake_case      :		Union[str, Any]         =							data_shrad.map(
	    partial(_snake_case	,  ctx_encoder=_snake_case	,  ctx_tokenizer=_snake_case	,  device=_snake_case	)	,  batched=_snake_case	,  batch_size=16	,  features=_snake_case	,  )
	dataset.save_to_disk(_snake_case	)






def 						lowercase				(	_snake_case		: List[str]	,  _snake_case		: Any	)	->Union[str, Any]:
	"""simple docstring"""





	__snake_case      :		Dict         =							[]

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

	__snake_case      :		str         =							concatenate_datasets(_snake_case	)
	faiss.omp_set_num_threads(96	)

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









"""simple docstring"""







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class  __A							(			_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 @register_to_config
 def __init__(						self					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A = False					,       )   ->	Optional[Any]:
     super().__init__()

     a											=nn.Linear(__A					,       __A					,       bias=__A		)

     a											=nn.Embedding(__A					,       __A		)
     a											=False

     a											=nn.Dropout(p=__A		)

     a											=TaConfig(
         d_model=__A					,       num_heads=__A					,       d_kv=__A					,       d_ff=__A					,       feed_forward_proj=__A					,       dropout_rate=__A					,       is_decoder=__A					,       is_encoder_decoder=__A					,       )
     a											=nn.ModuleList()
     for lyr_num in range(__A		):
         a											=TaBlock(__A		)
         self.encoders.append(__A		)

     a											=TaLayerNorm(__A		)
     a											=nn.Dropout(p=__A		)





 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	str:
     a											=self.input_proj(__A		)

     # terminal relative positional encodings
     a											=encoder_inputs.shape[1]
     a											=torch.arange(__A					,       device=encoder_inputs.device		)

     a											=encoder_inputs_mask.sum(-1		)
     a											=torch.roll(input_positions.unsqueeze(0		)					,       tuple(seq_lens.tolist()		)					,       dims=0		)
     x += self.position_encoding(__A		)

     a											=self.dropout_pre(__A		)

     # inverted the attention mask
     a											=encoder_inputs.size()
     a											=self.get_extended_attention_mask(__A					,       __A		)

     for lyr in self.encoders:
         a											=lyr(__A					,       __A		)[0]
     a											=self.layer_norm(__A		)

     return self.dropout_post(__A		), encoder_inputs_mask