

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 _a			(	SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      Any=1_00   , SCREAMING_SNAKE_CASE		:      Any=" "		)   ->   Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:       Optional[int]            =				text.split(_SCREAMING_SNAKE_CASE		)
 return [character.join(text[i : i + n]		).strip() for i in range(0   , len(_SCREAMING_SNAKE_CASE		)   , _SCREAMING_SNAKE_CASE		)]







def _a			(	SCREAMING_SNAKE_CASE		:      List[Any]		)   ->   int:

 """simple docstring"""






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







def _a			(	SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      List[Any]   , SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      Any   , SCREAMING_SNAKE_CASE		:      List[str]		)   ->   List[str]:

 """simple docstring"""






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

 __lowerCAmelCase:       List[Any]            =				"data_" + str(_SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       List[Any]            =				os.path.join(_SCREAMING_SNAKE_CASE   , _SCREAMING_SNAKE_CASE		)

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

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

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

 __lowerCAmelCase:       Any            =				data_shrad.map(
     partial(_SCREAMING_SNAKE_CASE   , ctx_encoder=_SCREAMING_SNAKE_CASE   , ctx_tokenizer=_SCREAMING_SNAKE_CASE   , device=_SCREAMING_SNAKE_CASE		)   , batched=_SCREAMING_SNAKE_CASE   , batch_size=16   , features=_SCREAMING_SNAKE_CASE   , )
 dataset.save_to_disk(_SCREAMING_SNAKE_CASE		)







def _a			(	SCREAMING_SNAKE_CASE		:      Any   , SCREAMING_SNAKE_CASE		:      str		)   ->   Tuple:

 """simple docstring"""






 __lowerCAmelCase:       List[Any]            =				[]

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

 __lowerCAmelCase:       int            =				concatenate_datasets(_SCREAMING_SNAKE_CASE		)
 faiss.omp_set_num_threads(96		)

 __lowerCAmelCase:       Optional[int]            =				faiss.IndexHNSWFlat(7_68   , 1_28   , faiss.METRIC_INNER_PRODUCT		)
 concat.add_faiss_index('embeddings'   , custom_index=_SCREAMING_SNAKE_CASE		)
 concat.get_index('embeddings'		).save(
     _SCREAMING_SNAKE_CASE		)  # since we load the index in to memory,we can directly update the index in the disk








import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig






class 							A_			(		snake_case__ ):
     _lowercase      :					Any       =     CustomConfig


     def __init__(							self      :		List[Any]      ,  UpperCAmelCase      :		Optional[Any] )							->     int:
      super().__init__(UpperCAmelCase )
      __lowerCAmelCase:       Tuple            =				torch.nn.Linear(config.hidden_size      ,  config.hidden_size )


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Tuple )							->     Union[str, Any]:
      return self.linear(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		str      ,  UpperCAmelCase      :		str )							->     Optional[int]:
      pass







class 							A_			(		snake_case__ ):
     _lowercase      :					str       =     NoSuperInitConfig


     def __init__(							self      :		List[Any]      ,  UpperCAmelCase      :		Union[str, Any] )							->     int:
      super().__init__(UpperCAmelCase )
      __lowerCAmelCase:       int            =				torch.nn.Linear(config.attribute      ,  config.attribute )


     def   UpperCAmelCase  (							self      :		Tuple      ,  UpperCAmelCase      :		Tuple )							->     Tuple:
      return self.linear(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Optional[Any]      ,  UpperCAmelCase      :		Any )							->     str:
      pass





