



"""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 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	Any=100  , UpperCamelCase__	:	int=" "							):
    _UpperCAmelCase							:						Tuple       							=					text.split(UpperCamelCase__							)
    return [character.join(text[i : i + n]							).strip() for i in range(0  , len(UpperCamelCase__							)  , UpperCamelCase__							)]





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

    _UpperCAmelCase							:						str       							=					'''data_''' + str(UpperCamelCase__							)
    _UpperCAmelCase							:						int       							=					os.path.join(UpperCamelCase__  , UpperCamelCase__							)

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

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

    _UpperCAmelCase							:						List[Any]       							=					Features(
        {'''text''': Value('''string'''							), '''title''': Value('''string'''							), '''embeddings''': Sequence(Value('''float32'''							)							)}							)  # optional, save as float32 instead of float64 to save space

    _UpperCAmelCase							:						Tuple       							=					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 				lowerCamelCase_		(UpperCamelCase__	:	Union[str, Any]  , UpperCamelCase__	:	Optional[int]							):
    _UpperCAmelCase							:						Dict       							=					[]

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

    _UpperCAmelCase							:						Dict       							=					concatenate_datasets(UpperCamelCase__							)
    faiss.omp_set_num_threads(96							)

    _UpperCAmelCase							:						Optional[int]       							=					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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


_lowerCAmelCase					:Dict  =      logging.get_logger(__name__)

_lowerCAmelCase					:Tuple  =      {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_lowerCAmelCase					:Any  =      {
    'vocab_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
    },
}

_lowerCAmelCase					:str  =      {
    'microsoft/mpnet-base': 512,
}

_lowerCAmelCase					:Union[str, Any]  =      {
    'microsoft/mpnet-base': {'do_lower_case': True},
}
class 							_UpperCAmelCase							(				a   ):





     '''simple docstring'''



     a__             =VOCAB_FILES_NAMES
     a__             =PRETRAINED_VOCAB_FILES_MAP
     a__             =PRETRAINED_INIT_CONFIGURATION
     a__             =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a__             =MPNetTokenizer
     a__             =['''input_ids''', '''attention_mask''']





     def __init__(      self ,			A=None ,			A=None ,			A=True ,			A="<s>" ,			A="</s>" ,			A="</s>" ,			A="<s>" ,			A="[UNK]" ,			A="<pad>" ,			A="<mask>" ,			A=True ,			A=None ,			**A ,			)							->      Dict:
         super().__init__(
             A ,			tokenizer_file=A ,			do_lower_case=A ,			bos_token=A ,			eos_token=A ,			sep_token=A ,			cls_token=A ,			unk_token=A ,			pad_token=A ,			mask_token=A ,			tokenize_chinese_chars=A ,			strip_accents=A ,			**A ,			)

         _UpperCAmelCase							:						List[str]       							=					json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
         if (
             pre_tok_state.get('''lowercase''' ,			A	) != do_lower_case
             or pre_tok_state.get('''strip_accents''' ,			A	) != strip_accents
         ):
             _UpperCAmelCase							:						Optional[int]       							=					getattr(A ,			pre_tok_state.pop('''type'''	)	)
             _UpperCAmelCase							:						List[Any]       							=					do_lower_case
             _UpperCAmelCase							:						List[Any]       							=					strip_accents
             _UpperCAmelCase							:						Tuple       							=					pre_tok_class(**A	)

         _UpperCAmelCase							:						Union[str, Any]       							=					do_lower_case





     @property
     def 				__lowerCAmelCase			(      self	)							->      str:
         if self._mask_token is None:
             if self.verbose:
                 logger.error('''Using mask_token, but it is not set yet.'''	)
             return None
         return str(self._mask_token	)





     @mask_token.setter
     def 				__lowerCAmelCase			(      self ,			A	)							->      str:
         _UpperCAmelCase							:						Optional[int]       							=					AddedToken(A ,			lstrip=A ,			rstrip=A	) if isinstance(A ,			A	) else value
         _UpperCAmelCase							:						List[Any]       							=					value





     def 				__lowerCAmelCase			(      self ,			A ,			A=None	)							->      str:
         _UpperCAmelCase							:						List[str]       							=					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
             return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      List[int]:
         _UpperCAmelCase							:						int       							=					[self.sep_token_id]
         _UpperCAmelCase							:						int       							=					[self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]





     def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      Tuple[str]:
         _UpperCAmelCase							:						str       							=					self._tokenizer.model.save(A ,			name=A	)
         return tuple(A	)


