


'''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 _UpperCamelCase			(	__A ,     __A=100 ,     __A=" "       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							text.split(_lowerCamelCase       )
      return [character.join(text[i : i + n]       ).strip() for i in range(0 ,     len(_lowerCamelCase       ) ,     _lowerCamelCase       )]







def _UpperCamelCase			(	__A       )				->						Optional[int]:


      '''simple docstring'''



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







def _UpperCamelCase			(	__A ,     __A ,     __A ,     __A ,     __A ,     __A       )				->						Dict:


      '''simple docstring'''



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

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

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

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

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

      UpperCamelCase__      =							data_shrad.map(
          partial(_lowerCamelCase ,     ctx_encoder=_lowerCamelCase ,     ctx_tokenizer=_lowerCamelCase ,     device=_lowerCamelCase       ) ,     batched=_lowerCamelCase ,     batch_size=16 ,     features=_lowerCamelCase ,     )
      dataset.save_to_disk(_lowerCamelCase       )







def _UpperCamelCase			(	__A ,     __A       )				->						Union[str, Any]:


      '''simple docstring'''



      UpperCamelCase__      =							[]

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

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

      UpperCamelCase__      =							faiss.IndexHNSWFlat(768 ,     128 ,     faiss.METRIC_INNER_PRODUCT       )
      concat.add_faiss_index("embeddings" ,     custom_index=_lowerCamelCase       )
      concat.get_index("embeddings"       ).save(
          _lowerCamelCase       )  # since we load the index in to memory,we can directly update the index in the disk










import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE     =	{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}

class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def __init__(						self   :      Dict						,						__lowerCamelCase   :      str=None						,						__lowerCamelCase   :      int=None						,						__lowerCamelCase   :      Optional[Any]=False						,						__lowerCamelCase   :      List[Any]=True						,						__lowerCamelCase   :      int=False						,						__lowerCamelCase   :      Optional[int]="<s>"						,						__lowerCamelCase   :      Union[str, Any]="</s>"						,						__lowerCamelCase   :      Dict="<unk>"						,						__lowerCamelCase   :      List[str]="<sep>"						,						__lowerCamelCase   :      Any="<pad>"						,						__lowerCamelCase   :      Optional[Any]="<cls>"						,						__lowerCamelCase   :      Union[str, Any]="<mask>"						,						__lowerCamelCase   :      List[str]=["<eop>", "<eod>"]						,						**__lowerCamelCase   :      Optional[Any]						,						)   ->					Optional[int]:
									A				:       Any														=    AddedToken(__lowerCamelCase						,						lstrip=__lowerCamelCase						,						rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase						,						__lowerCamelCase			) else mask_token

									super().__init__(
									    vocab_file=__lowerCamelCase						,						tokenizer_file=__lowerCamelCase						,						do_lower_case=__lowerCamelCase						,						remove_space=__lowerCamelCase						,						keep_accents=__lowerCamelCase						,						bos_token=__lowerCamelCase						,						eos_token=__lowerCamelCase						,						unk_token=__lowerCamelCase						,						sep_token=__lowerCamelCase						,						pad_token=__lowerCamelCase						,						cls_token=__lowerCamelCase						,						mask_token=__lowerCamelCase						,						additional_special_tokens=__lowerCamelCase						,						**__lowerCamelCase						,						)

									A				:       str														=    3
									A				:       str														=    do_lower_case
									A				:       int														=    remove_space
									A				:       Optional[int]														=    keep_accents
									A				:       Tuple														=    vocab_file
									A				:       int														=    False if not self.vocab_file else True

									try:
												import jieba
									except ModuleNotFoundError as error:
												raise error.__class__(
												    "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
												    "See https://pypi.org/project/jieba/ for installation."			)
									A				:       str														=    jieba
									A				:       Optional[Any]														=    str.maketrans(" \n"						,						"\u2582\u2583"			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]						,						__lowerCamelCase   :      List[int]						,						__lowerCamelCase   :      Optional[List[int]] = None			)   ->					List[int]:
									A				:       Dict														=    [self.sep_token_id]
									A				:       Tuple														=    [self.cls_token_id]
									if token_ids_a is None:
												return token_ids_a + sep + cls
									return token_ids_a + sep + token_ids_a + sep + cls







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      List[int]						,						__lowerCamelCase   :      Optional[List[int]] = None			)   ->					List[int]:
									A				:       Tuple														=    [self.sep_token_id]
									A				:       Dict														=    [2]

									if token_ids_a is None:
												return len(token_ids_a + sep			) * [0] + cls_segment_id
									return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1] + cls_segment_id







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      Optional[str] = None			)   ->					Tuple[str]:
									if not self.can_save_slow_tokenizer:
												raise ValueError(
												    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
												    "tokenizer."			)

									if not os.path.isdir(__lowerCamelCase			):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
												return
									A				:       List[Any]														=    os.path.join(
									    __lowerCamelCase						,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

									if os.path.abspath(self.vocab_file			) != os.path.abspath(__lowerCamelCase			):
												copyfile(self.vocab_file						,						__lowerCamelCase			)

									return (out_vocab_file,)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						__lowerCamelCase   :      Dict						,						*__lowerCamelCase   :      List[str]						,						**__lowerCamelCase   :      Tuple			)   ->					str:
									A				:       Union[str, Any]														=    [
									    " ".join([x.translate(self.translator			) for x in self.jieba.cut(__lowerCamelCase						,						cut_all=__lowerCamelCase			)]			)
									    for text in batch_text_or_text_pairs
									]
									return super()._batch_encode_plus(__lowerCamelCase						,						*__lowerCamelCase						,						**__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						*__lowerCamelCase   :      Optional[Any]						,						**__lowerCamelCase   :      Optional[int]			)   ->					Tuple:
									A				:       Optional[Any]														=    super()._decode(*__lowerCamelCase						,						**__lowerCamelCase			)
									A				:       Tuple														=    text.replace(" "						,						""			).replace("\u2582"						,						" "			).replace("\u2583"						,						"\n"			)
									return text