


'''simple docstring'''





import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin

@require_jieba
class        _a    (						snake_case_ , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   str       =						CpmAntTokenizer
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						False






     def     _lowercase     (			self   )							->      Any:
          super().setUp()

          _snake_case							=				[
              '<d>',
              '</d>',
              '<s>',
              '</s>',
              '</_>',
              '<unk>',
              '<pad>',
              '</n>',
              '我',
              '是',
              'C',
              'P',
              'M',
              'A',
              'n',
              't',
          ]
          _snake_case							=				os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"]   )
          with open(self.vocab_file ,"w" ,encoding="utf-8"   ) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )






     @tooslow
     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							=				CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"   )
          _snake_case							=				'今天天气真好！'
          _snake_case							=				['今天', '天气', '真', '好', '！']
          _snake_case							=				tokenizer.tokenize(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				'今天天气真好！'
          _snake_case							=				[tokenizer.bos_token] + tokens

          _snake_case							=				[6, 9_802, 14_962, 2_082, 831, 244]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   ) ,_SCREAMING_SNAKE_CASE   )

          _snake_case							=				tokenizer.decode(_SCREAMING_SNAKE_CASE   )
          self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )








'''simple docstring'''





import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


UpperCamelCase_						:      Any       =						logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase_						:      Optional[Any]       =						'''cuda''' if torch.cuda.is_available() else '''cpu'''





def      __a     (       _UpperCamelCase:   str ,						_UpperCamelCase:   Union[str, Any]=100 ,						_UpperCamelCase:   List[str]=" "   )						->			List[str]:

     """simple docstring"""




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





def      __a     (       _UpperCamelCase:   dict   )						->			dict:

     """simple docstring"""




     _snake_case							, _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      __a     (       _UpperCamelCase:   dict ,						_UpperCamelCase:   DPRContextEncoder ,						_UpperCamelCase:   DPRContextEncoderTokenizerFast   )						->			dict:

     """simple docstring"""




     _snake_case							=				ctx_tokenizer(
         documents["title"] ,						documents["text"] ,						truncation=_UpperCamelCase ,						padding="longest" ,						return_tensors="pt"   )["input_ids"]
     _snake_case							=				ctx_encoder(input_ids.to(device=_UpperCamelCase   ) ,						return_dict=_UpperCamelCase   ).pooler_output
     return {"embeddings": embeddings.detach().cpu().numpy()}





def      __a     (       _UpperCamelCase:   "RagExampleArguments" ,						_UpperCamelCase:   "ProcessingArguments" ,						_UpperCamelCase:   "IndexHnswArguments" ,						)						->			Dict:

     """simple docstring"""




     logger.info("Step 1 - Create the dataset"   )
     ######################################

     # The dataset needed for RAG must have three columns:
     # - title (string): title of the document
     # - text (string): text of a passage of the document
     # - embeddings (array of dimension d): DPR representation of the passage
     # Let's say you have documents in tab-separated csv files with columns "title" and "text"
     assert os.path.isfile(rag_example_args.csv_path   ), "Please provide a valid path to a csv file"

     # You can load a Dataset object this way
     _snake_case							=				load_dataset(
         "csv" ,						data_files=[rag_example_args.csv_path] ,						split="train" ,						delimiter="\t" ,						column_names=["title", "text"]   )

     # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

     # Then split the documents into passages of 100 words
     _snake_case							=				dataset.map(_UpperCamelCase ,						batched=_UpperCamelCase ,						num_proc=processing_args.num_proc   )

     # And compute the embeddings
     _snake_case							=				DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=_UpperCamelCase   )
     _snake_case							=				DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
     _snake_case							=				Features(
         {"text": Value("string"   ), "title": Value("string"   ), "embeddings": Sequence(Value("float32"   )   )}   )  # optional, save as float32 instead of float64 to save space
     _snake_case							=				dataset.map(
         partial(_UpperCamelCase ,						ctx_encoder=_UpperCamelCase ,						ctx_tokenizer=_UpperCamelCase   ) ,						batched=_UpperCamelCase ,						batch_size=processing_args.batch_size ,						features=_UpperCamelCase ,						)

     # And finally save your dataset
     _snake_case							=				os.path.join(rag_example_args.output_dir ,						"my_knowledge_dataset"   )
     dataset.save_to_disk(_UpperCamelCase   )
     # from datasets import load_from_disk
     # dataset = load_from_disk(passages_path)  # to reload the dataset

     ######################################
     logger.info("Step 2 - Index the dataset"   )
     ######################################

     # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
     _snake_case							=				faiss.IndexHNSWFlat(index_hnsw_args.d ,						index_hnsw_args.m ,						faiss.METRIC_INNER_PRODUCT   )
     dataset.add_faiss_index("embeddings" ,						custom_index=_UpperCamelCase   )

     # And save the index
     _snake_case							=				os.path.join(rag_example_args.output_dir ,						"my_knowledge_dataset_hnsw_index.faiss"   )
     dataset.get_index("embeddings"   ).save(_UpperCamelCase   )
     # dataset.load_faiss_index("embeddings", index_path)  # to reload the index

@dataclass
class        _a    :
     SCREAMING_SNAKE_CASE_							:   str       =						field(
         default=str(Path(__lowerCAmelCase							).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv"""							) , metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""} , )
     SCREAMING_SNAKE_CASE_							:   Optional[str]       =						field(
         default=__lowerCAmelCase , metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""} , )
     SCREAMING_SNAKE_CASE_							:   str       =						field(
         default="""facebook/rag-sequence-nq""" , metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""} , )
     SCREAMING_SNAKE_CASE_							:   str       =						field(
         default="""facebook/dpr-ctx_encoder-multiset-base""" , metadata={
             """help""": (
                 """The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"""
                 """ 'facebook/dpr-ctx_encoder-multiset-base'"""
             )
         } , )
     SCREAMING_SNAKE_CASE_							:   Optional[str]       =						field(
         default=str(Path(__lowerCAmelCase							).parent / """test_run""" / """dummy-kb"""							) , metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""} , )







@dataclass
class        _a    :
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						field(
         default=__lowerCAmelCase , metadata={
             """help""": """The number of processes to use to split the documents into passages. Default is single process."""
         } , )
     SCREAMING_SNAKE_CASE_							:   int       =						field(
         default=16 , metadata={
             """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
         } , )







@dataclass
class        _a    :
     SCREAMING_SNAKE_CASE_							:   int       =						field(
         default=7_68 , metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""} , )
     SCREAMING_SNAKE_CASE_							:   int       =						field(
         default=1_28 , metadata={
             """help""": (
                 """The number of bi-directional links created for every new element during the HNSW index construction."""
             )
         } , )


if __name__ == "__main__":
   logging.basicConfig(level=logging.WARNING)
   logger.setLevel(logging.INFO)

   UpperCamelCase_						:      List[str]       =						HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
   UpperCamelCase_     ,     UpperCamelCase_     ,     UpperCamelCase_						:      List[Any]       =						parser.parse_args_into_dataclasses()
   with TemporaryDirectory() as tmp_dir:
      UpperCamelCase_						:      str       =						rag_example_args.output_dir or tmp_dir
      main(rag_example_args, processing_args, index_hnsw_args)


