





'''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_						(						_UpperCAmelCase       :Any     ,	_UpperCAmelCase       :int=100     ,	_UpperCAmelCase       :Optional[int]=" "							)  ->							List[Any]:
     '''simple docstring'''


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

def     UpperCAmelCase_						(						_UpperCAmelCase       :Optional[int]							)  ->							Union[str, Any]:
     '''simple docstring'''


     A_      ,						A_					      =					[], []
     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     UpperCAmelCase_						(						_UpperCAmelCase       :List[str]     ,	_UpperCAmelCase       :List[Any]     ,	_UpperCAmelCase       :str     ,	_UpperCAmelCase       :Dict     ,	_UpperCAmelCase       :int     ,	_UpperCAmelCase       :Optional[int]							)  ->							Any:
     '''simple docstring'''


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

     A_					      =					'''data_''' + str(_UpperCAmelCase							)
     A_					      =					os.path.join(_UpperCAmelCase     ,	_UpperCAmelCase							)

     A_					      =					DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''							)
     A_					      =					ctx_encoder.to(device=_UpperCAmelCase							)

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

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

     A_					      =					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     UpperCAmelCase_						(						_UpperCAmelCase       :Optional[Any]     ,	_UpperCAmelCase       :Tuple							)  ->							List[str]:
     '''simple docstring'''


     A_					      =					[]

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

     A_					      =					concatenate_datasets(_UpperCAmelCase							)
     faiss.omp_set_num_threads(96							)

     A_					      =					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










import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
      from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


a__			:    Optional[Any]			      =		get_tests_dir('fixtures/test_sentencepiece.model')





@require_torch
@require_torchaudio
@require_sentencepiece
class UpperCAmelCase_							(   unittest.TestCase   ):


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					tempfile.mkdtemp()

       A_					      =					['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
       A_					      =					dict(zip(__snake_case   ,range(len(__snake_case   )   )   )   )
       A_					      =					Path(self.tmpdirname   )
       save_json(__snake_case   ,save_dir / VOCAB_FILES_NAMES['''vocab_file''']   )
       if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
            copyfile(__snake_case   ,save_dir / VOCAB_FILES_NAMES['''spm_file''']   )

       A_					      =					SpeechaTextTokenizer.from_pretrained(self.tmpdirname   )
       tokenizer.save_pretrained(self.tmpdirname   )

       A_					      =					{
           '''feature_size''': 2_4,
           '''num_mel_bins''': 2_4,
           '''padding_value''': 0.0,
           '''sampling_rate''': 1_6_0_0_0,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }
       save_json(__snake_case   ,save_dir / FEATURE_EXTRACTOR_NAME   )


  def 	__UpperCAmelCase    (       self   ,**__snake_case   ):



       """simple docstring"""





       return SpeechaTextTokenizer.from_pretrained(self.tmpdirname   ,**__snake_case   )


  def 	__UpperCAmelCase    (       self   ,**__snake_case   ):



       """simple docstring"""





       return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname   ,**__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       shutil.rmtree(self.tmpdirname   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.get_tokenizer()
       A_					      =					self.get_feature_extractor()

       A_					      =					SpeechaTextProcessor(tokenizer=__snake_case   ,feature_extractor=__snake_case   )

       processor.save_pretrained(self.tmpdirname   )
       A_					      =					SpeechaTextProcessor.from_pretrained(self.tmpdirname   )

       self.assertEqual(processor.tokenizer.get_vocab()   ,tokenizer.get_vocab()   )
       self.assertIsInstance(processor.tokenizer   ,__snake_case   )

       self.assertEqual(processor.feature_extractor.to_json_string()   ,feature_extractor.to_json_string()   )
       self.assertIsInstance(processor.feature_extractor   ,__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					SpeechaTextProcessor(
           tokenizer=self.get_tokenizer()   ,feature_extractor=self.get_feature_extractor()   )
       processor.save_pretrained(self.tmpdirname   )

       A_					      =					self.get_tokenizer(bos_token='''(BOS)'''   ,eos_token='''(EOS)'''   )
       A_					      =					self.get_feature_extractor(do_normalize=__snake_case   ,padding_value=1.0   )

       A_					      =					SpeechaTextProcessor.from_pretrained(
           self.tmpdirname   ,bos_token='''(BOS)'''   ,eos_token='''(EOS)'''   ,do_normalize=__snake_case   ,padding_value=1.0   )

       self.assertEqual(processor.tokenizer.get_vocab()   ,tokenizer_add_kwargs.get_vocab()   )
       self.assertIsInstance(processor.tokenizer   ,__snake_case   )

       self.assertEqual(processor.feature_extractor.to_json_string()   ,feature_extractor_add_kwargs.to_json_string()   )
       self.assertIsInstance(processor.feature_extractor   ,__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.get_feature_extractor()
       A_					      =					self.get_tokenizer()

       A_					      =					SpeechaTextProcessor(tokenizer=__snake_case   ,feature_extractor=__snake_case   )

       A_					      =					floats_list((3, 1_0_0_0)   )

       A_					      =					feature_extractor(__snake_case   ,return_tensors='''np'''   )
       A_					      =					processor(__snake_case   ,return_tensors='''np'''   )

       for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum()   ,input_processor[key].sum()   ,delta=1E-2   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.get_feature_extractor()
       A_					      =					self.get_tokenizer()

       A_					      =					SpeechaTextProcessor(tokenizer=__snake_case   ,feature_extractor=__snake_case   )

       A_					      =					'''This is a test string'''

       A_					      =					processor(text=__snake_case   )

       A_					      =					tokenizer(__snake_case   )

       for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]   ,encoded_processor[key]   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.get_feature_extractor()
       A_					      =					self.get_tokenizer()

       A_					      =					SpeechaTextProcessor(tokenizer=__snake_case   ,feature_extractor=__snake_case   )

       A_					      =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       A_					      =					processor.batch_decode(__snake_case   )
       A_					      =					tokenizer.batch_decode(__snake_case   )

       self.assertListEqual(__snake_case   ,__snake_case   )




  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.get_feature_extractor()
       A_					      =					self.get_tokenizer()

       A_					      =					SpeechaTextProcessor(tokenizer=__snake_case   ,feature_extractor=__snake_case   )

       self.assertListEqual(
           processor.model_input_names   ,feature_extractor.model_input_names   ,msg='''`processor` and `feature_extractor` model input names do not match'''   ,)



