

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(   lowercase_     ,   lowercase_=100     ,   lowercase_=" "				)      ->							List[str]:





  '''simple docstring'''





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



def   UpperCamelCase(   lowercase_				)      ->							Union[str, Any]:





  '''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(lowercase_				):
        titles.append(title if title is not None else """"""				)
        texts.append(lowercase_				)
  return {"title": titles, "text": texts}



def   UpperCamelCase(   lowercase_     ,   lowercase_     ,   lowercase_     ,   lowercase_     ,   lowercase_     ,   lowercase_				)      ->							Optional[int]:





  '''simple docstring'''





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

  snake_case_					= """data_""" + str(lowercase_				)
  snake_case_					= os.path.join(lowercase_     ,   lowercase_				)

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

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

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



def   UpperCamelCase(   lowercase_     ,   lowercase_				)      ->							int:





  '''simple docstring'''





  snake_case_					= []

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

  snake_case_					= concatenate_datasets(lowercase_				)
  faiss.omp_set_num_threads(96				)

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


import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
    pass

if is_speech_available():
    from transformers import EncodecFeatureExtractor, MusicgenProcessor


lowerCamelCase_					  =   random.Random()



def   UpperCamelCase(   lowercase_     ,   lowercase_=1.0     ,   lowercase_=None     ,   lowercase_=None				)      ->							List[str]:





  '''simple docstring'''





  if rng is None:
    snake_case_					= global_rng

  snake_case_					= []
  for batch_idx in range(shape[0]				):
    values.append([]				)
    for _ in range(shape[1]				):
      values[-1].append(rng.random() * scale				)

  return values







@require_torch
@require_sentencepiece
class 					__lowerCamelCase							(      unittest.TestCase      ):







      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        snake_case_					= """facebook/musicgen-small"""
        snake_case_					= tempfile.mkdtemp()







      def       lowerCAmelCase_       (	self				,    **lowerCamelCase  )		->			int:
        return TaTokenizer.from_pretrained(self.checkpoint				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    **lowerCamelCase  )		->			Dict:
        return EncodecFeatureExtractor.from_pretrained(self.checkpoint				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Tuple:
        shutil.rmtree(self.tmpdirname  )







      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        snake_case_					= self.get_tokenizer()
        snake_case_					= self.get_feature_extractor()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        processor.save_pretrained(self.tmpdirname  )
        snake_case_					= MusicgenProcessor.from_pretrained(self.tmpdirname  )

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

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







      def       lowerCAmelCase_       (	self  )		->			List[str]:
        snake_case_					= MusicgenProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()  )
        processor.save_pretrained(self.tmpdirname  )

        snake_case_					= self.get_tokenizer(bos_token="""(BOS)"""				,    eos_token="""(EOS)"""  )
        snake_case_					= self.get_feature_extractor(do_normalize=lowerCamelCase				,    padding_value=1.0  )

        snake_case_					= MusicgenProcessor.from_pretrained(
            self.tmpdirname				,    bos_token="""(BOS)"""				,    eos_token="""(EOS)"""				,    do_normalize=lowerCamelCase				,    padding_value=1.0  )

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

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







      def       lowerCAmelCase_       (	self  )		->			Tuple:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        snake_case_					= floats_list((3, 1000)  )

        snake_case_					= feature_extractor(lowerCamelCase				,    return_tensors="""np"""  )
        snake_case_					= processor(lowerCamelCase				,    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       lowerCAmelCase_       (	self  )		->			List[str]:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        snake_case_					= """This is a test string"""

        snake_case_					= processor(text=lowerCamelCase  )

        snake_case_					= tokenizer(lowerCamelCase  )

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







      def       lowerCAmelCase_       (	self  )		->			int:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

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

        snake_case_					= processor.batch_decode(sequences=lowerCamelCase  )
        snake_case_					= tokenizer.batch_decode(lowerCamelCase  )

        self.assertListEqual(lowerCamelCase				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Optional[int]:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

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







      def       lowerCAmelCase_       (	self  )		->			Optional[Any]:
        snake_case_					= self.get_feature_extractor(padding_side="""left"""  )
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        snake_case_					= [floats_list((1, x)  )[0] for x in range(5				,    20				,    5  )]
        snake_case_					= processor(lowerCamelCase  ).padding_mask

        snake_case_					= np.asarray(floats_list((3, 20)  )  )[:, None, :]
        snake_case_					= processor.batch_decode(lowerCamelCase				,    padding_mask=lowerCamelCase  )

        self.assertIsInstance(lowerCamelCase				,    lowerCamelCase  )

        for audio in decoded_audios:
          self.assertIsInstance(lowerCamelCase				,    np.ndarray  )

        self.assertTrue(decoded_audios[0].shape == (1, 10)  )
        self.assertTrue(decoded_audios[1].shape == (1, 15)  )
        self.assertTrue(decoded_audios[2].shape == (1, 20)  )