





import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = None
  a_        = None






  @property
  def 				lowercase		(				self :		Any     )							->						Union[str, Any]:
     return self.feat_extract_tester.prepare_feat_extract_dict()






  def 				lowercase		(				self :		Optional[int]     )							->						Dict:
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_dict     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'feature_size'     )     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'sampling_rate'     )     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'padding_value'     )     )






  def 				lowercase		(				self :		Optional[Any]     )							->						Union[str, Any]:
     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_common()
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_dict     )
     __lowerCAmelCase								=	feat_extract.model_input_names[0]

     __lowerCAmelCase								=	BatchFeature({input_name: speech_inputs}     )

     self.assertTrue(all(len(lowerCAmelCase_     ) == len(lowerCAmelCase_     ) for x, y in zip(lowerCAmelCase_					,  processed_features[input_name]     )     )     )

     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase_     )
     __lowerCAmelCase								=	BatchFeature({input_name: speech_inputs}					,  tensor_type='np'     )

     __lowerCAmelCase								=	processed_features[input_name]

     if len(batch_features_input.shape     ) < 3:
        __lowerCAmelCase								=	batch_features_input[:, :, None]

     self.assertTrue(
         batch_features_input.shape
         == (self.feat_extract_tester.batch_size, len(speech_inputs[0]     ), self.feat_extract_tester.feature_size)     )






  @require_torch
  def 				lowercase		(				self :		List[Any]     )							->						Union[str, Any]:
     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase_     )
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_dict     )
     __lowerCAmelCase								=	feat_extract.model_input_names[0]

     __lowerCAmelCase								=	BatchFeature({input_name: speech_inputs}					,  tensor_type='pt'     )

     __lowerCAmelCase								=	processed_features[input_name]

     if len(batch_features_input.shape     ) < 3:
        __lowerCAmelCase								=	batch_features_input[:, :, None]

     self.assertTrue(
         batch_features_input.shape
         == (self.feat_extract_tester.batch_size, len(speech_inputs[0]     ), self.feat_extract_tester.feature_size)     )






  @require_tf
  def 				lowercase		(				self :		Any     )							->						Tuple:
     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase_     )
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_dict     )
     __lowerCAmelCase								=	feat_extract.model_input_names[0]

     __lowerCAmelCase								=	BatchFeature({input_name: speech_inputs}					,  tensor_type='tf'     )

     __lowerCAmelCase								=	processed_features[input_name]

     if len(batch_features_input.shape     ) < 3:
        __lowerCAmelCase								=	batch_features_input[:, :, None]

     self.assertTrue(
         batch_features_input.shape
         == (self.feat_extract_tester.batch_size, len(speech_inputs[0]     ), self.feat_extract_tester.feature_size)     )






  def 				lowercase		(				self :		List[str]					,  lowerCAmelCase_ :		str=False     )							->						Any:
     def _inputs_have_equal_length(lowerCAmelCase_ :		Any     ):
        __lowerCAmelCase								=	len(input[0]     )
        for input_slice in input[1:]:
           if len(lowerCAmelCase_     ) != length:
              return False
        return True

     def _inputs_are_equal(lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		Tuple     ):
        if len(lowerCAmelCase_     ) != len(lowerCAmelCase_     ):
           return False

        for input_slice_a, input_slice_a in zip(lowerCAmelCase_					,  lowerCAmelCase_     ):
           if not np.allclose(np.asarray(lowerCAmelCase_     )					,  np.asarray(lowerCAmelCase_     )					,  atol=1e-3     ):
              return False
        return True

     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_dict     )
     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCAmelCase_     )
     __lowerCAmelCase								=	feat_extract.model_input_names[0]

     __lowerCAmelCase								=	BatchFeature({input_name: speech_inputs}     )

     __lowerCAmelCase								=	self.feat_extract_tester.seq_length_diff
     __lowerCAmelCase								=	self.feat_extract_tester.max_seq_length + pad_diff
     __lowerCAmelCase								=	self.feat_extract_tester.min_seq_length
     __lowerCAmelCase								=	self.feat_extract_tester.batch_size
     __lowerCAmelCase								=	self.feat_extract_tester.feature_size

     # test padding for List[int] + numpy
     __lowerCAmelCase								=	feat_extract.pad(lowerCAmelCase_					,  padding=lowerCAmelCase_     )
     __lowerCAmelCase								=	input_a[input_name]

     __lowerCAmelCase								=	feat_extract.pad(lowerCAmelCase_					,  padding='longest'     )
     __lowerCAmelCase								=	input_a[input_name]

     __lowerCAmelCase								=	feat_extract.pad(lowerCAmelCase_					,  padding='max_length'					,  max_length=len(speech_inputs[-1]     )     )
     __lowerCAmelCase								=	input_a[input_name]

     __lowerCAmelCase								=	feat_extract.pad(lowerCAmelCase_					,  padding='longest'					,  return_tensors='np'     )
     __lowerCAmelCase								=	input_a[input_name]

     # max_length parameter has to be provided when setting `padding="max_length"`
     with self.assertRaises(lowerCAmelCase_     ):
        feat_extract.pad(lowerCAmelCase_					,  padding='max_length'     )[input_name]

     __lowerCAmelCase								=	feat_extract.pad(
         lowerCAmelCase_					,  padding='max_length'					,  max_length=lowerCAmelCase_					,  return_tensors='np'     )
     __lowerCAmelCase								=	input_a[input_name]

     self.assertFalse(_inputs_have_equal_length(lowerCAmelCase_     )     )
     self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_     )     )
     self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_     )     )
     self.assertTrue(_inputs_are_equal(lowerCAmelCase_					,  lowerCAmelCase_     )     )
     self.assertTrue(len(input_a[0]     ) == pad_min_length     )
     self.assertTrue(len(input_a[1]     ) == pad_min_length + pad_diff     )
     self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]     ))     )
     self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)     )

     if feature_size > 1:
        self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size     )

     # test padding for `pad_to_multiple_of` for List[int] + numpy
     __lowerCAmelCase								=	feat_extract.pad(lowerCAmelCase_					,  pad_to_multiple_of=1_0     )
     __lowerCAmelCase								=	input_a[input_name]

     __lowerCAmelCase								=	feat_extract.pad(lowerCAmelCase_					,  padding='longest'					,  pad_to_multiple_of=1_0     )
     __lowerCAmelCase								=	input_a[input_name]

     __lowerCAmelCase								=	feat_extract.pad(
         lowerCAmelCase_					,  padding='max_length'					,  pad_to_multiple_of=1_0					,  max_length=lowerCAmelCase_     )
     __lowerCAmelCase								=	input_a[input_name]

     __lowerCAmelCase								=	feat_extract.pad(
         lowerCAmelCase_					,  padding='max_length'					,  pad_to_multiple_of=1_0					,  max_length=lowerCAmelCase_					,  return_tensors='np'					,  )
     __lowerCAmelCase								=	input_a[input_name]

     self.assertTrue(all(len(lowerCAmelCase_     ) % 1_0 == 0 for x in input_a     )     )
     self.assertTrue(_inputs_are_equal(lowerCAmelCase_					,  lowerCAmelCase_     )     )

     __lowerCAmelCase								=	pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
     self.assertTrue(all(len(lowerCAmelCase_     ) == expected_mult_pad_length for x in input_a     )     )
     self.assertEqual(input_a.shape[:2]					,  (batch_size, expected_mult_pad_length)     )

     if feature_size > 1:
        self.assertTrue(input_a.shape[2] == feature_size     )

     # Check padding value is correct
     __lowerCAmelCase								=	(np.ones(self.feat_extract_tester.feature_size     ) * feat_extract.padding_value).sum()
     self.assertTrue(
         abs(np.asarray(input_a[0]     )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)     )
         < 1e-3     )
     self.assertTrue(
         abs(
             np.asarray(input_a[1]     )[pad_min_length + pad_diff :].sum()
             - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)     )
         < 1e-3     )
     self.assertTrue(
         abs(
             np.asarray(input_a[2]     )[pad_min_length + 2 * pad_diff :].sum()
             - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)     )
         < 1e-3     )
     self.assertTrue(
         abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)     ) < 1e-3     )
     self.assertTrue(
         abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)     )
         < 1e-3     )






  def 				lowercase		(				self :		int					,  lowerCAmelCase_ :		List[Any]=False     )							->						Any:
     def _inputs_have_equal_length(lowerCAmelCase_ :		Dict     ):
        __lowerCAmelCase								=	len(input[0]     )
        for input_slice in input[1:]:
           if len(lowerCAmelCase_     ) != length:
              return False
        return True

     def _inputs_are_equal(lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		int     ):
        if len(lowerCAmelCase_     ) != len(lowerCAmelCase_     ):
           return False

        for input_slice_a, input_slice_a in zip(lowerCAmelCase_					,  lowerCAmelCase_     ):
           if not np.allclose(np.asarray(lowerCAmelCase_     )					,  np.asarray(lowerCAmelCase_     )					,  atol=1e-3     ):
              return False
        return True

     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_dict     )
     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCAmelCase_     )
     __lowerCAmelCase								=	feat_extract.model_input_names[0]

     __lowerCAmelCase								=	BatchFeature({input_name: speech_inputs}     )

     # truncate to smallest
     __lowerCAmelCase								=	feat_extract.pad(
         lowerCAmelCase_					,  padding='max_length'					,  max_length=len(speech_inputs[0]     )					,  truncation=lowerCAmelCase_     )
     __lowerCAmelCase								=	input_a[input_name]

     __lowerCAmelCase								=	feat_extract.pad(lowerCAmelCase_					,  padding='max_length'					,  max_length=len(speech_inputs[0]     )     )
     __lowerCAmelCase								=	input_a[input_name]

     self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_     )     )
     self.assertFalse(_inputs_have_equal_length(lowerCAmelCase_     )     )

     # truncate to smallest with np
     __lowerCAmelCase								=	feat_extract.pad(
         lowerCAmelCase_					,  padding='max_length'					,  max_length=len(speech_inputs[0]     )					,  return_tensors='np'					,  truncation=lowerCAmelCase_					,  )
     __lowerCAmelCase								=	input_a[input_name]

     __lowerCAmelCase								=	feat_extract.pad(
         lowerCAmelCase_					,  padding='max_length'					,  max_length=len(speech_inputs[0]     )					,  return_tensors='np'     )
     __lowerCAmelCase								=	input_a[input_name]

     self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_     )     )
     self.assertTrue(input_a.shape[1] == len(speech_inputs[0]     )     )

     # since truncation forces padding to be smaller than longest input
     # function can't return `np.ndarray`, but has to return list
     self.assertFalse(_inputs_have_equal_length(lowerCAmelCase_     )     )

     # truncate to middle
     __lowerCAmelCase								=	feat_extract.pad(
         lowerCAmelCase_					,  padding='max_length'					,  max_length=len(speech_inputs[1]     )					,  truncation=lowerCAmelCase_					,  return_tensors='np'					,  )
     __lowerCAmelCase								=	input_a[input_name]

     __lowerCAmelCase								=	feat_extract.pad(
         lowerCAmelCase_					,  padding='max_length'					,  max_length=len(speech_inputs[1]     )					,  truncation=lowerCAmelCase_     )
     __lowerCAmelCase								=	input_a[input_name]

     __lowerCAmelCase								=	feat_extract.pad(
         lowerCAmelCase_					,  padding='max_length'					,  max_length=len(speech_inputs[1]     )					,  return_tensors='np'     )
     __lowerCAmelCase								=	input_a[input_name]

     self.assertTrue(input_a.shape[1] == len(speech_inputs[1]     )     )
     self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_     )     )
     self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_     )     )
     self.assertTrue(_inputs_are_equal(lowerCAmelCase_					,  lowerCAmelCase_     )     )

     # since truncation forces padding to be smaller than longest input
     # function can't return `np.ndarray`, but has to return list
     self.assertFalse(_inputs_have_equal_length(lowerCAmelCase_     )     )
     self.assertTrue(len(input_a[-1]     ) == len(speech_inputs[-1]     )     )

     # padding has to be max_length when setting `truncation=True`
     with self.assertRaises(lowerCAmelCase_     ):
        feat_extract.pad(lowerCAmelCase_					,  truncation=lowerCAmelCase_     )[input_name]

     # padding has to be max_length when setting `truncation=True`
     with self.assertRaises(lowerCAmelCase_     ):
        feat_extract.pad(lowerCAmelCase_					,  padding='longest'					,  truncation=lowerCAmelCase_     )[input_name]

     # padding has to be max_length when setting `truncation=True`
     with self.assertRaises(lowerCAmelCase_     ):
        feat_extract.pad(lowerCAmelCase_					,  padding='longest'					,  truncation=lowerCAmelCase_     )[input_name]

     # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
     with self.assertRaises(lowerCAmelCase_     ):
        feat_extract.pad(lowerCAmelCase_					,  padding='max_length'					,  truncation=lowerCAmelCase_     )[input_name]

     # test truncation for `pad_to_multiple_of` for List[int] + numpy
     __lowerCAmelCase								=	1_2
     __lowerCAmelCase								=	feat_extract.pad(
         lowerCAmelCase_					,  padding='max_length'					,  max_length=len(speech_inputs[0]     )					,  pad_to_multiple_of=lowerCAmelCase_					,  truncation=lowerCAmelCase_					,  )
     __lowerCAmelCase								=	input_a[input_name]

     __lowerCAmelCase								=	feat_extract.pad(
         lowerCAmelCase_					,  padding='max_length'					,  max_length=len(speech_inputs[0]     )					,  pad_to_multiple_of=lowerCAmelCase_					,  )
     __lowerCAmelCase								=	input_a[input_name]

     # retrieve expected_length as multiple of pad_to_multiple_of
     __lowerCAmelCase								=	len(speech_inputs[0]     )
     if expected_length % pad_to_multiple_of != 0:
        __lowerCAmelCase								=	((len(speech_inputs[0]     ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

     self.assertTrue(len(input_a[0]     ) == expected_length     )
     self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_     )     )
     self.assertFalse(_inputs_have_equal_length(lowerCAmelCase_     )     )






  def 				lowercase		(				self :		Optional[int]     )							->						Optional[Any]:
     self._check_padding(numpify=lowerCAmelCase_     )






  def 				lowercase		(				self :		Any     )							->						List[Any]:
     self._check_padding(numpify=lowerCAmelCase_     )






  def 				lowercase		(				self :		Dict     )							->						Dict:
     self._check_truncation(numpify=lowerCAmelCase_     )






  def 				lowercase		(				self :		Dict     )							->						List[Any]:
     self._check_truncation(numpify=lowerCAmelCase_     )






  @require_torch
  def 				lowercase		(				self :		str     )							->						Any:
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_dict     )
     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_common()
     __lowerCAmelCase								=	feat_extract.model_input_names[0]

     __lowerCAmelCase								=	BatchFeature({input_name: speech_inputs}     )

     __lowerCAmelCase								=	feat_extract.pad(lowerCAmelCase_					,  padding='longest'					,  return_tensors='np'     )[input_name]
     __lowerCAmelCase								=	feat_extract.pad(lowerCAmelCase_					,  padding='longest'					,  return_tensors='pt'     )[input_name]

     self.assertTrue(abs(input_np.astype(np.floataa     ).sum() - input_pt.numpy().astype(np.floataa     ).sum()     ) < 1e-2     )






  @require_tf
  def 				lowercase		(				self :		Dict     )							->						Optional[int]:
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_dict     )
     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_common()
     __lowerCAmelCase								=	feat_extract.model_input_names[0]

     __lowerCAmelCase								=	BatchFeature({input_name: speech_inputs}     )

     __lowerCAmelCase								=	feat_extract.pad(lowerCAmelCase_					,  padding='longest'					,  return_tensors='np'     )[input_name]
     __lowerCAmelCase								=	feat_extract.pad(lowerCAmelCase_					,  padding='longest'					,  return_tensors='tf'     )[input_name]

     self.assertTrue(abs(input_np.astype(np.floataa     ).sum() - input_tf.numpy().astype(np.floataa     ).sum()     ) < 1e-2     )






  def 				lowercase		(				self :		int     )							->						Tuple:
     __lowerCAmelCase								=	self.feat_extract_dict
     __lowerCAmelCase								=	True
     __lowerCAmelCase								=	self.feature_extraction_class(**lowerCAmelCase_     )
     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_common()
     __lowerCAmelCase								=	[len(lowerCAmelCase_     ) for x in speech_inputs]
     __lowerCAmelCase								=	feat_extract.model_input_names[0]

     __lowerCAmelCase								=	BatchFeature({input_name: speech_inputs}     )

     __lowerCAmelCase								=	feat_extract.pad(lowerCAmelCase_					,  padding='longest'					,  return_tensors='np'     )
     self.assertIn('attention_mask'					,  lowerCAmelCase_     )
     self.assertListEqual(list(processed.attention_mask.shape     )					,  list(processed[input_name].shape[:2]     )     )
     self.assertListEqual(processed.attention_mask.sum(-1     ).tolist()					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		str     )							->						Optional[int]:
     __lowerCAmelCase								=	self.feat_extract_dict
     __lowerCAmelCase								=	True
     __lowerCAmelCase								=	self.feature_extraction_class(**lowerCAmelCase_     )
     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_common()
     __lowerCAmelCase								=	[len(lowerCAmelCase_     ) for x in speech_inputs]
     __lowerCAmelCase								=	feat_extract.model_input_names[0]

     __lowerCAmelCase								=	BatchFeature({input_name: speech_inputs}     )
     __lowerCAmelCase								=	min(lowerCAmelCase_     )

     __lowerCAmelCase								=	feat_extract.pad(
         lowerCAmelCase_					,  padding='max_length'					,  max_length=lowerCAmelCase_					,  truncation=lowerCAmelCase_					,  return_tensors='np'     )
     self.assertIn('attention_mask'					,  lowerCAmelCase_     )
     self.assertListEqual(
         list(processed_pad.attention_mask.shape     )					,  [processed_pad[input_name].shape[0], max_length]     )
     self.assertListEqual(
         processed_pad.attention_mask[:, :max_length].sum(-1     ).tolist()					,  [max_length for x in speech_inputs]     )








import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


_snake_case  :    Dict						    =  pytest.mark.integration



@require_faiss
class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  def 				lowercase		(				self :		List[Any]     )							->						Optional[Any]:
     __lowerCAmelCase								=	Dataset.from_dict({'filename': ['my_name-train' + '_' + str(lowerCAmelCase_     ) for x in np.arange(3_0     ).tolist()]}     )
     return dset






  def 				lowercase		(				self :		List[str]     )							->						Tuple:
     import faiss

     __lowerCAmelCase								=	self._create_dummy_dataset()
     __lowerCAmelCase								=	dset.map(
         lambda lowerCAmelCase_					,  lowerCAmelCase_     : {"vecs": i * np.ones(5					,  dtype=np.floataa     )}					,  with_indices=lowerCAmelCase_					,  keep_in_memory=lowerCAmelCase_     )
     __lowerCAmelCase								=	dset.add_faiss_index('vecs'					,  batch_size=1_0_0					,  metric_type=faiss.METRIC_INNER_PRODUCT     )
     __lowerCAmelCase	,	__lowerCAmelCase								=	dset.get_nearest_examples('vecs'					,  np.ones(5					,  dtype=np.floataa     )     )
     self.assertEqual(examples['filename'][0]					,  'my_name-train_29'     )
     dset.drop_index('vecs'     )






  def 				lowercase		(				self :		Optional[Any]     )							->						str:
     import faiss

     __lowerCAmelCase								=	self._create_dummy_dataset()
     dset.add_faiss_index_from_external_arrays(
         external_arrays=np.ones((3_0, 5)     ) * np.arange(3_0     ).reshape(-1					,  1     )					,  index_name='vecs'					,  batch_size=1_0_0					,  metric_type=faiss.METRIC_INNER_PRODUCT					,  )
     __lowerCAmelCase	,	__lowerCAmelCase								=	dset.get_nearest_examples('vecs'					,  np.ones(5					,  dtype=np.floataa     )     )
     self.assertEqual(examples['filename'][0]					,  'my_name-train_29'     )






  def 				lowercase		(				self :		int     )							->						Optional[Any]:
     import faiss

     __lowerCAmelCase								=	self._create_dummy_dataset()
     dset.add_faiss_index_from_external_arrays(
         external_arrays=np.ones((3_0, 5)     ) * np.arange(3_0     ).reshape(-1					,  1     )					,  index_name='vecs'					,  metric_type=faiss.METRIC_INNER_PRODUCT					,  )

     # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
     # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
     # see https://bugs.python.org/issue14243 and
     # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
     with tempfile.NamedTemporaryFile(delete=lowerCAmelCase_     ) as tmp_file:
        dset.save_faiss_index('vecs'					,  tmp_file.name     )
        dset.load_faiss_index('vecs2'					,  tmp_file.name     )
     os.unlink(tmp_file.name     )

     __lowerCAmelCase	,	__lowerCAmelCase								=	dset.get_nearest_examples('vecs2'					,  np.ones(5					,  dtype=np.floataa     )     )
     self.assertEqual(examples['filename'][0]					,  'my_name-train_29'     )






  def 				lowercase		(				self :		Union[str, Any]     )							->						List[Any]:
     __lowerCAmelCase								=	self._create_dummy_dataset()
     dset.add_faiss_index_from_external_arrays(
         external_arrays=np.ones((3_0, 5)     ) * np.arange(3_0     ).reshape(-1					,  1     )					,  index_name='vecs'     )
     dset.drop_index('vecs'     )
     self.assertRaises(lowerCAmelCase_					,  partial(dset.get_nearest_examples					,  'vecs2'					,  np.ones(5					,  dtype=np.floataa     )     )     )






  def 				lowercase		(				self :		Union[str, Any]     )							->						Tuple:
     from elasticsearch import Elasticsearch

     __lowerCAmelCase								=	self._create_dummy_dataset()
     with patch('elasticsearch.Elasticsearch.search'     ) as mocked_search, patch(
         'elasticsearch.client.IndicesClient.create'     ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk'     ) as mocked_bulk:
        __lowerCAmelCase								=	{'acknowledged': True}
        mocked_bulk.return_value([(True, None)] * 3_0     )
        __lowerCAmelCase								=	{'hits': {'hits': [{'_score': 1, '_id': 2_9}]}}
        __lowerCAmelCase								=	Elasticsearch()

        dset.add_elasticsearch_index('filename'					,  es_client=lowerCAmelCase_     )
        __lowerCAmelCase	,	__lowerCAmelCase								=	dset.get_nearest_examples('filename'					,  'my_name-train_29'     )
        self.assertEqual(examples['filename'][0]					,  'my_name-train_29'     )





@require_faiss
class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  def 				lowercase		(				self :		str     )							->						int:
     import faiss

     __lowerCAmelCase								=	FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT     )

     # add vectors
     index.add_vectors(np.eye(5					,  dtype=np.floataa     )     )
     self.assertIsNotNone(index.faiss_index     )
     self.assertEqual(index.faiss_index.ntotal					,  5     )
     index.add_vectors(np.zeros((5, 5)					,  dtype=np.floataa     )     )
     self.assertEqual(index.faiss_index.ntotal					,  1_0     )

     # single query
     __lowerCAmelCase								=	np.zeros(5					,  dtype=np.floataa     )
     __lowerCAmelCase								=	1
     __lowerCAmelCase	,	__lowerCAmelCase								=	index.search(lowerCAmelCase_     )
     self.assertRaises(lowerCAmelCase_					,  index.search					,  query.reshape(-1					,  1     )     )
     self.assertGreater(scores[0]					,  0     )
     self.assertEqual(indices[0]					,  1     )

     # batched queries
     __lowerCAmelCase								=	np.eye(5					,  dtype=np.floataa     )[::-1]
     __lowerCAmelCase	,	__lowerCAmelCase								=	index.search_batch(lowerCAmelCase_     )
     self.assertRaises(lowerCAmelCase_					,  index.search_batch					,  queries[0]     )
     __lowerCAmelCase								=	[scores[0] for scores in total_scores]
     __lowerCAmelCase								=	[indices[0] for indices in total_indices]
     self.assertGreater(np.min(lowerCAmelCase_     )					,  0     )
     self.assertListEqual([4, 3, 2, 1, 0]					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		List[Any]     )							->						List[str]:
     import faiss

     __lowerCAmelCase								=	FaissIndex(string_factory='Flat'     )
     index.add_vectors(np.eye(5					,  dtype=np.floataa     )     )
     self.assertIsInstance(index.faiss_index					,  faiss.IndexFlat     )
     __lowerCAmelCase								=	FaissIndex(string_factory='LSH'     )
     index.add_vectors(np.eye(5					,  dtype=np.floataa     )     )
     self.assertIsInstance(index.faiss_index					,  faiss.IndexLSH     )
     with self.assertRaises(lowerCAmelCase_     ):
        __lowerCAmelCase								=	FaissIndex(string_factory='Flat'					,  custom_index=faiss.IndexFlat(5     )     )






  def 				lowercase		(				self :		Union[str, Any]     )							->						Dict:
     import faiss

     __lowerCAmelCase								=	faiss.IndexFlat(5     )
     __lowerCAmelCase								=	FaissIndex(custom_index=lowerCAmelCase_     )
     index.add_vectors(np.eye(5					,  dtype=np.floataa     )     )
     self.assertIsInstance(index.faiss_index					,  faiss.IndexFlat     )






  def 				lowercase		(				self :		str     )							->						Any:
     import faiss

     __lowerCAmelCase								=	FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT     )
     index.add_vectors(np.eye(5					,  dtype=np.floataa     )     )

     # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
     # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
     # see https://bugs.python.org/issue14243 and
     # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
     with tempfile.NamedTemporaryFile(delete=lowerCAmelCase_     ) as tmp_file:
        index.save(tmp_file.name     )
        __lowerCAmelCase								=	FaissIndex.load(tmp_file.name     )
     os.unlink(tmp_file.name     )

     __lowerCAmelCase								=	np.zeros(5					,  dtype=np.floataa     )
     __lowerCAmelCase								=	1
     __lowerCAmelCase	,	__lowerCAmelCase								=	index.search(lowerCAmelCase_     )
     self.assertGreater(scores[0]					,  0     )
     self.assertEqual(indices[0]					,  1     )





@require_faiss
def   a_    ( lowerCAmelCase_      :			Union[str, Any]	):
   import faiss

   __lowerCAmelCase								=	FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT	)
   index.add_vectors(np.eye(5,	dtype=np.floataa	)	)

   __lowerCAmelCase								=	'index.faiss'
   __lowerCAmelCase								=	F"""mock://{index_name}"""
   index.save(lowerCAmelCase_,	storage_options=mockfs.storage_options	)
   __lowerCAmelCase								=	FaissIndex.load(lowerCAmelCase_,	storage_options=mockfs.storage_options	)

   __lowerCAmelCase								=	np.zeros(5,	dtype=np.floataa	)
   __lowerCAmelCase								=	1
   __lowerCAmelCase	,	__lowerCAmelCase								=	index.search(lowerCAmelCase_	)
   assert scores[0] > 0
   assert indices[0] == 1




@require_elasticsearch
class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  def 				lowercase		(				self :		Any     )							->						int:
     from elasticsearch import Elasticsearch

     with patch('elasticsearch.Elasticsearch.search'     ) as mocked_search, patch(
         'elasticsearch.client.IndicesClient.create'     ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk'     ) as mocked_bulk:
        __lowerCAmelCase								=	Elasticsearch()
        __lowerCAmelCase								=	{'acknowledged': True}
        __lowerCAmelCase								=	ElasticSearchIndex(es_client=lowerCAmelCase_     )
        mocked_bulk.return_value([(True, None)] * 3     )
        index.add_documents(['foo', 'bar', 'foobar']     )

        # single query
        __lowerCAmelCase								=	'foo'
        __lowerCAmelCase								=	{'hits': {'hits': [{'_score': 1, '_id': 0}]}}
        __lowerCAmelCase	,	__lowerCAmelCase								=	index.search(lowerCAmelCase_     )
        self.assertEqual(scores[0]					,  1     )
        self.assertEqual(indices[0]					,  0     )

        # single query with timeout
        __lowerCAmelCase								=	'foo'
        __lowerCAmelCase								=	{'hits': {'hits': [{'_score': 1, '_id': 0}]}}
        __lowerCAmelCase	,	__lowerCAmelCase								=	index.search(lowerCAmelCase_					,  request_timeout=3_0     )
        self.assertEqual(scores[0]					,  1     )
        self.assertEqual(indices[0]					,  0     )

        # batched queries
        __lowerCAmelCase								=	['foo', 'bar', 'foobar']
        __lowerCAmelCase								=	{'hits': {'hits': [{'_score': 1, '_id': 1}]}}
        __lowerCAmelCase	,	__lowerCAmelCase								=	index.search_batch(lowerCAmelCase_     )
        __lowerCAmelCase								=	[scores[0] for scores in total_scores]
        __lowerCAmelCase								=	[indices[0] for indices in total_indices]
        self.assertGreater(np.min(lowerCAmelCase_     )					,  0     )
        self.assertListEqual([1, 1, 1]					,  lowerCAmelCase_     )

        # batched queries with timeout
        __lowerCAmelCase								=	['foo', 'bar', 'foobar']
        __lowerCAmelCase								=	{'hits': {'hits': [{'_score': 1, '_id': 1}]}}
        __lowerCAmelCase	,	__lowerCAmelCase								=	index.search_batch(lowerCAmelCase_					,  request_timeout=3_0     )
        __lowerCAmelCase								=	[scores[0] for scores in total_scores]
        __lowerCAmelCase								=	[indices[0] for indices in total_indices]
        self.assertGreater(np.min(lowerCAmelCase_     )					,  0     )
        self.assertListEqual([1, 1, 1]					,  lowerCAmelCase_     )

