"""simple docstring"""


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 				a (						__snake_case      ):
    # to overwrite at feature extractactor specific tests
    _snake_case   :     Any   							=							None
    _snake_case   :     Optional[Any]   							=							None


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


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					self.feature_extraction_class(**self.feat_extract_dict    )
      self.assertTrue(hasattr(A_      , """feature_size"""    )    )
      self.assertTrue(hasattr(A_      , """sampling_rate"""    )    )
      self.assertTrue(hasattr(A_      , """padding_value"""    )    )


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.feat_extract_tester.prepare_inputs_for_common()
      _UpperCAmelCase         =					self.feature_extraction_class(**self.feat_extract_dict    )
      _UpperCAmelCase         =					feat_extract.model_input_names[0]

      _UpperCAmelCase         =					BatchFeature({input_name: speech_inputs}    )

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

      _UpperCAmelCase         =					self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_    )
      _UpperCAmelCase         =					BatchFeature({input_name: speech_inputs}      , tensor_type="""np"""    )

      _UpperCAmelCase         =					processed_features[input_name]

      if len(batch_features_input.shape    ) < 3:
        _UpperCAmelCase         =					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    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_    )
      _UpperCAmelCase         =					self.feature_extraction_class(**self.feat_extract_dict    )
      _UpperCAmelCase         =					feat_extract.model_input_names[0]

      _UpperCAmelCase         =					BatchFeature({input_name: speech_inputs}      , tensor_type="""pt"""    )

      _UpperCAmelCase         =					processed_features[input_name]

      if len(batch_features_input.shape    ) < 3:
        _UpperCAmelCase         =					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    lowerCAmelCase_     (    self		:     Optional[int]    ):
      _UpperCAmelCase         =					self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_    )
      _UpperCAmelCase         =					self.feature_extraction_class(**self.feat_extract_dict    )
      _UpperCAmelCase         =					feat_extract.model_input_names[0]

      _UpperCAmelCase         =					BatchFeature({input_name: speech_inputs}      , tensor_type="""tf"""    )

      _UpperCAmelCase         =					processed_features[input_name]

      if len(batch_features_input.shape    ) < 3:
        _UpperCAmelCase         =					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    lowerCAmelCase_     (    self		:     Dict      , __lowerCAmelCase		:     List[str]=False    ):




      def _inputs_have_equal_length(__lowerCAmelCase		:     int    ):
        _UpperCAmelCase         =					len(input[0]    )
        for input_slice in input[1:]:
          if len(A_    ) != length:
            return False
        return True

      def _inputs_are_equal(__lowerCAmelCase		:     List[str]      , __lowerCAmelCase		:     Optional[int]    ):
        if len(A_    ) != len(A_    ):
          return False

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

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

      _UpperCAmelCase         =					BatchFeature({input_name: speech_inputs}    )

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

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

      _UpperCAmelCase         =					feat_extract.pad(A_      , padding="""longest"""    )
      _UpperCAmelCase         =					input_a[input_name]

      _UpperCAmelCase         =					feat_extract.pad(A_      , padding="""max_length"""      , max_length=len(speech_inputs[-1]    )    )
      _UpperCAmelCase         =					input_a[input_name]

      _UpperCAmelCase         =					feat_extract.pad(A_      , padding="""longest"""      , return_tensors="""np"""    )
      _UpperCAmelCase         =					input_a[input_name]

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

      _UpperCAmelCase         =					feat_extract.pad(
          A_      , padding="""max_length"""      , max_length=A_      , return_tensors="""np"""    )
      _UpperCAmelCase         =					input_a[input_name]

      self.assertFalse(_inputs_have_equal_length(A_    )    )
      self.assertTrue(_inputs_have_equal_length(A_    )    )
      self.assertTrue(_inputs_have_equal_length(A_    )    )
      self.assertTrue(_inputs_are_equal(A_      , A_    )    )
      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
      _UpperCAmelCase         =					feat_extract.pad(A_      , pad_to_multiple_of=10    )
      _UpperCAmelCase         =					input_a[input_name]

      _UpperCAmelCase         =					feat_extract.pad(A_      , padding="""longest"""      , pad_to_multiple_of=10    )
      _UpperCAmelCase         =					input_a[input_name]

      _UpperCAmelCase         =					feat_extract.pad(
          A_      , padding="""max_length"""      , pad_to_multiple_of=10      , max_length=A_    )
      _UpperCAmelCase         =					input_a[input_name]

      _UpperCAmelCase         =					feat_extract.pad(
          A_      , padding="""max_length"""      , pad_to_multiple_of=10      , max_length=A_      , return_tensors="""np"""      , )
      _UpperCAmelCase         =					input_a[input_name]

      self.assertTrue(all(len(A_    ) % 10 == 0 for x in input_a    )    )
      self.assertTrue(_inputs_are_equal(A_      , A_    )    )

      _UpperCAmelCase         =					pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
      self.assertTrue(all(len(A_    ) == 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
      _UpperCAmelCase         =					(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    lowerCAmelCase_     (    self		:     Union[str, Any]      , __lowerCAmelCase		:     Tuple=False    ):




      def _inputs_have_equal_length(__lowerCAmelCase		:     Dict    ):
        _UpperCAmelCase         =					len(input[0]    )
        for input_slice in input[1:]:
          if len(A_    ) != length:
            return False
        return True

      def _inputs_are_equal(__lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     int    ):
        if len(A_    ) != len(A_    ):
          return False

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

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

      _UpperCAmelCase         =					BatchFeature({input_name: speech_inputs}    )

      # truncate to smallest
      _UpperCAmelCase         =					feat_extract.pad(
          A_      , padding="""max_length"""      , max_length=len(speech_inputs[0]    )      , truncation=A_    )
      _UpperCAmelCase         =					input_a[input_name]

      _UpperCAmelCase         =					feat_extract.pad(A_      , padding="""max_length"""      , max_length=len(speech_inputs[0]    )    )
      _UpperCAmelCase         =					input_a[input_name]

      self.assertTrue(_inputs_have_equal_length(A_    )    )
      self.assertFalse(_inputs_have_equal_length(A_    )    )

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

      _UpperCAmelCase         =					feat_extract.pad(
          A_      , padding="""max_length"""      , max_length=len(speech_inputs[0]    )      , return_tensors="""np"""    )
      _UpperCAmelCase         =					input_a[input_name]

      self.assertTrue(_inputs_have_equal_length(A_    )    )
      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(A_    )    )

      # truncate to middle
      _UpperCAmelCase         =					feat_extract.pad(
          A_      , padding="""max_length"""      , max_length=len(speech_inputs[1]    )      , truncation=A_      , return_tensors="""np"""      , )
      _UpperCAmelCase         =					input_a[input_name]

      _UpperCAmelCase         =					feat_extract.pad(
          A_      , padding="""max_length"""      , max_length=len(speech_inputs[1]    )      , truncation=A_    )
      _UpperCAmelCase         =					input_a[input_name]

      _UpperCAmelCase         =					feat_extract.pad(
          A_      , padding="""max_length"""      , max_length=len(speech_inputs[1]    )      , return_tensors="""np"""    )
      _UpperCAmelCase         =					input_a[input_name]

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

      # 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(A_    )    )
      self.assertTrue(len(input_a[-1]    ) == len(speech_inputs[-1]    )    )

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

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

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

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

      # test truncation for `pad_to_multiple_of` for List[int] + numpy
      _UpperCAmelCase         =					12
      _UpperCAmelCase         =					feat_extract.pad(
          A_      , padding="""max_length"""      , max_length=len(speech_inputs[0]    )      , pad_to_multiple_of=A_      , truncation=A_      , )
      _UpperCAmelCase         =					input_a[input_name]

      _UpperCAmelCase         =					feat_extract.pad(
          A_      , padding="""max_length"""      , max_length=len(speech_inputs[0]    )      , pad_to_multiple_of=A_      , )
      _UpperCAmelCase         =					input_a[input_name]

      # retrieve expected_length as multiple of pad_to_multiple_of
      _UpperCAmelCase         =					len(speech_inputs[0]    )
      if expected_length % pad_to_multiple_of != 0:
        _UpperCAmelCase         =					((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(A_    )    )
      self.assertFalse(_inputs_have_equal_length(A_    )    )


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      self._check_padding(numpify=A_    )


    def    lowerCAmelCase_     (    self		:     Dict    ):
      self._check_padding(numpify=A_    )


    def    lowerCAmelCase_     (    self		:     str    ):
      self._check_truncation(numpify=A_    )


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      self._check_truncation(numpify=A_    )


    @require_torch
    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.feature_extraction_class(**self.feat_extract_dict    )
      _UpperCAmelCase         =					self.feat_extract_tester.prepare_inputs_for_common()
      _UpperCAmelCase         =					feat_extract.model_input_names[0]

      _UpperCAmelCase         =					BatchFeature({input_name: speech_inputs}    )

      _UpperCAmelCase         =					feat_extract.pad(A_      , padding="""longest"""      , return_tensors="""np"""    )[input_name]
      _UpperCAmelCase         =					feat_extract.pad(A_      , 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    lowerCAmelCase_     (    self		:     Optional[int]    ):
      _UpperCAmelCase         =					self.feature_extraction_class(**self.feat_extract_dict    )
      _UpperCAmelCase         =					self.feat_extract_tester.prepare_inputs_for_common()
      _UpperCAmelCase         =					feat_extract.model_input_names[0]

      _UpperCAmelCase         =					BatchFeature({input_name: speech_inputs}    )

      _UpperCAmelCase         =					feat_extract.pad(A_      , padding="""longest"""      , return_tensors="""np"""    )[input_name]
      _UpperCAmelCase         =					feat_extract.pad(A_      , 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    lowerCAmelCase_     (    self		:     str    ):
      _UpperCAmelCase         =					self.feat_extract_dict
      _UpperCAmelCase         =					True
      _UpperCAmelCase         =					self.feature_extraction_class(**A_    )
      _UpperCAmelCase         =					self.feat_extract_tester.prepare_inputs_for_common()
      _UpperCAmelCase         =					[len(A_    ) for x in speech_inputs]
      _UpperCAmelCase         =					feat_extract.model_input_names[0]

      _UpperCAmelCase         =					BatchFeature({input_name: speech_inputs}    )

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


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					self.feat_extract_dict
      _UpperCAmelCase         =					True
      _UpperCAmelCase         =					self.feature_extraction_class(**A_    )
      _UpperCAmelCase         =					self.feat_extract_tester.prepare_inputs_for_common()
      _UpperCAmelCase         =					[len(A_    ) for x in speech_inputs]
      _UpperCAmelCase         =					feat_extract.model_input_names[0]

      _UpperCAmelCase         =					BatchFeature({input_name: speech_inputs}    )
      _UpperCAmelCase         =					min(A_    )

      _UpperCAmelCase         =					feat_extract.pad(
          A_      , padding="""max_length"""      , max_length=A_      , truncation=A_      , return_tensors="""np"""    )
      self.assertIn("""attention_mask"""      , A_    )
      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


__lowerCamelCase       :     Union[str, Any]   =     pytest.mark.integration



@require_faiss
class 					A__   (       __snake_case			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   Dataset.from_dict({"filename": ["my_name-train" + "_" + str(A_   ) for x in np.arange(30   ).tolist()]}   )
       return dset





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       import faiss

       UpperCamelCase	:					Dataset       	=   self._create_dummy_dataset()
       UpperCamelCase	:					List[Any]       	=   dset.map(
           lambda A_       ,							A_   : {"vecs": i * np.ones(5       ,							dtype=np.floataa   )}       ,							with_indices=A_       ,							keep_in_memory=A_   )
       UpperCamelCase	:					List[str]       	=   dset.add_faiss_index("vecs"       ,							batch_size=100       ,							metric_type=faiss.METRIC_INNER_PRODUCT   )
       UpperCamelCase	,    UpperCamelCase	:					Tuple       	=   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        __UpperCamelCase( self   ):
       '''simple docstring'''




       import faiss

       UpperCamelCase	:					Dataset       	=   self._create_dummy_dataset()
       dset.add_faiss_index_from_external_arrays(
           external_arrays=np.ones((30, 5)   ) * np.arange(30   ).reshape(-1       ,							1   )       ,							index_name="vecs"       ,							batch_size=100       ,							metric_type=faiss.METRIC_INNER_PRODUCT       ,							)
       UpperCamelCase	,    UpperCamelCase	:					int       	=   dset.get_nearest_examples("vecs"       ,							np.ones(5       ,							dtype=np.floataa   )   )
       self.assertEqual(examples["filename"][0]       ,							"my_name-train_29"   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       import faiss

       UpperCamelCase	:					Dataset       	=   self._create_dummy_dataset()
       dset.add_faiss_index_from_external_arrays(
           external_arrays=np.ones((30, 5)   ) * np.arange(30   ).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=A_   ) as tmp_file:
        dset.save_faiss_index("vecs"       ,							tmp_file.name   )
        dset.load_faiss_index("vecs2"       ,							tmp_file.name   )
       os.unlink(tmp_file.name   )

       UpperCamelCase	,    UpperCamelCase	:					List[str]       	=   dset.get_nearest_examples("vecs2"       ,							np.ones(5       ,							dtype=np.floataa   )   )
       self.assertEqual(examples["filename"][0]       ,							"my_name-train_29"   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dataset       	=   self._create_dummy_dataset()
       dset.add_faiss_index_from_external_arrays(
           external_arrays=np.ones((30, 5)   ) * np.arange(30   ).reshape(-1       ,							1   )       ,							index_name="vecs"   )
       dset.drop_index("vecs"   )
       self.assertRaises(A_       ,							partial(dset.get_nearest_examples       ,							"vecs2"       ,							np.ones(5       ,							dtype=np.floataa   )   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       from elasticsearch import Elasticsearch

       UpperCamelCase	:					Dataset       	=   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:
        UpperCamelCase	:					List[str]       	=   {"acknowledged": True}
        mocked_bulk.return_value([(True, None)] * 30   )
        UpperCamelCase	:					List[Any]       	=   {"hits": {"hits": [{"_score": 1, "_id": 29}]}}
        UpperCamelCase	:					Optional[Any]       	=   Elasticsearch()

        dset.add_elasticsearch_index("filename"       ,							es_client=A_   )
        UpperCamelCase	,    UpperCamelCase	:					List[str]       	=   dset.get_nearest_examples("filename"       ,							"my_name-train_29"   )
        self.assertEqual(examples["filename"][0]       ,							"my_name-train_29"   )



@require_faiss
class 					A__   (       __snake_case			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       import faiss

       UpperCamelCase	:					Optional[int]       	=   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       ,							10   )

       # single query
       UpperCamelCase	:					Any       	=   np.zeros(5       ,							dtype=np.floataa   )
       UpperCamelCase	:					Optional[Any]       	=   1
       UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   index.search(A_   )
       self.assertRaises(A_       ,							index.search       ,							query.reshape(-1       ,							1   )   )
       self.assertGreater(scores[0]       ,							0   )
       self.assertEqual(indices[0]       ,							1   )

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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       import faiss

       UpperCamelCase	:					List[str]       	=   FaissIndex(string_factory="Flat"   )
       index.add_vectors(np.eye(5       ,							dtype=np.floataa   )   )
       self.assertIsInstance(index.faiss_index       ,							faiss.IndexFlat   )
       UpperCamelCase	:					List[str]       	=   FaissIndex(string_factory="LSH"   )
       index.add_vectors(np.eye(5       ,							dtype=np.floataa   )   )
       self.assertIsInstance(index.faiss_index       ,							faiss.IndexLSH   )
       with self.assertRaises(A_   ):
        UpperCamelCase	:					List[str]       	=   FaissIndex(string_factory="Flat"       ,							custom_index=faiss.IndexFlat(5   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       import faiss

       UpperCamelCase	:					Dict       	=   faiss.IndexFlat(5   )
       UpperCamelCase	:					Union[str, Any]       	=   FaissIndex(custom_index=A_   )
       index.add_vectors(np.eye(5       ,							dtype=np.floataa   )   )
       self.assertIsInstance(index.faiss_index       ,							faiss.IndexFlat   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       import faiss

       UpperCamelCase	:					str       	=   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=A_   ) as tmp_file:
        index.save(tmp_file.name   )
        UpperCamelCase	:					int       	=   FaissIndex.load(tmp_file.name   )
       os.unlink(tmp_file.name   )

       UpperCamelCase	:					str       	=   np.zeros(5       ,							dtype=np.floataa   )
       UpperCamelCase	:					int       	=   1
       UpperCamelCase	,    UpperCamelCase	:					Dict       	=   index.search(A_   )
       self.assertGreater(scores[0]       ,							0   )
       self.assertEqual(indices[0]       ,							1   )



@require_faiss
def 		A_				(     _lowerCAmelCase     )						-> Optional[int]:
 import faiss

 UpperCamelCase	:					Union[str, Any]       	=   FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT     )
 index.add_vectors(np.eye(5							,   dtype=np.floataa     )     )

 UpperCamelCase	:					List[Any]       	=   "index.faiss"
 UpperCamelCase	:					List[str]       	=   F"""mock://{index_name}"""
 index.save(_lowerCAmelCase							,   storage_options=mockfs.storage_options     )
 UpperCamelCase	:					List[str]       	=   FaissIndex.load(_lowerCAmelCase							,   storage_options=mockfs.storage_options     )

 UpperCamelCase	:					List[str]       	=   np.zeros(5							,   dtype=np.floataa     )
 UpperCamelCase	:					Optional[int]       	=   1
 UpperCamelCase	,    UpperCamelCase	:					List[str]       	=   index.search(_lowerCAmelCase     )
 assert scores[0] > 0
 assert indices[0] == 1



@require_elasticsearch
class 					A__   (       __snake_case			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       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:
        UpperCamelCase	:					List[str]       	=   Elasticsearch()
        UpperCamelCase	:					Union[str, Any]       	=   {"acknowledged": True}
        UpperCamelCase	:					Union[str, Any]       	=   ElasticSearchIndex(es_client=A_   )
        mocked_bulk.return_value([(True, None)] * 3   )
        index.add_documents(["foo", "bar", "foobar"]   )

        # single query
        UpperCamelCase	:					str       	=   "foo"
        UpperCamelCase	:					Dict       	=   {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
        UpperCamelCase	,    UpperCamelCase	:					Tuple       	=   index.search(A_   )
        self.assertEqual(scores[0]       ,							1   )
        self.assertEqual(indices[0]       ,							0   )

        # single query with timeout
        UpperCamelCase	:					Dict       	=   "foo"
        UpperCamelCase	:					Optional[Any]       	=   {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
        UpperCamelCase	,    UpperCamelCase	:					str       	=   index.search(A_       ,							request_timeout=30   )
        self.assertEqual(scores[0]       ,							1   )
        self.assertEqual(indices[0]       ,							0   )

        # batched queries
        UpperCamelCase	:					Dict       	=   ["foo", "bar", "foobar"]
        UpperCamelCase	:					List[Any]       	=   {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
        UpperCamelCase	,    UpperCamelCase	:					Optional[int]       	=   index.search_batch(A_   )
        UpperCamelCase	:					str       	=   [scores[0] for scores in total_scores]
        UpperCamelCase	:					Optional[Any]       	=   [indices[0] for indices in total_indices]
        self.assertGreater(np.min(A_   )       ,							0   )
        self.assertListEqual([1, 1, 1]       ,							A_   )

        # batched queries with timeout
        UpperCamelCase	:					int       	=   ["foo", "bar", "foobar"]
        UpperCamelCase	:					List[Any]       	=   {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
        UpperCamelCase	,    UpperCamelCase	:					Union[str, Any]       	=   index.search_batch(A_       ,							request_timeout=30   )
        UpperCamelCase	:					Union[str, Any]       	=   [scores[0] for scores in total_scores]
        UpperCamelCase	:					Dict       	=   [indices[0] for indices in total_indices]
        self.assertGreater(np.min(A_   )       ,							0   )
        self.assertListEqual([1, 1, 1]       ,							A_   )
