





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


a__:      Any									=		pytest.mark.integration




@require_faiss
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self ):
        A__											=						Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(__lowerCamelCase ) for x in np.arange(30 ).tolist()]} )
        return dset



 def 				UpperCamelCase			(							self ):
        import faiss

        A__											=						self._create_dummy_dataset()
        A__											=						dset.map(
            lambda __lowerCamelCase,__lowerCamelCase : {"vecs": i * np.ones(5,dtype=np.floataa )},with_indices=__lowerCamelCase,keep_in_memory=__lowerCamelCase )
        A__											=						dset.add_faiss_index('''vecs''',batch_size=100,metric_type=faiss.METRIC_INNER_PRODUCT )
        A__					,	A__											=						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 ):
        import faiss

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



 def 				UpperCamelCase			(							self ):
        import faiss

        A__											=						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=__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 )

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



 def 				UpperCamelCase			(							self ):
        A__											=						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(__lowerCamelCase,partial(dset.get_nearest_examples,'''vecs2''',np.ones(5,dtype=np.floataa ) ) )




 def 				UpperCamelCase			(							self ):
        from elasticsearch import Elasticsearch

        A__											=						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:
               A__											=						{'''acknowledged''': True}
               mocked_bulk.return_value([(True, None)] * 30 )
               A__											=						{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
               A__											=						Elasticsearch()

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




@require_faiss
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self ):
        import faiss

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

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



 def 				UpperCamelCase			(							self ):
        import faiss

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



 def 				UpperCamelCase			(							self ):
        import faiss

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




 def 				UpperCamelCase			(							self ):
        import faiss

        A__											=						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 )
               A__											=						FaissIndex.load(tmp_file.name )
        os.unlink(tmp_file.name )

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


@require_faiss
def 	UpperCamelCase__(       UpperCamelCase__			:       Dict  )->Optional[int]:
       import faiss

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

       A__											=						'''index.faiss'''
       A__											=						f"mock://{index_name}"
       index.save(UpperCamelCase__ ,     storage_options=mockfs.storage_options  )
       A__											=						FaissIndex.load(UpperCamelCase__ ,     storage_options=mockfs.storage_options  )

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




@require_elasticsearch
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self ):
        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:
               A__											=						Elasticsearch()
               A__											=						{'''acknowledged''': True}
               A__											=						ElasticSearchIndex(es_client=__lowerCamelCase )
               mocked_bulk.return_value([(True, None)] * 3 )
               index.add_documents(['''foo''', '''bar''', '''foobar'''] )

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

               # single query with timeout
               A__											=						'''foo'''
               A__											=						{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
               A__					,	A__											=						index.search(__lowerCamelCase,request_timeout=30 )
               self.assertEqual(scores[0],1 )
               self.assertEqual(indices[0],0 )

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

               # batched queries with timeout
               A__											=						['''foo''', '''bar''', '''foobar''']
               A__											=						{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
               A__					,	A__											=						index.search_batch(__lowerCamelCase,request_timeout=30 )
               A__											=						[scores[0] for scores in total_scores]
               A__											=						[indices[0] for indices in total_indices]
               self.assertGreater(np.min(__lowerCamelCase ),0 )
               self.assertListEqual([1, 1, 1],__lowerCamelCase )












import random


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       List[Any]  )->tuple:
       A__					,	A__					,	A__											=						[], [], []
       for element in data:
              if element < pivot:
                     less.append(UpperCamelCase__  )
              elif element > pivot:
                     greater.append(UpperCamelCase__  )
              else:
                     equal.append(UpperCamelCase__  )
       return less, equal, greater


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       int  )->Optional[int]:
       # index = len(items) // 2 when trying to find the median
       #   (value of index when items is sorted)

       # invalid input
       if index >= len(UpperCamelCase__  ) or index < 0:
              return None

       A__											=						items[random.randint(0 ,     len(UpperCamelCase__  ) - 1  )]
       A__											=						0
       A__					,	A__					,	A__											=						_partition(UpperCamelCase__ ,     UpperCamelCase__  )
       A__											=						len(UpperCamelCase__  )
       A__											=						len(UpperCamelCase__  )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(UpperCamelCase__ ,     UpperCamelCase__  )
       # must be in larger
       else:
              return quick_select(UpperCamelCase__ ,     index - (m + count)  )





