

"""simple docstring"""



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


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



@require_faiss
class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Union[str, Any]       ):






         """simple docstring"""


         import faiss

         A__										=  self._create_dummy_dataset()
         A__										=  dset.map(
             lambda UpperCamelCase    ,						UpperCamelCase       : {"vecs": i * np.ones(5    ,						dtype=np.floataa       )}    ,						with_indices=UpperCamelCase    ,						keep_in_memory=UpperCamelCase       )
         A__										=  dset.add_faiss_index("""vecs"""    ,						batch_size=1_00    ,						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: Union[str, Any]       ):






         """simple docstring"""


         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=1_00    ,						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: Optional[Any]       ):






         """simple docstring"""


         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=UpperCamelCase       ) 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: Dict       ):






         """simple docstring"""


         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(UpperCamelCase    ,						partial(dset.get_nearest_examples    ,						"""vecs2"""    ,						np.ones(5    ,						dtype=np.floataa       )       )       )





    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         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=UpperCamelCase       )
              A__			,				A__										=  dset.get_nearest_examples("""filename"""    ,						"""my_name-train_29"""       )
              self.assertEqual(examples["""filename"""][0]    ,						"""my_name-train_29"""       )



@require_faiss
class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         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(UpperCamelCase       )
         self.assertRaises(UpperCamelCase    ,						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(UpperCamelCase       )
         self.assertRaises(UpperCamelCase    ,						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(UpperCamelCase       )    ,						0       )
         self.assertListEqual([4, 3, 2, 1, 0]    ,						UpperCamelCase       )

    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         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(UpperCamelCase       ):
              A__										=  FaissIndex(string_factory="""Flat"""    ,						custom_index=faiss.IndexFlat(5       )       )

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         import faiss

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





    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         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=UpperCamelCase       ) 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(UpperCamelCase       )
         self.assertGreater(scores[0]    ,						0       )
         self.assertEqual(indices[0]    ,						1       )





@require_faiss
def  _snake_case     (    UpperCAmelCase_  :					str			):
     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 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Optional[int]       ):






         """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:
              A__										=  Elasticsearch()
              A__										=  {"""acknowledged""": True}
              A__										=  ElasticSearchIndex(es_client=UpperCamelCase       )
              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(UpperCamelCase       )
              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(UpperCamelCase    ,						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(UpperCamelCase       )
              A__										=  [scores[0] for scores in total_scores]
              A__										=  [indices[0] for indices in total_indices]
              self.assertGreater(np.min(UpperCamelCase       )    ,						0       )
              self.assertListEqual([1, 1, 1]    ,						UpperCamelCase       )

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









"""simple docstring"""



from itertools import count





def  _snake_case     (    UpperCAmelCase_  :					int = 50			):
     A__										=  [1] * min_block_length

     for n in count(UpperCAmelCase_			):
          fill_count_functions.append(1			)

          for block_length in range(UpperCAmelCase_					,   n + 1			):
               for block_start in range(n - block_length			):
                    fill_count_functions[n] += fill_count_functions[
                        n - block_start - block_length - 1
                    ]

               fill_count_functions[n] += 1

          if fill_count_functions[n] > 100_0000:
               break

     return n


if __name__ == "__main__":
 print(f"""{solution() = }""")






