"""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


_a   :	Tuple 					=     pytest.mark.integration
@require_faiss
class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  Dataset.from_dict({"""filename""": ["""my_name-train""" + """_""" + str(a__						) for x in np.arange(30						).tolist()]}						)
       return dset






     def 					__A						(	self						):
       import faiss

       _lowerCAmelCase		: Dataset						    =  self._create_dummy_dataset()
       _lowerCAmelCase		: Dict						    =  dset.map(
           lambda a__       ,  a__						: {"vecs": i * np.ones(5       ,  dtype=np.floataa						)}       ,  with_indices=a__       ,  keep_in_memory=a__						)
       _lowerCAmelCase		: List[Any]						    =  dset.add_faiss_index("""vecs"""       ,  batch_size=100       ,  metric_type=faiss.METRIC_INNER_PRODUCT						)
       _lowerCAmelCase    ,			_lowerCAmelCase		: int						    =  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 					__A						(	self						):
       import faiss

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






     def 					__A						(	self						):
       import faiss

       _lowerCAmelCase		: 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						)

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






     def 					__A						(	self						):
       _lowerCAmelCase		: 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 					__A						(	self						):
       from elasticsearch import Elasticsearch

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

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







@require_faiss
class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def 					__A						(	self						):
       import faiss

       _lowerCAmelCase		: Dict						    =  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
       _lowerCAmelCase		: List[Any]						    =  np.zeros(5       ,  dtype=np.floataa						)
       _lowerCAmelCase		: Any						    =  1
       _lowerCAmelCase    ,			_lowerCAmelCase		: 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
       _lowerCAmelCase		: List[Any]						    =  np.eye(5       ,  dtype=np.floataa						)[::-1]
       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  index.search_batch(a__						)
       self.assertRaises(a__       ,  index.search_batch       ,  queries[0]						)
       _lowerCAmelCase		: int						    =  [scores[0] for scores in total_scores]
       _lowerCAmelCase		: int						    =  [indices[0] for indices in total_indices]
       self.assertGreater(np.min(a__						)       ,  0						)
       self.assertListEqual([4, 3, 2, 1, 0]       ,  a__						)






     def 					__A						(	self						):
       import faiss

       _lowerCAmelCase		: Tuple						    =  FaissIndex(string_factory="""Flat"""						)
       index.add_vectors(np.eye(5       ,  dtype=np.floataa						)						)
       self.assertIsInstance(index.faiss_index       ,  faiss.IndexFlat						)
       _lowerCAmelCase		: Dict						    =  FaissIndex(string_factory="""LSH"""						)
       index.add_vectors(np.eye(5       ,  dtype=np.floataa						)						)
       self.assertIsInstance(index.faiss_index       ,  faiss.IndexLSH						)
       with self.assertRaises(a__						):
         _lowerCAmelCase		: int						    =  FaissIndex(string_factory="""Flat"""       ,  custom_index=faiss.IndexFlat(5						)						)






     def 					__A						(	self						):
       import faiss

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






     def 					__A						(	self						):
       import faiss

       _lowerCAmelCase		: List[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						)
         _lowerCAmelCase		: Tuple						    =  FaissIndex.load(tmp_file.name						)
       os.unlink(tmp_file.name						)

       _lowerCAmelCase		: Optional[Any]						    =  np.zeros(5       ,  dtype=np.floataa						)
       _lowerCAmelCase		: int						    =  1
       _lowerCAmelCase    ,			_lowerCAmelCase		: List[str]						    =  index.search(a__						)
       self.assertGreater(scores[0]       ,  0						)
       self.assertEqual(indices[0]       ,  1						)







@require_faiss
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[int]			) ->   Tuple:
  import faiss

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

  _lowerCAmelCase		: Tuple						    =  """index.faiss"""
  _lowerCAmelCase		: Optional[int]						    =  f"mock://{index_name}"
  index.save(_lowerCamelCase    ,storage_options=mockfs.storage_options			)
  _lowerCAmelCase		: Optional[Any]						    =  FaissIndex.load(_lowerCamelCase    ,storage_options=mockfs.storage_options			)

  _lowerCAmelCase		: Dict						    =  np.zeros(5    ,dtype=np.floataa			)
  _lowerCAmelCase		: Union[str, Any]						    =  1
  _lowerCAmelCase    ,			_lowerCAmelCase		: Tuple						    =  index.search(_lowerCamelCase			)
  assert scores[0] > 0
  assert indices[0] == 1






@require_elasticsearch
class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def 					__A						(	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:
         _lowerCAmelCase		: Optional[Any]						    =  Elasticsearch()
         _lowerCAmelCase		: List[str]						    =  {"""acknowledged""": True}
         _lowerCAmelCase		: str						    =  ElasticSearchIndex(es_client=a__						)
         mocked_bulk.return_value([(True, None)] * 3						)
         index.add_documents(["""foo""", """bar""", """foobar"""]						)

         # single query
         _lowerCAmelCase		: List[str]						    =  """foo"""
         _lowerCAmelCase		: str						    =  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
         _lowerCAmelCase    ,			_lowerCAmelCase		: Union[str, Any]						    =  index.search(a__						)
         self.assertEqual(scores[0]       ,  1						)
         self.assertEqual(indices[0]       ,  0						)

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

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

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





"""simple docstring"""




from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class 		__A							:
     _UpperCamelCase							:    int
     _UpperCamelCase							:    Node | None	      =	None
     _UpperCamelCase							:    Node | None	      =	None







def 							SCREAMING_SNAKE_CASE  (    ) ->   Node | None:
  _lowerCAmelCase		: Tuple						    =  Node(1			)
  _lowerCAmelCase		: int						    =  Node(2			)
  _lowerCAmelCase		: int						    =  Node(3			)
  _lowerCAmelCase		: Any						    =  Node(4			)
  _lowerCAmelCase		: Dict						    =  Node(5			)
  return tree





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return [root.data, *preorder(root.left			), *preorder(root.right			)] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return postorder(root.left			) + postorder(root.right			) + [root.data] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return [*inorder(root.left			), root.data, *inorder(root.right			)] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   int:
  return (max(height(root.left			)    ,height(root.right			)			) + 1) if root else 0





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  if root is None:
    return output

  _lowerCAmelCase		: Union[str, Any]						    =  deque([root]			)

  while process_queue:
    _lowerCAmelCase		: Optional[Any]						    =  process_queue.popleft()
    output.append(node.data			)

    if node.left:
      process_queue.append(node.left			)
    if node.right:
      process_queue.append(node.right			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  def populate_output(_lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) -> None:
    if not root:
      return
    if level == 1:
      output.append(root.data			)
    elif level > 1:
      populate_output(root.left    ,level - 1			)
      populate_output(root.right    ,level - 1			)

  populate_output(_lowerCamelCase    ,_lowerCamelCase			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  def populate_output(_lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) -> None:
    if root is None:
      return
    if level == 1:
      output.append(root.data			)
    elif level > 1:
      populate_output(root.right    ,level - 1			)
      populate_output(root.left    ,level - 1			)

  populate_output(_lowerCamelCase    ,_lowerCamelCase			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   Sequence[Node | None] | list[Any]:
  if root is None:
    return []

  _lowerCAmelCase		: list[Sequence[Node | None]]						    =  []

  _lowerCAmelCase		: Tuple						    =  0
  _lowerCAmelCase		: Dict						    =  height(_lowerCamelCase			)

  for h in range(1    ,height_tree + 1			):
    if not flag:
      output.append(get_nodes_from_left_to_right(_lowerCamelCase    ,_lowerCamelCase			)			)
      _lowerCAmelCase		: Any						    =  1
    else:
      output.append(get_nodes_from_right_to_left(_lowerCamelCase    ,_lowerCamelCase			)			)
      _lowerCAmelCase		: Optional[int]						    =  0

  return output





def 							SCREAMING_SNAKE_CASE  (    ) ->   None:  # Main function for testing.
  _lowerCAmelCase		: int						    =  make_tree()
  print(f"In-order Traversal: {inorder(_lowerCamelCase			)}"			)
  print(f"Pre-order Traversal: {preorder(_lowerCamelCase			)}"			)
  print(f"Post-order Traversal: {postorder(_lowerCamelCase			)}"    ,"""\n"""			)

  print(f"Height of Tree: {height(_lowerCamelCase			)}"    ,"""\n"""			)

  print("""Complete Level Order Traversal: """			)
  print(level_order(_lowerCamelCase			)    ,"""\n"""			)

  print("""Level-wise order Traversal: """			)

  for level in range(1    ,height(_lowerCamelCase			) + 1			):
    print(f"Level {level}:"    ,get_nodes_from_left_to_right(_lowerCamelCase    ,level=_lowerCamelCase			)			)

  print("""\nZigZag order Traversal: """			)
  print(zigzag(_lowerCamelCase			)			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




