





import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
  from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
  _lowercase		:     int 					=None

if is_ray_available():
  import ray  # noqa: E402 # isort:skip
  from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
  _lowercase		:     Any 					=None
  _lowercase		:     Tuple 					=None
  _lowercase		:     List[Any] 					=None


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
    lowerCamelCase_  :    List[Any]   	=			unittest.skip('test requires Datasets, Faiss, psutil'	)(lowerCAmelCase__	)
  return test_case






@require_distributed_retrieval
class 	UpperCamelCase_     (     snake_case__       ):



     def      __a    (		self    :    Dict	):
       lowerCamelCase_  :    Optional[Any]   	=			tempfile.mkdtemp()
       lowerCamelCase_  :    Tuple   	=			8

       # DPR tok
       lowerCamelCase_  :    Any   	=			[
           '[UNK]',
           '[CLS]',
           '[SEP]',
           '[PAD]',
           '[MASK]',
           'want',
           '##want',
           '##ed',
           'wa',
           'un',
           'runn',
           '##ing',
           ',',
           'low',
           'lowest',
       ]
       lowerCamelCase_  :    List[str]   	=			os.path.join(self.tmpdirname					,			'dpr_tokenizer'	)
       os.makedirs(lowerCamelCase					,			exist_ok=lowerCamelCase	)
       lowerCamelCase_  :    int   	=			os.path.join(lowerCamelCase					,			DPR_VOCAB_FILES_NAMES['vocab_file']	)
       with open(self.vocab_file					,			'w'					,			encoding='utf-8'	) as vocab_writer:
         vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]	)	)

       # BART tok
       lowerCamelCase_  :    Optional[Any]   	=			[
           'l',
           'o',
           'w',
           'e',
           'r',
           's',
           't',
           'i',
           'd',
           'n',
           '\u0120',
           '\u0120l',
           '\u0120n',
           '\u0120lo',
           '\u0120low',
           'er',
           '\u0120lowest',
           '\u0120newer',
           '\u0120wider',
           '<unk>',
       ]
       lowerCamelCase_  :    Any   	=			dict(zip(lowerCamelCase					,			range(len(lowerCamelCase	)	)	)	)
       lowerCamelCase_  :    str   	=			['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
       lowerCamelCase_  :    Union[str, Any]   	=			{'unk_token': '<unk>'}

       lowerCamelCase_  :    int   	=			os.path.join(self.tmpdirname					,			'bart_tokenizer'	)
       os.makedirs(lowerCamelCase					,			exist_ok=lowerCamelCase	)
       lowerCamelCase_  :    List[Any]   	=			os.path.join(lowerCamelCase					,			BART_VOCAB_FILES_NAMES['vocab_file']	)
       lowerCamelCase_  :    Tuple   	=			os.path.join(lowerCamelCase					,			BART_VOCAB_FILES_NAMES['merges_file']	)
       with open(self.vocab_file					,			'w'					,			encoding='utf-8'	) as fp:
         fp.write(json.dumps(lowerCamelCase	) + '\n'	)
       with open(self.merges_file					,			'w'					,			encoding='utf-8'	) as fp:
         fp.write('\n'.join(lowerCamelCase	)	)



     def      __a    (		self    :    List[Any]	):
       return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname					,			'dpr_tokenizer'	)	)



     def      __a    (		self    :    Union[str, Any]	):
       return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname					,			'bart_tokenizer'	)	)



     def      __a    (		self    :    str	):
       shutil.rmtree(self.tmpdirname	)



     def      __a    (		self    :    Union[str, Any]	):
       lowerCamelCase_  :    Optional[int]   	=			Dataset.from_dict(
           {
               'id': ['0', '1'],
               'text': ['foo', 'bar'],
               'title': ['Foo', 'Bar'],
               'embeddings': [np.ones(self.retrieval_vector_size	), 2 * np.ones(self.retrieval_vector_size	)],
           }	)
       dataset.add_faiss_index('embeddings'					,			string_factory='Flat'					,			metric_type=faiss.METRIC_INNER_PRODUCT	)
       return dataset



     def      __a    (		self    :    Any					,			lowerCamelCase    :    bool					,			lowerCamelCase    :    Optional[Any]=1_23_45	):
       lowerCamelCase_  :    Optional[Any]   	=			self.get_dummy_dataset()
       lowerCamelCase_  :    Optional[int]   	=			RagConfig(
           retrieval_vector_size=self.retrieval_vector_size					,			question_encoder=DPRConfig().to_dict()					,			generator=BartConfig().to_dict()					,			)
       with patch('transformers.models.rag.retrieval_rag.load_dataset'	) as mock_load_dataset:
         lowerCamelCase_  :    Optional[Any]   	=			dataset
         lowerCamelCase_  :    List[str]   	=			RagPyTorchDistributedRetriever(
             lowerCamelCase					,			question_encoder_tokenizer=self.get_dpr_tokenizer()					,			generator_tokenizer=self.get_bart_tokenizer()					,			)
         if init_retrieval:
           retriever.init_retrieval(lowerCamelCase	)
       return retriever



     def      __a    (		self    :    Dict					,			lowerCamelCase    :    bool	):
       # Have to run in local mode because sys.path modifications at top of
       # file are not propogated to remote workers.
       # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
       ray.init(local_mode=lowerCamelCase	)
       lowerCamelCase_  :    Optional[int]   	=			RagConfig(
           retrieval_vector_size=self.retrieval_vector_size					,			question_encoder=DPRConfig().to_dict()					,			generator=BartConfig().to_dict()					,			)
       lowerCamelCase_  :    Any   	=			ray.remote(lowerCamelCase	)
       lowerCamelCase_  :    int   	=			[remote_cls.remote() for _ in range(1	)]
       with patch('transformers.models.rag.retrieval_rag.load_dataset'	) as mock_load_dataset:
         lowerCamelCase_  :    int   	=			self.get_dummy_dataset()
         lowerCamelCase_  :    Tuple   	=			RagRayDistributedRetriever(
             lowerCamelCase					,			question_encoder_tokenizer=self.get_dpr_tokenizer()					,			generator_tokenizer=self.get_bart_tokenizer()					,			retrieval_workers=lowerCamelCase					,			)
         if init_retrieval:
           retriever.init_retrieval()
       return retriever



     def      __a    (		self    :    List[str]					,			lowerCamelCase    :    bool					,			lowerCamelCase    :    bool					,			lowerCamelCase    :    List[Any]=1_23_45	):
       lowerCamelCase_  :    int   	=			self.get_dummy_dataset()
       lowerCamelCase_  :    Any   	=			RagConfig(
           retrieval_vector_size=self.retrieval_vector_size					,			question_encoder=DPRConfig().to_dict()					,			generator=BartConfig().to_dict()					,			index_name='custom'					,			)
       if from_disk:
         lowerCamelCase_  :    Any   	=			os.path.join(self.tmpdirname					,			'dataset'	)
         lowerCamelCase_  :    str   	=			os.path.join(self.tmpdirname					,			'index.faiss'	)
         dataset.get_index('embeddings'	).save(os.path.join(self.tmpdirname					,			'index.faiss'	)	)
         dataset.drop_index('embeddings'	)
         dataset.save_to_disk(os.path.join(self.tmpdirname					,			'dataset'	)	)
         del dataset
         lowerCamelCase_  :    str   	=			RagPyTorchDistributedRetriever(
             lowerCamelCase					,			question_encoder_tokenizer=self.get_dpr_tokenizer()					,			generator_tokenizer=self.get_bart_tokenizer()					,			)
       else:
         lowerCamelCase_  :    List[Any]   	=			RagPyTorchDistributedRetriever(
             lowerCamelCase					,			question_encoder_tokenizer=self.get_dpr_tokenizer()					,			generator_tokenizer=self.get_bart_tokenizer()					,			index=CustomHFIndex(config.retrieval_vector_size					,			lowerCamelCase	)					,			)
       if init_retrieval:
         retriever.init_retrieval(lowerCamelCase	)
       return retriever



     def      __a    (		self    :    Dict					,			lowerCamelCase    :    bool					,			lowerCamelCase    :    bool	):
       # Have to run in local mode because sys.path modifications at top of
       # file are not propogated to remote workers.
       # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
       ray.init(local_mode=lowerCamelCase	)
       lowerCamelCase_  :    Dict   	=			self.get_dummy_dataset()
       lowerCamelCase_  :    Dict   	=			RagConfig(
           retrieval_vector_size=self.retrieval_vector_size					,			question_encoder=DPRConfig().to_dict()					,			generator=BartConfig().to_dict()					,			index_name='custom'					,			)
       lowerCamelCase_  :    Any   	=			ray.remote(lowerCamelCase	)
       lowerCamelCase_  :    List[str]   	=			[remote_cls.remote() for _ in range(1	)]
       if from_disk:
         lowerCamelCase_  :    int   	=			os.path.join(self.tmpdirname					,			'dataset'	)
         lowerCamelCase_  :    Optional[int]   	=			os.path.join(self.tmpdirname					,			'index.faiss'	)
         dataset.get_index('embeddings'	).save(os.path.join(self.tmpdirname					,			'index.faiss'	)	)
         dataset.drop_index('embeddings'	)
         dataset.save_to_disk(os.path.join(self.tmpdirname					,			'dataset'	)	)
         del dataset
         lowerCamelCase_  :    List[Any]   	=			RagRayDistributedRetriever(
             lowerCamelCase					,			question_encoder_tokenizer=self.get_dpr_tokenizer()					,			generator_tokenizer=self.get_bart_tokenizer()					,			retrieval_workers=lowerCamelCase					,			index=CustomHFIndex.load_from_disk(
                 vector_size=config.retrieval_vector_size					,			dataset_path=config.passages_path					,			index_path=config.index_path					,			)					,			)
       else:
         lowerCamelCase_  :    List[str]   	=			RagRayDistributedRetriever(
             lowerCamelCase					,			question_encoder_tokenizer=self.get_dpr_tokenizer()					,			generator_tokenizer=self.get_bart_tokenizer()					,			retrieval_workers=lowerCamelCase					,			index=CustomHFIndex(config.retrieval_vector_size					,			lowerCamelCase	)					,			)
       if init_retrieval:
         retriever.init_retrieval()
       return retriever



     def      __a    (		self    :    Any					,			lowerCamelCase    :    RagRetriever					,			lowerCamelCase    :    np.array					,			lowerCamelCase    :    int	):
       lowerCamelCase_  :    List[Any]   	=			retriever.retrieve(lowerCamelCase					,			n_docs=lowerCamelCase	)
       self.assertEqual(retrieved_doc_embeds.shape					,			(2, n_docs, self.retrieval_vector_size)	)
       self.assertEqual(len(lowerCamelCase	)					,			2	)
       self.assertEqual(sorted(doc_dicts[0]	)					,			['embeddings', 'id', 'text', 'title']	)
       self.assertEqual(len(doc_dicts[0]['id']	)					,			lowerCamelCase	)
       self.assertEqual(doc_dicts[0]['id'][0]					,			'1'	)  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]['id'][0]					,			'0'	)  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()					,			[[1], [0]]	)



     def      __a    (		self    :    int	):
       lowerCamelCase_  :    List[Any]   	=			1
       lowerCamelCase_  :    Union[str, Any]   	=			np.array(
           [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,			dtype=np.floataa	)

       self.distributed_retriever_check(
           self.get_dummy_pytorch_distributed_retriever(init_retrieval=lowerCamelCase	)					,			lowerCamelCase					,			lowerCamelCase	)



     def      __a    (		self    :    Optional[int]	):
       lowerCamelCase_  :    Optional[Any]   	=			1
       lowerCamelCase_  :    int   	=			np.array(
           [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,			dtype=np.floataa	)

       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase					,			from_disk=lowerCamelCase	)					,			lowerCamelCase					,			lowerCamelCase					,			)



     def      __a    (		self    :    List[Any]	):
       lowerCamelCase_  :    List[str]   	=			1
       lowerCamelCase_  :    Union[str, Any]   	=			np.array(
           [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,			dtype=np.floataa	)

       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase					,			from_disk=lowerCamelCase	)					,			lowerCamelCase					,			lowerCamelCase					,			)



     @require_ray
     def      __a    (		self    :    Optional[Any]	):
       lowerCamelCase_  :    Optional[int]   	=			1
       lowerCamelCase_  :    Tuple   	=			np.array(
           [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,			dtype=np.floataa	)

       self.distributed_retriever_check(
           self.get_dummy_ray_distributed_retriever(init_retrieval=lowerCamelCase	)					,			lowerCamelCase					,			lowerCamelCase	)
       ray.shutdown()



     @require_ray
     def      __a    (		self    :    List[str]	):
       lowerCamelCase_  :    str   	=			1
       lowerCamelCase_  :    Optional[int]   	=			np.array(
           [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,			dtype=np.floataa	)
       with self.assertRaises(lowerCamelCase	):
         self.distributed_retriever_check(
             self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase					,			from_disk=lowerCamelCase	)					,			lowerCamelCase					,			lowerCamelCase					,			)
       ray.shutdown()





     @require_ray
     def      __a    (		self    :    List[Any]	):
       lowerCamelCase_  :    int   	=			1
       lowerCamelCase_  :    int   	=			np.array(
           [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,			dtype=np.floataa	)

       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase					,			from_disk=lowerCamelCase	)					,			lowerCamelCase					,			lowerCamelCase	)
       ray.shutdown()







class 	UpperCamelCase_     :



					def __init__(		self    :    List[Any]					,			lowerCamelCase    :    int	):
							lowerCamelCase_  :    Union[str, Any]   	=			val
							# Number of nodes in left subtree
							lowerCamelCase_  :    Dict   	=			0
							lowerCamelCase_  :    str   	=			None
							lowerCamelCase_  :    int   	=			None
							lowerCamelCase_  :    Dict   	=			None





					def      __a    (		self    :    Optional[int]					,			lowerCamelCase    :    List[Any]	):
							assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

							if self.val < other.val:
									lowerCamelCase_  :    int   	=			self.right
									lowerCamelCase_  :    Any   	=			None
									if self.right:
											lowerCamelCase_  :    Optional[Any]   	=			other
									lowerCamelCase_  :    Union[str, Any]   	=			other
									lowerCamelCase_  :    List[Any]   	=			self.left_tree_size * 2 + 1
									return self
							else:
									lowerCamelCase_  :    str   	=			other.right
									lowerCamelCase_  :    List[Any]   	=			None
									if other.right:
											lowerCamelCase_  :    Union[str, Any]   	=			self
									lowerCamelCase_  :    Optional[int]   	=			self
									lowerCamelCase_  :    Optional[int]   	=			other.left_tree_size * 2 + 1
									return other






class 	UpperCamelCase_     :



					def __init__(		self    :    List[str]					,			lowerCamelCase    :    Optional[int]=None					,			lowerCamelCase    :    str=None					,			lowerCamelCase    :    List[Any]=0	):
							lowerCamelCase_  :    Any   	=			heap_size
							lowerCamelCase_  :    int   	=			bottom_root
							lowerCamelCase_  :    Any   	=			min_node



					def      __a    (		self    :    int					,			lowerCamelCase    :    int	):

							# Empty heaps corner cases
							if other.size == 0:
									return None
							if self.size == 0:
									lowerCamelCase_  :    List[str]   	=			other.size
									lowerCamelCase_  :    Tuple   	=			other.bottom_root
									lowerCamelCase_  :    Tuple   	=			other.min_node
									return None
							# Update size
							lowerCamelCase_  :    Optional[Any]   	=			self.size + other.size

							# Update min.node
							if self.min_node.val > other.min_node.val:
									lowerCamelCase_  :    Optional[Any]   	=			other.min_node
							# Merge

							# Order roots by left_subtree_size
							lowerCamelCase_  :    Dict   	=			[]
							lowerCamelCase_	,			lowerCamelCase_  :    Optional[Any]   	=			self.bottom_root, other.bottom_root
							while i or j:
									if i and ((not j) or i.left_tree_size < j.left_tree_size):
											combined_roots_list.append((i, True)	)
											lowerCamelCase_  :    Tuple   	=			i.parent
									else:
											combined_roots_list.append((j, False)	)
											lowerCamelCase_  :    Optional[Any]   	=			j.parent
        # Insert links between them
							for i in range(len(lowerCamelCase	) - 1	):
									if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
											lowerCamelCase_  :    Dict   	=			combined_roots_list[i + 1][0]
											lowerCamelCase_  :    Optional[int]   	=			combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
							lowerCamelCase_  :    Any   	=			combined_roots_list[0][0]
							while i.parent:
									if (
									    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
									) or (
									    i.left_tree_size == i.parent.left_tree_size
									    and i.left_tree_size != i.parent.parent.left_tree_size
									):
											# Neighbouring Nodes
											lowerCamelCase_  :    Dict   	=			i.left
											lowerCamelCase_  :    int   	=			i.parent.parent

											# Merging trees
											lowerCamelCase_  :    str   	=			i.merge_trees(i.parent	)

											# Updating links
											lowerCamelCase_  :    Union[str, Any]   	=			previous_node
											lowerCamelCase_  :    List[Any]   	=			next_node
											if previous_node:
													lowerCamelCase_  :    List[str]   	=			i
											if next_node:
													lowerCamelCase_  :    Dict   	=			i
									else:
											lowerCamelCase_  :    List[str]   	=			i.parent
        # Updating self.bottom_root
							while i.left:
									lowerCamelCase_  :    Union[str, Any]   	=			i.left
							lowerCamelCase_  :    Any   	=			i

							# Update other
							lowerCamelCase_  :    Optional[int]   	=			self.size
							lowerCamelCase_  :    Optional[Any]   	=			self.bottom_root
							lowerCamelCase_  :    str   	=			self.min_node

							# Return the merged heap
							return self



					def      __a    (		self    :    Any					,			lowerCamelCase    :    Dict	):
							if self.size == 0:
									lowerCamelCase_  :    Tuple   	=			Node(lowerCamelCase	)
									lowerCamelCase_  :    Optional[int]   	=			1
									lowerCamelCase_  :    Optional[int]   	=			self.bottom_root
							else:
									# Create new node
									lowerCamelCase_  :    List[Any]   	=			Node(lowerCamelCase	)

									# Update size
									self.size += 1

									# update min_node
									if val < self.min_node.val:
											lowerCamelCase_  :    List[str]   	=			new_node
									# Put new_node as a bottom_root in heap
									lowerCamelCase_  :    int   	=			new_node
									lowerCamelCase_  :    Tuple   	=			self.bottom_root
									lowerCamelCase_  :    Any   	=			new_node

									# Consecutively merge roots with same left_tree_size
									while (
									    self.bottom_root.parent
									    and self.bottom_root.left_tree_size
									    == self.bottom_root.parent.left_tree_size
									):
											# Next node
											lowerCamelCase_  :    Any   	=			self.bottom_root.parent.parent

											# Merge
											lowerCamelCase_  :    int   	=			self.bottom_root.merge_trees(self.bottom_root.parent	)

											# Update Links
											lowerCamelCase_  :    Dict   	=			next_node
											lowerCamelCase_  :    Optional[Any]   	=			None
											if next_node:
													lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root



					def      __a    (		self    :    Dict	):
							return self.min_node.val



					def      __a    (		self    :    int	):
							return self.size == 0



					def      __a    (		self    :    List[Any]	):
							lowerCamelCase_  :    Tuple   	=			self.min_node.val

							# Last element in heap corner case
							if self.size == 1:
									# Update size
									lowerCamelCase_  :    Optional[Any]   	=			0

									# Update bottom root
									lowerCamelCase_  :    Any   	=			None

									# Update min_node
									lowerCamelCase_  :    List[str]   	=			None

									return min_value
							# No right subtree corner case
							# The structure of the tree implies that this should be the bottom root
							# and there is at least one other root
							if self.min_node.right is None:
									# Update size
									self.size -= 1

									# Update bottom root
									lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root.parent
									lowerCamelCase_  :    Union[str, Any]   	=			None

									# Update min_node
									lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root
									lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root.parent
									while i:
											if i.val < self.min_node.val:
													lowerCamelCase_  :    Union[str, Any]   	=			i
											lowerCamelCase_  :    Optional[int]   	=			i.parent
									return min_value
							# General case
							# Find the BinomialHeap of the right subtree of min_node
							lowerCamelCase_  :    Dict   	=			self.min_node.right
							lowerCamelCase_  :    Dict   	=			None
							lowerCamelCase_  :    Tuple   	=			bottom_of_new
							lowerCamelCase_  :    Dict   	=			1

							# Size, min_node and bottom_root
							while bottom_of_new.left:
									lowerCamelCase_  :    Any   	=			size_of_new * 2 + 1
									lowerCamelCase_  :    Tuple   	=			bottom_of_new.left
									if bottom_of_new.val < min_of_new.val:
											lowerCamelCase_  :    int   	=			bottom_of_new
        # Corner case of single root on top left path
							if (not self.min_node.left) and (not self.min_node.parent):
									lowerCamelCase_  :    Union[str, Any]   	=			size_of_new
									lowerCamelCase_  :    Union[str, Any]   	=			bottom_of_new
									lowerCamelCase_  :    Tuple   	=			min_of_new
									# print("Single root, multiple nodes case")
									return min_value
							# Remaining cases
							# Construct heap of right subtree
							lowerCamelCase_  :    Optional[Any]   	=			BinomialHeap(
							    bottom_root=lowerCamelCase					,			min_node=lowerCamelCase					,			heap_size=lowerCamelCase	)

							# Update size
							lowerCamelCase_  :    Union[str, Any]   	=			self.size - 1 - size_of_new

							# Neighbour nodes
							lowerCamelCase_  :    Union[str, Any]   	=			self.min_node.left
							lowerCamelCase_  :    Optional[Any]   	=			self.min_node.parent

							# Initialize new bottom_root and min_node
							lowerCamelCase_  :    Optional[int]   	=			previous_node or next_node
							lowerCamelCase_  :    Tuple   	=			next_node

							# Update links of previous_node and search below for new min_node and
							# bottom_root
							if previous_node:
									lowerCamelCase_  :    Dict   	=			next_node

									# Update bottom_root and search for min_node below
									lowerCamelCase_  :    int   	=			previous_node
									lowerCamelCase_  :    Tuple   	=			previous_node
									while self.bottom_root.left:
											lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root.left
											if self.bottom_root.val < self.min_node.val:
													lowerCamelCase_  :    Any   	=			self.bottom_root
							if next_node:
									lowerCamelCase_  :    Any   	=			previous_node

									# Search for new min_node above min_node
									lowerCamelCase_  :    List[str]   	=			next_node
									while i:
											if i.val < self.min_node.val:
													lowerCamelCase_  :    List[Any]   	=			i
											lowerCamelCase_  :    Dict   	=			i.parent
        # Merge heaps
							self.merge_heaps(lowerCamelCase	)

							return min_value



					def      __a    (		self    :    Optional[int]	):
							lowerCamelCase_  :    List[Any]   	=			self.bottom_root
							while top_root.parent:
									lowerCamelCase_  :    Tuple   	=			top_root.parent
							# preorder
							lowerCamelCase_  :    str   	=			[]
							self.__traversal(lowerCamelCase					,			lowerCamelCase	)
							return heap_pre_order



					def      __a    (		self    :    Dict					,			lowerCamelCase    :    Tuple					,			lowerCamelCase    :    Dict					,			lowerCamelCase    :    List[str]=0	):
							if curr_node:
									preorder.append((curr_node.val, level)	)
									self.__traversal(curr_node.left					,			lowerCamelCase					,			level + 1	)
									self.__traversal(curr_node.right					,			lowerCamelCase					,			level + 1	)
							else:
									preorder.append(('#', level)	)





					def __str__(		self    :    List[Any]	):
							if self.is_empty():
									return ""
							lowerCamelCase_  :    str   	=			self.pre_order()

							return "\n".join(('-' * level + str(lowerCamelCase	)) for value, level in preorder_heap	)


# Unit Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()
