


"""simple docstring"""




from __future__ import annotations

import math
import random
from typing import Any
class 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self      ):
				__a           =			[]
				__a           =			0
				__a           =			0

		def 			__UpperCAmelCase       (  self      ):
				return self.head == self.tail

		def 			__UpperCAmelCase       (  self							, _a      ):
				self.data.append(lowercase_      )
				__a           =			self.tail + 1

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.data[self.head]
				__a           =			self.head + 1
				return ret

		def 			__UpperCAmelCase       (  self      ):
				return self.tail - self.head





		def 			__UpperCAmelCase       (  self      ):
				print(self.data      )
				print('''**************'''      )
				print(self.data[self.head : self.tail]      )







class 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self							, _a      ):
				__a           =			data
				__a           =			None
				__a           =			None
				__a           =			1

		def 			__UpperCAmelCase       (  self      ):
				return self.data

		def 			__UpperCAmelCase       (  self      ):
				return self.left

		def 			__UpperCAmelCase       (  self      ):
				return self.right

		def 			__UpperCAmelCase       (  self      ):
				return self.height

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			data

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			node

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			node





		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			height







def        lowercase							(		lowerCAmelCase__ :     Optional[int]			) ->      int:

		if node is None:
				return 0
		return node.get_height()

def        lowercase							(		lowerCAmelCase__ :     List[Any]    ,					lowerCAmelCase__ :     Dict			) ->      int:

		if a > b:
				return a
		return b

def        lowercase							(		lowerCAmelCase__ :     List[str]			) ->      MyNode:
		print('''left rotation node:'''    ,					node.get_data()			)
		__a           =			node.get_left()
		assert ret is not None
		node.set_left(ret.get_right()			)
		ret.set_right(__UpperCAmelCase			)
		__a           =			my_max(get_height(node.get_right()			)    ,					get_height(node.get_left()			)			) + 1
		node.set_height(__UpperCAmelCase			)
		__a           =			my_max(get_height(ret.get_right()			)    ,					get_height(ret.get_left()			)			) + 1
		ret.set_height(__UpperCAmelCase			)
		return ret

def        lowercase							(		lowerCAmelCase__ :     int			) ->      MyNode:
		print('''right rotation node:'''    ,					node.get_data()			)
		__a           =			node.get_right()
		assert ret is not None
		node.set_right(ret.get_left()			)
		ret.set_left(__UpperCAmelCase			)
		__a           =			my_max(get_height(node.get_right()			)    ,					get_height(node.get_left()			)			) + 1
		node.set_height(__UpperCAmelCase			)
		__a           =			my_max(get_height(ret.get_right()			)    ,					get_height(ret.get_left()			)			) + 1
		ret.set_height(__UpperCAmelCase			)
		return ret

def        lowercase							(		lowerCAmelCase__ :     Any			) ->      MyNode:
		__a           =			node.get_left()
		assert left_child is not None
		node.set_left(left_rotation(__UpperCAmelCase			)			)
		return right_rotation(__UpperCAmelCase			)

def        lowercase							(		lowerCAmelCase__ :     List[str]			) ->      MyNode:
		__a           =			node.get_right()
		assert right_child is not None
		node.set_right(right_rotation(__UpperCAmelCase			)			)
		return left_rotation(__UpperCAmelCase			)

def        lowercase							(		lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Tuple			) ->      MyNode | None:

		if node is None:
				return MyNode(__UpperCAmelCase			)
		if data < node.get_data():
				node.set_left(insert_node(node.get_left()    ,					__UpperCAmelCase			)			)
				if (
				    get_height(node.get_left()			) - get_height(node.get_right()			) == 2
				):  # an unbalance detected
						__a           =			node.get_left()
						assert left_child is not None
						if (
						    data < left_child.get_data()
						):  # new node is the left child of the left child
								__a           =			right_rotation(__UpperCAmelCase			)
						else:
								__a           =			lr_rotation(__UpperCAmelCase			)
		else:
				node.set_right(insert_node(node.get_right()    ,					__UpperCAmelCase			)			)
				if get_height(node.get_right()			) - get_height(node.get_left()			) == 2:
						__a           =			node.get_right()
						assert right_child is not None
						if data < right_child.get_data():
								__a           =			rl_rotation(__UpperCAmelCase			)
						else:
								__a           =			left_rotation(__UpperCAmelCase			)
		__a           =			my_max(get_height(node.get_right()			)    ,					get_height(node.get_left()			)			) + 1
		node.set_height(__UpperCAmelCase			)
		return node

def        lowercase							(		lowerCAmelCase__ :     Union[str, Any]			) ->      Any:

		while True:
				__a           =			root.get_right()
				if right_child is None:
						break
				__a           =			right_child
		return root.get_data()

def        lowercase							(		lowerCAmelCase__ :     Optional[int]			) ->      Any:

		while True:
				__a           =			root.get_left()
				if left_child is None:
						break
				__a           =			left_child
		return root.get_data()

def        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Tuple			) ->      MyNode | None:
		__a           =			root.get_left()
		__a           =			root.get_right()
		if root.get_data() == data:
				if left_child is not None and right_child is not None:
						__a           =			get_left_most(__UpperCAmelCase			)
						root.set_data(__UpperCAmelCase			)
						root.set_right(del_node(__UpperCAmelCase    ,					__UpperCAmelCase			)			)
				elif left_child is not None:
						__a           =			left_child
				elif right_child is not None:
						__a           =			right_child
				else:
						return None
		elif root.get_data() > data:
				if left_child is None:
						print('''No such data'''			)
						return root
				else:
						root.set_left(del_node(__UpperCAmelCase    ,					__UpperCAmelCase			)			)
		else:  # root.get_data() < data
				if right_child is None:
						return root
				else:
						root.set_right(del_node(__UpperCAmelCase    ,					__UpperCAmelCase			)			)

		if get_height(__UpperCAmelCase			) - get_height(__UpperCAmelCase			) == 2:
				assert right_child is not None
				if get_height(right_child.get_right()			) > get_height(right_child.get_left()			):
						__a           =			left_rotation(__UpperCAmelCase			)
				else:
						__a           =			rl_rotation(__UpperCAmelCase			)
		elif get_height(__UpperCAmelCase			) - get_height(__UpperCAmelCase			) == -2:
				assert left_child is not None
				if get_height(left_child.get_left()			) > get_height(left_child.get_right()			):
						__a           =			right_rotation(__UpperCAmelCase			)
				else:
						__a           =			lr_rotation(__UpperCAmelCase			)
		__a           =			my_max(get_height(root.get_right()			)    ,					get_height(root.get_left()			)			) + 1
		root.set_height(__UpperCAmelCase			)
		return root
class 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self      ):
				__a           =			None

		def 			__UpperCAmelCase       (  self      ):
				return get_height(self.root      )

		def 			__UpperCAmelCase       (  self							, _a      ):
				print('''insert:''' + str(lowercase_      )      )
				__a           =			insert_node(self.root							, lowercase_      )

		def 			__UpperCAmelCase       (  self							, _a      ):
				print('''delete:''' + str(lowercase_      )      )
				if self.root is None:
						print('''Tree is empty!'''      )
						return
				__a           =			del_node(self.root							, lowercase_      )





		def __str__(  self							, ):  # a level traversale, gives a more intuitive look on the tree
				__a           =			''''''
				__a           =			MyQueue()
				q.push(self.root      )
				__a           =			self.get_height()
				if layer == 0:
						return output
				__a           =			0
				while not q.is_empty():
						__a           =			q.pop()
						__a           =			''' ''' * int(math.pow(2							, layer - 1      )      )
						output += space
						if node is None:
								output += "*"
								q.push(lowercase_      )
								q.push(lowercase_      )
						else:
								output += str(node.get_data()      )
								q.push(node.get_left()      )
								q.push(node.get_right()      )
						output += space
						__a           =			cnt + 1
						for i in range(100      ):
								if cnt == math.pow(2							, lowercase_      ) - 1:
										__a           =			layer - 1
										if layer == 0:
												output += "\n*************************************"
												return output
										output += "\n"
										break
				output += "\n*************************************"
				return output







def        lowercase							(		) ->      None:
		import doctest

		doctest.testmod()


if __name__ == "__main__":
				_test()
				lowercase_			     =  AVLtree()
				lowercase_			     =  list(range(1_0))
				random.shuffle(lst)
				for i in lst:
								t.insert(i)
								print(str(t))
				random.shuffle(lst)
				for i in lst:
								t.del_node(i)
								print(str(t))












'''simple docstring'''

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

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
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_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
							import faiss






@require_faiss
class 						a				(   _lowerCamelCase     ):


				def   A_ (		self    :					str					):
									snake_case_									=				tempfile.mkdtemp()
									snake_case_									=				8

									# DPR tok
									snake_case_									=				[
									    '''[UNK]''',
									    '''[CLS]''',
									    '''[SEP]''',
									    '''[PAD]''',
									    '''[MASK]''',
									    '''want''',
									    '''##want''',
									    '''##ed''',
									    '''wa''',
									    '''un''',
									    '''runn''',
									    '''##ing''',
									    ''',''',
									    '''low''',
									    '''lowest''',
									]
									snake_case_									=				os.path.join(self.tmpdirname       ,				'''dpr_tokenizer'''					)
									os.makedirs(lowercase_       ,				exist_ok=lowercase_					)
									snake_case_									=				os.path.join(lowercase_       ,				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
									snake_case_									=				[
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''\u0120''',
									    '''\u0120l''',
									    '''\u0120n''',
									    '''\u0120lo''',
									    '''\u0120low''',
									    '''er''',
									    '''\u0120lowest''',
									    '''\u0120newer''',
									    '''\u0120wider''',
									    '''<unk>''',
									]
									snake_case_									=				dict(zip(lowercase_       ,				range(len(lowercase_					)					)					)					)
									snake_case_									=				['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
									snake_case_									=				{'''unk_token''': '''<unk>'''}

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


				def   A_ (		self    :					Union[str, Any]					):
									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,				'''dpr_tokenizer'''					)					)


				def   A_ (		self    :					Union[str, Any]					):
									return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,				'''dpr_tokenizer'''					)					)


				def   A_ (		self    :					int					):
									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,				'''bart_tokenizer'''					)					)


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


				def   A_ (		self    :					str					):
									snake_case_									=				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    :					str					):
									snake_case_									=				self.get_dummy_dataset()
									snake_case_									=				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:
														snake_case_									=				dataset
														snake_case_									=				RagRetriever(
														    lowercase_       ,				question_encoder_tokenizer=self.get_dpr_tokenizer()       ,				generator_tokenizer=self.get_bart_tokenizer()       ,				)
									return retriever


				def   A_ (		self    :					str       ,				lowercase_    :					bool					):
									snake_case_									=				self.get_dummy_dataset()
									snake_case_									=				RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size       ,				question_encoder=DPRConfig().to_dict()       ,				generator=BartConfig().to_dict()       ,				index_name='''custom'''       ,				)
									if from_disk:
														snake_case_									=				os.path.join(self.tmpdirname       ,				'''dataset'''					)
														snake_case_									=				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
														snake_case_									=				RagRetriever(
														    lowercase_       ,				question_encoder_tokenizer=self.get_dpr_tokenizer()       ,				generator_tokenizer=self.get_bart_tokenizer()       ,				)
									else:
														snake_case_									=				RagRetriever(
														    lowercase_       ,				question_encoder_tokenizer=self.get_dpr_tokenizer()       ,				generator_tokenizer=self.get_bart_tokenizer()       ,				index=CustomHFIndex(config.retrieval_vector_size       ,				lowercase_					)       ,				)
									return retriever


				def   A_ (		self    :					Tuple					):
									snake_case_									=				Dataset.from_dict(
									    {
									        '''id''': ['''0''', '''1'''],
									        '''text''': ['''foo''', '''bar'''],
									        '''title''': ['''Foo''', '''Bar'''],
									        '''embeddings''': [np.ones(self.retrieval_vector_size + 1					), 2 * np.ones(self.retrieval_vector_size + 1					)],
									    }					)
									dataset.add_faiss_index('''embeddings'''       ,				string_factory='''Flat'''       ,				metric_type=faiss.METRIC_INNER_PRODUCT					)

									snake_case_									=				os.path.join(self.tmpdirname       ,				'''hf_bert_base.hnswSQ8_correct_phi_128.c_index'''					)
									dataset.save_faiss_index('''embeddings'''       ,				index_file_name + '''.index.dpr'''					)
									pickle.dump(dataset['''id''']       ,				open(index_file_name + '''.index_meta.dpr'''       ,				'''wb'''					)					)

									snake_case_									=				os.path.join(self.tmpdirname       ,				'''psgs_w100.tsv.pkl'''					)
									snake_case_									=				{sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
									pickle.dump(lowercase_       ,				open(lowercase_       ,				'''wb'''					)					)

									snake_case_									=				RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size       ,				question_encoder=DPRConfig().to_dict()       ,				generator=BartConfig().to_dict()       ,				index_name='''legacy'''       ,				index_path=self.tmpdirname       ,				)
									snake_case_									=				RagRetriever(
									    lowercase_       ,				question_encoder_tokenizer=self.get_dpr_tokenizer()       ,				generator_tokenizer=self.get_bart_tokenizer()					)
									return retriever


				def   A_ (		self    :					Optional[Any]					):
									snake_case_									=				1
									snake_case_									=				self.get_dummy_canonical_hf_index_retriever()
									snake_case_									=				np.array(
									    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]       ,				dtype=np.floataa					)
									snake_case_			,snake_case_			,snake_case_									=				retriever.retrieve(lowercase_       ,				n_docs=lowercase_					)
									self.assertEqual(retrieved_doc_embeds.shape       ,				(2, n_docs, self.retrieval_vector_size)					)
									self.assertEqual(len(lowercase_					)       ,				2					)
									self.assertEqual(sorted(doc_dicts[0]					)       ,				['''embeddings''', '''id''', '''text''', '''title''']					)
									self.assertEqual(len(doc_dicts[0]['''id''']					)       ,				lowercase_					)
									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    :					str					):
									snake_case_									=				self.get_dummy_canonical_hf_index_retriever()
									with tempfile.TemporaryDirectory() as tmp_dirname:
														with patch('''transformers.models.rag.retrieval_rag.load_dataset'''					) as mock_load_dataset:
																			snake_case_									=				self.get_dummy_dataset()
																			retriever.save_pretrained(lowercase_					)
																			snake_case_									=				RagRetriever.from_pretrained(lowercase_					)
																			self.assertIsInstance(lowercase_       ,				lowercase_					)
																			snake_case_									=				np.array(
																			    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]       ,				dtype=np.floataa					)
																			snake_case_									=				retriever.retrieve(lowercase_       ,				n_docs=1					)
																			self.assertTrue(out is not None					)


				def   A_ (		self    :					int					):
									snake_case_									=				1
									snake_case_									=				self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_					)
									snake_case_									=				np.array(
									    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]       ,				dtype=np.floataa					)
									snake_case_			,snake_case_			,snake_case_									=				retriever.retrieve(lowercase_       ,				n_docs=lowercase_					)
									self.assertEqual(retrieved_doc_embeds.shape       ,				(2, n_docs, self.retrieval_vector_size)					)
									self.assertEqual(len(lowercase_					)       ,				2					)
									self.assertEqual(sorted(doc_dicts[0]					)       ,				['''embeddings''', '''id''', '''text''', '''title''']					)
									self.assertEqual(len(doc_dicts[0]['''id''']					)       ,				lowercase_					)
									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					):
									snake_case_									=				self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_					)
									with tempfile.TemporaryDirectory() as tmp_dirname:
														retriever.save_pretrained(lowercase_					)
														snake_case_									=				RagRetriever.from_pretrained(lowercase_					)
														self.assertIsInstance(lowercase_       ,				lowercase_					)
														snake_case_									=				np.array(
														    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]       ,				dtype=np.floataa					)
														snake_case_									=				retriever.retrieve(lowercase_       ,				n_docs=1					)
														self.assertTrue(out is not None					)


				def   A_ (		self    :					str					):
									snake_case_									=				1
									snake_case_									=				self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_					)
									snake_case_									=				np.array(
									    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]       ,				dtype=np.floataa					)
									snake_case_			,snake_case_			,snake_case_									=				retriever.retrieve(lowercase_       ,				n_docs=lowercase_					)
									self.assertEqual(retrieved_doc_embeds.shape       ,				(2, n_docs, self.retrieval_vector_size)					)
									self.assertEqual(len(lowercase_					)       ,				2					)
									self.assertEqual(sorted(doc_dicts[0]					)       ,				['''embeddings''', '''id''', '''text''', '''title''']					)
									self.assertEqual(len(doc_dicts[0]['''id''']					)       ,				lowercase_					)
									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    :					Any					):
									snake_case_									=				self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_					)
									with tempfile.TemporaryDirectory() as tmp_dirname:
														retriever.save_pretrained(lowercase_					)
														snake_case_									=				RagRetriever.from_pretrained(lowercase_					)
														self.assertIsInstance(lowercase_       ,				lowercase_					)
														snake_case_									=				np.array(
														    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]       ,				dtype=np.floataa					)
														snake_case_									=				retriever.retrieve(lowercase_       ,				n_docs=1					)
														self.assertTrue(out is not None					)


				def   A_ (		self    :					Any					):
									snake_case_									=				1
									snake_case_									=				self.get_dummy_legacy_index_retriever()
									snake_case_									=				np.array(
									    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]       ,				dtype=np.floataa					)
									snake_case_			,snake_case_			,snake_case_									=				retriever.retrieve(lowercase_       ,				n_docs=lowercase_					)
									self.assertEqual(retrieved_doc_embeds.shape       ,				(2, n_docs, self.retrieval_vector_size)					)
									self.assertEqual(len(lowercase_					)       ,				2					)
									self.assertEqual(sorted(doc_dicts[0]					)       ,				['''text''', '''title''']					)
									self.assertEqual(len(doc_dicts[0]['''text''']					)       ,				lowercase_					)
									self.assertEqual(doc_dicts[0]['''text'''][0]       ,				'''bar'''					)  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]['''text'''][0]       ,				'''foo'''					)  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()       ,				[[1], [0]]					)


				def   A_ (		self    :					int					):
									snake_case_									=				self.get_dummy_legacy_index_retriever()
									with tempfile.TemporaryDirectory() as tmp_dirname:
														retriever.save_pretrained(lowercase_					)
														snake_case_									=				RagRetriever.from_pretrained(lowercase_					)
														self.assertIsInstance(lowercase_       ,				lowercase_					)
														snake_case_									=				np.array(
														    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]       ,				dtype=np.floataa					)
														snake_case_									=				retriever.retrieve(lowercase_       ,				n_docs=1					)
														self.assertTrue(out is not None					)


				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def   A_ (		self    :					List[str]					):
									import torch

									snake_case_									=				1
									snake_case_									=				self.get_dummy_canonical_hf_index_retriever()
									snake_case_									=				[[5, 7], [10, 11]]
									snake_case_									=				np.array(
									    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]       ,				dtype=np.floataa					)
									snake_case_									=				retriever(lowercase_       ,				lowercase_       ,				prefix=retriever.config.generator.prefix       ,				n_docs=lowercase_					)
									snake_case_			,snake_case_			,snake_case_									=				(
									    out['''context_input_ids'''],
									    out['''context_attention_mask'''],
									    out['''retrieved_doc_embeds'''],
									)
									self.assertEqual(retrieved_doc_embeds.shape       ,				(2, n_docs, self.retrieval_vector_size)					)
									self.assertIsInstance(lowercase_       ,				lowercase_					)
									self.assertIsInstance(lowercase_       ,				lowercase_					)
									self.assertIsInstance(lowercase_       ,				np.ndarray					)

									snake_case_									=				retriever(
									    lowercase_       ,				lowercase_       ,				prefix=retriever.config.generator.prefix       ,				n_docs=lowercase_       ,				return_tensors='''pt'''       ,				)
									snake_case_			,snake_case_			,snake_case_			,snake_case_									=				(  # noqa: F841
									    out['''context_input_ids'''],
									    out['''context_attention_mask'''],
									    out['''retrieved_doc_embeds'''],
									    out['''doc_ids'''],
									)
									self.assertEqual(retrieved_doc_embeds.shape       ,				(2, n_docs, self.retrieval_vector_size)					)
									self.assertIsInstance(lowercase_       ,				torch.Tensor					)
									self.assertIsInstance(lowercase_       ,				torch.Tensor					)
									self.assertIsInstance(lowercase_       ,				torch.Tensor					)


				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def   A_ (		self    :					Tuple					):
									snake_case_									=				self.get_dpr_ctx_encoder_tokenizer()
									snake_case_									=				1
									snake_case_									=				self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_					)
									retriever.set_ctx_encoder_tokenizer(lowercase_					)

									snake_case_									=				[[5, 7], [10, 11]]
									snake_case_									=				np.array(
									    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]       ,				dtype=np.floataa					)
									snake_case_									=				retriever(lowercase_       ,				lowercase_       ,				prefix=retriever.config.generator.prefix       ,				n_docs=lowercase_					)

									self.assertEqual(
									    len(lowercase_					)       ,				6					)  # check whether the retriever output consist of 6 attributes including tokenized docs
									self.assertEqual(
									    all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''')					)       ,				lowercase_					)  # check for doc token related keys in dictionary.

