




"""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 __lowercase(      lowercase__				):




					'''simple docstring'''


					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Union[str, Any]	       =				tempfile.mkdtemp()
								__lowerCamelCase					:	Union[str, Any]	       =				8

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

								__lowerCamelCase					:	List[str]	       =				os.path.join(self.tmpdirname						,	'bart_tokenizer'     )
								os.makedirs(__a						,	exist_ok=__a     )
								__lowerCamelCase					:	Dict	       =				os.path.join(__a						,	BART_VOCAB_FILES_NAMES['vocab_file']     )
								__lowerCamelCase					:	Optional[Any]	       =				os.path.join(__a						,	BART_VOCAB_FILES_NAMES['merges_file']     )
								with open(self.vocab_file						,	'w'						,	encoding='utf-8'     ) as fp:
											fp.write(json.dumps(__a     ) + '\n'     )
								with open(self.merges_file						,	'w'						,	encoding='utf-8'     ) as fp:
											fp.write('\n'.join(__a     )     )
					def     snake_case_				(    self     ):
								return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,	'dpr_tokenizer'     )     )
					def     snake_case_				(    self     ):
								return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,	'dpr_tokenizer'     )     )
					def     snake_case_				(    self     ):
								return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname						,	'bart_tokenizer'     )     )
					def     snake_case_				(    self     ):
								shutil.rmtree(self.tmpdirname     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Dict	       =				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     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				self.get_dummy_dataset()
								__lowerCamelCase					:	Tuple	       =				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					:	Any	       =				dataset
											__lowerCamelCase					:	Dict	       =				RagRetriever(
											    __a						,	question_encoder_tokenizer=self.get_dpr_tokenizer()						,	generator_tokenizer=self.get_bart_tokenizer()						,	)
								return retriever
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	Union[str, Any]	       =				self.get_dummy_dataset()
								__lowerCamelCase					:	Optional[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					:	List[Any]	       =				os.path.join(self.tmpdirname						,	'dataset'     )
											__lowerCamelCase					:	List[Any]	       =				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					:	Dict	       =				RagRetriever(
											    __a						,	question_encoder_tokenizer=self.get_dpr_tokenizer()						,	generator_tokenizer=self.get_bart_tokenizer()						,	)
								else:
											__lowerCamelCase					:	List[str]	       =				RagRetriever(
											    __a						,	question_encoder_tokenizer=self.get_dpr_tokenizer()						,	generator_tokenizer=self.get_bart_tokenizer()						,	index=CustomHFIndex(config.retrieval_vector_size						,	__a     )						,	)
								return retriever
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				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     )

								__lowerCamelCase					:	str	       =				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'     )     )

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

								__lowerCamelCase					:	Tuple	       =				RagConfig(
								    retrieval_vector_size=self.retrieval_vector_size						,	question_encoder=DPRConfig().to_dict()						,	generator=BartConfig().to_dict()						,	index_name='legacy'						,	index_path=self.tmpdirname						,	)
								__lowerCamelCase					:	Tuple	       =				RagRetriever(
								    __a						,	question_encoder_tokenizer=self.get_dpr_tokenizer()						,	generator_tokenizer=self.get_bart_tokenizer()     )
								return retriever
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				1
								__lowerCamelCase					:	Any	       =				self.get_dummy_canonical_hf_index_retriever()
								__lowerCamelCase					:	List[Any]	       =				np.array(
								    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]						,	dtype=np.floataa     )
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	List[Any]	       =				retriever.retrieve(__a						,	n_docs=__a     )
								self.assertEqual(retrieved_doc_embeds.shape						,	(2, n_docs, self.retrieval_vector_size)     )
								self.assertEqual(len(__a     )						,	2     )
								self.assertEqual(sorted(doc_dicts[0]     )						,	['embeddings', 'id', 'text', 'title']     )
								self.assertEqual(len(doc_dicts[0]['id']     )						,	__a     )
								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     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				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:
														__lowerCamelCase					:	Union[str, Any]	       =				self.get_dummy_dataset()
														retriever.save_pretrained(__a     )
														__lowerCamelCase					:	Optional[Any]	       =				RagRetriever.from_pretrained(__a     )
														self.assertIsInstance(__a						,	__a     )
														__lowerCamelCase					:	Dict	       =				np.array(
														    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]						,	dtype=np.floataa     )
														__lowerCamelCase					:	str	       =				retriever.retrieve(__a						,	n_docs=1     )
														self.assertTrue(out is not None     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[int]	       =				1
								__lowerCamelCase					:	Tuple	       =				self.get_dummy_custom_hf_index_retriever(from_disk=__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				np.array(
								    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]						,	dtype=np.floataa     )
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				retriever.retrieve(__a						,	n_docs=__a     )
								self.assertEqual(retrieved_doc_embeds.shape						,	(2, n_docs, self.retrieval_vector_size)     )
								self.assertEqual(len(__a     )						,	2     )
								self.assertEqual(sorted(doc_dicts[0]     )						,	['embeddings', 'id', 'text', 'title']     )
								self.assertEqual(len(doc_dicts[0]['id']     )						,	__a     )
								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     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[int]	       =				self.get_dummy_custom_hf_index_retriever(from_disk=__a     )
								with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(__a     )
											__lowerCamelCase					:	Tuple	       =				RagRetriever.from_pretrained(__a     )
											self.assertIsInstance(__a						,	__a     )
											__lowerCamelCase					:	int	       =				np.array(
											    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]						,	dtype=np.floataa     )
											__lowerCamelCase					:	int	       =				retriever.retrieve(__a						,	n_docs=1     )
											self.assertTrue(out is not None     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				1
								__lowerCamelCase					:	Union[str, Any]	       =				self.get_dummy_custom_hf_index_retriever(from_disk=__a     )
								__lowerCamelCase					:	str	       =				np.array(
								    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]						,	dtype=np.floataa     )
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	List[str]	       =				retriever.retrieve(__a						,	n_docs=__a     )
								self.assertEqual(retrieved_doc_embeds.shape						,	(2, n_docs, self.retrieval_vector_size)     )
								self.assertEqual(len(__a     )						,	2     )
								self.assertEqual(sorted(doc_dicts[0]     )						,	['embeddings', 'id', 'text', 'title']     )
								self.assertEqual(len(doc_dicts[0]['id']     )						,	__a     )
								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     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				self.get_dummy_custom_hf_index_retriever(from_disk=__a     )
								with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(__a     )
											__lowerCamelCase					:	Union[str, Any]	       =				RagRetriever.from_pretrained(__a     )
											self.assertIsInstance(__a						,	__a     )
											__lowerCamelCase					:	List[Any]	       =				np.array(
											    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]						,	dtype=np.floataa     )
											__lowerCamelCase					:	Tuple	       =				retriever.retrieve(__a						,	n_docs=1     )
											self.assertTrue(out is not None     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				1
								__lowerCamelCase					:	Optional[int]	       =				self.get_dummy_legacy_index_retriever()
								__lowerCamelCase					:	List[Any]	       =				np.array(
								    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]						,	dtype=np.floataa     )
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	List[str]	       =				retriever.retrieve(__a						,	n_docs=__a     )
								self.assertEqual(retrieved_doc_embeds.shape						,	(2, n_docs, self.retrieval_vector_size)     )
								self.assertEqual(len(__a     )						,	2     )
								self.assertEqual(sorted(doc_dicts[0]     )						,	['text', 'title']     )
								self.assertEqual(len(doc_dicts[0]['text']     )						,	__a     )
								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     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.get_dummy_legacy_index_retriever()
								with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(__a     )
											__lowerCamelCase					:	int	       =				RagRetriever.from_pretrained(__a     )
											self.assertIsInstance(__a						,	__a     )
											__lowerCamelCase					:	Optional[Any]	       =				np.array(
											    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]						,	dtype=np.floataa     )
											__lowerCamelCase					:	Union[str, Any]	       =				retriever.retrieve(__a						,	n_docs=1     )
											self.assertTrue(out is not None     )
					@require_torch
					@require_tokenizers
					@require_sentencepiece
					def     snake_case_				(    self     ):
								import torch

								__lowerCamelCase					:	List[Any]	       =				1
								__lowerCamelCase					:	List[Any]	       =				self.get_dummy_canonical_hf_index_retriever()
								__lowerCamelCase					:	Dict	       =				[[5, 7], [10, 11]]
								__lowerCamelCase					:	Optional[int]	       =				np.array(
								    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]						,	dtype=np.floataa     )
								__lowerCamelCase					:	Union[str, Any]	       =				retriever(__a						,	__a						,	prefix=retriever.config.generator.prefix						,	n_docs=__a     )
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	List[str]	       =				(
								    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(__a						,	__a     )
								self.assertIsInstance(__a						,	__a     )
								self.assertIsInstance(__a						,	np.ndarray     )

								__lowerCamelCase					:	List[str]	       =				retriever(
								    __a						,	__a						,	prefix=retriever.config.generator.prefix						,	n_docs=__a						,	return_tensors='pt'						,	)
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Optional[int]	       =				(  # 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(__a						,	torch.Tensor     )
								self.assertIsInstance(__a						,	torch.Tensor     )
								self.assertIsInstance(__a						,	torch.Tensor     )





					@require_torch
					@require_tokenizers
					@require_sentencepiece
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.get_dpr_ctx_encoder_tokenizer()
								__lowerCamelCase					:	Any	       =				1
								__lowerCamelCase					:	str	       =				self.get_dummy_custom_hf_index_retriever(from_disk=__a     )
								retriever.set_ctx_encoder_tokenizer(__a     )

								__lowerCamelCase					:	int	       =				[[5, 7], [10, 11]]
								__lowerCamelCase					:	int	       =				np.array(
								    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]						,	dtype=np.floataa     )
								__lowerCamelCase					:	List[Any]	       =				retriever(__a						,	__a						,	prefix=retriever.config.generator.prefix						,	n_docs=__a     )

								self.assertEqual(
								    len(__a     )						,	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')     )						,	__a     )  # check for doc token related keys in dictionary.









"""simple docstring"""
def 						UpperCAmelCase				(			A__:    Dict     )  ->     Optional[int]:
			stooge(A__							,				0							,				len(A__     ) - 1     )
			return arr





def 						UpperCAmelCase				(			A__:    Dict							,				A__:    List[str]							,				A__:    Optional[int]     )  ->     Any:
			if i >= h:
						return

			# If first element is smaller than the last then swap them
			if arr[i] > arr[h]:
						__lowerCamelCase       ,      __lowerCamelCase					:	Dict	       =				arr[h], arr[i]

			# If there are more than 2 elements in the array
			if h - i + 1 > 2:
						__lowerCamelCase					:	str	       =				(int)((h - i + 1) / 3     )

						# Recursively sort first 2/3 elements
						stooge(A__							,				A__							,				(h - t)     )

						# Recursively sort last 2/3 elements
						stooge(A__							,				i + t							,				(A__)     )

						# Recursively sort first 2/3 elements
						stooge(A__							,				A__							,				(h - t)     )


if __name__ == "__main__":
					a_				:    Tuple										=     input('''Enter numbers separated by a comma:\n''').strip()
					a_				:    Optional[Any]										=     [int(item) for item in user_input.split(''',''')]
					print(stooge_sort(unsorted))



