code: 
  
  
  
  """simple docstring"""
  
  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:
  							__A 		=			None
  
  if is_ray_available():
  							import ray  # noqa: E402 # isort:skip
  							from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
  else:
  							__A 		=			None
  							__A 		=			None
  							__A 		=			None
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Optional[Any]   ):
  		if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
  				snake_case		:						List[Any]								=				unittest.skip("test requires Datasets, Faiss, psutil"   )(lowercase_   )
  		return test_case
  @require_distributed_retrieval
  class 			lowerCamelCase__    (  a_			):
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				tempfile.mkdtemp()
  					snake_case		:						int								=				8
  
  					# DPR tok
  					snake_case		:						Any								=				[
  					    '[UNK]',
  					    '[CLS]',
  					    '[SEP]',
  					    '[PAD]',
  					    '[MASK]',
  					    'want',
  					    '##want',
  					    '##ed',
  					    'wa',
  					    'un',
  					    'runn',
  					    '##ing',
  					    ',',
  					    'low',
  					    'lowest',
  					]
  					snake_case		:						List[Any]								=				os.path.join(self.tmpdirname				, "dpr_tokenizer"							)
  					os.makedirs(SCREAMING_SNAKE_CASE				, exist_ok=SCREAMING_SNAKE_CASE							)
  					snake_case		:						Dict								=				os.path.join(SCREAMING_SNAKE_CASE				, 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		:						int								=				[
  					    'l',
  					    'o',
  					    'w',
  					    'e',
  					    'r',
  					    's',
  					    't',
  					    'i',
  					    'd',
  					    'n',
  					    '\u0120',
  					    '\u0120l',
  					    '\u0120n',
  					    '\u0120lo',
  					    '\u0120low',
  					    'er',
  					    '\u0120lowest',
  					    '\u0120newer',
  					    '\u0120wider',
  					    '<unk>',
  					]
  					snake_case		:						Tuple								=				dict(zip(SCREAMING_SNAKE_CASE				, range(len(SCREAMING_SNAKE_CASE							)							)							)							)
  					snake_case		:						str								=				['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
  					snake_case		:						List[Any]								=				{'unk_token': '<unk>'}
  
  					snake_case		:						str								=				os.path.join(self.tmpdirname				, "bart_tokenizer"							)
  					os.makedirs(SCREAMING_SNAKE_CASE				, exist_ok=SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[int]								=				os.path.join(SCREAMING_SNAKE_CASE				, BART_VOCAB_FILES_NAMES["vocab_file"]							)
  					snake_case		:						List[Any]								=				os.path.join(SCREAMING_SNAKE_CASE				, BART_VOCAB_FILES_NAMES["merges_file"]							)
  					with open(self.vocab_file				, "w"				, encoding="utf-8"							) as fp:
  							fp.write(json.dumps(SCREAMING_SNAKE_CASE							) + "\n"							)
  					with open(self.merges_file				, "w"				, encoding="utf-8"							) as fp:
  							fp.write("\n".join(SCREAMING_SNAKE_CASE							)							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				, "dpr_tokenizer"							)							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname				, "bart_tokenizer"							)							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					shutil.rmtree(self.tmpdirname							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Union[str, Any]								=				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 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=12_345							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				self.get_dummy_dataset()
  					snake_case		:						str								=				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		:						Any								=				dataset
  							snake_case		:						List[Any]								=				RagPyTorchDistributedRetriever(
  							    SCREAMING_SNAKE_CASE				, question_encoder_tokenizer=self.get_dpr_tokenizer()				, generator_tokenizer=self.get_bart_tokenizer()				, )
  							if init_retrieval:
  									retriever.init_retrieval(SCREAMING_SNAKE_CASE							)
  					return retriever
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					ray.init(local_mode=SCREAMING_SNAKE_CASE							)
  					snake_case		:						Tuple								=				RagConfig(
  					    retrieval_vector_size=self.retrieval_vector_size				, question_encoder=DPRConfig().to_dict()				, generator=BartConfig().to_dict()				, )
  					snake_case		:						Tuple								=				ray.remote(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Any								=				[remote_cls.remote() for _ in range(1							)]
  					with patch("transformers.models.rag.retrieval_rag.load_dataset"							) as mock_load_dataset:
  							snake_case		:						Any								=				self.get_dummy_dataset()
  							snake_case		:						List[Any]								=				RagRayDistributedRetriever(
  							    SCREAMING_SNAKE_CASE				, question_encoder_tokenizer=self.get_dpr_tokenizer()				, generator_tokenizer=self.get_bart_tokenizer()				, retrieval_workers=SCREAMING_SNAKE_CASE				, )
  							if init_retrieval:
  									retriever.init_retrieval()
  					return retriever
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=12_345							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				self.get_dummy_dataset()
  					snake_case		:						int								=				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		:						Optional[Any]								=				os.path.join(self.tmpdirname				, "dataset"							)
  							snake_case		:						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
  							snake_case		:						Dict								=				RagPyTorchDistributedRetriever(
  							    SCREAMING_SNAKE_CASE				, question_encoder_tokenizer=self.get_dpr_tokenizer()				, generator_tokenizer=self.get_bart_tokenizer()				, )
  					else:
  							snake_case		:						Optional[Any]								=				RagPyTorchDistributedRetriever(
  							    SCREAMING_SNAKE_CASE				, question_encoder_tokenizer=self.get_dpr_tokenizer()				, generator_tokenizer=self.get_bart_tokenizer()				, index=CustomHFIndex(config.retrieval_vector_size				, SCREAMING_SNAKE_CASE							)				, )
  					if init_retrieval:
  							retriever.init_retrieval(SCREAMING_SNAKE_CASE							)
  					return retriever
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					ray.init(local_mode=SCREAMING_SNAKE_CASE							)
  					snake_case		:						List[Any]								=				self.get_dummy_dataset()
  					snake_case		:						int								=				RagConfig(
  					    retrieval_vector_size=self.retrieval_vector_size				, question_encoder=DPRConfig().to_dict()				, generator=BartConfig().to_dict()				, index_name="custom"				, )
  					snake_case		:						Dict								=				ray.remote(SCREAMING_SNAKE_CASE							)
  					snake_case		:						List[Any]								=				[remote_cls.remote() for _ in range(1							)]
  					if from_disk:
  							snake_case		:						Union[str, Any]								=				os.path.join(self.tmpdirname				, "dataset"							)
  							snake_case		:						List[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
  							snake_case		:						Union[str, Any]								=				RagRayDistributedRetriever(
  							    SCREAMING_SNAKE_CASE				, question_encoder_tokenizer=self.get_dpr_tokenizer()				, generator_tokenizer=self.get_bart_tokenizer()				, retrieval_workers=SCREAMING_SNAKE_CASE				, index=CustomHFIndex.load_from_disk(
  							        vector_size=config.retrieval_vector_size				, dataset_path=config.passages_path				, index_path=config.index_path				, )				, )
  					else:
  							snake_case		:						List[str]								=				RagRayDistributedRetriever(
  							    SCREAMING_SNAKE_CASE				, question_encoder_tokenizer=self.get_dpr_tokenizer()				, generator_tokenizer=self.get_bart_tokenizer()				, retrieval_workers=SCREAMING_SNAKE_CASE				, index=CustomHFIndex(config.retrieval_vector_size				, SCREAMING_SNAKE_CASE							)				, )
  					if init_retrieval:
  							retriever.init_retrieval()
  					return retriever
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Any								=				retriever.retrieve(SCREAMING_SNAKE_CASE				, n_docs=SCREAMING_SNAKE_CASE							)
  					self.assertEqual(retrieved_doc_embeds.shape				, (2, n_docs, self.retrieval_vector_size)							)
  					self.assertEqual(len(SCREAMING_SNAKE_CASE							)				, 2							)
  					self.assertEqual(sorted(doc_dicts[0]							)				, ["embeddings", "id", "text", "title"]							)
  					self.assertEqual(len(doc_dicts[0]["id"]							)				, SCREAMING_SNAKE_CASE							)
  					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 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						str								=				1
  					snake_case		:						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=SCREAMING_SNAKE_CASE							)				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Any								=				1
  					snake_case		:						str								=				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=SCREAMING_SNAKE_CASE				, from_disk=SCREAMING_SNAKE_CASE							)				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, )
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Union[str, Any]								=				1
  					snake_case		:						Dict								=				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=SCREAMING_SNAKE_CASE				, from_disk=SCREAMING_SNAKE_CASE							)				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, )
  
  
  			@require_ray
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				1
  					snake_case		:						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=SCREAMING_SNAKE_CASE							)				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  					ray.shutdown()
  
  
  			@require_ray
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						str								=				1
  					snake_case		:						List[Any]								=				np.array(
  					    [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]				, dtype=np.floataa							)
  					with self.assertRaises(SCREAMING_SNAKE_CASE							):
  							self.distributed_retriever_check(
  							    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=SCREAMING_SNAKE_CASE				, from_disk=SCREAMING_SNAKE_CASE							)				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, )
  					ray.shutdown()
  
  
  
  			@require_ray
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				1
  					snake_case		:						List[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_ray_retriever(init_retrieval=SCREAMING_SNAKE_CASE				, from_disk=SCREAMING_SNAKE_CASE							)				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  					ray.shutdown()
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import random
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]   ):
  
  		def is_sorted(lowercase__  :     int   ):
  				for i in range(len(lowercase__   ) - 1   ):
  						if collection[i] > collection[i + 1]:
  								return False
  				return True
  
  		while not is_sorted(lowercase__   ):
  				random.shuffle(lowercase__   )
  		return collection
  
  
  if __name__ == "__main__":
  							__A 		=			input("Enter numbers separated by a comma:\n").strip()
  							__A 		=			[int(item) for item in user_input.split(",")]
  							print(bogo_sort(unsorted))
  
  