
'''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:
					__SCREAMING_SNAKE_CASE		:  List[Any]        =      None

if is_ray_available():
					import ray  # noqa: E402 # isort:skip
					from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
					__SCREAMING_SNAKE_CASE		:  int        =      None
					__SCREAMING_SNAKE_CASE		:  Tuple        =      None
					__SCREAMING_SNAKE_CASE		:  Tuple        =      None

def 				_snake_case (		lowercase						)       ->       Optional[int]:
				if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
								__a       :     List[str]								=	unittest.skip("""test requires Datasets, Faiss, psutil"""						)(lowercase						)
				return test_case
@require_distributed_retrieval
class  SCREAMING_SNAKE_CASE__       (  __UpperCamelCase   ):
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     int								=	tempfile.mkdtemp()
					__a       :     Any								=	8

					# DPR tok
					__a       :     Optional[int]								=	[
					    """[UNK]""",
					    """[CLS]""",
					    """[SEP]""",
					    """[PAD]""",
					    """[MASK]""",
					    """want""",
					    """##want""",
					    """##ed""",
					    """wa""",
					    """un""",
					    """runn""",
					    """##ing""",
					    """,""",
					    """low""",
					    """lowest""",
					]
					__a       :     Optional[int]								=	os.path.join(self.tmpdirname  ,    """dpr_tokenizer"""			)
					os.makedirs(__UpperCamelCase  ,    exist_ok=__UpperCamelCase			)
					__a       :     str								=	os.path.join(__UpperCamelCase  ,    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
					__a       :     Optional[int]								=	[
					    """l""",
					    """o""",
					    """w""",
					    """e""",
					    """r""",
					    """s""",
					    """t""",
					    """i""",
					    """d""",
					    """n""",
					    """\u0120""",
					    """\u0120l""",
					    """\u0120n""",
					    """\u0120lo""",
					    """\u0120low""",
					    """er""",
					    """\u0120lowest""",
					    """\u0120newer""",
					    """\u0120wider""",
					    """<unk>""",
					]
					__a       :     int								=	dict(zip(__UpperCamelCase  ,    range(len(__UpperCamelCase			)			)			)			)
					__a       :     Union[str, Any]								=	["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
					__a       :     str								=	{"""unk_token""": """<unk>"""}

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






					__a       :     str								=	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  ,    __UpperCamelCase  ,    __UpperCamelCase=1_2345			):



					'''simple docstring'''






					__a       :     Any								=	self.get_dummy_dataset()
					__a       :     Union[str, Any]								=	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:
									__a       :     List[Any]								=	dataset
									__a       :     Dict								=	RagPyTorchDistributedRetriever(
									    __UpperCamelCase  ,    question_encoder_tokenizer=self.get_dpr_tokenizer()  ,    generator_tokenizer=self.get_bart_tokenizer()  ,    )
									if init_retrieval:
													retriever.init_retrieval(__UpperCamelCase			)
					return retriever
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					ray.init(local_mode=__UpperCamelCase			)
					__a       :     Any								=	RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size  ,    question_encoder=DPRConfig().to_dict()  ,    generator=BartConfig().to_dict()  ,    )
					__a       :     Optional[int]								=	ray.remote(__UpperCamelCase			)
					__a       :     Optional[Any]								=	[remote_cls.remote() for _ in range(1			)]
					with patch("""transformers.models.rag.retrieval_rag.load_dataset"""			) as mock_load_dataset:
									__a       :     Optional[int]								=	self.get_dummy_dataset()
									__a       :     int								=	RagRayDistributedRetriever(
									    __UpperCamelCase  ,    question_encoder_tokenizer=self.get_dpr_tokenizer()  ,    generator_tokenizer=self.get_bart_tokenizer()  ,    retrieval_workers=__UpperCamelCase  ,    )
									if init_retrieval:
													retriever.init_retrieval()
					return retriever
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase=1_2345			):



					'''simple docstring'''






					__a       :     Tuple								=	self.get_dummy_dataset()
					__a       :     List[str]								=	RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size  ,    question_encoder=DPRConfig().to_dict()  ,    generator=BartConfig().to_dict()  ,    index_name="""custom"""  ,    )
					if from_disk:
									__a       :     Any								=	os.path.join(self.tmpdirname  ,    """dataset"""			)
									__a       :     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
									__a       :     Tuple								=	RagPyTorchDistributedRetriever(
									    __UpperCamelCase  ,    question_encoder_tokenizer=self.get_dpr_tokenizer()  ,    generator_tokenizer=self.get_bart_tokenizer()  ,    )
					else:
									__a       :     Dict								=	RagPyTorchDistributedRetriever(
									    __UpperCamelCase  ,    question_encoder_tokenizer=self.get_dpr_tokenizer()  ,    generator_tokenizer=self.get_bart_tokenizer()  ,    index=CustomHFIndex(config.retrieval_vector_size  ,    __UpperCamelCase			)  ,    )
					if init_retrieval:
									retriever.init_retrieval(__UpperCamelCase			)
					return retriever
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase			):



					'''simple docstring'''






					ray.init(local_mode=__UpperCamelCase			)
					__a       :     Union[str, Any]								=	self.get_dummy_dataset()
					__a       :     Optional[Any]								=	RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size  ,    question_encoder=DPRConfig().to_dict()  ,    generator=BartConfig().to_dict()  ,    index_name="""custom"""  ,    )
					__a       :     List[str]								=	ray.remote(__UpperCamelCase			)
					__a       :     Tuple								=	[remote_cls.remote() for _ in range(1			)]
					if from_disk:
									__a       :     int								=	os.path.join(self.tmpdirname  ,    """dataset"""			)
									__a       :     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
									__a       :     Tuple								=	RagRayDistributedRetriever(
									    __UpperCamelCase  ,    question_encoder_tokenizer=self.get_dpr_tokenizer()  ,    generator_tokenizer=self.get_bart_tokenizer()  ,    retrieval_workers=__UpperCamelCase  ,    index=CustomHFIndex.load_from_disk(
									        vector_size=config.retrieval_vector_size  ,    dataset_path=config.passages_path  ,    index_path=config.index_path  ,    )  ,    )
					else:
									__a       :     Optional[int]								=	RagRayDistributedRetriever(
									    __UpperCamelCase  ,    question_encoder_tokenizer=self.get_dpr_tokenizer()  ,    generator_tokenizer=self.get_bart_tokenizer()  ,    retrieval_workers=__UpperCamelCase  ,    index=CustomHFIndex(config.retrieval_vector_size  ,    __UpperCamelCase			)  ,    )
					if init_retrieval:
									retriever.init_retrieval()
					return retriever
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a							,	__a							,	__a       :     List[Any]								=	retriever.retrieve(__UpperCamelCase  ,    n_docs=__UpperCamelCase			)
					self.assertEqual(retrieved_doc_embeds.shape  ,    (2, n_docs, self.retrieval_vector_size)			)
					self.assertEqual(len(__UpperCamelCase			)  ,    2			)
					self.assertEqual(sorted(doc_dicts[0]			)  ,    ["""embeddings""", """id""", """text""", """title"""]			)
					self.assertEqual(len(doc_dicts[0]["""id"""]			)  ,    __UpperCamelCase			)
					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'''






					__a       :     int								=	1
					__a       :     Optional[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=__UpperCamelCase			)  ,    __UpperCamelCase  ,    __UpperCamelCase			)
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Tuple								=	1
					__a       :     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=__UpperCamelCase  ,    from_disk=__UpperCamelCase			)  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    )
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Any								=	1
					__a       :     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=__UpperCamelCase  ,    from_disk=__UpperCamelCase			)  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    )
	@require_ray
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Optional[int]								=	1
					__a       :     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_ray_distributed_retriever(init_retrieval=__UpperCamelCase			)  ,    __UpperCamelCase  ,    __UpperCamelCase			)
					ray.shutdown()
	@require_ray
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     List[str]								=	1
					__a       :     str								=	np.array(
					    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]  ,    dtype=np.floataa			)
					with self.assertRaises(__UpperCamelCase			):
									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__UpperCamelCase  ,    from_disk=__UpperCamelCase			)  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    )
					ray.shutdown()

	@require_ray
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     int								=	1
					__a       :     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=__UpperCamelCase  ,    from_disk=__UpperCamelCase			)  ,    __UpperCamelCase  ,    __UpperCamelCase			)
					ray.shutdown()

'''simple docstring'''







def 				_snake_case (		lowercase = 1_0_0_0						)       ->       int:
				__a       :     Any								=	[]
				__a       :     List[Any]								=	[]
				__a       :     Any								=	3
				__a       :     Optional[Any]								=	5
				__a       :     Optional[Any]								=	1
				while True:
								__a       :     List[str]								=	z * temp
								if result < n:
												zmulti.append(lowercase						)
												temp += 1
								else:
												__a       :     Any								=	1
												break
				while True:
								__a       :     Optional[int]								=	x * temp
								if result < n:
												xmulti.append(lowercase						)
												temp += 1
								else:
												break
				__a       :     Any								=	list(set(xmulti + zmulti						)						)
				return sum(lowercase						)


if __name__ == "__main__":
					print(f'''{solution() = }''')