2020-09-24 01:15:36 -04:00
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#
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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from typing import overload
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from typing import List, NamedTuple, Optional, Tuple, TypeVar
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import array
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from numpy import float64, int64, ndarray # type: ignore[import]
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from py4j.java_gateway import JavaObject # type: ignore[import]
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from pyspark.mllib._typing import VectorLike
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from pyspark.context import SparkContext
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from pyspark.rdd import RDD
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from pyspark.mllib.common import JavaModelWrapper
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from pyspark.mllib.stat.distribution import MultivariateGaussian
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from pyspark.mllib.util import Saveable, Loader, JavaLoader, JavaSaveable
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from pyspark.streaming.dstream import DStream
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T = TypeVar("T")
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class BisectingKMeansModel(JavaModelWrapper):
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centers: List[ndarray]
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def __init__(self, java_model: JavaObject) -> None: ...
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@property
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def clusterCenters(self) -> List[ndarray]: ...
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@property
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def k(self) -> int: ...
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@overload
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def predict(self, x: VectorLike) -> int: ...
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@overload
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def predict(self, x: RDD[VectorLike]) -> RDD[int]: ...
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@overload
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def computeCost(self, x: VectorLike) -> float: ...
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@overload
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def computeCost(self, x: RDD[VectorLike]) -> float: ...
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class BisectingKMeans:
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@classmethod
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def train(
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self,
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rdd: RDD[VectorLike],
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k: int = ...,
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maxIterations: int = ...,
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minDivisibleClusterSize: float = ...,
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seed: int = ...,
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) -> BisectingKMeansModel: ...
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class KMeansModel(Saveable, Loader[KMeansModel]):
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centers: List[ndarray]
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2020-11-24 19:27:04 -05:00
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def __init__(self, centers: List[VectorLike]) -> None: ...
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2020-09-24 01:15:36 -04:00
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@property
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def clusterCenters(self) -> List[ndarray]: ...
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@property
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def k(self) -> int: ...
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@overload
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def predict(self, x: VectorLike) -> int: ...
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@overload
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def predict(self, x: RDD[VectorLike]) -> RDD[int]: ...
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def computeCost(self, rdd: RDD[VectorLike]) -> float: ...
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def save(self, sc: SparkContext, path: str) -> None: ...
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@classmethod
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def load(cls, sc: SparkContext, path: str) -> KMeansModel: ...
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class KMeans:
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@classmethod
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def train(
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cls,
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rdd: RDD[VectorLike],
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k: int,
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maxIterations: int = ...,
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initializationMode: str = ...,
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seed: Optional[int] = ...,
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initializationSteps: int = ...,
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epsilon: float = ...,
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initialModel: Optional[KMeansModel] = ...,
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) -> KMeansModel: ...
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class GaussianMixtureModel(
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JavaModelWrapper, JavaSaveable, JavaLoader[GaussianMixtureModel]
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):
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@property
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def weights(self) -> ndarray: ...
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@property
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def gaussians(self) -> List[MultivariateGaussian]: ...
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@property
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def k(self) -> int: ...
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@overload
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def predict(self, x: VectorLike) -> int64: ...
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@overload
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def predict(self, x: RDD[VectorLike]) -> RDD[int]: ...
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@overload
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def predictSoft(self, x: VectorLike) -> ndarray: ...
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@overload
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def predictSoft(self, x: RDD[VectorLike]) -> RDD[array.array]: ...
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@classmethod
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def load(cls, sc: SparkContext, path: str) -> GaussianMixtureModel: ...
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class GaussianMixture:
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@classmethod
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def train(
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cls,
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rdd: RDD[VectorLike],
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k: int,
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convergenceTol: float = ...,
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maxIterations: int = ...,
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seed: Optional[int] = ...,
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initialModel: Optional[GaussianMixtureModel] = ...,
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) -> GaussianMixtureModel: ...
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class PowerIterationClusteringModel(
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JavaModelWrapper, JavaSaveable, JavaLoader[PowerIterationClusteringModel]
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):
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@property
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def k(self) -> int: ...
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def assignments(self) -> RDD[PowerIterationClustering.Assignment]: ...
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@classmethod
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def load(cls, sc: SparkContext, path: str) -> PowerIterationClusteringModel: ...
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class PowerIterationClustering:
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@classmethod
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def train(
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cls,
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rdd: RDD[Tuple[int, int, float]],
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k: int,
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maxIterations: int = ...,
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initMode: str = ...,
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) -> PowerIterationClusteringModel: ...
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class Assignment(NamedTuple("Assignment", [("id", int), ("cluster", int)])): ...
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class StreamingKMeansModel(KMeansModel):
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2020-11-24 19:27:04 -05:00
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def __init__(
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self, clusterCenters: List[VectorLike], clusterWeights: VectorLike
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) -> None: ...
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2020-09-24 01:15:36 -04:00
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@property
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def clusterWeights(self) -> List[float64]: ...
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centers: ndarray
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def update(
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self, data: RDD[VectorLike], decayFactor: float, timeUnit: str
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) -> StreamingKMeansModel: ...
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class StreamingKMeans:
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def __init__(
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self, k: int = ..., decayFactor: float = ..., timeUnit: str = ...
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) -> None: ...
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def latestModel(self) -> StreamingKMeansModel: ...
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def setK(self, k: int) -> StreamingKMeans: ...
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def setDecayFactor(self, decayFactor: float) -> StreamingKMeans: ...
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def setHalfLife(self, halfLife: float, timeUnit: str) -> StreamingKMeans: ...
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def setInitialCenters(
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self, centers: List[VectorLike], weights: List[float]
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) -> StreamingKMeans: ...
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def setRandomCenters(
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self, dim: int, weight: float, seed: int
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) -> StreamingKMeans: ...
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def trainOn(self, dstream: DStream[VectorLike]) -> None: ...
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def predictOn(self, dstream: DStream[VectorLike]) -> DStream[int]: ...
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def predictOnValues(
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self, dstream: DStream[Tuple[T, VectorLike]]
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) -> DStream[Tuple[T, int]]: ...
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class LDAModel(JavaModelWrapper, JavaSaveable, Loader[LDAModel]):
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def topicsMatrix(self) -> ndarray: ...
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def vocabSize(self) -> int: ...
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def describeTopics(
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self, maxTermsPerTopic: Optional[int] = ...
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) -> List[Tuple[List[int], List[float]]]: ...
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@classmethod
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def load(cls, sc: SparkContext, path: str) -> LDAModel: ...
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class LDA:
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@classmethod
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def train(
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cls,
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rdd: RDD[Tuple[int, VectorLike]],
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k: int = ...,
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maxIterations: int = ...,
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docConcentration: float = ...,
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topicConcentration: float = ...,
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seed: Optional[int] = ...,
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checkpointInterval: int = ...,
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optimizer: str = ...,
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) -> LDAModel: ...
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