# Stubs for pyspark.ml.util (Python 3.5)
#

from typing import Any, Dict, Generic, Optional, Type, TypeVar, Union

from pyspark.context import SparkContext
from pyspark.ml.param import Params
from pyspark.sql.context import SQLContext
from pyspark.sql.session import SparkSession

R = TypeVar("R", bound=MLReadable)
S = TypeVar("S")

basestring = Type[str]
unicode = Type[str]
long = Type[int]

class Identifiable:
    uid: str
    def __init__(self) -> None: ...

class BaseReadWrite:
    def __init__(self) -> None: ...
    def session(self, sparkSession: SparkSession) -> Union[MLWriter, MLReader]: ...
    @property
    def sparkSession(self) -> SparkSession: ...
    @property
    def sc(self) -> SparkContext: ...

class MLWriter(BaseReadWrite):
    shouldOverwrite: bool = ...
    def __init__(self) -> None: ...
    def save(self, path: str) -> None: ...
    def saveImpl(self, path: str) -> None: ...
    def overwrite(self) -> MLWriter: ...

class GeneralMLWriter(MLWriter):
    source: str
    def format(self, source: str) -> MLWriter: ...

class JavaMLWriter(MLWriter):
    def __init__(self, instance: JavaMLWritable) -> None: ...
    def save(self, path: str) -> None: ...
    def overwrite(self) -> JavaMLWriter: ...
    def option(self, key: str, value: Any) -> JavaMLWriter: ...
    def session(self, sparkSession: SparkSession) -> JavaMLWriter: ...

class GeneralJavaMLWriter(JavaMLWriter):
    def __init__(self, instance: MLWritable) -> None: ...
    def format(self, source: str) -> GeneralJavaMLWriter: ...

class MLWritable:
    def write(self) -> MLWriter: ...
    def save(self, path: str) -> None: ...

class JavaMLWritable(MLWritable):
    def write(self) -> JavaMLWriter: ...

class GeneralJavaMLWritable(JavaMLWritable):
    def write(self) -> GeneralJavaMLWriter: ...

class MLReader(BaseReadWrite, Generic[R]):
    def load(self, path: str) -> R: ...

class JavaMLReader(MLReader[R]):
    def __init__(self, clazz: Type[JavaMLReadable]) -> None: ...
    def load(self, path: str) -> R: ...
    def session(self, sparkSession: SparkSession) -> JavaMLReader[R]: ...

class MLReadable(Generic[R]):
    @classmethod
    def read(cls) -> MLReader[R]: ...
    @classmethod
    def load(cls, path: str) -> R: ...

class JavaMLReadable(MLReadable[R]):
    @classmethod
    def read(cls) ->  JavaMLReader[R]: ...

class JavaPredictionModel:
    @property
    def numFeatures(self) -> int: ...

class DefaultParamsWritable(MLWritable):
    def write(self) -> MLWriter: ...

class DefaultParamsWriter(MLWriter):
    instance: DefaultParamsWritable
    def __init__(self, instance: DefaultParamsWritable) -> None: ...
    def saveImpl(self, path: str) -> None: ...
    @staticmethod
    def saveMetadata(instance: DefaultParamsWritable, path: str, sc: SparkContext, extraMetadata: Optional[Dict[str, Any]] = ..., paramMap: Optional[Dict[str, Any]] = ...) -> None: ...

class DefaultParamsReadable(MLReadable[R]):
    @classmethod
    def read(cls) -> MLReader[R]: ...

class DefaultParamsReader(MLReader[R]):
    cls: Type[R]
    def __init__(self, cls: Type[MLReadable]) -> None: ...
    def load(self, path: str) -> R: ...
    @staticmethod
    def loadMetadata(path: str, sc: SparkContext, expectedClassName: str = ...) -> Dict[str, Any]: ...
    @staticmethod
    def getAndSetParams(instance: R, metadata: Dict[str, Any]) -> None: ...
    @staticmethod
    def loadParamsInstance(path: str, sc: SparkContext) -> R: ...

class HasTrainingSummary(Generic[S]):
    @property
    def hasSummary(self) -> bool: ...
    @property
    def summary(self) -> S: ...
