#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

"""
PySpark is the Python API for Spark.

Public classes:

  - :class:`SparkContext`:
      Main entry point for Spark functionality.
  - :class:`RDD`:
      A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.
  - :class:`Broadcast`:
      A broadcast variable that gets reused across tasks.
  - :class:`Accumulator`:
      An "add-only" shared variable that tasks can only add values to.
  - :class:`SparkConf`:
      For configuring Spark.
  - :class:`SparkFiles`:
      Access files shipped with jobs.
  - :class:`StorageLevel`:
      Finer-grained cache persistence levels.
  - :class:`TaskContext`:
      Information about the current running task, available on the workers and experimental.
  - :class:`RDDBarrier`:
      Wraps an RDD under a barrier stage for barrier execution.
  - :class:`BarrierTaskContext`:
      A :class:`TaskContext` that provides extra info and tooling for barrier execution.
  - :class:`BarrierTaskInfo`:
      Information about a barrier task.
  - :class:`InheritableThread`:
      A inheritable thread to use in Spark when the pinned thread mode is on.
"""

from functools import wraps
import types
from typing import cast, Any, Callable, Optional, TypeVar, Union

from pyspark.conf import SparkConf
from pyspark.rdd import RDD, RDDBarrier
from pyspark.files import SparkFiles
from pyspark.status import StatusTracker, SparkJobInfo, SparkStageInfo
from pyspark.util import InheritableThread, inheritable_thread_target
from pyspark.storagelevel import StorageLevel
from pyspark.accumulators import Accumulator, AccumulatorParam
from pyspark.broadcast import Broadcast
from pyspark.serializers import MarshalSerializer, CPickleSerializer
from pyspark.taskcontext import TaskContext, BarrierTaskContext, BarrierTaskInfo
from pyspark.profiler import Profiler, BasicProfiler
from pyspark.version import __version__
from pyspark._globals import _NoValue  # noqa: F401

T = TypeVar("T")
F = TypeVar("F", bound=Callable)


def since(version: Union[str, float]) -> Callable[[F], F]:
    """
    A decorator that annotates a function to append the version of Spark the function was added.
    """
    import re

    indent_p = re.compile(r"\n( +)")

    def deco(f: F) -> F:
        assert f.__doc__ is not None

        indents = indent_p.findall(f.__doc__)
        indent = " " * (min(len(m) for m in indents) if indents else 0)
        f.__doc__ = f.__doc__.rstrip() + "\n\n%s.. versionadded:: %s" % (indent, version)
        return f

    return deco


def copy_func(
    f: F,
    name: Optional[str] = None,
    sinceversion: Optional[Union[str, float]] = None,
    doc: Optional[str] = None,
) -> F:
    """
    Returns a function with same code, globals, defaults, closure, and
    name (or provide a new name).
    """
    # See
    # http://stackoverflow.com/questions/6527633/how-can-i-make-a-deepcopy-of-a-function-in-python
    assert isinstance(f, types.FunctionType)

    fn = types.FunctionType(
        f.__code__,
        f.__globals__,
        name or f.__name__,
        f.__defaults__,
        f.__closure__,
    )
    # in case f was given attrs (note this dict is a shallow copy):
    fn.__dict__.update(f.__dict__)
    if doc is not None:
        fn.__doc__ = doc
    if sinceversion is not None:
        fn = since(sinceversion)(fn)
    return cast(F, fn)


def keyword_only(func: F) -> F:
    """
    A decorator that forces keyword arguments in the wrapped method
    and saves actual input keyword arguments in `_input_kwargs`.

    Notes
    -----
    Should only be used to wrap a method where first arg is `self`
    """

    @wraps(func)
    def wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
        if len(args) > 0:
            raise TypeError("Method %s forces keyword arguments." % func.__name__)
        self._input_kwargs = kwargs
        return func(self, **kwargs)

    return cast(F, wrapper)


# To avoid circular dependencies
from pyspark.context import SparkContext

# for back compatibility
from pyspark.sql import SQLContext, HiveContext, Row  # noqa: F401

__all__ = [
    "SparkConf",
    "SparkContext",
    "SparkFiles",
    "RDD",
    "StorageLevel",
    "Broadcast",
    "Accumulator",
    "AccumulatorParam",
    "MarshalSerializer",
    "CPickleSerializer",
    "StatusTracker",
    "SparkJobInfo",
    "SparkStageInfo",
    "Profiler",
    "BasicProfiler",
    "TaskContext",
    "RDDBarrier",
    "BarrierTaskContext",
    "BarrierTaskInfo",
    "InheritableThread",
    "inheritable_thread_target",
    "__version__",
]
