#
# 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.
#

"""For internal use only; no backwards-compatibility guarantees."""

# pytype: skip-file

# TODO(https://github.com/apache/beam/issues/18399): Issue with dill + local
# classes + abc metaclass
# import abc
import inspect
from typing import TYPE_CHECKING
from typing import Any
from typing import Callable
from typing import Optional
from typing import TypeVar
from typing import Union
from typing import overload

from google.protobuf import message
from google.protobuf import wrappers_pb2

from apache_beam.internal import pickler
from apache_beam.portability.api import beam_runner_api_pb2
from apache_beam.utils import proto_utils

if TYPE_CHECKING:
  from apache_beam.runners.pipeline_context import PipelineContext

T = TypeVar('T')
RunnerApiFnT = TypeVar('RunnerApiFnT', bound='RunnerApiFn')
ConstructorFn = Callable[[Union['message.Message', bytes], 'PipelineContext'],
                         Any]


class RunnerApiFn(object):
  """Abstract base class that provides urn registration utilities.

  A class that inherits from this class will get a registration-based
  from_runner_api and to_runner_api method that convert to and from
  beam_runner_api_pb2.FunctionSpec.

  Additionally, register_pickle_urn can be called from the body of a class
  to register serialization via pickling.
  """

  # TODO(https://github.com/apache/beam/issues/18399): Issue with dill + local
  # classes + abc metaclass
  # __metaclass__ = abc.ABCMeta

  _known_urns: dict[str, tuple[Optional[type], ConstructorFn]] = {}

  # @abc.abstractmethod is disabled here to avoid an error with mypy. mypy
  # performs abc.abtractmethod/property checks even if a class does
  # not use abc.ABCMeta, however, functions like `register_pickle_urn`
  # dynamically patch `to_runner_api_parameter`, which mypy cannot track, so
  # mypy incorrectly infers that this method has not been overridden with a
  # concrete implementation.
  # @abc.abstractmethod
  def to_runner_api_parameter(
      self, unused_context: 'PipelineContext') -> tuple[str, Any]:
    """Returns the urn and payload for this Fn.

    The returned urn(s) should be registered with `register_urn`.
    """
    raise NotImplementedError

  @classmethod
  @overload
  def register_urn(
      cls,
      urn: str,
      parameter_type: type[T],
  ) -> Callable[[Callable[[T, 'PipelineContext'], Any]],
                Callable[[T, 'PipelineContext'], Any]]:
    pass

  @classmethod
  @overload
  def register_urn(
      cls,
      urn: str,
      parameter_type: None,
  ) -> Callable[[Callable[[bytes, 'PipelineContext'], Any]],
                Callable[[bytes, 'PipelineContext'], Any]]:
    pass

  @classmethod
  @overload
  def register_urn(
      cls,
      urn: str,
      parameter_type: type[T],
      fn: Callable[[T, 'PipelineContext'], Any]) -> None:
    pass

  @classmethod
  @overload
  def register_urn(
      cls,
      urn: str,
      parameter_type: None,
      fn: Callable[[bytes, 'PipelineContext'], Any]) -> None:
    pass

  @classmethod
  def register_urn(cls, urn, parameter_type, fn=None):
    """Registers a urn with a constructor.

    For example, if 'beam:fn:foo' had parameter type FooPayload, one could
    write `RunnerApiFn.register_urn('bean:fn:foo', FooPayload, foo_from_proto)`
    where foo_from_proto took as arguments a FooPayload and a PipelineContext.
    This function can also be used as a decorator rather than passing the
    callable in as the final parameter.

    A corresponding to_runner_api_parameter method would be expected that
    returns the tuple ('beam:fn:foo', FooPayload)
    """
    def register(fn):
      cls._known_urns[urn] = parameter_type, fn
      return fn

    if fn:
      # Used as a statement.
      register(fn)
    else:
      # Used as a decorator.
      return register

  @classmethod
  def register_pickle_urn(cls, pickle_urn):
    """Registers and implements the given urn via pickling.
    """
    inspect.currentframe().f_back.f_locals['to_runner_api_parameter'] = (
        lambda self, context:
        (pickle_urn, wrappers_pb2.BytesValue(value=pickler.dumps(self))))
    cls.register_urn(
        pickle_urn,
        wrappers_pb2.BytesValue,
        lambda proto, unused_context: pickler.loads(proto.value))

  def to_runner_api(
      self, context: 'PipelineContext') -> beam_runner_api_pb2.FunctionSpec:
    """Returns an FunctionSpec encoding this Fn.

    Prefer overriding self.to_runner_api_parameter.
    """
    urn, typed_param = self.to_runner_api_parameter(context)
    return beam_runner_api_pb2.FunctionSpec(
        urn=urn,
        payload=typed_param.SerializeToString() if isinstance(
            typed_param, message.Message) else typed_param)

  @classmethod
  def from_runner_api(
      cls: type[RunnerApiFnT],
      fn_proto: beam_runner_api_pb2.FunctionSpec,
      context: 'PipelineContext') -> RunnerApiFnT:
    """Converts from an FunctionSpec to a Fn object.

    Prefer registering a urn with its parameter type and constructor.
    """
    parameter_type, constructor = cls._known_urns[fn_proto.urn]
    return constructor(
        proto_utils.parse_Bytes(fn_proto.payload, parameter_type), context)