import ray
from ray.dag.base import DAGNodeBase
from ray.dag.py_obj_scanner import _PyObjScanner
from ray.util.annotations import DeveloperAPI

from typing import (
    Optional,
    Union,
    List,
    Tuple,
    Dict,
    Any,
    TypeVar,
    Callable,
)
import uuid
import asyncio

T = TypeVar("T")


@DeveloperAPI
class DAGNode(DAGNodeBase):
    """Abstract class for a node in a Ray task graph.

    A node has a type (e.g., FunctionNode), data (e.g., function options and
    body), arguments (Python values, DAGNodes, and DAGNodes nested within Python
    argument values) and options (Ray API .options() used for function, class
    or class method)
    """

    def __init__(
        self,
        args: Tuple[Any],
        kwargs: Dict[str, Any],
        options: Dict[str, Any],
        other_args_to_resolve: Dict[str, Any],
    ):
        """
        args:
            args (Tuple[Any]): Bound node arguments.
                ex: func_or_class.bind(1)
            kwargs (Dict[str, Any]): Bound node keyword arguments.
                ex: func_or_class.bind(a=1)
            options (Dict[str, Any]): Bound node options arguments.
                ex: func_or_class.options(num_cpus=2)
            other_args_to_resolve (Dict[str, Any]): Bound kwargs to resolve
                that's specific to subclass implementation without exposing
                as args in base class, example: ClassMethodNode
        """
        self._bound_args: Tuple[Any] = args or []
        self._bound_kwargs: Dict[str, Any] = kwargs or {}
        self._bound_options: Dict[str, Any] = options or {}
        self._bound_other_args_to_resolve: Optional[Dict[str, Any]] = (
            other_args_to_resolve or {}
        )
        # UUID that is not changed over copies of this node.
        self._stable_uuid = uuid.uuid4().hex
        # Cached values from last call to execute()
        self.cache_from_last_execute = {}

    def get_args(self) -> Tuple[Any]:
        """Return the tuple of arguments for this node."""

        return self._bound_args

    def get_kwargs(self) -> Dict[str, Any]:
        """Return the dict of keyword arguments for this node."""

        return self._bound_kwargs.copy()

    def get_options(self) -> Dict[str, Any]:
        """Return the dict of options arguments for this node."""

        return self._bound_options.copy()

    def get_other_args_to_resolve(self) -> Dict[str, Any]:
        """Return the dict of other args to resolve arguments for this node."""
        return self._bound_other_args_to_resolve.copy()

    def get_stable_uuid(self) -> str:
        """Return stable uuid for this node.
        1) Generated only once at first instance creation
        2) Stable across pickling, replacement and JSON serialization.
        """
        return self._stable_uuid

    async def get_object_refs_from_last_execute(self) -> Dict[str, Any]:
        """Gets cached object refs from the last call to execute().

        After this DAG is executed through execute(), retrieves a map between node
        UUID to a reference to the return value of the default executor on that node.
        """
        cache = {}
        for node_uuid, value in self.cache_from_last_execute.items():
            if isinstance(value, asyncio.Task):
                cache[node_uuid] = await value
            else:
                cache[node_uuid] = value

        return cache

    def clear_cache(self):
        self.cache_from_last_execute = {}

    def execute(
        self, *args, _ray_cache_refs: bool = False, **kwargs
    ) -> Union[ray.ObjectRef, ray.actor.ActorHandle]:
        """Execute this DAG using the Ray default executor _execute_impl().

        Args:
            _ray_cache_refs: If true, stores the the default executor's return values
                on each node in this DAG in a cache. These should be a mix of:
                - ray.ObjectRefs pointing to the outputs of method and function nodes
                - Serve handles for class nodes
                - resolved values representing user input at runtime
        """

        def executor(node):
            return node._execute_impl(*args, **kwargs)

        result = self.apply_recursive(executor)
        if _ray_cache_refs:
            self.cache_from_last_execute = executor.cache
        return result

    def _get_toplevel_child_nodes(self) -> List["DAGNode"]:
        """Return the list of nodes specified as top-level args.

        For example, in `f.remote(a, [b])`, only `a` is a top-level arg.

        This list of nodes are those that are typically resolved prior to
        task execution in Ray. This does not include nodes nested within args.
        For that, use ``_get_all_child_nodes()``.
        """

        # we use List instead of Set here because the hash key of the node
        # object changes each time we create it. So if using Set here, the
        # order of returned children can be different if we create the same
        # nodes and dag one more time.
        children = []
        for a in self.get_args():
            if isinstance(a, DAGNode):
                if a not in children:
                    children.append(a)
        for a in self.get_kwargs().values():
            if isinstance(a, DAGNode):
                if a not in children:
                    children.append(a)
        for a in self.get_other_args_to_resolve().values():
            if isinstance(a, DAGNode):
                if a not in children:
                    children.append(a)
        return children

    def _get_all_child_nodes(self) -> List["DAGNode"]:
        """Return the list of nodes referenced by the args, kwargs, and
        args_to_resolve in current node, even they're deeply nested.

        Examples:
            f.remote(a, [b]) -> [a, b]
            f.remote(a, [b], key={"nested": [c]}) -> [a, b, c]
        """

        scanner = _PyObjScanner()
        # we use List instead of Set here, reason explained
        # in `_get_toplevel_child_nodes`.
        children = []
        for n in scanner.find_nodes(
            [
                self._bound_args,
                self._bound_kwargs,
                self._bound_other_args_to_resolve,
            ]
        ):
            if n not in children:
                children.append(n)
        scanner.clear()
        return children

    def _apply_and_replace_all_child_nodes(
        self, fn: "Callable[[DAGNode], T]"
    ) -> "DAGNode":
        """Apply and replace all immediate child nodes using a given function.

        This is a shallow replacement only. To recursively transform nodes in
        the DAG, use ``apply_recursive()``.

        Args:
            fn: Callable that will be applied once to each child of this node.

        Returns:
            New DAGNode after replacing all child nodes.
        """

        replace_table = {}
        # CloudPickler scanner object for current layer of DAGNode. Same
        # scanner should be use for a full find & replace cycle.
        scanner = _PyObjScanner()
        # Find all first-level nested DAGNode children in args.
        # Update replacement table and execute the replace.
        for node in scanner.find_nodes(
            [
                self._bound_args,
                self._bound_kwargs,
                self._bound_other_args_to_resolve,
            ]
        ):
            if node not in replace_table:
                replace_table[node] = fn(node)
        new_args, new_kwargs, new_other_args_to_resolve = scanner.replace_nodes(
            replace_table
        )
        scanner.clear()

        # Return updated copy of self.
        return self._copy(
            new_args, new_kwargs, self.get_options(), new_other_args_to_resolve
        )

    def apply_recursive(self, fn: "Callable[[DAGNode], T]") -> T:
        """Apply callable on each node in this DAG in a bottom-up tree walk.

        Args:
            fn: Callable that will be applied once to each node in the
                DAG. It will be applied recursively bottom-up, so nodes can
                assume the fn has been applied to their args already.

        Returns:
            Return type of the fn after application to the tree.
        """

        class _CachingFn:
            def __init__(self, fn):
                self.cache = {}
                self.fn = fn
                self.fn.cache = self.cache
                self.input_node_uuid = None

            def __call__(self, node):
                if node._stable_uuid not in self.cache:
                    self.cache[node._stable_uuid] = self.fn(node)
                if type(node).__name__ == "InputNode":
                    if not self.input_node_uuid:
                        self.input_node_uuid = node._stable_uuid
                    elif self.input_node_uuid != node._stable_uuid:
                        raise AssertionError(
                            "Each DAG should only have one unique InputNode."
                        )
                return self.cache[node._stable_uuid]

        if not type(fn).__name__ == "_CachingFn":
            fn = _CachingFn(fn)

        return fn(
            self._apply_and_replace_all_child_nodes(
                lambda node: node.apply_recursive(fn)
            )
        )

    def apply_functional(
        self,
        source_input_list: Any,
        predictate_fn: Callable,
        apply_fn: Callable,
    ):
        """
        Apply a given function to DAGNodes in source_input_list, and return
        the replaced inputs without mutating or coping any DAGNode.

        Args:
            source_input_list: Source inputs to extract and apply function on
                all children DAGNode instances.
            predictate_fn: Applied on each DAGNode instance found and determine
                if we should apply function to it. Can be used to filter node
                types.
            apply_fn: Function to appy on the node on bound attributes. Example:
                apply_fn = lambda node: node._get_serve_deployment_handle(
                    node._deployment, node._bound_other_args_to_resolve
                )

        Returns:
            replaced_inputs: Outputs of apply_fn on DAGNodes in
                source_input_list that passes predictate_fn.
        """
        replace_table = {}
        scanner = _PyObjScanner()
        for node in scanner.find_nodes(source_input_list):
            if predictate_fn(node) and node not in replace_table:
                replace_table[node] = apply_fn(node)

        replaced_inputs = scanner.replace_nodes(replace_table)
        scanner.clear()

        return replaced_inputs

    def _execute_impl(self) -> Union[ray.ObjectRef, ray.actor.ActorHandle]:
        """Execute this node, assuming args have been transformed already."""
        raise NotImplementedError

    def _copy_impl(
        self,
        new_args: List[Any],
        new_kwargs: Dict[str, Any],
        new_options: Dict[str, Any],
        new_other_args_to_resolve: Dict[str, Any],
    ) -> "DAGNode":
        """Return a copy of this node with the given new args."""
        raise NotImplementedError

    def _copy(
        self,
        new_args: List[Any],
        new_kwargs: Dict[str, Any],
        new_options: Dict[str, Any],
        new_other_args_to_resolve: Dict[str, Any],
    ) -> "DAGNode":
        """Return a copy of this node with the given new args."""
        instance = self._copy_impl(
            new_args, new_kwargs, new_options, new_other_args_to_resolve
        )
        instance._stable_uuid = self._stable_uuid
        return instance

    def __getstate__(self):
        """Required due to overriding `__getattr__` else pickling fails."""
        return self.__dict__

    def __setstate__(self, d: Dict[str, Any]):
        """Required due to overriding `__getattr__` else pickling fails."""
        self.__dict__.update(d)

    def __getattr__(self, attr: str):
        if attr == "bind":
            raise AttributeError(f".bind() cannot be used again on {type(self)} ")
        elif attr == "remote":
            raise AttributeError(
                f".remote() cannot be used on {type(self)}. To execute the task "
                "graph for this node, use .execute()."
            )
        else:
            return self.__getattribute__(attr)
