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"""This module supports physical and logical plans in DataFusion."""

from __future__ import annotations

from typing import TYPE_CHECKING, Any

import datafusion._internal as df_internal

if TYPE_CHECKING:
    from datafusion.context import SessionContext

__all__ = [
    "ExecutionPlan",
    "LogicalPlan",
]


class LogicalPlan:
    """Logical Plan.

    A `LogicalPlan` is a node in a tree of relational operators (such as
    Projection or Filter).

    Represents transforming an input relation (table) to an output relation
    (table) with a potentially different schema. Plans form a dataflow tree
    where data flows from leaves up to the root to produce the query result.

    A `LogicalPlan` can be created by the SQL query planner, the DataFrame API,
    or programmatically (for example custom query languages).
    """

    def __init__(self, plan: df_internal.LogicalPlan) -> None:
        """This constructor should not be called by the end user."""
        self._raw_plan = plan

    def to_variant(self) -> Any:
        """Convert the logical plan into its specific variant."""
        return self._raw_plan.to_variant()

    def inputs(self) -> list[LogicalPlan]:
        """Returns the list of inputs to the logical plan."""
        return [LogicalPlan(p) for p in self._raw_plan.inputs()]

    def __repr__(self) -> str:
        """Generate a printable representation of the plan."""
        return self._raw_plan.__repr__()

    def display(self) -> str:
        """Print the logical plan."""
        return self._raw_plan.display()

    def display_indent(self) -> str:
        """Print an indented form of the logical plan."""
        return self._raw_plan.display_indent()

    def display_indent_schema(self) -> str:
        """Print an indented form of the schema for the logical plan."""
        return self._raw_plan.display_indent_schema()

    def display_graphviz(self) -> str:
        """Print the graph visualization of the logical plan.

        Returns a `format`able structure that produces lines meant for graphical display
        using the `DOT` language. This format can be visualized using software from
        [`graphviz`](https://graphviz.org/)
        """
        return self._raw_plan.display_graphviz()

    @staticmethod
    def from_proto(ctx: SessionContext, data: bytes) -> LogicalPlan:
        """Create a LogicalPlan from protobuf bytes.

        Tables created in memory from record batches are currently not supported.
        """
        return LogicalPlan(df_internal.LogicalPlan.from_proto(ctx.ctx, data))

    def to_proto(self) -> bytes:
        """Convert a LogicalPlan to protobuf bytes.

        Tables created in memory from record batches are currently not supported.
        """
        return self._raw_plan.to_proto()


class ExecutionPlan:
    """Represent nodes in the DataFusion Physical Plan."""

    def __init__(self, plan: df_internal.ExecutionPlan) -> None:
        """This constructor should not be called by the end user."""
        self._raw_plan = plan

    def children(self) -> list[ExecutionPlan]:
        """Get a list of children `ExecutionPlan` that act as inputs to this plan.

        The returned list will be empty for leaf nodes such as scans, will contain a
        single value for unary nodes, or two values for binary nodes (such as joins).
        """
        return [ExecutionPlan(e) for e in self._raw_plan.children()]

    def display(self) -> str:
        """Print the physical plan."""
        return self._raw_plan.display()

    def display_indent(self) -> str:
        """Print an indented form of the physical plan."""
        return self._raw_plan.display_indent()

    def __repr__(self) -> str:
        """Print a string representation of the physical plan."""
        return self._raw_plan.__repr__()

    @property
    def partition_count(self) -> int:
        """Returns the number of partitions in the physical plan."""
        return self._raw_plan.partition_count

    @staticmethod
    def from_proto(ctx: SessionContext, data: bytes) -> ExecutionPlan:
        """Create an ExecutionPlan from protobuf bytes.

        Tables created in memory from record batches are currently not supported.
        """
        return ExecutionPlan(df_internal.ExecutionPlan.from_proto(ctx.ctx, data))

    def to_proto(self) -> bytes:
        """Convert an ExecutionPlan into protobuf bytes.

        Tables created in memory from record batches are currently not supported.
        """
        return self._raw_plan.to_proto()