import json
import os
from typing import Callable

import yaml
from astropy import table

from ._dispatcher import override_common_keys
from .._dfs import DFS, dfs
from ..hash import generate_sha1_from_time

DAG_CONFIG_DIR = os.path.join(
    os.path.dirname(os.path.dirname(__file__)),
    "dag_config",
)


class BaseDAG:
    """Base class for all Directed Acyclic Graph (DAG) implementations.

    This class provides core functionality for DAG configuration, message generation,
    and execution management within the CSST dlist processing system.

    Attributes
    ----------
    dag : str
        Name of the DAG, must exist in DAG_MAP
    dag_cfg : dict
        Configuration loaded from YAML file
    dag_run_template : dict
        Message template structure loaded from JSON file
    dfs : DFS
        Data Flow System instance for execution

    Raises
    ------
    AssertionError
        If DAG name is not in DAG_MAP or config name mismatch
    """

    def __init__(
        self,
        dag: str,
        pattern: table.Table,
        dispatcher: Callable,
    ):
        """Initialize a DAG instance with configuration loading.

        Parameters
        ----------
        dag : str
            DAG name, must exist in DAG_MAP
        """
        # Set DAG name
        self.dag = dag
        self.pattern = pattern
        self.dispatcher = dispatcher

        # Load yaml and json config
        yml_path = os.path.join(DAG_CONFIG_DIR, f"{dag}.yml")
        json_path = os.path.join(DAG_CONFIG_DIR, f"{dag}.json")

        with open(yml_path, "r") as f:
            self.dag_cfg = yaml.safe_load(f)
        assert (
            self.dag_cfg["name"] == self.dag
        ), f"{self.dag_cfg['name']} != {self.dag}"  # , f"{dag_cfg} not consistent with definition in .yml file."
        with open(json_path, "r") as f:
            self.dag_run_template = json.load(f)

        # DFS instance
        self.dfs = dfs

    def filter_basis(self, plan_basis, data_basis):
        filtered_data_basis = table.join(
            self.pattern,
            data_basis,
            keys=self.pattern.colnames,
            join_type="inner",
        )
        # sort via obs_id
        filtered_data_basis.sort(keys=["dataset", "obs_id"])
        if len(filtered_data_basis) == 0:
            return plan_basis[:0], filtered_data_basis
        u_data_basis = table.unique(filtered_data_basis["dataset", "obs_id"])
        filtered_plan_basis = table.join(
            u_data_basis,
            plan_basis,
            keys=["dataset", "obs_id"],
            join_type="inner",
        )
        return filtered_plan_basis, filtered_data_basis

    def schedule(
        self,
        dag_group_run: dict,
        data_basis: table.Table,
        plan_basis: table.Table,
        force_success: bool = False,
        **kwargs,
    ) -> tuple[list, set]:
        # filter plan and data basis
        filtered_plan_basis, filtered_data_basis = self.filter_basis(
            plan_basis, data_basis
        )
        # dispatch tasks
        task_list = self.dispatcher(filtered_plan_basis, filtered_data_basis)
        for this_task in task_list:
            # only convert success tasks
            if force_success or this_task["success"]:
                dag_run = self.gen_dag_run(
                    **dag_group_run,
                    **this_task["task"],
                    **kwargs,
                )
                this_task["dag_run"] = dag_run
            else:
                this_task["dag_run"] = None
        return task_list

    @staticmethod
    def generate_sha1():
        """Generate a unique SHA1 hash based on current timestamp.

        Returns
        -------
        str
            SHA1 hash string
        """
        return generate_sha1_from_time(verbose=False)

    @staticmethod
    def gen_dag_group_run(
        dag_group: str = "-",
        batch_id: str = "-",
        priority: int = 1,
    ):
        """Generate a DAG group run configuration.

        Parameters
        ----------
        dag_group : str, optional
            Group identifier (default: "-")
        batch_id : str, optional
            Batch identifier (default: "-")
        priority : int, optional
            Execution priority (default: 1)

        Returns
        -------
        dict
            Dictionary containing:
            - dag_group: Original group name
            - dag_group_run: Generated SHA1 identifier
            - batch_id: Batch identifier
            - priority: Execution priority
        """
        return dict(
            dag_group=dag_group,
            dag_group_run=BaseDAG.generate_sha1(),
            batch_id=batch_id,
            priority=priority,
        )

    @staticmethod
    def force_string(d: dict):
        for k, v in d.items():
            d.__setattr__(k, str(v))
        return d

    def gen_dag_run(self, **kwargs) -> dict:
        """Generate a complete DAG run message.

        Parameters
        ----------
        kwargs : dict
            Additional keyword arguments to override.

        Returns
        -------
        dict
            Complete DAG run message

        Raises
        ------
        AssertionError
            If any key is not in the message template
        """
        # copy template
        dag_run = self.dag_run_template.copy()
        # update values
        dag_run = override_common_keys(dag_run, kwargs)
        # set hash
        dag_run = override_common_keys(
            dag_run,
            {"dag": self.dag, "dag_run": self.generate_sha1()},
        )
        # force values to be string
        dag_run = self.force_string(dag_run)
        return dag_run

    @staticmethod
    def push_dag_group_run(
        dag_group_run: dict,
        dag_run_list: list[dict],
    ):
        """Submit a DAG group run to the DFS system.

        Parameters
        ----------
        dag_group_run : dict
            Group run configuration
        dag_run_list : list[dict]
            List of individual DAG run messages

        Returns
        -------
        Any
            Result from dfs.dag.new_dag_group_run()
        """

        return dfs.dag.new_dag_group_run(
            dag_group_run=dag_group_run,
            dag_run_list=dag_run_list,
        )