import pickle
from pathlib import Path
from typing import List

import pandas as pd

from rdagent.components.runner import CachedRunner
from rdagent.components.runner.conf import RUNNER_SETTINGS
from rdagent.core.exception import FactorEmptyError
from rdagent.log import rdagent_logger as logger
from rdagent.scenarios.qlib.experiment.factor_experiment import QlibFactorExperiment

DIRNAME = Path(__file__).absolute().resolve().parent
DIRNAME_local = Path.cwd()

# class QlibFactorExpWorkspace:

#     def prepare():
#         # create a folder;
#         # copy template
#         # place data inside the folder `combined_factors`
#         #
#     def execute():
#         de = DockerEnv()
#         de.run(local_path=self.ws_path, entry="qrun conf.yaml")

# TODO: supporting multiprocessing and keep previous results


class QlibFactorRunner(CachedRunner[QlibFactorExperiment]):
    """
    Docker run
    Everything in a folder
    - config.yaml
    - price-volume data dumper
    - `data.py` + Adaptor to Factor implementation
    - results in `mlflow`
    """

    def develop(self, exp: QlibFactorExperiment) -> QlibFactorExperiment:
        """
        Generate the experiment by processing and combining factor data,
        then passing the combined data to Docker for backtest results.
        """
        if exp.based_experiments and exp.based_experiments[-1].result is None:
            exp.based_experiments[-1] = self.develop(exp.based_experiments[-1])

        if RUNNER_SETTINGS.cache_result:
            cache_hit, result = self.get_cache_result(exp)
            if cache_hit:
                exp.result = result
                return exp

        if exp.based_experiments:
            SOTA_factor = None
            if len(exp.based_experiments) > 1:
                SOTA_factor = self.process_factor_data(exp.based_experiments)

            # Process the new factors data
            new_factors = self.process_factor_data(exp)

            if new_factors.empty:
                # raise FactorEmptyException("No valid factor data found to merge.")
                logger.error("No valid factor data found to merge.")
                return None

            # Combine the SOTA factor and new factors if SOTA factor exists
            if SOTA_factor is not None and not SOTA_factor.empty:
                combined_factors = pd.concat([SOTA_factor, new_factors], axis=1).dropna()
            else:
                combined_factors = new_factors

            # Sort and nest the combined factors under 'feature'
            combined_factors = combined_factors.sort_index()
            new_columns = pd.MultiIndex.from_product([["feature"], combined_factors.columns])
            combined_factors.columns = new_columns

            # Save the combined factors to the workspace
            with open(exp.experiment_workspace.workspace_path / "combined_factors_df.pkl", "wb") as f:
                pickle.dump(combined_factors, f)

        result = exp.experiment_workspace.execute(
            qlib_config_name=f"conf.yaml" if len(exp.based_experiments) == 0 else "conf_combined.yaml"
        )

        exp.result = result
        if RUNNER_SETTINGS.cache_result:
            self.dump_cache_result(exp, result)

        return exp

    def process_factor_data(self, exp_or_list: List[QlibFactorExperiment] | QlibFactorExperiment) -> pd.DataFrame:
        """
        Process and combine factor data from experiment implementations.

        Args:
            exp (ASpecificExp): The experiment containing factor data.

        Returns:
            pd.DataFrame: Combined factor data without NaN values.
        """
        if isinstance(exp_or_list, QlibFactorExperiment):
            exp_or_list = [exp_or_list]
        factor_dfs = []

        # Collect all exp's dataframes
        for exp in exp_or_list:
            # Iterate over sub-implementations and execute them to get each factor data
            for implementation in exp.sub_workspace_list:
                message, df = implementation.execute(data_type="All")

                # Check if factor generation was successful
                if df is not None and "datetime" in df.index.names:
                    time_diff = df.index.get_level_values("datetime").to_series().diff().dropna().unique()
                    if pd.Timedelta(minutes=1) not in time_diff:
                        factor_dfs.append(df)

        # Combine all successful factor data
        if factor_dfs:
            return pd.concat(factor_dfs, axis=1)
        else:
            logger.error("No valid factor data found to merge.")
            return pd.DataFrame()  # Return an empty DataFrame if no valid data