import inspect

import pandas as pd

from .config import QUESTION2FILTERARGS, TEXTER_PREFIX, HELPER_PREFIX

# Utils to filter convo according to a phase
from .ta_filter_utils import filter_convo


def join_messages(
    grp: pd.DataFrame, texter_prefix: str = "texter", helper_prefix: str = "helper"
) -> str:
    """join messages from dataframe using texter an helper prefixes

    Args:
        grp (pd.DataFrame): conversation in DataFrame with each row corresponding to each **message**.
            Must have the following columns:
            - actor_role
            - message

        texter_prefix (str, optional): prefix to use as the texter. Defaults to "texter".
        helper_prefix (str, optional): prefix to use as the counselor (helper). Defaults to "helper".

    Returns:
        str: joined messages string separated by prefixes
    """

    if "actor_role" not in grp:
        raise Exception("Column 'actor_role' not in DataFrame")
    if "message" not in grp:
        raise Exception("Column 'message' not in DataFrame")

    roles = grp.actor_role.replace(
        {"texter": texter_prefix, "counselor": helper_prefix, "helper": helper_prefix}
    )
    messages = roles.str.strip() + ": " + grp.message.str.strip()
    return "\n".join(messages)


def _get_context(grp: pd.DataFrame, **kwargs) -> str:
    """Get context as a str taking into account message to delete, context marker
    and the type of question to use. This allows for better truncation later

    Args:
        grp (pd.DataFrame): conversation in DataFrame with each row corresponding to each **message**.
            Must have the following columns:
            - actor_role
            - message
            - `column`
        column (str): column name in which the marker of the problem is

    Returns:
        pd.DataFrame: joined messages string separated by prefixes
    """

    if "actor_role" not in grp:
        raise Exception("Column 'actor_role' not in DataFrame")
    if "message" not in grp:
        raise Exception("Column 'message' not in DataFrame")

    join_args = list(inspect.signature(join_messages).parameters)
    join_kwargs = {k: kwargs.pop(k) for k in dict(kwargs) if k in join_args}

    ## DEPRECATED
    # context_args = list(inspect.signature(get_context_on_marker).parameters)
    # context_kwargs = {k: kwargs.pop(k) for k in dict(kwargs) if k in context_args}

    return join_messages(grp, **join_kwargs)


def load_context(
    messages: pd.DataFrame,
    question: str,
    message_col: str,
    col_type: str,
    inference: bool = False,
    **kwargs,
) -> pd.DataFrame:
    """Load and filter conversation from messages given a question (with configured parameters of what phase that question is answered)

    Args:
        messages (pd.DataFrame): Messages dataframe with conversation_id, actor_role, `message_col` and phase prediction
        question (str): Question to get context to
        message_col (str): Column where messages are
        col_type (str): type of message_col, can be "individual" or "joined"
        base_dir (str, optional): Base directory to find model base args. Defaults to "../../".

    Raises:
        Exception: If question is not supported

    Returns:
        pd.DataFrame: filtered messages according to question configuration
    """

    if question not in QUESTION2FILTERARGS:
        raise Exception(f"Question {question} not supported")

    texter_prefix = TEXTER_PREFIX
    helper_prefix = HELPER_PREFIX
    context_data = messages.copy()

    def convo_cpc_get_context(grp, **kwargs):
        """Filter convo according to Convo Phase Classifier (CPC) predictions"""
        context_ = filter_convo(grp, **QUESTION2FILTERARGS[question])
        return _get_context(context_, **kwargs)

    if col_type == "individual":
        if "actor_role" in context_data:
            context_data.dropna(subset=["actor_role"], inplace=True)
        if "delete_message" in context_data:
            context_data.delete_message.replace({1: True}, inplace=True)
            context_data.delete_message.fillna(False, inplace=True)

        context_data = (
            context_data.groupby("conversation_id")
            .apply(
                convo_cpc_get_context,
                helper_prefix=helper_prefix,
                texter_prefix=texter_prefix,
            )
            .rename("q_context")
        )
    elif col_type == "joined":
        context_data = context_data.groupby("conversation_id")[[message_col]].max()
        context_data.rename(columns={message_col: "q_context"}, inplace=True)

    return context_data