import datetime
import os
from typing import Any, Callable, FrozenSet, Iterable, Optional, Union
import re

from sqlalchemy import func

from airflow.exceptions import AirflowException
from airflow.models import BaseOperatorLink, DagBag, DagModel, DagRun, TaskInstance
from airflow.operators.dummy import DummyOperator
from airflow.sensors.base import BaseSensorOperator
from airflow.utils.helpers import build_airflow_url_with_query
from airflow.utils.session import provide_session
from airflow.utils.state import State

DELIMITER = "."

# TO-DO: add exception in the source base class to ensure the delimiter is not used in the source/table names


class TableIngestionSensor(BaseSensorOperator):
    """
    Waits for table ingestion DAGs to complete for a
    specific execution_date

    :param external_source_tables: A map whose keys are the sources
        to wait for and the values are a list of regex expressions for each source.
        The regex expressions will be used to find matching DAGs. For example:

        external_source_tables = {
            "SourceX" : ["^ABC.*"], # table name starts with ABC followed by any character
            "SourceY" : [".*ABC$"], # table name ends with ABC preceded by any character
            "SourceZ" : [".*"], # wildcard for matching all tables for the given source
            "SourceK" : ["(.*)([^ABC]$)"], # wildcard for matching all tables for the given source AND don't end with ABC
            "SourceW" : ["ABC"], # table name must match exactly ABC
            "SourceM" : ["^ABC.*", "YXZ"] # table name starts with ABC followed by any character OR table name matches YX
        }

    :type external_source_tables: dict
    :param mode: How the sensor operates.
    :type mode: str
    :param allowed_states: Iterable of allowed states, default is ``['success']``
    :type allowed_states: Iterable
    :param failed_states: Iterable of failed or dis-allowed states, default is ``None``
    :type failed_states: Iterable
    :param execution_delta: time difference with the previous execution to
        look at, the default is the same execution_date as the current task or DAG.
        For yesterday, use [positive!] datetime.timedelta(days=1). Either
        execution_delta or execution_date_fn can be passed to
        ExternalTaskSensor, but not both.
    :type execution_delta: Optional[datetime.timedelta]
    """

    template_fields = ["external_source_tables"]
    ui_color = "#19647e"

    def __init__(
        self,
        *,
        external_source_tables: dict,
        mode: Optional[str] = "reschedule",
        allowed_states: Optional[Iterable[str]] = None,
        failed_states: Optional[Iterable[str]] = None,
        execution_delta: Optional[datetime.timedelta] = None,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.allowed_states = (
            list(allowed_states) if allowed_states else [State.SUCCESS]
        )
        self.failed_states = list(failed_states) if failed_states else []

        total_states = self.allowed_states + self.failed_states
        total_states = set(total_states)

        if set(self.failed_states).intersection(set(self.allowed_states)):
            raise AirflowException(
                f"Duplicate values provided as allowed "
                f"`{self.allowed_states}` and failed states `{self.failed_states}`"
            )

        self.execution_delta = execution_delta
        self.external_source_tables = external_source_tables

    @provide_session
    def poke(self, context, session=None):
        if self.execution_delta:
            dttm = context["execution_date"] - self.execution_delta
        else:
            dttm = context["execution_date"]

        dttm_filter = [dttm]

        count_allowed = self.get_count(
            dttm_filter, context, session, self.allowed_states
        )

        count_failed = -1
        if self.failed_states:
            count_failed = self.get_count(
                dttm_filter, context, session, self.failed_states
            )

        if count_failed == len(dttm_filter):
            pass  # TO-DO: define behaviour if any of the dependent DAGs have failed. Maybe provide list of essential tables?

        return count_allowed == 1

    def get_count(self, dttm_filter, context, session, states) -> int:
        """
        Get the count of records against dttm filter and states
        :param dttm_filter: date time filter for execution date
        :type dttm_filter: list
        :param session: airflow session object
        :type session: SASession
        :param states: task or dag states
        :type states: list
        :return: count of record against the filters
        """
        # TI = TaskInstance
        DR = DagRun

        external_dag_ids = self.get_external_dag_ids(context=context, session=session)

        expected_count = len(external_dag_ids)
        count = (
            session.query(func.count())
            .filter(
                DR.dag_id.in_(external_dag_ids),
                DR.state.in_(states),
                DR.execution_date.in_(dttm_filter),
            )
            .scalar()
        )

        self.log.info(
            "Current count of completed DAGs is %s. The expected DAG count is %s",
            count,
            expected_count,
        )

        return count / expected_count

    def get_external_dag_ids(self, context, session) -> list:
        """
        Retrieve a list of external DAG IDs based on the provided source & table combinations
        """
        schedule_interval = context["dag"].schedule_interval

        external_dag_ids = []

        # Query all active dags
        query = session.query(DagModel).filter(DagModel.is_active == True).all()  # noqa

        if len(query) == 0:
            raise AirflowException("No active dags found.")

        schedule_map = {}
        source_dag_map = {}
        for dag in query:
            dag_id = dag.dag_id
            schedule_map[dag_id] = dag.schedule_interval

            if len(dag_id.split(DELIMITER)) != 2:
                continue

            source = dag_id.split(DELIMITER)[0]

            if source in source_dag_map:
                source_dag_map[source].append(dag_id)
            else:
                source_dag_map[source] = [dag_id]

        if len(source_dag_map) == 0:
            raise AirflowException(
                "Unable to determine table ingestion DAGs. Make sure the period delimiter is used correctly."
            )

        for source, tables in self.external_source_tables.items():
            source_dags = source_dag_map.get(source, None)

            if source_dags is None:
                raise AirflowException(f"No active dags found for source {source}.")

            dags = []
            for regex in tables:
                try:
                    table_dags = [
                        dag
                        for dag in source_dags
                        if re.match(regex, dag.split(DELIMITER)[1])
                    ]
                except re.error:
                    raise AirflowException(
                        f"The regex expression '{regex}' is invalid."
                    )

                if not table_dags:
                    raise AirflowException(
                        f'No active dags found for source {source} using regex: "{regex}".'
                    )

                if not all(
                    [schedule_interval == schedule_map[dag] for dag in table_dags]
                ):
                    raise AirflowException(
                        f"Incompatible schedule intervals with that of the main DAG: {schedule_interval}."
                    )

                dags.extend(table_dags)

            dags = list(set(dags))
            external_dag_ids.extend(dags)

            self.log.info(
                "%s dependent DAGs found for source %s: %s.", len(dags), source, dags
            )

        return list(set(external_dag_ids))
