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"""
This serves as the bridge between the gem5 statistics exposed via PyBind11 and
the Python Stats model.
"""

from datetime import datetime
from typing import IO, List, Union

import _m5.stats
from m5.objects import *
from m5.ext.pystats.group import *
from m5.ext.pystats.simstat import *
from m5.ext.pystats.statistic import *
from m5.ext.pystats.storagetype import *

class JsonOutputVistor():
    """
    This is a helper vistor class used to include a JSON output via the stats
    API (`src/python/m5/stats/__init__.py`).
    """
    file: str
    json_args: Dict

    def __init__(self, file: str, **kwargs):
        """
        Parameters
        ----------

        file: str
            The output file location in which the JSON will be dumped.

        kwargs: Dict[str, Any]
            Additional parameters to be passed to the `json.dumps` method.
        """

        self.file = file
        self.json_args = kwargs

    def dump(self, roots: Union[List[SimObject], Root]) -> None:
        """
        Dumps the stats of a simulation root (or list of roots) to the output
        JSON file specified in the JsonOutput constructor.

        WARNING: This dump assumes the statistics have already been prepared
        for the target root.

        Parameters
        ----------

        roots: Union[List[Root], Root]]
            The Root, or List of roots, whose stats are are to be dumped JSON.
        """

        with open(self.file, 'w') as fp:
            simstat = get_simstat(root=roots, prepare_stats=False)
            simstat.dump(fp=fp, **self.json_args)

def get_stats_group(group: _m5.stats.Group) -> Group:
    """
    Translates a gem5 Group object into a Python stats Group object. A Python
    statistic Group object is a dictionary of labeled Statistic objects. Any
    gem5 object passed to this will have its `getStats()` and `getStatGroups`
    function called, and all the stats translated (inclusive of the stats
    further down the hierarchy).

    Parameters
    ----------
    group: _m5.stats.Group
        The gem5 _m5.stats.Group object to be translated to be a Python stats
        Group object. Typically this will be a gem5 SimObject.

    Returns
    -------
    Group
        The stats group object translated from the input gem5 object.
    """

    stats_dict = {}

    for stat in group.getStats():
        statistic = __get_statistic(stat)
        if statistic is not None:
            stats_dict[stat.name] = statistic

    for key in group.getStatGroups():
        stats_dict[key] = get_stats_group(group.getStatGroups()[key])

    return Group(**stats_dict)

def __get_statistic(statistic: _m5.stats.Info) -> Optional[Statistic]:
    """
    Translates a _m5.stats.Info object into a Statistic object, to process
    statistics at the Python level.

    Parameters
    ----------
    statistic: Info
        The Info object to be translated to a Statistic object.

    Returns
    -------
    Optional[Statistic]
        The Statistic object of the Info object. Returns None if Info object
        cannot be translated.
    """

    assert(isinstance(statistic, _m5.stats.Info))
    statistic.prepare()

    if isinstance(statistic, _m5.stats.ScalarInfo):
        return __get_scaler(statistic)
    elif isinstance(statistic, _m5.stats.DistInfo):
        return __get_distribution(statistic)
    elif isinstance(statistic, _m5.stats.FormulaInfo):
        # We don't do anything with Formula's right now.
        # We may never do so, see https://gem5.atlassian.net/browse/GEM5-868.
        pass
    elif isinstance(statistic, _m5.stats.VectorInfo):
        return __get_vector(statistic)

    return None

def __get_scaler(statistic: _m5.stats.ScalarInfo) -> Scalar:
    value = statistic.value
    unit = statistic.unit
    description = statistic.desc
    # ScalarInfo uses the C++ `double`.
    datatype = StorageType["f64"]

    return Scalar(
                  value=value,
                  unit=unit,
                  description=description,
                  datatype=datatype,
                 )

def __get_distribution(statistic: _m5.stats.DistInfo) -> Distribution:
    unit = statistic.unit
    description = statistic.desc
    value = statistic.values
    bin_size = statistic.bucket_size
    min = statistic.min_val
    max = statistic.max_val
    num_bins = len(value)
    sum_val = statistic.sum
    sum_squared = statistic.squares
    underflow = statistic.underflow
    overflow = statistic.overflow
    logs = statistic.logs
    # DistInfo uses the C++ `double`.
    datatype = StorageType["f64"]

    return Distribution(
                        value=value,
                        min=min,
                        max=max,
                        num_bins=num_bins,
                        bin_size=bin_size,
                        sum = sum_val,
                        sum_squared = sum_squared,
                        underflow = underflow,
                        overflow = overflow,
                        logs = logs,
                        unit=unit,
                        description=description,
                        datatype=datatype,
                        )

def __get_vector(statistic: _m5.stats.VectorInfo) -> Vector:
    to_add = dict()

    for index in range(statistic.size):
        # All the values in a Vector are Scalar values
        value = statistic.value[index]
        unit = statistic.unit
        description = statistic.subdescs[index]
        # ScalarInfo uses the C++ `double`.
        datatype = StorageType["f64"]

        # Sometimes elements within a vector are defined by their name. Other
        # times they have no name. When a name is not available, we name the
        # stat the index value.
        if str(statistic.subnames[index]):
            index_string = str(statistic.subnames[index])
        else:
            index_string = str(index)

        to_add[index_string] = Scalar(
                                      value=value,
                                      unit=unit,
                                      description=description,
                                      datatype=datatype,
                                      )

    return Vector(scalar_map=to_add)

def _prepare_stats(group: _m5.stats.Group):
    """
    Prepares the statistics for dumping.
    """

    group.preDumpStats()

    for stat in group.getStats():
        stat.prepare()

    for child in group.getStatGroups().values():
        _prepare_stats(child)


def get_simstat(root: Union[SimObject, List[SimObject]],
                prepare_stats: bool = True) -> SimStat:
    """
    This function will return the SimStat object for a simulation given a
    SimObject (typically a Root SimObject), or list of SimObjects. The returned
    SimStat object will contain all the stats for all the SimObjects contained
    within the "root", inclusive of the "root" SimObject/SimObjects.

    Parameters
    ----------
    root: Union[SimObject, List[SimObject]]
        A SimObject, or list of SimObjects, of the simulation for translation
        into a SimStat object. Typically this is the simulation's Root
        SimObject as this will obtain the entirety of a run's statistics in a
        single SimStat object.

    prepare_stats: bool
        Dictates whether the stats are to be prepared prior to creating the
        SimStat object. By default this is 'True'.

    Returns
    -------
    SimStat
        The SimStat Object of the current simulation.

    """
    stats_map = {}
    creation_time = datetime.now()
    time_converstion = None # TODO https://gem5.atlassian.net/browse/GEM5-846
    final_tick = Root.getInstance().resolveStat("finalTick").value
    sim_ticks = Root.getInstance().resolveStat("simTicks").value
    simulated_begin_time = int(final_tick - sim_ticks)
    simulated_end_time = int(final_tick)

    if prepare_stats:
        _m5.stats.processDumpQueue()

    for r in root:
        if isinstance(r, Root):
            # The Root is a special case, we jump directly into adding its
            # constituent Groups.
            if prepare_stats:
                _prepare_stats(r)
            for key in r.getStatGroups():
                stats_map[key] = get_stats_group(r.getStatGroups()[key])
        elif isinstance(r, SimObject):
            if prepare_stats:
                _prepare_stats(r)
            stats_map[r.get_name()] = get_stats_group(r)
        else:
            raise TypeError("Object (" + str(r) + ") passed is not a "
                            "SimObject. " + __name__ + " only processes "
                            "SimObjects, or a list of  SimObjects.")


    return SimStat(
                   creation_time=creation_time,
                   time_conversion=time_converstion,
                   simulated_begin_time=simulated_begin_time,
                   simulated_end_time=simulated_end_time,
                   **stats_map,
                  )