#!/usr/bin/env python3
"""
Evaluate accuracy of online model for DFA/PTA traces.

Usage:
PYTHONPATH=lib bin/eval-online-model-accuracy.py [options] <pta/dfa definition>

Options:
--accounting=static_state|static_state_immediate|static_statetransition|static_statetransition_immedate
    Select accounting method

--depth=<depth> (default: 3)
    Maximum number of function calls per run

--sleep=<ms> (default: 0)
    How long to sleep between simulated function calls.

--trace-filter=<transition,transition,transition,...>[ <transition,transition,transition,...> ...]
    Only consider traces whose beginning matches one of the provided transition sequences.
    E.g. --trace-filter='init,foo init,bar' will only consider traces with init as first and foo or bar as second transition,
    and --trace-filter='init,foo,$ init,bar,$' will only consider the traces init -> foo and init -> bar.
"""

import getopt
import json
import re
import runner
import sys
import time
import io
import itertools
import yaml
from automata import PTA
from codegen import *
from harness import OnboardTimerHarness
from dfatool import regression_measures

opt = dict()

if __name__ == '__main__':

    try:
        optspec = (
            'accounting= '
            'arch= '
            'app= '
            'depth= '
            'dummy= '
            'instance= '
            'repeat= '
            'run= '
            'sleep= '
            'timer-pin= '
            'trace-filter= '
            'timer-freq= '
            'timer-type= '
            'timestamp-type= '
            'energy-type= '
            'power-type= '
            'timestamp-granularity= '
            'energy-granularity= '
            'power-granularity= '
        )
        raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(' '))

        opt_default = {
            'depth' : 3,
            'sleep' : 0,
            'timer-freq' : 1e6,
            'timer-type' : 'uint16_t',
            'timestamp-type' : 'uint16_t',
            'energy-type' : 'uint32_t',
            'power-type' : 'uint16_t',
            'timestamp-granularity' : 1e-6,
            'power-granularity' : 1e-6,
            'energy-granularity' : 1e-12,
        }

        for option, parameter in raw_opts:
            optname = re.sub(r'^--', '', option)
            opt[optname] = parameter

        for key in 'depth sleep'.split():
            if key in opt:
                opt[key] = int(opt[key])
            else:
                opt[key] = opt_default[key]

        for key in 'timer-freq timestamp-granularity energy-granularity power-granularity'.split():
            if key in opt:
                opt[key] = float(opt[key])
            else:
                opt[key] = opt_default[key]

        for key in 'timer-type timestamp-type energy-type power-type'.split():
            if key not in opt:
                opt[key] = opt_default[key]

        if 'trace-filter' in opt:
            trace_filter = []
            for trace in opt['trace-filter'].split():
                trace_filter.append(trace.split(','))
            opt['trace-filter'] = trace_filter
        else:
            opt['trace-filter'] = None

    except getopt.GetoptError as err:
        print(err)
        sys.exit(2)

    modelfile = args[0]

    pta = PTA.from_file(modelfile)

    enum = dict()
    if '.json' not in modelfile:
        with open(modelfile, 'r') as f:
            driver_definition = yaml.safe_load(f)
        if 'dummygen' in driver_definition and 'enum' in driver_definition['dummygen']:
            enum = driver_definition['dummygen']['enum']

    pta.set_random_energy_model()

    runs = list(pta.dfs(opt['depth'], with_arguments = True, with_parameters = True, trace_filter = opt['trace-filter'], sleep = opt['sleep']))

    num_transitions = len(runs)

    if len(runs) == 0:
        print('DFS returned no traces -- perhaps your trace-filter is too restrictive?', file=sys.stderr)
        sys.exit(1)

    real_energies = list()
    real_durations = list()
    model_energies = list()
    for run in runs:
        accounting_method = get_simulated_accountingmethod(opt['accounting'])(pta, opt['timer-freq'], opt['timer-type'], opt['timestamp-type'],
            opt['power-type'], opt['energy-type'])
        real_energy, real_duration, _, _ = pta.simulate(run, accounting = accounting_method)
        model_energy = accounting_method.get_energy()
        real_energies.append(real_energy)
        real_durations.append(real_duration)
        model_energies.append(model_energy)

    measures = regression_measures(np.array(model_energies), np.array(real_energies))
    print('SMAPE {:.0f}%, MAE {}'.format(measures['smape'], measures['mae']))


    timer_freqs = [1e3, 2e3, 5e3, 1e4, 2e4, 5e4, 1e5, 2e5, 5e5, 1e6, 2e6, 5e6]
    timer_types = timestamp_types = power_types = energy_types = 'uint8_t uint16_t uint32_t uint64_t'.split()

    def config_weight(timer_freq, timer_type, ts_type, power_type, energy_type):
        base_weight = 0
        for var_type in timer_type, ts_type, power_type, energy_type:
            if var_type == 'uint8_t':
                base_weight += 1
            elif var_type == 'uint16_t':
                base_weight += 2
            elif var_type == 'uint32_t':
                base_weight += 4
            elif var_type == 'uint64_t':
                base_weight += 8
        return base_weight

    #sys.exit(0)

    mean_errors = list()
    for timer_freq, timer_type, ts_type, power_type, energy_type in itertools.product(timer_freqs, timer_types, timestamp_types, power_types, energy_types):
        real_energies = list()
        real_durations = list()
        model_energies = list()
        # duration in µs
        # Bei kurzer Dauer (z.B. nur [1e2]) performt auc uint32_t für Energie gut, sonst nicht so (weil overflow)
        for sleep_duration in [1e2, 1e3, 1e4, 1e5, 1e6]:
            runs = pta.dfs(opt['depth'], with_arguments = True, with_parameters = True, trace_filter = opt['trace-filter'], sleep = sleep_duration)
            for run in runs:
                accounting_method = get_simulated_accountingmethod(opt['accounting'])(pta, timer_freq, timer_type, ts_type, power_type, energy_type)
                real_energy, real_duration, _, _ = pta.simulate(run, accounting = accounting_method)
                model_energy = accounting_method.get_energy()
                real_energies.append(real_energy)
                real_durations.append(real_duration)
                model_energies.append(model_energy)
        measures = regression_measures(np.array(model_energies), np.array(real_energies))
        mean_errors.append(((timer_freq, timer_type, ts_type, power_type, energy_type), config_weight(timer_freq, timer_type, ts_type, power_type, energy_type), measures))

    mean_errors.sort(key = lambda x: x[1])
    mean_errors.sort(key = lambda x: x[2]['mae'])

    for result in mean_errors:
        config, weight, measures = result
        print('{}  -> {:.0f}% / {}'.format(
            config,
            measures['smape'], measures['mae']))

    sys.exit(0)