"""
Harnesses for various types of benchmark logs.

tbd
"""
import re
from .pubcode import Code128


class TransitionHarness:
    """
    TODO

    :param done: True if the specified amount of iterations have been logged.
    :param synced: True if `parser_cb` has synchronized with UART output, i.e., the benchmark has successfully started.
    :param traces: List of annotated PTA traces from benchmark execution. This list is updated during UART logging and should only be read back when `done` is True.
        Uses the standard dfatool trace format: `traces` is a list of `{'id': ..., 'trace': ...}` dictionaries, each of which represents a single PTA trace (AKA
        run). Each `trace` is in turn a list of state or transition dictionaries with the
        following attributes:
        * `isa`: 'state' or 'transition'
        * `name`: state or transition name
        * `parameter`: currently valid parameter values. If normalization is used, they are already normalized. Each parameter value is either a primitive
          int/float/str value (-> constant for each iteration) or a list of
          primitive values (-> set by the return value of the current run, not necessarily constant)
        * `args`: function arguments, if isa == 'transition'
    """

    def __init__(
        self,
        gpio_pin=None,
        gpio_mode="around",
        pta=None,
        log_return_values=False,
        repeat=0,
        post_transition_delay_us=0,
        energytrace_sync=None,
    ):
        """
        Create a new TransitionHarness

        :param gpio_pin: multipass GPIO Pin used for transition synchronization with an external measurement device, e.g. `GPIO::p1_0`. Optional.
            The GPIO output is high iff a transition is executing
        :param pta: PTA object. Needed to map UART output IDs to states and transitions
        :param log_return_values: Log return values of transition function calls?
        :param repeat: How many times to run the benchmark until setting `one`, default 0.
            When 0, `done` is never set.
        :param post_transition_delay_us: If set, inject `arch.delay_us` after each transition, before logging the transition as completed (and releasing
            `gpio_pin`). This artificially increases transition duration by the specified time and is useful if an external measurement device's resolution is
            lower than the expected minimum transition duration.
        """
        self.gpio_pin = gpio_pin
        self.gpio_mode = gpio_mode
        self.pta = pta
        self.log_return_values = log_return_values
        self.repeat = repeat
        self.post_transition_delay_us = post_transition_delay_us
        self.energytrace_sync = energytrace_sync
        self.reset()

    def copy(self):
        new_object = __class__(
            gpio_pin=self.gpio_pin,
            gpio_mode=self.gpio_mode,
            pta=self.pta,
            log_return_values=self.log_return_values,
            repeat=self.repeat,
            post_transition_delay_us=self.post_transition_delay_us,
            energytrace_sync=self.energytrace_sync,
        )
        new_object.traces = self.traces.copy()
        new_object.trace_id = self.trace_id
        return new_object

    def undo(self, undo_from):
        """
        Undo all benchmark runs starting with index `undo_from`.

        :param undo_from: index of measurements to be undone. Measurementh with a higher index (i.e., which happened later) will also be undone.

        Removes all logged results (nondeterministic parameter values and return values)
        of the current benchmark iteration. Resets `done` and `synced`,
        """
        for trace in self.traces:
            for state_or_transition in trace["trace"]:
                if "return_values" in state_or_transition:
                    state_or_transition["return_values"] = state_or_transition[
                        "return_values"
                    ][:undo_from]
                for param_name in state_or_transition["parameter"].keys():
                    if type(state_or_transition["parameter"][param_name]) is list:
                        state_or_transition["parameter"][
                            param_name
                        ] = state_or_transition["parameter"][param_name][:undo_from]

    def reset(self):
        """
        Reset harness for a new benchmark.

        Truncates `traces`, `trace_id`, `done`, and `synced`.
        """
        self.traces = []
        self.trace_id = 0
        self.repetitions = 0
        self.abort = False
        self.done = False
        self.synced = False

    def restart(self):
        """
        Reset harness for a new execution of the current benchmark.

        Resets `done` and `synced`.
        """
        self.repetitions = 0
        self.abort = False
        self.done = False
        self.synced = False

    def global_code(self):
        """Return global (pre-`main()`) C++ code needed for tracing."""
        ret = ""
        if self.gpio_pin != None:
            ret += "#define PTALOG_GPIO {}\n".format(self.gpio_pin)
            if self.gpio_mode == "before":
                ret += "#define PTALOG_GPIO_BEFORE\n"
            elif self.gpio_mode == "bar":
                ret += "#define PTALOG_GPIO_BAR\n"
        if self.log_return_values:
            ret += "#define PTALOG_WITH_RETURNVALUES\n"
            ret += "uint16_t transition_return_value;\n"
        ret += '#include "object/ptalog.h"\n'
        if self.gpio_pin != None:
            ret += "PTALog ptalog({});\n".format(self.gpio_pin)
        else:
            ret += "PTALog ptalog;\n"
        return ret

    def start_benchmark(self, benchmark_id=0):
        """Return C++ code to signal benchmark start to harness."""
        return "ptalog.startBenchmark({:d});\n".format(benchmark_id)

    def start_trace(self):
        """Prepare a new trace/run in the internal `.traces` structure."""
        self.traces.append({"id": self.trace_id, "trace": list()})
        self.trace_id += 1

    def append_state(self, state_name, param):
        """
        Append a state to the current run in the internal `.traces` structure.

        :param state_name: state name
        :param param: parameter dict
        """
        self.traces[-1]["trace"].append(
            {"name": state_name, "isa": "state", "parameter": param}
        )

    def append_transition(self, transition_name, param, args=[]):
        """
        Append a transition to the current run in the internal `.traces` structure.

        :param transition_name: transition name
        :param param: parameter dict
        :param args: function arguments (optional)
        """
        self.traces[-1]["trace"].append(
            {
                "name": transition_name,
                "isa": "transition",
                "parameter": param,
                "args": args,
            }
        )

    def start_run(self):
        """Return C++ code used to start a new run/trace."""
        return "ptalog.reset();\n"

    def _get_barcode(self, transition_id):
        barcode_bits = Code128("T{}".format(transition_id), charset="B").modules
        if len(barcode_bits) % 8 != 0:
            barcode_bits.extend([1] * (8 - (len(barcode_bits) % 8)))
        barcode_bytes = [
            255 - int("".join(map(str, reversed(barcode_bits[i : i + 8]))), 2)
            for i in range(0, len(barcode_bits), 8)
        ]
        inline_array = "".join(map(lambda s: "\\x{:02x}".format(s), barcode_bytes))
        return inline_array, len(barcode_bytes)

    def pass_transition(
        self, transition_id, transition_code, transition: object = None
    ):
        """
        Return C++ code used to pass a transition, including the corresponding function call.

        Tracks which transition has been executed and optionally its return value. May also inject a delay, if
        `post_transition_delay_us` is set.
        """
        ret = "ptalog.passTransition({:d});\n".format(transition_id)
        if self.gpio_mode == "bar":
            ret += """ptalog.startTransition("{}", {});\n""".format(
                *self._get_barcode(transition_id)
            )
        else:
            ret += "ptalog.startTransition();\n"
        if (
            self.log_return_values
            and transition
            and len(transition.return_value_handlers)
        ):
            ret += "transition_return_value = {}\n".format(transition_code)
            ret += "ptalog.logReturn(transition_return_value);\n"
        else:
            ret += "{}\n".format(transition_code)
        if self.post_transition_delay_us:
            ret += "arch.delay_us({});\n".format(self.post_transition_delay_us)
        ret += "ptalog.stopTransition();\n"
        return ret

    def stop_run(self, num_traces=0):
        return "ptalog.dump({:d});\n".format(num_traces)

    def stop_benchmark(self):
        return "ptalog.stopBenchmark();\n"

    def _append_nondeterministic_parameter_value(
        self, log_data_target, parameter_name, parameter_value
    ):
        if log_data_target["parameter"][parameter_name] is None:
            log_data_target["parameter"][parameter_name] = list()
        log_data_target["parameter"][parameter_name].append(parameter_value)

    # Here Be Dragons
    def parser_cb(self, line):
        # print('[HARNESS] got line {}'.format(line))
        if re.match(r"\[PTA\] benchmark stop", line):
            self.repetitions += 1
            self.synced = False
            if self.repeat > 0 and self.repetitions == self.repeat:
                self.done = True
                print("[HARNESS] done")
                return
        if re.match(r"\[PTA\] benchmark start, id=(\S+)", line):
            self.synced = True
            print("[HARNESS] synced, {}/{}".format(self.repetitions + 1, self.repeat))
        if self.synced:
            res = re.match(r"\[PTA\] trace=(\S+) count=(\S+)", line)
            if res:
                self.trace_id = int(res.group(1))
                self.trace_length = int(res.group(2))
                self.current_transition_in_trace = 0
            if self.log_return_values:
                res = re.match(r"\[PTA\] transition=(\S+) return=(\S+)", line)
            else:
                res = re.match(r"\[PTA\] transition=(\S+)", line)
            if res:
                transition_id = int(res.group(1))
                # self.traces contains transitions and states, UART output only contains transitions -> use index * 2
                try:
                    log_data_target = self.traces[self.trace_id]["trace"][
                        self.current_transition_in_trace * 2
                    ]
                except IndexError:
                    transition_name = None
                    if self.pta:
                        transition_name = self.pta.transitions[transition_id].name
                    print(
                        "[HARNESS] benchmark id={:d} trace={:d}: transition #{:d} (ID {:d}, name {}) is out of bounds".format(
                            0,
                            self.trace_id,
                            self.current_transition_in_trace,
                            transition_id,
                            transition_name,
                        )
                    )
                    print("          Offending line: {}".format(line))
                    return
                if log_data_target["isa"] != "transition":
                    self.abort = True
                    raise RuntimeError(
                        "Log mismatch: Expected transition, got {:s}".format(
                            log_data_target["isa"]
                        )
                    )
                if self.pta:
                    transition = self.pta.transitions[transition_id]
                    if transition.name != log_data_target["name"]:
                        self.abort = True
                        raise RuntimeError(
                            "Log mismatch: Expected transition {:s}, got transition {:s} -- may have been caused by preceding malformed UART output".format(
                                log_data_target["name"], transition.name
                            )
                        )
                    if self.log_return_values and len(transition.return_value_handlers):
                        for handler in transition.return_value_handlers:
                            if "parameter" in handler:
                                parameter_value = return_value = int(res.group(2))

                                if "return_values" not in log_data_target:
                                    log_data_target["return_values"] = list()
                                log_data_target["return_values"].append(return_value)

                                if "formula" in handler:
                                    parameter_value = handler["formula"].eval(
                                        return_value
                                    )

                                self._append_nondeterministic_parameter_value(
                                    log_data_target,
                                    handler["parameter"],
                                    parameter_value,
                                )
                                for following_log_data_target in self.traces[
                                    self.trace_id
                                ]["trace"][
                                    (self.current_transition_in_trace * 2 + 1) :
                                ]:
                                    self._append_nondeterministic_parameter_value(
                                        following_log_data_target,
                                        handler["parameter"],
                                        parameter_value,
                                    )
                                if "apply_from" in handler and any(
                                    map(
                                        lambda x: x["name"] == handler["apply_from"],
                                        self.traces[self.trace_id]["trace"][
                                            : (self.current_transition_in_trace * 2 + 1)
                                        ],
                                    )
                                ):
                                    for preceding_log_data_target in reversed(
                                        self.traces[self.trace_id]["trace"][
                                            : (self.current_transition_in_trace * 2)
                                        ]
                                    ):
                                        self._append_nondeterministic_parameter_value(
                                            preceding_log_data_target,
                                            handler["parameter"],
                                            parameter_value,
                                        )
                                        if (
                                            preceding_log_data_target["name"]
                                            == handler["apply_from"]
                                        ):
                                            break
                self.current_transition_in_trace += 1


class OnboardTimerHarness(TransitionHarness):
    """TODO

    Additional parameters / changes from TransitionHarness:

    :param traces: Each trace element (`.traces[*]['trace'][*]`) additionally contains
        the dict `offline_aggregates` with the member `duration`. It contains a list of durations (in us) of the corresponding state/transition for each
        benchmark iteration.
        I.e. `.traces[*]['trace'][*]['offline_aggregates']['duration'] = [..., ...]`
    :param remove_nop_from_timings: If true, remove the nop duration from reported timings
        (i.e., reported timings reflect the estimated transition/state duration with the timer call overhea dremoved).
        If false, do not remove nop durations, so the timings more accurately reflect the elapsed wall-clock time during the benchmark.
    """

    def __init__(self, counter_limits, remove_nop_from_timings=True, **kwargs):
        super().__init__(**kwargs)
        self.remove_nop_from_timings = remove_nop_from_timings
        self.trace_length = 0
        (
            self.one_cycle_in_us,
            self.one_overflow_in_us,
            self.counter_max_overflow,
        ) = counter_limits

    def copy(self):
        new_harness = __class__(
            (self.one_cycle_in_us, self.one_overflow_in_us, self.counter_max_overflow),
            gpio_pin=self.gpio_pin,
            gpio_mode=self.gpio_mode,
            pta=self.pta,
            log_return_values=self.log_return_values,
            repeat=self.repeat,
            energytrace_sync=self.energytrace_sync,
        )
        new_harness.traces = self.traces.copy()
        new_harness.trace_id = self.trace_id
        return new_harness

    def undo(self, undo_from):
        """
        Undo all benchmark runs starting with index `undo_from`.

        :param undo_from: index of measurements to be undone. Measurementh with a higher index (i.e., which happened later) will also be undone.

        Removes all logged results (durations, nondeterministic parameter values, return values)
        of the current benchmark iteration. Resets `done` and `synced`,
        """
        super().undo(undo_from)
        for trace in self.traces:
            for state_or_transition in trace["trace"]:
                if "offline_aggregates" in state_or_transition:
                    state_or_transition["offline_aggregates"][
                        "duration"
                    ] = state_or_transition["offline_aggregates"]["duration"][
                        :undo_from
                    ]

    def global_code(self):
        ret = "#define PTALOG_TIMING\n"
        ret += super().global_code()
        if self.energytrace_sync == "led":
            # TODO Make nicer
            ret += """\nvoid runLASync(){
    // ======================= LED SYNC ================================
    ptalog.passTransition(0);
    ptalog.startTransition();
    gpio.led_toggle(0);
    gpio.led_toggle(1);
    ptalog.stopTransition();

    for (unsigned char i = 0; i < 4; i++) {
        arch.sleep_ms(250);
    }

    ptalog.passTransition(0);
    ptalog.startTransition();
    gpio.led_toggle(0);
    gpio.led_toggle(1);
    ptalog.stopTransition();
    // ======================= LED SYNC ================================
}\n\n"""
        return ret

    def start_benchmark(self, benchmark_id=0):
        ret = ""
        if self.energytrace_sync == "led":
            ret += "runLASync();\n"
        ret += "ptalog.passNop();\n"
        if self.energytrace_sync == "led":
            ret += "arch.sleep_ms(250);\n"
        ret += super().start_benchmark(benchmark_id)
        return ret

    def stop_benchmark(self):
        ret = ""
        ret += super().stop_benchmark()
        if self.energytrace_sync == "led":
            ret += "runLASync();\n"
            ret += "arch.sleep_ms(250);\n"
        return ret

    def pass_transition(
        self, transition_id, transition_code, transition: object = None
    ):
        ret = "ptalog.passTransition({:d});\n".format(transition_id)
        if self.gpio_mode == "bar":
            ret += """ptalog.startTransition("{}", {});\n""".format(
                *self._get_barcode(transition_id)
            )
        else:
            ret += "ptalog.startTransition();\n"
        if (
            self.log_return_values
            and transition
            and len(transition.return_value_handlers)
        ):
            ret += "transition_return_value = {}\n".format(transition_code)
        else:
            ret += "{}\n".format(transition_code)
        if (
            self.log_return_values
            and transition
            and len(transition.return_value_handlers)
        ):
            ret += "ptalog.logReturn(transition_return_value);\n"
        ret += "ptalog.stopTransition();\n"
        return ret

    def _append_nondeterministic_parameter_value(
        self, log_data_target, parameter_name, parameter_value
    ):
        if log_data_target["parameter"][parameter_name] is None:
            log_data_target["parameter"][parameter_name] = list()
        log_data_target["parameter"][parameter_name].append(parameter_value)

    # Here Be Dragons
    def parser_cb(self, line):
        # print('[HARNESS] got line {}'.format(line))
        res = re.match(r"\[PTA\] nop=(\S+)/(\S+)", line)
        if res:
            self.nop_cycles = int(res.group(1))
            if int(res.group(2)):
                raise RuntimeError(
                    "Counter overflow ({:d}/{:d}) during NOP test, wtf?!".format(
                        res.group(1), res.group(2)
                    )
                )
        match = re.match(r"\[PTA\] benchmark stop, cycles=(\S+)/(\S+)", line)
        if match:
            self.repetitions += 1
            self.synced = False
            if self.repeat > 0 and self.repetitions == self.repeat:
                self.done = True
                prev_state_cycles = int(match.group(1))
                prev_state_overflow = int(match.group(2))
                prev_state_duration_us = (
                    prev_state_cycles * self.one_cycle_in_us
                    + prev_state_overflow * self.one_overflow_in_us
                )
                if self.remove_nop_from_timings:
                    prev_state_duration_us -= self.nop_cycles * self.one_cycle_in_us
                final_state = self.traces[self.trace_id]["trace"][-1]
                if "offline_aggregates" not in final_state:
                    final_state["offline_aggregates"] = {"duration": list()}
                final_state["offline_aggregates"]["duration"].append(
                    prev_state_duration_us
                )

                print("[HARNESS] done")
                return
        # May be repeated, e.g. if the device is reset shortly after start by
        # EnergyTrace.
        if re.match(r"\[PTA\] benchmark start, id=(\S+)", line):
            self.synced = True
            print("[HARNESS] synced, {}/{}".format(self.repetitions + 1, self.repeat))
        if self.synced:
            res = re.match(r"\[PTA\] trace=(\S+) count=(\S+)", line)
            if res:
                self.trace_id = int(res.group(1))
                self.trace_length = int(res.group(2))
                self.current_transition_in_trace = 0
            if self.log_return_values:
                res = re.match(
                    r"\[PTA\] transition=(\S+) prevcycles=(\S+)/(\S+) cycles=(\S+)/(\S+) return=(\S+)",
                    line,
                )
            else:
                res = re.match(
                    r"\[PTA\] transition=(\S+) prevcycles=(\S+)/(\S+) cycles=(\S+)/(\S+)",
                    line,
                )
            if res:
                transition_id = int(res.group(1))
                prev_state_cycles = int(res.group(2))
                prev_state_overflow = int(res.group(3))
                cycles = int(res.group(4))
                overflow = int(res.group(5))
                if overflow >= self.counter_max_overflow:
                    self.abort = True
                    raise RuntimeError(
                        "Counter overflow ({:d}/{:d}) in benchmark id={:d} trace={:d}: transition #{:d} (ID {:d})".format(
                            cycles,
                            overflow,
                            0,
                            self.trace_id,
                            self.current_transition_in_trace,
                            transition_id,
                        )
                    )
                if prev_state_overflow >= self.counter_max_overflow:
                    self.abort = True
                    raise RuntimeError(
                        "Counter overflow ({:d}/{:d}) in benchmark id={:d} trace={:d}: state before transition #{:d} (ID {:d})".format(
                            prev_state_cycles,
                            prev_state_overflow,
                            0,
                            self.trace_id,
                            self.current_transition_in_trace,
                            transition_id,
                        )
                    )
                duration_us = (
                    cycles * self.one_cycle_in_us + overflow * self.one_overflow_in_us
                )
                prev_state_duration_us = (
                    prev_state_cycles * self.one_cycle_in_us
                    + prev_state_overflow * self.one_overflow_in_us
                )
                if self.remove_nop_from_timings:
                    duration_us -= self.nop_cycles * self.one_cycle_in_us
                    prev_state_duration_us -= self.nop_cycles * self.one_cycle_in_us
                if duration_us < 0:
                    duration_us = 0
                # self.traces contains transitions and states, UART output only contains transitions -> use index * 2
                try:
                    log_data_target = self.traces[self.trace_id]["trace"][
                        self.current_transition_in_trace * 2
                    ]
                    if self.current_transition_in_trace > 0:
                        prev_state_data = self.traces[self.trace_id]["trace"][
                            self.current_transition_in_trace * 2 - 1
                        ]
                    elif self.current_transition_in_trace == 0 and self.trace_id > 0:
                        prev_state_data = self.traces[self.trace_id - 1]["trace"][-1]
                    else:
                        prev_state_data = None
                except IndexError:
                    transition_name = None
                    if self.pta:
                        transition_name = self.pta.transitions[transition_id].name
                    print(
                        "[HARNESS] benchmark id={:d} trace={:d}: transition #{:d} (ID {:d}, name {}) is out of bounds".format(
                            0,
                            self.trace_id,
                            self.current_transition_in_trace,
                            transition_id,
                            transition_name,
                        )
                    )
                    print("          Offending line: {}".format(line))
                    return
                if log_data_target["isa"] != "transition":
                    self.abort = True
                    raise RuntimeError(
                        "Log mismatch in benchmark id={:d} trace={:d}: transition #{:d} (ID {:d}): Expected transition, got {:s}".format(
                            0,
                            self.trace_id,
                            self.current_transition_in_trace,
                            transition_id,
                            log_data_target["isa"],
                        )
                    )
                if prev_state_data and prev_state_data["isa"] != "state":
                    self.abort = True
                    raise RuntimeError(
                        "Log mismatch in benchmark id={:d} trace={:d}: state before transition #{:d} (ID {:d}): Expected state, got {:s}".format(
                            0,
                            self.trace_id,
                            self.current_transition_in_trace,
                            transition_id,
                            prev_state_data["isa"],
                        )
                    )
                if self.pta:
                    transition = self.pta.transitions[transition_id]
                    if transition.name != log_data_target["name"]:
                        self.abort = True
                        raise RuntimeError(
                            "Log mismatch in benchmark id={:d} trace={:d}: transition #{:d} (ID {:d}): Expected transition {:s}, got transition {:s} -- may have been caused by preceding maformed UART output".format(
                                0,
                                self.trace_id,
                                self.current_transition_in_trace,
                                transition_id,
                                log_data_target["name"],
                                transition.name,
                                line,
                            )
                        )
                    if self.log_return_values and len(transition.return_value_handlers):
                        for handler in transition.return_value_handlers:
                            if "parameter" in handler:
                                parameter_value = return_value = int(res.group(4))

                                if "return_values" not in log_data_target:
                                    log_data_target["return_values"] = list()
                                log_data_target["return_values"].append(return_value)

                                if "formula" in handler:
                                    parameter_value = handler["formula"].eval(
                                        return_value
                                    )

                                self._append_nondeterministic_parameter_value(
                                    log_data_target,
                                    handler["parameter"],
                                    parameter_value,
                                )
                                for following_log_data_target in self.traces[
                                    self.trace_id
                                ]["trace"][
                                    (self.current_transition_in_trace * 2 + 1) :
                                ]:
                                    self._append_nondeterministic_parameter_value(
                                        following_log_data_target,
                                        handler["parameter"],
                                        parameter_value,
                                    )
                                if "apply_from" in handler and any(
                                    map(
                                        lambda x: x["name"] == handler["apply_from"],
                                        self.traces[self.trace_id]["trace"][
                                            : (self.current_transition_in_trace * 2 + 1)
                                        ],
                                    )
                                ):
                                    for preceding_log_data_target in reversed(
                                        self.traces[self.trace_id]["trace"][
                                            : (self.current_transition_in_trace * 2)
                                        ]
                                    ):
                                        self._append_nondeterministic_parameter_value(
                                            preceding_log_data_target,
                                            handler["parameter"],
                                            parameter_value,
                                        )
                                        if (
                                            preceding_log_data_target["name"]
                                            == handler["apply_from"]
                                        ):
                                            break
                if "offline_aggregates" not in log_data_target:
                    log_data_target["offline_aggregates"] = {"duration": list()}
                log_data_target["offline_aggregates"]["duration"].append(duration_us)
                if prev_state_data is not None:
                    if "offline_aggregates" not in prev_state_data:
                        prev_state_data["offline_aggregates"] = {"duration": list()}
                    prev_state_data["offline_aggregates"]["duration"].append(
                        prev_state_duration_us
                    )
                self.current_transition_in_trace += 1