# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0

"""Adapter that drives PlannerStateMachine via the PlannerReplayBridge.

The bridge (Rust, PyO3) runs the offline simulation step-by-step.
This adapter sits between the bridge and the planner state machine:

    Bridge.advance_to(tick_ms) → raw metrics dict
    Adapter._build_tick_input() → TickInput
    StateMachine.on_tick() → PlannerEffects
    Adapter → Bridge.apply_scaling(prefill, decode)

Supports both aggregated and disaggregated topologies. No I/O, no runtime
dependencies. Fully deterministic when used with offline replay.
"""

from __future__ import annotations

import logging
from dataclasses import dataclass, field
from typing import Any, Optional

from dynamo.common.forward_pass_metrics import (
    ForwardPassMetrics,
    QueuedRequestMetrics,
    ScheduledRequestMetrics,
)
from dynamo.planner.config.planner_config import PlannerConfig
from dynamo.planner.core.state_machine import PlannerStateMachine
from dynamo.planner.core.types import (
    FpmObservations,
    PlannerEffects,
    ScheduledTick,
    TickDiagnostics,
    TickInput,
    TrafficObservation,
    WorkerCapabilities,
    WorkerCounts,
)

logger = logging.getLogger(__name__)


@dataclass
class ScalingEvent:
    """Record of a single scaling decision."""

    at_s: float
    component: str  # "agg", "prefill", or "decode"
    from_count: int
    to_count: int
    reason: Optional[str] = None


@dataclass
class ReplayPlannerReport:
    """Enriched report combining trace metrics and planner diagnostics."""

    trace_report: dict[str, Any]
    scaling_events: list[ScalingEvent] = field(default_factory=list)
    diagnostics_log: list[TickDiagnostics] = field(default_factory=list)
    total_ticks: int = 0


def _build_fpm_from_dict(d: dict[str, Any]) -> ForwardPassMetrics:
    """Convert a bridge FPM snapshot dict into a ForwardPassMetrics struct."""
    return ForwardPassMetrics(
        worker_id=str(d["worker_id"]),
        dp_rank=0,
        wall_time=d["wall_time"],
        scheduled_requests=ScheduledRequestMetrics(
            num_prefill_requests=d["num_prefill_requests"],
            sum_prefill_tokens=d["sum_prefill_tokens"],
            var_prefill_length=d["var_prefill_length"],
            sum_prefill_kv_tokens=d["sum_prefill_kv_tokens"],
            num_decode_requests=d["num_decode_requests"],
            sum_decode_kv_tokens=d["sum_decode_kv_tokens"],
            var_decode_kv_tokens=d["var_decode_kv_tokens"],
        ),
        queued_requests=QueuedRequestMetrics(
            num_prefill_requests=d["num_queued_prefill"],
            sum_prefill_tokens=d["sum_queued_prefill_tokens"],
            var_prefill_length=d["var_queued_prefill_length"],
            num_decode_requests=d["num_queued_decode"],
            sum_decode_kv_tokens=d["sum_queued_decode_kv_tokens"],
            var_decode_kv_tokens=d["var_queued_decode_kv_tokens"],
        ),
    )


def _update_fpm_cache(
    cache: dict[tuple[str, int], ForwardPassMetrics],
    snapshots: list[dict[str, Any]],
    active_count: int,
) -> None:
    """Update a last-seen FPM cache with new snapshots and prune removed workers."""
    for snap in snapshots:
        fpm = _build_fpm_from_dict(snap)
        cache[(fpm.worker_id, fpm.dp_rank)] = fpm

    # Prune cache down to active_count entries. Workers are removed
    # highest-ID-first during scale-down, so keep the lowest IDs.
    while len(cache) > active_count:
        # Remove the highest worker ID entry
        worst_key = max(cache.keys(), key=lambda k: int(k[0]))
        del cache[worst_key]


class ReplayPlannerAdapter:
    """Drives the planner state machine using the PlannerReplayBridge.

    Supports both ``mode="agg"`` and ``mode="disagg"``.
    """

    def __init__(
        self,
        planner_config: PlannerConfig,
        bridge: Any,  # PlannerReplayBridge (Rust pyclass)
        capabilities: Optional[WorkerCapabilities] = None,
        warmup_observations: Optional[list[TrafficObservation]] = None,
    ) -> None:
        self._config = planner_config
        self._bridge = bridge
        self._sm = PlannerStateMachine(planner_config, capabilities)
        self._is_disagg = planner_config.mode == "disagg"

        # Last-seen FPM caches (separate for prefill/decode)
        self._prefill_fpm_cache: dict[tuple[str, int], ForwardPassMetrics] = {}
        self._decode_fpm_cache: dict[tuple[str, int], ForwardPassMetrics] = {}

        # Scaling targets — used as `expected` in WorkerCounts
        self._scaling_target_prefill: Optional[int] = None
        self._scaling_target_decode: Optional[int] = None

        if warmup_observations:
            self._sm.warm_load_predictors(warmup_observations)

    def run(self) -> ReplayPlannerReport:
        """Run the full replay with planner-in-the-loop."""
        next_tick = self._sm.initial_tick(0.0)
        scaling_events: list[ScalingEvent] = []
        diagnostics_log: list[TickDiagnostics] = []
        total_ticks = 0

        while True:
            tick_ms = next_tick.at_s * 1000.0
            result = self._bridge.advance_to(tick_ms)

            if result["is_done"]:
                break

            tick_input = self._build_tick_input(next_tick, result)
            effects: PlannerEffects = self._sm.on_tick(next_tick, tick_input)
            diagnostics_log.append(effects.diagnostics)
            total_ticks += 1

            # Clear scaling targets once active counts match
            active_p = result["active_prefill_count"]
            active_d = result["active_decode_count"]
            if (
                self._scaling_target_prefill is not None
                and active_p == self._scaling_target_prefill
            ):
                self._scaling_target_prefill = None
            if (
                self._scaling_target_decode is not None
                and active_d == self._scaling_target_decode
            ):
                self._scaling_target_decode = None

            if effects.scale_to is not None:
                self._apply_scaling(effects, result, tick_input.now_s, scaling_events)

            if effects.next_tick is None:
                break
            next_tick = effects.next_tick

        trace_report = self._bridge.finalize()
        return ReplayPlannerReport(
            trace_report=trace_report,
            scaling_events=scaling_events,
            diagnostics_log=diagnostics_log,
            total_ticks=total_ticks,
        )

    def _apply_scaling(
        self,
        effects: PlannerEffects,
        result: dict[str, Any],
        now_s: float,
        scaling_events: list[ScalingEvent],
    ) -> None:
        """Apply scaling decisions and record events."""
        scale = effects.scale_to
        assert scale is not None
        current_p = result["active_prefill_count"]
        current_d = result["active_decode_count"]
        target_p = scale.num_prefill if scale.num_prefill is not None else current_p
        target_d = scale.num_decode if scale.num_decode is not None else current_d

        if target_p == current_p and target_d == current_d:
            return

        self._bridge.apply_scaling(target_p, target_d)

        if self._is_disagg:
            if scale.num_prefill is not None and target_p != current_p:
                direction = "scale_up" if target_p > current_p else "scale_down"
                logger.info(
                    "Planner scaling prefill: %d -> %d at t=%.1fs (%s)",
                    current_p,
                    target_p,
                    now_s,
                    direction,
                )
                self._scaling_target_prefill = target_p
                scaling_events.append(
                    ScalingEvent(
                        at_s=now_s,
                        component="prefill",
                        from_count=current_p,
                        to_count=target_p,
                        reason=direction,
                    )
                )
            if scale.num_decode is not None and target_d != current_d:
                direction = "scale_up" if target_d > current_d else "scale_down"
                logger.info(
                    "Planner scaling decode: %d -> %d at t=%.1fs (%s)",
                    current_d,
                    target_d,
                    now_s,
                    direction,
                )
                self._scaling_target_decode = target_d
                scaling_events.append(
                    ScalingEvent(
                        at_s=now_s,
                        component="decode",
                        from_count=current_d,
                        to_count=target_d,
                        reason=direction,
                    )
                )
        else:
            direction = "scale_up" if target_d > current_d else "scale_down"
            logger.info(
                "Planner scaling: %d -> %d workers at t=%.1fs (%s)",
                current_d,
                target_d,
                now_s,
                direction,
            )
            self._scaling_target_decode = target_d
            scaling_events.append(
                ScalingEvent(
                    at_s=now_s,
                    component="agg",
                    from_count=current_d,
                    to_count=target_d,
                    reason=direction,
                )
            )

    def _feed_extra_fpm_to_regression(
        self,
        decode_snaps: list[dict[str, Any]],
        prefill_snaps: list[dict[str, Any]],
    ) -> None:
        """Feed accumulated FPM snapshots to regression, excluding the last
        per worker (which will be added by _observe_fpm via fpm_observations).
        This avoids double-counting the cached snapshot."""
        if not hasattr(self._sm, "_is_easy") or self._sm._is_easy:
            return  # easy mode has no regression models

        if self._sm._is_agg:
            # Exclude the last snapshot per worker (it's in the cache and
            # will be added by _observe_fpm)
            last_idx_per_worker: dict[int, int] = {}
            for i, snap in enumerate(decode_snaps):
                last_idx_per_worker[snap["worker_id"]] = i
            exclude = set(last_idx_per_worker.values())
            for i, snap in enumerate(decode_snaps):
                if i in exclude:
                    continue
                fpm = _build_fpm_from_dict(snap)
                if fpm.wall_time > 0.0:
                    self._sm._agg_regression.add_observation(fpm)
        else:
            if self._sm._has_prefill:
                last_idx: dict[int, int] = {}
                for i, snap in enumerate(prefill_snaps):
                    last_idx[snap["worker_id"]] = i
                exclude = set(last_idx.values())
                for i, snap in enumerate(prefill_snaps):
                    if i in exclude:
                        continue
                    fpm = _build_fpm_from_dict(snap)
                    if fpm.wall_time > 0.0:
                        self._sm._prefill_regression.add_observation(fpm)
            if self._sm._has_decode:
                last_idx = {}
                for i, snap in enumerate(decode_snaps):
                    last_idx[snap["worker_id"]] = i
                exclude = set(last_idx.values())
                for i, snap in enumerate(decode_snaps):
                    if i in exclude:
                        continue
                    fpm = _build_fpm_from_dict(snap)
                    if fpm.wall_time > 0.0:
                        self._sm._decode_regression.add_observation(fpm)

    def _build_tick_input(
        self, tick: ScheduledTick, result: dict[str, Any]
    ) -> TickInput:
        """Convert bridge result dict to planner TickInput."""
        now_s = result["now_ms"] / 1000.0

        worker_counts = None
        if tick.need_worker_states:
            active_p = result["active_prefill_count"]
            active_d = result["active_decode_count"]
            expected_p = (
                self._scaling_target_prefill
                if self._scaling_target_prefill is not None
                else active_p
            )
            expected_d = (
                self._scaling_target_decode
                if self._scaling_target_decode is not None
                else active_d
            )
            worker_counts = WorkerCounts(
                ready_num_prefill=active_p if self._is_disagg else None,
                ready_num_decode=active_d,
                expected_num_prefill=expected_p if self._is_disagg else None,
                expected_num_decode=expected_d,
            )

        fpm_observations = None
        if tick.need_worker_fpm:
            prefill_snaps = result.get("prefill_fpm_snapshots", [])
            decode_snaps = result.get("decode_fpm_snapshots", [])

            _update_fpm_cache(
                self._prefill_fpm_cache, prefill_snaps, result["active_prefill_count"]
            )
            _update_fpm_cache(
                self._decode_fpm_cache, decode_snaps, result["active_decode_count"]
            )

            # In offline replay, we accumulate many FPM snapshots per tick
            # (one per engine pass). Feed ALL non-idle snapshots directly to
            # the regression models for a representative fit. The last-per-worker
            # cache is only used for the FpmObservations dict (worker count
            # reconciliation), not as the sole regression input.
            prefill_dict = (
                dict(self._prefill_fpm_cache) if self._prefill_fpm_cache else None
            )
            decode_dict = (
                dict(self._decode_fpm_cache) if self._decode_fpm_cache else None
            )
            self._feed_extra_fpm_to_regression(decode_snaps, prefill_snaps)
            fpm_observations = FpmObservations(
                prefill=prefill_dict,
                decode=decode_dict,
            )

        traffic = None
        if tick.need_traffic_metrics:
            t = result.get("traffic", {})
            duration_s = t.get("duration_s", 0.0)
            if duration_s > 0:
                traffic = TrafficObservation(
                    duration_s=duration_s,
                    num_req=float(t.get("num_req", 0)),
                    isl=t.get("avg_isl", 0.0),
                    osl=t.get("avg_osl", 0.0),
                )

        return TickInput(
            now_s=now_s,
            traffic=traffic,
            worker_counts=worker_counts,
            fpm_observations=fpm_observations,
        )
