package audit

import (
	"context"
	"fmt"
	"log/slog"
	"strings"
	"sync"
	"sync/atomic"
	"time"

	llm "gitea.stevedudenhoeffer.com/steve/majordomo/llm"
)

// stepTextMax caps the per-step assistant-text preview persisted on a
// "step" event. Large enough to capture the model's reasoning around a
// (mis)fired tool call — the single best clue to WHY a model emitted a
// malformed call — but bounded so the longtext payload can't balloon.
const stepTextMax = 2000

// Writer wraps a Storage with the OnStep / OnTool callbacks suitable for
// wiring into the majordomo agent loop's step observer, tracking sequence
// numbers and tool-call counts internally.
//
// Why: the agent loop's observer hooks are unaware of run identity; the
// writer captures the runID + skill metadata at construction so the
// per-event callbacks stay simple. AppendLog failures are logged but
// never fatal — audit must not break user-visible execution.
//
// What: NewWriter(storage, runID) → use OnStep / OnTool / Close. Close
// records the final FinishRun. The executors translate each agent.Step
// into one OnStep call (1-indexed iteration, the step's *llm.Response)
// plus one OnTool call per executed tool.
//
// Test: see writer_test.go for sequence ordering and finish semantics.
type Writer struct {
	storage  Storage
	runID    string
	sequence atomic.Int32
	calls    atomic.Int32
	mu       sync.Mutex // guards Close idempotency + token tally
	closed   bool

	// V5 token accumulator — summed across each OnStep's resp.Usage.
	// Reads come from TokenStats() so the executor can pass them to
	// FinishRun. atomics-on-Int64 would also work, but mu already
	// guards Close + we need consistent multi-field reads anyway
	// (input + output + thinking). The mutex hot-path overhead is
	// negligible vs the LLM call latency that dominates step time.
	inputTokens    int64
	outputTokens   int64
	thinkingTokens int64

	// Per-step wall-clock + run-level model attribution (guarded by mu).
	// startedAt anchors the first step's duration; lastStepAt is the
	// previous step's observation time; resolvedModelLogged ensures the
	// one-shot "resolved_model" run-level event fires at most once.
	startedAt           time.Time
	lastStepAt          time.Time
	resolvedModelLogged bool
}

// NewWriter constructs a Writer. The caller is expected to have already
// called Storage.StartRun.
func NewWriter(storage Storage, runID string) *Writer {
	return &Writer{storage: storage, runID: runID, startedAt: time.Now()}
}

// OnStep records one agent-loop step: a "step" event with the iteration
// number and the response's text size.
//
// V5: also tallies per-step token usage. majordomo populates
// resp.Usage when the provider reports it; for providers that don't,
// the fields stay 0 and the tally stays at 0 — the formatter then
// renders "—" rather than a misleading "$0.00".
//
// Why we tally here vs in the agent loop: the loop's Result.Usage is a
// run total; the audit row needs the same numbers, but the writer also
// serves the live RunState accessor mid-run, so a per-step running sum
// is the right shape. Global usage attribution is handled by the llms
// package's instrumented models — the writer tally is strictly the
// per-run audit roll-up.
func (w *Writer) OnStep(iter int, resp *llm.Response) {
	if w == nil || w.storage == nil {
		return
	}
	now := time.Now()
	payload := map[string]any{"iter": iter}

	w.mu.Lock()
	// Per-step wall-clock: time since the previous observed step, or since
	// run start for the first step. A long gap localises a slow/hung model
	// call — the signal that was missing when an animate step-0 call hung
	// ~5 min. NOTE: this is step-to-step wall time (model call + the prior
	// step's tool execution), not pure model latency.
	prev := w.lastStepAt
	if prev.IsZero() {
		prev = w.startedAt
	}
	if !prev.IsZero() {
		payload["step_ms"] = now.Sub(prev).Milliseconds()
	}
	w.lastStepAt = now
	if resp != nil {
		w.inputTokens += int64(resp.Usage.InputTokens)
		w.outputTokens += int64(resp.Usage.OutputTokens)
		// Thinking/reasoning tokens are a first-class Usage field in
		// majordomo (populated by the providers that report them).
		w.thinkingTokens += int64(resp.Usage.ReasoningTokens)
	}
	// One-shot run-level served-model attribution: the FIRST step with a
	// resolved model name emits a "resolved_model" event so a run that
	// errors before producing a useful step still records which model
	// served it. resp.Model is failover-aware ("provider/model-id" of the
	// element that actually served), unlike the static configured head.
	logResolvedModel := ""
	if resp != nil && resp.Model != "" && !w.resolvedModelLogged {
		w.resolvedModelLogged = true
		logResolvedModel = resp.Model
	}
	w.mu.Unlock()

	if resp != nil {
		payload["text_len"] = len(resp.Text())
		// Served model + why generation stopped — the two scalars that turn
		// a "model misbehaved" guess into a fact. finish_reason on an
		// empty-tool-call step disambiguates truncation (length) from a
		// deliberate empty emission (tool_calls).
		if resp.Model != "" {
			payload["model"] = resp.Model
		}
		if resp.FinishReason != "" {
			payload["finish_reason"] = string(resp.FinishReason)
		}
		// Per-step token breakdown (OnStep already reads these into the run
		// total above; persisting the per-step slice costs nothing more).
		payload["in_tokens"] = resp.Usage.InputTokens
		payload["out_tokens"] = resp.Usage.OutputTokens
		if resp.Usage.ReasoningTokens > 0 {
			payload["thinking_tokens"] = resp.Usage.ReasoningTokens
		}
		if resp.Usage.CacheReadTokens > 0 {
			payload["cache_read_tokens"] = resp.Usage.CacheReadTokens
		}
		// The model's own narration accompanying this step — the smoking gun
		// for WHY a malformed tool call was emitted. Capped; suppressed when
		// the step fired a secret-bearing tool (mcp_call/email_send/http_*)
		// whose narration could echo the secret it's about to send.
		if t := strings.TrimSpace(resp.Text()); t != "" {
			if stepHasSecretTool(resp) {
				payload["text_redacted"] = true
			} else {
				payload["text"] = truncate(t, stepTextMax)
			}
		}
	} else {
		payload["text_len"] = 0
	}

	w.appendLog("step", payload)

	if logResolvedModel != "" {
		w.appendLog("resolved_model", map[string]any{"model": logResolvedModel})
	}
}

// stepHasSecretTool reports whether a step's response fired a tool whose
// surrounding narration could leak a secret (MCP args, email body/
// recipients, raw HTTP request). Mirrors the steps.go redaction list so
// the audit trace never persists secret-adjacent assistant text.
func stepHasSecretTool(resp *llm.Response) bool {
	if resp == nil {
		return false
	}
	for _, c := range resp.ToolCalls {
		switch c.Name {
		case "mcp_call", "email_send":
			return true
		}
		if strings.HasPrefix(c.Name, "http_") {
			return true
		}
	}
	return false
}

// TokenStats returns the running totals tallied from OnStep.
// Safe to call concurrently. Returned values are a snapshot at call
// time. Used by the executors to populate RunStats before Close
// finalises the audit row.
//
// Why: the executor needs the totals AND a model name to compute cost,
// but cost calculation is a different concern from audit persistence.
// Exposing this getter lets the cost calculation live in the executor
// where the model is known.
func (w *Writer) TokenStats() (input, output, thinking int64) {
	if w == nil {
		return 0, 0, 0
	}
	w.mu.Lock()
	defer w.mu.Unlock()
	return w.inputTokens, w.outputTokens, w.thinkingTokens
}

// OnTool records a "tool_call" event with the tool name and a
// "tool_result" event with the result length. Tool count is incremented
// for each call. The executors call this once per executed tool call
// from their step observers (call + matching result content).
func (w *Writer) OnTool(call llm.ToolCall, result string) {
	if w == nil || w.storage == nil {
		return
	}
	w.calls.Add(1)
	w.appendLog("tool_call", map[string]any{
		"name": call.Name,
		"args": string(call.Arguments),
		"id":   call.ID,
	})
	w.appendLog("tool_result", map[string]any{
		"name":      call.Name,
		"id":        call.ID,
		"result":    truncate(result, 4000),
		"truncated": len(result) > 4000,
	})
}

// LogEvent records a custom event mid-run. The executor uses this for
// diagnostic events (e.g. "compaction_setup" / "compaction_fired")
// outside the canonical step / tool_call / tool_result / error set.
// Nil-safe: no-op when receiver or storage is nil.
//
// Why: skill_run_logs is the only sink Steve can read from SQL, so
// diagnostics intended for post-hoc debugging belong here. slog goes
// to mort.log which is harder to reach from outside the host.
func (w *Writer) LogEvent(eventType string, payload map[string]any) {
	if w == nil || w.storage == nil {
		return
	}
	w.appendLog(eventType, payload)
}

// LogError records an "error" event mid-run. Distinct from the terminal
// status set by Close.
func (w *Writer) LogError(msg string) {
	if w == nil || w.storage == nil {
		return
	}
	w.appendLog("error", map[string]any{"message": msg})
}

// Close finishes the run. The caller assembles a RunStats; the writer
// fills in ToolCalls (which is bookkept on the writer itself) and
// hands the full record to FinishRun.
//
// Idempotent: subsequent calls are no-ops.
//
// Why a struct vs the old positional form: v5 adds four token + cost
// fields on top of the legacy six. The struct keeps call sites readable
// and lets future fields slot in without churning every caller.
//
// Why context.WithoutCancel: the run's terminal status MUST land in
// the audit row regardless of the run ctx's state. Pre-fix, child
// skill runs invoked via skill_invoke / skill_invoke_parallel inherited
// the parent agent's runCtx as their outer ctx; when the parent
// timed out at MaxRuntime, every in-flight child's FinishRun fired
// with that already-cancelled ctx and the row was left in
// status=running forever. Detaching here is defence in depth — the
// caller (skillexec.runInner / agentexec.runInner) ALSO detaches at
// the call site, but a cancelled ctx in the writer's hands MUST NOT
// drop the audit write. The short timeout (auditFinishTimeout) bounds
// the write so a hung DB doesn't pin the run goroutine indefinitely.
func (w *Writer) Close(ctx context.Context, stats RunStats) {
	if w == nil || w.storage == nil {
		return
	}
	w.mu.Lock()
	defer w.mu.Unlock()
	if w.closed {
		return
	}
	w.closed = true
	stats.ToolCalls = int(w.calls.Load())
	// Detach from the caller's deadline + cancellation. Run cleanup
	// must complete even when the run ctx is dead. The fresh
	// auditFinishTimeout caps how long we'll wait on the storage.
	finishCtx, cancel := context.WithTimeout(context.WithoutCancel(ctx), auditFinishTimeout)
	defer cancel()
	if err := w.storage.FinishRun(finishCtx, w.runID, stats); err != nil {
		slog.Warn("skillaudit: FinishRun failed", "run_id", w.runID, "error", err)
	}
}

// auditFinishTimeout caps how long Close will wait on the storage's
// FinishRun call after detaching from the caller's ctx. 10s is generous
// for a single-row UPDATE against MySQL — anything longer suggests a
// hung connection that the run goroutine shouldn't keep waiting on.
const auditFinishTimeout = 10 * time.Second

// ToolCallsCount returns how many tool invocations OnTool has seen so
// far. Useful for budget enforcement.
func (w *Writer) ToolCallsCount() int { return int(w.calls.Load()) }

func (w *Writer) appendLog(eventType string, payload map[string]any) {
	seq := int(w.sequence.Add(1))
	log := SkillRunLog{
		RunID:     w.runID,
		Sequence:  seq,
		EventType: eventType,
		Payload:   payload,
		CreatedAt: time.Now(),
	}
	if err := w.storage.AppendLog(context.Background(), log); err != nil {
		slog.Warn("skillaudit: AppendLog failed", "run_id", w.runID, "seq", seq, "type", eventType, "error", err)
	}
}

func truncate(s string, max int) string {
	if len(s) <= max {
		return s
	}
	return s[:max] + fmt.Sprintf("…[+%d bytes]", len(s)-max)
}