import { pruneMessages, type ModelMessage } from 'ai';

type Part = {
  type: string;
  toolCallId?: string;
  providerOptions?: Record<string, Record<string, unknown>>;
};

function itemId(part: Part): string | undefined {
  for (const options of Object.values(part.providerOptions ?? {})) {
    const id = options['itemId'];
    if (typeof id === 'string') return id;
  }
  return undefined;
}

const partsOf = (message: ModelMessage): Part[] =>
  message.role === 'assistant' && Array.isArray(message.content) ? (message.content as Part[]) : [];

/** The part again, with every provider `itemId` removed. */
function withoutItemId(part: Part): Part {
  const providerOptions: Record<string, Record<string, unknown>> = {};
  for (const [provider, options] of Object.entries(part.providerOptions ?? {})) {
    const { itemId: _dropped, ...rest } = options;
    if (Object.keys(rest).length > 0) providerOptions[provider] = rest;
  }
  const next: Part = { ...part };
  if (Object.keys(providerOptions).length > 0) next.providerOptions = providerOptions;
  else delete next.providerOptions;
  return next;
}

/**
 * Detaches assistant parts from reasoning items that pruning removed.
 *
 * A part carrying a provider `itemId` is not sent inline. The OpenAI responses
 * provider serialises it as `{ type: 'item_reference', id }`, pointing at an item
 * stored on their side, and that stored item depends on the `reasoning` item from
 * the same response. Send the reference without the reasoning and the request is
 * rejected with 400 "was provided without its required 'reasoning' item".
 *
 * The repair is to drop the `itemId`, not the part. Without it the same content is
 * serialised inline — a plain assistant message, a plain `function_call` — which
 * carries no dependency on anything stored. Verified against the provider's own
 * serialiser: `text` with an itemId goes out as `item_reference`, and the identical
 * part without one goes out as `output_text`.
 *
 * Dropping the part instead, which is what this used to do, cost the model its
 * memory of the turn: after compaction it could no longer see the tool results it
 * had just collected, so it called the same tools again until it hit the step limit.
 */
export function detachOrphanedItems(before: ModelMessage[], after: ModelMessage[]): ModelMessage[] {
  const survivingReasoning = new Set<string>();
  for (const message of after) {
    for (const part of partsOf(message)) {
      if (part.type !== 'reasoning') continue;
      const id = itemId(part);
      if (id) survivingReasoning.add(id);
    }
  }

  const orphaned = new Set<string>();
  for (const message of before) {
    const parts = partsOf(message);
    const reasoning = parts.filter((p) => p.type === 'reasoning').map(itemId);
    if (reasoning.length === 0) continue;
    if (reasoning.some((id) => id !== undefined && survivingReasoning.has(id))) continue;

    for (const part of parts) {
      const id = itemId(part);
      if (id) orphaned.add(id);
    }
  }

  if (orphaned.size === 0) return after;

  return after.map((message) => {
    const parts = partsOf(message);
    if (parts.length === 0) return message;

    let changed = false;
    const next = parts.map((part) => {
      const id = itemId(part);
      if (id === undefined || !orphaned.has(id)) return part;
      changed = true;
      return withoutItemId(part);
    });

    return changed ? ({ ...message, content: next } as ModelMessage) : message;
  });
}

export type PruneOptions = Parameters<typeof pruneMessages>[0];

const anyParts = (message: ModelMessage): Part[] =>
  Array.isArray(message.content) ? (message.content as Part[]) : [];

/**
 * Every part again with its provider `itemId` gone, so the history goes out inline
 * rather than as `item_reference` entries pointing at provider-side storage.
 *
 * A reference only resolves while the provider still holds that item; once it does
 * not, the request is rejected with 404 "Item with id '...' not found" and no retry
 * of the same history can succeed. The content is already in the local history, so
 * inlining loses nothing.
 */
export function detachProviderItems(messages: ModelMessage[]): ModelMessage[] {
  return messages.map((message) => {
    const parts = anyParts(message);
    if (parts.length === 0) return message;

    let changed = false;
    const next = parts.map((part) => {
      if (itemId(part) === undefined) return part;
      changed = true;
      return withoutItemId(part);
    });

    return changed ? ({ ...message, content: next } as ModelMessage) : message;
  });
}

/**
 * Drops tool results whose tool call is gone.
 *
 * The OpenAI responses API rejects a `function_call_output` with no `function_call`
 * carrying the same call id: 400 "No tool call found for function call output with
 * call_id ...". `pruneMessages({ toolCalls: 'before-last-3-messages' })` counts
 * messages, so the cut can land between an assistant tool-call and the tool message
 * answering it.
 *
 * The reverse pairing is left alone on purpose: a call still awaiting its result is
 * exactly what a suspended approval looks like, and dropping it would break resume.
 */
export function dropOrphanedResults(messages: ModelMessage[]): ModelMessage[] {
  const calls = new Set<string>();
  for (const message of messages) {
    for (const part of anyParts(message)) {
      if (part.type === 'tool-call' && part.toolCallId) calls.add(part.toolCallId);
    }
  }

  const cleaned: ModelMessage[] = [];
  for (const message of messages) {
    const parts = anyParts(message);
    if (parts.length === 0) {
      cleaned.push(message);
      continue;
    }

    const kept = parts.filter(
      (part) => !ANSWER_PARTS.has(part.type) || part.toolCallId === undefined || calls.has(part.toolCallId),
    );

    if (kept.length === parts.length) cleaned.push(message);
    else if (kept.length > 0) cleaned.push({ ...message, content: kept } as ModelMessage);
  }

  return cleaned;
}

/** pruneMessages, then repair the provider-item dependencies it breaks. */
export function prunePreservingItems(options: PruneOptions): ModelMessage[] {
  const pruned = pruneMessages(options);
  return dropOrphanedResults(detachOrphanedItems(options.messages, pruned));
}

/**
 * The messages a prune would discard, so they can be summarized before they go.
 *
 * Compaction keeps the model's *memory of a turn* — the tool tail it is told to
 * keep stays verbatim. What it does not keep is any statement of what was
 * dropped. So a decision from forty messages ago vanishes silently, and the model
 * contradicts it with full confidence, because as far as it can tell it never
 * said that.
 *
 * Identity is by reference, not by value: `prunePreservingItems` rebuilds the
 * surviving messages with `{ ...message }`, so a value comparison would report
 * every message as changed and no message as dropped. `Set` on the object
 * references is exact.
 *
 * Only messages that carry content worth summarizing are returned — an assistant
 * turn consisting of nothing but a dropped `reasoning` part is not a decision, and
 * summarizing "the model thought for a while" is worse than saying nothing.
 */
export function droppedBy(before: ModelMessage[], after: ModelMessage[]): ModelMessage[] {
  const surviving = new Set<ModelMessage>(after);
  return before.filter((message) => !surviving.has(message));
}

const ANSWER_PARTS = new Set(['tool-result', 'tool-error']);

function textOf(message: ModelMessage): string {
  const { content } = message;
  if (typeof content === 'string') return content;
  if (!Array.isArray(content)) return '';
  const chunks: string[] = [];
  for (const part of content as Part[]) {
    const p = part as Part & { text?: unknown; input?: unknown; output?: unknown };
    if (typeof p.text === 'string') chunks.push(p.text);
    // A tool call's input is the decision made: the path, the command, the patch.
    else if (p.type === 'tool-call' && p.input !== undefined) chunks.push(JSON.stringify(p.input));
    // A tool result is what came back. Without it a digest says what the model
    // asked for and nothing about the answer, which is the half a later
    // contradiction is usually argued from.
    else if (ANSWER_PARTS.has(p.type) && p.output !== undefined) {
      const rendered = typeof p.output === 'string' ? p.output : JSON.stringify(p.output);
      chunks.push(rendered);
    }
  }
  return chunks.join(' ').trim();
}

/**
 * A one-line-per-message digest of what a prune wants to drop.
 *
 * This is the *fallback* when no summarizer is available or the call fails: crude,
 * but it preserves the thing that matters — which tool touched which path, and in
 * what order — rather than the nothing that is there today. The summarizer, when
 * it runs, is a model and reads far better than this.
 */
export function digestOf(dropped: readonly ModelMessage[]): string {
  const lines: string[] = [];
  for (const message of dropped) {
    const text = textOf(message);
    if (!text) continue;
    const role = message.role === 'tool' ? 'result' : message.role;
    const clipped = text.length > 160 ? `${text.slice(0, 160)}...` : text;
    lines.push(`- (${role}) ${clipped}`);
  }
  return lines.join('\n');
}

export const PRUNED_SPAN_PREFIX = 'Earlier in this session, now compacted away:';

export function isPrunedSpanSummary(message: ModelMessage): boolean {
  return message.role === 'user' && typeof message.content === 'string' && message.content.startsWith(PRUNED_SPAN_PREFIX);
}

export function prunedSpanMessage(summary: string | undefined, dropped: readonly ModelMessage[]): ModelMessage | undefined {
  const body = summary?.trim() || digestOf(dropped);
  if (!body) return undefined;
  return { role: 'user', content: `${PRUNED_SPAN_PREFIX}\n\n${body}` };
}

/**
 * How many trailing messages keep their tool content, widest first.
 *
 * One agent step is two messages — the assistant's tool call and the tool message
 * answering it — so 64 is about 32 steps of memory.
 */
export function droppedSpan(before: ModelMessage[], after: ModelMessage[]): ModelMessage[] {
  const norm = (m: ModelMessage) => {
    const c = (m as { content?: unknown }).content;
    if (typeof c === 'string') return `${m.role}:${c}`;
    try {
      // Ignore reasoning parts and all providerOptions: a kept-but-detached
      // message (reasoning stripped, itemId removed) is not considered dropped.
      const filtered = Array.isArray(c)
        ? c.filter((p: unknown) => (p as { type?: string }).type !== 'reasoning')
        : c;
      const stripped = JSON.stringify(filtered, (k, v) => (k === 'providerOptions' ? undefined : k === 'itemId' ? undefined : v));
      return `${m.role}:${stripped}`;
    } catch {
      return `${m.role}:${String(c)}`;
    }
  };
  const keptNorm = new Set(after.map(norm));
  return before.filter((m) => !keptNorm.has(norm(m)));
}

const KEEP_LADDER = [64, 32, 16, 8, 4] as const;

/**
 * Token estimate used by the session harness — heuristic, not a tokenizer.
 * `len/4` undercounts tool envelopes (role + toolCallId + providerOptions);
 * `len/3.6 + 8*msgs` tracks cl100k closer without pulling a tokenizer.
 * Every display of its value must label it an estimate (e.g. "~N tokens (est.)").
 * Exported so session and tests share it.
 */
export function estimateTokens(messages: ModelMessage[]): number {
  return Math.round(JSON.stringify(messages).length / 3.6 + messages.length * 8);
}

export type FitOptions = {
  messages: ModelMessage[];
  /** Estimated tokens the wire history must come in under. */
  threshold: number;
  estimate: (messages: ModelMessage[]) => number;
};

/**
 * Prunes only as hard as the threshold requires.
 *
 * A fixed `before-last-3-messages` is catastrophic on an agent transcript, because
 * nearly every assistant and tool message there consists of nothing but tool parts:
 * stripping them empties the message, `emptyMessages: 'remove'` deletes it, and a
 * 405-message history collapses to five. Measured on a synthetic run of 202 steps —
 * two surviving tool calls out of 202.
 *
 * That is not a cost problem, it is a correctness one. The model loses its record of
 * what it already ran, so it runs it again, the history grows, the threshold is
 * crossed again, and the turn never converges. It looks like `git_status` and
 * `list_dir` being called in a circle with a compaction notice between them.
 *
 * So: drop reasoning first, since it is never needed on the wire, and only reach for
 * tool content if that was not enough — keeping as much of the recent tail as fits.
 * The widest rung that comes in under the threshold wins; if even the narrowest does
 * not, the narrowest is returned, because sending something is better than sending a
 * request that will be rejected for size.
 */
/** Head that must never be pruned: the initial user goal and first assistant ack. */
function headOf(messages: ModelMessage[]): ModelMessage[] {
  if (messages.length === 0) return [];
  const firstUser = messages.find((m) => m.role === 'user');
  if (!firstUser) return [];
  // Keep first user message; if an assistant immediately follows, keep it too (goal ack).
  const idx = messages.indexOf(firstUser);
  const next = messages[idx + 1];
  if (next && next.role === 'assistant' && idx === 0) return messages.slice(0, 2);
  return [firstUser];
}

function withHeadPreserved(all: ModelMessage[], pruned: ModelMessage[]): ModelMessage[] {
  const head = headOf(all);
  if (head.length === 0) return pruned;
  // identity + full content equality (not 80-char slice which collides)
  const headNorm = JSON.stringify(head[0]!.content);
  if (pruned.some((m) => m === head[0]! || JSON.stringify(m.content) === headNorm)) return pruned;
  return [...head, ...pruned];
}

export function pruneToFit({ messages, threshold, estimate }: FitOptions): ModelMessage[] {
  const withoutReasoning = withHeadPreserved(
    messages,
    detachProviderItems(prunePreservingItems({ messages, reasoning: 'all', emptyMessages: 'remove' })),
  );
  if (estimate(withoutReasoning) <= threshold) return withoutReasoning;

  let narrowest = withoutReasoning;
  for (const keep of KEEP_LADDER) {
    narrowest = withHeadPreserved(
      messages,
      detachProviderItems(
        prunePreservingItems({
          messages,
          reasoning: 'all',
          toolCalls: `before-last-${keep}-messages`,
          emptyMessages: 'remove',
        }),
      ),
    );
    if (estimate(narrowest) <= threshold) return narrowest;
  }
  return narrowest;
}

export { KEEP_LADDER };