const require_base = require("../messages/base.cjs");
const require_ai = require("../messages/ai.cjs");
const require_base$1 = require("../runnables/base.cjs");
require("../messages/index.cjs");
const require_language_models_chat_models = require("../language_models/chat_models.cjs");
//#region src/testing/fake_model_builder.ts
function deriveContent(messages) {
	return messages.map((m) => m.text).filter(Boolean).join("-");
}
let idCounter = 0;
function nextToolCallId() {
	idCounter += 1;
	return `fake_tc_${idCounter}`;
}
/**
* A fake chat model for testing, created via {@link fakeModel}.
*
* Queue responses with `.respond()` and `.respondWithTools()`, then
* pass the instance directly wherever a chat model is expected.
* Responses are consumed in first-in-first-out order — one per `invoke()` call.
* When all queued responses are consumed, further invocations throw.
*/
var FakeBuiltModel = class FakeBuiltModel extends require_language_models_chat_models.BaseChatModel {
	queue = [];
	_alwaysThrowError;
	_structuredResponseValue;
	_tools = [];
	_state = {
		callIndex: 0,
		calls: []
	};
	/**
	* All invocations recorded by this model, in order.
	* Each entry contains the `messages` array and `options` that were
	* passed to `invoke()`.
	*/
	get calls() {
		return this._state.calls;
	}
	/**
	* The number of times this model has been invoked.
	*/
	get callCount() {
		return this._state.calls.length;
	}
	constructor() {
		super({});
	}
	_llmType() {
		return "fake-model-builder";
	}
	_combineLLMOutput() {
		return [];
	}
	/**
	* Enqueue a response that the model will return on its next invocation.
	* @param entry A {@link BaseMessage} to return, an `Error` to throw, or
	*   a factory `(messages) => BaseMessage | Error` for dynamic responses.
	* @returns `this`, for chaining.
	*/
	respond(entry) {
		if (typeof entry === "function") this.queue.push({
			kind: "factory",
			factory: entry
		});
		else if (require_base.BaseMessage.isInstance(entry)) this.queue.push({
			kind: "message",
			message: entry
		});
		else this.queue.push({
			kind: "error",
			error: entry
		});
		return this;
	}
	/**
	* Enqueue an {@link AIMessage} that carries the given tool calls.
	* Content is derived from the input messages at invocation time.
	* @param toolCalls Array of tool calls. Each entry needs `name` and
	*   `args`; `id` is optional and auto-generated when omitted.
	* @returns `this`, for chaining.
	*/
	respondWithTools(toolCalls) {
		this.queue.push({
			kind: "toolCalls",
			toolCalls: toolCalls.map((tc) => ({
				name: tc.name,
				args: tc.args,
				id: tc.id ?? nextToolCallId(),
				type: "tool_call"
			}))
		});
		return this;
	}
	/**
	* Make every invocation throw the given error, regardless of the queue.
	* @param error The error to throw.
	* @returns `this`, for chaining.
	*/
	alwaysThrow(error) {
		this._alwaysThrowError = error;
		return this;
	}
	/**
	* Set the value that {@link withStructuredOutput} will resolve to.
	* @param value The structured object to return.
	* @returns `this`, for chaining.
	*/
	structuredResponse(value) {
		this._structuredResponseValue = value;
		return this;
	}
	/**
	* Bind tools to the model. Returns a new model that shares the same
	* response queue and call history.
	* @param tools The tools to bind, as {@link StructuredTool} instances or
	*   plain {@link ToolSpec} objects.
	* @returns A new RunnableBinding with the tools bound.
	*/
	bindTools(tools) {
		const merged = [...this._tools, ...tools];
		const next = new FakeBuiltModel();
		next.queue = this.queue;
		next._alwaysThrowError = this._alwaysThrowError;
		next._structuredResponseValue = this._structuredResponseValue;
		next._tools = merged;
		next._state = this._state;
		return next.withConfig({});
	}
	/**
	* Returns a {@link Runnable} that produces the {@link structuredResponse}
	* value. The schema argument is accepted for compatibility but ignored.
	* @param _params Schema or params (ignored).
	* @param _config Options (ignored).
	* @returns A Runnable that resolves to the structured response value.
	*/
	withStructuredOutput(_params, _config) {
		const { _structuredResponseValue } = this;
		return require_base$1.RunnableLambda.from(async () => {
			return _structuredResponseValue;
		});
	}
	async _generate(messages, options, _runManager) {
		this._state.calls.push({
			messages: [...messages],
			options
		});
		const currentCallIndex = this._state.callIndex;
		this._state.callIndex += 1;
		if (this._alwaysThrowError) throw this._alwaysThrowError;
		const entry = this.queue[currentCallIndex];
		if (!entry) throw new Error(`FakeModel: no response queued for invocation ${currentCallIndex} (${this.queue.length} total queued).`);
		if (entry.kind === "error") throw entry.error;
		if (entry.kind === "factory") {
			const result = entry.factory(messages);
			if (!require_base.BaseMessage.isInstance(result)) throw result;
			return { generations: [{
				text: "",
				message: result
			}] };
		}
		if (entry.kind === "message") return { generations: [{
			text: "",
			message: entry.message
		}] };
		const content = deriveContent(messages);
		return {
			generations: [{
				text: content,
				message: new require_ai.AIMessage({
					content,
					id: currentCallIndex.toString(),
					tool_calls: entry.toolCalls.length > 0 ? entry.toolCalls.map((tc) => ({
						...tc,
						type: "tool_call"
					})) : void 0
				})
			}],
			llmOutput: {}
		};
	}
};
/**
* Creates a new {@link FakeBuiltModel} for testing.
*
* Returns a chainable builder — queue responses, then pass the model
* anywhere a chat model is expected. Responses are consumed in FIFO
* order, one per `invoke()` call.
*
* ## API summary
*
* | Method | Description |
* | --- | --- |
* | `fakeModel()` | Creates a new fake chat model. Returns a chainable builder. |
* | `.respond(message)` | Queue an `AIMessage` (or any `BaseMessage`) to return on the next invocation. |
* | `.respond(error)` | Queue an `Error` to throw on the next invocation. |
* | `.respond(factory)` | Queue a function `(messages) => BaseMessage \| Error` for dynamic responses. |
* | `.respondWithTools(toolCalls)` | Shorthand for `.respond()` with tool calls. Each entry needs `name` and `args`; `id` is optional. |
* | `.alwaysThrow(error)` | Make every invocation throw this error, regardless of the queue. |
* | `.structuredResponse(value)` | Set the value returned by `.withStructuredOutput()`. |
* | `.bindTools(tools)` | Bind tools to the model. Returns a `RunnableBinding` that shares the response queue and call recording. |
* | `.withStructuredOutput(schema)` | Returns a runnable that produces the `.structuredResponse()` value. |
* | `.calls` | Array of `{ messages, options }` for every invocation (read-only). |
* | `.callCount` | Number of times the model has been invoked. |
*
* @example
* ```typescript
* const model = fakeModel()
*   .respondWithTools([{ name: "search", args: { query: "weather" } }])
*   .respond(new AIMessage("Sunny and warm."));
*
* const r1 = await model.invoke([new HumanMessage("What's the weather?")]);
* // r1.tool_calls[0].name === "search"
*
* const r2 = await model.invoke([new HumanMessage("Thanks")]);
* // r2.content === "Sunny and warm."
* ```
*/
function fakeModel() {
	return new FakeBuiltModel();
}
//#endregion
exports.FakeBuiltModel = FakeBuiltModel;
exports.fakeModel = fakeModel;

//# sourceMappingURL=fake_model_builder.cjs.map