import { NodeFileSystem } from "@effect/platform-node"
import { HttpRecorder, Redactor } from "@opencode-ai/http-recorder"
import { describe, expect } from "bun:test"
import { tool } from "ai"
import { Effect, Layer, Stream } from "effect"
import { FetchHttpClient } from "effect/unstable/http"
import path from "node:path"
import z from "zod"
import { Auth } from "@/auth"
import { Config } from "@/config/config"
import { Plugin } from "@/plugin"
import { Provider } from "@/provider/provider"
import { ModelID, ProviderID } from "@/provider/schema"
import { Filesystem } from "@/util/filesystem"
import { LLMClient, RequestExecutor } from "@opencode-ai/llm/route"
import { RuntimeFlags } from "@/effect/runtime-flags"
import type { Agent } from "../../src/agent/agent"
import { LLM } from "../../src/session/llm"
import { MessageV2 } from "../../src/session/message-v2"
import { MessageID, SessionID } from "../../src/session/schema"
import type { ModelsDev } from "@opencode-ai/core/models-dev"
import { TestInstance } from "../fixture/fixture"
import { testEffect } from "../lib/effect"

const OPENAI_CASSETTE = "session/native-openai-tool-call"
const ZEN_CASSETTE = "session/native-zen-tool-call"
const FIXTURES_DIR = path.join(import.meta.dir, "../fixtures/recordings")
const OPENAI_API_KEY = process.env.OPENCODE_RECORD_OPENAI_API_KEY ?? process.env.OPENAI_API_KEY
const CONSOLE_TOKEN = process.env.OPENCODE_RECORD_CONSOLE_TOKEN
const ZEN_ORG_ID = process.env.OPENCODE_RECORD_ZEN_ORG_ID
const ZEN_API_URL =
  process.env.OPENCODE_RECORD_ZEN_API_URL ?? "https://console.opencode.ai/proxy/connections/fixture/v1"

const shouldRecord = process.env.RECORD === "true"
const canRunOpenAI = shouldRecord
  ? Boolean(OPENAI_API_KEY)
  : HttpRecorder.hasCassetteSync(OPENAI_CASSETTE, { directory: FIXTURES_DIR })
const canRunZen = shouldRecord
  ? Boolean(CONSOLE_TOKEN && ZEN_ORG_ID)
  : HttpRecorder.hasCassetteSync(ZEN_CASSETTE, { directory: FIXTURES_DIR })

async function loadFixture(providerID: string, modelID: string) {
  const data = await Filesystem.readJson<Record<string, ModelsDev.Provider>>(
    path.join(import.meta.dir, "../tool/fixtures/models-api.json"),
  )
  const provider = data[providerID]
  if (!provider) throw new Error(`Missing provider in fixture: ${providerID}`)
  const model = provider.models[modelID]
  if (!model) throw new Error(`Missing model in fixture: ${modelID}`)
  return model
}

const openAIConfig = (model: ModelsDev.Provider["models"][string]): Partial<Config.Info> => ({
  enabled_providers: ["openai"],
  provider: {
    openai: {
      name: "OpenAI",
      env: ["OPENAI_API_KEY"],
      npm: "@ai-sdk/openai",
      api: "https://api.openai.com/v1",
      models: {
        [model.id]: JSON.parse(JSON.stringify(model)) as NonNullable<
          NonNullable<Config.Info["provider"]>[string]["models"]
        >[string],
      },
      options: {
        apiKey: OPENAI_API_KEY ?? "fixture-openai-key",
        baseURL: "https://api.openai.com/v1",
      },
    },
  },
})

const zenConfig = (model: ModelsDev.Provider["models"][string]): Partial<Config.Info> => ({
  enabled_providers: ["opencode"],
  provider: {
    opencode: {
      name: "OpenCode Zen",
      env: ["OPENCODE_CONSOLE_TOKEN"],
      npm: "@ai-sdk/openai-compatible",
      api: ZEN_API_URL,
      models: {
        [model.id]: JSON.parse(JSON.stringify(model)) as NonNullable<
          NonNullable<Config.Info["provider"]>[string]["models"]
        >[string],
      },
      options: {
        apiKey: CONSOLE_TOKEN ?? "fixture-console-token",
        headers: {
          "x-org-id": ZEN_ORG_ID ?? "fixture-org",
        },
      },
    },
  },
})

function recordedNativeLLMLayer(cassette: string, metadata: Record<string, unknown>) {
  const cassetteService = HttpRecorder.Cassette.fileSystem({ directory: FIXTURES_DIR }).pipe(
    Layer.provide(NodeFileSystem.layer),
  )
  // Only the HTTP client is recorded; RequestExecutor and the opencode LLM stack remain real.
  const recorder = HttpRecorder.recordingLayer(cassette, {
    mode: shouldRecord ? "record" : "replay",
    metadata,
    redactor: Redactor.compose(
      Redactor.defaults({
        url: {
          transform: (url) => url.replace(/\/proxy\/connections\/[^/]+\/v1/, "/proxy/connections/{connection}/v1"),
        },
      }),
      {
        response: (snapshot) => ({ ...snapshot, body: snapshot.body.replace(/wrk_[A-Z0-9]+/g, "wrk_redacted") }),
      },
    ),
  }).pipe(Layer.provide(FetchHttpClient.layer))
  const executor = RequestExecutor.layer.pipe(Layer.provide(recorder))
  const client = LLMClient.layer.pipe(Layer.provide(executor))

  const providerLayer = Provider.defaultLayer.pipe(
    Layer.provide(Auth.defaultLayer),
    Layer.provide(Config.defaultLayer),
    Layer.provide(Plugin.defaultLayer),
  )
  const llmLayer = LLM.layer.pipe(
    Layer.provide(Auth.defaultLayer),
    Layer.provide(Config.defaultLayer),
    Layer.provide(Provider.defaultLayer),
    Layer.provide(Plugin.defaultLayer),
    Layer.provide(client),
    Layer.provide(cassetteService),
    Layer.provide(RuntimeFlags.layer({ experimentalNativeLlm: true })),
  )

  return Layer.mergeAll(providerLayer, llmLayer)
}

const openAIIt = testEffect(
  recordedNativeLLMLayer(OPENAI_CASSETTE, {
    provider: "openai",
    protocol: "openai-responses",
    route: "openai-responses",
    tags: ["opencode", "native", "tool-call"],
  }),
)
const zenIt = testEffect(
  recordedNativeLLMLayer(ZEN_CASSETTE, {
    provider: "opencode",
    protocol: "openai-responses",
    route: "openai-responses",
    tags: ["opencode", "zen", "native", "tool-call"],
  }),
)
const recordedOpenAIInstance = canRunOpenAI ? openAIIt.instance : openAIIt.instance.skip
const recordedZenInstance = canRunZen ? zenIt.instance : zenIt.instance.skip

const writeConfig = (
  directory: string,
  model: ModelsDev.Provider["models"][string],
  config: (model: ModelsDev.Provider["models"][string]) => Partial<Config.Info> = openAIConfig,
) =>
  Effect.promise(() =>
    Bun.write(
      path.join(directory, "opencode.json"),
      JSON.stringify({ $schema: "https://opencode.ai/config.json", ...config(model) }),
    ),
  )

const getModel = (providerID: ProviderID, modelID: ModelID) =>
  Effect.gen(function* () {
    const provider = yield* Provider.Service
    return yield* provider.getModel(providerID, modelID)
  })

const collect = (input: LLM.StreamInput) =>
  Effect.gen(function* () {
    const llm = yield* LLM.Service
    return Array.from(yield* llm.stream(input).pipe(Stream.runCollect))
  })

describe("session.llm native recorded", () => {
  recordedOpenAIInstance("uses real RequestExecutor with HTTP recorder for native OpenAI tools", () =>
    Effect.gen(function* () {
      const test = yield* TestInstance
      const model = yield* Effect.promise(() => loadFixture("openai", "gpt-4.1-mini"))
      yield* writeConfig(test.directory, model)

      const sessionID = SessionID.make("session-recorded-native-tool")
      const agent = {
        name: "test",
        mode: "primary",
        prompt: "Call tools exactly as instructed.",
        options: {},
        permission: [{ permission: "*", pattern: "*", action: "allow" }],
        temperature: 0,
      } satisfies Agent.Info
      const resolved = yield* getModel(ProviderID.openai, ModelID.make(model.id))
      let executed: unknown

      const events = yield* collect({
        user: {
          id: MessageID.make("msg_user-recorded-native-tool"),
          sessionID,
          role: "user",
          time: { created: 0 },
          agent: agent.name,
          model: { providerID: ProviderID.make("openai"), modelID: ModelID.make(model.id) },
        } satisfies MessageV2.User,
        sessionID,
        model: resolved,
        agent,
        system: ["You must call the lookup tool exactly once with query weather. Do not answer in text."],
        messages: [{ role: "user", content: "Use lookup." }],
        toolChoice: "required",
        tools: {
          lookup: tool({
            description: "Lookup data.",
            inputSchema: z.object({ query: z.string() }),
            execute: async (args, options) => {
              executed = { args, toolCallId: options.toolCallId }
              return { output: "looked up" }
            },
          }),
        },
      })

      expect(events.filter((event) => event.type === "step-finish")).toHaveLength(1)
      expect(events.filter((event) => event.type === "finish")).toHaveLength(1)
      expect(events.some((event) => event.type === "tool-result")).toBe(true)
      expect(executed).toMatchObject({ args: { query: "weather" }, toolCallId: expect.any(String) })
    }),
  )

  recordedZenInstance("uses console-managed Zen config with native OpenAI-compatible tools", () =>
    Effect.gen(function* () {
      const test = yield* TestInstance
      const model = yield* Effect.promise(() => loadFixture("opencode", "gpt-5.2-codex"))
      yield* writeConfig(test.directory, model, zenConfig)

      const sessionID = SessionID.make("session-recorded-native-zen-tool")
      const agent = {
        name: "test",
        mode: "primary",
        prompt: "Call tools exactly as instructed.",
        options: {},
        permission: [{ permission: "*", pattern: "*", action: "allow" }],
      } satisfies Agent.Info
      const resolved = yield* getModel(ProviderID.opencode, ModelID.make(model.id))
      let executed: unknown

      const events = yield* collect({
        user: {
          id: MessageID.make("msg_user-recorded-native-zen-tool"),
          sessionID,
          role: "user",
          time: { created: 0 },
          agent: agent.name,
          model: { providerID: ProviderID.opencode, modelID: ModelID.make(model.id) },
        } satisfies MessageV2.User,
        sessionID,
        model: resolved,
        agent,
        system: ["You must call the lookup tool exactly once with query weather. Do not answer in text."],
        messages: [{ role: "user", content: "Use lookup." }],
        toolChoice: "required",
        tools: {
          lookup: tool({
            description: "Lookup data.",
            inputSchema: z.object({ query: z.string() }),
            execute: async (args, options) => {
              executed = { args, toolCallId: options.toolCallId }
              return { output: "looked up" }
            },
          }),
        },
      })

      expect(events.filter((event) => event.type === "step-finish")).toHaveLength(1)
      expect(events.filter((event) => event.type === "finish")).toHaveLength(1)
      expect(events.some((event) => event.type === "tool-result")).toBe(true)
      expect(executed).toMatchObject({ args: { query: "weather" }, toolCallId: expect.any(String) })
    }),
  )
})