import { afterEach, describe, expect, it, vi } from "vitest";
import * as authModule from "../agents/model-auth.js";
import { DEFAULT_GEMINI_EMBEDDING_MODEL } from "./embeddings-gemini.js";
import { createEmbeddingProvider, DEFAULT_LOCAL_MODEL } from "./embeddings.js";

vi.mock("../agents/model-auth.js", async () => {
  const { createModelAuthMockModule } = await import("../test-utils/model-auth-mock.js");
  return createModelAuthMockModule();
});

const importNodeLlamaCppMock = vi.fn();
vi.mock("./node-llama.js", () => ({
  importNodeLlamaCpp: (...args: unknown[]) => importNodeLlamaCppMock(...args),
}));

const createFetchMock = () =>
  vi.fn(async (_input?: unknown, _init?: unknown) => ({
    ok: true,
    status: 200,
    json: async () => ({ data: [{ embedding: [1, 2, 3] }] }),
  }));

const createGeminiFetchMock = () =>
  vi.fn(async (_input?: unknown, _init?: unknown) => ({
    ok: true,
    status: 200,
    json: async () => ({ embedding: { values: [1, 2, 3] } }),
  }));

function readFirstFetchRequest(fetchMock: { mock: { calls: unknown[][] } }) {
  const [url, init] = fetchMock.mock.calls[0] ?? [];
  return { url, init: init as RequestInit | undefined };
}

afterEach(() => {
  vi.resetAllMocks();
  vi.unstubAllGlobals();
});

function requireProvider(result: Awaited<ReturnType<typeof createEmbeddingProvider>>) {
  if (!result.provider) {
    throw new Error("Expected embedding provider");
  }
  return result.provider;
}

function mockResolvedProviderKey(apiKey = "provider-key") {
  vi.mocked(authModule.resolveApiKeyForProvider).mockResolvedValue({
    apiKey,
    mode: "api-key",
    source: "test",
  });
}

function mockMissingLocalEmbeddingDependency() {
  importNodeLlamaCppMock.mockRejectedValue(
    Object.assign(new Error("Cannot find package 'node-llama-cpp'"), {
      code: "ERR_MODULE_NOT_FOUND",
    }),
  );
}

function createLocalProvider(options?: { fallback?: "none" | "openai" }) {
  return createEmbeddingProvider({
    config: {} as never,
    provider: "local",
    model: "text-embedding-3-small",
    fallback: options?.fallback ?? "none",
  });
}

function expectAutoSelectedProvider(
  result: Awaited<ReturnType<typeof createEmbeddingProvider>>,
  expectedId: "openai" | "gemini" | "mistral",
) {
  expect(result.requestedProvider).toBe("auto");
  const provider = requireProvider(result);
  expect(provider.id).toBe(expectedId);
  return provider;
}

function createAutoProvider(model = "") {
  return createEmbeddingProvider({
    config: {} as never,
    provider: "auto",
    model,
    fallback: "none",
  });
}

describe("embedding provider remote overrides", () => {
  it("uses remote baseUrl/apiKey and merges headers", async () => {
    const fetchMock = createFetchMock();
    vi.stubGlobal("fetch", fetchMock);
    mockResolvedProviderKey("provider-key");

    const cfg = {
      models: {
        providers: {
          openai: {
            baseUrl: "https://api.openai.com/v1",
            headers: {
              "X-Provider": "p",
              "X-Shared": "provider",
            },
          },
        },
      },
    };

    const result = await createEmbeddingProvider({
      config: cfg as never,
      provider: "openai",
      remote: {
        baseUrl: "https://example.com/v1",
        apiKey: "  remote-key  ",
        headers: {
          "X-Shared": "remote",
          "X-Remote": "r",
        },
      },
      model: "text-embedding-3-small",
      fallback: "openai",
    });

    const provider = requireProvider(result);
    await provider.embedQuery("hello");

    expect(authModule.resolveApiKeyForProvider).not.toHaveBeenCalled();
    const url = fetchMock.mock.calls[0]?.[0];
    const init = fetchMock.mock.calls[0]?.[1] as RequestInit | undefined;
    expect(url).toBe("https://example.com/v1/embeddings");
    const headers = (init?.headers ?? {}) as Record<string, string>;
    expect(headers.Authorization).toBe("Bearer remote-key");
    expect(headers["Content-Type"]).toBe("application/json");
    expect(headers["X-Provider"]).toBe("p");
    expect(headers["X-Shared"]).toBe("remote");
    expect(headers["X-Remote"]).toBe("r");
  });

  it("falls back to resolved api key when remote apiKey is blank", async () => {
    const fetchMock = createFetchMock();
    vi.stubGlobal("fetch", fetchMock);
    mockResolvedProviderKey("provider-key");

    const cfg = {
      models: {
        providers: {
          openai: {
            baseUrl: "https://api.openai.com/v1",
          },
        },
      },
    };

    const result = await createEmbeddingProvider({
      config: cfg as never,
      provider: "openai",
      remote: {
        baseUrl: "https://example.com/v1",
        apiKey: "   ",
      },
      model: "text-embedding-3-small",
      fallback: "openai",
    });

    const provider = requireProvider(result);
    await provider.embedQuery("hello");

    expect(authModule.resolveApiKeyForProvider).toHaveBeenCalledTimes(1);
    const init = fetchMock.mock.calls[0]?.[1] as RequestInit | undefined;
    const headers = (init?.headers as Record<string, string>) ?? {};
    expect(headers.Authorization).toBe("Bearer provider-key");
  });

  it("builds Gemini embeddings requests with api key header", async () => {
    const fetchMock = createGeminiFetchMock();
    vi.stubGlobal("fetch", fetchMock);
    mockResolvedProviderKey("provider-key");

    const cfg = {
      models: {
        providers: {
          google: {
            baseUrl: "https://generativelanguage.googleapis.com/v1beta",
          },
        },
      },
    };

    const result = await createEmbeddingProvider({
      config: cfg as never,
      provider: "gemini",
      remote: {
        apiKey: "gemini-key",
      },
      model: "text-embedding-004",
      fallback: "openai",
    });

    const provider = requireProvider(result);
    await provider.embedQuery("hello");

    const { url, init } = readFirstFetchRequest(fetchMock);
    expect(url).toBe(
      "https://generativelanguage.googleapis.com/v1beta/models/text-embedding-004:embedContent",
    );
    const headers = (init?.headers ?? {}) as Record<string, string>;
    expect(headers["x-goog-api-key"]).toBe("gemini-key");
    expect(headers["Content-Type"]).toBe("application/json");
  });

  it("fails fast when Gemini remote apiKey is an unresolved SecretRef", async () => {
    await expect(
      createEmbeddingProvider({
        config: {} as never,
        provider: "gemini",
        remote: {
          apiKey: { source: "env", provider: "default", id: "GEMINI_API_KEY" },
        },
        model: "text-embedding-004",
        fallback: "openai",
      }),
    ).rejects.toThrow(/agents\.\*\.memorySearch\.remote\.apiKey:/i);
  });

  it("uses GEMINI_API_KEY env indirection for Gemini remote apiKey", async () => {
    const fetchMock = createGeminiFetchMock();
    vi.stubGlobal("fetch", fetchMock);
    vi.stubEnv("GEMINI_API_KEY", "env-gemini-key");

    const result = await createEmbeddingProvider({
      config: {} as never,
      provider: "gemini",
      remote: {
        apiKey: "GEMINI_API_KEY", // pragma: allowlist secret
      },
      model: "text-embedding-004",
      fallback: "openai",
    });

    const provider = requireProvider(result);
    await provider.embedQuery("hello");

    const { init } = readFirstFetchRequest(fetchMock);
    const headers = (init?.headers ?? {}) as Record<string, string>;
    expect(headers["x-goog-api-key"]).toBe("env-gemini-key");
  });

  it("builds Mistral embeddings requests with bearer auth", async () => {
    const fetchMock = createFetchMock();
    vi.stubGlobal("fetch", fetchMock);
    mockResolvedProviderKey("provider-key");

    const cfg = {
      models: {
        providers: {
          mistral: {
            baseUrl: "https://api.mistral.ai/v1",
          },
        },
      },
    };

    const result = await createEmbeddingProvider({
      config: cfg as never,
      provider: "mistral",
      remote: {
        apiKey: "mistral-key", // pragma: allowlist secret
      },
      model: "mistral/mistral-embed",
      fallback: "none",
    });

    const provider = requireProvider(result);
    await provider.embedQuery("hello");

    const { url, init } = readFirstFetchRequest(fetchMock);
    expect(url).toBe("https://api.mistral.ai/v1/embeddings");
    const headers = (init?.headers ?? {}) as Record<string, string>;
    expect(headers.Authorization).toBe("Bearer mistral-key");
    const payload = JSON.parse((init?.body as string | undefined) ?? "{}") as { model?: string };
    expect(payload.model).toBe("mistral-embed");
  });
});

describe("embedding provider auto selection", () => {
  it("prefers openai when a key resolves", async () => {
    vi.mocked(authModule.resolveApiKeyForProvider).mockImplementation(async ({ provider }) => {
      if (provider === "openai") {
        return { apiKey: "openai-key", source: "env: OPENAI_API_KEY", mode: "api-key" };
      }
      throw new Error(`No API key found for provider "${provider}".`);
    });

    const result = await createAutoProvider();
    expectAutoSelectedProvider(result, "openai");
  });

  it("uses gemini when openai is missing", async () => {
    const fetchMock = createGeminiFetchMock();
    vi.stubGlobal("fetch", fetchMock);
    vi.mocked(authModule.resolveApiKeyForProvider).mockImplementation(async ({ provider }) => {
      if (provider === "openai") {
        throw new Error('No API key found for provider "openai".');
      }
      if (provider === "google") {
        return { apiKey: "gemini-key", source: "env: GEMINI_API_KEY", mode: "api-key" };
      }
      throw new Error(`Unexpected provider ${provider}`);
    });

    const result = await createAutoProvider();
    const provider = expectAutoSelectedProvider(result, "gemini");
    await provider.embedQuery("hello");
    const [url] = fetchMock.mock.calls[0] ?? [];
    expect(url).toBe(
      `https://generativelanguage.googleapis.com/v1beta/models/${DEFAULT_GEMINI_EMBEDDING_MODEL}:embedContent`,
    );
  });

  it("keeps explicit model when openai is selected", async () => {
    const fetchMock = vi.fn(async (_input?: unknown, _init?: unknown) => ({
      ok: true,
      status: 200,
      json: async () => ({ data: [{ embedding: [1, 2, 3] }] }),
    }));
    vi.stubGlobal("fetch", fetchMock);
    vi.mocked(authModule.resolveApiKeyForProvider).mockImplementation(async ({ provider }) => {
      if (provider === "openai") {
        return { apiKey: "openai-key", source: "env: OPENAI_API_KEY", mode: "api-key" };
      }
      throw new Error(`Unexpected provider ${provider}`);
    });

    const result = await createEmbeddingProvider({
      config: {} as never,
      provider: "auto",
      model: "text-embedding-3-small",
      fallback: "none",
    });

    expect(result.requestedProvider).toBe("auto");
    const provider = requireProvider(result);
    expect(provider.id).toBe("openai");
    await provider.embedQuery("hello");
    const url = fetchMock.mock.calls[0]?.[0];
    const init = fetchMock.mock.calls[0]?.[1] as RequestInit | undefined;
    expect(url).toBe("https://api.openai.com/v1/embeddings");
    const payload = JSON.parse(init?.body as string) as { model?: string };
    expect(payload.model).toBe("text-embedding-3-small");
  });

  it("uses mistral when openai/gemini/voyage are missing", async () => {
    const fetchMock = createFetchMock();
    vi.stubGlobal("fetch", fetchMock);
    vi.mocked(authModule.resolveApiKeyForProvider).mockImplementation(async ({ provider }) => {
      if (provider === "mistral") {
        return { apiKey: "mistral-key", source: "env: MISTRAL_API_KEY", mode: "api-key" }; // pragma: allowlist secret
      }
      throw new Error(`No API key found for provider "${provider}".`);
    });

    const result = await createAutoProvider();
    const provider = expectAutoSelectedProvider(result, "mistral");
    await provider.embedQuery("hello");
    const [url] = fetchMock.mock.calls[0] ?? [];
    expect(url).toBe("https://api.mistral.ai/v1/embeddings");
  });
});

describe("embedding provider local fallback", () => {
  it("falls back to openai when node-llama-cpp is missing", async () => {
    mockMissingLocalEmbeddingDependency();

    const fetchMock = createFetchMock();
    vi.stubGlobal("fetch", fetchMock);

    mockResolvedProviderKey("provider-key");

    const result = await createLocalProvider({ fallback: "openai" });

    const provider = requireProvider(result);
    expect(provider.id).toBe("openai");
    expect(result.fallbackFrom).toBe("local");
    expect(result.fallbackReason).toContain("node-llama-cpp");
  });

  it("throws a helpful error when local is requested and fallback is none", async () => {
    mockMissingLocalEmbeddingDependency();
    await expect(createLocalProvider()).rejects.toThrow(/optional dependency node-llama-cpp/i);
  });

  it("mentions every remote provider in local setup guidance", async () => {
    mockMissingLocalEmbeddingDependency();
    await expect(createLocalProvider()).rejects.toThrow(/provider = "gemini"/i);
    await expect(createLocalProvider()).rejects.toThrow(/provider = "mistral"/i);
  });
});

describe("local embedding normalization", () => {
  async function createLocalProviderForTest() {
    return createEmbeddingProvider({
      config: {} as never,
      provider: "local",
      model: "",
      fallback: "none",
    });
  }

  function mockSingleLocalEmbeddingVector(
    vector: number[],
    resolveModelFile: (modelPath: string, modelDirectory?: string) => Promise<string> = async () =>
      "/fake/model.gguf",
  ): void {
    importNodeLlamaCppMock.mockResolvedValue({
      getLlama: async () => ({
        loadModel: vi.fn().mockResolvedValue({
          createEmbeddingContext: vi.fn().mockResolvedValue({
            getEmbeddingFor: vi.fn().mockResolvedValue({
              vector: new Float32Array(vector),
            }),
          }),
        }),
      }),
      resolveModelFile,
      LlamaLogLevel: { error: 0 },
    });
  }

  it("normalizes local embeddings to magnitude ~1.0", async () => {
    const unnormalizedVector = [2.35, 3.45, 0.63, 4.3, 1.2, 5.1, 2.8, 3.9];
    const resolveModelFileMock = vi.fn(async () => "/fake/model.gguf");

    mockSingleLocalEmbeddingVector(unnormalizedVector, resolveModelFileMock);

    const result = await createLocalProviderForTest();

    const provider = requireProvider(result);
    const embedding = await provider.embedQuery("test query");

    const magnitude = Math.sqrt(embedding.reduce((sum, x) => sum + x * x, 0));

    expect(magnitude).toBeCloseTo(1.0, 5);
    expect(resolveModelFileMock).toHaveBeenCalledWith(DEFAULT_LOCAL_MODEL, undefined);
  });

  it("handles zero vector without division by zero", async () => {
    const zeroVector = [0, 0, 0, 0];

    mockSingleLocalEmbeddingVector(zeroVector);

    const result = await createLocalProviderForTest();

    const provider = requireProvider(result);
    const embedding = await provider.embedQuery("test");

    expect(embedding).toEqual([0, 0, 0, 0]);
    expect(embedding.every((value) => Number.isFinite(value))).toBe(true);
  });

  it("sanitizes non-finite values before normalization", async () => {
    const nonFiniteVector = [1, Number.NaN, Number.POSITIVE_INFINITY, Number.NEGATIVE_INFINITY];

    mockSingleLocalEmbeddingVector(nonFiniteVector);

    const result = await createLocalProviderForTest();

    const provider = requireProvider(result);
    const embedding = await provider.embedQuery("test");

    expect(embedding).toEqual([1, 0, 0, 0]);
    expect(embedding.every((value) => Number.isFinite(value))).toBe(true);
  });

  it("normalizes batch embeddings to magnitude ~1.0", async () => {
    const unnormalizedVectors = [
      [2.35, 3.45, 0.63, 4.3],
      [10.0, 0.0, 0.0, 0.0],
      [1.0, 1.0, 1.0, 1.0],
    ];

    importNodeLlamaCppMock.mockResolvedValue({
      getLlama: async () => ({
        loadModel: vi.fn().mockResolvedValue({
          createEmbeddingContext: vi.fn().mockResolvedValue({
            getEmbeddingFor: vi
              .fn()
              .mockResolvedValueOnce({ vector: new Float32Array(unnormalizedVectors[0]) })
              .mockResolvedValueOnce({ vector: new Float32Array(unnormalizedVectors[1]) })
              .mockResolvedValueOnce({ vector: new Float32Array(unnormalizedVectors[2]) }),
          }),
        }),
      }),
      resolveModelFile: async () => "/fake/model.gguf",
      LlamaLogLevel: { error: 0 },
    });

    const result = await createLocalProviderForTest();

    const provider = requireProvider(result);
    const embeddings = await provider.embedBatch(["text1", "text2", "text3"]);

    for (const embedding of embeddings) {
      const magnitude = Math.sqrt(embedding.reduce((sum, x) => sum + x * x, 0));
      expect(magnitude).toBeCloseTo(1.0, 5);
    }
  });
});

describe("local embedding ensureContext concurrency", () => {
  afterEach(() => {
    vi.resetAllMocks();
    vi.resetModules();
    vi.unstubAllGlobals();
    vi.doUnmock("./node-llama.js");
  });

  it("loads the model only once when embedBatch is called concurrently", async () => {
    const getLlamaSpy = vi.fn();
    const loadModelSpy = vi.fn();
    const createContextSpy = vi.fn();

    const nodeLlamaModule = await import("./node-llama.js");
    vi.spyOn(nodeLlamaModule, "importNodeLlamaCpp").mockResolvedValue({
      getLlama: async (...args: unknown[]) => {
        getLlamaSpy(...args);
        await new Promise((r) => setTimeout(r, 50));
        return {
          loadModel: async (...modelArgs: unknown[]) => {
            loadModelSpy(...modelArgs);
            await new Promise((r) => setTimeout(r, 50));
            return {
              createEmbeddingContext: async () => {
                createContextSpy();
                return {
                  getEmbeddingFor: vi.fn().mockResolvedValue({
                    vector: new Float32Array([1, 0, 0, 0]),
                  }),
                };
              },
            };
          },
        };
      },
      resolveModelFile: async () => "/fake/model.gguf",
      LlamaLogLevel: { error: 0 },
    } as never);

    const { createEmbeddingProvider } = await import("./embeddings.js");

    const result = await createEmbeddingProvider({
      config: {} as never,
      provider: "local",
      model: "",
      fallback: "none",
    });

    const provider = requireProvider(result);
    const results = await Promise.all([
      provider.embedBatch(["text1"]),
      provider.embedBatch(["text2"]),
      provider.embedBatch(["text3"]),
      provider.embedBatch(["text4"]),
    ]);

    expect(results).toHaveLength(4);
    for (const embeddings of results) {
      expect(embeddings).toHaveLength(1);
      expect(embeddings[0]).toHaveLength(4);
    }

    expect(getLlamaSpy).toHaveBeenCalledTimes(1);
    expect(loadModelSpy).toHaveBeenCalledTimes(1);
    expect(createContextSpy).toHaveBeenCalledTimes(1);
  });

  it("retries initialization after a transient ensureContext failure", async () => {
    const getLlamaSpy = vi.fn();
    const loadModelSpy = vi.fn();
    const createContextSpy = vi.fn();

    let failFirstGetLlama = true;
    const nodeLlamaModule = await import("./node-llama.js");
    vi.spyOn(nodeLlamaModule, "importNodeLlamaCpp").mockResolvedValue({
      getLlama: async (...args: unknown[]) => {
        getLlamaSpy(...args);
        if (failFirstGetLlama) {
          failFirstGetLlama = false;
          throw new Error("transient init failure");
        }
        return {
          loadModel: async (...modelArgs: unknown[]) => {
            loadModelSpy(...modelArgs);
            return {
              createEmbeddingContext: async () => {
                createContextSpy();
                return {
                  getEmbeddingFor: vi.fn().mockResolvedValue({
                    vector: new Float32Array([1, 0, 0, 0]),
                  }),
                };
              },
            };
          },
        };
      },
      resolveModelFile: async () => "/fake/model.gguf",
      LlamaLogLevel: { error: 0 },
    } as never);

    const { createEmbeddingProvider } = await import("./embeddings.js");

    const result = await createEmbeddingProvider({
      config: {} as never,
      provider: "local",
      model: "",
      fallback: "none",
    });

    const provider = requireProvider(result);
    await expect(provider.embedBatch(["first"])).rejects.toThrow("transient init failure");

    const recovered = await provider.embedBatch(["second"]);
    expect(recovered).toHaveLength(1);
    expect(recovered[0]).toHaveLength(4);

    expect(getLlamaSpy).toHaveBeenCalledTimes(2);
    expect(loadModelSpy).toHaveBeenCalledTimes(1);
    expect(createContextSpy).toHaveBeenCalledTimes(1);
  });

  it("shares initialization when embedQuery and embedBatch start concurrently", async () => {
    const getLlamaSpy = vi.fn();
    const loadModelSpy = vi.fn();
    const createContextSpy = vi.fn();

    const nodeLlamaModule = await import("./node-llama.js");
    vi.spyOn(nodeLlamaModule, "importNodeLlamaCpp").mockResolvedValue({
      getLlama: async (...args: unknown[]) => {
        getLlamaSpy(...args);
        await new Promise((r) => setTimeout(r, 50));
        return {
          loadModel: async (...modelArgs: unknown[]) => {
            loadModelSpy(...modelArgs);
            await new Promise((r) => setTimeout(r, 50));
            return {
              createEmbeddingContext: async () => {
                createContextSpy();
                return {
                  getEmbeddingFor: vi.fn().mockResolvedValue({
                    vector: new Float32Array([1, 0, 0, 0]),
                  }),
                };
              },
            };
          },
        };
      },
      resolveModelFile: async () => "/fake/model.gguf",
      LlamaLogLevel: { error: 0 },
    } as never);

    const { createEmbeddingProvider } = await import("./embeddings.js");

    const result = await createEmbeddingProvider({
      config: {} as never,
      provider: "local",
      model: "",
      fallback: "none",
    });

    const provider = requireProvider(result);
    const [queryA, batch, queryB] = await Promise.all([
      provider.embedQuery("query-a"),
      provider.embedBatch(["batch-a", "batch-b"]),
      provider.embedQuery("query-b"),
    ]);

    expect(queryA).toHaveLength(4);
    expect(batch).toHaveLength(2);
    expect(queryB).toHaveLength(4);
    expect(batch[0]).toHaveLength(4);
    expect(batch[1]).toHaveLength(4);

    expect(getLlamaSpy).toHaveBeenCalledTimes(1);
    expect(loadModelSpy).toHaveBeenCalledTimes(1);
    expect(createContextSpy).toHaveBeenCalledTimes(1);
  });
});

describe("FTS-only fallback when no provider available", () => {
  it("returns null provider with reason when auto mode finds no providers", async () => {
    vi.mocked(authModule.resolveApiKeyForProvider).mockRejectedValue(
      new Error('No API key found for provider "openai"'),
    );

    const result = await createEmbeddingProvider({
      config: {} as never,
      provider: "auto",
      model: "",
      fallback: "none",
    });

    expect(result.provider).toBeNull();
    expect(result.requestedProvider).toBe("auto");
    expect(result.providerUnavailableReason).toBeDefined();
    expect(result.providerUnavailableReason).toContain("No API key");
  });

  it("returns null provider when explicit provider fails with missing API key", async () => {
    vi.mocked(authModule.resolveApiKeyForProvider).mockRejectedValue(
      new Error('No API key found for provider "openai"'),
    );

    const result = await createEmbeddingProvider({
      config: {} as never,
      provider: "openai",
      model: "text-embedding-3-small",
      fallback: "none",
    });

    expect(result.provider).toBeNull();
    expect(result.requestedProvider).toBe("openai");
    expect(result.providerUnavailableReason).toBeDefined();
  });

  it("returns null provider when both primary and fallback fail with missing API keys", async () => {
    vi.mocked(authModule.resolveApiKeyForProvider).mockRejectedValue(
      new Error("No API key found for provider"),
    );

    const result = await createEmbeddingProvider({
      config: {} as never,
      provider: "openai",
      model: "text-embedding-3-small",
      fallback: "gemini",
    });

    expect(result.provider).toBeNull();
    expect(result.requestedProvider).toBe("openai");
    expect(result.fallbackFrom).toBe("openai");
    expect(result.providerUnavailableReason).toContain("Fallback to gemini failed");
  });
});