import { describe, expect } from "bun:test"
import { Effect, Layer, Stream } from "effect"
import { HttpClientRequest } from "effect/http"
import { ai, ImageClient, Media } from "../src/effect.js"
import { Google, OpenAI, OpenAICompatible, OpenRouter, VercelAIGateway, XAI, ZAI } from "../src/providers.js"
import { it } from "./lib/effect.js"
import { dynamicResponse, json } from "./lib/http.js"

const layer = (handler: Parameters<typeof dynamicResponse>[0]) =>
  ImageClient.layer.pipe(Layer.provideMerge(dynamicResponse(handler)))

describe("Image", () => {
  for (const provider of [OpenAI, Google, XAI, ZAI]) {
    const model = provider.configure({ apiKey: "test", baseURL: "https://image.test" }).image("image-model")
    for (const body of ['{"data":42,"candidates":42,"opaque":{"nested":[1,2]},"trace":"outer"}', '{"invalid":']) {
      it.effect(`retains ${model.provider} image response body and decode cause: ${body}`, () =>
        Effect.gen(function* () {
          const error = yield* ai.image.generate({ model, prompt: "hello" }).pipe(Effect.flip)
          expect(error.reason._tag).toBe("InvalidProviderOutput")
          expect(error.message).toContain("invalid response")
          expect(error.reason.body).toBe(body)
          expect(error.reason.http).toMatchObject({ status: 200, headers: { "x-image-trace": "trace-1" } })
          expect(error.reason.http?.url).toStartWith("https://image.test/")
          expect(error.reason.cause).toBeInstanceOf(Error)
        }).pipe(
          Effect.provide(
            ImageClient.layer.pipe(
              Layer.provide(
                dynamicResponse((input) =>
                  Effect.succeed(
                    input.respond(body, {
                      headers: { "content-type": "application/json", "x-image-trace": "trace-1" },
                    }),
                  ),
                ),
              ),
            ),
          ),
        ),
      )
    }
  }

  it.effect("generates images through the OpenAI Images API", () =>
    Effect.gen(function* () {
      const response = yield* ai.image.generate({
        model: OpenAI.configure({
          apiKey: "test",
          baseURL: "https://api.openai.test/v1",
          queryParams: { "api-version": "v1" },
          headers: { "x-default": "yes" },
          http: { body: { deployment: "test" } },
        }).image("gpt-image-2"),
        prompt: "A robot tending a rooftop garden",
        n: 2,
        aspect: "1:1",
        resolution: "2K",
        providerOptions: {
          outputFormat: "jpeg",
          quality: "future-quality",
          output_format: "avif",
          outputCompression: 30,
          output_compression: 40,
          background: "opaque",
          native_default: true,
          future_option: true,
        },
        headers: { "x-request": "yes" },
        http: {
          body: { output_format: "webp", output_compression: 50, future_option: "http", request_metadata: "value" },
          query: { trace: "1" },
        },
      })

      expect(response.images).toHaveLength(2)
      expect(response.images[0].mediaType).toBe("image/webp")
      expect(response.images[0].source).toEqual({
        type: "bytes",
        data: Uint8Array.from([1, 2, 3]),
        mediaType: "image/webp",
      })
      expect(yield* response.images[0].bytes()).toEqual(Uint8Array.from([1, 2, 3]))
      expect(response.images[0].info).toEqual({ format: "webp", width: 2048, height: 2048 })
      expect(response.usage).toMatchObject({ type: "tokens", total: 12 })
      expect(response.providerMetadata).toEqual({
        openai: { outputFormat: "webp", size: "2048x2048", quality: "high", background: "opaque" },
      })
    }).pipe(
      Effect.provide(
        ImageClient.layer.pipe(
          Layer.provideMerge(
            dynamicResponse((input) =>
              Effect.gen(function* () {
                const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
                expect(request.url).toBe("https://api.openai.test/v1/images/generations?api-version=v1&trace=1")
                expect(request.headers.get("authorization")).toBe("Bearer test")
                expect(request.headers.get("x-default")).toBe("yes")
                expect(request.headers.get("x-request")).toBe("yes")
                expect(JSON.parse(input.text)).toEqual({
                  model: "gpt-image-2",
                  prompt: "A robot tending a rooftop garden",
                  n: 2,
                  size: "2048x2048",
                  quality: "future-quality",
                  background: "opaque",
                  output_format: "webp",
                  output_compression: 50,
                  native_default: true,
                  future_option: "http",
                  deployment: "test",
                  request_metadata: "value",
                })
                return input.respond(
                  JSON.stringify({
                    data: [{ b64_json: "AQID" }, { b64_json: "BAUG" }],
                    output_format: "webp",
                    size: "2048x2048",
                    quality: "high",
                    background: "opaque",
                    usage: { input_tokens: 4, output_tokens: 8, total_tokens: 12 },
                  }),
                  { headers: { "content-type": "application/json" } },
                )
              }),
            ),
          ),
        ),
      ),
    ),
  )

  it.effect("sends only model and prompt by default and sniffs bytes when output_format is missing", () =>
    Effect.gen(function* () {
      const openai = OpenAI.configure({ apiKey: "test", baseURL: "https://openai.test/v1" })
      expect(openai.image("gpt-image-2").route.id).toBe("openai-images")
      const response = yield* ai.image.generate({ model: openai.image("gpt-image-2"), prompt: "A lighthouse" }).pipe(
        Effect.provide(
          ImageClient.layer.pipe(
            Layer.provide(
              dynamicResponse((input) =>
                Effect.gen(function* () {
                  const web = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
                  expect(web.url).toBe("https://openai.test/v1/images/generations")
                  expect(JSON.parse(input.text)).toEqual({ model: "gpt-image-2", prompt: "A lighthouse" })
                  return input.respond(JSON.stringify({ data: [{ b64_json: "/9j/4AAQ" }] }), {
                    headers: { "content-type": "application/json" },
                  })
                }),
              ),
            ),
          ),
        ),
      )
      expect(response.images[0].source).toEqual({
        type: "bytes",
        data: Uint8Array.from([0xff, 0xd8, 0xff, 0xe0, 0x00, 0x10]),
        mediaType: "image/jpeg",
      })
      expect(response.providerMetadata).toEqual({ openai: {} })
    }),
  )

  it.effect("requests inline bytes by default where hosts would return URLs", () =>
    Effect.gen(function* () {
      const bodies: Array<unknown> = []
      const compatible = OpenAICompatible.configure({ apiKey: "test", baseURL: "https://compatible.test/v1" })
      const xai = XAI.configure({ apiKey: "test", baseURL: "https://api.xai.test/v1" })
      const generate = (
        model: Parameters<typeof ai.image.generate>[0]["model"],
        providerOptions?: Record<string, unknown>,
      ) =>
        ai.image.generate({ model, prompt: "A lighthouse", providerOptions }).pipe(
          Effect.provide(
            layer((input) => {
              bodies.push(JSON.parse(input.text))
              return Effect.succeed(json(input, { data: [{ b64_json: "AQID" }] }))
            }),
          ),
        )

      yield* generate(compatible.image("flux-schnell"))
      yield* generate(compatible.image("flux-schnell"), { response_format: "url" })
      yield* generate(compatible.image("openai/gpt-image-2"))
      yield* generate(compatible.image("image-alias"), { response_format: null })
      yield* generate(xai.image("grok-imagine-image"))
      yield* generate(xai.image("grok-imagine-image"), { responseFormat: "url" })

      expect(bodies).toEqual([
        { model: "flux-schnell", prompt: "A lighthouse", response_format: "b64_json" },
        { model: "flux-schnell", prompt: "A lighthouse", response_format: "url" },
        { model: "openai/gpt-image-2", prompt: "A lighthouse" },
        { model: "image-alias", prompt: "A lighthouse" },
        { model: "grok-imagine-image", prompt: "A lighthouse", response_format: "b64_json" },
        { model: "grok-imagine-image", prompt: "A lighthouse", response_format: "url" },
      ])
    }),
  )

  describe("OpenAI-style url results", () => {
    const generate = (data: unknown) =>
      ai.image
        .generate({
          model: OpenAI.configure({ apiKey: "test", baseURL: "https://openai.test/v1" }).image("dall-e-3"),
          prompt: "A lighthouse",
        })
        .pipe(Effect.provide(layer((input) => Effect.succeed(json(input, { data })))))

    it.effect("keeps an http url as a URL asset", () =>
      Effect.gen(function* () {
        const response = yield* generate([{ url: "https://cdn.image.test/result.png" }])
        expect(response.images[0].source).toEqual({ type: "url", url: "https://cdn.image.test/result.png" })
      }),
    )

    it.effect("decodes a data url inline with its media type", () =>
      Effect.gen(function* () {
        const response = yield* generate([{ url: "data:image/png;base64,iVBORw==" }])
        expect(response.images[0].source).toEqual({ type: "base64", data: "iVBORw==", mediaType: "image/png" })
        expect(response.images[0].mediaType).toBe("image/png")
      }),
    )

    it.effect("prefers b64_json when url is null", () =>
      Effect.gen(function* () {
        const response = yield* generate([{ b64_json: "/9j/4AAQ", url: null }])
        expect(response.images[0].source).toEqual({
          type: "bytes",
          data: Uint8Array.from([0xff, 0xd8, 0xff, 0xe0, 0x00, 0x10]),
          mediaType: "image/jpeg",
        })
      }),
    )

    it.effect("rejects an item with neither b64_json nor url", () =>
      Effect.gen(function* () {
        const error = yield* generate([{}]).pipe(Effect.flip)
        expect(error.reason._tag).toBe("InvalidProviderOutput")
        expect(error.message).toContain("has neither image data nor a URL")
      }),
    )
  })

  it.effect("classifies an OpenAI stream moderation error event as a content policy rejection", () =>
    Effect.gen(function* () {
      const error = yield* ai.image
        .stream({
          model: OpenAI.configure({ apiKey: "test", baseURL: "https://openai.test/v1" }).image("gpt-image-1-mini"),
          prompt: "A lighthouse",
        })
        .pipe(Stream.runDrain, Effect.flip)
      expect(error.reason._tag).toBe("ContentPolicy")
      expect(error.message).toContain("rejected by the safety system")
    }).pipe(
      Effect.provide(
        layer((input) =>
          Effect.succeed(
            input.respond(
              `event: error\ndata: ${JSON.stringify({
                type: "error",
                error: {
                  type: "image_generation_user_error",
                  code: "moderation_blocked",
                  message:
                    "Your request was rejected by the safety system. If you believe this is an error, contact us at help.openai.com and include the request ID req_123.",
                  param: null,
                },
                sequence_number: 0,
              })}\n\n`,
              { headers: { "content-type": "text/event-stream" } },
            ),
          ),
        ),
      ),
    ),
  )

  it.effect("ignores unknown OpenAI image stream events", () =>
    Effect.gen(function* () {
      const events = yield* ai.image
        .stream({
          model: OpenAI.configure({ apiKey: "test", baseURL: "https://openai.test/v1" }).image("gpt-image-1-mini"),
          prompt: "A lighthouse",
        })
        .pipe(Stream.runCollect)
      expect(Array.from(events).map((event) => event.type)).toEqual(["image", "finish"])
    }).pipe(
      Effect.provide(
        layer((input) =>
          Effect.succeed(
            input.respond(
              [
                { type: "image_generation.in_progress", sequence_number: 0 },
                {
                  type: "image_generation.completed",
                  b64_json: Buffer.from([0x89, 0x50, 0x4e, 0x47]).toString("base64"),
                  output_format: "png",
                },
              ]
                .map((event) => `data: ${JSON.stringify(event)}\n\n`)
                .join(""),
              { headers: { "content-type": "text/event-stream" } },
            ),
          ),
        ),
      ),
    ),
  )

  it.effect("lowers aspect to the nearest OpenAI size", () =>
    Effect.gen(function* () {
      const openai = OpenAI.configure({ apiKey: "test", baseURL: "https://openai.test/v1" })
      const response = yield* ai.image.generate({
        model: openai.image("gpt-image-1.5"),
        prompt: "A lighthouse",
        aspect: "16:9",
      })
      const conflict = yield* ai.image
        .generate({
          model: openai.image("gpt-image-2"),
          prompt: "A lighthouse",
          aspect: "16:9",
          providerOptions: { size: "1024x1024" },
        })
        .pipe(Effect.flip)
      expect(conflict.reason._tag).toBe("InvalidRequest")
    }).pipe(
      Effect.provide(
        layer((input) => {
          expect(JSON.parse(input.text)).toEqual({ model: "gpt-image-1.5", prompt: "A lighthouse", size: "1536x1024" })
          return Effect.succeed(json(input, { data: [{ b64_json: "AQID" }] }))
        }),
      ),
    ),
  )

  it.effect("computes a free size for gateway-hosted models", () =>
    Effect.gen(function* () {
      const response = yield* ai.image.generate({
        model: VercelAIGateway.configure({ apiKey: "test", baseURL: "https://gateway.test/v1" }).image(
          "bfl/flux-2-klein-4b",
        ),
        prompt: "A lighthouse",
        aspect: "16:9",
      })
    }).pipe(
      Effect.provide(
        layer((input) => {
          expect(JSON.parse(input.text)).toEqual({
            model: "bfl/flux-2-klein-4b",
            prompt: "A lighthouse",
            size: "1360x768",
          })
          return Effect.succeed(json(input, { data: [{ b64_json: "AQID" }] }))
        }),
      ),
    ),
  )

  describe("gateway and Google sizing", () => {
    const sent = (expected: Record<string, unknown>, response: unknown = { data: [{ b64_json: "AQID" }] }) =>
      layer((input) => {
        expect(JSON.parse(input.text)).toEqual(expected)
        return Effect.succeed(json(input, response))
      })
    const prompt = "A lighthouse"
    const openrouter = OpenRouter.configure({ apiKey: "test", baseURL: "https://openrouter.test/v1" })

    it.effect("sends OpenRouter an aspect ratio and resolution tier instead of pixels", () =>
      ai.image
        .generate({
          model: openrouter.image("google/gemini-3.1-flash-image"),
          prompt,
          aspect: "16:9",
          resolution: "0.5K",
        })
        .pipe(
          Effect.provide(
            sent({ model: "google/gemini-3.1-flash-image", prompt, aspect_ratio: "16:9", resolution: "512" }),
          ),
        ),
    )

    it.effect("leaves out a resolution OpenRouter doesn't list", () =>
      ai.image
        .generate({ model: openrouter.image("x/y"), prompt, aspect: "1:1", resolution: "8K" })
        .pipe(Effect.provide(sent({ model: "x/y", prompt, aspect_ratio: "1:1" }))),
    )

    for (const modelID of ["spacexai/grok-imagine-image", "xai/grok-imagine-image"])
      it.effect(`sizes Vercel AI Gateway ${modelID} through xAI provider options`, () =>
        ai.image
          .generate({
            model: VercelAIGateway.configure({ apiKey: "test", baseURL: "https://gateway.test/v1" }).image(modelID),
            prompt,
            aspect: "16:9",
            resolution: "2K",
          })
          .pipe(
            Effect.provide(
              sent({ model: modelID, prompt, providerOptions: { xai: { aspect_ratio: "16:9", resolution: "2k" } } }),
            ),
          ),
      )

    it.effect("names Google's half tier 512", () =>
      ai.image
        .generate({
          model: Google.configure({ apiKey: "test", baseURL: "https://google.test/v1beta" }).image(
            "gemini-3.1-flash-image",
          ),
          prompt,
          aspect: "1:1",
          resolution: "0.5K",
        })
        .pipe(
          Effect.provide(
            layer((input) => {
              expect(JSON.parse(input.text).generationConfig.imageConfig).toEqual({
                aspectRatio: "1:1",
                imageSize: "512",
              })
              return Effect.succeed(
                json(input, {
                  candidates: [{ content: { parts: [{ inlineData: { mimeType: "image/png", data: "AQID" } }] } }],
                }),
              )
            }),
          ),
        ),
    )
  })

  it.effect("preserves native snake_case and unknown request options", () =>
    ai.image
      .generate({
        model: OpenAI.configure({
          apiKey: "test",
          baseURL: "https://api.openai.test/v1",
        }).image("future-image-model"),
        prompt: "A lighthouse in fog",
        providerOptions: {
          outputFormat: "jpeg",
          output_format: "avif",
          outputCompression: 30,
          output_compression: 40,
          provider_future_option: { enabled: true },
        },
      })
      .pipe(
        Effect.tap((response) =>
          Effect.sync(() => {
            expect(response.images[0].mediaType).toBe("image/avif")
          }),
        ),
        Effect.provide(
          ImageClient.layer.pipe(
            Layer.provide(
              dynamicResponse((input) => {
                expect(JSON.parse(input.text)).toEqual({
                  model: "future-image-model",
                  prompt: "A lighthouse in fog",
                  output_format: "avif",
                  output_compression: 40,
                  provider_future_option: { enabled: true },
                })
                return Effect.succeed(
                  input.respond(JSON.stringify({ data: [{ b64_json: "AQID" }], output_format: "avif" }), {
                    headers: { "content-type": "application/json" },
                  }),
                )
              }),
            ),
          ),
        ),
      ),
  )

  it.effect("routes OpenAI byte inputs and masks through multipart edits", () =>
    ai.image
      .generate({
        model: OpenAI.configure({ apiKey: "test", baseURL: "https://api.openai.test/v1" }).image("future-model"),
        prompt: "Combine these images",
        images: [
          Media.bytes(Uint8Array.from([1, 2, 3]), "image/png"),
          Media.fromDataUrl("data:image/jpeg;base64,BAUG"),
        ],
        providerOptions: {
          mask: Media.bytes(Uint8Array.from([7, 8, 9]), "image/png"),
          quality: "high",
          future_option: true,
        },
        headers: { "content-type": "application/json" },
        http: { body: { quality: "low", model: "corrupt", prompt: "corrupt", image: "corrupt", "image[]": "corrupt" } },
      })
      .pipe(
        Effect.provide(
          ImageClient.layer.pipe(
            Layer.provide(
              dynamicResponse((input) =>
                Effect.gen(function* () {
                  const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
                  expect(request.url).toBe("https://api.openai.test/v1/images/edits")
                  expect(request.headers.get("content-type")).toStartWith("multipart/form-data; boundary=")
                  expect(input.text).toContain('name="model"\r\n\r\nfuture-model')
                  expect(input.text).toContain('name="prompt"\r\n\r\nCombine these images')
                  expect(input.text.match(/name="image\[\]"/g)).toHaveLength(2)
                  expect(input.text).toContain('name="mask"')
                  expect(input.text).toContain('name="quality"\r\n\r\nlow')
                  expect(input.text).not.toContain("corrupt")
                  return input.respond(JSON.stringify({ data: [{ b64_json: "AQID" }] }), {
                    headers: { "content-type": "application/json" },
                  })
                }),
              ),
            ),
          ),
        ),
      ),
  )

  it.effect("routes OpenAI URL and file inputs through JSON edits", () =>
    ai.image
      .generate({
        model: OpenAI.configure({ apiKey: "test", baseURL: "https://api.openai.test/v1" }).image("future-model"),
        prompt: "Combine these images",
        images: [Media.url("https://example.test/source.png"), Media.ref("openai", "file_123")],
        providerOptions: { mask: Media.ref("openai", "file_mask") },
        http: { body: { future_option: true } },
      })
      .pipe(
        Effect.provide(
          ImageClient.layer.pipe(
            Layer.provide(
              dynamicResponse((input) => {
                expect(JSON.parse(input.text)).toEqual({
                  model: "future-model",
                  prompt: "Combine these images",
                  images: [{ image_url: "https://example.test/source.png" }, { file_id: "file_123" }],
                  mask: { file_id: "file_mask" },
                  future_option: true,
                })
                return Effect.succeed(
                  input.respond(JSON.stringify({ data: [{ b64_json: "AQID" }] }), {
                    headers: { "content-type": "application/json" },
                  }),
                )
              }),
            ),
          ),
        ),
      ),
  )

  describe("OpenRouter image inputs", () => {
    const openrouter = OpenRouter.configure({ apiKey: "test", baseURL: "https://openrouter.test/v1" })
    const sent = (expected: Record<string, unknown>) =>
      layer((input) =>
        Effect.gen(function* () {
          const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
          expect(request.url).toBe("https://openrouter.test/v1/images/generations")
          expect(request.headers.get("content-type")).toBe("application/json")
          expect(JSON.parse(input.text)).toEqual(expected)
          return json(input, { data: [{ b64_json: "AQID" }] })
        }),
      )

    it.effect("sends a plain generation unchanged", () =>
      ai.image
        .generate({ model: openrouter.image("openai/gpt-image-1-mini"), prompt: "A lighthouse" })
        .pipe(Effect.provide(sent({ model: "openai/gpt-image-1-mini", prompt: "A lighthouse" }))),
    )

    it.effect("sends input images as input references on the generations endpoint", () =>
      ai.image
        .generate({
          model: openrouter.image("openai/gpt-image-1-mini"),
          prompt: "Combine these images",
          images: [Media.bytes(Uint8Array.from([1, 2, 3]), "image/png"), Media.url("https://example.test/source.png")],
          providerOptions: { quality: "low" },
        })
        .pipe(
          Effect.provide(
            sent({
              model: "openai/gpt-image-1-mini",
              prompt: "Combine these images",
              input_references: [
                { type: "image_url", image_url: { url: "data:image/png;base64,AQID" } },
                { type: "image_url", image_url: { url: "https://example.test/source.png" } },
              ],
              quality: "low",
            }),
          ),
        ),
    )

    it.effect("rejects a mask", () =>
      Effect.gen(function* () {
        const error = yield* ai.image
          .generate({
            model: openrouter.image("openai/gpt-image-1-mini"),
            prompt: "Paint the masked area",
            images: [Media.bytes(Uint8Array.from([1, 2, 3]), "image/png")],
            providerOptions: { mask: Media.bytes(Uint8Array.from([7, 8, 9]), "image/png") },
          })
          .pipe(Effect.flip)
        expect(error.reason._tag).toBe("UnsupportedOperation")
        expect(error.message).toContain("openrouter image edits take no mask")
      }).pipe(Effect.provide(layer(() => Effect.die("no request expected")))),
    )
  })

  it.effect("sends OpenAI edits as JSON to the ChatGPT backend, which rejects multipart", () =>
    ai.image
      .generate({
        model: OpenAI.configure({ apiKey: "test", baseURL: "https://chatgpt.com/backend-api/codex" }).image(
          "gpt-image-2",
        ),
        prompt: "Add a red cap",
        images: [Media.bytes(Uint8Array.from([1, 2, 3]), "image/png")],
      })
      .pipe(
        Effect.provide(
          layer((input) =>
            Effect.gen(function* () {
              const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
              expect(request.url).toBe("https://chatgpt.com/backend-api/codex/images/edits")
              expect(request.headers.get("content-type")).toBe("application/json")
              expect(JSON.parse(input.text)).toEqual({
                model: "gpt-image-2",
                prompt: "Add a red cap",
                images: [{ image_url: "data:image/png;base64,AQID" }],
              })
              return json(input, { data: [{ b64_json: "AQID" }] })
            }),
          ),
        ),
      ),
  )

  it.effect("routes ordered xAI image inputs through JSON edits", () =>
    ai.image
      .generate({
        model: XAI.configure({ apiKey: "test", baseURL: "https://api.xai.test/v1" }).image("future-model"),
        prompt: "Combine these images",
        images: [
          Media.bytes(Uint8Array.from([1, 2, 3]), "image/png"),
          Media.url("https://example.test/source.jpg"),
          Media.ref("xai", "file_123"),
        ],
      })
      .pipe(
        Effect.provide(
          ImageClient.layer.pipe(
            Layer.provide(
              dynamicResponse((input) => {
                expect(JSON.parse(input.text)).toEqual({
                  model: "future-model",
                  prompt: "Combine these images",
                  images: [
                    { url: "data:image/png;base64,AQID", type: "image_url" },
                    { url: "https://example.test/source.jpg", type: "image_url" },
                    { file_id: "file_123" },
                  ],
                  response_format: "b64_json",
                })
                return Effect.succeed(
                  input.respond(JSON.stringify({ data: [{ b64_json: "AQID", mime_type: "image/png" }] }), {
                    headers: { "content-type": "application/json" },
                  }),
                )
              }),
            ),
          ),
        ),
      ),
  )

  it.effect("uses xAI's singular image field for one input", () =>
    ai.image
      .generate({
        model: XAI.configure({ apiKey: "test", baseURL: "https://api.xai.test/v1" }).image("future-model"),
        prompt: "Edit this image",
        images: [Media.ref("xai", "file_123")],
      })
      .pipe(
        Effect.provide(
          ImageClient.layer.pipe(
            Layer.provide(
              dynamicResponse((input) => {
                expect(JSON.parse(input.text)).toEqual({
                  model: "future-model",
                  prompt: "Edit this image",
                  image: { file_id: "file_123" },
                  response_format: "b64_json",
                })
                return Effect.succeed(
                  input.respond(JSON.stringify({ data: [{ b64_json: "AQID", mime_type: "image/png" }] }), {
                    headers: { "content-type": "application/json" },
                  }),
                )
              }),
            ),
          ),
        ),
      ),
  )

  it.effect("decodes URL images and rejects items with neither data nor a URL", () =>
    Effect.gen(function* () {
      const model = XAI.configure({ apiKey: "test", baseURL: "https://api.xai.test/v1" }).image("future-model")
      const respond = (data: ReadonlyArray<object>) =>
        layer((input) =>
          Effect.succeed(input.respond(JSON.stringify({ data }), { headers: { "content-type": "application/json" } })),
        )
      const response = yield* ai.image
        .generate({ model, prompt: "A kite" })
        .pipe(Effect.provide(respond([{ url: "https://xai.test/a.png", mime_type: "image/png" }])))
      expect(response.images[0].source).toEqual({ type: "url", url: "https://xai.test/a.png", mediaType: "image/png" })
      const error = yield* ai.image
        .generate({ model, prompt: "A kite" })
        .pipe(Effect.provide(respond([{}])), Effect.flip)
      expect(error.reason._tag).toBe("InvalidProviderOutput")
      expect(error.message).toContain("xAI Images result 0 has neither image data nor a URL")
    }),
  )

  it.effect("lowers ordered Google image inputs into generateContent parts", () =>
    ai.image
      .generate({
        model: Google.configure({ apiKey: "test", baseURL: "https://google.test/v1beta" }).image("future-model"),
        prompt: "Combine these images",
        images: [
          Media.bytes(Uint8Array.from([1, 2, 3]), "image/png"),
          Media.fromDataUrl("data:image/jpeg;base64,BAUG"),
          Media.ref("google", "https://generativelanguage.googleapis.com/v1beta/files/123", "image/webp"),
        ],
      })
      .pipe(
        Effect.provide(
          ImageClient.layer.pipe(
            Layer.provide(
              dynamicResponse((input) => {
                expect(JSON.parse(input.text).contents[0].parts).toEqual([
                  { text: "Combine these images" },
                  { inlineData: { mimeType: "image/png", data: "AQID" } },
                  { inlineData: { mimeType: "image/jpeg", data: "BAUG" } },
                  {
                    fileData: {
                      mimeType: "image/webp",
                      fileUri: "https://generativelanguage.googleapis.com/v1beta/files/123",
                    },
                  },
                ])
                return Effect.succeed(
                  input.respond(
                    JSON.stringify({
                      candidates: [{ content: { parts: [{ inlineData: { mimeType: "image/png", data: "AQID" } }] } }],
                    }),
                    { headers: { "content-type": "application/json" } },
                  ),
                )
              }),
            ),
          ),
        ),
      ),
  )

  it.effect("rejects unsupported provider inputs before sending", () =>
    Effect.gen(function* () {
      const cases = [
        ai.image.generate({
          model: Google.configure({ apiKey: "test" }).image("model"),
          prompt: "edit",
          images: [Media.url("https://example.test/image.png")],
        }),
        ai.image.generate({
          model: ZAI.configure({ apiKey: "test" }).image("model"),
          prompt: "edit",
          images: [Media.bytes(Uint8Array.from([1]), "image/png")],
        }),
      ]
      yield* Effect.forEach(cases, (program) =>
        program.pipe(
          Effect.flip,
          Effect.tap((error) =>
            Effect.sync(() => expect(["InvalidRequest", "UnsupportedOperation"]).toContain(error.reason._tag)),
          ),
        ),
      )
    }).pipe(
      Effect.provide(
        ImageClient.layer.pipe(
          Layer.provide(dynamicResponse(() => Effect.die("unsupported input reached the network"))),
        ),
      ),
    ),
  )

  it.effect("generates images through the Google generateContent API", () =>
    Effect.gen(function* () {
      const response = yield* ai.image.generate({
        model: Google.configure({
          apiKey: "test",
          baseURL: "https://generativelanguage.test/v1beta/",
          headers: { "x-default": "yes" },
          http: { body: { labels: { deployment: "test" } }, query: { api: "v1" } },
        }).image("any-model-id"),
        prompt: "A robot tending a rooftop garden",
        aspect: "16:9",
        resolution: "2K",
        providerOptions: {
          seed: 42,
          thinkingLevel: "HIGH",
          includeThoughts: true,
          futureOption: true,
          imageConfig: { aspectRatio: "4:3", nativeImageOption: true },
          thinkingConfig: { thinkingLevel: "LOW", nativeThinkingOption: true },
        },
        headers: { "x-request": "yes" },
        http: {
          body: {
            safetySettings: [],
            generationConfig: {
              imageConfig: { aspectRatio: "3:2", httpImageOption: true },
              thinkingConfig: { includeThoughts: false, httpThinkingOption: true },
              futureOption: "http",
              httpOption: true,
            },
          },
          query: { trace: "1" },
        },
      })

      expect(response.images).toHaveLength(3)
      expect(yield* Effect.forEach(response.images, (image) => image.bytes())).toEqual([
        Uint8Array.from([1, 2, 3]),
        Uint8Array.from([4, 5, 6]),
        Uint8Array.from([7, 8, 9]),
      ])
      expect(response.images.map((image) => image.mediaType)).toEqual(["image/png", "image/jpeg", "image/webp"])
      expect(response.images[0].providerMetadata).toMatchObject({ google: { thoughtSignature: "signature-1" } })
      expect(response.images[1].providerMetadata).toMatchObject({
        google: { candidateIndex: 0, partIndex: 3, finishReason: "STOP" },
      })
      expect(response.images[2].providerMetadata).toMatchObject({ google: { candidateIndex: 7, partIndex: 0 } })
      expect(response.usage).toMatchObject({
        type: "tokens",
        input: 5,
        output: 10,
        details: { reasoningTokens: 3, google: { serviceTier: "STANDARD" } },
      })
      expect(response.providerMetadata).toEqual({
        google: {
          modelVersion: "gemini-3.1-flash-image",
          responseId: "response-1",
          promptFeedback: undefined,
          candidates: [
            {
              index: 0,
              finishReason: "STOP",
              finishMessage: undefined,
              safetyRatings: [{ category: "safe" }],
              citationMetadata: undefined,
              groundingMetadata: undefined,
              parts: [
                {
                  type: "inlineData",
                  mediaType: "image/png",
                  thought: undefined,
                  thoughtSignature: "signature-1",
                },
                { type: "text", text: "planning", thought: true, thoughtSignature: "text-signature" },
                {
                  type: "inlineData",
                  mediaType: "image/png",
                  thought: true,
                  thoughtSignature: "draft-signature",
                },
                {
                  type: "inlineData",
                  mediaType: "image/jpeg",
                  thought: undefined,
                  thoughtSignature: undefined,
                },
              ],
            },
            {
              index: 7,
              finishReason: undefined,
              finishMessage: undefined,
              safetyRatings: undefined,
              citationMetadata: undefined,
              groundingMetadata: undefined,
              parts: [
                {
                  type: "inlineData",
                  mediaType: "image/webp",
                  thought: undefined,
                  thoughtSignature: undefined,
                },
              ],
            },
          ],
        },
      })
    }).pipe(
      Effect.provide(
        ImageClient.layer.pipe(
          Layer.provideMerge(
            dynamicResponse((input) =>
              Effect.gen(function* () {
                const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
                expect(request.url).toBe(
                  "https://generativelanguage.test/v1beta/models/any-model-id:generateContent?api=v1&trace=1",
                )
                expect(request.headers.get("x-goog-api-key")).toBe("test")
                expect(request.headers.get("x-default")).toBe("yes")
                expect(request.headers.get("x-request")).toBe("yes")
                expect(JSON.parse(input.text)).toEqual({
                  contents: [{ role: "user", parts: [{ text: "A robot tending a rooftop garden" }] }],
                  generationConfig: {
                    responseModalities: ["IMAGE"],
                    imageConfig: {
                      aspectRatio: "3:2",
                      imageSize: "2K",
                      nativeImageOption: true,
                      httpImageOption: true,
                    },
                    seed: 42,
                    thinkingConfig: {
                      thinkingLevel: "LOW",
                      includeThoughts: false,
                      nativeThinkingOption: true,
                      httpThinkingOption: true,
                    },
                    futureOption: "http",
                    httpOption: true,
                  },
                  labels: { deployment: "test" },
                  safetySettings: [],
                })
                return input.respond(
                  JSON.stringify({
                    candidates: [
                      {
                        content: {
                          parts: [
                            {
                              inlineData: { mimeType: "image/png", data: "AQID" },
                              thoughtSignature: "signature-1",
                            },
                            { text: "planning", thought: true, thoughtSignature: "text-signature" },
                            {
                              inlineData: { mimeType: "image/png", data: "CgsM" },
                              thought: true,
                              thoughtSignature: "draft-signature",
                            },
                            { inlineData: { mimeType: "image/jpeg", data: "BAUG" } },
                          ],
                        },
                        finishReason: "STOP",
                        safetyRatings: [{ category: "safe" }],
                      },
                      {
                        index: 7,
                        content: { parts: [{ inlineData: { mimeType: "image/webp", data: "BwgJ" } }] },
                      },
                    ],
                    usageMetadata: {
                      promptTokenCount: 5,
                      candidatesTokenCount: 7,
                      thoughtsTokenCount: 3,
                      totalTokenCount: 15,
                      serviceTier: "STANDARD",
                    },
                    modelVersion: "gemini-3.1-flash-image",
                    responseId: "response-1",
                  }),
                  { headers: { "content-type": "application/json" } },
                )
              }),
            ),
          ),
        ),
      ),
    ),
  )

  it.effect("returns the image next to filtered Google candidates", () =>
    ai.image
      .generate({
        model: Google.configure({ apiKey: "test", baseURL: "https://generativelanguage.test/v1beta" }).image(
          "gemini-3.1-flash-image",
        ),
        prompt: "A robot tending a rooftop garden",
      })
      .pipe(
        Effect.tap((response) =>
          Effect.sync(() => {
            expect(response.images).toHaveLength(1)
            expect(response.providerMetadata?.google).toMatchObject({ promptFeedback: { blockReason: "OTHER" } })
          }),
        ),
        Effect.provide(
          ImageClient.layer.pipe(
            Layer.provide(
              dynamicResponse((input) =>
                Effect.succeed(
                  input.respond(
                    JSON.stringify({
                      promptFeedback: { blockReason: "OTHER" },
                      candidates: [
                        {
                          content: { parts: [{ inlineData: { mimeType: "image/png", data: "AQID" } }] },
                          finishReason: "STOP",
                        },
                        {
                          index: 1,
                          content: { parts: [{ text: "blocked" }] },
                          finishReason: "IMAGE_SAFETY",
                          finishMessage: "Blocked.",
                          safetyRatings: [{ category: "HARM_CATEGORY_DANGEROUS_CONTENT", blocked: true }],
                        },
                      ],
                    }),
                    { headers: { "content-type": "application/json" } },
                  ),
                ),
              ),
            ),
          ),
        ),
      ),
  )

  it.effect("reports a Google safety stop as a content policy refusal, with its diagnostics", () =>
    ai.image
      .generate({
        model: Google.configure({ apiKey: "test", baseURL: "https://generativelanguage.test/v1beta" }).image(
          "gemini-3.1-flash-image",
        ),
        prompt: "A robot tending a rooftop garden",
      })
      .pipe(
        Effect.flip,
        Effect.tap((error) =>
          Effect.sync(() => {
            expect(error.reason._tag).toBe("ContentPolicy")
            if (error.reason._tag !== "ContentPolicy") return
            expect(error.message).toContain("finish reasons: IMAGE_SAFETY")
            expect(JSON.parse(error.reason.body ?? "")).toEqual({
              promptFeedback: { blockReason: "SAFETY" },
              candidates: [
                {
                  finishReason: "IMAGE_SAFETY",
                  finishMessage: "The generated image was blocked by safety filters.",
                  safetyRatings: [{ category: "HARM_CATEGORY_DANGEROUS_CONTENT", blocked: true }],
                  content: { parts: [{ text: "blocked", thought: false }] },
                },
              ],
            })
          }),
        ),
        Effect.provide(
          ImageClient.layer.pipe(
            Layer.provide(
              dynamicResponse((input) =>
                Effect.succeed(
                  input.respond(
                    JSON.stringify({
                      candidates: [
                        {
                          content: { parts: [{ text: "blocked", thought: false }] },
                          finishReason: "IMAGE_SAFETY",
                          finishMessage: "The generated image was blocked by safety filters.",
                          safetyRatings: [{ category: "HARM_CATEGORY_DANGEROUS_CONTENT", blocked: true }],
                        },
                      ],
                      promptFeedback: { blockReason: "SAFETY" },
                    }),
                    { headers: { "content-type": "application/json" } },
                  ),
                ),
              ),
            ),
          ),
        ),
      ),
  )

  it.effect("rejects what a route cannot honor before sending anything", () =>
    Effect.gen(function* () {
      const openai = OpenAI.configure({ apiKey: "test" })
      const prompt = "A lighthouse"
      const errors = yield* Effect.all(
        [
          ai.image.start({ model: Google.configure({ apiKey: "test" }).image("gemini-3.1-flash-image"), prompt }),
          ai.image.generate({
            model: Google.configure({ apiKey: "test" }).image("gemini-3.1-flash-image"),
            prompt,
            n: 2,
          }),
          Stream.runCollect(ai.image.stream({ model: openai.image("gpt-image-2"), prompt, n: 2 })),
        ].map((effect) => Effect.flip(effect)),
      )
      expect(errors.map((error) => [error.reason._tag, "operation" in error.reason && error.reason.operation])).toEqual(
        [
          ["UnsupportedOperation", "image.start"],
          ["UnsupportedOperation", "media.n"],
          ["UnsupportedOperation", "media.n"],
        ],
      )
    }).pipe(Effect.provide(layer(() => Effect.die("an unsupported request reached the network")))),
  )
})