litellm/tests/image_gen_tests/test_image_generation.py

# What this tests?
## This tests the litellm support for the openai /generations endpoint

import logging
import os
import traceback
from unittest.mock import AsyncMock, MagicMock, patch

from dotenv import load_dotenv
from openai.types.image import Image

from litellm.caching import InMemoryCache

logging.basicConfig(level=logging.DEBUG)
load_dotenv()
import asyncio
import json
import logging
import tempfile

import pytest
from base_image_generation_test import BaseImageGenTest, TestCustomLogger

import litellm
from litellm._logging import verbose_logger

verbose_logger.setLevel(logging.DEBUG)


def get_vertex_ai_creds_json() -> dict:
    # Define the path to the vertex_key.json file
    print("loading vertex ai credentials")
    filepath = os.path.dirname(os.path.abspath(__file__))
    vertex_key_path = filepath + "/vertex_key.json"
    # Read the existing content of the file or create an empty dictionary
    try:
        with open(vertex_key_path, "r") as file:
            # Read the file content
            print("Read vertexai file path")
            content = file.read()

            # If the file is empty or not valid JSON, create an empty dictionary
            if not content or not content.strip():
                service_account_key_data = {}
            else:
                # Attempt to load the existing JSON content
                file.seek(0)
                service_account_key_data = json.load(file)
    except FileNotFoundError:
        # If the file doesn't exist, create an empty dictionary
        service_account_key_data = {}

    # Update the service_account_key_data with environment variables
    private_key_id = os.environ.get("VERTEX_AI_PRIVATE_KEY_ID", "")
    private_key = os.environ.get("VERTEX_AI_PRIVATE_KEY", "")
    private_key = private_key.replace("\\n", "\n")
    service_account_key_data["private_key_id"] = private_key_id
    service_account_key_data["private_key"] = private_key

    return service_account_key_data


def load_vertex_ai_credentials():
    # Define the path to the vertex_key.json file
    print("loading vertex ai credentials")
    filepath = os.path.dirname(os.path.abspath(__file__))
    vertex_key_path = filepath + "/vertex_key.json"

    # Read the existing content of the file or create an empty dictionary
    try:
        with open(vertex_key_path, "r") as file:
            # Read the file content
            print("Read vertexai file path")
            content = file.read()

            # If the file is empty or not valid JSON, create an empty dictionary
            if not content or not content.strip():
                service_account_key_data = {}
            else:
                # Attempt to load the existing JSON content
                file.seek(0)
                service_account_key_data = json.load(file)
    except FileNotFoundError:
        # If the file doesn't exist, create an empty dictionary
        service_account_key_data = {}

    # Update the service_account_key_data with environment variables
    private_key_id = os.environ.get("VERTEX_AI_PRIVATE_KEY_ID", "")
    private_key = os.environ.get("VERTEX_AI_PRIVATE_KEY", "")
    private_key = private_key.replace("\\n", "\n")
    service_account_key_data["private_key_id"] = private_key_id
    service_account_key_data["private_key"] = private_key

    # Create a temporary file
    with tempfile.NamedTemporaryFile(mode="w+", delete=False) as temp_file:
        # Write the updated content to the temporary files
        json.dump(service_account_key_data, temp_file, indent=2)

    # Export the temporary file as GOOGLE_APPLICATION_CREDENTIALS
    os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = os.path.abspath(temp_file.name)


class TestVertexAIGeminiImageGeneration(BaseImageGenTest):
    """Test Gemini image generation models (Nano Banana)"""

    def get_base_image_generation_call_args(self) -> dict:
        # comment this when running locally
        load_vertex_ai_credentials()

        litellm.in_memory_llm_clients_cache = InMemoryCache()
        return {
            "model": "vertex_ai/gemini-2.5-flash-image",
            "vertex_ai_project": "litellm-ci-cd",
            "vertex_ai_location": "us-central1",
            "n": 1,
            "size": "1024x1024",
        }


class TestBedrockNovaCanvasTextToImage(BaseImageGenTest):
    def get_base_image_generation_call_args(self) -> dict:
        litellm.in_memory_llm_clients_cache = InMemoryCache()
        return {
            "model": "bedrock/amazon.nova-canvas-v1:0",
            "n": 1,
            "size": "320x320",
            "imageGenerationConfig": {"cfgScale": 6.5, "seed": 12},
            "taskType": "TEXT_IMAGE",
            "aws_region_name": "us-east-1",
        }


class TestBedrockNovaCanvasColorGuidedGeneration(BaseImageGenTest):
    def get_base_image_generation_call_args(self) -> dict:
        litellm.in_memory_llm_clients_cache = InMemoryCache()
        return {
            "model": "bedrock/amazon.nova-canvas-v1:0",
            "n": 1,
            "size": "320x320",
            "imageGenerationConfig": {"cfgScale": 6.5, "seed": 12},
            "taskType": "COLOR_GUIDED_GENERATION",
            "colorGuidedGenerationParams": {"colors": ["#FFFFFF"]},
            "aws_region_name": "us-east-1",
        }


class TestOpenAIGPTImage1(BaseImageGenTest):
    def get_base_image_generation_call_args(self) -> dict:
        return {"model": "gpt-image-1"}




class TestAimlImageGeneration(BaseImageGenTest):
    def get_base_image_generation_call_args(self) -> dict:
        return {"model": "aiml/flux-pro/v1.1"}

    @pytest.mark.asyncio(scope="module")
    @pytest.mark.flaky(retries=0)
    async def test_basic_image_generation(self):
        """Test basic image generation"""
        from unittest.mock import AsyncMock, patch

        mock_aiml_response = {
            "created": 1703658209,
            "data": [{"url": "https://example.com/generated_image.png"}],
        }
        mock_response = MagicMock()
        mock_response.status_code = 200
        mock_response.json.return_value = mock_aiml_response
        mock_response.text = json.dumps(mock_aiml_response)
        mock_response.headers = {}

        with (
            patch(
                "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
                new_callable=AsyncMock,
            ) as mock_async_post,
            patch(
                "litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
            ) as mock_sync_post,
        ):
            mock_async_post.return_value = mock_response
            mock_sync_post.return_value = mock_response

            try:
                litellm.turn_on_debug()
                custom_logger = TestCustomLogger()
                litellm.logging_callback_manager._reset_all_callbacks()
                litellm.callbacks = [custom_logger]
                base_image_generation_call_args = (
                    self.get_base_image_generation_call_args()
                )
                litellm.set_verbose = True
                # Pass dummy api_key so validate_environment passes; HTTP is mocked
                response = await litellm.aimage_generation(
                    **base_image_generation_call_args,
                    prompt="A image of a otter",
                    api_key="test-key-mocked-no-credits-needed",
                )
                print("FAL AI RESPONSE: ", response)

                await asyncio.sleep(1)

                # assert response._hidden_params["response_cost"] is not None
                # assert response._hidden_params["response_cost"] > 0
                # print("response_cost", response._hidden_params["response_cost"])

                logged_standard_logging_payload = custom_logger.standard_logging_payload
                print(
                    "logged_standard_logging_payload", logged_standard_logging_payload
                )
                assert logged_standard_logging_payload is not None
                assert logged_standard_logging_payload["response_cost"] is not None
                assert logged_standard_logging_payload["response_cost"] > 0
                import openai
                from openai.types.images_response import ImagesResponse

                # print openai version
                print("openai version=", openai.__version__)

                response_dict = dict(response)
                if "usage" in response_dict:
                    response_dict["usage"] = dict(response_dict["usage"])
                print("response usage=", response_dict.get("usage"))

                assert (
                    response.data is not None
                )  # type guard for iteration (base fails here if None)
                for d in response.data:
                    assert isinstance(d, Image)
                    print("data in response.data", d)
                    assert d.b64_json is not None or d.url is not None
            except litellm.RateLimitError as e:
                pass
            except litellm.ContentPolicyViolationError:
                pass  # Azure randomly raises these errors - skip when they occur
            except litellm.InternalServerError:
                pass
            except Exception as e:
                if "Your task failed as a result of our safety system." in str(e):
                    pass
                else:
                    pytest.fail(f"An exception occurred - {str(e)}")


class TestGoogleImageGen(BaseImageGenTest):
    def get_base_image_generation_call_args(self) -> dict:
        return {"model": "gemini/gemini-3.1-flash-image"}




## AZURE AI DALL-E 3 is deprecated and new deployments cannot be made
# class TestAzureOpenAIDalle3(BaseImageGenTest):
#     def get_base_image_generation_call_args(self) -> dict:
#         return {
#             "model": "azure/dall-e-3",
#             "api_version": "2024-02-01",
#             "api_base": os.getenv("AZURE_AI_API_BASE"),
#             "api_key": os.getenv("AZURE_AI_API_KEY"),
#             "metadata": {
#                 "model_info": {
#                     "base_model": "azure/dall-e-3",
#                 }
#             },
#         }




@pytest.mark.asyncio
async def test_aiml_image_generation_with_dynamic_api_key():
    """
    Test that when api_key is passed as a dynamic parameter to aimage_generation,
    it gets properly used for AIML provider authentication instead of falling back
    to environment variables.

    This test validates the fix for ensuring dynamic API keys are respected
    when making image generation requests to the AIML provider.
    """
    from unittest.mock import AsyncMock, MagicMock, patch

    import httpx

    # Mock AIML response
    mock_aiml_response = {
        "created": 1703658209,
        "data": [{"url": "https://example.com/generated_image.png"}],
    }

    # Track captured arguments
    captured_headers = None
    captured_url = None
    captured_json_data = None

    def capture_post_call(*args, **kwargs):
        nonlocal captured_headers, captured_url, captured_json_data
        captured_url = kwargs.get("url") or (args[0] if args else None)
        captured_headers = kwargs.get("headers", {})
        captured_json_data = kwargs.get("json", {})

        # Create a mock response
        mock_response = MagicMock()
        mock_response.status_code = 200
        mock_response.json.return_value = mock_aiml_response
        mock_response.text = json.dumps(mock_aiml_response)
        return mock_response

    # Mock the HTTP client that actually makes the request (sync version for image generation)
    with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post") as mock_post:
        mock_post.side_effect = capture_post_call

        # Test with dynamic api_key
        test_api_key = "test-dynamic-api-key-12345"

        response = await litellm.aimage_generation(
            prompt="A cute baby sea otter",
            model="aiml/flux-pro/v1.1",
            api_key=test_api_key,  # This should be used instead of env vars
        )

        # Validate the response (mocked response processing might not populate data correctly)
        assert response is not None

        # The most important validations: API key and endpoint usage
        # These prove that the dynamic API key was properly used
        assert captured_headers is not None
        assert "Authorization" in captured_headers
        assert captured_headers["Authorization"] == f"Bearer {test_api_key}"
        print("TESTCAPTURED HEADERS", captured_headers)
        # Validate the correct AIML endpoint was called
        assert captured_url is not None
        assert "api.aimlapi.com" in captured_url
        assert "/v1/images/generations" in captured_url

        # Validate the request data
        assert captured_json_data is not None
        assert captured_json_data["prompt"] == "A cute baby sea otter"
        assert captured_json_data["model"] == "flux-pro/v1.1"


@pytest.mark.asyncio
async def test_aiml_openai_gpt_image_2_request_uses_openai_param_shape():
    """End-to-end check that ``aiml/openai/gpt-image-2`` keeps the upstream
    OpenAI request shape (``size``/``n``/``response_format``) instead of
    being remapped to the AI/ML flux schema (``image_size``/``num_images``/
    ``output_format``), and hits the correct upstream model name.
    """
    import json as _json
    from unittest.mock import MagicMock, patch

    mock_aiml_response = {
        "created": 1703658209,
        "data": [{"url": "https://example.com/gpt-image-2.png"}],
    }

    captured = {}

    def capture_post_call(*args, **kwargs):
        captured["url"] = kwargs.get("url") or (args[0] if args else None)
        captured["headers"] = kwargs.get("headers", {})
        captured["json"] = kwargs.get("json", {})
        mock_response = MagicMock()
        mock_response.status_code = 200
        mock_response.json.return_value = mock_aiml_response
        mock_response.text = _json.dumps(mock_aiml_response)
        return mock_response

    with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post") as mock_post:
        mock_post.side_effect = capture_post_call

        await litellm.aimage_generation(
            prompt="A T-Rex relaxing on a beach",
            model="aiml/openai/gpt-image-2",
            api_key="test-key-mocked-no-credits-needed",
            size="1024x1536",
            quality="high",
            response_format="b64_json",
            n=1,
        )

    assert captured["url"] is not None
    assert "api.aimlapi.com" in captured["url"]
    assert "/v1/images/generations" in captured["url"]

    body = captured["json"]
    assert body["model"] == "openai/gpt-image-2"
    assert body["prompt"] == "A T-Rex relaxing on a beach"
    assert body["size"] == "1024x1536"
    assert body["quality"] == "high"
    assert body["response_format"] == "b64_json"
    assert body["n"] == 1
    assert "image_size" not in body
    assert "num_images" not in body
    assert "output_format" not in body


@pytest.mark.asyncio
async def test_azure_image_generation_request_body():
    """Azure deployment URL selects the model; JSON body omits ``model`` (#26316)."""
    from litellm import aimage_generation

    test_dir = os.path.dirname(__file__)
    expected_path = os.path.join(test_dir, "request_payloads", "azure_gpt_image_1.json")
    with open(expected_path, "r") as f:
        expected_body = json.load(f)

    with patch(
        "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
        new_callable=AsyncMock,
    ) as mock_post:
        mock_post.side_effect = Exception("test")

        with pytest.raises(litellm.APIConnectionError):
            await aimage_generation(
                model="azure/gpt-image-1",
                prompt="test prompt",
                api_base="https://example.azure.com",
                api_key="test-key",
                api_version="2025-04-01-preview",
            )

        mock_post.assert_called_once()
        call_args = mock_post.call_args
        request_json = call_args.kwargs.get("json", {})
        assert request_json == expected_body