import logging import os import sys import traceback import asyncio from typing import Optional import pytest import base64 from io import BytesIO from unittest.mock import patch, AsyncMock import json from abc import ABC, abstractmethod sys.path.insert( 0, os.path.abspath("../..") ) # Adds the parent directory to the system path import litellm from litellm.utils import ImageResponse from litellm.integrations.custom_logger import CustomLogger from litellm.types.utils import StandardLoggingPayload # Configure pytest marks to avoid warnings pytestmark = pytest.mark.asyncio class TestCustomLogger(CustomLogger): def __init__(self): self.standard_logging_payload: Optional[StandardLoggingPayload] = None async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): self.standard_logging_payload = kwargs.get("standard_logging_object", None) pass class BaseLLMImageEditTest(ABC): """ Abstract base test class that enforces a common test across all image edit test classes. """ @property def image_edit_function(self): return litellm.image_edit @property def async_image_edit_function(self): return litellm.aimage_edit @abstractmethod def get_base_image_edit_call_args(self) -> dict: """Must return the base image edit call args""" pass @pytest.fixture(autouse=True) def _handle_rate_limits(self): """Fixture to handle rate limit errors for all test methods""" try: yield except litellm.RateLimitError: pytest.skip("Rate limit exceeded") except litellm.InternalServerError: pytest.skip("Model is overloaded") @pytest.mark.parametrize("sync_mode", [True, False]) @pytest.mark.flaky(retries=3, delay=2) @pytest.mark.asyncio async def test_openai_image_edit_litellm_sdk(self, sync_mode): """ Test image edit functionality with both sync and async modes. """ litellm._turn_on_debug() try: prompt = """ Create a studio ghibli style image that combines all the reference images. Make sure the person looks like a CTO. """ call_args = self.get_base_image_edit_call_args() call_args["prompt"] = prompt if sync_mode: result = self.image_edit_function(**call_args) else: result = await self.async_image_edit_function(**call_args) print("result from image edit", result) # Validate the response meets expected schema ImageResponse.model_validate(result) if isinstance(result, ImageResponse) and result.data: image_base64 = result.data[0].b64_json if image_base64: image_bytes = base64.b64decode(image_base64) # Save the image to a file with open("test_image_edit.png", "wb") as f: f.write(image_bytes) except litellm.ContentPolicyViolationError as e: pass # Get the current directory of the file being run pwd = os.path.dirname(os.path.realpath(__file__)) TEST_IMAGES = [ open(os.path.join(pwd, "ishaan_github.png"), "rb"), open(os.path.join(pwd, "litellm_site.png"), "rb"), ] SINGLE_TEST_IMAGE = open(os.path.join(pwd, "ishaan_github.png"), "rb") def get_test_images_as_bytesio(): """Helper function to get test images as BytesIO objects""" bytesio_images = [] for image_path in ["ishaan_github.png", "litellm_site.png"]: with open(os.path.join(pwd, image_path), "rb") as f: image_bytes = f.read() bytesio_images.append(BytesIO(image_bytes)) return bytesio_images class TestOpenAIImageEditGPTImage1(BaseLLMImageEditTest): """ Concrete implementation of BaseLLMImageEditTest for OpenAI image edits. """ def get_base_image_edit_call_args(self) -> dict: """Return base call args for OpenAI image edit""" return { "model": "gpt-image-1", "image": TEST_IMAGES, } class TestOpenAIImageEditDallE2(BaseLLMImageEditTest): """ Concrete implementation of BaseLLMImageEditTest for OpenAI DALL-E-2 image edits. DALL-E-2 only supports a single image (not an array). """ def get_base_image_edit_call_args(self) -> dict: """Return base call args for OpenAI DALL-E-2 image edit (single image only)""" return { "model": "dall-e-2", "image": SINGLE_TEST_IMAGE, } class TestAzureAIFlux2ImageEdit(BaseLLMImageEditTest): """ Concrete implementation of BaseLLMImageEditTest for Azure AI FLUX 2 image edits. FLUX 2 uses JSON with base64 image instead of multipart/form-data. """ def get_base_image_edit_call_args(self) -> dict: """Return base call args for Azure AI FLUX 2 image edit""" return { "model": "azure_ai/flux.2-pro", "image": SINGLE_TEST_IMAGE, "api_base": "https://litellm-ci-cd-prod.services.ai.azure.com", "api_key": os.getenv("AZURE_API_KEY"), "api_version": "preview", } @pytest.mark.flaky(retries=3, delay=2) @pytest.mark.asyncio async def test_openai_image_edit_litellm_router(): litellm._turn_on_debug() try: prompt = """ Create a studio ghibli style image that combines all the reference images. Make sure the person looks like a CTO. """ router = litellm.Router( model_list=[ { "model_name": "gpt-image-1", "litellm_params": { "model": "gpt-image-1", }, } ] ) result = await router.aimage_edit( prompt=prompt, model="gpt-image-1", image=TEST_IMAGES, ) print("result from image edit", result) # Validate the response meets expected schema ImageResponse.model_validate(result) if isinstance(result, ImageResponse) and result.data: image_base64 = result.data[0].b64_json if image_base64: image_bytes = base64.b64decode(image_base64) # Save the image to a file with open("test_image_edit.png", "wb") as f: f.write(image_bytes) except litellm.ContentPolicyViolationError as e: pass @pytest.mark.flaky(retries=3, delay=2) @pytest.mark.asyncio async def test_openai_image_edit_with_bytesio(): """Test image editing using BytesIO objects instead of file readers""" from litellm import image_edit, aimage_edit litellm._turn_on_debug() try: prompt = """ Create a studio ghibli style image that combines all the reference images. Make sure the person looks like a CTO. """ # Get images as BytesIO objects bytesio_images = get_test_images_as_bytesio() result = await aimage_edit( prompt=prompt, model="gpt-image-1", image=bytesio_images, ) print("result from image edit with BytesIO", result) # Validate the response meets expected schema ImageResponse.model_validate(result) if isinstance(result, ImageResponse) and result.data: image_base64 = result.data[0].b64_json if image_base64: image_bytes = base64.b64decode(image_base64) # Save the image to a file with open("test_image_edit_bytesio.png", "wb") as f: f.write(image_bytes) except litellm.ContentPolicyViolationError as e: pass @pytest.mark.asyncio async def test_azure_image_edit_litellm_sdk(): """Test Azure image edit with mocked httpx request to validate request body and URL""" from litellm import image_edit, aimage_edit # Mock response for Azure image edit mock_response = { "created": 1589478378, "data": [ { "b64_json": "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHggJ/PchI7wAAAABJRU5ErkJggg==" } ] } class MockResponse: def __init__(self, json_data, status_code): self._json_data = json_data self.status_code = status_code self.text = json.dumps(json_data) def json(self): return self._json_data with patch( "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", new_callable=AsyncMock, ) as mock_post: # Configure the mock to return our response mock_post.return_value = MockResponse(mock_response, 200) litellm._turn_on_debug() prompt = """ Create a studio ghibli style image that combines all the reference images. Make sure the person looks like a CTO. """ # Set up test environment variables test_api_base = "https://ai-api-gw-uae-north.openai.azure.com" test_api_key = "test-api-key" test_api_version = "2025-04-01-preview" result = await aimage_edit( prompt=prompt, model="azure/gpt-image-1", api_base=test_api_base, api_key=test_api_key, api_version=test_api_version, image=TEST_IMAGES, ) # Verify the request was made correctly mock_post.assert_called_once() # Check the URL call_args = mock_post.call_args expected_url = f"{test_api_base}/openai/deployments/gpt-image-1/images/edits?api-version={test_api_version}" actual_url = call_args.args[0] if call_args.args else call_args.kwargs.get('url') print(f"Expected URL: {expected_url}") print(f"Actual URL: {actual_url}") assert actual_url == expected_url, f"URL mismatch. Expected: {expected_url}, Got: {actual_url}" # Check the request body if 'data' in call_args.kwargs: # For multipart form data, check the data parameter form_data = call_args.kwargs['data'] print("Form data keys:", list(form_data.keys()) if hasattr(form_data, 'keys') else "Not a dict") # Validate that model and prompt are in the form data assert 'model' in form_data, "model should be in form data" assert 'prompt' in form_data, "prompt should be in form data" assert form_data['model'] == 'gpt-image-1', f"Expected model 'gpt-image-1', got {form_data['model']}" assert prompt.strip() in form_data['prompt'], f"Expected prompt to contain '{prompt.strip()}'" # Check headers headers = call_args.kwargs.get('headers', {}) print("Request headers:", headers) assert 'Authorization' in headers, "Authorization header should be present" assert headers['Authorization'].startswith('Bearer '), "Authorization should be Bearer token" print("result from image edit", result) # Validate the response meets expected schema ImageResponse.model_validate(result) if isinstance(result, ImageResponse) and result.data: image_base64 = result.data[0].b64_json if image_base64: image_bytes = base64.b64decode(image_base64) # Save the image to a file with open("test_image_edit.png", "wb") as f: f.write(image_bytes) @pytest.mark.asyncio async def test_openai_image_edit_cost_tracking(): """Test OpenAI image edit cost tracking with custom logger""" from litellm import image_edit, aimage_edit test_custom_logger = TestCustomLogger() litellm.logging_callback_manager._reset_all_callbacks() litellm.callbacks = [test_custom_logger] # Mock response for Azure image edit with usage data for cost tracking mock_response = { "created": 1589478378, "data": [ { "b64_json": "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHggJ/PchI7wAAAABJRU5ErkJggg==" } ], "usage": { "total_tokens": 1100, "input_tokens": 100, "input_tokens_details": { "image_tokens": 50, "text_tokens": 50 }, "output_tokens": 1000 } } class MockResponse: def __init__(self, json_data, status_code): self._json_data = json_data self.status_code = status_code self.text = json.dumps(json_data) def json(self): return self._json_data with patch( "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", new_callable=AsyncMock, ) as mock_post: # Configure the mock to return our response mock_post.return_value = MockResponse(mock_response, 200) litellm._turn_on_debug() prompt = """ Create a studio ghibli style image that combines all the reference images. Make sure the person looks like a CTO. """ # Set up test environment variables result = await aimage_edit( prompt=prompt, model="openai/gpt-image-1", image=TEST_IMAGES, ) # Verify the request was made correctly mock_post.assert_called_once() # Validate the response meets expected schema ImageResponse.model_validate(result) if isinstance(result, ImageResponse) and result.data: image_base64 = result.data[0].b64_json if image_base64: image_bytes = base64.b64decode(image_base64) # Save the image to a file with open("test_image_edit.png", "wb") as f: f.write(image_bytes) await asyncio.sleep(5) print("standard logging payload", json.dumps(test_custom_logger.standard_logging_payload, indent=4, default=str)) # check model assert test_custom_logger.standard_logging_payload["model"] == "gpt-image-1" assert test_custom_logger.standard_logging_payload["custom_llm_provider"] == "openai" # check response_cost assert test_custom_logger.standard_logging_payload["response_cost"] is not None assert test_custom_logger.standard_logging_payload["response_cost"] > 0 @pytest.mark.asyncio async def test_azure_image_edit_cost_tracking(): """Test Azure image edit cost tracking with custom logger""" from litellm import image_edit, aimage_edit test_custom_logger = TestCustomLogger() litellm.logging_callback_manager._reset_all_callbacks() litellm.callbacks = [test_custom_logger] # Mock response for Azure image edit with usage data for cost tracking mock_response = { "created": 1589478378, "data": [ { "b64_json": "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHggJ/PchI7wAAAABJRU5ErkJggg==" } ], "usage": { "total_tokens": 1100, "input_tokens": 100, "input_tokens_details": { "image_tokens": 50, "text_tokens": 50 }, "output_tokens": 1000 } } class MockResponse: def __init__(self, json_data, status_code): self._json_data = json_data self.status_code = status_code self.text = json.dumps(json_data) def json(self): return self._json_data with patch( "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", new_callable=AsyncMock, ) as mock_post: # Configure the mock to return our response mock_post.return_value = MockResponse(mock_response, 200) litellm._turn_on_debug() prompt = """ Create a studio ghibli style image that combines all the reference images. Make sure the person looks like a CTO. """ # Set up test environment variables result = await aimage_edit( prompt=prompt, model="azure/CUSTOM_AZURE_DEPLOYMENT_NAME", base_model="azure/gpt-image-1", image=TEST_IMAGES, ) # Verify the request was made correctly mock_post.assert_called_once() # Validate the response meets expected schema ImageResponse.model_validate(result) if isinstance(result, ImageResponse) and result.data: image_base64 = result.data[0].b64_json if image_base64: image_bytes = base64.b64decode(image_base64) # Save the image to a file with open("test_image_edit.png", "wb") as f: f.write(image_bytes) await asyncio.sleep(5) print("standard logging payload", json.dumps(test_custom_logger.standard_logging_payload, indent=4, default=str)) # check model assert test_custom_logger.standard_logging_payload["model"] == "CUSTOM_AZURE_DEPLOYMENT_NAME" assert test_custom_logger.standard_logging_payload["custom_llm_provider"] == "azure" # check response_cost assert test_custom_logger.standard_logging_payload["response_cost"] is not None assert test_custom_logger.standard_logging_payload["response_cost"] > 0 @pytest.mark.asyncio @pytest.mark.skip(reason="Recraft image edit API only tested locally") async def test_recraft_image_edit_api(): from litellm import aimage_edit import requests litellm._turn_on_debug() global TEST_IMAGES try: prompt = """ Create a studio ghibli style image that combines all the reference images. Make sure the person looks like a CTO. """ result = await aimage_edit( prompt=prompt, model="recraft/recraftv3", image=TEST_IMAGES, ) print("result from image edit", result) # Validate the response meets expected schema ImageResponse.model_validate(result) if isinstance(result, ImageResponse) and result.data: image_url = result.data[0].url # download the image image_bytes = requests.get(image_url).content with open("test_image_edit.png", "wb") as f: f.write(image_bytes) except litellm.ContentPolicyViolationError as e: pass def test_recraft_image_edit_config(): """ Test Recraft image edit configuration parameter mapping and request transformation. """ from litellm.llms.recraft.image_edit.transformation import RecraftImageEditConfig from litellm.types.images.main import ImageEditOptionalRequestParams from litellm.types.router import GenericLiteLLMParams config = RecraftImageEditConfig() # Test supported OpenAI params supported_params = config.get_supported_openai_params("recraftv3") expected_params = ["n", "response_format", "style"] assert supported_params == expected_params # Test parameter mapping (reuses OpenAI logic with filtering) image_edit_params = ImageEditOptionalRequestParams({ "n": 2, "response_format": "b64_json", "style": "realistic_image", "size": "1024x1024", # Should be dropped "quality": "high" # Should be dropped }) mapped_params = config.map_openai_params(image_edit_params, "recraftv3", drop_params=True) # Should only contain supported params assert mapped_params["n"] == 2 assert mapped_params["response_format"] == "b64_json" assert mapped_params["style"] == "realistic_image" assert "size" not in mapped_params # Should be dropped assert "quality" not in mapped_params # Should be dropped # Test request transformation (reuses OpenAI file handling) mock_image = b"fake_image_data" prompt = "winter landscape" litellm_params = GenericLiteLLMParams(api_key="test_key") data, files = config.transform_image_edit_request( model="recraftv3", prompt=prompt, image=mock_image, image_edit_optional_request_params={"strength": 0.7, "n": 1}, litellm_params=litellm_params, headers={} ) # Check data structure (like OpenAI but with Recraft additions) assert data["prompt"] == prompt assert data["strength"] == 0.7 # Recraft-specific parameter assert data["model"] == "recraftv3" # Check file structure (reuses OpenAI logic) assert len(files) == 1 assert files[0][0] == "image" # Field name (not image[] like OpenAI) assert files[0][1][1] == mock_image # Image data assert files[0][1][2] == "image/png" # Content type @pytest.mark.parametrize("sync_mode", [True, False]) @pytest.mark.flaky(retries=3, delay=2) @pytest.mark.asyncio async def test_multiple_vs_single_image_edit(sync_mode): """Test that both single and multiple image editing work correctly""" from litellm import image_edit, aimage_edit litellm._turn_on_debug() try: prompt = "Add a soft blue tint to the image(s)" # Test single image if sync_mode: single_result = image_edit( prompt=prompt, model="gpt-image-1", image=SINGLE_TEST_IMAGE, ) else: single_result = await aimage_edit( prompt=prompt, model="gpt-image-1", image=SINGLE_TEST_IMAGE, ) print("Single image result:", single_result) ImageResponse.model_validate(single_result) # Test multiple images if sync_mode: multiple_result = image_edit( prompt=prompt, model="gpt-image-1", image=TEST_IMAGES, ) else: multiple_result = await aimage_edit( prompt=prompt, model="gpt-image-1", image=TEST_IMAGES, ) print("Multiple images result:", multiple_result) ImageResponse.model_validate(multiple_result) # Both should return valid responses assert single_result is not None assert multiple_result is not None assert single_result.data is not None assert multiple_result.data is not None assert len(single_result.data) > 0 assert len(multiple_result.data) > 0 except litellm.ContentPolicyViolationError as e: pytest.skip(f"Content policy violation: {e}") @pytest.mark.flaky(retries=3, delay=2) @pytest.mark.asyncio async def test_multiple_image_edit_with_different_formats(): """Test multiple images editing with different file formats and types""" from litellm import aimage_edit litellm._turn_on_debug() try: prompt = "Create a cohesive artistic style across all images" # Test with mixed BytesIO and file objects mixed_images = [ SINGLE_TEST_IMAGE, # File object get_test_images_as_bytesio()[1] # BytesIO object ] result = await aimage_edit( prompt=prompt, model="gpt-image-1", image=mixed_images, ) print("Mixed format images result:", result) ImageResponse.model_validate(result) assert result is not None assert result.data is not None assert len(result.data) > 0 # Save result if available if result.data and result.data[0].b64_json: image_bytes = base64.b64decode(result.data[0].b64_json) with open("test_multiple_image_edit_mixed.png", "wb") as f: f.write(image_bytes) except litellm.ContentPolicyViolationError as e: pytest.skip(f"Content policy violation: {e}") @pytest.mark.flaky(retries=3, delay=2) @pytest.mark.asyncio async def test_image_edit_array_handling(): """Test that the image parameter correctly handles both single items and arrays""" from litellm import aimage_edit # Mock response mock_response = { "created": 1589478378, "data": [ { "b64_json": "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHggJ/PchI7wAAAABJRU5ErkJggg==" } ] } class MockResponse: def __init__(self, json_data, status_code): self._json_data = json_data self.status_code = status_code self.text = json.dumps(json_data) def json(self): return self._json_data with patch( "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", new_callable=AsyncMock, ) as mock_post: mock_post.return_value = MockResponse(mock_response, 200) prompt = "Test prompt" # Test 1: Single image (should be converted to list internally) result1 = await aimage_edit( prompt=prompt, model="gpt-image-1", image=SINGLE_TEST_IMAGE, ) # Test 2: Multiple images (already a list) result2 = await aimage_edit( prompt=prompt, model="gpt-image-1", image=TEST_IMAGES, ) # Both valid calls should succeed ImageResponse.model_validate(result1) ImageResponse.model_validate(result2) # Verify that both calls were made to the API assert mock_post.call_count == 2