Spaces:
Configuration error
Configuration error
File size: 14,580 Bytes
447ebeb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 |
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
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
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
# 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"),
]
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
@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(sync_mode):
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.
"""
if sync_mode:
result = image_edit(
prompt=prompt,
model="gpt-image-1",
image=TEST_IMAGES,
)
else:
result = await 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_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
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
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
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
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
|