rename examples to test_examples (#255)

* rename examples to test_examples

* rename

* fix tests

* fix tests

* fix tests

src/litserve/__init__.py

@@ -11,10 +11,10 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from litserve.__about__ import *  # noqa: F401, F403
+from litserve.__about__ import *  # noqa: F403
 from litserve.api import LitAPI
 from litserve.server import LitServer, Request, Response
-from litserve import examples
+from litserve import test_examples
 from litserve.specs.openai import OpenAISpec
 
-__all__ = ["LitAPI", "LitServer", "Request", "Response", "examples", "OpenAISpec"]
+__all__ = ["LitAPI", "LitServer", "Request", "Response", "test_examples", "OpenAISpec"]

src/litserve/server.py

@@ -128,7 +128,7 @@ def __init__(
             _msg = (
                 "middlewares must be a list of tuples"
                 " where each tuple contains a middleware and its arguments. For example:\n"
-                "server = ls.LitServer(ls.examples.SimpleLitAPI(), "
+                "server = ls.LitServer(ls.test_examples.SimpleLitAPI(), "
                 'middlewares=[(RequestIdMiddleware, {"length": 5})])'
             )
             raise ValueError(_msg)

tests/e2e/default_api.py

@@ -14,6 +14,6 @@
 import litserve as ls
 
 if __name__ == "__main__":
-    api = ls.examples.SimpleLitAPI()
+    api = ls.test_examples.SimpleLitAPI()
     server = ls.LitServer(api)
     server.run(port=8000)

tests/e2e/default_batching.py

@@ -14,6 +14,6 @@
 import litserve as ls
 
 if __name__ == "__main__":
-    api = ls.examples.SimpleBatchedAPI()
+    api = ls.test_examples.SimpleBatchedAPI()
     server = ls.LitServer(api, max_batch_size=4, batch_timeout=0.05)
     server.run(port=8000)

tests/e2e/default_openai_with_batching.py

@@ -1,4 +1,4 @@
-from litserve.examples.openai_spec_example import OpenAIBatchContext
+from litserve.test_examples.openai_spec_example import OpenAIBatchContext
 
 import litserve as ls
 

tests/e2e/default_openaispec.py

@@ -1,5 +1,5 @@
 from litserve import OpenAISpec
-from litserve.examples.openai_spec_example import TestAPI
+from litserve.test_examples.openai_spec_example import TestAPI
 import litserve as ls
 
 if __name__ == "__main__":

tests/e2e/default_openaispec_response_format.py

@@ -1,6 +1,6 @@
 import litserve as ls
 from litserve import OpenAISpec
-from litserve.examples.openai_spec_example import TestAPIWithStructuredOutput
+from litserve.test_examples.openai_spec_example import TestAPIWithStructuredOutput
 
 if __name__ == "__main__":
     server = ls.LitServer(TestAPIWithStructuredOutput(), spec=OpenAISpec())

tests/e2e/default_openaispec_tools.py

@@ -1,6 +1,6 @@
 import litserve as ls
 from litserve import OpenAISpec
-from litserve.examples.openai_spec_example import TestAPI
+from litserve.test_examples.openai_spec_example import TestAPI
 from litserve.specs.openai import ChatMessage
 
 

tests/e2e/default_spec.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from litserve.examples.openai_spec_example import TestAPI
+from litserve.test_examples.openai_spec_example import TestAPI
 from litserve.specs.openai import OpenAISpec
 
 import litserve as ls

tests/test_batch.py

@@ -185,7 +185,7 @@ def test_batched_loop():
     ],
 )
 def test_collate_requests(batch_timeout, batch_size):
-    api = ls.examples.SimpleBatchedAPI()
+    api = ls.test_examples.SimpleBatchedAPI()
     api.request_timeout = 5
     request_queue = Queue()
     for i in range(batch_size):

tests/test_examples.py

@@ -3,20 +3,20 @@
 from asgi_lifespan import LifespanManager
 from httpx import AsyncClient
 
-from litserve.examples.openai_spec_example import (
+from litserve.test_examples.openai_spec_example import (
     OpenAIWithUsage,
     OpenAIWithUsageEncodeResponse,
     OpenAIBatchingWithUsage,
     OpenAIBatchContext,
 )
-from litserve.examples.simple_example import SimpleStreamAPI
+from litserve.test_examples.simple_example import SimpleStreamAPI
 from litserve.utils import wrap_litserve_start
 import litserve as ls
 
 
 @pytest.mark.asyncio()
 async def test_simple_pytorch_api():
-    api = ls.examples.SimpleTorchAPI()
+    api = ls.test_examples.SimpleTorchAPI()
     server = ls.LitServer(api, accelerator="cpu")
     with wrap_litserve_start(server) as server:
         async with LifespanManager(server.app) as manager, AsyncClient(app=manager.app, base_url="http://test") as ac:
@@ -26,7 +26,7 @@ async def test_simple_pytorch_api():
 
 @pytest.mark.asyncio()
 async def test_simple_batched_api():
-    api = ls.examples.SimpleBatchedAPI()
+    api = ls.test_examples.SimpleBatchedAPI()
     server = ls.LitServer(api, max_batch_size=4, batch_timeout=0.1)
     with wrap_litserve_start(server) as server:
         async with LifespanManager(server.app) as manager, AsyncClient(app=manager.app, base_url="http://test") as ac:
@@ -36,7 +36,7 @@ async def test_simple_batched_api():
 
 @pytest.mark.asyncio()
 async def test_simple_api():
-    api = ls.examples.SimpleLitAPI()
+    api = ls.test_examples.SimpleLitAPI()
     server = ls.LitServer(api)
     with wrap_litserve_start(server) as server:
         async with LifespanManager(server.app) as manager, AsyncClient(app=manager.app, base_url="http://test") as ac:
@@ -46,15 +46,15 @@ async def test_simple_api():
 
 @pytest.mark.asyncio()
 async def test_simple_api_without_server():
-    api = ls.examples.SimpleLitAPI()
+    api = ls.test_examples.SimpleLitAPI()
     api.setup(None)
     assert api.model is not None, "Model should be loaded after setup"
     assert api.predict(4) == 16, "Model should be able to predict"
 
 
 @pytest.mark.asyncio()
 async def test_simple_pytorch_api_without_server():
-    api = ls.examples.SimpleTorchAPI()
+    api = ls.test_examples.SimpleTorchAPI()
     api.setup("cpu")
     assert api.model is not None, "Model should be loaded after setup"
     assert isinstance(api.model, torch.nn.Module)

tests/test_lit_server.py

@@ -203,7 +203,7 @@ def test_server_run(mock_uvicorn):
 @pytest.mark.skipif(sys.platform == "win32", reason="Test is only for Unix")
 @patch("litserve.server.uvicorn")
 def test_start_server(mock_uvicon):
-    server = LitServer(ls.examples.TestAPI(), spec=ls.OpenAISpec())
+    server = LitServer(ls.test_examples.TestAPI(), spec=ls.OpenAISpec())
     sockets = MagicMock()
     server._start_server(8000, 1, "info", sockets, "process")
     mock_uvicon.Server.assert_called()
@@ -213,7 +213,7 @@ def test_start_server(mock_uvicon):
 @pytest.mark.skipif(sys.platform == "win32", reason="Test is only for Unix")
 @patch("litserve.server.uvicorn")
 def test_server_run_with_api_server_worker_type(mock_uvicorn):
-    api = ls.examples.SimpleLitAPI()
+    api = ls.test_examples.SimpleLitAPI()
     server = ls.LitServer(api, devices=1)
     with pytest.raises(ValueError, match=r"Must be 'process' or 'thread'"):
         server.run(api_server_worker_type="invalid")
@@ -247,7 +247,7 @@ def test_server_run_with_api_server_worker_type(mock_uvicorn):
 @pytest.mark.skipif(sys.platform != "win32", reason="Test is only for Windows")
 @patch("litserve.server.uvicorn")
 def test_server_run_windows(mock_uvicorn):
-    api = ls.examples.SimpleLitAPI()
+    api = ls.test_examples.SimpleLitAPI()
     server = ls.LitServer(api)
     server.launch_inference_worker = MagicMock(return_value=[MagicMock(), [MagicMock()]])
     server._start_server = MagicMock()
@@ -273,7 +273,7 @@ def test_server_terminate():
         mock_manager.shutdown.assert_called()
 
 
-class IdentityAPI(ls.examples.SimpleLitAPI):
+class IdentityAPI(ls.test_examples.SimpleLitAPI):
     def predict(self, x, context):
         context["input"] = x
         return self.model(x)
@@ -283,7 +283,7 @@ def encode_response(self, output, context):
         return {"output": input}
 
 
-class IdentityBatchedAPI(ls.examples.SimpleBatchedAPI):
+class IdentityBatchedAPI(ls.test_examples.SimpleBatchedAPI):
     def predict(self, x_batch, context):
         for c, x in zip(context, x_batch):
             c["input"] = x
@@ -294,7 +294,7 @@ def encode_response(self, output, context):
         return {"output": input}
 
 
-class IdentityBatchedStreamingAPI(ls.examples.SimpleBatchedAPI):
+class IdentityBatchedStreamingAPI(ls.test_examples.SimpleBatchedAPI):
     def predict(self, x_batch, context):
         for c, x in zip(context, x_batch):
             c["input"] = x
@@ -305,7 +305,7 @@ def encode_response(self, output_stream, context):
             yield [{"output": ctx["input"]} for ctx in context]
 
 
-class PredictErrorAPI(ls.examples.SimpleLitAPI):
+class PredictErrorAPI(ls.test_examples.SimpleLitAPI):
     def predict(self, x, y, context):
         context["input"] = x
         return self.model(x)
@@ -354,16 +354,16 @@ def dummy_load_and_raise(resp):
 
 def test_custom_api_path():
     with pytest.raises(ValueError, match="api_path must start with '/'. "):
-        LitServer(ls.examples.SimpleLitAPI(), api_path="predict")
+        LitServer(ls.test_examples.SimpleLitAPI(), api_path="predict")
 
-    server = LitServer(ls.examples.SimpleLitAPI(), api_path="/v1/custom_predict")
+    server = LitServer(ls.test_examples.SimpleLitAPI(), api_path="/v1/custom_predict")
     url = server.api_path
     with wrap_litserve_start(server) as server, TestClient(server.app) as client:
         response = client.post(url, json={"input": 4.0})
         assert response.status_code == 200, "Server response should be 200 (OK)"
 
 
-class TestHTTPExceptionAPI(ls.examples.SimpleLitAPI):
+class TestHTTPExceptionAPI(ls.test_examples.SimpleLitAPI):
     def decode_request(self, request):
         raise HTTPException(501, "decode request is bad")
 
@@ -389,7 +389,7 @@ async def dispatch(self, request, call_next):
 
 
 def test_custom_middleware():
-    server = ls.LitServer(ls.examples.SimpleLitAPI(), middlewares=[(RequestIdMiddleware, {"length": 5})])
+    server = ls.LitServer(ls.test_examples.SimpleLitAPI(), middlewares=[(RequestIdMiddleware, {"length": 5})])
     with wrap_litserve_start(server) as server, TestClient(server.app) as client:
         response = client.post("/predict", json={"input": 4.0})
         assert response.status_code == 200, f"Expected response to be 200 but got {response.status_code}"
@@ -407,7 +407,7 @@ def test_starlette_middlewares():
         ),
         HTTPSRedirectMiddleware,
     ]
-    server = ls.LitServer(ls.examples.SimpleLitAPI(), middlewares=middlewares)
+    server = ls.LitServer(ls.test_examples.SimpleLitAPI(), middlewares=middlewares)
     with wrap_litserve_start(server) as server, TestClient(server.app) as client:
         response = client.post("/predict", json={"input": 4.0}, headers={"Host": "localhost"})
         assert response.status_code == 200, f"Expected response to be 200 but got {response.status_code}"
@@ -421,11 +421,11 @@ def test_middlewares_inputs():
     server = ls.LitServer(SimpleLitAPI(), middlewares=[])
     assert len(server.middlewares) == 1, "Default middleware should be present"
 
-    server = ls.LitServer(ls.examples.SimpleLitAPI(), middlewares=[], max_payload_size=1000)
+    server = ls.LitServer(ls.test_examples.SimpleLitAPI(), middlewares=[], max_payload_size=1000)
     assert len(server.middlewares) == 2, "Default middleware should be present"
 
-    server = ls.LitServer(ls.examples.SimpleLitAPI(), middlewares=None)
+    server = ls.LitServer(ls.test_examples.SimpleLitAPI(), middlewares=None)
     assert len(server.middlewares) == 1, "Default middleware should be present"
 
     with pytest.raises(ValueError, match="middlewares must be a list of tuples"):
-        ls.LitServer(ls.examples.SimpleLitAPI(), middlewares=(RequestIdMiddleware, {"length": 5}))
+        ls.LitServer(ls.test_examples.SimpleLitAPI(), middlewares=(RequestIdMiddleware, {"length": 5}))

tests/test_litapi.py

@@ -121,33 +121,33 @@ def test_batch_unbatch_stream():
 
 def test_decode_request():
     request = {"input": 4.0}
-    api = ls.examples.SimpleLitAPI()
+    api = ls.test_examples.SimpleLitAPI()
     assert api.decode_request(request) == 4.0, "Decode request should return the input 4.0"
 
 
 def test_decode_request_with_openai_spec():
-    api = ls.examples.TestAPI()
+    api = ls.test_examples.TestAPI()
     api._sanitize(max_batch_size=1, spec=ls.OpenAISpec())
     request = ChatCompletionRequest(messages=[{"role": "system", "content": "Hello"}])
     decoded_request = api.decode_request(request)
     assert decoded_request[0]["content"] == "Hello", "Decode request should return the input message"
 
 
 def test_decode_request_with_openai_spec_wrong_request():
-    api = ls.examples.TestAPI()
+    api = ls.test_examples.TestAPI()
     api._sanitize(max_batch_size=1, spec=ls.OpenAISpec())
     with pytest.raises(AttributeError, match="object has no attribute 'messages'"):
         api.decode_request({"input": "Hello"})
 
 
 def test_encode_response():
     response = 4.0
-    api = ls.examples.SimpleLitAPI()
+    api = ls.test_examples.SimpleLitAPI()
     assert api.encode_response(response) == {"output": 4.0}, 'Encode response returns encoded output {"output": 4.0}'
 
 
 def test_encode_response_with_openai_spec():
-    api = ls.examples.TestAPI()
+    api = ls.test_examples.TestAPI()
     api._sanitize(max_batch_size=1, spec=ls.OpenAISpec())
     response = "This is a LLM generated text".split()
     generated_tokens = []
@@ -164,7 +164,7 @@ def predict():
             yield {"content": token, "prompt_tokens": 4, "completion_tokens": 4, "total_tokens": 8}
 
     generated_tokens = []
-    api = ls.examples.TestAPI()
+    api = ls.test_examples.TestAPI()
     api._sanitize(max_batch_size=1, spec=ls.OpenAISpec())
 
     for output in api.encode_response(predict()):
@@ -174,12 +174,12 @@ def predict():
 
 
 def test_encode_response_with_custom_spec_api():
-    class CustomSpecAPI(ls.examples.TestAPI):
+    class CustomSpecAPI(ls.test_examples.TestAPI):
         def encode_response(self, output_stream):
             for output in output_stream:
                 yield {"content": output}
 
-    api = ls.examples.TestAPI()
+    api = ls.test_examples.TestAPI()
     api._sanitize(max_batch_size=1, spec=CustomSpecAPI())
     response = "This is a LLM generated text".split()
     generated_tokens = []
@@ -189,7 +189,7 @@ def encode_response(self, output_stream):
 
 
 def test_encode_response_with_openai_spec_invalid_input():
-    api = ls.examples.TestAPI()
+    api = ls.test_examples.TestAPI()
     api._sanitize(max_batch_size=1, spec=ls.OpenAISpec())
     response = 10
     with pytest.raises(TypeError, match="object is not iterable"):
@@ -200,14 +200,14 @@ def test_encode_response_with_openai_spec_invalid_predict_output():
     def predict():
         yield {"hello": "world"}
 
-    api = ls.examples.TestAPI()
+    api = ls.test_examples.TestAPI()
     api._sanitize(max_batch_size=1, spec=ls.OpenAISpec())
     with pytest.raises(HTTPException, match=r"Malformed output from LitAPI.predict"):
         next(api.encode_response(predict()))
 
 
 def test_format_encoded_response():
-    api = ls.examples.SimpleLitAPI()
+    api = ls.test_examples.SimpleLitAPI()
     sample = {"output": 4.0}
     msg = "Format encoded response should return the encoded response as a string"
     assert api.format_encoded_response(sample) == '{"output": 4.0}\n', msg
@@ -225,18 +225,18 @@ class Sample(BaseModel):
 
 
 def test_batch_torch():
-    api = ls.examples.SimpleLitAPI()
+    api = ls.test_examples.SimpleLitAPI()
     x = [torch.Tensor([1, 2, 3, 4]), torch.Tensor([5, 6, 7, 8])]
     assert torch.all(api.batch(x) == torch.stack(x)), "Batch should stack torch tensors"
 
 
 def test_batch_numpy():
-    api = ls.examples.SimpleLitAPI()
+    api = ls.test_examples.SimpleLitAPI()
     x = [np.asarray([1, 2, 3, 4]), np.asarray([5, 6, 7, 8])]
     assert np.all(api.batch(x) == np.stack(x)), "Batch should stack Numpy array"
 
 
 def test_device_property():
-    api = ls.examples.SimpleLitAPI()
+    api = ls.test_examples.SimpleLitAPI()
     api.device = "cpu"
     assert api.device == "cpu"

tests/test_specs.py

@@ -17,7 +17,7 @@
 from asgi_lifespan import LifespanManager
 from fastapi import HTTPException
 from httpx import AsyncClient
-from litserve.examples.openai_spec_example import (
+from litserve.test_examples.openai_spec_example import (
     OpenAIBatchingWithUsage,
     OpenAIWithUsage,
     OpenAIWithUsageEncodeResponse,