async def test_batch_predict_field_headers_async():
client = PredictionServiceAsyncClient(
credentials=credentials.AnonymousCredentials(), )
# Any value that is part of the HTTP/1.1 URI should be sent as
# a field header. Set these to a non-empty value.
request = prediction_service.BatchPredictRequest()
request.name = "name/value"
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(client._client._transport.batch_predict),
"__call__") as call:
call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
operations_pb2.Operation(name="operations/op"))
await client.batch_predict(request)
# Establish that the underlying gRPC stub method was called.
assert len(call.mock_calls)
_, args, _ = call.mock_calls[0]
assert args[0] == request
# Establish that the field header was sent.
_, _, kw = call.mock_calls[0]
assert (
"x-goog-request-params",
"name=name/value",
) in kw["metadata"]
async def test_batch_predict_async(transport: str = "grpc_asyncio"):
client = PredictionServiceAsyncClient(
credentials=credentials.AnonymousCredentials(),
transport=transport,
)
# Everything is optional in proto3 as far as the runtime is concerned,
# and we are mocking out the actual API, so just send an empty request.
request = prediction_service.BatchPredictRequest()
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(client._client._transport.batch_predict),
"__call__") as call:
# Designate an appropriate return value for the call.
call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
operations_pb2.Operation(name="operations/spam"))
response = await client.batch_predict(request)
# Establish that the underlying gRPC stub method was called.
assert len(call.mock_calls)
_, args, _ = call.mock_calls[0]
assert args[0] == request
# Establish that the response is the type that we expect.
assert isinstance(response, future.Future)
async def test_predict_flattened_async():
client = PredictionServiceAsyncClient(
credentials=credentials.AnonymousCredentials(), )
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(client._client._transport.predict),
"__call__") as call:
# Designate an appropriate return value for the call.
call.return_value = prediction_service.PredictResponse()
call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
prediction_service.PredictResponse())
# Call the method with a truthy value for each flattened field,
# using the keyword arguments to the method.
response = await client.predict(
name="name_value",
payload=data_items.ExamplePayload(image=data_items.Image(
image_bytes=b"image_bytes_blob")),
params={"key_value": "value_value"},
)
# Establish that the underlying call was made with the expected
# request object values.
assert len(call.mock_calls)
_, args, _ = call.mock_calls[0]
assert args[0].name == "name_value"
assert args[0].payload == data_items.ExamplePayload(
image=data_items.Image(image_bytes=b"image_bytes_blob"))
assert args[0].params == {"key_value": "value_value"}
async def test_predict_flattened_error_async():
client = PredictionServiceAsyncClient(
credentials=credentials.AnonymousCredentials(), )
# Attempting to call a method with both a request object and flattened
# fields is an error.
with pytest.raises(ValueError):
await client.predict(
prediction_service.PredictRequest(),
name="name_value",
payload=data_items.ExamplePayload(image=data_items.Image(
image_bytes=b"image_bytes_blob")),
params={"key_value": "value_value"},
)
def test_prediction_service_grpc_lro_async_client():
client = PredictionServiceAsyncClient(
credentials=credentials.AnonymousCredentials(),
transport="grpc_asyncio",
)
transport = client._client._transport
# Ensure that we have a api-core operations client.
assert isinstance(
transport.operations_client,
operations_v1.OperationsAsyncClient,
)
# Ensure that subsequent calls to the property send the exact same object.
assert transport.operations_client is transport.operations_client
async def test_batch_predict_flattened_error_async():
client = PredictionServiceAsyncClient(
credentials=credentials.AnonymousCredentials(), )
# Attempting to call a method with both a request object and flattened
# fields is an error.
with pytest.raises(ValueError):
await client.batch_predict(
prediction_service.BatchPredictRequest(),
name="name_value",
input_config=io.BatchPredictInputConfig(gcs_source=io.GcsSource(
input_uris=["input_uris_value"])),
output_config=io.BatchPredictOutputConfig(
gcs_destination=io.GcsDestination(
output_uri_prefix="output_uri_prefix_value")),
params={"key_value": "value_value"},
)
async def test_batch_predict_flattened_async():
client = PredictionServiceAsyncClient(
credentials=credentials.AnonymousCredentials(), )
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(client._client._transport.batch_predict),
"__call__") as call:
# Designate an appropriate return value for the call.
call.return_value = operations_pb2.Operation(name="operations/op")
call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
operations_pb2.Operation(name="operations/spam"))
# Call the method with a truthy value for each flattened field,
# using the keyword arguments to the method.
response = await client.batch_predict(
name="name_value",
input_config=io.BatchPredictInputConfig(gcs_source=io.GcsSource(
input_uris=["input_uris_value"])),
output_config=io.BatchPredictOutputConfig(
gcs_destination=io.GcsDestination(
output_uri_prefix="output_uri_prefix_value")),
params={"key_value": "value_value"},
)
# Establish that the underlying call was made with the expected
# request object values.
assert len(call.mock_calls)
_, args, _ = call.mock_calls[0]
assert args[0].name == "name_value"
assert args[0].input_config == io.BatchPredictInputConfig(
gcs_source=io.GcsSource(input_uris=["input_uris_value"]))
assert args[0].output_config == io.BatchPredictOutputConfig(
gcs_destination=io.GcsDestination(
output_uri_prefix="output_uri_prefix_value"))
assert args[0].params == {"key_value": "value_value"}