def test_batch_predict(
transport: str = "grpc", request_type=prediction_service.BatchPredictRequest
):
client = PredictionServiceClient(
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 = request_type()
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(client._transport.batch_predict), "__call__") as call:
# Designate an appropriate return value for the call.
call.return_value = operations_pb2.Operation(name="operations/spam")
response = client.batch_predict(request)
# Establish that the underlying gRPC stub method was called.
assert len(call.mock_calls) == 1
_, args, _ = call.mock_calls[0]
assert args[0] == prediction_service.BatchPredictRequest()
# Establish that the response is the type that we expect.
assert isinstance(response, future.Future)
def test_credentials_transport_error():
# It is an error to provide credentials and a transport instance.
transport = transports.PredictionServiceGrpcTransport(
credentials=credentials.AnonymousCredentials(),
)
with pytest.raises(ValueError):
client = PredictionServiceClient(
credentials=credentials.AnonymousCredentials(), transport=transport,
)
# It is an error to provide a credentials file and a transport instance.
transport = transports.PredictionServiceGrpcTransport(
credentials=credentials.AnonymousCredentials(),
)
with pytest.raises(ValueError):
client = PredictionServiceClient(
client_options={"credentials_file": "credentials.json"},
transport=transport,
)
# It is an error to provide scopes and a transport instance.
transport = transports.PredictionServiceGrpcTransport(
credentials=credentials.AnonymousCredentials(),
)
with pytest.raises(ValueError):
client = PredictionServiceClient(
client_options={"scopes": ["1", "2"]}, transport=transport,
)
def test_predict_flattened():
client = PredictionServiceClient(credentials=credentials.AnonymousCredentials(),)
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(client._transport.predict), "__call__") as call:
# Designate an appropriate return value for the call.
call.return_value = prediction_service.PredictResponse()
# Call the method with a truthy value for each flattened field,
# using the keyword arguments to the method.
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) == 1
_, 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"}
def test_predict_flattened_error():
client = PredictionServiceClient(credentials=credentials.AnonymousCredentials(),)
# Attempting to call a method with both a request object and flattened
# fields is an error.
with pytest.raises(ValueError):
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_transport_instance():
# A client may be instantiated with a custom transport instance.
transport = transports.PredictionServiceGrpcTransport(
credentials=credentials.AnonymousCredentials(),
)
client = PredictionServiceClient(transport=transport)
assert client._transport is transport
def test_prediction_service_host_with_port():
client = PredictionServiceClient(
credentials=credentials.AnonymousCredentials(),
client_options=client_options.ClientOptions(
api_endpoint="automl.googleapis.com:8000"
),
)
assert client._transport._host == "automl.googleapis.com:8000"
def test_prediction_service_auth_adc():
# If no credentials are provided, we should use ADC credentials.
with mock.patch.object(auth, "default") as adc:
adc.return_value = (credentials.AnonymousCredentials(), None)
PredictionServiceClient()
adc.assert_called_once_with(
scopes=("https://www.googleapis.com/auth/cloud-platform",),
quota_project_id=None,
)
def test_batch_predict_flattened_error():
client = PredictionServiceClient(credentials=credentials.AnonymousCredentials(),)
# Attempting to call a method with both a request object and flattened
# fields is an error.
with pytest.raises(ValueError):
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"},
)
def test_client_withDEFAULT_CLIENT_INFO():
client_info = gapic_v1.client_info.ClientInfo()
with mock.patch.object(
transports.PredictionServiceTransport, "_prep_wrapped_messages"
) as prep:
client = PredictionServiceClient(
credentials=credentials.AnonymousCredentials(), client_info=client_info,
)
prep.assert_called_once_with(client_info)
with mock.patch.object(
transports.PredictionServiceTransport, "_prep_wrapped_messages"
) as prep:
transport_class = PredictionServiceClient.get_transport_class()
transport = transport_class(
credentials=credentials.AnonymousCredentials(), client_info=client_info,
)
prep.assert_called_once_with(client_info)
def test_prediction_service_grpc_lro_client():
client = PredictionServiceClient(
credentials=credentials.AnonymousCredentials(), transport="grpc",
)
transport = client._transport
# Ensure that we have a api-core operations client.
assert isinstance(transport.operations_client, operations_v1.OperationsClient,)
# Ensure that subsequent calls to the property send the exact same object.
assert transport.operations_client is transport.operations_client
def test_batch_predict_flattened():
client = PredictionServiceClient(credentials=credentials.AnonymousCredentials(),)
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(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 the method with a truthy value for each flattened field,
# using the keyword arguments to the method.
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) == 1
_, 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"}
def test__get_default_mtls_endpoint():
api_endpoint = "example.googleapis.com"
api_mtls_endpoint = "example.mtls.googleapis.com"
sandbox_endpoint = "example.sandbox.googleapis.com"
sandbox_mtls_endpoint = "example.mtls.sandbox.googleapis.com"
non_googleapi = "api.example.com"
assert PredictionServiceClient._get_default_mtls_endpoint(None) is None
assert (
PredictionServiceClient._get_default_mtls_endpoint(api_endpoint)
== api_mtls_endpoint
)
assert (
PredictionServiceClient._get_default_mtls_endpoint(api_mtls_endpoint)
== api_mtls_endpoint
)
assert (
PredictionServiceClient._get_default_mtls_endpoint(sandbox_endpoint)
== sandbox_mtls_endpoint
)
assert (
PredictionServiceClient._get_default_mtls_endpoint(sandbox_mtls_endpoint)
== sandbox_mtls_endpoint
)
assert (
PredictionServiceClient._get_default_mtls_endpoint(non_googleapi)
== non_googleapi
)
def test_batch_predict_field_headers():
client = PredictionServiceClient(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._transport.batch_predict), "__call__") as call:
call.return_value = operations_pb2.Operation(name="operations/op")
client.batch_predict(request)
# Establish that the underlying gRPC stub method was called.
assert len(call.mock_calls) == 1
_, 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"]
def test_prediction_service_client_client_options_from_dict():
with mock.patch(
"google.cloud.automl_v1beta1.services.prediction_service.transports.PredictionServiceGrpcTransport.__init__"
) as grpc_transport:
grpc_transport.return_value = None
client = PredictionServiceClient(
client_options={"api_endpoint": "squid.clam.whelk"}
)
grpc_transport.assert_called_once_with(
credentials=None,
credentials_file=None,
host="squid.clam.whelk",
scopes=None,
ssl_channel_credentials=None,
quota_project_id=None,
client_info=transports.base.DEFAULT_CLIENT_INFO,
)
def test_prediction_service_client_get_transport_class():
transport = PredictionServiceClient.get_transport_class()
assert transport == transports.PredictionServiceGrpcTransport
transport = PredictionServiceClient.get_transport_class("grpc")
assert transport == transports.PredictionServiceGrpcTransport
def test_transport_grpc_default():
# A client should use the gRPC transport by default.
client = PredictionServiceClient(credentials=credentials.AnonymousCredentials(),)
assert isinstance(client._transport, transports.PredictionServiceGrpcTransport,)
