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_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_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_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"]