kserve/test/e2e/common/inference_pb2_grpc.py

# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
"""Client and server classes corresponding to protobuf-defined services."""

import grpc
import warnings

from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2
from . import inference_pb2 as inference__pb2

GRPC_GENERATED_VERSION = "1.64.1"
GRPC_VERSION = grpc.__version__
EXPECTED_ERROR_RELEASE = "1.65.0"
SCHEDULED_RELEASE_DATE = "June 25, 2024"
_version_not_supported = False

try:
    from grpc._utilities import first_version_is_lower

    _version_not_supported = first_version_is_lower(
        GRPC_VERSION, GRPC_GENERATED_VERSION
    )
except ImportError:
    _version_not_supported = True

if _version_not_supported:
    warnings.warn(
        f"The grpc package installed is at version {GRPC_VERSION},"
        + " but the generated code in inference_pb2_grpc.py depends on"
        + f" grpcio>={GRPC_GENERATED_VERSION}."
        + f" Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}"
        + f" or downgrade your generated code using grpcio-tools<={GRPC_VERSION}."
        + f" This warning will become an error in {EXPECTED_ERROR_RELEASE},"
        + f" scheduled for release on {SCHEDULED_RELEASE_DATE}.",
        RuntimeWarning,
        stacklevel=2,
    )


class InferenceAPIsServiceStub(object):
    """Missing associated documentation comment in .proto file."""

    def __init__(self, channel):
        """Constructor.

        Args:
            channel: A grpc.Channel.
        """
        self.Ping = channel.unary_unary(
            "/org.pytorch.serve.grpc.inference.InferenceAPIsService/Ping",
            request_serializer=google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString,
            response_deserializer=inference__pb2.TorchServeHealthResponse.FromString,
            _registered_method=True,
        )
        self.Predictions = channel.unary_unary(
            "/org.pytorch.serve.grpc.inference.InferenceAPIsService/Predictions",
            request_serializer=inference__pb2.PredictionsRequest.SerializeToString,
            response_deserializer=inference__pb2.PredictionResponse.FromString,
            _registered_method=True,
        )
        self.StreamPredictions = channel.unary_stream(
            "/org.pytorch.serve.grpc.inference.InferenceAPIsService/StreamPredictions",
            request_serializer=inference__pb2.PredictionsRequest.SerializeToString,
            response_deserializer=inference__pb2.PredictionResponse.FromString,
            _registered_method=True,
        )
        self.StreamPredictions2 = channel.stream_stream(
            "/org.pytorch.serve.grpc.inference.InferenceAPIsService/StreamPredictions2",
            request_serializer=inference__pb2.PredictionsRequest.SerializeToString,
            response_deserializer=inference__pb2.PredictionResponse.FromString,
            _registered_method=True,
        )


class InferenceAPIsServiceServicer(object):
    """Missing associated documentation comment in .proto file."""

    def Ping(self, request, context):
        """Check health status of the TorchServe server."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details("Method not implemented!")
        raise NotImplementedError("Method not implemented!")

    def Predictions(self, request, context):
        """Predictions entry point to get inference using default model version."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details("Method not implemented!")
        raise NotImplementedError("Method not implemented!")

    def StreamPredictions(self, request, context):
        """Streaming response for an inference request."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details("Method not implemented!")
        raise NotImplementedError("Method not implemented!")

    def StreamPredictions2(self, request_iterator, context):
        """Bi-direction streaming inference and response."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details("Method not implemented!")
        raise NotImplementedError("Method not implemented!")


def add_InferenceAPIsServiceServicer_to_server(servicer, server):
    rpc_method_handlers = {
        "Ping": grpc.unary_unary_rpc_method_handler(
            servicer.Ping,
            request_deserializer=google_dot_protobuf_dot_empty__pb2.Empty.FromString,
            response_serializer=inference__pb2.TorchServeHealthResponse.SerializeToString,
        ),
        "Predictions": grpc.unary_unary_rpc_method_handler(
            servicer.Predictions,
            request_deserializer=inference__pb2.PredictionsRequest.FromString,
            response_serializer=inference__pb2.PredictionResponse.SerializeToString,
        ),
        "StreamPredictions": grpc.unary_stream_rpc_method_handler(
            servicer.StreamPredictions,
            request_deserializer=inference__pb2.PredictionsRequest.FromString,
            response_serializer=inference__pb2.PredictionResponse.SerializeToString,
        ),
        "StreamPredictions2": grpc.stream_stream_rpc_method_handler(
            servicer.StreamPredictions2,
            request_deserializer=inference__pb2.PredictionsRequest.FromString,
            response_serializer=inference__pb2.PredictionResponse.SerializeToString,
        ),
    }
    generic_handler = grpc.method_handlers_generic_handler(
        "org.pytorch.serve.grpc.inference.InferenceAPIsService", rpc_method_handlers
    )
    server.add_generic_rpc_handlers((generic_handler,))
    server.add_registered_method_handlers(
        "org.pytorch.serve.grpc.inference.InferenceAPIsService", rpc_method_handlers
    )


# This class is part of an EXPERIMENTAL API.
class InferenceAPIsService(object):
    """Missing associated documentation comment in .proto file."""

    @staticmethod
    def Ping(
        request,
        target,
        options=(),
        channel_credentials=None,
        call_credentials=None,
        insecure=False,
        compression=None,
        wait_for_ready=None,
        timeout=None,
        metadata=None,
    ):
        return grpc.experimental.unary_unary(
            request,
            target,
            "/org.pytorch.serve.grpc.inference.InferenceAPIsService/Ping",
            google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString,
            inference__pb2.TorchServeHealthResponse.FromString,
            options,
            channel_credentials,
            insecure,
            call_credentials,
            compression,
            wait_for_ready,
            timeout,
            metadata,
            _registered_method=True,
        )

    @staticmethod
    def Predictions(
        request,
        target,
        options=(),
        channel_credentials=None,
        call_credentials=None,
        insecure=False,
        compression=None,
        wait_for_ready=None,
        timeout=None,
        metadata=None,
    ):
        return grpc.experimental.unary_unary(
            request,
            target,
            "/org.pytorch.serve.grpc.inference.InferenceAPIsService/Predictions",
            inference__pb2.PredictionsRequest.SerializeToString,
            inference__pb2.PredictionResponse.FromString,
            options,
            channel_credentials,
            insecure,
            call_credentials,
            compression,
            wait_for_ready,
            timeout,
            metadata,
            _registered_method=True,
        )

    @staticmethod
    def StreamPredictions(
        request,
        target,
        options=(),
        channel_credentials=None,
        call_credentials=None,
        insecure=False,
        compression=None,
        wait_for_ready=None,
        timeout=None,
        metadata=None,
    ):
        return grpc.experimental.unary_stream(
            request,
            target,
            "/org.pytorch.serve.grpc.inference.InferenceAPIsService/StreamPredictions",
            inference__pb2.PredictionsRequest.SerializeToString,
            inference__pb2.PredictionResponse.FromString,
            options,
            channel_credentials,
            insecure,
            call_credentials,
            compression,
            wait_for_ready,
            timeout,
            metadata,
            _registered_method=True,
        )

    @staticmethod
    def StreamPredictions2(
        request_iterator,
        target,
        options=(),
        channel_credentials=None,
        call_credentials=None,
        insecure=False,
        compression=None,
        wait_for_ready=None,
        timeout=None,
        metadata=None,
    ):
        return grpc.experimental.stream_stream(
            request_iterator,
            target,
            "/org.pytorch.serve.grpc.inference.InferenceAPIsService/StreamPredictions2",
            inference__pb2.PredictionsRequest.SerializeToString,
            inference__pb2.PredictionResponse.FromString,
            options,
            channel_credentials,
            insecure,
            call_credentials,
            compression,
            wait_for_ready,
            timeout,
            metadata,
            _registered_method=True,
        )