How to use the mlflow.protos.databricks_pb2.ErrorCode.Name function in mlflow

def test_log_batch_nonexistent_run(self):
        fs = FileStore(self.test_root)
        nonexistent_uuid = uuid.uuid4().hex
        with self.assertRaises(MlflowException) as e:
            fs.log_batch(nonexistent_uuid, [], [], [])
        assert e.exception.error_code == ErrorCode.Name(RESOURCE_DOES_NOT_EXIST)
        assert ("Run '%s' not found" % nonexistent_uuid) in e.exception.message

def test_rest_exception_error_code_and_no_message():
    exc = RestException({"error_code": ErrorCode.Name(RESOURCE_DOES_NOT_EXIST),
                         "messages": "something important."})
    assert "something important." in str(exc)
    assert "RESOURCE_DOES_NOT_EXIST" in str(exc)
    json.loads(exc.serialize_as_json())

def test_scoring_server_responds_to_malformed_json_input_with_stacktrace_and_error_code(
        sklearn_model, model_path):
    mlflow.sklearn.save_model(sk_model=sklearn_model.model, path=model_path)

    malformed_json_content = "this is,,,, not valid json"
    response = pyfunc_serve_and_score_model(
            model_uri=os.path.abspath(model_path),
            data=malformed_json_content,
            content_type=pyfunc_scoring_server.CONTENT_TYPE_JSON_SPLIT_ORIENTED)
    response_json = json.loads(response.content)
    assert "error_code" in response_json
    assert response_json["error_code"] == ErrorCode.Name(MALFORMED_REQUEST)
    assert "message" in response_json
    assert "stack_trace" in response_json

def test_validate_metric_name():
    for good_name in GOOD_METRIC_OR_PARAM_NAMES:
        _validate_metric_name(good_name)
    for bad_name in BAD_METRIC_OR_PARAM_NAMES:
        with pytest.raises(MlflowException, match="Invalid metric name") as e:
            _validate_metric_name(bad_name)
        assert e.value.error_code == ErrorCode.Name(INVALID_PARAMETER_VALUE)

def test_attempting_to_deploy_in_asynchronous_mode_without_archiving_throws_exception(
        pretrained_model):
    with pytest.raises(MlflowException) as exc:
        mfs.deploy(app_name="test-app",
                   model_uri=pretrained_model.model_uri,
                   mode=mfs.DEPLOYMENT_MODE_CREATE,
                   archive=False,
                   synchronous=False)

    assert "Resources must be archived" in exc.value.message
    assert exc.value.error_code == ErrorCode.Name(INVALID_PARAMETER_VALUE)

import json

from mlflow.protos.databricks_pb2 import INTERNAL_ERROR, TEMPORARILY_UNAVAILABLE, \
    ENDPOINT_NOT_FOUND, PERMISSION_DENIED, REQUEST_LIMIT_EXCEEDED, BAD_REQUEST, \
    INVALID_PARAMETER_VALUE, RESOURCE_DOES_NOT_EXIST, INVALID_STATE, RESOURCE_ALREADY_EXISTS, \
    ErrorCode

ERROR_CODE_TO_HTTP_STATUS = {
    ErrorCode.Name(INTERNAL_ERROR): 500,
    ErrorCode.Name(INVALID_STATE): 500,
    ErrorCode.Name(TEMPORARILY_UNAVAILABLE): 503,
    ErrorCode.Name(REQUEST_LIMIT_EXCEEDED): 429,
    ErrorCode.Name(ENDPOINT_NOT_FOUND): 404,
    ErrorCode.Name(RESOURCE_DOES_NOT_EXIST): 404,
    ErrorCode.Name(PERMISSION_DENIED): 403,
    ErrorCode.Name(BAD_REQUEST): 400,
    ErrorCode.Name(RESOURCE_ALREADY_EXISTS): 400,
    ErrorCode.Name(INVALID_PARAMETER_VALUE): 400
}


class MlflowException(Exception):
    """
    Generic exception thrown to surface failure information about external-facing operations.
    The error message associated with this exception may be exposed to clients in HTTP responses
    for debugging purposes. If the error text is sensitive, raise a generic `Exception` object
    instead.
    """

def get_experiment_by_name(self, experiment_name):
        try:
            req_body = message_to_json(GetExperimentByName(experiment_name=experiment_name))
            response_proto = self._call_endpoint(GetExperimentByName, req_body)
            return Experiment.from_proto(response_proto.experiment)
        except MlflowException as e:
            if e.error_code == databricks_pb2.ErrorCode.Name(
                    databricks_pb2.RESOURCE_DOES_NOT_EXIST):
                return None
            elif e.error_code == databricks_pb2.ErrorCode.Name(
                    databricks_pb2.REQUEST_LIMIT_EXCEEDED):
                raise e
            # Fall back to using ListExperiments-based implementation.
            for experiment in self.list_experiments(ViewType.ALL):
                if experiment.name == experiment_name:
                    return experiment
            return None

from mlflow.protos.databricks_pb2 import INTERNAL_ERROR, TEMPORARILY_UNAVAILABLE, \
    ENDPOINT_NOT_FOUND, PERMISSION_DENIED, REQUEST_LIMIT_EXCEEDED, BAD_REQUEST, \
    INVALID_PARAMETER_VALUE, RESOURCE_DOES_NOT_EXIST, INVALID_STATE, RESOURCE_ALREADY_EXISTS, \
    ErrorCode

ERROR_CODE_TO_HTTP_STATUS = {
    ErrorCode.Name(INTERNAL_ERROR): 500,
    ErrorCode.Name(INVALID_STATE): 500,
    ErrorCode.Name(TEMPORARILY_UNAVAILABLE): 503,
    ErrorCode.Name(REQUEST_LIMIT_EXCEEDED): 429,
    ErrorCode.Name(ENDPOINT_NOT_FOUND): 404,
    ErrorCode.Name(RESOURCE_DOES_NOT_EXIST): 404,
    ErrorCode.Name(PERMISSION_DENIED): 403,
    ErrorCode.Name(BAD_REQUEST): 400,
    ErrorCode.Name(RESOURCE_ALREADY_EXISTS): 400,
    ErrorCode.Name(INVALID_PARAMETER_VALUE): 400
}


class MlflowException(Exception):
    """
    Generic exception thrown to surface failure information about external-facing operations.
    The error message associated with this exception may be exposed to clients in HTTP responses
    for debugging purposes. If the error text is sensitive, raise a generic `Exception` object
    instead.
    """
    def __init__(self, message, error_code=INTERNAL_ERROR, **kwargs):
        """
        :param message: The message describing the error that occured. This will be included in the
                        exception's serialized JSON representation.
        :param error_code: An appropriate error code for the error that occured; it will be included
                           in the exception's serialized JSON representation. This should be one of

def get_experiment_by_name(self, experiment_name):
        try:
            req_body = message_to_json(GetExperimentByName(experiment_name=experiment_name))
            response_proto = self._call_endpoint(GetExperimentByName, req_body)
            return Experiment.from_proto(response_proto.experiment)
        except MlflowException as e:
            if e.error_code == databricks_pb2.ErrorCode.Name(
                    databricks_pb2.RESOURCE_DOES_NOT_EXIST):
                return None
            elif e.error_code == databricks_pb2.ErrorCode.Name(
                    databricks_pb2.REQUEST_LIMIT_EXCEEDED):
                raise e
            # Fall back to using ListExperiments-based implementation.
            for experiment in self.list_experiments(ViewType.ALL):
                if experiment.name == experiment_name:
                    return experiment
            return None

import json

from mlflow.protos.databricks_pb2 import INTERNAL_ERROR, TEMPORARILY_UNAVAILABLE, \
    ENDPOINT_NOT_FOUND, PERMISSION_DENIED, REQUEST_LIMIT_EXCEEDED, BAD_REQUEST, \
    INVALID_PARAMETER_VALUE, RESOURCE_DOES_NOT_EXIST, INVALID_STATE, RESOURCE_ALREADY_EXISTS, \
    ErrorCode

ERROR_CODE_TO_HTTP_STATUS = {
    ErrorCode.Name(INTERNAL_ERROR): 500,
    ErrorCode.Name(INVALID_STATE): 500,
    ErrorCode.Name(TEMPORARILY_UNAVAILABLE): 503,
    ErrorCode.Name(REQUEST_LIMIT_EXCEEDED): 429,
    ErrorCode.Name(ENDPOINT_NOT_FOUND): 404,
    ErrorCode.Name(RESOURCE_DOES_NOT_EXIST): 404,
    ErrorCode.Name(PERMISSION_DENIED): 403,
    ErrorCode.Name(BAD_REQUEST): 400,
    ErrorCode.Name(RESOURCE_ALREADY_EXISTS): 400,
    ErrorCode.Name(INVALID_PARAMETER_VALUE): 400
}


class MlflowException(Exception):
    """
    Generic exception thrown to surface failure information about external-facing operations.
    The error message associated with this exception may be exposed to clients in HTTP responses
    for debugging purposes. If the error text is sensitive, raise a generic `Exception` object
    instead.
    """
    def __init__(self, message, error_code=INTERNAL_ERROR, **kwargs):
        """
        :param message: The message describing the error that occured. This will be included in the
                        exception's serialized JSON representation.