# -*- coding: utf-8 -*-
# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from collections import OrderedDict
import os
import re
from typing import (
    Dict,
    Callable,
    Mapping,
    MutableMapping,
    MutableSequence,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
    cast,
)
import warnings

from google.cloud.aiplatform_v1beta1 import gapic_version as package_version

from google.api_core import client_options as client_options_lib
from google.api_core import exceptions as core_exceptions
from google.api_core import gapic_v1
from google.api_core import retry as retries
from google.auth import credentials as ga_credentials  # type: ignore
from google.auth.transport import mtls  # type: ignore
from google.auth.transport.grpc import SslCredentials  # type: ignore
from google.auth.exceptions import MutualTLSChannelError  # type: ignore
from google.oauth2 import service_account  # type: ignore

try:
    OptionalRetry = Union[retries.Retry, gapic_v1.method._MethodDefault, None]
except AttributeError:  # pragma: NO COVER
    OptionalRetry = Union[retries.Retry, object, None]  # type: ignore

from google.api_core import operation as gac_operation  # type: ignore
from google.api_core import operation_async  # type: ignore
from google.cloud.aiplatform_v1beta1.services.featurestore_service import pagers
from google.cloud.aiplatform_v1beta1.types import encryption_spec
from google.cloud.aiplatform_v1beta1.types import entity_type
from google.cloud.aiplatform_v1beta1.types import entity_type as gca_entity_type
from google.cloud.aiplatform_v1beta1.types import feature
from google.cloud.aiplatform_v1beta1.types import feature as gca_feature
from google.cloud.aiplatform_v1beta1.types import feature_monitoring_stats
from google.cloud.aiplatform_v1beta1.types import featurestore
from google.cloud.aiplatform_v1beta1.types import featurestore as gca_featurestore
from google.cloud.aiplatform_v1beta1.types import featurestore_monitoring
from google.cloud.aiplatform_v1beta1.types import featurestore_service
from google.cloud.aiplatform_v1beta1.types import operation as gca_operation
from google.cloud.location import locations_pb2  # type: ignore
from google.iam.v1 import iam_policy_pb2  # type: ignore
from google.iam.v1 import policy_pb2  # type: ignore
from google.longrunning import operations_pb2  # type: ignore
from google.protobuf import empty_pb2  # type: ignore
from google.protobuf import field_mask_pb2  # type: ignore
from google.protobuf import timestamp_pb2  # type: ignore
from .transports.base import FeaturestoreServiceTransport, DEFAULT_CLIENT_INFO
from .transports.grpc import FeaturestoreServiceGrpcTransport
from .transports.grpc_asyncio import FeaturestoreServiceGrpcAsyncIOTransport
from .transports.rest import FeaturestoreServiceRestTransport

try:
    from .transports.rest_asyncio import AsyncFeaturestoreServiceRestTransport

    HAS_ASYNC_REST_DEPENDENCIES = True
except ImportError as e:  # pragma: NO COVER
    HAS_ASYNC_REST_DEPENDENCIES = False
    ASYNC_REST_EXCEPTION = e


class FeaturestoreServiceClientMeta(type):
    """Metaclass for the FeaturestoreService client.

    This provides class-level methods for building and retrieving
    support objects (e.g. transport) without polluting the client instance
    objects.
    """

    _transport_registry = (
        OrderedDict()
    )  # type: Dict[str, Type[FeaturestoreServiceTransport]]
    _transport_registry["grpc"] = FeaturestoreServiceGrpcTransport
    _transport_registry["grpc_asyncio"] = FeaturestoreServiceGrpcAsyncIOTransport
    _transport_registry["rest"] = FeaturestoreServiceRestTransport
    if HAS_ASYNC_REST_DEPENDENCIES:  # pragma: NO COVER
        _transport_registry["rest_asyncio"] = AsyncFeaturestoreServiceRestTransport

    def get_transport_class(
        cls,
        label: Optional[str] = None,
    ) -> Type[FeaturestoreServiceTransport]:
        """Returns an appropriate transport class.

        Args:
            label: The name of the desired transport. If none is
                provided, then the first transport in the registry is used.

        Returns:
            The transport class to use.
        """
        # If a specific transport is requested, return that one.
        if (
            label == "rest_asyncio" and not HAS_ASYNC_REST_DEPENDENCIES
        ):  # pragma: NO COVER
            raise ASYNC_REST_EXCEPTION
        if label:
            return cls._transport_registry[label]

        # No transport is requested; return the default (that is, the first one
        # in the dictionary).
        return next(iter(cls._transport_registry.values()))


class FeaturestoreServiceClient(metaclass=FeaturestoreServiceClientMeta):
    """The service that handles CRUD and List for resources for
    Featurestore.
    """

    @staticmethod
    def _get_default_mtls_endpoint(api_endpoint):
        """Converts api endpoint to mTLS endpoint.

        Convert "*.sandbox.googleapis.com" and "*.googleapis.com" to
        "*.mtls.sandbox.googleapis.com" and "*.mtls.googleapis.com" respectively.
        Args:
            api_endpoint (Optional[str]): the api endpoint to convert.
        Returns:
            str: converted mTLS api endpoint.
        """
        if not api_endpoint:
            return api_endpoint

        mtls_endpoint_re = re.compile(
            r"(?P<name>[^.]+)(?P<mtls>\.mtls)?(?P<sandbox>\.sandbox)?(?P<googledomain>\.googleapis\.com)?"
        )

        m = mtls_endpoint_re.match(api_endpoint)
        name, mtls, sandbox, googledomain = m.groups()
        if mtls or not googledomain:
            return api_endpoint

        if sandbox:
            return api_endpoint.replace(
                "sandbox.googleapis.com", "mtls.sandbox.googleapis.com"
            )

        return api_endpoint.replace(".googleapis.com", ".mtls.googleapis.com")

    # Note: DEFAULT_ENDPOINT is deprecated. Use _DEFAULT_ENDPOINT_TEMPLATE instead.
    DEFAULT_ENDPOINT = "aiplatform.googleapis.com"
    DEFAULT_MTLS_ENDPOINT = _get_default_mtls_endpoint.__func__(  # type: ignore
        DEFAULT_ENDPOINT
    )

    _DEFAULT_ENDPOINT_TEMPLATE = "aiplatform.{UNIVERSE_DOMAIN}"
    _DEFAULT_UNIVERSE = "googleapis.com"

    @classmethod
    def from_service_account_info(cls, info: dict, *args, **kwargs):
        """Creates an instance of this client using the provided credentials
            info.

        Args:
            info (dict): The service account private key info.
            args: Additional arguments to pass to the constructor.
            kwargs: Additional arguments to pass to the constructor.

        Returns:
            FeaturestoreServiceClient: The constructed client.
        """
        credentials = service_account.Credentials.from_service_account_info(info)
        kwargs["credentials"] = credentials
        return cls(*args, **kwargs)

    @classmethod
    def from_service_account_file(cls, filename: str, *args, **kwargs):
        """Creates an instance of this client using the provided credentials
            file.

        Args:
            filename (str): The path to the service account private key json
                file.
            args: Additional arguments to pass to the constructor.
            kwargs: Additional arguments to pass to the constructor.

        Returns:
            FeaturestoreServiceClient: The constructed client.
        """
        credentials = service_account.Credentials.from_service_account_file(filename)
        kwargs["credentials"] = credentials
        return cls(*args, **kwargs)

    from_service_account_json = from_service_account_file

    @property
    def transport(self) -> FeaturestoreServiceTransport:
        """Returns the transport used by the client instance.

        Returns:
            FeaturestoreServiceTransport: The transport used by the client
                instance.
        """
        return self._transport

    @staticmethod
    def entity_type_path(
        project: str,
        location: str,
        featurestore: str,
        entity_type: str,
    ) -> str:
        """Returns a fully-qualified entity_type string."""
        return "projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}".format(
            project=project,
            location=location,
            featurestore=featurestore,
            entity_type=entity_type,
        )

    @staticmethod
    def parse_entity_type_path(path: str) -> Dict[str, str]:
        """Parses a entity_type path into its component segments."""
        m = re.match(
            r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/featurestores/(?P<featurestore>.+?)/entityTypes/(?P<entity_type>.+?)$",
            path,
        )
        return m.groupdict() if m else {}

    @staticmethod
    def feature_path(
        project: str,
        location: str,
        featurestore: str,
        entity_type: str,
        feature: str,
    ) -> str:
        """Returns a fully-qualified feature string."""
        return "projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature}".format(
            project=project,
            location=location,
            featurestore=featurestore,
            entity_type=entity_type,
            feature=feature,
        )

    @staticmethod
    def parse_feature_path(path: str) -> Dict[str, str]:
        """Parses a feature path into its component segments."""
        m = re.match(
            r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/featurestores/(?P<featurestore>.+?)/entityTypes/(?P<entity_type>.+?)/features/(?P<feature>.+?)$",
            path,
        )
        return m.groupdict() if m else {}

    @staticmethod
    def featurestore_path(
        project: str,
        location: str,
        featurestore: str,
    ) -> str:
        """Returns a fully-qualified featurestore string."""
        return "projects/{project}/locations/{location}/featurestores/{featurestore}".format(
            project=project,
            location=location,
            featurestore=featurestore,
        )

    @staticmethod
    def parse_featurestore_path(path: str) -> Dict[str, str]:
        """Parses a featurestore path into its component segments."""
        m = re.match(
            r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/featurestores/(?P<featurestore>.+?)$",
            path,
        )
        return m.groupdict() if m else {}

    @staticmethod
    def common_billing_account_path(
        billing_account: str,
    ) -> str:
        """Returns a fully-qualified billing_account string."""
        return "billingAccounts/{billing_account}".format(
            billing_account=billing_account,
        )

    @staticmethod
    def parse_common_billing_account_path(path: str) -> Dict[str, str]:
        """Parse a billing_account path into its component segments."""
        m = re.match(r"^billingAccounts/(?P<billing_account>.+?)$", path)
        return m.groupdict() if m else {}

    @staticmethod
    def common_folder_path(
        folder: str,
    ) -> str:
        """Returns a fully-qualified folder string."""
        return "folders/{folder}".format(
            folder=folder,
        )

    @staticmethod
    def parse_common_folder_path(path: str) -> Dict[str, str]:
        """Parse a folder path into its component segments."""
        m = re.match(r"^folders/(?P<folder>.+?)$", path)
        return m.groupdict() if m else {}

    @staticmethod
    def common_organization_path(
        organization: str,
    ) -> str:
        """Returns a fully-qualified organization string."""
        return "organizations/{organization}".format(
            organization=organization,
        )

    @staticmethod
    def parse_common_organization_path(path: str) -> Dict[str, str]:
        """Parse a organization path into its component segments."""
        m = re.match(r"^organizations/(?P<organization>.+?)$", path)
        return m.groupdict() if m else {}

    @staticmethod
    def common_project_path(
        project: str,
    ) -> str:
        """Returns a fully-qualified project string."""
        return "projects/{project}".format(
            project=project,
        )

    @staticmethod
    def parse_common_project_path(path: str) -> Dict[str, str]:
        """Parse a project path into its component segments."""
        m = re.match(r"^projects/(?P<project>.+?)$", path)
        return m.groupdict() if m else {}

    @staticmethod
    def common_location_path(
        project: str,
        location: str,
    ) -> str:
        """Returns a fully-qualified location string."""
        return "projects/{project}/locations/{location}".format(
            project=project,
            location=location,
        )

    @staticmethod
    def parse_common_location_path(path: str) -> Dict[str, str]:
        """Parse a location path into its component segments."""
        m = re.match(r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)$", path)
        return m.groupdict() if m else {}

    @classmethod
    def get_mtls_endpoint_and_cert_source(
        cls, client_options: Optional[client_options_lib.ClientOptions] = None
    ):
        """Deprecated. Return the API endpoint and client cert source for mutual TLS.

        The client cert source is determined in the following order:
        (1) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is not "true", the
        client cert source is None.
        (2) if `client_options.client_cert_source` is provided, use the provided one; if the
        default client cert source exists, use the default one; otherwise the client cert
        source is None.

        The API endpoint is determined in the following order:
        (1) if `client_options.api_endpoint` if provided, use the provided one.
        (2) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is "always", use the
        default mTLS endpoint; if the environment variable is "never", use the default API
        endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise
        use the default API endpoint.

        More details can be found at https://google.aip.dev/auth/4114.

        Args:
            client_options (google.api_core.client_options.ClientOptions): Custom options for the
                client. Only the `api_endpoint` and `client_cert_source` properties may be used
                in this method.

        Returns:
            Tuple[str, Callable[[], Tuple[bytes, bytes]]]: returns the API endpoint and the
                client cert source to use.

        Raises:
            google.auth.exceptions.MutualTLSChannelError: If any errors happen.
        """

        warnings.warn(
            "get_mtls_endpoint_and_cert_source is deprecated. Use the api_endpoint property instead.",
            DeprecationWarning,
        )
        if client_options is None:
            client_options = client_options_lib.ClientOptions()
        use_client_cert = os.getenv("GOOGLE_API_USE_CLIENT_CERTIFICATE", "false")
        use_mtls_endpoint = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto")
        if use_client_cert not in ("true", "false"):
            raise ValueError(
                "Environment variable `GOOGLE_API_USE_CLIENT_CERTIFICATE` must be either `true` or `false`"
            )
        if use_mtls_endpoint not in ("auto", "never", "always"):
            raise MutualTLSChannelError(
                "Environment variable `GOOGLE_API_USE_MTLS_ENDPOINT` must be `never`, `auto` or `always`"
            )

        # Figure out the client cert source to use.
        client_cert_source = None
        if use_client_cert == "true":
            if client_options.client_cert_source:
                client_cert_source = client_options.client_cert_source
            elif mtls.has_default_client_cert_source():
                client_cert_source = mtls.default_client_cert_source()

        # Figure out which api endpoint to use.
        if client_options.api_endpoint is not None:
            api_endpoint = client_options.api_endpoint
        elif use_mtls_endpoint == "always" or (
            use_mtls_endpoint == "auto" and client_cert_source
        ):
            api_endpoint = cls.DEFAULT_MTLS_ENDPOINT
        else:
            api_endpoint = cls.DEFAULT_ENDPOINT

        return api_endpoint, client_cert_source

    @staticmethod
    def _read_environment_variables():
        """Returns the environment variables used by the client.

        Returns:
            Tuple[bool, str, str]: returns the GOOGLE_API_USE_CLIENT_CERTIFICATE,
            GOOGLE_API_USE_MTLS_ENDPOINT, and GOOGLE_CLOUD_UNIVERSE_DOMAIN environment variables.

        Raises:
            ValueError: If GOOGLE_API_USE_CLIENT_CERTIFICATE is not
                any of ["true", "false"].
            google.auth.exceptions.MutualTLSChannelError: If GOOGLE_API_USE_MTLS_ENDPOINT
                is not any of ["auto", "never", "always"].
        """
        use_client_cert = os.getenv(
            "GOOGLE_API_USE_CLIENT_CERTIFICATE", "false"
        ).lower()
        use_mtls_endpoint = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto").lower()
        universe_domain_env = os.getenv("GOOGLE_CLOUD_UNIVERSE_DOMAIN")
        if use_client_cert not in ("true", "false"):
            raise ValueError(
                "Environment variable `GOOGLE_API_USE_CLIENT_CERTIFICATE` must be either `true` or `false`"
            )
        if use_mtls_endpoint not in ("auto", "never", "always"):
            raise MutualTLSChannelError(
                "Environment variable `GOOGLE_API_USE_MTLS_ENDPOINT` must be `never`, `auto` or `always`"
            )
        return use_client_cert == "true", use_mtls_endpoint, universe_domain_env

    @staticmethod
    def _get_client_cert_source(provided_cert_source, use_cert_flag):
        """Return the client cert source to be used by the client.

        Args:
            provided_cert_source (bytes): The client certificate source provided.
            use_cert_flag (bool): A flag indicating whether to use the client certificate.

        Returns:
            bytes or None: The client cert source to be used by the client.
        """
        client_cert_source = None
        if use_cert_flag:
            if provided_cert_source:
                client_cert_source = provided_cert_source
            elif mtls.has_default_client_cert_source():
                client_cert_source = mtls.default_client_cert_source()
        return client_cert_source

    @staticmethod
    def _get_api_endpoint(
        api_override, client_cert_source, universe_domain, use_mtls_endpoint
    ):
        """Return the API endpoint used by the client.

        Args:
            api_override (str): The API endpoint override. If specified, this is always
                the return value of this function and the other arguments are not used.
            client_cert_source (bytes): The client certificate source used by the client.
            universe_domain (str): The universe domain used by the client.
            use_mtls_endpoint (str): How to use the mTLS endpoint, which depends also on the other parameters.
                Possible values are "always", "auto", or "never".

        Returns:
            str: The API endpoint to be used by the client.
        """
        if api_override is not None:
            api_endpoint = api_override
        elif use_mtls_endpoint == "always" or (
            use_mtls_endpoint == "auto" and client_cert_source
        ):
            _default_universe = FeaturestoreServiceClient._DEFAULT_UNIVERSE
            if universe_domain != _default_universe:
                raise MutualTLSChannelError(
                    f"mTLS is not supported in any universe other than {_default_universe}."
                )
            api_endpoint = FeaturestoreServiceClient.DEFAULT_MTLS_ENDPOINT
        else:
            api_endpoint = FeaturestoreServiceClient._DEFAULT_ENDPOINT_TEMPLATE.format(
                UNIVERSE_DOMAIN=universe_domain
            )
        return api_endpoint

    @staticmethod
    def _get_universe_domain(
        client_universe_domain: Optional[str], universe_domain_env: Optional[str]
    ) -> str:
        """Return the universe domain used by the client.

        Args:
            client_universe_domain (Optional[str]): The universe domain configured via the client options.
            universe_domain_env (Optional[str]): The universe domain configured via the "GOOGLE_CLOUD_UNIVERSE_DOMAIN" environment variable.

        Returns:
            str: The universe domain to be used by the client.

        Raises:
            ValueError: If the universe domain is an empty string.
        """
        universe_domain = FeaturestoreServiceClient._DEFAULT_UNIVERSE
        if client_universe_domain is not None:
            universe_domain = client_universe_domain
        elif universe_domain_env is not None:
            universe_domain = universe_domain_env
        if len(universe_domain.strip()) == 0:
            raise ValueError("Universe Domain cannot be an empty string.")
        return universe_domain

    @staticmethod
    def _compare_universes(
        client_universe: str, credentials: ga_credentials.Credentials
    ) -> bool:
        """Returns True iff the universe domains used by the client and credentials match.

        Args:
            client_universe (str): The universe domain configured via the client options.
            credentials (ga_credentials.Credentials): The credentials being used in the client.

        Returns:
            bool: True iff client_universe matches the universe in credentials.

        Raises:
            ValueError: when client_universe does not match the universe in credentials.
        """

        default_universe = FeaturestoreServiceClient._DEFAULT_UNIVERSE
        credentials_universe = getattr(credentials, "universe_domain", default_universe)

        if client_universe != credentials_universe:
            raise ValueError(
                "The configured universe domain "
                f"({client_universe}) does not match the universe domain "
                f"found in the credentials ({credentials_universe}). "
                "If you haven't configured the universe domain explicitly, "
                f"`{default_universe}` is the default."
            )
        return True

    def _validate_universe_domain(self):
        """Validates client's and credentials' universe domains are consistent.

        Returns:
            bool: True iff the configured universe domain is valid.

        Raises:
            ValueError: If the configured universe domain is not valid.
        """
        self._is_universe_domain_valid = (
            self._is_universe_domain_valid
            or FeaturestoreServiceClient._compare_universes(
                self.universe_domain, self.transport._credentials
            )
        )
        return self._is_universe_domain_valid

    @property
    def api_endpoint(self):
        """Return the API endpoint used by the client instance.

        Returns:
            str: The API endpoint used by the client instance.
        """
        return self._api_endpoint

    @property
    def universe_domain(self) -> str:
        """Return the universe domain used by the client instance.

        Returns:
            str: The universe domain used by the client instance.
        """
        return self._universe_domain

    def __init__(
        self,
        *,
        credentials: Optional[ga_credentials.Credentials] = None,
        transport: Optional[
            Union[
                str,
                FeaturestoreServiceTransport,
                Callable[..., FeaturestoreServiceTransport],
            ]
        ] = None,
        client_options: Optional[Union[client_options_lib.ClientOptions, dict]] = None,
        client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
    ) -> None:
        """Instantiates the featurestore service client.

        Args:
            credentials (Optional[google.auth.credentials.Credentials]): The
                authorization credentials to attach to requests. These
                credentials identify the application to the service; if none
                are specified, the client will attempt to ascertain the
                credentials from the environment.
            transport (Optional[Union[str,FeaturestoreServiceTransport,Callable[..., FeaturestoreServiceTransport]]]):
                The transport to use, or a Callable that constructs and returns a new transport.
                If a Callable is given, it will be called with the same set of initialization
                arguments as used in the FeaturestoreServiceTransport constructor.
                If set to None, a transport is chosen automatically.
            client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]):
                Custom options for the client.

                1. The ``api_endpoint`` property can be used to override the
                default endpoint provided by the client when ``transport`` is
                not explicitly provided. Only if this property is not set and
                ``transport`` was not explicitly provided, the endpoint is
                determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment
                variable, which have one of the following values:
                "always" (always use the default mTLS endpoint), "never" (always
                use the default regular endpoint) and "auto" (auto-switch to the
                default mTLS endpoint if client certificate is present; this is
                the default value).

                2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable
                is "true", then the ``client_cert_source`` property can be used
                to provide a client certificate for mTLS transport. If
                not provided, the default SSL client certificate will be used if
                present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not
                set, no client certificate will be used.

                3. The ``universe_domain`` property can be used to override the
                default "googleapis.com" universe. Note that the ``api_endpoint``
                property still takes precedence; and ``universe_domain`` is
                currently not supported for mTLS.

            client_info (google.api_core.gapic_v1.client_info.ClientInfo):
                The client info used to send a user-agent string along with
                API requests. If ``None``, then default info will be used.
                Generally, you only need to set this if you're developing
                your own client library.

        Raises:
            google.auth.exceptions.MutualTLSChannelError: If mutual TLS transport
                creation failed for any reason.
        """
        self._client_options = client_options
        if isinstance(self._client_options, dict):
            self._client_options = client_options_lib.from_dict(self._client_options)
        if self._client_options is None:
            self._client_options = client_options_lib.ClientOptions()
        self._client_options = cast(
            client_options_lib.ClientOptions, self._client_options
        )

        universe_domain_opt = getattr(self._client_options, "universe_domain", None)

        (
            self._use_client_cert,
            self._use_mtls_endpoint,
            self._universe_domain_env,
        ) = FeaturestoreServiceClient._read_environment_variables()
        self._client_cert_source = FeaturestoreServiceClient._get_client_cert_source(
            self._client_options.client_cert_source, self._use_client_cert
        )
        self._universe_domain = FeaturestoreServiceClient._get_universe_domain(
            universe_domain_opt, self._universe_domain_env
        )
        self._api_endpoint = None  # updated below, depending on `transport`

        # Initialize the universe domain validation.
        self._is_universe_domain_valid = False

        api_key_value = getattr(self._client_options, "api_key", None)
        if api_key_value and credentials:
            raise ValueError(
                "client_options.api_key and credentials are mutually exclusive"
            )

        # Save or instantiate the transport.
        # Ordinarily, we provide the transport, but allowing a custom transport
        # instance provides an extensibility point for unusual situations.
        transport_provided = isinstance(transport, FeaturestoreServiceTransport)
        if transport_provided:
            # transport is a FeaturestoreServiceTransport instance.
            if credentials or self._client_options.credentials_file or api_key_value:
                raise ValueError(
                    "When providing a transport instance, "
                    "provide its credentials directly."
                )
            if self._client_options.scopes:
                raise ValueError(
                    "When providing a transport instance, provide its scopes "
                    "directly."
                )
            self._transport = cast(FeaturestoreServiceTransport, transport)
            self._api_endpoint = self._transport.host

        self._api_endpoint = (
            self._api_endpoint
            or FeaturestoreServiceClient._get_api_endpoint(
                self._client_options.api_endpoint,
                self._client_cert_source,
                self._universe_domain,
                self._use_mtls_endpoint,
            )
        )

        if not transport_provided:
            transport_init: Union[
                Type[FeaturestoreServiceTransport],
                Callable[..., FeaturestoreServiceTransport],
            ] = (
                FeaturestoreServiceClient.get_transport_class(transport)
                if isinstance(transport, str) or transport is None
                else cast(Callable[..., FeaturestoreServiceTransport], transport)
            )

            if "rest_asyncio" in str(transport_init):
                unsupported_params = {
                    "google.api_core.client_options.ClientOptions.credentials_file": self._client_options.credentials_file,
                    "google.api_core.client_options.ClientOptions.scopes": self._client_options.scopes,
                    "google.api_core.client_options.ClientOptions.quota_project_id": self._client_options.quota_project_id,
                    "google.api_core.client_options.ClientOptions.client_cert_source": self._client_options.client_cert_source,
                    "google.api_core.client_options.ClientOptions.api_audience": self._client_options.api_audience,
                }
                provided_unsupported_params = [
                    name
                    for name, value in unsupported_params.items()
                    if value is not None
                ]
                if provided_unsupported_params:
                    raise core_exceptions.AsyncRestUnsupportedParameterError(  # type: ignore
                        f"The following provided parameters are not supported for `transport=rest_asyncio`: {', '.join(provided_unsupported_params)}"
                    )
                self._transport = transport_init(
                    credentials=credentials,
                    host=self._api_endpoint,
                    client_info=client_info,
                )
                return

            import google.auth._default  # type: ignore

            if api_key_value and hasattr(
                google.auth._default, "get_api_key_credentials"
            ):
                credentials = google.auth._default.get_api_key_credentials(
                    api_key_value
                )

            # initialize with the provided callable or the passed in class
            self._transport = transport_init(
                credentials=credentials,
                credentials_file=self._client_options.credentials_file,
                host=self._api_endpoint,
                scopes=self._client_options.scopes,
                client_cert_source_for_mtls=self._client_cert_source,
                quota_project_id=self._client_options.quota_project_id,
                client_info=client_info,
                always_use_jwt_access=True,
                api_audience=self._client_options.api_audience,
            )

    def create_featurestore(
        self,
        request: Optional[
            Union[featurestore_service.CreateFeaturestoreRequest, dict]
        ] = None,
        *,
        parent: Optional[str] = None,
        featurestore: Optional[gca_featurestore.Featurestore] = None,
        featurestore_id: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> gac_operation.Operation:
        r"""Creates a new Featurestore in a given project and
        location.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_create_featurestore():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.CreateFeaturestoreRequest(
                    parent="parent_value",
                    featurestore_id="featurestore_id_value",
                )

                # Make the request
                operation = client.create_featurestore(request=request)

                print("Waiting for operation to complete...")

                response = operation.result()

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.CreateFeaturestoreRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.CreateFeaturestore][google.cloud.aiplatform.v1beta1.FeaturestoreService.CreateFeaturestore].
            parent (str):
                Required. The resource name of the Location to create
                Featurestores. Format:
                ``projects/{project}/locations/{location}``

                This corresponds to the ``parent`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            featurestore (google.cloud.aiplatform_v1beta1.types.Featurestore):
                Required. The Featurestore to create.
                This corresponds to the ``featurestore`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            featurestore_id (str):
                Required. The ID to use for this Featurestore, which
                will become the final component of the Featurestore's
                resource name.

                This value may be up to 60 characters, and valid
                characters are ``[a-z0-9_]``. The first character cannot
                be a number.

                The value must be unique within the project and
                location.

                This corresponds to the ``featurestore_id`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.api_core.operation.Operation:
                An object representing a long-running operation.

                The result type for the operation will be :class:`google.cloud.aiplatform_v1beta1.types.Featurestore` Vertex AI Feature Store provides a centralized repository for organizing,
                   storing, and serving ML features. The Featurestore is
                   a top-level container for your features and their
                   values.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([parent, featurestore, featurestore_id])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.CreateFeaturestoreRequest):
            request = featurestore_service.CreateFeaturestoreRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if parent is not None:
                request.parent = parent
            if featurestore is not None:
                request.featurestore = featurestore
            if featurestore_id is not None:
                request.featurestore_id = featurestore_id

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.create_featurestore]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Wrap the response in an operation future.
        response = gac_operation.from_gapic(
            response,
            self._transport.operations_client,
            gca_featurestore.Featurestore,
            metadata_type=featurestore_service.CreateFeaturestoreOperationMetadata,
        )

        # Done; return the response.
        return response

    def get_featurestore(
        self,
        request: Optional[
            Union[featurestore_service.GetFeaturestoreRequest, dict]
        ] = None,
        *,
        name: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> featurestore.Featurestore:
        r"""Gets details of a single Featurestore.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_get_featurestore():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.GetFeaturestoreRequest(
                    name="name_value",
                )

                # Make the request
                response = client.get_featurestore(request=request)

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.GetFeaturestoreRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.GetFeaturestore][google.cloud.aiplatform.v1beta1.FeaturestoreService.GetFeaturestore].
            name (str):
                Required. The name of the
                Featurestore resource.

                This corresponds to the ``name`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.cloud.aiplatform_v1beta1.types.Featurestore:
                Vertex AI Feature Store provides a
                centralized repository for organizing,
                storing, and serving ML features. The
                Featurestore is a top-level container
                for your features and their values.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([name])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.GetFeaturestoreRequest):
            request = featurestore_service.GetFeaturestoreRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if name is not None:
                request.name = name

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.get_featurestore]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def list_featurestores(
        self,
        request: Optional[
            Union[featurestore_service.ListFeaturestoresRequest, dict]
        ] = None,
        *,
        parent: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> pagers.ListFeaturestoresPager:
        r"""Lists Featurestores in a given project and location.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_list_featurestores():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.ListFeaturestoresRequest(
                    parent="parent_value",
                )

                # Make the request
                page_result = client.list_featurestores(request=request)

                # Handle the response
                for response in page_result:
                    print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.ListFeaturestoresRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.ListFeaturestores][google.cloud.aiplatform.v1beta1.FeaturestoreService.ListFeaturestores].
            parent (str):
                Required. The resource name of the Location to list
                Featurestores. Format:
                ``projects/{project}/locations/{location}``

                This corresponds to the ``parent`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.cloud.aiplatform_v1beta1.services.featurestore_service.pagers.ListFeaturestoresPager:
                Response message for
                   [FeaturestoreService.ListFeaturestores][google.cloud.aiplatform.v1beta1.FeaturestoreService.ListFeaturestores].

                Iterating over this object will yield results and
                resolve additional pages automatically.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([parent])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.ListFeaturestoresRequest):
            request = featurestore_service.ListFeaturestoresRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if parent is not None:
                request.parent = parent

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.list_featurestores]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # This method is paged; wrap the response in a pager, which provides
        # an `__iter__` convenience method.
        response = pagers.ListFeaturestoresPager(
            method=rpc,
            request=request,
            response=response,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def update_featurestore(
        self,
        request: Optional[
            Union[featurestore_service.UpdateFeaturestoreRequest, dict]
        ] = None,
        *,
        featurestore: Optional[gca_featurestore.Featurestore] = None,
        update_mask: Optional[field_mask_pb2.FieldMask] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> gac_operation.Operation:
        r"""Updates the parameters of a single Featurestore.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_update_featurestore():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.UpdateFeaturestoreRequest(
                )

                # Make the request
                operation = client.update_featurestore(request=request)

                print("Waiting for operation to complete...")

                response = operation.result()

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.UpdateFeaturestoreRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.UpdateFeaturestore][google.cloud.aiplatform.v1beta1.FeaturestoreService.UpdateFeaturestore].
            featurestore (google.cloud.aiplatform_v1beta1.types.Featurestore):
                Required. The Featurestore's ``name`` field is used to
                identify the Featurestore to be updated. Format:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}``

                This corresponds to the ``featurestore`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            update_mask (google.protobuf.field_mask_pb2.FieldMask):
                Field mask is used to specify the fields to be
                overwritten in the Featurestore resource by the update.
                The fields specified in the update_mask are relative to
                the resource, not the full request. A field will be
                overwritten if it is in the mask. If the user does not
                provide a mask then only the non-empty fields present in
                the request will be overwritten. Set the update_mask to
                ``*`` to override all fields.

                Updatable fields:

                -  ``labels``
                -  ``online_serving_config.fixed_node_count``
                -  ``online_serving_config.scaling``
                -  ``online_storage_ttl_days``

                This corresponds to the ``update_mask`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.api_core.operation.Operation:
                An object representing a long-running operation.

                The result type for the operation will be :class:`google.cloud.aiplatform_v1beta1.types.Featurestore` Vertex AI Feature Store provides a centralized repository for organizing,
                   storing, and serving ML features. The Featurestore is
                   a top-level container for your features and their
                   values.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([featurestore, update_mask])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.UpdateFeaturestoreRequest):
            request = featurestore_service.UpdateFeaturestoreRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if featurestore is not None:
                request.featurestore = featurestore
            if update_mask is not None:
                request.update_mask = update_mask

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.update_featurestore]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata(
                (("featurestore.name", request.featurestore.name),)
            ),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Wrap the response in an operation future.
        response = gac_operation.from_gapic(
            response,
            self._transport.operations_client,
            gca_featurestore.Featurestore,
            metadata_type=featurestore_service.UpdateFeaturestoreOperationMetadata,
        )

        # Done; return the response.
        return response

    def delete_featurestore(
        self,
        request: Optional[
            Union[featurestore_service.DeleteFeaturestoreRequest, dict]
        ] = None,
        *,
        name: Optional[str] = None,
        force: Optional[bool] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> gac_operation.Operation:
        r"""Deletes a single Featurestore. The Featurestore must not contain
        any EntityTypes or ``force`` must be set to true for the request
        to succeed.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_delete_featurestore():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.DeleteFeaturestoreRequest(
                    name="name_value",
                )

                # Make the request
                operation = client.delete_featurestore(request=request)

                print("Waiting for operation to complete...")

                response = operation.result()

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.DeleteFeaturestoreRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.DeleteFeaturestore][google.cloud.aiplatform.v1beta1.FeaturestoreService.DeleteFeaturestore].
            name (str):
                Required. The name of the Featurestore to be deleted.
                Format:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}``

                This corresponds to the ``name`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            force (bool):
                If set to true, any EntityTypes and
                Features for this Featurestore will also
                be deleted. (Otherwise, the request will
                only work if the Featurestore has no
                EntityTypes.)

                This corresponds to the ``force`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.api_core.operation.Operation:
                An object representing a long-running operation.

                The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated
                   empty messages in your APIs. A typical example is to
                   use it as the request or the response type of an API
                   method. For instance:

                      service Foo {
                         rpc Bar(google.protobuf.Empty) returns
                         (google.protobuf.Empty);

                      }

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([name, force])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.DeleteFeaturestoreRequest):
            request = featurestore_service.DeleteFeaturestoreRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if name is not None:
                request.name = name
            if force is not None:
                request.force = force

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.delete_featurestore]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Wrap the response in an operation future.
        response = gac_operation.from_gapic(
            response,
            self._transport.operations_client,
            empty_pb2.Empty,
            metadata_type=gca_operation.DeleteOperationMetadata,
        )

        # Done; return the response.
        return response

    def create_entity_type(
        self,
        request: Optional[
            Union[featurestore_service.CreateEntityTypeRequest, dict]
        ] = None,
        *,
        parent: Optional[str] = None,
        entity_type: Optional[gca_entity_type.EntityType] = None,
        entity_type_id: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> gac_operation.Operation:
        r"""Creates a new EntityType in a given Featurestore.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_create_entity_type():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.CreateEntityTypeRequest(
                    parent="parent_value",
                    entity_type_id="entity_type_id_value",
                )

                # Make the request
                operation = client.create_entity_type(request=request)

                print("Waiting for operation to complete...")

                response = operation.result()

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.CreateEntityTypeRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.CreateEntityType][google.cloud.aiplatform.v1beta1.FeaturestoreService.CreateEntityType].
            parent (str):
                Required. The resource name of the Featurestore to
                create EntityTypes. Format:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}``

                This corresponds to the ``parent`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            entity_type (google.cloud.aiplatform_v1beta1.types.EntityType):
                The EntityType to create.
                This corresponds to the ``entity_type`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            entity_type_id (str):
                Required. The ID to use for the EntityType, which will
                become the final component of the EntityType's resource
                name.

                This value may be up to 60 characters, and valid
                characters are ``[a-z0-9_]``. The first character cannot
                be a number.

                The value must be unique within a featurestore.

                This corresponds to the ``entity_type_id`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.api_core.operation.Operation:
                An object representing a long-running operation.

                The result type for the operation will be :class:`google.cloud.aiplatform_v1beta1.types.EntityType` An entity type is a type of object in a system that needs to be modeled and
                   have stored information about. For example, driver is
                   an entity type, and driver0 is an instance of an
                   entity type driver.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([parent, entity_type, entity_type_id])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.CreateEntityTypeRequest):
            request = featurestore_service.CreateEntityTypeRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if parent is not None:
                request.parent = parent
            if entity_type is not None:
                request.entity_type = entity_type
            if entity_type_id is not None:
                request.entity_type_id = entity_type_id

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.create_entity_type]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Wrap the response in an operation future.
        response = gac_operation.from_gapic(
            response,
            self._transport.operations_client,
            gca_entity_type.EntityType,
            metadata_type=featurestore_service.CreateEntityTypeOperationMetadata,
        )

        # Done; return the response.
        return response

    def get_entity_type(
        self,
        request: Optional[
            Union[featurestore_service.GetEntityTypeRequest, dict]
        ] = None,
        *,
        name: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> entity_type.EntityType:
        r"""Gets details of a single EntityType.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_get_entity_type():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.GetEntityTypeRequest(
                    name="name_value",
                )

                # Make the request
                response = client.get_entity_type(request=request)

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.GetEntityTypeRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.GetEntityType][google.cloud.aiplatform.v1beta1.FeaturestoreService.GetEntityType].
            name (str):
                Required. The name of the EntityType resource. Format:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}``

                This corresponds to the ``name`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.cloud.aiplatform_v1beta1.types.EntityType:
                An entity type is a type of object in
                a system that needs to be modeled and
                have stored information about. For
                example, driver is an entity type, and
                driver0 is an instance of an entity type
                driver.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([name])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.GetEntityTypeRequest):
            request = featurestore_service.GetEntityTypeRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if name is not None:
                request.name = name

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.get_entity_type]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def list_entity_types(
        self,
        request: Optional[
            Union[featurestore_service.ListEntityTypesRequest, dict]
        ] = None,
        *,
        parent: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> pagers.ListEntityTypesPager:
        r"""Lists EntityTypes in a given Featurestore.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_list_entity_types():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.ListEntityTypesRequest(
                    parent="parent_value",
                )

                # Make the request
                page_result = client.list_entity_types(request=request)

                # Handle the response
                for response in page_result:
                    print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.ListEntityTypesRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.ListEntityTypes][google.cloud.aiplatform.v1beta1.FeaturestoreService.ListEntityTypes].
            parent (str):
                Required. The resource name of the Featurestore to list
                EntityTypes. Format:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}``

                This corresponds to the ``parent`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.cloud.aiplatform_v1beta1.services.featurestore_service.pagers.ListEntityTypesPager:
                Response message for
                   [FeaturestoreService.ListEntityTypes][google.cloud.aiplatform.v1beta1.FeaturestoreService.ListEntityTypes].

                Iterating over this object will yield results and
                resolve additional pages automatically.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([parent])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.ListEntityTypesRequest):
            request = featurestore_service.ListEntityTypesRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if parent is not None:
                request.parent = parent

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.list_entity_types]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # This method is paged; wrap the response in a pager, which provides
        # an `__iter__` convenience method.
        response = pagers.ListEntityTypesPager(
            method=rpc,
            request=request,
            response=response,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def update_entity_type(
        self,
        request: Optional[
            Union[featurestore_service.UpdateEntityTypeRequest, dict]
        ] = None,
        *,
        entity_type: Optional[gca_entity_type.EntityType] = None,
        update_mask: Optional[field_mask_pb2.FieldMask] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> gca_entity_type.EntityType:
        r"""Updates the parameters of a single EntityType.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_update_entity_type():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.UpdateEntityTypeRequest(
                )

                # Make the request
                response = client.update_entity_type(request=request)

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.UpdateEntityTypeRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.UpdateEntityType][google.cloud.aiplatform.v1beta1.FeaturestoreService.UpdateEntityType].
            entity_type (google.cloud.aiplatform_v1beta1.types.EntityType):
                Required. The EntityType's ``name`` field is used to
                identify the EntityType to be updated. Format:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}``

                This corresponds to the ``entity_type`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            update_mask (google.protobuf.field_mask_pb2.FieldMask):
                Field mask is used to specify the fields to be
                overwritten in the EntityType resource by the update.
                The fields specified in the update_mask are relative to
                the resource, not the full request. A field will be
                overwritten if it is in the mask. If the user does not
                provide a mask then only the non-empty fields present in
                the request will be overwritten. Set the update_mask to
                ``*`` to override all fields.

                Updatable fields:

                -  ``description``
                -  ``labels``
                -  ``monitoring_config.snapshot_analysis.disabled``
                -  ``monitoring_config.snapshot_analysis.monitoring_interval_days``
                -  ``monitoring_config.snapshot_analysis.staleness_days``
                -  ``monitoring_config.import_features_analysis.state``
                -  ``monitoring_config.import_features_analysis.anomaly_detection_baseline``
                -  ``monitoring_config.numerical_threshold_config.value``
                -  ``monitoring_config.categorical_threshold_config.value``
                -  ``offline_storage_ttl_days``

                This corresponds to the ``update_mask`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.cloud.aiplatform_v1beta1.types.EntityType:
                An entity type is a type of object in
                a system that needs to be modeled and
                have stored information about. For
                example, driver is an entity type, and
                driver0 is an instance of an entity type
                driver.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([entity_type, update_mask])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.UpdateEntityTypeRequest):
            request = featurestore_service.UpdateEntityTypeRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if entity_type is not None:
                request.entity_type = entity_type
            if update_mask is not None:
                request.update_mask = update_mask

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.update_entity_type]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata(
                (("entity_type.name", request.entity_type.name),)
            ),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def delete_entity_type(
        self,
        request: Optional[
            Union[featurestore_service.DeleteEntityTypeRequest, dict]
        ] = None,
        *,
        name: Optional[str] = None,
        force: Optional[bool] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> gac_operation.Operation:
        r"""Deletes a single EntityType. The EntityType must not have any
        Features or ``force`` must be set to true for the request to
        succeed.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_delete_entity_type():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.DeleteEntityTypeRequest(
                    name="name_value",
                )

                # Make the request
                operation = client.delete_entity_type(request=request)

                print("Waiting for operation to complete...")

                response = operation.result()

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.DeleteEntityTypeRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.DeleteEntityTypes][].
            name (str):
                Required. The name of the EntityType to be deleted.
                Format:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}``

                This corresponds to the ``name`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            force (bool):
                If set to true, any Features for this
                EntityType will also be deleted.
                (Otherwise, the request will only work
                if the EntityType has no Features.)

                This corresponds to the ``force`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.api_core.operation.Operation:
                An object representing a long-running operation.

                The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated
                   empty messages in your APIs. A typical example is to
                   use it as the request or the response type of an API
                   method. For instance:

                      service Foo {
                         rpc Bar(google.protobuf.Empty) returns
                         (google.protobuf.Empty);

                      }

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([name, force])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.DeleteEntityTypeRequest):
            request = featurestore_service.DeleteEntityTypeRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if name is not None:
                request.name = name
            if force is not None:
                request.force = force

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.delete_entity_type]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Wrap the response in an operation future.
        response = gac_operation.from_gapic(
            response,
            self._transport.operations_client,
            empty_pb2.Empty,
            metadata_type=gca_operation.DeleteOperationMetadata,
        )

        # Done; return the response.
        return response

    def create_feature(
        self,
        request: Optional[
            Union[featurestore_service.CreateFeatureRequest, dict]
        ] = None,
        *,
        parent: Optional[str] = None,
        feature: Optional[gca_feature.Feature] = None,
        feature_id: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> gac_operation.Operation:
        r"""Creates a new Feature in a given EntityType.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_create_feature():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.CreateFeatureRequest(
                    parent="parent_value",
                    feature_id="feature_id_value",
                )

                # Make the request
                operation = client.create_feature(request=request)

                print("Waiting for operation to complete...")

                response = operation.result()

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.CreateFeatureRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.CreateFeature][google.cloud.aiplatform.v1beta1.FeaturestoreService.CreateFeature].
                Request message for
                [FeatureRegistryService.CreateFeature][google.cloud.aiplatform.v1beta1.FeatureRegistryService.CreateFeature].
            parent (str):
                Required. The resource name of the EntityType or
                FeatureGroup to create a Feature. Format for entity_type
                as parent:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}``
                Format for feature_group as parent:
                ``projects/{project}/locations/{location}/featureGroups/{feature_group}``

                This corresponds to the ``parent`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            feature (google.cloud.aiplatform_v1beta1.types.Feature):
                Required. The Feature to create.
                This corresponds to the ``feature`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            feature_id (str):
                Required. The ID to use for the Feature, which will
                become the final component of the Feature's resource
                name.

                This value may be up to 128 characters, and valid
                characters are ``[a-z0-9_]``. The first character cannot
                be a number.

                The value must be unique within an
                EntityType/FeatureGroup.

                This corresponds to the ``feature_id`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.api_core.operation.Operation:
                An object representing a long-running operation.

                The result type for the operation will be :class:`google.cloud.aiplatform_v1beta1.types.Feature` Feature Metadata information.
                   For example, color is a feature that describes an
                   apple.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([parent, feature, feature_id])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.CreateFeatureRequest):
            request = featurestore_service.CreateFeatureRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if parent is not None:
                request.parent = parent
            if feature is not None:
                request.feature = feature
            if feature_id is not None:
                request.feature_id = feature_id

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.create_feature]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Wrap the response in an operation future.
        response = gac_operation.from_gapic(
            response,
            self._transport.operations_client,
            gca_feature.Feature,
            metadata_type=featurestore_service.CreateFeatureOperationMetadata,
        )

        # Done; return the response.
        return response

    def batch_create_features(
        self,
        request: Optional[
            Union[featurestore_service.BatchCreateFeaturesRequest, dict]
        ] = None,
        *,
        parent: Optional[str] = None,
        requests: Optional[
            MutableSequence[featurestore_service.CreateFeatureRequest]
        ] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> gac_operation.Operation:
        r"""Creates a batch of Features in a given EntityType.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_batch_create_features():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                requests = aiplatform_v1beta1.CreateFeatureRequest()
                requests.parent = "parent_value"
                requests.feature_id = "feature_id_value"

                request = aiplatform_v1beta1.BatchCreateFeaturesRequest(
                    parent="parent_value",
                    requests=requests,
                )

                # Make the request
                operation = client.batch_create_features(request=request)

                print("Waiting for operation to complete...")

                response = operation.result()

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.BatchCreateFeaturesRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.BatchCreateFeatures][google.cloud.aiplatform.v1beta1.FeaturestoreService.BatchCreateFeatures].
            parent (str):
                Required. The resource name of the EntityType to create
                the batch of Features under. Format:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}``

                This corresponds to the ``parent`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            requests (MutableSequence[google.cloud.aiplatform_v1beta1.types.CreateFeatureRequest]):
                Required. The request message specifying the Features to
                create. All Features must be created under the same
                parent EntityType. The ``parent`` field in each child
                request message can be omitted. If ``parent`` is set in
                a child request, then the value must match the
                ``parent`` value in this request message.

                This corresponds to the ``requests`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.api_core.operation.Operation:
                An object representing a long-running operation.

                The result type for the operation will be :class:`google.cloud.aiplatform_v1beta1.types.BatchCreateFeaturesResponse` Response message for
                   [FeaturestoreService.BatchCreateFeatures][google.cloud.aiplatform.v1beta1.FeaturestoreService.BatchCreateFeatures].

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([parent, requests])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.BatchCreateFeaturesRequest):
            request = featurestore_service.BatchCreateFeaturesRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if parent is not None:
                request.parent = parent
            if requests is not None:
                request.requests = requests

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.batch_create_features]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Wrap the response in an operation future.
        response = gac_operation.from_gapic(
            response,
            self._transport.operations_client,
            featurestore_service.BatchCreateFeaturesResponse,
            metadata_type=featurestore_service.BatchCreateFeaturesOperationMetadata,
        )

        # Done; return the response.
        return response

    def get_feature(
        self,
        request: Optional[Union[featurestore_service.GetFeatureRequest, dict]] = None,
        *,
        name: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> feature.Feature:
        r"""Gets details of a single Feature.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_get_feature():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.GetFeatureRequest(
                    name="name_value",
                )

                # Make the request
                response = client.get_feature(request=request)

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.GetFeatureRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.GetFeature][google.cloud.aiplatform.v1beta1.FeaturestoreService.GetFeature].
                Request message for
                [FeatureRegistryService.GetFeature][google.cloud.aiplatform.v1beta1.FeatureRegistryService.GetFeature].
            name (str):
                Required. The name of the Feature resource. Format for
                entity_type as parent:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}``
                Format for feature_group as parent:
                ``projects/{project}/locations/{location}/featureGroups/{feature_group}``

                This corresponds to the ``name`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.cloud.aiplatform_v1beta1.types.Feature:
                Feature Metadata information.
                For example, color is a feature that
                describes an apple.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([name])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.GetFeatureRequest):
            request = featurestore_service.GetFeatureRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if name is not None:
                request.name = name

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.get_feature]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def list_features(
        self,
        request: Optional[Union[featurestore_service.ListFeaturesRequest, dict]] = None,
        *,
        parent: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> pagers.ListFeaturesPager:
        r"""Lists Features in a given EntityType.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_list_features():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.ListFeaturesRequest(
                    parent="parent_value",
                )

                # Make the request
                page_result = client.list_features(request=request)

                # Handle the response
                for response in page_result:
                    print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.ListFeaturesRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.ListFeatures][google.cloud.aiplatform.v1beta1.FeaturestoreService.ListFeatures].
                Request message for
                [FeatureRegistryService.ListFeatures][google.cloud.aiplatform.v1beta1.FeatureRegistryService.ListFeatures].
            parent (str):
                Required. The resource name of the Location to list
                Features. Format for entity_type as parent:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}``
                Format for feature_group as parent:
                ``projects/{project}/locations/{location}/featureGroups/{feature_group}``

                This corresponds to the ``parent`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.cloud.aiplatform_v1beta1.services.featurestore_service.pagers.ListFeaturesPager:
                Response message for
                   [FeaturestoreService.ListFeatures][google.cloud.aiplatform.v1beta1.FeaturestoreService.ListFeatures].
                   Response message for
                   [FeatureRegistryService.ListFeatures][google.cloud.aiplatform.v1beta1.FeatureRegistryService.ListFeatures].

                Iterating over this object will yield results and
                resolve additional pages automatically.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([parent])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.ListFeaturesRequest):
            request = featurestore_service.ListFeaturesRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if parent is not None:
                request.parent = parent

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.list_features]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # This method is paged; wrap the response in a pager, which provides
        # an `__iter__` convenience method.
        response = pagers.ListFeaturesPager(
            method=rpc,
            request=request,
            response=response,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def update_feature(
        self,
        request: Optional[
            Union[featurestore_service.UpdateFeatureRequest, dict]
        ] = None,
        *,
        feature: Optional[gca_feature.Feature] = None,
        update_mask: Optional[field_mask_pb2.FieldMask] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> gca_feature.Feature:
        r"""Updates the parameters of a single Feature.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_update_feature():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.UpdateFeatureRequest(
                )

                # Make the request
                response = client.update_feature(request=request)

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.UpdateFeatureRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.UpdateFeature][google.cloud.aiplatform.v1beta1.FeaturestoreService.UpdateFeature].
                Request message for
                [FeatureRegistryService.UpdateFeature][google.cloud.aiplatform.v1beta1.FeatureRegistryService.UpdateFeature].
            feature (google.cloud.aiplatform_v1beta1.types.Feature):
                Required. The Feature's ``name`` field is used to
                identify the Feature to be updated. Format:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature}``
                ``projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature}``

                This corresponds to the ``feature`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            update_mask (google.protobuf.field_mask_pb2.FieldMask):
                Field mask is used to specify the fields to be
                overwritten in the Features resource by the update. The
                fields specified in the update_mask are relative to the
                resource, not the full request. A field will be
                overwritten if it is in the mask. If the user does not
                provide a mask then only the non-empty fields present in
                the request will be overwritten. Set the update_mask to
                ``*`` to override all fields.

                Updatable fields:

                -  ``description``
                -  ``labels``
                -  ``disable_monitoring`` (Not supported for
                   FeatureRegistryService Feature)
                -  ``point_of_contact`` (Not supported for
                   FeaturestoreService FeatureStore)

                This corresponds to the ``update_mask`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.cloud.aiplatform_v1beta1.types.Feature:
                Feature Metadata information.
                For example, color is a feature that
                describes an apple.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([feature, update_mask])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.UpdateFeatureRequest):
            request = featurestore_service.UpdateFeatureRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if feature is not None:
                request.feature = feature
            if update_mask is not None:
                request.update_mask = update_mask

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.update_feature]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata(
                (("feature.name", request.feature.name),)
            ),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def delete_feature(
        self,
        request: Optional[
            Union[featurestore_service.DeleteFeatureRequest, dict]
        ] = None,
        *,
        name: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> gac_operation.Operation:
        r"""Deletes a single Feature.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_delete_feature():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.DeleteFeatureRequest(
                    name="name_value",
                )

                # Make the request
                operation = client.delete_feature(request=request)

                print("Waiting for operation to complete...")

                response = operation.result()

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.DeleteFeatureRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.DeleteFeature][google.cloud.aiplatform.v1beta1.FeaturestoreService.DeleteFeature].
                Request message for
                [FeatureRegistryService.DeleteFeature][google.cloud.aiplatform.v1beta1.FeatureRegistryService.DeleteFeature].
            name (str):
                Required. The name of the Features to be deleted.
                Format:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature}``
                ``projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature}``

                This corresponds to the ``name`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.api_core.operation.Operation:
                An object representing a long-running operation.

                The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated
                   empty messages in your APIs. A typical example is to
                   use it as the request or the response type of an API
                   method. For instance:

                      service Foo {
                         rpc Bar(google.protobuf.Empty) returns
                         (google.protobuf.Empty);

                      }

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([name])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.DeleteFeatureRequest):
            request = featurestore_service.DeleteFeatureRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if name is not None:
                request.name = name

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.delete_feature]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Wrap the response in an operation future.
        response = gac_operation.from_gapic(
            response,
            self._transport.operations_client,
            empty_pb2.Empty,
            metadata_type=gca_operation.DeleteOperationMetadata,
        )

        # Done; return the response.
        return response

    def import_feature_values(
        self,
        request: Optional[
            Union[featurestore_service.ImportFeatureValuesRequest, dict]
        ] = None,
        *,
        entity_type: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> gac_operation.Operation:
        r"""Imports Feature values into the Featurestore from a
        source storage.
        The progress of the import is tracked by the returned
        operation. The imported features are guaranteed to be
        visible to subsequent read operations after the
        operation is marked as successfully done.

        If an import operation fails, the Feature values
        returned from reads and exports may be inconsistent. If
        consistency is required, the caller must retry the same
        import request again and wait till the new operation
        returned is marked as successfully done.

        There are also scenarios where the caller can cause
        inconsistency.

         - Source data for import contains multiple distinct
          Feature values for    the same entity ID and
          timestamp.
         - Source is modified during an import. This includes
          adding, updating, or  removing source data and/or
          metadata. Examples of updating metadata  include but
          are not limited to changing storage location, storage
          class,  or retention policy.
         - Online serving cluster is under-provisioned.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_import_feature_values():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                avro_source = aiplatform_v1beta1.AvroSource()
                avro_source.gcs_source.uris = ['uris_value1', 'uris_value2']

                feature_specs = aiplatform_v1beta1.FeatureSpec()
                feature_specs.id = "id_value"

                request = aiplatform_v1beta1.ImportFeatureValuesRequest(
                    avro_source=avro_source,
                    feature_time_field="feature_time_field_value",
                    entity_type="entity_type_value",
                    feature_specs=feature_specs,
                )

                # Make the request
                operation = client.import_feature_values(request=request)

                print("Waiting for operation to complete...")

                response = operation.result()

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.ImportFeatureValuesRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.ImportFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.ImportFeatureValues].
            entity_type (str):
                Required. The resource name of the EntityType grouping
                the Features for which values are being imported.
                Format:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}``

                This corresponds to the ``entity_type`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.api_core.operation.Operation:
                An object representing a long-running operation.

                The result type for the operation will be :class:`google.cloud.aiplatform_v1beta1.types.ImportFeatureValuesResponse` Response message for
                   [FeaturestoreService.ImportFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.ImportFeatureValues].

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([entity_type])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.ImportFeatureValuesRequest):
            request = featurestore_service.ImportFeatureValuesRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if entity_type is not None:
                request.entity_type = entity_type

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.import_feature_values]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata(
                (("entity_type", request.entity_type),)
            ),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Wrap the response in an operation future.
        response = gac_operation.from_gapic(
            response,
            self._transport.operations_client,
            featurestore_service.ImportFeatureValuesResponse,
            metadata_type=featurestore_service.ImportFeatureValuesOperationMetadata,
        )

        # Done; return the response.
        return response

    def batch_read_feature_values(
        self,
        request: Optional[
            Union[featurestore_service.BatchReadFeatureValuesRequest, dict]
        ] = None,
        *,
        featurestore: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> gac_operation.Operation:
        r"""Batch reads Feature values from a Featurestore.

        This API enables batch reading Feature values, where
        each read instance in the batch may read Feature values
        of entities from one or more EntityTypes. Point-in-time
        correctness is guaranteed for Feature values of each
        read instance as of each instance's read timestamp.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_batch_read_feature_values():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                csv_read_instances = aiplatform_v1beta1.CsvSource()
                csv_read_instances.gcs_source.uris = ['uris_value1', 'uris_value2']

                destination = aiplatform_v1beta1.FeatureValueDestination()
                destination.bigquery_destination.output_uri = "output_uri_value"

                entity_type_specs = aiplatform_v1beta1.EntityTypeSpec()
                entity_type_specs.entity_type_id = "entity_type_id_value"
                entity_type_specs.feature_selector.id_matcher.ids = ['ids_value1', 'ids_value2']

                request = aiplatform_v1beta1.BatchReadFeatureValuesRequest(
                    csv_read_instances=csv_read_instances,
                    featurestore="featurestore_value",
                    destination=destination,
                    entity_type_specs=entity_type_specs,
                )

                # Make the request
                operation = client.batch_read_feature_values(request=request)

                print("Waiting for operation to complete...")

                response = operation.result()

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.BatchReadFeatureValuesRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.BatchReadFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.BatchReadFeatureValues].
            featurestore (str):
                Required. The resource name of the Featurestore from
                which to query Feature values. Format:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}``

                This corresponds to the ``featurestore`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.api_core.operation.Operation:
                An object representing a long-running operation.

                The result type for the operation will be :class:`google.cloud.aiplatform_v1beta1.types.BatchReadFeatureValuesResponse` Response message for
                   [FeaturestoreService.BatchReadFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.BatchReadFeatureValues].

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([featurestore])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.BatchReadFeatureValuesRequest):
            request = featurestore_service.BatchReadFeatureValuesRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if featurestore is not None:
                request.featurestore = featurestore

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[
            self._transport.batch_read_feature_values
        ]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata(
                (("featurestore", request.featurestore),)
            ),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Wrap the response in an operation future.
        response = gac_operation.from_gapic(
            response,
            self._transport.operations_client,
            featurestore_service.BatchReadFeatureValuesResponse,
            metadata_type=featurestore_service.BatchReadFeatureValuesOperationMetadata,
        )

        # Done; return the response.
        return response

    def export_feature_values(
        self,
        request: Optional[
            Union[featurestore_service.ExportFeatureValuesRequest, dict]
        ] = None,
        *,
        entity_type: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> gac_operation.Operation:
        r"""Exports Feature values from all the entities of a
        target EntityType.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_export_feature_values():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                destination = aiplatform_v1beta1.FeatureValueDestination()
                destination.bigquery_destination.output_uri = "output_uri_value"

                feature_selector = aiplatform_v1beta1.FeatureSelector()
                feature_selector.id_matcher.ids = ['ids_value1', 'ids_value2']

                request = aiplatform_v1beta1.ExportFeatureValuesRequest(
                    entity_type="entity_type_value",
                    destination=destination,
                    feature_selector=feature_selector,
                )

                # Make the request
                operation = client.export_feature_values(request=request)

                print("Waiting for operation to complete...")

                response = operation.result()

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.ExportFeatureValuesRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.ExportFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.ExportFeatureValues].
            entity_type (str):
                Required. The resource name of the EntityType from which
                to export Feature values. Format:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}``

                This corresponds to the ``entity_type`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.api_core.operation.Operation:
                An object representing a long-running operation.

                The result type for the operation will be :class:`google.cloud.aiplatform_v1beta1.types.ExportFeatureValuesResponse` Response message for
                   [FeaturestoreService.ExportFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.ExportFeatureValues].

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([entity_type])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.ExportFeatureValuesRequest):
            request = featurestore_service.ExportFeatureValuesRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if entity_type is not None:
                request.entity_type = entity_type

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.export_feature_values]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata(
                (("entity_type", request.entity_type),)
            ),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Wrap the response in an operation future.
        response = gac_operation.from_gapic(
            response,
            self._transport.operations_client,
            featurestore_service.ExportFeatureValuesResponse,
            metadata_type=featurestore_service.ExportFeatureValuesOperationMetadata,
        )

        # Done; return the response.
        return response

    def delete_feature_values(
        self,
        request: Optional[
            Union[featurestore_service.DeleteFeatureValuesRequest, dict]
        ] = None,
        *,
        entity_type: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> gac_operation.Operation:
        r"""Delete Feature values from Featurestore.

        The progress of the deletion is tracked by the returned
        operation. The deleted feature values are guaranteed to
        be invisible to subsequent read operations after the
        operation is marked as successfully done.

        If a delete feature values operation fails, the feature
        values returned from reads and exports may be
        inconsistent. If consistency is required, the caller
        must retry the same delete request again and wait till
        the new operation returned is marked as successfully
        done.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_delete_feature_values():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                select_entity = aiplatform_v1beta1.SelectEntity()
                select_entity.entity_id_selector.csv_source.gcs_source.uris = ['uris_value1', 'uris_value2']

                request = aiplatform_v1beta1.DeleteFeatureValuesRequest(
                    select_entity=select_entity,
                    entity_type="entity_type_value",
                )

                # Make the request
                operation = client.delete_feature_values(request=request)

                print("Waiting for operation to complete...")

                response = operation.result()

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.DeleteFeatureValuesRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.DeleteFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.DeleteFeatureValues].
            entity_type (str):
                Required. The resource name of the EntityType grouping
                the Features for which values are being deleted from.
                Format:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}``

                This corresponds to the ``entity_type`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.api_core.operation.Operation:
                An object representing a long-running operation.

                The result type for the operation will be :class:`google.cloud.aiplatform_v1beta1.types.DeleteFeatureValuesResponse` Response message for
                   [FeaturestoreService.DeleteFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.DeleteFeatureValues].

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([entity_type])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.DeleteFeatureValuesRequest):
            request = featurestore_service.DeleteFeatureValuesRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if entity_type is not None:
                request.entity_type = entity_type

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.delete_feature_values]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata(
                (("entity_type", request.entity_type),)
            ),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Wrap the response in an operation future.
        response = gac_operation.from_gapic(
            response,
            self._transport.operations_client,
            featurestore_service.DeleteFeatureValuesResponse,
            metadata_type=featurestore_service.DeleteFeatureValuesOperationMetadata,
        )

        # Done; return the response.
        return response

    def search_features(
        self,
        request: Optional[
            Union[featurestore_service.SearchFeaturesRequest, dict]
        ] = None,
        *,
        location: Optional[str] = None,
        query: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> pagers.SearchFeaturesPager:
        r"""Searches Features matching a query in a given
        project.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.cloud import aiplatform_v1beta1

            def sample_search_features():
                # Create a client
                client = aiplatform_v1beta1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1beta1.SearchFeaturesRequest(
                    location="location_value",
                )

                # Make the request
                page_result = client.search_features(request=request)

                # Handle the response
                for response in page_result:
                    print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1beta1.types.SearchFeaturesRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.SearchFeatures][google.cloud.aiplatform.v1beta1.FeaturestoreService.SearchFeatures].
            location (str):
                Required. The resource name of the Location to search
                Features. Format:
                ``projects/{project}/locations/{location}``

                This corresponds to the ``location`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            query (str):
                Query string that is a conjunction of field-restricted
                queries and/or field-restricted filters.
                Field-restricted queries and filters can be combined
                using ``AND`` to form a conjunction.

                A field query is in the form FIELD:QUERY. This
                implicitly checks if QUERY exists as a substring within
                Feature's FIELD. The QUERY and the FIELD are converted
                to a sequence of words (i.e. tokens) for comparison.
                This is done by:

                -  Removing leading/trailing whitespace and tokenizing
                   the search value. Characters that are not one of
                   alphanumeric ``[a-zA-Z0-9]``, underscore ``_``, or
                   asterisk ``*`` are treated as delimiters for tokens.
                   ``*`` is treated as a wildcard that matches
                   characters within a token.
                -  Ignoring case.
                -  Prepending an asterisk to the first and appending an
                   asterisk to the last token in QUERY.

                A QUERY must be either a singular token or a phrase. A
                phrase is one or multiple words enclosed in double
                quotation marks ("). With phrases, the order of the
                words is important. Words in the phrase must be matching
                in order and consecutively.

                Supported FIELDs for field-restricted queries:

                -  ``feature_id``
                -  ``description``
                -  ``entity_type_id``

                Examples:

                -  ``feature_id: foo`` --> Matches a Feature with ID
                   containing the substring ``foo`` (eg. ``foo``,
                   ``foofeature``, ``barfoo``).
                -  ``feature_id: foo*feature`` --> Matches a Feature
                   with ID containing the substring ``foo*feature`` (eg.
                   ``foobarfeature``).
                -  ``feature_id: foo AND description: bar`` --> Matches
                   a Feature with ID containing the substring ``foo``
                   and description containing the substring ``bar``.

                Besides field queries, the following exact-match filters
                are supported. The exact-match filters do not support
                wildcards. Unlike field-restricted queries, exact-match
                filters are case-sensitive.

                -  ``feature_id``: Supports = comparisons.
                -  ``description``: Supports = comparisons. Multi-token
                   filters should be enclosed in quotes.
                -  ``entity_type_id``: Supports = comparisons.
                -  ``value_type``: Supports = and != comparisons.
                -  ``labels``: Supports key-value equality as well as
                   key presence.
                -  ``featurestore_id``: Supports = comparisons.

                Examples:

                -  ``description = "foo bar"`` --> Any Feature with
                   description exactly equal to ``foo bar``
                -  ``value_type = DOUBLE`` --> Features whose type is
                   DOUBLE.
                -  ``labels.active = yes AND labels.env = prod`` -->
                   Features having both (active: yes) and (env: prod)
                   labels.
                -  ``labels.env: *`` --> Any Feature which has a label
                   with ``env`` as the key.

                This corresponds to the ``query`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.

        Returns:
            google.cloud.aiplatform_v1beta1.services.featurestore_service.pagers.SearchFeaturesPager:
                Response message for
                   [FeaturestoreService.SearchFeatures][google.cloud.aiplatform.v1beta1.FeaturestoreService.SearchFeatures].

                Iterating over this object will yield results and
                resolve additional pages automatically.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        has_flattened_params = any([location, query])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, featurestore_service.SearchFeaturesRequest):
            request = featurestore_service.SearchFeaturesRequest(request)
            # If we have keyword arguments corresponding to fields on the
            # request, apply these.
            if location is not None:
                request.location = location
            if query is not None:
                request.query = query

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.search_features]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("location", request.location),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # This method is paged; wrap the response in a pager, which provides
        # an `__iter__` convenience method.
        response = pagers.SearchFeaturesPager(
            method=rpc,
            request=request,
            response=response,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def __enter__(self) -> "FeaturestoreServiceClient":
        return self

    def __exit__(self, type, value, traceback):
        """Releases underlying transport's resources.

        .. warning::
            ONLY use as a context manager if the transport is NOT shared
            with other clients! Exiting the with block will CLOSE the transport
            and may cause errors in other clients!
        """
        self.transport.close()

    def list_operations(
        self,
        request: Optional[operations_pb2.ListOperationsRequest] = None,
        *,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> operations_pb2.ListOperationsResponse:
        r"""Lists operations that match the specified filter in the request.

        Args:
            request (:class:`~.operations_pb2.ListOperationsRequest`):
                The request object. Request message for
                `ListOperations` method.
            retry (google.api_core.retry.Retry): Designation of what errors,
                    if any, should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.
        Returns:
            ~.operations_pb2.ListOperationsResponse:
                Response message for ``ListOperations`` method.
        """
        # Create or coerce a protobuf request object.
        # The request isn't a proto-plus wrapped type,
        # so it must be constructed via keyword expansion.
        if isinstance(request, dict):
            request = operations_pb2.ListOperationsRequest(**request)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.list_operations]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def get_operation(
        self,
        request: Optional[operations_pb2.GetOperationRequest] = None,
        *,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> operations_pb2.Operation:
        r"""Gets the latest state of a long-running operation.

        Args:
            request (:class:`~.operations_pb2.GetOperationRequest`):
                The request object. Request message for
                `GetOperation` method.
            retry (google.api_core.retry.Retry): Designation of what errors,
                    if any, should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.
        Returns:
            ~.operations_pb2.Operation:
                An ``Operation`` object.
        """
        # Create or coerce a protobuf request object.
        # The request isn't a proto-plus wrapped type,
        # so it must be constructed via keyword expansion.
        if isinstance(request, dict):
            request = operations_pb2.GetOperationRequest(**request)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.get_operation]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def delete_operation(
        self,
        request: Optional[operations_pb2.DeleteOperationRequest] = None,
        *,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> None:
        r"""Deletes a long-running operation.

        This method indicates that the client is no longer interested
        in the operation result. It does not cancel the operation.
        If the server doesn't support this method, it returns
        `google.rpc.Code.UNIMPLEMENTED`.

        Args:
            request (:class:`~.operations_pb2.DeleteOperationRequest`):
                The request object. Request message for
                `DeleteOperation` method.
            retry (google.api_core.retry.Retry): Designation of what errors,
                    if any, should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.
        Returns:
            None
        """
        # Create or coerce a protobuf request object.
        # The request isn't a proto-plus wrapped type,
        # so it must be constructed via keyword expansion.
        if isinstance(request, dict):
            request = operations_pb2.DeleteOperationRequest(**request)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.delete_operation]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

    def cancel_operation(
        self,
        request: Optional[operations_pb2.CancelOperationRequest] = None,
        *,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> None:
        r"""Starts asynchronous cancellation on a long-running operation.

        The server makes a best effort to cancel the operation, but success
        is not guaranteed.  If the server doesn't support this method, it returns
        `google.rpc.Code.UNIMPLEMENTED`.

        Args:
            request (:class:`~.operations_pb2.CancelOperationRequest`):
                The request object. Request message for
                `CancelOperation` method.
            retry (google.api_core.retry.Retry): Designation of what errors,
                    if any, should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.
        Returns:
            None
        """
        # Create or coerce a protobuf request object.
        # The request isn't a proto-plus wrapped type,
        # so it must be constructed via keyword expansion.
        if isinstance(request, dict):
            request = operations_pb2.CancelOperationRequest(**request)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.cancel_operation]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

    def wait_operation(
        self,
        request: Optional[operations_pb2.WaitOperationRequest] = None,
        *,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> operations_pb2.Operation:
        r"""Waits until the specified long-running operation is done or reaches at most
        a specified timeout, returning the latest state.

        If the operation is already done, the latest state is immediately returned.
        If the timeout specified is greater than the default HTTP/RPC timeout, the HTTP/RPC
        timeout is used.  If the server does not support this method, it returns
        `google.rpc.Code.UNIMPLEMENTED`.

        Args:
            request (:class:`~.operations_pb2.WaitOperationRequest`):
                The request object. Request message for
                `WaitOperation` method.
            retry (google.api_core.retry.Retry): Designation of what errors,
                    if any, should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.
        Returns:
            ~.operations_pb2.Operation:
                An ``Operation`` object.
        """
        # Create or coerce a protobuf request object.
        # The request isn't a proto-plus wrapped type,
        # so it must be constructed via keyword expansion.
        if isinstance(request, dict):
            request = operations_pb2.WaitOperationRequest(**request)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.wait_operation]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def set_iam_policy(
        self,
        request: Optional[iam_policy_pb2.SetIamPolicyRequest] = None,
        *,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> policy_pb2.Policy:
        r"""Sets the IAM access control policy on the specified function.

        Replaces any existing policy.

        Args:
            request (:class:`~.iam_policy_pb2.SetIamPolicyRequest`):
                The request object. Request message for `SetIamPolicy`
                method.
            retry (google.api_core.retry.Retry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.
        Returns:
            ~.policy_pb2.Policy:
                Defines an Identity and Access Management (IAM) policy.
                It is used to specify access control policies for Cloud
                Platform resources.
                A ``Policy`` is a collection of ``bindings``. A
                ``binding`` binds one or more ``members`` to a single
                ``role``. Members can be user accounts, service
                accounts, Google groups, and domains (such as G Suite).
                A ``role`` is a named list of permissions (defined by
                IAM or configured by users). A ``binding`` can
                optionally specify a ``condition``, which is a logic
                expression that further constrains the role binding
                based on attributes about the request and/or target
                resource.

                **JSON Example**

                ::

                    {
                      "bindings": [
                        {
                          "role": "roles/resourcemanager.organizationAdmin",
                          "members": [
                            "user:mike@example.com",
                            "group:admins@example.com",
                            "domain:google.com",
                            "serviceAccount:my-project-id@appspot.gserviceaccount.com"
                          ]
                        },
                        {
                          "role": "roles/resourcemanager.organizationViewer",
                          "members": ["user:eve@example.com"],
                          "condition": {
                            "title": "expirable access",
                            "description": "Does not grant access after Sep 2020",
                            "expression": "request.time <
                            timestamp('2020-10-01T00:00:00.000Z')",
                          }
                        }
                      ]
                    }

                **YAML Example**

                ::

                    bindings:
                    - members:
                      - user:mike@example.com
                      - group:admins@example.com
                      - domain:google.com
                      - serviceAccount:my-project-id@appspot.gserviceaccount.com
                      role: roles/resourcemanager.organizationAdmin
                    - members:
                      - user:eve@example.com
                      role: roles/resourcemanager.organizationViewer
                      condition:
                        title: expirable access
                        description: Does not grant access after Sep 2020
                        expression: request.time < timestamp('2020-10-01T00:00:00.000Z')

                For a description of IAM and its features, see the `IAM
                developer's
                guide <https://cloud.google.com/iam/docs>`__.
        """
        # Create or coerce a protobuf request object.

        # The request isn't a proto-plus wrapped type,
        # so it must be constructed via keyword expansion.
        if isinstance(request, dict):
            request = iam_policy_pb2.SetIamPolicyRequest(**request)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.set_iam_policy]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("resource", request.resource),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def get_iam_policy(
        self,
        request: Optional[iam_policy_pb2.GetIamPolicyRequest] = None,
        *,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> policy_pb2.Policy:
        r"""Gets the IAM access control policy for a function.

        Returns an empty policy if the function exists and does not have a
        policy set.

        Args:
            request (:class:`~.iam_policy_pb2.GetIamPolicyRequest`):
                The request object. Request message for `GetIamPolicy`
                method.
            retry (google.api_core.retry.Retry): Designation of what errors, if
                any, should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.
        Returns:
            ~.policy_pb2.Policy:
                Defines an Identity and Access Management (IAM) policy.
                It is used to specify access control policies for Cloud
                Platform resources.
                A ``Policy`` is a collection of ``bindings``. A
                ``binding`` binds one or more ``members`` to a single
                ``role``. Members can be user accounts, service
                accounts, Google groups, and domains (such as G Suite).
                A ``role`` is a named list of permissions (defined by
                IAM or configured by users). A ``binding`` can
                optionally specify a ``condition``, which is a logic
                expression that further constrains the role binding
                based on attributes about the request and/or target
                resource.

                **JSON Example**

                ::

                    {
                      "bindings": [
                        {
                          "role": "roles/resourcemanager.organizationAdmin",
                          "members": [
                            "user:mike@example.com",
                            "group:admins@example.com",
                            "domain:google.com",
                            "serviceAccount:my-project-id@appspot.gserviceaccount.com"
                          ]
                        },
                        {
                          "role": "roles/resourcemanager.organizationViewer",
                          "members": ["user:eve@example.com"],
                          "condition": {
                            "title": "expirable access",
                            "description": "Does not grant access after Sep 2020",
                            "expression": "request.time <
                            timestamp('2020-10-01T00:00:00.000Z')",
                          }
                        }
                      ]
                    }

                **YAML Example**

                ::

                    bindings:
                    - members:
                      - user:mike@example.com
                      - group:admins@example.com
                      - domain:google.com
                      - serviceAccount:my-project-id@appspot.gserviceaccount.com
                      role: roles/resourcemanager.organizationAdmin
                    - members:
                      - user:eve@example.com
                      role: roles/resourcemanager.organizationViewer
                      condition:
                        title: expirable access
                        description: Does not grant access after Sep 2020
                        expression: request.time < timestamp('2020-10-01T00:00:00.000Z')

                For a description of IAM and its features, see the `IAM
                developer's
                guide <https://cloud.google.com/iam/docs>`__.
        """
        # Create or coerce a protobuf request object.

        # The request isn't a proto-plus wrapped type,
        # so it must be constructed via keyword expansion.
        if isinstance(request, dict):
            request = iam_policy_pb2.GetIamPolicyRequest(**request)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.get_iam_policy]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("resource", request.resource),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def test_iam_permissions(
        self,
        request: Optional[iam_policy_pb2.TestIamPermissionsRequest] = None,
        *,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> iam_policy_pb2.TestIamPermissionsResponse:
        r"""Tests the specified IAM permissions against the IAM access control
            policy for a function.

        If the function does not exist, this will return an empty set
        of permissions, not a NOT_FOUND error.

        Args:
            request (:class:`~.iam_policy_pb2.TestIamPermissionsRequest`):
                The request object. Request message for
                `TestIamPermissions` method.
            retry (google.api_core.retry.Retry): Designation of what errors,
                 if any, should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.
        Returns:
            ~.iam_policy_pb2.TestIamPermissionsResponse:
                Response message for ``TestIamPermissions`` method.
        """
        # Create or coerce a protobuf request object.

        # The request isn't a proto-plus wrapped type,
        # so it must be constructed via keyword expansion.
        if isinstance(request, dict):
            request = iam_policy_pb2.TestIamPermissionsRequest(**request)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.test_iam_permissions]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("resource", request.resource),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def get_location(
        self,
        request: Optional[locations_pb2.GetLocationRequest] = None,
        *,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> locations_pb2.Location:
        r"""Gets information about a location.

        Args:
            request (:class:`~.location_pb2.GetLocationRequest`):
                The request object. Request message for
                `GetLocation` method.
            retry (google.api_core.retry.Retry): Designation of what errors,
                 if any, should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.
        Returns:
            ~.location_pb2.Location:
                Location object.
        """
        # Create or coerce a protobuf request object.
        # The request isn't a proto-plus wrapped type,
        # so it must be constructed via keyword expansion.
        if isinstance(request, dict):
            request = locations_pb2.GetLocationRequest(**request)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.get_location]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def list_locations(
        self,
        request: Optional[locations_pb2.ListLocationsRequest] = None,
        *,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> locations_pb2.ListLocationsResponse:
        r"""Lists information about the supported locations for this service.

        Args:
            request (:class:`~.location_pb2.ListLocationsRequest`):
                The request object. Request message for
                `ListLocations` method.
            retry (google.api_core.retry.Retry): Designation of what errors,
                 if any, should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, str]]): Strings which should be
                sent along with the request as metadata.
        Returns:
            ~.location_pb2.ListLocationsResponse:
                Response message for ``ListLocations`` method.
        """
        # Create or coerce a protobuf request object.
        # The request isn't a proto-plus wrapped type,
        # so it must be constructed via keyword expansion.
        if isinstance(request, dict):
            request = locations_pb2.ListLocationsRequest(**request)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._transport._wrapped_methods[self._transport.list_locations]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response


DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo(
    gapic_version=package_version.__version__
)


__all__ = ("FeaturestoreServiceClient",)
