# -*- coding: utf-8 -*-
# Copyright 2022 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, Mapping, Optional, Sequence, Tuple, Type, Union
import pkg_resources

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]
except AttributeError:  # pragma: NO COVER
    OptionalRetry = Union[retries.Retry, object]  # 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_v1.services.featurestore_service import pagers
from google.cloud.aiplatform_v1.types import encryption_spec
from google.cloud.aiplatform_v1.types import entity_type
from google.cloud.aiplatform_v1.types import entity_type as gca_entity_type
from google.cloud.aiplatform_v1.types import feature
from google.cloud.aiplatform_v1.types import feature as gca_feature
from google.cloud.aiplatform_v1.types import featurestore
from google.cloud.aiplatform_v1.types import featurestore as gca_featurestore
from google.cloud.aiplatform_v1.types import featurestore_monitoring
from google.cloud.aiplatform_v1.types import featurestore_service
from google.cloud.aiplatform_v1.types import operation as gca_operation
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


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

    def get_transport_class(
        cls,
        label: 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:
            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")

    DEFAULT_ENDPOINT = "aiplatform.googleapis.com"
    DEFAULT_MTLS_ENDPOINT = _get_default_mtls_endpoint.__func__(  # type: ignore
        DEFAULT_ENDPOINT
    )

    @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
    ):
        """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 variabel 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.
        """
        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

    def __init__(
        self,
        *,
        credentials: Optional[ga_credentials.Credentials] = None,
        transport: Union[str, FeaturestoreServiceTransport, None] = None,
        client_options: Optional[client_options_lib.ClientOptions] = 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 (Union[str, FeaturestoreServiceTransport]): The
                transport to use. If set to None, a transport is chosen
                automatically.
            client_options (google.api_core.client_options.ClientOptions): Custom options for the
                client. It won't take effect if a ``transport`` instance is provided.
                (1) The ``api_endpoint`` property can be used to override the
                default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT
                environment variable can also be used to override the endpoint:
                "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). However, the ``api_endpoint`` property takes
                precedence if provided.
                (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable
                is "true", then the ``client_cert_source`` property can be used
                to provide client certificate for mutual TLS 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.
            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.
        """
        if isinstance(client_options, dict):
            client_options = client_options_lib.from_dict(client_options)
        if client_options is None:
            client_options = client_options_lib.ClientOptions()

        api_endpoint, client_cert_source_func = self.get_mtls_endpoint_and_cert_source(
            client_options
        )

        api_key_value = getattr(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.
        if isinstance(transport, FeaturestoreServiceTransport):
            # transport is a FeaturestoreServiceTransport instance.
            if credentials or client_options.credentials_file or api_key_value:
                raise ValueError(
                    "When providing a transport instance, "
                    "provide its credentials directly."
                )
            if client_options.scopes:
                raise ValueError(
                    "When providing a transport instance, provide its scopes "
                    "directly."
                )
            self._transport = transport
        else:
            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
                )

            Transport = type(self).get_transport_class(transport)
            self._transport = Transport(
                credentials=credentials,
                credentials_file=client_options.credentials_file,
                host=api_endpoint,
                scopes=client_options.scopes,
                client_cert_source_for_mtls=client_cert_source_func,
                quota_project_id=client_options.quota_project_id,
                client_info=client_info,
                always_use_jwt_access=True,
            )

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_create_featurestore():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1.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_v1.types.CreateFeaturestoreRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.CreateFeaturestore][google.cloud.aiplatform.v1.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_v1.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_v1.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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.CreateFeaturestoreRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)),
        )

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_get_featurestore():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1.GetFeaturestoreRequest(
                    name="name_value",
                )

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

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1.types.GetFeaturestoreRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.GetFeaturestore][google.cloud.aiplatform.v1.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_v1.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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.GetFeaturestoreRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)),
        )

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

        # Done; return the response.
        return response

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_list_featurestores():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1.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_v1.types.ListFeaturestoresRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.ListFeaturestores][google.cloud.aiplatform.v1.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_v1.services.featurestore_service.pagers.ListFeaturestoresPager:
                Response message for
                [FeaturestoreService.ListFeaturestores][google.cloud.aiplatform.v1.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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.ListFeaturestoresRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)),
        )

        # 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,
            metadata=metadata,
        )

        # Done; return the response.
        return response

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_update_featurestore():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1.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_v1.types.UpdateFeaturestoreRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.UpdateFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.UpdateFeaturestore].
            featurestore (google.cloud.aiplatform_v1.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``

                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_v1.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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.UpdateFeaturestoreRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)
            ),
        )

        # 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: Union[featurestore_service.DeleteFeaturestoreRequest, dict] = None,
        *,
        name: str = None,
        force: bool = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: float = None,
        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

            from google.cloud import aiplatform_v1

            def sample_delete_featurestore():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1.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_v1.types.DeleteFeaturestoreRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.DeleteFeaturestore][google.cloud.aiplatform.v1.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);

                      }

                   The JSON representation for Empty is empty JSON
                   object {}.

        """
        # 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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.DeleteFeaturestoreRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)),
        )

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_create_entity_type():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1.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_v1.types.CreateEntityTypeRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.CreateEntityType][google.cloud.aiplatform.v1.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_v1.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_v1.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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.CreateEntityTypeRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)),
        )

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_get_entity_type():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1.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_v1.types.GetEntityTypeRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.GetEntityType][google.cloud.aiplatform.v1.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_v1.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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.GetEntityTypeRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)),
        )

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

        # Done; return the response.
        return response

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_list_entity_types():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1.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_v1.types.ListEntityTypesRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.ListEntityTypes][google.cloud.aiplatform.v1.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_v1.services.featurestore_service.pagers.ListEntityTypesPager:
                Response message for
                [FeaturestoreService.ListEntityTypes][google.cloud.aiplatform.v1.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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.ListEntityTypesRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)),
        )

        # 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,
            metadata=metadata,
        )

        # Done; return the response.
        return response

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_update_entity_type():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1.UpdateEntityTypeRequest(
                )

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

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1.types.UpdateEntityTypeRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.UpdateEntityType][google.cloud.aiplatform.v1.FeaturestoreService.UpdateEntityType].
            entity_type (google.cloud.aiplatform_v1.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``

                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_v1.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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.UpdateEntityTypeRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)
            ),
        )

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

        # Done; return the response.
        return response

    def delete_entity_type(
        self,
        request: Union[featurestore_service.DeleteEntityTypeRequest, dict] = None,
        *,
        name: str = None,
        force: bool = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: float = None,
        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

            from google.cloud import aiplatform_v1

            def sample_delete_entity_type():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1.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_v1.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);

                      }

                   The JSON representation for Empty is empty JSON
                   object {}.

        """
        # 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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.DeleteEntityTypeRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)),
        )

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_create_feature():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                feature = aiplatform_v1.Feature()
                feature.value_type = "BYTES"

                request = aiplatform_v1.CreateFeatureRequest(
                    parent="parent_value",
                    feature=feature,
                    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_v1.types.CreateFeatureRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.CreateFeature][google.cloud.aiplatform.v1.FeaturestoreService.CreateFeature].
            parent (str):
                Required. The resource name of the EntityType to create
                a Feature. 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.
            feature (google.cloud.aiplatform_v1.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 60 characters, and valid
                characters are ``[a-z0-9_]``. The first character cannot
                be a number.

                The value must be unique within an EntityType.

                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_v1.types.Feature` Feature Metadata information that describes an attribute of an entity type.
                   For example, apple is an entity type, and color is a
                   feature that describes 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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.CreateFeatureRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)),
        )

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_batch_create_features():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                requests = aiplatform_v1.CreateFeatureRequest()
                requests.parent = "parent_value"
                requests.feature.value_type = "BYTES"
                requests.feature_id = "feature_id_value"

                request = aiplatform_v1.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_v1.types.BatchCreateFeaturesRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.BatchCreateFeatures][google.cloud.aiplatform.v1.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 (Sequence[google.cloud.aiplatform_v1.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_v1.types.BatchCreateFeaturesResponse`
                Response message for
                [FeaturestoreService.BatchCreateFeatures][google.cloud.aiplatform.v1.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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.BatchCreateFeaturesRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)),
        )

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_get_feature():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1.GetFeatureRequest(
                    name="name_value",
                )

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

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1.types.GetFeatureRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.GetFeature][google.cloud.aiplatform.v1.FeaturestoreService.GetFeature].
            name (str):
                Required. The name of the Feature 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_v1.types.Feature:
                Feature Metadata information that
                describes an attribute of an entity
                type. For example, apple is an entity
                type, and color is a feature that
                describes 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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.GetFeatureRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)),
        )

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

        # Done; return the response.
        return response

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_list_features():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1.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_v1.types.ListFeaturesRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.ListFeatures][google.cloud.aiplatform.v1.FeaturestoreService.ListFeatures].
            parent (str):
                Required. The resource name of the Location to list
                Features. 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.
            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_v1.services.featurestore_service.pagers.ListFeaturesPager:
                Response message for
                [FeaturestoreService.ListFeatures][google.cloud.aiplatform.v1.FeaturestoreService.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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.ListFeaturesRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)),
        )

        # 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,
            metadata=metadata,
        )

        # Done; return the response.
        return response

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_update_feature():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                feature = aiplatform_v1.Feature()
                feature.value_type = "BYTES"

                request = aiplatform_v1.UpdateFeatureRequest(
                    feature=feature,
                )

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

                # Handle the response
                print(response)

        Args:
            request (Union[google.cloud.aiplatform_v1.types.UpdateFeatureRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.UpdateFeature][google.cloud.aiplatform.v1.FeaturestoreService.UpdateFeature].
            feature (google.cloud.aiplatform_v1.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}``

                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``

                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_v1.types.Feature:
                Feature Metadata information that
                describes an attribute of an entity
                type. For example, apple is an entity
                type, and color is a feature that
                describes 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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.UpdateFeatureRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)
            ),
        )

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

        # Done; return the response.
        return response

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_delete_feature():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1.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_v1.types.DeleteFeatureRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.DeleteFeature][google.cloud.aiplatform.v1.FeaturestoreService.DeleteFeature].
            name (str):
                Required. The name of the Features to be deleted.
                Format:
                ``projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/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);

                      }

                   The JSON representation for Empty is empty JSON
                   object {}.

        """
        # 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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.DeleteFeatureRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)),
        )

        # 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: Union[featurestore_service.ImportFeatureValuesRequest, dict] = None,
        *,
        entity_type: str = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: float = None,
        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

            from google.cloud import aiplatform_v1

            def sample_import_feature_values():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                avro_source = aiplatform_v1.AvroSource()
                avro_source.gcs_source.uris = ['uris_value_1', 'uris_value_2']

                feature_specs = aiplatform_v1.FeatureSpec()
                feature_specs.id = "id_value"

                request = aiplatform_v1.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_v1.types.ImportFeatureValuesRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.ImportFeatureValues][google.cloud.aiplatform.v1.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_v1.types.ImportFeatureValuesResponse`
                Response message for
                [FeaturestoreService.ImportFeatureValues][google.cloud.aiplatform.v1.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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.ImportFeatureValuesRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)
            ),
        )

        # 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: Union[featurestore_service.BatchReadFeatureValuesRequest, dict] = None,
        *,
        featurestore: str = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: float = None,
        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

            from google.cloud import aiplatform_v1

            def sample_batch_read_feature_values():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                csv_read_instances = aiplatform_v1.CsvSource()
                csv_read_instances.gcs_source.uris = ['uris_value_1', 'uris_value_2']

                destination = aiplatform_v1.FeatureValueDestination()
                destination.bigquery_destination.output_uri = "output_uri_value"

                entity_type_specs = aiplatform_v1.EntityTypeSpec()
                entity_type_specs.entity_type_id = "entity_type_id_value"
                entity_type_specs.feature_selector.id_matcher.ids = ['ids_value_1', 'ids_value_2']

                request = aiplatform_v1.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_v1.types.BatchReadFeatureValuesRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.BatchReadFeatureValues][google.cloud.aiplatform.v1.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_v1.types.BatchReadFeatureValuesResponse`
                Response message for
                [FeaturestoreService.BatchReadFeatureValues][google.cloud.aiplatform.v1.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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.BatchReadFeatureValuesRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)
            ),
        )

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_export_feature_values():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                destination = aiplatform_v1.FeatureValueDestination()
                destination.bigquery_destination.output_uri = "output_uri_value"

                feature_selector = aiplatform_v1.FeatureSelector()
                feature_selector.id_matcher.ids = ['ids_value_1', 'ids_value_2']

                request = aiplatform_v1.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_v1.types.ExportFeatureValuesRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.ExportFeatureValues][google.cloud.aiplatform.v1.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_v1.types.ExportFeatureValuesResponse`
                Response message for
                [FeaturestoreService.ExportFeatureValues][google.cloud.aiplatform.v1.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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.ExportFeatureValuesRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)
            ),
        )

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

        .. code-block:: python

            from google.cloud import aiplatform_v1

            def sample_search_features():
                # Create a client
                client = aiplatform_v1.FeaturestoreServiceClient()

                # Initialize request argument(s)
                request = aiplatform_v1.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_v1.types.SearchFeaturesRequest, dict]):
                The request object. Request message for
                [FeaturestoreService.SearchFeatures][google.cloud.aiplatform.v1.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_v1.services.featurestore_service.pagers.SearchFeaturesPager:
                Response message for
                [FeaturestoreService.SearchFeatures][google.cloud.aiplatform.v1.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."
            )

        # Minor optimization to avoid making a copy if the user passes
        # in a featurestore_service.SearchFeaturesRequest.
        # There's no risk of modifying the input as we've already verified
        # there are no flattened fields.
        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),)),
        )

        # 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,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def __enter__(self):
        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()


try:
    DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo(
        gapic_version=pkg_resources.get_distribution(
            "google-cloud-aiplatform",
        ).version,
    )
except pkg_resources.DistributionNotFound:
    DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo()


__all__ = ("FeaturestoreServiceClient",)
