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

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.exceptions import MutualTLSChannelError  # type: ignore
from google.auth.transport import mtls  # type: ignore
from google.auth.transport.grpc import SslCredentials  # type: ignore
from google.oauth2 import service_account  # type: ignore

from google.ai.generativelanguage_v1beta import gapic_version as package_version

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

try:
    from google.api_core import client_logging  # type: ignore

    CLIENT_LOGGING_SUPPORTED = True  # pragma: NO COVER
except ImportError:  # pragma: NO COVER
    CLIENT_LOGGING_SUPPORTED = False

_LOGGER = std_logging.getLogger(__name__)

from google.longrunning import operations_pb2  # type: ignore

from google.ai.generativelanguage_v1beta.types import generative_service, safety
from google.ai.generativelanguage_v1beta.types import content
from google.ai.generativelanguage_v1beta.types import content as gag_content

from .transports.base import DEFAULT_CLIENT_INFO, GenerativeServiceTransport
from .transports.grpc import GenerativeServiceGrpcTransport
from .transports.grpc_asyncio import GenerativeServiceGrpcAsyncIOTransport
from .transports.rest import GenerativeServiceRestTransport


class GenerativeServiceClientMeta(type):
    """Metaclass for the GenerativeService 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[GenerativeServiceTransport]]
    _transport_registry["grpc"] = GenerativeServiceGrpcTransport
    _transport_registry["grpc_asyncio"] = GenerativeServiceGrpcAsyncIOTransport
    _transport_registry["rest"] = GenerativeServiceRestTransport

    def get_transport_class(
        cls,
        label: Optional[str] = None,
    ) -> Type[GenerativeServiceTransport]:
        """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 GenerativeServiceClient(metaclass=GenerativeServiceClientMeta):
    """API for using Large Models that generate multimodal content
    and have additional capabilities beyond text generation.
    """

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

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

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

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

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

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

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

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

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

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

        Returns:
            GenerativeServiceClient: 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:
            GenerativeServiceClient: 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) -> GenerativeServiceTransport:
        """Returns the transport used by the client instance.

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

    @staticmethod
    def cached_content_path(
        id: str,
    ) -> str:
        """Returns a fully-qualified cached_content string."""
        return "cachedContents/{id}".format(
            id=id,
        )

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

    @staticmethod
    def model_path(
        model: str,
    ) -> str:
        """Returns a fully-qualified model string."""
        return "models/{model}".format(
            model=model,
        )

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

        return api_endpoint, client_cert_source

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

        Raises:
            ValueError: If the configured universe domain is not valid.
        """

        # NOTE (b/349488459): universe validation is disabled until further notice.
        return True

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

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

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

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

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

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

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

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

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

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

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

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

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

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

        if CLIENT_LOGGING_SUPPORTED:  # pragma: NO COVER
            # Setup logging.
            client_logging.initialize_logging()

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

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

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

        if not transport_provided:
            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_init: Union[
                Type[GenerativeServiceTransport],
                Callable[..., GenerativeServiceTransport],
            ] = (
                GenerativeServiceClient.get_transport_class(transport)
                if isinstance(transport, str) or transport is None
                else cast(Callable[..., GenerativeServiceTransport], transport)
            )
            # initialize with the provided callable or the passed in class
            self._transport = transport_init(
                credentials=credentials,
                credentials_file=self._client_options.credentials_file,
                host=self._api_endpoint,
                scopes=self._client_options.scopes,
                client_cert_source_for_mtls=self._client_cert_source,
                quota_project_id=self._client_options.quota_project_id,
                client_info=client_info,
                always_use_jwt_access=True,
                api_audience=self._client_options.api_audience,
            )

        if "async" not in str(self._transport):
            if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(
                std_logging.DEBUG
            ):  # pragma: NO COVER
                _LOGGER.debug(
                    "Created client `google.ai.generativelanguage_v1beta.GenerativeServiceClient`.",
                    extra={
                        "serviceName": "google.ai.generativelanguage.v1beta.GenerativeService",
                        "universeDomain": getattr(
                            self._transport._credentials, "universe_domain", ""
                        ),
                        "credentialsType": f"{type(self._transport._credentials).__module__}.{type(self._transport._credentials).__qualname__}",
                        "credentialsInfo": getattr(
                            self.transport._credentials, "get_cred_info", lambda: None
                        )(),
                    }
                    if hasattr(self._transport, "_credentials")
                    else {
                        "serviceName": "google.ai.generativelanguage.v1beta.GenerativeService",
                        "credentialsType": None,
                    },
                )

    def generate_content(
        self,
        request: Optional[
            Union[generative_service.GenerateContentRequest, dict]
        ] = None,
        *,
        model: Optional[str] = None,
        contents: Optional[MutableSequence[content.Content]] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> generative_service.GenerateContentResponse:
        r"""Generates a model response given an input
        ``GenerateContentRequest``. Refer to the `text generation
        guide <https://ai.google.dev/gemini-api/docs/text-generation>`__
        for detailed usage information. Input capabilities differ
        between models, including tuned models. Refer to the `model
        guide <https://ai.google.dev/gemini-api/docs/models/gemini>`__
        and `tuning
        guide <https://ai.google.dev/gemini-api/docs/model-tuning>`__
        for details.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.ai import generativelanguage_v1beta

            def sample_generate_content():
                # Create a client
                client = generativelanguage_v1beta.GenerativeServiceClient()

                # Initialize request argument(s)
                request = generativelanguage_v1beta.GenerateContentRequest(
                    model="model_value",
                )

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

                # Handle the response
                print(response)

        Args:
            request (Union[google.ai.generativelanguage_v1beta.types.GenerateContentRequest, dict]):
                The request object. Request to generate a completion from
                the model.
            model (str):
                Required. The name of the ``Model`` to use for
                generating the completion.

                Format: ``models/{model}``.

                This corresponds to the ``model`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]):
                Required. The content of the current conversation with
                the model.

                For single-turn queries, this is a single instance. For
                multi-turn queries like
                `chat <https://ai.google.dev/gemini-api/docs/text-generation#chat>`__,
                this is a repeated field that contains the conversation
                history and the latest request.

                This corresponds to the ``contents`` 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, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.

        Returns:
            google.ai.generativelanguage_v1beta.types.GenerateContentResponse:
                Response from the model supporting multiple candidate
                responses.

                   Safety ratings and content filtering are reported for
                   both prompt in
                   GenerateContentResponse.prompt_feedback and for each
                   candidate in finish_reason and in safety_ratings. The
                   API: - Returns either all requested candidates or
                   none of them - Returns no candidates at all only if
                   there was something wrong with the prompt (check
                   prompt_feedback) - Reports feedback on each candidate
                   in finish_reason and safety_ratings.

        """
        # 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([model, contents])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

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

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

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

        # Validate the universe domain.
        self._validate_universe_domain()

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

        # Done; return the response.
        return response

    def generate_answer(
        self,
        request: Optional[Union[generative_service.GenerateAnswerRequest, dict]] = None,
        *,
        model: Optional[str] = None,
        contents: Optional[MutableSequence[content.Content]] = None,
        safety_settings: Optional[MutableSequence[safety.SafetySetting]] = None,
        answer_style: Optional[
            generative_service.GenerateAnswerRequest.AnswerStyle
        ] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> generative_service.GenerateAnswerResponse:
        r"""Generates a grounded answer from the model given an input
        ``GenerateAnswerRequest``.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.ai import generativelanguage_v1beta

            def sample_generate_answer():
                # Create a client
                client = generativelanguage_v1beta.GenerativeServiceClient()

                # Initialize request argument(s)
                request = generativelanguage_v1beta.GenerateAnswerRequest(
                    model="model_value",
                    answer_style="VERBOSE",
                )

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

                # Handle the response
                print(response)

        Args:
            request (Union[google.ai.generativelanguage_v1beta.types.GenerateAnswerRequest, dict]):
                The request object. Request to generate a grounded answer from the
                ``Model``.
            model (str):
                Required. The name of the ``Model`` to use for
                generating the grounded response.

                Format: ``model=models/{model}``.

                This corresponds to the ``model`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]):
                Required. The content of the current conversation with
                the ``Model``. For single-turn queries, this is a single
                question to answer. For multi-turn queries, this is a
                repeated field that contains conversation history and
                the last ``Content`` in the list containing the
                question.

                Note: ``GenerateAnswer`` only supports queries in
                English.

                This corresponds to the ``contents`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            safety_settings (MutableSequence[google.ai.generativelanguage_v1beta.types.SafetySetting]):
                Optional. A list of unique ``SafetySetting`` instances
                for blocking unsafe content.

                This will be enforced on the
                ``GenerateAnswerRequest.contents`` and
                ``GenerateAnswerResponse.candidate``. There should not
                be more than one setting for each ``SafetyCategory``
                type. The API will block any contents and responses that
                fail to meet the thresholds set by these settings. This
                list overrides the default settings for each
                ``SafetyCategory`` specified in the safety_settings. If
                there is no ``SafetySetting`` for a given
                ``SafetyCategory`` provided in the list, the API will
                use the default safety setting for that category. Harm
                categories HARM_CATEGORY_HATE_SPEECH,
                HARM_CATEGORY_SEXUALLY_EXPLICIT,
                HARM_CATEGORY_DANGEROUS_CONTENT,
                HARM_CATEGORY_HARASSMENT are supported. Refer to the
                `guide <https://ai.google.dev/gemini-api/docs/safety-settings>`__
                for detailed information on available safety settings.
                Also refer to the `Safety
                guidance <https://ai.google.dev/gemini-api/docs/safety-guidance>`__
                to learn how to incorporate safety considerations in
                your AI applications.

                This corresponds to the ``safety_settings`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            answer_style (google.ai.generativelanguage_v1beta.types.GenerateAnswerRequest.AnswerStyle):
                Required. Style in which answers
                should be returned.

                This corresponds to the ``answer_style`` 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, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.

        Returns:
            google.ai.generativelanguage_v1beta.types.GenerateAnswerResponse:
                Response from the model for a
                grounded answer.

        """
        # 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([model, contents, safety_settings, answer_style])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

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

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

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

        # Validate the universe domain.
        self._validate_universe_domain()

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

        # Done; return the response.
        return response

    def stream_generate_content(
        self,
        request: Optional[
            Union[generative_service.GenerateContentRequest, dict]
        ] = None,
        *,
        model: Optional[str] = None,
        contents: Optional[MutableSequence[content.Content]] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> Iterable[generative_service.GenerateContentResponse]:
        r"""Generates a `streamed
        response <https://ai.google.dev/gemini-api/docs/text-generation?lang=python#generate-a-text-stream>`__
        from the model given an input ``GenerateContentRequest``.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.ai import generativelanguage_v1beta

            def sample_stream_generate_content():
                # Create a client
                client = generativelanguage_v1beta.GenerativeServiceClient()

                # Initialize request argument(s)
                request = generativelanguage_v1beta.GenerateContentRequest(
                    model="model_value",
                )

                # Make the request
                stream = client.stream_generate_content(request=request)

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

        Args:
            request (Union[google.ai.generativelanguage_v1beta.types.GenerateContentRequest, dict]):
                The request object. Request to generate a completion from
                the model.
            model (str):
                Required. The name of the ``Model`` to use for
                generating the completion.

                Format: ``models/{model}``.

                This corresponds to the ``model`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]):
                Required. The content of the current conversation with
                the model.

                For single-turn queries, this is a single instance. For
                multi-turn queries like
                `chat <https://ai.google.dev/gemini-api/docs/text-generation#chat>`__,
                this is a repeated field that contains the conversation
                history and the latest request.

                This corresponds to the ``contents`` 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, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.

        Returns:
            Iterable[google.ai.generativelanguage_v1beta.types.GenerateContentResponse]:
                Response from the model supporting multiple candidate
                responses.

                   Safety ratings and content filtering are reported for
                   both prompt in
                   GenerateContentResponse.prompt_feedback and for each
                   candidate in finish_reason and in safety_ratings. The
                   API: - Returns either all requested candidates or
                   none of them - Returns no candidates at all only if
                   there was something wrong with the prompt (check
                   prompt_feedback) - Reports feedback on each candidate
                   in finish_reason and safety_ratings.

        """
        # 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([model, contents])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

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

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

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

        # Validate the universe domain.
        self._validate_universe_domain()

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

        # Done; return the response.
        return response

    def embed_content(
        self,
        request: Optional[Union[generative_service.EmbedContentRequest, dict]] = None,
        *,
        model: Optional[str] = None,
        content: Optional[gag_content.Content] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> generative_service.EmbedContentResponse:
        r"""Generates a text embedding vector from the input ``Content``
        using the specified `Gemini Embedding
        model <https://ai.google.dev/gemini-api/docs/models/gemini#text-embedding>`__.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.ai import generativelanguage_v1beta

            def sample_embed_content():
                # Create a client
                client = generativelanguage_v1beta.GenerativeServiceClient()

                # Initialize request argument(s)
                request = generativelanguage_v1beta.EmbedContentRequest(
                    model="model_value",
                )

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

                # Handle the response
                print(response)

        Args:
            request (Union[google.ai.generativelanguage_v1beta.types.EmbedContentRequest, dict]):
                The request object. Request containing the ``Content`` for the model to
                embed.
            model (str):
                Required. The model's resource name. This serves as an
                ID for the Model to use.

                This name should match a model name returned by the
                ``ListModels`` method.

                Format: ``models/{model}``

                This corresponds to the ``model`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            content (google.ai.generativelanguage_v1beta.types.Content):
                Required. The content to embed. Only the ``parts.text``
                fields will be counted.

                This corresponds to the ``content`` 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, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.

        Returns:
            google.ai.generativelanguage_v1beta.types.EmbedContentResponse:
                The response to an EmbedContentRequest.
        """
        # 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([model, content])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

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

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

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

        # Validate the universe domain.
        self._validate_universe_domain()

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

        # Done; return the response.
        return response

    def batch_embed_contents(
        self,
        request: Optional[
            Union[generative_service.BatchEmbedContentsRequest, dict]
        ] = None,
        *,
        model: Optional[str] = None,
        requests: Optional[
            MutableSequence[generative_service.EmbedContentRequest]
        ] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> generative_service.BatchEmbedContentsResponse:
        r"""Generates multiple embedding vectors from the input ``Content``
        which consists of a batch of strings represented as
        ``EmbedContentRequest`` objects.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.ai import generativelanguage_v1beta

            def sample_batch_embed_contents():
                # Create a client
                client = generativelanguage_v1beta.GenerativeServiceClient()

                # Initialize request argument(s)
                requests = generativelanguage_v1beta.EmbedContentRequest()
                requests.model = "model_value"

                request = generativelanguage_v1beta.BatchEmbedContentsRequest(
                    model="model_value",
                    requests=requests,
                )

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

                # Handle the response
                print(response)

        Args:
            request (Union[google.ai.generativelanguage_v1beta.types.BatchEmbedContentsRequest, dict]):
                The request object. Batch request to get embeddings from
                the model for a list of prompts.
            model (str):
                Required. The model's resource name. This serves as an
                ID for the Model to use.

                This name should match a model name returned by the
                ``ListModels`` method.

                Format: ``models/{model}``

                This corresponds to the ``model`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            requests (MutableSequence[google.ai.generativelanguage_v1beta.types.EmbedContentRequest]):
                Required. Embed requests for the batch. The model in
                each of these requests must match the model specified
                ``BatchEmbedContentsRequest.model``.

                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, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.

        Returns:
            google.ai.generativelanguage_v1beta.types.BatchEmbedContentsResponse:
                The response to a BatchEmbedContentsRequest.
        """
        # 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([model, requests])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

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

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

        # Validate the universe domain.
        self._validate_universe_domain()

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

        # Done; return the response.
        return response

    def count_tokens(
        self,
        request: Optional[Union[generative_service.CountTokensRequest, dict]] = None,
        *,
        model: Optional[str] = None,
        contents: Optional[MutableSequence[content.Content]] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> generative_service.CountTokensResponse:
        r"""Runs a model's tokenizer on input ``Content`` and returns the
        token count. Refer to the `tokens
        guide <https://ai.google.dev/gemini-api/docs/tokens>`__ to learn
        more about tokens.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.ai import generativelanguage_v1beta

            def sample_count_tokens():
                # Create a client
                client = generativelanguage_v1beta.GenerativeServiceClient()

                # Initialize request argument(s)
                request = generativelanguage_v1beta.CountTokensRequest(
                    model="model_value",
                )

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

                # Handle the response
                print(response)

        Args:
            request (Union[google.ai.generativelanguage_v1beta.types.CountTokensRequest, dict]):
                The request object. Counts the number of tokens in the ``prompt`` sent to a
                model.

                Models may tokenize text differently, so each model may
                return a different ``token_count``.
            model (str):
                Required. The model's resource name. This serves as an
                ID for the Model to use.

                This name should match a model name returned by the
                ``ListModels`` method.

                Format: ``models/{model}``

                This corresponds to the ``model`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]):
                Optional. The input given to the model as a prompt. This
                field is ignored when ``generate_content_request`` is
                set.

                This corresponds to the ``contents`` 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, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.

        Returns:
            google.ai.generativelanguage_v1beta.types.CountTokensResponse:
                A response from CountTokens.

                   It returns the model's token_count for the prompt.

        """
        # 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([model, contents])
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

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

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

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

        # Validate the universe domain.
        self._validate_universe_domain()

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

        # Done; return the response.
        return response

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

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

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

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

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

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

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

        # Validate the universe domain.
        self._validate_universe_domain()

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

        # Done; return the response.
        return response

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

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

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

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

        # Validate the universe domain.
        self._validate_universe_domain()

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

        # Done; return the response.
        return response


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


__all__ = ("GenerativeServiceClient",)