# -*- 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 re
from typing import (
    Callable,
    Dict,
    Mapping,
    MutableMapping,
    MutableSequence,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
)

from google.api_core import exceptions as core_exceptions
from google.api_core import gapic_v1
from google.api_core import retry_async as retries
from google.api_core.client_options import ClientOptions
from google.auth import credentials as ga_credentials  # 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.AsyncRetry, gapic_v1.method._MethodDefault, None]
except AttributeError:  # pragma: NO COVER
    OptionalRetry = Union[retries.AsyncRetry, object, None]  # type: ignore

from google.api_core import operation  # type: ignore
from google.api_core import operation_async  # type: ignore
from google.longrunning import operations_pb2  # type: ignore
from google.protobuf import field_mask_pb2  # type: ignore
from google.protobuf import timestamp_pb2  # type: ignore

from google.ai.generativelanguage_v1beta.services.model_service import pagers
from google.ai.generativelanguage_v1beta.types import tuned_model as gag_tuned_model
from google.ai.generativelanguage_v1beta.types import model, model_service
from google.ai.generativelanguage_v1beta.types import tuned_model

from .client import ModelServiceClient
from .transports.base import DEFAULT_CLIENT_INFO, ModelServiceTransport
from .transports.grpc_asyncio import ModelServiceGrpcAsyncIOTransport


class ModelServiceAsyncClient:
    """Provides methods for getting metadata information about
    Generative Models.
    """

    _client: ModelServiceClient

    # Copy defaults from the synchronous client for use here.
    # Note: DEFAULT_ENDPOINT is deprecated. Use _DEFAULT_ENDPOINT_TEMPLATE instead.
    DEFAULT_ENDPOINT = ModelServiceClient.DEFAULT_ENDPOINT
    DEFAULT_MTLS_ENDPOINT = ModelServiceClient.DEFAULT_MTLS_ENDPOINT
    _DEFAULT_ENDPOINT_TEMPLATE = ModelServiceClient._DEFAULT_ENDPOINT_TEMPLATE
    _DEFAULT_UNIVERSE = ModelServiceClient._DEFAULT_UNIVERSE

    model_path = staticmethod(ModelServiceClient.model_path)
    parse_model_path = staticmethod(ModelServiceClient.parse_model_path)
    tuned_model_path = staticmethod(ModelServiceClient.tuned_model_path)
    parse_tuned_model_path = staticmethod(ModelServiceClient.parse_tuned_model_path)
    common_billing_account_path = staticmethod(
        ModelServiceClient.common_billing_account_path
    )
    parse_common_billing_account_path = staticmethod(
        ModelServiceClient.parse_common_billing_account_path
    )
    common_folder_path = staticmethod(ModelServiceClient.common_folder_path)
    parse_common_folder_path = staticmethod(ModelServiceClient.parse_common_folder_path)
    common_organization_path = staticmethod(ModelServiceClient.common_organization_path)
    parse_common_organization_path = staticmethod(
        ModelServiceClient.parse_common_organization_path
    )
    common_project_path = staticmethod(ModelServiceClient.common_project_path)
    parse_common_project_path = staticmethod(
        ModelServiceClient.parse_common_project_path
    )
    common_location_path = staticmethod(ModelServiceClient.common_location_path)
    parse_common_location_path = staticmethod(
        ModelServiceClient.parse_common_location_path
    )

    @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:
            ModelServiceAsyncClient: The constructed client.
        """
        return ModelServiceClient.from_service_account_info.__func__(ModelServiceAsyncClient, info, *args, **kwargs)  # type: ignore

    @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:
            ModelServiceAsyncClient: The constructed client.
        """
        return ModelServiceClient.from_service_account_file.__func__(ModelServiceAsyncClient, filename, *args, **kwargs)  # type: ignore

    from_service_account_json = from_service_account_file

    @classmethod
    def get_mtls_endpoint_and_cert_source(
        cls, client_options: Optional[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 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.
        """
        return ModelServiceClient.get_mtls_endpoint_and_cert_source(client_options)  # type: ignore

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

        Returns:
            ModelServiceTransport: The transport used by the client instance.
        """
        return self._client.transport

    @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._client._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._client._universe_domain

    get_transport_class = ModelServiceClient.get_transport_class

    def __init__(
        self,
        *,
        credentials: Optional[ga_credentials.Credentials] = None,
        transport: Optional[
            Union[str, ModelServiceTransport, Callable[..., ModelServiceTransport]]
        ] = "grpc_asyncio",
        client_options: Optional[ClientOptions] = None,
        client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
    ) -> None:
        """Instantiates the model service async 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,ModelServiceTransport,Callable[..., ModelServiceTransport]]]):
                The transport to use, or a Callable that constructs and returns a new transport to use.
                If a Callable is given, it will be called with the same set of initialization
                arguments as used in the ModelServiceTransport 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 ``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 = ModelServiceClient(
            credentials=credentials,
            transport=transport,
            client_options=client_options,
            client_info=client_info,
        )

    async def get_model(
        self,
        request: Optional[Union[model_service.GetModelRequest, dict]] = None,
        *,
        name: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> model.Model:
        r"""Gets information about a specific ``Model`` such as its version
        number, token limits,
        `parameters <https://ai.google.dev/gemini-api/docs/models/generative-models#model-parameters>`__
        and other metadata. Refer to the `Gemini models
        guide <https://ai.google.dev/gemini-api/docs/models/gemini>`__
        for detailed model information.

        .. 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

            async def sample_get_model():
                # Create a client
                client = generativelanguage_v1beta.ModelServiceAsyncClient()

                # Initialize request argument(s)
                request = generativelanguage_v1beta.GetModelRequest(
                    name="name_value",
                )

                # Make the request
                response = await client.get_model(request=request)

                # Handle the response
                print(response)

        Args:
            request (Optional[Union[google.ai.generativelanguage_v1beta.types.GetModelRequest, dict]]):
                The request object. Request for getting information about
                a specific Model.
            name (:class:`str`):
                Required. The resource name of the model.

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

                Format: ``models/{model}``

                This corresponds to the ``name`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry_async.AsyncRetry): 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.ai.generativelanguage_v1beta.types.Model:
                Information about a Generative
                Language Model.

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

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, model_service.GetModelRequest):
            request = model_service.GetModelRequest(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._client._transport._wrapped_methods[
            self._client._transport.get_model
        ]

        # 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._client._validate_universe_domain()

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

        # Done; return the response.
        return response

    async def list_models(
        self,
        request: Optional[Union[model_service.ListModelsRequest, dict]] = None,
        *,
        page_size: Optional[int] = None,
        page_token: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> pagers.ListModelsAsyncPager:
        r"""Lists the
        ```Model``\ s <https://ai.google.dev/gemini-api/docs/models/gemini>`__
        available through the Gemini API.

        .. 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

            async def sample_list_models():
                # Create a client
                client = generativelanguage_v1beta.ModelServiceAsyncClient()

                # Initialize request argument(s)
                request = generativelanguage_v1beta.ListModelsRequest(
                )

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

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

        Args:
            request (Optional[Union[google.ai.generativelanguage_v1beta.types.ListModelsRequest, dict]]):
                The request object. Request for listing all Models.
            page_size (:class:`int`):
                The maximum number of ``Models`` to return (per page).

                If unspecified, 50 models will be returned per page.
                This method returns at most 1000 models per page, even
                if you pass a larger page_size.

                This corresponds to the ``page_size`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            page_token (:class:`str`):
                A page token, received from a previous ``ListModels``
                call.

                Provide the ``page_token`` returned by one request as an
                argument to the next request to retrieve the next page.

                When paginating, all other parameters provided to
                ``ListModels`` must match the call that provided the
                page token.

                This corresponds to the ``page_token`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry_async.AsyncRetry): 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.ai.generativelanguage_v1beta.services.model_service.pagers.ListModelsAsyncPager:
                Response from ListModel containing a paginated list of
                Models.

                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([page_size, page_token])
        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, model_service.ListModelsRequest):
            request = model_service.ListModelsRequest(request)

        # If we have keyword arguments corresponding to fields on the
        # request, apply these.
        if page_size is not None:
            request.page_size = page_size
        if page_token is not None:
            request.page_token = page_token

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

        # Validate the universe domain.
        self._client._validate_universe_domain()

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

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

        # Done; return the response.
        return response

    async def get_tuned_model(
        self,
        request: Optional[Union[model_service.GetTunedModelRequest, dict]] = None,
        *,
        name: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> tuned_model.TunedModel:
        r"""Gets information about a specific TunedModel.

        .. 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

            async def sample_get_tuned_model():
                # Create a client
                client = generativelanguage_v1beta.ModelServiceAsyncClient()

                # Initialize request argument(s)
                request = generativelanguage_v1beta.GetTunedModelRequest(
                    name="name_value",
                )

                # Make the request
                response = await client.get_tuned_model(request=request)

                # Handle the response
                print(response)

        Args:
            request (Optional[Union[google.ai.generativelanguage_v1beta.types.GetTunedModelRequest, dict]]):
                The request object. Request for getting information about
                a specific Model.
            name (:class:`str`):
                Required. The resource name of the model.

                Format: ``tunedModels/my-model-id``

                This corresponds to the ``name`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry_async.AsyncRetry): 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.ai.generativelanguage_v1beta.types.TunedModel:
                A fine-tuned model created using
                ModelService.CreateTunedModel.

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

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, model_service.GetTunedModelRequest):
            request = model_service.GetTunedModelRequest(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._client._transport._wrapped_methods[
            self._client._transport.get_tuned_model
        ]

        # 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._client._validate_universe_domain()

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

        # Done; return the response.
        return response

    async def list_tuned_models(
        self,
        request: Optional[Union[model_service.ListTunedModelsRequest, dict]] = None,
        *,
        page_size: Optional[int] = None,
        page_token: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> pagers.ListTunedModelsAsyncPager:
        r"""Lists created tuned models.

        .. 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

            async def sample_list_tuned_models():
                # Create a client
                client = generativelanguage_v1beta.ModelServiceAsyncClient()

                # Initialize request argument(s)
                request = generativelanguage_v1beta.ListTunedModelsRequest(
                )

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

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

        Args:
            request (Optional[Union[google.ai.generativelanguage_v1beta.types.ListTunedModelsRequest, dict]]):
                The request object. Request for listing TunedModels.
            page_size (:class:`int`):
                Optional. The maximum number of ``TunedModels`` to
                return (per page). The service may return fewer tuned
                models.

                If unspecified, at most 10 tuned models will be
                returned. This method returns at most 1000 models per
                page, even if you pass a larger page_size.

                This corresponds to the ``page_size`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            page_token (:class:`str`):
                Optional. A page token, received from a previous
                ``ListTunedModels`` call.

                Provide the ``page_token`` returned by one request as an
                argument to the next request to retrieve the next page.

                When paginating, all other parameters provided to
                ``ListTunedModels`` must match the call that provided
                the page token.

                This corresponds to the ``page_token`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry_async.AsyncRetry): 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.ai.generativelanguage_v1beta.services.model_service.pagers.ListTunedModelsAsyncPager:
                Response from ListTunedModels containing a paginated
                list of Models.

                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([page_size, page_token])
        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, model_service.ListTunedModelsRequest):
            request = model_service.ListTunedModelsRequest(request)

        # If we have keyword arguments corresponding to fields on the
        # request, apply these.
        if page_size is not None:
            request.page_size = page_size
        if page_token is not None:
            request.page_token = page_token

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

        # Validate the universe domain.
        self._client._validate_universe_domain()

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

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

        # Done; return the response.
        return response

    async def create_tuned_model(
        self,
        request: Optional[Union[model_service.CreateTunedModelRequest, dict]] = None,
        *,
        tuned_model: Optional[gag_tuned_model.TunedModel] = None,
        tuned_model_id: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> operation_async.AsyncOperation:
        r"""Creates a tuned model. Check intermediate tuning progress (if
        any) through the [google.longrunning.Operations] service.

        Access status and results through the Operations service.
        Example: GET /v1/tunedModels/az2mb0bpw6i/operations/000-111-222

        .. 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

            async def sample_create_tuned_model():
                # Create a client
                client = generativelanguage_v1beta.ModelServiceAsyncClient()

                # Initialize request argument(s)
                tuned_model = generativelanguage_v1beta.TunedModel()
                tuned_model.tuning_task.training_data.examples.examples.text_input = "text_input_value"
                tuned_model.tuning_task.training_data.examples.examples.output = "output_value"

                request = generativelanguage_v1beta.CreateTunedModelRequest(
                    tuned_model=tuned_model,
                )

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

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

                response = (await operation).result()

                # Handle the response
                print(response)

        Args:
            request (Optional[Union[google.ai.generativelanguage_v1beta.types.CreateTunedModelRequest, dict]]):
                The request object. Request to create a TunedModel.
            tuned_model (:class:`google.ai.generativelanguage_v1beta.types.TunedModel`):
                Required. The tuned model to create.
                This corresponds to the ``tuned_model`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            tuned_model_id (:class:`str`):
                Optional. The unique id for the tuned model if
                specified. This value should be up to 40 characters, the
                first character must be a letter, the last could be a
                letter or a number. The id must match the regular
                expression: ``[a-z]([a-z0-9-]{0,38}[a-z0-9])?``.

                This corresponds to the ``tuned_model_id`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry_async.AsyncRetry): 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_async.AsyncOperation:
                An object representing a long-running operation.

                The result type for the operation will be
                :class:`google.ai.generativelanguage_v1beta.types.TunedModel`
                A fine-tuned model created using
                ModelService.CreateTunedModel.

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

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, model_service.CreateTunedModelRequest):
            request = model_service.CreateTunedModelRequest(request)

        # If we have keyword arguments corresponding to fields on the
        # request, apply these.
        if tuned_model is not None:
            request.tuned_model = tuned_model
        if tuned_model_id is not None:
            request.tuned_model_id = tuned_model_id

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

        # Validate the universe domain.
        self._client._validate_universe_domain()

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

        # Wrap the response in an operation future.
        response = operation_async.from_gapic(
            response,
            self._client._transport.operations_client,
            gag_tuned_model.TunedModel,
            metadata_type=model_service.CreateTunedModelMetadata,
        )

        # Done; return the response.
        return response

    async def update_tuned_model(
        self,
        request: Optional[Union[model_service.UpdateTunedModelRequest, dict]] = None,
        *,
        tuned_model: Optional[gag_tuned_model.TunedModel] = None,
        update_mask: Optional[field_mask_pb2.FieldMask] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> gag_tuned_model.TunedModel:
        r"""Updates a tuned model.

        .. 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

            async def sample_update_tuned_model():
                # Create a client
                client = generativelanguage_v1beta.ModelServiceAsyncClient()

                # Initialize request argument(s)
                tuned_model = generativelanguage_v1beta.TunedModel()
                tuned_model.tuning_task.training_data.examples.examples.text_input = "text_input_value"
                tuned_model.tuning_task.training_data.examples.examples.output = "output_value"

                request = generativelanguage_v1beta.UpdateTunedModelRequest(
                    tuned_model=tuned_model,
                )

                # Make the request
                response = await client.update_tuned_model(request=request)

                # Handle the response
                print(response)

        Args:
            request (Optional[Union[google.ai.generativelanguage_v1beta.types.UpdateTunedModelRequest, dict]]):
                The request object. Request to update a TunedModel.
            tuned_model (:class:`google.ai.generativelanguage_v1beta.types.TunedModel`):
                Required. The tuned model to update.
                This corresponds to the ``tuned_model`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`):
                Required. The list of fields to
                update.

                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_async.AsyncRetry): 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.ai.generativelanguage_v1beta.types.TunedModel:
                A fine-tuned model created using
                ModelService.CreateTunedModel.

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

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, model_service.UpdateTunedModelRequest):
            request = model_service.UpdateTunedModelRequest(request)

        # If we have keyword arguments corresponding to fields on the
        # request, apply these.
        if tuned_model is not None:
            request.tuned_model = tuned_model
        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._client._transport._wrapped_methods[
            self._client._transport.update_tuned_model
        ]

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

        # Validate the universe domain.
        self._client._validate_universe_domain()

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

        # Done; return the response.
        return response

    async def delete_tuned_model(
        self,
        request: Optional[Union[model_service.DeleteTunedModelRequest, dict]] = None,
        *,
        name: Optional[str] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> None:
        r"""Deletes a tuned model.

        .. 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

            async def sample_delete_tuned_model():
                # Create a client
                client = generativelanguage_v1beta.ModelServiceAsyncClient()

                # Initialize request argument(s)
                request = generativelanguage_v1beta.DeleteTunedModelRequest(
                    name="name_value",
                )

                # Make the request
                await client.delete_tuned_model(request=request)

        Args:
            request (Optional[Union[google.ai.generativelanguage_v1beta.types.DeleteTunedModelRequest, dict]]):
                The request object. Request to delete a TunedModel.
            name (:class:`str`):
                Required. The resource name of the model. Format:
                ``tunedModels/my-model-id``

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

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, model_service.DeleteTunedModelRequest):
            request = model_service.DeleteTunedModelRequest(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._client._transport._wrapped_methods[
            self._client._transport.delete_tuned_model
        ]

        # 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._client._validate_universe_domain()

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

    async def __aenter__(self) -> "ModelServiceAsyncClient":
        return self

    async def __aexit__(self, exc_type, exc, tb):
        await self.transport.close()


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


__all__ = ("ModelServiceAsyncClient",)
