# -*- coding: utf-8 -*- # Copyright 2022 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from collections import OrderedDict import os import re from typing import ( Dict, Mapping, MutableMapping, MutableSequence, Optional, Sequence, Tuple, Type, Union, cast, ) 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_v1beta2 import gapic_version as package_version try: OptionalRetry = Union[retries.Retry, gapic_v1.method._MethodDefault] except AttributeError: # pragma: NO COVER OptionalRetry = Union[retries.Retry, object] # type: ignore from google.ai.generativelanguage_v1beta2.services.model_service import pagers from google.ai.generativelanguage_v1beta2.types import model, model_service from .transports.base import DEFAULT_CLIENT_INFO, ModelServiceTransport from .transports.grpc import ModelServiceGrpcTransport from .transports.grpc_asyncio import ModelServiceGrpcAsyncIOTransport from .transports.rest import ModelServiceRestTransport class ModelServiceClientMeta(type): """Metaclass for the ModelService 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[ModelServiceTransport]] _transport_registry["grpc"] = ModelServiceGrpcTransport _transport_registry["grpc_asyncio"] = ModelServiceGrpcAsyncIOTransport _transport_registry["rest"] = ModelServiceRestTransport def get_transport_class( cls, label: Optional[str] = None, ) -> Type[ModelServiceTransport]: """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 ModelServiceClient(metaclass=ModelServiceClientMeta): """Provides methods for getting metadata information about Generative Models. """ @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[^.]+)(?P\.mtls)?(?P\.sandbox)?(?P\.googleapis\.com)?" ) m = mtls_endpoint_re.match(api_endpoint) name, mtls, sandbox, googledomain = m.groups() if mtls or not googledomain: return api_endpoint if sandbox: return api_endpoint.replace( "sandbox.googleapis.com", "mtls.sandbox.googleapis.com" ) return api_endpoint.replace(".googleapis.com", ".mtls.googleapis.com") DEFAULT_ENDPOINT = "generativelanguage.googleapis.com" DEFAULT_MTLS_ENDPOINT = _get_default_mtls_endpoint.__func__( # type: ignore DEFAULT_ENDPOINT ) @classmethod def from_service_account_info(cls, info: dict, *args, **kwargs): """Creates an instance of this client using the provided credentials info. Args: info (dict): The service account private key info. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: ModelServiceClient: 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: ModelServiceClient: 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) -> ModelServiceTransport: """Returns the transport used by the client instance. Returns: ModelServiceTransport: The transport used by the client instance. """ return self._transport @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.+?)$", path) return m.groupdict() if m else {} @staticmethod def common_billing_account_path( billing_account: str, ) -> str: """Returns a fully-qualified billing_account string.""" return "billingAccounts/{billing_account}".format( billing_account=billing_account, ) @staticmethod def parse_common_billing_account_path(path: str) -> Dict[str, str]: """Parse a billing_account path into its component segments.""" m = re.match(r"^billingAccounts/(?P.+?)$", 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.+?)$", path) return m.groupdict() if m else {} @staticmethod def common_organization_path( organization: str, ) -> str: """Returns a fully-qualified organization string.""" return "organizations/{organization}".format( organization=organization, ) @staticmethod def parse_common_organization_path(path: str) -> Dict[str, str]: """Parse a organization path into its component segments.""" m = re.match(r"^organizations/(?P.+?)$", 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.+?)$", path) return m.groupdict() if m else {} @staticmethod def common_location_path( project: str, location: str, ) -> str: """Returns a fully-qualified location string.""" return "projects/{project}/locations/{location}".format( project=project, location=location, ) @staticmethod def parse_common_location_path(path: str) -> Dict[str, str]: """Parse a location path into its component segments.""" m = re.match(r"^projects/(?P.+?)/locations/(?P.+?)$", path) return m.groupdict() if m else {} @classmethod def get_mtls_endpoint_and_cert_source( cls, client_options: Optional[client_options_lib.ClientOptions] = None ): """Return the API endpoint and client cert source for mutual TLS. The client cert source is determined in the following order: (1) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is not "true", the client cert source is None. (2) if `client_options.client_cert_source` is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None. The API endpoint is determined in the following order: (1) if `client_options.api_endpoint` if provided, use the provided one. (2) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is "always", use the default mTLS endpoint; if the environment 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. """ if client_options is None: client_options = client_options_lib.ClientOptions() use_client_cert = os.getenv("GOOGLE_API_USE_CLIENT_CERTIFICATE", "false") use_mtls_endpoint = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto") if use_client_cert not in ("true", "false"): raise ValueError( "Environment variable `GOOGLE_API_USE_CLIENT_CERTIFICATE` must be either `true` or `false`" ) if use_mtls_endpoint not in ("auto", "never", "always"): raise MutualTLSChannelError( "Environment variable `GOOGLE_API_USE_MTLS_ENDPOINT` must be `never`, `auto` or `always`" ) # Figure out the client cert source to use. client_cert_source = None if use_client_cert == "true": if client_options.client_cert_source: client_cert_source = client_options.client_cert_source elif mtls.has_default_client_cert_source(): client_cert_source = mtls.default_client_cert_source() # Figure out which api endpoint to use. if client_options.api_endpoint is not None: api_endpoint = client_options.api_endpoint elif use_mtls_endpoint == "always" or ( use_mtls_endpoint == "auto" and client_cert_source ): api_endpoint = cls.DEFAULT_MTLS_ENDPOINT else: api_endpoint = cls.DEFAULT_ENDPOINT return api_endpoint, client_cert_source def __init__( self, *, credentials: Optional[ga_credentials.Credentials] = None, transport: Optional[Union[str, ModelServiceTransport]] = None, client_options: Optional[Union[client_options_lib.ClientOptions, dict]] = None, client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO, ) -> None: """Instantiates the model service client. Args: credentials (Optional[google.auth.credentials.Credentials]): The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. transport (Union[str, ModelServiceTransport]): The transport to use. 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. It won't take effect if a ``transport`` instance is provided. (1) The ``api_endpoint`` property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto switch to the default mTLS endpoint if client certificate is present, this is the default value). However, the ``api_endpoint`` property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the ``client_cert_source`` property can be used to provide client certificate for mutual TLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used. client_info (google.api_core.gapic_v1.client_info.ClientInfo): The client info used to send a user-agent string along with API requests. If ``None``, then default info will be used. Generally, you only need to set this if you're developing your own client library. Raises: google.auth.exceptions.MutualTLSChannelError: If mutual TLS transport creation failed for any reason. """ if isinstance(client_options, dict): client_options = client_options_lib.from_dict(client_options) if client_options is None: client_options = client_options_lib.ClientOptions() client_options = cast(client_options_lib.ClientOptions, client_options) api_endpoint, client_cert_source_func = self.get_mtls_endpoint_and_cert_source( client_options ) api_key_value = getattr(client_options, "api_key", None) if api_key_value and credentials: raise ValueError( "client_options.api_key and credentials are mutually exclusive" ) # Save or instantiate the transport. # Ordinarily, we provide the transport, but allowing a custom transport # instance provides an extensibility point for unusual situations. if isinstance(transport, ModelServiceTransport): # transport is a ModelServiceTransport instance. if credentials or client_options.credentials_file or api_key_value: raise ValueError( "When providing a transport instance, " "provide its credentials directly." ) if client_options.scopes: raise ValueError( "When providing a transport instance, provide its scopes " "directly." ) self._transport = transport else: import google.auth._default # type: ignore if api_key_value and hasattr( google.auth._default, "get_api_key_credentials" ): credentials = google.auth._default.get_api_key_credentials( api_key_value ) Transport = type(self).get_transport_class(transport) self._transport = Transport( credentials=credentials, credentials_file=client_options.credentials_file, host=api_endpoint, scopes=client_options.scopes, client_cert_source_for_mtls=client_cert_source_func, quota_project_id=client_options.quota_project_id, client_info=client_info, always_use_jwt_access=True, api_audience=client_options.api_audience, ) 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. .. 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_v1beta2 def sample_get_model(): # Create a client client = generativelanguage_v1beta2.ModelServiceClient() # Initialize request argument(s) request = generativelanguage_v1beta2.GetModelRequest( name="name_value", ) # Make the request response = client.get_model(request=request) # Handle the response print(response) Args: request (Union[google.ai.generativelanguage_v1beta2.types.GetModelRequest, dict]): The request object. Request for getting information about a specific Model. name (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.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.ai.generativelanguage_v1beta2.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." ) # Minor optimization to avoid making a copy if the user passes # in a model_service.GetModelRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. 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._transport._wrapped_methods[self._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),)), ) # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response 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.ListModelsPager: r"""Lists models available through the 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_v1beta2 def sample_list_models(): # Create a client client = generativelanguage_v1beta2.ModelServiceClient() # Initialize request argument(s) request = generativelanguage_v1beta2.ListModelsRequest( ) # Make the request page_result = client.list_models(request=request) # Handle the response for response in page_result: print(response) Args: request (Union[google.ai.generativelanguage_v1beta2.types.ListModelsRequest, dict]): The request object. Request for listing all Models. page_size (int): The maximum number of ``Models`` to return (per page). The service may return fewer models. If unspecified, at most 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 (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.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.ai.generativelanguage_v1beta2.services.model_service.pagers.ListModelsPager: 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." ) # Minor optimization to avoid making a copy if the user passes # in a model_service.ListModelsRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. 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._transport._wrapped_methods[self._transport.list_models] # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # This method is paged; wrap the response in a pager, which provides # an `__iter__` convenience method. response = pagers.ListModelsPager( method=rpc, request=request, response=response, metadata=metadata, ) # Done; return the response. return response def __enter__(self) -> "ModelServiceClient": 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() DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo( gapic_version=package_version.__version__ ) __all__ = ("ModelServiceClient",)