two version of R2R are here HEAD master

9 files changed, 1149 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/__init__.py
new file mode 100644
index 00000000..fdf8caba
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/__init__.py
@@ -0,0 +1,5 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/_constants.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/_constants.py
new file mode 100644
index 00000000..97a257ab
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/_constants.py
@@ -0,0 +1,8 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# Miscellaneous
+HTTPS = "https"
+HTTP = "http"
+WORKSPACE_BLOB_STORE = "workspaceblobstore"
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/_on_prem.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/_on_prem.py
new file mode 100644
index 00000000..e6c0dc3f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/_on_prem.py
@@ -0,0 +1,121 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from base64 import b64encode
+from pathlib import Path
+from typing import Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import Datastore as DatastoreData
+from azure.ai.ml._restclient.v2023_04_01_preview.models import DatastoreType
+from azure.ai.ml._restclient.v2023_04_01_preview.models import HdfsDatastore as RestHdfsDatastore
+from azure.ai.ml._schema._datastore._on_prem import HdfsSchema
+from azure.ai.ml._utils._experimental import experimental
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, TYPE
+from azure.ai.ml.entities._datastore.datastore import Datastore
+from azure.ai.ml.entities._datastore.utils import _from_rest_datastore_credentials_preview
+from azure.ai.ml.entities._util import load_from_dict
+
+from ._constants import HTTP
+from ._on_prem_credentials import KerberosKeytabCredentials, KerberosPasswordCredentials
+
+
+@experimental
+class HdfsDatastore(Datastore):
+    """HDFS datastore that is linked to an Azure ML workspace.
+
+    :param name: Name of the datastore.
+    :type name: str
+    :param name_node_address: IP Address or DNS HostName.
+    :type name_node_address: str
+    :param hdfs_server_certificate: The TLS cert of the HDFS server (optional).
+        Needs to be a local path on create and will be a base64 encoded string on get.
+    :type hdfs_server_certificate: str
+    :param protocol: http or https
+    :type protocol: str
+    :param description: Description of the resource.
+    :type description: str
+    :param tags: Tag dictionary. Tags can be added, removed, and updated.
+    :type tags: dict[str, str]
+    :param properties: The asset property dictionary.
+    :type properties: dict[str, str]
+    :param credentials: Credentials to use for Azure ML workspace to connect to the storage.
+    :type credentials: Union[KerberosKeytabCredentials, KerberosPasswordCredentials]
+    :param kwargs: A dictionary of additional configuration parameters.
+    :type kwargs: dict
+    """
+
+    def __init__(
+        self,
+        *,
+        name: str,
+        name_node_address: str,
+        hdfs_server_certificate: Optional[str] = None,
+        protocol: str = HTTP,
+        description: Optional[str] = None,
+        tags: Optional[Dict] = None,
+        properties: Optional[Dict] = None,
+        credentials: Optional[Union[KerberosKeytabCredentials, KerberosPasswordCredentials]],
+        **kwargs: Any
+    ):
+        kwargs[TYPE] = DatastoreType.HDFS
+        super().__init__(
+            name=name, description=description, tags=tags, properties=properties, credentials=credentials, **kwargs
+        )
+
+        self.hdfs_server_certificate = hdfs_server_certificate
+        self.name_node_address = name_node_address
+        self.protocol = protocol
+
+    def _to_rest_object(self) -> DatastoreData:
+        use_this_cert = None
+        if self.hdfs_server_certificate:
+            with open(self.hdfs_server_certificate, "rb") as f:
+                use_this_cert = b64encode(f.read()).decode("utf-8")
+        hdfs_ds = RestHdfsDatastore(
+            credentials=self.credentials._to_rest_object(),
+            hdfs_server_certificate=use_this_cert,
+            name_node_address=self.name_node_address,
+            protocol=self.protocol,
+            description=self.description,
+            tags=self.tags,
+        )
+        return DatastoreData(properties=hdfs_ds)
+
+    @classmethod
+    def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any) -> "HdfsDatastore":
+        res: HdfsDatastore = load_from_dict(HdfsSchema, data, context, additional_message)
+        return res
+
+    @classmethod
+    def _from_rest_object(cls, datastore_resource: DatastoreData) -> "HdfsDatastore":
+        properties: RestHdfsDatastore = datastore_resource.properties
+        return HdfsDatastore(
+            name=datastore_resource.name,
+            id=datastore_resource.id,
+            credentials=_from_rest_datastore_credentials_preview(properties.credentials),
+            hdfs_server_certificate=properties.hdfs_server_certificate,
+            name_node_address=properties.name_node_address,
+            protocol=properties.protocol,
+            description=properties.description,
+            tags=properties.tags,
+        )
+
+    def __eq__(self, other: Any) -> bool:
+        res: bool = (
+            super().__eq__(other)
+            and self.hdfs_server_certificate == other.hdfs_server_certificate
+            and self.name_node_address == other.name_node_address
+            and self.protocol == other.protocol
+        )
+        return res
+
+    def __ne__(self, other: Any) -> bool:
+        return not self.__eq__(other)
+
+    def _to_dict(self) -> Dict:
+        context = {BASE_PATH_CONTEXT_KEY: Path(".").parent}
+        res: dict = HdfsSchema(context=context).dump(self)
+        return res
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/_on_prem_credentials.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/_on_prem_credentials.py
new file mode 100644
index 00000000..b658851a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/_on_prem_credentials.py
@@ -0,0 +1,128 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from base64 import b64encode
+from typing import Any, Optional
+
+from azure.ai.ml._restclient.v2023_04_01_preview import models as model_preview
+from azure.ai.ml._utils._experimental import experimental
+from azure.ai.ml.entities._credentials import NoneCredentialConfiguration
+
+
+# TODO: Move classes in this file to azure.ai.ml.entities._credentials
+@experimental
+class BaseKerberosCredentials(NoneCredentialConfiguration):
+    def __init__(self, kerberos_realm: str, kerberos_kdc_address: str, kerberos_principal: str):
+        super().__init__()
+        self.kerberos_realm = kerberos_realm
+        self.kerberos_kdc_address = kerberos_kdc_address
+        self.kerberos_principal = kerberos_principal
+
+
+@experimental
+class KerberosKeytabCredentials(BaseKerberosCredentials):
+    def __init__(
+        self,
+        *,
+        kerberos_realm: str,
+        kerberos_kdc_address: str,
+        kerberos_principal: str,
+        kerberos_keytab: Optional[str],
+        **kwargs: Any,
+    ):
+        super().__init__(
+            kerberos_realm=kerberos_realm,
+            kerberos_kdc_address=kerberos_kdc_address,
+            kerberos_principal=kerberos_principal,
+            **kwargs,
+        )
+        self.type = model_preview.CredentialsType.KERBEROS_KEYTAB
+        self.kerberos_keytab = kerberos_keytab
+
+    def _to_rest_object(self) -> model_preview.KerberosKeytabCredentials:
+        use_this_keytab = None
+        if self.kerberos_keytab:
+            with open(self.kerberos_keytab, "rb") as f:
+                use_this_keytab = b64encode(f.read()).decode("utf-8")
+        secrets = model_preview.KerberosKeytabSecrets(kerberos_keytab=use_this_keytab)
+        return model_preview.KerberosKeytabCredentials(
+            kerberos_kdc_address=self.kerberos_kdc_address,
+            kerberos_principal=self.kerberos_principal,
+            kerberos_realm=self.kerberos_realm,
+            secrets=secrets,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: model_preview.KerberosKeytabCredentials) -> "KerberosKeytabCredentials":
+        return cls(
+            kerberos_kdc_address=obj.kerberos_kdc_address,
+            kerberos_principal=obj.kerberos_principal,
+            kerberos_realm=obj.kerberos_realm,
+            kerberos_keytab=obj.secrets.kerberos_keytab if obj.secrets else None,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, KerberosKeytabCredentials):
+            return NotImplemented
+        return (
+            self.kerberos_kdc_address == other.kerberos_kdc_address
+            and self.kerberos_principal == other.kerberos_principal
+            and self.kerberos_realm == other.kerberos_realm
+            and self.kerberos_keytab == other.kerberos_keytab
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
+
+
+@experimental
+class KerberosPasswordCredentials(BaseKerberosCredentials):
+    def __init__(
+        self,
+        *,
+        kerberos_realm: str,
+        kerberos_kdc_address: str,
+        kerberos_principal: str,
+        kerberos_password: Optional[str],
+        **kwargs: Any,
+    ):
+        super().__init__(
+            kerberos_realm=kerberos_realm,
+            kerberos_kdc_address=kerberos_kdc_address,
+            kerberos_principal=kerberos_principal,
+            **kwargs,
+        )
+        self.type = model_preview.CredentialsType.KERBEROS_PASSWORD
+        self.kerberos_password = kerberos_password
+
+    def _to_rest_object(self) -> model_preview.KerberosPasswordCredentials:
+        secrets = model_preview.KerberosPasswordSecrets(kerberos_password=self.kerberos_password)
+        return model_preview.KerberosPasswordCredentials(
+            kerberos_kdc_address=self.kerberos_kdc_address,
+            kerberos_principal=self.kerberos_principal,
+            kerberos_realm=self.kerberos_realm,
+            secrets=secrets,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: model_preview.KerberosPasswordCredentials) -> "KerberosPasswordCredentials":
+        return cls(
+            kerberos_kdc_address=obj.kerberos_kdc_address,
+            kerberos_principal=obj.kerberos_principal,
+            kerberos_realm=obj.kerberos_realm,
+            kerberos_password=obj.secrets.kerberos_password if obj.secrets else None,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, KerberosPasswordCredentials):
+            return NotImplemented
+        return (
+            self.kerberos_kdc_address == other.kerberos_kdc_address
+            and self.kerberos_principal == other.kerberos_principal
+            and self.kerberos_realm == other.kerberos_realm
+            and self.kerberos_password == other.kerberos_password
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/adls_gen1.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/adls_gen1.py
new file mode 100644
index 00000000..c2610703
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/adls_gen1.py
@@ -0,0 +1,106 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from pathlib import Path
+from typing import Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    AzureDataLakeGen1Datastore as RestAzureDatalakeGen1Datastore,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import Datastore as DatastoreData
+from azure.ai.ml._restclient.v2023_04_01_preview.models import DatastoreType
+from azure.ai.ml._schema._datastore.adls_gen1 import AzureDataLakeGen1Schema
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, TYPE
+from azure.ai.ml.entities._credentials import CertificateConfiguration, ServicePrincipalConfiguration
+from azure.ai.ml.entities._datastore.datastore import Datastore
+from azure.ai.ml.entities._datastore.utils import from_rest_datastore_credentials
+from azure.ai.ml.entities._util import load_from_dict
+
+
+class AzureDataLakeGen1Datastore(Datastore):
+    """Azure Data Lake aka Gen 1 datastore that is linked to an Azure ML workspace.
+
+    :param name: Name of the datastore.
+    :type name: str
+    :param store_name: Name of the Azure storage resource.
+    :type store_name: str
+    :param description: Description of the resource.
+    :type description: str
+    :param tags: Tag dictionary. Tags can be added, removed, and updated.
+    :type tags: dict[str, str]
+    :param properties: The asset property dictionary.
+    :type properties: dict[str, str]
+    :param credentials: Credentials to use for Azure ML workspace to connect to the storage.
+    :type credentials: Union[ServicePrincipalSection, CertificateSection]
+    :param kwargs: A dictionary of additional configuration parameters.
+    :type kwargs: dict
+    """
+
+    def __init__(
+        self,
+        *,
+        name: str,
+        store_name: str,
+        description: Optional[str] = None,
+        tags: Optional[Dict] = None,
+        properties: Optional[Dict] = None,
+        credentials: Optional[Union[CertificateConfiguration, ServicePrincipalConfiguration]] = None,
+        **kwargs: Any
+    ):
+        kwargs[TYPE] = DatastoreType.AZURE_DATA_LAKE_GEN1
+        super().__init__(
+            name=name, description=description, tags=tags, properties=properties, credentials=credentials, **kwargs
+        )
+
+        self.store_name = store_name
+
+    def _to_rest_object(self) -> DatastoreData:
+        gen1_ds = RestAzureDatalakeGen1Datastore(
+            credentials=self.credentials._to_datastore_rest_object(),
+            store_name=self.store_name,
+            description=self.description,
+            tags=self.tags,
+        )
+        return DatastoreData(properties=gen1_ds)
+
+    @classmethod
+    def _load_from_dict(
+        cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any
+    ) -> "AzureDataLakeGen1Datastore":
+        res: AzureDataLakeGen1Datastore = load_from_dict(
+            AzureDataLakeGen1Schema, data, context, additional_message, **kwargs
+        )
+        return res
+
+    @classmethod
+    def _from_rest_object(cls, datastore_resource: DatastoreData) -> "AzureDataLakeGen1Datastore":
+        properties: RestAzureDatalakeGen1Datastore = datastore_resource.properties
+        return AzureDataLakeGen1Datastore(
+            id=datastore_resource.id,
+            name=datastore_resource.name,
+            store_name=properties.store_name,
+            credentials=from_rest_datastore_credentials(properties.credentials),  # type: ignore[arg-type]
+            description=properties.description,
+            tags=properties.tags,
+        )
+
+    def __eq__(self, other: Any) -> bool:
+        res: bool = (
+            super().__eq__(other)
+            and self.name == other.name
+            and self.type == other.type
+            and self.store_name == other.store_name
+            and self.credentials == other.credentials
+        )
+        return res
+
+    def __ne__(self, other: Any) -> bool:
+        return not self.__eq__(other)
+
+    def _to_dict(self) -> Dict:
+        context = {BASE_PATH_CONTEXT_KEY: Path(".").parent}
+        res: dict = AzureDataLakeGen1Schema(context=context).dump(self)
+        return res
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/azure_storage.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/azure_storage.py
new file mode 100644
index 00000000..0fff1925
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/azure_storage.py
@@ -0,0 +1,337 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from pathlib import Path
+from typing import Any, Dict, Optional, Union
+
+from azure.ai.ml._azure_environments import _get_storage_endpoint_from_metadata
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AzureBlobDatastore as RestAzureBlobDatastore
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    AzureDataLakeGen2Datastore as RestAzureDataLakeGen2Datastore,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AzureFileDatastore as RestAzureFileDatastore
+from azure.ai.ml._restclient.v2023_04_01_preview.models import Datastore as DatastoreData
+from azure.ai.ml._restclient.v2023_04_01_preview.models import DatastoreType
+from azure.ai.ml._schema._datastore import AzureBlobSchema, AzureDataLakeGen2Schema, AzureFileSchema
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, TYPE
+from azure.ai.ml.entities._credentials import (
+    AccountKeyConfiguration,
+    CertificateConfiguration,
+    SasTokenConfiguration,
+    ServicePrincipalConfiguration,
+)
+from azure.ai.ml.entities._datastore.datastore import Datastore
+from azure.ai.ml.entities._datastore.utils import from_rest_datastore_credentials
+from azure.ai.ml.entities._util import load_from_dict
+
+from ._constants import HTTPS
+
+
+class AzureFileDatastore(Datastore):
+    """Azure file share that is linked to an Azure ML workspace.
+
+    :param name: Name of the datastore.
+    :type name: str
+    :param account_name: Name of the Azure storage account.
+    :type account_name: str
+    :param file_share_name: Name of the file share.
+    :type file_share_name: str
+    :param description: Description of the resource.
+    :type description: str
+    :param tags: Tag dictionary. Tags can be added, removed, and updated.
+    :type tags: dict[str, str]
+    :param endpoint: Endpoint to use to connect with the Azure storage account
+    :type endpoint: str
+    :param protocol: Protocol to use to connect with the Azure storage account
+    :type protocol: str
+    :param properties: The asset property dictionary.
+    :type properties: dict[str, str]
+    :param credentials: Credentials to use for Azure ML workspace to connect to the storage. Defaults to None.
+    :type credentials: Union[~azure.ai.ml.entities.AccountKeyConfiguration,
+        ~azure.ai.ml.entities.SasTokenConfiguration]
+    :param kwargs: A dictionary of additional configuration parameters.
+    :type kwargs: dict
+    """
+
+    def __init__(
+        self,
+        *,
+        name: str,
+        account_name: str,
+        file_share_name: str,
+        description: Optional[str] = None,
+        tags: Optional[Dict] = None,
+        endpoint: str = _get_storage_endpoint_from_metadata(),
+        protocol: str = HTTPS,
+        properties: Optional[Dict] = None,
+        credentials: Optional[Union[AccountKeyConfiguration, SasTokenConfiguration]] = None,
+        **kwargs: Any
+    ):
+        kwargs[TYPE] = DatastoreType.AZURE_FILE
+        super().__init__(
+            name=name, description=description, tags=tags, properties=properties, credentials=credentials, **kwargs
+        )
+        self.file_share_name = file_share_name
+        self.account_name = account_name
+        self.endpoint = endpoint
+        self.protocol = protocol
+
+    def _to_rest_object(self) -> DatastoreData:
+        file_ds = RestAzureFileDatastore(
+            account_name=self.account_name,
+            file_share_name=self.file_share_name,
+            credentials=self.credentials._to_datastore_rest_object(),
+            endpoint=self.endpoint,
+            protocol=self.protocol,
+            description=self.description,
+            tags=self.tags,
+        )
+        return DatastoreData(properties=file_ds)
+
+    @classmethod
+    def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any) -> "AzureFileDatastore":
+        res: AzureFileDatastore = load_from_dict(AzureFileSchema, data, context, additional_message)
+        return res
+
+    @classmethod
+    def _from_rest_object(cls, datastore_resource: DatastoreData) -> "AzureFileDatastore":
+        properties: RestAzureFileDatastore = datastore_resource.properties
+        return AzureFileDatastore(
+            name=datastore_resource.name,
+            id=datastore_resource.id,
+            account_name=properties.account_name,
+            credentials=from_rest_datastore_credentials(properties.credentials),  # type: ignore[arg-type]
+            endpoint=properties.endpoint,
+            protocol=properties.protocol,
+            file_share_name=properties.file_share_name,
+            description=properties.description,
+            tags=properties.tags,
+        )
+
+    def __eq__(self, other: Any) -> bool:
+        res: bool = (
+            super().__eq__(other)
+            and self.file_share_name == other.file_share_name
+            and self.account_name == other.account_name
+            and self.endpoint == other.endpoint
+            and self.protocol == other.protocol
+        )
+        return res
+
+    def __ne__(self, other: Any) -> bool:
+        return not self.__eq__(other)
+
+    def _to_dict(self) -> Dict:
+        context = {BASE_PATH_CONTEXT_KEY: Path(".").parent}
+        res: dict = AzureFileSchema(context=context).dump(self)
+        return res
+
+
+class AzureBlobDatastore(Datastore):
+    """Azure blob storage that is linked to an Azure ML workspace.
+
+    :param name: Name of the datastore.
+    :type name: str
+    :param account_name: Name of the Azure storage account.
+    :type account_name: str
+    :param container_name: Name of the container.
+    :type container_name: str
+    :param description: Description of the resource.
+    :type description: str
+    :param tags: Tag dictionary. Tags can be added, removed, and updated.
+    :type tags: dict[str, str]
+    :param endpoint: Endpoint to use to connect with the Azure storage account.
+    :type endpoint: str
+    :param protocol: Protocol to use to connect with the Azure storage account.
+    :type protocol: str
+    :param properties: The asset property dictionary.
+    :type properties: dict[str, str]
+    :param credentials: Credentials to use for Azure ML workspace to connect to the storage.
+    :type credentials: Union[~azure.ai.ml.entities.AccountKeyConfiguration,
+        ~azure.ai.ml.entities.SasTokenConfiguration]
+    :param kwargs: A dictionary of additional configuration parameters.
+    :type kwargs: dict
+    """
+
+    def __init__(
+        self,
+        *,
+        name: str,
+        account_name: str,
+        container_name: str,
+        description: Optional[str] = None,
+        tags: Optional[Dict] = None,
+        endpoint: Optional[str] = None,
+        protocol: str = HTTPS,
+        properties: Optional[Dict] = None,
+        credentials: Optional[Union[AccountKeyConfiguration, SasTokenConfiguration]] = None,
+        **kwargs: Any
+    ):
+        kwargs[TYPE] = DatastoreType.AZURE_BLOB
+        super().__init__(
+            name=name, description=description, tags=tags, properties=properties, credentials=credentials, **kwargs
+        )
+
+        self.container_name = container_name
+        self.account_name = account_name
+        self.endpoint = endpoint if endpoint else _get_storage_endpoint_from_metadata()
+        self.protocol = protocol
+
+    def _to_rest_object(self) -> DatastoreData:
+        blob_ds = RestAzureBlobDatastore(
+            account_name=self.account_name,
+            container_name=self.container_name,
+            credentials=self.credentials._to_datastore_rest_object(),
+            endpoint=self.endpoint,
+            protocol=self.protocol,
+            tags=self.tags,
+            description=self.description,
+        )
+        return DatastoreData(properties=blob_ds)
+
+    @classmethod
+    def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any) -> "AzureBlobDatastore":
+        res: AzureBlobDatastore = load_from_dict(AzureBlobSchema, data, context, additional_message)
+        return res
+
+    @classmethod
+    def _from_rest_object(cls, datastore_resource: DatastoreData) -> "AzureBlobDatastore":
+        properties: RestAzureBlobDatastore = datastore_resource.properties
+        return AzureBlobDatastore(
+            name=datastore_resource.name,
+            id=datastore_resource.id,
+            account_name=properties.account_name,
+            credentials=from_rest_datastore_credentials(properties.credentials),  # type: ignore[arg-type]
+            endpoint=properties.endpoint,
+            protocol=properties.protocol,
+            container_name=properties.container_name,
+            description=properties.description,
+            tags=properties.tags,
+        )
+
+    def __eq__(self, other: Any) -> bool:
+        res: bool = (
+            super().__eq__(other)
+            and self.container_name == other.container_name
+            and self.account_name == other.account_name
+            and self.endpoint == other.endpoint
+            and self.protocol == other.protocol
+        )
+        return res
+
+    def __ne__(self, other: Any) -> bool:
+        return not self.__eq__(other)
+
+    def _to_dict(self) -> Dict:
+        context = {BASE_PATH_CONTEXT_KEY: Path(".").parent}
+        res: dict = AzureBlobSchema(context=context).dump(self)
+        return res
+
+
+class AzureDataLakeGen2Datastore(Datastore):
+    """Azure data lake gen 2 that is linked to an Azure ML workspace.
+
+    :param name: Name of the datastore.
+    :type name: str
+    :param account_name: Name of the Azure storage account.
+    :type account_name: str
+    :param filesystem: The name of the Data Lake Gen2 filesystem.
+    :type filesystem: str
+    :param description: Description of the resource.
+    :type description: str
+    :param tags: Tag dictionary. Tags can be added, removed, and updated.
+    :type tags: dict[str, str]
+    :param endpoint: Endpoint to use to connect with the Azure storage account
+    :type endpoint: str
+    :param protocol: Protocol to use to connect with the Azure storage account
+    :type protocol: str
+    :param credentials: Credentials to use for Azure ML workspace to connect to the storage.
+    :type credentials: Union[
+        ~azure.ai.ml.entities.ServicePrincipalConfiguration,
+        ~azure.ai.ml.entities.CertificateConfiguration
+
+        ]
+    :param properties: The asset property dictionary.
+    :type properties: dict[str, str]
+    :param kwargs: A dictionary of additional configuration parameters.
+    :type kwargs: dict
+    """
+
+    def __init__(
+        self,
+        *,
+        name: str,
+        account_name: str,
+        filesystem: str,
+        description: Optional[str] = None,
+        tags: Optional[Dict] = None,
+        endpoint: str = _get_storage_endpoint_from_metadata(),
+        protocol: str = HTTPS,
+        properties: Optional[Dict] = None,
+        credentials: Optional[Union[ServicePrincipalConfiguration, CertificateConfiguration]] = None,
+        **kwargs: Any
+    ):
+        kwargs[TYPE] = DatastoreType.AZURE_DATA_LAKE_GEN2
+        super().__init__(
+            name=name, description=description, tags=tags, properties=properties, credentials=credentials, **kwargs
+        )
+
+        self.account_name = account_name
+        self.filesystem = filesystem
+        self.endpoint = endpoint
+        self.protocol = protocol
+
+    def _to_rest_object(self) -> DatastoreData:
+        gen2_ds = RestAzureDataLakeGen2Datastore(
+            account_name=self.account_name,
+            filesystem=self.filesystem,
+            credentials=self.credentials._to_datastore_rest_object(),
+            endpoint=self.endpoint,
+            protocol=self.protocol,
+            description=self.description,
+            tags=self.tags,
+        )
+        return DatastoreData(properties=gen2_ds)
+
+    @classmethod
+    def _load_from_dict(
+        cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any
+    ) -> "AzureDataLakeGen2Datastore":
+        res: AzureDataLakeGen2Datastore = load_from_dict(AzureDataLakeGen2Schema, data, context, additional_message)
+        return res
+
+    @classmethod
+    def _from_rest_object(cls, datastore_resource: DatastoreData) -> "AzureDataLakeGen2Datastore":
+        properties: RestAzureDataLakeGen2Datastore = datastore_resource.properties
+        return AzureDataLakeGen2Datastore(
+            name=datastore_resource.name,
+            id=datastore_resource.id,
+            account_name=properties.account_name,
+            credentials=from_rest_datastore_credentials(properties.credentials),  # type: ignore[arg-type]
+            endpoint=properties.endpoint,
+            protocol=properties.protocol,
+            filesystem=properties.filesystem,
+            description=properties.description,
+            tags=properties.tags,
+        )
+
+    def __eq__(self, other: Any) -> bool:
+        res: bool = (
+            super().__eq__(other)
+            and self.filesystem == other.filesystem
+            and self.account_name == other.account_name
+            and self.endpoint == other.endpoint
+            and self.protocol == other.protocol
+        )
+        return res
+
+    def __ne__(self, other: Any) -> bool:
+        return not self.__eq__(other)
+
+    def _to_dict(self) -> Dict:
+        context = {BASE_PATH_CONTEXT_KEY: Path(".").parent}
+        res: dict = AzureDataLakeGen2Schema(context=context).dump(self)
+        return res
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/datastore.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/datastore.py
new file mode 100644
index 00000000..bc933cfb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/datastore.py
@@ -0,0 +1,221 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access,redefined-builtin,arguments-renamed
+
+from abc import ABC, abstractmethod
+from os import PathLike
+from pathlib import Path
+from typing import IO, Any, AnyStr, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import Datastore as DatastoreData
+from azure.ai.ml._restclient.v2023_04_01_preview.models import DatastoreType
+from azure.ai.ml._utils.utils import camel_to_snake, dump_yaml_to_file
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, PARAMS_OVERRIDE_KEY, CommonYamlFields
+from azure.ai.ml.entities._credentials import (
+    AccountKeyConfiguration,
+    CertificateConfiguration,
+    NoneCredentialConfiguration,
+    SasTokenConfiguration,
+    ServicePrincipalConfiguration,
+)
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+from azure.ai.ml.entities._resource import Resource
+from azure.ai.ml.entities._util import find_type_in_override
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationErrorType, ValidationException
+
+
+class Datastore(Resource, RestTranslatableMixin, ABC):
+    """Datastore of an Azure ML workspace, abstract class.
+
+    :param name: Name of the datastore.
+    :type name: str
+    :param description: Description of the resource.
+    :type description: str
+    :param credentials: Credentials to use for Azure ML workspace to connect to the storage.
+    :type credentials: Optional[Union[
+        ~azure.ai.ml.entities.ServicePrincipalConfiguration,
+        ~azure.ai.ml.entities.CertificateConfiguration,
+        ~azure.ai.ml.entities.NoneCredentialConfiguration,
+        ~azure.ai.ml.entities.AccountKeyConfiguration,
+        ~azure.ai.ml.entities.SasTokenConfiguration
+
+        ]]
+    :param tags: Tag dictionary. Tags can be added, removed, and updated.
+    :type tags: dict[str, str]
+    :param properties: The asset property dictionary.
+    :type properties: dict[str, str]
+    :param kwargs: A dictionary of additional configuration parameters.
+    :type kwargs: dict
+    """
+
+    def __init__(
+        self,
+        credentials: Optional[
+            Union[
+                ServicePrincipalConfiguration,
+                CertificateConfiguration,
+                NoneCredentialConfiguration,
+                AccountKeyConfiguration,
+                SasTokenConfiguration,
+            ]
+        ],
+        name: Optional[str] = None,
+        description: Optional[str] = None,
+        tags: Optional[Dict] = None,
+        properties: Optional[Dict] = None,
+        **kwargs: Any,
+    ):
+        self._type: str = kwargs.pop("type", None)
+        super().__init__(
+            name=name,
+            description=description,
+            tags=tags,
+            properties=properties,
+            **kwargs,
+        )
+
+        self.credentials = NoneCredentialConfiguration() if credentials is None else credentials
+
+    @property
+    def type(self) -> str:
+        return self._type
+
+    def dump(self, dest: Union[str, PathLike, IO[AnyStr]], **kwargs: Any) -> None:
+        """Dump the datastore content into a file in yaml format.
+
+        :param dest: The destination to receive this datastore's content.
+            Must be either a path to a local file, or an already-open file stream.
+            If dest is a file path, a new file will be created,
+            and an exception is raised if the file exists.
+            If dest is an open file, the file will be written to directly,
+            and an exception will be raised if the file is not writable.
+        :type dest: Union[PathLike, str, IO[AnyStr]]
+        """
+        yaml_serialized = self._to_dict()
+        dump_yaml_to_file(dest, yaml_serialized, default_flow_style=False, **kwargs)
+
+    @abstractmethod
+    def _to_dict(self) -> Dict:
+        pass
+
+    @classmethod
+    def _load(
+        cls,
+        data: Optional[Dict] = None,
+        yaml_path: Optional[Union[PathLike, str]] = None,
+        params_override: Optional[list] = None,
+        **kwargs: Any,
+    ) -> "Datastore":
+        data = data or {}
+        params_override = params_override or []
+
+        context = {
+            BASE_PATH_CONTEXT_KEY: Path(yaml_path).parent if yaml_path else Path("./"),
+            PARAMS_OVERRIDE_KEY: params_override,
+        }
+
+        from azure.ai.ml.entities import (
+            AzureBlobDatastore,
+            AzureDataLakeGen1Datastore,
+            AzureDataLakeGen2Datastore,
+            AzureFileDatastore,
+            OneLakeDatastore,
+        )
+
+        # from azure.ai.ml.entities._datastore._on_prem import (
+        #     HdfsDatastore
+        # )
+
+        ds_type: Any = None
+        type_in_override = find_type_in_override(params_override)
+        type = type_in_override or data.get(
+            CommonYamlFields.TYPE, DatastoreType.AZURE_BLOB
+        )  # override takes the priority
+
+        # yaml expects snake casing, while service side constants are camel casing
+        if type == camel_to_snake(DatastoreType.AZURE_BLOB):
+            ds_type = AzureBlobDatastore
+        elif type == camel_to_snake(DatastoreType.AZURE_FILE):
+            ds_type = AzureFileDatastore
+        elif type == camel_to_snake(DatastoreType.AZURE_DATA_LAKE_GEN1):
+            ds_type = AzureDataLakeGen1Datastore
+        elif type == camel_to_snake(DatastoreType.AZURE_DATA_LAKE_GEN2):
+            ds_type = AzureDataLakeGen2Datastore
+        elif type == camel_to_snake(DatastoreType.ONE_LAKE):
+            ds_type = OneLakeDatastore
+        # disable unless preview release
+        # elif type == camel_to_snake(DatastoreTypePreview.HDFS):
+        #    ds_type = HdfsDatastore
+        else:
+            msg = f"Unsupported datastore type: {type}."
+            raise ValidationException(
+                message=msg,
+                error_type=ValidationErrorType.INVALID_VALUE,
+                target=ErrorTarget.DATASTORE,
+                no_personal_data_message=msg,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+        res: Datastore = ds_type._load_from_dict(
+            data=data,
+            context=context,
+            additional_message="If the datastore type is incorrect, change the 'type' property.",
+            **kwargs,
+        )
+        return res
+
+    @classmethod
+    def _from_rest_object(cls, datastore_resource: DatastoreData) -> "Datastore":
+        from azure.ai.ml.entities import (
+            AzureBlobDatastore,
+            AzureDataLakeGen1Datastore,
+            AzureDataLakeGen2Datastore,
+            AzureFileDatastore,
+            OneLakeDatastore,
+        )
+
+        # from azure.ai.ml.entities._datastore._on_prem import (
+        #     HdfsDatastore
+        # )
+
+        datastore_type = datastore_resource.properties.datastore_type
+        if datastore_type == DatastoreType.AZURE_DATA_LAKE_GEN1:
+            res_adl_gen1: Datastore = AzureDataLakeGen1Datastore._from_rest_object(datastore_resource)
+            return res_adl_gen1
+        if datastore_type == DatastoreType.AZURE_DATA_LAKE_GEN2:
+            res_adl_gen2: Datastore = AzureDataLakeGen2Datastore._from_rest_object(datastore_resource)
+            return res_adl_gen2
+        if datastore_type == DatastoreType.AZURE_BLOB:
+            res_abd: Datastore = AzureBlobDatastore._from_rest_object(datastore_resource)
+            return res_abd
+        if datastore_type == DatastoreType.AZURE_FILE:
+            res_afd: Datastore = AzureFileDatastore._from_rest_object(datastore_resource)
+            return res_afd
+        if datastore_type == DatastoreType.ONE_LAKE:
+            res_old: Datastore = OneLakeDatastore._from_rest_object(datastore_resource)
+            return res_old
+        # disable unless preview release
+        # elif datastore_type == DatastoreTypePreview.HDFS:
+        #     return HdfsDatastore._from_rest_object(datastore_resource)
+        msg = f"Unsupported datastore type {datastore_resource.properties.contents.type}"
+        raise ValidationException(
+            message=msg,
+            error_type=ValidationErrorType.INVALID_VALUE,
+            target=ErrorTarget.DATASTORE,
+            no_personal_data_message=msg,
+            error_category=ErrorCategory.SYSTEM_ERROR,
+        )
+
+    @classmethod
+    @abstractmethod
+    def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any) -> "Datastore":
+        pass
+
+    def __eq__(self, other: Any) -> bool:
+        res: bool = self.name == other.name and self.type == other.type and self.credentials == other.credentials
+        return res
+
+    def __ne__(self, other: Any) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/one_lake.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/one_lake.py
new file mode 100644
index 00000000..9bc06d92
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/one_lake.py
@@ -0,0 +1,153 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from abc import ABC
+from pathlib import Path
+from typing import Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import Datastore as DatastoreData
+from azure.ai.ml._restclient.v2023_04_01_preview.models import DatastoreType
+from azure.ai.ml._restclient.v2023_04_01_preview.models import LakeHouseArtifact as RestLakeHouseArtifact
+from azure.ai.ml._restclient.v2023_04_01_preview.models import NoneDatastoreCredentials as RestNoneDatastoreCredentials
+from azure.ai.ml._restclient.v2023_04_01_preview.models import OneLakeDatastore as RestOneLakeDatastore
+from azure.ai.ml._schema._datastore.one_lake import OneLakeSchema
+from azure.ai.ml._utils._experimental import experimental
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, TYPE
+from azure.ai.ml.entities._credentials import NoneCredentialConfiguration, ServicePrincipalConfiguration
+from azure.ai.ml.entities._datastore.datastore import Datastore
+from azure.ai.ml.entities._datastore.utils import from_rest_datastore_credentials
+from azure.ai.ml.entities._mixins import DictMixin, RestTranslatableMixin
+from azure.ai.ml.entities._util import load_from_dict
+
+
+@experimental
+class OneLakeArtifact(RestTranslatableMixin, DictMixin, ABC):
+    """OneLake artifact (data source) backing the OneLake workspace.
+
+    :param name: OneLake artifact name/GUID. ex) 01234567-abcd-1234-5678-012345678901
+    :type name: str
+    :param type: OneLake artifact type. Only LakeHouse artifacts are currently supported.
+    :type type: str
+    """
+
+    def __init__(self, *, name: str, type: Optional[str] = None):
+        super().__init__()
+        self.name = name
+        self.type = type
+
+
+@experimental
+class LakeHouseArtifact(OneLakeArtifact):
+    """LakeHouse artifact type for OneLake.
+
+    :param artifact_name: OneLake LakeHouse artifact name/GUID. ex) 01234567-abcd-1234-5678-012345678901
+    :type artifact_name: str
+    """
+
+    def __init__(self, *, name: str):
+        super(LakeHouseArtifact, self).__init__(name=name, type="lake_house")
+
+    def _to_datastore_rest_object(self) -> RestLakeHouseArtifact:
+        return RestLakeHouseArtifact(artifact_name=self.name)
+
+
+@experimental
+class OneLakeDatastore(Datastore):
+    """OneLake datastore that is linked to an Azure ML workspace.
+
+    :param name: Name of the datastore.
+    :type name: str
+    :param artifact: OneLake Artifact. Only LakeHouse artifacts are currently supported.
+    :type artifact: ~azure.ai.ml.entities.OneLakeArtifact
+    :param one_lake_workspace_name: OneLake workspace name/GUID. ex) 01234567-abcd-1234-5678-012345678901
+    :type one_lake_workspace_name: str
+    :param endpoint: OneLake endpoint to use for the datastore. ex) https://onelake.dfs.fabric.microsoft.com
+    :type endpoint: str
+    :param description: Description of the resource.
+    :type description: str
+    :param tags: Tag dictionary. Tags can be added, removed, and updated.
+    :type tags: dict[str, str]
+    :param properties: The asset property dictionary.
+    :type properties: dict[str, str]
+    :param credentials: Credentials to use to authenticate against OneLake.
+    :type credentials: Union[
+        ~azure.ai.ml.entities.ServicePrincipalConfiguration, ~azure.ai.ml.entities.NoneCredentialConfiguration]
+    :param kwargs: A dictionary of additional configuration parameters.
+    :type kwargs: dict
+    """
+
+    def __init__(
+        self,
+        *,
+        name: str,
+        artifact: OneLakeArtifact,
+        one_lake_workspace_name: str,
+        endpoint: Optional[str] = None,
+        description: Optional[str] = None,
+        tags: Optional[Dict] = None,
+        properties: Optional[Dict] = None,
+        credentials: Optional[Union[NoneCredentialConfiguration, ServicePrincipalConfiguration]] = None,
+        **kwargs: Any
+    ):
+        kwargs[TYPE] = DatastoreType.ONE_LAKE
+        super().__init__(
+            name=name, description=description, tags=tags, properties=properties, credentials=credentials, **kwargs
+        )
+        self.artifact = artifact
+        self.one_lake_workspace_name = one_lake_workspace_name
+        self.endpoint = endpoint
+
+    def _to_rest_object(self) -> DatastoreData:
+        one_lake_ds = RestOneLakeDatastore(
+            credentials=(
+                RestNoneDatastoreCredentials()
+                if self.credentials is None
+                else self.credentials._to_datastore_rest_object()
+            ),
+            artifact=RestLakeHouseArtifact(artifact_name=self.artifact["name"]),
+            one_lake_workspace_name=self.one_lake_workspace_name,
+            endpoint=self.endpoint,
+            description=self.description,
+            tags=self.tags,
+        )
+        return DatastoreData(properties=one_lake_ds)
+
+    @classmethod
+    def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any) -> "OneLakeDatastore":
+        res: OneLakeDatastore = load_from_dict(OneLakeSchema, data, context, additional_message, **kwargs)
+        return res
+
+    @classmethod
+    def _from_rest_object(cls, datastore_resource: DatastoreData) -> "OneLakeDatastore":
+        properties: RestOneLakeDatastore = datastore_resource.properties
+        return OneLakeDatastore(
+            name=datastore_resource.name,
+            id=datastore_resource.id,
+            artifact=LakeHouseArtifact(name=properties.artifact.artifact_name),
+            one_lake_workspace_name=properties.one_lake_workspace_name,
+            endpoint=properties.endpoint,
+            credentials=from_rest_datastore_credentials(properties.credentials),  # type: ignore[arg-type]
+            description=properties.description,
+            tags=properties.tags,
+        )
+
+    def __eq__(self, other: Any) -> bool:
+        res: bool = (
+            super().__eq__(other)
+            and self.one_lake_workspace_name == other.one_lake_workspace_name
+            and self.artifact.type == other.artifact["type"]
+            and self.artifact.name == other.artifact["name"]
+            and self.endpoint == other.endpoint
+        )
+        return res
+
+    def __ne__(self, other: Any) -> bool:
+        return not self.__eq__(other)
+
+    def _to_dict(self) -> Dict:
+        context = {BASE_PATH_CONTEXT_KEY: Path(".").parent}
+        res: dict = OneLakeSchema(context=context).dump(self)
+        return res
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/utils.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/utils.py
new file mode 100644
index 00000000..538f9590
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_datastore/utils.py
@@ -0,0 +1,70 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import Any, Optional, Union, cast
+
+from azure.ai.ml._restclient.v2023_04_01_preview import models
+from azure.ai.ml._restclient.v2024_07_01_preview import models as models2024
+from azure.ai.ml.entities._credentials import (
+    AccountKeyConfiguration,
+    CertificateConfiguration,
+    NoneCredentialConfiguration,
+    SasTokenConfiguration,
+    ServicePrincipalConfiguration,
+)
+from azure.ai.ml.entities._datastore._on_prem_credentials import KerberosKeytabCredentials, KerberosPasswordCredentials
+
+
+def from_rest_datastore_credentials(
+    rest_credentials: models.DatastoreCredentials,
+) -> Union[
+    AccountKeyConfiguration,
+    SasTokenConfiguration,
+    ServicePrincipalConfiguration,
+    CertificateConfiguration,
+    NoneCredentialConfiguration,
+]:
+    config_class: Any = NoneCredentialConfiguration
+
+    if isinstance(rest_credentials, (models.AccountKeyDatastoreCredentials, models2024.AccountKeyDatastoreCredentials)):
+        # we are no more using key for key base account.
+        # https://github.com/Azure/azure-sdk-for-python/pull/35716
+        if isinstance(rest_credentials.secrets, models2024.SasDatastoreSecrets):
+            config_class = SasTokenConfiguration
+        else:
+            config_class = AccountKeyConfiguration
+    elif isinstance(rest_credentials, (models.SasDatastoreCredentials, models2024.SasDatastoreCredentials)):
+        config_class = SasTokenConfiguration
+    elif isinstance(
+        rest_credentials, (models.ServicePrincipalDatastoreCredentials, models2024.ServicePrincipalDatastoreCredentials)
+    ):
+        config_class = ServicePrincipalConfiguration
+    elif isinstance(
+        rest_credentials, (models.CertificateDatastoreCredentials, models2024.CertificateDatastoreCredentials)
+    ):
+        config_class = CertificateConfiguration
+
+    return cast(
+        Union[
+            AccountKeyConfiguration,
+            SasTokenConfiguration,
+            ServicePrincipalConfiguration,
+            CertificateConfiguration,
+            NoneCredentialConfiguration,
+        ],
+        config_class._from_datastore_rest_object(rest_credentials),
+    )
+
+
+def _from_rest_datastore_credentials_preview(
+    rest_credentials: models.DatastoreCredentials,
+) -> Optional[Union[KerberosKeytabCredentials, KerberosPasswordCredentials]]:
+    if isinstance(rest_credentials, models.KerberosKeytabCredentials):
+        return KerberosKeytabCredentials._from_rest_object(rest_credentials)
+    if isinstance(rest_credentials, models.KerberosPasswordCredentials):
+        return KerberosPasswordCredentials._from_rest_object(rest_credentials)
+
+    return None